From 2fc25888ead9d4f8f98d31d63f0bf436b7f2205d Mon Sep 17 00:00:00 2001 From: Andrew Clark Date: Fri, 7 Aug 2020 15:31:20 -0400 Subject: [PATCH] new file structure and minting --- Alpha Explanation.ipynb | 32 + Trigger Function Explanation.ipynb | 32 + .../v1}/Aragon_Conviction_Voting_Model.ipynb | 0 {v1 => models/v1}/images/Aragon_v1.png | Bin .../v1}/images/bipartite-cv-compute.png | Bin .../v1}/images/stockflow-cv-trigger.png | Bin .../__pycache__/economyconfig.cpython-36.pyc | Bin .../__pycache__/economyconfig.cpython-37.pyc | Bin .../__pycache__/genesis_states.cpython-36.pyc | Bin .../__pycache__/genesis_states.cpython-37.pyc | Bin .../partial_state_update_block.cpython-36.pyc | Bin .../partial_state_update_block.cpython-37.pyc | Bin .../v1}/model/__pycache__/run.cpython-36.pyc | Bin .../v1}/model/__pycache__/run.cpython-37.pyc | Bin {v1 => models/v1}/model/economyconfig.py | 0 {v1 => models/v1}/model/genesis_states.py | 0 ...conviction_helper_functions.cpython-36.pyc | Bin ...conviction_helper_functions.cpython-37.pyc | Bin .../model/__pycache__/designed.cpython-36.pyc | Bin .../model/__pycache__/designed.cpython-37.pyc | Bin .../exogenousProcesses.cpython-36.pyc | Bin .../exogenousProcesses.cpython-37.pyc | Bin .../__pycache__/initialization.cpython-36.pyc | Bin .../__pycache__/initialization.cpython-37.pyc | Bin .../model/__pycache__/kpis.cpython-36.pyc | Bin .../model/__pycache__/kpis.cpython-37.pyc | Bin .../__pycache__/operatorentity.cpython-37.pyc | Bin .../__pycache__/participants.cpython-36.pyc | Bin .../__pycache__/participants.cpython-37.pyc | Bin .../__pycache__/proposals.cpython-36.pyc | Bin .../__pycache__/proposals.cpython-37.pyc | Bin .../subpopulation_clusters.cpython-37.pyc | Bin .../supportingFunctions.cpython-37.pyc | Bin .../model/__pycache__/system.cpython-36.pyc | Bin .../model/__pycache__/system.cpython-37.pyc | Bin .../model/conviction_helper_functions.py | 0 .../v1}/model/model/initialization.py | 0 {v1 => models/v1}/model/model/participants.py | 0 {v1 => models/v1}/model/model/proposals.py | 0 {v1 => models/v1}/model/model/system.py | 0 .../v1}/model/partial_state_update_block.py | 0 {v1 => models/v1}/model/run.py | 0 ...n Conviction Voting Model-checkpoint.ipynb | 0 ...n_Conviction_Voting_Model-checkpoint.ipynb | 0 .../Aragon_Conviction_Voting_Model.ipynb | 0 .../v2 workshop-copy}/images/Aragon_v2.png | Bin .../images/bipartite_cv_compute.png | Bin .../v2 workshop-copy}/images/cv_bipartite.png | Bin .../v2 workshop-copy}/images/snap/0.png | Bin .../v2 workshop-copy}/images/snap/1.png | Bin .../v2 workshop-copy}/images/snap/2.png | Bin .../v2 workshop-copy}/images/snap/3.png | Bin .../v2 workshop-copy}/images/snap/4.png | Bin .../images/stockflow_cv_trigger.png | Bin .../__pycache__/economyconfig.cpython-36.pyc | Bin .../__pycache__/economyconfig.cpython-37.pyc | Bin .../__pycache__/genesis_states.cpython-36.pyc | Bin .../__pycache__/genesis_states.cpython-37.pyc | Bin .../partial_state_update_block.cpython-36.pyc | Bin .../partial_state_update_block.cpython-37.pyc | Bin .../model/__pycache__/run.cpython-36.pyc | Bin .../model/__pycache__/run.cpython-37.pyc | Bin .../v2 workshop-copy}/model/economyconfig.py | 0 .../v2 workshop-copy}/model/genesis_states.py | 0 ...conviction_helper_functions.cpython-36.pyc | Bin ...conviction_helper_functions.cpython-37.pyc | Bin .../model/__pycache__/designed.cpython-36.pyc | Bin .../model/__pycache__/designed.cpython-37.pyc | Bin .../exogenousProcesses.cpython-36.pyc | Bin .../exogenousProcesses.cpython-37.pyc | Bin .../__pycache__/initialization.cpython-36.pyc | Bin .../__pycache__/initialization.cpython-37.pyc | Bin .../model/__pycache__/kpis.cpython-36.pyc | Bin .../model/__pycache__/kpis.cpython-37.pyc | Bin .../__pycache__/operatorentity.cpython-37.pyc | Bin .../__pycache__/participants.cpython-36.pyc | Bin .../__pycache__/participants.cpython-37.pyc | Bin .../__pycache__/proposals.cpython-36.pyc | Bin .../__pycache__/proposals.cpython-37.pyc | Bin .../subpopulation_clusters.cpython-37.pyc | Bin .../supportingFunctions.cpython-37.pyc | Bin .../model/__pycache__/system.cpython-36.pyc | Bin .../model/__pycache__/system.cpython-37.pyc | Bin .../model/conviction_helper_functions.py | 0 .../model/model/initialization.py | 0 .../model/model/participants.py | 0 .../model/model/proposals.py | 0 .../v2 workshop-copy}/model/model/system.py | 0 .../model/partial_state_update_block.py | 0 .../v2 workshop-copy}/model/run.py | 0 ...n Conviction Voting Model-checkpoint.ipynb | 0 ...n_Conviction_Voting_Model-checkpoint.ipynb | 0 .../v2}/Aragon_Conviction_Voting_Model.ipynb | 0 {v2 => models/v2}/images/Aragon_v2.png | Bin .../v2}/images/bipartite_cv_compute.png | Bin {v2 => models/v2}/images/cv_bipartite.png | Bin {v2 => models/v2}/images/snap/0.png | Bin {v2 => models/v2}/images/snap/1.png | Bin {v2 => models/v2}/images/snap/2.png | Bin {v2 => models/v2}/images/snap/3.png | Bin {v2 => models/v2}/images/snap/4.png | Bin .../v2}/images/stockflow_cv_trigger.png | Bin .../__pycache__/economyconfig.cpython-36.pyc | Bin .../__pycache__/economyconfig.cpython-37.pyc | Bin .../__pycache__/genesis_states.cpython-36.pyc | Bin .../__pycache__/genesis_states.cpython-37.pyc | Bin .../partial_state_update_block.cpython-36.pyc | Bin .../partial_state_update_block.cpython-37.pyc | Bin .../v2}/model/__pycache__/run.cpython-36.pyc | Bin .../v2}/model/__pycache__/run.cpython-37.pyc | Bin {v2 => models/v2}/model/economyconfig.py | 0 {v2 => models/v2}/model/genesis_states.py | 0 ...conviction_helper_functions.cpython-36.pyc | Bin ...conviction_helper_functions.cpython-37.pyc | Bin .../model/__pycache__/designed.cpython-36.pyc | Bin .../model/__pycache__/designed.cpython-37.pyc | Bin .../exogenousProcesses.cpython-36.pyc | Bin .../exogenousProcesses.cpython-37.pyc | Bin .../__pycache__/initialization.cpython-36.pyc | Bin .../__pycache__/initialization.cpython-37.pyc | Bin .../model/__pycache__/kpis.cpython-36.pyc | Bin .../model/__pycache__/kpis.cpython-37.pyc | Bin .../__pycache__/operatorentity.cpython-37.pyc | Bin .../__pycache__/participants.cpython-36.pyc | Bin .../__pycache__/participants.cpython-37.pyc | Bin .../__pycache__/proposals.cpython-36.pyc | Bin .../__pycache__/proposals.cpython-37.pyc | Bin .../subpopulation_clusters.cpython-37.pyc | Bin .../supportingFunctions.cpython-37.pyc | Bin .../model/__pycache__/system.cpython-36.pyc | Bin .../model/__pycache__/system.cpython-37.pyc | Bin .../model/conviction_helper_functions.py | 0 .../v2}/model/model/initialization.py | 0 {v2 => models/v2}/model/model/participants.py | 0 {v2 => models/v2}/model/model/proposals.py | 0 {v2 => models/v2}/model/model/system.py | 0 .../v2}/model/partial_state_update_block.py | 0 {v2 => models/v2}/model/run.py | 0 ...n_Conviction_Voting_Model-checkpoint.ipynb | 1727 +++++++++++++++++ .../v3/Aragon_Conviction_Voting_Model.ipynb | 1727 +++++++++++++++++ {v3 => models/v3}/images/Aragon_v2.png | Bin .../v3}/images/bipartite_cv_compute.png | Bin {v3 => models/v3}/images/cv_bipartite.png | Bin models/v3/images/snap/0.png | Bin 0 -> 70514 bytes {v3 => models/v3}/images/snap/1.png | Bin {v3 => models/v3}/images/snap/10.png | Bin {v3 => models/v3}/images/snap/11.png | Bin {v3 => models/v3}/images/snap/12.png | Bin {v3 => models/v3}/images/snap/13.png | Bin {v3 => models/v3}/images/snap/14.png | Bin {v3 => models/v3}/images/snap/15.png | Bin {v3 => models/v3}/images/snap/16.png | Bin {v3 => models/v3}/images/snap/17.png | Bin {v3 => models/v3}/images/snap/18.png | Bin {v3 => models/v3}/images/snap/19.png | Bin {v3 => models/v3}/images/snap/2.png | Bin {v3 => models/v3}/images/snap/20.png | Bin {v3 => models/v3}/images/snap/21.png | Bin {v3 => models/v3}/images/snap/22.png | Bin {v3 => models/v3}/images/snap/23.png | Bin {v3 => models/v3}/images/snap/24.png | Bin {v3 => models/v3}/images/snap/25.png | Bin {v3 => models/v3}/images/snap/26.png | Bin {v3 => models/v3}/images/snap/27.png | Bin {v3 => models/v3}/images/snap/28.png | Bin {v3 => models/v3}/images/snap/29.png | Bin {v3 => models/v3}/images/snap/3.png | Bin {v3 => models/v3}/images/snap/30.png | Bin {v3 => models/v3}/images/snap/31.png | Bin {v3 => models/v3}/images/snap/32.png | Bin {v3 => models/v3}/images/snap/33.png | Bin {v3 => models/v3}/images/snap/34.png | Bin {v3 => models/v3}/images/snap/35.png | Bin {v3 => models/v3}/images/snap/36.png | Bin {v3 => models/v3}/images/snap/37.png | Bin {v3 => models/v3}/images/snap/38.png | Bin {v3 => models/v3}/images/snap/39.png | Bin {v3 => models/v3}/images/snap/4.png | Bin {v3 => models/v3}/images/snap/40.png | Bin {v3 => models/v3}/images/snap/41.png | Bin {v3 => models/v3}/images/snap/42.png | Bin {v3 => models/v3}/images/snap/43.png | Bin {v3 => models/v3}/images/snap/44.png | Bin {v3 => models/v3}/images/snap/45.png | Bin {v3 => models/v3}/images/snap/46.png | Bin {v3 => models/v3}/images/snap/47.png | Bin {v3 => models/v3}/images/snap/48.png | Bin {v3 => models/v3}/images/snap/49.png | Bin {v3 => models/v3}/images/snap/5.png | Bin {v3 => models/v3}/images/snap/50.png | Bin {v3 => models/v3}/images/snap/51.png | Bin {v3 => models/v3}/images/snap/52.png | Bin {v3 => models/v3}/images/snap/53.png | Bin {v3 => models/v3}/images/snap/54.png | Bin {v3 => models/v3}/images/snap/55.png | Bin {v3 => models/v3}/images/snap/56.png | Bin {v3 => models/v3}/images/snap/57.png | Bin {v3 => models/v3}/images/snap/58.png | Bin {v3 => models/v3}/images/snap/59.png | Bin {v3 => models/v3}/images/snap/6.png | Bin {v3 => models/v3}/images/snap/7.png | Bin {v3 => models/v3}/images/snap/8.png | Bin {v3 => models/v3}/images/snap/9.png | Bin {v3 => models/v3}/images/snap/movie.mp4 | Bin .../v3}/images/stockflow_cv_trigger.png | Bin .../model/__pycache__/config.cpython-37.pyc | Bin 0 -> 1109 bytes .../__pycache__/economyconfig.cpython-36.pyc | Bin .../__pycache__/economyconfig.cpython-37.pyc | Bin .../__pycache__/genesis_states.cpython-36.pyc | Bin .../__pycache__/genesis_states.cpython-37.pyc | Bin 0 -> 563 bytes .../partial_state_update_block.cpython-36.pyc | Bin .../partial_state_update_block.cpython-37.pyc | Bin 0 -> 865 bytes .../v3}/model/__pycache__/run.cpython-36.pyc | Bin .../v3/model/__pycache__/run.cpython-37.pyc | Bin 0 -> 7972 bytes {v3 => models/v3}/model/config.py | 1 + {v3 => models/v3}/model/genesis_states.py | 12 +- ...conviction_helper_functions.cpython-36.pyc | Bin ...conviction_helper_functions.cpython-37.pyc | Bin 0 -> 14983 bytes .../model/__pycache__/designed.cpython-36.pyc | Bin .../model/__pycache__/designed.cpython-37.pyc | Bin .../exogenousProcesses.cpython-36.pyc | Bin .../exogenousProcesses.cpython-37.pyc | Bin .../__pycache__/initialization.cpython-36.pyc | Bin .../__pycache__/initialization.cpython-37.pyc | Bin .../model/__pycache__/kpis.cpython-36.pyc | Bin .../model/__pycache__/kpis.cpython-37.pyc | Bin .../__pycache__/operatorentity.cpython-37.pyc | Bin .../__pycache__/participants.cpython-36.pyc | Bin .../__pycache__/participants.cpython-37.pyc | Bin 0 -> 4936 bytes .../__pycache__/proposals.cpython-36.pyc | Bin .../__pycache__/proposals.cpython-37.pyc | Bin 0 -> 3193 bytes .../subpopulation_clusters.cpython-37.pyc | Bin .../supportingFunctions.cpython-37.pyc | Bin .../__pycache__/sys_params.cpython-37.pyc | Bin 0 -> 616 bytes .../model/__pycache__/system.cpython-36.pyc | Bin .../model/__pycache__/system.cpython-37.pyc | Bin 0 -> 3915 bytes .../model/conviction_helper_functions.py | 30 +- {v3 => models/v3}/model/model/participants.py | 28 +- {v3 => models/v3}/model/model/proposals.py | 7 +- models/v3/model/model/sys_params.py | 25 + {v3 => models/v3}/model/model/system.py | 74 +- .../v3}/model/partial_state_update_block.py | 12 +- {v3 => models/v3}/model/run.py | 1 + ...n_Conviction_Voting_Model-checkpoint.ipynb | 1518 --------------- v3/Aragon_Conviction_Voting_Model.ipynb | 1518 --------------- v3/images/snap/0.png | Bin 67076 -> 0 bytes v3/model/__pycache__/config.cpython-37.pyc | Bin 1094 -> 0 bytes .../__pycache__/genesis_states.cpython-37.pyc | Bin 474 -> 0 bytes .../partial_state_update_block.cpython-37.pyc | Bin 833 -> 0 bytes v3/model/__pycache__/run.cpython-37.pyc | Bin 7625 -> 0 bytes ...conviction_helper_functions.cpython-37.pyc | Bin 15006 -> 0 bytes .../__pycache__/participants.cpython-37.pyc | Bin 4975 -> 0 bytes .../__pycache__/proposals.cpython-37.pyc | Bin 3223 -> 0 bytes .../__pycache__/sys_params.cpython-37.pyc | Bin 504 -> 0 bytes .../model/__pycache__/system.cpython-37.pyc | Bin 3951 -> 0 bytes v3/model/model/sys_params.py | 22 - 256 files changed, 3631 insertions(+), 3135 deletions(-) create mode 100644 Alpha Explanation.ipynb create mode 100644 Trigger Function Explanation.ipynb rename {v1 => models/v1}/Aragon_Conviction_Voting_Model.ipynb (100%) rename {v1 => models/v1}/images/Aragon_v1.png (100%) rename {v1 => models/v1}/images/bipartite-cv-compute.png (100%) rename {v1 => models/v1}/images/stockflow-cv-trigger.png (100%) rename {v1 => models/v1}/model/__pycache__/economyconfig.cpython-36.pyc (100%) rename {v1 => models/v1}/model/__pycache__/economyconfig.cpython-37.pyc (100%) rename {v1 => models/v1}/model/__pycache__/genesis_states.cpython-36.pyc (100%) rename {v1 => models/v1}/model/__pycache__/genesis_states.cpython-37.pyc (100%) rename {v1 => models/v1}/model/__pycache__/partial_state_update_block.cpython-36.pyc (100%) rename {v1 => models/v1}/model/__pycache__/partial_state_update_block.cpython-37.pyc (100%) rename {v1 => models/v1}/model/__pycache__/run.cpython-36.pyc (100%) rename {v1 => models/v1}/model/__pycache__/run.cpython-37.pyc (100%) rename {v1 => models/v1}/model/economyconfig.py (100%) rename {v1 => models/v1}/model/genesis_states.py (100%) rename {v1 => models/v1}/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/designed.cpython-36.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/designed.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/exogenousProcesses.cpython-36.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/exogenousProcesses.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/initialization.cpython-36.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/initialization.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/kpis.cpython-36.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/kpis.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/operatorentity.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/participants.cpython-36.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/participants.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/proposals.cpython-36.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/proposals.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/supportingFunctions.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/system.cpython-36.pyc (100%) rename {v1 => models/v1}/model/model/__pycache__/system.cpython-37.pyc (100%) rename {v1 => models/v1}/model/model/conviction_helper_functions.py (100%) rename {v1 => models/v1}/model/model/initialization.py (100%) rename {v1 => models/v1}/model/model/participants.py (100%) rename {v1 => models/v1}/model/model/proposals.py (100%) rename {v1 => models/v1}/model/model/system.py (100%) rename {v1 => models/v1}/model/partial_state_update_block.py (100%) rename {v1 => models/v1}/model/run.py (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/.ipynb_checkpoints/Aragon Conviction Voting Model-checkpoint.ipynb (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/Aragon_Conviction_Voting_Model.ipynb (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/images/Aragon_v2.png (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/images/bipartite_cv_compute.png (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/images/cv_bipartite.png (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/images/snap/0.png (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/images/snap/1.png (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/images/snap/2.png (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/images/snap/3.png (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/images/snap/4.png (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/images/stockflow_cv_trigger.png (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/__pycache__/economyconfig.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/__pycache__/economyconfig.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/__pycache__/genesis_states.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/__pycache__/genesis_states.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/__pycache__/partial_state_update_block.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/__pycache__/partial_state_update_block.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/__pycache__/run.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/__pycache__/run.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/economyconfig.py (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/genesis_states.py (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/designed.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/designed.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/exogenousProcesses.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/exogenousProcesses.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/initialization.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/initialization.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/kpis.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/kpis.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/operatorentity.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/participants.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/participants.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/proposals.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/proposals.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/supportingFunctions.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/system.cpython-36.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/__pycache__/system.cpython-37.pyc (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/conviction_helper_functions.py (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/initialization.py (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/participants.py (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/proposals.py (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/model/system.py (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/partial_state_update_block.py (100%) rename {v2 workshop-copy => models/v2 workshop-copy}/model/run.py (100%) rename {v2 => models/v2}/.ipynb_checkpoints/Aragon Conviction Voting Model-checkpoint.ipynb (100%) rename {v2 => models/v2}/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb (100%) rename {v2 => models/v2}/Aragon_Conviction_Voting_Model.ipynb (100%) rename {v2 => models/v2}/images/Aragon_v2.png (100%) rename {v2 => models/v2}/images/bipartite_cv_compute.png (100%) rename {v2 => models/v2}/images/cv_bipartite.png (100%) rename {v2 => models/v2}/images/snap/0.png (100%) rename {v2 => models/v2}/images/snap/1.png (100%) rename {v2 => models/v2}/images/snap/2.png (100%) rename {v2 => models/v2}/images/snap/3.png (100%) rename {v2 => models/v2}/images/snap/4.png (100%) rename {v2 => models/v2}/images/stockflow_cv_trigger.png (100%) rename {v2 => models/v2}/model/__pycache__/economyconfig.cpython-36.pyc (100%) rename {v2 => models/v2}/model/__pycache__/economyconfig.cpython-37.pyc (100%) rename {v2 => models/v2}/model/__pycache__/genesis_states.cpython-36.pyc (100%) rename {v2 => models/v2}/model/__pycache__/genesis_states.cpython-37.pyc (100%) rename {v2 => models/v2}/model/__pycache__/partial_state_update_block.cpython-36.pyc (100%) rename {v2 => models/v2}/model/__pycache__/partial_state_update_block.cpython-37.pyc (100%) rename {v2 => models/v2}/model/__pycache__/run.cpython-36.pyc (100%) rename {v2 => models/v2}/model/__pycache__/run.cpython-37.pyc (100%) rename {v2 => models/v2}/model/economyconfig.py (100%) rename {v2 => models/v2}/model/genesis_states.py (100%) rename {v2 => models/v2}/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/designed.cpython-36.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/designed.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/exogenousProcesses.cpython-36.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/exogenousProcesses.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/initialization.cpython-36.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/initialization.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/kpis.cpython-36.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/kpis.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/operatorentity.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/participants.cpython-36.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/participants.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/proposals.cpython-36.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/proposals.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/supportingFunctions.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/system.cpython-36.pyc (100%) rename {v2 => models/v2}/model/model/__pycache__/system.cpython-37.pyc (100%) rename {v2 => models/v2}/model/model/conviction_helper_functions.py (100%) rename {v2 => models/v2}/model/model/initialization.py (100%) rename {v2 => models/v2}/model/model/participants.py (100%) rename {v2 => models/v2}/model/model/proposals.py (100%) rename {v2 => models/v2}/model/model/system.py (100%) rename {v2 => models/v2}/model/partial_state_update_block.py (100%) rename {v2 => models/v2}/model/run.py (100%) create mode 100644 models/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb create mode 100644 models/v3/Aragon_Conviction_Voting_Model.ipynb rename {v3 => models/v3}/images/Aragon_v2.png (100%) rename {v3 => models/v3}/images/bipartite_cv_compute.png (100%) rename {v3 => models/v3}/images/cv_bipartite.png (100%) create mode 100644 models/v3/images/snap/0.png rename {v3 => models/v3}/images/snap/1.png (100%) rename {v3 => models/v3}/images/snap/10.png (100%) rename {v3 => models/v3}/images/snap/11.png (100%) rename {v3 => models/v3}/images/snap/12.png (100%) rename {v3 => models/v3}/images/snap/13.png (100%) rename {v3 => models/v3}/images/snap/14.png (100%) rename {v3 => models/v3}/images/snap/15.png (100%) rename {v3 => models/v3}/images/snap/16.png (100%) rename {v3 => models/v3}/images/snap/17.png (100%) rename {v3 => models/v3}/images/snap/18.png (100%) rename {v3 => models/v3}/images/snap/19.png (100%) rename {v3 => models/v3}/images/snap/2.png (100%) rename {v3 => models/v3}/images/snap/20.png (100%) rename {v3 => models/v3}/images/snap/21.png (100%) rename {v3 => models/v3}/images/snap/22.png (100%) rename {v3 => models/v3}/images/snap/23.png (100%) rename {v3 => models/v3}/images/snap/24.png (100%) rename {v3 => models/v3}/images/snap/25.png (100%) rename {v3 => models/v3}/images/snap/26.png (100%) rename {v3 => models/v3}/images/snap/27.png (100%) rename {v3 => models/v3}/images/snap/28.png (100%) rename {v3 => models/v3}/images/snap/29.png (100%) rename {v3 => models/v3}/images/snap/3.png (100%) rename {v3 => models/v3}/images/snap/30.png (100%) rename {v3 => models/v3}/images/snap/31.png (100%) rename {v3 => models/v3}/images/snap/32.png (100%) rename {v3 => models/v3}/images/snap/33.png (100%) rename {v3 => models/v3}/images/snap/34.png (100%) rename {v3 => models/v3}/images/snap/35.png (100%) rename {v3 => models/v3}/images/snap/36.png (100%) rename {v3 => models/v3}/images/snap/37.png (100%) rename {v3 => models/v3}/images/snap/38.png (100%) rename {v3 => models/v3}/images/snap/39.png (100%) rename {v3 => models/v3}/images/snap/4.png (100%) rename {v3 => models/v3}/images/snap/40.png (100%) rename {v3 => models/v3}/images/snap/41.png (100%) rename {v3 => models/v3}/images/snap/42.png (100%) rename {v3 => models/v3}/images/snap/43.png (100%) rename {v3 => models/v3}/images/snap/44.png (100%) rename {v3 => models/v3}/images/snap/45.png (100%) rename {v3 => models/v3}/images/snap/46.png (100%) rename {v3 => models/v3}/images/snap/47.png (100%) rename {v3 => models/v3}/images/snap/48.png (100%) rename {v3 => models/v3}/images/snap/49.png (100%) rename {v3 => models/v3}/images/snap/5.png (100%) rename {v3 => models/v3}/images/snap/50.png (100%) rename {v3 => models/v3}/images/snap/51.png (100%) rename {v3 => models/v3}/images/snap/52.png (100%) rename {v3 => models/v3}/images/snap/53.png (100%) rename {v3 => models/v3}/images/snap/54.png (100%) rename {v3 => models/v3}/images/snap/55.png (100%) rename {v3 => models/v3}/images/snap/56.png (100%) rename {v3 => models/v3}/images/snap/57.png (100%) rename {v3 => models/v3}/images/snap/58.png (100%) rename {v3 => models/v3}/images/snap/59.png (100%) rename {v3 => models/v3}/images/snap/6.png (100%) rename {v3 => models/v3}/images/snap/7.png (100%) rename {v3 => models/v3}/images/snap/8.png (100%) rename {v3 => models/v3}/images/snap/9.png (100%) rename {v3 => models/v3}/images/snap/movie.mp4 (100%) rename {v3 => models/v3}/images/stockflow_cv_trigger.png (100%) create mode 100644 models/v3/model/__pycache__/config.cpython-37.pyc rename {v3 => models/v3}/model/__pycache__/economyconfig.cpython-36.pyc (100%) rename {v3 => models/v3}/model/__pycache__/economyconfig.cpython-37.pyc (100%) rename {v3 => models/v3}/model/__pycache__/genesis_states.cpython-36.pyc (100%) create mode 100644 models/v3/model/__pycache__/genesis_states.cpython-37.pyc rename {v3 => models/v3}/model/__pycache__/partial_state_update_block.cpython-36.pyc (100%) create mode 100644 models/v3/model/__pycache__/partial_state_update_block.cpython-37.pyc rename {v3 => models/v3}/model/__pycache__/run.cpython-36.pyc (100%) create mode 100644 models/v3/model/__pycache__/run.cpython-37.pyc rename {v3 => models/v3}/model/config.py (98%) rename {v3 => models/v3}/model/genesis_states.py (50%) rename {v3 => models/v3}/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc (100%) create mode 100644 models/v3/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc rename {v3 => models/v3}/model/model/__pycache__/designed.cpython-36.pyc (100%) rename {v3 => models/v3}/model/model/__pycache__/designed.cpython-37.pyc (100%) rename {v3 => models/v3}/model/model/__pycache__/exogenousProcesses.cpython-36.pyc (100%) rename {v3 => models/v3}/model/model/__pycache__/exogenousProcesses.cpython-37.pyc (100%) rename {v3 => models/v3}/model/model/__pycache__/initialization.cpython-36.pyc (100%) rename {v3 => models/v3}/model/model/__pycache__/initialization.cpython-37.pyc (100%) rename {v3 => models/v3}/model/model/__pycache__/kpis.cpython-36.pyc (100%) rename {v3 => models/v3}/model/model/__pycache__/kpis.cpython-37.pyc (100%) rename {v3 => models/v3}/model/model/__pycache__/operatorentity.cpython-37.pyc (100%) rename {v3 => models/v3}/model/model/__pycache__/participants.cpython-36.pyc (100%) create mode 100644 models/v3/model/model/__pycache__/participants.cpython-37.pyc rename {v3 => models/v3}/model/model/__pycache__/proposals.cpython-36.pyc (100%) create mode 100644 models/v3/model/model/__pycache__/proposals.cpython-37.pyc rename {v3 => models/v3}/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc (100%) rename {v3 => models/v3}/model/model/__pycache__/supportingFunctions.cpython-37.pyc (100%) create mode 100644 models/v3/model/model/__pycache__/sys_params.cpython-37.pyc rename {v3 => models/v3}/model/model/__pycache__/system.cpython-36.pyc (100%) create mode 100644 models/v3/model/model/__pycache__/system.cpython-37.pyc rename {v3 => models/v3}/model/model/conviction_helper_functions.py (95%) rename {v3 => models/v3}/model/model/participants.py (86%) rename {v3 => models/v3}/model/model/proposals.py (95%) create mode 100644 models/v3/model/model/sys_params.py rename {v3 => models/v3}/model/model/system.py (78%) rename {v3 => models/v3}/model/partial_state_update_block.py (85%) rename {v3 => models/v3}/model/run.py (96%) delete mode 100644 v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb delete mode 100644 v3/Aragon_Conviction_Voting_Model.ipynb delete mode 100644 v3/images/snap/0.png delete mode 100644 v3/model/__pycache__/config.cpython-37.pyc delete mode 100644 v3/model/__pycache__/genesis_states.cpython-37.pyc delete mode 100644 v3/model/__pycache__/partial_state_update_block.cpython-37.pyc delete mode 100644 v3/model/__pycache__/run.cpython-37.pyc delete mode 100644 v3/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc delete mode 100644 v3/model/model/__pycache__/participants.cpython-37.pyc delete mode 100644 v3/model/model/__pycache__/proposals.cpython-37.pyc delete mode 100644 v3/model/model/__pycache__/sys_params.cpython-37.pyc delete mode 100644 v3/model/model/__pycache__/system.cpython-37.pyc delete mode 100644 v3/model/model/sys_params.py diff --git a/Alpha Explanation.ipynb b/Alpha Explanation.ipynb new file mode 100644 index 0000000..1e67d9b --- /dev/null +++ b/Alpha Explanation.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Trigger Function Explanation.ipynb b/Trigger Function Explanation.ipynb new file mode 100644 index 0000000..1e67d9b --- /dev/null +++ b/Trigger Function Explanation.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/v1/Aragon_Conviction_Voting_Model.ipynb b/models/v1/Aragon_Conviction_Voting_Model.ipynb similarity index 100% rename from v1/Aragon_Conviction_Voting_Model.ipynb rename to models/v1/Aragon_Conviction_Voting_Model.ipynb diff --git a/v1/images/Aragon_v1.png b/models/v1/images/Aragon_v1.png similarity index 100% rename from v1/images/Aragon_v1.png rename to models/v1/images/Aragon_v1.png diff --git a/v1/images/bipartite-cv-compute.png b/models/v1/images/bipartite-cv-compute.png similarity index 100% rename from v1/images/bipartite-cv-compute.png rename to models/v1/images/bipartite-cv-compute.png diff --git a/v1/images/stockflow-cv-trigger.png b/models/v1/images/stockflow-cv-trigger.png similarity index 100% rename from v1/images/stockflow-cv-trigger.png rename to models/v1/images/stockflow-cv-trigger.png diff --git a/v1/model/__pycache__/economyconfig.cpython-36.pyc b/models/v1/model/__pycache__/economyconfig.cpython-36.pyc similarity index 100% rename from v1/model/__pycache__/economyconfig.cpython-36.pyc rename to models/v1/model/__pycache__/economyconfig.cpython-36.pyc diff --git a/v1/model/__pycache__/economyconfig.cpython-37.pyc b/models/v1/model/__pycache__/economyconfig.cpython-37.pyc similarity index 100% rename from v1/model/__pycache__/economyconfig.cpython-37.pyc rename to models/v1/model/__pycache__/economyconfig.cpython-37.pyc diff --git a/v1/model/__pycache__/genesis_states.cpython-36.pyc b/models/v1/model/__pycache__/genesis_states.cpython-36.pyc similarity index 100% rename from v1/model/__pycache__/genesis_states.cpython-36.pyc rename to models/v1/model/__pycache__/genesis_states.cpython-36.pyc diff --git a/v1/model/__pycache__/genesis_states.cpython-37.pyc b/models/v1/model/__pycache__/genesis_states.cpython-37.pyc similarity index 100% rename from v1/model/__pycache__/genesis_states.cpython-37.pyc rename to models/v1/model/__pycache__/genesis_states.cpython-37.pyc diff --git a/v1/model/__pycache__/partial_state_update_block.cpython-36.pyc b/models/v1/model/__pycache__/partial_state_update_block.cpython-36.pyc similarity index 100% rename from v1/model/__pycache__/partial_state_update_block.cpython-36.pyc rename to models/v1/model/__pycache__/partial_state_update_block.cpython-36.pyc diff --git a/v1/model/__pycache__/partial_state_update_block.cpython-37.pyc b/models/v1/model/__pycache__/partial_state_update_block.cpython-37.pyc similarity index 100% rename from v1/model/__pycache__/partial_state_update_block.cpython-37.pyc rename to models/v1/model/__pycache__/partial_state_update_block.cpython-37.pyc diff --git a/v1/model/__pycache__/run.cpython-36.pyc b/models/v1/model/__pycache__/run.cpython-36.pyc similarity index 100% rename from v1/model/__pycache__/run.cpython-36.pyc rename to models/v1/model/__pycache__/run.cpython-36.pyc diff --git a/v1/model/__pycache__/run.cpython-37.pyc b/models/v1/model/__pycache__/run.cpython-37.pyc similarity index 100% rename from v1/model/__pycache__/run.cpython-37.pyc rename to models/v1/model/__pycache__/run.cpython-37.pyc diff --git a/v1/model/economyconfig.py b/models/v1/model/economyconfig.py similarity index 100% rename from v1/model/economyconfig.py rename to models/v1/model/economyconfig.py diff --git a/v1/model/genesis_states.py b/models/v1/model/genesis_states.py similarity index 100% rename from v1/model/genesis_states.py rename to models/v1/model/genesis_states.py diff --git a/v1/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc b/models/v1/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc similarity index 100% rename from v1/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc rename to models/v1/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc diff --git a/v1/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc b/models/v1/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc rename to models/v1/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc diff --git a/v1/model/model/__pycache__/designed.cpython-36.pyc b/models/v1/model/model/__pycache__/designed.cpython-36.pyc similarity index 100% rename from v1/model/model/__pycache__/designed.cpython-36.pyc rename to models/v1/model/model/__pycache__/designed.cpython-36.pyc diff --git a/v1/model/model/__pycache__/designed.cpython-37.pyc b/models/v1/model/model/__pycache__/designed.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/designed.cpython-37.pyc rename to models/v1/model/model/__pycache__/designed.cpython-37.pyc diff --git a/v1/model/model/__pycache__/exogenousProcesses.cpython-36.pyc b/models/v1/model/model/__pycache__/exogenousProcesses.cpython-36.pyc similarity index 100% rename from v1/model/model/__pycache__/exogenousProcesses.cpython-36.pyc rename to models/v1/model/model/__pycache__/exogenousProcesses.cpython-36.pyc diff --git a/v1/model/model/__pycache__/exogenousProcesses.cpython-37.pyc b/models/v1/model/model/__pycache__/exogenousProcesses.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/exogenousProcesses.cpython-37.pyc rename to models/v1/model/model/__pycache__/exogenousProcesses.cpython-37.pyc diff --git a/v1/model/model/__pycache__/initialization.cpython-36.pyc b/models/v1/model/model/__pycache__/initialization.cpython-36.pyc similarity index 100% rename from v1/model/model/__pycache__/initialization.cpython-36.pyc rename to models/v1/model/model/__pycache__/initialization.cpython-36.pyc diff --git a/v1/model/model/__pycache__/initialization.cpython-37.pyc b/models/v1/model/model/__pycache__/initialization.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/initialization.cpython-37.pyc rename to models/v1/model/model/__pycache__/initialization.cpython-37.pyc diff --git a/v1/model/model/__pycache__/kpis.cpython-36.pyc b/models/v1/model/model/__pycache__/kpis.cpython-36.pyc similarity index 100% rename from v1/model/model/__pycache__/kpis.cpython-36.pyc rename to models/v1/model/model/__pycache__/kpis.cpython-36.pyc diff --git a/v1/model/model/__pycache__/kpis.cpython-37.pyc b/models/v1/model/model/__pycache__/kpis.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/kpis.cpython-37.pyc rename to models/v1/model/model/__pycache__/kpis.cpython-37.pyc diff --git a/v1/model/model/__pycache__/operatorentity.cpython-37.pyc b/models/v1/model/model/__pycache__/operatorentity.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/operatorentity.cpython-37.pyc rename to models/v1/model/model/__pycache__/operatorentity.cpython-37.pyc diff --git a/v1/model/model/__pycache__/participants.cpython-36.pyc b/models/v1/model/model/__pycache__/participants.cpython-36.pyc similarity index 100% rename from v1/model/model/__pycache__/participants.cpython-36.pyc rename to models/v1/model/model/__pycache__/participants.cpython-36.pyc diff --git a/v1/model/model/__pycache__/participants.cpython-37.pyc b/models/v1/model/model/__pycache__/participants.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/participants.cpython-37.pyc rename to models/v1/model/model/__pycache__/participants.cpython-37.pyc diff --git a/v1/model/model/__pycache__/proposals.cpython-36.pyc b/models/v1/model/model/__pycache__/proposals.cpython-36.pyc similarity index 100% rename from v1/model/model/__pycache__/proposals.cpython-36.pyc rename to models/v1/model/model/__pycache__/proposals.cpython-36.pyc diff --git a/v1/model/model/__pycache__/proposals.cpython-37.pyc b/models/v1/model/model/__pycache__/proposals.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/proposals.cpython-37.pyc rename to models/v1/model/model/__pycache__/proposals.cpython-37.pyc diff --git a/v1/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc b/models/v1/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc rename to models/v1/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc diff --git a/v1/model/model/__pycache__/supportingFunctions.cpython-37.pyc b/models/v1/model/model/__pycache__/supportingFunctions.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/supportingFunctions.cpython-37.pyc rename to models/v1/model/model/__pycache__/supportingFunctions.cpython-37.pyc diff --git a/v1/model/model/__pycache__/system.cpython-36.pyc b/models/v1/model/model/__pycache__/system.cpython-36.pyc similarity index 100% rename from v1/model/model/__pycache__/system.cpython-36.pyc rename to models/v1/model/model/__pycache__/system.cpython-36.pyc diff --git a/v1/model/model/__pycache__/system.cpython-37.pyc b/models/v1/model/model/__pycache__/system.cpython-37.pyc similarity index 100% rename from v1/model/model/__pycache__/system.cpython-37.pyc rename to models/v1/model/model/__pycache__/system.cpython-37.pyc diff --git a/v1/model/model/conviction_helper_functions.py b/models/v1/model/model/conviction_helper_functions.py similarity index 100% rename from v1/model/model/conviction_helper_functions.py rename to models/v1/model/model/conviction_helper_functions.py diff --git a/v1/model/model/initialization.py b/models/v1/model/model/initialization.py similarity index 100% rename from v1/model/model/initialization.py rename to models/v1/model/model/initialization.py diff --git a/v1/model/model/participants.py b/models/v1/model/model/participants.py similarity index 100% rename from v1/model/model/participants.py rename to models/v1/model/model/participants.py diff --git a/v1/model/model/proposals.py b/models/v1/model/model/proposals.py similarity index 100% rename from v1/model/model/proposals.py rename to models/v1/model/model/proposals.py diff --git a/v1/model/model/system.py b/models/v1/model/model/system.py similarity index 100% rename from v1/model/model/system.py rename to models/v1/model/model/system.py diff --git a/v1/model/partial_state_update_block.py b/models/v1/model/partial_state_update_block.py similarity index 100% rename from v1/model/partial_state_update_block.py rename to models/v1/model/partial_state_update_block.py diff --git a/v1/model/run.py b/models/v1/model/run.py similarity index 100% rename from v1/model/run.py rename to models/v1/model/run.py diff --git a/v2 workshop-copy/.ipynb_checkpoints/Aragon Conviction Voting Model-checkpoint.ipynb b/models/v2 workshop-copy/.ipynb_checkpoints/Aragon Conviction Voting Model-checkpoint.ipynb similarity index 100% rename from v2 workshop-copy/.ipynb_checkpoints/Aragon Conviction Voting Model-checkpoint.ipynb rename to models/v2 workshop-copy/.ipynb_checkpoints/Aragon Conviction Voting Model-checkpoint.ipynb diff --git a/v2 workshop-copy/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb b/models/v2 workshop-copy/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb similarity index 100% rename from v2 workshop-copy/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb rename to models/v2 workshop-copy/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb diff --git a/v2 workshop-copy/Aragon_Conviction_Voting_Model.ipynb b/models/v2 workshop-copy/Aragon_Conviction_Voting_Model.ipynb similarity index 100% rename from v2 workshop-copy/Aragon_Conviction_Voting_Model.ipynb rename to models/v2 workshop-copy/Aragon_Conviction_Voting_Model.ipynb diff --git a/v2 workshop-copy/images/Aragon_v2.png b/models/v2 workshop-copy/images/Aragon_v2.png similarity index 100% rename from v2 workshop-copy/images/Aragon_v2.png rename to models/v2 workshop-copy/images/Aragon_v2.png diff --git a/v2 workshop-copy/images/bipartite_cv_compute.png b/models/v2 workshop-copy/images/bipartite_cv_compute.png similarity index 100% rename from v2 workshop-copy/images/bipartite_cv_compute.png rename to models/v2 workshop-copy/images/bipartite_cv_compute.png diff --git a/v2 workshop-copy/images/cv_bipartite.png b/models/v2 workshop-copy/images/cv_bipartite.png similarity index 100% rename from v2 workshop-copy/images/cv_bipartite.png rename to models/v2 workshop-copy/images/cv_bipartite.png diff --git a/v2 workshop-copy/images/snap/0.png b/models/v2 workshop-copy/images/snap/0.png similarity index 100% rename from v2 workshop-copy/images/snap/0.png rename to models/v2 workshop-copy/images/snap/0.png diff --git a/v2 workshop-copy/images/snap/1.png b/models/v2 workshop-copy/images/snap/1.png similarity index 100% rename from v2 workshop-copy/images/snap/1.png rename to models/v2 workshop-copy/images/snap/1.png diff --git a/v2 workshop-copy/images/snap/2.png b/models/v2 workshop-copy/images/snap/2.png similarity index 100% rename from v2 workshop-copy/images/snap/2.png rename to models/v2 workshop-copy/images/snap/2.png diff --git a/v2 workshop-copy/images/snap/3.png b/models/v2 workshop-copy/images/snap/3.png similarity index 100% rename from v2 workshop-copy/images/snap/3.png rename to models/v2 workshop-copy/images/snap/3.png diff --git a/v2 workshop-copy/images/snap/4.png b/models/v2 workshop-copy/images/snap/4.png similarity index 100% rename from v2 workshop-copy/images/snap/4.png rename to models/v2 workshop-copy/images/snap/4.png diff --git a/v2 workshop-copy/images/stockflow_cv_trigger.png b/models/v2 workshop-copy/images/stockflow_cv_trigger.png similarity index 100% rename from v2 workshop-copy/images/stockflow_cv_trigger.png rename to models/v2 workshop-copy/images/stockflow_cv_trigger.png diff --git a/v2 workshop-copy/model/__pycache__/economyconfig.cpython-36.pyc b/models/v2 workshop-copy/model/__pycache__/economyconfig.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/__pycache__/economyconfig.cpython-36.pyc rename to models/v2 workshop-copy/model/__pycache__/economyconfig.cpython-36.pyc diff --git a/v2 workshop-copy/model/__pycache__/economyconfig.cpython-37.pyc b/models/v2 workshop-copy/model/__pycache__/economyconfig.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/__pycache__/economyconfig.cpython-37.pyc rename to models/v2 workshop-copy/model/__pycache__/economyconfig.cpython-37.pyc diff --git a/v2 workshop-copy/model/__pycache__/genesis_states.cpython-36.pyc b/models/v2 workshop-copy/model/__pycache__/genesis_states.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/__pycache__/genesis_states.cpython-36.pyc rename to models/v2 workshop-copy/model/__pycache__/genesis_states.cpython-36.pyc diff --git a/v2 workshop-copy/model/__pycache__/genesis_states.cpython-37.pyc b/models/v2 workshop-copy/model/__pycache__/genesis_states.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/__pycache__/genesis_states.cpython-37.pyc rename to models/v2 workshop-copy/model/__pycache__/genesis_states.cpython-37.pyc diff --git a/v2 workshop-copy/model/__pycache__/partial_state_update_block.cpython-36.pyc b/models/v2 workshop-copy/model/__pycache__/partial_state_update_block.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/__pycache__/partial_state_update_block.cpython-36.pyc rename to models/v2 workshop-copy/model/__pycache__/partial_state_update_block.cpython-36.pyc diff --git a/v2 workshop-copy/model/__pycache__/partial_state_update_block.cpython-37.pyc b/models/v2 workshop-copy/model/__pycache__/partial_state_update_block.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/__pycache__/partial_state_update_block.cpython-37.pyc rename to models/v2 workshop-copy/model/__pycache__/partial_state_update_block.cpython-37.pyc diff --git a/v2 workshop-copy/model/__pycache__/run.cpython-36.pyc b/models/v2 workshop-copy/model/__pycache__/run.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/__pycache__/run.cpython-36.pyc rename to models/v2 workshop-copy/model/__pycache__/run.cpython-36.pyc diff --git a/v2 workshop-copy/model/__pycache__/run.cpython-37.pyc b/models/v2 workshop-copy/model/__pycache__/run.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/__pycache__/run.cpython-37.pyc rename to models/v2 workshop-copy/model/__pycache__/run.cpython-37.pyc diff --git a/v2 workshop-copy/model/economyconfig.py b/models/v2 workshop-copy/model/economyconfig.py similarity index 100% rename from v2 workshop-copy/model/economyconfig.py rename to models/v2 workshop-copy/model/economyconfig.py diff --git a/v2 workshop-copy/model/genesis_states.py b/models/v2 workshop-copy/model/genesis_states.py similarity index 100% rename from v2 workshop-copy/model/genesis_states.py rename to models/v2 workshop-copy/model/genesis_states.py diff --git a/v2 workshop-copy/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc b/models/v2 workshop-copy/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc rename to models/v2 workshop-copy/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/designed.cpython-36.pyc b/models/v2 workshop-copy/model/model/__pycache__/designed.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/designed.cpython-36.pyc rename to models/v2 workshop-copy/model/model/__pycache__/designed.cpython-36.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/designed.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/designed.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/designed.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/designed.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/exogenousProcesses.cpython-36.pyc b/models/v2 workshop-copy/model/model/__pycache__/exogenousProcesses.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/exogenousProcesses.cpython-36.pyc rename to models/v2 workshop-copy/model/model/__pycache__/exogenousProcesses.cpython-36.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/exogenousProcesses.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/exogenousProcesses.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/exogenousProcesses.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/exogenousProcesses.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/initialization.cpython-36.pyc b/models/v2 workshop-copy/model/model/__pycache__/initialization.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/initialization.cpython-36.pyc rename to models/v2 workshop-copy/model/model/__pycache__/initialization.cpython-36.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/initialization.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/initialization.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/initialization.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/initialization.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/kpis.cpython-36.pyc b/models/v2 workshop-copy/model/model/__pycache__/kpis.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/kpis.cpython-36.pyc rename to models/v2 workshop-copy/model/model/__pycache__/kpis.cpython-36.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/kpis.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/kpis.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/kpis.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/kpis.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/operatorentity.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/operatorentity.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/operatorentity.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/operatorentity.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/participants.cpython-36.pyc b/models/v2 workshop-copy/model/model/__pycache__/participants.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/participants.cpython-36.pyc rename to models/v2 workshop-copy/model/model/__pycache__/participants.cpython-36.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/participants.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/participants.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/participants.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/participants.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/proposals.cpython-36.pyc b/models/v2 workshop-copy/model/model/__pycache__/proposals.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/proposals.cpython-36.pyc rename to models/v2 workshop-copy/model/model/__pycache__/proposals.cpython-36.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/proposals.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/proposals.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/proposals.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/proposals.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/supportingFunctions.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/supportingFunctions.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/supportingFunctions.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/supportingFunctions.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/system.cpython-36.pyc b/models/v2 workshop-copy/model/model/__pycache__/system.cpython-36.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/system.cpython-36.pyc rename to models/v2 workshop-copy/model/model/__pycache__/system.cpython-36.pyc diff --git a/v2 workshop-copy/model/model/__pycache__/system.cpython-37.pyc b/models/v2 workshop-copy/model/model/__pycache__/system.cpython-37.pyc similarity index 100% rename from v2 workshop-copy/model/model/__pycache__/system.cpython-37.pyc rename to models/v2 workshop-copy/model/model/__pycache__/system.cpython-37.pyc diff --git a/v2 workshop-copy/model/model/conviction_helper_functions.py b/models/v2 workshop-copy/model/model/conviction_helper_functions.py similarity index 100% rename from v2 workshop-copy/model/model/conviction_helper_functions.py rename to models/v2 workshop-copy/model/model/conviction_helper_functions.py diff --git a/v2 workshop-copy/model/model/initialization.py b/models/v2 workshop-copy/model/model/initialization.py similarity index 100% rename from v2 workshop-copy/model/model/initialization.py rename to models/v2 workshop-copy/model/model/initialization.py diff --git a/v2 workshop-copy/model/model/participants.py b/models/v2 workshop-copy/model/model/participants.py similarity index 100% rename from v2 workshop-copy/model/model/participants.py rename to models/v2 workshop-copy/model/model/participants.py diff --git a/v2 workshop-copy/model/model/proposals.py b/models/v2 workshop-copy/model/model/proposals.py similarity index 100% rename from v2 workshop-copy/model/model/proposals.py rename to models/v2 workshop-copy/model/model/proposals.py diff --git a/v2 workshop-copy/model/model/system.py b/models/v2 workshop-copy/model/model/system.py similarity index 100% rename from v2 workshop-copy/model/model/system.py rename to models/v2 workshop-copy/model/model/system.py diff --git a/v2 workshop-copy/model/partial_state_update_block.py b/models/v2 workshop-copy/model/partial_state_update_block.py similarity index 100% rename from v2 workshop-copy/model/partial_state_update_block.py rename to models/v2 workshop-copy/model/partial_state_update_block.py diff --git a/v2 workshop-copy/model/run.py b/models/v2 workshop-copy/model/run.py similarity index 100% rename from v2 workshop-copy/model/run.py rename to models/v2 workshop-copy/model/run.py diff --git a/v2/.ipynb_checkpoints/Aragon Conviction Voting Model-checkpoint.ipynb b/models/v2/.ipynb_checkpoints/Aragon Conviction Voting Model-checkpoint.ipynb similarity index 100% rename from v2/.ipynb_checkpoints/Aragon Conviction Voting Model-checkpoint.ipynb rename to models/v2/.ipynb_checkpoints/Aragon Conviction Voting Model-checkpoint.ipynb diff --git a/v2/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb b/models/v2/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb similarity index 100% rename from v2/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb rename to models/v2/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb diff --git a/v2/Aragon_Conviction_Voting_Model.ipynb b/models/v2/Aragon_Conviction_Voting_Model.ipynb similarity index 100% rename from v2/Aragon_Conviction_Voting_Model.ipynb rename to models/v2/Aragon_Conviction_Voting_Model.ipynb diff --git a/v2/images/Aragon_v2.png b/models/v2/images/Aragon_v2.png similarity index 100% rename from v2/images/Aragon_v2.png rename to models/v2/images/Aragon_v2.png diff --git a/v2/images/bipartite_cv_compute.png b/models/v2/images/bipartite_cv_compute.png similarity index 100% rename from v2/images/bipartite_cv_compute.png rename to models/v2/images/bipartite_cv_compute.png diff --git a/v2/images/cv_bipartite.png b/models/v2/images/cv_bipartite.png similarity index 100% rename from v2/images/cv_bipartite.png rename to models/v2/images/cv_bipartite.png diff --git a/v2/images/snap/0.png b/models/v2/images/snap/0.png similarity index 100% rename from v2/images/snap/0.png rename to models/v2/images/snap/0.png diff --git a/v2/images/snap/1.png b/models/v2/images/snap/1.png similarity index 100% rename from v2/images/snap/1.png rename to models/v2/images/snap/1.png diff --git a/v2/images/snap/2.png b/models/v2/images/snap/2.png similarity index 100% rename from v2/images/snap/2.png rename to models/v2/images/snap/2.png diff --git a/v2/images/snap/3.png b/models/v2/images/snap/3.png similarity index 100% rename from v2/images/snap/3.png rename to models/v2/images/snap/3.png diff --git a/v2/images/snap/4.png b/models/v2/images/snap/4.png similarity index 100% rename from v2/images/snap/4.png rename to models/v2/images/snap/4.png diff --git a/v2/images/stockflow_cv_trigger.png b/models/v2/images/stockflow_cv_trigger.png similarity index 100% rename from v2/images/stockflow_cv_trigger.png rename to models/v2/images/stockflow_cv_trigger.png diff --git a/v2/model/__pycache__/economyconfig.cpython-36.pyc b/models/v2/model/__pycache__/economyconfig.cpython-36.pyc similarity index 100% rename from v2/model/__pycache__/economyconfig.cpython-36.pyc rename to models/v2/model/__pycache__/economyconfig.cpython-36.pyc diff --git a/v2/model/__pycache__/economyconfig.cpython-37.pyc b/models/v2/model/__pycache__/economyconfig.cpython-37.pyc similarity index 100% rename from v2/model/__pycache__/economyconfig.cpython-37.pyc rename to models/v2/model/__pycache__/economyconfig.cpython-37.pyc diff --git a/v2/model/__pycache__/genesis_states.cpython-36.pyc b/models/v2/model/__pycache__/genesis_states.cpython-36.pyc similarity index 100% rename from v2/model/__pycache__/genesis_states.cpython-36.pyc rename to models/v2/model/__pycache__/genesis_states.cpython-36.pyc diff --git a/v2/model/__pycache__/genesis_states.cpython-37.pyc b/models/v2/model/__pycache__/genesis_states.cpython-37.pyc similarity index 100% rename from v2/model/__pycache__/genesis_states.cpython-37.pyc rename to models/v2/model/__pycache__/genesis_states.cpython-37.pyc diff --git a/v2/model/__pycache__/partial_state_update_block.cpython-36.pyc b/models/v2/model/__pycache__/partial_state_update_block.cpython-36.pyc similarity index 100% rename from v2/model/__pycache__/partial_state_update_block.cpython-36.pyc rename to models/v2/model/__pycache__/partial_state_update_block.cpython-36.pyc diff --git a/v2/model/__pycache__/partial_state_update_block.cpython-37.pyc b/models/v2/model/__pycache__/partial_state_update_block.cpython-37.pyc similarity index 100% rename from v2/model/__pycache__/partial_state_update_block.cpython-37.pyc rename to models/v2/model/__pycache__/partial_state_update_block.cpython-37.pyc diff --git a/v2/model/__pycache__/run.cpython-36.pyc b/models/v2/model/__pycache__/run.cpython-36.pyc similarity index 100% rename from v2/model/__pycache__/run.cpython-36.pyc rename to models/v2/model/__pycache__/run.cpython-36.pyc diff --git a/v2/model/__pycache__/run.cpython-37.pyc b/models/v2/model/__pycache__/run.cpython-37.pyc similarity index 100% rename from v2/model/__pycache__/run.cpython-37.pyc rename to models/v2/model/__pycache__/run.cpython-37.pyc diff --git a/v2/model/economyconfig.py b/models/v2/model/economyconfig.py similarity index 100% rename from v2/model/economyconfig.py rename to models/v2/model/economyconfig.py diff --git a/v2/model/genesis_states.py b/models/v2/model/genesis_states.py similarity index 100% rename from v2/model/genesis_states.py rename to models/v2/model/genesis_states.py diff --git a/v2/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc b/models/v2/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc similarity index 100% rename from v2/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc rename to models/v2/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc diff --git a/v2/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc b/models/v2/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc rename to models/v2/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc diff --git a/v2/model/model/__pycache__/designed.cpython-36.pyc b/models/v2/model/model/__pycache__/designed.cpython-36.pyc similarity index 100% rename from v2/model/model/__pycache__/designed.cpython-36.pyc rename to models/v2/model/model/__pycache__/designed.cpython-36.pyc diff --git a/v2/model/model/__pycache__/designed.cpython-37.pyc b/models/v2/model/model/__pycache__/designed.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/designed.cpython-37.pyc rename to models/v2/model/model/__pycache__/designed.cpython-37.pyc diff --git a/v2/model/model/__pycache__/exogenousProcesses.cpython-36.pyc b/models/v2/model/model/__pycache__/exogenousProcesses.cpython-36.pyc similarity index 100% rename from v2/model/model/__pycache__/exogenousProcesses.cpython-36.pyc rename to models/v2/model/model/__pycache__/exogenousProcesses.cpython-36.pyc diff --git a/v2/model/model/__pycache__/exogenousProcesses.cpython-37.pyc b/models/v2/model/model/__pycache__/exogenousProcesses.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/exogenousProcesses.cpython-37.pyc rename to models/v2/model/model/__pycache__/exogenousProcesses.cpython-37.pyc diff --git a/v2/model/model/__pycache__/initialization.cpython-36.pyc b/models/v2/model/model/__pycache__/initialization.cpython-36.pyc similarity index 100% rename from v2/model/model/__pycache__/initialization.cpython-36.pyc rename to models/v2/model/model/__pycache__/initialization.cpython-36.pyc diff --git a/v2/model/model/__pycache__/initialization.cpython-37.pyc b/models/v2/model/model/__pycache__/initialization.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/initialization.cpython-37.pyc rename to models/v2/model/model/__pycache__/initialization.cpython-37.pyc diff --git a/v2/model/model/__pycache__/kpis.cpython-36.pyc b/models/v2/model/model/__pycache__/kpis.cpython-36.pyc similarity index 100% rename from v2/model/model/__pycache__/kpis.cpython-36.pyc rename to models/v2/model/model/__pycache__/kpis.cpython-36.pyc diff --git a/v2/model/model/__pycache__/kpis.cpython-37.pyc b/models/v2/model/model/__pycache__/kpis.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/kpis.cpython-37.pyc rename to models/v2/model/model/__pycache__/kpis.cpython-37.pyc diff --git a/v2/model/model/__pycache__/operatorentity.cpython-37.pyc b/models/v2/model/model/__pycache__/operatorentity.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/operatorentity.cpython-37.pyc rename to models/v2/model/model/__pycache__/operatorentity.cpython-37.pyc diff --git a/v2/model/model/__pycache__/participants.cpython-36.pyc b/models/v2/model/model/__pycache__/participants.cpython-36.pyc similarity index 100% rename from v2/model/model/__pycache__/participants.cpython-36.pyc rename to models/v2/model/model/__pycache__/participants.cpython-36.pyc diff --git a/v2/model/model/__pycache__/participants.cpython-37.pyc b/models/v2/model/model/__pycache__/participants.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/participants.cpython-37.pyc rename to models/v2/model/model/__pycache__/participants.cpython-37.pyc diff --git a/v2/model/model/__pycache__/proposals.cpython-36.pyc b/models/v2/model/model/__pycache__/proposals.cpython-36.pyc similarity index 100% rename from v2/model/model/__pycache__/proposals.cpython-36.pyc rename to models/v2/model/model/__pycache__/proposals.cpython-36.pyc diff --git a/v2/model/model/__pycache__/proposals.cpython-37.pyc b/models/v2/model/model/__pycache__/proposals.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/proposals.cpython-37.pyc rename to models/v2/model/model/__pycache__/proposals.cpython-37.pyc diff --git a/v2/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc b/models/v2/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc rename to models/v2/model/model/__pycache__/subpopulation_clusters.cpython-37.pyc diff --git a/v2/model/model/__pycache__/supportingFunctions.cpython-37.pyc b/models/v2/model/model/__pycache__/supportingFunctions.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/supportingFunctions.cpython-37.pyc rename to models/v2/model/model/__pycache__/supportingFunctions.cpython-37.pyc diff --git a/v2/model/model/__pycache__/system.cpython-36.pyc b/models/v2/model/model/__pycache__/system.cpython-36.pyc similarity index 100% rename from v2/model/model/__pycache__/system.cpython-36.pyc rename to models/v2/model/model/__pycache__/system.cpython-36.pyc diff --git a/v2/model/model/__pycache__/system.cpython-37.pyc b/models/v2/model/model/__pycache__/system.cpython-37.pyc similarity index 100% rename from v2/model/model/__pycache__/system.cpython-37.pyc rename to models/v2/model/model/__pycache__/system.cpython-37.pyc diff --git a/v2/model/model/conviction_helper_functions.py b/models/v2/model/model/conviction_helper_functions.py similarity index 100% rename from v2/model/model/conviction_helper_functions.py rename to models/v2/model/model/conviction_helper_functions.py diff --git a/v2/model/model/initialization.py b/models/v2/model/model/initialization.py similarity index 100% rename from v2/model/model/initialization.py rename to models/v2/model/model/initialization.py diff --git a/v2/model/model/participants.py b/models/v2/model/model/participants.py similarity index 100% rename from v2/model/model/participants.py rename to models/v2/model/model/participants.py diff --git a/v2/model/model/proposals.py b/models/v2/model/model/proposals.py similarity index 100% rename from v2/model/model/proposals.py rename to models/v2/model/model/proposals.py diff --git a/v2/model/model/system.py b/models/v2/model/model/system.py similarity index 100% rename from v2/model/model/system.py rename to models/v2/model/model/system.py diff --git a/v2/model/partial_state_update_block.py b/models/v2/model/partial_state_update_block.py similarity index 100% rename from v2/model/partial_state_update_block.py rename to models/v2/model/partial_state_update_block.py diff --git a/v2/model/run.py b/models/v2/model/run.py similarity index 100% rename from v2/model/run.py rename to models/v2/model/run.py diff --git a/models/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb b/models/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb new file mode 100644 index 0000000..ff1746e --- /dev/null +++ b/models/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb @@ -0,0 +1,1727 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Aragon Conviction Voting Model - Version 3\n", + "\n", + "New to this model are the following elements:\n", + "\n", + "* Adding the realism that not all participant tokens are being allocated to proposals.\n", + "* Refactored parameters and system initialization to make more readable and consistent.\n", + "* Making the distinction between effective and total supply.\n", + "* Refining alpha calculations to more accurately reflect the 1Hive implementation. Discussion of alpha and its relation to alpha in the contract and how it relates to the timescales\n", + "* Updated differential specification and write-up to respect new state variables\n", + "* Moved all unit denominations to honey.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TODO:\n", + "* Move params to M. Pass params into confiction helper functions. Update run time, review conviction3 code (Andrew)\n", + "* Update diff spec (Andrew)\n", + "* Effective supply: sum of the holding of all agents - state variable (Andrew, Z review)\n", + "* Actual supply - state the gets initialized but grows whenever a mint happens (Andrew, Z review)\n", + "* Denominate the model in honey (Andrew, Z review)\n", + "* Add for next steps, to add a uniswap instance. Natural next step, add a paragraph about how it is a next step (Jeff)\n", + "* Factor the trigger function out. Trigger function notebook and how alpha notebook. (Andrew structure, Z work)\n", + "* Update all write-up, README.MD (Jeff)\n", + "* Directory: (Andrew)\n", + "* README\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# An Introduction to Conviction Voting\n", + "\n", + "Conviction Voting is an approach to organizing a communities preferences into discrete decisions in the management of that communities resources. Strictly speaking conviction voting is less like voting and more like signal processing. Framing the approach and the initial algorithm design was done by Michael Zargham and published in a short research proposal [Social Sensor Fusion](https://github.com/BlockScience/conviction/blob/master/social-sensorfusion.pdf). This work is based on a dynamic resource allocation algorithm presented in Zargham's PhD Thesis.\n", + "\n", + "The work proceeded in collaboration with the Commons Stack, including expanding on the pythin implementation to makeup part of the Commons Simulator game. An implemention of Conviction Voting as a smart contract within the Aragon Framework was developed by 1hive.org and is currently being used for community decision making around allocations their community currency, Honey.\n", + "\n", + "\n", + "## The Word Problem\n", + "\n", + "Suppose a group of people want to coordinate to make a collective decision. Social dynamics such as discussions, signaling, and even changing ones mind based on feedback from others input play an important role in these processes. While the actual decision making process involves a lot of informal processes, in order to be fair the ultimate decision making process still requires a set of formal rules that the community collecively agrees to, which serves to functionally channel a plurality of preferences into a discrete outcomes. In our case we are interested in a procedure which supports asynchronous interactions, an provides visibility into likely outcomes prior to their resolution to serve as a driver of good faith, debate and healthy forms of coalition building. Furthermore, participations should be able to show support for multiple initiatives, and to vary the level of support shown. Participants a quantity of signaling power which may be fixed or variable, homogenous or heterogenous. For the purpose of this document, we'll focus on the case where the discrete decisions to be made are decisions to allocate funds from a shared funding pool towards projects of interest to the community.\n", + "\n", + "## Converting to a Math Problem\n", + "\n", + "Let's start taking these words and constructing a mathematical representation that supports a design that meets the description above. To start we need to define participants.\n", + "\n", + "### Participants\n", + "Let $\\mathcal{A}$ be the set of participants. Consider a participant $a\\in \\mathcal{A}$. Any participant $a$ has some capacity to participate in the voting process $h[a]$. In a fixed quantity, homogenous system $h[a] = h$ for all $a\\in \\mathcal{A}$ where $h$ is a constant. The access control process managing how one becomes a participant determines the total supply of \"votes\" $S = \\sum_{a\\in \\mathcal{A}} = n\\cdot h$ where the number of participants is $n = |\\mathcal{A}|$. In a smart contract setting, the set $\\mathcal{A}$ is a set of addresses, and $h[a]$ is a quantity of tokens held by each address $a\\in \\mathcal{A}$. \n", + "\n", + "### Proposals & Shares Resources\n", + "Next, we introduce the idea of proposals. Consider a proposal $i\\in \\mathcal{C}$. Any proposal $i$ is associated with a request for resources $r[i]$. Those requested resources would be allocated from a constrained pool of communal resources currently totaling $R$. The pool of resources may become depleted because when a proposal $i$ passes $R^+= R-r[i]$. Therefore it makes sense for us to consider what fraction of the shared resources are being request $\\mu_i = \\frac{r[i]}{R}$, which means that thre resource depletion from passing proposals can be bounded by requiring $\\mu_i < \\mu$ where $\\mu$ is a constant representing the maximum fraction of the shared resources which can be dispersed by any one proposal. In order for the system to be sustainable a source of new resources is required. In the case where $R$ is funding, new funding can come from revenues, donations, or in some DAO use cases minting tokens.\n", + "\n", + "### Participants Preferences for Proposals\n", + "\n", + "Most of the interesting information in this system is distributed amongst the participants and it manifests as preferences over the proposals. This can be thought of as a matrix $W\\in \\mathbb{R}^{n \\times m}$.\n", + "![Replace this later](https://i.imgur.com/vxKNtxi.png)\n", + "\n", + "These private hidden signals drive discussions and voting actions. Each participant individually decides how to allocate their votes across the available proposals. Participant $a$ supports proposal $i$ by setting $x[a,i]>0$ but they are limited by their capacity $\\sum_{k\\in \\mathcal{C}} x[a,k] \\le h[a]$. Assuming each participant chooses a subset of the proposals to support, a support graph is formed.\n", + "![](https://i.imgur.com/KRh8tKn.png)\n", + "\n", + "## Aggregating Information\n", + "\n", + "In order to break out of the synchronous voting model, a dynamical systems model of this system is introduced.\n", + "\n", + "### Participants Allocate Voting Power\n", + "![](https://i.imgur.com/DZRDwk6.png)\n", + "\n", + "### System Accounts Proposal Conviction\n", + "![](https://i.imgur.com/euAei5R.png)\n", + "\n", + "### Understanding Alpha\n", + "* https://www.desmos.com/calculator/x9uc6w72lm\n", + "* https://www.desmos.com/calculator/0lmtia9jql\n", + "\n", + "\n", + "## Converting Signals to Discrete Decisions\n", + "\n", + "Conviction as kinetic energy and Trigger function as required activation energy.\n", + "\n", + "### The Trigger Function\n", + "\n", + "https://www.desmos.com/calculator/yxklrjs5m3\n", + "\n", + "Below we show a sweep of the trigger function threshold:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", + " return false;\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%javascript\n", + "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", + " return false;\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'beta': 0.2,\n", + " 'rho': 0.0025,\n", + " 'alpha': 0.875,\n", + " 'gamma': 0.001,\n", + " 'sensitivity': 0.75,\n", + " 'tmin': 0,\n", + " 'min_supp': 1,\n", + " 'base_completion_rate': 45,\n", + " 'base_failure_rate': 180,\n", + " 'base_engagement_rate': 0.3,\n", + " 'lowest_affinity_to_support': 0.3}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cadCAD.configuration.utils import config_sim\n", + "from model.model.sys_params import * \n", + "\n", + "sim_config = config_sim({\n", + " 'N': 1,\n", + " 'T': range(60), #day \n", + " 'M': params,\n", + "})\n", + "sim_config[0]['M']" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "for reference: max conviction = 5.25318713934522in log10 units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ] + } + ], + "source": [ + "from model.model.conviction_helper_functions import *\n", + "from model.model.sys_params import initial_values \n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "\n", + "supply = initial_values['supply']\n", + "alpha = sim_config[0]['M']['alpha']\n", + "\n", + "mcv = supply/(1-alpha)\n", + "print('for reference: max conviction = '+str(np.log10(mcv))+'in log10 units')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'beta': 0.2,\n", + " 'rho': 0.0025,\n", + " 'alpha': 0.875,\n", + " 'gamma': 0.001,\n", + " 'sensitivity': 0.75,\n", + " 'tmin': 0,\n", + " 'min_supp': 1,\n", + " 'base_completion_rate': 45,\n", + " 'base_failure_rate': 180,\n", + " 'base_engagement_rate': 0.3,\n", + " 'lowest_affinity_to_support': 0.3}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_config[0]['M']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "supply_sweep = trigger_sweep('effective_supply',trigger_threshold, sim_config[0]['M'], supply)\n", + "alpha_sweep = trigger_sweep('alpha',trigger_threshold, sim_config[0]['M'], supply)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "trigger_grid(supply_sweep, alpha_sweep)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resolving Passed Proposals\n", + "\n", + "![](images/stockflow_cv_trigger.png)\n", + "\n", + "\n", + "## Social Systems Modeling\n", + "\n", + "Subjective, exploratory modeling of the social system interacting through the conviction voting algorithm.\n", + "\n", + "### Sentiment\n", + "\n", + "Global Sentiment -- the outside world appreciating the output of the community\n", + "Local Sentiment -- agents within the system feeling good about the community\n", + "\n", + "### Social Networks\n", + "\n", + "Preferences as mixing process (social influence)\n", + "\n", + "### Relationships between Proposals\n", + "\n", + "Some proposals are synergistic (passing one makes the other more desireable)\n", + "Some proposals are (parially) substitutable (passing one makes the other less desirable)\n", + "\n", + "### Notion of Honey supply\n", + "#### Total supply = $S$\n", + "#### Effective supply = $E$\n", + "#### Funding Pool = $F$\n", + "#### Other supply = $L$, effectively slack. Funds could be in cold storage, in liquidity pools or otherwise in any address not actively participating in conviction voting.\n", + "$$S = F + E + L$$ \n", + "\n", + "System has the right to do direct mints:\n", + "$$F^+ = F + minted$$\n", + "$$S^+ = S + minted$$\n", + "\n", + "\n", + "Arrival of new funds which come from outside:\n", + "$$L+ = L - donated$$\n", + "$$F+ = F + donated$$\n", + "The above assumes the donated tokens were not in use for voting\n", + "$$L+ = L + tokens$$ that haven't been used in voting recently\n", + "$$E+ = E - tokens$$ that haven't been used in voting recently\n", + "$$L+ = L - tokens$$ that come into use\n", + "$$E+ = E - tokens$$ that come into use\n", + "\n", + "Tokens in $L$ or $E$ are defined at the level of the account holding them.\n", + "\n", + "Total supply $S$ can be made a param and the state supply should be only $E$, effective supply." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## cadCAD Overview\n", + "\n", + "In the cadCAD simulation [methodology](https://community.cadcad.org/t/differential-specification-syntax-key/31), we operate on four layers: **Policies, Mechanisms, States**, and **Metrics**. Information flows do not have explicit feedback loop unless noted. **Policies** determine the inputs into the system dynamics, and can come from user input, observations from the exogenous environment, or algorithms. **Mechanisms** are functions that take the policy decisions and update the States to reflect the policy level changes. **States** are variables that represent the system quantities at the given point in time, and **Metrics** are computed from state variables to assess the health of the system. Metrics can often be thought of as KPIs, or Key Performance Indicators. \n", + "\n", + "At a more granular level, to setup a model, there are system conventions and configurations that must be [followed.](https://community.cadcad.org/t/introduction-to-simulation-configurations/34)\n", + "\n", + "The way to think of cadCAD modeling is analogous to machine learning pipelines which normally consist of multiple steps when training and running a deployed model. There is preprocessing, which includes segregating features between continuous and categorical, transforming or imputing data, and then instantiating, training, and running a machine learning model with specified hyperparameters. cadCAD modeling can be thought of in the same way as states, roughly translating into features, are fed into pipelines that have built-in logic to direct traffic between different mechanisms, such as scaling and imputation. Accuracy scores, ROC, etc. are analogous to the metrics that can be configured on a cadCAD model, specifying how well a given model is doing in meeting its objectives. The parameter sweeping capability of cadCAD can be thought of as a grid search, or way to find the optimal hyperparameters for a system by running through alternative scenarios. A/B style testing that cadCAD enables is used in the same way machine learning models are A/B tested, except out of the box, in providing a side by side comparison of muliple different models to compare and contrast performance. Utilizing the field of Systems Identification, dynamical systems models can be used to \"online learn\" by providing a feedback loop to generative system mechanisms. \n", + "\n", + "\n", + "## Differential Specification \n", + "![](images/Aragon_v2.png)\n", + "\n", + "## Schema of the states - UPDATE\n", + "The model consists of a temporal in memory graph database called *network* containing nodes of type **Participant** and type **Proposal**. Participants will have *holdings* and *sentiment* and Proposals will have *funds_required, status*(candidate or active), *conviction* Tthe model as three kinds of edges:\n", + "* (Participant, participant), we labeled this edge type \"influencer\" and it contains information about how the preferences and sentiment of one participant influence another \n", + "* (Proposal, Proposal), we labeled this edge type \"conflict\" and it contains information about how synergistic or anti-synergistic two proposals are; basically people are likely to support multiple things that have synergy (meaning once one is passed there is more utility from the other) but they are not likely to pass things that have antisynergy (meaning once one is passed there is less utility from the other).\n", + "* The edges between Participant and Proposal, which are described below.\n", + " \n", + "\n", + "Edges in the network go from nodes of type Participant to nodes of type Proposal with the edges having the key *type*, of which all will be set to *support*. Edges from participant $i$ to proposal $j$ will have the following additional characteristics:\n", + "* Each pairing (i,j) will have *affinity*, which determines how much $i$ likes or dislikes proposal $j$.\n", + "* Each participant $i$, assigns its $tokens$ over the edges (i,j) for all $j$ such that the summation of all $j$ such that ```Sum_j = network.edges[(i,j)]['tokens'] = network.nodes[i]['holdings']```. This value of tokens for participants on proposals must be less than or equal to the total number of tokens held by the participant.\n", + "* Each pairing (i,j) will have *conviction* local to that edge whose update at each timestep is computed using the value of *tokens* at that edge.\n", + "* Each proposal *j* will have a *conviction* which is equal to the sum of the conviction on its inbound edges: ```network.nodes[j]['conviction'] = Sum_i network.edges[(i,j)]['conviction']```. \n", + "\n", + "\n", + "The other state variables in the model are *funds*, which is a numpy floating point, and effective supply, as supply.\n", + "\n", + "The system consists of 100 time steps without a parameter sweep or monte carlo.\n", + "\n", + " \n", + "## Partial State Update Blocks - TODO: UPDATE\n", + "\n", + "Each partial state update block is kind of a like a phase in a phased based board game. Everyone decides what to do and it reconciles all decisions. One timestep is a full turn, with each block being a phase of a timestep or turn. We will walk through the individaul Partial State update blocks one by one below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "{\n", + "# system.py: \n", + "'policies': { \n", + " 'random': driving_process\n", + "},\n", + "'variables': {\n", + " 'network': update_network,\n", + " 'funds':increment_funds,\n", + "}\n", + "```\n", + "\n", + "To simulate the arrival of participants and proposal into the system, we have a driving process to represent the arrival of individual agents. We use a random uniform distribution generator, over [0, 1), to calculate the number of new participants. We then use an exponential distribution to calculate the particpant's tokens by using a loc of 0.0 and a scale of expected holdings, which is calculated by .1*supply/number of existing participants. We calculate the number of new proposals by \n", + "```\n", + "proposal_rate = 1/median_affinity * (1+total_funds_requested/funds)\n", + "rv2 = np.random.rand()\n", + "new_proposal = bool(rv2<1/proposal_rate)\n", + "```\n", + "The network state variable is updated to include the new participants and proposals, while the funds state variable is updated for the increase in system funds. \n", + "[To see the partial state update code, click here](model/model/system.py)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "{\n", + " # participants.py \n", + " 'policies': {\n", + " 'completion': check_progress \n", + " },\n", + " 'variables': { \n", + " 'sentiment': update_sentiment_on_completion, #not completing projects decays sentiment, completing bumps it\n", + " 'network': complete_proposal\n", + " }\n", + "},\n", + "```\n", + "\n", + "In the next phase of the turn, [to see the logic code, click here](model/model/participants.py), the *check_progress* behavior checks for the completion of previously funded proposals. The code calculates the completion and failure rates as follows:\n", + "\n", + "```\n", + "likelihood = 1.0/(base_completion_rate+np.log(grant_size))\n", + "\n", + "failure_rate = 1.0/(base_failure_rate+np.log(grant_size))\n", + "if np.random.rand() < likelihood:\n", + " completed.append(j)\n", + "elif np.random.rand() < failure_rate:\n", + " failed.append(j)\n", + "```\n", + "With the base_completion_rate being 100 and the base_failure_rate as 200. \n", + "\n", + "The mechanism then updates the respective *network* nodes and updates the sentiment variable on proposal completion. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + " # proposals.py\n", + " 'policies': {\n", + " 'release': trigger_function \n", + " },\n", + " 'variables': { \n", + " 'funds': decrement_funds, \n", + " 'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment\n", + " 'network': update_proposals \n", + " }\n", + "},\n", + " ```\n", + " \n", + "The [trigger release function](model/model/proposals.py) checks to see if each proposal passes or not. If a proposal passes, funds are decremented by the amount of the proposal, while the proposal's status is changed in the network object." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "{ \n", + " # participants.py\n", + " 'policies': { \n", + " 'participants_act': participants_decisions\n", + " },\n", + " 'variables': {\n", + " 'network': update_tokens \n", + " }\n", + "}\n", + "```\n", + "\n", + "The Participants decide based on their affinity if which proposals they would like to support,[to see the logic code, click here](model/model/participants.py). Proposals that participants have high affinity for receive more support and pledged tokens than proposals with lower affinity and sentiment. We then update everyone's holdings and their conviction for each proposal.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model next steps\n", + "\n", + "The the model described above is the third iteration model that covers the core mechanisms of the Aragon Conviction Voting model. Below are next additional dynamics we can attend to enrich the model, and provide workstreams for subsequent iterations of this lab notebook.\n", + "\n", + "* Mixing of token holdings among participants\n", + "* Departure of participants\n", + "* Proposals which are good or no good together\n", + "* Affects of outcomes on sentiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration\n", + "Let's factor out into its own notebook where we review the config object and its partial state update blocks." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from model import config" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# pull out configurations to illustrate\n", + "sim_config,genesis_states,seeds,partial_state_update_blocks = config.get_configs()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'N': 1,\n", + " 'T': range(0, 60),\n", + " 'M': {'beta': 0.2,\n", + " 'rho': 0.0025,\n", + " 'alpha': 0.875,\n", + " 'gamma': 0.001,\n", + " 'sensitivity': 0.75,\n", + " 'tmin': 0,\n", + " 'min_supp': 1,\n", + " 'base_completion_rate': 45,\n", + " 'base_failure_rate': 180,\n", + " 'base_engagement_rate': 0.3,\n", + " 'lowest_affinity_to_support': 0.3},\n", + " 'subset_id': 0,\n", + " 'subset_window': deque([0, None]),\n", + " 'simulation_id': 0,\n", + " 'run_id': 0}]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_config" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'policies': {'random': },\n", + " 'variables': {'network': ,\n", + " 'effective_supply': }},\n", + " {'policies': {'random': },\n", + " 'variables': {'total_supply': ,\n", + " 'funds': }},\n", + " {'policies': {'completion': },\n", + " 'variables': {'sentiment': ,\n", + " 'network': }},\n", + " {'policies': {'release': },\n", + " 'variables': {'funds': ,\n", + " 'sentiment': ,\n", + " 'network': }},\n", + " {'policies': {'participants_act': },\n", + " 'variables': {'network': }}]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "partial_state_update_blocks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialization\n", + "To create the genesis_states, we create our in-memory graph database within networkx. \n", + "\n", + "\n", + "### Parameters\n", + "\n", + "Initial values are the starting values for the simulation and sys_params are global hyperparameters for the simulation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'initial_sentiment': 0.6,\n", + " 'n': 30,\n", + " 'm': 7,\n", + " 'initial_funds': 4867.21,\n", + " 'supply': 22392.22}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from model.model.sys_params import initial_values \n", + "\n", + "initial_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$n$ is initial participants, whereas $m$ is initial proposals" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'beta': 0.2,\n", + " 'rho': 0.0025,\n", + " 'alpha': 0.875,\n", + " 'gamma': 0.001,\n", + " 'sensitivity': 0.75,\n", + " 'tmin': 0,\n", + " 'min_supp': 1,\n", + " 'base_completion_rate': 45,\n", + " 'base_failure_rate': 180,\n", + " 'base_engagement_rate': 0.3,\n", + " 'lowest_affinity_to_support': 0.3}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_config[0]['M']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* $\\alpha$ : 0.875 The decay rate for previously accumulated conviction\n", + "* $\\beta$ = .2 Upper bound on share of funds dispersed in the example Trigger Function\n", + "* $\\rho$ = 0.002 Scale Parameter for the example Trigger Function\n", + "\n", + "* tmin = 7 unit days; minimum periods passed before a proposal can pass\n", + "* min_supp = 50 number of tokens that must be stake for a proposal to be a candidate" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# import libraries\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from model.model.conviction_helper_functions import * \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "network = initialize_network(initial_values['n'],initial_values['m'],\n", + " initial_values['initial_funds'],\n", + " initial_values['supply'],sim_config[0]['M'])\n", + "initial_funds = initial_values['initial_funds']\n", + "\n", + "genesis_states = { \n", + " 'network': network,\n", + " 'funds':initial_values['initial_funds'],\n", + " 'sentiment': initial_values['initial_sentiment'],\n", + " 'effective_supply': initial_values['supply']-initial_values['initial_funds'],\n", + " 'total_supply': initial_values['supply']\n", + "\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exploring the State Data Structure\n", + "\n", + "A graph is a type of temporal data structure that evolves over time. A graph $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$ consists of vertices or nodes, $\\mathcal{V} = \\{1...\\mathcal{V}\\}$ and is connected by edges $\\mathcal{E} \\subseteq \\mathcal{V} \\times \\mathcal{V}$.\n", + "\n", + "See *Schema of the states* above for more details\n", + "\n", + "\n", + "Let's explore!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n", + "proposals = get_nodes_by_type(network, 'proposal')\n", + "participants = get_nodes_by_type(network, 'participant')\n", + "supporters = get_edges_by_type(network, 'support')\n", + "influencers = get_edges_by_type(network, 'influence')\n", + "competitors = get_edges_by_type(network, 'conflict')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'type': 'participant',\n", + " 'holdings': 316.54596362304994,\n", + " 'sentiment': 0.1553685333489736}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#sample a participant\n", + "network.nodes[participants[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Count of Participants')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Let's look at the distribution of participant holdings at the start of the sim\n", + "plt.hist([ network.nodes[i]['holdings'] for i in participants])\n", + "plt.title('Histogram of Participants Token Holdings')\n", + "plt.xlabel('Amount of Honey')\n", + "plt.ylabel('Count of Participants')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Participants Social Network')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nx.draw_spring(network, nodelist = participants, edgelist=influencers)\n", + "plt.title('Participants Social Network')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'type': 'proposal',\n", + " 'conviction': 0,\n", + " 'status': 'candidate',\n", + " 'age': 0,\n", + " 'funds_requested': 2141.714746492425,\n", + " 'trigger': inf}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#lets look at proposals\n", + "network.nodes[proposals[0]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Proposals initially start without any conviction, and with the status of a candidate. If the proposal's amount of conviction is greater than it's trigger, then the proposal moves to active and it's funds requested are granted. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All initial proposal start with 0 conviction and state 'candidate'we can simply examine the amounts of funds requested" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "funds_array = np.array([ network.nodes[i]['funds_requested'] for i in proposals])\n", + "conviction_required = np.array([trigger_threshold(r, initial_funds, supply, alpha,sim_config[0]['M']) for r in funds_array])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Amount of Honey requested(as a Fraction of Funds available)')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAFACAYAAACx2ns2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3dd5hdVdn+8e9NAOlNggIhhBILKCJGUMQCguKLJIooVQVURI2g2MBXEVFfERXlh1giStdQBAwSRUXAgpCEIlIl0pIIUgQSQAKB5/fHWofsDHPOrBlm7zOZuT/Xta85u639nDMze529qiICMzMbuZbpdgBmZtZdzgjMzEY4ZwRmZiOcMwIzsxHOGYGZ2QjnjMDMbIRzRmBmNsI5IzAzG+GW7bRT0muBfYHXA+sC/wWuBy4ETo+Ih2uP0MzMaqV2PYsl/Rr4F/BLYBZwL7AC8CJge2BX4NiImNZMqGZmVodOGcHaEXF/x5MLjjEzs6GtbUawxEHShsD4iPi9pBWBZSNiQe3RmZlZ7fqsLJb0IeAc4Ed50xjg/DqDMjOz5pS0GvoY8DpgPkBE3AqsU2dQZmbWnJKMYGFEPNFakbQs4LGrzcyGiZKM4DJJnwdWlLQTcDZwQb1hmZlZU/qsLJa0DPAB4C2AgIuAE8Mz2piZDQtFrYbMzGz4atuzWNLf6VAXEBFb1BKRmZk1qlOHsg07nRgRd9YSkZmZNaq0Q9kLga1JTwgzI+KeugMzM7NmlHQo+yAwA9gN2B24QtIBdQdmZmbNKGk1dAuwbUQ8kNefD1weES9uID4zM6tZST+CB4DquEIL8jYzMxsGOrUaOjS/nA1cKemXpDqCScB1DcRmZmYN6DQxzar55z/z0vLL+sIxM7OmuUOZmdkI13GqSgBJo4HPApuTZigDICJ2qDEuMzNrSEll8RnAzcBGwJeBO4CZNcZkZmYNKmk+elVEvErSda1hJSTNjIhXNxKhmZnVqs+iIeDJ/PNuSbuQJrRfq76QzMysSSUZwVclrQ58CjgeWA34ZK1RmZlZY9xqyMxshOvUoex4Og9DfXAtEZmZWaM6tRqaBVzVYbGlkKRL80CCQ4KkF0j6o6QFkr7d7XgGm6SQtGm34+jNUPtbGKoknSzpq92Oo05tnwgi4pQmAxlJJN0BvAB4ilQZfzlwUETM6WZcAyHpZGBuRHxhgEkcCNwPrNbb9Kc5/b2BJ/JyFfDxiLh5gNcb8iTtB/wE+G9l88kRMbk7ES0maRxwO/Bo3nQ/8MOIOLpbMfVFUgDjI2J2t2MZqto+EUj6bv55gaRpPZfmQhy2do2IVYB1gX+TKuL7TVJJhX8tJI0ahGQ2BG7sYw7sY/JnNQa4Fzi5l1iU59ceLv4aEatUlq5nAj2skX8nuwNflLRTtwOygev0j3Na/vkt4Nu9LDYIIuJx4Bxgs9Y2SbtIukbSfElzJB1Z2TcuFzd8QNJdwB96S1fSJEnX5jT+KWnnyu4NJf0lF8f8VtLalfPOlnSPpIdzkc3mlX0nS/qBpOmSHgU+AOwDfFbSI5IuaBPLtpJm5jRnStq2lR7w/sr5O/bxWT0G/Ax4WT7/Uklfk/QX4DFg43bXqhz/dUkz8ufyS0lrVfZPlHSDpIfysS+t7PucpHn5M7tF0pvz9q0l/TWfc7ek70lavs3n8D+SbsxpzJP06U7vt5fz95P05x7bnil6yr+fEyRdmK9xpaRNKsfuJOnm/Nl8D1Bl36aSLsv77pd0ZklMETELuAHYspLWAZJukvSgpItUme2wZwz5mh/M+46UdHrl2Nbf+rJ5fXVJP8mf8zxJX219GWkXv6Q/5uT+lv/G9sjb357/Px6SdLmkLSrXfaWkq/NneCaVERWGrYjw0vBC6p29Y369EnAKcGpl/5uAl5My6i1ITwzvyPvGkSrxTwVWBlbsJf2tgYeBnXIa6wMvyfsuJQ0i+CJgxbx+dOXcA0gDDj4P+C5wbWXfyTnd1+V0V8jbvtrhva4FPAi8l1QUuVdef34lzU7nP7MfWIWUEfyp8l7uIg1/siypuK3TtS4F5pEykpWBXwCn530vIhV37AQsRxpWZTawPPBiYA6wXuV3sEl+/SrgNfl644CbgE9U4g9g0/z6buD1+fWawFZt3vN+wJ9LtvdI/2TSEPFb53jOAKbmfWuThpDfPb+/TwKLgA/m/T8H/rfye92uTWzj8jWXzeuvIWXC78zrk/Ln9tIcwxdI85eUxHBk6/fR5lrnAT/Kv7t1SBNmfbiv+KufUV5/JenJchtgFOnLyB2kv/nlgTtzbMvlWJ+kw9/ocFhKblrjSd9YbwRuay3dDnxpXvIf3SPAQ/mP7F/Ayzsc/13gO/l1659j4w7H/6h1fC/7LgW+UFn/KPCbNseuka+1el4/mUqGVdnW6Ub+XmBGj21/BfYrPP9k4PH8Wd0DTGPxTfhS4Kh+XOtSlsz0NiPVO4wCvgicVdm3DCnTeBOwab5x7Ags18fv9hPAeZX16o36LuDDpPqQTmnsR7pBPlRZXkNZRnBiZd//ADfn1+8DrqjsEzCXxTfhU4EpwJg+Ymv9/T1EqsMIUqlBqyn6r4EP9PgcHyMVAfYVw5G0yQhImfxCKl98SBn9JX3Fz7Mzgh8AX+lxzC3AG4E3kP4fVdl3OcM8IygpUz0pf3CLgO3zB356xzOsxDsiYg3St5fJwGVKc0MjaRtJl0i6T9LDwEGkb1NVnSqWN2DJocN7qs45/RjpmzaSRkk6WqkoaT4pw6LHtftbob0e6RtW1Z2kp5RS34qINSLihRExMSKq760aT8m15vTYtxzp/S1xbkQ8nY9dP1Il4ydIN6p7JU2VtB6ApBdJ+lUuTpsP/B/P/l21vIt0c74zF2O8tsN7viK/59ZyRYdjq3r93eb398x7j3SHq34WnyXdmGfk4rG+pqNdO6f9KVJmuVzeviFwXC5yeQj4T053/YIYOtkwX+PuSto/Ij0Z9Df+DYFPtdLJaW2Q41sPmJdja+n5NzXslGQEK0bExaQc8s6IOBLYpd6wRo6IeCoiziW1INoub/4Z6ZvvBhGxOvBDKuW5rVM7JDsH2KTD/nb2Jj3a7wisTvpGRo9r97xuXz0S/0X6x6saS/q2PRiq1y+51gY99j1JavmyxLmSlI+dBxARP4uI7fIxAXwjH/oD0qCM4yNiNeDzPPt3RU5jZkRMIt28zgfOKn6XyaOkosRWjC/sx7l3U3nvlffXiu2eiPhQRKxHemr5vvpo9pr/do8lPbF9NG+eQyquqWZiK0bE5X3F0PP9AdX3N4f0RLB2Jd3VImLzAcQ/B/hajxhXioif5xjXz7G1jO30OQwHJRnBQqXWGLdKmizpnSz+lmHPkZJJpDLjm/LmVYH/RMTjkrYm3aD74yfA/pLeLGkZSetLeknBeauS/tkeIP1D/l/BOf8GNu6wfzrwIkl7S1o2V9ZtBvyqIO3+KrnWvpI2k7QScBRwTkQ8Rbop75I/s+VI33QXApdLerGkHSQ9j3TT+y/wdE5vVWA+8Ej+jD/SW2CSlpe0j6TVI+LJfM7TvR3bwd+AzSVtKWkF0hNKqQvzubvlyteDqdxoJb1b0pi8+iApsyuN72hShf8KpC8thys3MsgVvO8uiQG4FniDpLFKw9oc3toREXcDvwW+LWm1/He9iaQ3FsTf82/0x8BB+clbklZWaqCxKqkocRFwsKTlJO1GqnMZ1koygkNIN4WDSRVj+5IqV+y5uUDSI6QbwteA90fEDXnfR4GjJC0AjqCf3xwjYgawP/AdUuXuZTz7m3JvTiU9Bs8j1QmVFEf8BNgsP2Kf30ssDwBvJ91YHyA9wr89Iu4vSLtfCq91Gqks/R5SsdzB+dxbSH/bx5OeEHYlNfF9glSJeHTefg/pG33rJvVpUka9gHSD6dTa5r3AHbkI6SBSi6v+vL9/kDKv3wO3An/ufMYS594PvDu/jwdIdX9/qRzyatKUtI+QnkYPiYjbCpO/kHTz/VBEnEd6Wpqa3+f1wNtKYoiI35E+v+tI/UV6fll4H6ky98Z8vXNIza/7iv9I4JT8N/qeSC2dPgR8L6czm1T/Qv5975bX/wPsAZxb+DkstUqGod4qIq5uKB6z2ki6lFQZeWK3Y7HEv5OhoeSJ4NtKbYK/IulltUdkZmaN6jMjiIjtSa2F7gN+JOnvkgY6nICZmQ0x/RqGWtLLSeWue0REr70nzcxs6dLnE4Gklyp1/f47qSLtctKYL2ZmNgyUVBb/FZgKnB0R/2okKjMza8xSN0PZ2muvHePGjet2GGZmS5Wrrrrq/ogY3du+PocwljQe+DqpY84zo/BFRKdORLUZN24cs2bN6salzcyWWpLaDpXhsYbMzEY4jzVkZjbClcxutcRYQ6ThBzzWkJnZMOGxhszMRrg+nwgiYmZ++QhpIDMzMxtGhtNk32ZmNgDOCMzMRjhnBGZmI1zJWEPH5BmBlpN0sdI8uvs2EZyZmdWvpPnoWyLis3mKyjtIs/f8EXcqM7N+GHfYhd0OYQl3HO3uUC0lRUOtzGIX0sBzD9cYj5mZNazkieBXkm4mTdj9EUmjSRN4m5nZMFAyQ9lhwLbAhIh4EngMmFR3YGZm1oy2TwSSdutlW3X13DoCMjOzZnUqGto1/1yH9ETwh7y+PWmWMmcEZmbDQNuMICL2B5D0W2CziLg7r68LnNxIdGZmVruSVkMbtDKB7N/A2JLEJe0s6RZJsyUd1sv+70i6Ni//kPRQYdxmZjZISloNXSzpIuDneX0P4Pd9nSRpFHACsBMwF5gpaVpE3Ng6JiI+WTn+48Ar+xG7mZkNgpLRRyfniuPX501TIuK8grS3BmZHxG0AkqaSWhvd2Ob4vYAvFaRrZmaDqOSJgIg4l/5XDq8PzKmszwW26e1ASRsCG7G4QtrMzBpSMtbQbpJulfSwpPmSFkiaP8hx7AmcExFPtYnhQEmzJM267777BvnSZmYjW0ll8THAxIhYPSJWi4hVI2K1gvPmARtU1sfkbb3Zk8V1EM8SEVMiYkJETBg9enTBpc3MrFRJRvDviLhpAGnPBMZL2kjS8qSb/bSeB0l6CbAm8NcBXMPMzJ6jkjqCWZLOBM4HFrY25nqDtiJiUZ7s/iJgFPDTiLhB0lHArIhoZQp7AlMjIgb0DszM7DkpyQhWI40v9JbKtqCg8jgipgPTe2w7osf6kQUxmJlZTUqaj3rCejOzYazPjEDSSaQngCVExAG1RGRmZo0qmo+g8noF4J3Av+oJx8zMmlZSNPSL6rqknwN/ri0iMzNrVEnz0Z7Gk4amNjOzYaCkjmABS9YR3AN8rraIzMysUZ1mKFs2IhZFxKpNBmRmZs3qVDQ0o/VC0vENxGJmZl3QKSOoTlD8uroDMTOz7uiUEXjIBzOzEaBTZfFLJF1HejLYJL8mr0dEbFF7dGZmVrtOGcFLG4vCzMy6pm1GEBF3NhmImZl1x0A6lJmZ2TDijMDMbIRrmxFIujj//EZz4ZiZWdM6VRavK2lbYKKkqSzZr4CIuLrWyMzMrBGdMoIjgC+SJp0/tse+AHaoKygzM2tOp1ZD5wDnSPpiRHylwZjMzKxBfVYWR8RXJE2U9K28vL00cUk7S7pF0mxJh7U55j2SbpR0g6Sf9Sd4MzN77kqGof46sDVwRt50iKRtI+LzfZw3CjgB2AmYC8yUNC0ibqwcMx44HHhdRDwoyfMcmJk1rGSqyl2ALSPiaQBJpwDXAB0zAlLmMTsibsvnTQUmATdWjvkQcEJEPAgQEff2L3wzM3uuSvsRrFF5vXrhOesDcyrrc/O2qhcBL5L0F0lXSNq5MG0zMxskJU8EXweukXQJqQnpG4Bey/sHeP3xwJtIrZP+KOnlEfFQ9SBJBwIHAowdO3aQLm1mZlBWWfxz4DXAucAvgNdGxJkFac8DNqisj8nbquYC0yLiyYi4HfgHKWPoGcOUiJgQERNGjx5dcGkzMytVVDQUEXdHxLS83FOY9kxgvKSNJC0P7AlM63HM+aSnASStTSoquq0wfTMzGwS1jTUUEYuAycBFwE3AWRFxg6SjJE3Mh10EPCDpRuAS4DMR8UBdMZmZ2bOV1BEMWERMB6b32HZE5XUAh+bFzMy6oCgjyH0CXlA9PiLuqisoMzNrTkmHso8DXwL+DTydNwfgqSrNzIaBkieCQ4AXu+zezGx4KqksngM8XHcgZmbWHSVPBLcBl0q6EFjY2hgRPYemNjOzpVBJRnBXXpbPi5mZDSN9ZgQR8WUASavk9UfqDsrMzJrTZx2BpJdJuga4AbhB0lWSNq8/NDMza0JJZfEU4NCI2DAiNgQ+Bfy43rDMzKwpJRnByhFxSWslIi4FVq4tIjMza1RRqyFJXwROy+v74oHhzMyGjZInggOA0aRhqM/Nrw+oMygzM2tOSauhB4GDG4jFzMy6oG1GIOm7EfEJSReQxhZaQkRM7OU0MzNbynR6ImjVCXyriUDMzKw72mYEEXFVfrllRBxX3SfpEOCyOgOzpde4wy7sdghLuOPoXbodgtmQVlJZ/P5etu03yHGYmVmXdKoj2AvYG9hIUnWu4VWB/9QdmJmZNaNTHcHlwN3A2sC3K9sXANfVGZSZmTWnbdFQRNyZexHvA1wZEZdFxGWkiejHlCQuaWdJt0iaLemwXvbvJ+k+Sdfm5YMDfB9mZjZAJXUEZ7F4ikqAp4Cz+zopz3N8AvA2YDNgL0mb9XLomRGxZV5OLIjHzMwGUUlGsGxEPNFaya9L5iXYGpgdEbflc6YCkwYWppmZ1aUkI7hP0jOdxyRNAu4vOG990jSXLXPztp7eJek6SedI2qAgXTMzG0QlGcFBwOcl3SVpDvA54MODdP0LgHERsQXwO+CU3g6SdKCkWZJm3XfffYN0aTMzg7Kxhv4JvGYAM5TNA6rf8MfkbdW0H6isnggc0yaGKaR5EZgwYcKzhrswM7OBKxmGGkm7AJsDK0gCICKO6uO0mcB4SRuRMoA9Sf0SqumuGxF359WJpBZJZmbWoD4zAkk/BFYCtid9a98dmNHXeRGxSNJk4CJgFPDTiLhB0lHArIiYBhyc6x8WkTqp7TfQN2JmZgNT8kSwbURsIem6iPiypG8Dvy5JPCKmA9N7bDui8vpw4PD+BGxmZoOrpLL48fzzMUnrAU8C69YXkpmZNankieACSWsA3wSuJs1N4MnrzcyGiY4ZgaRlgIsj4iHgF5J+BawQEQ83Ep2ZmdWuY9FQRDxNGiaitb7QmYCZ2fBSUkdwsaR3qdVu1MzMhpWSjODDpEHmFkqaL2mBpPk1x2VmZg3pNDHNayLiiohYtcmAzMysWZ2eCL7feiHprw3EYmZmXdApI6jWCaxQdyBmZtYdnZqPLiNpTVJm0Xr9TOYQEZ632MxsGOiUEawOXMXim//VlX0BbFxXUGZm1py2GUFEjGswDjMz65KiYaiHi3GHXdjtEJZwx9G7dDsEM7OifgRmZjaMOSMwMxvhiouGJK1DpRlpRNxVS0RmZtaoPp8IJE2UdCtwO3AZcAeFE9OYmdnQV1I09BXgNcA/ImIj4M3AFbVGZWZmjSnJCJ6MiAdIncqWiYhLgAk1x2VmZg0pyQgekrQK8EfgDEnHAY+WJC5pZ0m3SJot6bAOx71LUkhyBmNm1rCSjGAS8BjwSeA3wD+BXfs6SdIo0qQ2bwM2A/aStFkvx60KHAJcWR62mZkNlj5bDUVE69v/08Ap/Uh7a2B2RNwGIGkqKVO5scdxXwG+AXymH2mbmdkgqbMfwfrAnMr63LztGZK2AjaIiKHV5dfMbATpWocyScsAxwKfKjj2QEmzJM2677776g/OzGwE6VdGIGlNSVsUHj4P2KCyPiZva1kVeBlwqaQ7SE1Up/VWYRwRUyJiQkRMGD16dH9CNjOzPpR0KLtU0mqS1iINRf1jSccWpD0TGC9pI0nLA3sC01o7I+LhiFg7IsblkU6vACZGxKwBvRMzMxuQkieC1SNiPrAbcGpEbAPs2NdJEbEImAxcBNwEnBURN0g6StLE5xK0mZkNnpKxhpaVtC7wHuB/+5N4REwHpvfYdkSbY9/Un7TNzGxwlDwRHEX6Vj87ImZK2hi4td6wzMysKSX9CM4Gzq6s3wa8q86gzMysOX1mBJJWAD4AbM6Sw1AfUGNcZmbWkJKiodOAFwJvJQ1DPQZYUGdQZmbWnJLK4k0j4t2SJkXEKZJ+Bvyp7sAs8TzLZla3omGo88+HJL0MWB1Yp76QzMysSSVPBFMkrQl8gdQhbBXgi7VGZdawofTk5acua1pJq6ET88s/AhvXG46ZmTWtbdGQpH3zwHDt9m8iabt6wjIzs6Z0eiJ4PnCNpKuAq4D7SM1HNwXeCNwPtJ11zMzMlg5tM4KIOE7S94AdgNcBWwD/JY0b9N6IuKuZEM3MrE4d6wgi4ingd3kxM7NhqG1GIOl4INrtj4iDa4nIzMwa1akfwSxS3cAKwFakgeZuBbYElq8/NDMza0KnOoJTACR9BNguzy+ApB/insVmZsNGSc/iNYHVKuur5G1mZjYMlPQsPprUjPQSQMAbgCPrDMrMzJpT0rP4JEm/BrbJmz4XEffUG5aZmTWlZPJ6keYofkVE/BJYXtLWtUdmZmaNKKkj+D7wWmCvvL4AOKEkcUk7S7pF0mxJz+qFLOkgSX+XdK2kP0varDhyMzMbFCUZwTYR8THgcYCIeJCC5qOSRpEyjLcBmwF79XKj/1lEvDwitgSOAY7tT/BmZvbcFc1HkG/qASBpNPB0wXlbkya8vy0ingCmApOqB0TE/MrqynTowGZmZvUoaTX0/4DzgHUkfQ3YnbL5CNYH5lTW57K4wvkZkj4GHEp6ytihIF0zMxtEJa2GzsgjkL6Z1Hz0HRFx02AFEBEnACdI2ps0+c37ex4j6UDgQICxY8cO1qXNzIyyVkOnRcTNEXFCRHwvIm6SdFpB2vOADSrrY/K2dqYC7+htR0RMiYgJETFh9OjRBZc2M7NSJXUEm1dXcn3BqwrOmwmMl7SRpOWBPUlTXVbTGl9Z3YU0lpGZmTWo0+ijhwOfB1aU1KrUFfAEMKWvhCNikaTJwEXAKOCnEXGDpKOAWRExDZgsaUfgSeBBeikWMjOzenUadO7rwNclfT0iDh9I4hExHZjeY9sRldeHDCRdMzMbPCVFQ7+StDI8M4/xsZI2rDkuMzNrSElG8APgMUmvAD4F/BM4tdaozMysMSUZwaKICFJnsO/l5p6r1huWmZk1paRD2YJccfxe4PWSlgGWqzcsMzNrSskTwR7AQuCAPPz0GOCbtUZlZmaN6TMjyDf/XwDPy5vuJw05YWZmw0BJz+IPAecAP8qb1gfOrzMoMzNrTknR0MeA1wHzASLiVmCdOoMyM7PmlGQEC/Mw0gBIWhYPF21mNmyUZASXSWoNNbETcDZwQb1hmZlZU0oygsOA+4C/Ax8mDRnxhTqDMjOz5pTMR/A08OO8mJnZMNNnRiDpdnqpE4iIjWuJyMzMGlXSs3hC5fUKwLuBteoJx8zMmlbSoeyByjIvIr5LmkTGzMyGgZKioa0qq8uQnhBKniTMzGwpUHJD/3bl9SLgDuA9tURjZmaNK2k1tH0TgZiZWXeUFA0d2ml/RBw7eOGYmVnTSlsNvRqYltd3BWYAt/Z1oqSdgeNIk9efGBFH99h/KPBBUpHTfaShru8sjt7MrEbjDruw2yEs4Y6j62mnU5IRjAG2iogFAJKOBC6MiH07nSRpFHACsBMwF5gpaVpE3Fg57BpgQkQ8JukjwDGk+Q/MzKwhJUNMvAB4orL+RN7Wl62B2RFxWx60bippustnRMQlEfFYXr2ClOmYmVmDSp4ITgVmSGpNRvMO4JSC89YH5lTW5wLbdDj+A8CvC9I1M7NBVNJq6GuSfg28Pm/aPyKuGcwgJO1Lqot4Y5v9BwIHAowdO3YwL21mNuKVFA0BrATMj4jjgLmSNio4Zx6wQWV9TN62BEk7Av8LTIyIhb0lFBFTImJCREwYPXp0YchmZlaiZKrKLwGfAw7Pm5YDTi9IeyYwXtJGkpYH9mRxy6NW2q8kTYE5MSLu7U/gZmY2OEqeCN4JTAQeBYiIfwGr9nVSRCwCJgMXATcBZ0XEDZKOkjQxH/ZNYBXgbEnXSprWJjkzM6tJSWXxExERkgJA0sqliUfEdNJENtVtR1Re71ialpmZ1aPkieAsST8C1pD0IeD3eJIaM7Nho+MTgSQBZwIvAeYDLwaOiIjfNRCbmbUxUnq8WjM6ZgS5SGh6RLwc8M3fzGwYKikaulrSq2uPxMzMuqKksngbYB9Jd5JaDon0sLBFrZGZmVkjSjKCt9YehZmZdU3JEBMeFtrMbBgrHWLCzMyGKWcEZmYjXMlYQx+XtGYTwZiZWfNKJ6aZKeksSTvnTmZmZjZM9JkRRMQXgPHAT4D9gFsl/Z+kTWqOzczMGlBURxARAdyTl0XAmsA5ko6pMTYzM2tAn81HJR0CvA+4HzgR+ExEPClpGeBW4LP1hmhmZnUq6VC2FrBbz/4EEfG0pLfXE5aZmTWlpI7gS8AGkvYHkDS6NVVlRNxUc3xmZlazOqeqNDOzpUBtU1WamdnSoSQjeCK3Gur3VJVmZjb01TpVZe6Adouk2ZIO62X/GyRdLWmRpN37F7qZmQ2GktFHvyVpJ/o5VaWkUcAJwE7AXFLv5GkRcWPlsLtIndQ+PYDYzcxsEJQ0HyXf+Ps7VeXWwOyIuA1A0lRgEvBMRhARd+R9T/czbTMzGyQlrYZ2k3SrpIclzZe0QNL8grTXB+ZU1ufmbWZmNoSUPBEcA+zazT4Dkg4EDgQYO3Zst8IwMxuWSiqL/z3ATGAesEFlfUze1m8RMSUiJkTEhNGjRw8kCTMza6PkiWCWpDOB84GFrY0RcW4f580ExudeyPOAPYG9BxqomZnVoyQjWA14DHhLZVsAHTOCiFgkaTJwETAK+GlE3CDpKGBWREyT9GrgPNJoprtK+nJEbD6QN2JmZgNT0nx0/4EmHhHTgek9th1ReT2TVGRkZmZdUtJqaIyk8yTdm5dfSPLN28xsmCipLD4JmAasl5cL8jYzMxsGSjKC0RFxUkQsysvJgJvumJkNEyUZwQOS9pU0Ki/7Ag/UHZiZmTWjJCM4AHv5bTUAAAv4SURBVHgPab7iu4HdgQFXIJuZ2dBS0mroTtJ8BGZmNgy1zQgkHU+eg6A3EXFwLRGZmVmjOj0RzKq8/jLwpZpjMTOzLmibEUTEKa3Xkj5RXTczs+GjpLIYOhQRmZnZ0q00IzAzs2GqU2XxAhY/CaxUmYxGQETEanUHZ2Zm9etUR7Bqk4GYmVl3uGjIzGyEc0ZgZjbCtc0IJD2vyUDMzKw7Oj0R/BVA0mkNxWJmZl3QqWfx8pL2BraVtFvPnQVzFpuZ2VKgU0ZwELAPsAawa499fc5ZbGZmS4dOzUf/DPxZ0qyI+MlAEpe0M3AcafL6EyPi6B77nwecCryKNMfBHhFxx0CuZWZmA1PSaug0SQdLOicvH5e0XF8nSRoFnAC8DdgM2EvSZj0O+wDwYERsCnwH+EY/4zczs+eoJCP4Pukb+/fzshXwg4LztgZmR8RtEfEEMBWY1OOYSUBrMLtzgDdLUkngZmY2OPqcmAZ4dUS8orL+B0l/KzhvfWBOZX0usE27YyJikaSHgecD9xekb2Zmg6AkI3hK0iYR8U8ASRsDT9Ub1pIkHQgcmFcfkXRLk9fvxdoMQmalZgvCHHP9lrZ4wTE3ZSjEvGG7HSUZwWeASyTdRhpwbkPK5iyeB2xQWR+Tt/V2zFxJywKrkyqNlxARU4ApBddsRK5An9DtOPrDMddvaYsXHHNThnrMJXMWXyxpPPDivOmWiFhYkPZMYLykjUg3/D2BvXscMw14P6nz2u7AHyLCcx+YmTWo5ImAfOO/rj8J5zL/ycBFpOajP42IGyQdBcyKiGnAT0itkmYD/yFlFmZm1qCijGCgImI6ML3HtiMqrx8H3l1nDDUZMsVU/eCY67e0xQuOuSlDOma5JMbMbGTrsx+BpItLtpmZ2dKp0zDUK0haC1hb0pqS1srLOFL7/xEhfw4zJP1N0g2Svpy3byTpSkmzJZ0pafluxwod452cYw1Ja3c7zqoOMZ8h6RZJ10v6aUmP9qZ0iPknedt1uSf+Kt2OtaVdzJX9/0/SI92Kr6cOn/HJkm6XdG1etux2rC0dYpakr0n6h6SbJB3c7ViXEBG9LsAhwO3AQuC2/Pp24G/A5HbnDbeF1GR2lfx6OeBK4DXAWcCeefsPgY90O9Y+4n0lMA64A1i723EWxvw/eZ+Anw+Vz7iPmFerHHMscFi3Y+0r5rw+ATgNeKTbcRZ8xicDu3c7vn7GvD9pXLVl8r51uh1rdWn7RBARx0XERsCnI2LjiNgoL6+IiO+1O2+4iaT1LWm5vASwA2lYDEjDZLyjC+E9S7t4I+KaGKID+nWIeXreF8AMUl+UIaFDzPMhfQMEViT9rQwJ7WLO44J9E/hs14LrRYf/vSGrQ8wfAY6KiKfzcfd2KcRe9VlHEBHHS9pW0t6S3tdamghuqJA0StK1wL3A74B/Ag9FxKJ8yFyGUHFZz3gj4spux9SXTjHnIqH3Ar/pVny9aRezpJOAe4CXAMd3McRnaRPzZGBaRNzd3eiercPfxddy8dt3NMRmU2wT8ybAHpJmSfp17ps1ZJRUFp8GfAvYDnh1XoZsD7k6RMRTEbEl6Rvp1qR/8CGrZ7ySXtbtmPrSR8zfB/4YEX/qTnS9axdzROwPrAfcBOzRxRCfpZeY30Bqwj2kMqyWNp/x4aT/wVcDawGf62KIz9Im5ucBj0fqXfxj4KfdjLGnktFHJwCvi4iPRsTH8zK0KjoaEhEPAZcArwXWyMNiQO/DZ3RdJd6dux1LqZ4xS/oSMBo4tJtxddLb5xwRT5FG3H1Xt+LqpBLz9sCmwGxJdwAr5Q6eQ0r1M46Iu3MRzELgJNKXsyGnx9/FXBZP5nUesEW34upNSUZwPfDCugMZqiSNlrRGfr0isBPpm94lpGExIA2T8cvuRLikNvHe3N2oOmsXs6QPAm8F9mqVrQ4VbWK+RdKmeZuAiQyhz75NzFdFxAsjYlxEjAMeizQ/SNd1+LtYN28TqW7u+u5FuaQO/3/nkzJdgDcC/+hOhL0r6Vm8NnCjpBmkFkQARMTE2qIaWtYFTskVassAZ0XEryTdCEyV9FXgGtJwGUNBu3gPJlUGvhC4TtL0iPhgNwOtaBfzIuBO4K/pf55zI+KoLsZZ9ayYgQuBP0lajdR65G+kSsKhotfPucsxddLu7+IPkkaTPuNrSdPqDhXtYv4zcIakTwKPAEPlfw8o6Fks6Y29bY+Iy2qJyMzMGuUhJszMRrg+i4YkLWBx293lSe1iH42I1eoMzMzMmlEyH8Gqrde5cmYSqaecmZkNAwMqGpJ0TUS8soZ4zMysYSVFQ7tVVpch9St4vLaIzMysUSX9CHatLG8FFpCKh8xqJempPLrk9ZLOlrRSt2NqySNg7t7L9qMk7djL9jdJGnBTTUmf77F+eeX1N/NIl9+UdNBIGwLGnju3GrIhS9IjEbFKfn0GqfPTsZX9y1bGe2o6tpOBX0XEOX0dm49/E2kAx7cP8HrPfBa97HsYWCv3Zu5vul37DG3oKBlraIyk8yTdm5dfSBoyo0DaiPEnYNP8zfpPkqaROjquIOkkSX+XdI2k7QEk7Sfpl5IulXRrHqqCvO/Q/JRxvaRP5G0rS7pQaRz56yXtkbcfIWlm3jYlN5hoq/qkIGlnSTdLuhrYrXLMykrzK8zIMU+qxHyupN/kmI/J248GVsxPR2fkbY/kn9OAVYCrJO0h6UhJn877NslpXZU/s5dUYvyhpCuBY57zb8aWeiU9i08CfsbiuYX3zdt2qisosyqlMZ3exuLRR7cCXhYRt0v6FGn035fnG91vJb0oH7c18DLgMWCmpAtJTaH3B7Yh9Uy9UtJlwMbAvyJil3zN1XMa32v1ZlYagPHtwAUFMa9AGlxsB2A2cGZl9/8Cf4iIA5SGI5gh6fd535akuSMWkoasOD4iDpM0OQ9ktoSImJifFrbM1z2ysnsKcFBE3CppG9LgfTvkfWOAbQfyFGHDT0kdweiIOCkiFuXlZNIgYGZ1W1FpON9ZwF0sHsZjRkTcnl9vB5wOEBE3k4akaGUEv4uIByLiv6QBv7bLy3kR8WgeN/5c4PXA34GdJH1D0usj4uGcxvZKM9H9nXQT3bww9pcAt0fErXk+hdMr+94CHJbf26XACsDYvO/iiHg4Ih4HbgQ2LLzeEpRmRtsWODtf50ek4Q9aznYmYC0lTwQPSNqXNEMUwF7AA/WFZPaM//b8FpxLZh4tPL9nBVjbCrGI+IekrUizon1VaV7uY0jfoidExJz8bXuFwmt3IuBdEXHLEhvTt/aFlU1PUfY/2ptlSHNmtJvGsfQztBGg5IngAOA9pIk27iaNuLl/nUGZ9cOfgH0AcpHQWKB1g91JaZ7tFUmjVP4lH/8OSStJWhl4J2mguPVII2+eTpqtaysW3/Tvz9+wn9VKqIObgXGSNsnre1X2XQR8vFXfIKmkT86T6seczZFmSrtd0rvzNSTpFaXn28hS0rP4TtJwumZD0feBH+Sim0XAfhGxMN9jZwC/IJWHnx4Rs+CZFj8z8vknRsQ1kt4KfFPS08CTpPmRH5L0Y9Iwx/cAM0uDiojHJR0IXCjpMVIG1Oql/xXgu6RRYJchzQXeV2uiKfn4qyNin8Iw9iF9Nl8gDQ0zlTQiqtkSSkYf3Qj4OGni82cyjhE0DLUthSTtRyrSmdztWMyGupLyx/NJlXQXAENqchAzM3vuSp4IroyIbRqKx8zMGlaSEewNjAd+y5IzlF1db2hmZtaEkqKhlwPvJbWhbhUNBYs7ppiZ2VKs5IlgNrBZRDzRTEhmZtakkn4E1wNr1B2ImZl1R0nR0BrAzZJmsmQdgZuPmpkNAyUZwZf6PsTMzJZW/Z6PQNJ2wF4R8bF6QjIzsyYVDWiVx0LZmzQU9e2kbvtmZjYMtM0I8gBee+XlftJ46oqI7RuKzczMGtC2aCgPvvUn4AMRMTtvuy0iNm4wPjMzq1mn5qO7kYadvkTSjyW9mTSOupmZDSMlHcpWBiaRioh2AE4lzfD02/rDMzOzuvWr1ZCkNUkVxntExJtri8rMzBrT7+ajZmY2vJQMMWFmZsOYMwIzsxHOGYGZ2QjnjMDMbIRzRmBmNsL9fz+e/IlOkYo4AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar( proposals, funds_array/initial_funds)\n", + "plt.title('Bar chart of Proposals Funds Requested')\n", + "plt.xlabel('Proposal identifier')\n", + "plt.ylabel('Amount of Honey requested(as a Fraction of Funds available)')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Amount of Conviction')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar( proposals, conviction_required)\n", + "plt.title('Bar chart of Proposals Conviction Required')\n", + "plt.xlabel('Proposal identifier')\n", + "plt.ylabel('Amount of Conviction')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conviction is a concept that arises in the edges between participants and proposals in the initial conditions there are no votes yet so we can look at that later however, the voting choices are driven by underlying affinities which we can see now." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 55.73999999999998, 'Participant_id')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m = len(proposals)\n", + "n = len(participants)\n", + "\n", + "affinities = np.empty((n,m))\n", + "for i_ind in range(n):\n", + " for j_ind in range(m):\n", + " i = participants[i_ind]\n", + " j = proposals[j_ind]\n", + " affinities[i_ind][j_ind] = network.edges[(i,j)]['affinity']\n", + "\n", + "dims = (20, 5)\n", + "fig, ax = plt.subplots(figsize=dims)\n", + "\n", + "sns.heatmap(affinities.T,\n", + " xticklabels=participants,\n", + " yticklabels=proposals,\n", + " square=True,\n", + " cbar=True,\n", + " cmap = plt.cm.RdYlGn,\n", + " ax=ax)\n", + "\n", + "plt.title('Affinities between participants and proposals')\n", + "plt.ylabel('Proposal_id')\n", + "plt.xlabel('Participant_id')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run simulation\n", + "\n", + "Now we will create the final system configuration, append the genesis states we created, and run our simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from cadCAD.configuration import Experiment\n", + "\n", + "# Create configuration\n", + "exp = Experiment()\n", + "\n", + "exp.append_configs(\n", + " sim_configs=sim_config,\n", + " initial_state=genesis_states,\n", + " seeds=seeds,\n", + " partial_state_update_blocks=partial_state_update_blocks\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ___________ ____\n", + " ________ __ ___/ / ____/ | / __ \\\n", + " / ___/ __` / __ / / / /| | / / / /\n", + "/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n", + "\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\n", + "by cadCAD\n", + "\n", + "Execution Mode: local_proc\n", + "Configuration Count: 2\n", + "Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (60, 11, 1, 5)\n", + "Execution Method: local_simulations\n", + "SimIDs : [0, 1]\n", + "SubsetIDs: [0, 0]\n", + "Ns : [0, 0]\n", + "ExpIDs : [0, 0]\n", + "Total execution time: 91.82s\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from model.model.conviction_helper_functions import *\n", + "from model import run\n", + "from cadCAD import configs\n", + "pd.options.display.float_format = '{:.2f}'.format\n", + "\n", + "%matplotlib inline\n", + "\n", + "rdf = run.run(configs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the simulation has run successfully, we perform some postprocessing to extract node and edge values from the network object and add as columns to the pandas dataframe. For the rdf, we take only the values at the last substep of each timestep in the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "df= run.postprocessing(rdf,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
networkfundssentimenteffective_supplytotal_supplysimulationsubsetrunsubsteptimestep...funds_requestedshare_of_funds_requestedshare_of_funds_requested_alltriggersconviction_share_of_triggerageage_allconviction_alltriggers_allconviction_share_of_trigger_all
5(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4889.600.6017525.0122414.6100151...[1595.0421987193204, 2494.344628102797, 2363.4...[0.3497660585741865][0.3262110345490068, 0.5101324230220914, 0.483...[inf][0.0][1][1, 1, 1, 1, 1, 1, 1][0.0, 0.0, 0.0, 0.0, 7.215686217212743, 0.0, 0.0][inf, inf, inf, inf, inf, 226995.283259412, inf][0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
10(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4912.020.6017525.9922437.0300152...[1595.0421987193204, 2494.344628102797, 2363.4...[0.34816999918790076, 0.019712554176434215][0.32472246191355914, 0.5078045766743577, 0.48...[inf, 10794.257352359262][0.0, 0.16591253044853824][2, 1][2, 2, 2, 2, 2, 2, 2, 1][0.0, 0.0, 0.0, 0.0, 2179.0300378288057, 0.0, ...[nan, nan, nan, nan, inf, nan, nan, 10794.2573...[nan, nan, nan, nan, 0.0, nan, nan, 0.16591253...
15(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4934.450.6017525.9922459.4600153...[1595.0421987193204, 2494.344628102797, 2363.4...[0.34658686560572693, 0.019622920932386982, 0....[0.32324594459288075, 0.5054955825611662, 0.47...[inf, 10784.039844676328, 10038.988431151754][0.0, 0.16767410493517743, 0.0676098125520172][3, 2, 1][3, 3, 3, 3, 3, 3, 3, 2, 1][0.0, 0.0, 0.0, 0.0, 5890.347248494404, 0.0, 0...[nan, nan, nan, nan, inf, nan, nan, 10784.0398...[nan, nan, nan, nan, 0.0, nan, nan, 0.16767410...
20(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4956.910.6017525.9922481.9200154...[1595.0421987193204, 2494.344628102797, 2363.4...[0.3450165022212967, 0.019534010706450017, 0.0...[0.32178133745992704, 0.5032052137312398, 0.47...[inf, 10773.32486563405, 10032.570509508532, 1...[0.0, 0.29018493662687905, 0.12684949336988674...[4, 3, 2, 1][4, 4, 4, 4, 4, 4, 4, 3, 2, 1, 1][0.0, 0.0, 0.0, 0.0, 7518.415456879907, 0.0, 0...[nan, nan, nan, nan, inf, nan, nan, 10773.3248...[nan, nan, nan, nan, 0.0, nan, nan, 0.29018493...
25(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4979.400.6017525.9922504.4100155...[1595.0421987193204, 2494.344628102797, 2363.4...[0.3434587559158705, 0.01944581482940582, 0.01...[0.3203284977077525, 0.5009332468614979, 0.474...[inf, 10762.71208704474, 10026.210435699022, 1...[0.0, 0.3383280289679279, 0.19857705818729582,...[5, 4, 3, 2, 1, 1][5, 5, 5, 5, 5, 5, 5, 4, 3, 2, 2, 1, 1][0.0, 0.0, 0.0, 0.0, 8718.352575520581, 0.0, 0...[nan, nan, nan, nan, inf, nan, nan, 10762.7120...[nan, nan, nan, nan, 0.0, nan, nan, 0.33832802...
\n", + "

5 rows × 33 columns

\n", + "
" + ], + "text/plain": [ + " network funds sentiment \\\n", + "5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4889.60 0.60 \n", + "10 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4912.02 0.60 \n", + "15 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4934.45 0.60 \n", + "20 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4956.91 0.60 \n", + "25 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4979.40 0.60 \n", + "\n", + " effective_supply total_supply simulation subset run substep \\\n", + "5 17525.01 22414.61 0 0 1 5 \n", + "10 17525.99 22437.03 0 0 1 5 \n", + "15 17525.99 22459.46 0 0 1 5 \n", + "20 17525.99 22481.92 0 0 1 5 \n", + "25 17525.99 22504.41 0 0 1 5 \n", + "\n", + " timestep ... funds_requested \\\n", + "5 1 ... [1595.0421987193204, 2494.344628102797, 2363.4... \n", + "10 2 ... [1595.0421987193204, 2494.344628102797, 2363.4... \n", + "15 3 ... [1595.0421987193204, 2494.344628102797, 2363.4... \n", + "20 4 ... [1595.0421987193204, 2494.344628102797, 2363.4... \n", + "25 5 ... [1595.0421987193204, 2494.344628102797, 2363.4... \n", + "\n", + " share_of_funds_requested \\\n", + "5 [0.3497660585741865] \n", + "10 [0.34816999918790076, 0.019712554176434215] \n", + "15 [0.34658686560572693, 0.019622920932386982, 0.... \n", + "20 [0.3450165022212967, 0.019534010706450017, 0.0... \n", + "25 [0.3434587559158705, 0.01944581482940582, 0.01... \n", + "\n", + " share_of_funds_requested_all \\\n", + "5 [0.3262110345490068, 0.5101324230220914, 0.483... \n", + "10 [0.32472246191355914, 0.5078045766743577, 0.48... \n", + "15 [0.32324594459288075, 0.5054955825611662, 0.47... \n", + "20 [0.32178133745992704, 0.5032052137312398, 0.47... \n", + "25 [0.3203284977077525, 0.5009332468614979, 0.474... \n", + "\n", + " triggers \\\n", + "5 [inf] \n", + "10 [inf, 10794.257352359262] \n", + "15 [inf, 10784.039844676328, 10038.988431151754] \n", + "20 [inf, 10773.32486563405, 10032.570509508532, 1... \n", + "25 [inf, 10762.71208704474, 10026.210435699022, 1... \n", + "\n", + " conviction_share_of_trigger age \\\n", + "5 [0.0] [1] \n", + "10 [0.0, 0.16591253044853824] [2, 1] \n", + "15 [0.0, 0.16767410493517743, 0.0676098125520172] [3, 2, 1] \n", + "20 [0.0, 0.29018493662687905, 0.12684949336988674... [4, 3, 2, 1] \n", + "25 [0.0, 0.3383280289679279, 0.19857705818729582,... [5, 4, 3, 2, 1, 1] \n", + "\n", + " age_all \\\n", + "5 [1, 1, 1, 1, 1, 1, 1] \n", + "10 [2, 2, 2, 2, 2, 2, 2, 1] \n", + "15 [3, 3, 3, 3, 3, 3, 3, 2, 1] \n", + "20 [4, 4, 4, 4, 4, 4, 4, 3, 2, 1, 1] \n", + "25 [5, 5, 5, 5, 5, 5, 5, 4, 3, 2, 2, 1, 1] \n", + "\n", + " conviction_all \\\n", + "5 [0.0, 0.0, 0.0, 0.0, 7.215686217212743, 0.0, 0.0] \n", + "10 [0.0, 0.0, 0.0, 0.0, 2179.0300378288057, 0.0, ... \n", + "15 [0.0, 0.0, 0.0, 0.0, 5890.347248494404, 0.0, 0... \n", + "20 [0.0, 0.0, 0.0, 0.0, 7518.415456879907, 0.0, 0... \n", + "25 [0.0, 0.0, 0.0, 0.0, 8718.352575520581, 0.0, 0... \n", + "\n", + " triggers_all \\\n", + "5 [inf, inf, inf, inf, inf, 226995.283259412, inf] \n", + "10 [nan, nan, nan, nan, inf, nan, nan, 10794.2573... \n", + "15 [nan, nan, nan, nan, inf, nan, nan, 10784.0398... \n", + "20 [nan, nan, nan, nan, inf, nan, nan, 10773.3248... \n", + "25 [nan, nan, nan, nan, inf, nan, nan, 10762.7120... \n", + "\n", + " conviction_share_of_trigger_all \n", + "5 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] \n", + "10 [nan, nan, nan, nan, 0.0, nan, nan, 0.16591253... \n", + "15 [nan, nan, nan, nan, 0.0, nan, nan, 0.16767410... \n", + "20 [nan, nan, nan, nan, 0.0, nan, nan, 0.29018493... \n", + "25 [nan, nan, nan, nan, 0.0, nan, nan, 0.33832802... \n", + "\n", + "[5 rows x 33 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot('timestep','sentiment')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot('timestep',['funds', 'candidate_funds'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Funds are the total available funds, whereas candidate funds show how many funds are requested by candidate proposals." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "affinities_plot(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(x='timestep',y=['candidate_count','active_count','completed_count', 'killed_count', 'failed_count'],\n", + " kind='area')\n", + "plt.title('Proposal Status')\n", + "plt.ylabel('count of proposals')\n", + "plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(x='timestep',y=['candidate_funds','active_funds','completed_funds', 'killed_funds', 'failed_funds'], kind='area')\n", + "plt.title('Proposal Status weighted by funds requested')\n", + "plt.ylabel('Funds worth of proposals')\n", + "plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "nets = df.network.values" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "K = 55\n", + "N = 56\n", + "# K = 0\n", + "# N = 60\n", + "\n", + "snap_plot(nets[K:N], size_scale = 1/10,savefigs=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot('timestep',['effective_supply'])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot('timestep',['participant_count'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected effective_supply isn't changing since we don't have a secondary market." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.total_supply.plot(title='Total Supply')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deXhU5fXA8e8hIQlLICAhBIIENKCALCGJG+4VcCvuxY0QUKzFtrb9ubV1qUtrF7V1o6LsgoitVFpRwa1202SAAIlskS0JSwJhCUtClvP7Y25wwIRMkkkmM3M+z5Mnd95779zzOuF4573vIqqKMcaY0NDG3wEYY4xpOZb0jTEmhFjSN8aYEGJJ3xhjQoglfWOMCSGW9I0xJoRY0jemGYjIxSJS4O84jDmRJX0TkkRki4gcEZGDHj89/R2XMc0t3N8BGONH16jqR/4OwpiWZHf6xjicu//veLx+XETecLYTRURFJF1EtonIbhH5hcex7URklojsFZGvgNQT3vtBESkUkVIRWS8il7VYxYzxYHf6xjTMSGAA0B/IFJF3VHUt8BhwmvPTAXi/5gQRGQDcC6Sq6nYRSQTCWjhuYwC70zeh7W8iss/5+ZuX5/xKVY+o6ipgFTDUKb8ZeFpVS1Q1H3jB45wqIBIYKCJtVXWLqn7ts1oY0wCW9E0ou1ZVY5yfa708Z6fH9mGgo7PdE8j32Le1ZkNV84D7gMeBIhFZYA+Njb9Y0jfmG4eA9h6vezTg3B1Ab4/Xp3ruVNX5qjoS6AMo8NvGBmlMU1jSN+Yb2cA4EWkrIinAjQ04dyHwsIh0EZEE4Ic1O0RkgIhcKiKRQBlwBKj2ZeDGeMuSvjHfeAT3g9i9wK+A+Q0491e4m3Q2A0uBuR77IoFngN24m4e6Aw/7IF5jGkxsERVjjAkddqdvjDEhxJK+McaEEEv6xhgTQizpG2NMCPFqGgYR2QKU4h5ZWKmqKSLyFu7h6AAxwD5VHeYc/zAwyTn+R6r6oVM+BvgT7iHor6vqMye7brdu3TQxMbGhdTLGmJC2fPny3aoaW9u+hsy9c4mq7q55oarfq9kWkWeB/c72QGAcMAj3KMWPRKS/c+jLwOVAAZAlIotV9au6LpiYmIjL5WpAiMYYY0Rka137mjzhmogI7nlHLnWKxgILVLUc2CwieUCasy9PVTc55y1wjq0z6RtjjPEtb9v0FVgqIstFZPIJ+y4AdqnqRud1L46fg6TAKaur/DgiMllEXCLiKi4u9jI8Y4wx3vA26Y9U1WTgCmCKiFzose8W4E1fBaSq01Q1RVVTYmNrbZIyxhjTSF4lfVUtdH4XAYtwmmtEJBy4HnjL4/BCjp94KsEpq6vcGGNMC6k36YtIBxGJrtkGRgE5zu7vAOtU1XMB6MW4J62KFJG+QBKQCWQBSSLSV0QicD/sXey7qhhjjKmPNw9y44BF7ue1hAPzVfUDZ984TmjaUdVcEVmI+wFtJTBFVasARORe4EPcXTZnqGquT2phjDHGK616wrWUlBS1LpvGGNMwIrJcVVNq22cjco0xzaaqWnkzcxslh476OxTjsIXRjTHNQlV54u+5zP7fVg4cqeDui07zd0gGu9M3xjST6f/ezOz/uQeGrt9Z6udoTA270zfG+NySNTt4eslarjyrB6VllayzpN9q2J2+Mcanlm8t4b63shneO4bnbh7GmfGdyCs6SEWVLQvcGljSN8b4zObdh7hztouenaN4PT2VqLZhDIiL5mhVNVt2H/J3eAZL+sYYH9lzsJwJMzMREWZlpNG1QwQAA3pEA1gTTythSd8Y02RlFVXcOcfFzv1lvDY+hcRuHY7tO717R8LaiD3MbSXsQa4xpkmqqpX7FmSTnb+PV25NZkSfLsftj2obRuIp7e1Ov5WwO31jQsDXxQepbKYHqb9espYPcnfyiyvP5Iqz4ms95owenVi/60CzXN80jCV9Y4KUqvLpuiJunPpfLnv2n7zy2dc+v8bM/2xm+r83M+G8RCaN7FvncQN6RJNfcoSD5ZU+j8E0jCV9Y4JMVbXy91XbufKFf5MxK4sd+8s4LbYDb2XlU1Xtu7m2PszdyRP/+IpRA+N45OqBOJMy1uoM52Huhl3WxONvlvSNCSKbdx/i8uf/yQ/fXMnRyir+cNNQPrv/Yu77Tn8K9x3hv1/vrv9NvLBy215+vGAlQxNi+NO44YS1qTvhg7t5B3wzMvf1f21izB8/b7bmqmBnD3KNCRK7D5aTPiOTg+WVTL0tmdGDetDGScajBsUR074tb2Xlc0FS01ak27rnEJNmu+geHcXr6Sm0iwir95yELu1oHxHW5KT/bnYhT723FoCdB8pI6NK+Se8XiuxO35ggcPhoJZNmuygqLWN6egpXnBV/LOEDRIaHce2wXizN3cXeJsx4WXLoKBNmZlGtyqyMVLp1jPTqvDZthP5x0azb2fiHuZmbS7j/7dXHrrlzf1mj3yuUWdI3JsBVVSs/ejObNQX7eGHccIaf2qXW425O6c3RqmrezW7cKqVlFVXcNcdF4b4jvD4+hX6xHRt0/hk9olm/s5TGrOHxdfFB7prjIqFrO6bengzAdkv6jeJV0heRLSKyRkSyRcTlUf5DEVknIrki8juP8odFJE9E1ovIaI/yMU5Znog85NuqGBN6VJXHF+fy0dpdPP7dQYwa1KPOYwf27MRZvTrzlqugwYm3ulr56cJsVmzbyx+/N4yUxK4NjnVAj2j2Hq6guLS8QeftPlhOxswswtsIsyakHXsovGPfkQbHYBp2p3+Jqg6rWY1FRC4BxgJDVXUQ8AenfCDuZRQHAWOAV0QkTETCgJeBK4CBwC3OscaYRnr1803M/WIrd1/Yj/HnJtZ7/M0pCazdcYDc7Q1rZvnN+2tZssbdF//KOvri16cx0zEcOVrFnTXNVhNSOfWU9kRHtaVjZDg77E6/UZrSvHMP8IyqlgOoapFTPhZYoKrlqroZyAPSnJ88Vd2kqkeBBc6xxphGeDe7kGfeX8fVQ+J5cMwZXp3z3WG9iAxvw1tZ+V5fZ9Z/NvPav+rvi1+fhvbgqapW7ntrJasK9vGnccMZ1jvm2L74zlHs2G93+o3hbdJXYKmILBeRyU5Zf+ACEflSRP4pIqlOeS/A8y+qwCmrq/w4IjJZRFwi4iouLm5IXYwJGf/7eg//9/Yq0hK78oebhh730PZkOrdry5jBPXg3u5Cyiqp6j1+au5Nf/eMrLveiL359unaIIDY60us7/V8vWcuHubt45KqBjD6h2So+pp3d6TeSt0l/pKom426amSIiF+Lu7tkVOAe4H1goTfmLcKjqNFVNUdWU2NimdS0zJhht2FXK5Lku+pzSgdfGpxDVtv4uk56+l9KbA2WVfJi786THrdy2lx8tWMmQhBhe8KIvvjfO6BHt1XQMs5yRvhnnJzKxlm8X8Z2iLOk3kldJX1ULnd9FwCLcTTUFwDvqlglUA92AQqC3x+kJTlld5cYYL+06UMaEGZlEtQ1jVkYqndu3bfB7nNPvFHp3bcdCV91NPFv3uOfF7x4dxXQv++J7Y0BcNBt3HTzpyOCabxejBsbxy6tqf+wXHxPF7oPlHK20AVoNVW/SF5EOIhJdsw2MAnKAvwGXOOX9gQhgN7AYGCcikSLSF0gCMoEsIElE+opIBO6HvYt9XyVjgtPB8koyZmax70gFMyekNnpgUps2wk0jevOfvD3klxz+1v6avvhVDeyL740BPaIpr6xmy57aF1TJzt937NvFyUb6xneOQtX9P0HTMN6MyI0DFjktN+HAfFX9wEncM0QkBzgKpKu7H1iuiCwEvgIqgSmqWgUgIvcCHwJhwAxVzfV5jYwJQhVV1dzzxnLW7yplenoKg3t1btL73TAigec/2sBdc1yce9opnBnfiYHxnTj1lPbH+uLPv/PsBvfFr4/nw9zTTnjv/JLD3Dk7i9joSF4ff/JvF/Gd2wGwY38ZvbvaqNyGqDfpq+omYGgt5UeB2+s452ng6VrKlwBLGh6mMaFLVfn5O2v418bd/O6GIVw8oHuT37NXTDt+9d1BvLOikAWZ+RzxeKgrAi/fmtyovvj1SYrrSBtxd9v07Pq5/3AFE2ZmUlGlLJiQRmz0yb9d9IyJArAePI1gc+8Y08r98aONvL28gB9dlsTNqb3rP8FL489NZPy5iVRVK1v3HOKrHQdYt6OUwb06M2Zw3YO8msK9oEoH1ntMx1BeWcXkuS7yS44wd1Iap3ev/9tFD487fdMwlvSNacUWZuXzp483cuOIBH7ynaRmuUZYG6FfbEf6xXbk6iHNconjDOgRzdod7qRfXa088JfVfLm5hD+NG8bZ/U7x6j06RoYTHRVuo3IbwebeMaaV+mx9EQ8vWsMFSd34zfVnNamPfGsyoEc0W0sOc/hoJc8uW8+72du5f/QAxg771rCdk+rZuZ3Nv9MIdqdvTCuUU7ifKfNWMCAumlduS6ZtWPDcn53RIxpVeOq9tcz/chu3pPXmBxef1uD36dE5ymbabITg+UsyJkgU7D1MxqwsYtpHMDMjleiohvfFb80GOD145n+5jYv6x/Lk2MGN+hbTM8amYmgMu9M3phXZd9jdR768oop5d55NXKcof4fkc6d2bU90VDgJXdrz8m3JhDfyW0yPTu3YffAo5ZVVRIb7ZvBYa1G47whhIvTo7PvP35K+Ma1EWUUVk+csZ9uew8yZlEb/uGh/h9QswtoIf73nPOI6RdExsvEpKN7ptrlrfzmnnhI8ffUPlFUwcWYWivL+jy/0yfQXnqx5x5hWoLpa+dnbq8jcUsIfbh7KOV72YglU/eOi6dyuac1WPZ1um9uDqImnoqqaKfNW8HXxQR69epDPEz7Ynb4xrcJv3l/Le6t38PAVZ/DdoT39HU5AqGn6CJaHuarKo+/mHBuENzKpW7Ncx+70jfGzmc589enn9mHyhf38HU7AqBmVGyx3+tM+38SbmflMueQ0nw7CO5ElfWP86IOcHTzhzCj56DWDgqYvfktoHxFO53Zt2bEv8O/0l6zZwW+cBXF+dvmAZr2WJX1j/GT51hJ+vCCbYb1jeOEW38xXH2rcK2gFdtJfuW0vP3krmxF9ujRoQZzGsqRvjB98XXyQSbNd9Ixpx/T01AYvhGLcAn3ZxPySw9w1x0Vcpyim3TGiRf4OLOkb08KKS8uZMDOTMBFmZaTStUOEv0MKWD06twvYB7meM4vOzEjlFB+uW3AylvSNaUGHyiuZOCuL3aVHmTEhlT6ndPB3SAGtZ+co9hw66tV6v63J0cpq7pm3nG0lh3n1jhHfWlugOVnSN6aFVFZVc+/8FeRu389Ltw5naO8Yf4cU8OJj3H31A+luX1X5+aI1/PfrPfz2hiEtPibDkr4xLUBVeeTdHD5dX8yT1w7msjPj/B1SUIjvXLOYSuAk/Zc/zeMvywv48WVJXJ+c0OLX9yrpi8gWEVkjItki4nLKHheRQqcsW0Su9Dj+YRHJE5H1IjLao3yMU5YnIg/5vjrGtE4vfZJ3rA/2bWf38Xc4QeObpB8YD3PfzS7kD0s3cN3wXtzXTOsj1KchI3IvUdXdJ5Q9r6p/8CwQkYG4Fz0fBPQEPnIWTgd4GbgcKACyRGSxqn7VuNCNCQxvu/J5dtkGrk/uxf+Nat4+2KEmPoBW0MraUsL9b68mrW9XnrnBf+sjNMc0DGOBBapaDmwWkTwgzdmX56y5i4gscI61pG+C1ucbinn4nTWMPL0bz1w/xAZf+Vi7iDBi2rdt9Xf6m3cfYvIcFwld2jHtjhF+nRXU2zZ9BZaKyHIRmexRfq+IrBaRGSLSxSnrBeR7HFPglNVVfhwRmSwiLhFxFRcXe10RY1qb3O37ueeN5ZzevSNTb08mItweoTWH+M7tWvWo3L2HjjJxVhYiwsyMVGLa+7eLrrd/hSNVNRm4ApgiIhcCU4HTgGHADuBZXwSkqtNUNUVVU2JjY33xlsa0uIK9h8mYmUXndm2ZPTEt6BZCaU0aMiq3vLKKR/6WQ9aWkmaO6pvrTZ7ronDfEV4bP6JVdNH1qnlHVQud30UisghIU9XPa/aLyGvAP5yXhYDnbEEJThknKTcmaLgH3WRRVlHFX5x5403zie8cxcpte+s9rrpauf/t1SxetZ0jFVWkJnZt1rhU3Yu+Z23Zy4u3DGdEn+a9nrfqvdMXkQ4iEl2zDYwCckQk3uOw64AcZ3sxME5EIkWkL5AEZAJZQJKI9BWRCNwPexf7rirG+F9ZRRV3zXGxbc9hpo1PCdqFUFqTnjHt2Hu4giNHTz5A67llG1i8ajsdI8PJKdzf7HE9t2zDsUXfr2lF02V7c6cfByxyHkCFA/NV9QMRmSsiw3C3928B7gZQ1VwRWYj7AW0lMEVVqwBE5F7gQyAMmKGquT6ujzF+U12t/GyheyGUF28ZHvQLobQWPZxvUjsPlNG3W+3NJ29lbeOlT/O4Ja03XdpH8OrnmyirqGq2uW7eduXz4id5fC+lcYu+N6d6k77T22ZoLeV3nOScp4GnaylfAixpYIzGBIRfL1nLe2t28Isrz2xVd3bBrmbZxB37jtSa9D/fUMzPF+VwYf9Ynhg7mI++2kVVtbJhVylDEnw/Kvq/ebuP9dh66rrGLfrenKw7gTE+MP3fm3n935uZcF4id17Q19/hhJRvlk389sPcdTsP8IN5K0jq3pGXbx1O27A2DO7VGYCcwgM+j2XjrlLufmM5fbt14JXbk2nbyEXfm1Pri8iYAPPe6h089d5XjBnUg0euHtjq7uyC3TfLJh7fV3/XgTIyZmbRITKMmRmpx3pQJXRpR6eocHK2+7Zdv7i0nIxZWUSGu6/XqZX22LKkb0wTZG4u4ScLsxlxahf+OG6YLYTiB1Ftw+jaIeK4O/2D5ZVkzMziwJEKpqenHhu5CyAiDOrZmVwfPsw9crSKO+e42H2wnOnpKSR0ae+z9/Y1S/rGNFJeUSl3OaMsXxufYguh+FGPTlHHZtqsrKrmh/NXsH5XKS/dlnysOcfT4F6dWLuzlIqq6iZfu7pa+clb2awu2McL41r/7KmW9I1phF0HykifkUXbsDbMzkijiy2E4lc9Y6LYvu8Iqspji3P5dH0xT4wdxCUDutd6/OBenTlaWU1e0cEmX/s376/lg9yd/PKqgYwa1KPJ79fcLOkb00ClZRVkzMxi3+GjzMpIpXfX1vtVPlTEd27Hjv1lvPavTcz7cht3X9TvpLOZDupZ8zC3aU08c7/Yymv/2sz4c/sw8fzEJr1XS7Gkb0wDVFRV84N57qaDV24fUWvTgWl5PTpHsf9IBb9eso6rzornwdFnnPT4vt060D4ijNztje/B8+m6Ih57N4dLz+jOowH0AN+SvjFeUlUe/Otq/rVxN89cfxYX9be5oVqLnk5f/RF9uvDszUNpU88D9bA2wsD4To2+08/dvp9756/gzPhOvHjLcMJbYdfMugROpMb42bNLN/DOikJ+enl/bkrpXf8JpsVckBRLxvmJDXqgPrhXZ77acYCqam3QtXbsP8KkWS46tWvLjAmpdIhsjhnqm48lfWO8MO/Lrbz0aR7jUnvzw0tP93c45gTdOkby2DWD6NqAB+qDenbi8NEqNu8+5PU5B8srmTjLxcHySmZMSA3IyfQs6RtTj2Vf7eKRv7nbbp+6tvUNqzeNU/M8JtfLQVo1C9tv2FXKy7clc2Z8p+YMr9lY0jfmJFZs28sP31zBWb0689KtgdV2a07u9O4diQhv49XD3JquoJ+tL+bJsYMD+nmO/QUbU4fNuw9x52wXcZ2imD4hlfYRgdV2a06ubVgbzuwR7dXD3JquoN+/6DRuPfvUFoiu+VjSN6YWxaXlpM/IBGB2RhrdOkb6OSLTHAb27ExO4X5U636Y+/6aHe6uoEPieWB04C9sb0nfmBMcKq9k0uwsikrLmJ6eQmIdc7SbwDe4VycOlFVSsLf2hdVXbNvLfW9lk3xqDM/eVH9X0EBgSd8YDzUP63IK9/PSLckMP7WLv0MyzWjwSUbmbttzmLuc5r1gmlvJq6QvIltEZI2IZIuI64R9PxMRFZFuzmsRkRdEJE9EVotIssex6SKy0flJ921VjGkaVeWXf8vh0/XFPHntYL4zMM7fIZlmNqBHNGFt5FvTLO87fJQJszKprFZmZqRyShA17zXkydQlqrrbs0BEeuNeM3ebR/EVuNfFTQLOBqYCZ4tIV+AxIAX3EovLRWSxqta/orExLeCFj/NYkJXPvZecftJ5W0zwiGobRlL3jsctqFJeWcXdc5dTUHKEuZPSOC22ox8j9L2mNu88DzyAO4nXGAvMUbcvgBhnEfXRwDJVLXES/TJgTBOvb4xPLMzK5/mPNnBDcgI/G9Xf3+GYFjS41zcPc1WVh/66hi83l/D7m4ZwdhCuc+xt0ldgqYgsF5HJACIyFihU1VUnHNsLyPd4XeCU1VV+HBGZLCIuEXEVFxd7GZ4xjffp+iIeXrSGC5K68cwNZ9ngqxAzuGcn9hw6yq4D5Tz/0UYWrSzk/0b1Z+ywb6WnoOBt885IVS0Uke7AMhFZB/wcd9OOT6nqNGAaQEpKSsMmxTCmgVYX7GPKvBWc0SOaqbePaJVrmprmVTMy9+kla/n7qu3cnJLAlEuCd6oNr/7CVbXQ+V0ELAIuAvoCq0RkC5AArBCRHkAh4DkbVYJTVle5MX6xbc9hJs7Kokv7CGZOSKVjgE2cZXzjzPhOiMDfV21n5OndePq64P62V2/SF5EOIhJds4377j5LVburaqKqJuJuqklW1Z3AYmC804vnHGC/qu4APgRGiUgXEenivM+HzVMtY05uz8Fy0me6e2fMnphG9wCcOMv4RofIcM7s0Yn+cR155fbkoP+2582tTRywyPk/XzgwX1U/OMnxS4ArgTzgMJABoKolIvIkkOUc94SqljQ2cGMa68jRKibNdrF93xHm33U2p3cPrt4ZpuHmTkojqm1YwE2T3Bj11lBVNwFD6zkm0WNbgSl1HDcDmNGwEI3xncqqan745kpWFexj6m0jGNGnq79DMq1AMPXDr09wf48xxoOq8ujiXD5au4tffXcQYwa3/kWsjfE1S/omZLz8aR7znZkSx5+b6O9wjPELS/omJLztyucPSzdw3fBePDgm8GdKNKaxLOmboPfPDcU8/M4aRp7ejd/eMCSou+MZUx9L+iaorSnYzz1vLKd/XDRTb08mItz+5E1os38BJmjllxwmo2bwVUYq0VFt/R2SMX4X/J1STUgqOXSU9BmZVFRVs2Dy2cTZ4CtjALvTN35woKyCiqrqZnt/9+CrLAr2HeH19BRO7x7dbNcyJtBY0jctauOuUi783af8Zsm6Znn/msFX2fn7eGHcMFITbfCVMZ4s6ZsWs3N/GekzMtl3uIJ/bfT9tNmeg68ev2YQYwbH+/waxgQ6S/qmRew/UsGEmZkcKKvkmqE92Vh0kL2Hjvr0Gp6Dr9LPS/TpexsTLCzpm2bnXn7OxdfFB/nz7SMYf657KcKsLb6bb89z8NUDo23wlTF1saRvmlV1tfLThav4YlMJf7hpKCOTujEkoTMR4W18lvRPHHzVpo0NvjKmLpb0TbNRVZ587yveW72Dn195xrHl5yLDwxiWEEPm5qYn/ZrBV0k2+MoYr9i/ENNspn2+iZn/2cLE8/ty1wX9jtuX2rcLOdsPcKi8stHvv23PYTJmZdKlfQSzbPCVMV6xpG+axaKVBfzm/XVcPSSeX1515rfmu0lN7EpVtbJy275Gvf+JK1/Z4CtjvONV0heRLSKyRkSyRcTllD0pIqudsqUi0tMpFxF5QUTynP3JHu+TLiIbnZ/05qmS8bd/bSzm/rdXc26/U3j25qG1trGP6NOFNgKZjWjX91z56vXxKbbylTEN0JA7/UtUdZiqpjivf6+qQ1R1GPAP4FGn/AogyfmZDEwFEJGuwGPA2UAa8JizVq4JIjmF+/n+3OWc3r0jr44fQWR4WK3HRUe1ZWDPTmQ1sF3fPfhqBasL9vHCLcNJscFXxjRIo5t3VPWAx8sOgDrbY4E56vYFECMi8cBoYJmqlqjqXmAZMKax1zetz7Y9h5kwM4uY9hHMnphGp3ra2FMTu7Ji216OVno3JYOq8si7uXy0tohfjR3M6EG28pUxDeVt0ldgqYgsF5HJNYUi8rSI5AO38c2dfi8g3+PcAqesrvLjiMhkEXGJiKu42PejNk3zqGljr6iqZvbEVK/a2NMSu1JeWc2awv1eXeOFj/N4M3MbP7j4NO44p09TQzYmJHmb9EeqajLuppspInIhgKr+QlV7A/OAe30RkKpOU9UUVU2JjY31xVuaZnb4aCUTnTb26Q2Y4Cy1r7tpxpv++m9lbeP5jzZwQ3IC99vgK2Mazaukr6qFzu8iYBHuNnlP84AbnO1CoLfHvgSnrK5yE8AqqqqZMm8Fawr28WID29i7dYykX2yHetv1P1m3i58vyuHC/rE8c8NZtvKVMU1Qb9IXkQ4iEl2zDYwCckQkyeOwsUDNtImLgfFOL55zgP2qugP4EBglIl2cB7ijnDIToFSVXyxaw6fri3nq2rMY1Yg29rTErmRtKaG6Wmvdn52/jynzVjIwvhNTb0umbZj1MjamKbxZRCUOWOTcXYUD81X1AxH5q4gMAKqBrcD3neOXAFcCecBhIANAVUtE5EkgyznuCVX13eQrpsU9t2wDC10F/OiyJG49+9RGvUdqYlcWZOWzflcpZ8Z3Om7flt2HmDgri27REcyYkEqHSFvzx5imqvdfkapuAobWUn5DLYejqgpMqWPfDGBGA2M0rdDcL7by4id5jEvtzU++k1T/CXVI82jX90z6xaXljJ+RCcDsjDRioyObFrAxBrARuaYRPsjZyaPv5nDZGd156trBTWpjT+jSjvjOUcfNw3OovJKJs7IoLi1nenoK/WJt8JUxvmJJ3zRI1pYSfrRgJcN6x/DSrcmEN7GNXURIddr1VZWKqmrumbeCr3Yc4OXbhjP8VBu/Z4wvWdI3Xtuwq5RJs7JI6NKO6emptIuofbRtQ6X27cquA+Vs3XOYh/66hs83FPP0tYO59Iw4n7y/MeYb9mTMeGXH/iOkz8gksm0YszPS6NohwmfvneZ087z3zRXkFI34qakAABODSURBVB7gvu8kMS6tcQ+GjTEnZ3f6pl77D1eQPiOTg2WVzM5Io3fX9j59/6TuHencri05hQe4Ja03P76s8Q+GjTEnZ3f65qTKKqq4a46LzbsPMTsjjYE9O9V/UgO1aSN8L7U3RQfKeHJs0x4MG2NOzpK+qVNVtXLfgmwyt5Tw4i3DOe/0bs12rZ9feWazvbcx5hvWvGNqpao8vjiXD3J38sjVA7lmaE9/h2SM8QFL+qZWr3z2NXO/2MrdF/Zj0si+/g7HGOMjlvTNtyx05fP7D9dz3fBePDjmDH+HY4zxIUv65jifrNvFw++s4YKkbvz2hiG1LnVojAlclvTNMSu37eUH81a4Z7S8fQQR4fbnYUywsX/VBoBNxQeZOCuL7tFRzJiQSkeb0dKYoGRJ31B0oIzxMzJpI8KciTajpTHBzG7nQtyBsgrSZ2ZRcugoCyafQ2K3Dv4OyRjTjOxOP4SVV1Zx95zlbNxVytTbRzAkIcbfIRljmplXSV9EtojIGhHJFhGXU/Z7EVknIqtFZJGIxHgc/7CI5InIehEZ7VE+xinLE5GHfF8d463qauWnC1fxv017+N2NQ7iovy1Cb0woaMid/iWqOkxVU5zXy4DBqjoE2AA8DCAiA4FxwCBgDPCKiISJSBjwMnAFMBC4xTnWtDBV5cn3vuK91Tt4+IozuD45wd8hGWNaSKObd1R1qapWOi+/AGoyx1hggaqWq+pm3Gvlpjk/eaq6SVWPAgucY00Le/XzTcz8zxYyzk9k8oX9/B2OMaYFeZv0FVgqIstFZHIt+ycC7zvbvYB8j30FTlld5aYF/XV5Ac+8v46rh8TzyFUDbUZLY0KMt713RqpqoYh0B5aJyDpV/RxARH4BVALzfBGQ8z+VyQCnnmoLafjSZ+uLePCvqznvtFN49uahNtrWmBDk1Z2+qhY6v4uARbibahCRCcDVwG2qqs7hhUBvj9MTnLK6yk+81jRVTVHVlNhYe7joK6vy9/GDeStIiovm1TtGEBnum6UOjTGBpd6kLyIdRCS6ZhsYBeSIyBjgAeC7qnrY45TFwDgRiRSRvkASkAlkAUki0ldEInA/7F3s2+qY2mzefYiMWVl07RDB7IxUoqPa+jskY4yfeNO8Ewcsctp+w4H5qvqBiOQBkbibewC+UNXvq2quiCwEvsLd7DNFVasARORe4EMgDJihqrk+r5E5TlFpGeNnfAnAnIlpdO8U5eeIjDH+JN+0yrQ+KSkp6nK5/B1GwCotq2DctC/YVHyINyefw7DeNvjKmFAgIss9utcfx0bkBqnyyiq+/8Zy1u8s5ZXbky3hG2MAm3snKFVXK//39mr+k7eHZ28ayiUDuvs7JGNMK2F3+kFGVXnqvbX8fdV2HhxzBjeMsNG2xphvWNIPMtM+38SM/2wm4/xEvn+RjbY1xhzPkn4QeWdFAb+x0bbGmJOwpB8kPltfxAN/sdG2xpiTs6QfBGpG2/a30bbGmHpY0g9wm4oPkjEri1M6RjBroo22NcacnCX9AFazti3A7Iw0ukfbaFtjzMlZ0g9QNWvb7jl4lJkTUukX29HfIRljAoAl/QB0/Nq2yQy10bbGGC/ZiNwA47m27XM3D+ViG21rjGkAu9MPIKrKE/+wtW2NMY1nST+ATP3n18z67xYmjexra9saYxrFkn6AeNuVz+8+WM/YYT35xZVn2mhbY0yjWNIPAJ+uK+Khd9ZwQVI3fn+jjbY1xjSeJf1WbsW2vdwzbzlnxkcz9fYRRITbR2aMaTyvMoiIbBGRNSKSLSIup+wmEckVkWoRSTnh+IdFJE9E1ovIaI/yMU5Znog85NuqBJ+8olImzsoirlMUszLS6Bhpna2MMU3TkCxyiaru9nidA1wPvOp5kIgMxL3o+SCgJ/CRiPR3dr8MXA4UAFkislhVv2ps8MFs5/4yxk/PJLyNMGdiGt06Rvo7JGNMEGj0raOqrgVqe6A4FligquXAZmcB9TRnX56qbnLOW+Aca0n/BPsPV5A+I5MDZZUsmHwOfU7p4O+QjDFBwtsGYgWWishyEZlcz7G9gHyP1wVOWV3lxxGRySLiEhFXcXGxl+EFj7KKKu6a42LT7oNMu2MEg3t19ndIxpgg4m3SH6mqycAVwBQRubC5AlLVaaqaoqopsbGxzXWZVqmqWvnRmyvJ2lrCczcP47zTu/k7JGNMkPEq6atqofO7CFjEN801tSkEenu8TnDK6io3uEfb/vJvOSz9ahePXj2Qa4b29HdIxpggVG/SF5EOIhJdsw2Mwv0Qty6LgXEiEikifYEkIBPIApJEpK+IROB+2Lu4qRUIFn/8aCNvZm7jBxefRsb5ff0djjEmSHnzIDcOWOQ8sA0H5qvqByJyHfAiEAu8JyLZqjpaVXNFZCHuB7SVwBRVrQIQkXuBD4EwYIaq5vq+SoHnjS+28qePN3LTiATuHz3A3+EYY4KYqKq/Y6hTSkqKulwuf4fRrD7I2cE981Zw6YDuvHrHCMLDbPCVMaZpRGS5qqbUts8yjB99uWkPP1qQzfDeMbx0a7IlfGNMs7Ms4ydrdxzgzjkuTu3anunpqbSLsMXMjTHNz5K+H+SXHCZ9RiYdIsKZMzGNLh0i/B2SMSZEWNJvYXsOlpM+I5OyiirmTEqjZ0w7f4dkjAkhNoNXCzpUXsnE2S4K9x3hjTvPpn9ctL9DMsaEGLvTbyEVVdXcM28Fawr28dKtyaQmdvV3SMaYEGR3+i2gulp58C+r+XxDMc9cfxaXD4zzd0jGmBBld/ot4JkP1vHOykJ+dnl/xqWd6u9wjDEhzJJ+M5v2+ddM+3wT48/tw72Xnu7vcIwxIc6SfjN6Z0UBv16yjqvOiuexawbZYubGGL+zpN9MPltfxAN/Wc15p53Cc98bSpgtZm6MaQUs6TeDldv2cs8bK+gfF82rd4wgMtxG2xpjWgdL+j72dfFBJs7KIjY6klkTU4mOauvvkIwx5hhL+j5Us5h5mLOYeffoKH+HZIwxx7Gk7yP7j7gXM993+CizMtJI7GaLmRtjWh8bnOUDZRVV3DXbvZj5rIw0W8zcGNNqeXWnLyJbRGSNiGSLiMsp6yoiy0Rko/O7i1MuIvKCiOSJyGoRSfZ4n3Tn+I0ikt48VWpZlVXVxxYzf/57wzjfFjM3xrRiDWneuURVh3msxvIQ8LGqJgEfO68BrsC9Lm4SMBmYCu7/SQCPAWfjXlj9sZr/UQQqz8XMH79mEFcPscXMjTGtW1Pa9McCs53t2cC1HuVz1O0LIEZE4oHRwDJVLVHVvcAyYEwTru93zy3bwIKsfH546emkn5fo73CMMaZe3iZ9BZaKyHIRmeyUxanqDmd7J+4F1AF6Afke5xY4ZXWVH0dEJouIS0RcxcXFXobX8mb/dwsvfpLHuNTe/PTy/v4OxxhjvOLtg9yRqlooIt2BZSKyznOnqqqI+GSFdVWdBkwD98LovnhPX/vH6u08/vdcLh8Yx1PXDrbpFYwxAcOrO31VLXR+FwGLcLfJ73KabXB+FzmHFwK9PU5PcMrqKg8o/964m5+8lU1Kny68eMtwW8zcGBNQ6s1YItJBRKJrtoFRQA6wGKjpgZMOvOtsLwbGO714zgH2O81AHwKjRKSL8wB3lFMWMNYU7OfuuS76devI6+NTiWpr0ysYYwKLN807ccAipwkjHJivqh+ISBawUEQmAVuBm53jlwBXAnnAYSADQFVLRORJIMs57glVLfFZTZrZlt2HmDAzk5j2EcyemEbn9ja9gjEm8Ihqq2w2B9xt+i6Xy99hUFRaxg1T/8vBskre/v55nN69o79DMsaYOonIco/u9cexEbn1OFBWQfqMLHaXHmX+XWdbwjfGBDR7CnkSZRVVTJ7jYuOuUv58xwiGnxrQY8mMMcbu9OtSVa3ctyCbLzaV8MfvDeOi/rH+DskYY5rM7vRroao88m4OH+Tu5JdXncm1w781hswYYwKSJf1a/PGjjcz/chvfv+g07rygn7/DMcYYn7Gkf4K5X2zlTx9v5MYRCTw4ZoC/wzHGGJ+ypO9hyZodPPpuDped0Z1nrj/LplcwxgQdS/qO/+bt5r4F2Yw4tQsv3Zps0ysYY4KSZTYgp3A/k+cuJ7Fbe6anp9IuwqZXMMYEp5BP+lv3uKdX6NyuLXMmnm3TKxhjglpIJ/2i0jLumJ5JVbUye2IaPTpH+TskY4xpViE7OOtAWQUTZmRRXFrOm5PPsekVjDEhISTv9GumV9jgTK8wrHeMv0MyxpgWEXJ3+ja9gjEmlIXUnb7n9AqPXD3QplcwxoSckEr6zzvTK9xz8WlMGtnX3+EYY0yL8zrpi0iYiKwUkX84ry8VkRUikiMis0Uk3CkXEXlBRPJEZLWIJHu8R7qIbHR+0uu6VnOY+78tvPDxRm5OSeCB0Ta9gjEmNDXkTv/HwFoAEWkDzAbGqepg3Msl1iTxK4Ak52cyMNU5pyvwGHA27oXVH3PWym12/1i9nUcX5/KdM+P49XU2vYIxJnR5lfRFJAG4CnjdKToFOKqqG5zXy4AbnO2xwBx1+wKIEZF4YDSwTFVLVHWvc84YH9WjTv/euJufvJVNSp8uvHTrcJtewRgT0rzNgH8EHgCqnde7gXARqVmD8Uagt7PdC8j3OLfAKaur/DgiMllEXCLiKi4u9jK82q0u2Mfdc13069aR18enEtXWplcwxoS2epO+iFwNFKnq8poyda+mPg54XkQygVKgyhcBqeo0VU1R1ZTY2MZ3p9y8+xAZM7OIaR/BnElpNr2CMcbgXT/984HvisiVQBTQSUTeUNXbgQsARGQU0N85vpBv7voBEpyyQuDiE8o/a0rwdSk6UMYd079EgbmT0ojrZNMrGGMMeHGnr6oPq2qCqibivrv/RFVvF5HuACISCTwI/Nk5ZTEw3unFcw6wX1V3AB8Co0Ski/MAd5RT5nNREWEMiItmVkYq/WJtegVjjKnRlBG59ztNP22Aqar6iVO+BLgSyAMOAxkAqloiIk8CWc5xT6hqSROuX6dOUW2ZPiG1Od7aGGMCmrib51unlJQUdblc/g7DGGMCiogsV9WU2vZZ/0VjjAkhlvSNMSaEWNI3xpgQYknfGGNCiCV9Y4wJIZb0jTEmhFjSN8aYENKq++mLSDHuaZu90Q33RHDBIJjqAsFVn2CqCwRXfYKpLtC0+vRR1VonL2vVSb8hRMRV12CEQBNMdYHgqk8w1QWCqz7BVBdovvpY844xxoQQS/rGGBNCginpT/N3AD4UTHWB4KpPMNUFgqs+wVQXaKb6BE2bvjHGmPoF052+McaYeljSN8aYEBLwSV9ExojIehHJE5GH/B1PY4jIFhFZIyLZIuJyyrqKyDIR2ej87uLvOGsjIjNEpEhEcjzKao3dWU3tBeezWi0iyf6LvHZ11OdxESl0Pp9sZ+nQmn0PO/VZLyKj/RN17USkt4h8KiJfiUiuiPzYKQ/Iz+ck9Qm4z0dEokQkU0RWOXX5lVPeV0S+dGJ+S0QinPJI53Wesz+x0RdX1YD9AcKAr4F+QASwChjo77gaUY8tQLcTyn4HPORsPwT81t9x1hH7hUAykFNf7LhXVHsfEOAc4Et/x+9lfR4H/q+WYwc6f3ORQF/nbzHM33XwiC8eSHa2o4ENTswB+fmcpD4B9/k4/407OtttgS+d/+YLgXFO+Z+Be5ztHwB/drbHAW819tqBfqefBuSp6iZVPQosAMb6OSZfGQvMdrZnA9f6MZY6qernwInLXtYV+1hgjrp9AcSISHzLROqdOupTl7HAAlUtV9XNuJcITWu24BpIVXeo6gpnuxRYC/QiQD+fk9SnLq3283H+Gx90XrZ1fhS4FPiLU37iZ1Pzmf0FuExEpDHXDvSk3wvI93hdwMn/CForBZaKyHIRmeyUxal7QXmAnUCcf0JrlLpiD+TP616nyWOGR1NbwNTHaQ4YjvuOMuA/nxPqAwH4+YhImIhkA0XAMtzfRPapaqVziGe8x+ri7N8PnNKY6wZ60g8WI1U1GbgCmCIiF3ruVPd3uoDsWxvIsXuYCpwGDAN2AM/6N5yGEZGOwF+B+1T1gOe+QPx8aqlPQH4+qlqlqsOABNzfQM5oiesGetIvBHp7vE5wygKKqhY6v4uARbj/AHbVfLV2fhf5L8IGqyv2gPy8VHWX8w+0GniNb5oIWn19RKQt7gQ5T1XfcYoD9vOprT6B/PkAqOo+4FPgXNxNauHOLs94j9XF2d8Z2NOY6wV60s8Ckpwn3hG4H3As9nNMDSIiHUQkumYbGAXk4K5HunNYOvCufyJslLpiXwyMd3qJnAPs92hmaLVOaNe+DvfnA+76jHN6VvQFkoDMlo6vLk6b73Rgrao+57ErID+fuuoTiJ+PiMSKSIyz3Q64HPczik+BG53DTvxsaj6zG4FPnG9pDefvp9g+eAp+Je6n+F8Dv/B3PI2Ivx/uHgargNyaOuBur/sY2Ah8BHT1d6x1xP8m7q/UFbjbICfVFTvuHgsvO5/VGiDF3/F7WZ+5TryrnX988R7H/8Kpz3rgCn/Hf0JdRuJuulkNZDs/Vwbq53OS+gTc5wMMAVY6MecAjzrl/XD/jykPeBuIdMqjnNd5zv5+jb22TcNgjDEhJNCbd4wxxjSAJX1jjAkhlvSNMSaEWNI3xpgQYknfGGNCiCV9Y4wJIZb0jTEmhPw/7/hNdM1oHSIAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.funds.plot(title='Funds')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# # Run the following code , without the #, in the images/snap folder to make a movie\n", + "# # ffmpeg -r 10 -i %01d.png -vcodec mpeg4 -y movie.mp4\n", + "# %%HTML\n", + "# " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "We have created a conviction voting model that closely adheres to the 1Hive implementation. This notebook describes the use case, how the model works, and provides descriptions of how it fits together." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/models/v3/Aragon_Conviction_Voting_Model.ipynb b/models/v3/Aragon_Conviction_Voting_Model.ipynb new file mode 100644 index 0000000..ff1746e --- /dev/null +++ b/models/v3/Aragon_Conviction_Voting_Model.ipynb @@ -0,0 +1,1727 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Aragon Conviction Voting Model - Version 3\n", + "\n", + "New to this model are the following elements:\n", + "\n", + "* Adding the realism that not all participant tokens are being allocated to proposals.\n", + "* Refactored parameters and system initialization to make more readable and consistent.\n", + "* Making the distinction between effective and total supply.\n", + "* Refining alpha calculations to more accurately reflect the 1Hive implementation. Discussion of alpha and its relation to alpha in the contract and how it relates to the timescales\n", + "* Updated differential specification and write-up to respect new state variables\n", + "* Moved all unit denominations to honey.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TODO:\n", + "* Move params to M. Pass params into confiction helper functions. Update run time, review conviction3 code (Andrew)\n", + "* Update diff spec (Andrew)\n", + "* Effective supply: sum of the holding of all agents - state variable (Andrew, Z review)\n", + "* Actual supply - state the gets initialized but grows whenever a mint happens (Andrew, Z review)\n", + "* Denominate the model in honey (Andrew, Z review)\n", + "* Add for next steps, to add a uniswap instance. Natural next step, add a paragraph about how it is a next step (Jeff)\n", + "* Factor the trigger function out. Trigger function notebook and how alpha notebook. (Andrew structure, Z work)\n", + "* Update all write-up, README.MD (Jeff)\n", + "* Directory: (Andrew)\n", + "* README\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# An Introduction to Conviction Voting\n", + "\n", + "Conviction Voting is an approach to organizing a communities preferences into discrete decisions in the management of that communities resources. Strictly speaking conviction voting is less like voting and more like signal processing. Framing the approach and the initial algorithm design was done by Michael Zargham and published in a short research proposal [Social Sensor Fusion](https://github.com/BlockScience/conviction/blob/master/social-sensorfusion.pdf). This work is based on a dynamic resource allocation algorithm presented in Zargham's PhD Thesis.\n", + "\n", + "The work proceeded in collaboration with the Commons Stack, including expanding on the pythin implementation to makeup part of the Commons Simulator game. An implemention of Conviction Voting as a smart contract within the Aragon Framework was developed by 1hive.org and is currently being used for community decision making around allocations their community currency, Honey.\n", + "\n", + "\n", + "## The Word Problem\n", + "\n", + "Suppose a group of people want to coordinate to make a collective decision. Social dynamics such as discussions, signaling, and even changing ones mind based on feedback from others input play an important role in these processes. While the actual decision making process involves a lot of informal processes, in order to be fair the ultimate decision making process still requires a set of formal rules that the community collecively agrees to, which serves to functionally channel a plurality of preferences into a discrete outcomes. In our case we are interested in a procedure which supports asynchronous interactions, an provides visibility into likely outcomes prior to their resolution to serve as a driver of good faith, debate and healthy forms of coalition building. Furthermore, participations should be able to show support for multiple initiatives, and to vary the level of support shown. Participants a quantity of signaling power which may be fixed or variable, homogenous or heterogenous. For the purpose of this document, we'll focus on the case where the discrete decisions to be made are decisions to allocate funds from a shared funding pool towards projects of interest to the community.\n", + "\n", + "## Converting to a Math Problem\n", + "\n", + "Let's start taking these words and constructing a mathematical representation that supports a design that meets the description above. To start we need to define participants.\n", + "\n", + "### Participants\n", + "Let $\\mathcal{A}$ be the set of participants. Consider a participant $a\\in \\mathcal{A}$. Any participant $a$ has some capacity to participate in the voting process $h[a]$. In a fixed quantity, homogenous system $h[a] = h$ for all $a\\in \\mathcal{A}$ where $h$ is a constant. The access control process managing how one becomes a participant determines the total supply of \"votes\" $S = \\sum_{a\\in \\mathcal{A}} = n\\cdot h$ where the number of participants is $n = |\\mathcal{A}|$. In a smart contract setting, the set $\\mathcal{A}$ is a set of addresses, and $h[a]$ is a quantity of tokens held by each address $a\\in \\mathcal{A}$. \n", + "\n", + "### Proposals & Shares Resources\n", + "Next, we introduce the idea of proposals. Consider a proposal $i\\in \\mathcal{C}$. Any proposal $i$ is associated with a request for resources $r[i]$. Those requested resources would be allocated from a constrained pool of communal resources currently totaling $R$. The pool of resources may become depleted because when a proposal $i$ passes $R^+= R-r[i]$. Therefore it makes sense for us to consider what fraction of the shared resources are being request $\\mu_i = \\frac{r[i]}{R}$, which means that thre resource depletion from passing proposals can be bounded by requiring $\\mu_i < \\mu$ where $\\mu$ is a constant representing the maximum fraction of the shared resources which can be dispersed by any one proposal. In order for the system to be sustainable a source of new resources is required. In the case where $R$ is funding, new funding can come from revenues, donations, or in some DAO use cases minting tokens.\n", + "\n", + "### Participants Preferences for Proposals\n", + "\n", + "Most of the interesting information in this system is distributed amongst the participants and it manifests as preferences over the proposals. This can be thought of as a matrix $W\\in \\mathbb{R}^{n \\times m}$.\n", + "![Replace this later](https://i.imgur.com/vxKNtxi.png)\n", + "\n", + "These private hidden signals drive discussions and voting actions. Each participant individually decides how to allocate their votes across the available proposals. Participant $a$ supports proposal $i$ by setting $x[a,i]>0$ but they are limited by their capacity $\\sum_{k\\in \\mathcal{C}} x[a,k] \\le h[a]$. Assuming each participant chooses a subset of the proposals to support, a support graph is formed.\n", + "![](https://i.imgur.com/KRh8tKn.png)\n", + "\n", + "## Aggregating Information\n", + "\n", + "In order to break out of the synchronous voting model, a dynamical systems model of this system is introduced.\n", + "\n", + "### Participants Allocate Voting Power\n", + "![](https://i.imgur.com/DZRDwk6.png)\n", + "\n", + "### System Accounts Proposal Conviction\n", + "![](https://i.imgur.com/euAei5R.png)\n", + "\n", + "### Understanding Alpha\n", + "* https://www.desmos.com/calculator/x9uc6w72lm\n", + "* https://www.desmos.com/calculator/0lmtia9jql\n", + "\n", + "\n", + "## Converting Signals to Discrete Decisions\n", + "\n", + "Conviction as kinetic energy and Trigger function as required activation energy.\n", + "\n", + "### The Trigger Function\n", + "\n", + "https://www.desmos.com/calculator/yxklrjs5m3\n", + "\n", + "Below we show a sweep of the trigger function threshold:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", + " return false;\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%javascript\n", + "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", + " return false;\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'beta': 0.2,\n", + " 'rho': 0.0025,\n", + " 'alpha': 0.875,\n", + " 'gamma': 0.001,\n", + " 'sensitivity': 0.75,\n", + " 'tmin': 0,\n", + " 'min_supp': 1,\n", + " 'base_completion_rate': 45,\n", + " 'base_failure_rate': 180,\n", + " 'base_engagement_rate': 0.3,\n", + " 'lowest_affinity_to_support': 0.3}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cadCAD.configuration.utils import config_sim\n", + "from model.model.sys_params import * \n", + "\n", + "sim_config = config_sim({\n", + " 'N': 1,\n", + " 'T': range(60), #day \n", + " 'M': params,\n", + "})\n", + "sim_config[0]['M']" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "for reference: max conviction = 5.25318713934522in log10 units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ] + } + ], + "source": [ + "from model.model.conviction_helper_functions import *\n", + "from model.model.sys_params import initial_values \n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "\n", + "supply = initial_values['supply']\n", + "alpha = sim_config[0]['M']['alpha']\n", + "\n", + "mcv = supply/(1-alpha)\n", + "print('for reference: max conviction = '+str(np.log10(mcv))+'in log10 units')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'beta': 0.2,\n", + " 'rho': 0.0025,\n", + " 'alpha': 0.875,\n", + " 'gamma': 0.001,\n", + " 'sensitivity': 0.75,\n", + " 'tmin': 0,\n", + " 'min_supp': 1,\n", + " 'base_completion_rate': 45,\n", + " 'base_failure_rate': 180,\n", + " 'base_engagement_rate': 0.3,\n", + " 'lowest_affinity_to_support': 0.3}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_config[0]['M']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "supply_sweep = trigger_sweep('effective_supply',trigger_threshold, sim_config[0]['M'], supply)\n", + "alpha_sweep = trigger_sweep('alpha',trigger_threshold, sim_config[0]['M'], supply)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "trigger_grid(supply_sweep, alpha_sweep)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resolving Passed Proposals\n", + "\n", + "![](images/stockflow_cv_trigger.png)\n", + "\n", + "\n", + "## Social Systems Modeling\n", + "\n", + "Subjective, exploratory modeling of the social system interacting through the conviction voting algorithm.\n", + "\n", + "### Sentiment\n", + "\n", + "Global Sentiment -- the outside world appreciating the output of the community\n", + "Local Sentiment -- agents within the system feeling good about the community\n", + "\n", + "### Social Networks\n", + "\n", + "Preferences as mixing process (social influence)\n", + "\n", + "### Relationships between Proposals\n", + "\n", + "Some proposals are synergistic (passing one makes the other more desireable)\n", + "Some proposals are (parially) substitutable (passing one makes the other less desirable)\n", + "\n", + "### Notion of Honey supply\n", + "#### Total supply = $S$\n", + "#### Effective supply = $E$\n", + "#### Funding Pool = $F$\n", + "#### Other supply = $L$, effectively slack. Funds could be in cold storage, in liquidity pools or otherwise in any address not actively participating in conviction voting.\n", + "$$S = F + E + L$$ \n", + "\n", + "System has the right to do direct mints:\n", + "$$F^+ = F + minted$$\n", + "$$S^+ = S + minted$$\n", + "\n", + "\n", + "Arrival of new funds which come from outside:\n", + "$$L+ = L - donated$$\n", + "$$F+ = F + donated$$\n", + "The above assumes the donated tokens were not in use for voting\n", + "$$L+ = L + tokens$$ that haven't been used in voting recently\n", + "$$E+ = E - tokens$$ that haven't been used in voting recently\n", + "$$L+ = L - tokens$$ that come into use\n", + "$$E+ = E - tokens$$ that come into use\n", + "\n", + "Tokens in $L$ or $E$ are defined at the level of the account holding them.\n", + "\n", + "Total supply $S$ can be made a param and the state supply should be only $E$, effective supply." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## cadCAD Overview\n", + "\n", + "In the cadCAD simulation [methodology](https://community.cadcad.org/t/differential-specification-syntax-key/31), we operate on four layers: **Policies, Mechanisms, States**, and **Metrics**. Information flows do not have explicit feedback loop unless noted. **Policies** determine the inputs into the system dynamics, and can come from user input, observations from the exogenous environment, or algorithms. **Mechanisms** are functions that take the policy decisions and update the States to reflect the policy level changes. **States** are variables that represent the system quantities at the given point in time, and **Metrics** are computed from state variables to assess the health of the system. Metrics can often be thought of as KPIs, or Key Performance Indicators. \n", + "\n", + "At a more granular level, to setup a model, there are system conventions and configurations that must be [followed.](https://community.cadcad.org/t/introduction-to-simulation-configurations/34)\n", + "\n", + "The way to think of cadCAD modeling is analogous to machine learning pipelines which normally consist of multiple steps when training and running a deployed model. There is preprocessing, which includes segregating features between continuous and categorical, transforming or imputing data, and then instantiating, training, and running a machine learning model with specified hyperparameters. cadCAD modeling can be thought of in the same way as states, roughly translating into features, are fed into pipelines that have built-in logic to direct traffic between different mechanisms, such as scaling and imputation. Accuracy scores, ROC, etc. are analogous to the metrics that can be configured on a cadCAD model, specifying how well a given model is doing in meeting its objectives. The parameter sweeping capability of cadCAD can be thought of as a grid search, or way to find the optimal hyperparameters for a system by running through alternative scenarios. A/B style testing that cadCAD enables is used in the same way machine learning models are A/B tested, except out of the box, in providing a side by side comparison of muliple different models to compare and contrast performance. Utilizing the field of Systems Identification, dynamical systems models can be used to \"online learn\" by providing a feedback loop to generative system mechanisms. \n", + "\n", + "\n", + "## Differential Specification \n", + "![](images/Aragon_v2.png)\n", + "\n", + "## Schema of the states - UPDATE\n", + "The model consists of a temporal in memory graph database called *network* containing nodes of type **Participant** and type **Proposal**. Participants will have *holdings* and *sentiment* and Proposals will have *funds_required, status*(candidate or active), *conviction* Tthe model as three kinds of edges:\n", + "* (Participant, participant), we labeled this edge type \"influencer\" and it contains information about how the preferences and sentiment of one participant influence another \n", + "* (Proposal, Proposal), we labeled this edge type \"conflict\" and it contains information about how synergistic or anti-synergistic two proposals are; basically people are likely to support multiple things that have synergy (meaning once one is passed there is more utility from the other) but they are not likely to pass things that have antisynergy (meaning once one is passed there is less utility from the other).\n", + "* The edges between Participant and Proposal, which are described below.\n", + " \n", + "\n", + "Edges in the network go from nodes of type Participant to nodes of type Proposal with the edges having the key *type*, of which all will be set to *support*. Edges from participant $i$ to proposal $j$ will have the following additional characteristics:\n", + "* Each pairing (i,j) will have *affinity*, which determines how much $i$ likes or dislikes proposal $j$.\n", + "* Each participant $i$, assigns its $tokens$ over the edges (i,j) for all $j$ such that the summation of all $j$ such that ```Sum_j = network.edges[(i,j)]['tokens'] = network.nodes[i]['holdings']```. This value of tokens for participants on proposals must be less than or equal to the total number of tokens held by the participant.\n", + "* Each pairing (i,j) will have *conviction* local to that edge whose update at each timestep is computed using the value of *tokens* at that edge.\n", + "* Each proposal *j* will have a *conviction* which is equal to the sum of the conviction on its inbound edges: ```network.nodes[j]['conviction'] = Sum_i network.edges[(i,j)]['conviction']```. \n", + "\n", + "\n", + "The other state variables in the model are *funds*, which is a numpy floating point, and effective supply, as supply.\n", + "\n", + "The system consists of 100 time steps without a parameter sweep or monte carlo.\n", + "\n", + " \n", + "## Partial State Update Blocks - TODO: UPDATE\n", + "\n", + "Each partial state update block is kind of a like a phase in a phased based board game. Everyone decides what to do and it reconciles all decisions. One timestep is a full turn, with each block being a phase of a timestep or turn. We will walk through the individaul Partial State update blocks one by one below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "{\n", + "# system.py: \n", + "'policies': { \n", + " 'random': driving_process\n", + "},\n", + "'variables': {\n", + " 'network': update_network,\n", + " 'funds':increment_funds,\n", + "}\n", + "```\n", + "\n", + "To simulate the arrival of participants and proposal into the system, we have a driving process to represent the arrival of individual agents. We use a random uniform distribution generator, over [0, 1), to calculate the number of new participants. We then use an exponential distribution to calculate the particpant's tokens by using a loc of 0.0 and a scale of expected holdings, which is calculated by .1*supply/number of existing participants. We calculate the number of new proposals by \n", + "```\n", + "proposal_rate = 1/median_affinity * (1+total_funds_requested/funds)\n", + "rv2 = np.random.rand()\n", + "new_proposal = bool(rv2<1/proposal_rate)\n", + "```\n", + "The network state variable is updated to include the new participants and proposals, while the funds state variable is updated for the increase in system funds. \n", + "[To see the partial state update code, click here](model/model/system.py)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "{\n", + " # participants.py \n", + " 'policies': {\n", + " 'completion': check_progress \n", + " },\n", + " 'variables': { \n", + " 'sentiment': update_sentiment_on_completion, #not completing projects decays sentiment, completing bumps it\n", + " 'network': complete_proposal\n", + " }\n", + "},\n", + "```\n", + "\n", + "In the next phase of the turn, [to see the logic code, click here](model/model/participants.py), the *check_progress* behavior checks for the completion of previously funded proposals. The code calculates the completion and failure rates as follows:\n", + "\n", + "```\n", + "likelihood = 1.0/(base_completion_rate+np.log(grant_size))\n", + "\n", + "failure_rate = 1.0/(base_failure_rate+np.log(grant_size))\n", + "if np.random.rand() < likelihood:\n", + " completed.append(j)\n", + "elif np.random.rand() < failure_rate:\n", + " failed.append(j)\n", + "```\n", + "With the base_completion_rate being 100 and the base_failure_rate as 200. \n", + "\n", + "The mechanism then updates the respective *network* nodes and updates the sentiment variable on proposal completion. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + " # proposals.py\n", + " 'policies': {\n", + " 'release': trigger_function \n", + " },\n", + " 'variables': { \n", + " 'funds': decrement_funds, \n", + " 'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment\n", + " 'network': update_proposals \n", + " }\n", + "},\n", + " ```\n", + " \n", + "The [trigger release function](model/model/proposals.py) checks to see if each proposal passes or not. If a proposal passes, funds are decremented by the amount of the proposal, while the proposal's status is changed in the network object." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "{ \n", + " # participants.py\n", + " 'policies': { \n", + " 'participants_act': participants_decisions\n", + " },\n", + " 'variables': {\n", + " 'network': update_tokens \n", + " }\n", + "}\n", + "```\n", + "\n", + "The Participants decide based on their affinity if which proposals they would like to support,[to see the logic code, click here](model/model/participants.py). Proposals that participants have high affinity for receive more support and pledged tokens than proposals with lower affinity and sentiment. We then update everyone's holdings and their conviction for each proposal.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model next steps\n", + "\n", + "The the model described above is the third iteration model that covers the core mechanisms of the Aragon Conviction Voting model. Below are next additional dynamics we can attend to enrich the model, and provide workstreams for subsequent iterations of this lab notebook.\n", + "\n", + "* Mixing of token holdings among participants\n", + "* Departure of participants\n", + "* Proposals which are good or no good together\n", + "* Affects of outcomes on sentiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration\n", + "Let's factor out into its own notebook where we review the config object and its partial state update blocks." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from model import config" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# pull out configurations to illustrate\n", + "sim_config,genesis_states,seeds,partial_state_update_blocks = config.get_configs()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'N': 1,\n", + " 'T': range(0, 60),\n", + " 'M': {'beta': 0.2,\n", + " 'rho': 0.0025,\n", + " 'alpha': 0.875,\n", + " 'gamma': 0.001,\n", + " 'sensitivity': 0.75,\n", + " 'tmin': 0,\n", + " 'min_supp': 1,\n", + " 'base_completion_rate': 45,\n", + " 'base_failure_rate': 180,\n", + " 'base_engagement_rate': 0.3,\n", + " 'lowest_affinity_to_support': 0.3},\n", + " 'subset_id': 0,\n", + " 'subset_window': deque([0, None]),\n", + " 'simulation_id': 0,\n", + " 'run_id': 0}]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_config" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'policies': {'random': },\n", + " 'variables': {'network': ,\n", + " 'effective_supply': }},\n", + " {'policies': {'random': },\n", + " 'variables': {'total_supply': ,\n", + " 'funds': }},\n", + " {'policies': {'completion': },\n", + " 'variables': {'sentiment': ,\n", + " 'network': }},\n", + " {'policies': {'release': },\n", + " 'variables': {'funds': ,\n", + " 'sentiment': ,\n", + " 'network': }},\n", + " {'policies': {'participants_act': },\n", + " 'variables': {'network': }}]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "partial_state_update_blocks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialization\n", + "To create the genesis_states, we create our in-memory graph database within networkx. \n", + "\n", + "\n", + "### Parameters\n", + "\n", + "Initial values are the starting values for the simulation and sys_params are global hyperparameters for the simulation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'initial_sentiment': 0.6,\n", + " 'n': 30,\n", + " 'm': 7,\n", + " 'initial_funds': 4867.21,\n", + " 'supply': 22392.22}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from model.model.sys_params import initial_values \n", + "\n", + "initial_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$n$ is initial participants, whereas $m$ is initial proposals" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'beta': 0.2,\n", + " 'rho': 0.0025,\n", + " 'alpha': 0.875,\n", + " 'gamma': 0.001,\n", + " 'sensitivity': 0.75,\n", + " 'tmin': 0,\n", + " 'min_supp': 1,\n", + " 'base_completion_rate': 45,\n", + " 'base_failure_rate': 180,\n", + " 'base_engagement_rate': 0.3,\n", + " 'lowest_affinity_to_support': 0.3}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_config[0]['M']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* $\\alpha$ : 0.875 The decay rate for previously accumulated conviction\n", + "* $\\beta$ = .2 Upper bound on share of funds dispersed in the example Trigger Function\n", + "* $\\rho$ = 0.002 Scale Parameter for the example Trigger Function\n", + "\n", + "* tmin = 7 unit days; minimum periods passed before a proposal can pass\n", + "* min_supp = 50 number of tokens that must be stake for a proposal to be a candidate" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# import libraries\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from model.model.conviction_helper_functions import * \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "network = initialize_network(initial_values['n'],initial_values['m'],\n", + " initial_values['initial_funds'],\n", + " initial_values['supply'],sim_config[0]['M'])\n", + "initial_funds = initial_values['initial_funds']\n", + "\n", + "genesis_states = { \n", + " 'network': network,\n", + " 'funds':initial_values['initial_funds'],\n", + " 'sentiment': initial_values['initial_sentiment'],\n", + " 'effective_supply': initial_values['supply']-initial_values['initial_funds'],\n", + " 'total_supply': initial_values['supply']\n", + "\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Exploring the State Data Structure\n", + "\n", + "A graph is a type of temporal data structure that evolves over time. A graph $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$ consists of vertices or nodes, $\\mathcal{V} = \\{1...\\mathcal{V}\\}$ and is connected by edges $\\mathcal{E} \\subseteq \\mathcal{V} \\times \\mathcal{V}$.\n", + "\n", + "See *Schema of the states* above for more details\n", + "\n", + "\n", + "Let's explore!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n", + "proposals = get_nodes_by_type(network, 'proposal')\n", + "participants = get_nodes_by_type(network, 'participant')\n", + "supporters = get_edges_by_type(network, 'support')\n", + "influencers = get_edges_by_type(network, 'influence')\n", + "competitors = get_edges_by_type(network, 'conflict')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'type': 'participant',\n", + " 'holdings': 316.54596362304994,\n", + " 'sentiment': 0.1553685333489736}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#sample a participant\n", + "network.nodes[participants[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Count of Participants')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Let's look at the distribution of participant holdings at the start of the sim\n", + "plt.hist([ network.nodes[i]['holdings'] for i in participants])\n", + "plt.title('Histogram of Participants Token Holdings')\n", + "plt.xlabel('Amount of Honey')\n", + "plt.ylabel('Count of Participants')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Participants Social Network')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nx.draw_spring(network, nodelist = participants, edgelist=influencers)\n", + "plt.title('Participants Social Network')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'type': 'proposal',\n", + " 'conviction': 0,\n", + " 'status': 'candidate',\n", + " 'age': 0,\n", + " 'funds_requested': 2141.714746492425,\n", + " 'trigger': inf}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#lets look at proposals\n", + "network.nodes[proposals[0]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Proposals initially start without any conviction, and with the status of a candidate. If the proposal's amount of conviction is greater than it's trigger, then the proposal moves to active and it's funds requested are granted. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All initial proposal start with 0 conviction and state 'candidate'we can simply examine the amounts of funds requested" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "funds_array = np.array([ network.nodes[i]['funds_requested'] for i in proposals])\n", + "conviction_required = np.array([trigger_threshold(r, initial_funds, supply, alpha,sim_config[0]['M']) for r in funds_array])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Amount of Honey requested(as a Fraction of Funds available)')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar( proposals, funds_array/initial_funds)\n", + "plt.title('Bar chart of Proposals Funds Requested')\n", + "plt.xlabel('Proposal identifier')\n", + "plt.ylabel('Amount of Honey requested(as a Fraction of Funds available)')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Amount of Conviction')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar( proposals, conviction_required)\n", + "plt.title('Bar chart of Proposals Conviction Required')\n", + "plt.xlabel('Proposal identifier')\n", + "plt.ylabel('Amount of Conviction')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conviction is a concept that arises in the edges between participants and proposals in the initial conditions there are no votes yet so we can look at that later however, the voting choices are driven by underlying affinities which we can see now." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 55.73999999999998, 'Participant_id')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m = len(proposals)\n", + "n = len(participants)\n", + "\n", + "affinities = np.empty((n,m))\n", + "for i_ind in range(n):\n", + " for j_ind in range(m):\n", + " i = participants[i_ind]\n", + " j = proposals[j_ind]\n", + " affinities[i_ind][j_ind] = network.edges[(i,j)]['affinity']\n", + "\n", + "dims = (20, 5)\n", + "fig, ax = plt.subplots(figsize=dims)\n", + "\n", + "sns.heatmap(affinities.T,\n", + " xticklabels=participants,\n", + " yticklabels=proposals,\n", + " square=True,\n", + " cbar=True,\n", + " cmap = plt.cm.RdYlGn,\n", + " ax=ax)\n", + "\n", + "plt.title('Affinities between participants and proposals')\n", + "plt.ylabel('Proposal_id')\n", + "plt.xlabel('Participant_id')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run simulation\n", + "\n", + "Now we will create the final system configuration, append the genesis states we created, and run our simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from cadCAD.configuration import Experiment\n", + "\n", + "# Create configuration\n", + "exp = Experiment()\n", + "\n", + "exp.append_configs(\n", + " sim_configs=sim_config,\n", + " initial_state=genesis_states,\n", + " seeds=seeds,\n", + " partial_state_update_blocks=partial_state_update_blocks\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ___________ ____\n", + " ________ __ ___/ / ____/ | / __ \\\n", + " / ___/ __` / __ / / / /| | / / / /\n", + "/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n", + "\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\n", + "by cadCAD\n", + "\n", + "Execution Mode: local_proc\n", + "Configuration Count: 2\n", + "Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (60, 11, 1, 5)\n", + "Execution Method: local_simulations\n", + "SimIDs : [0, 1]\n", + "SubsetIDs: [0, 0]\n", + "Ns : [0, 0]\n", + "ExpIDs : [0, 0]\n", + "Total execution time: 91.82s\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from model.model.conviction_helper_functions import *\n", + "from model import run\n", + "from cadCAD import configs\n", + "pd.options.display.float_format = '{:.2f}'.format\n", + "\n", + "%matplotlib inline\n", + "\n", + "rdf = run.run(configs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the simulation has run successfully, we perform some postprocessing to extract node and edge values from the network object and add as columns to the pandas dataframe. For the rdf, we take only the values at the last substep of each timestep in the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "df= run.postprocessing(rdf,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
networkfundssentimenteffective_supplytotal_supplysimulationsubsetrunsubsteptimestep...funds_requestedshare_of_funds_requestedshare_of_funds_requested_alltriggersconviction_share_of_triggerageage_allconviction_alltriggers_allconviction_share_of_trigger_all
5(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4889.600.6017525.0122414.6100151...[1595.0421987193204, 2494.344628102797, 2363.4...[0.3497660585741865][0.3262110345490068, 0.5101324230220914, 0.483...[inf][0.0][1][1, 1, 1, 1, 1, 1, 1][0.0, 0.0, 0.0, 0.0, 7.215686217212743, 0.0, 0.0][inf, inf, inf, inf, inf, 226995.283259412, inf][0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
10(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4912.020.6017525.9922437.0300152...[1595.0421987193204, 2494.344628102797, 2363.4...[0.34816999918790076, 0.019712554176434215][0.32472246191355914, 0.5078045766743577, 0.48...[inf, 10794.257352359262][0.0, 0.16591253044853824][2, 1][2, 2, 2, 2, 2, 2, 2, 1][0.0, 0.0, 0.0, 0.0, 2179.0300378288057, 0.0, ...[nan, nan, nan, nan, inf, nan, nan, 10794.2573...[nan, nan, nan, nan, 0.0, nan, nan, 0.16591253...
15(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4934.450.6017525.9922459.4600153...[1595.0421987193204, 2494.344628102797, 2363.4...[0.34658686560572693, 0.019622920932386982, 0....[0.32324594459288075, 0.5054955825611662, 0.47...[inf, 10784.039844676328, 10038.988431151754][0.0, 0.16767410493517743, 0.0676098125520172][3, 2, 1][3, 3, 3, 3, 3, 3, 3, 2, 1][0.0, 0.0, 0.0, 0.0, 5890.347248494404, 0.0, 0...[nan, nan, nan, nan, inf, nan, nan, 10784.0398...[nan, nan, nan, nan, 0.0, nan, nan, 0.16767410...
20(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4956.910.6017525.9922481.9200154...[1595.0421987193204, 2494.344628102797, 2363.4...[0.3450165022212967, 0.019534010706450017, 0.0...[0.32178133745992704, 0.5032052137312398, 0.47...[inf, 10773.32486563405, 10032.570509508532, 1...[0.0, 0.29018493662687905, 0.12684949336988674...[4, 3, 2, 1][4, 4, 4, 4, 4, 4, 4, 3, 2, 1, 1][0.0, 0.0, 0.0, 0.0, 7518.415456879907, 0.0, 0...[nan, nan, nan, nan, inf, nan, nan, 10773.3248...[nan, nan, nan, nan, 0.0, nan, nan, 0.29018493...
25(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4979.400.6017525.9922504.4100155...[1595.0421987193204, 2494.344628102797, 2363.4...[0.3434587559158705, 0.01944581482940582, 0.01...[0.3203284977077525, 0.5009332468614979, 0.474...[inf, 10762.71208704474, 10026.210435699022, 1...[0.0, 0.3383280289679279, 0.19857705818729582,...[5, 4, 3, 2, 1, 1][5, 5, 5, 5, 5, 5, 5, 4, 3, 2, 2, 1, 1][0.0, 0.0, 0.0, 0.0, 8718.352575520581, 0.0, 0...[nan, nan, nan, nan, inf, nan, nan, 10762.7120...[nan, nan, nan, nan, 0.0, nan, nan, 0.33832802...
\n", + "

5 rows × 33 columns

\n", + "
" + ], + "text/plain": [ + " network funds sentiment \\\n", + "5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4889.60 0.60 \n", + "10 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4912.02 0.60 \n", + "15 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4934.45 0.60 \n", + "20 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4956.91 0.60 \n", + "25 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4979.40 0.60 \n", + "\n", + " effective_supply total_supply simulation subset run substep \\\n", + "5 17525.01 22414.61 0 0 1 5 \n", + "10 17525.99 22437.03 0 0 1 5 \n", + "15 17525.99 22459.46 0 0 1 5 \n", + "20 17525.99 22481.92 0 0 1 5 \n", + "25 17525.99 22504.41 0 0 1 5 \n", + "\n", + " timestep ... funds_requested \\\n", + "5 1 ... [1595.0421987193204, 2494.344628102797, 2363.4... \n", + "10 2 ... [1595.0421987193204, 2494.344628102797, 2363.4... \n", + "15 3 ... [1595.0421987193204, 2494.344628102797, 2363.4... \n", + "20 4 ... [1595.0421987193204, 2494.344628102797, 2363.4... \n", + "25 5 ... [1595.0421987193204, 2494.344628102797, 2363.4... \n", + "\n", + " share_of_funds_requested \\\n", + "5 [0.3497660585741865] \n", + "10 [0.34816999918790076, 0.019712554176434215] \n", + "15 [0.34658686560572693, 0.019622920932386982, 0.... \n", + "20 [0.3450165022212967, 0.019534010706450017, 0.0... \n", + "25 [0.3434587559158705, 0.01944581482940582, 0.01... \n", + "\n", + " share_of_funds_requested_all \\\n", + "5 [0.3262110345490068, 0.5101324230220914, 0.483... \n", + "10 [0.32472246191355914, 0.5078045766743577, 0.48... \n", + "15 [0.32324594459288075, 0.5054955825611662, 0.47... \n", + "20 [0.32178133745992704, 0.5032052137312398, 0.47... \n", + "25 [0.3203284977077525, 0.5009332468614979, 0.474... \n", + "\n", + " triggers \\\n", + "5 [inf] \n", + "10 [inf, 10794.257352359262] \n", + "15 [inf, 10784.039844676328, 10038.988431151754] \n", + "20 [inf, 10773.32486563405, 10032.570509508532, 1... \n", + "25 [inf, 10762.71208704474, 10026.210435699022, 1... \n", + "\n", + " conviction_share_of_trigger age \\\n", + "5 [0.0] [1] \n", + "10 [0.0, 0.16591253044853824] [2, 1] \n", + "15 [0.0, 0.16767410493517743, 0.0676098125520172] [3, 2, 1] \n", + "20 [0.0, 0.29018493662687905, 0.12684949336988674... [4, 3, 2, 1] \n", + "25 [0.0, 0.3383280289679279, 0.19857705818729582,... [5, 4, 3, 2, 1, 1] \n", + "\n", + " age_all \\\n", + "5 [1, 1, 1, 1, 1, 1, 1] \n", + "10 [2, 2, 2, 2, 2, 2, 2, 1] \n", + "15 [3, 3, 3, 3, 3, 3, 3, 2, 1] \n", + "20 [4, 4, 4, 4, 4, 4, 4, 3, 2, 1, 1] \n", + "25 [5, 5, 5, 5, 5, 5, 5, 4, 3, 2, 2, 1, 1] \n", + "\n", + " conviction_all \\\n", + "5 [0.0, 0.0, 0.0, 0.0, 7.215686217212743, 0.0, 0.0] \n", + "10 [0.0, 0.0, 0.0, 0.0, 2179.0300378288057, 0.0, ... \n", + "15 [0.0, 0.0, 0.0, 0.0, 5890.347248494404, 0.0, 0... \n", + "20 [0.0, 0.0, 0.0, 0.0, 7518.415456879907, 0.0, 0... \n", + "25 [0.0, 0.0, 0.0, 0.0, 8718.352575520581, 0.0, 0... \n", + "\n", + " triggers_all \\\n", + "5 [inf, inf, inf, inf, inf, 226995.283259412, inf] \n", + "10 [nan, nan, nan, nan, inf, nan, nan, 10794.2573... \n", + "15 [nan, nan, nan, nan, inf, nan, nan, 10784.0398... \n", + "20 [nan, nan, nan, nan, inf, nan, nan, 10773.3248... \n", + "25 [nan, nan, nan, nan, inf, nan, nan, 10762.7120... \n", + "\n", + " conviction_share_of_trigger_all \n", + "5 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] \n", + "10 [nan, nan, nan, nan, 0.0, nan, nan, 0.16591253... \n", + "15 [nan, nan, nan, nan, 0.0, nan, nan, 0.16767410... \n", + "20 [nan, nan, nan, nan, 0.0, nan, nan, 0.29018493... \n", + "25 [nan, nan, nan, nan, 0.0, nan, nan, 0.33832802... \n", + "\n", + "[5 rows x 33 columns]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot('timestep','sentiment')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot('timestep',['funds', 'candidate_funds'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Funds are the total available funds, whereas candidate funds show how many funds are requested by candidate proposals." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "affinities_plot(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(x='timestep',y=['candidate_count','active_count','completed_count', 'killed_count', 'failed_count'],\n", + " kind='area')\n", + "plt.title('Proposal Status')\n", + "plt.ylabel('count of proposals')\n", + "plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(x='timestep',y=['candidate_funds','active_funds','completed_funds', 'killed_funds', 'failed_funds'], kind='area')\n", + "plt.title('Proposal Status weighted by funds requested')\n", + "plt.ylabel('Funds worth of proposals')\n", + "plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "nets = df.network.values" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAFBCAYAAAAPCxQwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOxdd3wURf9+du9yKaQASSCEFgRf1CAoEKVJkaIi5RVFmgIqIEoRbIC8Ki/yKoIoYAR+oKAoiL4gSlFfkKYUhSgQihSldwiElMvlys7vjy03u7d7t9eSHOzDZ8nt7vSdmWe+3/nODEMIITBgwIABAwZuQrDlnQADBgwYMGCgvGCQoAEDBgwYuGlhkKABAwYMGLhpYZCgAQMGDBi4aWGQoAEDBgwYuGlhkKABAwYMGLhpYZDgDYbNmzejVq1a5Z2MkGHw4MH417/+FXQ4n376Kdq0aaP67sSJE2AYBk6nM+h4wo3hw4fjrbfeKu9kGPAChmHw119/lXcyDOiEQYIVGPHx8dLFsixiY2Ol+yVLlpR38lRht9vx0ksvoVatWoiPj0dGRgbGjBkjvc/IyMBPP/1UjikMLyZNmoSoqCjEx8ejcuXKaNWqFXbs2BFQWGrEPW/ePLz++uuhSKpXTJo0CU888USZ+wX4gY/FYkF8fDyqVq2Kzp0749ChQwGHZ8CANxgkWIFRVFQkXXXq1MHq1aul+wEDBpR38lTxzjvvICcnBzt37kRhYSE2b96Mpk2blneyyhR9+vRBUVERLl++jDZt2qBXr17wd0+KSJBKw4lXX30VRUVFOHPmDKpVq4bBgwd7uCGEgOO4sk+cgRsKBglGIEpLSzFmzBikp6cjPT0dY8aMQWlpqarb2bNn44477sCZM2dQWlqKl19+GXXq1EH16tUxfPhwlJSUAHCrUWfMmIFq1aqhRo0aWLRokRTO999/jzvuuAMJCQmoWbMm3nvvPdX4du3ahUceeQTp6elgGAYZGRkYOHAgAODJJ5/EqVOn0L17d8THx2PatGkAgN69eyMtLQ1JSUlo27YtDhw4oBp2YWEhOnTogNGjR4MQgkOHDqFz586oWrUqGjZsiK+//lpym5eXhx49eiAxMRH33HMP/v77b5/lunDhQqSnp6NGjRpS/i5cuIC4uDjk5eVJ7v744w+kpqbC4XB4DS8qKgqDBg3ChQsXkJeXh6lTp6J+/fpISEjAHXfcgZUrV0puP/30U7Ru3Rpjx45FcnIy+vTpg+HDh2PHjh2SVAnI1cPiN3v77beRkpKCjIwMmYZg7dq1uPvuu5GYmIjatWtj0qRJ0jtRBfzZZ5+hTp06SElJwX/+8x8AwI8//oi3334bX331FeLj49GkSRMpjbfccgsSEhJQr149VW2Elt9z586hR48eqFq1Kho0aIAFCxb4/B4AEBcXh/79+2P//v0AgPbt22PixIlo3bo14uLicOzYMWzfvh1ZWVlISkpCVlYWtm/fLvlv3749JkyYgHvuuQeJiYno2bMnrl69Kr1ftWoVMjMzUblyZbRv3x5//vmn9O7dd99FzZo1kZCQgIYNG2LDhg0AgJ07d6Jly5aoXLkyatSogZEjR8Jut6umX2+7MVCOIAYiAnXr1iXr168nhBDy+uuvk3vvvZdcvHiRXLp0ibRs2ZL861//IoQQsmnTJlKzZk1CCCH//ve/yd13300uXbpECCFkzJgxpHv37iQvL48UFBSQbt26kfHjx0v+TCYTef3114ndbidr164lsbGx5OrVq4QQQtLS0sjPP/9MCCHk6tWr5Pfff1dN51tvvUVq165NPvroI5Kbm0s4jtPMh4hPPvmEFBQUEJvNRl544QXSpEkT6d2gQYPIxIkTyZUrV0hWVhaZOHEiIYSQoqIiUqtWLbJw4ULicDjIH3/8QZKTk8mBAwcIIYT06dOH9O7dmxQVFZF9+/aR9PR00rp1a9U0Hz9+nAAgffv2JUVFRSQ3N5ekpKRI6XzooYfInDlzJPdjxowhI0eOVA3rzTffJAMGDCCEEGKz2cjLL79MateuTQgh5OuvvyZnz54lLpeLLFu2jMTFxZFz584RQghZtGgRMZlMZPbs2cThcBCr1UoWLVrkkWaxPAhxf7OxY8cSm81GNm/eTOLi4sihQ4ek97m5ucTlcpG9e/eSatWqkZUrV8ryPGTIEGK1WsmePXuIxWIhBw8e9MiHWN4JCQlS2OfOnSP79+/3WQYi7rvvPvLcc8+RkpISsnv3bpKSkkI2bNig6p/OY2FhIenXrx9p06YNIYSQdu3akdq1a5P9+/cTh8NBLly4QCpXrkwWL15MHA4HWbp0KalcuTK5cuWK5D49PZ3s27ePFBUVkV69eklpO3z4MImLiyPr1q0jdrudvPvuu6R+/fqktLSUHDp0iNSqVYucPXtWKq+//vqLEEJITk4O2bFjB3E4HOT48ePktttuIx988IGUfgDk6NGjhBD97cZA+cEgwQgBTR633HILWbt2rfTuxx9/JHXr1iWE8B1feno6GTt2LGndujXJz88nhBDCcRyJi4uTGjIhhGzfvp1kZGRI/mJiYojD4ZDep6amkh07dhBCCKlduzaZN28euX79utd0Op1Okp2dTVq1akUsFgupUaMG+fTTT1XzoYZr164RAFK6Bw0aRJ566imSmZlJpk2bJrlbtmyZ1DGKGDZsGJk0aRJxOp3EbDaTP//8U3o3YcIEnyRIu3/llVfI008/LcXVqlUrKX/Vq1cnv/32m2pYb775JomKiiJJSUkkNTWVdOjQgeTk5Ki6bdKkCfn2228JITwJimQpQi8JFhUVSe979+5NJk+erBrfCy+8QMaMGSPL8+nTp6X3WVlZ5Msvv5TyoSTBpKQksnz5cmK1WlXDp8uA9nvq1CnCsiwpKCiQno0fP54MGjRI1f+gQYNIdHQ0SUpKItWrVyfdu3eX6m27du3I66+/LrldvHgxycrKkvlv0aIFWbRokeR+3Lhx0rsDBw6QqKgo4nQ6yeTJk0nv3r2ldy6Xi6Snp5NNmzaRo0ePktTUVLJ+/Xpit9u95veDDz4g//znP6V7mgT1thsD5QdDHRqBOHfuHOrWrSvd161bF+fOnZPu8/PzMX/+fEyYMAFJSUkAgMuXL8NqtaJZs2aoXLkyKleujAcffBCXL1+W/CUnJ8NsNkv3cXFxKCoqAgCsWLEC33//PerWrYt27dppGnuYTCaMGDEC27ZtQ35+PiZOnIinn35apmai4XK5MH78eNSvXx+JiYnIyMgAAFy5ckVys3btWpSUlGD48OHSs5MnT+K3336T8lK5cmUsWbIEFy5cwOXLl+F0OlG7dm1ZGfmC0r1Ypj179sTBgwdx/PhxrF+/HklJSbjnnns0w3n88ceRn5+PS5cuYePGjWjWrBkAYPHixbjrrruk9O7fv1+WTzp+vahSpQoqVaqkmu7ffvsNHTp0QGpqKpKSkjBv3jxZfACQlpYm/aa/txKVKlXCV199hXnz5qFGjRp4+OGHdRurnDt3DlWrVkVCQoIsnWfPntX08/LLLyM/Px8XLlzAqlWrUL9+fekdXU7KtqAWtvK7OhwOXLlyxcMvy7KoXbs2zp49iwYNGmDmzJmYNGkSqlWrhr59+0rleuTIEXTr1g1paWlITEzEa6+95lGuIvS2GwPlB4MEIxDp6ek4efKkdH/q1Cmkp6dL91WqVMGaNWvw1FNPYdu2bQCAlJQUxMbG4sCBA8jPz0d+fj6uX7+u2ekpkZWVhe+++w6XLl3CP//5Tzz++OM+/cTGxmLEiBGoUqUKDh48CIA3H6exdOlSfPfdd/jpp59w/fp1nDhxAgBkhiRDhw7Fgw8+iK5du6K4uBgA37G1a9dOykt+fj6Kioowd+5cpKamwmw24/Tp07Iy8gWle7FMY2Ji8Pjjj+OLL77A559/jieffNJnWEqcPHkSQ4cORXZ2NvLy8pCfn49GjRrJ8qksG+W9Gq5duyaViTLd/fv3R48ePXD69Glcv34dw4cP122goxb3Aw88gPXr1+P8+fO47bbbMHToUF1+09PTcfXqVRQWFsrSWbNmTV1p8Ra+si2oha38rlFRUUhJSfHwSwjB6dOnJb/9+/fH1q1bcfLkSTAMg3HjxgEAnnvuOdx22204evQoCgoK8Pbbb2uWayDtxkDZwiDBCES/fv0wZcoUXL58GVeuXMHkyZM9TNLbt2+PJUuWoFevXti5cydYlsXQoUMxduxYXLp0CQBw9uxZ/O9///MZn91ux5IlS3D9+nVERUUhMTERLKtedWbOnInNmzejpKQETqcTn332GQoLC3H33XcDAKpXr45jx45J7gsLCxEdHY3k5GRYrVa89tprquFmZ2ejYcOG6N69O0pKStCtWzccOXIEn3/+ORwOBxwOB3bt2oU///wTJpMJvXr1wqRJk2C1WnHw4EF89tlnPvP51ltvwWq14sCBA1i0aBH69OkjvRs4cCA+/fRTrFq1KiASLC4uBsMwSE1NBQAsWrRIMvbQQvXq1XHmzBlNowsRb775Jux2O3755ResWbMGvXv3BsCXbdWqVRETE4OdO3di6dKlutNbvXp1nDhxQrK+vHjxIr777jsUFxcjOjpaWrajx2/t2rXRqlUrTJgwATabDbm5ufjkk0+CWkYhomvXrjhy5AiWLl0Kp9OJr776CgcPHkS3bt0kN1988QUOHjwIq9WKN954A4899hhMJhMef/xxrF27Fhs2bIDD4cCMGTMQHR2NVq1a4fDhw9i4cSNKS0sRExOD2NhYKb+FhYVITExEfHw8Dh06hLlz56qmzZ92Y6D8YHyRCMS//vUvNG/eHI0bN8add96Jpk2bqi4o79y5MxYuXIju3bvjjz/+wLvvvosGDRqgRYsWSExMRKdOnXD48GFdcX7++efIyMhAYmIi5s2bp7lOMS4uDi+99BLS0tKQkpKCjz76CCtWrMAtt9wCAJgwYQKmTJmCypUr47333sPAgQNRt25d1KxZE3fccQdatGihGi7DMJg/fz5q1aqFnj17IioqCuvWrcOyZcuQnp6OtLQ0jBs3TrKSzc7ORlFREdLS0jB48GA89dRTPvPYrl07NGjQAB07dsTLL7+MLl26SO9at24NlmXRtGlTXapVJe644w689NJLaNmyJapXr459+/ahdevWXv3cf//9yMzMlMpSDWlpaahSpQrS09MxYMAAzJs3D7fddhsAYM6cOXjjjTeQkJCAyZMn+yWFiESanJyMpk2bguM4vP/++0hPT0fVqlWxZcsWzc5f6RcAvvzyS5w4cQLp6el45JFH8O9//xudOnXSnR4tJCcnY82aNZgxYwaSk5Mxbdo0rFmzRlZeTz75JAYPHoy0tDTYbDbMnj0bANCwYUN88cUXGDVqFFJSUrB69WqsXr0aFosFpaWlGD9+PFJSUpCWloZLly7hnXfeAQC89957WLp0KRISEjB06FDZYEkJve3GQPmBIXr1IwYM3OS4//770b9/fwwZMqS8kwKAXyLxxBNP4MyZM+WdlAqL9u3b44knnqgw38xAxYPZtxMDBgzs2rULf/zxB7777rvyTooBAwZCCEMdasCADwwaNAidOnXCzJkzZRaOBgwYiHwY6lADBgwYMHDTwpAEDRgwYMDATQu/5gTF/QkNGDBgwICBSMKJEydUNzXwiwQzMjKQk5MTskQZMGDAgAEDZYHmzZurPjfUoQYMGDBg4KaFQYIGDBgwYOCmhUGCBgwYMGDgpoVBggYMGDBg4KaFQYIGDBgwYOCmhUGCBgwYMGDgpoVBggYMGDBg4KaFQYIGDBgwYOCmhUGCBgwYMGDgpoVBggYMGDBg4KaFQYIGDBgwYOCmhUGCBgwYMGDgpoVBggYMGDBg4KaFQYIGDBgwYOCmhUGCBgwYMGDgpoVBggYMGDBg4KaFQYIGDBgwYOCmhUGCBgwYMGDgpoW5vBNgwIABAwYiH06nE59++inOnz+Pfv36oUGDBuWdJF0wSNCAAQMGDASNAQMGYM2aNSgtLcX06dOxf/9+1KlTp7yT5ROGOtSAAQMGDASNb7/9FlarFS6XCxzHYcOGDT797NmzB82aNUNcXByaNWuGPXv2SO+WLl2KGjVqICMjA5s2bZKe//3332jVqhVcLldI0m2QoAEDBgwY8A8cB3ByEqpTpw4YhgEAEEJw6623eg3CbrejZ8+eeOKJJ3Dt2jUMGjQIPXv2hN1uh9PpxPjx4/HHH38gOzsbo0aNkvyNHj0aH3zwAUwmU0iyEtkkeOE0cHAvYC0ESksARyngdAAuJ39xrhBenP8X8eci4bsMGDBgIJSwFQK2IsB6ne9zAaxduxZNmzZHenotvPPOO2jTpo3XIDZv3gyn04kxY8YgOjoao0ePBiEEGzduRF5eHmrWrIkaNWqgU6dOOHbsGABg+fLlqFmzJu69996QZSWy5wRPnwEuXgQyMgCXHQiNdAyA8d858XLvE5FOVH6Ul59FG4AHP4IOY9iB1KFwhe0vjHJRCfpmLhOxf9IIy24F7EC1+DScPbsTeXlAUpLvUA8cOIDGjRtL0iMANG7cGAcOHECXLl2Ql5eHM2fOYPfu3cjMzERhYSGmTJmiS83qDyKbBLNa8iORkMNPUlI6l+4Zod4wVCOingGhey57p0xPOEnWj7D9TkYY022UiUbwFaxcdHsh/oWvGYzeMIjqT40HQcan4kbTWxkNqMV0U33Orpx85OXFwuEw47PP8jBwYLKM4JQoKipCkoItk5KSUFhYCJZlMXfuXDz22GOIjo7GggUL8Oabb2LUqFHIzc3F5MmTYbFYMGPGDDRq1CiorEQ2CQJAXBL/QUoKyjslKiBCnSSKuqlFbOIzjYpMAFnvILv3FXa4nvuhUQ+zAGMgAKh2wkT2R+VGpz+VuunhL1xxqflTIy49pMGo/FSrzAzAEIAwvus6LVypJoHIX/qV3rLHho2b8c++A8Bx0wA0xq+/jsLjjzfAV199BZZV7yPi4+NRUCDvtwsKCpCQkAAA6NixIzp27AgA2Lt3L3JycjB9+nRkZGRg69atOH36NIYMGYJff/01qLRHPgkCfAcdl8TPw9kKyyMB0K6cWtIZAHDUby0/GuEygnTJsJATFePZUBkxfWppIfK0cBokSzSe02kuU/INkFHLukON5E6fUXkm3WqQgMypihsPf2oEo+g0WR1xaRIJ9Y5RDEZ91mlvbUBD8iQUEcqSy8h5TfyrJkVKcVZMwpMgfEuH3Y7HBz4Fa0kxgBEAgJIS4IcfjmLFihXo3bu3qvfMzEzMmDEDhBBJYszNzcWIESNk7gghGDlyJD788ENcuXIFLpcLdevWRVpaGnJzc4POxo1BgiJYlidDeyngtAUSgFBZOfhXASm3YiOnG4z0XKUDUDYmkdTojkRq6CqNknCeYdI/FQNKr+SlJFGGAV8myrAVaZLlme546HvxL5VeWaddkUa8XjpvD6kdKuWj4k8t7GA6/YDj0uMvQHiQivBb9Z2358RP90oEMJBiAP470OEJ7iXVH5VPhoqfEKqN0eliIG8b1DdUa5ea+aHSWt7tg26zwu9dv/8Bh2AcQ6O4uBiLFy/WJMH27dvDZDJh9uzZGD58OBYsWAAAuP/++2XuPv74YzRt2hR33XUXnE4nSkpKcPDgQZw6dQq33HJL0FmKbBIkBHA6gago9z0ARFkAcxRgK4Yk4egCp2gDLH9JZKOj4mnp+CWC8CY1wk0SAddxldG/17CIovEGCxVJVEa0VIfPCM+Vz3x13rL5CLV0q3XGgDrp6uxsPb4LlSdCpUVtrlbpXkq7+NxHZy175wOa+fEjr4E+p0HnxVvePJ4LdYH14l7ZxpRtS7ynSUr6jlRbVqZdGgQy8nvVvAkSn/j9oYiTLh8pPIoIvdZNOm4iBV1+xKeIWzHQN5lMmkkzm7UpxmKx4Ntvv8WQIUMwfvx43H777fj2229hsVgkN1euXMGsWbOwfft2Kbzs7Gzcf//9iImJwaJFi4LJGZ8dQvTPhDdv3hw5OTlBRxoy/LgGOHESGPhEOUTug8x8+hWhDINVaXf0yJhqoAwL9Q5XcC8tkaBGpUo1qtRAWZVsKcLQ+k3nS61D94CiM/WQhunOiCZQ6rmyo1J1G2IoSVSTGFQ6YsmNL0Kh3oUMKlKkBxEx6s+V5Sq5VwlH9k6AGmF6Iyk998q4NeuJIu+yYlWGT/2m6zioMD0GLkQWnOyb0nWaJlO6fNS0I7RbpTbJ7UjlWRigSswiWCA2HigpgMvlQo36t+Hy5csy59HR0fj666/Ro0eP8KdVB7T4K7IlwZYtgMzMcoo8mErozS/nI2i6oemRchUjT7GxMi73e2+NTa2zZAS1scc7sfELYcpURgDUOhxlOpWjcbrByzpDtXC9dJRS2OJzRWctSRjCCJ+odTS+CMxH+XmkAdTgQ4NYZOmCd3ikQWd6OU79uTIviqyox6/lnwrHF2mxggaGfk/grk+S+lFBWOJvjvNBZMqBFK2JEMtKLA9RAyCEqxxQSt9RiINQZUQP8mTkR5WRFgG6G6u87FSfhxBeiU+ApRIgSnhxSTABaN26Nb799luZM6fTifbt24clmaFEZC+WT0oBatcDouLKOyUVBPSI3iRcLMCa+EsavYqL88HfM0LHYzIDpiiAFf6azPxvlnV3RIQDOCfgcgBOO+As5S+HjdqswA5wDsDl4t3KNhCgiFLWwMROiwOI6M8phCPE53GJblyQNhzwIAExTI7a+EAlPKdd+G1350E1Ppd7IwSxzKUyjuLLzWwBoqKFK8Z9WWKpK0b+TnIfDZij+TDMFj68qGhBxa9ymaLc38pkEr6ZyU0kEqGodNwyKKQXyYE46BHDEuoSa1ZcUXzHKObfJExJmKiLNcvrISF8ebqcQrnb3XXJbgVKrUBpMb8ou7SIn94oLRbelfDunML34lzuDlz6HnR9NrnJjnB8nE4qTqedrxdiPWIgtAsTlWeTuyzF+kSotiQb0zFyclUjLppwaAM3qY1QYYVb+lOLU0mAbBQQm+gmQAr79u3zeFapUiUcOHDAa7TDhg1Dw4YNwbIsPv30U9m7DRs2oF69ekhLS8OyZcuk5/n5+WjatCkKC0NjBBnZkqCIqCggKhiDmIqEYCo7rZ5xqT72cC+T1EIBhaSmlQDZCF34C6iM1JWNUSldKUbualKnUipQs+zTmsOiDRog/+kpfdKdYRg7LL9AE56KSg6MMP+mJYkq/IpQSpWSkMK58895KW/xu7KUWt5DbUg9k+bx6MESVfac2pw9406XcrzlAeKxDZgsHI9bkcjF/KrUS9pCVHMeE/LvoXxOlH5DSIZ6pD4R0fH8QMJh58vJEiNzX6PGHfj772OytBUX25GRUc9rEpo0aYI+ffpg3LhxHu/GjBmD1atXw+VyoUOHDujduzdMJhMmTJiA8ePHS0spgsWNQYIiLMKIudTKj+oqPMRGz8GjU494+JEfpapL9oIOUtExKqUblgXAKjp1aDduNZJU/pbdc1QalR0TFaWsMxSdqpUD7U+rAyKyPx7xin615mI9rFlpVqCJTIyHk5eLpqpckQaJaFTyoWpgoijfgKAcnNB5VwyOPKJQSmoMlRalY4V0TCg/sjJUulVOVyikLemxRhmommuEoH/wkDK9wGzhNRWiO0cJ/7fEDpiigegY2GzA3r1TAGwCUArAAaASGGYwfvopDU8+qR28uBwiJibG411xcbG0EN5isSAvLw8nTpzA8ePHMXfuXF1Z1YMbiwQB/mPFVOIbc0kRKjap0JVf0dB0pzsI8pQZRLBuNRVAdfpiGmnCgbxj85jn02jsXtNB/WXVwoY8bbSkEew3FkfcPsOi06m0aBXee/T1RHhGdZKyzlaD2L39pklMkogU+89CQWb+1CeaWKV6QQ86aIMsRf3zSAslwakaUilUtXR8RCxbogiX2m/XY5DAusMB4/5MsrIQBzM0SdHlq1KvZX+VA4swkVUo4Y/U53YkTA043Pc0XKWAtRRrVkYDaAxgL4CPAJwD8Diczn/igw/glQS9oVq1ati7dy8AgGVZVKlSBT179sRnn30WWIAaiGwSPHUMsBYDEXBmlX7423iCaGyyTsTXxqtiR+RvJL5IWoUwQ7YHrE7oJmy6I/V8HGDkAZZruECTdVnE5QpxPCLBBZIWP+tBJMAfqc/Ts+yPVEaKcPKuEjidAHALgBmyd1eu+BklhXnz5uGFF15ASUkJPv/8c8ydOxedOnWCzWbDAw88ALvdjkmTJqFdu3aBR4JIJ8HYGP8kjogHXfnClW/K8tNDcoFCeiQAYagRdShHxIxKo1VROTGKEbyW4YdMCqHC4byl3VcS6Xghl1wk9RoVP6NMh2qgPiLVI6lCVkT6pFwvYcqkNKF+iAYuSlUnoU9QUUhv4nt/4pb+iBKhVj4E6VSpoRC/h8ecISXxSmnX0ixoSXsVFAFJfYGjzb12ALEez00mQLHu3S/cdddd2Lx5MwDg/PnzeOmll7Bjxw60a9cOM2fORHp6Otq2bYuTJ0/C2x6lvhDZJJiazv8NyybaFRFl0RB9jKRVVVB64C+BK0bmknqLUpMxgHubKjrtNHEr1Gz0X9FyUhknR6vwFOo8ujNVKwtvWZO9o1SrSgMglpW/l94J+QHVsRPOTa70vCUotZ8M/nbotKRa5iI69YdmdTXtgka91cyqGqFSam3Zc6J4XwEJUZmXUBOfx4BOGORxLmTewaJ7VxdWf8+ipIQRoucQH8/ijTdCE/3YsWMxZcoUxMbGYt++fWjevDksFgscDgcuX76MatWqBRx2ZC+REBGXxJvu+hxFGyg/EOoSO3XB5F62jIOaE5J5p5Y5SMs0qCUUHHV+pGx3H0K5d8hN8G1F/Mbr0iWckWa3AnaFCb7L6ZYaPeawvFxQyQtdHtLSDWrZhkNccmLj02EXlgqISwZKi/i/jhIhP6XuJR6cC7xqm1rCoZz3pU3+lcsd6O8hfQs1iN+P9QyP8fIdgwZdj5TpUZFMQxK28n0FAj3gkxmKhShcjhOWOontj5MRoLgcZ+lXJrRosQrA3wAug2GWY9cuArVdzehlER9//DFsNhsIIXA4HFiwYAGaNm2KxMRE1KpVC6+++ip+/PFH2Gw2dOvWDfgMnXMAACAASURBVGPGjIHdbkeTJk2wYcMGlJaWIjk5GUuXLsXo0aMDympkS4I0GAaISyzHTbQjFb5Gv778KVVMtDpSVAnS0onwl9B/dUQnQ3l1REoJmCozmXEMVTaS0Q0tPcoCCTMUI3iVx768ucHI/qhbkZYXAtFORCikZTvKuhdsmIDb4Ihxb11HnwLBgCdFBm6VuCUOJhPQqdMB7NjRFzabDVWqJOPWWx9XjYpeFvHee+9h6NChACBtjTZr1iwMHz4cly9fRrdu3fDFF19gx44d2LlzJ37//XesWbMGffv2RY8ePfDJJ5+gqKgI06dPx5YtWwLK+o1DgiLETbRdTn60bMAHAu2U1dxTnaHP4EIxz0IRDj2/I5EO3Tkr5g8ZBfmLo1vl+jdQnYMHAiQYwC0l0iN3DzWomG4qTHGhNoF7gbiU7nCrK1W+7c1AOhUF9OBLOZ+pyz/xvCdK7YawxEh0K1VDKi6RFKW2x9+/9NJLOHHiCv788whmznxLMxn0soiRI0di8ODBqu5q1qyJJ598Eps2bULdunXx66+/ok2bNnjooYfw1VdfYfbs2ejbty9GjhyJV155BYmJiXpLQobIJsE/c4Fz5/nt0wyECPTcE9zkACC0PZ4fYSmlKf6hPKmEIjOJABXxKaVPj3TQBESEMFVO6ZCpnKi/YvgM3J2L5howlY3Sw8phykFHAPNb0gBCue8s4YuJXshusGNoQM+/Af5Jfb6kc1E1LoYpW0aiFRcDmKNAnHZhKpoApUVgAPyxOwFLlrwPAJgxA1i6VH9StfDzzz8jU9gaMzMzEx9++CFKSkqwYcMGZGZmIicnB4cPH0Z2dnbAcUT2nGCxFbh2rbxTcYOBJjy6Qwtlp0YTiI5GrUokovQjWiKKc4VaW6jRfrTmfqg8i2F7JsYdjziXJ27vRhwAcVLbb1UkMtDIq19BiHkX52Lt7m3mOCHvxOV/uAbkkI05qYGZXgL0pZ5Wzh3S7n3OKxJq3SB4q0zB77LPbbBaAasV+O9/fSfT4XDgP//5D5KTk1G5cmW0bNkS27Ztk96//PLLWLNmDRYuXIhly5ahUaNGePTRR5GVlYWPP/4Yzz//PEaPHo3Zs2dj9uzZaNu2LQYMGID8/HzfkVOIbBJs3gLo+Wh5p+ImgbcGKBhJ6CU1JdHedBBH4CZhj01hV44ocV/ROP6KinXvK2oS9gkVDVbCZgRGGdJopdsUxe9vGhXLG6SJhmkx8Xx6zZbwpDFURh8VFUTxw1dzolXhMrU4BZkRFyNX++skPmnISIh0eSaF4KHOJYiNBeLjAT37ZptMJjz99NO4fPkyrl27hnHjxqF79+5wOp349ttvMXPmTHz99ddYv349nn/+ebhcLowdOxb33Xcf/u///g/ff/892rZtC47jMH/+fGzYsAG33347pk6d6jtyCpGtDgXc+4Y6nYDdmAMMH7yRVUUgM3GehJ6voBs7kbtTdjgAZOvgpFEy5Y4TF3b7s9ZNDQTS5gSusl524As+vjNxydMsbqNVFvDb+CYU885hhqTuhPr8m9KtL2j6lf7z6k71ZD3RSpRzgXBOMJwLiE2Q3BKOAwOgcycWzz0HHDzIq0N9gWVZ1KhRAyzLguM4mEwmXLt2DcuXL8eoUaNQvXp19OrVC4DntmmTJk1Cly5dsGPHDqxevRqNGzdGVFQUsrKyMGvWLN+RU4h8EhRhNgPmJH6D17JsmAbKCYq5SwAy4xDN/kI5J0i/ok+H8CcdXiO8sUGvu5SsFultyUJRLoGSWQR9EzVO8pgHV/On4pHe1UVj4bwfx8jyfkwmMGYzGERL/sUF6oxgKDNrTjTmzgVsNiA3Fzh7Vj04u90OjuOkZRGNGjXC4cOH4XQ60bVrV4wYMQIrV67Eq6++qrlt2osvvohJkyYhLi4O9erVw65du1BUVITNmzf7fdr8jUOCIqIs/CWutTJwgyIcxjqBQBm/qMOiCVotrYK0yZqEW6VxgmKnFSlsrXj9RYjCkhbnh1OiLe9vHCKIVcKb1KfHmEU1bErTQd17hKaD/LzuvqLln2Fw/DiD0lLeyaVLci6m0aVLF2k5g7gs4n//+x/y8vIwefJkXL9+HV27dgXHcWjatCkSEhKwYsUKadu0LVu2YN26dTh37hyqVq2Kdu3a4eGHH0bt2rXRsGFDLF++3GceZUmP6JPlL5wGzl8AGt5GmQxTqq9yX7tkwICB4BFK8i9jyMZCClYIVNKT/Lv9eg9FQZBS0EFamir8nzhG0L5bZZw/D0ybBrzwgv7gRdx+++1YtmwZmjRpInt+/vx5PPTQQ0Ftm6bFX5FtGJPzB7DlZ3hYCFaIxbsG9BvKlEV8yueiMUAgTUAwEJEObhUuljo49kaGdMiuGWDMYTbUASjTjDDGESYoi0VmlKJBLN4MVSjjFHepiBbNnGq/xzCscDGySzeUm3CrLangOGRkmPDqq3MwaNAw9OlzQX/4FBwOB44dO+bxXG3btIyMDGnbtGAQ2STYrScwbEh5p8KAJsLVcYkNUXl5i09liYCHulEvBAMRabszags3UT0o246M2kpMy+pS97MKYB0pbWHnBL8soiItBSlHKPfXFP96G5R7IT3aGlNmmSluX8ZRu7sIAxOGDYLs1PKjtCDVyieA3Uf/wssvv4yFCxdi7NixqkFeuXIFrVu3RnJyMuLj45GZmYnNmzejpKQE7777Li5evIh7770XANCxY0cwDCNtm/bggw+ib9++sNvtaNmyJX777Tdp27S3334b77//fkDZjPw5wbgk/q+1CGW/wa+B8oEX45byhtoieN+egnhWTmBNvBRoFkieEGHfUzuCt56NYGgQhAQVUvLbSAUMGDaMgyGl5Ec/F0mYoaywCQfEVUZqaipYlkV0dDQaNGigGnR8fDwWLlyIW2+9FT///DOeeuopdOjQAVWqVMGdd96JtWvXIj09HUuWLIHDwa9HHDduHFatWoVvvvkGDMPghx9+wCOPPILOnTtj/vz5OHXqFFatWoWtW7cGlN3InhNUghCgtIQfkRswEDIw1F8taTMCzPENhA7KuT4tqEh3igeUW+k/+SONsEIOJflJhjwK8mNNkNldMGYgLh6EANnZx3Dw4Am8/np7pKd7VzRyHIe1a9eiR48euHjxonQSxPXr15GVlYXFixejZcuWcDgcMJvNePfdd1G5cmU8++yzmDdvHnJzczFnzhx0794d48ePR+vWrb3Gd2POCSrBMEBMHL9wl7nB52UMqIBWk6q8C1idKBKfN7Wf1nNG8fdGA8N3giZhPjTs84PlCCJoIKTt4bTn9KT5OqUqU8M9wzDg/ylqaihPhtCCUu0pPoPiucks7BuqlBRdgPU6Jk8GJky4BQsX3o9mzViUeFmp1rhxY8TExKBHjx4YMmSI7Cik1157Dc899xzS0tJkfho1aoSNGzeitLQUmzZtQmZmJlauXImUlBSfBOgNka8OpU3KORfACfM1LAu4xI7LwI0HpcWguDhey315SWkVZSlHuED4ecEbdSbCg7iI4rWKZCcZvQh1VCHhMWpzazTKalccD8nPC7HrwOrvnCgu5imlqAg4cQK4/XZ1t7m5ubDZbFi5ciXsdrv0PCcnB9u2bcOsWbNw5swZmZ+uXbvil19+QVZWFlq0aIG+ffuiY8eOWL9+PSZOnIhffvkFjRo1wsyZM2GxWHSlGYh0SfD7VUD2LP4sOFsRf/aa0+Y+l80gwBsYSovBG5VkDJQ5OE9LS6J6UUYvHkYkgpEKS1lmAu69Nr1tbxZuqMXvRUpVD4J4/O75sA2VKgExMUBSElCvnvdkxMTEoF+/fpg6dSr27t0LjuPw/PPPY9asWTCbPeUzhmEwdepU5ObmYv78+Zg6dSqGDx+OXbt2IScnB1u2bIHdbsfChQu9R6xAZJPgvfcAnTuWdyoM3JRg3MsEfFp/GqiwUNl7kzAMCBiPYZYnBFUmSxOeilWmqEYVf0vey5D4pHToGCz6mSYGAOE4DB94DtOnn8fTT/+J33/nEBOjz7+4LKKgoAA5OTno06cP0tLSkJWVBQCoVasWfvnlF5mfffv2Yfv27Rg2bBj27duHZs2agWEYZGVlITc3V3fagUgnweQ04LY7gej48k6JgYhAMPOCSoim6k73ae70UgHWBPem4mrpMFAuoMlOttbOF+HBY+mBzyUIMtJRLDUoy43AQ0l+RKECFsrx0skzKI2Nw8svN8CiRc2Rnf2mqvdff/0VW7duhd1uly2LOHXqFKpUqYLp06djz5492LNnj3RSvNlsRgk1wfjXX3+hdevW+OCDD8CyLOrVqyeFuWXLFr+3TYtsEhRhMvFLJWISyjslBsoMgZCZnq4uROBc0DakuRFUtwxP9OYYwBTDnypRQcldZpwCfTXAL7JzR+T+q6XuLEuElPzEMuTVxMX5BVJ5lhQVw5GQiX79rsFqJSgpKcX775/El196BlNaWooRI0YgOTkZNWvWxPfff49ly5Zh7ty5yMzMRFJSEtLS0pCSkoK5c+cCAD766CO8+OKLUhi9evVCly5dcM8990j36enpSE1NRV5eHoYNG6a3hADcCIYxNMRT5TkXYCvGjdHZGFBHWX5bmnCVc5DirjOCGxay89ZuXBD3mYoVBKpGKirLDmgEtZicjkftd3kd+6R31ZteYieEn/+kDlKOr5KE0hIbWJZBUWks7mnJ4tKlOwHMAZALq/U1DBH2MenXzx1cu3btpE2xRQwfPhyjR4/G119/LT3Ly8tD3bp1ce7cOdhsNvQTAlm+fDlatGiB+fPnS27NZjOWLVumL88quLFIkDbrtcQIh306yztVBiIe3mQHwRpZhEvsKFi5ufmNuqsKwwIw8YvmOZcwAAh/PgmgrW6kD6IVkxkOQgrCkjIsCBX5KfckFcqUc7nAms3SkCI6lp/0+++qBFy/Lp6wNVjya7UC48YBfftqR7lz507k5ORgzpw5MhIUpbozZ85g9+7dyMzMRGFhIaZMmYINGzboy6dORDYJHt4PXMkD7r6rvFNiwIAA0QjCdUNyngcIB4ADHOGRfn3u5SH1roxwKw484E0ADDZRVJykfIkPCB/5KWAymwXpWh7Ojz+4YLWqz6xdvAhcvQokJ3u+c7lceP7555GdnQ2WlftnWRZz587FY489hujoaCxYsABvvvkmRo0ahdzcXEyePBkWiwUzZsxAo0aNvOfLByKbBOMqAUmGpGfAQKSD0PNpOklFWkyu+TJEINJ/7vgU5FsuKCPy05SiBX/JVThpmaEaYmPVn8+ZMweNGzdGixYtVN937NgRHTvy1v979+5FTk4Opk+fjoyMDGzduhWnT5/GkCFD8Ouvv6pHoBORTYK16wG1YewbaqAcoZwrVL5mqFc3g2joHZIaUwsqHa5PVaYfxKkbNPHxqaA2/7kBJD9FGESFxbwu6hefcRyGDrLhv99Gw2qVOzGZgC5dgLg49SRs2LABW7Zswffffw8AuHr1Knbv3o09e/YgOzubiopg5MiR+PDDD3HlyhW4XC7UrVsXaWlpfi+HUENkk6CIOGGJhL2UXyxvwECZwUeH5M/myDcY/NoYGgqZyh+iCSUp0QveSYjDDhbhID+V5wxFcPwDMUwFKRIOYIDWLRx49lkgO9sOh8MEwASgGNWrx+H//s8zLcOGDcOWLVtw5MgRzJw5E3369AHAW3k+9thjsNlsSEtLg9VqxWOPPYbmzZujadOmaNSoEfr374/z58+jTZs2ePHFF6XlEG+//TZiYmJkVqR6cWOQoAhLNH857IDDy8Z1BgwYCA0YE98nOksBl4PnDVY4a1HD9F61i9ZLNuGa6yOU9aNyH83yRrDkp+FfOpYJBCAMwAq7l6p9NzEMl1PaEQdgeGOoSlXw/vvA5s0vYvfuuwEkw2z+CT/9NA7p6bU94m3SpAn69OmDcePGSUsiAMBiseDUqVP48ssvsXHjRqSnp6Nbt25YvXo1/v77b3zzzTcwmUz47LPP8Oyzz2LIkCFYsWIFjh8/HtQpEjcWCYowR/GbvTpKAZfdt3sDBoKBpPK8saU+NakBRJiTN5kAk0naGkwyHfHogBn/SYwmvhDtcyD7oez0y5v8RPVuIOSnw4/kgmGEnY4Y9WKVSYSCwRfLCgMdln9vjuG/PYBnnsnC2LHPA2BRp84t+Mc/ZqnGP2LECAD8tmk0Nm/ejP79++OZZ55BZmYmAGDy5MkYMGAAEhMTcfz4cbRv3x4DBw6E1WpFbm4uOnTogO7du2PGjBmqW63pQWQvlt+9C1jxFWC9Lr9KCgBboUGABsoG0obJAiJ8+zQCxQJz8QQEQoTDXF0gTjvgsPH30XFgouKk/TFBCBjhku2QwgRAgEDoilLMAyPmshwWsHsDzV/+qJKVe5d6Cd5jSKIkQLWwWOH4JPHsSIalNh5wATYrCgqAd98dBEK2wOFYjLNnd2DFCv9P8jlw4ACaNGki3Tdp0gQXL15EXl5e2E6RiGwSvHABOPJXeafCgAE5ImhNoBrhSZtHq3SqDMuCMZnBRkWDscSCMZnAlFoBh1WuRgx0h5RwzKGKe3cqpb2Kwn9SWfsmMt2gvgGhJHP3a7X9TT3jFolTDIMo/DMAwDnwzZdWXL0KOJ3NATwCmy0Or7/uf7KLioqQlJQk3Yu/CwsL0bVrV9SrVw9ZWVlISkpC37598e9//xvTpk3DxIkT0bZtWzz//POyUyn0ILJJ8KHuwMuvlncqDBio8PAq3XmAUd0QWmYtSEuGSmkvGATjXylJyUg5BOGHGjq3NZO+nfQPvi9CnV+oRm7UNmiEcHy4srrBCVukCRdHXSrh2EtdHtH4yUUA+JPnCwoKpHvxd0JCgnGKhCaiovit0mITUXGGdgYMiCjbOsn3q0JHxnHC5fLovGj43CeTJj2lipMNogsJldCntm1ZiPfq9BxAcO7yJfTlxR3noi5OQViKsESykxmvAFJ9EnfncZbyR8iVFgMlRfw0kK2Iv0qLQWxFQKmVd+Ow8XYSjlLen9MOOJ28sYvL5SY8yUiIBcOaIB0FRdUNiWRdToBzontXFywWgGX58o+LA0aO9L+cMzMzZduq7d27F9WrV0eyYrV9KE+RuHEMYxgGiEvkP2Cp1dguzUAFQXjUosrlBwzDeC5J0NpNxXfgns9CRShBGLmoLrkgRNi/VFzEDmFOFoo0iyRCh6FFlPS8GDV3KIZLCADOHR4BpG3ypGWjGupNtfjE5QeMpGz0eOfhy6TedauVkXSGoV6oLZGgy8rp4O9ZRrAEZlEjHVi27Bi6d88FxyWhY8dCvPhiD9Xg7XY7OI4fMDgcDthsNlgsFrAsi4EDB2Lw4MEYMGAA0tPTMWXKFAwePNgjjyNHjsTs2bOlUySys7OlUySaNm2qP6+4kUhQBMMAMZX4D2hYhxq4AaBGeN7c6D7xQG0xtLQejKh22PK0iB2jIBEoDYRoc3vlOjP35BKVBop06E6XnqeUSIl1h6tBCIJnRZK0GJh2J8ShkHKlcmUYeCjRVMlOMe9I1MuU8uT2q0VaGv5VyU8rbeIzD4MufWptxhzF968stT9uXBJ++OFNOJ2zARB8/70JgAOe5Qx06dIFW7ZsAQBJmouNjUVaWhrGjx+PV199FR06dEBxcTHMZjP27t2LatWqSWsAP/74Yxw6dAjVqlUDwK8v/Oabb5CamooWLVrcZKdIFBby1qAJxhFKBiIfvGChY45I0w3x6PPlc2PiQ0be0XoYZig7SJaSBsR74bcYBuunJSAj/SfcM1DrMKV3jEn4SRGjLulUrwGMTnfBGK6EQRL3Sn6+0hKoyphhpGURAIAofkuYJk2aIDY2Fg6HA/Xr19ccjG3evBkAf5huSkoKpk2bhmHDhiEnJwcdOnTAtm3b8OKLL6Jr164YNWoUGjdujMaNG6N///5IS0vD1atX8dJLL6F2bX4N4s19isTFM0Cx1SBBA+UCdfUceKnFo4MhACcSBq1Wg0BInJsIxA5eVPURQfXGCmQgk1Bo/aIQNydITpK0xHqmhQYjpoFXcfmCvHNjAUssP/3gLPXp1x2hTmiRXVkYuPhLeFoLzAOFl/DUQtZNfkGB4c+OdNjcgyCHFeCiMHjwYFSqVAnHjh3D008/7TOkq1evoqCgAE8++aQ0n3f77bfj4MGDaNKkCY4fP477778f0dHRuPXWW3Hq1CmUlpZixYoV2LZtW5D5cCOySbDB7bwkaOCmhb9bc4Ud9JyU8oWSYGQm+6zncy+qPjcR0WEq4tDs8JTPGfXHusAB9uJAPGojTMYtPuPTC19pClWd1Fiy4JEcPWGFrByJsDWloHKQ5gntYFwO7N79ODZuBAYO9B1S9erV0a9fPyxatAjDhw/Hzp07cfLkSbRp0wYA0KhRI6xbtw533303Tpw4gfr16+OZZ57B9OnTERUVFaL8RDoJArxlKMDb4zqNrdLKA6pNvqKRE+Egm2fy6pZWH3p3L1nLif4I4ReSOx383ElUtGfYahaYgUKP4UW4IBNCTQCCXB9Jk1844KtOqsXvT1rCXOeVoYec/HzNQ3oYFnn6z/6QwFrC4Oef+XMEfaFfv34YMmQIXnjhBQDA3LlzJTXne++9h+eeew4XLlzABx98gG3btiEhIQH16tVDz549kZ+fj5EjR6J3797686iCyCdBERYLf3Eu3jz4JgPxNUdS0UgpVGA0tnzycOfHnJWXclRKnvRaLOKwA5wLjCXaTX7K9AXbwZcn6Skhi9qPU1xkRjlCOLRUESr4U+cDlTrD1a6oOduApT9AZ/o8JpI9XquGazLL5pVLrFY8/eRP+PPvqmjQIBXAnV5jPXToEPr27YtvvvkGnTt3xtGjR9GtWzekp6fj4YcfRt26daUTJqxWK1q2bIl169Zh1KhR6NOnDx5++GE0atQIHTt2RNWqVXXkUx2RvU4wJwdYswawUSdHsCZh3WACAtTvBAWi5/JYSxTMxck6Ys0rHJCMJTzXmZXZFZ6caS8uF7Ou7DBZFmx0DNjYSmBM5tCmT/kdgzFqCDgNYQpXy1hTLwiRtnKTFnNLlqRq8THqVyDx6iUYLXhLB3Evipd5gZ9FJVrWimVErwX0WALCuLdI83YxghvKiMrhcKB1l65Y8NlAbNzYBe3atfC5ofX+/fvxj3/8Aw888ABYlkXDhg3x8MMP44cffvBwO3nyZAwdOhTVq1fHvn370Lx5cyQlJaFWrVr466/gdg2LXEkwOxsYN463UrrlFmD7VmFEyQnLTP1AREtJ7iYRMhVbYNFHJKQvr7cOUMQWtvKuKNIere4MJgmiGi2YfHj7PlqWpeUpWboT4Ud4gnWvihrer5wEqs71Bg9LYlDETbDt199w9O9jKCnhp6SsVismT56MdevWeQRFH6UUHR2NjRs3okOHDjh27BiWLVsGQgg+//xzREdH46GHHsKIESOwefNmbNu2DWPGjMFff/2FBx98ECtWrMDRo0dRp04dLF26FL/++itmz57td9YiVxLMzgasVqCwEOToUZA/D4BwThB6lCOODF3C7griWqaykJLKAcFLlXSxBBZGRYaaJO6tDpSZ5OlRH4OUUkKFoIiP7iyhv2MORJMRKukumDT4DlSHC36fT9nAFl4+Q6jz7ZEgsR8VpGyxDFiWFz4Y1i15W+J0DwqbNGmCOXPmoFmzZnj66acxevRoJCYmol27dnjkkUewb98+XL9+HceOHYPT6USvXr0wa9Ys/P777/j999+xfft2XL9+Ha1atcJrr72G2NhYTJ8+HVOmTAkom5ErCd59N8iJE0BpKf9RkuKB4gJIXR3DUPMMYkUWTM2jYoDoGO0RpN+QWQjoeF4R4U8j186XfiIk4ZkH0hW1UBc8lg4onQXY8YnhszpPkyDUlmYMC885Go3wpaUYJv0GP0GWt3pHFwV+YTQVhz8GSPoj1+82pAMyMSw/4vdIqzgH6pkuv+f8wtlm1MqNYUGpPwBLHIitCAzLgrAmMAzQqk0b1KtXD0ePHoXNZkNsbCwmTpyoGgV9lNI999yDOXPmaCbHZDKhcePGuPfee/HVV1+hTZs2yMrKwpIlSzB79my8+OKLGDlyJF555RUkJiYGlOXIJcGPPwaTng6cOgWMHw+k14K0z57LJexmIK7H4qiRjECIDrtwBpoZflduj4qi5T+c6jICdVP8QEE1Up+NTP296tZd3sJgGMgWXAcDfzp4kTjcnhH0txI1D+IuGn4Z4qjsPiLN18CTrKXwhTgIx9dnp4PPhimKP1NTGS5DfWPOxYcvLnJ3OtwDA1OUfC0i4QAXB8AFIn03k7AcgwFA7crEcfyaQbqOMoLkQCDsU+ng963kXJS0KIQnupfKUeOvNBCg/yp+A24pVBwoiP2CGJaYP5lf0Y8QAAMArKA3o8mMKOod5Ueq02J4lF8dkGhXrU2FSePiVeKkYbdKz/hPQRDlsuGbJT/hnnZfwma7irFje6BdO/+2LxOxdetWPPzwwygoKEBcXBxWrlwJgN9X9MMPP0RJSQk2bNiAzMxM5OTk4PDhw8jOzg4oLiCSSbBSJWDGDNkjBgBKbQDrXrRLxM4JhG9krBmqa7L0Viw/KmDY5oyocIk43xKGsKUIdObDkwB9kYsPiUcvVNLoX9mH4DuJHayYHjHcQIL2t94wJsBiAiwxvt3yHgCzgiAtXkib4Q0iCFF0F4TAvd+lcMgqywKsxe1GOpBVqAsmM0/Q0XGKSMS6QA0ACPWcJijxr0hKEjkCYIQ0ytSDKsTF8aQu3ypNDI/2Tw1AZBtM06prRToktwo1Kh2mSLrul+5BBbWJNRH9SG6Fe84lTO9wwibWHFXXGHd8sjoo/BDrKb3HKsO4BziSX8GPjz5AbGur18ahuHg0CAEWLgQC1E6iTZs2uH79Os6ePYsFCxYgIyMDAL9u8NFHH0WLFi1w2223ITs7Gz179sQnn3yC2bNnY/ny5ahduzY++ugjVK5cWXd8kUuCAPDXX0BesuaC7QAAIABJREFUHtC0KX+aBMCrORHDV4zSYv4Dmd2NUiJFzunuo1mz9j6BCsg6eh+VQ69UFAxZMn6OMAOMwCfU81oGqk6Nb6BZ9v6sFww0PQDcavgwDob8AT0e0ZscRRl6eJPlSyRERl4Guk+ZUEpeyvA906MvWEb9txKqakCF1kepFvQWDu2Wfk2HLW4F5xEvq7KxgmKQYjL72DPVS9pk4brzSGR1V5FimV+3Fkry43Li3ua8wB8fD9x3n39JU0PNmjXx4IMPom/fvvjjjz8AAGPHjsXYsWMBAB999BHatm0LjuMwf/587N69G++++y6mTp2KqVOn6o4ncknwgw+AiRP5Ur/1VmDbNv7kY87hNgVWAcMwghrUXakI4XgVjbhruskEwnoWjccxM34ToLpkJDvXTceIqyLC37Txg3z/OjXVOPwhQECuBQhmjkzml/BblSnnwgS1I4FQ53R8Y96foNIH3AYIetLjY56T0VvckipWa7Ag5Fey+BTdsdQzMVI/y1c5yKTjlL2DQgIL4jsqIalbNd7pDYd+rfLMM6QANQf+pocmd6pOqrYvSkB2P3CfLA+nHTBFgTGZ0fLW7ch5522cOFcFnXvUAki2vrrrBU6nE3///bfH84sXL2L+/PnYsWMHVq9ejcaNGyMqKgpZWVmYNWuWX3FELgm+9pp7feDRo8B3K4Ae3QMKimFYDWnRxY+23IN6hT9/CctLh63sPP3t3HWmq6zhN9kxwVtg6s6/V3cKqUbpXuaXkabnlOG71Vnw/PxapMiwcnWlqLlgGPcctrKOKOchxXk/cX6NcwlpYT3zpjbyp9V6TsGyWpQ+WJNcUiEc4HQBxMXnUdzfVFT7qRWN6lMvYFQINlB4q48i+Xl8EzWC8F2vy5T8fBGf6EbNnZ6pDKVqmRB+VySxg/y1L25PKcDtKQBOVwLS7wMy+smCOHLkCF566SXs2LED+fn5uHbtGpo2bYpGjRqBZVk8++yzWLBgAeLi4sBxHEpLS9G6dWsAPCE+8cQT+PHHHxEXF4fp06cjLi4O9erVw7p16/DOO++goKAAt9xyi77yEhC5JGixuEmQEEENGhqoSouSWoCTGrdqBfezkQYk3VQANax6vIB6sy+b+IOC6qgfQXe6Xs9y0xu2cp5Nj1+JMPnfRHbKg9BpqaobFZ0fw/K734g74BAOsNv4zbJZM/+cNfFaGLE7EQ11HDb+L2viN9mOskhpIgBPrM5S/mBXhuXfmy1uSYWBsLyJP7gVHMfPJ7Jm9+CUc/FGO8Tlbhcy6ZT+S5cbI5WNrBR8SbE625Yu8gs3oatJfHpAk52WE5aV90NmE+Cyuh1wNqD4hIe//Px8HDlyBHl5eQAgbZa9adMmtG/fHufOnUNUVBQIIahatSq6du2Kd955BwDwzTffgGEY/Pe//8WgQYNw/vx5AEBqaiqioqIwbdo0NGzYEMuXL9efV0QyCX76KdC/P98wOnUCOncMa3Tus8T4zkSqUorRtJ6z34KKn4pLDf6Y9QdKlv5KdxWK7Ggo8+B1zkj4zxupBYNAyVJdwy68c3eAqtKHEnrm7xgTEB3LX8q4pPlWljfS0TDUkeqdKFlGy14KlqsACON2I3a4RNDQOEoB4oRotANzNB83bX3qckJS4ciMWah78X8ZYXq7F9TNrKD+FUmXod6J7iUy4QmY0POeUhqootXbTkJlxBdgu6QHuwwAmGN5oSSlBbjLu8CiFISNBlOzm4ffe+65B4cPH5bur169iuTkZNx5J7/F2qOPPopr166p7jZz/PhxtG/fHp07d8Ybb7whnSA/evRoLF++XJIY/UXkkuAjjwDXrvEL5sV94zgOsBWD38g3vJAfsMnD05SZAyGMR2ULJynoDTswsvTW4/oXf7nAV571lImuMFTUW3rKRXcnCHf4jI40BROXLF5fnSq1fMFLXF7riDS3qPYcFOlp+GctvESJSvrUlTQpSmUpEifnSZoe95ybZGm/alauqoRMeLJn4FZXK5dxyP4qfsvmRBUk6zvzmq+8t2MiuSGEAM4SME4nXlv/P5j/noWaVc/imz1P4Yv770SqjyT8/PPPSEtLQ3JysvRs9+7dSElJQdWqVfHkk09iwoQJMJvNaNSoERYvXozBgwdj06ZNaNu2LVauXImUlJSACRCIZBIEgJgY/hLBskBcAv9xHXbhyI+yg6fERDVml1Oq3OGSFv0BHaeHVKvtC76IUA+5BpxfrTk0rXmcUCOURBYopE5Yp3t6PlJPGflDqCJxMCz4SVGXO87yhL9aCuV8vL/RqYUrj0RfODQ5eiNVEPe6Zy2SlsiVTp0irx6kykpuiGTd6jmIl2eNJ8ISmx3TP6gMp3MCACA6Gvj8c0A4DF4VZ86cwYgRI/D+++9Lz9q2bYv9+/ejbt26OHDgAPr06QOz2YwJEyaga9eu+OWXX5CVlYUWLVqgb9++6NixI9avX4+JEyfil19+QaNGjTBz5kxYLBbtiJV5IH6IBM2bN0dOTo7uwMscokGLy+5eQ1PBIC3o17Sa4nuscBFjuFSZ4dgyLeQlUN6dsz/wtzy9zV8FMbelK56KAj15og1E6N96oJByQ0J+wUrx/qj0dUKm+SEKEiYcbw3Kygfz9mIHEmqlwOHg44+JAaZNA0aNUo/j8uXLaNeuHQYMGKC5swwALFu2DNOnT8fvv//u8e6VV15B/fr1UadOHcyaNQs//vgjhg4diubNm2P48OEe7rX4K3L3DgV4i9Axo4HifP5w3ZICoLSInw+ogAQI8KTCsCZ4LLdwuwAAEM6FYPbklMaMRLG3p5a1HqN+SoNnwCphCHNOeq6A8uDjUmRE+6qIoEf9MgmAgjIP3vKm8X28hq9EJJWh3jzR7gE3ofkr+UK93snqt1pZ6fnOatD6BrT/YL+PZppE9awQtnjChIvvm8CJOxU5YYkz4d3/OBETw68TrFcPeOop9eiuXbuGLl26oEePHl4JkM+a+i5U+/btw/bt2zFs2DDs27cPzZo1g3g6vThXqBeRTYIbNgHfrHSv74tAaBKPZKJOAGFjcLXNqr2SnRbhQSNOrYaqFp7uhixvoHqPSPIHMlIkFXRzb386QW/E421govw+vhApRKcGfwkMUCcQz4Ddbj2mN3SQHx2+vwRNh+OLSDXSqAqNukcIB8IJl8sF4nKCOJ28EOGw8VbAdsHCV9inljGZeUtgl9Mdt9kMhjVh1FgGnTqNQnLyo1i0aDfi4z2TUlBQgAceeACtW7dWXdD+ww8/4OLFiwD48wbfeust9OzZU5EdgpEjR2L27NlgWRb16tXD1q1bYbfbsWXLFr+XSEQ2Cc76EDj6l2wpw40AGSGwLG8SLi1WdBO+b7LTksZ8dcg+OkRvkoLHpfDnbxko0+9BlvpVWmV6EkYwkpee8G5EqU4PAiEV2q8vyPYmdbtX8+lRgsHUHW/fRZlnLelfeXH8lmrE5QRxOUCcduEv/1taxuIs5TcZIRwv9ZktYCyxYKKFyxIDxhQFRhiY88t++C3kGEs0fx+XhD179mDDhk9w8uQ3eO+9t1WzuXLlSuzatQuLFi1CfHy8dJ06dQoAsGHDBjRu3BiVKlVC165d0atXL7z22muyMBYtWoRGjRqhWbNmAIBevXohPT0dqampyMvLw7Bhw/wr+htqTtBeWubGMAGBEO3KDvUGFygY8X+GCrgi938e1ZEJSXorxJyl3lF7IIhUUtMLIv0XeoikpxWt0rme8ADv39LbYMcjPXTjpVJGhEGxKNmJRjTSUg3C7+xDhM3SiRAvdUBuQLYHUTH8EhUxTbGJAMOguLgYTZs2xbFjx7B8+XIPCa68ocVfkW0dqoQlmr84F2CzoiyWSniFt0ZA/D76FwzdGJSqGo+4CADGvRJK8lbBOku1MgpDGsOydETrhcsl00641c1w96AG2elDuMhPUzUqthjPcvZ4okdq8+We9if9pk/FoIhOWKfqPslDsOaUJD/x9A5q/s5kAlhLaNq9JY7frADgN0lwOUGvjaxUqRIWLVqEo0ePokuXLsHHV0a4sUgQcFcIk5lfNBuKBhSkFOG3b8bb1mHqbzwruTihTIl/hBPaiL7NwsOCMiK9QOF1hxcKqi5Efwr1vHy3Hx9xq4VXgcpHHWqSSpAI9xyupuSnQX56CczDswrZiu1SVFnS6xEZN8ER8Z41u92Jp0dI78R1k1EAEx2GtszwJ36obdateLZ161Z06dIFDMNgxYoVWLVqVYjTEh5ENgmuXg38vBn41wT3KRI0wt2QVCbO/YrXK9kFD9mcGSBft0gt0wjrukW1uQwtdXB5Qa8RCX2r5l8lT/7UQJ8nX0hJqUBlByBkBBju9uotapVnXslPb1rF3W9A1X2a9FgWhDFBGkiIexY7S/lBvEiColQXFQ1YYsNfBxhhqzs/7C0OHToEALBardi/f3+4UhZyRDYJjhoJnDwF9OwO3H2X8FBU/VF/ZfNK1KSyvxWJuMMi0r2GO2WnWQE6LvnWa8L2byp5UHsW0JZOWhP95VkWARCe258OiUf69rxbXeVGAO/KcV+qbw1fFaDO6UJ5kR/DCFHrHATqTaes/TOCCtMdJ9/2hPrEuXgDFY4DGEJJdrEAU6nsvyFr5rfEC+D0h759+2Lp0qU4evQo5s6dG4bEhQeRTYJLlgI5vwFNGss7H34IB+l8M4AW0yi1hG8rORWK8K7SArwavfiFMmgAHrvcaKmJtKRFPaRXVqDn3ADfZe63ikuH2ksWpt6y0EmW8HPOUnWeGKgwUqWe7xNqgqTqrepgT6t96zmHkhGmIGSDTQaSRTfDupdziZbTLH8gcrkPWExmfs4viHRUqlQJ999/P1JTU/GPf/wjhIkLLyKbBFu35i9A2De0UKGaoiuj96D8VmX6DtI/qKkIy3KELMSt1hg1pUVFmsusGXs1OFJ5plaugZS1zCwdocuwn0tHfEGbKEV1nAui8YTHvHEQ8eqGPxKVXviyyKSWPXi4IIQavGrNy1KnsEOjjGmCo/1TUhVT0ZZzmYUTQkLwfdevX4+pU6fCarXi8uXL2LhxYwgSGH5ENgnSYFkgLon/7bQD9hKobU8WENmVxSitPAnQW3zEU0qRqXYAYcKeA1FMlEsdi1Ll5E95ei0HBh6Sn7ID0gojIPN1uOPzp0rQ/kJJnhrQu0G1e/NoqHTcFKlwLv7rsSZtLQfhAJaV1pKpIlxSnZ6wRclPbW7XS3kRrXZJS3eK9Mh8lLd2RAtRMe5jq0KElJQUcByHmJgYVK9ePWThhhuRTYJOJ2C3A3Fx8uemKCCaAbHbBAtR6NJxl7tKgkZ5W2x6SYfHE4YFMYnlK0qIrOq8KeMrPr+gorrywyjJb/hFfFSHG4iGtAygdhKKxz3DyIlAPEAXLrfBhjTPJWoIOL5tsqxgWKGjDgUCnd/aY9AGeFgBewyOGek/dTCsvL+o4FbPEsJAfiLuvPNOPPPMMzhy5AheeeWVkIcfLkQuCR44ALRpAxQWgrz+OvDiKGk0KhGeMOlMXA5+hGqxAKzZPVqtiJW0rCE1XiaonkmmFGW8zC1q+itjhIyIqXKTEd+NU7c8DKosKio9WtISD8iVOwD9tYl0OLX3uTbZoElPmVLk5u0Lex57JgzeVM5U1B4cqwzCKup3j4oVllGEL31DhgzBf//7X9hsNuzYsQNHjhxBWlpa2OILFSJ327RZs0Dy8/mFyW8LW/RwwqnXtmLA6eB3RIiJA1spCUxcAhhzNE+ARDyJWlyUWr5ZKVNIC3CFe2ldUmijUdvyTDU5KpefMVF5UFxlAuIuU4Aq3zBe5QkvaVLd5k66GHk9kAaiijMI6eUDEOqEII1KdUTc71K85yOHOOepWY8UYcvAMGBM7o3tdW0iT+T+KyYBMrxmjGEQ7o7uxx9/RElJCQghYFkWu3fvDmt8oULkSoJ33w3ExvIbvta/RdghwQKAGoEKDcdTR2+iKrJLNk9SMStyEFCOosORP51GJe5RPd8gtXxpSov+pl2vez3zdGrGNPS7skR5E2GQUC1FqX66dyCRS2tuNbtbWiPSdyEckaswCeEHxSwrDxtBTHtEispTBsJPCYnTQmB4S1DxCmAphBbuu+8+rF27FjabDU6nUzotvqIjcklw+HAwSUnAqVPAM88A5mgQp03YLo3h9d4mk0SEEsTOWBq5sW4yJBAaWASrtJTqzfIgPZ/GKYLkoOJH1RJVJd6g529p4tMTlE9jkzJCqAnQG7kHEpYobWkZ0WhBxQCFfiJtFSZzA0qipNIAgSyVlpgc5+4PVI2ohEqhlACVZR6J/YIEFVIUd5wJkhQXL16Mfv364dixY5g+fTpq1aoVmiSHGZGrDmUYoH9/YPx4IDUViI4FU6kKmKrpYColgYniD36E0w4UXgMpus5bjLqc8Dg5AHBP8hMi7MHnqhjqJ19QpjHU6k1fqjg19aMe1Z2G6lLvkUpBn/zgrXzo8Cra59dS/fq6vIUXEAi/fZfLJdc2sCahM/UzXKG+0G3SU0WuUteVYJj/Z+/N4+yoyoT/76m69/btTiedhYSIEFkMiASCQIAAxgQU54MaQFFRFFAgE5EJiyDigiIgm87L5sbMAAPyDgJjXkEWQZKwBFDAgYRBWRMFwUCS7nR3uvsuVef3x1nqVN2qu3R3kNbfA5Xurjp7nXqe8+wI4aWKZONcYcp+de7LMNTZFipIGRe9umnNxj5ofFcehME+lZd1qF8FyM7KyVqtKLVTApYuXcpvfvMb/vCHP3DmmWemVl2/fj0HHnggU6ZMYeLEicydO5eVK1fa59dffz2+78cyTKxYsUJ1W61y9NFHM3HiRP7pn/6J3t5eW+973/teLEN9KzB2OcFSCf7rv6C7GxYuhJ12UveFgOI49QLLQ2rz59uig2IYqpc9uBnpeYi2DmhrR3r61KiD0gqBDnkklfj07SDzl/YfRo3Ts+JJt+0GENPhtMAVDgMa+i0acVjKOGIO/cMdx1g79L9liFnEiZ3mspqrmv6+GvcYHe7Sasd6T3nnmaNLfNuWU0xys4bTteWlZiATotjULsbQRgoDbVSoCZ3wlF4xl1drUh5Q96slm0EC4M9//jPlcoUgCPjLX15Lbbqzs5Nrr72WmTNnIoTgl7/8JR/72Md44403yOUUOZo7dy4PP/xwTd1f/OIXCCFYv349xxxzDNdccw1nnnkma9as4fbbb0+t0wyMTSIYBLBgAaxaBZUKnHsuPPgAvGdnxcHVAeF50DEBOiZEqL88pLjF8iAIH9HeiSzqoLGOiEcY0QG8NQQxhihEww+t9TbtzRHUH0Wus0mIWytqhJqC9JLEMtPIwRD1Lak3HW3Y0sTOPUA0Mw5jmBLn2+rXQb3LurFrE89SiV8GR9io76znQohUK1FrT2DHkrZfEodK/aftNs2/0O2i/uhS+nsLQIaK4FVL+m/nWxnshbZOunt9/u//XUQY3o4QaxgYuJqlS+HII+NNFYtFdtllFwDCMMT3fbq7u9m4cSPTpk2rO4w1a9Ywf/58crkcCxYssBnklyxZwg9+8ANLRFuFsSkOfeIJWL0aNm+Gchm5eTPyBz9AhimiTkmNaCUpZhGFImLiVLxpM/CmvhM6OqE0AG++inz9ZeT6V2GgX7UXVvUVjD4iSooeYyKtUWivpfFaIZJzK03UNoxxjTKY5MPKOjFbjJqaQPftTvjS3uFo7LtG4tMW+lHG1vq7ICnCrDMEsLq/mAg8Y541x68GIvP0Tg0Xl7K3IRKZNnGlWZIK7T8Y34vuoc1x4QJsZghtyCPDwBEH14qFFeOZsSfqXaMEqa4lpX7OODXg+eenEIaPIuVfKZeP4phjYNOm9Hb22GMPisUiCxcu5MQTT4wRwP/5n/9hq622Yuedd+b888+nWlVZM2bNmsWyZcsolUosX76c3XbbjaVLl7LVVltxoIkcNgwYm5xg1iktCIAwLjasqw/xkI41mnvf5RallMhKCfo2wuY+9QFMmIxsHweGO6zTT9ppF9Loh8h60BhGstHrIpK3KXFwwT18W5pWhyt0Km3RDBqtwGgfqIYzj0Y63NQqEuELpMxGJXXTU9Xh+uztlDrDektZOu2030cDdHt1W3U5y4hdxLkZMdhSKtwXhsiwio2I5XlYlU2yebed4UCKqLdGgSIlt//Ko1yOV83nYflyOOKIxJiEoKOjg3w+j+d5PP/88/bZiy++yOTJk+np6aG7u5vLL78cz/P4xje+waGHHsqZZ55JR0cH22yzDZdccglHHHEE9913H/Pnz+eVV17hwx/+MJdffjmFQtJPNRvGJie4996wzz7Q2Qnt7ernaacoi6dKWSl1K9oCqjykuLrBfhjohb5u6N0Aveth0xvQ/Tr0vKF8C+2pLO5PJIRA5NsQXVMR79wJsc2OyI7xyIF+ZH83lDbD0GZkf48yxElAral3aH5LXPq/FI4lukJkEEQnxgxDgrrcb7NGE2MFGk1Bi7KTJ/b0oinc4mjCaJ7U63EqwxmT+TPtytiPBEoqksoVEXF7meOuswa1HGXU7kjmFxvD2whquUlPSzj0T+3HiOcp63cT/QWh8M5gH2zeBJt71M/BPmRpEBmkGPfQACcI7ZvpeUgiH03C0B5qorB7kvGdtUY0UsKkSelzffrpp9m8eTNDQ0O88cYbPP300wB84QtfYPXq1fT19fHcc88xffp0fvKTnwDK8GbPPfekVCpxwAEHcPzxx7N48WLuuOMOnnrqKf74xz9SLpe59tprW1r3scsJ3ncf/Pd/Q08P4rDDYLvt1LOgihzarOTXRn9XKQFVQOfjSnJ/RkQRVPV9DzyhCKLwACM2i/QkAgmdE+2HrE7Eejk3vYksDSI6J0F7p2rHcosj/fiEGpsBHbezFZP0v5VV26hwWe4xtG45RxKQ0e+IM2g00/9IYAsj6eEZpyQg10Iezyb1jJJa7rHlveNKg9LGMIahZi2Mz1+hGLttQ9iFobLojB3wtSpDqDyF0vPj7mFZ4CmXMrOyQigCefZXSpz1jXY2b1b1czmYNg3e//7G86lUKrz88svMnj2bnYyBox6/53kMDSkDHVcnuPPOO/Pggw+yaNEidtttNxYuXEg+n2fOnDmWoDYLY5MIgvIB2nlnJXR2jxt+DjGuS734SglZLakN4umNol8i1YoijqF2lrem3YCs2rZUeRIiNwHCXTpprVCREiZMQejNJvu7oVpBjJsACEUc821QbKc5bN4AhAd+NkPfLKLXD7cckggDJXqW+kAwkrm3grv1fJtBosMlijDM2bxFCHlUiF1NgYwSWSLHRhxvQmUgW3hv9cegZYl/B8SvFYi4O1/jiDqo3hDLqnYNCx2E52lc52sCKUTN5yeE4J9PKFOtwncubKe3T/CRj8CPf1yruXrssccA5VgfhiFTp07lr3/9K/vttx8Ad999N2vXruXss8+mr68P3/c56aSTAKUTvOGGGzjuuOP46U9/yhe+8AV++ctfsvXWW7N27VrK5TIPPPAAe+21V0trNTaJoJRw/PFw223qyDFuHPz2t/CO6dq/JbIQFUIo4gRWfGMJX3tnQicRKrPgcikiCH5OiR3MJjKZoj0/MgsXXpxAGkGQkIogG/0TyoADPwdDA8j+TYogTpgU5zSjAZlGR7BUSQpeBxKIYjT1Y5ELyt9my21R7lfKKMh04iDxVuoYtwixq6nQDPHTXIYjLqvXnkwpM2LiZwhvQp/19wJ2pqMlcRA6mS/5zNWSUjrcpFC4V98XAk5ZXOZfr2xjY7fPaafBO95R20apVGKnnXZi3bp15HI5uru7mTp1qjWMuf/++7nxxhsJgoBtt92W7bffnq9//esAHHbYYTz00EO8+93vZurUqXz1q1/lkEMO4e677+aQQw6xusIrr7yytenLFr6cffbZhyeeeKKlDrYIrFwJH/6wsg4FxRUe9XG49t9aakaGgeIIpdQiBZdDEfo7CrWesax0jIUiFNqtXs+KEFzxm+1Abxr7IRoxREIsWikjh/oj9w0pI1FubMBv8YnW7a+BaXcaDA+R0bq4s2HRt0j8G0vdJY1c763puwHUHUU9wxW3jAuy5heiPd7MgFR7IzJMMnslSfz+DiCa2pbZu7FVko4daqw7GfthyhpxqigUNGHUthTVMg/8rovHnu7kK19RhjGNIAgCJkyYwGOPPZYaZu3mm2/mlltu4Re/+EXNs7POOouddtqJGTNmcMUVV3DPPfdw0kknsc8++7B48eKa8ln0a2xygm++GQ+JFATw+l9bbkZ4UUR8KaVSLgeBVTxrgYxyttfcJBh1g/7gJVAZVD/zbdE9T0ScpB2nsejSJ2U0Ac0XEPnJsXEIKlq8GsZ9E8MQvLhhR8MPxRAzo+NsFlEk9aZm9g2Ru9T/D5OTtfQtiXgb6/lq25Kjxwc06tNNJ/Q38J9MBf3u6+6QZhBtvTKhCZWm9lZDgkvKnm3F4MXV972NDhrNQpzOZK/raJC/rGNJ7J45lMswMi50D71GeuXnEb5Q33YYKv/qXF6HYPMQ7eOZP+cZ5r/nBfA+BbTV9Js6RpEdPKFarfLSSy/V3F+9ejWPPPIIl1xyCZdddhl77703Qoh/IJ3gAQdEiFBKlU/w6E+OqEkhhLK0yjlsf7WsCVke6fv2g1P0y+GICu2A3liW0OVUOKIgiCItGIJoxUQhyqVDWOIkhIgU3CZ0WxhEukndrwwDGNysxtfWrjJmZCEDc9+PxBcWjLi0aQ6vGeSeLKOJQoucbCZxb/J0PGzUOEKk2pIedpT6SFp3OgVHvW/VSRghz4TiR7qHlfiTbMzuGFu0PpYR6A9HAlk647d2FHWhlbEI4amDt3PgJwxVKyLCUQgQnoccLClr0baiOgCW/wz3HwwIWLcc9q+10vzf//1fzj33XFavXs0LL7zAhz70Id75zney6667AvDv//7vLFy4kGnTprH//vvz29/+llNPPRVQBPFzn/scd999N77vs3Qsbm9VAAAgAElEQVTpUjzPY4cdduDss89m4sSJPPXUUy3rBMemi8S0aUok+sEPwl57wfe/D4tPUSF8vCbpuiRi7RNKeyEEws8h2joQhXb18ktDUBqEyhDWVcF8uJogCyEQubxyp/B8RK6gCJrnR2GIqpWIgHu+5R6UQloTUItENOE0hiQy1IRTE62O8SpEHAI2vUn45qvIgV47tiyImbF7nhqrSDFxN0S56QXN7NF0nKknGl6fjUdUezXhTjLKRGskc2r4TlIMTkTyEvVSHKWUNVfdcp5yO5EyVl4POoP4phDFkYKzn+q6FTnxPuteJk1TM2VJ3z9vR2jm3aeC7+CgpM1AcRyiTeHHASbw4ys2sHlAEFSG+O2K13n88drm1q1bx8MPP8wrr7xCLpdj/fr1/OpXvyKvZacrV65k9913p1gs8tRTTwFwwQUXAFHYtO9///tMnjyZx3UH73vf++jt7eXCCy9kw4YNLFq0qKW1GZucIMBuu8G999beL45TP3XsUBstfQSITXie4rYM214tK44PlNjU5RKzjEuMGTMayZrLJAE2Ih5DBI3bg6un1GJR06aNeCIEdE1FGEOdvh7kUD+0j4dxExwXDS99bI3m31S5qExdImLaGiG31Ejklt1ec3MeNiEcZb1tqs6s1bE1U9417GkWXGmMOz6365Q2ozJ11il1HCnCPfMtZK65rN9PrD/z7lp5f9LpokG9LWSoE2ttOPvDrdsAYla7ei6ybTyHHARPPTWPFxZ8lz1mrOJbt17AxvPhwQeVW7eBgw8+mHXr1gFw0EEHceKJJzJz5kz7/LrrrmPTpk3MmTOHG264gblz51IsKsmYcZE46aSTCILAhk0744wzuP3224cdNWbsEsE0kFLJs6tOBHRj/tusLqxBGWttaixOgyoMDQIS/BwyX8AqjtMi25g2Yv0ILS3UHJ6UIDTHVy0rYh5K5VaRK6TucSFEpCedMBnGa7eR0iCyWkYUiirSRGlAcbfFjobc4nChJnB1g49yOJ9sqzquVuYzIk5wlAhglp6koXtGq0iwVeJn2k9y9UmRaEqbdpzDXt86+7XJJ5ktCKVnb/bdRwfRup3X9BERQjOake4X6YxhBAQQ0uum7Ofkt3T/3QM880wnQ0OC/3P3V2LPzj4bfvOb1obx9a9/nS996Us1WemNi8Txxx/P8uXLmTdv3qiETRub4lCAclmJQ9+5DTy4DPq7VRqQyoASKxqLpSCKy6fk2xoMIUpeSZDSlZPVXEJnrxfFcSrSermkItSUB1U8RanjARrjAdteSls4IinPRwhftVkcp+KZ4sFAH/SsU+lOnPkkxT12mm3teOO6lGFPoU35K7YVob8b+cYrSsSbAvXEQGljT73M+jWApkV1DB9lNCXectZuJH3F9tkIxtt0WfcaDpfYSp209yqEmnOgvjuZaHNEa9kiNBJ1161r371suOeGHU3IwTEqumjr+z8+JoO3ttAKS6mzSlQj9zD7SM3/oYc9+vvTqz/6aO29RYsWscsuu7By5cqazA8///nPufHGG7ngggvYYYcdYs8OO+wwXnrpJTo6OlixYgXz5s3jvPPO49JLL+WII47gne98JyeffDLlZPy2BjB2ieCqVfDww/Da63Dt9YrbCwLt8KlPqp6vfFmM+4N7Wm0Fkad9Qmkncs9TwbiLncpYJtDGK0MDUK1oJBUiG2S6iBo0IZQUQRSepwjuhK2grUNtysE+RXSlM0ZjUEMCAZiUKMJDdE5CTNsOCm0qnFx/j1pD4zJSByKEmx6KaUvDqBLNjLm2OpdYn156PrstSeTTxj7cq/mOnIOdEEjPrxFLjiZqHq2xZ667nosbJmy4e/qteM8j2WMtXRYHaaM8fQAw0opJkyTFeLAaCxMm1N6bPXs2P/rRjxg3blzsfhiGXHjhhSxevDg19Nnjjz9Oe3s7AwMDHHvssRx77LEsXryYFStWsGzZMp599tl/oLBpALvvDvvtB3/4Axx7jLpnCJ+BRvoZIYiZW7v3G0EzYtOC4r4AJTYtDSjC6PvIQtH2nyU2re1Li0plqCLSeB4UtcO/EEgT+9QkB5YhahtLkvpAO2MTLNyAp5FatawIYltRGRulraXbZhiq06LnR47xaVNJ+2sLswlu82mm+aMBLRHMRntnBCKt0TiENN2GgxTD0hB+vmD9ZrNVdBmt19Xjtgb1clDWM8dvdi+MyHCr2b6HI9LeEiCzrXYNITz6k1W+/h1J8kNub4cUdz2+/OUvA+Al8F5vby/PPPMMb7zxBtddd529v+2223Lrrbfy2muvcdBBB9HW1saOO+7I2rVrWbRoEQcddBALFiygq6vrH8hFAqCtDR54IPrbIO7KkM4m4YhtYptERl95zd7RooVW9CNNgogZxoRRkG8BMl+MxWCs+5EJAcIHqWOaGjGrUIF2rRuGazwD2tCmCcbfEDCvEJlKlwYVQTWWrq55vJ2f4rRjK2fXPvpA4isrSd4cNcvQYSDU4fZd43LSoJ1RNQJqomzTHFIrhMjE2dWWxX6bwwrIAClFa8jZFZ/WGUc9Apf1d7PPstofNmQeuETyZNYC0ROtbYhmQTMDzYzCftF6PtOnww3/PsDnT+hgqKTu5fNw4IFwzjm19cvlMqFWGQRBwNDQEIVCga6uLl57TSXjXbNmDQcccAAATz75JFOnTuX555/nqquuYmBggO9973t89KMf5fe//z39/f3kcrlhh00bu+LQJBiDlY4u6JykLCNtLFCHIIbaN8+8bhM8u8ZwxnBSdbZFQ3EqqZtbCA9RaFf6ufbx6uZgnxJLlgZjJtp152uInnELCatRWLjQJAJG6R4SIrrGoEvJUK2rtro1JuR2WYwIKXFitO4XVqTbXK+NdHXNlq87s3puB63oPPUVFx2J+M/ENRwYqTiz2THUWKKSvlYIYc3mpZRs2rAp/n5bCchgO3f12/WJWCvvOgtG5g6UObg4QTMHRdfewBwKa1QudUcbtdHKELPwkXNF7h6NCaA1Bkp5dtSRVS45f7ONErP11sp4Py2j0aGHHkp7ezt9fX1cf/31tLe38+CDDyKEYPr06UyfPp2pU6fa8ltvvTWFQoFZs2bxiU98gl122YW2tjb+9V//lSVLlnDTTTfR399PR0cHy5cv51Of+lQLi/T3RAQNhCb+p7bYNNHRzQbyPEU0hI7WYg1oKhEBkZpzcYljXQOarO0TpmzEFASayyPax6s4o/mCGvvAJigNxFImZbYDepzal0dlOSWZEsodf23am4yvy0SMQK2d8DxEtQz9G1V6KssVJlbEfFxOotB6vojNIKC431cTxC7jqvtORhNS9k2m3gUHMbtjHQbU82HLIpIprVhDm1TDKDtFQdeUrqaJUfJdR4T1rUVFwzk8JRrIIHrUfg8j3mPNHcYa+zHW3ouN0Rg3BVWFD6v6CspQLSMrWkXipIuzfVWrfOrjQ0ye3I/nhZxxxvrMc9CKFSuQUnLggQdy3XXXIaVk/vz56TOXMpYx/vTTT+eVV17hxRdf5JZbbmHevHl4nke5XGZwcJAvf/nL/OhHP2ppdceuOFRK+Pa34dln4eKLYJtp2WXt5vSjuhAhcOEDWo9miEioHdOlrm8IKWkbPQtdGcGBHXTUXsachOcroxcgFrc0X0Dm22J1a1qx4k6jE9SE0BB0t++Uj7GxKE2XaOuIDHNKg4pom0DjbcV4P854ZRggA5V7Dj8X576oXa1sMG3K2jm5/TWYW3bzW0DeVA/5ad1wTRnhHE0sotX/ZHHGpr1WhpY+qIySzn0pFVJEqGhLQjZ8h62ILpsD4bz+Vtpy5xLtI+niBsOBNaxPbA8P9zA1ykew1sAe0jIOI4l1kKEyijPJCKTncdyXPk1//yMUCiHnnuvzwQ8+khoPdDRg3bp1XHPNNTz66KPccccd7LHHHjaV0hVXXNFSW2OXCD76qIoUUypB3od//2l22eRGttycNjQxok+JumezQrhGNi5h1Mg2KUZN+2Dce1KAaH6rC+EpPVy+TZ3hzAnNz2l/wQjpp358wteUJYzMmw0H3Gis5lbG2KTUXHZ7Z5QzcWgz9G5WFqht7UrPaZz+dd/CI8pJltQ1NlqPtDvCQR51kH+9Vd+i4bYaEr8U7kDUItdY+TqzEcn6tpnRQLG63aAaBXnIa3mXDKA0hPTz6TkGG7XchD6yoUFRncNlSun47+4+krIOZ5py8DLNSZR1bPLAYuptaQuwJiE2ipHsfXNoF4Inn3mOlY88wmad1EAIwXe/+11uvfXWWJX169fzsY99jOeee47e3l5ee+01ZsyYwfz58/E85ad52WWXcemllwJw7LHHcvXVVzNhwgQbNu2ee+6ho6ODyy67jI6ODnbYYQfuvfdeLrroInp7e9lxxx1bmsbYFYfOmKHcHtoK8L49I7Fm2mVY/GrVYfcd/Vmg43ea7RE6Icxc3aGn/fb8vKOH05kogkrkk2hECzVikGYRkUOkNBcqPB+RL6hQbrlCJMLV/o8yqGbL9a2o1FiXBo7Y14EmRC4xMa57eb6yMp2wFYzrUn0NDShOsVqODg+xmKta1+j5Kvdjf49ad3foSV1UmohxuGJWPf9m/AZbhkzi5uyztOcuck17P2ldJa8Gcxgx0fe1OqFaidxzhKekA7k8NhBzC2sX823NuFrZn8MWPyakCbWQOEDad5b4Zu1MTLskxtIKPhg+ZOp0U9U6TUK+aFUjCAF+kYqR8GiQUqb663V2dhIEAd3d3QRBwJo1azjkkENYvnw5ADfccANnn302GzZsAODGG29kO50w3YRNM4T19ddfB2Dq1Knk83kuvfRSli9fzte+9rWWpjN2ieC228ILL8Bjv4WvnqPihvo57JHOMQ5RhMJwfCkEyWRqN+/Q6A2N2Mk4itrI6noDmaDYfg6rjzOE14AMI2Lb9KZ3Pt6MD9rEJrUb0XyI1TKyPKREj6ZHl3P1fOxrt8QwbB1ZxEdbOzM/B8UOReRyhYj4mvinMT2QhFwB0TlRraOxnsVF6M3pAdMgk0A2Yy0bG0Md4pKFeF2EY/dda8SvhsglrlZhpFyhJUy5vBXdU60iqxU1IuuPaubUWvCARvPNmn+r9VLbGeEBITP2as09898o8Id1DtjNBohoaU9UhsCtGwyxz+6zmDlzJsViO0IIOjo6+OpXv1pTtVgs8rvf/Q4pJUEQcPvttwNYsemvf/1rzjnnHNv2b37zG9rbVYICEzbtQx/6EOeeey4vv/wyAEuWLOG2226ju7ubxx57jG233baFxRvLRBCUbe4ee6jfczllGTp+srIObdMRXCyhclMBadGm5dy0xajxdatWNXdXVXvLc7iosJpOFA0x8vwo2KyUgIgC0IYyMsBJItARgI0wA2rOhTY13tIADPQig4riEg2C9bwoeLfRgaYZ0iSgdSRs9AyaaHs5hHHlSCmnEKhGCyaNlB1PJFay1rOBjsQzAhg1Yx2cNTH6ElfXl7aumvjVN2b420EWgk4lHLmcIopODfvbFjZ6Gc11yuJCm72GxX0m2xFZK19bKzrMNUlKQ+2vPNCjjO+GNiudfqWk8IRjiNeIYNpvREpyIuSCb95DEFyM73+DOXMe5oADskOZ7bHHHhSLRRYuXMiJJ55ok+r+7//+L7Nnz7blZs+ezbp169iwYQOzZs1i2bJllEolli9fzm677TYqYdPGrk6wHpj0H57hCh3uw2ZkkBEBCIPE5k2c3g2eFUJzKg4hxeja/FoRQ/IkLAR4+eheYLLUm03sjKHRh5CBoC3iFkBxnNqwYaAsOasVZFu7IjA2dZNHZEijiKE0qZ3q9FMD0snb10SddOTqiJPMAcI+NAlrhXNblZFSRrpS/XDEp2s9jlRCqN9RDaozvphuuqt673GEh59WIYugZUGzo8uMc9pk/bcEssScifsypdyo7KU0SPbrjim1ePZImhZxG3UO8Wgt0sVpsurgN6I1sod8j9psPZKrf9xFpbIEUCYbb76pEv6kwapVqxgaGmLp0qUxsWl/fz9dXV32b/N7X1+fzSw/Z84c9t9/f44++mgOOeQQ7rvvPr7xjW/w0EMPMWvWLC6//HIKab4ZGTB2OUEp4Uc/gjPOgNdf16ebTeoqb3YMQUSUFFJ4Sv9XLUecnBHd5LRzeK6guErPOKNrYxRjMlwp6QwSIvLRE74qa9IhBY4oFqgxoDGWZ9aB3hBjjfgNATL6xuHo7zDT12mhOiYoLtnPawOWN2GgD1ktRxyI8JDWmCYgTXdYV7ToeemIpklIFR+l6VfUAiQ4biKsKyO/pxFxVY3WNlneiFfd9/4WGUPUcCUZuqAtwWU2I0oblh9eot0s38u4iNGZO4n1SB9Y1JfJwhINwP7bkkg1rQ/nsuUbiCJH1YexAVhpkkkFV2hXqZLMpdPKKRVMlA3HHrIrZd43O6BdJduhsxMmTqzfZ7FY5DOf+QwXX3yxjfLS2dlJb2+vLWN+Hz9+PEIILr74YlatWsU111zDxRdfzOLFi3n88cd54okneOCBB4YVNm3sEsGlS+GsM+HKK+HoT2vjCwdRGXGnET8GjqGLZ0Sbgc46UYl0dq7uzM87hLGglP6+JhKBIYhaD2LEoEoeZrkqG8w7pidyiYVM3HMQuudFCXmldES0TYgAnbWIfWh+TjnoT5iqLDjLJejdAH0bVbYKAKFFl1rEKmSAkGFz6DypeM+6WgAXiUV8VaINo4MyBlIuOH/HkA8NkBe1SK4R0pMZfpOjDUkCUDO2kep9mgWrj2pOrNxI/Bwrn1hH16k7ff3Nt5MtkswkoMJLifna+ntM3SfSEeHXeQdvCdGzeMEdcMpBzxgUGpwTJOwHgkDhjjBESBCez3kXtnH88WtZsOB57r9/MNVRPg0qlYrV7+22226xsGdPP/00W2+9NVOmTInVMZnlFy1axOrVq2OZ5U2KpWZh7IpDS6XoBQ4NKT2ekCCFY+giAF/9ndxUrkW+EQWYF+37kdi0BmknYpPaujpvoQ1dpjdSKEEE8S9W6DGZz80yOwnRmT2ZipiYTW3CqurD4TIzP6+kiEdK1Y2fQ2gXB2kOBJt7IKgiCx02Yz1SqlilshrNcThRQUzfowCxnvU4JOg1ctZJysj9IiZq1qBjrUZiMJlsvbVxtaj/ShyFmoYtQM6GN2v3MBemrO8ojSWV/5ayccBul9OrCzLxe+pRq7Zdt1a9PozEIlnPuSfNgcJ2X9tHM9bOzYGsv4mSLku2bfWehRDqgG6+mXGT+PXdd/Of/6mitSxZsjcPPvhgTbP33HMPZ511Fn/5y18IgoCuri7Wr1/PfvvtB8C8efM49dRT+eEPf8imTZtYsGABxx9/vK1/2mmncf311xOGITfffLPNLH/uueeyadMmuru7/4HCpn3qU/DVr8LRR8N/3aw5Jk18qlWoBk4IMXNlRHCBiHjlCoCnCGKlpEWnDidXU09EXGOuoDePfi78aFwxsZ1O8eS6aSQ3sGlX1IoZpRBIP4f08ypyP1IRsbR2dFu1ojLPisjU9H1EWzuic5Jyc8jlVLqmTW8q4xoJUvhI4Smib0K0Bc7psJlrC0LydG9F4c46IEQ0bnB8GDUiqlZoGC6vEbQwz0ac6JaCNPHpsDh113hpGFx+s5DGvdX2lSICH9a+c9ptIMlolss2KoNG3K49FIeSmKW55s7ihivx9GnNSCrqzik5v7TvVvg6JKUTiaswDjyPRx99lMHBQQYGBnjyySdTu/Z9n0qlQqVSwfd9xo8fj+/71jBmwYIFHHHEEbb8u971Ls477zwAfve73/Hkk09yySWXsNNOO3HXXXcBcMghh7Bu3Tp+9rOfDSuzvJAtyEj22WcfnnjiiZY6eMvBiA0rg3oD6RdqCESaq4Nr1GI3gXA2gSagVg+YNNBoclzql7jIzn4EwmAjlMN+BmdnN6XhAlNOwu7zqGI0J3vKdLieNC4JHJn/kDoUCE99BDqQtjBtmDXLOvEOBxq0UWNMMJw+Xcfv5P1KKXIBqHOCHyk04yjeqL6BEbs/NDsn24/ZR6ME7mGxRWi21kje2lvRR+O+E2tv/xSZ33GzkForg+u1+1YIhN/G83/6M3PnzqW3t5dvfvObfPvb367bVxiG3HnnnSxcuJB169ZZQgjw4osvMnPmzNh+/vnPf85TTz3FRRddxD333MOVV17JXXfdxSmnnMIBBxzAZz/72br9ZdGvsSsOzQLDpkttAGMJGDq/oLHO1MTA+hK67hKxBuOno6AKgUb8NkdhxoZL/aBFpD/U45AyMMdWXT6MygrPEZ1ix2J7NGOWODFSnfJuv6Z+8iNCxDe6XS9P00tPuZwUO5VLQqUElX5AIoudyv1Er5sgQ1Q6DMTWEkIfLkHynU/AJWzWaEnGXWUMZ+melsNqxO2be1JG6+dA2oyaNSxphvAaIlbjIJ/RdytjSe85ZX836sldp+Q3kiHJyN4/UX9p40td79Qhpe/XZtZsy/C+9foR8buxP50D0TD6yDx4p7XvEEIZlJj5zq05//y/smrVIIsWpSQSdGCPPfbgj3/8I5VKJeYiUQ922203rrrqKgYHB7n//vvZbbfdeOKJJ3juuee4+uqrm5xhLfz9EMFAcyuJiCMRESEidkbX5hIjlzgacaZxrUjj3AyHGCn0HBGBsSyVjTelECCc12D93jRRkYEiulJzdkZEGqufEt4NIutJ27shhK4VY8rHHxMhuuVDRBiotSoU1XyqZRjU2ekLRaQnULpD7X6RJBq2m1HgHkaRGwMi5/nY2IQKBRZ1Gv0QGgGZ50EViVBppQwxCoKWrGZrSrn1mmgjbV1F4mdm3eE+11bWol4p+93QmOjVtF+vTB3iaOiDwx1l7bsoZmjzB41RFe8ndfaj0WSL5ev2akSzVj0TZYYxP++93+Oss/KUSnmefx6WLUtvatGiRZRKJSqVCv/8z//M3Llz7bObb76Zb3/72zal0nHHHcdVV13FhAkTmDVrFp7n0dnZycSJE1m2bBlf+tKX+I//+A+OPfZY7r//fubPn88Pf/hDJjYyTXVg7OoEAf79J3DMZ6DnTRjqi0I12SthlWmcxI3uzmRSr5Z1RvoEB2Z0hIWiyhRfaFd/+3ntciGwlqCGY6hWVMSWSjmS36Mcu5X+L92y0/BuwmRq8HII4Uch2qw7gIn0XtbzTbRnxm3GZwi6lcaGcZ1YrK6o/zEKba2q2xZCqBBu7eNVyDThKQtTbTUmZRA54FrruBDZYgSRTEgRBTd/1YZ+y9JbxstpQ3xXjA1qPYUmgFKqiPugrYndg0iL0KSuNTU8WjS6psDOUWRYa6aNC+whSYJNtSXN3/qKifFbGlGzRUX8cvtxdd+N5lRn/zfaJ02PrY6OsenyrV7J9lP6SXUtMZeJUOWbgBeJIA9hgBdE/n71gjGZzPJ77703+++/f8xF4sADD2TlypX8z//8DwDVapVvfvObADbSzMDAAF/84hdZsmQJ8+bNY9OmTdx6662sWrWKXXfdlYsvvrj5d8NY5wR/+m/wxO/hW1+H7dxQORlnX1cvZzgom4VdRtahwtPIK8FxATGuC/cE6TjdC92XMXwxpydPR0tJOspnBY82YiDDBVrGTRK3gKxGnEmNeFYk/vScvxW3K804aiRbzSAhh8B6vs07SKjF0UJEBrUh0VrY6tEpvR6MHr/XeotNoTvfTFIigwrCBpZ2EWtjXkxmlMmqmSraS21gmCsoM0SNCQ7VEroM7CfrWN1mjyzOkddfPhl9A8nxmRKuHit1ICIuzE1KL+y/Ihr3KEoiRh2yRMvuzzRoNCdN+OJr6fGbh4sMDanqL78Mr7wCOuxnDExm+WJRJWI2LhKzZ8+2cUJ7enoAZUjz4osvAipsmsksv9dee/HTn/6Uu+++myOPPJL3ve99TJky5R8siwTAr+7SK7094IpB0/QUdcCelgxBMqLQqnrJXi5bpOUSR4eYCRO1xjXICasRovAcAxsjwrSnsgxxp8CxdDXiUWKxv/VxXP3uJR8kxy/i7gNGvGv8KE0wgbofjENUQ8eASHg6Mo2IDgQCINQBy0O7Di2hkVEWgcaaHkadaCQREha5yEFKpg3VrLXwE3MxKFhE5YxYtaWxZPS5pUDK+DokRpJBRhPPs8GuQDNTaKDLS+OW0/tL1Et5Kk3Qiwxt3dsS6hHGFuolRaFL78hx9U+LVtD15z/DJz4Bv/tdvJnHHnuMarXKvvvuSxAE3Hnnnaxbt866SEgpWbZsGYcffjgAt912G7fccgsQ1wmef/75HHzwwTz77LN0d3fT3d1Nf38/K1asaDmLxNgmgltvrS4XQuPEaUSF9bd0zR2h/zEGE0Y3aEStwrdcnS2eBjYRrSEyRrfoGOKEyiBGeiIRn1QTuZihC1F7LuEyZcNQ+0MaIgRIo49LGalFsM4JOsHlxjhUNwRY1kfj6byMoJxobVQePT8dxk4SYDnZqhIb4+VU/Mk0P7sMxXwtF5nNbeCUGA40RhPpnEqaw7U0emArgag66Yc0yg/DpgN823abHusoghFx1omOU288TdG1JrjIZt/rcNcmWU9C/LCq93mUUVGk1vvbQIOdMZIDksYjjzwi0FmUACXocnzeLZRKJZYsWcLLL7/M0NAQ3d3d3HnnnWyzzTYA/OlPf+KDH/ygLT84OMjixYt59dVXbWb5WbNmMTg4yL/9279x+OGH85//+Z986UtfYtKkSXR1dfHAAw+0NIWxrRNMA89X2QvaxhEzOAHM5yS1Rsi9qLk0CIHwPRVOKJdXjE8YICollWE9DMgK0RW7hKd8+/IFZC6v/pZ6RJLIL8gQp1xOEw0tVrSh3hIEwc9BLo8otCmZvStuDapOGDfHStbUVb9ExFCvUSq3ZYi0q1tJWTV7mVB1RocKNoKPkCbLvO9ExfGgUkZu7kWWBqw+1Y41S7cRIxRxfiR+JdJMaQKUwhunzm00wfZh0tHEjLJ0mVQjneYgaw9uETCSCwB/DrkAACAASURBVBMhqV65mnoN9FBR4cxmJbXcXbybOmmERnDVhg30nXFGpDn1PYyWbq9paLSLG2Kv6MDmGhCZ38OQGduqkGkuTJ1a29MHPvABnn76afr6+thvv/342te+xrx58+zz7bffPqZrfPTRR2OWo6effjovvfQSr732ms0sH4Yh69evZ2BggNNOO40bb7yxpdUZ25zgyy/DunWw334KGVYrKiK6i/CMOE794XAPYK0j0zglB2KBnbUI0gbpDsMolqixDK3ZsImTrPCUW4GP5eaEdEWc2rrU9Jdz3SnCWpN94UXztWb8uj/XwlWgDYB0PzG9Z/QBx4eeIX40HK2AVN1pEjG5kVxM/aCqetXWu9L3wdfBBytlKA0iczllkOQnxKZGH9G0VZ+DUhNlRZKjbJXwZPXbpOgpzXHazs0dU9rvLUAkKGyGvCTGnDaX2CGlwXk6Wb/JNXaPZ3XHl9ltfQI5mpBsL6trazFs6o3qKJoE1186Nk6BSvztHpLNz+RIpbV5OOlEj+t/Ds8/rxoLQ8ENN4x8mNVqlZdeeqnm/v+fWR7g7rvhE59Aeh4csgD+bwr1t+LCMELWrttADQekfxqxX22D+qfDgZiwYjGC6BCzVKLojM9wc/rvKPKLY1BjTnJGZGnFlFJzkE7ItqRhTCKMmDCnOjNegjhBTRKEtI1vx2KQsraytXkC/VrDoiS4YeBAxyCU0eEkl0PmJqi+KiXo70EaLj+XEpQwjbhk9Z3GlYwEXGwXO1gNsw/NocZCgqX+ni0mzAZdPqiCn8skMLFW6xiS2NJhNdvAq0VoWrTZYE0bueGkPtff5GgQpkwMEtv3gRaPx7+VLU4YjerCirMTOC+GGl0uMFSJcww+FR60T6A4Ps+jK6vcfeFX6Fnfz/u/cBI77rt/atflcplQW4tXKhWGhoYoFAp4nsdNN93E+9//fmbMmMGf/vQnvvGNb3DIIYfUtHHGGWfwne98x2aWf/zxx/8BdYLXXQeDg+pd3XFnJOoLQ3WSsfot4SB3qU3ZiV66I95LfPmJDjMIoyEGUtZwdzXETHiJWIeaiBmimSCI1j/RcG7mROYa6ThETph6GOd5d8yawOBFhEbo+yafopSYaDV2DDUJcNPWQBvBANZK1sRSNbFchUNkU5CXTOMU0XPJF1SORCkVh9i7XpUpdEBbEUwuxWZhJJxbIw5muG07z9N6SHPmTuqf6oJx6Dfg1//0myKOtm85bALYNMFLrVy/dr1VSa3ZgiFS0/stwUXXHCnddTPfWyyWbYv9tQpJsbvLHUrQJt1q75hkAkODEJZU2Y5J1jK64PVx+Ht/qMq3bw+kE8FDDz3U6u1MEOzly5czf/58nn32Wc4++2y6u7uZNGkShx12GBdddFGs/rJly+jp6eHII48EYN999+UjH/kI2223Hbvssgu33XZbS0swdsOm3XADnHyyEnzuuivcdyfxrZJEtoYgOOJQp6i9Z35PGqXUmIo7YoKUpmKcpSFoLkGJObK7DSQ4MX1PyARBdLnMZHm3nUxwJi1l9Lf7e9J6MevUWLcbI45V+iKVqinBBdatrxCDDSInvAj5l4dgaEC1XShCe2drBLGe+LNZzq0ON9EqpBK/4daPGaw4EGgr6gZEsBVoxXl8RERvS0JSFD2SpkY4FNu7q7pJaTm1n+GK9GsGIaPDrbHydqFadizcHVhzE2x8HHb/DhSad1h/K+DvL2zascfC9tsjXn0VjjgCOjrUiysNKkdym9ImImR2c1u3BSfws+GUzN4JRVTfsd602kZLK8wvhhiZfxwiaUQHbmi2auSOIFyRaXKzubq3WO7BsIbLrKuHrCGuSfGaM36bTNMNjqz7sIvkfmQpnJ19pOsZLsQQxapGxp5niW0m8oi5cYQIczrVAQwkqGhB/T0QVlUot7YOvfR1UFI9RDEcJJIpQq7fdjrxc99fc9xerIQwBl/EibIhfjqAgci50XDqtx0bp/GnFSL+XTmqgpo6zY79bwGObrmhmLVRUw2eN5prRPOSXKneB/p9psmqRkz8knvY+PrmCtG9chmCEhRr9051u08zMOVjTCjUD5v2doKxbR06bx589rOKAIJ6ScUOGDcRxk0CrxAFQq6UbU484fkq8kGhDdFWRBTbEcUOhTiLOoVQoahOQAZpVKtRNJRqNfLr0whdViuRH6DxCUwG69aWnDY/oW5bBlWV3DaoRJFVINIfmNROhtCZGKi5gnpuHNNjCXgTBDCNwGZ9MEZf5/mKQGFcN8ycAISiSUGADFTfMqjqaCH1PkShouHkCtqa1VfvpVqO3FBixRPE2oxJCEziXyFDRL6ImDAFJm6tymzugc2boqTBemxbFFzdSb3LrZJoIjqOJKUYrYNpy7W+tKATqAJKxJwJMnWcsYTGhrt0Y6um1THjEukJfxteWxqa5OBFnaupbupcafOO1klnoYhxX9L+TG2v4WQarbFUB8zBXhjs0xG2BhVeG+yt2c8LFy5k4sSJNj9gPVi/fj0HHnggU6ZMYeLEicydO5eVK1fa5/fffz877LAD06dP5+abb7b3e3p62Guvvejr62tmhg1h7HKCYQj/7//B+vWwcCFMn65eSLmkTikGHPNzaYNqGzl33MG9hpOS+p6Xj+cftMWFDVFmz+yGA40ZSzhO5BAhECFQDvp6y7qGNaZqJqenxxaLeuNyiNpsO1nPFZc0g1iEiOsxXaMagdYH5LDZJoIKhJ7+eL2EXpKUOUAy0IAlFsm67jtRJwSsflMGIHWfhaIT27QCpQHVfr4NWVEh3cgXtFl7Clh96TAQb5o4O61Yyr23ghtK9iFB6VxjN3WQhNQaGsIwUhn4uWjvJttKgbQ4nZnc10g5m1ZgFAhtoxYacpH1rFlTfzfcYnrPqdzicOcpQygPxO8N9kL7BCSCm26C3/52W4SYynnntXPppbVu3C50dnZy7bXXMnPmTIQQ/PKXv+RjH/sYb7zxBrlcjtNOO4077riDIAhYsGABn/zkJ/F9n3POOYevfe1rjB8/fnjzSMDYJYKf+xzcfrv6+M45B/n4ozB969qPRkrFpQwNwFCvEpeGoRLPtRURbe2a49MxMT0jmhPRTmth01hn74gqqp++jkQTDQwl9DKIQMQNayxBC3XWCs19hloEavIUxgJUa9cLAKliOBqCaA1TmgFXF1HjjqHHqomPkGDNxXw/Jm5DBlAJnMNEWuSdeHt2LqlrnnwnmuP1PB04PSKG9h3m8vYgJI0Ir5CHIFB7whOQL8ZdMNIMVurtAVf/lqybIKZ/K+KXBalnfzfAeta8zV6qlCKJhKcJoLY8jfWjD4YyftP2k8k1Jss3QxRbQfKjqNNtBuq1Otpi1rTyEmrmPGJXkcFezr2oi3/9VxgYuAa4hv/6L/jNb+DZZ6GrK71asVhkl112ASAMQ3zfp7u7m40bNzJt2jQ2b97MrFmzACgUCmzYsIG1a9eyZs0afvzjH49szA6MTSI4MAC33GLjb0op4fY74KQTFHIb7NcBtU3GgzaF6Dq20QFgU8SCxsG8MqQQuEsUR/JRJBG+jBNCc46zzyGy+IxZmoZKeO23RdyhxHEBERHHqBqBnBFjauJQNQSxjmGOO2YpqUkRZAiPkrPpkRs3EaLIMsYE2+gCBRERt7ojI+Ylgzq4/aY991J+F1i9oyG+mjgJh/OXPtA+TpUf2gyVIWS+qEThrnFNDeecEDXbMs69IEgEzn77Eb96YF9Hxv6QlZIKDSeEOkBKCUObEYV2ZeWbYnSTxeEIqMkOH6sXbySq18z3OBwuMu1bf4u41GERSGdfZnGDoNarYQYNp2z2QGoPhb298P3vw9BQVKxSgZ4eZcR/2mnZzUF2WqVp06bZwNqe5zFp0iQbIWY0YWwSwUIB8vlYEGo5vlPF6/Q8xLgJME4rZlPEWqmbQWiiQd7hxHR2eRmCn0cYogijQxSl/ce94XxcGh15rshTW4pWtQuCJSQR9xRDYEJoYupFYkYjFjZcVBZBTEUGmpBXKlEWC+14L/18XDRr5mCIhzGSsamJFIEWYSLYgF2OFpBMjCDGDWkw+RrVgkXGSKab9k5lWVotw0CvCuXWpnXEhiDatdBEFlG7PuYw4kdi1paI32hwH8NEzM3WEoDIt6nyQwPIXAGRy0FxnNJDDQ2ov7UvZ9b5JtZvGMTE4cJ9llXPrHVibMOCNHVBZsfuqBwJRrPvbpjvp9GeqUcAVbdN9KvdkmSKmkCkrJFp84UXfQqFOBEExas88kgtEbzjjjs455xzWLt2LXvssQc333wzO+64I0uXLqVcjvTTYRiy5557MmfOHG688Uauvvpqent72XvvvWlra+O9730v559/Ph/4wAf43ve+R7FY5Iwzzmg8zwSMTSKYy8GNN8LnPw9BgPjwh+G4E1S0mOoQFkEZPzvDCVmk6FiOpiWidQmI9a2SyGo5spzM5bApjpqE9G1aT4gh4z+tAtsHmVP6x3oO+m4vnmc3rRC5eAxTYwGmOcTMk7lrYJMvRPofE1u1YqxyHWMeY7gDkY7PNRDxBJJ8vI8sNwz3FGo4z3rIx7w/970TJnwzncS3uQJ0FtQ6lYdg8yaVI7Ctw3KIqtkEoda+XTGjkBRCWTvSBFeZoiuLzSULhoFYWyF6mfeLHRo5ekjtZyqKHXZMMhYTtba96FziHFpErdVjsm4Wl9qUSDUNhs3RSecTHUWusJEEJK1KnWdNt+KqS4Kqs6/9OBFNcOTbbRtSLtVKSIpF0NJMCy+88ALHHHMMd911F/vvvz+XXXYZCxcu5I9//COf+cxn2HXXXdlzzz155plnbJaJRx55hDfffJMTTjiBOXPm0NHRwYQJE9hvv/34/Oc/z4oVK7j99tt5+OGHm51pfNrDqvV2gKOOUnz4+vXwi18gcjlEx3jEhKmICVsp61A/rzimoKyQcnEcjOuCzknQ0QUd45URhZ9TO8VYkpYGlTHF0GZFWIOyI3LUyC4I1LOhfmU1NdSv6lTK1ifO5BdUxiLKatIIFLOvZFxT95n+T4aRLtH3FTEy4ilN1ES1omKcGmIhowipFsmauJW5gib2UtWtlBFBNbKmNZfIyjWm/Yna2tUaF4qa2xPRx4ThQvVsPBE5z7suK4B1xUhaUyZ0hTbGIIroqHVJrKd5Ljyk8CN9l1mHFKtNIQSirR0xfjJ0TVVrNNgPvRtgaHOsn0j0q9sLAn3fDToQRqHZYpBCAE2byftNWpumQdo+S4PYexZNWm0CENbUE56X6n4hg2rNGNx+m5lH2pibqdfMGrylhjj1xjCK40j9Zmmwdibur+er77JSdowK4zBtGnzy42Xa2+NjLhTgpJPiZX/961/z/ve/n4MOOohcLsfZZ5/NX/7yF+s8X6lUWL16Needdx6XXnqprXf66aczf/58PvjBD/LMM89w5JFH0tPTQ6VSYfHixfzgBz8glxseTzc2OUED+by6XAiqUBpUsTgFitBBZBk6qLPPC1+Ju0yA53ziI9QINuImtU7NpjIyEWFCyLUpRCkAWdY+hn6UgNaFpL7KhjmTzk+w29MQ52oZCFW71oDHRJGR6qdvQiERcYnGUjRND+hyvIANo2Q5xEqMQ4yXdX6XCWFMrN20c5aTfy6Z39BwUJJaQx6XQ0oznpGSKO6h/pmsg1DpjaxFqT6w2HyN7rvQfxuH4VCLVksD2sK0iHTyBgohVPxTM0vLOSfXoPbU3FA/2wIMi8sbTUMQI3FI3oboewiM7thLJWw1TWZ1lXIvldtstu4WMoh5O0NDLtIYPRmc4LkqDQX/8eMhJkyQ/PCninvbfnu44w54xzvi7b3xxhs88cQTTJ06lVKpxC677EIQBPz+97/niSeeYN26dVxxxRW8/vrrNpvE3XffzdDQEJ/97Gc55ZRTKJfLnHvuuZxwwgn09/fzjne8gwceeIDf/va3/0Di0CRUq8p0Ny1jud7UQvhRdnhQQWwrQ7C5W51yPA/ZNi7mGygMp+Vnizyta0DyQmd/N0RMyuh0lcurNvUzGQRqI3opMTeFiFk46k61O0I5IsS+Ec960SnSz1lkLxMO+nVjmgoRNwoyfnxJHWKj06pIECEzdkgXZbpEnTAystFixfpi2gQxNS4pifikVsQjBDadkSW+YJMzeil9WYMlrTcOqoogCgG5NjW+MCAMArxcPjbeeGsph52a+TSPjJshejVBwtP63MIQ61l/U9FhyOHwG9WtUy66X3vQqEsck6LYxJhGkzQmR/Y24D2bg6RLGRr/SUnOC/n8URWuu1El1t1771pRKMCsWbPo6enh1ltvpVAocNRRR1GpVPj2t7/NnDlz+OIXv8iPfvQjG9llzz335IQTTuDxxx/nscceo7OzkxkzZvDqq6/yk5/8hMmTJ/Mv//IvHH744ey44468+OKLXH755RQKjV117LRGtCh/a7jwu3DAXOjbmE4AIVOEJDwP0daB6JyMmDQdxk9RO3NgE2z4C2x8HTnQi6xWrBgzDYQQyuI036baax8PHV2IcROhOB6KnUpE2FbUhE9zEv2blEN3aVAZuhiu0YhZS4NKNOukT4qJq9w+i53KWVxbvpoUPYoJEYo4GmtX3wNkxF1qUW0qEhQoYuDnkLmCSgUFygG9UkKWh9RVGdLO/tW4SNKKKpsX3dmOre+gUO821OJZKa2IN7Ye7t8YS9DE9jaWi0Koda6aHJE6oo2NNxtGh4usMZvDSVuHEv/KUL1XKfEMd2hE61pEGr9ccbckJioXkRg82X/zos34GsUOPKNJ8GLvtRnhZByS7y+zG2rnXL9O7RN3jWuL1+fM3QAWI72S8xmr4GY/kaUh3re3z4c/DO99L/zLv6TX+dSnPsVNN93E17/+dY455hiOOeYYAH7wgx+wfPlyfvazn/HpT3+a2bNn06V9K4QQvOtd72Lt2rWceuqprFmzhquuuoqddtqJc845h5NPPpltt92WBx98kHK5zLXXXtvSPMY2EXzpZXj2D8pScYQgPB/RNg4xfgpiyjuVLsjzIqK4/hXk5m6F/Bt8EKARvxF35ttUfsO2cZAvKuKVL9gchYpj08YlaI4xX9Dm59pBtW8jsne9IsyVIZVvL7XvWom/VeF4XhQtJ6/zIyIQYRWRokcU+j/biJsj0Izb6O8M92qc0wc0kR/ohfKAIo46VFfquskEITCXEMpazXNCxsmgMZFKWYnY5fs6P6QyjLGcUiwijbEsNQeRbJKDn1ME0fpJ6sDfbe1AqHSK5cFI75k60lpwCWKs94aErA5JaVL31tSlOXRZ1hGP0t5hC1ezMDwiMoJDQOqayYz36VarrVd3X27BazQg2Y7NMN/eyRHHTOLee+H55+GjH4Xnnktv46ijjuKZZ55hw4YNzJs3DyEECxYsoLe3l40bN/KrX/2K6dOnM2fOHECJUH/1q18xa9Ysli1bRqlUYunSpfT19TF16lTCMOSQQw5BCMGcOXNYtWpVS3Ma2+LQa6+H/9AbulKGqnZnGAUQfg78ThWHEhTnVB5USL2ibYGNGX2urYbrSN10RsxoM0UojkOYpLcCzZE44ko/D35eiWqBmL6uOqAQtPAsYYrMm53zsiPaiQmdjLiXSOyZlvmi5kM2Iko/Ee3GWs4WlF8maOtPEfVp5lDv5O1aVhoxldGjxuKIBk6zekwZAQEaIgHhRBm1IljPIbQBUhpir+eRlirKRFJxReh+Htq1CLU8qKIaGS4ybbzNiEObIGTpd0dRsGdDAnrKCsLeb2L8ow0pfVoOOAn6Xo3bgNtGVntu8aTY3BjPJYy3suqPDETsR7KvEbbauFxibf66DpYtg5IO1lWpwPXXQyIBBABPPvmktQA98cQTmTt3Lu95z3uQUjJjxgwuuugiDj74YF555RX23XdfAHbeeWdmzpzJQw89xD777MP69ev52c9+xle+8hVOOeUUvvvd7/Lggw+ybt06TjjhhJbmPLaJIEQbrtCmLgNhqMRRQSIuYsZGybxrHni+5uY61L0wUER3sA/Kb2qOr6hEn/liXDeXOXYd4cXomNw0RJUh/Vzp5mJBtg0hLRQTUwsja0xrCENETGQY5S6Tem5CRGXwIecrriiUkWGN1QUars/5AFyC6IONdGPcNqSn9LEuchAiwsepYti4TlTafojdR2iLVkuopBPTVapLO/tLsH5QNTkXk+Dqz4yu0eoOXUMacy+MDi5uahrj/+j70bqZg1OoxeLlAZUSqtgRzTsmtpTR/VaIS7Kuajj+fKSEyo5LRN1saQKY0nYy7VBMv+f4E9ZwMSltS6d+w36SbaQY+eiagNE31h5Eml0tmfZXi3Sv6TdjDANTG3EOqBra84Eur3ooFCRdXem9nXrqqTz11FMMDg6y1157ce+99+pmBRMnTiSXyzF9+nSGHMfDKVOmIITg4osvZuedd+bJJ5/knnvuYfHixWy99dbWqnT8+PG0tbWl9psFY1scmgZSKj3MUF8tASQpXopM7NNFa+plx8y+PV+J0vIFRPt4RNc02Go76JqmOKCBXiU6Xf8qsm8DsrQ5PXCzlVHqy4ga8206kHdnFI2jUtLuF0OOT6CouYTwlKgzV4j0gxgaJyKLUog4TavT0b8HWmTpCWQup/SAnlD3qhWl+6tWVOBsE+zbFQ8LT9XJF5QeEYEMq1G9MLRBttPFyrX3ondh32LEeUpQ+sNcxEVLhwsw71XKyKjHzjVyX0l9N+6esmvmO4cIk9rKWVczLsvFKzGuMIccA56vHPS7pimR6dBm2LRevWs7DEHyQFAzpixw61pjrcRz98/6raWDXRcZ7YMwaEkkOqw+k/p9Pf7YXrcTiw4Utl9HJJ8Eoa18k23G2nKHA7F9bIvGLpF4ktJGE1czULMWafNopj+Dk7Sxl4r0VI2SBJg5a2Zj4mTBhSf+jGJ+kM5iPztOfZ6TP/uH1DHecccdzJw5k7POOovHH3+ccePG2We77babjRKz/fbbc//997P11lszZcoUW+aLX/wiixcvtrkIn332Wb7whS+wadMmvvWtb/HCCy80uVoKxi4n+PDDKoNETw986Uvw3fNs4Gy7YcxHKqWKArK5R6XbkSG0dSA6xkduEmlivyxIICAhRJQZomOCOhFVy8rherAPeteraCqFouIS820REUptVxM51xrLhHULqhGXaJ67rg8GOSgqEBGCUOnkBMRipEYnOpdDEtgTq0GkRroniMKfmb7CIBqzIRSGc9QBtq1lpqmX6dhf7x0I+1OkWW9CxLlBRHis2NQQN8NRZ6cRir3hNM7XcIexiDQiZT6maxcNmROz/un50D5eXUEVBvq0tXKHw2UmkkLV2as1CNO16HNSINWt0wDsIUMIHb1Fi4lbTK47LEJI/V2S9ly634fh1oOqEmn6kYg9Cwek3bVjd8T2gPrWvFruXTD8+dYbRxqMtB/bnxBOEgJ1iJNCc7xBBfAQ7RPgL7dy5ryTOfo957Bx82R23eaP5FftBDPihLC3t5cPf/jDHHjggVx88cU1/R177LEcf/zxHHPMMWyzzTZccMEFHH/88fG5Sckpp5zClVdeied57LDDDlx99dWUy2UeeOAB9tprr5bmODY5wXIZDjsMXnkF+vqQP7wa+Yvb7GmUMEQO9SM3/hW5/i/KoKRShs7J8M6dEdvtipj2LvV3vmg3seVSpHOylcm/jcN69mlX6fZ8neh1ArJzihaBhcpYZP2r0PNX6N8Y+ZwZsCd456MNA0UAzQeca7NhqaiWoaSd+l1HfSvGC209YYxtkohKRCYwNT+FsrS0lxHRGqKfy0XO+n5ejb9aUoYxvRvUtblHEW4po7pCKCOcVIOc5k+0dcEeJLTlp+V8sw1r0k7dmeMwa2M5Qadtx6o3ZWCJn045P6d8W4vj1IGhWlbPq2VliduElWI0mbCWU/L8Gk5nOBCz/BUCMCLwbBjWO8zqv8UrGoRjiernIleNoALCcIpxbrap+biHFC/Bvev3IE2c3xHAqHKNrpQrhfuN3pGIHcpteT+PyLUrSUbPagg2s+2Uv7DHjNXkcxVk38s1fS5dupTHH3+c6667jmKxiBCCYrHIn//8ZwBWr14NKFeKyZMn09fXx3nnnQdAtVrl6KOPZty4caxZs4aZM2cC8PGPf5yenh66urrYsGEDixYtamlNxyYn2N2tCKGBUhn5wvMK8XoqZ5/ItcH4vEJK2vBEDvYRZVcwXKLxD5NxzhGpTkD5NkS+TZvBS5WRwpwYzUcUuHkDNYfmGMDEPngT97M8iCyXlBgsDFQWg1xbxC26TvZJ/xzDgfi5iJsxXFa1or4C43Po5RyuRUYRIMx4Nfcnq2U1D8+LQsKFTt7EGr0eEVfkjin0IO+peRg9mdFVlocUt16tqlBMOR16zSYcdgxyEHH9GkR9J6FpQxKHg8O8byNS9YiJHpPvLdllWttWJ2LaDxGkc4aJwaXf9vNKgqB/x0et02B/FBS+Xtt6PlJKpRoQEdJvFjI5INNvtYINk9cgW/2wuM2am9mBoJuFtNrCfkfSOYTqA3WL3G1t40YFYb49863ob9hIUUZ8NEjput7DeuuYsa8s7rNlKlAuc8u97+UjW3cwrk2lWgpDWLNhe8b9VWW5M3Dcccdx3HHH0d3dzdy5c23KpBkzZtj277rrLvbYYw9eeuklDj30UJYuXcrRRx/NL37xC4QQ9Pb2cswxx3DNNddw5pln8sorr9DR0UFfX9+wosaMTSI4dSrsuCO8+KIyQ8rnER/4APhtylrLIOtcHkRktZb2Wl3xR/zjks7XYn4RKg+dORmpSlgjDaHLWI4jyeFpLk9HIBFGFB6GilMy1qfBRiecWV4TJZPKyIga3Q9TgvTAa1NjCKUiYCYMnCs6teKghOFOvg03hCfuqdXLaUMO5RNItaItQJWrh+IANeLwzXj0/APtvuLrCDpiXIQUNNdOUI3Keb6aC6jIO66voOGwdeQeCdqKN3FASCIVw5kZlw5LXS+GnAAAIABJREFUtFQUIIl0ci+i1ibTyKEOpBxUpBGVWleSBqQ11L1abkLvQ1PPz0HnRKzuuzygxKh5xxggeSgQQkkPDCQCVteDhmtgRGVVHUw935pRQst9j5AA1u8weZgS0TqNwODHrWXFpMkcmkakbA+nPlixeYM2a55G9VIJfjODTlvnjPk/+XiJEy44lhsX/ZIPzrqPapAnkB6fvOJWZj4CP/95bZ1zzjmHJUuWcMstt8Tuf/WrX7W/77LLLhx++OGsXLmSo48+mjVr1jB//nxyuRwLFiywrhBLliz5Bwyb5nlKJ/id78C6dYgvf1llmTdgjB60IYeyEo3idwrPj1wPfM+hcfEPIFXvlNwchktKljEnymY+HM+LrAZR4hjKQ4owlvphMEjoE/OY/GwxHZdtT2Bzu1liVI1i/1ldYi7O3cXGlPhI/Ry0dyLaO7PnYdsRRt4EnnPyDYzu0OgDhbYqbQMSyNOKj4JonkJH2dEO7yo0nu4zqGCRVtJ1QYtya9r3fCg4aZNc6UCoRXspHGJdMHM1B48arlPU2RciJVKNSMdaQiiL0qK2NB3sU324vop2LCI+h5FyNgaqes1zuZpA2c1DNqJ/y6EuV91ILpD9vDHZdoiti0skgAkjKHHFrPXbrL+ewz5GJFUHmiu8/mc5BgZ9jvw/S9ljxiq6Ojbx9J9m0zvYxTOvRl5DoLLJH3zwwTz77LOMGzcOKSX7779/rN2XX36ZJUuWsGLFCoaGhjj44IMBJSI9/fTTOfvss/F9n9NPP52lS5ey1VZb8ac//Ymf//znXHnllS1Pa2wSQYDJkyFrwoEmIqjIMMphWYVLi52gZKj0aDoyiwzDyCIvl0fm2xSXYa0QI4JR14gmi7BYsV5yJE49qYl0cZy6QEXjrwypOQ30AiEy3x4TnWaOxxAP90RrcvqVB9U9Izatl2OwEdTTf3ke4MddKFziVmMgo9ZPJJG5jYWqiahXiAwehI9N5GuteDTB1ZFsYoQ9bZ5uW3ZO0hr+iGYIYvJQlGlII5xnCa6t7nqmgDGsQe/p0mAUpEFojkLqFFxJgpg1h2bGYAmfOZDFHzc3g7cJAXShZa4vQ2jscvDDAVeaYdtxJFS26bd4DWUUQUtKSV+fp4VGglV/nh0rGgSRlgWgvb2dMAx58MEHmTt3LrvvvjtXXXUVF1xwAblcjnK5zIc+9CG+/OUvs/vuu3PnnXfyve99D4CtttqKwcFBtttuOzzPY+3atdx2223893//NwceeCA77bQTJ5988j9Y2DQXwlDp1wY2QXkzDT9BKZXxR74N0TEB0TkJb8IUxPhJiM4udaIOAtjcC/09yL6NyN4N0LcBBjZpIwWduaBaVdEyAuMrkwFCxDd2yphSq/k5FRptwlaIrbaFSe9QBLA8pAxsNryqjH8G+xXBTLbpugkYq8h8Uc0xXwQ8xUmVNmtXjHi4tqbAintTrli5hGGNSbVUreiMG0a/6qj5jVLeiohNnbIyrAkDpajP5RFezlH2e5EBhImIb3SypSHNGdd7X/qnEUOD427g1HN1ljXzddbBuMFYVw5DEMOobKP1bPgaPJUBw7jXGB2x0JFdENawKxNc/Xg9iFkzJ+/RtJFT1vU3gRGIPePgvC9jrJa0Kq9zNWxXpJXegqLijF6EEHzkn6p0jkvve/bseI6Da6+9ln333ZcDDjjAcpIDAwNs3LgRgOuvv55tttmGQqHAzTffzF133cU+++wDwNq1a/nc5z7H6tWrueSSS1i2bBmLFy/m1FNPZdq0aTz88MP/YGHTwhAWL4a5+8MzTym/QNdsP3kZ7sOkN6qWo/Qg1XKEhDXXJ4RAFNoQ47oQE6bgTZyKN1GnaeroUptwsB96Nyp/wE0bkL3roXcDcrBP+cWhRJsyqGjfuqpjlDL8DSv8vPZRnApTtoWJ0xWSLw/AxteQhijqtD+ZwbKFiFIxFdpVMABjdVopRVan1RRfupYHncF5eS5B9CPEXalEjv8uURQkCKJvCSLGuMdBNjWrrLl82opR+inIQPyGaxJgLIKFUCbiUr9bHYBAvWsnak49omUIos1Habhzk3aqwTo2SSDtwcCIzq2+Gs0dO36ywwHXQCJmLDEcMIce97fhw7BGUZcAjmBEKaJ402Ja+EPQ5M1a9ppIs40ODvVmLVs/2CbBZNFJwBEfq7LD9iFthXjb7e1w+eXxsvfffz9Lly4ln8/j+z7PPPMM+Xye7373uwA89thjVKtVzjzzTHp6eizRA+VD+NBDDzE4OMjNN99MqVRir7324rnnnuOjH/0oww2bJmQLplb77LOPje79N4dVq2DffZWV6KIT4AeXqfuW8DkWoHZvGMMHEf2UGZ+cFfPrsjERUraow/oIajGrNM7YmjshX9RiTAeBO+239vE6IpJY/xWoDEZ6RU9Hlym0K2OWZrNA2Ag2ibBuSd/E0Qb3HZqPrm7mC2HLS3PIMGK/jHG2mjInNcSWMR6yiZmNSFcfuowcKDGG1L7tntU6oHpZPhoPtnER80ugDajybfExuoXT9koawUhJJFy37xFAsofR4oEE2Lm9NXyVhko5yvoyCha8SYjNpVrWvsbavamBVW9tYzLCZxiRKHzycx3ce786RLe3w333wYEHxqv29PQwadIk2tuVempoaIgddtiB3//+93R1dTF+/Hj6+/vJ5/Pk83mGhobwfZ/+/n48z2POnDk8/fTT5HI5br31Vi655BKOOuooLrzwQrq6ugjDkBNOOIFvfetbNcPOol9jVyf47nfD9u+CNWuVz2DMP044p68GWyTzgxUpX5qMTtKZh3wR5Z9jvKaj0Waxzu79PUhX7CiUH59sG6fM31M2ZgrJzehfux50dKkS1VKkT6yWHMf9dii0YUOy1Rj9eE2HdWvaCKgZsMjfczilMLZWhvBIQ3TMQcLzzBE74hBFSj7FVszDpaxdeyG0i4pTDOJE24hfZajmoi1FpSHoNW0qrlPgHuLE6K6t6cr84quoQID2SRRKHw4RsqtnKWiMnWwwdQXDIyDRh9VIyDec9usefNw5itoxtAotj89kHrH5S8No77sGVkAsfi3DINZG8gJR1hrzHnPKYDDNeEqYXl18pmF8scy5Z4Y8/OhkAA49tJYAAkycOBGAVatW8e53v5v58+fz3HPPsXbtWmbPnk2o7TJcnV4QBPzhD3/gueee4z3veQ8nn3wyl1xyCddffz3z5s3jPe95D5s2baJarTJt2jQrWm16OVoq/XaCjg7443Nx0yNQm8WY3JsQY+a++kXfkhGScamaq4BO+wwanbLrfGg2+oKfg2Kno9dWBEgaX7rNmxQXWakoa8FCO6LYgSwUow/CJVoiWxAiQDvXt0XRbCpl7Y7RA71l7a/ncIpZossaAxvNIRpHeMMl5qLwZanooIbIZJSzZQX4XoSYGxnWxAiiVHl2w8ASxHocop3bMEBArZtELM+hhxU+ZnFNwgi+QuUuISRphjSjSRJtW7lCtMbGkMiISxOEwa6QE20Fp1zTfbptOXdbeQOJL9h50MIqNSrb4p4Y9vvRuEy635k50HmeiiYkhDKaswdA7e5kFyJzRWrB98ErQk7nGjXrUCnpxMfY8H/JfJ4uJyjyBbbZuYtdd4XXX4cPfai56a5YsYJ3v/vdvPzyy8yePZszzjiDlStXsmzZMtauXcuOO+5IZ6eySDcuEieddBLd3d1ceuml3HTTTbz//e9nwYIF3Hffffz617/miiuuaK5zDWOXCBpIRuE3XKCJtOHeV79k740kMrYm8wmRaQrtjNo27bfwGeh+hHZDQLshRGc8ERHIqk7U62mH9FxBi12rRDo2cypXAxTOoIUQWjRa1F2HKqtBZVBFsAkqSOO0X2iHXCGbKNqAADrlkxGbDiV8E5MEp0mEklYqyt7uWbGpCEOkrDgE7v9j783j7SqqfPFv7TPdKSMZGIMQXttpEpQhEBACgTDIqDQ8EIjGDiBBoUFMawB/+hQVGqOA2P5AAgi08lRCN2KDMiVNGGzGAAGZEpAICUkgudO5Z9i73h9Vq/aq2rXP2efmBgi48jk55+6hpl27vmutWkNgrjXPI8ib65VxDE8S3KIbRFZKAFyg1yfdBjOB4sVHsmtV7kd9PbREqW+RPAZsWvVZ2mUxgjR1BSQZAjnGHTIB8i7zmKw/syTXYE+u2dvkPd9Igs1Km7KHtgnEGWSpmUAAQOcIABLo3Qj57hrl/jN8tDoucvzGFioTMMHoqxW1N20ssPW7S5bXUQTkSxCOylYCOPhg4C9vqEvmzQMmTQJmzLCrevTRRwEABxxwAKIowtixY7F69Wrss88+AIDTTjsNCxYswL333oulS5di5513hpQSkyZNwqpVq3DTTTdh9uzZ+OlPf4rjjz8ef/jDH7DNNtvgxRdfRG9vLxYvXoydd945e9/xYQBBTvWaMg5pRlkmNi2grViLcdAk4LT2JR3VlgWuPvCMfwsDOJ1wSeYKMFFWcjk1DgN9kAP9KtxWoQRRUlKeCm+WM2ULoV1ISkpHrwLi6v3EnnXKgMLsY7apaDycoTANDIB8AMBRnSZ8E/2q05Y4fyMRKS5UknQsldQkyH/N7NU5HwRAwAGRXe9Gq2lEg10gSaqmMoxDvYA3wowQAPQCJ1UoPyNJGhWZK11HmjHySL2JdmfoB4FfFCkDiQJTf9I+rMNMtCrNbbY9Zk7vE6hloewtE0DXSAgKmtDfA6z7K2RlAAiESujdNUoxw/6708s1zDGTQLmBmpQmIIJk612lP8TrfxFmeOt14MknkyBYqVQwceJErFmzBvl8Hu+++y7Gjh2LcePGAVAO8rfccgvOOussrFixAn/3d3+H2267DcViEUceeSQefPBBTJo0CVJK/OAHP8AhhxyCe+65B0cddRRGjRqF0aNH45FHHsk8kgC2YMMYQFkQ9vYCpTyUUymjD/BkN0QqDM4zc6MebtFnGV5Yhehv/9Q26i0K1dbfo9wgQh3uqk2lhxJF7YhPmdd1iZbjPiWFJUAstgE5DooN+mki0dO+GIV18zi3M2ootKddaxnWaImLO+h79vzs6x1AfC8WZyBeaKg3jfYCDZPFwvX5JEoiMoDh+Sw3lcgdh+fHrNd1ZCBHdTY0NW5+aoXpHWwVg7gn63sgpdRbHT3KZQwSotQJDBsd7zu2QrT9QLXRu0xuStSCIIdPHDAOf/6zWpY7OlR+QS3gpVIYhhg+fDgeffRRTJkyxRxfunQpjjjiCKxevdqoQ12aN28eJk6ciAkTJuDKK6/E3XffjTPOOAN77bUXzjrrrMT1Hz7DGAA47FBg8RLg5eeBbbZp4cahmoaNAajpeZ+VI4GdVYwrYVLZTHoUsK/Ri6OglzqfBwojIDpHsGKlDsBdVpJjzzsa8CoApDLS6RwB0aa+MWy0Unkax/2NqhwCxGJbHIPR6afkiy93V3HCuolGe3XwP7nE1Vziy4GBsGtYo/frGkqIsKXXZgukJ7FqZuLO+KR+krqHDiBSVgplRkPzokFEmkGBX5N9JYqNG5Qgw6qqlxZao54foug07wkNEgBTjK2GEuyylqm2O9pN9CkASiPQ3wO8uxqIIsXwdo3U+Vcb16hiJNvBH2RlQNkVFNtBMYxFxwjcdNNfMXPmk+jpGY5/+qcB7LPP4U1aG9fhymK/+MUvcPzxx6cC4LPPPouHH34Yl112GS6//HLsueeeIBcJSsWUlbZsENxtCrB6tWI7NsuU29T7h4ijdPddfC8qN0ygCC0Js/aIREN9qYgtWaWMrQCF3o2saVPqcg/kxor6Xauo8rtGQXSMVFx/WNMGPe9CIoj3HAttsYUbbdhT+4IAgqtO3bBuxqmcSTdOfxISaJr0T/uXNAbGsKYeMx08ATEHROPuQICoHefTgC6LGpW3K41yea2SYhIfaQZYUmCTokmrShMRafi4t0wNpFApYyOOqBLXH0VOMG11rWRt+eBKhQzMWhivwQT0Zrofu6yWS/IVLuJ3ClBbHl0jIIaNBKABp9IPvLNGuXDlSzqtXLtTjH8MROcIyP5u9SzzRdCcnD//dLz77h8RRRGuu64N8+e/im233da6d/ny5Zg8eTLa29shhEC1WkVnZycmTZoEAPjxj3+MK6+8Eq+//jpGjx6N888/H5dffjny+Tzq9TpOO+003HXXXcjlcrj99ttNKqWvf/3rGDlyJJ5++umPSColoit/ArzwZ2DbHYHOUUDHSKDUpYMFZ+ka35PbjGSkM7aoDbYtXlUeO+dypRw4SQVJUgHPKUjqP60yFELoxMEqUk0wZjsE205EsOOuKhXVsNHqRevrhux5F3KgH1IysBlQjvt4568qnVKlHyqrAszH1JvTfoxtnSqZcK6gFtNqv5JQq2XIes0COeEd1wbjZI4HTsQactCvagf9MB5Tyo2YK2jnfN03Auywbo9zi2CTSNHlfOJyCaTJSEHHNpVO5Bp6xiZtVAS1TcDGx5l/YjAfoRNMA7FET/UnfNx0+wmMpVRJloHEx7t/2+izOagBAPraPNjVY1NXnubPKNDJtQvK6b5e0wEwyhD1ijo3cgyCMdsjGDVOvRM970Cufwty4zrARMSi+cjaW69AFEvGWlRCAgN96O7uRqRVpxQJxqU1a9aY352dnTjqqKPw2GOPoaDDyhx77LG4+OKLMWHCBLz00ktYtmyZiQdKWSR++MMfYvTo0XjssccAALvvvju6u7vxve997yOUSimNhIhdEIJcbLqfStL5HgQlNEaeFyjTC5vxpfb2R6afkw3OeZshnN+B/16SIDvsRKTKF1JJhkIEkDKCiEKg3A30rld7gUEeklJe5bTDrpb6BGBAWsqili7qxjfR+PtZGSFYX1NUU16ibBqkMiW1aV271pjMHUEMljknBmq9pq/VgBnEbWpNQmiiegR0uUzqAyCkhJSUKJcxfvrvOKM9U5VmqCorJQKTe9os+XMRAsoSEdoKMQZs5U7ZYH+4oXHPEJBu53vAFrdEmR5VKtMnYILpE/EtE230IgCgrROifRgQ5NQYlPuAShkyl4do6wAKJWts6HkIADKs4Uc/+AEOPfpolMtlfP7zn8fEiRMTzTlYB8MmP0GXJk6ciIkTJ+L000/H+vXrEQQBXnnlFQC2i0QYhiYyzFe/+lXccccd+JTPMTEDfbhAkKQHHtrHZ0hiYZ9nRUg1PvFQYu5lfYVcsMlA74WxT6v7GuxFUH8KyxmX90xGkY5NOgBByYDzBQWCUkJWq7Grh95nFBTRolCIJY8oVFytmxHD5y6TlbhETNJSGMbSnrWPqMBE5rTkQ2rAqA6E0gYkIZSv56BV5ymIZdSKOhCAATvJwFCDj3EP0apSAEpT4khUzUCmFQaDlZPaM2oXALM/G4YAojhPplVU83q9dWVlSoZAumxWQpaRS5Qx1FKvu2fOSc99EUXGmlzxXoFKo1YpQwZ5ZW3u7DHvveduePj+N7HsReCUU7oaNnv69OmIogj77bcffvSjH+FjH/uYOffLX/4SZ511Fnp6ejBmzBgsWLAAgMoiQS4SDzzwAKZPn26ySAwWAIEPCwhWq0C9bB/zSkXcglTAXmD0b8Zlx08xyyRsFaDY9aatjRZCdj5tMRoMmDplDQLCG5dvLlYqNO4HqbJ46Gg29QGIqK4AsdSmVNpRpKxZy91AuU+nPmoH2siaNU7IK+oqM7gJTh3k0sEn8Xz9PTSRVAzI8X1EJvUJaGs5PacE4iDbQkubZuHJvqAJ9/pGizi/loxkzHymvWDd7oQhTex+4S+3UZ3QZZMPYwv9c8qXUu8nIherWVvMT+gdoUEyj0MFPZmBb3OpeFshmke5JEAKqMD7IgohobZMXMbi0KM7sOZtgdGjgU9/2l/FkiVLMG3aNPT39+Piiy/G0UcfjaefftrkAzzllFNwyimn4OWXX8ZNN92E8ePHA4BxkZg6dSqmTZuGk08+2bhIXHTRRXjwwQcxefLklrNIbNkuEv94PPDQw8B/3wdst519zhgU6L9psUqTwIZSykqbzI1UmemFNbmngRokQ5+GBPQ2cRzNHZRcmKxPw5pWu7Ybdwxolw1Z6dNm4FryLxTVfmJ7l5IeCaCCvFK5prgbpC6aUoOZD4hMWqc4Rqng+6vQUge/zpIm47iLCcbG1xaXsiyWpOWg94D6wdti9geJARTse/NT5plCJvktguym0KbUMihpL/XC9wkYm7zHslJW72SkGNlatYqLLv0hrvi3T6BePxB77fUN3HDDdzB58uSG5aS5SBDdeuut+PWvf41FixYlzv3NRQIAli0D1qwB1q6LXST43gN3HeBEANHwQZOkyEmy4w0mZ5qE5p3QPkklco677UgBRun7Te0RkMQE+Ixr2ELoh4oW+tuM2B6RdWcQQLjJhQkQu9dpsFPRbMSw0cDI8YobhVT7eLUKc/WoKsfhYgmyrUtJGKHiYFEoKIu4fN7fL9fwgiRBnvIo0BILAMhQJW8GDNgKsuK0pEmfm4auizNrzVSRrYy3JSGywOIEfiQh0nkAjRP/JioARAEStdbnQVYyOSCh6ghrcezLjMT1PVmoKWvq7oNnrL9lajgXMhZstn1arKuB0CCKbTqovgq28aXz5uH/3nY76loj9/jjwKc+dR9eeuklI8mlkU+iJKrX63j11VcTx//mIkH0p/9RIPgP/xBHNqjXtCoqihd3Dg7WtzODBPuRmGAEZLyMQagfM5FbLnuFaUIn2ibs9kMS9nkWM7s96rTHIEG69QDGoTsi4KRC2KJp4mX66lWLvm9EEr0Ockq6a9Pq0zCMo9lsfFupS407RruyLm3vssqRmlOlPUQBoeZKuQcY6IWs6pyChRLQ0QkUO1QuPr6/KESspnPbKSN1nFSv3EFfBrEEQ89O5PRQS3/8U5p7piKu4txE4vuUfM5ICbVXyBiTRol/LZKArMZVwJ1dTZrU4Jy3HNpzBtT+oYldGimJfBPqM3VmcJHYrMDXjHjdg50Wvv412R5QRodMy9L7LiAE3nl3A371m9tQqVSsO6rVKhYuXIgLL7zQHFu+fDlqtRqmTJmCcrmMiy++GNttt51xkbjuuutw7LHHYty4cXj++efxgx/8AIcfbvsbSinxla98BVdddZVxkbj66qtRrVaxZMmSll0ktmwQ3Gor9QFAmcZjZ91QGWEYMARSwc/LbUn7FopIk8pVZeWafZKfD9SAJJCkSLZUTKKu5BXpt/vGwK0fMEYfzTxQUqRe3+Jhd92+z7o+CFTOw2KHuo7iqdbKKug4wKLZqI17Edhh4XShgIwg2zohwpqR9GStArzzFuRAn3KBKLYr/6q2YXH+QTjsjwF8GhBPgHGfYQ2g5lik52cYAQiTgJhF9d2AUtW9nLjFphW1hkudKdFzTAB7oU41MIRpqY1NSABATlsxmr1XqRglZrCRaItXC+KU687BQbQv632bDSiBjJJ8AwpyirlMS+1U7AAE8OaGVSgWiwkQHBgYwIsvvmgdW7NmDebOnYtVq1ahs7MT++23H+68807jIvHQQw/hoosuQm9vL8aOHYsTTzwR3/3ud60ybrjhBkyePBl77rknAOD444/HokWLMHbsWEybNq1lF4kte0/QR2Qkw8HKWIsKxJm8SeUTxFx8K3tbDV0VnL8bkg8Y2eJjATgtnoI5SQvnPt4OyXCMcfVp7W6JMvHUhiIPc62W+CFaBqSM01RVtbQYBCx/Y5sCxbR7CbCiuhbiuP9gBbKqgwUIxRGLjhFAR5cyxAFi6Zj2+9y55NlHtGJ6khrUGODw6wJbepZIn6teCRwxuEnEZTYgAcTGPe6ZQaZ22hyzLLV8Gj9WQtYckkYa3IR2DZVieMhBcjDAKHKKiUxxhymXyxg7diz6+vqs452dnbjyyisxZ86cwbR0yOnDuSco9V5QoQAM9MY5seic5CpRyRYVZk5vqRaaTV1pz+7EhGJqS/43LUypwOmqbLkaipeor+P9MomCVR2Cg6MX77gaU3gWTQdIrWukc51vgaRrmWoNNFT29RLJY37K8OIKoUAup4xjTO7G6oCKodjzjvJ/IvVpnpILx/eajBiR9m2M6ooLznepcHPa/1QCChwHyio/YySBUgmi0A7kdC6+eg2yNgBRraj5piVJIfIwyX95JguTB1MDFEmQMgLqTEIEMW3S7rsZ1KQKWz0Sx4/QupbKc27k6Xyg2wKtOpV0H821GPg3JYLKppCtUQiYEieKmQjdZ77zvTkktizXZ6l3yNuWyTLavSdU66sI1H49B8MoQntbGy655BJ848JvoFJW0mBQCLDNNtvgc5/7XCute19oy40Ys3EjsNPHgM5O4Lb/G+cOpKSvln+XjhBCySKFiDN/kwQQsgDPYWh/U5JL2gejhTONePQVNyO6UXfR3/p8TpvcawdtKQJI/eLGLwLdT3E28zpyh9DLEQF/xNpNbY/iRU+wNjHp0pI8jP+d7RsXA1zcdinc9sb/bEMO4Xx054wE5HzoXt0nISMV/ULLj9RnoSXeWAJTvnMiX4ToGA4xcjwwdgIwYqwCsnIv8M5fId99S0W7qfQrUKJxCAI1X0odaj+Ssq7XKsBAL0S1DCGEiaYjRo6FaB+uktFGEWStCgmpYq6OGAMMG6nGsncDorf/gmj1Ssi1b6i665V4ztar6juK4I1sE0mIsAZRr0GEUdxvyPgjhD3KfN65EqolKbmSK2Mmac4EeSUVGAZHQiKClCGisA5ZqyhjpgbkzgBbVhtaMuXT+2b2+pRaXkqZCjJN2+cb1xY/aWPR6pjIDJ/kTTL5aVpRpMCwv1sJH/19wIByYTrvn/8ZHZ/rAD4GYByQ2z+Hy359GTo6OqwiHnzwQXR1dVkfIQRuu+02AMCNN96IXC5nnV+8eDEAZSRz8sknY+TIkTjiiCPQ3d1tyv3+97+PH/3oRy2MWkxbriT4zDPA22tVqKtbfw0cerA+EcDKHQep1VR0I5NW+CIQOAvEUJFvcjlUqaIiAAAgAElEQVTHEuoXw60hbqv5KZJlNtnnsKVj9rdP2mP1xXd7pAQ+tsboA3H53DjGEjSEKdWt1+LNrXY6fYk4NysSTbOtXVX5AogBpWOEklZqFR1xfyNQWwuZL7A9xRLM/h4xBHmKYKMZppo2CKFsGJopSUToAJRRwfDRKvcb9aNWBSr9kD3vAr0bVBnDRqmoHXkdVzVHgQB0cHEz7iFQjyymKDFOlvbB0U40IgcopXtO5GJmSz9cQUCpVa7m7w8AmfYnGABo9wsm8YqMsU1bttBNub/BmmOdSakvaysySZSZ3XEkUO2DlCxaVLkbhUkFYIL6s1Qsoa2rLXHnAQccgN7eXvP34sWLccwxx+CII44wx/bdd18sXbo0cS+FTVu3bh1OPfVUXHvttfja176GlStX4o477vDek4W2XBDcZx9g5kzghReAeV8Hip1AVIslB8UGq2vT5pm74G/KxG7h3qZX6rK9AaKz1JOmpk0ZB0t9ZcBHxn8LjWI+KYKvva41LrteGE7caZO/A14wjk+ngKPTNnPEGjMZc+MsuTC4437fRqC7qkCPpYwyKuYgAIISkNd9jeoQiYwY5CSv25MABGFCz4muUcC4Caq9VE65FzIKIQiYK2VE9apqR1unsmDN50F7jSJ0g4EbjgRJ2ULa7fAcbUoUls2ACYGihHGc1xabmw0MUwAkwVT6bqUfpN42+6UC0kpP1ZpU5h3nRm0ZlNVvzPgl2uYpb5OAkpUnPL/4vT89ZAFm/dcZEBDYc5s9cdjEw5rW+Ytf/AInnHACOjuTeVJdorBp+XweM2bMMGHTzj33XCxYsMA427dKWy4IFovAHXekn5dS7QXJ0OGGafZ4uGTLGIUvIoxocZb6O3Wxju9NnecuSLg3DJHfVSoPSQDn7UMTBsIyxmBj6pPqIs2YeBvD74W/LcK1Kk0Zb1e1I4B4D02we6WdIw1QC16xHSi2qyvIcb86oEzBw5oCRALFfFHJlwJxxgsCAYopasK65dXGQxDE7aSqmYpSAPHeZFtn3EspIfMFBXQQQBRC9qwH+rpV+zqGqSSqxTYYYx4GiPHUUvM6NkZizE7DsW0ABFyisRIF0/4hkxhZ9ou4YGGX0wrpezJJObwu77mcLpLNBwCQESQD8eaA6GgpWqVWxsF7qUicTLYiubZlqTVmLpJGR5AS//h3R2Nszwt4/NU6/nnWzsg3Mb7q6+vDb3/7W/zud7+zjj/11FMYM2YMRo8ejVmzZmH+/PnI5/N/C5uWmaJIxaQ0xiOwc7z5VCIEaoIvng3IVV0yYxNpSUJ0Df2OF3zhHs9sjcqAnFWUUFklC2iw3jXusPesqyZLu1YEUCmJrJZ4GBAHJCX/I64zBlrP4mlUsPwW9zo9foHbUhsQRCC0awVz3KfkwtpxX2rHfeWOUYAgS1GSjmlvNgw1MMGoNk1YN596222XECpPY65g5pvIF4GO4eoKIZQEWe5VlqyAkjCLbUAQ6l5pUMznk24bTP0sYl1mgzY5ZFSkYfw3BV6XTEKk/UIuHRqDtez1tSTpWTc2X+oFjQvVYwILqBKlcQlpMkKD0tikHEu9v4WTrjaklaL4HWxuJN8t4HMnTcBbqwNM3hY4vEk6wUWLFmHMmDG45557cMopp6Cnpwc77LADHn74YUyZMgULFy7El7/8Zfzrv/4rfv7zn+Okk07Cgw8+iD322ANr1qzBZZddhs985jMf8bBpnGo1oNbvSFG04KU4glu/44XAkvCaTMrmg6fK5Vy9parii7Nv4ddtkBwAvC3wHJesb4B9v08KzbQ5LnXkmZQ6E1wm5xa1yilTQOm4vmRJjiQF9m31G5mfo6fixmd5cuHqgOqblUcxD8tVggCBGyyR8QtP2mskdLvH/kboZxtpa1MwYNPlyVAZ3cTF6TaFOjOHyKn9TwpYTUwdl+b5vDHzN2XMJL/HaSd/J613k/09GBVnln28IdCqmDYI4U2e3NJ7lIXeKxuFrLc6fyfkyXoF37liPO65B7j9dmDs2MblzZw5E5VKBYVCATfccAMmTJiA5cuXY5dddkFbWxumTJmCz3/+87jhhhuwevVqrF27FrlcDnPnzsWMGTPw2GOP/S1sGu6/H3jyceD0Lyo3CSB+ibnqhQOjuzga4tIBe5kdDirmqt37WUnCKTeBFUyaE1SuMIuP3W7d5ihKlsUlWdN0vuBEbPtMWF8+gTLz6yH5D+mMq9smxlQwSa2lhLjJg9ZX4pzw3MfnAB/jNKOFRguQlMpIpdSpotQAKvsFAaJ23JcUyaZYiuOXBgFAyYTJctdkxNCAmMshabitxtHLw1OOSLKoNYG+A2U9TNatRl2s1LQyyKs5H+SUBFmvKhwtFFXaHFJjhmG84HPVrcvs8OwVVhM1iArE9RNFap6auaHBXGZgXCzG0nuB8P/OQp4yBStHWoy1tJYMq22bQkMFpq1W28J19B5LKSEKbfjWt4Bvfav5vW+88QYWL16MYrGIZ599FjvuuCMAWLFG+/r6MGHCBLS1taFYLGL9+vV47bXXsHLlSpx99tm48sor/xY2DZ/+tHpBt9kaOPZodYxeVCv+pgs6sBdrTuyFTXKfWUDPT9Ir5TnluuWkWT82qptNSiDn1EhSEu3XMOkzYUUIVjdz6UgsTiLb+uJKBpvjBW/UEN+eKydiTHwA6i2LcxFQ6sr2PNDWpcAjDLXTfj/Q+45aNCmSTbFNgWggYqwzUmJd+R4KEatNKWOF3WCnnVKpcJXONX7OUYQ48wW5v2hrU4DlTxQQxXZjVBPzOZEC2HpVWbOKACgUVTABYxSjXTwkgEKxcY5BS1UqQSpUCRlHVCAVZBRCCGH25FoGls0IIs7I20yo0Mc8136gSAjPGpd+LeAwHsLuYxQBMw8GnnwSuOceYOrUZDEvvfQS5s2bh3vvvRdSStRqNfzsZz/DLbfcguHDh2PmzJn41re+hfHjx+Ptt9/GySefjDPPPBO/+93vMGzYMBx55JEYGBjAgQceOGSZ5bdsEDzlZOB/HgOm7ok4DxwRBxaRPO4h/4RIqnhMCWwSpGuVHRbRuQ9unc7EShqFuLfa55oCMjdg8J7mUiiVT+CpfgsDnPqbWksSVkJ1K2O3Biq7kSFMWrs3hZq96C2X73mGpEYUAZAPtHZCP7+wpkBxoBfoWaekMNpPLJSMJESxq1WZ0AY2Fea76ahZ09ojoMGT3Bm0BFavqZM8WXCg0yuxJMHCqGg1A0RJlKHnBLeGzasUOzQOsl4FajVIIWIpVKtqjeRg2s9if0KqAdCcAYGp4JkvUp8TSZo0cO8dWS1K7JVLuFGR3g9Q9I5IVvVyRkO9vl6JJUvUI7rvPj8IbtiwAcceeyyef/55TJ8+Hddffz2uu+46vPXWW3j55Zcxbdo0/OpXv0K5XDZr2xNPPIGbb74ZZ599NsaPH48TTjgBl19+OebMmYOFCxdameX/FjaNE1n31atILNiaiTeqTUP2+fgsm9gi8WMzEqlCNcectndJ3LPhokVyIrvUbK+TQDCKlO8bNEBmNuCh9utvAcQBmZ36uQrX7BvRQ+ASKNBQat5c0uUQ7ScJ6HGVEgirsaFNraL25Gg/sdAGBCwYNCXsJfUpJDOucaXEJvOSq0RNWQR2VA4FLJBxXTzgAx8bqlOr360Fn6s+SToM65BhTTMKRVOGlDrogUB8D5vLZhRpL9UY1qR02avmNv8lr83CJA1iHkjrV7JNm2MVabWVg2mD6Y215y3xi9tG4U9/Ar73PWDUqMZl3H777Tj++OMBAOvWrcNWW22Fc845B2EY4t5778VNN92EfffdF7VaDWvXrsWee+6J+fPn4+abb8aBBx6It956C//93/+N3XbbDfPnz29qIfrh3BP0kZQ6Fx2ltlELqDHm4IYiUquKJHHGFvLBgB9/AWI9kV1WgkTyp/UeeDj3xHTUC0AOEIkU0IOgZtKOdCNoSB2RJj5vAVtib9PtEzP2aCr9igZvI0mi3OzeN/YkhXKGwFMfuzwTaGQhPRZSt01JdLZLgJlLQgBBmyU9qUwXA8pxv74WyBchTdzTkrIGJYq46pTcIZzoPtZ4OMBFKskcYjBk+4hGZZoT8XmKyGTV4e4BCmuq21oH/QyDHEROR50hQCZGKwggo1DlqgtDiDatntXisbqGFl0OlCSFN3neHFBdw7hmRi3u8WZzR78jxETb2xTOZbzIFmmTAM/EhuWMpU/L4BYizFphLW1BgIMPBnbaCRg5snlbdtttNwDA2LFjsZVOhCCEwEMPPYS5c+di6623Nteef/75OOOMM7B06VI8++yzmDBhAmbMmIHf//736Orq+puLBAA1SSv9amEAxyo2TcgqDxJWBnK6zuzLkNOvu6i6i7V+sYVk64BIn0wcL9yXKiHluZQE6KHY7DenvDV6Wex4AUir3rf/5q07Xjz9IKUB1Ei8tKCmMASuRCnryefCTfN5wlvdDHDpM6HWTbY80ecG+1epT0sIA3boHKmAlBz3tY+izJdi69N8STnRk4GN1CAV1hH7Juq9RKNi5POZg2KgsoiTylpyQCRw1YY3xpiHuVjkCkCpEzKsKRBP9Q3je8f6OdGBnIqEI7WUJ9qHxbfVq0pFm89DVrWBTqHNfo6AkiwBQOqckV4Hfel5cHRKxtdkgaPU9zTl4gyXyhbqb5SDr3kdmmjti3QarUjGa5kpW6hj5r0ASKPjbr1UKhJTpkhUqwLXXAPMmtW4LaVSCcViEVOmTEGlUsGKFStwyy23YNSoUTjnnHOwatUqAMC9996LgYEBfPvb38b8+fMhpURfXx8++clPoqenB1dcccUmuUhsuSDY3a1GeeVK4IeXQu7/qeRCShwupDZFD+LjlEKHnq+J8BEAedpHAcwLazbtaVF0JkIiiokjKRH51IHqgPVlrnEtFblRiat6TJTlk750myR3dXAqJq7V08JMr13mlzNZp/eapuWxBb6hRAk2BrrsIG9LATz2KtilLoiQqs5d9F1mSgDksC8tEPIxSQxIteM+MEqr9nVmjO71QFRXPop5AsWCnWTWSIk19TcL62YYCwP8ui1mncsBgnwJmcQJsH1EnYw4CpXGpU/vDYogDmIvaGziOWx8aK1+s3ZwZ3s6RqHupARyNLZ1lSBZSpP7UYZ1dTxfhAnfRmNJZWZiGgcjjw0lCZhIOw0AcTAAmEq0b0zSPm8L/+bML80lpj0SYR35vEClApRKjatcu3YtDjvsMJx33nlYtmwZttpqK4wbNw7Dhw/H9ddfb0V/+frXv4477rgDQghceumlOPTQQzFnzhwcfvjh+MIXvoAnn3wSjz/+OJYsWYIzzjgD119/vddFIo22XBD80Y8g77oLolaDnPVFYMWLMadKE95azGhi0QPVUTz4gmS9JElpg5sDE8W/aCGNYJO0f3KOM8FNErixhTwBmk47+T6aWcT5MWoP+Y8Jew/ISE0sawG1y6pbMGMGT7+YsIFmi71LHNSt8eMF+8h5bpaK1fzn3MIkOx8fIljGeN6OiI8tC/BtpBpmaEKMlFVEFH8i9nyIoeLB1qlOeg6BUO4YJR1aijvu96xTbSmUtJGNShmFQkm93VJq0HDCupHLhqsKIwq0m0ZO2HMkrPMBBkCuOyKeQ6Q5Md3jiynvNyvHPHd3vjhgTXNWBDDh5aVkWUAQS/ja9SN2O9Fjx9TU7yfkpcIY3+/k40unh6oBlkYgBxSzb7nIutIUyDA0Y10vlTBnzg/x6qsD2HnnIwDs5b333XffxWGHHYZjjz0Wl156qTm+YcMGjB49GieddBIAIAzV2r1mzRr85S9/MW4UhxxyCH73u9/hrLPOws9//vOPsItEZ6fiAIUA2kpARed6y3HVJU1+rupA/JtAib+bRHzt9UpGPi5NJsuwfzgTzznnLs6kshOwgZEMTCxunnzwckgHHbaw0gtGRdDCkAYeVE+CA3XAnEtOlorXU5YLYtY+k1uN9I9nU7LBOm6eYpCkXjCVwMaMTaxFR+i9qDRVH1LGhrW3YQxNmotayjFSJ+2BCQaiuo5im04UPFqpEclxv18nF+b7iTzaDKkzawPqbxPWLW9F0ZHUbuobPZtAP9Mo0labIgbTIG/OKYmQ7TH6DGusIXL97iQsYDbPn+ZMLgZOSu1kSeeqPKktWpUxkjZAglBgLgLlumLtCUamLS2DTQNmT1IbMpYh2G/JGSYpHWYUCbXkkBJXh+dy8XPq74es9en0Ze1AeweOnTkTS5cuRaVSwd13X4rHH38cf//3f28V193djcMPPxyf+tSnLAAEgBEjRuDNN980f7/xxhvYe++98cQTT2As87z/W2Z5on/+Z4i33wZeeQX4/veB0XoT1UgVkfJrCqv+yUecqm+FbrZJvjnI28YG9buce4YXQXJxTS941m2tGgf4pNlmQGWpcj0Aaak0nf5ZQJjWDtXHRA8SUmou9pXjLfbtZ2YhIy3J5LNo+mh0fxu4rsQgw+skAJfahaGo9hQpw0W1DPRtUGNh9hPbIIolGGDVYd1kXRuSERPAFzzdJ/NIJFQfg3wM3jLUWwYEerQf6TG6MUEDqB+AkXjMeLhjKOKLaZvBZSoMKBLTwXa1hQDyDBBzeV1tFM+LsM6yqPM5lHyAiSMuo5wR8LLAl2DvulUqqZlZXUMOiEGg5o0LvB0dQEW7x+RykFLigQceQKRj8kZRhAceeCABgrfffjsee+wxLF++HDfeeKM5/vzzz2PChAmWMczAwAAAYPz48ZZ69G+Z5bOQL4SalLEvoWQqhqaThi3U3FCjGbWygGaRcHwAlK1QdaU7ibO17INDKXs6xCWTiT2A+Lk6xhOWfMNBMyH9yuR4pz6jJtKz3VrryzruNcDJONesW5jUIKAk3bCuJcVKbLxCqlO9pxYbCoVGnSi8Yd141zUjKQEIreYlozLK7G4Aj0A3iqWthDUrEI+nr1+ecaN7zCHHcjhzFgsZg7tH2kq0xbdf34QsQB4iarR8Dykg5opeMOS0yy67YMWKFZBSoqOjA3fddRemT58+dG3YRPpouEjUqsqAgEuDlnqOXkzffg2XZtiCJNyXspGEIz3vZSvSGrvZWbjTSaRcI5Nco2lNGpf9XpJHinSByPpLxM/JPC99fRCoBdsJip1cdITnpNssAhDvSuxps1SSB9ieFq/XXeATdUuYCCpgKr+GjeT3+p5lfEyQZFcoQXYA5DAvB/ogyt1AT1VJdDqSDQptEAUCRcqbqI3IyOI0R0ZkxGzotgbCNj6j50QGOgYQmXO+5ZaRS++y93k4x8x+pO4nvfP03sMD5Lws4fzU97nO7sl9+lgD0eiZmbNNGeQURsB3JetPwv3C+XuTQDGsAmWWXkwInaVHAu1dAIA//OEP+MIXvoDVq1fjggsu+EABYCPKwiJ9cKm3F3jzTWBgAOjboEJUEUdrDGSgHljAfJPI8s0yj3d8y0h6tDK0p3xMHjW2EPBoG2ahIJVZlPKBLXVSexp+uETL1SZMPaelJJFWrzcLfWSPEf+Af7ISk8pVBz39U8Y3kgSMSEKGIWS9phZSSn8UUMb1ApAvQAQ5JDKqN2wK7w8fB3qO7jNgbQ08z4T6YxZCYojcsQ2RYLgSTJKn5WbcXeKMWhK4vU9KBGqfcNhoYNQ2wJgJwPCt1Jzt7wbWvwH5zpuQfRsgwxpkvqiNcjpg0kWRUU69pt6rti6g1AUEhbgPgZYiyXKV8jHqgN6gbAy0F0kxVGsVVW5dG/NEnvnn+7jjYMLNaQMgCSgpN0wZSz5m2uRGBDAxfRPzlz91Yb7TSerbZeK5JN+mDP31dEEIYT7eFkhpPoOmehUod6u956iuxrSq1JYTJ07EYd87DGO/MRYnfuFE7+3NMsufddZZ1rlSqYRhw2KXmfPOOw+jRo3Cvvvua1woAOCXv/wlzj333EF1actVh/b0ADvvrFwlfnE9cMxRsSWosSDT13JpMFVySqFNkpLoxXEWKIvbd65PSBq+IhnHycui99Wn+kzrh7XASvt440bw0lkbdCMEG2frcg3SfGFxJa+UtmZ6Ela5TnNprFLHfzNTozF122Q9E1fyQPIciPGBZwziPnsXSEuaYD6K1bJa9LiPYqEtlpLCegzuZGCTS1GdWs2OHEaVqUyNhMi0NoFTHt97tY4nLSkT9TJVtzRMb7a5QOyledFaXBtSn771vqSX2bS2RoY5m1ttWuoCcjl8/OqP46X1L+Hhf3oY++6wb9PbKLP86tWrvYl1Z8+ejSAIcP311+N//ud/cMEFF+Dee+/FxRdfjHK5jKuvvhobN27EQQcdhCVLlmD48OGpdX341KEbNigAlBJ49lngqCP0Cc2B65+GLC7bo96Q0n+uEblqkcQ+knPcqsdbYPMFmp/yqUIsLPEtqpz0S9dIXejdi2zC8ZLUy/6XWooTvE6jumL1CMR7e7wxTRkHutQjTQ0V3qXuy7oV6E6lGnf4ynCLSJmrrA1mhM3Y6HFi9Qi6R0pwa2BzNsEwiTh0W+dI9dxqFbXN0POOctwndwyThDhQEkFY02mlpPZNdCwKTSWBUqlaDvjuXiFzadDm+P4wcWxMUvb+THBucx+T1KV2FGfALRoCnMuQZAfF1LPGqKbx/U1WjoZrCy85MYtbVJtKXZd1XaUX6BiBn077I55Z9Qp2H9scAIHGmeX7+vpw22234c477wSgMsvvv//+KJVKOOSQQ3DVVVcBAC666CLMmzevIQA2oi0XBHfYAbj5ZmD5cmDePKCrK37ZjaNv3QQEBujLs5Dx4M6WOizxI/6bLfSpAOpOSnffMYsQ3mxCesoQ8ckmZWRAh4TwwRZO34LsLUPoUFlgGMoDDfjv8VIqQL4HlGhTo4qzNioLUCpqOltEikIujUkzBTOp091TFEL7IJaATihQrGvL041rteN+mxX3VEC/gxRFhgfqzuXtPgqhze91O3iYNmKUcgUYCa5WYYCYNKyRVn9MJfZ+MZUrAwV4BIYSMP60prQ0xsVhUvgYD1KyasJaNqSmAJnyt5c1ZuMXL4E2Q+3uRQoh8OPLyrjo/+yIfH5HXLst8PjjallOo7TM8kS33XYbxo4da/YWd911V/zkJz9BuVzGfffdh1133RWPP/44XnzxRVx99dXpFTWhLRcEAeB//2/7b64eGRgAENkvdbO5yV+eVMlJf0vfNRlArVFDTFtd6UYXzVSMBs8ZRyroYvcl3JQ9AKetVpSZRD2mUYg7wBdmyYqTsJgHLum12t6sDEVDktZXovxW29O0umaAtAmg1+D6lo6756SEIAmu1AEl4YfKR7c2AJR7gCjSeRR1hoxcEQLab7BecUAxH++xmvq0lBjkYlA0cUs1iELHaK1R3FQyqknvXypzQAYzpEKmYOUNrUqzjBd/DzadS2sZIBkYNwRI5/mmrmie+SoZGEop8c3vtKFcVuf++lfgjjuAU07x19nR0YF6vY5arYabb74ZBx10kHVNtVrF3LlzkcvlTB077LAD1q1bh66uLmy//fb405/+hOOPPx4LFy7Efvvthw0bNmD33XfHT3/6U4zMErxU05ZtGONSFKkXcaAHsbVeBvIZHiSMIkT8wvHoINYnH38EJUZN4yQ9H8ORRrr9+kN/yxBSRmoBYMeEDG2jF5+hi49S+6gNA6RKixOVexBteBvh6tcQvfYcopXPQa56GXLtKsiN64CBfuU8LQSETpejvnXqHN/YkfqOA5/pF/udapzjPD/3WfoMXxLGPdZgwDAQQsQRXGg/qpVPFkobc/bhLXNayUfOT64RRRbjkmYfuG0OIHIFiI5hECPGQozZHhi9LVDsUIC3YTXw7puQvWRkU1JGNvmi6mFtAKjocHD1qm0IQ88gl4fMF9QHUIZSYQ3W/p+Ucb7DsA6hgx+YTxbjGhpdMqiREiZzx6AYLEdSfK8p4zzkxjINe+msIT5L1FKbXWdHR3pxy5YtwwEHHIBvfvObWLhwYeL8RRddhP7+fnSwQq655hp85jOfQX9/P7bffntcfPHFmD59Op588kk8++yzWLZsGSZNmpRwwm9GW7YkSBRFKk9bKwYvaZJe4rCHN5K+40RMqjGLYgoQpkhOVtlMVeXNS8alIOtWaX9bkziWjNVWUQhUK5CU1qeuQ2OV2iFKHRDFNqB9mAJaQbZw1DYZ/ybDJKt+l5qK4ynXsfIaSn4uEA1yERqM6touINmuJlf4yhD6wpintzn3bCW1AM5SxvM3QbSnmF6WyOWVyXz7MLUXF9Z1cmEVDFyKwA4EHuRA4dhkVI2ZSxPWDfE7lKOINVBMFyg+KzFYBIjMJcPdk2y6RaDPERBCIo5Ik7MZnUGA46bqK943IhVxvWo/G8TS5M0/78MJp3WiVgMOPhg45pj04t566y0sXrwY11xzTeLcypUrceONN2LXXXfFhg0brOOf+cxnUCqVsMcee+C2227Diy++iD322AMzZsxAoVDA1KlTceWVV7bWtZau/qDRFVcAJ50IvP1XgNRrDT8R/C4ADY57ucYUKcKAA+IXRCKlLiqHAIm9yI60KUUAKXJQptvqJbQSq3KwTZG6JIRqfhhC1iqQ1QGVsqY6oMCrUIToHAExajzEuO0hxm2PYMRWEG3tEIGAiDSHTQ7VJLFxdRQ3XOAuHC0BkSvrtCpp+aTItE/a83SLzLJ0ueUkpd2kpJcs1+65/mWkUr2gJ6TTDG40Jhycx7WGf9wQZ9569O9cHii0A8VOJf2RG4RW2wsoUBTtSlLEmB2AkeOUJDjQD7z7FuS7byLq3YCoVlF9pUgztQpQ6TOuGCLS0p2e+yKXi4NrW9kQZAx+gAJEcrsgRjBtDsn4qUhAu+vod08ESgsT1SHDOmQUpelzICGd55ycHUNN3D3C+mDQbKCfKEA5YvCj39ttV1fCew7YaScWfMdDRx11FIIgwLx58/Daa69Z58455xwUi0UceeSR1vHJkyfj3nvvRblcxq233oovfvGLuO6663DQQcm6VDgAACAASURBVAfhhRdeQG9vLxYvXoydd965tS61dPUHjS6cD1SqwGc/o1wkAEc6ATbv1CNKqaPZ4snb57lUHYolSzWx9THf1Ua4lGB/2JeZTOIxtbblJZu225Y8As1q8UrcMnxLhPT+NOUPCTXqg48Eq1pYh5NtcqQ0S5pD47nJGRqAMVR6nCL3Pqddvva4UnqqIJ11bCUQRiygdsMr47IJuNqHqf6ENQjyO+zfqICUGdoIUkvWNUgSkxXkVdxPktooEwIFOg+pvyKOiBNqCZEzE1n6bp4HYmaapEPyP7Zv8Pffc+VQ0aZmlWilXWZmO9sQ/3F7Dv3aVXvhQiWj+IiyPcyePRtXXXUVdtllF3R2duIf/uEfcMwxx2DdunXYsGEDRowYgdWrV2PrrbfGFVdcgTlz5uDcc8/FpEmT0N3djVmzZuGUU07BAw88gP333x+jRo3CuHHj8Kc//am1vm+xfoIA8KtfQaUxvgRoa1MT38qGIGPu0CKR8uemTNEGC4spmklujUryGNwIR00qvYuoMNf7JDC/Qq7FPjfiopupDX3nmoC64aFTQUNYX3ZZHtCy1GFNymXDGd/ja6ran1KO1SFkJCECRzr3tK4xpbXNBTkmzUp2r7cmR3vgJT7PJDvmu1QDkHZnkDICoprdB/deN1uEBixZqyowlMyyOwyVtGksT0tOgAqSXHNxuihTDzEMThAEIexx9QRMj9+3BmTGXH9nDs82CNKuI5vKzg8lAMevhGbtpMQjj+Rx6HFdEAI48kjg179uXMbAwABWrFiBqVOn4pFHHsHzzz+PU089Fc899xwmTZqEnXbaCf39/fjjH/+IGTNmYO3atcjlcpg7dy5mzJiB3/zmN5gzZw5WrFiBp59+Gtdccw0OO+wwXHDBBTjiiCMS9X34/AQB4HOfUx8iSqUipYoeU6/FL5qUUCKJI3WQYz3AXtBWKGURtgIEO9M3BSiSRznoke8jLSz6BeRl6YwDgiJkNJQ0rEanUyPA5ntSZIDhk2AEO55F0jBGGKyNqbfpeo00KZ1zaVJ6s0aI5DXUP/pTOCcFABHYxo6DXnqElqBdnzjzn26LAEQKA5Lp2bFiASR9CRuUKSUg6wDqjPGM4uehxyNOEcWl4Chuf76gkgS3dzIIliyKjJYS6zUVJJwAMV9Q6vmorsJ6BXkn+LfQ/ogylhCt6E763Sc3Kg6I7th5x9aVDuuwXSzQnDFMH2lWRhDXQapo32WZammVhL9gp09CCOy3X4hnn6hi1dtFZEn03tbWho9//OMQQiCKIqxZswZRFOHAAw9EEARYu3YtAODwww9HLpfD+vXr8dprr2HlypU47rjjIKXEEUccgblz52KvvfaCEAJ77bUXnnnmGS8IptGWDYI+qvSrzVsAZv/NTGgZTyAjCbAFhSa/y+U1UXckuXDJ1DHNyT/5WVt4BA0AtOch0jjPVlQjrgFI4jwtVk2I2kovK9htXt5iU/haV5oR8Kc6ktaXeTbS90yztc+yoZPJhZJBZJPyU8hI8PThjIaun48xpGqH4HOQzeW06C2kMiSJKwqVuwMAiJzac8vl9T4flCqxXtfJa3mUmJwCplxeGcUE/o0gYbWL97XBteSO0dapmxzFKaP6NmjHfeajKAIIGaotEtoHpTblApg8kfROkURJEi0QJ9t2AbGZIQ25WZD6FtAqWrufkupvlXQbBQhwZRIQW2B6/Oc974WQyXlOV3kYhUWLQvznXcDvfw/4fNeXL1+OWq2GKVOmoFwu4+KLL0atVsPUqVNRr9dxyimnYMGCBQCA6dOnY8OGDfj3f/93nHbaaRg1ahSOO+44XHvttZg1a5Zxot9pp52wePFizJ49Gw899FDL4dM+PCCYBn4C8SRVJ60v9VvCcKZcmkmonejGRhOKFi/fcV9Qaz3xKKYicdJCKI42p2JjmqDD/F4ZsnJ8JJ2fHPB530zzUtruHk5hCvgCl3q5dNrCfjeSQOA5J93+eSo2/RTxPEhrjwTi9FVCn3U5+eRCkRwakfJIfJKpllIIWEIVM1PWdaCHejVmRChWaqld+ekVigoQKgOQVbLsrcYMGCWa1WAgCm3K6b1QVHMrnwdQ0O3WMmvaXArUfQagEqNA/d888ggAiCBQ/S4ps3lJyYWrA0B5nQJyZnkKCIh61QZssmzMaYnRMpgLbWBJA8TUBurE05F+mUgNm0lVKmyhq6n1M0n/DBDRABCbPZdm533z1t1iAHDnXXksXQq8/bYfBNesWYO5c+di1apV6OzsxH777YfnnnsOO+ywA26//XZUq1WTTulf/uVf8OUvfxkXXnghbrnlFvzsZz/DzJkzcfXVV6O/vx+zZs3Ct7/9bXzpS1/CiSeeiLFjx+Koo47CZz/72cZ9cbu+xe4JlsvAiScCL70IXPNTYO+9YfYASQ1jnhOT7izSFxgAcM+nTQzOobvlub+QMqGl3T4AQxLDb8iItyVFcmqpvRxwh7ifm1CeTOOA4wtgnhUxSy5Dwe/nzAVXmcUnfeyQrivxw0vCN089+4+mLNNeEfN5bjUNjEK8Z1qVZt6DuS3Dug7ura2eZaStV9u0byIgorrqty+sm5HkQgfA4nPSZKnP2B8zfyKQqtQrRWUqJ0s9LUqIgyWa8jKuU4oCfn//Y3jllW7MmvUpbLXVVi0XO2nSJNx66634xCc+YR1/66238OlPfxqPPPIIDjzwQFxxxRXYdtttMX36dLz++uuZ1s4P357gf/wH8MD9QH8Z+MbFwD3/ZXNqlrFAGmhp8gJg2rHk8dSSzTrJH1ByobKWmUbPkspr+EJkXZyE9dUU5IbsRWKSdQvkWqJxDl6aPHgsQAC0UCe0NActSZDqlKfvMQsbYonADTBguQbQIiNZW7iUraT4RiNmC6OSHST1k298koAaSwGmMOun+SthKMUWTdaGBOSZd8oBeTJIcXMFRhTkwSGvUdTQLs6xj6KK1SUplimpTwFIShkFAUF5Fo1kWABEAJkv6OHhwSZU+2UUQjC1sQJR6ocjzfE+Sj1fyFCPrEqzjkHDPUqpK+WAzfY+Xe1U5jpzKuqPG8CcSI8pggBoH47zzz8f1113HYQQ+MEPOvDcc89ZGeGzUK1Ww4oVKxIgeP755+OSSy5Be3s7nn32Wey1114oFouo1WpYu3Ytxo0b11I9nLZcEJw8WT3jjg5g6l7xXoTh6ug/d5HQ1OIL2FRgttIyqcnN9+x81pxNuZfEZry5sWl7rWvcBd0FFAj4Fy5Wsbv4comHXx5FSo2k1Xrqo52h6+7xWqwGDvV+U73uXFOPw2blCkBJOe6jrQOi1KEd+sl6sKishCnWpN7rEjKW3qSxDoQ6X6tC5IsASnoh1Htbmchd8QZJCSaJ/ekudubPyLqGIMxcR+Ndq6q/c3mIvFaFBgEEAhjVb5qmgr64FoUMoIQAUHfAlUmdfL4nmClHjUbH+L73EJDIFYD2gnLclzJ23K+Ugeo7ysWC9hSjAAi19Gj2EvOJuKYiitR8Isaaq0xzOSQyxrj9zjEjFxnCUpW20nd3zlh7+/RfED8vMMlWuvenkAxVYOy8VqO7VOrUDF8eURTh6quvRqhV8WEYYtGiRfjSl76UWvyjjz6Ker2OvffeG2EY4qqrrsKaNWuwzz77WNfdc889GBgYwNFHHw1A7QHef//92GGHHVCpVAYlcXLackFwyhTgmWdUkLoDDkj6/JBVWYurlB/sSOQPnBQwmuOnUEv+OwEpEUURwmoN+WIRQa7B/sBgNs0BRwJ2yuFm8ZJnzWZSBy12xrWEW/pxrpKVaaQpzSmGYQL8ZETfkeKgzf5WHigWY2kiyCEwKir2yatvkWJw0ZCCnBl/I+l49koGv+yKeMHhlrJDSR6AjNWZ0jNf9N8E5qX2WAkr2H2cOfQuiK6mwCUW2cVYZYexsQ1nCvm3pVLm7XI0OPw+amNCmmX9dRkyn1VmLq8YKPJRpEDgA9opn5ILF0oA6mo/EYjnJPXXaCHC2GeTrEyz7CGSIY7UIEWSpWHkBzEj00CR9g8JFAFYdgdZpNH6gPoUO/U+sqZ8gVWv4oH29PSorgRB0/idlUoF5557LlasWIFCoYApU6bg97//Pbbddlvrmnnz5uE///M/zbGf/OQnmDNnDiqVCv7t3/4NuUZe+Rloy90TTKN6TblHZKBE12UEVMqQG9dD9m5Q5tdt7RBdI4HOEUCxLfO+nVl8GaXem/IIJL0gRkKqMSkrVJITfeo1nXaGpKqaLVVRORRHMZcHCgUrOS0lQBVkFcjByEhJbrzUgL3wOlaoJbEwiaIhQGSQNjOMvTXufL+PLfhDq4TzkTCCAmvVoCgxj/ii6bEAtfqWKkXy48L5OQi12SYTAaCrjZCen1otyVV9FlPHPgK2pMUkVOEs/pJAjPYTawPqHSi2K+lZBIqJE4F6TwKWDYMb1gA2Q5rVqIZHMeIO+EP1DHzGXRbjJpq3EVD9L3XGQketosYpCPBf//VfOPGE41Gt1XDMUUfhN7fdvskANZT04dsTBIBKBejvB0aNUgt+tc97mQ+QEEWQlT6grxso9wIQEJ3Dga6REOMnINh6x8zNkO6iG0UxQFFW9LAGWauqdkYajMxLKmyn31xeqXLyBfUC5otAeydMRH0NgsI4DivOVLKFIqANfQIqC9QauFcMCWVQEyZeSiQWOqXxS/PdZFKRBKReiIRW/YH2rRC8d2u5IQ44me9QRBI4X1ApwgmgF+EgG5Bbz5gkP4+UZY1/lDickNaoXQaE+DkZvwdg99sFOuRRm0JBlb1fqn8YFT5iqYy3SS/QiTGSnn7otgogft86Rqj5RGDY1w3Uq5D5IlBq1wBbVdFsOMNIkhx/fjJSkXWk1BKl3kNNaCQ0WLuqUpLeNnUS+yRFfow0PhJx+3x1ykglKBB5KL9IqDHqGIEjJ72DjdcFGKi3o2vM60nt3AeUtlwQ/MMfgM9+VoHKP34WuO4avwoE0IBXVkG2KwNqoneNANq6INqHJa+XOkUM7W9Vq8oEvdIPVPohy33694COb1hTaoIgb39r0BGWRKU/3LcqV9Am6wX73iBOIwJAg2Q+VXJU7/d7vuJnIEss0oecl5J9eUmSBABYHKyWbIVWTcfFMImJjlqSpoCxnEltV6vE701HQS9TRmdIVcaimAy+RSmqUqDx+NO+KX+fuPWhAfmsUmSKpoN+RCFQq0FWtZuTjkUr8kW1X2f2aG2pvqX5LlIYs5T7BRx3DKkd96vacZ+yYhTbgZwydhEQ6h2muKZSatUwMXUhM4why1TP/CNVKc1hARgfx6F6vxPPnzMWfBvE1qIYivQ+PR2v9AN/+Q3yKKMrD2DjC0BtA1Aclaj6zDPPxJIlS/Dyyy/j+uuvx+zZs825G2+8EXPmzEF7e7s5duedd+Kggw5CvV7HaaedhrvvvhvTpk3Dr3/9a5NI9/vf/z7a2trw1a9+teWh2HJB8IwzQMmr5B13Ao8/Aey1pwKwehUo96kH1dYFFEoQxRIQBMqPqFYBNq6DLJdV5oS65urIasuAVcEGsvZOoGuEA2oFSCHgqjpFApAd6cC1YuULD7/enYgJY5kPIui5NATqQDKqkTJeaHTuOa+a2bdXJt2/fVILkQua9NUINO1yLOmOS+2BAIKCjlgSGW3A0Ctqad4w0Lcc61P6791XZuX55rVPihQUSk7GEq5ZOJ1g3EX9jpr6fAxAk/FptLOT2TpVA0Lifc45PorkuF8G+vsUKBbbgajN+B9aDC6gx0DvI0Z1mBCPtBfuawvlVTSaAWapOZTvvstsu9I22Q/4QFtKJSzscCKw+l7VxmG7AAX/nuAnPvEJnHTSSfj617/uPb/vvvti6dKlieOLFi2CEALr1q3DqaeeimuvvRZf+9rXsHLlStxxxx3ee7LQlguCDkXlHqBnAwIZQkRSLTCFNj2nVQxC0dYBgQZJrlokWqA9PHWDl86RPvhmvzW/0lRQH26KpSQZj6EQ8Z4lkG1fNnFNiiRgVZ7GtDRXz8azwJW29DPne6jg+OrumbC5QEA1aJJ2n6TzPRiy1LPaYzGsKm2JDvYgIJTaj7sQBAJGmokL089SxK4WpuONGJQGbUseZL8dcPe+oxmrSnXcL+vkwiFkqUOtQYEApIAoaE1PIABEMSiGob1Xz2OZEmMsgpiJiBxV6WCp0XvkWvS6GgHXD1FKhGOPwS1//V/oe/svOGz2Udglpfwvf/nLAFTYtFZo5cqVOOigg5DP5zFjxgw888wzAIBzzz0XCxYsQD4/ODjbckHw+uuB444DqlWIk09G7vDj7AcX1pWqcjOQT52VfNyuqk2kXplKHxnQM/8xYmqvzToOPimP1G7Jq2Nc5Iu08C/YEhAGNPW5kINoA2mTikrtuwd0N4k89Wg8khxMybrTpUJBLfLW/R5pLsFo6HHzhhkcCkYwhRlNkxrN8QxMEy8yyKnwbhTijRz3CRgFIEudsUVlkFfxUrUTv9IShGa/H0AcFNxI4QQ8FOmG+SvyvqX2yTrI0rl5e8S+Uph2p+z//9o85n1zHwwM7IMdfgW8/npK0U3oqaeewpgxYzB69GjMmjUL8+fPRz6fx+TJk3HTTTdh9uzZeOCBBzB9+nTcfvvtGDNmDD6VJVhpCm25IDhzJrBhgzKO6eqKj0eRAr9BBcNuTNnAr9kZ6V/gPgKAR2MiiZuta6tV2gPVXO97GzlHWl98sZFmb0SH04oitWcVhSo5bEDWsiwSSMOqOGNEAdE91zRU0XJVmEdqpPMmd6DDfFk5MpVkIakhCWtpxNeFYbxPDgkR5JUrQVsnREEv5OQeQddx9afpgsMY8gXeqEd15SYjPHNYl6wvFgPhquq4RK7LlaxsnwWx2z7u5mEBcsqzYccbOu5Xy0CQhyy16zBuAiKnDeAInCJm2U3SN3ehEALKOEXG7QwCpK4tCUbTw2CkkmdueJiw1W8HqGkvkXfeyVCsh6ZPn47nnnsOO+64I5YvX46TTjoJ+Xwe8+fPx5FHHokHH3wQU6dOxbRp03DyySfjkEMOwT333IOLLroIDz74ICZPnowrrrgCxWIxc50fLheJgV7HGGLoyDdM6n3iCxZ/EaV7pXPzRwH0AKUyYwta4HCSdFXT8UhZ4Fq+1q2HBTDL8io0dbEgtR73rWxSrussTQtWAhClU1QDtZ4qMMa+hPEDk8BCvT9FYES+n8biMdCKz6GSOgdJvn5aZv5A/A5ygGXPwAcGQHLsvfNG+u9vNCE8Wg5Je2gEiLWKAsBiyfgkmmDkUto5G11AtMZBzxdSn2Yl394vP0Cqaio3EKrdpAHR4ene7m7D4Yf2YNWqIn64oI4vzPYYHTLaf//9cfrpp1uGMS7deuutuPzyy/HEE08kzs2bNw8TJ07EhAkTcOWVV+Luu+/GGWecgb322gtnnXVW4voPp4sEUX8/gFrTy1om6Y3w6Gzd+Rf1TVPhvJ+UYdFuQrbErL+F0EuwVpENhVprENfKpsDBqCng+aiBr1tqo1KkQsBZlFzuPb7BjDmXnEywgroCMZ53j/addCBpkTaP06Q5I2XS4sjuN3tXJL01GgN9HwVckNB7aI6kZpgBDwAJxE7rfD8xsR/K/5YWTxBLoYxJ4AwI76tVJncraNBNxoQIgTi5cMdwBYr1qjK00YkAJKWL0sY1KlgEY1qMhJhnYc1EPHbGbSSI609rHwFrsR229qABDeitplwB1VwHrrzqKry+5BI8dH43wgh48a8CnzvpePx/3/4uJk2a1Ly81KYJrwDy7LPP4uGHH8Zll12Gyy+/HHvuuSeEEJg6dSqWLVvWUh1bNgi+9grw5pvAJz/R/NpWSPqlA2H953LliCfPFgV6LrUOgDbo+Um852pO3i69eNSrQHVAqTlzBdsXE80UQ4LxB47UkeC6pb0QNSs32erG/dGBD2Stolx2+pVRGKKaysZOarj2LhVajpy9wVRnVJa7R+e2i0ulqdoNh7FwVYxud7m/noAaw4gBTMjKcqVXS8oDGLawA64UyK7hkl4C6NDaO+xKTa7q1B1brppmbRZCKFeQYhvQqSXFunbHKHcrZqbYZoJZIMhDiEDPg5oaL4p/CnLMz8X9MxqYBtJhFCpNWkEHG2/W/67RAICBSgUHz5iBp59+Gs98r4wOrYX8+NYSq25ehKlT78Zdd92FAw44wNxarVYRRRGklKjVahgYGECxWEQQBLjrrruwxx57YPz48fjzn/+M7373uzjxxBOtqqWU+MpXvoKrrroKQRBgp512wtVXX41qtYolS5Zgjz32aNx2h7ZsEHzlFeDVV4cGBPWE5Ry1hIQQgaVNsV5wzjl+hCgBem62cKRJemxxatXyr1l7AHsPCRLWPhO5tLR1piwFzcDIw/QkwI5JREEOAIsqQnOIO1GbJK/qAilgYqnKWlXFuKxXdTolqSSDkgrrJSilUlsHxEgVPFgkVFm6jRR71QI0NJeGzV6SZ0ws6Y/eBc/Y8HExH+Ya4bpKSAkIGT9LRLEBR+p+KZeMrZfVIw06/aN9NEvCY8/UM7fNvXRtGnDya0z9nvY49wkhdCzcNqBzpLq9VgaqFaBvIyAj5Y6RL8YuXFGk9thRiwHRsjKVfjcLl2oDCoCLnbFrh4/0/V/96lfx1FNPYWBgAG+sBz42Bsjr9I1vvivR19eHo48+Gm+88Ybx6TvssMOwZMkSAMDDDz+MM888Ew888AAOOugg3HfffZg9ezZ6e3sxfvx4nHbaabjwwgutqm+44QZMnjwZe+65JwDg+OOPx6JFizB27FhMmzYNZ555Znq7fV35UO0JcspiIEMvVBQptRHYCwqBOGWNiPeyPsqgF4VAtQLZ36389vIFiLZOJXGYPQpHfeQuhvyalkiwtrD4rbRfYaLw6Geo88aZZ8iNUoQuKWqhHV6A8ZG0FzsGPJKf56pLUl/yzOEBBwhdtyVNcxGI9YNHl6EoRLztBCwcUNzF2e24qd9RgQL+94GAgYMJZZeQ0hqThvVbdTlASu2BUKDJ1aaWCpX1wwxbK/MvTeJ1y4fTVk85/FhE/r9u5gdaa3g7+TMWSpNRLSvAqlXU+VK7yqGoE/kKYgR5phTAfibNVJ8UNi7lmp6eHowfPx5l7a+97SjglrOB7UcD/2cR8O8Pqes6Oztx2WWXGdeI94s+3HuCnKRUEyT07BFyDpisryBUjjChde5SRx9JSx/yESAJmGg5cqBfhZXL5ZUzc7ENYtR4nZYocRf707dQ8EXPuc9ZCLlEbqwNKdoGgUMQKDUa2N4dUxc2Jr4o8YVe2H/bgxLXwRdiJuFImlv1mvGbozoEGZtYGdgJyIR/7fQ2ndrJJChqLgcYY1ThumVwEEG86HLi+3nU90FZXHPADGxmkoMqtYN8MaWMgTOihZtJiGkSIX+OXFK1DLK0tEnMSEOmjAMRB34ZF8f33biqlcbQPGuPJMm1SqQhkaw+YloodRf0+uTzUaz0KeZUBMpHkdwxwjpEvcb2gnOmLItxdCmsAeUaUNRlVatKSmzvAoTAH//4R8s37813gYO/lyymr68PCxcufN9BMI0+XCBYqyq1AScf8OlFVObirNomaseHAfgSHGoj4IEKNxfWIQf6VKSdsAaUOiBK7Url1t5ljwvFQ7RIeH66x3xcsojfebeddF0uD74YJZ8QSQMp9dMCQzFFqfOWxEbqN8nmQ9L4Q0qp5lm9qpzEuYopr6PA1Ktq4aFEqhRGq1hSrgVUn1nUucrNlvysvnFVbyQBNAN6HzHGRLrSHAORwJX6HEmMgxdJfWBtdL9dYGgofXLSbTILtdMeSN0PLnF66g6bSJrEVBBz56pHeZlmHqcwynSJVRa1FfFYULQgrlLmhVCfuZaAt4m2HlwfxSiMnfbDEMgXFChCmtRPwgCibmMUwh4LRtV+oGo4LGW809aJDRs2IHLTtKXQhg0bMl33ftCWD4JSKl14rT/+m5/TqXzMw80XIeklZzQk4NdIzeK2S/1ocG9j4LLvdf5OkA08xlzeySqh9pjUi2THLBV2WezLD3Rp9dKPyDJUQb2q/O7yBYigYLJb2NKmQPM+svoSYyL92gEPGWnOPggTwDwKGeAGtsopyEO06XyG7jM1sSNTa4YKpZXWV0eC4sHXwb75c0gs5vQtU/5mUqRr3embtw2Jt0XYbeVAGui9wECoomlhpbBi1sdjrZpWt5E6XetJfp0LTFryCjnoSXu8AcasuGU5YwsNrkA8P82+p3u7w0gYZoTKYX0DYM/xeM4oUOxSH0A77peBgYpinkptmvGNbECkQqMICYDnIKzX1PHjxyPIGCR76623znTd+0FbLgi+8w4wdSrwxhvATTcAxxwVnwtr6gEHQpkQFwsxF++QeczuucQCAaQvHGmUlHrsShsBSOAYczW61wPg7Jik9hrVog67RPr+TZWAE4YOJDFVgIE+yP5eoLwR6N2orusYAXQOgyh1Ah3DgXwezV3NOQfODzv7G/RJ8BTx85KJxV6PSajzHUYRTJJUkyoqMHFlG7eUm8x72pFpAWcLW2Lvjfef2i9N1QlmgS+ULog1Uiu6vmHWXhz7bbWNimdgZYxaItbWLO+O0x56P3wgysdIUnsY02hJ0mzcfODP+y94SiN3mDxMA5d4zbhBAwhgJG/TrcAu1FJri7jN5iDVS3PekdrM47XHVQQ5k0fRSi5cq6p4yqX2mPEIAuWjyCRVtwUAgEovDj30UPT3Z2EsBU499ewM170/tOWC4OLFwOrVKoPD1f8GHHmEerj5gg5aCzMZJFPBRFGoeDPBXixOCTDgEgZ/8T33eu9/78mAnjlAm/AitiZrqZ2agxa0n8VVhVCm+v3dQH8PZLlbWbHJKDbT7xwGjNgqmRWjlfoT9zkcsEkDw85aqvBIcbC1KlCrqKzrgI74kofMF1QYq0K7kkjpPg4SfC/SRCFhNVpqQKY6C5JMgv1bxO10VW/8A8/fcWdTxs1zzFp4wRZnSU/AQQAAEnBJREFUBpJGYk15Tyxq1A66XwOCT5K19jdhj6l5vg2YUWN8I5PnfKrXBHi6ak2BONg4NYP3i5+TdjmJfiPug1fqpG/OcLj3c2KGNLyPfMioSn6tvl4ALLmwDmRBjvthTWnKokLM9On2CW4pqvt8zdUBwnBbACt8DbXGa8GCA/DFLwIdTujmp59+GnPmzMELL7yASZMmYeHChfjkJz8JAPjlL3+JCy64AKVSCTfccANmzJgBAHj11Vcxa9YsPPjgg0OSr3DLBcEDDlB5BKtV4JxzgWGj2Un14GSlrBY+IbDm1dcgZITx/2vi+9bkzUXS/i9JIiXTQis1RIBETe2DGcCR8TqVzwPDR0MMj5/D0O2vJqWGWKWrkwvXtUuBTndlonAUlDEPCiUI+t05HAFF49ALqFp3fBIP7MVUkim66aVjySl0RgB3EXMkBlmHvQg6kohX8tKGJeZ4yrVNh5MW0AhJcI1iFbnFXPC2uQs1b0+TOsHqQeg8Vw9gGXLAmEvG5ku3w6joHCAygMXAlY7zNpgC6ZmngK/VTs4g03jI+JiryeHjYcXwJIB1xsJVSVvPnoEvB0gz1rxvfBx12PdcAWgv6OKl3qbQthU6nZWULFGAEOjtkfjGN/MAJqA5CJbw1ls5/OIXwNy58dFqtYrjjjsO5513Hs4++2xcc801OO644/Dyyy8jCAJ84xvfwJNPPoknnngC55xzDp577jkAKmD2j3/84yFL2LvlguDYscBf/qIWJDdOXK2GaKAPIhDo37ARa15egZ33bs2B8oNO6v2QsYQDwQxIhMd6EzB+WHEJMFms6cXQFnnSkjYc6cFagNX3JuMdN3rQZcYvZEVJm/WKArdaTR3P5ZRzeKGk9hHbOoDOESano+D9pHbTl1lMBFt/bGkyG5HUtCnh+lIWdg6+qQPsSnG8aXwxTLmG183B1TwP+o34utQ22D9TUTFrGSQhNeyDu8gjpd/6mDHkSAGZhnXpxgnEY+U7Z4jUnc77Q8/TaAr0va562MdgWFIgtdWdt2ntDuK+CnatxXsJxTgWSvpStcZwGyQRAbfcWkAQCAAzATwKYMBTJ1EXBga2wb/+K3DWWfHjX7x4Mer1Os477zwIIXDuuefihz/8Ie6//37svvvu2G677bDNNttg5syZWLFCAe1vf/tbbLfddthnn30a1NcabbkgCCiOjwNgFKG2cT3yxQJkFOG1J5bhY3vtvgUBoAs2MRnQMwcIxAIgKCITEBm1KEAbjtI1fjB575gfE08BlBXtfCo/Rw0mJZT0Vu5WUU/6e+zf9apSqXYMU8mP2zoh2rqA4Vsp61WS5iDtsq0+x9JeUj3GQdHTfiNZeaSttN/mXla/l6T3Z/wsuHTGGRLfMdNoG0h9lqbUXx94pi2k1vi12JfEpUNQRkNyJK7kH+yw8Pxm93vB2F9d42Z6wE26B6gN0jNGKWPjA8nUJrAbXWmRDlrzSReo1wJJvyMVSP36m7ZFXx8AnAnA4xdhqB3A+QByePtt4KWXgI9/XJ1Zvnw5dtttN2tN2W233bB8+XIcdthhWL9+PVatWoWnnnoKu+66K3p6enDJJZfgvvvua9LZ1mjLBcFHHgHOPVdlkvj85zFw5hkodbUhXyxgzcuvYqsdd8DOe+/5freyRZL2L1cSIFWVEM7+mgYbqf2qiGjjnfZGARj3BhN5Xij1HYEhoNQjjbIiGNUc7Y9RqCZnjwtQCY77e4ByjwE3We7Wx3pVSK/2YTrU1zBg5HgEW+8MtHcoC9G4Uo8E4SzUEkhKZAyorL3MDGA2FEQcf+peH/fHc6RvbuhD+eMSDMVmaHMWymzUYm7IdCjTSbfuRqDhLcplHtKq0wct0Eghoa/POiyp4Jn2LH3g2AIl9jybXE97iEEAWQ9VmMF80czPdeuJ2RwL4GYAn4eSBrnJaweAfQD8CwAgn5dYu1YYEOzt7cWIESOsakeMGIGenh4EQYCf/exnOOGEE1AqlfDzn/8c3/rWt3DOOefgmWeewXe+8x0Ui0UsWLAAkydPbn08GG2ZILhiBXDoodCsCOSll6IU1dE354sQQmDrv9vlfW5g62RAT0qgWobs2aDTDBUh2llUFlJfWm+P7chszpBxg5XahvaVAhXbCFq6S+jXSQp0jEGcVksptaNuv9qDrZaBShmyWtaOu0JJcKV25dw7bCQCHf8wDtyccYSsRYCAjXPRzl5VqtDBJcIsVadJNB7JPMFhu+RKKk7/fWpsKWHcBVqilOsbLfibjZpIUtlPeC5tlQFotS0OJaQxtD6mkm5uNu6SfWUSO4eGjLGWUOBHDJzW7hRLfLD+EcDOUBLhnVDJDHYGMA/AP4Fgplarob091tx1dXWhu7vbqra7uxvDhqnsE4cccggOOeQQAMCyZcvw+OOP4/LLL8fHPvYxLF26FG+88QZOP/10PProo5vU1S0TBO+7z1oQRH8/wl/+Cl3zvvo+Nqo1SihY+B5VqQOi1NEAIHwSY4PrjJl/WlYEEYMHtYGKjEJIWYcVqoxLM1R+LgfROfz/tXc+sU1kdxz/jv/O2CShSdgllC4pu0sLlD8F0UP/CDaHnBbtVgi0QrQnVqJSe0F7QCBx2guiSCXQAwIRVITYQw9pD7CHVZVDxa1auodsuyVSSillF4OTEMcZx/br4c2M34zHztj5Z2e+HwlsP78Zv3Hk953fe78/wLquqs9p3EGmlmVW7w7W84bwb7YGrPQTlTt9oRzodVapWnqEssTosc6qrqHe2Bv8bhr6LltJeDz47sFZz+v2Wej9RZzDu9fmi/p3VH4nQawrdXi+ehbk81E5uK6DlNLmGl+NNud8HsplmcTbPi5uAPEEenq/BL56C4BdSPmHAP5Y97Lz+RLefrvyeufOnbh06ZJ0urE++4svvqjKLGMnzL5y5QoymQxKpRK2bNmCjRs3OtXlF0N7imB3d9Udc/T114GolVDWpjQfOEB6uZFr6lawtTUmEddd11FPLCrWne3qb0qLy5RVqyuVqu1Cp3H3D9xxgFF+KNYk7wSHl22PRWvpsGwFtM8XKqKnAU5JnmhMCWr3LuN4JhzXHqGyf1eVmWSR+Hk71mpThc27vGgnH3YtR3qFrcUIKgrC26eOaDgPDYhPkHO48AiL89RvCa/O+85DpKrZHwEnMYF9I6SOVfXE9LY5z9XvpZbQeEXGp821Z+sRsqprXUHs7Y64NbdY4xgf/w2AP6MiggsxD+ATfP31T9HZKZXw0KFDiEajGBoawqlTp3D9+nUAwMDAgOvIGzduYN++fdi7dy+KxSLy+TzGxsbw+PFjbN26ddGX2J4i+N57wN69wMOH8rWmAVd/L5PIApWyIKuI20JThCAak96LqBWgrkHYAmTOQuRzsnbX3KxcvtTTQNKAFteBdd+C1rXB/zzOZ8sfnHCytMxLAS3MQZgyL6jIz1hji8sq4fEkkNRlMLueAtLrK0Ln3Yezr6tWDJyvQDbyRSqTjXc/rVab736aLbqqxRZA1KomdOFxaffp04woNGTVLLWwKH2CCEuk2XP4jMPPk7NKaOB5XKjNR9x9RUVpE0pbxLrexVpRa4WUuzjukydP8M03fwHwOYADAIJUcS8AuIQrVz7H0NAQACCRSGBkZAQnT57EmTNnsH37doyMjLiqwmcyGVy+fBkPHjwAAMRiMVy9ehUDAwPQdR3Dw8OLvrz2rSJRLAKffgpMTQHvvANs2iR/AHM5yCrZK4tb9AAnnkyUreXCuE/YglJ13a7uLWAtX8Yq6aQgYO/HuaoIKPt1QmhAcQ7IWxlaZqeAWdvTclqOw+iQe4u6FDfNzjeop2WWCPVqvFakb5B3tSNM/S/JOqezP1nHOqvp+Vhj8ql7I2Bfk/K8qk9AYXEelkJYGjiHq0+Nc1SdZ5H4CZTTvkDbguKmHAvrOtaaFbVGOXv2LC5cuIByuQfA3wC8BiBZ54hZAL8C8Ad0dnbi+fPnLqFbKdZeFYlYDHj33crrgilFYIVwiV6pKJ1DhJCelfG49N5MJF39BeDOmVnIQxSL0sKLxuQGdFyXz+3lS2vZUeb6K0HkZyrelbNKOIGV1Bapjoq49fQh8u23gGQaiMeheS05AFVWE3wmEfUYNSbO747dd4ILimciVLPzNyMKC1ksQc7h6tNi+H3nZZ+/yXJbUVVtEeW7D7kVtQa5deuWlTj7OYB9AD4B8GNIE1oVw2lIC/AkgD8BAIQQGB0dxeDg4IqOuR7tK4KADJp+9QrQlyZzQD1colcuW96Qc/L3nNDlEqVlTQmrjyiass/crMy0EpVVzLVYAkjqUrDsUAdNg4Dm7PmJwpzi1alYg9EotFSHDAqP2JlgguT+VCYov7tr79UKoOJxGmSSVEQ0oliIjtemx2pc6xNfy1pR1o2Fy6uWVhQJzsuXL5VXGciA+TcB/BrAjyCF8CmA6wDuQQ1bMk0Tz549W7GxBqG9RXD/PuAf/wT+Ogr8YOeSnlpATgNCvWNWC7kaaSlGynvCrgReKjkxdFosAXSlpOVqT0Clkoyfy01XQgvMWblPZwulnbUhkYSWMKyaXt6cn1IUK04mnni9wPtpitDW3E/T3OcOsp/WyqzYXhQCCo16k+LTj1YUaRFKJb/tpnHIoPj6FAqFwOWXVor2FsGZGTn5mKbSaFkdsagMCi8HLJ/j/g+Ym0X5xVO5bLmuC+jqlWJUKsrKCLkpYDojPz+ZsrKZpCH0FGAkoZWK0uEk9wJi+gUwMynre2nSuUUz7P24dTK0oKcPSKas0iS2qCnxebUKd9oi5hSSVdoAj2Cp1pi17+hdCm2FSbUlrCi/No8VFWh/ipC1Q7lcXrSITUxMLM1gloj2FsGHfweyWWDLFnd7QAcZ776emH4BMZOVYtfVC63vu9JSm8kC//2X7K+n5R5bqhPo2gCtOA8xk4WYzEBMjsnPTaYgjLSzNxfp3ghsetMKXUgqguTnWKKKm5DenOpE7mudadUhEE5ZlmWYiBu1ogJZVrSiCGl1nj59GmDrpT6tVmC3vUWws1P+U6njIOMSPQiIfA5iOisrgHd2S8/J0rwUsuf/kXW2kob0qozrwFQG4tm/ZVaUhCEtP0Nac5ENm4E3vi/3Bu3q4RpQSV1mjcCe8O2irX7Li44rv93egBemy0NVsQiDWFF+bbSiCCEWk5OT0HUdOStbVzNks9klHNHiaW8RVCmVANMdG+gWPQCleelNOZeTVZfLJWgoyZ6zU04OTZGfBcwcAA2abi11GuuA3s3QNn9PBqVXfxIqQobaFptXHOot4bnCBYDaVpSnzaZRKwpQqnEr1+Ncm/dYQkiYSKXqZbIKhp0WrVVoXxH87DPggw+kd+gvfwH87rcyjMBjCYn5OSD3Sj6PaNDmC9KNPJeFKBSgFQtyKdFIywD0dBe0195wx+MBStC30uaLJWRCsfZoRRFC1gB9fX01HGOCkUqlsGPHjiUc0eJpTxE0TeD99ysJtO/eBX72E+DIz6XYzc1Iaw4CWtGUoicEIomk3MszvMHhNfDuq9W0opppI4SQ9sIwDBw9ehS3b99GA3lWHMrlMo4fP74MI2ue9hTBqSl3dW+zAPHoK4iX/4MWlVUXtO4+aNEoXGm7vMuRdWOqCCGEeDl9+jTu3LnTlEV45MgRrF+/fhlG1TztKYIbNgA7dgBjY4BpQkskoB09Dnxn+2qPjBBC1jR79uxBf38/xsfHGz72/PnzyzCixeFTvKwN0DRgdBT46CPgww/l8127VntUhBASCu7evduwg8zg4CC2bdu2TCNqnva0BAEZGvHxx6s9CkIICR0HDhzArl27AtfzSyaTuHjx4jKPqjna0xIkhBCyqty8eROpVGrBfoZh4MSJE9i9e/cKjKpxKIKEEEIaZv/+/bh37x46Ojqg63rV+5qmIZ1O49ixY7h27doqjDAYFEFCCCFNcfDgQTx69Ajnzp1Db28vkskkDMNAIpHA4cOHcf/+fQwPDyMaXf5KP83SvkV1CSGEtAzlchnZbBamaaK7u9vXOlxN1l5RXUIIIS1DJBJBT0/Pag+jYbgcSgghJLRQBAkhhIQWiiAhhJDQQhEkhBASWiiChBBCQgtFkBBCSGihCBJCCAktFEFCCCGhhSJICCEktFAECSGEhBaKICGEkNBCESSEEBJaKIKEEEJCC0WQEEJIaKEIEkIICS0UQUIIIaGlocryvb296O/vX8bhEEIIIUvPxMQEMplMVXtDIkgIIYSsJbgcSgghJLRQBAkhhIQWiiAhhJDQQhEkhBASWiiChBBCQgtFkBBCSGihCBJCCAktFEFCCCGhhSJICCEktPwfNDmEyZtwF3sAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "K = 55\n", + "N = 56\n", + "# K = 0\n", + "# N = 60\n", + "\n", + "snap_plot(nets[K:N], size_scale = 1/10,savefigs=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEGCAYAAACHGfl5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3de3hV9Z3v8feXJBAQCLeI3JSL3IQmUaLgVCxeUI/1lBmPitRWbZl6mNraeZza2tPRwbae2hHHU23V0cGqU7Uo6IB2ZtTx1vEuSJKC3BFlQ0IgmAuXcMv3/LFXcAMJue6svVc+r+fJk7V/a6+1vz+y2Z+91m9dzN0REZHOrUvYBYiISPgUBiIiojAQERGFgYiIoDAQEREgM+wCWmvAgAE+fPjwsMsQEUkry5Yt2+HuuUe3p20YDB8+nKVLl4ZdhohIWjGzTxtq124iERFRGIiIiMJARERI4zGDhhw4cIBYLEZtbW3YpUg7yc7OZujQoWRlZYVdikikRSoMYrEYvXr1Yvjw4ZhZ2OVIG7k7FRUVxGIxRowYEXY5IpEWqd1EtbW19O/fX0EQEWZG//79taUn0gEiFQaAgiBi9PcU6RiRCwMRkajau/8Qd7ywks8q9rT7uhUGIiJp4vnlW/jd25soq27/XacKgw7w7LPPMn78eM477zwAZs2aRV5eHvfee2+L1lNZWckDDzxw+PHWrVu54oor2rXWZNq0aRMTJ04MuwyRtOTu/O7tT5gwuDdnDu/b7uuP1NFEqWr+/Pk88sgjnHPOOZSVlfHhhx+yfv36Fq+nPgy++93vAjB48GAWLlzY3uWKSAp6a/0O1pXv4p4r85MylhbZMLjjhZV8vLW6Xdd52uDe/MP/nHDc5/z+97/nvvvuY//+/UyePJnBgwfz1ltvMXv2bL72ta/x0ksvsWXLFgoKCrj//vsZPHgwN954I9u3b6dHjx488sgjjBs3jm3btjFnzhw2btwIwIMPPsh9993Hhg0bKCgoYPr06dx4441cdtllrFixgilTpjB//nwmTIjXN23aNObNm8f48eP5/ve/z4oVKzhw4ABz585lxowZDda+cuVKvvWtb7F//37q6upYtGgRWVlZh18DYN68eezatYu5c+cybdo08vPzefPNNzl48CCPPvooZ511FnPnzmXDhg2sX7+eHTt28KMf/YjvfOc7R7zWueeey3333UdBQQEA55xzDr/97W/Jz89v099IJKp+9/YmBvTsxmX5g5Kyfu0makerVq1iwYIFvP322xQVFZGRkcGIESMoLCzkySef5O6772bJkiWMGjWKoqIipk6dyg033MD999/PsmXLmDdv3uFv/TfddBNf+cpXKC4u5qOPPmLChAncddddh5e9++67j3jtmTNn8swzzwBQWlpKaWkphYWF3HnnnZx//vl88MEHvP7669xyyy3s3r27wfofeughfvCDH1BUVMTSpUsZOnRok33es2cPRUVFPPDAA3z7298+3F5SUsJrr73Gu+++y89+9jO2bt16xHKzZ8/mscceA2Dt2rXU1tYqCEQasXH7Ll5bXc43ppxMt8yMpLxGZLcMmvoGnwyvvvoqy5Yt48wzzwRg7969nHjiiY0+f9euXbzzzjtceeWVh9v27dsHwGuvvcYTTzwBQEZGBjk5OXz++eeNruuqq67ioosu4o477uCZZ545PJbw8ssvs2TJEubNmwfEz8X47LPPGD9+/DHrOPvss7nzzjuJxWJcfvnljB49usk+z5o1C4h/06+urqayshKAGTNm0L17d7p37855553HBx98cHgrAODKK6/k5z//OXfffTePPvoo119/fZOvJdJZPf7OJrpmdOGayack7TUiGwZhcHeuu+46fvnLXx7RPm3atAafX1dXR58+fSgqKmrzaw8ZMoT+/ftTUlLCggULeOihhw7XtGjRIsaOHdvkOr7+9a8zefJk/vjHP3LppZfyz//8z4wZM4a6urrDzzn6BLCj913WP26svV6PHj2YPn06ixcv5plnnmHZsmXN76xIJ1K19wDPLotxWf4gcnt1S9rraDdRO7rgggtYuHAh5eXlAOzcuZNPP23w0uEA9O7dmxEjRvDss88C8Q/u4uLiw+t68MEHATh06BBVVVX06tWLmpqaRtc3c+ZM/vEf/5Gqqiry8vIAuPjii7n//vtxdwCWL1/e6PIbN25k5MiR3HTTTcyYMYOSkhIGDhxIeXk5FRUV7Nu3jxdffPGIZRYsWADAW2+9RU5ODjk5OQAsXryY2tpaKioqeOONNw5vLSX667/+a2666SbOPPNM+vZt/6MjRKLg2aWb2bP/EN/+cnIvyaIwaEennXYav/jFL7jooovIy8tj+vTplJaWHneZJ598kvnz55Ofn8+ECRNYvHgxAL/+9a95/fXX+dKXvsSkSZP4+OOP6d+/P1/+8peZOHEit9xyyzHruuKKK/jDH/7AVVdddbjttttu48CBA+Tl5TFhwgRuu+22Rmt55plnmDhxIgUFBaxYsYJrr72WrKwsbr/9ds466yymT5/OuHHjjlgmOzub008/nTlz5jB//vzD7Xl5eZx33nlMmTKF2267jcGDBx/zepMmTaJ3795861vfOu6/kUhndajOeeydTZw1vB8Th+Qk9bWs/htjuiksLPSj73S2atWqBveFS3LUH7FUWFh4RPvcuXPp2bMnP/zhD4+7/NatW5k2bRqrV6+mS5fGv5fo7yqd1X+uKGPO75fx0DfO4JKJ7XMUkZktc/fCo9u1ZSCheOKJJ5g8eTJ33nnncYNApDP73dufMKRPd6afdlLSX0sDyJ3QSy+9xI9//OMj2kaMGMHzzz/fovW88cYbDbbPnTu3yWWvvfZarr322ha9nkgUvLSyjBdLjr/7GODgoTre/2QnP710PBldkn/BxsiFgbvrSpdNuPjii7n44ovDLqNZ0nU3pkhDSmKVfO+pj8jp3pXe2U1//E46pS9XnTmsAyqLWBhkZ2dTUVGhexpERP3NbbKzs8MuRaTNamoP8P2nl5Pbsxv//oOp9OnRNeySjhCpMBg6dCixWIzt27eHXYq0k/rbXoqkM3fn/zy/gtjne1lww5SUCwJoRhiY2aPAZUC5u08M2hYA9Wcx9QEq3b3AzIYDq4A1wbz33H2OmfUC/jthtUOB37v735pZN+AJYBJQAcx0902t6UxWVpZujygiKWfBh5t5oXgrt1w8lsLh/cIup0HN2TJ4DPgN8Q9sANx9Zv20md0DVCU8f4O7FyQ8xt1rgIKEZZYBzwUPZwOfu/upZnY18CtgJiIiEbB2Ww1zX1jJOacO4G++MirschrV5DF97v4nYGdD8yy+Y/4q4OnmvqCZjQFO5IsthRnA48H0QuAC0w5/EYmAvfsPceOTH9GzWyb/NDOfLh1wVFBrtfUA76nANndfl9A2wsyWm9mbZja1gWWuBhb4F4eJDAE2A7j7QeJbGf0bejEzu8HMlprZUo0LiEiq+8UfP2b99l3cO7OAE3ul9oEQbQ2DWRy5VVAKnOzupwM3A0+ZWe+jlrmaFmxJJHL3h9290N0Lc3NzW1WwiEhH2Ll7Pws+3Mw3Jp/C1NGp/3nV6jAws0zgcmBBfZu773P3imB6GbABGJOwTD6QGcyrtwUYlrDOHOIDySIiaeuF4q0crHOumXJy2KU0S1u2DC4EVrt7rL7BzHLNLCOYHgmMBjYmLHP0lgTAEuC6YPoK4DXXmUYikuae+yjGaYN6M+6ko3eOpKYmw8DMngbeBcaaWczMZgezGtrdcy5QYmZFxAeD57h74uBzQ4PN84H+Zrae+K6lW1veDRGR1LG+vIbiWBWXnzEk7FKarclDS919ViPt1zfQtghYdJx1jWygrRa4soGni4ikpec+2kJGF+NrBcdeuj1V6XKRIiLtqK7OeX75Fs4dPSDljyBKpDAQEWlH722soLSqlsvPSK/LqCgMRETa0aKPttCrWybTTxsYdiktojAQEWkne/Yf5D9WlPLVvEFkZ2WEXU6LKAxERNrJyyu3sWf/obTbRQQKAxGRdrPooxjD+nWn8JS+YZfSYgoDEZF2UFZVy9vrd/BXBUNS+oJ0jVEYiIi0g8VFW6hz+Ks03EUEEbvTmYikt08rdvPIf2/kUF36XZHmjTXbOePkPowYcELYpbSKwkBEUsYT737KU+9/xoCe3cIupcW6mPGdqcdcZCFtKAxEJGW8u6GCySP68/QNU8IupdPRmIGIpITKPftZVVbN2aMavLeVJJnCQERSwvuf7MQdpoxUGIRBYSAiKeG9jRVkZ3Uhf1hO2KV0SgoDEUkJ726oYNIpfemWmV6XcYgKhYGIhO7z3ftZXVbD2dpFFBqFgYiE7v1P4rc913hBeBQGIhK69zbupHtWBnlD+4RdSqelMBCR0L27oYLC4X3pmqmPpLDoX15EQlWxax9rttVoF1HIFAYiEqr3P9kJaLwgbAoDEQnVexsr6NE1g7yhOr8gTAoDEQlVfLygH1kZ+jgKk/71RSQ0O3btY135Lp1fkAIUBiISmvc21p9f0C/kSkRhICKheXdDBSd0zeBLQzReEDaFgYiE5r2NFZw5oh+ZGi8Inf4CIhKK8upaNmzfrfGCFKE7nUko6uqcLZV78fS71a20kzfWlgM6vyBVKAykw22t3MuNT33E8s8qwy5FQtY7O5MJg3uHXYagMJAO9ta6Hdz0h+XsP1jH3391PH17dA27JAnRqSf21HhBimgyDMzsUeAyoNzdJwZtC4CxwVP6AJXuXmBmw4FVwJpg3nvuPidYpivwG2AaUAf81N0XmVk34AlgElABzHT3Te3ROUkddXXOA2+s555X1jL6xJ48+I1JjMrtGXZZIhJozpbBY8Q/xJ+ob3D3mfXTZnYPUJXw/A3uXtDAen5KPFDGmFkXoP7A4tnA5+5+qpldDfwKmNnA8pJiKvfsp2rvgSafd+BQHb/899W8urqcGQWD+eXlX6JHV22UiqSSJv9Huvufgm/8xzAzA64Czm/Ga30bGBessw7YEbTPAOYG0wuB35iZuWtoMZXt3neQL9/1Grv3H2rW87MyjJ/NmMA3p5xC/G0jIqmkrV/PpgLb3H1dQtsIM1sOVAN/7+7/bWb1d6z4uZlNAzYA33P3bcAQYDOAux80syqgP1+ExWFmdgNwA8DJJ5/cxtKlLf68pYrd+w/x3WmjOPXEpnf3TBySw5iBvTqgMhFpjbaGwSzg6YTHpcDJ7l5hZpOAfzOzCcHrDAXecfebzexmYB7wzZa8mLs/DDwMUFhYqC2HEBVvjh8JNPucEfTv2S3kakSkrVo9jG9mmcDlwIL6Nnff5+4VwfQy4lsAY4gPDO8Bngue+ixwRjC9BRiWsM6c4PmSwkpiVQzt211BIBIRbTmm60JgtbvH6hvMLNfMMoLpkcBoYGOw//8F4kcSAVwAfBxMLwGuC6avAF7TeEHqK45Vkq/71YpERpNhYGZPA+8CY80sZmazg1lXc+QuIoBzgRIzKyI+GDzH3XcG834MzDWzEuK7h/4uaJ8P9Dez9cDNwK1t6ZAkX8WufcQ+30v+MF1cTCQqmnM00axG2q9voG0RsKiR539KPCyObq8FrmyqDkkdJbH4kcR52jIQiQyd+ictVhyrxCx+hJCIRIPCQFqsJFbFqbk96dlNJ46JRIXCQFrE3SmJVZI/TLuIRKJEYSAtsqVyLzt27Sd/qHYRiUSJwkBaRIPHItGkMJAWKY5V0jWjC+MG6dISIlGiMJAWKd5cyfhBveiWmRF2KSLSjhQG0mx1dc6KLdXaRSQSQQoDabaNO3axa99B8jR4LBI5CgNptuLN8cHjAh1WKhI5CgNptuJYJSd0zWCkblcpEjkKA2m24lgVE4fkkNFFdyoTiRqFgTTL/oN1rNparV1EIhGlMJBmWV1Wzf5DdTqSSCSiFAbSLMWHzzzWkUQiUaQwkGYp2VxJvxO6MrRv97BLEZEkUBhIs5TEqsgfmoOZBo9FokhhIE3ave8g68prNF4gEmEKA2nSx6XV1LnGC0SiTGEgTVpdVgPA+EG9Q65ERJJFYSBNWl1aTa/sTAblZIddiogkicJAmrSmrIbxJ/XW4LFIhCkM5LjcnTVlNYw9STezEYkyhYEc15bKvdTsO6gwEIk4hYEc15pg8HicwkAk0hQGclz1RxKNURiIRJrCQI5rdVkNQ/p0p3d2VtiliEgSKQzkuNaUVWsXkUgnoDCQRu07eIiN23czbpDCQCTqFAbSqA3luzlY54w9SWcei0SdwkAatWZbNaAjiUQ6gybDwMweNbNyM1uR0LbAzIqCn01mVhS0DzezvQnzHkpY5g0zW5Mw78SgvVuwvvVm9r6ZDW//bkprrC6roWtGF0YMOCHsUkQkyTKb8ZzHgN8AT9Q3uPvM+mkzuweoSnj+BncvaGRd17j70qPaZgOfu/upZnY18Ctg5rGLSkdbXVrDqBN7kpWhDUiRqGvyf7m7/wnY2dA8i1+s5irg6TbUMAN4PJheCFxgughOSlhTVqNdRCKdRFu/8k0Ftrn7uoS2EWa23MzeNLOpRz3/d8EuotsSPvCHAJsB3P0g8a2M/g29mJndYGZLzWzp9u3b21i6HE/lnv2UVdcqDEQ6ibaGwSyO3CooBU5299OBm4GnzKz+UJRr3P1LxANkKvDNlr6Yuz/s7oXuXpibm9vG0uV46s881jWJRDqHVoeBmWUClwML6tvcfZ+7VwTTy4ANwJjg8Zbgdw3wFHBWsNgWYFjCOnOAitbWJe3ji2sS6bBSkc6gLVsGFwKr3T1W32BmuWaWEUyPBEYDG80s08wGBO1ZwGVA/dFJS4DrgukrgNfc3dtQl7SD1WU19OmRxcDe3cIuRUQ6QJNHE5nZ08A0YICZxYB/cPf5wNUcO3B8LvAzMzsA1AFz3H2nmZ0AvBQEQQbwX8AjwTLzgX81s/XEB6qvbnu3pK3WlFUzdmAv3dBGpJNoMgzcfVYj7dc30LYIWNRA+25gUiPrqQWubKoO6Th1dfEb2lwxaWjYpYhIB9EB5HKMLZV72b3/EOMGabxApLNQGMgxdCSRSOejMJBjrC6NX5NozECFgUhnoTCQY6zeVsPJ/XrQs1tzrlYiIlGgMJBjrCmr0S4ikU5GYSBHqD1wiE927NZlKEQ6Ge0HiKAdu/bx2c49rVp28849HKpznXks0skoDCLoG//y/uEjglpr4hCFgUhnojCImKq9B1hdVsPVZw7jkokntWodfXt05ZT+uqGNSGeiMIiYP8fi9xn6at4gpo7WlV1FpHk0gBwxxbFKAPKG9Am5EhFJJwqDiCneXMmIASeQ0yMr7FJEJI0oDCKmJFZF3tCcsMsQkTSjMIiQbdW1lFXXkj9Uu4hEpGUUBhFSvDk+XpA/TFsGItIyCoMIKYlVkdHFOG2QwkBEWkZhECHFsUrGDuxF964ZYZciImlGYRAR7k5JrEq7iESkVRQGEfFpxR6q9h4gT4PHItIKCoOIqD/ZTEcSiUhrKAwionhzFdlZXRgzsGfYpYhIGlIYRERJrJIJg3PIzNCfVERaTp8cEXDwUB0rtlZpF5GItJrCIALWbttF7YE6HUkkIq2mMIiAkvorlWrLQERaSWEQAcWxKnpnZzK8f4+wSxGRNKUwiIDizZXkD+uDmYVdioikKYVBmqs9cIg122o0eCwibaIwSHMrt1ZzqM51DwMRaROFQZqrHzzOH6YtAxFpPYVBmiveXMlJvbMZ2Ds77FJEJI01GQZm9qiZlZvZioS2BWZWFPxsMrOioH24me1NmPdQA+tbctS6+pnZK2a2Lvjdt7061xnoNpci0h6as2XwGHBJYoO7z3T3AncvABYBzyXM3lA/z93nJC5nZpcDu45a/63Aq+4+Gng1eCzN8OdYFRt37OasEf3CLkVE0lyTYeDufwJ2NjTP4scyXgU83dR6zKwncDPwi6NmzQAeD6YfB/6yqXVJ3D+9soY+PbKYeeawsEsRkTTX1jGDqcA2d1+X0DbCzJab2ZtmNjWh/efAPcCeo9Yx0N1Lg+kyYGBjL2ZmN5jZUjNbun379jaWnt6Wffo5r6/Zzv8+dxS9srPCLkdE0lxbw2AWR24VlAInu/vpxLcCnjKz3mZWAIxy9+ePtzJ3d8CPM/9hdy9098Lc3Nw2lp7e7n1lLf1P6Mp1f3FK2KWISAS0OgzMLBO4HFhQ3+bu+9y9IpheBmwAxgBnA4Vmtgl4CxhjZm8Ei20zs0HBOgcB5a2tqbN4f2MFb63fwd9MG0WPrplhlyMiEdCWLYMLgdXuHqtvMLNcM8sIpkcCo4GN7v6guw929+HAOcBad58WLLYEuC6Yvg5Y3IaaIs/dueeVtZzYqxvfmKKtAhFpH805tPRp4F1grJnFzGx2MOtqjh04PhcoCQ41XQjMcfcGB58T3AVMN7N1xAPmrpZ0oLN5e30FH3yykxvPO5XsrIywyxGRiGhyH4O7z2qk/foG2hYRP9T0eOvbBExMeFwBXNBUHVK/VbCGwTnZXH2WjiASkfajM5DTyBtrt7P8s0q+d/5oumVqq0BE2o/CIE24O/e+spZh/bpzZeHQsMsRkYhRGKSJ9eW7KIlVccO5o8jSTe9FpJ3pUyVNbKncC8D4k3qFXImIRJHCIE2UVdUCcFKOrk4qIu1PYZAmyqprMYMTeykMRKT9KQzSRFlVLQN6dqNrpv5kItL+9MmSJkqrajlJN7ARkSRRGKSJsqpajReISNIoDNJEWXUtgxQGIpIkCoM0sGf/Qar2HtCWgYgkjcIgDRw+rFRjBiKSJAqDNKBzDEQk2RQGaaCsOh4Gg3K6h1yJiESVwiANlGo3kYgkmcIgDZRV1ZLTPYvuXXXZahFJDoVBGtBhpSKSbAqDNKATzkQk2RQGaUCXohCRZFMYpLj9B+vYsWuftgxEJKkUBimuvKb+sFKFgYgkj8IgxX1xwpnOMRCR5FEYpDidYyAiHUFhkOK2VetSFCKSfAqDFFdaVUuPrhn0zs4MuxQRiTCFQYqrP8fAzMIuRUQiTGGQ4kqr9mq8QESSTmGQ4rZV6xwDEUk+hUEKO1TnbNN1iUSkAygMUljFrn0crHPtJhKRpFMYpLBSnXAmIh2kyTAws0fNrNzMViS0LTCzouBnk5kVBe3DzWxvwryHEpb5TzMrNrOVZvaQmWUE7f3M7BUzWxf87puMjqajL+5wpi0DEUmu5mwZPAZcktjg7jPdvcDdC4BFwHMJszfUz3P3OQntV7l7PjARyAWuDNpvBV5199HAq8FjQfc+FpGO02QYuPufgJ0NzbP4we9XAU83Yz3VwWQm0BXw4PEM4PFg+nHgL5taV2dRWlVLVobRr0fXsEsRkYhr65jBVGCbu69LaBthZsvN7E0zm5r4ZDN7CSgHaoCFQfNAdy8NpsuAgY29mJndYGZLzWzp9u3b21h66ttWXcvA3tl06aITzkQkudoaBrM4cqugFDjZ3U8HbgaeMrPe9TPd/WJgENANOP/olbm788UWwzHc/WF3L3T3wtzc3DaWnvpKq/ZqvEBEOkSrw8DMMoHLgQX1be6+z90rgullwAZgTOJy7l4LLCa+ewhgm5kNCtY5iPiWg1B/KQodSSQiydeWLYMLgdXuHqtvMLPchKOERgKjgY1m1jPhAz8T+CqwOlhsCXBdMH0d8aDo9Nw9uN1lt7BLEZFOoDmHlj4NvAuMNbOYmc0OZl3NsQPH5wIlwaGmC4E57r4TOAFYYmYlQBHxb//1h53eBUw3s3XEA+auNvYpEqr2HmDfwTptGYhIh2jyusjuPquR9usbaFtE/FDTo9u3AWc2sp4K4IKm6uhs6k8405iBiHQEnYGconSOgYh0JIVBitLtLkWkIykMUlRZdS1dDHJ7aQBZRJJPYZCiyqr2kturG1kZ+hOJSPLpkyZFxQ8r1S4iEekYCoMUta26VoPHItJhFAYpqrSqlkE6x0BEOojCIAXt2neQmtqD2jIQkQ6jMEhBZTqsVEQ6WJNnIHcGL68s41/f+5SDhxq9YGqHqtl3ANAJZyLScTp1GBw4VMev/mM1//LWJ5zSvwcDe6XGh2+PrEwuHH8iE4fkhF2KiHQSnTYMtlbu5XtPfcRHn1Vy/V8M5yeXjqNbZkbYZYmIhKJThsHra8q5eUERBw45v/36GXw1b1DYJYmIhKrThcFvXlvHvJfXMu6kXjxwzRmMzO0ZdkkiIqHrdGEwfMAJzCwcxh0zJpCdpd1CIiLQCcPgsrzBXJY3OOwyRERSis4zEBERhYGIiCgMREQEhYGIiKAwEBERFAYiIoLCQEREUBiIiAhg7qlx2eaWMrPtwKfNeOoAYEeSy+lIUepPlPoC0epPlPoC6k+iU9w99+jGtA2D5jKzpe5eGHYd7SVK/YlSXyBa/YlSX0D9aQ7tJhIREYWBiIh0jjB4OOwC2lmU+hOlvkC0+hOlvoD606TIjxmIiEjTOsOWgYiINEFhICIi0Q4DM7vEzNaY2XozuzXselrCzB41s3IzW5HQ1s/MXjGzdcHvvmHW2BJmNszMXjezj81spZn9IGhPuz6ZWbaZfWBmxUFf7gjaR5jZ+8H7bYGZdQ271pYwswwzW25mLwaP07I/ZrbJzP5sZkVmtjRoS7v3WT0z62NmC81stZmtMrOzk9GfyIaBmWUAvwX+B3AaMMvMTgu3qhZ5DLjkqLZbgVfdfTTwavA4XRwE/s7dTwOmADcGf4907NM+4Hx3zwcKgEvMbArwK+Bedz8V+ByYHWKNrfEDYFXC43Tuz3nuXpBwLH46vs/q/Rr4T3cfB+QT/xu1f3/cPZI/wNnASwmPfwL8JOy6WtiH4cCKhMdrgEHB9CBgTdg1tqFvi4Hp6d4noAfwETCZ+BmhmUH7Ee+/VP8BhgYfKucDLwKWrv0BNgEDjmpLy/cZkAN8QnCwTzL7E9ktA2AIsDnhcSxoS2cD3b00mC4DBoZZTGuZ2XDgdOB90rRPwS6VIqAceAXYAFS6+8HgKen2fvt/wI+AuuBxf9K3Pw68bGbLzOyGoC0t32fACGA78LtgF96/mNkJJKE/UQ6DSPP4V4K0Oy7YzHoCi4C/dffqxHnp1Cd3P+TuBcS/UZ8FjAu5pFYzs8uAcndfFnYt7eQcdz+D+C7iG83s3MbhFFMAAAO8SURBVMSZ6fQ+AzKBM4AH3f10YDdH7RJqr/5EOQy2AMMSHg8N2tLZNjMbBBD8Lg+5nhYxsyziQfCkuz8XNKd1n9y9Enid+G6UPmaWGcxKp/fbl4Gvmdkm4A/EdxX9mjTtj7tvCX6XA88TD+t0fZ/FgJi7vx88Xkg8HNq9P1EOgw+B0cEREV2Bq4ElIdfUVkuA64Lp64jvd08LZmbAfGCVu/9Twqy065OZ5ZpZn2C6O/Gxj1XEQ+GK4Glp0RcAd/+Juw919+HE/5+85u7XkIb9MbMTzKxX/TRwEbCCNHyfAbh7GbDZzMYGTRcAH5OM/oQ9QJLkwZdLgbXE9+f+NOx6Wlj700ApcID4t4PZxPfjvgqsA/4L6Bd2nS3ozznEN2VLgKLg59J07BOQBywP+rICuD1oHwl8AKwHngW6hV1rK/o2DXgxXfsT1Fwc/Kys/3+fju+zhD4VAEuD99u/AX2T0R9djkJERCK9m0hERJpJYSAiIgoDERFRGIiICAoDERFBYSCdXHBFyO8G04PNbGESX6vAzC5N1vpF2kJhIJ1dH+C7AO6+1d2vaOL5bVFA/NwKkZSj8wykUzOzPwAziF8Fch0w3t0nmtn1wF8CJwCjgXlAV+CbxC9hfam77zSzUcQvlZ4L7AG+4+6rzexK4B+AQ0AVcCHxk7e6E7+swy+JXx30fmAikAXMdffFwWv/FfErVg4Bfu/udyT5n0I6ucymnyISabcCE929ILia6osJ8yYSv7pqNvEP8h+7++lmdi9wLfErfT4MzHH3dWY2GXiA+LV9bgcudvctZtbH3feb2e1Aobt/D8DM/i/xSz98O7i8xQdm9l/Ba58VvP4e4EMz+6O7L03mP4R0bgoDkca97u41QI2ZVQEvBO1/BvKCK7D+BfBs/NJLAHQLfr8NPGZmzwDP0bCLiF8g7ofB42zg5GD6FXevADCz54hfzkNhIEmjMBBp3L6E6bqEx3XE/+90IX7N/4KjF3T3OcGWwleBZWY2qYH1G/C/3H3NEY3x5Y7ef6v9uZJUGkCWzq4G6NWaBT1+P4ZPgvEBLC4/mB7l7u+7++3Eb04yrIHXegn4fnBFV8zs9IR504P73HYnPnbxdmtqFGkuhYF0asGumLfNbAVwdytWcQ0w28zqr5I5I2i/O7gp+wrgHeJX0XwdOC24UftM4OfEB45LzGxl8LjeB8Tv/VACLNJ4gSSbjiYSSTHB0USHB5pFOoK2DERERFsGIiKiLQMREUFhICIiKAxERASFgYiIoDAQERHg/wO+nVNgFXaQQAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot('timestep',['effective_supply'])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot('timestep',['participant_count'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected effective_supply isn't changing since we don't have a secondary market." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.total_supply.plot(title='Total Supply')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.funds.plot(title='Funds')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# # Run the following code , without the #, in the images/snap folder to make a movie\n", + "# # ffmpeg -r 10 -i %01d.png -vcodec mpeg4 -y movie.mp4\n", + "# %%HTML\n", + "# " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "We have created a conviction voting model that closely adheres to the 1Hive implementation. This notebook describes the use case, how the model works, and provides descriptions of how it fits together." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/v3/images/Aragon_v2.png b/models/v3/images/Aragon_v2.png similarity index 100% rename from v3/images/Aragon_v2.png rename to models/v3/images/Aragon_v2.png diff --git a/v3/images/bipartite_cv_compute.png b/models/v3/images/bipartite_cv_compute.png similarity index 100% rename from v3/images/bipartite_cv_compute.png rename to models/v3/images/bipartite_cv_compute.png diff --git a/v3/images/cv_bipartite.png b/models/v3/images/cv_bipartite.png similarity index 100% rename from v3/images/cv_bipartite.png rename to models/v3/images/cv_bipartite.png diff --git a/models/v3/images/snap/0.png b/models/v3/images/snap/0.png new file mode 100644 index 0000000000000000000000000000000000000000..b740eef08b0419b071ea9677e4bf8d17aa58ab49 GIT binary patch literal 70514 zcmdRWWmr{F*X^OZyE~;@y1Tnu1f;vWyFmvvUJZJB<=9+WNF~&TJ+L}sO=;Y`y7z|5ASzZ?ggBu1PGN{Pl5gZIc9`FyMx2%dj zD)7@Suo~g0YXDyMvFvjn^}ntBsF`i@T4DlP#^^ zGcRu^cQ<}6J}zDkT1Our4^eLJ|MPP$cdzH%Rp+8VU@%&kioA?|K=Gl4e+Z$%W0>EG z7h5$C^z2DGr*Lm)aOc-7%EQs;Q%A>_C56ui)^27o}qbA)beut&-HCOf1WLsC z&8+bOW9Kmm_f_$}hkw5-lpBrIUkadUiJ!?A-XdK` zrjOgogoKDU8oo}NEfL%LR3Qf!|9mJyn3T~tQb%8((ZcuF((td(O*OY*L7>Yxn-x2u zWnn=P^FNliv7r-kUrnjiD9ZD1nJR;NSpk0~r|^sfY(HY<-HQ zY^*`A?o)rTKUg_CuvM^#)&5`xPEITp6&1&y?>Mn3Mas$jz}<@a>_^O3DMjN($;D$= zc<;^Dn|8313%MjT4?dWB73Js0Q%eNV^77(cEqmiUJlwjM?eutT$bx&S0+*)yz-8VQ z(bXlgy|bfo{BXU6Q(ap-vf&Yc#m&x+9*vAO5{p5S0iFb0O=Z~qKV@a*Au9jn$lNDJ zs%}#EmwN4PE97<)X$oFmeE%*FqCo_K@2Il$(I!8SZVCtA9(87BWksc=U>3$%AN}a) zx;^gAEGS6$(ec7=p;~1@Ob6-i`}492%@R>%6_w%ehYK3mP`cDZLg!i-g;nSuqRp+X z`Dzscw$#pLFARKqeC=jS2?N)dyftvA6-w#U|G8TM#|R`0;_EwV7%mA({KNgN-Q&YO zEaLCcPrJ>rM8}mL!OhK0kHc@gE^7l+CMG7zs;aVvhQ#mO*SK!n*9Ih*)e9nmkGoM4 z6BGBpzh!x2+0Wi@)!|IU$Iq|R?e(nPeT_Q&;im5Lc*TEB;p0oe%#4hPI4la=(Kswv zM2VPx<+GW*dh>2R=cW3{Oa`S9x6YZDxtVJ^%N+IXn2I zKi|ABAk)0ZC$Zh-d)D1=t4I16+%~60X5F_A8nUQ#j}KRaQ{bgExGZ|)QR(bI6!2CD zUmY`YbEgv$5@xuq^kklGO{hJnGZO^f|M{A@;>Pmg>l>!l*4AxsEMjnF7Zw&K=cFFh zQR$!uwn5|>Y3!M~_bWJmeWM}yZ=WHns7OsDZSg~Vf|le>40ux9qK}G}0UJnQPY4cH z%4MP_L1LH!yQ~b>sZhj|o-QV%vXWl6$&j-RM9-#&$RthpolI7C_EaPS3MyU9<-uY+ zSf3(7BBIRD+rOEYmp%(UzArut&`5<9!)U=nOyICF3Jd2~S63?^gzQ(uPZmG*X1oHY zijF}kO&{y`N8vR_kl|=!aBbgypPb@u6YuZ zlNo-0dClP0Tha&4OEoxm)$Q#$l%hTfON|D}#S+1W?BI0&OU9K|*3`uQ-fD;9DMGn@ zeYUmz?x!0AO~`3vP8e)^f4(XP_x|X;^vy8sFe;n|HFShbdi`Es9#7DOGeTh*b4q0u z6eLO}=Eva#PRZ2V9Ej2pPfBnF+2nk5G2pzE*xTDHKiJyZCX8-mtk%`mW>Jax;mV>m zSo8`N3cFKxhu!yl^${)6%ql6-Fzs+w;2_3m(5XVcxm?sTxTuH&k%?LomJmoE+a z#g)#YShfd!D3y_703nKAkcEoD;S5Xs~Ouu zZoEJw7>w4=i%E@YfjX#KH zkc6`}ii9$&l+v@7mh{`g?t^SQfA;TQMdL`)KHlv~)dasp{PT_iVFyKyiab)p|CsXE zm)B2LzP>gi0g;(~d~tEX8~zZYUcdtfmI4+rlg~Zs+)-)r)&WXKAm~z1XhLxFxvlc< zm{?dwU4+!GJUu;G+1cS?uZ&ya*TNqqz!N5efOR4dE>LGqA?1bt`OZ}?EKHJFvpAuy zEX=glp>o5kn#g6BDX4bV*QXl`Z?jpnHbK(PKGy{q zlf$G9k%Y^9XlEvW!$(x7*U#02>?UfV-?&yH`0p4fwVrp^p0BJ1sliou?Ue=#j0WDF zO=OmprK{z0*=)T}b^P**3}$_{Io|dAO>gz(@!FtOo|b4>f4y!EI=ew*^~>Y6uv;uD zQ6Fh%XLfKBc>tI&34vwgCPM{T z*(i<@kWoHgUYyTxGe}9%Ko<*8fc9Wm=mC|5=JdOA;&koIH=H)_uyA;#}e?MwZNtpM~x7sscZE)&<(j-@xMN(EAzPj z=UXh|2@kW6V+5FB90XIs8;jn`t}as8T5HFPubR_p;3blhl6d@&&7pelBD4kacN^b1 zQcZBHx}B}<>Gf9nRxy%d3d55OBmJ{`~&Bb$4^ob@c8hkI%lg zMcCrvqUWDq286`K(+ePEH7uOYhzHs@>tkQvz0uLp0f6A_R1I|)oK&Vk@Wc~eUYmKI zZ>#WNMtg3(hl7;{A3t68`{^nYbjga2j(!Tx{foR8v>@Sq9{~wZelYBw=h-x&`9KJn z(Hrw&aPTD57k+lSrvvl~A;E9m-Ymv>;{ads`W+gBa*PJ^_h`w1@oU4rzdv3yZa6%DUS%^Hx4+sirmCv?>9v_KAFnF-|x>QFK)h=xeq&cK;Z$PBp$$74E+=UCrSeR^$MUpT6_*J zF62=jWep7^7>8-QGT+}%vH*>G?tUQCsKUU5vgM(lSlG=D$t#n$$e{Qk59PapoQg+4 z5Dnmq?ZHA#&!Sm>5EM4DZ3BG)XY>QN$6|yeBn+aWrl#+zCfosbn+Hi4$}~bnSfGqv zZ)a;onJUs%f&?$(wT+s`Wr1F|nAkk<4;@rc`A)YLMq#UvGbAWNn?5HAuLdBbOZ+7T zn1wq0&hwQ;FENFXOT_5LPzo78E?6(nLu{OGjDG9#WB{-my0UNIjM)ZbV`IUYk_Y_& zHkD{hxA$IS?Z+}dyYf5hkWAFE!G}NG0`9AnsT9KROZ-<8L1k&=Vm(>z<|pPdPdHxb z-2zy0sP|+5I=AHt$;8le)oGS^T%X!lc!oXP?15}1ZEH)v`?095Ta%ZI3!A(D5+C5F z41ikGCI6v5F?=<|*|`i4E8ww#uT?LMn$CT))qk}*3N}Nh)6HV(l?j3WvL}jZyJH18 z*CkTlte)83fRt6It7PER27oiWO5)`mcEjDr?XX7NK zg3g4xy1Fevf1F=g_LCDQBh}Q@kV=J#g9ky*jssbL1MD9htnX?y*cbpLfb^jp@!eVl ztsg=#k#s?S7?3fSx4Xc_&JTu%W0LVz_hrLJSka%25ooP{DCFNM$w5yBkbf(YrL>-B z?Su4q^!H8z04NWCbYO(vouPn`?mgE`0!^bVsvMQ`Py`Mr>8RkQM!^4mMw|wk>x2-} zU)oTY&Ev5+l7WZ?I`5$&4UEBE#PNHy|9lq_sXUTMz@jfKc$$pJ&&QXMolVdtPxe18 z`?E+8M@^E#OBMfV+ShAiWupFn`Kr5*;D2ZbdKxIT0fJz*Oe!5f&kC7nTBCdQ4$_bGAuq5vf zzrRHR|4Yu?oD$T&H~<8cPe9U0A{TU~Wne%k5^~KdeZ0R~gE#_nE+gdy*Z=Yyr#O@- z9$xs3dUe>Oad0qPS&!eZJA<%5msJI7c)(^Ni^tIqL1-_*bH=Q=#V;O=0W5q5KrXGA z7zJo^v+h!U}K_w^Na8SYmP)#W)h_mlvR?Km+HuWnlXe}^7&f~RvPb(@)4p1C2 z9U_IWo4lnZwey!(iQvx4_3AW5{0>QB);H%nP}_eDa!1B9rnWsqtui<+OGG|CSUweM zst;zw+1k@HuM>hyP5rQ({4xcK2o$L(Y`brfOBEimN8Bl1I%xkKxCmOq7f@&1kPf9g zt_6xEdIp?XAVEOf2*_ZS zY9D{$&=(=TH9cp@%ox?u)fLS@CaYNG(PyYEg{uhW3a(Sv5>fe*-g2+0NV@2nlc;z~ z^vc{PnC>CAnJi_MC*hY?DvHeex=qDJGcv6DaUZ?V**C=@+dJbU8$4TY=gxUNtlLu7 za{$bg&5Gj8&dSm@Hb%{R#tYDf_}_1K32ABXqmVF8cZ=L@m+EyUKYknswb`cCn8)rt zrhxMjYyB&EAvAbtbNojcRrF~wCU#oc62_#S1_hT*{?sIOn8^uk?-h|>SLtnq4$fPc z9WAWhZ$$pD(vD)CRBq3~2DiCq!;KyTmqhh&{hfZ(2h1Vd2%dr8qMYUp%#o8;|8iH` zCgM_y&gKRXhCCNx0`Hp#(?+(^g@*uQM9MQbVF09qjyCN{Ck8q?NSQF${`pFkWYS(H`yy{o>^ADlK=;NQgI`H8UBgao5DGpz3L; zK9Wrvq87iBHi06E^g@L<0uL6Eg})w8lV&@fM6|!s+u3;a;e$Hh5)fi=Rct?iRZqZU zus=qnBYo<{D0z1(2cRwzIIp0lD43d(dwF|D&%RHUiJ~{oW04DHTW?3gkI*)GdTj*9 z)@jR7N0D-AUW1@5#GJXbpC<3T&z_p)QEIawMTDT*yj{bn69;F-Jl#U%l(kcM9IzjJ z#+_?vQfHB=*k>u~j?+NJzEicS#d&BgdLOa1u*EGMkdbt1_NSzCZ%pe-6_F>y7WN?a zX#?$;=`&5PR$NjUZe!~Ffi0~&#gef8i~N^26$_@TN6~I?M@}pTAK~JyDdnmFzD*|K z{=zs6iomEnpABFINF_r=h&ha5FsN)iJC=O7wi9suLFm8YOH!=K0RlQ)5R6IwrsW8Bz>uX9A;2v@PMs@ilqxMqqvWfmOKz=!+l znL9uDwI!kQ3>kw+MPIR9KYk8%E_W`(ceU*XwZo^$_kD!u!LLIXi2;mU7TFpF*Efa- zma4ti%Sz8VgZB_#-kdQsM@rVo?2VhPikD*eM*i8f{Qf@2Rjsd1MfG2rzL{aI1vw($ zdGO~xH2kvb`-+5YE*duRB-H*d8lrCWNETsZbqQv?pM(q5U#jpIb+Em+ZC(HAJ~*C1 zCkJ(G%VD=i*8moSv&AS3VPH_GTS9OB*!m6+T_D(ykeE1JsgN8sMOH>7dpQ>)46xMG zr%z>+$ocNNkVQQ4Nw4Lf${$C62>C};ff;S!H1RGva0B3lj@7wPNj0oR;UHMv7>cDto01T zW?Eknwj@gVOf?7%#(p}I@O2{Y zj4B6?4gzgV6Q#E5nodHr_A_~$bNg+j+)QG)9*U;EL;bRNT+2kQh#Gua)c{Ww9yo}~ z9Jv2$R>%*^4hZvjrosw&d+t0fRXFFb2$h>4SrQ>$Caf}=a}2$_RwM6EO1>Cg`Qgqz&(~i*20?^n-3z8Ew$8D`9iRH+)eyD^_R!Ae%Crz}d4lejp)t*5 zgoOP$wit%7Jiw3Kp>W1e7v1DJpqs(}-S7GJy+O$3XK;e`IBYs7_&oUotHOW+@ z$UfdE{xAF&hUPjU6Rf*yjOr={^~r)Wzv<{g0(**OyMM0uPPrkezLs|49c1loPbgbu z8#y0;)?Gq~0J(cDWQmo0YSy$3bVk==w+m1ny`#igsWBi|d{X0gA zMP4s20|u^_9*zjY4XN5Tv1b4DlJ!&~TmbjE&n%68`}gP~z_3A=iBiO4=tVn#EOOvu zxQzqqvcC6XQ3#a-f=*dY4Z`!)dp9>Xjz8Mj&@trf5Xru0l_%7*(z26A7bL}4vx~hh z^=y#*>%pD9BnuZ?U7HL0j%sm@QJ=Duo2tWR!KQfZtXrqcuKo6K@#SbG7CG(X0bA%o zi4Cb3$DlMpX_6{-3eRILQzH9alD2AYR>ny8#atkp`&n;Wj_InLtFlD)?%Dn@zQNz( zAH@<|n)zORJ8+w=SOpxE>1LUe<9CfqYCmN4y{QS#Pbxw*bHe15r6xCWnY;HMXd6s4#GhBy=xLr~hIXA5)wguF^{nxXz@hX;k|gg{?c!%U(l~hcv`4 z>gy=%CYYuFkhkKkPo7IfKveUJpRlAc7vV^&34y*%iux=mtYg!Ui7LgE&>lO-unQ_9_esCG46XL-!Cc_A(TCv5@kt$c0Kmo4 zl5C*r>!PG*W`4hA;py4hj=O5xO3TfS!=hO{1nv|kM-!{~4Q_q8hOSqX%KTRGdQfj`!$sj2P?_Ip(rtbgo{NyolWD-Z zk-oI&$Ec1avV0|AUK7zl_`*;T!4cD%J0cxcjBqMaJR80ljkBJM*k1vtS_sX+BA%J( zH|QckyxjA$P-5c<@&p%rabL_9Vgx)7hupC1p8-*hE^V4)U*9*`Ds8(n4k(+(^2ohI zd8N6}?9#*VCxp^^8!qofmvpwT$=D9Mr#SsEQWmk)ZyClRnufW>w}lZ}3G* zU>+C|WNrRNZ?4IhAZl(Smc&w8MFmar@y`20i5Lm!#iBksi~_O<*1M9XlFeLU(d!qf z6s62E{EhUHF)abb;$|e*efoS+&MQ(jh!JK}kLSy(G?t=TWuWtvHwmvVZzEnEy<%je zuV%#v{~&-a-<iWG23bS-f7RyPHq+Gt6=4a;T&e~v6#OlPW2Ul%wWRf35K;Z#B+t3LMo!CI;3G?=AZk5{;t zeZN|K>kH(Etpj2!;GibI#cl$-{l!;ofV$-YvHs(oYce24M}AV*4B&oeEOo1f=H(9NucF<+eQqU~)p&E+$HdxEhWX&P-(GFHOE3B--oaP!C`z&a z^M^+A3SDQ{@7|RGR&Z!Kh56b9WfKvaq}!fc_N&8{!A2y8;d7PMF?f}GxHNN{slu`Z z_upFWcmZh!QH*N?p^mG4PXV6~C@M%n1-u3lrJp@_CN5t7Skdu6N(Bm5DcoQ9V!p%M zmhV!cUlL>m*5y4_o)M^LBBb;Ut9J3Hk?r=M)HrojxO@M5fvGstp%dOO7HweWAdg&P zx24ZqdkVvXdSL;}`Fot4xtHzMq76wNdV(6VYV!498a|y=%`p>oF-D(rk?BZ9@l}n+ zL+%XK{s~4`X9{nvvaCwWQ+8i1D#BDIsA9$p-Au&>)B*Uv8?841bL!~N9oyk zzFOCi^!u`zid zvtR%wi#_b_ObKuq5V?DeSpv)j|KkM!npNbAn^HEjMvKiTa!y_z{nMvpfWsIEk_*DL zv%OlB04Z{U8pni2I|I>x;^4p?n}K?}sIETR!b0L{{-iD`H=enN(T+=+p{?%~OYdb7+t68L#ddDiM@V$(e&E(H-k*AqP5EtJaOha*@N zklk{-n6Q}%J~b@D9e$f90lb_+BjeT=Ga94RBunBmpfKC)f2x4dx~2-f_`-onBS{IA z07byeosN@OSp%6CVnD+l@BJZG5YR~qfVn*d(sOxzJ?dhe)DKyKGAU+Nv!uF*F`S-8G=4@d0TdyVB!(2E><2 z(47YWRq^z*krf2}_kVVBE2dE<8d(M+0o5W%O*U?ld05Ij(tGdbXc038^>UeDc^gLC zE>TKj!&bxgP$AwBL(iWGBRXTN(MtFEgeV`wW1xAE(NJ7JXnT#hT#~#?&Xb$EFgk5B zEu#dxFk67hcAs!;Bqv%cQkDOM7ZAQV(d7yH?;zl7B& zSi|mG#o*NP`fy;N2Q%)`57+HDy_l0?(ECQt&(BXl6Lw7pIua>V8FfuA8BhepxQ z&u_?6{%^5$D^Zu<>pnft=tySq9yyF>b&3QDX^O8^87nUtOzD%nV@=KK=fm1}!|KJ8 zZGxHKQ>BaKv$&loQo=lSf4Unm@_m|YvXV&%B-yh)I(FR^p*tDd3GYJQ&PaXQ5Lsvm ze_2y%I7_u~96I|1-+6^LphbHkl(kky%Mq_L&5k=2v}imDZVU(lu<1(pWvM=0zTol6+5|IOG3C zBf>HqaX?fgTX3xTNjQGuPEIsKQJom)#CsMJ^fB+Wnpa(x4ZJ81+Y zez$MGXkZ~O>>|Etv-DfXkR0ImoLb~?t1=)y{dvtycrj}eSA87w@2+U!HMirx+=q)_ znt$vi=2dgNBRw>EiZK#2GM4ev5D?0}S%~K(P$S(FDWwc<}%s4N-!T4kDhb6yc2xK7FHa7AXBAchhM zYMvLM7+utDX0Co#u-oXPnV38eQ)lLJ;3t(=dl*Ohm151x|3Xt>cNWP-h*p?HB-l_w zq;~~7ZL9hjUMjiZ2ym1D330yTg%Ko9J~4VT*3>-E*zNiQ$WsXbBG3WO9%vn-VDq%~ z^<#iKa~t~O4oX^;9Cu7_I`}-I=_%ifMoNr3NMvcVjX>=FIX87YR4Uoim0?+Ls(FS#(2G{e|qi-uoaN1(^)pPco)2+3(6k6h94^Ju8;NQ7=O7JB@l+SAaTy<99A( z0Mye5hdtZ{ieG+`lW{Go9k5A_3}Go2tIZzbAV(!VClZ?y>(y7n#~_NFU^qj4&MRjB3X5v0F>)fEQ!0GX6d1|P$uzo*HwKYN_xz_a5+!7GkiVex3M@QCKqusfj1<8000-&Fkn9CCU3y?p zQ_JIs935^}q7-(c1aev|(EX4xM*#&GHpGxF9tF&Fb`N(qBEG)}p`U`gN6uN9EG5v7 z!Yyx)p~w=90yeQ+rbL+%&HalDN6Tza@5>PP zu`-*nYc!^xk_@3;2y$R_jit>DrJ~e~mv@O+&V&Z4I1xQ#Q|gkw4jiWNWgdy{O4#g3 zvS%UdNA=aG+u(KfejzaJY($AOHHXpqd+66mF)PCmtDQXIeoP<=-Ma`ONxZ($iof1ALFCeqz5fbUB9`aRj=7v;y*z^$Vj50f-=h6JIGYC(l{DQZj|I_ySq;!72DSByMB!?y zGa1*_KKrB`{|cFrVH+ZtGy&L)b!8KaT91iq;d{6QRda(~9F^teFu-u`FV^8eDsOHs z60F{!i5Swx3(EiowgEg4?NqZ-@fk2fMja ziEnS8r~BykeRokr69gV!t8hs9y#_gHsEq?S{8(bUfp9z6sgdjgDmCAk8j~$PpaPMn zF(WeyOW$zGubY1os_w?q@5I!)+$9|%94%wlk(sh^D25Le9O7>CR@#1OsyXIRi^;hg z{C84qC6h3-mmay^8JlnTqbP{mF21;#<6ade5g|4zva`=% zJ0f-z6sIX2`3F+nR{MW0vb1H1DSNEO)=g{9idn#?FwT?FapE|3n%Z2b5rg{xmx0mr zM-UV5FYm$EIVu2!j;b|U6`-yN@Wk)InsQ3a%*@DvD(!i^{1kM3mjv)810heL<8*Ve zr{?z2fgf~4wcK;;U9HL)Lccr4P~$^IXT%JU>8V%kQHIG)V$=><5@nNN%c75D@o}IM-LPFT5>4lLcMrY%xR60Fnpbvtd(wz# zKFC$tB4~X6q5~dyTtaW1mB1=$0&^Rr;q&1W*7W>ef?gEhCiiFRG=XSFy^3Q#g>XF3 z)Td`V6akw@9r2a(Dn&s>i9n#-$P5cT-l6;RCdK0kw)naR3{yx)zqmRi5eJp#hRvF< z2WlwjrM-aa=W2HwkYyL5@T{e4Fc z;}#g?E&)!U*yUw&^-^&%pzZTJe2@mJYv0}s3eXw1*x^*-s-F)@S4V%ka1vEfFjaqZ z!zWL>5Pp13xNXM8%5c7zQ;8HlWgy~ISV3-U_qlAYkRJs?p~=LY`fL8cFHR+P{^b$C zas^;_tko(@0}#SxrH80atL#0HI!;0R*QiuZkf{;X@f)tg=8(dwcclhsr}mH}nCC@IN$douI-bQH&PXPOA~21~$~ z2P_z2-0|9S_SxkLA~uOJ{W(?~?nBar!%FenbGwtmq<23^$DpN{_Q{GaUsR)i{GNOfGQ$NP76l76u_R|U1mz>QI} zgA3$&A>iq>H>?iww96<*RTBt*dKfV}^WGOUXQ)`D1Zym!YH<0{dsv1dFLXvPEjyCG zs0nSn{LPT_r^<`wH_FfV4l-VZz!>j1lYRfE%q+o9c$ z+_qQ8y>N`9E)Qr@_X5}l2OScdkVg};ii1u|MU5Ud1SBh9AGiiayjIUGMaY*9vY>H) zkRW7M54rd#%u}d-efW0n6nxlDX3!b8+9IXVNKr4b;R_R^CGjYo74?g%!sV}Y1|uLx zMOL5pwbn=qHo(ajWD6IVNtMgHv?MIM3s-!Rv;2wF?-c}r5&J^9r|8b+;t{Y9? zjV>l}i$PTef$^@Nl|!+R;)F;v*y zJ0}5X%m7%;w+f$^$^ak$vp(OR0sC# zULVK+&1S71!}yHIx}R%M6&+?)ArpZ-jl2*y1y3JxD*!(Z?|p`dC>t|sZb1t~nwGPx zc~9%g&FXtz8C24ZtfPz(MCl0W{UqHF2n@Szl*BE;2p_E;>a%i#^MlYgr1JaQKOuEL#0Tm$)^!>CW^OkSWzLmbqstllw%4PZ}3JD z(}uE{Mk-%9xaTm_@|)r*dM4T&w{L_N%C);oUK(9_e8`Gz3B zh?IPU6NfM2#BLru4sT0NO3KK|VO&Wb1Rgr5<6CMn!uhXVACQq}kOyh8R$~-c)92s0 znu$N&U9{=3UDCFxoK_qU%``(;fwcbglzk;ILT3fn)PYl3El$;mn-p=&icMuo`6=Q? zJh^D6VD>=CCC&i1Y>8F9qT7;?V~j^gS6k08nprg(iIN5T#y2$!>iO#_1=w}=9$F$&iDHqua{Y}nI&qHvau>L z&~iW3y>m55xcYTTDeZB5&UCpmPPJFbr72^a)FR)OP1X$44aN80^UOO}n#?6eU{bgf z$atMFsG2O}q9EOn>%Y}<=7*oAyIQu0EmG3gnmn_^EIy^2fHeA&mI_JXGghL#g=T~| zMB;J~M?AYZPftcrOuHN`22|3spN&T=y(B=tQEId82xZmE?DE94gBZzeDeH#M-?%BrnNc>rLLuj} zk5tf~HBJo`HcLefSM7RU{j7@omqy3+*bYicwaaC|-o{Mzs&7+nrcYdjWj)Y_XQmI< zPfQeB5FKDb3==>M zA9Ri&6B}TnF{#DBF{FbDfiYkps?lR?dkcLAJZ+;?b62LaHsUCZH}@LH(Kw#G3$9!- zV0C$4@=V&5x4oS@rQYw`d3yp?6)hesR*a)}@Qx_|)i!=Ea+1%!W`1d1j&h=Je3J}n z)N_TbcG3brma&QquNtpsqyen+`cs;`C(27RIp^Ddn-omR9}K3np15xE<3w|XI=jB7 z9KUMj=#X+V#}B*S&=4tk3x^x>*pC*0P`ZQZbFhE}N(LNE?+}KXx$l50CgbJHBrwRZY+t8LNPPaK9PK@x zDl$DypLK;@n(`vn+5W|jx80_k;_7@5sROH+a$XklSnx(CDe~_+CR&DjmSukJ-#j(` zDJs`0cvV~n`ps}Dn*PJ2Z(?NduT;YZ7Vzt*3v2+r0*hb`Q!4-yTC_q!#Mto&pi}!w znc}+IM+OToa8p%92mZh~VBUhzIk@zpcdkEAHq4$4m=oy0hyD2U1%%J|BQ*vt& z!^#QWpk&FoZ9S7x!Y1|=Wnzn9O}SUbF4~c`Irib|y@-$-A~Nw9H4-iViF)B1F1RGlJ_GS{?14KzYu<&C;7J4vk5aIgs9UUDV9JnVr(78aKen8t-`k$=+ zyQq(vWRbxm#t8T1S{1)$MmQ5x(w$dk)3+9Q7jo{gH&L9M@1?I=dTGkB*@94vk;3#t zBW(H->F01h%K;l*@{e>qb9=?7vLfyJc(r|i`=5aqSV4{ zvtkVozjT6#RJnIFQLz@Z*i5m`<49@F@4qe`9?5UD{>&8xkNCPrEDr09y&0L9pc&5V-_7fOSjpxpJYp22 z!fBnlJv+qKw}v+IQ)n~l_tNreD#y%td_-o}^Gb}>+|8ZX+GAi!;tlHp_3L9yX0%Fj z&VrDDTS?QrnjJQi9)b;}fGFSQB=XhfV#LPTKgc+0w%TJFEk|G1JT6b{SlKq}s=;!D zxttWR+<}M+G&O|42M*CIz;XbYCIQL?R$WLOmRC~Rs)(l^0gknnzzYYLpYN~$lo|#t zb1XFA0-3bIcIwn=QgK-H5Q5RDCk}I^NB&>vjqpFrBEK>$Z^7N(*U6B=iSW?G4a#9i z!?YMVM&C58ZOh1-h8tGP^=U;UR#b?4$$0!4JEw5_K!+weM^`qsJR$v-!L~rKDW_#Ze}0D-BV#ODm>v9*HFB7tTPlO&=WeBH*QX>b+YI zrWykN9n|p#T{L06Y zBlby;@T%7`Oi=d&Eb%t97{1QthtV@`6rwB;b!d}_=V7y!AbmSa+;}Uhl})>p=YWYZ z_mn*K6xgwu?~$cs@;ZZ*NRYy81c>6Lkd^;2d_57WM&RS+klMUbZW2NHRme5CcQ^9g zVQD(mMy5durbe0Y8WkYI7G@sIPmLNB5M%@7S?os#ITsfWz&+KIBZcNHNL(#Pg9UM9 zp0MYu0LZ@!_`J`6Qda?5lF8}m5in}O6a3c&A_^coqGjL?5m+$foSjLg;dCkF>{9Gv zQKS?aoa(Pp?Q^9wOotA%ZzeYDGgG7!uzNj_Uy_9xxuqvn?no8?;)D0qyOWj?)GbR@ zzwiI`#bR~Dnsk!b90eg^>P==7`LxrsXBM9C4;-uwo$|+wNdJ4HR8~l z)M^)8ZXx#gj+>y>_qU<=-RUSRE2}gxR09?gEn>~+aui2O9{4H1SRydHsRPzv9gI{= z&dy@9a?xg~wCFNfzsLN}fDBW0h5fc{R~wr}i*dJqMb`MD?b47#irxC#Zc4pJqR1M4 zTuTkA&+ce8EHpD2CsB!8e_`M1d;GdsBGsz`YTjk(sK1@7Jj_G3@Ep7YE9ca(w^6j5 zmdZsNSoEkmJ|rSG(lF}|On!diT=n4=(g(K!d zgkREx^x>|sdr@eJ3k(ZGmUGyk+v&U#4HygI^!Vx8Ne9g0kPQY1%TbV82|2dG)E7ix z>Ax{I@ej#Vi-LifcxlVF-KeAg?FFFz)XoMDc-12%7X0!Lg7lPF1^C+!Eu$z z+rN^L=#!#BG`Vuk`h=J!d|_!k2q{A>OBbfdjhdPbh6ZQZzbsruPYxLI z&;BcEfKe|g7z~ibXOTNPY3Vmes19lByZNVPxomqQh#d@(+Iv1BcB^IO3nN6W%vY>KhdHJAym9$>|Xy*~fIUkwvP zd8mzN_HWc|e8;US?I-0?#(`5u#iJ0(TR4CI@2&p7^310c#K+kU^F!BKG zu$>bZz(EXg(_kr344W-e0lsAbV?3YXXPo+g*Nc_WpL>FeASTulL+b+1oR{u zV=Sln@$`4g(Lu3N;b=2z@vjfX?~j&!Ynxte2!H1tHnaXUJ80yO-k^A3xtpI^yO*=k z``P65?#{G&vc=1{roP*n^osIbTN>*g-FTpsXIQE?nv;#hMXk_z-RB_9;WpD8N#ksD zT{_BjAGopcJ)Pw>ef`@8qSRk{+}8T2LfQdM>GU^&Aq0X^rxwjKId>s=W&4UBOG+Cs zYn%&l!;BbPqwhMn-L%+-j@V$DM(_(K2z>vhqM)%Y%b)`$Ac!FVKVz5Q5jo_40c0M~ zjUj0Ua>Rhq=^;-_Fp@P7#<^kQbdq4+<{Hda;DJedz)my#SAOmB{e24FhM^n)ZQ!L& z9yrIx?Phdr?NZ6WmBAt>uUAdZGL6V4?{r4GdYg&NxS|ged==Ce5%iY3=sG&;S3Ql4 ze~;yy*CNF`uTqV4t3;lgHF>}3=uhDN7AS~HXhzR_*Am3maj{!|UVm&e;eRX^JToP2 z6~n{wr4)MLez7V1J*`dIi5{l)YF78b+9+;kt%%c2B{s69FHx$Ch;m|%rOn}LJ;(b@ zQEY6-jfC$Y=Rqx>f+08c6=8@u5%3GiYAqiAnELqJc7L3D>(;(^|l@=525nw znD{~9SAb^r0s{kqpC+##4C_M#4;aFg15!CE9rQK?J$^??{;Po~uv0L}9$`azcQyDp z4(5eDfcy%B=2R;yD|x`Qlz_`OT%f{#Ei;nmLjyL%PlHlxJfgnvqW+jKe2(9aUC+ul z>L$&%m%d$#N-9fI!+I$AgQA-Epz2SQbm+)>vFV>R7XePfs>yfDwRl=-WK)kJZ|563^m+-8hpJA$_W zjj(IzFE3f$jmZ>~`rxy0GMk`YlBJKY${&bS@+(#okD2&3&8`yScB9x(>qj27?X;u_qHg04SEv=;9AK$2AC!ZOh7INvJ8&@s7jYi z0zG5Hv5@zFi7^O&Q@HohmC#B7XawYM^DNSPh3%|$hIH81H)j~Tr# z88j_Zql-C(2y`h*&oEfn_OZlx+AhCr{UZtQ9x^KZlau&{M}NsIXj;La1=asLB~1W{ zAfLn1s)W5GWLVAI@^^wjXZ>GF!UL!4=bU}=iK_pWas}8_pRwVy?5Vt@zA7%VcN%my zeA5`C9?v5S|@eVumn-E=XqS$rN%7E7~7se;GS*p~)# zn?({@8_BmP@{Q??cR=R?ZLpk|SKFlv7`Oq`t1_TpI0Ivnwj(iUK=g!|SnwVIDPa8B z4$xST6Xt)^!<+~-+yr}k#E1gY2mIkSX><5TPb$BS$@I49x-oe{hkU)p<&7-jdpN4P zc>Sg$jDZAgoNnY|tJhBjUI!LyGvvqS~eQ$ko$(@^D(VdW_{PV%&7{3r@>c4!Q@sD96%ub}&6Cn3 zc!|JDEcyMlpz;haIoYsb)w75COJCXA8#;7+W=eOg>1mXUzqTm&&NV|D>s#rGaJ3o; zimAHkTVPJW@#`BZKz5gRzr}=cn7zY<9B)9(KR!FoHL{es|Mv%KCqBFs!~hHpLmW0$ z5+Ky=z^FU*amV!&uPbb%faFiSRgs$^4<0L7@a4dlj#D3jhOcyXMEupFLGG@t2D5JgGNUxF12R zC|frr*M420wc-@X`CI&R7vNm^|LFgTc5nSUZPn2}`8RDd`wZRmVbmLEks*Fxh1%jd z5FB-x*%!fw#bX@7wzK)cjfOE;i&3SC;(1DYe+&$TLxCje=QP^@E`BECnMoZOZw>;p zO%}V~%m8-+1*ijldh&L2YnI^v(-Q(t@a-zM+RG?mEA%+C0!1)mQ>W&sitfa0>>Uwu z#p=)s?3{~G2FE<_IO!W0R#gftH&@31UJ=KkJ$s{qSHT`sTJcio060WtYSft@n+^gc zTyF6sL_NJw3SoOu(j4<;@G%`yurLE~sS{szU4j5%6LqKVTBFaSe?aqhX$niamz+`R z0#WNKVkn34q84;EZ;YtRO(Bqa=Gxs8_)k??`3DTKCm3K|(9oZK2ddpayTegnPq)z9 z-e-(=e^=EKh=M*%f`HM?NE`dp76fF*0ZP$luC+H+8kmqxKcjL#r`pjD#(?3G)iF4t zGcjtoVe~jQEF_Njx{nF+iaRC-jTT=WIpWbQT#BSTf73DdMwetxOwGA+5HD9mfgrWu6%9B|>05q`Z- zXEb7{H!293K!+T`{33~)c01w!#JGf5gMqTsXfRaC>1LnH^Rm13TO$Amk^RiA z0;Q>_uWy$d;EaX``?LmrruzW6f#^z&{#>Qz*Ux-;12gcb1p}vcKG1H0{Qy!5WPll@ zyu5sDVqy^ZJFoQ51U8DZ20Di$%;15j=i|ut&^R=>*8!pMZJ@^h;-v&Q8E^Wbtl}*d zAJTAj)dd958;*B)Q7KmSe0$Cn*yZ;8y3r`PfZJZNs${T8JTK7(!k|kvEZrs z2-L6c!VbbMFe)ss=vXWY^$}o$c|aBmsJDj!1WO&j)Ifo&>B|3f9uZXpQ)cvX;}|s- zFr>(OhO|lwzY0F%Aa3F&Rp7_9!Geu+qs!iC^z+I!v%-tv4>q`opM^|NE-1DwO*uX0 zL9d>OwT&sManQBk&bT~y7D(CL9^^bwzm+Hr=Y<1rz*E6rVs(c}6hhZ`$TsiOW)NC( zR%_jqvD;N(f4F3vctmPrs?^KTKd%=s^eq4^yZxh@lE63@)kzcfzjQ-(f=Fo0BFvpu z$C34A<5Nd|`gZPo`G|mEjJ5Edse!>DR#3O_j(Zpmdc(>?2`K$$qJbzc<|EU?co$H{ zSJeJx_RH0tEkHl_sT(5>8Uga7RA(4z#hsQFGRFkt3v@AG&n)4rZehOi=0weC+uquyeDv}T=($vU1+5p=&zi5q=3tN-JF4=0Bl}WjBomO?v!7XD} zEzY^bun8(Utv)qUs`NYK2}?vPAvOD{0G%koHQ15r8j-TV5J2kEj-n-R;XnN~-0f3< z1}4Trs%mPVIvja$1j1Kf6t3HP_*M8fSCevnMge`{W8SNV`7`poP+2+bgiA2PQ1vn{ zN3n!N#zYmK4+poZSBXYVU{doW15y4&U9OyzuSh11gnJf3!FBLxmow`1X{Zo-vcXfs zSW$|AXB%lSewWSEglcJ>SYfnvP~n@JDwf(@ifeiZhN$S3-wJR90}b2&fRO;sK7g`d z3X6{G4}6~UM%tuI5g+GGkPql(_Da~OtI5MO)%a>e?k6y4hub|^*kmi@;lZ3B&7(sYw6JnkRdgQ3U;8=t(zc3C{Ykw&F3?-nv_SiR6 z8ENGgB7cF%-d;1Eh4$0}_d$%sI}vhcuIlP7`=c>~J|OH5y6C!9=a>mWHk zjD!hj1c|x9nyeu)v4gtad9QY9tl8<%a+pu#^^pC8ZR)|QRt0pb!;_n;$T$qB%5r2% zFwMIr8Eu~FO@pJu%U%9M+m2Cd|-K zJV)>$_Mt-DQlC~|Bqppx9H2Y>9v6v~q7wYoPb?I%A8?);0<*Kv22LuLP((*Z=l3oG z7U=tWlXj!<&F#OCUyca6cTNN4D=j8{Cx5<+hetr_(!$B>LEyTwEBz4XkI>Oe(&^Er zRg*?=3h}YlSG6isS-+9z;Gs*+Wl`}`)-#9LWm5Y=x6N#D0Qc2eb@NcM1J_F+9=afs z4ff;!67@8gG)C54eB)BJ>xevz`>0$y4%2v|kJ(86BIexP9;CGYPEQ5S+GP=KjCv9K z37+~Cs9?!}v|Kkx(Lkw(>|91G|g|Pt!1O$OZ01f1a94IM%eE<8^paZjN_FVCCdFfzLO@% z**b7^>gtfseM>=(viV71$>zinZ$W@V6!Uu)Tp}hgmyZTe z3_E}qd8EU2*e(IVs&>fd2L$Rp6dd8!DHuES@SDR&aCN7ZNXJhn}ilCdi89zW%B9 zJ-(t_J6chFd+z88!9j)X^-GHcI=jyqwzmTQ(m2 zaarE+_;9XBogES!j;f6q4izN)18f8e^XMnOu!;(%GATv^TombU5QsE$dg5?o5)ASN z8N$>+1Y0Dr4obN%Fjzzz<4eDA;)UDyju)nHM~|c0r|O#(oo?Ubm$xC4FKT#wvb}R| z|HAGRN1E;F8ISwBUraDBu42Sb`*S4jpeshYN`#LADAl$jzBe`W&S z*$!MnNMWBQ3=QP$I~p7R1BMGJ0KyU!{3o^dm6GJ;_vj}hX6yiyCxh=h6N=EQlftIq z>n;u~*{6+kl+YD%Mb8zc zaQ_ncj@K*;I4_lWSucN4+wcu3EXmwf1jA`weQdZC?i_d*2NX*vjrbr+^*!hL6xt$* zL`|bixGud-a7NHw;t=b?SU>7LV>uU*{qOmqX#rt&He}N@^rQ9D6_M_Vj(S^+8Py0w zXsO?2msLli;p+4%wn|2$i7kfcVX|+f=bTf$v*!ix$Ewdh2*RO}f4<2*J09uc{S9eR z?C|jevK#v1xLdKPwz#-$8O3rtW8`}ZSWyxltL8Xh1I$B@ejS~is{n}zXwxhCq5+fR zXS~Cuoe%+=p$>09l|g#|#s3OYPFy#no)+0}7f!1ipVKka1+U$U?FL3jIFz+ zvP4&Ug+S166ELbsfEF zu>7Vu9HX_+pxZk|B%IWSRx$Plc( z^m^)3{X(en8p>k-X>N``|BgygL*qaPhP0`1yJ@CI3nh0RfTs*4au|ew!?X`TcB0Yx zKK|u?7*|%em>gw)KLiAiQk`$wY=BL67|2A^B+CLqPS&Q!dQ7ogYQvI-u5(@lAtxek zW|q-1EhFp?W+u+6N|fvPSdI*z?E~!Mq%m2zXU>5`!FzD|v}ijLm{7i|1*J7>ocZIh zG4EMi$%eTzP+}{svs1r-uWp7$7`+>v{Tw2Gb)OYUStwhaB=5!|9r_MJ(WE4$`-0li z&hy{%B;G-9Ic?tarfn%mHy?LhcbUrt16DbUXU=qkc>n!l zJ0JfY2PI3FIF5zp?32D%(+J&6XufzMeTYLaxDIR6E~6yJeW!Fj8h%83_8sHxOa9fg z5sBRvRHo13OuwL+H7Z^a?&B;NHSBrjiI+5pL}6f-*ruS)C-FMFS(059fSR4S`T?`h zRUj@aCMMQ(JwouAy8`pwK`=A|I`zBz`)h!%$pWBN{BQot1slJz0F1&#>U7S1;4n@A z9k9{!+T@dg^%;J62fTQyOsVZa_p7V(SU&-`^_}GPF+fZSWw&n7sPlZWF)xyEt!jTo zGC-3S{G0I6o~%oNq&S-Ty&npHK5W4i7&&Lu9R&njHpY2amEw>$4_9{t0}_arTEwT+ zX4EB${Ou_iKNFm&wGr#TyWZq)>t#;b7c5NK_rGfBz(3H)(kMZDi)VO!$N#jJ?`gk9?Emx=O0&;Z#L@P5pI=T=$bK zoHk=Q((Xx2fgxxq5)_;7k#05T}&ud++oPbUxFLFFZ_0pNIBNCkn z1}i~$Yp-lq%44?$77XLhDYnf}u!7P`m7DW{%$(TX8q|63n<-K-NEyjIx!NTG z>!lv9uMilGt_>QMByKR#<1^d~=Plbpov83YLLdr0verAY zg`gFmI-(B#ldD9dRz2{Y$*#0j=_(08%=r4S1sm{5vFI zpKcgHYCHfep(VAoDBF)bCq@5(6=QS(O#?S@lRTX$1&rhLTz&EXH(}TSczZrkmE#Aj zpMD6R^JYysTMo{Myd>bZ0tSDlV=NAYuMe$Pu_tT2lntm@qBOfu8os7*hCY^})M#{# ze_Uumu>Oh>M3Ypk=>-BptOz&;S;F`t@$r$Q1aBDQKOlu*#30As6(UI$(=dQE6ZmQuNU+}$(brwa^z{* zOm;0{9Yu5RA0a$S3MrjYdDFlCn_Nm%<=rS!ZqwYxfl$=eJ2b4dGc|3Uu`ccB+frGV zELS%v&Gs+>+$;MoJiw`y&g)*m5#0{pZrXt-3@+%0ed}h*dFyr#aP7{(2t9rqeD16fcp0k_+H;JKwsO{(0nq1-IL|_uo_04<5^IQ+yYyr2S@T= z*MR(G7>oGcx$R+F$3`+Va2K|C5%eWh4DFc@zB#A9$}Ktsgt=C2FcXQYcb$1I?1=wD zhR-4{%Ai9_9`;QBh(t@S;$YY3cpb5PUK?or9Ul=TVXmXC(Ri*^`jtR#lQKr{vihVG ze33{(U=XV{8lmA3r$At9@`t()IjcOY52eiOJ2Gj;TFPo?{kOzeBAY5cycgqfeCG-p zOm^eN7R)79l-HW5(VUBYdP9{^mKai42mo5q2e{Q!@GZ^(Yu#aruEP#a?~}w-W8*Y$ zRJO-j1Rk4((=e9iXDq>KUWU4UM)0N1^LjVqM*Ggz&LzmP2M;9z6YA>OP}f1yQ$^gD zwpf)3Ch!}-ZD6!Z%gP1?uKUmTKa|RN>O!~na{@}@Sw%#p^~dwn=Ir28IH>=A)jPw| z>2%?b_+bp9cC{Hx4WbOAvK8*xVXIKwmsmU?C3G%+p#1k=^BrS^C&Af2oAt!PQ5g2q z1qJFmuB%-6FK-uT7x5OT_T8-caLFYP8e&0m<2m-je}Ken_|W)IZ43rNF3jJQEo!{TJ)#bR2!6v5$`8&qz( zxZ)i$o3)6LxEoa?E6X*k7G`9SuG?`#IM~r`P1eY@c>6gFg%P4rS$0&;jyG=nc8lDi zMQe$~9!byAI^TJ}NOe0d$>5UB1n)|9ge5=yXE%9)tJ@@)f_97|1)2`hlJw;7P081I_+BNru@beYa zbJ^&|;NbY}|GWSvs{AviVMHE(fcKIG&{%x39U0G{MoXfGnyh6hp8HV-41D?@dr_j4 zRLFe0s7^X4OwKW6eaN5&X2{lfQA0fLZ{)<3@q@=vm%sJP={y`~c#1fhsMfp`Nb;Hi zWm2gY!wrDJ1$XNIf&-@Vy9w}kzW_0k^ig-1&t~iR{QO`5;j0}ASyN7sRS*R&4%o&3a2kEPyUCiOOdV86?KFDMZpR9FJ zdOu~;z}1zQlZ?>3+rDDDYN?(>ZHwctHMX90G@oGWSrTUe;6T@k^n~>4z_QqfJN1=ZmU7x z$n8!)OtNlkAb{#k1I%#XPO~B~&H&hu6_}L0ymp+`d=3nO@Lm{!Lt7va2vTLB`<3RL zzzv*wut9yH3VeIfY;A83>tRG37Jt$E+2d7zHC8G@xtYGNS20zI;Mv;SFev_{Lqvi= zeGA+qGO% za=DjDu#(O6$Qm@*=6z}YMV-zFTWsn0uqg#I*O9$)hlVD~mXUCQa}sR~1%*0)R`T~~ zzt!zy6O#W{o?1*S=)?6<(`05^kih>ob2;?-zBVBHtKW$nhxSQK)DYl5m;q!=fIq%f z7OX&!xvTucMLR-lyB&$Y4e`128-D!w4|IC`sAAH{iPHUbd_*!?1DW{wAU^HBzIM6CyH7BD24?* zGVISi_d1e{mGTw-(YIjb_Y(XYp_d)Xgt#9Pwl-Djo_yx`Q6k}vNMg=Tx#hC#K;bw1 zunIe}?xy{SkDU5jHoNJOE;z$AuZJ()%2nKU6~vD2zEW+w-EG%)6iTEp*3UxMVRiN* zBuMqMwPhAwuDnrJo&1scMSAKZKTP2HWua2&+3LI-Fe}whFm=`e%QP}3Q2-vp%;!1Z z{wE0Gw{aMe$qX{zU*=Cw2QM%0XHe$$aB2_WRqg>V_=9lr*gx5qzSMxfiS@wjwFR2l zBjQiRx2vnctF<$y^ZUqk1c>via;1u#T3_wxm!Cd5_Foc8{Y0&)U1rq!+oH0^wWwxC z=*1wFtF0)oKm=wT;rQ%iNthy-Ii;`{!PR-yV)o&Wo-bXj@yI0%=eZNYV9>?QakmP1oD_Y0_#h!634Q+7e|`afkcLICQD?Wx zngdii0eN-SV4au9FXu1;PrxrlcUZ#a21-8Tk%g!0c0Lk_u$e3b?e>6Caxx;(3>?3V ziOxObQgT+G%SFkWmx1Ozi7}f8dr_q^UKS}}QpjuwQ=rmWsn&_(F z5foP!uUp_&EEF6`#a>&h$-LMiG{QODT9^DQ1ZPB>l=QKL0#(m(2eWU6{=^)dNA>S9 zdb4Q!=7v|jvb5I%eea^Y%r@%i@iltPsdBX^jC_6zowTvq1Bs0P`M+;H??f^;Vh)aB znBN6@UJt%P{-L}waCH1S!+(qD*mVuPqW?X+yBDBuy8!&un>E{7XW%U}x4b+8>@pbx z_7!|?Q-C3ILj^>RyvoWmk}(O0ylNysCVO5dy%>+5rrHU&H^~&2H1wLR*826XElr-HmZ6_fCC?ZDL2i)XQ@;U^r$8XwdIFgn=OGG*+ylqMNOAYg~k%$ zNU5pu>dMaDZ8D{|ToHU0-leCP*&^ham^CI{+8eq*)_F^5rYA8sMS200Zy;6LXr zxKKKX^dZ=Hq;zalR&;T2>+~nkX_}|`e&T>NUjq+Hq;GZ>HAFP9riRJDM}cxrA7Gfk z5ITW<1_dZSyN!QGxknY1H6}(zKQ8?)eSGf8?$6{{ zrsJ$426B4__J`}!L!(@DxfEzq zsx|i`+wdG`1h`5St+rVT2+0#-bWq1gp4>0x>dKZ54yt_*?Pyq9XOpH29F3xNz1kQz z_owK+ zQPCg`sZyD@Kze7brAnFeP^P1g69w3>Wk%Odh(`-oXJ7G)HzU3geDkK8z@pL^^3_u; znDy=npCZj`+t$PwV%n>CQIy0`eNZUb`U1n!X-WjWcxyZXN~EpU63bK{q~VhgSxKrX ziBP%GmkPeGmXH|a;itqOBlTYNMhPii(-T<@)k11f}vBeq5Q6oL)y_ zw7JN;&PLo(NS5=+^Q7x+Bo=+Vs>`217?(}=6|!P8z-4&CmdcvK@(ygu z_+O<6pfc?P{fY5bmzQUbRJ)#qlT-ZHfPWLO$5KKz*6yP<-0HGV53M_^mOHyqMw!;D z=I4iZg&nA5PCJk~aTejyWkd_bHIA^f)&v3w`gr|;L~e@)BU!;m^o2O2*sbh_!iVc7 z?zTGhyNZPvi@~#~ZmpL|arN~y3aM6S;fSuXKO?O&h4<`-J%y${&2dQ~lYNkdaE^M) z^sDWd?Z?-6+TS?RbUki$g}2mE{#kE2Dd6o8Dlil*eV93qlyqx^rU;frCriV`i519Gi5^VbiHe=xg4RNlb`HD4+e5nDcPZ*JTh~POVus+5zn(ClSjD6=d(t# z{emqza^6T4R0#MW$Al+4$~n93M-fcKM{#5Sq^Ib)PI2%*9~W*oj}ig805NCsJeH>3 zoT{#ADy-mhStOb_*lMiwVKOMy~Ehh?K^n(T0!e z4=evlgdJJ|y&i?^ok_T<8S8+4*ppI!-+`4R%IL{VB$y4WszZ&D-$jdh9zK>yY|d6} zcMclZW%lQ?GuBz}?%M<8<1#6W9Gp6FO?(3)#P0C{-QTtgc3SY!doeGB7eOmp|9BhG zem%FO&k5IeZ06yA@fx%BN=3|L55BuS;ejS--Ut?K$QMz`9yFIddAht1Ol&~5%y7A4 zosvm%x4Y>xIkmS&sG}m|j7<3J=W2x$M4jsun;geKV3EBj4BP*`_eI~K`+iv~8Axs2 z*2nop0~wrHU`Ao!bIxHgnbr>g>8|+?O1)OiQ=I`Y9fT8b#a!-}G}(aVDg>P8!19EA zK;r~TiRjo^WIz^x0m#$_OdZD{Zy%pk-~#JrjQqw5n@-E$AYOyN>JcEXw^%dRdbL?H zek6L+&fEK*agN4IHmSFnU;6g&+Eo~~kWLdMyZ@h+I0A=ByaifasD}hwvOe`!lK3+Wytl4!~(PyfDNJun_^*XW0DD`QT zIxENj=pIdhqT&1ZV;Oz*jBw{R6Agh6i&tpfQ2E`%zl}{tOfCvOLe~0}wa~7(EOU>| zy!kp28?M2nMUd_MoT#n{wm7%;Q*An{xr5k3R7G&j@!Y~|-&39}&gJDhDMseu1#tz< zPTka&cojr@tiF-Mg8L^#CR|y?tCl>UxqEn^LxFE@ZUQF-04(R;j~CMdZ~$Oj8%~e_ zDUlOcUjX(8a50+kWixLEE3gIM->(?L7y+uv{;d~N_nm6WSHtO*o`>d?5;maFYFP*R zm_F0qklLKW-Oy*AzX{VU$ST1DPQJ8Izszj7E@zLCVt)VPj$9i%^@dE(`|Xch8OIj9 z@&l3FWc{w>yWmzd%GwZBGr^p1_!4Uaf01}xiBziVz3*S!)*)s)BJCk5T|PD;m|L+J@7dmmFmL!oMDOMsT1%8(*J}xO&JLY#E5niLp=3^O&|4bhiXH| zC~Gu~i}dm-<=eZkHr1*@l~^30vpAc>MV+f3lDAVC0hRZv>v%ihWNZ&MQv$c!G*@Bn z$iJtc!>z29)IQ4YE%66LPh;aSkb1b*E{w)XsgY#UBIUcWnLj$t?0-7E}zKa^T^<2rpM$>TFwR@!o$9K(dJ`=8O zWpx>fm`E%)uQb1_l756k%!abE_Q`-|(SF1*L)uJ-aB^(TgCX95en#%1BL&@e(RjAb z1(x8=Q{=f**xey!So!b-J0D2XmZFwPB2dL75h+nDN+))XMrLWkzUrZIlb|w+{brCGrL2F{ixQ_wCN3X>3JtPWMsw8 zYx!pjqc1}_>9hTdi>`uA!&kzM)*QuUT|oOmj$2}=deN5wKxBsa@CRgrSo%5ey2Ff; z0L0!gV7-CWf2W`Lv9FDmGc5-|(rF5CU;nN%hHSK$GFfdfPm-nly!RYB&pGLg7e;Cn z>xk3tNOE@Ii7o~$%;M3&VSf%CDr9XT*cH2J< z|8Ej}K#(2IHpEpoIYCvjL-oyRsMJ;~Ar<^ut*!ZL{ZtbOyFFfjjcG732J{b;Ic&t^$sRFyFW} zJAuT?fDMV_1}J4J0>_?m^!K8EU>OfCKK`d2h^5{-AV5f8U;n}J*2mB=cZxv>Knp!= z2L!|lK3gI4?4y1T`+Kfq9Is}@P*{NE6;to43%qp?9v(2&6`|dc5(;9Q{;^`XQkto7 zO6TZ-C?cMOsp={Fp~)zfm4&vs-{k69lh-CY!(C`=ifPKKM|*;Z4!W7c=%KTnC;Jjh zgI&gw$C6^X9JqyQ8uNq%&iXvEb9~9XuwtFhOGU%9I%5l&!eyL?qRl8p7=9JMoJK{e z@Lfx@>DcQeyhKJCi=_BOa}k1q+W}NXPu^RK=KL*YnNxc!TyyjB9UD(<(TNhTBagA) zoReIZAce%BV3T2cqAh$boiE~EnnY_c_16G8Qs4V}qBKV}%2C4sK{!ZiDXbn`%Q{i! zi>8%kdE#X8!Zw(_Y8Bhg zIQ`DClI

j0&D}DVh*;Y<`rbcxK=lR%lfw?tsyF6r^~ZX6YKV`Ty|z|0_h2 zfly{^NjUY(_)-~Xqgb^e{Yprmt0lEk&gb_w)%qV};v_}hfLz4CwPzpsP3n2HofR(_ zN4U90&we7OagiBZO#CbiioY;H!aK3d;?k%&GhR3?&acx@X+)r|ziJ$_q|Z#T^1;oe z3#3P;r774CRGbd(u(Ao&7KKa6F2&@ES>eD~BLhP^(lQVN5vQquYK2yD1OAjVw~C1T zb|F}O&OJB9F^9{t%=p+Fh{|R;&$L?7ggM==yV<$9>_e6?BqNkjcsi8J_K_K-zg44KFnS22J=0D-6*%BwxGu%EJUtay)fB-dhp zpA&+5`op0o-TA+#`_1pSxgX^P&A^HBhBZbD##gvxW?$|)3YX}?k1L(&9zxWJ* zScB>7^19^)G}M1PC;?+Y82vMk0yv1essq-nOPv8OBSt70*S2rt6hpv8`_bnHMPLH* z5jardcdB-^R68&yH5jm2#Y3gwTk)+7S`n7T)m+>uFx>M1{zoaSNpX=xDKfRDnv%Gl zX-ytioIlg6l^#odw&L}EhcyLPowep-BwjJ9v{2aHEuD*yDq4M$hnsG50qacU*y5d^ zxGrCTYod$m*nxUSiGm~D9zLAvlk^U4fum^Cm+6zbbXx?`8#hOZooQZDadrnMUtA7Q zg@zR_Q!QE(^NeKHaY_*T^+_GK`--PEuAYBm{7CoGLox`0WVy$6&7Sm^Rmub>-A~Dl z8Uf6(#I)k}!|B~n`s+LEDQUXXX$xw6Hg#j5>}exLM#%a>(li~gW5gw8`~Ew7zh_e?-ohab%Uv2*(~q2Th1GPQ0& zk2*91j$inIzHhKLY8gr588@u8e$E1NhR3x9PR#JF9C;G#Y=zn@cp5WNPif2^* zv|Tje3UuNOq{`DkNn@Y2a?v|YZ5x~RhZ-;T zVP}FPO#g`I#e`MzgW`>EVYW}>nBcuehQV)AF_mY|64W2P+<%j$bpbDP4I-gs-=oj^~b6k_M2UXNRqDulW)u9vWCS(e^f)18yr>%p*f{J>1#c5aN+yX692FS zj{XpkSRDk$_rLVNZw}LzW(|PD5cXLLy{vY;na|bM(<4to43@~*gPFqN^7iJ{PcIJj zJ)?JApw~n1l@{i~2THzjN*RxY%Dv@D6bUWv#j`SxYUm3_kHgQPWt)te_^_L%aI>gp z-#GM^_}`F=4smtpe^pwLQ)g$W69V zAEn*0mCT5~IilrBPRgLo(0Qhhs^r;`@LF^R2ZMF!6+TE~k2H7gJH4sQC69Rk*Xb|) z(tWN(vR`9cLMj2)dvf}hi)6)@D;CRk6IVU=&uG`3^+MT@bIi|U{GO%Fm#Ct6NooJ4 zt9d?NdmC()NTaZeDbU@Ffc0H}KFzGjvXqA)6_n56b4|x79W)Hg=eThdsL?)m@puAH zV%zg+WoDK!Yxx~3zdT#?U443wf5G}BtAvuPt z7?m_@#prE|T_qt9)S>F?At{bunTx)K1Ui#h?<5PZ!7?2mCBY!;I1m%ZEo-L3T=K0j z$6Z`KL69h-R_+ix_fMd}Z^=~oLoAs<00}z!_xpD+{1E)c4>sbB8Uh&Rp)5j|-P?OrjWQmgu z6H2VWgVsgwhyUSB?hH!6lNc2ztAe}H-%|iU|3>XJX_Au-w=wn| zS_CzF4k~k0o6=zD)3PmzLHJ816Qs~gIko*98F8(PuhJ+X@#^y8?7kgysBZbe zwMMNFR~;K$e{xf8@asYCS;n1_B{f3%AoB-K1SyK~4;=A#|9b#DGj)~~+3{-Va!1!N?0iF+2hY+_^Q$E{O=gI`8H4DD0HVt$-?@) zd#3oL%ZBGf#i5(Y=5&6a&e3)gM-%t1ct+2r z79>g3AU<(1!*0b+t?;yKdvcu_7KMmZe)_c+v9=3>_pc?Qyv08g6-pCMCiKt#!CBPD z+Z)VW`WAQ%LeSY|{9@<@keAmBx$jB9-n>iz9Gdvped#Bk&U!$?5Czp&dofSnE$}B& zWZ@3ey)P#PQG~T%w!-_D0h4ag0lHLJ8XQLR2*(&)C0fB1AtV9$KvQW0qH}3qy&K1_ zib;L%y|doi1tkf^=-1r~NXahSR~iN8zz&jVF?F%gAJU$@tTExpP!Obx;1gn<$c*_3 zD%gP|8N`y=RupLH*zIE?vL#4MRn1ozt}HZK;s?=M&BgU*ckRW(7R|JP4hUgCQc3&>hp{MxGU2g($ch_4=8}n;0F6#ruVr-7j*>=ph5p5^u6l;jLEuv z|Gu!fsQ`2x0EohhCu-<7RgkV@H4~2L-jKC;$bGBZ6jA;M3p9o#mz8++K&xIhV-7SY zS~VG~Wa}~RZtys%q&EmHR~@50EgwO}hiQk^d{FsC3?Bp~T^J{aS1wUE4aeJmFv6|Q z1*NqtwRh4LotI^0uTCr=L^Ps^Zp5=%fljb@p#J)cU8oar*q0F}eaik%5-u{kB*saW zY{pWa882dQNU_bvp6bCfsL`YovHgD*6W%61FOFAuuBHd*>OBV6m3q+?l)LL6LMG=_P9`_yMqo!{U z#^bG5#9K4TXX~slzzAM}=lDNcKR-YJ27rA}Yb!k?Kt=rb7Vz^3me~3N?qt)HLSRWR z@8{CW+U|#pDF(nP1?(EP1ZEaMITr(DZ0n1c@Yb{bOne>eaIf3;hsV>Prvr6FT}5*% zzU?@n7*&%ma8tsV1ZFqG&O@Id57MNR)gxA~)?V=5*0Fe(>bglg zp!1*Kj>K)V#cPMq%D}fgq#tfVEZvrO;GB1tmW^ey+NDu%g4Vxe8~mD&Az(J}3m^V;+jk=oiNqc8=H zU1%4@NET=6Z?fWkFJA55UmPBBmhPjetM+~nJQ?wJUn^~GS?o?@PH8frRt$ML6$52o z&xsSKX7;6vo4vSC#N*BakY|Ky@e#!Ok+`CL(||J})xf*_@ckoKoA}Q~=MF}$us1Pb z1K7JG{C(^00JG?F&AKoWOi=)Sy`lZj=hDHzv^o=(pkvfV^@8K*87J50OPZS~Q)N>{ z7fCbg;Urbpc*U`nB4c?_bQ*IhFPlLSw}=9Ia8r!qs7ZW7OGC34#GB4w0Y#hfAK_8b zq`4i6RIx*l z!Lp#-2@2+VjWgWD-}A;C>wT3HRys|qMhXP&J4J#k+xu3iCGv>q>yLrhaihJ-jEcB1 zrvs6V7~Ee&@%5RQ)gxRH1+j>ml=8R*XA0>nH~e-qkF~dFUV5F-`7SyGY*r3j(v@|i zH0iKHLqlmW;XQLk6}UEsUba8{KNaNM_dDuOndGw{SZs10!Lz&q#>b-;DZnz#(GPI< zpcQ?XpFdCaH=QjhP7%c16YHNDcSygNE38HsbdYb!`TmPQW`ivxgH~O3`pz*V?-`*L zmJyCukWL!DEHvlja43F=f5I)UnC6Xpn|MjSaDjcu-}~q$+T|5x@huD!xqLAS-RxN7H#%? zGYj6*u7`@Jxu-$S$6S9_KJGF>u`P}~b`ZbW`{kA_s&l~0cGQ@jwnSK{bDEr zi>K%EsDFVi*KB}492g^OPYb@-eY)^~_0WJ12p31+O{MA`Fh?JsoB$^CI4GlSzsCb| zl7FxOZ-l7xZvz5wr0B}YF?$S_Y&t<^`8J^YUUblJe5byyP84qmCFR~#E-mY}^jE?! zUQCPRGYLv6G07?>vtkm7;A!YYqTpunQyPO3aciG60}RdbPE?HMcdaiz^WH3Hi(0J& zNnMmQ*jn+NH6*qgCsozbZLAf-`|WjE8DN=43B9Db+|%uB*!wsp6VvN-8mT6m3Ijo zkpkF0_9lR;@r|an1-bjqU0GE?oQ04gkyf;3+k^Mob=%N&!=Ngd8QO3tqJhkx_ebDZ z`<&BhD-l8Ibtx9;U7x2SD1&z@_RSGI)}cK=tyx74DTgb=R{S=hLJ;`RoIhkIBw1sVD&ZV z6G-x|;pCyumQ%7gh4LMAp}!?>aF&TDd?TGFNn2D5olVCrYY*~vAY3qo^TdE+j_I@T zW0o{L`WGo|tj9H(1r}T&Bk}`LeVeuH)Qaq0P&~h2XCHnoV@dO}SSNNv*<=`FXf0X> zR({uzOia8dJdODal|%Ko-9_M3WUwpU$$kUezYAzB&{zfpf2&t#fXsmz;Gp9aG7|VqpZtGmozH+J-`_e&Ah>@7 zSUX!DfkO5-5NbI-JF~S2MKb>D5@Zbfarx2zd|&58CZAhlAl+W1NDEDP>uUYehJfe$ zMt^tce~HgIsc}anO~WBbDi$+;Lm^}EIfm1u?E|mncfyS7n8D3gtu@}=8bl!bKqhjcXUgRs zAH0ZT9#=VZ3T~CUe9X6QUJUm>^n^!(PWx=SgH>th*rX(-8 zpU#kWz1Yucf$yU2Cn@Zw$d>!MPkrr|>pIO7R_k|e8x4rRp10C{zbS(N=i`Qd=kwDv z56r)(LT10SwKz%H;bKIjKIK~U)X;qNnPx0B`}2&w1r`P1h8@wZu}rCB()51O&pcI*!Wsa6ej;uGQ0N2E;mrdnMGS0hd>E;+ z&j3dJ*Aac@c|?uVb8yX%l&Vb9k^uVM)Gy_h0bG6F%Ux3j6-l6o;8M7}7XFy~LX=Qe z|AmMDesNGuaEW2+irf^IE!ab!HuJ8TEH~Yt+Z^(nXeRU1-xkjp&{tD(2wV@ zPg9P6RxR@@>AQ96wafs|EI28mnD2HeFtq|OcB}yF1%L!k0*rnz;B*B@(t*Hcga(T# zeE-)yk~q6bWFS1n%?$8zO!gdQX?Gs@{uE(gx)A(b0dH0q`NtZlpF8G}7P2 zT7=prhbeWVLV?y3jUnn9+c-nk@D#0`5i09>-YT(?KjaLKhBjJ;ADD$^YO|}%0aYJs z9Lr}xTP}lU1{;^yc#=TCg;p@eIs&zR4DkgGt+j)k{E+e|Z`)gmz@w@^j#hUeSx*Kx zT-a@@;H;oxGRYcZGMQOVnfOkPvjWml7AnU&>vT7%G}N^J3tF$Yk)hPcdwg;tJQ8&< zudpnfSSDJChxtLKiQ9j}JFsQHkVz_W94RVRCLg&8nOM(t1ESg%jnSkn4K191<}Ftm zLnD>iJqX*NgmiabyXe9wPBGxSUF}=TIbyM%rywA+U^liy z?-vEX`KN_y!_%WSe0Ik_)CR|_NN=Gwq*Iizj1WXgf%b?gW+ZB5aw=gWNsi5_r;aTmF9DqquSutZjd1Sy_03n0 z-S*HZJd7o=zB~dm5Zf5^`-grkcD#Hv=?IcF^FREhAc+y_lFSE1b8Q{QLE&2XgXI$U zp-|Gb=2>xUl}^D!=3Ob1<&^MkD<_gc@yV%W^dDob29REK^jSE>5>p>k#Ftac>iO32OQ|| z#LctUv+jA%dCg48qN+rPtMC{8sNsS*@I#fNzZKbWI(HI81P`)3UU$4>2i7(upo#N> zFEcIR8x$KS{F$>LY5<#69g$2(;&It>q=&(Gt1@dEt^5?E3}cNdXATk&)oKObWgN)bzl44@bk9llN|p)c@4 ziMp;%8g&$E$e!4%V@}8n`{QD$5kYZUTYN{sY2VS2PKnztJO)4B zoWYhhSWm)v;Z)#fXGo>3s~&KVs`vq)zu0ZUUHA!K+Cbzx{Hu%t*4RmUw20u>M%2C; zx3T0TLcWT>>{w#R->LEt;x#pQ9U5x)=3Dt95-5%0mU@+cG|<`YecTEUXMs!#52WN{}U zQ%W?t+lbvZN@*1h(wOrTLNyLZDzH3*`@-^Y!XZALgaG7Z<(#BZ6ORd+6&bw zI8uBKC4A|lV|*n~(JLzpBrjqzSMg>?!hT9$JB#87O?Pmx26+uB8tO23hVu_o#T=G- zDOHvFaEKON(E6tvMB-PfQNtHhlXC~~c&h0+y0C0Jr*I9Jf%^$I){DWNQ|u4u^2QWo0xc9 z>MBi*;}$)v{Zb9<(7iJVqtIGtP&rN}F5N329mPVmYQ51TEjf@QV{_)MW|E-{MY@bK z@0Y|RrnCHi8C9X0NAXp1<>GbAe(`4=Gl8U$KQ&|ZRp3_o8>2jbBcf94{?*}C85d$) z%Db6g)09|+MUNdPRg=M{byM7kIQ)PdceboT@64xW>y_%xnY!xSd`8F<@WXAQ2u-<$;cQSQ^JozPy24paJgHXw*)zwJl)b9;N0@V+w? z-1~wy|J}4aO9HE zPLjW+!Fyg-b?neHb-a@QnwJ>fk=B=)TOd5jK=4?-Nkm$RX0RJ)+IN=u`?P^ICm!a5 zmsKVzjsPl8fS(e%W#L?fA~rSeJH;0^?Xo#sJPos!Ll2fK@Z5YH%+Ys6`h&Fa{5gsA z-R8->zRdmxq4d=Pm=!Tps4#8(eBE4B=&+r};`|%lPRx-xnfEV0#${3a@=5j2J*@R( zpBMz{%vy~8ydYhu*5PP3`!G|cLY7>xJos5Y`6YwG4gZ!{R%nq5G0C%%(iGbLUoC=~ z4lVk{^$+KUT1>s*Dgm7&7mZwtHJRSdYbj`z>jFn$&t$fc$KCdep3#p1e`ra6*nlk4 zZWx~W7Vya*0Ov>{NPB~r6X@is1<}gCVc(J&uQ;E7bCLmg`DyD;0u;My3@UK&QR7X# zP8;PLnF5_!FFM}#8XvBHR)_;v%6I-QU zMZ}8{hCR0ZaJ!s;n<-&rC&t$oE@To0C83ADorb}Bo zEplFu`1{nqg;vGyF)Udx)_){apXX_w^UrS^Fjq$jG*g^y5~<7#-teczN|$-nWRfKc z7sSU4kjh8KR&i$yC>cfKK96a3y<)yZe{U~)Vkk?}W9-&K$H4L;kx2AM;8nro9mx=; zXra=D<#`kFPiwNIbIz}%jt9m)>c8;b$2-L?9Zal42x+^Lu81qQY$XzlsW)0$rXwZL z!Atb-cu&#N{d}qT5L883>iOrDAhb^e{ec8rgi=C5NYIt)B~VB+0bg{k*q=2FyYs~4 z+!9cmvwe@t!EZOBvZg%-no5ISy^}S>GobN3fH?l--Zs&+WV-rYbeJE<@5sjc25U^) zEsRQ(a+V*HI{?*kOeB?K^opjS^&i3R{+sZ>q<+|WI6XM;_g8fT2AbVIak6ZuaxRmF z$5_n`)7vL-o6YPWtVlJv(l)&BqM1mgPIcvMIya2&(fjFbiqFnidqqowZ9$M&pn(4% zu^N>{I}^8hc#eMSm_3K|GLiBy{C5ydg0SwZW5D^O=1RvNGo1909{!fcov3UOy}b7* zEK$T0SD&rTMiXDp#)p*XoD`a?13&wqblkh?@=LPJGiI+(@jOHLyWv|D%c|RmuqsR? z2kqO%`C=hYgGHQ=-#0AKUVY^frQr#Ykvz`)7I%B3Us?U?z1f>SrJxF3SGsn0+--;V*i@zh!e$iPx%RZUOA=&na@Qnu#<^`+sYDH z0jAN8!JoTpg!8Z)FW4+%CbDv_8faIdQF7v1(-luM((ViPW%y~7C2)QX8%`0Xm@(L& zXy9Bs>}#Iy{xZ}j;ZB_`9NvyXRR8L$N(=)-LId+tmbi)L^)z)Oj5dj7?LlH1%b2XT zo7`al#Yz)$lW1812{8wcOXp;iOcs^((R&50Iy)Iy-!atw4Oh#*g&55KhM0mV52o>} zL96omAKqLREw2$x)5o7+PWTK!i8%L}{_shg$7&cIY8}M{ehw(Q`DXpS8RXrDkDcvQ_BSAt4T?XW4ghlf z{g3H@;aAL=r)eM_lk3~F|K zJRLMzCsik88Cg|pqCJje-#5~-HW$z_J*w5eJ z@%++M?K!x^TYSsS?4nW{Fc6UBQbcrrm>D!Hut*`BX~Ldy^D5S}_DH}A9ZrYT4YQ17 zgk-E z)3MFSM7Nk(^!MKgvX9=fOD~rlMGiAd1oMJT>4i<6SsGm78XASf-hNHhIv$TAxpEQy z^xh6XzEJ!7*$bHjjl8n9u#1#3-d9n7LWxKiX=1gb1l20%W_yqft&dQwIZFQoebKzF zASiyU)Ysi|#yedlz(Uhl`1T@&0w)-~Qmc!rQL1@vXsqeB;JwAB<)DwU=EH#Tpqxr1 z8rQkr*l5|v6ra19lE1*&mjGy%L>pm3Z=lzu3QJ(-yo~O#Egu z1-e}L{A#~sm*ExnNVs}`=h$J6MDL{U@_4U!HNO%%T-ldMgeEuEr^o!!jZcFAEaYTs zwQb!O&iF6RGT_scrG$vO|CeNm$H;l^4Y2kHJx$5bxH0f9Wdr#654(E{0WSR> z&ri1?XJNz(s!ojdW6l2300MDy8*(lXle;g%IqQI_sr$Ox^%A&;U$z;d4s$Sq!YP(e zSq&_(KD$`Ia^7<8u$WL_DUN|{Xj3R&O{8x0q7oifc!TR;*~6wtMMW)}LR?@KB8;Oq zKK9eM4$F6`(w-%}U>oGviDw40%_z@oSFwNCACFSWy%Vy`~$d5Wj2CmYBZBnw{ zRm=0(0s~rhr8qHNKVMEQy^XAodP!pgR#q=CGVs3sJG!3{H#LzWKzwLya*rIaVMvl* z_)*D+JlAySp-r_=JqJ0ZmO&;5S+{5-)fy2tX~id#q#NV*&B&jNp%u6e4~bD?Xh=ot zO;+y*Z-O2cI}OiFxc=TRk}8nIHBzqTVyolAdPOBmdvr=6Twwpg+v03--|tKodJsFP zbGx#Bx4!>z_th_W-tDkD@NNJuivcoVUNrW*9O8Q-hsMLef`iOsf*ZQfGzP&`=$|j! zl)gc2K)@0dec*FiH-KsEf4z`0ZbRX{1p_b?wQ2i;{6c;1#F!%JpmF-);gdus$$jkV zsY42vEao+>z(0bvf1G1^McNTl-!7(da8`fCe7@nhe&1~`P`{^L+ohRk zvvotW_ca>5`Y7lc_e%HtzG66iS6|QA;c~JPk}7o>sSvO+kIq#Wk|^~virtwH&NtMb zuC%dfguj2Dy``^a6 zy=gALZ;iy~?CUzYrBch@#OW<_kFQB#4S$T^NQ@#MbMcY*INHRjBtPwl%>VUQN>ANL z+`_I0f%WD%pFg0Nra-?nAd#J5Ss#TRVp%_$`~24q0y5heOs`_VO?GZ+X#{MaktCZ8 z_YMm`+OD;_K`&B(Yy^7&;CtC$zEHHcw?pmG0H*2apf2+ zL2+-q+pnTgZ0RXPZ0Nd#p~gmo?Ct6|O3!2i!FO#@S=`3@O$FVsJ06F7WHoZ~I8+nf ze7B61HhfsqvC{qF>KlV3T@*O4b`wB@)p@xOkyys8URor6i)nR*rm7IR?<51+J9Kw! zwSa=A$fr#3e|Jw zf%y2{rmKJktXxcjjq}}=kVlAW&b287BYBC+=&7!CGBfK(a9&n;<4S*k!grytV>qwa zH5Zp1c?64Yv8(Ds*JVX5dB2y6N9`K}z9*mSYuV1e+OTMHc#LN6Amat2pUMm6#JtIC2;}Z{v>C> zC0ZEvpPL)^3GR`%lYC21EZ6Tb)>@#9NST>!))fpxrBN@I(^k+OS->MRc7Q{92f1?@ zGaRQnF{Y5UZc4i`G`UpxyOT7vWEp_Nf|VmgT(10@79)TBbpq|TpAH-v3RF1q_!JQ_ zIy{S|ee#Ui2BvuQ$z!8YB#^a7JYR&kN9~i%%8p>CRcO$S^mWk6A>fTZEHp_$mpph;P2j z@Qw7jjL!75)PZc(S%+xeV42Sd5tR=UwzLu!IvuS3QfP#exvY&IxvJP^rk3=$|Hn^| z#d9v7&$zR~@x5Ck)iy&#-^eoMi=44a#b4L^gz`Imk-yeP3E&M?H#&&Bm$>(i#^Vr7 zBYp9dgf-Rv(eV@GCyLz8ZGBsDSczvJmmKJ*0gdR5v$qVMLGQ&)J?9PM9MIvg%D62F z(7QqW<^LyO)nJ2o#yt{x*1nH2<6X{qZ@X6%XP|*ihJbk-MI&`&18)5Gd(`QV##nyS z`EHk3RC?zrYh)2B%4yt;W47XC)3H^ETH8cpGBI3f4;EaChj>??|2lJ%r@LsFdvq9B z>c(3>8<<#fOsx+Mr`#z%ku|=jSrPeqV6loLzXAWn<2Od!*~}qacr%L&9Pa2`%*PP> zFVYG>W--rMUVb^_Ei+Hl|L!Ff>+ZC2>&GPMkZgP;Stj>^nat9J%YzT;BT0?U{Ertj z^xE-?BZksUvEK`tUy`5d__ub)yknygk4$vuN38Ij^k~bJ{4puRUl2H+&+%$qk>k@- zC-;wcQPr_3bU`SzOm0I}*Q-@MUk1eG>GY5?Sv&vP2_Yi-C;XLA`C&E8(k{$@`6t(Q zPGotAw^rU%Ca>We<^TDG;z$KR9tP@Mt6B$Ss0{GoLuj z6%44b%K1N>)@7;0Q#3gI4vYbVl3l~e8CNRkTQap%e=q}0*(leTg+aJVY)+na*&lmh zo>=sOQ5w#4h5c3hbcYuu(57so!lQ%kru>g`K!~d>Tu@g0(~g9MogHDxAm>43Kuo#65Ha#E27I@MWIuK zeN@|Zs`h@_w~**;87AiP<&Duh_3tGAqHK{Ze`}^VDAH!zq105L`IZE6S4o{8eK*H1 zlCn>bKE^jz3i-?zX*+C=k}LFSh*Cfxv;_smGvB6$Q^x+({bB&-Ri?$N8=Y?RQHwW* z4Zf6$vXWVDX}EldUf>sURucHcG=#jat{g6OpNl>u=p+KnY9g;pya-+xzl8z&EHE!a zaURed^w-C;4i9h-{tc9Ftf1=Vl^9@@2EKd|0P6sg_Zl#=0(R810CdMF@~aUKJ3{@06mZIJ^olVVBp?p2SAG<3~3z zW?Enn`5kBxa@z6?^=5fscUY5QfV21A*k|p^dm#c6mwx@I>gI5zb4~a<(IRwb{?3ZH zK*UFaKGo)rG>Ph0I4`vUgI5MF-Z?@(=ywdot zycYXf`(D4ol))Y-b1uAgz9SJ$g&AmFA|kJ!Ni@M;tYgJ9e)1NJGT>!ApKVOkS-chq z#)%T!+3Mu9s!D5QsrBGx0_qxnV@q1;pWskArwjy^uJwChr<-? zbx#5zd*`)QoFLH3`o*RBM}%~KFDOKqfEua690=D7qUQH&2jU|j0MWLaqynv}2Y23m zKmoB?X>@?L+B}5vOtQD}x+N2K`qDy!Q3R${asm`5f5MmXV`vxS6fGHh%2 z7?B4_b~!ORs*4raAQwz3IJ9~szpLmI=2xQn)z=#}RozW8NiGu5FaWb8Nn^{#O~w^T zaoh59piW*Z9g<)!%Us|+XK8pRGqXEsc$2C4H69c5+Lql&Vws`OdRD0yhqAI^;D*{I z+NqI%Qg$QCvykQ zJ^@1>_|2#S{R5cCw%ZQA>=aTyF%4j3F<7IO=z|03WBl2ZK%`zJu&tz~9v0wjA*Gi; zrdky1cKPhC(`XBdb-K?N`ZVWd7*7H%gm>vxv9knICK3?4wzXt9XoZS@y)aHD~fL z@(reOQ#3?p#4Em{Y>y*j;*OZ_{`F-+IM3HWoCK@c4d+TF52Nh2%O!Vv_}q;$NuQz; zopH-Kj$}jR>~=8xa$6UlUuuN1(AWbHDq*Km)MT;YV)yV$S9!3WtBVy+za3E`lizO- z^0f~=!m=$b1OB&t!AQfEH-G+cfsNzJYO1fJy?qqujCW`62X4+o_?%Lw%_k((1xsrYjp(jI37E|?9{Wf`M)YN=%%FN;_uzxEyCLFN6VFs@cc|#ZiIm zn-dDZHKCXl?nYGhkJHu^_4g_R_U9dl`jTt`>;qJb^1orV7D=usJjTqqCcTwEo0|BQ zSoqaXwAn?7{ynsYV2U-SL10z01#yD(D)leV-uGmIs_iSl0jvkWHjr#oEBnP`oMQ?$ z2p|Hq1${!r&V8_;1QRHw3d(DR5>B9hm&NZ=U|0>^xw9I&_zD@aJiQA^z`Wb>aGRsm z%o_Tpl9a5@l4YCDk0cPcbyMp{ z?v~!;pr&((cv+}~>tfU<-8ek_X*5a&FRZWT6P^n~sqvhDyNl3!BOkbE&SjsOeilBK z)$K1tH;=Qes_mN@LNh*JVi@Sccrw2tN&Y&senI0Q=h%1K$11IUK*D#T$$T_8vV0Nv z&F>2bBhzn$<{xdbChxDRPScAM%f}wzf+W?>_i!~s#cQ5(YMswd=XM}Zv<5I(e;<~5_G4IU>}xzQKoXxsQU0fZ zab9%e&y80-*5~nbJk1POD2WcJzv5Q-f3;i?9|nZIlacJ8mbDYb!5OVkpFeSk{9`N? zYf>pI%ebY~#$Jz8qPms7et2?AZ!1iGQ(F_+7gG;$3H^B_;~RHeGeVO`*odh7IN# zEqf`79W{(eC6zCw>I4aVz$YLn3|jst35(qLTUT1ZGL(m2jmUWz0+WIJ2^mF+s%@^# zZnp*=D7M?CGnMR z$y}r6$Lg-wreesXa#llRGs+;bb$Ny)MDhR`f_u1*e1Ar=_m9`FQJBO1?=T??9#`LC zB|(#b3tQcFhf3O#7W}RXcEXrGtl*vb7N={O%KjuAgBq;uL|vsXa0|mgpn#JCCeUeZ zRquQ42ry;h5U5cY1L)+vUAG&t;Hn8_yGEqoCNrq@gHSCL++hLgG@wMq7(mgX0BQ+k z-6a3&7Kne!0=f*e(I)5phR}!2OACmU`gJA`rh~iz13!CS_3K!-8M>{;8+!7EG=v)Q zi?~2EIKiB^O8cl>TKeune3)?lpWvQY*7Wn?wY0Urpt`2<+Mwl|C@L!})WU8bKMj8I z8Vl+_wQ4T67tPM+!axu5gY;_UF!?5aP$^ZE@Wjb<1qt-WVMG4FGU_o&&?=D-WU!hjWODyj?23M8cZd{hhR+Na z4XYFQUA$^QwUK4grCH2Y{T#2%CjXQjPjX2S88vfe6Rzr!)!GJv9HxA@&Zo>h@ULTT zoNt0VS+XrSGlhXF#QMafSYq%b5XaX0#B1vtV=~q2a$PQfjs{W$;=LaQv_ow;fjkzd ze=wSuzo8+5Dd7_~z?p^(ynP=Sx%#F9W5^N*7zOAk{4S(aGhd=;D?|6pz0{FpA<$YS z1#&NZ(D4xtdMG9;pXq@3Av;nC!rmcsePG1{0%Hd@=*WvJ)JTQPtebM zHi6Vp^2mdcswFRLDg`h8mwaym9K(;uoe4r)8Bab?70(-yi;dZ!;}*=Lpp%lHi!@6& zK1nvscBsBlo)4jl)F9)+4E*=iFik{QH$aMgt5};Tf=Jv?ki!3u76_gUk(sr7GA9Ub zBUhPrt(3`_WD2-afNIGo&^WXOR0HT9IUXEcZ~*Q30(`7hgV&Ve14IzPQ%&uP6X3-i*2bCE!ufv`fIWie*2iGGsR zv{01%Ea&@IZvE}i@*lh|B-#cS zjJL;wgX^4L7v7$bC`Z*5FWI6MG1fcUZcp761thgcVp3`AquR*JTop*kW=aK{CHvFI z#FM5_4OCR56B-tNp)5Fjd5(gC3(<+Ij=()_NGop^SyB7L3{j>9k6>p9Nfgb{#k=_V z`&Xn(WtMcSeU#QVl0OLWSEab)wKFmMfk^4R_7lhqY6E5 zP5d}n1XS~o*-^gUM9Iqdmq)Q16vtIV!Z+fp9CVJ|v37@J9!x6Bes{=AdB*7)2fm&| z6g!uuiMl02bEyv}by?PV41t7Hs;}3*87|LO5>OAs#kJ4;qw$#j5^=b$go2k`6owl~ zcIQkosEqtzE{G$RB(;S#-rc7<$r6{+0r9oAd2)eLr7|9l!(D5ODn^kevlVSZ8SWCL zoI00_SGBdv{g!e#`h+1-Z6Ev}+jh#1sx+0~1ROww&@`ejp{0t5htGp4{f^c7Rle=* zo{Dg_afeP^AiC@U^60-e*KAjEGuhb^&z_x+XOuW{I5izL7*_{9F<|w&*!~{0Kz+8xU?f|9poImKo7V$_8`+;FV?h=Wx~=3KIs9w{8(&(Z2+0z&UM-GIM-blnHi- zv2%N_^7nfRc>0&}UtTcHp=Dhb-`psav+q8f!Vu917hmeW;(h$rUlL~~N&|{fnG!Hq#yJW|6 z`9lNAtHvwvC&J#HCrWt?QYPj}kF-C5pwc@zANn9ec&Y&A;tmE=?7<%FF#P^q<&J

OA@`yOe2eZaDu| z$xlT{6e}Zkik23+c5;e*;+LA1XEy%f#&zE$&%^6ADj!KsC)Px~Y z--50X5JI%2^(*TuBRA~u@n;J(qv$^}=^9sO;S3ybB&W8@s(#ld8vg#J>0?>1?0(~} zjAL&ZX4rQ`I_i|#gFL+h<|v!_7-8%n7?E$inxP-vux2Z?A<8=2in^1MB?O3}smUw6 zjxQizB}gM61%etSdb(G>U0yae$`O+7&ic}6i7F}F^V+o?c2i2z{y1{OnZ}o0Px^`0tfYHq1$n@f4ESB z0T`~iY$u4=Ue`f_#%QECaG|{E5{=w94T|W5kYFq{Wc^CXE@#RsWx2Hw-Ck4kKd!?z zBO2V}bj}2M%hx5<@r2Uv#9g>a&_4^?V8Hg1rn_?Jv%EY*DsS*UwCBHui7Kms)X$gb zu4oli8H}$>wrP}#J0h)&X7Z_zvL3VWwxXz$?|$!Ny!3k#dzq+dd%W#ci?<6Ev z3cpRYDy@=b`}13O9?I>5e5_zkTAcNSUrCqw`dJvY+G)T~&K9kl8JlD*E3_w%v2^Wo zMw%Cu21XMahvfNEJRfBc#4>_iJ<7d}s52GX=p<3gdLL;}sq>pvjfb~q3PtMnU4mx| zgEazI%!Z(S&R`S+Hm)78g&qc?e>}*kt?a^y0-x90G3L@O&=NQdd@^B|J0oyGZi%0T zy;uO@Ap&?_ezYnYd(O&v17VNb=l5>fS4HO^9JtTkq8Bp*y@8uZ&Mi6qQ$+ZOJr4EC z$-NQUW6D_*@wypD;e2hS!6bAtxe@Sx_}$1R$y1C!S7{7n>UT7gzDKuD$1pgWv)wZM z&56GdVEG|Ei2@}rq*J@Cq?NRYBU3vx11n0^uvAdKeCx4tMjNSTDv4pIH5O%fc!@B* zfi10QFYh+!p>Ngl1}Pc_|MbI^&VGh8!##V*X~|5)V0SDz)alGR57y20j$X!)hU-3hH=%l5m$8ie zv_xsBR#76#-!|7`Vv23`+t}n>x(K0@`tqA;1{V!7s!8vPh`x;4u-#asl$#Y%6U*x~ zshe0G*FsO-(7O#EKu7)E2NI9>%)kPXg~4rFJdn}{OQ!r|WGA=N@wmZjF@rK@r{l9w zyQ&@=N5L07gwDsHL|e8ym*g^x!A%R3jdh+*&N=wGft|E zk~h(a<3qTEG~)`x>5UBTjSnGjhuCrB2$fX(HDowngl4Nqdbz-1+ved|FSgrN7Pkx2 zUAf(9?s$$XuJDJmpHaC=yoh{91Bc6b!&qXbPO+FQO#Z%d8xrVas5;IL3CEq8MZp)* z-ct9qB>yw^i>2)qCB z{h-j4k5nE1{>VWEzv1&9F5o(ARkK{TN~B_e1z@0}fCqVo%m@I<9Ggf=R5L={+u}f+ zoe1R0a@-PI&)QF*z&A^<4E6qZe{cyBxsXW8H;<&s%$KRPeh5G*PX+%Ba2m9#_3N#_ zC8e3rL2rMKtk*D-8N*g~>zc$CL|pLNebeJEN{@swipgVoHhY==Nx4|_L3E#r6q|Kb z3u<-=j#?gvd#fqAvKi3h1)+V!PAd0ek9l$L2kJg7Hk z+twx8$G$?_PC1$ClWOEV@z26w3XV(igF|(XX;W4CrF#m2zu1q^VII*j zJ-HD;fPyBuSfEAafBlLFJ>{X@Upq9HKrQ`Q5X&JA243hGho>Qmv-x!PzMAJv>rX_$ zt0o2`8Y5&2bZL(i$i;HKJAJL1#b0gnBAWe!#*BhB-9X>i>sZvmj83IJyg_3>)(=J) zCIs0%q)AvrKenx41lhlY2q~c*h;LwidmHC=?eObOG|%OH1Pzu)b=n=ZA!1S7oUUF| zBN04Xpj8&^P~!)bx`KAX?bc^=gocVx1mq!{*aRF&g>;@l7|Wy2H;-#=yFU~fEq#+n z=83j0PGF6E(-d42@o})(tZH!FUrkeQxqBbm4 zf0(_?qVxH8OnO=E&J#~`h#q-JXyJ?V zRuUa=LNz4CYNsNP9M|9?rwm7om%2CCxZkV-#xAx|^CdY-PXC1`zMW{w%xFa8D+SA57-s~Rv<*mY2 z3#WQgPBszU3DAd(m3*q!LjH`)7?Xj#Zr6R)B}CWEwq#fSesneM8_!A?qwYy1@;peX z4S-Ok5h+vw_S@-q&C}NZZmx;^pIQQt@y4vxAI$8>eEN;ousYoyG-hFkM3~&}qFnD_xh$(-8{9cBrp%}} z{Z-^T%Kxxx7Puk9@|h`|N^%0gDs@xLFtIVe zjW*m0dgityRx1XFDR>`x0wP2OX_}A}>Vi5H+Bzp@f6g?D6MO^xR!Fo_hyFt)kw#9wQXWC;#3ccMIY3+-G9)+!Kg6qB+^8 zRf)*)0DoctD6FD^N-aRxH=v}3hr;q8z3~DYAQ)hmW7g@-4Y8r~04OD^-BIA;jG~=c zUw0XM0zUSSluw5W>~V&wMm2PI6TH2vPI_gPMu8Y)zIEa_y<_A^ZV0w#p-(K{f8oW@#f(}IJf&X z!C~B&VayBFOWTvx?}YuM1qE1f)xK7n=-tCc!4z8o&C`*9+%1q+(+DktvrcRXcj6xf zH@r`bc_DF0CO>k%Gf^h9lI$TQMXT>(mgSc8x=`~yIcyiHF7beTc^SD6`71t! zV2I~iH{LU9&5vv)Ox)^eH+{3}FKLjNia)6DTH2~e7)wc(>(%U^V?i=*7>OYEw`dHP zF>&>3M$I#7Z9RRNRyj)LoKCDfN+}uZZgiTzLQ~ibb=ye8l@QEB9=__b_ zQV>9sUflv7gd4DYYylKG*9g=l%4X93(v34*&8X!Kxw_c3t}&2spqzsUph9m0`T1fa zIlS2zd(Dp0d|!1d{6&zD8B$D+q;4!d(hy>$)7*YW$Cm5Z;G3TtC9jSfx?l>v&L@|Y zs1YNn_}la<3)OOYIademxsjoRC_rRHQC1{ie70&T{43h8?d+e~aakcIQ zY|7_#R7id-j^kn3r-Fpwep>R6KgN!ZBif~_T}_6-TS!kw*J$;&qEvM0s<*x1L8V6i z%xWWhUE!!;RN7!Zc_8&6p2jmYm^1-i>4)^nuM094W&E_sAe7*OFt_X_!mKO|bR7s? zpYFq(V#=Zo{>Nh2@5&krnY#Pr2l6h&DGRO>cl0a!%&y5nNvVvYJ$GFaV%9LWPfNtu z*vlSSVs6WW1DT8Wz#$0BnEe)3SR~m;+oYh*0f>vdwi8?ybI>LWFnRO}uC^!}aUc`M z_L9FZ%1zh)h45|WXN1FzcdPX{Q8*_}oV{DxgNRps!ns<`0f%C%0D9u49t)NdfnVKtEN8Nga z^Hlo=GGS$5hy1j@rNg=nqrt+&Fgy0Q?=1|=U=RK(s>q1ZhFoq~8`-U?FjIq>&l6h` zU;3sB>7iZkQ8=QUu40Iv$ch3n>T+KDN|;~XazcHe-UR}0Tu_gTF!zr-naJQKw$GV666yO9o*%9(4jrEVX^*j--mL=Y}IC(wq~1hK3k3?d4*57 z&v)+?temIOy#Fo;|MOX#8WrH~K0Y}a07NFEzhK++H9-P6X^D7k(V+?sV3b$@D)Z>U zRUlecT1J85P|?d_+B%QUdtDlM#}Ljh7^8Y7JXUxPO46PfPYEyleo{<3$BzYN>!oNK z(4*PcZSbAd-BwjHsC4MVYcfYyrFj!!Bhk@6)7J1J8cuIJ=+^xuycHG;2wv4D#g?UN zbVBf0E8Xp1aWiuq@!w0(3ZhccN=k5WMP?{do7UbyXumT&7xsdW4@m?`XV^t z=P@dE3cSIYURqU95?8E?B=)hXgXY9R@*7gsCpJqH+faTXYuzG?zXAxR>8jF2PtqLZ zB%w{G*G+Us1_JzM#|ZIg3s$gbWw^y|C=NW*q2HH_$W*#|!jo(Gi&6{j$erKNZL)89 zFsk($Rl=!m5DIoG3HN~k#bV+!cPmH{LWj4!(o!U7@|q&x&I0JO2q5}42s#99{+{&w zV+{ae!cI}LdOs+$h!VS5h68PZeKzSP5z~JAyp)%94%v=;d5hqs88c3UeY&>`HoT$5(x7M5-g+xfWvJPta}N>$*-8rKVf1e3BQUZQENIMI1_jQo>7 zv?FzJ(%~|n+xwdDGK!Y)~9#or^~~8Ac}*R zs*DgW4Fd%QgZ7~yfQPUGVyl#q5h?Vd4w@LRT0u`$9az9XD@WUmF#)7*2$YpTg{&ub zXOgoXO7GupTYrG;ib53--F6%fSJ)v)vi%+ALk2k}%YMcBqk&RCvp_I`Y9^j;4Oi^P z^*8JLOv-6P^agXD-CMHGkE*ZD#DjURPN?s(j^Zj;8HRPal&XuvTEB%ZzS_>dSQ|Nj zQ0}}e43|gylrpARJYQZg4`F#kfGK#lmX80eQC_=3CPY{XO-tL06q<{NoND}^X`!9!WgLdK4-w?d|E6=Bi2<*`sbetL0A zppVBFg*teRC+GMjvn{{R*w);bZTS0#;EU%e=`TD8tO|y4O^s>jl`dJ*G6*Hr`m6n3 ze<;97f$DO%*b?OGUwPjJ&8S27OuzvO$jgM#Xe;D(z5L_D0~gTxfRjD{HJc^qmhlGh zcF>HWoDB*cp!d{gLVk?ge~rFUEk8&EZ|uF|Om$iRm7odNy>L=L4Oer|HISUv_|Cc~ zwAa2@WFWy`k)@_gbuh1}jr(hf>xkDv4q0Eo<4BQBoo4l6TNyEH2f=k7d-Dq`qot}J z_&8EvACBOy=g8$V&*Zy@FjDSTD@AZJ(Xn@uePPhJq0RGr5dwU__}#% zj&}Lm)G5AzDXlQUD}=TldncWsJq>*9p?6lgcvnJ+u*<%B1^^Jjgs;a1V})O6B1#t| zUGJ74rJ~0o#iENste{IpV2v-0Y;m(|h?q8sOfxmQs#-QPBDxfir-(dFk{-am#YNG7 zW`z&Ki-q{b54ZJz0uZUKxuJld{mHCIkokR~tvv6@_xIFJVt~D6E#&7wq}BdJ2KcPI z!+{pF1K2{tATSWeJ1vR_HpAy!&dXe6KD&uTU`Pr^q7bys*lTgd7TvSn3Z#H5Ig1v= zBEvx z4KDj3%TNVZU)qb+ss)jyBavFwutOj`v+IyO{oE&_NFE>N6ISCpX^Eb?5=pnb7l#I= zMO-TyY$9hO<{Qawey_Cl^=E;cJ59&Db8WIDS4_$| z=}Spl{;m9*6IYl)n2elSqw)*@#}!pVD75)A8206%R68B~{9*8Nk2O#YABHjBoK9wJa+Fho;m} zhtQ+=rUUlN*j4$T8iBCgw_@h=Wy3aWfaogt?`{VMad_Fi)z{MMSFhQe2xe0qF^^O?dnMV@N*6CPhyJj z3-aP-EwnQ3{jCTZ?aCNBw_(EHeL<9gFqUG8Ci)?=ze}$vyajIuJ?GIRF|niZXF~IA zzbAOo+AESLGKjmv7+Jc`{leqDv1fUTL3E{6Dp)(+5V)ZWcaP}Rq3jUFv$v)eBh+qD zQ}S$93Cd8BhDc@6iuUR<%p_!c3}grNny zi&Ri37UH`V6e6JpA0sH26VeOa@TaPWQHAw_{8bpJjIso!FZ@g3p}iLbWl?pWpsNfR zygqYT0C&LW|13_2$Hxy@vG5Q+CDtcL3&;oyzuG7Z7Op;`d;8Ss_@}wrjyseGTvam8wR9Hhy|xti!~Eq_fznPsUxQO`7{$tsmgoIy5mV78s$!UV#m>gO5-#`} zS>}^(2mNYSKK(}P=x>jB^B2{cMQ0BXA77Pgs360=J0vR)N+G*#`dBcLZh122rzA9+ z92`GSMX-qT$Z*^xTWGm^l@V&0*ASIFX6P^!=EGDUhkqxmgDhMF=K07W9)nw zc@UZn&W;JAZAxKWi5zNKs8p8AnwHUT3Rcgb3`ht{VuO)9&Q3H%@;~x<>#01vWF1^M zfBSN88$ErD@YppawAP0_)BabiCrP#9uu}%JQJK|#7euje=S`O`3$9LyqHZPZk}=#w z=0Yobu{H`=*#o4r6u|6)3_sLB4W4u@D2buYae)Q$pQ9o;7zPTS9R__2)Wx!9W|ZRh z-!uxq1?rWt+Ym#WqH`M+h$G1R>?vW4mW82aVXj}b z)?`1O*P!$^vt+Lxp*vp|g&(o~l*&~FM)ZbdxtJsBQ^l0Y9}`<;$Zj1cJN9pGC3MB6 z9dRz@${R1+xU*|X-B;o+G6qrV{AF|E z1W{9mi#5(MVyG}JIqjXdg`EBDs{_8idu#VWSU&%(2;Ti=vsWsWfpYSrR)m+As96}w zro5GsjjbgK675F}dX&$KA{Y=iF34x)MpxDE;;v}V`xBg#^d(}r7UcJxlEtto%JG%P z0B!+ox5|FM;RE%IHm^9cLY>qDkys<3dBIgK@E#bEz|7bRCyfmCDG7q2OQ2MdL5=Kp z{V%$MfNc!-qr^W!fGz}tcR@ICa(6As(ZquR^Y83Os`GZV0{zhJYdQ0wSng6~ybZ$% zd&^n41ufCL5bG{FC5O-~IJ}`Ad+K=egTZ@lN38pg zC?Bv!71h|BV4B7&jJT{2HF`eR1>44UsgTWAU+y40j{3Ywi&^-4u+~o}LP<&Udf0m- zA?hPL+J#FIiLJeKT#BA7ejv?_UsV2f-(tDCFJ`dj84*3XVT{PxZIzA-b4p`kyfSgP zv{Ki)w&Tp({`SQ2h)Psi)W$4%^<@vbsw)^o$r=%wFET|!-0M5B%;y5EBcku$cMF=U zdFaD){oE_IHjuJc9v05 zzVVwMy1PSZq)P!oV(9Mfp}R#Ih7geM?rs4YQbLfHP7&$uE(QP3{Lb##cYDsB%B%V6Bm@JsxFg;oN0nH_u!>;By~peo+3ufD zBForSuN{#NU5OSrU1g?|=3EY`M03F6vn(^ZIrONrw5}7ZpAt`aAF&;4={%lJ895*V zlLfoVT&SvwCsqo3)go1#hH1RX0^m?}2p8f`hs6noY&j=qm9VjsD-f3bVCdml{IR{x#0|A-Nk zvHY>9@|KvjUj~MK?W}HTeoOR=HkyeXGH?Fd%7iMJfT)*D7GWneJaFCm}kEHHar>=KIQKndAnF^lsQLR$oRXVo@qeQ>8oV5R#ycn73N3TJeIHK zzkL?@8CVg}>vow+_(qRiKnY;M=(4(Ses=tXiqB^G?j`;|=!QJrdXi#Mgmua-D>e_b znyrO%fM3!(GzZ|Z((Xo^@@zK)$v%*QXp%(D;*t_1IHv~KLyNt4UXp&aYm=X=9)&`c z!qWZXO_kSza58L`LK#;wnv%j9M9-|*XDB;=8nn$Slo~Un7?;?qP-Z-tCd);?4;g+` z`aU@#!W|fhUy1SyBRbFcefVDKFAhZfGsR>3FWSjyOZlXq7fsfQyp%H0*&u8^5pmmh zkNMUC%86EbED|3;+dSN8kF=w|!D>1R?}d|bFrD#H5M@SlD&vNSH{Zy~T77rgZ$a%7 z5G&b}MioZI&+_cv0oL?qO?SR;hkgW=p1$h3x?b%^+WyM1@pta7ed2T^@KGOWdOs`c ziweayv23q5(~(-=Q{RJs3YfPR(K_8!89lE|4)MeU1QiEF#Q$9c^EG-~p;%n^U<3%m zXdiu`TU9HW&y@-U0^0voD5*^#=ZI}p_)@<&AZ7S+oG-yUU`*bxnXkDbK=~zq3;wws zl}!PJvS>vaW0LM?L99Tl1cHCi;3h&~WrimDq%|m*j2tU3 z89`^&RZgm@7)-1_c!QY84MrAXq0fmhp+M90O1>PkBQVee1d z7Nl?79*Gu%g;h|QLP^YKd0Q5lhc}SuDns1Myn9yR@R#*wJL6h8_S4OK33PL@RZ@9- zv9NMK5*F{8uD#0o!RG3@bijiC-F)pGwq*}5>8qd(>9dn@zOe$Y1%ril&8l$oev6DO zAiEr|L^iGuU@SWtvw+rtp%D`>w_K6%vLDllyuAj!JF7Kj3UU{9`=QauRM|*7yXaxh zqvN4iq>`MxKj2Qv67$QuUQM2|o1S85WUTmZzT#2&C=%AI_{V2U_I$>G>OBK8mFL`;yb%IawXwh|x8`#>A`;NbdIvYI#(Tu;#Mkn~+F*>k0O$Ov+Ul3j3F zCCR~Y!C9Sqt+Xg_vj02AsqZ3rZtOY3Fq-JAYDtI14f=O~|F68jyu4$MK?R3owd{1P zgJfT|jMDpl)GbCntELvmCVYHp-yQhY4mm|PM~J7uH=a*#-y7}8->f*0z{aPp;yqSg zd1I%9zf~uc(4Vz0E4@{D&w;7J6rx#WhHq`;vIEK9MfWal!DM!+Bi=I#gBkUgM3yUv-l7 z8^ymEVYapcM~0t&BUN1sJ?50!S2uu=k0RmRpIvCOfxFRC?X*gwzX-5lsk1o<8%ak? za>XgUMtNN|`&)}x@(*n~RFy@&)^Th$&Jng1ltXf-4ekjemWe<+oY*-L!z>y<^Lpn4 zr0}MCppat#`CHJ;H?>K+!OBdxLA+_~NGLwY5rjO?&E?H+1_#VF@5%8rn;iNK1xm|7 zS(mLD{5D%;+#v!|C#N!)bmv=Rqiy^Dj-vvC+e_Tq#ndQLB+VPeY zxCHb7uA{g_yYV2uz@yX6fUkmrpI^gm4?$|)XyO>ZA6aBwF-bz z{h=%~zQkS;vBm_5%dQQk?qFV;v&QVl=2Y@+3-vedO8ilhLw0oi{s01KD4Lqb4{HPWCgYc{NY`Qb~mL_o(nR_8xBg_=_lHw&}EWPyKzKea@ zg;zAe$V{>-Ta2PK{YkPNPmfZ;WB8h~4hWE;aBSxivVx+R1kFE{I7p?6F+^$p_|865 z8;6VdUANT5X_FH5eiR-jY{PL!(2$@V5l-ZA)r^vEuS4p!rKHDrq=EyoKY0$hTO#6j z29MZggoC0BkX{~q$?pWlcH-4(YNU!(p0^vEo`xfG6KeGYGNfC{HZ*O{xU?Q9QSs*Z z41N;qJmdfy;f-@Z!;EC>oD0E~XiT97{+$B-!& zD4ZW~sQ{l;z*{~*c$@0h=PGN|!YEHH!=`4$j>>JHY`WPHDVW^7C9Pb|8)JcqUoNkz z`%_tGXIyEpjP^*^KG-PbZF=myt_Hd`dt&NBMZJ_|+A^Ku3;Y3r!oW;KLx~o*IRT1) z%>9ftK^fW6Tda$Xmz0V@T=+O11eCFd%UrZS>zVcLNAVx~GAmkz?wpulpb5>vD?y3o zicHh=;hnURfImDiGO`6P4|5W1dIs+V(h&7 z$WcF-`hTZ5b;uM>65vbH$IGXDp~#bfZj~RhuL{zWxOX{-Q+=P=9v46B$Effs)Z^a2(0FgZd5ja$}TX688I$K%;Fq{O^23`6A zNy~rx7hpXIE>iEEtI~4cNz-dtfThYdjiMk~$c}9fHUu7uXW#5z?BP@5y?mT0oyBD#b@j4eZCqfeYQ8SzUL+Nm{ z+Va)=m>*5=+jkhtewUVJa4ep-KU!M9!YyFN31$hLo0PP0?XuI!!XqkUe}Y; z&&=fI|Dn%_33D|1iEo7+1Vxng=%kh9amF3FuaXCM9_@RpAuD1L&C~S@TM?HXc+%44 z?d9gakWyI@?R$hwITaSLfQ2m1OV)ll>*@9R<1X31A`KXlf*UeIY2gu7=K%i0U=N;X z2t@S3XXOhQ0GDD5xXxvOqqeSfH5vdO#Qdkpnvxjl(0=>BwE$=vAIVI?|Hnkr4&#Eo zzd?)R%Ei;J2GWK~j2S_4!UzIqa}0`AB|kSue(kkuzldg1;%~g+U7MF}ON9h4Wn?LD z^1@nnm~vWvrHderJ6tHP@C7J^Uq8Ru!NcG`3`;<+G1QMtarP*Eqm(VR)jmQ&3FX!H z%cZ{)t1#)lZPWCtrp@zmV#1f@DE!dRzSV@$wj5dY9L|q^`R;hF5}Hb}jIOD)z%Ngj zLb9#1qL#eOLWGDSj91k|A0I{JfRXf?$l%c>AI-uhyHx5@yv6nWZq0K?eY)Hx&92PE z#^&8G{D1ELxKGPE{u`1^CHGNHqCfIQso-Yq4?nHR04(t)&GQD08z2mT^8w+c4%t}p z5THo61ZI|lK*TE?nz;^`0tZSJQUDzWd{MiRs-^l32~Pz2bhkzWFDcvHx6T2Bd=;$Q zwJ|6ZbY5d7QO4Vi46Im))}El;QDlP>qlm8Vg$Vpy{kT;dcj$yd=(B!^V^P zqv;)XTC+WenvyCMGuhbFy@N#yj~81NADQmh{RUY%VXYO_YS1{?7I#treWsovXLJIO z2{iOM+~%b8J43>@`@XvgKH}R;`Sryf7vn*oQPr*04*>$0Hvv!3H^73B0OVJZ0Qf&@ z;H(S>KynyBTJ`-F&A%uB$N>S|?tmX56L8D_H}n0wE13D(78m{sfH;>DI971r12#DF z2CjG^ya~Mj3zP$%1zI%pie>a=(J!?=4{&Fvs?&dB@Jh54?KPd!Ww9n`K}3ExD~Q69 zEGM*|bjHBR@rbf*eGrQjN^kWh^-Zm8g%(5auD_-gbRz|V{4QT+hDVmXHm<7mz%T9% zYo4dQrr#IYsBQi^@kgz2<$p=3hEf5$3F{n~m znP`tdhp`v2n|nn=45|bJW$P?1!QF+)$gSRd=;<8MvFU_I{3MLIAYL=|!-vZHK0g%S zSNE+`&ry8o_;vfLkH23Dvf)OZF8K~Eq~RVlm~c$x@+#BV>&oIUN>tmeS!X%er-k=G zD%2#=k6X=vm1CvF{4W_Rh3nOYg$Mxgx`i)E8%+;)c4-iA0Kp~F_{ebbE@=I{A5V@e z15P@-0FDH6Adu+oY1?gt>Ja!09RsO8LIK6x`_L)9E;&V%66*<3=Ex zf8)w_6l$~#7!4>NnR~`|_%*y4+ucUyc*jS?9}J9qka0lGdvr5zCv~+M>Jj8OML2a; zqLvl8*_WJ6S1q*e4fV1K&MSnI7czVWX6}@~z$TlDQbs-|OIW2`Y7NVuUvI=l)E6Z1 zmO4bPdnS(WH1Bhk@-cp5$4u))0!8I6dyehgh|#37HJUDtIQph$<|39Gn^FuQKgZFU z!(#4W-(cJE(+gEO)B~&~j{fx9Ivi0Bm6oP^70Wdb{hnIcKos!7rcgq--Vetke_ z%%a0-H%NRWonkZWEJ!|dO?yv;g6?Kbh;Ib(`@1K4_Azn9S5?oK{G&{a4RNSHGWq*#w@5FGS=W6OcErbtDVCgdVmflc*_^F69G~tI{>m z^(9GOYb|&cM*8vFNVDiVRGB;+kVIqRvmqtwG#%FsVv{pv&qHW2oktJ4)U`d?3((Z3 zP8RqamyI#!0#OSIOTbTSBA4i(C|dO=F4ix;3L_tDkifoqh#oe+nCMY{^QcZuPCx8F zP4i`9IYShXzR#f^exFSW%96*)QP5#?Dypfvd9Cmd8-E&vpCg)Kvln=^@Y@39wrAqY zVS@Sfsf7l4vDP3A{vMsL*4aNk{;$jh`P)+nt zLOI8*DZ!Pm3qMY*5X7YJNrvS$a3TM#a!~Z@Cc2%M^RIHmh5EmsN=n)l}T>+%{=jT z#IcM;vKQ92VX-@|X|{+D2N=|>{;K&68mB_yVQR%0FBX->r(PT$NIU8jJ%fQ!TWVeD zy)1(bR!Q;;#+ww^fMh785<)r8QN;thIm-a4%0dM4c9!=jf-j%EyC;e)@%A^m&l+=6 zToa86#nJOjtm&*(Y6$X*6etK1zNB$h`XFgg7We+f#y4i|Ki0&OJg`PYjl)QW@e46U zQL|z7gj&Tf@e=)_idF18(D7R_lN0S-VNbTh1ghR#`2^NbkR44t+$sl@tuMmy0lu>E zIdHQZP;7ir2R`V)8jX+^u2|nnR^pQI_kRZuRXGO8r+`qqa^3-c%nrs;EozG90!y1* z5d+b!cwES#>4jX3@( zie5a9_%&44hO;h|t{xHcrLUGY;)V;G)kto~fE4}c=KPJahD|O|6)>1TJsog;*qt2i zh@kypEVpG+Q{Cg~8}(K9BPs$iBKf>)c6n%b8|g>v!f=qfcW;_PUO@M&Ojfx#*`fRH zJN(3966kHH$4Um@hn3H%A&2iq2SRU&OF-zbYr9**t50p^^wDzx^n=+o%AUaVaIo!Z zf|r4eqE9oeWyafkLf`+-wHdxRH_wP}$6ZAtwhrX@t8PiB_Te11*-PNA0{FkOl{y#Z zwDcIp9245J=R2d+vn@j3dY)ei+@bxk$Dhx6wr7{4fX$H+pohPfoKhWgTaYFh~4r zCB2rkSvdZz`;vv!(w=^pj)#rycBIBN$--?qF*KJVnBl}zq=7@Dh~+J`MendAVezbktJsD^8g_jaE5Zh zD=f((C~&8(-}U?xZ2>ZWja*-MXmnE&`pE2}8&P0l1O#syojhfTC$nwOPb zJDDi(kU#H3L}_(4r;dgYu^ev(8YT+65GNCu#ENA2+pxTX%hx$**SWB6B~v5Mr(!y= zRieP_*e?FlfSv@-d#Rf6t0bOKM929;7T$#W;)6yI>@l59=5nj=K?oi8lbHyiuqG}s zzrc`lL?@6`Ye)>SrF4K%dxPUS860~{1gy*ebtwCR0dn*wq7Xkd#B`i6ss)gMRQ7yj zgdFcC?!D9vcEF$s=@xT#D}nkGx7VPgTwU=XEBd+FzZ_CO zh(Gmu;};P!R1szW3{5sp6DtTd>7Rv{h%b|vCRX^FiscRwA3v`@q&^9M>lN*i-_Vco z#M5Fb8D6l4?sWsIK~41sS<2WSN^DLQYsRKk9+xOLh`TXh|5wV=fjFUkZ*kctw3|NC z7vcmWvSH0pw&`JGSgR8;n<_Gr3YT3dUH!JwOhPU-jSAO&3c0Pr|LX17PKWz8>r7pm z@U59TYad1-*#<3DqS0g~qj*B9bzE@j+uX#3N)y!Zdm)M*G|1gA6ya@+97&0^F0<-j zauP9%s@PTq?x|0_$@xM1Ets@HYr?XX57LmOMVz^s#0PqHheeQ)&a~6pe)(kT;2=KD zB(Ksr!m?E<6+6Z^`kmF9$*Tfhgnt-if@0r@5xV~g@EVn8V!`)i>5;GGJtPldc-0ja zw}?Vs*;?ZG<4ECT1uYf6ig#*pVEHz2iX39kk1l zOrJA^Ww1|Nb!tYDyJqmTAL+&dAMc>?uKHRIp|q~Sj#C8b(q-Z+t!#Qz%tt7HR%ulk z5W!(qVmcn$XLtVHo3^w{95a!*8OedZ3dzVIed^kDr;oZSLjydYKE)d*mqQzK6`b{I zzMrYcNpNe)+REz;7H492%dl2!7GB)XcC!W-wA~aTJk)AhMVE&KNzsY!m?a99kI;`X72;?+FMSeX*lLfCy1E{_)6q83^i;2GYe2oo!U8)Z zsZ*y_J_6AH?|_N%{Bn$Y>+$IG(DBbN63YorhhRS~M}SqUs=Fy}{c&$coHU zeD-4iHaO+NzWGbaPhlM-$<4qCA+hM~HZ#=6Kg2Agzd|a4P+z2{%Ket&qH)zA(G^V=nVC@8%WC2yMhvTVs) z%jWzE_m-A6bvdCE9~n_L`xDmeq~`Z}((Eo3rI%JcFWm0BODJj-y;iu7w)^O; zs-)^9LO`WZ7CRL+T97m^OGHtZ-lSy~p(RIX=BFrXodZbKwEwo@bq6KFBA$j~xMjjO z&fq7reg8k)Z=G&6mYB-^as!FzAnm1QT|0OuxnCHTIJw6QpteP>e!K=jfymxt9p>QQzFOCx+P}*{4J+o^+zU7yKl0V<(bKtvu2(TP7*N zdAacP(w^&^-M;%(1O+vwEYC*@l@!YH3v6W~Zoc8Fa~I;xIBLKGi$bfE*C=D1sCS_W zBkGwU47cf_0aFvN$ZsHv5FbQ5uwQnU-Hr$k&$RpcfA7}J{7`ZH@87b>ZlEU(HuiDY ziy)M*&wh&2D*M12pf8ixlE?4b?ngF17NGl+6Dt_n9!YWkUr-u=+Ka{m6xMKQAzZ{S zmd2!SkLlb_`__}W!90vF{@^e=K`sfgM*^kaMfqp%=bsb@X6s^zP&rHbPdRmtH9E^k z^TucXR6=rwW&w>!Hm=f%}4hgD2RCrS+T`va|h=(qvl~pUj4NSF}od?j-Nk5%trwhj0xl_=ngWpc?SQ*^?+JW_M)MJJ&wDsWFOC5-_VH|;wt#tAxFWY z5qm90?isTo+nUQlR3_l&L5WE+%>o;ywN-9$aJ2)QAb$1lQiSP^gVj@){*B06$b2<@ zP+MU<_m5lh7ktY=;pz?~x)??9a=|^c(sqf|AiGh~+W1W#lS1gmxhE=6fgH=Dnx2 zPZCI&Efr$0p4l8e74+9*?Bb_H3Zy|XPMq?$i!ZgwM8E@Kd{;dR5a(^A8U2?|s)nyr z6sxd~jeb_6!|cs&H>ENdjvn&1-EGJXCHkhCuX*|th_NhYW{{svAKB6{HayeTd|X=O z!=N)`)RCGa4T59ii9OhiqTFw+Thw2MP9}`&@iREO>X|^EY_y znG2X6&VBtlSB~)+bO~T&Ha8+^2H_qKAn}(PZgmaEha2{R@Pq};7e)f;-M5(MyE(nD z#Har*>&ictA(=nzdF({Jnj}T>JN#EY1N2%=yzH38J@lD<2{OZ+5^A$@;urBr5Vv_zpEJJRCoyOr_D(Z9*t0 zoCU$prNymO!OzLvVgB;7fj`I}4})+)jHtc2*{hrbWxRK||C+HkUO`#{7Zk%L=^6D- zBZu+xAtEcpqH5GoXP~WC&r6u8iZLu1^N5M1Ow$PMyF7`AKBKllM;x*LJmQ--84=^A z$+pTI2MMbvlS}9ByfnL%8y$-B80Zw@O45x8a)&{z8L@R4^9(q`PbGnGl=>_u=U%st zcfEI}bh~s^e#*andxt^yo?}Iz(6h9oOBc18q|lXtmSHD8iF=UCSfliINh1bfx`{6> zNDwQ-0SOZtlh^1NnQ;AN_H?Ls0nkCs0ey~ZEt;4B>hRHZoNG7x^x|S9(7sXv_K=8+ z17J|q-(WRD2-q%Kp`UjPU_gEJiA1H-oSKCHTG*Z+%o8^<=^ z*T%0i(Bwh=1jA$0f>w%Ko0t_wu!Myf^42;o>aIy_w24+Bt?uvbB}!C_DwBJr zD5hNCHdn;o>~#5tmWKPfW;65R^L^XOtHCVW;W*83E>?%47pBmQq-L^rs3OS)d(}#s zW|u!U5YW&~TA5Tl38HSox{%1%9My3 zBQGJa;z@{l;mCY>I%e8UKeiG36}QT!*I4ANuA0zaed*^Omt8bNN1t~OuSpeCG&tFN zt99lcG&@I&Dv2I2Es%Z7iF<>X$F&QH1-N#y0ZKZM^7s;<8g`wqtfzkoIgvy9XG?H? zhF5hY{O+CmPwNbd#i+X&u@(Q&+)`~K#_MAO58TY!&$Qa{JVjoonsPJ>N;H=Qv>2Io zLLP-?AJfKm(q4Nu;#kc2-RY@akhZ8`=sP|tFX829(AwFoT}jQHHwN(XNcjwH2?19#stsCy+gu!GC0aVf5rnO0s(6h zJaJ;iwE8t{Foah$=La&{yPLJk%nn152=|244h-udn1W;rbZC#-rFjjgslv31CyF}@ ztJVov15(ZMK0zHr0me;HYe1s!dlN3aZgQpqd<7)dn^{CDUYV+^^zX8Vf+M?9Gxt)8 zRl5dV)w$o>qgg+f^v=~dY!pdX6U)}*>^=8_7&E1dc1?}n3)=w=jjcEp0V)JhM@rTKXStPUP)cQchE|$c7 zFR^6jg9;tqa#(+zUHAkjCTKloo6DwvRW+ZlIUZ^IUiBFyQb56l*Rltw_aWc9C zCfhfR>x=T^n=MLDc5beg>rH1~&#H+yB7?@OBWy*tnR78#LmVU@435X}-rL{IBn1+N* z1}(=3%(AovJ-N_LAfCMY!Bd`N_nK4d*+#R}@%KSaE6%4@nI-2PFy($%FSxSU-n^{A zVqwU#E?c)XN!a8_At(<^+3*rmXkWqqE5cxHo3)T77EV1_M0~uv!oRa$p4=vU)I1Og zGI`-M!J>YwNdta&nh_EbZIQ3&oP81J1TK{=!{Q}7OZ5G5+Ws98p_H52J~h?vSdj`r zex2Ntzhe<;{W!DqmU6xrw^RD@StSih6oJdgs{;)Y8-6w|{7SI@yR!a>9N|4~7>gI; zi~f*+m*XbmLTD3s@BUC-E1VNwTqS=RFilXXym}!^pq#9o!p6qqZcWvin)AbdImsTu z7R$)uEbgvgnOkKZ!zP3~l`D#0lf9$ce8H6Zk25ST+7@CRYe>Jaz;PBo`_fN5fOd39 zUelj^)=pu@X(eEN@(;I^AcP9dV~*m{F${SpO(;!_x$=?`+LEPypBXG4^7Yc=Jx^ z`sP6I;XGSd|NK8xYTt0bwuew$L#)>nj9TBMH7b2xe>3G!#o$>dL=C06i`tManLNSg}=bUvz=KR*2i2jf|_8yPf?un5eaQKQB#v1O5zq&*Vd2~jsF zAoWieR-eneH^A7`YbtAxpz-&~fHp>(yhgG!S^hu_moR{PMw-r?-_QYEE{v6|9QtJR z{p7ul>tCeFHFsOB!Ew(YhCbg&Z57mdB|soTH{f2@MopHrQ1MmcaG1mGFhdGeONQMF znkQ8GcH?AfSOzep>;+UWBKveWsD!*-ddnYn7gLb(LFiKu!)ch_VX5ncTiczdMZ}x0 z2?4{Eia~YVrXY^?23#wN_&??<_M^p;KdujMFjn!+GHO zSt0BwTf0sDp8q+)apyYq{*R^b=_95??gY>&&# zJaa41GtEk}^GideRZhYvdXp}M(w#bQ6tD~O<`3UdIu2V9S>+HvvwSmS&p#2mj!tfcrv0IM0TMICr9aB&O|Wn z`i#OWEEBC2s`Je;6 zY4KMAnTQ8j70}P~suQ%cKEwB!H6t5a<^$)9P_My09R;Jw{19$5`PU0yT?44SmhOxZ z?6~(g%T~*$T1bTR(o#7zp!oYIN%w<-S0Ls>A@ua~1tX!qn`FH!?HAKPQMVQU4i^|5 zy@V&l8+(rP!#Bk6DH8)aV!#&FS}y>=0@h|eh=7R*7qGzzf!H9o@SQWA(o!ez7GG-Y zHG3{5WfW93u8XiM#G}XGUuz(6G&K#$yK@;2xeQZ1d`fH#NBV{>A3o$q#1o|Nj=^J4 zdoVCX>N}GxKz;Dd5ITRyy!ArRZ_0X*+e$_u=O!Al)llWds;629OcgzVa ziEC-_I#Mvw@gIkE4l|UkJ!bN5%Ohk(cpVI4e7D?rXB#z_H}OYma@G#>x~txAX$YdP zNei3LYqdYY-R#EvP&eR@G4PgD@lg{t=tjjzfY7>P2*yVTy+#nhg!F2?(jY@cZTSY6 z%-t3YF1Fy54gj?Xs6a)~>D*4+_ckNN|I(fI{0lk;1jb7M(gTiOfdE!Z{YFWI6S(Q$ z36MiB0bWeXxkXA~PJqr5pMV4*E^2WXy&OQaBHn<#O5d>bKj}t6D8v%r1*Rov=-&}( zS?G!IDPSyMs`wfHARgbdOd29JgjAA~05)+s9A>7}Yu!^_4Yf!ALl;do;1~o4b7NpiVjLl&jv9V_?Ff+ukD)Z_GFaa+5Y8ddp}4)xW+qc zMThb41(*1B$;;m3-6Kg`b44t9WO=QhHWu?`n(&-`4l z13u<*FA*k0^{kWc|L*a+?NYFK!RX95Amd}7EoxqP_fGhqJp{Ej$t2%$>k-oGq~T9I zXQNV8Jyl29ZROp{B9Pyq68W04+`I^(E1YA9YoaHgr#?y0sARTHz*5JE|Kkt!NI(!Y zI9(X>(~MJ~XAd z8}cl5b=jAC*doLpu(9(b9d(+sa2bBjGON&fDqFb0IcoiKrg|2Qb4s4B=<}p72VncX z2>t!CpFf(4X7Y88MS@H5e*s$`iljkEy&RC z)FsD1J~kOPEDKX-=b3d0rJ#s~E_S&spN<%BKZ6dW27yX6I-FvM`nzpD7tdy&WelAO7D zP9o&9&PhXj>0t3bvu8|`tL{fJfogt57XAB|I#s}mb{a$*Kxi3u6q^-nsEbSxh6W3X zj+K-*h$N|wReG%*#Dz93^!FdkTdYyV%A5LM{J)7M#v+9!rc^H0P9>~Vag=GPf9L{{ zxuhq$=*^PNuXKww{U!XtwC>AT#qvy3NQea5;Y(6@_IM4EI%!e^b%q_0PMu7eimcXT zVJY}S6hpU18{@HSu|pF}54N`5G#F0eJrq6e+jn_u{ypNo9re|d_A3(Q>DIbuxC>Vi z=dLZNwbw*x?*fac_q}wh4E@5JzVA0qRM!`+E>Zn;p?u^rQt%PEKmBGfd30;A|0I*! z-?_LO^XRP|^h%tTFa7o=^4i`XB){)y6#D}xL@P%*EMNWT_SnJX%fePHBW!W{ z849Fj3eWG5N%==ZyuIgNcwXNuhE%4hFO;(9&yXWCoD*am4;q|MnkUKFiYpLQ74q$rF&yr= zwtSeW7he`N1?#0{StZ^3zFTn6`Z(VC)$SF|2TnP^k97m+Y?yt&D&penO{uVjSKOCrd34JIQFWx#)z|)P|^iS5b^Qj`KDA^KnO3FS!Lg)7M=mqNS?S&o< z1J(=3T6f@5r#E->06jqT9UL4yzrV0_0`!T~8B6)}_pAQBt6jbQk(aP?1ifJHlwriQ zCfb^)Uy@qkw3I2UF_k2R^%6^nRuhujo=qPXS89#B=Vrk(P}0Q^ueHzx*tdTo6SiYC zxo%C(Ka>zSe@JBVaaRsc>RZN)v29#1CeB&&bmk+@9I;F8{f_+dxkeW|_cK`FQid%h z&Re`rH|H!Uo3o>OfD}Ae&HR4D-Tv-iHkRd$)jS8Rfh8~$U8(e^=Y4GdWXcysyLDgV z%5k}sKp7c(B1NO`9jgO|8jc@RJKvrHXVLk%U@*eD5EKa%s_o7VY71xA6B`0DnL)Hk z@(?R4-+?H#hQL1`Vq;_5s{3sDD2%%UDDYm2fI#0Zc#XUKNPxhXDX$m({rxfB zUvKN^H8>Cpf-sd;5YE0LIW02=gFJt@$_j~jxlvM`u(e0CI6-%B}cw&CqMcehNGNZ{8%KAi>!LSr@%uHX*;5O=e z9xOH2{K?}8E{BUY#h#7+z0Mi7xavH7-{G@6YO;2wUfY5+r5s` z)876O0`4U-gmX)IB14jXdwDFNN)<-{h2xXIcrmcPz8)Rl{_=!96pEc4bb~pS!R!eZ zH7?fUGFnaayu8bu+952nR>7!AV99QpT&exhOqizg1E!BCf2i4Ttd6D<2Hib=_|%uX zIbWq8HqFwk9f^&Zu#^P}-G0_)X6fm4Je$WX+;pe3O4gQsQ>-5qzgRxxV0=VmHfE@? z5chGcCS8{pV`40WJ57&b82TWH@RzbTf%fA$EA$9-DuH9fr$RKDFKX1-h+5PoySY{8g{ zo+Q=}gLXArtg<{9T~c^DPi?m8YHoohF}o^rzE4;0AN%O3g%!2gl|9_1f`!rAB~xNV z%s9W4&DjK9VtWEbrk;~>_u<0(M`Fy6y*nAY(YK`EvM7WeGRtS|3|=%=VqSMIZ8!YnPVK19tAvDWH4Hj0c4ES+aHWZan_xm8HB)_l4h|u zk5zZpIir~~g81(#F>lPcx3vCUru$qrW0o&07o zP2|}^xA*jmqjM5ha}T9Y9rNtx>F`wz(ycS2s9?p22D;6~==NUuVu6&9qcX8NT}W$} zOv4wgXRWF&+ATs+^r!$_3t@J4sP_k@$&s0_#7%qulDt}yBd)7sk9xIHH-Gk6>!ya& zaf(YBdM4rIga~$X-=UryZcwT#nApVi=AWMw*9DEm#IyJOUWJmCk4fn?qKD~g1UUo$ zE*)t~N(ykM2(cUKy|()DlZkqI)83Upn>7tI`+{npR1{R^d!Qnek)o3^#$?AblK(|Z z6;~yx0ObcY2q)Wx(~I&5x;j235Uhq;rWZ`eCK<6znxjX3$hpzphXP&JO|2L&*I>s~ zuXbl&+%nF&)0dRblrKB9Zo-VnBdyc1Gb8wW{2It6F7@8$4o3KU+R7K(Yl-pcL5TKF zQ7pl3Y8PuJP9)Og5p%`%x(;mm1N_soeY8G0w^#r zke}KzG)b|n!Ez}6zh6a-;El@q(;YiT^Vup&&@IvW@MPK2__(|yE~4RdYVj*%`cI`R zW3Rt{+W;InHS^8I+4cQ*R*eiSPFr*8A~xu1q?0{k6mK2{BJsyM8r<{a9b8ZE^n9qRilsf7^_VKtXwN zF;pXpDT$8p0u7hCx89@^aq&utIi=0#Vv{eST4;HekTD#;se0wBFHx1Y!?&Oa^K}48 zzSQM^hZ$S#&Jr4qFG*?h@A;|C`|Kq^Z!A3WoBxwtPM4>;1+Y%qKAbGG#qy97OGu*A zskoo7ONjVgqXV^$6yUB%9%01p2h%6@0<0tVf6sx~cz6tyCeMoE<1^hdUKn)Y&Dc90 z6hECv%k7JS5_M77SY^Rf!vuIGPANu02{pAz2fPyVHflw5I>u&oWZ(zFNuz1$!V{+? z34muNH2m-3B(Y=?@g?Q$iqHvxUy}2`Cn@?!FaQ7kLuEyymt9?5`)6lngB$q3V<=62 r|BlR;rL5yG1%JKpTBiS>hxtqMMkstF$BoP%1bivTs7lvMLPGxs0~R7% literal 0 HcmV?d00001 diff --git a/v3/images/snap/1.png b/models/v3/images/snap/1.png similarity index 100% rename from v3/images/snap/1.png rename to models/v3/images/snap/1.png diff --git a/v3/images/snap/10.png b/models/v3/images/snap/10.png similarity index 100% rename from v3/images/snap/10.png rename to models/v3/images/snap/10.png diff --git a/v3/images/snap/11.png b/models/v3/images/snap/11.png similarity index 100% rename from v3/images/snap/11.png rename to models/v3/images/snap/11.png diff --git a/v3/images/snap/12.png b/models/v3/images/snap/12.png similarity index 100% rename from v3/images/snap/12.png rename to models/v3/images/snap/12.png diff --git a/v3/images/snap/13.png b/models/v3/images/snap/13.png similarity index 100% rename from v3/images/snap/13.png rename to models/v3/images/snap/13.png diff --git a/v3/images/snap/14.png b/models/v3/images/snap/14.png similarity index 100% rename from v3/images/snap/14.png rename to models/v3/images/snap/14.png diff --git a/v3/images/snap/15.png b/models/v3/images/snap/15.png similarity index 100% rename from v3/images/snap/15.png rename to models/v3/images/snap/15.png diff --git a/v3/images/snap/16.png b/models/v3/images/snap/16.png similarity index 100% rename from v3/images/snap/16.png rename to models/v3/images/snap/16.png diff --git a/v3/images/snap/17.png b/models/v3/images/snap/17.png similarity index 100% rename from v3/images/snap/17.png rename to models/v3/images/snap/17.png diff --git a/v3/images/snap/18.png b/models/v3/images/snap/18.png similarity index 100% rename from v3/images/snap/18.png rename to models/v3/images/snap/18.png diff --git a/v3/images/snap/19.png b/models/v3/images/snap/19.png similarity index 100% rename from v3/images/snap/19.png rename to models/v3/images/snap/19.png diff --git a/v3/images/snap/2.png b/models/v3/images/snap/2.png similarity index 100% rename from v3/images/snap/2.png rename to models/v3/images/snap/2.png diff --git a/v3/images/snap/20.png b/models/v3/images/snap/20.png similarity index 100% rename from v3/images/snap/20.png rename to models/v3/images/snap/20.png diff --git a/v3/images/snap/21.png b/models/v3/images/snap/21.png similarity index 100% rename from v3/images/snap/21.png rename to models/v3/images/snap/21.png diff --git a/v3/images/snap/22.png b/models/v3/images/snap/22.png similarity index 100% rename from v3/images/snap/22.png rename to models/v3/images/snap/22.png diff --git a/v3/images/snap/23.png b/models/v3/images/snap/23.png similarity index 100% rename from v3/images/snap/23.png rename to models/v3/images/snap/23.png diff --git a/v3/images/snap/24.png b/models/v3/images/snap/24.png similarity index 100% rename from v3/images/snap/24.png rename to models/v3/images/snap/24.png diff --git a/v3/images/snap/25.png b/models/v3/images/snap/25.png similarity index 100% rename from v3/images/snap/25.png rename to models/v3/images/snap/25.png diff --git a/v3/images/snap/26.png b/models/v3/images/snap/26.png similarity index 100% rename from v3/images/snap/26.png rename to models/v3/images/snap/26.png diff --git a/v3/images/snap/27.png b/models/v3/images/snap/27.png similarity index 100% rename from v3/images/snap/27.png rename to models/v3/images/snap/27.png diff --git a/v3/images/snap/28.png b/models/v3/images/snap/28.png similarity index 100% rename from v3/images/snap/28.png rename to models/v3/images/snap/28.png diff --git a/v3/images/snap/29.png b/models/v3/images/snap/29.png similarity index 100% rename from v3/images/snap/29.png rename to models/v3/images/snap/29.png diff --git a/v3/images/snap/3.png b/models/v3/images/snap/3.png similarity index 100% rename from v3/images/snap/3.png rename to models/v3/images/snap/3.png diff --git a/v3/images/snap/30.png b/models/v3/images/snap/30.png similarity index 100% rename from v3/images/snap/30.png rename to models/v3/images/snap/30.png diff --git a/v3/images/snap/31.png b/models/v3/images/snap/31.png similarity index 100% rename from v3/images/snap/31.png rename to models/v3/images/snap/31.png diff --git a/v3/images/snap/32.png b/models/v3/images/snap/32.png similarity index 100% rename from v3/images/snap/32.png rename to models/v3/images/snap/32.png diff --git a/v3/images/snap/33.png b/models/v3/images/snap/33.png similarity index 100% rename from v3/images/snap/33.png rename to models/v3/images/snap/33.png diff --git a/v3/images/snap/34.png b/models/v3/images/snap/34.png similarity index 100% rename from v3/images/snap/34.png rename to models/v3/images/snap/34.png diff --git a/v3/images/snap/35.png b/models/v3/images/snap/35.png similarity index 100% rename from v3/images/snap/35.png rename to models/v3/images/snap/35.png diff --git a/v3/images/snap/36.png b/models/v3/images/snap/36.png similarity index 100% rename from v3/images/snap/36.png rename to models/v3/images/snap/36.png diff --git a/v3/images/snap/37.png b/models/v3/images/snap/37.png similarity index 100% rename from v3/images/snap/37.png rename to models/v3/images/snap/37.png diff --git a/v3/images/snap/38.png b/models/v3/images/snap/38.png similarity index 100% rename from v3/images/snap/38.png rename to models/v3/images/snap/38.png diff --git a/v3/images/snap/39.png b/models/v3/images/snap/39.png similarity index 100% rename from v3/images/snap/39.png rename to models/v3/images/snap/39.png diff --git a/v3/images/snap/4.png b/models/v3/images/snap/4.png similarity index 100% rename from v3/images/snap/4.png rename to models/v3/images/snap/4.png diff --git a/v3/images/snap/40.png b/models/v3/images/snap/40.png similarity index 100% rename from v3/images/snap/40.png rename to models/v3/images/snap/40.png diff --git a/v3/images/snap/41.png b/models/v3/images/snap/41.png similarity index 100% rename from v3/images/snap/41.png rename to models/v3/images/snap/41.png diff --git a/v3/images/snap/42.png b/models/v3/images/snap/42.png similarity index 100% rename from v3/images/snap/42.png rename to models/v3/images/snap/42.png diff --git a/v3/images/snap/43.png b/models/v3/images/snap/43.png similarity index 100% rename from v3/images/snap/43.png rename to models/v3/images/snap/43.png diff --git a/v3/images/snap/44.png b/models/v3/images/snap/44.png similarity index 100% rename from v3/images/snap/44.png rename to models/v3/images/snap/44.png diff --git a/v3/images/snap/45.png b/models/v3/images/snap/45.png similarity index 100% rename from v3/images/snap/45.png rename to models/v3/images/snap/45.png diff --git a/v3/images/snap/46.png b/models/v3/images/snap/46.png similarity index 100% rename from v3/images/snap/46.png rename to models/v3/images/snap/46.png diff --git a/v3/images/snap/47.png b/models/v3/images/snap/47.png similarity index 100% rename from v3/images/snap/47.png rename to models/v3/images/snap/47.png diff --git a/v3/images/snap/48.png b/models/v3/images/snap/48.png similarity index 100% rename from v3/images/snap/48.png rename to models/v3/images/snap/48.png diff --git a/v3/images/snap/49.png b/models/v3/images/snap/49.png similarity index 100% rename from v3/images/snap/49.png rename to models/v3/images/snap/49.png diff --git a/v3/images/snap/5.png b/models/v3/images/snap/5.png similarity index 100% rename from v3/images/snap/5.png rename to models/v3/images/snap/5.png diff --git a/v3/images/snap/50.png b/models/v3/images/snap/50.png similarity index 100% rename from v3/images/snap/50.png rename to models/v3/images/snap/50.png diff --git a/v3/images/snap/51.png b/models/v3/images/snap/51.png similarity index 100% rename from v3/images/snap/51.png rename to models/v3/images/snap/51.png diff --git a/v3/images/snap/52.png b/models/v3/images/snap/52.png similarity index 100% rename from v3/images/snap/52.png rename to models/v3/images/snap/52.png diff --git a/v3/images/snap/53.png b/models/v3/images/snap/53.png similarity index 100% rename from v3/images/snap/53.png rename to models/v3/images/snap/53.png diff --git a/v3/images/snap/54.png b/models/v3/images/snap/54.png similarity index 100% rename from v3/images/snap/54.png rename to models/v3/images/snap/54.png diff --git a/v3/images/snap/55.png b/models/v3/images/snap/55.png similarity index 100% rename from v3/images/snap/55.png rename to models/v3/images/snap/55.png diff --git a/v3/images/snap/56.png b/models/v3/images/snap/56.png similarity index 100% rename from v3/images/snap/56.png rename to models/v3/images/snap/56.png diff --git a/v3/images/snap/57.png b/models/v3/images/snap/57.png similarity index 100% rename from v3/images/snap/57.png rename to models/v3/images/snap/57.png diff --git a/v3/images/snap/58.png b/models/v3/images/snap/58.png similarity index 100% rename from v3/images/snap/58.png rename to models/v3/images/snap/58.png diff --git a/v3/images/snap/59.png b/models/v3/images/snap/59.png similarity index 100% rename from v3/images/snap/59.png rename to models/v3/images/snap/59.png diff --git a/v3/images/snap/6.png b/models/v3/images/snap/6.png similarity index 100% rename from v3/images/snap/6.png rename to models/v3/images/snap/6.png diff --git a/v3/images/snap/7.png b/models/v3/images/snap/7.png similarity index 100% rename from v3/images/snap/7.png rename to models/v3/images/snap/7.png diff --git a/v3/images/snap/8.png b/models/v3/images/snap/8.png similarity index 100% rename from v3/images/snap/8.png rename to models/v3/images/snap/8.png diff --git a/v3/images/snap/9.png b/models/v3/images/snap/9.png similarity index 100% rename from v3/images/snap/9.png rename to models/v3/images/snap/9.png diff --git a/v3/images/snap/movie.mp4 b/models/v3/images/snap/movie.mp4 similarity index 100% rename from v3/images/snap/movie.mp4 rename to models/v3/images/snap/movie.mp4 diff --git a/v3/images/stockflow_cv_trigger.png b/models/v3/images/stockflow_cv_trigger.png similarity index 100% rename from v3/images/stockflow_cv_trigger.png rename to models/v3/images/stockflow_cv_trigger.png diff --git a/models/v3/model/__pycache__/config.cpython-37.pyc b/models/v3/model/__pycache__/config.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..58e4b3db6c65eff8fa6770eea134fc3a3cc275e5 GIT binary patch literal 1109 zcmZ`&y>iqr5VmZe&-Z^v0tpE)E@+Nn?h4urLr9>46ojEr<54W-qC}ECl8(vIhL$&= zfRci@;3-5&l~!R zibR0@I}}KUI7A*tB9bwVYm7t-C*X@kTc$Xb9o(t?Ezy-d+^caS`f`8=a)^g=ghz6W z$KY?%R7~UqPiou|H{=H1sBu?pz7E)kw%JYEyF%<1?SG7}5ZYx=q`k5l483 z^{xWE%XZK2uq`%WJ8XjvKlr*vbo@TRcPTm!rju(pNpLhpu6@V~E{Ooxv0O6BgeB9^ z#fQAGE`Gs{g;4j|WyLgvm7T_Ja#Ai8%~h#ou8E?hbbVIketT{#t6aAz)hS=(hC_rD z1v4f;5oK|4jUX-X4Hrx?!wq~+EHmKQt%$aqh{lsID++;TlMkZ@*L!|z~FQ2WzQ@3|PNK67o_w1E!6>ryYizfK+bWlJq3 zP=%^;iCtBYNqZLE0)saTLJ#&2XMgFT{V#)e{uP=nEf+?&JwfVC#~c6i)l%S{|2IOd z5FXb@oSBu$;Zq`wYe5q(m`j?az6o3|X&MvvUeyhH?E9>i*+uNK!b4J340^b!+SFpK vm2c3N=SlY<-BPf}zH`k3Fe&Q8lcI6hN82y~M2ghDGo2*Jo0B*?&uC?5k70}F_uOEC2{wQ%exc8dxl3x5HG*je}= z`~`1_35j38#3i9coaA?YcTaau_Cc%FbTBTSHLI6L&>xa^f1rG z*e*`W8l>SFLgd>S3FcZASJtn^v(u(sfPNo(hL0fmoiJ~d!LceW;h(v4Ww=b^(RN&!ivJJY%Eva`!Ol>? ZZutdz1tBD1!zC^Wy*3F*6Hh?eeuQUJv literal 0 HcmV?d00001 diff --git a/v3/model/__pycache__/partial_state_update_block.cpython-36.pyc b/models/v3/model/__pycache__/partial_state_update_block.cpython-36.pyc similarity index 100% rename from v3/model/__pycache__/partial_state_update_block.cpython-36.pyc rename to models/v3/model/__pycache__/partial_state_update_block.cpython-36.pyc diff --git a/models/v3/model/__pycache__/partial_state_update_block.cpython-37.pyc b/models/v3/model/__pycache__/partial_state_update_block.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..19f95e4311f7cfd0e37e406c0656588a7182e865 GIT binary patch literal 865 zcmZ`%JCD>b5Kgk2P4-X8qiyjsMgrTokDv83!xY!;Ht913?oOM1U&3w&wG&66_Rw|mgu%SiQq%%$KR+@f z*o_2>?v>My$&l8kyMyAkU(~woBsifJrpzrUCt4I{ie3B6fCOe?J27*#&C&}WA!Wym z!zLY5&Qey71s}GL$g0&G#wPNE*(Z#x7U?LJb2XWeA=DEqVNKCKSPhMOaIUkm>Xul~VUmGJxExw}v%%z4uxj#lW0?)_!u; u@Cvp%z8tmc|BbG@Q=h-We4O=b|2x{JDDoJ;Y&J>aMLNh)pLrd9c>V^c%NM)= literal 0 HcmV?d00001 diff --git a/v3/model/__pycache__/run.cpython-36.pyc b/models/v3/model/__pycache__/run.cpython-36.pyc similarity index 100% rename from v3/model/__pycache__/run.cpython-36.pyc rename to models/v3/model/__pycache__/run.cpython-36.pyc diff --git a/models/v3/model/__pycache__/run.cpython-37.pyc b/models/v3/model/__pycache__/run.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6e70f54ff1fa5d479159889a6a2bb64b71dd985a GIT binary patch literal 7972 zcmd5>&2!U66xT|&{1MwpAP_?M5Z@R`LVj>9g>WF z3Nz)xf&U=6wf~Z?J#p@hOZ(o+**FO__F!=2(R%l%-F|6$9zJ;56ka(I$0?j6#?iqpp3zk!XQBT<^g86iiuA8=5&)6BFQFBwXvw)g_ z=IorTkqPqk5qkulj#P=4^>QzWUGR$Dh*yAI(IY==<elXj++0ZomKwY&w_09#<)50N#r=9Sa1qN(>_?Zmbt)+1NgY3IG=-CBci;MO0dhV`UBC;AT=avt> ztaCx@R#L6WlKxO+XJ~T7x?P|^3U-ENhC?i8SQ`GA35lH#qKX?hbs&so0EjbKzbR;} zH@rBv;0pH%YFEzMg>Jp#oa`6d*`?h+oQhbB^ScAI$C>-wNmO;{Dh&m<3xb9`Vouek zfoEqqcMWHhvg{yuAX>opy zgdf)CP>+Dmt=>&?bF3N8Hr8VU{&UbFFF>N@Oj6Jbq@?F{b_~jI{)MZe<0H7xn$_Fa z0Byl9V@TU*hmv^JJ>EVcC(@yLqC?zD7IpVV4p5-dfwfG4UhScqqMsTbNdJoC~I zusvd?!ZHfWDlDh4yuwBlR!~?`VI_r)Dr`();|e>bu;U7wP}m8DomAMQ!cHk{N@1rJ zHm$HT3OlQ?a|$~zu{7{tP6HdBCCeFAzM$G%RBbLvEDLpVvdv{x=ZdOxRbkf@c3oj* zh22otO@-Z(SRVQxk^RqL8GLwlatl##q)@*SkdA#t%zAm%3>Id7}}%?ONgMa9*3Ks zgG6Xsx-U?84ODj+36t80`1Vy4L~tH zY{Y4oF?T&4uTkMhHJrtDN31t!nXuC!Nb0eZM|I-tPXDOPKX7Xs``%}_;6xR65~?Xe z@m*l>l!TsvjL^E?1kk$kCN}@?O=DUU~X=l@b&cOXae6vf~w;me-4cYm9a&}}Qt;~W1#jy*J*yCN=!gr?` ziXpMbCQzpnkn~L2EL8q5($FU0jI~Wps^<@;7@K;C_Jz~CGM*aHo<$Hr6urFUmnH^v5uz1jyKiyPN zevMiYeBL_Qx3Z@n5Git89&qUaGbyqlNl zgFEYA(x0#5j_m*zZymCFo2sBpNtRUAKE;<@MxChY?3=FGXWsY3af4vbWM&Y=_P^EB zwc5)F=GSTzw#a*&-h49z;!$Se?%9sI+@9XunsOA~ou@~OV#BTVK6`vL1bR_sV%NB& z88~@|IzCG6+5Y5t&4+9)GP!Hhm*b<%BuZxBU49fNK9nTfne3UQ1*rU~qa(^poV3*C znja(J9m*B%3iZs@;~{W`G83)}I{^)?%0A!4-2R}370QJ;!<&n+E!T*Ry1T|o&{3S} z94^2nd7}}mv+D?^~(uC zXCqLwFUZrq*)AfMXwaZ=g4j8j;c;44VR+VI7b~te|7c;BhBZH=yJwxO2v3tkPBW<@ mk3;2ApcLOV%H5%6A3)d5fIh^ne~x$-2MEf;X(FB{p7;;;L|G^R literal 0 HcmV?d00001 diff --git a/v3/model/config.py b/models/v3/model/config.py similarity index 98% rename from v3/model/config.py rename to models/v3/model/config.py index 0fabb4f..aff08fe 100644 --- a/v3/model/config.py +++ b/models/v3/model/config.py @@ -15,6 +15,7 @@ from .model.sys_params import * sim_config = config_sim({ 'N': 1, 'T': range(60), #day + 'M': params, }) seeds = { diff --git a/v3/model/genesis_states.py b/models/v3/model/genesis_states.py similarity index 50% rename from v3/model/genesis_states.py rename to models/v3/model/genesis_states.py index b5b91bb..6aeda9d 100644 --- a/v3/model/genesis_states.py +++ b/models/v3/model/genesis_states.py @@ -1,13 +1,19 @@ from .model.conviction_helper_functions import * from .model.sys_params import * +# current hack until sim_config +initial_params = { + 'beta': 0.2, # maximum share of funds a proposal can take + 'rho': 0.0025, # tuning param for the trigger function + 'alpha': 0.875, # timescale set in days with 3 day halflife (from comments in contract comments) +} genesis_states = { 'network':initialize_network(initial_values['n'],initial_values['m'], initial_values['initial_funds'], - initial_values['supply']), + initial_values['supply'],initial_params), 'funds':initial_values['initial_funds'], 'sentiment': initial_values['initial_sentiment'], - 'effective_supply': initial_values['supply'], - 'funds_arrival': 0 + 'effective_supply': initial_values['supply']-initial_values['initial_funds'], + 'total_supply': initial_values['supply'] } diff --git a/v3/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc b/models/v3/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc similarity index 100% rename from v3/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc rename to models/v3/model/model/__pycache__/conviction_helper_functions.cpython-36.pyc diff --git a/models/v3/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc b/models/v3/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..68bd3feedb5d55ffcad15def3e504afc4e6a297d GIT binary patch literal 14983 zcmd6OTZ|l6dS2bSyZSQs;c#YnQIn!5wJxM?tKH>N616MI>y4HcMDA+0Y;C)zPtCB0 zJ>A1oHB@s_vv5oXOF5CjI1h=}i}lb5;>b2OvEvx_OM+Ys8-B471bHX~L4Z6MO#Big z$irrVUz`Nu`(5`Iz9 zwk%~SyK4nDo=(@m&-lP$$uLTwPG7)M?b5P+w4I)MLnR|bjz`rC>P0-yDEo@lxb%R~x!iElLO9s&_tL^fYiq03 zF0dM(5`Ki+m+*@KP%E(#`=o;}r7t1ptXPS&XqmdCuxu%J!8&FsZxO%5 zT}FPdm=v#Av9*bHRRL=kcmGDEj?bGry>{H`_v-PrR$NzM9O|u3FN{#uVRWtERrU40 zu5au9c0X!$qxxFd>21_o?RK~whw5BiW=XxZFm9!;zSe)>V51Lk$ns)NZ##86z4b;Z zDC_XXP8eZOT3FxdsVFT*JKNjcTWO)y-M-dJi`y;T+KSRbbgiYsv^?CwN0y$&d_Pp@ zul2XW^R0HbrLU`2+&ZsA?CSh$y0y{oHNVvF-R#KAG{4@DvCH#YeHC`2^EY2Gxh!pu z`dtgV+o5h^J2Eahw|y&}i*;vX1GU5Tt5`e|)GqjT&3?l696gUZ%>#PC6KXTzhc*1k zk=;Qi#-S3O3dat|Z4b~<1Epw39ooF?-Hy!09wsJpocvOjoVe5OY`1#x2Hs0Pz+AW6Z{x(;t!}7` zs7%W^?e0$4Ylo>9-`Wlvg>-HsjGH~2Mbuoo)nuh6k^o(9MycOYs>y{w1YE9BN=w;> z^b&dn#i+Bf)r!*E*bbu9?xgl+S{p9b)Hel5Oftvmm$FxEnzw3ZvmQlTw1~v=Do(|& z*uFh&FXCM1?NwW!M*Uy1(X#&H)i$@u58sSGIo1I(!((*@*AfTE`tvy8KyvTw@o3!q zSohughGK0{D~}OviN|BafrD0Ya0S-LQMx!vccR8SaFm7JpOyo8JzSU9>-R2vvZDpo zuXjNn^|3?sbFvmpPW`-(UdAgnih2bP?ITHxy46$tt<>XFA7lM-)8_=6Q5}O~^XVe} z2z%7oRLv(ne(|19EW$c|GU}^p}@FOM`F6pg2{!&4b>?@ml*O z(5D=t%-}0ul~wt^t12s&hvOE?wl>S`B?nu@!B#NmsHXN%%@k^;C;IU4zm?JOOluBI z_0Y3_U3YGhDFgiNFp9vu$+F{XVZGkcXahy}*X!ftbFLy2y-kKomLC};%AkKCX8?nG ze{C~t$I`hMzTX_1qeRTTD@KM0djQ*V z&OMuYWJw2@#t$nOyPYU*_qVoR`7&qXv_)(8@r|$t1~LJGa~Hw;AQE3eCLg28ScH7i zauoLB&K8o?&tav{p!vc7#YZSkbMxSU>q5)7(qb%-Oe>&=Oq8W13dz2X8x`={5ITY| zQo}ao1xTOzEE7_Vp;;1WTH5J#fCyXTlo~7_c!}XL3dyEkL^7>&xYs&_Viyj9!{^XD zA~&#R7r{`c`9nHu*PLZAReqNoeG%h>;Fh;ZmL$o67?1Uu{ zrz#1lvgD&gL1@~_AY((+Y^#viQKn$^R23SNcYD+q#2ACD)f5DEF)nNtLEy#v_Fidl zFDZiP@9z27ji30L0QQs8bw~eMMy{w8BA>Zv_0G!Z3N+1WH4TP*!`3wA_X~?2tV{n~huXPL#SW=rS_` zr{+-Q)iw4mWvZp&5+%;}Jd@{`yujo$NE%iB67!#BLgF&Y=1>?5v8ToMwSK1^j%2a^ zJjYfv1+pfAY3i-|5|b$=btWf~1V@aZ2KOH4=tgLNfxRZ-mChc7PZOP@Cy;>Tp+QYM zAb6(ENf3XHKS=9EXVIQ_GV-V9G?dvnuO`2asN{Xe`i{Nsh}`z?TALQ-HuP4v^~A@? zyf=q-OTQitV%>sfBb>3m)`~(^U%OTBLAI$n)y>1D-NsPPjf4+PB2FV(YBLJEkbaO` z=!wd)xh6!~!xHN)$b)*j-`&~jQ7lpvL;LM$><1bvcydc@t`_8>41QzK+S=}hMhXwL zsQSzGhcxFWG8z4J{qelt;|(*(gedd?)dhn0G!hYot9v#!v>B1pEIY3aq&ebw;`Pf7IK8&KUC~+LCy^e=|g9$NjqJpZt2(=H9 zk@>B8DgWhnf0dsc!#>87=F}b(17*XgI;-qGt5-wG zfo}f1a@az&+bzhcjAXRgmZ#5SQdqBj4eO?$!glLc>TK;CB$pY!{ZRV1y7#%CX(X2G zfhv5v25<+39)1(pAzRJu!H>a?Y(c?}w=hAD9g>$?G4#Z~oS5$i z`D&J@zl!`2%0v&kSA_GdA_GIt{*JY78{R`_(uXeW1qWzQ*nMgO(gbt_O$>ycYiISo zh6*_eV(N4x1ED^ix=s%Jmkt4q?d>JL-+e4tG{>5%76 zPmdOE%EIGdGo7!}B)=ZsN=rJt3B5jS*bq$Ebf_AxS)zfd(vpl1m4RrsxBzqZ;3EG5 z{Uef=d-(KwQd(<$#+z;1`B%4q81`oMd-5uqXTS3RA0KCb6)&Z zhPk;7+Fa}R;iJ{#tR**YKjrRSN|d|KL%Ve8!9qXfMo{?K!CQ7oCn;H{y8LXwuR3kM=Gq~C)mD>TBV<+)%!vpr-*}V!d6`rZRDzTjukW?0} z#9y{H;1h#!3h&g73-4Nk3m8+BG3PJ_y)dRGV?Y;)k1@q#)`77wop1EOhh60e=D6+P!Dxkrcj0R`1?-}fS4ipVl3_a&1$9G!5 zyFhBFDn?L{mNJjt5zOT$M^tScZ?LyyO2{3R++0?_9GCYiY6|r$adoqXU@hM&B~tUIse}Ad-mXBg8V)Ez32C8TsNtK2It^5umOdm3Ep~GJCBxy z>&`t}|1hrZ&!|PfbZMf;5y0`pu>TTDC)Lq-Hl9<<$w|z2;+Xg;k6LO4>ggl#Z1Tu$ zJ6Yr$y&nw6ah+AHVwO`#yP>pF9I891RGCbzWL-7=6eiKJe?fdpGmlf0^ZqZx*r8T?&ut%02hS8fGi+FW$Fo zD?YNh1Rh<+J1r$=Fn&>;{jh}k^8QivI9Eq*S$dZDSJV?(&FTi^hqN6txx~HaX+JJ+ z9#;*V|I}?8*zhENPpPLrWXxwu6@eR5nC%2-Bb3xLkTmCVN%I`oh4>eZcY%_|Xgw6` z56Dc!yD-7T$!S1=HV2Gqat>uUt{HqNOr}v zfB}ck1BHqo+K;70_&Pc_;c)6B7z1Auy-@I1cj3rKXmvw}@Ddl-Tb(Y7-Uj4gP`uvh zvQ${>w%XS2d)U+%neuRKvT9GXL7k!;p#K4n$;4@EOcUtVW)(~3V zeIoPyUx{1Sjn@S+g;=75Vwk?2`x;ZH-`lNqw%{C#&J!ul?|SF9dmCvX?rdC(fl1Z1 zwf>;l>9wzg(dEWTqoC7@PYd#%5{S~@K!uoesf&XYk&nP3q6?7r+ub% z#t!1qPH&KUO1IwUmSYU8$A>W6)kx(| zOYQzn584~fC+uw?fK3%KC;?K`c2fI|pp1!|GG&(Kbip)h<16gG>*5sqe2t$jr6xTFOe{C zI&TCE6Rk}-KcjuwTF6M$t@4ni{BI15{s|Jx^Wk&!#ngh5h1^L=&%)m#Y8P6T9rmd} z30p*4CHqY~^T;hB{wp3#`?$n{(JnRPDZ?78;^p*^GG@&0tZQ--L!P2%#~FUjez<1E zE;_4F`t;w&Uat;K8X5p(QDQz{MkaCeY;5hrjDgDH_Lh;yH2~&ua|6)}I4SJE?I0jo zkG&Z20tPoBW@j0aOgxA_n9oqhUZOfiPeUdeE<;N;YVmHU`;oqdrNFpbJ^em%;%r8g z-n|*N^*uIu>tGkWj-hZ;2D@h2Q4%==7j1oVG5R+w&l8_7U}?`f`d3ji@rry+(POV7 z!^rB2wGU~0-`;obTOZ(+5<2A=nvs^w7YrZ49oSNI+A(xv^j1W5QC>mFJkuRv`r<{4 z*^34xv*+fJrDKl*;srb-`ru<|(syBH-6nwS%i!7EFW8R_si6M>@~H=Md295tnkRD` z6X~BpW_NiDobLJ8$Hkd(y$si+ZTIPfS!%b;dP4|{D7!-lb2bDZS07$BH_IAD*gL#P zW5Nal3>fjsw21I8uH7^W`fW6T+u+`edCww3fp8TCJ}>&TwsU8Yfr9=uP9uk1G)sS* zyP+Ckx%0sP4CNK{yM+Ik96LXkEDR0uA3~ zSPpubCpFahVUuT;4Px_fb0C5a5-1JcLKNOuCztko*eYnrY?U%>6(9Ow1q4vWvzj>2 z*egj@Y)=wKiR}qOf;Ya7dof>z{^ldtW~;)7_Rl$cHRx59&1zEGtYIb>UVtLnX(Pa% z5pB+gUJq=9#^&$OBGPYibE?9essfj4AESpczytIuK?soPUu_hFst5@qL4&GD3R8fq z?#2%9K6&jmSg>mtnmVn)?lYNn-a%*|7p>lhgr5kr*TH#5SG0m^9vNtMR4_drv+tNWUN6LG6usFC^z&j*Y~5HHR^8&Y(>2&C|wI%F~zHWRsRawgW@~to86!4 z>D%mFyd^IJF(}W*bn2*eeV3Dcz~q;h{2eBLmkC|^L1pxEL7@W$Km~3l0J=&mRkec6&4Hz&##Z~X=fH}IA_st!;47J{}lZnSbz52FAVMP z71WSr3hurQ8U(hA{Vl-)+Ir{6sBxPFw)cG(ILi{Q_9%FUh(X6J)WSAr2pgefTIcj` zmNq@$vOktDkL4?4`Rd>|;sS6SHojo_uj~;wVTJBZ4em$_XhB-yy=hR%G+Jho8HB?f z1zeX)f3x70z+l);QkOWl3$(Hk+@1qx^^;mMy$*4b%)x#cB<+2^2-NlQtFi*i%l{>F39=Gv9$Ug@0No}|)6t&h(9^r-3f za6>Kb$E|L*VmdwSAi--1S!b;Mzq|^!`0sN0*O)MvqyIS*ZbUOeAi?Hcc+enjI=D01 z3A^AmzR^31xMxI5r2s`a)JFf{El+5OLk%P?$SxZzh_7)l)a&dXCU(;{5p0T=)EiLo zmo5uG@)wQ3*tdV19BdQaESKDg%4yB1A$q_s1uuWI zP*3Mkp0!U7xf!_{c%E>(gCjhQVQ(|$w3Bl{K7CjJORSc4Fm$5IZaEk)L@9B;+2s8I)9Bfk8^oj9iBycU-YH zzy~2st$j%GEC&yJmcxBI=90=L{CC;Cy)tN~lF)YG&oY2NQ~Q<~DH|2Z_=Dkar@gNKF-r$9 zWG2TD3Sv;ue#v(6GE-D#0z6-WOGYKJP=3<|?z{60qU2pQ@$fljDCjF7uj z#HoPGZ6(|Z-~l1#WUCUMMYwcJ$Wy^W;6y>ef-T~a7;9J2c<)A9*+kMTg+eD^GkcF&Tp7P0$kzWZ$6 zY`=hhCCS#3cbQJ6^LJ5<4IjBYa@jSU2NJIMQ=DPL#l1h>e;=3QdqAehpuF%v;mX2> zNwWs^MNlvdnG?iMpoOOoqlNl2_1AbWzb5CjSU{xltRV#?pGX1?2DPf6ezKMH)K%x5&Yv0>U#& zCof*cO>xoaJ@)!H>>!m!DV+&wM#eT}v7jo8nSv%U25xH*842_6IKdw?5ppMp;YHeh zHgMZ(&x-U5{Gpsh!*gtS?jZdRMcIb#sQ*La{TwE;a6ih2I?68?w6kiT6-9LkY`Ox! zLSA1RYneuy{&S2SUZtEvfh6wY7yXrx_`VIh+8X>kEGihTa1dPGLktEwmAOpGYd{DF z!-o#tkJlB!yKzm1e;6_UJ{Y#+CUjid^!LkP4~T#c>v3K2#yo7O3gR{VZ-XC~_Nx*_ z;a_lYO)|62%ZQ_V1G^AC6|t1BfOx^xVeKO>fc;cS#HuXwGDd*?D9nl7vBanVV=tz} z+tH)8Y3!_qozL(uJ#uHGmW=w&qjd@znFGe*sRgp5+kike(;^%wcZRST2yCXIp;hn0 z&pv$LDap<74?=3m-0X&)J&jpr*;g#fquHKDHOmjx@GVv*M&T`?AD+wLR|foMhVWYz z_>CfCbAT}ZhV&s~+VMQ{OX>vkH~!(fh(Cd0oZS5daS?y&zXOb4wjRF*F0=iZ+ilW5 zab3-X8yM%@z)wCCH!q$=m0X;faFBSY6{pNkj685Wc+O|}$Xx4;a--@59){FN1mZHe z2vaS0nVil3T|ieW{FD5P%edDjcb}M;2?)oy+{Kk|Jo2xyj+-1R4L{^1ipMF4#fjn=eJpD%8y&f{0X%~be=Ju1_3U7eg4SJruhby~ccRIvG7W{j*5kY}i*DHd6f?ro%ro50TK1iSwMuIgo=& zs}cTXb?Y2&6G!qtDSOe;=j=e8%j~wTI=hc{s<*=)I3)Cr;Xg)*I!~$P~t1h$$g8f%zcdsZ|cZx2Jvu< zOI3V>;#m>%LiAFRTmr^13CUA5jz<3?{;;>x4PPNKMlT|vr9;bwU*Gd|&FrS;f-QL+DU!KF96+)!kqA=6nInGzLWn~jkPsJgi#X=Q?^XAFY`lm-dbg^( zUcIV%uj;+udo{1s>kh-?e)w|xgVT)tgBsIE$Hz^ST5q{8_iEJE4%ZFhI&KGw2QrW zS3my2-wz+$=or+4r-ovoypEFm9+fQl+8H)6QYNwReSKo~*u+99kKdE#E%w$gCbl$o zHEEw=ehJ0s>%yH;OpLAgmSleU3`_MVC0Ti@t+R7%;wUOc9ivuLeb;zmK4sGBGx46Z zo>rm1;e8X|^+!Dcl5cS-lh&wzSoAY{+L2jF>ZPM3v%F3k?DMQi zBa^n^dn2Bt-0xw>@O*kBTix~&-kz`67GBD;Wkv0JK{ygzkv82H}h3;xgUZtlbb zzUg&BPxO5+^)>~^wr##Dyj~o&Uyq~x0K0?FpT;RpU~>R1!en#*+xb^fL&!Af1Vb#G zTpk`|jm{45^xKfICvaXQTJjt!X4;yg8=9dx+KSfD*7P;hHI#-fh@oQ=7oJlnWfWz* z0!Cigt_j;UmF-&Us{|`9ry6JmzF@t+veNM%Wd-*92Uw;l&0SqOux4wEjo+4K(0?|m zN=H_^y0rV2xDAd{-X@!emDk2sr3JoAle+4w6EFH;_r5J_r`al`z?d0*i|wWN*xv1X zYhJ)dhnQbmW=ciKA`^Tg#1^U@Nn| zZa0X6^gs}lW|f3TX)r)V_CTO#`n=;ED9hh4MV*+GtHF2Yt|l5pHx(^wk|)mQ@xqN$ zVin!uD^#sfwNBhtJBWs(G#9qEAZ#Ll@_=CSa)3`7i^BWP&@indo>%W6GrO_qaN(kv zduFN64>EJ#3rAeJUS6Fe_T?#Nki#g@dI{v@Dk|kLa27|0~yNtIZE7DnK zzS%d#9XOgz?bC3k`=yB;)rf+)Wm3yG;8ymeCabch45)lZ?cf}r^8KLGE5P+nH-MR? zkVoP?h#U2jPD8wiu9GHeHK!Zr1Dr=unqwRXDYRBgA{a5&|y12&&A7T37P{ zQV(p2fQ$XL0_;7CJ^q_C;O2jQ#1LSuGZ?X}9kCpARuOm+GD}Yk1k(ya=Dz?#GDGcS#;zUBn!aqzV=kZ zIt|$X4}b@&{5k&utu7ll|9$B^v1L_-@O=Q3BW;plZ)++|F=u@~=ad87Wmrl>h4tp) zy9y2740=26%o{JcJJg;!z5D&^+oJ=;cRPM(ku5~C;0kyGK6iU0BE|V?k_5fTP2)a~ z61?y~T;NqaxhZ`Wr=_5$L*_^ifed*Hg`VUER6U%;TW2RYgz`}q)Z$ndf1FP0AUlD`~pwO&y^ZU%%3WFpid%GUdTC}jm4lgd^OdHS^ zBrwM0@>UpkkjGrd$8=bck?^SJ^*AA-N7Hx~Ckv+{E;!oi}1`Og1m8IR^N;;0(PX;QDf6Nf$`T#}ud z;&3t*7HM2X1sHX@f?QrKQ?-IBTTkLn;DvK7x8oT3notsDrGYmVwA~xc6XA_iR$(P` zW@eO#?-2F7s9Md>9dlMLc2FtVDo+(Jc;0Hg!dDhhy{ z9MmS&@e`Hx{pt~n9Cr~*`~`CwM}Sym>;Obr#Vw{LYl~~G(kgSV>K2PueBwApJJv>S zH>n?^R6)rhwSiUYtGVp+8R~$~$H@0?A#>bOQofDAL7Hevlcv%Hxh%mmW`+iEPmEgN zSz6?oG?7&@f0+RMu_mthE3;9jwkXrPrZT;yBOTe^l3Z4SdiAiSpz#(@IwBZS!vBgQ z+*OGj-{;~Wj)1jO7g4!iy2As33~+i!aFO%4-0SSjE?*0HtL&M8=a}FBe|Rf4G0zNX z6(pSuwF-|u0P_@Mzg|TC95_ybR^@YoqC;j+wLK>Cmj%!N22H=2i>shlTmwNt9PnYb z*O}>s!yWI7NhT4(o`Ln7nD#iVD?|rqP}DjJ*gpcj*vK!jE~)dst^2$Ax|ux)A_dBs z)ek~kuT=mpvi~_aXQgB`$OFR4iJ8Nay5n|GcLmnYf*~Prs`Q7*4yzhJS z({?*%^!(|){@*){{l_+z!a@CG6ul22nBWt3z=b2+iSxyx?H+i-6aH`5fiD8lKplus zM5r4g7ERQl;GeTz>nrTQKKqJ8_PF8i3_R+iZ=e)J^uG2c6|R4tHU}H8-H}*-_5g!af0WIai){9m}G;~WH6S4Bb`)5qNPl- zVItGPI4M<87CN0IWvaE*NuiRwsCE;(+%$4o{kl*`#vjgdp-o2)hjLJ5Pi0@v%5rjQ z!qo?}n znG56OrE&8#H=S3%=@&{!)w^MWGF9nR8xN|L#?hY|t`!BN@)!0j%#2oMYPCs&fCf9b zxv;D%`cEY6o5)l{Uw&w!wXv#6yF_cH)CRR1_V1Y6SgtZ|mW#hIbr+*Af3Y_%rgASG zOj30O7p8kk;*9owqS8^3_di$1;giWyu2tPFPfcg#7wBpoOyJT# zf?!_A>5Uza(;M?H-@tp5w;<7vk-!Pv(24E1$M2xO%R8?6E>;i7KlG4=iakMXfY zEFqN;i^#W#ZJgY(*xa6ngtSA%D=ffnu z9;o*c>-;2DG7)m1)@Qj)3FG_PG#kh&qZ|Z&ZE3VEwFY-EztmzMqtZk-LhvAf3|QP zg^`8AfN)O_0hi){7+l|j7=cG0-<|UdhO-8T3t~79Cid1nh>zbp#~#kwRUwwBh{WX+ zag4M3z9LuSj4Un*BZ?gSGTxtLx*8PI^1&x)YtkTKXM3}fh^Ma+5gCO=AE;RZZFhfC z4AO~yfREMM*IPixZ!PFvwP2|)zpABr7t>#_rXpW_in7$tL~F$?XO1w0=U*T8e6@uS z^#+7-^=xX~iOkI=ao&1KI_b4v7o_SfTKa7g6x9cBtYTjGi&+I$1|8?4gYK*D6~U_a z=zspOPy?ynrVV-|$jvL4+i<@9f86>IlQnsaIYb|D`3y)`c@~S#F7Nna-2q z$syAC?t%|3s@7S}G?5XjY><^{UfF;$jb#o&Mzy(r0aZG}Wzw&TBbng`2C{eX6C0$n=8tp-1~X!uz#TtJbMg2kiSrmr88*rO%xG(A!8 zFRs*muo;=*sdsVFRZta>|8hW0OOW> zmnp_3aikffnhoA@Vz0|@y4y(bF;5)NecQF^=R>UOMYu`Lrse5^er{j7Dvjl&l!|hg zy+CLaF2m>qSF00y8L$~l5yBO1b}19C+1iBrxTMS`@&P3b-GZ4odHTu~&EVOWf$jmbBgaWBOU3&@-w-<|2 zQ;Yky%yU`sw{P0`lJeJ+505TSY0BGn2>nX=-;3X0pUj?aGe6VDgNx>%l}{iWOW)FX zPzN*SNbMI(Q-?(6OHzlNC`Ir9d{}U_60X95Q$phiBx~E_$&6T*&5g<>bHeDXAdcbj zI=di3mIdol-9x51;jEph%ifgc6|*iQi$yzJl{vGnYYGk5v*E^2! zqSHwd8*dPf$6?aN(o{QP^giY=>p8W=H&)^BK!LT}VC9+#Pnz z&Uog7%iU&A9qpbw!aH-aq8<4d1y7s`}nF%7V574-5p%W9&M)>tZ$A5Np|H zybtX->FO4cNY^1v3({;Y5!nVJ+n{gF5!ukyocjzI;;x%16RmBQpVR`fJM1Zc!hy^> zNuq1%<{7ur+yV~}&vq74rLA;FY^*V?+QgZ+)7!#1W{~Z3;o!?po9RZnncfmxfApa5 z?MEM$+j#EE;mI%_v?ns@M@qF164?%gps6_eq&*3x9`=WmFxIO5sy>W{vVAa(h3d4! zSmZ1+nItM4sa?C_1zEKu12!cU#dIf$6#fgX%sLt+d zh97mf46xsy*cb2b9VX*wFYJ#(c?{-oPeu@F?+qCalDPL~5}yqFdWhkNi3SjRV{9~1 zdnd2wqj{@lN=M_}$?S{rozYO~elniyZ;{+|37I?7>DZUGXgFW0|HLgv5nyoj$K;Tv zR?&3u&aZ#7WVgUzC0l)7rE_VxX;|y($>`xU0=@!jtI@`}G_pbCT$9(1{>J6e#i+7IDQ?S+{njdp{5{DmLNHO z4d9xHmr!9O7UEwM@kdxTN9TfUWY~NKTYIJfPwQRr2D5c-qIZ~#$R(AjTFSzO@`dxS zUSFGytK@|g0I_bIcw&4B_ejRZr5?B>$;hk>BHgo|(t9xL>DeSQfto%T$Z&F)6RkBY ziG@)VOS-@O5`o~4BQXqPQ&7{fDGcH19yT9DofnLc8--(KT&Qtk9QCf@s#^^uoFW|c zB%HJh2)`v?qJ2tBvoh5yNT_aoAxti0clFW|7nE*!?n9AO-4f)pSSmyX>qp&{rCOFS zx`#b^BEL!}m*4hHi}vX@R4Xx3QqRt`m(5T(eX8HP9qEUbnWm%kq-n zmQXa4MWR-%^Y#wHzD}eykWN98TgWd{Lov$wlWANF9~Ds4eKgE%@-lBafm7yXXO#y| z9esel%3WUPzT@JnMKttfaJiVnH*aE9i*FDwMwkyAp8w>Jv2Wj_-SLnsldx~%RRQ7* zk_qNZeANzNR@S#cib7yX@n3 zR>4<*R969SRXgDI(_&gp*_oGl4}bj;v&91?`r7GfTEm(N(z^b>eLmQEsnKs*$^O=%h1 zP2o7284uxL8s(vKnN3W6I-xv{8(9cHz)D48Gmnfnu;%V!RCZjx%B#FpAVa^6ah&fXT zdWsoEqA@76%H?|)E!9STuoRhKgOZ+$MJR7cO&-*SzYb|ISC^N&+q+mHe?^T&yL6+( z7H!M<-yyG))O4jBkKbZjW&gw#$RuHoj=YDt|EugaRQyk}Th9MzF13a1))xP8gXr&L zuG2)_JRMJFc^E(rk431AGZAK|e_0qGMx#k2DF@m@S(#w&r>7`1PEAGOFT-q?s&bVV z3uHMq$5@|~WRzCT7B5LBUor1X8xQWF_J)P*lLaUWnAZC#eR;v#!OKB2@Po>~0l%V~ AO#lD@ literal 0 HcmV?d00001 diff --git a/v3/model/model/conviction_helper_functions.py b/models/v3/model/model/conviction_helper_functions.py similarity index 95% rename from v3/model/model/conviction_helper_functions.py rename to models/v3/model/model/conviction_helper_functions.py index 2fb9839..dfb88a3 100644 --- a/v3/model/model/conviction_helper_functions.py +++ b/models/v3/model/model/conviction_helper_functions.py @@ -5,17 +5,17 @@ import matplotlib.pyplot as plt import matplotlib.colors as colors import matplotlib.cm as cmx import seaborn as sns -from .sys_params import * -def trigger_threshold(requested, funds, supply, alpha): +def trigger_threshold(requested, funds, supply, alpha, params): ''' Function that determines threshold for proposals being accepted. ''' + share = requested/funds - if share < sys_params['beta']: - threshold = sys_params['rho']*supply/(sys_params['beta']-share)**2 * 1/(1-alpha) + if share < params['beta']: + threshold = params['rho']*supply/(params['beta']-share)**2 * 1/(1-alpha) return threshold else: return np.inf @@ -83,7 +83,7 @@ def gen_new_participant(network, new_participant_holdings): a_rv = a_rv = np.random.uniform(-1,1,1)[0] network.edges[(i, j)]['affinity'] = a_rv - network.edges[(i,j)]['tokens'] = a_rv*network.nodes[i]['holdings'] + network.edges[(i,j)]['tokens'] = 0 network.edges[(i, j)]['conviction'] = 0 network.edges[(i,j)]['type'] = 'support' @@ -92,7 +92,7 @@ def gen_new_participant(network, new_participant_holdings): -def gen_new_proposal(network, funds, supply, funds_requested): +def gen_new_proposal(network, funds, supply, funds_requested,params): ''' Definition: Driving processes for the arrival of proposals. @@ -118,7 +118,7 @@ def gen_new_proposal(network, funds, supply, funds_requested): network.nodes[j]['funds_requested'] =funds_requested - network.nodes[j]['trigger']= trigger_threshold(funds_requested, funds, supply,sys_params['alpha']) + network.nodes[j]['trigger']= trigger_threshold(funds_requested, funds, supply, params['alpha'],params) participants = get_nodes_by_type(network, 'participant') proposing_participant = np.random.choice(participants) @@ -424,10 +424,10 @@ def affinities_plot(df, dims = (8.5, 11) ): -def trigger_sweep(field, trigger_func,supply=10**9): +def trigger_sweep(field, trigger_func,params,supply=10**9): ''' ''' - xmax= sys_params['beta'] + xmax= params['beta'] if field == 'effective_supply': share_of_funds = np.arange(.001,xmax,.001) @@ -442,8 +442,8 @@ def trigger_sweep(field, trigger_func,supply=10**9): sof = share_of_funds[sof_ind] for ts_ind in range(len(total_supply)): ts = total_supply[ts_ind] - tc = ts /(1-sys_params['alpha']) - trigger = trigger_func(sof, 1, ts,sys_params['alpha']) + tc = ts /(1-params['alpha']) + trigger = trigger_func(sof, 1, ts, params['alpha'],params) demo_data_Z0[sof_ind,ts_ind] = np.log10(trigger) demo_data_Z1[sof_ind,ts_ind] = trigger demo_data_Z2[sof_ind,ts_ind] = trigger/tc #share of maximum possible conviction @@ -454,7 +454,7 @@ def trigger_sweep(field, trigger_func,supply=10**9): 'log10_share_of_max_conv':demo_data_Z3, 'total_supply':total_supply, 'share_of_funds':share_of_funds, - 'alpha':sys_params['alpha']} + 'alpha':params['alpha']} elif field == 'alpha': #note if alpha >.01 then this will give weird results max alpha will be >1 alpha = np.arange(0,.5,.001) @@ -471,7 +471,7 @@ def trigger_sweep(field, trigger_func,supply=10**9): ts = supply a = alpha[a_ind] tc = ts /(1-a) - trigger = trigger_func(sof, 1, ts, a) + trigger = trigger_func(sof, 1, ts, a, params) demo_data_Z4[sof_ind,a_ind] = np.log10(trigger) demo_data_Z5[sof_ind,a_ind] = trigger demo_data_Z6[sof_ind,a_ind] = trigger/tc #share of maximum possible conviction @@ -542,7 +542,7 @@ def trigger_grid(supply_sweep, alpha_sweep): cb1.set_label('log10 of conviction to trigger') -def initialize_network(n,m, initial_funds, supply): +def initialize_network(n,m, initial_funds, supply, params): ''' Definition: Function to initialize network x object @@ -584,7 +584,7 @@ def initialize_network(n,m, initial_funds, supply): r_rv = gamma.rvs(3,loc=0.001, scale=500) network.nodes[j]['funds_requested'] = r_rv - network.nodes[j]['trigger']= trigger_threshold(r_rv, initial_funds, initial_supply,sys_params['alpha']) + network.nodes[j]['trigger']= trigger_threshold(r_rv, initial_funds, initial_supply, params['alpha'],params) for i in range(n): network.add_edge(i, j) diff --git a/v3/model/model/participants.py b/models/v3/model/model/participants.py similarity index 86% rename from v3/model/model/participants.py rename to models/v3/model/model/participants.py index 9fa86a0..e12515c 100644 --- a/v3/model/model/participants.py +++ b/models/v3/model/model/participants.py @@ -22,9 +22,9 @@ def check_progress(params, step, sL, s): for j in proposals: if network.nodes[j]['status'] == 'active': grant_size = network.nodes[j]['funds_requested'] - likelihood = 1.0/(sys_params['base_completion_rate']+np.log(grant_size)) + likelihood = 1.0/(params['base_completion_rate']+np.log(grant_size)) - failure_rate = 1.0/(sys_params['base_failure_rate']+np.log(grant_size)) + failure_rate = 1.0/(params['base_failure_rate']+np.log(grant_size)) if np.random.rand() < likelihood: completed.append(j) elif np.random.rand() < failure_rate: @@ -109,27 +109,23 @@ def participants_decisions(params, step, sL, s): proposals = get_nodes_by_type(network, 'proposal') candidates = [j for j in proposals if network.nodes[j]['status']=='candidate'] - gain = .01 - delta_holdings={} + delta_holdings = {} proposals_supported ={} for i in participants: - engagement_rate = .03*network.nodes[i]['sentiment'] + engagement_rate = params['base_engagement_rate']*network.nodes[i]['sentiment'] - #engagement_rate = .3*network.nodes[i]['sentiment'] if np.random.rand() cutoff: support.append(j) @@ -139,7 +135,7 @@ def participants_decisions(params, step, sL, s): delta_holdings[i] = 0 proposals_supported[i] = [j for j in candidates if network.edges[(i,j)]['tokens']>0 ] - return({'delta_holdings':delta_holdings, 'proposals_supported':proposals_supported}) + return({'delta_holdings':delta_holdings,'proposals_supported':proposals_supported}) # Mechanisms def update_tokens(params, step, sL, s, _input): @@ -156,7 +152,7 @@ def update_tokens(params, step, sL, s, _input): participants = get_nodes_by_type(network, 'participant') for i in participants: - network.nodes[i]['holdings'] = network.nodes[i]['holdings']+delta_holdings[i] + network.nodes[i]['holdings'] = network.nodes[i]['holdings'] + delta_holdings[i] supported = proposals_supported[i] total_affinity = np.sum([ network.edges[(i, j)]['affinity'] for j in supported]) for j in candidates: @@ -168,12 +164,12 @@ def update_tokens(params, step, sL, s, _input): prior_conviction = network.edges[(i, j)]['conviction'] current_tokens = network.edges[(i, j)]['tokens'] - network.edges[(i, j)]['conviction'] =current_tokens+sys_params['alpha']*prior_conviction + network.edges[(i, j)]['conviction'] =current_tokens+params['alpha']*prior_conviction for j in candidates: network.nodes[j]['conviction'] = np.sum([ network.edges[(i, j)]['conviction'] for i in participants]) total_tokens = np.sum([network.edges[(i, j)]['tokens'] for i in participants ]) - if total_tokens < sys_params['min_supp']: + if total_tokens < params['min_supp']: network.nodes[j]['status'] = 'killed' key = 'network' diff --git a/v3/model/model/proposals.py b/models/v3/model/model/proposals.py similarity index 95% rename from v3/model/model/proposals.py rename to models/v3/model/model/proposals.py index 51e4f16..264ff44 100644 --- a/v3/model/model/proposals.py +++ b/models/v3/model/model/proposals.py @@ -1,7 +1,6 @@ import numpy as np from .conviction_helper_functions import * import networkx as nx -from .sys_params import * # Behaviors @@ -21,8 +20,8 @@ def trigger_function(params, step, sL, s): if network.nodes[j]['status'] == 'candidate': requested = network.nodes[j]['funds_requested'] age = network.nodes[j]['age'] - threshold = trigger_threshold(requested, funds, supply, sys_params['alpha']) - if age > sys_params['tmin']: + threshold = trigger_threshold(requested, funds, supply, params['alpha'],params) + if age > params['tmin']: conviction = network.nodes[j]['conviction'] if conviction >threshold: accepted.append(j) @@ -118,7 +117,7 @@ def update_proposals(params, step, sL, s, _input): affinities = [network.edges[(i,p)]['affinity'] for p in proposals if not(p in accepted)] if len(affinities)>1: max_affinity = np.max(affinities) - force = network.edges[(i,j)]['affinity']-sys_params['sensitivity']*max_affinity + force = network.edges[(i,j)]['affinity']-params['sensitivity']*max_affinity else: force = 0 diff --git a/models/v3/model/model/sys_params.py b/models/v3/model/model/sys_params.py new file mode 100644 index 0000000..d30ccba --- /dev/null +++ b/models/v3/model/model/sys_params.py @@ -0,0 +1,25 @@ +import numpy as np + +# Initial values +initial_values = { + 'initial_sentiment': 0.6, + 'n': 30, #initial participants + 'm': 7, #initial proposals + 'initial_funds': 4867.21, # in honey, as of 8-5-2020 + 'supply': 22392.22, # Honey total supply balance as of 8-5-2020 +} + +# Parameters +params = { + 'beta': [0.2], # maximum share of funds a proposal can take + 'rho': [0.0025], # tuning param for the trigger function + 'alpha': [0.875], # timescale set in days with 3 day halflife (from comments in contract comments) + 'gamma': [0.001], # expansion of supply per per day + 'sensitivity': [.75], + 'tmin': [0], #unit days; minimum periods passed before a proposal can pass + 'min_supp': [1], #number of tokens that must be stake for a proposal to be a candidate + 'base_completion_rate': [45], + 'base_failure_rate': [180], + 'base_engagement_rate' :[0.3], + 'lowest_affinity_to_support': [0.3], +} \ No newline at end of file diff --git a/v3/model/model/system.py b/models/v3/model/model/system.py similarity index 78% rename from v3/model/model/system.py rename to models/v3/model/model/system.py index af105c7..fe13a5a 100644 --- a/v3/model/model/system.py +++ b/models/v3/model/model/system.py @@ -4,7 +4,6 @@ import pandas as pd from .conviction_helper_functions import * import networkx as nx from scipy.stats import expon, gamma -from .sys_params import * @@ -31,8 +30,6 @@ def driving_process(params, step, sL, s): len_parts = len(participants) supply = s['effective_supply'] - funds_arrival = supply * 0.0001 - expected_holdings = .01*supply/len_parts if new_participant: h_rv = expon.rvs(loc=0.0, scale=expected_holdings) @@ -58,7 +55,7 @@ def driving_process(params, step, sL, s): new_proposal = bool(rv2<1/proposal_rate) new_proposal_ct = int(1-median_affinity)+1 - expected_request = sys_params['beta']*s['funds']/10 + expected_request = params['beta']*s['funds']/10 new_proposal_requested = [expon.rvs(loc=expected_request/10, scale=expected_request) for ct in range(new_proposal_ct)] sentiment = s['sentiment'] @@ -69,18 +66,18 @@ def driving_process(params, step, sL, s): scale_factor = 1 #this shouldn't happen but expon is throwing domain errors - if sentiment>.4: - funds_arrival = expon.rvs(loc = 0, scale = scale_factor) - else: - funds_arrival = 0 + # if sentiment>.4: + # funds_arrival = expon.rvs(loc = 0, scale = scale_factor) + # else: + # funds_arrival = 0 return({'new_participant':new_participant, #True/False 'new_participant_holdings':new_participant_holdings, #funds held by new participant if True 'new_proposal':new_proposal, #True/False 'new_proposal_ct': new_proposal_ct, #int 'new_proposal_requested':new_proposal_requested, #list funds requested by new proposal if True, len =ct - 'funds_arrival':funds_arrival}) #quantity of new funds arriving to the communal pool - + # 'funds_arrival':funds_arrival #quantity of new funds arriving to the communal pool (donations or revenue) + }) # Mechanisms def update_network(params, step, sL, s, _input): @@ -102,7 +99,7 @@ def update_network(params, step, sL, s, _input): if new_proposal: for ct in range(_input['new_proposal_ct']): funds_req = _input['new_proposal_requested'][ct] - network= gen_new_proposal(network,funds,supply, funds_req) + network= gen_new_proposal(network,funds,supply, funds_req,params) #update age of the existing proposals proposals = get_nodes_by_type(network, 'proposal') @@ -111,7 +108,7 @@ def update_network(params, step, sL, s, _input): network.nodes[j]['age'] = network.nodes[j]['age']+1 if network.nodes[j]['status'] == 'candidate': requested = network.nodes[j]['funds_requested'] - network.nodes[j]['trigger'] = trigger_threshold(requested, funds, supply, sys_params['alpha']) + network.nodes[j]['trigger'] = trigger_threshold(requested, funds, supply, params['alpha'],params) else: network.nodes[j]['trigger'] = np.nan @@ -120,44 +117,47 @@ def update_network(params, step, sL, s, _input): return (key, value) -def increment_funds(params, step, sL, s, _input): - ''' - Increase funds by the amount of the new particpant's funds. - ''' - funds = s['funds'] - funds_arrival = _input['funds_arrival'] - - #increment funds - funds = funds + funds_arrival - - key = 'funds' - value = funds - - return (key, value) def increment_supply(params, step, sL, s, _input): ''' - Increase funds by the amount of the new particpant's funds. + Increase supply by the amount of the new particpant's funds. ''' supply = s['effective_supply'] - funds_arrival = _input['funds_arrival'] - #increment funds - supply = supply + funds_arrival #/2 * 0.0001 + if _input['new_participant_holdings']: + supply = supply + _input['new_participant_holdings'] - key = 'supply' + key = 'effective_supply' value = supply return (key, value) -def fund_arrival_check(params, step, sL, s, _input): +# Behaviors +# Substep 2 +def minting_rule(params, step, sL, s): + supply = s['total_supply'] + tokens_to_mint = params['gamma'] * supply + return ({'mint':tokens_to_mint}) + +# Mechanisms +def mint_to_supply(params, step, sL, s, _input): ''' - Increase funds by the amount of the new particpant's funds. ''' + mint = _input['mint'] + supply = s['total_supply'] + + key = 'total_supply' + value = supply + mint - funds_arrival = _input['funds_arrival'] - - key = 'funds_arrival' - value = funds_arrival + return (key, value) + +def mint_to_funds(params, step, sL, s, _input): + ''' + ''' + mint = _input['mint'] + funds = s['funds'] + + key = 'funds' + value = funds + mint return (key, value) \ No newline at end of file diff --git a/v3/model/partial_state_update_block.py b/models/v3/model/partial_state_update_block.py similarity index 85% rename from v3/model/partial_state_update_block.py rename to models/v3/model/partial_state_update_block.py index 04434e8..63044c5 100644 --- a/v3/model/partial_state_update_block.py +++ b/models/v3/model/partial_state_update_block.py @@ -11,9 +11,17 @@ partial_state_update_blocks = [ }, 'variables': { 'network': update_network, - 'funds':increment_funds, 'effective_supply': increment_supply, - 'funds_arrival' : fund_arrival_check, + } + }, + { + 'policies': { + 'random': minting_rule + }, + 'variables': { + 'total_supply': mint_to_supply, + 'funds':mint_to_funds, + } }, { diff --git a/v3/model/run.py b/models/v3/model/run.py similarity index 96% rename from v3/model/run.py rename to models/v3/model/run.py index 643feee..963b030 100644 --- a/v3/model/run.py +++ b/models/v3/model/run.py @@ -42,6 +42,7 @@ def postprocessing(df, sim_ind=-1): # Extract information from dataframe df['conviction'] = df.network.apply(lambda g: np.array([g.nodes[j]['conviction'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate'])) + df['participant_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'participant') if g.nodes[j]['type']=='participant'])) df['candidate_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate'])) df['candidate_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate'])) df['killed_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='killed'])) diff --git a/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb b/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb deleted file mode 100644 index c03657e..0000000 --- a/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb +++ /dev/null @@ -1,1518 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Aragon Conviction Voting Model - Version 3\n", - "\n", - "New to this model are the following elements:\n", - "\n", - "* Adding the realism that not all participant tokens are being allocated to proposals.\n", - "* Refactored parameters and system initialization to make more readable and consistent.\n", - "* Making the distinction between effective and total supply.\n", - "* Refining alpha calculations to more accurately reflect the 1Hive implementation. Discussion of alpha and its relation to alpha in the contract and how it relates to the timescales\n", - "* Updated differential specification and write-up to respect new state variables\n", - "* Moved all unit denominations to honey.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# An Introduction to Conviction Voting\n", - "\n", - "Conviction Voting is an approach to organizing a communities preferences into discrete decisions in the management of that communities resources. Strictly speaking conviction voting is less like voting and more like signal processing. Framing the approach and the initial algorithm design was done by Michael Zargham and published in a short research proposal [Social Sensor Fusion](https://github.com/BlockScience/conviction/blob/master/social-sensorfusion.pdf). This work is based on a dynamic resource allocation algorithm presented in Zargham's PhD Thesis.\n", - "\n", - "The work proceeded in collaboration with the Commons Stack, including expanding on the pythin implementation to makeup part of the Commons Simulator game. An implemention of Conviction Voting as a smart contract within the Aragon Framework was developed by 1hive.org and is currently being used for community decision making around allocations their community currency, Honey.\n", - "\n", - "\n", - "## The Word Problem\n", - "\n", - "Suppose a group of people want to coordinate to make a collective decision. Social dynamics such as discussions, signaling, and even changing ones mind based on feedback from others input play an important role in these processes. While the actual decision making process involves a lot of informal processes, in order to be fair the ultimate decision making process still requires a set of formal rules that the community collecively agrees to, which serves to functionally channel a plurality of preferences into a discrete outcomes. In our case we are interested in a procedure which supports asynchronous interactions, an provides visibility into likely outcomes prior to their resolution to serve as a driver of good faith, debate and healthy forms of coalition building. Furthermore, participations should be able to show support for multiple initiatives, and to vary the level of support shown. Participants a quantity of signaling power which may be fixed or variable, homogenous or heterogenous. For the purpose of this document, we'll focus on the case where the discrete decisions to be made are decisions to allocate funds from a shared funding pool towards projects of interest to the community.\n", - "\n", - "## Converting to a Math Problem\n", - "\n", - "Let's start taking these words and constructing a mathematical representation that supports a design that meets the description above. To start we need to define participants.\n", - "\n", - "### Participants\n", - "Let $\\mathcal{A}$ be the set of participants. Consider a participant $a\\in \\mathcal{A}$. Any participant $a$ has some capacity to participate in the voting process $h[a]$. In a fixed quantity, homogenous system $h[a] = h$ for all $a\\in \\mathcal{A}$ where $h$ is a constant. The access control process managing how one becomes a participant determines the total supply of \"votes\" $S = \\sum_{a\\in \\mathcal{A}} = n\\cdot h$ where the number of participants is $n = |\\mathcal{A}|$. In a smart contract setting, the set $\\mathcal{A}$ is a set of addresses, and $h[a]$ is a quantity of tokens held by each address $a\\in \\mathcal{A}$. \n", - "\n", - "### Proposals & Shares Resources\n", - "Next, we introduce the idea of proposals. Consider a proposal $i\\in \\mathcal{C}$. Any proposal $i$ is associated with a request for resources $r[i]$. Those requested resources would be allocated from a constrained pool of communal resources currently totaling $R$. The pool of resources may become depleted because when a proposal $i$ passes $R^+= R-r[i]$. Therefore it makes sense for us to consider what fraction of the shared resources are being request $\\mu_i = \\frac{r[i]}{R}$, which means that thre resource depletion from passing proposals can be bounded by requiring $\\mu_i < \\mu$ where $\\mu$ is a constant representing the maximum fraction of the shared resources which can be dispersed by any one proposal. In order for the system to be sustainable a source of new resources is required. In the case where $R$ is funding, new funding can come from revenues, donations, or in some DAO use cases minting tokens.\n", - "\n", - "### Participants Preferences for Proposals\n", - "\n", - "Most of the interesting information in this system is distributed amongst the participants and it manifests as preferences over the proposals. This can be thought of as a matrix $W\\in \\mathbb{R}^{n \\times m}$.\n", - "![Replace this later](https://i.imgur.com/vxKNtxi.png)\n", - "\n", - "These private hidden signals drive discussions and voting actions. Each participant individually decides how to allocate their votes across the available proposals. Participant $a$ supports proposal $i$ by setting $x[a,i]>0$ but they are limited by their capacity $\\sum_{k\\in \\mathcal{C}} x[a,k] \\le h[a]$. Assuming each participant chooses a subset of the proposals to support, a support graph is formed.\n", - "![](https://i.imgur.com/KRh8tKn.png)\n", - "\n", - "## Aggregating Information\n", - "\n", - "In order to break out of the synchronous voting model, a dynamical systems model of this system is introduced.\n", - "\n", - "### Participants Allocate Voting Power\n", - "![](https://i.imgur.com/DZRDwk6.png)\n", - "\n", - "### System Accounts Proposal Conviction\n", - "![](https://i.imgur.com/euAei5R.png)\n", - "\n", - "### Understanding Alpha\n", - "* https://www.desmos.com/calculator/x9uc6w72lm\n", - "* https://www.desmos.com/calculator/0lmtia9jql\n", - "\n", - "\n", - "## Converting Signals to Discrete Decisions\n", - "\n", - "Conviction as kinetic energy and Trigger function as required activation energy.\n", - "\n", - "### The Trigger Function\n", - "\n", - "https://www.desmos.com/calculator/yxklrjs5m3\n", - "\n", - "Below we show a sweep of the trigger function threshold:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", - " return false;\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%javascript\n", - "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", - " return false;\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "for reference: max conviction = 5.25318713934522in log10 units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", - " import pandas.util.testing as tm\n" - ] - } - ], - "source": [ - "from model.model.conviction_helper_functions import *\n", - "from model.model.sys_params import initial_values,sys_params \n", - "\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "\n", - "supply = initial_values['supply']\n", - "alpha = sys_params['alpha']\n", - "\n", - "mcv = supply/(1-alpha)\n", - "print('for reference: max conviction = '+str(np.log10(mcv))+'in log10 units')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "supply_sweep = trigger_sweep('effective_supply',trigger_threshold, supply)\n", - "alpha_sweep = trigger_sweep('alpha',trigger_threshold, supply)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "

" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "trigger_grid(supply_sweep, alpha_sweep)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Resolving Passed Proposals\n", - "\n", - "![](images/stockflow_cv_trigger.png)\n", - "\n", - "\n", - "## Social Systems Modeling\n", - "\n", - "Subjective, exploratory modeling of the social system interacting through the conviction voting algorithm.\n", - "\n", - "### Sentiment\n", - "\n", - "Global Sentiment -- the outside world appreciating the output of the community\n", - "Local Sentiment -- agents within the system feeling good about the community\n", - "\n", - "### Social Networks\n", - "\n", - "Preferences as mixing process (social influence)\n", - "\n", - "### Relationships between Proposals\n", - "\n", - "Some proposals are synergistic (passing one makes the other more desireable)\n", - "Some proposals are (parially) substitutable (passing one makes the other less desirable)\n", - "\n", - "### Notion of Honey supply\n", - "#### Total supply = $S$\n", - "#### Effective supply = $E$\n", - "#### Funding Pool = $F$\n", - "#### Other supply = $L$, effectively slack. Funds could be in cold storage, in liquidity pools or otherwise in any address not actively participating in conviction voting.\n", - "$$S = F + E + L$$ \n", - "\n", - "System has the right to do direct mints:\n", - "$$F^+ = F + minted$$\n", - "$$S^+ = S + minted$$\n", - "\n", - "\n", - "Arrival of new funds which come from outside:\n", - "$$L+ = L - donated$$\n", - "$$F+ = F + donated$$\n", - "The above assumes the donated tokens were not in use for voting\n", - "$$L+ = L + tokens$$ that haven't been used in voting recently\n", - "$$E+ = E - tokens$$ that haven't been used in voting recently\n", - "$$L+ = L - tokens$$ that come into use\n", - "$$E+ = E - tokens$$ that come into use\n", - "\n", - "Tokens in $L$ or $E$ are defined at the level of the account holding them.\n", - "\n", - "Total supply $S$ can be made a param and the state supply should be only $E$, effective supply." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## cadCAD Overview\n", - "\n", - "In the cadCAD simulation [methodology](https://community.cadcad.org/t/differential-specification-syntax-key/31), we operate on four layers: **Policies, Mechanisms, States**, and **Metrics**. Information flows do not have explicit feedback loop unless noted. **Policies** determine the inputs into the system dynamics, and can come from user input, observations from the exogenous environment, or algorithms. **Mechanisms** are functions that take the policy decisions and update the States to reflect the policy level changes. **States** are variables that represent the system quantities at the given point in time, and **Metrics** are computed from state variables to assess the health of the system. Metrics can often be thought of as KPIs, or Key Performance Indicators. \n", - "\n", - "At a more granular level, to setup a model, there are system conventions and configurations that must be [followed.](https://community.cadcad.org/t/introduction-to-simulation-configurations/34)\n", - "\n", - "The way to think of cadCAD modeling is analogous to machine learning pipelines which normally consist of multiple steps when training and running a deployed model. There is preprocessing, which includes segregating features between continuous and categorical, transforming or imputing data, and then instantiating, training, and running a machine learning model with specified hyperparameters. cadCAD modeling can be thought of in the same way as states, roughly translating into features, are fed into pipelines that have built-in logic to direct traffic between different mechanisms, such as scaling and imputation. Accuracy scores, ROC, etc. are analogous to the metrics that can be configured on a cadCAD model, specifying how well a given model is doing in meeting its objectives. The parameter sweeping capability of cadCAD can be thought of as a grid search, or way to find the optimal hyperparameters for a system by running through alternative scenarios. A/B style testing that cadCAD enables is used in the same way machine learning models are A/B tested, except out of the box, in providing a side by side comparison of muliple different models to compare and contrast performance. Utilizing the field of Systems Identification, dynamical systems models can be used to \"online learn\" by providing a feedback loop to generative system mechanisms. \n", - "\n", - "\n", - "## Differential Specification \n", - "![](images/Aragon_v2.png)\n", - "\n", - "## Schema of the states - UPDATE\n", - "The model consists of a temporal in memory graph database called *network* containing nodes of type **Participant** and type **Proposal**. Participants will have *holdings* and *sentiment* and Proposals will have *funds_required, status*(candidate or active), *conviction* Tthe model as three kinds of edges:\n", - "* (Participant, participant), we labeled this edge type \"influencer\" and it contains information about how the preferences and sentiment of one participant influence another \n", - "* (Proposal, Proposal), we labeled this edge type \"conflict\" and it contains information about how synergistic or anti-synergistic two proposals are; basically people are likely to support multiple things that have synergy (meaning once one is passed there is more utility from the other) but they are not likely to pass things that have antisynergy (meaning once one is passed there is less utility from the other).\n", - "* The edges between Participant and Proposal, which are described below.\n", - " \n", - "\n", - "Edges in the network go from nodes of type Participant to nodes of type Proposal with the edges having the key *type*, of which all will be set to *support*. Edges from participant $i$ to proposal $j$ will have the following additional characteristics:\n", - "* Each pairing (i,j) will have *affinity*, which determines how much $i$ likes or dislikes proposal $j$.\n", - "* Each participant $i$, assigns its $tokens$ over the edges (i,j) for all $j$ such that the summation of all $j$ such that ```Sum_j = network.edges[(i,j)]['tokens'] = network.nodes[i]['holdings']```. This value of tokens for participants on proposals must be less than or equal to the total number of tokens held by the participant.\n", - "* Each pairing (i,j) will have *conviction* local to that edge whose update at each timestep is computed using the value of *tokens* at that edge.\n", - "* Each proposal *j* will have a *conviction* which is equal to the sum of the conviction on its inbound edges: ```network.nodes[j]['conviction'] = Sum_i network.edges[(i,j)]['conviction']```. \n", - "\n", - "\n", - "The other state variables in the model are *funds*, which is a numpy floating point, and effective supply, as supply.\n", - "\n", - "The system consists of 100 time steps without a parameter sweep or monte carlo.\n", - "\n", - " \n", - "## Partial State Update Blocks - TODO: UPDATE\n", - "\n", - "Each partial state update block is kind of a like a phase in a phased based board game. Everyone decides what to do and it reconciles all decisions. One timestep is a full turn, with each block being a phase of a timestep or turn. We will walk through the individaul Partial State update blocks one by one below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "{\n", - "# system.py: \n", - "'policies': { \n", - " 'random': driving_process\n", - "},\n", - "'variables': {\n", - " 'network': update_network,\n", - " 'funds':increment_funds,\n", - "}\n", - "```\n", - "\n", - "To simulate the arrival of participants and proposal into the system, we have a driving process to represent the arrival of individual agents. We use a random uniform distribution generator, over [0, 1), to calculate the number of new participants. We then use an exponential distribution to calculate the particpant's tokens by using a loc of 0.0 and a scale of expected holdings, which is calculated by .1*supply/number of existing participants. We calculate the number of new proposals by \n", - "```\n", - "proposal_rate = 1/median_affinity * (1+total_funds_requested/funds)\n", - "rv2 = np.random.rand()\n", - "new_proposal = bool(rv2<1/proposal_rate)\n", - "```\n", - "The network state variable is updated to include the new participants and proposals, while the funds state variable is updated for the increase in system funds. \n", - "[To see the partial state update code, click here](model/model/system.py)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "{\n", - " # participants.py \n", - " 'policies': {\n", - " 'completion': check_progress \n", - " },\n", - " 'variables': { \n", - " 'sentiment': update_sentiment_on_completion, #not completing projects decays sentiment, completing bumps it\n", - " 'network': complete_proposal\n", - " }\n", - "},\n", - "```\n", - "\n", - "In the next phase of the turn, [to see the logic code, click here](model/model/participants.py), the *check_progress* behavior checks for the completion of previously funded proposals. The code calculates the completion and failure rates as follows:\n", - "\n", - "```\n", - "likelihood = 1.0/(base_completion_rate+np.log(grant_size))\n", - "\n", - "failure_rate = 1.0/(base_failure_rate+np.log(grant_size))\n", - "if np.random.rand() < likelihood:\n", - " completed.append(j)\n", - "elif np.random.rand() < failure_rate:\n", - " failed.append(j)\n", - "```\n", - "With the base_completion_rate being 100 and the base_failure_rate as 200. \n", - "\n", - "The mechanism then updates the respective *network* nodes and updates the sentiment variable on proposal completion. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - " # proposals.py\n", - " 'policies': {\n", - " 'release': trigger_function \n", - " },\n", - " 'variables': { \n", - " 'funds': decrement_funds, \n", - " 'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment\n", - " 'network': update_proposals \n", - " }\n", - "},\n", - " ```\n", - " \n", - "The [trigger release function](model/model/proposals.py) checks to see if each proposal passes or not. If a proposal passes, funds are decremented by the amount of the proposal, while the proposal's status is changed in the network object." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "{ \n", - " # participants.py\n", - " 'policies': { \n", - " 'participants_act': participants_decisions\n", - " },\n", - " 'variables': {\n", - " 'network': update_tokens \n", - " }\n", - "}\n", - "```\n", - "\n", - "The Participants decide based on their affinity if which proposals they would like to support,[to see the logic code, click here](model/model/participants.py). Proposals that participants have high affinity for receive more support and pledged tokens than proposals with lower affinity and sentiment. We then update everyone's holdings and their conviction for each proposal.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model next steps\n", - "\n", - "The the model described above is the third iteration model that covers the core mechanisms of the Aragon Conviction Voting model. Below are next additional dynamics we can attend to enrich the model, and provide workstreams for subsequent iterations of this lab notebook.\n", - "\n", - "* Mixing of token holdings among participants\n", - "* Departure of participants\n", - "* Proposals which are good or no good together\n", - "* Affects of outcomes on sentiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuration\n", - "Let's factor out into its own notebook where we review the config object and its partial state update blocks." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from model import config" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# pull out configurations to illustrate\n", - "sim_config,genesis_states,seeds,partial_state_update_blocks = config.get_configs()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'N': 1,\n", - " 'T': range(0, 60),\n", - " 'M': [{}],\n", - " 'subset_id': 0,\n", - " 'subset_window': deque([0, None]),\n", - " 'simulation_id': 0,\n", - " 'run_id': 0}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim_config" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'policies': {'random': },\n", - " 'variables': {'network': ,\n", - " 'funds': ,\n", - " 'effective_supply': ,\n", - " 'funds_arrival': }},\n", - " {'policies': {'completion': },\n", - " 'variables': {'sentiment': ,\n", - " 'network': }},\n", - " {'policies': {'release': },\n", - " 'variables': {'funds': ,\n", - " 'sentiment': ,\n", - " 'network': }},\n", - " {'policies': {'participants_act': },\n", - " 'variables': {'network': }}]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "partial_state_update_blocks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialization\n", - "To create the genesis_states, we create our in-memory graph database within networkx. \n", - "\n", - "\n", - "### Parameters\n", - "\n", - "Initial values are the starting values for the simulation and sys_params are global hyperparameters for the simulation.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'initial_sentiment': 0.6,\n", - " 'n': 30,\n", - " 'm': 7,\n", - " 'initial_funds': 4867.21,\n", - " 'supply': 22392.22}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from model.model.sys_params import initial_values,sys_params \n", - "\n", - "initial_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$n$ is initial participants, whereas $m$ is initial proposals" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'beta': 0.2,\n", - " 'rho': 0.0025,\n", - " 'alpha': 0.875,\n", - " 'sensitivity': 0.75,\n", - " 'tmin': 0,\n", - " 'min_supp': 1,\n", - " 'base_completion_rate': 45,\n", - " 'base_failure_rate': 180}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sys_params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* $\\alpha$ : 0.875 The decay rate for previously accumulated conviction\n", - "* $\\beta$ = .2 Upper bound on share of funds dispersed in the example Trigger Function\n", - "* $\\rho$ = 0.002 Scale Parameter for the example Trigger Function\n", - "\n", - "* tmin = 7 unit days; minimum periods passed before a proposal can pass\n", - "* min_supp = 50 number of tokens that must be stake for a proposal to be a candidate" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# import libraries\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from model.model.conviction_helper_functions import * \n", - "\n", - "\n", - "#initializers\n", - "network = genesis_states['network']\n", - "initial_funds = genesis_states['funds']\n", - "initial_sentiment = genesis_states['sentiment']" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'network': ,\n", - " 'funds': 4867.21,\n", - " 'sentiment': 0.6,\n", - " 'effective_supply': 22392.22,\n", - " 'funds_arrival': 0}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genesis_states" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Exploring the State Data Structure\n", - "\n", - "A graph is a type of temporal data structure that evolves over time. A graph $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$ consists of vertices or nodes, $\\mathcal{V} = \\{1...\\mathcal{V}\\}$ and is connected by edges $\\mathcal{E} \\subseteq \\mathcal{V} \\times \\mathcal{V}$.\n", - "\n", - "See *Schema of the states* above for more details\n", - "\n", - "\n", - "Let's explore!" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n", - "proposals = get_nodes_by_type(network, 'proposal')\n", - "participants = get_nodes_by_type(network, 'participant')\n", - "supporters = get_edges_by_type(network, 'support')\n", - "influencers = get_edges_by_type(network, 'influence')\n", - "competitors = get_edges_by_type(network, 'conflict')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'type': 'participant',\n", - " 'holdings': 520.2734462358999,\n", - " 'sentiment': 0.29980234113414117}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#sample a participant\n", - "network.nodes[participants[0]]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Count of Participants')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Let's look at the distribution of participant holdings at the start of the sim\n", - "plt.hist([ network.nodes[i]['holdings'] for i in participants])\n", - "plt.title('Histogram of Participants Token Holdings')\n", - "plt.xlabel('Amount of Honey')\n", - "plt.ylabel('Count of Participants')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Participants Social Network')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb4AAAE+CAYAAADyPXUxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOydd1hUx9fHzy69SN2lF0FQUUDBhoKCWKKCCootiGDvSuyoCBq7YoslVjQqlqiJiCXW2LFiLxGxgopKk77s/b5/EPZ1pS2wqL8wn+fhcbl3Zu7MZb3fe2bOOcMDAGIwGAwGo4bA/9YdYDAYDAbja8KEj8FgMBg1CiZ8DAaDwahRMOFjMBgMRo2CCR+DwWAwahRM+BgMBoNRo2DCx/gu0dTUpISEhDLLvHz5kjQ1NUksFn+lXn0/dO7cmbZt2yZT2dq1a9PJkyeruUfVT3h4OPXv3/9bd4PxH4AJH6NS1K5dm9TU1EhTU5MMDQ0pKCiIMjMzK9WWh4cHbdq0SepYZmYmWVtbl1nPwsKCMjMzSUFBoVLXrQg8Ho/i4+MrVffgwYPUuHFj0tLSIoFAQJ6envTs2bMq9efo0aMUGBhYpTaIiIKCgojH49HVq1clx+Lj44nH48lUf+vWreTm5lblfjAYXxMmfIxKc+jQIcrMzKSbN2/S9evXae7cuRWqD4A4jqum3n0fxMfH04ABAygiIoLS09Pp2bNnNHr06K8i1rKip6dHM2fO/NbdKJOCgoJv3QXGfwgmfIwqY2pqSp07d6Z79+5RamoqeXt7k1AoJF1dXfL29qbXr19Lynp4eNCMGTPI1dWV1NXVKSAggM6fP09jxowhTU1NGjNmDBFJW1g5OTk0ceJEsrS0JG1tbXJzc6OcnBx6/vw58Xg8yUPRw8ODQkJCqHnz5qSlpUXdu3enlJQUybV79epFRkZGpK2tTW3atKH79+9LzgUFBdHo0aPJy8uLatWqRS1atKCnT58SEVGbNm2IiKhRo0akqalJe/bsoQ8fPpC3tzfp6OiQnp4etW7dukQRv3XrFllZWVG7du2Ix+NRrVq1qGfPnmRhYUFERHl5eRQcHEwmJiZkYmJCwcHBlJeXJ6n/ubVYp04dOnbsmGSsRVby06dPydPTk/T19UkgEJC/vz+lpaXJ/PcLDAykO3fu0NmzZ0s8n56eToMHDyZjY2MyNTWlmTNnklgspocPH9KIESPo8uXLpKmpSTo6OvTs2TPS0dGR3IuhQ4eSgYGBpK2AgABasWIFERElJSVRt27dSE9Pj2xsbGjjxo2ScuHh4eTn50f9+/cnLS0t2rp1q1SfRCIR9evXj3r27En5+fkyj5XBIGLCx5ADr169oiNHjpCTkxNxHEcDBw6kFy9e0MuXL0lNTU0iZkVs376dNmzYQJ8+faKtW7dS69atafXq1ZSZmUmrV68u1v6kSZPoxo0bdOnSJUpJSaHFixcTn1/yV/e3336jLVu20Js3b0hRUZHGjRsnOde5c2d68uQJJScnk7OzM/n7+0vV3b17N4WFhVFqairZ2NjQjBkziIjo3LlzRER0+/ZtyszMpD59+lBERASZmZnR+/fv6d27dzR//vwSpwednZ3p0aNH9NNPP9GZM2eKTQfPmzePYmNj6datW3T79m26evWqxHK+evUqDRgwgJYsWUJpaWl07tw5ql27drFrAKCQkBBKSkqihw8f0qtXryg8PLzE+1MS6urqNH36dMl4vyQoKIgUFRUpPj6e4uLi6Pjx47Rp0yays7OjX3/9lVq2bEmZmZmUlpZGVlZWpKWlRXFxcZJ7p6mpSQ8fPiQiorNnz5K7uzsREfXt25fMzMwoKSmJ9u3bR9OnT6fTp09Lrnvw4EHy8/OjtLQ0qb9VTk4O+fj4kIqKCu3du5eUlZVlHiuDQUREYDAqgaWlJTQ0NKCtrQ0LCwuMHDkS2dnZxcrFxcVBR0dH8ru7uztCQ0Olyri7u2Pjxo1Sx4gIT548gVgshqqqKm7dulWs7WfPnoGIIBKJJO1MnTpVcv7+/ftQUlJCQUFBsbqpqakgIqSlpQEAAgMDMXjwYMn5w4cPo169esX6U0RoaCi6desmdaw0Ll++jF69ekEgEEBFRQWBgYH49OkTAMDa2hqHDx+WlD127BgsLS0BAMOGDUNwcHCJbZZ0z4r4448/0LhxY8nvlpaWOHHiRIllAwMDMWPGDOTm5sLc3BxHjhzBkydPUPRoePv2LZSVlaX+tlFRUfDw8AAAREZGwtXVVarN/v37IyIiAm/evEHdunUxefJkrFu3DgkJCdDW1oZYLMbLly/B5/ORkZEhqTdt2jQEBgYCAMLCwtC6dWupdsPCwtC1a1e0adMGY8eOBcdxJY6JwSgPZvExKs2ff/5JaWlp9OLFC1q7di2pqalRdnY2DR8+nCwtLUlLS4vatGlDaWlpUp6X5ubmMl/jw4cPlJubS3Xq1JGp/OdtW1pakkgkog8fPpBYLKZp06ZRnTp1SEtLS2I5ffjwQVLeyMhI8lldXb1MZ53JkyeTjY0NdezYkaytrWnhwoWllnVxcaG9e/fS+/fv6fz583Tu3DmaN28eERVO91laWkr1OSkpiYgKLWlZxv3u3Tvq27cvmZqakpaWFvXv319qXLKgoqJCoaGhFBoaKnX8xYsXJBKJyNjYmHR0dEhHR4eGDx9OycnJpbbl7u5Of//9N507d47atGlDHh4edPbsWTp79iy1bt2a+Hw+JSUlkZ6eHtWqVUtq7ImJiZLfS/qexMbG0p07d2jatGkyO+AwGF/ChI8hVyIiIujx48d05coVysjIkEwT4rNNQL58YJX1ABMIBKSqqipZbyuPV69eST6/fPmSlJSUSCAQUFRUFB08eJBOnjxJ6enp9Pz582L9qgi1atWiiIgISkhIoOjoaFq2bBmdOnWq3HrNmjWjHj160L1794iIyMTEhF68eCHVZxMTEyIqfPDLMu7p06cTj8eju3fvUkZGBu3YsaNS4xo4cCClpaXRgQMHJMfMzc1JRUWFPnz4QGlpaZSWlkYZGRmS9dGS/nbu7u50/vx5+vvvv8nd3Z3c3Nzo4sWLUtOcJiYmlJKSQp8+fZIau6mpqeT3ktru2LEjhYSEULt27ejdu3cVHiODQcSEjyFnPn36RGpqaqSjo0MpKSk0e/bscusYGhqWGrPH5/Np0KBBNGHCBEpKSiKxWEyXL1+WcgD5nB07dtCDBw8oOzubZs2aRX5+fqSgoECfPn0iFRUV0tfXp+zsbJo+fXqFxvVlH2NiYig+Pp4AkLa2NikoKJS47njhwgXauHGjxEJ69OgRRUdHk4uLCxER9evXj+bOnUvv37+nDx8+0Jw5cySxaoMHD6bIyEg6deoUcRxHiYmJ9OjRo2LX+PTpE2lqapK2tjYlJibSkiVLKjS2IhQVFWn27Nm0aNEiyTFjY2Pq2LEjTZw4kTIyMojjOHr69KnEEcbQ0JBev34t5WBia2tLampqtGPHDnJ3dyctLS0yNDSk/fv3S4TP3NycWrVqRSEhIZSbm0t37tyhzZs3yxSnN2XKFPrxxx+pXbt2FbZsGQwiJnwMORMcHEw5OTkkEAjIxcWFOnXqVG6d8ePH0759+0hXV1fKGaWIpUuXkoODAzVr1oz09PRo6tSppYZBBAQEUFBQEBkZGVFubi6tWrWKiIgGDBhAlpaWZGpqSg0aNJAIj6yEh4dTYGAg6ejo0N69e+nJkyfUvn170tTUpJYtW9KoUaOobdu2xerp6OhQdHQ0OTg4kKamJnXq1Il8fX1pypQpREQ0c+ZMatq0KTk6OpKDgwM5OztLQguaN29OkZGR9NNPP5G2tja5u7tLWYdFhIWF0c2bN0lbW5u8vLyoR48eFRrb5/Tr14+MjY2ljv3222+Un59PDRo0IF1dXfLz86M3b94QEZGnpyc1bNiQjIyMSCAQSOq4u7uTvr6+ZLrS3d2dAJCzs7OkzK5du+j58+dkYmJCvr6+NHv2bGrfvr1M/QwNDSUfHx9q3769lOcugyELPFR2rofB+M7w8PCg/v3705AhQ751VxgMxncMs/gYDAaDUaNgwsdgMBiMGgWb6mQwGAxGjYJZfAwGg8GoUTDhYzAYDEaNggkfg8FgMGoUTPgYDAaDUaNgwsdgMBiMGgUTPgaDwWDUKJjwMRgMBqNGwYSPwWAwGDUKJnwMBoPBqFEw4WMwGAxGjYIJH4PBYDBqFEz4GAwGg1GjYMLHYDAYjBoFEz4Gg8Fg1CiY8DEYDAajRsGEj8FgMBg1CiZ8DAaDwahRMOFjMBgMRo1C8Vt3gMFg/EtyMtHWrUR37hClpxNpaxM5OhINHEgkFH7r3jEY/xl4APCtO8Fg1GiuXSNasIDo6NHC33Nz//+cmhoRQNS5M1FICFGzZt+mjwzGfwgmfAzGt2TdOqJJk4hycgoFrjR4vEIRXLqUaOTIr9c/BuM/CJvqZDC+FUWil51dflmgsNykSYW/M/FjMCoNs/gYjG/BtWtEHh6yid6XqKsTnT1L1LSp3LvFYNQEmFcng/EtWLCgcHqzMuTkFNZnMBiVgll8DMbXJjmZyNJS2omloqiqEr18ybw9GYxKwCw+BuNrs3Vr1dvg8eTTDoNRA2HOLQzG1+bOnapZe0SF051378qnP/81WDwkoxzYVCeD8bXp2pUoJqbKzZzT1qZlHh5kbGxMJiYmZGJiIvVZIBAQn1+DJnVYPCRDRpjwMRhfm/79iXburHIz7zt1ovNDh9KbN28oKSmJkpKSpD5nZGSQoaFhMUH88vN/QiBZPCSjArCpTgbja+PoSNi/n3hVme5UUyOhpyf16NGj1CJ5eXn09u3bYoJ44cIFKaHMyMggIyOjMsXRxMSE9PX1v0+BZPGQjArCLD4G4ysCgM7t20cuffqQSlX+68nRqzM3N5fevn1bquX45s0bSkxMpKysLIkF+aU4fimQPB6vyv2SCRYPyagETPgY8oE5FJTLhQsXaObMmZSUlESntLXJ7MYN4lXmvx+PR+TrS7R/v/w7WQZFAlmaOBZ9zsrKIiMjozKtRxMTE9LT05NZID09PSkoKIgCAgKk6/ToQfTnn2VPb5bGN7qPjG8PEz5G1WAOBeVy7do1Cg0NpcePH9OsWbMoICCAFOPi/rOWSk5OjkwCmZ2dLRHCsqZZlZWVSVdXl1RVVcnR0ZG2b99OderUYfGQjErDhI9ReZhDQZncvn2bZs2aRTdu3KCZM2fSoEGDSFlZ+f8LVGRt6l/EKirELV5MSuPGVUOPvy45OTkSISxrmjUrK4sKCgqo6FGloKBAAwcOpI22tkRhYVUTPjU1otmziSZPltOoGP8TgMGoDGvXAurqQKHkyfajrl5Y7z/OgwcP0KtXLxgZGWHFihXIyckpVubChQuoV68eZgoEhfeFxyv73vF4gLo6xikrQ1lZGVOmTMGrV6++wei+PseOHYO6ujp4PB5UVVWhqamJYcOGAf7+Ffv+lfYTEPCth8j4ynyHLlqM755r1ypsqRDR/3vTXb9ePf36xsTHx1NAQAC5u7tT06ZNKT4+nsaPH0+qqqpEVOjY8tdff5GzszO1b9+eHj9+TE87dCictvT1LZx2U1OTblRNrfC4ry/R2bP0zteX8vPzadmyZWRjY0NeXl6Unp7+DUb79RCJRFRQUEB+fn507NgxSk9Pp/Xr1xeuJcuD1FT5tMP4n4GFMzAqjjwSLP+HHApevHhBc+fOpT/++IPGjRtHa9asIS0trWLlnjx5Qp07d5ZM2amqqpKXl1fhWt3+/UTv3xc6CN29W/gw1tUlcnAgCgqSrEH5+vpSTEwMZWVlEY/Ho4SEhO8zxECOeHl50adPn6SmiVNTU+lTRgZZyOMCurryaIXxPwQTPkbFSE4udGSp7NIwQHTkSOFD/n/coSApKYnmz59Pu3btohEjRtA///xDenp6pZavW7cuRUdHk4+PD3EcRwoKCtT0cwcVobDctSZ3d3cqKCggFRUVAkB79uyhWrVqyWtI3yU8Ho9EIhGdOXOGTp06RadPn6bHjx/TcmNjClRUJKWCgso3rqZW+HLBqFH8t18VGfKHJVim5ORkmjhxIjk4OJCqqio9evSI5s2bV6boFXHlyhVq1qwZ1alThziOI1tb2wpd28jIiGrXrk3Dhg2jFStWUK9evSglJaWyQ/luycvLo3PnzlFYWBi1bt2aDA0Nad68eaShoUHLly+nDx8+0JALF0hJsYrv7kChRc2oUTCLj1ExanCC5ZSUFIqIiKBff/2VfvzxR7p79y6ZmJjIXP/QoUO0bds2un79OtWqVYtu375dqWnKBw8eSOrFx8dTjx496Pjx49Ieo/9jiMViunnzpsSiu3z5MtnZ2ZGnpyeFhoaSq6sraWhoSFcyMCgMlalKHF+XLv/zMw+MisPCGRgVQ04Jlsnbm+jQoaq38xXIyMigFStW0KpVq6hHjx40c+ZMsrCo2OpSfHw8tWrViqKjo8nFxUVufROLxdSzZ0/S0dGhyMjIr5cxpYoAoHv37tHp06fp9OnTdO7cOTIzMyNPT0/y9PQkd3d30tHRKbP+y5cv6cG2bdR50aJKxUNCXZ1433E8JKP6YBYfo2Joa8unnX8dCjIzM+nGjRsUGxtLDRs2JG9vb/m0LweysrJo9erVFBERQZ06daLY2FiysbGpVDs9evSg2bNny1X0iApj2nbu3Elt2rShBQsW0PTp0+XavrwAQE+fPpUI3ZkzZ6hWrVrk6elJ/fr1ow0bNpChoWGZbaSnp9PmzZvp+PHjdOXKFUpLSyMVFRXKWb6ceBX0Ms7h82mvoyP1d3IihaoOjvG/x7eLpGD8T7JoEaCqWrW4KTU17G7SBDo6OlBUVISWlhYUFBQwbdq0bz06AEBOTg6WL18OIyMj9O7dGw8ePKh0WxzH4ccff8SAAQPAcZwceylNYmIizM3NsWfPnmq7RkV5/fo1fvvtNwQFBcHCwgLGxsbo378/tmzZgufPn1e4vRs3boDH44GIQETg8/lwdXWFgoICbo8cKVM8JMfjoUBFBbkrVqBdu3bo3bs38vLyqmH0jO8ZJnyMivHuXdWFT1UVmxYsgIKCguQhpqioiBEjRuDmzZsQi8XfZGh5eXlYt24dTE1N0b17d9y6davKbf7yyy9o1KgRsrKy5NDDsomLi4NAIMDly5er/Vol8f79e/z+++8YOXIk6tatCz09PfTs2RNr1qzBo0eP5CL8I0eOlHxniEgihGlpacC1a0CPHoXfTzW1Yi9bUFXFRw8PNCFC586d8ezZM3Tr1g3e3t4lJhlg/HdhwseoOL6+5WcaKSsDSY8eAIDr169DV1cXfD4fioqKGDp0KOrVqwc9PT306NEDv/zyC+7fv1+tlhIAiEQibNmyBbVr18YPP/yAK1euyKXdixcvwsDAAPHx8XJpTxYOHToEY2NjJCQkVPu10tPTERMTgwkTJqBx48bQ0tJCly5dsHTpUsTFxcn9BebXX3+FUCiEUCiUEj8rKyvpgsnJwOLFeN6mDQ4SQezvDyxeDCQn4+PHj+Dz+eDxeNDQ0MCSJUvQp08feHp64tOnT3LtL+P7hQkfo+JcvVrxdGVFU01qariwYoXkIfP8+XNYWlqiefPmkuYTExOxY8cODBo0CJaWljA0NES/fv2wceNGxMfHy00ICwoKsHPnTtja2sLd3R3nz5+XS7sA8ObNG5iamiImJkZubcrKypUr0aBBg0IrSI5kZ2fj1KlTmDFjBlxcXKChoYG2bdti7ty5uHTpEvLz8+V6vSLy8/MxcuRI2NnZ4Z9//kFGRgYEAoHE2hs2bFiJ9erVqwciws2bNyXHOI6DoqKilMU4c+ZMDB48GC1btkRqamq1jIHxfcGEj1E5Kpmr89rgwZL1GaFQCHd3d4SEhODly5elXiohIQGbNm2Cv78/jI2NYWFhgaCgIPz222+VylfJcRz279+Phg0bomXLljh58qRcrcr8/Hy0adMGs2bNklubFYHjOIwePRodOnSokhjl5+fj0qVLmDt3Ltq2bQsNDQ24uLhgxowZOHXqFLKzs+XY65JJTk6Gu7s7vL29kZ6eDgA4fvw4DAwMMHnyZPB4POzdu7dYvdjYWKioqID+ndb8HAMDA4nwDRs2DFlZWRCLxRg/fjwaN26M5OTkah8X49vChI9ReYrET4YEy5y6On5t3Bhdu3ZFnTp1pKaq6tevL7PwcByHhw8fYs2aNejZsyf09fVha2uL4cOHY8+ePXj37l2ZdWNiYuDk5ARnZ2ccPny4WqZRJ06ciE6dOqGgoEDubcuKSCRC586dMXz4cJnHKBaLERcXh6VLl6JLly7Q0tJCo0aNMGHCBMTExEiE52tx+/Zt1K5dGyEhIZJ7+fDhQwiFQvz999+SMiWJe9u2bSXfLxUVFdy/f19yrkWLFqhbty769OmDQYMGSY5zHIeZM2fCzs4Or1+/rubRMb4lTPgYVUMGh4J3bm4Y6OAAVVVVEBGUlJSkvPP2799f6cuLxWLcunULy5Ytg7e3N7S1teHg4IBx48bhzz//RGpqKjiOw4kTJ+Di4gJ7e3scOHCg2tYN9+7dCysrK3z8+LFa2q8I6enpcHBwQERERInnOY7Do0ePpF4i6tati5EjR+L333/H+/fvv3KP/599+/ZBIBBg165dkmMfPnyAjY0NNm/eXGbd+Ph4EBHU1dUljlMBn+3AkJqaCpFIhIyMDJibm+P06dNS9RcuXAhra2s8e/ZMrmNifD8w4WPIh38dChAQAFHnznju7i5xKFBQUCjmkFD04+XlBaFQiIiICLmIkUgkwpUrV7BgwQJ06NABampq0NTUhK6uLqZOnVqtVsuDBw8gEAik1pS+NS9evICJiQn++OMPye9btmxB//79YWJiAnNz8ypNG8sbsViMsLAwWFhY4MaNG5LjeXl58PDwwKRJk8ptIy8vD8eOHcPChQuhoaGB5cuXlzqVfvDgQdjY2BSbtl29ejXMzc3x6NGjqg2I8V3ChI8hd8LDw0FEEm9GJSWlEkXP2dkZHMchISEBzZs3h5eXl9ysjCtXrqBjx46SqbLQ0FC0adMGGhoacHV1RWhoKE6fPi03N/b09HTUq1cPkZGRcmlPXrx9+xZz586FqqoqzMzMIBQK0adPH6xfvx5Pnjypdo/ZivDp0yf4+vrC1dUVb9++lRznOA5Dhw6Ft7d3haaP//77b+jp6SE6OrrMcn5+fggJCSl2PDIyEsbGxnIJa2F8XzDhY8gdOzs7EBFatmwJjuOgrKxcTPQcHR3h5OSEjIwMAIVv6ZMmTYK5uTnOnTtX6WvHxcWha9euMDMzw6+//losODkrKwsnTpxASEgIWrRoAQ0NDXh6elbJM5HjOPTs2RPDhw+vdL/lRWpqKv7880+MGzcO9vb20NbWRrdu3TBw4EAYGBiU6UT0LUlISICDgwOGDBmC3NxcqXPLli2Do6Oj5LsiKxcuXIC+vj6ioqLKLJeUlAShUIjbt28XO7d3714YGBggNja2QtdmfN8w4WPIlcePH0vW8tTV1bF161Yp93FFRUXo6Ojg4MGDGDp0KH744QcpsTly5AgMDQ0xZ86cCr3d379/X7Lr+cqVK2W25NLS0nDo0CFJLFqtWrXQuXNnLFmyBDdu3JCpD4sXL0azZs2KPbC/BpmZmfjrr78wdepUNGvWDJqamujQoQMWLFiAq1evQiQSScouWrQIjRo1qrCAVDenT5+GoaEhfvnll2IWaExMDIyNjSuV6eXy5csQCoXYsGFDuWU3bNiAZs2alfj3jomJgVAoxJkzZyrcB8b3CRM+hlyZOHGiVEYWTU1NqKmpoUGDBtDW1sb58+dx7tw5GBkZITExEd7e3sXSeb1+/Rru7u7w9PREUlJSmdd78uQJ+vfvD6FQiMWLFyMzM7NK/X///j327duHUaNGwc7ODrq6uvDx8cGqVatw7969Yg/mU6dOwdDQEC9evKjSdWUlLy8P586dQ3h4uGTq1s3NDbNmzcLZs2fLFF+O4zBkyBB4eXl9U4/Tz/uzevVqGBoa4tSpU8XO3717F0KhEJcuXapU+1evXoWBgQGWLVtWblmxWIw2bdpgxYoVJZ4/ffo0hEIhjhw5Uqm+ML4vmPAx5Mrw4cPRqFEj6OjowM/PD6tWrcKrV6/AcRzWrFkDb29vAMDUqVPRvXt3fPr0CS1atMD06dOl2ikoKEBYWBiMjIxw7NixYtd5/vw5Bg8eDH19fcyZM6fanFaSkpKwc+dODB48GFZWVjAwMEDfvn2xfv16iYCfPHmyWq4NFN6Ha9euYdGiRfjhhx9Qq1YtNGnSBJMnT8axY8cqnG0kPz8fnp6eGD9+fDX1WDby8vIwdOhQ2Nvb4+nTp8XOv3v3DrVr18aOHTsq1X5KSgoiIiKgra0Nb29vLF68WCqkoSQePXoEfX39Uq3LS5cuwcDAAPv27atUnxjfD0z4GNVCjx49ij0gcnJyYGJigps3byI3NxeNGjXCli1bkJycDFtbW6xZs6ZYO2fOnIGpqSmmTZuG/Px8JCYmYtSoUdDT08OMGTOQkpLytYYEAHj27Bm2bNmCfv36QUlJCdra2ggMDMS2bdvksn7GcRzu3buHVatWoXv37tDR0UGDBg0wduxY/PHHH3IZb0pKCurXr4/Vq1dXua3K8PbtW7i6usLHx6fEadecnBy0atUKM2fOrPQ19uzZI8nMoqioCAUFBanQiNL4+eef0aVLl1KdfuLi4mBkZIRt27ZVum+Mbw8TPka1MHr0aKxatarY8WXLlqFnz54ACqeyBAIBEhIS8PTpUxgbG+PAgQPF6iQnJ8PT0xPGxsbQ0dHBpEmTvnl2jVGjRsHHxwcPHz7EunXr0KtXLwgEAtjY2GDYsGHYvXu3lGdiaXAch6dPn2Ljxo3o27cvDA0NYWVlhSFDhiAqKgpv3ryplv4/ffoURkZGOHz4cLW0Xxo3btyAhYUFZs2aVWIuT47jEBAQAD8/vyrl+iwoKICpqalkyl1XV1cmx6W8vDzY29uXKZIPHz6EmZkZ1q5dW+n+Mb4tTPgY1cK8efNK3GYoMzMTBgYGuHfvHgBg6dKlcHNzQ0FBAa5fvw6hUIgLFy5Iyn/8+BEhISHQ0zKzs9YAACAASURBVNODq6sr9PX1JTFp34pt27bB1ta2WC5MsViM27dvY/ny5ejWrRu0tbXRsGHDYtZaUS7SgQMHwtLSEkZGRvD398fmzZu/SnLpIi5evFiqN2N1sHv3bgiFQvz++++llpk/fz6aNGkil90sNm3aBCKCgoIC5s6dK3O9y5cvw8jIqMwkBE+fPoWVlRUWL15c5X4yvj5M+BjVQmRkJAYMGFDiuQULFsDf3x9AoVh4eHhg4cKFAIC//voLhoaGuHLlCsLDw6Gvr4+hQ4dKnEcuX76M2rVrY8yYMd9kK5mirX/u3r1bblmRSISrV69i4YQJWGZsjB08HmL4fOzk87Ha0hJrZ8/GgwcPvmksXVRUFCwsLMp1IqoKYrEY06dPR+3atcuMidu/fz/MzMzkli7syZMnkixBFc2kM2bMGAwcOLDMMq9evUK9evUwa9as7yoeklE+TPgY1cJff/2F9u3bl3guPT0dAoEA//zzD4BCRxWBQIC4uDhkZmaiV69e4PP58PPzK3FLn5SUFPTo0QNOTk6SNr4GKSkpsLa2LjcuLCMjA4cPH8YKf3+c0tZGDhFy+XypdG45fD5yiHBGTw/rBg36akmfS2LOnDlo2rRptewZmJ6ejq5du8Ld3b3M6ekbN25AIBDg+vXrcrt2QkICjIyMMGbMmArXLUpnVpK36ee8e/cOjRo1wk8//cTE738IJnyMauHu3bto0KBBqefDwsKkEgRv2rQJRkZGMDQ0RJ8+fRAcHAxHR8dSt9Yp8hIVCASV9vyrCGKxGF5eXiV6Q+bk5OD06dOYOXMmWrVqBQ0NDUTY2iJPSQlcOQm8xTwe8hQVscjKSrLNz88//4yLFy9W2zY/X1K0rtajRw+57qH35MkT2NnZYeTIkWWOJTExEWZmZmVOgVaG1zdvYq6WFuDvD3h7F/67aFFhej0ZiI6OLjGd2ZekpKTAxcUFQ4cO/S7CRBjlw4SPUS18/PgRurq6ZZ7X09PD48ePsWbNGpiamsLY2FgyPcpxHEaNGgVPT89i2Vc+Jy4uDnXr1sXAgQOrHMNXFnPmzIGbmxvy8/MhEolw+fJlzJs3D+3atYOmpiZatGiBkJAQnDhxAnkrV1Zqy6bsZctw+PBhTJw4EU5OTqhVqxY6deqExYsX4/r169X6UM3NzUXr1q0xZcoUubRXtHXQr7/+Wma5rKwsNG3atEJrcOVy9Srg6wtORQXZX97nfxOnw9e3sFw59OrVq8S16i/JyMiAh4cHfvzxx6/2wsKoPEz4GNUCx3FQUVEp9W25aNucoof71atX8f79e5iYmEiy5RcUFMDHxwc//vhjmZbIp0+fEBAQADs7O5nW3irK4cOHYWBggPDwcMkOEI6OjggODkZ0dLS0VVqFTXqhrl6428W/fPjwAfv378eYMWPQoEED6OjooHv37li5ciXu3r0r96m1ot0PNm7cWOk2OI7D8uXLYWRkhLNnz5ZZViwWo1evXujfv7/8xlKBrbKgrl5YvgzevHkjswNQdnY2unTpgu7du3+TLD4M2WHCx6g2ateuXSw4uaCgADt27ICNjQ1cXV1Rq1YtJCYmSs4fOXIEFhYWEjHJzs6Gq6srJk+eXO71tm7dCoFAgA0bNlTpQcpxHB4/fox169ahS5cu4PF4MDMzw/Dhw7F3796yQyl8fct/6Jb1MO7Ro9Sm37x5g6ioKAwZMgR16tSBgYEBevfujV9//RX//POPXMTj8ePHMDAwqFRQfm5uLoKCgtCoUSOZUozNmjULLVu2lJ+TUiU3Ry5P/DZu3FhqOrMvycvLg5+fHzp27Fgta6YM+cCEj1FttGrVCufPnwdQ+Hb/+++/o0GDBmjZsqXEaeCnn35CcHCwVL2RI0dK7Z/28eNH1K9fv9R0Up/z8OFDODo6ok+fPqWuD5bEy5cvsXXrVgQEBMDU1BSmpqbw9/eHpaUlwsLCZGvk3bvCabTKiF7Rj6qqzGtQz58/R2RkpKTPZmZmGDBgACIjI6uUQu3vv/+GUCjEgwcPZK6TlJQEFxcX9OrVS6Yp56ioKFhaWsoU6ygTcrS0v4TjOLi7u8v0/QMKZzMCAwPh5uZWoe8g4+vBhI9Rbfj5+WH37t2Ijo5G48aN4ezsjCNHjkhZJomJidDV1ZXaOT0zMxO2trZSzg7Pnz+Hqakp9uzZU+51s7OzMWLECFhbW+NaKQ+0d+/eYc+ePRg2bBhsbGwgEAikrCexWIyBAweib9++sltSixZVXfjU1Ar3MawgHMfhn3/+wa+//orevXtDKBSiTp06GDp0aKUC4bdu3Qpra2uZEgVcvXoVZmZm+Pnnn2W6V0XJo+/cuVOhPpVJNVraQKElXFY6sy8Ri8UYPXo0mjRpgg8fPshjhAw5woSPUS1wHAcfHx9YWFjA3t4ef/zxR6kPxVGjRmHq1KlSx2JjY2FgYCAVX3br1i0IhUL8/fffMvVh7969EAqFWLZsGVJTU3Hw4EGMHz8eDg4O0NbWRteuXbF8+XLcvn272Bri+vXr0bBhw4rlwvT3r5ro/fvzj4sLfvvtN+zatQv79+9HdHQ0jh49ilOnTuHcuXO4fPkybty4gTt37uDhw4d4+vQpXr58ibdv3+Ljx4/IyMhAdnY2bt26hZUrV0qlPhszZgwOHDggU1zbjBkz0KpVqzKnIrdv3w6hUIg///xTpltUtDHuoUOHZL6tRaxYsQI//PCDJPmBhK9kac+dO7fMdGZfwnEcpk6dCnt7+2rLwMOoHDwAIAajiORkoq1bie7cIUpPJ9LWJnJ0JBo4kEgoLLHK48ePafr06bRnzx5SVFSkc+fOUWhoKD148IBcXFzo4MGDxOfzS73kixcvyNnZmZ48eUJ6enqS42FhYXT16lU6cuQI8Xg8IiI6deoU9evXj06fPk329valtpmdnU0XL16kAwcO0Pbt2yk3N5fc3Nzohx9+IE9PT2rSpAkpKiqWWPfq1avk7e1NFy5coLp16xY7f//+fRo1ahT98MMP9MMPP5CTk1Ph+Lp2JYqJKbVPsnLL3JyWtmlDIpGIRCIR5efnF/tc0rGSPvP5fFJWViYlJSXi8/nEcRyJRCLKy8sjZWVlqlWrFuno6JCuri6pqqpKyhb9XLt2jRQUFMjT01NyTllZmRQUFOj8+fP06NEjCgwMJAsLC0mdz9v4/LNYLKZx48aRl5cXBQUFlVm26HPR352IaPDgwRQZGUmqqqrUpUsXWrJkCVlZWREtXkwUFkaUm1v5m66mRjR7NtHkyaUWyc/PpyZNmtCMGTOob9++MjULgObPn0/btm2jkydPkoWFReX7yJAbTPgYhVy7RrRgAdHRo4W/f/4QUVMrfC/u3JkoJISoWTPJqdzcXHJwcKDnz59TSEgIxcbGUnx8PIWFhREAOnnyJO3YsaPcyw8ZMoTMzMwoPDxcckwkElGrVq1o0KBBNHLkSMnxqKgomjZtGl28eJHMzc2JqPChdPXqVTp9+jSdPn2arl+/To0bNyZPT09q3bo1HT58mA4cOEA7d+6k1q1bl9qP9+/fU9OmTWnFihXk6+tbYpmHDx+So6Mj8fl8UlBQILFYTHXr1qW7jRoR7dxZ7ljLJSCA6LffqtwMABKLxSUKZlZWFt28eZMuX75MsbGx9ODBA7K2tqZGjRqRvb092djYEJ/Pp8zMTJo7dy7Z2dlR586dSSQSUXp6Ou3cuZNEIhH5+PiQoqJiuQKdn59PcXFxpKCgQJaWluUKd35+PhUUFJCCgoJEBPPy8igvL09qjOvXr6dh5859tft+5coV8vHxofv370u9pJXHihUraMWKFXTixAmytbWtak8ZVeUbWpuM74UquIAPGTJEsvEsn8/HmjVrJHF3J0+ehKenp0xdePLkCQQCQbHthYq2inn8+LHU8UWLFsHa2hqzZ89Gp06dUKtWLTg7O2PSpEk4cuRIiVOUMTExMDQ0xM8//1yih15BQQHatWtXYtxWVlYWLl68iJUrV8Lf3x88Hg9EhQmQFRQU4Ofn903X+KpKdnY2Tp06hRkzZqBly5bQ0NCAh4cHZs+ejYMHD8LS0hK//fYbHj16hLp162LcuHFSm9yWx5QpU+Du7l5mTOaXcByH/Px8ZGZmIjU1Fd26dQNR4QbHurq6CAkJKXSk8faWyxQz/t0yqzzGjh1bbjqzkti0aRNMTEyqJeSGUTGY8NV0quACvnr1aikBUFZWxrp16yRN379/H/Xr15e5K/7+/pg/f36x47/88guaN2+O27dv45dffoGvry90dXWhp6cHExMT7Nq1S+ZcjK9evUKbNm3Qrl27YusuISEhaNeuHTIyMhAbG4vVq1cjKCgI9vb2UFNTQ9OmTTFixAhs2rQJFhYWICKoqKggODgY69atw8GNG7+qV2d1kpGRgSNHjmDSpElwdnaGuro6FBUVoaqqihkzZlQomD4yMhJ16tSpspNHUFAQzM3NsXXrVukgcTmtreIzT+KyyMjIgIWFRbnpzEoiKioKhoaGpTpdMb4OTPhqMlV0AR/epAmEQiHs7Ozg6OgIJycnLF26VNJ8amoqtLS0ZO7O/fv3YWBgIHGHT0hIwKZNm9CvXz8oKytDW1sbgwYNws6dO5GUlASxWIzevXtXeAsbkUiEWbNmwdjYGIcOHcK1a9cwfPhwaGhooGHDhlBTU4OTkxOGDh2K9evX4/r168UCkps2bQoigqqqKpSUlEBEaNq0aZW8CwuIcK9evW+Ws7M0OI5DeHg4NDU1oaKiAhsbG+jo6KBbt25YsWIF7ty5U+r9P3v2LAwMDPDw4cMq96OgoKDk6yxaBO4rW9qypjMriT///BNCoVAS6sP4+jDhq8lUsws4x3FQU1OT2TMyKSkJzZs3R/PmzVG7dm0YGhqiX79+2LRpk2T366tfpJnKycmBu7s7xo0bV663XX5+Pm7evIkNGzZg+PDhqFu3Lng8HnR1daGqqorJkyfjypUrMgVUX7hwAQoKChJr19TUtHCcVXiZEKupYWq7drC2tsaRI0dkumfVTXZ2Nvr37w9nZ2e8fPkS69evh62tLR48eIBdu3ZJhYP06tUL69atw+PHj8FxHOLj42FoaIjjx4/LpS9FOT2NjY1hZWUFGxsb6OnpIXzUKORU9ntcBUtb1nRmJXH8+HEIhUK53RtGxWDCV1P5Si7gderUKXUHhY8fP+LAgQMYM2YM7OzsoKuri7Zt20JLSws3b94sJmR79uxB3bp1i2XESE1NRcOGDbFkyRLJMZFIhNu3b2PLli0YNWoUmjdvDnV1dTRo0AADBgzAqlWrcOnSJdy/fx+ampqwtraWKej78ePHCAoKgp6eHuzt7cHn86Guri5t0VQxg8jRo0dRp04d9OzZE69evSq3T9XF69ev0bRpU/Tt21fqnk+cOLHYet2LFy+wdetWDBgwAGZmZjAyMoK2tjb69+8vc+xbeWRkZEhNrRf91K9fH8lubuUmBC/L0r5br17FQlfw/+nMytpqqSwuXLhQoVAQhvxgwldT+UqOGK1bt5bE3X369AlHjx6VrBuVloS5a9euWL16dYnt+fv7Y/To0VLHCgoKcOrUKejp6aFjx45o2bIl1NXVUa9ePfj7+2P58uU4f/58sQcbx3Ho168fgoKCsHDhQhgYGJT6ELp16xZ69+4NgUCA2bNnIyUlBc+ePYOioiJ2795dvMK/4ieWxXIuIW1WdnY2Zs2aBX19fURERFTIkUQeXL58GSYmJliwYEGxF5CiHKqBgYElWtn5+flo3bo1PDw80KdPHxgYGMDa2hqDBw+WTFNXhuPHj0NdXV1K9IYMGQKO45B6/DiyKyl8YjU1zOzUCWZmZoiKiqpQ6reKpDMrievXr8PQ0BA7d+6sVH1G5WDCV1P5Cg4Bubm5aNu2LXx9feHq6goNDQ24u7tj9uzZOH/+fKkefleuXIG5uXmJ5z9+/AhjY2NMmTIFwcHBcHNzg6amJmxsbNCpUydoampi6dKlxbxDS2LlypVwcnKSrNMUbXI7duxYyZpebGwsunbtCiMjIyxZsgQZGRnF+lMabw4dQrSSEjgVlcKXhC9fGlRVC6eLy3B0ePz4Mdq3bw9HR0dcvHix3DHJg8jISAiFwjKDzDMzM+Hs7Ix58+YVOzd27Fh07NhRItYcx+HevXtYtWoVfHx8oKurCzs7O4wePRr79u0r0+mF4zhs2bIFJiYmICJoa2tLRK979+6Ii4vD0KFDoaCggF3u7uCqYGlfuHABTk5OcHNzQ1xcnEz3qiid2fLly2UqXxL37t2DqakpNmzYUOk2GBWDCV9NpRpcwEUiEa5cuYL58+ejffv20NTUhJGRETw9PXH8+PEKJe3t2LEjNmzYgCdPnmDXrl2YOHEiPDw8oKWlBSMjI6ipqSE0NBQnT55ESkqKpN7Zs2chFArLfXCdP38eBgYGSEhIkDqekpICHx8f2NraomXLlrCwsMDq1asr5cQwbdo0jBs3rnA6ePFivPTwQKyBQeHLwuLFMq8pcRyHXbt2wcTEBEOGDKm2FFgikQjBwcGSNbzySExMhLm5uVQaubVr16J+/fpITU0ttV5BQQFu3LiBJUuWSHboaNy4MSZMmICYmBikp6dDJBIhPDwc2tra4PP5MDc3h1AoxMqVK+Hi4gItLS0IhUKoqamBiKCkpFRodVXR0i4oKMD69ethYGCAkSNHynSvK5rOrCSePHkCS0vLKgkoQ3aY8NVU5GTxJbi5ISIiAl27doW2tjYcHBwwfvx4HDx4EKmpqViyZAkmTpxYbnc4jkNCQgJ+//13TJ06Fc7OzuDz+TAzM4Ovry/mzZuHv/76S/IgCg4ORq9evUqcltq7dy9MTU3x7NmzEq+VlJQEExOTYg4kHMchJiYGLVu2hFAohKamJrZt21bxe4vCqUqhUCgVf3j48GF06tSpUu0BQFpaGsaOHQtDQ0Ns2bJFrpvGfvz4ER06dEDHjh2lXiTKoyiN3OXLl3HixAkYGhriyZMnFbp2fn4+Ll68iLlz56JNmzZQVFQEj8cDn8+HpaUl9PT0MGfOHIm1LRKJYGdnJ3Eu4vF4mDRpErKysuDu7o4ge3scVlWFWFm59P34yrG0P378iDFjxkAoFGLNmjXlTmXOnTsXnTt3rtIOGS9evICtra3MOU8ZlYcJX01FDmt8WUSYwudDRUUFLi4uiIiIKPbWu2PHDvTr10/qGMdxePnyJQ4cOIDp06ejY8eOkpi8bt26Yc6cOThy5AhatWqF7du3l9j97OxsNGjQoNTd11euXIl69eoVe2PPz8+Hm5sbZs+eLTlWUFCAvXv3onHjxnB0dMTu3btRUFAg2eR28ODBFd5iJjIyspjIHTt2DB06dKhQOyVx48YNNGvWDG5ubnIJhr5//z5sbGwwceLESq0lxsTEQCgUQl9fX+Y8ql+SmJiIbt26QUFBAVpaWnB0dISamhpMTEygrq6ONm3aIDw8HOfOnUNeXh4OHDggET11dXXs27dPEltpb28PBwcHIDkZP2tr45OvLy7o6iK2bt0KWdoAcPv2bXh4eKBRo0Zl7i+Yl5cHe3t7REVFVWr8Rbx58wb29vaYMmUKE79qhAlfTUUOXp2cigqQnIyXL19iy5Yt6Nu3LwQCAerWrYvRo0fjzz//xKFDh9CyZUscPHgQoaGh6Ny5MwwMDGBgYAAvLy+EhYUhOjpaak++Ik6cOIH69euX+rZ98+ZNCIXCUr0xJ0+ejFatWklNUwYHB6NLly4Qi8XIz8/H1q1bUb9+fbRo0QLR0dHFHjYZGRno378/GjRoILPIcBwHZ2dnHD58uNh4ZM1kUx4FBQVYu3YtBAIBJk+eXGGPxCKio6MhFAorbdkChdaRQCCAiYlJhbfhuX37Nlq1agUejwdjY2PJzhKDBg2S/F2LnKImT56MJk2aSDIF2dvbw8jICPr6+pIpTyKClZUVFixYAACwsrLC/fv3oaioCEVFxQpbo0Dh33PPnj2wsLBA3759S/W0jY2NhZGRUZWnoj98+ICmTZti5MiRcrXqGf8PE76aTBXi+MSlxPElJSVh5cqVaN++PQQCgcT93MbGBoGBgdi3bx9evXol09ssx3Fo0aJFmVsRzZs3D23bti3xASEWi/Hjjz/Cx8cHBQUF2LVrF6ytrZGUlIR169ahdu3aaNu2LU6ePFlmfziOQ2RkpMyb3F68eBE2NjbF+nTq1Cl4eHiUM+qK8fbtW/Tv3x8WFhZl7oDxJRzHYd68eTA1NUVsbGylr5+fn4+2bdti0qRJGDNmDDp06CCdVaUU/vrrL9SvXx88Hg92dnYIDg6GqakpevbsWeb64r59+8Dn89GxY0eMGzcO9evXh4KCAvh8Pj7PIFQ082BjY4OVK1dKxLJJkyaVFpPMzEyEhoZCX18f8+bNKzHec+zYsQgKCqpU+5+Tnp6O1q1bY8CAAV/do7cmwISvJnP1KrgvvQ1l/MkkQkslJXh4eKBr165o3bo1TExMoKOjg/bt22PatGnYt28frl27BhUVFUyYMAEODg7Q0dGBr68v1q5di/j4+HK7GBMTA0dHx1IfViKRCK1atSrVKSAvLw/t2rVD3759oa+vjwkTJsDExARdunSpsJfkgwcP4ODggL59+5bpNdq3b98S+/P333+jdevWFbqmrJw+fRr169eHt7d3qWubRWRlZaFPnz5o3rx5iZa2rHAch6FDh8Lb2xsFBQUQiUTo3Lkzhg8fXqIAcxyHzZs3w8TEBDweD66urliyZAlsbW3Rrl27YskJvmT79u3g8/kYP3681PF3796hffv2UFZWlkx/+vn5Ye3atbCysoKLi4tEFNXU1KrsQJKQkAAfHx9YW1vj4MGDUmPNyMiAubl5pdKZfUlWVhY6duyInj17VijHKaN8mPDVMLKzs3Hy5ElMnz4dLVq0wDhlZeTw+RUSvRwFBUz4LJ6Kz+dDUVGx1L3KNDU1JULx5s0bbN++HQEBATAyMoKVlRWGDRuGffv2lehUwXEcnJyccPDgwVLHFB8fD4FAUHyftn+5e/cuFBQUoKKiAj8/P9y8ebOSd+//N7mtU6cOrl+/Xux8YmIidHR0SvRqPH/+PFq1alXpa5dHXl4e5s2bB319fcyfP7/Eh+WLFy/g5OSEgIAAmTLUlMXy5cvh6OgoFeKRkZEBBwcHqdR1IpEIYWFh0NLSgoKCAnx8fBAVFQUnJyc0bdoUJ06cKPda69evB4/HQ0hISLFzaWlpEAqFuH//Ptzd3TF8+HBs27YNQUFBUFRURFEicSKChoYGAmTMyVkeRVZrp06d8OjRI8nxQ4cOVTqd2Zfk5ubC19cXnTt3/u5S2f0vw4TvP06Rx9zPP/8MDw8PaGhooGXLlpg5cyZOnz5d+PCTcXeGAiLkKSriysCBePz4MZydnSXip6ioWGqGlrp165aYq5HjONy9excRERGSGLzmzZtj5syZOHv2rOTBvX//fjRr1qzMabyNGzeicePGUg/75ORkTJs2DUpKSrC2toaJiQkiIyOrdkP/pWiT2+XLl0v1a9asWRgxYkSJdS5duoQWLVrI5fplkZCQAC8vL9jZ2eHMmTOS4+fPn4exsTGWLl1aZceJw4cPw9jYuEQX/qLNZqOiojB06FCoqKhARUUFI0aMwOnTp+Hh4YF69eph3759MvVj+fLl4PF4+Pnnn0s8P2vWLAQGBuLTp0/Q0tLC+/fvJeccHR2xcOFChIaGgsfjwcDAAFZWVhg0aBB27NhRJYsXKPz/FRERAX19fUyaNEnygte7d+9KpzP7EpFIBH9/f7i7uxeLI2VUDiZ8/zHEYjFu3rwpFSPl5OSEiRMn4vDhw8X+42RkZODs2bPYERyMy6amyP7XW1NqPU9FpTAJ8Bcu4GfOnIG6ujqUlJTA5/PRrFmzEr0fPTw8ZJr6yc3NxalTpzBt2jQ0adIEWlpa8Pb2xooVK1CnTh0cPXq01Locx6Fr164ICQnB69evERwcDF1dXbRo0QKNGjVCbm4uHj58CENDwzLbqQhPnz5F06ZN0a1bN3z48AF5eXkwMjIq1fK8cuVKYSLrrwDHcThw4ADMzc0REBCApUuXwsDAAMeOHaty23fv3oVQKMSlS5dKPP/69Wu0adMGRARNTU3MmTMHt27dQvfu3WFubo7NmzfLvG41f/588Hg8KQvyc96/fw89PT0kJCRgx44d6NKli9R5JycniWVuZmaGZ8+e4f79+5JdPvT09FC/fn2MHDkSv//+u5RoVoQ3b95g4MCBMDY2xtatW5GYmAiBQIAOHTrg9evXpVd8967Qw9rfvzAm1t+/8PcvPE/FYjGGDRuGFi1aVCjchFEyTPj+x+E4Dg8fPsTq1avRo0cP6OnpoV69ehg1alSxrBif7ykXEBAAOzs7qKuro0WLFhg9ejRGjx6NLs2aoWDhwsIga2/vcoOtXVxcMHDgQDx69EgS+/blA7Ffv36lhh2Uxfv377F7924MHjwY+vr6UFZWRlBQEKKiopBcQn+uXLkCNTU11KpVCxMmTMDu3bthZGQk5YV34cIFCASCEqcpK0NeXh4mTJgAc3NzzJo1q0yvzevXr8PJyUku15WVlJQUNG7cGAoKCggPD690aq0i3r17h9q1a5f494yLi0PLli3B4/FgamqKMWPGwMjICH5+fhAKhYiIiKjQ9OrMmTPB4/Gw9osg88+ZNGkSRo4cCQDo0qVLsX41a9YMV65cAQC4ubkVC7coKCjAzZs3sXTpUnTp0gVaWlpo1KgRfvrpJ0RHR1fYSzU2NhbNmjWDi4sL3N3dQURo27Ztccv26tVC5zJV1eLe1UWxhr6+heX+heM4TJgwAY0aNcK7d+8q1C+GNEz4/gd5/vw5Nm/eDH9/fxgbG8PCwgIDBw7E9u3bJW+X2dnZ5e4pd+vWLSkPvEGDBmHlypUV6kteXp7kP3WRcwOPx0NoaKikzMSJE7G4ipurikQiWFpaYty4cejaTfwSQgAAIABJREFUtSu0tLTg5OSEKVOmYPPmzejXrx/09fXRq1cvWFhY4MGDBzAyMirR0vzjjz9gbGyMp0+fVqlPnxMTEwMlJSX8+OOPpYpLXFwcHB0d5XbN8nj//j3atm0rceRxdXVF8+bNcePGjUq1l5ubC1dXV8ycOVPq+NGjRyUemg0aNMDx48fx7t07jBs3DmpqajA0NKxwsu0JEyaAx+Nh69atpZZJTEyEnp4eEhMTkZycDC0trWJhHS4uLhInpv79+5fZHlA4dXn58mXMmzcP7dq1k0y/T5s2TebsQ2KxGBEREZJlACUlJen4vips/MxxHMLCwlCvXr1vmsD8fx0mfOUh41REdfLmzRtERUVhyJAhsLa2hoGBAfr27YsNGzYgPj4eOTk5uHbtGtatW4fBgwejUaNGMu0p9zlisRhGRkaVinP6krVr10JBQQFNmzZFVlYWIiIiEBwcXOV2t2zZgnbt2gEofEBt3LgR9evXh5KSEpSVleHp6YmlS5eia9euEAgEZYrt2rVrYWtrW6LlWBmuXbsGU1NTuLm5oX379sU2uQWAO3fuoGHDhnK5XnncuXMHVlZWmDp1qkSIxWIxNm/eDAMDA4wbN65C1gzHcQgICJDsfchxHDZu3AhjY2OJh+bt27eRnp6OWbNmQU9PD+PGjcPbt28xdOhQeHl5yWxtjhgxAnw+v8wwFgAYOXIkJk2aBKDw79m3b99iZVxdXXHu3DkAhRZkeHi4zGMGCre9OnPmDGbNmgU3NzdoaGigdevWCAsLw9mzZ0v9P+Xr61ssxCIxMbHKO3cUsXTpUlhZWcn15a0mwYSvNCoxFSEvUlJS8Mcff2Ds2LFo2LAhdHR00L17d6xcuRI3b97EjRs3sGHDBgwbNgzOzs5QU1ODo6MjBg4ciDVr1si8p9zn3LhxA3Xr1pXbGP755x8YGBhAU1MT4eHh6N27d5XbzM/Ph6WlJdatW4dOnTrB1NQUy5cvR2ZmJlJSUrB//36MGDECtWrVAhHB3d0d27ZtK3U3gOnTp6N58+aSjW+rwoABA7Bw4UKIRCKEhobC2Ni42F5rFd2RvrIcOHAAAoGg1Iz/Hz58wJAhQ2BiYoLdu3fL5GAyf/58NGnSBKmpqcU8NBMTE5GTk4OIiAgYGBggMDBQKqQiPz8f7dq1K8xbWg4DBgwAn88v04sXKFxf1dPTk6zJubm5ITo6ulg5d3d3iYPPpk2bqhxj9+nTJxw7dgxTpkxBs2bNoKmpifbt22P+/PmIjY2VrF2OHz8eLi4uqFevHtTV1cHj8bCwZ88qbfz8ZYq1devWwczMTKa8qgxpmPCVRBWmIipDZmam5D9T06ZNoampiY4dO2L+/PmIiorCxo0by9xTrqLptEpizpw5crHKPkckEsHLyws8Hg/m5uZVaovjOBw/fhy2trZQU1PD+vXrS3zbjoyMRL169bBlyxZoaWnBy8sLurq6sLe3x08//YSjR49K7hfHcQgMDISXl1eVgoTfvXsHHR0dqfXUU6dOwcTEBNOnT5e0/ejRI9ja2lb6OuUhFosxe/ZsmJub41oZeSiLuHDhAhwcHNChQ4dSPXKBQq9aU1NT9OvXT8pDMyMjAyKRCJs3b4a5uTm6d+9eanab1NRU2NnZ4Zdffin1Or169YKCgoJMm7MOGDAAYWFhAAqn/vX19UsM3/D09JSES5w8eVLuCQRSU1Nx8OBBjB8/Hg4ODtDW1oa3tzciIiIQFxcnyRDk4PB/7H13WJPZ9vVOAiSEGkhCL9IRREGkSrEAooANR8SOOjrYy6hgwTJe7N1xbGPBBiN2bIgFO9YRKzYcKwyiIh2S9f3B8F4jLRRn5v4+1/PkgZyct+bN2fvsvc9aLfCHs3OTCz9v27YNurq6jVqi8/8jvhm+L9FEoQiggptx3759VdqLi4tx9uxZxMTEMOGTtm3bIjIyEtHR0YiMjJRbU66p4OrqKtd6qoZgzpw5DGtGfc9fIpFg//79aNOmDWxtbRmJmuryVDdu3IBQKMTdu3cBAFOmTEFISAhKS0tx+fJlzJkzB15eXlBVVUX79u0RGxuLy5cvw9/fn9F1awjmzZuHiIiIKu1ZWVnw9/eHp6cnnj9/jkePHsHc3LxBx6gLnz59Qs+ePeHu7l5tmLUmfF6OP3PmzCqRgqSkJCgpKYHFYkFDQwNz5sxBaWkppFIp9uzZAxsbG/j4+NRY4fk5nj59Cl1d3SpUbgDQpUsXKCgoMGHJ2nDv3j2IRCJm6UBsbCyGDx9ebV8/Pz+mkvXx48cwNTWtc/+NQXZ2NhISEjBixAhYWVlBW1sbPXv2RMwPP6CoIQbv81cNws+JiYkQi8VyfQffUIFvhu9zpKU1WShizZo1UFBQYLgm09LSMH/+fPj5+UFVVRX29vYICgpC9+7d4e7uzmjKhYWFYfHixThz5oxcmnJNgcrCgNpygI1Bfn4+uFwuxGIxVFRUcP78+Tq3KS8vx86dO2Fvbw8nJyckJiYy7C3Lly9Hjy+833fv3qFZs2YyeaHi4mK0bNkSGzdulOmbl5eHgwcPYvTo0bCxsYG2tjYEAgFCQkLkUmH/HGVlZTA0NKxRBkkikTAit+vWrfsqA++zZ8/g4OCAiIgI+b7DavLW76OjMTgoCObm5jh27BiuX7/OrNPU1NSEoaEhI7h78uRJtGnTBo6Ojjh27Fi9HIYLFy5AJBLh999/B1Ax627fvj0UFRXrZG6pRGhoKBYsWMC8b9GiRY0E0oGBgYyhLSkpgZKS0t9KAfby5UvExcUhvnXrqkoR9X3VIvx89OhRiESiJmGM+f8B3wzf52gEd2VlKEIikWDcuHEMNyCLxYKamhoMDQ3h6OgIOzs7qKmpoVmzZujVqxcWLFhQRVPu78a2bdvQrVu3r3qMyoXFQUFBYLFYiI6OrrZfSUkJNm7cCAsLC3h6euLIkSNVBtaCggLo6OgwYTWJRILAwEBMmDChyv7S09MhFAprLQL4448/sHTpUqioqEBVVRXW1tYYNWoUDh48WOeC4d9++w1t27at6/Jx4cIFGBgYQFVVtUkdjNOnT0NHRwcrVqyo2wDJkbe+Z2MDt79YTrhcLgYOHIjhw4dDUVERWlpaaNeuHSwtLbF79+4Gc17u2rULxsbGePnyJTw8PMDlchlDWBeuX78OPT09Jlx9+/ZtGBoa1nguQUFBMvlCAwODejs3TYK/Qfj5zJkzdQoIf0MFvhm+SjSFWgGPhw4ODqis5Kp8aWlpVasp929B7969sWHDhq96DBsbGyYEuX79enA4HDg5OTGhz8LCQqxatQrGxsbw8/OrVQIGAObPn8/IHcXExMDb27tGcuQlS5bA09OzzqrCjIwM6OjoYNmyZZg/fz7at28PVVVVtG3bFnPmzMGlS5eqzBa8vb3rrD6sxO3bt8Hj8eDk5NRk1bNisVi+EHU92HkK2WxM19YGl8tlmFcqnbiwsDC5SKjrwpw5c8Dn88Hj8WTovupCYGAgVq9ezbyPiorCjz/+WGP/rl27Yu/evcx7T0/POp+txqJNmzbo3r07EhMT/1s49RWEn6vDlStXIBaL5X4m/3/FN8NXiSbSp5uhrAwzMzOYmppCWVkZLBYLU6ZM+aevrkaUlZVBIBA0mrqpLnxeZABU5Ft0dHTA5/MxYsQI6OrqomvXrnKHu/Ly8iAUCrFu3ToYGBjUmteSSCTw9fVlpGpqw+XLlyESiRjFgoKCAhw7dowh2RYIBOjRowfWrl2LpKQk6Ovry20IXr9+DR0dHaxatQpCobDB2m0lJSUYPnw47Ozs5CL6bkjeupDNRvrIkYy6RuVLQ0Oj0RyflUKyCgoKCAgIkHvmeO7cOZiYmDBFLFKpFCYmJjWGmQGgR48e+O2335j3ffv2bZQEkzwwNDQEEYHP50NBQQHNmjXD+y5dvvqMrxK///479PT08Ouvv37V6/xfxjfDV4mvEIqQSqV49uxZg2mQ/g6kpqaiVatWX/04/fr1kxlw3r17h5kzZzKM+kOGDKn3PseOHQsejydXzjAzMxNCoVCu6rfDhw9DV1dXRj29Eq9fv8a2bdvQv39/KCsrQyAQYPjw4UhMTKyWmPpzZGVlQSgUAqgoxLG0tMTQoUPrVZWblZUFLy8vhISEyMfb2Ii8dTmPhzALC7i5uYHP58Pe3h4//vhjo0K1JSUlsLCwgLq6Oh4/fgxvb29Mnjy5zu2kUim8vb1lBvMLFy7A1ta21hDvd999h127djHvp02bJiNC3BhIpVK8fv0aKSkpWLNmDUaNGoUOHTowM+RKcmx9fX3kRkU12rGuLcf3JR48eAAjI6Naq2j/fwabvqECHz82yW7Sz52j2bNn0+rVq2n37t2UkZFBmZmZ9OzZM8rLyyMATXKcpkJSUhJ16dLlqx9HX1+fXr9+TW/fvqXJkyeTpaUlvXr1iu7cuUMbNmygLVu2kJOTE+Xn58u1v8LCQkpJSSE2m00GBgZ19jcxMaGlS5dSv379qLi4uNa+Xbp0oblz51JgYCBlZWXJfKanp0f9+/en5cuXE5fLpb1795KVlRVt2LCBjIyMyM3NjWbOnEnnzp2jsrIymW05HA5JJBIiInJ0dKTr169TUVERubi40N27d+u8hlu3bpGLiwv5+PjQvn37SE1Nrc5tKDaWqKio7n7VgFNSQrscHOjSpUv06tUr8vHxoW3bttHu3bsb9BwXFhaSpaUlvXv3jh49ekTm5ua0d+9e2rt3L23YsKHWbZOTkykrK4v69+/PtO3cuZPCw8OJxWLVfA2f3XMiIlNTU8rMzKzXeZeXl9OjR4/o4MGDtHDhQho8eDC5ubmRQCAgBwcHiomJoVu3bpGZmRlNmjSJBg8eTCwWi5SVlWnGjBn0xx9/kGDcuHods1oARIMGydXV2tqaUlNTafny5RQbG9v4Y/9fwz9tef81aKIZ3z1nZ8yYMQORkZHo3bs3OnbsCCcnJ5iYmEBVVRWKiorQ0dGBra0t2rZti65duyIiIgI//vgjYmNjsX79eiQmJuLMmTNIT0/Hq1evvlq1JVBREfd3lEHHxMQwocLRo0dXKTB4/PgxdHV1oaKiUmcORiqVYsCAAQgPD0dUVFSNpezVbRcaGlptEUx1mDVrFpycnKqdWS1atAj9+vWTaSsqKkJKSgqmTJkCJycnqKurIzg4GCtXrsT9+/eRm5sLdXX1Kuf066+/QigUYuPGjTXOXhISEiAUCpGQkCDXuQNokrz1lyX0aWlpaN26Nby9vZmcrTzIy8uDnp4eRCIR3r17J/PZw4cPoaOjU2OuUiqVwtnZWSZvVVpaCpFIVGeo98tIQ3JyMtq1a1dt34KCAty4cQM7duzAjBkzEBoaCjs7O/B4PJiamqJTp04YP3481q9fj9TU1BojOfv374eRkRHDEcqge3dIG1E8VxgYiBs3buD169coLCyUq5r21atXaN68OaKjoxutyPF/Cd8MXyWaIMcHZWVkDBuGjRs34scff4Sfnx9MTU2xZ88e5jDFxcV4/fo10tPTcfbsWezduxcbNmzA/Pnz8eOPP2LIkCHo1q0bvLy80Lx5c+jo6EBRUREqKiowNjaGo6MjOnbsiO+++w4//PADpk+fjmXLlmHbtm04fPgwLl26hIyMDLx7967O3Mnz58+hra3daOLi2pCRkYGIiAioqqrCysoKb9++rbGvRCJB165dwWKxag1/rV27Fvb29sjPz0d2djYEAoHcvIU5OTnQ19eXq+xbKpVi6NChCAgIkMnjlZeXw9TUtOrA9gWys7Oxa9cuREREwMjICAYGBlBQUMCuXbuqDJp3796Fvb09+vTpI7OMRSKRYPr06XXmsqo79zsDBqCwoQNtLeG18vJyrF69GkKhEFOmTKmT+SY3NxcikQh6eno1hmcrKxKrYyHZu3cvWrZsKfM8HzlyBG5ubnXeh0GDBsmERx89egQTExOkpqZi/fr1GD9+PDp16gRTU1PweDzY29sjNDQUM2bMwM6dO3Hjxo0mIYiQSqXYNWEC8hv6PfD5+LFdOxBV0J9xOBxwOBwEBATUeew///wTjo6OGDNmTIMrcf+v4Zvhq0QTeMelHA7M/qLLqnxxudxGz6ikUiny8vLw7NkzXLt2DcePH8fOnTuxevVqzJ49G2PGjEHfvn3RqVMnuLi4wNzcHJqamuBwONDS0oKlpSXc3NzQpUsXDBgwAOPHj8fcuXPRp08ftG3bFidPnsTNmzfx/Plz5OfnN4lnePv2bYSFhUEoFGLWrFk4cuQI3N3d5dp248aN4HA4aNWqVZUF75XFJ58zjUycOLGKKndtOHLkCIyNjevMyQH/ZZ8ZMGAAc18OHDgAFxcXuY8HVHyHN27cgKKiIkOy7eTkhKlTpyIlJQXFxcUoLCzE999/DwsLC1y7dg15eXno2rUrvLy85GbjLy8vx88//wwdHR3ENUEEA0Q1FlS8efMG4eHhMDExqZFiLCsrCwKBAMbGxnUakC1btqBZs2Yy11peXg47OzscPnxYpm+/fv2wcuXKGvclkUjw7Nkz+Pv7o1evXhg2bBjatm0LbW1tEBFcXV0xaNAgLFiwAAcPHkRGRsZXWd/3/PlzrFu3Dvr6+iAiRGtpQaqsXG+jh59/Rn5+PgQCATO28Hg8uUnl379/Dw8PD0RERHxVR/d/Bd8M3+doxDo+KYuFm+bm0NDQgIqKCkNQy2Kx0KdPHyQmJjaJ51gflJWVITs7Gw8ePMCFCxdw8OBBbN68GYsXL0Z0dDRMTEzg4uKCdu3awcHBAQYGBlBWVgaXy4W+vj5atGgBHx8f9OjRA8OGDcPUqVOxaNEibNq0Cfv378e5c+dw7949ZGVlMYPGlStXEBISAl1dXSxcuJDx8J88eQITExO5z72S5ePz0GdWVhaMjIyqDLKvX7+GQCCodTb5JX744YcqocqakJ+fDxcXF2btYceOHbFt2za5j1WJ4uJiKCoqAqgI1aWmpmLGjBlwdXWFmpoaOnXqhCVLlmDhwoUQCATQ1dXFsGHDqqXi+hIFBQWYPn06VFVVweFwEBISgoL27ZvG8NVRQn/y5ElYWVkhJCRERpj2xYsXUFdXh4WFhVzXAFQUn7i7uzOVo9u3b4e7u7uMM1ZQUAANDQ28ffsWxcXFSE9PR0JCAubMmYM+ffqgVatW4PP5MDAwgIGBAXx9fbFmzRqcOnUKr1+/hr6+Pv744w+5zqcxcHZ2hqKioowj/OTJk0ZRIp48eRLKysogIrDZbAwePFhup+jTp09o3749evfu3SRLUv6X8c3wfY5GVMBJeDysHjQIJ06cwMSJE8H5axFwhw4dsGbNGnTo0AHq6uoIDQ3Frl27/nEl5aKiIqipqVXJtwAVa+pevHiBW7duISUlBb/99ht++eUXzJs3DxMmTMDAgQMRHBwMDw8PWFtbQ1tbG2w2mwm/GBsbo2PHjggPD8eoUaMQExODxYsXQ1FREUePHkVaWhqePHmCDx8+1Dq7lEgk6NatG1gsFiZNmoR27dph2rRp1fYdOXKkXNWBlcjPz4elpaXcObPs7GxYWlpixowZ0NHRaVDetbS0FBwOp9rPKkm2hw8fzige8Pl8ODk51UpCnJ2djYiICCgpKTHr7pjv9G9YNF2J4uJizJ07F9ra2pg/fz4ePnwIFRUVNG/evF4zKYlEgt69eyMsLAwlJSUwNzfHqVOn8OHDB1y6dAm//vorgoODIRKJYGFhAS6XC2tra3Tr1g1Tp07F1q1bkZaWxoSLIyMjZdb9AYCHh4dc1GiNQWlpKQIDAxmDV7kG8u3bt3B1dcVYT08UdekC8Hgo53KrjCWoRvi5Ev379wcRYf/+/ZgwYQK0tbWxePFiuZyLoqIiBAcHIzg4uNHLUv6X8c3wfYkGcnX+/sMPIKrQ3lJQUIC2tjaTl2vdujV2796NN2/eYNOmTejcuTPU1NQQHByMrVu3/iOsLceOHYOnp2ej9iGVSpGUlAQPDw9YWlpi1apVuHfvHq5cuYIjR44gLi4Oy5cvx8yZMzFy5EgoKSnBx8cHTk5OMDU1hZqaGhQUFCAWi2FjYwNPT0+EhIRg8ODBmDRpEmJjY7Fu3TpERkYyhuDOnTuM1NHnzsPz58+hpaVVL3KAy5cvQywWy72G8cmTJ+Dz+QgNDa33vQIqBvXa6smkUilWrFgBHR0dbN++HatWrYKFhQVYLBbMzc0xceJEHDt2DAUFBXj8+DE6d+4MDocDNTU1xMTEoLCwkNnXp0+fcKFbNxQ1MscnVVZG4Zw5ePbsGW7evIlTp07Veo+fPHkCLy8vsFgsWFpa1iunJJVK8fLlSxw+fBimpqYwNjaGQCCAnp4eVFRU0Lp1a/Tt2xc2NjYYNWoU7t27V+dgP3r0aCxfvlymLTw8vEEzdnnx7NkzuLm5QVdXlxkTeDweJkyYwMzWWrRoASJCycuXwMKFSLOxwQ1DQxwRCvF4xIhaZc8+fvwok7e8f/8+OnXqBCsrq2p5UL9EaWkpevfujQ4dOnw17t9/O74ZvurQwFCEp6cn4+EpKCggKCgIEokEBw4cQNu2bWFqaoqVK1ciPz8f79+/R1xcHLp16wZ1dXUEBARg/fr1TaYPVxdGjx6NefPmNWhbiUSCPXv2wNHRES1atMDu3bvlyhvY2dnh9u3bMm0lJSV48+YN7ty5g9TUVOzbtw8bN27EggULMHnyZAwZMgQuLi5MQr/SeyYiqKuro1WrVujQoQN69eoFGxsbtG3bFkuXLsXWrVtx+PBhXLx4EQ8fPkROTk615xgTE4OAgAC58pofPnyAmpoaBAKBXGsHqwMRVWsMiouLERERgRYtWuDp06cyn+3btw8CgQAdO3aEg4MDE0ZXVVVFdHS0TNjq0aNHGDduHLS0tDCoc2eUKyk1yvAVEUFEFfmkSimi9evX13h9v//+O5SUlGBtbQ1DQ0MMHDiwyjNdVlaGBw8eYN++fYiNjcWAAQPQpk0bqKmpQSwWw8fHB+Hh4WCz2ejbty+eP3/O3LN3795BXV1dbh7bcePGYcmSJTJt0dHRmDNnjlzb1xfx8fEQCoXQ1dWFkpISjh07BldXVygpKTFGj8Vigc1mg81mM05EXl4ew1q0adOmBh07KSkJlpaW6Ny5c51sOOXl5YiIiICHh4dcue7/a/hm+GrC1asVoQYer6Ky7fMBoVKP74tQxI0bN2Qe7i+ZEy5duoQePXpAKBRi+vTpTE7q06dPSEhIwHfffQcNDQ34+vpi9erVX41NRSqVwszMDLdu3arXdmVlZdi2bRtsbW3h4uKCAwcO1Muj/5wpX17cv38fQqEQV69ehUQigbe3N+Nc8Pl8nDlzBidOnMCuXbswe/Zs8Pl8/DhwIHY6OiJFXx+pmprYp6qKmTwedNhsaGlpwcLCAq6urujcuTP69u0LHR0dBAUFYc2aNdi9ezeSk5Nx48YNZGZm4tOnT4xRXLFiBb777jscPXoUYrG4QTpoHA6nSn7lzZs3cHd3R48ePar1wKVSKdatW8fwvzZr1gzTp0/HyJEjYW1tDZFIBB8fH2a5yJQpU/6ba2sk/+wLFxeZPJWiomKNLDlpaWlQVFRkxIJfv36Nvn37Qk1NDYGBgejevTtsbW3B5XJhZmaGLl26YOLEidi4cSMuXLggE3ZfunQp2rVrB5FIJLO8Zf369fWacU+aNKmKIPH69eurVdNoDPLz8zF06FAYGxtDTU0NIpGIWbLz8eNHhIaGMmQNlS9lZWUZ3cLDhw9DIBAw4roNQUlJCRYtWgRtbW1MnDixVrFhiUSCMWPGwNHR8W9zuP8t+Gb46kJ2dkU5d//+FUn+/v0r3tfwoPj7+4PD4WDLli2wsLDAsGHDqhS1ZGRkYMSIEdDU1MSwYcNkvLPCwkLs378f/fv3h0AggIeHB5YsWSJTNNBYPHjwAAYGBnJXbxYXF+OXX35Bs2bN4Ovri+Tk5AZVfg4cOLBeNEqfPn2Cra0twyP6/v17piqv0rmQoYNLS8NVIyOUKShUS8Is5fFQ3LkzMn/7DRcvXsShQ4ewZcsWTJkyBcrKyggLC0OvXr3Qvn17tGzZEoaGhuDz+VBSUmI8+FatWqF79+7w9vaGuro6pk2bhk2bNmHfvn1ITU3F3bt38fbt2xqLB5SUlGRyK1evXoWRkRFmzZpVxYkoLS3F6tWrIRaLweFw4OrqisGDB0NfXx/Jycn4+PEjVq5ciWbNmsHQ0BBt2rSBUCiEjY0NxowZg0OHDiH/9OlGK46sXr2aEVPV19eHSCTC1KlTkZmZCalUiqysLKxcuRJsNhumpqbw9/eHkZERI5Ds7+8PQ0NDWFpaIj4+XiYkW9P3rqOjg99//x0nTpyAjo4OU8Xr6+srw71ZF6ZMmYL//Oc/Mm0nTpxA+/bt5d5HXbh16xZsbGzQrl07KCgowMXFpUoI9u3bt1BVVZUxfGpqalX0C93d3WFtbd3oc3r79i0iIiKgq6uLjRs31uigSqVSREdHw9bW9qvTFv6b8M3wNTGePXuG/fv3A6jw9MLDw2FnZ1ftYt+srCzMnDkTIpEIXbt2rRI+KykpwdGjRzFkyBAIhUI4OzsjNja2VtFQebBkyRIMGzaszn75+flYunQpDAwM0LlzZ1y4cKFRx42KisJPP/0kV1+pVIrvvvtOhsrs7du3CAoKgoGBARP2JKKKmetf4ek6FwjXIB68evVqtGnTplqDVVhYiLi4OFhbWzPFPuvWrYO/vz9EIhHCw8MRHBwMT09P2NjYQCQSQUFBAWpqajA1NUXr1q3h7++PPn36QEFBAVOnTsWKFSsQGRkJdXV1LFiwAI8fP8b79+8hlUrx6dMnREVFQVVVlQmZf64usXnzZqioqIDH4yE0NBSpqamMIyKRSJCSkoJevXoxSvSxJiYoUVSsv9H77B5FRkaCiLB69WpMnjwZdnZ2UFBQYPJXRARTU1MsWrQIhw8fxpMnT2RCyxKJBBs2bIAG6gUpAAAgAElEQVRYLMa4ceNqLe766aefGAJyoGKGZmlpidu3b0NLS6tehUXR0dGYO3euTFtGRgbMzMzk3kdNkEqlDO9q5frT77//vtq+oaGhmDp1KrKysqCqqsrk/75cC3rs2DEoKCjUa81mbbh69So8PDzQunXrWsPzsbGxMDc3l5mB/l/GN8P3lSGVSrFp06ZamTkKCgqwZs0amJmZwd3dHXv37q2SjyorK0NKSgoiIyOhq6uLFi1aYNasWbhz5069Z1/t27dnjHN1+PDhA+bNmwexWIzQ0NAmU3deuXIlIiMj5eq7dOlStG7dusbKs8LCQly5cgUBAQGYyOejrL65rC8GdqlUioCAAMyaNava43Xu3LmKrp9UKkVkZCTat29fxcOXSCR4//49njx5grS0NBw9ehTbt28Hl8vF5MmT4eTkBBUVFbi7u6N169YwNTWFqqoqk79ksVgQi8UICAjAoEGDMGHCBAwYMADNmzeHhoYGevbsCUdHR7Rp04ZxhP7880+4u7uDy+XKyGIpKSlhmbU18qlCfaHWYhYWCxIeD1eHDMGsWbPQu3dvODg4gMfjQSQSwd/fH2PGjMHatWtx5MgRDBo0CEQV5NWrVq2qM/eWnZ2NwYMHw9DQEAkJCVWe3dzcXAiFwirO3aRJk2BmZoZBgwbVuv8vMWPGjCrfaVFREZSUlBq1ni0nJwchISFwcnKCl5cX2Gx2jQoniYmJsLKyQmFhIQ4dOgQnJydIJBJcvXq1iqOVm5sLLpeL1q1bN9l6O6lUih07dsDQ0BDh4eE1kj2sWrUKRkZG9VLL+F/FN8P3N6EmZo7PUV5ejt9++w0uLi6wtLTE2rVrqw0LSSQSnDt3DuPGjYORkRGsra0RHR2N69ev12kEP378CFVV1WpzSX/++SemTZsGbW1t9O/fv0E5rNqwZ88euXT/zp49Cx0dHfm8z7Q0lDeUeOAL8eBXr15BLBZX8cIfPXoEoVBY7XdRXl6Obt26ITw8XK58p7q6Ovz8/ODr68uwtzx48ACBgYFQUFCAqqoqxo8fj6tXryI1NRXbt29H7969oa2tDV1dXXh7e6Nr167w8fGBvb09M6tTUlKCgYGBDEFy5cvAwADq6upIjIrCKzc3lCkooJjNlrkXBUQoJMJeFguhpqbo0aMHoqOjERcXh2vXrlX7vOzevRtsNhvDhw/H2bNn0atXLwgEAowcObLOZyc1NRV2dnYICAiQoR2LiorC0KFDq/SXSCTQ0NCAv79/vRy92bNnY/r06VXa9fT05Gb7+RKnT5+GoaEhRo0aBVNTU/D5/BpVRXJzc6Gvr49z584BADp06IC4uLha96+trQ1PT08sXbq0QedXEz59+oRp06ZBS0sLc+fOrfZ5/vXXX6Gnp8foI5aUlPxtgth/J74Zvr8RXzJz1ASpVIqzZ88iKCgIYrEYs2fPrpEXUCqVIi0tDVOmTIG5uTmaNWuGSZMm4dKlS9UOxImJifDz85Npe/XqFcaPH88oDdQm2toYXLx4Ea6urrX2efXqFfT09OQvgmkC8eDPER8fDysrK5m87Pjx42tdI1hYWAgPD49adeGACk5KNpuNIUOGoLS0FOfOnYOzszM4HA7EYjHWrFnDzADu3r3L5IH79OmDixcv1jjgnz9/HkZGRoiIiMC0adNkjB6LxYKCggKsra2ZwgsdNhsTibCVCIdYLOzgcDCJCMK/+js6OiIiIgIrVqzArl27cOLECVy/fh2ZmZnIy8uDVCrFli1bwGKxqvCevnjxglnr2KFDB+zbt6/GdXylpaVYuHAhtLW1MXv2bGZJSnVCsffu3YOOjg5at25dr2rkn376CVFRUVXa3d3dGWMkL8rKyjBjxgzo6elh9erVUFFRgZGRUa3qK4MHD8bIkSMB/FcuqK4lGG5ubti5cye0tbW/Sujx6dOn6NGjB0On+OVzFR8fD7FYjLNnz8LFxQXe3t5Nfg7/NL4Zvn8A8fHxEIlEWLZsWZ3e6927dzFkyBDGk37y5EkFvdqCBRULlIOCKv4uWABpVhZu3bqFGTNmoHnz5jAwMMDo0aNx5swZJmwSERHBrGt6+vQphg8fDoFAgPHjx+Ply5df9bozMzNhZGRU4+clJSXw8PCokpOpEV+BhBmoEObt/9ei7U+fPkFLS6vO4qKcnBzY2NhUWTNWiWPHjkEsFkNNTQ3r16+HmZkZOBwOLCwssHfvXkilUpSXl2P//v3o0KEDdHV1MXPmTLkKDkpKSnDx4kWYm5uDzWbD0dFRxviZmJggMjISK1euRHJyMl6+fMkcLycnBw8fPoSFhQXTn81mQygUQklJCfr6+rCysoKNjQ1T7FOZXxUKhfD29ka3bt0wZMgQTJ48GQsWLMDGjRsRHx+P6dOno2XLljAwMMBPP/1UY+Xg8+fP0a1bN2hqatYYEZgxYwbGjx+PV69ewdjYGLt3767zvgAVuavqnJY+ffrUOfP6HJmZmfD09ISfnx9WrFgBDoeDDh061DrLP3HiBExMTJh8ZkREhFzPdr9+/bB582b85z//QadOnb4auXRKSgrs7e3Rrl07ZoZXib1790JRUZEhRqgPK9L/Ar4Zvn8IT548gbOzM4KDg+VadP369WusHjgQhxQVUcJmV12fVbnEonv3CgYaVHjJc+fORatWraCjo4Pvv/8eWlpaOHToEPr37w9tbW1MmzbtbytlrqTsqmmwGDNmDLP2US40EbF4JQlzdnY2oqOjoaKiwrDMrF27Vq7wLFAxOBoYGMioCEilUixevBh6enoYM2YMs4bLxcWFKTZ49+4dFi5cCFNTU7i6umL79u3Vzgo+fvyItLQ0bN26FVOnTkW3bt1gbW0NLpcLHR0dcLlcODs7Q0FBAba2tkw1qDyMHgYGBjLGUlFRES9evMCuXbuYnJyxsTFcXFxARBg7dixu376N06dPY8+ePVi3bh1iY2MxadIkDB48GCEhIUyxj0AgYHKXKioqsLa2hp+fH8LCwhAZGYkZM2YgJiaGySP6+fnhwoULyM3NhUQigVQqhbm5Oa7+FZa+desWRCKRXBy4ixYtwsSJE6u0R0VFye1g7dmzByKRCAsXLsTIv8R56xKX/vTpE0xNTXH06FEAFYVsmpqacv3WZs2ahWnTpqG0tBQODg7YsWOHXOfZEJSVlWHNmjUQiUSIjIxETk4OJBIJunTpwoTNlZSUqg+71uCA17b4/t+Cb4bvH0RJSQkmTJgAIyOjuimUGlm1+PjxY4SFhYHNZoPFYsHJyQnx8fFfVfKoOgiFwmq5BXfs2AFzc/P6LaZtIkquwtBQtG3bFoqKisyaNT09PQiFQpiammLz5s3IzMxEVlYWPn78WCvP4c2bNyESiXDmzBkUFRWhd+/eEIvFUFZWhoKCAng8Hk6fPg2ggsh72LBh0NTURP/+/ZGWlsaIm546dYoRN+3YsSMMDAzA5/PRqlUr9OnTB7Nnz0ZCQgLS09OxYcMG6Onp4fbt21iyZAmsrKygpqYGY2NjuZfBfF5qz+PxMH/+fJnPpVIpxowZAyKCnZ0d1NXV0bp1a0RFReHUqVN1PkdSqRRPnz7F5MmToaenBxsbG/zwww9YsmQJYmJi0Lx5czRv3hwdO3aErq4u2Gw2eDwe2Gw2NDQ0oKioCDc3NwQFBWHgwIHo1q0b1NTUMHfuXCQkJCAlJQW3bt3CixcvZMLUS5curZbAfN26dXWKHxcUFOD777+HmZkZLly4AA8PDygoKMgouteEMWPGYMCAAcz72bNnV5u7rA6VeV2ggvtWV1e3XoxEDcG7d+8watQoiEQiLF68GE5OTjJFUvr6+v/tnJZW4WBXUqvV4YD/G/HN8P0LcPjwYejo6GDu3LnVV3I1kEat0vhduHCBoUnz9vbGgwcPsHz5cnh5eUFTUxPh4eF/G4m2g4NDlVLt27dvQygUVgm31ImgoCYxfIf/cgY+n/FUMm1Url3T1dVlaOgqeUlVVVUhEolgbGwMKysrtGzZEm5ubmjVqhVT6l+ZZ7O1tUVkZCQ0NDTQtWtXmJubQ01NDR4eHujRowc6duwIW1tbqKioQENDA05OTggLC8OsWbOwZ88eZGRkVDsTXrduHQwMDHD//n2cOHECurq6mDFjBuzt7dGrV68al9J8CS8vL3h6eoLP58PQ0LDK51FRUWCxWFi3bh2ACqft7NmzmD59OlxcXJhF6kuXLkV6enqt4bny8nIcOHAAfn5+EIvFGDFiBAQCgQx137179+Dj4wNHR0d069YNkZGROHfuHPbv349NmzZh0aJF6NixIzQ1NREUFMQs4NfX12cGbAMDA+jr68PAwAChoaH4/vvvERUVhcWLF2PixIlo2bIlzp8/j/v37yM7O1smF5meng47OzuEh4fjwYMH0NXVhbq6ulwFX+fPn4eenh6zIL+4uBi6urq4c+dOndsCFcbOycmJeT927FgMHDhQrm0bi/T0dLRv3x52dnZISEjApk2b4OLiAjabXXE9jSDY/jeBBQD1Vq/9hibHy5cvqW/fvqSoqEhxcXGkp6dX8cHVq0S+vkSFhfXep4TLpUg7O0p+/56mTJlCv/76K82bN486duzI9Hn79i3t27ePEhMT6erVq+Tv7089e/akLl26yKfwXU906tSJxowZQ507dyYioo8fP5KzszPFxMRQv379at22vLyc7ty5Q1euXKErV65Q9717Kfjjx0afU1lYGD2bPZu2b99OK1asoPz8fDI2NiaiivtjampKRETZ2dmUl5dHQqGQRCIRCYVCEggEpKmpSZqamqSmpkafPn2ixMREev78ORER+fr6Ups2bSgzM5OuX79OT58+JQ6HQ1KplPh8Pqmrq5OKigrxeDxSVFQkDodDZWVlVFRUREVFRVRcXMz8T0SkrKxMPB6PlJWVqbi4mD58+EB2dnbE5XLp5s2bZGlpSU+fPqWQkBASiUT0+PFjOnv2LHXu3Jnatm1LfD6f2f7LF4/Ho4ULF9LWrVvp+vXrZG9vTwoKCjR+/HhatWoVbdmyRUYB/XO8f/+eTp06RSdOnKDk5GQqLi4mPz8/8vPzo44dO5Kurm612z18+JBCQkLoxYsXFBgYSKNGjSJfX19isVgEgLZt20YRERHUu3dv+vnnn0lTU5MAUGpqKnl7e9PYsWPpwYMHlJSURIqKikREBIAKCwvp3bt3tHbtWrp79y7169eP3r17x7wyMzPpxIkT5ODgwLR9/PiRVFVVSVFRkT58+EC2trYkFovpzJkzpKmpSVOnTiUDAwPS1tYmbW1t0tLSIm1tbVJTU2NU4IuLi8nR0ZHmzp1LoaGhRES0detW2rFjB504cUKu5zE3N5eaNWtGHz58IBaLRfn5+WRvb08bN26U+e1+LQCg/fv308SJE6lly5a0ZMkSMjMzI1q7lmjSpPqNRXw+0eLFRD/88PVOuAH4Zvj+RZBIJDR37lxav349bd68mQICAoh69CDav7/Cj6rv/ojopbMzGVy6RO/fvycLCwvKzs4mLpdbbf+cnBw6ePAgJSYm0vnz58nHx4d69uxJISEhJBAIGnl1FYiIiCAPDw8aOnQoSaVS6tGjBxkZGdGqVatk+gGgly9fMkbuypUrdPPmTTI0NCRXV1dydXWl4AcPyGD9emIVFzf8hJSV6aCzMy1ls+njx4/04sULys3NJS8vL7pz5w4dPHiQunfvTteuXSNjY2MqLS2lnJwcys7OpqysLMrOzqa3b9/SxYsX6dSpU5SXl0dERGw2m6RSKRERMyiqqalRSUkJde3aldzc3MjIyIjEYjHp6OiQWCwmTU1Npm91+Nwgrlq1irZu3Uq//PILcblcGjZsGLm4uNCxY8do7NixZGhoyBjO58+fU0JCAmlra5O7uzuVl5fLGNTPjWxBQQG9fPmSFBQUCABJJBLm3DU1NWUMb3WGs/L/oqIievHiBT19+pSePHlC2tra1LJlS2rTpg05OzuThoYGKSsr04sXL2jo0KGUkpJCJ0+epI0bNxKLxaJRo0ZR//796dKlSzR58mRyc3Oj/fv306JFi4jH41GvXr1o165dFBoaSl27diUjIyNau3btf+9fdjbRli2UkZhIRW/fUksvLyIHB6LBg4lEIiouLiYNDQ0qKioiNptNRER//vknDR48mJ49e0YxMTHM+bRo0YKCgoLo/fv3jJHMzc1l/i8tLWWM4KdPn6i8vJwCAwMZ4/jzzz/TgAEDyM/PjzGa2trapKSkVON3raWlRQ8fPiSRSEREREeOHKHRo0dTeno68fn8hj/v9UBxcTEtXbqUlixZQnOCgykyIYFYfzlg9QKfT3T2LJGzc9OfZAPxzfD9C3HmzBnq168fDe/enaZv2ECskpIG76uUzaaHycn0++vXlJiYSPv27ZNruw8fPtDhw4cpMTGRTp06Re7u7tSzZ0/q1q0b82NsCKZPn05KSko0c+ZMio2NpUOHDtGZM2eopKSErl27JmPoysvLGSPn6upKbdq0IU1Nzf/uLDubyMSEqDGGj8ejAb6+FHfsGNOkrKxMISEhpKenR8uWLaPY2FhKTk6mEydO0MuXL+nBgwd0//59unv3Lp0/f54ePXpEUqmUOBwOKSoq0tChQ4nH49GpU6fo4cOHpKmpSStWrCAANHLkSOrVqxdJJBIZ45mdnU1FRUUkEolkjOHn/1f+3b17N+3du5dOnTpFBgYG1KtXL+LxeHTp0iWaO3cu9e3bt8plFhYW0rhx4+j06dMUHx9PTk5ONd6S5s2b06NHjyg0NJQSEhJo9+7d5OPjU8VIVmc4v2wrKiqiwsJCevnyJb148YLevHlDeXl5pKqqSnw+nz58+EBKSkqkpKTE9JdKpYzjwOFwSFVVlYyNjUkikVBmZiYVFhYSAFJUVKTevXsTn8+nffv2UcuWLamvlRW1PXeOTO/fJ2KxSKGsjLkuCZdLLIDyvb3p08iR5DR8OJ05c4bMzc3p8uXL1K9fPwoNDaXY2FgaMmQI7dy5k+bPn0+TJ0+u9REqKSmhd+/e0YULF+j777+nxYsXk1QqpXfv3tHNmzfp+PHj5OPjI2Msc3NzicvlyhjCz2eR27Zto4EDB5KbmxvTPmXKFLKwsKCFCxc24EGvG4WFhfT+/XsyMDCQaX/16hW9cXcnxxcviNOQHbNYRN27EyUmNsl5NgW+Gb5/Kf7880866OVFfTMyiNeIr6hMQYHmKyvTGj6f+vTpQ0uXLq11VlEd8vPz6ejRo7Rnzx46fvw4OTo6UmhoKHXv3p309fXrta+ff/6Zbt++TY6OjjRlyhQKDAykO3fu0NOnT6lly5Yyhs7U1LTOc5V07UqsgweJXa+z+At//SCxZw+NGjWKfv31Vyr+y4iy2WwKCAggS0tLys7OpkOHDlFpaSmJRCKysrKisrIyunnzJpWUlJCNjQ0pKysTn88nT09P2rZtG1lYWNDo0aMpODiYCeHu3r2b7O3t6bfffiM7O7sqp1NcXEx//vmnjDGs/D8rK4uysrIoPT2dcnJyiIhIXV2dOBwOlZSUkKKiIpmZmVFwcHC1RrMyHLd7924aPXo0zZw5k0aNGlXt/U1ISKDevXsTi8Wi5ORk6tChQ0Pubo3Iy8ujM2fOUFxcHO3fv58EAgF17NiRCY3q6upSUVERpaenU7t27UhFRYVsbGyoe/fudP78eUpKSiKJREJsNptcXFyoT58+9ObNGypatozml5eTkkRS6/MgIaISFot+JKLNPB6VlJSQVColLpdLKioq9OnTJyorK6NmzZqRWCyudob7ZZuSkhKtWLGC/P39yd/fn2mfM2cOeXp6Uv/+/WX6c7lcKikpkTGGn88kd+7cSVpaWqShocG05eTkUF5eHmlqapJYLGaMZHWG88s2Pp9f529p06ZNNHz4cBo7dizNmTOHVFRUKj5oIgeT/viDqBFOc1Pim+H7FwP9+hFrx45G76esTx8SHDxIhoaGxOVyadKkSRQWFsbkROqDoqIiOnHiBCUmJtLhw4fJ1taWevbsST179iQTE5Nqt3n16hUzi0tKSqL79+8Ti8WiDh06UEhICLm6upKDg0OtoZ/qkJGRQbO6dKHNz54R96+QXH0AZWW6+/PPdI2I7t27R/Hx8fTHH38Qi8UiLpdL+vr6lJ2dTdra2uTj40MHDhyg7t27U0JCApWVlVGHDh3ohx9+oFGjRpGqqiq9efOGevXqRaNGjSIHBwfmOMXFxRQQEECtWrWiU6dO0Y4dO2Q+l+tcAZo6dSodPXqUTp48SUKhkHbu3EkTJ04kGxsbKikpoT59+tCff/5ZxWhmZ2dTeXk5YwxVVVUpPT2dtLS0aODAgWRqaipjLAcNGkTHjx8nkUhE2dnZ9b6v8sLPz4969epFAQEBlJycTMnJyZSSkkJisZj8/PxIRUWFLly4QMePH6fffvuNli5dSrdu3SIOh8PkOMvLy+ngwYMU/PIlSSZMIE49BudiDofma2vT+RYtaPPmzZSbm0s+Pj7E4XAoMTGRNDU1a53Jft5+7tw5ev78ObVr145pf/fuHV27do1sbW1lwtSfz2y/NKCV77OyskhBQYEcHBxk+jx69Ihu3bpFffr0IalUSmVlZVRWVkYlJSXMfgsKCig/P5/y8vLo48eP9OHDByIixhAKhcJqDWTls6mkpER8Pp/WrFlDYWFhxFq0iCgmpnGGT1mZaPZsoh9/bPg+mhDfDN+/GcHBRIcPN3o37zw8qGNhId24cYOOHTtGixYtokePHtG4ceNo2LBhpK6u3qD9lpaW0qlTp2jPnj104MABMjU1paCgILKwsJAxdiUlJcwsjs/n08yZM2nOnDk0ceLEBl9TfHw8jR49mubOnUvfS6XEqmfSvZCIopSU6Ly9Pdna2pKtrS1ZWVlRSkoKnT17lpYuXUqBgYEkkUgoPj6eZs6cSU+ePCEiIhcXF1qzZg3Fx8fT8uXLSUNDg6ZMmUJDhgwhLS2tao/3/v178vLyotzcXEpKSiJHR0e5zxUAjR8/ns6dO0cnTpwgbW1tun//Pnl7e9N3331HV65codTU1FpzP4WFhTLG8NWrVxQXF0fp6enk5uZGEomE3r59SxkZGVReXk4KCgpUXl5Ozs7OZG5uXmvolZkZ1AOnT5+mYcOG0f3792UcMIlEQjdv3qTk5GRatmwZ5eXlkaurK/n7+1NZWRnNmzePpFIpKSkpkZeXFxkZGdHMwEAyGTiwQQVgxWw2KV26REeys6lHjx7UsmVLunDhQr2csIcPH5Knpyddu3aNKYQiIho7dizx+XyKjY2tdrvqcq2V7w8cOECXLl2i0aNHy7QXFhbSL7/8Qs2aNSMHB4c6DfKXbRKJhJSUlJhCKg6Hw8wEP378SGWfhYaJiJydnemXggJqff9+ve9tFfTvT7RtW+P30xT4O0tIv6GeaKJ1atft7BAdHS2z62vXriEsLAxaWlqYPHlyg1hbysvLkZ6ejo0bN2LIkCEwMzNjyvi1tbXRs2dPHD16VKYMPzw8HDwer8FsFMXFxYiMjIS5ubkMeXb56tWQ8HiQ1HEvJCwWyrlc5Pz0U7XLA86dO8coh1+/fh0dOnSAoqIi+Hw+JkyYAHt7ewiFQmaZQlhYWK2aZ5/jxYsXUFRUlJ+ZBhUclSNGjICrqyuzxvH9+/ewtLTEyJEjYWho2CjGnYMHD0IsFmPevHlwcXEBj8fDrVu3cPnyZRARfH19sWPHDixbtgxRUVGIiIhAUFAQ2rRpAxMTE/B4PPD5fDRr1gyurq4ICQnB0KFDER0dzVCepaSk4M6dO8jOzmYWpLu7u2P79u01ntf79++hrq6OFy9e4MiRIxg/fryMnp2CggK8vb1hZGSE0wIBJA2krZMQ4a6NDVgsltzr7L78ftq2bYuVK1fKtH/48AECgaDBfKAXL15EmzZtqv0sIyOjwXRmZWVl+PTpE7Kzs/H8+XM8fPgQt27dwqVLl6ClpcXcXy0tLYSFhWH58uV4ZGvbJOMQgoIadC++Br7N+P6FKCsro+PHj9PH6dOpx++/k3JjdqasTEs0NMg9MZE8PDyqfJyZmUnLli2juLg4CgkJoUmTJpG9vX21u3r9+jUzi0tLS6Nr166Rrq6uTF6uZcuWpKCgQBcvXqTExERKTEwkPp9PPXv2JA6HQ/Hx8fT06VMqLi4mDqd+qfKnT59SaGgoCQQCCgsLo+fPn9P9+/fpwYMH9OTJE/LX0qKhf/5JnaRSYnM4MoUNpKxc8fPr3JkoKqrGCrOwsDASCAR06dIlun//PqmpqdG0adPIycmJ1q1bR0lJSVRQUEAGBgY0ceJEOn78OF24cIECAwMpPDycAgICap0ttGjRgl6+fEl79uypM3cmkUho2LBh9OjRI0pKSiJ1dXWSSCQUHBxMGhoadPLkSTp27Bi1bt26XvfxS2RmZpKdnR0VFxfT+fPnyd3dnYiIDA0NKTs7m3JycmqMCgCg/Pz8anOS1bVVFrYUFRVR27ZtSUdHp9rZ5JkzZ+jcuXN08OBB5jiV1ZcsFovYbDZJJBJK3bOHPPr0Ic4XM5X6oIiIflu8mAY0IAKxZs0a2rVrF6WmpjLnR0S0dOlSunbtGu3cubNB55STk0OWlpaUm5tbbW4uNjaWUlNT6ciRI/XO2X+OgoICyszMpKdPn9KkSZMoIyND5vORI0fS6g8fiJog5fJtxvcNVSCVSnH+/Hn88MMPEAqFcHd3x6bYWEi53EZ5WVIuF1YCQZ0SJ+/evcNPP/0EXV1dBAYGIikpCWfOnMHChQvRs2dPGBoaQktLC4GBgZg1axaOHj0qo5hd23WlpaVh4MCBYLPZDN/joUOHahXHzMrKwtmzZ/HLL79g7NixaNmyJdhsNhQUFGBvb4/vvvsOMTEx2LVrF27duoXCwkJcvnwZZmZmKH/zBnkzZ2K3kpJc4sFAhSe8fPlysNlsKCoqwsTEBFu3bsXmzZvh7OwMMzMzzJw5Ew4ODvD29qBzaZgAACAASURBVEazZs0YDsbs7Gz8/PPP8PLygra2NoYNG4bTp09Xe31ubm5YvXo1RCJRhY5gLecTHh6Odu3aIT8/n2mfOnUqPDw8YGRkJBeDSF0oKyuDjY0NVFVVMXLkSOjr6+PkyZMAKnguORwOVq9e3ejjVKK4uBh2dnZYsmQJjh8/jri4OCxevBiTJ0/GwIEDERgYiNatW4PL5TKahhYWFgxVWuVLUVERY8aMaRLaunIul6Gtqw8yMzOhra2N+/fvy7SXlZXBxMSkispHfSCVSqGhoVEjY4u8dGalpaV4/PgxkpOTsX79ekRFRSEsLAyurq4Qi8Xg8XiwtrZGp06d4OPjA0VFRbBYLHC5XMyePbti3GhiasB/A74Zvn8Yd+/eRXR0NExNTWFjY4O5c+fKqiM0Un3ggp4efH198eDBgxqZWSQSCe7cuYNNmzZhyJAhMDQ0BIvFAp/PR6dOnbB161Y8evSoweHJnJwcmJqaIiEhAbdu3YKOjg7MzMxgYGCAAQMGIDY2FgsXLsSQIUPg6ekJLS0taGpqwt3dHQMHDoSXlxfEYjESExNrNeB9+/bF4sWLAQA3btyAg4NDneeWn5+P+fPnQyAQMOTMu3btQnR0NKOHd/jwYZw7dw56enpYuHAhpFIpIiIiqg2NPX/+HAsWLECrVq1gYGCACRMm4Nq1a8y98/T0RGpqKuLj42FgYFBtuKq0tBShoaEICAiQkY7ZvXs3jI2N4ejoKLegb20oKiqCmZkZNDQ0GDLs5ORk6OnpYfr06SgsLGSclaYiSo6Pj4ezs3Ot+3v9+jU0NTVRUFCA9+/f4+HDhzh8+DDDhMPhcMBms2FlZdVk6QD8RUouLyr1G6tTitizZw88PDzqfW++ROvWrXH58uUaP79y5Qp0dHSQnp6O8+fPIy4uDnPmzMHgwYPh6+sLExMTKCkpwcTEBL6+vhg8eDDmzJmDuLg4nD9/Hq9evZJxzk6cOAEWi4UWLVrIavJ9JTL4fxLfDN8/gJcvX2LRokVo1aoV9PX1MXHiRNy4caP6wSAtrf50ZZWzPT4fzn95yHw+n/GgDx48iP379yMqKgrt27eHuro6zM3NER4ejhUrVuDy5csoLCzEgQMH0LZtW5iammLFihUyMw95UV5eDj8/PwwcOBDx8fGYNWsW9PT0YGpqCi6XC3V1daipqYHP58PLywuLFi3CixcvIJVK8fz5c7i5uSE4OLjO2eXbt2+hqanJ9Dt06BACAwNr7J+VlYVx48ZBWVkZioqKzH3o1KkTBAIBRo0axfz4N27cCJFIhKSkJGb7vLw8NGvWDAcOHKjxGHfv3sX06dNhbm4OKysrxMTEwNnZmeHqXL58OaytrWW8+uLiYoSEhCA4OFhGhPfmzZvQ1taGv78/+vbt22hDVFBQwMziv5TVefv2Lfz8/ODl5QVXV1coKSnVzSUrB8rKymBtbY3jx4/X2m/ZsmVVKLoePnwIogpFiJkzZ/43d9ZEtHX1zT9t2bIFrVq1qpa31dPTEwkJCfXaX3UICwvD9u3bkZubi+vXr2PPnj1YtGgRIiMj0alTJ1hbW4PD4YDH48HV1RVhYWGIiorC+vXrkZycjCdPntTKK/slCgoKEBcXV71z2cTyX/80vhm+vwnv37/Hxo0b0a5dOwgEAkRERCAlJUU+leUGcnWWrlgBHo/HyMgQVUjOCAQCBAQEYObMmUhKSqpVTwwALl26hB49ekAoFGLatGk1SpTk5ubiwoUL2LRpEyZNmoQuXbowzPw2Njbo3r07oqOj4evri6ioKBlx08ePH2PhwoVwcXGBtrY2w8MYGxsr1yA/d+5cDBs2jHn/yy+/VDsje/ToEcLCwsDlcqGkpISwsDDMnz8fxsbG4PP5WLlyJSO8WVZWhtGjR8PKyqpKOAuoKITR1dWtlnT7c0ilUly5cgVjx46FkpISLCwssHjxYrx48QKTJk2Ch4cHCgsLUVhYiE6dOqFnz54yigrZ2dkwNTVFz5494e7uXqMqvbz4+PEjdHV1IRaLayQFl0gk+M9//gNNTU0QEXr27NmoYwLA5s2b4e3tXef32aZNmyrGUSKR4PLly1V/L//AjO/NmzcQiUQyxVWVSEtLg7GxcY0ahNWhsLAQ9+7dQ1JSElavXo0JEyage/fujOKGmpoaHBwc0LVrV4wfPx4rV67EoUOHcOfOHbx9+xYmJiZITk6W+3gNQiMc8C8Fn/8N+Gb4viKKioqQmJiIHj16QF1dHd27d8eePXsaNnDJSQ5bToQSRUXs8PJi9Nn4fD6IKqoQDQ0NGXmX+iIjIwPDhw+Huro6AgMDER0djREjRsDHxwc6OjpQU1NDmzZt0L9/f/znP/9BdHQ0dHV1q1S2zZw5EzExMdUeo6ysDJGRkdDU1ETLli3lItEuLS2FgYGBTM5s+vTpmDVrFvP+ypUraNeuHZSUlMDn8zF06FCMHj0aIpEIXbp0gbW1Nfbu3cv0z8nJQfv27dGpU6daFSOmTp2KkJAQuWdgHTp0wPz58xEREQGBQAAfHx+4uLjA398f7du3R58+fWQGzdLSUvj6+iIkJAQmJiaN1kXLycmBUCiEgYFBtarqX+L8+fOMlFJDKxSBipmsiYlJneKvGRkZEIvF1RqOnJwcvHz5UlYJognyT2VKSvXKP/Xs2bNacVugomp50aJFMm1lZWV49uwZUlJSsHHjRkybNg3h4eFwd3eHrq4uuFwurKysEBAQgBEjRmDBggVISEhATEwMQkND63y2kpKSYGZm9vVJ5htJlv9vwjfD18SQSCQ4deoUIx7r6+uLDRs2yDDPNxhXr1aEDHi8imTxZw9YEZuNIiIkEsFdURFBQUEIDQ3F7Nmzcfv2bbBYLISHhyMuLg5isRhTpkyRyR99idLSUty7dw+JiYmYN28e+vXrh9atWzOKBJWl7C1atMCyZcuY8GQlHj16VKNm2v9j77rDorje7l3qLp1lC0vvXVBAwIaKKNgoigUUCxiKShFRAsZCEHtFsRdURGNJ7A0ssUZiR41iNyYCSlQUEGHnfH/w24nr7sJSzGfKeZ59Hpi5M3tndua+977lnFWrVomtzkR49uwZunTpAj8/P1q37Pnz51ixYgV69OgBLS0thISEYNu2bXRiCQDs2LEDXbp0ETvX6NGjsXr1auzfvx9t2rSBqqoqdHR0EBMTg4CAALDZbCQmJuLevXu4ePEizMzM6NVEUVERLCwsMGnSpEZX5DU1NXBxccG6desabCeCn58frdH2/v17/PDDDwgMDAQh9Vp1W7ZsEXMpx8XFoUOHDuByuU1Xr/gEz58/h46ODszMzJo0+YqMjISCggIMDQ1x//79Zn338uXLG3Q9i5Ceno64uDip+7p3707H91RUVMBkMvFVYGDLDZ+Sktzxp127dsHW1lbs/lEUhefPn2Pv3r1QV1fHlClTEBkZCR8fH5ibm0NFRQXGxsbw9vbGyJEjMWPGDGzatAmnT5/Gr7/+KjPJ6+zZs/D09JSrX0OHDpUquNvq+IeoM/xn+FoBFEXh6tWrSE5OhqGhIVxcXDBv3rwWzZA/RVVVFc6dO4dFixZhTGAgMnV1sU1ZGRe4XFxt0wa3Ro/GD2vWgBACbW1tODs7g8Vi4cSJEwDqZ4UiF15JSQkGDRoEGxsbHD16lBY3TU1NFRM3tbKyQv/+/TFp0iRs2LAB58+fFzPglZWVWL58OSwsLNChQwc6+aSyshLOzs7Izs6Wei379u1D3759xbaJJHVmyqivA4AXL15g/fr1tMRS//79kZOTAy8vL7GYSk1NDRwdHaGnp0fL04waNQpOTk6wt7fHihUrxFY74eHhmPe/Gf+ePXvA4XCapM5dVFQEDocjnpQkA71798aBAwfo/1+9egUvLy8MGzYMxsbGsLGxgba2NkJDQxEfHw8zMzMIBALs27dP7v5Iw5MnT6CpqQkbGxu5hGk/Rnl5ORgMBrS0tMDhcORWPxehsrISAoEAly9fbrAdRVGwtbXFhQsXpO5fsWIFLY4qyuz88ccfURcQ0Gj9pqyPkMHAJTOzRq/h9evXOHXqFB3/HT9+PPr27QsHBweoqamBw+FAIBDAysoKKSkpWLVqFY4dO4Z79+41+X6LUFpaCj09PbnalpSUgMfjSXW/tjoamIDTenwDBnxx7s2P8Z/hawEePXqEzMxMODg4wNTUFKmpqXJrbjUEoVCIO3fuYNOmTRg7dizc3NygpqYGNzc3jB07Fps2bcKdO3ckDMS+ffvogUGkSSYQCFBQUIDnz5/T4qZxcXHw9fWlC1Y5HA4GDhwoJm7alBVBXV0ddu7cCQ8PDzr1PCwsTKaL5ueff0a7du3oY6dPnw4DAwPaSMuDV69eYcuWLejevTsYDAZ69eqFpUuXIjU1Fdra2mAwGDA3N8eAAQOgp6eHgIAAFBQUSPRJpIz98uVLfPvttzAyMkJhMwQ0Fy5ciE6dOslcIebk5MDf3x8cDgdt2rSBr68vrly5Ajc3N8TFxYGiKPz2228wNTXF0qVLkZycDCUlJSgqKsLLywunTp2SX5n+E9y/fx9qampwcnJqUuzpYzg6OkJTUxNLly6FpaUloqKiGvQYfIy5c+fKFSO8fPkyzM3NJX6jmzdvIi4uDrq6unS8msVi4dSpU7h9+zZCrazwXlGxeas9VVWM9fBAdXU17ty5g8OHD2PFihWYNGkSQkJC4OrqCl1dXairq0NHRwfm5uZISEjAkiVLsHfvXty4cQMVFRWorKwEh8NBcXFxs+6vNFAUBU1NTbm9RRs2bICbm1uzf+Mmo6ys3kUcHi532dCXgv8MXxPx4sULrFixAp06dQKHw0FsbCzOnj3boiy7srIyHDhwAFOnTkWvXr1od9SQIUOwaNEinDt3Tq5BJi8vj47niT6WlpZgMBhgsVjo1KkTxowZg4ULF+LQoUN4+PAhXr58iVGjRsHU1LTRbLvGQFEUEhMToampCS6Xi/T0dKmJM7/99hv4fD5KSkrQo0cPdO/eHc+fP2/Wd3711VdISEiAv78/PSiKxGJ1dHSQnJyMhw8fyjw+IyMDo0aNQkhICLy8vPD77783qx9CoRDdu3fH7Nmzpe5fsmQJnY4vWq04ODggOTlZ7Nm5ffs29PT0wGaz4enpicGDB2P27NlwcXGBoaEhJk6ciMuXL8v9vN26dQtMJhPu7u7NNpwAsGnTJigoKKBv37548+YNQkND4eTk1KjI7evXr8HlcuUSw504cSKmTJkCoD4+npubiy5dutDlFY8fP8ZXX30FBoOBNWvWIDY2FioqKlBSUsKhgIAmx5/eKyoi08gIioqKtIejZ8+eiIqKwuzZs7F9+3ZcvHgRZWVlOHLkCExNTWXGRVetWoX+/fs3/cY2gnbt2sk9EaMoCj4+Pli4cGGr9+Ofhv8MnxyorKzEtm3b0K9fP2hra2Po0KHYv39/s1wY1dXVuHDhApYsWYLQ0FC6jsrX1xdTpkzBvn37Gk1gqKqqwtWrV5GXl4dp06Zh0KBBcHJygpKSEj2wKikpYfDgwTh9+jSKioro9HRZ7lfRiz1q1KhmxyPPnz8PLpeL+/fv49atW3Scc9y4cWJuwNraWigqKkIgEGDq1KnyZbbK+D5lZWWoqKhAWVmZLjQ3MTEBg8GAjo4OOnbsiIULF+Lx48cSx3/48AH6+vqwsbHBqFGjWpwt+eTJE3A4HAmFeaA+nieiOlNSUoKuri6++eYbCQMmKvBWUlKCq6ur2DN28+ZNTJkyBRYWFrCxscGMGTNw9+5dmf25cuUKVFRU0Llz5xYZPaDesKuqqkJNTQ1PnjwBRVFYt24dOBwONmzYINMQT58+HSNGjGj0/HV1dTAwMMDBgwcxceJEcLlc9OrVC7t37xZLyX/27Bnc3d2hoKAABoNBJ239+uuvKM/MRBWDgbrG3JuE4B0h2OPvjyNHjkBVVbXBtP+3b982ODEUCoWws7NrksdCXgwaNAh5eXlyt7937x709PQanOz9h/8Mn0zU1tbi8OHDGD58OLS1teHn54fNmzeLJVU0BoqiUFxcjC1btmD8+PFo37491NTU0K5dO8TExGDjxo24ffu2zEHp5cuXOHPmDNasWYOkpCT07t0bZmZmYDKZcHR0REhICL755hts3boVV65cwfnz55GWlobjx4+DEEIXJQN/pqfz+XyZ8aKKigqMHz8eBgYGYhmO8qCkpARGRkbYv3+/2Pbff/8dqamp0NPTQ0hICC5cuIDMzEwoKCg0yNUoCxRF4cyZM/D29oaSkhIUFBTQtm1b6OrqYsCAATh16hTu3r0LCwsL1NTU4NChQ4iMjASHw4Gbmxtmz55Nu6OmT58OZWVlLF68uNUKtLds2QJHR0epRnTVqlVQUFAAIURqIoKoMN7NzQ36+vrg8/lS44YUReGnn35CfHw89PX14ebmhoULF4pxdoomBb6+vq1yXQAQFBQELS0telUG1BtjBwcHDBs2TOLdePHiBdhsdqOxz5qaGkybNg0aGhp04pW0JJqKigps3rwZmpqatNEjhEBDQwNqampgMBjwVFTEWT4fHxQV8UFZWWb86ceFC2FqaopXr16Bx+M1uNKPi4vDqFGjZO4/fPgwnJ2dW+0Z+hhpaWn49ttvm3TM7Nmz4efn91n680/Bf4bvI4gGlLi4OPD5fHh4eGDp0qVyp5C/fPkSBw8exLRp0+Dn5wddXV2YmJhg0KBBWLBgAc6cOSORciwUCvH48WMcPnwYixYtQlRUFLp06QIOhwMtLS14enpi1KhRmDNnDvbu3Yvi4uJGffjff/89+Hw+Fi1aJLHv7NmzMDExQWJionha+Ec4c+YMbGxsMGjQILmuvba2Fl27dsXUqVNltqmoqEBGRgaYTCa0tLRgZmbWICvFpxAKhdi9ezccHBxoYmRlZWXo6Ojg66+/xpMnT+i2J06cgLe3t0Qfjx8/jrFjx0JfXx+GhoZQUFBAVFRUqw4QFEUhJCQESUlJEvuKi4vBYDBgaWkp9djly5fD3NwcXC4Xv/zyC1asWAFra2s6w1UaamtrkZ+fj9GjR9NZxElJSVBUVERgYGCrXRdQ74YlhIDL5YqtRCsrKzFmzBhYW1uLJVckJycjNjZW5vkePnyI1NRU8Pl8CAQCDBkyBDdv3sTRo0exatUqpKSkYPDgwXB3d4eenh6UlZWhqKgIOzs7Ov6pqqqK2NhYKCkpwc3N7c9+/S/+9LRbN1wSCKTGn8aNG4fBgwejffv2UrOPgfr3RSAQNEig4Ofnh40bN8p5F5uGDRs2yLVi/hgfPnyAi4tLsyaW/xb8fQxfaWl9zc6wYfWB1GHD6v9vhUDq3bt3MW3aNFhZWcHa2hozZsxoNEj9/v17/PTTT1i6dCnCwsJgaWkJLS0t+Pj4IDU1FXv27BGLW71//x43b97Ezp078e233yIsLAzt2rWDmpoaDAwM0KNHD4wfPx7Lly/H8ePH8fvvvzd7QI6MjMSECRNkFtKWl5cjKCgIbm5uuHfvntRzVFVVISUlBTweD1u2bGmwL8nJyfDz82vQZXnu3DkYGxtj4sSJ2LRpE7S0tGBkZIT169fLNMBAvWt45cqVEAgEYLFY0NbWBp/Ph7m5OczMzKTWLm3ZsgWhoaFSz/fhwwdER0dDIBBATU0NRkZGsLGxQVpaWpNiZw3h5cuXMDAwwPHjx+lt9+/fh6mpKeLj46XGM0+dOgU9PT1wuVwcO3aM3p6WlgYPDw+5WHOqq6vxzTff0DHE/v37Iy8vr1mMO7JgaGgIHo+H7777TmJfXl4euFwuli1bhmfPnoHNZkt4HUQJYW3atAGLxYK9vT1cXV3pEgULCwv06NEDY8aMQWZmJvLy8rBv3z507twZXbp0wfr162kWnYiICGhoaIAQgtGjR0vtr62tLQghUl3dVVVVcHJyQvv27aW6E6urq2Fra4tdu3bJvB+3bt0Cn89vsZtcFk6fPo0OHTo0+bjCwkLw+fxGySn+rfjyDV9hYT1dDpMpWa8jcl0EB9e3k4K3b99KDUg/f/4cixcvhru7O/T19ZGYmIiff/5Z6sBHURTu3buH3NxcxMXFwcPDA2pqanBxcUFUVBTWr1+Pmzdvoq6uDq9fv8ZPP/2EjRs3YvLkyQgICIC1tTVdpBoYGIiUlBTk5OTg4sWLckvayAuKoiAQCFBcXAxvb29s27ZNZrtly5aBw+E0GEO4dOkSnJ2d0adPHzx9+lRi/86dO2FmZiaTTJeiKMyfPx88Hk/MDRoVFYX4+Hj4+flBIBBg9uzZYrHFP/74AzNmzICWlhZUVVXBZrOhoaGBQYMG4cyZM+jVqxdycnKkfuecOXOQnJwssb2srAze3t7o168fwsPDMXPmTJpVZfLkybC0tISZmRkmTpyICxcutCgudvjwYZiYmODVq1e4c+cOXAQCXBgwQOrE7fHjx+DxeDAxMZEghKYoCiNGjEDfvn3lWukrKChg9OjRePPmDTZt2gR/f39oa2sjLCys2XHpjzFjxgyoqqqiW7duEvtE99LW1hZcLhcdOnRAdHQ0evXqBXNzc7oGT1NTEx07dkRKSgrWr1+PjIwMeHh4SL2+/fv3g8/n49tvv6UnQIWFhRAKhejXrx8YDAZWrVolta9nz56FqqoqGAyGzFVTUVERWCyW1BV6ampqo9moUVFRYkQJrY3ff/8dXC63WccmJiY2ebX4b8GXbfhaWCz54sULmJmZISwsDEA9VVNOTg569uwJHR0djBw5EseOHZN44crLy3H48GHMmDEDvXv3hp6eHoyMjDBw4EDMmzcPp06dwr1791BQUIBly5Zh3Lhx8PHxgUAggLq6OlxdXTFs2DBkZGRg165duHXrVosHHHlx5coVWFtbA6gvb3Bzc2twFSNqHxkZKZP5oaamBhkZGeBwOFi5ciVtEG7fvg0Oh4NLly5JPe6PP/5AQEAAPDw8JGbcM2bMwDfffAMAuH79OsLDw2kqt2HDhoHJZEJZWRm6urp0IogohnXnzh1wuVyZs+y4uDgsXrxYbNu1a9dgZmaG1NRUuoThU6oxiqJw7do1TJ06Ffb29jA0NERcXBxOnTrVrAScsWPHYkKXLjioqopaZWWpEzdKVRUF2toYaGKCcePGST3Phw8f4OfnhzFjxsj8LXNzc6GgoIDx48dL7CstLUV2djY6deoEPT09REdHN7s84uXLlzQbUFpaGhITExEYGAhnZ2doampCR0cH9vb2UFBQgIaGBoKDg+Hp6QktLS1ER0fjxo0bEuccPHiwhPF6//49EhISYGJigtOnT2PatGmwtLTEvXv38ObNG1haWoLFYsl0UQL1ShiiOCCTyRRzh3+MIUOGgMPhiL2jV65cAZfLbTDb+MWLF9DR0Wkxm05DoCgK6urqzZogi5JyPvYg/Id6fLmGr4X0OK9evYKNjQ0dC+jXrx+0tLQQEBCA7777ji4PqKmpQWFhIZYtW4bhw4fD2toampqa6NatGyZNmoTly5djw4YNmD17NkaOHAkPDw9oaWmBy+XC29sb0dHRWLx4MY4cOYInT560OHuupcjIyEBCQgKAeteSra0tTp061eAxFRUVGDZsGBwcHFBUVCSz3a1bt+Dp6YmuXbvi6tWrsLOzw/r166W2LSwshJmZGRITE6Ua/TVr1iAiIoL+//r16/D396eTP5SUlGBpaYlNmzZJGLj4+HiZlFEAMGDAALGC9l27dtGqC0B9XVm4HNyMt2/fRkZGBtq2bQsej4eoqCgcO3ZMbuLf+8nJePe/LMKGnts6Us+8U9eA/M/bt2/h5uYmdXWxbt06MBgMuZg7Hj9+jDlz5sDZ2RlGRkYS5RGfyth8/fXXGDJkCDw8PMDlcsFkMsFisaCqqgpnZ2csWLAAu3fvxpUrV2hqt8GDB6N79+7gcrlQUlLCwIEDZSaFVVRUQEtLS8xjcOfOHbRt2xYDBgxAaWkpIiMj4e7ujtLSUly/fh0aGhowNDRskCP12rVrdL2fSBx5woQJUtsePHgQXC4XkyZNou9B27ZtZXoURMjMzJTpYm1NuLi4NFr8LwuHDh36a+jM/mb4Mg1fCwlR3548CQsLC3oQJaReSfrFixd48OAB8vLykJCQAC8vL6ipqcHR0RFBQUGIjIzEV199hQEDBtBJFObm5ujTpw8mTpyItWvX4uzZszLdel8CvLy8xGZ4q1evRj85mOcpisKGDRvA4XCwZs0amSuLuro6LFy4ECoqKvD09JRYCVEUhaysLHC5XOzevVvm9x04cAB+fn44fvw4OnbsCFVVVTpZoW/fvhg7diwEAgF8fX1x9OhRuj8VFRXQ1dWV6nYVwdPTE+fOnYNQKMS0adNgYmJCDxx1dXUwNTVtcpH6/fv3MXfuXJpEe9SoUdi/f7/M+OSjyZNR2YxntyGKp5KSElhYWGDt2rX0tqysLDAYDJncp59CKBTi2bNnOHPmDGbNmgVvb29oaGjQ8VNlZWWYmZmhe/fuiIiIQEZGBnJzc3Hu3Dk67iySr9HV1aUHVKFQiPz8fPTs2RMMBgMjR47EpUuX8OTJE3Tq1Al+fn5SDdXmzZvp5/PjZ3DVqlV4+/Yt+vbtCz8/P7x9+xZbt26FoqIiunXr1qjbt7q6GocOHUJqaiq8vLywb98+mXRrt27dgqWlJQwNDZGfn49Zs2Y1mhVZU1MDAwODBjUVWwsDBw5sMlvOxwgNDaWN+n+ox5dp+FoogXFeIBAr4hYxmejp6YHD4aBt27bo1KkT2rdvDyMjI5pzUpq46d8JL168gJaWlthgXFVVBR6PJ1VdQBpu376NNm3aYMiQITTF2adYsGAB2rRpA29vb3h4eNCrxNevXyMkJATt2rVrkNOxtrYWs2fPhrKyMm3wNDQ0kJKSIpZWXlNTg5ycHDg5OcHZ2RmbN29GVlYWBjQicWJoaIibN28iODgYnTp1EnNF/fDDD3LzH8rC06dPsWTJEnTp0gU6ltjIewAAIABJREFUOjoIDQ0VI9G+vm5d043ex8avAaqn4uJi6OvrY//+/Zg7dy4YDAbmzp1L76coCuXl5bh06RJ27tyJefPmITY2lpaxYTKZ4PP58PLyQmhoKNLS0rBmzRpkZWVhxIgR4PP5cHd3lyiP+BQiKaslS5Zg3rx5sLS0hLOzM9q1a4f09HSxtrW1tUhLS5NI+AEAf39/5OXl4fXr1wgNDYWjoyOKiopQVlYGT09PjBw5Eh8+fMCECRPAYDCkxm4bwq5duxAcHNxgm3fv3oHJZCI/Px88Hg+6urpSk2E+Rm5uLnx8fJrUl+YiJSWlRfqLJSUlMtUk/q348gxfK4ge1ikrg/OJ4SOEQEtLCx06dEBERATmz5+P/fv34/79+80uoP7SsGXLFqkp7DNmzJBKCi0LVVVViImJgaWlpYSSw8mTJ8Hn8+ki5jVr1oDD4SA6OhoWFhaIjY2VGXt79+4dvRoUsZiYm5tj48aNDcZAKYrCoUOH0K1bNygpKSEmJkamUa6rq4OSkhIcHR0xZswYiRWZj49Pq6Z5f0qi7e3tjb2KihB+Ru2yU6dO0dR0ISEhmDBhAoKCguDi4gItLS1oaWnBxcUFQUFBtIzNgQMHcOvWrUZdXrW1tTh27BhdHtG9e3esWbNGLJ2foij06dOHLi8YNWoULly4gMuXL0MgEMj8jmPHjtGkBbW1tSgtLYW2tjZO/s9DExMTg6qqKjx8+BDW1tZIS0vDhw8f4O3tDUVFxWatenbu3NnoRAkAuFwufvvtNxgYGMDJyanB1R5FUXB3d28xf6q8WLduXYN1hPJgw4YNcHV1/evozL5wfHmGrxVkRqoIwVwuF05OTmCz2XQ22V+VYPL/hdDQUKxZs0Zie1lZmdRkjsawY8cOcLlcLFq0CBRF4dmzZxAIBDhy5Ag9MFAUhTlz5tAM9NIkj8rKyjBlyhRaDFdBQYEezJrymxw/fhwWFhYYPHgw2Gw2Jk+eLJYuD9QrfCsoKGDZsmUSg9etW7egr6//2Z6DXbt2wUxNDe+ba/T+96GYTDwqLERBQQHWrl2LtLQ0hIaGwsvLC3w+n6ZmU1RURGhoKObNm4edO3fi0qVLKC8vb7W6RJGsVkhICC3SGx4eDhsbG1hbW4MQAj6fT//mffr0wbJlyxo85/Pnz9GjRw906dIF6enpdPxU5Ba/fPkyDAwMsHz5cpSUlMDAwACampoNxp4bgryGr3379khKSkKHDh3g7u7e4HWcOXMGVlZWf1k8/9SpU+jcuXOLziGiM1uwYEEr9ervjS/P8H0GYcnLly9j6tSp/2gmg9raWrDZbJnuqejoaEybNq3J53348CE8PDzQp08fuLu7Y9asWQgODkZQUBAqKioQFhaGNm3a4JdffsHWrVvB5/MxadIkVFVV4f79+4iIiICysjJdpzV69Gja9WhkZCQz004agoKCsHLlSrpf8fHx0NXVxahRo1BUVIRly5aBzWbTWa2fIjY2tln3QB6IEiQexMS0eOJWSQhmamuja9euGDVqFNLT07F582acOXMGI0eOBIPBwNatW7F27VpYWFh89qzC8+fPIzQ0FCwWCwKBABoaGggLC4OBgQGsrKwQERGBs2fPwtTUtMGaTBHq6uqQkpICBoMBY2Nj+hk4duwYHRs+e/YsmEwmrK2tZa7u5cGOHTvkIsju06cPNDQ0cOfOHRQXF4PD4UjNQAXqY26NGfjWxLNnz8Dn81t8nv/ozP7El2f4+vVrFcNX3qkTpkyZAktLS9ot9E/ObDpz5gxcXFxk7heVAEi9B42QA9TU1KBdu3ZgsVjIyckBk8mkY0URERFi5ywtLUXPnj2hpqZGr0z09PQwd+5ciWxIDw8PmRI0n+Lx48dgs9kSNZnl5eWYMWMGWCwWNDQ0kJiYKJUs+PXr19DR0ZFYIbYG9uzZAx6PV38tn1ERfPTo0VBQUBCjk5sxYwZcXV2bRKUnD968eYPs7Gw4OzvDysoK8+fPp4uhS0tLaTkqUbG8k5OT3JqEhw4dAofDgaqqKgwNDZGUlISNGzeCx+Ph9OnTyM7OhoKCAoKCglq8qtqxYwdCQkIabENRFMzMzODn50dvy8nJgaOjo0Sc/+HDh9DT05NLxLe1QFEU1NTUWuU3/o/OrB5fnuFrpYFjE5GM8eXn5+P58+f/yB89NTUVaWlpDbYJCAigV0wA5CYHOJSeDmtra+zcuVNCD03EakFRFA4fPixGlq2oqIg+ffrIfGEDAwMbzPz8GCkpKUhMTJTYXlJSgk6dOqF///7IysoCj8ejNeM+jmcsXrwYQ4YMkeu75MGDBw8wd+5cTJ8+HTwe789axlaauOGTTNyhQ4dCUVERhw4dEttOURTGjBkDPz8/ucssGsKlS5cwZswY6OjoICQkBAUFBTKNj1AohLKyMvT09KCoqAgjIyMkJyfjypUrUt+x9+/fIykpCcbGxhgzZgxiY2Px4sUL2NvbQ0VFBYcPH6ZXtC1J5vgY3333XaOGb+PGjXSfRKAoCkOHDsXYsWPF2iYlJTU5waY10KZNm1ZJTvmPzqweX57ha4UYX62KCiZ+YvSUlJTg7e1NS7507twZ0dHRWLp0Ka1X93c2iM7Ozjh37lyDbX788UdYW1vXD2RykgNQDAYqCcGvU6bQtVEff1gsFjZs2EDHnhgMBnx8fHDnzh28evUKERERMDExoVXHP0ZsbKxcLqOqqiqpWmeXL1+GiYkJpk2bRg/OKSkpCAsLQ6dOnWBmZoasrCxUVFTAysoKZ8+ebcIdbRhHjx6lFQJEShN+fn6fZcUXGBgIJSUlnDx5Umpfamtr0bdvX4wcObJZz/Dbt2+xZs0auLm5wdTUFJmZmXLLRPXu3RvKysowNDTEjRs3kJaWBjMzM9jZ2SE9PZ3+zYqLi+Hq6orAwEC8ePECDg4OOHXqFOLj4+Hk5IS0tDQoKipKNe4twXfffYdBgwbJ3P/8+XNwuVxkZWWJrfiAei+BmZkZ9uzZA6C+lIbNZjfJPd9aCA4Oxs6dO1vlXP/RmX2Jhq8VsjrBZOLY1q1gMpn0AK2goIA2bdogISEBubm5OHDgAJYtW4bY2FjaIOrq6v4tDeLTp0+hp6fXaHYqRVFo3749rkVHN4scYGuXLtDQ0IC/vz/U1dXFDKCysjIiIyOlMkwcO3YMZmZmGDlypFh2YEZGRoOF6CJs3LgRvXv3Ftu2bds2cDgcicEgPDwcGzZsAFCvUDBgwABoaWlBX1+/2Zp/n+LXX3/FyJEjxa6fwWCge/furTJxo5hMXA8Px/bt22Fra0uTaTeEd+/ewcPDo9FV/8e4fv06YmNjoauri8DAQBw6dKjJGc4LFy4EIQTW1ta0YaYoChcuXEBcXBx4PB7Mzc2hrq6OzMxMUBSF69evw9jYGAMHDkTXrl1x7do16OnpQVtbG8bGxoiOjm61UqLt27c3aPgGDBiAtLQ03Lx5E3Z2dhL7z507Bz6fj2fPnmHp0qUNnutzYtKkSZg1a1arne/fTmf25Rk+oEV1fEIGAyd0dTF58mQkJyfTciV5eXm4ePEiMjMz0b17d6irq6Nz586YPn06zpw5g5qaGpSWluLEiRN/O4O4evVqmpatMRzLzES1gkLzBmU1NZQdOgQjIyOxQV9PT6/Rgert27eIj4+HQCCg3aPr16/HyJEjGzyOoii4urri4MGDAOrda6mpqTAzM5NaPOzj4yNB0eTt7Y1u3bpBR0cHX331Fe7cuSPXvRLh0aNHyMnJwejRo2FhYQF1dXWwWCxawZ7FYv1JkdYKE7cPSkoS5Ti2traN9rOsrAzW1tbIzs6W2aaqqgo5OTno0KEDDA0NMX36dJkajY2hrq4OTk5O0NHRoUVzP4Yo+cnY2BgBAQHQ0dGBj48PevToAUNDQwwaNAh79uyBsrIy2rVrh+rqarx58wZDhw5FmzZtcPv27Wb162Ns375dol8i7Nq1C3Z2dqiursbbt2/BYrGkvtPffvstunfvDnNz8wYp0j4nPmU6ain+7XRmX6bhawFzS7WiItw+WoWoqKhAXV1dYiZbWVmJY8eOYfLkyXB1dYWWlhb69u2LRYsW4caNG2Lp+l+6QQwICJDbZy8MDGxUqFPmpIIQfP+RDppImJTBYMDd3V2ubLGzZ8/C1tYWAwcOxNatW9GzZ88G258/fx6WlpYQCoV48+YN+vXrh65du8qU6rG1tRVT+y4uLgaXy0VVVRVKS0sxbdo0cLlcBAYGSnV9ijQU165di+HDh8PExAQ8Hg+DBg3C8uXLMWnSJJiZmeHBgwdITEyEiooKDhw4IH6SFhIwCIODoaenJ7aaHDRokFyMQQ8ePIBAIJDQU7x9+zYSEhKgp6eH3r17Y8+ePS2u6crNzUWHDh3w9ddf0zydolX1zz//DCsrK3z11Vd08lN1dTVWrVoFBoMBZWVl2NnZgRCC4cOHi52XoiisXbsWHA4HGzdubNH7tG3bNqmGr7y8HAKBQOwZ4HA4UjNk6+rq4ODgACMjo/+3ya40qa2W4t9MZ/ZlGj6gWVyddaqqiP5kpqygoCCXVtbLly+xc+dOREdHw9LSEnw+H6GhoVi/fr1Un/6XYhCrq6uhqakpH41aK6xGahQU8POhQ3j16hUePXqEq1evorCwEAsWLACXy5UrDlFdXY3U1FSw2exGB5OwsDAsWrQIxcXFsLe3R2xsbINJHBoaGmLu1oSEBHz99ddibSorK+msxA4dOiArKwvZ2dkYOnQoDAwMYGhoiLCwMKxevRq//PIL3b/Zs2fD0tKSfh5EFHgSKCxErYpK89ycamoYbGkJNTU1dO7cGaqqqlBVVcXQoUOhra2N4cOH48yZMw3es0uXLoHD4eDEiRPIy8uDt7c3+Hw+0tLS8OjRI5nHNQUfPnyApaUlTpw4gcrKSigoKMDf3x8ZGRmYP38+uFyuGF8qUC9ay+fzwePx4O/vDwaDAXt7e2hra2PYsGE4ePCg2G9bVFQEBwcHDB8+vNkZjdu2bZOa1DRy5EjExcWJbXN3d5epESki2r548WKz+tFSPH36FAYGBq1+3n8rndmXa/gAuRMwhAwG3hGCaEJgYGCAT5NauFwu1q5d26TU6EePHmHdunUYOnQouFwurK2tERMTg127djUoSimPQYyKiqINYkt094D6JIuOHTvK17gV4k9gseoFPaWgsLBQjIGjMRQUFEBRURH+/v5SJxfPnz+Hjo4Odu3aBR6PJ1N+RoQ3b95AXV2dvp9v374Fm80Wo58SCoW4ceMGli1bhoEDB0JLSwsqKirQ1NREeHg4bt68KfF7UBSFGTNmwNbWVq5yiJ9//hlj//dMNtXoTePxoKmpiSdPnqCqqgouLi40687Lly+xaNEi2NjYwNHREcuWLaOJoT/GvXv3EBISAgaDAS8vL+zcubPVi/bXrFmDHj160P+7u7vD3t4eTCYTHTp0kKD8On36NHg8Hv0uMJlMerVVUlKCZcuWoUOHDuBwOIiJicHp06chFApRWVmJyMhI2NjY4OrVq03uZ15enoThO3LkCMzMzCRKEkJCQqSyw1y5cgVGRkbYtm0bLC0tW710RB4IhUKwWKxW1VYE6ktTeDzev47O7Ms2fEA9b+GAAfUDNoslOQgzmTimqUm7Nz/97NixA1euXIGXlxe8vLya9fIIhUJcv34dCxcuRO/evaGpqQl3d3ekpKQgPz9frkH+cxnE+Ph4+VO/P2ONmQivX7/G4MGD4ezs3Cg/KEVRUFFRwYwZM8DhcJCdnS02OZkxYwa8vLygr6+PH3/8sdHLu337NmxsbOj/s7OzERQUhMuXL2PRokUIDAwEm82GpaUlIiIisGnTJjx+/BgUReHHH39Ev379wOPxkJ6eTme8URSF1NRUODk5NVoo/v79eyxZsoR+9g727y+3rBalpoY0NlvMXQjUZ2x+usKlKAonTpzA4MGDoa2tjYiICJw/fx47duyAr68vOBwOkpOTMWfOHJiamrZ67WJ1dTWMjIzEVkfTpk0DIQQCgYDOghRh9+7d4HK5yM7OBiEEPB5PZqLRw4cPMWvWLLRp0wbGxsaYNGkSrl69itzcXHA4HCxfvrxJE8WtW7di6NCh9P8VFRUwNTXF0aNHJdqK7tmnGDFiBL19zJgx/29JIY6Ojrh+/Xqrn3fjxo3/OjqzL9/wiVBWVr/SCA+vr3EKD6//v6wMMTExUo1ecHAw9PX16dnj2rVrwePxkJCQ0CI2iJqaGlojrGPHjtDQ0ICPjw9mzZqFwsLCJmXGtcQgUhQFS0tL+Y35Z6oxk3ZNq1evBofDaVTaxcTEBA8fPsTt27fRoUMHeHt7o7i4GG/evAGLxYKtrW2jhMEiHDt2DN26dcPFixcxd+5caGhoQF1dHXZ2doiOjkZeXl6DxMtAPa1ZZGQkdHV1MXbsWERERKBt27aNpn6vWLECWlpa+DguV1tb2+DE7b2iIsBkojYgAL25XHA4nAa9CdJQWFiIbt26QVFREerq6oiMjBTra2ZmJlxcXFpV8Hjx4sUICAgAUP8uJCcnw9DQECwWC4GBgejTpw/ddvny5TAwMEBmZiYUFBSgpaUl9wB748YNOpHJzs4OCQkJcHBwQHBwsJhocUPYunUrQkND6f/Hjx8vU0po+fLliImJEdsm8jqIfpd3797B1tYWW7dulev7WxNNqXttCiiKQo8ePf5VdGZ/H8PXAObMmSNh9ETp70ePHgWPx6NT3F+8eIHIyEgYGBhg27ZtrRJ3e/PmDfbv34+EhAQ4OjpCV1cXwcHByM7Oxt27d5v9HSKDuHz5cqkGUTTjz8/Pl2+F+Bes+D7GjRs3YG9vj/DwcJnuIS8vL9rlVVdXhyVLlkBXV5dW0mjMtVNTU4OzZ89i1qxZdPG8k5MTAgICYGxs3OwShmfPnsHV1RWKiooIDAxsVMbo02fQ1dVVvMEnE7dcBQVMJASXDh8Gj8eDvr6+3JOx2tpa7N27F3369AGbzUZ8fDyKiopw5MgRBAUFQVdXFzExMbh27RooisLYsWPh4+PTKu7Ot2/fgs/n4/r167h37x7c3d3Rv39/vHjxAsOHD4dAIICenh4ePHiA1NRUWFtbY8yYMWAwGLCzs5NQmJcHItq08ePHg8fjgc/nQ1dXF/v372/02NzcXNrwnTlzBgYGBtKNZmkpbo0aheMGBmLsRbOTkhAbGyvW9MqVK+BwOH859dfEiROlrkhbA/82OrN/hOHLycmh2UIIIdDV1YWhoSEtcfPLL7/AysoKSUlJ9Grs3LlzcHZ2Ro8ePZqc3t4Yfv/9d+Tm5mLUqFEwMjKCkZERRo0ahdzc3FapJRMZxAEDBsDe3l7MIHbq1IleIUoYxM8c45OGd+/eNRij+VQ0trCwEPr6+lBVVYWFhYUEX2J1dTVOnTqF9PR0+Pj4QENDA+3atUNiYiLCwsIQHx8PoH523FhMUBbq6uowZswYdOzYEc+ePcPixYthYmICb29v7N+/X2qsWOTGE30+Taj5GEKhEIyPsmMNDAzkyqx79uwZZsyYASMjI3h6emLjxo1Sj3v27BnS09NhZGQELy8vbNiwAf3790dYWFiLKcBmzpyJoUOH0q7HrKws+vl6+vQpCCEYNmwYnJyc0L59e3To0AGKiorYuHEjtLW1m0yU/ilqa2tx9OhR+Pj4gMFgwNLSEmvWrJG5AszNzUVYWBiqq6thY2MjuWL6iL1IqKoqHnNlsVBNCCp8fevbfYSFCxfCy8vrL3UPrly5UoxdprUxZ84c9OrV64sp0/qc+EcYvrNnz0JbWxuBgYHQ1dVFUFAQ2rVrB09PT1oip7y8HD4+PujTpw89s66trcXixYuhp6eHKVOmfJa0XoqicPfuXWRnZyM4OBi6urpwdHREQkIC9u/f36JAua+vL3744Qf6/49XiNHR0ejYsSPYbPafBjEoqMXKAWAyaQ7PpiAvLw8cDkdCNWH8+PFYsmQJgHpZJZEahJGREVatWgUOh4Pw8HCkpaXB29sb6urqaN++PZKTk7F//36x5A4RE8yjR4/AZrOblQhQW1uL8PBwdOvWTSz54cOHD9i6dSvatm0Le3t7rF+/niZkPnv2LBQUFKCgoID8/HwYGxs3qHp/8eJFMSNpa2vbIC3Y4cOH6Wc7JiZGbte2aGXYu3dvsNlsCASCFtWC/fHHH9DT00NQUBBsbW2l9sPMzAxsNhvKysrQ19eHhoYGioqKkJeXB39//2Z/tzTcvXsXtra24PP50NTURGBgIL777jux93jLli0ICwvD119/LUldJmfyHBgMCZFgoVAIPz8/fPPNN616TQ2hoKAA3bp1+2znF9GZbdmy5bN9x5eCf4Th+xhZWVno3LkzbG1t4enpifDwcHqg/fDhA2JjY+Hg4CCWhv7s2TMMGTIEZmZmcrlPWoK6ujoUFhZi1qxZ9IqlY8eOmDZtGk6fPi23O6qiogIaGhoyyXInT55M10t9PMh+T0iz6/jk0YprCPfu3YOrq6tYjGbWrFmYNGkSkpOTYWFhgZ9++gl+fn7w9vZGx44dwWQyoaurCw6HgyVLljQ4UQgICMD333+PSZMmYcKECU3u34cPHzBkyBD07NlT5iSIoigUFBTAz88PAoEAX3/9NVRUVEAIkbsYeNiwYRKu+U9joSUlJZg1axbMzMzQrl07rF69ukWTpEePHiExMRGKioqwtLREXl6eXEoKH2P06NHQ0tJCRESE1ElFSUkJjIyMoKCgAJFkkWiS2a9fP2zevLnZ/ZeFDx8+IDU1FQKBAJMnT0avXr2go6OD4cOH49ChQzTrj0RCTTPKpT41fs+fP4e+vn6Dk5zWxOPHj2FkZPRZv+Pnn38WpzNrhMD+74p/nOGrq6uDm5sb0tPTweFw4ODggHmfuOaWL18OPp8v8cAeO3YM1tbWCAwMlDuhoqWoqqpCfn4+UlJS4ObmBk1NTfTu3RsLFy7EtWvXZK4Evv/+e/j6+so877Vr1ySMXlBQEEoPHGg2OQDViDq4PHj//j0SEhJgamqKc+fOYcGCBWCz2TAxMUG7du1ozb6kpCTk5+fj3bt3oCgK27ZtA5/Px8SJE2UaJTc3N/z444/Q09NrUAFeVr+CgoLQt29fmUK6n6KwsBAsFguEEHh5ecnF4VhcXEwbBgaDARUVFWRnZ6O2thZCoRAFBQUYNGgQtLW1ERkZicLCwlZ1PRUXF4PNZsPJyQlcLheTJk3CvXv3GjxGKBQiPT0dDAZDJrfqvXv3YGlpCX9/fxBCYGpqSmvIvXz5ElpaWp+1DODo0aMQCASYNm0afvvtN2RlZaFDhw7Q0NCAqqoqpkyZ8ue71AKCDHzyDhw8eBDGxsZNTkpqDurq6sBkMj97wfmECRMwrXdvuQjsP3UB/13wjzN8wJ+zljlz5sDZ2Rn6+vo05ZUIx44dA4/Hw9q1a8W2v3//HhkZGdDT08Ps2bP/cvHa8vJy7Nq1CzExMbCysgKXy8XQoUOxbt06seLjMWPG/EmT9RGEQiFmzpwpxqWppKQkHhRvxmy3khBMYLHoGOKSJUvkT6r5CC9fvsQPP/yA/v3707JFKioqmDZtGk6dOoXMzEyEy0ieKSsrQ2hoKKysrKTOsvl8PubNm4e+ffvK3R+gPm7Yp08fBAcHy/17UxQFX19fEELg7e2NiRMngs1mIywsTKYrsqioCKqqqvD09IRQKMTgwYOhqKiIFy9eYP78+bC2toaTkxOWL1/eqlmYn+Lq1avgcrnIzc1FcnIyOBwOevbsid27d0uUTpSWlqJ3797Q19eXqQJ+8eJF6Ovro2PHjmAwGLCwsIC9vT0EAgGKioqwcuXKVlXGkAWRyK23tzedvSsqP3J0dISxsTEmT56MV927g2oBs86nXo+EhAQMGDDgL4mN2dnZNVuUV15UL16MSgYDwma4gP8u+EcaPgCIi4tDZGQkAgMDERoaCi6XK8H9d/fuXdjY2CAxMVGiBOHBgwfo06cP7OzscOLEib+y62J4/Pgx1q9fj9DQUPB4PFhaWiIqKgq6urpiLBK1tbVISkqCiooKGAwGtLW1MWHCBBBCEBISIvlSNiO+0VCWqSyDWFJSgh07dmDcuHFwcnKCpqYm/Pz8MGLECGhpacHY2Bjq6uooKSlBXV0dzMzMGmXH2LdvH4yMjBATE0O70j58+ABFRUW0adMGR44ckfv+VlZWomfPnhgyZEiTZH2mT58OQggMDQ3pba9fv8bcuXNhYGAAX19fHD16lL7vP//8M1RUVNC1a1cIhUJQFIW5c+eCEEIXz587d+4vSywoKCgAl8tFUVERqqurkZubiy5dukAgEOCbb77BkydPkJ+fDwMDA4wdOxZsNltqHaNIW8/CwgLKyso4ePAgLl68CAaDgYkTJ2LcuHHo0qUL9u7d+5dcV11dHWbOnAk+n4+VK1dCQ0MDQUFBAOqzjGfGx6O6ua5+0eeTOPf79+/h4uKCNWvWfPbr69+/v1hcv9XRCi7gvwP+sYbv9evXMDAwoF0REyZMgJWVlYRL4o8//oCvry/8/f0lZtkUReGHH36AiYkJhg0b1mrs/s0FRVG4ceMGkpKSoKamBi0tLbRt2xZOTk60RI5AIMD27dtpQ75hwwbZrpGPasw+KClJd2cMGNCoe/NjgzhixAjY2dmByWRCQUEBioqKMDExQUBAAJYtW4YnT55gzpw5NE9iaWkpVFVVYWBggPT0dHh4eMh1L169eoUxY8bAxMQEhw4dwpMnT6CnpwcbGxu5Mxffvn2Lrl27Ijw8vEm1l0ePHqXdlNLcojU1NcjJyYGTkxNcXFwwdepUKCkpwd/fH69evcLSpUvh4OAAS0tLEELkUqj4HNi6dSuMjY3x9OlTetvNmzcxbtw4Wmx45syZiIyMlJqlumHDBnA4HGhpaYHNZoulwnM4HAwfPhza2trQ1dX9yz0nJ0+ehIqKCuzt7TFs2LA/d8ydC+ozZDbfunULenq+3cc8AAAgAElEQVR6jZI2tBQTJkzA/PnzP8/JW9EF/KXjH2v4gHotLicnJ5w4cQL6+vqIiopCjx49JGb2tbW1GD9+POzt7aXGht69e4eUlBQ6K7Gp0i2tjZkzZyIqKgr9+vWj0+KZTCZUVVXRvXt3ZGZm4qeffpI/1bqsDMVRUdiqpIQqX18xcoDG8PjxY2zatAmRkZGwsrICm81GYGAgFi1ahPz8fOTn59OF+SLuSUVFRbi7uyMqKgqLFy+GsrIyNm/eDFVVVfTv379JKeIFBQUwNzeHv78/dHR0kJWVJddxr1+/RseOHTFmzJgm/Z4PHz6kXbSNlcFQFIXMzEzandu+fXtoa2tjyJAhOHnyJCiKApPJhLu7u9zf39qYP38+HB0d6ezYBw8ewMPDA35+fliyZAmcnZ2hoKCAr7/+mmaAoSgKGRkZ4PP5UFZWhouLi8QEYMKECdDR0YGDgwMd6/urr4vD4cDExETcKLdSLev7wYPx/v17sRX6qlWr0LZt2yYnDTUF2dnZjUpUNYTKykqpNHcAWkyu3pLEt78aDAAg/1AAIL179yY9evQgVVVV5McffyQqKirE2tqaLFu2TKL9ypUrSXp6Otm+fTvp1q2bxP7bt2+TsWPHkoqKCrJy5Uri6en5F1yFOJ48eUKcnZ1JRUUFYTAYxNPTk6xevZo4OzuTt2/fktOnT5OCggJy/Phx8uuvv5Ju3boRX19f0qNHD2Jra0sYDIbEOSmKIiYmJqSsrIwkJSWROXPmSP1uAOThw4fkxx9/pD9VVVWka9euxNvbm3Tt2pU4OTkRBQUFiWOfPXtGgoODiZWVFZk9ezZ59OgRuX37Nrl16xbZuHEjUVFRIRUVFURbW5swmUwSExNDOnfuTBwdHYm+vr7UfotQWVlJAgICyIkTJ0hOTg4ZMWJEg+1fvXpF/Pz8iIeHB8nKypLaX1nfY2hoSN68eUO2b99OhgwZ0mD7nTt3kiFDhhAdHR2ipqZG9PT0yNOnT0lUVBRJSEggBgYGpE2bNuTRo0fk3bt3cvWhtQGATJgwgVy7do1ERESQ5ORkkpaWRhISEgiDwSDDhg0jOjo6RCgUku+++450796d1NbWkkuXLpGSkhIyYsQIsmnTJonzVlRUEB0dHaKvr0/U1dVJcXFxg79Ja+Lx48fE3d2dlJeXEyaTSWpqaoiysjKJjo4mWY8eEXLgQIu/47CSEglkMAhFUURNTY2oqakRFotFysvLiZqaGrG3t6e3q6mpEXV1dbH/m7JNVVWVvnf5+flkzpw55Pjx483q95w5c0h6ejpJS0sjycnJhMVi1e8oKyPE1JSQ9++bf1OYTEKePiWEy23+Of4i/KMNHyGEPHjwgHh6epLCwkISERFBunTpQnbu3EmSkpJIVFSURPsTJ06Q0NBQkpGRIXU/ALJ161YyadIkEhgYSGbNmkXYbPZnv46bN2+S8PBwcu3aNUIIIf7+/mTVqlXE1NRU5jElJSXkxIkTpKCggBQUFBCKomgj2KNHD2JgYEAIISQ7O5tMmDCBACDq6urk2bNnRENDgwAgxcXFYoaOoijStWtX+mNnZ9fogHbhwgUSEhJC4uLiSEpKikT7zp07Ex6PR0xNTUm/fv3IsmXLyLFjx4i5uTkpLS0lQqGQODo6EgcHB+Lo6Ej/LRAI6HP16tWLPHz4kKioqBA7OzuSnZ1NBAKBRF9evnxJevbsSXx8fMiCBQvkHowBEFdXV3Lt2jUyfvx4qRMnEa5evUomT55MCgoKiJmZGVm9ejXx9fUlCgoK5NGjR2TJkiVky5YtJDAwkCgpKZF169aRmpoaoqKiIldfWhtv374lzs7O5OXLl+TkyZPE3d2dEEJIUVER8fX1Jffv3yeampqkrKyM9OrVi9y8eZMIhUISGBhI1q5dS7gyBjo7Ozvy6NEjYmhoSLZt2/bZJooAyMuXL8njx4/Jw4cPydSpUwmbzSaFhYXk0+HtgpUV8bp/v+VfGh5OyObNpLa2llRXV5PKykpSVVVFfv/9dzJo0CAyYcIE4ujoSKqqqsQ+onbStlVWVpLq6mqJdnV1dbQRVFFRIaWlpaRt27ZNNqLq6uokJyeH5OXlESaTSVgsFsnIyCAxMTFEceFCQqZPb5nhY7EISU8nZNKklt/fz43/l3XmX4yMjAz069cPT58+BZ/Px7Zt28DlcmUSHxcXF8PW1hbx8fEy3W6vXr3CuHHjwOfzsXHjxhYzYsjC2bNn6ViQoqIiunbtKqFGLg9EOnMrVqzAwIEDoaurCwcHB4wePRqqqqogpD693tDQECEhIRg8eDD4fD6MjY0xfPhwrF27FsXFxU1OvtiwYQO4XG6D9ZFBQUFQV1cXE0Q9f/48TE1NER8fj6dPn+LkyZN0Uk3Xrl3B4XCgo6ODTp06YfTo0VBWVsbAgQPx8OFDpKWlgcvlSmi5lZSUwNHREampqU2+jujoaBBC0L59e6n73717h/Xr16N9+/Zgs9lgMBiIjIyUeb7y8nLMnDmTFrNNT0//f2HMuHr1KmxtbTF8+HB07twZCQkJdD9ELmtRf728vKCpqQlVVVWsXr0ao0ePho6ODoYOHYpTp05J9D84OBgMBgOZmZmNCg43BIqi8OLFCxQWFmLHjh2YN28exo4diz59+sDBwQFqampgs9lwdXWFq6sreDwelixZQpeNEFJPnt2/f3+strJCdUtJHBphLzp58iQEAkGjpObyora2FhUVFXj+/Dnu3r0LZWVlnD59GidOnMCBAwewY8cO5OTkYMWKFZg/fz7S09ORkpKCuLg4REREYOjQoQgICICvry/4fD59T0SfmTNn/uV0hv/f+FcYvvfv38POzg4//PAD9u3bB1NTU+zevRv6+voy9clevXqFXr16wc/PT7ZPHPXaZ+3bt0fnzp0l6LVagr1790JfXx+E1Au+pqamorKyEqGhoVi9enWLz19XV4eLFy/C2tpa4kVgsVhYtWpVi7TbamtrkZCQAGtr60aVtLt16wZnZ2eJ7X/88QeCg4Ph6uqK4uJiif2lpaU4efIkwsPDwWKxYGdnRxvEtm3bQk9PD3Z2dsjNzcWlS5dgY2PTLAOTl5cHQuqp8D6d4BQVFWH8+PFgs9no168fxo0bBwaD0SBl2ccoLy8HIQQaGhpwd3fH9u3b/xIaLIqisHTpUnA4HJpw+dWrV3B0dMT8+fNx8eJFGBkZobq6Gk+ePIGFhQVUVFSgr68vluT1xx9/YOnSpbC3t4ednR0WL16M8vJyUBQFc3NzuoZOW1tbZq2byLD9/PPP2LlzJ+bPn49x48ahb9++cHR0hLq6OnR1ddGuXTsMGDAASUlJyMrKwr59+3Djxg06s/f3338Hl8uly0m4XK7Es709Kwu1nyZyNfUjB3tRWloa/P39P8uE2MbGRkxsuSkIDQ2FgoICmEwmAgICcOLECZw/fx5PXFxax/A1QmD/peBfYfiA+lmYsbEx3r59i/j4eAwcOBCLFy9GmzZtZBbW1tbWIj4+HnZ2dlIHXhHq6uqwcuVKcLlcTJw4sUWFuuvWrYOOjg49GC5dupQeCGtra8Fms8VWRk1BbW0tLRrbv39/6OrqwtTUFF26dAGLxULbtm1hbm4OdXV1sFgs+Pv7Y/78+bh69WqTXuDy8nL4+vqiV69ejbLoUxQFLpcrs86LoigsX75cbID+FO3bt4ejoyOOHz8O4E+DuHTpUnh6ekJJSQkMBgNMJhOdOnXCV199RZdd/Pbbbw0awqKiIjAYDCgqKtIToOrqamzevBmdOnWCgYEBpk6diidPnmDx4sVgMBjIyMiQ5zbR0NHRgaGhIfbs2YNOnTrB3NwcWVlZra69JkJZWRn69u2L9u3bSyRz/frrrzA2NoaNjQ1WrFiB69evQ09PDwwGA126dJFplCmKwunTpxEWFgZtbW306dMHJiYmCAkJgbGxMUJCQpCYmIhdu3ZhwYIFGD9+PPr16wcnJydoaGjQhi04OBgTJkzA0qVLsXfvXly/fl2umkaKohAcHIyJEydiy5YtCAoKApPJpJO/WCzWn2UuwcGtWscnDR8+fICnp6fUWtuWom/fvk0qDxEKhXj8+DGOHDmC4OBgODo6wtPTE3w+n55wnTU3/2/F90/FiBEjMHHiRLx//x7t2rXDihUr6Fq/hgb21atXg8fj0QOrLJSWlmLkyJEwMjLCzp075V5ZCIVCzJo1C2pqaiCEgMvl4rvvvpM4/uzZs3BxcZHrnED9y3f+/HnMnj0b/v7+0NLSgqOjI2JjY7F9+3aaxFuUXSgaaL///nu4urpi165dGDt2LGxsbMDhcDB48GCsWbOmQQb3mzdvwtLSEklJSXKtXPLz82FkZISwsLAG2129ehU2NjY0Xdb58+dpqSZzc3NYWFhIzbJ88OABDA0NYW5uDg8PD2zevFmmy/RTg/jq1SvaDXzx4kXcuXMHSUlJ4HA46NWrF77//ns6Q3jWrFlgMBjNknbp0aMHFBQU6P/Pnz+PAQMGgMPh4Jtvvmk1lxkAHD9+HIaGhpg8ebLMEoMVK1aAkHo2GtEzmZiYKPOcFEWhvLwcly9fxu7du/Htt99CIBBAWVmZpnNTU1ODqqoqgoKCkJiYiCVLlmDPnj24du1agx4VeVD2P2kydXX1/2PvvONjytv3P+m9z6RMikiTkEIQIoKIFhHRRW9B9BCiRwkhSIiyLGKx2GD1aLFYZRerl1V3satGL4kUmXn//rA5a0zKJLHP8/0963q95o85Z06dcz73527XhaGhIWFhYaxevZoHDx6gpaWFpqYmP/zww98bVKBsX66np3LZ/u+//45YLC6XBmhJGDFiBElJSUrLc3JyuHTpEps2bWLatGl06dJFYEOSSqUEBwczZMgQFi1axIEDB7h///5/lcD+v4l/leHLzMxEIpFw4cIFbty4gVgs5uzZs9SvX5+JEyeWuO2hQ4eEptjScPToUTw9PWnevHmJdFCF4cBCajFHR8cSBVfHjx9fYs9Xbm4uR48eJT4+niZNmmBoaIiPjw/Dhw9ny5YtPCkmPPPs2TNMTU2F7wUFBbi4uHDs2DFh2R9//MGqVavo1q0bVlZWODk50b9/fzZu3Cjw+u3YsQOxWMyaNWuKPcdP0bp1a6Kjo1Ui33379i09evTAw8ODYcOGoaGhIbRGaGtrK/GW3rhxA3t7e7766itkMhkLFy7EwsKC2bNnKxjlQg/xY4NY6OWIRCJcXV1xcXHB1NSUwYMHK/2nkyZNQk1Nja/K2cRbqOzwqeG+efMmUVFRmJmZMWDAgAqpiOTn5zNhwgSkUmmRIqyFKAxRfqwesXbtWl68eMG5c+fYunUrycnJDBs2jLCwMLy8vDAyMsLY2BgfHx/Cw8MZNmwYhoaGLFmyhKVLl6Knp4empibm5uYqvT+q4P79+yxatIhGjRphaGiIrq4u06ZNU4q2dOzYseiceDkatfO0tJgskZQpBbBu3Trc3d0/q/c+e/Zs2rRpw4oVK4iJiSE0NBRnZ2d0dHRwd3enbdu2jB8/njVr1vDLL7+oJneVmVlxw1dOAvv/Bv5Vhg9g+fLl1K1bF5lMxpo1a/Dw8ODOnTtUqlSJtLS0Ere9desW7u7uDB06tFRvJj8/n3nz5mFhYcGUKVMUVNpzcnLo1q2b0A/m7e2tUszex8dH0K6DDzyfBw8eZMqUKTRq1AgDAwNq1qzJqFGj2LFjh8pinRcvXsTT01Nh2ZIlSwgPDy/y93K5nMuXLzN//nxatWqFkZGRwMS/YMEClbkE79y5g4WFhZB/UxVr1qzBwMBAkKIqLGL4+AX/9ddfkUqlpKamKh2zSZMm1KxZkwsXLhR7jKCgIESiD3Rvrq6uNGvWjAYNGggeYr169ejfvz+NGjVCTU2N5OTkchennD17Fg0NjWKZ/jMzM4mLi0MikRAeHq7wDAB8//33JQ6sd+7coWWtWqxwc+Nd+/ZFkg2/fPmS8+fPM2HCBAWjV+itGRkZ4e3tTevWrRk+fDjJycls27aN8+fPK3ls+/btUyAimDVrFrq6uoSFhaGvr0/t2rVZuXJlmY3B7du3mTdvHv7+/piZmdGjRw+2bdtGt27dBDmqT7FixYriFSnKwV60cOFC7OzsuHLlisrn3b179zL33hUUFHD79m12795NUlIS/fv3JzAwELFYLJBX9OrVi1mzZrF9+3auX79eJuahIvEv6uP71xk+mUxGvXr1BK227t27079/f86fP49YLObMmTOKG3zCTp7XqRMr3dzo0LChSobl3r17dOjQAWdnZzZt2kTz5s2FgaVhw4ZCuFGV/ZiZmbFnzx4mTJhAQEAABgYG1K1bl7Fjx7J79+5y8zvu3r2b5s2bKyzLzs5GIpFw48aNErfNysqiQ4cOeHh4EBMTQ2BgIAYGBjRs2JD4+HhOnDhR7CQhNjaWUaNG8erVKwwNDct0znPnzlXI39jb2wvrLly4gLW1dbHyKnK5nNTUVCQSCZMnTxYajvPz89m6datQ8GNqalqkl/XkyRMOHz5M/fr1EYlEeHh4KBnEBQsWkJGRUWoOsfC46urqpbLWZGdns3jxYpycnPD392fr1q1cunQJkUhEaGhokcc5kJBAurY27zU1lfTmctXVyVVTY6emJg309KhWrZowGROJREKOzMzMrFQF+o/Rs2dPQWqq8Po0NDSE/PXatWsJCwvD3NycoUOHlsg9ee3aNWbMmEGNGjWQSCRERkayd+9eIUy7Z88eHB0di1UpWbFiRYnVtR+zF8n19JRDd0WwF61btw5LS0tOnDih0v14/fo1Tk5ORaqnZ2dnc/78eb777jumTJlCp06d8Pb2Fp7ppk2bMmzYML766isOHTrEw4cPuXXrFo6Ojiodu0z4wtzyv41Lly4hkUh4/Pgxb968wcXFhbS0NLZs2YK9vf0HY/SRQOWnIQC5nh75GhrsNzDg7ubNSvt/9eoVUVFRApvF/fv3cXNzEwaUkJAQlQpgXr16xe7du4mNjaVy5cpoaGgIYdmMjIxiX/ayYvny5UUODpMmTSIqKqrY7f744w+qV69Ojx49FJg73r59y+7duxk1ahTe3t6YmJjQunVrFi5cyNWrV5HL5bx79w6xWMytW7eQy+Xo6+uXqSho27ZtFFa8WlhYCEbjzJkzWFlZKYjbFocHDx4QHh6Oq6srffv2RSqV4unpKXg5JdFs9ezZE3V1dYUig0KDuHjxYgYPHqyQQyzNIBZylqqCgoICNm/ejJ+fH4aGhqipqaGnp8fgwYPZvn07CxYsYMiQIUwwNydLVLoMlVxNDbm+PvG2toLBnzlzJseOHSMnJ4cxY8ZQr149hahFcXj37h2mpqZKE7oGDRrg7e3NwIEDmTZtGvBBuDYuLg6pVEpAQADffvst79694/z580yaNAkPDw+kUilDhw7l8OHDShOoN2/e4ODgUKIcVHHPthKePPmQn+rRg4e1arFRR4fDLVsiKya/mp6ejlgsLjFsXAi5XM6ePXswMzNj1qxZREdH06JFCxwdHdHV1aVatWq0b9+eSZMmCRXIJb3b79+/R0dH559hiPnC1fm/jTFjxtC9e3fgw2ApkUi4ffs206ZNY7aj4wcJnlLcfpmaGtkiEVc/CrPIZDKaNm2Kuro6kydPxt3dHZFIhJaWFgMGDGDSpElYWFgwd+5cpdDE8+fP2b59OyNHjsTX1xcDAwMaNWrElClTCAgIYNWqVf/IvYiLiyMuLk5p+ePHjzE1NS0yN3j06FGsra2ZN29eqR7N48eP2bBhA/369cPBwQEbGxv8/f3x9vYWWPRdXFyK9i6L0QM7sXMntra2PHr0iKioKHR1dRk2bBgSiYTt27eXes0FBQXs2rWLVq1aYWhoiL6+Ph06dBA4T0uqnO3YsSMaGhoq6++pYhBr166NSCQSJgKf4vXr11y8eJEdO3aQkpLCyJEjadGihVJYskaNGkRERDDa0JAcdfUyDWBZIhE7i8iHyWQyunbtSps2bUqld9u0aVORcllHjhxBTU2No0ePYmdnp2DEcnNzmTVrFo6Ojqirqwuafz///HOJRWdDhgwpVVh3+fLl5VItv3v3Lv7+/oSEhBSbGz927JhQiAYfnqlbt26xa9cu5s6dS9++fQUxaFNTUxwcHLC2tmb27Nns3LmTW7dulbt1xcXFpUI53xJRAYHe/1/wrzV8WVlZODg4CJWaycnJ1KlTh/eLFpGroVGmASNbJOLwX+X406ZNQ1dXVyFcFB8fr/AC37p1i+bNm+Pu7s706dMZNmwY3t7eGBkZ0bRpU2bMmMGxY8eEGV1ubi7GxsY8e/bsH7kX/fr1K5ZZvl+/fsIMvRDLly9HIpGUSQWhEIWN9HZ2dtSvXx9zc3Pc3d2RSqXEx8f/Ha4tweP+VA9s6tSptG3bFk1NTWrVqlXsQAUfvLz4+HgcHByoXbs2qampZGVl8ejRI6HIKDExsdjtQ0ND0dTU5OjRo2W+9k/xsUFs2LCh8Lzo6ekhlUpxdHTEzs4OIyMjdHV1qVq1KqGhoQwZMoS5c+fSq1cv1NTUBP7TQg+4iYkJ77W1yxWyKk5zMS8vj+DgYAYPHlziRKdNmzbFTtDMzMyIjo7G39+f77//nsOHDzNs2DBsbW1xd3dn4sSJ7Nixg9jYWCwtLWncuDGbNm0q0vM+evQoUqm01HTD119/XS7DBx9CtOPGjcPW1pbDhw8Ly7Oysjh79izr16+nf//+6OrqIpVK0dXVpVKlSrRo0YLo6GiWLVvGjz/+SGZmJnK5nIKCAho1avShYbyCaNGixT8rmv1RCBgVQ8D/P+Ffa/jgQxWim5ubQDYbHRBAXjmbW9+pqdH9L++u8KOlpcW6deuE4z18+JC0tDQGDRqEh4cHenp66Orq4uvry+7du4tNTu/fvx9/f/9/7D40b95cSa+wEL/++itWVlbk5OSQn5/PkCFDqFKlSqm5v5Jw/PhxXF1dkclkFBQUcObMGXx8fISertmOjuRpapbea/XXjHNRtWoYGhqyd+9exo4dqzRQyWQy9u/fT7t27TA1NWXAgAGcPXtW4Zx8fX0RiURERERgZ2fHgAEDFHKmMpmMxo0bo6WlxS/lEN98+/Ytly9fZteuXSxatIiYmBjat2+Pr68v5ubmwmTJ2tqaPn36MHDgQPr27UuHDh2oV6+ekoc4f/580tPTOXnyJI8ePeLOnTu0atWKmjVr8rZp03+kSOH169f4+PiQkJBQ5PoXL15gbGxcbK554MCBGBsb06hRI7S1talevTrx8fFFEhzk5uaSlpZGo0aNsLKyYvz48UIbzbt373B1dWXr1q2l3vevv/6a/v37l/q7TyGXy3n06BGHDh1i6NCh6Ovr4+zsjL29PXp6enh7e9OpUyfi4uKYP38+dnZ2TJ06tdTox71797C0tOTnn38u8zl9jGHDhv0jPYJK+CgETKtWZSKw/7+Mf7Xhgw+0TIUeTW7LlqXmQ4r7FIhEfP+XwTMzM8PS0hJTU1OaN29OZGQkrq6umJmZ0bp1a5KSkjh9+rRARTR69GgkEglLly4tMpQ0YsSIzzJLLA6enp5cvHix2PUtW7YkKSmJRo0a0bJlywqLpEZERCgUP8Dfciv5KSkUfFKEoUqIbk/r1sK+9u3bh7W1NaNHjyYhIQEnJyd8fHxYunRpkaXdQ4YMQSQSCS0Vr169YsCAAdjb25Oeno5MJsPf3x8dHZ1i79Pbt2+5cuUK6enpLF68mNGjR9O+fXtq1qyJhYUFenp6eHh4EBISwqBBg0hMTGTjxo2cOnWKzMxMCgoK0NLSUtD3+xSFHuKSJUuEkKlEIsHQ0BBtbW28vLxYMmUKBVpa5TN6hZ8SytIfPHiAo6Mjq1evVlq3YsUK2rdvr7Ds3bt3bN++nR49egjEDIMGDcLMzKxIJZSicO3aNUaOHImFhQUtWrSgXbt2dOjQQaVtly1bVqLhe//+PTdu3GD79u3Mnj2b3r17U6dOHUxMTBCLxdSvX5/IyEji4uLw9vbGz8+PP/74Q2k/Dx48wNPTk5EjR5ZK9rBt2zYqV65cofcoJSWFIUOGlHv7fzv+9YavUMft9xMnKtzHUqClRaegIIyNjbG1tUUsFtOuXTtSUlK4cOFCiS/EpUuXqF+/PrVr11aqLHVxcfnsTbAfw9TUtMQw6sqVK9HS0iI2NrbCkkwPHjzA1NRUqQR+7ty5JHfpUu6qsgJdXTh9GrlczuHDh2ndujUaGhpYWVmxc+fOYmfiGzduRCQSYWVlpfT/HDp0CEdHR8zMzNDV1SU9PZ3du3ezZMkSRo8eTYcOHahVqxZisRhdXV3c3d1p0aIFUVFRzJ49m7S0NMGwqdLq4OXlpdDIXhrev3/P5MmTsbS0JDExkSVLlrC1bl1y/2EuymvXrmFlZcXevXsVlgcFBbFlyxbevHlDWloaHTt2FDy8RYsWce/ePapWrUpwcDCjR49mzJgxKl8rfDCiU6dORUtLC2tra6ZOnVoqi9GyZcsYMGAAb9684ZdffmHt2rVMmDCBtm3b4uHhgY6ODk5OTrRs2ZKYmBhWrFjBsWPHiqxiLSgoYPr06VhbW7Nnzx6l9S9evMDf359evXqVmruLioqia9eu5W6B2b17N82aNSvXtl/wxfABMG/ePJa7ulZYoPKdSMRab2+6deuGubm50sBQGuRyOatXr8bKyoohQ4bw8uVLbty4gVQq/ccIjLOystDV1S12/1u2bEEsFlOpUqXPklOYMmVKkZWi69ev55StbblDdDI1Na56eFClShWqVq1KSkoKT58+FXTjigrlXr9+ncKQdGZmJlevXmXPnj0sWbKEMWPG0L59e4V8rVQqpXnz5gwcOJBZs2aRlpbGyZMnefz48Wf5f2JiYhCJREph2KJw9+5dAgICaNq0qaJA8n+IbPj48eMK7T9XrlxBX19f6Ots0aIFy5cvJzMzU2G71atXo6mpybVr15BIJEUK+Y1jr/sAACAASURBVBaH/Px8fHx8WLt2LRcvXmTw4MGYmZkRHh7O3r17KSgo4P79+/zwww8sXrxYCMvr6+ujr69P9erViYiIYNq0aWzcuJGLFy+qVKn6KY4cOYKdnR2jR49Wyj9mZWXRokULWrduXeK+s7OzqVq1KmvXri3z8eEDOYOTk1O5tv2CL4YP+PBC7TI1/awDxpEjR7C2tmbRokVlHhSfP39OVFQUNjY2dOvWTbVy7HLixo0bODs7Ky2XyWRMnToVe3t7Tp8+zfr161ViVykJeXl5WFtbF9n8e3zr1gp7Ku9EImo7OjJq1Cj27NkjNEgXVhL26dOHHTt28NVXXxEdHS1URJqbm6Ojo4ObmxvNmjVjwIABgsHU19fn3Llz/Pzzz1StWpU2bdoIgqyfGxs3bkRbW5vRo0eX+Lvvv/8eiUTCnDlzlLzU/ObNP89zrALZ8DfffIOJiQmBgYHo6Ojg4ODA2rVrS6Qgk8lk6OjokJycTLNmzYrttSwKM2bMoEWLFuTm5nL16lW2bt1KXFycQK2mpqYm9LYOHDiQ+fPnM2TIELp27frZyaKfPn1KaGgofn5+ShR+eXl5RERE0LBhwxLDmRcvXhRaesqKvLw8dHR0/uPK9v8r+GL4/sLzgIDPPmDcvn2batWqERUVVS5WhVOnTmFkZES1atXKzcZeGg4ePEjDhg0Vlr19+5Z27drh7+8veBP5+fnY29srN/iXARs2bCAoKKjIdU9Gj+ZdBQ2fTEeHH1u1IiwsDHt7ezQ0NDAyMsLQ0BAtLS2B8aJz584YGxsjEomIi4vj4cOHCgNjdnY2Dg4OmJmZKXgsubm5TJ48GYlEQmpq6mf3wm/fvo2Ojg41a9Yscn12djYDBgzAycmJU6dOUVBQwMWLF1m+fDl9+/alatWqbChjRXJZPb579+6RkpJCgwYNMDExoWbNmlhbW+Pp6cmBAwdUus7w8HCcnJzYunUr9erVK/Z3r1694uTJk3zzzTf069cPbW1tQfHBxcWFsLAwxowZQ2pqKj///DM//PADkZGRmJqa0rFjRw4ePMiSJUtK7EWtCORyOUlJSUgkEr7//nuFdTKZjMGDB1OjRg0lr/djpKSkULt27XKND05OTiWS539B8fhi+ArxmUJEB+3sGDlyJIsXL2bfvn2cO3eOli1bEhQUVOZ2hDdv3mBoaMi8efMQi8XExsZ+dsb+tWvXKhBE3759G29vb/r27avUIDtv3jy6dOlS7mPVq1evSPYKgPzOnT/L/c+wtqZ///7MnDmTVatWkZSUxMCBA/H29hZIugspzorqAXv9+jU2NjZIJJJiZXQuXLhAzZo1adq0aYWkmz6FXC7HwMAAfX19pXWXL1+mSpUqBAYGMmrUKIKCgjAyMsLNzY2ePXuyZMkSzp49S0FCwmcnG/7tt99ITEykTp06mJub06tXL3bs2CGEKQcOHIiWlpZqnJDA1atXEYlE3L59G1tbW/bv38/+/ftJSUlh0KBBBAUFYW1tLVDwdenSBXt7eyIjI7ly5UqpjduvXr1i8eLFeHp6Ymlpib+//z/WCgTwyy+/4OTkxKBBgxRCt3K5nLi4OFxdXbl7926R28rlclq2bKmyjNXHaNasWbHV2F9QMr4YvkIkJlY4xyfT0eFijx4kJiYyYMAAgoODcXR0REtLCxMTE/T09OjcuTNz585l69atXLx4sURDtm3bNqEZ+NGjR3Tv3h0HBwe2bdv22byNhIQEYmNjgb+JuFNSUorc/6tXrzA3Ny+yqq00nD17Fnt7++KT/q1afXaP+1NkZmbSpUsXPtYdjIiI4JtvvuHevXs8f/4ciUSCjY1NqSwy79+/JzExEbFYzMKFCz9bKK1x48aIRCIyMzM5ceIEycnJ1KpVC3V1dfT19WnevDlTpkxhz549RQ/mn4FsWK6ry7WjR5k+fTo+Pj5YWloyYMAA9u/fX6RnEhcXh4eHB61atSr2/83NzeXy5cts3ryZ+Ph49PT0MDc3FzzxoKAgBg0axMKFC8nIyODPP/8UnsGUlBQCAwPLfI/lcjkxMTG4ublhYmJC9+7dOX78+D+SL3/16hWdOnXCx8dHqbE8JSUFOzu7YqM2mZmZSKXSUtVfPsXgwYNJSUkp9zn/m/HF8BXiMw0YRZWB5+XlcePGDUaOHImhoSFt2rQhLCwMDw8PdHV1sba2JiAggJ49ezJt2jTWrVvHiRMn6N69u5L8yOHDh/Hw8CA0NJTff/+9wpc9ZMgQFixYwJIlS7C0tCw1XBUTE8OoUaPKfJw+ffoU2/8F/EeKMn788UdEIhFGRkY8e/aMVq1aYWtrS/PmzTEzM0NdXR1DQ0M2bNigMsH3jRs3qF+/PgEBAeVm0pDL5dy+fZsNGzbg5+dHYcGNl5cXlStXxsHBgT179qg+8Ldti6y8RUIiEfsNDbGzs2P48OEcOXKkxEpeuVyOq6srP/30E82bN6dHjx4cP36c1NRUxowZQ6tWrXBxcUFHR4cqVaoQHh7OuHHjCA8PR09Pj19//RVTU9NiJxq3b9/GwsKi3H2jhYobz549Izk5GTc3N6pVq8aiRYsq3JbzKeRyOcuWLStSoeTbb7/FysqKkydPFrnt/v37sbOzKxMn6vz58xk2bFiFzvnfii+G72NUQKBSJhKxR1+fZcuWlRiKOXbsGNbW1ixYsAC5XI5MJuPevXv8+OOPpKamMmHCBDp16oSvr6+QrK9RowYdOnRg7NixrFixgv379zNu3DgsLCyIj4+vEGdfWFgYTZs2pWrVqir1Vf3xxx+YmZmVadAolD0qiVGFxERy/sEy/IcPH6Kuro66urqQt5TL5axYsUJoV7CzsyMhIYFmzZphaGiIn58f48eP5+DBgyVWH8pkMkEsNyEhodR8zZs3bzh48CAzZ84kLCwMS0tLbGxsaNu2Lb169UJPT486depgb29PdHR0mf7fgoICkiIiyp0vzdPU5PI335RoZGUyGXfu3GHv3r1ER0djbGwsKAeoq6sjlUrp2bMnCQkJbN26latXryoVYeTk5KCurs727dtp165dkZJOcrmcJk2alMikUxoKaeI+3ufhw4fp3Lkzpqam9O3bl19++eWzeoGXLl3C3d2dnj17KnBupqenI5FIiqW6i4mJITw8XOVz2bVrV9GSS19QKr4Yvo9RQXbyCytXEhISgp2dHQsXLiy2nPnOnTt4enrSv3//Yquyzp8/j4uLC8+fP+eXX35hw4YNxMfH07t3bwIDA5FKpejo6GBoaIiBgQFt2rQhJSWF9PR0rl27ptJgmZmZiaGhIYGBgSrnZwC6du3K3LlzVf797Nmz6dmzZ7Hrz58/z5hevXhXUW+vGI/7/fv3GBkZIRKJlKjGbt++jZ6eHjo6OkRERAieR25uLocPH2bixInUqVMHQ0NDmjZtyuzZszlz5kyRXtDdu3dp1qwZNWrUEPouZTIZV65cYeXKlURGRuLp6Ym+vj4BAQGMGjWKTZs2KYT17t27h4aGBmpqaqSnp6t8j+FDfrJly5YEBweTNW9emZ9l+Se8i+/evePixYts3LiRqVOnEhERQfXq1dHX18fW1pYmTZrg4+NDSEgIP/zwAw8ePODRo0c4OTmxYsWKUs/X39+fmjVrcuDAAby8vJQG/NTUVHx9fcvNZwmwaNEiBcP3MR4/fsysWbOoXLkyvr6+fP3115+N+D0rK4s+ffpQpUoVBfmro0ePIpFI2FwEuX1eXh6+vr4q6zpeu3YNFxeXz3K+/zZ8MXyf4jOwk58+fZo2bdpgbW3NnDlzinyZ3rx5Q1hYGA0bNiwyVzNjxoxiNcYKkZ2dzeXLl5k4cSLm5ua4uLjQsGFDnJ2d0dbWxsHBgaCgICIjI5k1axabN2/m3LlzvH79mnPnzlGpUiUMDAz4888/y3SLzp49i52dnUqVaAUFBTg4OHD6E06/rKwsUlNT8fPzE+iezjk6ljtEVxLVVqHiwqeew9WrV9HT06NGjRq8ffuW/v374+LiUmQf3cuXL9m+fTtDhw7F3d0dc3NzOnTowNKlSxWIpTMzMxkxYgT6+vo4OjpibGyMi4sL3bt3Z9GiRZw+fbrYyc6ff/5JYGAgWlpaqKmplSmnVWQF8V/PcmlRDLmaGgU6Ohzt2pVRo0bRsmVLoXqyatWqtGvXjgkTJrB27VpOnz4tTA4KCgqwtrbm2rVrCudy8+ZNrK2tS+373L9/P2pqarx580YIlxbiwYMHgmh0RbBo0aJSGU5kMhn79u2jTZs2mJmZMWjQoBKZjMqCb7/9FrFYzFdffSU8I+fPn8fGxoavv/5a6ffXr19HLBarpPeXm5uLjo5OxXX4/oX4YviKwldfIdfXL52+rBR28osXL9K5c2ckEokiAfNfKCgoIDY2FmdnZ6XEd7169VSSPClEdnY2kyZNQiwWk5ycTE5ODr///jsZGRksXbqUmJgY2rRpg5eXF9ra2qipqeHs7IyamhqTJk1izZo1HD9+nEePHqkUagkKClKpB2v79u3UrVtX+H7p0iWGDBmCubk5rVq1YteuXYL3tD46utxcqcXpgfXr149CvbqPcfHiRXR0dKhXr56CgUlLS0MikRRb4FOI+/fvk5qaSkhICMbGxoJYq76+Pg0bNhTImF1dXVXiZdy6dSuWlpbMmjWL8PBw1NTUOHbsWKnbwd/h84ULFwrnnJ2dzdatWxng68v3IhG5amrkfdLqkKOmRo5IxA5NTXp7etK7d29mz57Njh07uHHjRqkD6g8//ICvr2+R606ePIlEIik2p1UIY2Njxo4dS1JSkqCWIpfLadOmTbHCvGXBwoULy0Ttdf/+faZNm4adnR3+/v6sXr26XE3uH+PGjRtUr16d9u3bCz2Ot27donLlyiQkJBTp6Xp5eanU3O/o6Kgy9dsX/I0vhq84nD7N44AActTUlKo934k+VHDe9vWloJQXGz6EJHr27ImFhQWTJk1S8vBWr16NRCIRaJCePXuGsbFxuXJ3169fp0mTJnh7eyspdctkMiZOnChomH3//feYmpoyefJkunbtSp06dRCLxRgYGODt7U3btm0ZPXo0S5cuJSMjg9u3bwthp/T0dKpXr16qkQwODmbVqlWsXr0af39/bG1tiYuLK7IyNC0tjeU1alQ4RFeItWvXIhKJsLOzU1j+yy+/oKWlRXBwcJHn/Ntvv1GrVi3Cw8OFlga5XM7du3dJS0tj5MiR+Pv7o6+vj7e3N/3792fGjBmMHz+esLAwTExM8Pb2Jjo6mvHjx2NlZUV0dHSRFbzv3r1j0KBBODo6CsKmCQkJGBgYFBui+xiFz87evXt5/fo133zzDcHBwejp6WFhYUFhBaudjg6x6uqsEYm44ODAdT8/bg0cyOPLl8ud3+rbty/z5s0rdn16ejrW1tYlFqb07dsXKysrnj17homJCU+fPmXTpk14eHh8Fr25lJQUhg4dWubt3r9/z44dOwgJCcHCwoLo6Gglz7YsyMnJYejQoVSuXJlTp04Bf/N7xsTEKPwHcrmcjh07qlS40qRJk3KppPzb8cXwlYL+bdqwOyhIgZ38cMuWhP5Vfefm5qZyqPC3336jf//+mJubM2bMGB5/JHL5008/YWNjQ3JyMt9++y2tPyJdLivkcjlpaWlIpVL69u3L06dPef36Na1bt6Z+/fpCQ+3PP/9cpOr3q1evOHv2LJs2bWLWrFlERkbSqFEj7O3thebhZs2aCWGhHTt2cOXKFaWZ8c6dO4UBOCQkhO3bt5eYrzl69CgBAQEq64HJ/9JDLMroXbx4EZFIhLa2NtnZ2cLyY8eOoampSatSmEmeP39Ohw4dMDU1JTAwEGtra6ysrAgPD2fWrFkcPny42HzQ+/fvOXHiBPHx8TRs2BADAwMsLS0xMzMjJSVF8KSuXLmCp6cnnTt3VogGHDhwAHNzc7y8vIrcv1wu5+HDh3Tp0gVzc3P8/f2RSCSoqakJxSVOTk5oampSKFW0evVqgammJGOlKnJycjAzMyuVK3PFihU4OTkpPOsf49GjR4hEIs6dO0fPnj2ZMmUK1tbWFVYvKERKSkqFKx/v3LnDhAkTsLa2pmHDhnz33XflNspbtmxBIpEwd+5cZDIZz58/x9/fn969eyu8Gy9evMDBwaHUcHFUVBSLFi0q17n8m/HF8JWChw8fKsXcZTIZzZs3F2bThoaGghilKvjjjz8YOnQoZmZmDB8+XBBjvXv3Lt7e3jg7O7NkyZIKn/vr16+Jjo7GwsICGxsbpWKaTZs20a6YvFhxyMnJ4dq1a6SnpxMREUGlSpVo2bIlVapUQUdHBxsbG6pUqYKVlRVaWlpUqVKFHTt2qNQe8Ntvv1G5cuUPX1TQA3vZuDERRST3X758KWjrfextZGRkoKGhQceOHRV+L5PJuHr1KqtWrWLAgAH4+Pigr6+Pv78/rVu3xsTEhNGjR5e7yCIrK4t9+/bRrl07tLS00NLSomrVqhgYGDBt2jSlXN6LFy/Q1dVFV1eXmzdvsnPnThITE+nTpw/+/v4YGxujrq6OhoYGmpqagtdw/vx5CgoKOH78OCKRSGjUNzExwcbGRtDsc3BwqHAV49atW1WmsJs6dSq+vr7Ftiy4ubkREhLCiRMnMDQ0LDW3XRYsWLDgs5X85+XlsXnzZoKDg7G0tCQ2NrZcYcY7d+5Qt25dQeQ2KyuL5s2bEx4erhDePHbsGFZWVkpq9h8jKSmJESNGlOt6/s34YvhUwOLFiwkMDFQYLB4/fiwYPpFIhJqampLUTml4+PAhMTExmJmZMXDgQO7cucOrV6/Q1tamTp06ZerpKQ4HDhzAzMyMypUrU7duXQWVh4r2AeXk5GBlZcWVK1e4du0aw4cPx8zMjNq1a9O3b190dHQIDQ2lRo0aGBsbY2ZmRq1atejcuTMTJkxg1apVHDlyhPv37yOTycjOzkZHR0dxUP5LD+x+48YcMjBQ0AMrqpy7oKAAS0tLRCIRmzZtEpbv2LEDdXV1evXqxbNnz9i9ezeTJ0+madOmmJqa4uTkRNeuXUlJSeHUqVMKM/p79+7RoEEDgoODFQmhy4G7d+9SqVIlNDQ0CAgIwMnJCUtLS1q0aEH//v0ZOnQo7du3Fwy3nZ0dISEh9OvXj44dO+Lu7o66ujqVK1fmu+++K5YA4ddff8Xe3l4olPn4WdXV1a2wR9WhQ4dixYs/hVwuJzIykubNmxeZN1y2bBlaWlrs2rULbW1ttm/fXqFz+xgLFiz4rIa0EDdv3mT06NGIxWKaNWvGli1bylRkkp+fT2xsLHZ2dvz444/k5eXRuXNnGjVqpFBhPWXKFJo0aaI0OcrOziY5OZmgoCBBQumf5PT9X8MXw6cCCgoKqFWrFt98843Cch0dHWEwadu2bZmrIwvx5MkTJkyYgLm5OSEhIVSpUoVx48bh5OSkUnVXUZDL5SxYsAArKysOHz6MTCZj5cqVWFpaMmLECF6/fk1MTEyFeqRyc3Pp0KED1tbWWFtbK4iFLly4kE6dOimcz5MnTzhx4gTr1q1j2rRp9OzZk3r16mFlZYWenh5Vq1ZFU1OTQYMGsXjxYvbu3cvNmzfJz88nODgYkUik4DUsXbpUSV27QYMGiEQiIa+Tn5/PzJkzUVNTw9XVFRcXF4yNjQkODmbixIns3LmzRC7FQrx//564uDhsbGzKVHT08fVv374dKysrGjZsSOvWrdHT00NPTw9tbW3s7Oywt7dHX18fa2tr7O3t0dTUpFmzZtSqVQsLCwtatWqFubl5kQURn+L27duYm5tz//59HBwc8PHxwcHBARMTE0xMTCoUUXj16hXGxsbFUroVhffv3xMaGkqvXr2Uzl0mk6GlpYW5uTnR0dGEh4eX+9w+xfz58/8Rw1eInJwc1q1bJ7QYTZ48uUzMRnv37hUklvLy8hg0aBC+vr7CM/n+/XsCAgKE9qEjR46Qk5PDkydPFJRDdHR0yjzx/jfji+FTEWfPnhWS8IWwtbXFwcEBY2NjJaNYHrx48YLAwED09PTo0qULCQkJSCSSMvdz5ebm0qdPH7y8vJSY458+fUpkZCRSqRR/f/8yseMX4saNG8TExCCRSGjUqBH6+voKRl8mk+Hm5qbUM1cS3r59y8WLF7GzsyMmJoaBAwfSpEkTKleujLa2tvCC+/n5MWfOHLZu3Ur//v0ZP368sI9x48YhEolwcnIiJiaGgIAAYXLi5eXFypUruXz5coU0BQ8dOoStrS3jxo0rcoafn5/PtWvX2LZtG7NmzaJXr17Url0bHR0d1NTUqFq1Kv379yc5OVm4Bmtra7777jtkMhkXLlwgKipK6DtUU1PDzc2NVq1aYWJiUmT/V1EYM2YMo0aNIiMjAzc3N96/f8+oUaPK1H9ZHL755ptyGaesrCz8/PyYOHGi0rrC9+jt27cq5Q5Vxfz58/9jocArV64wbNgwoWI5PT1dpWftwYMHBAUF0ahRI+7fv8/kyZNxc3MT+D3v3r0reJYikUhQnl+5ciX6+vqC4StMmXxB6fhi+MqAESNGKBAb5+bmkp+fT9WqVTEzM/ssVWg+Pj5kZGQwe/ZswTsQi8XMmzdPpbzMo0eP8Pf3p127diU24/78888YGBjg6+urEtVWXl4eaWlpBAUFYWVlxdixY4X8xqBBgxQGs/379+Pt7V2uPFJwcLASs8WgQYOEfJWenh7Dhg0Tcm8aGhoYGhoKA4BIJMLHx4eBAwcSGRkptGt8TmRmZhIcHIyXlxdJSUmMHz+eNm3a4O7ujo6ODs7OzoSGhhITE8PcuXOpUaMGAQEBRQ5MhRqMhdW0tra2REdHs2TJEqysrJBIJPTq1UuoFDUwMCA4OJhZs2Zx+vTpIgfW7OxsQe6mdu3apKWlAR+IB8qr//YxmjZtWqac9sd48uQJrq6uCk3aR44cEcLTjx49YsiQIcTFxVX4PAGSk5P/4zmw7OxsVq1ahZ+fHw4ODsTHx5eYp4MPUaVp06ZhbW3N3r17WbBgAfb29ly9epV169YJIWsNDQ1mzJgBfHh2mjZtikgkwsPD4z9xaf8z+GL4yoDXr19ja2tbJPuHKjpqpeHevXuYm5sLRRTZ2dnMnz8fS0tLjI2NCQ0NLdG4nj59WmgGV6X52dHRkYkTJyIWi5kwYYJC9WMhbt26RWxsLJaWljRu3JiNGzcqNWDfvHkTsVgs5JvCwsJUYu4oCj169GD16tXC9/v37wtFGSKRCA0NDQIDA6lRowbq6uq4ubnRvn17wTuKjo4mIiICOzs7oarzY8q35cuXc/DgQf74449SZ+NyuZx79+5x4MABgQGkcePGSKVS9PT0BPaciIgINm3axKVLlxSKE3bs2IGVlRUzZsxQOJZMJuPYsWNER0fj4OCAq6srY8aMoX///ojFYlasWMHbt2+FUJafn59QFfnq1St27NjB8OHDhQlXIeXXzZs3kcvlpKamEhoayrZt2/Dx8RGehaImFWXFo0ePMDExKfJZURW///47NjY2bN26lXfv3uHq6iqEgfv06cPly5eRSqWfpTE7OTmZ6OjoCu+nvDh37hwDBw7E1NSU9u3bk5GRUeK7+eOPP2JnZ8eYMWNYtWoVFhYWSjnaNm3aCL/PzMxEU1OzXC0b/2Z8MXxlxObNm6lWrZrS4D9nzhw0NDRKndmVhOXLlxcp+5OTk0NycjJ6enqYmpoWmfxfv349YrG4WNmfTyGXy9HR0eHdu3c8ePCAiIgIHB0d2blzJ3l5eWzatIng4GAkEgmjR48uVferbdu2LF68mN9//x0LC4tyD4xjx44lISGB58+fs3fvXkaOHIlUKhU8PmtrayIiIjhx4gQeHh789NNP6OnpIRKJhIKN+Ph4odjoxYsXnD59mrS0NGbMmEGfPn1o0KABtra26OrqUqVKFVq0aEG3bt3o27cvPXr0ICwsDF9fXwwNDQWvOyoqigULFrBv3z7++OMPYfA6ceIEjo6ODB06VDB6hT1blSpVEthI8vPzOXDgAFFRUVhbW+Pl5cXUqVO5/Ekf3aVLl6hVqxb169cXDN+OHTuKvV8PHjxg7dq19OrVC1tbW+zs7IR2GXd3d3bu3Cn8tlq1ahVmJElJSaFHKersquDMmTOIxWK6du0q5ILHjRuHsbExAPXr11fSuCsPkpKS/quGrxCvX79m6dKl+Pj44OzszJw5c4rlri0Uua1Ro4YQ8v64StfKyurvH2dm8ufQoR9kvVq1+kD2nphYJHXfF/yNL4avjJDL5YSEhDB79myldc7OzuUO8cEHgc6Scm65ubmEhoaiqamJr68v+/fv5/3798TGxlK5cmUuXbqk8rGePHmCubm5wrK1a9cKauR16tQpU7/S8ePHcXZ2ZtSoUcTExKh8HvAhgX/u3Dm++uorateujampKUZGRgQFBTF+/Hi2b99O7969cXBwUNjOxMSESpUqIRKJSE5OBmD8+PGoqakVSQf18uVLTpw4wapVq4iNjSU0NJRKlSqhqamJWCzG0dGRypUrY2lpiba2Nra2tjRq1EigfNu0aRNnz55VYuB5+fIl7du3p3r16uzZswdvb286dOjA48ePSU9Pp0+fPlhYWFC7dm1mz55d6iTi1KlTQhhXX1+fPn36qHQf5XI5GzZsQCKR4Ovri4aGBtWqVSM6Opr09HQsLCxUKuQpCXXq1GHv3r0V2kchUlJSUFNTEyIo2dnZqKmpsW/fPtavX18swUBZkJSUxMiRIyu8n88FuVzOyZMn6d27NyYmJnTp0oUjR44UWfBTSLVX+NHU1CQkJARdXd0PvMJt235o9/lUVeavdh/atv3wuy9QwhfDVw4USqV8KkJ6+fJlNDQ0ytRQmpeXx6FDh3jx4gXGxsYqtTCsWbMGY2NjpFIpJiYmeHl5lax8UATOnTuHt7c3+fn5fP/99zRr1gyxWMzw4cOF3r+EhIRieSU/hVwux8/PD0NDw1Llkh48eMCWLVsYM2YMDRo00xQIaQAAIABJREFUwNDQkKpVq9K3b19Bx/DTMGRkZCROTk7C96ysLCEEVBj6GT58OOrq6oJntmDBAqKiomjUqBFWVlYYGhpSq1YtunfvzowZM9iyZQu//vprkcb9/fv33L59mwMHDrBs2TJGjx5N27ZthTybWCymTp06dO3alcmTJ7Nq1Sqh+CAoKIguXboIze/z589XudJvy5YtiMViNm/ezLRp09DR0UFXV5erV6+qtH2XLl2YN28eLi4uZGRkcPLkSWbOnEnDhg0RiUTUq1ePKVOmcOzYsTKHEn/77TckEslnCUHm5+fj7e3NgAEDqFSpEg8ePACgZs2a1K1bl9zcXCwtLcstR1SIefPm/Z8yfB/jxYsXpKSk4OHhgYeHhxChKMSrV69ISEhAX19fICOIiYlBriLBQ2mUiv9mfDF85URCQgKhoaFKM7VOnTqhp6ensjbbr7/+ipqaGlpaWhgZGbFw4UKVjNimTZvQ1NTEzc0NLy8vfHx82Lx5s8rExitXrsTZ2RkbGxsCAwNZt26dQn7q9u3bhIaG4u7urrJAZlRUFGZmZgrL3r17x/Hjx5k3bx4dOnTA3t4eCwsLQkNDiY+P58CBAwoe1PHjxxW4PQvRs2dPXF1dhe+jRo1CJBIhkUiYPn06zs7OQnWbVCqlcePGDB48mEWLFnHgwAHu3bv32aRn5HI5jx8/5vjx46xZs4YRI0ZgYWGhkIvU09OjSZMmCpRvv//+e7FN8HK5nJkzZ2JnZ8eZM2eAD+FAOzs7wSOdMWNGiUbn4cOHmJqasmDBAho3bqyw7sGDB1hZWbF//35iY2Px9fXFyMiIli1bkpyczMWLF0u9P/Hx8WXivSxtXyEhIcjlchISEvDx8eH169ds374ddXV1cnJyGDt2bIWN1ty5c8ulH/mfhFwu58iRI8JkqXfv3pw8eVL4P16+fEmHDh2oXLkyl4cMqTCJ/hd8MXzlRl5eHh4eHko5tYcPH6Kvr0+1atVUChPm5eUJs7nCAo3SSs737t2LRCJh9uzZVK9enV69erF161b8/Pzw8PBg3bp1RQ6w79+/Z9u2bbRo0QIDAwM8PT2LVYWGv3vPHBwc6NatW4nN23K5HC8vL8RiMXFxcQwZMoSaNWuir69PrVq1GDp0KOvWrVNQMigKt2/fVghpPnv2jOPHj+Pn54e5uTmhoaFIpVLhfrVu3Zpq1aqhpqZGUlLSZxcXLQ5Pnz4lNTUVf39/1NTUcHR0ZNmyZTx79oyHDx8SFhaGVCplxIgRREZGEhQUhIODA9ra2jg7O9O8eXMGDx5MUlISmzZtolWrVtSoUUPwfODDs1FYrfrTTz/RokULqlevzrlz54o8p2nTphEZGYm9vb3A+1mIs2fP4uPjo3QNmzZtYuDAgTg7O2NpaUmXLl1ITU0VSukLIZfLcXd3V1BQKC9+/fVXxGKx0AIjl8sZPHgwwcHB5OXlYWBgwJQpU4TISkVIoufOnVvm0Pt/E0+ePGHOnDk4Ozvj4+PD0qVLefPmDXK5nC3jxn2g6CvPpxgS938rvhi+CuDIkSPY2dkpUTGNHTsWR0dHlRtnHR0dBW+ldevWxVYbyuVy5s6di7W1tcDcn5WVRbt27QgICODx48dkZGQQGBiIi4sLqamp5OXlcffuXSZNmoRUKiUgIIC1a9cyduxYpk6dqtL5ZWVlMW7cOMRiMYsWLRLO7+XLl2RkZDB9+nTq1q2Luro6pqam2NrakpSUxE8//aTSoCWTybh9+zZ79uwhMTERdXV16tevj0QiwdjYWCgLt7Ky4uuvvxY8q1atWtGuXTs0NDT44YcfVLqWiuDhw4csWbKExo0bY2xsTLVq1TA2Nmb9+vVF/r4oSZrc3FyuX79Oeno6KSkpREZGCjlNbW1tpFIpgYGB9O7dm/j4eJydndHT02PSpEnI5XLWrFmDpaUl48ePV/DQ8/PzkUqlxMbGEhYWpnQue/bsoXnz5iVe3507d1i5ciURERFIJBJcXFyIiori+++/5/Dhwzg6OlbYay4oKKBu3bpKmnMFBQW0adOGrl270rVrV6RSKQAhISEKVb5lxZw5c/6/MnyFkMlkZGRk0L59e0xNTRkwYAAvg4LKLZRdkmzXvxFfDF8F0bt3b6VwzIsXLzA3N0cqlZZYkVeIwl6cwMDAYnNqOTk5dO/enRo1aijli2QyGZMnT8bR0VGo2jt48CA+Pj7o6elhYGDAoEGDuHz5ssJ5r1y5UuXrfP/+PVu2bMHNzQ1zc3McHR0xNDSkQYMGxMbGUq9ePaZPn87bt28Ri8VFchi+e/eOCxcukJaWxpQpU+jcubNwjnZ2djRp0oRhw4ZhYGDA1q1befjwoTDQtm7dGm9vb0xMTBCJRHTr1g1HR0c0NTWVVCg+J+7cuUNSUhIBAQGYmprSrVs3Vq5cScOGDWnQoEGpbD1FSdIU4tKlS1SqVInJkycjk8koKCjgjz/+4NChQ6xYsYJx48bh7Ows9HDp6Ojg5ORE69atqVKlCjY2NixevJh79+7x3XffERAQgJWVVZEadqtWrSpRDPhTyGQyLl68SFJSEiEhIWhpaWFtbc3YsWPJyMgotxe2YMECGjRoUGRI/t27d9SrV4+BAwciEom4evUqO3fupE6dOuU6FnwwfBVtM/pv48GDBySNHUtOeb29wk8xQs3/RnwxfBXE06dPsbS0VAo/zZo1S2j2Lo2Fol+/fkgkkmJbAO7fv0/t2rXp1KlTiW0C3333HWZmZnTu3FnQE5s0aRItWrRAKpUyf/58YfumTZuWWJ336NEjtm/fzrhx42jUqBGGhoZUqVKFnj170qdPH8RiMZGRkTx//pz79+9jZmbG69evkcvljBgxgrZt2/L1118zcuRIQkJCFIRN27dvz8SJE/n22285c+aMksfs6empUHafmJiIp6enUN4/fPhwpFIpGhoaSgK3nwPXr18nISGBmjVrChyIe/bsITc3l/T0dKysrJg2bZrKDDCF7Q2Ojo6CPt2uXbuQSCTFeovwN2NHYQhcXV2dAQMGCJRvDRs2REtLC319fdTU1ISCp+joaBYtWsSePXu4efMmeXl5zJo1i9jY2HLdD5lMhq2tLatXryYuLo569ephYGBA48aNmTlzJqdOnVLpXhS2upRU1frs2TPc3d2xsLAgPDycgoIC7O3tufTDDx/K9Lt1K1PZfmJi4v/3hg+AxEQlebQyf/T0PvDcfsEXw/c5UKgk/vHLn52djVQqZfDgwTRo0ODvdZmZSi+wPDGRgmLyZydPnsTW1paZM2cWG2YqKChg165dtGrVCmNjYwwMDBgxYoTC78+dO0e7du2wsrJi9uzZuLm5CR5gTk4OP//8M8nJyXTu3JlKlSphZmZGSEgIU6dOZd++fUrqCs+ePaNHjx6YmppStWpV3N3dqVevHubm5hgbG6OhoUGXLl1ITExk586d3Lx5U2V1g2bNmikYZSsrKyGn5+vri5+fH+rq6p9FXgc+hJAvXrxIXFwc1apVw8bGhiFDhnDo0CHhnHNzcxkxYgQODg4qC8R+ikKx2dDQUKytrZXycJ8iNzcXFxcX4dqNjIyUvNvnz58TFhaGhoYGBgYGLF68mLlz5xIVFaVA+WZkZISrqysDBgxgzpw5bNmyhQsXLpTI7lOIH3/8EW9vb4Vlr1+/ZteuXYwYMYJq1aphamoq9HJev35d6VmVy+UEBwczR4WB9+7duxgbG6OpqYns5EmueXiQr6FRrrL9xMRExowZU+ox/8+jW7eKGb3Cz2fowfxfwBfD9xkgk8moX78+S5cuVVj+1Vdf0bRpUxo3bszy/v3L3HdTSGW1c+dOXrx4wZo1axT2X6gWbW9vj5+fH6mpqWRlZXHv3j18fX3p2bOnkorz5cuXCQsLQyQSUbNmTXx9fdHX18fX15dBgwaxZs0abty4IQxcWVlZnD17lvXr1zN58mQ6duwoeF+VKlWiTp06qKurY29vT2pqKo8fP0Yul9O7d2+BWqms6N27N6mpqcAHFYyPqyXV1dXR1dXF29u7QoUWcrmcU6dOERsbi4uLC46OjsTExPDTTz8pheGuX79O9erVadu2bZmImT9FXl4enTp1ElTaVemp+/PPP/m4j6uoUHhkZCSenp4YGBjQt29fpZBqfn4+oaGhxMbGsnjxYkaOHCkUBenp6WFpaYm/vz89evRg6tSpfPvtt/z8889kZmYil8sZMGBAkX2rH+Phw4esW7eO3r17Y2dnh52dHb179+bbb7/l4cOHrFy5kpo1a6o8+Tlz5gwDRSLyNDVLz2uVULY/e/bs/w3D16rV5zF8pehQ/lvwxfB9Jly+fBmJRKJQ+Zifn4+zszNnIiPJFolUfoELFi9m1KhRODs7c+XKFXJycvD19UVNTY2bN2+yZ88ewsPDMTMzIyoqSkFqqBDZ2dl07NiR2rVrs3nzZmbMmEFYWBgSiQQbGxvU1dWpVasWRkZGjB49msuXL3P48GGWLl3KiBEjaNasGQ4ODujq6uLl5UXHjh2Ji4tjw4YNnDt3TqAnW79+PY0bN2bZsmVIJBJGjRrFmzdvuHz5MtbW1uXiL50wYQLx8fEAzJw5k4+beEUiEXXq1EEqlSpVHpaGgoICjhw5wogRI7C3t6dKlSpMmDCBs2fPFulNy+VyVq1ahVgsZunSpRUq7Hj69CkNGjSgdevWvHjxgvHjx2Nra6tSq0iNGjUQiURKhAPwweMzMTHB1NSUK1euMHjwYGxtbZXYfRo1alRkAZBMJuP+/fscOXKEVatWMXHiRCIiIqhVqxZmZmYYGhqioaFBixYtiI2N5euvv+bgwYPcvXu3xCKsGzdusGTJEtq2basQAdi5c6eC7E6x+Oor3pW1kKMI4zd79uxyh3j/T+GLx/dZ8cXwfUaMHTuWrl27Kiw71acP79TVy/Rw5qirM79KFZ4/f45MJiM0NBRdXV3U1dXR09OjZs2aLF++XClMVVBQwOXLl1mxYgX9+vXD09MTLS0tdHR06NmzJxs3buTIkSMsWbIECwsLevfuTfXq1dHW1kZNTQ2pVErXrl2ZN28e6enp/Pbbb6XmburWrcu2bduAD7yBhTP+TZs20axZM8FzKxZFhH6Ph4czpndv7ty5I9A0fUrbpKGhoVJzfX5+PhkZGQwcOBArKyt8fHyYPn16iW0c8KF5uEuXLlSrVk2hKKg8uHr1Ks7OzsTGxircz4yMDGxsbJg8eXKJnlBMTAzu7u54enoqrZs3bx7u7u4KuopHjhzB1dWVzp07Cz2hHh4e5bqODRs2UL169SIp33R0dAQR2WHDhrFgwQJ27drF1atXhUiDXC6ndevW9OvXj4SEBBo3boyBgQH16tXDy8uLefPmKf+Pv/xS9l61j43fR3nfiuQ2/08hMVE5UlTWz5ccn4Avhu8zIisrC0dHRw4cOPBhwS+/IC/nCyz/6wUOCwtTGvwLCYszMzPZuXMnEyZMEErsXV1diYiIIDY2lunTpzN27Fj8/PzQ0NBAS0uLypUr4+fnh729PcuXL+fo0aM8ffqUe/fuMWLECMzMzBg6dKhK2oKnT5/GwcFBadA+evQonp6e1KxZE2dn56I9pRIol95ra5Onrs4eXV1qiUQCQ0thWX9GRgaWlpbFnldOTg47d+6kV69emJubU6dOHRITE7l165ZK/+PJkydxcnIiKiqqQmTMAPv27UMikRQrW/Xo0SOaNGlCYGBgsUVQaWlptKtfn0QLC4UJgmzWLKrb2mJkZKTUY/nu3TtiY2OxsrJi/fr1mJmZlUvYOCIiQqn14ONjXLlyhR07dpCcnMyQIUNo0aIFLi4ugsZg1apVMTU1Zfr06WzcuJEzZ87w8OFDdu3aJRTsaGlp0axZM+bNm8eFCxeQt2lTOitJSVGTj8r2Z82axdixY8t83f/nkJlZccP3papTwBfD95mRnp6Oq6vrhxlv27YVeoELwsMxNjYW1LgLP1WqVKFy5coYGxtTq1YtQkJCCAsLE2bienp6VK9enYiICKZOnUpaWhobNmzA1taWhIQEvvnmm//X3nnHNXW9f/xJCBDCEgggS1BAFAVEUVQUB63WgUrd4kBrRay11lVQ0ToRt61iW0frxJ+7xaqt7bdSrQttrdZVRS2igoCA7JDcz++PlNQYQgIJKnLer1dekDvOPfcmuZ97nvMMjBw5stL+Z2RkYObMmbCyssL48eOrTD8WERGhdu5HIpFgxYoVMDAwwIgRI5Td37VMuSQlQjGPhyXOznB3d8fAgQMBABcuXEDr1q2VjldQUIC9e/di6NChsLS0RHBwMNatW1et4sAymQzLli2Dra2tXhIkf/7557C3t9dYl1Amk2HJkiWwt7dHUlKS8soLF1Dw9tsoIULxC9en3MgIJUS46ump1rkjJSVFEeBfnQKpAPDs2TOt0+i9SHl5OS5dugRra2vMmjVLJeVbRWLxitG7SCRCz5490c7NTa9u+0uXLn0zhA/Q+X7C4vj+gwlfLfDuu+9i+YwZenlCixk/Hi/OcYlEIohEItja2iI4OBgTJkzA6tWrcezYMdy7d09t2rL09HQEBATAz89Po4t3dnY2YmNjYWNjg9GjR+PGjRtK6588eYIGDRpovCmuXbsWdnZ2cHd3x9GjR/8TvWqOfufb2ysqVxw6dAihoaHIzc3F9u3bMWDAAFhYWKBHjx748ssvFSPi6vDo0SO89dZb6NSpU7UF4kUkEgkmTZoEb29vjXlLn+fUqVNo1KgRpk6dKjf//XutNM0NcxpyMt65cwfm5uYQi8X48ssvtZ6r3LFjB/r06aN1/19k5MiRlVZG4DgOrVq1Uvled+3aFYiPh8zYWLffzXMmvaVLlyI6OrrG5/BaoUcTcH2HBwDE0Cvp6em0uWlTmsdxxC8rq3lDJib0TZMmNPbaNaXFjo6OdOXKFbKxsal2k8XFxeTr60symYzOnj1LDRs2rHL7vLw82rBhA61bt466detGc+fOJR8fH1q2bBn9/ffftHXr1ir3l0gk1KRJE4qJiaGf4uIoMSODhDJZ9fvN49GSt9+mMZ9/TjExMXT+/HnKz8+njh07Uu/evSkkJIQsLCwIAHEcRxzHKf3/4vvn/z99+jQtWrSI+vfvT2PGjCEDAwOt9qts3bNnz2jNmjXE5/Np8uTJJBQKq9VGYWEh7dmzh/qmp9PCoiIykkq1vkYSgYC+69KFfvPxUWozMzOTTp48SV27dqXk5GQSCAQUFBREpqamVfbr9OnT5OzsTE5OTkrrZDIZyWQykkqlKv9LpVLiOI7y8/Pp8ePH5OLiothXJpMp2sjKyiKO45T6b29vTxlvvUW0a1e1vx8qjBpFtH07LV26lAoKCiguLk73Nl8HNm4kmjGDqLhY+31EIqKVK4miomqvX3UMJny1xI02baj577/r3M4Zd3da4+9P9+/fp9u3b1N+fj5ZWVlRjx49anST5ziOrl69Snw+n549e0bNmjUjExMTjftJpVLKy8ujp0+fkrGxMZWVlZGdnR0JBAKNxy4pKSGZTEb7OY76chwZ1OA6yIjoMBENIiI+n08CgYDMzc1JIBAQn88nPp9PPB6v0v/VrePxePT48WN6+vQpeXh4kKWlpVb7qVtXVFSkeJjw9/dX9K26/WqUmUlR//d/JHxBGLShTCCg5b17011ra4UQPXjwgG7fvk3t27cnqVRKd+7codTUVHJzcyN7e3vFdlKplMrLy0kqlVJpaSmlpaWRnZ0dyWQyKi8vp/LycpJIJCSVSsnQ0JAMDQ3JyMhI8TI0NCRjY2Pi8Xh0584dcnZ2JnNzcyXhqzhGRkYGlb3wUOjm5kb3WrYkOnKkBt+QF+jblygpiZYuXUqFhYW0dOlS3dt8XagQv5J/jcLq4PGITEyY6FWC4FV34E3Fy8FBL+04mZpS7969FTfFwsJCkkgkZG9vX62b6fPvIyMjacaMGZSZmUmrVq2i6Oho6t69u9r9nv9fIpHQokWL6PDhw+Tp6UkfffQRtW3btsr9nj17Rm/5+lJoaSnxJZIaXQcDIgrl86n43j2KnDuXunXrRmPHjq3xdb19+zYNGzaM2rdvT1u2bKly9AyAysrKqKysjEpLSyv9e+7cOVq8eDENHz6cgoODq9z2+b+VLVv2999kWAPRIyIylEqp+/nzdDkoiIyNjUkoFJKBgQHZ2NhQs2bNSCgUUlBQEBUWFtLevXvp8ePHFBkZSe7u7iQUChX7JCUl0dWrV2nNmjVUXl5OhYWFVFhYSPn5+ZSbm0tPnz6lrKwsxSs7O5uysrIoPT2diouLydjYmMzNzcnW1pZsbW1JLBYr/re1taUdO3bQd999R0RExsbGtH79eho3bhzR6NE1Om8VrKwUn90bR1QUUdu2RHFxREePygWupOS/9SYmckHs3ZsoJoYoIODV9fU1hQlfLcFv0EAv7bj6+el0g6+M/Px86tu3Lzk6OlLv3r1pwIABJJVKac6cOcTj8TTun5OTQ5s3b6bS0lKaPn06NW7cmGJjY6lr166V7m9lZUWrfH1JdvYs8XXoN3g8KtywgW7dukWBgYF0+vTpKgVE3d8bN27Q77//Tp6enlRYWEj9+/evUpgkEgkZGRkpROF5gTA2Nqa8vDxKS0uj1q1bU1ZWFn3//feVbmtpaamy74t/zYqLya9fvxpfJz4RBeXnU9AXXxDZ2hIRKZn7ZDIZ5eTkUHZ2Nr311luUmJhICxcupMDAQGrevDnl5ORQVlYWnT17loRCIfn6+pKhoaGSaD0vYs2bN1daduvWLYqMjKS//vqLGlTxG2jWrBl99913NHLkSFq3bh1ZW1vLV/j6Eh04QFRaWsMrQPIbv48PEcmFT5vvdJ0jIEB+nbKyiL75hujqVaLcXLng+/gQRUQoPn+GKkz4ags9/4D1hUQiodzcXLK3tyciojZt2tD58+dpwIABdP36ddqyZQuZmJio3f/atWt0/fp1GjZsGBkZGdHYsWNp9+7dNHHiRBKLxTR37lx65513VG423cViMqzB3N7zGMtkdDohgS5LJFRUVKRRSCr+t7CwINt/bwL79u2jtLQ0Wrp0KXl5eakVs+f/GhkZEZ+vKkUymYxmzJhBR48epb/++ouaNm2q0/kREdHy5USVHKs6lMtk9NOwYXTYw4OysrLo/PnzJJVKadOmTZSXl0cNGjRQEq/+/fvT+fPn6caNGzRp0iRycXGhixcv0oULF8jR0bHK78PzlJSUUK9evSghIaFK0SMiatmyJWVnZ6uOtCMiiObPr+GZ/wsgb+df3kjhq8DWlmjmzFfdi7pHrbvP1Ff0EHdTyuPhxO7deiugCsirDbi4uKgsLy4uxrBhw9CuXTs8evRI7f5RUVGYN2+eynKpVIrExES0aNECbdq0waFDh5S9S/WYcsnMzKzadfdSUlLg4eGB999/X5F1Rhfy8/PRq1cvhISEqOQx1Qk9Zei47OuLhIQE7Nu3D926dcPy5cuRmZlZZSHcHTt2wM7ODl26dMF7771X7a7PmjULQ4cO1fUK6NVtf+HChZgzZ47ufWK8UTDhq010+AFzPB7S2raFr68vfH19sXv3bq3zHFaFugrngPzmt2jRIri4uODSpUsq6/Py8tCgQQOlYqkvIpPJcPDgQbRu3Ro+Pj7Ys2ePPFuJnm7oZz09YWxsrLHixfP9WbFiBWxtbbF3717tLpIG7t69ixYtWmDixIlVVkSvEbWQkzE4OBj/+9//tDp8RkYGLCws4OzsXK1k3CkpKbC3t9cq/6hG9Oi2v3DhQsydO1f3PjHeKJjw1SZ6+AFzHIfvv/8enTp1QpMmTbBx40aVxNPVYc+ePRg0aFCV2+zfvx9isRj79u1TWr527Vqtn+gr+t2+fXt4eXnh4pAhOpdVKTMwwHfBwTA1NYWNjQ1cXV0RHh6OhIQEXLlyRSV+MSMjAz179kSHDh1w7969al0ndZw6dQoNGzbEZ599pteRuIJayMno5eWF69eva3X4K1euwMXFBfv374ejoyMmT56sUjbqRcrKyuDj44OdO3fqdOpK1CDes7JYxgULFjDhY6jAhK+20dMPGJDfdCtK2ixbtky7ZL8vsGrVKnz00Ucat/v999/h4uKCBQsWgOM4yGQyeHp6VrskD8dx+OmnnzCgY0eU1NR89e+rXCDAyX9NdxzH4ebNm9i8eTMiIiLg6emJBg0aoFevXli8eLGiUv2cOXP0MlIG5NUybG1tq6xjqDO1kJPR0tJS66oS0dHRityWT58+RUREBFxdXfHDDz+o3WfhwoXo3bu3/h8EtMzwU1V1hgULFiA2Nla//WLUeZjwvQz+/QHLNN2wNGTgqODPP/9EeHg4bGxsEBMTUy3z0scff4wVK1Zote2jR48QGBiIoUOH4vDhw2jVqpVuFQo6d9Z8DdS8pES427o1vv76a4xSk2E+IyMDe/fuRUBAAAwNDSEUChEYGIjp06fj4MGDNTbDyWQyfPLJJ2jSpInG5NY6o+ecjCUlJTAyMtLqc5PJZHB1dVWp4H78+HG4uroiIiJCZT7z2rVrEIvF1UoNVy1SUuRzdkKhXNBfFHihUL5eTVaSTz/9lAkfQwXd3McY2hEVRZScTOjfn0p5PJIaGSmt5oyNiYRCorAwouRkjcGmvr6+tHPnTrpw4QLl5eVRs2bNaPLkyXT//n2NXXn48CE5OTlp1W0HBwc6efIkGRgY0JgxY2jkyJE6echZr1hBkhp6LJYS0Y3+/avsf2FhIa1YsYIaNmxIjx49opycHIqPjycrKyvatGkTNW3alJo2bUrjxo2jLVu20K1btwhAlcctLCykgQMH0pkzZ+j8+fPk7e1do/5rjZ0dUa9e8tismsDjyeO3/vVizczMJDs7O60+t7Nnz5KpqSn5+voqLe/ZsyddvXqVzMzMqGXLlnTo0CEiknu1jhs3jhYtWkQuLi41668mKtz209KIFiwgGjWKjgsElNKsmfx9Wpq6y2wPAAAgAElEQVR8fRWxam+0VyejZrxq5a1vnNy3D0usrCAZNgzo2xc327XDV56eOmVNz8jIQHR0NKytrREeHl5l+ZmOHTtqTJr8Irdv34ZIJIKTkxNSdMj3N2PGDEwxMkKZoWG1Tb8fGBhg3759iIqKwueff67S9s6dOyEWi6uce5NKpfjzzz+RkJCAESNGwNXVFWKxGP3798fy5ctx5swZpfqBaWlp8PPzw9ixY7UqgaQ39Ojccf78eQQEBGh12EmTJmksHnzq1Ck0bdoUgwcPxsKFC9GlSxe1uWFri6ioKIjFYq22nT9/fqVeyIz6DRO+V8CIESMUGePLysrQpEkTrb3uqiIvLw9xcXFo2LAh+vbtW2mFcldX12olTgaAadOmYebMmTh48CDEYjH+7//+r9p92759O0QiEYYMGQIuIQFlhoYazZ5SIkiFQiAhAUZGRkhKSkK/fv1w8OBBRbsFBQUYPXo0vLy8Ki3Iq4kHDx5gz549mDx5Mvz9/WFqaorOnTsrShpVzHG+dPQ0N/ztt99qlWhaIpHA1tZWq+9GcXExIiMjwePxsGLFipd+fXJycsDj8XDu3DmN286fPx/z58+v/U4x6hRM+F4Bjx8/hlgsVozMdu3ahcDAQL3dQIqLi5GQkIDGjRujc+fOOHr0qMJBxcjIqFpeoYWFhbCxscHdu3cBAH/88QcaNWqE+fPna/2kf+7cOYhEIrRu3Voxosr54Qd8a2gIzthYZe6miAgSAwP8amuL5FWrAAACgQAnTpxAmzZtcP78eQDApUuX4OnpiXHjxuklNg+Qx+fFxMTAxMQEvr6+MDMzQ4sWLRAZGYnt27fj7t27L+9Grwfnjq+++kqrmLyjR4+qDXN5EY7jEBISgo8++gi+vr7o3bt37c3xqcHb2xshISEat5s3bx4TPoYKTPheEQkJCejUqRNkMhlkMhl8fX1x+PBhvR6jvLwcu3btgo+PD/z8/PDFF1/AxsamWm189dVX6Nevn9Kyx48fo3379hg8eLDGQq1paWlo0KABGjZsqFLCaMKECVg2fbrcA3HUKHns2ahRyImJwayxY2FkZITu3bsjPT0dBgYGilCCBw8eYPXq1bC1tUViYmK1zqcqZDIZ5s2bB1dXV/z5558A5COhlJQUrF27FoMGDYKDgwMcHBwwePBgrF27FhcvXtSb12il6OjcsXDhQsyePVvjYUaOHInPPvtMqy5t3rwZAQEBKC8vR1lZGRYtWgSxWIyNGze+NLPnN998A4FAoPHaz5s3D59++ulL6ROj7sCE7xUhk8kQGBiILVu2AJAXsPX29pYHe+sZjuNw5MgR+Pn5wcjICF988YVWoz6O4+Dj44Mff/xRZV1JSQlGjhyJNm3aID09vdL9CwsL4eHhATMzM9y8eVNl/c2bN2Fra6tWPKdNm4bAwEBYWVmBx+Ph4MGDMDQ0RK9evRAYGFhtk21VFBUVYciQIejQoUOV9fw4jkNqaiq2b9+OCRMmoEWLFjA3N0dISAjmzZuHH3/8UWPcW4148kTxgPCHiwtutmsnf69hbviDDz7QKGiFhYWwtLTUqo7hw4cPYWtriytXrigtv3btGgIDA9GlSxetK93rgkwmg7GxMdasWVPldrGxsUz4GCow4XuF/PHHH7Czs0NWVhY4jkOnTp3wzTff1Nrxvv32W3To0AF9+vSBg4MD4uPjq4wFTE5OhpeXl9qneI7jEBcXBycnJ1x4oQK4TCZDjx49IBQK8dNPP6k9RmhoKDZu3Fjpui+//BLjx49HZmYmiAgmJibg8XiIjIzUa8aUhw8fIiAgAOHh4TVKDpCTk4MjR44gOjoanTt3hqmpKfz9/TF58mTs2bNH6ywz2pKQkIBx48Zpte3AgQM1zskmJiaiZ8+eGtviOA79+vVT6ywilUqxevVq2NjYYOXKlbXyEPc8/fv3R5MmTarcJjY2FgsWLKjVfjDqHkz4XjEff/wxIiIiAMg95lxdXZU8C/XJhg0bEBkZCUAeCzhixAjY2Nhg9uzZlca4DR48uFIPyhc5fPgwxGKxktlx+vTpEAqFSNAQk5icnAxPT89KxTUpKQm9e/eGRCIBEcHCwgI2NjYQi8UIDw/XS0zdpUuX4OzsjMWLF+tt7q60tBRnzpzB8uXL0b9/f62yzFSHlJQU+Pr6arVtp06dkJycXOU2oaGh2LZtm8a29uzZA29vb43fzzt37qBbt25o165dlR7GunL9+nUQEf755x+128ydO5cJH0MFJnyvmGfPnsHZ2RknT54EAPTp0wfr1q2rlWPNnj0bCxcuVFqWmpqKiRMnwsrKCh988IEitdeDBw9gZWWldXaYy5cvw9XVFXPnzsU333wDoVCIqKgojftxHIeAgIBK5zcvXboEb29vtGvXDkSEVatWISwsDPn5+YiLi4OdnR0GDhxYI29OADhw4ADEYjH2799fo/21RVOWmZMnT2qcK32esrIyiEQirfbx8PCo1MxcQXZ2NiwsLDR+zllZWWjYsKFWnpSA/Jy/+uoriMVifPrpp7UWDuLo6Ijw8HC16+fMmaPynWcwmPC9Bhw4cADNmzdHWVkZLl++DHt7+1qZJxo9ejS2bt1a6brHjx/jk08+gbW1NUaOHIkJEyZg8uTJ1Wo/IyMDLVu2BJ/PR3BwsNamrj179qBTp04qyxMSEsDj8bBmzRoQEeLi4pT6VFhYiNWrV8PR0RGhoaEKb09NcByHJUuWwNnZGRcvXtTu5PRMRkYGDh48qJjHFIlECAwMxLRp07TKMtO2bVucPn1a43HMzc2rrGTx5ZdfYvDgwRrbCQ8Px8cff6xxuxd58OAB+vTpAx8fH51iQNUxf/58mJqaql0/Z84cLFq0SO/HZdRtmPC9BnAchz59+mDp0qUA5HF+tfGUGhISUmXORQDIzc3FwoULwefz0a1bt0pjAdWRlpYGc3NzmJmZwdfXV+u5rfLycri6uipGE4WFhRg7diw8PDwgEAhQVlYGIsK0adMQFxensn9JSQk2bNiARo0a4e23364yQP95p5yqqky8bIqKinDy5EksXrwYvXr1gqWlJTw9PREREYHNmzfjxo0bSqbYSZMmaXTsKCoqgrGxcZUm3C5duuDQoUNVtnPkyBE0adKkxiEjHMdh165dsLe3x8yZM1FcXFyjdiqjpKQEfD5frUf07NmzmfAxVGDC95pw79492NjYIDU1FXfu3IGNjY2K+7+ueHl5aTUvtmPHDnTr1g0bNmyAm5sbgoODFbGA6igsLETjxo1haWmJBw8eID4+Ho6OjlqbxtasWYPBgwfjjz/+gJeXF8aMGYOCggI4OjoiNTUVRIQRI0Zg+/btatsoKyvD5s2b4e7ujuDgYJw4cUKpzxkZGejQoQMGDRpULdPiq0Amk+HKlStISEhAeHi4IstMv379sHz5csyZM0djpYy7d++iUaNGatenpaXBysqqyjm7/Px8uLi44Oeff67xuVSQmZmJoUOHwtPTU+O8Y3Vo37492rRpU+m62bNna8xGw6h/MOF7jVi2bBl69eoFjuMQFRWF6dOn661tjuNgamqqVQHXwMBAfPvttwDko7GdO3eiZcuW8PPzQ2JiokrslEwmQ/fu3WFsbKxkOvz2228hFouxa9cujcfMz8+HSCSClZWVUnkbPz8/fP755yAitG/fHsePH9fYVnl5OXbs2IFmzZohMDAQR44cUcxBxsbGvvQUW/qiIsvMhx9+iGbNmoHH46Fz586Ijo7GkSNHVBJInzlzBu3atVPb3ooVKzQGt0+cOBHvv/++XvpfweHDh+Hk5IRJkybpxaR//Phx8Hi8Sh9mYmJimPAxVGDC9xohkUjQokUL7Nu3D48ePYK1tbXeXOHz8vJgZmam0XPxwoULcHV1VZmf4zgOSUlJ6NixI9zd3ZViAadMmQJDQ0OV+n2AvL6bm5sb5syZo1Zwnjx5oii3NGbMGKV1BgYGICLFi8fjoUWLFlqZ3aRSKfbu3Qs3NzcIBAJMnTq1zorei0ilUpiZmeHgwYOYN28eQkJCFFlmJkyYgO3bt2Pjxo0IDQ1V20arVq2qTJV38uRJODs7V7vavTbk5uZi3LhxaNSokV7KPFlYWCA6OlpleUxMDJYsWaJz+4w3CyZ8rxm//vornJyckJ+fj+joaIwfP14v7V67dg1eXl4atxs9ejTi4+M19rF3796KDCaGhoZVpoXKzMxEUFAQwsLCUFBQoLTu559/hpOTE2bNmoXU1FRYWVkpjVyaN28OHo+nJH6dOnXSKvSA4zisXLkSDg4OWL58OQICAuDt7Y1du3bVeozZy6Bz5844ceKE4n15eTkuXryItWvXYvDgwbCwsICJiQkGDRqkkmXm2rVrcHR0VHsdioqK4OHhge+++65Wz+HHH3+Em5sbxowZo3XNwMoYO3Ys7O3tVZZHR0cz4WOowITvNWTcuHH46KOP8PTpU4jF4ird0bXlhx9+0Jjb8MmTJ2jQoAGys7O1anPbtm3g8/kwNDRUGwtYQWlpKSIiIuDn54d//vkHEokEs2fPhoODg5LDzahRo5QcWKZPn6406jMxMdGqmnpZWRnGjRunOB4gF8Ljx48jKCgInp6e2Lp1q14D4V8206ZNUzhEVcb8+fMxefJkbN++HZGRkUpZZjp37ox3331XbRjDzJkzMWzYsNrquhIFBQWYMmUKHBwcahxa8vjxY/B4PJw5cwaJiYmKbELR0dFVXiNG/YQJ32tIVlYW7O3tcenSJcTFxWnlbq6JrVu3YvTo0VVus2TJEq0zgqSnp8PMzAw+Pj64ceNGpbGAL1IxArOzs0PLli3Rs2dPlTRZly9fhqOjoyLua/PmzXB0dFQIn7oiuiUlJZg6dSqePXuGrKwsBAcHo1+/fiojzIp+/PLLLwgJCYGbmxs2btxYa0kDapPExESEhYWpXT9x4kSsX79eaVlOTg6SkpLQoEEDRTWKVq1aKWWZuXDhAuzt7fFEh1JZNeH06dPw8vLCwIED8fjxY6334zgO586dg7m5Ofh8PgwMDLBnzx4AwCeffMKEj6ECK0T7GiIWi2nZsmU0ceJE+uCDD+j06dN06dIlndpMT08nZ2dnteulUilt3LiRJk+erLGt4uJi6tixIwmFQkpOTqZmzZrRxo0b6dq1a2RmZkZt2rShUaNG0V9//aW0H4/Ho0aNGlFpaSndu3ePRowYQfb29krb+Pn5kbe3NyUmJhKRvBhuw4YNicfjkZ2dHX388ceV9ikxMZE+++wz6tGjBwUGBlL79u3p0KFDZGZmprItj8ejrl270k8//US7d++mpKQkcnd3p3Xr1lFxcbHG839daNu2LaWkpKhdn5mZqXJ9ra2tydbWluzs7OjSpUuUk5NDCQkJ5OrqSomJidSqVSsKCgoid3d32r9/P125coVkMlltnwoREQUFBdHly5epadOm5OfnR9u3b9dYKJiI6OLFi9S+fXsqKCggjuPIzMyMHBwcFOtZIVqGCq9aeRmVw3EcgoODsX79eiQkJKBHjx46tRcZGYkNGzaoXb9//34EBQVpbEcmk6Fz584QCoVqkxHn5uZi6dKlsLe3R2hoKM6cOYPCwkKMHz8eHh4eSElJwdWrV9G4cWNER0erOJwcO3YMPj4+4DIykP7RR/jW3Bw/iURAeDgQH6+SmJnjOLi7uytGhW+99ZYWV0SZixcvIiwsDA0bNkR8fHztJJrWMxzHwdraWu3oqGPHjjh16pTK8ilTpqhN3LxgwQJ06dIFmzZtwtixY+Hp6QlLS0u88847WLx4MX755ZeXEgpy6dIltGrVCu+8806VKckqWLJkCUxMTEBEMDY2xt9//w0AmDVrVqWxn4z6DRO+15hr165BLBbj/v37Oher7dOnjyJEoTK6du2qVYmfqKgoRW08TRQXF2PDhg1wdHSEiYkJunfvjvz8fBQUFKBp06ZISkpC586d0b9/fyWTJHf+PE6Ym0NqaAiZUFh5KZ6wMHmlcsjzfRoZGSk5wBw4cECLq6LKlStXMGzYMNja2mLhwoXIzc2tUTsvix49eqh1QGnSpIlCACooLy+Hvb09bt26pbL9X3/9BbFYrOJJnJmZiYMHD2L69OmKLDPt2rXDtGnTcODAAa2qOtQEiUSCJUuWQCwWY8OGDRo9cjdv3gw+nw8iUnyfZs2ahWXLltVK/xh1FyZ8rzkxMTEYNmyYzsVqW7VqpTY919WrV+Hg4KAxn+JXX30FgUCgdd02juOwfv162NjYYOLEiWjZsiVatWqFkSNHwtDQEFZWVrh//z7GjRsHX19f3L9/X1F8VVaN4qteXl4gIhgaGsLX11fhIaoLN2/exJgxY2BjY4M5c+boPZmAvpgzZw5iY2MrXWdqaqoycv3xxx8REBCgsq1UKkVgYCC++OILjcesyDKzZMkSRZYZDw8PREREYNOmTSpZZnTl+vXr6NChA4KDg1WE/EUq0ttVCN/MmTOZ8DFUYML3mlNUVAQ3NzccO3YMvr6+GtNLqUMsFqt9Mo+MjNRYs+z06dMQCARah1dkZ2ejX79+aNOmjeJmJZPJcPDgQQgEAhAR+Hw+fH19UVpailWrVmGWhQWkL47wNL1EIpwcNgzHjh2rFQeV1NRUTJgwAVZWVpgxY0a1nC5eBocPH0avXr1UlhcUFMDExERFgMaMGYPVq1erbL969Wp07dq1RnGOlWWZsbGxQb9+/RAfH4/ffvtN589GKpVi7dq1sLGxwfLly6ssQBsaGorEdeuA+HhcbN4ct5s3V2smZ9RPmPDVAb7//nu4u7vjwIEDNSpWW1JSAiMjo0pvarm5uWjQoAEePXqkdv8HDx5AJBIhKChIqxtjReDz9OnTVUaRW7ZsUQhfxat79+7AhQsoNzaunug9J37qKpDri7S0NHz44YewsrLChx9+iLS0tFo9nrY8fPgQYrFYReDu3LmDxo0bKy0rLi6u9LNOTU2FjY2NXgvIPp9lxt/fHyKRCJ06dcInn3yCpKSkGsfspaamIiQkBAEBASrFcAEAFy7gYWAgSnk8uUlcg5mcUT9hwldHGDRoEGJjY2tUrDY1NRWurq6VrluzZg2GDx+udt+ioiI4OjrC2dlZY3Lh8vJyxMbGomHDhjh69Gil2xw6dAiDBw/GqFGj8P7772P48OGYNGmS/GakybxZldnz3Xe1vh668PjxY8yYMQNWVlaYMGGCXqvA1xRHR0eVEJLTp0+jffv2Ssv27dunEsvJcRy6d++OlStX1mofnz17hh9//BHz589HSEgIzM3N4e3tjffffx/btm1Damqq1uZRjuOwadMmiMVizJs377+Hq3/N5Fw1zOSM+gkTvjpCenq6Iu9ldYvV/vrrr+jYsaPKcplMBg8PD7UVGDiOQ2BgIExNTTWa+O7fv4+goCC8/fbb1TcHZmaqPp1X9yUUvlQzVlZWFubMmQMbGxuMGTNGL0kGakr//v2xd+9epWUHDhzAgAEDlJaFhYVhy5YtSss2bdqEtm3bVmk6rA0qssysW7cOgwcPhoODAxo2bIhBgwZhzZo1SElJUUkusGjRIvTt21cxf5eeno7Q0FC0aNECd2fNkotZdS0FTPzqJUz46hBr165Ft27dql2sdvfu3RgyZIjK8qNHj8Lf31/tk/Z7770HgUCgsY7a/v37YWtri/j4+JrlwoyP1134TEyA5curf2wdqSjjZGtri2HDhlVufqtlFi9ejJkzZyot27BhAyIjIxXvc3NzYWFhoeSlWvEwVZtV0rWF4zjcvXtXKcuMmZkZunfvjtjYWPzwww/w9vaGQCCAh4eHUjaeHxYvRlFNvzcvwUzOeP1gwleHKC8vh7+/PxYvXlytYrUrVqyotIhor1691BamXb9+Pfh8fpWVFYqKihAZGYkmTZpoXQS2UsLDdRO9iteoUTXvg448e/YM8fHxsLe3x4ABA15qgdvjx4+ja9euSsvmzZunlD91y5YtSlleOI5DaGholTlWXzU5OTk4cuQIYmJiEBQUpJgT5vF4EIlESEpKkm8YFqbZvPkamMkZrw9M+OoY58+fV5iEtC1WO3XqVKxatUpp2e3btyEWiyudtzt58iQMDAwwa9YstW1evXoVLVq0wPDhw9Xme9Savn31I3x9++rWDz1QVFSEdevWwcnJCb1798aZM2dq/ZjZ2dmwsLBQGm1PmDABCc+Z8UJCQpSqZ+zZswctWrTQGMLyuvDbb79BKBSCiGBkZAQejwdbW9s6aSZnvHpYyrI6Rrt27ejdd98lgUBA69ato+zsbI37pKenk5OTk9KyhIQEeu+998jExERpeVpaGvXs2ZN69OhB8fHxKm0BoI0bN1K3bt1oxowZtGvXLrKwsNDtpCwtddu/Aisr/bSjAyKRiKZMmUKpqanUr18/Gj58OIWEhNDJkycJQK0c08bGhsRiMd26dUuxLDMzk/h8Po0ZM4Z27NhBFy9epD59+hARUXZ2Nk2dOpW2bt1KRkZGtdInffP06VOys7OjkSNH0pYtW+jevXv05MkTom++0b1xHk8/7TDqDq9aeRnVJy8vD46OjggLC9OqWG2HDh1w+vRpxfuCggJYW1ureAIWFRXBzs4Onp6elTo75OTkICwsDP7+/npz5pBIJLgxdixKDQzq5ByfJiQSCb7++mt4enqiU6dOOH78uF6DuysYOnQotm3bpngfGBiIrVu3QiAQwNjYGAKBAGPHjsU///yD8PBwTJs2Te99eCW8AWZyxsuHjfjqIJaWlrRq1Sq6efMmff3115Senl7l9hUjvry8PAJAu3btos6dO5Obm5tiGwDUqVMnKisro5SUFBIIBEptnDp1ivz9/cnV1ZXOnj1LXl5eNe4/x3H022+/0QcffEBOTk407coVMtA1kTBAFBGhWxu1gKGhIUVERNCNGzdo0qRJNG3aNAoMDKTvvvtOryPAFxNWZ2Zmkp+fH/H5fCorKyOpVErbtm2jnTt30rlz52jRokV6O/YrJT9fP+3k5uqnHUbd4BULL6OGcByHt99+G927d68ym4pUKoWhoSHKysrg5uYGV1dXODg4/OcY8C/h4eEwNDRUGclJpVJ8+umnsLe3x5EjR3Tq89WrVxETEwNXV1c0b94cixcv/i8Oro7E8emKTCbDgQMH4O/vD19fX+zdu1cvRXGTk5MRGBgIQP7dEAqFKCgogLGxMYjkdQx37NgBFxcXnXK+vnawER+jBjDhq8P8/fffsLKygrW1tVrT46NHj2BnZwcAcHNzU3jFCYVCtG7dGgUFBVi5ciX4fL6KsKWlpSE4OBjdu3fHw4cPa9THe/fuIS4uDj4+PnBxccGsWbNw+fJlVXPfhQvVj8OqeNVBl3SO43DkyBEEBgaiWbNm2LFjh06xdAUFBRCJRCgrK0N+fj7MzMwAAJaWlorPNjIyEhMmTNDXKbwe1OFQGMargwlfHWfBggXw9vZWW6w2JSUFrVu3BgD4+fmBSO4SXpE2zNjYGDweT6V0y6FDh2BnZ4elS5dWe0Ty5MkTbNiwAUFBQRCLxZg4cSJ+/fVXzTF+/2beqLbo1eEgZI7jcOLECQQHB6NJkybYtGlTjT0tO3t54cGUKcgPDVWUcdrWsiV2rV2LX375Bc7OzsjLy9PzGbximFcnowYw4avjlJaWwsPDA1ZWVpXGjh06dAihoaEA5E4uRARTU1O0bt1aIYJEpMjKX1xcjEmTJqFx48Y4e/as1v149uwZtm/frsjWP2LECHz//fcq2Tc0UiF+9TDtVHJyMnr06IFGjRph/fr1KCkpAQB8/vnniI6OVr/jhQtAWBjKDAxQbmioMprhjI3xg6kpkl8IaXljqCdmcob+YML3BvDzzz/D2tpaJQ8jIA9Ej4qKAgD4+PhAJBLh+vXrMDc3VxI+IsKvv/6Kli1bYsiQIVrVoSstLcXhw4cxZMgQWFhYoG/fvkhMTERhYaFuJ5SSIr8ZCYVyM9SLZimhUL6+jpk3teXcuXMIDQ2Fg4MDli5dCjMzMwiFQqU4PAVaPijI3oDRsVrqmZmcoTtM+N4Qhg8fDktLS7njQmamfO4jPBzX3d1xxc8PiI/H5RMn8M8//6C4uFhJ8Hg8Hng8HgwNDbFp06Yq3e2lUin+97//Yfz48bC2tkZwcDC++OILZGdn6/+knjyRz72MGiUPTh81Sv6+npil/vjjD/j4+Cg+J5FIpFyPrh6ahtXCrgWjGjDhe0PIyMhAF5EIJ8zNwQmFVZZkSTtwADweDwMHDkTTpk0VN1ahUFhp0U6O43Dp0iVMnz4dTk5OaNWqFZYvX/7alOZ5UykuLoaFhYXSQ4qVlZU8Uw4b5ahSj83kjOrBA4CXGD3BqC02bqTyqVOJL5GQQVXb8XhEJiZEK1fSl3w+TZw4kYyMjEggEFBpaSnxeDzKy8sjMzMzun37Nu3evZt2795NUqmURowYQcOHDydvb++XdVb1mszMTBo6dChJJBISCARUVlZGWVlZlJSURC1iY4kOH5bfyqsLj0cUFkZ04ID+O/2quXiRKC6O6OhR+XmWlPy3zsREfr169yaKiSEKCHh1/WS8UpjwvQls3Eg0YwZRcbH2+4hElDJ0KG3gOAoLC6M///yTli1bRgBo4MCBdOvWLXrw4AENHTqURowYQe3atSOerkHmDP3w5AmRqytRaWnN2xAKidLSiGxt9dev14msLHkasqtX5cHpVlZEPj7yJAdv6jkztIYJX10nJYWoa9fqiV4FIhFRcjIlXLhAU6dOpfLyciIisre3px07dlC3bt1UMrgwXgOWLyeaP1834TMxIVqwgGjmTP31i8GoI7C7Wl0nLk7ZnFMdSkqoaO5c+uCHH4jP55OJiQlJJBLKysoif39/JnqvK1eu6CZ6RPLvzNWr+ukPg1HHYHe2usyTJ0THjtVsnoeICCDT5GTKuHqV7hUW0v379+nevXt0//59YoaA1xiWn5LB0AkmfHUZPZVksT92jOxnzqT27cTK3QEAAAG2SURBVNvr3h6j9nmDyjgxGK8CVp2hLsNMXvUTX1+5c4oumJjInT0YjHoIE766DDN51U/0UX4JeC3LODEYLwMmfHUZZvKqn9jZEfXqJY9Tqwk8njyWjbn1M+opTPjqMszkVX+JiZF/djXBxES+P4NRT2HCV5dhJq/6S9u2RCtXymMxq4NIJN+PZS1h1GOY8NVlmMmrfhMV9Z/4afoO8Hj/iV5U1MvpH4PxmsIyt9R19JC5hT3913FYfkoGo1ow4XsTqGGuTvb0/4bB8lMyGFrBhO9NoUL8SkqqzuTyXHUGJnoMBqM+woTvTYKZvBgMBkMjTPjeRJjJi8FgMNTChI/BYDAY9QoWzsBgMBiMegUTPgaDwWDUK5jwMRgMBqNewYSPwWAwGPUKJnwMBoPBqFcw4WMwGAxGvYIJH4PBYDDqFUz4GAwGg1GvYMLHYDAYjHoFEz4Gg8Fg1CuY8DEYDAajXsGEj8FgMBj1CiZ8DAaDwahXMOFjMBgMRr2CCR+DwWAw6hVM+BgMBoNRr2DCx2AwGIx6BRM+BoPBYNQrmPAxGAwGo17BhI/BYDAY9QomfAwGg8GoV/w/C5Zbh+LrWHMAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw_spring(network, nodelist = participants, edgelist=influencers)\n", - "plt.title('Participants Social Network')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'type': 'proposal',\n", - " 'conviction': 0,\n", - " 'status': 'candidate',\n", - " 'age': 0,\n", - " 'funds_requested': 1671.6565260937566,\n", - " 'trigger': inf}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#lets look at proposals\n", - "network.nodes[proposals[0]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Proposals initially start without any conviction, and with the status of a candidate. If the proposal's amount of conviction is greater than it's trigger, then the proposal moves to active and it's funds requested are granted. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All initial proposal start with 0 conviction and state 'candidate'we can simply examine the amounts of funds requested" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "funds_array = np.array([ network.nodes[i]['funds_requested'] for i in proposals])\n", - "conviction_required = np.array([trigger_threshold(r, initial_funds, supply, alpha) for r in funds_array])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Amount of Honey requested(as a Fraction of Funds available)')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar( proposals, funds_array/initial_funds)\n", - "plt.title('Bar chart of Proposals Funds Requested')\n", - "plt.xlabel('Proposal identifier')\n", - "plt.ylabel('Amount of Honey requested(as a Fraction of Funds available)')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Amount of Conviction')" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar( proposals, conviction_required)\n", - "plt.title('Bar chart of Proposals Conviction Required')\n", - "plt.xlabel('Proposal identifier')\n", - "plt.ylabel('Amount of Conviction')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Conviction is a concept that arises in the edges between participants and proposals in the initial conditions there are no votes yet so we can look at that later however, the voting choices are driven by underlying affinities which we can see now." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 55.73999999999998, 'Participant_id')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "m = len(proposals)\n", - "n = len(participants)\n", - "\n", - "affinities = np.empty((n,m))\n", - "for i_ind in range(n):\n", - " for j_ind in range(m):\n", - " i = participants[i_ind]\n", - " j = proposals[j_ind]\n", - " affinities[i_ind][j_ind] = network.edges[(i,j)]['affinity']\n", - "\n", - "dims = (20, 5)\n", - "fig, ax = plt.subplots(figsize=dims)\n", - "\n", - "sns.heatmap(affinities.T,\n", - " xticklabels=participants,\n", - " yticklabels=proposals,\n", - " square=True,\n", - " cbar=True,\n", - " cmap = plt.cm.RdYlGn,\n", - " ax=ax)\n", - "\n", - "plt.title('Affinities between participants and proposals')\n", - "plt.ylabel('Proposal_id')\n", - "plt.xlabel('Participant_id')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "Now we will create the final system configuration, append the genesis states we created, and run our simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "from cadCAD.configuration import Experiment\n", - "\n", - "# Create configuration\n", - "exp = Experiment()\n", - "\n", - "exp.append_configs(\n", - " sim_configs=sim_config,\n", - " initial_state=genesis_states,\n", - " seeds=seeds,\n", - " partial_state_update_blocks=partial_state_update_blocks\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " ___________ ____\n", - " ________ __ ___/ / ____/ | / __ \\\n", - " / ___/ __` / __ / / / /| | / / / /\n", - "/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n", - "\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\n", - "by cadCAD\n", - "\n", - "Execution Mode: local_proc\n", - "Configuration Count: 3\n", - "Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (60, 1, 1, 5)\n", - "Execution Method: local_simulations\n", - "SimIDs : [0, 1, 2]\n", - "SubsetIDs: [0, 0, 0]\n", - "Ns : [0, 0, 0]\n", - "ExpIDs : [0, 0, 0]\n", - "Total execution time: 86.42s\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from model.model.conviction_helper_functions import *\n", - "from model import run\n", - "from cadCAD import configs\n", - "pd.options.display.float_format = '{:.2f}'.format\n", - "\n", - "%matplotlib inline\n", - "\n", - "rdf = run.run(configs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the simulation has run successfully, we perform some postprocessing to extract node and edge values from the network object and add as columns to the pandas dataframe. For the rdf, we take only the values at the last substep of each timestep in the simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "df= run.postprocessing(rdf,0)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
networkfundssentimenteffective_supplyfunds_arrivalsimulationsubsetrunsubsteptimestep...funds_requestedshare_of_funds_requestedshare_of_funds_requested_alltriggersconviction_share_of_triggerageage_allconviction_alltriggers_allconviction_share_of_trigger_all
4(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4867.500.6022392.220.2900141...[1671.6565260937566, 1944.3791991826024, 2177....[0.1131283127104601, 0.4776341120726794][0.3434325991383445, 0.399461965817948, 0.4473...[59352.21276113208, inf][0.006034361745133491, 0.0][1, 1][1, 1, 1, 1, 1, 1, 1][0.0, 0.0, 0.0, 358.15272217479924, 323.890220...[inf, inf, inf, 59352.21276113208, inf, inf, 1...[0.0, 0.0, 0.0, 0.006034361745133491, 0.0, 0.0...
8(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4867.600.6022392.220.1000142...[1671.6565260937566, 1944.3791991826024, 2177....[0.11312598437087308, 0.47762428169171994][0.3434255308130342, 0.39945374432956693, 0.44...[59343.15532804561, inf][0.011316155171822826, 0.0][2, 2][2, 2, 2, 2, 2, 2, 2, 1][0.0, 0.0, 0.0, 671.5363540777486, 607.2941638...[nan, nan, nan, 59343.15532804561, inf, nan, n...[nan, nan, nan, 0.011316155171822826, 0.0, nan...
12(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4868.170.6022392.220.5700143...[1671.6565260937566, 1944.3791991826024, 2177....[0.11311263200004616, 0.4775679072295611][0.34338499596098976, 0.3994065964125596, 0.44...[59339.97441911249, inf][0.015937772829376525, 0.0][3, 3][3, 3, 3, 3, 3, 3, 3, 2, 1][0.0, 0.0, 0.0, 945.7470319928292, 855.2726140...[nan, nan, nan, 59339.97441911249, inf, nan, n...[nan, nan, nan, 0.015937772829376525, 0.0, nan...
16(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4868.520.6022392.220.3500144...[1671.6565260937566, 1944.3791991826024, 2177....[0.11310448685868632, 0.4775335179836871][0.3433602691087939, 0.3993778354940812, 0.447...[59321.73774141947, inf][0.01998730010804558, 0.0][4, 4][4, 4, 4, 4, 4, 4, 4, 3, 2, 1][0.0, 0.0, 0.0, 1185.681375168525, 1084.771104...[nan, nan, nan, 59321.73774141947, inf, nan, n...[nan, nan, nan, 0.01998730010804558, 0.0, nan,...
20(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4869.750.6022392.221.2300145...[1671.6565260937566, 1944.3791991826024, 2177....[0.11307595030892517, 0.4774130350623014][0.34327363843942293, 0.399277071450208, 0.447...[59310.61722951841, inf][0.023530760437823733, 0.0][5, 5][5, 5, 5, 5, 5, 5, 5, 4, 3, 2, 1][0.0, 0.0, 0.0, 1395.6239254472584, 1285.58228...[nan, nan, nan, 59310.61722951841, inf, nan, n...[nan, nan, nan, 0.023530760437823733, 0.0, nan...
\n", - "

5 rows × 33 columns

\n", - "
" - ], - "text/plain": [ - " network funds sentiment \\\n", - "4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4867.50 0.60 \n", - "8 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4867.60 0.60 \n", - "12 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4868.17 0.60 \n", - "16 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4868.52 0.60 \n", - "20 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4869.75 0.60 \n", - "\n", - " effective_supply funds_arrival simulation subset run substep \\\n", - "4 22392.22 0.29 0 0 1 4 \n", - "8 22392.22 0.10 0 0 1 4 \n", - "12 22392.22 0.57 0 0 1 4 \n", - "16 22392.22 0.35 0 0 1 4 \n", - "20 22392.22 1.23 0 0 1 4 \n", - "\n", - " timestep ... funds_requested \\\n", - "4 1 ... [1671.6565260937566, 1944.3791991826024, 2177.... \n", - "8 2 ... [1671.6565260937566, 1944.3791991826024, 2177.... \n", - "12 3 ... [1671.6565260937566, 1944.3791991826024, 2177.... \n", - "16 4 ... [1671.6565260937566, 1944.3791991826024, 2177.... \n", - "20 5 ... [1671.6565260937566, 1944.3791991826024, 2177.... \n", - "\n", - " share_of_funds_requested \\\n", - "4 [0.1131283127104601, 0.4776341120726794] \n", - "8 [0.11312598437087308, 0.47762428169171994] \n", - "12 [0.11311263200004616, 0.4775679072295611] \n", - "16 [0.11310448685868632, 0.4775335179836871] \n", - "20 [0.11307595030892517, 0.4774130350623014] \n", - "\n", - " share_of_funds_requested_all \\\n", - "4 [0.3434325991383445, 0.399461965817948, 0.4473... \n", - "8 [0.3434255308130342, 0.39945374432956693, 0.44... \n", - "12 [0.34338499596098976, 0.3994065964125596, 0.44... \n", - "16 [0.3433602691087939, 0.3993778354940812, 0.447... \n", - "20 [0.34327363843942293, 0.399277071450208, 0.447... \n", - "\n", - " triggers conviction_share_of_trigger age \\\n", - "4 [59352.21276113208, inf] [0.006034361745133491, 0.0] [1, 1] \n", - "8 [59343.15532804561, inf] [0.011316155171822826, 0.0] [2, 2] \n", - "12 [59339.97441911249, inf] [0.015937772829376525, 0.0] [3, 3] \n", - "16 [59321.73774141947, inf] [0.01998730010804558, 0.0] [4, 4] \n", - "20 [59310.61722951841, inf] [0.023530760437823733, 0.0] [5, 5] \n", - "\n", - " age_all \\\n", - "4 [1, 1, 1, 1, 1, 1, 1] \n", - "8 [2, 2, 2, 2, 2, 2, 2, 1] \n", - "12 [3, 3, 3, 3, 3, 3, 3, 2, 1] \n", - "16 [4, 4, 4, 4, 4, 4, 4, 3, 2, 1] \n", - "20 [5, 5, 5, 5, 5, 5, 5, 4, 3, 2, 1] \n", - "\n", - " conviction_all \\\n", - "4 [0.0, 0.0, 0.0, 358.15272217479924, 323.890220... \n", - "8 [0.0, 0.0, 0.0, 671.5363540777486, 607.2941638... \n", - "12 [0.0, 0.0, 0.0, 945.7470319928292, 855.2726140... \n", - "16 [0.0, 0.0, 0.0, 1185.681375168525, 1084.771104... \n", - "20 [0.0, 0.0, 0.0, 1395.6239254472584, 1285.58228... \n", - "\n", - " triggers_all \\\n", - "4 [inf, inf, inf, 59352.21276113208, inf, inf, 1... \n", - "8 [nan, nan, nan, 59343.15532804561, inf, nan, n... \n", - "12 [nan, nan, nan, 59339.97441911249, inf, nan, n... \n", - "16 [nan, nan, nan, 59321.73774141947, inf, nan, n... \n", - "20 [nan, nan, nan, 59310.61722951841, inf, nan, n... \n", - "\n", - " conviction_share_of_trigger_all \n", - "4 [0.0, 0.0, 0.0, 0.006034361745133491, 0.0, 0.0... \n", - "8 [nan, nan, nan, 0.011316155171822826, 0.0, nan... \n", - "12 [nan, nan, nan, 0.015937772829376525, 0.0, nan... \n", - "16 [nan, nan, nan, 0.01998730010804558, 0.0, nan,... \n", - "20 [nan, nan, nan, 0.023530760437823733, 0.0, nan... \n", - "\n", - "[5 rows x 33 columns]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df.plot('timestep','sentiment')" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df.plot('timestep',['funds', 'candidate_funds'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Funds are the total available funds, whereas candidate funds show how many funds are requested by candidate proposals." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdsAAAKXCAYAAADKCOC+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd5hU9dnG8e+zi1RBpCkIiooNUVCJ3Yhi713sRH2NGuyVaNQYe4mxK/Yo9h7FGkVsKGooUlREEBBEkSpN4Hn/OGfN7LrLs2zhHMj9ua65YGfumfnNbDlzzvmd+5i7IyIiIrWnKOsBiIiIrOi0sBUREallWtiKiIjUMi1sRUREapkWtiIiIrVMC1sREZFapoVtLTOzK8zsRzObnH59oJmNN7PZZraZmQ03s26VfKwlZs3sFTM7rmZG/utj9jezE2vyMf/XpN/rdYLMmmmueFmNK2tm9qCZXZH1OGqLmbmZdch6HJIPdbIewIrMzNYEzgHWcvcp6dU3AL3c/YX0640r+3ju/mvWzC4DOrj70QW371ntQdcgM+sPPOLu92Y9lmWlvNfs7itH93P3b4EwVxPMzIH13H30sng+EdHCtratCUwtWNACrAUMz2g8UkvMzADLehz/i0ree3dfnPVYRCrk7rpU4wJcCHwNzAJGAAem1+8CzAUWA7OBx9J/HfgZ+DrNjQV2Sf9/GfAk8M/08YYDXQuea2z6uHsAC4Bf0scckt7eHzixIH88MBKYBrxGsoYNyULhJmAKMBMYBnSq4PX1B64GPk6zLwDNCm7fGvgAmA4MAbql118JLALmpWO8DfgrcGt6+0rp+3B9+nWDNNtsSY+b3rYKcB8wCZgIXAEUp7f1BN4j2YIwDfgG2HMJ37+xQO/0ezcNeACon962KvAS8EN620tA2zLvzZXA++n3um/Z15zmnGQrRMnrvBEYB8xIx9oAaJ/m6lTyfX8KmJw+xgBg44LbHgRuB14m+Tn6CFg3vW0A//0ZnA0cDrRIX9t04CfgXaCogvfrZmB8OqZPgR0KbruMJf/8bgZ8lt72BPA4cEUFz9MzfV9vS1/jKKD7Et77DsC2wKA0PwjYdil+jvdLxzs9zW5UcNsFJD9ns4AvSsYBbAl8mN5nUjrWugX3K/y+70XyMzYrfaxzs/7bpcuyvWQ+gOX9AhwKtCHZ/314+kesdXpbN2BCmfyvv4Dp12MpvbCdl/5iFqd/HAYuIftImcfuT7qwBfYHRgMbkWzBuBj4IL1t9/QPZVOSBe9GJWMu5/X1T/84dAIaAc+UPC+wBjA1HW8RsGv6dcuy40m/3hkYlv5/W5IPKR8V3Dakko/7HHB3Op5W6R/QP6a39ST5EPJ/6Xt4CvAdyZpPea9vLPA50A5oRvLH+4r0tubAwUBDoDHJAu75Mu/NtyS7AuqQfIAo9ZrLfs9JFoL909dYnL4P9Sh/YVvu+57efnw6pnrAP4DBBbc9mL5fW6bj6gs8voSfwauBu9LxrwTssIT36+j0falDsotkMv/9cHIZFfz8AnVJPmCclT7HIen3aUkL24UF+cNJFqLNKnjvVyP5QHRM+vUR6dfNK/FzvD7J7+2u6XOdT/K7UxfYgOTDRZs0257/fnDZguRDYZ30+pHAmRV83yeRfjAh+RC3edZ/u3RZtpfMB7CiXYDBwP7p/7ux9AvbNwtu6wjMXUJ2SQvbV4ATCm4rAuaQbMbeGfgy/UNR7hpMmce8psyYFqR/TC8AHi6Tfw04rux40q9L1l6bk2wR+DMwgWRf5V+BW9JchY+b/lGdDzQouO0I4O30/z2B0QW3NUzf89UreH1jgZMLvt6LdKtDOdkuwLQy783lFX0Pyn7P0+/BXKBzOY/dnt8ubMt938u5b9P0vqukXz8I3FvmNY1aws/g5SRreh3Ke93Bz8e0ktfDEn5+gd9T5kMPyZaLJS1sy+Y/Bo4p770nWch+XOYxPgR6VuLn+C/Ak2V+VyaS/P52INkCtAuwUvBenAk8V977TPLB4I9Ak6V9j3VZMS6ajVxNZnasmQ02s+lmNp3kk3OLajzk5IL/zwHqm1lV9q2vBdxcMK6fSNZi13D3t0g2ed0OTDGzPmbWZAmPNb7g/+NIPv23SJ/j0JLnSJ9ne6B1eQ/i7nOBT4AdSf74vkPyB3e79Lp3CsZe0eOulT7/pILb7iZZwy3x63vo7nPS/y5p8lHZ19cGwMwamtndZjbOzGaSbIJtWmbGcOF9Iy2A+iRr9JVR7vtuZsVmdo2ZfZ2Oa2zB45co+3O0pNd/Pcma3OtmNsbMLqwoaGbnmtlIM5uRvverBM9b8vPbBpjonix5Cl7TkpSXb1PwdeH706acxxtHsgWhvHzhz3Gp+3qy73c8ye/KaJKF6GUkvyuPm1nJz8f6ZvaSmU1Ovw9XUfHv/sEkH3rGmdk7ZrZNxS9bVkRa2FaDma0F3AP0Itlc1ZRkk+SymCjjwe3jSTatNi24NHD3DwDc/RZ334LkE/76wHlLeKx2Bf9fk2Tz34/pczxc5jkaufs1SxjjOyRr1puR7Fd7h2Sz9pYkC7OSsVf0uONJ1mxbFNzWxAtmaldB2df3Xfr/c0g2I27l7k1IPiBA6e9v2de4pO/LjyRr9utWcVwl7/uRJLsJdiFZ2LUvZ1yV5u6z3P0cd1+HZN/l2WbWvWzOzHYg2cR6GLBq+vM+o5LPOwlYI53MVPialqS8/HcFXxe+19+RfBArtCbJGmqJit7PUvdNn7NdyX3d/VF33z7NOHBtGr2TZF/yeunPx5+p4L1w90Huvj/Jh8LnSfZty/8QLWyrpxHJL98PAGb2B5I122Xhe6C9mVX0PbwL6G1mG6djW8XMDk3//zsz28rMSiYpzSOZyFWRo82so5k1JNnk+LS7LwIeAfY1s93Tta36ZtbNzNoWjLHs8aXvAMcCI9x9AelmV+Abd/8hzVT4uO4+CXgduNHMmphZkZmta2Y7Vu5tK9efzKytmTUDLiKZvAPJPtG5wPT0tksr8VjlvWbg1zWm+4G/m1mb9LVtY2b1Knisit73xiQfOKaSbCa/qnIvs/wxmtk+ZtYhXcjMIJnkVd7PQ2OS/ag/AHXM7BJgSVtECn2Y3vd0M1vJzA4i+YC1JK0K8oeSzC3oV0G2H7C+mR1pZnXM7HCSD5IvFWQqej+fBPY2s+7p78Q5JO/vB2a2gZntnH6P5vHfSY8l78dMYLaZbUgyP+A3zKyumR1lZqu4+y/pfTRz+n+MFrbV4O4jSGaWfkjyB2wTkgk2y8JT6b9Tzeyzcsb2HMkn8MfTTVyfAyXH4TYhWSOfRrL5bCrJpsSKPEyyH3AyyWbQ09PnGE+yhvVnkj/A40nWkEt+rm4GDjGzaWZ2S3rdByT7bkvWYkeQ/BEr+boyj3ssyeSVkhnET1PBputKepRkAT6GZBNvSdHCP9Kx/ggMBF6txGOV95oLnUsy+3sQyab9a6n497Dc951ktu84kjWvEenYlsZlwEPpZvjDgPWAN0lmJ38I3OHub5dzv9dI3oMv0+efRyU3o6cfrA4i2Rf7E8mEp2eDu32Uju1HkpnHh7j71AoefyqwD8mCcirJGvg+7v5jQayin+MvSCZ+3Zo+177AvumY6wHXpNdPJvkA0Dt9vHNJtjLMIvl9KvmQVp5jgLHp7+LJwFHBa5cVjJXeJSLyv8XMxpJMaHoz67EU+l8sBClkZj1Jvi/b19Dj9ed/+P2U7GnNVkREpJZpYSsiIlLLtBlZRESklmnNVkREpJZpYSsiIlLLlvuz/tQ79/fhdvCrjuwYPs6pL04IM30PjY7Bh2Pf/jHMXL/VqmFms5bx4Yt71o+7EaY0qR9mvpk5Nsxs+UFU9gOH14t3SfT78NswM3vf6PBLmPfK52EGoOnsmWHm1P02CDPXj/45zBTvGU+c/ebwu8JM6x3bhZl6e8Y/07ZW/DhPtb89zMwc/Jt+i9846qn4+1r/4K5hZt4zn4SZnz79Psz4ovhnsUm7xmGmsl46O/5+1K+zUpipu9kr8ZMN3SeMzF73pTDTY9EXtVq+Y6dsnck+Sr9zYC7PvpXJmm1aUvCxmQ2x5ITof02vX9vMPjKz0Wb2hJnVzWJ8IiIiNSmrzcjzgZ3dvTNJufseZrY1yQH+N7l7B5KyghMyGp+IiEiNyWRh64nZ6Zclp/Vyks7cp9PrHwIOyGB4IiJSTVZkmVzyKrMJUmkv7GCS01e9QVKTN93dF6aRCZQ+Y4eIiMhyKbMJUmkBeBcza0pyMvANK3tfMzsJOAmgeNcOFG9anVpcERGpaXley8xC5of+uPt04G1gG5JzhZZ8AGhL6dNjFd6nj7t3dfeuWtCKiEjeZTUbuWW6RouZNQB2BUaSLHQPSWPHAS9kMT4REZGalNVm5NYkp/gqJlngP+nuL5nZCJJTwl0B/Ae4L6PxiYhINWgzcmmZLGzdfSiwWTnXjyE+oXQp/zhu4zBzcotNwswum8wKMxtMWRBmLpwflx/MfGxSmGnYvGGY+ebcuCSgXe/bwsyDN00JM1v92DvM/Hn7a8LMsAs6hZkPDns+zKyz+SphBuDnfePijydXiw/nXvzpT2Fm5K7lnb62NHunZ5hp9+C7YebTv7wRZv5yweZh5pXr1gszI68aEmZOOz6ectF7zaZhZq1Lzggzbf/0lzBz6ym/+fPyG1PmLAoz6zWNiygAureNn2/Xp18LM8W3dQ4zQ4rieaPzL10/zMiylbdSi15poYWbWYssxiYiItWnQ39Ky2ozckmpxWwzWwl4z8xeAd4HXgL6ZzQuERGRGpfVZmQHflNq4e7/ATDL76cTERGJ6e94abkptXD3j7Iai4iISG3KbGHr7ovcvQvJ8bRbmlk8cyZlZieZ2Sdm9sl7T4+ovUGKiIjUgMxPsefu083sbWAPoFLnTXP3PkAfgDuHnZLJaZxERKRieZ6slIU8lVqMymIsIiIitS1vpRanA+cDqwNDzayfu5+Y0RhFRKSKtGZbmiUTg5dfza7dM3wBn526d/g4C3a+I8xsf3RckLBK43ph5stdto3H03dAmKl32jFhptE1t4eZ8RN+CTNrtY9f15Cz9wsz7T8fH2Zs/Y3CDBPHxRlgpfvfDDPjxsRlJc2fvyzMfLTmOWHmhx/j37ebH94izLxTPy49GXfToDBT76XTwszZA94LM4913T7MNL71sTAz+/xTw8zioQPjx3nokzAz9qrdwszUTg+FGYCdvrszzCz+sF+YublhXK6z8S5vh5nGjeMF3TbfjKrVpWH983fMZOEy77p3crmUz2TN1szqAwOAeukYnnb3S82sL9AV+AX4GPiju8dLAhERyRWt2ZaW1WzkklKLzkAXYA8z2xroS3KqvU2ABoA2IYuIyHIvb6UWv25nMbOPSQ4LEhERWa5lduhPOjnqU6ADcHthqUVa4XgMELeSi4hI7mgzcml5LbW4Axjg7uWe/qSw1GL+R/GEGxERkSxltrAt4e7TSU4avweAmV0KtATOXsJ9+rh7V3fvWm+rdstmoCIiUmk6609puSq1MLMTgd2BI9x9cRZjExERqWl5K7VYCIwDPkzPGPGsu1+e0RhFRKSK8ryWmYWsZiMPBTYr5/qlHs/+m7cJMwt3uyvMXHfNNmFmyvYHhZleHz4fZt6t81OYuaNL0zBz0Kp/DjMju8XlB607Nw4zX54Tv/b1//p4mJnZvFmYqbf1HmGmshs+Fhy6dZgZdtLLYWbkd3Gxw6hXdqrUmCITB02JM/fH7aYdOjUKM1/s8o8w03vGwjBjH+4eZrbqvHqYmX933zDzu5bxn4mBt1e4F+pXjbrFvz8b/e03f6bKNaf3dWGmbqcWYebE9yeGmZXfjcts3l1lXpiRZSurzcj1zexjMxtiZsPN7K/p9fel1w01s6fNbOUsxiciIlKTstqMXFJqMTs9zOc9M3sFOMvdZwKY2d+BXsA1GY1RRESqSJuRS8tbqUXJgtZIGqSW7+JmERERMjz0x8yKzWwwMAV4o6TUwsweACaT1DbemtX4RESk6swsk0te5a7Uwt3/ALQBRgKHl3ffwlKLL17WaXBFRCTfcldqkV63CHgcOLiC+/xaarHB3hsum4GKiEilqdSitDyVWnxhZh3S6wzYD9Bqq4iILPdyU2oBvAy8a2ZNAAOGAKdkND4REZEak6tSC2C7pX2sDVZdKcys2+eQMHP7S4PDzI/bTQsz9736VZi56OahYWbPq7YIM/1f3yHMdD7n/TDz06L4YPuGc+MflUYt4xKFfx28Zpg5eEr8Hi5+58MwA1C8515hZpN/xAUAI/d+LszcfXVcoPHUXseFmW5rfBRmhp3zeZh54u2fw8yed24SZj4/7oQwM2JG/DO919pxecrXO20aZk6fNC7MNLT6YabdweuHmUYjJoQZgLF/i7+vrSd8Hz/ONYPCzEpX1Qszbfd/Msww4Io4Uw153qSbhVyVWhTcfouZza7o/iIiIsuTXJVauPtAM+sKrJrRuEREpAZozba0TNZsPfGbUot0H+71wPlZjEtERKQ2ZLVmS7pg/RToANzu7h+Z2RnAi+4+Kc8HJ4uIyJJpzba0PJVa/B44lEq0RhWWWnz87IjaHqqIiEi15KnUYieStdzRZjYWaGhmoyu4z6+lFlse1HHZDVZERKQKMtmMbGYtgV/cfXpBqcW17r56QWa2u3fIYnwiIlI92oxcWm5KLdz9pYzGIiIiUqvyVmpRmKnUiePP+3JO/Hwrxwem//jBxDAz+cH4IPAZp8UH5a++6qwwc8yUX8LMCSe/F2bW/+jiMFPnL3eEmVH14sOex5x5fJjZ/JHHw8xa17weZlp+1ivMAKw5/LMwM2KLdmGmuBI7XB79dm6Y8e+GhZkLPxkeZl768ZYwM273C8LMER2bhZkHR74YZk5qG/fRdFxvnTBD/fjXvu8XX4cZn/pNmOl3UVwMMv/rM8IMwCtrXB9mPh96QJhZ8G18VtHVtrwvzGxyzHphprZpzba0XJVamNmDZvaNmQ1OL12yGJ+IiEhNylWpRXrbee7+dEbjEhGRGqA129Ky2ozswG9KLbIYi4iISG3L7NAfMys2s8HAFOANdy9pX7/SzIaa2U1mFjdui4iI5FyeSi06Ab2BDYHfAc2Acmd4FJZa3PPWl8tszCIiUjk6eXxpeSq12MPdJ6W9yfOBB4AtK7jPr6UW/7dzfJosERGRLGU1G7mlmTVN/19SajHKzFqn1xlwABDPzRcRkdwxs0wueZWrUgszeyttlzJgMHByRuMTERGpMZZMDF5+9XilZ/gCdmrXIHycDVaND+7v1mLbMDONuPzBKrFBYbWLwvMx8K/TtwkzTerGr33B4oVhZuML+oeZZtf2CDOj9rotzIx7Yb8wc0wlT0Dx1fD4+zHug8lhpuefO4eZv506uFJjitT9PC4/2GVU/LqYNz+M2MaVKD/4aUYYGXjYs2Fm6tt7h5n6xXXDzK5XvR1mJt8Yf04fNvWLMLNpi43CDMCchTPDzMonPxZmXr8krhb4Zmb8+3r6/V+FmcZ3vlWrq4Gr37Z/JguXyb1eyOXqbd5KLczMrjSzL81spJmdnsX4REREalLeSi02AtoBG7r7YjNrldH4RERkBWVmewA3A8XAve5+TZnb1wQeApqmmQvdvV91njNvpRanAEe6++I0NyWL8YmISPXk9TCcdK7Q7SQTcycAg8zsRXcv3Dd1MclcojvNrCPQD2hfnefNW6nFusDh6TG0r5hZ9m3aIiKyItkSGO3uY9x9AfA4sH+ZjANN0v+vAnxX3SfNW6lFPWCeu3cF7gHuL+++haUWX7+iUgsRkbzJcanFGsD4gq8npNcVugw42swmkKzVnlbd9yNXpRYkL7pkWuNzQLnnqysstVh3T5VaiIhIonBlLL2cVIWHOQJ40N3bAnsBD5tZtZaXmeyzTY+l/cXdpxeUWlwLPA/sBHwD7AhotVVEZDlUlNGqnLv3AfosITKRZCJuibbpdYVOIFkBxN0/NLP6QAuS3Z5VkrdSi/eAvmZ2FskEqhMzGp+IiKyYBgHrmdnaJAvZHsCRZTLfAt2BB81sI6A+8EN1njSr2chDgc3KuX46EB/5XuDNEfHrf/aN+IDzymzrn3dKkzDTdPacMPPyDo+Hme0f6Bpm1jrgxTCzw3EdwkzPrq3DzAGdGoeZH0fFGyLW771VmOl4zYAwc8XxcckEwE8nbBxmdn3gjTDzxlVDwswm/fYJM0/v9FKY2ePtsh+yf2vmuxPCzOsXbBJmDl28OMx8c0485q0/PjfMvLDW9WGm+9sHhhm/6aIw893hf4uf6887h5kn2l8ZZgBOuyGey/nKTXuEmX3+8laY+XrApDDz7iv7hpm9wsSKyd0Xmlkv4DWSw3rud/fhZnY58Im7vwicA9yTrvg50NOr2QCV1Wbk+sAAkglRdYCn3f1SM+sOXE+ytjub5AWOzmKMIiJSdcU57ilOj5ntV+a6Swr+PwLYriafM6sJUiWlFp2BLsAeZrY1cCdwVDpL+VGSY51ERESWa3krtajxY5tERGTZK85pqUVWspogVdLi8SnQAbjd3T8ysxOBfmY2F5gJbJ3V+ERERGpK3kotzgL2So9tegD4e3n3LTyOat7Ab5fdoEVEpFKKzTK55FWeSi32BDqntY0ATwDlntOusNSi/tZrLqORioiIVE1Wp9hraWZN0/+XlFqMBFYxs5JKqJLrRERElmt5K7X4P+AZM1sMTAOOz2h8IiJSDcWZbzfNl7yVWjxH0olcaRMXrBRm6p6wa5gpui0+VWGfuWPDzCE3DAszI97eKcxs+vOiMLP+M38IM1Narhtmpq8UFxucvEl8IH3rB98MMxu1XjnMvPzYtDCz2Y1xOQbAakO/CDOTN2gbZk64OT618qPPDwozv5zULMws7nFEmGnc8KkwM37WL2HG5/wYZho0bxBm5t74TJjZ5fj4ff76zLhAY+6s58PMqgPPDjMjti13SkhpI+O/HQCftekUZtp+EReRTBw/K8xMmhR3KzSr3yjMyLKVt1KLnYEbgLokM5VPcPeFWYxRRESqLs+TlbKQp1KLbYGHgB7u3gkYBxyX0fhERERqTJ5KLRYBC9y9pGD3DaA3cN+yH6GIiFSH1mxLy2wXtpkVm9lgklMWvQF8DNQxs5IG/kMofRokERGR5VJuSi2AjUlOdXSTmX0MzCJZ2/2NwlKLez8eu6yGLCIiUiWZ1TWWSE8g/zawh7vfAOwAYGa7AetXcJ9fTw48/+oDqnXaIxERqXnqRi4tT6UWo8ysVXpdPeAC4K4sxiciIlKT8lZqcb2Z7ZNed6e7x2dSFhGR3CnWim0peSu1OA84b2keq846q4SZC6bHB5MPfXd6mFll9MAw06JDvTBzQyV+CPdsH5cfzGm9QZi59tOXw8zYmfGhzPeOmhlmvhk3L8xc1GW1MNP9go3CzITdbgozAI+/vyDMHHxV/HwHXhk3h97Ze8MwU+/wjcMM82eHka7z58aPMyLO3D7/hzDzjzfi342ui+O9OZ22bR5mho2LCzQ6vHtFmJkwb1yYabpW4zDTo233MANwy1evh5nvF8RFNYeNjb/3+x4Tv4/z3/w6zHB0HJGak+k+23TN9hNgorvvY2a9gDOBdYGW7h7X24iISO5on21pWbdXnkHpkw28D+xCUmghIiKyQsjyONu2wN7AvSXXuft/3H1sVmMSERGpDVluRv4HcD4Q7zgREZHlihqkSsvq0J99gCnu/mkV7/9rqcU9b34Z30FERCRDWa3ZbgfsZ2Z7AfWBJmb2iLtXan5cYanFoieOU6mFiEjOaIJUaZms2bp7b3dv6+7tSSoa36rsglZERGR5k/Vs5FLM7HQzm0DSlzzUzO6N7iMiIvlTbNlc8ioP3cj9gf7p/28Bblma+9cfGB+8PX/fzcPM81/HW6MP3iEurPDrzokzC34OM4yJ90XvMujJMLN6o7ph5oa7R4SZQZ/NCDN1KvHT9KcPvgszjbaMi0pOu2rr+MmAiyrx2j64ZVSY+eTNHcPMfp12DzNvTvskzPxz4Gth5vbucYHGtg3jzPjDrw8zLWf8PcysdfvdYebQTq3CzLpT54SZA954LMwctN+HYWafs9YOM6xUP84A38+JCysqsyDY/dS4qGbhVnExyu7jvw0zcQ2H1KRM12zT0+z9x8xeSr/ua2ZfmNnnZna/ma2U5fhERERqQtabkcuWWvQFNgQ2ARoAJ2YxKBERqZ7iIsvkkld5K7Xo5ymSk8m3zWp8IiIiNSWXpRbp5uNjSNZ8RURkOaNSi9LyWmpxBzDA3d+t4P6/llosHja51sYpIiJSE3JXamFmlwItgT9WdOfCUouVztpepRYiIjmjNdvSclVqYWYnArsDR7j74izGJiIiUtOyno1c1l3AasCHZjbYzC7JekAiIiLVlbdSi6Uez4Ljd4lD8xaEkW33XjXMDH13eph5fNI7YeawUfPCzJtdmoeZ7RY1CjO9Om8fZs6txHT5/zvhgzDz0L3xc93etHWY+WnjLmFmjavuDDMAh30wNcysOuzYMHPiEU+FmXk7TAoz+86ZFWZmdojfo8l//1eYWXxhXETSYus2YaZBr7+FmR9nx7+6RX88KszctdqaYWbroXFRSfceLcPMmAu6hZkd/3FrmAF4o+e2YWaVuiuHmW4HxH8/GLZvGLlu7fi5altx3lblMpa3Uov7zGyImQ01s6fNLPufGBERkWrKes22pNSiSfr1We4+E8DM/g70Aq7JaGwiIlJFmiBVWt5KLUoWtEbSIKWZxiIistzLXamFmT0A7AWMAOJWfxERyZ08VydmIXelFu7+B6ANyeblwyu4/6+lFn2erqgXQ0REJB+y2oxcUmoxFngc2NnMHim50d0XpdcfXN6d3b2Pu3d1964nHbLFshiviIhIlWWyGdndewO9AcysG3AucIyZdXD30ek+2/2A+ESjIiKSO5ogVVrWs5ELGfCQmTVJ/z8EOCXbIYmIiFSfJWezW36td/8h4QvYfZ24sGLmgkVh5sG6TcLMlNsGhpmbz940zFy9zRFh5qute4WZNtvGpQVWiYkMT53YIczsuubmYWbB4rjQ4+PJX4SZQxfEpQUAP98ZlwSs3ix+nLnT5oaZl/vGBRp9/rl1mIVt5iYAACAASURBVHlm443DzA8XPBdmvh8xLczUey8+ZfT0+XPCTPP68e/G9n37h5mJXdYLM8UPl3t+klIeO6trmNn7prgcA+CHaw8IM3V7/DPMtOkVj2nU1muHmUP/9UmY2aV90zBz84731OqqZ49XemaycHl8zwdzuUqdq1KLgutvMbPZWY1LRAQqt6AVqYysNyOXLbXAzLoC8aqoiIjklvbZlparUgszKwauJzn+VkREZIWQ5WbkklKLwlPp9QJedPe40V1ERGQ5kZtSCzNrAxwKhKfZKCy1mPHOmFocqYiIVEVxkWVyyaus9tmWlFrsBdQn2Wc7HJgPjE4Os6WhmY12999Mg3X3PkAfqNxsZBERkSzlptTC3fcpzJjZ7PIWtCIikn+aIFWaTu8rIiJSy7I+9Ad37w/0L+f6Sp04fsGixWHmlqLGYcY2bh9mXpj/TZhpN2x6mLlqw+3DzFGvxQfJ930nPtXvan3uCTPtWzYMM2/1+SrMNDxznTBjzeOD9js9HBc2HA+c0D1+vhtvuTDMzHjqkTAzbs+4g7t1k1fDzDlT44KIX57/KMw0e/zyMDP6+/fCzKa9nw4zw66IjzVtfXm/MPPyhb8PM//ZPv5ePNNvjzAzcHL8Pn92dAfeGjtjyaF/vc+LT8a/93/vvVmY+WVxXJyz7vixYeazH+PHOb1d9nvXirUqV4reDlkuVWZBK7Ik4YIWKrWgFamMXDVImdmDZvaNmQ1OL12yHJ+IiEhNyHoz8m8apIDz3D3etiUiIrmlCVKl5apBSkREZEWUtwYpgCvNbKiZ3WRm9cq7Y2GpxewB2qciIpI3xZbNJa9y0yCV6g1sCPwOaAZcUN793b2Pu3d1964r/z6e3SoiIpKl3DRImdkj7n50evt8M3sAODej8YmISDUUaZ9tKZms2bp7b3dv6+7tgR7AW+5+tJm1BrCkr/EA4PMsxiciIlKTsp6NXFZfM2sJGDAYODnj8YiIiFSbuWffNFId1316cvgC/vX1zPBx3ll99fjJFsdtVYsnTA0zq3//U5j58ayzw8yYbmeFmXU3jtuhZq8Ul3U1ODJuUPpk7UZhZmzbvmHm0LF/CjNMj5u6AKhf7hy7Up7peH+Y2WFa7zDT8vV3w8zsvfYKMw3qxN+PoRudEWa6XLplmGk3c1aYmTQkPuPlP8+Ifz56PRW3kDVvE7e9XXdqvMGrqBLb7NZuH2/m3PhPm8YPBDT97ocw8/NVl4SZemfHmcat4/folWO3DjNbrnZJrW7nPeOd/8tk4XLzjvfkcvt13kotzMyuNLMvzWykmZ2e5fhERERqQtabkcuWWvQE2gEbuvtiM2uV1cBERKTqcnxq2UzkrdTiFOByd18M4O5TshibiIhITcpyzbak1KJwB8S6wOFmdiDwA3C6u8c7ekREJFfyXDCRhbyVWtQD5rl7V+AeoNyZK4UNUh89O6KWRysiIlI9WW1GLim1GAs8DuxsZo8AE4Bn08xzQLlTAQsbpLY6qOOyGK+IiEiV5arUAnge2CmN7Qh8mcX4RESkeoqKLJNLXmU9G7msa0iKLc4CZgMnZjweERGRast8Yevu/YH+6f+nk8xQrrRBk+eFmQFrtw8zdR8dEGYe+kd84HqzsfuHmcPGxEUL9qeLwswhF2weZha3ahNmhqwdlyisvO+jYWbTPeOTQrSdfl6YYU4c8R/GxCHghsbxz8f5c+8IM3/s/1iYadsm/nV65dn4VM3v77xTmBnxQvcws9E98dzC+h3jgoSBr8cFIlMviMtT6tSP35/HrhoeZjr9sV2YeeHW8WFmjU1WDTOLvv85zABMb9MyzMw9/9ows3D+wjAz/KO4FKfxmZ3DTG3TBKnSMl3Ymlkx8Akw0d33MbN3+e/s5FbAx+5+QGYDFBERqQFZr9mWKrVw9x1KbjCzZ4AXMhqXiIhUQ453n2Yib6UWJbc1AXYmmTAlIiKyXMuyG7mk1KK8dv8DgH+7e3wGARERkZzLW6lFiSOACmekFJZajHlVRweJiORNsWVzyau8lVpgZi2ALYGXK7pzYanFOnusvyzGKyIiUmWZTJBy995AbwAz6wacm5ZaABwCvOTu8TEbIiKSS0WW49XMDGR6PtsK9GAJm5BFRESWN1kf+lOq1CL9utvS3P+xaYvCTP8Tnw0zryzyMPPsWzuEmVsHfh9mph85MMw8d+tGYebpl0aFmWfu/zDMrPxlXMTx5nXbhJkbtj8yzLxX9+Qwc8wZq4WZn64+J8wA7NTxgjDzywUzwswtP8UbWhadfXyYGTMjnmD/bYO42OCoMfHP/R7bxaeD/nzgj2HGeqwTZuq33yvMfDP6kzBT98sbw0y96XG5zIPd3gwzh3y3IMw80K15mAFYd+e3wkz3j04IM6eOi4tINh0yOcz0nxX/bejYLP69l5qTt1KL7sD1JGvcs4Ge7j46yzGKiMjSy/NkpSxkvRm5pNSixJ3AUe7eBXgUuDiTUYmIiNSgvJVaOGmbFLAK8N2yHpeIiFRfkWVzyassNyOXlFoUNqGfCPQzs7nATGDrLAYmIiJSk/JWanEWsJe7twUeAP5ewf1/LbW45614QoGIiCxbxWaZXPIqqzXbklKLvYD6QBMzexnY0N0/SjNPAK+Wd2d37wP0AVj4yDHxNGIREZEMZbJm6+693b2tu7cnOa72LWB/YBUzK6mE2pXSk6dERESWS5kfZ1vC3Rea2f8Bz5jZYmAaEB+4KCIiuZPnyUpZMPfleyvs6y02CF/A12/vHD7OH16MJz5v0nqlMHNylxZhZu+11w0zGzbYMMw0u/mOMDNt7LQwM7dtXCLx4mXxQfK7Hdc6zIwfGB+Qv/6Qm8LMWQOeCTMA9/7rizAz7dpzw8xP+/41zKy2T4cwM29YXMjQ4Herh5ln/vhZmFlzQo8w07FZpzDz+rcDwszUeXERxwlvTgkzk07sHmZa3dgvzHxzelxAs9lVFdav/2rEXw4KMwBztrsrzBx0dvxe33rusDCz653xvNFF3/8cZuqe/nStLg6v+/TkTBYu529xVy4X83krtdgZuAGoC3wKnODu8W+xiIjkikotSstNqYWZFQEPAT3cvRMwDjguw7GJiIjUiDyVWjQHFrh7yQlq3wAOzmJsIiJSPUVF2VzyKsuhlZRaLE6//hGoY2Zd068PAdplMTAREZGalJtSC09mavUAbjKzj4FZQLmnNiksteg3b/oyGbOIiEhV5anU4pH0BPI7AJjZbsD65d25sNSiMrORRURk2cpzm1MWclNq4e5Hm1krADOrB1wAxPPpRUREci43pRap89JNzEXAne4en5FZRERyR6UWpWW+sHX3/kD/9P/nAectzf2Hv7lTmDnmmqFh5oKTOoaZm9ZqEmb2abFtmLnk89fCzPdzKlHGUInCijU3bxNmDmzRKMz0G9EzzOw0OD4g/+3zdg0z/dueFWYW9d0+zACcfmBcDnLCm8+FmcdfujrMtLk33hDz+KXbhZkOp7weZg6f+pcww6SxYWS1J/8ZP0yb5mFm0aTZYWbkPcPDzBZj49ITq8Rf8QVTF4eZma3i1/Xvte4MMwAHHN8qzDy+Y9sw0+nb3cLMlGPuCzP3/7lLmLkoTEhNymxha2Zj+e8kqIXu3tXMmpGcgKA9MBY4zN3jJYqIiOSKSi1Ky/qopJ3cvYu7lxzucyHwb3dfD/h3+rWIiMhyLeuFbVn7k7RIkf57QIZjERERqRFZ7rN14HUzc+Du9HCe1dx9Unr7ZCBuyBcRkdzRBKnSslyz3d7dNwf2BP5kZr8vvDEtuSj3GNrCUosPnxmxDIYqIiJSdZmt2br7xPTfKWb2HLAl8L2ZtXb3SWbWGij3nFyFpRY3Dc7mNE4iIlIxlVqUllVdYyMza1zyf2A34HPgRf57pp/jgBeyGJ+IiEhNymrNdjXgOUs++dQBHnX3V81sEPCkmZ1Acoq9wzIan4iIVIP22ZaWycLW3ccAncu5firQfWke64GhP4WZO7q3DjNvbbFFmGl166thZsBxDcLMwO/mhJlX69QPM3ddcmyYeW7Ol2HmwAYdwoyPih/nrQYrh5m5fd4JM5Mmx3sGHh0wNswATCF+H1sunhtmBp1+RpiZd+LaYWbDVcut+y6l1ZPbhJnBs4aEmVm7PRJmJt/dLcxMuX9wmFn94d5hZvgZ54eZRTuWe+6RUuZ1iItatvs0LrI5/7J4vsf+w44PMwBHjYl/Pw6+YWCYufWU+WGm2fPxyVcePSD+u3jR78KI1KAsz2c71syGmdlgM/skve5QMxtuZosLTrUnIiKyXMu6rnEnd/+x4OvPgYOAuzMaj4iI1AA1SJWW9cK2FHcfCWCaxSYiIiuQLI+zLSm1+NTMTspwHCIiUsOKzDK5VIaZ7WFmX5jZaDOrsBbYzA42M6+J3Zq5LbVYksJSi5/eHlN7IxQRkRWKmRUDt5MsezoCR5jZb077lh6eegbwUU08b2YL28JSC6Ck1KKy9+3j7l3dvWuzndaprSGKiEgVFVs2l0rYEhjt7mPcfQHwOEkvf1l/A64F5tXE+5G3UgsREZHatAYwvuDrCel1vzKzzYF27v5yTT1pVmu2qwHvmdkQ4GPg5bTU4kAzmwBsA7xsZvFZ1kVERFKFuxnTy1LNCTKzIuDvwDk1Oq6k73/5NbLLhuEL2Gjg9eHjjJo7Kswc9+qnYeb9ufHnl+It1gsztvG2YeanE68IMxNu3CvMFBFve9l4djxxfaO344P2//3MuDDjj/1fmCk+5r4wA1C8Uvz9GNgvLgDo2n2VMPP1ZzPDzPgJ8e9bj+e2DzO2dtswM+/pT8LM5PcmhJlvHt47zHS/8q0wU7dR3TDTYZP4RF9rN42LSnr94eMws1/PVmFm5MdxgQTAJr9vEWbW6NA8zBzRsVmYuWx6cZiZ81JcsrHybf+u1cM+Hv+yVyYLlx7r37bE12Vm2wCXufvu6de9Adz96vTrVYCvgdnpXVYHfgL2c/f4l6oCmR36Y2ZjgVnAImChu3c1s7+RbDtfTHISgp7u/l1WYxQRkRXOIGA9M1sbmAj0AI4sudHdZwC/fnoys/7AudVZ0EL2x9mWLbW43t3/AmBmpwOXACdnMjIREamyyh6Gs6y5+0Iz6wW8BhQD97v7cDO7HPjE3V+sjefNemFbirsXbodrRAXnsxUREakqd+8H9Ctz3SUVZLvVxHNmubAtKbVw4O70HLWY2ZXAscAMYKcMxyciIlWU1zXbrOSu1MLdL3L3dkBfoFd5dyycbfbk1MpNYBAREclKnkst+gIHV3DfX0stDmvetHYHKiIiUk2ZbEZOiyyK3H1WQanF5Wa2nrt/lcb2B+LjcUREJHeKLMsNp/mT1T7b1YDn0rP71AEeTUstnjGzDUgO/RmHZiKLiMgKIJOFrbuPATqXc325m42XpPVHfwsz+7/2VJh5/KMfw0yX7m3CzKId45cwfpcKTzLxq7XfjIsN3rp4szBzwCsjwswX1w0KM3V2jA+2/+WW+HtRdPTCMHPUm4+HmRc7Nw4zADO23yjMDDtvQZjpdm9cEtDxq7PCzDqH/j3MzH0rLv6YNnpImHn9hrhu/I+zZ4SZd4pXCjMDX4/nTnQYfmmYaXHWzWFm8BZrh5ltbu0SZuZ4XLKxUae5YQZg+px4La5ftzXCzAnPfBFnTt4nzGz37eQwE9eZVI8mSJWW2Xq+mY01s2FmNtjMPilz2znpaY3iWhYREZGcy/o427KlFphZO5J9uN9mMyQREakurdmWlsc92DcB56NCCxERWUFkubAtKbX4tOSsDGa2PzDR3eMdUiIiIsuJvJVa/JmkD3mJCkstHrz3zdoep4iILKUis0wueZXZPtvCUgszew7YEVgbGJIeEtQW+MzMtnT3yWXu2wfoAzB9/lPa3CwiIrmWq1ILd29VkBkLdC07gUpERPKvKJdTgrKTq1KLjMYiIiJSq3JValEm074yj9VopSZh5r0v4pXjn/9yZJi58+PPwszcxXFBQqftmoeZhrfEB/eP/XZ+mCk6ND64v9sfOoQZGzstzDxd99QwM3FQfCKnh+vGhRW+cdswA9DyP1+HmY9fGB9mBjyyW5hZsOplYebAz44JM890eTjMXHlf/H19e934vd7y4jXDTMd3vwozTY9tF2YOe79m5ld8e+hWYebrO+LP7o2mzgkzT931faXGtNKIP4aZ/QbHBTNrt4l/9tdqtEGYef+IMFLr8rz/NAu5KrUws8vMbGJ63WAz2yur8YmIiNSU3JVaADe5+w2ZjEZERKQWZL2wFRGRFZA2I5eWq1KLVC8zG2pm95vZqlkNTkREpKbkrdTiTmBdoAswCbixvDsWllrc26ffMhuwiIhUTpEVZXLJqzyVWmzp7gNKbjeze4CXKrjvr6UWvyx+TaUWIiKSa7kqtTCz1u4+KY0dCHyexfhERKR6tM+2tFyVWpjZw2bWhWR/7lggPnhNREQk53JVauHu8RH/ZfxyXrm7dUt56dn41Lhjjh0TZl5pFZdIFI95Pcz8fNEZYWbCovhg+joN4iKBqz97Psy0bdkozHRbv0WYOfTCo8LM1HP7hpmiBy8OMz5pZJgBmLrvemGmOXeGmXs2KnePRimtV6/EJ/mNtgsjtz0Slx/ce3FcsHJEo7ph5ul9DgkzG/z0SZgZde7hYWaNc+8NM4uOit+feZfH8zTO7bF2mNmlxzthpnjM0WEGYPPXPwozn+0Uv7azZ38RZvyrgWGm9RuVOHFar7iERmpOZvts0+7jWcAiYKG7d02vPw34U3r9y+5+flZjFBGRqtFm5NKyPs62VKmFme0E7A90dvf5Ztaq4ruKiIgsH7Je2JZ1CnCNu8+HZKZyxuMREZEq0JptaXkrtVgf2MHMPjKzd8zsdxmOT0REpEZkuWa7vbtPTDcVv2Fmo9LxNAO2Bn4HPGlm67h7qWNp04XzSQC37rIRx29auTPAiIjIspHngoksZPZuFJZaAM8BWwITgGc98TGwGPjNNFh37+PuXd29qxa0IiKSd5ksbM2skZk1Lvk/SanF58DzwE7p9esDdYH4ZLQiIiI5lrdSi7rA/Wb2ObAAOK7sJmQREcm/IjRBqpAt78uyCbP7hC9gzMy41GKHhc3CTPvX3gszI0bMCjMNT9oxzFi7jcLM9DNvDTOr3HBimLnqq3fDzBfTfgkz55z5QZhps2n8Pq+yfbxr4LNDOoYZgA0v7h9mmhy2cZj5cYetwkzL6bPDzIQ/xMUO7Z79S5h5e93eYabDV2eGmTXPezDM1GtcL8zMPSsutWDxwjBSdMWjYWbRnp3CzOTuW4eZNtY8zCx+780wA9D8068rlYucvOOaYeaneYvDzNyFceafu91fq0vDDyZdmsnCZdvWf83lUj5XpRZm9gSwQRppCkx39y4ZDVFERKpIh/6UlvVxtqVKLdz914/HZnYjMCOTUYmIiNSgrBe25bJkZ+5hwM5Zj0VERJaeDv0pLW+lFiV2AL53968yGJeIiEiNynJhu727bw7sCfzJzH5fcNsRwGMV3dHMTjKzT8zsk773D6goJiIikguZbUYuLLUws5JSiwFmVgc4CNhiCfftA/SBys1GFhGRZUsTpErLW6kFwC7AKHefkMXYREREalquSi3S23qwhE3IIiKSf1qzLS2Tha27jwE6V3Bbz6V5rGZXPBdmWlzZK8z8cudDYaa4QfzDM+T8bcNM+5NfDDOL+v6mEvo3Jr8zOcyssdK8MPPcl9PDzIz5i8LM1vutHmZ+3mWTMLPyW5+HmdVfHhZmAL7aOS4JmPbwkDDTpPteYWbWZQ+EmdmT54SZRS+/EGZmzIj3nnw4aXiYqYwPzts1zCz4Z1z+sAbzw8z89dcIMy2/+C7MjN0zLqzY86Vnw8zLXrk/kYsrUSIxbMAPYeaTwzYMMwcMiY+I/Lp7/Hsmy1beSi26AHcB9YGFwKnpCQlERGQ5okN/Ssv6ONtSpRbAdcBf3f0VM9sr/bpbJiMTERGpIXn76OFAk/T/qwDx9iIREZGcy3LNtqTUwoG708N5zgReM7MbSD4IxDtARUQkdzRBqrS8lVqcApzl7u2As4D7yrtjYanF/YPHL7sRi4iIVEFmC9vCUgugpNTiOKBkiuBT6XXl3bePu3d1967Hd2m3LIYrIiJLoQjL5JJXeSu1+A4oOdnrzoC6kUVEZLmXq1ILM5sN3JxWNs4Dyp6gQERElgPaZ1tarkot3P09ltCJXJ4pF+0dZtbu9bcws+i4HcPM58/G5Qf1V5sUZoZ/PTPM/DL3xzBTb+BxYWZcpxvCzEfXbBVm/rVNszBzdYP4x2nyrR+FmVPPLbfvpJTr19wgzACMPiwumujw8jlh5omvXwkzf1g1LpqY8/KpYebj398RZvZ7JJ472PTRUWFm8dVx4cvca/qGmdmXHRtmfvdKvzCz5sT4d+OTFyeGmUFHDAozTw+IS2GK580KMwBdtlsrzKyxYZswM3BxXB5ja8XFHw2OeDTM0O/sOCM1Jm+lFp1JSi1WBsYCR7l7/NsnIiKSY3krtbgXONfd3zGz44HzgL9kMzQREakqNUiVlrd3Y32g5AS1bwAHZzgWERGRGpHlwrak1OJTMyuZCDUc2D/9/6GAjusREVkOFZllcsmrvJVaHA+camafAo2BBeXdsbDU4tEH3l92IxYREamCzPbZFpZamNlzwJbufgPJMbeY2fpAuVON02rHPgBjZ90WTwEVEZFlyrTPtpRclVqYWav0uiLgYpKZySIiIsu1rD56rAa8Z2ZDgI+Bl939VeAIM/sSGEXSJhUfJCkiIpJz5r58b4Wtd+7vwxfw4QW7hY+z2S9Nw8wVE4aFmU1aNAwza+0dFyQcfO7GYebYTi3CzCUz4j0FF638S5i55uGhYcavOSPMvNvpr2Fm8zHXhhm76M4wA+DzFoaZRtfF4x6z4JswM3PB7DAzduYPYabtynGBSJfXvwwzUw/qHmZaNYzLGKbOi0sk9n7upTDz6htx4cvQ174PMzbokDDzz5Hxcz32diW+p53XDDMARTtsHWbm3xf/3ndcPf59HXPoYWFmsxefDTP/OfqJWp1N9PWMf2SycFl3lTNzOUsqs43qZtbUzJ42s1FmNtLMtjGzZmb2hpl9lf67albjExERqSlZ7sG+GXjV3TckqW4cCVwI/Nvd1wP+nX4tIiLLGbOiTC55ldUEqVWA35Oer9bdF7j7dJJjbB9KYw8BB2QxPhERkZqU1aE/awM/AA+kfcifAmcAq7l7yc6WySQTqUREZDmjusbSsno36gCbA3e6+2bAz5TZZOzJzK1yd7AXllosGhpPhBAREclSVgvbCcAEdy8539rTJAvf782sNUD675Ty7uzufdy9q7t3Ld609TIZsIiISFVlsrB198nAeDMrOSlpd2AE8CJQcpLW44AXMhieiIhUk1GUySWvsjzF3mlAXzOrC4wB/kCy8H/SzE4AxgHxAWUiIiI5t9yXWqx+2/7hC2jVpF74OGO/mRZm/nlMpzCzRasNwsxr4+KCiNP7jgwzI3sfGmYWLi73XA6l7X1fGClaKf7E2PTVP4eZnZ+KD7b/dJ24SOC2RnGBBMBLX88KM8/1nxxm5l7XM8yMnj46zGz+5OdhZuzDI8LMei+fGWa+P/X+MNO6ZRhhxIfTw8yOf1gvzEzpdXqYeX+d0+LnOqB5mGm8euMwM/V38Zht1VXCDMD3V78ZZr67c58wM32Th8PM1j3XCTMNzjs8zFjznrVa/jB+9l2ZLFzarXyySi0KVVBqcaiZDTezxWbWNauxiYiI1KQsNyOXlFockm5KbghMBw4C7s5wXCIiUk153n+ahUwWtgWlFj0hKbUgOXft9PT2LIYlIiJSK7L66FFYavEfM7s3PdWeiIjICie3pRZLUlhqMef9sbU0RBERqaoiK8rkkld5K7WolMJSi4bbta+N8YmIiNSYTPbZuvtkMxtvZhu4+xf8t9RCRERWAHk+A08Wsnw3SkothgJdgKvM7EAzmwBsA7xsZq9lOD4REZEasdyXWhT9aZvwBRyx74bh4zw8L/7c0bNh/F4tWhxnHn0hLqz48NI9wsxud78fZqZ1XCPMFO++Z5hZ8OBTYabuiUeFma9++SbMfDFtYpi56dO4iALgxffKrdcupeGJO4SZ+c8MCjM7r79ymHl07+3CzFo/zgkz7d8cGGbG7rtbmGHuzDAy4ZTHw8xP38ZjXrgoHs6/H+0WZs5rFpc6/O6DT8PMI3tV7lD+9ne8F2a+O617mNns5pfCzNQ1W4WZNtNnhJkvTz42zDStd2itHvbx/ZwHM1m4rNawdss6qipvpRbXp18PNbPnzKxpVuMTEanMglakMrLcjFxSarEh0BkYCbwBdHL3TYEvgd4Zjk9ERKRG5K3U4vWC2EDgkGU+OBERqTZNkCotz6UWxwOvLPuhiYiI1KxcllqY2UXAQqBveXcuLLXw4d8vi/GKiMhSUKlFabkrtTCznsA+wFFewVTpwlIL23i1ZTFeERGRKstVqYWZ7QGcD+zo7vGxBCIikktGcdZDyJUsT7FXUmpRFxgD/AEYBNQD3kjP/DPQ3U/ObogiIiLVt9yXWvh3t4Uv4O+TPw8f5/RB08PMSkceHI9nypgw826DWWGmab34JEi3D/k6zCyqxPf3sPVahJld664dZgB+eeyNMFPvy7i04oRDNg4zXVrWDTMnD50dZhYM/SHM3NmjfZi5/oNJYeaOPeP3cfjUuWFm1frxHqBRPy0MM706rx9mXhwT/5xd8kRc1DL3hLhkY1iLyu3Z2qBplzAzcM2zwsyWx68bj6nXVmHmd7Z6mGHi+DDy2OZPhpkjF98VZmYtjP+eNV7pwFotf5g677FMFi7N6x+Ry1KLLNdsZQWTtwWtrJjytqCV8uV5slIWMlvYpu1Q9wKdACc51GcvYH9gMTAF6Onu32U1RhERkZoQLmzNbBbJwrBc7t6kis9d0iB1SLrftiEwABMJjQAAIABJREFU3N3/kj7v6cAlgPbZiogsZyzTgsL8CRe27t4YwMz+BkwCHgYMOApoXZUnXUKDVKFGLGEhLyIisrxYms3I+7l754Kv7zSzISRrn0ursEGqM/ApcIa7/2xmVwLHAjOAnarw2CIikjHtsy1tad6Nn83sKDMrNrMiMzuKpPmpKipskHL3i9y9HUl7VK/y7lzYINXnkfg0cyIiIllamoXtkcBhwPfp5dD0uqqosEGqQF+g3GNtChukTjo6Pj+oiIhIliq9Gdndx5LMFK62JTRIrefuX6Wx/YFRNfF8IiKybOmsP6VVZjby+e5+nZndSjkTltz99Co+d3kNUvea2QYkh/6MQzORRURkBVCZNduSaphPavKJ3X0w0LXM1XFFUxm/PPF2mLn4+6lh5tT6K4eZT38eHmY6XBIXO8z8149hZsejVg0zX3wyM8x03WeNMLNFq7ggYvuN4sd569K4qWuNGzqGmReGxmdyem/V+mEGoMW2cbPPj5vG3/uTHxgdZrZ+Nm4I6jorLrfpt2XzMLPdfu+FmT99fkGYmb1y/HN21GVxq9HF68Xfj+bPx2N+tec2YWbktP+EmQN6tQ8zn38wJcy89M2EMAPQomPTMDN0u/h9PODstcLMu2ueEmY6f31lmKltRTr0p5TKHPrzr/Tfh5aUM7Nb3f20yj5xeaUW7v5hets5wA1AS3ePl0wiIiI5VpMNUks7U6m8UgvMrB2wG/BtDY5NRESWIe2zLS2Td6Og1OI+SEot3L2kOfsmktPsqdBCRERWCFl99CgstfiPmd1rZo3MbH9gorsPyWhcIiIiNa4mF7ZLc1qj8kotLgP+TCUaqQpLLe798JuqjFVERGpRkRVlcsmrmhzZzUuRrajUYm1giJmNBdoCn5n99kSRhaUWJ25TufOsioiIZKUyx9n+iyWf9We/9N8HK/ukFZRafObu3QuedyzQVbORRUSWPzrrT2mVmY18Qy09d3mlFiIiIiucyhxn+05tPHEFpRaFt7evzOO8tF+7MDP6zPgoovrX7BxmtvIW8YCuOTSMFF05McwsGj0vfq5dZ4eRt7ZfJ8xsMX5+mLGF8XD2/nfcSdLLfgoz589pGD9ZneI4A9S5OS5SGHDxrmGm4cltw8zipx8OM7ftHpdsDPtiepjpfmBcRrF4xg9hptUN/wwzb9+zd5iZ99nkMLP9zBlhZu7mj4aZLa7fMsycsH38d2GVib+EmcvmVK48ZZPn3g0zD3xzVJhpMD8uWFn38vgU4vX+8UiY4fyl7hBaKnnef5qFSr8bZraemT1tZiPMbEzJpapPbGZN08cbZWYjzWwbM7vMzCaa2eD0sldVH19ERCQvluajxwPAncBCkvPM/hOoxMenCpWUWmwIdOa/tZA3uXuX9NKvGo8vIiKSC0uzsG3g7v8GzN3HuftlQLx9qRxBqYWIiCznjKJMLnm1NCObb0n/1ldm1svMDgTiHQzlK7fUIr2tl5kNNbP7zSzeMSUiIpJzS7OwPYOkv/h0YAvgGOC4Kj5veaUWF5Jspl4X6AJMAm4s786FpRavPz6sikMQEZHakudSCzPbw8y+MLPRZnZhObfXM7Mn0ts/MrP21X4/Kht090HuPtvdJwAnAD3dfWAVn7fcUgt3/97dF7n7YuAeoNxph4WlFrv12KSKQxARkf81ZlYM3A7sCXQEjjCzsuf+PAGY5u4dSPr6r63u8y7NbORHzaxJurn3c2CEmZ1XlSd198nA+PRE8ZCUWowws9YFsQPT5xERkeWMWVEml0rYEhjt7mPcfQHwOLB/mcz+QMlpZZ8GupvZ0lQS/8bSbEbu6O4zgQOAV0j2ux5TjecuKbUYSrLZ+CrgOjMbll63E3BWNR5fRESkrDWA8QVfT0ivKzfj7guBGUDz6jzp0pzPdiUzW4lkYXubu/9iZlU+DV4FpRZLvfA+/vGRYWbuBg3izKS4AGDewx+GmU7rxYUMr9/7RZixPoeEmW+2XC/MbHJP/zAz9L4RYabLjm+GmbHXHRtmzn83LrWYcv8HYWbwjb8PM1C5wortfmkaj6ldszDTea/WYebgd+NCkzFfzQkzja44Ocy8M++rMPPdlPhXuOEDg8PMoHtHhZniqzcPM8Ne2SnMHPTauDAz9fD4c7pvGD8O0+O/CwBDuu0bZp4s7hVmetzbKcwMOir+27Bvh0Zh5vUwsXwys5OAkwqu6uPufbIaT4mlWbO9GxgLNAIGmNlawMyqPnF5pRbp9ael1w03s+uq+vgiIpId82wuhXN60kvZBe1EoLBirG16XbkZM6sDrAJMrc77Uek1W3e/Bbil4KpxZhZ/DK1YSanFIWk/csP08fYHOrv7fDNrVY3HFxERKWsQsJ6ZrU2yUO0BHFkm8yLJ0TYfAocAb7l7lbfkwlIsbNMiiktJyigA3gEuJ9mWvVQKSi16QlJqASwws1OAa9x9fnr9lKV9bBERyQFfnM3zBtOY3H2hmfUCXgOKgfvdfbiZXQ584u4vkhQuPWxmo4GfSBbI1bI0+2zvJ5kdfFj69TEkFY4HVeF5C0stOgOfkhzHuz6wg5ldCcwDznX3QVV4fBERkXKlVcD9ylx3ScH/5wHxWWWWwtLss13X3S9Np0uPcfe/AvEpZcpXUalFHeD/27vvMKnqs43j34cqqKCgKIqKqIANUbBrsHcFe4tBjbFHMcZoYk0swRajsQVLxNeOsRcQUVGjiKhIkaaoiCJgAymCsM/7xzkLs8suz/ZzFu/PdZ1rZ8/eM/Obndk9M6fcpxWwA3AB8FhZu1sXllosfHdqFYcgIiK1xouymXKqMgvb+Wa2S/E3ZrYzML+K91tmqUU6/wlPDAeKgGXOa1e4AbzJtvGpz0RERLJUmdXIZwD90+2tRrIeu0p1je7+tZl9YWad3H0CaakF8AnJ8bWvmllHoAnwTVXuQ0REJC8qszfySGArM2uRfl/lw35SxaUWTYDJwEkkq5PvNbMxwEKgd3X3ABMRkQzkeJVuFqyiyzIza02yN/IugANvAn9z92ode1RdRc+cEj6ABzs2DW/n1y03DzPvNog/ZLdeqUWY+fCbz8LM2YPjA+57d1kzzLRpHm8puH/M92Fm3Kivw8wn4+Iyhg+Gxu/RZr+7V5g5ds3twwzATzc+EmYWXHZSmGn52cQw84fpn4WZY056I8w0Gx6vMLrp/Y/DzO2vTQszU/8U/67Pe+2DMPPs1luHGVZZZovQMnxYXGgyf+/9w0zba24NMz9+/WOYKfrryWEGYPBmZZ4zpYQGH8T7km5+zithZvTNu4WZKT/Gj+2UzW+vVv1gaNGgbD4oNdq3dh9XFVVmNfIjwOvA4en3xwOPAvFfaxnMbDXgbmALkoX3yUAfoLgveTXgB3fvWpXbFxGRDOmTbQmVWdi2dfcrC76/ysyOrsZ9L1Nq4e5Lbs/MbqQKx/CKiIjkTWUWti+Z2THAY+n3R5AcFFxp5ZVaFPzcSI7n3aMqty8iIhkr0ifbQpU59Od3wEMkC8Xi0xKdZmY/mllld5YqLLX4wMzuTk/dV2xXYLq7x03qIiIiOVeZk8ev6u4N3L1ROjVI563q7vFeQSWVV2pR7Fjg4fKuXFhq0W9QfLYRERGRLFVmNTJmdghLu5Ffc/fnqni/ZZVaXJTeRyOSCshu5V05PYtDP6jY3sgiIlLHtINUCRX+ZGtmfUn6iz9Kp3PN7O9VuVN3/xr4wsyK9zwuLrWAZO/m8e6uHkYREVkhVOaT7QFAV/fk7YqZ9Qc+AP5cxfsuq9QCkrMrlLsKWURE6gF9si2hUquRSY59/S693LI6d5w2UnUvY/6JlbmdF34dlwTsu1+rMGP37BZmbv5ffAKim3fbM8xcM+ztMDP+5F+HmT0GPBZmBh5+cJh5Y+rgMDPyxwVhptPW8ab7j5s2DDMd+n8UZo49faMwA/DzN3F9d8M+d4QZPz4ubahIYcXcufFWj+2m/hRm/rNX/PpYMOpfYebDNv8OM69esG6Y8VVXDzMvbRQXoxz+VlweMmfN+O/5hy3jzvTPhrwXZmauulKYAZj6evx3f+Ta24WZbQ6uwD6hL38SRmZ8Ge+zekrc4yM1qDIL22uAD8zsVZJu5F9RcqemSimn1GI+cCewErAIODM9IYGIiNQn+mRbQoUWtmbWgOQMPDsA26azL0y3vVbVMqUWJMfw/tXdXzSzA4DrgN2qcR8iIiKZq9DC1t2LzOxP7v4Y8Ex177S8Ugszc6B4XWRL4Kvq3peIiEjWKrMa+WUz+yNJH/Lc4pnu/l35VylXYanFVsB7JHs69wEGmdkNJHtK71SF2xYRkaypQaqEyjRIHQ2cCQwFRhRMVVFeqcUZwHnuvh5wHnBPWVcuLLUYuPCHKg5BRESkblRmYbsZcBvwITAS+BdQ1f3Zyiq12IbkZPRPpPMGAGXuvufu/dy9u7t336/JalUcgoiI1BovymbKqcosbPsDmwK3kCxoN0vnVdpySi2+Anqk8/YA1I0sIiL1XmW22W7h7psVfP+qmcUHRJavrFKLp4Gb08rGn4BTq3H7IiKSlRx/ysxCZRa275vZDu4+DMDMtqfq22zLK7V4k+V0Ipel8+aNw8zi+34XZla76e4w8/6gaWGmy2fxNuSRvQ8NMy2seZh5+pHJYabVBh+EmYf2PyrMnNH0iTAz4I3PwkzbNcMIo56Jf8+cHkcAWlwfB7d//skwc+X68Z/K7n+Oiy+6Nl4UZm5cLS7iKPr8hTBzctPFYWbYN78PM2MOuSvMtG3zWXxfV34eZnpduE18O0fFr8V1N24WZtpfsmuYsalfhhmAST/Ez+uqDVYJM7/vFv+BHLFx/C+y9S1Vra2X2lKZhW034C0zm5J+vz4wwcxGA+7uXSpzx+WUWswjKbVYBfgMON7dK3v6PhERkVypzMJ2vxq+77JKLQYDf3T3oWZ2MnABcGkN36+IiNQ2rUYuocILW3eP1wFV0HJKLToCr6exwcAgtLAVEZF6rrInIqgp5ZVajAV6Ak8BRwLrZTQ+ERGpBvd4n4HaYJnca6wyh/7UpPJKLU4GzjSz94BVgYVlXbmw1OKR6VUpsBIREak7WX2yLavU4iJ3vxTYByBdpXxgWVd2935AP4CPd9wiPmeZiIjULdU1lpDJJ9vySi3MrA0sOcvQJSR7JouIiNRrWa1GhqWlFqOAriTnyz3WzCYC40napP6T4fhERERqhLnX77Wwjc/bJXwAP67ROrydSQ9PCDNbvndjPKCG8Zr5P779eJg5qEObMNPju7jQw7boEWba3nFzmPnqmMPCzKhdrgkzrYb3iTOX/jfMNOtZsVruTy8ZEmY+uG/3MDN8+k9hZsrsMncxKOGuZ6aEmXFX7BZmtmtTgUKGM/8cZs45Zsswc+MX8WOfcN27YeaJu+PX4sXbxgUr9tl7YebT3g+EmVfu2CXMtGm+UpgBOKTVtmGmaHTcAfTlVa+GmXY3Hx5mfPq3YabBr/5Rq/sS+bf3ZbJwsdYn5nIfqUw+2ZpZJzMbWTDNNrM+ZnakmY01syIzK90uJSIiUi9lsoOUu08gWXWMmTUEvgSeJCm2OAz4dxbjEhGRGqJSixKy2hu50J7AJ4WlGWa5XAsgIiJSJXlY2B4DPJz1IEREpAbpk20JWe6NTNqJfAjJieIrc70lpRZFo7+uncGJiIjUkEwXtsD+wPvuPr0yV3L3fu7e3d27N9hy7VoamoiISM3IejXysWgVsojIikerkUvI7JOtma0M7A08UTDvUDObCuwIPG9mg7Ian4iISE2p96UWY7t0Dh/A5sOvD2/nkwWfhJl1V9kkzMz9OT7X/ayFM8PMlB/jNevHPftRmLlz3w3DzJMfzwoz9z0xNswcsn/HMPPuVz+GmSmnnhlmXl/n7DADsNGkc8PMem+MCjMf77RxmOn2z4FhZvT5vcJM21tfDjNX9IhLTy7Ztsxq8RKKzokLTVo3+znM/GDNwsw6reKjDM7aY4Mwc2G3fcPMfePi3+FWB8QFEgve7hlmAPZuFJ+g7FdvvhVmBk+aG2bmjo8LK3beM968Nv7Ex2u31OLrO7MptVj79FwezpLJauS0E/nRglkdgMuAdYGDSc728wlwkrv/UPcjFBERqTlZnYhggrt3dfeuQDdgHkmpxWBgC3fvAkwE4r45ERHJHy/KZsqprPdGhoJSC3d/yd0XpfOHAe0yHJeIiEiNyMPCtrxSi5OBF+t4LCIiIjUul6UWZnYxsAh4sJzrLSm1GPCdNumKiOSOViOXkPVxtsuUWpjZicBBwJ5ezq7S7t4P6AcV2xtZREQkS1kvbEuUWpjZfsCfgB7uPi+zUYmISPXk+FNmFnJVagHcCqwKDE7Pc3tnJoMTERGpQZl9snX3uUDrUvPi5oBStjtknTCz8r/7hZkZZ58TZh5vHhcpdJn+uzDz0IRJYebyteMCjc8brRRmGs1bLc4c+XyYOadtfJz4buvEJ4X4Yb/uYebhSU+Fmb/13TLMALx02F1hZuNjOoSZCQ9/H2Ze/r+pYWbD6Q+EmWlx5wnXHLF9HJo6IYw8cO6mYeazC98NM59+Gg/6uyt2DDO2QZcww9efh5HtD4sLK7a9Y5cwU/Tql/F4gDf2bhxmXt9xuzAz59VnwsyaLRaHmTZz4iKSWlekT7aF8lZq0RroCRQBM4AT3f2ruh+hiIhIzclkYevuE4CuAGbWEPiSpNTie3e/NJ1/DskC+PQsxigiIlJTst5BCgpKLUrNXxnQnsYiIvWRdpAqIQ8L2xKlFmZ2NfAbYBawe1aDEhERqSm5K7Vw94vdfT2SQosy90gqLLVY9L426YqI5I5KLUrIuq5xmVKLAg8Ch5d1JXfv5+7d3b17o23ivZFFRESylPVq5NKlFpu4e/FxMT2B8ZmMSkREqkeH/pSQ2cK2oNTitILZfdPDgoqAz9GeyCIisgLIW6lFmauNl+ed8/YJM5uNjssW5l92U5hZNHrvMLPxmGlhpnmzuCDi5Injwsz8dZuGmf7PvB9m9h99Rpg5YMR7Yeb5HTYMM20eicczeWLc1HnSyovCDEDbwzqGmWM3axlmvr1xVJjZ9l+7hZn/Hh6XLdi38Wmcb/v4nTBz1jrbhpmGHe4JM+3PaxNmFtzzpzDz6FrXhpljT41f9688FZ98ZMs9WoeZWfvGf88tX3wpzAAs7vZ4mNn9lm3CzB8v3DrMHDwqLhC5bY+4sEPqVibbbM2sU1rHWDzNNrM+BT8/38zczNbIYnwiIlJNRZ7NlFN5K7XAzNYD9gGmZDE2ERGRmpb1DlKwbKnFTSRn/nk6uyGJiEi1aAepErI+9AcKSi3MrCfwpbt/mO2QREREak5uSi3MrDnwF5I+5Oh6S0otBvQfXtvDFBGRyioqymbKqaxXIy8ptTCzLYENgQ/NDKAd8L6ZbefuJXYndvd+QD+AMd/2ze8WcREREbJf2C4ptXD30cCSYwzM7DOgu7t/k83QREREakbeSi1ERGRFkOPDcLJg7vX7FzLv/H3CB7D+Oo3D21lp1SZh5tvJ34eZSaPmhJl1/tIjzBTttF+YWbA4Ln9YeXG8Wb7d/feGmWcO7RZmtlmpU5gZ2vnSeDwfxcVhG1nbMANw2ojBYWb4Vz+GmQkfzQgz363cIszMHf9tmFljlbiwo/XGcWnDgh8XhJkxb8Qrjv5zzfZh5vIN49fHmv8XH2Cw3lqrhJn3e+wYZhass0mY2eWR+8PMn7aLCz0Ajti4Vxwa8lQYGXhEXKKx3YFx/UCje/uEmdWaHhm361SDj7sqk4WLbXpJrT6uqsrkk21ayfhowawOJDtGrQb8DiiuSPmLu79Qx8MTEZHqyvHOSlnIW6nFScBN7n5DFuMSERGpDVnvIAUFpRbpXsgiIlLf6ZNtCbkqtUidbWajzOxeM1s9q0GJiIjUlNyUWqSz7gA2IlnFPA24sZzrLSm1uHfU1DoZq4iISFVlvRp5SakFQPFXADO7C3iurCsVllpUZG9kERGpYzr0p4SsVyMvKbUAMCtxPMehwJg6H5GIiEgNy1upxXVm1hVw4DNUeCEiUj9pB6kSMlvYuvtcoHWpeSdU9na22GzVMHPm5nEBwO+22CbMvDt9fJi5Y0ZcNHGlxb/2l6bEB7fvv3JcIrH1oLjU4dMGK4WZolZxaUHRkDLX+pfgww8PMy2bxgftv7fV5WEG4N8jrgwzoxZ+HGa6vfd8mGn2/fQw02nPdmHmkFbNw8wl58cn4Oj+2plhZtRjt4eZozbpEGbmX/dkmJm+dVwQ0aBLXEYxtvlPYWbzKWPDzHtvfh5mDp1TsSMkGnSIFywDN2kWZsYM2T3M7L/BdmGmZd9+YWb25UeGGak5maxGNrNOZjayYJptZn3Sn/3ezMab2Vgzuy6L8YmISDUVeTZTTuWq1MLMdgd6Alu5+wIzq1hXmoiISI5lvYMUFJRaAGcAfd19AYC7x4W0IiIiOZeHhW1hqUVHYFcze8fMhprZthmOS0REqkonjy8hb6UWjYBWwA7ABcBjVkaHY2Gpxeyhn9bZeEVERKoi60+2JUotgKnAE54YDhQBy+ya6u793L27u3dv0WPDOhyuiIhUiD7ZlpD1wrZEqQXwFLA7gJl1BJoA8Qk3RUREcixvpRb3Avea2RhgIdDb6/vZ7UVEfoGy+ted13PHWX1flh32XO/wAdyy287h7by/dpnnPCjhkzfjA877tIsPOGdhXHzxYZPZYebZyfFB+cee+78w0+6gjcJMs6lxYcMFv+4SZp6c8F2YGdcwLtmw5hV7n3hjh/i2LmjTOb6/dTYPM78Z8nCY6d/tV2FmyLwJYWan20aGmdbMDzPPn71TmNm9efz7KRrySpgZu0dcwrLl1z+HmX4+LcycvFmvMFN007/DzNAj45INgB4DJoWZxp3ispb5+x4YZpqP/yDMzOm8VZhZtfGhtbpcKhp2YSYLlwY7XJvL5W0mn2zNrBPwaMGsDsBlwI5A8V/kasAP7t61jocnIiJSo3JVauHu/yzOmNmNwKwsxiciItWU452VspD1KfagZKkFAOnhPkcBe2Q2KhERkRqSh4VtYalFsV2B6e4ebwgREZH80SfbEvJWalGs9CFBpa+3pNTi04ETa3OIIiIi1Zb1J9vSpRaYWSPgMKDcc7q5ez+gH1Rsb2QREaljOT4DTxbyVmoBsBcw3t2nZjAeERGRGpfZwrag1OKJUj8qaxuuiIhIvVXvSy3wV8MHsNKFV4Q3M+/ay8LMG+3OCjMbbtYszKx3b+8wM7FnfMD9PmdsGmYmr9YizCx49+sw0651fJz43UfEpQW9PolLCxrsdViYYW5cjgGwyYBHw8x9fx0TZnZ6709hZs6lD4SZb8Z/G2bWeblvmJnwQ1xq0XD3/mFm0xv2DDPj/jgkzGz+1tVhZuLul4aZ9x+MD0A49qYRYebVS+LHtWjrx8NMl91ahRmAV64qd6vXEmc++UmYGXZaPO4jnhkeZvqe82GYOeCHCbVbavFan2xKLXb7Zy5LLTL5ZGtmncxsZME028z6mFlXMxuWzhthZhWoYxIREcm3XJVaAHcBf3X3F83sAOA6YLcsxigiItWgQ39KyHoHKShZauFA8XrPlsBXmY1KRESkhmR96A+U3CGqDzDIzG4geSMQt6SLiEj+6NCfEvJWanEGcJ67rwecB9xTzvWWlFr06/dc3QxWRESkirL+ZFu61KI3cG56eQBwd1lXKiy1qMjeyCIiIlnKemFbutTiK6AH8BrJSQjUjSwiUh9pB6kSMlvYFpRanFYw+3fAzWll40/AqVmMTUREpCbV+1KLV9buFD6A3Yf+Jrydg8ePjzMbrRJmPvp2UZj55+Y7hhnmzw4jC9bZJMwc+NRDYea8bmuHmW7nvRlm+vbpEmaO7rhOmNluyOQw80qf+MB+gA3Wj3dL+GFW/Dew3yntw8xeXeLf44Bd9g0zk21mmPly89vDzC5vnhJmNn11WJj5R5/RYWa3324QZlaeF7+mrzp5qzDzq8OGhpldnjoizNzWOC4Y2XL/V8MMwOX/3CbM/G3nDmFm1DfxmNZYqWGYOfvZT8PMN+c/X7ulFgPPyKbUYr87VGpRbDmlFluZ2dtmNtrMnjWzuP5IREQk5/JWavE48Ed3H2pmJwMXAHHHm4iI5IsO/Skhb6UWHYHX0/mDgcMzG5WIiEgNycPCtrDUYizQM718JLBeJiMSERGpQXkrtTgZONPM3gNWBRaWc70lpRbPzfuhbgYrIiIVV1SUzZRTWR9nW6LUwt3HA/sAmFlH4MCyrlRYalGRvZFFRESylPXCtkSphZm1cfcZZtYAuAS4M7ORiYhI1eX4U2YWMluNXFBq8UTB7GPNbCIwnqRN6j9ZjE1ERH55zKyVmQ02s0np19WXk21hZlPN7NaK3HZmn2zdfS7QutS8m4GbK3M7rz+9W5gpaj4nzDw/MG6G/N/6q4WZh6+PDyZf/fwpYea7LeN9w7457YEw8+VR7cPMEbePCDO9Tt00zAx664swc9LmG4aZEXttFGZajNmIPW6Jyw1WWSsuIpneq0eYafxUfF8LF8fv5F/t/Ncws/WBcTnGun02CzNDGsQFCZPei89iucnnp4WZ5k3jgpVGf742zBy5SXw7Z988N8y8tMmWYeased+HGd7bipnn9A9jQ36O/5Uu7DcyzOx69A5hpujDj8MMf6pA5vw4Ui3189Cfi4Ah7t7XzC5Kv7+wnOyVLD16JpTlJ9vzzGysmY0xs4fNbCUz29DM3jGzj83s0XQHKpFlVGRBK1JdFVnQygqlJ1D8pPcHepUVMrNuwFrASxW94awapNYFzgG6u/sWQEOSQ4CuBW5y942B74HfZjE+ERH5RVrL3aell78mWaCWkO5TdCPwx8rccJY7SDUCmpnZz0BzYBrJmX6OS3/eH7gCuCOT0YmISNVltIOUmZ1KyZPY9EuPYCn++ctAWdtqLi78xt3dzMpaF34m8IK7TzWreA31GiVgAAAgAElEQVRzVnWNX5rZDcAUYD7JR/H3gB/cvbjJfyqwbhbjExGR+qnE+c7L/vle5f3MzKabWVt3n2ZmbYEZZcR2BHY1szOBVYAmZjbH3S9a3riyWo28Osm68Q2BdYCVgf0qcf0lpRbvPTWulkYpIiJV5Ys9k6mangF6p5d7A08v87jcj3f39d29Pcmq5PujBS1kt4PUXsCn7j7T3X8mOfxnZ2C19Fy2AO1ITlCwDHfv5+7d3b17t17xXrIiIiIV0BfY28wmkSyn+gKYWXczu7s6N5zVNtspwA5m1pxkNfKewAjgVeAI4BHKeVchIiL1QD089MfdvyVZHpWePwJY5gTR7n4fcF9FbjuTT7bu/g7J6fTeB0an4+hHcjzTH8zsY5JjcO/JYnwiIiI1KctSi8uBy0vNngxsl8FwREREak3W3cjVtlmruPdi0zNfDjODbtsjzHz3U9xc8+JOcfPToQvjXeJPbRCvgnnugHhn7a86xo1NdtABYebFOfGOaA+t2jrMLHro/TDzv7+PCTNNz2kfZgCu2St+Pj5eLV7BM/H034SZllPi1p43Gw0PM6cf3j7MTJm1IMz8ZXGZJ80q4aC9Nw4zDQ++L8z8tNcGYWbBkTuGGfv8xzDz/MxFYebTnteEmQ0HXxdmVl43biADaPTGxDDz821Xhpl5feKWrcYdW4WZ1VqGkdpX/Z2VVih5a5B60MwmpPPuNbPGWY1PRESkpuStQepBoDOwJdCMMjZIi4hI/nmRZzLlVZ4apL5y9yU9k2Y2nOTwHxERkXotNw1SpRa0jYETgHOzGJ+IiFSTttmWkJsGKTP7dUHkduB1d3+jnOsvaZB6+ZF4ZxoREZEs5alBaicAM7scWBP4Q3lXLmyQ2uuYLepkwCIiIlWVqwYpMzsF2BfY092zOWWEiIhU32L9Cy+U1Tbbd8ysuEFqEfABSYPUXOBz4O301EVPuPvfshijiIhITTH3+r0R+7kWncIHsMfpcbHDStdeEGa+PfbSMPPTDz+FmYZN4/c43386O8xssfNqYWbx4duGGebNDyMNeixTF7qM37wzOMxcvVNcENa0YfMw88zkYWEGoH2L+Oj+I+6PizbeHvB5mOnw3k1hxonf7T8zeWCY6f7boWFm9hPHhJkur08KM1v+OCfMjF49/j032P/wMDN+TrwPRosmq4eZ73e8IcyMe2KfMHPERoeGGYA1/3ljmLEG8blPpx+5b5jxkfHvaMa9I8PM2o8Pq/jJWKvg57uPzWTh0viUh2v1cVVVrkotCn52i5nFf+EiIiL1QN5KLTCz7kD81lVERPJrsWcz5VRmn2xZWmrRiLTUwswaAtcDf8pwXCIiIjUqq1PsfQkUl1pMA2alpRZnA8+4+7QsxiUiIlIb8lRq8RvgSOBfFbj+klKLgQt/qN3BiohI5RV5NlNO5anU4q/AxsDHZvYZ0Dw9ifwyCkst9msS75ErIiKSpTyVWvzD3Zd8qjWzOe4en2xTRERyx3O8s1IWstpm+w5QXGoxOh1HvyzGIiIiUtsyO8Weu18OXL6cn69SkdtpNLpnmFnpg2/DzOjNzwozG3xwZZiZNqfMNd8lzO3eP8xsf/MuYabhkHFh5tY1F4WZczrFv8Pn2vwxzDzYu3WYefOLWWHm0xFfhplBf+4RZgD2/CZ+iT938vZhpsNFfcLMNg/cG2ZGfBoXiHR7OS7Q+Oa/R4aZds3ahJmiXnHpSY+hj4aZe9s0CTMHHnVFmLnlnC3DzPEnvhlm9jspPjvnh23iFWcd7r01zAC89cinYWbjW+O/s3l9nwozzQ7qFGaGvfB9mOkVJqqpSHWNhXJVamFm95nZp2Y2Mp26ZjU+ERGRmpLJJ9uCUovN3H2+mT1GWmoBXODuj2cxLhERkdqQ2WpklpZa/ExaapHhWEREpCZpB6kS8lZqAXC1mY0ys5vMrGkW4xMREalJeSq1+DXwZ6AzsC3QCriwnOsvKbV44aEP62jUIiJSUV7kmUx5ladSi53cfZonFgD/Aco8H1thqcUBx21Vh8MWERGpvDyVWowws7buPs2SM8f3AuITN4qISP5om20JmSxs3f0dMysutVgEfEBSavGima0JGDASOD2L8YmIiNSkvJVa7FHZ2xn0eXzw9sQNK3DA/dvxcv3kwc+FmVdHTw8zA14/NMzw7tdh5Ju5jcPMtu/NDDM9/3BpmDnwnRPCTNEt8e/n7ovjlRV7HxmXY+z493fDDIDv3yHMtP39O2Gm1/lxacGbQ2eEmaKW8Wtx/ddvCDPtrr0jzFz6q6lh5ort1wozdzwWP2dtNo0LNE7stUmYeWZSXEDTp0289euNs+N/JR18zTDzSZe4QALg5xMahplLFn0TZv6+eG6YaTMxfl4veHP3MCN1K2+lFmZmV5vZRDMbZ2bnZDU+ERGpBp08voS8lVoYsB7Q2d2LzCx+uywiIpJzeSu1uAo4zt2LANw9Xi8nIiK5k+fDcLKQt1KLjYCj02NoXzSzeCOPiIhIzuWt1KIp8JO7dwfuAso8jUphqcXoZ8bX1bBFRKSiFhdlM+VUrkotgKnpZYAngS5lXbmw1GLLQzrXyYBFRESqKlelFsBsYHfgU6AHMDGj8YmIiNSYvJVaNAMeNLPzgDnAKVmMT0REqkc7SJWUt1KLBcCBlbmdf2y6fZixpquGme8rcH6hVs1Gh5lvt+8Yj2fOyvGddds8jLRYY/UwM376d2Hm4+8XxJkTHw4zE1/6bZjZcNsRYWbRntuEmZH73R1mALr22TPMbPT6sWFm4FkXh5k5Xy0MMz123jDMvDn/izCz+oJFYea6beI/JR89PMzMXXW1MDOtd1wiMef8p8LMwGv3CzObHLx3mPnx4gFhZvjf4uKH7t3j1w9Ao7nzwsyzk+KziI79X1zS07nPIWHmwhlxCYvUrcwWtumn11MAB0YDJwGDgeIlYxtguLv3ymaEIiJSZTkumMhCrkot3H3Xgsx/gaezGJ+IiEhNyqyukaWlFo1YWmoBgJm1IOlJjtc7iYiI5FxWO0h9aWbFpRbzgZfSUotivYAh7j47i/GJiEg1aQepEvJWalHsWKDcPXIKSy363f967Q5WRESkmrLaQWpJqQWAmRWXWjxgZmsA2wHlnofO3fuRHCqEz7xbb59ERHLGtYNUCVlts11SamFmRlJqMS792RHAc+7+U0ZjExERqVF5K7WA5FR7fbMYl4iI1BBtsy0hb6UWuPtulbmdWS1ahpnRG18aZjabtMxQlnH59tuGmcl73xpmOvx5hzBzdMO4tOCm28aEmVabtgoz7XZZN8yMenZqmFnL4pfT/y59P8y8unlcQvL5LTuHGYB714gfm8+eFmZarNMizKx7+4Vh5oi3Hg0zkzreEGbW26hJmGnX8v4w8/gf4tfQFt+cGGZmbHJHmGl10Nph5u/vTggzDz09Lsx8ddOpYWb78/uFma9ubBdmANpus2OY+d+j/w4zK18Rv66fbDA9zPR77fMwc13F/oSkhmR26I+ZnWdmY81sjJk9bGYrmdmeZva+mY00szfNbOOsxiciIlJTstobubjUoru7bwE0JFl9fAdwvLt3BR4CLslifCIiUk06xV4JeSu1cKB4fV1LCoouRERE6qtclVqY2SnAC2Y2n+R0e/HGTRERyR2d9aekvJVanAcc4O7tgP8A/yjn+ktKLe67++W6GraIiEiV5KnUYmdgK3d/J808Cgws68qFpRY/LBigt08iInmjUosS8lRq8RHQ0syKTwi7N0uLLkREROqtvJVaTAX+a2ZFwPfAyVmMT0REpCblrdTiyXSqsH98MDjMXHHbLmHG5v0cZta4+L4w89Ob14cZf/yRMDN6/tww8/V9PcNMozOfDzNtTo/LOrquGpco3Dn5ozDTswJlDJfNil+WDXbvFWYA5l18c5hZ6ZDNw8z2m7cJM3NOvyrM/Onm34aZZkd9GmaeP7NzmNnoH++GmZ8XxyUsX+37pzAzeODuYWbnZ+JilAd33CfM9P8mLnz5aH5c/DC35ephpnH/QWEGwLfbJMw83+/LMHP0yfE+oQe0j9sovj+9a5ipbdpBqqS8lVrskZZajDGz/ulhQSIiIvVankotjgP6A8ek8z4HemcxPhERqR5f7JlMeZWnUou5wEJ3n5j+fDBweFaDExERqSmZLGzd/UuguNRiGjALeAxoZGbd09gRwHpZjE9ERKrHizyTKa9yU2oBHE/Sj3yTmQ0HfgQWl3P9JaUW7z+lo4NERCTfslqNvKTUwt1/Bp4AdnL3t919V3ffDngdmFjWld29n7t3d/fu2/TatA6HLSIiUnlZ7e27pNSCpBt5T2CEmbVx9xlm1hS4ELg6o/GJiEg1FOV4Z6UsZLXN9h2guNRidDqOfsAFZjYOGAU86+6vZDE+ERGRmpS3UosL0qnC+o2ID17/+yffhpkFa8QHuBedvm+YaX3DLWFm2OOfhxnO2iyMdGsY7z+2sNvaYWbcH+JikHET4nepfXrFB9IX/X6bMHPxKnHByMHfDAszAJ2mzQkzzTp2CjN9DnouzLQ8Nn4NnTVsaJi57vpzwsymXeM/k8vv6RFmDn76/jDz3wq8hs7a6uAw03T1sWFm4l5XhpkpT8RlLhs2ahZmLjuwXZi5vlXF9tFc2Dkuhllt8sIwM/W0MuvgS2j0xMZhZu3JH4cZWseR6sjzzkpZyLLU4ty0vGKsmfVJ57Uys8FmNin9Gv/3EhERybms9kbeAvgdsB2wFXCQmW0MXAQMcfdNgCHp9yIiUs94UVEmU15l9cl2U+Add5/n7ouAocBhJIcD9U8z/YGKFeCKiIjkWFYL2zHArmbWOt0j+QCSAou13H1amvkaWCuj8YmIiNSYrE6xN87MrgVeIqlpHEmpAgt3dzMrcwu7mZ0KnAqw6jFdaL5z+9odsIiIVEqee4qzkNkOUu5+j7t3c/dfkZy7diIw3czaAqRfZ5Rz3SWlFlrQiohI3mV26E9BgcX6JNtrdyCpb+wN9E2/Pp3V+EREpOp06E9JWZ4v9r9m1hr4GTjL3X8ws77AY2b2W5JT7B2V4fhERERqhLnX73cfT3zSJ3wAB2+4f3g7U36MT2jQ/MSHw8w3k2aFmYrYcvQdYebtDmeEmZtv3yHMvDD8yzAz66T4dzhjjVZhZs2X3ogzk6aFmZ9m/RRmAKyBhZnPPolvq9XxW8Zjej0uK1n415PDzLxF8WuoWaNVw8zfRwwJM3/bIS6IaHZ2XDTx0ds/hJnO/zkyzPx455thZtU/7BVm/vXT1DBz0aPjw8zP8+OCFYCFl54aZto+9N8w8/mChmHm6xcmh5kNni3dF1SG5ofGfxzVMPOYnTJZuKz5yFu1+riqKm+lFkem3xcVnGpPRESkXstbqcUYku23r2cxLhERkdqQ1TbbJaUWAGY2FDjM3a9Lv89oWCIiUhO0g1RJeSu1EBERWeFkdYq9cUBxqcVAyii1WB4zO9XMRpjZiJceGV1LoxQRkaoqKvJMprzKW6lFRa+7pNRin2PivURFRESylLdSCxERWQGorrGkvJVaHAr8C1gTeN7MRrp7fMZ2ERGRHMtsYevuu5Yx70ngycrczqKieFNvk7PiA7wrUn4w8I34wP2dT1g/zLRfv2mYmblgTpjZ/5QNwsz3a8f7nY16dFiYWXxpvLq+/V+uDjNXHdU5zHz+8oIw88Pk2WEGoHmbZmFm5W3ik0vZWmuEmZ++Hhtmml7UL8ys1jguNtio/Uph5qMR38fj6TQ9zCzef4sw89h58Xga947/tPtetX2YOffw/wszv907ft3v92b82Dc4Jn69Akw+5Pow89nQvmFmxoGXhpn1r9kvzFwxemCc2f7QMCM1J2+lFteb2XgzG2VmT5rZalmNT0REqs6LPJMpr/JWajEY2MLdu5DsMPXnLMYnIiJSk3JZapEaBhyRxeBERKR68vwpMwt5LrU4GXixzkcmIiJSw3JZamFmFwOLgAfLun5hqcXLj4ypgxGLiEhl+GLPZMqr3JVamNmJwEHA8V7O+f8KSy32OibeU1JERCRLuSq1MLP9gD8BPYq354qIiNR3eSu1uBVoCgxOz/wzzN1Pz3CMIiJSBV5UlPUQciVvpRYbV/Z2up30WpiZ1yM+wH3I7zYNM3v/qW2YOf2T8WFmwRufx5nG8VPTZ6/2Yca67hFmNtz1zTDTYNhLYWZe773CzIAucSHByp+dFWZ+XH2VMAPw9U8zw8ymn8yKb2jtNmHk+J7tw8x3P/0cZt5eOX5sQ38Vly10mPVymPmoeRih4QNxWcekP3YJM+ccs2GYmfl4fF89Ht8zzOz8aFy1fu+ouBDn/QfXCTMAU3aLXx8XfDkhzPzjL93CzP2vjwozf903LryRupXlauRzSY61NeAud/+nmV0J9ASKgBnAie7+VVZjFBGRqsnzzkpZyFupxfXu3sXduwLPAZdlMT4REZGalOdSi5UBvTUSEamHVGpRUu5KLczsajP7AjgefbIVEZEVQO5KLdz9Yndfj6TQ4uyyrl9YavHI19/V0ahFRESqJnelFgUeBA4v57pLSi2OWbtVbQ9VREQqqajIM5nyKstT7LVJvxaXWjxkZpsURHoC8XE0IiIiOZe3Uot7zKwTyaE/nwMqtBARqYd06E9JeSu1KHO18fJs1zMurPhiZtxkstd6cflDx/7/CTMTt+wYZubvGI+nybdfhJlDTxoaZqZ1Ghlmfpz6Y5g5d7+vw8y/zo9P0nTUwjvDDIsXhpEGC6fFtwNs1iTuSVk04akwc86cGWHm2dnxP5cnTvkgzMya87cw0/SAv4eZ71+4Osx8uPX5YabL0D+GmWMP+WeYueSxU8JMg2seCTOzFsZ/P8fuGpc6HHzFymHm1jmLwgzAcZ22CTPzLh0QZg69YPsws/9hr4eZvfeIxyN1K8vVyOea2RgzG2tmfdJ5V5jZl2Y2Mp0OyGp8IiJSdV7kmUx5lckn21KlFguBgWb2XPrjm9z9hizGJSIiUhtyVWqR0VhERERqVe5KLYCzzWyUmd1rZqtnND4REakGnTy+pLyVWtwBbAR0BaYBN5Z1/cJSi5+GTambQYuIiFRRrkot3H26uy929yLgLpJtumVdd0mpxUo7rF+XwxYRkQrQDlIl5a3UovCEsYeSrG4WERGp1/JWavEvM+tKcrafz4DTMhyfiIhUUZ4/ZWYhb6UWJ1T2dmZ2WifMvHP7m2FmhzPGhpkvxs8MM98dF/dynL/NE2Hm2BvjA/fn9y1zLXsJT+8dj+fu0S/EtzP8+zDTau+1wsx3t90RZn485dAws9qtT4cZgG9Hx8/ZXvuuG2bebxGXJLD32mHk4It/CDN3jns1zPz++M3i8TRpHkb2Pm2TMDPzsrvDjL90eZhZeHdcWFF0+r5h5lfjJ4WZpw45OMwsfj4uqRk27acwA3Ddm8+FmRFXxn+LrRbEr48tPrkizAybPyHM7BAmpCblqtQinf97Mxufzr9uebchIiJSU8yslZkNNrNJ6dcyj4gxs+vSZdQ4M7vFzCy67byVWqxHcgKCrdx9QfF2XRERqV/yfBjOclwEDHH3vmZ2Ufr9hYUBM9sJ2Bnoks56E+gBvLa8G87qk+2SUgt3XwQUl1qcAfR19wUA7h4X0oqIiNSMnkD/9HJ/oFcZGQdWApoATYHGwPTohvNWatExnf+OmQ01s20zGp+IiFRDVuezLexhSKdTKzHstdy9+CwnXwPL7Iji7m8Dr5J0QUwDBqXdEcuVyWpkdx9nZsWlFnNZWmrRCGhFsu1+W+AxM+vg7iXWR6S/vFMB7jhzZ07dt3NdDl9ERHLK3fsB/cr7uZm9DJS1N+PFpW7HzWyZdeFmtjHJ2tl26azBZraru7+xvHFluTfyPcA9AGZ2DTAV6Aw8kS5ch5tZEbAGMLPUdZf8MoueOaVebhgQEVmRFcUHVGTC3fcq72dmNt3M2rr7tLT3oaxNmYcCw9x9TnqdF4EdgeUubHNVagE8Beyezu9Isk78m6zGKCIivyjPAL3Ty72Bso4xnAL0MLNGZtaYZOeocDVyZgtbklKLj4BnSUstgHuBDmY2BngE6F16FbKIiEgt6QvsbWaTgL3S7zGz7mZWfMD548AnwGjgQ+BDd382umGr78uyde48NHwAz94Uvungyy/j38NDA3YMMxf0GRZmuo29OcwcOejhMPPbE94OM7N/DCMcc2qrMDO/XVxYsdqs+ID8ubt0DDPWNj7i63c/TAszAHetHZdRTDwh/l1vvmv8O1q8cHGY+dtJW4WZS9bdPMxY2zhTNHBAmLmuAgfXrb9tXLLR4tODwsy9Y+KVVDffOjrMrPfEpWFm9nm3hJkjKlBmMmLSt2EG4Jtdtwgzz7WNXx8LFy8KM/MXxbfz5Zw4c1H3O8NjQ6tj9BadM1m4bDlmfK0+rqrKVamFmT1qZiPT6TMzG5nV+ERERGpKrkot3P3ogsyNwKwsxiciItWT1x2kspK3UgsA0uqro4B4/Z6IiEjOZXXozxjg6vSsP/NJSi1GFPx8V2C6u8eN4yIikjs66U9JmXyyTds2ikstBrK01KLYsSznU21hQ8i8Nz6rzaGKiIhUW2Y7SLn7Pe7ezd1/BXwPTAQws0Ykq5QfXc51+7l7d3fv3nzX9nUyXhERkarKrEHKzNq4+4yCUovi0yvuBYx396lZjU1ERKpHO0iVlNnClqTUojXwM0tLLQCOQTtGiYjICiTLbuRdy5l/YmVuZ43mjcPM1rftHWa6bbtTmBn/8dAw07xZGGHK/heFmTPHzA8zs36K7+vAedeFmfb33x9mztmwzHMol2Avx0dqjb3o9TCzyYc3hpm/H391mAFosPrYMPOX28p8KZbwWvuyestL6nD6wDDTpEt8X8+ucWWYOWhgXCLxze7xa7r3cbeFmbW/vTjMfLTbNWFm5iXbhJl/Xdo9zGy3evz3s8excVvHH7qtE2YOGPJJmAF448iwQIibb+kWZgYdemKYeXmNs8PMQb3i1yv3xpHq0CfbkvJWatHVzIalpRYjzGy7rMYnIiJSU3JVagFcB/zV3V80swPS73fLYowiIlJ1+mRbUlarkZeUWgCYWXGphQMt0kxL4KtshiciIlJz8lZq0QcYZGY3kKzijjc6iYiI5FzeSi3OAM5z9/WA80hPLl9aYanFd69OrqNRi4hIRRUVZTPlVd5KLXoDT6SRASTbdMu67pJSi1a7d6ibAYuIiFRRlnsjt0m/FpdaPESyjbZHGtkDUDeyiEg9pE+2JeWq1MLMfgfcnFY2/gScmuH4REREaoS51+9TM7yzUefwAcwccmB4Owc2XK9GxnP9jPFh5uFx34eZhhbf17tbdA4ztlGXMDPl0KvCTMetW4SZWWu0CjMzXvk8zHTbf90ws3enNcIMwAPd4xKJYdvGxR9rt28aZt6+c5cw07PDPmGmUYMmYeaNtn3CzHZT46KJj2eNCzPdLnsuzEweH5ew3Pa3+LD5xhV44b/y+ewwM+zY08LMUc/3DzMA9w+bGWYeP759mPn1rFXDzNNrLggzR906Isw0rkDZz9yrXqnAf5mqe2O9+H9zbdj1i/G1+riqKm+lFluZ2dtmNtrMnjWz+D+8iEgtqciCVqQiMlnYliq12Ao4yMw2Bu4GLnL3LYEngQuyGJ+IiEhNylupRUeguDx3MDAIuDSTEYqISJXleWelLGS1GnkMsKuZtTaz5iSlFusBY4GeaebIdJ6IiEi9lrdSi5OBM83sPWBVkt7kZRSWWjw1+4eyIiIikiEd+lNSrkot3H28u+/j7t1Izmlb5vmtCksterVYrS6HLSIiUmmZHWdrZm3cfUZBqcUOBfMaAJcAd2Y1PhERqbr6flhpTcvsky1JqcVHwLOkpRbAsWY2ERhP0ib1nwzHJyIiUiPqfakFRUPCB+CfDA9v5sXG34WZ/SbOCzMjt24bZhYuXhRmuq65bZg5YeCAMLND25XCTNNG8THgh1/+fphpOeCSMNNsbHxA/vzNu4cZgC363xdm7rxwTJi55Z64bOHZdeJ99Zo89HqYWbD/VmHGNu8UZvZ9J35Nn/2bd8PMHf8XP/Z+e+8cZi57O76v+54YG2b82vPCzJTD+4aZLXapWOnJzCYrh5nVf5wVZmbMjj+3jL9stzCz/tkvhJlBf9smzJzQbo8wwyqH12r5wytrd8pk4bLH1xNyWWqRZV2jSJVVZEErsjw1taCVsuV5Z6Us1OpqZDO718xmmNmYgnmtzGywmU1Kv66ezjczu8XMPjazUWYWv30TERGpB2p7m+19wH6l5l0EDHH3TYAh6fcA+wObpNOpwB21PDYREaklOvSnpFpd2Lr760DpjaE9geIG8P5Ar4L593tiGLCamcUbQEVERHIui72R13L3aenlr4G10svrAl8U5Kam85ZRWGrRr198RhIREalb+mRbUqY7SLm7m1ml91hz935AP6BCeyOLiIhkKYtPttOLVw+nX2ek87+kZBdyu3SeiIhIvZbFwvYZoHd6uTfwdMH836R7Je8AzCpY3SwiIvWIViOXVKurkc3sYWA3YA0zmwpcDvQFHjOz3wKfA0el8RdIzv7zMTAPOKk2xyYiIlJXanVh6+7HlvOjPcvIOnBWpe+kKG5jerrhjDAzekbcDrXfvPi+Rs78Ksxst9b6Yean064PMwedv1mYOe61b8NM41NPCTNz1p0YZr496PIws/6GTcLM6KuahZknb4zbiADeHbrMS20Zl/d6JczM3PrjMFO0Vlxcc0JR/Do7c6fbw8ygl3uHmfk/HB1mZkx4McxcNTxu/bpm57hl6vbXvw4z351za5iZeG/powmXdfdPc8LM/GtHh5lXrtotzABMbxafEOWQh4eGmQff+D7MtGzaNMw0ueyfYWbhPw4PM9WR50+ZWchTqUVnM3vbzBaY2R9rc1wiIiJ1KU+lFt8B5wA31PKYRESklmmbbUm5KbVw9xnu/i7wc22OSUREpDcYAKAAAA8vSURBVK7lqdRCRERkhZTl+WyLd4qqdClFiQapu+JTUomISN3SauSSsmiQmm5mbd19WqlSiwor0SC1aJAapEREJNfyVGohIiIriCLPZsqr3JRamNnawAigBVBkZn2Azdx9dm2OUUREpLZZstm0/nJeDR/A/IuuC29npb02DjMNdj8kzHzZ6y9hxhrE5QfvvzorzKxVgRKFlx7qEWa2X7tVmPnXB3FN9fnd47KOHle+HGbGXRMfbN+kYXxgP0D7EXEZxXUtFoaZ84bGWzua7BaXjMzrsm2YOfqFx8PMnXvuGmZaXvBImFn54I5hxjbZIMxMPPr+MNPp1cvCTNMr49NYvzIgfi7WbBOvtPt40uIwc9ipa4cZgFl/vyjMHPz0g2Fm0E67hJkBP8aFLoeP/DHMNDzyvvgfSDU83rRTJguXIxZMqNXHVVV5KrU43sxGmdloM3vLzLaqzbGJiIjUlTyVWnwK9HD3LYErKd4BSkREpJ6r7W7k182sfanZPUm240JSavEacKG7v1WQGUZyij0REamH8nwYThbyWmrxWyBuSBcREakHcldqYWa7kyxsLyzveiVKLfo9V8ujFBGRylKpRUm5KrUwsy7A3cD+7l7uueEKSy0qsjeyiIhIlnJTamFm6wNPACe4e3zyVBERyS19si0pN6UWwGVAa+B2MwNY5O7da3N8IiIidcLdV7gJOFUZZfJwf8r8MjN5HFNFx62pdqbMB1ArDwpGKKNMHu5PmV9mJo9jqui4NdXOlOneyCIiIr8EWtiKiIjUshV1YVuRqkdlfpmZur4/ZX6Zmbq+v5oct9SCen/WHxERkbxbUT/ZioiI5IYWtiIiIrUsi7rGGmVmnUnOJLRuOutL4Bl3H1dL97cdSa3zu2a2GckpBMe7+wvl5O9399/UxlgqwsyaAMcAX7n7y2Z2HLATMA7o5+4/ZzU2EZFfinq9zdbMLgSOBR4Bpqaz25EsXB5x976VvL3OJAvtd9x9TsH8/dx9oJldDuxP8iZlMLA98CqwNzAo/b7ETQK7A68AuPsh5dzvLsB2wBh3fymdtz0wzt1nm1kzkvP+bgN8BFzj7rPM7BzgSXf/YjmP6cF0vM2BH4BVSGox9yR5/nunuQ7AYcB6wGJgIvCQu88Of3Hyi2Nmbdx9RpwMb6e1L6cHXWpGTT1fUg1ZH+hbnYlkgdC4jPlNgEkVvI2T0q/nABOAp4DPgJ4FmffTr6OBhiQLrtlAi3R+M2AU8D7wAElFZY/067T0co+C2xtecPl3wEiSKsv/ARel88cCjdLL/YB/ArukuSfS+bOAr4A3gDOBNct4fKPSr42A6UDD9Hsr+Nk5wEvAJcBbwG3A1SQL9t2yfp4r+ZpoU0O30zrDx9CSpNZ0PPAd8C3Jmoi+wGoVuP6L6dcWwN+B/wOOK5W5Pf26NnBH+py3Bq5IX+ePAW3TTKtSU+v0b2R1oFWa2a/U+O9J/yYeIjmtJun410gvdwcmAx+T1Lb2KP5bS1+HGy3n8XUneZP7AMmbw8Hp38K7wNZpZhXgb+nf0SxgJsl5sk8suJ0ngF8DqyznvhoBpwED08cziuT0n6eT/u+p4O+5us9pv/Rrc+BPwAXASsCJJH3z1xU/joo8X5oy+LvOegDVGnzywt2gjPkbABMqeBtT0q+jC16s7YERwLnp9x8Ufi19Of1+JMk28PPSP/6u6fzJZdxn4e28S7qQBFYGRqeXxxVk3i99X8W3k97nPiT/3Gam/xR6A6ummTEkbz5WB35k6T/HlYrvI33sxQvh5sBr6eX1Cx57nS0A0su/5IXAIJJTTK5d6vdxIfBS+v025UzdgGlp5r/pY+tF8g/5v0DTwtdU+nr5Pcmak1HpfayXzns6zRQBn5aafk6/Ti79GiU5c9dVJH+H5wFPFb/OCjKvAtumlzuSthult3kDMAUYnl5/nVK/5+Eka5iOBb4Ajkjn7wm8nV5+mmRB1A74A3ApsAnQn2TNECSbnB4neT0/BhwKNCl1Xw+TvA53SG+rXXr5DuDRSvyeK/Kcln49F76up6aZx4AbgduBIcCtwK7A9cD/VfT50lT3U+YDqNbgk+2lH5O80yw+7d7AdF7hP9lR5UyjgQVpZmyp214lva1/sHTh9g7QPL3coCDbkpL/bNoBA9I/hClljPtDkgVCa0pVqLF04TaApZ+6/wN0Ty93BN5NL5deCDcGDkn/QcxM551HsvD4nOQT7BDgrvSxX55mRhf8c1i9cEwkq7Yr+s+iRhYA6eVf8kKg3DeKxT8jWdX/Sjre0tP8NDOy1HUvJll70pqlC4HCN35TSuWLX/fnp8/HlgU/+7RU9v3S1yvjdsaxdG3NsFKZ0WXczq4kC5Wv08d1agXGXPz382Gp+cV/Mw1I9rEozLYATgBeIHnz8x9gn/RnE5fzXEysxO+5os/pZEq+nou/X1jqd2np78UKvi9eUxU+X5rqfsp8ANV+AMkfzw7A4em0A+mntILMdKAryT/Zwqk9yY5DpP+4upa6XiPgfmBx+n3TcsawRuELu2D+gaT/QEvN/6zgj2gySz+prVLwx9QSuA/4hGQh/3OaHQpslWY+WM7vpXnB5XVIFwzAasARwHYFPz+XZIF2F8kn1+KF/JrA6+nlOlsAlH5s/PIWAi+RrCpcqyC3FskbjpfT78cAm5TzfHxR8LgalPrZiSSfqj8vPR7gqrIee3q5+A3kP4BVKfUJiWSfiT+kz8lk0oVA+rPihcDv08e2B8maiptJNrH8laWfyt4v4/E0JHlj/Z/0+7dJ1uYcSfImslc6vwdL3xy9BeySXj4EGFTG67Ws+2pNsor4leLXQ3o/hW+uGwBHk+zbUdHfc0We00nA+sFzOrJg3r2lMoXPZfHzdVNZz5emup8yH0CdPMhk1eEu5fzsofRrOwo+tZXK7FxH42wObFhqXgtgK5JPh2uV+lnHGrzvzUkWwp3L+XmdLQDSeb/khcDqwLUkb3y+J1nNOS6dV7x6/AigUzm/6+L7vQ7Yq4yf70e6TwPJKu1ltlkCGwOPlzH/EJIF0Nel5l9eaireNLI2cH9BbjfgUZJNIKNJPk2eytLtn49U4LW6FcmalheBzunz9UP6GtqpIDM8/f29Wfy7InkDeU56+fUK3Ff7dLwzSfYRmQTMSOdtWInfc0We07NI30iXcVu/T7/eXc7ztRHwZkWfL011P2U+AE31Yyr1z+K7Uv8sVk8zNbIASL/Py0Kg+BNvTS0EupRaCHRM5y9ZCKTfdwb2Kv07oOTmkc4kq6irktm/qrdDskPgFjU8nspmNq1gJvodbsfSzQabkbwxO6Cc57d1Oj0QvA52SW9nn4J55wDrBddrCvyG9O8DOI5kU9RZLH0zEmbS+R2AP6avwX+TvKFsUZW/fU01M2U+AE31fyJd7VwXmeXlSi0E6mxMNZ2hYnvGVyTz+xrK1NR91eTtjK+BzOUkb9BGkOy09wrJNvTXgYvTzDNlTHOKL6eZihxdUHjkwBmkO+SVev4fJHnD9yzJDoRPkmxLvg/oX4HMfQWPfTArwNEFK9KU+QA01f+JMnYCq61MXd9fFhkqtme8MjWTKfdQvvRyeDgfFTu6oCJHDlTkML2KZMKjCzTV/VTvG6SkbpjZqPJ+RLLttsYydX1/ecuQbNOeA+Dun5nZbsDjZrZBmlOmZjKL3H0xMM/MPvG0wMXd55tZUZrpTrID4cXABe4+0szmu/vQgueugZmtTrIwNXefmd7OXDNblGbc3YtI9g14ycwas3TP9RtINiM0SBvfViZZSLYk2VzTlORIAyqYgWRhvDidv0o6gCnp/UoGtLCViloL2JdkW2MhI1lVVZOZur6/vGWmm1lXdx8J4O5zzOwg4F5gS2VqLLPQzJq7+zySHRCTJ8KsJclhZaQLyJvMbED6dTrL/t9sCbyXPoduZm3dfZqZrcLSBbsVXsGTmtRngGfMrHk6+x6SVd8NSRbuA8xsMskRFo9UInM38K6ZvUOy5/y16eNak2TBLFnI+qO1pvoxUbE9umskU9f3l8NMuGe8MjWSqdShfOnPyjycr5zskqMLqOCRAwSH6VUis9yjCzTV/VSvu5FFRETqA51iT0REpJZpYSsiIlLLtLAVERGpZVrYipTBzHqZ2WYF3//NzPZaTr67md1SC+M40czWCTJ3F4611HVvrekxiUjl6dAfkVLMrBHJWYmeI2ndwd0vW9513H0ESXFCTTuRpHP6q+Xc9ym1cL8iUoP0yVZWSGbW3szGm9mDZjbOzB43s+ZmdpmZvWtmY8ysn5lZmn/NzP5pZiNITq5wCHC9mY00s43M7D4zOyLNbmtmb5nZh2Y23MxWNbPdzOy59OdXmNn/mdnbZjbJzH6Xzl/FzIaY2ftmNtrMehaMdZyZ3WVmY83sJTNrlt5fd+DBdBzNynmsr5lZ9/TySWY20cyGAzvX7m9ZRCpKC1tZkXUiOSH9piR1fGcCt7r7tu6+BUkt30EF+Sbu3t3dryYpHLjA3bu6+yfFgbS951GS6r+tSIru55dx311IziK0I3BZuir4J+BQd98G2B24sXhhT3JO29vcfXOSkxcc7u6Pk3xaPj4dR1n3s4SZtSU5U9HOJGX4y6xaFpFsaGErK7Iv3P1/6eUHSBZAu5vZO2Y2mmRhuHlB/tEK3GYnYJq7vwvg7rPdfVEZuafdfb67f0NyztvtSFqErrGksvFlYF2WVjR+6mnbEUkbUfuKPsgC25P04M5094UVfDwiUge0zVZWZKUbW5zkpO/d3f0LM7sCWKng53Nr+b6PJ+nA7ebuP5vZZwX3v6Agu5jkU7eIrCD0yVZWZOub2Y7p5eNIzh8L8E3aW3vEcq77I8nJ6EubALQ1s20B0u21Zb1p7WlmK5lZa5IzxbxL0qE7I13Q7g5sUIHHUN44yvIO0MPMWqeF80dW8HoiUsv0yVZWZBOAs8zsXpK9iu8AVifZu/drkgVgeR4B7jKzcyhYKLv7QjM7GvhXusPSfJLttqWNIll9vAZwpbt/ZWYPAs+mq7BHkBTKR+4D7jSz+cCOy9tu60n5/RXA2yTbfUeWlxWRuqVuZFkhmVl74Ll0R6i6vu8rgDnufkNd37eI5JNWI4uIiNQyfbIVqSfM7Elgw1KzL3T3QVmMR0QqTgtbERGRWqbVyCIiIrVMC1sREZFapoWtiIhILdPCVkREpJZpYSsiIlLL/h+boG3/QFyozAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "affinities_plot(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df.plot(x='timestep',y=['candidate_count','active_count','completed_count', 'killed_count', 'failed_count'],\n", - " kind='area')\n", - "plt.title('Proposal Status')\n", - "plt.ylabel('count of proposals')\n", - "plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df.plot(x='timestep',y=['candidate_funds','active_funds','completed_funds', 'killed_funds', 'failed_funds'], kind='area')\n", - "plt.title('Proposal Status weighted by funds requested')\n", - "plt.ylabel('Funds worth of proposals')\n", - "plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "nets = df.network.values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "K = 55\n", - "N = 56\n", - "# K = 0\n", - "# N = 60\n", - "\n", - "snap_plot(nets[K:N], size_scale = 1/10,savefigs=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.effective_supply.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.funds_arrival.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.funds.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.sentiment.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# # Run the following code , without the #, in the images/snap folder to make a movie\n", - "# # ffmpeg -r 10 -i %01d.png -vcodec mpeg4 -y movie.mp4\n", - "# %%HTML\n", - "# " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "We have created a conviction voting model that closely adheres to the 1Hive implementation. This notebook describes the use case, how the model works, and provides descriptions of how it fits together." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/v3/Aragon_Conviction_Voting_Model.ipynb b/v3/Aragon_Conviction_Voting_Model.ipynb deleted file mode 100644 index c03657e..0000000 --- a/v3/Aragon_Conviction_Voting_Model.ipynb +++ /dev/null @@ -1,1518 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Aragon Conviction Voting Model - Version 3\n", - "\n", - "New to this model are the following elements:\n", - "\n", - "* Adding the realism that not all participant tokens are being allocated to proposals.\n", - "* Refactored parameters and system initialization to make more readable and consistent.\n", - "* Making the distinction between effective and total supply.\n", - "* Refining alpha calculations to more accurately reflect the 1Hive implementation. Discussion of alpha and its relation to alpha in the contract and how it relates to the timescales\n", - "* Updated differential specification and write-up to respect new state variables\n", - "* Moved all unit denominations to honey.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# An Introduction to Conviction Voting\n", - "\n", - "Conviction Voting is an approach to organizing a communities preferences into discrete decisions in the management of that communities resources. Strictly speaking conviction voting is less like voting and more like signal processing. Framing the approach and the initial algorithm design was done by Michael Zargham and published in a short research proposal [Social Sensor Fusion](https://github.com/BlockScience/conviction/blob/master/social-sensorfusion.pdf). This work is based on a dynamic resource allocation algorithm presented in Zargham's PhD Thesis.\n", - "\n", - "The work proceeded in collaboration with the Commons Stack, including expanding on the pythin implementation to makeup part of the Commons Simulator game. An implemention of Conviction Voting as a smart contract within the Aragon Framework was developed by 1hive.org and is currently being used for community decision making around allocations their community currency, Honey.\n", - "\n", - "\n", - "## The Word Problem\n", - "\n", - "Suppose a group of people want to coordinate to make a collective decision. Social dynamics such as discussions, signaling, and even changing ones mind based on feedback from others input play an important role in these processes. While the actual decision making process involves a lot of informal processes, in order to be fair the ultimate decision making process still requires a set of formal rules that the community collecively agrees to, which serves to functionally channel a plurality of preferences into a discrete outcomes. In our case we are interested in a procedure which supports asynchronous interactions, an provides visibility into likely outcomes prior to their resolution to serve as a driver of good faith, debate and healthy forms of coalition building. Furthermore, participations should be able to show support for multiple initiatives, and to vary the level of support shown. Participants a quantity of signaling power which may be fixed or variable, homogenous or heterogenous. For the purpose of this document, we'll focus on the case where the discrete decisions to be made are decisions to allocate funds from a shared funding pool towards projects of interest to the community.\n", - "\n", - "## Converting to a Math Problem\n", - "\n", - "Let's start taking these words and constructing a mathematical representation that supports a design that meets the description above. To start we need to define participants.\n", - "\n", - "### Participants\n", - "Let $\\mathcal{A}$ be the set of participants. Consider a participant $a\\in \\mathcal{A}$. Any participant $a$ has some capacity to participate in the voting process $h[a]$. In a fixed quantity, homogenous system $h[a] = h$ for all $a\\in \\mathcal{A}$ where $h$ is a constant. The access control process managing how one becomes a participant determines the total supply of \"votes\" $S = \\sum_{a\\in \\mathcal{A}} = n\\cdot h$ where the number of participants is $n = |\\mathcal{A}|$. In a smart contract setting, the set $\\mathcal{A}$ is a set of addresses, and $h[a]$ is a quantity of tokens held by each address $a\\in \\mathcal{A}$. \n", - "\n", - "### Proposals & Shares Resources\n", - "Next, we introduce the idea of proposals. Consider a proposal $i\\in \\mathcal{C}$. Any proposal $i$ is associated with a request for resources $r[i]$. Those requested resources would be allocated from a constrained pool of communal resources currently totaling $R$. The pool of resources may become depleted because when a proposal $i$ passes $R^+= R-r[i]$. Therefore it makes sense for us to consider what fraction of the shared resources are being request $\\mu_i = \\frac{r[i]}{R}$, which means that thre resource depletion from passing proposals can be bounded by requiring $\\mu_i < \\mu$ where $\\mu$ is a constant representing the maximum fraction of the shared resources which can be dispersed by any one proposal. In order for the system to be sustainable a source of new resources is required. In the case where $R$ is funding, new funding can come from revenues, donations, or in some DAO use cases minting tokens.\n", - "\n", - "### Participants Preferences for Proposals\n", - "\n", - "Most of the interesting information in this system is distributed amongst the participants and it manifests as preferences over the proposals. This can be thought of as a matrix $W\\in \\mathbb{R}^{n \\times m}$.\n", - "![Replace this later](https://i.imgur.com/vxKNtxi.png)\n", - "\n", - "These private hidden signals drive discussions and voting actions. Each participant individually decides how to allocate their votes across the available proposals. Participant $a$ supports proposal $i$ by setting $x[a,i]>0$ but they are limited by their capacity $\\sum_{k\\in \\mathcal{C}} x[a,k] \\le h[a]$. Assuming each participant chooses a subset of the proposals to support, a support graph is formed.\n", - "![](https://i.imgur.com/KRh8tKn.png)\n", - "\n", - "## Aggregating Information\n", - "\n", - "In order to break out of the synchronous voting model, a dynamical systems model of this system is introduced.\n", - "\n", - "### Participants Allocate Voting Power\n", - "![](https://i.imgur.com/DZRDwk6.png)\n", - "\n", - "### System Accounts Proposal Conviction\n", - "![](https://i.imgur.com/euAei5R.png)\n", - "\n", - "### Understanding Alpha\n", - "* https://www.desmos.com/calculator/x9uc6w72lm\n", - "* https://www.desmos.com/calculator/0lmtia9jql\n", - "\n", - "\n", - "## Converting Signals to Discrete Decisions\n", - "\n", - "Conviction as kinetic energy and Trigger function as required activation energy.\n", - "\n", - "### The Trigger Function\n", - "\n", - "https://www.desmos.com/calculator/yxklrjs5m3\n", - "\n", - "Below we show a sweep of the trigger function threshold:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", - " return false;\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%javascript\n", - "IPython.OutputArea.prototype._should_scroll = function(lines) {\n", - " return false;\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "for reference: max conviction = 5.25318713934522in log10 units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", - " import pandas.util.testing as tm\n" - ] - } - ], - "source": [ - "from model.model.conviction_helper_functions import *\n", - "from model.model.sys_params import initial_values,sys_params \n", - "\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "\n", - "supply = initial_values['supply']\n", - "alpha = sys_params['alpha']\n", - "\n", - "mcv = supply/(1-alpha)\n", - "print('for reference: max conviction = '+str(np.log10(mcv))+'in log10 units')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "supply_sweep = trigger_sweep('effective_supply',trigger_threshold, supply)\n", - "alpha_sweep = trigger_sweep('alpha',trigger_threshold, supply)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "trigger_grid(supply_sweep, alpha_sweep)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Resolving Passed Proposals\n", - "\n", - "![](images/stockflow_cv_trigger.png)\n", - "\n", - "\n", - "## Social Systems Modeling\n", - "\n", - "Subjective, exploratory modeling of the social system interacting through the conviction voting algorithm.\n", - "\n", - "### Sentiment\n", - "\n", - "Global Sentiment -- the outside world appreciating the output of the community\n", - "Local Sentiment -- agents within the system feeling good about the community\n", - "\n", - "### Social Networks\n", - "\n", - "Preferences as mixing process (social influence)\n", - "\n", - "### Relationships between Proposals\n", - "\n", - "Some proposals are synergistic (passing one makes the other more desireable)\n", - "Some proposals are (parially) substitutable (passing one makes the other less desirable)\n", - "\n", - "### Notion of Honey supply\n", - "#### Total supply = $S$\n", - "#### Effective supply = $E$\n", - "#### Funding Pool = $F$\n", - "#### Other supply = $L$, effectively slack. Funds could be in cold storage, in liquidity pools or otherwise in any address not actively participating in conviction voting.\n", - "$$S = F + E + L$$ \n", - "\n", - "System has the right to do direct mints:\n", - "$$F^+ = F + minted$$\n", - "$$S^+ = S + minted$$\n", - "\n", - "\n", - "Arrival of new funds which come from outside:\n", - "$$L+ = L - donated$$\n", - "$$F+ = F + donated$$\n", - "The above assumes the donated tokens were not in use for voting\n", - "$$L+ = L + tokens$$ that haven't been used in voting recently\n", - "$$E+ = E - tokens$$ that haven't been used in voting recently\n", - "$$L+ = L - tokens$$ that come into use\n", - "$$E+ = E - tokens$$ that come into use\n", - "\n", - "Tokens in $L$ or $E$ are defined at the level of the account holding them.\n", - "\n", - "Total supply $S$ can be made a param and the state supply should be only $E$, effective supply." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## cadCAD Overview\n", - "\n", - "In the cadCAD simulation [methodology](https://community.cadcad.org/t/differential-specification-syntax-key/31), we operate on four layers: **Policies, Mechanisms, States**, and **Metrics**. Information flows do not have explicit feedback loop unless noted. **Policies** determine the inputs into the system dynamics, and can come from user input, observations from the exogenous environment, or algorithms. **Mechanisms** are functions that take the policy decisions and update the States to reflect the policy level changes. **States** are variables that represent the system quantities at the given point in time, and **Metrics** are computed from state variables to assess the health of the system. Metrics can often be thought of as KPIs, or Key Performance Indicators. \n", - "\n", - "At a more granular level, to setup a model, there are system conventions and configurations that must be [followed.](https://community.cadcad.org/t/introduction-to-simulation-configurations/34)\n", - "\n", - "The way to think of cadCAD modeling is analogous to machine learning pipelines which normally consist of multiple steps when training and running a deployed model. There is preprocessing, which includes segregating features between continuous and categorical, transforming or imputing data, and then instantiating, training, and running a machine learning model with specified hyperparameters. cadCAD modeling can be thought of in the same way as states, roughly translating into features, are fed into pipelines that have built-in logic to direct traffic between different mechanisms, such as scaling and imputation. Accuracy scores, ROC, etc. are analogous to the metrics that can be configured on a cadCAD model, specifying how well a given model is doing in meeting its objectives. The parameter sweeping capability of cadCAD can be thought of as a grid search, or way to find the optimal hyperparameters for a system by running through alternative scenarios. A/B style testing that cadCAD enables is used in the same way machine learning models are A/B tested, except out of the box, in providing a side by side comparison of muliple different models to compare and contrast performance. Utilizing the field of Systems Identification, dynamical systems models can be used to \"online learn\" by providing a feedback loop to generative system mechanisms. \n", - "\n", - "\n", - "## Differential Specification \n", - "![](images/Aragon_v2.png)\n", - "\n", - "## Schema of the states - UPDATE\n", - "The model consists of a temporal in memory graph database called *network* containing nodes of type **Participant** and type **Proposal**. Participants will have *holdings* and *sentiment* and Proposals will have *funds_required, status*(candidate or active), *conviction* Tthe model as three kinds of edges:\n", - "* (Participant, participant), we labeled this edge type \"influencer\" and it contains information about how the preferences and sentiment of one participant influence another \n", - "* (Proposal, Proposal), we labeled this edge type \"conflict\" and it contains information about how synergistic or anti-synergistic two proposals are; basically people are likely to support multiple things that have synergy (meaning once one is passed there is more utility from the other) but they are not likely to pass things that have antisynergy (meaning once one is passed there is less utility from the other).\n", - "* The edges between Participant and Proposal, which are described below.\n", - " \n", - "\n", - "Edges in the network go from nodes of type Participant to nodes of type Proposal with the edges having the key *type*, of which all will be set to *support*. Edges from participant $i$ to proposal $j$ will have the following additional characteristics:\n", - "* Each pairing (i,j) will have *affinity*, which determines how much $i$ likes or dislikes proposal $j$.\n", - "* Each participant $i$, assigns its $tokens$ over the edges (i,j) for all $j$ such that the summation of all $j$ such that ```Sum_j = network.edges[(i,j)]['tokens'] = network.nodes[i]['holdings']```. This value of tokens for participants on proposals must be less than or equal to the total number of tokens held by the participant.\n", - "* Each pairing (i,j) will have *conviction* local to that edge whose update at each timestep is computed using the value of *tokens* at that edge.\n", - "* Each proposal *j* will have a *conviction* which is equal to the sum of the conviction on its inbound edges: ```network.nodes[j]['conviction'] = Sum_i network.edges[(i,j)]['conviction']```. \n", - "\n", - "\n", - "The other state variables in the model are *funds*, which is a numpy floating point, and effective supply, as supply.\n", - "\n", - "The system consists of 100 time steps without a parameter sweep or monte carlo.\n", - "\n", - " \n", - "## Partial State Update Blocks - TODO: UPDATE\n", - "\n", - "Each partial state update block is kind of a like a phase in a phased based board game. Everyone decides what to do and it reconciles all decisions. One timestep is a full turn, with each block being a phase of a timestep or turn. We will walk through the individaul Partial State update blocks one by one below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "{\n", - "# system.py: \n", - "'policies': { \n", - " 'random': driving_process\n", - "},\n", - "'variables': {\n", - " 'network': update_network,\n", - " 'funds':increment_funds,\n", - "}\n", - "```\n", - "\n", - "To simulate the arrival of participants and proposal into the system, we have a driving process to represent the arrival of individual agents. We use a random uniform distribution generator, over [0, 1), to calculate the number of new participants. We then use an exponential distribution to calculate the particpant's tokens by using a loc of 0.0 and a scale of expected holdings, which is calculated by .1*supply/number of existing participants. We calculate the number of new proposals by \n", - "```\n", - "proposal_rate = 1/median_affinity * (1+total_funds_requested/funds)\n", - "rv2 = np.random.rand()\n", - "new_proposal = bool(rv2<1/proposal_rate)\n", - "```\n", - "The network state variable is updated to include the new participants and proposals, while the funds state variable is updated for the increase in system funds. \n", - "[To see the partial state update code, click here](model/model/system.py)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "{\n", - " # participants.py \n", - " 'policies': {\n", - " 'completion': check_progress \n", - " },\n", - " 'variables': { \n", - " 'sentiment': update_sentiment_on_completion, #not completing projects decays sentiment, completing bumps it\n", - " 'network': complete_proposal\n", - " }\n", - "},\n", - "```\n", - "\n", - "In the next phase of the turn, [to see the logic code, click here](model/model/participants.py), the *check_progress* behavior checks for the completion of previously funded proposals. The code calculates the completion and failure rates as follows:\n", - "\n", - "```\n", - "likelihood = 1.0/(base_completion_rate+np.log(grant_size))\n", - "\n", - "failure_rate = 1.0/(base_failure_rate+np.log(grant_size))\n", - "if np.random.rand() < likelihood:\n", - " completed.append(j)\n", - "elif np.random.rand() < failure_rate:\n", - " failed.append(j)\n", - "```\n", - "With the base_completion_rate being 100 and the base_failure_rate as 200. \n", - "\n", - "The mechanism then updates the respective *network* nodes and updates the sentiment variable on proposal completion. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - " # proposals.py\n", - " 'policies': {\n", - " 'release': trigger_function \n", - " },\n", - " 'variables': { \n", - " 'funds': decrement_funds, \n", - " 'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment\n", - " 'network': update_proposals \n", - " }\n", - "},\n", - " ```\n", - " \n", - "The [trigger release function](model/model/proposals.py) checks to see if each proposal passes or not. If a proposal passes, funds are decremented by the amount of the proposal, while the proposal's status is changed in the network object." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "{ \n", - " # participants.py\n", - " 'policies': { \n", - " 'participants_act': participants_decisions\n", - " },\n", - " 'variables': {\n", - " 'network': update_tokens \n", - " }\n", - "}\n", - "```\n", - "\n", - "The Participants decide based on their affinity if which proposals they would like to support,[to see the logic code, click here](model/model/participants.py). Proposals that participants have high affinity for receive more support and pledged tokens than proposals with lower affinity and sentiment. We then update everyone's holdings and their conviction for each proposal.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Model next steps\n", - "\n", - "The the model described above is the third iteration model that covers the core mechanisms of the Aragon Conviction Voting model. Below are next additional dynamics we can attend to enrich the model, and provide workstreams for subsequent iterations of this lab notebook.\n", - "\n", - "* Mixing of token holdings among participants\n", - "* Departure of participants\n", - "* Proposals which are good or no good together\n", - "* Affects of outcomes on sentiment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuration\n", - "Let's factor out into its own notebook where we review the config object and its partial state update blocks." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from model import config" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# pull out configurations to illustrate\n", - "sim_config,genesis_states,seeds,partial_state_update_blocks = config.get_configs()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'N': 1,\n", - " 'T': range(0, 60),\n", - " 'M': [{}],\n", - " 'subset_id': 0,\n", - " 'subset_window': deque([0, None]),\n", - " 'simulation_id': 0,\n", - " 'run_id': 0}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim_config" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'policies': {'random': },\n", - " 'variables': {'network': ,\n", - " 'funds': ,\n", - " 'effective_supply': ,\n", - " 'funds_arrival': }},\n", - " {'policies': {'completion': },\n", - " 'variables': {'sentiment': ,\n", - " 'network': }},\n", - " {'policies': {'release': },\n", - " 'variables': {'funds': ,\n", - " 'sentiment': ,\n", - " 'network': }},\n", - " {'policies': {'participants_act': },\n", - " 'variables': {'network': }}]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "partial_state_update_blocks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialization\n", - "To create the genesis_states, we create our in-memory graph database within networkx. \n", - "\n", - "\n", - "### Parameters\n", - "\n", - "Initial values are the starting values for the simulation and sys_params are global hyperparameters for the simulation.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'initial_sentiment': 0.6,\n", - " 'n': 30,\n", - " 'm': 7,\n", - " 'initial_funds': 4867.21,\n", - " 'supply': 22392.22}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from model.model.sys_params import initial_values,sys_params \n", - "\n", - "initial_values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$n$ is initial participants, whereas $m$ is initial proposals" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'beta': 0.2,\n", - " 'rho': 0.0025,\n", - " 'alpha': 0.875,\n", - " 'sensitivity': 0.75,\n", - " 'tmin': 0,\n", - " 'min_supp': 1,\n", - " 'base_completion_rate': 45,\n", - " 'base_failure_rate': 180}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sys_params" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* $\\alpha$ : 0.875 The decay rate for previously accumulated conviction\n", - "* $\\beta$ = .2 Upper bound on share of funds dispersed in the example Trigger Function\n", - "* $\\rho$ = 0.002 Scale Parameter for the example Trigger Function\n", - "\n", - "* tmin = 7 unit days; minimum periods passed before a proposal can pass\n", - "* min_supp = 50 number of tokens that must be stake for a proposal to be a candidate" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# import libraries\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from model.model.conviction_helper_functions import * \n", - "\n", - "\n", - "#initializers\n", - "network = genesis_states['network']\n", - "initial_funds = genesis_states['funds']\n", - "initial_sentiment = genesis_states['sentiment']" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'network': ,\n", - " 'funds': 4867.21,\n", - " 'sentiment': 0.6,\n", - " 'effective_supply': 22392.22,\n", - " 'funds_arrival': 0}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genesis_states" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Exploring the State Data Structure\n", - "\n", - "A graph is a type of temporal data structure that evolves over time. A graph $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$ consists of vertices or nodes, $\\mathcal{V} = \\{1...\\mathcal{V}\\}$ and is connected by edges $\\mathcal{E} \\subseteq \\mathcal{V} \\times \\mathcal{V}$.\n", - "\n", - "See *Schema of the states* above for more details\n", - "\n", - "\n", - "Let's explore!" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n", - "proposals = get_nodes_by_type(network, 'proposal')\n", - "participants = get_nodes_by_type(network, 'participant')\n", - "supporters = get_edges_by_type(network, 'support')\n", - "influencers = get_edges_by_type(network, 'influence')\n", - "competitors = get_edges_by_type(network, 'conflict')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'type': 'participant',\n", - " 'holdings': 520.2734462358999,\n", - " 'sentiment': 0.29980234113414117}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#sample a participant\n", - "network.nodes[participants[0]]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Count of Participants')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Let's look at the distribution of participant holdings at the start of the sim\n", - "plt.hist([ network.nodes[i]['holdings'] for i in participants])\n", - "plt.title('Histogram of Participants Token Holdings')\n", - "plt.xlabel('Amount of Honey')\n", - "plt.ylabel('Count of Participants')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Participants Social Network')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw_spring(network, nodelist = participants, edgelist=influencers)\n", - "plt.title('Participants Social Network')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'type': 'proposal',\n", - " 'conviction': 0,\n", - " 'status': 'candidate',\n", - " 'age': 0,\n", - " 'funds_requested': 1671.6565260937566,\n", - " 'trigger': inf}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#lets look at proposals\n", - "network.nodes[proposals[0]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Proposals initially start without any conviction, and with the status of a candidate. If the proposal's amount of conviction is greater than it's trigger, then the proposal moves to active and it's funds requested are granted. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All initial proposal start with 0 conviction and state 'candidate'we can simply examine the amounts of funds requested" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "funds_array = np.array([ network.nodes[i]['funds_requested'] for i in proposals])\n", - "conviction_required = np.array([trigger_threshold(r, initial_funds, supply, alpha) for r in funds_array])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Amount of Honey requested(as a Fraction of Funds available)')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar( proposals, funds_array/initial_funds)\n", - "plt.title('Bar chart of Proposals Funds Requested')\n", - "plt.xlabel('Proposal identifier')\n", - "plt.ylabel('Amount of Honey requested(as a Fraction of Funds available)')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Amount of Conviction')" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar( proposals, conviction_required)\n", - "plt.title('Bar chart of Proposals Conviction Required')\n", - "plt.xlabel('Proposal identifier')\n", - "plt.ylabel('Amount of Conviction')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Conviction is a concept that arises in the edges between participants and proposals in the initial conditions there are no votes yet so we can look at that later however, the voting choices are driven by underlying affinities which we can see now." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 55.73999999999998, 'Participant_id')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "m = len(proposals)\n", - "n = len(participants)\n", - "\n", - "affinities = np.empty((n,m))\n", - "for i_ind in range(n):\n", - " for j_ind in range(m):\n", - " i = participants[i_ind]\n", - " j = proposals[j_ind]\n", - " affinities[i_ind][j_ind] = network.edges[(i,j)]['affinity']\n", - "\n", - "dims = (20, 5)\n", - "fig, ax = plt.subplots(figsize=dims)\n", - "\n", - "sns.heatmap(affinities.T,\n", - " xticklabels=participants,\n", - " yticklabels=proposals,\n", - " square=True,\n", - " cbar=True,\n", - " cmap = plt.cm.RdYlGn,\n", - " ax=ax)\n", - "\n", - "plt.title('Affinities between participants and proposals')\n", - "plt.ylabel('Proposal_id')\n", - "plt.xlabel('Participant_id')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run simulation\n", - "\n", - "Now we will create the final system configuration, append the genesis states we created, and run our simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "from cadCAD.configuration import Experiment\n", - "\n", - "# Create configuration\n", - "exp = Experiment()\n", - "\n", - "exp.append_configs(\n", - " sim_configs=sim_config,\n", - " initial_state=genesis_states,\n", - " seeds=seeds,\n", - " partial_state_update_blocks=partial_state_update_blocks\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " ___________ ____\n", - " ________ __ ___/ / ____/ | / __ \\\n", - " / ___/ __` / __ / / / /| | / / / /\n", - "/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n", - "\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\n", - "by cadCAD\n", - "\n", - "Execution Mode: local_proc\n", - "Configuration Count: 3\n", - "Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (60, 1, 1, 5)\n", - "Execution Method: local_simulations\n", - "SimIDs : [0, 1, 2]\n", - "SubsetIDs: [0, 0, 0]\n", - "Ns : [0, 0, 0]\n", - "ExpIDs : [0, 0, 0]\n", - "Total execution time: 86.42s\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from model.model.conviction_helper_functions import *\n", - "from model import run\n", - "from cadCAD import configs\n", - "pd.options.display.float_format = '{:.2f}'.format\n", - "\n", - "%matplotlib inline\n", - "\n", - "rdf = run.run(configs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the simulation has run successfully, we perform some postprocessing to extract node and edge values from the network object and add as columns to the pandas dataframe. For the rdf, we take only the values at the last substep of each timestep in the simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "df= run.postprocessing(rdf,0)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
networkfundssentimenteffective_supplyfunds_arrivalsimulationsubsetrunsubsteptimestep...funds_requestedshare_of_funds_requestedshare_of_funds_requested_alltriggersconviction_share_of_triggerageage_allconviction_alltriggers_allconviction_share_of_trigger_all
4(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4867.500.6022392.220.2900141...[1671.6565260937566, 1944.3791991826024, 2177....[0.1131283127104601, 0.4776341120726794][0.3434325991383445, 0.399461965817948, 0.4473...[59352.21276113208, inf][0.006034361745133491, 0.0][1, 1][1, 1, 1, 1, 1, 1, 1][0.0, 0.0, 0.0, 358.15272217479924, 323.890220...[inf, inf, inf, 59352.21276113208, inf, inf, 1...[0.0, 0.0, 0.0, 0.006034361745133491, 0.0, 0.0...
8(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4867.600.6022392.220.1000142...[1671.6565260937566, 1944.3791991826024, 2177....[0.11312598437087308, 0.47762428169171994][0.3434255308130342, 0.39945374432956693, 0.44...[59343.15532804561, inf][0.011316155171822826, 0.0][2, 2][2, 2, 2, 2, 2, 2, 2, 1][0.0, 0.0, 0.0, 671.5363540777486, 607.2941638...[nan, nan, nan, 59343.15532804561, inf, nan, n...[nan, nan, nan, 0.011316155171822826, 0.0, nan...
12(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4868.170.6022392.220.5700143...[1671.6565260937566, 1944.3791991826024, 2177....[0.11311263200004616, 0.4775679072295611][0.34338499596098976, 0.3994065964125596, 0.44...[59339.97441911249, inf][0.015937772829376525, 0.0][3, 3][3, 3, 3, 3, 3, 3, 3, 2, 1][0.0, 0.0, 0.0, 945.7470319928292, 855.2726140...[nan, nan, nan, 59339.97441911249, inf, nan, n...[nan, nan, nan, 0.015937772829376525, 0.0, nan...
16(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4868.520.6022392.220.3500144...[1671.6565260937566, 1944.3791991826024, 2177....[0.11310448685868632, 0.4775335179836871][0.3433602691087939, 0.3993778354940812, 0.447...[59321.73774141947, inf][0.01998730010804558, 0.0][4, 4][4, 4, 4, 4, 4, 4, 4, 3, 2, 1][0.0, 0.0, 0.0, 1185.681375168525, 1084.771104...[nan, nan, nan, 59321.73774141947, inf, nan, n...[nan, nan, nan, 0.01998730010804558, 0.0, nan,...
20(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...4869.750.6022392.221.2300145...[1671.6565260937566, 1944.3791991826024, 2177....[0.11307595030892517, 0.4774130350623014][0.34327363843942293, 0.399277071450208, 0.447...[59310.61722951841, inf][0.023530760437823733, 0.0][5, 5][5, 5, 5, 5, 5, 5, 5, 4, 3, 2, 1][0.0, 0.0, 0.0, 1395.6239254472584, 1285.58228...[nan, nan, nan, 59310.61722951841, inf, nan, n...[nan, nan, nan, 0.023530760437823733, 0.0, nan...
\n", - "

5 rows × 33 columns

\n", - "
" - ], - "text/plain": [ - " network funds sentiment \\\n", - "4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4867.50 0.60 \n", - "8 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4867.60 0.60 \n", - "12 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4868.17 0.60 \n", - "16 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4868.52 0.60 \n", - "20 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4869.75 0.60 \n", - "\n", - " effective_supply funds_arrival simulation subset run substep \\\n", - "4 22392.22 0.29 0 0 1 4 \n", - "8 22392.22 0.10 0 0 1 4 \n", - "12 22392.22 0.57 0 0 1 4 \n", - "16 22392.22 0.35 0 0 1 4 \n", - "20 22392.22 1.23 0 0 1 4 \n", - "\n", - " timestep ... funds_requested \\\n", - "4 1 ... [1671.6565260937566, 1944.3791991826024, 2177.... \n", - "8 2 ... [1671.6565260937566, 1944.3791991826024, 2177.... \n", - "12 3 ... [1671.6565260937566, 1944.3791991826024, 2177.... \n", - "16 4 ... [1671.6565260937566, 1944.3791991826024, 2177.... \n", - "20 5 ... [1671.6565260937566, 1944.3791991826024, 2177.... \n", - "\n", - " share_of_funds_requested \\\n", - "4 [0.1131283127104601, 0.4776341120726794] \n", - "8 [0.11312598437087308, 0.47762428169171994] \n", - "12 [0.11311263200004616, 0.4775679072295611] \n", - "16 [0.11310448685868632, 0.4775335179836871] \n", - "20 [0.11307595030892517, 0.4774130350623014] \n", - "\n", - " share_of_funds_requested_all \\\n", - "4 [0.3434325991383445, 0.399461965817948, 0.4473... \n", - "8 [0.3434255308130342, 0.39945374432956693, 0.44... \n", - "12 [0.34338499596098976, 0.3994065964125596, 0.44... \n", - "16 [0.3433602691087939, 0.3993778354940812, 0.447... \n", - "20 [0.34327363843942293, 0.399277071450208, 0.447... \n", - "\n", - " triggers conviction_share_of_trigger age \\\n", - "4 [59352.21276113208, inf] [0.006034361745133491, 0.0] [1, 1] \n", - "8 [59343.15532804561, inf] [0.011316155171822826, 0.0] [2, 2] \n", - "12 [59339.97441911249, inf] [0.015937772829376525, 0.0] [3, 3] \n", - "16 [59321.73774141947, inf] [0.01998730010804558, 0.0] [4, 4] \n", - "20 [59310.61722951841, inf] [0.023530760437823733, 0.0] [5, 5] \n", - "\n", - " age_all \\\n", - "4 [1, 1, 1, 1, 1, 1, 1] \n", - "8 [2, 2, 2, 2, 2, 2, 2, 1] \n", - "12 [3, 3, 3, 3, 3, 3, 3, 2, 1] \n", - "16 [4, 4, 4, 4, 4, 4, 4, 3, 2, 1] \n", - "20 [5, 5, 5, 5, 5, 5, 5, 4, 3, 2, 1] \n", - "\n", - " conviction_all \\\n", - "4 [0.0, 0.0, 0.0, 358.15272217479924, 323.890220... \n", - "8 [0.0, 0.0, 0.0, 671.5363540777486, 607.2941638... \n", - "12 [0.0, 0.0, 0.0, 945.7470319928292, 855.2726140... \n", - "16 [0.0, 0.0, 0.0, 1185.681375168525, 1084.771104... \n", - "20 [0.0, 0.0, 0.0, 1395.6239254472584, 1285.58228... \n", - "\n", - " triggers_all \\\n", - "4 [inf, inf, inf, 59352.21276113208, inf, inf, 1... \n", - "8 [nan, nan, nan, 59343.15532804561, inf, nan, n... \n", - "12 [nan, nan, nan, 59339.97441911249, inf, nan, n... \n", - "16 [nan, nan, nan, 59321.73774141947, inf, nan, n... \n", - "20 [nan, nan, nan, 59310.61722951841, inf, nan, n... \n", - "\n", - " conviction_share_of_trigger_all \n", - "4 [0.0, 0.0, 0.0, 0.006034361745133491, 0.0, 0.0... \n", - "8 [nan, nan, nan, 0.011316155171822826, 0.0, nan... \n", - "12 [nan, nan, nan, 0.015937772829376525, 0.0, nan... \n", - "16 [nan, nan, nan, 0.01998730010804558, 0.0, nan,... \n", - "20 [nan, nan, nan, 0.023530760437823733, 0.0, nan... \n", - "\n", - "[5 rows x 33 columns]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df.plot('timestep','sentiment')" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df.plot('timestep',['funds', 'candidate_funds'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Funds are the total available funds, whereas candidate funds show how many funds are requested by candidate proposals." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "affinities_plot(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df.plot(x='timestep',y=['candidate_count','active_count','completed_count', 'killed_count', 'failed_count'],\n", - " kind='area')\n", - "plt.title('Proposal Status')\n", - "plt.ylabel('count of proposals')\n", - "plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df.plot(x='timestep',y=['candidate_funds','active_funds','completed_funds', 'killed_funds', 'failed_funds'], kind='area')\n", - "plt.title('Proposal Status weighted by funds requested')\n", - "plt.ylabel('Funds worth of proposals')\n", - "plt.legend(ncol = 3,loc='upper center', bbox_to_anchor=(0.5, -0.15))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "nets = df.network.values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "K = 55\n", - "N = 56\n", - "# K = 0\n", - "# N = 60\n", - "\n", - "snap_plot(nets[K:N], size_scale = 1/10,savefigs=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.effective_supply.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.funds_arrival.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.funds.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.sentiment.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# # Run the following code , without the #, in the images/snap folder to make a movie\n", - "# # ffmpeg -r 10 -i %01d.png -vcodec mpeg4 -y movie.mp4\n", - "# %%HTML\n", - "# " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "We have created a conviction voting model that closely adheres to the 1Hive implementation. This notebook describes the use case, how the model works, and provides descriptions of how it fits together." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/v3/images/snap/0.png b/v3/images/snap/0.png deleted file mode 100644 index 82730d83ec002f81ce0b3f050fb752c89a83278a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 67076 zcmc$_1y>zS&^3B+cRRSdyZb?cy9I~f9v}n{4#C~sAxO~RE`bCO79>dU;BMcX=l$M$ z??1S+7O;RbJ=5J)yLRoWj@D39z(6HI1%W^qZxkU~AP`JH@Fk6m2>b;L4WA460smg+ zjSez!`6F9L0l%ZTDH^^9fzWWFUof+4flt66g*@d9JhfeIJU>`?yaPE~c)B^cdOF!# zQhC4gcyI6O!pp(K!Occx=jrJt!pZsn{-49u!5ESy3ZWC7ryC43yOEC^VLrkIkB%@|8I8^`rnx&;mF}H)v1@WH|Qg{07 z-$Rv!BjO`bRGNc-L%xRA!cETlE!@8d?R|M)-O*7I3C^MTdHMJQtGu)NLFb2x_;t_e zWmkPa*lPN}YYE9h&ku&=ziT0cK^g;raQ1>&DOycOLq=CEKDm8nVD;RS^FF z`H&yj^y4l8I)zyK%Ea9em-7mr|Eb@`V@AUJ*XP^qyX^$50M{Sc{9Nd8;WB^BUUGv3 zIXaJdp7s?at8aMtzV~sV```VETriE051*!MCo1XbS%2hF#fydvJia6;P*mMq$Rthd zx?L~mTb*t8SJu~$HdqXDS&g8dK3fb%{0S#At=vL=?QXySn`5ymaCmeyQ=`urODc$o zM##2QVik^ad%WD_KBp=efUC^@Imfk!n8?_zzoLh;kJ7(*h6#E@g*s)f$9HC*)9%}w zJ0B8~-NH9+obn0^k+&_cFOS<*75NuElCMvfHQikrzr9zT4Z3`|3E7RpZf?B0F21u0 z9aff9ch{;Dq%f)jcOkVpuSRSqNK)7q1PFAjrW6wv&jvBR9QD4Sk&}}zHCVvith!37 zhZ|I6cP0t_7D=GO@hS&=+Ms=3M!YhFaqanZ?yu;2fkJdeM;np3wLio&t8 zv+FCA4t3kh`w$Vh4$MEg-hY?X&ffl$1sE*g1nzmcu7?A0be+l6zHP%yVNjI`2oMjv zX)1sL{S@9K;}aAtsj5OTyBi>C@V~#9wrXo@+YZB1>r2x&MHvCF`<;qI*KU8V0t-|8 zO5^F}w)eJmttAp8H+m&6?){gyms{gLb95l*2)d5MlZ&3s6};dhk$YkT*2!f#ge>-Qx4qta zyAdQgl0cbIQ`dnS@OU%4GoIqU{4T?yKLo*42jy;V@QSw+7;~oHOcI?`U^0U`Q!+?G zK8_@OWJDgFms&sTQj%ia8n;I8%fL~6Z~RP&0(3n~6cR>&U;mv*x^F>@gPyO! zyf)}0q@?bDvuul7S{D3X#~BNT#*%2E)~u2%gmc>SSa-XA(zX#V9g0{~R0I=A;mc&| ze=Y~=E0+80_S=25NWb0`EaXO2*S>|ibN|| z#d4Iwk8SnqJG9jfFILfi?b}PYJ-6Z`U@OkvCmvEhJ8Y}iM#`77NT>NqO{b$@Ut+mO zY$h}N8C0`}ypNaqS5{V*4k`+M)^+|9ah06-x}++&HstY*EC*O)Wjfs2mXI))^t4S`bo4Zn5I7WpPCO9!d}P){ubj@c=%t*G^rx-&ncdOR(JD(N zB4AH+!$N`LW9jX>9FfTobOBof9=!#8uk5_H;{CF>++`+;Dt`($@H~m zy@Bf@>f+Zr-c#bYKC4ri+@U7!!vsa65B*qyeLp2%{ed+B-pSLfN}lW<)`w0xh$~FFbfn`u9!OpZnt@UxVGZ#xwG7VITUVf zYinZGE^h!(D4%;|DGUW~DUn)s>Fc`*1Bn7#z_o0;eXlLCB<`qy6?FRd`&%twJ5G-` zC+j!t?Ceelvt_jp`yp=_(A%@2A8^fnDx=zFA$CMj{{jy3FPW0OrzW zf9Uc^9BKE_^`A&eV1M2M;P|$|90oe__2Gik-PxaGqTajkwm^{6?)S9V#_z8#eAP&* zA^fmuVwhrQQUXCQ{-@mlSf*o;i==XoxcUR{@ZXAUk0#i5dY@Rva{uk%P-aM3r{uAU z?S6SUasp6vJ@DKoclYw+>GR9;V-o@zA=e0)l*bx5N8C?fwcGE*vKJvLf;V6#P43$x zjdpzB5i!Ze{sGQDeTh8yGf$KlXtD{{ zp_EU-%dJ1Mv$G9eU!Ipy7<2mwIZeqrPJmc8{_?aZ$vYy1f@_G&Lf;RV=;`zOpTd!A z9uw?E1-E&p{*ga2xd%V|JX);3U8fZH<$GK^4cyC9NQBSH&R%+bIeq=A!$RQee@#;P zSogLqKC65j@W!r1s4IHU$PtDA3j)438nBH3&tHPRROy?Pu8ma`JcfoGmyYq(n|>rp zYXEz0P9(jL4Fqg|lDR%Q)_4E%bG^UVoosd1W5QozZ~Hv}MgCLqQFjAnWi-G0-Hhe$ zX{?6O`9M*`Ue7rtcB}!5p zdX&ggDK0621D%aZzTl`!KJ!IjlE(nkNX7RvNDp@nIC^{cxbYfq(&-IQAlD)2UYKRS z3EviK|JQNkHZ6@*)YynK7=Zx?a@y!wTeW$4x>u~2Yd`)v(Y5%i&g64A8js-4g{gY{ZogZ36BCpD4k8ZYC_FVG znCHh^1#N9So2mShM=_9{i_6VR{YzJ9WlhbXRf2fLa+9sqX5Xi~i;Q05MuT^A<*LxQ z3WPrYqu=fBR^tJ6-~N&uue6{+qhpG?M06;o$e5pcw_1O1c!L#EP~E*h#cG=?=gK0V zDm2cK3c3sdJ06?GYrFL~$8{v|@g(x{r|48?9i{&njGE-LCp5eRD<$~bO?JkMUI!X6 z#O-ivr2!6NezMZCehc8gtg`aw+kn&cZf@6gF=PMpVd&Chf}lYYHyS>j(+s)|7K?Qz zOMu&r=a^zcgBzZ@*rzXV+qP<(ehz%w%U|70dYtRS_%1Efm6#c-F z@3V!{bvA$o+${L3)({RPK|OwTFn3$ND=m)P0S}(P18&!r z0AAvKvJyUsDLVNfzuyt>NdjH;!@sM#H*Ra%6>7u8wz;3Kj~1 zXEp-h1W@!m|4*hi@OZYtll&+V~+2f-kh@JJp z*}1xoR%(`jKyZlYebu@(T_>xnt2m#Br`uf3O0}4OyA5I#jdnl&>vZ2QinN?9eFM0W z(DT*YIbc_tMUfOT5(Nqp1*1@2`C%h~DE-}^B=2B=`15kp)@!le^-*Xq_dV#$aTVb1L)-3bM$O3Y_kMoy>4r`to8M9F zdKU>YXx5lh_48B^U1U{~AyeJEx4%k=ZMF1`g z0!EQ5(&&SFBlPV75`EkH5lB!PEr(Ho_3#G>!02Mn4L^WFKtv?E+?yKx%-Iv=FOkss zuLG^o0DXHTZt(H6HzC8+7iHOr@Z5(CNM%Gf07C)TaB)u$1rRhTY<^@DbhQ3^TC>x^ zdSKKFy<5NSiL{CrMpPhkhsTtBawGGa*DQ*-&{h*hfD#>mdxf>F`=S9fK@?Ns&ODY} zlrSzXPUwCo_kl1Jp}Wmc z_t`g$f1oK#(9>v;&uJXMhRf}@Dof(4nOaDI^n@0g*=@IP#}5MGssUhZ0AKDomSD{H zp^=X#J208xfo{s`(T z@E>kYK7YLY`v(Z8GQdFRO$E>Bmw$g;n$)9lc3}f(EDOw;Cks9By2cC!1Wk9up3VmE z0E>(Ze17-?f+&cd@)V}pO5M~F#%2ny#;X1m{pff3=lL4vjpfCG$#WvE&xke z`hOr7Ts!*|6)_?a55Oi46o~*9sC7~X+9Zrd<7fww3OH5XXx$h=69H)UYI?~#xAcg8 z;EM-j>OeAs=h}Tu2mooc1E-v<>^5GQTBL)W=O<_$#aQ6aWAiijKwHSc&Td}9Q$*+g zrttCqo8EiYFepBw{Fm$VIiNWHszq^jm}bW%19C%vxjaR)D?#s&-Ja{?9wozj=zxw5 zT*pn*bd^9NQq(vllTsJ~Tq6=_sv-ZW2#;)hDF45H=$z9EVAq)v0V2>e6pC|WdzTBQ zT5?f3|5xc~i34`p;$mlf%%;hW42iS*3YWrnpIYqp_j|4%-J-u%I`u35o1K0rcZ4!$ zH-424YWy|`1cakTw?8t_h^1?4viQGiFuDYUrdHPt(<@(FTgG*t?Kqa&O27Z-zrcD) z@sY6mcGuNmB9XaC5$Mf}&kLZqtRg;Lo;cLY0PAa2AV-JW&%bCKYRz-dZDU9XtmN53 zePBxrYYlpfY6xpOj4KDMR_7;UMDyAe)>)_`vv>A<8bk@j4D>+|2mnvp{Es6Fv3{%$ zIq;0RAeBeN*C1-N($!!mRJV(4Z0H_K&;uV!ka$2=`TZefoKC=LNn=-6H)4fB8Wgo8 z)ddq3mD{YnscF$cK2`>^SoQDWKMe>bmY5e205{O45GJPRaRWlUr@BlmwYRS;%_LZjthmmhH zYt6xXFsns*lPU6fz_RUp^vqN}zpAREgxcJb^8WsQpL5$<#G)5yIY?(Rs3(jhcX4Fe@6DLgp$;A0`IQeJMPy;(2p@V@LG3ud5P)fY(~tD=5)XDN z`C{KzCaS=|79=s52P^nhwYt$XIZ?nE2?R;5{d_aSCpW|tmCUUMjG&e-A_CRQD#52+ zmzdB%3uG!VKv*fPz+YR{T;=bFgao~t3r@jn>0?VcW6PY5rZ zR-Py(Vk<|H6b|>=#-yH7<$Mb9&OpUPrbtI5Pe;b9EnVi~>>46uWez>NHioUvDVU4eEAWo`_s7@2MPl&HPc^rJ$!55F<2!Cuw5SnbcX>Q98g%(A=unSTfp>$HmOz#$SHI^W46HnH zmP$ElO*I*?_*v);_S)x15!62}LG>_ywYpk~>NBdeFYt_7?id)}8 z(P2*zW=2eht-JVH; z(q%bWmq8|rHoLvY!-t}X2ze|dEA-9}ryTcz0L2-l40>X&t9-0<_S)ZlB@){9G@Y98 zhQ%I8FD#ymzd+_F_+u7Mmhe|aIZ)0r7j$W8D$8blpOLrum=S6ZYZIG7^Zx#vH}C0M zQRhPiVBGsjfF=s0x=Z-!NpR+?0>E}otAEs8lXX@I@$%}km0&##zyZk+WUId4C=}5g z;1fO{KD_tN!hDgC^NZ*oPt?luh7Io5E?uuI=NRJhjI|3LD8wMS?WXA$*VS=cbwdMg`L_>qD$W(Mp$#;-n-|1tLZF22DxVk8-xrsl z^H0YwTsptEuS1WcFI`MD|IBAAFsxfz2e)HlQJw%wS8PdF{*b&j5P$xR_K`k-G_5=k zYKJ7D?+hE*YWA-I>H?whlN`R96s>1JM%N2V(G{St#lxUs;T67rCjuEF)Q0Ci*kYRE zY6mgsAqAg1e_^T@_5P4q|KFZpapk%VpvXZoCw94ful8BKRC)jqTdvi2!~SQ;B0IO) zGDejqXX~|XJ5c6oYyv;edgabhTBDRW`r?;Ec&(To*V=NLiOlMMi--KL>rxFM@mQ_S zgM6xx5=}s0BL^}bvzCls^Z>eE8dBx_DNQRMS?ot#saB=objA&CP+|(RTYd6ERD&FN zue<6Xe8jf8KI<6UnH}JuW+RHwbjp~9x?UVb43K7S0^Ahd3cc=V;47O-7EjsAL^ZNs z&_F9$wl5rps?6?z+ZM0c= zio4}+6K66B{>R50k)MDWN%+5C#F;C>xCy%_`HaLUmf3fiGHDE-)5u^rfWN-j z%0WsEv9Ej4%>>Cybix$8(P(x;z@)nXSZfONKuJjnYC8~y-TPpWHrGCE3=>hKpanuYT8zYh^Rwa{09-$oGw6N-AQ8|M65xj&4xCjqS7NyE zr9Sky+dIk1?v_#XAANC)|LyGvH-3w!!W=;fpG}mWqFUJTuKEJX2hpf_i%?&c9^RAcQTJr}^?&E2sNo<-r7ZmDJ(#?8Dj8a$?H>G6 zX)4Ss*Ea+Qv*uwnK#V)*v;?Drl8xSiYQS`U$7Hwx^Itr0M)Oi)@c|LL7Ru6i>1M*9#q~-Lx9S4wZ>TbSv zK%7&k`eV;*7W4Oq5mczLix$OB%^?qW9?YgqCB7DkDr`kU>`XJMw5fSl5(_7mULKuJ zw(cwo%{~t4+{=-v59wOZ^))1>4T$?YaHV=O_*URxi#MMVE7Y88YHy%%eFg!lM;XUs z(H07awn&ADE6XcBOP_!RJBYr1T_7%FY|0bEWwpL{oGHCJeJ48D{o&9(3oSTWr<&QQ ztR;Fs*4?Y}@L&Sz;gc5StQ~+t>eFO{^DaH{b-qzr7>QzG`1a+io$CejRr7W1P95Pl zP?qG02V^qajpx4E5OQCPi#)W+jJ0cC7*c!BE+=H|s|sj!pi0Ox$v*im>Va zV>%y2T#BXneiHJ_`u_O(?ybNg4xRlu@Q9N3YhoPMGM21~qxkY4Z(@;Bd|cjGC#Pe;R(D3=%cY+pT(NxEB>J)3-KpHTqpArme#M3kr( z@6#R2V)DmPMF1s7E)lzPp9fF#m&G#A$+B)nR>J{d7N+=rkfl7CD~wmA^ZijGPt6KQ z2gmkg5pj;+Ri0#Tn&ra~vct=hZRtx?w|ps7rCFt(&qsMN)gV3;88~G;^qVJJjekra zt;QsX(O!5|coAphTqsXs+K6W(w<*N3FPHVsZ7iXt7*Mo0+|+A4dqnG_JW`I5)2)AWDwJBX5_f*Qf<R}r{+X+kDq<*F^%!ZF=#=@2wNc3n zM0>uYMhZr}L+w{<)kgL<-W9Su*82KpQh+V$?>YhBU%gR<;SjGr9;=)v#qDbzf?UnR zND-52AZ`3l8-!N0fYKU-28S%@>D=eAssbpb{!i?PF}N_KIOx|@*^XDC&rXlUr^N`I zGUxl*re7rQci-Hg5M_U5PBUCt%2eVt`;utSsaEz-tU#lAo^QK{ee`+ZbCd@9YRh(@ zYXPU$YAA9e>qNLnj8ZoR{Rtrf;H*#v*1i?P+FX%Dr5)E!MUD_RatmVAheIYz456<`N?=O8C*lZ zKw}miGjvaf(@_n~d}AUd88p`ZIi7y!+~bLqI5P%%{&jmWrA%66pHUWU1WeW+3d>cT z>EC7lqj`7OWDLItPS=d_EvSML`A3>`4wD!_#sVtPn@~}tQp3g-0#H%PKv3;lG=B?A zAJlj!T<_cpnfJ^Ts+u*;g*@}0`#ff8Hs$qZ>l4GMQwoILt_|l~nfz_Nb~W>15EA{d^x0D~v0ZW^nUt2Fl;=G(CAiowH>0 z&mflMP`mr~^zt%LVNPc=4Ed2GxLqOnZ2uqm62*;O3MR*)kdg!A9{XBm&*IsEyZ)uQ zY`3Dos(Q3-cOkqrj>c`W^qb^!a0PIp*#@E-T<7vQdsP2 zT2`@4%k(Nksmu)!zVtuprWLi)_FEvGaaOvd1Zn6~lj`ZY4t5CutfTBdqlY#e% zI7u(sv$L5RT;7b#b@*+Wm;xb*iotX$Vbs`AFqAs(B1=tkLir*|$>IhPHf#F?9>jn$ z9qY#P-7~7Ws=tj}C7<|cjSdt{ZvRAKs^yXpr#0eiQ_^|`vt5fU5*~gJ~tL=Cs+26QaoIF z7tVssoEd1T`zCsbKedM)IozSkR1D1ZMx{(Et-yN|R^nTAkQ?>7sE#t3WlyhW=li`pBhE!ArMVu90kYSUf=ST{Kb-h#B& zcssKE_6P{`B+jVI-j4m}8^@mQsK222eOVG!kjf-29V@bx)^BZx*kqR2=$O>16eN!m zns*MD{9zwa0@BfR$?*0&wpSIPd4~f5peHM^qyeJ(D4+ytp6u$xuF&^0=7Y*r{&@9H z1yT4s*uOrOYk0|92J+D1@~~@$#@t6w)I7htCu-3{Ti>s;DX*3t2dj^^S`9zTUJ}2Q z;ex4Xf5_x75;QRfC1~?9BKi36~B5qF{jj{B!g-fzy@dJ$&rUtVns5JXmjtxepU3zMmF zG$f7+4%JLFbhcP_cH2umO;Ig$-byt*DjPHt(S4%&G!y27+q5J@CnNuAzDTT&kB6se z^^4N0ddPCwSXeo!q=SP}lNAe;hV0lu1DTS*vDrT@-~BHgbAScvQ*fpM*Sb(S1uA{8 z)d+;Sv?zyE6mg)En9sZc6F z!S}26Nh0%JWkg9LQ;_36_wD3FG~qxtPF3a+F->b*%^HESBlNYa&~=#%t@?;48#$Bd z*fSmOBvyPYHW^go&UkL9$ZgOPlS-xm7~?I^?3HC**p% z{%1vRSe(|ohyiU|Rv-rfWZh)=H1+y~<$l#Q<)pC)-(-?4USnyh!&dl&KGciM`67nw&9F^E zSUQGUtaNro6|$+XpCAxlwD-*4Fvuqu>x$xeVT$5W3k=F~2C~KJAuTTl3vk*OWTB6D z$U0(om8yLc{Mdkocn86blMm>Tn)r&HFj@m3ie~4B$VIa~I-lR}eJ5?7qtDA{dHF_M zua?yUx8tL@r4E@&{PphcGGmXwTEcc$AM4;W$^}iAo^7GL7L_SJzxn5y4N{GAL$h4P zmMkXFJVu}IY}WQS)lrK{SQxiUV^FphDck*oUwrPXHqfjpQRCTGm9gdqI(UEVa3NMk zTQYzLCc4%?G?1m3)C!QRqO|{-L>Wpm>q*M^k4v)1_8ZkiRD*Hq1&0Gb_T+V^f!dU@ z-$@IAi$GTOUlZ0afxeEPAwiJcW0!bTvvBdYO9p;ZaKkLUFtMrAqrbC`EF&?p7yyy+B?Ay8&EI5gH)+SQG2U|>TMvdLhCDrO*Ta>Ed)UKS!I@{65^4(YYwz9^_htC31b;g+Ov%TVA@G>H_D4pv&%ZGG& z6Lb%;q1mk=(#ls41Sisfi3u;B5bco05|45Dg19)JyeI#33tw8Q>1jCOmDv;6A8^Mq z#6(ZhK%#>i&&n>cq#FBdq$esD<$a&utzgl zUh>gthp{-M8aTN9%SUw#Ih-m+*3=A6f2b|Atc4IUKf>W|^v0 zXvz!o&NB_FsreT}>(Vp7enmhl29o#V>JAa`j=bcD9eukQaJOrtC@y2z?dpdo#y-R}6iCgmJSV}8Y=#KkmzEG`u zcQ%OS{++qPXU*z{FmIvDY?*b_*s}|xy-0OqTPS$Ppef5LzMl6N__cY8O0P!DQEWj3&gl2zm25Ix20Pu zXlBzk0>_d$1TOU?t>8U#a*3xsbv=}l6BK{@Mhvubj6yp-=F~*LtW2C2*)G7ts0F@0 zZ}i=q^vzfGSwzBsR3-+f@g%TDz!JVEr*2w#iUC~|UnpG%3#S4w^>k2XvRG+wM~?un`dCR!{?@Y&@&W8G=o&|dz6 zywS~zO%?Y_&nK~Y|nUzVbD&|uQ4e8sz3-=7I$-#X;(s@(L)^MA||V5hG= zFXLlgsP;h}MCM)!`SEC|VWNXGp12lLZijy%B|6790*?xsj}b+IpF2v*;Z1yBNklQAik>tWNwp5ON|!1*$G0h-kC|-&ZT)% zpZpl$#frjp$uRKCcuS=C2r2kA$hkSST59ioJGf&=?6sX^UA7>K<_ELvyJNZ|v1V6vlpjyN!AnzeWCl2aaWuqCB>Xh_=%=jbXt$R*}on zwCxBLAemW;Y;ax`0NNla6v(}FS~9e_9a4Zsv!^u~OYXuCw5Jj>TSV8&zo{bQgb~5- z(SP+mt2@2@hT4tvFY@HnSz%+!AF=TvVrxnh<+D~qFrCuh2b9k}mR=!32*eQ!H4+)le&%(_UHUf znYAX>zmm+g&l~ocga~7X`18Yy!`ep_$_e5_R`6tM=RGX4gNZY|(G)860eRMnas=AB zG5nbbTJRMD%JDUTR;iIgx6~@cGoO#`WL?t(Q#?#+IC4yvoo-2SCtaBY$xwzvW}`~raH>C z2S=_?c`{jZ{8C9r$Nia@o%WOfu_=qafQVoxQA9h6S+dgBp?KFaGldZ_)H2gRaK<$| z40S^gOM2?qhz{NVXO=*VbCL=}BhZho@5f9~BH$Pj)+$igO>SrZ$yT9|2?<-U2+T-V z&!4RgiOrETj^jro`op^|KR8aLA?b#V+wGU3ANk-A{OJK~^)+7(=d+PIFKuy~-PjM! zJUy6UbTbFh>vqqdI&Z`JU9axhUW2ANVBqnqBIvaKdhj6KIePx+dun-eZG`=&QgxVl zwuEVl)O`VgxpFJ}pJ$zBIDeXFZIK8Uv-BXcrd-+o+GZ<&gw-?dj2--!1dk0 zd_S7HgF0wPn}EDe`;XjMQT74N%l+|U$ANP^GT<}3?#_EDYT7w3 zXXBdkj*VO2GPpeF|JadDQ|htBKfF9oY=}+BMMx)l1uG6W^o=WI9(GUR%;-?F zE>fZqU*q$Qr5KDfC@GXkx%>WFRM1JxaHv9lpuOK3Ru!iiPd^N;BVDKJQ&3~6!eRE7 z#>9`iM{ZHsBRSeV9x?MioBM9Qw)s!>D%!cBQ!M~Lt>k^7YkLb3YKktjpf zC|yUAB_*RjMUn;EEW*G|Pq%$&zbmgKf7@A$hd>L*in~wAqE(>dda3e^fU{{BOzqN4 zEVod_0tv-q`f8u`QhKgDxocueMjcc1P+}kLX#%;yDI3SL-lLLZ35efg=Yyq?b7!OR z@d^oN+|tx30PXySEa)4QKThUHIm(3d>jbGpDx~kQ#*WZa`|?9XyEqhC^t`@@lia7} zxIpX2{IOF3|K4+{;(2*_0Uhuv&>}xjT?+~%I-A$-i=oeD(n%nXCPgmw^(0$f#LJpB z(E#ybIF0OazA}h-ANQ>W`H%%5-T)GEe8@Hz-*H<(*D?ZayFp$psWADllA5b34u@q? zDg%l(jr($Agv0w%?6NENXv=xU&-fK}JdOE?X8;;epz1tE9=Xws4F*xr#eT~4FDe&0 zpr&Cgc8W6nl7zg;^CDNJ3x=^!1y?cKe8eSAl8QKtDs#e5i*gKEvlX+zkZ~fV9?0gB znT+PvF(u){r5fU3BLbM;OenNRbjXt1)7yK13*DM0D;r$qckRH?7 zHaU{kkgPyCaV1Ta82Z~aICs?#;rtFJiQk69z+xON5okFcID8L&O}J3RIUNC7o{KcE{|_$@o+Fj2O5v33fIHHa$~HT_8e> zSp_t1+I@CE!698<0{40E1t$HE<$P8JO~#KB_i5}tJ&p>I+6KlZp#eRW7xWqtskP_+5L(8e{)Y4aN=K zCqVI%n8JR>eP3QRiGE?G7=@l(H4MGXJ0jS!d58`*<|^@Q@jA*!Io=kv*@`xVS&;C% zZJdTfM%GU^+%jYwhp@~f@^?BiI_yL`M;JyN{@!TZZyY%qc&x$@N@VI& z1Z|7}D(5VAxrwY|uAU`je}C;PYwH&2kiw z6N?q{YTY`(|by={K|El+Y+@N~<5vhw`4$nU3wA{XKmN{`n(^ z``^HO4L4)HX7mW0a+$Ig&3X7S5wf-U1x&sOW?C>v&UtR$>cI)~%nyB`GGga-{T{*J zeKIYCa1dmE7cL~nJS}yYk^L#fW6tYc%sh6LRm=#8S*JLl>x!PP$jg=_{DGIQDL`K@ z=3jic7CUJjDTQwJs@8X%6Je6y0!x>ClOBJrg?~&i82~#}jXnbA__l)!ZAOfO&;Tj6 z575%+4s@I9`PozW=F3^$du%q(1v{_$Oier1F?WA)kP?hN`w0jabS%Dp)l3c(BeX~r z8hw(1uZ+icXR@)c57yu$4^@Pu(aOcz-OibGR--l`i_hfiG{IIQ(wtX!Jt0}swuX`lZTJv9gV1Ht?b03QQz zp0?k8yN{4x5H@1n)^r9__I+T{B7+-3_=O4uZ~!8x8|f_Q5gkP0+fS7ovv`?`juUq_r1e#r}U{ONLMX&r& zspZILf}~Io$RnTUO_MJ1h^xnL9NVbf8&m!lChGP8t?9Ql3XN>`g_L#WujXR+pa9Lh zXQaE5d7tDI^rXC`>lE~bL$Us-!S1kXE_s4mP)w9!a~ZFyJ=%B~Os26SE)L5yjcbvj z-(M3PW|A|brmXTP%*)w$=|v`?KcThw4kii3L(_=A{WevP>)Ok4EiiDv{D1WPvi|Z) z{UYH1RcV~Ec&P&klAZ_$(q%LXhRHDTp4Oj+~BQ;C1%k zj7yw})5}Z_0*8)+Vy* zh*XCxMakG(CN}o@jkIGpP!}_JzK7kZp-}-7Im&v>%z(k+Xg0?{iAlSteFRSdA)Fs( zP+ZPQE-aH%Itr#q9OtXUw?doKo&!Q41fHMD0b=%0G@knybtDzxXRq!-(~uR_NL;Y? ztVawF{+SChTLGL=TX2g@((Aw9u%)7()}MuiF~vY5n{eItLGoo@gPT&TREGBgwg&f$ zi)S~l67jU^qQpqC6yb_&h^&_PwB%pi`Vuc6LCi7C3!}4+UF=h9juBs0_~$E=6r;3K z zUJmP|DiPxMGOFe<&@#qLPG_qYO~?6F2~l2^gKYs0!Qd>(pU53`n$5h7%l=s)FHs5-YgC`ZSZ0psn0 z;JC&%-q?gOGRDmn5sVKNlR6t#Y^K5Oeo1{7MYI<;aNA*wS50B)!=)HqQ+dmnl$we zk5~V{C`T58Utze#C$Hj{@dTB;)!)s4Lf!U8FmQg;GkZfDI6}(}oX7zBk`Z)G(;!nc z{i->^RtX5+r-VUQm}io&aJfN&=dIO|=uCO0+I<&>=6eDS{)FE4a z@8G!upM1K2GsZv*5U{%j&2~6Id%EGtyN~;ZbUytEz<~jG7$oW}_*NH$!^44FlyW82 zBn~$E2x_K4*^%~;sc=6E`IA@#{`seHN%%V_f{?KJD3$Jm8ak)M zMl=8Nql=zee0Bs|EZ=L0LKkN-w+Lez8O%#lG{fEPJZMe1AlpMLa!T#bop=N!)}+Nw zJDg{}y{q$ZMyV^hCmPS8nxBr~ut}OJ#-?Qs`%95V1hy}vmm=h;27cVix!;$(UPnicu#!CI{#j8Q?eKwnCWvM2W;;WejH)m z=Nj~(@X84}YnIfp2=r6F-|1tC4j)4nX z>H{xUvTfC;o-}Mr;s2ofm>TG=<#w27asp2H0tYykYqW}_)M@)s@PhTtu6qeTpWEZY zAt~W%(9&p`XER9g-iB0ZDPzlKTpq&)4&rLlWNy3BIIY3{>cNw@ay6GU?X-UTEA-1N zwmje8HZRDxYKO<{7N3KRTPngP_wTV2=8VY=N2G&tg8c_)^Ph(C`T1H%)q?bl-u!Pb z0GBVHY#1RvKM{mdFJ^xp34#A>c@E>keI{!Ojnuk7`>LefV$HqbMTGK;>{KuE%YCY8 zc|pxenLPcn7L!ttmVQ1@zND)e|13W9l2Ytr(Kp64xEGDiW6L~yh-*4Ed6eTwC@e0dwp1JS{D(AA@Af$Tt|XcP@NV$ z8gwUu^Hzf)c~jf%qOMzINUy*Vg~6(gXMru%xLou>2zQY+m}c6Y{15pg0->#xT2WyX zM+GGDY?TGHD?N}_pJb;H#}Y#$`jrIy^dLMyTZy@aK&NRW@XEF_Rix)p4YRSO89;B} zrEy(3%U+SYWTPJU=`gT6@JtC1w&5Eb z7dfDP?!Xa3`By`$B@7OR6~}ZBP+kd%U!dID6;7~pRDemT z>&6X06dYW+!#Pzfh9t@tOHd*52t|kU14jcA(9sODs;|Phb!4@)qTvfP2DR!w%D+jH zFJ)P%L1KX=19yEBD2&odbyUN`w=Np17`8=eap3E3D{@qf(Yj#o*n&|Y`0lBTcNIt! z!mP0iUeim|n|1jj<7!#9m8}%UFBb+rSNOQgMnuKm3~VK+zUP5O4LI4ISso3D=fnw; zFFybAX~!Hrlzo(?8h6de4BP`Xbro8gU^^LzlaiVGU)VYnFlHabd7N~gl?K!GIFX7_ z!QaMUEvC3xQ3n@3&JefMIg>%ZK;TAtuwiN|7LS2??N>za%Y$j{!y~Lp$BQUf=mI(O z?XP~5E3yV>+TQVNVo-@I@3bp+Y zi)A_4Fpl(7S$IQUj{Bu5iHV=Huu-*oX@v9{pQcrFF4yx%lwuVkxy|p}zYGKF66?Pq zWfBJVdn7G`Dpm3Pbjg-=*bKXO0;#xVw+rd+EC(}w^Z(RW*>B!-0Vkq?Q-0>Le}H;m zJ@oX1szS)|)!e6H*MYm$jo?tMs#;9Q4E6oG4!6Ai<3o8V=?lssB%+6}2D^0n#WOS( zQr1Ux{QfhE_h(YoF+A_qCcL1>A7pGn55oTU9jxws`+&2~P z=L=Nf#u*Cha7-`Hxn`8Px|OTujrD$A)FQVjYs97L#Dcnvt9>~fRs@`q;pG-gZ;ADj z8>e`ynEnq>=NKMY6RzuyZQHgvv7L@FvF(YS%)}Ghm>3h=PQKWj*fw`(@9UhO{ja;$ zs_Lq@-sipzs%X0%yk_(gTd$sx7jpmb6pN!`qAJ-Gi*O0?YfS}iNor+W;6JwSDppG9 zt3}j?`)_qN(4dhQkIQQ_BZ`B5u`1*DHcU`%PaG8dD^4|t1jS`FePo61M%-xSsh<;1 z#a#pEx)IZ;5hI5XsT9lT#o($}5ebr`i~%DbNezxI{7Ebc4!p6y;1NFDaZ6B*X-`Y; z(Pbf_1ju-ne5pg04h5qF1Ye{<0XT!$sT@{f3&GK~Zn`qM-agGiHoh`) zDh(@6WEONLGctsfKpZyqA-H%6u6<%pP$ieYZ%hlPL4_SkZb=Ml6*IzK4psC=%qwVZ zsF&bxKzEK|*~j}Eqf2oI8y{*ZWjzk{uHU#}k}ImGN8oX>9+5BpQ`3h|@+hA2SMw&g zULE+;*s&mzco*CCykjXiE~!etn#?TI2)6BfSaC&HJ|J?C$s+Z9)|VVzt}wwVL{uu? z=%z%TSomI0&R2;nwZHEtN&o5Nw_g}6-MG>@AClVFkkPd=z=)@`r<_IysKqXnCeuVK zp&Y1i*A#NDT3T z#y=%0&1Z8NQ{#{4?eK>JMFru<4+DQ8H92E#^Rl~CUP6UX%ly6)BRV;S3!1-RNwEtn z;r4!bXOtj9RFJw1hpUOnD95~);=mekW_d!{QYmGVlKprve3E$oyl9IVA;jIb9Z zhnEfW7;Z77%uoepgYJ}m*|woC0`}q9CR{{?UG1BX;=kA+U(}U9x&HWna=dLCQ+NA? zM1zTOA;yb?)Cq93wYgCp0qFOva>a}xMZ0@ZaDPU89iJ%V^4u~S*O5{f0ZK~54Ctl3 zWwumYmhn?4A6)6!`p_M>19P6v2Q_Rb6LVK#^2~wR3NGx3CH@RmP;F4Pc9B~HgE1@L zpOv@Yn*;yj-ju)ZiDyOTL*Lwk84pHN9Ldb)FYBv-S-t5M{f@vj{cwg&4m_Y?oXQN9 zo)`SC5h;b?E1Q1Q*TGlsnBR6BZl}w;5TE4 z=UBjROj+rws6}K85GDg+;-+9zjfWXWn3j7WwBOy&E}ve5cmdlWXhC@Z|njQ2l<1+5vK@f4Y-+%9+6kXm`(a|xd~<50jeK5cEiJ2p%7is8}R<^aQMUu3j01$U??U=>*EG{3)&OhhMm5Lw_WqyP&ts3kEMD z?b}S(S&kJkyE%j^l<}Nne#BCRa*5!Hh5CN4!kC@PAO=Mn3Jz)=HuymtA`n-|uIGNq zFb))CZu#2q9L8_Ymt{fXL7>AK9RoMnt1xkQ=g4O5^*`)!eG9z*Mkog!!IK?$#LXYQ z*K$_yOZ_rR8^d6cObQ~%jriO;~2KUGTc+{G7=FBPxw2*%|pbLMJs2we=A~~$6vaZ%QqzLTju5f zLO0xrspteA(3rzzRM)JEWUiLR8yir`_5bGi@sjSj^j#n3gkC(`hFsM@=R`sLel;A; z6pbzgV*-*w=pI+XDG2tzwdSN{DY79}Dr>5tccTohiA?Q?mXfU6p(t>1Sk)?)@)QIS z<>pe+XnZbs`4q;W)3&d3{CVRNtId3=4a>MV=*08jk*7?;CrM5W;s2tx9qCBNLs7V= z7!m)2X^yPZDME_WV9DK0CR9yD;YotGctpJu4zx8y>zjB~?=BccxHBkQv~z>FJHv`> zp)5N1d*fKMo33{$Q3Lkwd@%VLHuUjsREq8y>EZ|B0u>LL33#&c+c}W6)rd&LcheI* zF$sq&66kFTn4lQeMjZP18GOEsNRb1H?rx;V?E*aG09c(Ng}fL6LnQ}_@eed?LygCd zv01@rxkTr-d7S%`W=uJWy*neTZ@VR;_xRMwrMPw&&4*Cx<)6sAjrchy3{=HeOMf%pQn)v5xwDwEs_+NMdQ5*&R2-6IL2DF|~1!VLD!rEr{FT=`X1NB~ewEN}~y z%m0exmv_)dhFgP2c$3Zm4lq64mS^A_9ATms zz(%Er!$N};Ceid358!o3^=Pwi)MJ*+V+;63%p$x7zUlXT5lHRSGdBswX@ zL0#vR=8=mYBZtHPep`NoE<|DP#$T?(jZCsB^N7Nbfbi4 z3$jWG;Lg@#-luU`NV?=@Re<9xPuL#PKs)6Xfc?%!Ze#k zM0tvcXXM=fHNy%fFTvN)JrzyDdB*^$Wj275t2YA1Cuvq3=}ymep*AE+rM%^VCL1j? z!cS(-bku1De9OT!drANBC~&T;LEYG!^^J+AMXlQHzEsL#=o)qThj&x&>to-%e98*q zNqqGo(_+Gd`Z6)76+ovmP((&57;v>6(!fFNqvrUjZ#^BE=Udxg*?IN&KOgtkb~~> zBRf=vS)D1kn+*?O9^`1=44aQVPxD=hW82<_cv45;dWQ?0M;x5Yz-%&jpI0f6zV2Kl z)Wbqcm)nTtAAYh!L+f$&oMg)e*X0S>XJ>tB1`xIP@_Y1I@xj`izv^dkI0fLoB0*Qe zYQf;#?BtWtlOW32pYUfn~J1I%LHTjdKXL{8NCnrCWVpm1hR4A8?3vK6hbDo3? zX}oSaCGB!F>rMZ9Xkr@E=Uz^(sR?ywkwRZdJ2V*bP333!tB3LBIEnJHaB)?Zm{k3$ zjH7#c+IYM^v;SedHip7xeo%$$yX-`a2g5`{k5^uR&96m%E=HUW5vUPRWRvoPu=`(5 zB?@KF)d>60RMqd@dxJmQ8@Maw=0+sUAv4{0Lx){}CO?koHt)QAo?Ic|5@bI*H#`G9 z+(vBgZH!bwLQ99M%bpil0`9j3rymPN8>CWPx_5Av^L`W`Tf=#SeXkwpCYhiTz@~?J zM}&yAGZL3U-h3&(=D;%+)|GHN=4Aq>IX!Li*-t8Kfa1_-*x>ar(OsZ$TFEc6U& zRDWAz3lMS|CIa20gYqiipU=62YWtl@aW+~ikObd@*lK;DWtyYhP+HnJQ$j`2e%Cly zjKCtQIHc^LYhe=6FM^3_!K*g+sP#_74=6y6@g$(tdf%$&FVi0pHdX0HSvr0~Ei(FI zsQKTk5pr52p;kaH?nE7_v#U3$ktiV|{8bI43($1S_HC42Qx5k+D@(`z6s&P4f=iW{ zr>#Vrhy}|&_Z1d^f4JrsT4LJ(dpz$3_k+2S0#uVGh93PjmE5z`;WqS9;oU_`IHlpd z=Lha-GuymrMW|}zdvA(|ZATUilFmR$@|U~!X~3$-0LqZ9=*#8;dE&_>SrQn@s`G#5 z<<$-~kXquSlSS&1xxTrgYiN`=>8V3~(Re@+D$pH{hSv8m5K*o0|H=m2osZxg7JxF$ zPa$F$WC29&mRe5LC+~2vxA=i45&FmiwQIXc*tHAd1@yL3M>5#~e;1@Ebv09=kr*?z9*zmafQK{|^epTwY@8 z6(sd|v0}5M+s}y1oD-CyJEo(*sxeygnn*IDup36BtW)}UTvB$jkuxoCc0)atu-#4R zn9Jb{BAoE1+aE?G9Y~xHVKa`T%i(cPpN|BPz&m^;oW69U1}RFor@|pQ%;NILY4`1C zf^j18n74T@H!LPicD*{_u0Ne4xjhbj4%#IFIX?=#ZHJ5s*UH+>51B^&G&H3IVzQ{& zUrl;%YqkFG-dWx!W!kMSRDhnpjBg-T%>ammQPII3ks}^ljc|8~($Mr`SW^Q-96$)d zn^Zrtm?g8xhAaHj0IC@wPK;h3v1T3w)YkEX;{TNmZ4E`E*)|Ds-#AG3t{w0aU&&H8Pm3Awf!*yUHuo`f0&*8G9{ zwD_5<+$u!Qw(au*^+aMXdVDdfLBtGuaPuEsI`Thb4n90;eALzSp|!YC?+jgtJ1K0a z0@+B`h52$A@d#Q)szKUCWq0vlYb9JA0Dkd8URo+Kkf2Jky_ih>b)TYiVlH<$&GvO}4L;&x~T-svTv zh77PrUr#~pt&7b;!h6-qsK3Cgog1|`_2RNZo`wAAN$Ei5yvM5A&3I?pYoi=SHBlp> zZ^N8Qq8Jk4pQmwfc`rJu*M$_~plwp65Yey`KZWDCz+eOakYPVkEc*F3uVtu$13Hwe z1O!elA|cZ5+A;t({w zOnPrM(&RnH6cXQhz>UTTGRvVgMt_qtE2A5rme`W5T$KA;rLpeZi&Vx&S&#TE*I_W? zABN(}V{gG~M08rFqM9X7T?s1*=<;jG2dvNE>G=U4a;(+s5I1N`W62#FY%Hq|CS){WgdfH_BQahpie$9S=MuTVPx8Ec zdobCCXU6T4B+LrW>Fe1Fxqc{?Z`InYR={6aXmSSPggg`xob;Gov8C~u)HNjV%H@Z~ zRE_mbdcNdV_V!5GfMrVd$9r^#7ek73S!z+H~Z z6nz(&S`tvbU9B+nx7Bz(DJ>dx(kH_)S<3iLCa+?eGHeCZ25z4S55%QK%ow>^n`{C& z3TkL^QHHN-v5ek~V)B3Dii0zJ?0Kc8(Z%{|K?*(s2HAcj25-+By#Mr4w?oJOkfkVK z*L?q8kqX|(dkpcB^!@5(?&a!p<@JVFHj@6+(aeHq;~UO|Q?3CleZh`rLEuQt8GKYQ z<5_SdEp0IA68veT%UFJ3OEu|*GBQes5i}TzIx~x#=$;g|{y|sTMRj%g#M3+4b&Mxk zvc~ssh{$sazCs)u-;<@yEX;g&{k9%EVQf1wErJ;;!$$I+I_xWXVeylUak60ftmVe@ z&pPDNcUfBuG)rYFh-KPgE!|8Zwec6+^x3y4JyCjQbYtf%GH@5GcPTC#-Y9<$O|dQ; zf}0X|Q8>3qrUTV=qhhg>7fsCQpiKyjN3UQ1_V`6k79}*A23%*WXYzL&5d)j*!f20G zb)zv40H=LSn)yg5D5ixm+$Xh=FfcIAAmZ*01zHR8gGg6ODgsU)yBDp{mOp&#!G@np3oZK{@>eP1pKv}| zS8Rw%nX|{;Nk+ITd3_|bOakH5l#9cVPlJ)_3FQO8=9Gs5+VDd}msoD_wmF@)I~>bo zr@BDq$Kb?r#s>kVq4YfV;e_XD`Ga))IEtx^ydXv-_AB#VnZoLfqY#%&v9$qK@u$sT$M^iDLay~$XGa-b}{;fvYV z8VGJtLTKAC4JVHyp$;{iMCQpWC=D1Ke5Ubb#>#AAzf7pJFAQ&IwlD;ca zWPwPPWx4C&$Yp{L>;H>RKO%_mqE=Ce<&yH*!zS_-^m0jLnRLtb=#lzyMFXf z{A8xwXNh>_bj&oHHSL!&h8NL-sk&5oVlKuSl1-W~xzm$7?1}^^2P>?VONKd4eb*a& zxjstGPhO60QU)RjVP_FKi75!u(d$56K5hT=Tr<8LcC2m(!|6CBsa+uO=lr$jnI6w+OU|Xn0)w*P@M#9Zt~CzmJWbydA8W*`2oRU2{tPYb7#=F-^$Wp z!~M|J;1A9;>vm>9ZU)$m$0Ghjo81jTt`-5F z6G@p(Yg)Avv7j>&mTOk9+(SNf{qynz;QAjw2}koP;ibT5eOlwfK{&c*S#6Ff7tNbo z_94|I9TNl&Yg}y#>Fcl^kK{##Vi8u$ON_$Mxfrt*dz(^PH=g*h7*|6xC75!>Y)$e) zmHYfd+0_m_g>MrW@9YxxbLK8tQgcu-0#bIHSyvq)&J>I$BXr4sfW=$Q!i-ilR#Wj( zxxLg_CgF#u_xKc3k*FbT(>1*e=a%qjy@~y%c=Y4~T3v7aqY)d5ZW#Xe2qI0xRcoZ1 zEd7bmFhD;SY{Gi#C1R2=@?jL{9R>t`F(=#4{(_L>+JPXBZ2cD@amg(;n49l-y7K`N z#D4QU@=xSf)D!?fcG3j(MiT99NuqUW%Yo6|+CGQ|xE0F3%BDgCBpr6<@cRvL)p+2# z+Np0YVCzx+MP>!i17p)qNCqhRJ}FDI0|az>FT8hbrAgW!aRUQUE*=s~+fQoXta zzhOjvrVWKTBx%Pue6`g7mmSNA~AxC|~1 z5a<^9oPJ1*x^Xg{;&}Y?AW2<$lN%y8g_wLs63-L5C+)>l7Sv~q4T-{i>Z88mrgl)q z1LW7)9nxj3*o4oGOn(DG4F{MF414@@{oxL4x@~yVvA^3V za>@rzieo~?s5<*w2a3LGd%>rRpk~#SN6`1R=DKt4zrMepKF~k?_M<#kG7Vs;5Wtth zL$_c=3hYRIOe)J?FfaQj))sH(pzVNQDXqno$$z7u{$Ws3Rim>sjFulfwRSGbO{!U9 zl^EGdHCQRWDP3V6|C@k%l0-)Z=DM(<)#6wmE>q>nSIn4))TOxY`KZf+Pb~KYu8lY0 z#5DnOzJoJ1@#Gx$ng)mGB238xbM>}E>$IK_oM*CKH~ zi-V`*_TQr=j@x;Ouu=vXmTY{JV4kW@Ia-kUAPXRUUSK4~X1G=Ub%>2((>X^u`W{EE zWpH^J7aytc$REUSUvzSOakOow;x%v$w4y_LA)~+lNX{2QIc1g;$JR?uI7pxhZ>buO zkY$aoRCu|~i{UNcvwGRKFrj9i@R1x9 z(Gm%n*||7;@j-Nu;{%uDb!ANGef+%aklLInyJkKU)MzkFKF)gm!Cewn=&hzFQabvj-b z==xCj6YuL}YtzVFoy9dz{RcE`@@mFpx;B$g0m=YWYrN6m7Oy##6pN#~|5cT0?bN zYv4Z|noA{yp1_SJ>&uGZkyRj;=<7|}FyO^ZQnxbo{LX{$^`UuZRQL_eb*c+iE8vNj z&}HWEdezTEIsqPVAVG5*9vk!Yp>-mn>B&Xgs}z@hs6`O`2&YSGtAo(VZxp-Iu+@46 zOEtj3G#AjL=Zd<7w4YokYZCRc>a%1MKCk26qn88viqt!0!A=Ca|IRN+@c?E!Wj%kd z|5Oz#s3Z-$r&xj)qZ?V#vzqhIYp%7y>DwYz>K}+Y_s-E0zkI9p%;EcU$5q{p#_DQn zWEAPl3aOb_+D)f6pqj2{0mpAt^tto`^=#Ls69Op}X`jX=HS}%FRc(FXF%z5YJM(|i zX|JZ>Gh$2chYNd&g6k%Bc8KR50wZKEVve>oRJ@MFwYUHE%qc?{e)2nA^}b)80@#{R z<1b0U=wn{?XG-^A`7vkoX9k=XlPRVZ~*_sQS3s1y`6u+INjnf_ZT|7^C7?N=y>je zy^@=IvnFSB@!IwnXz3Iq8#KQXeLPRI$c+~*aJt!O-X?O=tgl;y=5uK^yn9T@_1ntJ zQ4hpQ85U_VB)@171`!Plr`bx8xr zvTc}T5S1FYm&a`s-ub)?+jXMp*_xYv1+vr$>BmCAeL(X4C7FGZKkqDn8g;EBVP^x2 zJ5HK-;8QW{w|^EILYQgn*a-N1_ngbzgskHFg7NCRFKF1d$%|(CrffHNLA@{0`xyxMQV5&wde(;SmEVWce1g9nz(iYa^>TtpbL`o>CyBhCz4y^G{B#0u%` zT!B(agp?_#e(DgiLKozbo-F#-fv5tX!I@KCs9QoGoNxVy zO2ca(&!lEp?Fluy-AT70*aq*TuZNhSeKbe^!F3C-ObNoQGIVO>Ay9;pC3A7pn?RjA z)4R8rAttxw>Q_J*xKEt!2SF>JOY9&T zVAQ^GA(Q_g=3vVLd(lGi+hSTA%jm(bMb||VA#f5ie~k)5l(;g_ofWM}jNZ|YKZ}!g z`~tdG7G8tkW>KVlze=!pLVke>I(|Mm%HCFItPw?|F)!avr3Gy+8Xv*glg`b&=a;oU zxudyevjVSzeB9DFR?CDOMQ{CxI6cpwOQ=55LPt!IOblO2>b(7Q%|HpCg&g3fqgI)5 zp;iasr^|v??NLPcPRcEF@1N#PFrQp+L648Cf2Eq|-^$ecDOdH)Z+#V@MaZC!6_1+> zmxD0U7yb2m91&noH^4ED=Q%k56=&{^_#UEM+d*34W~BoRfe0^n6+vD7LG1C?a{J-GM{^sowu6!I~=3V zcJFmf09Z=o-Yy3Zvf79u+P2Jef+j`1s52q9{!OP?+qq(81+5+EpPtKGy7oTw4gd}IzshVd~Eh@WAoR(jiU$_WWgQIn7`$qIEj!^W~> z7BWtEN4vP2vVjXfqWGiXt_#3L>igQ(b=SgY3k%@9qah$~YWXV-nK8jOPwVamLLmPy z!nT0+w>jPyqueCZzDZ~9yQ{s+5S)j#t}TY=#orKZb0bYC8#OtwI!jYTVj6jHZOm8A z@&N>Y=@$MXl9)sXRrcPNT(LCrOw8USmF}@f6SsO}*U+K5pEI4?2Yt}Sai&%A84j3w>_bD$<$ z_L*fHwYtT6k8EST!$mhG`kmpz1ts~Rq8p(;*2MV4QKDjd>uZ%Q<7A53$=r8(yn6O- z(v(wh^>O0RgT*6j+J3DCp4P!rH>CnzLSQ*lCqK+f;|td!%O^5KM%=b0u#FSdwtxs6 z;mh4U8v76Qj+#0#_Bm6&qC*tkW=H-I3obbgKn#4)X6{FP`v&Jb2%eN(Atl7KHKJ}kDE)(aE}swkONT_sA>E-WMVBq=TGTpNf8}HitTIoR*El-H zYa7)BnrYsD#74w#6uJb0h8zk#V~0L)Mj#wW`I-T$MQ;VPZTV~7BGo#gU)LN=}G zgY$9?yh8*W(12d9q?^Mx{nyzDe~1TeA1`bX7aYE^ifO;ZFYJZ!-b=hb<4?F^ILnqnpecfO6Rh_!xY4K}Cd$Vm4M{HB_)MA20W~z? z&k}s6y@D_Vz2D(`;9^f7=>LdeD_0ob){B=hhT}(h)=e>^z=T-9t*(5HhOlwsSbChI z{_f0-B4F0;a?>&O8YuD*gmW|5L8JC`$TW&3cG;3cLv>K`nr%qFNPwYat!ssA{l3A><7+d4@^@ZYx~h+28TRn$K)C#N>Q>o2qkwS0{gfdj z+A~+waHk%b=TRWH?V>+6I?>7eRyJ0Xyx3Y9%M7uHH=b5vpgP`~AePw#TUI*%UJz(X z5i5=4pOBLcF!CU<-6++ves?Cn%+pK!&vj^+Kx+k`NFG9jQz*NbC|3OYUeDh_XhNt? zn}oQjyQCB7Owp?De7Jp;qr2D9J5YB{Unz>OG!@p^d5cKAI$iT&J!!FAcfOC{121NJ zC!mk1VUhPJFXSpQ_YG3l)=Ve-OGV=e>S61WdZk+1D-xd)#CRH<3Ibul)9Q!?vBJBc z!yLF2seX3QwfsnnW&~<*Uj%XLUwOiiKD65Gj_cE{YrfoD^SLXvcdY|q^DQp0O8{?Iq=0Tr#@uLx=g9g2GMUoCUDE7j}XiqCsB11Kh zRpp%-?32-LI8I_Yb@6C#Cly-%77@BatG3>x2;DBaO&75()wws^=C~G9|8nz*I^9kNb-yD$tuXauZq1t)TJ@=x-?|o0MAor^YppR?;I6V& z;EE`yUz%RI!u-nG(?UZH30>EJzDKhf_tP_F#m=nl^Dg_Za#}L6WM4URgg}R@NYH8! zD#<|gLIBfWN)dtiD)AQM~vjqu~G`|@83%R3FX z*FJ;Ow3nsHu6xDktS3m&=cMY7a~J*;?|MNFOLhWN1=H?$q&o%nk@=(7uNcF zZ3jk}Sk|^WI8Gvtuv*j8inHCQWC&!n0k**~Uk-zj6aWXpLLt!uVgh;J7BRu3!|Ga@ z`d1v-Y-a^*b4)@~CZ>ygkU0X92>XHa3VE)tZ(>yt5_ebzH%2K6uC7LKrIaVjIDj8o z^q3;)jWTR9N!K5u$KUaFW&Yjp1?_MphYk?UD{}V%Ugo3+193=+CI`o_5@poxeEc zvN$))$oiQAfq_-?+l1$csif~|x^j%rT*V#tG{>_A_9w$!a3!j5`7t``ShnX4nL}a| zYdwFxEbbND%kH3D#2jP5Y9G57&hCg-QBkSh^C3lsW)>vusI5(2c5OmHnI#yVb8G}f z-UV}$P?}`^+VSDo0VQdFz@xqo1oP(~3Rb^eBZv>P>%1G&*PuhQY|n_hxD8(9O3{lf}0{H?Kldh8X#0T1dhrPyn|C;>XjKu>AKlN2I4UomJL9^ z?UB4vWyh&K|6oa6vgzk5nSv~-f@ypnKYLgH_jix+yIIe5?^faO(P5rdaGS(bKs|SO z277;peDNq<+X|t{ng}CBm#HtAFb>J{`G5}x+R}uL4T%2K!!aco0+NWmegChSe|6O! zE`7K`cB2!18_sN2%8+R^6O#Q34S3}9$4%tH$cs9Ux{-5cX zEFZvKSR$)YqOB1KL+_qBBA|N>y=^?DT=6>*u{km51fc(bqbc))vtDs^GMbptd2fsX zPF^iVMlM>UB(Se@>yzyu$6W}p9e67gb-wwmxvOXPMg^upmm4$`CSCNg%8@Cbawx}6 z>X_l&_bwALZh7e&5N628x~tfD@vbE4Fr;fgohwi0C_CnZUebil z=`F}x+Y*6c^$0Xy*}86_0~-S+E%ExUm!EJ~nj$=Sd;izeBMY5HD2SBKs#7GJa&ORb&G+)u=&t<&!^xXKxt zlON18+@eg6Hfox8BQ_R#FyYp)>RxlPXs4DhQR zLCSem#(p65B#kZg%wk2gZ0pYkW9Ahj?79+qEe7u1ND>pKvarhnxC2_z_7rlhf#kYd zr7nI^u`9}_(7zmF(UJ{w#2F$%y-O1!6$^bJ(`SS@GT$B_iK~0lC`>TusuzY3@OJqL zp86g7ta)qTVT#J>SE?D7>(lWfF6-a*lHT$(tWKbag<8ga{Rs#&jHM*O>@(mQxXjFw zvSu5gM94l#V4d#383`~{%q3(R$f)wsF8diMBbRIparZtj*5sg0cdVkN8{y~h9Fd7p z4qN$yUKH>T(j)5@h6rUDNA~vs|N3wap@SHx2Jy-9o=9J} zM4yz?#`<9snZkY<;=Z3lYZ4iSVaZ*HbV`?itPv463h5i3zE}#Hf5?-Ecu``(Q#T-B zZ&X7uhPOTmQC6JZyy?u9%XwAEhW3h!qvo!3qb#y|TKeMU-(d zr9AHZ27~u;4q(gllq6bPl*-*+tw7FZgP>l}Ai%}HF@gGnx<|^;_a2FR+xVFed`gu7 zn68g$hTYiq&~dt};;P0Jce>fjsK{N9;}G_~<<&&!D8uW*q-4MWMI>MANl+WF(uCv| z=c3BCDZ$Y7QsbV1|AJxziHolPHbfSec*e&U5V=XU1EFc~}0 zE$MPO>t-J|8JiS(DS;w~gW4_qA-*t)Gj>Xg0X2HB__L7~z-pvkVEHd_F-y7(5;qGT z5mD6s1-*_aPx_}K?eG>c&o-~cCJdLo-;rWdct%}NBYqIjiP6;^;E`iQg`Jt#rTjhs z(yd&BM0vX)ndKqEe>BTgRh=nDgS3ip>tHJ0*e{97a*GS#rs#J>$C97H06zyPzu_v2ac$v?|}cWVFX2tMOIr0z}s zwgX+gIIRSyXQ+EFKo>rbt%rWbG3@GnVUyq8FLmuz-ubaC(_M z?CAVUTbcz@e|g@`NPorfziiB3Mrr_nXTuHSKcQET=f+tmjDvn?mJ!bdh zHi(+1I4y5fB#nEV%)*Xs{iYe3{?e-pkGfDHjUduMA^=m_WXAozznMgk)G|WHze2f{ zL1DC#(7?np;v6n>-yv7nRSZDxF4>hvSad^jDXy{s_z=ROz&2)W#vTl-=fPMe{_TZ% z0>x6{h8jL!$NQf~p;G^S=mo@nkA1;WYc)AhxwM>{5tFA#>qr7`2rjJJ2zAWu#)iY- zMH}{4O8=4cIvbpVHLLb7bHZdy9!i)WTVl%GoJ4`66VRm0nnQav6pVi>PuG(nE-BRi zMDxDVhA7hrvl@7T75V)TT~?55_56dsE!8i3BzT@@!wxsfc}2|VNyn-xANo&PgY&7q zkq!mie`DGMRDLt2NToIl_?2Z4eeaK3B+Vc@-$m;ou^ot`+gn|wiSUK*@;c1^Ss2R? z-wkrP)`DCbJFx;6Js^ixplx+4G)Mt0wE6tskFUEJlBXV++67I$e~}E;|GWAA{$m5H z8w60fnsoHX09oERzHG=1YxZXib4p*D(^?Rl^ReRt2M2a2CP^KYKEDHCtYKnFi2DoR z0q`qh7eFpnbo!{pTAYWT7|Zq_@E! zr2f3=rs~+QaYTE8$wYRl)5quALsO7Mdg$tk6&glS6y$}s{8E|IY9wreG?!nBL+35O z$A6&xWA7l2VKhjLiw#OilOpqO1HKe;E!)8e({|j>tJZ(q&nedSZsC-QsOVG_!R8A` z8#h{AEP%V$89*Tpgg{d2csv4HZCsj#bl{k_guCj!iCOD4m8Q*RT$-lBk>pBUk_(#; zF)9Jb+~DiO2%eb?D1el+mUP{*ml{*RSg67<^3*)tR~d z|GfayjiR@Vn#^F4>t-co%3Mn)kaS|LW2lhF0!4AE<#KqpJZ*h<`9KJ0TOlbm9PBrB z?LqR2mua-wV=n{(hbA)Q_BZgeTSuCm6ojtmfHe#0j{Pg~+Q+hC)04Y0r~NieL98)b z(?~9;T*!9d?Z-6b?VX2TBn}ta(;Q;y(qMr&1Z}i3uos7XG3>htl?CB(pRXYi`yn2| zeN?90#<2Pr`Ivc?Ta_TP)q@jK<>~{*)n!fWM^Y2Ik!CN9iM+1o-I8s0wst_$Bh%R* zWD&JM^L(%oJAUj^fmoW&d&;1cv0F~AbO#H2^qj^GcJuOZE!@0J_I`_gwj^3Ve`r_) zL61-PvyMZ~iCmN{S7Z>{&lu%vF(Y&tA!|7)uW$k_hr2x8&qyG-sO;B_Db{))H~c^c z3A!u50ADhnuV-z%^4Gitv?aSb(dxzrslYM6Ov>w^Df{hWxvND0*v}l^;oVH1AOg!&H6|zM2h$ErwUWF;l*o+Td^AZ!MV)Lu`fkDSad6;NG0h zRkPE(3A8_bI3us1)ZX|(;8~%R9JBT!bO!mKFxl1dt}h0^LM}n7!=kOB#!Cgvy65%k zu=5`7NmT7govPi+5N6Hm=y|f?)oL#PRKDE-fftR?bs0VL=W1Sr_t?aXKo0+xznETMd-Tgu@eO(PK;xi0O78ecD`aYQUvy)4Am{*pdy-J zEZ+g{m!b7bR}M1C2aPkgBZ7pnUdP4Ca`Gx9?`U}|;1=E3%YWwJ+T~&vV=85oR^x#0 zMz78&R@BdjxzcQs$83YWt(WytRePN_p?JhwrusS#JAjel~~!$+w?L6iWmK}5=9KMaAO+urvxP$rlYad0I8JxLNVTEMFwcz z^c+Rv%xyGUJB0RH**+g-{Do%{4(G~swXDEGGfO)`gwMNYwYo!^*nuhLPw4-=ciU^< z4cbk%)MEQ9Q|->fn00-kQijQq;D4RNJMMEV{*FSmerGU`-NmMO0Bg5b>znm* z#Y}Bf)5&>d`^OmViI{{Eb69KKa18lyx;)I>MD6y=AWGU{mgTPpgz1N|Q=!_k`T}K^vm)H!k@0BQ_?^SQh zzi@(Dkgs?5>nQvsd{*Syzyy7+S-ZGszZ`-Qx&rfkY~MnnpCbWHm00f1nu5$Onv9QoWAZK#={e&zLjV*zVJ?iBG0g}{|CqNdlJTV)OdHS>kk3c zwJsl=vmZmF%TMNdy&A155syoM`$E-zuL8VR;tFcRVi0mElKzokF_@Gtv#k(90TJe} zn`BQIOgRYx$G|kM{}E5BlFDyKGBn%WC)jLkwdaJWDvf-j9RfNl)Y!jlIDj9z3GW?V z3ltz)3~nWN$x}rsGWkO@l~}glKYIN=$d!+}(78Ti*As>4iAJimTXE5MzF*6~Z&xYn zKtup_ITJYW5`c7|foq2#iSID?n%%B?VGT%2TX)&@`sIm5h6Kri4bDK)`j(q9hB}Z# zHrd16%?(Fe?;IitI_UQ?a_5|0kC5S zd<*UumFT-tXn&Z91$DIG6q*d9yX zkVcyo%OL`rbMt?YPH`k5v43gdGEM3O@ z-0(J>0L+6lIj8!%q?4oU*1b2$NyF-EE?(XjH#eXPHaV_o+#ogVZ zxVuAfcZ!~{&)yfg$z`4|>zixLcZ^ZU6ij_>sd6`~3R}>3di2~S@lBaXlBnBN(`kG` zg#6KpaA|1;+umijZu4kZfd}cc?|8=U*@>HrD+~$&%gE{vO)0V08S7b1Kl0N|tZ&fg z@b!65e^_@>^nRYp8T!m~1NV>;gJ(PZuDc@U9k7YeZ)q;a_xTu{i-{Ejju`puzVw)| zHTVfbPXx%*Ow%(1MtudzZoh zL;ZWk^&i)m%V!*G#@%zV!?K%C&)35uh7$@M6)IKR)p@9nUh zFR2T!fxY_Fep%anQ?Du@(A56dYyDxLh+b)bjnH^B%4EHDOE0F%9UnVq#4Ei$e-lTf zT6$sem!yD88>`M3+Nmim)h=vua{s1^(QQ||*8SwqqNl)(V(@eO*}AlBo}#~Q`RO6P zCjPi4_P80L{;X^#_;HlS1+I;aqkxOy+J)8gxnEG=h6m`{H)4LCQtjQ~7JFd=_hsW2 z@c9;}10>&r`7E;ktoLT}WpMz@e^I*10H{3})80|Y#Jr7|tVp=5Xxe5g{ySddE01$5 zp4|Zg`&E21{e{T5eJZJLPOF|il_I(c#48GCw$P`}E8zRs;U`v&e-wgIMBdo9UO&(6z4s2_*A;p56QPQ>) zKv5N&Y{urW<+QsyG-Ik82fxGB_Ut7s;X0zZ8;-8o)j9sh44P_IEfE1DUWg;hz)#aW zmCyoq&+l|@E({v$fQn%K`l;Ef#SQlYU(#PIkelbM-8nMnTdTSy4pE6(Y~^Gd(WK~c|%GPSn0@ex0K#ngu$L-hv;99 zzR#w5I#7-DTo$8RO#3ez_G1<#EhaFX31BJVurOej)?JVRRfhO(xE7a6tVe3K9PFp{ z$PqGma&mJ~q(v>%nX`@ep_ugaDsqdG*BF(h4b*<-0!E8u>K7~I7qhXUF8)2lI^J?) z8;?TQ*GyRoL~C9?DA1^6==V;_YYhI9h0qSt1$=CKkqXzvNdBk5n zfm=a<*qk-gJoS=x@B#6Tap^hb#lmC5hK+=XXavsB8d`64p$5x)^rKk1p)D_jEtio55r3WmF%Y-k zbL0#Nm+sMJT{+hGHuxq;<`Ub4XEA$V6L%t^@P3VOezf_!bpo8@HR>H3&$M3)Y=Os} z^`n7e6E_}3ZOnkai|m`X!%u}57tE6zmA`U=Yc}8Zp8LtvJGuYW{$<``Ttv)fUAcx6 zj{9~Ni#wvD=kj|CoXb=@q;_YBkHS9+gZ9gdquO;kYq~nFv)jYYT(lJWA2i=LX!rM| zwIw3|>ERy2KY8@meyK-Ffd6y%pPn2bt_Tw-ReiZ!?BK8dpbs#s`GE6xJv>4aJdB~# zh9iNrdXEAqQ>{A=hdmtEu#x+_uO$;q`E1em23WDKp{>t3r8CHNmg&H)`M%%g>`jZJ z49*tGUgx3sz=G?~fi{h+PrUD0tA7#Vtl0f6b2t*-40EnV0ypTAAQb3WWaZx3a^nPO zbSQ3u&zDpfY`w$HvggQPet7{-lP(w>SnbnonO{)6{ol1@XcOS_IRO>TwTuvrAgwL{ zjq_0+ZWV!IDKV>bRFXRW9Vsy~Dllb>txHi(C)c(Q9(zT3)s%|1Nv(LO_el<8LNB)f z3G!VOUnbuw4Qaw(R;cFb`?U7plT7iGTRw2^_fA{ORrX zISGo6S!!(KzFY6h*FA4n#TgERnf1&=vG04{XFexzpI^$$i>Am^Jw`0$6CNO$50UG1 z>p-kB$;=90E)|hAat;y~aGzjKc{*TGMJHXgDy)$amu6+|kxjB0mf}~(75yEO!)Eyk zq2h0j$n~1?pYIS{&XhuS#?&O`TrAem>?-|0?FMW7l~(-C66n{TXUHYy>~=nCKUZ_R zHt!1)vvY2U$De$bu2FTI2AFAw)3fY@H~V8VR_s?t1+iVZfErC*FGgWN%O3J}UZ}q) z3WwIAmHf3c#l8^Bj>L|n@v-X2zxDu*F2|x%C(~e>?UxP9WwFEBpvG;0bfGFeuUNae z$!$mqZqDp9B6@5|;>hI+!Yd^SmOb<1(rTpXZrP(AB$pwb=ECY@Mh@+M<-K~%YGX*+ znXc)9cTyYhhN8Z>K|qeF5T`xGf+ZYi^rHRbB@~e=LK708W@l_r`f4UG+*93U58wLY z_e?*|{X!PSpdHbv@Pe`FdKDnm+hDu6CB>RT|4{zQpI=00sz7;v_G(6xM!gU7kUgsQ z{grp50@KigE>&hrWIOm{|aq9eqi1$ffOPg%FJ#(^QZ8S5tPO&4W zXbH!jp0U2}k`jm42putu=^x@K0~JNd!`zQxzRIB{aBsX_^M<>}!;0nq*yvJCV>~w} z8|mzCX7W1m5Q5kO+;NSHd_c0z63>hp*&?{<%-T>&f5-aRb=iI*y;-hs?I7Q1jq4xF zV9qoLp&FSQM?%r)H$(?>wSlanQYl(Bvpe1O?qmsE4X8$>(%`K5T`$Th#d2pd5CSI&08^4eyoh!La#b`9-PJmE0~UxZjKR5~112 zr>Qm+K6$i43t7zTc=MZdy)%PL?Y=)PNUS-l264=Pnl_>`cywxSPsXoU&I1tkpK+$d z*0mT;(ZYO4XlSkL@obg0?xLBOQ-IMrEbRI3oWzyFA1>-+jKG6bd!2}liW%44T$(q{ z>!3rnpHexHh7wQbKf?<%zN>HwA1`D7QHmg^0^=FWN)ZsMzzl&A>IY0RYy&~nyvCtz zP@nnkQ61og#9Jf~3dmFzPx=-O-gFcz9t##K4b0A@V_tw);oW7?{1u2otvaDKfQ-~b zSwk}0$Nlqd1E|NA{!of+%*)0C<<70EnRegM)j&5au0ftNJ-oP z1OdRA+K5S0H$Wx;vJMrJrWBd!cE;r;=A2?#1hxlqd3Z5j&l{biJXvho9eZj(} z^w8BPm)C*^T({3|U&lpl%T_cg=ER3x=-w7?aMzv1Uw?NE6=EmffO`tzVpvAfmA?`C z;n*~&2x3B<4)x4gUIFKp7X(+#cGH5a0WO%l3c0?hKUnM5<@`mz>XH06LHz@Zy5ASDHoSwNrosc1zNo;NknZ4yPbs1-$K5jauBJ3{>?6;T zQKx+yIfF=uS5-n&`{Q4%P8iJ-LjB=Le31D_$>%h6S-S=|Y;qV&6#k>qMx3dMSZ$d{ zDEW;6Uo>vNI`J4KF|4u>Gu10kuE=3>fgnJ0>bKD3ZjZY#l}r2RpplSuvetK}b#uKv z#djuQDUkj+(EtZT(UwMFs@I21Qxd`i4q{TEDhq_Kq2(8em)6_JN7N&TyWL=E_KT00 zMT6$wjS|V`Qt^-}20v&8ZsKAqj+vnBoddx^4|qA-FPlr!_U;e~Y_if+-%U569OLskYeUh-FA-e>PqoIZ|mJUyVtKpG8s-rJkL$|^11@b^Ha0b=>V7o4T zgntQ|(dZA(tdxs$$?>(#&?5lr&w8d*yUfSrJS?_9yx9zeTjj1+2u5MlilkCA{y^}% zcaGZPw=ryR0dA6NAx}3+0u5|l3~g6N*yS3Tr2WNQYb|l6_xY{ob65BF0Rq$D&+aD< z_qU@j%l8G6Mvl*SN3#_irKslN#^zDKi&^K!2OzP)lpzTFx8|5jt~^3~U%UZBpqALR zHv?S-n(A?&j1dWBI?lRT#cxQuW=FO6FYffub5-hubF~B{6R{3Eqq1RdM(>}9b_~$jiV?@@V?sq$S!w6iU zYB4BUdHdPczw-gjP>f^_Naf=V{gm3Wy|1}>nDV=v6KA+y49)=k%kA_m={|m>iO2Z0 zMl{n=_xb+Y+1K92F(sGBGt( z==c)KyL&%qMmR<1G!g+;(~cWX?N|DH=a7tPXG9Q0#Jf=hav^NUyhZlfJGCz<1r(;( znV4Lazt%LxyTKv%0skGWL)Vq_`hRnb9>X)l|Fcz6{jCVM2Mzawr=G~%} z=2x<@owy24f37FI>8k0j@7kRleRP2=Mq;8+9eXQ60U*G6)h9#Wu$yWU`?qs?qozzBIwtdfDc0_&jcW-l}S* z1YTNPt(gwBoX~`e||`=_L>_npVcA?rTbxa`v2D8#MBB745`v@12XO zSxOvwP_CPgEmADvG8iJb;`B7WW4m-WPc1`O<{V|JE;tf;+D(MdO9*~+0UoHdcpUNYJNM-TYh_rHGU2` zm2|agW?C81c@ArZ-4+2}mCmjrT|QRKcl@CtLNS-e`m(rMyh&F;S0uiSKx1ljlFO_L zj8De>B}85RpA>=uQ39^3S&KR#1Wj(RQA$(N&U#3rr~$F{4dK_pS+hRayuk%VN5FT? zrd1pI*JDLL=h6uSSn$8vXx(biaTZxEuc3yn5BrKB9itEOPy{lf+>yziG0ZzK_o&1G z&TJ-fBZURp*W{Owq^tzpsRxmcrZ!e(;yKlMaxoZ^l8e6#u!csZJg0jLQ@{}$We15> zcH;CTkot!qs>{8sy>|B1MdiED0WpE~rMJreu#EAnBTe($mt3)SHB;E;T)cS)ig zk`n~BD@ncXN(;Q?BH-p)o%b-n!ZXfRE)4X6X2iZ{jPamryE#mYkzoIfpogOpqvY?6 zS>)u|g5zdF?fe$O@Uwx)g^jmjt_3KU(41ND;HI z%dt{IcAOo%ah{B%l`xnOwdH=AFC)?O*p-&EU25{+(Q!G?V<35H7Ndn-1COO#5qKhc zz`!(~zdI}$_u@4O7x(5g1?`K-rGo=&D!5bc^Oq5vNlLr^uwT6s^Ib1;iMU4af#)95 zuI3)gz#p=QUXO8NtDMrERdsnVNQJ5uv<-1Phv>gS^_y=Dk5QjVt-h@}-fJdhwr-Cr ziG&I>+1bkqVm!bN2E_xlhv8TOE)|l`8ux}r-<`xsK>JYnA<1+u359;-S3`2M%Op39 zWtJP3P9>JbM0zlFwg+gz#!ddeu|_aqGeN~-88d=+JNe&3j$op6gEoVDR|6fArrDcH z>_su4hD^u$Va}vS<;o9+1o~xG#xLK8HS0rhU_N@uPZQ~#ytFj&F5;6I9J~7JEun7~hU| z0bln%#CP7BnK-9s1Dlkp$K<|p?lM+ZP&uU4lx2P}-uqCQ%WhR=*i~+^mZ!yPUB+V3 zs;Bb|k3u0_*N=T5H2a-rcuWvo_8CwU{giL=5r697nuHrS!zptEgnt=D&DD=8K#FJf zdRMq22c6E*O%vTuiBRUb(dqs=QPgsugl`a%TquQX7xR}zjB>#5m4n108DNFvU-_~I z-<^AKsM5+1%(`Cn6Ljw4)QZ*oX~6z?iv0)^kHp#m@AP_Ti!;Hm5{!lssKA1P9^EZ? zzK7J0vxAI)=58O8;o~bWIO1?yNYWkHSVoRq$R4stxiueIMxK2}Xvr`4|Kho1Vu26t zm=oddWcfIrf`MqFdNcX$!Pr0jesxlVYG+@{$D2(HhC-LMtS4Ey`ookl`P`cd} zKU_PG=abQ@rliaVviOrg;a?)5s+8!B$kxWloKYq++E_u+CNwu#y@+it2pdkyKrh=8 z`j;qEG0{>gnEpv80KMH&_j%F~3GW5R3fBLttxr^C;7UZKK2$%8wqpYS5@}(C| zt22F^q`>$&W8u6nbTEmra}DWCFICvsx0>bOQ>tMFS6MCOnN}7*fVb9O$eIk)5q6^n zxXGUz-Ro*dCfrX;1*XbQ*Y62OP8HrCV*M)rW#GhU>zQpD$=K8bMs#z*lkYZ3RrmW3 zFyQ5D+h$uOU$vfR_*MoNZN>g-8A^=IcZYr3uD0oRn;>l*30Xccl-qg^kAm!suY*@k z7Gx}#DUwKrweunkN%H_=F5;tMrDdxxsfmDMOuiOUF8eko(;~F}MlO{6-_RDygRBC) z%2m$@(>1+W5t=Bq2?YKRRy*&o$Q(`*ZuWlfExO!@xaru`6+2Ejz=jrFF{(KZ#H~4< zlVW7w;hR}Pt1uE)mEUOM5XB5@J(iJe8p`$ksTV~9{!EfdMpjc_Eo>%;y=wATn;Ysb|HJ^ z+(>tr4HNQdwVq-mvM~0K`g4DG!B7C|5MG&r{cnb0G#-8=z|2K6x1QpgIe6%Cc;a-R zmIie4Zev3^Bx_l=w6W?LhOT3S7A1`{!>u;dmS#tF6DkAru?iDQ(aV5x`RwR|FED9= z8gpVrQMbc`72J9bT2&(_C;yZ-4V6g198DI<(^7W`02Xk2_@ZMQ(66s!g__F&yvlFv z<%`SYqmlj3)evQr#R09u1G!7DNI)*zlpuXBm{rZYl6&c~DOGK#$*z~H+o3We-DGzRzGHlix7h;7M%l@V0h zEy@p_T5gb~Jp{}A@2sPQ0ZN2N)-Z9Y=y*1{@@%4UT?vknL6~hHeUUqpXJ{j609Ur1YxMpG}L^m!&k9W#3*4=IWP8Nm zNtz$G2nXI}D{K-m%8G*5L@*hS$IGOi8_gP_MFt>41UYX^3^w?#| z(Ea7b**q;z($85{%yL!whg+G*N*kRr@X%$K&QHDM7n{v20YPp_zp`oJ?lxmpT?_M} z=6s%8JKbGr1K0k^>b-s_-C~^?v{OZ)num(eY+QO7i#jtNz5H`ihyBrPfOXTas>bAa zc0CqUD{YTvGMc%L^J?z$RNZ5h-_|XuO4xY0ubt5TSFZoZ0_>QrsiUGXV$34)l%{sE zhaml0DZ`qEN*q^|0mj_Tj2=!{GK#Z1A&K=ER4WHINoww1lvnpoVC#k4b8cb0WIoW^ zwU4dIqxmljR&5Vw$y81$c^M<_ak+IEp__(ONS7wj41pd=<@Nqj!Eq0G{AwujsvdX@ zNJWTq$_Fb;s|1dq81*d}TIu9C8I~ zb;UJrzFxxI%E;5oeWkW3Xn#72N!0OEa^sD#U&5{Tll7VR4rn0B$oJ+mP2_&nQLT|| zffZ0$-zqq;UFWM)RXpn*S>wZy?PqsP?J$uSi8R_-=i}zn;W@K9l7yOJwBx+gFe$e{4=q3%dJiH_JO^^G4>6s8hVaLFz62s2eb+7`e9Uf%8O>~O3X)PN=Sj#H-XzX<{RWAYc%s)|`$9Hd2W<~>qI@QeGD z{xD(K;(%n;B#A}!%?BaU4DViQ9VgHgMhpZ>?alAlLG#FCr&pn4T8#~U(;nEr*8kji z{fbeEY1S-Nl%-*9s+fWHbOW#rWtqg&5KWUVrurNjcnGZJsrhyHq%_YtG|ndCVfp)C z7g!jzgwm)CD#Xl-3sj)Y4_>n<7fpLWut}uCq~)p0{k6*IY_kq{r<|3k%71RTy6x@| zJ%}^?M;>}ZY+L?92wNd3VQ^n$c;O#py1=CuAf370Tzl;G!jP%MmdfF$EN}GvEQVi0 zf`~9DAz*KZr5j}s_AriQiZV=gwE@`a{n{t}#a%Q1&duYLQS%VNhoK{Xx1qaqH7xl4 zh4;Jx9qerSB1!NGVA#q3M{AXU0D0Mt;xVS%q3Gy{%5{LV-EsTM`8FMO&AA7i53#vL zoq15+CUcfc4#ys7lc2FAR;mlr(tzq}MGNgK5*DuPeaJ97;D1&nfwkXloGrQ^dDYSQgfaIC~UZ-fI!X* z02x#ds3S6gLQ01-(A7HqM;>N#f|I!n?r9UZ&2yZ3R4SD;;x9{eKsXQuSCgiDEQ8Ed zVtN$g?TSceOu;s~d)*u1zvOQN{iC_gNl9qp3oc+e36sjxp4)v3QTXn+pr*kO$kEcR)2O<6t z0U)OoZ!WmuXM!Gdh)~9*B17mM@`2=|q#l$gG~9>JK`+n1 z@%dE;9fhKx(TS6eJYS6G7{?lbn3o$Xjcs9KQO1b`TunlqH`72(%MG|IME_WJ(uDv0 zfMteB{(B^2h{ul8KY&znfi~pi${=~O9uf$4;@A;Q4G=JID%$eBp0=|dt}~nV7Q~vy zCJ4kd`&}6-m8vnkmF|eh zHR(mQKYyK0Mli@z?KxpvV>W`O_v5(elQsSS=lU+BzKI@p95tyCMJY;|FB3cy$V>&w zi1KVUa8ITlGaN-j-Mavdq>)}n;cC*)n0mM4D6^rZ8pExH?yT%JI3?ky>d5}XlapTF zRb(w?cq_AZ<6N0JL&l=TzT!?u#vNr+Mc=#3cFfl>sV&QNVA<{?ZGpVBkB)f3uBED_ zh@$qCw?G@3)<23|js~PLp(5l#0Wj!}8Odljw(T+W9v5#H`}7XVuG-@= z25>#@b-E_B0>6pp>}|UqJ*%O+vvd$hp&fzkGMSXBBg%>S9Jy-;0_rg1TZ|d>uCBp2 z)o>s|^LUet4flS|_RE0bORyr|eI-85|3Y^5J`%f2DCrGo%mex{M$w8H(@(Dr1~v`5 z>~Mv5>H&??D@0gW^oe=ALnq^_AX+Cf=Ni_g^G*Mibs#mkbmNpF!%CRy>u#Kbb3O-0 zljvt`8XYG%mO8eDYIJpZ8GA#Cq21TNXjz-&v2a;c^v@}VmY-d~d#zGYuO5=SeW7;1 z&Lw)8hD4$qJ8+$DP?;wwk&>3C^AgYzis=e6w3dZ$+_H7crLigmOFv0RbD>zeK5R5y z^9DkcOzNz#4penR1;c$OOc;jXNxpc59lsUTYzZ(AnAe zn#l4FYrxFGm;1<|_(SB>t`;pB>0qpP+&G(}R0(LOvE)J_o#4e*;9u|>xUd$JMQt=y z)ajCoGt(K{w>LkL@!=HVc_?D}<1Gh<<#Ap1>WHwr*CFQ#*8Tly1_y_LrAv^}`n4s8 z!_Z`eU-!vA0Wl!8I71MV@{mO7X7l!)duHCocWSAzs&dB42n_NKLZI;Ze*&A}6t1!K z3zcaqY(jXz$E{!wt|dS=1q4HkNZ{l8&nP8PKlBOeOD7FOv9Ip7CWESUGAz#S?Qm`* zolkDkyu-^#h%e`C3DA7)CwzP#&VO5@eUKhV@Oe;lG0ef-;71ceX+ePVtz(XW;vdog604lBVP`gbU8!8tey8{UEX8Fj%C<){ANks9zF zT7aJ&jNoV}@V1HA`_gMmH$~_LYK>gp4EXA(-<8evmc;51BpHg=0!;|c8Vs;R7d;eX# zPR^58eO0BiZj&7@*v6|G6_Xw%@<}(GSqerbDDYS#J&rez?4z4`9pDo zV3@@N=pfK^$O0$lxc$-mPjd07PxLf}Y&3iAoMx@5>|-G6(q{5g8fi+wYk7Yr?iS>! z$oyMA20C7%q`wSG3ZrD~BvYMqtTE@#BB(0y)<>5Kj}blP?72zJ7N)YwOg2{LtW8ya z1_u=AG2c5pW%+D^{ZANIsT96jL9^IHpsx!&SJa}W2terLv-oqdEEGPNJpt!xG3^sf z6Cxo3<3nKTC^$$~93WOfmPODQtC6*UoICt0_JSzsAm~-bg3)p$p(SV}eO^vPjMck^ zHb}L>2(UozJ{Oxbuj9n??PZp&u)1;sG6IWp^#=lh@1VGr_X)MrSa8bDR@`>Y3IbZdrv+}^^;e>@R7R#doD0NuUr;MN#mb-CphdZaH2XDUBxcclB{NCxV8qC z4FW-J1qB5jM|q)(MzP=!tZGV+m-C)YuMD8?v~W+7Axh~V6}Q5M)u7{qG2v#`4ygMEbJS_TL=RhC?cczSu^YA zgU!OO(Gj*sF$Q!pyv9axs?y)u9=sbZ>cdmO*jVbf??g9zhUH~a6`dqc)gSPBJ{$lc zOWYc>?0@UCGBP^wY>r-T74St+YRSBbZ`*$t6ivlJvO*Ybkj5>>eq86Qu^T%0m8*cM6m4Doa_fech^mK zZGV@S$y7q@umtlO9jI$Sb>N2Y-DKN49`9Xy<~gBjcbSetsjZ_(zWu1AvK~HX_yJ6K z?C@WDqJZh*sJ$3oWXnvaq5nC>iD3==_#OYog=DW9du{V>-7#0+#hGc~>EpAdeWT;x zV;0T~FgG9MBJ80I(2`?vj*nYRf93xo#56@aC+K$nR( z0h%D-G}5l;A{nVcN7aOEBY7ai8R?d;BwAG5E>RAqHjKkXu)Qn%R-un1teDSD7=J-d zy20pFS$v4l3azl1q^0Lq8cDZ+6=Ro<*r6P|x&=$5-A^aqYBs7adp3&+V&$9t)QcDJ z?-*}zNAvqJKuMjN&IALbSW&7B&R?;mGzZ$6R#(ADZ}_Ke#MIO@mCYPJ`^O0wh-5YW zU)31k2#(?f=XbqaqRgLOnCkx(5dE_(#m)5_Q}l5@R4&)G(So`djuXzL*05GCT4%9v z;YJVQ!jwLV{yAnkDAl(iN49)UxipR|1S?b3Q(_qI)qiigB*wdeHE zM+u)@x~phcwGLrc1QB$G?zf>9<6b0M%G5a0USt@zbqZ>m7T`=g8I>WVE61B2HU9${ zz|86tz{HRjsG!3!cqE11Z0JPk`(tF^7CaEKKwKIy_)(%dwYdn-yge&o4^!ph}ZtwU&vlKPJIlj z6bc`%%G&84yEZK*y|tWXnimaBWa)J&@halmGZTRbvqiw(F?w`1#ne@d+ykn`>pl() zkq--EBLbzQ)*MuG1`2wc77Ccfy1lmqQY@A0k#`kDGopMb(38NBYkg#Qg73%?kM-c6 z%%X-eit`f&fig)?IpJPwFFD-DWCs?)I9o+Dhj9E5=J;j z#YD4ToqpE~)n#f!)V2GdJ99a?_BdYKMAK=vJ{ZP8)d>txWC3wM5|KfkF4O&enRlo} zDAt-d|7`d~7JPLqw2mNjUj~9zUoVnm?Kyiw*kle~xYG`EZ~(By;3gKUs<$u=b~RG! z87YTodC3B16mJy{v}1yhkZqrm2dkr_%CF>1^mAAQx`&Su%qBI}2h*a5J^FKZMty|q zA&qP(aG06VNr@sM5%`5g4U8b6nnLgZ{3n77(%R`wbS0=Xh8t}=x}(JU5(e58ux+<> z5M`F|X2_S{`RzYVOiSyY(l(*6J*%^SPQ|g#h)7*;9-kY{ap;e9v3bmXI|6_WanH*) ziR3Tu3#R$Q*C;*641Jbrw~zOq@9o;th$psje~Nu{r{X_~rj>t$oaThsa=Y0;$9Kb+ z`dJouYdD=B884#@9O7#TuKsR)X$#9ley>jBIfBIXmQxbRoi%|dRMfDWKj^Op(0c?w z>PPU%bwha)F^VAJTy-2oq)Q|FiWl(ep_B+at*HCw1Iab-lG?uDRv;TOL`**){ShI@ z2QsUaJ*-i{j`@KjQ-cr0XpbO`J&7T5W2%CM$dbwkcQ9x{$}ttF)52NFfCN6Qa*M=p zyG$r}zgS8)rha1r78NPe(oqq~yZW2q@RwBe@k@EDytM_rRJA}2VaDX4wB-QMv&l4 zpkfu~Me79&*drO7+O@-Q=5yoZv5xDN@mqG@`NgQ7KGo zG+5FOo+|aZaLMThH-(0^x*VE-yVwoqHsDtOLiFl705@L@zW(}e4e9CP-jUwou`h8Z z9&}`qC!HhTtV_zp2COaQhl^PtwdG%A+{XkaKoA2bZyu5To09bqbz<-Wb1*;L4PG_@Ne}?hd*7{ ziC{Zbh~mGo)j*)ah~3CfC(AC?D{5-3+3~%ls|G2}mkYViDc9wneO}VF9L@mjM1k zUA+*G53bjDhuL(-bSl0>8nl0GN|{`lZWU32v(zOcC6-QhS9o^D z%bJ!tFWjEM=xC7O>kf*?+o@sq|IiOGfrf8%j<3`KGte~~O+A+vSca=3O!A#2+nxqH z)|C@~2jXK6uMn*p-* zsmlVZP|HEuLGoEp8fkDP-nCR4@rdGUZRjMOrqINqtsMs+fEx4b;%%~G;pC6#@fZkG zxex0}Mb^VJ1m%Xc<(ER!>kxKf1H_mcmwsNfT?C4xrghWu;|I5mXXe6`4F4C*S03761OFBcAn3qt=9PFc)N z+jIumgXiYSTX>YH&z+!BxtOKqn^d~M6=cHKcv4oTHfE(}-q)AiqZwP@vC*HP;f#kp0rL2RM@?zWRqNB3)<5P9jVD*Ijm<_>&U|)jQ{Mt0;%3{D-?xx; zFm`?ayGBS>DFj``&xe**pikayfbV+>yyxtE&qoEHe#TnxFje)*DBT1Ybw`(*O18tv zN^*X?)|v^%P)}4&zPWS&C9;{9oF-?%4oO5W99gLYW6n%9zLtO3Yyb+QDG_>DYamy; zJL>%cK&sR6z zHa8N@SY9^>X8)j04->)Gtu4`(kC2fb)-MwfC9|O`clQst=v;xnbGaOrYtu+CTC_@5 zB2b&ePOwdPY1l|o?iLX0>xpszz9uHWUaBtNsuK6>qIe0`$xwnsg=2@d|M+4D8;U`} z9gNYa*kU@fn#-zJ+IFsix5a_7Os$a75)gC0<@V=VEy%=Ol;a-t4oe~jqw$hZdJzV8$Co^QG(Ocz)7Ze zhUn9gcEZBnY_|}VTgHPQt%1HLRmHDeVc9cZ0g-&*yW0Qp)+|lG{x#1qVar(WKF3f+ z;D2i%#{NC{pR~QNvPF7r?&0w~6-(3X9ep`qLAapJfkQ58XIbW`!lY7kD7b07z8Vp< z8sdXdm_n~49%KKXFMZR-KSqzcsx9M5Ps5?lOb()9KMY>Ne?T=Gj*nN+Sd3gy?qTbd z^9~0Vj%hcisQ0^{mm!{oq*L~b#^Z{G1U(3t%sP}h0Rj#DF}@zPh>T>>1&kS)Qip_k z?V#bCI=lRQgFLHQhONbc!2U_fa6or0!h%{qf(+d^n9Ar6C-%h%5fqTDJvpoYD7m0{ zukLoA<}x`>GEvff7IEqw^pl+X z+nqd&MTz@pVbR6{2ZWUkue_-kHUDA^vUR@R8j@`V5EDA)ALg1qd`Wj^&?b@Bfq{F& zNirJU49AP)o6&Q4Yrg7M$b(U$3T}AREjV-pB1XVB70#Y+jJiKa%@VOizQcHYa_%vE zp5x~n+MFc3Uy`CF7dr&cC0P&emUj&q=Gvx!z?8@lHrd&Wj6 zOe!ER%wK8^Hgu70nD{2&fkTZLLdG7U^rZ^9KaldO*GC{~&d(V_gYSWN-E#)B+Pq-EY;ssNbOu=5hw7g6@F_ zZ&7fX3|QKXcJ_4U!jU&{JA$JI2>uICAAf!cD9#2QIK)AH+hZU(7vt1`!AN5}SgyN& z+Fd4Iv8+ly74tc6Sg1SJ4XrhT}@bt-lG8b7boFb$crbQcg>29fn46qEeF9^;2Cqa;Cr5n}-%9<;FbZ@Ba zMDrubK6urZb0g^!rnLY%zLWEzHp7!5ETeT=LveTGdlm?-VoeBvzq?&xlIXTVovSvr!bkmz#7L z)SpJW&DQQNJ>)};i`?cJBQU^Y3SHGD|BJWEv0B{*U-ONJR~;4y<_mn`p})-1+k} z;8C?#Ii9n4+qo3J;-PYUb@gYXD_s^f$&i+1ooOkYFB(|SkJo4Ua!TEv9dFQ{e;t*+TAT|GPDxnU+b$)X<_70&yj6{Hj4JMerVC0=UeVKoDGl zm}QzI16AGmuquJh%++WYSQHQH6|Ri=@}=e<*2&k>4l8BcJ6J+VrMpg1qzn^I2AvfU z%LDau*0O*12n{Q9KP<0ZeYOx3P?rFr(bwqNPRfK$uSlke0^Z^~*fqidARWUzh%(hK zl)e_XnfirYX8irE3+by-1(WfKXpZOXz~4JY(<}k0C;yZN@I_*MN22d95{F&f!af6* z(HL7U^%+0VeHQA>$Ih zg1bZEP4_lLCGT-cEtkvwD`w4RMfuO^BkTWT0j|P&7otcXXlwF)i~I&e%5{pQ4W=jB z+EU`181aPWPiCEw>h$KWgG*IXBBaVK$Olp3u7C?4alS;LWt(D@-N+@qKy~J>p!Sqk zfZuJNPt6v#V5nk_CmpoKgqe{PAQdL$tmW8f=oJXjVN(HTOU+1N$s*;#nm`l5!p2qy z)S1hYQ=ozc{I^l`@C7`TDex%=OLLX6)fIb`DH}dPN+mFRDm`ze=$Rj%SxvV-vysR2 z!!~oX!9cl@*)nS~%HZgRp=Zk5hfCT=QW#NK5}3qBe`(w%fx+)lI7noqv{2wq5;zT; ziCKWU#6}D=^A|o9aK8zDo(k>Z`UO8}?1|XpojiOwHxeo{C9>Ky{h#1jVEtEWvA_+8 zQdv(rt^|sV4c^9BcY*D8LoQZFSBDjgP-&gj%@e5cgl6kPDu)%DBcCrztC@LkUol9) zrx2>L2g+b48c8yp&DU+LTMW!NxGW`PafT_{XnZ)rwNxJa6xZ*SA!YTe)tP30LZcx&6N4wbgIdkGpD}385Um7B3txk9|8jqc}wTJ40 z%jMHH5AMMDb~4QsQwwgy*-o>}7H)_ui7rm&NfeX02f3U27mcv&a8}M|QLMqiU*H=2GB6{N>WeE>XUTQ0&6ZhbceXK7X_m*xVOq0vO z(Xt)fUF(l=(?^xQW!hGsn5AQ84G%$=P~yFRbPPw%x>ZhLGj0^C*Mlf=xzB%2=k#QW zevdcVF*o?jMRMt6&hXYPJ6q$QQY1dgRZU>m?z=s*$tM{p(h4x>~W<*P5{Gm=IK7zT==&W+ad zdo~Rl1Posr+}lNv-j}ovH4FBcN2HSJ>4EfVRlB`qx!8JZI$dYDOndD|4;jz&{Hb7L1 zB8M)exqrKLgn>(COxbQ+VG*c==O8LGSSQu`nEq#B*qe+r%<}j*3qNJv3I3dAlDh4D z;H&`0KZsz)pQ}TmG1TPCi$$3@64VGa5_A~HeM*NJH_2@MH#gN3Ta-hMF{tB~LUzpg z&F5e6Z`0t(*@V})*RWiM6mqfqp7Wyp6_4RV@S90Pg$;L}L1`@Iu0l<3D1v_n!-sX02hwO)-*Nxt*?ZmuWm zzp|iJ3c_+`a*{H@KJt7DfzZD~25C2}mLV7^Q}E2t&ILi5TYyMZP^N~9FbUytW5%%q z^!LnbT}-0-VdBBf8WL#7ss7EnRSq5NRnSbm4ANhvInIt&)HXmvbdZ-P9FTafjKir~ z_bBYnQ6)mzH1zXdsbIQWi!S#JD6o_;YZ~c6uEl3^`CfE&U*@Ajy0 z=0)wh?{=3V$ghq!>Wz7-*NkjF#UIKbz2={68hti@ZAwjaN7IwRFKDZkw_D6=)tm+& z9J4rfCzm8D2x@3V6%nAM=^yi0wnnF@bp?qBk{cHG5rEC;b$+2K5g`Y4DJ^$@0^%JY z6+m@M@aqGm!_O!;{={PTT#0>7NfRFis`(n_0%UXd zuag)+FNY)FRGIl@pRCt<$jngzh72U5BSI*TXk#dMpE*ntmB6#1njgs`5W20uPe?L| znb#U>t-*gnOFv-{&6nfh;uU9~KOV_+mUun_g}haOD8z;9a$DaZuVjPZbF8!Af+;~5 z-3~wewHWv8{IdnvK+;P^8|Io3u1W7h-mPBl!%PUk=Q`yx?(y#a-hNv{J5A?g+F83) zzC|trTCWR(>%fY4#)Y{*TqfTh)X(2oXj*Rq$Y&B6ikz3~Ss{fL8K8-)-W^l6gud)G z9uMaY2tBijfkmM3Mkt(VW3p}=k2{O;S1(tgV{|UGvI^NB;8~1flt3++7E?I?mh^*D z7=*VOs_=m@NM|wupT&Yoq)Fqi2AYkfpGS!8U_pA%{D*wC-YaR-By*yU>^z`>=11d%N}1V;kfsVbE}GDQ#ayjkyt^OHHQj&t0$rR;i3-_R%mZ@ zy!0ByJMciGw8dfq!fs=1EcwMwgx`NMAF;||fQrwFu)7?!A<-ECQ{4{r;e3thF;&I8 zml`=asIm6lHbpVoU2T6 z9yhC`Ec=d^jd8*#n`P#@J z5cL0wI}4^bqIJ>E;0^(TLvVsS!JPoX-Q5Z91czY3-6g@@CFtNT3GM`UcYVz{_r8Dd z>QO*Z1$6cF^zOa)Cu?=k&3JG=Mx?|a*YQ_rKw#bMC@2fDk;ZZ_tZp%d>%{F9M0-Bc z1G}7!M<@m*(T~hiUG9m*tW30g#;+)02cS`T%!(G^*R(&|l_=h-5 zXiL||mAu#_#M}kTjei-h@7xVs@OOZFkLa=8-sCW=NkgFyR}$X<#bLnhoC}uN0w~W7 z?0h-wYUa+MkrWlu4ZR_?KrG#du!^hAH~8p-9Lt_+~1E69p=(GbG)U1(yAPg7K^%BU_pj|Nv2~wRLHk^tbj#YvGi1skI8ro zRui~ya>@K$gInJiWE7j*q-eyXIyI0OHlwBsI$($nK+4D?`5J7^tU>})BE zN$NB}DwrGxS1q*%zz04`V2jPx>!~i;7a(4SyRdX9CncAud0#~g7_Ie5ZvHWA+AQ7d zj0_kv-@GgEi@7~%sbf-~=W;;~F^vh)7gssxp>51)Ps9|=IS4ecRZPp2#Q>>S!uZ~a zZ4-a%eFp)hVIPTf?BwfHWH z)D*MvXK*TsMas+X5CP@qfZY-NJbdWU$|0>Bum$RBe|F)*FGongNcNs?0$cQw0RB9v zGAmZoz6|ygjCgYd<%waBK+-0rsp{;w;%RZ8bUK~7K?d4%A+4|t5We6z`Q?O$HEj~s zEpEv_b-G1XxPUyJ_t~L!+K8)>4 za9YC!3dG@=my~9$Q47)Q0^Vh}CEHKsI!$Q6$iKbaJgS@MUwb)Tv)e1ge|vn|_0@R3 zBa}Tt-dsW#TQ^&-_QZcO$gtbW-J3thbN=B|O$bj|2wgeakxu{{`V;0#SX2L8u4 zt91h7U1*z)-Al-__ycJcIX*ZMxXb{`r7d7eo)7h{bLZPCvMUr|>0ZyUhst>*I=dKw zP^ExEiP!`UKjEBGlgZ z8P2p@ZOKElu3UEL}MdMgV(pt6!%q1)dX)qaF&@1f4R+&!6YM6K0PX3!PB*>F++Z(xLO=&JSvnk z88uj#I$PE&{+E$TIo}NJL5cRLGY+`gnGFdC(+i)J=D8tpB8DDO2Nnmf2OMExdKRny z1_cRA4jq-V18P23etEycKcqIC$Ra{Bj5F;H%9lMqy?sLJY&N6~S@ngFVD7|NdR+*W z%;8!L{8_mKs!zp?W*y`?>jCf^K*!kPq@0tf^#Tld{irSgovJ4hzxm_7-1Lup3Rm+!U#>ZpTKHVD5 z(CNs+m++D~m|Qmns+-THpAmBlY*qhutizmie4_VcO#WiMfn4Gxh9)K`^RwfJNh%D8 ztwA1-0b5k@Rjt1D1;UW4s(w$WgPI}Zx{|>gN%04bi?g65A}vT^QF+teG;6W;Bb}#~ zQ|V3`GM07MQKTdOM+rAJ7n|?B(GAwje++xIK9rEtFQR6}2+>S-bUlzZx`}Sa_G}>P zjLKV%&z*l4wY)AdcgMy>Xy%eIb~06<%xGPhY%*O+<(MfhRt-2%x6c0*lB6=$(nM-C zF=pMVZJN(3pCI&=fbB~EPFU93ym6$BR5((GYgx3jNx4irI1z)q|0zf?0G;@b_332} zeKV)ihjmr^o=v`aL-eTnHD{Zh`0^k=J(+k@-t$>hq7v^s@ZLLZV-G0Z6Hnc#;cZ~UclSkvC znB$-n5>FI&eGi^Wq5H4g=a0BXMKPyKa47epT<|C&VoLJX18F`t3-E8u)wc!d8->#6x@n5lo&X^?;lq5V{?J7&jfE_gKwsDHf`>I%Cv zT)mL@ojy=Bx{mMM?zdhr^2T7NE9IvP5+)s~r7=;^(2V;d@d;aK@zcI>b)93dJ9_zx zK|URgb)n}q5`8;=}L)7Rqy(a7cBt5q(9BnW@5m$j%ivGe+leC2QG%~h=D?AE0F zS@i5hKW_!HEUZQeZsgt^d>4AiS*p*)Loi99^UF`K`@oghg_1Sq^Gbv>k+~6( zPqIkzuyrks7lp0RwDrdjal>J^bH$rd$lW??d71^|(KAm3Q6s{7B_zm*z{7(%RNb_js$~PiS0e4NAmx6u1|dqhNDg(FKK_0*%NFFQ zWY{kxgdCGwcuVa z!FsF??-etT=in5F+uKS=)a&!1ruRPV{Pns5`48vS?daM1hm|sP=o&nRxdG9Od3^lTHkJL>nxk|!9=W_-n6H9Z|c^~2J~d_lq%w{BYK=7=1KN59kF#q&eh6--_bKq z&4yh+$YCXQRIq`><9tPPpN#w`S&N`;Vjj50U(@lTnaU6t!m?cdq1 z8K=+3MAqmQRHHj$KDbUl$k7GQybN={kW}Iq(P^?=ZGIT?J?dpw&VWWq!Gq5=Aw2wY zEE!onQ@_7nI{Ou~;75ysoxRP$3ZJ;maTIn0H9090yMt-2txZe)*?D%BmTnyTmmkZT z;&to!vB-2eJPmPoMnwFVQAQ8iNka=fS?Kf5*Oze>As5RPhqk~PY?MnjaJYyCeOHen z`82@Wuv9hlD)nnaYG@0Duj=_-r|vgSlE;B1$K~7czMQ9~paFOk3DfqHPi&q~=L6@b zuP>)>^Z+F~TiqZ1C5S*087^4}wCoZj(644-&UsXEvEQa|%5kc=hG@iKOoSj+F+^=D zX|@+&78dts#M&1fREyydFX7%pdT%O4l9?H$EI`Jr+)+ER95GPdyj$`r3+8)w7`|%C ze~xd0;O~u-Mr#0fLgu=(&#w3q>Fs4q62!4hBQL-qLY8;5dV(*CzI?9ey!X^ zaNIe{D41seXFnk8!|O+trm+`S^YZGinYn(mjbSxXCwD!^-d3eOo4Ld25|SDWPCAojkn!NXn}@}x)e8|#u|qMGXka0RDcsBa+oT~${gU37Emw5pBxtrDqHJ% z7HURAf9|p6-Z*{1z2P+`+xRp8x7hIGwDSrAkwrXZ7eERR1DLt^?#qtwXuNy5s4J9` zh13Xc^c*!sFO*55u;1XP-GBcIfEbxWQFBeyDX)>287>ZDyukpMJ=dx z!ONoakG03Qu;l)_))*&Z@j0WM5J*S)-O`ZNlD=rdWoTArOT*xA1?5p3F=?B^=9M$< zdi3oT?^=T-;;I2dFoV^E8I-)dlVQbd5raY+HHNbKOPqZ`U5rOfQC{9SrWXnkuc4_l z_lQwirA&>mw2F+gSnWsvV`|RsDNr?Ud4aZP(4$xqru8;2r75xhU5Py^7NtYNjrU8| zt!uGe?R`xR9qe>FT2<4M@JwM@mgL@jtZh9q21ifwbcfB<>OWU8P2cO7v-Rty^=jww z-q09+6vyAtGe7ZZz~S|`=N=EAu1JB>jld3kJB{lc zGW(P~?QIlHc`wsnY3?TTi|9i)QY6EfJj#VQ!rLYV7-&5&u0Wi{`t0w}NH9jF^)x(< z-%)}sFO?)iGcv;(n#|nxg6|mK)=@%~QXw(x(EMQ#-Ib{LV)Q(^R0-E1)r>v*SCzC{ zcZo04k_2~ihj#r{mCx`D{D(j*>L_{dHdmy4ExP9^+i_xTfNH*i&@)H2&y5z5>$Fl{ zXn5O5?S3FBd>B9TpPJfAS)aePEKZ4_Sa4l#OL7z08KK6?-#m6Km!nd-*0GRH_9=NS zf=>IXImT8)mg54ijoW|VLfR3Hnt-ecTzHZtBHMo$E*nN`mqbRzb~=i9AAR1JcY&OpXAud5WCey-(JKSpZHBPjHW_8v(?LEtKhJw`pO_>0{TVZ`Sgb4dU|NXgyNz`cC`1A z?@g1LpmH@k@`pg7&nI;9*AV> z0-P{imhWUtr-#-k;y;~W7S8(8Xf1;{SonNYErO=o%`pPo+I#rb&A$7PBKs~&vmQOV z|KEVcc6mDtOD(L2hvA`yiAtEJr~HjvlWm-G0L8?QI$zG}2Xx^wM6JI(5{RT_&%y;C z5;^-oWzvPD*eucID&_I@?+boxwp%L?QN{lwhyNy+JkRdRAJ3C|5G+0@J-|KHY@#4e zl}iNPMlyXxK@K}yh59Rgtf6@Vy&-P9zc41MjWDyOeB7l`iQxUjjAPQt{KWHeJRsBq z`#}gLhkHBXZtz@^j47#3f~}UY9)Eb+eC8jI@fxfaP?86vjL3lIE2dLzAnQJa9C$bf z&36Q9yT)-Ck%M$AEWPJsO+AO>hXi6;$oZ=Y+$a|rN!V;>P=C8Hz7a*@<~nyI(KHuj zqO64w*!XA3PGL(Oa-G*(<)G*4-)~)OoCGc8ay5S`K%{rImBMD2fQBqB(6N_lj(2cl zVcP`eJ}OiY4=cIuRLflQYYBl#_VV3o-8B)RM^eiapndl0#46z^8!50r;loYCS$^c` zk$HnV9}B#8A$I(L6B7!AvnFqgD_RC{8zHiML-Y=&e2(Qh6__AOxrP8ns&_Q@GmD__ zqz==_oF>SLUx@kY(JZ5jCB>y%X_JV)H~ewzx9k8IB633MZ4FnE3#a_fk$E@@^Hkm6 z{-TZ6FS($$=ixv%SP839wO~x&jLsv6VU*e&Z>g0c96jEfoW*U%Kulh*Chktal7_Dd zf}ui>Ppn}wRHhdRwS7Mh(5gD}f#j#HaWEoWkJo8CF1ty=bm$x&O=5sjgDGk0k8tbUe^W@qb3*(@o|?ldER2fy z#ANRFBg{8>xc6g}GVe*pN3dCkWj>=s!BZp@S(&rlLK08cZRcqWz9~NkE?Bud5_(gO zgAqyg#AkC)t3M89%fQnSTfAF`{_-m`TeOiAN_HH^Gi~~lRjxPhTFquZo#jg6CZlRR z4pA&}iJa#KNJV_TBk~h1>l_S5iMe;VzH=uE(U5ZjJ1$)YQF3I98V#De_{T6w8WKiAa?3XWYjT_ha&{CWVO}88_c*-t> zUR~Y=7HE9cVa^@4jYHeJUMCWR5Fr_mHc*}(=3XMtElrZ45nN$;;Y8Dw1^L>}t^M?Z zf)d8RlerFU6CV@r!3&3mtnNlyCs$tMu#$0|7VEW7be*A`R@IC&;;%)2`P3-9&#dn) z$p$YgYt)!kzoLXqe=|qqc{HNuqXfqub@Ra;@DnF_* z$Oy~dQr_vVudz39nh0H;EE(KwUlqt=*@_XV-fACfprk{SAM0^1dKE7*l95rJU3D(U zJa)W_9l73#9z6z|^o&l{r=%0y`2@y0833^6yRyy)dgwqED}J*Z-ngiGp9 zQfL#K=rRhQ+^q!GSOYsaetb*OIzr(bXi%@y{BJbw1Fl|Ho9HQ`T#iVYy3XV(fj`|M z8oi4oLr2RY5^T2NvH^vR6a4Mgay2|Y?Ho2$XdEH_uJ+JTLdC{bk0nkLGg>9II(qrH zBjs``a|syqd6Usju(jn_Nf{QoAbsu${|1(IPHW$yV1sCcFI##Ug~F{yA_nT#!#f0h z80)h9RV4KM=kTc7A(lS?VxQ|=SYQ&91Me2_4Kq1x{a zXbX@qPxDkStkok0QeI9C|jTD7{$q?j3=KSb@ zQt~8eGW9P*$u{mU+gh9m>N+&nnkCH;RVt2{=z8$l6Cp|S1@F;S_;bw~Zj6e^t*9w$ z12KC(>o)jUu`%R zN-|rPYs9Yar=(%gW0OoHIb==H-+=n$9(YE>M1zKS#Eg+y-Q`O$nTR+kJ8E8? zwkDQ9)i9*-`vH86hH<%Ze~xC0(_szt%9V9-m<h(TP$lgh6S7c{sk$u=S4}S46jw;awYfgUm8xMHqS0Kv-OIsx1dUczG9EfhvK{$ zz`>vrCZZ?&aLgq7@3S&o2w(x&`Z3+aRBv7X!D-RlW@_8rUs-zL9cL?!r@Yw?aX49i zZ}cM*RMre_nkdaQ)>Lf;vNg;j483+uhJQ7?$fr*vxX|4ibFOU(L(*|C$1$>RT~s278)8Nw1RYW z2OjKdfK2AD>KEF?f25D8tR;X@*_z~Pay{6*#4C}I?Cr-QfSX0OOh5v5QxvCN=Yv<> z0&|b-@E&jD8PNSR09eXu zzFk{?&&}`b{D7N8?&err5O^_g+Y0VTaEy8fY89I z`hkVF`Qbh9bt71%h#|(T5qe5qqz-G+skdxx?eh=+C}c*+Q$;4-kR@h0!ha{}BLCR(wapH;V+nSXrnb%{+=HLZ6MrMddV%d`m4s!JzO2b0|v z?5q@BeeHQj>3Js8o$>u0G2nsDFq^J4aKp!>3e*B!C1z51Tek|MzRzmqG+6IwPurbx z9&}2Z%RR)t6qVPlc!HJ@aY@1)9e%Fi2|EfSgSlQw9>%687Q1LJmemB8**u9em7)u4 z$4oriWCud<_BM49r^V>pA8c@FR4t6xI@@4lIz3ot?oHyEsoVNQ;liMAjUY;O5*7yi zs+}sFxwAz$WxRL%=#wbi7srji@Tl!Lf0V16V>@tYc^!zAGA#1*&;`_9n&!}h(u}X^ z{UB99MT^dHEwrHbmeGlCNZTp~-jBiW9(cBD3$V3s#*-}f)gi0j@%vLnn-7Oo13-i9 z!cALva>*_pyPx3M)8&eRq;<#SPEArefu|HhUJ2Bd zT%CZVWB6DI(l(y13DJP*@6xHh83q*!2W-)$`6E8&0=uGzU*lFK4&>687{o=560G8D zF1hwCXzh4#A+yCWFf+b=x+aa9oTNA=b+)S!J+C*i<(|*3%dUU6=bZ+QYj*$TC>a(H zgig=lv2bist9h!(s5NkdRA1GABHCixhKK0DEq1Hb5=)2m)lT#~CxJ`x4KnrE9u0;cBWA?> zE>d%~RHbg}CU69jWlL2_b_rX?e;fQ{TkNGOAkT-Tt7$7-b+Y!zGLJ@;dRL+;1tp0T zjKM3HF0__7{NPx#EBRYJ81z>)NqVj9nXXs#K(qIs>%j`ednQIaxeo}V%hvQn-){Cb zJFPiSeqRyT{O)m^xBVx=)u_;7M3qTAyc5Rs1td)@MK6p^7HtA#V81I%?nu=wbk|!= zEoIk{nK=a+mKDTXqNB{4;7elRAZu1eDKHp@>~}$_{+`W(FQQFgA1s7DY%VwPbh2?; z#-Y;GTDO%oWV>?)9SiHKNqg-abnt^&?A%9x!=u3+qz-?Q28~V+VrQ=Xt-P$@C!Mvs z?h!-VD?|f1!x4e@^fet3%IP@?chyViTAk9eWVE5N>{EPhy7zN1Gy{o|t5qzHSk7~z z5;B+3CCPxFO!&^1s6k4@f?l8#kIT$BZ}ssJHyo;C8p|HhF%6?qIE$YzYP}a5U|6YX zV{MLjZP%40^Hw_il=nT9IurqUC9`bYOl+8q#G()uy?0u>B(!P*Fk$sn>b;`W;@_=w zM!U%TT-ica^@FyBnn|3S*3dri?ro}93@;c-<*Oc5`%$0#hL?(`^Z(gacTGj>dzJV5 zZUc9wthQ$8^L#$qc8nj)%a(%b%Vb$4jG%iPF38}3@#`0rX8>l>~PH+pz zVg+332 z>ID2|2)a6QNfu5~UTyU14ByjbAB$+wyF0+Sv*;p6dD<$kbesrq_0bjFKQxIL;kj4U z`1NCL`3gr6Y6&j+UDq;XWwH}wnXpi6gjF1JNuE>2Kh42vk{KYF(w#3ZsRu?)%^^VxR~>tB_F;EX=6yUm~HJ;KO|q(yRIdR%q!;xK+gL$FhbPYNYzd2et( zY}9 zppL}FbT&aPHWg5##H`;W24^x8QF3*iHJW~NVZM7xjc~!@{6<4y8lbyFAo!WIEGJIg z7AakqVod)FD=EFdQ21BS`?N=->f}+@oC32LX=Nb`ystNPMEG!Ub7mq*T>2p#HcIga zF&s27Zuw|CY1*Zn1-~fZ<3r2^D+(9Q0uIPysg{g}9|(F2UGCG#kdfRh$Yjj<*w8&- zPs_rdsf1Tob(ZHd7N7F3=x)S_qPiWe-e-Jd*vn0u5z9z5AQu|9smt;0HL={sBTkwzu%=Ti6S@#9!a=eISrs z$Gy4t@mB+}2JJpTrxU8+dyxS0$2IVNN0aFQIC2aVn%GeM{BVgurbu0Zkf4ME8n^x( z>jFbsD@$sj+^<-5}dilhNFAu=v$dW6hex zx+W7|=mUjhzU=$Sq*7U#Ul93Nc*+v2`;EBWohXxQuZWth5G0qBoh+&-Qm`pVQo(S(I+nAI#!{fMx0`k;-FoH^r{psd z;Hx6v)!x9wkgLKy)J*Kgh|~!NN~Sy$>6~1NwAnYIgGEivgF{K8&lV4%APg?UFacw% z95A{raj;#V^8((QegJR?9I((-)qTTy0|d(RZe(WCALN-pZN}ZPyw%-;ed@d?a~wk%@Mr+20#3&mx)0 zCfYF^WUR!vWf$w)P`G(=kLDyUop*@-WpAcT#Q~u;xY7K%rab?IQ9d~sUzC!Oy(Y*A z!A0ZMNF2`q&96+|b9F{IJc;I$%#*#2mnVsE76@FHNWu5@x?9{hBJYx8P7fQRLHDbT z2-JO4m2U>hBZAUC0vrgx?V`-OyuBE~@4avnP#eoZ%+c7~4e4~?fk6xwHW;lBjsFxE z|HxWX)0g@n`9Btxd?ln1&I2c1s)%kQZCh{0tI1{jE61gUiT1GxP*`vre6fJK8Hs(r z6V{Uh124GB){k!?j1VxTAVj?OeB%BeyCz^L?ggkoZ=%{w z2sC=H40AgSNE2W&?TlrF#&~a0K`E(|2cASGHk7~1%Ley035 zD;J4D3uvka#MYUNXALm>B9l)A_W2_+x!{8RS1opqBOGe9f+V9&w5x37Y)@5T3n%_= zHDc%zSII8tT@4blU+BI@{ail%mZQ z73u)5XAHVWzXO?rxUv1ow-xxoL@pR-qwEugw=3@MsfU22G1?;A6Aoaiv#fqQWNZV} ze;z=x`9BrgK;0%_mLfR;0HN~P+5>E!LE8l|BtEJ<{YS0LEGy1k*8#LKao5^SN?@xD zQgBkIk!{g-+GJ4SgM6Ac?@IX3*m9TQMw*RGeO!d-@2MQ$pqJ%`_pgXLfB)W`;$uZ! z5elfdihe|y6xvC_IuMIH#@%h6ljZ%C8#q3jqOltiCmRO4LdvsQPjI5Ec05t6Vr#^E zMQ&9R>|PSdA$``}DHupB_+XKGxvHgEms}|WW`J|x^B1YgMe?@-k-${5gADso*3-XY z^51WL0>mxKk&%&xb9(N5fIm3~NE938_}p~;cu)U^_WV*12z;XZo&_~u{grX-LHz`T zS95xSVIB!INdY4nFf&#{-Z1n)SPUOs=!u1?{p#c zk=H!YFoWPkv;$<;lTkB4gsaJv1W|re>Ag?zd;Hf9C-js(D(^SPfJb#Y)Quz zro(0wqZK(|JZ(1V#HinBfYyjj4~~3XmWg^r zZCR`9e1PQ$noS&-I9K1!^F6sqb%*mL-jOd1fLfH7ebIQwJ&ic%u52F~5$sr21)coC zb-wVtrPHysiR6GVA)Oy`B)Z!AlPxGBn7?qPAQG{vxpZ1B!;XR6vWqwcK$=jb!^Y3G z!1d3bv0xP4O##5az7?|yOxS53lCoD+a4~dh_+%>^+Xa{JO`XiyR)WGh;AifAn_B^; za|Vy)@nQovU~U9}mMj2mm_fe{4;V@x0N^kh5s*9g0NNeS|H?4q=K}QyH0PiEElAm=6awub~+5)eH^g;Qm*=k$S$9>e&&iraQ#1oU#1T$1qYg4Y{ zzi+%Rb%uvoAM>QQIg38gG;Bl4WJ>)ViiqQ%vrE^AdxS<>{&_2qspyf|{zxw@C64Wn znE0K|N22%$In9t0jL$(o<>P}pC14tdqO3YUS5R z%jJmXO+d zYA(mrI*j~1fw3_;1;V1q-y>hLQTsxLJ?E`#8cm)ZcWd}0dd-{EWiLaM&|WQ;z1Qy! zLXUs{kV_*6EFIN@8oOz@K2sUZf5{d9V~?u^Z!H;E}aTXv>m&}l>g7BH;p zRr*mT(L8W(Wsa4mK-MBT!JLiqSl-uTr`J{wInq zbXuL}`ACAI)nM+(@Zs0yTXCNrt@PiYPIX`SGzK1?`yVs1+waRJ3M35H7fet8tQKuO zi}@sY$*sQx8=62mbN)P1@{!#6-1`t)E37_;495|Cr>+0aFPkLU`drShE1}EQ`RD%Q zKCz7pHMvs7M~fV5)L+W#HEBt7ij~5ubixg#F=hGVFKBT9oU7-PhGrFLeKjONZF`!iz;|Iq1n^Y8NYUBa{ zU(k{td^p}(PTpGc{^NU9$cYB#QtUSkQ8J@Wh;8EnoQAF=EMStDaV(>*GLg;uX%lGQ ziRyh*Crs0dC$k^SER|eX+UjB~pXS@bUKjNTkJtfe=m)#5mDArGXFrPp-WR=EA)d}h zS3Xf2Nk6zYSPT=f9mS$f~IntkJ zcUC>T76mukTAoV*(kXp=;nuQ>UF&!EK_Momn4+&5Vx)cPKY1IU)-@(ApHM(DS_|rM z?*`oe^rmweog}+Ce!e?TK;TEE1VocH{Xztv+k#+#Czn6H znwc&ly11NaA~)F1Dy1OIzIZACCAa5Jkp(qo*;yC-r9XRDfdDj!V_X{+Nlh$UoEFYK znevkDmM6bz3lW-%jGpV(lOhyer~9jf3-e+hVAAzukKrsHmwv&)pt(vzw! zAmPG&xnO?91fElpOrVaJhr{NFgR=8CAl}WA{lME|!0C4+Fx?Z%q%a*HbWk%R?1U%& zI}28Z4SV$ZfES%!XVq1&=XCgI(W+CHbTabh(J~Uwa)cp}dtE{P1-|8D)pDk}Vg@Yp zfPJI;n$=$RH(0Qr@@enO->$7J8s!$fWKUlX#F$1+Lsuj)q$zHHjo9#)^MXZPQWC`LE&$K%*W%V4;|0P_M5;HxDU8#zrWqYP0LQmp!JT&lQ-1Lc|y(;3^ESG zpzVl8=YyK}q0XA9pP$fMS>_wZMzhmbfW%`R?465ML7D(;n(R9ey$@6%!%2|qIdG^i zHDQP1KTrV;vRx1esyyg9X-iWSE6VrYm0UX^s#|x}u3=^DOU5E$x~cPzi-By4_VNq9 z%(hDqOZ~$q1F^DxM5t65jYzkS8g_$H;I;GQy3tx~ z*kA6?IbdU5d|$-<<3K2Q5?R?o*AUB~FS^|=uKZ%7ryy?v2>-|GM;L7OCv?yn>>2y+ z1@PW|cv^>lWAU(Bj1!G!@!{@fdoiJLuY*94-@DlnKy=OUUw80MdJGWYXucfh8@mOJ z59mO80qxCs3xWi~B0B&)P2&vP#x4Nu^WAJD+0|7hVUF5k!3;9jS=p%k0D<=}^5r{s zbrblx)9&x>O59SJ(n7sq<^i__0>MH_i;JkXNK=ckTlJ}s0L$XL(zqAN-W00Wp^jzj3onL2+y zE61NHaeNJwqvm)itc9RQ(eA%!#iIVxV?3XsaubmCb^G8yL+)|+F)x)wLNR~EK+|um zu@69(7a!jfc|{So#}zVt zHF3>@vk#kk(a~hWdZfVgQ3+rE9hpuXQyzsmwyUSF5i?zW>=0d@`Iu;l>H#7QaLbP% zTK<_gbqZQ%pLk0a5qadUB#f+u|D39bzfkq^y@B(UpPZ`7!s^!S(;!B2&ztD>{242X zlxCPFS2 z9X=4yQURt7c5nrLhO9m{w*}f+>zeJJI<~SDS+I?h(_d+te{-ELoU>I1Yv-J;CoS|q zn>t@W#ep0(GZsTWaA9aog9a^x9<>sAqsHJ^SXlh+y*CN5=6|ggfVzO+0)NsH4-7zn zc8~J>>`Az%U7|`i@41`hxD_Yafy{WQkA(5xAcj ztaBj3gSqX*<1rqR3HiS6fQ&QuMi_-+*$j*Gj_hbA4;H``p7(t`ep{gP z1B;+k0k4}7JL^hW{ejf~o()&?o7mxP|NFMD<9~g+ES@`@w=S!w2FASw*XRn~|2^@3 z2Irm8F5m&#`ZMEC1NOn>{~SS!R=E1@mFLIGssHcUn7;+6G5+_>{D1W_UeQMa WLo7NA-t~fj4`~T`@k&v{!2bgWZ<2KY diff --git a/v3/model/__pycache__/config.cpython-37.pyc b/v3/model/__pycache__/config.cpython-37.pyc deleted file mode 100644 index 4b2d950880c680ef5412ba83516c061e512c06b9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1094 zcmZ`&&2H2%5RS9i-R%D^{ck~v#Dzs@_rSG6^an1W9%$8rFOeI&Ter3o+1{$lwOn}v z4shf(IPwU-a_TE^Vw_M`NZ`nmFEjBs%NA~x{<>((4(^n=A-b}Md$N!Fa)1YN zh=<^B(pZe-2#-qK5)b4GUMX=~teymHK$~ohc5V^vehP09UZ*|QxZL=>Nt<5~M|g{M zZUel{wl8d^8jw3fZ%jb%wD%UdAMA2NjE-O;MJT;aTuASxToX=9jAwsf@ z8IzugJiEF>kQVs*Gp3l~27V`&8Sv~BL|aZomRc3jv z5}PON#%hw;#9pwZN;=o1403f2QdAgFLb)jfStMK~BvH9#XL)`#DG5Qn_Ofc-g(CEd zH@?uXfnM|2o1gpBi(IlP$pq0?AO@LgR^(>7r^zf==|Qfp;e?>J^fb3z&8FASr!t4* zn^wh5iiPXUn7tS1F_0R+`=H(p^#;7hk6Z**U$`c%dO(NZb+K38pQWz(&$^mRpbAyt z8g@}YChb{t9SmO02tC+4ocyJS_O}IZ|0^_^TP}=ldV<)SmN)+1l~UmC|93&D5FVCW zoS214;a4J!t3wCQm|g`A*ZAFyop)td9{c7h<#jc7Q{X-+3I>f^RclHSmWo$s-Sfn| nkY+B}3*WBh378ml;fc{O?4nJWfNLQNQ9l~i8es&;KXLsRrxq#Q diff --git a/v3/model/__pycache__/genesis_states.cpython-37.pyc b/v3/model/__pycache__/genesis_states.cpython-37.pyc deleted file mode 100644 index a5adc9375795d5f64b51c017494bfb94de2e26d0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 474 zcmY*VJx{|h5VhMTO)I4;Y#kYcP=myV5aMI&z(U2)#j@&aYt?Zg+YKr!e}g~3U-HV- zg&)AgB_SZrx@X_{-Sd<0;&@0fJ}=MMd54gXs%Rs`!wsgLAqXOD+;yu zM{V1Jb}~aa;2=o?-`C)tbF=k9GcovO_%W8;^wba4M?<|%hluha4(txcr=MW@ByM^|{WQ*d4V Rq#YwfH0q9MNF&T@eFLM?l1l&p diff --git a/v3/model/__pycache__/partial_state_update_block.cpython-37.pyc b/v3/model/__pycache__/partial_state_update_block.cpython-37.pyc deleted file mode 100644 index a20ab5fef2921e2622aae6b961bc93d93c1c7d21..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 833 zcmZ`%y^h;347L*|&VPIvx*Sk+by1v$ZbfnE&)%iQ4O#@9#rTBCk|8NB@-Epr^_8@C z>ML|AWjnnq&`K~#k<>@>lRvE2iwM`ZH?L?qiJ~9ja9JrD@9?t{1&??vqc6RV2R%YP zg0YApfeB0@<>Pb0lkoeHzp>(1JYL87LPk!A6C^JU1Oy(m$ zYqS)l0PE*1GlJbqG#90=8wpNmg(-6j%843!;}ET3 z%2~?F{=%nxM^?4wAaCr+I;hF6KRwMR+rejaqb-w`Y&rHQR7D%3OR$z~jtyrH=rAPN z3RM~iebH_zl07>5amg+OF4NNT0m=i}bSj`$Ccr))VE;@>wW5y%kgLIlaHZ>wWX_1H z0u$;Yy;@jD!)@LuZj zV82IZ6J8>SUw6fWuAyM1WafaZvBCiMUc5J~!tp+8)rzijdZ!(dT(qx??g5JbSGZ|T fe)bl#juLLe?`L12n8mo$>39@}y_g28m%aWC1Jn)O diff --git a/v3/model/__pycache__/run.cpython-37.pyc b/v3/model/__pycache__/run.cpython-37.pyc deleted file mode 100644 index e392e3b6458e25f3e0e0450cbb8c108ed84406b7..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7625 zcmd5>&2!U66xT|&{1MwpAP_?M5R{J^XiO>nVlos+%B{51c6!jXqfxB2qePa>?mEO7 z`xIu%p&WYaF*){M(zPdMdh3l#``*ggI0)oGr_r3k?d%M!xN5x`ZgX@>n zP3N}>P5T22JC^~O2XJ!_5>N9;pxMOJJtNS+$8st#8Yw$PFrN<64bwIo89PHXYHn(F z7Elw=oSl<3GC{sEW{<(su^RERUhX-u3trJ1^9qnFdgME;TzUnPv?s3!q-7G58<3dQ zD6IMQvK|+|T&LBR@T2gn$fNOuDlSB!pz8uEXA>-9kk2Fyc@=xZD+_lD?ghB{JxGMM zsdb4U9kQZ(+XV`wU}sonIK*;>rQwg6kl6Vks=9&G0K!-XfH;Ho zTY|=V(~EP9u5cftcIB*H=+`UG$$qh&UE1x#sfzVDzdJyClDW^EL{*2b(NJ)^AZW-V z=G1%|cy^Xk&S8K&*1g*A8UrKKUVl+pjvBP$Rs)xMYgq7eMakINyk!G?+}lyYV1x&_w;G4L#Dw*WD9tr>Vj!_hL?J-ZNoD! zJ&)}XGZmImSXN;iDjWqPPVzM>b$4wTv6CngG+9(d(+umDSf+hWYca}Of#B$j z=u>W0SgW)RJ_$`5(hsc~iyE?7NX4^=ty5>;{#bgGSU%0_G zSxO;b)4%FZQ@BvYCKs+~@ixFc zw9A;=jwkC>I8qH~sqKh%la>iP3xcE`J9$(m&hGS&%KQblwy|$M`voVeu+vaY5sGtx zE2Jd!9At#nHxoeX&P;6n*Gyc7hH@s(zG)^pq{qL`rl2A_1qlpe8vk^52BDb*Qlpe? zvspktfct;Z%y3jNp+-PMc5$Daem0o}4^bSu1c^PaOayk6Rw(vRb~NU@OJ6)*YlR+n z7=7BJus3=G^En4&8v&mNI}aVj25&VEClzHLAXN@v52^3i4r6oG4}u}6tBi&^ouRuN z&KinKSSuuo9y2?H^<@;7@K;C_J!Zaq9rYW^mp>W}fyq8H zpf97$o-g-UJm|~sd=sg;{xF-!-O=zisNtcwp5Y~m9y7x$qkcmf-p8XMa45q=nLWen zv3T@`m&31+075B)zx#bM8tMkMJrtMN-ni2Bn3>;ARBN zC|+am3rv2=o!I*&*ZYDa!;$s*NXZ)PRRv{AvZSi^8NTc?>O?hX-|LEf?%$p`ZV>Ew znHdGK!v`z+zVgMs04DK?uB zFn2i{gQ9&&o oq>4PVlgCz4eBT&%2b!ISu3G`UkD+Uhcov5T%EQSQo_(GA2cDf1Pyhe` diff --git a/v3/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc b/v3/model/model/__pycache__/conviction_helper_functions.cpython-37.pyc deleted file mode 100644 index ed802a87e6f1a13030588d9f524aeff79761e5b5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 15006 zcmd5@TaX;rS?=5P^jvm!_M*M%;*l)L9$S`dId&XHwrodsZnj8Sij_2R#=UcTcV~BQ zI^8SJEX$kp7v-Jc%bL>6#`qp*J;G z$J=NcEz>nymTT#%KI7Wlo^3g9E~(463T`3E7h4l9CgDDlZdsVZYEE6!L`K-xHFsJ( zDjbo!uDdhh{h}a>$jyogQ9|23F)7M;&xt89jrY8GOw5XX=&>N?#5`&i#e!JGdr5pi zEQ$SS*)PtB1L7cZ2gD(97upVr4~iq=ZqytSXT>pb9J#y1-W_y>VQ8R4?*%ncmjY}i?m4Jul23S3IWqaZ9|h!B4;dWkuj^Oy2x74ggK+_ z*Mv2TCo&h1?`NXSMJ?3Vjf;RCc42RSJYT`zvzzUD*yyw?;hGm#gdh5{)oA+x%F++k zI!#eo>Bve~cDkLwYX+5NztLW;c=fv94SjL4!n4HIvLAY}Dc3r;4Qy1>CtRqSaX#1z zYF$rytspkq-PmljSE{y~lm3-WKfnZWW@WQ2g4hl=yWQqiobj67H80KtYo7Gu++Y`P zYH|v5zV*_nwNA@F<<*;>+z?*qosvFwcIqkVt#;bA4|UpC8~j1F7ds(#dg|)Kr&=B1 zH~6(Vp=_+KqIR%w5vvA_>6W7x^?P;8kTa;eO(#>Qg#NP0 z!$)-;g%F1&^lL`n?1hneUVEw8w|Xm_`z#$sWNjG15*>&zB3+oT8+|*nBR!&$HKWaH z^V)_bmm+P)jI1bg%g{BnYwI}tD6@hi7n!qo#%k=d+Lfit+LiKUtd2v@ZlBwA$el`~ z-3S|Av$5@0+J5*-M{ZPlm7r5c-Jrr3tA3ykwt&M68}&xlYlo}!rSu2fHJhC}PO0uS zed(Yo&f#R6n|{0Q$5y!2^{bh9YSj;GZ2&*0EpOGRQWA0iO|Ay9;|Wotg+KvD)&2xHz(dAl4hPz8)6`i`C>+Mhulq)AV!6C)U(g)sk6h(qI;eX622% zp4T0{q|f3=r}ZUW-i`VnQlqB*;9Q-yNqqAjxM7;)g41su?NhHCgIO&p~;R%7it%FOmh`9RM5EBtw#_L+Bg zv_SZkCP<<(a;Q#9&P=DVllGB|_{6F$m++E=^RX?xw&=8Ci{A18)gM%S4pB3zLr`ft zT_7ve<1jT9(n27Qt0(7N~0{1aM;{R!hb0aib+ zUDL1WeFI=OBIA}0pc8mOU7#C)HZ<3*oeU1h#1W+RpeN8@S^_Qgz7m4sMD`jT^yhHA z+D;bq$%n`@I3#>T?v^R?i<*VwX3EyqbJU9uwtzW_M%BvpBIp{WVS5n=pC7B4Kuu|^ zhEUD!$o>jjMb}|NmlV`yR zAsC-PCLJU9p+6H-oD2MR*k~b%ofKB`7@BYYH~yIZByAr2aD^#*E4D+1WSj?eB*HAt zl6dUMu$l+w^`SE`TUAVyKY*xJ68Xq`DPamGa*eZ_?FJB`HA=Vc!mf`P93vBOYz0KL zOvaYC2mQ|M0f)!1SU@Xk=_9GvwoFALutZ6b9V&1fZ}72@}x~ z$qh-^QY6fxPmibsQWaSyGAcPJDUzd74g{!3oJx5jeUw3tp(P57+BM7%;-of^Vu*2K z0zx|z+UwaJ2Vz(6+r67n1`OavA8YL7qO4*7c-tF>{3|BbJk>J6PoCA<$9Z%?ltoFD zz#KA*THlErtTfh-cR8$~ub_PoYo<^$Jyt`k;Ohx1nA!f<_s9oE*+8n#{cm^xYiwtf z=BL!QGyh*nTs{CClP4)5^>L^r$WthG@T9bTbb!+dX!ay*qFg3yCR+IjigB(E+1McF z1T8l7!p$HyJ?J%)45!*a)s-dcolP`M#Un^hmJd^Mnv(ZX@+gvOQNEw@L{;(wl*~{< z6f>00Oz*K>U+XmL{!lo}Gw4;#Ns`c#)K@t{$y1aNP0PEGxcijQ2Jar_=S=_dEcF_P zS6to=pBg#^#B4zD(56ZT$emK-07$<`4r^pIIZ)3C-`3;ZTzAEXv~qH<)eF@bicym}7Opi=KNH(PBIiX@1k2{$D61ML+Y z*<-uQgEZv9&-A=jx9KYxJkY2rk5}%{qVG*4^ohznX}^1_YLYQQNXSKK6C6Rpf^ey? z6KEsYjzU}1Zs_1(Rw^2+y3+r2#=5F8q#cM#eKnrh11P%)>Kn9LA}o*HvG2Vo3X2ks zW3^}SlFw2?m^W5IYCWU<>&Wo@+BBDc_{J~O-*?Xq3^DphYxPYZ)u2<^5rd@4f;t+r*!2QewE){dW*`@QBZj0%VWXr=|KaC95g0aKF9`gcha`j95JPxIzd0)VwXqyX0Q6AB=^ zo^YQ~`dy#~Rv4g$wl`|9_ODRGwP-2e#tgl8QHwfG<{^A~#BTop&lq;_R#V&XZLlL* zkg?|Ut&T}ozuWZe+`M#a5rdjeiV0^XY}&Lhs-{}v zK1>y7dAu(aM3coCnC0Dz{1N&GL|U2!CGH~L1A@za=57z3XNAWa?*_IJ!e#d>f4j@f~)BOzGQy;t$@AJ_=V7WOExpF%EBk?q`!lBC40 zhcm= z_Xc1aExQ2dG5}u~2OxVF$YDfWi-5*%p~SmjC*FnBQ8e@}q&1{Rrhv<&obDKX6dkfW zPRVXxz=(Bm2cURpl|~U}jR5L?D(=!GO2965Nz@ore~m7?K$Y730M#eaIsjE_FfiqQ z9W9}D!&F|jP)E+VX1oSV?=@i5YiLhroJGhk~CO zv>9l%ggw@3qZR%-^vgzBk%h?kD%aRiW>&kUlj1*14pygaiEQ8DHs=;;E=~$qMkB_% zhv;jFInW;=9#PH=o`B%SBKio4=m?q+gtINJ8^$`A2bGn`R#q%n1Ke1C$usRdeF?sVXrm7}aBHErMH?oE;?H;g-WPm1ndySEhX zyohE+Y2zG`xBkz=t+N)MI;@DvD}_0a96ZTVLN=T5I;6@lqbZ*TtH4g9rc7cD^ST7Ot)^T4pwko4R}}EbL5*S-^B|tj9jU@!+8U97>18d{_>r#6olk z^BvsJKFfJcEJ8iKD=bHMUDKmknxp;g!8lrH3F{d0IIpumT}K>9`hIb&??LYS0{1<1 zN8cC6`rgHTKgWG5cl5n)tnXp|)~nq2$nL&3^($9_Nl!#Gz`RHM1s(}!i@00CPGo^W z^Mr9c{wR+}iN68wo5qztybNyGukbi>p9VHgGBzH@eACfk%y$e>JQD5SnT%); z3lZEq(P05EYjj}428@0bRd!~g!{j`|oIkpZ09aJP+1||mn`gbm!7z-$L;HiD9P0Fvfp zDrruGU9fjic^631D6NOY`fXw+>|GdR;?%?c6uoH_{1z}G}xD0r)zaN{GS zx+;D6i0u`x(L~W&g&cJ4jYgA7ndPQe-;ktIph2`Z=uhwYS*Zkl@^^j7~$q^Q3_&Yf$B`>3bO}f~`K{CJ* zL_}->(!SdaU59LMI#u|_X#atmyFmUH3VOz7z14F|7vZw^n%5ZHyd_pa&;LI z>>!jBqz+lJ0+_p9zbydD#_A^G(MG!$TS9uT(3V3CEb`84O>e8S8OD~^YXq*{Lv&;V z?=AJt2HsUf?N;GdU+cV5JqEnn+LJa z=hGtKi{b^@;!|uOG*=;*5W2XguT2jJ4MI{FL^kOC}>$w{ioP@}C$T{4NsBa^Q1x z*wliOh1?-dkHg=>Y8P4-4C<4I5;lvreDaixY2@Y*3uX_det_e`Xy=;Il!6*d?B%qO zQv8_y%cjb4Jb8jVJI3H&?T6OP>$b53rBD7W_Ihbx(vSf_EQ-zN3&=!%8Lk}C%Pj{Uwo*Ye5jjMd#(*wI`+sQp20gHe|QK@`Ub45YXl(u0(ds< z7wkueRFFT9d~AVSo*QA=&!h4hCX#eS+Y2ply3^-J#hG%w4Avyu?!5`KSod~8nB~NE zf-ud7@Z-{*%cf>o)rP%87id)2fc!8+UK!g6{^Gh#H6wol4d6DoH^blQ+6V-~RpdH! z*~jaq&L9N^`HM6S6P&G<{v7Rw&`2| zUX@=jMA`KsW-{Rgu+dI70_+*q<{aqtz(#0n&Q2Lof0dgOdCG|baH;qK0g1r~+JdS2pSHlT}}a1^Y6F#)jA1eju^V8wl*vtTfI`gibu z#3+Lerz{$7coqrrchLW~_Wd_LIN)1kwmWzjcb@|dsta};PB7U1IT38$I=C!cN!WsI%AEn>3PlM)J9le7?66+95nl36sAGthORpQvG7@ zi`)Y1pYdPzCxEpRXemV{k;P7d>wNKV65O(g@OVkpU9J*nC&Z-9nA4CJO*B6SmuYfMNodeQ7Oh-|t+8&w#`9)6oq2@59Q36$OHe zDB_s_uPCLwf^I;#Wq)Ob!i-m;5%NXI)gQh2Z-4Xr@+Z!zK=WtNNiI`D$^u7{aba$v z*;#$)!Bn$iy(^yLo0Ft)k;X@k{CHk?@=aR# zX-a;Nl5eIPk-ty52_)O_oAauH-36Gz0YOeNW2)jsv<(USY%Sm~j$(&@3% z!)`VJ#E9>h$hdYGP$4$J@{F*qzM5p&*r@l7l^z)@Jvvr;znk%xo&6GlY8U^SNWU=v zQ}2G0xYs(mX(n+dQcR0R5s?G>C!tGE8R<02W&O~AlMzP)zhh3fdxV8CMa-qf95GVv zM{mcJ-@$6Jx$cK`LPq){A)dv(q!px>L?fG*WtTM?(gM5 zGx>yOprsK(+$OCO+S{+<#l(U9%ajKQ)rE@fg^P5LnBwnPv%Yb3+U&gWG(&{2bbE9H0CPB)CbRY0J(l0bW3+K%qSIK<3KCn$#^HoN*#3{+Pxk1aTVqcc6t6d(lGW zfyz^K7r(-6>4TMvESD;eW6m9#?hvzTBCTPxn8M=;u1aFg+vR6cxt5IIzW;63N~TX1 z<6i+hj$2Tqe=2Ist~5O$e_K*8oGDV#;h&-iX9@^UDV%iWGOV$CM*b9a{S)fIl}aI< zbc;&H)_5_uz>BGZBw}<;uNNo@^Jg@{pHsr*PC^bIr0#>RSzmsTC8X=5au#*nhp6G9 z-Sj(qBF-)Z4Zj=L6l>Wh>jV1j!E2(o}(|Pz5()!{^3$bwdml!*U6451B zB5@N>@Xt)*J34G?t@kplDHyGA4qWP6H#n|NL?h@55Nu{>YS8?`%(_h)Y{Wkp0eKU%p%p93DaZ37&P4kWXuu(Kj|j-UA8=RI^^ zqYzP?LG_(R>jX41M8Cq4n4xQ)gSJ_?POc9i(__d?f}Q1W!NWfI+9K!5Vjq4gG^g%& zL&KiHETqfj*`}ONb~LP6xTA)?VR38}z7g8ti3EB%K(91_-V#Hvl=w9!!)d^Gzc}zZ zqS@gL@^j)K<*$70G9pi47l*b#&(7iZ{G));OV)7K4z7RmZ0~TdjoZgAstvC?C#oWt z&gh+shk%xRWopbdLP{*VV}5MRfyRU55WS>M^@^(Eh$#z7l7#FmISV5#b(S1Yeq%sR zR(K}qhc2W3n$&Tkq9x2V$=S|MeC5u6l8VHE2da4EKhRHk>zU!QE=;UdDdlA4vm^8W z5@UG^lf3Ki{nYS#6X^fo90pg_9P{*=l|PM_n?2w7u=KiXO4a@Vsu-mFdmMH7g>>|h zD;{((@>7%$V;Q-9dVq>&C}IBq+1dQ#nQA%K+p*rF2s;15h<;cl0wt9y=;c~#bd~&r zG~g~O!x5}HDsC*lOf84Gr$o=#K<~3ucOTVFDqCfEo0g6qAIoS!7!Xh2dE$o&Ky*x6 z$sunq?x#-U$>UBcyulW!(tJ&w_%5!h)7{k)`!R8AmH!v0AJlL7G`~;lA?$+}qXpJJ zN>#f2uzFETT4{2nbW!zeRlii#k;-3D9mrda63ge~>^3Ll?5Tq)1o*Ah)=9cY9H`SF z_(SaDw-Z~+gFA(rqofEn8_W2~8s%bJy;)kiqwOmGA-6DxJ25h@-eS*<+@^+X;Css* z*_OYKlCs!{IQdxlM>l+JU!JEr#*2?p?m0>>QbImmzNo{O8Q8(i9#wV?vSWoU3|389 zcrh$TgyevVapMg?*4u3QPY^{0k0K#shm05cheWiTLcW}za_(_XIA?ix@R-9sgV#i@;U;^xuFQ5^$`w+W5kia00~ZgLtKcs4naFuY|xL?H`{{e~2G5w4-HWN)^;Csf{>S$vdcYgSuv~Fo{{cP+=b61zn z8I9#|n_WZHW+fKd7MPtFEPqBzaz~~tJl5B>bK2NdrD%1mw3y^}tt0!fCf%+k-jnuG zUOJC;UE8b8YKo|-$^5+Lt|o1nXN6xISfqRKnp?wlOZfYK*s1kJ)Z%f>*K9-b=!-1b3S5IcnMFJRcYJvgMr|xsBWZ%i4zt{k_9OBAEBO>v~~^YLZ$6ZZTPm# zqx{F?qI6}kZAhnUi`$UMWqDG3sCsF5RoaldG%l;UGRdM2%Fi5GI<2i@7HBgo-_-V! zyV~CEyV~%UYPmUU=}udYD{3ZX^ijYVONl9$WQDn`_{-e920cANPgh|KCM!X%I&8}- z=JFG)rKIGhYk6IbbmQrfirVKL>yf^9`mTmly9-KPV$?Ep%a zX)&=Yb-Z@l5B+2$h^J{G=3(OZkdQtQ`7Fj;-bkr@-4bOIQuYP|p4*$KP`Rbb(j_v- zSwCJ-KPA3|YOzMiIwhw`+HU$`e~@I8EiFto7C=2XOr{P3C6Snz-C1g;x5QV}(@(8- zBwAcNi)?0+xh@~2*1i`ExU#t{A)`o_r{ExiQRI3a#qm`n%3xqDt^vb=k+?9H6}(-+ z8yQf|ROKjHMeYKbmLXn5yO|-;t`I1cA?@O2YDnYg#|1p| zc)GG6-F1ywT~pkFp*fU44P&~O8#`f%O5nUK$}tmW<%~H7FAR+U>R1vpxsBL1$fwfQ}qdhJs_MtAzhZ@eUf;Ra>O_mj?ifC6^ zCp@V>6XVEvpkdYqE2&uni}&=$I%ZUrRaS-%|AdwCz9g&AkRw?-D#)_(^hZEgNfv1A zy&J5O&0^Bm(!8&!1f-!`tg5{K^1-``7q9!Bo!ZP^FV%J^KgB}ryVth|BPDk`dS_8A zKz1VK^B8iq?STlDAvx{2Y>w zCbhTDj&V-AnnZlX=)819PNvk~H}*_G)DVhgg%)V(6Bg^bA`!17qn-GO#W`i0 z{hw#`i}|c@Iu(RBiPxNNQF>|$=eQXU`u#{CPOQ7i>!<@2RWuhXl+ebBB_!#39JPEe zXiinOxgA9a;e?u2n(KK(u?o5R@`>R48uddCCv|7)6Q^d!^F#41s{1yQ#u5)ZUWb!o zRH3N2NG&#|CZ7ZUqXIb<%H^sE zM&+9I+E#mnE#d1*{M5<Q*pHc{Q8+yoWL<`UrvHErggmYL;)q!_Z1}wUUge2zrUm=ou@3k)qXtOjWIRA~Rl+ z#CUwWb`b!IYiwoK>eMEMgx6F^SUofl7FOl5^60Av4MoUXJZ=fUPoD%YtAaOG#K-r! z7)2qFJ2e{>BqlrD7YG%nUlBC~M>X!Xc4nVy3)oePP53#+g8vV@;tKki0atL2it9Jf?QyOvq=Omcw@xDa6O>Q(@C%Gf%lY5N{r!C0)am)5VsvVE{Q%$L$_3Z$ z&#*ep#e<##v0`<7Ixc_BuW-Y-#8UUqUQzp{$?}$ zH>!Q%M*^|t)D#+()<6hCccy8LQr6AHi5(AN>$%LM&Np8HyL?Pn-^aYNZwGMNAx=|@ z?}ZwkXOVY^(8h|ff|}=1egUNweKm`0UqfsB-e84;UVo%qT6%6i+vW}r`dl>ebBX#n zAWoghN8S*>VGJ{$XY>Z@TV8#K2nv-I{{?lu5Q(dpT+QFv#BT|MfM2IDCnonW3A=Nq N>6~@G?yNhP{tZG*X+Z!0 diff --git a/v3/model/model/__pycache__/proposals.cpython-37.pyc b/v3/model/model/__pycache__/proposals.cpython-37.pyc deleted file mode 100644 index 9d71bdca83a87ddb6bf3d0b992ba1461183f863c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3223 zcmaJ@&2J<}6|d^A>F)XPcy{BBS!7yKfJ|cSWRn9BYYAjQfj|x<%1TD7QEykxOxrWv z?W!8%v3gEMTCcs111t0H0kqgn3X}$Cmh8)8oVXPSXmY z)m2v6Dc#ajHVe1Og|q6FUHFFe$By`WGq(eCH_DB&XL|i-PTBs9Qc{Lx=MGtgnWx+% z57zd|pllytzY8z3 zk0j?&I*xdpOrt{NMK0r6RK!wp8Ra6%a2j;cArSjLXsED&8ru+OZPjjDUaQuo(3 z0$q)p-4+5rjuFydLh?OUN4cj%58?c3tJusT8e;DaYb zgBk3803S7W5wnP04-kvkZJq5Hly0qj#M}}1E_lDQO7{(Kz8H=TAjGRBW$!6U{K!RI^!&xsx{BQ@nwJkJ-Iit@2(tu119GZ$N3vAIfQ zz#$a_tlDB5D)9{zcTs#3g!V>hRxH%bh2O$4`jb0<8>)X&N z(Q$}tEN_Q&3vF+SH=wVvXBQ_xv7yM{gQ)EJlqhmZm<4>|))w^?eP%r&V`|tJtondl z(9h_q)%1J7%6{q9{XXo}f}04GU3wQ3EFCf*n5s|?+MER!_A1Ca*aH6cvBg@zsR8h2 zyX=@d2|ub#hiq-2kRS)(gCIsfKrWZNASR?Fk6vBTO9FQdY|fDFC~&gV^nic-(K>P9 zu5E#iDFPH%Z^Y8p-kSnV4R>U4i5L-R;IG1iSt?bM&x`vXLR+E+9y#A%6i7lN2_BK6 zTZ{lTHPH4BW_c3Nte>r1a|De%BAR#SWrhJFR5)=vkGVun8_ zFa*!Pel&E&CVYr35ZacDxwdCK(;LWt&60RF?7qlJ#cQ~92L*cd;Y(|um!o{4fT4k! zv&rG$dH0%Z#UB389p@qeM#K$XM}cNuo7}kb-T!0OpTcB`79$oChi|Od^?h2jt}5 zLvr@dO9JV@KOpF~nlb-?DC@{>+Q9)i`wK$j(Vw1D7DB&s3A>Ep?+k)?J@e5WK$M#x_wc*58KI;5O1-J9AjLYAYYL zxkPAe!dROPE{uLv51i#3g8Z)yF?KGkRTq+#t^wh#^Ixoqaebe4EZ>b%C8H`JN00|3 z(>R*|EvA_v!aB4^`Qsx9>3bC)8dNn=O}8N;sx(Q9I8(-z#@N6LYfM1~FdnB_s+N!*x|~h899jli znifD2sHCRp4IVH6JB6V&0lZp!>e@E)MW{DPhxk73*amdT6fzw6u|dPnhCu~I3&_LM$_k|>dW7|PQ!WoXu;pldesK@?zJbQd z0ODO7GM8p>*}r+(-@v#b;5yEjNDi?E;!ThCtj8itTs zE#}3t;-I@%@}()C6cmg&apo<42#6qM#L8tx_8ue@Wd`C z0R$XLr~@5v!2=%_VDZ|g9SC3vy0DD>572`?tXv1w1?QNAs}Hu>bsP9Qt%1lrVzHSaW4Wim;BdPHKB?{44Jk!x`!4mY^&(usz8r$!g5h{shTTz+;m;d!UV+4pX1_(Gr535B{BD*=& zMR_!?zGMuHCWwLL=pcvjF+h-0euMvm3pr#t4e}3s%J-^CN~4T@2;2f|d#|cqeQ(uI ztJM<2_q+WU`=72d_HUXjJ_m!J;FkzAf(brmJ#KramlKZ2jor7ib+4Dlx;rjR{GLB4 z_KKWYdI@w-XuI6-Z+0Ey zg?FcMWV~T8nFIru81P-guYg|;zvS=W7hE_xCvt7J!M0iIDt62tIUlpsQ#tK{=5DhS zcAuwtaJg;v&{6KWBfR@gTCk%$M*aimkXuQ8UAPJEhwPkbe}kpPY}X@C7eD4{N#%7( z6tSbM3fjl170^pDvl-P8iY+w38K zz=6;@Nup|c^FFuIY=Z~Lr#lO&^qSrg8>|QTSeG z8b~!7jHW@PlFke2C>+Vo;VcqKw-ZDnW0CPRPJ(f=YY%)()-TD>O)&{0HJYF?g(y_- z#qz}XxX7}b@};u}WS;cVJ~Bu+4okj*qcPr)R?3iiZhI8P@PaNrL{s-rdi>Ejy zpQu315>p&NsF4U%2vhf>ScD0v&hEDiKkjnrpvU@#8k37CUD8A7DJ zS7b1ZqyCLJIvowv2*X>k0uXzr-`$(wtns`}&Jq<)cBf~bmG;M@L=ED}^q@@wlk|CZ z?^%(7+Mk2EyFZQx0O$aN1-~gIv*|RJ$~3~m!w~nKhJ9KZpABESvGbpQy}WgO_$4JaUDaCYl%Vo8$0w$+o~?C2KyZ)VVa= z)UAE>Wq4;60%!rnRk3F-jjYo+)8&<`_b#qBo;~F4#}v$rdplIY6Vl|dY)y=Uo|R@j z<>bZZFtBZ4CHRNV5+CBm^=s(ldNw8-H92{@1j!jF0M|fdg)T#}5dVsZ2iP`8=P?<} zsPR1R|KmsWeR{o{H+48&|B_>v%_|H2qj*f-nV5>DEW9b(IG^Va1rkIl+3RDJ71{o7}Kbv6x+pUiF#WiUO;h}KG$#N0TH zB;g{zLg3^lp%?{`$tAOi$&C;LN^l%{TQEtChl_?`_nOJi7hI@%YMkUv!;@ZRE?r;3 zm3x5s+wxgDv$!-pQ@I3E(yJ{5%LMK6PXV7s{kpgsc|k1hK<9oqPco^j4Po zTf*!f_2sGjI$d9yyT~X^diCsi{e{=`EUpWj6S+gjt*KZAl1FiDBHSFv8+KMV#cme!KGIZ6H_ze){-GwXP!ekI^kKuLa$hPe%1;tj`lO1$JW zx$o4_`{;eA!d+hDdB?@PMO5@9@VQvSoHwwq#n*`&Bdq7>B-e5H;!j@34Fiwv!AIUt zV&A|oaS@u3xe#Si550q)sszet1?^I`koxF7^hNE<1B~++mlV^UE};rQRUqZvUBy8u zw2`}SvUjhgWz2l!&oX!`$^ma)7j)&mn|gPC`3|#n3DpqQyQjK}9Z_1--m$A|NE)am zM0o?}FXsCf;|ge1B$apvMbnwO^qPI5U3pChYrW#q zU*wO8ha$cyF7tuOorGt`L-3k~S$JJ$AXA%7Df{DD7ROuInNUVz9$7K09zPeO651xO z@MexIeh1?kzvi^C>N@i0;9pubp-Pe3S~bOprB%CgtA-7tvh$$%0yK{*SNm4;xqGi# z1->2)WEdo2RyTESpLI}1b%IGei&Q5*v@@t-UTRUL^*qWjsX#; zHHdbJ^L3iOL5&6H0-^ed#G#rj3rp{VCm}OoF2KnVJb>U$%>NGva#tX5zbpu>Z0I5aMB$P&PBapwO5@r%nqP{3Wwmu5zxbt1vZekaqYjS3!O2JTR(cKmB2)NV74l~G`zX3+AD6 diff --git a/v3/model/model/sys_params.py b/v3/model/model/sys_params.py deleted file mode 100644 index 1183008..0000000 --- a/v3/model/model/sys_params.py +++ /dev/null @@ -1,22 +0,0 @@ -import numpy as np - -# Initial values -initial_values = { - 'initial_sentiment': 0.6, - 'n': 30, #initial participants - 'm': 7, #initial proposals - 'initial_funds': 4867.21, # in honey, as of 8-5-2020 - 'supply': 22392.22, # Honey total supply balance as of 8-5-2020 -} - -# Parameters -sys_params = { - 'beta': 0.2, # maximum share of funds a proposal can take - 'rho': 0.0025, # tuning param for the trigger function - 'alpha': 0.875, # timescale set in days with 3 day halflife (from comments in contract comments) - 'sensitivity': .75, - 'tmin': 0, #unit days; minimum periods passed before a proposal can pass - 'min_supp': 1, #number of tokens that must be stake for a proposal to be a candidate - 'base_completion_rate': 45, - 'base_failure_rate': 180, -} \ No newline at end of file