From 7590d041e0acc1759765f00f8cce83fe68ca5909 Mon Sep 17 00:00:00 2001
From: wangz539 <wangz539@berkeley.edu>
Date: Wed, 4 Mar 2026 10:12:16 -0800
Subject: [PATCH 1/4] atc-138matlab with redtag fix

---
 +plotters/main_plot_functionality.m           |   52 +
 +plotters/plt_gantt_chart.m                   |  164 ++
 +plotters/plt_heatmap_breakdowns.m            |   46 +
 +plotters/plt_histograms.m                    |   34 +
 +plotters/plt_recovery_trajectory.m           |   47 +
 +preprocessing/fn_create_fnc_filters.m        |  177 ++
 +preprocessing/fn_define_door_racking.m       |   36 +
 +preprocessing/fn_populate_damage_per_tu.m    |   24 +
 +preprocessing/fn_simulate_damage_per_side.m  |   35 +
 +preprocessing/fn_simulate_temp_worker_days.m |   87 +
 +preprocessing/main_preprocessing.m           |   58 +
 .../fn_building_level_system_operation.m      |  133 ++
 +recovery/+functionality/fn_building_safety.m |  306 +++
 .../fn_calc_subsystem_recovery.m              |   81 +
 .../fn_calculate_functionality.m              |   78 +
 .../+functionality/fn_calculate_reoccupancy.m |   99 +
 .../+functionality/fn_check_habitability.m    |   59 +
 .../fn_combine_comp_breakdown.m               |   33 +
 .../fn_extract_recovery_metrics.m             |  337 ++++
 +recovery/+functionality/fn_story_access.m    |  152 ++
 +recovery/+functionality/fn_tenant_function.m |  459 +++++
 +recovery/+functionality/fn_tenant_safety.m   |  171 ++
 +recovery/+functionality/main_functionality.m |   68 +
 +recovery/+impedance/fn_contractor.m          |   58 +
 .../+impedance/fn_default_surge_factor.m      |   51 +
 +recovery/+impedance/fn_engineering.m         |   87 +
 +recovery/+impedance/fn_financing.m           |   73 +
 +recovery/+impedance/fn_inspection.m          |   61 +
 +recovery/+impedance/fn_permitting.m          |   53 +
 +recovery/+impedance/main_impeding_factors.m  |  332 ++++
 .../fn_allocate_workers_stories.m             |  107 ++
 .../fn_allocate_workers_systems.m             |  191 ++
 .../fn_calc_system_repair_time.m              |  159 ++
 .../fn_format_gantt_chart_data.m              |  111 ++
 .../+repair_schedule/fn_prioritize_systems.m  |   87 +
 .../fn_restructure_repair_schedule.m          |   96 +
 .../fn_set_repair_constraints.m               |   58 +
 .../+repair_schedule/main_repair_schedule.m   |  151 ++
 .gitignore                                    |   38 +-
 README.md                                     |  188 +-
 build_example_inputs.py                       |   60 +
 demo.ipynb                                    |    1 -
 driver_PBEErecovery.m                         |   48 +
 driver_convert_PELICUN.m                      |  298 +++
 fn_red_tag.m                                  |  140 ++
 inputs/Inputs2Copy/build_inputs.m             |  355 ++++
 inputs/Inputs2Copy/optional_inputs.m          |   67 +
 .../example_inputs}/ICSB/building_model.json  |    0
 .../example_inputs}/ICSB/comp_ds_list.csv     |    0
 .../example_inputs}/ICSB/comp_population.csv  |    0
 .../ICSB/damage_consequences.json             |    0
 .../ICSB/simulated_damage.json                |    0
 .../example_inputs/ICSB/simulated_inputs.mat  |  Bin 0 -> 2256246 bytes
 .../example_inputs}/ICSB/tenant_unit_list.csv |    0
 .../RCSW_1story/building_model.json           |    0
 .../RCSW_1story/comp_ds_list.csv              |    0
 .../RCSW_1story/comp_population.csv           |    0
 .../RCSW_1story/damage_consequences.json      |    0
 .../RCSW_1story/simulated_damage.json         |    0
 .../RCSW_1story/simulated_inputs.mat          |  Bin 0 -> 147204 bytes
 .../RCSW_1story/tenant_unit_list.csv          |    0
 .../haseltonRCMF_12story/building_model.json  |    0
 .../haseltonRCMF_12story/comp_ds_list.csv     |    0
 .../haseltonRCMF_12story/comp_population.csv  |    0
 .../damage_consequences.json                  |    0
 .../simulated_damage.json                     |    0
 .../haseltonRCMF_12story/simulated_inputs.mat |  Bin 0 -> 3252456 bytes
 .../haseltonRCMF_12story/tenant_unit_list.csv |    0
 .../haseltonRCMF_4story/building_model.json   |    0
 .../haseltonRCMF_4story/comp_ds_list.csv      |    0
 .../haseltonRCMF_4story/comp_population.csv   |    0
 .../damage_consequences.json                  |    0
 .../haseltonRCMF_4story/simulated_damage.json |    0
 .../haseltonRCMF_4story/simulated_inputs.mat  |  Bin 0 -> 1046153 bytes
 .../haseltonRCMF_4story/tenant_unit_list.csv  |    0
 .../001/general_inputs.json                   |    1 +
 .../001/pelicun_data/CMP_QNT.csv              |   34 +
 .../001/pelicun_data/DL_summary.csv           |  501 +++++
 .../001/pelicun_data/DMG_sample.csv           |  501 +++++
 .../pelicun_data/DV_bldg_repair_sample.csv    |  501 +++++
 .../001/pelicun_data/input.json               |   72 +
 .../001/tenant_unit_list.csv                  |   17 +
 main_PBEErecovery.m                           |   79 +
 plotModel_PBEErecovery.m                      |   61 +
 plotModel_PBEErecovery.py                     |   45 -
 plotters/main_plot_functionality.py           |   65 -
 plotters/other_plot_functions.py              |  398 ----
 pyproject.toml                                |   36 -
 requirements.txt                              |    5 -
 schema/Inputs.json                            |  541 ++++++
 schema/Optional Inputs.json                   |  515 +++++
 schema/Outputs.json                           |  385 ++++
 src/atc138/__init__.py                        |    0
 src/atc138/cli.py                             |   26 -
 src/atc138/data/default_inputs.json           |   81 -
 src/atc138/driver.py                          |  198 --
 src/atc138/engine.py                          |  103 -
 src/atc138/functionality/__init__.py          |    0
 .../fn_calculate_functionality.py             |   80 -
 .../functionality/fn_calculate_reoccupancy.py |   97 -
 .../functionality/fn_check_habitability.py    |   59 -
 .../main_functionality_function.py            |   65 -
 .../other_functionality_functions.py          | 1688 -----------------
 src/atc138/impedance/__init__.py              |    0
 .../impedance/main_impedance_function.py      |  317 ----
 .../impedance/other_impedance_functions.py    |  427 -----
 src/atc138/input_builder.py                   |  476 -----
 src/atc138/preprocessing/__init__.py          |    0
 .../preprocessing/main_preprocessing.py       |   64 -
 src/atc138/preprocessing/preprocessing_fns.py |  358 ----
 src/atc138/red_tag.py                         |  168 --
 src/atc138/repair_schedule/__init__.py        |    0
 .../repair_schedule/main_repair_schedule.py   |  160 --
 .../other_repair_schedule_functions.py        |  900 ---------
 {src/atc138/data => static_tables}/README.md  |    0
 .../component_attributes.csv                  |    0
 .../damage_state_attribute_mapping.csv        |    0
 .../impeding_factors.csv                      |    0
 .../data => static_tables}/subsystems.csv     |    0
 .../atc138/data => static_tables}/systems.csv |    0
 .../temp_repair_class.csv                     |    0
 .../tenant_function_requirements.csv          |    0
 122 files changed, 8674 insertions(+), 5956 deletions(-)
 create mode 100644 +plotters/main_plot_functionality.m
 create mode 100644 +plotters/plt_gantt_chart.m
 create mode 100644 +plotters/plt_heatmap_breakdowns.m
 create mode 100644 +plotters/plt_histograms.m
 create mode 100644 +plotters/plt_recovery_trajectory.m
 create mode 100644 +preprocessing/fn_create_fnc_filters.m
 create mode 100644 +preprocessing/fn_define_door_racking.m
 create mode 100644 +preprocessing/fn_populate_damage_per_tu.m
 create mode 100644 +preprocessing/fn_simulate_damage_per_side.m
 create mode 100644 +preprocessing/fn_simulate_temp_worker_days.m
 create mode 100644 +preprocessing/main_preprocessing.m
 create mode 100644 +recovery/+functionality/fn_building_level_system_operation.m
 create mode 100644 +recovery/+functionality/fn_building_safety.m
 create mode 100644 +recovery/+functionality/fn_calc_subsystem_recovery.m
 create mode 100644 +recovery/+functionality/fn_calculate_functionality.m
 create mode 100644 +recovery/+functionality/fn_calculate_reoccupancy.m
 create mode 100644 +recovery/+functionality/fn_check_habitability.m
 create mode 100644 +recovery/+functionality/fn_combine_comp_breakdown.m
 create mode 100644 +recovery/+functionality/fn_extract_recovery_metrics.m
 create mode 100644 +recovery/+functionality/fn_story_access.m
 create mode 100644 +recovery/+functionality/fn_tenant_function.m
 create mode 100644 +recovery/+functionality/fn_tenant_safety.m
 create mode 100644 +recovery/+functionality/main_functionality.m
 create mode 100644 +recovery/+impedance/fn_contractor.m
 create mode 100644 +recovery/+impedance/fn_default_surge_factor.m
 create mode 100644 +recovery/+impedance/fn_engineering.m
 create mode 100644 +recovery/+impedance/fn_financing.m
 create mode 100644 +recovery/+impedance/fn_inspection.m
 create mode 100644 +recovery/+impedance/fn_permitting.m
 create mode 100644 +recovery/+impedance/main_impeding_factors.m
 create mode 100644 +recovery/+repair_schedule/fn_allocate_workers_stories.m
 create mode 100644 +recovery/+repair_schedule/fn_allocate_workers_systems.m
 create mode 100644 +recovery/+repair_schedule/fn_calc_system_repair_time.m
 create mode 100644 +recovery/+repair_schedule/fn_format_gantt_chart_data.m
 create mode 100644 +recovery/+repair_schedule/fn_prioritize_systems.m
 create mode 100644 +recovery/+repair_schedule/fn_restructure_repair_schedule.m
 create mode 100644 +recovery/+repair_schedule/fn_set_repair_constraints.m
 create mode 100644 +recovery/+repair_schedule/main_repair_schedule.m
 create mode 100644 build_example_inputs.py
 delete mode 100644 demo.ipynb
 create mode 100644 driver_PBEErecovery.m
 create mode 100644 driver_convert_PELICUN.m
 create mode 100644 fn_red_tag.m
 create mode 100644 inputs/Inputs2Copy/build_inputs.m
 create mode 100644 inputs/Inputs2Copy/optional_inputs.m
 rename {examples => inputs/example_inputs}/ICSB/building_model.json (100%)
 rename {examples => inputs/example_inputs}/ICSB/comp_ds_list.csv (100%)
 rename {examples => inputs/example_inputs}/ICSB/comp_population.csv (100%)
 rename {examples => inputs/example_inputs}/ICSB/damage_consequences.json (100%)
 rename {examples => inputs/example_inputs}/ICSB/simulated_damage.json (100%)
 create mode 100644 inputs/example_inputs/ICSB/simulated_inputs.mat
 rename {examples => inputs/example_inputs}/ICSB/tenant_unit_list.csv (100%)
 rename {examples => inputs/example_inputs}/RCSW_1story/building_model.json (100%)
 rename {examples => inputs/example_inputs}/RCSW_1story/comp_ds_list.csv (100%)
 rename {examples => inputs/example_inputs}/RCSW_1story/comp_population.csv (100%)
 rename {examples => inputs/example_inputs}/RCSW_1story/damage_consequences.json (100%)
 rename {examples => inputs/example_inputs}/RCSW_1story/simulated_damage.json (100%)
 create mode 100644 inputs/example_inputs/RCSW_1story/simulated_inputs.mat
 rename {examples => inputs/example_inputs}/RCSW_1story/tenant_unit_list.csv (100%)
 rename {examples => inputs/example_inputs}/haseltonRCMF_12story/building_model.json (100%)
 rename {examples => inputs/example_inputs}/haseltonRCMF_12story/comp_ds_list.csv (100%)
 rename {examples => inputs/example_inputs}/haseltonRCMF_12story/comp_population.csv (100%)
 rename {examples => inputs/example_inputs}/haseltonRCMF_12story/damage_consequences.json (100%)
 rename {examples => inputs/example_inputs}/haseltonRCMF_12story/simulated_damage.json (100%)
 create mode 100644 inputs/example_inputs/haseltonRCMF_12story/simulated_inputs.mat
 rename {examples => inputs/example_inputs}/haseltonRCMF_12story/tenant_unit_list.csv (100%)
 rename {examples => inputs/example_inputs}/haseltonRCMF_4story/building_model.json (100%)
 rename {examples => inputs/example_inputs}/haseltonRCMF_4story/comp_ds_list.csv (100%)
 rename {examples => inputs/example_inputs}/haseltonRCMF_4story/comp_population.csv (100%)
 rename {examples => inputs/example_inputs}/haseltonRCMF_4story/damage_consequences.json (100%)
 rename {examples => inputs/example_inputs}/haseltonRCMF_4story/simulated_damage.json (100%)
 create mode 100644 inputs/example_inputs/haseltonRCMF_4story/simulated_inputs.mat
 rename {examples => inputs/example_inputs}/haseltonRCMF_4story/tenant_unit_list.csv (100%)
 create mode 100644 inputs/example_pelicun_inputs/001/general_inputs.json
 create mode 100644 inputs/example_pelicun_inputs/001/pelicun_data/CMP_QNT.csv
 create mode 100644 inputs/example_pelicun_inputs/001/pelicun_data/DL_summary.csv
 create mode 100644 inputs/example_pelicun_inputs/001/pelicun_data/DMG_sample.csv
 create mode 100644 inputs/example_pelicun_inputs/001/pelicun_data/DV_bldg_repair_sample.csv
 create mode 100644 inputs/example_pelicun_inputs/001/pelicun_data/input.json
 create mode 100644 inputs/example_pelicun_inputs/001/tenant_unit_list.csv
 create mode 100644 main_PBEErecovery.m
 create mode 100644 plotModel_PBEErecovery.m
 delete mode 100644 plotModel_PBEErecovery.py
 delete mode 100644 plotters/main_plot_functionality.py
 delete mode 100644 plotters/other_plot_functions.py
 delete mode 100644 pyproject.toml
 delete mode 100644 requirements.txt
 create mode 100644 schema/Inputs.json
 create mode 100644 schema/Optional Inputs.json
 create mode 100644 schema/Outputs.json
 delete mode 100644 src/atc138/__init__.py
 delete mode 100644 src/atc138/cli.py
 delete mode 100644 src/atc138/data/default_inputs.json
 delete mode 100644 src/atc138/driver.py
 delete mode 100644 src/atc138/engine.py
 delete mode 100644 src/atc138/functionality/__init__.py
 delete mode 100644 src/atc138/functionality/fn_calculate_functionality.py
 delete mode 100644 src/atc138/functionality/fn_calculate_reoccupancy.py
 delete mode 100644 src/atc138/functionality/fn_check_habitability.py
 delete mode 100644 src/atc138/functionality/main_functionality_function.py
 delete mode 100644 src/atc138/functionality/other_functionality_functions.py
 delete mode 100644 src/atc138/impedance/__init__.py
 delete mode 100644 src/atc138/impedance/main_impedance_function.py
 delete mode 100644 src/atc138/impedance/other_impedance_functions.py
 delete mode 100644 src/atc138/input_builder.py
 delete mode 100644 src/atc138/preprocessing/__init__.py
 delete mode 100644 src/atc138/preprocessing/main_preprocessing.py
 delete mode 100644 src/atc138/preprocessing/preprocessing_fns.py
 delete mode 100644 src/atc138/red_tag.py
 delete mode 100644 src/atc138/repair_schedule/__init__.py
 delete mode 100644 src/atc138/repair_schedule/main_repair_schedule.py
 delete mode 100644 src/atc138/repair_schedule/other_repair_schedule_functions.py
 rename {src/atc138/data => static_tables}/README.md (100%)
 rename {src/atc138/data => static_tables}/component_attributes.csv (100%)
 rename {src/atc138/data => static_tables}/damage_state_attribute_mapping.csv (100%)
 rename {src/atc138/data => static_tables}/impeding_factors.csv (100%)
 rename {src/atc138/data => static_tables}/subsystems.csv (100%)
 rename {src/atc138/data => static_tables}/systems.csv (100%)
 rename {src/atc138/data => static_tables}/temp_repair_class.csv (100%)
 rename {src/atc138/data => static_tables}/tenant_function_requirements.csv (100%)

diff --git a/+plotters/main_plot_functionality.m b/+plotters/main_plot_functionality.m
new file mode 100644
index 0000000..dc2f6b5
--- /dev/null
+++ b/+plotters/main_plot_functionality.m
@@ -0,0 +1,52 @@
+function [] = main_plot_functionality(functionality, save_dir, p_gantt)
+% Plot function and occupancy loss and recovery at for a single model at a 
+% single intensity levels
+%
+% Parameters
+% ----------
+% functionality: structure
+%   main output data strcuture of the functional recovery assessment. 
+%   Loaded directly from the output mat file.
+% save_dir: str
+%   Save directory for plots. Plots will save directly to this location as
+%   png files.
+% p_gantt: int
+%   percentile of functional recovery time to plot gantt chart
+%
+% Returns
+% -------
+% 
+
+%% Initial Setup
+% Import Packages
+import plotters.*
+
+% Set plot variables to use
+recovery = functionality.recovery;
+impede = functionality.impeding_factors.breakdowns;
+schedule = functionality.building_repair_schedule;
+workers = functionality.worker_data;
+full_repair_time = max(schedule.repair_complete_day.per_story,[],2);
+
+%% Plot Performance Objective Grid for system and component breakdowns
+plot_dir = [save_dir filesep 'breakdowns'];
+plt_heatmap_breakdowns( recovery, plot_dir )
+
+%% Plot Performance Target Distribution Across all Realizations
+plot_dir = [save_dir filesep 'histograms'];
+plt_histograms( recovery, plot_dir )
+
+%% Plot Mean Recovery Trajectories
+plot_dir = [save_dir filesep 'recovery trajectories'];
+plt_recovery_trajectory( recovery, full_repair_time, plot_dir)
+
+%% Plot Gantt Charts
+plot_dir = [save_dir filesep 'gantt_charts'];
+fr_time = functionality.recovery.functional.building_level.recovery_day;
+p_val = prctile(fr_time,p_gantt);
+p_idx = find(abs(fr_time - p_val) == min(abs(fr_time - p_val)),1); % Find the index of the first realization that matches the selected percentile
+plot_name = ['prt_' num2str(p_gantt)];
+plt_gantt_chart( p_idx, recovery, full_repair_time, workers, schedule, impede, plot_dir, plot_name )
+
+end
+
diff --git a/+plotters/plt_gantt_chart.m b/+plotters/plt_gantt_chart.m
new file mode 100644
index 0000000..e620dd7
--- /dev/null
+++ b/+plotters/plt_gantt_chart.m
@@ -0,0 +1,164 @@
+function [] = plt_gantt_chart( p_idx, recovery, full_repair_time, workers, schedule, impede, plot_dir, plot_name )
+% Plot gantt chart for a single realization of the functional recovery
+% assessment
+%
+% Parameters
+% ----------
+% p_idx: int
+%   realization index of interest
+% recovery: structure
+%   data structure containing recovery trajectory data from the functional
+%   recovery assessment
+% full_repair_time: array [num reals x 1] 
+%   simulated realization of full repair time 
+% workers: structure
+%   data structure containing work allocation data from the functional
+%   recovery assessment
+% schedule: structure
+%   data structure containing repair schedule data from the functional
+%   recovery assessment
+% impede: structure
+%   data structure containing impedance time data from the functional
+%   recovery assessment
+% plot_dir: str
+%   Save directory for plots. Plots will save directly to this location as
+%   png files.
+% plot_name: str
+%   name of file to save plot as (does not include the file type extension)
+%
+% Returns
+% -------
+% 
+%% Intial Setup
+if ~exist(plot_dir,'dir')
+    mkdir(plot_dir)
+end
+
+imps = fieldnames(impede);
+sys = fieldnames(impede.contractor_mob);
+
+%% Format Gantt Chart Data
+% Collect Recovery Trajectory
+recovery_trajectory.reoc = recovery.reoccupancy.recovery_trajectory.recovery_day(p_idx,:);
+recovery_trajectory.func = recovery.functional.recovery_trajectory.recovery_day(p_idx,:);
+recovery_trajectory.level_of_repair = recovery.reoccupancy.recovery_trajectory.percent_recovered;
+recovery_trajectory.ful_rep = ones(1,length(recovery_trajectory.level_of_repair)) * ceil(full_repair_time(p_idx));
+
+% Collect Worker Data
+worker_data.total_workers = workers.total_workers(p_idx,:);
+worker_data.day_vector = workers.day_vector(p_idx,:);
+
+% Collect Impedance Times
+sys_imp_times = [];
+labs = [];
+for s = 1:length(sys)
+    for i = 1:length(imps)
+        if isfield(impede.(imps{i}),'complete_day')
+            duration = impede.(imps{i}).complete_day(p_idx) - impede.(imps{i}).start_day(p_idx);
+            if duration > 0
+                sys_imp_times(i,:) = [impede.(imps{i}).start_day(p_idx), duration];
+                labs{i} = [upper(imps{i}(1)) strrep(imps{i}(2:end),'_',' ')];
+            end
+        elseif isfield(impede.(imps{i}),sys{s})
+            duration = impede.(imps{i}).(sys{s}).complete_day(p_idx) - impede.(imps{i}).(sys{s}).start_day(p_idx);
+            if duration > 0
+                sys_imp_times(end+1,:) = [impede.(imps{i}).(sys{s}).start_day(p_idx), duration];
+                labs{end+1} = [upper(sys{s}(1)) sys{s}(2:end) ' ' strrep(imps{i},'_',' ')];
+            end
+        end
+    end
+end
+labs_imp = flip(labs);
+y_imp = flip(sys_imp_times);
+
+% Collect Repair Times 
+sys_repair_times = [];
+labs = [];
+for s = 1:length(sys) % WARNING: This assumes the system order in the output of repair schedule is the same order has the impedance breakdowns
+    duration = schedule.repair_complete_day.per_system(p_idx,s) - schedule.repair_start_day.per_system(p_idx,s);
+    if duration > 0
+        sys_repair_times(end+1,:) = [schedule.repair_start_day.per_system(p_idx,s),duration];
+        labs{end+1} = [upper(sys{s}(1)) sys{s}(2:end) ' Repairs'];
+    end
+end
+labs_rep = flip(labs);
+y_rep = flip(sys_repair_times);
+
+%% Plot Gantt
+x_limit = max(ceil(max(recovery_trajectory.ful_rep)/10)*10,1);
+
+% Set Plot Layout
+pos = [0.25    0.63     0.7    0.34;...
+       0.25    0.41     0.7    0.19;...
+       0.25    0.24     0.7    0.14;...
+       0.25    0.065    0.7    0.14];
+
+% Impedance Time
+subplot('Position',pos(1,:))
+x = categorical(labs_imp);
+x = reordercats(x,labs_imp);
+G = barh(x,y_imp,'stacked');
+G(1).HandleVisibility = 'off';
+G(2).DisplayName = 'Repair Time';
+set(G(1),'Visible','off')
+G(2).FaceColor = [0.6, 0.6, 0.6];
+G(2).FaceAlpha = 0.5;
+G(2).EdgeAlpha = 0;
+% set(gca,'YColor','k')
+fn_format_subplot(gca,x_limit,[],[],'Impedance Time')
+
+% Repair Time
+subplot('Position',pos(2,:))
+x = categorical(labs_rep);
+x = reordercats(x,labs_rep);
+H = barh(x,y_rep,'stacked');
+H(1).HandleVisibility = 'off';
+H(2).DisplayName = 'Repair Time';
+set(H(1),'Visible','off')
+H(2).FaceColor = [0.1, 0.1, 0.1];
+H(2).FaceAlpha = 0.5;
+H(2).EdgeAlpha = 0;
+% set(gca,'YColor','k')
+fn_format_subplot(gca,x_limit,[],[],'Repair Time')
+
+% Workers
+subplot('Position',pos(3,:))
+hold on
+plot(worker_data.day_vector, worker_data.total_workers)
+fn_format_subplot(gca,x_limit,{'Number of', 'Workers'},[],'Workers')
+
+% Plot Recovery Trajectory
+subplot('Position',pos(4,:))
+hold on
+plot(recovery_trajectory.reoc, recovery_trajectory.level_of_repair,'-r','LineWidth',1.5,'DisplayName','Re-Occupancy')
+plot(recovery_trajectory.func, recovery_trajectory.level_of_repair,'-b','LineWidth',1.5,'DisplayName','Functional')
+plot(recovery_trajectory.ful_rep, recovery_trajectory.level_of_repair,'-k','LineWidth',1.5,'DisplayName','Fully Repaired')
+ylim([0,1])
+lgd = legend('location','northeast');
+set(lgd,'position',[0.07    0.11    0.05   0.05])
+fn_format_subplot(gca,x_limit,'Fraction of Floor Area','Days After Earthquake','Building Recovery State')
+
+% Set and Save plot
+set(gcf,'position',[10,10,800,600])
+saveas(gcf,[plot_dir filesep plot_name],'png')
+close
+
+end
+
+function [] = fn_format_subplot(ax,x_limit,y_lab,x_lab,tle)
+    ax.XGrid = 'on';
+    ax.XMinorGrid = 'on';
+    xlim([0,x_limit])
+    box on
+    set(gca,'fontname','times')
+    set(gca,'fontsize',9)
+    if ~isempty(y_lab)
+        ylabel(y_lab)
+    end
+    if ~isempty(x_lab)
+        xlabel(x_lab)
+    else
+        set(gca,'XTickLabel',[])
+    end
+    title(tle)
+end
\ No newline at end of file
diff --git a/+plotters/plt_heatmap_breakdowns.m b/+plotters/plt_heatmap_breakdowns.m
new file mode 100644
index 0000000..ce8b60b
--- /dev/null
+++ b/+plotters/plt_heatmap_breakdowns.m
@@ -0,0 +1,46 @@
+function [] = plt_heatmap_breakdowns( recovery, plot_dir )
+% Plot the time and percent of realizations that each system and/or 
+% component is impeding function as a heatmap
+%
+% Parameters
+% ----------
+% recovery: structure
+%   data structure containing recovery breakdown data from the functional
+%   recovery assessment
+% plot_dir: str
+%   Save directory for plots. Plots will save directly to this location as
+%   png files.
+%
+% Returns
+% -------
+% 
+%% Initial Setup
+if ~exist(plot_dir,'dir')
+    mkdir(plot_dir)
+end
+
+var = {'component_breakdowns', 'system_breakdowns'};
+labs = {'comp_names', 'system_names'};
+plt_ht = [550, 350];
+
+%% Plot Heatmaps
+fnc_states = fieldnames(recovery);
+for fs = 1:length(fnc_states)
+    for v = 1:length(var)
+        y_labs = recovery.(fnc_states{fs}).breakdowns.(labs{v});
+        data = recovery.(fnc_states{fs}).breakdowns.(var{v});
+        perform_targ_labs = recovery.(fnc_states{fs}).breakdowns.perform_targ_days;
+        h = heatmap(perform_targ_labs, strrep(y_labs,'_',' '), round(data,2));
+        h.MissingDataColor = 1-h.MissingDataColor;
+        fnc_lab = [upper(fnc_states{fs}(1)) fnc_states{fs}(2:end) ' Recovery'];
+        title(['Fraction of Realizations Affecting Building ' fnc_lab])
+        xlabel('Recovery Time After Earthquake (days)')
+        set(gca,'fontname','times')
+        set(gcf,'position',[10,10,650,plt_ht(v)])
+        saveas(gcf,[plot_dir filesep var{v} '_' fnc_states{fs}],'png')
+        close
+    end
+end
+
+end
+
diff --git a/+plotters/plt_histograms.m b/+plotters/plt_histograms.m
new file mode 100644
index 0000000..27652de
--- /dev/null
+++ b/+plotters/plt_histograms.m
@@ -0,0 +1,34 @@
+function [] = plt_histograms( recovery, plot_dir )
+% Plot all realizations of building level recovery as a histogram
+%
+% Parameters
+% ----------
+% recovery: structure
+%   data structure containing building-level recovery data from the 
+%   functional recovery assessment
+% plot_dir: str
+%   Save directory for plots. Plots will save directly to this location as
+%   png files.
+%
+% Returns
+% -------
+% 
+%% Initial Setup
+if ~exist(plot_dir,'dir')
+    mkdir(plot_dir)
+end
+
+%% Plot Histograms
+fnc_states = fieldnames(recovery);
+for fs = 1:length(fnc_states)
+    histogram(recovery.functional.building_level.recovery_day)
+    fnc_lab = [upper(fnc_states{fs}(1)) fnc_states{fs}(2:end) ' Recovery'];
+    xlabel([fnc_lab ' Time (days)'])
+    ylabel('Number of Realizatons')
+    box on
+    saveas(gcf,[plot_dir filesep fnc_states{fs}],'png')
+    close
+end
+
+end
+
diff --git a/+plotters/plt_recovery_trajectory.m b/+plotters/plt_recovery_trajectory.m
new file mode 100644
index 0000000..7af714a
--- /dev/null
+++ b/+plotters/plt_recovery_trajectory.m
@@ -0,0 +1,47 @@
+function [] = plt_recovery_trajectory( recovery, full_repair_time, plot_dir)
+% Plot mean recovery trajectories
+%
+% Parameters
+% ----------
+% recovery: structure
+%   data structure containing recovery trajectory data from the functional
+%   recovery assessment
+% full_repair_time: array [num reals x 1] 
+%   simulated realization of full repair time 
+% plot_dir: str
+%   Save directory for plots. Plots will save directly to this location as
+%   png files.
+%
+% Returns
+% -------
+% 
+
+%% Initial Setup
+if ~exist(plot_dir,'dir')
+    mkdir(plot_dir)
+end
+
+% Calculate mean recovery times
+reoc = mean(recovery.reoccupancy.recovery_trajectory.recovery_day);
+func = mean(recovery.functional.recovery_trajectory.recovery_day);
+full = mean(full_repair_time);
+level_of_repair = recovery.functional.recovery_trajectory.percent_recovered;
+
+%% Plot Recovery Trajectory
+hold on 
+plot(reoc, level_of_repair,'r','LineWidth',1.5,'DisplayName','Re-Occupancy') 
+plot(func, level_of_repair,'b','LineWidth',1.5,'DisplayName','Functional') 
+plot([full full], [0 1],'k','LineWidth',1.5,'DisplayName','Fully Repaired') 
+xlim([0,ceil((full+1)/10)*10])
+xlabel('Days After Earthquake')
+ylabel('Fraction of Floor Area')
+box on
+grid on
+legend('location', 'Northwest');
+set(gca,'fontname','times')
+set(gcf,'position',[10,10,500,300])
+saveas(gcf,[plot_dir filesep 'recovery trajectory'],'png')
+close
+
+end
+
diff --git a/+preprocessing/fn_create_fnc_filters.m b/+preprocessing/fn_create_fnc_filters.m
new file mode 100644
index 0000000..22a09f1
--- /dev/null
+++ b/+preprocessing/fn_create_fnc_filters.m
@@ -0,0 +1,177 @@
+function [ fnc_filters ] = fn_create_fnc_filters( comp_ds_table )
+% Define function filter arrays that allow rapid sampling of simulated
+% damage for use within the fault tree analysis
+%
+% Parameters
+% ----------
+% comp_ds_table: table
+%   various component attributes by damage state for each component 
+%   within the performance model. Can be populated from the 
+%   component_attribites.csv and damage_state_attribute_mapping.csv 
+%   databases in the static_tables directory.  Each row of the table 
+%   corresponds to each column of the simulated component damage arrays 
+%   within damage.tenant_units.
+%
+% Returns
+% -------
+% fnc_filters: struct of arrays
+%   each filter is a 1 x num_comp_ds array that can be used to sample
+%   select types of damage from the damage.tenant unit or damage.story
+%   simulated damage arrays.
+%
+
+%% Impedance filters
+fnc_filters.permit_rapid = strcmp(comp_ds_table.permit_type, 'rapid');
+fnc_filters.permit_full = strcmp(comp_ds_table.permit_type, 'full');
+fnc_filters.redesign = comp_ds_table.redesign == 1;
+
+%% Building level filters
+% combine all damage state filters that have the potential to affect
+% function or reoccupancy, other than structural safety damage (for repair
+% prioritization)
+fnc_filters.affects_reoccupancy = comp_ds_table.affects_envelope_safety | ...
+                                  comp_ds_table.ext_falling_hazard | ...
+                                  comp_ds_table.int_falling_hazard | ...
+                                  comp_ds_table.global_hazardous_material | ...
+                                  comp_ds_table.local_hazardous_material | ...
+                                  comp_ds_table.affects_access; 
+                              
+fnc_filters.affects_function = fnc_filters.affects_reoccupancy | ...
+                               comp_ds_table.damages_envelope_seal | ...
+                               comp_ds_table.obstructs_interior_space | ...
+                               comp_ds_table.impairs_system_operation;
+
+% Define when building has resolved its red tag (when all repairs are complete that may affect red tags)
+% get any components that have the potential to cause red tag
+fnc_filters.red_tag = comp_ds_table.safety_class > 0; 
+
+% Define when the building requires shoring from external falling hazards
+fnc_filters.requires_shoring = logical(comp_ds_table.requires_shoring);
+
+% Define when the building has issues with internal flooding
+fnc_filters.causes_flooding = logical(comp_ds_table.causes_flooding);
+
+%% System dependent filters
+% fire suppresion system damage that affects entire building
+fnc_filters.fire_building = comp_ds_table.system == 9 & strcmp(comp_ds_table.service_location,'building') & comp_ds_table.impairs_system_operation;
+
+% fire suppresion damage that affects each tenant unit
+fnc_filters.fire_unit = comp_ds_table.system == 9 & ~comp_ds_table.subsystem_id == 23 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation; % pipe and brace branches (not spinkler heads)
+fnc_filters.fire_drops = comp_ds_table.subsystem_id == 23 & comp_ds_table.impairs_system_operation;
+
+% Hazardous materials
+fnc_filters.global_hazardous_material = comp_ds_table.global_hazardous_material;
+fnc_filters.local_hazardous_material = comp_ds_table.local_hazardous_material;
+
+% Stairs
+fnc_filters.stairs = comp_ds_table.affects_access & comp_ds_table.system == 4;
+
+% Exterior enclosure damage
+fnc_filters.exterior_safety_lf = strcmp(string(comp_ds_table.unit),'lf') & comp_ds_table.affects_envelope_safety; % Components with perimeter linear feet units
+fnc_filters.exterior_safety_sf = strcmp(string(comp_ds_table.unit),'sf') & comp_ds_table.affects_envelope_safety; % Components with perimeter square feet units
+fnc_filters.exterior_safety_all = fnc_filters.exterior_safety_lf | fnc_filters.exterior_safety_sf;
+
+% Exterior Falling hazards
+fnc_filters.ext_fall_haz_lf = strcmp(string(comp_ds_table.unit),'lf') & comp_ds_table.ext_falling_hazard; % Components with perimeter linear feet units
+fnc_filters.ext_fall_haz_sf = strcmp(string(comp_ds_table.unit),'sf') & comp_ds_table.ext_falling_hazard; % Components with square feet units
+fnc_filters.ext_fall_haz_ea = strcmp(string(comp_ds_table.unit),'each') & comp_ds_table.ext_falling_hazard; % Components with "each" units
+fnc_filters.ext_fall_haz_all = fnc_filters.ext_fall_haz_lf | ...
+                                fnc_filters.ext_fall_haz_sf | ...
+                                fnc_filters.ext_fall_haz_ea;
+
+% Exterior enclosure envelope seal damage
+fnc_filters.exterior_seal_lf = strcmp(string(comp_ds_table.unit),'lf') & comp_ds_table.damages_envelope_seal; % Components with perimeter linear feet units
+fnc_filters.exterior_seal_sf = strcmp(string(comp_ds_table.unit),'sf') & comp_ds_table.damages_envelope_seal; % Components with perimeter square feet units
+fnc_filters.exterior_seal_ea = strcmp(string(comp_ds_table.unit),'each') & comp_ds_table.damages_envelope_seal; % Components with perimeter square feet units
+fnc_filters.exterior_seal_all = fnc_filters.exterior_seal_lf | ...
+                                fnc_filters.exterior_seal_sf | ...
+                                fnc_filters.exterior_seal_ea;
+
+% Roofing components
+fnc_filters.roof_structure       = comp_ds_table.subsystem_id == 21 & comp_ds_table.affects_roof_function;
+fnc_filters.roof_weatherproofing = comp_ds_table.subsystem_id == 22 & comp_ds_table.affects_roof_function;
+
+% Interior falling hazards
+fnc_filters.int_fall_haz_lf = comp_ds_table.int_falling_hazard & strcmp(string(comp_ds_table.unit), 'lf') & strcmp(comp_ds_table.interior_area_conversion_type, 'component'); % Interior components with perimeter feet units
+fnc_filters.int_fall_haz_sf = comp_ds_table.int_falling_hazard & strcmp(string(comp_ds_table.unit), 'sf') & strcmp(comp_ds_table.interior_area_conversion_type, 'component'); % Interior components with area feet units
+fnc_filters.int_fall_haz_ea = comp_ds_table.int_falling_hazard & strcmp(string(comp_ds_table.unit), 'each') & strcmp(comp_ds_table.interior_area_conversion_type, 'component'); % Interior components with "each" for units (just scale directly)
+fnc_filters.int_fall_haz_bay = comp_ds_table.int_falling_hazard & strcmp(comp_ds_table.interior_area_conversion_type, 'bay'); % structural damage that does not cause red tags but affects function
+fnc_filters.int_fall_haz_build = comp_ds_table.int_falling_hazard & strcmp(comp_ds_table.interior_area_conversion_type, 'building'); % structural damage that does not cause red tags but affects funciton (this one should only be tilt-ups)
+fnc_filters.int_fall_haz_all = fnc_filters.int_fall_haz_lf | ...
+                                fnc_filters.int_fall_haz_sf | ...
+                                fnc_filters.int_fall_haz_ea | ...
+                                fnc_filters.int_fall_haz_bay | ...
+                                fnc_filters.int_fall_haz_build;
+
+% Flag structural damage that causes interior falling hazards
+fnc_filters.vert_instabilities = comp_ds_table.system == 1 & comp_ds_table.int_falling_hazard;
+
+% Interior function damage 
+fnc_filters.interior_function_lf = comp_ds_table.obstructs_interior_space & strcmp(string(comp_ds_table.unit), 'lf') & strcmp(comp_ds_table.interior_area_conversion_type, 'component'); 
+fnc_filters.interior_function_sf = comp_ds_table.obstructs_interior_space & (strcmp(string(comp_ds_table.unit), 'sf')) & strcmp(comp_ds_table.interior_area_conversion_type, 'component');  
+fnc_filters.interior_function_ea = comp_ds_table.obstructs_interior_space & (strcmp(string(comp_ds_table.unit), 'each')) & strcmp(comp_ds_table.interior_area_conversion_type, 'component'); 
+fnc_filters.interior_function_bay = comp_ds_table.obstructs_interior_space & strcmp(comp_ds_table.interior_area_conversion_type, 'bay');
+fnc_filters.interior_function_build = comp_ds_table.obstructs_interior_space & strcmp(comp_ds_table.interior_area_conversion_type,'building');  
+fnc_filters.interior_function_all = fnc_filters.interior_function_lf | ...
+                                    fnc_filters.interior_function_sf | ...
+                                    fnc_filters.interior_function_ea | ...
+                                    fnc_filters.interior_function_bay | ...
+                                    fnc_filters.interior_function_build;  
+
+% Elevators
+fnc_filters.elevators    = comp_ds_table.system == 5 & comp_ds_table.impairs_system_operation & comp_ds_table.subsystem_id ~= 2;
+fnc_filters.elevator_mcs = comp_ds_table.system == 5 & comp_ds_table.impairs_system_operation & comp_ds_table.subsystem_id == 2;
+
+% Electrical system
+fnc_filters.electrical_main = comp_ds_table.system == 7 & comp_ds_table.subsystem_id == 1 & strcmp(comp_ds_table.service_location,'building') & comp_ds_table.impairs_system_operation;
+fnc_filters.electrical_unit = comp_ds_table.system == 7 & comp_ds_table.subsystem_id == 1 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation;
+
+% Potable Water Plumbing
+fnc_filters.water_main = comp_ds_table.system == 6 & comp_ds_table.subsystem_id == 8 & strcmp(comp_ds_table.service_location,'building') & comp_ds_table.impairs_system_operation;
+fnc_filters.water_unit = comp_ds_table.system == 6 & comp_ds_table.subsystem_id == 8 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation; 
+
+% Sanitary Plumbing
+fnc_filters.sewer_main = comp_ds_table.system == 6 & comp_ds_table.subsystem_id == 9 & strcmp(comp_ds_table.service_location,'building') & comp_ds_table.impairs_system_operation;
+fnc_filters.sewer_unit = comp_ds_table.system == 6 & comp_ds_table.subsystem_id == 9 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation; 
+
+% HVAC: Control System
+fnc_filters.hvac.building.hvac_control.mcs = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 2 & comp_ds_table.impairs_system_operation;
+fnc_filters.hvac.building.hvac_control.control_panel = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 20 & comp_ds_table.impairs_system_operation ;
+
+% HVAC: Ventilation
+fnc_filters.hvac.tenant.hvac_ventilation.duct_mains = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 24 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation;
+fnc_filters.hvac.tenant.hvac_ventilation.duct_braches = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 4 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation;
+fnc_filters.hvac.tenant.hvac_ventilation.in_line_fan = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 5 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation; 
+fnc_filters.hvac.tenant.hvac_ventilation.duct_drops = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 6 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation;
+fnc_filters.hvac.tenant.hvac_ventilation.ahu = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 19 & comp_ds_table.impairs_system_operation;
+fnc_filters.hvac.tenant.hvac_ventilation.rtu = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 17 & comp_ds_table.impairs_system_operation;
+
+% HVAC: Heating
+fnc_filters.hvac.building.hvac_heating.piping = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 10 & comp_ds_table.impairs_system_operation; 
+fnc_filters.hvac.tenant.hvac_heating.vav = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 7 & comp_ds_table.impairs_system_operation; 
+
+% HVAC: Cooling
+fnc_filters.hvac.building.hvac_cooling.piping = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 11 & comp_ds_table.impairs_system_operation; 
+fnc_filters.hvac.building.hvac_cooling.chiller = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 15 & comp_ds_table.impairs_system_operation; 
+fnc_filters.hvac.building.hvac_cooling.cooling_tower = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 16 & comp_ds_table.impairs_system_operation; 
+fnc_filters.hvac.tenant.hvac_cooling.vav = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 7 & comp_ds_table.impairs_system_operation; 
+
+% HVAC: Exhaust
+fnc_filters.hvac.tenant.hvac_exhaust.exhaust_fan = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 18 & comp_ds_table.impairs_system_operation;
+
+% Data system
+fnc_filters.data_main = comp_ds_table.system == 11 & strcmp(comp_ds_table.service_location,'building') & comp_ds_table.impairs_system_operation;
+fnc_filters.data_unit = comp_ds_table.system == 11 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation;
+
+% Horizontal Egress: Fire break partitions
+fnc_filters.fire_break = comp_ds_table.system == 3 & comp_ds_table.weakens_fire_break; % only collect interior fire break partitions
+
+%% Flip orientation of fnc_filters to match orientation of damage data [reals x ds]
+names = fieldnames(fnc_filters);
+for fn = 1:length(names)
+    tmp_fnc_filt.(names{fn}) = fnc_filters.(names{fn})';
+end
+fnc_filters = tmp_fnc_filt; % assign to damage data structure
+
+end
+
diff --git a/+preprocessing/fn_define_door_racking.m b/+preprocessing/fn_define_door_racking.m
new file mode 100644
index 0000000..25b981b
--- /dev/null
+++ b/+preprocessing/fn_define_door_racking.m
@@ -0,0 +1,36 @@
+function [ damage_consequences ] = fn_define_door_racking( damage_consequences, num_stories )
+% Define building level damage consquences when not specificed by the user.
+%
+% Parameters
+% ----------
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+%   tags
+% num_stories: int
+%   Integer number of stories in the building being assessed
+%
+% Returns
+% -------
+% damage_consequences.racked_stair_doors_per_story: array, num real x num stories
+%   simulated number of racked stairwell doors at each story
+% damage_consequences.racked_entry_doors_side_1: array, num real x 1
+%   simulated number of racked entry doors on one side of the building
+% damage_consequences.racked_entry_doors_side_2: array, num real x 1
+%   simulated number of racked entry doors on the other side of the building
+
+%% Set door racking damage if not provided by user
+[num_reals, ~] = size(damage_consequences.simulated_replacement_time);
+
+% Assume there are no racked doors if not specified by the user
+if ~isfield(damage_consequences,'racked_stair_doors_per_story')
+    damage_consequences.racked_stair_doors_per_story = zeros(num_reals,num_stories); % 
+end
+if ~isfield(damage_consequences,'racked_entry_doors_side_1')
+    damage_consequences.racked_entry_doors_side_1 = zeros(num_reals,1); % array, num real x num stories
+end
+if ~isfield(damage_consequences,'racked_entry_doors_side_2')
+    damage_consequences.racked_entry_doors_side_2 = zeros(num_reals,1); % array, num real x num stories
+end
+
+end
+
diff --git a/+preprocessing/fn_populate_damage_per_tu.m b/+preprocessing/fn_populate_damage_per_tu.m
new file mode 100644
index 0000000..1d6d627
--- /dev/null
+++ b/+preprocessing/fn_populate_damage_per_tu.m
@@ -0,0 +1,24 @@
+function [ damage ] = fn_populate_damage_per_tu( damage )
+% Check to see if the damage.tenant_units variable has been defined. If
+% not, assume its the same as damage.story. This assumption creates a 1:1
+% coupling between stories and tenant_units (one tenant unit per story)
+%
+% Parameters
+% ----------
+% damage.story: array of struct
+%   contains simulated per component and damage state damage info
+%   disagregated by story
+%
+% Returns
+% -------
+%   contains simulated per component and damage state damage info
+%   disagregated by tenant unit
+%
+
+%% If tenant unit damage is not provided by the user, assume its the same as per story damage
+if ~isfield(damage,'tenant_units')
+    damage.tenant_units = damage.story;
+end
+
+end
+
diff --git a/+preprocessing/fn_simulate_damage_per_side.m b/+preprocessing/fn_simulate_damage_per_side.m
new file mode 100644
index 0000000..6dd9d01
--- /dev/null
+++ b/+preprocessing/fn_simulate_damage_per_side.m
@@ -0,0 +1,35 @@
+function [ damage ] = fn_simulate_damage_per_side( damage )
+% Simulate damage per side for the exterior falling hazard check, if not 
+% provided by the user. Component location within a story is typically not 
+% Provided in most PBEE assessments. Therefore, this script make the rough 
+% assumptions to distribute damage to 4 sides, randomly.
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains simulated damage info and damage state attributes
+%
+% Returns
+% -------
+% damage: struct
+%   contains simulated damage info and damage state attributes
+%
+
+%% Simulate damage per side, if not provided by the user
+if ~isfield(damage.tenant_units{1},'qnt_damaged_side_1')
+    [num_reals,~] = size(damage.tenant_units{1}.qnt_damaged);
+    
+    % Randomly split damage between 4 sides
+    % (this will only matter for cladding components)
+    ratio_damage_per_side = rand(num_reals,4); % assumes square footprint
+    ratio_damage_per_side = ratio_damage_per_side ./ sum(ratio_damage_per_side,2); % force it to add to one
+
+    % Assing damage
+    for tu = 1:length(damage.tenant_units)
+        for s = 1:4
+            damage.tenant_units{tu}.(['qnt_damaged_side_' num2str(s)]) = ...
+                ratio_damage_per_side(:,s).*damage.tenant_units{tu}.qnt_damaged;
+        end
+    end
+end
+
diff --git a/+preprocessing/fn_simulate_temp_worker_days.m b/+preprocessing/fn_simulate_temp_worker_days.m
new file mode 100644
index 0000000..ea45ae9
--- /dev/null
+++ b/+preprocessing/fn_simulate_temp_worker_days.m
@@ -0,0 +1,87 @@
+function [ damage, temp_repair_class ] = fn_simulate_temp_worker_days( damage, temp_repair_class, repair_time_options )
+% Simulate Temporary Repair Times for each component, if not already
+% defined by the user. In a perfect system this should be done alongside 
+% the other full repair time simulation. However, most PBEE assessments do
+% not contain information on temp repair times per component when they are
+% simulating damage and consequences. Therefore, this is decoupled from the
+% rest of the assessment and simulated here (if not already provided by the
+% user)
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains simulated damage info and damage state attributes
+% repair_time_options.allow_shoring: logical
+%   flag indicating whether or not shoring should be considered as a
+%   temporary repair for local stability issues for structural components
+% temp_repair_class: table
+%   attributes of each temporary repair class to consider
+%
+% Returns
+% -------
+% damage: struct
+%   contains simulated damage info and damage state attributes
+% temp_repair_class: table
+%   attributes of each temporary repair class to consider
+%
+
+%% Define Temporary Repair Times Options
+% Turn of temp repairs if specificied by the user
+if ~repair_time_options.allow_tmp_repairs
+    damage.comp_ds_table.tmp_repair_class = zeros(size(damage.comp_ds_table.tmp_repair_class));
+end
+
+% Set up temp_repair_class based on user inputs
+if ~repair_time_options.allow_shoring
+    temp_repair_class(temp_repair_class.id == 5,:) = []; % Remove shoring from table
+end
+
+%% Simulate temp repair worker days per component 
+% if not already specified by the user
+if ~isfield(damage.tenant_units{1},'tmp_worker_day')
+    % Find total number of damamged components
+    total_damaged = damage.tenant_units{1}.qnt_damaged;
+    for tu = 2:length(damage.tenant_units)
+        total_damaged = total_damaged + damage.tenant_units{tu}.qnt_damaged;
+    end
+
+    % Aggregate the total number of damaged components accross each damage
+    % state in a component
+    tmp_worker_days_per_unit = [];
+    for c = 1:height(damage.comp_ds_table) % for each comp ds
+        comp = damage.comp_ds_table(c,:);
+        if comp.tmp_repair_class > 0 % For damage that has temporary repair
+            filt = strcmp(damage.comp_ds_table.comp_id,comp.comp_id)';
+            total_damaged_all_ds = sum(total_damaged(:,filt),2);
+
+            % Interpolate to get per unit temp repair times
+            if comp.tmp_repair_time_lower_qnty == comp.tmp_repair_time_upper_qnty
+                tmp_worker_days_per_unit(:,c) = comp.tmp_repair_time_lower;
+            else
+                tmp_worker_days_per_unit(:,c) = ...
+                    interp1([comp.tmp_repair_time_lower_qnty, comp.tmp_repair_time_upper_qnty],...
+                            [comp.tmp_repair_time_lower,comp.tmp_repair_time_upper],...
+                            min(max(total_damaged_all_ds,comp.tmp_repair_time_lower_qnty),comp.tmp_repair_time_upper_qnty));
+            end
+
+        else
+            tmp_worker_days_per_unit(:,c) = NaN(size(total_damaged(:,1)));
+        end
+    end
+
+    % Simulate uncertainty in per unit temp repair times
+    % Assumes distribution is lognormal with beta = 0.4
+    % Assumes time to repair all of a given component group is fully correlated, 
+    % but independant between component groups 
+    sim_tmp_worker_days_per_unit = lognrnd(log(tmp_worker_days_per_unit),0.4,size(tmp_worker_days_per_unit));
+
+    % Allocate per unit temp repair time among tenant units to calc worker days
+    % for each component
+    for tu = 1:length(damage.tenant_units)
+        damage.tenant_units{tu}.tmp_worker_day = ...
+            damage.tenant_units{tu}.qnt_damaged .* sim_tmp_worker_days_per_unit;
+    end
+end
+
+end
+
diff --git a/+preprocessing/main_preprocessing.m b/+preprocessing/main_preprocessing.m
new file mode 100644
index 0000000..fe33f95
--- /dev/null
+++ b/+preprocessing/main_preprocessing.m
@@ -0,0 +1,58 @@
+function [damage, temp_repair_class, damage_consequences] = ...
+    main_preprocessing(comp_ds_table, damage, repair_time_options, temp_repair_class, damage_consequences, num_stories)
+% Parameterize variables and simplifying assumptions to expedite the ATC138
+% recovery assessment
+%
+% Parameters
+% ----------
+% comp_ds_table: table
+%   various component attributes by damage state for each component 
+%   within the performance model. Can be populated from the 
+%   component_attribites.csv and damage_state_attribute_mapping.csv 
+%   databases in the static_tables directory.  Each row of the table 
+%   corresponds to each column of the simulated component damage arrays 
+%   within damage.tenant_units.
+% damage: struct
+%   contains simulated damage info and damage state attributes
+% repair_time_options.allow_shoring: logical
+%   flag indicating whether or not shoring should be considered as a
+%   temporary repair for local stability issues for structural components
+% temp_repair_class: table
+%   attributes of each temporary repair class to consider
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+% num_stories: int
+%   Integer number of stories in the building being assessed
+%
+% Returns
+% -------
+% damage: struct
+%   contains simulated damage info and damage state attributes
+% temp_repair_class: table
+%   attributes of each temporary repair class to consider
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+%
+%% Import Packages
+import preprocessing.fn_populate_damage_per_tu
+import preprocessing.fn_simulate_damage_per_side
+import preprocessing.fn_create_fnc_filters
+import preprocessing.fn_simulate_temp_worker_days
+import preprocessing.fn_define_door_racking
+
+%% Define simulated damage in each tenant unit if not provided by the user
+[ damage ] = fn_populate_damage_per_tu( damage );
+
+%% Simulate damage per side, if not provided by the user
+[ damage ] = fn_simulate_damage_per_side( damage );
+
+%% Combine compoment attributes into recovery filters to expidite recovery assessment
+[ damage.fnc_filters ] = fn_create_fnc_filters( comp_ds_table );
+
+%% Simulate Temporary Repair Times for each component
+[ damage, temp_repair_class ] = fn_simulate_temp_worker_days( damage, temp_repair_class, repair_time_options );
+
+%% Set door racking damage if not provided by user
+[ damage_consequences ] = fn_define_door_racking( damage_consequences, num_stories );
+
+end
\ No newline at end of file
diff --git a/+recovery/+functionality/fn_building_level_system_operation.m b/+recovery/+functionality/fn_building_level_system_operation.m
new file mode 100644
index 0000000..e1b5ff0
--- /dev/null
+++ b/+recovery/+functionality/fn_building_level_system_operation.m
@@ -0,0 +1,133 @@
+function [ system_operation_day ] = fn_building_level_system_operation( damage, ...
+    damage_consequences, building_model, utilities, functionality_options )
+% Calculate the day certain systems recovery building-level opertaions
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage, loss, and repair time data for each 
+%   component in the building
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+%   tags and repair costs ratios
+% building_model: struct
+%   general attributes of the building model
+% utilities: struct
+%   data structure containing simulated utility downtimes
+% functionality_options: struct
+%   recovery time optional inputs such as various damage thresholds
+%
+% Returns
+% -------
+% system_operation_day.building: struct
+%   simulation of the day operation is recovered for various systems at the
+%   building level
+% system_operation_day.comp: struct
+%   simulation number of days each component is affecting building system
+%   operations
+
+%% Initial Setep
+% import packages
+import recovery.functionality.fn_calc_subsystem_recovery
+
+% Initialize Variables
+num_stories = building_model.num_stories;
+num_reals = length(damage_consequences.red_tag);
+num_comps = height(damage.comp_ds_table);
+
+
+system_operation_day.comp.elev_quant_damaged = zeros(num_reals,num_comps);
+system_operation_day.comp.elev_day_repaired = zeros(num_reals,num_comps);
+system_operation_day.comp.electrical_main = zeros(num_reals,num_comps);
+system_operation_day.comp.water_potable_main = zeros(num_reals,num_comps);
+system_operation_day.comp.water_sanitary_main = zeros(num_reals,num_comps);
+system_operation_day.comp.elevator_mcs = zeros(num_reals,num_comps);
+system_operation_day.comp.data_main = zeros(num_reals,num_comps);
+
+
+%% Loop through each story/TU and quantify the building-level performance of each system (e.g. equipment that severs the entire building)
+for tu = 1:num_stories
+    damaged_comps = damage.tenant_units{tu}.qnt_damaged;
+    initial_damaged = damaged_comps > 0;
+    total_num_comps = damage.tenant_units{tu}.num_comps;
+    repair_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day;
+    
+    %% Elevators
+    % Assumed all components affect entire height of shaft
+    system_operation_day.comp.elev_quant_damaged = ...
+        max(system_operation_day.comp.elev_quant_damaged, damage.tenant_units{tu}.qnt_damaged .* damage.fnc_filters.elevators);
+    system_operation_day.comp.elev_day_repaired = ...
+        max(system_operation_day.comp.elev_day_repaired, repair_complete_day .* damage.fnc_filters.elevators);
+    
+    % Motor Control system - Elevators
+    system_operation_day.comp.elevator_mcs = ...
+        max(system_operation_day.comp.elevator_mcs, repair_complete_day .* damage.fnc_filters.elevator_mcs);
+    
+    %% Electrical
+    system_operation_day.comp.electrical_main = ...
+        max(system_operation_day.comp.electrical_main, repair_complete_day .* damage.fnc_filters.electrical_main);
+
+    %% Water
+    % Potable
+    system_operation_day.comp.water_potable_main = ...
+        max(system_operation_day.comp.water_potable_main, repair_complete_day .* damage.fnc_filters.water_main);
+    
+    % Sanitary
+    system_operation_day.comp.water_sanitary_main = ...
+        max(system_operation_day.comp.water_sanitary_main, repair_complete_day .* damage.fnc_filters.sewer_main);
+    
+    %% HVAC
+    building_hvac_subsystems = fieldnames(damage.fnc_filters.hvac.building);
+    for s = 1:length(building_hvac_subsystems)
+        subsys_label = building_hvac_subsystems{s};
+        if not(isfield(system_operation_day, 'building')) ...
+                || not(isfield(system_operation_day.building, subsys_label))
+            % Initialize variables if not already initialized
+            system_operation_day.building.(subsys_label) = zeros(num_reals, 1);
+            system_operation_day.comp.(subsys_label) = zeros(num_reals, num_comps);
+        end
+
+        % go through each subsystem and calculate how long entire building operation is impaired
+        subs = fieldnames(damage.fnc_filters.hvac.building.(subsys_label));
+        for b = 1:length(subs)
+            filt = damage.fnc_filters.hvac.building.(subsys_label).(subs{b})';
+            
+            [repair_day] = fn_calc_subsystem_recovery( filt, damage, ...
+                 repair_complete_day, total_num_comps, damaged_comps );
+            
+            comps_breakdown = filt .* initial_damaged .* repair_day;
+            
+            % combine with previous stories
+            system_operation_day.building.(subsys_label) = ...
+                max(system_operation_day.building.(subsys_label), repair_day);
+            
+            system_operation_day.comp.(subsys_label) = ...
+                max(system_operation_day.comp.(subsys_label), comps_breakdown);
+        end
+    end
+    
+    %% Data
+    system_operation_day.comp.data_main = ...
+        max(system_operation_day.comp.data_main, repair_complete_day .* damage.fnc_filters.data_main);
+end
+
+%% Calculate building level consequences for systems where any major main damage leads to system failure
+system_operation_day.building.electrical_main = max(system_operation_day.comp.electrical_main,[],2);  % any major damage to the main equipment fails the system for the entire building
+system_operation_day.building.water_potable_main = max(system_operation_day.comp.water_potable_main,[],2);  % any major damage to the main pipes fails the system for the entire building
+system_operation_day.building.water_sanitary_main = max(system_operation_day.comp.water_sanitary_main,[],2);  % any major damage to the main pipes fails the system for the entire building
+system_operation_day.building.elevator_mcs = max(system_operation_day.comp.elevator_mcs,[],2); % any major damage fails the system for the whole building so take the max
+system_operation_day.building.data_main = max(system_operation_day.comp.data_main,[],2);  % any major damage to the main equipment fails the system for the entire building
+
+%% Account for Extermal Utilities impact on system Operation
+% Electricity
+system_operation_day.building.electrical_main = max(system_operation_day.building.electrical_main,utilities.electrical);
+
+% Potable water
+system_operation_day.building.water_potable_main = max(system_operation_day.building.water_potable_main,utilities.water);
+
+% Assume hvac control runs on electricity and heating system runs on gas
+system_operation_day.building.hvac_control = max(system_operation_day.building.hvac_control,system_operation_day.building.electrical_main);
+system_operation_day.building.hvac_heating = max(system_operation_day.building.hvac_heating,utilities.(functionality_options.heat_utility));
+
+end
+
diff --git a/+recovery/+functionality/fn_building_safety.m b/+recovery/+functionality/fn_building_safety.m
new file mode 100644
index 0000000..8526dad
--- /dev/null
+++ b/+recovery/+functionality/fn_building_safety.m
@@ -0,0 +1,306 @@
+function [ recovery_day, comp_breakdowns ] = fn_building_safety( ...
+    damage, building_model, damage_consequences, utilities, ...
+    functionality_options, impeding_temp_repairs )
+% Check damage that would cause the whole building to be shut down due to
+% issues of safety
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage, loss, and repair time data for each 
+%   component in the building
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+%   tags and repair costs ratios
+% building_model: struct
+%   general attributes of the building model
+% utilities: struct
+%   data structure containing simulated utility downtimes
+% functionality_options: struct
+%   recovery time optional inputs such as various damage thresholds
+% impeding_temp_repairs: struct
+%   contains simulated temporary repairs the impede occuapancy and function
+%   but are calulated in parallel with the temp repair schedule
+%
+% Returns
+% -------
+% recovery_day: struct
+%   simulation of the number of days each fault tree event is affecting building
+%   safety
+% comp_breakdowns: struct
+%   simulation of each components contributions to each of the fault tree events 
+
+%% Initial Setup
+num_reals = length(damage_consequences.red_tag);
+num_units = length(damage.tenant_units);
+num_comps = height(damage.comp_ds_table);
+
+% Initialize parameters
+recovery_day.red_tag = zeros(num_reals, 1);
+recovery_day.shoring = zeros(num_reals, 1);
+recovery_day.hazardous_material = zeros(num_reals, 1);
+recovery_day.fire_suppression = zeros(num_reals, 1);
+
+% check if building has fire supprsion system
+fs_exists = any(damage.fnc_filters.fire_building);
+
+%% Check damage throughout the building that can affect the entire building
+for tu = 1:num_units
+    % Grab tenant and damage info for this tenant unit
+    repair_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day;
+    repair_complete_day_w_tmp = damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp;
+    
+    is_damaged = damage.tenant_units{tu}.qnt_damaged > 0 & damage.tenant_units{tu}.worker_days > 0;
+    
+    %% Red Tags
+    % The day the red tag is resolved is the day when all damage (anywhere in building) that has
+    % the potentail to cause a red tag is fixed (ie max day)
+    if any(damage.fnc_filters.red_tag)
+        recovery_day.red_tag = max(recovery_day.red_tag, ...
+            damage_consequences.red_tag .* max(repair_complete_day(:,damage.fnc_filters.red_tag),[],2)); 
+    end
+    
+    % Componet Breakdowns
+    comp_breakdowns.red_tag(:,:,tu) = recovery_day.red_tag .* damage_consequences.red_tag_impact;
+    
+    %% Local Shoring
+    if any(damage.fnc_filters.requires_shoring) && functionality_options.include_local_stability_impact
+        % Any unresolved damage (temporary or otherwise) that requires shoring,
+        % blocks occupancy to the whole building
+        recovery_day.shoring = max(recovery_day.shoring, ...
+            max(repair_complete_day_w_tmp(:,damage.fnc_filters.requires_shoring),[],2)); 
+    
+        % Componet Breakdowns (the time it takes to shore or fully repair each
+        % component is the time it blocks occupancy for)
+        comp_breakdowns.shoring(:,:,tu) = damage.fnc_filters.requires_shoring .* repair_complete_day_w_tmp .* is_damaged;
+    end
+    
+    %% Day the fire suppression system is operating again (for the whole building)
+    if ~functionality_options.fire_watch && fs_exists
+        % any major damage fails the system for the whole building so take the max
+        recovery_day.fire_suppression = max(recovery_day.fire_suppression,max(repair_complete_day(:,damage.fnc_filters.fire_building),[],2));
+    
+        % Consider utilities (assume no backup water supply)
+        recovery_day.fire_suppression = max(recovery_day.fire_suppression,utilities.water); 
+
+        % Componet Breakdowns
+        comp_breakdowns.fire_suppression(:,:,tu) = damage.fnc_filters.fire_building .* repair_complete_day;
+    end
+
+    %% Hazardous Materials
+    % note: hazardous materials are accounted for in building functional
+    % assessment here, but are not currently quantified in the component
+    % breakdowns
+    if any(damage.fnc_filters.global_hazardous_material)
+        % Any global hazardous material shuts down the entire building
+        recovery_day.hazardous_material = max(recovery_day.hazardous_material, max(repair_complete_day(:,damage.fnc_filters.global_hazardous_material),[],2)); 
+    end
+end
+
+%% Building Egress
+% Calculate when falling hazards or racking of doors affects the building
+% safety due to limited entrance and exit door access
+
+% Simulate a random location of the doors on two sides of the building for
+% each realization. Location is defined at the center of the door as a
+% fraction of the building width on that side
+% This is acting a random p value to determine if the unknown location of
+% the door is within the falling hazard zone
+door_location = rand(num_reals,building_model.num_entry_doors);
+
+% Assign odd doors to side 1 and even doors to side two
+door_numbers = 1:building_model.num_entry_doors;
+door_side = ones(1,building_model.num_entry_doors);
+door_side(rem(door_numbers, 2) == 0) = 2;
+
+% Determine the quantity of falling hazard damage and when it will be
+% resolved
+day_repair_fall_haz = zeros(num_reals,building_model.num_entry_doors);
+fall_haz_comps_day_rep = zeros(num_reals,num_comps,num_units,building_model.num_entry_doors);
+comp_affected_lf = zeros(num_reals,num_comps,num_units);
+scaffold_filt = logical(damage.comp_ds_table.resolved_by_scaffolding');
+for tu = 1:num_units
+    tmp_or_full_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp;
+    
+    % Effect of falling hazards on building safety are resolved either by
+    % full repair, local temp repair, or erecting scaffolding. Whatever
+    % occurs first
+    isdamaged = 1*(damage.tenant_units{tu}.qnt_damaged > 0);
+    isdamaged(isdamaged == 0) = NaN; % mark undamaged cases as NaN to help combine factors below
+    scaffold_day = impeding_temp_repairs.scaffold_day .*  isdamaged(:,scaffold_filt);
+    complete_day_w_scaffold = tmp_or_full_complete_day;
+    complete_day_w_scaffold(:,scaffold_filt) = ...
+        min(tmp_or_full_complete_day(:,scaffold_filt),scaffold_day);
+    repair_complete_day_w_tmp(:,:,tu) = complete_day_w_scaffold;
+end
+
+% Loop through component repair times to determine the day it stops affecting re-occupancy
+num_repair_time_increments = sum(damage.fnc_filters.ext_fall_haz_all)*num_units; % possible unique number of loop increments
+edge_lengths = [building_model.edge_lengths; building_model.edge_lengths];
+for i = 1:num_repair_time_increments 
+    % Calculate the falling hazards per side
+    for tu = 1:num_units
+        for side = 1:4 % assumes there are 4 sides
+            lf_affected_direct_scale_all_comps = damage.comp_ds_table.exterior_falling_length_factor' .* ...
+                damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.(['qnt_damaged_side_' num2str(side)]);
+            
+            lf_affected_sf_all_comps = damage.comp_ds_table.exterior_falling_length_factor' .* ...
+                damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.(['qnt_damaged_side_' num2str(side)]) ...
+                ./ building_model.ht_per_story_ft(tu);
+
+            comp_affected_lf(:,damage.fnc_filters.ext_fall_haz_lf, tu) = lf_affected_direct_scale_all_comps(:,damage.fnc_filters.ext_fall_haz_lf);
+            comp_affected_lf(:,damage.fnc_filters.ext_fall_haz_sf, tu) = lf_affected_sf_all_comps(:,damage.fnc_filters.ext_fall_haz_sf);
+            comp_affected_lf(:,damage.fnc_filters.ext_fall_haz_ea, tu) = lf_affected_direct_scale_all_comps(:,damage.fnc_filters.ext_fall_haz_ea);
+
+            comp_affected_ft_this_story = comp_affected_lf(:,:,tu);
+            % Assumes cladding components do not occupy the same perimeter space
+            % Don't exxceed edge lengths (can happen for things like chimneys)
+            affected_ft_this_story = min(sum(comp_affected_ft_this_story,2), edge_lengths(side,tu));
+
+            affected_ratio.(['side_' num2str(side)])(:,tu) = min((affected_ft_this_story) ./ edge_lengths(side,tu),1);
+        end
+    end
+
+    % Calculate the time increment for this loop
+    delta_day = min(min(repair_complete_day_w_tmp(:,damage.fnc_filters.ext_fall_haz_all,:),[],3),[],2);
+    delta_day(isnan(delta_day)) = 0;
+    if sum(delta_day) == 0
+        break % everything has been fixed
+    end
+    
+    % Go through each door to determine which is affected by falling hazards
+    for d = 1:building_model.num_entry_doors
+        % Combine affected areas of all stories above the first using SRSS
+        % HARDCODED ASSUMPTIONS: DOORS ONLY ON TWO SIDES
+        fall_haz_zone = min(sqrt(sum(affected_ratio.(['side_' num2str(door_side(d))])(:,2:end) .^2,2)),1);
+
+        % Augment the falling hazard zone with the door access zone
+        % add the door access width to the width of falling hazards to account
+        % for the width of the door (ie if any part of the door access zone is
+        % under the falling hazard, its a problem)
+        door_access_zone = functionality_options.door_access_width_ft / building_model.edge_lengths(door_side(d),1); 
+        total_fall_haz_zone = fall_haz_zone + 2*door_access_zone; % this is approximating the probability the door is within the falling hazard zone
+
+        % Determine if current damage affects occupancy
+        % if the randonmly simulated door location is with falling hazard zone
+        affects_door = door_location(:,door_side(d)) < total_fall_haz_zone;
+
+        % Add days in this increment to the tally
+        day_repair_fall_haz(:,d) = day_repair_fall_haz(:,d) + affects_door .* delta_day;
+
+        % Add days to components that are affecting occupancy
+        fall_haz_comps_day_rep(:,:,:,d) = fall_haz_comps_day_rep(:,:,:,d) + (comp_affected_lf > 0) .* damage.fnc_filters.ext_fall_haz_all .* affects_door .* delta_day;
+    end
+    
+    % Change the comps for the next increment
+    repair_complete_day_w_tmp = repair_complete_day_w_tmp - delta_day;
+    repair_complete_day_w_tmp(repair_complete_day_w_tmp <= 0) = NaN;
+end
+
+% Determine when racked doors are resolved
+day_repair_racked = zeros(num_reals, building_model.num_entry_doors);
+side_1_count = 0;
+side_2_count = 0;
+for d = 1:building_model.num_entry_doors
+    if door_side(d) == 1
+        side_1_count = side_1_count + 1;
+        day_repair_racked(:,d) = impeding_temp_repairs.door_racking_repair_day .* (damage_consequences.racked_entry_doors_side_1 >= side_1_count);
+    else 
+        side_2_count = side_2_count + 1;
+        day_repair_racked(:,d) = impeding_temp_repairs.door_racking_repair_day .* (damage_consequences.racked_entry_doors_side_2 >= side_2_count);
+    end
+end
+door_access_day = max(day_repair_racked,day_repair_fall_haz);
+
+% Find the days until door egress is regained from resolution of both
+% falling hazards or door racking
+cum_days = 0;
+entry_door_access_day = zeros(num_reals,1);
+door_access_day_nan = door_access_day;
+door_access_day_nan(door_access_day_nan == 0) = NaN;
+num_repair_time_increments = building_model.num_entry_doors; % possible unique number of loop increments
+for i = 1:num_repair_time_increments
+    num_accessible_doors = sum(door_access_day <= cum_days,2);
+    entry_door_accessible = num_accessible_doors >= max(functionality_options.min_egress_paths,functionality_options.egress_threshold*building_model.num_entry_doors); 
+            
+    % Bean counting for this iteration
+    delta_day = min(door_access_day_nan,[],2);
+    delta_day(isnan(delta_day)) = 0;
+    door_access_day_nan = door_access_day_nan - delta_day;
+    cum_days = cum_days + delta_day;
+    
+    % Save recovery time increments
+    entry_door_access_day = entry_door_access_day + delta_day .* ~entry_door_accessible;
+end
+
+% Save recovery day values
+recovery_day.entry_door_access = entry_door_access_day;
+
+% Determine when Exterior Falling Hazards or doors actually contribute to re-occupancy
+recovery_day.falling_hazard = min(recovery_day.entry_door_access,max(day_repair_fall_haz,[],2));
+recovery_day.entry_door_racking = min(recovery_day.entry_door_access,max(day_repair_racked,[],2));
+
+% Component Breakdown
+comp_breakdowns.falling_hazard = min(recovery_day.entry_door_access, max(fall_haz_comps_day_rep,[],4));
+
+%% Fire Safety
+if ~functionality_options.fire_watch && fs_exists
+    comp_breakdowns_local_fire = zeros(num_reals,num_comps,num_units);
+    fire_safety_day = zeros(num_reals,num_units); % Day the local fire sprinkler system becomes operational
+    filt_fs_drop = damage.fnc_filters.fire_drops;
+    filt_fs_branch = damage.fnc_filters.fire_unit;
+    for tu = 1:num_units
+        repair_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day;
+        repair_complete_day(repair_complete_day == 0) = NaN; % Make sure zero repair days are NaN
+        damaged_comps = damage.tenant_units{tu}.qnt_damaged;
+        num_drops = max(damage.tenant_units{tu}.num_comps .* filt_fs_drop,[],2); % Assumes drops are all in one performance group
+        num_branches = max(damage.tenant_units{tu}.num_comps .* filt_fs_branch,[],2); % Assumes branches are all in one performance group
+        if sum([num_drops,num_branches]) > 0 % If there are any of these components on in this tenant unit
+            % Loop through component repair times to determine the day it stops affecting re-occupanc
+            num_repair_time_increments = sum(filt_fs_drop | filt_fs_branch); % possible unique number of loop increments
+            for i = 1:num_repair_time_increments 
+                % Calculate the ratio of damaged drops and branches on this story
+                ratio_damaged_drop = sum(damaged_comps .* filt_fs_drop,2) ./ num_drops; % assumes comps are not simeltaneous
+                ratio_damaged_branch = sum(damaged_comps .* filt_fs_branch,2) ./ num_drops; % assumes comps are not simeltaneous
+
+                % Determine if fire drops and branches are adequately operating
+                fire_drop_operational = functionality_options.local_fire_damamge_threshold >= ratio_damaged_drop;
+                fire_branch_operational = functionality_options.local_fire_damamge_threshold >= ratio_damaged_branch;
+                local_fire_operational = fire_drop_operational & fire_drop_operational;
+                
+                % Add days in this increment to the tally
+                delta_day = min(repair_complete_day(:,filt_fs_drop),[],2);
+                delta_day(isnan(delta_day)) = 0;
+                fire_safety_day(:,tu) = fire_safety_day(:,tu) + ~local_fire_operational .* delta_day;
+
+                % Add days to components that are affecting occupancy
+                contributing_drops = ((damaged_comps .* filt_fs_drop) > 0)  .* ~fire_drop_operational; % count all components that contributed to non operational fire drops
+                contributing_branches = ((damaged_comps .* filt_fs_drop) > 0)  .* ~fire_branch_operational; % count all components that contributed to non operational fire drops
+                contributing_comps = max(contributing_drops,contributing_branches);
+                comp_breakdowns_local_fire(:,:,tu) = comp_breakdowns_local_fire(:,:,tu) + contributing_comps .* delta_day;
+
+                % Change the comps for the next increment
+                repair_complete_day = repair_complete_day - delta_day;
+                repair_complete_day(repair_complete_day <= 0) = NaN;
+                fixed_comps_filt = isnan(repair_complete_day);
+                damaged_comps(fixed_comps_filt) = 0;
+            end
+        end
+    end
+
+    % Inoperable fire drips on any story shuts down entire building
+    fire_safety_day_building = max(fire_safety_day,[],2);
+    
+    % Combine parts of fire suppression system
+    recovery_day.fire_suppression = max(recovery_day.fire_suppression,fire_safety_day_building);
+    comp_breakdowns.fire_suppression = max(comp_breakdowns.fire_suppression,comp_breakdowns_local_fire);
+end
+
+%% Delay Red Tag recovery by the time it takes to clear the tag
+sim_red_tag_clear_time = ceil(lognrnd(log(functionality_options.red_tag_clear_time),...
+                                 functionality_options.red_tag_clear_beta,num_reals,1));
+recovery_day.red_tag = recovery_day.red_tag + sim_red_tag_clear_time .* damage_consequences.red_tag;
+comp_breakdowns.red_tag = comp_breakdowns.red_tag + sim_red_tag_clear_time .* (comp_breakdowns.red_tag > 0);
+
+end
+
diff --git a/+recovery/+functionality/fn_calc_subsystem_recovery.m b/+recovery/+functionality/fn_calc_subsystem_recovery.m
new file mode 100644
index 0000000..345ea12
--- /dev/null
+++ b/+recovery/+functionality/fn_calc_subsystem_recovery.m
@@ -0,0 +1,81 @@
+function [subsys_repair_day] = fn_calc_subsystem_recovery(subsys_filt, damage, ...
+    repair_complete_day, total_num_comps, damaged_comps)
+% Check whether the components of a particular subsystem have redundancy
+% or not and calculate the day the subsystem recovers opertaions
+%
+% Parameters
+% ----------
+% subsys_filt: logical array [1 x num_comps_ds]
+%   indentifies which rows in the damage.comp_ds_info are the components of interest
+% damage: struct
+%   contains per damage state damage, loss, and repair time data for each 
+%   component in the building
+% repair_complete_day: array [num_reals x num_comps_ds]
+%   day reapairs are complete for each components damage state for a given
+%   tenant unit
+% total_num_comps: array [1 x num_comps_ds]
+%   total number of each component damage state in this tenant unit
+% damaged_comps: array [num_reals x num_comps_ds]
+%   total number of damaged components in each damage state in this tenant unit
+%
+% Returns
+% -------
+% subsys_repair_day: array [num reals x 1]
+%   The day this subsystem stops affecting functoin in a given tenant unit
+
+%% Initial Setup
+[num_reals, ~] = size(repair_complete_day);
+is_redundant = max(damage.comp_ds_table.parallel_operation(subsys_filt)); % should all be the same within a subsystem
+any_comps = any(subsys_filt);
+
+%% Check if the componet has redundancy
+if any_comps
+    if is_redundant
+        %% go through each component in this subsystem and find number of damaged units
+        comps = unique(damage.comp_ds_table.comp_idx(subsys_filt));
+        num_tot_comps = zeros(1,length(comps));
+        num_damaged_comps = zeros(num_reals,length(comps));
+        for c = 1:length(comps)
+            this_comp = subsys_filt & (damage.comp_ds_table.comp_idx == comps(c))';
+            num_tot_comps(c) = max(total_num_comps .* this_comp); % number of units across all ds should be the same
+            num_damaged_comps(:,c) = max(damaged_comps .* this_comp,[],2);
+        end
+
+        %% sum together multiple components in this subsystem
+        subsystem_num_comps = sum(num_tot_comps);
+        subsystem_num_damaged_comps = sum(num_damaged_comps,2);
+        ratio_damaged = subsystem_num_damaged_comps ./ subsystem_num_comps;
+
+        %% Check failed component against the ratio of components required for system operation
+        % system fails when there is an insufficient number of operating components
+        n1_redundancy = max(damage.comp_ds_table.n1_redundancy(subsys_filt)); % should all be the same within a subsystem
+        if subsystem_num_comps == 0 % No components at this level
+            subsystem_failure = zeros(num_reals,1);
+        elseif subsystem_num_comps == 1 % Not actually redundant
+            subsystem_failure = subsystem_num_damaged_comps == 1;
+        elseif n1_redundancy
+            % These components are designed to have N+1 redundncy rates,
+            % meaning they are designed to lose one component and still operate at
+            % normal level
+            subsystem_failure = subsystem_num_damaged_comps > 1;
+        else
+            % Use a predefined ratio
+            redundancy_threshold = max(damage.comp_ds_table.redundancy_threshold(subsys_filt)); % should all be the same within a subsystem
+            subsystem_failure = ratio_damaged > redundancy_threshold;
+        end
+
+        %% Calculate recovery day and combine with other subsystems for this tenant unit
+        % assumes all quantities in each subsystem are repaired at
+        % once, which is true for our current repair schedule (ie
+        % system level at each story)
+        subsys_repair_day = max(subsystem_failure .* subsys_filt .* repair_complete_day,[],2); 
+    else % This subsystem has no redundancy
+        % any major damage to the components fails the subsystem at this
+        % tenant unit
+        subsys_repair_day = max(repair_complete_day .* subsys_filt,[],2);
+    end
+else % No components were populated in this subsystem
+    subsys_repair_day = zeros(num_reals,1);
+end
+
+end % Function
diff --git a/+recovery/+functionality/fn_calculate_functionality.m b/+recovery/+functionality/fn_calculate_functionality.m
new file mode 100644
index 0000000..8485f7b
--- /dev/null
+++ b/+recovery/+functionality/fn_calculate_functionality.m
@@ -0,0 +1,78 @@
+function [functional, recovery_day, comp_breakdowns] = fn_calculate_functionality(damage, damage_consequences, utilities, ...
+    building_model, subsystems, reoccupancy, functionality_options, tenant_units, impeding_temp_repairs )
+% Calcualte the loss and recovery of building functionality based on global building
+% damage, local component damage, and extenernal factors
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage, loss, and repair time data for each 
+%   component in the building
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+%   tags and repair costs ratios
+% utilities: struct
+%   data structure containing simulated utility downtimes
+% building_model: struct
+%   general attributes of the building model
+% subsystems: table
+%   data table containing information about each subsystem's attributes
+% reoccupancy: struct
+%   contains data on the recovery of tenant- and building-level function, 
+%   recovery trajectorires, and contributions from systems and components 
+% functionality_options: struct
+%   recovery time optional inputs such as various damage thresholds
+% tenant_units: table
+%   attributes of each tenant unit within the building
+% impeding_temp_repairs: struct
+%   contains simulated temporary repairs the impede occuapancy and function
+%   but are calulated in parallel with the temp repair schedule
+%
+% Returns
+% -------
+% functionality: struct
+%   contains data on the recovery of tenant- and building-level function, 
+%   recovery trajectorires, and contributions from systems and components 
+
+%% Initial Set Up
+% import packages
+import recovery.functionality.fn_building_level_system_operation
+import recovery.functionality.fn_tenant_function
+import recovery.functionality.fn_extract_recovery_metrics
+import recovery.functionality.fn_combine_comp_breakdown
+
+%% Define the day each system becomes functionl - Building level
+[ system_operation_day ] = fn_building_level_system_operation( damage, damage_consequences, ...
+    building_model, utilities, functionality_options );
+
+%% Define the day each system becomes functionl - Tenant level
+[ recovery_day.tenant_function, comp_breakdowns.tenant_function ] = fn_tenant_function( ...
+    damage, building_model, system_operation_day, subsystems, ...
+    tenant_units, impeding_temp_repairs, functionality_options );
+
+%% Combine Checks to determine per unit functionality
+% Each tenant unit is functional only if it is occupiable
+day_tentant_unit_functional = reoccupancy.tenant_unit.recovery_day;
+
+% Go through each of the tenant function branches and combines checks
+fault_tree_events = fieldnames(recovery_day.tenant_function);
+for i = 1:length(fault_tree_events)
+    day_tentant_unit_functional = max(day_tentant_unit_functional,recovery_day.tenant_function.(fault_tree_events{i}));
+end
+
+%% Reformat outputs into functionality data strucutre
+[ functional ] = fn_extract_recovery_metrics( day_tentant_unit_functional, ...
+    recovery_day, comp_breakdowns, damage.comp_ds_table.comp_id', ...
+    damage_consequences.simulated_replacement_time );
+
+% get the combined component breakdown between reoccupancy and function
+functional.breakdowns.component_combined = fn_combine_comp_breakdown( ...
+    damage.comp_ds_table, ...
+    functional.breakdowns.perform_targ_days, ... % assumes names are consistent in both objects
+    functional.breakdowns.comp_names, ...        % assumes names are consistent in both objects
+    reoccupancy.breakdowns.component_breakdowns_all_reals, ...
+    functional.breakdowns.component_breakdowns_all_reals ...
+);
+
+end
+
diff --git a/+recovery/+functionality/fn_calculate_reoccupancy.m b/+recovery/+functionality/fn_calculate_reoccupancy.m
new file mode 100644
index 0000000..a2978f9
--- /dev/null
+++ b/+recovery/+functionality/fn_calculate_reoccupancy.m
@@ -0,0 +1,99 @@
+function [reoccupancy, recovery_day, comp_breakdowns] = fn_calculate_reoccupancy(...
+    damage, damage_consequences, utilities, building_model,...
+    functionality_options, tenant_units, impeding_temp_repairs )
+% Calcualte the loss and recovery of building re-occupancy 
+% based on global building damage, local component damage, and extenernal factors
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage, loss, and repair time data for each 
+%   component in the building
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+%   tags and repair costs ratios
+% utilities: struct
+%   data structure containing simulated utility downtimes
+% building_model: struct
+%   general attributes of the building model
+% subsystems: table
+%   attributes of building subsystems; data provided in static tables
+%   directory
+% functionality_options: struct
+%   recovery time optional inputs such as various damage thresholds
+% tenant_units: table
+%   attributes of each tenant unit within the building
+% impeding_temp_repairs: struct
+%   contains simulated temporary repairs the impede occuapancy and function
+%   but are calulated in parallel with the temp repair schedule
+%
+% Returns
+% -------
+% reoccupancy: struct
+%   contains data on the recovery of tenant- and building-level reoccupancy, 
+%   recovery trajectorires, and contributions from systems and components 
+
+%% Initial Set Up
+% Import packages
+import recovery.functionality.fn_building_safety
+import recovery.functionality.fn_story_access
+import recovery.functionality.fn_tenant_safety
+import recovery.functionality.fn_extract_recovery_metrics
+    
+%% Stage 1: Quantify the effect that component damage has on the building safety
+[ recovery_day.building_safety, comp_breakdowns.building_safety ] = ...
+    fn_building_safety( damage, building_model, damage_consequences, ...,
+    utilities, functionality_options, impeding_temp_repairs );
+
+%% Stage 2: Quantify the accessibility of each story in the building
+[ recovery_day.story_access, comp_breakdowns.story_access ] = ...
+    fn_story_access( damage, building_model, damage_consequences, ...
+    functionality_options, impeding_temp_repairs );
+
+%% Stage 3: Quantify the effect that component damage has on the safety of each tenant unit
+[ recovery_day.tenant_safety, comp_breakdowns.tenant_safety ] = ...
+    fn_tenant_safety( damage, building_model, functionality_options, tenant_units );
+
+%% Combine Check to determine the day the each tenant unit is reoccupiable
+% Check the day the building is Safe
+building_safety_fields = fieldnames(recovery_day.building_safety);
+% the old hard coded ones excluded 'falling_hazard' and 'entry_door_racking'
+% building_safety_fields = {'red_tag', 'shoring', 'hazardous_material', 'entry_door_access', 'fire_suppression'};  
+day_building_safe = 0;
+
+for i_field = 1:length(building_safety_fields)
+    building_safety_field = building_safety_fields{i_field};
+    day_building_safe = max(day_building_safe, recovery_day.building_safety.(building_safety_field));
+end
+
+% Check the day each story is accessible
+story_access_fields = fieldnames(recovery_day.story_access);
+% story_access_fields = {'stairs', 'stair_doors', 'flooding', 'horizontal_egress'};  % old hard coded ones
+day_story_accessible = 0;
+
+for i_field = 1:length(story_access_fields)
+    story_access_field = story_access_fields{i_field};
+    day_story_accessible = max(day_story_accessible, recovery_day.story_access.(story_access_field));
+end
+
+% Check the day each tenant unit is safe
+tu_safety_fields = fieldnames(recovery_day.tenant_safety);
+% tu_safety_fields = {'interior', 'exterior', 'hazardous_material'};  % old hard coded ones
+day_tenant_unit_safe = 0;
+
+for i_field = 1:length(tu_safety_fields)
+    tu_safety_field = tu_safety_fields{i_field};
+    day_tenant_unit_safe = max(day_tenant_unit_safe, recovery_day.tenant_safety.(tu_safety_field));
+end
+
+% Combine checks to determine when each tenant unit is re-occupiable
+day_tentant_unit_reoccupiable = max(max(day_building_safe, day_story_accessible), day_tenant_unit_safe); 
+
+%% Reformat outputs into occupancy data strucutre
+[ reoccupancy ] = fn_extract_recovery_metrics( day_tentant_unit_reoccupiable, ...
+     recovery_day, comp_breakdowns, damage.comp_ds_table.comp_id', ...
+     damage_consequences.simulated_replacement_time );
+
+
+end
+
diff --git a/+recovery/+functionality/fn_check_habitability.m b/+recovery/+functionality/fn_check_habitability.m
new file mode 100644
index 0000000..15c7236
--- /dev/null
+++ b/+recovery/+functionality/fn_check_habitability.m
@@ -0,0 +1,59 @@
+function [ reoccupancy ] = fn_check_habitability( damage, damage_consequences, reoc_meta, func_meta, habitability_requirements )
+% Overwrite reocuppancy with additional checks from the functionality check
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage, loss, and repair time data for each 
+%   component in the building
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+%   tags and repair costs ratios
+% reoc_meta: struct
+%   meta data from reoccupancy assessment
+% func_meta: struct
+%   meta data from functionality assessment
+% habitability_requirements: struct
+%   basic requirements for habitability beyond basic reoccupancy.
+%
+% Returns
+% -------
+% reoccupancy: struct
+%   contains data on the recovery of tenant- and building-level reoccupancy, 
+%   recovery trajectorires, and contributions from systems and components 
+
+
+import recovery.functionality.fn_extract_recovery_metrics
+
+% Functionality checks to adopt onto reoccupancy requirements for
+% habitability:
+    % heating, cooling, vent, exhaust, potable water, sanitary, electrical
+recovery_day = reoc_meta.recovery_day;
+comp_breakdowns = reoc_meta.comp_breakdowns;
+habitability_list = fieldnames(habitability_requirements);
+% habitability_list = {'electrical', 'water_potable', 'water_sanitary', 'hvac_ventilation', 'hvac_heating', 'hvac_cooling', 'hvac_exhaust'};
+for i = 1:length(habitability_list)
+    if habitability_requirements.(habitability_list{i}) % If this system is required for habitability
+        recovery_day.habitability.(habitability_list{i}) = func_meta.recovery_day.tenant_function.(habitability_list{i});
+        comp_breakdowns.habitability.(habitability_list{i}) = func_meta.comp_breakdowns.tenant_function.(habitability_list{i});
+    end
+end
+
+% Go through each of the tenant function branches and combines checks
+day_tentant_unit_reoccupiable = 0;
+fault_tree_events_LV1 = fieldnames(recovery_day);
+for i = 1:length(fault_tree_events_LV1)
+    fault_tree_events_LV2 = fieldnames(recovery_day.(fault_tree_events_LV1{i}));
+    for j = 1:length(fault_tree_events_LV2)
+        day_tentant_unit_reoccupiable = max(day_tentant_unit_reoccupiable,...
+            recovery_day.(fault_tree_events_LV1{i}).(fault_tree_events_LV2{j}));
+    end
+end
+
+% Reformat outputs into reoccupancy data strucutre
+[ reoccupancy ] = fn_extract_recovery_metrics( day_tentant_unit_reoccupiable, ...
+    recovery_day, comp_breakdowns, damage.comp_ds_table.comp_id', ...
+    damage_consequences.simulated_replacement_time );
+
+end
+
diff --git a/+recovery/+functionality/fn_combine_comp_breakdown.m b/+recovery/+functionality/fn_combine_comp_breakdown.m
new file mode 100644
index 0000000..bc5353b
--- /dev/null
+++ b/+recovery/+functionality/fn_combine_comp_breakdown.m
@@ -0,0 +1,33 @@
+function combined = fn_combine_comp_breakdown(comp_ds_table, perform_targ_days, comp_names, reoccupancy, functional)
+% get the combined reoccupancy/functionality effect of components
+%
+% Parameters
+% ----------
+% comp_ds_table: table
+%   contains basic component attributes
+% perform_targ_days: array
+%   recovery time horizons to consider
+% comp_names: cell array
+%   string ids of components to attribte recovery time to
+% reoccupancy: array [num reals x num comp_ds]
+%   realizations of reoccupancy time broken down for each damage state of
+%   each component
+% functional: array [num reals x num comp_ds]
+%   realizations of functional recovery time broken down for each damage
+%   state of each component
+%
+% Returns
+% -------
+% combined: array
+%   Probability of recovering within time horizon for each component
+%   considering both consequenses from reoccupancy and functional recovery
+
+%% Method
+combined = zeros(length(comp_names),length(perform_targ_days));
+max_reocc_func = max(reoccupancy, functional);
+for c = 1:length(comp_names)
+    comp_filt = strcmp(comp_ds_table.comp_id, comp_names{c}); % find damage states associated with this component    
+    combined(c,:) = mean(max(max_reocc_func(:,comp_filt'), [], 2) > perform_targ_days,1);
+end
+
+end
diff --git a/+recovery/+functionality/fn_extract_recovery_metrics.m b/+recovery/+functionality/fn_extract_recovery_metrics.m
new file mode 100644
index 0000000..85b9316
--- /dev/null
+++ b/+recovery/+functionality/fn_extract_recovery_metrics.m
@@ -0,0 +1,337 @@
+function [ recovery ] = fn_extract_recovery_metrics( tentant_unit_recovery_day, ...
+   recovery_day, comp_breakdowns, comp_id, simulated_replacement_time )
+% Reformant tenant level recovery outcomes into outcomes at the building level, 
+% system level, and compoennt level
+%
+% Parameters
+% ----------
+% tentant_unit_recovery_day: array [num_reals x num_tenant_units]
+%   simulated recovery day of each tenant unit
+% recovery_day: struct
+%   simulation of the number of days each fault tree event is affecting
+%   recovery
+% comp_breakdowns: struct
+%   simulation of each components contributions to each of the fault tree events 
+% comp_id: cell array [1 x num comp damage states]
+%   list of each fragility id associated with the per component damage
+%   state structure of the damage object. With of array is the same as the
+%   arrays in the comp_breakdowns structure
+% simulated_replacement_time: array [num_reals x 1]
+%   simulated time when the building needs to be replaced, and how long it
+%   will take (in days). NaN represents no replacement needed (ie
+%   building will be repaired)
+%
+% Returns
+% -------
+% recovery.tenant_unit.recovery_day: array [num_reals x num_tenant_units]
+%   simulated recovery day of each tenant unit
+% recovery.building_level.recovery_day: array [num_reals x 1]
+%   simulated recovery day of the building (all tenant units recovered)
+% recovery.building_level.initial_percent_affected: array [num_reals x 1]
+%   simulated fraction of the building with initial loss of
+%   reoccupancy/function
+% recovery.recovery_trajectory.recovery_day: array [num_reals x num recovery steps]
+%   simulated recovery trajectory y-axis
+% recovery.recovery_trajectory.percent_recovered: array [1 x num recovery steps]
+%   recovery trajectory x-axis
+% recovery.breakdowns.system_breakdowns: array [num fault tree events x target days]
+%   fraction of realizations affected by various fault tree events beyond
+%   specific target recovery days
+% recovery.breakdowns.component_breakdowns: array [num components x target days]
+%   fraction of realizations affected by various components beyond
+%   specific target recovery days
+% recovery.breakdowns.perform_targ_days: array [0 x target days]
+%   specific target recovery days
+% recovery.breakdowns.system_names: array [num fault tree events x 1]
+%   name of each fault tree event
+% recovery.breakdowns.comp_names: array [num components x 1]
+%   fragility IDs of each component
+
+
+%% Initial Setup
+num_units = size(tentant_unit_recovery_day,2);
+
+% Define performance targets
+perform_targ_days = [0, 3, 7, 14, 30, 60, 90, 120, 182, 270, 365]; % Number of days for each performance target stripe
+
+% Determine replacement cases
+replace_cases = ~isnan(simulated_replacement_time);
+
+%% Post process tenant-level recovery times
+% Overwrite NaNs in tenant_unit_day_functional
+% Only NaN where never had functional loss, therefore set to zero
+tentant_unit_recovery_day(isnan(tentant_unit_recovery_day)) = 0;
+
+% Overwrite building replacment cases to replacement time
+tentant_unit_recovery_day(replace_cases,:) = simulated_replacement_time(replace_cases)*ones(1,num_units);
+
+%% Save building-level outputs to occupancy structure
+% Tenant Unit level outputs
+recovery.tenant_unit.recovery_day = tentant_unit_recovery_day;
+
+% Building level outputs
+recovery.building_level.recovery_day = max(tentant_unit_recovery_day,[],2);
+recovery.building_level.initial_percent_affected = mean(tentant_unit_recovery_day > 0,2); % percent of building affected, not the percent of realizations
+recovery.building_level.perform_targ_days = perform_targ_days;
+recovery.building_level.prob_of_target = mean(recovery.building_level.recovery_day > perform_targ_days);
+
+% Save specific breakdowns for red tags
+if isfield(recovery_day, 'building_safety')
+    red_tag_day = recovery_day.building_safety.red_tag;
+    red_tag_day(replace_cases,:) = simulated_replacement_time(replace_cases);
+    recovery.building_level.recovery_day_red_tag = red_tag_day;
+end
+
+%% Recovery Trajectory -- calcualte from the tenant breakdowns
+recovery.recovery_trajectory.recovery_day = sort([tentant_unit_recovery_day, tentant_unit_recovery_day],2);
+recovery.recovery_trajectory.percent_recovered = sort([(0:(num_units-1)), (1:num_units)])/num_units;
+
+% partial recovery
+pct_recovered_targets = [0.1, 0.5, 0.75, 0.8, 1];
+
+% order the tennant recovery days so it's easier to see at what time the required numnber of units
+% are required
+ordered_tennant_repair_days = sort(tentant_unit_recovery_day, 2);
+        
+for i_pct = 1:length(pct_recovered_targets)
+    pct_recovered = pct_recovered_targets(i_pct);
+    recovery.partial{i_pct}.pct_recovered = pct_recovered;
+    recovery.partial{i_pct}.perform_targ_days = perform_targ_days;
+    for i_targ_day = 1:length(perform_targ_days)
+        targ_day = perform_targ_days(i_targ_day);
+        % get the percentage of tenant units recovered at the given day
+        pct_recovered_per_real = mean(sum(tentant_unit_recovery_day <= targ_day, 2) / num_units, 2);
+        recovery.partial{i_pct}.prob_of_target(i_targ_day) = mean(pct_recovered_per_real < pct_recovered);
+        
+        % how many units need to be repaired to meet the percent required 
+        reqd_units = ceil(num_units * pct_recovered);
+        
+        recovery.partial{i_pct}.reqd_units = reqd_units;
+        recovery.partial{i_pct}.mean = mean(ordered_tennant_repair_days(:, reqd_units));
+        recovery.partial{i_pct}.median = median(ordered_tennant_repair_days(:, reqd_units));
+        recovery.partial{i_pct}.fractile_75 = prctile(ordered_tennant_repair_days(:, reqd_units), 75);
+        recovery.partial{i_pct}.fractile_90 = prctile(ordered_tennant_repair_days(:, reqd_units), 90);
+        
+    end
+end
+
+% histogram information - this is really complicated set up of the bins and labels, but it works
+[ hist_bins, hist_labels ] = get_hist_bins_and_labels(24);
+
+n_bins = size(hist_bins, 1);
+hist_freqs = zeros(n_bins, 1);
+all_days = recovery.building_level.recovery_day;
+for i_bin = 1:n_bins
+    hist_freqs(i_bin) = mean(all_days > hist_bins(i_bin, 1) & all_days <= hist_bins(i_bin, 2));
+end
+
+recovery.building_level.hist_data.labels = hist_labels;
+recovery.building_level.hist_data.bins = hist_bins;
+recovery.building_level.hist_data.freqs = hist_freqs;
+
+%% Format and Save Component-level breakdowns
+% Find the day each ds of each component stops affecting recovery for any story
+
+% Combine among all fault tree events
+component_breakdowns_per_story = 0;
+fault_tree_events_LV1 = fieldnames(comp_breakdowns);
+for i = 1:length(fault_tree_events_LV1)
+    fault_tree_events_LV2 = fieldnames(comp_breakdowns.(fault_tree_events_LV1{i}));
+    for j = 1:length(fault_tree_events_LV2)
+        component_breakdowns_per_story = max(component_breakdowns_per_story,...
+            comp_breakdowns.(fault_tree_events_LV1{i}).(fault_tree_events_LV2{j}));
+    end
+end
+
+% Combine among all stories
+% aka time each component's DS affects recovery anywhere in the building
+component_breakdowns = max(component_breakdowns_per_story,[],3);
+
+% Ignore repalcement cases
+component_breakdowns(replace_cases,:) = []; 
+
+%% Format and Save System-level breakdowns
+% Find the day each system stops affecting recovery for any story
+
+% Combine among all fault tree events
+system_breakdowns = [];
+fault_tree_events_LV1 = fieldnames(recovery_day);
+for i = 1:length(fault_tree_events_LV1)
+    fault_tree_events_LV2 = fieldnames(recovery_day.(fault_tree_events_LV1{i}));
+    for j = 1:length(fault_tree_events_LV2)
+        % Combine among all stories or tenant units to represent the events
+        % effect anywhere in the building 
+        building_recovery_day = max(recovery_day.(fault_tree_events_LV1{i}).(fault_tree_events_LV2{j}),[],2);
+        
+        % Ignore repalcement cases
+        building_recovery_day(replace_cases,:) = []; 
+        
+        % Save per "system", which typically represents the fault tree level 2
+        if isfield(system_breakdowns,fault_tree_events_LV2{j})
+            % If this "system" has already been defined in another fault
+            % tree branch, combine togeter by taking the max (i.e., max
+            % days this system affects recovery anywhere in the building)
+            system_breakdowns.(fault_tree_events_LV2{j}) = ...
+                max(system_breakdowns.(fault_tree_events_LV2{j}),building_recovery_day);
+        else
+            system_breakdowns.(fault_tree_events_LV2{j}) = building_recovery_day;
+        end
+    end
+end
+
+%% Format breakdowns as performance targets
+system_names = fieldnames(system_breakdowns);
+
+% pre-allocating variables
+comps = unique(comp_id);
+recovery.breakdowns.system_breakdowns = zeros(length(system_names),length(perform_targ_days));
+recovery.breakdowns.component_breakdowns = zeros(length(comps),length(perform_targ_days));
+
+% Calculate fraction of realization each system affects recovery for each
+% performance target time
+for s = 1:length(system_names)
+    recovery.breakdowns.system_breakdowns(s,:) = mean(system_breakdowns.(system_names{s}) > perform_targ_days);
+end
+
+% get the "Level 2" (and "Level 1") breakdowns (aggregating them up into more broad categories for 
+% ATC-138 report by committee)
+
+system_grouping_L2.structural = {
+    'red_tag';
+    'shoring';
+};
+
+system_grouping_L2.mep = {
+    'hazardous_material';
+    'fire_suppression';
+    'flooding';  % reocc and func
+    'electrical';
+    'water_potable';
+    'water_sanitary';
+    'hvac_ventilation';
+    'hvac_heating';
+    'hvac_cooling';
+    'hvac_exhaust';
+    'roof';
+};
+
+system_grouping_L2.architectural = { ...
+    'elevators';  % reocc and func
+    'entry_door_access';
+    'falling_hazard';
+    'entry_door_racking';
+    'stairs';
+    'stair_doors';
+    'horizontal_egress';
+    'exterior';  % reocc and func
+    'interior';  % reocc and func
+};
+
+[ recovery.breakdowns.system_breakdowns_L2, system_names_L2 ] = get_grouped_system_metrics(system_grouping_L2, system_breakdowns, perform_targ_days);
+
+system_grouping_L1.structural = system_grouping_L2.structural;
+system_grouping_L1.non_struct = [system_grouping_L2.mep; system_grouping_L2.architectural];
+
+[ recovery.breakdowns.system_breakdowns_L1, system_names_L1 ] = get_grouped_system_metrics(system_grouping_L1, system_breakdowns, perform_targ_days);
+
+% Calculate fraction of realization each component affects recovery for each
+% performance target time
+for c = 1:length(comps)
+    comp_filt = strcmp(comp_id,comps{c}); % find damage states associated with this component
+    recovery.breakdowns.component_breakdowns(c,:) = mean(max(component_breakdowns(:,comp_filt'),[],2) > perform_targ_days);
+end
+
+% store this so we can properly overalap the reoccupancy and functionality
+recovery.breakdowns.component_breakdowns_all_reals = component_breakdowns;
+
+% Save other variables
+recovery.breakdowns.perform_targ_days = perform_targ_days;
+recovery.breakdowns.system_names = system_names;
+recovery.breakdowns.system_names_L1 = system_names_L1;
+recovery.breakdowns.system_names_L2 = system_names_L2;
+recovery.breakdowns.comp_names = comps';
+
+end
+
+
+function [hist_bins, bin_labels] = get_hist_bins_and_labels(max_months)
+% get the bins with an isolated bin for 0 days up to the max months specified (last interval is
+% (max_months, 1e6]
+
+months = round((1:max_months) * 365 / 12, 2);
+days = [3, 7, 14];
+inclusive_uppers = [days, months];
+n_bins_tot = length(inclusive_uppers) + 2;  % include zero and greater than 2 years
+
+hist_bins = zeros(n_bins_tot, 2);
+hist_bins(1, 1) = -1;  % first bin catches zeros only
+bin_labels = cell(n_bins_tot, 1);
+bin_labels{1} = '0 Days';
+
+for i_day = 1:length(days)
+    if i_day == 1
+        lower_day = 0;
+    else
+        lower_day = inclusive_uppers(i_day - 1);
+    end
+    upper_day = inclusive_uppers(i_day);
+    bin_labels{i_day + 1} = sprintf('(%.0fd, %.0fd]', lower_day, upper_day);
+end
+
+for i_month = 1:length(months)
+    if i_month == 1
+        upper_month = round(months(i_month) / (365 / 12));
+        bin_labels{i_month + length(days) + 1} = sprintf('(%.0fd, %.0fm]', days(end), upper_month);
+    else
+        lower_month = round(months(i_month - 1) / (365 / 12));
+        upper_month = round(months(i_month) / (365 / 12));
+        bin_labels{i_month + length(days) + 1} = sprintf('(%.0fm, %.0fm]', lower_month, upper_month);
+    end
+end
+
+bin_labels{end} = sprintf('>%.0fm', max_months);
+
+% make the bins and labels
+for i_upper = 1:length(inclusive_uppers)
+    upper_bin = inclusive_uppers(i_upper);
+    hist_bins(i_upper + 1, 2) = upper_bin;
+    hist_bins(i_upper + 2, 1) = upper_bin;
+end
+
+hist_bins(n_bins_tot, 2) = 1e6;
+
+end
+
+
+function [system_breakdowns_per_day, system_names] = get_grouped_system_metrics(system_grouping, system_breakdowns, perform_targ_days)
+% aggregate to different levels
+
+system_names = fieldnames(system_grouping);
+grouped_system_breakdowns = struct();
+for i_group = 1:length(system_names)
+    group_name = system_names{i_group};
+    group_systems = system_grouping.(group_name);
+    for i_sub_field = 1:length(group_systems)
+        system = group_systems{i_sub_field};
+        
+        if not(isfield(grouped_system_breakdowns, group_name))
+            % initialize
+            grouped_system_breakdowns.(group_name) = 0;
+        end
+        
+        if isfield(system_breakdowns, system)
+            % take the enveolpe of all the systems in this group
+            grouped_system_breakdowns.(group_name) = max(grouped_system_breakdowns.(group_name), system_breakdowns.(system));
+        end
+        
+    end
+    
+end
+
+system_breakdowns_per_day = zeros(length(system_names), length(perform_targ_days));
+for s = 1:length(system_names)
+    system_breakdowns_per_day(s,:) = mean(grouped_system_breakdowns.(system_names{s}) > perform_targ_days);
+end
+
+end
+
diff --git a/+recovery/+functionality/fn_story_access.m b/+recovery/+functionality/fn_story_access.m
new file mode 100644
index 0000000..bce390b
--- /dev/null
+++ b/+recovery/+functionality/fn_story_access.m
@@ -0,0 +1,152 @@
+function [ recovery_day, comp_breakdowns ] = fn_story_access(damage, ...
+    building_model, damage_consequences, functionality_options, impeding_temp_repairs )
+% Check each story for damage that would cause that story to be shut down due to
+% issues of access
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage, loss, and repair time data for each 
+%   component in the building
+% building_model: struct
+%   general attributes of the building model
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+%   tags and repair costs ratios
+% functionality_options: struct
+%   recovery time optional inputs such as various damage thresholds
+% impeding_temp_repairs: struct
+%   contains simulated temporary repairs the impede occuapancy and function
+%   but are calulated in parallel with the temp repair schedule
+%
+% Returns
+% -------
+% recovery_day: struct
+%   simulation of the number of days each fault tree event is affecting access
+%   in each story
+% comp_breakdowns: struct
+%   simulation of each components contributions to each of the fault tree events 
+
+%% Initial Setup
+num_reals = length(damage_consequences.red_tag);
+num_units = length(damage.tenant_units);
+num_stories = length(damage.tenant_units);
+num_comps = height(damage.comp_ds_table);
+
+% Pre-allocate data
+recovery_day.stairs = zeros(num_reals,num_units);
+recovery_day.stair_doors = zeros(num_reals,num_units);
+recovery_day.flooding = zeros(num_reals,num_units);
+recovery_day.horizontal_egress = zeros(num_reals,num_units);
+comp_breakdowns.stairs = zeros(num_reals,num_comps,num_units);
+comp_breakdowns.horizontal_egress = zeros(num_reals,num_comps,num_units);
+
+%% Horizontal Egress - Fire breaks
+if any(damage.fnc_filters.fire_break)
+    for tu = 1:num_stories
+        % Grab tenant and damage info for this tenant unit
+        repair_complete_day_w_tmp = damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp;
+
+        % Any significant damage to fire breaks in the story impairs the horizontal egress
+        recovery_day.horizontal_egress(:,tu) = ...
+            max(recovery_day.horizontal_egress(:,tu),...
+                max(repair_complete_day_w_tmp(:,damage.fnc_filters.fire_break),[],2)); 
+
+        % Componet Breakdowns
+        comp_breakdowns.horizontal_egress(:,:,tu) = damage.fnc_filters.fire_break .* repair_complete_day_w_tmp;
+    end
+end
+
+%% STORY FLOODING
+for tu = flip(1:num_stories) % Go from top to bottom
+    is_damaged = damage.tenant_units{tu}.qnt_damaged > 0;
+    flooding_this_story = any(is_damaged(:,damage.fnc_filters.causes_flooding),2); % Any major piping damage causes interior flooding
+    flooding_cleanup_day = flooding_this_story .* impeding_temp_repairs.flooding_cleanup_day;
+
+    % Save clean up time per component causing flooding
+    comp_breakdowns.flooding(:,:,tu) = damage.fnc_filters.causes_flooding .* is_damaged .* flooding_cleanup_day;
+
+    % This story is not accessible if any story above has flooding
+    recovery_day.flooding(:,tu) = max([flooding_cleanup_day,recovery_day.flooding(:,(tu+1):end)],[],2);
+end
+
+%% STAIRS AND STAIRDOORS
+if num_stories == 1 
+    return % Re-occupancy of one story buildigns is not affected by stairway access
+end
+
+% Augment damage filters with door data
+damage.fnc_filters.stairs = logical([damage.fnc_filters.stairs, 0]);
+damage.fnc_filters.stair_doors = logical([zeros(1,num_comps), 1]);
+
+%% Go through each story and check if there is sufficient story access (stairs and stairdoors)
+% if stairs don't exist on a story, this will assume they are rugged (along with the stair doors)
+for tu = 1:num_stories
+    % Augment damage matrix with door data
+    racked_stair_doors = min(damage_consequences.racked_stair_doors_per_story(:,tu),building_model.stairs_per_story(tu));
+    damage.tenant_units{tu}.qnt_damaged = [damage.tenant_units{tu}.qnt_damaged, racked_stair_doors];
+    door_repair_day = (racked_stair_doors > 0) .* impeding_temp_repairs.door_racking_repair_day;
+    damage.tenant_units{tu}.recovery.repair_complete_day = [damage.tenant_units{tu}.recovery.repair_complete_day, door_repair_day];
+
+    % Quantify damaged stairs on this story
+    repair_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day;
+    damaged_comps = damage.tenant_units{tu}.qnt_damaged;
+
+    % Make sure zero repair days are NaN
+    repair_complete_day(repair_complete_day == 0) = NaN;
+
+    % Step through each unique component repair time and determine when
+    % stairs stop affecting story access
+    stair_access_day = zeros(num_reals,1); % day story becomes accessible from repair of stairs
+    stairdoor_access_day = zeros(num_reals,1); % day story becomes accessible from repair of doors
+    filt_all = damage.fnc_filters.stairs | damage.fnc_filters.stair_doors;
+    num_repair_time_increments = sum(filt_all); % possible unique number of loop increments
+    for i = 1:num_repair_time_increments
+        % number of functioning stairs
+        num_dam_stairs = sum(damaged_comps .* damage.fnc_filters.stairs,2); % assumes comps are not simeltaneous
+        num_racked_doors = sum(damaged_comps .* damage.fnc_filters.stair_doors,2); % assumes comps are not simeltaneous
+        functioning_stairs = building_model.stairs_per_story(tu) - num_dam_stairs;
+        functioning_stairdoors = building_model.stairs_per_story(tu) - num_racked_doors;
+
+        % Required egress with and without operational fire suppression system
+        required_stairs = max(functionality_options.min_egress_paths,functionality_options.egress_threshold .* building_model.stairs_per_story(tu));
+
+        % Determine Stair Access
+        sufficient_stair_access  = functioning_stairs >= required_stairs;
+
+        % Determine Stair Door Acces
+        sufficient_stairdoor_access  = functioning_stairdoors >= required_stairs;
+
+        % Add days in this increment to the tally
+        delta_day = min(repair_complete_day(:,filt_all),[],2);
+        delta_day(isnan(delta_day)) = 0;
+        stair_access_day = stair_access_day + ~sufficient_stair_access .* delta_day;
+        stairdoor_access_day = stairdoor_access_day + ~sufficient_stairdoor_access .* delta_day;
+
+        % Add days to components that are affecting occupancy
+        contributing_stairs = ((damaged_comps .* damage.fnc_filters.stairs) > 0)  .* ~sufficient_stair_access; % Count any damaged stairs for realization that have loss of story access
+        contributing_stairs(:,end) = []; % remove added door column
+        comp_breakdowns.stairs(:,:,tu) = comp_breakdowns.stairs(:,:,tu) + contributing_stairs .* delta_day;
+
+        % Change the comps for the next increment
+        repair_complete_day = repair_complete_day - delta_day;
+        repair_complete_day(repair_complete_day <= 0) = NaN;
+        fixed_comps_filt = isnan(repair_complete_day);
+        damaged_comps(fixed_comps_filt) = 0;
+    end
+
+    % This story is not accessible if this or any story below has insufficient stair egress
+    recovery_day.stairs(:,tu) = max([stair_access_day,recovery_day.stairs(:,1:(tu-1))],[],2);
+    
+    % For the third story and above, the story below is not accessible if
+    % there is insufficient stair egress at this story
+    if tu >= 3
+        recovery_day.stairs(:,(tu-1)) = max([stair_access_day,recovery_day.stairs(:,(tu-1))],[],2);
+    end
+    
+    % Damage to doors only affects this story
+    recovery_day.stair_doors(:,tu) = stairdoor_access_day;
+end
+
+end % end function
+
diff --git a/+recovery/+functionality/fn_tenant_function.m b/+recovery/+functionality/fn_tenant_function.m
new file mode 100644
index 0000000..ddce78e
--- /dev/null
+++ b/+recovery/+functionality/fn_tenant_function.m
@@ -0,0 +1,459 @@
+function [ recovery_day, comp_breakdowns ] = fn_tenant_function( damage, ...
+    building_model, system_operation_day, subsystems, ...
+    tenant_units, impeding_temp_repairs, functionality_options )
+% Check each tenant unit for damage that would cause that tenant unit 
+% to not be functional
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage, loss, and repair time data for each 
+%   component in the building
+% building_model: struct
+%   general attributes of the building model
+% system_operation_day.building: struct
+%   simulation of the day operation is recovered for various systems at the
+%   building level
+% system_operation_day.comp: struct
+%   simulation number of days each component is affecting building system
+%   operations
+% subsystems: table
+%   data table containing information about each subsystem's attributes
+% tenant_units: table
+%   attributes of each tenant unit within the building
+% impeding_temp_repairs: struct
+%   contains simulated temporary repairs the impede occuapancy and function
+%   but are calulated in parallel with the temp repair schedule
+% functionality_options: struct
+%   recovery time optional inputs such as various damage thresholds
+%
+% Returns
+% -------
+% recovery_day: struct
+%   simulation of the number of days each fault tree event is affecting
+%   function in each tenant unit
+% comp_breakdowns: struct
+%   simulation of each components contributions to each of the fault tree events 
+
+%% Initial Setup
+% Initialize Variables
+num_units = length(damage.tenant_units);
+[num_reals, num_comps] = size(damage.tenant_units{1}.qnt_damaged);
+num_stories = building_model.num_stories;
+recovery_day.elevators = zeros(num_reals,num_units);
+recovery_day.exterior = zeros(num_reals,num_units);
+recovery_day.roof = zeros(num_reals,num_units);
+recovery_day.interior = zeros(num_reals,num_units);
+recovery_day.electrical = zeros(num_reals,num_units);
+recovery_day.flooding = zeros(num_reals,num_units);
+comp_breakdowns.elevators = zeros(num_reals,num_comps,num_units);
+comp_breakdowns.electrical = zeros(num_reals,num_comps,num_units);
+
+%% STORY FLOODING
+for tu = flip(1:num_stories) % Go from top to bottom
+    is_damaged = damage.tenant_units{tu}.qnt_damaged > 0;
+    flooding_this_story = any(is_damaged(:,damage.fnc_filters.causes_flooding),2); % Any major piping damage causes interior flooding
+    flooding_recovery_day = flooding_this_story .* impeding_temp_repairs.flooding_repair_day;
+
+    % Save clean up time per component causing flooding
+    comp_breakdowns.flooding(:,:,tu) = damage.fnc_filters.causes_flooding .* is_damaged .* flooding_recovery_day;
+
+    % This story is not accessible if any story above has flooding
+    recovery_day.flooding(:,tu) = max([flooding_recovery_day,recovery_day.flooding(:,(tu+1):end)],[],2);
+end
+
+%% SYSTEM SPECIFIC CONSEQUENCES
+for tu = 1:num_units
+    damaged_comps = damage.tenant_units{tu}.qnt_damaged;
+    initial_damaged = damaged_comps > 0;
+    total_num_comps = damage.tenant_units{tu}.num_comps;
+    unit = tenant_units(tu,:);
+    repair_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day;
+    repair_complete_day_w_tmp = damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp;
+    
+    %% Elevators
+    if unit.is_elevator_required
+        comps_day_repaired = system_operation_day.comp.elev_day_repaired;
+        comps_day_repaired(comps_day_repaired == 0) = NaN;
+        comps_quant_damaged = system_operation_day.comp.elev_quant_damaged;
+        elev_function_recovery_day = zeros(num_reals,1);
+        elev_comps_day_fnc = zeros(num_reals,num_comps);
+        num_repair_time_increments = sum(damage.fnc_filters.elevators); % possible unique number of loop increments
+        % Loop through each unique repair time increment and determine when
+        % stops affecting function
+        for i = 1:num_repair_time_increments
+            % Take the max of component damage to determine the number of
+            % shafts/cabs that are damaged/non_operational
+            % This assumes that different elevator components are correlated
+            num_damaged_elevs = max(comps_quant_damaged,[],2); % this assumes elevators are in one performance group if simeltaneous
+            num_damaged_elevs = min(num_damaged_elevs,building_model.num_elevators); % you can never have more elevators damaged than exist
+            
+            % If elevators are in mutliple performance groups and those
+            % elevators have simultaneous damage states, it is not possible
+            % to count the number of damaged elevators without additional
+            % information
+            num_elev_pgs = length(unique(damage.comp_ds_table.comp_idx(damage.fnc_filters.elevators)));
+            is_sim_ds = any(damage.comp_ds_table.is_sim_ds(damage.fnc_filters.elevators)');
+            if (num_elev_pgs > 1) && is_sim_ds
+                error('PBEE_Recovery:Function','Elevator Function check does not handle multiple performance groups with simultaneous damage states')
+            end
+
+            % quantifty the number of occupancy needing to use the elevators
+            % all occupants above the first floor will try to use the elevators
+            building_occ_per_elev = sum(building_model.occupants_per_story(2:end)) ./ (building_model.num_elevators - num_damaged_elevs); 
+            
+            % elevator function check
+            % do tenants have sufficient elevator access need based on
+            % elevators that are still operational
+            affects_function = building_occ_per_elev > max(unit.occ_per_elev);
+
+            % Add days in this increment to the tally
+            delta_day = min(comps_day_repaired(:,damage.fnc_filters.elevators),[],2);
+            delta_day(isnan(delta_day)) = 0;
+            elev_function_recovery_day = elev_function_recovery_day + affects_function .* delta_day;
+            
+            % Add days to components that are affecting occupancy
+            any_elev_damage = comps_quant_damaged > 0; % Count any component that contributes to the loss of function regardless of by how much 
+            elev_comps_day_fnc = elev_comps_day_fnc + any_elev_damage .* affects_function .* delta_day;
+
+            % Change the comps for the next increment
+            comps_day_repaired = comps_day_repaired - delta_day;
+            comps_day_repaired(comps_day_repaired <= 0) = NaN;
+            fixed_comps_filt = isnan(comps_day_repaired);
+            comps_quant_damaged(fixed_comps_filt) = 0;
+        end
+        power_supply_recovery_day = max(system_operation_day.building.elevator_mcs,system_operation_day.building.electrical_main);
+        recovery_day.elevators(:,tu) = max(elev_function_recovery_day,power_supply_recovery_day); % electrical system and utility
+        power_supply_recovery_day_comp = max(system_operation_day.comp.elevator_mcs,system_operation_day.comp.electrical_main);
+        comp_breakdowns.elevators(:,:,tu) = max(elev_comps_day_fnc,power_supply_recovery_day_comp);
+    end
+    
+    %% Exterior Enclosure 
+    % Perimeter Cladding (assuming all exterior components have either lf or sf units)
+    area_affected_lf_all_comps = damage.comp_ds_table.exterior_surface_area_factor' .* damage.comp_ds_table.unit_qty' .* building_model.ht_per_story_ft(tu) .* damage.tenant_units{tu}.qnt_damaged;
+    area_affected_direct_scale_all_comps = damage.comp_ds_table.exterior_surface_area_factor' .* damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.qnt_damaged;
+   
+    comp_affected_area = zeros(num_reals,num_comps);
+    comp_affected_area(:,damage.fnc_filters.exterior_seal_lf) = area_affected_lf_all_comps(:,damage.fnc_filters.exterior_seal_lf);
+    comp_affected_area(:,damage.fnc_filters.exterior_seal_sf) = area_affected_direct_scale_all_comps(:,damage.fnc_filters.exterior_seal_sf);
+    comp_affected_area(:,damage.fnc_filters.exterior_seal_ea) = area_affected_direct_scale_all_comps(:,damage.fnc_filters.exterior_seal_ea);
+    
+    comps_day_repaired = repair_complete_day;
+    ext_function_recovery_day = zeros(num_reals,1);
+    all_comps_day_ext = zeros(num_reals,num_comps);
+    num_repair_time_increments = sum(damage.fnc_filters.exterior_seal_all); % possible unique number of loop increments
+    % Loop through each unique repair time increment and determine when stops affecting function
+    for i = 1:num_repair_time_increments
+        % Determine the area of wall which has severe exterior encolusure damage 
+        area_affected = sum(comp_affected_area,2); % Assumes cladding components do not occupy the same perimeter area
+        percent_area_affected = min(area_affected / unit.perim_area,1); % normalize it
+        
+        % Determine if current damage affects function for this tenant unit
+        % if the area of exterior wall damage is greater than what is
+        % acceptable by the tenant 
+        affects_function = percent_area_affected > unit.exterior; 
+        
+        % Add days in this increment to the tally
+        delta_day = min(comps_day_repaired(:,damage.fnc_filters.exterior_seal_all),[],2);
+        delta_day(isnan(delta_day)) = 0;
+        ext_function_recovery_day = ext_function_recovery_day + affects_function .* delta_day;
+        
+        % Add days to components that are affecting occupancy
+        any_area_affected_all_comps = comp_affected_area > 0; % Count any component that contributes to the loss of occupance regardless of by how much
+        all_comps_day_ext = all_comps_day_ext + any_area_affected_all_comps .* affects_function .* delta_day;
+        
+        % Change the comps for the next increment
+        % reducing damage for what has been repaired in this time increment
+        comps_day_repaired = comps_day_repaired - delta_day;
+        comps_day_repaired(comps_day_repaired <= 0) = NaN;
+        fixed_comps_filt = isnan(comps_day_repaired);
+        comp_affected_area(fixed_comps_filt) = 0;
+    end
+    
+    recovery_day.exterior(:,tu) = ext_function_recovery_day;
+        comp_breakdowns.exterior(:,:,tu) = all_comps_day_ext;
+        
+    if unit.story == num_stories % If this is the top story, check the roof for function
+        % Roof structure check
+        [ all_comps_day_roof_struct, roof_structure_recovery_day ] = check_roof_function( ...
+            damage.fnc_filters.roof_structure, ...
+            subsystems.redundancy_threshold(subsystems.id == 21), ...  % structure threshold
+            repair_complete_day_w_tmp, ...
+            damage.tenant_units{tu}.qnt_damaged, ...
+            damage.tenant_units{tu}.num_comps ...
+        );
+        
+        % Roof seatherproofing check
+        [ all_comps_day_roof_weather, roof_weather_recovery_day] = check_roof_function( ...
+            damage.fnc_filters.roof_weatherproofing, ...
+            subsystems.redundancy_threshold(subsystems.id == 22), ...  % seal threshold
+            repair_complete_day_w_tmp, ...
+            damage.tenant_units{tu}.qnt_damaged, ...
+            damage.tenant_units{tu}.num_comps ...
+        );
+
+        % Combine branches
+        recovery_day.roof(:,tu) = max(roof_structure_recovery_day, roof_weather_recovery_day);
+        comp_breakdowns.roof(:,:,tu) = max(all_comps_day_roof_struct, all_comps_day_roof_weather);
+    end
+    
+    %% Interior Area
+    area_affected_lf_all_comps           = damage.comp_ds_table.interior_area_factor' .* damage.comp_ds_table.unit_qty' .* building_model.ht_per_story_ft(tu) .* damage.tenant_units{tu}.qnt_damaged;
+    area_affected_direct_scale_all_comps = damage.comp_ds_table.interior_area_factor' .* damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.qnt_damaged;
+    area_affected_bay_all_comps          = damage.comp_ds_table.interior_area_factor' .* building_model.struct_bay_area_per_story(tu) .* damage.tenant_units{tu}.qnt_damaged;
+    area_affected_build_all_comps        = damage.comp_ds_table.interior_area_factor' .* sum(building_model.area_per_story_sf) .* damage.tenant_units{tu}.qnt_damaged;
+    
+    repair_complete_day_w_tmp_w_instabilities = repair_complete_day_w_tmp;
+    if tu > 1
+        area_affected_below = damage.comp_ds_table.interior_area_factor' .* building_model.struct_bay_area_per_story(tu-1) .* damage.tenant_units{tu-1}.qnt_damaged;
+        area_affected_bay_all_comps(:,damage.fnc_filters.vert_instabilities) ...
+            = max(area_affected_below(:,damage.fnc_filters.vert_instabilities),area_affected_bay_all_comps(:,damage.fnc_filters.vert_instabilities));
+        repair_time_below = damage.tenant_units{tu-1}.recovery.repair_complete_day_w_tmp;
+        repair_complete_day_w_tmp_w_instabilities(:,damage.fnc_filters.vert_instabilities) ...
+            = max(repair_time_below(:,damage.fnc_filters.vert_instabilities),repair_complete_day_w_tmp(:,damage.fnc_filters.vert_instabilities));
+    end
+
+    comp_affected_area = zeros(num_reals,num_comps);
+    comp_affected_area(:,damage.fnc_filters.interior_function_lf) = area_affected_lf_all_comps(:,damage.fnc_filters.interior_function_lf);
+    comp_affected_area(:,damage.fnc_filters.interior_function_sf) = area_affected_direct_scale_all_comps(:,damage.fnc_filters.interior_function_sf);
+    comp_affected_area(:,damage.fnc_filters.interior_function_ea) = area_affected_direct_scale_all_comps(:,damage.fnc_filters.interior_function_ea);
+    comp_affected_area(:,damage.fnc_filters.interior_function_bay) = area_affected_bay_all_comps(:,damage.fnc_filters.interior_function_bay);
+    comp_affected_area(:,damage.fnc_filters.interior_function_build) = area_affected_build_all_comps(:,damage.fnc_filters.interior_function_build);
+
+    frag_types_in_check = unique(damage.comp_ds_table.comp_type_id(damage.fnc_filters.interior_function_all));
+    comps_day_repaired = repair_complete_day_w_tmp_w_instabilities;
+
+    int_function_recovery_day = zeros(num_reals,1);
+    int_comps_day_repaired = zeros(num_reals,num_comps);
+    num_repair_time_increments = sum(damage.fnc_filters.interior_function_all); % possible unique number of loop increments
+    % Loop through each unique repair time increment and determine when stops affecting function
+    for i = 1:num_repair_time_increments
+        % Quantify the affected area (based on srss of differenct component
+        % types)
+        diff_comp_areas = [];
+        for cmp = 1:length(frag_types_in_check)
+            filt = strcmp(damage.comp_ds_table.comp_type_id,frag_types_in_check{cmp})'; % check to see if it matches the first part of the ID (ie the type of comp)
+            diff_comp_areas(:,cmp) = sum(comp_affected_area(:,filt),2);
+        end
+        area_affected = sqrt(sum(diff_comp_areas.^2,2)); % total area affected is the srss of the areas in the unit
+        percent_area_affected = min(area_affected / unit.area, 1); % no greater than the total unit area
+    
+        % Determine if current damage affects function for this tenant unit
+        % affects function if the area of interior damage is greater than what is
+        % acceptable by the tenant 
+        affects_function = percent_area_affected > unit.interior; 
+        
+        % Add days in this increment to the tally
+        delta_day = min(comps_day_repaired(:,damage.fnc_filters.interior_function_all),[],2);
+        delta_day(isnan(delta_day)) = 0;
+        int_function_recovery_day = int_function_recovery_day + affects_function .* delta_day;
+        
+        % Add days to components that are affecting occupancy
+        any_area_affected_all_comps = comp_affected_area > 0; % Count any component that contributes to the loss of occupance regardless of by how much
+        int_comps_day_repaired = int_comps_day_repaired + any_area_affected_all_comps .* affects_function .* delta_day;
+        
+        % Change the comps for the next increment
+        % reducing damage for what has been repaired in this time increment
+        comps_day_repaired = comps_day_repaired - delta_day;
+        comps_day_repaired(comps_day_repaired <= 0) = NaN;
+        fixed_comps_filt = isnan(comps_day_repaired);
+        comp_affected_area(fixed_comps_filt) = 0;
+    end
+    recovery_day.interior(:,tu) = int_function_recovery_day;
+    comp_breakdowns.interior(:,:,tu) = int_comps_day_repaired;
+    
+    %% Potable Water System
+    % determine effect on funciton at this tenant unit
+    % any major damage to the branch pipes (small diameter) failes for this tenant unit
+    tenant_sys_recovery_day = max(repair_complete_day .* damage.fnc_filters.water_unit,[],2); 
+    recovery_day.water_potable(:,tu) = max(system_operation_day.building.water_potable_main,tenant_sys_recovery_day);
+
+    % distribute effect to the components
+    comp_breakdowns.water_potable(:,:,tu) = max(system_operation_day.comp.water_potable_main, repair_complete_day .* damage.fnc_filters.water_unit);
+    
+    % In taller buildings, water needs to be pumped to reach upper stories
+    % and therefore requires electrical power
+    if unit.story > functionality_options.water_pressure_max_story
+        electrical_failure_controls = system_operation_day.building.electrical_main > recovery_day.water_potable(:,tu);
+        recovery_day.water_potable(:,tu) = max(recovery_day.water_potable(:,tu),system_operation_day.building.electrical_main);
+        comp_breakdowns.water_potable(:,:,tu) = max(comp_breakdowns.water_potable(:,:,tu) .* ~electrical_failure_controls,...
+                                                    system_operation_day.comp.electrical_main .* electrical_failure_controls);
+    end
+    
+    %% Sanitary Waste System
+    % determine effect on funciton at this tenant unit
+    % any major damage to the branch pipes (small diameter) failes for this tenant unit
+    tenant_sys_recovery_day = max(repair_complete_day .* damage.fnc_filters.sewer_unit,[],2); 
+    recovery_day.water_sanitary(:,tu) = max(system_operation_day.building.water_sanitary_main,tenant_sys_recovery_day);
+
+    % distribute effect to the components
+    comp_breakdowns.water_sanitary(:,:,tu) = max(system_operation_day.comp.water_sanitary_main, repair_complete_day .* damage.fnc_filters.sewer_unit);
+
+    % Sanitary waste operation at this tenant unit depends on the 
+    % operation of the potable water system at this tenant unit
+    recovery_day.water_sanitary(:,tu) = max(recovery_day.water_sanitary(:,tu),recovery_day.water_potable(:,tu));
+    comp_breakdowns.water_sanitary(:,:,tu) = max(comp_breakdowns.water_sanitary(:,:,tu), comp_breakdowns.water_potable(:,:,tu));
+    
+    %% Electrical Power System
+    % Does not consider effect of backup systems
+    % Just electical power within the tenant unit (building power is
+    % assessed in fn_builing_level_system_operation)
+    if unit.is_electrical_required
+        % determine effect on funciton at this tenant unit 
+        % any major damage to the unit level electrical equipment fails for this tenant unit
+        tenant_sys_recovery_day = max(repair_complete_day .* damage.fnc_filters.electrical_unit,[],2);
+        recovery_day.electrical(:,tu) = max(system_operation_day.building.electrical_main,tenant_sys_recovery_day);
+        
+        % distribute effect to the components
+        comp_breakdowns.electrical(:,:,tu) = max(system_operation_day.comp.electrical_main, repair_complete_day .* damage.fnc_filters.electrical_unit);
+    end
+    
+    %% HVAC System
+    % HVAC: Control System
+    recovery_day_hvac_control = system_operation_day.building.hvac_control; % Electrical power is counted in here 
+    comp_breakdowns_hvac_control = system_operation_day.comp.hvac_control;
+
+    % HVAC: Ventilation
+    dependancy.recovery_day = recovery_day_hvac_control;
+    dependancy.comp_breakdown = comp_breakdowns_hvac_control;
+    [recovery_day.hvac_ventilation(:,tu), comp_breakdowns.hvac_ventilation(:,:,tu)] = ...
+        subsystem_recovery('hvac_ventilation', damage, repair_complete_day, ...
+                     total_num_comps, damaged_comps, initial_damaged, dependancy);
+
+    % HVAC: Heating
+    dependancy.recovery_day = max(recovery_day.hvac_ventilation(:,tu),system_operation_day.building.hvac_heating);
+    dependancy.comp_breakdown = max(comp_breakdowns.hvac_ventilation(:,:,tu),system_operation_day.comp.hvac_heating);
+    [recovery_day.hvac_heating(:,tu), comp_breakdowns.hvac_heating(:,:,tu)] = ...
+        subsystem_recovery('hvac_heating', damage, repair_complete_day, ...
+                     total_num_comps, damaged_comps, initial_damaged, dependancy);
+
+    % HVAC: Cooling
+    dependancy.recovery_day = max(recovery_day.hvac_ventilation(:,tu),system_operation_day.building.hvac_cooling);
+    dependancy.comp_breakdown = max(comp_breakdowns.hvac_ventilation(:,:,tu),system_operation_day.comp.hvac_cooling);
+    [recovery_day.hvac_cooling(:,tu), comp_breakdowns.hvac_cooling(:,:,tu)] = ...
+        subsystem_recovery('hvac_cooling', damage, repair_complete_day, ...
+                     total_num_comps, damaged_comps, initial_damaged, dependancy);
+
+    % HVAC: Exhast
+    dependancy.recovery_day = recovery_day_hvac_control;
+    dependancy.comp_breakdown = comp_breakdowns_hvac_control;
+    [recovery_day.hvac_exhaust(:,tu), comp_breakdowns.hvac_exhaust(:,:,tu)] = ...
+        subsystem_recovery('hvac_exhaust', damage, repair_complete_day, ...
+                     total_num_comps, damaged_comps, initial_damaged, dependancy);
+                 
+    %% Data
+    if unit.is_data_required && any(damage.fnc_filters.data_unit | damage.fnc_filters.data_main)
+        % determine effect on funciton at this tenant unit
+        % any major damage to the unit level electrical equipment failes for this tenant unit
+        tenant_sys_recovery_day = max(repair_complete_day .* damage.fnc_filters.data_unit,[],2);
+        recovery_day.data(:,tu) = max(system_operation_day.building.data_main,tenant_sys_recovery_day);
+        
+        % Consider effect of external water network
+        recovery_day.data = max(recovery_day.data,system_operation_day.building.electrical_main);
+        
+        % distribute effect to the components
+        comp_breakdowns.data(:,:,tu) = max(system_operation_day.comp.data_main, repair_complete_day .* damage.fnc_filters.data_unit);
+    end      
+                 
+    %% Post process for tenant-specific requirements 
+    % Zero out systems that are not required by the tenant
+    % Still need to calculate above due to dependancies between options
+    if ~unit.is_water_potable_required
+        recovery_day.water_potable = zeros(num_reals,num_units);
+        comp_breakdowns.water_potable = zeros(num_reals,num_comps,num_units);
+    end
+    if ~unit.is_water_sanitary_required
+        recovery_day.water_sanitary = zeros(num_reals,num_units);
+        comp_breakdowns.water_sanitary = zeros(num_reals,num_comps,num_units);
+    end
+    if ~unit.is_hvac_ventilation_required
+        recovery_day.hvac_ventilation = zeros(num_reals,num_units);
+        comp_breakdowns.hvac_ventilation = zeros(num_reals,num_comps,num_units);
+    end
+    if ~unit.is_hvac_heating_required
+        recovery_day.hvac_heating = zeros(num_reals,num_units);
+        comp_breakdowns.hvac_heating = zeros(num_reals,num_comps,num_units);
+    end
+    if ~unit.is_hvac_cooling_required
+        recovery_day.hvac_cooling = zeros(num_reals,num_units);
+        comp_breakdowns.hvac_cooling = zeros(num_reals,num_comps,num_units);
+    end
+    if ~unit.is_hvac_exhaust_required
+        recovery_day.hvac_exhaust = zeros(num_reals,num_units);
+        comp_breakdowns.hvac_exhaust = zeros(num_reals,num_comps,num_units);
+    end
+end
+
+
+
+end % Function
+
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%% SUBFUNCTIONS %%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%
+function [recovery_day_all, comp_breakdowns_all] = subsystem_recovery(...
+    subsystem, damage, repair_complete_day, total_num_comps, damaged_comps, ...
+    initial_damaged, dependancy)
+
+% import packages
+import recovery.functionality.fn_calc_subsystem_recovery
+
+% Set variables
+recovery_day_all = dependancy.recovery_day;
+comp_breakdowns_all = dependancy.comp_breakdown;
+
+% Go through each component group in this subsystem and determine recovery
+% based on impact of system operation at the tenant unit level
+subs = fieldnames(damage.fnc_filters.hvac.tenant.(subsystem));
+for b = 1:length(subs)
+    filt = damage.fnc_filters.hvac.tenant.(subsystem).(subs{b})';
+    [recovery_day] = fn_calc_subsystem_recovery( filt, damage, repair_complete_day, total_num_comps, damaged_comps );
+    comps_breakdown = filt .* initial_damaged .* recovery_day;
+    recovery_day_all = max(recovery_day_all,recovery_day); % combine with previous stories
+    comp_breakdowns_all = max(comp_breakdowns_all,comps_breakdown);
+end
+
+end % Function
+
+
+function [ all_comps_day_roof, roof_recovery_day ] = check_roof_function(roof_sys_filter, damage_threshold, repair_complete_day_w_tmp, qnt_damaged, num_comps)
+% Check the roof area for function (seal and function)
+
+num_comp_damaged = roof_sys_filter .* qnt_damaged;
+num_roof_comps = roof_sys_filter .* num_comps;
+
+comps_day_repaired = repair_complete_day_w_tmp;
+roof_recovery_day = zeros(size(repair_complete_day_w_tmp, 1), 1);
+all_comps_day_roof = zeros(size(repair_complete_day_w_tmp));
+num_repair_time_increments = sum(roof_sys_filter); % possible unique number of loop increments
+
+% Loop through each unique repair time increment and determine when stops affecting function
+for i = 1:num_repair_time_increments
+    % Determine the area of roof affected 
+    percent_area_affected = sum(num_comp_damaged,2) / sum(num_roof_comps,2); % Assumes roof components do not occupy the same area of roof
+
+    % Determine if current damage affects function for this tenant unit
+    % if the area of exterior wall damage is greater than what is
+    % acceptable by the tenant 
+    affects_function = percent_area_affected >= damage_threshold; 
+
+    % Add days in this increment to the tally
+    delta_day = min(comps_day_repaired(:, roof_sys_filter),[],2);
+    delta_day(isnan(delta_day)) = 0;
+    roof_recovery_day = roof_recovery_day + affects_function .* delta_day;
+
+    % Add days to components that are affecting function
+    any_area_affected_all_comps = num_comp_damaged > 0; % Count any component that contributes to the loss of function regardless of by how much
+    all_comps_day_roof = all_comps_day_roof + any_area_affected_all_comps .* affects_function .* delta_day;
+
+    % Change the comps for the next increment
+    % reducing damage for what has been repaired in this time increment
+    comps_day_repaired = comps_day_repaired - delta_day;
+    comps_day_repaired(comps_day_repaired <= 0) = NaN;
+    fixed_comps_filt = isnan(comps_day_repaired);
+    num_comp_damaged(fixed_comps_filt) = 0;
+end
+
+end
\ No newline at end of file
diff --git a/+recovery/+functionality/fn_tenant_safety.m b/+recovery/+functionality/fn_tenant_safety.m
new file mode 100644
index 0000000..b9d9c4f
--- /dev/null
+++ b/+recovery/+functionality/fn_tenant_safety.m
@@ -0,0 +1,171 @@
+function [ recovery_day, comp_breakdowns ] = fn_tenant_safety( damage, ...
+    building_model, functionality_options, tenant_units )
+% Check each tenant unit for damage that would cause that tenant unit 
+% to be shut down due to issues of locay safety
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage, loss, and repair time data for each 
+%   component in the building
+% building_model: struct
+%   general attributes of the building model
+% functionality_options: struct
+%   recovery time optional inputs such as various damage thresholds
+% tenant_units: table
+%   attributes of each tenant unit within the building
+%
+% Returns
+% -------
+% recovery_day: struct
+%   simulation of the number of days each fault tree event is affecting safety
+%   in each tenant unit
+% comp_breakdowns: struct
+%   simulation of each components contributions to each of the fault tree events 
+
+%% Initial Setup
+[num_reals, num_comps] = size(damage.tenant_units{1}.qnt_damaged);
+num_units = length(damage.tenant_units);
+comp_types_interior_check = unique(damage.comp_ds_table.comp_type_id(damage.fnc_filters.int_fall_haz_all));
+
+% go through each tenant unit and quantify the affect that each system has on reoccpauncy
+for tu = 1:num_units
+    % Grab tenant and damage info for this tenant unit
+    unit = tenant_units(tu,:);
+    repair_complete_day_w_tmp = damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp; % day each component (and DS) is reparied of this TU
+
+    %% Exterior Enclosure 
+    % Calculated the affected perimeter area of exterior components
+    % (assuming all exterior components have either lf or sf units)
+    area_affected_all_linear_comps       = damage.comp_ds_table.exterior_surface_area_factor' .* damage.comp_ds_table.unit_qty' .* building_model.ht_per_story_ft(tu) .* damage.tenant_units{tu}.qnt_damaged;
+    area_affected_all_direct_scale_comps = damage.comp_ds_table.exterior_surface_area_factor' .* damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.qnt_damaged;
+    
+    % construct a matrix of affected areas from the various damaged component types
+    comp_affected_area = zeros(num_reals,num_comps);
+    comp_affected_area(:,damage.fnc_filters.exterior_safety_lf) = area_affected_all_linear_comps(:,damage.fnc_filters.exterior_safety_lf);
+    comp_affected_area(:,damage.fnc_filters.exterior_safety_sf) = area_affected_all_direct_scale_comps(:,damage.fnc_filters.exterior_safety_sf);
+    
+    % Go each possible unique repair time contributing to interior safety check
+    % Find when enough repairs are complete such that interior damage no
+    % longer affects tenant safety
+    comps_day_repaired = repair_complete_day_w_tmp; % define as initial repair day considering tmp repairs
+    ext_repair_day = zeros(num_reals,1);
+    all_comps_day_repaired = zeros(num_reals,num_comps);
+    num_repair_time_increments = sum(damage.fnc_filters.exterior_safety_all); % possible unique number of loop increments
+    for i = 1:num_repair_time_increments
+        
+        % Quantify Affected Area
+        area_affected = sum(comp_affected_area,2); % Assumes cladding components do not occupy the same perimeter area
+        percent_area_affected = area_affected / unit.perim_area;
+
+        % Check if this is sufficent enough to cause as tenant safety issue
+        affects_occupancy = percent_area_affected > functionality_options.exterior_safety_threshold;
+
+        % Determine step increment based on the component with the shortest repair time
+        delta_day = min(comps_day_repaired(:,damage.fnc_filters.exterior_safety_all),[],2);
+        delta_day(isnan(delta_day)) = 0;
+        
+        % Add increment to the tally of days until the interior damage
+        % stops affecting occupancy
+        ext_repair_day = ext_repair_day + affects_occupancy .* delta_day;
+        
+        % Add days to components that are affecting occupancy
+        any_area_affected_all_comps = (damage.fnc_filters.exterior_safety_all .* comp_affected_area) > 0; % Count any component that contributes to the loss of occupancy regardless of by how much
+        all_comps_day_repaired = all_comps_day_repaired + any_area_affected_all_comps .* affects_occupancy .* delta_day;
+        
+        % Reduce compent damaged for the next increment based on what was
+        % repaired in this increment
+        comps_day_repaired = comps_day_repaired - delta_day;
+        comps_day_repaired(comps_day_repaired <= 0) = NaN;
+        fixed_comps_filt = isnan(comps_day_repaired);
+        comp_affected_area(fixed_comps_filt) = 0;
+    end
+    
+    % Save exterior recovery day for this tenant unit
+    recovery_day.exterior(:,tu) = ext_repair_day;
+    comp_breakdowns.exterior(:,:,tu) = all_comps_day_repaired;
+    
+    %% Interior Falling Hazards
+    % Convert all component into affected areas
+    area_affected_all_linear_comps       = damage.comp_ds_table.interior_area_factor' .* damage.comp_ds_table.unit_qty' .* building_model.ht_per_story_ft(tu) .* damage.tenant_units{tu}.qnt_damaged;
+    area_affected_all_direct_scale_comps = damage.comp_ds_table.interior_area_factor' .* damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.qnt_damaged;
+    area_affected_all_bay_comps          = damage.comp_ds_table.interior_area_factor' .* building_model.struct_bay_area_per_story(tu) .* damage.tenant_units{tu}.qnt_damaged;
+    area_affected_all_build_comps        = damage.comp_ds_table.interior_area_factor' .* sum(building_model.area_per_story_sf) .* damage.tenant_units{tu}.qnt_damaged;
+    
+    % Checking damage that affects components in story below
+    repair_complete_day_w_tmp_w_instabilities = repair_complete_day_w_tmp;
+    if tu > 1
+        area_affected_below = damage.comp_ds_table.interior_area_factor' .* building_model.struct_bay_area_per_story(tu-1) .* damage.tenant_units{tu-1}.qnt_damaged;
+        area_affected_all_bay_comps(:,damage.fnc_filters.vert_instabilities) ...
+            = max(area_affected_below(:,damage.fnc_filters.vert_instabilities),area_affected_all_bay_comps(:,damage.fnc_filters.vert_instabilities));
+        repair_time_below = damage.tenant_units{tu-1}.recovery.repair_complete_day_w_tmp;
+        repair_complete_day_w_tmp_w_instabilities(:,damage.fnc_filters.vert_instabilities) ...
+            = max(repair_time_below(:,damage.fnc_filters.vert_instabilities),repair_complete_day_w_tmp(:,damage.fnc_filters.vert_instabilities));
+    end
+
+    % construct a matrix of affected areas from the various damaged component types
+    comp_affected_area = zeros(num_reals,num_comps);
+    comp_affected_area(:,damage.fnc_filters.int_fall_haz_lf) = area_affected_all_linear_comps(:,damage.fnc_filters.int_fall_haz_lf);
+    comp_affected_area(:,damage.fnc_filters.int_fall_haz_sf) = area_affected_all_direct_scale_comps(:,damage.fnc_filters.int_fall_haz_sf);
+    comp_affected_area(:,damage.fnc_filters.int_fall_haz_ea) = area_affected_all_direct_scale_comps(:,damage.fnc_filters.int_fall_haz_ea);
+    comp_affected_area(:,damage.fnc_filters.int_fall_haz_bay) = area_affected_all_bay_comps(:,damage.fnc_filters.int_fall_haz_bay);
+    comp_affected_area(:,damage.fnc_filters.int_fall_haz_build) = area_affected_all_build_comps(:,damage.fnc_filters.int_fall_haz_build);
+    
+    % Go each possible unique repair time contributing to interior safety check
+    % Find when enough repairs are complete such that interior damage no
+    % longer affects tenant safety
+    comps_day_repaired = repair_complete_day_w_tmp_w_instabilities; % define as initial repair day considering tmp repairs
+    int_repair_day = zeros(num_reals,1);
+    all_comps_day_repaired = zeros(num_reals,num_comps);
+    num_repair_time_increments = sum(damage.fnc_filters.int_fall_haz_all); % possible unique number of loop increments
+    for i = 1:num_repair_time_increments
+        % Quantify Affected Area
+        diff_comp_areas = [];
+        for cmp = 1:length(comp_types_interior_check)
+            filt = strcmp(damage.comp_ds_table.comp_type_id,comp_types_interior_check{cmp})'; % check to see if it matches the first part of the ID (ie the type of comp)
+            diff_comp_areas(:,cmp) = sum(comp_affected_area(:,filt),2);
+        end
+        area_affected = sqrt(sum(diff_comp_areas.^2,2)); % total area affected is the srss of the areas in the unit
+        
+        % Determine if current damage affects occupancy
+        percent_area_affected = min(area_affected / unit.area,1);
+        affects_occupancy = percent_area_affected > functionality_options.interior_safety_threshold;
+        
+        % Determine step increment based on the component with the shortest repair time
+        delta_day = min(comps_day_repaired(:,damage.fnc_filters.int_fall_haz_all),[],2);
+        delta_day(isnan(delta_day)) = 0;
+        
+        % Add increment to the tally of days until the interior damage
+        % stops affecting occupancy
+        int_repair_day = int_repair_day + affects_occupancy .* delta_day;
+        
+        % Add days to components that are affecting occupancy
+        any_area_affected_all_comps = (damage.fnc_filters.int_fall_haz_all .* comp_affected_area) > 0; % Count any component that contributes to the loss of occupancy regardless of by how much
+        all_comps_day_repaired = all_comps_day_repaired + any_area_affected_all_comps .* affects_occupancy .* delta_day;
+        
+        % Reduce compent damaged for the next increment based on what was
+        % repaired in this increment
+        comps_day_repaired = comps_day_repaired - delta_day;
+        comps_day_repaired(comps_day_repaired <= 0) = NaN;
+        fixed_comps_filt = isnan(comps_day_repaired);
+        comp_affected_area(fixed_comps_filt) = 0;
+    end
+    
+    % Save interior recovery day for this tenant unit
+    recovery_day.interior(:,tu) = int_repair_day;
+    comp_breakdowns.interior(:,:,tu) = all_comps_day_repaired;
+    
+    %% Hazardous Materials
+    % note: hazardous materials are accounted for in building functional
+    % assessment here, but are not currently quantified in the component
+    % breakdowns
+    if any(damage.fnc_filters.local_hazardous_material)
+        % Any local hazardous material shuts down the entire tenant unit
+        recovery_day.hazardous_material(:,tu) = max(repair_complete_day_w_tmp(:,damage.fnc_filters.local_hazardous_material),[],2); 
+    else
+        recovery_day.hazardous_material(:,tu) = zeros(num_reals,1);
+    end
+end
+
+end % end function
+
diff --git a/+recovery/+functionality/main_functionality.m b/+recovery/+functionality/main_functionality.m
new file mode 100644
index 0000000..c828665
--- /dev/null
+++ b/+recovery/+functionality/main_functionality.m
@@ -0,0 +1,68 @@
+function [ recovery ] = main_functionality( damage, building_model, ...
+     damage_consequences, utilities, functionality_options, ...
+     tenant_units, subsystems, impeding_temp_repairs )
+% Calculates building re-occupancy and function based on simulations of
+% building damage and calculates the recovery times of each recovery state
+% based on a given repair schedule
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage, loss, and repair time data for each 
+%   component in the building
+% building_model: struct
+%   general attributes of the building model
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+%   tags and repair costs ratios
+% utilities: struct
+%   data structure containing simulated utility downtimes
+% functionality_options: struct
+%   recovery time optional inputs such as various damage thresholds
+% tenant_units: table
+%   attributes of each tenant unit within the building
+% subsystems: table
+%   attributes of building subsystems; data provided in static tables
+%   directory
+% impeding_temp_repairs: struct
+%   contains simulated temporary repairs the impede occuapancy and function
+%   but are calulated in parallel with the temp repair schedule
+%
+% Returns
+% -------
+% recovery.reoccupancy: struct
+%   contains data on the recovery of tenant- and building-level reoccupancy, 
+%   recovery trajectorires, and contributions from systems and components 
+% recovery.functional: struct
+%   contains data on the recovery of tenant- and building-level function, 
+%   recovery trajectorires, and contributions from systems and components 
+
+%% Import Packages
+import recovery.functionality.fn_calculate_reoccupancy
+import recovery.functionality.fn_calculate_functionality
+import recovery.functionality.fn_check_habitability
+
+%% Calaculate Building Functionality Restoration Curves
+% Downtime including external delays
+[recovery.reoccupancy, reoc_meta.recovery_day, reoc_meta.comp_breakdowns] = ...
+    fn_calculate_reoccupancy( damage, damage_consequences, utilities, ...
+    building_model, functionality_options, tenant_units, impeding_temp_repairs );
+
+[recovery.functional, func_meta.recovery_day, func_meta.comp_breakdowns] =  ...
+    fn_calculate_functionality( damage, damage_consequences, ...
+    utilities, building_model, subsystems, recovery.reoccupancy, ...
+    functionality_options, tenant_units, impeding_temp_repairs);
+
+%% Habitability Checks
+% Overwrite reocuppancy with additional checks from the functionality check
+if isfield(functionality_options,'habitability_requirements')
+    [ recovery.reoccupancy ] = fn_check_habitability( damage, damage_consequences, ...
+         reoc_meta, func_meta, functionality_options.habitability_requirements );
+end
+
+% delete all the extra per-realization data
+recovery.reoccupancy.breakdowns = rmfield(recovery.reoccupancy.breakdowns, 'component_breakdowns_all_reals');
+recovery.functional.breakdowns = rmfield(recovery.functional.breakdowns, 'component_breakdowns_all_reals');
+
+end
+
diff --git a/+recovery/+impedance/fn_contractor.m b/+recovery/+impedance/fn_contractor.m
new file mode 100644
index 0000000..e769a01
--- /dev/null
+++ b/+recovery/+impedance/fn_contractor.m
@@ -0,0 +1,58 @@
+function contractor_mob_imped = fn_contractor( num_reals, surge_factor, ...
+    sys_repair_trigger, trunc_pd, contractor_options )
+% Simulute contractor mobilization time
+%
+% Parameters
+% ----------
+% num_sys: int
+%   number of building systems considered in the assessment
+% num_reals: int
+%   number of Monte Carlo simulations assessed
+% surge_factor: number
+%   amplification factor for impedance time based on a post disaster surge
+%   in demand for skilled trades and construction supplies
+% sys_repair_trigger: logical array [num_reals x num_systems]
+%   systems that require repair for each realization
+% trunc_pd: matlab normal distribution object
+%   standard normal distrubtion, truncated at upper and lower bounds
+% contractor_options: struct
+%   various options that controll the contracting impedance time
+%
+% Returns
+% -------
+% contractor_mob_imped: array [num_reals x num_sys]
+%   Simulated contractor mobilization time for each system
+
+%% Define contractor distribution parameters
+switch contractor_options.contractor_relationship
+    case 'retainer'
+        med = surge_factor * contractor_options.contractor_retainer_time;
+        beta = 0.4;
+    case 'good'
+        med = (1 + 0.5*(surge_factor-1)) * 3;
+        beta = 0.4;
+    case 'none'
+        med = surge_factor * 42;
+        beta = 0.8;
+    otherwise
+        error('PBEE_Recovery:RepairSchedule', 'Invalid contractor relationship type, "%s", for impedance factor simulation', contractor_relationship)
+end
+
+%% Set median for each realization
+contr_med = med * ones(num_reals,1);
+
+%% Simulate Impedance Time
+prob_sim = rand(num_reals, 1); % This assumes systems are correlated
+x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
+contractor_mob_imped = exp(x_vals_std_n * beta + log(contr_med));
+
+% Only use the simulated values for the realzation and system that
+% require permitting
+contractor_mob_imped = contractor_mob_imped .* sys_repair_trigger;
+
+% Amplify by the surge factor
+% Assume impedance always takes a full day
+contractor_mob_imped = ceil(contractor_mob_imped);
+
+end
+
diff --git a/+recovery/+impedance/fn_default_surge_factor.m b/+recovery/+impedance/fn_default_surge_factor.m
new file mode 100644
index 0000000..50cbeb7
--- /dev/null
+++ b/+recovery/+impedance/fn_default_surge_factor.m
@@ -0,0 +1,51 @@
+function [ surge_factor ] = fn_default_surge_factor( is_dense_urban_area, pga, pga_de )
+% This function determines the surge factor based on the intensity of shaking
+%
+% Parameters
+% ----------
+% is_dense_urban_area: bool
+%   if the building is in a dense urban area or not
+% pga: number
+%   peak ground acceleration control point 1 (below which surge factor is 1)
+% pga_de: number
+%   peak ground acceleration at the design intensity
+%
+%
+% Returns
+% -------
+% surge_factor: number
+%   surge factor which amplified impedance factor times
+%
+% Notes
+% -----
+%
+%     |                                         _____________ 3.0
+%     |                                       /              
+%     |                                      /               
+%     |                                     /                
+%     |                                    /                 
+%     |                                   /                  
+%     |                                  /     ______________ 1.5
+%     |                                 /    / |             
+%     |                                /   /   |             
+%     |                               /  /     |             
+%     |                              / /       |             
+%     |                             //         |             
+% 1.0 |  __________________________/           |             
+%     |                            |           |             
+%     |                            |           |             
+%     -----------------------------+-----------+-------------
+%                                pga_1       pga_2
+
+if is_dense_urban_area
+    max_surge = 3;
+else
+    max_surge = 1.5;
+end
+
+pga_1 = max(0.2, pga_de);
+pga_2 = pga_1 * 1.5;
+surge_factor = interp1([pga_1, pga_2], [1, max_surge], min(max(pga,pga_1),pga_2));
+
+end
+
diff --git a/+recovery/+impedance/fn_engineering.m b/+recovery/+impedance/fn_engineering.m
new file mode 100644
index 0000000..d25a18c
--- /dev/null
+++ b/+recovery/+impedance/fn_engineering.m
@@ -0,0 +1,87 @@
+function [ eng_mob_imped, eng_design_imped ] = fn_engineering( ...
+    num_reals, repair_cost_ratio, building_value, ...
+    surge_factor, redesign_trigger, is_engineer_on_retainer, user_options, ...
+    design_min, design_max, trunc_pd, beta, impeding_factor_medians )
+% Simulute permitting time
+%
+% Parameters
+% ----------
+% num_reals: int
+%   number of Monte Carlo simulations assessed
+% repair_cost_ratio: array [num_reals x 1]
+%   simulated building repair cost; normalized by building replacemnt
+%   value.
+% building_value: number
+%   replacment value of building, in USD, non including land
+% surge_factor: number
+%   amplification factor for impedance time based on a post disaster surge
+%   in demand for skilled trades and construction supplies
+% redesign_trigger: logical array [num_reals x num_sys]
+%   is redesign required for the given system
+% is_engineer_on_retainer: logical
+%   is there a pre-arranged agreement with an engineer for priorization of
+%   redesign
+% user_options: struct
+%   contains paramters of system design time function, set by user
+% design_min: number
+%   lower bound on the median design time
+% design_max: number
+%   upper bound on the median design time
+% trunc_pd: matlab normal distribution object
+%   standard normal distrubtion, truncated at upper and lower bounds
+% beta: number
+%   lognormal standard deviation (dispersion)
+% impeding_factor_medians: table
+%   median delays for various impeding factors
+%
+% Returns
+% -------
+% eng_mob_imped: array [num_reals x num_sys]
+%   Simulated enginering mobilization time for each system
+% eng_design_imped: array [num_reals x num_sys]
+%   Simulated enginering design time for each system
+
+% Notes
+% ------
+% assumes engineering mobilization and re-design time are independant, but
+% are correlated between each system. In other words, you will have the same 
+% designers for the structural, stairs, exterior, and whatever other systems 
+% need design time, but the time it takes to spin up an engineer is not
+% related to the time it takes for them to complete the re-design.
+
+%% Engineering Mobilization Time
+% Mobilization medians
+eng_mob_medians = ...
+    impeding_factor_medians(strcmp(impeding_factor_medians.factor,'engineering mobilization'),:);
+
+if is_engineer_on_retainer
+    filt = strcmp(eng_mob_medians.category,'retainer');
+else
+    filt = strcmp(eng_mob_medians.category,'default');
+end
+median_eng_mob = surge_factor * eng_mob_medians.time_days(filt); % days
+
+% Truncated lognormal distribution (via standard normal simulation)
+prob_sim = rand(num_reals, 1); % This assumes systems are correlated
+x_vals_std_n = icdf(trunc_pd, prob_sim);
+eng_mob_time = exp(x_vals_std_n * beta + log(median_eng_mob));
+% Assume impedance always takes a full day
+eng_mob_imped = ceil(eng_mob_time .* redesign_trigger);
+
+%% Engineering Design Time
+% Calculate System Design Time
+RC_total = repair_cost_ratio .* building_value;
+SDT = RC_total * user_options.f / ...
+    (user_options.r * user_options.t * user_options.w);
+design_med = min(max(SDT, design_min), design_max);
+
+% Truncated lognormal distribution (via standard normal simulation)
+% Assumes engineering design time is independant of mobilization time
+prob_sim = rand(num_reals,1); % This assumes systems are correlated
+x_vals_std_n = icdf(trunc_pd ,prob_sim);
+eng_design_time = exp(x_vals_std_n * beta + log(design_med));
+% Assume impedance always takes a full day
+eng_design_imped = ceil(eng_design_time .* redesign_trigger);
+
+end
+
diff --git a/+recovery/+impedance/fn_financing.m b/+recovery/+impedance/fn_financing.m
new file mode 100644
index 0000000..b2ea15c
--- /dev/null
+++ b/+recovery/+impedance/fn_financing.m
@@ -0,0 +1,73 @@
+function financing_imped = fn_financing( capital_available_ratio, ...
+    funding_source, surge_factor, sys_repair_trigger, repair_cost_ratio, ...
+    trunc_pd, beta, impeding_factor_medians )
+% Simulute financing time
+%
+% Parameters
+% ----------
+% capital_available_ratio: number
+%   liquidable funding on hand to make repairs immediately after the
+%   damaging event. Normalized by building replacment value.
+% funding_source: string
+%   accepted values: {'sba', 'private', 'insurance'}
+%   type of funding source for required funds greater than the capital on
+%   hand
+% surge_factor: number
+%   amplification factor for impedance time based on a post disaster surge
+% sys_repair_trigger: logical array [num_reals x num_systems]
+%   systems that require repair for each realization
+% repair_cost_ratio: array [num_reals x 1]
+%   simulated building repair cost; normalized by building replacemnt
+%   value.
+% trunc_pd: matlab normal distribution object
+%   standard normal distrubtion, truncated at upper and lower bounds
+% beta: number
+%   lognormal standard deviation (dispersion)
+% impeding_factor_medians: table
+%   median delays for various impeding factors
+%
+% Returns
+% -------
+% financing_imped: array [num_reals x num_sys]
+%   Simulated financing time for each system
+
+%% Define financing distribution parameters
+% Median financing times
+finance_medians = ...
+    impeding_factor_medians(strcmp(impeding_factor_medians.factor,'financing'),:);
+
+% Required Financing
+financing_trigger = repair_cost_ratio > capital_available_ratio;
+
+% Financing Type
+switch funding_source
+    case 'sba'  % SBA Backed Loans
+        filt = strcmp(finance_medians.category,'sba');
+        median = finance_medians.time_days(filt) * surge_factor; %days
+    case 'private'  % Private loans 
+        filt = strcmp(finance_medians.category,'private');
+        median = finance_medians.time_days(filt); % days, not affected by surge
+    case 'insurance'  % Insurance
+        filt = strcmp(finance_medians.category,'insurance');
+        median = finance_medians.time_days(filt); % days, not affected by surge
+    otherwise 
+        error('PBEE_Recovery:RepairSchedule', 'Invalid financing type, "%s", for impedance factor simulation', funding_source)
+end
+
+%% Simulate
+% Truncated lognormal distribution (via standard normal simulation)
+num_reals = length(repair_cost_ratio);
+prob_sim = rand(num_reals, 1);
+x_vals_std_n = icdf(trunc_pd, prob_sim);
+financing_time = exp(x_vals_std_n * beta + log(median));
+
+% Only use realizations that require financing
+financing_time(~financing_trigger) = 0;
+
+% Affects all systems that need repair
+% Assume impedance always takes a full day
+financing_imped = ceil(financing_time .* sys_repair_trigger);
+
+
+end
+
diff --git a/+recovery/+impedance/fn_inspection.m b/+recovery/+impedance/fn_inspection.m
new file mode 100644
index 0000000..ed41b4a
--- /dev/null
+++ b/+recovery/+impedance/fn_inspection.m
@@ -0,0 +1,61 @@
+function inspection_imped = fn_inspection( is_essential_facility, ...
+    is_borp_equivalent, surge_factor, sys_repair_trigger, ...
+    inpsection_trigger, trunc_pd, beta, impeding_factor_medians )
+% Simulute inspection time
+%
+% Parameters
+% ----------
+% is_essential_facility: logical
+%   is the building deemed essential by the local jurisdiction
+% is_borp_equivalent: logical
+%   does the building have an inspector on retainer (BORP equivalent)
+% surge_factor: number
+%   amplification factor for impedance time based on a post disaster surge
+%   in demand for skilled trades and construction supplies
+% sys_repair_trigger: logical array [num_reals x num_systems]
+%   systems that require repair for each realization
+% inpsection_trigger: logical array [num_reals x 1]
+%   defines which realizations require inspection
+% trunc_pd: matlab normal distribution object
+%   standard normal distrubtion, truncated at upper and lower bounds
+% beta: number
+%   lognormal standard deviation (dispersion)
+% impeding_factor_medians: table
+%   median delays for various impeding factors
+%
+% Returns
+% -------
+% inspection_imped: array [num_reals x num_sys]
+%   Simulated inspection time for each system
+
+%% Define inspection distribtuion parameters
+inspection_medians = ...
+    impeding_factor_medians(strcmp(impeding_factor_medians.factor,'inspection'),:);
+    
+if is_borp_equivalent
+    filt = strcmp(inspection_medians.category,'borp');
+    median = inspection_medians.time_days(filt) * surge_factor; 
+elseif is_essential_facility
+    filt = strcmp(inspection_medians.category,'essential');
+    median = inspection_medians.time_days(filt) * surge_factor;
+else
+    filt = strcmp(inspection_medians.category,'default');
+    median = inspection_medians.time_days(filt) * surge_factor;
+end
+
+%% Simulate 
+% Truncated lognormal distribution
+num_reals = length(inpsection_trigger);
+prob_sim = rand(num_reals, 1);
+x_vals_std_n = icdf(trunc_pd, prob_sim);
+inspection_time = exp(x_vals_std_n * beta + log(median));
+
+% Only use realizations that require inpsection
+inspection_time(~inpsection_trigger) = 0;
+
+% Affects all systems that need repair
+% Assume impedance always takes a full day
+inspection_imped = ceil(inspection_time .* sys_repair_trigger);
+
+end
+
diff --git a/+recovery/+impedance/fn_permitting.m b/+recovery/+impedance/fn_permitting.m
new file mode 100644
index 0000000..6016283
--- /dev/null
+++ b/+recovery/+impedance/fn_permitting.m
@@ -0,0 +1,53 @@
+function [permitting_rapid, permitting_full] = fn_permitting( num_reals, ...
+    sys_repair_trigger, trunc_pd, beta, impeding_factor_medians )
+% Simulute permitting time
+%
+% Parameters
+% ----------
+% num_reals: int
+%   number of Monte Carlo simulations assessed
+% sys_repair_trigger: struct
+%   contains simulation data indicate if rapid permits or full permits are
+%   required for each system
+% trunc_pd: matlab normal distribution object
+%   standard normal distrubtion, truncated at upper and lower bounds
+% beta: number
+%   lognormal standard deviation (dispersion)
+% impeding_factor_medians: table
+%   median delays for various impeding factors
+%
+% Returns
+% -------
+% permitting_rapid: array [num_reals x num_sys]
+%   Simulated time to receive rapid permits, where applicable for each system
+% permitting_full: array [num_reals x num_sys]
+%   Simulated time to for full review and permit process, where applicable for each system
+
+%% Define permitting distribution parameters
+% Find the median permit time for each system
+permit_medians = ...
+    impeding_factor_medians(strcmp(impeding_factor_medians.factor,'permitting'),:);
+
+% Rapid Permits
+filt = strcmp(permit_medians.category,'rapid');
+rapid_permit_median = permit_medians.time_days(filt); % days
+
+% Full Permits
+filt = strcmp(permit_medians.category,'full');
+full_permit_median = permit_medians.time_days(filt); % days
+
+%% Simulate
+% Rapid Permits
+prob_sim = rand(num_reals, 1); % This assumes systems are correlated
+x_vals_std_n = icdf(trunc_pd, prob_sim); % Truncated lognormal distribution (via standard normal simulation)
+rapid_permit_time = exp(x_vals_std_n * beta + log(rapid_permit_median));
+permitting_rapid = ceil(rapid_permit_time .* sys_repair_trigger.rapid_permit); % Assume impedance always takes a full day
+
+% Full Permits - simulated times are independent of rapid permit times
+prob_sim = rand(num_reals, 1); % This assumes systems are correlated
+x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
+full_permit_time = exp(x_vals_std_n * beta + log(full_permit_median));
+permitting_full = ceil(full_permit_time .* sys_repair_trigger.full_permit); % Assume impedance always takes a full day
+
+end
+
diff --git a/+recovery/+impedance/main_impeding_factors.m b/+recovery/+impedance/main_impeding_factors.m
new file mode 100644
index 0000000..2b73edc
--- /dev/null
+++ b/+recovery/+impedance/main_impeding_factors.m
@@ -0,0 +1,332 @@
+function [impeding_factors] = main_impeding_factors(damage, ...
+    impedance_options, repair_cost_ratio_total, repair_cost_ratio_engineering, ...
+    inpsection_trigger, systems, tmp_repair_class, building_value, impeding_factor_medians, include_flooding_impact)
+% Calculate ATC-138 impeding times for each system given simulation of damage
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage and loss data for each component in the building
+% impedance_options: struct
+%   general impedance assessment user inputs such as mitigation factors
+% repair_cost_ratio_total: array [num_reals x 1]
+%   total repair cost per realization normalized by building replacement
+%   value
+% repair_cost_ratio_engineering: array [num_reals x 1]
+%   repair cost per realization that is used for engineering impedance (structural/stairs/envelope)
+% inpsection_trigger: logical array [num_reals x 1]
+%   defines which realizations require inspection
+% num_stories: int
+%   Total number of building stories
+% systems: table
+%   data table containing information about each system's attributes
+% building_value: number
+%   The replacement value of the building, in USD
+% impeding_factor_medians: table
+%   median delays for various impeding factors
+%
+% Returns
+% -------
+% impedingFactors.time_sys: array [num_reals x num_sys]
+%   Simulated total impeding time for each system
+% impedingFactors.breakdown: struct
+%   feilds: 'inspection', 'financing', 'eng_mob', 'design', 'permitting', 'contractor_mob'
+%   The simulated start day and complete day for each impeding factor,
+%   broken down per system where applicable
+%   
+%
+% Notes
+% -----
+% Correlation: Assuming factor-to-factor simulations are independent, but
+% within a given factor, simulated system impedance time is fully
+% correlated
+
+
+%% Initial Setup
+% Import packages
+import recovery.impedance.fn_contractor
+import recovery.impedance.fn_engineering
+import recovery.impedance.fn_financing
+import recovery.impedance.fn_inspection
+import recovery.impedance.fn_permitting
+import recovery.impedance.fn_default_surge_factor
+
+% Initialize parameters
+num_reals = length(inpsection_trigger);
+num_sys = height(systems);
+
+% Pre-allocate each type of impedance
+duration.inspection = zeros(num_reals, num_sys);
+duration.financing = zeros(num_reals, num_sys);
+duration.permit_rapid = zeros(num_reals, num_sys);
+duration.permit_full = zeros(num_reals, num_sys);
+duration.contractor_mob = zeros(num_reals, num_sys);
+duration.eng_mob = zeros(num_reals, num_sys);
+duration.design = zeros(num_reals, num_sys);
+duration.long_lead = zeros(num_reals, num_sys);
+
+% Create basic trucated standard normal distribution for later simulation
+pd = makedist('normal','mu',0,'sigma',1);
+th_low = -impedance_options.impedance_truncation;
+th_high = impedance_options.impedance_truncation;
+trunc_pd = truncate(pd,th_low,th_high);
+beta = impedance_options.impedance_beta;
+
+%% Calculate Demand Surge (if applicble)
+if impedance_options.demand_surge.include_surge
+    surge_factor = fn_default_surge_factor( ...
+        impedance_options.demand_surge.is_dense_urban_area, ...
+        impedance_options.demand_surge.site_pga, ...
+        impedance_options.demand_surge.pga_de ...
+    );
+else
+    surge_factor = 1;
+end
+    
+%% Parse through damage to determine which systems require repair
+sys_repair_trigger.any = zeros(num_reals, num_sys);
+sys_repair_trigger.rapid_permit = zeros(num_reals, num_sys);
+sys_repair_trigger.full_permit = zeros(num_reals, num_sys);
+sys_repair_trigger.redesign = zeros(num_reals, num_sys);
+sys_repair_trigger.flooding = zeros(num_reals, 1);
+for sys = 1:num_sys
+    sys_filt = damage.comp_ds_table.system' == sys; 
+    for tu = 1:length(damage.tenant_units)
+        is_damaged = damage.tenant_units{tu}.qnt_damaged > 0 & damage.tenant_units{tu}.worker_days > 0; % There is damage that needs to be fixed
+        % Track if any damage exists that requires repair (assumes all
+        % damage requires repair)
+        sys_repair_trigger.any(:,sys) = max( ...
+            sys_repair_trigger.any(:,sys), ...
+            max(is_damaged .* sys_filt, [], 2) ...
+        );
+        
+        % Track if any damage exists that requires rapid permit per system
+        sys_repair_trigger.rapid_permit(:,sys) = max( ...
+            sys_repair_trigger.rapid_permit(:,sys), ...
+            max(is_damaged .* (sys_filt & damage.fnc_filters.permit_rapid), [], 2) ...
+        );
+    
+        % Track if any damage exists that requires full permit per system
+        sys_repair_trigger.full_permit(:,sys) = max( ...
+            sys_repair_trigger.full_permit(:,sys), ...
+            max(is_damaged .* (sys_filt & damage.fnc_filters.permit_full), [], 2) ...
+        );
+    
+        % Track if any systems require redesign
+        sys_repair_trigger.redesign(:,sys) = max( ...
+            sys_repair_trigger.redesign(:,sys), ...
+            max(is_damaged .* (sys_filt & damage.fnc_filters.redesign), [], 2) ...
+        );
+    
+        % Track if any damage exists that triggers flooding
+        sys_repair_trigger.flooding = max( ...
+            sys_repair_trigger.flooding, ...
+            max(is_damaged .* (sys_filt & damage.fnc_filters.causes_flooding), [], 2) ...
+        );
+    end
+end
+
+%% Simulate impedance time for each impedance factor 
+if impedance_options.include_impedance.inspection
+    duration.inspection = fn_inspection( ...
+        impedance_options.mitigation.is_essential_facility, ...
+        impedance_options.mitigation.is_borp_equivalent, ...
+        surge_factor, sys_repair_trigger.any, ...
+        inpsection_trigger, trunc_pd, beta, impeding_factor_medians );
+end
+
+if impedance_options.include_impedance.financing
+    duration.financing = fn_financing( ...
+        impedance_options.mitigation.capital_available_ratio, ...
+        impedance_options.mitigation.funding_source, ...
+        surge_factor, sys_repair_trigger.any, ...
+        repair_cost_ratio_total, trunc_pd, beta, impeding_factor_medians );
+end
+
+if impedance_options.include_impedance.permitting
+    [duration.permit_rapid, duration.permit_full ]= fn_permitting( ...
+        num_reals, sys_repair_trigger, trunc_pd, beta, impeding_factor_medians );
+end
+
+if impedance_options.include_impedance.contractor
+    duration.contractor_mob = fn_contractor( num_reals, surge_factor, ...
+        sys_repair_trigger.any, trunc_pd, impedance_options.mitigation );
+end
+
+if impedance_options.include_impedance.engineering
+    [ duration.eng_mob, duration.design ] = fn_engineering( num_reals, ...
+        repair_cost_ratio_engineering, building_value, surge_factor, ...
+        sys_repair_trigger.redesign, ...
+        impedance_options.mitigation.is_engineer_on_retainer, ...
+        impedance_options.system_design_time, ...
+        impedance_options.eng_design_min_days', ...
+        impedance_options.eng_design_max_days', ...
+        trunc_pd, beta, impeding_factor_medians);
+end
+
+if impedance_options.include_impedance.long_lead
+    for sys = 1:num_sys
+        sys_filt = damage.comp_ds_table.system' == sys; 
+        
+        % Simulate long lead times. Assume long lead times are correlated among
+        % all components within the system, but independant between systems
+        prob_sim = rand(num_reals, 1);
+        x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
+        sim_long_lead = exp(x_vals_std_n * beta + log(damage.comp_ds_table.long_lead_time'));
+        
+        for tu = 1:length(damage.tenant_units)
+            is_damaged = damage.tenant_units{tu}.qnt_damaged > 0 & damage.tenant_units{tu}.worker_days > 0;
+            
+            % Track if any damage exists that requires repair (assumes all
+            % damage requires repair). The long lead time for the system is
+            % the max long lead time for any component within the system
+            duration.long_lead(:,sys) = max( ...
+                duration.long_lead(:,sys), ...
+                max(is_damaged .* sys_filt .* sim_long_lead, [], 2) ...
+            );
+        end
+    end
+end
+
+%% Aggregate experienced impedance time for each system/sequence and realization 
+% Figure out when each impeding factor finishes
+start_day.inspection = zeros(num_reals,num_sys);
+complete_day.inspection = duration.inspection;
+
+start_day.financing = complete_day.inspection;
+complete_day.financing = start_day.financing + duration.financing;
+
+start_day.eng_mob = max(complete_day.inspection,start_day.financing);
+complete_day.eng_mob = start_day.eng_mob + duration.eng_mob;
+
+start_day.design = complete_day.eng_mob;
+complete_day.design = start_day.design + duration.design;
+
+start_day.permit_rapid = complete_day.design;
+complete_day.permit_rapid = start_day.permit_rapid + duration.permit_rapid;
+
+start_day.permit_full = complete_day.design;
+complete_day.permit_full = start_day.permit_full + duration.permit_full;
+
+start_day.contractor_mob = max(complete_day.inspection,start_day.financing);
+complete_day.contractor_mob = start_day.contractor_mob + duration.contractor_mob;
+
+start_day.long_lead = max(complete_day.inspection,start_day.financing);
+complete_day.long_lead = start_day.long_lead + duration.long_lead;
+
+% Combine all impedance factors by system
+impede_factors = fieldnames(complete_day);
+impeding_factors.time_sys = 0;
+for i = 1:length(impede_factors)
+    impeding_factors.time_sys = max(impeding_factors.time_sys,...
+        complete_day.(impede_factors{i}));
+end
+    
+%% Simulate Impeding Factors for Temporary Repairs
+% Determine median times for each system
+switch impedance_options.mitigation.contractor_relationship
+    case 'retainer'
+        temp_impede_med = surge_factor*tmp_repair_class.impeding_time'; % days
+    case 'good'
+        temp_impede_med = surge_factor*tmp_repair_class.impeding_time'; % days
+    case 'none'
+        temp_impede_med = surge_factor*tmp_repair_class.impeding_time_no_contractor'; % days
+    otherwise
+        error('PBEE_Recovery:RepairSchedule', 'Invalid contractor relationship type, "%s", for impedance factor simulation', contractor_relationship)
+end
+
+% Find the which realization have damage that can be resolved by temp
+% repairs
+tmp_repair_class_trigger = zeros(num_reals, height(tmp_repair_class));
+for sys = 1:height(tmp_repair_class) 
+    sys_filt = damage.comp_ds_table.tmp_repair_class' == sys; 
+    for tu = 1:length(damage.tenant_units)
+        is_damaged = damage.tenant_units{tu}.qnt_damaged > 0 & damage.tenant_units{tu}.worker_days > 0;
+        % Track if any damage exists that requires repair (assumes all
+        % damage requires repair)
+        tmp_repair_class_trigger(:,sys) = max( ...
+            tmp_repair_class_trigger(:,sys), ...
+            max(is_damaged .* sys_filt, [], 2) ...
+        );
+    end
+end
+
+% Simulate Impedance Time
+prob_sim = rand(num_reals, 1); % This assumes systems are correlated
+x_vals_std_n = icdf(trunc_pd, prob_sim); % Truncated lognormal distribution (via standard normal simulation)
+tmp_impede_sys = exp(x_vals_std_n * beta + log(temp_impede_med));
+
+% Only use the simulated values for the realzation and system that
+% trigger temporary repair damage
+tmp_impede_sys = tmp_impede_sys .* tmp_repair_class_trigger;
+
+% Assume impedance always takes a full day
+impeding_factors.temp_repair.time_sys = ceil(tmp_impede_sys);
+
+%% Simulate impeding factors and temp repair that occur in parallel with temp repair scheudl
+% Temporary scaffolding for falling hazards
+prob_sim = rand(num_reals, 1);
+x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
+scaffold_impede_time = ceil(surge_factor*exp(x_vals_std_n * beta + log(impedance_options.scaffolding_lead_time))); % always round up
+
+prob_sim = rand(num_reals, 1); % repair time is not correlated to impedance time
+x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
+scaffold_repair_time = exp(x_vals_std_n * beta + log(impedance_options.scaffolding_erect_time)); 
+impeding_factors.temp_repair.scaffold_day = ceil(scaffold_impede_time + scaffold_repair_time); % round up (dont resolve issue on the same day repairs are complete)
+
+% Door Unjamming
+prob_sim = rand(num_reals, 1);
+x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
+impeding_factors.temp_repair.door_racking_repair_day = ceil(surge_factor*exp(x_vals_std_n * beta + log(impedance_options.door_racking_repair_day))); % always round up
+
+% Interior Flooding
+if include_flooding_impact
+	% Flooding Cleanup
+	prob_sim = rand(num_reals, 1);
+	x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
+	impeding_factors.temp_repair.flooding_cleanup_day = ...
+	    sys_repair_trigger.flooding .* ...
+	    ceil(surge_factor*exp(x_vals_std_n * beta + log(impedance_options.flooding_cleanup_day))); % always round up
+	
+	% Repair Flooding Damage
+	prob_sim = rand(num_reals, 1);
+	x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
+	impeding_factors.temp_repair.flooding_repair_day = ...
+	    sys_repair_trigger.flooding .* ...
+	    ceil(surge_factor*exp(x_vals_std_n * beta + log(impedance_options.flooding_repair_day))); % always round up
+else % zero out the flooding impedance (cleanup and repair)
+    impeding_factors.temp_repair.flooding_cleanup_day = zeros(num_reals, 1); % Flooding Cleanup
+    impeding_factors.temp_repair.flooding_repair_day = zeros(num_reals, 1); % Repair Flooding Damage
+end
+
+%% Format Impedance times for Gantt Charts
+% Full repair
+impeding_factors.breakdowns.full.inspection.start_day = max(start_day.inspection,[],2);
+impeding_factors.breakdowns.full.inspection.complete_day = max(complete_day.inspection,[],2);
+impeding_factors.breakdowns.full.financing.start_day = max(start_day.financing,[],2);
+impeding_factors.breakdowns.full.financing.complete_day = max(complete_day.financing,[],2);
+impeding_factors.breakdowns.full.contractor_mob.start_day = max(start_day.contractor_mob,[],2);
+impeding_factors.breakdowns.full.contractor_mob.complete_day = max(complete_day.contractor_mob,[],2);
+impeding_factors.breakdowns.full.eng_mob.start_day = max(start_day.eng_mob,[],2);
+impeding_factors.breakdowns.full.eng_mob.complete_day = max(complete_day.eng_mob,[],2);
+impeding_factors.breakdowns.full.design.start_day = max(start_day.design,[],2);
+impeding_factors.breakdowns.full.design.complete_day = max(complete_day.design,[],2);
+impeding_factors.breakdowns.full.permit_rapid.start_day = max(start_day.permit_rapid,[],2);
+impeding_factors.breakdowns.full.permit_rapid.complete_day = max(complete_day.permit_rapid,[],2);
+impeding_factors.breakdowns.full.permit_full.start_day = max(start_day.permit_full,[],2);
+impeding_factors.breakdowns.full.permit_full.complete_day = max(complete_day.permit_full,[],2);
+
+% Represent long lead times per system
+for s = 1:height(systems)
+    impeding_factors.breakdowns.long_lead.(systems.name{s}).start_day = start_day.long_lead(:,s);
+    impeding_factors.breakdowns.long_lead.(systems.name{s}).complete_day = complete_day.long_lead(:,s);
+end
+
+% Temporary Repairs
+for tmp = 1:height(tmp_repair_class)
+    impeding_factors.breakdowns.temp.(tmp_repair_class.name_short{tmp}).start_day = zeros(num_reals,1);
+    impeding_factors.breakdowns.temp.(tmp_repair_class.name_short{tmp}).complete_day = impeding_factors.temp_repair.time_sys(:,1);
+end
+
+
+end
diff --git a/+recovery/+repair_schedule/fn_allocate_workers_stories.m b/+recovery/+repair_schedule/fn_allocate_workers_stories.m
new file mode 100644
index 0000000..1dd2285
--- /dev/null
+++ b/+recovery/+repair_schedule/fn_allocate_workers_stories.m
@@ -0,0 +1,107 @@
+function [repair_start_day, repair_complete_day, max_workers_per_story] = fn_allocate_workers_stories(...
+    total_worker_days, required_workers_per_story, average_crew_size, max_crews_building, max_workers_per_building)
+% For a given building system, allocate crews to each story to
+% determine the system level repair time on each story; not considering
+% impeding factors or construction constriants with other systems.
+%
+%
+% Parameters
+% ----------
+% total_worker_days: [num reals x num stories]
+%   The total worker days needed to repair damage each story for this
+%   sequence
+% required_workers_per_story: array [num reals x num stories]
+%   Number of workers required to repair damage in each story
+% average_crew_size: array [num reals x num stories]
+%   Average crew size required to repair damage in each story
+% max_crews_building: logical array [num reals x 1]
+%   maximum number of crews allowed in the building for this system
+% max_workers_per_building: int
+%   maximum number of workers allowed in the buildings at a given time
+%
+% Returns
+% -------
+% repair_start_day: matrix [num reals x num stories]
+%   The number of days from the start of repair of this system until the repair of this system 
+%   starts on each story
+% repair_complete_day: matrix [num reals x num stories]
+%   The number of days from the start of repair of this system until each story is
+%   repaired for damage in this system
+% max_workers_per_story: matrix [num reals x num stories]
+%   number of workers required for the repair of this story and system as
+%
+% Notes
+% -----
+
+
+%% Initial Setup
+[num_reals, num_stories] = size(total_worker_days);
+repair_complete_day = zeros(num_reals,num_stories);
+repair_start_day = nan(num_reals,num_stories);
+max_workers_per_story = zeros(num_reals,num_stories);
+
+%% Allocate workers to each story
+% Loop through iterations of time reduce damage on each story based on
+% assigned workers
+iter = 0;
+while sum(sum(total_worker_days)) > 0.01
+    iter = iter + 1; 
+    if iter > 1000 % keep the while loop pandemic contained
+        error('PBEE_Recovery:RepairSchedule', 'Could not converge worker allocations for among stories in sequence');
+    end
+
+    % Determine the available workers in the building
+    available_workers_in_building = max_workers_per_building*ones(num_reals, 1);
+    assigned_workers_per_story = zeros(num_reals,num_stories); 
+    assigned_crews_per_story = zeros(num_reals,num_stories); 
+
+    % Define where needs repair
+    needs_repair = total_worker_days > 0;
+
+    % Defined Required Workers
+    required_workers_per_story = needs_repair .* required_workers_per_story;
+    
+    % Assign Workers to each story -- assumes that we wont drop the number
+    % of crews in order to meet worker per sqft limitations, and instead
+    % wait until more workers are made available
+    for s = 1:num_stories
+        % Are there enough workers to assign a crew
+        sufficient_workers = required_workers_per_story(:,s) <= available_workers_in_building;
+        
+        % Assign Workers to this story
+        assigned_workers_per_story(sufficient_workers,s) = required_workers_per_story(sufficient_workers,s);
+        assigned_crews_per_story(:,s) = assigned_workers_per_story(:,s) ./ average_crew_size(:,s);
+        assigned_crews_per_story(isnan(assigned_crews_per_story)) = 0;
+        num_crews_in_building = sum(assigned_crews_per_story,2);
+        exceeded_max_crews = num_crews_in_building > max_crews_building;
+        assigned_workers_per_story(exceeded_max_crews,s) = 0; % don't assign workers if we have exceeded the number of crews allowed in the building
+        
+        % Define Available Workers
+        available_workers_in_building = available_workers_in_building - assigned_workers_per_story(:,s);  
+    end
+    
+    % Define the start of repairs for each story
+    start_repair_filt = isnan(repair_start_day) & (assigned_workers_per_story > 0);
+    max_day_completed_so_far = max(repair_complete_day,[],2) .* ones(num_reals,num_stories);
+    repair_start_day(start_repair_filt) =  max_day_completed_so_far(start_repair_filt);
+    
+    % Calculate the time associated with this increment of the while loop
+    in_progress = assigned_workers_per_story > 0; % stories where work is being done
+    total_repair_days = inf(size(in_progress)); % pre-allocate with inf's becuase we take a min later
+    total_repair_days(in_progress) = total_worker_days(in_progress) ./ assigned_workers_per_story(in_progress);
+    delta_days = min(total_repair_days,[],2); % time increment is whatever in-progress story that finishes first
+    delta_days(isinf(delta_days)) = 0; % Replace infs from real that has no repair with zero
+    delta_worker_days = assigned_workers_per_story .* delta_days; % time increment is whatever in-progress story that finishes first
+    total_worker_days(in_progress) = max(total_worker_days(in_progress) - delta_worker_days(in_progress),0);
+    indx_neg_remaining = total_worker_days < 0.001; % find instances of remaining time that are excessively small and don't represent realistic amount of work
+    total_worker_days(indx_neg_remaining) = 0;  % zero remaining work that is negligible as defined above
+    
+    % Define Start and Stop of Repair for each story in each sequence
+    repair_complete_day = repair_complete_day + delta_days .* needs_repair;
+    
+    % Max Crew Size for use in later function
+    max_workers_per_story = max(max_workers_per_story,assigned_workers_per_story);
+end
+
+end
+
diff --git a/+recovery/+repair_schedule/fn_allocate_workers_systems.m b/+recovery/+repair_schedule/fn_allocate_workers_systems.m
new file mode 100644
index 0000000..3276cb8
--- /dev/null
+++ b/+recovery/+repair_schedule/fn_allocate_workers_systems.m
@@ -0,0 +1,191 @@
+function [repair_complete_day_per_system, worker_data] = ...
+    fn_allocate_workers_systems(systems, sys_repair_days, sys_crew_size, ...
+    max_workers_per_building, sys_idx_priority_matrix, ...
+    sys_constraint_matrix, condition_tag, sys_impeding_factors)
+% Stager repair to each system and allocate workers based on the repair
+% constraints, priorities, and repair times of each system
+%
+% Parameters
+% ----------
+% system_repair_days: [num reals x num systems]
+%   Number of days from the start of repair of each to the completion of
+%   the system (assuming all sequences start on day zero)
+% sys_crew_size: array [num reals x num systems]
+%   required crew size for each system
+% max_workers_per_building: int
+%   Maximum number of workers that can work in the building at the same time 
+% sys_idx_priority_matrix: matrix [num reals x systems]
+%   worker allocation order of system id's prioritized for each realiztion
+% sys_constraint_matrix: array [num reals x num_sys]
+%   array of system ids which define which systems (column index) are delayed by the
+%   system ids (array values)
+% condition_tag: logical array [num reals x 1]
+%   true/false if the building is red tagged
+% sys_impeding_factors: array [num_reals x num_sys]
+%   maximum impedance time (days) for each system. Pass in empty array when
+%   calculating repair times (i.e. not including impeding factors).
+%
+% Returns
+% -------
+% repair_complete_day_per_system: matrix [num reals x num systems]
+%   Number of days from the start of each sequence to the completion of the
+%   sequence considering the allocation of workers to each sequence (ie
+%   some sequences start before others)
+% worker_data.total_workers: array [num reals x varies]
+%   total number of workers in the building at each time step of the worker
+%   allocation algorthim. The number of columns varies with the number of
+%   increments of the worker allocation algorithm.
+% worker_data.day_vector: array [num reals x varies]
+%   Day of each time step of the worker allocation algorthim. The number of 
+%   columns varies with the number of increments of the worker allocation algorithm.
+%
+% Notes
+% -----
+
+%% Initial Setup
+% Initialize Variables
+[num_reals, num_sys] = size(sys_repair_days);
+priority_system_complete_day = zeros(num_reals,num_sys);
+day_vector = zeros(num_reals, 0);
+total_workers = zeros(num_reals, 0);
+
+% Re-order system variables based on priority
+[ priority_sys_workers_matrix ] = fitler_matrix_by_rows( sys_crew_size, sys_idx_priority_matrix );
+[ priority_sys_constraint_matrix ] = fitler_matrix_by_rows( sys_constraint_matrix, sys_idx_priority_matrix );
+[ priority_sys_repair_days ] = fitler_matrix_by_rows( sys_repair_days, sys_idx_priority_matrix );
+if isempty(sys_impeding_factors)
+    priority_sys_impeding_factors = zeros(num_reals, num_sys);
+else
+    [ priority_sys_impeding_factors ] = fitler_matrix_by_rows( sys_impeding_factors, sys_idx_priority_matrix );
+end
+
+% Round up days to the nearest day
+% Provides an implicit change of trade delay, as well as help to reduce the
+% number of delta increments in the following while loop
+priority_sys_repair_days = ceil(priority_sys_repair_days);
+priority_sys_impeding_factors = ceil(priority_sys_impeding_factors);
+
+%% Assign workers to each system based on repair constraints
+iter = 0;
+current_day = zeros(num_reals,1);
+priority_sys_waiting_days = priority_sys_impeding_factors;
+while sum(sum(priority_sys_repair_days)) > 0.01
+    iter = iter + 1; 
+    if iter > 1000 % keep the while loop pandemic contained
+        error('PBEE_Recovery:RepairSchedule', 'Could not converge worker allocations for among systems');
+    end
+    
+    % zero out assigned workers matrix
+    assigned_workers = zeros(num_reals,num_sys);
+    
+    % limit available workers to the max that can be on any one given story
+    % this ensures next system cannot start until completely unblocked on
+    % every story. Need to update for taller buildings which could start
+    % next sequence on lower story once previous sequence was far enough
+    % along
+%     available_workers = min(max(max_workers_per_story),max_workers_per_building)*ones(num_reals, 1);  % currently assumes all stories have the same crew size limitation (uniform sq ft for each story)
+    available_workers = max_workers_per_building*ones(num_reals, 1);
+    
+    % Define what systems are waiting to begin repairs
+    sys_blocked = zeros(num_reals,num_sys);
+    sys_incomplete = (priority_sys_repair_days > 0);
+%     constraining_systems = unique(priority_sys_constraint_matrix(priority_sys_constraint_matrix ~=0));
+    for s = 1:num_sys % Loop over each system that may constrain something
+        constrained_systems = priority_sys_constraint_matrix == s; % These systems are contrained by looped system
+        constraining_sys_filt = sys_idx_priority_matrix == s; % location in matrix of this system
+        is_constraining_sys_incomplete = max(sys_incomplete .* constraining_sys_filt,[],2); % Vec of relizations for looped system
+        sys_blocked = sys_blocked | (constrained_systems .* is_constraining_sys_incomplete); % System is constrained if blocked by an incomplete system
+    end
+    
+    % Need to wait for impeding factors or other repairs to finish
+    is_waiting = (current_day < priority_sys_impeding_factors) | sys_blocked; % assuming impeding factors are the only constraints
+    
+    % Define where needs repair
+    % System needs repair if there are still repairs days left and it is
+    % not waiting to be unblocked
+    needs_repair = (priority_sys_repair_days > 0) & ~is_waiting;
+
+    % Defined Required Workers
+    required_workers = needs_repair .* priority_sys_workers_matrix;
+    
+    % Assign Workers to each system
+    for s = 1:num_sys
+        % Assign Workers to this systems
+        enough_workers = required_workers(:,s) <= available_workers;
+        assigned_workers(enough_workers,s) = min(required_workers(enough_workers,s), available_workers(enough_workers));
+
+        % Define Available Workers
+        % when in series limit available workers to the workers in this system 
+        % (occurs for structural systems when the building is red tagged
+        is_sturctural = strcmp(systems.name{s},'structural');
+        in_series = condition_tag & is_sturctural;
+        available_workers(in_series & assigned_workers(:,s) > 0) = 0; 
+        % when not in series, calc the remaining workers
+        available_workers(~in_series) = available_workers(~in_series) - assigned_workers(~in_series,s); 
+    end
+    
+    % Calculate the time associated with this increment of the while loop
+    in_progress = assigned_workers > 0; % sequences where work is being done
+    total_repair_days = inf(size(in_progress)); % pre-allocate with inf's becuase we take a min later
+    total_repair_days(in_progress) = priority_sys_repair_days(in_progress);
+    total_waiting_days = priority_sys_waiting_days;
+    total_waiting_days(total_waiting_days == 0) = inf; % Convert zeros to inf such that zeros are not included in the min in the next step
+    total_time = min(total_repair_days,total_waiting_days); % combime repair time and waiting time
+    delta_days = min(total_time,[],2); % time increment is whatever in-progress story that finishes first
+    delta_days(isinf(delta_days)) = 0; % Replace infs from real that has no repair with zero
+    
+    % Reduce waiting time
+    priority_sys_waiting_days = max(priority_sys_waiting_days - delta_days,0);
+    
+    % Reduce time needed for repairs
+    delta_days_in_progress = delta_days .* in_progress; % change in repair time for all sequences being worked on
+    priority_sys_repair_days(in_progress) = max(priority_sys_repair_days(in_progress) - delta_days_in_progress(in_progress),0);
+    
+    % Define Start and Stop of Repair for each sequence
+    priority_system_complete_day = priority_system_complete_day + delta_days .* (needs_repair | is_waiting);
+    
+    % Define Cummulative day of repair
+    day_vector = [day_vector, current_day];
+    current_day = current_day + delta_days;
+    
+    % Save worker data data over time
+    total_workers = [total_workers, sum(assigned_workers,2), sum(assigned_workers,2)];
+    day_vector = [day_vector, current_day];
+end
+    
+% Untangle system_complete_day back into system table order
+[~, sys_idx_untangle_matrix] = sort(sys_idx_priority_matrix, 2);
+[ repair_complete_day_per_system ] = fitler_matrix_by_rows( priority_system_complete_day, sys_idx_untangle_matrix );
+
+% Save worker data matrices
+worker_data.total_workers = total_workers;
+worker_data.day_vector = day_vector;
+end
+
+
+function [ filtered_values ] = fitler_matrix_by_rows( values, filter )
+% Use a identiry matrix to filter values from another matrix by rows
+
+% Parameters
+% ----------
+% values: matrix [n x m]
+%   values to filter by row
+% filter: matrix [n x m]
+%   array indexes to grab from each row of values
+%
+% Returns
+% -------
+% filtered_values: matrix [n x m]
+%   values filtered by rows
+
+% Method
+x2t = values.';
+idx1 = filter.';
+y4         = zeros(size(idx1));
+for r = 1:size(values,1)
+    y4(:, r) = x2t(idx1(:, r), r);
+end
+filtered_values = y4.';
+
+
+end
\ No newline at end of file
diff --git a/+recovery/+repair_schedule/fn_calc_system_repair_time.m b/+recovery/+repair_schedule/fn_calc_system_repair_time.m
new file mode 100644
index 0000000..9f31c3e
--- /dev/null
+++ b/+recovery/+repair_schedule/fn_calc_system_repair_time.m
@@ -0,0 +1,159 @@
+function [schedule] = fn_calc_system_repair_time(damage, repair_type, systems, max_workers_per_building, max_workers_per_story)
+% Determine the repair time for each system if repaired in isolation 
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage and loss data for each component in the building
+% repair_type: string
+%   String identifier indicating whether the repair are temporary or full
+% systems: table
+%   data table containing information about each system's attributes
+% max_workers_per_building: int
+%   maximum number of workers allowed in the building at once
+% max_workers_per_story: array [1 x num_stories]
+%   maximum number of workers allowed in each story at once
+%
+% Returns
+% -------
+% schedule.per_system{sys}.repair_start_day [num reals x num stories]
+%   The number of days from the start of repair of a specifc system until
+%   the start of repair of each story for that given system (i.e. the day 
+%   of the start of repairs relative the to start of this system ... e.g.
+%   story 1 should always be zero)
+% schedule.per_system{sys}.repair_complete_day [num reals x num stories]
+%   The number of days from the start of repair of a specifc system until
+%   each story is fully repaired for that given system.
+% schedule.per_system{sys}.max_num_workers_per_story [num reals x num stories]
+%   The number of workers required to repair each story of this system
+% schedule.system_totals.repair_days [num reals x num systems]
+%   The number of days required to repair each system in isolation
+% schedule.system_totals.num_workers [num reals x num systems]
+%   The number of workers required for the repair of each system
+%
+% Notes
+% -----
+
+%% Initial Setup
+% Import Packages
+import recovery.repair_schedule.fn_allocate_workers_stories
+
+% General Varaible
+[num_reals, ~] = size(damage.tenant_units{1}.worker_days);
+schedule.system_totals.repair_days = zeros(num_reals,height(systems));
+schedule.system_totals.num_workers = zeros(num_reals,height(systems));
+
+%% Allocate workers to each story for each system
+% Repair finish times assumes all sequences start on day zero
+for sys = 1:height(systems)
+    % Define the Crew workers and total workers days for this sequence
+    % in arrays of [num reals by num stories]
+    [ total_worker_days, num_workers, average_crew_size, max_crews_building ] ...
+        = fn_repair_sequence_parameters( ...
+            damage, ...
+            repair_type, ...
+            systems.id(sys), ...
+            systems.num_du_per_crew(sys), ...
+            systems.max_crews_per_comp_type(sys), ...   
+            max_workers_per_story, ...
+            max_workers_per_building ...
+            );
+    
+    % Allocate workers to each story and determine the total days until
+    % repair is complete for each story and sequence
+    [ ... 
+        schedule.per_system{sys}.repair_start_day, ...
+        schedule.per_system{sys}.repair_complete_day, ...
+        schedule.per_system{sys}.max_num_workers_per_story ...
+    ] = fn_allocate_workers_stories( ...
+        total_worker_days, num_workers, average_crew_size, max_crews_building, max_workers_per_building);
+
+    % How many days does it take to complete each system in isloation
+    schedule.system_totals.repair_days(:,sys) = max(schedule.per_system{sys}.repair_complete_day,[],2);
+    schedule.system_totals.num_workers(:,sys) = max(schedule.per_system{sys}.max_num_workers_per_story,[],2);
+end 
+
+end
+
+
+function [total_worker_days, num_workers, average_crew_size, max_crews_building] = fn_repair_sequence_parameters( ...
+    damage, repair_type, sys, num_du_per_crew, max_crews_per_comp_type, max_workers_per_story, max_workers_per_building)
+% Define crew sizes, workers, and repair times for each story of a given
+% system. Based on worker limiations, and component worker days data from
+% the FEMA P-58 assessment.
+
+% Define Repair Type Variables (variable within the damage object)
+if strcmp(repair_type,'full')
+    repair_time_var = 'worker_days';
+    system_var = 'system';
+    crew_size_var = 'crew_size';
+elseif strcmp(repair_type,'temp')
+    repair_time_var = 'tmp_worker_day';
+    system_var = 'tmp_repair_class';
+    crew_size_var = 'tmp_crew_size';
+else
+    error('Unexpected Repair Type')
+end
+
+% Define Initial Parameters
+num_stories = length(damage.tenant_units);
+[num_reals, num_comps] = size(damage.tenant_units{1}.worker_days);
+sequence_filt = damage.comp_ds_table.(system_var)' == sys; % identifies which ds idices are in this seqeunce  
+comp_types = unique(damage.comp_ds_table.comp_idx(sequence_filt)); % Types of components in this system
+
+% Pre-allocatate variables
+total_worker_days = zeros(num_reals,num_stories);
+is_damaged_building = zeros(num_reals,num_comps);
+num_damaged_comp_types = zeros(num_reals,num_stories);
+num_damaged_units = zeros(num_reals,num_stories);
+average_crew_size = zeros(num_reals,num_stories);
+
+for s = 1:num_stories
+    % Define damage properties of this system at this story
+    num_damaged_units(:,s) = sum(sequence_filt .* damage.tenant_units{s}.qnt_damaged,2);
+    is_damaged = damage.tenant_units{s}.qnt_damaged > 0 & damage.tenant_units{s}.(repair_time_var) > 0;
+    is_damaged_building = is_damaged_building | is_damaged;
+    
+    for c = 1:length(comp_types)
+        num_damaged_comp_types(:,s) = num_damaged_comp_types(:,s) + any((damage.comp_ds_table.comp_idx' == comp_types(c)) .* is_damaged,2);
+    end
+
+    % Caluculate total worker days per story per sequeces
+    total_worker_days(:,s) = sum(damage.tenant_units{s}.(repair_time_var)(:,sequence_filt),2); % perhaps consider doing when we first set up this damage data structure
+    
+    % Determine the required crew size needed for these repairs
+    repair_time_per_comp = damage.tenant_units{s}.(repair_time_var) ./  damage.comp_ds_table.(crew_size_var)';
+    average_crew_size(:,s) = total_worker_days(:,s) ./ sum(repair_time_per_comp(:,sequence_filt),2);
+end
+    
+% Define the number of crews needed based on the extent of damage
+num_crews = ceil(num_damaged_units / num_du_per_crew);
+num_crews = min(num_crews,max_crews_per_comp_type .* num_damaged_comp_types);
+num_crews = min(num_crews,ceil(num_damaged_units)); % Safety check: num crews should never be greater than the number of damaged components
+
+% Round up total worker days to the nearest day to speed up the worker 
+% allocation loop and implicitly consider change of trade delays
+total_worker_days = ceil(total_worker_days);
+
+% Round crew sizes such that we have a realistic size (still implicitly
+% averaged based on type of damage)
+average_crew_size(isnan(average_crew_size)) = 0;
+average_crew_size = round(average_crew_size);
+
+% Limit the number of crews based on the space limitations at this story
+% and the assumed crew size
+worker_upper_lim = min(max_workers_per_story,max_workers_per_building);
+max_num_crews_per_story = max(floor(worker_upper_lim ./ average_crew_size),1);
+num_crews = min(num_crews,max_num_crews_per_story);
+
+% Calculate the total number of workers per story for this system
+num_workers = average_crew_size .* num_crews;
+
+% Repeat calc of number of uniquely damaged component types for the whole
+% building
+num_damaged_comp_types = zeros(num_reals,1);
+for c = 1:length(comp_types)
+    num_damaged_comp_types = num_damaged_comp_types + any((damage.comp_ds_table.comp_idx' == comp_types(c)) .* is_damaged_building,2);
+end
+max_crews_building = max_crews_per_comp_type .* num_damaged_comp_types;
+end
diff --git a/+recovery/+repair_schedule/fn_format_gantt_chart_data.m b/+recovery/+repair_schedule/fn_format_gantt_chart_data.m
new file mode 100644
index 0000000..7c0607c
--- /dev/null
+++ b/+recovery/+repair_schedule/fn_format_gantt_chart_data.m
@@ -0,0 +1,111 @@
+function [ repair_schedule ] = fn_format_gantt_chart_data( damage, systems, simulated_replacement_time )
+% Reformat data from the damage structure into data that is used for the
+% gantt charts
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage and loss data for each component in the building
+% systems: table
+%   data table containing information about each system's attributes
+% simulated_replacement_time: array [num_reals x 1]
+%   simulated time when the building needs to be replaced, and how long it
+%   will take (in days). NaN represents no replacement needed (ie
+%   building will be repaired)
+%
+% Returns
+% -------
+% repair_schedule: struct
+%   Contians reformated repair schedule data for gantt chart plots for
+%   bothy repair time and downtime calculations. Data is reformanted to
+%   show breakdowns by component, by story, by system, by story within each
+%   system, and by system within each story.
+%
+% Notes
+% -----
+% Since this all has to do with plotting gantt charts, a form of this
+% (whether the damage structure or a higher level repair schedule
+% structure) should be output and this reformatting logic moved outside the
+% functional recovery assessment and into the data visuallation logic
+
+%% Initial Setup
+num_stories = length(damage.tenant_units);
+[num_reals, ~] = size(damage.tenant_units{1}.recovery.repair_start_day);
+comps = unique(damage.comp_ds_table.comp_id);
+
+% Determine replacement cases
+replace_cases = ~isnan(simulated_replacement_time);
+
+%% Reformate repair schedule data into various breakdowns
+% Per component
+repair_schedule.repair_start_day.per_component = nan(num_reals,length(comps));
+repair_schedule.repair_complete_day.per_component = zeros(num_reals,length(comps));
+for c = 1:length(comps)
+    comps_filt = strcmp(damage.comp_ds_table.comp_id',comps{c});
+    for s = 1:num_stories
+        repair_schedule.repair_start_day.per_component(:,c) = min([repair_schedule.repair_start_day.per_component(:,c), damage.tenant_units{s}.recovery.repair_start_day(:,comps_filt)],[],2);
+        repair_schedule.repair_complete_day.per_component(:,c) = max([repair_schedule.repair_complete_day.per_component(:,c), damage.tenant_units{s}.recovery.repair_complete_day(:,comps_filt)],[],2);
+    end
+    repair_schedule.component_names{c} = comps{c};
+end
+
+% Per Story
+repair_schedule.repair_start_day.per_story = nan(num_reals,num_stories);
+repair_schedule.repair_complete_day.per_story = zeros(num_reals,num_stories);
+for s = 1:num_stories
+    repair_schedule.repair_start_day.per_story(:,s) = min([repair_schedule.repair_start_day.per_story(:,s), damage.tenant_units{s}.recovery.repair_start_day],[],2);
+    repair_schedule.repair_complete_day.per_story(:,s) = max([repair_schedule.repair_complete_day.per_story(:,s), damage.tenant_units{s}.recovery.repair_complete_day],[],2);
+end
+
+% Per Repair System
+repair_schedule.repair_start_day.per_system = nan(num_reals,height(systems));
+repair_schedule.repair_complete_day.per_system = zeros(num_reals,height(systems));
+for sys = 1:height(systems)
+    sys_filt = damage.comp_ds_table.system' == systems.id(sys); % identifies which ds idices are in this seqeunce  
+    for s = 1:num_stories
+        repair_schedule.repair_start_day.per_system(:,sys) = min([repair_schedule.repair_start_day.per_system(:,sys), damage.tenant_units{s}.recovery.repair_start_day(:,sys_filt)],[],2);
+        repair_schedule.repair_complete_day.per_system(:,sys) = max([repair_schedule.repair_complete_day.per_system(:,sys), damage.tenant_units{s}.recovery.repair_complete_day(:,sys_filt)],[],2);
+    end
+end
+repair_schedule.system_names = systems.name;
+
+% Per system per story
+num_sys_stories = num_stories * height(systems);
+repair_schedule.repair_start_day.per_system_story = nan(num_reals,num_sys_stories);
+repair_schedule.repair_complete_day.per_system_story = zeros(num_reals,num_sys_stories);
+id = 0;
+for s = 1:num_stories
+    for sys = 1:height(systems)
+        id = id + 1;
+        sys_filt = damage.comp_ds_table.system' == systems.id(sys); % identifies which ds idices are in this seqeunce  
+        repair_schedule.repair_start_day.per_system_story(:,id) = min([repair_schedule.repair_start_day.per_system_story(:,id), damage.tenant_units{s}.recovery.repair_start_day(:,sys_filt)],[],2);
+        repair_schedule.repair_complete_day.per_system_story(:,id) = max([repair_schedule.repair_complete_day.per_system_story(:,id), damage.tenant_units{s}.recovery.repair_complete_day(:,sys_filt)],[],2);
+    end
+end
+
+% Per story per system
+num_sys_stories = num_stories * height(systems);
+repair_schedule.repair_start_day.per_story_system = nan(num_reals,num_sys_stories);
+repair_schedule.repair_complete_day.per_story_system = zeros(num_reals,num_sys_stories);
+id = 0;
+for sys = 1:height(systems)
+    for s = 1:num_stories
+        id = id + 1;
+        sys_filt = damage.comp_ds_table.system' == systems.id(sys); % identifies which ds idices are in this seqeunce  
+        repair_schedule.repair_start_day.per_story_system(:,id) = min([repair_schedule.repair_start_day.per_story_system(:,id), damage.tenant_units{s}.recovery.repair_start_day(:,sys_filt)],[],2);
+        repair_schedule.repair_complete_day.per_story_system(:,id) = max([repair_schedule.repair_complete_day.per_story_system(:,id), damage.tenant_units{s}.recovery.repair_complete_day(:,sys_filt)],[],2);
+    end
+end
+
+
+%% Overwrite realization for demo and replace cases
+formats = fieldnames(repair_schedule.repair_start_day);
+for f = 1:length(formats)
+    repair_schedule.repair_start_day.(formats{f})(replace_cases,:) = 0;
+    
+    [~, format_width] = size(repair_schedule.repair_complete_day.(formats{f})); % apply replacement time to all comps / systems / stories / etc
+    repair_schedule.repair_complete_day.(formats{f})(replace_cases,:) = simulated_replacement_time(replace_cases)*ones(1,format_width);
+end
+
+end
+
diff --git a/+recovery/+repair_schedule/fn_prioritize_systems.m b/+recovery/+repair_schedule/fn_prioritize_systems.m
new file mode 100644
index 0000000..0efff24
--- /dev/null
+++ b/+recovery/+repair_schedule/fn_prioritize_systems.m
@@ -0,0 +1,87 @@
+function [ sys_idx_priority_matrix ] = fn_prioritize_systems( systems, repair_type, damage, tmp_repair_complete_day, impeding_factors )
+% Determine the priority of worker allocation for each system and realization
+% based on default table priorities, whether they have the potential to 
+% affect reoccupancy or function and whether they are resolved by 
+% temporary repairs.
+
+% Parameters
+% ----------
+% systems: table
+%   data table containing information about each system's attributes
+% damage: struct
+%   contains per damage state damage and loss data for each component in the building
+% tmp_repair_complete_day: array [num_reals x num_comp]
+%   contains the day (after the earthquake) the temporary repair time is 
+%   resolved per damage state damage and realization. Inf represents that
+%   there is not temporary repair time available for a given components
+%   damage.
+%
+% Returns
+% -------
+% sys_idx_priority_matrix: index array [num reals x num systems]
+%   row index to filter system matrices to be prioritized for each
+%   realiztion. Priority is left to right.
+% 
+% Notes
+% ------
+% The checks done here are only able to implicitly check for which systems
+% affect funtion the most (this prioritizes which have the potential to
+% affect function). To explicitly check which systems have the biggest
+% impact on function and prioritize those, this check would need to be
+% coupled with the function assessment
+
+%% Initial Setup
+% Define Repair Type Variables (variable within the damage object)
+if strcmp(repair_type,'full')
+    system_var = 'system';
+elseif strcmp(repair_type,'temp')
+    system_var = 'tmp_repair_class';
+else
+    error('Unexpected Repair Type')
+end
+
+% initialize variables
+num_sys = height(systems);
+[num_reals, ~] = size(damage.tenant_units{1}.qnt_damaged);
+
+% Find which components potentially affect reoccupancy accross any tenant unit
+affects_reoccupancy = zeros(num_reals, height(damage.comp_ds_table));
+for s = 1:length(damage.tenant_units)
+    affects_reoccupancy = affects_reoccupancy | (damage.fnc_filters.affects_reoccupancy & damage.tenant_units{s}.qnt_damaged);
+end
+
+% Find which components potentially affect function accross any tenant unit
+affects_function = zeros(num_reals, height(damage.comp_ds_table));
+for s = 1:length(damage.tenant_units)
+    affects_function = affects_function | (damage.fnc_filters.affects_function & damage.tenant_units{s}.qnt_damaged);
+end
+
+%% Define ranks for each system 
+sys_affects_reoccupancy = zeros(num_reals, num_sys); % only prioritize the systems that potentially affect function
+sys_affects_function = zeros(num_reals, num_sys); % only prioritize the systems that potentially affect function
+sys_tmp_repaired = zeros(num_reals, num_sys); % dont prioitize the systems that are completely resolved by temporary repairs
+for sys = 1:num_sys
+    sys_filter = damage.comp_ds_table.(system_var)' == sys;
+    if any(sys_filter) % Only if this system is present
+        sys_affects_reoccupancy(:,sys) = any(affects_reoccupancy(:,sys_filter),2); % any damage that potentially affects reoccupancy in this system
+        sys_affects_function(:,sys) = any(affects_function(:,sys_filter),2); % any damage that potentially affects function in this system
+        if ~isempty(tmp_repair_complete_day) % Only if temp repair data is passed in
+            all_sys_tmp_repaired = all(tmp_repair_complete_day(:,sys_filter)<inf | isnan(tmp_repair_complete_day(:,sys_filter)),2); % is every single damaged component resolved by temp repairs
+            tmp_repair_quick = max(tmp_repair_complete_day(:,sys_filter),[],2) < impeding_factors.time_sys(:,sys); % Are the temp repairs for this system resolved before impeding factors are complete
+            sys_tmp_repaired(:,sys) = all_sys_tmp_repaired & tmp_repair_quick; % damage is quickly resolved by temp repair
+        end
+    end
+end
+prioritize_system_reoccupancy = sys_affects_reoccupancy & ~sys_tmp_repaired;
+prioritize_system_function_only = sys_affects_function & ~prioritize_system_reoccupancy & ~sys_tmp_repaired;
+non_priorities = ~prioritize_system_reoccupancy & ~prioritize_system_function_only;
+
+sys_priority_matrix = prioritize_system_reoccupancy   .* (100 + systems.priority') + ... % First prioritize reoccpancy repairs (100 classifies first priority bank, important for sort function below)
+                      prioritize_system_function_only .* (200 + systems.priority') + ... % then priotize repairs that only affect function (200 classifies second priority bank)
+                      non_priorities                  .* (300 + systems.priority'); % finally, repair the systems that dont affect reoccupancy or function (300 classifies third priority bank)
+
+% use rank matrix to get row indeces of priorities
+[~, sys_idx_priority_matrix] = sort(sys_priority_matrix, 2);
+
+end
+
diff --git a/+recovery/+repair_schedule/fn_restructure_repair_schedule.m b/+recovery/+repair_schedule/fn_restructure_repair_schedule.m
new file mode 100644
index 0000000..205ff67
--- /dev/null
+++ b/+recovery/+repair_schedule/fn_restructure_repair_schedule.m
@@ -0,0 +1,96 @@
+function [ damage ] = fn_restructure_repair_schedule( damage, system_schedule, ...
+    repair_complete_day_per_system, systems, repair_type, simulated_red_tags)
+% Redistribute repair schedule data from the system and story level to the component level for use 
+% in the functionality assessment (ie, put repair schedule data into the
+% damage object)
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage and loss data for each component in the building
+% system_schedule: Structure
+%   repair time data for each system in isolation
+% repair_complete_day_per_system: matrix [num reals x num systems]
+%   Number of days from the start of each sequence to the completion of the
+%   sequence considering the allocation of workers to each sequence (ie
+%   some sequences start before others)
+% systems: table
+%   data table containing information about each system's attributes
+% tmp_repair_complete_day: array [num_reals x num_comp]
+%   contains the day (after the earthquake) the temporary repair time is 
+%   resolved per damage state damage and realization
+%
+% Returns
+% -------
+% damage: struct
+%   contains per damage state damage and loss data for each component in
+%   the building, including repair schedule data.
+%
+% Notes
+% -----
+% In the repair start day outputs:
+%    - Zero = Starts immediately
+%    - NaN = Repairs never started (because not damaged, has no temp repair, or controlled by red tag)
+% In the repair complete day outputs:
+%    - NaN = No damage (or controlled by red tag)
+%    - Inf = Is damaged, but has no System/RepairClass assignment (or no temp repair)
+% 
+
+%% Initialize Parameters
+num_sys = height(systems);
+num_units = length(damage.tenant_units);
+
+% Define Repair Type Variables (variable within the damage object)
+if strcmp(repair_type,'full')
+    repair_time_var = 'worker_days';
+    system_var = 'system';
+elseif strcmp(repair_type,'temp')
+    repair_time_var = 'tmp_worker_day';
+    system_var = 'tmp_repair_class';
+else
+    error('Unexpected Repair Type')
+end
+
+% Initialize recovery field
+for tu = 1:num_units
+    % Set to inf as a null repair time (will remain inf for components with
+    % no attributed system) - matters for temp repairs, shouldnt matter for
+    % full repair
+    damage.tenant_units{tu}.recovery.repair_start_day = nan(size(damage.tenant_units{tu}.qnt_damaged));
+    damage.tenant_units{tu}.recovery.repair_complete_day = inf(size(damage.tenant_units{tu}.qnt_damaged));
+    
+    % if not damaged, set repair complete time to NaN
+    is_damaged = damage.tenant_units{tu}.qnt_damaged > 0 & damage.tenant_units{tu}.(repair_time_var) > 0;
+    damage.tenant_units{tu}.recovery.repair_complete_day(~is_damaged) = NaN; 
+end
+
+%% Redistribute repair schedule data
+for sys = 1:num_sys
+    % Calculate system repair times on each story
+    system_duration = max(system_schedule.per_system{sys}.repair_complete_day,[],2); % total repair time spent in this system over all stories
+    start_day = repair_complete_day_per_system(:,sys) - system_duration;
+    story_start_day = start_day + system_schedule.per_system{sys}.repair_start_day;
+    story_complete_day = start_day + system_schedule.per_system{sys}.repair_complete_day;
+    
+    % Do not perform temporary repairs when building is red tagged
+    if strcmp(repair_type,'temp') && any(simulated_red_tags)
+        story_start_day(simulated_red_tags,:) = NaN;
+        story_complete_day(simulated_red_tags,:) = NaN;
+    end
+
+    % Re-distribute to each tenant unit
+    sys_filt = damage.comp_ds_table.(system_var)' == systems.id(sys); % identifies which ds idices are in this seqeunce  
+    for tu = 1:num_units
+        is_damaged = damage.tenant_units{tu}.qnt_damaged(:,sys_filt) > 0 & damage.tenant_units{tu}.(repair_time_var)(:,sys_filt) > 0;
+        is_damaged = is_damaged*1;
+        is_damaged(is_damaged == 0) = NaN;
+
+        % Re-distribute repair days to component damage states
+        damage.tenant_units{tu}.recovery.repair_start_day(:,sys_filt) = is_damaged .* story_start_day(:,tu);
+        damage.tenant_units{tu}.recovery.repair_complete_day(:,sys_filt) = is_damaged .* story_complete_day(:,tu);
+        
+    end
+end
+
+end
+
diff --git a/+recovery/+repair_schedule/fn_set_repair_constraints.m b/+recovery/+repair_schedule/fn_set_repair_constraints.m
new file mode 100644
index 0000000..1f76e42
--- /dev/null
+++ b/+recovery/+repair_schedule/fn_set_repair_constraints.m
@@ -0,0 +1,58 @@
+function [ sys_constraint_matrix ] = fn_set_repair_constraints( systems, repair_type, conditionTag )
+% Define a constraint matrix to be used by repair schedule
+%
+% Delelops a matrix of various constriants between each system (i.e. what
+% systems need to be repaired before others) for each realization
+
+% Parameters
+% ----------
+% systems: table
+%   data table containing information about each system's attributes
+% conditionTag: logical array (num_reals x 1)
+%   Is the building red tagged for each realization
+%
+% Returns
+% -------
+% sys_constraint_matrix: array [num reals x num_sys]
+%   array of system ids which define which systems (column index) are delayed by the
+%   system ids (array values)
+%
+% Notes
+% -----
+% Shortcoming: as implemented, each system can only be constrained by one
+%              other system
+% Example:
+%   [0 0 1 0 0 0 0 6 0]
+%   Interiors (column 3) are blocked by struture (value of 1 in the 3rd column)
+%   HVAC (column 8) is blocked by plumbing (value of 6 in the 8th column)
+
+%% Initial Setup
+num_sys = height(systems);
+num_reals = length(conditionTag);
+sys_constraint_matrix = zeros(num_reals, num_sys);
+
+%% Interior Constraints
+if strcmp(repair_type,'full') 
+    % Interiors are delayed by structural repairs
+    interiors_idx = find(strcmp(systems.name,'interior'));
+    structure_idx = find(strcmp(systems.name,'structural'));
+    sys_constraint_matrix(:,interiors_idx) = structure_idx;
+
+    % Red Tag Constraints
+    % All systems blocked by structural when red tagged
+    sys_constraint_matrix(logical(conditionTag),~strcmp(systems.name,'structural')) = structure_idx;
+elseif strcmp(repair_type,'temp')
+    % All temp repairs are blocked by shoring
+    shoring_id = 5;
+    shoring_filt = systems.id == shoring_id;
+    if any(shoring_filt) % If shoring is considred as a temp repair measure
+        shoring_idx = find(shoring_filt);
+        sys_constraint_matrix(:,~shoring_filt') = shoring_idx; % all classes that are not shoring idx are blocked by the shoring idx
+    end
+else
+    error('Unexpected Repair Type')
+end
+
+
+end
+
diff --git a/+recovery/+repair_schedule/main_repair_schedule.m b/+recovery/+repair_schedule/main_repair_schedule.m
new file mode 100644
index 0000000..9fbf9a2
--- /dev/null
+++ b/+recovery/+repair_schedule/main_repair_schedule.m
@@ -0,0 +1,151 @@
+function [damage, worker_data, building_repair_schedule ] = main_repair_schedule(...
+    damage, building_model, simulated_red_tags, repair_time_options, ...
+    systems, tmp_repair_class, impeding_factors, simulated_replacement_time)
+% Determine the repair time for a given damage simulation.
+%
+% Simulation of system and building level repair times based on a
+% simulation of impedance factor and repair scheduling algorithm.
+% Simulates the repair times for all realizations of a single ground motion
+% or intensity level.
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage and loss data for each component in the building
+% building_model: struct
+%   data structure containing general information about the building
+% simulated_red_tags: logical array [num_realization x 1]
+%   for each realization of the Monte Carlo simulation, is the structure
+%   expected to be red-tagged
+% repair_time_options: struct
+%   general repair time options such as mitigation factors
+% systems: table
+%   attributes of structural and nonstructural building systems; data 
+%   provided in static tables directory
+% tmp_repair_class: table
+%   data table containing information about each temporary repair class
+%   attributes. Attributes are similar to those in the systems table.
+% impeding_factors: struct
+%   simulated impedance times
+% simulated_replacement_time: array [num_reals x 1]
+%   simulated time when the building needs to be replaced, and how long it
+%   will take (in days). NaN represents no replacement needed (ie
+%   building will be repaired)
+%
+% Returns
+% -------
+% damage: struct
+%   contains per damage state damage and loss data for each component in the building
+% worker_data: struct
+%   simulated building-level worker allocations throught the repair process
+% building_repair_schedule: struct
+%   simulations of the building repair schedule, broken down by component,
+%   story, and system
+
+
+%% Initial Setup
+% Import Packages
+import recovery.repair_schedule.*
+
+% Define the maximum number of workers that can be on site, based on REDI
+max_workers_per_building = ...
+    min(max(floor(sum(building_model.area_per_story_sf) * ...
+    repair_time_options.max_workers_per_sqft_building + 10), ...
+    repair_time_options.max_workers_building_min), ...
+    repair_time_options.max_workers_building_max);
+                          
+%% Determine repair schedule per system for Temporary Repairs 
+% Define the maximum number of workers that can be on any given story
+max_workers_per_story = ...
+    ceil(building_model.area_per_story_sf * ...
+    repair_time_options.max_workers_per_sqft_story_temp_repair); 
+
+% Temporary Repairs
+repair_type = 'temp';
+[tmp_damage, tmp_worker_data] = fn_schedule_repairs(...
+    damage, repair_type, tmp_repair_class, ...
+    max_workers_per_building, max_workers_per_story, ...
+    impeding_factors.temp_repair, simulated_red_tags, []);
+
+% Calculate the max temp repair complete day for each component (anywhere
+% in building)
+tmp_repair_complete_day = nan(size(damage.tenant_units{1}.tmp_worker_day));
+% NaN = Never damaged
+% Inf  = Damage not resolved by temp repair
+for tu = 1:length(tmp_damage.tenant_units)
+    tmp_repair_complete_day = ...
+        max(tmp_repair_complete_day,...
+            tmp_damage.tenant_units{tu}.recovery.repair_complete_day);
+end
+
+%% Determine repair schedule per system for Full Repairs 
+% Define the maximum number of workers that can be on any given story
+max_workers_per_story = ...
+    ceil(building_model.area_per_story_sf * ...
+    repair_time_options.max_workers_per_sqft_story); 
+
+% Full Repairs
+repair_type = 'full';
+[damage, worker_data] = fn_schedule_repairs(...
+    damage, repair_type, systems, ...
+    max_workers_per_building, max_workers_per_story, impeding_factors, ...
+    simulated_red_tags, tmp_repair_complete_day);
+
+%% Combine temp and full repair schedules
+for tu = 1:length(tmp_damage.tenant_units)
+    % Repair time is the lesser of the full repair and temp repair times
+    damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp = ...
+        min(damage.tenant_units{tu}.recovery.repair_complete_day,...
+            tmp_damage.tenant_units{tu}.recovery.repair_complete_day);
+    % Temporary Repair Times control if temporary repair times are less
+    % than the full repair time
+    tmp_day_controls = tmp_damage.tenant_units{tu}.recovery.repair_complete_day < ...
+        damage.tenant_units{tu}.recovery.repair_complete_day;
+    % Repair start day is set to the temp repair start day when temp
+    % repairs control
+    damage.tenant_units{tu}.recovery.repair_start_day_w_tmp = damage.tenant_units{tu}.recovery.repair_start_day;
+    damage.tenant_units{tu}.recovery.repair_start_day_w_tmp(tmp_day_controls) = ...
+        tmp_damage.tenant_units{tu}.recovery.repair_start_day(tmp_day_controls);
+end
+
+%% Format Outputs 
+% Format Start and Stop Time Data for Gantt Chart plots 
+% This is also the main data structure used for calculating full repair time
+% outputs
+[ building_repair_schedule.full ] = fn_format_gantt_chart_data( damage, systems, simulated_replacement_time );
+[ building_repair_schedule.temp ] = fn_format_gantt_chart_data( tmp_damage, systems, simulated_replacement_time );
+
+end
+
+
+
+function [damage, worker_data] = ...
+    fn_schedule_repairs(damage, repair_type, systems, max_workers_per_building, ...
+    max_workers_per_story, impeding_factors, simulated_red_tags, tmp_repair_complete_day)
+
+    %% Initial Setup
+    % Import Packages
+    import recovery.repair_schedule.*
+
+    %% Step 1 - Calculate the start and finish times for each system in isolation
+    % based on REDi repair sequencing and Yoo 2016 worker allocations
+    [ system_schedule ] = fn_calc_system_repair_time(damage, repair_type, systems, max_workers_per_building, max_workers_per_story);
+
+    %% Step 2 - Set system repair priority
+    [ sys_idx_priority_matrix ] = fn_prioritize_systems( systems, repair_type, damage, tmp_repair_complete_day, impeding_factors );
+
+    %% Step 3 - Define system repair constraints
+    [ sys_constraint_matrix ] = fn_set_repair_constraints( systems, repair_type, simulated_red_tags );
+
+    %% Step 4 - Allocate workers among systems and determine the total days until repair is completed for each sequence
+    [repair_complete_day_per_system, worker_data] = fn_allocate_workers_systems(...
+        systems, system_schedule.system_totals.repair_days, ...
+        system_schedule.system_totals.num_workers, max_workers_per_building, ...
+        sys_idx_priority_matrix, sys_constraint_matrix, simulated_red_tags, ...
+        impeding_factors.time_sys );
+    
+    %% Step 5 - Format outputs for Functionality calculations
+    [ damage ] = fn_restructure_repair_schedule( damage, system_schedule, ...
+                 repair_complete_day_per_system, systems, repair_type, simulated_red_tags);
+
+end
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index d601d32..12d2f02 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,36 +1,2 @@
-# Compiled python modules
-*.pyc
-
-# VSCode folders
-/.vscode
-
-# PyCharm folders
-/.idea
-/.cache
-
-# coverage
-.coverage
-
-# Python egg metadata, regenerated from source files by setuptools
-*.egg-info/
-.eggs/
-*.egg
-
-# Temporary jupyter files
-/.ipynb_checkpoints/
-*.ipynb_checkpoints
-
-# Output folder
-outputs/
-
-# Folder with original Matlab code
-/Matlab_code/
-
-.ai/
-Comparison_scripts/
-
-# All new files coming out of buildInputs
-inputs/example_inputs/*/optional_inputs.json
-inputs/example_inputs/*/optional_inputs.py
-inputs/example_inputs/*/simulated_inputs.json
-inputs/example_inputs/*/build_input.py
+outputs/*
+*.asv
\ No newline at end of file
diff --git a/README.md b/README.md
index 4ac0652..e6f778f 100644
--- a/README.md
+++ b/README.md
@@ -1,122 +1,84 @@
-# Functional-Recovery---Python
-This is translation of Matlab codebase into Python for quantifying building-specific functional recovery and reoccupancy based on a probabilistic performance-based earthquake engineering framework.
+# PBEE-Recovery
+Matlab codebase for quantifying building-specific functional recovery and reoccupancy based on a probabilistic performance-based earthquake engineering framework.
 
-## Requirements
+### Method Description
+The method for quantifying building-specific functional recovery is based on the performance-based earthquake engineering framework. To quantify building function, the method maps component-level damage to system-level performance, and system-level performance to building function using a series of fault trees that describe the interdependencies between the functions of various building components. The method defines the recovery of function and occupancy at the tenant unit level, where a building can be made up of one-to-many tenant units, each with a possible unique set of requirements to regain building function; the recovery state of the building is defined as an aggregation of all the tenant units within the building. The method propagates uncertainty through the assessment using a Monte Carlo simulation. Details of the method are fully described in Cook, Liel, Haselton, and Koliou, 2022. "A Framework for Operationalizing the Assessment of Post Earthquake Functional Recovery of Buildings", Earthquake Spectra.
 
-- **Python Version**: 3.9 or later (recommend 3.9)
-- **Package Manager**: pip (comes with Python)
+### Implementation Details
+The method is developed as part of the consequence module of the Performance-Based Earthquake Engineering framework and uses simulations of component damage from the FEMA P-58 method as an fundamental input. Therefore, this implementation will not perform a FEMA P-58 assessment, and instead, expects the simulations of component damage, from a FEMA P-58 assessment to be provided as inputs. Along with other information about the building, the buildings tenant units, and some analysis options, this implementation will perform the functional recovery assessment method, and provide simulated recovery times for each realization provided. The implementation runs an assessment for a single building at a single intensity level. The implementation of the method does not handle demo and replace conditions and predicts building function based on component damage simulation and recovery times assuming damage will be repaired in-kind. Building failure, demo, and replacement conditions can be handled as a post-process by either overwriting realizations where global failure occurs or only inputting realizations that are scheduled for repair.
 
-### Installation
-
-The ATC-138 Functional Recovery Assessment tool is distributed as a Python package. Install it using pip:
-
-
-```bash
-# Create and activate a virtual environment (recommended)
-python -m venv .venv
-
-# Activate virtual environment
-# On Windows (PowerShell):
-.\.venv\Scripts\Activate.ps1
-# On macOS/Linux:
-source .venv/bin/activate
-
-# Install the package in editable mode
-pip install -e .
-```
-
-
-### Verify Installation
-
-After installation, verify that the CLI is available:
-
-```bash
-atc138 --help
-```
-
-You should see the command help output with available options.
+The method is built using Matlab v2017a; running this implementation using other versions Matlab may not perform as expected.
 
 ## Running an Assessment
-
-An assessment can be run directly from the command line, or as imported within a Python workflow. If `simulated_inputs.json` does not exist, it will be created using default inputs within `src/atc138/data`. Various assessment options can be overridden by placing them in file `optional_inputs.json` file within the input directory. This file can be customized for each assessment if desired and will be set as default values if not specified.
-
-### Running from the command line
-
-With the input directory containing the necessary inputs, perform an assessment by running:
-
-```bash
-python -m atc138.cli dir/to/inputs dir/to/outputs
-```
-
- For example, the ICSB example case is run with:
-
-```bash
-python -m atc138.cli ./examples/ICSB ./examples/ICSB/output
-```
-
-### Imported via Python script
-
-Ensure that the `src/` directory is on the path of the main script. Then:
-
-```python
-from src.atc138 import driver
-
-example_dir = './examples/ICSB'
-output_dir = './examples/ICSB/output'
-
-driver.run_analysis(example_dir, output_dir, seed=985)
-```
+ - **Step 1**: Build the inputs matlab data file of simulated inputs. Title the file "simulated_inputs.mat" and place it in a directory of the model name within the "inputs" drirectory. This matlab data file can either be constructed manually following the inputs schema or using the build script as discussed in the _Building the Inputs File section_ below.
+ - **Step 2**: Open the matlab file "driver_PBEErecovery.m" and set the "model_name", "model_dir", and "outputs_dir" variables.
+ - **Step 3**: Run the script
+ - **Step 4**: Simulated assessment outputs will be saved as a Matlab data file in a directory of your choice
 
 ## Example Inputs
-Four example inputs are provided to help illustrate both the construction of the inputs file and the implementation. These files are located in the `examples/` directory and can be run through the assessment by setting the variable names accordingly above.
+Several example inputs are provided to help illustrate both the construction of the inputs file and the implementation. These files are located in the inputs/example_inputs directory and can be run through the assessment by setting the variable names accordingly in **step 2** above.
 
 ## Definition of I/O
-A brief description of the various input and output variables are provided below. A detailed schema of all expected input and output subfields is provided in the schema directory.
+A brief description of the various input and output variables are provided below. A detailed schema of all expected input and output subfields is provided in the PBEE-Recovery/schema directory.
 
 ### Inputs
-  - **impedance_options**: Python dictionary
-   Python dictionary containing optional method inputs for the assessment of impeding factors
- - **repair_time_options**: Python dictionary
-   Python dictionary containing optional method inputs for the assessment of the repair schedule
- - **functionality_options**: Python dictionary
-   Python dictionary containing optional method inputs for the assessment of building function, such as functionality limit state thresholds
- - **building_model**: Python dictionary
-   Python dictionary containing general information about the building such as the number of stories and the building area
- - **damage**: Python dictionary
-   Python dictionary containing simulated damage, simulated repair time, and component attribute data associated with each component's damages state in the building
- - **damage_consequences**: Python dictionary
-   Python dictionary containing simulated building consequences, such as red tags and repair costs ratios
- - **functionality['utilities']**: Python dictionary
-   Python dictionary containing simulated utility downtimes
- - **tenant_units**: Python dictionary
-   Python dictionary that contains the attributes and functional requirements of each tenant unit in the building
+  - **impedance_options**: [_struct_]
+   data structure containing optional method inputs for the assessment of impeding factors
+ - **repair_time_options**: [_struct_]
+   data structure containing optional method inputs for the assessment of the repair schedule
+ - **functionality_options**: [_struct_]
+   data structure containing optional method inputs for the assessment of building function, such as functionality limit state thresholds
+ - **building_model**: [_struct_]
+   data structure containing general information about the building such as the number of stories and the building area
+ - **damage**: [_struct_]
+   data structure containing simulated damage, simulated repair time, and component attribute data associated with each component's damages state in the building
+ - **damage_consequences**: [_struct_]
+   data structure containing simulated building consequences, such as red tags and repair costs ratios
+ - **functionality.utilities**: [_struct_]
+   data structure containing simulated utility downtimes
+ - **tenant_units**: [_table_]
+   datatable that contains the attributes and functional requirements of each tenant unit in the building
 
 ### Outputs
- - **functionality['recovery']**: Python dictionary
-   Python dictionary containing the simulated tenant- and building-level functional recovery and reoccupancy outcomes
- - **functionality['building_repair_schedule']**: Python dictionary
-   Python dictionary containing the simulated building repair schedule
- - **functionality['worker_data']**: Python dictionary
-   Python dictionary containing the simulation of allocated workers throughout the repair process
- - **functionality['impeding_factors']**: Python dictionary
-   Python dictionary containing the simulated impeding factors delaying the start of system repair
-
-## Manually building the Inputs File
-By default, the inputs file are built from a simpler set of building inputs, taking advantage of default assessment assumptions and component, system, and tenant attributes contained within the _data_ directory. If you would like to manually modify the data tables listed below for a specific model, simply copy the files to the input directory and modify them.
+ - **functionality.recovery**: [_struct_]
+   data structure containing the simulated tenant- and building-level functional recovery and reoccupancy outcomes
+ - **functionality.building_repair_schedule**: [_struct_]
+   data structure containing the simulated building repair schedule
+ - **functionality.worker_data**: [_struct_]
+   data structure containing the simulation of allocated workers throughout the repair process
+ - **functionality.impeding_factors**: [_struct_]
+   data structure containing the simulated impeding factors delaying the start of system repair
+
+## Building the Inputs File
+Instead of manually defining the inputs matlab data file based on the inputs schema, the inputs file can be built from a simpler set of building inputs, taking advantage of default assessment assumptions and component, system, and tenant attributes contained within the _static_tables_ directory.
+
+### Instructions
+ - **Step 1**: Copy the scripts build_inputs.m and optional_inputs.m from the _Inputs2Copy_ directory to the directory where you want to build the simulated_inputs.mat inputs file.
+ - **Step 2**: Add the requried building specific input files listed below to the same directory
+ - **Step 3**: Modify the optional_inputs.m file as needed.
+ - **Step 4**: Make sure the variable _static_data_dir_ in build_inputs.m is correctly pointing to the location of the static data directory
+ - **Step 5**: Run the build script
+
+#### Option for Customizing Static Data 
+If you would like to modify the static data tables listed below for a specifc model, simply copy the static data tables listed below to the build script directory, modify the files, and set the _static_data_dir_ vairable to the point to the location of the modified files (same directory as the build script).
 
 ### Required Building Specific Data
-Each file listed below contains data specific to the building performance model and simulated damage given for a specific level of shaking. Each file listed will need to be created for each unique assessment and saved in the root directory of the build script. Data are contained in either json  or csv format.
+Each file listed below contains data specific to the building performance model and simulated damage given for a specific level of shaking. Each file listed will need to be created for each unique assessment and saved in the root directory of the build script. Data are contained in either json structures or csv tables.
  - **building_model.json**: Basic properties of the building and performance model. Contains all variables within the _building_model_ structure defined in the inputs schema.
  - **tenant_unit_list.csv**: Table that lists each tenant unit within the building; one row per tenant unit. This table requires the following attributes:
      - id: [int or string] unique identifier for this tenant unit
      - story: [int] building story where this tenant unit is located (ground floor is listed at 1)
      - area: [number] total gross plan area of the tenant unit, in square feet
      - perim_area: [number] total exterior perimeter area (elevation) of the tenant unit, is square feet
-     - occupancy_id: [int] foreign key to the _occupancy_id_ attribute of the tenant_function_requirements.csv table in the _data_ directory
+     - occupancy_id: [int] foreign key to the _occupancy_id_ attribute of the tenant_function_requirements.csv table in the _static_tables_ directory
  - **comp_ds_list.csv**: Table that lists each component and damage state populated in the building performance model; one row per each component's damage state. This table requires the following attributes:
      - comp_id: [string] unique FEMA P-58 component identifier
      - ds_seq_id: [int] interger index of the sequential parent damage state (i.e., damage state 1, 2, 3, 4);
      - ds_sub_id: [int] interger index for the mutually exlusive of simeltaneous sub damage state; use 1 to indicate a sequential damage state with no sub damage state.
+  - **comp_ds_list.csv**: Table that lists the quantity and location of each component in the building performance model. This table requires the following attributes:
+     - story: [int] story location of the component
+     - dir: [int] direciton of the component (1, 2, or 3 for nondirectional);
+     - <1 attribute per comp>: [str] each component should be listed at its own column with string representation of the component id as the column header.
  - **damage_consequences.json**: Building-level and story-level simulated properties of building damage. Contains all variables within the _damage_consequences_ structure defined in the inputs schema.
  - **simulated_damage.json**: Component-level simulated damage properties. Contains all variables within the _damage.tenant_units_ structure defined in the inputs schema. Each variable containing realization of component damage should be defined uniquely for each tenant unit (shown as "tu" below). Each tenant_unit cell should contain the following variables:
      - tenant_unit{tu}.qnt_damaged: [array: simulations × damage states] The number of damaged components in each component damage state for each realization of the simulation.
@@ -128,20 +90,40 @@ Each file listed below contains data specific to the building performance model
      - tenant_unit{tu}.num_comps: [array: 1 × damage states] The total number of components associated with each damage state (should be uniform for damage state of the same component stack).
 
 ### Optional Building Specific Data
-The file(s) listed below contain data that is optional for the assessment. If the files do not exist, the method will make simplifying assumptions to account for the missing data (as noted below). Save in the input directory of your analysis.
- - **utility_downtime.json**: Regional utility simulated downtimes for gas, water, and electrical power networks. Contains all variables within the _functionality['utilities']_ dictionary defined in the inputs schema.
+The file(s) listed below contain data that is optional for the assessment. If the files do not exist, the method will make simplifying assumptions to account for the missing data (as noted below). Save in the root directory of the build script.
+ - **utility_downtime.json**: Regional utility simulated downtimes for gas, water, and electrical power networks. Contains all variables within the _functionality.utilities_ structure defined in the inputs schema.
 
 ### Default Optional Inputs
-The Python file listed below defines additional assessment inputs based on set of default values. Place this file in the input directory of your analysis.
- - **optional_inputs.json**: Defines default variables for the impedance_options, repair_time_options, functionality_options, and regional_impact variables listed in the inputs schema.
-
-<!-- How does a user modify the static tables (now data) without touching the original data files? -->
+The matlab file listed below defines additional assessment inputs based on set of default values. Copy the file from the _Inputs2Copy_ directory, place it in the root directory of the build script, and modify it as you see if (or build the script programmatically)
+ - **optional_inputs.m**: Defines default variables for the impedance_options, repair_time_options, and functionality_options variables listed in the inputs schema.
 
 ### Static Data
-The csv tables listed below contain default component, damage state, system, and tenant function attributes that can be used to populate the required assessment inputs according to the methodology. These are located in the _data_ directory. To override the static data with custom versions, copy modified sheets and place them in the input directory.
+The csv tables listed below contain default componet, damage state, system, and tenant function attributes that can be used to populate the required assessment inputs according to the methodology. Either point the _static_data_dir_ variable in build_inputs.m to the location of these tables in the _static_tables_ directory, or copy and modify them as you see fit and place them in the root directory of the build script.
  - **component_attributes.csv**: Attributes of components in the FEMA P-58 fragility database that are required for the functional recovery assessment.
  - **damage_state_attribute_mapping.csv**: Attributes of damage state in the FEMA P-58 fragility database and their affect on function and reoccupancy.
  - **subsystems.csv**: Attributes of each default subsystem considered in the method.
  - **tenant_function_requirements.csv**: Default tenant requirements for function for various occupancy classes.
- - **systems.csv**: Attributes of each default ssytem considered in the method.
- - **temp_repair_class.csv**: Attributes of each temprary repair class considered in the method.
+
+## Converting Inputs from PELICUN Outputs
+Convert outputs from SimCenter's PELICUN v3 tool into PBEE-Recovery inputs. Specifically; build the four input files: _comp_ds_list.csv_, _damage_consequences.json_, _building_model.json_, and _simulated_damage.json_ from PELICUN I/O
+
+### Instructions
+ - **Step 1**: Create a model input directory in the _inputs_ directory
+ - **Step 2**: Copy the PELICUN I/O files into a directory titled _pelicun_data_ within the model input directory.
+ - **Step 3**: Create the files _tenant_unit_list.csv_ and _general_inputs.json_ and place them into the model input directory.
+ - **Step 4**: Make sure the input variables at the begining of the file _driver_convert_PELICUN.m_ are all up to date.
+ - **Step 5**: Run the file _driver_convert_PELICUN.m_
+ - **Step 6**: Run the build script following the instructions above
+
+### PELICUN Files
+Place each of the following files in the _pelicun_results_ directory within the model input directory.
+ - **DMG_sample.csv**: Realizations of damaged components for each damage state of each performance group.
+ - **DV_bldg_repair_sample.csv**: Realizations of repair time (worker days) for each damage state of each performance group.
+ - **DL_summary.csv**: Realizations of consequence data at the building level (including repair cost).
+ - **CMP_QNT.csv**: Component population data.
+ - **input.json**: Pelicun config file.
+ 
+ ### Required Additoinal Inputs
+ Place each of the following files in the _treads_data_ directory within the model input directory.
+ - **general_inputs.json**: Json containing other misc data needed for recovery assessment.
+ - **tenant_unit_list.csv**: Table that lists each tenant unit within the building; see description above.
\ No newline at end of file
diff --git a/build_example_inputs.py b/build_example_inputs.py
new file mode 100644
index 0000000..30c64e8
--- /dev/null
+++ b/build_example_inputs.py
@@ -0,0 +1,60 @@
+"""
+Go through example inputs and rebuild simulated input file
+"""
+import os
+import shutil
+import subprocess
+import time
+
+def build_inputs(input_dir, build_file_dir):
+    """
+    Parameters
+    ----------
+    input_dir: str
+        location of the example input directories
+    build_file_dir: str
+        location of the build scripts to copy
+
+    Returns
+    -------
+    None, writes simulated_inputs.m in each example inputs directory
+    """
+
+    # Define files to copy
+    bld_script = build_file_dir + '/build_inputs.m'
+    opt_script = build_file_dir + '/optional_inputs.m'
+
+    # Go through each example input folder
+    dirs = os.listdir(input_dir)
+    for d in dirs:
+        # Copy files over to example input directory
+        shutil.copyfile(bld_script, input_dir + '/' + d + '/build_inputs.m')
+        shutil.copyfile(opt_script, input_dir + '/' + d + '/optional_inputs.m')
+
+        # Delete old simulated inputs file (if it exists)
+        file_exists = os.path.exists(input_dir + '/' + d + '/simulated_inputs.mat')
+        if file_exists:
+            os.remove(input_dir + '/' + d + '/simulated_inputs.mat')
+
+        # Execute Matlab build script
+        bld_path = os.path.abspath(input_dir + '/' + d + '/build_inputs.m')
+        exc_str = "run('" + bld_path + "'); exit;"
+        p = subprocess.run(["matlab", "-nosplash", "-nodesktop", "-r", exc_str])
+
+        time.sleep(60)  # low tech way of getting python to wait until matlab is finished
+
+        # Check if the input file has been successfully created
+        file_exists = os.path.exists(input_dir + '/' + d + '/simulated_inputs.mat')
+        if file_exists:
+            # Delete build scripts
+            os.remove(input_dir + '/' + d + '/build_inputs.m')
+            os.remove(input_dir + '/' + d + '/optional_inputs.m')
+        else:
+            raise Exception("Matlab process failed to build inputs")
+
+if __name__ == '__main__':
+
+    # Copy fragilities table over from original location to public repo (with some omissions)
+    example_input_dir = 'inputs/example_inputs'
+    build_file_dir = 'inputs/Inputs2Copy'
+    build_inputs(example_input_dir, build_file_dir)
diff --git a/demo.ipynb b/demo.ipynb
deleted file mode 100644
index d2292da..0000000
--- a/demo.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"code","execution_count":1,"id":"1fce86e9-d68d-489b-83df-4855fe002aed","metadata":{"id":"1fce86e9-d68d-489b-83df-4855fe002aed","executionInfo":{"status":"ok","timestamp":1674609674963,"user_tz":480,"elapsed":455,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}}},"outputs":[],"source":["import json, os, sys\n","import numpy as np\n","import pandas as pd\n","\n","pd.set_option('display.max_rows',100)"]},{"cell_type":"code","source":["# connect with Google Drive and change to the folder with ATC 138 scripts\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","main_dir = '/content/drive/MyDrive/SimCenter/FunctionalRecovery/ATC138/'\n","\n","os.chdir(main_dir)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EwswFIkCtCuu","executionInfo":{"status":"ok","timestamp":1674609722093,"user_tz":480,"elapsed":47132,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"2794626b-452a-4a04-fe6c-dbc8b314c508"},"id":"EwswFIkCtCuu","execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":3,"id":"a74c086e-9e47-4cc0-9600-330c1fdb5657","metadata":{"id":"a74c086e-9e47-4cc0-9600-330c1fdb5657","executionInfo":{"status":"ok","timestamp":1674609722093,"user_tz":480,"elapsed":2,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}}},"outputs":[],"source":["#model_name = 'RCSW_1story'\n","#model_name = 'haseltonRCMF_4story'\n","#model_name = 'haseltonRCMF_12story'\n","model_name = 'ICSB'\n","\n","model_dir = f'./inputs/{model_name}/'"]},{"cell_type":"markdown","id":"a17aa617-7a52-4310-a740-fe393878ab8c","metadata":{"id":"a17aa617-7a52-4310-a740-fe393878ab8c"},"source":["## Inputs\n","\n","Each file listed below contains data specific to the building performance model and simulated damage given **for a specific level of shaking**. Each file listed will need to be created for each unique assessment and saved in the root directory of the build script. \n","\n","Data are contained in either json or csv format."]},{"cell_type":"markdown","id":"212f99d5-cdbc-4751-8959-0945e9b5b95b","metadata":{"id":"212f99d5-cdbc-4751-8959-0945e9b5b95b"},"source":["---\n","### Building Model\n","\n","Provides general information about the building:\n","\n","- **building_value**: [number] [USD]\n","<br> Total replacement value of the building\n","- **num_stories**: [number]\n","<br> number of stories in the building, does not include basement levels\n","- **total_area_sf**: [number] [ft2]\n","<br> total interior area of the entire building\n","- **area_per_story_sf**: [array] [ft2]\n","<br> interior area for each story in the building, starting from the lowest level\n","- **ht_per_story_ft**: [array] [ft2]\n","<br> elevation height for each story in the building, starting from the lowest level\n","- **edge_lengths**: [array] [ft]\n","<br> external length of two orthogonal sides of the building, for each story in the building, starting from the lowest level\n","- **struct_bay_area_per_story**: [array] [ft]\n","<br> length of the typical structural bay, from centerline to centerline, for each story in the building, starting from the lowest level\n","- **num_entry_doors**: [number]\n","<br> number of external building exits/entrances\n","- **num_elevators**: [number]\n","<br> number of elevator shafts in the building\n","- **stairs_per_story**: [array]\n","<br> number of stairways on each story in the building, starting from the lowest level\n","- **occupants_per_story**: [array]\n","<br> total number of occuapnts (persons) that occupy each story of this building, starting from the lowest level"]},{"cell_type":"code","execution_count":4,"id":"d2398246-e9ef-44c7-a794-005d4af8148b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d2398246-e9ef-44c7-a794-005d4af8148b","executionInfo":{"status":"ok","timestamp":1674609723161,"user_tz":480,"elapsed":1070,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"54de60d7-7cb1-43ae-9222-5772e3f4d20a"},"outputs":[{"output_type":"stream","name":"stdout","text":["{\n","  \"building_value\": 15507252.123410186,\n","  \"num_stories\": 6,\n","  \"total_area_sf\": 56250,\n","  \"area_per_story_sf\": [\n","    9375,\n","    9375,\n","    9375,\n","    9375,\n","    9375,\n","    9375\n","  ],\n","  \"ht_per_story_ft\": [\n","    14.5,\n","    13.5,\n","    13.5,\n","    13.5,\n","    13.5,\n","    13.167\n","  ],\n","  \"edge_lengths\": [\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ],\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ],\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ],\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ],\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ],\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ]\n","  ],\n","  \"struct_bay_area_per_story\": [\n","    585.9375,\n","    585.9375,\n","    585.9375,\n","    585.9375,\n","    585.9375,\n","    585.9375\n","  ],\n","  \"num_entry_doors\": 2,\n","  \"num_elevators\": 2,\n","  \"stairs_per_story\": [\n","    2,\n","    2,\n","    2,\n","    2,\n","    2,\n","    2\n","  ],\n","  \"occupants_per_story\": [\n","    37.5,\n","    37.5,\n","    37.5,\n","    37.5,\n","    37.5,\n","    37.5\n","  ]\n","}\n"]}],"source":["with open(model_dir+'building_model.json') as f:\n","    building_model = json.load(f)\n","    \n","print(json.dumps(building_model, indent=2))"]},{"cell_type":"markdown","id":"647aa5af-b19b-4dbb-ad49-0d0d90e42d65","metadata":{"id":"647aa5af-b19b-4dbb-ad49-0d0d90e42d65"},"source":["---\n","### Tenant Units\n","\n","Table that lists each tenant unit within the building; one row per tenant unit. This table requires the following attributes:\n","\n","- **id**: [int or string] \n","<br> unique identifier for this tenant unit\n","- **story**: [int] \n","<br> building story where this tenant unit is located (ground floor is listed at 1)\n","- **area**: [float] [ft2]\n","<br> total gross plan area of the tenant unit\n","- **perim_area**: [float] [ft2] \n","<br>total exterior perimeter area (elevation) of the tenant unit\n","- **occupancy_id**: [int] \n","<br>foreign key to the occupancy_id attribute of the tenant_function_requirements.csv table in the static_tables directory\n","\n","Other attributes listed in the input schema - I assume these have defaults:\n","- **exterior**: [float]\n","<br> tolerance for damage to exterior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the perimeter area of severely damaged exterior components within the tenant-unit to the total perimeter area of the tenant unit (i.e., what percent of the exterior walls must be sufficiently damaged to cause tenants to not be able to function in this space).\n","- **interior**: [float]\n","<br> tolerance for damage to interior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the interior plan area of severely damaged interior components within the tenant-unit to the total plan area of the tenant unit (i.e., what percent of the interior space must be hindered to cause tenants to not be able to function in this space).\n","- **occ_per_elev**: [float]\n","<br> max number of occupants per elevator allowed for function\n","- **is_elevator_required**: [bool]\n","<br> is an operational elevator required for tenant function\n","- **is_electrical_required**: [bool] \n","<br> is an operational electrical system required for tenant function\n","- **is_water_required** [bool]\n","<br> is an operational plumbing system required for tenant function\n","- **is_hvac_required**: [bool] \n","<br> is an operational hvac system required for tenant function"]},{"cell_type":"code","execution_count":5,"id":"f48855f7-364b-49c6-8ca5-765d0b41ce8a","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"id":"f48855f7-364b-49c6-8ca5-765d0b41ce8a","executionInfo":{"status":"ok","timestamp":1674609723489,"user_tz":480,"elapsed":330,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"a8837fbd-901c-46f5-d01b-ac95123824ef"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    story  area  perim_area  occupancy_id\n","id                                       \n","1       1  9375        4800             0\n","2       2  9375        4800             1\n","3       3  9375        4800             1\n","4       4  9375        4800             1\n","5       5  9375        4800             1\n","6       6  9375        4800             1"],"text/html":["\n","  <div id=\"df-09671f02-6915-4375-8d02-63ba2a804035\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style 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<td>3</td>\n","      <td>9375</td>\n","      <td>4800</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>9375</td>\n","      <td>4800</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>9375</td>\n","      <td>4800</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>9375</td>\n","      <td>4800</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09671f02-6915-4375-8d02-63ba2a804035')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 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building performance model; one row per each component's damage state. \n","\n","This table requires the following attributes:\n","- **comp_id**: [string] \n","<br> unique FEMA P-58 component identifier\n","- **ds_seq_id**: [int] \n","<br> integer index of the sequential parent damage state (i.e., damage state 1, 2, 3, 4)\n","- **ds_sub_id**: [int] \n","<br> integer index for the mutually exlusive or simeltaneous sub damage state; use 1 to indicate a sequential damage state with no sub damage state."]},{"cell_type":"code","execution_count":6,"id":"0ca695c3-3b6d-4175-8917-5d706f838056","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"0ca695c3-3b6d-4175-8917-5d706f838056","executionInfo":{"status":"ok","timestamp":1674609723677,"user_tz":480,"elapsed":211,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"0cc60e66-dcb3-4c79-a07a-8976a0626fce"},"outputs":[{"output_type":"stream","name":"stdout","text":["(93, 2)\n"]},{"output_type":"execute_result","data":{"text/plain":["            ds_seq_id  ds_sub_id\n","comp_id                         \n","B1041.021a          1          1\n","B1041.021a          2          1\n","B1041.021a          3          1\n","B1041.021a          3          2\n","B1041.021b          1          1\n","B1041.021b          2          1\n","B1041.021b          3          1\n","B1041.021b          3          2\n","B1041.111a          1          1\n","B1041.111a          2          1\n","B1041.111a          3          1\n","B1041.111b          1          1\n","B1041.111b          2          1\n","B1041.111b          3          1\n","B1041.121b          1          1\n","B1041.121b          2          1\n","B1044.003           1          1\n","B1044.003           2          1\n","B1044.003           3          1\n","B1044.011           1          1\n","B1044.011           2          1\n","B1044.011           3          1\n","B2011.201a          1          1\n","B2011.201a          1          2\n","B2011.201b          1          1\n","C1011.001a          1          1\n","C1011.001a          2          1\n","C1011.001a          3          1\n","C2011.011b          1          1\n","C2011.011b          2          1\n","C2011.011b          3          1\n","C3011.001a          1          1\n","C3011.002a          1          1\n","C3011.002a          2          1\n","C3032.001a          1          1\n","C3032.001a          2          1\n","C3032.001a          3          1\n","C3032.001b          1          1\n","C3032.001b          2          1\n","C3032.001b          3          1\n","C3032.001c          1          1\n","C3032.001c          2          1\n","C3032.001c          3          1\n","C3032.001d          1          1\n","C3032.001d          2          1\n","C3032.001d          3          1\n","C3034.001           1          1\n","D1014.012           1          1\n","D1014.012           1          2\n","D1014.012           1          3\n","D1014.012      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   1          1\n","D3041.103c          1          2\n","D3041.103c          1          3\n","D3052.013l          1          1\n","D3052.013l          1          2\n","D3052.013l          1          3\n","D3052.013l          1          4\n","D3067.011a          1          1\n","D4011.022a          1          1\n","D4011.022a          2          1\n","D4011.032a          1          1\n","D4011.032a          2          1\n","D5011.011a          1          1\n","D5012.013a          1          1\n","D5012.021d          1          1\n","D5012.031d          1          1"],"text/html":["\n","  <div id=\"df-83e64e1e-9515-4537-8b7b-3177f15cbc7d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ds_seq_id</th>\n","      <th>ds_sub_id</th>\n","    </tr>\n","    <tr>\n","      <th>comp_id</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>B1041.021a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>B1041.021a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>B1041.021a</th>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>B1041.021a</th>\n","      <td>3</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>B1041.021b</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>B1041.021b</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>B1041.021b</th>\n","      <td>3</td>\n","      <td>1</td>\n","    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 <th>C1011.001a</th>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C2011.011b</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C2011.011b</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C2011.011b</th>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3011.001a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3011.002a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3011.002a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001a</th>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001b</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001b</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001b</th>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001c</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001c</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001c</th>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001d</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001d</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3032.001d</th>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>C3034.001</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D1014.012</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D1014.012</th>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>D1014.012</th>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>D1014.012</th>\n","      <td>1</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>D2021.012a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2021.012a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2021.012b</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2021.012b</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2021.022a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2021.022a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2022.012a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2022.012a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2022.012b</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2022.012b</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2022.022a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2022.022a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2031.022a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2031.022b</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2031.022b</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2051.022a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2051.022a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D2051.022b</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3031.011b</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3031.021b</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.002c</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.011c</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.011c</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.012c</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.012c</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.032c</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.103c</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.103c</th>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.103c</th>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>D3052.013l</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3052.013l</th>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>D3052.013l</th>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>D3052.013l</th>\n","      <td>1</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>D3067.011a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D4011.022a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D4011.022a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D4011.032a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D4011.032a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D5011.011a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D5012.013a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D5012.021d</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D5012.031d</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83e64e1e-9515-4537-8b7b-3177f15cbc7d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 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Within each dictionary, the following variables need to be defined:\n","- **qnt_damaged**: [array: simulations × damage states] \n","<br>The number of damaged components in each component damage state for each realization of the simulation.\n","- **cost**: [array of float: sim x ds]\n","<br> simulated repair cost of each component's damage state\n","- **worker_days**: [array: simulations × damage states] \n","<br>The number of single worker days required to repair all damage to this damage state of this component at this story for each realization.\n","- **qnt_damaged_side_1**: [array: simulations × damage states] \n","<br>The number of damaged components in each component damage state assocaited with side 1 of the building; set to zero if not associated with a particular side. This is only for exterior cladding components.\n","- **qnt_damaged_side_2**: [array: simulations × damage states] \n","<br>The number of damaged components in each component damage state assocaited with side 2 of the building; set to zero if not associated with a particular side. This is only for exterior cladding components.\n","- **qnt_damaged_side_3**: [array: simulations × damage states] \n","<br>The number of damaged components in each component damage state assocaited with side 3 of the building; set to zero if not associated with a particular side. This is only for exterior cladding components.\n","- **qnt_damaged_side_4**: [array: simulations × damage states] \n","<br>The number of damaged components in each component damage state associated with side 4 of the building; set to zero if not associated with a particular side. This is only for exterior cladding components.\n","- **num_comps**: [array: 1 × damage states] \n","<br>The total number of components associated with each damage state (should be uniform for damage state of the same component stack)."]},{"cell_type":"code","execution_count":9,"id":"4b9133ea-2e31-494e-82e1-d69f52dff05d","metadata":{"tags":[],"colab":{"base_uri":"https://localhost:8080/"},"id":"4b9133ea-2e31-494e-82e1-d69f52dff05d","executionInfo":{"status":"ok","timestamp":1674609724742,"user_tz":480,"elapsed":775,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"007f84eb-e5cd-42b8-9aeb-80e893523cfc"},"outputs":[{"output_type":"stream","name":"stdout","text":["qnt_damaged (500, 93)\n","worker_days (500, 93)\n","qnt_damaged_side_1 (500, 93)\n","qnt_damaged_side_2 (500, 93)\n","qnt_damaged_side_3 (500, 93)\n","qnt_damaged_side_4 (500, 93)\n","num_comps (93,)\n"]}],"source":["with open(model_dir+'simulated_damage.json') as f:\n","    sim_dmg = json.load(f)\n","    \n","for key, item in sim_dmg[0].items():\n","    print(key, np.array(item).shape)"]},{"cell_type":"code","execution_count":10,"id":"72f316ea-2b48-43d6-aede-962e16dac0b5","metadata":{"id":"72f316ea-2b48-43d6-aede-962e16dac0b5","executionInfo":{"status":"ok","timestamp":1674609724742,"user_tz":480,"elapsed":4,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}}},"outputs":[],"source":["#pd.DataFrame(sim_dmg[0]['cost'])"]},{"cell_type":"markdown","id":"3ca062e5-f99d-4342-bc89-2b6f5834ac87","metadata":{"id":"3ca062e5-f99d-4342-bc89-2b6f5834ac87"},"source":["---\n","### Optional Inputs\n","\n","Details are described in the Inputs.json schema file"]},{"cell_type":"code","execution_count":11,"id":"9d08a5e9-d209-482d-864c-2dd239743bf7","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9d08a5e9-d209-482d-864c-2dd239743bf7","executionInfo":{"status":"ok","timestamp":1674609725078,"user_tz":480,"elapsed":339,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"d4476ffc-e908-4639-8dbb-8c1162035710"},"outputs":[{"output_type":"stream","name":"stdout","text":["{\n","  \"impedance_options\": {\n","    \"include_impedance\": {\n","      \"inspection\": true,\n","      \"financing\": true,\n","      \"permitting\": true,\n","      \"engineering\": true,\n","      \"contractor\": true\n","    },\n","    \"system_design_time\": {\n","      \"f\": 0.04,\n","      \"r\": 175,\n","      \"t\": 1.3,\n","      \"w\": 8\n","    },\n","    \"mitigation\": {\n","      \"is_essential_facility\": false,\n","      \"is_borp_equivalent\": false,\n","      \"is_engineer_on_retainer\": false,\n","      \"is_contractor_on_retainer\": false,\n","      \"funding_source\": \"private\",\n","      \"capital_available_ratio\": 0.1\n","    },\n","    \"impedance_beta\": 0.6,\n","    \"impedance_truncation\": 2\n","  },\n","  \"repair_time_options\": {\n","    \"temp_repair_beta\": 0.6,\n","    \"max_workers_per_sqft_story\": 0.001,\n","    \"max_workers_per_sqft_building\": 0.00025,\n","    \"max_workers_building_min\": 20,\n","    \"max_workers_building_max\": 260\n","  },\n","  \"functionality_options\": {\n","    \"door_racking_repair_day\": 3,\n","    \"egress_threshold\": 0.5,\n","    \"egress_threshold_wo_fs\": 0.75,\n","    \"required_ratio_operating_hvac_main\": 0.6667,\n","    \"required_ratio_operating_hvac_unit\": 0.6667,\n","    \"exterior_safety_threshold\": 0.1,\n","    \"interior_safety_threshold\": 0.25,\n","    \"door_access_width_ft\": 9\n","  },\n","  \"regional_impact\": {\n","    \"surge_factor\": 1\n","  }\n","}\n"]}],"source":["with open(model_dir+'optional_inputs.json') as f:\n","    options = json.load(f)\n","    \n","print(json.dumps(options, indent=2))"]},{"cell_type":"markdown","id":"89c23f41-64dc-4858-ba49-85ee845fc824","metadata":{"id":"89c23f41-64dc-4858-ba49-85ee845fc824"},"source":["## Analysis"]},{"cell_type":"code","execution_count":12,"id":"59046d7f-9e53-423d-8168-46f6d71e3ef8","metadata":{"id":"59046d7f-9e53-423d-8168-46f6d71e3ef8","executionInfo":{"status":"ok","timestamp":1674609725625,"user_tz":480,"elapsed":562,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}}},"outputs":[],"source":["from inputs.Inputs2Copy.build_input import build_input\n","from driver_PBEErecovery import run_analysis\n","os.chdir(main_dir)"]},{"cell_type":"code","execution_count":13,"id":"1187ae15-8fdc-4b1d-a037-5f18dcec8730","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1187ae15-8fdc-4b1d-a037-5f18dcec8730","executionInfo":{"status":"ok","timestamp":1674609731255,"user_tz":480,"elapsed":5632,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"b7b812dc-7ccf-4863-a609-f725bb068d77"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/SimCenter/FunctionalRecovery/ATC138/inputs/ICSB\n","Component ID B1041.021a does not belong to a subsystem\n","Component ID B1041.021a does not belong to a subsystem\n","Component ID B1041.021a does not belong to a subsystem\n","Component ID B1041.021a does not belong to a subsystem\n","Component ID B1041.021b does not belong to a subsystem\n","Component ID B1041.021b does not belong to a subsystem\n","Component ID B1041.021b does not belong to a subsystem\n","Component ID B1041.021b does not belong to a subsystem\n","Component ID B1041.111a does not belong to a subsystem\n","Component ID B1041.111a does not belong to a subsystem\n","Component ID B1041.111a does not belong to a subsystem\n","Component ID B1041.111b does not belong to a subsystem\n","Component ID B1041.111b does not belong to a subsystem\n","Component ID B1041.111b does not belong to a subsystem\n","Component ID B1041.121b does not belong to a subsystem\n","Component ID B1041.121b does not belong to a subsystem\n","Component ID B1044.003 does not belong to a subsystem\n","Component ID B1044.003 does not belong to a subsystem\n","Component ID B1044.003 does not belong to a subsystem\n","Component ID B1044.011 does not belong to a subsystem\n","Component ID B1044.011 does not belong to a subsystem\n","Component ID B1044.011 does not belong to a subsystem\n","Component ID B2011.201a does not belong to a subsystem\n","Component ID B2011.201a does not belong to a subsystem\n","Component ID B2011.201b does not belong to a subsystem\n","Component ID C1011.001a does not belong to a subsystem\n","Component ID C1011.001a does not belong to a subsystem\n","Component ID C1011.001a does not belong to a subsystem\n","Component ID C2011.011b does not belong to a subsystem\n","Component ID C2011.011b does not belong to a subsystem\n","Component ID C2011.011b does not belong to a subsystem\n","Component ID C3011.001a does not belong to a subsystem\n","Component ID C3011.002a does not belong to a subsystem\n","Component ID C3011.002a does not belong to a subsystem\n","Component ID C3032.001a does not belong to a subsystem\n","Component ID C3032.001a does not belong to a subsystem\n","Component ID C3032.001a does not belong to a subsystem\n","Component ID C3032.001b does not belong to a subsystem\n","Component ID C3032.001b does not belong to a subsystem\n","Component ID C3032.001b does not belong to a subsystem\n","Component ID C3032.001c does not belong to a subsystem\n","Component ID C3032.001c does not belong to a subsystem\n","Component ID C3032.001c does not belong to a subsystem\n","Component ID C3032.001d does not belong to a subsystem\n","Component ID C3032.001d does not belong to a subsystem\n","Component ID C3032.001d does not belong to a subsystem\n","Component ID C3034.001 does not belong to a subsystem\n","Component ID D4011.022a does not belong to a subsystem\n","Component ID D4011.022a does not belong to a subsystem\n"]}],"source":["%reload_ext autoreload\n","%autoreload 2\n","\n","os.chdir(model_dir)\n","build_input('simulated_inputs.json')\n","os.chdir(main_dir)"]},{"cell_type":"code","execution_count":14,"id":"71f870d8-bd26-4f66-a196-e3e9c06dfaff","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"71f870d8-bd26-4f66-a196-e3e9c06dfaff","executionInfo":{"status":"ok","timestamp":1674609740321,"user_tz":480,"elapsed":9082,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"05803b87-eb3a-46e7-fb0f-c4779f1a7a2b"},"outputs":[{"output_type":"stream","name":"stdout","text":["time to run 8.95s\n"]}],"source":["%reload_ext autoreload\n","%autoreload 2\n","\n","run_analysis(model_name)"]},{"cell_type":"markdown","id":"bdf2d49d-6936-43c7-83d3-a407ea309bcf","metadata":{"id":"bdf2d49d-6936-43c7-83d3-a407ea309bcf"},"source":["## Outputs"]},{"cell_type":"code","execution_count":15,"id":"a99fae91-4439-4d62-ac31-20c3384a6729","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a99fae91-4439-4d62-ac31-20c3384a6729","executionInfo":{"status":"ok","timestamp":1674609740321,"user_tz":480,"elapsed":18,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"b3f53ea8-d315-42e2-c130-34f814237401"},"outputs":[{"output_type":"stream","name":"stdout","text":["utilities ()\n","impeding_factors ()\n","worker_data ()\n","building_repair_schedule ()\n","recovery ()\n"]}],"source":["with open(f'./outputs/{model_name}/recovery_outputs.json') as f:\n","    outputs = json.load(f)\n","    \n","for key, item in outputs.items():\n","    print(key, np.array(item).shape)"]},{"cell_type":"code","execution_count":16,"id":"2491e900-a086-47b9-99ae-aa81db8aff37","metadata":{"id":"2491e900-a086-47b9-99ae-aa81db8aff37","executionInfo":{"status":"ok","timestamp":1674609740523,"user_tz":480,"elapsed":219,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}}},"outputs":[],"source":["from plotModel_PBEErecovery import plot_results"]},{"cell_type":"code","execution_count":17,"id":"b17eabe1-fc69-45b5-a9cd-f362b5087a1f","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"b17eabe1-fc69-45b5-a9cd-f362b5087a1f","executionInfo":{"status":"ok","timestamp":1674609758215,"user_tz":480,"elapsed":17693,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"ffdb3f20-15b8-45a6-d98e-6582e9898bda"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.font_manager:findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n","WARNING:matplotlib.font_manager:findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x576 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAlkAAAILCAYAAADbvom7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydd3hVRfrHP28KJKEkQEJoCaEpKkWq2EDXgmBBf4oFC+6qq2tZu2DHLhYUZS0rIrhrdy0oTRFQUUSBEHqXFiCEEEIvSd7fHzMJ917SgHvTznye5z6558ycOfOde+7Ne2bmfEdUFYfD4XA4HA5HcAmr6Ao4HA6Hw+FwVEdckOVwOBwOh8MRAlyQ5XA4HA6HwxECXJDlcDgcDofDEQJckOVwOBwOh8MRAlyQ5XA4HA6HwxECXJDlOAQROV1EllbAeY8VkbkiskNE/lmO500RERWRCLs9QUQGhuA8C0XkjGCXe6SI4T0RyRaR3+2+f4hIhojsFJEGQTzX1SLyXbDKCyZWa0v7frSIPF1CXhWR1vb9WyLyaHnV0+FwVD1ckFXJEZHVIrLH/iMoeDUJ8jkK/3EAqOrPqnpsMM9RRh4ApqpqHVV9LTBRRKaJyF7bBltE5AsRaRzsSqhqH1UdczRlFPXPWlVPUNVpR1W5I6vLGfYzHhSQdBpwDtBMVbuLSCQwDDhXVWuratYRns8vaAVQ1Q9U9dwjFlHy+Xy/I9kiMk5Eksp6vNW66nDPq6q3qOpTh3tcafi0X8H3fbWIDA72ebxCwPWxyX43a1d0vRzewAVZVYML7T+CgtcG30Tff2ZVnObAwlLy3K6qtYHWQG3gpZDXquozENgKXBewvzmwWlV32e1EIIrSP4PKyIX2umgMZACvV3B9gkGc1XQZ8KiInFPRFarCFFwfJwKdgAcruD5lohr9tnsWF2RVUeyd7m0ishxYbvcNF5F1IrJdRGaLyOk++cNF5CERWWmH42aLSJKI/GSzpNk7vStsz8d6n2OPs71I2+yQ10U+aaNF5F+292CHiMwUkVYl1PsiW8Y2W+Zxdv8U4ExghK3HMSXpV9VtwFeYH82CstuKyPcislVElorI5T5p54tIqm2bdSIypIQ6ThORG+37gnYpeGnBkJ+IfGbvjHNE5CcROcHu/ztwNfCAPeYbu3+1iJxt39cUkVdFZIN9vSoiNW3aGSKyXkTuFZHNIrJRRP7qU7++IrLItne6iNxXgpZamH/StwFtRKSr3X8DMBI42dbxI6BgiHib/TxKa9NoEXlZRNbYNpguItHATz7l7BSRk0XkehGZ7nOsisgtIrLcXgv/EhGxaeG23C0i8qeI3C4BPWPFoap7gc+B44v6PO12UXVpTRGIyP22/TeIyN8C0gp7K8vwmTUQkW/s9feHiDztW4dSNM3CBL6+1/rfRGSxmJ67SSLS3CftFHuOHPv3FJ+0+mKGiDfYY7/ySesnZrh+u5jfifPs/sLr1m4PEZH/2vcFvW5/t2Vu9L0eRaS7iMywn/FGERkhIjUC2r7I68Cm32R17rDXfGf7mfwv4LN4TUSGl6EtNwGTAtqyh4j8as+fJj5D+qW0100issJ+N8aKHWEQkTdFxO/mT0S+FpF77PsmIvI/Ecm01/c/ffINEZHPReS/IrIdGCwiu8Vn6N62QaaYnmdHZUdV3asSv4DVwNlF7Ffge6A+EG33XQM0ACKAe4FNQJRNux+YDxwLCNARaOBTVmufss8A1tv3kcAK4CGgBvAXYAdwrE0fDWQB3e15PwA+LkbLMcAuzBBVJGZ4cAVQw6ZPA24soS0K063OycDXdrsWsA74q61HJ2ALcLyPpvaYG4sOmN6Oi21aim2DiJLqAfwdWALUtdt/A+oANYFXgbk+eUcDTxf3WQJPAr8BDYEE4FfgKZ+65to8kUBfYDdQz6ZvBE637+sBnUtos2tt/nDgG+B1n7Trgek+24HtUFqb/su2VVNb/im2LfzKKeZcCnwLxAHJQCZwnk27BVgENLP6JgeWV0K7xgBjgPeLum5KqEvrwM8NOA9znbSzbfFhCXlL+8w+tq8YTAC4zrcOAXoCP4cetqxL7HY/zPfmOPu5PAL8atPqA9n2c48ArrLbBd/1ccAntl0jgV52f3cgB/PdDLOfaduifoOAIcB/A+r6kW2j9vazLPg8utj6R9i8i4G7yngd9AfSgW6Y36zWmN7XxpjfkTibLwLYDHQpw/XRDPM7ONxuN8X8fvW1us+x2wmltNdfMN+Fzphr/nXgJ5vW036+4vMd3QM0seeYDTyG+T1tCawCevu07QHgYps3GhgP/MNHzyv4fI/dq3K/KrwC7lXKB2R+IHYC2+zrK7tfgb+Ucmw20NG+Xwr0KyZfSUHW6ZhgLcwn/SNgiH0/Ghjpk9YXWFLMeR4FPvXZDrM/omfY7WmUHmTtxvwzUGAukGzTrgB+Dsj/NvB4MWW9Crxi36dQSpCFmb+0GTimmPLibBmxPu1SUpC1Eujrk9YbM3RX0P578A9SNgM97Pu1wM3YYK+Ua2Ay8Kp9fxXmn1ik3b6ekoOsYtvUfnZ7Cq6vgDx+5RRzLgVO89n+FBhs308BbvZJOzuwvBK+IweADUD7gOvmSIKsUcDzPvmOKSFvsZ8ZJgA9gL0xsWlPU3qQtc2WqZhh8YJ/2hOAGwK+R7sxAci1wO8B5c2wmhsD+djAr4jP9ZUS2re0IKutT/oLwLvFlHUX8GUZr4NJwJ3FlDMBuMm+vwBYVMJ3oOD62GHP9wMHA7RBwH8C8k/CDLGX1F7vAi/4bNe2n3EKJiBcC/S0aTcBU+z7k4C1AWU9CLzn07Y/BaRfAfxi34djfo+7l/bdd6/K8XLDhVWDi1U1zr4u9tm/zjeTiNxnu9ZzRGQbEAvE2+QkzD/2w6UJsE5V8332rcHcARawyef9bswPTnFlrSnYsGWuCyirNP6pqrGY3qh6mDtTMP9gTrJd/tus/quBRgAicpKITLXd7DmY3pL4Iso/BDGTqD8FBqrqMrsvXESet8Mq2zE/5JS1TALawr73faAhS1VzfbZ92/VSTDC7RkR+FJGTS6j3mZjeRYCvMXOuzi9jHUtq03hb1pFcUwUUd900wf/a9rvOi+FiVY2zdbod+FFEGh1F3Yqqx5riMlqK+8wSML0th6sp3h5/LyaIKxgeag4M9/lMtmL+sTfl0OuqoN5NMb8BW1U1u4hzHenvQwGB7VQwdHaMiHwrZlh9O/Ash35HirsOSqrTGEzPPfbvf0qp38WqWgfTjm196tAc6B9wjZ+GCbBKaq/A37KdmB6wpmqioY8xNzUAAzj4HWwONAk430OY+ZAFBF4bXwPHi0gLTE9bjqr+XopeRyXBBVlVGy14I2b+1QPA5Zg7rzhMj0/B/IZ1QLFzpUpgA5AkIr7XSjKmB+pIymruU2fB/JAddlmqOh/TG1Awh2Md8KNPMBqn5iGBf9hDPgTGAkk2SHuLg21TLGLmGH2F6Q2a4JM0ADNsczYmmE0pOKSgiqUU7dcWmDbdUExeP1T1D1Xthxlq/AoTABbFtZjv+DcisgkzLBGFuUsvCyW16RZgL0VfU6VpL42NHAyewVwjZUJV81T1CyAP888SzNBSjE+2sgZfGwPOnVzWegSQiRlKPGxNVs8wTFvfanevw/T0+X4u0ar6K4deVwX1TrfH1ReRuCJOVdLvQ1naL7CdCq7lNzFD7G1UtS4moCj1e1eGOn0FdBCRdpierA+KyeeHqv6I6YEsmDO1DtOT5duWtVT1eUpur8DfslqYKQwFv2UfAZeJmSt3ElAwh2wd8GfA+eqoal/fagbUeS/mO34N5jtdWkDpqES4IKv6UAfzQ54JRIjIY0Bdn/SRwFMi0kYMHXwmU2Zg5gYUxUzM3eUDIhJpJ4VeiLlTO1w+Bc4XkbPspM17gX2Y+UhHwhjMHeBFmHkdx4jItbaekSLSTezEekz7bFXVvSLSHRMklYVRmOHPFwL217F1z8L8A3o2IL2kNgXzI/yIiCSISDxmjsZ/S6uMiNQQ4zkVq6oHgO2YIY2iGAg8gZnkW/C6FOgrZfPAKrZNbS/kKGCYncgbLmaCe03MNZhfiv6S+BS4U0Sa2n9wgdYTxWKv7X6YXs7Fdvdc4P9EJEbMBPcbDqMe14vI8SISgxkmPWxUNQ/4Ahhi69CWQ5/0LI3nMd/BKMwNwoNy8EGLWBHpb/ONx3xmA0QkQkSuwMwB+1ZVN2KG2d4QkXr28+xpj3sX+Kv9bobZtm9r0+YCV9r8XTEPUgTyqNV2AmYO3yd2fx3MNbrTlvePIo4tjpHAfSLSxX6urW3QUhB4fI65efpdVdceRrmvAueISEfMd+5CEeltr+EoMQ8xNCulvT7CtNeJ9pp/Fpipqqtt/VIxNyIjgUlqHtQB+B3YISKDxDw4Ei4i7USkWyl1fh8z5HsRLsiqUrggq/owCZgILMN0Y+/Fv9t5GOafxneYH713MZMqwcwDGGO7ry/3OQZV3Y8JqvpgfjTeAK5T1SWHW0FVXYq5G3vdlnUh5tHq/Ydblk/dhgOPquoO4FzgSsxd5iZgKGZSKphegCdFZAcmoCmu9yeQK4FLxP8Jw9MxP3prMHeuizCT2H15F9PFv018nkjy4WlgFjAPMxF3jt1XFq4FVtvhl1swQ3h+iEgPzJ32v1R1k89rLGbS9FWBxwRShja9z9b9D8yQ1VDM3L3dwDPAL1Z/jzLqKuAdzHU6D0jFBA65mN6p4vhGRHZiru1nMEO7BVYUrwD7MYHvGMre6zEB8w95CqbNphymDl9ux/R4bsL8k/wIE6SXlXGYOZY3qeqXmLb+2F4DCzDfT9R4m12AuYHJwvRuX6CqW2w512LmDi3BzBm7yx73OyY4egXTA/4jB3tqHsX0KGVjgvYPi6jfj5g2+gF4SVULjGfvw9zQ7MB8rp8UcWyRqOpnmM/yQ3v8V5iJ/QWMwUy0P6ygQ1UzMd/fx1R1HaZH+iHMzcE6zENCBf8bi2uvyZh2+R+mx7MV5nviy4eYnu7C9rIB9wWYG54/ORiIxZZS518wNy5zVLW0YWtHJaJgIqXD4XBUSkSkD/CWqgYOg1VZRGQo0EhVyzp0WykRkRRMsBAZMB+tPM6djAl+Gqnq9vI8d0UgxlblQ1UdWdF1cZQd15PlcDgqFXYYpa8d7mqKGab7sqLrdTSI8RvrYIe9umOGLKu0popEzBzRezB2MV4IsLph7CLK3BPoqBy4IMvhcFQ2BDMslY0ZLlyMGeKtytTBzMvahflH+TLmqTHHYWInmW/HPGl3RPPkqhIiMgZjxXKXHcJ3hAARGSXGSHhBMekixvR2hYjME5HOZSrXDRc6HA6Hw+HwMvahhp0YI+N2RaT3Be7A2OechDG0Pam0cl1PlsPhcDgcDk+jqj9hHuApjn6YAExV9TcgTkQal1auW3zyMBAR1+3ncDgcDs+hqmX1N6uuNMX/if31dt/Gkg4qU0+WiOSJWTg0TUTmiP+CoxPtY9rfBhzTQsxiwStE5BPxWRTUpl8qZnHQggVrG4hx5N4pIiNKqEt9MQvWLrd/69n9V9tx0vliFvvs6HNMiWOtlYHevXuzZMkSli9fzqBBh9oCJScnM3nyZNLS0pg6dSpNmx40Sc/NzSU1NZXU1FS+/rpqTvNw+p1+r+r3snYoXX9SUhJTpkxhzpw5pKWl0adPHwAGDBhQqD01NZW8vDw6dux4yPGVnZiYGFJSUkhJSaFevXqHpEdHR5OcnEybNm2oXdt/MY34+HiaN29O8+bND0mrykR3ul2D/RKziPksn9ffy0NLmeZkichOVa1t3/cGHlLVXnb7LIwZ482qeoHPMZ8CX6jqxyLyFpCmqm/atDoY35cawO2qOstOZuyEWYy1nareXkxdXsCYSj4vIoMx7uaDbOC3WFWz7SPfQwrGS0sbay0rBT1ZUac9eqRFFElYmDD/wzs5/+4xpGduZ/o7NzPwic9YsjqzMM8HT17O+F+X8cHEufTq3ILr+nbihqe/ACBz0sMk9H4mqHUqir3Tnwq6dnD6nf7Kr3/v9KcAb3/3AaIuCa57QFiYMH/EZZz/xETSs3Yx/YWLGDhsGkvWbyvMM+KWU0n7M4t3Ji2hbbM4vnrkXNre4m9zd0JyPT4dfDYn3PpZUOtXwN4vbyRtbfDnnOfl5XHn9Zfw6NA3qJ+QyIO3XcudDz9LUvODPr6bN21gz+5djP30P3Q9pScn9zwbgNm//cy4Lz7i4ede48D+Awy59+889uKbxNQKfrDVMblOufZkRXe6PeijRntSR5RlhY8UjHFvUXOy3gamqepHdnspZt3do+/JCqAu5qkfAFT1B4xRnG9lBLNK+ed21xjMquIFPIUx09vrU84uVZ3uu68Y+tny/MpV1V991pj6DZ8lLIobaxWRm0TkD9tD9z8xrs7lTrfjmrEyfSurN2ZzIDePz36YzwWntfXL0zalIT/OWQXAj3P+PCS9KuP0O/1e1e9l7QDdWiewcuN2Vmfs4EBuPp9NX8UF3f1XL1KgbowZCImNqcHGrbsPKefy01vy2fRV5VHloLJi6UIaNUkisUkzIiMjOfWMc5n1yzS/PA0bNaF5yzZImH+MsH7NnxzfoRPh4RFERUeT3LINc/840sUzKhkSFvzX0TMWuM4+ZdgDs4ZkiQEWlD3IirbDhUuwy7OUkr8BsM3HnK5g7BL72GOSqo4r47kDSfQRtgn/hTULuAGzHEJpfKGq3VS1I+Yx8bIutxFUmiTUYf3mnMLt9MztNI2v65dn/opN9Ot5PAD9eh5H3VpR1K9rDNujakQw/Z2b+fGtm7jw9Kr3A+z0O/1e1e9l7QBNGsSwPmtX4XZ61m6a1q/ll+eZT+ZwZc9WrHjnSr585FzuGTnjkHIuO7Uln1bBIGvrls00aHjwX1j9hESysjJLOOIgKa3aMPePGezbu4ftOdksnDuLrMyMUFW12iMiHwEzgGNFZL2I3CAit4jILTbLeMz6ryswqxfcWkxRfpR14vseVT3RVuRk4H0RaaeH6f9gDeSGYdZgOmpUVQMno4vImZhg6bSij/KjnYg8DcRhVn6fFIx6hYIH/zWJV+4+n2v6dOKXtNWkb84hL99IP7b/MDZs2UFK43pMHH49C1Zm8OeGohaOr7o4/U6/V/V7WTvA5ae14r9TlzN87AJOOqYh797Ziy53fUHBf59ubRLYvS+XRWurl+7S6Nj1ZFYsXcTDd/6NurH1OOb49oSFhVd0tYKDlP8ce1UtcakxG+/cdrjlHvbThao6Q8yCtgmYtZyKIgvzeGOE7c1qhlnjrQ5mztU0M6JII2CsiFykqrPKWIUMEWmsqhvt45OFdRCRDpietj52Da/SGA1crKppInI9cEZRmewEuZBNktuQuYNmDQ8uXdU0oS7pW/xNjDdm7eDKR8yazLWia3Bxr+PJ2WlGVjdsMaO1qzdm89Pc1Zx4TOMq9UPr9Dv9XtXvZe0AG7J206zBwZ6rpg1iSN+6yy/PwLOOod9T5v535rLNREWGE183iswc0wb9T6uavVgA9eMbkrX5YO/T1swMGjRIKPPxl159A5debQZgXn3mIRo3Sy7lCEd5c9gDlWJWUg/HBFJFYiO+qRxcrX0g8LWq5qhqvKqmqGoKZu7U4QRYYMZFC9b7Goh1TbbrWH0BXKuqy8pYVh1go4hEUsQiuz56/q2qXQ+jjofFrCXptG5Wn+aN44iMCKf/We0ZN91//eUGsTHYwJT7rzmdMeNTAYirHUWNyPDCPCe3S2bx6rJ1N1cWnH6n36v6vawdYNaKTFo3rkvzhrWJjAij/2ktGffHWr8867bs5IwOTQA4tmksUTXCCwMsEbj0lBZVcj4WQOtjj2dj+joyNqZz4MABfpn2HV1P6VWmY/Py8tiRYx4QWLNqOWv/XEHHroe7FnslpXLOyToiytqTFS0ic+17waxwnwcgIj8DbYHaIrIeuEFVJwGDMKvEP41ZGuPd0k4iIqsxE+triMjFwLmqukhERmIWiJ0FPA98KiI3AGuAy+3hj2Hmgr1hf5ByCwIjO9Z6BhBv6/i4qr6LWUV9Jmb19ZmYoKvcycvL5+5XxvHNy9cRHhbGmHFzWLw6k0dv+AtzlqQz7pel9OyUwpN/PwdFmZ62hruGGceMtikJvH7fReSrEibCSx/87PdkUlXA6Xf6varfy9oB8vKVu0fO4JvHziM8TBjzwzIWr9vGo1d2Zs7KLYz7Yy2DR//OG7eexh0XnoAq3PT6z4XHn3Z8I9Zn7WJ1RtVcbSY8PIIb7niAZwbfTn5+Hmee14+klFZ8PPpNWh1zPN1O6cWKJQt5cch97Nq5ndkzfubTMW/zyrufkZeXy6N33whATEwt7hj8FOHh1cT6sgKGC0OFW1bnMAiVhUNVIVSP8FcVnH7v6g+VhUNVIVQWDlWFUFk4VBXK3cKh2z3Bt3D4Y1iFRG4uyDoMnOO7w+FwOLxIuQZZ3e8LfpD1+0sVEmSVqW9RRPKA+ZihwjyMgeivNm0i0AOYHmBG2gL4GDOENxszV2q/T/qlGB+tbtaMtEHBNjC6BDPS+phV7FOA1cDl1oD0aswQpWB8u/6hqmn2mFHABcBmX5MxEZkG3HeYc8JCcjd7TvfWvHRnX8LDhNHfzuGlD372S09OjOWtBy8hPi6G7O17+NtT/yM900yQ3TltCAtWmcmT6zJy6P/gh0GvH4S2J8Ppd/ors/5Q9mRVdu0Q2p6sczo15aW/9SA8LIzRk5fy0pfz/NKT4mvxzh09ia1Vk/Aw4dH//sGkOeu5smcr7urXvjBf++b1Ofm+r5i3uqTl546MUPZkpf7+K++98RL5+Xmc1ediLrnqr37pi+bNYfQbL7Fm1QrueuTZQjNSgP++8xpzZk4H4NKrb+TUM88NSR07JpfzTJpqNFx4JBYOvYHngILZeS9iHd8DjhkKvOLj+H4D4Ov4fidmHlQBezFzpNrZV3EMBn7wcXwfjAmu/gR6+Ti+/xuzUjaYpwhHAO+XUW+5EhYmvHrPBX6uz9/+ssRvfsVzt/Xmg4lzC12fn7z57ELX5z37DtDjb29WVPWPGqff6feqfi9rB6v/plP8HN+//WOtn+P7oMtO5H+//nmI4/vHP63k459WAgcd30MRYIWSvLw83n39eT/H966n9PJzfI9v2IjbHniCsZ/+x+/Y2b/9zKrlS3jx7Q8LHd87dT8lJI7vjiPH047vlmut0eoCEeleyrlDguddn51+p9+j+r2sHZzju3N8L4Zq9HSh1x3fAWJsL92twKgjrNNR4XnXZ6ff6feofi9rB+f47hzfqz9ed3wH+MiW9ZOI1BWROFXd5psh1GakZcHrrs9Ov9PvVf1e1g7O8b04nON71cDrju9geqNL2kZV/w38O1RPF3re9dnpd/o9qt/L2sE5vjvH92KowOG9YON1x3eAK+zxp2FW1c4pJX/Q8bzrs9Pv9HtUv5e1g3N8d47v1R+vO74D7BWRVCAS+FsZ2yOoeN712el3+j2q38vawTm+O8f3YqhGw4XOjPQwcI7v3nX8Bqffy/qd47tzfHeO7+VoRnrqw8E3I/3lmcrr+F7JzEj7A0OA44DuBUONJR0vIs8A1wH1VPWITURC6fjeu3dvhg8fTnh4OCNHjmTo0KF+6cnJyYwaNYqEhAS2bt3KNddcQ3p6OgC5ubnMnz8fgLVr19KvX79QVTNkOP1Ov1f1e1k7lK4/KSmJMWPGEBcXR3h4OIMHD2bChAkMGDCA+++/vzBfhw4d6Ny5M2lpaeUt4aiIiYmhYcOGAOTk5JCd7T+nLjo6moSEBGrWrMnGjRvZuXNnYVp8fDy1apk5bVlZWX5pwaZcg6zTHg1+kDX9qUodZO0sCE6sGelDqtrLbp+FNSMNCLI+Bb7wMSNNU1VfM9JxQA1MwDZLRGoBnbBmpCUEWccB+cDb+Li1l3S8iPTADC0uD0aQFey72bAwYf6Hd/oZEg584jO/rv8Pnryc8b8uKzQkvK5vp0JDwsxJD5PQ+5mg1qkoQtWT4fQ7/ZVdf6h6sqqCdghdT1ZYmDB/xGV+ZqQDh03zMyMdccuppP2ZdYgZqS8FZqQn3PpZUOtXQKh6svLy8rjz+kv8zEjvfPhZPzPSzZs2sGf3LsZ++h+6ntKz0PF99m8/M+6Lj3j4udcKzUgfe/HNkJiRlntP1umPBT/I+vnJCgmyqpwZqaouVtWlRewv9nhV/c3HW8u3nheKyEwRSRWRySJSlOdWyPG8IaHT7/R7VL+XtYMzI3VmpNWfqmhGGkymAz1UtRNmaPOBiqiE5w0JnX6n36P6vawdnBmpMyMthmrk+F6lzUiDQDPgE+u3VQOz/mGlxOuGhE6/0+9V/V7WDs6MtDiqtRlpNaIqmpEGk9eBYao6VkTOwEyoP4RQO7573pDQ6Xf6Parfy9rBmZE6M9JicGakFWpGGkxiMcFfQR2LRFX/XeC5FQo8b0jo9Dv9HtXvZe3gzEidGWkxhEnwXxVElTMjFZFLMD1QCcA4EZmrqr1LOf4FYAAQY+s4UlWHYHquPhORbGAK0KKM7RFUPG9I6PQ7/R7V72Xt4MxInRlp9ceZkR4GzozUu2aU4PR7Wb8zI3VmpM6MtBwtHP7yTPAtHKY8XHl9shyGUJqROhwOh8NRWXFB1pFRpjlZIpJnLRzSRGSOiJzikzZRRLaJyLcBx7SwHlQrROQTEakRkH6piKiIFKwv2EBEporIThEZUUJd+ovIQhHJLzi2tONF5BkRWSciOwP2jxaRy6gE9O7dmyVLlrB8+XIGDRp0SHpycjKTJ08mLS2NqVOn0rRp08K03NxcUlNTSU1N5euvvy7PagcNp9/p96p+L2uH0vUnJSUxZcoU5syZQ1paGn369AFgwIABhdpTU1PJy8ujY8eO5V39oyYmJoaUlBRSUlKoV6/eIenR0dEkJyfTpk0batf2NxqNj4+nefPmNG/e/JC0Ko1I8F8VxJFYOPQGngMKZue9iHV8DzhmKPCKj+P7DYCv4/udwEyf/HuBR7GO7SXUZQHwfxjHd19KOv4bYASwvESVZSQUrs9vvOvr+nwXk9Kb+s2veOXJy/no12V8MPFHenVuwQvvflvo+rxnXy4n3/FFyOpXQCgdv51+p78y60N9LXYAACAASURBVA+l43tl1w6hdXx/w8/x/X4m7e7m5/j++C2n8uWyLN6ZNMc4vo/+hLa3fMoXe+CLJ2cDBY7vLVna8g6iWhZ3tiPHOb7XCXqZJeLlpwupRo7vlrNFZJaILBORC4rJE1I87/rs9Dv9HtXvZe3gHN+d43v1x+uO7wApQHfgfOAtEYkq7wp43vXZ6Xf6Parfy9rBOb47x/di8PhwYXVyfAf4VFXzgeUisgpjRzG3lGPKHa+7Pjv9Tr9X9XtZOzjH9+Ko1o7v1Wi40OuO72B6o0vado7vIcbpd/q9qt/L2sE5vjvH9+qP1x3fAfqLSJiItAJaAkXN93KO7yHE6Xf6varfy9rBOb47x/di8OBwYXV1fAdYC/xuj7tFVUuceB8KPO/67PQ7/R7V72Xt4BzfneN79ceZkR4GzvHdu47f4PR7Wb9zfHeO787xvRzNSM8bFnwz0on3VF7HdxHJA+ZjerHygNtV9VebNhHoAUxX1Qt8jmkBfIx50nA2cK2q7vdJvxRj8dDN9lA1KNgGRqvq7cXUpT9mzcHjgO4FQ40icg7wPFAD2A/cr6pTbNozwHVAPVWt7VPWaOBbVf2cMhBKx/fevXszfPhwwsPDGTlyJEOHDvVLT05OZtSoUSQkJLB161auueYa0tPN2ta5ubnMnz8fgLVr19KvX79QVTNkOP1Ov1f1e1k7lK4/KSmJMWPGEBcXR3h4OIMHD2bChAkMGDCA+++/vzBfhw4d6Ny5M2lpaeUt4aiIiYmhYcOGAOTk5JCd7T+nLjo6moSEBGrWrMnGjRvZufOgp3Z8fDy1apk5bVlZWX5pwaZcg6w+rwQ/yJpwd4UEWdXJjHQLcKGqbhCRdsAkrG0Ezow0KHjZjBKcfi/rd2akzozUmZE6joTqZEaaqqob7OZCzDyymjbNmZFWYpx+p9+r+r2sHZwZqTMjLQYJC/6rgqiuZqSXAnNUdV8Z8qbgzEgrFKff6feqfi9rB2dG6sxIqz/VzoxURE7A9JKdW8ZDnBlpFcDpd/q9qt/L2sGZkRZH9TYjrTjLhWBTrcxIRaQZ8CVwnaquLONhzoy0gnH6nX6v6veydnBmpM6MtBiqkeN7tTEjFZE4YBwwWFV/OYxDnRlpBeP0O/1e1e9l7eDMSJ0ZafWnOpmR3g60Bh4Tkcdskeeq6mZnRlq5cfqdfq/q97J2cGakzoy0GKpRT5YzIz0MnBmpd80owen3sn5nRurMSJ0ZaTn6ZF34RvB9sr65tfKakToMoTQjdTgcDoejslKuQdZFbwY/yBr7jwoJso66T05E8qy9Q5qIzBGRU3zSJorINhH5NuCYFiIyU0RWiMgnIlIjIP1SEVER6RqwP1lEdorIfcXUpchyRaSm3V5h01Ps/u627gX1v+Ro2+NI6d27N0uWLGH58uUMGjTokPTk5GQmT55MWloaU6dOpWnTpoVpubm5pKamkpqaytdff12e1Q4aTr/T71X9XtYOpetPSkpiypQpzJkzh7S0NPr06QPAgAEDCrWnpqaSl5dHx44dy7v6R01MTAwpKSmkpKRQr169Q9Kjo6NJTk6mTZs21K7tbzQaHx9P8+bNad68+SFpVZpq5JN11D1ZIrKzYKka6wb/kKr2sttnYd3gA5bc+RT4wscNPk1Vfd3gx2GWx7ndd1K8iHyOefpvpqq+VERdiixXRG4FOqjqLSJyJXCJql4hIjHAflXNFZHGQBrQxMffK7D8kAwXhoUJ8z/0dX2+mYFPfOY3v+KDJy9n/K/L+GDiXHp1bsF1fTsVuj5nTnqYhN7PBLVORRFKx2+n3+mvzPpD6fhe2bVDaB3f5/s5vl/EwGHT/BzfR9xyKml/ZvHOpCXG8f2Rc2l7y6d+5RjH97M54dbPglq/ApzjezkPF/Z7O/g9WV/fXDV7sgIIiRu8Pe5i4E+Mm/shlFJuP7uNTT9LRERVd/sEVFEUYd9QHnje9dnpd/o9qt/L2sE5vjvH92IQCf6rgghGkBVyN3gRqY15WvGJIynX/l0HYNNzbH5E5CQRWYhZAPuW4nqxQonnXZ+dfqffo/q9rB2c47tzfK/+BON5z/Jwgx+CWWx6pwQ5IlXVmcAJInIcMEZEJlSEjUNpeN312el3+r2q38vawTm+F0f1dnyvPhYOQTXVCJUbPHAScJn1u4oD8kVkr6qOKEO52L9JwHoRiQBiCTBTVdXFIrLT1sHPHNU5vocWp9/p96p+L2sH5/juHN+LoRotqxPUcDFUbvCqerrP/leBZwMCrGLLte/H2m1s+hRVVfs0YoSte3OMqerqIursHN9DiNPv9HtVv5e1g3N8d47v1Z9g9GSVixt8cYjIeOBGVd1QQrnvAv8RkRXAVuBKu/80YLCIHADygVtVdcuR1uVI8bzrs9Pv9HtUv5e1g3N8d47vRRPsaUEViTMjPQyc47t3Hb/B6feyfuf47hzfneN7+Vk41LrsvaAHJrs+/2vVdHwXkTzMk3kC5GG8rX61aROBHsD0AJ+sFsDHmCf8ZgPXqup+n/RLMVYL3QJ8spKBRcCQYnyyiixXRHpihhk7AFeq6uc+xxRZx2K0hiwi7d27N8OHDyc8PJyRI0cydOhQv/Tk5GRGjRpFQkICW7du5ZprriE93Uw5y83NZf78+QCsXbuWfv36haqaIcPpd/q9qt/L2qF0/UlJSYwZM4a4uDjCw8MZPHgwEyZMYMCAAdx///2F+Tp06EDnzp1JS0srbwlHRUxMDA0bNgQgJyeH7Gz/OXXR0dEkJCRQs2ZNNm7cyM6dOwvT4uPjqVXLzGnLysrySws2Lsg6MrxiRpqC8fC6DxgbEGQVWcditDozUo+aUYLT72X9zozUmZE6M9JyDLL6hyDI+qxigixPmJGq6mpVnYeZd+VHUXUsbzxvSOj0O/0e1e9l7eDMSJ0ZafXHK2aklRrPGxI6/U6/R/V7WTs4M1JnRlo0IhL0V0XheTPSqoLXDQmdfqffq/q9rB2cGWlxVGcz0ur0f94rZqRHhTMjDS1Ov9PvVf1e1g7OjNSZkVZ/vGJGelQ4M9LQ4vQ7/V7V72Xt4MxInRlp0bjhQn8qvRmpiHQDvgTqAReKyBOqekIpdSw3PG9I6PQ7/R7V72Xt4MxInRlp9ceZkR4GzozUu2aU4PR7Wb8zI3VmpM6MtPwsHGKv+k/QA5Ocj66tmj5ZXiKUZqQOh8PhcFRWyjXIGhCCIOvDigmyyjQnS0TyrE1DmojMEZFT7P4TRWSGiCwUkXkicoXPMS1EZKaIrBCRT0Skht3f05aRKyKXBZxnoohsE5FvS6jLPSKyyJ7vBzELOxekDRSR5fY10Gf/MyKyTkRCZ4d7lPTu3ZslS5awfPlyBg0adEj6sGHDSE1NJTU1laVLlxa6Anfs2JFff/2VBQsWkJaWxuWXX17eVQ8KR6ofYMKECWRnZ/PNN9+UZ5VDRmltAdC/f38WLlzIggUL+OCDD8q5hqElJiaGlJQUUlJSqFev3iHpsbGxNG/enOTkZJKSkqhRo0YF1DJ4lKZXRGjcuDEpKSkkJSUREWGGhMLCwmjWrBmtW7cudAyvjpTWPtUdr+uv6pSpJ0uKcXUXkWMwc86Xi0gTzFI2x6nqtlC5r4vImRjH990i8g/gDFW9QkTqA7OArhj/utlAF1XNFpEewBpgeYGOI6GgJ+v4h4I7ZStMYNzdp3HTe7PJ2L6XT/7Rg/s/mcfKzF1F5h/QI4njmtTl0S8W0rxBDAqszdpNQp2afHZbDy589Rd27M0t8tijYdGzvWn/6PdBLzdM4Js7T+HvY1LJ2L6Xj27uzqDPFrCqGP1XnZRE28Z1ePyrRQCc1LIeUZHhXNa1KXd8ELolNeY/dQ7nv/V7yMoH0xZvX9GeR8YtI2vXfl75v+N5YfJK1m076M3bpG5NBp3Tioe+Wcqu/XnERkWQE4LPO5Bxt3Rn3ILQ+vDk5+Xx7O0DuOXxV4hrkMArD9zEtfc8TqOkFoV59u7eRVSMeSJtwe/T+WXil9z82Mshrdf57YyX0a79h/gZHxV5eXlcfP55vPnOKBIbJXL1Ff157sWXadWqdWGeTz/+kGVLl/LI408wcfw4pv4wmaEvv8Ke3btZsmQxK5YvZ+WKZQx++LGg1s2XWjXM/fieA+XbmZ+Xl8dF5/fm7XfeIzExkQFXXMbzLw6jVevWpR8cRKIjpdy1Q+XSX549WXFX/zfojb3tg2sqb09WAIWu7qq6TFWX2/cbMLYNCaF0X1fVqapaYPn7G8aqAaA38L2qblXVbOB74Dx7zG+qujGwLBG50Pa2pYrIZBFJDMxTHrRvFsu6rbtZn72HA3nK+HmbOPO44u9M+3ZozPg0I2dN1m7WZpnmyNyxj60791OvVtW6s2/XLJa1W/eQnr2H3Dxl4vwMzmxb/GPMfdonMmH+psLtmauy2bUvrzyqGnKOaViLjdv3kbFjH7n5yk8rttIjxf/utfdxCYxbuJld+43m8giwyou1KxYT37gp8Y2aEBEZSafTzmLB79P98hQEWAD79+01j5hVURbMn0dScjLNkpKIjKxB7z59mTblB78806b8wIX9zKIYZ5/bm99nzkBViY6JoVPnLtSsWbW+74fDgvnzSEpqbtqnRg3O63s+06b+UPqB1QSv668OlPVRhIInCKOAxpgAyg8R6Y5Zb3Al5ee+fgMwwb5vCqzzSSvLOacDPVRVReRG4AHg3qDXshQS60axMedgT0XG9r10SIotMm/juCia1Y9m5qqth6S1b1aXiHBhXRHLTlRmEuvUJCNAf/tmxeiPjaJpvWh+L0J/daBBTA0ydxaulc6WXfs5tqG/A3aT2CgAXujXljARPpydzpx1/t5KVZVtWZnENTh4gxHbIIG1yxcfkm/6hC+YNvYT8nJzufWJV8uzikFl8+YMEhs1LtxOTGzEgvlpAXk208jmiYiIoHbtOmzbts0TQ0ebMzJo1LhR4XbDxETmz5tXgTUqX7yq34tmpCW6uotIY+A/GPuG/PJoIBG5BjM0WDZTkaJpBnxi618DszZipaZv+0Z8tyCD/IDO1Pg6NXjusvY89L8FVOdnGc5rn8j3Czcfot9LhIcJTWKjePCbpcTXiuT5i47j9s8WFPZseYHT+vwfp/X5P2b/9D3fff4+V//z4YquksPhCBLVKcg67OFCVZ0BFLi6IyJ1gXHAw6r6m81W6L5ut4Pmvm7PeTbwMMasdJ/dnQ4k+WQryzlfB0aoanvgZkxPXVHn+7uIzDq6WhdPxva9NI49eOrEulFk5OwrMm+fDo0YP89/5LNWzXDevK4zr32/gnnrcoo8rjKTsWMfiQH6N28vWv957Rv5DRVWN7J27yeh9sHhn/haNcjadcA/z679zFyzjbx8JWPHfjbk7C3s3arqxDVIYFvWwcUicrIyia0fX2x+M5z4c7HplZ2GDRPJ2HTw+5yRsYmEhokBeRqyyebJzc1l584dxMXFlWs9K4qGiYls2njw+745I4PExAqZ1VEheF1/deCwgyxfV3f7xOCXwPu+E9hD6b4uIp2AtzEBlu/SPZOAc0WknojUA861+0oiloOB2MDiMoXa8X1B+naSG8TQtF40keFC3w6NmLrk0FWJWsTHUDc6krlrDwZSkeHCa1efyNjUDXy3sGouDrowfTvN60fTNC6KiHDhvPaJTFtyqKliSnwMdaMiSKuCgWRZWbZ5F01ia5JYpwYRYULP1vWZucZ/mZQZq7Np37gOAHWjImgSG8Wm7XuLKq7KkdS6LZkb15OVsYHcAwdInf4DJ3Q7zS9P5oaDswIWzZ5BfONmgcVUGU5o1561a9eQvn49Bw7sZ9KE8Zxxpv9sjF5n/oVvvv4KgMnfTaLbST2q1Z1+SZj2Wc369es4sH8/E8ePo9eZh8xWqbZ4Vb8XHd+LdHUXkauAnkADEbnepl+vqnMJovu6iDwJzFLVscCLQG3gM9twa1X1IlXdKiJPAX/YejypqlttuS8AA4AYW+5IVR2CWXj6MxHJBqYABx9hKkfy8pVnvlnCv6/vTJgIX85JZ+XmXdx+VisWpm9nqg04+nRozIR5/r04vds1oktKPeJiIrm4s1l64uH/LWTJxqpjnJeXrzw7bilvXteJ8DDhqzkbWJm5i1v/0pJF6duZtnQLAH3aN2JiEU+3jb6hCynxtYipEc73957G418v4tcVVXPOVr7CW9PX8mTfYwkT+H7pFtZm7+Xqrk1Ynrmb39dsY8667XRuFssbl7cjP19577d17KgmE//DwyO49Ma7efvJe8nPz+eks86ncXILJnw0kqRWbWnX/TR+nvAFy+bNIjw8gpjadRhwR9UdKoyIiGDQQ49y6803kJ+XT79LLqVV6za8MeI1jj+hHWec+Rcu/r/LeOTBB7ioz7nUjY3l+ReHFR7f99y/sGvnLg4cOMDUKT/wxr/f9XsysaoTERHBgw8/xj/+fiP5+XlcfMmltG7dpqKrVW54XX91wJmRHgahsnCoKoTKwqGqUB4WDpWZ8rBwqKyEysKhqlBRFg6VhYqycKgslLeFQ4OBHwW9sbPGXFV5Hd9FJA+Yj+nFygNuV9VfReRE4E2MrUMe8IyqfmKPaQF8jHnScDZwraruF5GemEWeOwBXBvhkTQR6ANNL8Mm6B7gRyAUygb+p6hqbNhB4xGZ9WlXH2P3PANcB9Xx9skRkNPCtbx1KaYcK+5a9++67XHDBBWzevJn27dtXVDUqjOquv3fv3gwfPpzw8HBGjhzJ0KFD/dKTk5MZNWoUCQkJbN26lWuuuYb0dDPSnZSUxMiRI0lKSkJV6du3L2vWrKkIGUdMafqHDRvGmWeeCRhzxoYNGxY+XTd06FDOP/98wsLC+P7777nzzjvLvf5HQ4EegJycHD+jXYC4uDhiY83Ttnl5eWzatInc3FwiIiJo0sT0XosI27ZtIyen+g2ll9Y+1Z3Kor88g6z46z8O+v/aLaOvrNRBVrU0Iz3SICvqxNvKkj2onNq5Fbt272PkU9fRtf+z5X5+gL1z/1Uh2qES6T/5waCXGxYmzP/kHs6/cxTpm7cz/d1bGfj4JyxZfXBe3gdPX8X4X5bwwYRUenVpyXXnd+GGJz8DYNKIGxk6ZhpT/lhBrega5Ocre/YdKO50R8zeGc/R9B9fBr3cMIGfhpzDgNd+YeO2PYwbdAa3jZrF8k1FD3n/9YyWnNAslvv+m0qXlvV55JITuHSYmfz+5b09ef7rRcxYviWodUx/8xIAfl+1Lajl5uflcd+NlzH42RHUj2/IY/8cyG2Dn6Zp85aFeRalzaLVse2oGRXF5G8/Z/G8Odzx0LPkHjiAqhJZowZ79+xm8M1X8fgrI6nXoHiPuSOle0sz0d6ZkZYvlUm/C7KODE+bkVrOFpFZIrJMRIoM7CoDv8xZydacquV/FUyqs/5uxzdj5fosVm/I5kBuHp9NnscFpx/nl6dtSkN+nL0KgB9nrypMb5vSkIjwMKb8sQKAXXv2hyTACiUnptRjdeZO1mbt5kCe8vXs9ZzbsVGx+ft1bcrXs9YDoKrUjAynRkQYNSLCiQgXMndUnYcAVi5dSGLjZjRs3JSIyEh69DqX2TN+8stzfMeu1IwyT4+2btuerVtM8B0RGUmkXVLowIH9qFa/oUyvm3F6Vb+XJ75XNzNSgBSgO9AKmCoirVW16vxKO6o8TRJiWZ9xcJgnPTOH7scn+eWZv2IT/c44gX99+iv9ep1A3VpR1K8bTZvkBmzbuZePn72a5k3qMfWPFTzy5iTyq5CRWOO4aDZm7ync3pS9l04pRRttNq0fTVKDWvyy1DwMMufPbH5dlsns5/ogAqN/XMWKTZV2idJDyM7KpH7CwUfy68c3ZOXShcXm/3HSWDp2PblwOyszg5cevZuMjeu46oZ/hqQXqyLxqhlnAV7VX52eni1rT9YeVT1RVdtieofeF59W8DEj/auW0+2Ujxnpi0dZ1Keqmm975FZhnm50OCoVD44Yz+kntmDG6Ns5vVML0jfnkJevRISHc2rHFAaPGM9pN7xBiyb1ubZv54qubsjo16UZ41M3FJrRpiTUok2jOnR7eCJdH5rIqcck0L1Vg4qtZIiY/sMEVi1fzPmXXVu4r0FCIs+99SEvj/qCnyePIyc7qwJr6HBUbUTkPBFZKiIrRGRwEenJIjLVLsU3T0T6llam181IwczfKmk75GakDm+zITOHZokHlxFqmhBLeqb/Mjkbt+zgyoc+4OTrR/D4298BkLNzL+mbc5i3fCOrN2STl5fP2J8XceKxoeg0Dh0bt+2hcb3owu1G9aLYmLOnyLwXdW3KV3aoEOC8jo2Z82c2u/flsXtfHlMXZtClZdVZbqZegwS2Zh58YnPrls1F9kYtmPM7Yz9+j3uGvFQ4RBhYTrOUVixdMPeQtKqM1804PatfQvAq7ZQi4cC/gD7A8cBVInJ8QLZHMB0znYArgTdKK9frZqQA/UUkTERaAS2BpYEZQm1G6vA2sxan07pZPM0b1yMyIpz+Z3dg3HT/9foaxMYUdqHff10vxnw72x67ntjaUcTHmfUNz+jSiiV/HmpkW5lJW7ONFg1rk9QghshwoV+XZnw/71BX/1aJtYmNqcFsn3Ur07P30KNNA8LDhIgwoUebeJZXoeHClscez6YN69i8KZ3cAwf47cfv6NzjdL88q1csZdTrz3HPkJeIjatfuD8rM8MskA3s2rGdZQvn0rhZ83Ktf6jxqhlnAV7XX850B1ao6ipV3Y9xR+gXkEcx89LBmJlvKK1Qr5uRAqwFfrcNd0tlnY815rnrOb1LG+LjarNi4lM89dZ4xnw1o6KrVW5UZ/15efncPWws37zyV8LDhTHfzmbxn5t59MazmbNkPeOmL6Fn55Y8ecu5qML0uX9y18tjAcjPVx4cMYHxr/0NESF1STqjxv5RyhkrF3n5yqOfzOOD208hLEz4ZMYalm3cwX0XtCVtzTa+t8so9evajLE+vVgA4+akc+ox8Ux+5C+oKtMWbWZyFVp2KTw8goG33s8LD/+T/Px8ep17Ic1SWvH5+2/Tos1xdDm5Jx+NfI29e/bw2jPmydYGCY2494mX2bBuNR/+ezgioAp9L72GpBbVx4gUnBmnV/VX0JysouZ1nxSQZwjwnYjcAdQCzi6tUGdGehhUpIVDZaAiLRwqA6GycKgqhMrCoSoQKguHqkJFWThUFpwZaflaODS66fOgN3bGyP43A3/32fVvVf13wYaIXAacp6o32u1rgZNU9XafPPdg4qaXReRkTOdRu5Lmorsg6zCoSDNSh8PhcDgqiqoeZG1657IS62+DpiGq2ttuPwigqs/55FmICcTW2e1VQI+AqUt+lHlOlog0EpGPRWSliMwWkfEicoyITBSRbSLybUD+FiIy087S/8TO30JEeorIHBHJtZGj7zFFlhWQp6Ytb4UtP8Un7UG7f6k1TS3YP0pENovIgrLqrWzExMSQkpJCSkpKodO1l3D6nX6v6veydnD6vai/gnyy/gDa2NilBmZi+9iAPGuBs2wdj8PYWmWWVGiZ5mRZu4YvgTGqeqXd1xFIxMyRigFuDjhsKPCKj+P7DZgleNYC12Mc3wMprixfbgCyVbW1iFxpz3OFfQrgSuAEoAkwWUSOUdU8YDQwAni/LHpLw7kely9Ov9Nf0fqjI82PtJe/++Bt/V699uHg51+dUdVcEbkd88BcODBKVRcGzAm/F3hHRO7GTIK/XksZDixrT9aZwAFVfcunQmmq+nNRLu02KAuJ4ztmtv8Y+/5z4Cx7vn7Ax6q6T1X/BFZgnhZAVX8CtgYWJCI3icgfIpImIv8TkZhSzl0heNX1twCn3+n3qn4vawen36v6K6gnC1Udr6rHqGorVX3G7nvMBlio6iJVPVVVO1rv0O9KK7OsQVY7zFqAZSWUju+FTwDY8nPs+Y7E8f0LVe2mqh2BxZheskpHUa6/GRkZJRxRvXD6nX6v6veydnD6Pau/AnyyQkVZLRyqK+2sxUQcxhaiLL5aDofD4XA4HKVS1p6shUCXwyg3lI7vhc7utvxYe74jcXwfDdyuqu2BJzCT2A5BKtjx3bOuvxan3+n3qn4vawen36v6K2q4MBSUNciaAtQUkUKPCRHpICKnF5U5lI7vmNn+A+37y4Ap9nxjgSvt04ctgDYYk9GSqANsFJFI4OriMlW047vXXX+dfqffq/q9rB2cfq/rrw6UabhQVVVELgFeFZFBwF5gNXCXFOPSTugc398F/iMiKzCT2a+0dVwoIp8Ci4Bc4Db7ZCEi8hFwBhBvy31cVd8FHgVmYh7BnIkJuiodXnX9LcDpd/q9qt/L2sHp96r+iux5CjbOjPQwEGtG6lXnX+d67PR7VX9FWRhUFpx+7177UP6O70m3fR30xl73r34VErl5feL7EeEFz5Di8LJ2cPqdfqffq3hZu+PIKXOQJSKNgFeBbsA2IAOzWOJwzOLKecAzqvqJzd8Cs4p1A4z9w7Wqul9EetpyOgBXqurnPueYCPQApqvqBcXUoybGVLQLZsL7Faq6WkQaYHyzugGjA9Ybega4DqinqrV99o8GvvWtQ1k4+/XgL0zcNTmWW09PIUyECYs288mcQxf37tm6Ptd1b4YqrMrazXPfraBVfAz/PKMFMZHh5Kvy4awN/LgiK+j1A5h8x8kh0Q5Ov9NfufVPvuNkwNvffYDz3vgt6GV3SYrlltOaEybCxMWb+Sx14yF5Tm9Vn2u6NkNRVmXt5oXJKwF46vxjaZtYm4UbdzBkwrKg162Aibf2YPbqnJCUPfePX3n/zZfJz8/nzPP60e/K6/3Sx33+AVMnfk1YeDh1Y+O4+d7HSEhsDMBzD93BisULOLbdiTzw1CshqR9Al5TYkJVdJNUonj1ax/c44DpVXS4iTYDZIjJJVbdRzo7vmHlij2I8vdoFHPMNxvF9eVn0ljdhAnf0asGgrxezZed+Rlzejhl/ZrM2e09hnqaxU1ZlUwAAIABJREFUUVzVpSl3/W8hO/flERdtPrq9ufm88P1K0nP20qBWJP+6vD2z1m5j1/68ipJz2Dj9Tr9X9XtZOxj9t52ewkPfLGHLrv0Mv/QEZq7e5qe/SWxNrujUhHu/XMjO/XnERh/8t/W/uRupGRFGn+MbVkT1j5r8vDzeG/ECDz0/ggbxiTx8x0C6nNyTZs1bFuZJaX0sz4x4n5pRUXz/zed8OPI17nzYLKd3Yf9r2bd3Lz+M9+bC7VWBo3V8/1FVl9vtDcBmIKEiHN9VdZeqTscEW4Hl/qaqh94eGc4WkVkiskxEiuw9CzXHJtZmQ85eNm3fR26+Mm15Fqe09F+jqs8JDRk7fxM795kf0G17jM9r+ra9pOcYyVm7DrBtzwHioiPLV8BR4vQ7/V7V72XtAMc0tPp3GP0/rthKjxR//ecd15BvFmaw0waPOXtyC9Pmpm9n94GqE1QGsmLpQho1SSKxcTMiIiM5udc5zPr1R788J5zYlZpRxl2o9XHt2Zp5cC3idp26Ex1Tq1zrXB5UJwuHsg4Xlur4LiLdgRrASsrR8V1EChzftxxheSmY5XdaAVNFpLWqHhKohZL4WjXI3LG/cHvLzv20Taztl6dZnPmSvXrpCYQJvP/7emat9e++PrZhLSLDwtiQU67VP2qcfqffq/q9rB2s/l0++nft59iG/kFDU6v/pYuPJzxM+O8f65m9LjRDd+VN9pZMGiQc9L1qkJDIiiULis0/beLXdOx2SnlUrUKpTk8XlrUnq0REpDHwH+CvqnpID1Ul51NVzbc9cqswFhJ+VLQZKUB4mNA0Nop7v1zEs5NWcPeZLalVI7wwvX5MJIPOac1LP6ykOj4D4/Q7/V7V72XtAOFi9A8au5jnv1/BnWe08NPvFX6ePJ5VyxZzYf9rK7oqjsPgqB3fRaQuMA54WFULZkVWhOP7kRL4u3TI71SozUi37NpPQp0ahdvxtWuwxefuDswd7m+rs8nLVzbt2Ef6tr2Fd3gxkeE8fUFb3vttHYszdoaqmiHD6Xf6varfy9rB6q/lo79WDbJ2HTgkT4H+jAL9sUUuzlHlqBefQFbmwbUIszIzqNcg4ZB88+fM5KuP3uO+J14mskaNQ9KrG9VpuPBoHd97YSbEv+/7hF4FOb4fKf1FJExEWgEtgaVHW8HDZWnGTprGRtGoTk0iwoQz2jRgxp/Zfnl+WbWVDk3rAlA3KoKmcVFs3L6PiDBhSN9j+H5pJj+v3FreVQ8KTr/T71X9XtYOsGzzTprERZFo9fdqXZ/fVvvrn/FndpH6qwOtjj2eTelr2bwxndwDB5jx4/d0ObmnX54/Vyxl5PDnuO/Jl4mtV7+Calq+VKcg62gd338DegINROR6m/16VZ1LOTu+2+NXY+wkaojIxcC5qrpIRF4ABgAxttyRqjrEHrYWs/xOXeCW8p6PBZCvMOKn1TzXry1hIkxatJk1W/cwsHszlm3exYzV2cxam0OX5DhGDuhAvsI7v65lx95czjomnvZN6lA3KoLebc0d0Is/rGTllt3lLeOIcfqdfq/q97J2MPrf/Hk1T19wLOEifLckk7XZe7i2W1OWZe5i5uptzF6XQ+ekWN6+ogN5qrw7Yy079pnpvi9efBxJcdFERYbzn2s78cq0VcypQvO1wsMjuP72B3juoX+Sn5/HGb0vIimlFZ+NeYsWxxxH15N78eE7w9m7Zw/DnxoMQIOGjbj/yWEADLnnJjasW83ePXu4bcD5/P2eR+jY9eSKlOQIwDm+HwYFju+h8gqq7ITSJ6kq4PR7V38ofbKqAqH0yaoKhNInqyrQJSW2XB3fW9w9LuiByZ+vnF8h3VllDrKKMSMdCryCGXaMBF4vsHkQkS7AaCAaGA/caXvE+mNMTI8DuqvqLJu/WDPRgHrUBz7BPBW4GrhcVbOtbcRwoC+wG9OjNsceU6rJaRnboMIi0piYGBo2NF4wOTk5ZGdnl3JE9cLpd/q9qt/L2sHpryz6XZB1ZATDjPRkVd0nIrWBBSIy1npmvQnchFl4eTxwHjABWAD8H/B2wGlKMhP1ZTDwg6o+LyKD7fYgoA/Qxr5Osuc/yR5TFpPTMlPea1jl5eVx0fm9efud90hMTGTAFZfx/IvDaNW6dbnWo6LW73L6nf6K1l9Ra/dVBu3g9Hv52ofyX1LIixYOJZmRFsxArFlQnrV0qGtNQBWzDE6BGeliVT1kcnlJZqIB+JqRFpqc2v3vq+E3zNONjW3ZRZqcishjIvKHiCwQkX9LJf1kF8yfR1JSc5olJRFZowbn9T2faVN/qOhqlRtOv9PvVf1e1g5Ov1f1V6eJ72UNsoo1IxWRJBGZhzEIHWp7sZpiDEgLCKYZaaKPe/smoMDJrdCk9DDOOUJVu6lqO8ywZoU4vpfG5owMGjVuVLjdMDGRjIyMEo6oXjj9Tr9X9XtZOzj9XtdfHThqM1JVXaeqHYDWwEARSSztmGBhe8mOpg/3TBGZKSLzMcsAnRCcmjkcDofD4TgSRP6fvfMOj6rY//D73U0HkpCekIRI7yJFEQtNKYJi13tRr4oFUS92RARRfiqogIiCgqCoWBAE9dKU3kSBUEIJJPQUkpBA6AnZnd8f56RsCCHA7iabPe/z5IGcnZ2dz2FDZqe8Y/+vyuKKZaSF6CNY24Cb0ISh0SUetqeMNKNwGlD/s/AgpyJJaUVeU0R8gInAvUqplsAUoEzDXWUb38PCwzmcfrjo+8yMDMLDndaXrXSM/EZ+d83vztnByO/u+asDVyojvUlEfPXvawM3Arv06bzjItJBX+f0CI6RkZaUnP4GPCIaHYDccg6FhuIO1RF90f69FyroaOP7xWjeoiUHD+4nJeUQ5/LzWTh/Hp26dK2s5jgdI7+R313zu3N2MPK7a/7qtCbrSmWkc4HPdLWBAB8ppRL0pw2kWOGwQP9Cr2cCEArME5HNSqke+mP7KVsm+iXwua57GAXMFJH+wAHgfv315qPpG5LRFA6PFba/HMnpFLTRt8PA+grfNSfj4eHBkKHDeeapJ7BaLdx51z00aNCwspvlNIz8Rn53ze/O2cHI7675q+YWtMvDkJFeAoWerMrYylsVqKxtzFUFI7/75q8shUFVwcjvvu990PI705PV6LWFdr/Zuz/oWXU9WQa2ONsZUpVw5+xg5DfyG/ndFXfO7myqqE3psqjw7kIRiRCRH0Vkj4hsFJH5ItJJROJFZLOIbBeRASXKtxWRBBFJFpFPCh1UInKfXtYqIu1KlA8WkWUiclJEPi2nHUEi8qeIJOl/1tavi/46ySKyVUTa6Ndbi8hf+mtuFZEHLudGVTZ+fn7ExcURFxdH7dq1K7s5TsfIb+R31/zunB2M/O6e39VxB+P7aeARpVSSiEQBG0VkkVLqWEWyl4VhPXYuRn4jf2XnN4znRn53fe9DZRjfnfpyDqXaG9+VUruVUkn6a6ShKR9C9XYaxncXwMhv5HfX/O6cHYz87prfZBK7f1ValgqWqxbGdxG5FvAC9uiXDOO7C2DkN/K7a353zg5GfnfPXx1wG+O7Prr2LfCYUsqqXzaM7wYGBgYGBlUIw/heBlXZ+C4i/sA8YKg+lWgY310II7+R313zu3N2MPK7e/7qQLU3vouIF9qi/W+UUrNK1GMY310EI7+R313zu3N2MPK7a37D+O5axvf7gZuBYBF5VL/2qFJqs2F8dw2M/EZ+d83vztnByO+u+avmFrTLwzC+XwKG8d2wHhv53TO/YTw38rtrdnC+8b3lsD/tfrMTRt5atY3vIhIBfAy0B44BGcBoYBzatKMnMKFQ8yAibSkeyZoPDNJHxEai6RasaOupHlVKpYlIPzTflQAngGeUUlvKaMdVwI9AMNqOx4eVUvki4o2mimgLZAMPKKX2i0gwMEtv99dKqecu4f6UiSOcIT169GD8+PGYzWa+/PJLRo8ebfN4TEwM06dPJzAwELPZzOuvv86CBQsAaNmyJV988QX+/v5YrVbat29PXl5eWS9zxTjKl3K5+du3b8/kyZMBbYh5xIgRzJ071yFtBOf4Yi52L8aOHUuXLl0ATVQYFhbmNEmho/IX5gDIzc3l6NGjNo8HBAQQGBiIUgqlFBkZGeTn5+Pj41P0PBEhOzubkydPOqSN4Jj8F8suIkRERODt7Y3FYiE9PZ2CggL8/PwICQlBRFBKkZWVxZkzZ+zevpJUhvX8YvfHWVSW8b2q5HcmVdSmdFlUaCRLX1e1Fk1GWtiJKpSRrispIwU66p2mf4D/Uiwj/UQptUBE/JVSx/U6/gs0U0oNEJGOwE6l1FER6QWMUEpdV0ZbZgK/KKV+FJHPgS1KqUkiMhBopdf1IHCXUuoBEakBXIMuOb2STlbhSJbfPdMut4oyMZmELZ/cze3vLCI15zSrRt3Oox8vJzElt6jMhKc7smVfNl/+sYsm0QH88satNBs4C7NJWPvhHTzxyUoSDhwlqKY3x07nY7Xa/1PX6dmPU/uhGXav1yTC+g/7cPfopaTlnGHJOz148rM17Eo7XlRm3OPXsvVADl8tSaZxlD8/vdKZ1i/9hq+XmfwCKxarIjzAh5Xv3Uaz5+dgcUD+o9/1o9XwxXavtyQmgd/+ez1Pf7OJjON5fP9Ue16ftZ29WafKLP+v66JpElGLt37d6dB2AWx95xZmb06ze71Wq4Uxgx6m/5sf4R8cymdDBvDgoGGER8cVlTl7+hQ+fjUA2LFhDesWzeXxoR+Sn3cWs4cHZrMHx49m88mr/RnyxSzMZvueGHZP6ygATpy1XqTkpWGxWLjr9p5MnDyN8PBwHv7Xfbw3egz16hfLJmf++D3JSbt4Y9jbLFowj2VLFzPqw3Ek7txBcHAwoWHhJCft5rlnnmDh4pV2bV8htXy05buGjNS5VKX8zhzJuvqtJXa/2Vve7lYpPbfKkJEeL1FvDXQFg1JqrVKqsIu+Dtvdiej1CppqoXABe2kZaaGkdBbQTUSkPMmpiEwSkQ36kTtvV/Be2J12DULYe/gE+zNPcq7Ayqw1e+nTPtamjFLg7+cFaH+mH9U+sd5ydR22HThKwgHt1uWczHNIB8uRtK0fzL6MkxzIOsU5i5Vf1h2gV1vbf36lFLV8PAEt/+FjWv4z+ZaiDpW3lxlXn/5uUcefQzlnSD16lgKLYuG2DDo3Cblg+Z4twlmQ4NrenEPJiQRH1CEoPAoPD0+u7tiVnevX2JQp7GAB5J89W/RJ18vbp6hDVXAu3+U+AW/ftpWY2Fiio2Pw9PSie8/bzpNNrli+hD53aP/Ndbu1B//8/RdKKZo0bUZomLbTrH6DhuSdzSM/P9/pGRyJu8o4C3H3/NWBin7cK1dGiqZHaAC8qo9itaMcGamIvIu24zAXrQNXmv7oC+VLEQwcU0oVlFFvkYxUKVUgIrl6+SPl5BqqlMoRETOwRERaKaW2llPeIUQF+ZFypHikIjX7NO0ahtqUeW/mJn4b1oMBvZri5+1Bn3cWAdAgyh+lFL++2Z0Qfx9mrdnLuF+3ObX9V0pkbV9Sc4rzp+Wcpm19247F6F8SmD24K091b4yftwd3jSr+j6Zt/WAmPHEd0SE1eObzvxwyiuUswvx9OJxb/HkgMzePltH+ZZaNDPChTm1f/tmX46zmOYTjOVkEBBe/3/2DQzmUtOO8cn8tnMPqeT9jKTjHE8PHFV0/mLSD2ZM+4FjWYe5/fqjdR7EcibYlP7Lo+/DwCLYl2K6SyMrILCrj4eFBzZq1OHbsmM0U8ZI/F9GkaTO8vLyc03AnUZaMM2Gr0/+LrjTcNb+LfVYql0qRkSqlhiqlYoAZgM30nYh0QetkDb7StlWA+0UkHtiEJiJt5oTXvCzuu7Ee3y1PotHTM7n7vT/58vmbEQEPs4nrm4Tz+PgV3PLmPG6/ti6dW0ZevEIX457r4/hh1V5aDJrLAx8t5/MBHYt+EDfuyabjkPnc8tYiXri9Od6eV/y2dgl6tgxn8Y5MXLhPeUlc3/MuXp3wPT37Pc3S2d8WXY9t2IwXx37Ns+9/wfI5MziX75j1iFWVPclJfPLxGN4YXmmD8QYGBhegsmWkM4B7Cr8RkVbAl0BfpVR2GeWz0c4kLPyoWrLeIhmp/niAXr5M9AX0rwDd9E7iPCpJRpqWc5rokOLpkDrBfqTn2K7BeaRbQ2av3Q/AP7uz8PEyE1LLh9TsU6zZmUH2iTzO5FtYtCmF1lcFO6qpDiH96BnqBBXnjwryI/3oaZsyD3Wqx9y/DwKwPvkI3p5mgmt525TZnXacU3nnaBod6PhGO4jM42eJCCh+G4YFeJNxouxOQ3WYKgTwDwolNzur6Pvj2VkEBIVesHyrjl3ZsX71edfDouvi5eNLxqF9DmmnI9COSUkv+j4j43DRFGAhoeFhRWUKCgo4efIEgYHaezzj8GFeefE53nl3NDExtksMqgPuLuN01/zVyZPldBmpiJSUfPQFEvXrscAvaLsFd5fVCH191zKKxaGlZaSFktJ7gaWq/AU6/sApIFcffet1oYKOlpFuTD5C/Uh/6obVxNPDxL031GPe+kM2ZVKOnKKLPkLVuE4APp5mso6fZfHmVJrH1sbXy4zZJNzULIKdKccc1VSHEL83m3oRtYgNrYGn2cTdHeqyMN62T56SfZqbm2v/uTSK8sfb08SR43nEhtbArB/+GR3sR8NIfw5eYJG4K7A97QSxQX7UCfTBwyz0bBHOisTzZ7zjQvyo5ePBlkO5ZdTiWkTXb8yR9BRyMtMpKDjHlrVLadquo02ZI+nFqw92xa8jJFJbJZCTmY7Foq0eOJp1mKy0g9QOjcBVaNa8JYcOHCA1JYVz5/L5Y+F8OnW2lU126tyV//2m7Zhd8uci2l/bARHhxPHjDHruaZ4f9DKtr2lTGc13OO4q4yzEXfNXp2N1nC4jBUaJSGM0hcMBYIB+fTjaGqqJeq+zoLBjIyLzgSf00bLBwI8i8n9o03xT9edPBb4VkWQgB3iwsP0XkJxuEZFNaJ28Q4DtSlsnYrEqXv5yHb++2R2zSfhmaRI7U47x5gPXEL/nCPM3HGLI9H/4dMANPNenOUopnv5sFQDHTuUz4fdtrBx9OyhYFJ/CoviUi7xi1cJiVbz2zQZmvdoFs0mYsXIviam5DLm7JZv25bBwUyrDvo/n4/7X8UzPJigFz01eB0CHRqG80KcZ5ywKq1K8On0DOSddd7rIYlW8P38Xkx6+BpMJ5m5KZ0/WKQZ2qcf2tOOs2KV1uHq2CGfRNtcfxQIwmz244/FBTHv3VZTVSrsuvQiPuYo/f5pGnfqNadbuBv5aOIfkhI2YzWZ8a9bivmeHALA/MYEVc7/HbDYjJhN9+79ADX/XGcn08PDgtTeG8dwz/bFYrPS98x7qN2jIpM8+oVmzFnTq0pW+d93LsDdeo2/v7gQEBPDeB2MB+OnHGRw6eJApX0xkyhcTAfjs86kEBbvWSHZ5uKuMsxB3z18dMGSkl4CjFA6ugqMUDq6CMxQOVRlHKRxcAUcpHFyFylI4VBUMGalzFQ5tRy6z+83eOKxLpYxnGZ2sS6Cwk2VgYGBgYOBOGJ2sy6PC27BEJEJEfhSRPSKyUUTmi0gnEYkXkc26a2pAifJtRSRBRJJF5BN9bRYiMlJEturP+UNEovTr/fTrCSKyVpedltWOq0Tkb73en0Q7ABoR8da/T9Yfj9OvB4vIMhE5KSKflqrLcWroS6RHjx4kJiaSlJTE4MHnb6yMiYlh6dKlxMfHs2XLFnr10paQ1a1bl9OnT7Np0yY2bdrEpEmTnN10u3C5+W+55RY2bNjA1q1b2bBhQ5EJ3dW4WH6A++67j+3bt7Nt2zZmzCgeUYyJiWHRokXs2LGD7du3U7duXWc12yH4+fkRFxdHXFxcmSb7gIAA6tatS2xsLDExMS6vLbhYXhEhMjKSuLg4YmJi8PDQVnmYTCaio6Np0KBBkRG8OnKx+1Pdccf81WlNllsb30XkpFKqZoVvlj6S5f/gNxV9SoUwiRA/ri93vreY1OzTLHu3F/0nrGZXavGi5vFPdGDr/hymLt5N4zoB/Dy4K63+O4fYkBr89FpXrn/td7u2qSyO//gIQY98b/d6TSL880Fv7vlgGWk5Z1g8ojtPTVprY3wf+1h7Eg4c5aulmvH9x5c6cc0rv9MytjZZx89y+NgZmtQJYNarnWnxwq/lvNrlk/PNv+kwaoXd6zUJ/PRkewb9lEDmiTym/ecahv+WyP7s4h2W0bV9eLdvM577YSsn8gqo7efJ0dPnAPjsX634+q+DrN9/DF9PE1YFeQX2n9Za93onFmzLtHu9JbFaLPzfc/9i4FvjCAwOY8xrT/Cfl0YQEXNVUZmS9veEf1azeuEvPDN8rEPb1auF1ok5lWd/4/udfXoyafI0wiPC6ffgfbz/wRjqlzK+7969izeHv83CBfNYtmQxoz8ax5nTp0lM3ElychJ7knbz+tDhdm1bSWp4G8Z3w/juvJGs9u8ut/vNXj+0c5UeyaqWxne9znH6KNwSEbnwvnEH0rZBcLHx3WLll78O0LtdjE0ZpRS1fAuN554cLqU4cGXa1AuyMb7P+fsgvdqUNr5TZHyv5etZZHxPOHi06O+Jqbn4eJrx8nAtT1azyFqkHDtDWu5ZCqyKxTuzuLmh7eLlvldHMis+jRN5+k46vYMVF+yH2SSs36/tKD1zzuqQDpazOJC8k9DIaEIi6uDh6UmbG28h4R9bXYON/T3vjMtZ3kuyLUE3vsdoxvcevc43vi9ftoTbdeP7LSWM775+flzTpi3eLj6SVx7ubjx39/zVAXc3vtcANiilXhSR4cBblJKjOoOo2n6kZpc0vp+iXQNb4/n7s7cyZ0g3nurRmBreHvR9r3gBdt3Qmqx6vzfHz5zj/37azF+7HDvaYG8ia/uRmlPcadSM77adjA/mJDDr1S48eWsj/Lw9uPuDpefVc3u7GLYeOEq+i3UyQmt5k3m8eEdk5ok8mkfWsikTU9sXgC/6XY3JJExdfYB1+44SG+TLybMFvH9nM6ICfVi//ygTV+xzWUFpbnYWgcHFU1+BwaEcKMP+vmrBbJb99hOWggKefXu8M5toVzIzMwiPKGV837qlVJlMIiLKN75XV9zVeF6Iu+Z34c9N5+Huxncr8JP+9+/QPF9Vkns7xvH9yj00e+4X7v1gKV8MvAEROHzsDM2fn81NQ+Yx9NsNfPn8jUUjXtWJuzvU5YfV+2j54q88MGY5k5663uYHsXEdf9564Gpe+np95TXSgXiYhJjavgz8YSvDf0vk9Z6NqOmtudGujglgwrK9PD49nqhAH3q3dB1P1OVyU697GD5pJrc/PIA/Zk2/+BMMDAwMKgG3Nb5fKEZZFx1ufD96mjrBJY3vNYoOgC7k4S4NmPPXAQDWJx3Bx9NMcC0f8gusHD2pHQq7eV8O+zJO0KDUKEhVJ/3oaeoE+RV9rxnfbfM/1Kk+c//RjO8b9mRrxveamvE9qrYv3/z3JgZOXsf+zCqzl6HCZJ3II8y/2F4fVsubrJO2B/1mnshjVXI2FqsiPfcsh3JOE1Pbl8wTeSRlnCQt9ywWBSuTsmkcXuFlhlWOgOBQjmUXj8Qeu4j9XZtOXOWMpjmEsLBwMg6XMr6XMnqHhYVx+HDZxvfqjrsazwtx1/yG8Z1qYXwHLX9hXf8Gzj+rA8cb3+P3ZFM/ohZ1Q2tqxvPr6zJ/4/nG904ttBGKRlH+eHuZOXL8LMG1vDHpb6C4sJrUj/Bnf4ZrdTQ27cuhXngtYkM04/td18WyYJOtUDUl+xSdmunG90h/fDxNHDmRh7+fJz+81ImRM7fwT1J5M8NVl53pJ4ip7UtkgA8eJuGWpqGsSrb9fLAyKZs2Mdov1gBfD2KC/Eg9dpad6Seo6eNBoD562bZuIPuOuK7xPrZBE7LSD5GdkUbBuXPEr15Mi/Y32JTJTCv+2dixcS2hkect33QZmrdoycESxvdFC+bTuQzj+++68X1xCeO7O+CuxvNC3DV/ddpd6M7G9x1ox+pcKyJvApnAAxW5H/bGYlW88vU//DKkG2aT8N3yZBJTcnnj3qvZtC+bBRtTGPrdRj55sgMDb2uKUjBw0loAbmgazhv3Xc25AitKKV6c+jdHT+Vf5BWrFharYvC3G/j51c6YTcL3K/eyK/U4r9/Vks37NeP78B82Me7xaxnQozFKwbNf/g3Ak7c04qrwWrzStwWv9G0BwL0fLuPIBc77q4pYFIz5M5mP72+BSYT/JRxm35HTPHljXXYePsHq5BzW7TvKtVfV5vv+bbEq+HT5Xo6f1ZYmTli2lwkPtkRESDx8gl+3HL7IK1ZdzGYP7nniJSa98xJWq5UO3XoTGVuP+T98SUz9JrS89kZWLZjN7q0bMJs98K1Zi37PD63sZl82Hh4eDH5jGAMH9MdqsdL3Ls34PvHTT2jWvAWdu3Tlzrvv5c0hr3HHbd3xDwhg1AfFOylv69GVUydPce7cOZYtXcLEyVNtdia6Ou5uPHf3/NUBQ0Z6CThK4eAqOErh4Co4SuHgKjhD4VBVcZTCwVWoLIVDVcEwvjtX4XD96JV2v9l/Db656hvfRSQC+BhoDxwDMoDRwDi0qTdPYEIJl1Zbikez5gOD9FGxkWhThVa0EaRH9V2J/dBGqgQ4ATyjlLLdaqPVexXwI9rI10a0KcZ8EfFG00W0RVuP9YBSar+IBKNpHdoDX5d0ZV0K4kDje48ePRg/fjxms5kvv/yS0aNH2zweExPD9OnTCQwMxGw28/rrr7NgwQLat2/P5MmTC9vHiBEjmDt3rqOa6TAuln/s2LFFolE/Pz/CwsKKdleNGjWK3r17AzBy5Ehmzpzp3MbbgYvlB01GOmLECJRSbNmyhX79+gHaOp2EBG0A+eDBg/Tt29epbbc3hf++ALm5uRw9etTm8YCAAAIDA1FKoZQiIyOD/HzXGr0tycXyiggRERF4e3tjsVhIT0/HDuPwAAAgAElEQVSnoKAAk8lEVFQUPj4+HD9+nMzM6tkBvtj9qe5UlfxGJ+vyqHAnq7oKSS/pZumdLHuf32cSYf2Hfbh79FLScs6w5J0ePPnZGhsZ57jHr2XrgRy+WqLJOH96pTOtX/oNXy8z+QVWLFZFeIAPK9+7jWbPz8HigD38R7/rR+TTs+1er0lg9TvdeXD8atKPnmH+kC4MnLqepPQTZZZ/vHM9WsQE8tK38XRrEcGT3erTb8JavDxMzH7pZu7/eBUnzxaU+dwrIf2Le7jxI/svsjYJfP94W16ctY2sE/lM6deat+clsj+nePF/dKAPb9/ehEEzEziZZyHQ15NjZzRX1qLnr6fHhL/s3q7SrH7lJuY5+FBqq8XCe8/9mwFvjSMwOJRxrz3Jwy+9dUEZ6bZ/VrNm4RyeHj7Goe3q3UJbD3gq3wEy0t49mTRFl5E+cB/vf1iGjHTXLt58620WztdlpGNKyEiTktiT7GAZqZchIzVkpM7rZHX8wP6drLWvVU4n61IUDtVSSCoiPUU7GmiLiFSK5a1t/WAbGecv6w7Qq21pGacqknH6+3kVCTjP5FuKOlTeXmZccfr3mrgg9mee4uCR05yzKH5dn0KPVpEXLH9n+xjmbtAWxjeKrMW6JG3X3Zl8CztTc+nS3LV23zSNqEXqsbOk5+ZRYFUs2ZXFjQ1sPWG3t4pgzuZ0TuZZAIo6WNWNg8k7CYmsQ0hEFB6enlxzYze2lSsjPevSUp0yZaRLS8lIly7h9r66jLR7GTJSb0NGWl1x1/zVaXdhRWWkUA2FpLrhfQpws1Jqn4gElVXO0UTW9iU1p3hHmCbjtJWRjv4lgdmDu/JU98b4eXtw16jiH7S29YOZ8MR1RIfU4JnP/3LIKJYjiajtQ1oJZUP6sTO0uarsf4o6Qb7EhNRgdaI2NbIjJZeX+jTliz+T8PUy07FRKLvTj5f53KpKaE0vMkss1M86kUfTC8hIJz7YCpPAtL8O8o9ueffyMDGl39VYrIoZ61NYlZzjvMbbmWOlZKQBwaEcTNp5XrnVC35huS4jHfj2x85sol0pU0aaYMhIC3FXGWch7p6/OnApnawLopQ6BLQS7bDnuSIyqwLPGQoMFZEhaELStwofKyEkdbQctAOwUim1T29Tlf3tdM/1cfywai+fLUikfYMQPh/QkY5D5qEUbNyTTcch82kU5c9nT13P4q1p5J2rngt072wXw7z41CKj+YqdmVwdV5vfXutE9sk8Nu7LdrlOZkUwixAd6MvzMxMIq+nFhAdb8ej0eE7mWbhvynqOnMwnMsCb8fe1ZE/WadJyyzxFqtpwY6+7ubHX3Wxc+Sd/zPqGfv913R2GBgYGtrjw4PR5XMp0obsISc/D0TLS9KNnqBNUPAWiyThtzyZ8qFM95v6tyTjXJx/RZJy1vG3K7E47zqm8czSNdi1R4eGjZ4nSR2oAIgN9z5ORFtK3XTRz19s6xD5ZsItb313Kg+PXIAh7XUxImnUyn7AS/5ahtbw5UlpGejKPNXt0GenxPFJyzhAdqN2zwrLpuXlsPpRLo7AauCqBpWSkudlZBASFXLC8Np1YzWSkYYaMtBB3lXEW4q75q9N04aV0sqqjkHQdcLO+W5ELTRc6XEa6N5t6EbWIDdVknHd3qMvCeNv+aEr2aW7W1xo1ivLH29PEkeN5xIbWwGzS3kDRwX40jPTnYJZrySg3HzjKVWE1iQn2w9Ms9G0fzR9b088r1yC8JgE1PNmwt3jA0SRQu4a2JqVpHX+a1vFnxQ7X2mWVePgE0YG+RPp742ESujUOZfUe20HVVcnZtI4JADQZaXSQL2m5Z6npbcbTLEXXW9TxZ3+26x4eHtOgCVnpKUUy0k2rl9C8ve2AdpaNjPQvQlxdRnqwlIy0lGyyU5eu/P6rLiP9YxHtrzNkpO6Cu+evDlR4urC6Ckn1TuMvImJC00ncWtF7Yi8sVsVr32xg1qtdMJuEGSv3kpiay5C7W7JpnybjHPZ9PB/3v45nejZBKXhu8joAOjQK5YU+zThnUViV4tXpG8g56ToiTtDyD/1pM9//9wbMJuHHtQfYnX6CV29vypYDx4o6XH3bx/DrelsTvKfZxJxXbgbgxJkCnv9qg8tNF1oUjFu6hzH3tMBkgnnbMtiffZr+HWNJzDjJmj05/LP/GNfWrc23j7bBYlVMWrGP42cLaBFVi1duaYBS2hD7jH9SbHYluhqajPRFvnjnZaxWK9d1601k7FUs0GWkLa69kVULfimSkfrVrMW/q4OM9OkKyEh76TLSD0vISLsbMtLqjLvmr04fIgwZ6SXgKIWDq+AohYOr4CiFg6vgDIVDVcVRCgdXobIUDlUFQ0bqXIXDzWPX2P1mr3zphqrtyTJwrIzUwMDAwMCgquLMTlancfbvZK14sXI6WRVekyUiESLyo4jsEZGNIjJfRDrpjqnNIrJdRAaUKN9WRBJEJFlEPtHXZSEiI0Vkq/6cP/QdiYhIP/16goisFU10WlY7rhKRv/V6fxIRL/26t/59sv54nH49WESWichJEfm0nHyPlve4o+nRoweJiYkkJSUxePDg8x6PiYlh6dKlxMfHs2XLFnr16gWAp6cn06ZNY+vWrWzevJlOnTo5u+l24XLzA7Rs2ZK1a9eybds2tm7dire393nPr+pcLD9oxvft27ezbds2ZswoHk0dNWoUCQkJJCQkcP/99zuryXbFz8+PuLg44uLiylQTBAQEULduXWJjY4mJicHLS1uH5+PjQ2xsLLGxsdStW5eaNWs6u+lXzMWyiwiRkZHExcURExODh4e2ysNkMhEdHU2DBg2KjODVkYvdn+qOO+avTgvfK7QmS+8gzUGzvT+oXyu0vV9f0vYuIr/p66YmAU9SbHvvibYm60Ol1DC9jv+ircMaAOwDOpWwvU8GzrO9ox/jU8L23l9/rf7AUaVUA9Fs76PRDnw+CwxDt71f2u0pG5/bJ9qjmiJMJmHipH/Re/jvpGafYvWYV1h0ojWJh4qPT3jr2U7MSTzClAXraBJTm7lf/UiTJ2fw9G3NMceEce3QZYQG+DL3y5+58eVZOGKA8uzvA6lx31d2r9ckwsTxd3HH//1BavZpVr7/GovzriMxNbeozNtPXc+vyTl8+ecWmtQJYPbXM2n+3CztQOnRt/PEp6vYdmAXQTW98er7BR4OuAGnfn6Ma962vwjQJDDl2esZ+N0mMo7n8d0LQ0mKvZN9R4oXsMcE+fLOPS14+ttNnDhbQG2/GK55ewk3Ngym03UxPDZjC54ewpSxU0i7ZiCn8i12b+emt7rx8+Y0u9drtVoYN+hhHnvzI/yDQ5k0ZAAPDBpGWHRcUZmSlvedG9bw96K5PDr0Q/LzzmL28MBs9uD40Ww+fbU/g7+YhdlsFztNEfe1jgIM47thfHcuVSm/weVR0ZGs6mp7f0xEdot2/M8NFbwXdqd9wzD2pOeyP+ME5wqs/LwqmT7XxdmUUUrh76sZ3wP8vEjP0X4BN4kJYvlWbSdiVu4Zck/l0baBa32qbdcghL2HT7A/8yTnLFZmrd1H7/axNmWUglp+xcb7QsVFt6uj2HbwKNsOaG+dnJN5WF1sCrxFHX9Sjp4m9dhZCqyKRdsz6dw41KbM3W2imLkhhRP6cUFHT2vG93ohNYg/cAyLUpw9ZyUp8yQdS9niqzopyYkERdQhKDwKDw9PWnXsys71a2zK2FjezxZb3r28fYo6VAXn8l1OsGMY38vHXY3nhbhrfhH7f1UWFe1klWt7F5GtaKb10fooVh0uYnsXkUNAP7SRrNJcse0dzSR/wd82ekfwbbTO1Y1AswuVdTRRwTVIOVKsXUg9coo6wbauo3d/2MCDnRuRPO1h5rzVm5cmawuwE/Yfoc91cZhNQt3wWlxTP5ToENeaMokK8iMlu0T+7FNEBfnZlHn35808eFN9dk26j9lDbuGVaX8D0CAyAKVg7hu3snrU7bxwh10GK51KaC1vDucW7wjNPJ5n480CiA3yo26wH9Mea8P0x9vSsb5mG9mdcZKODYLw8TAR6OtJu7jahPu71nTp8ZwsAoKLO5X+waHk5mSdV27dwjmMef7fLJrxOX0e+2/R9UNJOxj/0qNMePkx+j75kt1HsRxJWcb3rMyMUmXKNr67A2UZzzMy3Gfzhbvnrw5ciierTJRSh5RSrdCO1PmPiFzUlKaUGqqUikETkdoc1izFtveyF6bYj+uA5UqpLKVUPvCTg1/virj/5gZ8t3QXDR7/lrvensfUF7shAtP/TCT1yEnWjL2XD5+4gXWJh7FYq98OqPtuuIrvlifT+Jmfuef9xXz5/E2IgIdZuL5JGP0nrOTW4fO5/dpYOre48LmHroqHSYgJ8uOp6ZsY8st23uzThJreHqzbm8OapGy+erwt793TnK0puS43kldROvS8i5cnfE+Pfk+zfPa3RddjGjZj0Niveeb9L1gxZwbn8l1LYWJgYGBLdVqTVdFOltva3vX6HGp8T8s+RXRI8chVnZAapGbbCkX/c2tTZq9OBuDvXRn4eHkQ4u+rObamrqXDCz9z/7sLCazhTVJaLq5EWs5pokuM3NUJrkFajq1Q8z9dG/LLX/sA+CcpC29PMyG1fEjLPs2anRlkn8jjTL6FPzalcPUFzj2sqmSdyCMioHj0Kczf2+YsQ4CM43ms3HWEAqsi7dhZDuacJjZYM75PXX2Af01ez8DvNiPAgWzX8mT5B4WSm108cnU8O4uAoNALlm/ZsSs71q8+73pYdF28fXzJOLTPIe10BIbxvXzc1XheiLvmd8fpwupoe/8b6KTvPvQE7rtQQUcb3zckZdIgKpC64bXw9DBx300NmPf3fpsyh7JO0rmV1m9tHB2Ij6eZrNwz+Hp54Oet9Tm7to6mwGq1WTDvCmzcc4T6kf7UDa2Jp9nEvR2vYv4G26NzDh05RecW2uLjxnUCtPzHz7J4SyrNY2rj62XGbBJubBpBYoprdTK3p54gJsiPqEAfPExCj+ZhrNhte6b58l1ZtI3TfrEG+noSG+RH6tEzmEQzvQM0DKtBw/CarNtTZY/gLJM69RuTnZ5CTmY6BQXn2Lp2KU3adbQpcyS9ePXBrvh1BEdqqwRyMtOxWPR1almHyUo7SO3QCFwFw/hePu5uPHf3/NWBCi1eqMa29xHAX8AxYHNF7oUjsFgVL36xit9H9MFsEqYvTmTnoaMM+3d74pOzmPfPfl6ftpaJz3Xi+b6tUAqeHL8UgNBAX34f0QerUqRln6L/WNdbFGmxKl6eto65Q2/FbBK+XZbMzpRjvHl/a+L3ZDN/4yHe+GY9E57uyHO9m6GApydqIxnHTuUzYd52Vr7fB6Vg0aYUFm1KKf8FqxgWpRi9YDef9WuNSYTfNqexN+sUAzpfxY60E6zcfYS1e3LoUD+IWc9ch8Wq+HhxMrlnCvAym5j6qDbIfCqvgDfn7MDiYtOFZrMHtz8+iK/ffRVltdKmSy/CY65i8U/TqFO/MU3b3cC6hXPYk7ARk9mMb81a3PvsEAAOJCawcu73mMxmxGTijv4vUMPfdUZ5DON7+bir8bwQd81vqkYfIgwZ6SWgdybtrnBwFRylcHAVHKVwcBUcpXBwBRylcHAVDOO7YXx3poz01k/X2f1m//lcB9c0votIBPAx0B5tRCgD3WWFNh3pCUwo1D+ISFuKR7jmA4P0kbKRaNOHVrQzBB/VR64KX6c92qjTg0qpQoVDyXZcqN4gtEXtcWijb/frLq4mwFdAG2CoUuqjCmR12E9Zjx49GD9+PGazmS+//JLRo0fbPB4bG8u0adMIDQ0lJyeHhx56iNRUbTnaqFGj6N27NwAjR45k5syZjmqmw7iS/KNHj6Z3796YTCb+/PNPBg0aVBkRroiL5Y+JiWH69OkEBgZiNpt5/fXXWbBggc3jO3bsYMSIEYwZM8bZzb9i/Pz8ioSaubm5HD1a9pR3zZo1iYqK4sCBA+Tl5VGrVi0bQaO3tzcHDx4kL891Fr9fLLuIEBERgbe3NxaLhfT0dAoKCvDz8yMkJAQRQSlFVlYWZ8641nq8ilDR90Z1parkd2Ynq/tn9u9k/fFs5XSyrmivs5MkpYiIGa3j9kc5zblQva8DS5RSo0Tkdf37wWhTiv+l2LNVYXyuef5Sn1IuJpMwccoweg/8jNSMY6z+7hUWJfmTuK94weO40Y/xw4rtzPjfr3Rq35APvphD/2Hf0vPGZrTr1JkOj32Ot6cHf0z5gGVpdTlx6jwt2BVzdtMEfNq/bPd6TSZh4peD6f3cZFIzc1k9fRCL9gWTuK94q/K49x/mh9U7mTFvAZ3aNeCDyb/Sf8QPdGhZl5t69OHa/tro4tIpz3Lr056sit9j93aeXT+GgH9/e/GCl4hJhElj7uDO95eQlnOaZSMHs8x6A7tKyFjf6X8dv+/LYdqS7TSuE8DP02fS6oW5RY9PGHQTi7dn43PNvwj4d2u7txEg9/uHGTJvl93rVVYLi94bwI0DRuIXGMzScS9x7cOv4h9h60o7d/Y0a6e8g9VSQNcXx1I71nbaJDdtP39Ne5eXPv6f3dv4fu/GAJzMs7+M9K4+PZk4WZORPvTgfbz/wRjqlZKRJu3exdDhb7NowTyWLlnM6I/GkbhzB8HBwYSGhZOctJtnBzzBoiUr7dq+Qmp6GzJSQ0bqPKrTmsMrVTg4XFKq8zwwG22E6zzKqxdbSWmRvFQplamUWg+cu6zkdqR9i7rsSclif2o25wos/Lwonj6dW9qUaVIvghXrtf0AK9Yn0aeT9njTehGsjt+DxWLl9Nl8EpLS6N6xqdMzXAntm8eyJyWb/Wk5Wv4/NtPn5uY2ZZpcFc6K9UkArNiQXPS4Ary9PPHyNOPt6YGHh5nMnBPOjnBFtK0fzN6MExzI0mSss9ft57a2ti5epaCWLqP19/Uk/WjxiEXvttEcyDzlcgv+C8k5mESNkEhqhkRg8vAk+pqbSdv293nldiyYQaOu92Dy8CyznkObVhJ9zU2Obq5d2ZawlejSMtJSssnly5bQ5w7tv7Nut/ZgvS4jbdK0WdFOxPoNGpJ3No/8/HynZ3Ak7irjLMTd8zsbEekpIrtEO57v9QuUuV9Edoh2lOD3F6vzSjtZDpeUikgd4C60kaoLUV694fpuR4DDQJXb/xoVGkjK4WK5YGrmMeqEBdiUSdidSt+u2nGOfbu2wr+mD0EBfmzdrXWqfH08CQ6sQad2DYkOd52FvwBRoQGkZJTKH1oqf1IafbtoHcu+nVsU5f874QArNyazb/5b7FswnMXrdrFrf5l98SpLZJAfqdnFyoq0nNNE1raVsY76ZSv333gV2yfcxc+vdeG16esBqOHtwaDbmzP6l61ObbM9OXMsG7/AkKLvfQOCOZNra185eiiZ08eyiGze/oL1pGxaRUwb1zq7Myszo0g0ChAWHkFmKdlkVgVkpEv+XESTps2KznSsLri7jNNd85vE/l8XQ58x+wzohSYn/5eINCtVpiEwBLhBKdUceOGiWS4jf4Wwo6T0Y2CwUuqKx+n1US6XXL04ZNxcbmrbgL++f42b2jQgNeMYFotiybpEFq7ZwbKvXmT6e//h7637sVhdMmK5DBn/P25qU5+/vn2Rm9rU1/NbqRcdTOO4cBr0GUn93iPp3K4BN7S+qrKba3fuvT6OH1bupfnzc7jvg2V8MbAjIvD6Pa2YuGAnp/IKLl6Ji6KsVrb+OpVWfftfsEzOgV2YvbwJiKzrxJZVDfYkJ/HJuDEMfevtym6KgYErcy2QrJTaqwvKf0SbCSvJk8BnhUcAKqUu+on+Ss+f2E6xs6pMlFJpIlIoKV1DxSWl84G3gHZoygaAEOA2ESlQSs0tUb48+WmGiEQqpdL1acVLHubQ/WBPXbTgZZKWdYzoiOLRpzphgaRm2k79pB85zoOvaLaKGr5e3NmtNbkntSmjD6b+wQdTteVqX7/7CEkHXGskJy0r12b0rU5YIKlZZeQfrM361vD14s4uLck9eZbH7+zAP9sOcOqMNk2yaO0urmtZlzWbXUdImZ5zmjrBxSNXUUF+RWczFvJQ5/rcO1rTdqxPPoKPp5ngWt60rR9C32tjeedfbQjw88KqFHnnLEz5s0zVXJXENzCY08eKvWBncrPxDSg+Easg7wzHDx9g5advAHD2xFHWTv0/OvZ/s2hd1qH4lcRcc7NzG24HQsPCi0SjAJkZhwkrJZsM1WWk4RER58lIMw4f5uUXnuOd90YTE2O7hq064K4yzkLcNX8lrckqOppPJwXtZJiSNAIQkTWAGRihlFpYXqVXOpLlcEmpUuoqpVScUioO7eDngaU6WJRXL7aS0pLy0grjcBnp9oM0iAmlblQQnh5m7uvRhnkrEmzKBAfWKHrjvfr4rUz/dR2gLRoPCtB+QbdoGEWLhlEsXpfoqKY6hA07DtEgJqQ4f/fWzFu13aZMcIBfcf5HuzL9d2267NDho9zUph5mswkPs4mb2tQjcZ9rdTLj92ZTP6IWdUNr4Gk2cU+HOBZstHV9pWSfolMLbdqgUZQ/3p5mjhzP47aRf9Dqhbm0emEukxYmMubXbS7VwQKoHdOQk1lpnMo+jLXgHCmbVhLV/Nqixz19a3D7/31Pr+FT6TV8KkF1G9t0sJTVSsqW1US7YCereYuWHDpgKyPt1LmUjLRzV/73m/Zf3pI/F9H+Wk1GeuL4cf777NM8/8LLtL6mTWU03+G4u4zTXfM7wvheeHJLia/LGTjxABoCnYF/AVNEpNz1OVc0kuUkSekFEZHNSqnCrVQXrBeYKSL99Xrv158bAWxAk5RaReQFoFmpBfhOwWKx8uLoWfz+2UDMJhPTf1vHzr2HGTbgNuJ3HGTeym3c3LYh7zyvCTdXx+/hhVE/A+DpYWbxVG1a+MSpszz+5rdYLK7l8rFYrLz44Rx+/+RJTcb6+3p27s1g2FM9iN95iHmrdnBz2wa8M7AXCli9aS8vfPALAL8s3Uqndg3Y8P3LKAV/rktk/uodlRvoErFYFa9+vZ7Zg7thNgnfrdhDYmoub9zTik37clgQn8KbM+IZ/8R1DOzZFIVi4Bd/VXaz7YbJbKb1PQNY/cVbKKuVuOtuwT+yLtsXfEftmIZEtSj9YdKWI3u34xcYSs0Q1zG9F1IoI312gCYjvUOXkU7SZaSddBnpsCGvccdt3QkICOD9DzQZ6U8/zODQoYNM+XwiUz7XdtdO/GIqQcHB5b2kS+GuMs5C3D2/PVFKTQYml1Ok6Gg+nbJm2lKAv5VS54B9IrIbrdO1/kKVGjLSS6BIRmpnhYOr4CiFg6vgKIWDq+AohYMr4CiFg6tQWQqHqoIhI3WujLTPF+vtfrP/93T7ctuvn3m8G+iG1rlaD/xbKbW9RJmewL+UUv8RkRC0U2daX+CsZe05Rier4jhSRmpgYGBgYFBVcWYn647J9u9k/fZU+Z0sABG5DW2znRmYppR6V0TeATYopX7TlyONQfNwWoB3lVI/llfnFa3JEhGLiGzWfRFbRORlESl0YgWLyDIROSkin5Z6XlsRSdBdFJ/oDUdE7tPrsorIeWugRCRWr++VC7TnKhH5W6/3JxHx0q/fLCLxIlIgIveWKN9aRP7SX3OriDxwJffDkfj5+REXF0dcXJyN4dpdMPIb+d01vztnByO/u+d3Jkqp+UqpRkqp+kqpd/Vrw5VSv+l/V0qpl5RSzZRSLS/WwYIr3114pnBNlIiEAd+jrXF6C2191jA0l1aLUs+7kJ19G3A38MUFXm8sxWutymI0ME4p9aOIfA7011/rIPAoULpzdhp4RCmVJCJRwEYRWaSUOkY5GNZj52LkN/JXdv5C47U7/+yDe+d31/c+GMb3K8FunizdF/EU8JyIiFLqlFJqNVpnq4iLWN93KqXKXPQhIncC+9C0EWU9LkBXtB2IYGt336+U2oq2qL5km3crpZL0v6eh6R1CLzm8g3F366+R38jvrvndOTsY+d09f3XArjJSpdRetLnMsHKKlWt9Lwv9/MPBQHm2vWDgmFKq0Mp40XpLvca1gBdg/0PvrhB3tf4WYuQ38rtrfnfODkZ+d83vCIVDZXGl04XOYgTaNOBJRwwj6qNr3wL/sYdZ3sDAwMDAwODyMFWj6UK7drJEpB7aivvybJDl2dkvxHXAvSLyARCI5rU6q5QquaA+GwgUEQ99NKsi9SIi/sA8YKhSat0FyjjU+H4x3NX6W4iR38jvrvndOTsY+d09f3XAbtOFIhIKfA58qsrxQlzEzn6h59xUwvr+MfBeqQ5W4bmEyyg+5ueidnd99+Ec4Bul1KwLlXO08f1iuKv1txAjv5HfXfO7c3Yw8rtrfmO6sBhfEdkMeAIFaFNuYwsfFJH9aLsNvfSF692VUju4gJ1dt8dPQFt8Pk83uvcorwEiMh94Ql+4PhjtnMP/Q5OETdXLtEfrTNUGbheRt/UTtO8HbgaCReRRvcpHlVKbr+Sm2Bt3t/4a+Y387prfnbODkd/d81cHDBnpJVAoI3VX869hPTbyu2v+ylIYVBWM/O773gfnG9/v/Sre7jd71mNtKmU8y1UWvlcpnO0MqUq4c3Yw8hv5jfzuijtndzbVaN37xTtZImIBEiieEvwGbaefVURuRTuA2QvIB15VSi3Vn9eW4inB+cAg/UDpD4Hb9fJ7gMeUUsdEJBjNcdUe+Fop9dwF2hME/ATEoR1Gfb9S6qiINAG+AtqgLWL/SC8fo7c5HFDAZKXU+Eu5SaXZfMDxZ0hvXr+WryZ+hNVqpVuvO7nzwUdtHv/frO9YsuBXzGYz/gG1eeaV4YSGRzq0Ta3r+rttdjDyu3P+1nX9tba5ef5tKSftXnf8P2uY9ulHWK0WbrntLu7+92M2j2/fspFpn43hwN4kXhr2Ph073VL02DdffMzGdauxKitXt+1A/+dedYjIskV0TY6cPGf3egHWrVnFxx+NwmKxcPtd9/DIYx5NkCsAACAASURBVE/aPJ6fn8/IYUNI3LmdgMBARo4aQ2SUZidK3r2L0e++zelTJxGTianf/oS3t7fd2xhS09PudboLFVn4fkYp1Vpfw3Qr0AvN6A5wBLhdKdUSbaF5ydNzC63uDfWvnvr1P4EWSqlWaIcxDtGvFxriyzwypwSvA0uUUg2BJfr3ADnAf4GPSpUvAF5WSjUDOgDPikizCuSuNKwWC1MnjOaN9z5h3Jc/s2bZIlIO7LUpE9egCaM++5aPJv9Ih5u78d2UTyqptfbFnbODkd/I7175LRYLU8aP5s1RExj/1WxWLV3Iof22eUPDI3l+8Ahu6tbT5nriti3s3LaFsV/+xMdTfyZ513a2b9nozOZfMRaLhY9Gv8uYCZ/z/ezfWLxwPvv2JtuU+X3ubGr5+/Pzbwt5oN8jTByvLXsuKCjg7Tdf57Whw5kx6zc+m/w1Hh7VY3LKJGL3r0rLcimFy7C6b9IXnINmYvcVEe+LWN3/KCEMXYeuc7iQIb4M+qLZ3MHW6p6plFoP2HzcUEqlK6Xi9b+fAHaiS0pF5EkRWa+fuzhbRPwu5X44iuRd24mIiiE8MhoPT086du7O+rUrbMq0aN0Obx8fABo2bUFOVvUQ1LlzdjDyG/ndK39y4jYi60QTERWNp6cnN3btwT9rl9uUCYuIIq5+I0wm219XInAuP4+CgnMUnMvHUlBAYO0g5zXeDuzYlkB0dAx1omPw9PTilh63sWr5Mpsyq5YvpVefvgB06dadDevXoZTin3Vrqd+wEQ0bNQEgIDAQs9ns9AwG5XPJCodyrO73APFKqTwqbnV/nPLPIiyLcF0DAXAYbRqwQohIHHAN2pmJAL8opdorpa5G63z1v8S2OIScI5kEhxbHCg4JI+fIhdVjSxf8SutrOzqjaQ7HnbODkd/I7175s49kERxWbDQPDgkjJ6s8zWIxjZtfTYvW7el/b3f639eD1u2vJ7puPUc11SFkZWUQHlE81RsaFk5WZkapMpmER2j3yMPDgxo1a5F77BiHDuxHRHhh4JM8+u97+e7rqU5tuyMRB3xVFnbxZIlIc7TDmZ++hOcMRZvKm3G5r6uPklVoF4J+NM9s4AWlVOHCihYiskpEEoB+QPMLPPcpEdlwue10JCsXz2fv7p3ccd8jld0Up+PO2cHIb+R37/zpqQdJObiPKTMXMmXmQhI2rWfH1vjKbpbTsFgsbN0cz4h3P+Dzqd+yYtkSNvxdpk/b5RARu39VFpfcySptdReRaDQH1SNKqcJz/8q1uutOqj5Av/LEpRcgQ5+OLDwO56Ife0TEE62DNUMp9UuJh74GntPXlL0N+JT1fGfLSINCwsguMQWQfSSToJDzj4PcGv83c76fxmvvjMXTy8tZzXMo7pwdjPxGfvfKHxwSSnZmsdE8+0gmQaHlHX1bzN+rltGoWUt8ff3w9fWjzbU3sGvHVkc11SGEhoaTcTi96PuszAxCw8JLlQkj47B2jwoKCjh18gQBgYGEhofTuk1bAmvXxsfXl4433sSuxB1Obb/BxbmkTlZpq7uIBKIdSfO6UmpNYbnyrO4i0hN4DbhDKXX6Mtr8G9oie6iY1V3QpKQ7lVJjSz1cC0jXO2H9LqMtDqF+42akpx4iMz2VgnPnWLv8D9pdf7NNmX3JiUz5+D1ee2csAS62DqE83Dk7GPmN/O6Vv0GT5qSnHiIjPZVz586xeuki2l/fqULPDQmPYMeWjVgsBRQUnGP7lo1Ex17l4Bbbl6bNW5By6CBpqSmcO5fP4kXzubFTF5syN3XqwoL/ab/mli35g7btr0NEuO76G9iTnMTZM2coKChg08YNxNWrXxkx7I5J7P9VWVxURlqGwuFbYKyucHgTbXdgUomndFdKZYpIO2yt7s/rHbNkwBvtrEGAdUqpAfpr7Uc3xAPH9Lp2iMiXwOdKqQ266mEmEAscQFM45IhIBLBBf74VOAk0A1oBq/QMhYc/v6GUmi8iz6B1+LLQ1mnVUko9Ws69UOCcbdzxf69m+qSxWK0WuvS4g7v79eenrz+nfqOmtOvYiZGvDeTgvmQCg0IACAkLZ/DIcQ5tk7O28FfF7GDkd+f8zlQ4VOX8jlA4bFy3mmkTP8JqsdKt1x3c+9AT/PDVJOo3asa1N3QiKXE7o4e/zKmTx/H08qZ27WDGfzULi8XC5PHvs2NrPCLCNe078tjAl+3ePnCswmHt6pWM/2gUFquVPnfcxaNPPM2USRNo0qw5N3XqSl5eHu8Me53diTvxDwjgnfc/ok50DAAL5/3Ot19NARE63nATz75wsc35l0dITU+nykj7fbvZ7jLSGQ+3rpSulmF8vwSc2cmqijjrl2xVxcjvvvmd2cmqijiyk+UKOLKT5Qo4u5P10Hdb7N4x+e6hq6tmJ6s6y0hFZDnwilKqQovaCztZlYGfnx9hYdpahdzcXI4ePVpZTakUjPxGfnfN787ZwchfVfI7s5P18Az7d7K+7Vc5nayKmMvOKKVaA4hIGPA92pTcWxTLSNNEpAWwiGJVQ6GM9G+0TlZPtGnDP4EhSqkCERmNNt04mGIZaQv960IUykhHicjr+veDKZaR3lmqfKGMNF5EagEbReRP/aDqy8LZZ1hZLBbu6N2DL6Z8RXh4OP9+4F5GfTiW+g0aOLUdlXV+l5HfyF/Z+Svr7L6qkB2M/O783gfjSKErwa1lpDoPi8hmEdkmItdeyv1wFtsSthITU5fomBg8vbzoeVtvli9bUtnNchpGfiO/u+Z35+xg5HfX/G6tcKhmMlIAP32kbiAw7RLb4hQyMzKIiCwW9oWFh5OR4bqW50vFyG/kd9f87pwdjPzumr867S60y0FHJWSk3S/hOXaRkVZ0ndQFZKQAP+h1rRQRfxEJVEodu9w2GRgYGBgYGBjAZXSy7Cwj7Xa5MlKlVLodZKRwvjH+vPaIyFNo06SVQlh4OIfTi4V9mRkZhIdXeADP5THyG/ndNb87Zwcjv7vmr8zpPXvj7jJSgAf0cjcCuUqp3NIFnG18L03zFi05eHA/KSmHOJefz8L58+jUpWtlNcfpGPmN/O6a352zg5Hf3fNXByoykuUrIpspJSPVH3sOaAAMF5Hh+rXu+gL5gdjKSAvXXn2KJiP9U++tlikjFZE7KUNGiqaMmCki/dFlpPpzbWSkIvICxTLSh4EEPQfoMlL972dFZJOe7/EK3A+n4+HhwZChw3nmqSewWi3cedc9NGjQsLKb5TSM/EZ+d83vztnByO+u+avPOJYhI70kCtd/VcZW3qpAZW1jrioY+d03f2UpDKoKRn73fe+Dlt+Znqwnftpm95v95QMtqqwny6AU7uwMcefsYOQ38hv53RV3zm5w+Vy0k+VCxvd+aFJSAU4AzyiltpRnfK/KXMzyGxgYSEBAAKAJ6w4fPkxBQcF59bgi7pwd3DP/xTL7+voSGhqKt7c36enpnDxZfLxLSEgINWrUACA7O9vmMVfgcrMXXi/Ey8uL9PR0Tp065bzG24Er+bf38PAgPDwcDw/tV1lqaqrL/SxcLL+IEBERgbe3NxaLhfT09KKMXl5ehIeHYzJpy6sPHjxIdZidqkbr3quV8X0f0EnvcPUCJgPX4QDj+yYHn19mtVh44bG7GTr6M4JDwhny3CMMeuNdouvWKyqzbfMGGjZpgbePD3/8PosdWzbywpvvO7Rd19T1d9vsoOXffNDx+Qc9ejdvjv6M4NBwhjz7CIOGlpP/t1ls37KRF4c5Pn/rWH+2HDxh93otFguDHr2LYaMnEhQazpBnH2bQ0PeIKZE583AaZ06f4reZ39Ku481cf/MtAGxct4p5v/zA0Pc/4Vz+OUa8/BTDP5yEX42adm3j1bG1ANieat8OnMVi4dlH7mLEhxMJDg3ntQEP8dKw94mJs81++tRJfv3pW9rf0ImOnW45r54Tx3MZ+FBfZi76C28fX7u2EaB5He1+7kq3bwfOYrEw4KE7GTlmEsGh4bz0dD9eHf4+sXH1i8pkpKdx+vRJ5vz4Ddfd0IkbOt9a9NiQQU9w/0NPcE37Dpw5fRoxCT4OyN84sgZZJ+x/dqHFYuHBu3rz8cQphIWH88TDDzDivQ+5ql6x0f2XmT+QnLyb1954i8WL5rNi2RJGjhpDQUEBj/e7j2Ej36dhoybkHjtGzVq1MJvNdm9naC1Pu9fpLlQn4/tapVThR4CS9V7Q+C4iT4rIehHZIiKzRcTvUu6Ho0jetZ3wqBjCI6Px8PSkY+furF+7wqZMi9bt8PbxAaBh0xZkZ1UPQZ07Zwctf0RUDOFRJfKvKT9/zhHXzl8ys6enJzd07s6GNcttyoRFRFG3XkOklFUw5cA+mrW6BrPZAx9fX2LrNWTz+rVObP2VkZS4jcioaCL07Dd27cE/ZWSPq98IMV34v+u/ViymzbU3OKSD5UiSdm4jsk5MUf6bu/bg79XLbcqER0ZxVRn5D+7fg8Vi4Zr2HQDw9fNzSAfLkezcnkB0TAx1omPw9PSiW/fbWLV8mU2ZVSuWclufvgB07tadjf+sQynFP+vWUr9hIxo2agJAQOD/s3fe8VFU6x9+3pCEECAEUpYWCAJSVVBARbpKERT5KeK9KBYUxYYFBUSxoFhQVCwoIIpevHa9KKAoHREE6ZDQEQghnRJpyeb8/pjZZDekuy075+tnPyY7Z2bfZ3ZJTmbOeU6kRzpYvog2vvu/8X14Ucctwvj+rVKqo1LqIozO1/By1uKRZKanEhVTgBUVHUtWevE6sCUL/ke7Tp29UZrHY2V2MPljnfhjYsnMKJ5/8U//o13Hys1fmLlOjI2MjLQy7RvftDkb1/7OmdOnOH4si20b11WqTndmehrRsQVG76iYWDJK+LwXlxVLfqbLlX3cWZpXkpGeSrTL591GRnrZ3vukgweoXqMmk556jFHDb2bWtDew2+2eKtUjSUtNIdZWL//7WJuNtEKf37S0VGJtxmckODiY6jVqcuzoUQ4e2I+I8Mj9d3PHv29kzuwPvVq7JyPi/oevEnDGdxHpidFZ6lLo+aKM721F5AUgEqiBcbuzUmXFr/PZszOBZ1+f7utSvB4rswMs/3U+e3ck8OwUa/IDXNThcnbv2M74UXcSUas257e+gKCgwPhrvqzJzEjjwN7dtO94ua9L8Wry7Lls37yBt2b+l5jYurzy3BgW/TSX3v0H+bo0r8Sea2fzxvXM/OQLwsLCeGjkcFq0akOHTpf5ujQdp5T7Spabje9DK2p8N4/jYnwXkQuBmcBApVSG0/PFGd8/Bh5QSl0APAeEFcM8QkTWlbPOCqdOdKzLX+MZ6anUji584RA2r1/Dt5/N4onnpxASGuqt8jwaK7ODyZ/qxJ+WSp2oIvj/XMN3n83iiYmVn78wc2ZaClFRMSXs4Zobhg7ntQ/+y4RX30MpRb2GjTxRpkdSJzqG9NQCo3dGWipRRXzeS8pvS37h0i49CQ6ufONmoqJjSXf5vKcQFV229z4qxkaTZudTt35DqgQHc1mXnuzZmeipUj2SmFgbqSnJ+d+npqQQE+N6cyYmJpbUFOMzkpuby9/ZJ6gVGUmszcZF7S8hsnZtwqpV4/IrurIjscJDjf0qQSJuf/iMpTyN/dn4LiKNgG+BW5VSO51qLsn4XhNINjthQ4t7QW8b35u2aM2RpIOkJieRm5PDqqUL6XB5N5c2+3YnMvPNSTzx/BRq1a7jrdI8Hiuzg8GfXJi/cyH+XYnMCCD+ZiZzSnISOTk5/LZ0IR06dy/Tvna7nRPHjKVG/9q7iwP7dnNRh8rzl3zzlm1c2Fcu/pmOZWR3ZOXin+h6ZV8PVejZNG/ZhsOHDnDE5F+++Gc6XdGjzPv+nX2CY0czAdi8fi2NnCYMVIa0bN2WQwcPcDjpEDk5Z1m0cD5duvd0adOle0/m/2gsbLJ00UIu6XgpIkKny69g7+5dnD51itzcXDauX0eTJk2LehkdH6ZUGWkRCodPgSmmwuEpjNmBu5x26a2UShWRDrga3x80O2a7MYzvjitNRRrfgaMUYXw3VQ9fAo0wje9KqUyzzQ3mcwC5SqkO5nI5K0yGPHPbk0qp+SIyEqPDl4YxTqumUur2Es6FAs/PLgTYsGYls6dNIS/PTo8+1/F/Q4fz5cfvc975rejQuTsTn7iPg/t2E1knGoDoWBtPTHzDozV5Y3Yh+Cc7eGd2IcD6NSuZ/Z7B37Ovwf/Fx+/T1MH/+H0c2LebyKgC/jFe4PfU7EIwmD9+73WTeSA3DB3O5x9Po+n5renYuTu7E7cx+dnR/J19nJCQqkTWieKND7/i7NkzPHGv8fdReHh17n74SZo0a+H2+jw1uxDgz9Ur+fDd18jLy+PKftcx+Ja7+GzWNJq1aE2nK7qzK3Ebrzz9GNnZxwkJrUrt2lFM/fhrwJh5OO7BO5jxxYL8afyeiKdmFwKsW72CGW8b/FddM5Aht97Ffz58j+YtW3PpFT3YmbCNSU8/SvaJ44SGGu/9e7O/AWDD2tXMem8KSimatmjFA6OfJiTE/Vf0PDW7EGDVyuVMff1l7PY8BgwcxG3D72HGtLdp2boNXbv34syZM0x8eiw7dyQQUasWz016jQYN4wD4ef4PfPLRDESEy6/oyv2jRnukxpiaIV6Vkd737Xa3eyje+7/WPrmcpY3v5Yg3O1n+GG91svw13upk+Ws82cny93iyk1UZ4slOVmWIJztZlSHe7mTd/12C2zsm7w5q5Z+dLC/KSIs9VqF6ipORtgQ+Ai4GxiulXjPbu01GWniQvTvTp08f3nrrLapUqcLMmTN55ZVXXLY3atSIWbNmERMTQ2ZmJrfccgtJScYwt9zcXLZs2QIYMrqBAwd6qkyPRfNrfqvyW5kdSuePi4tj9uzZRJqKgrFjx7JgwQIaN25MQkICO3bsAGD16tWMHDnSFwhuiz+LiHUnq2IpSycrWylVw/zaISP9TSn1jIi0B1KcZaRKKYeD6g/gIQpkpFOVUgtEpDew2ElGilJqTEnHKlTPq0Cmk4y0trl/LNAYw5uV5dTJqgfUc5aRAtdXREbq6GSFdXm6vLuWmKAgYctno+j/yGyS0o6zcsY93PbcVyTuL5jKPOf5m5i/aidzftpI94ubMOya9gx/wRjDn/bzeGL6vOjWmorK6ZUT3c4Oml/z+z//6ZUTAQjr+oxbjxsUJGyZ8yD9H/3UYJ9+N7c99w2JfzmxPzeY+b/vZM5Pmwz2fu0Y/uJ3AKT99CQxfSe5taaicnrFcwDUuOljtx43SIQNbw5i4IsLSco4ybKX+nPHW8vZkXQsv83Uuy9n0/5MPvxlBy0a1OKbsVfR9sFvaBRTna/GXMmlo+e6taaikv3l7Wz0goh51B2miLiMIuZtm/7kES+ImNs1jvBqJ+tBD3Sy3vZRJ8ufZKRFHquIMoqTkaYqpdYCLtd0K4OMtGOrhuxJymR/chY5uXa+WrSFAV1aurRpGR/LsvV7AVi2ft852ytzNL/mtyp/x1YNCrFvZUAX1zFlLeNjWLZ+HxBY7AAdmkWzN+U4+1OzybHn8c2qfQzoGOfSRqGIqGaMs6oVHsqRrIrMl/L/5Et5yyFizqxETjirxl9lpM7HKpyyyEiLjL/KSOvH1ORQasFfbklpx2kQHeHSZsvuIwzs1hqAgd1aEVE9jDoRht04LDSYlTPuYdn7d3Nt18r3A1jza36r8tePjuBQasEVkqS04zSIKcyewsBurQAHe1VX9ukjWDbtLq6thJ2venXCScooGOeVlHGSerWru7SZ9NUmhnQ9j8T3buTrsVcy+qM1+dsax9Rg5csDWPBMHzq3LJ/6wt9SlIg5swQx7eIAEzE7J5CM734nIy3PsYqSkZbwepVaRjru3Z9545H+3NKvPb9t2k9S6jHseQZ6i8FTOJx+gvh6tfnprdvZuieFfYezSjli5Yrm1/xW5R/33kLeeOQabunbjt82/0VS6vEC9pveKGB/8za27g0sdoDBVzRhzrLdvP3jdjo1j2HGA13pNPp/HMk6Rev7vyEz+wztmtThv6N70Wn0/zhxKvAHqC//dT57A1jEHORDQ7u7U+5OlptlpFc6y0iLOVbhpIhIPaVUcmEZaQk1lyQjvV4ptcmsqUcx+4/AuE3qkRxOO0HD2Fr53zeIiSAp3fX+f3LGCW5+6nMAqlcL5frurTmWbSzzeDjdmPG1PzmL5Rv30+78epXqB63m1/xW5T+cfpyGsQVXrhrERJCUVhT7F4DJ3q0E9uaVhx0gOfMkDaIKrlw1iAonOct1BuOwns0Z9NIvAPyxK42qIVWIqhlG+vHTZGYbNzs27stkX8oJmtWLYMPeDCpjihIx1ylGxPzdZ7N49vXplV5EbIX4jYy0uGMVkSJlpCXU7Pcy0nWJSTRrWIfG9SIJCa7C4CsvYN5KV3NxVK3w/Euej9/SldnzNwAQWSOM0JAq+W0ub9uIhP1lW/vLX6L5Nb9V+dclHqZZwygn9rbM+22HSxsX9qFdime/IK5SsQP8uSedpnUjaBxTg5AqQdzQuQnz1h1yaXMwPZsebY31/Vo0qEVYSBXSj58mumbVfJN3fGwNmtaLYH9K5VWMFCkiLkLEHEgi4uISJO5/+CpluZJVTUQ2UkhGam57AGgGTBCRCeZzvc0B8vfhKiN1jL16B0NG+ov5g8MhIy32WM4yUgzNw5ciMhxTRgogInWBdRgy0zwReRhoDVwI3ApsMTnAlJECT2OMz8qXkZbhfLg9dnsej7wxjx9eH0aVoCBmz1tPwv40nh7ei/WJScz7bQfd2sfz/IirUShWbvqLh6f8CBiDYt8efR15ShEkwmtzVrjMyqoM0fya36r8dnsej7w5nx9eu5UqQcLs+RsM9jt7sn7HYYO9XTzP33MlSmGwvzEPcLAPIC9PERQkvDZnpcusxMoQe55i9Kw1fP/kVQQFBfHp0l0kHjrK+MHt2LA3g/l/HuTJT9fxzj2dub9/a5SCe6cZf4N3bmXjqZvak2PPI08pHp7xO1l/n/UxUcVTpUowdz7wOC+Oe9CQ8va5jrj4pi4i4v9Mn8rpU6eYMnEs4D0RsbfjyzFU7o6WkZYjnlI4VJZ4agp/ZYnmty6/pxQOlSWeUjhUlnhD4eDP8bbC4bEfdri9Y/L6tS3805OlUxBPykh1dHR0dHT8Nd7sZD3+o/s7WZMH+KaTVeqYLBGxi8hGEdlm+qQeE5Egc9vVIvKniGwx/9/Lab9LzOd3i8hUc2wUIjJZRBJFZLOIfGeOxSrxWIXqqSMiv4jILvP/tc3nW4rI7yJyRkRGO7WPE5ElIrLdZBjltG2pGGss+n3Cw8OJj48nPj6e2rVr+7ocr0fza36r8luZHTS/1fkre8oyJuuUUqoduBjfI4BngHTgWmdLOwU+rGnA3RQY3/tijMv6BRjnZHwfB4wp5VjOGQsscjK+jzX3z8QwzF9fqH0u8Jiz8V1EfqmI8T3/hOR494KW3W7nuv59+GDGR9hsNv495EZenjyFps2aebWOaiHidXbQ/Jrf9/zVQow/gq38bx+szW/Vzz4UvP/eSgANybK28d3MreaVuq0i0qk858Nb2bplM3FxjWkYF0dIaCh9r+nP0iWLfF2W16L5Nb9V+a3MDprfqvxBIm5/+IylvDsEmPEdINy8UncfMKusx/JmUlNSqFuvbv73sTYbKSnWWU5B82t+q/JbmR00v9X5AyFWN74D/Nc81nIRiRCRSKXU0TJi6Ojo6Ojo6Lgx5b7648cpN4ubje9DK2p8N9v/U+M7QOFO2jmdNhEZISLrSnsdTyXWZuNI8pH871NTUrDZynwBr9JH82t+q/JbmR00v9X5AyFWN74DDDHbdQGOKaWOFW7gaeN7aWnT9gIOHNjPoUMHyTl7lp/mz6N7zyInXwZkNL/mtyq/ldlB81uVX8T9D1/F6sZ3gNMissHku7MM58PrCQ4OZtz4CYwccRd5eXauH3QDzZo193VZXovm1/xW5bcyO2h+q/L7cqC6u6NlpOWIY/yXL6by+kN8NY3ZX6L5rcvvK4WBv0TzW/ezDwa/N2WkT/+0y+0ne2Lf5j7puZV6JUtE7MAWCq5kfQK8oZTKE5GrMa4shQJngceVUovN/S6h4ErWfGCUeYtxMnCt2X4PcIdS6mhJxypUTx3gCyAe2A/cpJTKEpGhGL4sAU4AI5VSm0QkzqzZhjHearpS6q3ynijneMIZEh4eTmysMWHz2LFjZGVluWyPjIykVq1agOFOOXLkCLm5hgkjODgYm81GcLDxdiYlJeVvc3c85UvR/JrfqvyVhR38iz84OJj69esDxlp3R48e5dixc0Z7uC2++uyLCHXr1qVq1arY7XaSk5Pz32ObzUZYWBgAqampnDp1yiM1ejsBdCEroGSk+4DuZoerHzAduBQPyEiX78is6K5FJs9uZ/y9N/HoxKnUjorlhUfvYMTjE6nfqEl+m8TNf9Lk/DZUDQtjyfxv2LFlPfeOeRGAV8eNpP9Nt9Om/aWcPnUSkSCqmv/w3JluLeq4nR00v+b3f/5uLeoA1v63D/DHHvdOvM6z2xl9142MnfQOdaJjmTDqNu4f8wINGp+X32b7pnU0bdGWqmFh/Prj1yRsWc+D4yaRm5ODUoqQ0FBOnzrJ2Hv/xTNTZlI7KsatNQJ0ahrJ4aNFGYX+Wex2O7feOIDX3plOTGxd7r3tZp5+4VXiz2ua3+b7rz9nz66dPDZuAosWLmDl0kU8M+k1vvvqv+xI2MbYCS+QlZnBmIdH8v7HnxMU5P65efUji1JW6pQlgSQjXaWUcvwJ4HzcYmWkInK3iKw1lwv6RkTCy3M+3JV9u7YTW68hMXUbEBwSQqduV7NxzXKXNi0vvCT/h2fTFm3JyjAmVR4+sI88u5027S8FIKxauEd+yHoyml/zW5XfyuwAe3Zuw1a/IbH1DP7Luvfmz9Wu/K0v6pDP1azlBWSmG/zBISGEhIYCkJNzFqXyvFu8G5K4bQsNQxu1FgAAIABJREFUGjaifoM4QkJC6NW7H78tX+LS5rdlS+jb/zoAuve6mj/XrkEpxV/79nBxB+O9r10niho1ItiRsM3rDJ5IkLj/4TOW8u5QSWSkw4s6bhEy0m+VUh2VUhdhdL6GF3EsjycrI43a0QWns3ZULFkZacW2X/HLD1xwyeUApCQdILx6Td6dNIbnRg3jq1lvk2e3e7xmd0bza36r8luZHSArPY06MQU/wutEl8y/bOFcLupwef73GWkpjBv5b0YNu5YBg4d55CqWJ5OWlkqMrUA2GhNrIy0tpdg2wcHB1KhRg2PHjtK0eQt+W76E3NxckpMOsSNxO6kpRwiEWNr4XlScBKL3lGOf0mSkpR7LvErmMkBORHpidJbGFHq+KBlpWxFZISJbgKFAm7LW76v8vmQBf+1OoM//3QKAPc/Oru0buenOh3hqyizSjiTx26J5Pq7Sc9H8mt+q/FZmB1i5eAF7dybQ/4Zb85+LirHx0rTPeP3Db1nx6zyOZWX4sELvpt+1g4iJtXHPbTfzzhuv0PbCizxyq1Dnn6Xcxnc3y0ivrKiMVCmVXFhGKiIXAjOBfkqpDKfni5ORfgxcbw6Qvx3oUQzzCIzbpB5J7agYstILnKpZGalF/kW2feMfzPvyY554aRohIaHmvrHENTmfmLrGhcL2l3Vn746tnirVI9H8mt+q/FZmB6gdHUOm05WbzPSi+bdu+IO5n3/E+Fffz79F6HKcqBgaNm7Kjq0b6dT1So/W7M7ExMSS5nT1KS01hZgYW5FtYm11yc3NJTs7m1q1IhERHni04FrC/cNvIa5RvLdK92gCaeB7wMhIRaQR8C1wq1Jqp9NxS5KR1gSSzU7Y0OJe0NMy0vjmrUg5fJC0I4fJzcnhj+W/cFGnri5tDuzZwafvvsKDT08mIrJO/vNNmrfi5N8nOHHMGI6WsHkd9ZwGzVaGaH7Nb1V+K7MDnHd+a44cPkjqkSRyc3JYvWwhF1/myr9/9w5mTX2JR595jVpO/BlpKZw9cxqAv08cZ+f2jdRr2Nir9f/TtGjdlkMH/yI56RA5OTksXriAzl17uLTp3K0HP82bC8Cyxb9wcYdOiAinT5/i1CnjV+i6NauoUqWKy4B5Hf9IqZ6sIhQOnwJTTIXDUxizA3c57eIQiHbAVUb6oNkx240hI3VcaVqtlLq3lGPly0hFJAr4EmiEKSNVSmWabW4wnwPIVUp1ME3uK0wGx8jIJ5VS80VkJEaHLw1jnFZNpdTtJZwLBe6fYQSwed0qvpjxBnl5eVxx1QAGDLmD7/8znfjmLWl3aTdef+oBDv21h8ja0QDUibHx4NOvAbBtwxq+nDUVFDRu2oJhD4wjOCTE7TV6anYZaH7N79/8nppdCP7PDp6bXQiw8Y/f+M/0KeTZ8+je+1oG/utOvv7kA5qc34pLLuvGS+Pu5+D+PUTWiQIgKqYujz37OlvWr+GzGW8hAkrB1dcOptc1g9xeH3hudiHA6t+W886UV8nLs9Pv2kHceucIZn3wDi1ateGKbj05c+YMk54Zx66diURE1GLCi69Sv0EcyYeTeOKhe5EgITomlieeep669ep7pMb6kVW96sl6cdFut3uyxl/ZzCfXx7SMtBzxZCerMsSTv2QrQzS/dfk92cmqDPFkJ6syxJOdrMoQb3eyJi3a4/aOyZNXNtWdLH+Po5Olo6Ojo6NjpehOVsVSpoHvHrC+T8TwXeVhDFy/3ZSQtgQ+Ai4GxiulXiumnibA50AU8CfGOKyzIvIocJdZYxpwp1LqLxFphyFHjcAYtP+iUuqLcpwnn6c0K3CgR/NrfqvyW5kdNL8V+X3ptXJ3ynQlS0SylVI1zK8d1vfflFLPiEh7IMXZ1K6Ucsg+/wAeosD6PlUptUBEIhwaBRF5CGhtjsuKBRpjCEazSuhkfYnhuPpcRN4HNimlppn6hjVKqZPmeKseSqkhInI+hvFhl4jUx+iYtVJKlevat6/WLrTb7VzXvw8fzPgIm83Gv4fcyMuTp9C0WTOv1uGr9bs0v+b3Nb+v1u7zB3bQ/Fb+7IP31y58dYn7r2Q90dM3V7IqIiN1h/X9uNMhq2O6rpRSqUqptUBOca9vzhbsBXxtPuVsfV/iNGPR2fq+Uym1y/z6MMbVsxjzeBNM6/tWEZluHt+vsnXLZuLiGtMwLo6Q0FD6XtOfpUsW+bosr0Xza36r8luZHTS/1fkDIRUyl7nD+i4iL4rIQQx1woRyvHwUcNRpaZ7ibPLFWd87YdzadHi43jGt720xbmsOKEctXklqSgp16xVYgWNtNlJSUkrYI7Ci+TW/VfmtzA6a36r8IuL2h6/iNj1sea3vSqnxSqk4DOP7A+6qw6zlFqADMLnQ8/UwFBR3qIKFrnqKyBrT+t6LSmB919HR0dHR0fH/VKiT5Q7ru1PmYFwBK2sygEgRcQzaL2yTvwoYjyE7PeP0fASG7HS8Umq1+VwY8B5wo1LqAmAGcM4KqyIyQkTWlaNGtybWZuNIcoEVODUlBZutqCUbAzOaX/Nbld/K7KD5rcpv6QWi3WR9b+50yIFAYllf3xzftQS40XzK2freHvgAo4PlvNxOKEYn8BOl1NdOh3N0qNLNtQ1vpIh42vheWtq0vYADB/Zz6NBBcs6e5af58+jes5evyvF6NL/mtyq/ldlB81uVX8T9D1+lrGsXVhORjRSyvpvbHgCaARNExDG2qrfZybkPV+u7Y4zUyyLSAkPh8BdwL4CI1AXWYagW8kTkYYyZh8dFZD5wlzlwfQzwuYi8AGzAWDYHjNuDNYCvzHuwB5RS1wE3Ad2AKHONQjC0ERtFZAawFTgCrC3j+fBqgoODGTd+AiNH3EVenp3rB91As2bNS98xQKL5Nb9V+a3MDprf6vyBEC0jLUd8pXDwl/hqGrO/RPNbl99XCgN/iea37mcfvK9weHPFPref7Ie7NvFPGWkgi0hFZD/QQSmVXo5zlv8Dx5vxFyGdL9hB82t+6/L7CztYm9/qn30rRET6Am9h2BNmKqVeLqbdDRgaqY5KqRLHa5flduEppVQ788AOEWkE8AyQDlzrLCKlQKcwDbibAhFpX4zbhZOVUk+bx3sIQ99wL5CJIS69vpR6XsHo5DlEpMPN19qA0WFyiEhfBYYAJzEG5OeLSEXk5/KKSF1OiBbyeTWaX/P7ml/LODW/VT/74P0Opi8GqotIFeBd4GoMNdRaEZmrlNpeqF1NYBRG36bUlGvge6CJSM08ISJbROQPEfHuJ7eMsbqQTvNrfqvyW5kdNL9V+X008L0TsFsptVcpdRbjjtnAItpNxLjYc7osB62I8T2QRKQAx0x9wzvAm+WoxWuxqpDOEc2v+a3Kb2V20PxW5/dyGgAHnb4/p38hIhcDcUqpeWU9qFtkpJVYRArwX6f/X+7OWnR0dHR0dHTKlyDE7Q+H79LpMaI8NYlIEIZV4bHysZQzgSIidYoq5uv8aBmpb6P5Nb9V+a3MDprf6vzujMN36fSYXqhJEhDn9H3hfktNoC2w1Jw0dxkwV0RKdGiWq5MVYCJSR4Y4/f/3Yl5Xy0h9GM2v+a3Kb2V20PxW5ffRmKy1QHMRaWL2G24G5jo2KqWOKaWilVLxSql4jLHf17ljdmHAikjNr2uLyGbgDPCvMpwPr8fqQjrNr/mtym9ldtD8VuX3xexCpVSuiDyAYUmoAsxSSm0TkeeBdUqpuSUfoehoGWk5omWkWsin+a3Jr2Wcmt+q7OB9Gen7v+93+8m+9/J4/5SR6pwbX0np/CFWZgfNr/k1v1VjZXZvJ8iXiw26Ob4wvj+LISlNM1/iSaXUfBGJwjSoAh8rpYqcdSgidYAvgHhgP3CTUipLRIZi3EoU4AQwUim1SUTizJptGAPbpyul3irzGfJRSrP8VqtWjZiYGKpWrUpycjLZ2dm+KNMjsTI7aH7Nb11+K7KXxiwi1K1bl6pVq2K320lOTiY3Nzd/e3BwMPHx8WRkZGgbvB/GF8Z3MDpphZfNOQ08jTF6v20J9YwFFimlXhaRseb3Y4B9QHezw9UPmA5citExfEwptd40tf4pIr8UtriWJwczy+Qgq3Dsdju33XQtr06dTkysjfvu+BfjJ75CfJOm+W2OHE7i77//5qvPPubyrj3o3qu3R2sCiKsTZll20PxW5o+rEwZY+98+wMHMM6W0rHgM9gEme13uu+Nmxk98tQj2bL76bLaXP/tVyfw7t/SG5Yzdbuem669h6rSZxNps3DF0CBNfmkyTpgVe7K+//C97du5gzFPP8stP81m65FdefGVK/vZxox9GBNpccCFDh93p9hoB6lT37k2vALqQ5X3jewnH/lsptZLSLaoDMUzv4Gp8X6WUcnTjnY3vyUqp9ebXJ4AEzI6giNwtImtFZJOIfCMi4WU9F55M4vatNGjYiPoNGhISEkLPq/uyavkSlzZ16zegafPzMdQdgRMrs4Pm1/zW5U/cvsVkjzPZ+xXD3gIJkN/C27duoWFcIxo0jCMkJJSr+/Rj+dLFLm1WLF3MNdcavz57XtWbdX+sxjGWetmSX6nfoIFLpywQEiTi9ofPWMq7gzuM7xidtM0iMktEapezBJupiAA4gnEbsHCKM77HA+0pWHPoW6VUR6XURRidr+HlrMUjSU9LISa2ACsm1kZ6WmoJewROrMwOml/zW5c/PS2VmNgCu7nBHth287TUFGJtzkb3uqQVer/TUlOw1TXaBAcHU6NGTY4dPcrJk3/z6UcfMvye+7xas0754gvj+zSgKdAOSAZer+jrmlfJXGYhiEhPjM7SmELP1wC+AR52Wj+xrYisEJEtGEv8tKloLTo6Ojo6Ot7KzPff5eZbhhEeXt3Xpbg9PvJkeSTlvtH6T43vSqkUp2PNAH4sZwkpIlJPKZVs3pZ0Fo9eCMwE+imlMpyeD8HoYM1RSn3rdKyPgevNAfK3Az2KYR6BcZvUK4mOsZGWWvAXXFpqCtExhS8cBmaszA6aX/Nblz86Jpa01AK7ucEe2HbzmFgbqSnORvcjxBR6v2NibaQcOUKsrS65ublkZ5+gVmQk27ZuZvGvC3nnzdfJPnGCoCAhNLQqg28e6m0MtyeQboT7wvhez+mQg4Ct5ax5LobpHVyN742Ab4FblVI7nWoWDGFpglJqSqFj1QSSzU5YsZ9MbxvfW7ZqQ9LBv0g+fIicnByW/PITnbv28NbL+zRWZgfNr/mty9+yVdtC7AsCnr1Vm7YcPPAXh5MOkZNzll9+XkDXHj1d2nTt3pP5P3wPwJJfF9Kh46WICB/M+g/fz/+V7+f/ypCht3Lb8BEB0cEKtJQqIy1C4fApMMVUODwFjAN2Oe3SWymVaq7n8zEFxvcHzY7Zpxi3ChWGguEexxgrcz2gCAwlxFHzWNtFZCbwvlJqnal6+BJohGGMv0kplWm2ucF8DiBXKdVBRLoAK0wGx8LQDm3ESOAJDJ3EGqCmUur2Es6FAs/PMAJYs2oF777xKnl5dvoNuJ6hd4zgo+nv0qJlazp360ni9q08M+Zhsk8cJyS0KnWiopn13+88WpM3ZpeBf7KD5rcyv7dmF4K/83tudiHAmlXLndgHmezv0KJlGyf2UWSfOEFIaKjJ/r1HawLPzS4EWLViGW+89jJ5eXkMGDiIO+66l+nvvU3L1m3o1qMXZ86c4bmnxrBzRwIREZFMfPk1GjSMcznGjPffITw83KOzC70pI5297qDbZaS3dYjzyU1DbXwvR7zZyfLHeOuXrL9G81uX35udLH+MtzpZ/hpPdrIqQ3Qnq+LRnaxyxNHJ0tHR0dHRsVK82cn6xAOdrGE+6mRZ2vguIkuB0aWtol04vljD6rcVy3nl5RfJs+cx6IbBDL/ba+Pw8+PL9bs0v+b3Jb8v1+7zNTtofit/9sH7SwoF0rI6ZRn4fkop1U4p1Qa4GuiHYXuHAuP7BRiD0D912s9hfG9uPvo6bXvDPGY7pdR88zmH8X10KfU4jO/NgUXm91BgfL8AmIhhfIcC43tr4DLgfhFpXQZuv4ndbmfSi8/z3vsz+W7uPH6a/yN7du/2dVlei+bX/FbltzI7aH6r8wdCLG18N3OriGwUka0i0qnUk+CDbN2ymbi4xjSMiyMkNJS+1/Rn6ZJFvi7La9H8mt+q/FZmB81vVX7xwMNXsbrxHSDcXJvxPmBWOWvxSlJTUqhbz9kKbCMlJbBNyM7R/JrfqvxWZgfNb3X+QIjVje8A/zWPtRyIMN1fFNp3hIiUa9yWjo6Ojo6OTvkTSMb3cney3GF8V0rZlVJ5wAygvLfoUhxC0xKM7wPLaHyHQp20Ir73uoy0cGJtNo4kO1uBU7DZAtuE7BzNr/mtym9ldtD8VuUXEbc/fBWrG98BhpjtugDHlFLHylmPx9Om7QUcOLCfQ4cOknP2LD/Nn0f3nr18XZbXovk1v1X5rcwOmt/q/IGQsqxdWE1ENlLI+G5uewBoBkwQkQnmc73NAfL34Wp8d4yRelVEXIzvjhdyNr6LyPUUYXzHUEZ8KSLDMY3v5u4TgCjgPbPXmmtefboCuBXYYnKAqY0wvz4tIhtMPs/ocv9hgoODGTd+AiNH3EVenp3rB91As2bNfV2W16L5Nb9V+a3MDprfqvyBtHahlpGWIw4Zqa98Kb6OL10x/hDNb11+X3qi/CGa37qffTD4vSkj/WJDkttP9pD2DfxTRgqlCkk7UeCkEuBZpdR35n59gbcwZiPOVEq9bD7/IdDBbL8TuF0plS0i3YA3gQuBm5VSXxdTT3Gi08HAs0AroJNDMlqS6FREspVSNcpyHhzxtpgNIDw8nNhYY0LnsWPHyMrKKmUPz8QX7KD5Nb91+f2FHazNb/XPvjfjyzFU7k6ZOlmYQlIAEYkFPsO4rfcMxpiqDkqpXHO81SYR+QHjduC7GALTQ8BaEZmrlNoOPOKY4SciUzBuO74MHABup3QhqUN0ugajk9UX43bkVuD/gA8KtXeITtuaj38Ub/9FY7fbua5/Hz6Y8RE2m41/D7mRlydPoWmzZl6tw1d/zWl+ze9rfl9dyfEHdtD8Vv7sg/c7mIHTxaqYJ6uwkPSkUsqxcmYYBbPzOgG7lVJ7lVJngc8xRKI4dbAE42qUMp/fr5TaDOQV9/oliU6VUglKqR1F1Fyi6FRE3hCRbSKyyBzc71exqpDOEc2v+a3Kb2V20PxW5w+EVGh8WWEhqYhcKiLbMG4p3mt2uhoAB512cxGSishHGDLRlsDb5Xj50kSn5U11YJ25bNAyCpYM8ptYXUin+TW/VfmtzA6a36r8llU4FBel1Bqzk9IRGCciYWXY5w6gPsYyN0PcUUcFk4ex4DTAf4AuPqxFR0dHR0dHJ0BSoU5WYSGpI0qpBCAbY9xTEhDntDlfSOrU3o5xG/GGcrx8saJTN+WcG+++Nr5bVUjniObX/FbltzI7aH6r8gd54OGrVMT4XlhI2kREgs1tjTFu/+0H1gLNze2hwM3AXDHSzGwvwHVAYllfvyTRaQUTBNxofv1vYGURr+lT47vVhXSaX/Nbld/K7KD5rcofSLcLyzq7sCQhaRdgrIjkYNx6u08plQ4gIg8AP2OM35qllNomIkHAbBGJwJhEsAkYabbviLFET23gWhF5zrwNiYhsdMxwpBjRqYgMwhjfFQPMM/fpY27bTxGiU+BvoJOIPIVxZc6Xty6LjFWFdI5ofs1vVX4rs4Pmtzp/IETLSMsRLSPVQj7Nb01+LePU/FZlB+/LSL/ffMTtJ/v6C+v6r4xUxzW+ktL5Q6zMDppf82t+q8bK7N5OALlIyzYmS0TsIrLRdEltEpHHzNt+iEgnc9tGc9sgp/36isgOEdktImOdnv/QbLtZRL4WkRrm891EZL2I5IrIjedWkr//JSKyxTzuVHNsFiIyWUQSzeN+J8YC1ohIlIgsEZFsEXmn0LGyy3PCfJXw8HDi4+OJj4+ndu3avi7H69H8mt+q/FZmB81vdf7KnjLdLhSnpWekwPj+m1LqGREJB846G98x1AwKY8mcfOM78C9zweeIQsb3VKXUyyISjzFuajQwt4Rldf4AHqLA+D5VKbVARHoDi81aXgFQSo0RkepAe0zje0WX1fHV7UJ/sv5a3Xqs+a3Jr43nmt+qn33w/u3CH7akuP1kX3uBzSfXxwLN+L7QqZbVmKoHbXyv3NH8mt+q/FZmB81vdf5ASCAb3+/EnHVYSrTx3c+j+TW/VfmtzA6a36r8Iu5/+CoBaXwXkfEYqok5ZWiuje86Ojo6Ojp+EvHAf75KwBnfReR2YAAwVFXMT6GN734Wza/5rcpvZXbQ/FbnD4QElPFdRPoCTwDXKaVOlvGQ2vju59H8mt+q/FZmB81vVf5Aul0YUMZ34B2gKvCLaXVYrZS619x/P9r4Ximj+TW/VfmtzA6a3+r8gRBtfC9HtPFdW481vzX5tfFc81uVHbyvcPhpW5rbT3bfNjH+a3wXETvGzEHHlaxPgDeUUnki0gmY7mgKPKuU+s7cry/wFsaVrJlKqZcLHXcqcKeTg6sb8CZwIXBzCZ6sSyi4kjUfGGXeuqyDMYg9HuOW5U1KqSwRGQqMMes7AYxUSm0qC3tR8YX5Nzw8nNjYWACOHTtGVlaW12sA31mPNb/mtyq/v7CDtfmt/tn3ZgLJ+F7W24WnHLfqnGSkERi6g61AB2cZqYj8gDGA/F2cZKQiMte8RYeIdMC4LeicA8DtGDLSkjINuJsCGWlfjFuGY4FFpth0rPn9GGAf0N3scPXD6BReWkb2c0+GFvJ5NZpf8/uaX8s4Nb9VP/uglxT6J/GJjFREqgCTMQapOx/7H8lIzePPNr+eTYGkdJVSytH9z5eUmscbZi7Ds0lEPi3v+fBGrC6k0/ya36r8VmYHzW9V/kAa+O4rGekDGMvmJFfg5UuSkdqcjnkEKGqu63DMgfIi0gZ4CuillLoIGFWBejweqwrpHNH8mt+q/FZmB81vdf5ASFlvF5YYpdQaoI2ItMKYOVisaV1E6gODgR7ueO0SalKOgepOr90To5PlEI72Ar5yzIZUSmV6siYdHR0dHR2dkuNLeai74wsZaXugGbDb1CqEi8jucrx8STLSFPN2ouO2Yn59InIhMBMYqJTKKMfraRmpj6P5Nb9V+a3MDprfqvxB4v6Hz1jKu8M/lZEqpeYppeoqpeKVUvHASaVUmUfxlSQjBeYCt5lf30aBpLQR8C1wq1Jqp9PhFgODRSTKbFenmNfUMlIfRvNrfqvyW5kdNL/V+QMhXpeRlvQibpCRvgx8KSLDgb+Am8znJwBRwHumpDRXKdXBlKO+CCwzNRUbMGY3+lWsLqTT/JrfqvxWZgfNb1X+QLpdqGWk5YiWkWohn+a3Jr+WcWp+q7KD92WkixMz3H6ye7WM8l8ZqY5rrOwMsTI7aH7Nr/mtGiuzezuWk5FWIuP7YOBZoBXQSSm1zmwfBXwNdAQ+Vko9UBZub6Y0q29kZCS1atUCDEHdkSNHyM019GTR0dFUr14dgIyMDLKzs71YuXvyT/gBgoKCaNy4MX///TepqS7zMSpFSuOvVq0aMTExVK1aleTkZJf3uHnz5pw5cwaA3NxcDh8+7L3C3ZTS+EWEunXrUrVqVex2O8nJyeTm5hIcHEx8fDxnz54F4PTp05Xy/S8pVjR+O0fzW48/kG4XBprxfSvwf8AHhdqfBp7GmPXYtozMxebn7e79IZ5ntzPxvn9x/3NvEBkVy+TH7+L2x56lXlyT/DY7t6wn/vzWhFYNY8WC79i1dQN3Pv48W9etYukPXzJywmvk5uQw9akHeeD5t6gWXt2tNQL0aR3Lmj1H3X7cPLudx++6kTGT3qFOdCwTRt3G/WNeoEHj8/LbbN+0jqYt2lI1LIxff/yaxC3reWDcpPztn77/OieOZVG9Zi1uu+9xt9cIcGnTSHYc+dvtx7Xb7dw79HomTplGVIyNR0cM5fFnXqJRfNP8NinJhzl5MpvvPv+ES6/ozhU9rs7fNrhPZ35csdHtdRVOi7rVycjOLb1hOWO32xky6Breem8msTYbd94yhOdfmkyT8wrmw3zz5X/ZvWsHY8Y/yy8/z2fZ4l954ZUpJB9OYvSokcz5aq7b63JOVA3jR6WVjedgbX5tfNepSALK+K6USlBK7Sii5r+VUisxOluFj9dXRNabxnefqHT/2pVAdL2GRNdtQHBICJd0uYota1a6tDn/gosJrRoGQHyLNhzNSAPgyMH9NGvdjipVgqkaVo36jZuSsH611xn+Sfbs3IatfkNi6xn8l3XvzZ+rl7u0aX1RB6qGGfzNWl5AZnpBR3ffrgSOZWXS9uLLvFq3u7IrYSv1GsRRt35DQkJC6HZlH9asXOrSxlavPk2ano9Ihawrfp3tW7fQsGEjGjSMIyQklKv69GP50sUubVYsXcw1A4yFHXpe2Zt1a1djhfGkVjV+O6L5rclvaYUD+LXxvVwxdRQzgBtM4/vgihznn+ZoZhq1o2Pzv4+MiuFoZlqx7X//9UdaX2wsvdggvhnbN6zh7JnTZB8/yq6t68lKr1y3S7LS06gTU+B+qRMdS1ZG8fzLFs7lwg6XA5CXl8dnM97i33c95PE6PZWM9FSiYwv4o2JsZKQVz184Z8+e5ZG7/83oe4fx+4olnijRo0lLSyG2rpPVOrYuaYVu+aWlpWAz2wQHB1OjRk2OHTWuqh5OSmLYv/6PkXcNY+N6n+nsPBKrG781vzX5xQP/+SoBa3wvYy4Dliul9kHlML6vXfozB3cn8tCL7wDQqn0nDuxOYMqYe6lRK5ImLdoSFFTFx1V6Lr8tXsC+nQmMf/V9ABb9+DUXdezs0kmzWmZ9OZ+omFiOHD7E+IdHEH9eM+o1iCt9xwBIVHQM389fRK3ISBK3b2PMYw/y2VdzqV6jhq9L09HR0alYJ6sk47uIlMf4DqbxvRxC0pKM7x6JiIzAuEXqkUTWiXG5+nQ0I43IOjHntEv0QdkLAAAgAElEQVTctJafv/6EUS+8Q0hIaP7zfQbfRp/BhoP149efJbZ+5foFWzs6hsy0gr/OMtNTqR11Lv/WDX8w9/OPePLV9wkJNfh3JWxh57aNLPrxG06fPkluTi5hYdUYcqffzW0oNlHRsaSnFvBnpKUQFXMuf7H7xxhXQevWb0jbdh3YuyuxUnWyYmJspB5xslqnHiEmNvacNilHjhBrq0tubi7Z2SeoFRmJiBBqfhZatm5Dg4ZxHDiwn1at//HQS7+IVY3fjmh+a/IH0uzCQDO+lzergW4i0sSs3yfG90bNW5KWfJD0lMPk5uTw58pfuaDTFS5tDu7dyRfvTWbEky9TM7JgvkCe3c7fx48BkLR/N4f/2kPL9h09VapHct75rTly+CCpR5LIzclh9bKFXHxZV5c2+3fv4KOpL/HIM69RK7LgbbpvzETe/OQH3pj9P/511yi6XHVNpepgATRv2YbDhw5w5HASOTk5LF/0M52u6FGmfbNPHCfHnFl37GgWCVs2Ehd/Xil7+VdatWnLwYN/cTjpEDk5Z/n15wV07d7TpU2X7j2Z/+P3ACxZtJBLOl6KiJCVlYndbgcg6dBBDh74i/oNGp7zGpU1Vjd+a35r8wdCAsr4LiKDgLeBGGCeuU8fc9t+jBmRoSJyPdBbKbXdvEr1rRgjilMxZkN6NVWqBDP47kd577lHUfY8LruqP/Uance8z2bSqFlLLujUhe8/fpczp08x69WnAagdY+Oe8a9gt+fy5pP3AxAWHs6whydQpUrl0p9VqRLMsJGPM/mph8iz59Gt97U0bNyUbz75gCbnt+Liy7rx+YdTOX36FG9PGgdAVExdHn32dR9X7p5UCQ7m3ofH8Mzo+8jLy+OqawbSuElT/vPhezRv0ZpLu/RgZ8I2Jj31KNknjrN21XLmzHqf9z75hoP79/Luay8iQYLKU9w49A6XWYmVIcHBwTw2ZjwP3383eXl5DLhuEOc1bc70aW/TqnUbunbvxbXX38BzT4/hxuv6EFErkokvvQbAxvXrmDHtbYKDg5GgIJ548hlq1Yr0MZH7YlXjtyOa35r8AXQhSxvfyxOH8d3dCofKEk8pHCpLPKVwqCzxlMKhMsRXCgd/iTa+a+O7N43vv+8+6vaTfXmzSJ/03crUyQoAGenVGOsahgJngceVUouLOHRp58Fj/8oqKmMMCgqifv36hIWFcfz48YATMTpiRSGfczS/dfmtzA6a31/4dSerYrGKjDQduFYpdVhE2mLcwqyQ9gHg5Fn3vv92u52B/fvw/oyPsNW1MXTIjbw0eQpNmxYMVfvi8zns2rGDp555np/mz2Pxol949fU3OXXyJImJ29m9axe7d+9i3PgJbq3NOeGh4nb2sqQs58cbCQ/VQkKr8msZp+a36mcfvC8jDaTbhVaRkW5QSjnWGtmGMcasqnk8n8tIt27ZTFwjUzgXEkqffv1Zuti1lKWLF3PtwEEAXNW7D3+s+R2lFNXCw2l/cQdCq1b1ReleSVnOTyDHqkJCR6zMb2V20PxW5w+EWFFGegOwXil1xl9kpKmpKdR1kjHabDZSU1OKaFMPKJAxHj1qjcvmZTk/gRyrCgkdsTK/ldlB81uWXzzw8FEsJSMVkTbAK0Bv86lKJyPV0dHR0dEJ5ATSAtEVupJVkowUKI+MdD+mjLQcL18hGamINMTQQwxTSu0px+shIiNExGPrdcTG2jjiJGNMSUkhNtZWRBvjwp9DxhgZWXhIW2CmLOcnkGNVIaEjVua3MjtofqvzB0IsISMVkUhgHjBWKfWb0ya/kJE6hHNJhw6Sk3OWnxecK5zr3rMXP/zvOwB+XfgzHS+9DAkkLW4JKcv5CeRYXUhoZX4rs4Pmtyq/iPsfvopVZKQPYFw9myAijul3vZVSqf4gIw0ODmbskxMYec9d5NntDDSFc++98xat27SlR88rGfR/NzJ+3ONc2+9qImrV4pXJb+Tv3693L/7OziYnJ4cli39l2vRZXp9558kUd36sEqsKCR2xMr+V2UHzW50/EKJlpOWIw5PlC42BP8RXCgd/ia8UDv4SKwsZtYxT81uVHbwvI12795jbT3bH82r5tSdLxynhoda4TVdUrMwO3vfF+Fs0v+a3aqzM7vUE0Kku05gsEbGLyEYR2Wb6pB4zb68hIp3MbRvNbYOc9usrIjtEZLeIjC3iuFNFJNvp+26msypXRG4soZ5LRGSLedyp5tgsRGSiiGw2a1lozmRERIaaz28RkVUiclHZT5F30qdPHxITE9m1axdjxow5Z3ujRo349ddf2bRpE0uWLKFBA8Na0aNHDzZs2JD/OHXqFAMHDvR2+f84ml/zV4QfIC4ujp9//pnt27ezbds2Gjdu7M3S/3H+CXtubm7+e/+//5U4NNVvUxp/XFwcixcvZv369WzatIl+/foB0LhxY06ePJnPP23aNG+X7vaEh4cTHx9PfHw8tWufO7EpMjKSxo0b07hxYxo2bEhwsL5O4u8p67I62U5L3ziM778ppZ4RkXDgrLPxHaiPISXdiZPxHfhXIeP7KGCQ07HjMUzyozEGyRe3rM4fwEMUGN+nKqUWiEiEUuq42eYhoLVS6l4R6QwkKKWyRKQfxtI/l5b7ZJm3C8M6lSakL1+CgoQtX42h/4PTSUo9xsqPH+K2p+eQuK9g8uacSbcwf2UCc+b/SfdLmjLs2o4Mf/Zzl+PUjqjG1q/H0uzaFzh1JsetNQKc/uM1wi59ovSG5UxQkLDly8fp/9BMg/+jB7jt6f+SuN+J/8WhzP8tgTnz1xv8Azow/LkvXI5TO6IaW796gmbXTfIM/5pXCbvs3F8C/zRBQcKWL0bTf9SHBv+sB7htQmH+fzP/t8QC/v6XMPz5L12OY/A/TrPrXvIM/+pXCOv8pNuPGxQkbPn8Efo//BFJqcdZOXMktz37BYn70/LbzJl4M/NX7WDOgg10v/g8hvW/mOETjR8PP789nFc+WcritXuoXi2UvDzldv7TqyYBENblabceNyhI2PLZKPo/MpuktOOsnHEPtz33lSv78zcxf9VO5vy0ke4XN2HYNe0Z/sK3AKT9PJ6YPi+6taaicnrlRABq3PSxW48bJMKGNwcx8MWFJGWcZNlL/bnjreXsSDqW32bq3ZezaX8mH/6ygxYNavHN2Kto++A3NIqpzldjruTS0XPdWlNRyf7ydjb+ddyjr5FntzPqjv/jqVfeJSraxrgHhjHqyRdp2Pi8/DZbN66jecu2VA0LY+EPX7Nt05888tRLHq0LoF3jCK/eLly377jbbxd2aBLhk+tjgWZ8d/5XUN1Ri1JqlVLKYe5cjZMCQkSGmVe5NonIp+U9H+5Ix9aN2HMonf2HM8nJtfPVLxsZ0K2NS5uWTWwsW2eYLpb9ueec7QCDel3Iwt8TPfIL1pPp2DqOPYcynPg3MaBba5c2Br9h3jD4W59znEE9L2Dh6h2Vn//XIvjjy8j/eyXkb9WQPYcy2X84y+BftJkBXVu5tGnZJJZlf+4FYNn6vfnbW8bHEFwliMVrjXPz96mzlYq/Y6uG7EnKZH+yg30LA7q0dGnTMj6WZesd7PvO2V6Z06FZNHtTjrM/NZscex7frNrHgI5xLm0UiohqIQDUCg/lSNZJX5Tq8ezesY269eOw1WtIcEgInXv0Zu2qZS5t2rbrQNWwMACat2pLZlpgikkDaXZhwBnfReRFETkIDAWKWshvOAWzEdsATwG9TOP7qArU849TPzaCQylH879PSj1Gg5haLm227EpmYM8LABjYoy0R1cOoExHu0mbw1e34cuFGzxfs5tSPqcWh1NL4DzOwR1sABvZoUwz/RZWUP4JDqQV/uRv8ES5ttuxOLuDvXgz/VRfx5S+bPF+wm3Mu//Ei3v8jDOxudCwHdm9t8lejeVw0R7NP8/mkf/P7R/cz6f6+BAVVngEd9WNqurKnHadBdOH3/ggDzU71wG6t8tkBwkKDWTnjHpa9fzfXdq18na96dcJJyvg7//ukjJPUq13dpc2krzYxpOt5JL53I1+PvZLRH63J39Y4pgYrXx7Agmf60LllrNfq9kQy01OJiilwYEVFx5KZnlps+8UL/ke7Tp29UZrOP0iFOlmFo5RaY6oWOgLjRCSsuLZOxve33fHaRdQyXikVB8zB6Mw5v3ZPjE6W455PL+Arh3LCn43v46b+SNf25/H7Jw/T9eLzSEo9ij2v4IJf3aiatGlal19Wn7N0Y0Bk3Nvz6Hrxefw++yG6tj+PpNRjxfDv9GGVnsu4t+fRtX2TUvhtgcv/7gKD/6P76dquicmvCK4SxBUXxTP2nQV0uWsaTerX5tZrLvZ1uW7NuHd/pmu7eH7/cCRd28XnswO0GDyFLnd/wG3Pfc3kB/vRpH7gCYoHX9GEOct20/K+r7nx5UXMeKArInAk6xSt7/+GLmN/ZNwna/nwwW7UNK94BXqW/zqfvTsTuG7wMF+X4pEE0Ko6FZtdWJLx3RzIXlbjO5jG93IISctqfJ+DMV7rGbPmC4GZQD+lVEYZXwtz3xEYt0g9ksOpx2loi8z/vkFsLZLSjrm0SU4/zs1jPwGgerVQru95AceyT+dvv+Gqi5i7bCu59mLvtPptDqcdo2FsafwnuHmscTe3SP4rL2Tusm2VlP84DWMLrtwY/K7jP5LTT3DzuP8ADv62AcwfUfT7/+RngMnfow3Hsk+TlHaczbuS2X/YGA0wd3kCndrEMZs/vQfwD3I47YQre0wESemF3vuME9z8lDH+snq1UK7v3jr/vT+cfgKA/clZLN+4n3bn12Pf4cqzpmly5kkaRBVcuWoQFU5y1t8ubYb1bM6gl34B4I9daVQNqUJUzTDSj58mM/sMABv3ZbIv5QTN6kWwYW+5frz7TepEx5LhdPsvIz2VOtHnXp3bvH4N3302i2dfn05IaKg3S9SpQALK+C4izpa2gUCi+Xwj4FvgVqWU85/6i4HBIhJltvOJ8X1dwkGaxUXTuF5tQoKrMPjqdsxbvt2lTVSt8HzD++O39WL2D2tdtt/Uu3LeKgRYl3CIZnFRTvwXMW9FgksbV/6egc1/1UXMW1HC+z+sB7N/dF3l6aarK+etQoB1iUk0a+jEf+WFzFuZ6NLGhf/W7syeZ3Si1iUcolaNMKIjjVunPS45z2XCgL/HYK9D43qRJvsFJbPf0pXZ8zcAEFkjjNCQKvltLm/biASnAfOVIX/uSadp3Qgax9QgpEoQN3Ruwrx1h1zaHEzPpkfbegC0aFCLsJAqpB8/TXTNqgSZ5yU+tgZN60WwP+WE1xnclaYtWpOcdJDU5CRyc3JYtXQhHS7v5tJm3+5EZrw5iSeen0Kt2kX+ugqMBNClrIAyvgMvi0gLs46/gHvN5ycAUcB75g+rXKVUB6XUNhF5EVgmInZgA3B7Gc+J22K35/HIa9/zw9S7qRIUxOwf/iBhXwpPj+jN+oRDzFuxnW6XNOX5+/qhFKzcsJeHJ3+Xv3+jerVpGBvJCnNwbGWLwf8/fnhruMH/41qD/+6rWZ94iHkrEuh2cVOev68vSilWbtzHw5O/z9/f4K/Fig37fEhR8djteTzy+lx+ePNOk38dCftSDf6EQ8xbmUC3i8/j+ZEO/v08/JoTf93aNLRVcv43fuCHKbdTpYow+8f1Bv9dV7I+MYl5KxPp1r4Jz9/b2/j8b9rPw68bM8ry8hTj3l3A/LeGIwIbdhxm1lyPLTPq9hjs8/jh9WHGez9vPQn703h6eC+D/bcddGsfz/MjrkahWLnpLx6e8iNgDPp/e/R15ClFkAivzVnhMiuxMsSepxg9aw3fP3kVQUFBfLp0F4mHjjJ+cDs27M1g/p8HefLTdbxzT2fu798apeDeacbKaJ1b2Xjqpvbk2PPIU4qHZ/xO1t9nfUxU8VSpEsydDzzOi+MeJC/PTs8+1xEX35QvPn6fpue3okPn7vxn+lROnzrFlImGESk61saYiW+UcuTKl0BaIFob38sRTykcKks8pXCoLPGUwqGyxFMKh8oQTykcKks8pXCoLPGGwsGf422Fw4a/Tri9Y9K+cc1S6xeRvsBbGBeGZiqlXi60/VHgLoyLTWnAnUqpv0o8Zhk9WXaMmYOOK1mfAG8opfJEpBMw3dEUw0H1XRkLnmoW6fBkdQPeBC4Ebi7Bk/Uixm3C2o59zeermrVdAmQAQ5RS+0uqsTxxdLJ8kfDwcGJjjfvzx44dIyur8oy7cEc0v+a3Kr+V2UHz+wu/NztZGw+4v5PVrlHJnSxTLVWs29Ns0xNYo5Q6KSIjgR5KqSElHbestwtPOW7VOclIIzAGlW8FOjjLSEXkBwxH1bvOBYvI3EIy0sJTYQ5g3K4r7VLRD8A7wK5Czw8HspRSzUTkZuAVYEhxNTr5vcoVb69hZbfbua5/Hz6Y8RE2m41/D7mRlydPoWkz7y4C7av1uzS/5vc1v6/W7vMHdtD8Vv7sg2WWFMp3ewKIiMPtmd/JUkotcWq/GriltINWOhmp2W51MY6tgcBs8+uvgStLqdEvZKSlZeuWzcTFNaZhXBwhoaH0vaY/S5cs8nVZXovm1/xW5bcyO2h+q/L7aNx7SW7PopLv3CwplVFGWlLyX9Os4RjGgPcia/QXGWlpSU1JoW69uvnfx9pspKQEpum3qGh+zW9Vfiuzg+a3LL8HelkiMkJE1jk9KqxlEpFbgA4YF4tKjFtWl1RKrQHaiEgrYLaIFNu7c5KR9nDHa5c1xdRYaWSkOjo6Ojo6OhWLUmo6BWOzi0pxbk+XiMhVwHigu1LqTGmvW6ErWSXJSIHyyEj3Y8pIK1JHEcl/TdPdVQtjAHxxNZYpjh6wm2osd2JtNo4kH8n/PjUlBZvNVsIegRXNr/mtym9ldtD8VuUXD/xXhhTp9nSpS6Q98AFwnTl0qtRUOhlpKZkL3GZ+fSOwuJQa/UJGWlratL2AAwf2c+jQQXLOnuWn+fPo3rOXr8rxejS/5rcqv5XZQfNbnd+bMYcYOdyeCcCXpkvzeRG5zmw2GagBfCUiG0VkbjGHy0+llJGKyKvAvzGugh3C0EM8C3wIfGpeGcvE6NiVVGO6P8hIS0twcDDjxk9g5Ii7yMuzc/2gG2jWrHnpOwZINL/mtyq/ldlB81uVX3w0mVEpNR9jOT7n5yY4fX1VeY+pZaTliMOT5YupvP4QX01j9pdofuvy+0ph4C/R/Nb97IPB701P1tZD2W4/2W0b1vBJ180tA9+tFos4Q4qMldlB82t+zW/VWJldp+KpyJgsu3kvcpvplnpMRILMbZ3MbRvNbYOc9usrIjtEZLeIjHV6/mMR2ee0n+OW4FDTX7VFRFaJyP+3d95xcpXVG/8+yYaQJaQ3SmCBIKGjgChIVZDeq0hRinRQUEKLkaII2AClKgj+EAQBkd57MYQWQu8tDUgCgdTN+f1x3kkmy+5my8zc3bnnm89+MnPvO3fe504797znfd61m+jPCpKeSse9LtV/IWkTSc9Imitp96L2y6ftBQ2HFe17R9KA1p6TSlBbW0tdXR11dXX07dvQw7X6Cf2hP6/686wdQn8u9edwgehiSu7+Dvy8kSV03sanSE6RtA0+9XKDRvrzW3yJn2slXYwbhF1E0+7x44Fvm9ksST2BF1NfPmrxCQjX44oS+kN/1vrD8Tz05/W9D5HFaw9tsnAoUAr392aO/biZFRZpehK3gFgIScK9rgoB2t+BndPjG3WPN7PZRd4W3WnnOagEeXX9LRD6Q39e9edZO4T+vOrPyMKhLLQ7wCiB+zvAWWlo8A/yRZ4b0pR9fX9galFgtygbfFIfh0p6IfXpt63JYmVBbl1/E6E/9OdVf561Q+jPq36p9H9ZUfIsjpk9lWwX1gdOkrT4Ih5yEu5btT7QDzixeKd81euDGm5vZx/fN7O1cFPUAyRVv7tbEARBEAQVpd1BVjvd3zGz8ebMAq7AhxYLx14LuBzYycwWcm5PfAL0KRiN0oQNflOkDNaLwMaL0BiO7xkS+kN/XvXnWTuE/rzqr6K69/YFWe11f0/tlkr/C6+nejHdXw64EdjPzF5r7PnNTb4ewN3dwd3e/7OIPi8rqUe63Rc3Kn21uceE43u2hP7Qn1f9edYOoT+3+qsoymrL7MJSu7//XwrWBDwHFCwVRuI1V3/x+Iu5hUBH0u3AwSkTdSJwraQzccf2v6Y2TbnHrwr8LhmLCjjPzMa24TxUjLy6/hYI/aE/r/rzrB1Cf971VwPh+N4KwvE9XI9Dfz71h+N56M+rdqi84/sr478s+ckevlRtOL53FvLsGZJn7RD6Q3/ozyt51h60nRYFWWnx5LEsGCK8CjcAnSfpm7hRKPjw2ygzuyk9bmvgT/gQ4eVmdnbaLuBMYA+8aP4iMztf0s+BfYv6tiow0Mw+bdCfFXCvrf7AGLxua7akTYA/AmsBexcbnEq6E/gW8KiZbd/SE9QYWVzRPPbIw/z27LOYVz+PXXbbg4MOObTifcjyai70h/4s9WeZyclaO4T+PL/3ofIBZpaWC6WmpYXvM8xsnVTTtCWwDe7wDgtc3tcBtgYukVQjqSvu8r4NsBqwj6TV0mMOxGcbDjezVfGACTM7Nz3POri1w0MNA6xEweV9GDAFt3iABS7v1zTymHOB/Vqot0NRX1/Pr886nb9cfDk33XIbd95+K2++8UbW3aoYoT/051V/nrVD6M+r/iqqe2/97MISubwfDpxuZvOKjtmQfYB/NtzYFpf3tO8+4PNGjjdS0mhJL0q6NB2/Q5FX198CoT/051V/nrVD6M+7/mqgTRYOJXB5XwnYS9LTku6QtNB0CUm1eFbs3408fZtc3pvhQjNb38zWAHoA7RpKLAd5df0tEPpDf17151k7hP7c6q+iVFZJHN/b4PLeHZiZLBkuA/7WYP8OwGNNDBWWms0lPSVpLJ4hW70CzxkEQRAEQZXTpiCrvS7vePbpxnT7JrxQvZi9aWSoMNEul/diUjD4F2B3M1sTD/i+EiCG43u2hP7Qn1f9edYOoT+v+nO9QHQpXN6Bm4HN0+1NgdeKjt87bWvUub0tLu/NUAioPpbUs+iYDZ8zHN8zJPSH/rzqz7N2CP151V9NC0S31Cer1C7vZ+NO7z/FM18HFz3XLsDdZvZFcQfa6fKOpEfwALCnpA+Ag8zsLkmX4TMkJ+CBYYcj766/oT/051V/nrVD6M+7/mogHN9bQTi+h+tx6M+n/nA8D/151Q6Vd3x/c9KMkp/slQb16LiO782ZkRa1WQ54CTcjPS9ta8qM9CjgOHyW4cCizNdw4ArgG8ApheM00p+mzEh/hmfF5gKTgR+b2buS1gEuAnrhtWRnmdl1LTtFX6Ucxmy1tbUMGjQIgGnTpjFlypSF9vfu3Zs+ffpgZpgZEydOZPbs2dTU1FBXV8fs2bMBmDlzJpMmNeaIURrKZUoX+kN/XvV3Fu3QsfTX1tYyYMAAJGFmTJ48mRkzZpS8fwWyeu9LYsiQIXTv3p36+nrGjx/P3Lk+uX7w4MEsvrhXvUyaNKms+oO20dLhwhnJIBRJg3Czz14sMCQFHz68o3CnyIx0S7zQfbSkW8zsJeAx4FbgwQbP8ylwDMn3qhkKZqTXSroYNyO9CB86XM/MvpR0OHAOsBfwJbC/mb0uaWlgjKS7zGxqC/UvxP+NeX/RjVrBvHn1XPjTA9jv5HPo1X8gl51yBLsdfQoDl62b32bWl1/QvXYJAF59+nFG33MLPzzpbKZOnsA155zCEef+taR9aox91x1acu0Q+kN/x9e/77o+hyfPn32Am18YX9Ljzquv55xjfsghI39H734DuWDET/jBcSMZPLRufpuZX37B4kn/uNGP8cRdN3Pwqefy4Vuv0bNPP3r3G8CE997i8jN/zqmXNub60352XmspPpgyq+THra+vZ/89tufcCy5l4KAhHH7g3px6xjnUrbjS/Db/ueFa3nrjNX46YiT3330Hjz50HyPPOo+br/8nr748jhNHnsmUTz9hxHGHc9GV19KlS0lMAxZi2b7dS37MZulwbpVtp91mpACSdgbeBsYVNW3SjNTMnjWzdxo7tpmNBuY09fyLMCN9wMy+TNufxGceYmavmdnr6fZH+KzIgel4mZuRfvjGK/Qbsgx9By9N15purP7tzXnl6ccXalP4kgWYPWtmVS07EPpDf17151k7wPtvvMyAIcvQf/DS1HTrxtobbcG40Y8u1GbxhfTPmP/7u8yKX6N3vwEADB66AnNmz2LunNmV6npJeOWlsSyz7HIsvcxQunXrxhZbbsPjDz+wUJvHHn6ArbbbEYBNt9iSZ0Y/hZnx7ttv8vX1NgCgb7/+9FyyF6++PO4rz9EZqabZhW1aINrM3kqZqkGSvsAL0bcETihq1pgZ6QZt7WgRLTUjPYiizFqBtNbiYsCbadOFZnZ62nc1bkb63xL0s8V8PuVjevUfOP9+r/4D+fCNl7/S7n9338yTt91A/dy57H/qgpHUqZMncMmIn9C9Ry2b7/Ujlh/e0BGjYxP6Q39e9edZO8C0Tz+m94BB8+/37j+Q91//qv7H77iJh2/9F/Vz53DoqD9+Zf/YJx9imRW+Rk23xcra31Lz8aRJDBq8wGx0wKDBvDzuhYXbTJ7EoEHepmtNDUv07Mln06ay0sqr8PgjD/DdrbZh0qQJvPbKS0yeOIFVV1+zohqC5mlTkNWAUfjQ3fSOsiKNpB8C6+FWEMXbl8JnRh5QVE+2uaRfALVAPzwbV9Egq6V8c6ud+eZWOzP2sft45KZ/sPMRI+jZpx/HXXANtUv25qO3XuO6343kiHP/utDVb7UQ+kN/XvXnWTvAhtvswobb7MKzj9zD/TdcxV5Hnzx/34T33+b2f1zCIac1WsJbtWyzwy68+85bHHbg3gweshSrr7k2XbqWfqgwCzpIKFESSmFGugFwjqR38GL2k1Nhe3NmpO2hWTNSSd8DTgF2NLNZRdt7AbfhBVKlkEUAACAASURBVPVPpm0dwox0yb4D+OyTyfPvf/bJZJbsO6DJ9msUDSnUdFuM2iV7A7D0il+j7+Cl+WT8B+XqalkI/aE/r/rzrB2gd78BTPt4QbH+tE8m06tf0/rX3ui7Cw0nTv1kEledcyp7H30y/Ye0Z3W1bBgwaBCTJi4wG/140kQGDlzYbHTAwEFMmuRt6ufO5Yvp0+nVuw9da2o48qcnctk/buDM8y5g+vTPWbaoli3oGLTbjNTMNjazOjOrA/4I/NrMLqR5M9I205wZqaSvA5fgAdb8T256/puAq8zshqLDdQgz0mVWGs4nEz5kyqTx1M+dw7gnHmCVdTdcqE3xl+drzz5Jv/SF8sVnU5k3rx6AKRM/4tMJH9B38FLl6mpZCP2hP6/686wdYNlhw/l4/Ad8OnE8c+fM4fnH7me19TdaqM3kIv2vPPME/YcsC8CMLz7nil+PYJt9f0Ld8M45RDZ81TX48P13Gf/RB8yZM4f777mDb2+y2UJtNtx4M+6+zX86H7r/Hr6+3jeRxMyZM5gxw0uQn37qcbp27bpQwXxnpoqWLiyJGWmjmNncpsxIJR0D/AIYArwg6XYzO1jSEOBpfObiPEnHAauZ2WctMSMFzgV6Atenocv3zGxHYE9gE6C/pANT2wPN7LmOYEbapWtXtj3waP7xmxOxefNYZ7NtGDS0jgeuv4KlV1iFVdbbkP/dfTNvj32GLjU19FiiJzsffiIA7778Ag9efyVdamqQxHYHHUePnr2ykNFmQn/oz6v+PGsH6Nq1hp0OPo7LzzyBefPmsf4W2zJk6Arcde1fWXal4ay+/kY8fseNvPHCmPn69zr6JMDrtD6e8CH33vB37r3h7wAcctp59OzdN0tJraJrTQ1Hn3AyJx5zGPXz6tlmh11YYcVhXHHJhXxt1dXZaJPN2XbHXfn1qJP44W7bsmSv3px25jkATP30U35x7GF06SIGDBzESaN+k7GaElJFw4VhRtoKCmak5ZjG3hko1xT+zkLoz6/+clk4dBbKZeHQWSiXhUNnYdm+3StqRvrOJzNLHpjU9V88k9AtgqxWUAiygiAIgiBPVDLIeveTWSX/rV2+f/dMgqwW1WRJqpf0nKRxkp6XdLykLg3aLCdpuqQTirZtLelVSW9IGlG0/ai0zSQNKNo+XNITkmYVH6eR/qwg6al0jOtSzRWSDpM0NvX1UUmrpe1bShqT9o2R1OFW2KytraWuro66ujr69v1qulsSSy21FHV1dQwdOpSamoVHemtqahg2bFijj60GFnV+qp3Qn1/9edYOoT+P+qtpgegWZbIkTTeznul2wfH9MTP7ZVGbGwADnjKz85KP1msUOb4D+5jZS6lAfQru+L5e0bI6g4DlcXPRKc0sq/Mv4MYix/fnzewiSb3M7LPUZkd8seqt0/NNNLOPJK0B3GVmrZ6KUshkTfly7qKator6+nr22GlbLrj4cgYNHsyB++7FGb85lxVXGja/zQ3X/ZM3Xn+VEaeO4u47b+eh++/lrHMWlMWNOOE4BKy+5lr88IAfl7R/BfrW1mSyfld9fT07bvd9LrnsCgYPHswP9tqds8/9PSsNG7boB5eQrNYvC/3Z689q7b6OoB1Cf57f+1D5tQvf+7T0mazl+nXgTFYxHdzx/bOipkvgQV/h+T5K28fhhfzd0/EukvR0ytL9quVnonS89OJYlh26HMssO5Ru3RZjy+9vw8MP3r9Qm4cfvJ/tdvDVhrb43laM/t+TFALkh+6/l6WXXmahoKyaeHHsCwwdujzLDh1Kt8UWY+ttt+PBB+7LulsVI/TnV3+etUPoz6v+appd2CafLDN7C58xOChZH5wINAxQGnN8L4WRSbOO75KOlPQmvm7hMY08fjfgmSIPrVOSPcNawKaSKm6ZPGnSRAYPWeD6O2jwECY3WOh18qSJDEptampq6NlzSaZNncqXX37BVVf+lYMPO6Kifa4kkyZOZMhSxednMBMnTsywR5Ul9OdXf561Q+jPu/5qoBT2sKNIju8lOFa7MbM/m9lKeOB3avE+Savji0v/pGjznpKewa0gVgdWq1RfS8FlF/+Zffbdn9oqdHkOgiAI8kc11WS1aVmdRhzfd5d0DtAH97eaCYyhzI7vKZvV1HGvBS4q6vOyuCHp/mb2Ztq2Ar7e4vpmNkXSlTTh+I4PkZaFQYMGM3HCAtffSRMnMHDQoIXaDBw0mEkTJjB48BDmzp3L9Omf07tPH8aNfYEH7rmbC//4Oz7//HO6dBHdu3dnj733LVd3K86gwYOZML74/Exk8ODBzTyiugj9+dWfZ+0Q+vOrv3qMsqrN8X3loqbbAa+n7X3wJXVGmNljRW16AV8A0yQNBrZp4jnL6vi+6upr8P577/LRhx8wZ85s7rnrDjbZdPOF2my86ebc9t+bAbj/3rtZb/0NkMSlV/yDm++4l5vvuJe9992PAw46tKoCLIDV11iT9957hw8+eJ85s2dz5+23senmHW6CaNkI/fnVn2ftEPrzrr8aqDbH96PS2oVz8NmLBxS2A8OAkZJGpm1bmdnzkp4FXsHrx4oDsIpRU1PDCSNO4ZjDD2HevHnssNMurDhsZS75ywWsutrqbLLZFuy4y26MOuVEdtvh+/Tq1Yczf5ufxVBramo46ZSRHH7owcybV8/Ou+zGsGErL/qBVULoz6/+PGuH0J9X/dW0QHSYkbaCclk4dBaysnDoKGQ1jbujkGf9WVkYdBRCf37f+1B5C4cPp84u+cleps9iHdPxXVI9MJYFWayr8EL3eZLqgJeBV1PzJ83ssPS4dYErgR7A7cCxZmaS9sCL5VcFvmlmT6f2/XFbhvWBK83sqCb60w+4DqgD3gH2TPVUw4ErgG/gMwbPK3rM34DtgUlmtkbLTk2jz53Zp6y2tpZBqU5r2rRpTJkyJauuZELoD/151Z9n7RD6O4r+SgZZH5UhyFo6oyCrJcOFM8xsHVjIiLQXUDAifbOwvwEXAYcAT+FB1tbAHfhizLsClzRoPxM4DVgj/TXFCOA+Mzs7uciPwIcPP8UtG3Zu5DFXAhfiAWK7CUO+yhL6Q3/W+sOMM/Tn9b0PC17/SlFNw4WtKnxvzIi0MSQtBfQysydTofpVLDAMfdnMXm34GDP7wswexYOt5tgJNyCFhY1ImzQyNbOH8SCsYT8PkTQ6LRX0b0m1i3juTMirIV2B0B/686o/z9oh9OddfzXQFsf3+UakadMKkp6V9JCkjdO2ZXCT0AKlMiIFGGxmhaXgJwDtmc96o5mtb2Zr48OeB7W7d2Ug74Z0oT/051V/nrVD6M+rfpXhX1a0ySeriPHAcmb2SarBujkZflaEVOPVnhzuGmmGYh+gJz4TMgiCIAiCoN20xSdrvhGpmc0ys08AzGwM8CbwNdwcdNmih5XKiBRgYhqOLAxLTlpE++a4EjjKzNbElwX6ihFpep5DJT3djudpF/k1pHNCf+jPq/48a4fQn1v9VbR4YauCrIZGpJIGSuqa9q0IrAy8lYbzPpP0rVS7tT/JMLQE3MIC/6v5RqRtZElgvKRuQJMOnuU2I10UeTekC/2hP6/686wdQn9e9VdRjNWi4cLmjEg3AU6XNAeYBxxmZoUC8yNYYOFwR/pD0i7ABcBA4DZJz5nZ99O+d/CZi4tJ2hk3DH1J0uXAxcnu4WzgX5IOAt4F9kyPbc7I9J/AZsAASR8AvzSzv+KzGZ8CJqf/l2zNyasUeTWkKxD6Q39e9edZO4T+vOuvBsKMtBUU6r/yakoXhnyhP6/6w4wz9OdVO1TejHTS56U/2YOWrLAPRSKCrFaQpRlpEARBEGRFBFlto0U1WZLqJT0naVzylDpeUpe0r07SjLT/OUkXFz1uXUljJb0h6fyCt5akPdKx5klar6j9lpLGpMeMkdTo4LOkfpLukfR6+r9v2j5c0hOSZkk6oaj94pL+l/o+TtKv2na6sqO2tpa6ujrq6uro27dv1t2pOKE/9OdVf561Q+jPo/48WjhUyvX9Y2AHM/tI0hq4pUJj/lqtdX2fBWxhZtNTkfujku4wsydbqH8hwvW4soT+0J+1/nA8D/15fe9D5R3fM61ULzFtMSMtp+v7s2b2Ubo7Di+6797I4Vvl+m7O9HS3W/qz1M+RyfX9RUmXNqcpK/Lu+hv6Q39e9edZO4T+vOuvBlodZEHFXN93A54xs1mN7Gu167ukrmmW5CTgHjN7Ku26MLm+r4HPhNy+FX2sCHl1/S0Q+kN/XvXnWTuE/rzqz5uFw6Iouet7evxvga0W1balru9mVg+sI6kPcJOkNczsRWBzSb8AaoF+eAbtv+3pfxAEQRAEbaPjjSe1nTZlssrp+i5pWeAmYH8ze7OJZm12fTezqcADwNaSFgf+AuyeXN8voxHX93B8z5bQH/rzqj/P2iH0511/NdCWZXXK5vqesky3ASPM7LFmmrbK9T31sU+63QPYEniFBQHVx5J6Ars39vhwfM+W0B/686o/z9oh9OdVfx5nF1bK9f0oYBgwUtLIdIytzGxSe1zfgaWAv6dgsAvwLzO7NT3mMny24wRgdAvPR0XJu+tv6A/9edWfZ+0Q+vOuvxoIM9JWEI7v4Xoc+vOpPxzPQ39etUPlHd+nfFlf8pPdt7Zrx3R8l1QPjGVBFusq4A9mNk9SHfAyULBjeNLMDkuPW5cFWazbgWPT8OIewChgVeCbKTOFpC3xDNViwGzg52Z2fyP96QdcB9QB7wB7mtkUSfviXlkCPgcON7Pn02P+hs8anJRmETamsw64tan9qU1mn7La2loGDfLJnNOmTWPKlClZdSUTQn/oz6v+PGuH0N9R9EeQ1TZaMlzYWYxI3wY2TQHXNsClwAbpMVcCF+IBYrsJQ77KEvpDf9b6w4wz9Of1vQ8ZmJFWEa0qfO/gRqSPm1khxH+SopmNZvYw7gbfsJ/rpqV2ngeObEZ6puTdkC70h/686s+zdgj9edUvlf4vK9ri+N4ZjEgPIhXZL4IrgKPNbO1W9K3i5NWQrkDoD/151Z9n7RD6866/GmivGWmHMyKVtDkeZH1nEc/TB+iTslzgMya3aVOngyAIgiAoCVlaLpSatvhkdVgjUklrAZcDOxX6VQrCjDRbQn/oz6v+PGuH0J9X/bkdLuzIRqSSlgNuBPYzs9cWpSU5v0+VVMh47dtM2zAjzZDQH/rzqj/P2iH0511/NdCS4cJOYUQKjAT6A39JNflzC4GRpH8CmwEDJH0A/NLM/gr8CPhbGnK8u4XnrOLk3ZAu9If+vOrPs3YI/XnVXz2DhWFG2irCjDQM+UJ/PvWHGWfoz6t2qLwZ6ecz55X8ZC+5eJeOaUYaLCBLM9IgCIIgyIqKBlmzyhBkdc8myFpkTZakeknPSRqXPKWOl9Ql7auTNCPtf07SxUWPW1fSWElvSDq/4KslaY90rHmS1itqv6WkMekxYyQ1OvAsqZ+keyS9nv7vm7bvJOmF1I+ni2qtkHSnpKmSbm1G52bN7c+a2tpa6urqqKuro2/fvll3p+KE/tCfV/151g6hP4/687ZAdGdxfL8PuCUV5K8F/AsYnh5zLlAL/KQFehdJuB5XltAf+rPWH47noT+v730Ix/f2UE2O79NtwdjnEsD8T4SZ3YevZ9iwn1tLekXSM3jg1yHJq+tvgdAf+vOqP8/aIfTnVX9uLRygYzu+S9pF0iu4FcSPm3sCSYsDlwE7AOsCQ5prnyV5d/0N/aE/r/rzrB1Cf971VwOtDrIaUHB8/zrwM+AaSb3ac8Aix/dFDu2lzFVxxuomMxuOZ7fOWMTDhwNvm9nr6Tj/aHuvgyAIgiAoBSrDX1ZUleN7gbRUzoqSBrRYWPP9Csf3DAn9oT+v+vOsHUJ/bvVXUZRVTY7vw4pmMH4D6A40t7TOK0CdpJXS/X2aahiO79kS+kN/XvXnWTuE/rzrrzSpTvvV5IowopH93SVdl/Y/JaluUcesJsf33YD9U19mAHsVCuElPYIPD/ZMju8Hmdldkg5NffgSeARYsgXno+Lk1fW3QOgP/XnVn2ftEPrzqj8Ly4WUMPozsCVeRz5a0i1m9lJRs4OAKWY2TNLeeGnTXs0eN8xIW044vofrcejPp/5wPA/9edUOlXd8nzmXkp/sxWuaj9wkfRsYlZI+SDoJwMx+U9TmrtTmCUk1+OS7gdZMINWSTFbQgDx7huRZO4T+0B/680qeteeEZYD3i+5/AGzQVBszmytpGr5m8sdNHTSCrFZQyUi+KSQdamaXZt2PrMiz/jxrh9Af+vOrP2/aF5V1agupPOjQok2XVuKcttfCIag8hy66SVWTZ/151g6hP/TnlzxrLwmFCWxFfw0DrA+BoUX3G3NFmN8mDRf2pvkJdhFkBUEQBEGQe0YDK0taQdJiwN64m0Exxe4GuwP3N1ePBTFcGARBEARBzkk1Vkfh6yZ3Bf5mZuMknQ48bWa3AH8Frpb0BvApHog1SwRZnY/cjMs3QZ7151k7hP7Qn1/yrL1imNntwO0Nto0suj0T2KM1xwwLhyAIgiAIgjIQNVlBEARBEARlIIKsIAiCIOjkFJaVCzoWEWTlHElV9x4oWsMyl186Rfqr7rVtDcWvf5otlBvy/toXyMt3gCQVLSO3btb9CRYQH8ScIumbAGY2L+u+lJLCl42kLfF1Nbvk5YsWFtK/HXBJYQH3vNHgR2c/YKe8BFqSuhc+15JWl7R61n3Kggbvge0lfSvrPpWLIp0/Bs6U1CfjLgWJCLLyywmSzs26E6UmBRg7An8AHjazeUVfQFUfbCX92wO/AW4ws/o86G5I0Wt+JPAzfAr27Gx7VX4kDQcOk1Qj6QjgGuA2SSMX8dCqo+g9cAxwBjAp2x6VF0kb4TPfjjOzqXm9wOpoRJCVExoZPvgjUJ9FX8pJcuHdC9gNeEjSdyVdJmnpFIBUZcDRQNf3gIPxVeS3A64tZC6rVX+BBkOESwP7AbsC70vaTdIISd/IrIPlZw1gdeBE4PvAN4CNgW0lnZplx7Igve/3B75jZm9J2ljSXpK6Zd239lJcFpCytBsCSwG7SOqa1wusjkZYOFQ5kgbhq4SPk7Q5MAN4GugO3AlcbmZ/z7KPpSINBzwD/An4Or6Q59vAKkAPYFszm5tdD8tL0v8WcAzwHWAW8DiwPL6I6Z5mNiu7HlYOSWvhr/0IYAvgFaAbMBmYZWYjMuxeyZHUw8xmpNs/BNbHPwO7m9kkScOAq4AHzezkDLtaViR1KS6BkNQfOBtYAs9krQp8jp+HC7PpZftpMBTa38w+SRfSBwFrAg+Z2b8btg0qT5iRVjHpam1f4OuSzgYGAMcBTwL/A04CDpF0PTCjCj6IRwDvm9nhaYjgATMbK2l54HygJzA10x6WlwOAxc3sR5J2AV40s9eT/ivxQOujLDtYLiStYmavptvr4sNDewKXA+8Ad5nZe5IOAjZs+GPcmZHUE9hC0ofAcsBs4F789d5D0o1m9oakHwF/kTTAzD7OsMtlIdWizUq3Vwe6pM//tcDOuFv3y8BR+EVmp6XBcPj2kl4E/mdml6Vh4o3T+bimCr7XOzdmFn9V/AeshV/NXwQsgy9ouSHwIPAfYAqwSuEzm3V/W6lNDe6vB/ypwbY9gOeBXbLubwX0DwEuBoYUbdsdeAHYOev+lusc4BeLzwAXpm0DgKvxcoguRW0PTu3WyLrfJT4HSwK7AGPwTGaPtH0PvCzgKGCZtK1b1v0t0zkYDhyb3gtHpM/8u8DJDdrtn87Taln3uQSaDwYeAoYB/waeBY5M+44HfgssmXU/8/4XNVlVSqEGy8xeAG4CJgCnAsub2ePAlsAo4AbgXEmLWfp0dhbMzCRtluquFsODidXT1V2BDYBTzeymaqtPSPo3l7SzpOXNbAI+LLp/UbOVgZ+b2c3Vpr+A+RDwVsB6ks7Bf2gnA8Wz7IYDg4EDzOzFzDpbQgqvp5l9jg+FLY1nqL+Rtl+PX0ythWc7ugLVOlzeVC3ajpJOldQt1eLtBBxoZi9l19X2k7KX4MH1Nvhw6InA/pIOM7PfAWen90aQIVGTVYUUTeNfC5jHgiGiI/HCyL+a2ZjUdgl8Jt4x5usydSrS9Pyf4CuojwVeBI7Gr+g+K2pXlXUJknYGfgjMxLU/gteg7Gtm72XZt3LT8DWVNAC4Aw8k5uKZjM/wbNck4Fwzm55FX0tNg5qc3mY2TVJfYAdgU+AmM7tV0orAJsDtZlZ1s+taUYt2Lz7jtmtnfA809f0laVk8e72fmU2RdAt+kfEDM6vm0ohOQwRZVYqkrYHfA9fiU9jXBmqBHfFC8AvM7FlJ3wf+DGzYGb6EiwLI4fgq6JPx4ZKV8S/RmcDmwCZJX9XU3sBC+tcBvgA+Tl+uS+HB8lQ8k7Wrmd1ZmGWUZZ/LQYMgY0f8dX8Pr7/6N561OgoYCPTD6/OqIuhsoP0EYDv8s3ABPnx0FB5oCNf+IzP7NKPulo1CLRpQqEXrhk/s2Qt4ArjRzMZLWgUvl9jDzD7Jqr9tpVDYnm4fjQ8P9gHOwyd3/Af/jl8Lfy8cZVVYc9dZiSCrykhDCEPxmpQfAV/DZ9ttaD4DZRheDH+D+YzDVfDZVu9k1efWIveBGoVnbWrwzNxzad+a+Ayb4Xgd1oys+lku5LYMvwFuxH9M/2Rm96fXfiBwGG5b8J3OeNXeGiT9DL9wuB237TgMD7RuBx4xs19k17vyIunbeAnAqfhw2S+BQ9N7YXu8JuvcahkebYikJXG7klOBvsDqZjZD0h7ARsAbeEbvQ0ndzGxOht1tE+n7+cdmdqKko/AC/oOB64BnzCf5jMSHR5dLbZ/LrsdBQ6ImqwqQtJik2nR3CbyY/Ubgu8DpwPYpwNoJGI+P1Y9LV8SvdrIAaz08wNoBmIYPhZwgaUMAMxuLF/p/QBW+v9MQ8Cj8inUKUAf8UtK25kwys9Px+rS+mXW0AkgaCmxsZpsBvfD39ktmNgU/P99IGb6qQ27Hch4+o+xZM7sany18saQdzexWPINVdQFWzmrRBgPbJQ29gL3xC6iPgeNSpvp0PHu3aQRYHY+q+xHKG3LzzW8D30s1CWfgQwS7AL/CM1ivS1ofv+KrszTNuTPUKMmdq4uNA+cCh+JeMDvgdWZzgZMkfSe12QjYDB9G7NRIWlzSkpIKU84/xjN1KwEH4tmbJ4DfpgxXwYDxm0CHf31bQyOF+wLmSvo9/oO6t5nNkrQP7hG2pZmNr3Q/y0Ej2h/GZ0quL2m5dMF0Pf6ZP72oMLqqaKQW7TFgNTxz+eOUwQO/yHgcz2TVd4bvusYws4fxmZJL4cHkvfgs6h3T9/jhKcM1J4rcOybhk9XJMbO5kubixd7D8SUV3pX7AT2Cf+HOxtPMvzSzcRl2t1WkuqsRwNKSJuD+XpeYOxn/GjjezB6VtBUwBzcZBK9T2CLNtuu0SFoVD5r7AtMkvQmcZGYfSfo58Dtz/6P38R+UQr3Je8Dm1RJgwMImk5K+hhe1fwi8jl/Fb2RmM+Vrtx0J3G9mX2TW4RLSILDYGc9WTzCzoyWdjwdWv5L0rpn9n6RbqnGYuLFaNEmFWrSrcXuaXSXtRieuRZP0PbyA/zU8eByAZ+nOBB7Ds7X1ct+zI3B7lqqpO602IsjqxBR96TyBp82/BHpKGmpmb8odwHfFzQmPNbOHmpql0tFItQjX4maSTwPr4hm7dSQdBiwGnCHpdDybc4CZPZ/0vZVVv0tF0v8P4FLgATxztR++Dt12eKbmQEmG+wPtY2Zjkv5OHVw2RlGAdSywLfAqPlx6P/499l9Jd+PZzR+Y2cSs+lpqigKLY/B6yoeAPSXtYWaHSfoT7on0c+C9as1oFJ2Hb+OTW36G16JdjteiXVBUi3ZKZwywEjPw2sq1cZ+7lfGSj1skbQtcky406vBZlK9l1tNgkUTheyelECzJp/B+iAcd6+LT+V/FHc774EvqdKoPoaTlgEfxL8qr0zbhafJjgSfM7M+SrsCHvG82s5sy63CJSa/pGDxT94+i7QPx1/UjMzte0hm4L9ajZnZzNr0tL5LWBr5MQ97bAr/AZ5Tdgr/vDwcWx72CZgCvmtmbWfW3lMjtFz43s8lyq5Vr8YultyT1wK0JHsPfExcCZ1RT9rIxUi3amcDdZvartG0P4CzghBSIVM2MYkmD8SzWcfj33EWSFsdLAXpY2DR0eCKT1QkpfIlI2gYvbL8b98Mahf/g7Ipf3X0fr83qbKyGez7NvwJIAeUY4C48W/Fn8+VjFk/DRJ0iQ9dCeuNBVl1hQ3rNJ0u6EBiRCl5PK3ovVJP+Yn6Kn4fN8Nq7G/HZVd3wgGOepGGW1mmrFuQWHaOBsyRdkUoAeuPrUL6VZtFdjVuVzMOHjaqORt7XC9Wi4ctoXS83Iz5d0v14Rr9TowXWK5PN7A5JnwGjUnB9sZl9iWezgw5OFL53ItIHjPTD8h3gHNwTqQueVr4U/xK6AB9C/JGZjc6ou+3hIVzLhml4CJg/ZDQGWEm+8CuWDFSrLMB4GTgZWDEVdc8fLsPrzQbjS+jM315l+gsTOjCzA9P9E4E3gR8DB5nZ91NwfRTw03R1X01MxIOJZfGhwSXwz8QlKQADL4ReUT45ourc/BvWoknaF9jMzI7Glw/6FbB8avd/+EzT6dWQxUoBVuG7XqnA/yz8YmOxLPsWtI4IsjoJkvoBx8tdncFnzu2Dfwlvif/4LIVnsN4zs8vN7J7O9uWbvlBm4Bmre4FVJB1bpGNZPNCoOv8rmK9/Hj476nygj6TfFelfBTeerEr9AJK2BE6WL/QM/mM6DK9L+zfwP0kHpyL3H+ETADrdagWNUfQ6T8XfA0vj2ekj8SLoXwO3SvozcAy+ZNTMaguy4Su1aCfhNUrHSLrYzI7B3fx/i/sCFiwdOhUpePx1c21SFl9m9iCwVwwRdi4iyOoEpKv6L/BC6CUkbWRmd+C1V/vgtTtPsCDL8bXCYzvbl2/R8CnIxwAAEU5JREFUF8oMfImUe/FZk/umgtffAVemdHlVIJ+CX/D4sfR/caDVFzg1TWT4A/CXTlzU2xLWxou4fyNfNulxfPZoHfAX4D7cA24tqmstwi1wG4Jh6f0/Cvf+mo1nLg8BrsG1XwpsbZ18Db7GkLRiqj8sLPu1JT6x4xe4T1R/ScfhQ8mf4O+NTkf6zF8MHNGSQCvdrIqLiTwRNVkdnJS5Oh84L82e+zk+w64mzRZcAlgrXQGvic+yeSXLPrcGuQP9PvgQ2Utm9lJxoCXpDrze7Af4l+2+ZnZ7tdQgyWcR3gQ8Jl/Yem7REOA8SYVA6zTc52yXatJfTEGTmZ0naR4wHZ9RuRjwEj5c8pr5Ytf/wSfudPqhIZgfTPwG/ww/nyY1TMUL21/E69H2B04BLjezV7PqaznJWS1aX+AA/CLiofSdPn+FgsY+49X2mc8Dkcnq+CyGByAj5bONLsO/hH4oaQ38i/l7wEh8PcLOFGANx5eH6IvPDptfpJ8CrS5FQ4fXANuZ2e2F/Rl0uaSkAOtK/Id0aaBfoQaj0KYoo3U6XnNSNfqLkTv2nyF39AevuSk4XNfgV/DTgd9JWikFY1URYAGYe3odjtdTgs8o2xr3STsHfw/cgZ+LqsniNkJuatHM7D5gTBrm/D7wXUnnFTXpn03PglISFg6dAEmb4kMFtbg3zMf4Gm1fw2fZPS+pp5lN7ywZjlS4/k/gGjO7UtKueKbqXGCepaV+CnoaFMF2Co3NIang3nyWuYHk5XhQ8QMza3YJkGrQ35B0AXEm7n013szOlHQRPrtqZHq/jMSXy9nIqsgHq4B86ZR1cBuWxfAh0+PwH9vfm6/BV1tNQ+UFij7nPfDM7TL45J1ZeJ3pjvj74z+4R9bu1TJUqjSTUG7XcDted/gefvF8WLXUG+aVCLI6OJJ2wIdJbsZrVQz/8p2Au7wPw/1hOl2NjqRvmdmT6fYj+JDIS/iV6u/N7JEs+1duJG1qZg+l22viNSbnmNkrqiKvn0WhBTYUvXAvtKPwIONneBbnb2Z2S2o7wMw+zq635SUFWmvhwdU7ZvbLlK2pmqHRhqRatBWAh8xXMFgGz9y+hteYTsRrEVfAJwFMMbP3supvOdACywbkdg1f4stCjc22Z0F7iSCrAyOpC/Bn4D9mdqekOnz4ZF080PoU6G9mb2fWyTbQMICQG05umWpx+uCGo9PM7I+ZdbKMNBZApSv4/wPeNrPjs+lZ5SnKYDR8T1yMLx8zAM/cHm1mU6sxi9eQ9LlfC7+Imm1mh2fcpbKRhgPvJ9Wi4cOjU/HJLsW1aNPwWrT3M+pqSSi6oFgc6Ipb79xsZtPS/m8DNwBbWSdaAi1omqjJ6tgYbky5FUAaQnsKX2bhfHxR0E4VYMFCnk+F+8+b2Xnp9lTcnmBQFn2rBI0EWIXZlD8HNtKCha6rFklD0w/skrDQsjld0v3D8KDzfXwZnYJvVqcPsLSIaftFdXgX4QtgV/NnIW+1aD1SLeZVwO/xerPBRfsXx2svI8CqEmJ2YQei6Kp+ffzDNgGfTfQHSUea2Z9xb5gx+HBap/NKKk6Lp/s9inVI+iZek/LTLPpXbhrRX3Cs74IvevwssIakx6ohoGgM+dqLJ+PDQK9L+lthtly6yu9iZvNS9vYeYERnHA5vDC2Ytr+4JMzs5MbapfPwDDDWzKrS2bsoK/k8IHyIeCn8YmMmXos22MzukvRIZ69FS7OHlwNWxbNVNfisycmFNmb2QDa9C8pFDBd2MCTtiE/X/w++RtsleLB1IT7L8FvAkWb238w6WQLkhpPb4NnUn6bgcnO8yPVYM7s10w6Wmcb0p+27ApPM7NEs+1cu0mt8EW7b0R1fAuopM/t3ToYCv4v/uD6Or2xwrzUybb+az0XhwqLBtqquRUvZyJuBX+Jrbr6c7v/dzG7Msm9BeYnhwg6E3BPrMHxWyXg8m3VPKo7+Nl4Av7WZ/bczTl2WtIKkzSQ9gM8k/DpecyGYfxW3m5nd2hn1LYrm9Bf0mtmN1RpgJb6OW408myY9vA/smXGfKoa1cNp+FQdY3wWOlntfzSdld5/HC9yXlXSROZ0+wAIws0n4MOA9aVbkUDxz/WCmHQvKTgRZHYsaYBJwEHAg7mb9qaStgQGpdukl6HxfwpL2wLNz3wOuxa/oegIPFIaIAMzsufR/p9K3KBalv9r0NoWZ/R43Xy0wmrQQeMrg9MqkYxXEzD5Ow8aT8XqzzSWdLOmHwHmqvnUYgfnZ26uAZwuF3kX7ChMfqrkWrThg/CNQXy3D4EHTRE1WhhQNDQwxswlmNlnSB8AvgG3N7HVJm+HeUXtl2tn28wLuzvykmc2VtBs+VHIbfLUYvAppVn8eKLzfzeyjos1TgcISKvsBK0s608xmZ9LJCmHui9TV3O9r3QbT9qvKFyllaWuAnXC7mXvla7H2AGrN7PXiWrxqrUUrKgnoi2dwf5PuV+3QcBBBVqakAGtbYJSk8fhsk9vxK54/SboZz2qdaJ3ceC8VNhcvBbItbjaYC/KuH5rMTtYDUyUdhFt37FltAVYLp+1/TpVO20+v+xxJHwGzU4bqDnySx2YpqL7SipaTwk1IqxIzmyLpNJLGCLCqmyh8z4CiDFYffFmVs4DNcGuGO3Gbhk3wYZQPzeyRarrakfQD4Bgz+1bWfcmCvOlvLshIGY1X8GV0fmRmL2fZ13KQrCqWxW0JpuClAGua2Wtp/+bAu2b2VmadrACSjsJrSx/Bh8ouk7QR8C88c/98ph0MgjIQmawMSAHWd3F/lE/NbDQwOk3xLXgC3WxF1gbVEmAlHgS+AJAvitrsMjJVyIPkS38PSQ2DjCdww9lPJT0BjKzSACum7SfM7EJJ2+P1iPunbY/JF/uu9s9AkFMik5UBkr6FFz9fh//gXGZmp6Z9x+FLixxvVbg+W4GGflF5Iy/6mwgy9sQtHKZW2cXDQsS0/caRdBfu9/crYAN8+aStrZO7uQdBY0SQVWEkrQUcCTxsvjDwarjr731m9svUZjmrsrW5gvwRQcZX1qRbDjgBGJWHWWWNDBPvY2aXp31n4DVJG+LF8J265jQImiKGCyvPcPyq/gtJd5vZS5IOAf4pqZuZnRwBVlANmNkkSRs3CDLy5g3UcNr+u3kIsBJfGSaW9LiZvWRmp8FXV3wIgmojgqwyU1TkPhyYbGb/kjQBNx3dUtKdZvaypH2BqvcICnJHnoOM3E7bb6YWbXyDplVlVxEEDYnhwgqQTPj+gc8cnIn7YG0A7IevQH+LmX2SXQ+DoLykIGMUcFbKcFV1kNEYyWh1VrX5PzUkhomDYAGRySoTRRms3sAawC7Ae8BPcEfjw/E6hQOBu7LqZxBUgvAGAjP7LOs+VIIYJg6CBUQmq4yk5XB2BoYBx+Ou38sAhwJr4gFWNzP7OKs+BkEQlJriTKWkG/Fh4p9m3K0gqDixdmGJkVST/t8AGAk8jtda7Q70MbMPgMtwA8YVIsAKgqDaaK4WLct+BUGliUxWiZC0Am4sOi3dvhq43cx+LakOuABfRuKPyYCxe7XXZgRBEOSlFi0IGiMyWaVjJeDdtFTOe8DTwH6S1jGzd/DFgb8DnJBcvuMLJwiCqsfMPovvuyCvRCarhKQarAuA9VJG6xTcvX2UmT2fCkAHmdnTmXY0CIIgCIKyE0FWiZG0LXA+sC6+dMSJwBbAL8zsuSz7FgRBEARB5QgLhxJjZren2s6n8SzWb4FuuF1DEARBEAQ5ITJZZSINHV4BDDezaVn3JwiCIAiCyhJBVhmRtB3whZk9mHVfgiAIgiCoLBFkVYA8LiESBEEQBHkngqwgCIIgCIIyED5ZQRAEQRAEZSCCrCAIgiAIgjIQQVYQBEEQBEEZiCArCCqIpHpJz0l6UdJ/0zJMnQZJT6X+vydpcrr9nKQNJd1Qxue9WdKTDbbtIellSQ9I2kzShu18jjpJM4o0PSdp/1Ye40BJSxfdf0fSgPb0qwXPeaWk3cv5HEEQtI0wIw2CyjLDzNYBkPR34EjgrGy75DNg8Ykw85prZ2YbpPYH4stHHVW0+/Ey9a0PvoLCdEkrmtlbaddBwCFm9qikUcD01vQhrSE6t8HmNwuvTxv62RU4EHgR+KgtxwiCoLqITFYQZMcTwDIAklaSdKekMZIekTQ8bR8s6SZJz6e/DdP2n6Vs2IuSjkvbzpZ0ZOHgkkZJOiHd/rmk0ZJekPSrtK1O0quSrsIDg9Mk/bHo8YdI+kNLhKRjvZhuH5gyT/ekTM5Rqb/PSnpSUr/mNDfCrsB/gWuBvdNjR+ILrv9V0vXAYcBPU/ZpY0kDJf07aR4taaOic3K1pMeAq1uiLT3uIklPSxpXOH9p+zuSfivpGWAffJWH/0v96JGaHS3pGUlji17X/pLuTse7XNK7kgYUn8fU7oQUQBZej9HpffBvSbWN9POMlNnq2thrHgRBhTGz+Iu/+KvQHzA9/d8VuB7YOt2/D1g53d4AuD/dvg44rugxvfGszlhgCaAnMA74evp7qOi5XgKGAlsBlwLCL6xuBTYB6oB5wLdS+57Am0C3dP9xYM0mdBwIXFh0vw54sWjfG8CSwEBgGnBY2veHIj2Nam7kue4BNga+Bowt2v4gnk0DGAWcULTvGuA76fZywMtF7cYAPRp5njpgBvBc0d/GaV+/otfgQWCtdP8dfF3Sr/SpaP/R6fYRwOXp9vnAyHR7O8CAAcXnMe07AV9gHqB/0fYzi457JbA7cC5wcXqdG33Ns37/x1/85e0vhguDoLL0kPQcnsF6GbhHUk9gQ+B6H7UDoHv6fwtgfwAzqwemSfoOcJOZfQEg6UY8GDhf0qBUEzQQmGJm70s6Fv/RfTYdsyewMvAe8K6ZPZmOP13S/cD2kl7Gg62xbdT5gJl9DnwuaRqeiQIPDtdahOb5SBqc+vqomZmkOZLWMLMXG7ZtwPeA1YqO3Ss9J8AtZjajicc1NVy4p6RD8RKLpYDVgBfSvusW0Zcb0/9j8KwceJC7K4CZ3SZpyiKOAbCGpDOBPvhreFfRvtOAp8zsUABJW9H4a/5wC54nCIISEUFWEFSWGWa2ThrquQuvyboSmNrEj3truR7PagxhwY+/gN+Y2SXFDSXVAV80ePzlwMnAK/jam21lVtHteUX35+HfO11omeY9gb7A2ylg6oUPy52yiMd1wTN0M4s3pmM01NwsklbAM0rrm9kUSVcCixc1WdTxCtrrWfR37lwWLuMofp4rgZ3N7PlUE7dZ0b7RwLqS+pnZpzTxmgdBUFmiJisIMsDMvgSOAY4HvsSDiD3Ai9AlrZ2a3gccnrZ3ldQbeATYWVKtpCWAXdI28MBqbzzQuj5tuwv4cSGTI2kZSYOa6NdT+BDjD4B/llByw+f5jKY1F7MPPqRaZ2Z1+FDp3o20+xwfnixwN3B04Y6k9gSwvfBAalrKrG3TTNuG/WiKh/FzjKRt8EASYCIwKNVsdQe2L3rMksB4Sd2AfRsc707gbOA2SUvSitc8CILyEUFWEGSEmT2LDzntg/9oHiTpebzGaqfU7Fhgc0lj8eGm1czsGTyr8T/gKbzO59l0zHH4j/GHZjY+bbsbr1F6Ih3nBpoPBP4FPGZmLRnCag9NaQbmZ9qWB+ZbN5jZ23iws0GDY/0X2KVQ+I4HsOulou+X8ML4lrCSFrZwOMbMnseH3V7Bz+NjzTz+SuDiBoXvjfErYBNJ4/Bhw/eSvjnA6fhre096zgKn4a/3Yw22kx57PXAZcAsedLfmNQ+CoAzE2oVBECyEpFuBP5jZfVn3JS9IegcvmP84674EQVA6IpMVBAHgflSSXsPrxiLACoIgaCeRyQqCIAiCICgDkckKgiAIgiAoAxFkBUEQBEEQlIEIsoIgCIIgCMpABFlBEARBEARlIIKsIAiCIAiCMhBBVhAEQRAEQRn4f1vqJfsNTVVWAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x576 with 2 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}}],"source":["%reload_ext autoreload\n","%autoreload 2\n","\n","plot_results(model_name)"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.0"},"colab":{"provenance":[],"toc_visible":true}},"nbformat":4,"nbformat_minor":5}
\ No newline at end of file
diff --git a/driver_PBEErecovery.m b/driver_PBEErecovery.m
new file mode 100644
index 0000000..187559c
--- /dev/null
+++ b/driver_PBEErecovery.m
@@ -0,0 +1,48 @@
+% This script facilitates the performance based functional recovery and
+% reoccupancy assessment of a single building for a single intensity level
+
+% Input data consists of building model info and simulated component-level
+% damage and conesequence data for a suite of realizations, likely assessed
+% as part of a FEMA P-58 analysis. Inputs are read in as matlab variables
+% direclty from matlab data files, as well as loaded from csvs in the
+% static_tables directory.
+
+% Output data is saved to a specified outputs directory and is saved into a
+% single matlab variable as at matlab data file.
+
+clear
+close all
+clc 
+rehash
+
+%% Define User Inputs
+% model_name = 'haseltonRCMF_4story'; % Name of the model;
+% model_name = 'haseltonRCMF_12story'; % Name of the model;
+% model_name = 'ICSB'; % Name of the model;
+% model_name = 'RCSW_1story'; % Name of the model;
+                     % inputs are expected to be in a directory with this name
+                     % outputs will save to a directory with this name
+model_dir = ['inputs' filesep 'example_inputs']; % Directory where the simulated inputs are located
+outputs_dir = ['outputs' filesep model_name]; % Directory where the assessment outputs are saved
+
+%% Load FEMA P-58 performance model data and simulated damage and loss
+load([model_dir filesep model_name filesep 'simulated_inputs.mat'])
+
+%% Load required static data
+systems = readtable(['static_tables' filesep 'systems.csv']);
+subsystems = readtable(['static_tables' filesep 'subsystems.csv']);
+impeding_factor_medians = readtable(['static_tables' filesep 'impeding_factors.csv']);
+tmp_repair_class = readtable(['static_tables' filesep 'temp_repair_class.csv']);
+
+%% Run Recovery Method
+[functionality, damage_consequences] = main_PBEErecovery(damage, damage_consequences, ...
+    building_model, tenant_units, systems, subsystems, tmp_repair_class, ...
+    impedance_options, impeding_factor_medians, repair_time_options, ...
+    functionality, functionality_options);
+
+%% Save Outputs
+if ~exist(outputs_dir,'dir')
+    mkdir(outputs_dir)
+end
+save([outputs_dir filesep 'recovery_outputs.mat'],'functionality')
+fprintf('Recovery assessment of model %s complete\n',model_name)
diff --git a/driver_convert_PELICUN.m b/driver_convert_PELICUN.m
new file mode 100644
index 0000000..3c3afc1
--- /dev/null
+++ b/driver_convert_PELICUN.m
@@ -0,0 +1,298 @@
+%% Script Converts PELICUN outputs into PBEE-Recovery tool inputs
+
+% Required inputs
+%    - comp_ds_list.csv
+%    - tenant_unit_list.csv
+%    - input_parameters.json
+%    - DL_model.json
+%    - IF_delays.csv
+%    - DV_rec_cost_agg.csv
+%    - DV_rec_time.csv
+%    - DMG.csv
+
+% Assumptions: (specific to 16 story treads model)
+%    - assume rugged entry doors
+%    - Stairs run the whole length of the building
+%    - Residential occupancy
+%    - uniform 10ft story ht
+%    - 33ft x 33ft structural bay area
+%    - square building (100ft sides)
+%    - 16 stories (4 basement, 12 above grade)
+%    - Stability IF longer than 10 days = red tag
+
+
+clear all
+close all
+clc
+rehash
+
+%% Define Inputs
+model_name = '001'; % Name of the model;
+                     % inputs are expected to be in a directory with this name
+                     % outputs will save to a directory with this name
+model_dir = ['inputs' filesep 'example_pelicun_inputs' filesep model_name]; % Directory where the simulated inputs are located
+
+%% Load Pelicun Outputs
+pelicun_dir = [model_dir filesep 'pelicun_data'];
+
+% Pull basic model info from Pelicun Inputs
+fileID = fopen([pelicun_dir filesep 'input.json'],'r');
+pelicun_inputs = jsondecode(fscanf(fileID,'%s'));
+fclose(fileID);
+num_stories = str2double(pelicun_inputs.DL.Asset.NumberOfStories);
+total_cost = str2double(pelicun_inputs.DL.Losses.BldgRepair.ReplacementCost.Median);
+plan_area = str2double(pelicun_inputs.DL.Asset.PlanArea);
+
+% Pull components from DL_model.json
+comps = readtable([pelicun_dir filesep 'CMP_QNT.csv']);
+
+% Pull repair cost realizations
+DV_rec_cost_agg = readtable([pelicun_dir filesep 'DL_summary.csv']);
+
+% Pull realizations of damaged components
+DMG = readtable([pelicun_dir filesep 'DMG_sample.csv']);
+frag_col_filt = ~cellfun(@isempty,regexp(DMG.Properties.VariableNames,'^B_')) | ...
+                ~cellfun(@isempty,regexp(DMG.Properties.VariableNames,'^C_')) | ...
+                ~cellfun(@isempty,regexp(DMG.Properties.VariableNames,'^D_')) | ...
+                ~cellfun(@isempty,regexp(DMG.Properties.VariableNames,'^E_')) | ...
+                ~cellfun(@isempty,regexp(DMG.Properties.VariableNames,'^F_'));
+DMG = DMG(:,frag_col_filt); % Filt to only component level damage
+DMG_ids = DMG.Properties.VariableNames;
+
+% Pull realization of repair time per componentDS
+DV = readtable([pelicun_dir filesep 'DV_bldg_repair_sample.csv']);
+rec_time_filt = ~cellfun(@isempty,regexp(DV.Properties.VariableNames,'^TIME'));
+DV_time = DV(:,rec_time_filt); % Filt to only component level damage
+repair_cost_filt = ~cellfun(@isempty,regexp(DV.Properties.VariableNames,'^COST'));
+DV_cost = DV(:,repair_cost_filt); % Filt to only component level damage
+
+%% Load ATC 138 model input data
+tenant_unit_list = readtable([model_dir filesep 'tenant_unit_list.csv']);
+ds_attributes = readtable(['static_tables' filesep 'damage_state_attribute_mapping.csv']);
+
+% general model inputs
+fileID = fopen([model_dir filesep 'general_inputs.json'],'r');
+general_inputs = jsondecode(fscanf(fileID,'%s'));
+fclose(fileID);
+
+%% Develop building_model.json
+% Count the number of stairs in the building
+stair_filt = contains(comps.ID,'C.20.11'); 
+if any(stair_filt)
+    num_stairs = min(comps.Theta_0(stair_filt)); % Assumes number of vertical egress routes is the min number of stairs on any story. This is faulty logic and wont hold true for all comp tables 
+else
+    num_stairs = 0;
+end
+% Count the number of elevator bays in the building
+elev_filt = contains(comps.ID,'D.10.14'); 
+if any(elev_filt)
+    num_elev_bays = max(max(comps.Theta_0(elev_filt)),0);  % Assumes the number of elevator bays is the max on any story
+else
+    num_elev_bays = 0;
+end
+
+% Set Variables
+building_model.building_value = total_cost; % num
+building_model.num_stories = num_stories; % int
+building_model.area_per_story_sf = plan_area*ones(num_stories,1); % num_stories x 1 array
+building_model.ht_per_story_ft = general_inputs.typ_story_ht_ft*ones(num_stories,1); % num_stories x 1 array
+building_model.edge_lengths = [general_inputs.length_side_1_ft; general_inputs.length_side_2_ft].*ones(1, num_stories); % 2 x num_stories array (json flips the others)
+building_model.struct_bay_area_per_story = general_inputs.typ_struct_bay_area_ft*ones(num_stories,1); % num_stories x 1 array
+building_model.num_entry_doors = general_inputs.num_entry_doors; % int
+building_model.num_elevators = num_elev_bays; % int
+building_model.stairs_per_story = num_stairs*ones(num_stories,1); % num_stories x 1 array
+building_model.occupants_per_story = general_inputs.peak_occ_rate*building_model.area_per_story_sf; % num_stories x 1 array
+
+% Write file
+fileID = fopen([model_dir filesep 'building_model.json'],'w');
+fprintf(fileID,'%s',jsonencode(building_model));
+fclose(fileID);
+
+%% Develop damage_consequences.json
+% Pull data from Pelicun structure 
+sim_repair_cost = DV_rec_cost_agg.repair_cost_;
+sim_replacement_time = NaN(height(DV_rec_cost_agg),1);
+sim_replacement_filt = DV_rec_cost_agg.collapse | DV_rec_cost_agg.irreparable;
+sim_replacement_time(sim_replacement_filt) = ...
+    mean([DV_rec_cost_agg.repair_time_parallel(sim_replacement_filt),...
+    DV_rec_cost_agg.repair_time_parallel(sim_replacement_filt)],2); % Take the average of parallel and series rep times (they should be the same)
+
+% Set Variables
+damage_consequences.repair_cost_ratio_total = sim_repair_cost / building_model.building_value;  % array, num real x 1
+damage_consequences.simulated_replacement_time = sim_replacement_time;
+
+% Write file
+fileID = fopen([model_dir filesep 'damage_consequences.json'],'w');
+fprintf(fileID,'%s',jsonencode(damage_consequences));
+fclose(fileID);
+
+%% Build comp_population.csv
+comp_population.story = zeros(num_stories*3,1);
+comp_population.dir = zeros(num_stories*3,1);
+for c = 1:height(comps)
+    idx = 1;
+    comp = comps(c,:);            
+    frag_id = comp.ID{1};
+    frag_id([2,5]) = []; % Remove extra periods
+    frag_id = strrep(frag_id,'.','_');
+    comp_population.(frag_id) = zeros(num_stories*3,1);
+    loc = comp.Location{1};
+    dir = comp.Direction{1};
+    for s = 1:num_stories
+        if  strcmp(loc,'all')
+            is_story = true;
+        elseif  strcmp(loc,'roof')
+            is_story = s == num_stories;
+        elseif  contains(loc,'--')
+            is_story = s >= str2double(loc(1)) & s<= str2double(loc(end));
+        else
+            loc_vec = str2double(strsplit(loc));
+            is_story = ismember(s,loc_vec);
+        end
+        
+        for d = 1:3
+            comp_population.story(idx) = s;
+            comp_population.dir(idx) = d;
+            dir_str = strrep(dir,'0','3'); % replace nondirection identifier
+            dir_vec = str2double(strsplit(dir_str,','));
+            is_dir = ismember(d,dir_vec);
+
+            if is_story && is_dir
+                % Assign to component population table (add duplicates)
+                comp_population.(frag_id)(idx) = comp_population.(frag_id)(idx) + comp.Theta_0;
+            end
+
+            idx = idx + 1;
+        end
+    end
+end
+
+% Convert to table and save
+comp_population = struct2table(comp_population);
+writetable(comp_population, [model_dir filesep 'comp_population.csv']);
+
+%% Build comp_ds_list.csv
+idx = 0;
+unique_comps = unique(comps.ID); % Only one entry per unique component id
+for c = 1:length(unique_comps)
+    frag_id = unique_comps{c};
+    frag_id([2,5]) = []; % Remove extra periods
+    ds_filt = ~cellfun(@isempty,regexp(frag_id,ds_attributes.fragility_id_regex));
+    ds_match = ds_attributes(ds_filt,:);
+    for ds = 1:height(ds_match)
+        idx = idx + 1;
+        comp_ds_list.comp_id{idx,1} = frag_id;
+        comp_ds_list.ds_seq_id(idx,1) = ds_match.ds_index(ds);
+        if strcmp(ds_match.sub_ds_index{ds},'NA')
+            comp_ds_list.ds_sub_id(idx,1) = 1;
+        else
+            comp_ds_list.ds_sub_id(idx,1) = str2double(ds_match.sub_ds_index{ds});
+        end
+    end
+end
+
+% Convert to table and save
+comp_ds_list = struct2table(comp_ds_list);
+writetable(comp_ds_list, [model_dir filesep 'comp_ds_list.csv']);
+
+%% Develop simulated damage.json
+[num_reals,~] = size(DMG(:,2:end));
+
+% Set Variables
+count = 0;
+for s = 1:num_stories
+    % Initialize variables
+    simulated_damage.story(s).qnt_damaged = zeros(num_reals,height(comp_ds_list));
+    simulated_damage.story(s).worker_days = zeros(num_reals,height(comp_ds_list));
+    simulated_damage.story(s).repair_cost = zeros(num_reals,height(comp_ds_list));
+    simulated_damage.story(s).qnt_damaged_dir_1 = zeros(num_reals,height(comp_ds_list));
+    simulated_damage.story(s).qnt_damaged_dir_2 = zeros(num_reals,height(comp_ds_list));
+    simulated_damage.story(s).qnt_damaged_dir_3 = zeros(num_reals,height(comp_ds_list));
+    simulated_damage.story(s).num_comps = zeros(height(comp_ds_list),1);
+       
+    % Go through each ds in PELICUN outputs and assign to simualated damage
+    % data structure
+    for c = 1:length(DMG_ids)
+        % Identify attributes of column IDS
+        DMG_id = strsplit(DMG_ids{c},'_');
+        frag_id = [DMG_id{1} DMG_id{2} DMG_id{3} '.' DMG_id{4}];
+        frag_filt = strcmp(comp_ds_list.comp_id,frag_id);
+        loc_id = str2double(DMG_id{end-2});
+        dir_id = str2double(DMG_id{end-1});
+        if dir_id == 0
+            dir_id = 3; % Change nondirection indexing
+        end
+        ds_id = str2double(DMG_id{end});
+        
+        % Find the associated column in the repair time table
+        frag_id_DV = [DMG_id{1} '_' DMG_id{2} '_' DMG_id{3} '_' DMG_id{4} '_' num2str(ds_id) '_' DMG_id{5} '_' DMG_id{6}];
+        DV_filt = contains(DV_time.Properties.VariableNames,frag_id_DV);
+        
+        % Assign simulated damage to new structure
+        if loc_id == s  && ds_id > 0 && any(frag_filt) % Only if there are components on this story
+            comp_ds = comp_ds_list(frag_filt,:);
+            seq_ds_filt = comp_ds_list.ds_seq_id == comp_ds.ds_seq_id(ds_id);
+            sub_ds_filt = comp_ds_list.ds_sub_id == comp_ds.ds_sub_id(ds_id);
+            sim_dmg_idx_filt = (frag_filt & seq_ds_filt & sub_ds_filt)';
+            if sum(sim_dmg_idx_filt) == 1 && sum(DV_filt) == 1 % if you find matching components
+                % Assign Damage data
+                dmg_data = DMG{:,c};
+                dmg_data(isnan(dmg_data)) = 0; % Change blank cases to no damage
+                simulated_damage.story(s).qnt_damaged(:,sim_dmg_idx_filt) = ...
+                    simulated_damage.story(s).qnt_damaged(:,sim_dmg_idx_filt) + dmg_data; % add number of damaged component amongst directions and multiple comps of the same frag id
+                
+                % Assign Repair time data
+                repair_time_data = DV_time{:,DV_filt};
+                repair_time_data(isnan(repair_time_data)) = 0; % Change blank cases to no damage
+                simulated_damage.story(s).worker_days(:,sim_dmg_idx_filt) = ...
+                    simulated_damage.story(s).worker_days(:,sim_dmg_idx_filt) + repair_time_data; % add the repair time amongst directions and multiple comps of the same frag id
+                
+                % Assign Repair Cost data
+                repair_cost_data = DV_cost{:,DV_filt};
+                repair_cost_data(isnan(repair_cost_data)) = 0; % Change blank cases to no damage
+                simulated_damage.story(s).repair_cost(:,sim_dmg_idx_filt) = ...
+                    simulated_damage.story(s).repair_cost(:,sim_dmg_idx_filt) + repair_cost_data; % add the repair time amongst directions and multiple comps of the same frag id
+                
+                % Assign Damage Data Per Direction
+                for dir = 1:3
+                    if dir_id == dir % only for the direction associated with this DMG column
+                        simulated_damage.story(s).(['qnt_damaged_dir_' num2str(dir)])(:,sim_dmg_idx_filt) = ...
+                        simulated_damage.story(s).(['qnt_damaged_dir_' num2str(dir)])(:,sim_dmg_idx_filt) + dmg_data; % add number of damaged component amongst directions and multiple comps of the same frag id
+                    end
+                end
+            else
+                error('Location in new damage state structure could not be found for this compoennt DS')
+            end
+        end
+    end
+    
+    % Assign component quantities
+    for c = 1:height(comps)
+        comp = comps(c,:);
+        loc = comp.Location{1};
+        if  strcmp(loc,'all')
+            is_story = true;
+        elseif  strcmp(loc,'roof')
+            is_story = s == num_stories;
+        elseif  contains(loc,'--')
+            is_story = s >= str2double(loc(1)) & s<= str2double(loc(end));
+        else
+            loc_vec = str2double(strsplit(loc));
+            is_story = ismember(s,loc_vec);
+        end
+        
+        if is_story
+            frag_id = comp.ID{1};
+            frag_id([2,5]) = []; % Remove extra periods
+            frag_filt = strcmp(comp_ds_list.comp_id,frag_id)';
+            simulated_damage.story(s).num_comps(frag_filt) = ...
+                simulated_damage.story(s).num_comps(frag_filt) + comp.Theta_0;
+        end
+    end
+end
+            
+% Write file
+fileID = fopen([model_dir filesep 'simulated_damage.json'],'w');
+fprintf(fileID,'%s',jsonencode(simulated_damage));
+fclose(fileID);
+
diff --git a/fn_red_tag.m b/fn_red_tag.m
new file mode 100644
index 0000000..c029c0a
--- /dev/null
+++ b/fn_red_tag.m
@@ -0,0 +1,140 @@
+e function [ red_tag, red_tag_impact, inspection_tag ] = ...
+    fn_red_tag( calculate_red_tag, damage, comps, simulated_replacement_time )
+% Perform the ATC-138 functional recovery time assessement given similation
+% of component damage for a single shaking intensity
+%
+% Parameters
+% ----------
+% calculate_red_tag: logical
+%   flag to indicate whether on not to calculate red tags based on
+%   component damage. Typically assumed to be FALSE for small wood light
+%   frame type structures.
+% damage: struct
+%   contains per damage state damage and loss data for each component in the building
+% comps: struct
+%   data structure component population info
+% simulated_replacement_time: array [num_reals x 1]
+%   simulated time when the building needs to be replaced, and how long it
+%   will take (in days). NaN represents no replacement needed (ie
+%   building will be repaired)
+%
+% Returns
+% -------
+% red_tag: logical array [num_reals x 1]
+%   indicates the realizations that have a red tag
+% red_tag_impact: logical array [num_reals x num_comp_ds]
+%   indicates the realizations of various component damage states that
+%   contribute to the cause of red tag
+% inspection_tag: logical array [num_reals x 1]
+%   indicates the realizations that require inspection
+
+%% Initial Setup
+% Check to see if any components need the red tag check
+% if none of the components are assinged to structuctural systems, then
+% skip the red tag calc
+if ~any(comps.comp_table.structural_system)
+    calculate_red_tag = false;
+end
+
+%% Method
+if calculate_red_tag
+    % Simulate Red Tags
+    sc_ids = [1 2 3 4];
+    sc_thresholds = [0.5 0.25 0.1 0];
+    [ red_tag, red_tag_impact ] = simulate_tagging( damage, comps, sc_ids, sc_thresholds );
+
+    % Inspection is flagged for 50% of the red tag thresholds
+    [ inspection_tag, ~ ] = simulate_tagging( damage, comps, sc_ids, 0.5*sc_thresholds );
+else
+    % Do not calculate red tags based on component damage
+    [num_reals,num_comp_ds] = size(damage.tenant_units{1}.qnt_damaged);
+    red_tag = false(num_reals,1);
+    red_tag_impact = zeros(num_reals,num_comp_ds);
+    inspection_tag = false(num_reals,1);
+end
+
+% Account for global red tag cases
+replace_case = ~isnan(simulated_replacement_time);
+red_tag(replace_case) = 1;
+
+end
+
+function [ red_tag, red_tag_impact ] = simulate_tagging( damage, comps, sc_ids, sc_thresholds )
+
+% Simulate uncertainty in inspector threhsold
+[num_reals,~] = size(damage.story{1}.qnt_damaged_dir_1);
+% sc_beta = [0.5 0.5 0.5 0.5];
+% sc_mins = [0.05 0.05 0.05 0];
+% sc_max = [0.75 0.75 0.75 0.75];
+% p_inpsector = rand(num_reals,1); % Simulate inspector "conservatism"
+% sc_sim = max(min(logninv(p_inpsector,log(sc_thresholds),sc_beta),sc_max),sc_mins);
+
+red_tag_impact = zeros(size(damage.tenant_units{1}.qnt_damaged)); % num reals by num comp_ds
+
+% Go through each structural system and calc the realizations where the
+% building is red tagged
+for sc = 1:length(sc_ids)
+    for s = 1:length(damage.story)
+        sc_filt = damage.comp_ds_table.safety_class' >= sc_ids(sc);
+        
+        for dir = 1:3 % Fix assume there are three direction, where direction 3 = nondirectional
+            sc_dmg = damage.story{s}.(['qnt_damaged_dir_' num2str(dir)]) .* sc_filt;
+            num_comps = comps.story(s).(['qty_dir_' num2str(dir)]);
+
+            % For each structural system
+            structural_systems = unique([damage.comp_ds_table.structural_system; damage.comp_ds_table.structural_system_alt]);
+            structural_systems(structural_systems == 0) = []; % do not include components not assigned to a structural system
+            sys_tag = false(num_reals, length(structural_systems));
+
+            for sys = 1:length(structural_systems)
+                ss_filt_ds = damage.comp_ds_table.structural_system' == structural_systems(sys) | damage.comp_ds_table.structural_system_alt' == structural_systems(sys);
+                ss_filt_comp = comps.comp_table.structural_system' == structural_systems(sys) | comps.comp_table.structural_system_alt' == structural_systems(sys);
+
+                % Check damage among each series within this structural system
+                series = unique(damage.comp_ds_table.structural_series_id(ss_filt_ds));
+
+                ser_dmg = zeros(num_reals, length(series));
+                ser_qty = zeros(num_reals, length(series));
+
+                for ser = 1:length(series)
+                    ser_filt_ds = damage.comp_ds_table.structural_series_id' == series(ser); 
+                    ser_filt_comp = comps.comp_table.structural_series_id' == series(ser); 
+
+                    % Total damage within this series and system
+                    ser_dmg(:,ser) = sum(sc_dmg(:,ser_filt_ds & ss_filt_ds),2); 
+
+                    % Total number of components within this series and system
+                    ser_qty(:,ser) = sum(num_comps(:,ser_filt_comp & ss_filt_comp),2); 
+                end
+
+                % Check if this system is causing a red tag
+                sys_dmg = max(ser_dmg,[],2);
+                sys_qty = max(ser_qty,[],2);
+                sys_ratio = sys_dmg ./ sys_qty;
+                sys_tag(:,sys) = sys_ratio > sc_thresholds(sc);
+%                 sys_tag(:,sys) = sys_ratio > sc_sim(:,sc); % when using simulated safety class thresholds
+                
+                % Calculate the impact that each component has on red tag
+                % (boolean, 1 = affects red tag, 0 = does not affect)
+                % Take all damage that is part of this system at this story
+                % in this direction that is damaged to this safety class
+                % level, only where damage exceeds tagging threshold
+                red_tag_impact = max(red_tag_impact, sys_tag(:,sys) .* ss_filt_ds .* sc_filt .* (sc_dmg>0));
+            end
+            
+            % Combine across all systems in this direction
+            dir_tag(:,dir) = max(sys_tag,[],2);
+        end
+
+        % Combine across all directions at this story
+        story_tag(:,s) = max(dir_tag,[],2);
+    end
+    
+    % Combine across all stories for this safety class
+    sc_tag(:,sc) = max(story_tag,[],2);
+end
+
+% Combine all safety class checks into one simulated red tag
+red_tag =  max(sc_tag,[],2);
+
+end
\ No newline at end of file
diff --git a/inputs/Inputs2Copy/build_inputs.m b/inputs/Inputs2Copy/build_inputs.m
new file mode 100644
index 0000000..d379548
--- /dev/null
+++ b/inputs/Inputs2Copy/build_inputs.m
@@ -0,0 +1,355 @@
+% This script builds the matlab datafile inputs required for the
+% reoccupancy and functional recovery assessment of a building given
+% simulated damage.
+
+% Currently, this script will build the inputs data file for an example
+% building model. Follow the instructions below to custumize this build
+% script for other assessments.
+
+% Instructions
+% ----------
+% Step 1: Place this script in the directory where you want the
+% simulated_inputs.mat assessment inputs file to be written
+% Step 2: Add the requried building specific input files listed below to
+% the same directory
+% Step 3: Copy the optional_inputs.m file from the example inputs directory
+% to the same directory and modify if needed.
+% Step 4: Make sure the variable "static_data_dir" is correctly pointing to
+% the location of the static data directory
+% Step 5: Run the build script
+
+% Option for customizing static data 
+% ----------
+% If you would like to modify the static data tables listed below for a 
+% specifc model, simply copy the subsequent static data tables to this 
+% input directory, modify them, and set the static_data_dir to this input 
+% directory.
+
+% Required building specific data (inputs directory)
+% ----------
+% building_model.json
+% tenant_unit_list.csv
+% comp_ds_list.csv
+% damage_consequences.json
+% simulated_damage.json
+
+% Optional building specific data (inputs directory)
+% ----------
+% utility_downtime.json
+
+% Static data (static data directory)
+% ----------
+% component_attributes.csv
+% damage_state_attribute_mapping.csv
+% subsystems.csv
+% tenant_function_requirements.csv
+
+% Static data (copy to this inputs directory)
+% ----------
+% optional_inputs.m
+
+clear
+close all
+clc 
+rehash
+
+%% DEFINE USER INPUTS
+% This is currently set to the default directory, relative to this example
+% inputs directory. If the location of this directory differs, updat the 
+% static_data_dir variable below. 
+static_data_dir = ['..' filesep '..' filesep '..' filesep 'static_tables'];
+
+%% PULL STATIC DATA
+% Load required data tables
+component_attributes = readtable([static_data_dir filesep 'component_attributes.csv']);
+damage_state_attribute_mapping = readtable([static_data_dir filesep 'damage_state_attribute_mapping.csv']);
+subsystems = readtable([static_data_dir filesep 'subsystems.csv']);
+tenant_function_requirements = readtable([static_data_dir filesep 'tenant_function_requirements.csv']);
+
+%% LOAD BUILDING DATA
+% This data is specific to the building model and will need to be created
+% for each assessment. Data is formated as json structures or csv tables
+
+% Building Model: Basic data about the building being assessed
+building_model = jsondecode(fileread('building_model.json'));
+
+% Transponse building model inputs to go from vertically oriented story
+% data to horizontally oriented
+names = fieldnames(building_model);
+for fn = 1:length(names)
+    building_model.(names{fn}) = building_model.(names{fn})';
+end
+
+% Translate edge length back to vertically oriented (2 sides) for single
+% story buildings
+% if building_model.num_stories == 1
+building_model.edge_lengths = building_model.edge_lengths';
+% end
+
+% List of tenant units within the building and their basic attributes
+tenant_unit_list = readtable('tenant_unit_list.csv');
+
+% List of component and damage states ids associated with the damage matrix
+comp_ds_list = readtable('comp_ds_list.csv');
+
+% List of component and damage states in the performance model
+comp_population = readtable('comp_population.csv');
+
+% Read header row unaltered by matlab
+fid = fopen('comp_population.csv');
+comp_header = strsplit(fgetl(fid), ',');
+fclose(fid);
+comp_list = strrep(comp_header(3:end),'_','.');
+building_model.comps.comp_list = comp_list;
+
+% Go through each story and assign component populations
+drs = unique(comp_population.dir);
+for s = 1:building_model.num_stories
+    for d = 1:length(drs)
+        filt = comp_population.story == s & comp_population.dir == drs(d);
+        building_model.comps.story{s}.(['qty_dir_' num2str(drs(d))]) = comp_population{filt,3:end};
+    end
+end
+
+ % Set comp info table
+for c = 1:length(comp_list)
+    % Find the component attributes of this component
+    comp_attr_filt = strcmp(string(component_attributes.fragility_id),comp_list{c});
+    if sum(comp_attr_filt) ~= 1
+        error('Could not find component attrubutes')
+    else
+        comp_attr = component_attributes(comp_attr_filt,:);
+    end
+    
+    comp_info.comp_id{c,1} = comp_list{c};
+    comp_info.comp_idx(c,1) = c;
+    comp_info.structural_system(c,1)  = comp_attr.structural_system;
+    comp_info.structural_system_alt(c,1)  = comp_attr.structural_system_alt;
+    comp_info.structural_series_id(c,1)  = comp_attr.structural_series_id;
+end
+building_model.comps.comp_table = struct2table(comp_info);
+
+%% LOAD SIMULATED DATA
+% This data is specific to the building performance at the assessed hazard
+% intensity and will need to be created for each assessment. 
+% Data is formated as json structures.
+
+% Simulated damage consequences - various building and story level
+% consequences of simulated data, for each realization of the monte carlo
+% simulation.
+damage_consequences = jsondecode(fileread('damage_consequences.json'));
+
+% Simulated utility downtimes for electrical, water, and gas networks - 
+% for each realization of the monte carlo simulation.
+if exist('utility_downtime.json','file')
+    functionality = jsondecode(fileread('utility_downtime.json'));
+else
+    % If no data exist, assume there is no consequence of network downtime
+    num_reals = length(damage_consequences.repair_cost_ratio_total);
+    functionality.utilities.electrical = zeros(num_reals,1);
+    functionality.utilities.water = zeros(num_reals,1);
+    functionality.utilities.gas = zeros(num_reals,1);
+end
+
+% Simulated component damage per tenant unit for each realization of the
+% monte carlo simulation
+sim_damage = jsondecode(fileread('simulated_damage.json'));
+
+% Transform damage per story array to cell array to work with later code
+if isfield(sim_damage,'story')
+    for s = 1:length(sim_damage.story)
+        damage.story{s} = sim_damage.story(s);
+        if isfield(damage.story{s},'num_comps')
+            damage.story{s}.num_comps = damage.story{s}.num_comps'; 
+        end
+    end
+end
+
+% Transform damage per tenant unit array to cell array to work with later code
+if isfield(sim_damage,'tenant_units')
+    for tu = 1:length(sim_damage.tenant_units)
+        damage.tenant_units{tu} = sim_damage.tenant_units(tu);
+        if isfield(damage.tenant_units{tu},'num_comps')
+            damage.tenant_units{tu}.num_comps = damage.tenant_units{tu}.num_comps'; 
+        end
+    end
+end
+
+
+%% LOAD DEFAULT OPTIONAL INPUTS
+% various assessment otpions. Set to default options in the
+% optional_inputs.m file. This file is expected to be in this input
+% directory. This file can be customized for each assessment if desired.
+optional_inputs
+
+%% PULL ADDITIONAL ATTRIBUTES FROM THE STATIC DATA TABLES
+% Preallocate tenant unit table
+tenant_units = tenant_unit_list;
+tenant_units.exterior = zeros(height(tenant_units),1);
+tenant_units.interior = zeros(height(tenant_units),1);
+tenant_units.occ_per_elev = zeros(height(tenant_units),1);
+tenant_units.is_elevator_required = zeros(height(tenant_units),1);
+tenant_units.is_electrical_required = zeros(height(tenant_units),1);
+tenant_units.is_water_potable_required = zeros(height(tenant_units),1);
+tenant_units.is_water_sanitary_required = zeros(height(tenant_units),1);
+tenant_units.is_hvac_ventilation_required = zeros(height(tenant_units),1);
+tenant_units.is_hvac_heating_required = zeros(height(tenant_units),1);
+tenant_units.is_hvac_cooling_required = zeros(height(tenant_units),1);
+tenant_units.is_hvac_exhaust_required = zeros(height(tenant_units),1);
+tenant_units.is_data_required = zeros(height(tenant_units),1);
+
+% Pull default tenant unit attributes for each tenant unit listed in the
+% tenant_unit_list
+for tu = 1:height(tenant_unit_list)
+    fnc_requirements_filt = tenant_function_requirements.occupancy_id == tenant_units.occupancy_id(tu);
+    if sum(fnc_requirements_filt) ~= 1
+        error('Tenant Unit Requirements for This Occupancy Not Found')
+    end
+    tenant_units.exterior(tu) = tenant_function_requirements.exterior(fnc_requirements_filt);
+    tenant_units.interior(tu) = tenant_function_requirements.interior(fnc_requirements_filt);
+    tenant_units.occ_per_elev(tu) = tenant_function_requirements.occ_per_elev(fnc_requirements_filt);
+    if tenant_function_requirements.is_elevator_required(fnc_requirements_filt) && ...
+            tenant_function_requirements.max_walkable_story(fnc_requirements_filt) < tenant_units.story(tu)
+        tenant_units.is_elevator_required(tu) = 1;
+    else
+        tenant_units.is_elevator_required(tu) = 0;
+    end
+    tenant_units.is_electrical_required(tu) = tenant_function_requirements.is_electrical_required(fnc_requirements_filt);
+    tenant_units.is_water_potable_required(tu) = tenant_function_requirements.is_water_potable_required(fnc_requirements_filt);
+    tenant_units.is_water_sanitary_required(tu) = tenant_function_requirements.is_water_sanitary_required(fnc_requirements_filt);
+    tenant_units.is_hvac_ventilation_required(tu) = tenant_function_requirements.is_hvac_ventilation_required(fnc_requirements_filt);
+    tenant_units.is_hvac_heating_required(tu) = tenant_function_requirements.is_hvac_heating_required(fnc_requirements_filt);
+    tenant_units.is_hvac_cooling_required(tu) = tenant_function_requirements.is_hvac_cooling_required(fnc_requirements_filt);
+    tenant_units.is_hvac_exhaust_required(tu) = tenant_function_requirements.is_hvac_exhaust_required(fnc_requirements_filt);
+    tenant_units.is_data_required(tu) = tenant_function_requirements.is_data_required(fnc_requirements_filt);
+end
+
+
+% Pull default component and damage state attributes for each component 
+% in the comp_ds_list
+for c = 1:height(comp_ds_list)
+    
+    % Find the component attributes of this component
+    comp_attr_filt = strcmp(string(component_attributes.fragility_id),comp_ds_list.comp_id{c});
+    if sum(comp_attr_filt) ~= 1
+        error('Could not find component attrubutes')
+    else
+        comp_attr = component_attributes(comp_attr_filt,:);
+    end
+    
+    % Find the damage state attributes of this componnt
+    ds_comp_filt = ~cellfun(@isempty,regexp(comp_ds_list.comp_id{c},damage_state_attribute_mapping.fragility_id_regex));
+    ds_seq_filt = damage_state_attribute_mapping.ds_index == comp_ds_list.ds_seq_id(c);
+    if comp_ds_list.ds_sub_id(c) == 1
+        ds_sub_filt = ismember(damage_state_attribute_mapping.sub_ds_index,{'1','NA'});
+    else
+        ds_sub_filt = strcmp(damage_state_attribute_mapping.sub_ds_index,num2str(comp_ds_list.ds_sub_id(c)));
+    end
+    ds_filt = ds_comp_filt & ds_seq_filt & ds_sub_filt;
+    
+    if sum(ds_filt) ~= 1
+        error('Could not find damage state attrubutes')
+    else
+        ds_attr = damage_state_attribute_mapping(ds_filt,:);
+    end
+    
+    %% Populate data for each damage state
+    % Basic Component and DS identifiers
+    comp_ds_info.comp_id{c,1} = comp_ds_list.comp_id{c};
+    comp_ds_info.comp_type_id{c,1} = comp_ds_list.comp_id{c}(1:5); % first 5 characters indicate the type
+    comp_ds_info.comp_idx(c,1) = c;
+    comp_ds_info.ds_seq_id(c,1) = ds_attr.ds_index;
+    comp_ds_info.ds_sub_id(c,1) = str2double(strrep(ds_attr.sub_ds_index{1},'NA','1'));
+
+    % Set Component Attributes
+    comp_ds_info.system(c,1) = comp_attr.system_id;
+    comp_ds_info.subsystem_id(c,1) = comp_attr.subsystem_id;
+    comp_ds_info.structural_system(c,1)  = comp_attr.structural_system;
+    comp_ds_info.structural_system_alt(c,1)  = comp_attr.structural_system_alt;
+    comp_ds_info.structural_series_id(c,1)  = comp_attr.structural_series_id;
+    comp_ds_info.unit{c,1} = comp_attr.unit{1};
+    comp_ds_info.unit_qty(c,1) = comp_attr.unit_qty;
+    comp_ds_info.service_location{c,1} = comp_attr.service_location{1};
+
+    % Set Damage State Attributes
+    comp_ds_info.is_sim_ds(c,1) = ds_attr.is_sim_ds;
+    comp_ds_info.safety_class(c,1) = ds_attr.safety_class;
+    comp_ds_info.affects_envelope_safety(c,1) = ds_attr.affects_envelope_safety;
+    comp_ds_info.ext_falling_hazard(c,1) = ds_attr.exterior_falling_hazard;
+    comp_ds_info.int_falling_hazard(c,1) = ds_attr.interior_falling_hazard;
+    comp_ds_info.global_hazardous_material(c,1) = ds_attr.global_hazardous_material;
+    comp_ds_info.local_hazardous_material(c,1) = ds_attr.local_hazardous_material;
+    comp_ds_info.weakens_fire_break(c,1) = ds_attr.weakens_fire_break;
+    comp_ds_info.affects_access(c,1) = ds_attr.affects_access;
+    comp_ds_info.damages_envelope_seal(c,1) = ds_attr.damages_envelope_seal;
+    comp_ds_info.affects_roof_function(c,1) = ds_attr.affects_roof_function;
+    comp_ds_info.obstructs_interior_space(c,1) = ds_attr.obstructs_interior_space;
+    comp_ds_info.impairs_system_operation(c,1) = ds_attr.impairs_system_operation;
+    comp_ds_info.causes_flooding(c,1) = ds_attr.causes_flooding;
+    comp_ds_info.interior_area_factor(c,1) = ds_attr.interior_area_factor;
+    comp_ds_info.interior_area_conversion_type(c,1) = ds_attr.interior_area_conversion_type;
+    comp_ds_info.exterior_surface_area_factor(c,1) = ds_attr.exterior_surface_area_factor;
+    comp_ds_info.exterior_falling_length_factor(c,1) = ds_attr.exterior_falling_length_factor;
+    comp_ds_info.crew_size(c,1) = ds_attr.crew_size;
+    comp_ds_info.permit_type(c,1) = ds_attr.permit_type;
+    comp_ds_info.redesign(c,1) = ds_attr.redesign;
+    comp_ds_info.long_lead_time(c,1) = impedance_options.default_lead_time * ds_attr.long_lead;
+    comp_ds_info.requires_shoring(c,1) = ds_attr.requires_shoring;
+    comp_ds_info.resolved_by_scaffolding(c,1) = ds_attr.resolved_by_scaffolding;
+    comp_ds_info.tmp_repair_class(c,1) = ds_attr.tmp_repair_class;
+    comp_ds_info.tmp_repair_time_lower(c,1) = ds_attr.tmp_repair_time_lower;
+    comp_ds_info.tmp_repair_time_upper(c,1) = ds_attr.tmp_repair_time_upper;
+    if comp_ds_info.tmp_repair_class(c,1) > 0 % only grab values for components with temp repair times
+        time_lower_quantity = ds_attr.time_lower_quantity;
+        time_upper_quantity = ds_attr.time_upper_quantity;
+        if iscell(time_lower_quantity)
+            time_lower_quantity = str2double(time_lower_quantity);
+        end
+        if iscell(time_upper_quantity)
+            time_upper_quantity = str2double(time_upper_quantity);
+        end
+        comp_ds_info.tmp_repair_time_lower_qnty(c,1) = time_lower_quantity;
+        comp_ds_info.tmp_repair_time_upper_qnty(c,1) = time_upper_quantity;
+    else
+        comp_ds_info.tmp_repair_time_lower_qnty(c,1) = NaN;
+        comp_ds_info.tmp_repair_time_upper_qnty(c,1) = NaN;
+    end
+    comp_ds_info.tmp_crew_size(c,1) = ds_attr.tmp_crew_size;
+
+    % Subsystem attributes
+    subsystem_filt = subsystems.id == comp_attr.subsystem_id;
+    if comp_ds_info.subsystem_id(c,1) == 0
+        % No subsytem
+        comp_ds_info.n1_redundancy(c,1) = 0;
+        comp_ds_info.parallel_operation(c,1) = 0;
+        comp_ds_info.redundancy_threshold(c,1) = 0;
+    elseif sum(subsystem_filt) ~= 1
+        error('Could not find damage state attrubutes')
+    else
+        % Set Damage State Attributes
+        comp_ds_info.n1_redundancy(c,1) = subsystems.n1_redundancy(subsystem_filt);
+        comp_ds_info.parallel_operation(c,1) = subsystems.parallel_operation(subsystem_filt);
+        comp_ds_info.redundancy_threshold(c,1) = subsystems.redundancy_threshold(subsystem_filt);
+    end
+end
+damage.comp_ds_table = struct2table(comp_ds_info);
+
+%% Check missing data
+% Engineering Repair Cost Ratio - Assume is the sum of all component repair
+% costs that require redesign
+if ~isfield(damage_consequences,'repair_cost_ratio_engineering')
+    eng_filt = logical(damage.comp_ds_table.redesign');
+    damage_consequences.repair_cost_ratio_engineering = zeros(size(damage_consequences.repair_cost_ratio_total));
+    for s = 1:length(sim_damage.story)
+        damage_consequences.repair_cost_ratio_engineering = ...
+            damage_consequences.repair_cost_ratio_engineering + ...
+            sum(sim_damage.story(s).repair_cost(:,eng_filt),2);
+    end
+end
+
+%% SAVE INPUTS AS MATLAB DATAFILE
+save('simulated_inputs.mat',...
+    'building_model','damage','damage_consequences','functionality',...
+    'functionality_options','impedance_options',...
+    'repair_time_options','tenant_units')
+
diff --git a/inputs/Inputs2Copy/optional_inputs.m b/inputs/Inputs2Copy/optional_inputs.m
new file mode 100644
index 0000000..0d586e3
--- /dev/null
+++ b/inputs/Inputs2Copy/optional_inputs.m
@@ -0,0 +1,67 @@
+% Impedance Time Options
+impedance_options.include_impedance.inspection = true;
+impedance_options.include_impedance.financing = true;
+impedance_options.include_impedance.permitting = true;
+impedance_options.include_impedance.engineering = true;
+impedance_options.include_impedance.contractor = true;
+impedance_options.include_impedance.long_lead = false;
+impedance_options.system_design_time.f = 0.04;
+impedance_options.system_design_time.r = 200; 
+impedance_options.system_design_time.t = 1.3;
+impedance_options.system_design_time.w = 8;
+impedance_options.eng_design_min_days = 14;
+impedance_options.eng_design_max_days = 365;
+impedance_options.mitigation.is_essential_facility = false;
+impedance_options.mitigation.is_borp_equivalent = false;
+impedance_options.mitigation.is_engineer_on_retainer = false;
+impedance_options.mitigation.contractor_relationship = 'good';
+impedance_options.mitigation.contractor_retainer_time = 3;
+impedance_options.mitigation.funding_source = 'private';
+impedance_options.mitigation.capital_available_ratio = 0.02;
+impedance_options.impedance_beta = 0.6;
+impedance_options.impedance_truncation = 2;
+impedance_options.default_lead_time = 182;
+impedance_options.demand_surge.include_surge = 1;
+impedance_options.demand_surge.is_dense_urban_area = 1;
+impedance_options.demand_surge.site_pga = 1;
+impedance_options.demand_surge.pga_de = 1;
+impedance_options.scaffolding_lead_time = 5;
+impedance_options.scaffolding_erect_time = 2;
+impedance_options.door_racking_repair_day = 3;
+impedance_options.flooding_cleanup_day = 5;
+impedance_options.flooding_repair_day = 90;
+
+% Repair Schedule Options
+repair_time_options.max_workers_per_sqft_story = 0.001;
+repair_time_options.max_workers_per_sqft_story_temp_repair = 0.005;
+repair_time_options.max_workers_per_sqft_building = 0.00025;
+repair_time_options.max_workers_building_min = 20;
+repair_time_options.max_workers_building_max = 260;
+repair_time_options.allow_tmp_repairs = 1;
+repair_time_options.allow_shoring = 1;
+
+% Functionality Assessment Options
+functionality_options.calculate_red_tag = 1;
+functionality_options.red_tag_clear_time = 7;
+functionality_options.red_tag_clear_beta = 0.6;
+functionality_options.red_tag_options.tag_coupling_beams_over_height = true;
+functionality_options.red_tag_options.ignore_coupling_beam_for_red_tag = false;
+functionality_options.include_local_stability_impact = 1;
+functionality_options.include_flooding_impact = 1;
+functionality_options.egress_threshold = 0.5;
+functionality_options.fire_watch = true;
+functionality_options.local_fire_damamge_threshold = 0.25;
+functionality_options.min_egress_paths = 2;
+functionality_options.exterior_safety_threshold = 0.1;
+functionality_options.interior_safety_threshold = 0.25;
+functionality_options.door_access_width_ft = 9;
+functionality_options.habitability_requirements.electrical = 0;
+functionality_options.habitability_requirements.water_potable = 0;
+functionality_options.habitability_requirements.water_sanitary = 0;
+functionality_options.habitability_requirements.hvac_ventilation = 0;
+functionality_options.habitability_requirements.hvac_heating = 0;
+functionality_options.habitability_requirements.hvac_cooling = 0;
+functionality_options.habitability_requirements.hvac_exhaust = 0;
+functionality_options.water_pressure_max_story = 4;
+functionality_options.heat_utility = 'gas';
+
diff --git a/examples/ICSB/building_model.json b/inputs/example_inputs/ICSB/building_model.json
similarity index 100%
rename from examples/ICSB/building_model.json
rename to inputs/example_inputs/ICSB/building_model.json
diff --git a/examples/ICSB/comp_ds_list.csv b/inputs/example_inputs/ICSB/comp_ds_list.csv
similarity index 100%
rename from examples/ICSB/comp_ds_list.csv
rename to inputs/example_inputs/ICSB/comp_ds_list.csv
diff --git a/examples/ICSB/comp_population.csv b/inputs/example_inputs/ICSB/comp_population.csv
similarity index 100%
rename from examples/ICSB/comp_population.csv
rename to inputs/example_inputs/ICSB/comp_population.csv
diff --git a/examples/ICSB/damage_consequences.json b/inputs/example_inputs/ICSB/damage_consequences.json
similarity index 100%
rename from examples/ICSB/damage_consequences.json
rename to inputs/example_inputs/ICSB/damage_consequences.json
diff --git a/examples/ICSB/simulated_damage.json b/inputs/example_inputs/ICSB/simulated_damage.json
similarity index 100%
rename from examples/ICSB/simulated_damage.json
rename to inputs/example_inputs/ICSB/simulated_damage.json
diff --git a/inputs/example_inputs/ICSB/simulated_inputs.mat b/inputs/example_inputs/ICSB/simulated_inputs.mat
new file mode 100644
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diff --git a/examples/ICSB/tenant_unit_list.csv b/inputs/example_inputs/ICSB/tenant_unit_list.csv
similarity index 100%
rename from examples/ICSB/tenant_unit_list.csv
rename to inputs/example_inputs/ICSB/tenant_unit_list.csv
diff --git a/examples/RCSW_1story/building_model.json b/inputs/example_inputs/RCSW_1story/building_model.json
similarity index 100%
rename from examples/RCSW_1story/building_model.json
rename to inputs/example_inputs/RCSW_1story/building_model.json
diff --git a/examples/RCSW_1story/comp_ds_list.csv b/inputs/example_inputs/RCSW_1story/comp_ds_list.csv
similarity index 100%
rename from examples/RCSW_1story/comp_ds_list.csv
rename to inputs/example_inputs/RCSW_1story/comp_ds_list.csv
diff --git a/examples/RCSW_1story/comp_population.csv b/inputs/example_inputs/RCSW_1story/comp_population.csv
similarity index 100%
rename from examples/RCSW_1story/comp_population.csv
rename to inputs/example_inputs/RCSW_1story/comp_population.csv
diff --git a/examples/RCSW_1story/damage_consequences.json b/inputs/example_inputs/RCSW_1story/damage_consequences.json
similarity index 100%
rename from examples/RCSW_1story/damage_consequences.json
rename to inputs/example_inputs/RCSW_1story/damage_consequences.json
diff --git a/examples/RCSW_1story/simulated_damage.json b/inputs/example_inputs/RCSW_1story/simulated_damage.json
similarity index 100%
rename from examples/RCSW_1story/simulated_damage.json
rename to inputs/example_inputs/RCSW_1story/simulated_damage.json
diff --git a/inputs/example_inputs/RCSW_1story/simulated_inputs.mat b/inputs/example_inputs/RCSW_1story/simulated_inputs.mat
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diff --git a/examples/RCSW_1story/tenant_unit_list.csv b/inputs/example_inputs/RCSW_1story/tenant_unit_list.csv
similarity index 100%
rename from examples/RCSW_1story/tenant_unit_list.csv
rename to inputs/example_inputs/RCSW_1story/tenant_unit_list.csv
diff --git a/examples/haseltonRCMF_12story/building_model.json b/inputs/example_inputs/haseltonRCMF_12story/building_model.json
similarity index 100%
rename from examples/haseltonRCMF_12story/building_model.json
rename to inputs/example_inputs/haseltonRCMF_12story/building_model.json
diff --git a/examples/haseltonRCMF_12story/comp_ds_list.csv b/inputs/example_inputs/haseltonRCMF_12story/comp_ds_list.csv
similarity index 100%
rename from examples/haseltonRCMF_12story/comp_ds_list.csv
rename to inputs/example_inputs/haseltonRCMF_12story/comp_ds_list.csv
diff --git a/examples/haseltonRCMF_12story/comp_population.csv b/inputs/example_inputs/haseltonRCMF_12story/comp_population.csv
similarity index 100%
rename from examples/haseltonRCMF_12story/comp_population.csv
rename to inputs/example_inputs/haseltonRCMF_12story/comp_population.csv
diff --git a/examples/haseltonRCMF_12story/damage_consequences.json b/inputs/example_inputs/haseltonRCMF_12story/damage_consequences.json
similarity index 100%
rename from examples/haseltonRCMF_12story/damage_consequences.json
rename to inputs/example_inputs/haseltonRCMF_12story/damage_consequences.json
diff --git a/examples/haseltonRCMF_12story/simulated_damage.json b/inputs/example_inputs/haseltonRCMF_12story/simulated_damage.json
similarity index 100%
rename from examples/haseltonRCMF_12story/simulated_damage.json
rename to inputs/example_inputs/haseltonRCMF_12story/simulated_damage.json
diff --git a/inputs/example_inputs/haseltonRCMF_12story/simulated_inputs.mat b/inputs/example_inputs/haseltonRCMF_12story/simulated_inputs.mat
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diff --git a/examples/haseltonRCMF_12story/tenant_unit_list.csv b/inputs/example_inputs/haseltonRCMF_12story/tenant_unit_list.csv
similarity index 100%
rename from examples/haseltonRCMF_12story/tenant_unit_list.csv
rename to inputs/example_inputs/haseltonRCMF_12story/tenant_unit_list.csv
diff --git a/examples/haseltonRCMF_4story/building_model.json b/inputs/example_inputs/haseltonRCMF_4story/building_model.json
similarity index 100%
rename from examples/haseltonRCMF_4story/building_model.json
rename to inputs/example_inputs/haseltonRCMF_4story/building_model.json
diff --git a/examples/haseltonRCMF_4story/comp_ds_list.csv b/inputs/example_inputs/haseltonRCMF_4story/comp_ds_list.csv
similarity index 100%
rename from examples/haseltonRCMF_4story/comp_ds_list.csv
rename to inputs/example_inputs/haseltonRCMF_4story/comp_ds_list.csv
diff --git a/examples/haseltonRCMF_4story/comp_population.csv b/inputs/example_inputs/haseltonRCMF_4story/comp_population.csv
similarity index 100%
rename from examples/haseltonRCMF_4story/comp_population.csv
rename to inputs/example_inputs/haseltonRCMF_4story/comp_population.csv
diff --git a/examples/haseltonRCMF_4story/damage_consequences.json b/inputs/example_inputs/haseltonRCMF_4story/damage_consequences.json
similarity index 100%
rename from examples/haseltonRCMF_4story/damage_consequences.json
rename to inputs/example_inputs/haseltonRCMF_4story/damage_consequences.json
diff --git a/examples/haseltonRCMF_4story/simulated_damage.json b/inputs/example_inputs/haseltonRCMF_4story/simulated_damage.json
similarity index 100%
rename from examples/haseltonRCMF_4story/simulated_damage.json
rename to inputs/example_inputs/haseltonRCMF_4story/simulated_damage.json
diff --git a/inputs/example_inputs/haseltonRCMF_4story/simulated_inputs.mat b/inputs/example_inputs/haseltonRCMF_4story/simulated_inputs.mat
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Ly#wsG!Q}q{xAmK0

literal 0
HcmV?d00001

diff --git a/examples/haseltonRCMF_4story/tenant_unit_list.csv b/inputs/example_inputs/haseltonRCMF_4story/tenant_unit_list.csv
similarity index 100%
rename from examples/haseltonRCMF_4story/tenant_unit_list.csv
rename to inputs/example_inputs/haseltonRCMF_4story/tenant_unit_list.csv
diff --git a/inputs/example_pelicun_inputs/001/general_inputs.json b/inputs/example_pelicun_inputs/001/general_inputs.json
new file mode 100644
index 0000000..d21a422
--- /dev/null
+++ b/inputs/example_pelicun_inputs/001/general_inputs.json
@@ -0,0 +1 @@
+{"num_entry_doors":2,"typ_struct_bay_area_ft":1089,"typ_story_ht_ft":10,"length_side_1_ft":100,"length_side_2_ft":100,"peak_occ_rate":0.0031}
\ No newline at end of file
diff --git a/inputs/example_pelicun_inputs/001/pelicun_data/CMP_QNT.csv b/inputs/example_pelicun_inputs/001/pelicun_data/CMP_QNT.csv
new file mode 100644
index 0000000..5b81f48
--- /dev/null
+++ b/inputs/example_pelicun_inputs/001/pelicun_data/CMP_QNT.csv
@@ -0,0 +1,34 @@
+ID,Units,Location,Direction,Theta_0,Blocks,Family,Theta_1,Comment
+B.10.41.001a,ea,"3, 4","1,2",2,2,N/A,,"24x24 ACI 318 SMF, beam on one side"
+B.10.41.002a,ea,1,"1,2",2,2,,,"24x36 ACI 318 SMF, beam on one side"
+B.10.41.002a,ea,2,"1,2",1,,,,"24x36 ACI 318 SMF, beam on one side"
+B.10.41.002b,ea,2--4,"1,2",3,3,,,"24x36 ACI 318 SMF, beam on both sides"
+B.10.41.003a,ea,2,"1,2",1,,,,"36x36 ACI 318 SMF, beam on one side"
+B.10.41.003b,ea,1,"1,2",3,3,,,"36x36 ACI 318 SMF, beam on both sides"
+B.10.49.031,ea,all,0,15,15,,,Post-tensioned concrete flat slabs- columns with shear reinforcing 0<Vg/Vo<0.4
+B.20.22.031,ft2,1,1,5040,168,,,"Midrise stick-built curtain wall, Config: Monolithic, Lamination: Not laminated, Glass Type: Annealed, Details: 1/4 in. (6 mm) AN monolithic; glass-frame clearance = 0.43 in. (11 mm); aspect ratio = 6:5 sealant = dry"
+B.20.22.031,ft2,1,2,3360,112,,,"Midrise stick-built curtain wall, Config: Monolithic, Lamination: Not laminated, Glass Type: Annealed, Details: 1/4 in. (6 mm) AN monolithic; glass-frame clearance = 0.43 in. (11 mm); aspect ratio = 6:5 sealant = dry"
+B.20.22.031,ft2,2--4,1,4680,156,,,"Midrise stick-built curtain wall, Config: Monolithic, Lamination: Not laminated, Glass Type: Annealed, Details: 1/4 in. (6 mm) AN monolithic; glass-frame clearance = 0.43 in. (11 mm); aspect ratio = 6:5 sealant = dry"
+B.20.22.031,ft2,2--4,2,3120,104,,,"Midrise stick-built curtain wall, Config: Monolithic, Lamination: Not laminated, Glass Type: Annealed, Details: 1/4 in. (6 mm) AN monolithic; glass-frame clearance = 0.43 in. (11 mm); aspect ratio = 6:5 sealant = dry"
+B.30.11.011,ft2,roof,0,5832,59,,,"Concrete tile roof, tiles secured and compliant with UBC94"
+C.10.11.001a,ft,1,"1,2",660,7,,,"Wall Partition, Type: Gypsum with metal studs, Full Height, Fixed Below, Fixed Above"
+C.10.11.001a,ft,2--4,"1,2",891,9,,,"Wall Partition, Type: Gypsum with metal studs, Full Height, Fixed Below, Fixed Above"
+C.30.11.002c,ft,all,"1,2",113,2,,,"Wall Partition, Type: Gypsum + Ceramic Tile, Full Height, Fixed Below, Slip Track Above w/ returns (friction connection)"
+C.30.32.003b,ft2,all,0,21600,36,,,"Suspended Ceiling, SDC D,E (Ip=1.0), Area (A): 250 < A < 1000, Vert & Lat support"
+C.30.34.002,ea,all,0,648,24,,,Independent Pendant Lighting - seismically rated
+D.20.21.023a,ft,all,0,1230,2,,,"Cold or Hot Potable Water Piping (dia > 2.5 inches), SDC D,E,F, PIPING FRAGILITY"
+D.20.21.023b,ft,all,0,1230,2,,,"Cold or Hot Potable Water Piping (dia > 2.5 inches), SDC D,E,F, BRACING FRAGILITY"
+D.20.22.013a,ft,all,0,1920,2,,,"Heating hot Water Piping - Small Diameter Threaded Steel - (2.5 inches in diameter or less), SDC D, E, or F, PIPING FRAGILITY"
+D.20.22.023a,ft,all,0,760,,,,"Heating hot Water Piping - Large Diameter Welded Steel - (greater than 2.5 inches in diameter), SDC D, E, or F, PIPING FRAGILITY"
+D.20.22.023b,ft,all,0,760,,,,"Heating hot Water Piping - Large Diameter Welded Steel - (greater than 2.5 inches in diameter), SDC D, E, or F, BRACING FRAGILITY"
+D.20.31.013b,ft,all,0,1230,2,,,"Sanitary Waste Piping - Cast Iron w/flexible couplings, SDC D,E,F, BRACING FRAGILITY"
+D.20.61.013b,ft,all,0,1920,2,,,"Steam Piping - Small Diameter Threaded Steel - (2.5 inches in diameter or less), SDC D, E, or F, BRACING FRAGILITY"
+D.30.31.013i,ea,roof,0,1,,,,Chiller - Capacity: 350 to <750 Ton - Equipment that is either hard anchored or is vibration isolated with seismic snubbers/restraints - Combined anchorage/isolator & equipment fragility
+D.30.31.023i,ea,roof,0,1,,,,Cooling Tower - Capacity: 350 to <750 Ton - Equipment that is either hard anchored or is vibration isolated with seismic snubbers/restraints - Combined anchorage/isolator & equipment fragility
+D.30.41.021c,ft,all,0,1620,2,,,"HVAC Stainless Steel Ducting less than 6 sq. ft in cross sectional area, SDC D, E, or F"
+D.30.41.022c,ft,all,0,430,,,,"HVAC Stainless Steel Ducting -  6 sq. ft cross sectional area or greater, SDC D, E, or F"
+D.30.41.032c,ea,all,0,19.44,20,,,"HVAC Drops / Diffusers without ceilings - supported by ducting only - No independent safety wires, SDC D, E, or F"
+D.30.41.041b,ea,all,0,16,2,,,"Variable Air Volume (VAV) box with in-line coil, SDC C"
+D.30.52.013i,ea,roof,0,4,4,,,Air Handling Unit - Capacity: 10000 to <25000 CFM - Equipment that is either hard anchored or is vibration isolated with seismic snubbers/restraints - Combined anchorage/isolator & equipment fragility
+D.40.11.023a,ft,all,0,4320,5,,,"Fire Sprinkler Water Piping - Horizontal Mains and Branches - Old Style Victaulic - Thin Wall Steel - with designed bracing, SDC D, E, or F (OSHPD or sim), PIPING FRAGILITY"
+D.40.11.033a,ea,all,0,194,2,,,"Fire Sprinkler Drop Standard Threaded Steel - Dropping into unbraced lay-in tile SOFT ceiling - 6 ft. long drop maximum, SDC D, E, or F"
diff --git a/inputs/example_pelicun_inputs/001/pelicun_data/DL_summary.csv b/inputs/example_pelicun_inputs/001/pelicun_data/DL_summary.csv
new file mode 100644
index 0000000..1e1f399
--- /dev/null
+++ b/inputs/example_pelicun_inputs/001/pelicun_data/DL_summary.csv
@@ -0,0 +1,501 @@
+#,repair_cost-,repair_time-parallel,repair_time-sequential,collapse,irreparable
+0,12641648.444969147,13494.513696542552,13494.513696542552,0.0,1.0
+1,8847517.298934922,16051.418611689896,16051.418611689896,0.0,1.0
+2,18045498.745757017,17493.901589809582,17493.901589809582,0.0,1.0
+3,2163818.8196495427,579.7407747030634,1568.0878137380307,0.0,0.0
+4,13825441.850770922,16616.049826108814,16616.049826108814,1.0,0.0
+5,2706784.535966555,833.3909699936039,1641.9889025910168,0.0,0.0
+6,10621565.364381785,11219.390926360787,11219.390926360787,0.0,1.0
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diff --git a/inputs/example_pelicun_inputs/001/pelicun_data/DMG_sample.csv b/inputs/example_pelicun_inputs/001/pelicun_data/DMG_sample.csv
new file mode 100644
index 0000000..db9d7b7
--- /dev/null
+++ b/inputs/example_pelicun_inputs/001/pelicun_data/DMG_sample.csv
@@ -0,0 +1,501 @@
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diff --git a/inputs/example_pelicun_inputs/001/pelicun_data/DV_bldg_repair_sample.csv b/inputs/example_pelicun_inputs/001/pelicun_data/DV_bldg_repair_sample.csv
new file mode 100644
index 0000000..f446fbf
--- /dev/null
+++ b/inputs/example_pelicun_inputs/001/pelicun_data/DV_bldg_repair_sample.csv
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diff --git a/inputs/example_pelicun_inputs/001/pelicun_data/input.json b/inputs/example_pelicun_inputs/001/pelicun_data/input.json
new file mode 100644
index 0000000..2e71395
--- /dev/null
+++ b/inputs/example_pelicun_inputs/001/pelicun_data/input.json
@@ -0,0 +1,72 @@
+{
+    "DL": {
+        "Asset": {
+            "ComponentAssignmentFile": "CMP_QNT.csv",
+            "ComponentDatabase": "FEMA P-58",
+            "NumberOfStories": "4",
+            "OccupancyType": "COM1",
+            "PlanArea": "15000"
+        },
+        "Damage": {
+            "CollapseFragility": {
+                "CapacityDistribution": "lognormal",
+                "CapacityMedian": "521.219",
+                "DemandType": "SA_1.13",
+                "Theta_1": "0.5"
+            },
+            "DamageProcess": "FEMA P-58",
+            "IrreparableDamage": {
+                "DriftCapacityLogStd": "0.3",
+                "DriftCapacityMedian": "0.015"
+            }
+        },
+        "Demands": {
+            "Calibration": {
+                "ALL": {
+                    "DistributionFamily": "lognormal"
+                },
+                "PID": {
+                    "DistributionFamily": "lognormal",
+                    "TruncateLower": "",
+                    "TruncateUpper": "0.06"
+                }
+            },
+            "CollapseLimits": {
+                "PID": "0.06"
+            },
+            "DemandFilePath": "demands_s4.csv",
+            "InferResidualDrift": {
+                "1": "0.0075",
+                "2": "0.0075",
+                "method": "FEMA P-58"
+            },
+            "SampleSize": "500"
+        },
+        "Losses": {
+            "BldgRepair": {
+                "ConsequenceDatabase": "FEMA P-58",
+                "MapApproach": "Automatic",
+                "ReplacementCost": {
+                    "Distribution": "normal",
+                    "Median": "12500000",
+                    "Theta_1": "0.3",
+                    "Unit": "USD_2011"
+                },
+                "ReplacementTime": {
+                    "Distribution": "lognormal",
+                    "Median": "15000",
+                    "Theta_1": "0.4",
+                    "Unit": "worker_day"
+                }
+            }
+        }
+    },
+    "GeneralInformation": {
+        "units": {
+            "force": "kips",
+            "length": "in",
+            "temperature": "C",
+            "time": "sec"
+        }
+    }
+}
diff --git a/inputs/example_pelicun_inputs/001/tenant_unit_list.csv b/inputs/example_pelicun_inputs/001/tenant_unit_list.csv
new file mode 100644
index 0000000..485c746
--- /dev/null
+++ b/inputs/example_pelicun_inputs/001/tenant_unit_list.csv
@@ -0,0 +1,17 @@
+id,story,area,perim_area,occupancy_id
+1,1,206800,18200,0
+2,2,206800,18200,0
+3,3,206800,18200,0
+4,4,206800,18200,0
+5,5,206800,18200,7
+6,6,206800,18200,7
+7,7,206800,18200,7
+8,8,206800,18200,7
+9,9,206800,18200,7
+10,10,206800,18200,7
+11,11,206800,18200,7
+12,12,206800,18200,7
+13,13,206800,18200,7
+14,14,206800,18200,7
+15,15,206800,18200,7
+16,16,206800,18200,7
diff --git a/main_PBEErecovery.m b/main_PBEErecovery.m
new file mode 100644
index 0000000..5be3918
--- /dev/null
+++ b/main_PBEErecovery.m
@@ -0,0 +1,79 @@
+function [functionality, damage_consequences] = main_PBEErecovery(damage, damage_consequences, ...
+    building_model, tenant_units, systems, subsystems, tmp_repair_class, ...
+    impedance_options, impeding_factor_medians, repair_time_options, ...
+    functionality, functionality_options)
+% Perform the ATC-138 functional recovery time assessement given similation
+% of component damage for a single shaking intensity
+%
+% Parameters
+% ----------
+% damage: struct
+%   contains per damage state damage and loss data for each component in the building
+% damage_consequences: struct
+%   data structure containing simulated building consequences, such as red
+%   tags and repair costs ratios
+% building_model: struct
+%   general attributes of the building model
+% tenant_units: table
+%   attributes of each tenant unit within the building
+% systems: table
+%   data table containing information about each system's attributes
+% subsystems: table
+%   attributes of building subsystems; data provided in static tables
+%   directory
+% tmp_repair_class: table
+%   data table containing information about each temporary repair class
+%   attributes. Attributes are similar to those in the systems table.
+% impedance_options: struct
+%   general impedance assessment user inputs such as mitigation factors
+% impeding_factor_medians: table
+%   median delays for various impeding factors
+% repair_time_options: struct
+%   general repair time options such as mitigation factors
+% functionality.utilities
+%   data structure containing simulated utility downtimes
+% functionality_options: struct
+%   recovery time optional inputs such as various damage thresholds
+%
+%
+% Returns
+% -------
+% functionality: struct
+%   contains data on the recovery of tenant- and building-level function, 
+%   recovery trajectorires, and contributions from systems and components, 
+%   simulated repair schedule breakdowns and impeding times.
+
+%% Import Packages
+import preprocessing.main_preprocessing
+import recovery.impedance.main_impeding_factors
+import recovery.repair_schedule.main_repair_schedule
+import recovery.functionality.main_functionality
+
+%% Combine compoment attributes into recovery filters to expidite recovery assessment
+[damage, tmp_repair_class, damage_consequences] = main_preprocessing(...
+    damage.comp_ds_table, damage, repair_time_options, tmp_repair_class, ...
+    damage_consequences, building_model.num_stories);
+
+%% Calculate Red Tags
+[ damage_consequences.red_tag, damage_consequences.red_tag_impact, damage_consequences.inspection_trigger ] ...
+    = fn_red_tag( functionality_options.calculate_red_tag, damage, building_model.comps,...
+    damage_consequences.simulated_replacement_time, functionality_options.red_tag_options );
+
+%% Simulate ATC 138 Impeding Factors
+[functionality.impeding_factors] = main_impeding_factors(damage, impedance_options, ...
+    damage_consequences.repair_cost_ratio_total, damage_consequences.repair_cost_ratio_engineering, ...
+    damage_consequences.inspection_trigger, systems, tmp_repair_class, ...
+    building_model.building_value, impeding_factor_medians, functionality_options.include_flooding_impact);
+
+%% Construct the Building Repair Schedule
+[damage, functionality.worker_data, functionality.building_repair_schedule ] = ...
+    main_repair_schedule(damage, building_model, damage_consequences.red_tag, ...
+    repair_time_options, systems, tmp_repair_class, ...
+    functionality.impeding_factors, damage_consequences.simulated_replacement_time);
+
+%% Calculate the Recovery of Building Reoccupancy and Function
+[ functionality.recovery ] = main_functionality( damage, building_model, ...
+    damage_consequences, functionality.utilities, functionality_options, ...
+    tenant_units, subsystems, functionality.impeding_factors.temp_repair );
+
+end
diff --git a/plotModel_PBEErecovery.m b/plotModel_PBEErecovery.m
new file mode 100644
index 0000000..2c4067f
--- /dev/null
+++ b/plotModel_PBEErecovery.m
@@ -0,0 +1,61 @@
+% Plot Functional Recovery Plots For a Single Model and Single Intensity
+clear all
+close all
+clc
+rehash
+
+%% Define User inputs
+model_name = '16-story_RCSW_475yr_Example'; % Name of the model;
+                     % inputs are expected to be in a directory with this name
+                     % outputs will save to a directory with this name
+outputs_dir = ['outputs' filesep model_name]; % Directory where the assessment outputs are saved
+plot_dir = [outputs_dir filesep 'plots']; % Directory where the plots will be saved
+p_gantt = 50; % percentile of functional recovery time to plot for the gantt chart
+              % e.g., 50 = 50th percentile of functional recovery time
+
+%% Import Packages
+import plotters.main_plot_functionality
+
+%% Load Assessment Output Data
+load([outputs_dir filesep 'recovery_outputs.mat'])
+
+
+%% Create plots for UBC Study
+data_RO = functionality.recovery.reoccupancy;
+data_FR = functionality.recovery.functional;
+mkdir(plot_dir)
+
+% Functional Recovery Histogram
+histogram(data_FR.building_level.recovery_day,'Normalization','probability')
+ylabel('Percent of Relizations')
+xlabel('Days after earthquake')
+saveas(gcf,[plot_dir filesep 'FR_hist.png'])
+close
+
+% Recovery Trajectory
+hold on
+plot(data_FR.recovery_trajectory.recovery_day,data_FR.recovery_trajectory.percent_recovered,'color',[0.8 0.8 0.8],'linewidth',0.5)
+plot(median(data_FR.recovery_trajectory.recovery_day),data_FR.recovery_trajectory.percent_recovered,'-k','linewidth',1.5)
+plot(prctile(data_FR.recovery_trajectory.recovery_day,10),data_FR.recovery_trajectory.percent_recovered,'--k','linewidth',1.5)
+plot(prctile(data_FR.recovery_trajectory.recovery_day,90),data_FR.recovery_trajectory.percent_recovered,'--k','linewidth',1.5)
+box on
+ylabel('Percent of Functional Tenant Units')
+xlabel('Days after earthquake')
+saveas(gcf,[plot_dir filesep 'Recovery_trajectory.png'])
+close
+
+% Table
+outs = table;
+outs.Return_Period = 475;
+outs.Median_Downtime_FR = median(data_FR.building_level.recovery_day);
+outs.Robustness_RO = mean(data_RO.building_level.recovery_day == 0);
+outs.Robustness_FR = mean(data_FR.building_level.recovery_day == 0);
+outs.Rapidity_RO = mean(data_RO.building_level.recovery_day <= 120); % less than 4 months ~ 120 days
+outs.Rapidity_FR = mean(data_FR.building_level.recovery_day <= 120); % less than 4 months ~ 120 days
+writetable(outs, [plot_dir filesep 'summary_outs.csv'])
+
+% %% Create plot for single intensity assessment of PBEE Recovery
+% main_plot_functionality( functionality, plot_dir, p_gantt )
+
+
+
diff --git a/plotModel_PBEErecovery.py b/plotModel_PBEErecovery.py
deleted file mode 100644
index a738f4c..0000000
--- a/plotModel_PBEErecovery.py
+++ /dev/null
@@ -1,45 +0,0 @@
-def plot_results(model_name):
-    """
-    Plot Functional Recovery Plots For a Single Model and Single Intensity
-    
-    Parameters
-    ----------
-    model_name: string
-        Name of the model. Inputs are expected to be in a directory with this 
-        name. Outputs will save to a directory with this name
-    
-    """
-    import os
-    import json
-    import pandas as pd
-    
-    # from plotters import main_plot_functionality
-    # Plot Functional Recovery Plots For a Single Model and Single Intensity
-    
-    ## Define User inputs
-    
-    # Load systems information
-    systems = pd.read_csv(os.path.join(os.path.dirname(__file__), 'static_tables', 'systems.csv'))
-    systems = systems['name']
-    
-    # outputs will save to a directory with this name
-    outputs_dir = os.path.join(os.path.dirname(__file__), 'outputs/'+model_name) # Directory where the assessment outputs are saved
-    plot_dir = outputs_dir +'/plots' # Directory where the plots will be saved
-    p_gantt = 50 # percentile of functional recovery time to plot for the gantt chart
-                  #e.g., 50 = 50th percentile of functional recovery time
-    
-    ## Import Packages
-    from plotters import main_plot_functionality
-    
-    ## Load Assessment Output Data
-    f = open(os.path.join(outputs_dir, 'recovery_outputs.json'))
-    functionality= json.load(f)
-    
-    ## Create plot for single intensity assessment of PBEE Recovery
-    main_plot_functionality.main_plot_functionality(functionality, plot_dir, p_gantt, systems)
-
-if __name__ == '__main__':
-
-    model_name = 'ICSB'
-
-    plot_results(model_name)
\ No newline at end of file
diff --git a/plotters/main_plot_functionality.py b/plotters/main_plot_functionality.py
deleted file mode 100644
index b05aeb0..0000000
--- a/plotters/main_plot_functionality.py
+++ /dev/null
@@ -1,65 +0,0 @@
-def main_plot_functionality(functionality, save_dir, p_gantt, systems):
-    '''Plot function and occupancy loss and recovery at for a single model at a 
-    single intensity levels
-    
-    Parameters
-    ----------
-    functionality: dictionary
-     main output data strcuture of the functional recovery assessment. 
-     Loaded directly from the output mat file.
-    save_dir: str
-     Save directory for plots. Plots will save directly to this location as
-     png files.
-    p_gantt: int
-     percentile of functional recovery time to plot gantt chart
-    
-    Returns
-    -------'''
-     
-    import numpy as np
-    import os
-    
-    ## Initial Setup
-    # Import Packages
-    from plotters import other_plot_functions
-    
-    # Set plot variables to use
-    recovery = functionality['recovery']
-    impede = functionality['impeding_factors']['breakdowns']['full']
-    schedule = functionality['building_repair_schedule']
-    workers = functionality['worker_data']
-    full_repair_time = np.nanmax(np.array(schedule['full']['repair_complete_day']['per_story']), axis=1)
-    
-    if os.path.exists(save_dir) == False:
-        os.mkdir(save_dir)
-    ## Plot Performance Objective Grid for system and component breakdowns
-
-    plot_dir = os.path.join(save_dir,'breakdowns')
-    other_plot_functions.plt_heatmap_breakdowns(recovery, plot_dir)
-    
-    ## Plot Performance Target Distribution Across all Realizations
-    plot_dir = os.path.join(save_dir,'histograms')
-    other_plot_functions.plt_histograms(recovery,plot_dir)
-    
-    ## Plot Mean Recovery Trajectories
-    plot_dir = os.path.join(save_dir,'recovery_trajectories')
-    other_plot_functions.plt_recovery_trajectory( recovery, full_repair_time, plot_dir)
-    
-    # Plot Gantt Charts
-    plot_dir = os.path.join(save_dir,'gantt_charts')
-    fr_time = np.array(functionality['recovery']['functional']['building_level']['recovery_day'])
-    if len(np.where(fr_time == np.percentile(fr_time,p_gantt))[0])>0:
-        p_idx = np.where(fr_time == np.percentile(fr_time,p_gantt))[0][0] # Find the index of the first realization that matches the selected percentile
-        
-    else:
-        diff = abs(fr_time - np.percentile(fr_time,p_gantt))
-        p_idx = np.where(diff == min(diff))[0][0]
-        from scipy.stats import percentileofscore
-        p_gantt = percentileofscore(fr_time, fr_time[p_idx])
-    
-        
-    plot_name = 'prt_'+str(p_gantt)
-    other_plot_functions.plt_gantt_chart(p_idx, recovery, full_repair_time, workers, schedule, impede, plot_dir, plot_name, systems)
-    
-
-
diff --git a/plotters/other_plot_functions.py b/plotters/other_plot_functions.py
deleted file mode 100644
index 6280b4f..0000000
--- a/plotters/other_plot_functions.py
+++ /dev/null
@@ -1,398 +0,0 @@
-def plt_heatmap_breakdowns(recovery, plot_dir):
-    '''Plot the time and percent of realizations that each system and/or 
-    component is impeding function as a heatmap and as lineplots
-    
-    Parameters
-    ----------
-    recovery: dictionary
-     dictionary containing recovery breakdown data from the functional
-     recovery assessment
-     
-    plot_dir: str
-     Save directory for plots. Plots will save directly to this location as
-     png files.
-    
-    Returns
-    -------'''
-    
-    import seaborn as sb
-    import matplotlib.pyplot as plt
-    import os
-    import numpy as np
-    
-    
-    ## Initial Setup
-    if os.path.exists(plot_dir) == False:
-        os.mkdir(plot_dir)
-    plot_dir=plot_dir +'/'
-    
-    perform_targ_days = recovery['reoccupancy']['breakdowns']['perform_targ_days']
-    perform_targ_labs = ['Immediately', '>3 Days', '>7 Days', '>2 Weeks', '>1 Month', '>2 Months', '>3 Months', '>4 Months', '>6 Months', '>9 Months', '>1 Year', ]
-    
-    #FZ# Maximum day for recovery amongs all tenant units and all realizations
-    recovery_day_max = perform_targ_days[-1]
-   
-    if recovery_day_max <= 365:
-        perform_targ_labs = perform_targ_labs # Number of days for each performance target stripe
-    if recovery_day_max > 365:
-        num_years = (int(np.floor((recovery_day_max)/365)))
-        
-        # If number of complete years is more than 2
-        for yr in range(num_years-1):
-            quarters =[1,2,3]
-            for qtr in quarters:
-                if yr == 0:
-                    perform_targ_labs.append('>' + str(yr+1) +' Year & ' + str(qtr*3)+ ' months')
-                if yr > 0:
-                    perform_targ_labs.append('>' + str(yr+1) +' Years & ' + str(qtr*3)+ ' months')    
-            perform_targ_labs.append('>' + str(yr+2) +' Years')
-        
-        # For final incomplete year
-        num_quarters = int(np.floor((recovery_day_max - num_years*365)/90))
-        compl_yr = perform_targ_labs[-1]
-        for qtr in range(num_quarters):
-            perform_targ_labs.append(compl_yr + ' & '+str((qtr+1)*3) + ' Months')
-
-        perform_targ_labs.append(perform_targ_labs[-1] + ' & ' + str(int(perform_targ_days[-1] - perform_targ_days[-2])) + ' Days')    
-
-    
-    var = ['component_breakdowns', 'system_breakdowns']
-    labs = ['comp_names', 'system_names']
-    plt_ht = [10, 8]
-    
-    ## Plot Heatmaps
-    fnc_states = list(recovery.keys())
-    for fs in range(len(fnc_states)):
-        
-        perform_targ_days = recovery[fnc_states[fs]]['breakdowns']['perform_targ_days']
-        perform_targ_labs = ['Immediately', '>3 Days', '>7 Days', '>2 Weeks', '>1 Month', '>2 Months', '>3 Months', '>4 Months', '>6 Months', '>9 Months', '>1 Year', ]
-        
-        #FZ# Maximum day for recovery amongs all tenant units and all realizations
-        recovery_day_max = perform_targ_days[-1]
-       
-        if recovery_day_max <= 365:
-            perform_targ_labs = perform_targ_labs # Number of days for each performance target stripe
-        if recovery_day_max > 365:
-            num_years = (int(np.floor((recovery_day_max)/365)))
-            
-            # If number of complete years is more than 2
-            for yr in range(num_years-1):
-                quarters =[1,2,3]
-                for qtr in quarters:
-                    if yr == 0:
-                        perform_targ_labs.append('>' + str(yr+1) +' Year & ' + str(qtr*3)+ ' months')
-                    if yr > 0:
-                        perform_targ_labs.append('>' + str(yr+1) +' Years & ' + str(qtr*3)+ ' months')    
-                perform_targ_labs.append('>' + str(yr+2) +' Years')
-            
-            # For final incomplete year
-            num_quarters = int(np.floor((recovery_day_max - num_years*365)/90))
-            compl_yr = perform_targ_labs[-1]
-            for qtr in range(num_quarters):
-                perform_targ_labs.append(compl_yr + ' & '+str((qtr+1)*3) + ' Months')
-    
-            perform_targ_labs.append(perform_targ_labs[-1] + ' & ' + str(int(perform_targ_days[-1] - perform_targ_days[-2])) + ' Days')        
-        
-        # system breakdown for line plots
-        sys_bkdn = np.array(recovery[fnc_states[fs]]['breakdowns'][var[1]])
-        systs = recovery[fnc_states[fs]]['breakdowns'][labs[1]]
-        # no. of systems in one plot (total 3 plots)
-        num_systems = int(np.floor(len(systs)/3))
-        
-        # Plot 1
-        plt.figure(figsize=([6,6]))
-        for syst in range(num_systems):
-            plt.plot(perform_targ_days, sys_bkdn[syst], marker ='o', label=systs[syst])
-        plt.ylim([0,1])
-        plt.xlabel('Recovery Time After Earthquake (Days')
-        plt.ylabel('Fraction of Realizations Affecting Building')
-        plt.title(var[1] +' '+fnc_states[fs] + ' lineplot 1')
-        plt.legend()
-        plt.grid()
-        plt.savefig(plot_dir + var[1] +' '+fnc_states[fs] + ' lineplot 1.png', dpi=300)
-        
-        # Plot 2
-        plt.figure(figsize=([6,6]))
-        for syst in range(num_systems):
-            plt.plot(perform_targ_days, sys_bkdn[num_systems + syst], marker ='o', label=systs[num_systems + syst])
-        plt.ylim([0,1])
-        plt.xlabel('Recovery Time After Earthquake (Days')
-        plt.ylabel('Fraction of Realizations Affecting Building')
-        plt.title(var[1] +' '+fnc_states[fs] + ' lineplot 2')
-        plt.legend()
-        plt.grid()
-        plt.savefig(plot_dir + var[1] +' '+fnc_states[fs] + ' lineplot 2.png', dpi=300)        
-        
-        # Plot 3
-        plt.figure(figsize=([6,6]))
-        for syst in range(len(systs) - num_systems*2):
-            plt.plot(perform_targ_days, sys_bkdn[2*num_systems + syst], marker ='o', label=systs[2*num_systems + syst])
-        plt.ylim([0,1])
-        plt.xlabel('Recovery Time After Earthquake (Days')
-        plt.ylabel('Fraction of Realizations Affecting Building')
-        plt.title(var[1] +' '+fnc_states[fs] + ' lineplot 3')
-        plt.legend()
-        plt.grid()
-        plt.savefig(plot_dir + var[1] +' '+fnc_states[fs] + ' lineplot 3.png', dpi=300)
-        
-        
-        # Heat maps
-        for v in range(len(var)):
-            y_labs = recovery[fnc_states[fs]]['breakdowns'][labs[v]]
-            data = np.array(recovery[fnc_states[fs]]['breakdowns'][var[v]])
-            
-            plt.rcParams["font.family"] = "Times New Roman"
-            plt.figure(figsize=(plt_ht))
-            h = sb.heatmap(data, cmap='Blues', linewidths = 0.5, linecolor = 'black', annot = np.round(data,2), xticklabels = perform_targ_labs, yticklabels = y_labs, clip_on = False)
-            fnc_lab = fnc_states[fs][0].upper() + fnc_states[fs][1:len(fnc_states[fs])]+' Recovery'
-            title = 'Fraction of Realizations Affecting Building ' + fnc_lab
-            h.set(xlabel  = 'Recovery Time After Earthquake')
-            # h.set(ylabel = labs[v])
-            h.set_title(title, x=0.5, y=1.05)
-            plt.xticks(rotation=90)
-            plt.subplots_adjust(bottom=0.15)
-            
-            plt.savefig(plot_dir + var[v] +' '+fnc_states[fs] + '.png', dpi=300)
-
-    return
-
-
-def plt_histograms( recovery, plot_dir ):
-    
-    '''Plot all realizations of building level recovery as a histogram
-    
-    Parameters
-    ----------
-    recovery: dictionary
-      dictionary containing building-level recovery data from the 
-      functional recovery assessment
-     
-    plot_dir: str
-      Save directory for plots. Plots will save directly to this location as
-      png files.
-    
-    Returns
-    -------'''
-    
-    import seaborn as sb
-    import matplotlib.pyplot as plt
-    import os
-    
-    ## Initial Setup
-    if os.path.exists(plot_dir) == False:
-        os.mkdir(plot_dir)
-    plot_dir=plot_dir +'/'
-    
-    ## Plot Histograms
-    fnc_states = list(recovery.keys())
-    for fs in range(len(fnc_states)):
-        plt.rcParams["font.family"] = "Times New Roman"
-        plt.figure(figsize=(6,4))
-        sb.histplot(data = recovery[fnc_states[fs]]['building_level']['recovery_day'])
-        fnc_lab = fnc_states[fs][0].upper() + fnc_states[fs][1:len(fnc_states[fs])]+' Recovery'        
-        plt.xlabel(fnc_lab + ' Time (days)')
-        plt.ylabel('Number of Realizatons')
-        plt.savefig(plot_dir + fnc_states[fs] + '.png', dpi=300)
-        
-    return
-
-
-def plt_recovery_trajectory( recovery, full_repair_time, plot_dir):
-    '''Plot mean recovery trajectories
-    
-    Parameters
-    ----------
-    recovery: dictionary
-      dictionary containing recovery trajectory data from the functional
-      recovery assessment
-     
-    full_repair_time: array [num reals x 1] 
-      simulated realization of full repair time 
-    
-    plot_dir: str
-      Save directory for plots. Plots will save directly to this location as
-      png files.
-    
-    Returns
-    -------'''
-     
-    
-    import matplotlib.pyplot as plt
-    import os
-    import numpy as np
-    
-    ## Initial Setup
-    if os.path.exists(plot_dir) == False:
-        os.mkdir(plot_dir)
-    plot_dir=plot_dir +'/'
-    
-    
-
-    # Calculate mean recovery times
-    reoc = np.mean(np.array(recovery['reoccupancy']['recovery_trajectory']['recovery_day']), axis=0)
-    func = np.mean(np.array(recovery['functional']['recovery_trajectory']['recovery_day']), axis=0)
-    med = np.median(np.array(recovery['functional']['recovery_trajectory']['recovery_day']), axis=0)
-    per_10 = np.percentile(np.array(recovery['functional']['recovery_trajectory']['recovery_day']), 10, axis=0)
-    per_90 = np.percentile(np.array(recovery['functional']['recovery_trajectory']['recovery_day']), 90, axis=0)
-    full = np.mean(full_repair_time)
-    level_of_repair = recovery['functional']['recovery_trajectory']['percent_recovered']
-    
-    # Plot Recovery Trajectory
-    plt.rcParams["font.family"] = "Times New Roman"
-    plt.figure(figsize=(6,4)) 
-    plt.plot(reoc, level_of_repair,'r-', linewidth = 1.5, label = 'Re-Occupancy') 
-    plt.plot(func, level_of_repair,'b-', linewidth = 1.5, label= 'Functional') 
-    plt.plot([full, full], [0, 1],'k-', linewidth = 1.5, label= 'Fully Repaired') 
-    plt.xlim([0,np.ceil((full+1)/10)*10])
-    plt.xlabel('Days After Earthquake')
-    plt.ylabel('Fraction of Floor Area')
-    plt.legend(loc='upper left')
-    plt.grid()
-    plt.show()
-    
-    plt.savefig(plot_dir + 'recovery_trajectory.png', dpi=300)
-    
-    plt.figure(figsize=(6,4))
-    for i in range(len(recovery['functional']['recovery_trajectory']['recovery_day'])):
-        plt.plot(recovery['functional']['recovery_trajectory']['recovery_day'][i] , level_of_repair, color = 'lightgrey', linewidth = 0.5)
-    plt.plot(med, level_of_repair,'r-', linewidth = 1.5, label= 'Median')
-    plt.plot(per_10, level_of_repair,'b--', linewidth = 1.0, label= '10th percentile')
-    plt.plot(per_90, level_of_repair,'b--', linewidth = 1.0, label= '90th percentile')
-    plt.xlim([0,np.ceil((max(max(recovery['functional']['recovery_trajectory']['recovery_day'])))/10)*10])
-    plt.title('Functional Recovery Trajectories')
-    plt.xlabel('Days After Earthquake')
-    plt.ylabel('Fraction of Floor Area')
-    plt.legend(loc='upper left')
-    plt.grid()    
-
-    plt.savefig(plot_dir + 'functional_recovery_trajectories.png', dpi=300)
-    
-    return
-
-
-def plt_gantt_chart(p_idx, recovery, full_repair_time, workers, schedule, impede, plot_dir, plot_name, systems):
-    '''Plot gantt chart for a single realization of the functional recovery
-    assessment
-    
-    Parameters
-    ----------
-    p_idx: int
-      realization index of interest
-     
-    recovery: dictionary
-      dictionary containing recovery trajectory data from the functional
-      recovery assessment
-    
-    full_repair_time: array [num reals x 1] 
-      simulated realization of full repair time 
-    
-    workers: dictionary
-      data structure containing work allocation data from the functional
-      recovery assessment
-    
-    schedule: dictionary
-      dictionary containing repair schedule data from the functional
-      recovery assessment
-    
-    impede: dictionary
-      data structure containing impedance time data from the functional
-      recovery assessment
-    
-    plot_dir: str
-      Save directory for plots. Plots will save directly to this location as
-      png files.
-     
-    plot_name: str
-      name of file to save plot as (does not include the file type extension)
-    
-    Returns
-      -------'''
-
-    import matplotlib.pyplot as plt
-    import os
-    import numpy as np
-    
-    ## Initial Setup
-    if os.path.exists(plot_dir) == False:
-        os.mkdir(plot_dir)
-    plot_dir=plot_dir +'/'
-    
-    imps = list(impede.keys())
-    sys = list(systems)
-    
-    ## Format Gantt Chart Data
-    recovery_trajectory = {
-        'reoc' : np.array(recovery['reoccupancy']['recovery_trajectory']['recovery_day'])[p_idx,:],
-        'func' : np.array(recovery['functional']['recovery_trajectory']['recovery_day'])[p_idx,:],
-        'level_of_repair' : np.array(recovery['reoccupancy']['recovery_trajectory']['percent_recovered']),
-        'ful_rep' : np.ones(len(recovery['reoccupancy']['recovery_trajectory']['percent_recovered'])) * np.ceil(full_repair_time[p_idx])
-        }
-    
-    # Collect Worker Data
-    worker_data = {
-        'total_workers' : np.array(workers['total_workers'])[p_idx,:],
-        'day_vector' : np.array(workers['day_vector'])[p_idx,:]
-        }
-    
-    # Collect Impedance Times
-    sys_imp_times = []
-    labs = []
-    for i in range(len(imps)):
-        duration = impede[imps[i]]['complete_day'][p_idx] - impede[imps[i]]['start_day'][p_idx]
-        # if duration > 0:
-        sys_imp_times.append([impede[imps[i]]['start_day'][p_idx], duration])
-        labs.append(imps[i][0].upper() + imps[i][1:len(imps[i])].replace('_',' '))
-     
-    labs_imp = np.array(labs)
-    y_imp = np.array(sys_imp_times)
-
-    # Collect Repair Times 
-    sys_repair_times = []
-    labs = []
-    for s in range(len(sys)): 
-        duration = schedule['full']['repair_complete_day']['per_system'][p_idx][s] - schedule['full']['repair_start_day']['per_system'][p_idx][s]
-        if duration > 0:
-            sys_repair_times.append([schedule['full']['repair_start_day']['per_system'][p_idx][s], duration])
-            labs.append(sys[s][0].upper() + sys[s][1:len(sys[s])] + ' Repairs')
-
-    labs.reverse()    
-    labs_rep = labs
-    y_rep = np.flip(np.array(sys_repair_times), axis=0)
-    ## Plot Gantt
-    
-    plt.rcParams["font.family"] = "Times New Roman"
-    # Impedance Time
-    fig, (G1, G2, G3, G4) = plt.subplots(4, 1, sharex=True, figsize = (10,12), gridspec_kw={'height_ratios': [2, 1, 1, 1]})
-    G1.barh(labs_imp, y_imp[:,0], color = 'white')    
-    G1.barh(labs_imp, y_imp[:,1], left = y_imp[:,0], color='lightgrey')
-    G1.set_title('Impedance Time')
-    G1.grid(which = 'major', axis = 'x', alpha=0.5)
-    # G1.set_xlabel('Days After Earthquake')
-    
-    # Repair Time
-    G2.barh(labs_rep, y_rep[:,0], color = 'white')
-    G2.barh(labs_rep, y_rep[:,1], left = y_rep[:,0], color='grey')
-    G2.set_title('Repair Time') 
-    G2.grid(which = 'major', axis = 'x', alpha=0.5)    
-    
-    
-    # Workers
-    G3.plot(worker_data['day_vector'], worker_data['total_workers'])
-    G3.set_ylabel('Number of workers')
-    G3.set_title('Number of workers')
-    G3.grid(which = 'major', axis = 'x', alpha=0.5) 
-    
-    # Plot Recovery Trajectory
-    G4.plot(recovery_trajectory['reoc'], recovery_trajectory['level_of_repair'],'r-', linewidth = 1.5, label = 'Re-Occupancy')
-    G4.plot(recovery_trajectory['func'], recovery_trajectory['level_of_repair'],'b-', linewidth = 1.5, label = 'Functional')
-    G4.plot(recovery_trajectory['ful_rep'], recovery_trajectory['level_of_repair'],'k-', linewidth = 1.5, label = 'Fully Repaired')
-    G4.set_ylim([0,1])
-    G4.set_xlabel('Days After Earthquake')
-    G4.set_ylabel('Fraction of Floor Area')
-    G4.set_title('Building Recovery State')
-    G4.grid(which = 'major', axis = 'x', alpha=0.5)
-    G4.legend()
-    fig.subplots_adjust(left=0.20)
-    fig.savefig(plot_dir + plot_name + '.png', dpi=300)
-    
-    return
diff --git a/pyproject.toml b/pyproject.toml
deleted file mode 100644
index 0fdd6fe..0000000
--- a/pyproject.toml
+++ /dev/null
@@ -1,36 +0,0 @@
-[build-system]
-requires = ["setuptools>=61.0", "wheel"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "atc138"
-version = "0.1.0"
-description = "Functional Recovery Assessment (ATC-138)"
-readme = "README.md"
-requires-python = ">=3.9"
-license = {file = "LICENSE"}
-authors = [
-  {name = "Dustin Cook", email = "dustin.cook@nist.gov"},
-]
-classifiers = [
-  "Programming Language :: Python :: 3",
-  "License :: OSI Approved :: BSD License",
-  "Operating System :: OS Independent",
-]
-dependencies = [
-  "numpy",
-  "pandas",
-  "scipy",
-  "matplotlib",
-  "seaborn",
-]
-
-[project.scripts]
-atc138 = "atc138.cli:main"
-
-[project.urls]
-"Homepage" = "https://github.com/NHERI-SimCenter/Functional-Recovery-Python"
-"Bug Tracker" = "https://github.com/NHERI-SimCenter/Functional-Recovery-Python/issues"
-
-[tool.setuptools.packages.find]
-where = ["src"]
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index af3fc8b..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-numpy
-pandas
-scipy
-matplotlib
-seaborn
\ No newline at end of file
diff --git a/schema/Inputs.json b/schema/Inputs.json
new file mode 100644
index 0000000..c0d41c6
--- /dev/null
+++ b/schema/Inputs.json
@@ -0,0 +1,541 @@
+{
+	"definitions": {
+		"arrayOfNumbers": {
+			"type": "array",
+			"items": {
+				"type": "number"
+			}
+		},
+		"arrayOfArraysOfNumbers": {
+			"type": "array",
+			"items": {
+				"$ref": "#/definitions/arrayOfNumbers"
+			}
+		}
+	},
+	"properties": {
+		"impedance_options": {
+			"type": "object",
+			"description": "general impedance module inputs",
+			"properties": {
+				"include_impedance": {
+					"type": "object",
+					"description": "logical switches for defining which impedance factors to consider in the assessment",
+					"properties": {
+						"inspection": {
+							"type": "logical",
+							"description": "flag to include inpection delays"
+						},
+						"financing": {
+							"type": "logical",
+							"description": "flag to include financing delays"
+						},
+						"permitting": {
+							"type": "logical",
+							"description": "flag to include permitting delays"
+						},
+						"engineering": {
+							"type": "logical",
+							"description": "flag to include engineering mobilization and design delays"
+						},
+						"contractor": {
+							"type": "logical",
+							"description": "flag to include contractor mobilization delays"
+						},
+						"long_lead": {
+							"type": "logical",
+							"description": "flag to include long lead delays"
+						}
+					}
+				},
+				"system_design_time": {
+					"type": "object",
+					"description": "variables of formula to calculate median system design time",
+					"properties": {
+						"f": {
+							"type": "number",
+							"description": "design fee ratio: ratio of building value charged by engineering firm. Default to 0.04 for repair projects"
+						},
+						"r": {
+							"type": "number",
+							"description": "engineering rate: Average hourly rate of engineering work. Default to $175 per hour"
+						},
+						"t": {
+							"type": "number",
+							"description": "team factor: accounts for parallel time between multiple designers spent on project. Default to 1.3"
+						},
+						"w": {
+							"type": "number",
+							"description": "average workday: average number of hours per day spent on project: Default to 8 hours"
+						}
+					}
+				},
+				"eng_design_min_days":{
+					"type": "number",
+					"description": "Minumum number of days for the median engineering design time"
+				},
+				"eng_design_max_days":{
+					"type": "number",
+					"description": "Maximum number of days for the median engineering design time"
+				},
+				"mitigation": {
+					"type": "object",
+					"description": "general impedance assessment inputs such as mitigation factors",
+					"properties": {
+						"is_essential_facility": {
+							"type": "logical",
+							"description": "is the building deemed as essential by the local jurisdiction"
+						},
+						"is_borp_equivalent": {
+							"type": "logical",
+							"description": "are the BORP, or BORP equivalent, post-earthquake strategies in place to limit inspection delays"
+						},
+						"is_engineer_on_retainer": {
+							"type": "logical",
+							"description": "is an engineer contracted to be on retainer in case of post-earthquake damage"
+						},
+						"contractor_relationship": {
+							"type": "string",
+							"description": "string identifier that defines the state of existing relationships with contractors to perform repairs. Options include <'retainer', 'good', 'none'>"
+						},
+						"contractor_retainer_time": {
+							"type": "number",
+							"description": "time, in days, from onset of damage to when repairs start previosly agreed upon in retainer agreement. Only applies when 'retainer' contractor_relationship is selected"
+						},
+						"funding_source": {
+							"type": "string",
+							"description": "what is the source of funding for repair costs beyond that of the capital of the owner"
+						},
+						"capital_available_ratio": {
+							"type": "number",
+							"description": "ratio of the building value that the onwer has immediately available for repairs"
+						}
+					}
+				},
+				"impedance_beta": {
+					"type": "number",
+					"description": "lognormal uncetainty of impedance times",
+				},
+				"impedance_truncation": {
+					"type": "number",
+					"description": "Truncation bounds of lognormal distribution of impedance time, input as the number of standard deviations above and below the mean. Default to 2",
+				},
+				"default_lead_time": {
+					"type": "number",
+					"description": "Lead time, in days, to replace select components (equipment or other less readily available components). This default lead time will be applied to all components with the long_lead flag in the inputs builder. To add custom lead times per component, update the long_lead_time attribute in the damage.comp_ds_table field.",
+				},
+				"demand_surge": {
+					"type": "object",
+					"description": "general user input options for defining the demand surge",
+					"properties": {
+						"include_surge": {
+							"type": "logical",
+							"description": "include demand surge when simulating impedance times"
+						},
+						"is_dense_urban_area": {
+							"type": "logical",
+							"description": "is within a dense urban area, typically defined by a site within a metropolitan statistical area (MSA) with a population of over 1 million"
+						},
+						"site_pga": {
+							"type": "number",
+							"description": "peak ground acceleration (PGA) in g at the building site for the ground shaking intensity of interest"
+						},
+						"pga_de": {
+							"type": "number",
+							"description": "peak ground acceleration (PGA) in g associated with the design eartquake (DE). Method of combining ground motion pairs (e.g., max direction vs geomean) should match the field site_pga"
+						}
+					}
+				},
+				"scaffolding_lead_time": {
+					"type": "number",
+					"description": "The number of days it takes to get workers to the site to erect scaffolding",
+				},
+				"scaffolding_erect_time": {
+					"type": "number",
+					"description": "The number of days it takes for workers to errect scaffolding to protect doorways and walkways from falling hazards",
+				},
+				"door_racking_repair_day": {
+					"type": "number",
+					"description": "days after the earthquake required to unjam all racked doorways"
+				},
+				"flooding_cleanup_day": {
+					"type": "number",
+					"description": "days after the earthquake required to shut off leaks and dry out water"
+				},
+				"flooding_repair_day": {
+					"type": "number",
+					"description": "days after the earthquake required to repair all damage caused by interior flooding"
+				}
+			}
+		},
+		"repair_time_options": {
+			"type": "object",
+			"description": "general repair schedule module inputs",
+			"properties": {
+				"temp_repair_beta": {
+					"type": "number",
+					"description": "lognormal uncetainty of temporary repair times. Default to 0.6",
+				},
+				"max_workers_per_sqft_story": {
+					"type": "number",
+					"description": "Maximum density of workers on a given story. Default to 0.001 from FEMA P-58",
+				},
+				"max_workers_per_sqft_building": {
+					"type": "number",
+					"description": "Maximum density of workers for the entire building after the first 10. Default to 0.00025 from REDi",
+				},
+				"max_workers_building_min": {
+					"type": "number",
+					"description": "Lower limit on the maximum number of workers for the entire building (i.e., max number of workers is never less than this). Default to 20 from REDi",
+				},
+				"max_workers_building_max": {
+					"type": "number",
+					"description": "Upper limit on the maximum number of workers for the entire building (i.e., max number of workers is never more than this). Default to 260 from REDi",
+				},
+				"allow_tmp_repairs": {
+					"type": "logical",
+					"description": "Flag to allow temporay repair measures to be used to restore building reoccupancy and basic function",
+				},
+				"allow_shoring": {
+					"type": "logical",
+					"description": "Flag to allow shoring to be used as a temporary repair for mitigating local structural stability issues. Setting allow_tmp_repairs to zero overwrites this flag",
+				}
+			}
+		},
+		"functionality_options": {
+			"type": "object",
+			"description": "general functionality module inputs",
+			"properties": {
+				"calculate_red_tag": {
+					"type": "logical",
+					"description": "flag to indicate whether or not to include the assessment of red tags based on component damage"	
+				}, 
+				"red_tag_clear_time": {
+					"type": "number",
+					"description": "days after all red tag repairs are done before occupancy can be regained (i.e. time for permitting office to clear the tag)"
+				},
+				"red_tag_clear_beta": {
+					"type": "number",
+					"description": "lognormal dispersion defining the uncertaitny in the red tag clear time"
+				},
+				"include_local_stability_impact": {
+					"type": "logical",
+					"description": "flag to consider the effect of local structural stability issues (that would require shoring) on building occupancy"
+				},
+				"egress_threshold": {
+					"type": "number",
+					"description": "ratio of building egress paths required for building safety and access"
+				},
+				"fire_watch": {
+					"type": "logical",
+					"description": "will a fire watch be set if fire sprinkler systems are damaged"
+				},
+				"local_fire_damamge_threshold": {
+					"type": "number",
+					"description": "ratio of building fire sprinkler branches or drops that can be damaged with a given story without significantly disrupting the operation of the active fire supression system"
+				},
+				"min_egress_paths": {
+					"type": "number",
+					"description": "minimum number of egress pathways that must be maintained at each story to maintain occupancy"
+				},
+				"required_ratio_operating_hvac_main": {
+					"type": "number",
+					"description": "ratio of hvac units, which service the entire building, required to maintain an operational hvac system; not applied to components with n+1 redundancy"
+				},
+				"required_ratio_operating_hvac_unit": {
+					"type": "number",
+					"description": "ratio of hvac units, which service an individual tenant unit, required to maintain an operational hvac system; not applied to components with n+1 redundancy"
+				},
+				"exterior_safety_threshold": {
+					"type": "number",
+					"description": "damage theshold that triggers an exterior safety issue; threshold is defined in terms of the ratio of damaged area to perimeter area of a given tenant unit"
+				},
+				"interior_safety_threshold": {
+					"type": "number",
+					"description": "damage theshold that triggers an interior safety issue; threshold is defined in terms of the ratio of damaged area to interior area of a given tenant unit"
+				},
+				"door_access_width_ft": {
+					"type": "number",
+					"description": "width of door access zone, centerlined on the doorway, required for safe access. A safe door access zone must be free of falling hazards"
+				},
+				"heat_utility": {
+					"type": "string",
+					"description": "string identifier of the type of utility that is used to heat the building; string should match on of the fields in functionality.utilities"
+				},
+				"habitability_requirements": {
+					"type": "object",
+					"description": "flags to add additional system check on the reoccupancy time",
+					"properties": {
+						"electrical": {
+							"type": "logical",
+							"description": "electrical power required for reoccupancy/habitability"
+						},
+						"water_potable": {
+							"type": "logical",
+							"description": "potable water required for reoccupancy/habitability"
+						},
+						"water_sanitary": {
+							"type": "logical",
+							"description": "sanitary waste system required for reoccupancy/habitability"
+						},
+						"hvac_ventilation": {
+							"type": "logical",
+							"description": "active ventilation required for reoccupancy/habitability"
+						},
+						"hvac_heating": {
+							"type": "logical",
+							"description": "heating required for reoccupancy/habitability"
+						},
+						"hvac_cooling": {
+							"type": "logical",
+							"description": "cooling required for reoccupancy/habitability"
+						},
+						"hvac_exhaust": {
+							"type": "logical",
+							"description": "exhaust systems required for reoccupancy/habitability"
+						}
+					}
+				}
+			}
+		},
+		"building_model": {
+			"type": "object",
+			"description": "general information about the building such as the number of stories and the building area",
+			"properties": {
+				"building_value": {
+					"type": "number",
+					"description": "Total replacement value of the building, in USD"
+				},
+				"num_stories": {
+					"type": "number",
+					"description": "number of stories in the building, does not include basement levels"
+				},
+				"area_per_story_sf": {
+					"type": "#/definitions/arrayOfNumbers",
+					"description": "interior area, in square feet, for each story in the building, starting from the lowest level"
+				},
+				"ht_per_story_ft": {
+					"type": "#/definitions/arrayOfNumbers",
+					"description": "elevation height, in feet, for each story in the building, starting from the lowest level"
+				},
+				"edge_lengths": {
+					"type": "#/definitions/arrayOfArraysOfNumbers",
+					"description": "external length of two orthogonal sides of the building, in feet, for each story in the building, starting from the lowest level"
+				},
+				"struct_bay_area_per_story": {
+					"type": "#/definitions/arrayOfNumbers",
+					"description": "length of the typical structural bay, from centerline to centerline, in feet, for each story in the building, starting from the lowest level"
+				},
+				"num_entry_doors": {
+					"type": "number",
+					"description": "number of external building exits/entrances"
+				},
+				"num_elevators": {
+					"type": "number",
+					"description": "number of elevator shafts in the building"
+				},
+				"stairs_per_story": {
+					"type": "#/definitions/arrayOfNumbers",
+					"description": "number of stairways on each story in the building, starting from the lowest level"
+				},
+				"occupants_per_story": {
+					"type": "#/definitions/arrayOfNumbers",
+					"description": "total number of occuapnts (persons) that occupy each story of this building, starting from the lowest level"
+				},
+				"comps": {
+					"type": "object",
+					"description": "component population information",
+					"properties": {
+						"story": {
+							"type": "array",
+							"description": "component quantities at each story",
+							"items": {
+								"type": "object",
+								"properties": {
+									"qty_dir_1": {								
+										"description": "total number of component units for each component stack in direction 1 of this story",
+										"$ref": "#/definitions/arrayOfNumbers",
+									"qty_dir_2": {
+										"description": "total number of component units for each component stack in direction 2 of this story",
+										"$ref": "#/definitions/arrayOfNumbers"
+									},
+									"qty_dir_3": {
+										"description": "total number of component units for each component stack in direction 3 (nondirectional) of this story",
+										"$ref": "#/definitions/arrayOfNumbers"
+									}
+								}
+							}
+						},
+						"comp_list": {
+							"description": "fragility ID for each component stack",
+							"$ref": "#/definitions/arrayOfStrings"
+						},
+						"comp_table": {
+							"type": "table",
+							"description": "various component attributes. Can be populated from the component_attribites.csv database in the static_tables directory"
+						}
+					}
+				}
+			}
+		},
+		"damage": {
+			"type": "object",
+			"description": "simulated damage, simulated repair time, and compoennt attribute data associated with each component's damages state in the building",
+			"properties": {
+				"comp_ds_table": {
+					"type": "table",
+					"description": "various component attributes by damage state. Can be populated from the component_attribites.csv and damage_state_attribute_mapping.csv databases in the static_tables directory"
+				},
+				"story": {
+					"type": "array",
+					"description": "simulated component damage, broken down by each story in the building",
+					"items": {
+						"type": "object",
+						"properties": {
+							"qnt_damaged": {
+								"description": "simulation of the quantity of damaged components in each component damage state",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"cost": {
+								"description": "simulated repair cost of each component's damage state",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"worker_days": {
+								"description": "simulated worker days required to repair each component's damage state",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"qnt_damaged_dir_1": {
+								"description": "simulated number of damaged component damage states associated with damage in direction 1",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"qnt_damaged_dir_2": {
+								"description": "simulated number of damaged component damage states associated with damage in direction 2",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"qnt_damaged_dir_3": {
+								"description": "simulated number of damaged component damage states associated with nondirectional damage",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"num_comps": {
+								"description": "total number of components populated within each tenant unit associated with each damage state",
+								"$ref": "#/definitions/arrayOfNumbers"
+							}
+						}
+					}
+				}
+			}
+		},
+		"damage_consequences": {
+			"type": "object",
+			"description": "general impedance assessment inputs such as mitigation factors",
+			"properties": {
+				"simulated_replacement_time": {
+					"description": "simulated replacement time in days for all realizations where replacement is required, NaN = building does not need replaced and is scheduled for repairs. Note: to not consider building replacement, pass in an array of NaNs", 
+					"$ref": "#/definitions/arrayOfNumbers"
+				},
+				"repair_cost_ratio_total": {
+					"description": "simulated building repair cost of structural and nonstructural repairs (does not include tenant contents); normalized by the replacement value", 
+					"$ref": "#/definitions/arrayOfNumbers"
+				},
+				"repair_cost_ratio_engineering": {
+					"description": "simulated building repair cost of repairs that require engineering redesign; normalized by the replacement value", 
+					"$ref": "#/definitions/arrayOfNumbers"
+				}
+			}
+		},
+		"functionality": {
+			"type": "object",
+			"description": "simulated outputs of the performance based functional recovery methodology",
+			"properties": {
+				"utilities": {
+					"type": "object",
+					"description": "simulated utility downtimes at this building site",
+					"properties": {
+						"electrical": {
+							"description": "simulated electrical power network downtime", 
+							"$ref": "#/definitions/arrayOfNumbers"
+						},
+						"water": {
+							"description": "simulated water network downtime", 
+							"$ref": "#/definitions/arrayOfNumbers"
+						},
+						"gas": {
+							"description": "simulated natural gas network downtime", 
+							"$ref": "#/definitions/arrayOfNumbers"
+						}
+					}
+				}
+			}
+		},
+		"tenant_units": {
+			"type": "table",
+			"description": "attributes and functional requirements of each tenant unit in the building",
+			"properties": {
+				"id": {
+					"type": "number",
+					"description": "tenant unit unique id"
+				},
+				"story": {
+					"type": "number",
+					"description": "story where each tenant unit is located"
+				},
+				"area": {
+					"type": "number",
+					"description": "floor area, in square feet, of the tenant unit"
+				},
+				"perim_area": {
+					"type": "number",
+					"description": "perimeter area, in square feet, of the tenant unit"
+				},
+				"occupancy_id": {
+					"type": "number",
+					"description": "unique id key linking each tenant unit to its associated tenant function requirements"
+				},
+				"exterior": {
+					"type": "number",
+					"description": "tolerance for damage to exterior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the perimeter area of severely damaged exterior components within the tenant-unit to the total perimeter area of the tenant unit (i.e., what percent of the exterior walls must be sufficiently damaged to cause tenants to not be able to function in this space)."
+				},
+				"interior": {
+					"type": "number",
+					"description":  "tolerance for damage to interior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the interior plan area of severely damaged interior components within the tenant-unit to the total plan area of the tenant unit (i.e., what percent of the interior space must be hindered to cause tenants to not be able to function in this space)."
+				},
+				"occ_per_elev": {
+					"type": "number",
+					"description": "max number of occupants per elevator allowed for function"
+				},
+				"is_elevator_required": {
+					"type": "logical",
+					"description": "is an operational elevator required for tenant function"
+				},
+				"is_electrical_required": {
+					"type": "logical",
+					"description": "is an operational electrical system required for tenant function"
+				},
+				"is_water_potable_required": {
+					"type": "logical",
+					"description": "is an operational potable water plumbing system required for tenant function"
+				},
+				"is_water_sanitary_required": {
+					"type": "logical",
+					"description": "is an operational sanitary waste plumbing system required for tenant function"
+				},
+				"is_hvac_ventilation_required": {
+					"type": "logical",
+					"description": "is an operational hvac system required for tenant function"
+				},
+				"is_hvac_heating_required": {
+					"type": "logical",
+					"description": "is an operational hvac system required for tenant function"
+				},
+				"is_hvac_cooling_required": {
+					"type": "logical",
+					"description": "is an operational hvac system required for tenant function"
+				},
+				"is_hvac_exhaust_required": {
+					"type": "logical",
+					"description": "is an operational hvac system required for tenant function"
+				}
+			}
+		}
+	}
+}
\ No newline at end of file
diff --git a/schema/Optional Inputs.json b/schema/Optional Inputs.json
new file mode 100644
index 0000000..474ac5b
--- /dev/null
+++ b/schema/Optional Inputs.json	
@@ -0,0 +1,515 @@
+{
+	"definitions": {
+		"arrayOfNumbers": {
+			"type": "array",
+			"items": {
+				"type": "number"
+			}
+		},
+		"arrayOfArraysOfNumbers": {
+			"type": "array",
+			"items": {
+				"$ref": "#/definitions/arrayOfNumbers"
+			}
+		}
+	},
+	"properties": {
+		"impedance_options": {
+			"type": "object",
+			"description": "general impedance module inputs",
+			"properties": {
+				"include_impedance": {
+					"type": "object",
+					"description": "logical switches for defining which impedance factors to consider in the assessment",
+					"properties": {
+						"inspection": {
+							"type": "logical",
+							"description": "flag to include inpection delays"
+						},
+						"financing": {
+							"type": "logical",
+							"description": "flag to include financing delays"
+						},
+						"permitting": {
+							"type": "logical",
+							"description": "flag to include permitting delays"
+						},
+						"engineering": {
+							"type": "logical",
+							"description": "flag to include engineering mobilization and design delays"
+						},
+						"contractor": {
+							"type": "logical",
+							"description": "flag to include contractor mobilization delays"
+						},
+						"long_lead": {
+							"type": "logical",
+							"description": "flag to include long lead delays"
+						}
+					}
+				},
+				"system_design_time": {
+					"type": "object",
+					"description": "variables of formula to calculate median system design time",
+					"properties": {
+						"f": {
+							"type": "number",
+							"description": "design fee ratio: ratio of building value charged by engineering firm. Default to 0.04 for repair projects"
+						},
+						"r": {
+							"type": "number",
+							"description": "engineering rate: Average hourly rate of engineering work. Default to $175 per hour"
+						},
+						"t": {
+							"type": "number",
+							"description": "team factor: accounts for parallel time between multiple designers spent on project. Default to 1.3"
+						},
+						"w": {
+							"type": "number",
+							"description": "average workday: average number of hours per day spent on project: Default to 8 hours"
+						}
+					}
+				},
+				"eng_design_min_days":{
+					"type": "number",
+					"description": "Minumum number of days for the median engineering design time"
+				},
+				"eng_design_max_days":{
+					"type": "number",
+					"description": "Maximum number of days for the median engineering design time"
+				},
+				"mitigation": {
+					"type": "object",
+					"description": "general impedance assessment inputs such as mitigation factors",
+					"properties": {
+						"is_essential_facility": {
+							"type": "logical",
+							"description": "is the building deemed as essential by the local jurisdiction"
+						},
+						"is_borp_equivalent": {
+							"type": "logical",
+							"description": "are the BORP, or BORP equivalent, post-earthquake strategies in place to limit inspection delays"
+						},
+						"is_engineer_on_retainer": {
+							"type": "logical",
+							"description": "is an engineer contracted to be on retainer in case of post-earthquake damage"
+						},
+						"contractor_relationship": {
+							"type": "string",
+							"description": "string identifier that defines the state of existing relationships with contractors to perform repairs. Options include <'retainer', 'good', 'none'>"
+						},
+						"contractor_retainer_time": {
+							"type": "number",
+							"description": "time, in days, from onset of damage to when repairs start previosly agreed upon in retainer agreement. Only applies when 'retainer' contractor_relationship is selected"
+						},
+						"funding_source": {
+							"type": "string",
+							"description": "what is the source of funding for repair costs beyond that of the capital of the owner"
+						},
+						"capital_available_ratio": {
+							"type": "number",
+							"description": "ratio of the building value that the onwer has immediately available for repairs"
+						}
+					}
+				},
+				"impedance_beta": {
+					"type": "number",
+					"description": "lognormal uncetainty of impedance times",
+				},
+				"impedance_truncation": {
+					"type": "number",
+					"description": "Truncation bounds of lognormal distribution of impedance time, input as the number of standard deviations above and below the mean. Default to 2",
+				},
+				"default_lead_time": {
+					"type": "number",
+					"description": "Lead time, in days, to replace select components (equipment or other less readily available components). This default lead time will be applied to all components with the long_lead flag in the inputs builder. To add custom lead times per component, update the long_lead_time attribute in the damage.comp_ds_table field.",
+				},
+				"demand_surge": {
+					"type": "object",
+					"description": "general user input options for defining the demand surge",
+					"properties": {
+						"include_surge": {
+							"type": "logical",
+							"description": "include demand surge when simulating impedance times"
+						},
+						"is_dense_urban_area": {
+							"type": "logical",
+							"description": "is within a dense urban area, typically defined by a site within a metropolitan statistical area (MSA) with a population of over 1 million"
+						},
+						"site_pga": {
+							"type": "number",
+							"description": "peak ground acceleration (PGA) in g at the building site for the ground shaking intensity of interest"
+						},
+						"pga_de": {
+							"type": "number",
+							"description": "peak ground acceleration (PGA) in g associated with the design eartquake (DE). Method of combining ground motion pairs (e.g., max direction vs geomean) should match the field site_pga"
+						}
+					}
+				},
+				"scaffolding_lead_time": {
+					"type": "number",
+					"description": "The number of days it takes to get workers to the site to erect scaffolding",
+				},
+				"scaffolding_erect_time": {
+					"type": "number",
+					"description": "The number of days it takes for workers to errect scaffolding to protect doorways and walkways from falling hazards",
+				}
+			}
+		},
+		"repair_time_options": {
+			"type": "object",
+			"description": "general repair schedule module inputs",
+			"properties": {
+				"temp_repair_beta": {
+					"type": "number",
+					"description": "lognormal uncetainty of temporary repair times. Default to 0.6",
+				},
+				"max_workers_per_sqft_story": {
+					"type": "number",
+					"description": "Maximum density of workers on a given story. Default to 0.001 from FEMA P-58",
+				},
+				"max_workers_per_sqft_building": {
+					"type": "number",
+					"description": "Maximum density of workers for the entire building after the first 10. Default to 0.00025 from REDi",
+				},
+				"max_workers_building_min": {
+					"type": "number",
+					"description": "Lower limit on the maximum number of workers for the entire building (i.e., max number of workers is never less than this). Default to 20 from REDi",
+				},
+				"max_workers_building_max": {
+					"type": "number",
+					"description": "Upper limit on the maximum number of workers for the entire building (i.e., max number of workers is never more than this). Default to 260 from REDi",
+				},
+				"allow_tmp_repairs": {
+					"type": "logical",
+					"description": "Flag to allow temporay repair measures to be used to restore building reoccupancy and basic function",
+				},
+				"allow_shoring": {
+					"type": "logical",
+					"description": "Flag to allow shoring to be used as a temporary repair for mitigating local structural stability issues. Setting allow_tmp_repairs to zero overwrites this flag",
+				}
+			}
+		},
+		"functionality_options": {
+			"type": "object",
+			"description": "general functionality module inputs",
+			"properties": {
+				"calculate_red_tag": {
+					"type": "logical",
+					"description": "flag to indicate whether or not to include the assessment of red tags based on component damage"	
+				}, 
+				"red_tag_clear_time": {
+					"type": "number",
+					"description": "days after all red tag repairs are done before occupancy can be regained (i.e. time for permitting office to clear the tag)"
+				},
+				"red_tag_clear_beta": {
+					"type": "number",
+					"description": "lognormal dispersion defining the uncertaitny in the red tag clear time"
+				},
+				"include_local_stability_impact": {
+					"type": "logical",
+					"description": "flag to consider the effect of local structural stability issues (that would require shoring) on building occupancy"
+				},
+				"door_racking_repair_day": {
+					"type": "number",
+					"description": "days after the earthquake required to repaired all racked doorways"
+				},
+				"egress_threshold": {
+					"type": "number",
+					"description": "ratio of building egress paths required for building safety and access"
+				},
+				"fire_watch": {
+					"type": "logical",
+					"description": "will a fire watch be set if fire sprinkler systems are damaged"
+				},
+				"local_fire_damamge_threshold": {
+					"type": "number",
+					"description": "ratio of building fire sprinkler branches or drops that can be damaged with a given story without significantly disrupting the operation of the active fire supression system"
+				},
+				"min_egress_paths": {
+					"type": "number",
+					"description": "minimum number of egress pathways that must be maintained at each story to maintain occupancy"
+				},
+				"required_ratio_operating_hvac_main": {
+					"type": "number",
+					"description": "ratio of hvac units, which service the entire building, required to maintain an operational hvac system; not applied to components with n+1 redundancy"
+				},
+				"required_ratio_operating_hvac_unit": {
+					"type": "number",
+					"description": "ratio of hvac units, which service an individual tenant unit, required to maintain an operational hvac system; not applied to components with n+1 redundancy"
+				},
+				"exterior_safety_threshold": {
+					"type": "number",
+					"description": "damage theshold that triggers an exterior safety issue; threshold is defined in terms of the ratio of damaged area to perimeter area of a given tenant unit"
+				},
+				"interior_safety_threshold": {
+					"type": "number",
+					"description": "damage theshold that triggers an interior safety issue; threshold is defined in terms of the ratio of damaged area to interior area of a given tenant unit"
+				},
+				"door_access_width_ft": {
+					"type": "number",
+					"description": "width of door access zone, centerlined on the doorway, required for safe access. A safe door access zone must be free of falling hazards"
+				},
+				"heat_utility": {
+					"type": "string",
+					"description": "string identifier of the type of utility that is used to heat the building; string should match on of the fields in functionality.utilities"
+				}
+			}
+		},
+		"building_model": {
+			"type": "object",
+			"description": "general information about the building such as the number of stories and the building area",
+			"properties": {
+				"building_value": {
+					"type": "number",
+					"description": "Total replacement value of the building, in USD"
+				},
+				"num_stories": {
+					"type": "number",
+					"description": "number of stories in the building, does not include basement levels"
+				},
+				"area_per_story_sf": {
+					"type": "#/definitions/arrayOfNumbers",
+					"description": "interior area, in square feet, for each story in the building, starting from the lowest level"
+				},
+				"ht_per_story_ft": {
+					"type": "#/definitions/arrayOfNumbers",
+					"description": "elevation height, in feet, for each story in the building, starting from the lowest level"
+				},
+				"edge_lengths": {
+					"type": "#/definitions/arrayOfArraysOfNumbers",
+					"description": "external length of two orthogonal sides of the building, in feet, for each story in the building, starting from the lowest level"
+				},
+				"struct_bay_area_per_story": {
+					"type": "#/definitions/arrayOfNumbers",
+					"description": "length of the typical structural bay, from centerline to centerline, in feet, for each story in the building, starting from the lowest level"
+				},
+				"num_entry_doors": {
+					"type": "number",
+					"description": "number of external building exits/entrances"
+				},
+				"num_elevators": {
+					"type": "number",
+					"description": "number of elevator shafts in the building"
+				},
+				"stairs_per_story": {
+					"type": "#/definitions/arrayOfNumbers",
+					"description": "number of stairways on each story in the building, starting from the lowest level"
+				},
+				"occupants_per_story": {
+					"type": "#/definitions/arrayOfNumbers",
+					"description": "total number of occuapnts (persons) that occupy each story of this building, starting from the lowest level"
+				},
+				"comps": {
+					"type": "object",
+					"description": "component population information",
+					"properties": {
+						"story": {
+							"type": "array",
+							"description": "component quantities at each story",
+							"items": {
+								"type": "object",
+								"properties": {
+									"qty_dir_1": {								
+										"description": "total number of component units for each component stack in direction 1 of this story",
+										"$ref": "#/definitions/arrayOfNumbers",
+									"qty_dir_2": {
+										"description": "total number of component units for each component stack in direction 2 of this story",
+										"$ref": "#/definitions/arrayOfNumbers"
+									},
+									"qty_dir_3": {
+										"description": "total number of component units for each component stack in direction 3 (nondirectional) of this story",
+										"$ref": "#/definitions/arrayOfNumbers"
+									}
+								}
+							}
+						},
+						"comp_list": {
+							"description": "fragility ID for each component stack",
+							"$ref": "#/definitions/arrayOfStrings"
+						},
+						"comp_table": {
+							"type": "table",
+							"description": "various component attributes. Can be populated from the component_attribites.csv database in the static_tables directory"
+						}
+					}
+				}
+			}
+		},
+		"damage": {
+			"type": "object",
+			"description": "simulated damage, simulated repair time, and compoennt attribute data associated with each component's damages state in the building",
+			"properties": {
+				"comp_ds_table": {
+					"type": "table",
+					"description": "various component attributes by damage state. Can be populated from the component_attribites.csv and damage_state_attribute_mapping.csv databases in the static_tables directory"
+				},
+				"tenant_units": {
+					"type": "array",
+					"description": "simulated component damage, broken down by each tenant unit in the building",
+					"items": {
+						"type": "object",
+						"properties": {
+							"qnt_damaged": {
+								"description": "simulation of the quantity of damaged components in each component damage state",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"cost": {
+								"description": "simulated repair cost of each component's damage state",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"worker_days": {
+								"description": "simulated worker days required to repair each component's damage state",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"qnt_damaged_side_1": {
+								"description": "simulated number of damaged component damage states associated with side 1",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"qnt_damaged_side_2": {
+								"description": "simulated number of damaged component damage states associated with side 2",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"qnt_damaged_side_3": {
+								"description": "simulated number of damaged component damage states associated with side 3",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"qnt_damaged_side_4": {
+								"description": "simulated number of damaged component damage states associated with side 4",
+								"$ref": "#/definitions/arrayOfArraysOfNumbers"
+							},
+							"num_comps": {
+								"description": "total number of components populated within each tenant unit associated with each damage state",
+								"$ref": "#/definitions/arrayOfNumbers"
+							}
+						}
+					}
+				}
+			}
+		},
+		"damage_consequences": {
+			"type": "object",
+			"description": "general impedance assessment inputs such as mitigation factors",
+			"properties": {
+				"simulated_replacement": {
+					"description": "simulated replacement time in days for all realizations where replacement is required, NaN = building does not need replaced and is scheduled for repairs. Note: to not consider building replacement, pass in an array of NaNs", 
+					"$ref": "#/definitions/arrayOfNumbers"
+				},
+				"racked_stair_doors_per_story": {
+					"description": "simulated number of racked stairway doors per story", 
+					"$ref": "#/definitions/arrayOfArraysOfNumbers"
+				},
+				"racked_entry_doors_side_1": {
+					"description": "simulated number of racked entry doors on one side of the building", 
+					"$ref": "#/definitions/arrayOfNumbers"
+				},
+				"racked_entry_doors_side_2": {
+					"description": "simulated number of racked entry doors on one side of the building", 
+					"$ref": "#/definitions/arrayOfNumbers"
+				},
+				"repair_cost_ratio_total": {
+					"description": "simulated building repair cost of structural and nonstructural repairs (does not include tenant contents); normalized by the replacement value", 
+					"$ref": "#/definitions/arrayOfNumbers"
+				},
+				"repair_cost_ratio_engineering": {
+					"description": "simulated building repair cost of repairs that require engineering redesign; normalized by the replacement value", 
+					"$ref": "#/definitions/arrayOfNumbers"
+				}
+			}
+		},
+		"functionality": {
+			"type": "object",
+			"description": "simulated outputs of the performance based functional recovery methodology",
+			"properties": {
+				"utilities": {
+					"type": "object",
+					"description": "simulated utility downtimes at this building site",
+					"properties": {
+						"electrical": {
+							"description": "simulated electrical power network downtime", 
+							"$ref": "#/definitions/arrayOfNumbers"
+						},
+						"water": {
+							"description": "simulated water network downtime", 
+							"$ref": "#/definitions/arrayOfNumbers"
+						},
+						"gas": {
+							"description": "simulated natural gas network downtime", 
+							"$ref": "#/definitions/arrayOfNumbers"
+						}
+					}
+				}
+			}
+		},
+		"tenant_units": {
+			"type": "table",
+			"description": "attributes and functional requirements of each tenant unit in the building",
+			"properties": {
+				"id": {
+					"type": "number",
+					"description": "tenant unit unique id"
+				},
+				"story": {
+					"type": "number",
+					"description": "story where each tenant unit is located"
+				},
+				"area": {
+					"type": "number",
+					"description": "floor area, in square feet, of the tenant unit"
+				},
+				"perim_area": {
+					"type": "number",
+					"description": "perimeter area, in square feet, of the tenant unit"
+				},
+				"occupancy_id": {
+					"type": "number",
+					"description": "unique id key linking each tenant unit to its associated tenant function requirements"
+				},
+				"exterior": {
+					"type": "number",
+					"description": "tolerance for damage to exterior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the perimeter area of severely damaged exterior components within the tenant-unit to the total perimeter area of the tenant unit (i.e., what percent of the exterior walls must be sufficiently damaged to cause tenants to not be able to function in this space)."
+				},
+				"interior": {
+					"type": "number",
+					"description":  "tolerance for damage to interior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the interior plan area of severely damaged interior components within the tenant-unit to the total plan area of the tenant unit (i.e., what percent of the interior space must be hindered to cause tenants to not be able to function in this space)."
+				},
+				"occ_per_elev": {
+					"type": "number",
+					"description": "max number of occupants per elevator allowed for function"
+				},
+				"is_elevator_required": {
+					"type": "logical",
+					"description": "is an operational elevator required for tenant function"
+				},
+				"is_electrical_required": {
+					"type": "logical",
+					"description": "is an operational electrical system required for tenant function"
+				},
+				"is_water_potable_required": {
+					"type": "logical",
+					"description": "is an operational potable water plumbing system required for tenant function"
+				},
+				"is_water_sanitary_required": {
+					"type": "logical",
+					"description": "is an operational sanitary waste plumbing system required for tenant function"
+				},
+				"is_hvac_ventilation_required": {
+					"type": "logical",
+					"description": "is an operational hvac system required for tenant function"
+				},
+				"is_hvac_heating_required": {
+					"type": "logical",
+					"description": "is an operational hvac system required for tenant function"
+				},
+				"is_hvac_cooling_required": {
+					"type": "logical",
+					"description": "is an operational hvac system required for tenant function"
+				},
+				"is_hvac_exhaust_required": {
+					"type": "logical",
+					"description": "is an operational hvac system required for tenant function"
+				}
+			}
+		}
+	}
+}
\ No newline at end of file
diff --git a/schema/Outputs.json b/schema/Outputs.json
new file mode 100644
index 0000000..bef16e7
--- /dev/null
+++ b/schema/Outputs.json
@@ -0,0 +1,385 @@
+{
+	"definitions": {
+		"numberOrNaN": {
+		  "oneOf": [
+			{ 
+			  "type": "number"
+			},
+			{ 
+			  "type": "string",
+			  "pattern": "NaN"
+			}
+		  ]
+		},
+		"arrayOfNumbers": {
+		  "type": "array",
+		  "items": {
+			"type": "number"
+		  }
+		},
+		"arrayOfNumbersOrNaN": {
+		  "type": "array",
+		  "items": {
+			"$ref": "#/definitions/numberOrNaN"
+		  }
+		},
+		"arrayOfStrings": {
+		  "type": "array",
+		  "items": {
+			"type": "string"
+		  }
+		},
+		"arrayOfArraysOfNumbers": {
+		  "type": "array",
+		  "items": {
+			"$ref": "#/definitions/arrayOfNumbers"
+		  }
+		},
+		"arrayOfArraysOfNumbersOrNaN": {
+		  "type": "array",
+		  "items": {
+			"$ref": "#/definitions/arrayOfNumbersOrNaN"
+		  }
+		},
+		"summaryImpedingFactors": {
+		  "type": "object",
+		  "properties": {
+			"start_day": {
+			  "$ref": "#/definitions/arrayOfArraysOfNumbers"
+			},
+			"complete_day": {
+			  "$ref": "#/definitions/arrayOfArraysOfNumbers"
+			}
+		  }
+		},
+		"summaryImpedingFactorsSystem": {
+		  "type": "object",
+		  "properties": {
+			"structural": {
+			  "$ref": "#/definitions/summaryImpedingFactors"
+			},
+			"exterior": {
+			  "$ref": "#/definitions/summaryImpedingFactors"
+			},
+			"interior": {
+			  "$ref": "#/definitions/summaryImpedingFactors"
+			},
+			"stairs": {
+			  "$ref": "#/definitions/summaryImpedingFactors"
+			},
+			"elevators": {
+			  "$ref": "#/definitions/summaryImpedingFactors"
+			},
+			"plumbing": {
+			  "$ref": "#/definitions/summaryImpedingFactors"
+			},
+			"electrical": {
+			  "$ref": "#/definitions/summaryImpedingFactors"
+			},
+			"hvac": {
+			  "$ref": "#/definitions/summaryImpedingFactors"
+			},
+			"fire": {
+			  "$ref": "#/definitions/summaryImpedingFactors"
+			}
+		  },
+		  "required": [
+			"structural",
+			"exterior",
+			"stairs"
+		  ],
+		  "additionalProperties": false
+		}
+	},
+	"properties": {
+		"functionality": {
+			"type": "object",
+			"description": "simulated outputs of the performance based functional recovery methodology",
+			"properties": {
+				"recovery": {
+					"type": "object",
+					"description": "simulated tenant- and building-level functional recovery and reoccupancy outcomes",
+					"properties": {
+						"reoccupancy": {
+							"type": "object",
+							"description": "simulated tenant- and building-level reoccupancy outcomes",
+							"properties": {
+								"building_level" :{
+									"type": "object",
+									"description": "simulated building-level reoccupancy outcomes",
+									"properties": {
+										"recovery_day": {
+											"description": "simulated building reoccupancy time, in days",
+											"$ref": "#/definitions/arrayOfNumbers"
+										},
+										"initial_percent_affected": {
+											"description": "simulated portion of building that is non-occupiable, initially after the earthquake",
+											"$ref": "#/definitions/arrayOfNumbers"
+										}
+									}
+								},
+								"tenant_unit": {
+									"type": "object",
+									"description": "simulated tenant-unit-level reoccupancy outcomes",
+									"properties": {
+										"recovery_day": {
+											"description": "simulated reoccupancy time, in days, for each tenant-unit in the building",
+											"$ref": "#/definitions/arrayOfArraysOfNumbers"
+										}
+									}
+								},
+								"recovery_trajectory": {
+									"type": "object",
+									"description": "simulated recovery trajectory vectors; tracks the recovery of occupancy over time",
+									"properties": {
+										"recovery_day": {
+											"description": "simulated reoccupancy time, in days, for each step of the recovery trajectory (x-axis)",
+											"$ref": "#/definitions/arrayOfArraysOfNumbers"
+										},
+										"percent_recovered": {
+											"description": "each step of the recovery trajectory; step increment is equal to the inverse of the number of tenant-units in the building",
+											"$ref": "#/definitions/arrayOfNumbers"
+										}
+									}
+								},
+								"breakdowns": {
+									"type": "object",
+									"description": "deagregated reoccupancy times for various recovery targets (in days)",
+									"properties": {
+										"system_breakdowns": {
+											"description": "fraction of realization affected by loss of reoccupancy anywhere in the building, broken down by systems-level fault tree events",
+											"$ref": "#/definitions/arrayOfArraysOfNumbers"
+										},
+										"component_breakdowns": {
+											"description": "fraction of realization affected by loss of reoccupancy anywhere in the building, broken down by component",
+											"$ref": "#/definitions/arrayOfArraysOfNumbers"
+										},
+										"perform_targ_days": {
+											"description": "recovery targets of interest(in days)",
+											"$ref": "#/definitions/arrayOfNumbers"
+										},
+										"system_names": {
+											"description": "names of each of the systems in the systems_breakdowns field",
+											"$ref": "#/definitions/arrayOfStrings"
+										},
+										"comp_names": {
+											"description": "names of each of the components in the component_breakdowns field",
+											"$ref": "#/definitions/arrayOfStrings"
+										}
+									}
+								}
+							}
+						},
+						"functional": {
+							"type": "object",
+							"description": "simulated tenant- and building-level functional recovery outcomes",
+							"properties": {
+								"building_level": {
+									"type": "object",
+									"description": "simulated tenant- and building-level functional recovery outcomes",
+									"properties": {
+										"recovery_day": {
+											"description": "simulated building functional recovery time, in days",
+											"$ref": "#/definitions/arrayOfNumbers"
+										},
+										"initial_percent_affected": {
+											"description": "simulated portion of building that is non-functional, initially after the earthquake",
+											"$ref": "#/definitions/arrayOfNumbers"
+										}
+									}
+								},
+								"tenant_unit": {
+									"type": "object",
+									"description": "simulated tenant-unit-level functional recovery outcomes",
+									"properties": {
+										"recovery_day": {
+											"description": "simulated functional recovery time, in days, for each tenant-unit in the building",
+											"$ref": "#/definitions/arrayOfArraysOfNumbers"
+										}
+									}
+								},
+								"recovery_trajectory": {
+									"type": "object",
+									"description": "simulated recovery trajectory vectors; tracks the recovery of function over time",
+									"properties": {
+										"recovery_day": {
+											"description": "simulated functional recovery time, in days, for each step of the recovery trajectory (x-axis)",
+											"$ref": "#/definitions/arrayOfArraysOfNumbers"
+										},
+										"percent_recovered": {
+											"description": "each step of the recovery trajectory; step increment is equal to the inverse of the number of tenant-units in the building",
+											"$ref": "#/definitions/arrayOfNumbers"
+										}
+									}
+								},
+								"breakdowns": {
+									"type": "object",
+									"description": "deagregated functional recovery times for various recovery targets (in days)",
+									"properties": {
+										"system_breakdowns": {
+											"description": "fraction of realization affected by loss of function anywhere in the building, broken down by systems-level fault tree events",
+											"$ref": "#/definitions/arrayOfArraysOfNumbers"
+										},
+										"component_breakdowns": {
+											"description": "fraction of realization affected by loss of function, not including reoccupancy, anywhere in the building, broken down by component",
+											"$ref": "#/definitions/arrayOfArraysOfNumbers"
+										},
+										"perform_targ_days": {
+											"description": "recovery targets of interest(in days)",
+											"$ref": "#/definitions/arrayOfNumbers"
+										},
+										"system_names": {
+											"description": "names of each of the systems in the systems_breakdowns field",
+											"$ref": "#/definitions/arrayOfStrings"
+										},
+										"comp_names": {
+											"description": "names of each of the components in the component_breakdowns field",
+											"$ref": "#/definitions/arrayOfStrings"
+										},
+										"component_combined": {
+											"description": "fraction of realization affected by loss of function, including reocupancy, anywhere in the building, broken down by component",
+											"$ref": "#/definitions/arrayOfArraysOfNumbers"
+										}
+									}
+								}
+							}
+						}
+					}
+				},
+				"building_repair_schedule": {
+					"type": "object",
+					"description": "simulated building repair schedule",
+					"properties": {
+						"full": {
+							"type": "object",
+							"description": "full building repairs required to bring building back to pre-event conditions",
+							"properties": {
+								"repair_start_day": {
+									"type": "object",
+									"description": "the day after the earthquake that each repair starts",
+									"properties":{
+										"per_component":{
+											"description": "repair start day of each component",
+											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
+										},
+										"per_story":{
+											"description": "repair start day of each story",
+											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
+										},
+										"per_system":{
+											"description": "repair start day of each system",
+											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
+										},
+										"per_system_story":{
+											"description": "repair start day of each story within each system",
+											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
+										},
+										"per_story_system":{
+											"description": "repair start day of each system on each story",
+											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
+										}
+									}
+								},
+								"repair_complete_day": {
+									"type": "object",
+									"description": "the day after the earthquake that each repair is finished",
+									"properties":{
+										"per_component":{
+											"description": "repair complete day of each component",
+											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
+										},
+										"per_story":{
+											"description": "repair complete day of each story",
+											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
+										},
+										"per_system":{
+											"description": "repair complete day of each system",
+											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
+										},
+										"per_system_story":{
+											"description": "repair complete day of each story within each system",
+											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
+										},
+										"per_story_system":{
+											"description": "repair complete day of each system on each story",
+											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
+										}
+									}
+								},
+								"component_names": {
+									"description": "names of each of the components in the per_component breakdown field",
+									"$ref": "#/definitions/arrayOfStrings"
+								},
+								"system_names": {
+									"description": "names of each of the systems in the per_system breakdown field",
+									"$ref": "#/definitions/arrayOfStrings"
+								}
+							}
+						},
+						"temp": {
+							"type": "object",
+							"description": "temporary building repairs only required to restore basic function",
+							"properties": {
+								"shares properties with full repair object"
+							}
+						}
+					}
+				},
+				"worker_data": {
+				    "type": "object",
+				    "description": "simulated worker allocations throughout the repair schedule",
+				    "properties": {
+						"total_workers": {
+							"description": "simulated worker allocated in the building for any repair at various time steps",
+							"$ref": "#/definitions/arrayOfArraysOfNumbers"
+						},
+						"day_vector": {
+							"description": "various time steps, in days after the earthquake, associated with the total_workers field",
+							"$ref": "#/definitions/arrayOfArraysOfNumbers"
+						}
+				    }
+				},
+				"impeding_factors": {
+				    "type": "object",
+				    "description": "simulated impeding factor times",
+				    "properties": {
+						"time_sys": {
+							"description": "simulated total impeding time for each system",
+							"$ref": "#/definitions/arrayOfArraysOfNumbers"
+						},
+						"breakdowns": {
+							"type": "object"
+							"description": "simulated impeding factor times broken down by factor",
+							"properties": {
+								"inspection": {
+									"description": "simulated inpsection time",
+									"$ref": "#/definitions/summaryImpedingFactors"
+								},
+								"financing": {
+									"description": "simulated financing time",
+									"$ref": "#/definitions/summaryImpedingFactors"
+								},
+								"contractor_mob": {
+									"description": "simulated contractor mobilization time",
+									"$ref": "#/definitions/summaryImpedingFactors"
+								},
+								"eng_mob": {
+									"description": "simulated engineering mobilization time for each system",
+									"$ref": "#/definitions/summaryImpedingFactorsSystem"
+								},
+								"design": {
+									"description": "simulated engineering design time for each system",
+									"$ref": "#/definitions/summaryImpedingFactorsSystem"
+								},
+								"permitting": {
+									"description": "simulated permitting time for each system",
+									"$ref": "#/definitions/summaryImpedingFactorsSystem"
+								}
+							}
+					    }
+				    }
+				}
+			}
+		}
+	}
+}
\ No newline at end of file
diff --git a/src/atc138/__init__.py b/src/atc138/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/atc138/cli.py b/src/atc138/cli.py
deleted file mode 100644
index 5934609..0000000
--- a/src/atc138/cli.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import argparse
-import sys
-import os
-from .driver import run_analysis
-
-def main():
-    parser = argparse.ArgumentParser(description="Run ATC-138 Functional Recovery Assessment")
-    parser.add_argument("input_dir", help="Path to the directory containing input files (e.g., simulated_inputs.json)")
-    parser.add_argument("output_dir", help="Path to the directory where outputs will be saved")
-    parser.add_argument("--seed", type=int, help="Random seed for reproducibility", default=None)
-
-    args = parser.parse_args()
-
-    # Validate inputs
-    if not os.path.isdir(args.input_dir):
-        print(f"Error: Input directory '{args.input_dir}' does not exist.", file=sys.stderr)
-        sys.exit(1)
-
-    try:
-        run_analysis(args.input_dir, args.output_dir, seed=args.seed)
-    except Exception as e:
-        print(f"Error running analysis: {e}", file=sys.stderr)
-        sys.exit(1)
-
-if __name__ == "__main__":
-    main()
diff --git a/src/atc138/data/default_inputs.json b/src/atc138/data/default_inputs.json
deleted file mode 100644
index 1a7d4e6..0000000
--- a/src/atc138/data/default_inputs.json
+++ /dev/null
@@ -1,81 +0,0 @@
-{
-  "impedance_options": {
-    "include_impedance": {
-      "inspection": true,
-      "financing": true,
-      "permitting": true,
-      "engineering": true,
-      "contractor": true,
-      "long_lead": false
-    },
-    "system_design_time": {
-      "f": 0.04,
-      "r": 200,
-      "t": 1.3,
-      "w": 8
-    },
-    "eng_design_min_days": 14,
-    "eng_design_max_days": 365,
-    "mitigation": {
-      "is_essential_facility": false,
-      "is_borp_equivalent": false,
-      "is_engineer_on_retainer": false,
-      "contractor_relationship": "good",
-      "contractor_retainer_time": 3,
-      "funding_source": "private",
-      "capital_available_ratio": 0.02
-    },
-    "impedance_beta": 0.6,
-    "impedance_truncation": 2,
-    "default_lead_time": 182,
-    "demand_surge": {
-      "include_surge": 1,
-      "is_dense_urban_area": 1,
-      "site_pga": 1,
-      "pga_de": 1
-    },
-    "scaffolding_lead_time": 5,
-    "scaffolding_erect_time": 2,
-    "door_racking_repair_day": 3,
-    "flooding_cleanup_day": 5,
-    "flooding_repair_day": 90
-  },
-  "repair_time_options": {
-    "max_workers_per_sqft_story": 0.001,
-    "max_workers_per_sqft_story_temp_repair": 0.005,
-    "max_workers_per_sqft_building": 0.00025,
-    "max_workers_building_min": 20,
-    "max_workers_building_max": 260,
-    "allow_tmp_repairs": 1,
-    "allow_shoring": 1
-  },
-  "functionality_options": {
-    "calculate_red_tag": 1,
-    "red_tag_clear_time": 7,
-    "red_tag_clear_beta": 0.6,
-    "red_tag_options": {
-      "tag_coupling_beams_over_height": true,
-      "ignore_coupling_beam_for_red_tag": false
-    },
-    "include_local_stability_impact": 1,
-    "include_flooding_impact": 1,
-    "egress_threshold": 0.5,
-    "fire_watch": true,
-    "local_fire_damamge_threshold": 0.25,
-    "min_egress_paths": 2,
-    "exterior_safety_threshold": 0.1,
-    "interior_safety_threshold": 0.25,
-    "door_access_width_ft": 9,
-    "habitability_requirements": {
-      "electrical": 0,
-      "water_potable": 0,
-      "water_sanitary": 0,
-      "hvac_ventilation": 0,
-      "hvac_heating": 0,
-      "hvac_cooling": 0,
-      "hvac_exhaust": 0
-    },
-    "water_pressure_max_story": 4,
-    "heat_utility": "gas"
-  }
-}
\ No newline at end of file
diff --git a/src/atc138/driver.py b/src/atc138/driver.py
deleted file mode 100644
index 3fbb0ec..0000000
--- a/src/atc138/driver.py
+++ /dev/null
@@ -1,198 +0,0 @@
-def run_analysis(input_dir, output_dir, seed=None):
-
-    '''This script facilitates the performance based functional recovery and
-    reoccupancy assessment of a single building for a single intensity level
-    
-    Input data consists of building model info and simulated component-level
-    damage and conesequence data for a suite of realizations, likely assessed
-    as part of a FEMA P-58 analysis. Inputs are read from json files, as well 
-    as loaded from csvs in the static_tables directory.
-    
-    Output data is saved to a specified outputs directory and is saved into a
-    single json output file.
-    
-        Main file for running the code
-    
-    Parameters
-    ----------
-    input_dir: string
-        Path to the directory containing the input files (simulated_inputs.json).
-    
-    output_dir: string
-        Path to the directory where the output file (recovery_outputs.json) will be saved.
-    
-    seed: int
-        Random seed to be passed to the Numpy random engine. Default behavior
-        is set as None and will not pass a random seed.
-    """'''
-    
-    import time
-    start_time = time.time() # For runtime calculation
-    
-    ''' ######################  Caution ########################'''
-    # Ignoring runtime warnings
-    import warnings
-    warnings.filterwarnings('ignore')
-    ''' ########################################################'''
-        
-    ## 1. Import Python modules to be used
-    import os
-    import json
-    import numpy as np
-    import math
-    import pandas as pd
-    from scipy.stats import truncnorm
-    from .input_builder import load_custom_static_tables
-    
-    ## 2. Define User Inputs
-    # Input/Output directories are passed as arguments
-    
-    ## 3. Load FEMA P-58 performance model data and simulated damage and loss
-    # Check if simulated_inputs.json exists, if not build it
-    sim_inputs_path = os.path.join(input_dir, 'simulated_inputs.json')
-    
-    if os.path.exists(sim_inputs_path):
-        f = open(sim_inputs_path)
-        simulated_inputs = json.load(f)
-    else:
-        print(f"simulated_inputs.json not found in {input_dir}. Building from raw inputs...")
-        from .input_builder import build_simulated_inputs
-        simulated_inputs = build_simulated_inputs(input_dir)
-        
-        # Save simulated inputs
-        with open(sim_inputs_path, 'w') as f:
-            json.dump(simulated_inputs, f)
-    
-    building_model = simulated_inputs['building_model']
-    damage = simulated_inputs['damage']
-    damage_consequences = simulated_inputs['damage_consequences']
-    functionality = simulated_inputs['functionality']
-    functionality_options = simulated_inputs['functionality_options']
-    impedance_options = simulated_inputs['impedance_options']
-    repair_time_options = simulated_inputs['repair_time_options']
-    tenant_units = simulated_inputs['tenant_units']
-    
-    # Change story indices in damage['tenant_units'], damage['story'] building_model['comps']['story'] to int from string
-    # (This ensures compatibility if JSON keys were strings)
-    damage_ten_units = []
-    if ('tenant_units' in damage.keys()) == True:
-        for tu in range(len(damage['tenant_units'])):
-            # Handle list vs dict if necessary, but assuming list structure from builder
-            if isinstance(damage['tenant_units'], list): # list
-                 damage_ten_units.append(damage['tenant_units'][tu])
-            elif str(tu) in damage['tenant_units']: # string key
-                 damage_ten_units.append(damage['tenant_units'][str(tu)])
-            elif tu in damage['tenant_units']: # integer key
-                 damage_ten_units.append(damage['tenant_units'][tu])
-            
-        damage['tenant_units'] = damage_ten_units  
-    
-    damage_story = []    
-    for s in range(len(damage['story'])):
-        if isinstance(damage['story'], list): # list
-            damage_story.append(damage['story'][s])
-        elif str(s) in damage['story']: # string key
-            damage_story.append(damage['story'][str(s)])
-        elif s in damage['story']: # integer key
-            damage_story.append(damage['story'][s])
-    
-    damage['story'] = damage_story 
-    
-    bldg_comps_story = []
-    for s in range(len(building_model['comps']['story'])):
-        if isinstance(building_model['comps']['story'], list): # list
-             bldg_comps_story.append(building_model['comps']['story'][s])
-        elif str(s) in building_model['comps']['story']: # string key
-             bldg_comps_story.append(building_model['comps']['story'][str(s)])
-        elif s in building_model['comps']['story']: # integer key
-             bldg_comps_story.append(building_model['comps']['story'][s])
-        
-    building_model['comps']['story'] = bldg_comps_story
-    
-    ## 4. Load required static data
-    # Static data is bundled with the package, so use __file__
-    # First, check if custom versions exist within input directory
-
-    pkg_dir = os.path.dirname(__file__)
-    static_data_dir = os.path.join(pkg_dir, 'data')
-
-    subsystems = load_custom_static_tables(
-        input_dir, static_data_dir, 'subsystems.csv')
-    systems = load_custom_static_tables(
-        input_dir, static_data_dir, 'systems.csv')
-    impeding_factor_medians = load_custom_static_tables(
-        input_dir, static_data_dir, 'impeding_factors.csv')
-    tmp_repair_class = load_custom_static_tables(
-        input_dir, static_data_dir, 'temp_repair_class.csv')
-
-    
-    ## 5. Run Recovery Method
-    from .engine import main_PBEE_recovery
-
-    # set a seed
-    # this seed propagates through the entire subfunctions
-    # so if subfunction requires a distinct random variate, a seed instance will have to be used instead
-    if seed is not None:
-        np.random.seed(seed)
-    
-    functionality, damage_consequences = main_PBEE_recovery(damage, 
-                                                            damage_consequences, 
-                                                            building_model, 
-                                                            tenant_units, 
-                                                            systems, 
-                                                            subsystems, 
-                                                            tmp_repair_class,
-                                                            impedance_options, 
-                                                            impeding_factor_medians, 
-                                                            repair_time_options,
-                                                            functionality, 
-                                                            functionality_options)
-           
-    # 6. Save Outputs
-    # Ensure output directory exists
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir)
-    
-    # Covert arrays to list for writing to json file   
-    fnc_keys_1 = list(functionality.keys())
-    for k_1 in fnc_keys_1:
-        if type(functionality[k_1]) == np.ndarray: 
-            functionality[k_1] = functionality[k_1].tolist() 
-        if type(functionality[k_1]) == dict:
-            fnc_keys_2 = list(functionality[k_1].keys())    
-       
-            for k_2 in fnc_keys_2:
-                if type(functionality[k_1][k_2]) == np.ndarray: 
-                    functionality[k_1][k_2] = functionality[k_1][k_2].tolist()
-                if type(functionality[k_1][k_2]) == dict:
-                    fnc_keys_3 = list(functionality[k_1][k_2].keys())    
-       
-                    for k_3 in fnc_keys_3:
-                        if type(functionality[k_1][k_2][k_3]) == np.ndarray: 
-                            functionality[k_1][k_2][k_3] = functionality[k_1][k_2][k_3].tolist()
-                        if type(functionality[k_1][k_2][k_3]) == dict:
-                            fnc_keys_4 = list(functionality[k_1][k_2][k_3].keys())
-     
-                            for k_4 in fnc_keys_4:
-                                if type(functionality[k_1][k_2][k_3][k_4]) == np.ndarray: 
-                                    functionality[k_1][k_2][k_3][k_4] = functionality[k_1][k_2][k_3][k_4].tolist()
-                                if type(functionality[k_1][k_2][k_3][k_4]) == dict:
-                                    fnc_keys_5 = list(functionality[k_1][k_2][k_3][k_4].keys())
-        
-                                    for k_5 in fnc_keys_5:
-                                        if type(functionality[k_1][k_2][k_3][k_4][k_5]) == np.ndarray: 
-                                            functionality[k_1][k_2][k_3][k_4][k_5] = functionality[k_1][k_2][k_3][k_4][k_5].tolist()
-                                        if type(functionality[k_1][k_2][k_3][k_4][k_5]) == dict:
-                                            fnc_keys_6 = list(functionality[k_1][k_2][k_3][k_4][k_5].keys())
-    
-    output_json_object = json.dumps(functionality)
-    
-    with open(os.path.join(output_dir, "recovery_outputs.json"), "w") as outfile:
-        outfile.write(output_json_object)
-    
-    end_time = time.time()
-    
-    # print('Recovery assessment of model ' + model_name + ' complete') # model_name no longer available
-    print('Recovery assessment complete')
-    print('time to run '+str(round(end_time - start_time,2))+'s')
-
diff --git a/src/atc138/engine.py b/src/atc138/engine.py
deleted file mode 100644
index d2b455b..0000000
--- a/src/atc138/engine.py
+++ /dev/null
@@ -1,103 +0,0 @@
-def main_PBEE_recovery(damage, damage_consequences, building_model, 
-                      tenant_units, systems, subsystems, tmp_repair_class, 
-                      impedance_options, impeding_factor_medians, 
-                      repair_time_options, functionality, 
-                      functionality_options):
-    '''Perform the ATC-138 functional recovery time assessement given similation
-    of component damage for a single shaking intensity
-    
-    Parameters
-    ----------
-    damage: struct
-      contains per damage state damage and loss data for each component in the building
-    
-    damage_consequences: dictionary
-      dictionary containing simulated building consequences, such as red
-      tags and repair costs ratios
-    
-    building_model: dictionary
-      general attributes of the building model
-   
-    tenant_units: DataFrame
-      attributes of each tenant unit within the building
-    
-    systems: DataFrame
-      data table containing information about each system's attributes
-    
-    subsystems: DataFrame
-      attributes of building subsystems; data provided in static tables
-      directory
-    
-    tmp_repair_class: DataFrame
-      data table containing information about each temporary repair class
-      attributes. Attributes are similar to those in the systems table.
-    
-    impedance_options: dictionary
-      general impedance assessment user inputs such as mitigation factors
-    
-    impeding_factor_medians: DataFrame
-      median delays for various impeding factors
-    
-    repair_time_options: dictionary
-      general repair time options such as mitigation factors
-    
-    functionality['utilities']
-      dictionary containing simulated utility downtimes
-    
-    functionality_options: dictionary
-      recovery time optional inputs such as various damage thresholds
-    
-    
-    Returns
-    -------
-    functionality: dictionary
-      contains data on the recovery of tenant- and building-level function, 
-      recovery trajectorires, and contributions from systems and components, 
-      simulated repair schedule breakdowns and impeding times.'''
-    
-    ## Import Packages
-    import numpy as np
-    from .preprocessing import main_preprocessing
-    from .red_tag import fn_red_tag
-    from .impedance import main_impedance_function
-    from .repair_schedule import main_repair_schedule
-    from .functionality import main_functionality_function
-    
-    ## Combine compoment attributes into recovery filters to expidite recovery assessment
-    damage, tmp_repair_class, damage_consequences = main_preprocessing.main_preprocessing(damage['comp_ds_table'], 
-                                                damage , repair_time_options, tmp_repair_class, damage_consequences, 
-                                                building_model['num_stories'])
-    
-    ## Calculate Red Tags
-    RT, RTI, IT = fn_red_tag(functionality_options['calculate_red_tag'], 
-                                    damage, building_model['comps'],
-                                    np.array(damage_consequences['simulated_replacement_time']),
-                                    functionality_options['red_tag_options'])
-    
-    damage_consequences['red_tag'] = RT 
-    damage_consequences['red_tag_impact'] = RTI 
-    damage_consequences['inspection_trigger'] = IT
-    
-    ## Simulate ATC 138 Impeding Factors
-    functionality['impeding_factors'] = main_impedance_function.main_impeding_factors(damage, impedance_options, 
-                                          damage_consequences['repair_cost_ratio_total'],
-                                          damage_consequences['repair_cost_ratio_engineering'], 
-                                          damage_consequences['inspection_trigger'],
-                                          systems, tmp_repair_class, 
-                                          building_model['building_value'], 
-                                          impeding_factor_medians,
-                                          functionality_options['include_flooding_impact'])
-    
-    ## Construct the Building Repair Schedule
-    damage, functionality['worker_data'], functionality['building_repair_schedule'] = main_repair_schedule.main_repair_schedule(damage, building_model, damage_consequences['red_tag'], 
-        repair_time_options, systems, tmp_repair_class, functionality['impeding_factors'], 
-        damage_consequences['simulated_replacement_time'])
-    
-    ## Calculate the Recovery of Building Reoccupancy and Function
-    functionality['recovery'] = main_functionality_function.main_functionality(damage, building_model, 
-                                damage_consequences, functionality['utilities'], 
-                                functionality_options, tenant_units, subsystems, 
-                                functionality['impeding_factors']['temp_repair'])
-    
-    return functionality, damage_consequences
-
diff --git a/src/atc138/functionality/__init__.py b/src/atc138/functionality/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/atc138/functionality/fn_calculate_functionality.py b/src/atc138/functionality/fn_calculate_functionality.py
deleted file mode 100644
index 27d808d..0000000
--- a/src/atc138/functionality/fn_calculate_functionality.py
+++ /dev/null
@@ -1,80 +0,0 @@
-
-def fn_calculate_functionality(damage, damage_consequences, utilities, 
-                               building_model, subsystems, reoccupancy, 
-                               functionality_options, tenant_units, impeding_temp_repairs):
-    '''Calcualte the loss and recovery of building functionality based on global building
-    damage, local component damage, and extenernal factors
-
-    Parameters
-    ----------
-    damage: dictionary
-     contains per damage state damage, loss, and repair time data for each 
-     component in the building
-    damage_consequences: dictionary
-     data structure containing simulated building consequences, such as red
-     tags and repair costs ratios
-    utilities: dictionary
-     data structure containing simulated utility downtimes
-    building_model: dictionary
-     general attributes of the building model
-    subsystems: DataFrame
-     data table containing information about each subsystem's attributes
-    reoccupancy: dictionary
-     contains data on the recovery of tenant- and building-level function, 
-     recovery trajectorires, and contributions from systems and components 
-    functionality_options: dictionary
-     recovery time optional inputs such as various damage thresholds
-    tenant_units: DataFrame
-     attributes of each tenant unit within the building
-    impeding_temp_repairs: dictionary
-     contains simulated temporary repairs the impede occuapancy and function
-     but are calulated in parallel with the temp repair schedule
-    
-    Returns
-    -------
-    functionality: dictionary
-     contains data on the recovery of tenant- and building-level function, 
-     recovery trajectorires, and contributions from systems and components''' 
-    
-    import numpy as np
-    
-    ## Initial Set Up
-    # import packages
-    from . import other_functionality_functions
-    
-    ## Define the day each system becomes functionl - Building level
-    system_operation_day = other_functionality_functions.fn_building_level_system_operation(damage, 
-                                                              damage_consequences,
-                                                              building_model, 
-                                                              utilities, 
-                                                              functionality_options)
-    
-    ## Define the day each system becomes functionl - Tenant level
-    recovery_day = {}
-    comp_breakdowns = {}
-    recovery_day['tenant_function'], comp_breakdowns['tenant_function'] = other_functionality_functions.fn_tenant_function(damage,
-        building_model, system_operation_day, subsystems, tenant_units, impeding_temp_repairs, functionality_options)
-    
-    ## Combine Checks to determine per unit functionality
-    # Each tenant unit is functional only if it is occupiable
-    day_tenant_unit_functional = reoccupancy['tenant_unit']['recovery_day']
-    
-    # Go through each of the tenant function branches and combines checks
-    fault_tree_events = list(recovery_day['tenant_function'].keys())
-    for i in range(len(fault_tree_events)):
-        day_tenant_unit_functional = np.fmax(day_tenant_unit_functional, recovery_day['tenant_function'][fault_tree_events[i]])
-    
-    ## Reformat outputs into functionality data strucutre
-    functional = other_functionality_functions.fn_extract_recovery_metrics(day_tenant_unit_functional,
-        recovery_day, comp_breakdowns, damage['comp_ds_table']['comp_id'], 
-        damage_consequences['simulated_replacement_time'])
-    
-    ## get the combined component breakdown between reoccupancy and function
-    functional['breakdowns']['component_combined'] = other_functionality_functions.fn_combine_comp_breakdown(damage['comp_ds_table'], 
-    functional['breakdowns']['perform_targ_days'], # assumes names are consistent in both objects
-    functional['breakdowns']['comp_names'], # assumes names are consistent in both objects
-    reoccupancy['breakdowns']['component_breakdowns_all_reals'],
-    functional['breakdowns']['component_breakdowns_all_reals'])
-    
-    return functional, recovery_day, comp_breakdowns
-
diff --git a/src/atc138/functionality/fn_calculate_reoccupancy.py b/src/atc138/functionality/fn_calculate_reoccupancy.py
deleted file mode 100644
index 836ee00..0000000
--- a/src/atc138/functionality/fn_calculate_reoccupancy.py
+++ /dev/null
@@ -1,97 +0,0 @@
-def fn_calculate_reoccupancy(damage, damage_consequences, utilities, 
-                         building_model, functionality_options, 
-                         tenant_units, impeding_temp_repairs):
-    '''Calcualte the loss and recovery of building re-occupancy 
-    based on global building damage, local component damage, and extenernal factors
-
-    Parameters
-    ----------
-    damage: dictionary
-      contains per damage state damage, loss, and repair time data for each 
-      component in the building
-    damage_consequences: dictionary
-      data structure containing simulated building consequences, such as red
-      tags and repair costs ratios
-    utilities: dictionary
-      data structure containing simulated utility downtimes
-    building_model: dictionary
-      general attributes of the building model
-    subsystems: DataFrame
-      attributes of building subsystems; data provided in static tables
-      directory
-    functionality_options: dictionary
-      recovery time optional inputs such as various damage thresholds
-    tenant_units: DataFrame
-      attributes of each tenant unit within the building
-    impeding_temp_repairs: dictionary
-     contains simulated temporary repairs the impede occuapancy and function
-     but are calulated in parallel with the temp repair schedule
-    
-    Returns
-    -------
-    reoccupancy: dictionary
-     contains data on the recovery of tenant- and building-level reoccupancy, 
-     recovery trajectorires, and contributions from systems and components''' 
-    
-    ## Initial Set Up
-    import numpy as np
-    # Import packages
-    
-    from . import other_functionality_functions
-        
-    ## Stage 1: Quantify the effect that component damage has on the building safety
-    recovery_day={}
-    comp_breakdowns={}
-    
-    recovery_day['building_safety'], comp_breakdowns['building_safety'] = other_functionality_functions.fn_building_safety(damage, building_model, 
-                                                                                                                    damage_consequences, utilities, functionality_options
-                                                                                                                    ,impeding_temp_repairs)
-    
-    ## Stage 2: Quantify the accessibility of each story in the building
-    recovery_day['story_access'], comp_breakdowns['story_access'] = other_functionality_functions.fn_story_access( damage, 
-                                                                                    building_model, damage_consequences, 
-                                                                                    functionality_options, impeding_temp_repairs)
-    
-    # Delete added door column to damage ['comps'] and damage[qnt_damaged]
-    if len(damage['tenant_units']) !=1: #FZ# Story is accessible on day zero for 1 story building
-        for i in range(len(damage['tenant_units'])):
-            # damage['tenant_units'][i]['num_comps'].pop(-1) 
-            damage['tenant_units'][i]['recovery']['repair_complete_day'] = damage['tenant_units'][i]['recovery']['repair_complete_day'][:,0:len(damage['comp_ds_table']['comp_id'])]
-            for j in range(len(damage['tenant_units'][0]['qnt_damaged'])):
-                damage['tenant_units'][i]['qnt_damaged'][j].pop(-1)
-        
-        damage['fnc_filters']['stairs'] = damage['fnc_filters']['stairs'][0:len(damage['comp_ds_table']['comp_id'])]   
-        damage['fnc_filters']['stair_doors'] = damage['fnc_filters']['stair_doors'][0:len(damage['comp_ds_table']['comp_id'])]
-    
-    ## Stage 3: Quantify the effect that component damage has on the safety of each tenant unit
-    recovery_day['tenant_safety'], comp_breakdowns['tenant_safety'] = other_functionality_functions.fn_tenant_safety( damage, building_model, functionality_options, tenant_units)
-    
-    ## Combine Check to determine the day the each tenant unit is reoccupiable
-    # Go through each of the building safety checks and combine them to check the day the building is safe (max of all checks)
-    fault_tree_events_building_safety = list(recovery_day['building_safety'].keys())
-    day_building_safe = recovery_day['building_safety'][fault_tree_events_building_safety[0]]
-    for i in range(1, len(fault_tree_events_building_safety)):
-        day_building_safe = np.fmax(day_building_safe, recovery_day['building_safety'][fault_tree_events_building_safety[i]])
-
-    # Go through each of the story access checks and combine them to check the day each story is accessible (max of all checks)
-    fault_tree_events_story_access = list(recovery_day['story_access'].keys())
-    day_story_accessible = recovery_day['story_access'][fault_tree_events_story_access[0]]
-    for i in range(1, len(fault_tree_events_story_access)):
-        day_story_accessible = np.fmax(day_story_accessible, recovery_day['story_access'][fault_tree_events_story_access[i]])
-    
-    # Go through each of the tenant unit safety checks and combine them to check the day each tenant unit is safe (max of all checks)
-    fault_tree_events_tenant_unit_safe = list(recovery_day['tenant_safety'].keys())
-    day_tenant_unit_safe = recovery_day['tenant_safety'][fault_tree_events_tenant_unit_safe[0]]
-    for i in range(1, len(fault_tree_events_tenant_unit_safe)):
-        day_tenant_unit_safe = np.fmax(day_tenant_unit_safe, recovery_day['tenant_safety'][fault_tree_events_tenant_unit_safe[i]])
-
-    # Combine checks to determine when each tenant unit is re-occupiable
-    day_tenant_unit_reoccupiable = np.fmax(np.fmax(day_building_safe.reshape(len(day_building_safe),1), day_story_accessible), day_tenant_unit_safe)
-    
-    ## Reformat outputs into occupancy data strucutre
-    reoccupancy = other_functionality_functions.fn_extract_recovery_metrics(day_tenant_unit_reoccupiable, 
-                                              recovery_day, comp_breakdowns, 
-                                              damage['comp_ds_table']['comp_id'],
-                                              damage_consequences['simulated_replacement_time'])
-
-    return reoccupancy, recovery_day, comp_breakdowns
\ No newline at end of file
diff --git a/src/atc138/functionality/fn_check_habitability.py b/src/atc138/functionality/fn_check_habitability.py
deleted file mode 100644
index 17da486..0000000
--- a/src/atc138/functionality/fn_check_habitability.py
+++ /dev/null
@@ -1,59 +0,0 @@
-def fn_check_habitability( damage, damage_consequences, reoc_meta, func_meta, 
-                          habitability_requirements):
-    '''Overwrite reocuppancy with additional checks from the functionality check
-    
-    Parameters
-    ----------
-    damage: dictionary
-      contains per damage state damage, loss, and repair time data for each 
-      component in the building
-    damage_consequences: dictionary
-      data structure containing simulated building consequences, such as red
-      tags and repair costs ratios
-    reoc_meta: dictionary
-      meta data from reoccupancy assessment
-    func_meta: dictionary
-      meta data from functionality assessment
-    habitability_requirements: dictionary
-      basic requirements for habitability beyond basic reoccupancy.
-    
-    Returns
-    -------
-    reoccupancy: dictionary
-      contains data on the recovery of tenant- and building-level reoccupancy, 
-      recovery trajectorires, and contributions from systems and components''' 
-    
-    import numpy as np
-    from . import other_functionality_functions
-    
-    num_reals = len(damage_consequences['red_tag'])
-    # Functionality checks to adopt onto reoccupancy requirements for
-    # habitability:
-        # heating, cooling, vent, exhaust, potable water, sanitary, electrical
-    recovery_day = reoc_meta['recovery_day']
-    comp_breakdowns = reoc_meta['comp_breakdowns']
-    habitability_list = list(habitability_requirements.keys())
-    # habitability_list = {'electrical', 'water_potable', 'water_sanitary', 'hvac_ventilation', 'hvac_heating', 'hvac_cooling', 'hvac_exhaust'};
-    for i in range(len(habitability_list)):
-        if habitability_requirements[habitability_list[i]] ==1: # If this system is required for habitability
-            recovery_day['habitability'][habitability_list[i]] = func_meta['recovery_day']['tenant_function'][habitability_list[i]]
-            comp_breakdowns['habitability'][habitability_list[i]] = func_meta['comp_breakdowns']['tenant_function'][habitability_list[i]]
-   
-    # Go through each of the tenant function branches and combines checks
-    day_tenant_unit_reoccupiable = np.zeros([num_reals,1])
-    fault_tree_events_LV1 = list(recovery_day.keys())
-    for i in range(len(fault_tree_events_LV1)):
-        fault_tree_events_LV2 = list(recovery_day[fault_tree_events_LV1[i]].keys())
-        for j in range(len(fault_tree_events_LV2)):
-            day_tenant_unit_reoccupiable = np.fmax(day_tenant_unit_reoccupiable, 
-                                               recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]].reshape(num_reals, int(np.size(recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]])/num_reals)))
-
-    
-    # Reformat outputs into reoccupancy data strucutre
-    reoccupancy = other_functionality_functions.fn_extract_recovery_metrics(day_tenant_unit_reoccupiable, 
-                                              recovery_day, comp_breakdowns, 
-                                              damage['comp_ds_table']['comp_id'],
-                                              damage_consequences['simulated_replacement_time'])
-    
-    return reoccupancy
-
diff --git a/src/atc138/functionality/main_functionality_function.py b/src/atc138/functionality/main_functionality_function.py
deleted file mode 100644
index ee492d0..0000000
--- a/src/atc138/functionality/main_functionality_function.py
+++ /dev/null
@@ -1,65 +0,0 @@
-def main_functionality(damage, building_model, damage_consequences, 
-                       utilities, functionality_options, tenant_units, 
-                       subsystems, impeding_temp_repairs):
-    '''Calculates building re-occupancy and function based on simulations of
-    building damage and calculates the recovery times of each recovery state
-    based on a given repair schedule
-    
-    Parameters
-    ----------
-    damage: dictionary
-     contains per damage state damage, loss, and repair time data for each 
-     component in the building
-    building_model: dictionary
-     general attributes of the building model
-    damage_consequences: dictionary
-     data structure containing simulated building consequences, such as red
-     tags and repair costs ratios
-    utilities: dictionary
-     data structure containing simulated utility downtimes
-    functionality_options: dictionary
-     recovery time optional inputs such as various damage thresholds
-    tenant_units: DataFrame
-     attributes of each tenant unit within the building
-    subsystems: DataFrame
-     attributes of building subsystems; data provided in static tables
-     directory
-    
-    Returns
-    -------
-    recovery['reoccupancy']: dictionary
-     contains data on the recovery of tenant- and building-level reoccupancy, 
-     recovery trajectorires, and contributions from systems and components 
-    recovery['functional']: dictionary
-     contains data on the recovery of tenant- and building-level function, 
-     recovery trajectorires, and contributions from systems and components''' 
-    
-    from . import fn_calculate_reoccupancy
-    from . import fn_calculate_functionality
-    from . import fn_check_habitability
-    ## Calaculate Building Functionality Restoration Curves
-    # Downtime including external delays
-    recovery = {}
-    reoc_meta = {}
-    recovery['reoccupancy'], reoc_meta['recovery_day'], reoc_meta['comp_breakdowns'] = fn_calculate_reoccupancy.fn_calculate_reoccupancy(damage, damage_consequences, utilities,
-        building_model, functionality_options, tenant_units, impeding_temp_repairs)
-    
-    func_meta = {}
-    recovery['functional'], func_meta['recovery_day'], func_meta['comp_breakdowns'] =  fn_calculate_functionality.fn_calculate_functionality(damage, damage_consequences, utilities,
-        building_model, subsystems, recovery['reoccupancy'], functionality_options, tenant_units, impeding_temp_repairs)
-    
-    # # delete all the extra per-realization data
-    del recovery['reoccupancy']['breakdowns']['component_breakdowns_all_reals']
-    del recovery['functional']['breakdowns']['component_breakdowns_all_reals']
-    
-    
-    ## Habitability Checks
-    # Overwrite reocuppancy with additional checks from the functionality check
-    if 'habitability_requirements' in functionality_options.keys():
-        recovery['reoccupancy'] = fn_check_habitability.fn_check_habitability(damage, 
-                                                        damage_consequences, 
-                                                        reoc_meta, func_meta, 
-                                                        functionality_options['habitability_requirements'])
- 
-    return recovery
-
diff --git a/src/atc138/functionality/other_functionality_functions.py b/src/atc138/functionality/other_functionality_functions.py
deleted file mode 100644
index 103fede..0000000
--- a/src/atc138/functionality/other_functionality_functions.py
+++ /dev/null
@@ -1,1688 +0,0 @@
-'''
-Other functionality functions
-'''
-
-def fn_building_safety(damage, building_model, damage_consequences, utilities,
-                       functionality_options, impeding_temp_repairs):
-    '''Check damage that would cause the whole building to be shut down due to
-     issues of safety
-    
-    Parameters
-    ----------
-    damage: dictionary
-     contains per damage state damage, loss, and repair time data for each 
-     component in the building
-    
-    damage_consequences: dictionary
-     data structure containing simulated building consequences, such as red
-     tags and repair costs ratios
-     
-    building_model: dictionary
-     general attributes of the building model
-     
-    utilities: dictionary
-     data structure containing simulated utility downtimes
-     
-    functionality_options: dictionary
-     recovery time optional inputs such as various damage thresholds
-    
-    impeding_temp_repairs: dictionary
-     contains simulated temporary repairs the impede occuapancy and function
-      but are calulated in parallel with the temp repair schedule
-    
-    Returns
-    -------
-    recovery_day: dictionary
-     simulation of the number of days each fault tree event is affecting building
-     safety
-     
-    comp_breakdowns: dictionary
-     simulation of each components contributions to each of the fault tree events 
-     
-    system_operation_day: dictionary
-     simulation of recovery of operation for various systems in the building'''
-    
-    import numpy as np
-    ## Initial Setup
-    num_reals = len(damage_consequences['red_tag'])
-    num_units = len(damage['tenant_units'])
-    num_comps = len(damage['comp_ds_table']['comp_id'])
-    
-    ## Calculate effect of red tags and fire suppression system
-    # Initialize parameters
-    recovery_day = {'red_tag' : np.zeros(num_reals), 
-                    'shoring' : np.zeros(num_reals), 
-                    'hazardous_material' : np.zeros(num_reals), 
-                    'fire_suppression' : np.zeros(num_reals)}
-    
-    # check if building has fire supprsion system
-    fs_exists = any(damage['fnc_filters']['fire_building'])
-    
- 
-    # Check damage throughout the building
-    comp_breakdowns = {'red_tag' : np.empty([num_reals,num_comps,num_units]),
-                       'shoring' : np.empty([num_reals,num_comps,num_units]),
-                       'fire_suppression' : np.empty([num_reals,num_comps,num_units])
-                       }
-    
-
-    for tu in range(num_units):
-        # Grab tenant and damage info for this tenant unit
-        repair_complete_day = damage['tenant_units'][tu]['recovery']['repair_complete_day'].copy()
-        repair_complete_day_w_tmp = damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp'].copy()
-        
-        is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0, np.array(damage['tenant_units'][tu]['worker_days']) > 0)
-        ## Red Tags
-        # The day the red tag is resolved is the day when all damage (anywhere in building) that has
-        # the potential to cause a red tag is fixed (ie max day)
-        if any(damage['fnc_filters']['red_tag']):
-            recovery_day['red_tag'] = np.fmax(recovery_day['red_tag'],
-                                          np.array(damage_consequences['red_tag'])*np.nanmax(repair_complete_day[:,damage['fnc_filters']['red_tag']], axis=1))
-   
-        # Component Breakdowns
-        
-        comp_breakdowns['red_tag'][:,:,tu] = recovery_day['red_tag'].reshape(num_reals,1) * damage_consequences['red_tag_impact']
-        
-        
-        ## Local Shoring
-        if np.logical_and(any(damage['fnc_filters']['requires_shoring']), functionality_options['include_local_stability_impact']):
-            #Any unresolved damao documentatige (temporary or otherwise) that requires shoring,
-            # blocks occupancy to the whole building
-            recovery_day['shoring'] = np.fmax(recovery_day['shoring'],
-                                      np.nanmax(repair_complete_day_w_tmp[:,damage['fnc_filters']['requires_shoring']], axis=1)) 
-        
-            # Componet Breakdowns (the time it takes to shore or fully repair each
-            # component is the time it blocks occupancy for)
-            comp_breakdowns['shoring'][:,:,tu] = damage['fnc_filters']['requires_shoring'] * repair_complete_day_w_tmp * is_damaged
-
-    
-    
-        ## Day the fire suppression system is operating again (for the whole building)
-        if np.logical_not(functionality_options['fire_watch']) and fs_exists:
-            # any major damage fails the system for the whole building so take the max
-            recovery_day['fire_suppression'] = np.fmax(recovery_day['fire_suppression'], np.amax(repair_complete_day[:,damage['fnc_filters']['fire_building']], axis=1))
-    
-            # Consider utilities (assume no backup water supply)
-            recovery_day['fire_suppression'] = np.fmax(recovery_day['building']['fire'], np.array(utilities['water'])) # Assumes building does not have backup water supply
-        
-            # Componet Breakdowns
-            comp_breakdowns['fire_suppression'][:,:,tu] = damage['fnc_filters']['fire_building'] * repair_complete_day
-
-
-        ## Hazardous Materials
-        # note: hazardous materials are accounted for in building functional
-        #assessment here, but are not currently quantified in the component
-        # breakdowns
-        if any(damage['fnc_filters']['global_hazardous_material']):
-            # Any global Nohazardous material shuts down the entire building
-            recovery_day['hazardous_material'] = np.fmax(recovery_day['hazardous_material'], np.amax(repair_complete_day[:,damage['fnc_filters']['global_hazardous_material']], axis=1))
-
-    
-    ## Building Egress
-    '''Calculate when falling hazards or racking of doors affects the building
-    safety due to limited entrance and exit door access'''
-    
-    '''Simulate a random location of the doors on two sides of the building for
-    each realization. Location is defined at the center of the door as a
-    fraction of the building width on that side
-    This is acting a random p value to determine if the unknown location of
-    the door is within the falling hazard zone'''
-    
-    door_location = np.random.rand(num_reals , building_model['num_entry_doors'])
-    
-    # Assign odd doors to side 1 and even doors to side two
-    door_numbers = np.linspace(1,building_model['num_entry_doors'], building_model['num_entry_doors'])
-    door_side = np.ones(building_model['num_entry_doors'])
-    door_side[np.remainder(door_numbers, 2) == 0] = 2
-    door_side = door_side.astype('int')
-    
-    # Determine the quantity of falling hazard damage and when it will be resolved
-    day_repair_fall_haz = np.zeros([num_reals,building_model['num_entry_doors']])
-    fall_haz_comps_day_rep = np.zeros([num_reals,num_comps,num_units,building_model['num_entry_doors']])
-    comp_affected_lf = np.zeros([num_reals,num_comps,num_units])
-    scaffold_filt = damage['comp_ds_table']['resolved_by_scaffolding'].astype(bool)
-    
-    repair_complete_day_w_tmp = np.empty([num_reals,num_comps,num_units])
-    for tu in range(num_units):
-        tmp_or_full_complete_day = damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp'].copy()
-
-        '''Effect of falling hazards on building safety are resolved either by
-        full repair, local temp repair, or erecting scaffolding. Whatever
-        occurs first'''
-        isdamaged = 1*(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0).astype('float')
-        isdamaged[isdamaged == 0] = np.nan # mark undamaged cases as NaN to help combine factors below
-        
-        scaffold_day = impeding_temp_repairs['scaffold_day'].reshape(num_reals,1) *  isdamaged[:,scaffold_filt]
-        complete_day_w_scaffold = tmp_or_full_complete_day
-        complete_day_w_scaffold[:,scaffold_filt] = np.fmin(tmp_or_full_complete_day[:,scaffold_filt], scaffold_day)
-        repair_complete_day_w_tmp[:,:,tu] = complete_day_w_scaffold
-
-    # Loop through component repair times to determine the day it stops affecting re-occupancy
-    num_repair_time_increments = np.sum(damage['fnc_filters']['ext_fall_haz_all'])*num_units # possible unique number of loop increments
-    edge_lengths = np.row_stack((np.array(building_model['edge_lengths']), np.array(building_model['edge_lengths'])))
-    
-    affected_ratio={}
-    for side in range(4):
-        affected_ratio['side_'+str(side+1)]=np.zeros([num_reals, num_units]) #FZ# Initiated with zeros. Check later if it works
-    for i in range(num_repair_time_increments):
-        # Calculate the falling hazards per side
-        for tu in range(num_units):
-            for side in range(4): # assumes there are 4 sides
-                lf_affected_direct_scale_all_comps = damage['comp_ds_table']['exterior_falling_length_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged_side_' + str(side+1)]  #FZ# +1 done to account for python indexing starting with 0.       
-                lf_affected_sf_all_comps = damage['comp_ds_table']['exterior_falling_length_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged_side_' + str(side+1)] / building_model['ht_per_story_ft'][tu]
-                
-                comp_affected_lf[:,damage['fnc_filters']['ext_fall_haz_lf'],tu] = lf_affected_direct_scale_all_comps[:,damage['fnc_filters']['ext_fall_haz_lf']]
-                comp_affected_lf[:,damage['fnc_filters']['ext_fall_haz_sf'],tu] = lf_affected_sf_all_comps[:,damage['fnc_filters']['ext_fall_haz_sf']]
-                comp_affected_lf[:,damage['fnc_filters']['ext_fall_haz_ea'],tu] = lf_affected_direct_scale_all_comps[:,damage['fnc_filters']['ext_fall_haz_ea']]    
-                
-                comp_affected_ft_this_story = comp_affected_lf[:,:,tu].copy()
-                affected_ft_this_story = np.fmin(np.sum(comp_affected_ft_this_story,axis = 1), edge_lengths[side,tu]) # Assumes cladding components do not occupy the same perimeter space
-                
-                affected_ratio['side_'+str(side+1)][:,tu] = np.fmin((affected_ft_this_story)/ edge_lengths[side,tu],1)
-
-
-        # Calculate the time increment for this loop
-        delta_day = np.nanmin(np.nanmin(repair_complete_day_w_tmp[:,damage['fnc_filters']['ext_fall_haz_all'],:],axis =2), axis=1)
-        delta_day[np.isnan(delta_day)] = 0
-        if sum(delta_day) == 0:
-            break # everything has been fixed
-        
-        
-        # Go through each door to determine which is affected by falling
-        # hazards
-        for d in range(building_model['num_entry_doors']):
-            # Combine affected areas of all stories above the first using SRSS
-            # HARDCODED ASSUMPTIONS: DOORS ONLY ON TWO SIDES
-            fall_haz_zone = np.fmin(np.sqrt(np.sum(affected_ratio['side_'+str(door_side[d])][:,1:num_units]**2, axis=1)),1)
-    
-            '''Augment the falling hazard zone with the door access zone
-            add the door access width to the width of falling hazards to account
-            for the width of the door (ie if any part of the door access zone is
-            under the falling hazard, its a problem)'''
-            door_access_zone = functionality_options['door_access_width_ft'] / building_model['edge_lengths'][door_side[d]-1][0] #FZ# -1 done to account for python indexing starting from zero
-            total_fall_haz_zone = fall_haz_zone + 2*door_access_zone # this is approximating the probability the door is within the falling hazard zone
-    
-            '''Determine if current damage affects occupancy
-            if the randonmly simulated door location is with falling hazard zone'''
-            affects_door = door_location[:,door_side[d]-1] < total_fall_haz_zone #FZ# -1 done to account for python indexing starting from zero
-    
-            # Add days in this increment to the tally
-            day_repair_fall_haz[:,d] = day_repair_fall_haz[:,d] + affects_door * delta_day
-    
-            # Add days to components that are affecting occupancy
-            # fall_haz_comps_day_rep[:,:,:,d] = fall_haz_comps_day_rep[:,:,:,d] + ((1 * comp_posing_falling_hazard).transpose(2,0,1) * (affects_door * delta_day).reshape(num_reals,1)).transpose(1,2,0) #FZ# Transpose and reshape done to align nd arrays
-            fall_haz_comps_day_rep[:,:,:,d] = fall_haz_comps_day_rep[:,:,:,d] + ((comp_affected_lf > 0 * damage['fnc_filters']['ext_fall_haz_all'].reshape(num_comps,1)).transpose(2,0,1) * (affects_door * delta_day).reshape(num_reals,1)).transpose(1,2,0) #FZ# Transpose and reshape done to align nd arrays      
-        
-        # Change the comps for the next increment
-        repair_complete_day_w_tmp = (repair_complete_day_w_tmp.transpose(2,0,1) - delta_day.reshape(num_reals,1)).transpose(1,2,0)
-        repair_complete_day_w_tmp[repair_complete_day_w_tmp <= 0] = np.nan
-
-    
-    # Determine when racked doors are resolved
-    day_repair_racked = np.zeros([num_reals, building_model['num_entry_doors']])
-    side_1_count = 0
-    side_2_count = 0
-    for d in range(building_model['num_entry_doors']):
-        if door_side[d] == 1:
-            side_1_count = side_1_count + 1
-            day_repair_racked[:,d] = impeding_temp_repairs['door_racking_repair_day'] * (np.array(damage_consequences['racked_entry_doors_side_1']) >= side_1_count)
-        else:
-            side_2_count = side_2_count + 1
-            day_repair_racked[:,d] = impeding_temp_repairs['door_racking_repair_day'] * (np.array(damage_consequences['racked_entry_doors_side_2']) >= side_2_count)
-
-    door_access_day = np.fmax(day_repair_racked, day_repair_fall_haz)
-    
-    '''Find the days until door egress is regained from resolution of both
-    falling hazards or door racking'''
-    cum_days = np.zeros(num_reals)
-    entry_door_access_day = np.zeros(num_reals)
-    door_access_day_nan = door_access_day.copy() #FZ# Be cautious! Numpy arrays are mutable
-    door_access_day_nan[door_access_day_nan == 0] = np.nan
-    num_repair_time_increments = np.array(building_model['num_entry_doors']) # possible unique number of loop increments
-    for i in range(num_repair_time_increments):
-        num_accessible_doors = np.sum(door_access_day <= cum_days.reshape(num_reals,1), axis=1)
-        entry_door_accessible = num_accessible_doors >= max(functionality_options['min_egress_paths'], functionality_options['egress_threshold'] * building_model['num_entry_doors'])
-        # Bean counting for this iteration
-        delta_day = np.nanmin(door_access_day_nan, axis=1);
-        delta_day[np.isnan(delta_day)] = 0
-        door_access_day_nan = door_access_day_nan - delta_day.reshape(num_reals,1)
-        cum_days = cum_days + delta_day
-        
-        # Save recovery time increments
-        entry_door_access_day = entry_door_access_day + delta_day * np.logical_not(entry_door_accessible)
-        
-    # Save recovery day values
-    recovery_day['entry_door_access'] = entry_door_access_day
-  
-    # Determine when Exterior Falling Hazards or doors actually contribute to re-occupancy
-    recovery_day['falling_hazard'] = np.fmin(recovery_day['entry_door_access'], np.nanmax(day_repair_fall_haz, axis=1))
-    recovery_day['entry_door_racking'] = np.fmin(recovery_day['entry_door_access'], np.nanmax(day_repair_racked,axis=1))
-    
-    # Component Breakdown
-    comp_breakdowns['falling_hazard'] = (np.fmin(recovery_day['entry_door_access'], (np.nanmax(fall_haz_comps_day_rep, axis=3)).transpose(2,1,0))).transpose(2,1,0) #FZ# Transpose done to align the nd array fopr operation
-    
-    
-    ## Fire Safety
-    if np.logical_not(functionality_options['fire_watch']) and fs_exists:
-        comp_breakdowns_local_fire = np.zeros([num_reals,num_comps,num_units])
-        fire_safety_day = np.zeros([num_reals,num_units]) # Day the local fire sprinkler system becomes operational
-        filt_fs_drop = damage['fnc_filters']['fire_drops']
-        filt_fs_branch = damage['fnc_filters']['fire_unit']
-        for tu in range(num_units):
-            repair_complete_day = damage['tenant_units'][tu]['recovery']['repair_complete_day'].copy()
-            repair_complete_day[repair_complete_day == 0] = np.nan # Make sure zero repair days are NaN
-            damaged_comps = damage['tenant_units'][tu]['qnt_damaged']
-            num_drops = max(np.array(damage['tenant_units'][tu]['num_comps'])* filt_fs_drop) # Assumes drops are all in one performance group
-            num_branches = max(np.array(damage['tenant_units'][tu]['num_comps']) * filt_fs_branch) # Assumes branches are all in one performance group
-            
-            if (num_drops + num_branches) > 0: # If there are any of these components on in this tenant unit
-                # Loop through component repair times to determine the day it stops affecting re-occupanc
-                num_repair_time_increments = sum(filt_fs_drop | filt_fs_branch) # possible unique number of loop increments
-                
-                for i in range(num_repair_time_increments):
-                    # Calculate the ratio of damaged drops and branches on this story
-                    ratio_damaged_drop = np.sum(damaged_comps * filt_fs_drop, axis=1) / num_drops # assumes comps are not simeltaneous
-                    ratio_damaged_branch = np.sum(damaged_comps * filt_fs_branch, axis=1) / num_branches # assumes comps are not simeltaneous
-    
-                    # Determine if fire drops and branches are adequately operating
-                    fire_drop_operational = functionality_options['local_fire_damamge_threshold'] >= ratio_damaged_drop
-                    fire_branch_operational = functionality_options['local_fire_damamge_threshold'] >= ratio_damaged_branch
-                    local_fire_operational = fire_drop_operational & fire_branch_operational
-                    
-                    # Add days in this increment to the tally
-                    delta_day = np.nanmin(repair_complete_day[:,filt_fs_drop], axis=1);
-                    delta_day[np.isnan(delta_day)] = 0
-                    fire_safety_day[:,tu] = fire_safety_day[:,tu] + np.logical_not(local_fire_operational) * delta_day
-    
-                    # Add days to components that are affecting occupancy
-                    contributing_drops = ((damaged_comps * filt_fs_drop) > 0)  * np.logical_not(fire_drop_operational).reshape(num_reals,1) #count all components that contributed to non operational fire drops
-                    contributing_branches = ((damaged_comps * filt_fs_drop) > 0)  * np.logical_not(fire_branch_operational).reshape(num_reals,1) # count all components that contributed to non operational fire drops
-                    contributing_comps = np.amax(contributing_drops, contributing_branches)
-                    comp_breakdowns_local_fire[:,:,tu] = comp_breakdowns_local_fire[:,:,tu] + contributing_comps * delta_day.reshape(num_reals,1)
-    
-                    # Change the comps for the next increment
-                    repair_complete_day = repair_complete_day - delta_day.reshape(num_reals,1)
-                    repair_complete_day[repair_complete_day <= 0] = np.nan
-                    fixed_comps_filt = np.isnan(repair_complete_day)
-                    np.array(damaged_comps)[fixed_comps_filt] = 0
-  
-        # Inoperable fire drips on any story shuts down entire building
-        fire_safety_day_building = np.nanmax(fire_safety_day, axis=1)
-        
-        # Combine parts of fire suppression system
-        recovery_day['fire_suppression'] = np.fmax(recovery_day['fire_suppression'], fire_safety_day_building)
-
-        comp_breakdowns['fire_suppression'] = np.fmax(comp_breakdowns['fire_suppression'],comp_breakdowns_local_fire)
-    
-    ## Delay Red Tag recovery by the time it takes to clear the tag
-    sim_red_tag_clear_time = np.ceil(np.random.lognormal(np.log(functionality_options['red_tag_clear_time']),
-                                                                functionality_options['red_tag_clear_beta'], num_reals))
-    recovery_day['red_tag'] = recovery_day['red_tag'] + sim_red_tag_clear_time * damage_consequences['red_tag']
-    comp_breakdowns['red_tag'] = comp_breakdowns['red_tag'] + (sim_red_tag_clear_time.reshape(num_reals,1) * (comp_breakdowns['red_tag'] > 0).transpose(2,0,1)).transpose(1,2,0)
-    
-    return recovery_day, comp_breakdowns
-
-
-def fn_story_access(damage, building_model, damage_consequences, 
-                    functionality_options, impeding_temp_repairs):
-    
-    '''Check each story for damage that would cause that story to be shut down due to
-    issues of access
-    
-    Parameters
-    ----------
-    damage: dictionary
-     contains per damage state damage, loss, and repair time data for each 
-     component in the building
-     
-    building_model: dictionary
-     general attributes of the building model
-     
-    damage_consequences: dictionary
-     data structure containing simulated building consequences, such as red
-     tags and repair costs ratios
-     
-    functionality_options: dictionary
-     recovery time optional inputs such as various damage thresholds
-    
-    impeding_temp_repairs: dictionary
-     contains simulated temporary repairs the impede occuapancy and function
-     but are calulated in parallel with the temp repair schedule
-    
-    Returns
-    -------
-    recovery_day: dictionary
-     simulation of the number of days each fault tree event is affecting access
-     in each story
-     
-    comp_breakdowns: dictionary
-     simulation of each components contributions to each of the fault tree events''' 
-    
-    import numpy as np
-    
-    ## Initial Setup
-    num_reals = len(damage_consequences['red_tag'])
-    num_units = len(damage['tenant_units'])
-    num_stories = len(damage['tenant_units'])
-    num_comps = len(damage['comp_ds_table']['comp_id'])
-    
-    # Pre-allocate data
-    recovery_day={}
-    comp_breakdowns={}
-    recovery_day['stairs'] = np.zeros([num_reals,num_units])
-    recovery_day['stair_doors'] = np.zeros([num_reals,num_units])
-    recovery_day['flooding'] = np.zeros([num_reals,num_units])
-    recovery_day['horizontal_egress'] = np.zeros([num_reals,num_units])
-    comp_breakdowns['stairs'] = np.zeros([num_reals,num_comps,num_units])
-    comp_breakdowns['flooding'] = np.zeros([num_reals,num_comps,num_units])
-    comp_breakdowns['horizontal_egress'] = np.zeros([num_reals,num_comps,num_units])
-    
-    
-    ## Horizontal Egress - Fire breaks
-    if any(damage['fnc_filters']['fire_break']):
-        for tu in range(num_stories):
-            # Grab tenant and damage info for this tenant unit
-            repair_complete_day_w_tmp = damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp'].copy()
-    
-            # Any significant damage to fire breaks in the story impairs the horizontal egress
-            recovery_day['horizontal_egress'][:,tu] = np.fmax(recovery_day['horizontal_egress'][:,tu], np.nanmax(repair_complete_day_w_tmp[:, damage['fnc_filters']['fire_break']], axis=1))
-    
-            # Componet Breakdowns
-            comp_breakdowns['horizontal_egress'][:,:,tu] = damage['fnc_filters']['fire_break'].reshape(1,num_comps) * repair_complete_day_w_tmp
-
-     
-    ## STORY FLOODING
-    for tu in reversed(range(num_stories)): # Go from top to bottom
-        is_damaged = np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0
-        flooding_this_story = np.any(is_damaged[:,damage['fnc_filters']['causes_flooding']], axis=1); # Any major piping damage causes interior flooding
-        flooding_cleanup_day = flooding_this_story * impeding_temp_repairs['flooding_cleanup_day']
-    
-        # Save clean up time per component causing flooding
-        comp_breakdowns['flooding'][:,:,tu] = damage['fnc_filters']['causes_flooding'].reshape(1,num_comps) * is_damaged * flooding_cleanup_day.reshape(num_reals,1)
-    
-        # This story is not accessible if any story above has flooding
-        recovery_day['flooding'][:,tu] = np.nanmax(np.column_stack((flooding_cleanup_day, recovery_day['flooding'][:,(tu+1):num_units])), axis=1)
-
-    # Go through each story and check if there is sufficient story access (stairs and stairdoors)
-    ## STAIRS AND STAIRDOORS
-    if num_stories == 1: 
-        return recovery_day, comp_breakdowns # Re-occupancy of one story buildigns is not affected by stairway access
-
-
-    # Augment damage filters with door data
-    damage['fnc_filters']['stairs'] = np.append(damage['fnc_filters']['stairs'], np.array([False]))
-    damage['fnc_filters']['stair_doors'] = np.append(np.zeros(num_comps), 1).astype(dtype=bool)
-    
-    ## Go through each story and check if there is sufficient story access (stairs and stairdoors)
-    # if stairs don't exist on a story, this will assume they are rugged (along with the stair doors)
-    for tu in range(num_stories):
-        # Augment damage matrix with door data
-        racked_stair_doors = np.fmin(np.array(damage_consequences['racked_stair_doors_per_story'])[:,tu], building_model['stairs_per_story'][tu])
-        damage['tenant_units'][tu]['qnt_damaged'] = (np.column_stack((np.array(damage['tenant_units'][tu]['qnt_damaged']), racked_stair_doors))).tolist() #FZ# Converted back to alist to keep it consistent with other objects in the dictionary damage['tenant_units'][tu]
-        door_repair_day = 1*(racked_stair_doors > 0) * impeding_temp_repairs['door_racking_repair_day']
-        damage['tenant_units'][tu]['recovery']['repair_complete_day'] = np.column_stack((damage['tenant_units'][tu]['recovery']['repair_complete_day'], door_repair_day))
-    
-        # Quantify damaged stairs on this story
-        repair_complete_day = damage['tenant_units'][tu]['recovery']['repair_complete_day'].copy()
-        damaged_comps = np.array(damage['tenant_units'][tu]['qnt_damaged']).copy()
-        
-        # Make sure zero repair days are NaN
-        repair_complete_day[repair_complete_day == 0] = np.nan
-    
-        '''Step through each unique component repair time and determine when
-        stairs stop affecting story access'''
-        stair_access_day = np.zeros(num_reals) # day story becomes accessible from repair of stairs
-        stairdoor_access_day = np.zeros(num_reals) # day story becomes accessible from repair of doors
-        filt_all = damage['fnc_filters']['stairs'] | damage['fnc_filters']['stair_doors']
-        num_repair_time_increments = sum(filt_all) # possible unique number of loop increments
-        for i in range(num_repair_time_increments):
-            # number of functioning stairs
-            num_dam_stairs = np.sum(damaged_comps * damage['fnc_filters']['stairs'], axis=1) # assumes comps are not simeltaneous
-            num_racked_doors = np.sum(damaged_comps * damage['fnc_filters']['stair_doors'], axis=1) # assumes comps are not simeltaneous
-            functioning_stairs = building_model['stairs_per_story'][tu] - num_dam_stairs
-            functioning_stairdoors = building_model['stairs_per_story'][tu] - num_racked_doors
-    
-            # Required egress with and without operational fire suppression system
-            required_stairs = max(functionality_options['min_egress_paths'] ,functionality_options['egress_threshold'] * building_model['stairs_per_story'][tu])
-    
-            # Determine Stair Access
-            sufficient_stair_access = functioning_stairs >= required_stairs
-
-            # Determine Stair Door Acces
-            sufficient_stairdoor_access = functioning_stairdoors >= required_stairs
- 
-            # Add days in this increment to the tally
-            delta_day = np.nanmin(repair_complete_day[:,filt_all], axis=1)
-            delta_day[np.isnan(delta_day)] = 0
-            stair_access_day = stair_access_day + np.logical_not(sufficient_stair_access)* delta_day
-            stairdoor_access_day = stairdoor_access_day + np.logical_not(sufficient_stairdoor_access) * delta_day
-    
-            # Add days to components that are affecting occupancy
-            contributing_stairs = ((damaged_comps * damage['fnc_filters']['stairs'] > 0) * np.logical_not(sufficient_stair_access.reshape(len(sufficient_stair_access),1))) # Count any damaged stairs for realization that have loss of story access
-            contributing_stairs = np.delete(contributing_stairs, -1, 1) # remove added door column
-            comp_breakdowns['stairs'][:,:,tu] = comp_breakdowns['stairs'][:,:,tu] + contributing_stairs * delta_day.reshape(len(delta_day),1)
-    
-            # Change the comps for the next increment
-            repair_complete_day = repair_complete_day - delta_day.reshape(len(delta_day),1)
-            repair_complete_day[repair_complete_day <= 0] = np.nan
-            fixed_comps_filt = np.isnan(repair_complete_day)
-            damaged_comps[fixed_comps_filt] = 0
-           
-        ## This story is not accessible if this or any story below has insufficient stair egress
-        recovery_day['stairs'][:,tu] = np.nanmax(np.column_stack((stair_access_day, recovery_day['stairs'][:,0:(tu)])), axis=1)
-        
-        # For the third story and above, the story below is not accessible if
-        # there is insufficient stair egress at this story
-        if tu >= 2:
-            recovery_day['stairs'][:,(tu-1)] = np.nanmax(np.column_stack((stair_access_day, recovery_day['stairs'][:,(tu-1)])), axis=1)
-    
-        # Damage to doors only affects this story
-        recovery_day['stair_doors'][:,tu] = stairdoor_access_day
-   
-    return recovery_day, comp_breakdowns
-
-
-def fn_tenant_safety( damage, building_model, functionality_options, 
-                     tenant_units):
-    '''Check each tenant unit for damage that would cause that tenant unit 
-    to be shut down due to issues of locay safety
-    
-    Parameters
-    ----------
-    damage: dictionary
-     contains per damage state damage, loss, and repair time data for each 
-     component in the building
-     
-    building_model: dictionary
-     general attributes of the building model
-     
-    functionality_options: dictionary
-     recovery time optional inputs such as various damage thresholds
-     
-    tenant_units: DataFrame
-     attributes of each tenant unit within the building
-    
-    Returns
-    -------
-    recovery_day: dictionary
-     simulation of the number of days each fault tree event is affecting safety
-     in each tenant unit
-    
-    comp_breakdowns: dictionary
-     simulation of each components contributions to each of the fault tree events''' 
-    
-    import numpy as np
-    
-    ##Initial Setup
-    num_reals, num_comps = np.shape(damage['tenant_units'][0]['qnt_damaged'])
-    num_units = len(damage['tenant_units'])
-    comp_types_interior_check = np.unique(damage['comp_ds_table']['comp_type_id'][damage['fnc_filters']['int_fall_haz_all']])
-    
-    recovery_day={}
-    recovery_day['exterior'] = np.zeros([num_reals, num_units])
-    recovery_day['interior'] = np.zeros([num_reals, num_units])    
-    recovery_day['hazardous_material'] = np.zeros([num_reals, num_units])     
-    
-    comp_breakdowns = {}
-    comp_breakdowns['exterior'] = np.empty([num_reals,num_comps,num_units])
-    comp_breakdowns['interior'] = np.empty([num_reals,num_comps,num_units])
-    
-    # go through each tenant unit and quantify the affect that each system has on reoccpauncy
-    for tu in range(num_units):
-        # Grab tenant and damage info for this tenant unit
-        unit={}
-        for key in list(tenant_units.keys()):
-            unit[key] = tenant_units[key][tu]
-        repair_complete_day_w_tmp = tuple(map(tuple,(damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp']))) # day each component (and DS) is reparied of this TU
-        #FZ# Made tuple to bypass the issue of mutable numpy array
-        '''#Exterior Enclosure 
-           Calculated the affected perimeter area of exterior components
-           (assuming all exterior components have either lf or sf units)'''
-        area_affected_all_linear_comps = damage['comp_ds_table']['exterior_surface_area_factor'] * damage['comp_ds_table']['unit_qty'] * building_model['ht_per_story_ft'][tu] * damage['tenant_units'][tu]['qnt_damaged']
-        area_affected_all_direct_scale_comps = damage['comp_ds_table']['exterior_surface_area_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged']
-        
-        # construct a matrix of affected areas from the various damaged component types
-        comp_affected_area = np.zeros([num_reals,num_comps])
-        comp_affected_area[:,damage['fnc_filters']['exterior_safety_lf']] = area_affected_all_linear_comps[:,damage['fnc_filters']['exterior_safety_lf']]
-        comp_affected_area[:,damage['fnc_filters']['exterior_safety_sf']] = area_affected_all_direct_scale_comps[:,damage['fnc_filters']['exterior_safety_sf']]
-        
-        '''Go each possible unique repair time contributing to interior safety check
-           Find when enough repairs are complete such that interior damage no
-           longer affects tenant safety'''
-        comps_day_repaired = repair_complete_day_w_tmp # define as initial repair day considering tmp repairs
-        comps_day_repaired = np.array(comps_day_repaired)
-        ext_repair_day = np.zeros(num_reals)
-        all_comps_day_repaired = np.zeros([num_reals,num_comps])
-        num_repair_time_increments = np.sum(damage['fnc_filters']['exterior_safety_all']) # possible unique number of loop increments
-        for i in range(num_repair_time_increments):
-            
-            # Quantify Affected Area
-            area_affected = np.sum(comp_affected_area, axis=1) # Assumes cladding components do not occupy the same perimeter area
-            percent_area_affected = area_affected / unit['perim_area'] #FZ# Fraction area
-    
-            # Check if this is sufficent enough to cause as tenant safety issue
-            affects_occupancy = percent_area_affected > functionality_options['exterior_safety_threshold']
-    
-            # Determine step increment based on the component with the shortest repair time
-            delta_day = np.nanmin(comps_day_repaired[:,damage['fnc_filters']['exterior_safety_all']], axis=1)
-            delta_day[np.isnan(delta_day)] = 0
-            
-            # Add increment to the tally of days until the interior damage stops affecting occupancy
-            ext_repair_day = ext_repair_day + affects_occupancy * delta_day
-            
-            # Add days to components that are affecting occupancy
-            any_area_affected_all_comps = (damage['fnc_filters']['exterior_safety_all'] * comp_affected_area) > 0 # Count any component that contributes to the loss of occupancy regardless of by how much
-            all_comps_day_repaired = all_comps_day_repaired + any_area_affected_all_comps * affects_occupancy.reshape(num_reals,1) * delta_day.reshape(num_reals,1)
-            
-            # Reduce compent damaged for the next increment based on what was repaired in this increment
-            comps_day_repaired = comps_day_repaired - delta_day.reshape(num_reals,1)
-            comps_day_repaired[comps_day_repaired <= 0] = np.nan
-            fixed_comps_filt = np.isnan(comps_day_repaired)
-            comp_affected_area[fixed_comps_filt] = 0
-
-        
-        # Save exterior recovery day for this tenant unit
-        recovery_day['exterior'][:,tu] = ext_repair_day
-        comp_breakdowns['exterior'][:,:,tu] = all_comps_day_repaired
-        
-        ## Interior Falling Hazards
-        # Convert all component into affected areas
-        area_affected_all_linear_comps = damage['comp_ds_table']['interior_area_factor'] * damage['comp_ds_table']['unit_qty'] * building_model['ht_per_story_ft'][tu] * damage['tenant_units'][tu]['qnt_damaged']
-        area_affected_all_direct_scale_comps   = damage['comp_ds_table']['interior_area_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged']
-        area_affected_all_bay_comps    = damage['comp_ds_table']['interior_area_factor'] * building_model['struct_bay_area_per_story'][tu] * damage['tenant_units'][tu]['qnt_damaged']
-        area_affected_all_build_comps  = damage['comp_ds_table']['interior_area_factor'] * sum(building_model['area_per_story_sf']) * damage['tenant_units'][tu]['qnt_damaged']
-        
-        # Checking damage that affects components in story below
-        repair_complete_day_w_tmp_w_instabilities = repair_complete_day_w_tmp
-        repair_complete_day_w_tmp_w_instabilities = np.array(repair_complete_day_w_tmp_w_instabilities) #FZ# coverted to array from tuple
-        if tu > 0: #FZ# changed to zero to account for python indexing starting from 0.
-            area_affected_below = damage['comp_ds_table']['interior_area_factor'] * building_model['struct_bay_area_per_story'][tu-1] * damage['tenant_units'][tu-1]['qnt_damaged']
-            area_affected_all_bay_comps[:,damage['fnc_filters']['vert_instabilities']] = np.fmax(area_affected_below[:,damage['fnc_filters']['vert_instabilities']], area_affected_all_bay_comps[:,damage['fnc_filters']['vert_instabilities']])
-            repair_time_below = damage['tenant_units'][tu-1]['recovery']['repair_complete_day_w_tmp']
-            repair_complete_day_w_tmp_w_instabilities[:,damage['fnc_filters']['vert_instabilities']] = np.fmax(repair_time_below[:,damage['fnc_filters']['vert_instabilities']],np.array(repair_complete_day_w_tmp)[:,damage['fnc_filters']['vert_instabilities']])
-        
-    
-        # construct a matrix of affected areas from the various damaged component types
-        comp_affected_area = np.zeros([num_reals,num_comps])
-        comp_affected_area[:,damage['fnc_filters']['int_fall_haz_lf']] = area_affected_all_linear_comps[:,damage['fnc_filters']['int_fall_haz_lf']]
-        comp_affected_area[:,damage['fnc_filters']['int_fall_haz_sf']] = area_affected_all_direct_scale_comps[:,damage['fnc_filters']['int_fall_haz_sf']]
-        comp_affected_area[:,damage['fnc_filters']['int_fall_haz_ea']] = area_affected_all_direct_scale_comps[:,damage['fnc_filters']['int_fall_haz_ea']]        
-        comp_affected_area[:,damage['fnc_filters']['int_fall_haz_bay']] = area_affected_all_bay_comps[:,damage['fnc_filters']['int_fall_haz_bay']]
-        comp_affected_area[:,damage['fnc_filters']['int_fall_haz_build']] = area_affected_all_build_comps[:,damage['fnc_filters']['int_fall_haz_build']]
-        
-        '''Go each possible unique repair time contributing to interior safety check
-        Find when enough repairs are complete such that interior damage no
-        longer affects tenant safety'''
-        comps_day_repaired = repair_complete_day_w_tmp_w_instabilities # define as initial repair day considering tmp repairs
-        int_repair_day = np.zeros(num_reals)
-        all_comps_day_repaired = np.zeros([num_reals,num_comps])
-        num_repair_time_increments = np.sum(damage['fnc_filters']['int_fall_haz_all']) # possible unique number of loop increments
-        for i in range(num_repair_time_increments):
-            # Quantify Affected Area
-            diff_comp_areas = np.empty([num_reals, len(comp_types_interior_check)])
-            for cmp in range(len(comp_types_interior_check)):
-                filt = damage['comp_ds_table']['comp_type_id'] == comp_types_interior_check[cmp] # check to see if it matches the first part of the ID (ie the type of comp)
-                diff_comp_areas[:,cmp] = np.sum(comp_affected_area[:,filt], axis=1)
-            
-            area_affected = np.sqrt(np.sum(diff_comp_areas**2, axis=1)) # total area affected is the srss of the areas in the unit
-            
-            # Determine if current damage affects occupancy
-            percent_area_affected = np.fmin(area_affected / unit['area'], 1)
-            affects_occupancy = percent_area_affected > functionality_options['interior_safety_threshold']
-            
-            # Determine step increment based on the component with the shortest repair time
-            delta_day = np.nanmin(comps_day_repaired[:,damage['fnc_filters']['int_fall_haz_all']], axis=1)
-            delta_day[np.isnan(delta_day)] = 0
-            
-            # Add increment to the tally of days until the interior damage
-            # stops affecting occupancy
-            int_repair_day = int_repair_day + affects_occupancy * delta_day
-            
-            # Add days to components that are affecting occupancy
-            any_area_affected_all_comps = (damage['fnc_filters']['int_fall_haz_all'] * comp_affected_area) > 0 # Count any component that contributes to the loss of occupancy regardless of by how much
-            all_comps_day_repaired = all_comps_day_repaired + any_area_affected_all_comps * affects_occupancy.reshape(num_reals,1) * delta_day.reshape(num_reals,1)
-            
-            # Reduce compent damaged for the next increment based on what was repaired in this increment
-            comps_day_repaired = comps_day_repaired - delta_day.reshape(num_reals,1)
-            comps_day_repaired[comps_day_repaired <= 0] = np.nan
-            fixed_comps_filt = np.isnan(comps_day_repaired)
-            comp_affected_area[fixed_comps_filt] = 0
-        
-        
-        # Save interior recovery day for this tenant unit
-        recovery_day['interior'][:,tu] = int_repair_day
-        comp_breakdowns['interior'][:,:,tu] = all_comps_day_repaired
-        
-        ''' Hazardous Materials
-          note: hazardous materials are accounted for in building functional
-          assessment here, but are not currently quantified in the component
-          breakdowns'''
-        if any(damage['fnc_filters']['local_hazardous_material']):
-            # Any local hazardous material shuts down the entire tenant unit
-            recovery_day['hazardous_material'][:,tu] = np.nanmax(repair_complete_day_w_tmp[:,damage['fnc_filters']['local_hazardous_material']], axis=1) 
-        else:
-            recovery_day['hazardous_material'][:,tu] = np.zeros(num_reals)
-    
-    return recovery_day, comp_breakdowns
-
-    
-#########################functional recovery sub functions ########################################
-
-def fn_calc_subsystem_recovery(subsys_filt, damage,
-    repair_complete_day, total_num_comps, damaged_comps):
-    '''Check whether the components of a particular subsystem have redundancy
-    or not and calculate the day the subsystem recovers opertaions
-    
-    Parameters
-    ----------
-    subsys_filt: logical array [1 x num_comps_ds]
-     indentifies which rows in the damage.comp_ds_info are the components of interest
-    
-    damage: struct
-     contains per damage state damage, loss, and repair time data for each 
-     component in the building
-    
-    repair_complete_day: array [num_reals x num_comps_ds]
-     day reapairs are complete for each components damage state for a given
-     tenant unit
-    
-    total_num_comps: array [1 x num_comps_ds]
-     total number of each component damage state in this tenant unit
-    
-    damaged_comps: array [num_reals x num_comps_ds]
-     total number of damaged components in each damage state in this tenant unit
-    
-    Returns
-    -------
-    subsys_repair_day: array [num reals x 1]
-     The day this subsystem stops affecting functoin in a given tenant unit'''
-
-
-    import numpy as np    
-    ## Initial Setup
-    num_reals = len(repair_complete_day)
-    if len(damage['comp_ds_table']['parallel_operation'][subsys_filt])>0:
-        is_redundant = max(damage['comp_ds_table']['parallel_operation'][subsys_filt]) # should all be the same within a subsystem
-    else: 
-        is_redundant = 0
-    any_comps = any(subsys_filt)
-    
-    ## Check if the componet has redundancy
-    if any_comps:
-        if is_redundant == 1:
-            ## go through each component in this subsystem and find number of damaged units
-            comps = np.unique(damage['comp_ds_table']['comp_idx'][subsys_filt])
-            num_tot_comps = np.zeros(len(comps))
-            num_damaged_comps = np.zeros([num_reals,len(comps)])
-            for c in range(len(comps)):
-                this_comp = np.logical_and(subsys_filt, damage['comp_ds_table']['comp_idx'] == comps[c])
-                num_tot_comps[c] = max(total_num_comps * this_comp) # number of units across all ds should be the same
-                num_damaged_comps[:,c] = np.nanmax(damaged_comps * this_comp, axis=1)
-
-    
-            ## sum together multiple components in this subsystem
-            subsystem_num_comps = sum(num_tot_comps)
-            subsystem_num_damaged_comps = np.sum(num_damaged_comps, axis=1);
-            ratio_damaged = subsystem_num_damaged_comps / subsystem_num_comps
-    
-            ## Check failed component against the ratio of components required for system operation
-            # system fails when there is an insufficient number of operating components
-            n1_redundancy = max(damage['comp_ds_table']['n1_redundancy'][subsys_filt]) # should all be the same within a subsystem
-            if subsystem_num_comps == 0: # No components at this level
-                subsystem_failure = np.zeros([num_reals,1])
-            elif subsystem_num_comps == 1: # Not actually redundant
-                subsystem_failure = subsystem_num_damaged_comps == 1;
-            elif n1_redundancy ==1:
-                # These components are designed to have N+1 redundncy rates,
-                # meaning they are designed to lose one component and still operate at
-                # normal level
-                subsystem_failure = subsystem_num_damaged_comps > 1
-            else:
-                # Use a predefined ratio
-                redundancy_threshold = max(damage['comp_ds_table']['redundancy_threshold'][subsys_filt]) # should all be the same within a subsystem
-                subsystem_failure = ratio_damaged > redundancy_threshold
-    
-            ## Calculate recovery day and combine with other subsystems for this tenant unit
-            # assumes all quantities in each subsystem are repaired at
-            # once, which is true for our current repair schedule (ie
-            # system level at each story)
-            subsys_repair_day = np.nanmax(subsystem_failure.reshape(len(subsystem_failure),1) * subsys_filt.reshape(1, len(subsys_filt)) * repair_complete_day, axis=1) 
-        
-            '''else: This subsystem has no redundancy
-                any major damage to the components fails the subsystem at this
-                tenant unit'''
-        else: 
-            subsys_repair_day = np.nanmax(repair_complete_day * subsys_filt.reshape(1, len(subsys_filt)), axis=1)
-
-    else: # No components were populated in this subsystem
-        subsys_repair_day = np.zeros(num_reals)
-
-    return subsys_repair_day
-
-
-def fn_building_level_system_operation( damage, damage_consequences, 
-                                        building_model, utilities, 
-                                        functionality_options):
-    
-    '''Calculate the day certain systems recovery building-level opertaions
-    
-    Parameters
-    ----------
-    damage: dictionary
-      contains per damage state damage, loss, and repair time data for each 
-      component in the building
-     
-    damage_consequences: dictionary
-      dictionary containing simulated building consequences, such as red
-      tags and repair costs ratios
-     
-    building_model: dictionary
-      general attributes of the building model
-    
-    utilities: dictionary
-      dictionary containing simulated utility downtimes
-     
-    functionality_options: dictionary
-      recovery time optional inputs such as various damage thresholds
-    
-    Returns
-    -------
-    system_operation_day['building']: dictionary
-      simulation of the day operation is recovered for various systems at the
-      building level
-     
-    system_operation_day['comp']: dictionary
-      simulation number of days each component is affecting building system
-      operations'''
-    
- 
-    # import packages
-    from . import other_functionality_functions
-    import numpy as np
-    
-    system_operation_day = {'building' : {}, 'comp' : {}}
-    ## Initial Setep
-    num_stories = building_model['num_stories']
-    num_reals = len(damage_consequences['red_tag'])
-    num_comps = len(damage['comp_ds_table']['comp_id'])
-    
-    system_operation_day['building']['hvac_main'] = np.zeros(num_reals)
-    
-    system_operation_day['comp']['elev_quant_damaged'] = np.zeros([num_reals,num_comps])
-    system_operation_day['comp']['elev_day_repaired'] = np.zeros([num_reals,num_comps])
-    system_operation_day['comp']['electrical_main'] = np.zeros([num_reals,num_comps])
-    system_operation_day['comp']['water_potable_main'] = np.zeros([num_reals,num_comps])
-    system_operation_day['comp']['water_sanitary_main'] = np.zeros([num_reals,num_comps])
-    system_operation_day['comp']['elevator_mcs'] = np.zeros([num_reals,num_comps])
-    system_operation_day['comp']['data_main'] = np.zeros([num_reals,num_comps])
-    
-    ## Loop through each story/TU and quantify the building-level performance of each system (e.g. equipment that severs the entire building)
-    for tu in range(num_stories):
-        damaged_comps = np.array(damage['tenant_units'][tu]['qnt_damaged'])
-        initial_damaged = damaged_comps > 0
-        total_num_comps = np.array(damage['tenant_units'][tu]['num_comps'])
-        repair_complete_day = damage['tenant_units'][tu]['recovery']['repair_complete_day']
-        
-        ## Elevators
-        # Assumed all components affect entire height of shaft
-        system_operation_day['comp']['elev_quant_damaged'] = np.fmax(
-            system_operation_day['comp']['elev_quant_damaged'], 
-            damage['tenant_units'][tu]['qnt_damaged'] * 
-            damage['fnc_filters']['elevators'])
-        
-        system_operation_day['comp']['elev_day_repaired'] = np.fmax(
-            system_operation_day['comp']['elev_day_repaired'], 
-            repair_complete_day * damage['fnc_filters']['elevators'])
-        
-        # Motor Control system - Elevators
-        system_operation_day['comp']['elevator_mcs'] = np.fmax(
-            system_operation_day['comp']['elevator_mcs'], 
-            repair_complete_day * damage['fnc_filters']['elevator_mcs'])
-        
-        ## Electrical
-        system_operation_day['comp']['electrical_main'] = np.fmax(
-            system_operation_day['comp']['electrical_main'], 
-            repair_complete_day * damage['fnc_filters']['electrical_main'])
-        
-
-        ## Water
-        system_operation_day['comp']['water_potable_main'] = np.fmax(
-            system_operation_day['comp']['water_potable_main'], 
-            repair_complete_day * damage['fnc_filters']['water_main'])
-        
-        system_operation_day['comp']['water_sanitary_main'] = np.fmax(
-            system_operation_day['comp']['water_sanitary_main'], 
-            repair_complete_day * damage['fnc_filters']['sewer_main'])        
-        
-        
-        ## HVAC
-        building_hvac_subsystems = list(damage['fnc_filters']['hvac']['building'].keys())
-        for s in range(len(building_hvac_subsystems)):
-            # Initialize variables
-            subsys_label = building_hvac_subsystems[s]
-
-            if ('building' in system_operation_day.keys()) == False or ('building' in system_operation_day.keys() and (subsys_label in system_operation_day['building']) ==  False):
-                # Initialize variables if not already initialized
-                system_operation_day['building'][subsys_label] = np.zeros(num_reals)
-                system_operation_day['comp'][subsys_label] = np.zeros([num_reals,num_comps])
-                
-            # go through each subsystem and calculate how long entire building
-            # operation is impaired
-            subs = list(damage['fnc_filters']['hvac']['building'][subsys_label].keys())
-                
-            for b in range(len(subs)):
-                filt = damage['fnc_filters']['hvac']['building'][subsys_label][subs[b]]
-                
-                repair_day = other_functionality_functions.fn_calc_subsystem_recovery( filt, damage,
-                     repair_complete_day, total_num_comps, damaged_comps)
-                
-                comps_breakdown = filt.reshape(1, num_comps) * initial_damaged * repair_day.reshape(num_reals,1)
-                
-                # combine with previous stories
-                system_operation_day['building'][subsys_label] = np.fmax(system_operation_day['building'][subsys_label], repair_day)
-                system_operation_day['comp'][subsys_label] = np.fmax(system_operation_day['comp'][subsys_label], comps_breakdown)
-                
-        ## Data
-        system_operation_day['comp']['data_main'] = np.fmax(system_operation_day['comp']['data_main'], repair_complete_day * damage['fnc_filters']['data_main'])
-        
-    ## Calculate building level consequences for systems where any major main damage leads to system failure
-    system_operation_day['building']['electrical_main'] = np.nanmax(system_operation_day['comp']['electrical_main'], axis=1)  # any major damage to the main equipment fails the system for the entire building
-    system_operation_day['building']['water_potable_main'] = np.nanmax(system_operation_day['comp']['water_potable_main'], axis=1)  # any major damage to the main pipes fails the system for the entire building
-    system_operation_day['building']['water_sanitary_main'] = np.nanmax(system_operation_day['comp']['water_sanitary_main'], axis=1) # any major damage fails the system for the whole building so take the max
-    system_operation_day['building']['elevator_mcs'] = np.nanmax(system_operation_day['comp']['elevator_mcs'], axis=1) # any major damage fails the system for the whole building so take the max
-    system_operation_day['building']['data_main'] = np.nanmax(system_operation_day['comp']['data_main'], axis=1)  # any major damage to the main equipment fails the system for the entire building
-   
-    ## Account for External Utilities impact on system Operation
-    # Electricity
-    system_operation_day['building']['electrical_main'] = np.fmax(system_operation_day['building']['electrical_main'] , utilities['electrical'])
-   
-    # Potable water
-    system_operation_day['building']['water_potable_main'] = np.fmax(system_operation_day['building']['water_potable_main'], utilities['water'])
-    
-    # Assume hvac control runs on electricity and heating system runs on gas
-    system_operation_day['building']['hvac_control'] = np.fmax(system_operation_day['building']['hvac_control'], system_operation_day['building']['electrical_main'])
-    system_operation_day['building']['hvac_heating'] = np.fmax(system_operation_day['building']['hvac_heating'], utilities[functionality_options['heat_utility']])
-
-    return system_operation_day
-
-
-def fn_tenant_function( damage, building_model, system_operation_day, 
-                        subsystems, tenant_units, impeding_temp_repairs, functionality_options):
-    
-    '''Check each tenant unit for damage that would cause that tenant unit 
-    to not be functional
-    
-    Parameters
-    ----------
-    damage: dictionary
-      contains per damage state damage, loss, and repair time data for each 
-      component in the building
-    
-    building_model: dictionary
-      general attributes of the building model
-      system_operation_day.building: struct
-      simulation of the day operation is recovered for various systems at the
-      building level
-     
-    system_operation_day.comp: dictionary
-      simulation number of days each component is affecting building system
-      operations
-    
-    subsystems: DataFrame
-      data table containing information about each subsystem's attributes
-    
-    tenant_units: DataFrame
-      attributes of each tenant unit within the building
-    
-    impeding_temp_repairs: dictionary
-      contains simulated temporary repairs the impede occuapancy and function
-      but are calulated in parallel with the temp repair schedule
-    
-    functionality_options: dictionary
-      recovery time optional inputs such as various damage thresholds
-      
-    Returns
-    -------
-    recovery_day: dictionary
-      simulation of the number of days each fault tree event is affecting
-      function in each tenant unit
-    
-    comp_breakdowns: dictionary
-      simulation of each components contributions to each of the 
-      fault tree events''' 
-
-    import numpy as np
-    import sys
-    
-    '''Subfunction'''
-    def subsystem_recovery(subsystem, damage, repair_complete_day, 
-                           total_num_comps, damaged_comps, 
-                           initial_damaged, dependancy):
-
-        # import packages
-        from . import other_functionality_functions
-        
-        # Set variables
-        recovery_day_all = dependancy['recovery_day'].copy()
-        comp_breakdowns_all = dependancy['comp_breakdown'].copy()
-        
-        # Go through each component group in this subsystem and determine recovery
-        # based on impact of system operation at the tenant unit level
-        subs = list(damage['fnc_filters']['hvac']['tenant'][subsystem].keys())
-        for b in range(len(subs)):
-            filt = damage['fnc_filters']['hvac']['tenant'][subsystem][subs[b]]
-            recovery_day = other_functionality_functions.fn_calc_subsystem_recovery(filt, damage, repair_complete_day, total_num_comps, damaged_comps)
-            comps_breakdown = filt * initial_damaged * recovery_day.reshape(len(recovery_day),1)
-            recovery_day_all = np.fmax(recovery_day_all, recovery_day) # combine with previous stories
-            comp_breakdowns_all = np.fmax(comp_breakdowns_all,comps_breakdown)
-    
-        return recovery_day_all, comp_breakdowns_all
-    '''Subfunction ends'''
-    
-    '''Subfunction'''
-    def check_roof_function(roof_sys_filter, damage_threshold, repair_complete_day_w_tmp, qnt_damaged, num_comps):
-
-        # Check the roof area for function (seal and function)
-        num_comp_damaged = roof_sys_filter * qnt_damaged
-        num_roof_comps = roof_sys_filter * num_comps
-        
-        comps_day_repaired = np.array(repair_complete_day_w_tmp)
-        roof_recovery_day = np.zeros(np.shape(comps_day_repaired)[0])
-        all_comps_day_roof = np.zeros(np.shape(comps_day_repaired))
-        num_repair_time_increments = sum(roof_sys_filter) # possible unique number of loop increments
-
-        # Loop through each unique repair time increment and determine when stops affecting function
-        for i in range(num_repair_time_increments):
-            # Determine the area of roof affected 
-            percent_area_affected = np.sum(num_comp_damaged, axis = 1) / sum(num_roof_comps) # Assumes roof components do not occupy the same area of roof            
-            # Determine if current damage affects function for this tenant unit
-            # if the area of exterior wall damage is greater than what is
-            # acceptable by the tenant 
-            affects_function = percent_area_affected >= damage_threshold
-
-            # Add days in this increment to the tally
-            delta_day = np.nanmin(comps_day_repaired[:, roof_sys_filter],axis = 1)
-            delta_day[np.isnan(delta_day)] = 0
-            roof_recovery_day = roof_recovery_day + affects_function * delta_day
-
-            # Add days to components that are affecting function
-            any_area_affected_all_comps = num_comp_damaged > 0 # Count any component that contributes to the loss of function regardless of by how much
-            all_comps_day_roof = all_comps_day_roof + any_area_affected_all_comps * (affects_function * delta_day).reshape(len(delta_day),1)
-
-            # Change the comps for the next increment
-            # reducing damage for what has been repaired in this time increment
-            comps_day_repaired = comps_day_repaired - delta_day.reshape(len(delta_day),1)
-            comps_day_repaired[comps_day_repaired <= 0] = np.nan
-            fixed_comps_filt = np.isnan(comps_day_repaired)
-            num_comp_damaged[fixed_comps_filt] = 0
-
-        return all_comps_day_roof, roof_recovery_day
-    '''Subfunction ends'''
-    
-    ## Initial Setup
-    num_units = len(damage['tenant_units'])
-    num_reals, num_comps = np.shape(np.array(damage['tenant_units'][0]['qnt_damaged']))
-    num_stories = building_model['num_stories']
-    
-    recovery_day = {
-        'elevators' : np.zeros([num_reals,num_units]),
-        'exterior' : np.zeros([num_reals,num_units]),
-        'roof': np.zeros([num_reals,num_units]),
-        'interior' : np.zeros([num_reals,num_units]),
-        'electrical' : np.zeros([num_reals,num_units]),
-        'flooding' : np.zeros([num_reals,num_units]),
-        'water_potable' : np.zeros([num_reals,num_units]),
-        'water_sanitary' : np.zeros([num_reals,num_units]),
-        'hvac_ventilation' : np.zeros([num_reals,num_units]),     
-        'hvac_cooling' : np.zeros([num_reals,num_units]),
-        'hvac_heating' : np.zeros([num_reals,num_units]),
-        'hvac_exhaust' : np.zeros([num_reals,num_units]),
-        'data' : np.zeros([num_reals,num_units]),
-        }
-    
-    comp_breakdowns = {
-        'elevators' : np.zeros([num_reals,num_comps,num_units]),
-        'electrical' : np.zeros([num_reals,num_comps,num_units]),
-        'exterior' : np.zeros([num_reals,num_comps,num_units]),
-        'roof' : np.zeros([num_reals,num_comps,num_units]),
-        'interior' : np.zeros([num_reals,num_comps,num_units]),
-        'flooding' : np.zeros([num_reals,num_comps,num_units]),
-        'water_potable' : np.zeros([num_reals,num_comps,num_units]),
-        'water_sanitary' : np.zeros([num_reals,num_comps,num_units]),
-        'hvac_ventilation' : np.zeros([num_reals,num_comps,num_units]),
-        'hvac_cooling' : np.zeros([num_reals,num_comps,num_units]),
-        'hvac_heating' : np.zeros([num_reals,num_comps,num_units]),
-        'hvac_exhaust' : np.zeros([num_reals,num_comps,num_units]),
-        'data' : np.zeros([num_reals,num_comps,num_units]),        
-        }
-    
-    ## Go through each tenant unit, define system level performacne and determine tenant unit recovery time
-    ## STORY FLOODING
-    for tu in reversed(range(num_stories)): # Go from top to bottom
-        is_damaged = np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0
-        flooding_this_story = np.any(is_damaged[:,damage['fnc_filters']['causes_flooding']], axis=1) # Any major piping damage causes interior flooding
-        flooding_recovery_day = flooding_this_story * impeding_temp_repairs['flooding_repair_day']
-    
-        # Save clean up time per component causing flooding
-        comp_breakdowns['flooding'][:,:,tu] = damage['fnc_filters']['causes_flooding'] * is_damaged * flooding_recovery_day.reshape(num_reals,1)
-    
-        # This story is not accessible if any story above has flooding
-        if tu < num_stories-1: #FZ# If this story id not top story
-            recovery_day['flooding'][:,tu] = np.nanmax(np.column_stack((flooding_recovery_day, recovery_day['flooding'][:,(tu+1):num_stories])), axis = 1)
-        else: #FZ# If this story is top story
-            recovery_day['flooding'][:,tu] = flooding_recovery_day                         
-    
-    ## SYSTEM SPECIFIC CONSEQUENCES    
-    for tu in range(num_units):
-        damaged_comps = np.array(damage['tenant_units'][tu]['qnt_damaged'])
-        initial_damaged = damaged_comps > 0
-        total_num_comps = np.array(damage['tenant_units'][tu]['num_comps'])
-        unit={}
-        for key in list(tenant_units.keys()):
-            unit[key] = tenant_units[key][tu]        
-        
-        repair_complete_day = tuple(map(tuple,damage['tenant_units'][tu]['recovery']['repair_complete_day'])) #FZ# Made tuple to bypass the issue of mutable numpy array
-        repair_complete_day_w_tmp = tuple(map(tuple, damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp'])) #FZ# Made tuple to bypass the issue of mutable numpy array
-        
-        ## Elevators
-        if unit['is_elevator_required'] == 1:
-            comps_day_repaired= tuple(map(tuple,system_operation_day['comp']['elev_day_repaired'])) #FZ# Made tuple to bypass the issue of mutable numpy array
-            comps_day_repaired = np.array(comps_day_repaired)
-            comps_day_repaired[comps_day_repaired == 0] = np.nan
-            comps_quant_damaged = tuple(map(tuple,system_operation_day['comp']['elev_quant_damaged'])) #FZ# Made tuple to bypass the issue of mutable numpy array
-            comps_quant_damaged = np.array(comps_quant_damaged)
-            elev_function_recovery_day = np.zeros(num_reals)
-            elev_comps_day_fnc = np.zeros([num_reals,num_comps])
-            num_repair_time_increments = sum(damage['fnc_filters']['elevators']) # possible unique number of loop increments
-            # Loop through each unique repair time increment and determine when
-            # stops affecting function
-            for i in range(num_repair_time_increments):
-                '''Take the max of component damage to determine the number of
-                shafts/cabs that are damaged/non_operational
-                This assumes that different elevator components are correlated'''
-                num_damaged_elevs = np.nanmax(comps_quant_damaged, axis=1) # this assumes elevators are in one performance group if simeltaneous
-                num_damaged_elevs = np.fmin(num_damaged_elevs, building_model['num_elevators']) # you can never have more elevators damaged than exist
-                
-                '''If elevators are in mutliple performance groups and those
-                elevators have simultaneous damage states, it is not possible
-                to count the number of damaged elevators without additional
-                information'''
-                num_elev_pgs = len(np.unique(damage['comp_ds_table']['comp_idx'][damage['fnc_filters']['elevators']]))
-                is_sim_ds = any(damage['comp_ds_table']['is_sim_ds'][damage['fnc_filters']['elevators']])
-                if (num_elev_pgs > 1) and is_sim_ds:
-                    sys.exit('Error! PBEE_Recovery:Function','Elevator Function check does not handle multiple performance groups with simultaneous damage states')
-                
-    
-                # quantifty the number of occupancy needing to use the elevators
-                # all occupants above the first floor will try to use the elevators
-                building_occ_per_elev = sum(building_model['occupants_per_story'][1:len(building_model['occupants_per_story'])]) / (building_model['num_elevators'] - num_damaged_elevs) 
-                
-                # elevator function check
-                # do tenants have sufficient elevator access need based on
-                # elevators that are still operational
-                # affects_function = building_occ_per_elev > max(unit['occ_per_elev'])
-                affects_function = building_occ_per_elev > unit['occ_per_elev'] #FZ# Check. Why max is done . Only a single value per unit
-                # Add days in this increment to the tally
-                delta_day = np.nanmin(comps_day_repaired[:,damage['fnc_filters']['elevators']], axis=1)
-                delta_day[np.isnan(delta_day)] = 0
-                elev_function_recovery_day = elev_function_recovery_day + affects_function * delta_day
-                
-                # Add days to components that are affecting occupancy
-                any_elev_damage = comps_quant_damaged > 0 # Count any component that contributes to the loss of function regardless of by how much 
-                elev_comps_day_fnc = elev_comps_day_fnc + any_elev_damage * affects_function.reshape(num_reals,1) * delta_day.reshape(num_reals,1)
-    
-                # Change the comps for the next increment
-                comps_day_repaired = comps_day_repaired - delta_day.reshape(num_reals,1)
-                comps_day_repaired[comps_day_repaired <= 0] = np.nan
-                fixed_comps_filt = np.isnan(comps_day_repaired)
-                comps_quant_damaged[fixed_comps_filt] = 0
-            
-            power_supply_recovery_day = np.fmax(system_operation_day['building']['elevator_mcs'], system_operation_day['building']['electrical_main'])
-            
-            recovery_day['elevators'][:,tu] = np.fmax(elev_function_recovery_day, power_supply_recovery_day) # electrical system and utility
-            power_supply_recovery_day_comp = np.fmax(system_operation_day['comp']['elevator_mcs'], system_operation_day['comp']['electrical_main'])
-            comp_breakdowns['elevators'][:,:,tu] = np.fmax(elev_comps_day_fnc, power_supply_recovery_day_comp)
-        
-        
-        ## Exterior Enclosure 
-        # Perimeter Cladding (assuming all exterior components have either lf or sf units)
-        area_affected_lf_all_comps = damage['comp_ds_table']['exterior_surface_area_factor'] * damage['comp_ds_table']['unit_qty'] * building_model['ht_per_story_ft'][tu] * damage['tenant_units'][tu]['qnt_damaged']
-        area_affected_direct_scale_all_comps = damage['comp_ds_table']['exterior_surface_area_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged']
-       
-        comp_affected_area = np.zeros([num_reals,num_comps])
-        comp_affected_area[:,damage['fnc_filters']['exterior_seal_lf']] = area_affected_lf_all_comps[:,damage['fnc_filters']['exterior_seal_lf']]
-        comp_affected_area[:,damage['fnc_filters']['exterior_seal_sf']] = area_affected_direct_scale_all_comps[:,damage['fnc_filters']['exterior_seal_sf']]
-        comp_affected_area[:,damage['fnc_filters']['exterior_seal_ea']] = area_affected_direct_scale_all_comps[:,damage['fnc_filters']['exterior_seal_ea']]
-        
-        comps_day_repaired = np.array(repair_complete_day)
-        ext_function_recovery_day = np.zeros(num_reals)
-        all_comps_day_ext = np.zeros([num_reals,num_comps])
-        num_repair_time_increments = sum(damage['fnc_filters']['exterior_seal_all']) # possible unique number of loop increments
-        # Loop through each unique repair time increment and determine when stops affecting function
-        for i in range(num_repair_time_increments):
-            # Determine the area of wall which has severe exterior encolusure damage 
-            area_affected = np.sum(comp_affected_area, axis=1) # Assumes cladding components do not occupy the same perimeter area
-            percent_area_affected = np.fmin(area_affected / unit['perim_area'], 1) # normalize it. #FZ# Should be fraction area?
-            
-            # Determine if current damage affects function for this tenant unit
-            # if the area of exterior wall damage is greater than what is
-            # acceptable by the tenant 
-            affects_function = percent_area_affected > unit['exterior'] 
-            
-            # Add days in this increment to the tally
-            delta_day = np.nanmin(comps_day_repaired[:,damage['fnc_filters']['exterior_seal_all']], axis=1)
-            delta_day[np.isnan(delta_day)] = 0
-            ext_function_recovery_day = ext_function_recovery_day + affects_function * delta_day
-            # Add days to components that are affecting occupancy
-            any_area_affected_all_comps = comp_affected_area > 0 # Count any component that contributes to the loss of occupance regardless of by how much
-            all_comps_day_ext = all_comps_day_ext + any_area_affected_all_comps * affects_function.reshape(num_reals,1) * delta_day.reshape(num_reals,1)
-            
-            # Change the comps for the next increment
-            # reducing damage for what has been repaired in this time increment
-            comps_day_repaired = comps_day_repaired - delta_day.reshape(num_reals,1)
-            comps_day_repaired[comps_day_repaired <= 0] = np.nan
-            fixed_comps_filt = np.isnan(comps_day_repaired)
-            comp_affected_area[fixed_comps_filt] = 0
-            
-        recovery_day['exterior'][:,tu] = ext_function_recovery_day
-        comp_breakdowns['exterior'][:,:,tu] = all_comps_day_ext        
-
-        if unit['story'] == num_stories: # If this is the top story, check the roof for function
-            #Roof structure check
-            all_comps_day_roof_struct, roof_structure_recovery_day = check_roof_function(damage['fnc_filters']['roof_structure'],
-                                                                                        np.array(subsystems['redundancy_threshold'][subsystems['id'] == 21])[0],
-                                                                                        repair_complete_day_w_tmp,
-                                                                                        damage['tenant_units'][tu]['qnt_damaged'],
-                                                                                        damage['tenant_units'][tu]['num_comps'])
-            
-            # Roof seatherproofing check
-            all_comps_day_roof_weather, roof_weather_recovery_day = check_roof_function(damage['fnc_filters']['roof_weatherproofing'],
-                                                                                        np.array(subsystems['redundancy_threshold'][subsystems['id'] == 22])[0],
-                                                                                        repair_complete_day_w_tmp,
-                                                                                        damage['tenant_units'][tu]['qnt_damaged'],
-                                                                                        damage['tenant_units'][tu]['num_comps'])           
-           
-            # Combine branches
-            recovery_day['roof'][:,tu] = np.fmax(roof_structure_recovery_day, roof_weather_recovery_day)
-            comp_breakdowns['roof'][:,:,tu] = np.fmax(all_comps_day_roof_struct, all_comps_day_roof_weather)
-        
-        ## Interior Area
-        area_affected_lf_all_comps    = damage['comp_ds_table']['interior_area_factor'] * damage['comp_ds_table']['unit_qty'] * building_model['ht_per_story_ft'][tu] * damage['tenant_units'][tu]['qnt_damaged']
-        area_affected_direct_scale_all_comps    = damage['comp_ds_table']['interior_area_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged']
-        area_affected_bay_all_comps   = damage['comp_ds_table']['interior_area_factor'] * building_model['struct_bay_area_per_story'][tu] * damage['tenant_units'][tu]['qnt_damaged']
-        area_affected_build_all_comps = damage['comp_ds_table']['interior_area_factor'] * sum(building_model['area_per_story_sf']) * damage['tenant_units'][tu]['qnt_damaged']
-        
-        repair_complete_day_w_tmp_w_instabilities = np.array(repair_complete_day_w_tmp)
-        if tu > 0: #FZ# changed to 0 to account for python index starting from 0.
-            area_affected_below = damage['comp_ds_table']['interior_area_factor'] * building_model['struct_bay_area_per_story'][tu-1] * damage['tenant_units'][tu-1]['qnt_damaged']
-            area_affected_bay_all_comps[:,damage['fnc_filters']['vert_instabilities']] = np.fmax(
-                area_affected_below[:,damage['fnc_filters']['vert_instabilities']],area_affected_bay_all_comps[:,damage['fnc_filters']['vert_instabilities']])
-            repair_time_below = damage['tenant_units'][tu-1]['recovery']['repair_complete_day_w_tmp']
-            repair_complete_day_w_tmp_w_instabilities[:,damage['fnc_filters']['vert_instabilities']] = np.fmax(
-                repair_time_below[:,damage['fnc_filters']['vert_instabilities']], np.array(repair_complete_day_w_tmp)[:,damage['fnc_filters']['vert_instabilities']])
-
-    
-        comp_affected_area = np.zeros([num_reals,num_comps])
-        comp_affected_area[:,damage['fnc_filters']['interior_function_lf']] = area_affected_lf_all_comps[:,damage['fnc_filters']['interior_function_lf']]
-        comp_affected_area[:,damage['fnc_filters']['interior_function_sf']] = area_affected_direct_scale_all_comps[:,damage['fnc_filters']['interior_function_sf']]
-        comp_affected_area[:,damage['fnc_filters']['interior_function_ea']] = area_affected_direct_scale_all_comps[:,damage['fnc_filters']['interior_function_ea']]        
-        comp_affected_area[:,damage['fnc_filters']['interior_function_bay']] = area_affected_bay_all_comps[:,damage['fnc_filters']['interior_function_bay']]
-        comp_affected_area[:,damage['fnc_filters']['interior_function_build']] = area_affected_build_all_comps[:,damage['fnc_filters']['interior_function_build']]
-    
-        frag_types_in_check = np.unique(damage['comp_ds_table']['comp_type_id'][damage['fnc_filters']['interior_function_all']])
-        comps_day_repaired = repair_complete_day_w_tmp_w_instabilities.copy()
-    
-        int_function_recovery_day = np.zeros(num_reals)
-        int_comps_day_repaired = np.zeros([num_reals,num_comps])
-        num_repair_time_increments = sum(damage['fnc_filters']['interior_function_all']) # possible unique number of loop increments
-        # Loop through each unique repair time increment and determine when stops affecting function
-        for i in range(num_repair_time_increments):
-            # Quantify the affected area (based on srss of differenct component
-            # types)
-            diff_comp_areas = np.empty([num_reals, len(frag_types_in_check)])
-            for cmp in range(len(frag_types_in_check)):
-                filt = damage['comp_ds_table']['comp_type_id'] == frag_types_in_check[cmp] # check to see if it matches the first part of the ID (ie the type of comp)
-                diff_comp_areas[:,cmp] = np.sum(comp_affected_area[:,filt], axis=1)
-            
-            area_affected = np.sqrt(np.sum(diff_comp_areas**2, axis=1)) # total area affected is the srss of the areas in the unit
-            percent_area_affected = np.fmin(area_affected / unit['area'], 1) # no greater than the total unit area
-        
-            # Determine if current damage affects function for this tenant unit
-            # affects function if the area of interior damage is greater than what is
-            # acceptable by the tenant 
-            affects_function = percent_area_affected > unit['interior'] 
-            
-            # Add days in this increment to the tally
-            delta_day = np.nanmin(comps_day_repaired[:,damage['fnc_filters']['interior_function_all']], axis=1)
-            delta_day[np.isnan(delta_day)] = 0
-            int_function_recovery_day = int_function_recovery_day + affects_function * delta_day
-            
-            # Add days to components that are affecting occupancy
-            any_area_affected_all_comps = comp_affected_area > 0 # Count any component that contributes to the loss of occupance regardless of by how much
-            int_comps_day_repaired = int_comps_day_repaired + any_area_affected_all_comps * affects_function.reshape(num_reals,1) * delta_day.reshape(num_reals,1)
-            
-            # Change the comps for the next increment
-            # reducing damage for what has been repaired in this time increment
-            comps_day_repaired = comps_day_repaired - delta_day.reshape(num_reals,1)
-            comps_day_repaired[comps_day_repaired <= 0] = np.nan
-            fixed_comps_filt = np.isnan(comps_day_repaired)
-            comp_affected_area[fixed_comps_filt] = 0
-            
-        recovery_day['interior'][:,tu] = int_function_recovery_day
-        comp_breakdowns['interior'][:,:,tu] = int_comps_day_repaired
-        
-        # Water and Plumbing System
-        # determine effect on funciton at this tenant unit
-        # any major damage to the branch pipes (small diameter) failes for this tenant unit
-        tenant_sys_recovery_day = np.nanmax(np.array(repair_complete_day) * damage['fnc_filters']['water_unit'], axis=1) 
-        recovery_day['water_potable'][:,tu] = np.fmax(system_operation_day['building']['water_potable_main'],tenant_sys_recovery_day)
-              
-        # distribute effect to the components
-        comp_breakdowns['water_potable'][:,:,tu] = np.fmax(system_operation_day['comp']['water_potable_main'], np.array(repair_complete_day) * damage['fnc_filters']['water_unit'])
-        
-        # In taller buildings, water needs to be pumped to reach upper stories
-        #and therefore requires electrical power
-        if unit['story'] > functionality_options['water_pressure_max_story']:
-            electrical_failure_controls = system_operation_day['building']['electrical_main'] > recovery_day['water_potable'][:,tu]
-            recovery_day['water_potable'][:,tu] = np.fmax(recovery_day['water_potable'][:,tu], system_operation_day['building']['electrical_main'])
-            comp_breakdowns['water_potable'][:,:,tu] = np.fmax(comp_breakdowns['water_potable'][:,:,tu] * np.logical_not(electrical_failure_controls).reshape(num_reals,1), 
-                                                               system_operation_day['comp']['electrical_main'] * electrical_failure_controls.reshape(num_reals,1))
-        
-        
-        ## Sanitary Waste System
-        # determine effect on funciton at this tenant unit
-        # any major damage to the branch pipes (small diameter) failes for this tenant unit
-        tenant_sys_recovery_day = np.nanmax(repair_complete_day * damage['fnc_filters']['sewer_unit'], axis=1) 
-        recovery_day['water_sanitary'][:,tu] = np.fmax(system_operation_day['building']['water_sanitary_main'], tenant_sys_recovery_day)
-    
-        # distribute effect to the components
-        comp_breakdowns['water_sanitary'][:,:,tu] = np.fmax(system_operation_day['comp']['water_sanitary_main'], np.array(repair_complete_day) * damage['fnc_filters']['water_unit'])
-    
-        # Sanitary waste operation at this tenant unit depends on the 
-        # operation of the potable water system at this tenant unit
-        recovery_day['water_sanitary'][:,tu] = np.fmax(recovery_day['water_sanitary'][:,tu],recovery_day['water_potable'][:,tu])
-        comp_breakdowns['water_sanitary'][:,:,tu] = np.fmax(comp_breakdowns['water_sanitary'][:,:,tu], comp_breakdowns['water_potable'][:,:,tu])
-  
-        ## Electrical Power System
-        # Does not consider effect of backup systems
-        if unit['is_electrical_required'] == 1:
-            # determine effect on funciton at this tenant unit
-            # any major damage to the unit level electrical equipment fails for this tenant unit
-            tenant_sys_recovery_day = np.nanmax(np.array(repair_complete_day) * damage['fnc_filters']['electrical_unit'], axis=1)
-            recovery_day['electrical'][:,tu] =np.fmax(system_operation_day['building']['electrical_main'], tenant_sys_recovery_day)
-                      
-            # distribute effect to the components
-            comp_breakdowns['electrical'][:,:,tu] = np.fmax(system_operation_day['comp']['electrical_main'], np.array(repair_complete_day) * damage['fnc_filters']['electrical_unit'])
-
-        ## HVAC System
-        # HVAC: Control System
-        recovery_day_hvac_control = system_operation_day['building']['hvac_control'] # Electrical power is counted in here
-        comp_breakdowns_hvac_control = system_operation_day['comp']['hvac_control']
-        
-        dependancy = {}
-        # HVAC: Ventilation
-        dependancy['recovery_day'] = recovery_day_hvac_control
-        dependancy['comp_breakdown'] = comp_breakdowns_hvac_control
-        recovery_day['hvac_ventilation'][:,tu], comp_breakdowns['hvac_ventilation'][:,:,tu] = subsystem_recovery('hvac_ventilation',
-                                                                         damage, 
-                                                                         repair_complete_day,
-                                                                         total_num_comps, 
-                                                                         damaged_comps, 
-                                                                         initial_damaged, 
-                                                                         dependancy)                               
-
-        #HVAC: Heating
-        dependancy['recovery_day'] = np.fmax(recovery_day['hvac_ventilation'][:,tu], system_operation_day['building']['hvac_heating'])
-        dependancy['comp_breakdown'] = np.fmax(comp_breakdowns['hvac_ventilation'][:,:,tu], system_operation_day['comp']['hvac_heating'])
-        recovery_day['hvac_heating'][:,tu], comp_breakdowns['hvac_heating'][:,:,tu] = subsystem_recovery('hvac_heating', 
-                                                                     damage, 
-                                                                     repair_complete_day, 
-                                                                     total_num_comps, 
-                                                                     damaged_comps, 
-                                                                     initial_damaged, 
-                                                                     dependancy)
-    
-        # HVAC: Cooling
-        dependancy['recovery_day'] = np.fmax(recovery_day['hvac_ventilation'][:,tu],system_operation_day['building']['hvac_cooling'])
-        dependancy['comp_breakdown'] = np.fmax(comp_breakdowns['hvac_ventilation'][:,:,tu], system_operation_day['comp']['hvac_cooling'])
-        recovery_day['hvac_cooling'][:,tu], comp_breakdowns['hvac_cooling'][:,:,tu] = subsystem_recovery('hvac_cooling', 
-                                                                     damage, 
-                                                                     repair_complete_day, 
-                                                                     total_num_comps, 
-                                                                     damaged_comps, 
-                                                                     initial_damaged, 
-                                                                     dependancy)
-    
-        # HVAC: Exhaust
-        dependancy['recovery_day'] = recovery_day_hvac_control
-        dependancy['comp_breakdown'] = comp_breakdowns_hvac_control
-        recovery_day['hvac_exhaust'][:,tu], comp_breakdowns['hvac_exhaust'][:,:,tu] = subsystem_recovery('hvac_exhaust', 
-                                                  damage, 
-                                                  repair_complete_day, 
-                                                  total_num_comps, 
-                                                  damaged_comps, 
-                                                  initial_damaged, 
-                                                  dependancy)    
-
-        ## Data
-        if unit['is_data_required'] == 1 and any(damage['fnc_filters']['data_unit'] | damage['fnc_filters']['data_main']):
-        # determine effect on funciton at this tenant unit
-        # any major damage to the unit level electrical equipment fails for this tenant unit
-            tenant_sys_recovery_day = np.nanmax(repair_complete_day * damage['fnc_filters']['data_unit'], axis=1)
-            recovery_day['data'][:,tu] = np.fmax(system_operation_day['building']['data_main'], tenant_sys_recovery_day)
-
-            # Consider effect of external power network
-                 
-            recovery_day['data'] = np.fmax(recovery_day['data'], system_operation_day['building']['electrical_main'].reshape(num_reals,1))
-    
-            # distribute effect to the components
-            comp_breakdowns['data'][:,:,tu] = np.fmax(system_operation_day['comp']['data_main'], np.array(repair_complete_day) * damage['fnc_filters']['data_unit'])
-
-        ## Post process for tenant-specific requirements 
-        # Zero out systems that are not required by the tenant
-        # Still need to calculate above due to dependancies between options
-        if unit['is_water_potable_required'] != 1:
-            recovery_day['water_potable'] = np.zeros([num_reals,num_units])
-            comp_breakdowns['water_potable'] = np.zeros([num_reals,num_comps,num_units])
-
-        if unit['is_water_sanitary_required'] != 1:
-            recovery_day['water_sanitary'] = np.zeros([num_reals,num_units])
-            comp_breakdowns['water_sanitary'] = np.zeros([num_reals,num_comps,num_units])
-
-        if unit['is_hvac_ventilation_required'] != 1:
-            recovery_day['hvac_ventilation'] = np.zeros([num_reals,num_units])
-            comp_breakdowns['hvac_ventilation'] = np.zeros([num_reals,num_comps,num_units])
-
-        if unit['is_hvac_heating_required'] != 1:
-            recovery_day['hvac_heating'] = np.zeros([num_reals,num_units])
-            comp_breakdowns['hvac_heating'] = np.zeros([num_reals,num_comps,num_units])
-
-        if unit['is_hvac_cooling_required'] != 1:
-            recovery_day['hvac_cooling'] = np.zeros([num_reals,num_units])
-            comp_breakdowns['hvac_cooling'] = np.zeros([num_reals,num_comps,num_units])
-
-        if unit['is_hvac_exhaust_required'] != 1:
-            recovery_day['hvac_exhaust'] = np.zeros([num_reals,num_units])
-            comp_breakdowns['hvac_exhaust'] = np.zeros([num_reals,num_comps,num_units])
-
-    return recovery_day, comp_breakdowns    
-
-
-def fn_combine_comp_breakdown(comp_ds_table, perform_targ_days, comp_names, reoccupancy, functional):
-    '''get the combined reoccupancy/functionality effect of components
-    
-    Parameters
-    ----------
-    comp_ds_table: table
-      contains basic component attributes
-    perform_targ_days: array
-      recovery time horizons to consider
-    comp_names: cell array
-      string ids of components to attribte recovery time to
-    reoccupancy: array [num reals x num comp_ds]
-      realizations of reoccupancy time broken down for each damage state of
-      each component
-    functional: array [num reals x num comp_ds]
-      realizations of functional recovery time broken down for each damage
-      state of each component
-    
-    Returns
-    -------
-    combined: array
-      Probability of recovering within time horizon for each component
-      considering both consequenses from reoccupancy and functional recovery'''
-    
-    import numpy as np
-    ## Method
-    combined = np.zeros([len(comp_names),len(perform_targ_days)])
-    max_reocc_func = np.fmax(reoccupancy, functional)
-    for c in range(len(comp_names)):
-        comp_filt = comp_ds_table['comp_id'] == comp_names[c] # find damage states associated with this component    
-        combined[c,:] = np.mean(np.nanmax(max_reocc_func[:,comp_filt], axis=1).reshape(len(max_reocc_func),1) > np.array(perform_targ_days).reshape(1, len(perform_targ_days)), axis=0)
-
-    return combined
-
-
-def fn_extract_recovery_metrics( tenant_unit_recovery_day, 
-                                 recovery_day, comp_breakdowns, comp_id, 
-                                 simulated_replacement_time):
-    '''Reformant tenant level recovery outcomes into outcomes at the building level, 
-    system level, and compoennt level
-    
-    Parameters
-    ----------
-    tenant_unit_recovery_day: array [num_reals x num_tenant_units]
-     simulated recovery day of each tenant unit
-     
-    recovery_day: dictionary
-     simulation of the number of days each fault tree event is affecting
-     recovery
-    comp_breakdowns: dictionary
-     simulation of each components contributions to each of the fault tree events 
-     
-    comp_id: cell array [1 x num comp damage states]
-     list of each fragility id associated with the per component damage
-     state structure of the damage object. With of array is the same as the
-     arrays in the comp_breakdowns structure
-     
-    simulated_replacement_time: array [num_reals x 1]
-     simulated time when the building needs to be replaced, and how long it
-     will take (in days). NaN represents no replacement needed (ie
-     building will be repaired)
-    Returns
-    -------
-    recovery['tenant_unit']['recovery_day']: array [num_reals x num_tenant_units]
-    simulated recovery day of each tenant unit
-    
-    recovery['building_level']['recovery_day']: array [num_reals x 1]
-    simulated recovery day of the building (all tenant units recovered)
-    
-    recovery['building_level']['initial_percent_affected']: array [num_reals x 1]
-    simulated fraction of the building with initial loss of
-    reoccupancy/function
-    
-    recovery['recovery_trajectory']['recovery_day']: array [num_reals x num recovery steps]
-    simulated recovery trajectory y-axis
-    
-    recovery['recovery_trajectory']['percent_recovered']: array [1 x num recovery steps]
-    recovery trajectory x-axis
-    
-    recovery['breakdowns']['system_breakdowns']: array [num fault tree events x target days]
-    fraction of realizations affected by various fault tree events beyond
-    specific target recovery days
-    
-    recovery['breakdowns']['component_breakdowns']: array [num components x target days]
-    fraction of realizations affected by various components beyond
-    specific target recovery days
-    
-    recovery['breakdowns']['perform_targ_days']: array [0 x target days]
-    specific target recovery days
-    
-    recovery['breakdowns']['system_names']: array [num fault tree events x 1]
-    name of each fault tree event
-    
-    recovery['breakdowns']['comp_names']: array [num components x 1]
-    fragility IDs of each component'''
-    
-    import numpy as np
-    
-    recovery={'tenant_unit' : {}, 'building_level' : {}, 'recovery_trajectory' : {}, 'breakdowns' : {}, 'partial' : {}}
-    
-    ## Initial Setup
-    num_units = np.size(tenant_unit_recovery_day,1)
-
-    # Define performance targets
-    perform_targ_days = [0, 3, 7, 14, 30, 60, 90, 120, 182, 270, 365]
-    #FZ# Maximum day for recovery amongs all tenant units and all realizations
-    recovery_day_max = max(np.nanmax(tenant_unit_recovery_day, axis=1))
-
-    #FZ# Append perform target days with more milestones if repair time exceeds 1 year  
-    if recovery_day_max <= 365:
-        perform_targ_days = perform_targ_days # Number of days for each performance target stripe
-    if recovery_day_max > 365:
-        num_years = (int(np.floor((recovery_day_max)/365)))
-        
-        # If number of complete years is more than 2
-        for yr in range(num_years-1):
-            quarters =[1,2,3]
-            for qtr in quarters:
-                perform_targ_days.append(365*(yr+1) + qtr * 90)
-            perform_targ_days.append(365*(yr+2))
-        
-        # For final incomplete year
-        num_quarters = int(np.floor((recovery_day_max - perform_targ_days[-1])/90))
-        for qtr in range(num_quarters):
-            perform_targ_days.append(perform_targ_days[-1] +  90)
-        perform_targ_days.append(recovery_day_max)
-        
-        
-    # Determine replacement cases
-    replace_cases = np.logical_not(np.isnan(simulated_replacement_time))
-    ''' Post process tenant-level recovery times
-      Overwrite NaNs in tenant_unit_day_functional
-      Only NaN where never had functional loss, therefore set to zero'''
-    tenant_unit_recovery_day[np.isnan(tenant_unit_recovery_day)] = 0
-    
-    # Overwrite building replacment cases to replacement time
-    tenant_unit_recovery_day[replace_cases,:] = np.array(simulated_replacement_time)[replace_cases].reshape(len(np.array(simulated_replacement_time)[replace_cases]),1)* np.ones([1,num_units])   
-   
-    ## Save building-level outputs to occupancy structure
-    # Tenant Unit level outputs
-    recovery['tenant_unit']['recovery_day'] = tenant_unit_recovery_day
-    
-    # Building level outputs
-    recovery['building_level']['recovery_day'] = np.nanmax(tenant_unit_recovery_day, axis=1)
-    recovery['building_level']['initial_percent_affected'] = np.mean(tenant_unit_recovery_day > 0, axis=1) # percent of building affected, not the percent of realizations
-    
-    ## Recovery Trajectory -- calcualte from the tenant breakdowns
-    recovery['recovery_trajectory']['recovery_day'] = np.sort(np.column_stack((tenant_unit_recovery_day, tenant_unit_recovery_day)), axis=1)
-    recovery['recovery_trajectory']['percent_recovered'] = np.sort(np.concatenate((np.arange(num_units), np.arange(1, num_units+1)))/num_units)
-    recovery['building_level']['perform_targ_days'] = perform_targ_days
-    recovery['building_level']['prob_of_target'] = np.mean(recovery['building_level']['recovery_day'].reshape(len(recovery['building_level']['recovery_day']),1) > np.array(perform_targ_days).reshape(1,len(perform_targ_days)), axis=0)
-    
-    
-    # Save specific breakdowns for red tags
-    if 'building_safety' in recovery_day.keys():
-        red_tag_day = recovery_day['building_safety']['red_tag']
-        red_tag_day[replace_cases] = np.array(simulated_replacement_time)[replace_cases]
-        recovery['building_level']['recovery_day_red_tag'] = red_tag_day
-
-    
-    ## Recovery Trajectory -- calcualte from the tenant breakdowns
-    recovery['recovery_trajectory']['recovery_day'] = np.sort(np.column_stack((tenant_unit_recovery_day, tenant_unit_recovery_day)), axis =1)
-    recovery['recovery_trajectory']['percent_recovered'] = np.sort(np.concatenate((np.arange(0, (num_units)), np.arange(1, (num_units+1))))) / num_units
-
-    #partial recovery
-    pct_recovered_targets = [0.1, 0.5, 0.75, 0.8, 1]
-    
-    # order the tennant recovery days so it's easier to see at what time the required numnber of units
-    # are required
-    ordered_tenant_repair_days = np.sort(tenant_unit_recovery_day, axis = 1)
-    
-    for i_pct in range(len(pct_recovered_targets)):
-        recovery['partial'][i_pct] = {}
-        target_recovery_ratio_units = pct_recovered_targets[i_pct]
-        recovery['partial'][i_pct]['target_recovery_ratio_units'] = target_recovery_ratio_units
-        recovery['partial'][i_pct]['target_recovery_day'] = perform_targ_days;
-        recovery['partial'][i_pct]['prob_of_target'] = {}
-        for i_targ_day in range(len(perform_targ_days)):
-            targ_day = perform_targ_days[i_targ_day]
-            # get the percentage of tenant units recovered at the given day
-            # pct_recovered_per_real = np.mean(np.sum(tenant_unit_recovery_day <= targ_day, axis=1) / num_units, axis=1);
-            pct_recovered_per_real = np.sum(tenant_unit_recovery_day <= targ_day, axis=1) / num_units
-            recovery['partial'][i_pct]['prob_of_target'][i_targ_day] = np.mean(pct_recovered_per_real < target_recovery_ratio_units)
-           
-            # how many units need to be repaired to meet the percent required 
-            reqd_units = int(np.ceil(num_units * target_recovery_ratio_units))
-            recovery['partial'][i_pct]['reqd_units'] = reqd_units
-            recovery['partial'][i_pct]['mean'] = np.mean(ordered_tenant_repair_days[:, reqd_units-1]) #FZ reqd_units - 1 because pytho indexing start from zero.
-            recovery['partial'][i_pct]['median'] = np.percentile(ordered_tenant_repair_days[:, reqd_units-1], 50)
-            recovery['partial'][i_pct]['fractile_75'] = np.percentile(ordered_tenant_repair_days[:, reqd_units-1], 75)
-            recovery['partial'][i_pct]['fractile_90'] = np.percentile(ordered_tenant_repair_days[:, reqd_units-1], 90)
-
-
-
-    ## Format and Save Component-level breakdowns
-    # Find the day each ds of each component stops affecting recovery for any story
-    
-    # Combine among all fault tree events
-    component_breakdowns_per_story = 0
-    fault_tree_events_LV1 = list(comp_breakdowns.keys())
-    for i in range(len(fault_tree_events_LV1)):
-        fault_tree_events_LV2 = list(comp_breakdowns[fault_tree_events_LV1[i]].keys())
-        for j in range(len(fault_tree_events_LV2)):
-            component_breakdowns_per_story = np.fmax(component_breakdowns_per_story, 
-                                                 comp_breakdowns[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]])
-
-    
-    # Combine among all stories
-    # aka time each component's DS affects recovery anywhere in the building
-    component_breakdowns = np.nanmax(component_breakdowns_per_story,axis=2)
-    
-    # Ignore repalcement cases
-    # component_breakdowns[replace_cases,:] = np.nan #FZ# conveted to nan
-    component_breakdowns = np.delete(component_breakdowns, replace_cases, 0)
-    ## Format and Save System-level breakdowns
-    # Find the day each system stops affecting recovery for any story
-    
-    # Combine among all fault tree events
-    system_breakdowns = {}
-    fault_tree_events_LV1 = list(recovery_day.keys())
-    for i in range(len(fault_tree_events_LV1)):
-        fault_tree_events_LV2 = list(recovery_day[fault_tree_events_LV1[i]].keys())
-        for j in range(len(fault_tree_events_LV2)):
-            # Combine among all stories or tenant units to represent the events
-            # effect anywhere in the building 
-            if len(np.shape(recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]]))>1:
-                building_recovery_day = np.nanmax(recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]], axis=1)
-            else:
-                building_recovery_day = recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]].reshape(len(recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]]),1)
-
-            # Ignore repalcement cases
-            if len(np.shape(building_recovery_day))>1:
-                # building_recovery_day[replace_cases,:] = np.nan #FZ# conveted to nan
-                building_recovery_day = np.delete(building_recovery_day, replace_cases, 0)
-            else:
-                # building_recovery_day[replace_cases] = np.nan #FZ# conveted to nan
-                building_recovery_day = np.delete(building_recovery_day, replace_cases)
-        
-            # Save per "system", which typically represents the fault tree level 2
-            if fault_tree_events_LV2[j] in system_breakdowns.keys():
-                # If this "system" has already been defined in another fault
-                # tree branch, combine togeter by taking the max (i.e., max
-                # days this system affects recovery anywhere in the building)
-                system_breakdowns[fault_tree_events_LV2[j]] =  np.fmax(
-                    system_breakdowns[fault_tree_events_LV2[j]],building_recovery_day.reshape(len(building_recovery_day),1))
-            else:
-                system_breakdowns[fault_tree_events_LV2[j]] = building_recovery_day.reshape(len(building_recovery_day),1)
-
-    
-    ## Format breakdowns as performance targets
-    system_names = list(system_breakdowns.keys())
-    
-    # pre-allocating variables
-    comps = np.unique(comp_id)
-    recovery['breakdowns']['system_breakdowns'] = np.zeros([len(system_names),len(perform_targ_days)])
-    recovery['breakdowns']['component_breakdowns'] = np.zeros([len(comps),len(perform_targ_days)])
-    
-    # Calculate fraction of realization each system affects recovery for each
-    # performance target time
-    for s in range(len(system_names)):
-        recovery['breakdowns']['system_breakdowns'][s,:] = np.mean(system_breakdowns[system_names[s]] > perform_targ_days, axis=0)
-
-    
-    # Calculate fraction of realization each component affects recovery for each
-    # performance target time
-    for c in range(len(comps)):
-        comp_filt = comp_id == comps[c] # find damage states associated with this component
-        recovery['breakdowns']['component_breakdowns'][c,:] = np.nanmean(np.max(component_breakdowns[:,comp_filt], axis=1).reshape(len(component_breakdowns),1) > perform_targ_days, axis=0)
-
-    # store this so we can properly overalap the reoccupancy and functionality
-    recovery['breakdowns']['component_breakdowns_all_reals'] = component_breakdowns
-    
-    # Save other variables
-    recovery['breakdowns']['perform_targ_days'] = perform_targ_days
-    recovery['breakdowns']['system_names'] = system_names
-    recovery['breakdowns']['comp_names'] = comps
-    
-    return recovery
\ No newline at end of file
diff --git a/src/atc138/impedance/__init__.py b/src/atc138/impedance/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/atc138/impedance/main_impedance_function.py b/src/atc138/impedance/main_impedance_function.py
deleted file mode 100644
index 0bb3334..0000000
--- a/src/atc138/impedance/main_impedance_function.py
+++ /dev/null
@@ -1,317 +0,0 @@
-def main_impeding_factors(damage, impedance_options, repair_cost_ratio_total, 
-                          repair_cost_ratio_engineering, inspection_trigger, 
-                          systems, tmp_repair_class, building_value, 
-                          impeding_factor_medians, include_flooding_impact):
-
-    '''Calculate ATC-138 impeding times for each system given simulation of damage
-    
-    Parameters (taken from Dustin's MATLAB file)
-    ----------
-    damage: dictionary
-      contains per damage state damage and loss data for each component in the building
-    
-    impedance_options: dictionary
-      general impedance assessment user inputs such as mitigation factors
-    
-    repair_cost_ratio: array [num_reals x 1]
-      total repair cost per realization normalized by building replacement
-      value
-    
-    inspection_trigger: logical array [num_reals x 1]
-      defines which realizations require inspection
-    
-    num_stories: int
-      Total number of building stories
-    
-    systems: DataFrame
-      data table containing information about each system's attributes
-    
-    building_value: number
-      The replacement value of the building, in USD
-   
-    impeding_factor_medians: table
-      median delays for various impeding factors
-   
-    surge_factor: number
-      amplification factor for imepding times due to materials and labor
-      impacts due to regional damage
-    
-    Returns
-    -------
-    impedingFactors['time_sys']: array [num_reals x num_sys]
-      Simulated total impeding time for each system
-    
-    impedingFactors['breakdown']: dictionary
-      feilds: 'inspection', 'financing', 'eng_mob', 'design', 'permitting', 'contractor_mob'
-      The simulated start day and complete day for each impeding factor,
-      broken down per system where applicable
-      
-    
-    Notes
-    -----
-    Correlation: Assuming factor-to-factor simulations are independent, but
-    within a given factor, simulated system impedance time is fully
-    correlated'''
-    
-    import numpy as np
-    from scipy.stats import truncnorm
-    
-    from . import other_impedance_functions
-
-    # Initialize parameters
-    num_reals = len(inspection_trigger)
-    num_sys = len(systems) # used height in MATLAB - Correlate with MATLAB at the time of running
-    
-    # Preallocate each impedance time
-    duration={
-    'inspection': np.zeros([num_reals, num_sys]),
-    'financing': np.zeros([num_reals, num_sys]),
-    'permit_rapid': np.zeros([num_reals, num_sys]),
-    'permit_full': np.zeros([num_reals, num_sys]),
-    'contractor_mob': np.zeros([num_reals, num_sys]),
-    'eng_mob': np.zeros([num_reals, num_sys]),
-    'design': np.zeros([num_reals, num_sys]),  
-    'long_lead': np.zeros([num_reals, num_sys]), 
-        }
-    
-    # System repair trigger
-    # are there any repairs needed for this system
-    # sys_repair_trigger = system_repair_time > 0
-    
-    # Create basic trucated standard normal distribution for later simulation
-    th_low = -impedance_options['impedance_truncation']
-    th_high = impedance_options['impedance_truncation']  
-    mu = 0
-    sigma = 1
-    trunc_pd = truncnorm((th_low - mu)/sigma, (th_high - mu)/sigma, loc=mu, scale=sigma)
-    beta = impedance_options['impedance_beta']
-
-    ## Calculate Demand Surge (if applicble)
-    if impedance_options['demand_surge']['include_surge'] ==1:
-        surge_factor = other_impedance_functions.fn_default_surge_factor(impedance_options['demand_surge']['is_dense_urban_area'], 
-                                               impedance_options['demand_surge']['site_pga'],
-                                               impedance_options['demand_surge']['pga_de'])
-    else:
-        surge_factor = 1
-
-
-    ## Parse through damage to determine which systems require repair
-    
-    sys_repair_trigger = {
-        'any': np.zeros([num_reals, num_sys]),
-        'rapid_permit' : np. zeros([num_reals, num_sys]),
-        'full_permit' : np.zeros([num_reals, num_sys]),
-        'redesign' : np. zeros([num_reals, num_sys]),
-        'flooding' : np. zeros(num_reals)
-        }
-    for sys in range(num_sys):
-        sys_filt = np.array(damage['comp_ds_table']['system']) == sys+1 #FZ +1 is done to coorrelate with python indexing starting from 0. 
-        for tu in range(len(damage['tenant_units'])): 
-            is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0, np.array(damage['tenant_units'][tu]['worker_days']) > 0) # There is damage that needs to be fixed
-            # Track if any damage exists that requires repair (assumes all
-            # damage requires repair)
-            sys_repair_trigger['any'][:,sys] = np.maximum(sys_repair_trigger['any'][:,sys] , np.amax((np.multiply( 1*(is_damaged) , 1*(sys_filt) )), axis=1))
-            
-            # Track if any damage exists that requires rapid permit per system
-            sys_repair_trigger['rapid_permit'][:,sys] = np.maximum(sys_repair_trigger['rapid_permit'][:,sys] , np.amax((np.multiply( 1*(is_damaged) , ( 1*(sys_filt) & 1*(damage['fnc_filters']['permit_rapid'])))), axis=1))
-            
-            # Track if any damage exists that requires full permit per system
-            sys_repair_trigger['full_permit'][:,sys] = np.maximum(sys_repair_trigger['full_permit'][:,sys] , np.amax((np.multiply( 1*(is_damaged) , ( 1*(sys_filt) & 1*(damage['fnc_filters']['permit_full'])))), axis=1))            
-            
-            # Track if any systems require redesign
-            sys_repair_trigger['redesign'][:,sys] = np.maximum(sys_repair_trigger['redesign'][:,sys] , np.amax((np.multiply( 1*(is_damaged) , ( 1*(sys_filt) & 1*(damage['fnc_filters']['redesign'])))), axis=1))         
-
-            # Track if any damage exists that triggers flooding
-            sys_repair_trigger['flooding'] = np.maximum(sys_repair_trigger['flooding'], np.amax((np.multiply( 1*(is_damaged) , ( 1*(sys_filt) & 1*(damage['fnc_filters']['causes_flooding'])))), axis=1)) 
-    
-    # other_impedance_functions.
-    # Simulate impedance time for each impedance factor 
-        
-    if impedance_options['include_impedance']['inspection'] == True:
-       
-        duration['inspection'] = other_impedance_functions.fn_inspection(impedance_options['mitigation']['is_essential_facility'],
-            impedance_options['mitigation']['is_borp_equivalent'], 
-            surge_factor, sys_repair_trigger['any'], inspection_trigger,
-            trunc_pd, beta, impeding_factor_medians)
-    
-    if impedance_options['include_impedance']['financing'] == True:
-       
-        duration['financing'] = other_impedance_functions.fn_financing(impedance_options['mitigation']['capital_available_ratio'],
-            impedance_options['mitigation']['funding_source'], 
-            surge_factor, sys_repair_trigger['any'], repair_cost_ratio_total,
-            trunc_pd, beta, impeding_factor_medians)
-        
-    if impedance_options['include_impedance']['permitting'] == True:
-       
-        duration['permit_rapid'], duration['permit_full'] = other_impedance_functions.fn_permitting(num_reals,
-            sys_repair_trigger, trunc_pd,
-            beta, impeding_factor_medians)
-    
-    
-    if impedance_options['include_impedance']['contractor'] == True:    
-        duration['contractor_mob'] = other_impedance_functions.fn_contractor(num_reals,
-            surge_factor, sys_repair_trigger['any'], trunc_pd, impedance_options['mitigation'])
-    
-    if impedance_options['include_impedance']['engineering'] == True:     
-        
-        duration['eng_mob'], duration['design'] = other_impedance_functions.fn_engineering( num_reals,
-            repair_cost_ratio_engineering, building_value, surge_factor,
-            sys_repair_trigger['redesign'],
-            impedance_options['mitigation']['is_engineer_on_retainer'],
-            impedance_options['system_design_time'], impedance_options['eng_design_min_days'],
-            impedance_options['eng_design_max_days'], trunc_pd, beta, impeding_factor_medians)
-
-    if impedance_options['include_impedance']['long_lead'] == True:
-        for sys in range(num_sys):
-            sys_filt = damage['comp_ds_table']['system'] == sys+1   #FZ +1 is done to coorrelate with python indexing starting from 0.
-            
-            # Simulate long lead times. Assume long lead times are correlated among
-            # all components within the system, but independant between systems
-            prob_sim = np.random.rand(num_reals, 1) # Truncated lognormal distribution (via standard normal simulation)
-            x_vals_std_n = trunc_pd.ppf(prob_sim) 
-            sim_long_lead = np.exp(x_vals_std_n * beta + np.log(np.array(damage['comp_ds_table']['long_lead_time'])))
-            
-            for tu in range(len(damage['tenant_units'])):
-                is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0, np.array(damage['tenant_units'][tu]['worker_days']) > 0)
-                
-                #Track if any damage exists that requires repair (assumes all
-                # damage requires repair). The long lead time for the system is
-                # the max long lead time for any component within the system
-                duration['long_lead'][:,sys] = np.maximum(duration['long_lead'][:,sys], 
-                                                np.nanmax(is_damaged * sys_filt * sim_long_lead, axis=1))
-
-
-
-    ## Aggregate experienced impedance time for each system/sequence and realization 
-    # Figure out when each impeding factor finishes
-    start_day = {'inspection' : np.zeros([num_reals,num_sys])}
-    complete_day = {'inspection' : duration['inspection']}
-    
-    start_day['financing'] = complete_day['inspection']
-    complete_day['financing'] = start_day['financing'] + duration['financing']
-    
-    start_day['eng_mob'] = np.maximum(complete_day['inspection'] ,start_day['financing'])
-    complete_day['eng_mob'] = start_day['eng_mob'] + duration['eng_mob']
-    
-    start_day['design'] = complete_day['eng_mob']
-    complete_day['design'] = start_day['design'] + duration['design']
-    
-    start_day['permit_rapid'] = complete_day['design']
-    complete_day['permit_rapid'] = start_day['permit_rapid'] + duration['permit_rapid']
-    
-    start_day['permit_full'] = complete_day['design'];
-    complete_day['permit_full'] = start_day['permit_full'] + duration['permit_full']
-    
-    start_day['contractor_mob'] = np.maximum(complete_day['inspection'], start_day['financing'])
-    complete_day['contractor_mob'] = start_day['contractor_mob'] + duration['contractor_mob']
-    
-    # if impedance_options['include_impedance']['long_lead'] == True:
-    start_day['long_lead'] = np.maximum(complete_day['inspection'] ,start_day['financing'])
-    complete_day['long_lead'] = start_day['long_lead'] + duration['long_lead']
-    
-    # Combine all impedance factors by system
-    impede_factors = np.array(list(complete_day.keys()));
-    impeding_factors = {'time_sys' : 0}
-    for i in range(len(impede_factors)):
-        impeding_factors['time_sys'] = np.maximum(impeding_factors['time_sys'],
-            complete_day[impede_factors[i]])
-    
-    ## Simulate Impeding Factors for Temporary Repairs
-    # Determine median times for each system
-    if impedance_options['mitigation']['contractor_relationship'] == 'retainer':
-        temp_impede_med = surge_factor * np.array(tmp_repair_class['impeding_time']) # days
-    elif impedance_options['mitigation']['contractor_relationship'] == 'good':
-        temp_impede_med = surge_factor * np.array(tmp_repair_class['impeding_time']) # days
-    elif impedance_options['mitigation']['contractor_relationship'] == 'none':
-        temp_impede_med = surge_factor * np.array(tmp_repair_class['impeding_time_no_contractor']) # days        
-    else:
-        sys.exit('error! PBEE_Recovery:RepairSchedule. Invalid contractor relationship type for impedance factor simulation')
-
-    # Find the which realization have damage that can be resolved by temp repairs
-    tmp_repair_class_trigger = np.zeros([num_reals, len(tmp_repair_class)])
-    for sys in range(len(tmp_repair_class)): 
-        sys_filt = np.array(damage['comp_ds_table']['tmp_repair_class']) == sys+1
-        for tu in range(len(damage['tenant_units'])):
-            is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0 , np.array( damage['tenant_units'][tu]['worker_days']) > 0)
-            # Track if any damage exists that requires repair (assumes all damage requires repair)
-            tmp_repair_class_trigger[:,sys] = np.maximum(tmp_repair_class_trigger[:,sys], np.nanmax(is_damaged * sys_filt, axis = 1))
-   
-    # Simulate Impedance Time
-    prob_sim = np.random.rand(num_reals, 1) # This assumes systems are correlated
-    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
-    tmp_impede_sys = np.exp(x_vals_std_n * beta + np.log(temp_impede_med))
-    
-    # Only use the simulated values for the realzation and system that
-    # trigger temporary repair damage
-    tmp_impede_sys = tmp_impede_sys * tmp_repair_class_trigger
-    
-    # Assume impedance always takes a full day
-    impeding_factors['temp_repair'] = {}
-    impeding_factors['temp_repair']['time_sys'] = np.ceil(tmp_impede_sys)
-    
-    ## Simulate impeding factors and temp repair that occur in parallel with temp repair schedule
-    # Temporary scaffolding for falling hazards
-    prob_sim = np.random.rand(num_reals)
-    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
-    scaffold_impede_time = np.ceil(surge_factor * np.exp(x_vals_std_n * beta + np.log(impedance_options['scaffolding_lead_time']))) # always round up
-    prob_sim = np.random.rand(num_reals) # repair time is not correlated to impedance time
-    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
-    scaffold_repair_time = np.exp(x_vals_std_n * beta + np.log(impedance_options['scaffolding_erect_time'])) 
-    impeding_factors['temp_repair']['scaffold_day'] = np.ceil(scaffold_impede_time + scaffold_repair_time) # round up (dont resolve issue on the same day repairs are complete)   
-    
-    # Door Unjamming
-    prob_sim = np.random.rand(num_reals)
-    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
-    impeding_factors['temp_repair']['door_racking_repair_day'] = np.ceil(surge_factor * np.exp(x_vals_std_n * beta + np.log(impedance_options['door_racking_repair_day']))) # always round up
-    
-    # Interior Flooding
-    
-    if include_flooding_impact == 1:
-        # Flooding Cleanup
-        prob_sim = np.random.rand(num_reals)
-        x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
-        impeding_factors['temp_repair']['flooding_cleanup_day'] = sys_repair_trigger['flooding'] * np.ceil(surge_factor * np.exp(x_vals_std_n * beta + np.log(impedance_options['flooding_cleanup_day']))) # always round up
-    
-        # Repair Flooding Damage
-        prob_sim = np.random.rand(num_reals)
-        x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
-        impeding_factors['temp_repair']['flooding_repair_day'] = sys_repair_trigger['flooding'] * np.ceil(surge_factor * np.exp(x_vals_std_n * beta + np.log(impedance_options['flooding_repair_day']))) # always round up
-
-
-    else: # zero out the flooding impedance (cleanup and repair)
-        impeding_factors['temp_repair']['flooding_cleanup_day'] = np.zeros(num_reals)
-        impeding_factors['temp_repair']['flooding_repair_day'] = np.zeros(num_reals)
-
-    ## Format Impedance times for Gantt Charts                                                     
-    # Full repair
-    impeding_factors['breakdowns'] = {}
-    
-    impeding_factors['breakdowns']['full'] = {'inspection':{'start_day': np.nanmax(start_day['inspection'],1), 
-                                                  'complete_day': np.nanmax(complete_day['inspection'],1)}} 
-    impeding_factors['breakdowns']['full']['financing'] = {'start_day': np.nanmax(start_day['financing'],1), 
-                                                  'complete_day': np.nanmax(complete_day['financing'],1)}                                    
-    impeding_factors['breakdowns']['full']['contractor_mob'] = {'start_day': np.nanmax(start_day['contractor_mob'],1), 
-                                                  'complete_day': np.nanmax(complete_day['contractor_mob'],1)}                                    
-    impeding_factors['breakdowns']['full']['eng_mob'] = {'start_day': np.nanmax(start_day['eng_mob'],1), 
-                                                  'complete_day': np.nanmax(complete_day['eng_mob'],1)}    
-    impeding_factors['breakdowns']['full']['design'] = {'start_day': np.nanmax(start_day['design'],1), 
-                                                  'complete_day': np.nanmax(complete_day['design'],1)}                                                        
-    impeding_factors['breakdowns']['full']['permit_rapid'] = {'start_day': np.nanmax(start_day['permit_rapid'],1), 
-                                                  'complete_day': np.nanmax(complete_day['permit_rapid'],1)}     
-    impeding_factors['breakdowns']['full']['permit_full'] = {'start_day': np.nanmax(start_day['permit_full'],1), 
-                                                  'complete_day': np.nanmax(complete_day['permit_full'],1)}    
-    
-    # Represent long lead times per system
-    # if impedance_options['include_impedance']['long_lead'] == True:
-    impeding_factors['breakdowns']['long_lead']= {}    
-    for s in range(len(systems)):
-        impeding_factors['breakdowns']['long_lead'][systems['name'][s]] = {'start_day' : start_day['long_lead'][:,s],
-                                                                           'complete_day' : complete_day['long_lead'][:,s]}
-    
-    # Temporary Repairs
-    impeding_factors['breakdowns']['temp'] = {}
-    for tmp in range(len(tmp_repair_class)):
-        impeding_factors['breakdowns']['temp'][tmp_repair_class['name_short'][tmp]] = {'start_day' : np.zeros(num_reals),
-                                                                                       'complete_day' : impeding_factors['temp_repair']['time_sys'][:,0]}
-        
-    return impeding_factors
\ No newline at end of file
diff --git a/src/atc138/impedance/other_impedance_functions.py b/src/atc138/impedance/other_impedance_functions.py
deleted file mode 100644
index fe0ce59..0000000
--- a/src/atc138/impedance/other_impedance_functions.py
+++ /dev/null
@@ -1,427 +0,0 @@
-import numpy as np
-import sys
-
-def fn_inspection(is_essential_facility, is_borp_equivalent, surge_factor, 
-                  sys_repair_trigger, inspection_trigger, trunc_pd, beta, 
-                  impeding_factor_medians):
-    '''Simulutes inspection time
-   
-    Parameters
-    ----------
-    is_essential_facility: logical
-    is the building deemed essential by the local jurisdiction
-      
-    is_borp_equivalent: logical
-    does the building have an inspector on retainer (BORP equivalent)
-      
-    surge_factor: number
-    amplification factor for impedance time based on a post disaster surge
-    in demand for skilled trades and construction supplies
-      
-    sys_repair_trigger: logical array [num_reals x num_systems]
-      systems that require repair for each realization
-      
-    inpsection_trigger: logical array [num_reals x 1]
-    defines which realizations require inspection
-      
-    trunc_pd: python (scipy) normal distribution object
-    standard normal distrubtion, truncated at upper and lower bounds
-      
-    beta: number
-    lognormal standard deviation (dispersion)
-      
-    impeding_factor_medians: DataFrame
-    median delays for various impeding factors
-   
-    Returns
-    -------
-    inspection_imped: array [num_reals x num_sys]
-    Simulated inspection time for each system '''
-    
-    ## Define inspection distribtuion parameters
-    inspection_medians = impeding_factor_medians.loc[impeding_factor_medians['factor'] == 'inspection']
-        
-    if is_borp_equivalent == True:
-        filt = np.array(inspection_medians['category']) =='borp'
-        # BORP equivalent is not affected by surge
-        median = np.array(inspection_medians['time_days'])[filt] 
-    elif is_essential_facility == True:
-        filt = np.array(inspection_medians['category']) =='essential'
-        # BORP equivalent is not affected by surge
-        median = np.array(inspection_medians['time_days'])[filt] * surge_factor
-    else:
-        filt = np.array(inspection_medians['category']) =='default'
-        median = np.array(inspection_medians['time_days'])[filt] * surge_factor        
-        
-    ## Simulate 
-    # Truncated lognormal distribution
-    num_reals = len(inspection_trigger)
-    prob_sim = np.random.rand(num_reals, 1)
-    x_vals_std_n = trunc_pd.ppf(prob_sim)
-    inspection_time = np.exp(x_vals_std_n * beta + np.log(median))
-    
-    # Only use realizations that require inpsection
-    inspection_time[~inspection_trigger.astype(dtype=bool)] = 0
-    
-    # Affects all systems that need repair
-    # Assume impedance always takes a full day
-    inspection_imped = np.ceil(inspection_time * sys_repair_trigger) 
-    
-    return inspection_imped
-
-def fn_financing(capital_available_ratio, funding_source, surge_factor, 
-                 sys_repair_trigger, repair_cost_ratio, trunc_pd, beta, 
-                 impeding_factor_medians):
-    
-    '''Simulutes financing time
-     
-    Parameters
-    ----------
-    capital_available_ratio: number
-    liquidable funding on hand to make repairs immediately after the
-    damaging event. Normalized by building replacment value.
-    
-    funding_source: string
-    accepted values: {'sba', 'private', 'insurance'}
-    type of funding source for required funds greater than the capital on
-    hand
-    
-    surge_factor: number
-    amplification factor for impedance time based on a post disaster surge
-    sys_repair_trigger: logical array [num_reals x num_systems]
-    systems that require repair for each realization
-   
-    repair_cost_ratio: array [num_reals x 1]
-    simulated building repair cost; normalized by building replacemnt
-    value.
-    
-    trunc_pd: python (scipy) normal distribution object
-    standard normal distrubtion, truncated at upper and lower bounds
-    
-    beta: number
-    lognormal standard deviation (dispersion)
-    
-    impeding_factor_medians: DataFrame
-    median delays for various impeding factors
-   
-    Returns
-    -------
-    financing_imped: array [num_reals x num_sys]
-    Simulated financing time for each system '''
-    
-    ## Define financing distribution parameters
-    # Median financing times
-    finance_medians = impeding_factor_medians.loc[impeding_factor_medians['factor'] == 'financing']
-    
-    # Required Financing
-    financing_trigger = np.array(repair_cost_ratio) > capital_available_ratio
-    
-    if funding_source == 'sba': # SBA Backed Loans
-        filt = np.array(finance_medians['category']) =='sba'
-        # BORP equivalent is not affected by surge
-        median = np.array(finance_medians['time_days'])[filt] * surge_factor # days
-        
-    elif funding_source == 'private': # SBA Backed Loans
-        filt = np.array(finance_medians['category']) =='private'
-        # BORP equivalent is not affected by surge
-        median = np.array(finance_medians['time_days'])[filt] # days, not affected by surge
-
-    elif funding_source == 'insurance': # SBA Backed Loans
-        filt = np.array(finance_medians['category']) =='insurance'
-        # BORP equivalent is not affected by surge
-        median = np.array(finance_medians['time_days'])[filt] # days, not affected by surge        
-        
-    else: # Review this in relation to what is desired by MATLAB code
-        print('Error! PBEE_Recovery:RepairSchedule - Invalid financing type for impedance factor simulation-funding_source')
-
-    ## Simulate
-    # Truncated lognormal distribution (via standard normal simulation)
-    num_reals = len(repair_cost_ratio)
-    prob_sim = np.random.rand(num_reals, 1)
-    x_vals_std_n = trunc_pd.ppf(prob_sim)
-    financing_time = np.exp(x_vals_std_n * beta + np.log(median))    
-
-    
-    # Only use realizations that require financing
-    financing_time[~financing_trigger] = 0
-
-    # Affects all systems that need repair
-    # Assume impedance always takes a full day
-    financing_imped = np.ceil(financing_time * sys_repair_trigger)
-    
-    return financing_imped 
-
-
-def fn_permitting( num_reals, sys_repair_trigger, trunc_pd, 
-                  beta, impeding_factor_medians):
-    
-    '''Simulutes permitting time
-    
-    Parameters
-    ----------
-    num_reals: int
-    number of Monte Carlo simulations assessed
-    
-    surge_factor: number
-    amplification factor for impedance time based on a post disaster surge
-    in demand for skilled trades and construction supplies
-    
-    sys_repair_trigger: dictionary
-    contains simulation data indicate if rapid permits or full permits are
-    required for each system
-    
-    trunc_pd: python (scipy) normal distribution object
-    standard normal distrubtion, truncated at upper and lower bounds
-    
-    beta: number
-    lognormal standard deviation (dispersion)
-    
-    impeding_factor_medians: DataFrame
-    median delays for various impeding factors
-    
-    Returns
-    -------
-    permitting_imped: array [num_reals x num_sys]
-    Simulated permitting time for each system'''
-    
-    ## Define permitting distribution parameters
-    # Find the median permit time for each system
-    permit_medians = impeding_factor_medians.loc[impeding_factor_medians['factor'] == 'permitting']   
-       
-    # Rapid Permits
-    filt = np.array(permit_medians['category']) =='rapid'
-    rapid_permit_median = np.array(permit_medians['time_days'])[filt] # days
-    
-    # Full Permits
-    filt = np.array(permit_medians['category']) =='full'
-    full_permit_median = np.array(permit_medians['time_days'])[filt]  # days
-    
-    ## Simulate
-    # Rapid Permits
-    prob_sim = np.random.rand(num_reals, 1) # This assumes systems are correlated
-    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
-    rapid_permit_time = np.exp(x_vals_std_n * beta + np.log(rapid_permit_median))
-    permitting_rapid = np.ceil(rapid_permit_time * sys_repair_trigger['rapid_permit']) # Assume impedance always takes a full day
-    
-    # Full Permits - simulated times are independent of rapid permit times
-    prob_sim = np.random.rand(num_reals, 1); # This assumes systems are correlated
-    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
-    full_permit_time = np.exp(x_vals_std_n * beta + np.log(full_permit_median))
-    permitting_full = np.ceil(full_permit_time * sys_repair_trigger['full_permit'])
-       
-    return permitting_rapid, permitting_full
-
-def fn_contractor(num_reals, surge_factor, sys_repair_trigger, trunc_pd, contractor_options):
-
-    '''Simulutes contractor mobilization time
-    
-    Parameters
-    ----------
-    num_sys: int
-    number of building systems considered in the assessment
-    
-    num_reals: int
-    number of Monte Carlo simulations assessed
-    
-    surge_factor: number
-    amplification factor for impedance time based on a post disaster surge
-    in demand for skilled trades and construction supplies
-    sys_repair_trigger: logical array [num_reals x num_systems]
-    systems that require repair for each realization
-    
-    systems: DataFrame
-    data table containing information about each system's attributes
-    
-    contractor_options: 
-    various options that controll the contracting impedance time
-    
-    Returns
-    -------
-    contractor_mob_imped: array [num_reals x num_sys]
-    Simulated contractor mobilization time for each system'''
-    
-    ## Define contractor distribution parameters
-    if contractor_options['contractor_relationship'] == 'retainer':
-        med = surge_factor * contractor_options['contractor_retainer_time']
-        beta = 0.4
-    elif contractor_options['contractor_relationship'] == 'good':
-        med = (1 + 0.5*(surge_factor-1)) * 3
-        beta = 0.4
-    elif contractor_options['contractor_relationship'] == 'none':
-        med = surge_factor * 42
-        beta = 0.8
-    else:
-        sys.exit('error! PBEE_Recovery:RepairSchedule. Invalid contractor relationship type for impedance factor simulation')
-
-    ## Set median for each realization
-    contr_med = med * np.ones([num_reals,1])
-    
-    ## Simulate Impedance Time
-    prob_sim = np.random.rand(num_reals, 1) # This assumes systems are correlated
-    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
-    contractor_mob_imped = np.exp(x_vals_std_n * beta + np.log(contr_med))
-    
-    # Only use the simulated values for the realzation and system that require permitting
-    contractor_mob_imped = contractor_mob_imped * sys_repair_trigger
-
-    # Amplify by the surge factor
-    # Assume impedance always takes a full day
-    contractor_mob_imped = np.ceil(contractor_mob_imped)
-    
-    return contractor_mob_imped
-
-
-def fn_engineering(num_reals, repair_cost_ratio, building_value, surge_factor, 
-                   redesign_trigger, is_engineer_on_retainer, user_options, 
-                   design_min, design_max, trunc_pd, beta, 
-                   impeding_factor_medians):
-       
-    '''Simulutes permitting time
-      
-    Parameters
-    ----------
-    num_reals: int
-    number of Monte Carlo simulations assessed
-    
-    repair_cost_ratio: array [num_reals x 1]
-    simulated building repair cost; normalized by building replacemnt
-    value.
-    
-    building_value: number
-    replacment value of building, in USD, non including land
-    
-    surge_factor: number
-    amplification factor for impedance time based on a post disaster surge
-    in demand for skilled trades and construction supplies
-    
-    redesign_trigger: logical array [num_reals x num_sys]
-    is redesign required for the given system
-    
-    is_engineer_on_retainer: logical
-    is there a pre-arranged agreement with an engineer for priorization of
-    redesign
-    
-    user_options: dictionary
-    contains paramters of system design time function, set by user
-    design_min: row vector [1 x n_systems]
-    lower bound on the median for each system
-    
-    design_max: row vector [1 x n_systems]
-    upper bound on the median for each system
-    
-    trunc_pd: python (scipy) normal distribution object
-    standard normal distrubtion, truncated at upper and lower bounds
-    
-    beta: number
-    lognormal standard deviation (dispersion)
-    
-    impeding_factor_medians: DataFrame
-    median delays for various impeding factors
-   
-    Returns
-    -------
-    eng_mob_imped: array [num_reals x num_sys]
-    Simulated enginering mobilization time for each system
-    
-    eng_design_imped: array [num_reals x num_sys]
-    Simulated enginering design time for each system
- 
-    Notes
-    ------
-    assumes engineering mobilization and re-design time are independant, but
-    are correlated between each system. In other words, you will have the same 
-    designers for the structural, stairs, exterior, and whatever other systems 
-    need design time, but the time it takes to spin up an engineer is not
-    related to the time it takes for them to complete the re-design.'''
-    
-    
-    ## Engineering Mobilization Time
-    # Mobilization medians
-    eng_mob_medians = impeding_factor_medians.loc[impeding_factor_medians['factor'] == 'engineering mobilization']      
-    
-    if is_engineer_on_retainer == True:
-        
-        
-        filt = np.array(eng_mob_medians['category']) =='retainer'
-        
-    else:
-        filt = np.array(eng_mob_medians['category']) =='default'
-        
-    median_eng_mob = surge_factor * np.array(eng_mob_medians['time_days'])[filt] # days
-    
-    # Truncated lognormal distribution (via standard normal simulation)
-    prob_sim = np.random.rand(num_reals, 1) # This assumes systems are correlated
-    x_vals_std_n = trunc_pd.ppf(prob_sim)
-    eng_mob_time = np.exp(x_vals_std_n * beta + np.log(median_eng_mob))
-
-    # Assume impedance always takes a full day
-    eng_mob_imped = np.ceil(eng_mob_time * redesign_trigger)
-    
-    ## Engineering Design Time
-    ## Calculate System Design Time
-    RC_total = np.array(repair_cost_ratio) * building_value;
-    SDT = RC_total * user_options['f'] / (user_options['r'] * user_options['t'] * user_options['w'])     
-    
-    design_med=np.fmin(np.fmax(SDT, design_min), design_max).reshape(len(SDT),1)
-     
-    # Truncated lognormal distribution (via standard normal simulation)
-    # Assumes engineering design time is independant of mobilization time
-    prob_sim = np.random.rand(num_reals,1) # This assumes systems are correlated
-    x_vals_std_n = trunc_pd.ppf(prob_sim)
-    eng_design_time = np.exp(x_vals_std_n * beta + np.log(design_med))
-    # Assume impedance always takes a full day
-    eng_design_imped = np.ceil(eng_design_time * redesign_trigger)
-    
-    return eng_mob_imped, eng_design_imped  
-
-def fn_default_surge_factor(is_dense_urban_area, pga, pga_de):
-    '''This function determines the surge factor based on the intensity of shaking
-      
-       Parameters
-       ----------
-       is_dense_urban_area: bool
-         if the building is in a dense urban area or not
-       pga: number
-         peak ground acceleration control point 1 (below which surge factor is 1)
-       pga_de: number
-         peak ground acceleration at the design intensity
-      
-      
-       Returns
-       -------
-       surge_factor: number
-         surge factor which amplified impedance factor times
-      
-       Notes
-       -----
-      
-           |                                         _____________ 3.0
-           |                                       /              
-           |                                      /               
-           |                                     /                
-           |                                    /                 
-           |                                   /                  
-           |                                  /     ______________ 1.5
-           |                                 /    / |             
-           |                                /   /   |             
-           |                               /  /     |             
-           |                              / /       |             
-           |                             //         |             
-       1.0 |  __________________________/           |             
-           |                            |           |             
-           |                            |           |             
-           -----------------------------+-----------+-------------
-                                      pga_1       pga_2'''
-
-    if is_dense_urban_area:
-        max_surge = 3
-    else:
-        max_surge = 1.5
-    
-    pga_1 = max(0.2, pga_de);
-    pga_2 = pga_1 * 1.5;
-    surge_factor = np.interp(min(max(pga, pga_1), pga_2), [pga_1, pga_2], [1, max_surge])
-    
-    return surge_factor
-
-    
\ No newline at end of file
diff --git a/src/atc138/input_builder.py b/src/atc138/input_builder.py
deleted file mode 100644
index 9cb9c7c..0000000
--- a/src/atc138/input_builder.py
+++ /dev/null
@@ -1,476 +0,0 @@
-
-import numpy as np
-import json
-import pandas as pd
-import os
-import re
-import sys
-
-def clean_types(obj):
-    """
-    Recursively convert numpy types to native Python types for JSON serialization,
-    preserving NaN as float('nan') for Numpy compatibility in the engine.
-    """
-    if isinstance(obj, dict):
-        return {k: clean_types(v) for k, v in obj.items()}
-    elif isinstance(obj, list):
-        return [clean_types(i) for i in obj]
-    elif isinstance(obj, np.ndarray):
-        return clean_types(obj.tolist())
-    elif isinstance(obj, (np.int64, np.int32, int)):
-        return int(obj)
-    elif isinstance(obj, (np.float64, np.float32, float)):
-         # Preserving NaN for numpy compatibility in downstream engine
-         return float(obj)
-    elif pd.isna(obj): 
-        # Handle standalone pandas/numpy NaNs/NaTs
-        return float('nan') 
-    return obj
-
-def recursive_update(d, u):
-    """
-    Recursively update dictionary d with values from u.
-    """
-    for k, v in u.items():
-        if isinstance(v, dict) and k in d and isinstance(d[k], dict):
-            recursive_update(d[k], v)
-        else:
-            d[k] = v
-    return d
-
-def load_custom_static_tables(model_dir, static_dir, filename):
-    path = os.path.join(model_dir, filename)
-    if os.path.exists(path):
-        print(f"found custom {filename} in inputs. Overriding static tables...")
-    else:
-        path = os.path.join(static_dir, filename)
-    return pd.read_csv(path)
-
-
-def build_simulated_inputs(model_dir):
-    """
-    Generates simulated_inputs dictionary from raw input files in the model directory.
-    Based on the original build_inputs.py script.
-    
-    Parameters
-    ----------
-    model_dir: string
-        Path to the directory containing raw input files.
-        
-    Returns
-    -------
-    simulated_inputs: dict
-        The complete dictionary of inputs.
-    """
-        
-    ''' PULL STATIC DATA
-    If static data tables exist in the input directory, use those. Else, use
-    the defaults in src/atc138/data/
-    '''
-    
-    static_data_dir = os.path.join(os.path.dirname(__file__), 'data')
-
-    component_attributes = load_custom_static_tables(
-        model_dir, static_data_dir, 'component_attributes.csv')
-
-    damage_state_attribute_mapping = load_custom_static_tables(
-        model_dir, static_data_dir, 'damage_state_attribute_mapping.csv')
-
-    subsystems = load_custom_static_tables(
-        model_dir, static_data_dir, 'subsystems.csv')
-
-    tenant_function_requirements = load_custom_static_tables(
-        model_dir, static_data_dir, 'tenant_function_requirements.csv')
-    
-    
-    ''' LOAD BUILDING DATA
-    This data is specific to the building model and will need to be created
-    for each assessment. Data is formated as json structures or csv tables'''
-    
-    # 1. Building Model: Basic data about the building being assessed
-    with open(os.path.join(model_dir, 'building_model.json'), 'r') as f:
-        building_model = json.load(f)
-    
-    # If number of stories is 1, change individual values to lists in order to work with later code
-    if building_model['num_stories'] == 1:
-        for key in ['area_per_story_sf', 'ht_per_story_ft', 'occupants_per_story', 'stairs_per_story', 'struct_bay_area_per_story']:
-            if not isinstance(building_model[key], list):
-                building_model[key] = [building_model[key]]
-    if building_model['num_stories'] == 1:
-        for key in ['edge_lengths']:
-            if not isinstance(building_model[key][0], list):
-                building_model[key] = [[building_model[key][0]], [building_model[key][1]]]
-    
-    # 2. List of tenant units within the building and their basic attributes
-    tenant_unit_list = pd.read_csv(os.path.join(model_dir, 'tenant_unit_list.csv'))
-    
-    
-    # 3. List of component and damage states ids associated with the damage
-    comp_ds_list = pd.read_csv(os.path.join(model_dir, 'comp_ds_list.csv'))
-    
-    # 4. List of component and damage states in the performance model
-    comp_population = pd.read_csv(os.path.join(model_dir, 'comp_population.csv'))
-    comp_header = list(comp_population.columns)
-    comp_list = np.array(comp_header[2:len(comp_header)])
-    comp_list= np.char.replace(np.array(comp_list),'_','.')
-    comp_list = comp_list.tolist()
-    # Remove suffixes from repated entries
-    for i in range(len(comp_list)):
-        if len(comp_list[i]) > 10:
-            comp_list[i]=comp_list[i][0:10]
-    building_model['comps'] = {'comp_list' : comp_list} #FZ# Component list has been added to building model dictionary.
-    
-    # Go through each story and assign component populations
-    drs = np.unique(np.array(comp_population['dir']))
-    
-    building_model['comps']['story'] = {}
-    for s in range (building_model['num_stories']):
-        building_model['comps']['story'][s] = {}
-        for d in range(len(drs)):
-            # [FIX] Robust key generation and missing data handling
-            current_dir = drs[d]
-            filt = np.logical_and(np.array(comp_population['story']) == s+1, np.array(comp_population['dir']) == current_dir)
-            
-            # Format key identifier using integer representation of direction to ensure consistency (e.g. qty_dir_1 not qty_dir_1.0)
-            try:
-                dir_key_suffix = str(int(current_dir))
-            except:
-                dir_key_suffix = str(current_dir)
-            
-            qty_data = comp_population.to_numpy()[filt,2:len(comp_header)]
-            
-            if qty_data.shape[0] > 0:
-                building_model['comps']['story'][s]['qty_dir_' + dir_key_suffix] = qty_data.tolist()[0]
-            else:
-                # Missing data for this story/direction, fill with zeros to avoid crashes
-                num_comps = len(comp_header) - 2
-                building_model['comps']['story'][s]['qty_dir_' + dir_key_suffix] = [0] * num_comps
-    
-    
-    # Set comp info table
-    comp_info = {'comp_id': [], 'comp_idx': [], 'structural_system': [], 'structural_system_alt': [], 'structural_series_id': []}
-    for c in range(len(comp_list)):
-        # Find the component attributes of this component
-        comp_attr_filt = component_attributes['fragility_id'] == comp_list[c]
-        if np.logical_not(sum(comp_attr_filt) == 1):
-            sys.exit('error!.Could not find component attrubutes')
-        else:
-            comp_attr = component_attributes.to_numpy()[comp_attr_filt,:]
-        comp_info['comp_id'].append(comp_list[c])
-        comp_info['comp_idx'].append(c) 
-        
-        # [FIX] Scalar extraction: Use scalar indexing [0, col] instead of slicing [0, [col]] to avoid array-to-scalar conversion errors
-        comp_info['structural_system'].append(float(comp_attr[0, component_attributes.columns.get_loc('structural_system')]))
-        comp_info['structural_system_alt'].append(float(comp_attr[0, component_attributes.columns.get_loc('structural_system_alt')]))
-        comp_info['structural_series_id'].append(float(comp_attr[0, component_attributes.columns.get_loc('structural_series_id')]))
-    
-    building_model['comps']['comp_table'] = comp_info
-    
-    
-    ''' LOAD SIMULATED DATA
-    This data is specific to the building performance at the assessed hazard intensity 
-    and will need to be created for each assessment. 
-    Data is formated as json structures.'''
-    
-    # 1. Simulated damage consequences - various building and story level consequences of simulated data, for each realization of the monte carlo simulation.
-    with open(os.path.join(model_dir, 'damage_consequences.json'), 'r') as f:
-        damage_consequences = json.load(f)
-    
-    # 2. Simulated utility downtimes for electrical, water, and gas networks for each realization of the monte carlo simulation.
-    # If file exists load it 
-    utility_path = os.path.join(model_dir, 'utility_downtime.json')
-    if os.path.exists(utility_path):
-        with open(utility_path, 'r') as f:
-            functionality = json.load(f)
-    # else If no data exist, assume there is no consequence of network downtime
-    else:
-        num_reals = len(damage_consequences["repair_cost_ratio_total"])
-        functionality = {'utilities' : {'electrical':[], 'water':[], 'gas':[]} } 
-    
-        for real in range(num_reals):
-            functionality['utilities']['electrical'].append(0)
-            functionality['utilities']['water'].append(0)
-            functionality['utilities']['gas'].append(0)
-    
-    
-    # 3. Simulated component damage per tenant unit for each realization of the monte carlo simulation
-    # 3. Simulated component damage per tenant unit for each realization of the monte carlo simulation
-    with open(os.path.join(model_dir, 'simulated_damage.json'), 'r') as f:
-        sim_damage = json.load(f)
-    
-    # Write in individual dictionaries part of larger 'damage' dictionary 
-    damage = {'story' : {}, 'tenant_units' : {}}
-    
-    if 'story' in list(sim_damage.keys()):
-        for tu in range(len(sim_damage['tenant_units'])):
-            damage['tenant_units'][tu] = sim_damage['tenant_units'][tu]
-    
-    
-    if 'tenant_units' in list(sim_damage.keys()):
-        for s in range(len(sim_damage['story'])):
-            damage['story'][s] = sim_damage['story'][s]
-        
-    ''' OPTIONAL INPUTS
-    Various assessment otpions. Set to default options in the
-    optional_inputs.json file. This file is expected to be in this input
-    directory. This file can be customized for each assessment if desired.'''
-    
-    # Load defaults first, then merge user overrides
-    pkg_dir = os.path.dirname(__file__)
-    defaults_path = os.path.join(pkg_dir, 'data', 'default_inputs.json')
-    with open(defaults_path, 'r') as f:
-        options = json.load(f)
-
-    user_options_path = os.path.join(model_dir, 'optional_inputs.json')
-    if os.path.exists(user_options_path):
-        with open(user_options_path, 'r') as f:
-            user_options = json.load(f)
-        options = recursive_update(options, user_options)
-        
-    functionality_options = options['functionality_options']
-    impedance_options = options['impedance_options']
-    repair_time_options = options['repair_time_options']
-
- 
-    
-    # Preallocate tenant unit table
-    tenant_units = tenant_unit_list.copy() # copy to avoid SettingWithCopy if passed dataframe
-    tenant_units['exterior'] = np.zeros(len(tenant_units))
-    tenant_units['interior'] = np.zeros(len(tenant_units))
-    tenant_units['occ_per_elev'] = np.zeros(len(tenant_units))
-    tenant_units['is_elevator_required'] = np.zeros(len(tenant_units))
-    tenant_units['is_electrical_required'] = np.zeros(len(tenant_units))
-    tenant_units['is_water_potable_required'] = np.zeros(len(tenant_units))
-    tenant_units['is_water_sanitary_required'] = np.zeros(len(tenant_units))
-    tenant_units['is_hvac_ventilation_required'] = np.zeros(len(tenant_units))
-    tenant_units['is_hvac_heating_required'] = np.zeros(len(tenant_units))
-    tenant_units['is_hvac_cooling_required'] = np.zeros(len(tenant_units))
-    tenant_units['is_hvac_exhaust_required'] = np.zeros(len(tenant_units))
-    tenant_units['is_data_required'] = np.zeros(len(tenant_units))  
-    '''Pull default tenant unit attributes for each tenant unit listed in the
-    tenant_unit_list'''
-    for tu in range(len(tenant_unit_list)):
-        occ_id = tenant_units.loc[tu, 'occupancy_id'] # Use .loc for pandas safety
-        fnc_requirements_filt = tenant_function_requirements['occupancy_id'] == occ_id
-        if sum(fnc_requirements_filt) != 1:
-            raise ValueError(f'error! Tenant Unit Requirements for Occupancy ID {occ_id} Not Found')
-        
-        # Accessing filtered rows. Original input builder used filtered Series assignment.
-        req_row = tenant_function_requirements[fnc_requirements_filt].iloc[0]
-        
-        tenant_units.loc[tu, 'exterior'] = req_row['exterior']
-        tenant_units.loc[tu, 'interior'] = req_row['interior']
-        tenant_units.loc[tu, 'occ_per_elev'] = req_row['occ_per_elev']
-        
-        story = tenant_units.loc[tu, 'story']
-        if req_row['is_elevator_required'] == 1 and req_row['max_walkable_story'] < story:
-            tenant_units.loc[tu, 'is_elevator_required'] = 1
-        else:
-            tenant_units.loc[tu, 'is_elevator_required'] = 0
-    
-        tenant_units.loc[tu, 'is_electrical_required'] = req_row['is_electrical_required']
-        tenant_units.loc[tu, 'is_water_potable_required'] = req_row['is_water_potable_required']
-        tenant_units.loc[tu, 'is_water_sanitary_required'] = req_row['is_water_sanitary_required']
-        tenant_units.loc[tu, 'is_hvac_ventilation_required'] = req_row['is_hvac_ventilation_required']
-        tenant_units.loc[tu, 'is_hvac_heating_required'] = req_row['is_hvac_heating_required']
-        tenant_units.loc[tu, 'is_hvac_cooling_required'] = req_row['is_hvac_cooling_required']
-        tenant_units.loc[tu, 'is_hvac_exhaust_required'] = req_row['is_hvac_exhaust_required']
-        tenant_units.loc[tu, 'is_data_required'] = req_row['is_data_required']    
-    '''Pull default component and damage state attributes for each component 
-    in the comp_ds_list'''
-    
-    ## Populate data for each damage state
-    comp_ds_info = {'comp_id' : [], 
-                    'comp_type_id' : [], 
-                    'comp_idx' : [], 
-                    'ds_seq_id' : [], 
-                    'ds_sub_id' : [],
-                    'system' : [],
-                    'subsystem_id' : [],
-                    'structural_system' : [],
-                    'structural_system_alt' : [],
-                    'structural_series_id' : [],
-                    'unit' : [],
-                    'unit_qty' : [],
-                    'service_location' : [],
-                    'is_sim_ds' : [],
-                    'safety_class' : [],
-                    'affects_envelope_safety' : [],
-                    'ext_falling_hazard' : [],
-                    'int_falling_hazard' : [],
-                    'global_hazardous_material' : [],
-                    'local_hazardous_material' : [],
-                    'weakens_fire_break' : [],
-                    'affects_access' : [],
-                    'damages_envelope_seal' : [],
-                    'affects_roof_function' : [],
-                    'obstructs_interior_space' : [],
-                    'impairs_system_operation' : [],
-                    'causes_flooding' : [],
-                    'interior_area_factor' : [],
-                    'interior_area_conversion_type' : [],
-                    'exterior_surface_area_factor' : [],
-                    'exterior_falling_length_factor' : [],
-                    'crew_size' : [],
-                    'permit_type' : [],
-                    'redesign' : [],
-                    'long_lead_time' : [],
-                    'requires_shoring' : [],
-                    'resolved_by_scaffolding' : [],
-                    'tmp_repair_class' : [],
-                    'tmp_repair_time_lower' : [],
-                    'tmp_repair_time_upper' : [],
-                    'tmp_repair_time_lower_qnty' : [],
-                    'tmp_repair_time_upper_qnty' : [],
-                    'tmp_crew_size' : [],
-                    'n1_redundancy' : [],
-                    'parallel_operation' :[],
-                    'redundancy_threshold' : []
-                    }
-    
-    for c in range(len(comp_ds_list)):
-        
-        # Find the component attributes of this component
-        comp_attr_filt = component_attributes['fragility_id'] == comp_ds_list['comp_id'][c]
-        if sum(comp_attr_filt) != 1:
-            raise ValueError('error! Could not find component attrubutes')
-        else:
-            comp_attr = component_attributes[comp_attr_filt].iloc[0] # Changed to Series access for robust scalar extraction
-                  
-        ds_comp_filt = []
-        for frag_reg in range(len(damage_state_attribute_mapping["fragility_id_regex"])):
-            regex_str = damage_state_attribute_mapping["fragility_id_regex"][frag_reg]
-            cid = comp_ds_list["comp_id"][c]
-            match = re.search(regex_str, cid)
-            
-
-            # Mapping components with attributes - Cjecks are based on mapping, comp_id, seq_id and sub_id
-
-            # Matching element ID using information contained in damage_state_attribute_mapping ["fragility_id_regex"]
-            if match and match.string == cid:
-                ds_comp_filt.append(True)
-            else:
-                ds_comp_filt.append(False)    
-        
-        ds_comp_filt = np.array(ds_comp_filt) # Convert to array for boolean indexing
-
-        ds_seq_filt = damage_state_attribute_mapping['ds_index'] == comp_ds_list['ds_seq_id'][c]
-        if comp_ds_list['ds_sub_id'][c] == 1:
-            ds_sub_filt = np.logical_or(damage_state_attribute_mapping['sub_ds_index'] ==1, damage_state_attribute_mapping['sub_ds_index'].isnull())
-        else:
-            ds_sub_filt = damage_state_attribute_mapping['sub_ds_index'] == comp_ds_list['ds_sub_id'][c]
-        
-        ds_filt = ds_comp_filt & ds_seq_filt & ds_sub_filt
-        
-        if sum(ds_filt) != 1:
-             raise ValueError('error!, Could not find damage state attrubutes')
-        else:
-            ds_attr = damage_state_attribute_mapping[ds_filt].iloc[0] # Series access
-        
-        ## Populate data for each damage state
-        # Basic Component and DS identifiers
-        comp_ds_info['comp_id'].append(comp_ds_list['comp_id'][c])
-        comp_ds_info['comp_type_id'].append(comp_ds_list['comp_id'][c][0:5]) # first 5 characters indicate the type
-        comp_ds_info['comp_idx'].append(c)
-        comp_ds_info['ds_seq_id'].append(ds_attr['ds_index'])
-        
-        sub_id = ds_attr['sub_ds_index']
-        if pd.isna(sub_id): sub_id = 1.0
-        comp_ds_info['ds_sub_id'].append(sub_id)
-            
-        # Set Component Attributes
-        comp_ds_info['system'].append(comp_attr['system_id'])
-        comp_ds_info['subsystem_id'].append(comp_attr['subsystem_id'])
-        comp_ds_info['structural_system'].append(comp_attr['structural_system'])
-        comp_ds_info['structural_system_alt'].append(comp_attr['structural_system_alt']) # component_attributes.csv does not have structural_system_alt field
-        comp_ds_info['structural_series_id'].append(comp_attr['structural_series_id'])
-        comp_ds_info['unit'].append(comp_attr['unit']) #FZ# Check w.r.t. matlab output
-        comp_ds_info['unit_qty'].append(comp_attr['unit_qty'])
-        comp_ds_info['service_location'].append(comp_attr['service_location']) #FZ# Check w.r.t. matlab output
-                   
-        # Set Damage State Attributes
-        # Map fields (legacy mapping logic preserved where simple)
-        comp_ds_info['is_sim_ds'].append(ds_attr['is_sim_ds'])
-        comp_ds_info['safety_class'].append(ds_attr['safety_class'])
-        comp_ds_info['affects_envelope_safety'].append(ds_attr['affects_envelope_safety'])
-        comp_ds_info['ext_falling_hazard'].append(ds_attr['exterior_falling_hazard'])
-        comp_ds_info['int_falling_hazard'].append(ds_attr['interior_falling_hazard'])
-        comp_ds_info['global_hazardous_material'].append(ds_attr['global_hazardous_material'])
-        comp_ds_info['local_hazardous_material'].append(ds_attr['local_hazardous_material'])
-        comp_ds_info['weakens_fire_break'].append(ds_attr['weakens_fire_break'])
-        comp_ds_info['affects_access'].append(ds_attr['affects_access'])
-        comp_ds_info['damages_envelope_seal'].append(ds_attr['damages_envelope_seal'])
-        comp_ds_info['affects_roof_function'].append(ds_attr['affects_roof_function'])
-        comp_ds_info['obstructs_interior_space'].append(ds_attr['obstructs_interior_space'])
-        comp_ds_info['impairs_system_operation'].append(ds_attr['impairs_system_operation'])
-        comp_ds_info['causes_flooding'].append(ds_attr['causes_flooding'])
-        comp_ds_info['interior_area_factor'].append(ds_attr['interior_area_factor'])
-        comp_ds_info['interior_area_conversion_type'].append(ds_attr['interior_area_conversion_type'])    
-        comp_ds_info['exterior_surface_area_factor'].append(ds_attr['exterior_surface_area_factor'])
-        comp_ds_info['exterior_falling_length_factor'].append(ds_attr['exterior_falling_length_factor'])                
-        comp_ds_info['crew_size'].append(ds_attr['crew_size'])
-        comp_ds_info['permit_type'].append(ds_attr['permit_type'])
-        comp_ds_info['redesign'].append(ds_attr['redesign'])
-        comp_ds_info['long_lead_time'].append(impedance_options['default_lead_time'] * ds_attr['long_lead'])
-        comp_ds_info['requires_shoring'].append(ds_attr['requires_shoring'])
-        comp_ds_info['resolved_by_scaffolding'].append(ds_attr['resolved_by_scaffolding'])
-        comp_ds_info['tmp_repair_class'].append(ds_attr['tmp_repair_class'])
-        comp_ds_info['tmp_repair_time_lower'].append(ds_attr['tmp_repair_time_lower'])
-        comp_ds_info['tmp_repair_time_upper'].append(ds_attr['tmp_repair_time_upper'])
-        
-        tmp_class = ds_attr['tmp_repair_class']
-        if tmp_class > 0:
-            comp_ds_info['tmp_repair_time_lower_qnty'].append(ds_attr['time_lower_quantity'])
-            comp_ds_info['tmp_repair_time_upper_qnty'].append(ds_attr['time_upper_quantity'])
-        else:
-            comp_ds_info['tmp_repair_time_lower_qnty'].append(np.nan)
-            comp_ds_info['tmp_repair_time_upper_qnty'].append(np.nan)
-    
-        comp_ds_info['tmp_crew_size'].append(ds_attr['tmp_crew_size'])
-    
-        # Subsystem attributes
-        sub_id = comp_attr['subsystem_id']
-        subsystem_filt = subsystems['id'] == sub_id
-        if sub_id == 0:
-            # No subsytem
-            comp_ds_info['n1_redundancy'].append(0)
-            comp_ds_info['parallel_operation'].append(0)
-            comp_ds_info['redundancy_threshold'].append(0)
-        elif sum(subsystem_filt) != 1:
-            sys.exit('error! Could not find damage state attrubutes')
-        else:
-            sub_row = subsystems[subsystem_filt].iloc[0]
-            comp_ds_info['n1_redundancy'].append(sub_row['n1_redundancy'])
-            comp_ds_info['parallel_operation'].append(sub_row['parallel_operation'])
-            comp_ds_info['redundancy_threshold'].append(sub_row['redundancy_threshold'])
-    
-    damage['comp_ds_table'] = comp_ds_info
-    
-    ## Check missing data
-    # Engineering Repair Cost Ratio - Assume is the sum of all component repair
-    # costs that require redesign
-    ## Check missing data
-    # Engineering Repair Cost Ratio - Assume is the sum of all component repair
-    # costs that require redesign
-    if 'repair_cost_ratio_engineering' not in damage_consequences:
-        eng_filt = np.array(damage['comp_ds_table']['redesign']).astype(bool)
-        # Re-calc using numpy arrays
-        costs = np.zeros(len(damage_consequences['repair_cost_ratio_total']))
-        if 'story' in sim_damage:
-             for s in range(len(sim_damage['story'])):
-                 story_costs = np.array(sim_damage['story'][s]['repair_cost'])
-                 costs += np.sum(story_costs[:, eng_filt], axis=1)
-        damage_consequences['repair_cost_ratio_engineering'] = costs.tolist()
-    
-
-    # Convert tenant_units dataframe to dictionary
-    tenant_units_dict = tenant_units.to_dict(orient='list')
-         
-    # Export output as simulated_inputs.json file 
-    
-    simulated_inputs = {'building_model' : building_model, 'damage' : damage, 'damage_consequences' : damage_consequences, 'functionality' : functionality, 'functionality_options' : functionality_options, 'impedance_options' : impedance_options, 'repair_time_options' : repair_time_options, 'tenant_units' : tenant_units_dict}
-    
-    # [FIX] Type cleaning using recursive helper (enables JSON serialization while preserving NaNs)
-    simulated_inputs = clean_types(simulated_inputs)
-    
-    return simulated_inputs
diff --git a/src/atc138/preprocessing/__init__.py b/src/atc138/preprocessing/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/atc138/preprocessing/main_preprocessing.py b/src/atc138/preprocessing/main_preprocessing.py
deleted file mode 100644
index 28ccaf5..0000000
--- a/src/atc138/preprocessing/main_preprocessing.py
+++ /dev/null
@@ -1,64 +0,0 @@
-def main_preprocessing(comp_ds_table, damage, repair_time_options, temp_repair_class, damage_consequences, num_stories):
-    '''Parameterize variables and simplifying assumptions to expedite the ATC138
-    recovery assessment
-
-    Parameters
-    ----------
-    comp_ds_table: DataFrame
-      various component attributes by damage state for each component 
-      within the performance model. Can be populated from the 
-      component_attribites.csv and damage_state_attribute_mapping.csv 
-      databases in the static_tables directory.  Each row of the table 
-      corresponds to each column of the simulated component damage arrays 
-      within damage['tenant_units'].
-    
-    damage: dictionary
-      contains simulated damage info and damage state attributes
-    
-    repair_time_options['allow_shoring']: logical
-      flag indicating whether or not shoring should be considered as a
-      temporary repair for local stability issues for structural components
-    
-    temp_repair_class: DataFrame
-      attributes of each temporary repair class to consider
-    
-    damage_consequences: dictionary
-      dictionary containing simulated building consequences, such as red
-    
-    num_stories: int
-      Integer number of stories in the building being assessed
-
-    Returns
-    -------
-    damage: dictionary
-      contains simulated damage info and damage state attributes
-    
-    temp_repair_class: DataFrame
-      attributes of each temporary repair class to consider
-    
-    damage_consequences: dictionary
-      dictionary containing simulated building consequences, such as red'''
-    
-    import numpy as np
-    from . import preprocessing_fns
-    
-    ## Define simulated damage in each tenant unit if not provided by the user
-    damage = preprocessing_fns.fn_populate_damage_per_tu(damage)
-    
-    ##Simulate damage per side, if not provided by the user
-    damage = preprocessing_fns.fn_simulate_damage_per_side(damage)
-    
-    ## Combine compoment attributes into recovery filters to expidite recovery assessment
-    damage['fnc_filters'] = preprocessing_fns.fn_create_fnc_filters(comp_ds_table)
-    
-    ## Simulate Temporary Repair Times for each component
-    damage, temp_repair_class = preprocessing_fns.fn_simulate_temp_worker_days(damage, temp_repair_class, repair_time_options)
-    
-    ## Set door racking damage if not provided by user
-    damage_consequences = preprocessing_fns.fn_define_door_racking(damage_consequences, num_stories)
-    
-    return damage, temp_repair_class, damage_consequences
-
-    
-  
-    
\ No newline at end of file
diff --git a/src/atc138/preprocessing/preprocessing_fns.py b/src/atc138/preprocessing/preprocessing_fns.py
deleted file mode 100644
index ccf4d0d..0000000
--- a/src/atc138/preprocessing/preprocessing_fns.py
+++ /dev/null
@@ -1,358 +0,0 @@
-import numpy as np
-
-def fn_populate_damage_per_tu(damage):
-    
-    '''Check to see if the damage.tenant_units variable has been defined. If
-    not, assume its the same as damage.story. This assumption creates a 1:1
-    coupling between stories and tenant_units (one tenant unit per story)
-    
-    Parameters
-    ----------
-    damage['story']: dictionary
-    contains simulated per component and damage state damage info
-    disagregated by story
-    
-    Returns
-    -------
-    contains simulated per component and damage state damage info
-    disagregated by tenant unit'''
-
-# If tenant unit damage is not provided by the user, assume its the same as per story damage
-
-    if ('tenant_units' in damage.keys()) == False:
-        damage['tenant_units'] = damage['story']
-        
-    return damage
-
-def fn_simulate_damage_per_side(damage):
-    '''Simulate damage per side for the exterior falling hazard check, if not 
-    provided by the user. Component location within a story is typically not 
-    Provided in most PBEE assessments. Therefore, this script make the rough 
-    assumptions to distribute damage to 4 sides, randomly.
-    
-    Parameters
-    ----------
-    damage: dictionary
-      contains simulated damage info and damage state attributes
-    
-    Returns
-    -------
-    damage: dictionary
-      contains simulated damage info and damage state attributes'''
-    
-    
-    # Simulate damage per side, if not provided by the user
-    if ('qnt_damaged_side_1' in damage['tenant_units'][0].keys()) == False:
-        num_reals = len(damage['tenant_units'][0]['qnt_damaged'])
-        
-        # Randomly split damage between 4 sides
-        # (this will only matter for cladding components)
-        ratio_damage_per_side = np.random.rand(num_reals,4) # assumes square footprint
-        ratio_damage_per_side = ratio_damage_per_side / np.sum(ratio_damage_per_side, axis=1).reshape(num_reals,1) # force it to add to one
-    
-        # Assing damage
-        for tu in range(len(damage['tenant_units'])):
-            for s in range(4):
-                damage['tenant_units'][tu]['qnt_damaged_side_' +str(s+1)] = ratio_damage_per_side[:,s].reshape(num_reals, 1) * np.array(damage['tenant_units'][tu]['qnt_damaged'])
-                
-    return damage
-
-def fn_create_fnc_filters(comp_ds_table):
-    '''Define function filter arrays that allow rapid sampling of simulated
-    damage for use within the fault tree analysis
-
-    Parameters
-    ----------
-    comp_ds_table: DataFrame
-      various component attributes by damage state for each component 
-      within the performance model. Can be populated from the 
-      component_attribites.csv and damage_state_attribute_mapping.csv 
-      databases in the static_tables directory.  Each row of the table 
-      corresponds to each column of the simulated component damage arrays 
-      within damage.tenant_units.
-
-    Returns
-    -------
-    fnc_filters: dictionary
-      each filter is a 1 x num_comp_ds array that can be used to sample
-      select types of damage from the damage['tenant unit'] or damage['story']
-      simulated damage arrays.'''
-
-
-    # Convert damage['comp_ds_table'] lists to numpy arrays
-    import numpy as np    
-    
-    comp_ds_table_keys = list(comp_ds_table.keys())
-    
-    for key in comp_ds_table_keys:
-        comp_ds_table[key] = np.array(comp_ds_table[key])
-        
-    fnc_filters = {}  
-
-
-    ## Impedance filters
-    # fnc_filters['permit_rapid'] = strcmp(comp_ds_table.permit_type, 'rapid');
-    # fnc_filters.permit_full = strcmp(comp_ds_table.permit_type, 'full');
-    # fnc_filters.redesign = comp_ds_table.redesign == 1;
-    fnc_filters['permit_rapid'] = comp_ds_table['permit_type'] == 'rapid'
-    fnc_filters['permit_full'] = comp_ds_table['permit_type'] == 'full'
-    fnc_filters['redesign'] = comp_ds_table['redesign'] == 1   
-    
-    # Building level filters
-    '''combine all damage state filters that have the potential to affect
-    function or reoccupancy, other than structural safety damage (for repair
-    prioritization)'''
-    
-    fnc_filters['affects_reoccupancy'] = comp_ds_table['affects_envelope_safety'].astype('bool') | comp_ds_table['ext_falling_hazard'].astype('bool')  | comp_ds_table['int_falling_hazard'].astype('bool')  | comp_ds_table['global_hazardous_material'].astype('bool')  | comp_ds_table['local_hazardous_material'].astype('bool') | comp_ds_table['affects_access'].astype('bool')
-    fnc_filters['affects_function'] = fnc_filters['affects_reoccupancy'] | comp_ds_table['damages_envelope_seal'].astype('bool') | comp_ds_table['obstructs_interior_space'].astype('bool') | comp_ds_table['impairs_system_operation'].astype('bool') 
-    
-    # Define when building has resolved its red tag (when all repairs are complete that may affect red tags)
-    # get any components that have the potential to cause red tag
-    fnc_filters['red_tag'] = comp_ds_table['safety_class'] > 0
-    
-    # Define when the building requires shoring from external falling hazards
-    fnc_filters['requires_shoring'] = comp_ds_table['requires_shoring'].astype('bool')
-    
-    # Define when the building has issues with internal flooding
-    fnc_filters['causes_flooding'] = comp_ds_table['causes_flooding'].astype('bool')
-    
-    ## System dependent filters
-    # fire suppresion system damage that affects entire building
-    fnc_filters['fire_building'] = np.logical_and(comp_ds_table['system'] == 9, np.logical_and(comp_ds_table['service_location'] == 'building' , comp_ds_table['impairs_system_operation'] == 1))
-    
-    # fire suppresion damage that affects each tenant unit
-    fnc_filters['fire_unit'] = np.logical_and(comp_ds_table['system'] == 9, np.logical_and(comp_ds_table['subsystem_id'] == 23, np.logical_and(comp_ds_table['service_location'] == 'unit' , comp_ds_table['impairs_system_operation'] == 1))) # pipe and brace branches (not spinkler heads)
-    fnc_filters['fire_drops'] = np.logical_and(comp_ds_table['subsystem_id'] == 23, comp_ds_table['impairs_system_operation'] == 1)
-    
-    # Hazardous materials
-    fnc_filters['global_hazardous_material'] = comp_ds_table['global_hazardous_material'].astype('bool')
-    fnc_filters['local_hazardous_material'] = comp_ds_table['local_hazardous_material'].astype('bool')
-    
-    # Stairs
-    fnc_filters['stairs'] = np.logical_and(comp_ds_table['affects_access'] == 1, comp_ds_table['system'] == 4)
-    
-    # Exterior enclosure damage
-    fnc_filters['exterior_safety_lf'] = np.logical_and(comp_ds_table['unit'] == 'lf', comp_ds_table['affects_envelope_safety'] == 1) # Components with perimeter linear feet units
-    fnc_filters['exterior_safety_sf'] = np.logical_and(comp_ds_table['unit'] == 'sf' , comp_ds_table['affects_envelope_safety'] == 1) # Components with perimeter square feet units
-    fnc_filters['exterior_safety_all'] = fnc_filters['exterior_safety_lf'] | fnc_filters['exterior_safety_sf']
-    
-    # Exterior Falling hazards
-    fnc_filters['ext_fall_haz_lf'] = np.logical_and(comp_ds_table['unit'] =='lf', comp_ds_table['ext_falling_hazard'] == 1) # Components with perimeter linear feet units
-    fnc_filters['ext_fall_haz_sf'] = np.logical_and(comp_ds_table['unit'] == 'sf', comp_ds_table['ext_falling_hazard'] == 1) # Components with square feet units
-    fnc_filters['ext_fall_haz_ea'] = np.logical_and(comp_ds_table['unit'] == 'each', comp_ds_table['ext_falling_hazard'] == 1) # Components with "each" units
-    fnc_filters['ext_fall_haz_all'] = fnc_filters['ext_fall_haz_lf'] | fnc_filters['ext_fall_haz_sf'] | fnc_filters['ext_fall_haz_ea']
-    
-    # Exterior enclosure envelope seal damage
-    fnc_filters['exterior_seal_lf'] = np.logical_and(comp_ds_table['unit'] == 'lf', comp_ds_table['damages_envelope_seal'] == 1) # Components with perimeter linear feet units
-    fnc_filters['exterior_seal_sf'] = np.logical_and(comp_ds_table['unit'] == 'sf', comp_ds_table['damages_envelope_seal'] == 1) # Components with perimeter square feet units
-    fnc_filters['exterior_seal_ea'] = np.logical_and(comp_ds_table['unit'] == 'each', comp_ds_table['damages_envelope_seal'] == 1) # Components with perimeter each units    
-    fnc_filters['exterior_seal_all'] = fnc_filters['exterior_seal_lf'] | fnc_filters['exterior_seal_sf'] | fnc_filters['exterior_seal_ea']
-    
-    # Roofing components
-    fnc_filters['roof_structure'] = np.logical_and(comp_ds_table['subsystem_id'] == 21, comp_ds_table['affects_roof_function'] == 1)
-    fnc_filters['roof_weatherproofing'] = np.logical_and(comp_ds_table['subsystem_id'] == 22, comp_ds_table['affects_roof_function'] == 1)
-    
-    # Interior falling hazards
-    fnc_filters['int_fall_haz_lf'] = np.logical_and(comp_ds_table['int_falling_hazard'] == 1, np.logical_and(comp_ds_table['unit'] == 'lf', comp_ds_table['interior_area_conversion_type'] == 'component')) # Interior components with perimeter feet units
-    fnc_filters['int_fall_haz_sf'] = np.logical_and(comp_ds_table['int_falling_hazard'] == 1, np.logical_and(comp_ds_table['unit'] == 'sf', comp_ds_table['interior_area_conversion_type'] == 'component')) # Interior components with area feet units
-    fnc_filters['int_fall_haz_ea'] = np.logical_and(comp_ds_table['int_falling_hazard'] == 1, np.logical_and(comp_ds_table['unit'] == 'each', comp_ds_table['interior_area_conversion_type'] == 'component')) # Interior components with "each" for units (just scale directly)    
-    fnc_filters['int_fall_haz_bay'] = np.logical_and(comp_ds_table['int_falling_hazard'] == 1, comp_ds_table['interior_area_conversion_type'] == 'bay') # structural damage that does not cause red tags but affects function
-    fnc_filters['int_fall_haz_build'] = np.logical_and(comp_ds_table['int_falling_hazard'] == 1, comp_ds_table['interior_area_conversion_type'] == 'building') # structural damage that does not cause red tags but affects funciton (this one should only be tilt-ups)
-    fnc_filters['int_fall_haz_all'] = fnc_filters['int_fall_haz_lf'] | fnc_filters['int_fall_haz_sf'] | fnc_filters['int_fall_haz_ea'] | fnc_filters['int_fall_haz_bay'] | fnc_filters['int_fall_haz_build'] 
-    fnc_filters['vert_instabilities'] = np.logical_and(comp_ds_table['system'] == 1, comp_ds_table['int_falling_hazard'] == 1)
-    
-    # Interior function damage 
-    fnc_filters['interior_function_lf'] = np.logical_and(comp_ds_table['obstructs_interior_space'] == 1, np.logical_and(comp_ds_table['unit'] == 'lf', comp_ds_table['interior_area_conversion_type'] == 'component')) 
-    fnc_filters['interior_function_sf'] = np.logical_and(comp_ds_table['obstructs_interior_space'] == 1, np.logical_and(comp_ds_table['unit'] == 'sf', comp_ds_table['interior_area_conversion_type'] == 'component')) 
-    fnc_filters['interior_function_ea'] = np.logical_and(comp_ds_table['obstructs_interior_space'] == 1, np.logical_and(comp_ds_table['unit'] == 'each', comp_ds_table['interior_area_conversion_type'] == 'component'))     
-    fnc_filters['interior_function_bay'] = np.logical_and(comp_ds_table['obstructs_interior_space'] == 1, comp_ds_table['interior_area_conversion_type'] == 'bay')
-    fnc_filters['interior_function_build'] = np.logical_and(comp_ds_table['obstructs_interior_space'] == 1, comp_ds_table['interior_area_conversion_type'] == 'building')  
-    fnc_filters['interior_function_all'] = fnc_filters['interior_function_lf'] | fnc_filters['interior_function_sf'] | fnc_filters['interior_function_ea'] | fnc_filters['interior_function_bay'] | fnc_filters['interior_function_build'] 
-    
-    # Elevators
-    fnc_filters['elevators'] = np.logical_and(comp_ds_table['system'] == 5, np.logical_and(comp_ds_table['impairs_system_operation'], comp_ds_table['subsystem_id'] != 2))
-    fnc_filters['elevator_mcs'] = np.logical_and(comp_ds_table['system'] == 5, np.logical_and(comp_ds_table['impairs_system_operation'], comp_ds_table['subsystem_id'] == 2))
-    
-    # Electrical system
-    fnc_filters['electrical_main'] = np.logical_and(comp_ds_table['system'] == 7, np.logical_and(comp_ds_table['subsystem_id'] == 1, np.logical_and(comp_ds_table['service_location'] == 'building', comp_ds_table['impairs_system_operation'] == 1)))
-    fnc_filters['electrical_unit'] = np.logical_and(comp_ds_table['system'] == 7, np.logical_and(comp_ds_table['subsystem_id'] == 1, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1)))
-    
-    # Water and Plumbing
-    # Potable Water Plumping
-    fnc_filters['water_main'] = np.logical_and(comp_ds_table['system'] == 6, np.logical_and(comp_ds_table['subsystem_id'] == 8, np.logical_and(comp_ds_table['service_location'] == 'building', comp_ds_table['impairs_system_operation'] == 1)))
-    fnc_filters['water_unit'] = np.logical_and(comp_ds_table['system'] == 6, np.logical_and(comp_ds_table['subsystem_id'] == 8, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1)))
-    
-    # Sanitary Plumbing
-    fnc_filters['sewer_main'] = np.logical_and(comp_ds_table['system'] == 6 , np.logical_and(comp_ds_table['subsystem_id'] == 9, np.logical_and(comp_ds_table['service_location'] == 'building', comp_ds_table['impairs_system_operation'] ==1)))
-    fnc_filters['sewer_unit'] = np.logical_and(comp_ds_table['system'] == 6 , np.logical_and(comp_ds_table['subsystem_id'] == 9, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] ==1)))
-
-  
-    # HVAC
-    fnc_filters['hvac'] = {}
-    
-    fnc_filters['hvac'] = {'building': 
-                        {'hvac_control': {},
-                         'hvac_heating': {},
-                         'hvac_cooling': {}
-                         },
-                       'tenant':
-                        {'hvac_ventilation': {},
-                         'hvac_heating': {},
-                         'hvac_cooling': {},
-                         'hvac_exhaust': {}
-                         }
-                        }
-                                                
-    
-    # HVAC: Control System
-    
-    fnc_filters['hvac']['building']['hvac_control']['mcs'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 2, comp_ds_table['impairs_system_operation'] == 1))
-    fnc_filters['hvac']['building']['hvac_control']['control_panel'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 20, comp_ds_table['impairs_system_operation'] == 1))
-
-    # HVAC: Ventilation
-    fnc_filters['hvac']['tenant']['hvac_ventilation']['duct_mains'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 24, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1)))
-    fnc_filters['hvac']['tenant']['hvac_ventilation']['duct_braches'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 4, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1)))
-    fnc_filters['hvac']['tenant']['hvac_ventilation']['in_line_fan'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 5, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1))) 
-    fnc_filters['hvac']['tenant']['hvac_ventilation']['duct_drops'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 6, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1)))
-    fnc_filters['hvac']['tenant']['hvac_ventilation']['ahu'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 19, comp_ds_table['impairs_system_operation'] == 1))
-    fnc_filters['hvac']['tenant']['hvac_ventilation']['rtu'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 17, comp_ds_table['impairs_system_operation'] == 1))
-
-    # HVAC: Heating
-    fnc_filters['hvac']['building']['hvac_heating']['piping'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 10, comp_ds_table['impairs_system_operation'] == 1))
-    fnc_filters['hvac']['tenant']['hvac_heating']['vav'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 7, comp_ds_table['impairs_system_operation'] == 1))
-
-    # HVAC: Cooling
-    fnc_filters['hvac']['building']['hvac_cooling']['piping'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 11, comp_ds_table['impairs_system_operation'] == 1))
-    fnc_filters['hvac']['building']['hvac_cooling']['chiller'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 15, comp_ds_table['impairs_system_operation'] == 1))
-    fnc_filters['hvac']['building']['hvac_cooling']['cooling_tower'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 16, comp_ds_table['impairs_system_operation'] == 1))
-    fnc_filters['hvac']['tenant']['hvac_cooling']['vav'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 7, comp_ds_table['impairs_system_operation'] == 1))
-
-    # HVAC: Exhaust
-    fnc_filters['hvac']['tenant']['hvac_exhaust']['exhaust_fan'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 18, comp_ds_table['impairs_system_operation'] == 1))
-    
-    # Data system
-    fnc_filters['data_main'] = np.logical_and(comp_ds_table['system'] == 11, np.logical_and(comp_ds_table['service_location'] == 'building', comp_ds_table['impairs_system_operation'] == 1))
-    fnc_filters['data_unit'] = np.logical_and(comp_ds_table['system'] == 11, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] ==1))
-    
-    # Horizontal Egress: Fire break partitions
-    fnc_filters['fire_break'] = np.logical_and(comp_ds_table['system'] == 3, comp_ds_table['weakens_fire_break'] == 1) # only collect interior fire break partitions
-    
-    return fnc_filters
-
-    
-def fn_simulate_temp_worker_days(damage, temp_repair_class, repair_time_options):
-    '''Simulate Temporary Repair Times for each component, if not already
-        defined by the user. In a perfect system this should be done alongside 
-        the other full repair time simulation. However, most PBEE assessments do
-        not contain information on temp repair times per component when they are
-        simulating damage and consequences. Therefore, this is decoupled from the
-        rest of the assessment and simulated here (if not already provided by the
-        user)
-        
-        Parameters
-        ----------
-        damage: dictionary
-          contains simulated damage info and damage state attributes
-        repair_time_options['allow_shoring']: logical
-          flag indicating whether or not shoring should be considered as a
-          temporary repair for local stability issues for structural components
-        temp_repair_class: DataFrame
-          attributes of each temporary repair class to consider
-        
-        Returns
-        -------
-        damage: dictionary
-          contains simulated damage info and damage state attributes
-        temp_repair_class: DataFrame
-          attributes of each temporary repair class to consider'''
-        
-        
-    ## Define Temporary Repair Times Options
-    # Turn of temp repairs if specificied by the user
-    if ('allow_tmp_repairs' in repair_time_options.keys()) == False:
-        damage['comp_ds_table']['tmp_repair_class'] = np.zeros(np.shape(damage['comp_ds_table']['tmp_repair_class']))
- 
-    # Set up temp_repair_class based on user inputs
-    if ('allow_shoring' in repair_time_options.keys()) == False:
-        temp_repair_class.drop(temp_repair_class.index[temp_repair_class['id'] == 5], inplace=True)
-
-    ## Simulate temp repair worker days per component
-    # if not already specified by the user
-    if ('tmp_worker_day' in damage['tenant_units'][0].keys()) == False:
-        # Find total number of damamged components
-        total_damaged = np.array(damage['tenant_units'][0]['qnt_damaged']).copy()
-        for tu in range(len(damage['tenant_units'])-1):
-            total_damaged = total_damaged + np.array(damage['tenant_units'][tu+1]['qnt_damaged'])
-    
-        # Aggregate the total number of damaged components accross each damage
-        # state in a component
-        num_reals = len(damage['tenant_units'][0]['qnt_damaged']) 
-        tmp_worker_days_per_unit = np.zeros([num_reals, len(damage['comp_ds_table']['comp_id'])])
-        for c in range(len(damage['comp_ds_table']['comp_id'])): # for each comp ds
-            # comp = comp_ds_table(c,:);
-            if damage['comp_ds_table']['tmp_repair_class'][c] > 0: # For damage that has temporary repair
-                filt = np.array(damage['comp_ds_table']['comp_id']) == damage['comp_ds_table']['comp_id'][c]
-                total_damaged_all_ds = np.sum(total_damaged[:,filt], axis=1)
-            
-                if float(damage['comp_ds_table']['tmp_repair_time_lower_qnty'][c]) == float(damage['comp_ds_table']['tmp_repair_time_upper_qnty'][c]):
-                    tmp_worker_days_per_unit[:,c] = damage['comp_ds_table']['tmp_repair_time_lower'][c]
-                else:
-                # Interpolate to get per unit temp repair times
-                    tmp_worker_days_per_unit[:,c] = np.interp(np.minimum(np.maximum(total_damaged_all_ds, float(damage['comp_ds_table']['tmp_repair_time_lower_qnty'][c])), float(damage['comp_ds_table']['tmp_repair_time_upper_qnty'][c])),
-                                np.array([float(damage['comp_ds_table']['tmp_repair_time_lower_qnty'][c]), float(damage['comp_ds_table']['tmp_repair_time_upper_qnty'][c])]),
-                                np.array([damage['comp_ds_table']['tmp_repair_time_lower'][c], damage['comp_ds_table']['tmp_repair_time_upper'][c]]),
-                                )
-            else:
-                tmp_worker_days_per_unit[:,c] = np.nan
-
-        '''Simulate uncertainty in per unit temp repair times
-        Assumes distribution is lognormal with beta = 0.4
-        Assumes time to repair all of a given component group is fully correlated, 
-        but independant between component groups''' 
-        
-        sim_tmp_worker_days_per_unit = np.random.lognormal(np.log(tmp_worker_days_per_unit), 0.4, np.shape(tmp_worker_days_per_unit))
-        
-        # Allocate per unit temp repair time among tenant units to calc worker days
-        # for each component
-        for tu in range(len(damage['tenant_units'])):
-            damage['tenant_units'][tu]['tmp_worker_day'] = np.array(damage['tenant_units'][tu]['qnt_damaged']) * sim_tmp_worker_days_per_unit
-
-    return damage, temp_repair_class
-
-def fn_define_door_racking(damage_consequences, num_stories):
-    '''Define building level damage consquences when not specificed by the user.
-    
-    Parameters
-    ----------
-    damage_consequences: dictionary
-      dictionary containing simulated building consequences, such as red
-      tags
-    num_stories: int
-      Integer number of stories in the building being assessed
-    
-    Returns
-    -------
-    damage_consequences['racked_stair_doors_per_story']: array, num real x num stories
-      simulated number of racked stairwell doors at each story
-    damage_consequences['racked_entry_doors_side_1']: array, num real x 1
-      simulated number of racked entry doors on one side of the building
-    damage_consequences['racked_entry_doors_side_2']: array, num real x 1
-      simulated number of racked entry doors on the other side of the building'''
-    
-    ## Set door racking damage if not provided by user
-    num_reals = len(damage_consequences['simulated_replacement_time'])
-    
-    # Assume there are no racked doors if not specified by the user
-    if ('racked_stair_doors_per_story' in damage_consequences.keys()) == False:
-        damage_consequences['racked_stair_doors_per_story'] = (np.zeros([num_reals,num_stories])).tolist() #num real x num stories
-
-    if ('racked_entry_doors_side_1' in damage_consequences.keys()) == False:
-        damage_consequences['racked_entry_doors_side_1'] = list(np.zeros(num_reals)) 
-
-    if ('racked_entry_doors_side_2' in damage_consequences.keys()) == False:
-        damage_consequences['racked_entry_doors_side_2'] = list(np.zeros(num_reals))
-    
-    return damage_consequences
-
-
-
diff --git a/src/atc138/red_tag.py b/src/atc138/red_tag.py
deleted file mode 100644
index 18603f5..0000000
--- a/src/atc138/red_tag.py
+++ /dev/null
@@ -1,168 +0,0 @@
-def fn_red_tag( calculate_red_tag, damage, comps, simulated_replacement_time, red_tag_options ):
-    '''Perform the ATC-138 functional recovery time assessement given similation
-    of component damage for a single shaking intensity
-    
-    Parameters
-    ----------
-    calculate_red_tag: logical
-      flag to indicate whether on not to calculate red tags based on
-      component damage. Typically assumed to be FALSE for small wood light
-      frame type structures.
-    damage: dictionary
-      contains per damage state damage and loss data for each component in the building
-    comps: dictionary
-      data structure component population info
-    simulated_replacement_time: array [num reals x 1]
-      Time 
-    
-    Returns
-    -------
-    red_tag: logical array [num_reals x 1]
-      indicates the realizations that have a red tag
-    red_tag_impact: logical array [num_reals x num_comp_ds]
-      indicates the realizations of various component damage states that
-      contribute to the cause of red tag
-    inspection_tag: logical array [num_reals x 1]
-      indicates the realizations that require inspection'''
-    
-    import numpy as np
-    
-    def simulate_tagging(damage, comps, sc_ids, sc_thresholds, red_tag_options ):
-        
-        red_tag_impact = np.zeros(np.shape(damage['tenant_units'][0]['qnt_damaged'])) # num reals by num comp_ds
-        red_tag_impact_cb = np.zeros(np.shape(damage['tenant_units'][0]['qnt_damaged'])) # num reals by num comp_ds, specific for counting red tags for coupling beams
-        
-        num_reals = len(red_tag_impact)
-        # Go through each structural system and calc the realizations where the
-        # building is red tagged
-        
-        sc_tag = np.zeros([num_reals,len(sc_ids)])
-        for sc in range(len(sc_ids)):
-            story_tag = np.zeros([num_reals,len(damage['story'])])
-
-            # resets the coupling beam counter for every safety class
-            coupling_beam_dmg = np.zeros([num_reals,3]) # three directions
-            coupling_beam_qty = np.zeros([1,3]) # three directions
-            red_tag_impact_cb_sc = np.zeros(np.shape(damage['tenant_units'][0]['qnt_damaged'])) # num reals by num comp_ds
-            
-            for s in range(len(damage['story'])):
-                sc_filt = np.array(damage['comp_ds_table']['safety_class']) >= sc_ids[sc]
-                
-                dir_tag = np.zeros([num_reals, 3])
-                for direc in [1,2,3]: # Fix assume there are three direction, where direction 3 = nondirectional
-                    sc_dmg = np.array(damage['story'][s]['qnt_damaged_dir_' + str(direc)]) * sc_filt
-                    num_comps = np.array(comps['story'][s]['qty_dir_' + str(direc)])
-        
-                    # For each structural system
-                    structural_systems = np.unique(np.array(damage['comp_ds_table']['structural_system'] + damage['comp_ds_table']['structural_system_alt']))
-                    structural_systems = np.delete(structural_systems, 0) # do not include components not assigned to a structural system
-                    
-                    sys_tag = np.zeros([num_reals, len(structural_systems)]).astype(bool)
-                    
-                    if red_tag_options['ignore_coupling_beam_for_red_tag']:
-                          # just ignore system 12 (coupling beams)
-                          structural_systems = structural_systems[structural_systems != 12]
-
-                    for sys in range(len(structural_systems)):
-                        ss_filt_ds = np.logical_or(np.array(damage['comp_ds_table']['structural_system']) == structural_systems[sys], np.array(damage['comp_ds_table']['structural_system_alt']) == structural_systems[sys])
-                        ss_filt_comp = np.logical_or(np.array(comps['comp_table']['structural_system']) == structural_systems[sys], np.array(comps['comp_table']['structural_system_alt']) == structural_systems[sys])
-        
-                        # Check damage among each series within this structural system
-                        series = np.unique(np.array(damage['comp_ds_table']['structural_series_id'])[ss_filt_ds])
-                        ser_dmg = np.zeros([num_reals,len(series)])
-                        ser_qty = np.zeros([1,len(series)])
-                        for ser in range(len(series)):
-                            ser_filt_ds = np.array(damage['comp_ds_table']['structural_series_id']) == series[ser] 
-                            ser_filt_comp = np.array(comps['comp_table']['structural_series_id']) == series[ser] 
-        
-                            # Total damage within this series and system
-                            ser_dmg[:,ser] = np.sum(sc_dmg[:,ser_filt_ds & ss_filt_ds], axis=1)
-        
-                            # Total number of components within this series and system
-                            ser_qty[:,ser] = np.sum(num_comps[ser_filt_comp & ss_filt_comp])
-        
-                        # Check if this system is causing a red tag
-                        if structural_systems[sys] == 12 and bool(red_tag_options.get('tag_coupling_beams_over_height', False)):
-                            # HARED CODED CHECK FOR COUPLING BEAMS
-                            # just do counts for now instead of adding to red tag per story check
-                            coupling_beam_dmg[:, direc-1] = coupling_beam_dmg[:,direc-1] + np.nanmax(ser_dmg, axis = 1)
-                            coupling_beam_qty[0, direc-1] = coupling_beam_qty[0,direc-1] + np.nanmax(ser_qty, axis = 1)
-                            red_tag_impact_cb_sc = np.fmax(red_tag_impact_cb_sc, ss_filt_ds.reshape(1,-1) * sc_filt.reshape(1,-1) * (sc_dmg>0))
-                        else:
-                            sys_dmg = np.nanmax(ser_dmg, axis = 1)
-                            sys_qty = np.nanmax(ser_qty, axis = 1)
-                            sys_ratio = sys_dmg / sys_qty
-                            sys_tag[:,sys] = sys_ratio > sc_thresholds[sc]
-                          
-                            '''Calculate the impact that each component has on red tag
-                            (boolean, 1 = affects red tag, 0 = does not affect)
-                            Take all damage that is part of this system at this story
-                            in this direction that is damaged to this safety class
-                            level, only where damage exceeds tagging threshold'''
-                            red_tag_impact = np.fmax(red_tag_impact, 1*sys_tag[:,sys].reshape(-1,1) * ss_filt_ds.reshape(1,-1) * sc_filt.reshape(1,-1) * (sc_dmg>0))
-                    
-                    
-                    # Combine across all systems in this direction
-                    dir_tag[:,direc-1] = np.nanmax(sys_tag, axis = 1) #FZ# -1 done to account for python ndexing starting from zero
-                
-        
-                # Combine across all directions at this story
-                story_tag[:,s] = np.nanmax(dir_tag, axis = 1)
-    
-            
-            # Combine across all stories for this safety class
-            sc_tag[:,sc] = np.nanmax(story_tag, axis = 1)
-
-            # Additional Hard Coded Check for Coupling Beams
-            if np.any(np.array(damage['comp_ds_table']['structural_system']) == 12):
-                CB_damage_ratio = coupling_beam_dmg / coupling_beam_qty
-                CB_tag_sc = np.nanmax(CB_damage_ratio, axis = 1) > sc_thresholds[sc]
-                sc_tag[:,sc] = np.fmax(sc_tag[:,sc], CB_tag_sc) # add it back to sum
-                red_tag_impact_cb = np.fmax(
-                    red_tag_impact_cb,
-                    red_tag_impact_cb_sc * sc_tag[:, sc].reshape(-1, 1)
-                )
-       
-        red_tag_impact = np.fmax(red_tag_impact, red_tag_impact_cb)
-    
-        # Combine all safety class checks into one simulated red tag
-        red_tag =  np.nanmax(sc_tag, axis = 1)
-        
-        return red_tag, red_tag_impact
-    
-    ##Initial Setup
-    '''Check to see if any components need the red tag check
-    if none of the components are assinged to structural systems, then
-    skip the red tag calc'''
-    if any(comps['comp_table']['structural_system']) == False:
-        calculate_red_tag = False
-    else:
-        calculate_red_tag = True
-    
-    ## Method
-    if calculate_red_tag == True:
-        # Simulate Red Tags
-        sc_ids = np.array([1, 2, 3, 4])
-        sc_thresholds = np.array([0.5, 0.25, 0.1, 0])
-        red_tag, red_tag_impact = simulate_tagging(damage, comps, sc_ids, sc_thresholds, red_tag_options )
-    
-        # Inspection is flagged for 50% of the red tag thresholds
-        inspection_thresholds = 0.5*sc_thresholds
-        inspection_tag, _ = simulate_tagging(damage, comps, sc_ids, inspection_thresholds, red_tag_options )
-    
-    else:
-        
-        # Do not calculate red tags based on component damage
-        num_reals, num_comp_ds = np.shape(np.array(damage['tenant_units'][0]['qnt_damaged']))
-        red_tag = np.zeros(num_reals)
-        red_tag_impact = np.zeros([num_reals,num_comp_ds])
-        inspection_tag = np.zeros(num_reals)
-    
-    
-    # Account for global red tag cases
-    replace_case = np.logical_not(np.isnan(np.array(simulated_replacement_time)))
-    red_tag[replace_case] = 1
-    
-    return red_tag, red_tag_impact, inspection_tag
-    
-    
diff --git a/src/atc138/repair_schedule/__init__.py b/src/atc138/repair_schedule/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/atc138/repair_schedule/main_repair_schedule.py b/src/atc138/repair_schedule/main_repair_schedule.py
deleted file mode 100644
index 1b788d2..0000000
--- a/src/atc138/repair_schedule/main_repair_schedule.py
+++ /dev/null
@@ -1,160 +0,0 @@
-def main_repair_schedule(damage, building_model, simulated_red_tags, 
-                         repair_time_options, systems, tmp_repair_class, 
-                         impeding_factors, simulated_replacement_time):
-    
-    '''Determine the repair time for a given damage simulation.
-      
-    Simulation of system and building level repair times based on a
-    simulation of impedance factor and repair scheduling algorithm.
-    Simulates the repair times for all realizations of a single ground motion
-    or intensity level.
-      
-    Parameters
-    ----------
-    damage: dictionary
-      contains per damage state damage and loss data for each component in the building
-      
-    building_model: dictionary
-      data structure containing general information about the building
-      
-    simulated_red_tags: logical array [num_realization x 1]
-      for each realization of the Monte Carlo simulation, is the structure
-      expected to be red-tagged
-      
-    repair_time_options: dictionary
-      general repair time options such as mitigation factors
-      
-    systems: DataFrame
-      attributes of structural and nonstructural building systems; data 
-      provided in static tables directory
-    
-    tmp_repair_class: DataFrame
-     data table containing information about each temporary repair class
- 
-    impeding_factors: dictionary
-      simulated impedance times
-      
-    surge_factor: number
-      amplification factor for temporary repair times due to materials and
-      labor impacts due to regional damage
-      
-    Returns
-    -------
-    damage: dictionary
-      contains per damage state damage and loss data for each component in the building
-    
-    worker_data: dictionary
-      simulated building-level worker allocations throught the repair process
-    
-    building_repair_schedule: dictionary
-      simulations of the building repair schedule, broken down by component,
-      story, and system'''
-
-
-    ## Initial Setup
-    # Import Packages
-    import math
-    import numpy as np
-    
-    from . import other_repair_schedule_functions
-    
-    ## initial Setup
-    # Define the maximum number of workers that can be on site, based on REDI
-    max_workers_per_building = min(max(math.floor(sum(building_model['area_per_story_sf'])* repair_time_options['max_workers_per_sqft_building'] + 10), repair_time_options['max_workers_building_min'])
-           , repair_time_options['max_workers_building_max'])
-
-    def fn_schedule_repairs(damage, repair_type, systems, max_workers_per_building, 
-                            max_workers_per_story, impeding_factors, 
-                            simulated_red_tags, tmp_repair_complete_day):
-        
-        ## Step 1 - Calculate the start and finish times for each system in isolation
-        # based on REDi repair sequencing and Yoo 2016 worker allocations
-        system_schedule = other_repair_schedule_functions.fn_calc_system_repair_time(damage, repair_type, systems, max_workers_per_building, max_workers_per_story)
-                                  
-        ## Step 2 - Set system repair priority
-        sys_idx_priority_matrix = other_repair_schedule_functions.fn_prioritize_systems( systems, repair_type, damage, tmp_repair_complete_day, impeding_factors)
-                                  
-        ## Step 3 - Define system repair constraints
-        sys_constraint_matrix= other_repair_schedule_functions.fn_set_repair_constraints( systems, repair_type, simulated_red_tags)
-        
-        ## Step 4 - Allocate workers among systems and determine the total days until repair is completed for each sequence
-        repair_complete_day_per_system, worker_data = other_repair_schedule_functions.fn_allocate_workers_systems(systems, system_schedule['system_totals']['repair_days'], system_schedule['system_totals']['num_workers'],
-             max_workers_per_building, sys_idx_priority_matrix, sys_constraint_matrix,
-            simulated_red_tags, impeding_factors['time_sys'])
-                                  
-        ## Step 5 - Format outputs for Functionality calculations
-        damage_recovery = other_repair_schedule_functions.fn_restructure_repair_schedule( damage, system_schedule,
-                     repair_complete_day_per_system, systems, repair_type, simulated_red_tags)
-        
-        return damage_recovery, worker_data
-    
-    
-    ## Determine repair schedule per system for Temporary Repairs 
-    # Define the maximum number of workers that can be on any given story
-    max_workers_per_story = np.ceil(np.array(building_model['area_per_story_sf']) * repair_time_options['max_workers_per_sqft_story_temp_repair'])
-    
-    # Temporary Repairs
-    repair_type = 'temp'
-    tmp_damage, tmp_worker_data = fn_schedule_repairs(damage, repair_type, 
-                                                      tmp_repair_class, 
-                                                      max_workers_per_building, 
-                                                      max_workers_per_story,
-                                                      impeding_factors['temp_repair'], 
-                                                      simulated_red_tags, [])
-    
-    tmp_damage = tmp_damage.copy()
-    # Calculate the max temp repair complete day for each component (anywhere in building)
-    tmp_repair_complete_day = np.empty(np.shape(damage['tenant_units'][0]['tmp_worker_day']))
-    tmp_repair_complete_day[:] = np.nan
-    # NaN = Never damaged
-    # Inf  = Damage not resolved by temp repair
-
-    for tu in range(len(tmp_damage)):
-        tmp_repair_complete_day = np.fmax(tmp_repair_complete_day, tmp_damage[tu]['repair_complete_day'])
-    
-    ## Determine repair schedule per system for Full Repairs 
-    # Define the maximum number of workers that can be on any given story
-    max_workers_per_story = np. ceil(np.array(building_model['area_per_story_sf']) * repair_time_options['max_workers_per_sqft_story'])
-       
-    # Full Repairs   
-    repair_type = 'full'
-    full_damage, worker_data = fn_schedule_repairs(damage, repair_type, systems, 
-                                              max_workers_per_building, 
-                                              max_workers_per_story, 
-                                              impeding_factors, 
-                                              simulated_red_tags, 
-                                              tmp_repair_complete_day)
-
-
-    ## Combine temp and full repair schedules
-    for tu in range(len(full_damage)):
-        damage['tenant_units'][tu]['recovery'] = full_damage[tu]
-        # Repair time is the lesser of the full repair and temp repair times
-        damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp'] = np.fmin(damage['tenant_units'][tu]['recovery']['repair_complete_day'],
-                                                                                  tmp_damage[tu]['repair_complete_day'])
-        
-        # Temporary Repair Times control if temporary repair times are less than the full repair time
-        tmp_day_controls = tmp_damage[tu]['repair_complete_day'] < damage['tenant_units'][tu]['recovery']['repair_complete_day']
-        # Repair start day is set to the temp repair start day when temp repairs control
-        damage['tenant_units'][tu]['recovery']['repair_start_day_w_tmp'] = damage['tenant_units'][tu]['recovery']['repair_start_day'].copy()
-        damage['tenant_units'][tu]['recovery']['repair_start_day_w_tmp'][tmp_day_controls] = tmp_damage[tu]['repair_start_day'][tmp_day_controls].copy()
-
-
-    
-    temporary_damage = { 'tenant_units': {} }
-    for tu in range(len(full_damage)):
-        temporary_damage['tenant_units'][tu]={}
-        temporary_damage['tenant_units'][tu]['recovery'] = tmp_damage[tu]
-    
-    temporary_damage['comp_ds_table'] = damage['comp_ds_table'].copy()
-    
-    ## Format Outputs 
-    # Format Start and Stop Time Data for Gantt Chart plots 
-    # This is also the main data structure used for calculating full repair time outputs
-    building_repair_schedule = {}
-    building_repair_schedule['full'] = other_repair_schedule_functions.fn_format_gantt_chart_data(damage, systems, simulated_replacement_time)
-    building_repair_schedule['temp'] = other_repair_schedule_functions.fn_format_gantt_chart_data(temporary_damage, systems, simulated_replacement_time)
- 
-    return damage, worker_data, building_repair_schedule
-    
-
diff --git a/src/atc138/repair_schedule/other_repair_schedule_functions.py b/src/atc138/repair_schedule/other_repair_schedule_functions.py
deleted file mode 100644
index 4a1aa41..0000000
--- a/src/atc138/repair_schedule/other_repair_schedule_functions.py
+++ /dev/null
@@ -1,900 +0,0 @@
-
-"""
-Other repair schedule functions called by the main repair schedule function
-"""
-import numpy as np
-
-def fn_allocate_workers_stories(total_worker_days, required_workers_per_story, 
-                                average_crew_size, max_crews_building, 
-                                max_workers_per_building):
-
-    '''For a given building system, allocate crews to each story to
-    determine the system level repair time on each story; not considering
-    impeding factors or construction constraints with other systems.
-      
-      
-    Parameters
-    ----------
-    total_worker_days: [num reals x num stories]
-      The total worker days needed to repair damage each story for this
-      sequence
-      
-    required_workers_per_story: array [num reals x num stories]
-      Number of workers required to repair damage in each story
-      
-    average_crew_size: array [num reals x num stories]
-      Average crew size required to repair damage in each story
-      
-    max_crews_building: logical array [num reals x 1]
-      maximum number of crews allowed in the building for this system
-      
-    max_workers_per_building: int
-      maximum number of workers allowed in the buildings at a given time
-      
-    Returns
-    -------
-    repair_start_day: array [num reals x num stories]
-      The number of days from the start of repair of this system until the repair of this system 
-      starts on each story
-      
-    repair_complete_day: array [num reals x num stories]
-      The number of days from the start of repair of this system until each story is
-      repaired for damage in this system
-      
-    max_workers_per_story: array [num reals x num stories]
-      number of workers required for the repair of this story and system as
-      
-    Notes
-    -----'''
-    import sys
-    
-    ## Initial Setup
-    num_reals, num_stories = np.shape(total_worker_days)
-    repair_complete_day = np.zeros([num_reals,num_stories])
-    repair_start_day = np.empty([num_reals,num_stories])
-    repair_start_day[:] = np.nan
-    max_workers_per_story = np.zeros([num_reals,num_stories])
-    
-    ## Allocate workers to each story
-    # Loop through iterations of time reduce damage on each story based on assigned workers
-    iter = 0;
-    while sum(sum(total_worker_days)) > 0.01:
-        iter = iter + 1; 
-        if iter > 1000: # keep the while loop pandemic contained
-            # error('PBEE_Recovery:RepairSchedule', 'Could not converge worker allocations for among stories in sequence');
-            sys.exit('Error! could not converge worker allocations for among stories in sequence')
-    
-        # Determine the available workers in the building
-        available_workers_in_building = max_workers_per_building * np.ones([num_reals])
-        assigned_workers_per_story = np.zeros([num_reals,num_stories])
-        assigned_crews_per_story = np.zeros([num_reals,num_stories]) 
-    
-        # Define where needs repair
-        needs_repair = total_worker_days > 0
-    
-        # Defined Required Workers
-        required_workers_per_story = needs_repair * required_workers_per_story.copy()
-        
-        '''Assign Workers to each story -- assumes that we wont drop the number
-        of crews in order to meet worker per sqft limitations, and instead
-        wait until more workers are made available'''
-        
-        for s in range(num_stories):
-            # Are there enough workers to assign a crew
-            sufficient_workers = required_workers_per_story[:,s] <= available_workers_in_building
-            
-            # Assign Workers to this story
-            assigned_workers_per_story[sufficient_workers,s] = required_workers_per_story[sufficient_workers,s]
-            assigned_crews_per_story[:,s] = assigned_workers_per_story[:,s] / average_crew_size[:,s]
-            assigned_crews_per_story[np.isnan(assigned_crews_per_story)] = 0
-            num_crews_in_building = np.sum(assigned_crews_per_story, axis=1)
-            exceeded_max_crews = num_crews_in_building > max_crews_building
-            assigned_workers_per_story[exceeded_max_crews,s] = 0 # don't assign workers if we have exceeded the number of crews allowed in the building
-            
-            # Define Available Workers
-            available_workers_in_building = available_workers_in_building - assigned_workers_per_story[:,s]  
-        
-        
-        # Define the start of repairs for each story
-        start_repair_filt = np.isnan(repair_start_day) & (assigned_workers_per_story > 0)
-        
-        max_day_completed_so_far = np.transpose(np.multiply(np.amax(repair_complete_day, axis=1), np.ones([num_stories,num_reals]))) #FZ# transpose done to align the arrays for operation
-         
-        repair_start_day[start_repair_filt] =  max_day_completed_so_far[start_repair_filt]
-        
-        # Calculate the time associated with this increment of the while loop
-        in_progress = assigned_workers_per_story > 0 # stories where work is being done
-        total_repair_days = np.empty(np.shape(in_progress)) 
-        total_repair_days[:] = np.inf # pre-allocate with inf's becuase we take a min later
-        total_repair_days[in_progress] = total_worker_days[in_progress] / assigned_workers_per_story[in_progress]
-        delta_days = np.amin(total_repair_days,axis=1) # time increment is whatever in-progress story that finishes first 
-        delta_days[np.isinf(delta_days)] = 0 # Replace infs from real that has no repair with zero       
-        delta_worker_days = np.transpose(np.multiply(np.transpose(assigned_workers_per_story), delta_days)) # time increment is whatever in-progress story that finishes first #FZ# transpose done fto align the arrays for operation
-        total_worker_days[in_progress] = np.maximum(total_worker_days[in_progress] - delta_worker_days[in_progress], 0)
-        indx_neg_remaining = total_worker_days < 0.001 # find instances of remaining time that are excessively small and don't represent realistic amount of work
-        total_worker_days[indx_neg_remaining] = 0  # zero remaining work that is negligible as defined above
-        
-        # Define Start and Stop of Repair for each story in each sequence
-        repair_complete_day = repair_complete_day + np.transpose(np.multiply(delta_days, np.transpose(1*needs_repair))) #FZ# transpose done fto align the arrays for operation
-         
-        # Max Crew Size for use in later function
-        max_workers_per_story = np.maximum(max_workers_per_story , assigned_workers_per_story)
-    
-    return repair_start_day, repair_complete_day, max_workers_per_story
-    
-
-def fn_calc_system_repair_time(damage, repair_type, systems, max_workers_per_building, max_workers_per_story):
-    ''' From Dustin's work
-    Determine the repair time for each system if repaired in isolation 
-      
-       Parameters
-       ----------
-       damage: dictionary
-         contains per damage state damage and loss data for each component in the building
-       
-       repair_type: string
-         String identifier indicating whether the repair are temporary or full
-  
-       systems: DataFrame
-         data table containing information about each system's attributes
-         
-       max_workers_per_building: int
-         maximum number of workers allowed in the building at once
-         
-       max_workers_per_story: array [1 x num_stories]
-         maximum number of workers allowed in each story at once
-      
-       Returns
-       -------
-       schedule['per_system'][sys]['repair_start_day'] [num reals x num stories]
-         The number of days from the start of repair of a specifc system until
-         the start of repair of each story for that given system (i.e. the day 
-         of the start of repairs relative the to start of this system ... e.g.
-         story 1 should always be zero)
-         
-       schedule['per_system'][sys]['repair_complete_day'] [num reals x num stories]
-         The number of days from the start of repair of a specifc system until
-         each story is fully repaired for that given system.
-         
-       schedule['per_system'][sys]['max_num_workers_per_story'] [num reals x num stories]
-         The number of workers required to repair each story of this system
-         
-       schedule['system_totals']['repair_days'] [num reals x num systems]
-         The number of days required to repair each system in isolation
-         
-       schedule['system_totals']['num_workers'] [num reals x num systems]
-         The number of workers required for the repair of each system
-      
-       Notes
-       -----'''
-       
-    def fn_repair_sequence_parameters(damage, repair_type, sys, num_du_per_crew, 
-                                      max_crews_per_comp_type, 
-                                      max_workers_per_story, 
-                                      max_workers_per_building):
-        
-        '''Define crew sizes, workers, and repair times for each story of a given
-        system. Based on worker limiations, and component worker days data from
-        the FEMA P-58 assessment.'''
-        
-        # Define Repair Type Variables (variable within the damage object)
-        if repair_type == 'full':
-            repair_time_var = 'worker_days'
-            system_var = 'system'
-            crew_size_var = 'crew_size'
-        elif repair_type == 'temp':
-            repair_time_var = 'tmp_worker_day'
-            system_var = 'tmp_repair_class'
-            crew_size_var = 'tmp_crew_size'
-        else:
-            sys.exit('error!. Unexpected Repair Type')
-
-
-        # Define Initial Parameters
-        num_stories = len(damage['tenant_units']) 
-        num_reals, num_comps = np.size(damage['tenant_units'][0]['worker_days'],0), np.size(damage['tenant_units'][0][repair_time_var],1)
-        sequence_filt = np.array(damage['comp_ds_table'][system_var]) == sys # identifies which ds indices are in this seqeunce.
-        comp_types = np.unique(np.array(damage['comp_ds_table']['comp_idx'])[sequence_filt]) # Types of components in this system
-        
-        # Pre-allocate variables
-        total_worker_days = np.zeros([num_reals,num_stories])
-        is_damaged_building = np.zeros([num_reals,num_comps], dtype=bool)
-        num_damaged_comp_types = np.zeros([num_reals,num_stories])
-        num_damaged_units = np.zeros([num_reals,num_stories])
-        average_crew_size = np.zeros([num_reals,num_stories])
-        
-        for s in range(num_stories):
-            # Define damage properties of this system at this story
-            num_damaged_units[:,s] = np.sum((1*sequence_filt) * damage['tenant_units'][s]['qnt_damaged'], axis=1) #FZ# Total number of damaged components of one system in one story
-            is_damaged = np.logical_and(np.array(damage['tenant_units'][s]['qnt_damaged']) > 0, np.array(damage['tenant_units'][s][repair_time_var]) > 0) #FZ# No
-            is_damaged_building = is_damaged_building | is_damaged              #FZ# compiling for all stories. if there is damage at any story, building is damaged
-            
-            for c in range(len(comp_types)):
-                num_damaged_comp_types[:,s] = num_damaged_comp_types[:,s] + np.any((1* np.array(damage['comp_ds_table']['comp_idx']) == comp_types[c]) * (1*is_damaged), axis=1) #FZ# number of types of components damaged in a story in each realization
-            
-        
-            # Calculate total worker days per story per sequences
-            total_worker_days[:,s] = np.sum(np.array(damage['tenant_units'][s][repair_time_var])[:,sequence_filt], axis=1) # perhaps consider doing when we first set up this damage data structure
-            
-            # Determine the required crew size needed for  these repairs
-            repair_time_per_comp = np.array(damage['tenant_units'][s][repair_time_var]) / np.array(damage['comp_ds_table'][crew_size_var])
-            average_crew_size[:,s] = total_worker_days[:,s] / np.sum(repair_time_per_comp[:,sequence_filt], axis=1)
-        
-            
-        # Define the number of crews needed based on the extent of damage
-        num_crews = np.ceil(num_damaged_units / num_du_per_crew)
-        num_crews = np.fmin(num_crews, max_crews_per_comp_type * num_damaged_comp_types)
-        num_crews = np.fmin(num_crews, np.ceil(num_damaged_units)) # Safety check: num crews should never be greater than the number of damaged components
-        
-        # Round up total worker days to the nearest day to speed up the worker 
-        # allocation loop and implicitly consider change of trade delays
-        total_worker_days = np.ceil(total_worker_days)
-        
-        # Round crew sizes such that we have a realistic size (still implicitly
-        # averaged based on type of damage)
-        average_crew_size[np.isnan(average_crew_size)] = 0
-        average_crew_size = np.round(average_crew_size)
-        
-        # Limit the number of crews based on the space limitations at this story
-        # and the assumed crew size
-        worker_upper_lim = np.fmin(max_workers_per_story , max_workers_per_building)
-        max_num_crews_per_story = np.fmax(np.floor(worker_upper_lim / average_crew_size), 1)
-        num_crews = np.fmin(num_crews, max_num_crews_per_story) 
-        
-        # Calculate the total number of workers per story for this system
-        num_workers = average_crew_size* num_crews
-        
-        # Repeat calc of number of uniquely damaged component types for the whole building
-        num_damaged_comp_types = np.zeros([num_reals])
-        
-        for c in range(len(comp_types)):
-            num_damaged_comp_types = num_damaged_comp_types + np.any( (1* np.array(damage['comp_ds_table']['comp_idx']) == comp_types[c]) * (1*is_damaged_building), axis=1)                                                
-                                                                   
-        max_crews_building = max_crews_per_comp_type * num_damaged_comp_types
-            
-        return total_worker_days, num_workers, average_crew_size, max_crews_building
-    
-    # General Variable
-    num_reals = len(damage['tenant_units'][0]['worker_days'])
-    schedule = {'system_totals' : {'repair_days' : np.zeros([num_reals,len(systems)])}}
-    schedule['system_totals']['num_workers'] = np.zeros([num_reals,len(systems)])
-    
-    ## Allocate workers to each story for each system
-    # Repair finish times assumes all sequences start on day zero
-    schedule['per_system']={}    
-    for syst in range(len(systems)):
-        schedule['per_system'][syst]={}
-        # Define the Crew workers and total workers days for this sequence
-        # in arrays of [num reals by num stories]
-        total_worker_days, num_workers, average_crew_size, max_crews_building = fn_repair_sequence_parameters(damage, repair_type,
-            systems['id'][syst], 
-            systems['num_du_per_crew'][syst],
-            systems['max_crews_per_comp_type'][syst],
-            max_workers_per_story,
-            max_workers_per_building
-        )
-        # Allocate workers to each story and determine the total days until
-        # repair is complete for each story and sequence
-
-        # other_repair_schedule_functions.
-        AA,BB,CC = fn_allocate_workers_stories(total_worker_days, num_workers, 
-                                      average_crew_size, max_crews_building, 
-                                      max_workers_per_building)
-
-        schedule['per_system'][syst]['repair_start_day']=AA
-        schedule['per_system'][syst]['repair_complete_day']=BB
-        schedule['per_system'][syst]['max_num_workers_per_story']=CC
-    
-        # How many days does it take to complete each system in isloation
-        schedule['system_totals']['repair_days'][:,syst] = np.amax(schedule['per_system'][syst]['repair_complete_day'], axis=1)
-        schedule['system_totals']['num_workers'][:,syst] = np.amax(schedule['per_system'][syst]['max_num_workers_per_story'], axis=1)
-        
-    return schedule
-
-
-def fn_prioritize_systems( systems, repair_type, damage, tmp_repair_complete_day, impeding_factors):
-    '''Determine the priority of worker allocation for each system and realization
-    based on default table priorities, whether they have the potential to 
-    affect function and whether they are resolved by temporary repairs.
-       
-    Parameters
-    ----------
-    systems: DataFrame
-      data table containing information about each system's attributes
-    
-    repair_type: string
-      String identifier indicating whether the repair are temporary or full
-      
-    damage: dictionary
-      contains per damage state damage and loss data for each component in the building
-      
-    tmp_repair_complete_day: array [num_reals x num_comp]
-      contains the day (after the earthquake) the temporary repair time is 
-      resolved per damage state damage and realization. Inf represents that
-      there is not temporary repair time available for a given components
-      damage.
-    
-    iimpeding_factors: dictionary
-      simulated impedance times   
-    Returns
-    -------
-    sys_idx_priority_matrix: index array [num reals x num systems]
-      row index to filter system matrices to be prioritized for each
-      realiztion. Priority is left to right.
-    
-    Notes
-    ------
-    The checks done here are only able to implicitly check for which systems
-    affect funtion the most (this prioritizes which have the potential to
-    affect function). To explicitly check which systems have the biggest
-    impact on function and prioritize those, this check would need to be
-    coupled with the function assessment'''
-    
-    import sys
-    
-    ## Initial Setup
-    # Define Repair Type Variables (variable within the damage object)
-    if repair_type == 'full':
-        system_var = 'system'
-    elif repair_type == 'temp':
-        system_var = 'tmp_repair_class'
-    else:
-        sys.exit('error!. Unexpected Repair Type')
-
-
-    # initialize variables
-    num_sys = len(systems)
-    num_reals = np.size(damage['tenant_units'][0]['qnt_damaged'],0)    
-    
-    # Find which components potentially affect reoccupancy accross any tenant unit
-    affects_reoccupancy = np.zeros([num_reals, len(damage['comp_ds_table']['comp_id'])], dtype=bool)
-    for s in range(len(damage['tenant_units'])):
-        affects_reoccupancy = np.logical_or(affects_reoccupancy, np.logical_and(damage['fnc_filters']['affects_reoccupancy'], np.array(damage['tenant_units'][s]['qnt_damaged']) > 0))
-
-    
-    # Find which components potentially affect function accross any tenant unit 
-    for s in range(len(damage['tenant_units'])):
-        # affects_function = zeros(num_reals, len(damagecomp_ds_table));
-        affects_function = np.zeros([num_reals, len(damage['comp_ds_table']['comp_id'])], dtype=bool)
-        affects_function = np.logical_or(affects_function, np.logical_and(damage['fnc_filters']['affects_function'], np.array(damage['tenant_units'][s]['qnt_damaged']) > 0))
-
-       
-    ## Define ranks for each system 
-    sys_affects_reoccupancy = np.zeros([num_reals, num_sys]) # only prioritize the systems that potentially affect reoccupancy
-    sys_affects_function = np.zeros([num_reals, num_sys]) # only prioritize the systems that potentially affect function
-    sys_tmp_repaired = np.zeros([num_reals, num_sys]) # dont prioitize the systems that are completely resolved by temporary repairs
-    
-    for syst in range(num_sys):
-        sys_filter = damage['comp_ds_table'][system_var] == syst+1 #FZ# +1 done to account for python indexing starting from 0
-        if any(sys_filter): # Only if this system is present
-            sys_affects_reoccupancy[:,syst] = np.any(affects_reoccupancy[:,sys_filter], axis = 1); # any damage that potentially affects function in this system    
-            sys_affects_function[:,syst] = np.any(affects_function[:,sys_filter], axis = 1); # any damage that potentially affects function in this system
-            # check if the list (array) is non-empty
-            if list(tmp_repair_complete_day): # Only if temp repair data is passed in
-                all_sys_tmp_repaired = np.all(np.logical_or(tmp_repair_complete_day[:,sys_filter] < np.inf, np.isnan(tmp_repair_complete_day[:,sys_filter])), axis=1) # is every single damaged component resolved by temp repairs
-                tmp_repair_quick = np.nanmax(tmp_repair_complete_day[:,sys_filter], axis=1) < impeding_factors['time_sys'][:,syst] # Are the temp repairs for this system resolved before impeding factors are complete
-                sys_tmp_repaired[:,syst] = np.logical_and(all_sys_tmp_repaired, tmp_repair_quick) # damage is quickly resolved by temp repair
-
-    prioritize_system_reoccupancy = np.logical_and(sys_affects_reoccupancy == 1, sys_tmp_repaired == 0)
-    prioritize_system_function_only = np.logical_and(sys_affects_function == 1, np.logical_and(prioritize_system_reoccupancy, sys_tmp_repaired == 0))
-    non_priorities = np.logical_and(np.logical_not(prioritize_system_reoccupancy), np.logical_not(prioritize_system_function_only))
-    
-    '''First prioritize reoccpancy repairs (100 classifies first priority bank, 
-    important for sort function below), then priotize repairs that only 
-    affect function (200 classifies second priority bank). 
-    Finally, repair the systems that dont affect reoccupancy or 
-    function (300 classifies third priority bank)'''
-    
-    sys_priority_matrix = prioritize_system_reoccupancy * (100 + np.array(systems['priority'])) + prioritize_system_function_only * (200 + np.array(systems['priority'])) + non_priorities * (300 + np.array(systems['priority']))
-
-    
-    # use rank matrix to get row indices of priorities
-    sys_idx_priority_matrix = np.argsort(sys_priority_matrix, axis=1)
-    
-    return sys_idx_priority_matrix
-
-
-def fn_set_repair_constraints(systems, repair_type, conditionTag):
-    '''Define a constraint matrix to be used by repair schedule
-    
-    Develops a matrix of various constriants between each system (i.e. what
-    systems need to be repaired before others) for each realization
-    
-    Parameters
-    ----------
-    systems: DataFrame
-     data table containing information about each system's attributes
-
-    repair_type: string
-      String identifier indicating whether the repair are temporary or full
-      
-    conditionTag: logical array (num_reals x 1)
-     Is the building red tagged for each realization
-    
-    Returns
-    -------
-    sys_constraint_matrix: array [num reals x num_sys]
-     array of system ids which define which systems (column index) are delayed by the
-     system ids (array values)
-    
-    Notes
-    -----
-    Shortcoming: as implemented, each system can only be constrained by one
-                  other system
-    Example:
-    [0 0 1 0 0 0 0 6 0]
-    Interiors (column 3) are blocked by struture (value of 1 in the 3rd column)
-    HVAC (column 8) is blocked by plumbing (value of 6 in the 8th column)'''
-    
-    import sys
-    #Initial Setup
-    num_sys = len(systems)
-    num_reals = len(conditionTag)
-    sys_constraint_matrix = np.zeros([num_reals, num_sys])
-    
-    # Interior Constraints
-    if repair_type == 'full':
-        # Interiors are delayed by structural repairs
-        interiors_idx = np.where(np.array(systems['name']) == 'interior')[0][0] #FZ [0] is done to convert tuple to np array # [0][0] to get scalar
-        structure_idx = np.where(np.array(systems['name']) == 'structural')[0][0]
- 
-        sys_constraint_matrix[:,interiors_idx] = structure_idx+1 #FZ# +1 is done to replace with the system id which starts with 1, but python indexing starts at 0. 
-        
-        # Red Tag Constraints
-        # All systems blocked by structural when red tagged
-        for rt in np.where(conditionTag)[0]:
-            for st in np.where(np.array(systems['name']) != 'structural')[0]:
-                sys_constraint_matrix[rt, st] = structure_idx+1
-    
-    elif repair_type == 'temp':
-        shoring_id = 5
-        shoring_filt = systems['id'] == shoring_id
-        if np.any(shoring_filt): # If shoring is considred as a temp repair measure
-            shoring_idx = np.where(shoring_filt)[0][0]
-            sys_constraint_matrix[:,np.logical_not(shoring_filt)] = shoring_idx + 1 # all classes that are not shoring idx are blocked by the shoring idx. #FZ# +1 is done to replace with the system id which starts with 1, but python indexing starts at 0. 
-
-    else:
-        sys.exit('error. Unexpected Repair Type')
-    
-    return sys_constraint_matrix
-
-
-def fn_allocate_workers_systems(systems, sys_repair_days, sys_crew_size, 
-                                max_workers_per_building, 
-                                sys_idx_priority_matrix, 
-                                sys_constraint_matrix, 
-                                condition_tag, 
-                                sys_impeding_factors):
-
-
-    '''Stager repair to each system and allocate workers based on the repair
-    constraints, priorities, and repair times of each system
-    
-    Parameters
-    ----------
-    system_repair_days: [num reals x num systems]
-     Number of days from the start of repair of each to the completion of
-     the system (assuming all sequences start on day zero)
-     
-    system_repair_days: [num reals x num systems]
-     Number of days from the start of repair of each to the completion of
-     the system (assuming all sequences start on day zero)
-    
-    sys_crew_size: array [num reals x num systems]
-     required crew size for each system
-     
-    max_workers_per_building: int
-     Maximum number of workers that can work in the building at the same time 
-     
-    sys_idx_priority_matrix: matrix [num reals x systems]
-     worker allocation order of system id's prioritized for each realiztion
-     
-    sys_constraint_matrix: array [num reals x num_sys]
-     array of system ids which define which systems (column index) are delayed by the
-     system ids (array values)
-     
-    condition_tag: logical array [num reals x 1]
-     true/false if the building is red tagged
-     
-    sys_impeding_factors: array [num_reals x num_sys]
-     maximum impedance time (days) for each system. Pass in empty array when
-     calculating repair times (i.e. not including impeding factors).
-    
-    Returns
-    -------
-    repair_complete_day_per_system: matrix [num reals x num systems]
-     Number of days from the start of each sequence to the completion of the
-     sequence considering the allocation of workers to each sequence (ie
-     some sequences start before others)
-     
-    worker_data['total_workers']: array [num reals x varies]
-     total number of workers in the building at each time step of the worker
-     allocation algorthim. The number of columns varies with the number of
-     increments of the worker allocation algorithm.
-     
-    worker_data['day_vector']: array [num reals x varies]
-     Day of each time step of the worker allocation algorthim. The number of 
-     columns varies with the number of increments of the worker allocation algorithm.
-    
-    Notes
-    -----'''
-    
-    import sys
-    
-    def fitler_matrix_by_rows( values, filter_by):
-    # Use a identiry matrix to filter values from another matrix by rows
-    
-        '''Parameters
-        ----------
-        values: matrix [n x m]
-          values to filter by row
-        filter: matrix [n x m]
-          array indexes to grab from each row of values
-        
-        Returns
-        -------
-        filtered_values: matrix [n x m]
-          values filtered by rows'''
-        
-        # Method
-        x2t = np.transpose(values)
-        idx1 = np.transpose(filter_by)
-        y4 = np.zeros(np.shape(idx1))
-        for r in range(np.size(values,0)):
-            y4[:, r] = x2t[idx1[:, r], r]
-        filtered_values = np.transpose(y4)
- 
-        return filtered_values
-
-    
-    ## Initial Setup
-    # Initialize Variables
-    num_reals, num_sys = np.shape(sys_repair_days)
-    priority_system_complete_day = np.zeros([num_reals,num_sys])
-    day_vector = np.zeros([num_reals,0])
-    total_workers = np.zeros([num_reals,0])
-    
-    # Re-order system variables based on priority
-    priority_sys_workers_matrix = fitler_matrix_by_rows(sys_crew_size, sys_idx_priority_matrix)
-    priority_sys_constraint_matrix = fitler_matrix_by_rows(sys_constraint_matrix, sys_idx_priority_matrix)
-    priority_sys_repair_days = fitler_matrix_by_rows(sys_repair_days, sys_idx_priority_matrix)
-    if sys_impeding_factors.size == 0:
-        priority_sys_impeding_factors = np.zeros([num_reals, num_sys])
-    else:
-        priority_sys_impeding_factors = fitler_matrix_by_rows(sys_impeding_factors, sys_idx_priority_matrix)
-
-    
-    # Round up days to the nearest day
-    # Provides an implicit change of trade delay, as well as help to reduce the
-    # number of delta increments in the following while loop
-    priority_sys_repair_days = np.ceil(priority_sys_repair_days)
-    priority_sys_impeding_factors = np.ceil(priority_sys_impeding_factors) #FZ# Already rounded. Check requirement
-    
-    ## Assign workers to each system based on repair constraints
-    iter = 0
-    current_day = np.zeros(num_reals)
-    priority_sys_waiting_days = priority_sys_impeding_factors.copy()
-    while sum(sum(priority_sys_repair_days)) > 0.01:
-        iter = iter + 1; 
-        if iter > 1000: # keep the while loop pandemic contained
-            sys.exit('error (PBEE_Recovery:RepairSchedule, Could not converge worker allocations for among systems')
-
-        
-        # zero out assigned workers matrix
-        assigned_workers = np.zeros([num_reals,num_sys])
-        
-        # limit available workers to the max that can be on any one given story
-        # this ensures next system cannot start until completely unblocked on
-        # every story. Need to update for taller buildings which could start
-        # next sequence on lower story once previous sequence was far enough
-        # along
-        available_workers = max_workers_per_building * np.ones(num_reals)
-        
-        # Define what systems are waiting to begin repairs
-        sys_blocked = np.zeros([num_reals,num_sys]).astype(bool)
-        sys_incomplete = priority_sys_repair_days > 0
-        for s in range(num_sys): # Loop over each system that may constrain something
-            constrained_systems = priority_sys_constraint_matrix == s+1 # These systems are constrained by looped system #FZ# s+1 is done to correlate with +1 done in deriving constraint matrix
-            constraining_sys_filt = sys_idx_priority_matrix == s; # location in matrix of this system
-            is_constraining_sys_incomplete = np.amax(sys_incomplete * constraining_sys_filt, axis=1) # Vec of relizations for looped system
-            sys_blocked = sys_blocked | np.transpose(np.transpose(constrained_systems) * is_constraining_sys_incomplete) # System is constrained if blocked by an incomplete system. #FZ# transpose done to align arrays for the operation
-        
-        # Need to wait for impeding factors or other repairs to finish
-        is_waiting = np.transpose(current_day < np.transpose(priority_sys_impeding_factors)) | sys_blocked # assuming impeding factors are the only constraints. #FZ# transpose done to align arrays for the operation
-        
-        # Define where needs repair
-        # System needs repair if there are still repairs days left and it is
-        # not waiting to be unblocked
-        needs_repair = (priority_sys_repair_days>0) & np.logical_not(is_waiting)
-    
-        # Defined Required Workers
-        required_workers = needs_repair * priority_sys_workers_matrix
-        
-        # Assign Workers to each system
-        for s in range(num_sys):
-            # Assign Workers to this systems
-            enough_workers = required_workers[:,s] <= available_workers
-            assigned_workers[enough_workers,s] = np.fmin(required_workers[enough_workers,s], available_workers[enough_workers])
-    
-            # Define Available Workers
-            # when in series limit available workers to the workers in this system 
-            # (occurs for structural systems when the building is red tagged
-            is_structural = systems['name'][s] == 'structural'
-            in_series = np.logical_and(condition_tag, is_structural)
-            available_workers[np.logical_and(in_series, assigned_workers[:,s] > 0)] = 0 #FZ# Python index 0 is structural system
-            # when not in series, calc the remaining workers
-            available_workers[np.logical_not(in_series)] = available_workers[np.logical_not(in_series)] - assigned_workers[np.logical_not(in_series),s]
-
-        
-        # Calculate the time associated with this increment of the while loop
-        in_progress = assigned_workers > 0 # sequences where work is being done
-        total_repair_days = np.empty(np.shape(in_progress)) # pre-allocate with inf's becuase we take a min later
-        total_repair_days[:] = np.inf
-        total_repair_days[in_progress] = priority_sys_repair_days[in_progress]
-        total_waiting_days = priority_sys_waiting_days.copy()   #FZ# Check is.copy() worke here
-        
-        total_waiting_days[total_waiting_days == 0] = np.inf # Convert zeros to inf such that zeros are not included in the min in the next step
-        
-        total_time = np.fmin(total_repair_days,total_waiting_days) # combime repair time and waiting time
-        delta_days = np.amin(total_time,axis=1) # time increment is whatever in-progress story that finishes first
-        delta_days[np.isinf(delta_days)] = 0 # Replace infs from real that has no repair with zero
-                 
-        # Reduce waiting time
-        priority_sys_waiting_days = np.maximum(np.transpose(np.transpose(priority_sys_waiting_days) - delta_days), 0) #FZ# transpose done to align arrays for the operation
-        
-        # Reduce time needed for repairs
-        delta_days_in_progress = np.transpose(delta_days * np.transpose(in_progress)) # change in repair time for all sequences being worked on #FZ# transpose done to align arrays for the operation
-        priority_sys_repair_days[in_progress] = np.maximum(priority_sys_repair_days[in_progress] - delta_days_in_progress[in_progress], 0)
-        
-        # Define Start and Stop of Repair for each sequence
-        priority_system_complete_day = priority_system_complete_day + np.transpose(delta_days * np.transpose(needs_repair | is_waiting)) #FZ# transpose done to align arrays for the operation
-        
-        # Define Cummulative day of repair
-        day_vector = np.column_stack((day_vector, current_day))
-        current_day = current_day + delta_days
-        
-        # Save worker data data over time
-        # total_workers = [total_workers, np.sum(assigned_workers,axis=1), np.sum(assigned_workers,axis=1)] #FZ# why assigned workers stored twice - Review
-        total_workers = np.column_stack((total_workers, np.sum(assigned_workers,axis=1), np.sum(assigned_workers,axis=1)))
-        day_vector = np.column_stack((day_vector, current_day))
-
-        
-    # Untangle system_complete_day back into system table order
-    
-    sys_idx_untangle_matrix = np.argsort(sys_idx_priority_matrix, axis=1)
-    
-    repair_complete_day_per_system = fitler_matrix_by_rows(priority_system_complete_day, sys_idx_untangle_matrix)
-    
-    # Save worker data matrices
-    worker_data ={'total_workers' : total_workers, 'day_vector' : day_vector}
-
-    return repair_complete_day_per_system, worker_data 
-
-
-def fn_restructure_repair_schedule( damage, system_schedule,
-    repair_complete_day_per_system, systems, repair_type, simulated_red_tags):
-    
-    '''Redistribute repair schedule data from the system and story level to the component level for use 
-    in the functionality assessment (ie, put repair schedule data into the
-    damage object)
-    
-    Parameters
-    ----------
-    damage: dictionary
-     contains per damage state damage and loss data for each component in the building
-     
-    system_schedule: dictionary
-     repair time data for each system in isolation
-     
-    repair_complete_day_per_system: matrix [num reals x num systems]
-     Number of days from the start of each sequence to the completion of the
-     sequence considering the allocation of workers to each sequence (ie
-     some sequences start before others)
-     
-    systems: DataFrame
-     data table containing information about each system's attributes
-    
-    repair_type: string
-      String identifier indicating whether the repair are temporary or full
-      
-    simulated_red_tags: logical array [num_reals x 1]
-      indicates the realizations that have a red tag
-    
-    Returns
-    -------
-    damage: dictionary
-     contains per damage state damage and loss data for each component in
-     the building, including repair schedule data.
-    
-    % Notes
-    -----
-    In the repair start day outputs:
-       - Zero = Starts immediately
-       - NaN = Repairs never started (because not damaged, has no temp repair, or controlled by red tag)
-    In the repair complete day outputs:
-       - NaN = No damage (or controlled by red tag)
-       - Inf = Is damaged, but has no System/RepairClass assignment (or no temp repair)'''
-    
-    import sys
-    
-    ## Initialize Parameters
-    num_sys = len(systems)
-    num_units = len(damage['tenant_units'])
-    
-    # Define Repair Type Variables (variable within the damage object)
-    if repair_type == 'full':
-        repair_time_var = 'worker_days'
-        system_var = 'system'
-    elif repair_type == 'temp':
-        repair_time_var = 'tmp_worker_day'
-        system_var = 'tmp_repair_class'
-    else:
-        sys.exit('Unexpected Repair Type')
-
-    # Initialize recovery field
-    damage_recovery = {}
-    for tu in range(num_units):
-        # Set to inf as a null repair time (will remain inf for components with
-        # no attributed system) - matters for temp repairs, shouldnt matter for
-        # full repair
-        damage_recovery[tu]={}
-        damage_recovery[tu]['repair_start_day'] = np.empty(np.shape(np.array(damage['tenant_units'][tu]['qnt_damaged'])))
-        damage_recovery[tu]['repair_start_day'][:] = np.nan
-        damage_recovery[tu]['repair_complete_day'] = np.empty(np.shape(np.array(damage['tenant_units'][tu]['qnt_damaged'])))
-        damage_recovery[tu]['repair_complete_day'][:] = np.inf
-        
-        # if not damaged, set repair complete time to NaN
-        is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0, np.array(damage['tenant_units'][tu][repair_time_var]) > 0)
-        damage_recovery[tu]['repair_complete_day'][np.logical_not(is_damaged)] = np.nan
-
-
-   ## Redistribute repair schedule data
-    for syst in range(num_sys):
-        # Calculate system repair times on each story
-        system_duration = np.nanmax(system_schedule['per_system'][syst]['repair_complete_day'], axis=1) # total repair time spent in this system over all stories
-        start_day = repair_complete_day_per_system[:,syst] - system_duration
-        story_start_day = start_day.reshape(len(start_day),1) + system_schedule['per_system'][syst]['repair_start_day']
-        story_complete_day = start_day.reshape(len(start_day),1) + system_schedule['per_system'][syst]['repair_complete_day']
-        
-        # Do not perform temporary repairs when building is red tagged
-        if np.logical_and(repair_type == 'temp', np.any(simulated_red_tags)):
-            story_start_day[simulated_red_tags.astype(bool),:] = np.nan
-            story_complete_day[simulated_red_tags.astype(bool),:] = np.nan
-   
-        # Re-distribute to each tenant unit
-        sys_filt = damage['comp_ds_table'][system_var] == systems['id'][syst] # identifies which ds idices are in this seqeunce  
-        for tu in range(num_units):
-            is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged'])[:,sys_filt] > 0, np.array(damage['tenant_units'][tu][repair_time_var])[:,sys_filt] > 0)
-            is_damaged = (is_damaged * 1).astype(float)
-            is_damaged[is_damaged == 0] = np.nan
-    
-            # Re-distribute repair days to component damage states
-            damage_recovery[tu]['repair_start_day'][:,sys_filt] = is_damaged* story_start_day[:,tu].reshape(len(story_start_day[:,tu]),1)
-            damage_recovery[tu]['repair_complete_day'][:,sys_filt] = is_damaged * story_complete_day[:,tu].reshape(len(story_complete_day[:,tu]),1)
-            
-    return damage_recovery
-
-
-def fn_format_gantt_chart_data( damage, systems, simulated_replacement_time):
-    '''Reformat data from the damage structure into data that is used for the
-    gantt charts
-    
-    Parameters
-    ----------
-    damage: dictionary
-     contains per damage state damage and loss data for each component in the building
-    
-    systems: DataFrame
-     data table containing information about each system's attributes
-    
-    simulated_replacement_time: array [num_reals x 1]
-     simulated time when the building needs to be replaced, and how long it
-     will take (in days). NaN represents no replacement needed (ie
-     building will be repaired)
-    
-Returns
-    -------
-    repair_schedule: dictionary
-     Contians reformated repair schedule data for gantt chart plots for
-     both repair time and downtime calculations. Data is reformanted to
-     show breakdowns by component, by story, by system, by story within each
-     system, and by system within each story.
-    
-    Notes
-    -----
-    Since this all has to do with plotting gantt charts, a form of this
-    (whether the damage structure or a higher level repair schedule
-    structure) should be output and this reformatting logic moved outside the
-    functional recovery assessment and into the data visuallation logic'''
-    
-    ## Initial Setup
-    num_stories = len(damage['tenant_units'])
-    num_reals = np.size(damage['tenant_units'][0]['recovery']['repair_start_day'],0)
-    comps = np.unique(damage['comp_ds_table']['comp_id'])
-       
-    # Determine replacement cases
-    replace_cases = np.logical_not(np.isnan(simulated_replacement_time))
-
-    ## Reformat repair schedule data into various breakdowns
-    # Per component
-    repair_schedule = {'repair_start_day' : {}, 'repair_complete_day' : {}, 'component_names':[]}
-    repair_schedule['repair_start_day']['per_component'] = np.empty([num_reals, len(comps)])
-    repair_schedule['repair_start_day']['per_component'][:] = np.nan
-    repair_schedule['repair_complete_day']['per_component'] = np.zeros([num_reals,len(comps)])
-    for c in range(len(comps)):
-        comps_filt = damage['comp_ds_table']['comp_id'] == comps[c]
-        for s in range(num_stories):
-            repair_schedule['repair_start_day']['per_component'][:,c] = np.nanmin(np.column_stack((repair_schedule['repair_start_day']['per_component'][:,c], damage['tenant_units'][s]['recovery']['repair_start_day'][:,comps_filt])), axis=1)
-            repair_schedule['repair_complete_day']['per_component'][:,c] = np.nanmax(np.column_stack((repair_schedule['repair_complete_day']['per_component'][:,c], damage['tenant_units'][s]['recovery']['repair_complete_day'][:,comps_filt])), axis=1)
-
-        repair_schedule['component_names'].append(str(comps[c]))
-
-    
-    # Per Story
-    repair_schedule['repair_start_day']['per_story'] = np.empty([num_reals, num_stories])
-    repair_schedule['repair_start_day']['per_story'][:] = np.nan
-    repair_schedule['repair_complete_day']['per_story'] = np.zeros([num_reals,num_stories])
-    for s in range(num_stories):
-        repair_schedule['repair_start_day']['per_story'][:,s] = np.nanmin(np.column_stack((repair_schedule['repair_start_day']['per_story'][:,s], damage['tenant_units'][s]['recovery']['repair_start_day'])), axis=1)
-        repair_schedule['repair_complete_day']['per_story'][:,s] = np.nanmax(np.column_stack((repair_schedule['repair_complete_day']['per_story'][:,s], damage['tenant_units'][s]['recovery']['repair_complete_day'])), axis=1)
-    
-    
-    # Per Repair System
-    repair_schedule['repair_start_day']['per_system'] = np.empty([num_reals, len(systems)])
-    repair_schedule['repair_start_day']['per_system'][:] = np.nan
-    repair_schedule['repair_complete_day']['per_system'] = np.zeros([num_reals,len(systems)])
-    for sys in range(len(systems)):
-        sys_filt = damage['comp_ds_table']['system'] == systems['id'][sys] # identifies which ds idices are in this seqeunce  
-        for s in range(num_stories):
-            repair_schedule['repair_start_day']['per_system'][:,sys] = np.nanmin(np.column_stack((repair_schedule['repair_start_day']['per_system'][:,sys], damage['tenant_units'][s]['recovery']['repair_start_day'][:,sys_filt])), axis=1)
-            repair_schedule['repair_complete_day']['per_system'][:,sys] = np.nanmax(np.column_stack((repair_schedule['repair_complete_day']['per_system'][:,sys], damage['tenant_units'][s]['recovery']['repair_complete_day'][:,sys_filt])), axis=1)
-
-
-    repair_schedule['system_names'] = np.array(systems['name'])
-    
-    # Per system per story
-    num_sys_stories = num_stories * len(systems)
-    repair_schedule['repair_start_day']['per_system_story'] = np.empty([num_reals,num_sys_stories])
-    repair_schedule['repair_start_day']['per_system_story'][:] = np.nan
-    repair_schedule['repair_complete_day']['per_system_story'] = np.zeros([num_reals,num_sys_stories])
-    id = -1; #FZ# -1 done to account for python indexing starting from 0
-    for s in range(num_stories):
-        for sys in range(len(systems)):
-            id += 1
-            sys_filt = damage['comp_ds_table']['system'] == systems['id'][sys] # identifies which ds idices are in this seqeunce  
-            repair_schedule['repair_start_day']['per_system_story'][:,id] = np.nanmin(np.column_stack((repair_schedule['repair_start_day']['per_system_story'][:,id], damage['tenant_units'][s]['recovery']['repair_start_day'][:,sys_filt])), axis=1)
-            repair_schedule['repair_complete_day']['per_system_story'][:,id] = np.nanmax(np.column_stack((repair_schedule['repair_complete_day']['per_system_story'][:,id], damage['tenant_units'][s]['recovery']['repair_complete_day'][:,sys_filt])), axis=1)
-
-    
-    # Per story per system
-    num_sys_stories = num_stories * len(systems)
-    repair_schedule['repair_start_day']['per_story_system'] = np.empty([num_reals,num_sys_stories])
-    repair_schedule['repair_start_day']['per_story_system'][:] = np.nan
-    repair_schedule['repair_complete_day']['per_story_system'] = np.zeros([num_reals,num_sys_stories])
-    
-    id = -1; #FZ# -1 done to account for python indexing starting from 0
-    for sys in range(len(systems)):
-        for s in range(num_stories):
-            id += 1
-            sys_filt = damage['comp_ds_table']['system'] == systems['id'][sys] # identifies which ds idices are in this seqeunce 
-            repair_schedule['repair_start_day']['per_story_system'][:,id] = np.nanmin(np.column_stack((repair_schedule['repair_start_day']['per_story_system'][:,id], damage['tenant_units'][s]['recovery']['repair_start_day'][:,sys_filt])), axis=1)
-            repair_schedule['repair_complete_day']['per_story_system'][:,id] = np.nanmax(np.column_stack((repair_schedule['repair_complete_day']['per_story_system'][:,id], damage['tenant_units'][s]['recovery']['repair_complete_day'][:,sys_filt])), axis=1)
-    
-    # Overwrite realization for demo and replace cases
-    formats = list(repair_schedule['repair_start_day'].keys())
-    for f in range(len(formats)):
-        repair_schedule['repair_start_day'][formats[f]][replace_cases,:] = 0
-        
-        format_width = np.size(repair_schedule['repair_complete_day'][formats[f]], 1) # apply replacement time to all comps / systems / stories / etc
-        repair_schedule['repair_complete_day'][formats[f]][replace_cases,:] = np.array(simulated_replacement_time)[replace_cases].reshape(len(np.array(simulated_replacement_time)[replace_cases]),1) * np.ones([1,format_width])
-
-    return repair_schedule
-
-
-
-
-    
-
-    
-    
-    
\ No newline at end of file
diff --git a/src/atc138/data/README.md b/static_tables/README.md
similarity index 100%
rename from src/atc138/data/README.md
rename to static_tables/README.md
diff --git a/src/atc138/data/component_attributes.csv b/static_tables/component_attributes.csv
similarity index 100%
rename from src/atc138/data/component_attributes.csv
rename to static_tables/component_attributes.csv
diff --git a/src/atc138/data/damage_state_attribute_mapping.csv b/static_tables/damage_state_attribute_mapping.csv
similarity index 100%
rename from src/atc138/data/damage_state_attribute_mapping.csv
rename to static_tables/damage_state_attribute_mapping.csv
diff --git a/src/atc138/data/impeding_factors.csv b/static_tables/impeding_factors.csv
similarity index 100%
rename from src/atc138/data/impeding_factors.csv
rename to static_tables/impeding_factors.csv
diff --git a/src/atc138/data/subsystems.csv b/static_tables/subsystems.csv
similarity index 100%
rename from src/atc138/data/subsystems.csv
rename to static_tables/subsystems.csv
diff --git a/src/atc138/data/systems.csv b/static_tables/systems.csv
similarity index 100%
rename from src/atc138/data/systems.csv
rename to static_tables/systems.csv
diff --git a/src/atc138/data/temp_repair_class.csv b/static_tables/temp_repair_class.csv
similarity index 100%
rename from src/atc138/data/temp_repair_class.csv
rename to static_tables/temp_repair_class.csv
diff --git a/src/atc138/data/tenant_function_requirements.csv b/static_tables/tenant_function_requirements.csv
similarity index 100%
rename from src/atc138/data/tenant_function_requirements.csv
rename to static_tables/tenant_function_requirements.csv

From 9fdf1340e33f31bc014d4d75aadb92a8a387a99e Mon Sep 17 00:00:00 2001
From: wangz539 <wangz539@berkeley.edu>
Date: Wed, 4 Mar 2026 10:15:39 -0800
Subject: [PATCH 2/4] Revert "atc-138matlab with redtag fix"

This reverts commit 7590d041e0acc1759765f00f8cce83fe68ca5909.
---
 +plotters/main_plot_functionality.m           |   52 -
 +plotters/plt_gantt_chart.m                   |  164 --
 +plotters/plt_heatmap_breakdowns.m            |   46 -
 +plotters/plt_histograms.m                    |   34 -
 +plotters/plt_recovery_trajectory.m           |   47 -
 +preprocessing/fn_create_fnc_filters.m        |  177 --
 +preprocessing/fn_define_door_racking.m       |   36 -
 +preprocessing/fn_populate_damage_per_tu.m    |   24 -
 +preprocessing/fn_simulate_damage_per_side.m  |   35 -
 +preprocessing/fn_simulate_temp_worker_days.m |   87 -
 +preprocessing/main_preprocessing.m           |   58 -
 .../fn_building_level_system_operation.m      |  133 --
 +recovery/+functionality/fn_building_safety.m |  306 ---
 .../fn_calc_subsystem_recovery.m              |   81 -
 .../fn_calculate_functionality.m              |   78 -
 .../+functionality/fn_calculate_reoccupancy.m |   99 -
 .../+functionality/fn_check_habitability.m    |   59 -
 .../fn_combine_comp_breakdown.m               |   33 -
 .../fn_extract_recovery_metrics.m             |  337 ----
 +recovery/+functionality/fn_story_access.m    |  152 --
 +recovery/+functionality/fn_tenant_function.m |  459 -----
 +recovery/+functionality/fn_tenant_safety.m   |  171 --
 +recovery/+functionality/main_functionality.m |   68 -
 +recovery/+impedance/fn_contractor.m          |   58 -
 .../+impedance/fn_default_surge_factor.m      |   51 -
 +recovery/+impedance/fn_engineering.m         |   87 -
 +recovery/+impedance/fn_financing.m           |   73 -
 +recovery/+impedance/fn_inspection.m          |   61 -
 +recovery/+impedance/fn_permitting.m          |   53 -
 +recovery/+impedance/main_impeding_factors.m  |  332 ----
 .../fn_allocate_workers_stories.m             |  107 --
 .../fn_allocate_workers_systems.m             |  191 --
 .../fn_calc_system_repair_time.m              |  159 --
 .../fn_format_gantt_chart_data.m              |  111 --
 .../+repair_schedule/fn_prioritize_systems.m  |   87 -
 .../fn_restructure_repair_schedule.m          |   96 -
 .../fn_set_repair_constraints.m               |   58 -
 .../+repair_schedule/main_repair_schedule.m   |  151 --
 .gitignore                                    |   38 +-
 README.md                                     |  188 +-
 build_example_inputs.py                       |   60 -
 demo.ipynb                                    |    1 +
 driver_PBEErecovery.m                         |   48 -
 driver_convert_PELICUN.m                      |  298 ---
 .../ICSB/building_model.json                  |    0
 .../ICSB/comp_ds_list.csv                     |    0
 .../ICSB/comp_population.csv                  |    0
 .../ICSB/damage_consequences.json             |    0
 .../ICSB/simulated_damage.json                |    0
 .../ICSB/tenant_unit_list.csv                 |    0
 .../RCSW_1story/building_model.json           |    0
 .../RCSW_1story/comp_ds_list.csv              |    0
 .../RCSW_1story/comp_population.csv           |    0
 .../RCSW_1story/damage_consequences.json      |    0
 .../RCSW_1story/simulated_damage.json         |    0
 .../RCSW_1story/tenant_unit_list.csv          |    0
 .../haseltonRCMF_12story/building_model.json  |    0
 .../haseltonRCMF_12story/comp_ds_list.csv     |    0
 .../haseltonRCMF_12story/comp_population.csv  |    0
 .../damage_consequences.json                  |    0
 .../simulated_damage.json                     |    0
 .../haseltonRCMF_12story/tenant_unit_list.csv |    0
 .../haseltonRCMF_4story/building_model.json   |    0
 .../haseltonRCMF_4story/comp_ds_list.csv      |    0
 .../haseltonRCMF_4story/comp_population.csv   |    0
 .../damage_consequences.json                  |    0
 .../haseltonRCMF_4story/simulated_damage.json |    0
 .../haseltonRCMF_4story/tenant_unit_list.csv  |    0
 fn_red_tag.m                                  |  140 --
 inputs/Inputs2Copy/build_inputs.m             |  355 ----
 inputs/Inputs2Copy/optional_inputs.m          |   67 -
 .../example_inputs/ICSB/simulated_inputs.mat  |  Bin 2256246 -> 0 bytes
 .../RCSW_1story/simulated_inputs.mat          |  Bin 147204 -> 0 bytes
 .../haseltonRCMF_12story/simulated_inputs.mat |  Bin 3252456 -> 0 bytes
 .../haseltonRCMF_4story/simulated_inputs.mat  |  Bin 1046153 -> 0 bytes
 .../001/general_inputs.json                   |    1 -
 .../001/pelicun_data/CMP_QNT.csv              |   34 -
 .../001/pelicun_data/DL_summary.csv           |  501 -----
 .../001/pelicun_data/DMG_sample.csv           |  501 -----
 .../pelicun_data/DV_bldg_repair_sample.csv    |  501 -----
 .../001/pelicun_data/input.json               |   72 -
 .../001/tenant_unit_list.csv                  |   17 -
 main_PBEErecovery.m                           |   79 -
 plotModel_PBEErecovery.m                      |   61 -
 plotModel_PBEErecovery.py                     |   45 +
 plotters/main_plot_functionality.py           |   65 +
 plotters/other_plot_functions.py              |  398 ++++
 pyproject.toml                                |   36 +
 requirements.txt                              |    5 +
 schema/Inputs.json                            |  541 ------
 schema/Optional Inputs.json                   |  515 -----
 schema/Outputs.json                           |  385 ----
 src/atc138/__init__.py                        |    0
 src/atc138/cli.py                             |   26 +
 {static_tables => src/atc138/data}/README.md  |    0
 .../atc138/data}/component_attributes.csv     |    0
 .../data}/damage_state_attribute_mapping.csv  |    0
 src/atc138/data/default_inputs.json           |   81 +
 .../atc138/data}/impeding_factors.csv         |    0
 .../atc138/data}/subsystems.csv               |    0
 .../atc138/data}/systems.csv                  |    0
 .../atc138/data}/temp_repair_class.csv        |    0
 .../data}/tenant_function_requirements.csv    |    0
 src/atc138/driver.py                          |  198 ++
 src/atc138/engine.py                          |  103 +
 src/atc138/functionality/__init__.py          |    0
 .../fn_calculate_functionality.py             |   80 +
 .../functionality/fn_calculate_reoccupancy.py |   97 +
 .../functionality/fn_check_habitability.py    |   59 +
 .../main_functionality_function.py            |   65 +
 .../other_functionality_functions.py          | 1688 +++++++++++++++++
 src/atc138/impedance/__init__.py              |    0
 .../impedance/main_impedance_function.py      |  317 ++++
 .../impedance/other_impedance_functions.py    |  427 +++++
 src/atc138/input_builder.py                   |  476 +++++
 src/atc138/preprocessing/__init__.py          |    0
 .../preprocessing/main_preprocessing.py       |   64 +
 src/atc138/preprocessing/preprocessing_fns.py |  358 ++++
 src/atc138/red_tag.py                         |  168 ++
 src/atc138/repair_schedule/__init__.py        |    0
 .../repair_schedule/main_repair_schedule.py   |  160 ++
 .../other_repair_schedule_functions.py        |  900 +++++++++
 122 files changed, 5956 insertions(+), 8674 deletions(-)
 delete mode 100644 +plotters/main_plot_functionality.m
 delete mode 100644 +plotters/plt_gantt_chart.m
 delete mode 100644 +plotters/plt_heatmap_breakdowns.m
 delete mode 100644 +plotters/plt_histograms.m
 delete mode 100644 +plotters/plt_recovery_trajectory.m
 delete mode 100644 +preprocessing/fn_create_fnc_filters.m
 delete mode 100644 +preprocessing/fn_define_door_racking.m
 delete mode 100644 +preprocessing/fn_populate_damage_per_tu.m
 delete mode 100644 +preprocessing/fn_simulate_damage_per_side.m
 delete mode 100644 +preprocessing/fn_simulate_temp_worker_days.m
 delete mode 100644 +preprocessing/main_preprocessing.m
 delete mode 100644 +recovery/+functionality/fn_building_level_system_operation.m
 delete mode 100644 +recovery/+functionality/fn_building_safety.m
 delete mode 100644 +recovery/+functionality/fn_calc_subsystem_recovery.m
 delete mode 100644 +recovery/+functionality/fn_calculate_functionality.m
 delete mode 100644 +recovery/+functionality/fn_calculate_reoccupancy.m
 delete mode 100644 +recovery/+functionality/fn_check_habitability.m
 delete mode 100644 +recovery/+functionality/fn_combine_comp_breakdown.m
 delete mode 100644 +recovery/+functionality/fn_extract_recovery_metrics.m
 delete mode 100644 +recovery/+functionality/fn_story_access.m
 delete mode 100644 +recovery/+functionality/fn_tenant_function.m
 delete mode 100644 +recovery/+functionality/fn_tenant_safety.m
 delete mode 100644 +recovery/+functionality/main_functionality.m
 delete mode 100644 +recovery/+impedance/fn_contractor.m
 delete mode 100644 +recovery/+impedance/fn_default_surge_factor.m
 delete mode 100644 +recovery/+impedance/fn_engineering.m
 delete mode 100644 +recovery/+impedance/fn_financing.m
 delete mode 100644 +recovery/+impedance/fn_inspection.m
 delete mode 100644 +recovery/+impedance/fn_permitting.m
 delete mode 100644 +recovery/+impedance/main_impeding_factors.m
 delete mode 100644 +recovery/+repair_schedule/fn_allocate_workers_stories.m
 delete mode 100644 +recovery/+repair_schedule/fn_allocate_workers_systems.m
 delete mode 100644 +recovery/+repair_schedule/fn_calc_system_repair_time.m
 delete mode 100644 +recovery/+repair_schedule/fn_format_gantt_chart_data.m
 delete mode 100644 +recovery/+repair_schedule/fn_prioritize_systems.m
 delete mode 100644 +recovery/+repair_schedule/fn_restructure_repair_schedule.m
 delete mode 100644 +recovery/+repair_schedule/fn_set_repair_constraints.m
 delete mode 100644 +recovery/+repair_schedule/main_repair_schedule.m
 delete mode 100644 build_example_inputs.py
 create mode 100644 demo.ipynb
 delete mode 100644 driver_PBEErecovery.m
 delete mode 100644 driver_convert_PELICUN.m
 rename {inputs/example_inputs => examples}/ICSB/building_model.json (100%)
 rename {inputs/example_inputs => examples}/ICSB/comp_ds_list.csv (100%)
 rename {inputs/example_inputs => examples}/ICSB/comp_population.csv (100%)
 rename {inputs/example_inputs => examples}/ICSB/damage_consequences.json (100%)
 rename {inputs/example_inputs => examples}/ICSB/simulated_damage.json (100%)
 rename {inputs/example_inputs => examples}/ICSB/tenant_unit_list.csv (100%)
 rename {inputs/example_inputs => examples}/RCSW_1story/building_model.json (100%)
 rename {inputs/example_inputs => examples}/RCSW_1story/comp_ds_list.csv (100%)
 rename {inputs/example_inputs => examples}/RCSW_1story/comp_population.csv (100%)
 rename {inputs/example_inputs => examples}/RCSW_1story/damage_consequences.json (100%)
 rename {inputs/example_inputs => examples}/RCSW_1story/simulated_damage.json (100%)
 rename {inputs/example_inputs => examples}/RCSW_1story/tenant_unit_list.csv (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_12story/building_model.json (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_12story/comp_ds_list.csv (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_12story/comp_population.csv (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_12story/damage_consequences.json (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_12story/simulated_damage.json (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_12story/tenant_unit_list.csv (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_4story/building_model.json (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_4story/comp_ds_list.csv (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_4story/comp_population.csv (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_4story/damage_consequences.json (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_4story/simulated_damage.json (100%)
 rename {inputs/example_inputs => examples}/haseltonRCMF_4story/tenant_unit_list.csv (100%)
 delete mode 100644 fn_red_tag.m
 delete mode 100644 inputs/Inputs2Copy/build_inputs.m
 delete mode 100644 inputs/Inputs2Copy/optional_inputs.m
 delete mode 100644 inputs/example_inputs/ICSB/simulated_inputs.mat
 delete mode 100644 inputs/example_inputs/RCSW_1story/simulated_inputs.mat
 delete mode 100644 inputs/example_inputs/haseltonRCMF_12story/simulated_inputs.mat
 delete mode 100644 inputs/example_inputs/haseltonRCMF_4story/simulated_inputs.mat
 delete mode 100644 inputs/example_pelicun_inputs/001/general_inputs.json
 delete mode 100644 inputs/example_pelicun_inputs/001/pelicun_data/CMP_QNT.csv
 delete mode 100644 inputs/example_pelicun_inputs/001/pelicun_data/DL_summary.csv
 delete mode 100644 inputs/example_pelicun_inputs/001/pelicun_data/DMG_sample.csv
 delete mode 100644 inputs/example_pelicun_inputs/001/pelicun_data/DV_bldg_repair_sample.csv
 delete mode 100644 inputs/example_pelicun_inputs/001/pelicun_data/input.json
 delete mode 100644 inputs/example_pelicun_inputs/001/tenant_unit_list.csv
 delete mode 100644 main_PBEErecovery.m
 delete mode 100644 plotModel_PBEErecovery.m
 create mode 100644 plotModel_PBEErecovery.py
 create mode 100644 plotters/main_plot_functionality.py
 create mode 100644 plotters/other_plot_functions.py
 create mode 100644 pyproject.toml
 create mode 100644 requirements.txt
 delete mode 100644 schema/Inputs.json
 delete mode 100644 schema/Optional Inputs.json
 delete mode 100644 schema/Outputs.json
 create mode 100644 src/atc138/__init__.py
 create mode 100644 src/atc138/cli.py
 rename {static_tables => src/atc138/data}/README.md (100%)
 rename {static_tables => src/atc138/data}/component_attributes.csv (100%)
 rename {static_tables => src/atc138/data}/damage_state_attribute_mapping.csv (100%)
 create mode 100644 src/atc138/data/default_inputs.json
 rename {static_tables => src/atc138/data}/impeding_factors.csv (100%)
 rename {static_tables => src/atc138/data}/subsystems.csv (100%)
 rename {static_tables => src/atc138/data}/systems.csv (100%)
 rename {static_tables => src/atc138/data}/temp_repair_class.csv (100%)
 rename {static_tables => src/atc138/data}/tenant_function_requirements.csv (100%)
 create mode 100644 src/atc138/driver.py
 create mode 100644 src/atc138/engine.py
 create mode 100644 src/atc138/functionality/__init__.py
 create mode 100644 src/atc138/functionality/fn_calculate_functionality.py
 create mode 100644 src/atc138/functionality/fn_calculate_reoccupancy.py
 create mode 100644 src/atc138/functionality/fn_check_habitability.py
 create mode 100644 src/atc138/functionality/main_functionality_function.py
 create mode 100644 src/atc138/functionality/other_functionality_functions.py
 create mode 100644 src/atc138/impedance/__init__.py
 create mode 100644 src/atc138/impedance/main_impedance_function.py
 create mode 100644 src/atc138/impedance/other_impedance_functions.py
 create mode 100644 src/atc138/input_builder.py
 create mode 100644 src/atc138/preprocessing/__init__.py
 create mode 100644 src/atc138/preprocessing/main_preprocessing.py
 create mode 100644 src/atc138/preprocessing/preprocessing_fns.py
 create mode 100644 src/atc138/red_tag.py
 create mode 100644 src/atc138/repair_schedule/__init__.py
 create mode 100644 src/atc138/repair_schedule/main_repair_schedule.py
 create mode 100644 src/atc138/repair_schedule/other_repair_schedule_functions.py

diff --git a/+plotters/main_plot_functionality.m b/+plotters/main_plot_functionality.m
deleted file mode 100644
index dc2f6b5..0000000
--- a/+plotters/main_plot_functionality.m
+++ /dev/null
@@ -1,52 +0,0 @@
-function [] = main_plot_functionality(functionality, save_dir, p_gantt)
-% Plot function and occupancy loss and recovery at for a single model at a 
-% single intensity levels
-%
-% Parameters
-% ----------
-% functionality: structure
-%   main output data strcuture of the functional recovery assessment. 
-%   Loaded directly from the output mat file.
-% save_dir: str
-%   Save directory for plots. Plots will save directly to this location as
-%   png files.
-% p_gantt: int
-%   percentile of functional recovery time to plot gantt chart
-%
-% Returns
-% -------
-% 
-
-%% Initial Setup
-% Import Packages
-import plotters.*
-
-% Set plot variables to use
-recovery = functionality.recovery;
-impede = functionality.impeding_factors.breakdowns;
-schedule = functionality.building_repair_schedule;
-workers = functionality.worker_data;
-full_repair_time = max(schedule.repair_complete_day.per_story,[],2);
-
-%% Plot Performance Objective Grid for system and component breakdowns
-plot_dir = [save_dir filesep 'breakdowns'];
-plt_heatmap_breakdowns( recovery, plot_dir )
-
-%% Plot Performance Target Distribution Across all Realizations
-plot_dir = [save_dir filesep 'histograms'];
-plt_histograms( recovery, plot_dir )
-
-%% Plot Mean Recovery Trajectories
-plot_dir = [save_dir filesep 'recovery trajectories'];
-plt_recovery_trajectory( recovery, full_repair_time, plot_dir)
-
-%% Plot Gantt Charts
-plot_dir = [save_dir filesep 'gantt_charts'];
-fr_time = functionality.recovery.functional.building_level.recovery_day;
-p_val = prctile(fr_time,p_gantt);
-p_idx = find(abs(fr_time - p_val) == min(abs(fr_time - p_val)),1); % Find the index of the first realization that matches the selected percentile
-plot_name = ['prt_' num2str(p_gantt)];
-plt_gantt_chart( p_idx, recovery, full_repair_time, workers, schedule, impede, plot_dir, plot_name )
-
-end
-
diff --git a/+plotters/plt_gantt_chart.m b/+plotters/plt_gantt_chart.m
deleted file mode 100644
index e620dd7..0000000
--- a/+plotters/plt_gantt_chart.m
+++ /dev/null
@@ -1,164 +0,0 @@
-function [] = plt_gantt_chart( p_idx, recovery, full_repair_time, workers, schedule, impede, plot_dir, plot_name )
-% Plot gantt chart for a single realization of the functional recovery
-% assessment
-%
-% Parameters
-% ----------
-% p_idx: int
-%   realization index of interest
-% recovery: structure
-%   data structure containing recovery trajectory data from the functional
-%   recovery assessment
-% full_repair_time: array [num reals x 1] 
-%   simulated realization of full repair time 
-% workers: structure
-%   data structure containing work allocation data from the functional
-%   recovery assessment
-% schedule: structure
-%   data structure containing repair schedule data from the functional
-%   recovery assessment
-% impede: structure
-%   data structure containing impedance time data from the functional
-%   recovery assessment
-% plot_dir: str
-%   Save directory for plots. Plots will save directly to this location as
-%   png files.
-% plot_name: str
-%   name of file to save plot as (does not include the file type extension)
-%
-% Returns
-% -------
-% 
-%% Intial Setup
-if ~exist(plot_dir,'dir')
-    mkdir(plot_dir)
-end
-
-imps = fieldnames(impede);
-sys = fieldnames(impede.contractor_mob);
-
-%% Format Gantt Chart Data
-% Collect Recovery Trajectory
-recovery_trajectory.reoc = recovery.reoccupancy.recovery_trajectory.recovery_day(p_idx,:);
-recovery_trajectory.func = recovery.functional.recovery_trajectory.recovery_day(p_idx,:);
-recovery_trajectory.level_of_repair = recovery.reoccupancy.recovery_trajectory.percent_recovered;
-recovery_trajectory.ful_rep = ones(1,length(recovery_trajectory.level_of_repair)) * ceil(full_repair_time(p_idx));
-
-% Collect Worker Data
-worker_data.total_workers = workers.total_workers(p_idx,:);
-worker_data.day_vector = workers.day_vector(p_idx,:);
-
-% Collect Impedance Times
-sys_imp_times = [];
-labs = [];
-for s = 1:length(sys)
-    for i = 1:length(imps)
-        if isfield(impede.(imps{i}),'complete_day')
-            duration = impede.(imps{i}).complete_day(p_idx) - impede.(imps{i}).start_day(p_idx);
-            if duration > 0
-                sys_imp_times(i,:) = [impede.(imps{i}).start_day(p_idx), duration];
-                labs{i} = [upper(imps{i}(1)) strrep(imps{i}(2:end),'_',' ')];
-            end
-        elseif isfield(impede.(imps{i}),sys{s})
-            duration = impede.(imps{i}).(sys{s}).complete_day(p_idx) - impede.(imps{i}).(sys{s}).start_day(p_idx);
-            if duration > 0
-                sys_imp_times(end+1,:) = [impede.(imps{i}).(sys{s}).start_day(p_idx), duration];
-                labs{end+1} = [upper(sys{s}(1)) sys{s}(2:end) ' ' strrep(imps{i},'_',' ')];
-            end
-        end
-    end
-end
-labs_imp = flip(labs);
-y_imp = flip(sys_imp_times);
-
-% Collect Repair Times 
-sys_repair_times = [];
-labs = [];
-for s = 1:length(sys) % WARNING: This assumes the system order in the output of repair schedule is the same order has the impedance breakdowns
-    duration = schedule.repair_complete_day.per_system(p_idx,s) - schedule.repair_start_day.per_system(p_idx,s);
-    if duration > 0
-        sys_repair_times(end+1,:) = [schedule.repair_start_day.per_system(p_idx,s),duration];
-        labs{end+1} = [upper(sys{s}(1)) sys{s}(2:end) ' Repairs'];
-    end
-end
-labs_rep = flip(labs);
-y_rep = flip(sys_repair_times);
-
-%% Plot Gantt
-x_limit = max(ceil(max(recovery_trajectory.ful_rep)/10)*10,1);
-
-% Set Plot Layout
-pos = [0.25    0.63     0.7    0.34;...
-       0.25    0.41     0.7    0.19;...
-       0.25    0.24     0.7    0.14;...
-       0.25    0.065    0.7    0.14];
-
-% Impedance Time
-subplot('Position',pos(1,:))
-x = categorical(labs_imp);
-x = reordercats(x,labs_imp);
-G = barh(x,y_imp,'stacked');
-G(1).HandleVisibility = 'off';
-G(2).DisplayName = 'Repair Time';
-set(G(1),'Visible','off')
-G(2).FaceColor = [0.6, 0.6, 0.6];
-G(2).FaceAlpha = 0.5;
-G(2).EdgeAlpha = 0;
-% set(gca,'YColor','k')
-fn_format_subplot(gca,x_limit,[],[],'Impedance Time')
-
-% Repair Time
-subplot('Position',pos(2,:))
-x = categorical(labs_rep);
-x = reordercats(x,labs_rep);
-H = barh(x,y_rep,'stacked');
-H(1).HandleVisibility = 'off';
-H(2).DisplayName = 'Repair Time';
-set(H(1),'Visible','off')
-H(2).FaceColor = [0.1, 0.1, 0.1];
-H(2).FaceAlpha = 0.5;
-H(2).EdgeAlpha = 0;
-% set(gca,'YColor','k')
-fn_format_subplot(gca,x_limit,[],[],'Repair Time')
-
-% Workers
-subplot('Position',pos(3,:))
-hold on
-plot(worker_data.day_vector, worker_data.total_workers)
-fn_format_subplot(gca,x_limit,{'Number of', 'Workers'},[],'Workers')
-
-% Plot Recovery Trajectory
-subplot('Position',pos(4,:))
-hold on
-plot(recovery_trajectory.reoc, recovery_trajectory.level_of_repair,'-r','LineWidth',1.5,'DisplayName','Re-Occupancy')
-plot(recovery_trajectory.func, recovery_trajectory.level_of_repair,'-b','LineWidth',1.5,'DisplayName','Functional')
-plot(recovery_trajectory.ful_rep, recovery_trajectory.level_of_repair,'-k','LineWidth',1.5,'DisplayName','Fully Repaired')
-ylim([0,1])
-lgd = legend('location','northeast');
-set(lgd,'position',[0.07    0.11    0.05   0.05])
-fn_format_subplot(gca,x_limit,'Fraction of Floor Area','Days After Earthquake','Building Recovery State')
-
-% Set and Save plot
-set(gcf,'position',[10,10,800,600])
-saveas(gcf,[plot_dir filesep plot_name],'png')
-close
-
-end
-
-function [] = fn_format_subplot(ax,x_limit,y_lab,x_lab,tle)
-    ax.XGrid = 'on';
-    ax.XMinorGrid = 'on';
-    xlim([0,x_limit])
-    box on
-    set(gca,'fontname','times')
-    set(gca,'fontsize',9)
-    if ~isempty(y_lab)
-        ylabel(y_lab)
-    end
-    if ~isempty(x_lab)
-        xlabel(x_lab)
-    else
-        set(gca,'XTickLabel',[])
-    end
-    title(tle)
-end
\ No newline at end of file
diff --git a/+plotters/plt_heatmap_breakdowns.m b/+plotters/plt_heatmap_breakdowns.m
deleted file mode 100644
index ce8b60b..0000000
--- a/+plotters/plt_heatmap_breakdowns.m
+++ /dev/null
@@ -1,46 +0,0 @@
-function [] = plt_heatmap_breakdowns( recovery, plot_dir )
-% Plot the time and percent of realizations that each system and/or 
-% component is impeding function as a heatmap
-%
-% Parameters
-% ----------
-% recovery: structure
-%   data structure containing recovery breakdown data from the functional
-%   recovery assessment
-% plot_dir: str
-%   Save directory for plots. Plots will save directly to this location as
-%   png files.
-%
-% Returns
-% -------
-% 
-%% Initial Setup
-if ~exist(plot_dir,'dir')
-    mkdir(plot_dir)
-end
-
-var = {'component_breakdowns', 'system_breakdowns'};
-labs = {'comp_names', 'system_names'};
-plt_ht = [550, 350];
-
-%% Plot Heatmaps
-fnc_states = fieldnames(recovery);
-for fs = 1:length(fnc_states)
-    for v = 1:length(var)
-        y_labs = recovery.(fnc_states{fs}).breakdowns.(labs{v});
-        data = recovery.(fnc_states{fs}).breakdowns.(var{v});
-        perform_targ_labs = recovery.(fnc_states{fs}).breakdowns.perform_targ_days;
-        h = heatmap(perform_targ_labs, strrep(y_labs,'_',' '), round(data,2));
-        h.MissingDataColor = 1-h.MissingDataColor;
-        fnc_lab = [upper(fnc_states{fs}(1)) fnc_states{fs}(2:end) ' Recovery'];
-        title(['Fraction of Realizations Affecting Building ' fnc_lab])
-        xlabel('Recovery Time After Earthquake (days)')
-        set(gca,'fontname','times')
-        set(gcf,'position',[10,10,650,plt_ht(v)])
-        saveas(gcf,[plot_dir filesep var{v} '_' fnc_states{fs}],'png')
-        close
-    end
-end
-
-end
-
diff --git a/+plotters/plt_histograms.m b/+plotters/plt_histograms.m
deleted file mode 100644
index 27652de..0000000
--- a/+plotters/plt_histograms.m
+++ /dev/null
@@ -1,34 +0,0 @@
-function [] = plt_histograms( recovery, plot_dir )
-% Plot all realizations of building level recovery as a histogram
-%
-% Parameters
-% ----------
-% recovery: structure
-%   data structure containing building-level recovery data from the 
-%   functional recovery assessment
-% plot_dir: str
-%   Save directory for plots. Plots will save directly to this location as
-%   png files.
-%
-% Returns
-% -------
-% 
-%% Initial Setup
-if ~exist(plot_dir,'dir')
-    mkdir(plot_dir)
-end
-
-%% Plot Histograms
-fnc_states = fieldnames(recovery);
-for fs = 1:length(fnc_states)
-    histogram(recovery.functional.building_level.recovery_day)
-    fnc_lab = [upper(fnc_states{fs}(1)) fnc_states{fs}(2:end) ' Recovery'];
-    xlabel([fnc_lab ' Time (days)'])
-    ylabel('Number of Realizatons')
-    box on
-    saveas(gcf,[plot_dir filesep fnc_states{fs}],'png')
-    close
-end
-
-end
-
diff --git a/+plotters/plt_recovery_trajectory.m b/+plotters/plt_recovery_trajectory.m
deleted file mode 100644
index 7af714a..0000000
--- a/+plotters/plt_recovery_trajectory.m
+++ /dev/null
@@ -1,47 +0,0 @@
-function [] = plt_recovery_trajectory( recovery, full_repair_time, plot_dir)
-% Plot mean recovery trajectories
-%
-% Parameters
-% ----------
-% recovery: structure
-%   data structure containing recovery trajectory data from the functional
-%   recovery assessment
-% full_repair_time: array [num reals x 1] 
-%   simulated realization of full repair time 
-% plot_dir: str
-%   Save directory for plots. Plots will save directly to this location as
-%   png files.
-%
-% Returns
-% -------
-% 
-
-%% Initial Setup
-if ~exist(plot_dir,'dir')
-    mkdir(plot_dir)
-end
-
-% Calculate mean recovery times
-reoc = mean(recovery.reoccupancy.recovery_trajectory.recovery_day);
-func = mean(recovery.functional.recovery_trajectory.recovery_day);
-full = mean(full_repair_time);
-level_of_repair = recovery.functional.recovery_trajectory.percent_recovered;
-
-%% Plot Recovery Trajectory
-hold on 
-plot(reoc, level_of_repair,'r','LineWidth',1.5,'DisplayName','Re-Occupancy') 
-plot(func, level_of_repair,'b','LineWidth',1.5,'DisplayName','Functional') 
-plot([full full], [0 1],'k','LineWidth',1.5,'DisplayName','Fully Repaired') 
-xlim([0,ceil((full+1)/10)*10])
-xlabel('Days After Earthquake')
-ylabel('Fraction of Floor Area')
-box on
-grid on
-legend('location', 'Northwest');
-set(gca,'fontname','times')
-set(gcf,'position',[10,10,500,300])
-saveas(gcf,[plot_dir filesep 'recovery trajectory'],'png')
-close
-
-end
-
diff --git a/+preprocessing/fn_create_fnc_filters.m b/+preprocessing/fn_create_fnc_filters.m
deleted file mode 100644
index 22a09f1..0000000
--- a/+preprocessing/fn_create_fnc_filters.m
+++ /dev/null
@@ -1,177 +0,0 @@
-function [ fnc_filters ] = fn_create_fnc_filters( comp_ds_table )
-% Define function filter arrays that allow rapid sampling of simulated
-% damage for use within the fault tree analysis
-%
-% Parameters
-% ----------
-% comp_ds_table: table
-%   various component attributes by damage state for each component 
-%   within the performance model. Can be populated from the 
-%   component_attribites.csv and damage_state_attribute_mapping.csv 
-%   databases in the static_tables directory.  Each row of the table 
-%   corresponds to each column of the simulated component damage arrays 
-%   within damage.tenant_units.
-%
-% Returns
-% -------
-% fnc_filters: struct of arrays
-%   each filter is a 1 x num_comp_ds array that can be used to sample
-%   select types of damage from the damage.tenant unit or damage.story
-%   simulated damage arrays.
-%
-
-%% Impedance filters
-fnc_filters.permit_rapid = strcmp(comp_ds_table.permit_type, 'rapid');
-fnc_filters.permit_full = strcmp(comp_ds_table.permit_type, 'full');
-fnc_filters.redesign = comp_ds_table.redesign == 1;
-
-%% Building level filters
-% combine all damage state filters that have the potential to affect
-% function or reoccupancy, other than structural safety damage (for repair
-% prioritization)
-fnc_filters.affects_reoccupancy = comp_ds_table.affects_envelope_safety | ...
-                                  comp_ds_table.ext_falling_hazard | ...
-                                  comp_ds_table.int_falling_hazard | ...
-                                  comp_ds_table.global_hazardous_material | ...
-                                  comp_ds_table.local_hazardous_material | ...
-                                  comp_ds_table.affects_access; 
-                              
-fnc_filters.affects_function = fnc_filters.affects_reoccupancy | ...
-                               comp_ds_table.damages_envelope_seal | ...
-                               comp_ds_table.obstructs_interior_space | ...
-                               comp_ds_table.impairs_system_operation;
-
-% Define when building has resolved its red tag (when all repairs are complete that may affect red tags)
-% get any components that have the potential to cause red tag
-fnc_filters.red_tag = comp_ds_table.safety_class > 0; 
-
-% Define when the building requires shoring from external falling hazards
-fnc_filters.requires_shoring = logical(comp_ds_table.requires_shoring);
-
-% Define when the building has issues with internal flooding
-fnc_filters.causes_flooding = logical(comp_ds_table.causes_flooding);
-
-%% System dependent filters
-% fire suppresion system damage that affects entire building
-fnc_filters.fire_building = comp_ds_table.system == 9 & strcmp(comp_ds_table.service_location,'building') & comp_ds_table.impairs_system_operation;
-
-% fire suppresion damage that affects each tenant unit
-fnc_filters.fire_unit = comp_ds_table.system == 9 & ~comp_ds_table.subsystem_id == 23 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation; % pipe and brace branches (not spinkler heads)
-fnc_filters.fire_drops = comp_ds_table.subsystem_id == 23 & comp_ds_table.impairs_system_operation;
-
-% Hazardous materials
-fnc_filters.global_hazardous_material = comp_ds_table.global_hazardous_material;
-fnc_filters.local_hazardous_material = comp_ds_table.local_hazardous_material;
-
-% Stairs
-fnc_filters.stairs = comp_ds_table.affects_access & comp_ds_table.system == 4;
-
-% Exterior enclosure damage
-fnc_filters.exterior_safety_lf = strcmp(string(comp_ds_table.unit),'lf') & comp_ds_table.affects_envelope_safety; % Components with perimeter linear feet units
-fnc_filters.exterior_safety_sf = strcmp(string(comp_ds_table.unit),'sf') & comp_ds_table.affects_envelope_safety; % Components with perimeter square feet units
-fnc_filters.exterior_safety_all = fnc_filters.exterior_safety_lf | fnc_filters.exterior_safety_sf;
-
-% Exterior Falling hazards
-fnc_filters.ext_fall_haz_lf = strcmp(string(comp_ds_table.unit),'lf') & comp_ds_table.ext_falling_hazard; % Components with perimeter linear feet units
-fnc_filters.ext_fall_haz_sf = strcmp(string(comp_ds_table.unit),'sf') & comp_ds_table.ext_falling_hazard; % Components with square feet units
-fnc_filters.ext_fall_haz_ea = strcmp(string(comp_ds_table.unit),'each') & comp_ds_table.ext_falling_hazard; % Components with "each" units
-fnc_filters.ext_fall_haz_all = fnc_filters.ext_fall_haz_lf | ...
-                                fnc_filters.ext_fall_haz_sf | ...
-                                fnc_filters.ext_fall_haz_ea;
-
-% Exterior enclosure envelope seal damage
-fnc_filters.exterior_seal_lf = strcmp(string(comp_ds_table.unit),'lf') & comp_ds_table.damages_envelope_seal; % Components with perimeter linear feet units
-fnc_filters.exterior_seal_sf = strcmp(string(comp_ds_table.unit),'sf') & comp_ds_table.damages_envelope_seal; % Components with perimeter square feet units
-fnc_filters.exterior_seal_ea = strcmp(string(comp_ds_table.unit),'each') & comp_ds_table.damages_envelope_seal; % Components with perimeter square feet units
-fnc_filters.exterior_seal_all = fnc_filters.exterior_seal_lf | ...
-                                fnc_filters.exterior_seal_sf | ...
-                                fnc_filters.exterior_seal_ea;
-
-% Roofing components
-fnc_filters.roof_structure       = comp_ds_table.subsystem_id == 21 & comp_ds_table.affects_roof_function;
-fnc_filters.roof_weatherproofing = comp_ds_table.subsystem_id == 22 & comp_ds_table.affects_roof_function;
-
-% Interior falling hazards
-fnc_filters.int_fall_haz_lf = comp_ds_table.int_falling_hazard & strcmp(string(comp_ds_table.unit), 'lf') & strcmp(comp_ds_table.interior_area_conversion_type, 'component'); % Interior components with perimeter feet units
-fnc_filters.int_fall_haz_sf = comp_ds_table.int_falling_hazard & strcmp(string(comp_ds_table.unit), 'sf') & strcmp(comp_ds_table.interior_area_conversion_type, 'component'); % Interior components with area feet units
-fnc_filters.int_fall_haz_ea = comp_ds_table.int_falling_hazard & strcmp(string(comp_ds_table.unit), 'each') & strcmp(comp_ds_table.interior_area_conversion_type, 'component'); % Interior components with "each" for units (just scale directly)
-fnc_filters.int_fall_haz_bay = comp_ds_table.int_falling_hazard & strcmp(comp_ds_table.interior_area_conversion_type, 'bay'); % structural damage that does not cause red tags but affects function
-fnc_filters.int_fall_haz_build = comp_ds_table.int_falling_hazard & strcmp(comp_ds_table.interior_area_conversion_type, 'building'); % structural damage that does not cause red tags but affects funciton (this one should only be tilt-ups)
-fnc_filters.int_fall_haz_all = fnc_filters.int_fall_haz_lf | ...
-                                fnc_filters.int_fall_haz_sf | ...
-                                fnc_filters.int_fall_haz_ea | ...
-                                fnc_filters.int_fall_haz_bay | ...
-                                fnc_filters.int_fall_haz_build;
-
-% Flag structural damage that causes interior falling hazards
-fnc_filters.vert_instabilities = comp_ds_table.system == 1 & comp_ds_table.int_falling_hazard;
-
-% Interior function damage 
-fnc_filters.interior_function_lf = comp_ds_table.obstructs_interior_space & strcmp(string(comp_ds_table.unit), 'lf') & strcmp(comp_ds_table.interior_area_conversion_type, 'component'); 
-fnc_filters.interior_function_sf = comp_ds_table.obstructs_interior_space & (strcmp(string(comp_ds_table.unit), 'sf')) & strcmp(comp_ds_table.interior_area_conversion_type, 'component');  
-fnc_filters.interior_function_ea = comp_ds_table.obstructs_interior_space & (strcmp(string(comp_ds_table.unit), 'each')) & strcmp(comp_ds_table.interior_area_conversion_type, 'component'); 
-fnc_filters.interior_function_bay = comp_ds_table.obstructs_interior_space & strcmp(comp_ds_table.interior_area_conversion_type, 'bay');
-fnc_filters.interior_function_build = comp_ds_table.obstructs_interior_space & strcmp(comp_ds_table.interior_area_conversion_type,'building');  
-fnc_filters.interior_function_all = fnc_filters.interior_function_lf | ...
-                                    fnc_filters.interior_function_sf | ...
-                                    fnc_filters.interior_function_ea | ...
-                                    fnc_filters.interior_function_bay | ...
-                                    fnc_filters.interior_function_build;  
-
-% Elevators
-fnc_filters.elevators    = comp_ds_table.system == 5 & comp_ds_table.impairs_system_operation & comp_ds_table.subsystem_id ~= 2;
-fnc_filters.elevator_mcs = comp_ds_table.system == 5 & comp_ds_table.impairs_system_operation & comp_ds_table.subsystem_id == 2;
-
-% Electrical system
-fnc_filters.electrical_main = comp_ds_table.system == 7 & comp_ds_table.subsystem_id == 1 & strcmp(comp_ds_table.service_location,'building') & comp_ds_table.impairs_system_operation;
-fnc_filters.electrical_unit = comp_ds_table.system == 7 & comp_ds_table.subsystem_id == 1 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation;
-
-% Potable Water Plumbing
-fnc_filters.water_main = comp_ds_table.system == 6 & comp_ds_table.subsystem_id == 8 & strcmp(comp_ds_table.service_location,'building') & comp_ds_table.impairs_system_operation;
-fnc_filters.water_unit = comp_ds_table.system == 6 & comp_ds_table.subsystem_id == 8 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation; 
-
-% Sanitary Plumbing
-fnc_filters.sewer_main = comp_ds_table.system == 6 & comp_ds_table.subsystem_id == 9 & strcmp(comp_ds_table.service_location,'building') & comp_ds_table.impairs_system_operation;
-fnc_filters.sewer_unit = comp_ds_table.system == 6 & comp_ds_table.subsystem_id == 9 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation; 
-
-% HVAC: Control System
-fnc_filters.hvac.building.hvac_control.mcs = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 2 & comp_ds_table.impairs_system_operation;
-fnc_filters.hvac.building.hvac_control.control_panel = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 20 & comp_ds_table.impairs_system_operation ;
-
-% HVAC: Ventilation
-fnc_filters.hvac.tenant.hvac_ventilation.duct_mains = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 24 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation;
-fnc_filters.hvac.tenant.hvac_ventilation.duct_braches = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 4 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation;
-fnc_filters.hvac.tenant.hvac_ventilation.in_line_fan = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 5 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation; 
-fnc_filters.hvac.tenant.hvac_ventilation.duct_drops = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 6 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation;
-fnc_filters.hvac.tenant.hvac_ventilation.ahu = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 19 & comp_ds_table.impairs_system_operation;
-fnc_filters.hvac.tenant.hvac_ventilation.rtu = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 17 & comp_ds_table.impairs_system_operation;
-
-% HVAC: Heating
-fnc_filters.hvac.building.hvac_heating.piping = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 10 & comp_ds_table.impairs_system_operation; 
-fnc_filters.hvac.tenant.hvac_heating.vav = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 7 & comp_ds_table.impairs_system_operation; 
-
-% HVAC: Cooling
-fnc_filters.hvac.building.hvac_cooling.piping = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 11 & comp_ds_table.impairs_system_operation; 
-fnc_filters.hvac.building.hvac_cooling.chiller = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 15 & comp_ds_table.impairs_system_operation; 
-fnc_filters.hvac.building.hvac_cooling.cooling_tower = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 16 & comp_ds_table.impairs_system_operation; 
-fnc_filters.hvac.tenant.hvac_cooling.vav = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 7 & comp_ds_table.impairs_system_operation; 
-
-% HVAC: Exhaust
-fnc_filters.hvac.tenant.hvac_exhaust.exhaust_fan = comp_ds_table.system == 8 & comp_ds_table.subsystem_id == 18 & comp_ds_table.impairs_system_operation;
-
-% Data system
-fnc_filters.data_main = comp_ds_table.system == 11 & strcmp(comp_ds_table.service_location,'building') & comp_ds_table.impairs_system_operation;
-fnc_filters.data_unit = comp_ds_table.system == 11 & strcmp(comp_ds_table.service_location,'unit') & comp_ds_table.impairs_system_operation;
-
-% Horizontal Egress: Fire break partitions
-fnc_filters.fire_break = comp_ds_table.system == 3 & comp_ds_table.weakens_fire_break; % only collect interior fire break partitions
-
-%% Flip orientation of fnc_filters to match orientation of damage data [reals x ds]
-names = fieldnames(fnc_filters);
-for fn = 1:length(names)
-    tmp_fnc_filt.(names{fn}) = fnc_filters.(names{fn})';
-end
-fnc_filters = tmp_fnc_filt; % assign to damage data structure
-
-end
-
diff --git a/+preprocessing/fn_define_door_racking.m b/+preprocessing/fn_define_door_racking.m
deleted file mode 100644
index 25b981b..0000000
--- a/+preprocessing/fn_define_door_racking.m
+++ /dev/null
@@ -1,36 +0,0 @@
-function [ damage_consequences ] = fn_define_door_racking( damage_consequences, num_stories )
-% Define building level damage consquences when not specificed by the user.
-%
-% Parameters
-% ----------
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-%   tags
-% num_stories: int
-%   Integer number of stories in the building being assessed
-%
-% Returns
-% -------
-% damage_consequences.racked_stair_doors_per_story: array, num real x num stories
-%   simulated number of racked stairwell doors at each story
-% damage_consequences.racked_entry_doors_side_1: array, num real x 1
-%   simulated number of racked entry doors on one side of the building
-% damage_consequences.racked_entry_doors_side_2: array, num real x 1
-%   simulated number of racked entry doors on the other side of the building
-
-%% Set door racking damage if not provided by user
-[num_reals, ~] = size(damage_consequences.simulated_replacement_time);
-
-% Assume there are no racked doors if not specified by the user
-if ~isfield(damage_consequences,'racked_stair_doors_per_story')
-    damage_consequences.racked_stair_doors_per_story = zeros(num_reals,num_stories); % 
-end
-if ~isfield(damage_consequences,'racked_entry_doors_side_1')
-    damage_consequences.racked_entry_doors_side_1 = zeros(num_reals,1); % array, num real x num stories
-end
-if ~isfield(damage_consequences,'racked_entry_doors_side_2')
-    damage_consequences.racked_entry_doors_side_2 = zeros(num_reals,1); % array, num real x num stories
-end
-
-end
-
diff --git a/+preprocessing/fn_populate_damage_per_tu.m b/+preprocessing/fn_populate_damage_per_tu.m
deleted file mode 100644
index 1d6d627..0000000
--- a/+preprocessing/fn_populate_damage_per_tu.m
+++ /dev/null
@@ -1,24 +0,0 @@
-function [ damage ] = fn_populate_damage_per_tu( damage )
-% Check to see if the damage.tenant_units variable has been defined. If
-% not, assume its the same as damage.story. This assumption creates a 1:1
-% coupling between stories and tenant_units (one tenant unit per story)
-%
-% Parameters
-% ----------
-% damage.story: array of struct
-%   contains simulated per component and damage state damage info
-%   disagregated by story
-%
-% Returns
-% -------
-%   contains simulated per component and damage state damage info
-%   disagregated by tenant unit
-%
-
-%% If tenant unit damage is not provided by the user, assume its the same as per story damage
-if ~isfield(damage,'tenant_units')
-    damage.tenant_units = damage.story;
-end
-
-end
-
diff --git a/+preprocessing/fn_simulate_damage_per_side.m b/+preprocessing/fn_simulate_damage_per_side.m
deleted file mode 100644
index 6dd9d01..0000000
--- a/+preprocessing/fn_simulate_damage_per_side.m
+++ /dev/null
@@ -1,35 +0,0 @@
-function [ damage ] = fn_simulate_damage_per_side( damage )
-% Simulate damage per side for the exterior falling hazard check, if not 
-% provided by the user. Component location within a story is typically not 
-% Provided in most PBEE assessments. Therefore, this script make the rough 
-% assumptions to distribute damage to 4 sides, randomly.
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains simulated damage info and damage state attributes
-%
-% Returns
-% -------
-% damage: struct
-%   contains simulated damage info and damage state attributes
-%
-
-%% Simulate damage per side, if not provided by the user
-if ~isfield(damage.tenant_units{1},'qnt_damaged_side_1')
-    [num_reals,~] = size(damage.tenant_units{1}.qnt_damaged);
-    
-    % Randomly split damage between 4 sides
-    % (this will only matter for cladding components)
-    ratio_damage_per_side = rand(num_reals,4); % assumes square footprint
-    ratio_damage_per_side = ratio_damage_per_side ./ sum(ratio_damage_per_side,2); % force it to add to one
-
-    % Assing damage
-    for tu = 1:length(damage.tenant_units)
-        for s = 1:4
-            damage.tenant_units{tu}.(['qnt_damaged_side_' num2str(s)]) = ...
-                ratio_damage_per_side(:,s).*damage.tenant_units{tu}.qnt_damaged;
-        end
-    end
-end
-
diff --git a/+preprocessing/fn_simulate_temp_worker_days.m b/+preprocessing/fn_simulate_temp_worker_days.m
deleted file mode 100644
index ea45ae9..0000000
--- a/+preprocessing/fn_simulate_temp_worker_days.m
+++ /dev/null
@@ -1,87 +0,0 @@
-function [ damage, temp_repair_class ] = fn_simulate_temp_worker_days( damage, temp_repair_class, repair_time_options )
-% Simulate Temporary Repair Times for each component, if not already
-% defined by the user. In a perfect system this should be done alongside 
-% the other full repair time simulation. However, most PBEE assessments do
-% not contain information on temp repair times per component when they are
-% simulating damage and consequences. Therefore, this is decoupled from the
-% rest of the assessment and simulated here (if not already provided by the
-% user)
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains simulated damage info and damage state attributes
-% repair_time_options.allow_shoring: logical
-%   flag indicating whether or not shoring should be considered as a
-%   temporary repair for local stability issues for structural components
-% temp_repair_class: table
-%   attributes of each temporary repair class to consider
-%
-% Returns
-% -------
-% damage: struct
-%   contains simulated damage info and damage state attributes
-% temp_repair_class: table
-%   attributes of each temporary repair class to consider
-%
-
-%% Define Temporary Repair Times Options
-% Turn of temp repairs if specificied by the user
-if ~repair_time_options.allow_tmp_repairs
-    damage.comp_ds_table.tmp_repair_class = zeros(size(damage.comp_ds_table.tmp_repair_class));
-end
-
-% Set up temp_repair_class based on user inputs
-if ~repair_time_options.allow_shoring
-    temp_repair_class(temp_repair_class.id == 5,:) = []; % Remove shoring from table
-end
-
-%% Simulate temp repair worker days per component 
-% if not already specified by the user
-if ~isfield(damage.tenant_units{1},'tmp_worker_day')
-    % Find total number of damamged components
-    total_damaged = damage.tenant_units{1}.qnt_damaged;
-    for tu = 2:length(damage.tenant_units)
-        total_damaged = total_damaged + damage.tenant_units{tu}.qnt_damaged;
-    end
-
-    % Aggregate the total number of damaged components accross each damage
-    % state in a component
-    tmp_worker_days_per_unit = [];
-    for c = 1:height(damage.comp_ds_table) % for each comp ds
-        comp = damage.comp_ds_table(c,:);
-        if comp.tmp_repair_class > 0 % For damage that has temporary repair
-            filt = strcmp(damage.comp_ds_table.comp_id,comp.comp_id)';
-            total_damaged_all_ds = sum(total_damaged(:,filt),2);
-
-            % Interpolate to get per unit temp repair times
-            if comp.tmp_repair_time_lower_qnty == comp.tmp_repair_time_upper_qnty
-                tmp_worker_days_per_unit(:,c) = comp.tmp_repair_time_lower;
-            else
-                tmp_worker_days_per_unit(:,c) = ...
-                    interp1([comp.tmp_repair_time_lower_qnty, comp.tmp_repair_time_upper_qnty],...
-                            [comp.tmp_repair_time_lower,comp.tmp_repair_time_upper],...
-                            min(max(total_damaged_all_ds,comp.tmp_repair_time_lower_qnty),comp.tmp_repair_time_upper_qnty));
-            end
-
-        else
-            tmp_worker_days_per_unit(:,c) = NaN(size(total_damaged(:,1)));
-        end
-    end
-
-    % Simulate uncertainty in per unit temp repair times
-    % Assumes distribution is lognormal with beta = 0.4
-    % Assumes time to repair all of a given component group is fully correlated, 
-    % but independant between component groups 
-    sim_tmp_worker_days_per_unit = lognrnd(log(tmp_worker_days_per_unit),0.4,size(tmp_worker_days_per_unit));
-
-    % Allocate per unit temp repair time among tenant units to calc worker days
-    % for each component
-    for tu = 1:length(damage.tenant_units)
-        damage.tenant_units{tu}.tmp_worker_day = ...
-            damage.tenant_units{tu}.qnt_damaged .* sim_tmp_worker_days_per_unit;
-    end
-end
-
-end
-
diff --git a/+preprocessing/main_preprocessing.m b/+preprocessing/main_preprocessing.m
deleted file mode 100644
index fe33f95..0000000
--- a/+preprocessing/main_preprocessing.m
+++ /dev/null
@@ -1,58 +0,0 @@
-function [damage, temp_repair_class, damage_consequences] = ...
-    main_preprocessing(comp_ds_table, damage, repair_time_options, temp_repair_class, damage_consequences, num_stories)
-% Parameterize variables and simplifying assumptions to expedite the ATC138
-% recovery assessment
-%
-% Parameters
-% ----------
-% comp_ds_table: table
-%   various component attributes by damage state for each component 
-%   within the performance model. Can be populated from the 
-%   component_attribites.csv and damage_state_attribute_mapping.csv 
-%   databases in the static_tables directory.  Each row of the table 
-%   corresponds to each column of the simulated component damage arrays 
-%   within damage.tenant_units.
-% damage: struct
-%   contains simulated damage info and damage state attributes
-% repair_time_options.allow_shoring: logical
-%   flag indicating whether or not shoring should be considered as a
-%   temporary repair for local stability issues for structural components
-% temp_repair_class: table
-%   attributes of each temporary repair class to consider
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-% num_stories: int
-%   Integer number of stories in the building being assessed
-%
-% Returns
-% -------
-% damage: struct
-%   contains simulated damage info and damage state attributes
-% temp_repair_class: table
-%   attributes of each temporary repair class to consider
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-%
-%% Import Packages
-import preprocessing.fn_populate_damage_per_tu
-import preprocessing.fn_simulate_damage_per_side
-import preprocessing.fn_create_fnc_filters
-import preprocessing.fn_simulate_temp_worker_days
-import preprocessing.fn_define_door_racking
-
-%% Define simulated damage in each tenant unit if not provided by the user
-[ damage ] = fn_populate_damage_per_tu( damage );
-
-%% Simulate damage per side, if not provided by the user
-[ damage ] = fn_simulate_damage_per_side( damage );
-
-%% Combine compoment attributes into recovery filters to expidite recovery assessment
-[ damage.fnc_filters ] = fn_create_fnc_filters( comp_ds_table );
-
-%% Simulate Temporary Repair Times for each component
-[ damage, temp_repair_class ] = fn_simulate_temp_worker_days( damage, temp_repair_class, repair_time_options );
-
-%% Set door racking damage if not provided by user
-[ damage_consequences ] = fn_define_door_racking( damage_consequences, num_stories );
-
-end
\ No newline at end of file
diff --git a/+recovery/+functionality/fn_building_level_system_operation.m b/+recovery/+functionality/fn_building_level_system_operation.m
deleted file mode 100644
index e1b5ff0..0000000
--- a/+recovery/+functionality/fn_building_level_system_operation.m
+++ /dev/null
@@ -1,133 +0,0 @@
-function [ system_operation_day ] = fn_building_level_system_operation( damage, ...
-    damage_consequences, building_model, utilities, functionality_options )
-% Calculate the day certain systems recovery building-level opertaions
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage, loss, and repair time data for each 
-%   component in the building
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-%   tags and repair costs ratios
-% building_model: struct
-%   general attributes of the building model
-% utilities: struct
-%   data structure containing simulated utility downtimes
-% functionality_options: struct
-%   recovery time optional inputs such as various damage thresholds
-%
-% Returns
-% -------
-% system_operation_day.building: struct
-%   simulation of the day operation is recovered for various systems at the
-%   building level
-% system_operation_day.comp: struct
-%   simulation number of days each component is affecting building system
-%   operations
-
-%% Initial Setep
-% import packages
-import recovery.functionality.fn_calc_subsystem_recovery
-
-% Initialize Variables
-num_stories = building_model.num_stories;
-num_reals = length(damage_consequences.red_tag);
-num_comps = height(damage.comp_ds_table);
-
-
-system_operation_day.comp.elev_quant_damaged = zeros(num_reals,num_comps);
-system_operation_day.comp.elev_day_repaired = zeros(num_reals,num_comps);
-system_operation_day.comp.electrical_main = zeros(num_reals,num_comps);
-system_operation_day.comp.water_potable_main = zeros(num_reals,num_comps);
-system_operation_day.comp.water_sanitary_main = zeros(num_reals,num_comps);
-system_operation_day.comp.elevator_mcs = zeros(num_reals,num_comps);
-system_operation_day.comp.data_main = zeros(num_reals,num_comps);
-
-
-%% Loop through each story/TU and quantify the building-level performance of each system (e.g. equipment that severs the entire building)
-for tu = 1:num_stories
-    damaged_comps = damage.tenant_units{tu}.qnt_damaged;
-    initial_damaged = damaged_comps > 0;
-    total_num_comps = damage.tenant_units{tu}.num_comps;
-    repair_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day;
-    
-    %% Elevators
-    % Assumed all components affect entire height of shaft
-    system_operation_day.comp.elev_quant_damaged = ...
-        max(system_operation_day.comp.elev_quant_damaged, damage.tenant_units{tu}.qnt_damaged .* damage.fnc_filters.elevators);
-    system_operation_day.comp.elev_day_repaired = ...
-        max(system_operation_day.comp.elev_day_repaired, repair_complete_day .* damage.fnc_filters.elevators);
-    
-    % Motor Control system - Elevators
-    system_operation_day.comp.elevator_mcs = ...
-        max(system_operation_day.comp.elevator_mcs, repair_complete_day .* damage.fnc_filters.elevator_mcs);
-    
-    %% Electrical
-    system_operation_day.comp.electrical_main = ...
-        max(system_operation_day.comp.electrical_main, repair_complete_day .* damage.fnc_filters.electrical_main);
-
-    %% Water
-    % Potable
-    system_operation_day.comp.water_potable_main = ...
-        max(system_operation_day.comp.water_potable_main, repair_complete_day .* damage.fnc_filters.water_main);
-    
-    % Sanitary
-    system_operation_day.comp.water_sanitary_main = ...
-        max(system_operation_day.comp.water_sanitary_main, repair_complete_day .* damage.fnc_filters.sewer_main);
-    
-    %% HVAC
-    building_hvac_subsystems = fieldnames(damage.fnc_filters.hvac.building);
-    for s = 1:length(building_hvac_subsystems)
-        subsys_label = building_hvac_subsystems{s};
-        if not(isfield(system_operation_day, 'building')) ...
-                || not(isfield(system_operation_day.building, subsys_label))
-            % Initialize variables if not already initialized
-            system_operation_day.building.(subsys_label) = zeros(num_reals, 1);
-            system_operation_day.comp.(subsys_label) = zeros(num_reals, num_comps);
-        end
-
-        % go through each subsystem and calculate how long entire building operation is impaired
-        subs = fieldnames(damage.fnc_filters.hvac.building.(subsys_label));
-        for b = 1:length(subs)
-            filt = damage.fnc_filters.hvac.building.(subsys_label).(subs{b})';
-            
-            [repair_day] = fn_calc_subsystem_recovery( filt, damage, ...
-                 repair_complete_day, total_num_comps, damaged_comps );
-            
-            comps_breakdown = filt .* initial_damaged .* repair_day;
-            
-            % combine with previous stories
-            system_operation_day.building.(subsys_label) = ...
-                max(system_operation_day.building.(subsys_label), repair_day);
-            
-            system_operation_day.comp.(subsys_label) = ...
-                max(system_operation_day.comp.(subsys_label), comps_breakdown);
-        end
-    end
-    
-    %% Data
-    system_operation_day.comp.data_main = ...
-        max(system_operation_day.comp.data_main, repair_complete_day .* damage.fnc_filters.data_main);
-end
-
-%% Calculate building level consequences for systems where any major main damage leads to system failure
-system_operation_day.building.electrical_main = max(system_operation_day.comp.electrical_main,[],2);  % any major damage to the main equipment fails the system for the entire building
-system_operation_day.building.water_potable_main = max(system_operation_day.comp.water_potable_main,[],2);  % any major damage to the main pipes fails the system for the entire building
-system_operation_day.building.water_sanitary_main = max(system_operation_day.comp.water_sanitary_main,[],2);  % any major damage to the main pipes fails the system for the entire building
-system_operation_day.building.elevator_mcs = max(system_operation_day.comp.elevator_mcs,[],2); % any major damage fails the system for the whole building so take the max
-system_operation_day.building.data_main = max(system_operation_day.comp.data_main,[],2);  % any major damage to the main equipment fails the system for the entire building
-
-%% Account for Extermal Utilities impact on system Operation
-% Electricity
-system_operation_day.building.electrical_main = max(system_operation_day.building.electrical_main,utilities.electrical);
-
-% Potable water
-system_operation_day.building.water_potable_main = max(system_operation_day.building.water_potable_main,utilities.water);
-
-% Assume hvac control runs on electricity and heating system runs on gas
-system_operation_day.building.hvac_control = max(system_operation_day.building.hvac_control,system_operation_day.building.electrical_main);
-system_operation_day.building.hvac_heating = max(system_operation_day.building.hvac_heating,utilities.(functionality_options.heat_utility));
-
-end
-
diff --git a/+recovery/+functionality/fn_building_safety.m b/+recovery/+functionality/fn_building_safety.m
deleted file mode 100644
index 8526dad..0000000
--- a/+recovery/+functionality/fn_building_safety.m
+++ /dev/null
@@ -1,306 +0,0 @@
-function [ recovery_day, comp_breakdowns ] = fn_building_safety( ...
-    damage, building_model, damage_consequences, utilities, ...
-    functionality_options, impeding_temp_repairs )
-% Check damage that would cause the whole building to be shut down due to
-% issues of safety
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage, loss, and repair time data for each 
-%   component in the building
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-%   tags and repair costs ratios
-% building_model: struct
-%   general attributes of the building model
-% utilities: struct
-%   data structure containing simulated utility downtimes
-% functionality_options: struct
-%   recovery time optional inputs such as various damage thresholds
-% impeding_temp_repairs: struct
-%   contains simulated temporary repairs the impede occuapancy and function
-%   but are calulated in parallel with the temp repair schedule
-%
-% Returns
-% -------
-% recovery_day: struct
-%   simulation of the number of days each fault tree event is affecting building
-%   safety
-% comp_breakdowns: struct
-%   simulation of each components contributions to each of the fault tree events 
-
-%% Initial Setup
-num_reals = length(damage_consequences.red_tag);
-num_units = length(damage.tenant_units);
-num_comps = height(damage.comp_ds_table);
-
-% Initialize parameters
-recovery_day.red_tag = zeros(num_reals, 1);
-recovery_day.shoring = zeros(num_reals, 1);
-recovery_day.hazardous_material = zeros(num_reals, 1);
-recovery_day.fire_suppression = zeros(num_reals, 1);
-
-% check if building has fire supprsion system
-fs_exists = any(damage.fnc_filters.fire_building);
-
-%% Check damage throughout the building that can affect the entire building
-for tu = 1:num_units
-    % Grab tenant and damage info for this tenant unit
-    repair_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day;
-    repair_complete_day_w_tmp = damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp;
-    
-    is_damaged = damage.tenant_units{tu}.qnt_damaged > 0 & damage.tenant_units{tu}.worker_days > 0;
-    
-    %% Red Tags
-    % The day the red tag is resolved is the day when all damage (anywhere in building) that has
-    % the potentail to cause a red tag is fixed (ie max day)
-    if any(damage.fnc_filters.red_tag)
-        recovery_day.red_tag = max(recovery_day.red_tag, ...
-            damage_consequences.red_tag .* max(repair_complete_day(:,damage.fnc_filters.red_tag),[],2)); 
-    end
-    
-    % Componet Breakdowns
-    comp_breakdowns.red_tag(:,:,tu) = recovery_day.red_tag .* damage_consequences.red_tag_impact;
-    
-    %% Local Shoring
-    if any(damage.fnc_filters.requires_shoring) && functionality_options.include_local_stability_impact
-        % Any unresolved damage (temporary or otherwise) that requires shoring,
-        % blocks occupancy to the whole building
-        recovery_day.shoring = max(recovery_day.shoring, ...
-            max(repair_complete_day_w_tmp(:,damage.fnc_filters.requires_shoring),[],2)); 
-    
-        % Componet Breakdowns (the time it takes to shore or fully repair each
-        % component is the time it blocks occupancy for)
-        comp_breakdowns.shoring(:,:,tu) = damage.fnc_filters.requires_shoring .* repair_complete_day_w_tmp .* is_damaged;
-    end
-    
-    %% Day the fire suppression system is operating again (for the whole building)
-    if ~functionality_options.fire_watch && fs_exists
-        % any major damage fails the system for the whole building so take the max
-        recovery_day.fire_suppression = max(recovery_day.fire_suppression,max(repair_complete_day(:,damage.fnc_filters.fire_building),[],2));
-    
-        % Consider utilities (assume no backup water supply)
-        recovery_day.fire_suppression = max(recovery_day.fire_suppression,utilities.water); 
-
-        % Componet Breakdowns
-        comp_breakdowns.fire_suppression(:,:,tu) = damage.fnc_filters.fire_building .* repair_complete_day;
-    end
-
-    %% Hazardous Materials
-    % note: hazardous materials are accounted for in building functional
-    % assessment here, but are not currently quantified in the component
-    % breakdowns
-    if any(damage.fnc_filters.global_hazardous_material)
-        % Any global hazardous material shuts down the entire building
-        recovery_day.hazardous_material = max(recovery_day.hazardous_material, max(repair_complete_day(:,damage.fnc_filters.global_hazardous_material),[],2)); 
-    end
-end
-
-%% Building Egress
-% Calculate when falling hazards or racking of doors affects the building
-% safety due to limited entrance and exit door access
-
-% Simulate a random location of the doors on two sides of the building for
-% each realization. Location is defined at the center of the door as a
-% fraction of the building width on that side
-% This is acting a random p value to determine if the unknown location of
-% the door is within the falling hazard zone
-door_location = rand(num_reals,building_model.num_entry_doors);
-
-% Assign odd doors to side 1 and even doors to side two
-door_numbers = 1:building_model.num_entry_doors;
-door_side = ones(1,building_model.num_entry_doors);
-door_side(rem(door_numbers, 2) == 0) = 2;
-
-% Determine the quantity of falling hazard damage and when it will be
-% resolved
-day_repair_fall_haz = zeros(num_reals,building_model.num_entry_doors);
-fall_haz_comps_day_rep = zeros(num_reals,num_comps,num_units,building_model.num_entry_doors);
-comp_affected_lf = zeros(num_reals,num_comps,num_units);
-scaffold_filt = logical(damage.comp_ds_table.resolved_by_scaffolding');
-for tu = 1:num_units
-    tmp_or_full_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp;
-    
-    % Effect of falling hazards on building safety are resolved either by
-    % full repair, local temp repair, or erecting scaffolding. Whatever
-    % occurs first
-    isdamaged = 1*(damage.tenant_units{tu}.qnt_damaged > 0);
-    isdamaged(isdamaged == 0) = NaN; % mark undamaged cases as NaN to help combine factors below
-    scaffold_day = impeding_temp_repairs.scaffold_day .*  isdamaged(:,scaffold_filt);
-    complete_day_w_scaffold = tmp_or_full_complete_day;
-    complete_day_w_scaffold(:,scaffold_filt) = ...
-        min(tmp_or_full_complete_day(:,scaffold_filt),scaffold_day);
-    repair_complete_day_w_tmp(:,:,tu) = complete_day_w_scaffold;
-end
-
-% Loop through component repair times to determine the day it stops affecting re-occupancy
-num_repair_time_increments = sum(damage.fnc_filters.ext_fall_haz_all)*num_units; % possible unique number of loop increments
-edge_lengths = [building_model.edge_lengths; building_model.edge_lengths];
-for i = 1:num_repair_time_increments 
-    % Calculate the falling hazards per side
-    for tu = 1:num_units
-        for side = 1:4 % assumes there are 4 sides
-            lf_affected_direct_scale_all_comps = damage.comp_ds_table.exterior_falling_length_factor' .* ...
-                damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.(['qnt_damaged_side_' num2str(side)]);
-            
-            lf_affected_sf_all_comps = damage.comp_ds_table.exterior_falling_length_factor' .* ...
-                damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.(['qnt_damaged_side_' num2str(side)]) ...
-                ./ building_model.ht_per_story_ft(tu);
-
-            comp_affected_lf(:,damage.fnc_filters.ext_fall_haz_lf, tu) = lf_affected_direct_scale_all_comps(:,damage.fnc_filters.ext_fall_haz_lf);
-            comp_affected_lf(:,damage.fnc_filters.ext_fall_haz_sf, tu) = lf_affected_sf_all_comps(:,damage.fnc_filters.ext_fall_haz_sf);
-            comp_affected_lf(:,damage.fnc_filters.ext_fall_haz_ea, tu) = lf_affected_direct_scale_all_comps(:,damage.fnc_filters.ext_fall_haz_ea);
-
-            comp_affected_ft_this_story = comp_affected_lf(:,:,tu);
-            % Assumes cladding components do not occupy the same perimeter space
-            % Don't exxceed edge lengths (can happen for things like chimneys)
-            affected_ft_this_story = min(sum(comp_affected_ft_this_story,2), edge_lengths(side,tu));
-
-            affected_ratio.(['side_' num2str(side)])(:,tu) = min((affected_ft_this_story) ./ edge_lengths(side,tu),1);
-        end
-    end
-
-    % Calculate the time increment for this loop
-    delta_day = min(min(repair_complete_day_w_tmp(:,damage.fnc_filters.ext_fall_haz_all,:),[],3),[],2);
-    delta_day(isnan(delta_day)) = 0;
-    if sum(delta_day) == 0
-        break % everything has been fixed
-    end
-    
-    % Go through each door to determine which is affected by falling hazards
-    for d = 1:building_model.num_entry_doors
-        % Combine affected areas of all stories above the first using SRSS
-        % HARDCODED ASSUMPTIONS: DOORS ONLY ON TWO SIDES
-        fall_haz_zone = min(sqrt(sum(affected_ratio.(['side_' num2str(door_side(d))])(:,2:end) .^2,2)),1);
-
-        % Augment the falling hazard zone with the door access zone
-        % add the door access width to the width of falling hazards to account
-        % for the width of the door (ie if any part of the door access zone is
-        % under the falling hazard, its a problem)
-        door_access_zone = functionality_options.door_access_width_ft / building_model.edge_lengths(door_side(d),1); 
-        total_fall_haz_zone = fall_haz_zone + 2*door_access_zone; % this is approximating the probability the door is within the falling hazard zone
-
-        % Determine if current damage affects occupancy
-        % if the randonmly simulated door location is with falling hazard zone
-        affects_door = door_location(:,door_side(d)) < total_fall_haz_zone;
-
-        % Add days in this increment to the tally
-        day_repair_fall_haz(:,d) = day_repair_fall_haz(:,d) + affects_door .* delta_day;
-
-        % Add days to components that are affecting occupancy
-        fall_haz_comps_day_rep(:,:,:,d) = fall_haz_comps_day_rep(:,:,:,d) + (comp_affected_lf > 0) .* damage.fnc_filters.ext_fall_haz_all .* affects_door .* delta_day;
-    end
-    
-    % Change the comps for the next increment
-    repair_complete_day_w_tmp = repair_complete_day_w_tmp - delta_day;
-    repair_complete_day_w_tmp(repair_complete_day_w_tmp <= 0) = NaN;
-end
-
-% Determine when racked doors are resolved
-day_repair_racked = zeros(num_reals, building_model.num_entry_doors);
-side_1_count = 0;
-side_2_count = 0;
-for d = 1:building_model.num_entry_doors
-    if door_side(d) == 1
-        side_1_count = side_1_count + 1;
-        day_repair_racked(:,d) = impeding_temp_repairs.door_racking_repair_day .* (damage_consequences.racked_entry_doors_side_1 >= side_1_count);
-    else 
-        side_2_count = side_2_count + 1;
-        day_repair_racked(:,d) = impeding_temp_repairs.door_racking_repair_day .* (damage_consequences.racked_entry_doors_side_2 >= side_2_count);
-    end
-end
-door_access_day = max(day_repair_racked,day_repair_fall_haz);
-
-% Find the days until door egress is regained from resolution of both
-% falling hazards or door racking
-cum_days = 0;
-entry_door_access_day = zeros(num_reals,1);
-door_access_day_nan = door_access_day;
-door_access_day_nan(door_access_day_nan == 0) = NaN;
-num_repair_time_increments = building_model.num_entry_doors; % possible unique number of loop increments
-for i = 1:num_repair_time_increments
-    num_accessible_doors = sum(door_access_day <= cum_days,2);
-    entry_door_accessible = num_accessible_doors >= max(functionality_options.min_egress_paths,functionality_options.egress_threshold*building_model.num_entry_doors); 
-            
-    % Bean counting for this iteration
-    delta_day = min(door_access_day_nan,[],2);
-    delta_day(isnan(delta_day)) = 0;
-    door_access_day_nan = door_access_day_nan - delta_day;
-    cum_days = cum_days + delta_day;
-    
-    % Save recovery time increments
-    entry_door_access_day = entry_door_access_day + delta_day .* ~entry_door_accessible;
-end
-
-% Save recovery day values
-recovery_day.entry_door_access = entry_door_access_day;
-
-% Determine when Exterior Falling Hazards or doors actually contribute to re-occupancy
-recovery_day.falling_hazard = min(recovery_day.entry_door_access,max(day_repair_fall_haz,[],2));
-recovery_day.entry_door_racking = min(recovery_day.entry_door_access,max(day_repair_racked,[],2));
-
-% Component Breakdown
-comp_breakdowns.falling_hazard = min(recovery_day.entry_door_access, max(fall_haz_comps_day_rep,[],4));
-
-%% Fire Safety
-if ~functionality_options.fire_watch && fs_exists
-    comp_breakdowns_local_fire = zeros(num_reals,num_comps,num_units);
-    fire_safety_day = zeros(num_reals,num_units); % Day the local fire sprinkler system becomes operational
-    filt_fs_drop = damage.fnc_filters.fire_drops;
-    filt_fs_branch = damage.fnc_filters.fire_unit;
-    for tu = 1:num_units
-        repair_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day;
-        repair_complete_day(repair_complete_day == 0) = NaN; % Make sure zero repair days are NaN
-        damaged_comps = damage.tenant_units{tu}.qnt_damaged;
-        num_drops = max(damage.tenant_units{tu}.num_comps .* filt_fs_drop,[],2); % Assumes drops are all in one performance group
-        num_branches = max(damage.tenant_units{tu}.num_comps .* filt_fs_branch,[],2); % Assumes branches are all in one performance group
-        if sum([num_drops,num_branches]) > 0 % If there are any of these components on in this tenant unit
-            % Loop through component repair times to determine the day it stops affecting re-occupanc
-            num_repair_time_increments = sum(filt_fs_drop | filt_fs_branch); % possible unique number of loop increments
-            for i = 1:num_repair_time_increments 
-                % Calculate the ratio of damaged drops and branches on this story
-                ratio_damaged_drop = sum(damaged_comps .* filt_fs_drop,2) ./ num_drops; % assumes comps are not simeltaneous
-                ratio_damaged_branch = sum(damaged_comps .* filt_fs_branch,2) ./ num_drops; % assumes comps are not simeltaneous
-
-                % Determine if fire drops and branches are adequately operating
-                fire_drop_operational = functionality_options.local_fire_damamge_threshold >= ratio_damaged_drop;
-                fire_branch_operational = functionality_options.local_fire_damamge_threshold >= ratio_damaged_branch;
-                local_fire_operational = fire_drop_operational & fire_drop_operational;
-                
-                % Add days in this increment to the tally
-                delta_day = min(repair_complete_day(:,filt_fs_drop),[],2);
-                delta_day(isnan(delta_day)) = 0;
-                fire_safety_day(:,tu) = fire_safety_day(:,tu) + ~local_fire_operational .* delta_day;
-
-                % Add days to components that are affecting occupancy
-                contributing_drops = ((damaged_comps .* filt_fs_drop) > 0)  .* ~fire_drop_operational; % count all components that contributed to non operational fire drops
-                contributing_branches = ((damaged_comps .* filt_fs_drop) > 0)  .* ~fire_branch_operational; % count all components that contributed to non operational fire drops
-                contributing_comps = max(contributing_drops,contributing_branches);
-                comp_breakdowns_local_fire(:,:,tu) = comp_breakdowns_local_fire(:,:,tu) + contributing_comps .* delta_day;
-
-                % Change the comps for the next increment
-                repair_complete_day = repair_complete_day - delta_day;
-                repair_complete_day(repair_complete_day <= 0) = NaN;
-                fixed_comps_filt = isnan(repair_complete_day);
-                damaged_comps(fixed_comps_filt) = 0;
-            end
-        end
-    end
-
-    % Inoperable fire drips on any story shuts down entire building
-    fire_safety_day_building = max(fire_safety_day,[],2);
-    
-    % Combine parts of fire suppression system
-    recovery_day.fire_suppression = max(recovery_day.fire_suppression,fire_safety_day_building);
-    comp_breakdowns.fire_suppression = max(comp_breakdowns.fire_suppression,comp_breakdowns_local_fire);
-end
-
-%% Delay Red Tag recovery by the time it takes to clear the tag
-sim_red_tag_clear_time = ceil(lognrnd(log(functionality_options.red_tag_clear_time),...
-                                 functionality_options.red_tag_clear_beta,num_reals,1));
-recovery_day.red_tag = recovery_day.red_tag + sim_red_tag_clear_time .* damage_consequences.red_tag;
-comp_breakdowns.red_tag = comp_breakdowns.red_tag + sim_red_tag_clear_time .* (comp_breakdowns.red_tag > 0);
-
-end
-
diff --git a/+recovery/+functionality/fn_calc_subsystem_recovery.m b/+recovery/+functionality/fn_calc_subsystem_recovery.m
deleted file mode 100644
index 345ea12..0000000
--- a/+recovery/+functionality/fn_calc_subsystem_recovery.m
+++ /dev/null
@@ -1,81 +0,0 @@
-function [subsys_repair_day] = fn_calc_subsystem_recovery(subsys_filt, damage, ...
-    repair_complete_day, total_num_comps, damaged_comps)
-% Check whether the components of a particular subsystem have redundancy
-% or not and calculate the day the subsystem recovers opertaions
-%
-% Parameters
-% ----------
-% subsys_filt: logical array [1 x num_comps_ds]
-%   indentifies which rows in the damage.comp_ds_info are the components of interest
-% damage: struct
-%   contains per damage state damage, loss, and repair time data for each 
-%   component in the building
-% repair_complete_day: array [num_reals x num_comps_ds]
-%   day reapairs are complete for each components damage state for a given
-%   tenant unit
-% total_num_comps: array [1 x num_comps_ds]
-%   total number of each component damage state in this tenant unit
-% damaged_comps: array [num_reals x num_comps_ds]
-%   total number of damaged components in each damage state in this tenant unit
-%
-% Returns
-% -------
-% subsys_repair_day: array [num reals x 1]
-%   The day this subsystem stops affecting functoin in a given tenant unit
-
-%% Initial Setup
-[num_reals, ~] = size(repair_complete_day);
-is_redundant = max(damage.comp_ds_table.parallel_operation(subsys_filt)); % should all be the same within a subsystem
-any_comps = any(subsys_filt);
-
-%% Check if the componet has redundancy
-if any_comps
-    if is_redundant
-        %% go through each component in this subsystem and find number of damaged units
-        comps = unique(damage.comp_ds_table.comp_idx(subsys_filt));
-        num_tot_comps = zeros(1,length(comps));
-        num_damaged_comps = zeros(num_reals,length(comps));
-        for c = 1:length(comps)
-            this_comp = subsys_filt & (damage.comp_ds_table.comp_idx == comps(c))';
-            num_tot_comps(c) = max(total_num_comps .* this_comp); % number of units across all ds should be the same
-            num_damaged_comps(:,c) = max(damaged_comps .* this_comp,[],2);
-        end
-
-        %% sum together multiple components in this subsystem
-        subsystem_num_comps = sum(num_tot_comps);
-        subsystem_num_damaged_comps = sum(num_damaged_comps,2);
-        ratio_damaged = subsystem_num_damaged_comps ./ subsystem_num_comps;
-
-        %% Check failed component against the ratio of components required for system operation
-        % system fails when there is an insufficient number of operating components
-        n1_redundancy = max(damage.comp_ds_table.n1_redundancy(subsys_filt)); % should all be the same within a subsystem
-        if subsystem_num_comps == 0 % No components at this level
-            subsystem_failure = zeros(num_reals,1);
-        elseif subsystem_num_comps == 1 % Not actually redundant
-            subsystem_failure = subsystem_num_damaged_comps == 1;
-        elseif n1_redundancy
-            % These components are designed to have N+1 redundncy rates,
-            % meaning they are designed to lose one component and still operate at
-            % normal level
-            subsystem_failure = subsystem_num_damaged_comps > 1;
-        else
-            % Use a predefined ratio
-            redundancy_threshold = max(damage.comp_ds_table.redundancy_threshold(subsys_filt)); % should all be the same within a subsystem
-            subsystem_failure = ratio_damaged > redundancy_threshold;
-        end
-
-        %% Calculate recovery day and combine with other subsystems for this tenant unit
-        % assumes all quantities in each subsystem are repaired at
-        % once, which is true for our current repair schedule (ie
-        % system level at each story)
-        subsys_repair_day = max(subsystem_failure .* subsys_filt .* repair_complete_day,[],2); 
-    else % This subsystem has no redundancy
-        % any major damage to the components fails the subsystem at this
-        % tenant unit
-        subsys_repair_day = max(repair_complete_day .* subsys_filt,[],2);
-    end
-else % No components were populated in this subsystem
-    subsys_repair_day = zeros(num_reals,1);
-end
-
-end % Function
diff --git a/+recovery/+functionality/fn_calculate_functionality.m b/+recovery/+functionality/fn_calculate_functionality.m
deleted file mode 100644
index 8485f7b..0000000
--- a/+recovery/+functionality/fn_calculate_functionality.m
+++ /dev/null
@@ -1,78 +0,0 @@
-function [functional, recovery_day, comp_breakdowns] = fn_calculate_functionality(damage, damage_consequences, utilities, ...
-    building_model, subsystems, reoccupancy, functionality_options, tenant_units, impeding_temp_repairs )
-% Calcualte the loss and recovery of building functionality based on global building
-% damage, local component damage, and extenernal factors
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage, loss, and repair time data for each 
-%   component in the building
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-%   tags and repair costs ratios
-% utilities: struct
-%   data structure containing simulated utility downtimes
-% building_model: struct
-%   general attributes of the building model
-% subsystems: table
-%   data table containing information about each subsystem's attributes
-% reoccupancy: struct
-%   contains data on the recovery of tenant- and building-level function, 
-%   recovery trajectorires, and contributions from systems and components 
-% functionality_options: struct
-%   recovery time optional inputs such as various damage thresholds
-% tenant_units: table
-%   attributes of each tenant unit within the building
-% impeding_temp_repairs: struct
-%   contains simulated temporary repairs the impede occuapancy and function
-%   but are calulated in parallel with the temp repair schedule
-%
-% Returns
-% -------
-% functionality: struct
-%   contains data on the recovery of tenant- and building-level function, 
-%   recovery trajectorires, and contributions from systems and components 
-
-%% Initial Set Up
-% import packages
-import recovery.functionality.fn_building_level_system_operation
-import recovery.functionality.fn_tenant_function
-import recovery.functionality.fn_extract_recovery_metrics
-import recovery.functionality.fn_combine_comp_breakdown
-
-%% Define the day each system becomes functionl - Building level
-[ system_operation_day ] = fn_building_level_system_operation( damage, damage_consequences, ...
-    building_model, utilities, functionality_options );
-
-%% Define the day each system becomes functionl - Tenant level
-[ recovery_day.tenant_function, comp_breakdowns.tenant_function ] = fn_tenant_function( ...
-    damage, building_model, system_operation_day, subsystems, ...
-    tenant_units, impeding_temp_repairs, functionality_options );
-
-%% Combine Checks to determine per unit functionality
-% Each tenant unit is functional only if it is occupiable
-day_tentant_unit_functional = reoccupancy.tenant_unit.recovery_day;
-
-% Go through each of the tenant function branches and combines checks
-fault_tree_events = fieldnames(recovery_day.tenant_function);
-for i = 1:length(fault_tree_events)
-    day_tentant_unit_functional = max(day_tentant_unit_functional,recovery_day.tenant_function.(fault_tree_events{i}));
-end
-
-%% Reformat outputs into functionality data strucutre
-[ functional ] = fn_extract_recovery_metrics( day_tentant_unit_functional, ...
-    recovery_day, comp_breakdowns, damage.comp_ds_table.comp_id', ...
-    damage_consequences.simulated_replacement_time );
-
-% get the combined component breakdown between reoccupancy and function
-functional.breakdowns.component_combined = fn_combine_comp_breakdown( ...
-    damage.comp_ds_table, ...
-    functional.breakdowns.perform_targ_days, ... % assumes names are consistent in both objects
-    functional.breakdowns.comp_names, ...        % assumes names are consistent in both objects
-    reoccupancy.breakdowns.component_breakdowns_all_reals, ...
-    functional.breakdowns.component_breakdowns_all_reals ...
-);
-
-end
-
diff --git a/+recovery/+functionality/fn_calculate_reoccupancy.m b/+recovery/+functionality/fn_calculate_reoccupancy.m
deleted file mode 100644
index a2978f9..0000000
--- a/+recovery/+functionality/fn_calculate_reoccupancy.m
+++ /dev/null
@@ -1,99 +0,0 @@
-function [reoccupancy, recovery_day, comp_breakdowns] = fn_calculate_reoccupancy(...
-    damage, damage_consequences, utilities, building_model,...
-    functionality_options, tenant_units, impeding_temp_repairs )
-% Calcualte the loss and recovery of building re-occupancy 
-% based on global building damage, local component damage, and extenernal factors
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage, loss, and repair time data for each 
-%   component in the building
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-%   tags and repair costs ratios
-% utilities: struct
-%   data structure containing simulated utility downtimes
-% building_model: struct
-%   general attributes of the building model
-% subsystems: table
-%   attributes of building subsystems; data provided in static tables
-%   directory
-% functionality_options: struct
-%   recovery time optional inputs such as various damage thresholds
-% tenant_units: table
-%   attributes of each tenant unit within the building
-% impeding_temp_repairs: struct
-%   contains simulated temporary repairs the impede occuapancy and function
-%   but are calulated in parallel with the temp repair schedule
-%
-% Returns
-% -------
-% reoccupancy: struct
-%   contains data on the recovery of tenant- and building-level reoccupancy, 
-%   recovery trajectorires, and contributions from systems and components 
-
-%% Initial Set Up
-% Import packages
-import recovery.functionality.fn_building_safety
-import recovery.functionality.fn_story_access
-import recovery.functionality.fn_tenant_safety
-import recovery.functionality.fn_extract_recovery_metrics
-    
-%% Stage 1: Quantify the effect that component damage has on the building safety
-[ recovery_day.building_safety, comp_breakdowns.building_safety ] = ...
-    fn_building_safety( damage, building_model, damage_consequences, ...,
-    utilities, functionality_options, impeding_temp_repairs );
-
-%% Stage 2: Quantify the accessibility of each story in the building
-[ recovery_day.story_access, comp_breakdowns.story_access ] = ...
-    fn_story_access( damage, building_model, damage_consequences, ...
-    functionality_options, impeding_temp_repairs );
-
-%% Stage 3: Quantify the effect that component damage has on the safety of each tenant unit
-[ recovery_day.tenant_safety, comp_breakdowns.tenant_safety ] = ...
-    fn_tenant_safety( damage, building_model, functionality_options, tenant_units );
-
-%% Combine Check to determine the day the each tenant unit is reoccupiable
-% Check the day the building is Safe
-building_safety_fields = fieldnames(recovery_day.building_safety);
-% the old hard coded ones excluded 'falling_hazard' and 'entry_door_racking'
-% building_safety_fields = {'red_tag', 'shoring', 'hazardous_material', 'entry_door_access', 'fire_suppression'};  
-day_building_safe = 0;
-
-for i_field = 1:length(building_safety_fields)
-    building_safety_field = building_safety_fields{i_field};
-    day_building_safe = max(day_building_safe, recovery_day.building_safety.(building_safety_field));
-end
-
-% Check the day each story is accessible
-story_access_fields = fieldnames(recovery_day.story_access);
-% story_access_fields = {'stairs', 'stair_doors', 'flooding', 'horizontal_egress'};  % old hard coded ones
-day_story_accessible = 0;
-
-for i_field = 1:length(story_access_fields)
-    story_access_field = story_access_fields{i_field};
-    day_story_accessible = max(day_story_accessible, recovery_day.story_access.(story_access_field));
-end
-
-% Check the day each tenant unit is safe
-tu_safety_fields = fieldnames(recovery_day.tenant_safety);
-% tu_safety_fields = {'interior', 'exterior', 'hazardous_material'};  % old hard coded ones
-day_tenant_unit_safe = 0;
-
-for i_field = 1:length(tu_safety_fields)
-    tu_safety_field = tu_safety_fields{i_field};
-    day_tenant_unit_safe = max(day_tenant_unit_safe, recovery_day.tenant_safety.(tu_safety_field));
-end
-
-% Combine checks to determine when each tenant unit is re-occupiable
-day_tentant_unit_reoccupiable = max(max(day_building_safe, day_story_accessible), day_tenant_unit_safe); 
-
-%% Reformat outputs into occupancy data strucutre
-[ reoccupancy ] = fn_extract_recovery_metrics( day_tentant_unit_reoccupiable, ...
-     recovery_day, comp_breakdowns, damage.comp_ds_table.comp_id', ...
-     damage_consequences.simulated_replacement_time );
-
-
-end
-
diff --git a/+recovery/+functionality/fn_check_habitability.m b/+recovery/+functionality/fn_check_habitability.m
deleted file mode 100644
index 15c7236..0000000
--- a/+recovery/+functionality/fn_check_habitability.m
+++ /dev/null
@@ -1,59 +0,0 @@
-function [ reoccupancy ] = fn_check_habitability( damage, damage_consequences, reoc_meta, func_meta, habitability_requirements )
-% Overwrite reocuppancy with additional checks from the functionality check
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage, loss, and repair time data for each 
-%   component in the building
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-%   tags and repair costs ratios
-% reoc_meta: struct
-%   meta data from reoccupancy assessment
-% func_meta: struct
-%   meta data from functionality assessment
-% habitability_requirements: struct
-%   basic requirements for habitability beyond basic reoccupancy.
-%
-% Returns
-% -------
-% reoccupancy: struct
-%   contains data on the recovery of tenant- and building-level reoccupancy, 
-%   recovery trajectorires, and contributions from systems and components 
-
-
-import recovery.functionality.fn_extract_recovery_metrics
-
-% Functionality checks to adopt onto reoccupancy requirements for
-% habitability:
-    % heating, cooling, vent, exhaust, potable water, sanitary, electrical
-recovery_day = reoc_meta.recovery_day;
-comp_breakdowns = reoc_meta.comp_breakdowns;
-habitability_list = fieldnames(habitability_requirements);
-% habitability_list = {'electrical', 'water_potable', 'water_sanitary', 'hvac_ventilation', 'hvac_heating', 'hvac_cooling', 'hvac_exhaust'};
-for i = 1:length(habitability_list)
-    if habitability_requirements.(habitability_list{i}) % If this system is required for habitability
-        recovery_day.habitability.(habitability_list{i}) = func_meta.recovery_day.tenant_function.(habitability_list{i});
-        comp_breakdowns.habitability.(habitability_list{i}) = func_meta.comp_breakdowns.tenant_function.(habitability_list{i});
-    end
-end
-
-% Go through each of the tenant function branches and combines checks
-day_tentant_unit_reoccupiable = 0;
-fault_tree_events_LV1 = fieldnames(recovery_day);
-for i = 1:length(fault_tree_events_LV1)
-    fault_tree_events_LV2 = fieldnames(recovery_day.(fault_tree_events_LV1{i}));
-    for j = 1:length(fault_tree_events_LV2)
-        day_tentant_unit_reoccupiable = max(day_tentant_unit_reoccupiable,...
-            recovery_day.(fault_tree_events_LV1{i}).(fault_tree_events_LV2{j}));
-    end
-end
-
-% Reformat outputs into reoccupancy data strucutre
-[ reoccupancy ] = fn_extract_recovery_metrics( day_tentant_unit_reoccupiable, ...
-    recovery_day, comp_breakdowns, damage.comp_ds_table.comp_id', ...
-    damage_consequences.simulated_replacement_time );
-
-end
-
diff --git a/+recovery/+functionality/fn_combine_comp_breakdown.m b/+recovery/+functionality/fn_combine_comp_breakdown.m
deleted file mode 100644
index bc5353b..0000000
--- a/+recovery/+functionality/fn_combine_comp_breakdown.m
+++ /dev/null
@@ -1,33 +0,0 @@
-function combined = fn_combine_comp_breakdown(comp_ds_table, perform_targ_days, comp_names, reoccupancy, functional)
-% get the combined reoccupancy/functionality effect of components
-%
-% Parameters
-% ----------
-% comp_ds_table: table
-%   contains basic component attributes
-% perform_targ_days: array
-%   recovery time horizons to consider
-% comp_names: cell array
-%   string ids of components to attribte recovery time to
-% reoccupancy: array [num reals x num comp_ds]
-%   realizations of reoccupancy time broken down for each damage state of
-%   each component
-% functional: array [num reals x num comp_ds]
-%   realizations of functional recovery time broken down for each damage
-%   state of each component
-%
-% Returns
-% -------
-% combined: array
-%   Probability of recovering within time horizon for each component
-%   considering both consequenses from reoccupancy and functional recovery
-
-%% Method
-combined = zeros(length(comp_names),length(perform_targ_days));
-max_reocc_func = max(reoccupancy, functional);
-for c = 1:length(comp_names)
-    comp_filt = strcmp(comp_ds_table.comp_id, comp_names{c}); % find damage states associated with this component    
-    combined(c,:) = mean(max(max_reocc_func(:,comp_filt'), [], 2) > perform_targ_days,1);
-end
-
-end
diff --git a/+recovery/+functionality/fn_extract_recovery_metrics.m b/+recovery/+functionality/fn_extract_recovery_metrics.m
deleted file mode 100644
index 85b9316..0000000
--- a/+recovery/+functionality/fn_extract_recovery_metrics.m
+++ /dev/null
@@ -1,337 +0,0 @@
-function [ recovery ] = fn_extract_recovery_metrics( tentant_unit_recovery_day, ...
-   recovery_day, comp_breakdowns, comp_id, simulated_replacement_time )
-% Reformant tenant level recovery outcomes into outcomes at the building level, 
-% system level, and compoennt level
-%
-% Parameters
-% ----------
-% tentant_unit_recovery_day: array [num_reals x num_tenant_units]
-%   simulated recovery day of each tenant unit
-% recovery_day: struct
-%   simulation of the number of days each fault tree event is affecting
-%   recovery
-% comp_breakdowns: struct
-%   simulation of each components contributions to each of the fault tree events 
-% comp_id: cell array [1 x num comp damage states]
-%   list of each fragility id associated with the per component damage
-%   state structure of the damage object. With of array is the same as the
-%   arrays in the comp_breakdowns structure
-% simulated_replacement_time: array [num_reals x 1]
-%   simulated time when the building needs to be replaced, and how long it
-%   will take (in days). NaN represents no replacement needed (ie
-%   building will be repaired)
-%
-% Returns
-% -------
-% recovery.tenant_unit.recovery_day: array [num_reals x num_tenant_units]
-%   simulated recovery day of each tenant unit
-% recovery.building_level.recovery_day: array [num_reals x 1]
-%   simulated recovery day of the building (all tenant units recovered)
-% recovery.building_level.initial_percent_affected: array [num_reals x 1]
-%   simulated fraction of the building with initial loss of
-%   reoccupancy/function
-% recovery.recovery_trajectory.recovery_day: array [num_reals x num recovery steps]
-%   simulated recovery trajectory y-axis
-% recovery.recovery_trajectory.percent_recovered: array [1 x num recovery steps]
-%   recovery trajectory x-axis
-% recovery.breakdowns.system_breakdowns: array [num fault tree events x target days]
-%   fraction of realizations affected by various fault tree events beyond
-%   specific target recovery days
-% recovery.breakdowns.component_breakdowns: array [num components x target days]
-%   fraction of realizations affected by various components beyond
-%   specific target recovery days
-% recovery.breakdowns.perform_targ_days: array [0 x target days]
-%   specific target recovery days
-% recovery.breakdowns.system_names: array [num fault tree events x 1]
-%   name of each fault tree event
-% recovery.breakdowns.comp_names: array [num components x 1]
-%   fragility IDs of each component
-
-
-%% Initial Setup
-num_units = size(tentant_unit_recovery_day,2);
-
-% Define performance targets
-perform_targ_days = [0, 3, 7, 14, 30, 60, 90, 120, 182, 270, 365]; % Number of days for each performance target stripe
-
-% Determine replacement cases
-replace_cases = ~isnan(simulated_replacement_time);
-
-%% Post process tenant-level recovery times
-% Overwrite NaNs in tenant_unit_day_functional
-% Only NaN where never had functional loss, therefore set to zero
-tentant_unit_recovery_day(isnan(tentant_unit_recovery_day)) = 0;
-
-% Overwrite building replacment cases to replacement time
-tentant_unit_recovery_day(replace_cases,:) = simulated_replacement_time(replace_cases)*ones(1,num_units);
-
-%% Save building-level outputs to occupancy structure
-% Tenant Unit level outputs
-recovery.tenant_unit.recovery_day = tentant_unit_recovery_day;
-
-% Building level outputs
-recovery.building_level.recovery_day = max(tentant_unit_recovery_day,[],2);
-recovery.building_level.initial_percent_affected = mean(tentant_unit_recovery_day > 0,2); % percent of building affected, not the percent of realizations
-recovery.building_level.perform_targ_days = perform_targ_days;
-recovery.building_level.prob_of_target = mean(recovery.building_level.recovery_day > perform_targ_days);
-
-% Save specific breakdowns for red tags
-if isfield(recovery_day, 'building_safety')
-    red_tag_day = recovery_day.building_safety.red_tag;
-    red_tag_day(replace_cases,:) = simulated_replacement_time(replace_cases);
-    recovery.building_level.recovery_day_red_tag = red_tag_day;
-end
-
-%% Recovery Trajectory -- calcualte from the tenant breakdowns
-recovery.recovery_trajectory.recovery_day = sort([tentant_unit_recovery_day, tentant_unit_recovery_day],2);
-recovery.recovery_trajectory.percent_recovered = sort([(0:(num_units-1)), (1:num_units)])/num_units;
-
-% partial recovery
-pct_recovered_targets = [0.1, 0.5, 0.75, 0.8, 1];
-
-% order the tennant recovery days so it's easier to see at what time the required numnber of units
-% are required
-ordered_tennant_repair_days = sort(tentant_unit_recovery_day, 2);
-        
-for i_pct = 1:length(pct_recovered_targets)
-    pct_recovered = pct_recovered_targets(i_pct);
-    recovery.partial{i_pct}.pct_recovered = pct_recovered;
-    recovery.partial{i_pct}.perform_targ_days = perform_targ_days;
-    for i_targ_day = 1:length(perform_targ_days)
-        targ_day = perform_targ_days(i_targ_day);
-        % get the percentage of tenant units recovered at the given day
-        pct_recovered_per_real = mean(sum(tentant_unit_recovery_day <= targ_day, 2) / num_units, 2);
-        recovery.partial{i_pct}.prob_of_target(i_targ_day) = mean(pct_recovered_per_real < pct_recovered);
-        
-        % how many units need to be repaired to meet the percent required 
-        reqd_units = ceil(num_units * pct_recovered);
-        
-        recovery.partial{i_pct}.reqd_units = reqd_units;
-        recovery.partial{i_pct}.mean = mean(ordered_tennant_repair_days(:, reqd_units));
-        recovery.partial{i_pct}.median = median(ordered_tennant_repair_days(:, reqd_units));
-        recovery.partial{i_pct}.fractile_75 = prctile(ordered_tennant_repair_days(:, reqd_units), 75);
-        recovery.partial{i_pct}.fractile_90 = prctile(ordered_tennant_repair_days(:, reqd_units), 90);
-        
-    end
-end
-
-% histogram information - this is really complicated set up of the bins and labels, but it works
-[ hist_bins, hist_labels ] = get_hist_bins_and_labels(24);
-
-n_bins = size(hist_bins, 1);
-hist_freqs = zeros(n_bins, 1);
-all_days = recovery.building_level.recovery_day;
-for i_bin = 1:n_bins
-    hist_freqs(i_bin) = mean(all_days > hist_bins(i_bin, 1) & all_days <= hist_bins(i_bin, 2));
-end
-
-recovery.building_level.hist_data.labels = hist_labels;
-recovery.building_level.hist_data.bins = hist_bins;
-recovery.building_level.hist_data.freqs = hist_freqs;
-
-%% Format and Save Component-level breakdowns
-% Find the day each ds of each component stops affecting recovery for any story
-
-% Combine among all fault tree events
-component_breakdowns_per_story = 0;
-fault_tree_events_LV1 = fieldnames(comp_breakdowns);
-for i = 1:length(fault_tree_events_LV1)
-    fault_tree_events_LV2 = fieldnames(comp_breakdowns.(fault_tree_events_LV1{i}));
-    for j = 1:length(fault_tree_events_LV2)
-        component_breakdowns_per_story = max(component_breakdowns_per_story,...
-            comp_breakdowns.(fault_tree_events_LV1{i}).(fault_tree_events_LV2{j}));
-    end
-end
-
-% Combine among all stories
-% aka time each component's DS affects recovery anywhere in the building
-component_breakdowns = max(component_breakdowns_per_story,[],3);
-
-% Ignore repalcement cases
-component_breakdowns(replace_cases,:) = []; 
-
-%% Format and Save System-level breakdowns
-% Find the day each system stops affecting recovery for any story
-
-% Combine among all fault tree events
-system_breakdowns = [];
-fault_tree_events_LV1 = fieldnames(recovery_day);
-for i = 1:length(fault_tree_events_LV1)
-    fault_tree_events_LV2 = fieldnames(recovery_day.(fault_tree_events_LV1{i}));
-    for j = 1:length(fault_tree_events_LV2)
-        % Combine among all stories or tenant units to represent the events
-        % effect anywhere in the building 
-        building_recovery_day = max(recovery_day.(fault_tree_events_LV1{i}).(fault_tree_events_LV2{j}),[],2);
-        
-        % Ignore repalcement cases
-        building_recovery_day(replace_cases,:) = []; 
-        
-        % Save per "system", which typically represents the fault tree level 2
-        if isfield(system_breakdowns,fault_tree_events_LV2{j})
-            % If this "system" has already been defined in another fault
-            % tree branch, combine togeter by taking the max (i.e., max
-            % days this system affects recovery anywhere in the building)
-            system_breakdowns.(fault_tree_events_LV2{j}) = ...
-                max(system_breakdowns.(fault_tree_events_LV2{j}),building_recovery_day);
-        else
-            system_breakdowns.(fault_tree_events_LV2{j}) = building_recovery_day;
-        end
-    end
-end
-
-%% Format breakdowns as performance targets
-system_names = fieldnames(system_breakdowns);
-
-% pre-allocating variables
-comps = unique(comp_id);
-recovery.breakdowns.system_breakdowns = zeros(length(system_names),length(perform_targ_days));
-recovery.breakdowns.component_breakdowns = zeros(length(comps),length(perform_targ_days));
-
-% Calculate fraction of realization each system affects recovery for each
-% performance target time
-for s = 1:length(system_names)
-    recovery.breakdowns.system_breakdowns(s,:) = mean(system_breakdowns.(system_names{s}) > perform_targ_days);
-end
-
-% get the "Level 2" (and "Level 1") breakdowns (aggregating them up into more broad categories for 
-% ATC-138 report by committee)
-
-system_grouping_L2.structural = {
-    'red_tag';
-    'shoring';
-};
-
-system_grouping_L2.mep = {
-    'hazardous_material';
-    'fire_suppression';
-    'flooding';  % reocc and func
-    'electrical';
-    'water_potable';
-    'water_sanitary';
-    'hvac_ventilation';
-    'hvac_heating';
-    'hvac_cooling';
-    'hvac_exhaust';
-    'roof';
-};
-
-system_grouping_L2.architectural = { ...
-    'elevators';  % reocc and func
-    'entry_door_access';
-    'falling_hazard';
-    'entry_door_racking';
-    'stairs';
-    'stair_doors';
-    'horizontal_egress';
-    'exterior';  % reocc and func
-    'interior';  % reocc and func
-};
-
-[ recovery.breakdowns.system_breakdowns_L2, system_names_L2 ] = get_grouped_system_metrics(system_grouping_L2, system_breakdowns, perform_targ_days);
-
-system_grouping_L1.structural = system_grouping_L2.structural;
-system_grouping_L1.non_struct = [system_grouping_L2.mep; system_grouping_L2.architectural];
-
-[ recovery.breakdowns.system_breakdowns_L1, system_names_L1 ] = get_grouped_system_metrics(system_grouping_L1, system_breakdowns, perform_targ_days);
-
-% Calculate fraction of realization each component affects recovery for each
-% performance target time
-for c = 1:length(comps)
-    comp_filt = strcmp(comp_id,comps{c}); % find damage states associated with this component
-    recovery.breakdowns.component_breakdowns(c,:) = mean(max(component_breakdowns(:,comp_filt'),[],2) > perform_targ_days);
-end
-
-% store this so we can properly overalap the reoccupancy and functionality
-recovery.breakdowns.component_breakdowns_all_reals = component_breakdowns;
-
-% Save other variables
-recovery.breakdowns.perform_targ_days = perform_targ_days;
-recovery.breakdowns.system_names = system_names;
-recovery.breakdowns.system_names_L1 = system_names_L1;
-recovery.breakdowns.system_names_L2 = system_names_L2;
-recovery.breakdowns.comp_names = comps';
-
-end
-
-
-function [hist_bins, bin_labels] = get_hist_bins_and_labels(max_months)
-% get the bins with an isolated bin for 0 days up to the max months specified (last interval is
-% (max_months, 1e6]
-
-months = round((1:max_months) * 365 / 12, 2);
-days = [3, 7, 14];
-inclusive_uppers = [days, months];
-n_bins_tot = length(inclusive_uppers) + 2;  % include zero and greater than 2 years
-
-hist_bins = zeros(n_bins_tot, 2);
-hist_bins(1, 1) = -1;  % first bin catches zeros only
-bin_labels = cell(n_bins_tot, 1);
-bin_labels{1} = '0 Days';
-
-for i_day = 1:length(days)
-    if i_day == 1
-        lower_day = 0;
-    else
-        lower_day = inclusive_uppers(i_day - 1);
-    end
-    upper_day = inclusive_uppers(i_day);
-    bin_labels{i_day + 1} = sprintf('(%.0fd, %.0fd]', lower_day, upper_day);
-end
-
-for i_month = 1:length(months)
-    if i_month == 1
-        upper_month = round(months(i_month) / (365 / 12));
-        bin_labels{i_month + length(days) + 1} = sprintf('(%.0fd, %.0fm]', days(end), upper_month);
-    else
-        lower_month = round(months(i_month - 1) / (365 / 12));
-        upper_month = round(months(i_month) / (365 / 12));
-        bin_labels{i_month + length(days) + 1} = sprintf('(%.0fm, %.0fm]', lower_month, upper_month);
-    end
-end
-
-bin_labels{end} = sprintf('>%.0fm', max_months);
-
-% make the bins and labels
-for i_upper = 1:length(inclusive_uppers)
-    upper_bin = inclusive_uppers(i_upper);
-    hist_bins(i_upper + 1, 2) = upper_bin;
-    hist_bins(i_upper + 2, 1) = upper_bin;
-end
-
-hist_bins(n_bins_tot, 2) = 1e6;
-
-end
-
-
-function [system_breakdowns_per_day, system_names] = get_grouped_system_metrics(system_grouping, system_breakdowns, perform_targ_days)
-% aggregate to different levels
-
-system_names = fieldnames(system_grouping);
-grouped_system_breakdowns = struct();
-for i_group = 1:length(system_names)
-    group_name = system_names{i_group};
-    group_systems = system_grouping.(group_name);
-    for i_sub_field = 1:length(group_systems)
-        system = group_systems{i_sub_field};
-        
-        if not(isfield(grouped_system_breakdowns, group_name))
-            % initialize
-            grouped_system_breakdowns.(group_name) = 0;
-        end
-        
-        if isfield(system_breakdowns, system)
-            % take the enveolpe of all the systems in this group
-            grouped_system_breakdowns.(group_name) = max(grouped_system_breakdowns.(group_name), system_breakdowns.(system));
-        end
-        
-    end
-    
-end
-
-system_breakdowns_per_day = zeros(length(system_names), length(perform_targ_days));
-for s = 1:length(system_names)
-    system_breakdowns_per_day(s,:) = mean(grouped_system_breakdowns.(system_names{s}) > perform_targ_days);
-end
-
-end
-
diff --git a/+recovery/+functionality/fn_story_access.m b/+recovery/+functionality/fn_story_access.m
deleted file mode 100644
index bce390b..0000000
--- a/+recovery/+functionality/fn_story_access.m
+++ /dev/null
@@ -1,152 +0,0 @@
-function [ recovery_day, comp_breakdowns ] = fn_story_access(damage, ...
-    building_model, damage_consequences, functionality_options, impeding_temp_repairs )
-% Check each story for damage that would cause that story to be shut down due to
-% issues of access
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage, loss, and repair time data for each 
-%   component in the building
-% building_model: struct
-%   general attributes of the building model
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-%   tags and repair costs ratios
-% functionality_options: struct
-%   recovery time optional inputs such as various damage thresholds
-% impeding_temp_repairs: struct
-%   contains simulated temporary repairs the impede occuapancy and function
-%   but are calulated in parallel with the temp repair schedule
-%
-% Returns
-% -------
-% recovery_day: struct
-%   simulation of the number of days each fault tree event is affecting access
-%   in each story
-% comp_breakdowns: struct
-%   simulation of each components contributions to each of the fault tree events 
-
-%% Initial Setup
-num_reals = length(damage_consequences.red_tag);
-num_units = length(damage.tenant_units);
-num_stories = length(damage.tenant_units);
-num_comps = height(damage.comp_ds_table);
-
-% Pre-allocate data
-recovery_day.stairs = zeros(num_reals,num_units);
-recovery_day.stair_doors = zeros(num_reals,num_units);
-recovery_day.flooding = zeros(num_reals,num_units);
-recovery_day.horizontal_egress = zeros(num_reals,num_units);
-comp_breakdowns.stairs = zeros(num_reals,num_comps,num_units);
-comp_breakdowns.horizontal_egress = zeros(num_reals,num_comps,num_units);
-
-%% Horizontal Egress - Fire breaks
-if any(damage.fnc_filters.fire_break)
-    for tu = 1:num_stories
-        % Grab tenant and damage info for this tenant unit
-        repair_complete_day_w_tmp = damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp;
-
-        % Any significant damage to fire breaks in the story impairs the horizontal egress
-        recovery_day.horizontal_egress(:,tu) = ...
-            max(recovery_day.horizontal_egress(:,tu),...
-                max(repair_complete_day_w_tmp(:,damage.fnc_filters.fire_break),[],2)); 
-
-        % Componet Breakdowns
-        comp_breakdowns.horizontal_egress(:,:,tu) = damage.fnc_filters.fire_break .* repair_complete_day_w_tmp;
-    end
-end
-
-%% STORY FLOODING
-for tu = flip(1:num_stories) % Go from top to bottom
-    is_damaged = damage.tenant_units{tu}.qnt_damaged > 0;
-    flooding_this_story = any(is_damaged(:,damage.fnc_filters.causes_flooding),2); % Any major piping damage causes interior flooding
-    flooding_cleanup_day = flooding_this_story .* impeding_temp_repairs.flooding_cleanup_day;
-
-    % Save clean up time per component causing flooding
-    comp_breakdowns.flooding(:,:,tu) = damage.fnc_filters.causes_flooding .* is_damaged .* flooding_cleanup_day;
-
-    % This story is not accessible if any story above has flooding
-    recovery_day.flooding(:,tu) = max([flooding_cleanup_day,recovery_day.flooding(:,(tu+1):end)],[],2);
-end
-
-%% STAIRS AND STAIRDOORS
-if num_stories == 1 
-    return % Re-occupancy of one story buildigns is not affected by stairway access
-end
-
-% Augment damage filters with door data
-damage.fnc_filters.stairs = logical([damage.fnc_filters.stairs, 0]);
-damage.fnc_filters.stair_doors = logical([zeros(1,num_comps), 1]);
-
-%% Go through each story and check if there is sufficient story access (stairs and stairdoors)
-% if stairs don't exist on a story, this will assume they are rugged (along with the stair doors)
-for tu = 1:num_stories
-    % Augment damage matrix with door data
-    racked_stair_doors = min(damage_consequences.racked_stair_doors_per_story(:,tu),building_model.stairs_per_story(tu));
-    damage.tenant_units{tu}.qnt_damaged = [damage.tenant_units{tu}.qnt_damaged, racked_stair_doors];
-    door_repair_day = (racked_stair_doors > 0) .* impeding_temp_repairs.door_racking_repair_day;
-    damage.tenant_units{tu}.recovery.repair_complete_day = [damage.tenant_units{tu}.recovery.repair_complete_day, door_repair_day];
-
-    % Quantify damaged stairs on this story
-    repair_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day;
-    damaged_comps = damage.tenant_units{tu}.qnt_damaged;
-
-    % Make sure zero repair days are NaN
-    repair_complete_day(repair_complete_day == 0) = NaN;
-
-    % Step through each unique component repair time and determine when
-    % stairs stop affecting story access
-    stair_access_day = zeros(num_reals,1); % day story becomes accessible from repair of stairs
-    stairdoor_access_day = zeros(num_reals,1); % day story becomes accessible from repair of doors
-    filt_all = damage.fnc_filters.stairs | damage.fnc_filters.stair_doors;
-    num_repair_time_increments = sum(filt_all); % possible unique number of loop increments
-    for i = 1:num_repair_time_increments
-        % number of functioning stairs
-        num_dam_stairs = sum(damaged_comps .* damage.fnc_filters.stairs,2); % assumes comps are not simeltaneous
-        num_racked_doors = sum(damaged_comps .* damage.fnc_filters.stair_doors,2); % assumes comps are not simeltaneous
-        functioning_stairs = building_model.stairs_per_story(tu) - num_dam_stairs;
-        functioning_stairdoors = building_model.stairs_per_story(tu) - num_racked_doors;
-
-        % Required egress with and without operational fire suppression system
-        required_stairs = max(functionality_options.min_egress_paths,functionality_options.egress_threshold .* building_model.stairs_per_story(tu));
-
-        % Determine Stair Access
-        sufficient_stair_access  = functioning_stairs >= required_stairs;
-
-        % Determine Stair Door Acces
-        sufficient_stairdoor_access  = functioning_stairdoors >= required_stairs;
-
-        % Add days in this increment to the tally
-        delta_day = min(repair_complete_day(:,filt_all),[],2);
-        delta_day(isnan(delta_day)) = 0;
-        stair_access_day = stair_access_day + ~sufficient_stair_access .* delta_day;
-        stairdoor_access_day = stairdoor_access_day + ~sufficient_stairdoor_access .* delta_day;
-
-        % Add days to components that are affecting occupancy
-        contributing_stairs = ((damaged_comps .* damage.fnc_filters.stairs) > 0)  .* ~sufficient_stair_access; % Count any damaged stairs for realization that have loss of story access
-        contributing_stairs(:,end) = []; % remove added door column
-        comp_breakdowns.stairs(:,:,tu) = comp_breakdowns.stairs(:,:,tu) + contributing_stairs .* delta_day;
-
-        % Change the comps for the next increment
-        repair_complete_day = repair_complete_day - delta_day;
-        repair_complete_day(repair_complete_day <= 0) = NaN;
-        fixed_comps_filt = isnan(repair_complete_day);
-        damaged_comps(fixed_comps_filt) = 0;
-    end
-
-    % This story is not accessible if this or any story below has insufficient stair egress
-    recovery_day.stairs(:,tu) = max([stair_access_day,recovery_day.stairs(:,1:(tu-1))],[],2);
-    
-    % For the third story and above, the story below is not accessible if
-    % there is insufficient stair egress at this story
-    if tu >= 3
-        recovery_day.stairs(:,(tu-1)) = max([stair_access_day,recovery_day.stairs(:,(tu-1))],[],2);
-    end
-    
-    % Damage to doors only affects this story
-    recovery_day.stair_doors(:,tu) = stairdoor_access_day;
-end
-
-end % end function
-
diff --git a/+recovery/+functionality/fn_tenant_function.m b/+recovery/+functionality/fn_tenant_function.m
deleted file mode 100644
index ddce78e..0000000
--- a/+recovery/+functionality/fn_tenant_function.m
+++ /dev/null
@@ -1,459 +0,0 @@
-function [ recovery_day, comp_breakdowns ] = fn_tenant_function( damage, ...
-    building_model, system_operation_day, subsystems, ...
-    tenant_units, impeding_temp_repairs, functionality_options )
-% Check each tenant unit for damage that would cause that tenant unit 
-% to not be functional
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage, loss, and repair time data for each 
-%   component in the building
-% building_model: struct
-%   general attributes of the building model
-% system_operation_day.building: struct
-%   simulation of the day operation is recovered for various systems at the
-%   building level
-% system_operation_day.comp: struct
-%   simulation number of days each component is affecting building system
-%   operations
-% subsystems: table
-%   data table containing information about each subsystem's attributes
-% tenant_units: table
-%   attributes of each tenant unit within the building
-% impeding_temp_repairs: struct
-%   contains simulated temporary repairs the impede occuapancy and function
-%   but are calulated in parallel with the temp repair schedule
-% functionality_options: struct
-%   recovery time optional inputs such as various damage thresholds
-%
-% Returns
-% -------
-% recovery_day: struct
-%   simulation of the number of days each fault tree event is affecting
-%   function in each tenant unit
-% comp_breakdowns: struct
-%   simulation of each components contributions to each of the fault tree events 
-
-%% Initial Setup
-% Initialize Variables
-num_units = length(damage.tenant_units);
-[num_reals, num_comps] = size(damage.tenant_units{1}.qnt_damaged);
-num_stories = building_model.num_stories;
-recovery_day.elevators = zeros(num_reals,num_units);
-recovery_day.exterior = zeros(num_reals,num_units);
-recovery_day.roof = zeros(num_reals,num_units);
-recovery_day.interior = zeros(num_reals,num_units);
-recovery_day.electrical = zeros(num_reals,num_units);
-recovery_day.flooding = zeros(num_reals,num_units);
-comp_breakdowns.elevators = zeros(num_reals,num_comps,num_units);
-comp_breakdowns.electrical = zeros(num_reals,num_comps,num_units);
-
-%% STORY FLOODING
-for tu = flip(1:num_stories) % Go from top to bottom
-    is_damaged = damage.tenant_units{tu}.qnt_damaged > 0;
-    flooding_this_story = any(is_damaged(:,damage.fnc_filters.causes_flooding),2); % Any major piping damage causes interior flooding
-    flooding_recovery_day = flooding_this_story .* impeding_temp_repairs.flooding_repair_day;
-
-    % Save clean up time per component causing flooding
-    comp_breakdowns.flooding(:,:,tu) = damage.fnc_filters.causes_flooding .* is_damaged .* flooding_recovery_day;
-
-    % This story is not accessible if any story above has flooding
-    recovery_day.flooding(:,tu) = max([flooding_recovery_day,recovery_day.flooding(:,(tu+1):end)],[],2);
-end
-
-%% SYSTEM SPECIFIC CONSEQUENCES
-for tu = 1:num_units
-    damaged_comps = damage.tenant_units{tu}.qnt_damaged;
-    initial_damaged = damaged_comps > 0;
-    total_num_comps = damage.tenant_units{tu}.num_comps;
-    unit = tenant_units(tu,:);
-    repair_complete_day = damage.tenant_units{tu}.recovery.repair_complete_day;
-    repair_complete_day_w_tmp = damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp;
-    
-    %% Elevators
-    if unit.is_elevator_required
-        comps_day_repaired = system_operation_day.comp.elev_day_repaired;
-        comps_day_repaired(comps_day_repaired == 0) = NaN;
-        comps_quant_damaged = system_operation_day.comp.elev_quant_damaged;
-        elev_function_recovery_day = zeros(num_reals,1);
-        elev_comps_day_fnc = zeros(num_reals,num_comps);
-        num_repair_time_increments = sum(damage.fnc_filters.elevators); % possible unique number of loop increments
-        % Loop through each unique repair time increment and determine when
-        % stops affecting function
-        for i = 1:num_repair_time_increments
-            % Take the max of component damage to determine the number of
-            % shafts/cabs that are damaged/non_operational
-            % This assumes that different elevator components are correlated
-            num_damaged_elevs = max(comps_quant_damaged,[],2); % this assumes elevators are in one performance group if simeltaneous
-            num_damaged_elevs = min(num_damaged_elevs,building_model.num_elevators); % you can never have more elevators damaged than exist
-            
-            % If elevators are in mutliple performance groups and those
-            % elevators have simultaneous damage states, it is not possible
-            % to count the number of damaged elevators without additional
-            % information
-            num_elev_pgs = length(unique(damage.comp_ds_table.comp_idx(damage.fnc_filters.elevators)));
-            is_sim_ds = any(damage.comp_ds_table.is_sim_ds(damage.fnc_filters.elevators)');
-            if (num_elev_pgs > 1) && is_sim_ds
-                error('PBEE_Recovery:Function','Elevator Function check does not handle multiple performance groups with simultaneous damage states')
-            end
-
-            % quantifty the number of occupancy needing to use the elevators
-            % all occupants above the first floor will try to use the elevators
-            building_occ_per_elev = sum(building_model.occupants_per_story(2:end)) ./ (building_model.num_elevators - num_damaged_elevs); 
-            
-            % elevator function check
-            % do tenants have sufficient elevator access need based on
-            % elevators that are still operational
-            affects_function = building_occ_per_elev > max(unit.occ_per_elev);
-
-            % Add days in this increment to the tally
-            delta_day = min(comps_day_repaired(:,damage.fnc_filters.elevators),[],2);
-            delta_day(isnan(delta_day)) = 0;
-            elev_function_recovery_day = elev_function_recovery_day + affects_function .* delta_day;
-            
-            % Add days to components that are affecting occupancy
-            any_elev_damage = comps_quant_damaged > 0; % Count any component that contributes to the loss of function regardless of by how much 
-            elev_comps_day_fnc = elev_comps_day_fnc + any_elev_damage .* affects_function .* delta_day;
-
-            % Change the comps for the next increment
-            comps_day_repaired = comps_day_repaired - delta_day;
-            comps_day_repaired(comps_day_repaired <= 0) = NaN;
-            fixed_comps_filt = isnan(comps_day_repaired);
-            comps_quant_damaged(fixed_comps_filt) = 0;
-        end
-        power_supply_recovery_day = max(system_operation_day.building.elevator_mcs,system_operation_day.building.electrical_main);
-        recovery_day.elevators(:,tu) = max(elev_function_recovery_day,power_supply_recovery_day); % electrical system and utility
-        power_supply_recovery_day_comp = max(system_operation_day.comp.elevator_mcs,system_operation_day.comp.electrical_main);
-        comp_breakdowns.elevators(:,:,tu) = max(elev_comps_day_fnc,power_supply_recovery_day_comp);
-    end
-    
-    %% Exterior Enclosure 
-    % Perimeter Cladding (assuming all exterior components have either lf or sf units)
-    area_affected_lf_all_comps = damage.comp_ds_table.exterior_surface_area_factor' .* damage.comp_ds_table.unit_qty' .* building_model.ht_per_story_ft(tu) .* damage.tenant_units{tu}.qnt_damaged;
-    area_affected_direct_scale_all_comps = damage.comp_ds_table.exterior_surface_area_factor' .* damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.qnt_damaged;
-   
-    comp_affected_area = zeros(num_reals,num_comps);
-    comp_affected_area(:,damage.fnc_filters.exterior_seal_lf) = area_affected_lf_all_comps(:,damage.fnc_filters.exterior_seal_lf);
-    comp_affected_area(:,damage.fnc_filters.exterior_seal_sf) = area_affected_direct_scale_all_comps(:,damage.fnc_filters.exterior_seal_sf);
-    comp_affected_area(:,damage.fnc_filters.exterior_seal_ea) = area_affected_direct_scale_all_comps(:,damage.fnc_filters.exterior_seal_ea);
-    
-    comps_day_repaired = repair_complete_day;
-    ext_function_recovery_day = zeros(num_reals,1);
-    all_comps_day_ext = zeros(num_reals,num_comps);
-    num_repair_time_increments = sum(damage.fnc_filters.exterior_seal_all); % possible unique number of loop increments
-    % Loop through each unique repair time increment and determine when stops affecting function
-    for i = 1:num_repair_time_increments
-        % Determine the area of wall which has severe exterior encolusure damage 
-        area_affected = sum(comp_affected_area,2); % Assumes cladding components do not occupy the same perimeter area
-        percent_area_affected = min(area_affected / unit.perim_area,1); % normalize it
-        
-        % Determine if current damage affects function for this tenant unit
-        % if the area of exterior wall damage is greater than what is
-        % acceptable by the tenant 
-        affects_function = percent_area_affected > unit.exterior; 
-        
-        % Add days in this increment to the tally
-        delta_day = min(comps_day_repaired(:,damage.fnc_filters.exterior_seal_all),[],2);
-        delta_day(isnan(delta_day)) = 0;
-        ext_function_recovery_day = ext_function_recovery_day + affects_function .* delta_day;
-        
-        % Add days to components that are affecting occupancy
-        any_area_affected_all_comps = comp_affected_area > 0; % Count any component that contributes to the loss of occupance regardless of by how much
-        all_comps_day_ext = all_comps_day_ext + any_area_affected_all_comps .* affects_function .* delta_day;
-        
-        % Change the comps for the next increment
-        % reducing damage for what has been repaired in this time increment
-        comps_day_repaired = comps_day_repaired - delta_day;
-        comps_day_repaired(comps_day_repaired <= 0) = NaN;
-        fixed_comps_filt = isnan(comps_day_repaired);
-        comp_affected_area(fixed_comps_filt) = 0;
-    end
-    
-    recovery_day.exterior(:,tu) = ext_function_recovery_day;
-        comp_breakdowns.exterior(:,:,tu) = all_comps_day_ext;
-        
-    if unit.story == num_stories % If this is the top story, check the roof for function
-        % Roof structure check
-        [ all_comps_day_roof_struct, roof_structure_recovery_day ] = check_roof_function( ...
-            damage.fnc_filters.roof_structure, ...
-            subsystems.redundancy_threshold(subsystems.id == 21), ...  % structure threshold
-            repair_complete_day_w_tmp, ...
-            damage.tenant_units{tu}.qnt_damaged, ...
-            damage.tenant_units{tu}.num_comps ...
-        );
-        
-        % Roof seatherproofing check
-        [ all_comps_day_roof_weather, roof_weather_recovery_day] = check_roof_function( ...
-            damage.fnc_filters.roof_weatherproofing, ...
-            subsystems.redundancy_threshold(subsystems.id == 22), ...  % seal threshold
-            repair_complete_day_w_tmp, ...
-            damage.tenant_units{tu}.qnt_damaged, ...
-            damage.tenant_units{tu}.num_comps ...
-        );
-
-        % Combine branches
-        recovery_day.roof(:,tu) = max(roof_structure_recovery_day, roof_weather_recovery_day);
-        comp_breakdowns.roof(:,:,tu) = max(all_comps_day_roof_struct, all_comps_day_roof_weather);
-    end
-    
-    %% Interior Area
-    area_affected_lf_all_comps           = damage.comp_ds_table.interior_area_factor' .* damage.comp_ds_table.unit_qty' .* building_model.ht_per_story_ft(tu) .* damage.tenant_units{tu}.qnt_damaged;
-    area_affected_direct_scale_all_comps = damage.comp_ds_table.interior_area_factor' .* damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.qnt_damaged;
-    area_affected_bay_all_comps          = damage.comp_ds_table.interior_area_factor' .* building_model.struct_bay_area_per_story(tu) .* damage.tenant_units{tu}.qnt_damaged;
-    area_affected_build_all_comps        = damage.comp_ds_table.interior_area_factor' .* sum(building_model.area_per_story_sf) .* damage.tenant_units{tu}.qnt_damaged;
-    
-    repair_complete_day_w_tmp_w_instabilities = repair_complete_day_w_tmp;
-    if tu > 1
-        area_affected_below = damage.comp_ds_table.interior_area_factor' .* building_model.struct_bay_area_per_story(tu-1) .* damage.tenant_units{tu-1}.qnt_damaged;
-        area_affected_bay_all_comps(:,damage.fnc_filters.vert_instabilities) ...
-            = max(area_affected_below(:,damage.fnc_filters.vert_instabilities),area_affected_bay_all_comps(:,damage.fnc_filters.vert_instabilities));
-        repair_time_below = damage.tenant_units{tu-1}.recovery.repair_complete_day_w_tmp;
-        repair_complete_day_w_tmp_w_instabilities(:,damage.fnc_filters.vert_instabilities) ...
-            = max(repair_time_below(:,damage.fnc_filters.vert_instabilities),repair_complete_day_w_tmp(:,damage.fnc_filters.vert_instabilities));
-    end
-
-    comp_affected_area = zeros(num_reals,num_comps);
-    comp_affected_area(:,damage.fnc_filters.interior_function_lf) = area_affected_lf_all_comps(:,damage.fnc_filters.interior_function_lf);
-    comp_affected_area(:,damage.fnc_filters.interior_function_sf) = area_affected_direct_scale_all_comps(:,damage.fnc_filters.interior_function_sf);
-    comp_affected_area(:,damage.fnc_filters.interior_function_ea) = area_affected_direct_scale_all_comps(:,damage.fnc_filters.interior_function_ea);
-    comp_affected_area(:,damage.fnc_filters.interior_function_bay) = area_affected_bay_all_comps(:,damage.fnc_filters.interior_function_bay);
-    comp_affected_area(:,damage.fnc_filters.interior_function_build) = area_affected_build_all_comps(:,damage.fnc_filters.interior_function_build);
-
-    frag_types_in_check = unique(damage.comp_ds_table.comp_type_id(damage.fnc_filters.interior_function_all));
-    comps_day_repaired = repair_complete_day_w_tmp_w_instabilities;
-
-    int_function_recovery_day = zeros(num_reals,1);
-    int_comps_day_repaired = zeros(num_reals,num_comps);
-    num_repair_time_increments = sum(damage.fnc_filters.interior_function_all); % possible unique number of loop increments
-    % Loop through each unique repair time increment and determine when stops affecting function
-    for i = 1:num_repair_time_increments
-        % Quantify the affected area (based on srss of differenct component
-        % types)
-        diff_comp_areas = [];
-        for cmp = 1:length(frag_types_in_check)
-            filt = strcmp(damage.comp_ds_table.comp_type_id,frag_types_in_check{cmp})'; % check to see if it matches the first part of the ID (ie the type of comp)
-            diff_comp_areas(:,cmp) = sum(comp_affected_area(:,filt),2);
-        end
-        area_affected = sqrt(sum(diff_comp_areas.^2,2)); % total area affected is the srss of the areas in the unit
-        percent_area_affected = min(area_affected / unit.area, 1); % no greater than the total unit area
-    
-        % Determine if current damage affects function for this tenant unit
-        % affects function if the area of interior damage is greater than what is
-        % acceptable by the tenant 
-        affects_function = percent_area_affected > unit.interior; 
-        
-        % Add days in this increment to the tally
-        delta_day = min(comps_day_repaired(:,damage.fnc_filters.interior_function_all),[],2);
-        delta_day(isnan(delta_day)) = 0;
-        int_function_recovery_day = int_function_recovery_day + affects_function .* delta_day;
-        
-        % Add days to components that are affecting occupancy
-        any_area_affected_all_comps = comp_affected_area > 0; % Count any component that contributes to the loss of occupance regardless of by how much
-        int_comps_day_repaired = int_comps_day_repaired + any_area_affected_all_comps .* affects_function .* delta_day;
-        
-        % Change the comps for the next increment
-        % reducing damage for what has been repaired in this time increment
-        comps_day_repaired = comps_day_repaired - delta_day;
-        comps_day_repaired(comps_day_repaired <= 0) = NaN;
-        fixed_comps_filt = isnan(comps_day_repaired);
-        comp_affected_area(fixed_comps_filt) = 0;
-    end
-    recovery_day.interior(:,tu) = int_function_recovery_day;
-    comp_breakdowns.interior(:,:,tu) = int_comps_day_repaired;
-    
-    %% Potable Water System
-    % determine effect on funciton at this tenant unit
-    % any major damage to the branch pipes (small diameter) failes for this tenant unit
-    tenant_sys_recovery_day = max(repair_complete_day .* damage.fnc_filters.water_unit,[],2); 
-    recovery_day.water_potable(:,tu) = max(system_operation_day.building.water_potable_main,tenant_sys_recovery_day);
-
-    % distribute effect to the components
-    comp_breakdowns.water_potable(:,:,tu) = max(system_operation_day.comp.water_potable_main, repair_complete_day .* damage.fnc_filters.water_unit);
-    
-    % In taller buildings, water needs to be pumped to reach upper stories
-    % and therefore requires electrical power
-    if unit.story > functionality_options.water_pressure_max_story
-        electrical_failure_controls = system_operation_day.building.electrical_main > recovery_day.water_potable(:,tu);
-        recovery_day.water_potable(:,tu) = max(recovery_day.water_potable(:,tu),system_operation_day.building.electrical_main);
-        comp_breakdowns.water_potable(:,:,tu) = max(comp_breakdowns.water_potable(:,:,tu) .* ~electrical_failure_controls,...
-                                                    system_operation_day.comp.electrical_main .* electrical_failure_controls);
-    end
-    
-    %% Sanitary Waste System
-    % determine effect on funciton at this tenant unit
-    % any major damage to the branch pipes (small diameter) failes for this tenant unit
-    tenant_sys_recovery_day = max(repair_complete_day .* damage.fnc_filters.sewer_unit,[],2); 
-    recovery_day.water_sanitary(:,tu) = max(system_operation_day.building.water_sanitary_main,tenant_sys_recovery_day);
-
-    % distribute effect to the components
-    comp_breakdowns.water_sanitary(:,:,tu) = max(system_operation_day.comp.water_sanitary_main, repair_complete_day .* damage.fnc_filters.sewer_unit);
-
-    % Sanitary waste operation at this tenant unit depends on the 
-    % operation of the potable water system at this tenant unit
-    recovery_day.water_sanitary(:,tu) = max(recovery_day.water_sanitary(:,tu),recovery_day.water_potable(:,tu));
-    comp_breakdowns.water_sanitary(:,:,tu) = max(comp_breakdowns.water_sanitary(:,:,tu), comp_breakdowns.water_potable(:,:,tu));
-    
-    %% Electrical Power System
-    % Does not consider effect of backup systems
-    % Just electical power within the tenant unit (building power is
-    % assessed in fn_builing_level_system_operation)
-    if unit.is_electrical_required
-        % determine effect on funciton at this tenant unit 
-        % any major damage to the unit level electrical equipment fails for this tenant unit
-        tenant_sys_recovery_day = max(repair_complete_day .* damage.fnc_filters.electrical_unit,[],2);
-        recovery_day.electrical(:,tu) = max(system_operation_day.building.electrical_main,tenant_sys_recovery_day);
-        
-        % distribute effect to the components
-        comp_breakdowns.electrical(:,:,tu) = max(system_operation_day.comp.electrical_main, repair_complete_day .* damage.fnc_filters.electrical_unit);
-    end
-    
-    %% HVAC System
-    % HVAC: Control System
-    recovery_day_hvac_control = system_operation_day.building.hvac_control; % Electrical power is counted in here 
-    comp_breakdowns_hvac_control = system_operation_day.comp.hvac_control;
-
-    % HVAC: Ventilation
-    dependancy.recovery_day = recovery_day_hvac_control;
-    dependancy.comp_breakdown = comp_breakdowns_hvac_control;
-    [recovery_day.hvac_ventilation(:,tu), comp_breakdowns.hvac_ventilation(:,:,tu)] = ...
-        subsystem_recovery('hvac_ventilation', damage, repair_complete_day, ...
-                     total_num_comps, damaged_comps, initial_damaged, dependancy);
-
-    % HVAC: Heating
-    dependancy.recovery_day = max(recovery_day.hvac_ventilation(:,tu),system_operation_day.building.hvac_heating);
-    dependancy.comp_breakdown = max(comp_breakdowns.hvac_ventilation(:,:,tu),system_operation_day.comp.hvac_heating);
-    [recovery_day.hvac_heating(:,tu), comp_breakdowns.hvac_heating(:,:,tu)] = ...
-        subsystem_recovery('hvac_heating', damage, repair_complete_day, ...
-                     total_num_comps, damaged_comps, initial_damaged, dependancy);
-
-    % HVAC: Cooling
-    dependancy.recovery_day = max(recovery_day.hvac_ventilation(:,tu),system_operation_day.building.hvac_cooling);
-    dependancy.comp_breakdown = max(comp_breakdowns.hvac_ventilation(:,:,tu),system_operation_day.comp.hvac_cooling);
-    [recovery_day.hvac_cooling(:,tu), comp_breakdowns.hvac_cooling(:,:,tu)] = ...
-        subsystem_recovery('hvac_cooling', damage, repair_complete_day, ...
-                     total_num_comps, damaged_comps, initial_damaged, dependancy);
-
-    % HVAC: Exhast
-    dependancy.recovery_day = recovery_day_hvac_control;
-    dependancy.comp_breakdown = comp_breakdowns_hvac_control;
-    [recovery_day.hvac_exhaust(:,tu), comp_breakdowns.hvac_exhaust(:,:,tu)] = ...
-        subsystem_recovery('hvac_exhaust', damage, repair_complete_day, ...
-                     total_num_comps, damaged_comps, initial_damaged, dependancy);
-                 
-    %% Data
-    if unit.is_data_required && any(damage.fnc_filters.data_unit | damage.fnc_filters.data_main)
-        % determine effect on funciton at this tenant unit
-        % any major damage to the unit level electrical equipment failes for this tenant unit
-        tenant_sys_recovery_day = max(repair_complete_day .* damage.fnc_filters.data_unit,[],2);
-        recovery_day.data(:,tu) = max(system_operation_day.building.data_main,tenant_sys_recovery_day);
-        
-        % Consider effect of external water network
-        recovery_day.data = max(recovery_day.data,system_operation_day.building.electrical_main);
-        
-        % distribute effect to the components
-        comp_breakdowns.data(:,:,tu) = max(system_operation_day.comp.data_main, repair_complete_day .* damage.fnc_filters.data_unit);
-    end      
-                 
-    %% Post process for tenant-specific requirements 
-    % Zero out systems that are not required by the tenant
-    % Still need to calculate above due to dependancies between options
-    if ~unit.is_water_potable_required
-        recovery_day.water_potable = zeros(num_reals,num_units);
-        comp_breakdowns.water_potable = zeros(num_reals,num_comps,num_units);
-    end
-    if ~unit.is_water_sanitary_required
-        recovery_day.water_sanitary = zeros(num_reals,num_units);
-        comp_breakdowns.water_sanitary = zeros(num_reals,num_comps,num_units);
-    end
-    if ~unit.is_hvac_ventilation_required
-        recovery_day.hvac_ventilation = zeros(num_reals,num_units);
-        comp_breakdowns.hvac_ventilation = zeros(num_reals,num_comps,num_units);
-    end
-    if ~unit.is_hvac_heating_required
-        recovery_day.hvac_heating = zeros(num_reals,num_units);
-        comp_breakdowns.hvac_heating = zeros(num_reals,num_comps,num_units);
-    end
-    if ~unit.is_hvac_cooling_required
-        recovery_day.hvac_cooling = zeros(num_reals,num_units);
-        comp_breakdowns.hvac_cooling = zeros(num_reals,num_comps,num_units);
-    end
-    if ~unit.is_hvac_exhaust_required
-        recovery_day.hvac_exhaust = zeros(num_reals,num_units);
-        comp_breakdowns.hvac_exhaust = zeros(num_reals,num_comps,num_units);
-    end
-end
-
-
-
-end % Function
-
-
-
-%%%%%%%%%%%%%%%%%%%%%%%%
-%%%%% SUBFUNCTIONS %%%%%
-%%%%%%%%%%%%%%%%%%%%%%%%
-function [recovery_day_all, comp_breakdowns_all] = subsystem_recovery(...
-    subsystem, damage, repair_complete_day, total_num_comps, damaged_comps, ...
-    initial_damaged, dependancy)
-
-% import packages
-import recovery.functionality.fn_calc_subsystem_recovery
-
-% Set variables
-recovery_day_all = dependancy.recovery_day;
-comp_breakdowns_all = dependancy.comp_breakdown;
-
-% Go through each component group in this subsystem and determine recovery
-% based on impact of system operation at the tenant unit level
-subs = fieldnames(damage.fnc_filters.hvac.tenant.(subsystem));
-for b = 1:length(subs)
-    filt = damage.fnc_filters.hvac.tenant.(subsystem).(subs{b})';
-    [recovery_day] = fn_calc_subsystem_recovery( filt, damage, repair_complete_day, total_num_comps, damaged_comps );
-    comps_breakdown = filt .* initial_damaged .* recovery_day;
-    recovery_day_all = max(recovery_day_all,recovery_day); % combine with previous stories
-    comp_breakdowns_all = max(comp_breakdowns_all,comps_breakdown);
-end
-
-end % Function
-
-
-function [ all_comps_day_roof, roof_recovery_day ] = check_roof_function(roof_sys_filter, damage_threshold, repair_complete_day_w_tmp, qnt_damaged, num_comps)
-% Check the roof area for function (seal and function)
-
-num_comp_damaged = roof_sys_filter .* qnt_damaged;
-num_roof_comps = roof_sys_filter .* num_comps;
-
-comps_day_repaired = repair_complete_day_w_tmp;
-roof_recovery_day = zeros(size(repair_complete_day_w_tmp, 1), 1);
-all_comps_day_roof = zeros(size(repair_complete_day_w_tmp));
-num_repair_time_increments = sum(roof_sys_filter); % possible unique number of loop increments
-
-% Loop through each unique repair time increment and determine when stops affecting function
-for i = 1:num_repair_time_increments
-    % Determine the area of roof affected 
-    percent_area_affected = sum(num_comp_damaged,2) / sum(num_roof_comps,2); % Assumes roof components do not occupy the same area of roof
-
-    % Determine if current damage affects function for this tenant unit
-    % if the area of exterior wall damage is greater than what is
-    % acceptable by the tenant 
-    affects_function = percent_area_affected >= damage_threshold; 
-
-    % Add days in this increment to the tally
-    delta_day = min(comps_day_repaired(:, roof_sys_filter),[],2);
-    delta_day(isnan(delta_day)) = 0;
-    roof_recovery_day = roof_recovery_day + affects_function .* delta_day;
-
-    % Add days to components that are affecting function
-    any_area_affected_all_comps = num_comp_damaged > 0; % Count any component that contributes to the loss of function regardless of by how much
-    all_comps_day_roof = all_comps_day_roof + any_area_affected_all_comps .* affects_function .* delta_day;
-
-    % Change the comps for the next increment
-    % reducing damage for what has been repaired in this time increment
-    comps_day_repaired = comps_day_repaired - delta_day;
-    comps_day_repaired(comps_day_repaired <= 0) = NaN;
-    fixed_comps_filt = isnan(comps_day_repaired);
-    num_comp_damaged(fixed_comps_filt) = 0;
-end
-
-end
\ No newline at end of file
diff --git a/+recovery/+functionality/fn_tenant_safety.m b/+recovery/+functionality/fn_tenant_safety.m
deleted file mode 100644
index b9d9c4f..0000000
--- a/+recovery/+functionality/fn_tenant_safety.m
+++ /dev/null
@@ -1,171 +0,0 @@
-function [ recovery_day, comp_breakdowns ] = fn_tenant_safety( damage, ...
-    building_model, functionality_options, tenant_units )
-% Check each tenant unit for damage that would cause that tenant unit 
-% to be shut down due to issues of locay safety
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage, loss, and repair time data for each 
-%   component in the building
-% building_model: struct
-%   general attributes of the building model
-% functionality_options: struct
-%   recovery time optional inputs such as various damage thresholds
-% tenant_units: table
-%   attributes of each tenant unit within the building
-%
-% Returns
-% -------
-% recovery_day: struct
-%   simulation of the number of days each fault tree event is affecting safety
-%   in each tenant unit
-% comp_breakdowns: struct
-%   simulation of each components contributions to each of the fault tree events 
-
-%% Initial Setup
-[num_reals, num_comps] = size(damage.tenant_units{1}.qnt_damaged);
-num_units = length(damage.tenant_units);
-comp_types_interior_check = unique(damage.comp_ds_table.comp_type_id(damage.fnc_filters.int_fall_haz_all));
-
-% go through each tenant unit and quantify the affect that each system has on reoccpauncy
-for tu = 1:num_units
-    % Grab tenant and damage info for this tenant unit
-    unit = tenant_units(tu,:);
-    repair_complete_day_w_tmp = damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp; % day each component (and DS) is reparied of this TU
-
-    %% Exterior Enclosure 
-    % Calculated the affected perimeter area of exterior components
-    % (assuming all exterior components have either lf or sf units)
-    area_affected_all_linear_comps       = damage.comp_ds_table.exterior_surface_area_factor' .* damage.comp_ds_table.unit_qty' .* building_model.ht_per_story_ft(tu) .* damage.tenant_units{tu}.qnt_damaged;
-    area_affected_all_direct_scale_comps = damage.comp_ds_table.exterior_surface_area_factor' .* damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.qnt_damaged;
-    
-    % construct a matrix of affected areas from the various damaged component types
-    comp_affected_area = zeros(num_reals,num_comps);
-    comp_affected_area(:,damage.fnc_filters.exterior_safety_lf) = area_affected_all_linear_comps(:,damage.fnc_filters.exterior_safety_lf);
-    comp_affected_area(:,damage.fnc_filters.exterior_safety_sf) = area_affected_all_direct_scale_comps(:,damage.fnc_filters.exterior_safety_sf);
-    
-    % Go each possible unique repair time contributing to interior safety check
-    % Find when enough repairs are complete such that interior damage no
-    % longer affects tenant safety
-    comps_day_repaired = repair_complete_day_w_tmp; % define as initial repair day considering tmp repairs
-    ext_repair_day = zeros(num_reals,1);
-    all_comps_day_repaired = zeros(num_reals,num_comps);
-    num_repair_time_increments = sum(damage.fnc_filters.exterior_safety_all); % possible unique number of loop increments
-    for i = 1:num_repair_time_increments
-        
-        % Quantify Affected Area
-        area_affected = sum(comp_affected_area,2); % Assumes cladding components do not occupy the same perimeter area
-        percent_area_affected = area_affected / unit.perim_area;
-
-        % Check if this is sufficent enough to cause as tenant safety issue
-        affects_occupancy = percent_area_affected > functionality_options.exterior_safety_threshold;
-
-        % Determine step increment based on the component with the shortest repair time
-        delta_day = min(comps_day_repaired(:,damage.fnc_filters.exterior_safety_all),[],2);
-        delta_day(isnan(delta_day)) = 0;
-        
-        % Add increment to the tally of days until the interior damage
-        % stops affecting occupancy
-        ext_repair_day = ext_repair_day + affects_occupancy .* delta_day;
-        
-        % Add days to components that are affecting occupancy
-        any_area_affected_all_comps = (damage.fnc_filters.exterior_safety_all .* comp_affected_area) > 0; % Count any component that contributes to the loss of occupancy regardless of by how much
-        all_comps_day_repaired = all_comps_day_repaired + any_area_affected_all_comps .* affects_occupancy .* delta_day;
-        
-        % Reduce compent damaged for the next increment based on what was
-        % repaired in this increment
-        comps_day_repaired = comps_day_repaired - delta_day;
-        comps_day_repaired(comps_day_repaired <= 0) = NaN;
-        fixed_comps_filt = isnan(comps_day_repaired);
-        comp_affected_area(fixed_comps_filt) = 0;
-    end
-    
-    % Save exterior recovery day for this tenant unit
-    recovery_day.exterior(:,tu) = ext_repair_day;
-    comp_breakdowns.exterior(:,:,tu) = all_comps_day_repaired;
-    
-    %% Interior Falling Hazards
-    % Convert all component into affected areas
-    area_affected_all_linear_comps       = damage.comp_ds_table.interior_area_factor' .* damage.comp_ds_table.unit_qty' .* building_model.ht_per_story_ft(tu) .* damage.tenant_units{tu}.qnt_damaged;
-    area_affected_all_direct_scale_comps = damage.comp_ds_table.interior_area_factor' .* damage.comp_ds_table.unit_qty' .* damage.tenant_units{tu}.qnt_damaged;
-    area_affected_all_bay_comps          = damage.comp_ds_table.interior_area_factor' .* building_model.struct_bay_area_per_story(tu) .* damage.tenant_units{tu}.qnt_damaged;
-    area_affected_all_build_comps        = damage.comp_ds_table.interior_area_factor' .* sum(building_model.area_per_story_sf) .* damage.tenant_units{tu}.qnt_damaged;
-    
-    % Checking damage that affects components in story below
-    repair_complete_day_w_tmp_w_instabilities = repair_complete_day_w_tmp;
-    if tu > 1
-        area_affected_below = damage.comp_ds_table.interior_area_factor' .* building_model.struct_bay_area_per_story(tu-1) .* damage.tenant_units{tu-1}.qnt_damaged;
-        area_affected_all_bay_comps(:,damage.fnc_filters.vert_instabilities) ...
-            = max(area_affected_below(:,damage.fnc_filters.vert_instabilities),area_affected_all_bay_comps(:,damage.fnc_filters.vert_instabilities));
-        repair_time_below = damage.tenant_units{tu-1}.recovery.repair_complete_day_w_tmp;
-        repair_complete_day_w_tmp_w_instabilities(:,damage.fnc_filters.vert_instabilities) ...
-            = max(repair_time_below(:,damage.fnc_filters.vert_instabilities),repair_complete_day_w_tmp(:,damage.fnc_filters.vert_instabilities));
-    end
-
-    % construct a matrix of affected areas from the various damaged component types
-    comp_affected_area = zeros(num_reals,num_comps);
-    comp_affected_area(:,damage.fnc_filters.int_fall_haz_lf) = area_affected_all_linear_comps(:,damage.fnc_filters.int_fall_haz_lf);
-    comp_affected_area(:,damage.fnc_filters.int_fall_haz_sf) = area_affected_all_direct_scale_comps(:,damage.fnc_filters.int_fall_haz_sf);
-    comp_affected_area(:,damage.fnc_filters.int_fall_haz_ea) = area_affected_all_direct_scale_comps(:,damage.fnc_filters.int_fall_haz_ea);
-    comp_affected_area(:,damage.fnc_filters.int_fall_haz_bay) = area_affected_all_bay_comps(:,damage.fnc_filters.int_fall_haz_bay);
-    comp_affected_area(:,damage.fnc_filters.int_fall_haz_build) = area_affected_all_build_comps(:,damage.fnc_filters.int_fall_haz_build);
-    
-    % Go each possible unique repair time contributing to interior safety check
-    % Find when enough repairs are complete such that interior damage no
-    % longer affects tenant safety
-    comps_day_repaired = repair_complete_day_w_tmp_w_instabilities; % define as initial repair day considering tmp repairs
-    int_repair_day = zeros(num_reals,1);
-    all_comps_day_repaired = zeros(num_reals,num_comps);
-    num_repair_time_increments = sum(damage.fnc_filters.int_fall_haz_all); % possible unique number of loop increments
-    for i = 1:num_repair_time_increments
-        % Quantify Affected Area
-        diff_comp_areas = [];
-        for cmp = 1:length(comp_types_interior_check)
-            filt = strcmp(damage.comp_ds_table.comp_type_id,comp_types_interior_check{cmp})'; % check to see if it matches the first part of the ID (ie the type of comp)
-            diff_comp_areas(:,cmp) = sum(comp_affected_area(:,filt),2);
-        end
-        area_affected = sqrt(sum(diff_comp_areas.^2,2)); % total area affected is the srss of the areas in the unit
-        
-        % Determine if current damage affects occupancy
-        percent_area_affected = min(area_affected / unit.area,1);
-        affects_occupancy = percent_area_affected > functionality_options.interior_safety_threshold;
-        
-        % Determine step increment based on the component with the shortest repair time
-        delta_day = min(comps_day_repaired(:,damage.fnc_filters.int_fall_haz_all),[],2);
-        delta_day(isnan(delta_day)) = 0;
-        
-        % Add increment to the tally of days until the interior damage
-        % stops affecting occupancy
-        int_repair_day = int_repair_day + affects_occupancy .* delta_day;
-        
-        % Add days to components that are affecting occupancy
-        any_area_affected_all_comps = (damage.fnc_filters.int_fall_haz_all .* comp_affected_area) > 0; % Count any component that contributes to the loss of occupancy regardless of by how much
-        all_comps_day_repaired = all_comps_day_repaired + any_area_affected_all_comps .* affects_occupancy .* delta_day;
-        
-        % Reduce compent damaged for the next increment based on what was
-        % repaired in this increment
-        comps_day_repaired = comps_day_repaired - delta_day;
-        comps_day_repaired(comps_day_repaired <= 0) = NaN;
-        fixed_comps_filt = isnan(comps_day_repaired);
-        comp_affected_area(fixed_comps_filt) = 0;
-    end
-    
-    % Save interior recovery day for this tenant unit
-    recovery_day.interior(:,tu) = int_repair_day;
-    comp_breakdowns.interior(:,:,tu) = all_comps_day_repaired;
-    
-    %% Hazardous Materials
-    % note: hazardous materials are accounted for in building functional
-    % assessment here, but are not currently quantified in the component
-    % breakdowns
-    if any(damage.fnc_filters.local_hazardous_material)
-        % Any local hazardous material shuts down the entire tenant unit
-        recovery_day.hazardous_material(:,tu) = max(repair_complete_day_w_tmp(:,damage.fnc_filters.local_hazardous_material),[],2); 
-    else
-        recovery_day.hazardous_material(:,tu) = zeros(num_reals,1);
-    end
-end
-
-end % end function
-
diff --git a/+recovery/+functionality/main_functionality.m b/+recovery/+functionality/main_functionality.m
deleted file mode 100644
index c828665..0000000
--- a/+recovery/+functionality/main_functionality.m
+++ /dev/null
@@ -1,68 +0,0 @@
-function [ recovery ] = main_functionality( damage, building_model, ...
-     damage_consequences, utilities, functionality_options, ...
-     tenant_units, subsystems, impeding_temp_repairs )
-% Calculates building re-occupancy and function based on simulations of
-% building damage and calculates the recovery times of each recovery state
-% based on a given repair schedule
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage, loss, and repair time data for each 
-%   component in the building
-% building_model: struct
-%   general attributes of the building model
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-%   tags and repair costs ratios
-% utilities: struct
-%   data structure containing simulated utility downtimes
-% functionality_options: struct
-%   recovery time optional inputs such as various damage thresholds
-% tenant_units: table
-%   attributes of each tenant unit within the building
-% subsystems: table
-%   attributes of building subsystems; data provided in static tables
-%   directory
-% impeding_temp_repairs: struct
-%   contains simulated temporary repairs the impede occuapancy and function
-%   but are calulated in parallel with the temp repair schedule
-%
-% Returns
-% -------
-% recovery.reoccupancy: struct
-%   contains data on the recovery of tenant- and building-level reoccupancy, 
-%   recovery trajectorires, and contributions from systems and components 
-% recovery.functional: struct
-%   contains data on the recovery of tenant- and building-level function, 
-%   recovery trajectorires, and contributions from systems and components 
-
-%% Import Packages
-import recovery.functionality.fn_calculate_reoccupancy
-import recovery.functionality.fn_calculate_functionality
-import recovery.functionality.fn_check_habitability
-
-%% Calaculate Building Functionality Restoration Curves
-% Downtime including external delays
-[recovery.reoccupancy, reoc_meta.recovery_day, reoc_meta.comp_breakdowns] = ...
-    fn_calculate_reoccupancy( damage, damage_consequences, utilities, ...
-    building_model, functionality_options, tenant_units, impeding_temp_repairs );
-
-[recovery.functional, func_meta.recovery_day, func_meta.comp_breakdowns] =  ...
-    fn_calculate_functionality( damage, damage_consequences, ...
-    utilities, building_model, subsystems, recovery.reoccupancy, ...
-    functionality_options, tenant_units, impeding_temp_repairs);
-
-%% Habitability Checks
-% Overwrite reocuppancy with additional checks from the functionality check
-if isfield(functionality_options,'habitability_requirements')
-    [ recovery.reoccupancy ] = fn_check_habitability( damage, damage_consequences, ...
-         reoc_meta, func_meta, functionality_options.habitability_requirements );
-end
-
-% delete all the extra per-realization data
-recovery.reoccupancy.breakdowns = rmfield(recovery.reoccupancy.breakdowns, 'component_breakdowns_all_reals');
-recovery.functional.breakdowns = rmfield(recovery.functional.breakdowns, 'component_breakdowns_all_reals');
-
-end
-
diff --git a/+recovery/+impedance/fn_contractor.m b/+recovery/+impedance/fn_contractor.m
deleted file mode 100644
index e769a01..0000000
--- a/+recovery/+impedance/fn_contractor.m
+++ /dev/null
@@ -1,58 +0,0 @@
-function contractor_mob_imped = fn_contractor( num_reals, surge_factor, ...
-    sys_repair_trigger, trunc_pd, contractor_options )
-% Simulute contractor mobilization time
-%
-% Parameters
-% ----------
-% num_sys: int
-%   number of building systems considered in the assessment
-% num_reals: int
-%   number of Monte Carlo simulations assessed
-% surge_factor: number
-%   amplification factor for impedance time based on a post disaster surge
-%   in demand for skilled trades and construction supplies
-% sys_repair_trigger: logical array [num_reals x num_systems]
-%   systems that require repair for each realization
-% trunc_pd: matlab normal distribution object
-%   standard normal distrubtion, truncated at upper and lower bounds
-% contractor_options: struct
-%   various options that controll the contracting impedance time
-%
-% Returns
-% -------
-% contractor_mob_imped: array [num_reals x num_sys]
-%   Simulated contractor mobilization time for each system
-
-%% Define contractor distribution parameters
-switch contractor_options.contractor_relationship
-    case 'retainer'
-        med = surge_factor * contractor_options.contractor_retainer_time;
-        beta = 0.4;
-    case 'good'
-        med = (1 + 0.5*(surge_factor-1)) * 3;
-        beta = 0.4;
-    case 'none'
-        med = surge_factor * 42;
-        beta = 0.8;
-    otherwise
-        error('PBEE_Recovery:RepairSchedule', 'Invalid contractor relationship type, "%s", for impedance factor simulation', contractor_relationship)
-end
-
-%% Set median for each realization
-contr_med = med * ones(num_reals,1);
-
-%% Simulate Impedance Time
-prob_sim = rand(num_reals, 1); % This assumes systems are correlated
-x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
-contractor_mob_imped = exp(x_vals_std_n * beta + log(contr_med));
-
-% Only use the simulated values for the realzation and system that
-% require permitting
-contractor_mob_imped = contractor_mob_imped .* sys_repair_trigger;
-
-% Amplify by the surge factor
-% Assume impedance always takes a full day
-contractor_mob_imped = ceil(contractor_mob_imped);
-
-end
-
diff --git a/+recovery/+impedance/fn_default_surge_factor.m b/+recovery/+impedance/fn_default_surge_factor.m
deleted file mode 100644
index 50cbeb7..0000000
--- a/+recovery/+impedance/fn_default_surge_factor.m
+++ /dev/null
@@ -1,51 +0,0 @@
-function [ surge_factor ] = fn_default_surge_factor( is_dense_urban_area, pga, pga_de )
-% This function determines the surge factor based on the intensity of shaking
-%
-% Parameters
-% ----------
-% is_dense_urban_area: bool
-%   if the building is in a dense urban area or not
-% pga: number
-%   peak ground acceleration control point 1 (below which surge factor is 1)
-% pga_de: number
-%   peak ground acceleration at the design intensity
-%
-%
-% Returns
-% -------
-% surge_factor: number
-%   surge factor which amplified impedance factor times
-%
-% Notes
-% -----
-%
-%     |                                         _____________ 3.0
-%     |                                       /              
-%     |                                      /               
-%     |                                     /                
-%     |                                    /                 
-%     |                                   /                  
-%     |                                  /     ______________ 1.5
-%     |                                 /    / |             
-%     |                                /   /   |             
-%     |                               /  /     |             
-%     |                              / /       |             
-%     |                             //         |             
-% 1.0 |  __________________________/           |             
-%     |                            |           |             
-%     |                            |           |             
-%     -----------------------------+-----------+-------------
-%                                pga_1       pga_2
-
-if is_dense_urban_area
-    max_surge = 3;
-else
-    max_surge = 1.5;
-end
-
-pga_1 = max(0.2, pga_de);
-pga_2 = pga_1 * 1.5;
-surge_factor = interp1([pga_1, pga_2], [1, max_surge], min(max(pga,pga_1),pga_2));
-
-end
-
diff --git a/+recovery/+impedance/fn_engineering.m b/+recovery/+impedance/fn_engineering.m
deleted file mode 100644
index d25a18c..0000000
--- a/+recovery/+impedance/fn_engineering.m
+++ /dev/null
@@ -1,87 +0,0 @@
-function [ eng_mob_imped, eng_design_imped ] = fn_engineering( ...
-    num_reals, repair_cost_ratio, building_value, ...
-    surge_factor, redesign_trigger, is_engineer_on_retainer, user_options, ...
-    design_min, design_max, trunc_pd, beta, impeding_factor_medians )
-% Simulute permitting time
-%
-% Parameters
-% ----------
-% num_reals: int
-%   number of Monte Carlo simulations assessed
-% repair_cost_ratio: array [num_reals x 1]
-%   simulated building repair cost; normalized by building replacemnt
-%   value.
-% building_value: number
-%   replacment value of building, in USD, non including land
-% surge_factor: number
-%   amplification factor for impedance time based on a post disaster surge
-%   in demand for skilled trades and construction supplies
-% redesign_trigger: logical array [num_reals x num_sys]
-%   is redesign required for the given system
-% is_engineer_on_retainer: logical
-%   is there a pre-arranged agreement with an engineer for priorization of
-%   redesign
-% user_options: struct
-%   contains paramters of system design time function, set by user
-% design_min: number
-%   lower bound on the median design time
-% design_max: number
-%   upper bound on the median design time
-% trunc_pd: matlab normal distribution object
-%   standard normal distrubtion, truncated at upper and lower bounds
-% beta: number
-%   lognormal standard deviation (dispersion)
-% impeding_factor_medians: table
-%   median delays for various impeding factors
-%
-% Returns
-% -------
-% eng_mob_imped: array [num_reals x num_sys]
-%   Simulated enginering mobilization time for each system
-% eng_design_imped: array [num_reals x num_sys]
-%   Simulated enginering design time for each system
-
-% Notes
-% ------
-% assumes engineering mobilization and re-design time are independant, but
-% are correlated between each system. In other words, you will have the same 
-% designers for the structural, stairs, exterior, and whatever other systems 
-% need design time, but the time it takes to spin up an engineer is not
-% related to the time it takes for them to complete the re-design.
-
-%% Engineering Mobilization Time
-% Mobilization medians
-eng_mob_medians = ...
-    impeding_factor_medians(strcmp(impeding_factor_medians.factor,'engineering mobilization'),:);
-
-if is_engineer_on_retainer
-    filt = strcmp(eng_mob_medians.category,'retainer');
-else
-    filt = strcmp(eng_mob_medians.category,'default');
-end
-median_eng_mob = surge_factor * eng_mob_medians.time_days(filt); % days
-
-% Truncated lognormal distribution (via standard normal simulation)
-prob_sim = rand(num_reals, 1); % This assumes systems are correlated
-x_vals_std_n = icdf(trunc_pd, prob_sim);
-eng_mob_time = exp(x_vals_std_n * beta + log(median_eng_mob));
-% Assume impedance always takes a full day
-eng_mob_imped = ceil(eng_mob_time .* redesign_trigger);
-
-%% Engineering Design Time
-% Calculate System Design Time
-RC_total = repair_cost_ratio .* building_value;
-SDT = RC_total * user_options.f / ...
-    (user_options.r * user_options.t * user_options.w);
-design_med = min(max(SDT, design_min), design_max);
-
-% Truncated lognormal distribution (via standard normal simulation)
-% Assumes engineering design time is independant of mobilization time
-prob_sim = rand(num_reals,1); % This assumes systems are correlated
-x_vals_std_n = icdf(trunc_pd ,prob_sim);
-eng_design_time = exp(x_vals_std_n * beta + log(design_med));
-% Assume impedance always takes a full day
-eng_design_imped = ceil(eng_design_time .* redesign_trigger);
-
-end
-
diff --git a/+recovery/+impedance/fn_financing.m b/+recovery/+impedance/fn_financing.m
deleted file mode 100644
index b2ea15c..0000000
--- a/+recovery/+impedance/fn_financing.m
+++ /dev/null
@@ -1,73 +0,0 @@
-function financing_imped = fn_financing( capital_available_ratio, ...
-    funding_source, surge_factor, sys_repair_trigger, repair_cost_ratio, ...
-    trunc_pd, beta, impeding_factor_medians )
-% Simulute financing time
-%
-% Parameters
-% ----------
-% capital_available_ratio: number
-%   liquidable funding on hand to make repairs immediately after the
-%   damaging event. Normalized by building replacment value.
-% funding_source: string
-%   accepted values: {'sba', 'private', 'insurance'}
-%   type of funding source for required funds greater than the capital on
-%   hand
-% surge_factor: number
-%   amplification factor for impedance time based on a post disaster surge
-% sys_repair_trigger: logical array [num_reals x num_systems]
-%   systems that require repair for each realization
-% repair_cost_ratio: array [num_reals x 1]
-%   simulated building repair cost; normalized by building replacemnt
-%   value.
-% trunc_pd: matlab normal distribution object
-%   standard normal distrubtion, truncated at upper and lower bounds
-% beta: number
-%   lognormal standard deviation (dispersion)
-% impeding_factor_medians: table
-%   median delays for various impeding factors
-%
-% Returns
-% -------
-% financing_imped: array [num_reals x num_sys]
-%   Simulated financing time for each system
-
-%% Define financing distribution parameters
-% Median financing times
-finance_medians = ...
-    impeding_factor_medians(strcmp(impeding_factor_medians.factor,'financing'),:);
-
-% Required Financing
-financing_trigger = repair_cost_ratio > capital_available_ratio;
-
-% Financing Type
-switch funding_source
-    case 'sba'  % SBA Backed Loans
-        filt = strcmp(finance_medians.category,'sba');
-        median = finance_medians.time_days(filt) * surge_factor; %days
-    case 'private'  % Private loans 
-        filt = strcmp(finance_medians.category,'private');
-        median = finance_medians.time_days(filt); % days, not affected by surge
-    case 'insurance'  % Insurance
-        filt = strcmp(finance_medians.category,'insurance');
-        median = finance_medians.time_days(filt); % days, not affected by surge
-    otherwise 
-        error('PBEE_Recovery:RepairSchedule', 'Invalid financing type, "%s", for impedance factor simulation', funding_source)
-end
-
-%% Simulate
-% Truncated lognormal distribution (via standard normal simulation)
-num_reals = length(repair_cost_ratio);
-prob_sim = rand(num_reals, 1);
-x_vals_std_n = icdf(trunc_pd, prob_sim);
-financing_time = exp(x_vals_std_n * beta + log(median));
-
-% Only use realizations that require financing
-financing_time(~financing_trigger) = 0;
-
-% Affects all systems that need repair
-% Assume impedance always takes a full day
-financing_imped = ceil(financing_time .* sys_repair_trigger);
-
-
-end
-
diff --git a/+recovery/+impedance/fn_inspection.m b/+recovery/+impedance/fn_inspection.m
deleted file mode 100644
index ed41b4a..0000000
--- a/+recovery/+impedance/fn_inspection.m
+++ /dev/null
@@ -1,61 +0,0 @@
-function inspection_imped = fn_inspection( is_essential_facility, ...
-    is_borp_equivalent, surge_factor, sys_repair_trigger, ...
-    inpsection_trigger, trunc_pd, beta, impeding_factor_medians )
-% Simulute inspection time
-%
-% Parameters
-% ----------
-% is_essential_facility: logical
-%   is the building deemed essential by the local jurisdiction
-% is_borp_equivalent: logical
-%   does the building have an inspector on retainer (BORP equivalent)
-% surge_factor: number
-%   amplification factor for impedance time based on a post disaster surge
-%   in demand for skilled trades and construction supplies
-% sys_repair_trigger: logical array [num_reals x num_systems]
-%   systems that require repair for each realization
-% inpsection_trigger: logical array [num_reals x 1]
-%   defines which realizations require inspection
-% trunc_pd: matlab normal distribution object
-%   standard normal distrubtion, truncated at upper and lower bounds
-% beta: number
-%   lognormal standard deviation (dispersion)
-% impeding_factor_medians: table
-%   median delays for various impeding factors
-%
-% Returns
-% -------
-% inspection_imped: array [num_reals x num_sys]
-%   Simulated inspection time for each system
-
-%% Define inspection distribtuion parameters
-inspection_medians = ...
-    impeding_factor_medians(strcmp(impeding_factor_medians.factor,'inspection'),:);
-    
-if is_borp_equivalent
-    filt = strcmp(inspection_medians.category,'borp');
-    median = inspection_medians.time_days(filt) * surge_factor; 
-elseif is_essential_facility
-    filt = strcmp(inspection_medians.category,'essential');
-    median = inspection_medians.time_days(filt) * surge_factor;
-else
-    filt = strcmp(inspection_medians.category,'default');
-    median = inspection_medians.time_days(filt) * surge_factor;
-end
-
-%% Simulate 
-% Truncated lognormal distribution
-num_reals = length(inpsection_trigger);
-prob_sim = rand(num_reals, 1);
-x_vals_std_n = icdf(trunc_pd, prob_sim);
-inspection_time = exp(x_vals_std_n * beta + log(median));
-
-% Only use realizations that require inpsection
-inspection_time(~inpsection_trigger) = 0;
-
-% Affects all systems that need repair
-% Assume impedance always takes a full day
-inspection_imped = ceil(inspection_time .* sys_repair_trigger);
-
-end
-
diff --git a/+recovery/+impedance/fn_permitting.m b/+recovery/+impedance/fn_permitting.m
deleted file mode 100644
index 6016283..0000000
--- a/+recovery/+impedance/fn_permitting.m
+++ /dev/null
@@ -1,53 +0,0 @@
-function [permitting_rapid, permitting_full] = fn_permitting( num_reals, ...
-    sys_repair_trigger, trunc_pd, beta, impeding_factor_medians )
-% Simulute permitting time
-%
-% Parameters
-% ----------
-% num_reals: int
-%   number of Monte Carlo simulations assessed
-% sys_repair_trigger: struct
-%   contains simulation data indicate if rapid permits or full permits are
-%   required for each system
-% trunc_pd: matlab normal distribution object
-%   standard normal distrubtion, truncated at upper and lower bounds
-% beta: number
-%   lognormal standard deviation (dispersion)
-% impeding_factor_medians: table
-%   median delays for various impeding factors
-%
-% Returns
-% -------
-% permitting_rapid: array [num_reals x num_sys]
-%   Simulated time to receive rapid permits, where applicable for each system
-% permitting_full: array [num_reals x num_sys]
-%   Simulated time to for full review and permit process, where applicable for each system
-
-%% Define permitting distribution parameters
-% Find the median permit time for each system
-permit_medians = ...
-    impeding_factor_medians(strcmp(impeding_factor_medians.factor,'permitting'),:);
-
-% Rapid Permits
-filt = strcmp(permit_medians.category,'rapid');
-rapid_permit_median = permit_medians.time_days(filt); % days
-
-% Full Permits
-filt = strcmp(permit_medians.category,'full');
-full_permit_median = permit_medians.time_days(filt); % days
-
-%% Simulate
-% Rapid Permits
-prob_sim = rand(num_reals, 1); % This assumes systems are correlated
-x_vals_std_n = icdf(trunc_pd, prob_sim); % Truncated lognormal distribution (via standard normal simulation)
-rapid_permit_time = exp(x_vals_std_n * beta + log(rapid_permit_median));
-permitting_rapid = ceil(rapid_permit_time .* sys_repair_trigger.rapid_permit); % Assume impedance always takes a full day
-
-% Full Permits - simulated times are independent of rapid permit times
-prob_sim = rand(num_reals, 1); % This assumes systems are correlated
-x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
-full_permit_time = exp(x_vals_std_n * beta + log(full_permit_median));
-permitting_full = ceil(full_permit_time .* sys_repair_trigger.full_permit); % Assume impedance always takes a full day
-
-end
-
diff --git a/+recovery/+impedance/main_impeding_factors.m b/+recovery/+impedance/main_impeding_factors.m
deleted file mode 100644
index 2b73edc..0000000
--- a/+recovery/+impedance/main_impeding_factors.m
+++ /dev/null
@@ -1,332 +0,0 @@
-function [impeding_factors] = main_impeding_factors(damage, ...
-    impedance_options, repair_cost_ratio_total, repair_cost_ratio_engineering, ...
-    inpsection_trigger, systems, tmp_repair_class, building_value, impeding_factor_medians, include_flooding_impact)
-% Calculate ATC-138 impeding times for each system given simulation of damage
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage and loss data for each component in the building
-% impedance_options: struct
-%   general impedance assessment user inputs such as mitigation factors
-% repair_cost_ratio_total: array [num_reals x 1]
-%   total repair cost per realization normalized by building replacement
-%   value
-% repair_cost_ratio_engineering: array [num_reals x 1]
-%   repair cost per realization that is used for engineering impedance (structural/stairs/envelope)
-% inpsection_trigger: logical array [num_reals x 1]
-%   defines which realizations require inspection
-% num_stories: int
-%   Total number of building stories
-% systems: table
-%   data table containing information about each system's attributes
-% building_value: number
-%   The replacement value of the building, in USD
-% impeding_factor_medians: table
-%   median delays for various impeding factors
-%
-% Returns
-% -------
-% impedingFactors.time_sys: array [num_reals x num_sys]
-%   Simulated total impeding time for each system
-% impedingFactors.breakdown: struct
-%   feilds: 'inspection', 'financing', 'eng_mob', 'design', 'permitting', 'contractor_mob'
-%   The simulated start day and complete day for each impeding factor,
-%   broken down per system where applicable
-%   
-%
-% Notes
-% -----
-% Correlation: Assuming factor-to-factor simulations are independent, but
-% within a given factor, simulated system impedance time is fully
-% correlated
-
-
-%% Initial Setup
-% Import packages
-import recovery.impedance.fn_contractor
-import recovery.impedance.fn_engineering
-import recovery.impedance.fn_financing
-import recovery.impedance.fn_inspection
-import recovery.impedance.fn_permitting
-import recovery.impedance.fn_default_surge_factor
-
-% Initialize parameters
-num_reals = length(inpsection_trigger);
-num_sys = height(systems);
-
-% Pre-allocate each type of impedance
-duration.inspection = zeros(num_reals, num_sys);
-duration.financing = zeros(num_reals, num_sys);
-duration.permit_rapid = zeros(num_reals, num_sys);
-duration.permit_full = zeros(num_reals, num_sys);
-duration.contractor_mob = zeros(num_reals, num_sys);
-duration.eng_mob = zeros(num_reals, num_sys);
-duration.design = zeros(num_reals, num_sys);
-duration.long_lead = zeros(num_reals, num_sys);
-
-% Create basic trucated standard normal distribution for later simulation
-pd = makedist('normal','mu',0,'sigma',1);
-th_low = -impedance_options.impedance_truncation;
-th_high = impedance_options.impedance_truncation;
-trunc_pd = truncate(pd,th_low,th_high);
-beta = impedance_options.impedance_beta;
-
-%% Calculate Demand Surge (if applicble)
-if impedance_options.demand_surge.include_surge
-    surge_factor = fn_default_surge_factor( ...
-        impedance_options.demand_surge.is_dense_urban_area, ...
-        impedance_options.demand_surge.site_pga, ...
-        impedance_options.demand_surge.pga_de ...
-    );
-else
-    surge_factor = 1;
-end
-    
-%% Parse through damage to determine which systems require repair
-sys_repair_trigger.any = zeros(num_reals, num_sys);
-sys_repair_trigger.rapid_permit = zeros(num_reals, num_sys);
-sys_repair_trigger.full_permit = zeros(num_reals, num_sys);
-sys_repair_trigger.redesign = zeros(num_reals, num_sys);
-sys_repair_trigger.flooding = zeros(num_reals, 1);
-for sys = 1:num_sys
-    sys_filt = damage.comp_ds_table.system' == sys; 
-    for tu = 1:length(damage.tenant_units)
-        is_damaged = damage.tenant_units{tu}.qnt_damaged > 0 & damage.tenant_units{tu}.worker_days > 0; % There is damage that needs to be fixed
-        % Track if any damage exists that requires repair (assumes all
-        % damage requires repair)
-        sys_repair_trigger.any(:,sys) = max( ...
-            sys_repair_trigger.any(:,sys), ...
-            max(is_damaged .* sys_filt, [], 2) ...
-        );
-        
-        % Track if any damage exists that requires rapid permit per system
-        sys_repair_trigger.rapid_permit(:,sys) = max( ...
-            sys_repair_trigger.rapid_permit(:,sys), ...
-            max(is_damaged .* (sys_filt & damage.fnc_filters.permit_rapid), [], 2) ...
-        );
-    
-        % Track if any damage exists that requires full permit per system
-        sys_repair_trigger.full_permit(:,sys) = max( ...
-            sys_repair_trigger.full_permit(:,sys), ...
-            max(is_damaged .* (sys_filt & damage.fnc_filters.permit_full), [], 2) ...
-        );
-    
-        % Track if any systems require redesign
-        sys_repair_trigger.redesign(:,sys) = max( ...
-            sys_repair_trigger.redesign(:,sys), ...
-            max(is_damaged .* (sys_filt & damage.fnc_filters.redesign), [], 2) ...
-        );
-    
-        % Track if any damage exists that triggers flooding
-        sys_repair_trigger.flooding = max( ...
-            sys_repair_trigger.flooding, ...
-            max(is_damaged .* (sys_filt & damage.fnc_filters.causes_flooding), [], 2) ...
-        );
-    end
-end
-
-%% Simulate impedance time for each impedance factor 
-if impedance_options.include_impedance.inspection
-    duration.inspection = fn_inspection( ...
-        impedance_options.mitigation.is_essential_facility, ...
-        impedance_options.mitigation.is_borp_equivalent, ...
-        surge_factor, sys_repair_trigger.any, ...
-        inpsection_trigger, trunc_pd, beta, impeding_factor_medians );
-end
-
-if impedance_options.include_impedance.financing
-    duration.financing = fn_financing( ...
-        impedance_options.mitigation.capital_available_ratio, ...
-        impedance_options.mitigation.funding_source, ...
-        surge_factor, sys_repair_trigger.any, ...
-        repair_cost_ratio_total, trunc_pd, beta, impeding_factor_medians );
-end
-
-if impedance_options.include_impedance.permitting
-    [duration.permit_rapid, duration.permit_full ]= fn_permitting( ...
-        num_reals, sys_repair_trigger, trunc_pd, beta, impeding_factor_medians );
-end
-
-if impedance_options.include_impedance.contractor
-    duration.contractor_mob = fn_contractor( num_reals, surge_factor, ...
-        sys_repair_trigger.any, trunc_pd, impedance_options.mitigation );
-end
-
-if impedance_options.include_impedance.engineering
-    [ duration.eng_mob, duration.design ] = fn_engineering( num_reals, ...
-        repair_cost_ratio_engineering, building_value, surge_factor, ...
-        sys_repair_trigger.redesign, ...
-        impedance_options.mitigation.is_engineer_on_retainer, ...
-        impedance_options.system_design_time, ...
-        impedance_options.eng_design_min_days', ...
-        impedance_options.eng_design_max_days', ...
-        trunc_pd, beta, impeding_factor_medians);
-end
-
-if impedance_options.include_impedance.long_lead
-    for sys = 1:num_sys
-        sys_filt = damage.comp_ds_table.system' == sys; 
-        
-        % Simulate long lead times. Assume long lead times are correlated among
-        % all components within the system, but independant between systems
-        prob_sim = rand(num_reals, 1);
-        x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
-        sim_long_lead = exp(x_vals_std_n * beta + log(damage.comp_ds_table.long_lead_time'));
-        
-        for tu = 1:length(damage.tenant_units)
-            is_damaged = damage.tenant_units{tu}.qnt_damaged > 0 & damage.tenant_units{tu}.worker_days > 0;
-            
-            % Track if any damage exists that requires repair (assumes all
-            % damage requires repair). The long lead time for the system is
-            % the max long lead time for any component within the system
-            duration.long_lead(:,sys) = max( ...
-                duration.long_lead(:,sys), ...
-                max(is_damaged .* sys_filt .* sim_long_lead, [], 2) ...
-            );
-        end
-    end
-end
-
-%% Aggregate experienced impedance time for each system/sequence and realization 
-% Figure out when each impeding factor finishes
-start_day.inspection = zeros(num_reals,num_sys);
-complete_day.inspection = duration.inspection;
-
-start_day.financing = complete_day.inspection;
-complete_day.financing = start_day.financing + duration.financing;
-
-start_day.eng_mob = max(complete_day.inspection,start_day.financing);
-complete_day.eng_mob = start_day.eng_mob + duration.eng_mob;
-
-start_day.design = complete_day.eng_mob;
-complete_day.design = start_day.design + duration.design;
-
-start_day.permit_rapid = complete_day.design;
-complete_day.permit_rapid = start_day.permit_rapid + duration.permit_rapid;
-
-start_day.permit_full = complete_day.design;
-complete_day.permit_full = start_day.permit_full + duration.permit_full;
-
-start_day.contractor_mob = max(complete_day.inspection,start_day.financing);
-complete_day.contractor_mob = start_day.contractor_mob + duration.contractor_mob;
-
-start_day.long_lead = max(complete_day.inspection,start_day.financing);
-complete_day.long_lead = start_day.long_lead + duration.long_lead;
-
-% Combine all impedance factors by system
-impede_factors = fieldnames(complete_day);
-impeding_factors.time_sys = 0;
-for i = 1:length(impede_factors)
-    impeding_factors.time_sys = max(impeding_factors.time_sys,...
-        complete_day.(impede_factors{i}));
-end
-    
-%% Simulate Impeding Factors for Temporary Repairs
-% Determine median times for each system
-switch impedance_options.mitigation.contractor_relationship
-    case 'retainer'
-        temp_impede_med = surge_factor*tmp_repair_class.impeding_time'; % days
-    case 'good'
-        temp_impede_med = surge_factor*tmp_repair_class.impeding_time'; % days
-    case 'none'
-        temp_impede_med = surge_factor*tmp_repair_class.impeding_time_no_contractor'; % days
-    otherwise
-        error('PBEE_Recovery:RepairSchedule', 'Invalid contractor relationship type, "%s", for impedance factor simulation', contractor_relationship)
-end
-
-% Find the which realization have damage that can be resolved by temp
-% repairs
-tmp_repair_class_trigger = zeros(num_reals, height(tmp_repair_class));
-for sys = 1:height(tmp_repair_class) 
-    sys_filt = damage.comp_ds_table.tmp_repair_class' == sys; 
-    for tu = 1:length(damage.tenant_units)
-        is_damaged = damage.tenant_units{tu}.qnt_damaged > 0 & damage.tenant_units{tu}.worker_days > 0;
-        % Track if any damage exists that requires repair (assumes all
-        % damage requires repair)
-        tmp_repair_class_trigger(:,sys) = max( ...
-            tmp_repair_class_trigger(:,sys), ...
-            max(is_damaged .* sys_filt, [], 2) ...
-        );
-    end
-end
-
-% Simulate Impedance Time
-prob_sim = rand(num_reals, 1); % This assumes systems are correlated
-x_vals_std_n = icdf(trunc_pd, prob_sim); % Truncated lognormal distribution (via standard normal simulation)
-tmp_impede_sys = exp(x_vals_std_n * beta + log(temp_impede_med));
-
-% Only use the simulated values for the realzation and system that
-% trigger temporary repair damage
-tmp_impede_sys = tmp_impede_sys .* tmp_repair_class_trigger;
-
-% Assume impedance always takes a full day
-impeding_factors.temp_repair.time_sys = ceil(tmp_impede_sys);
-
-%% Simulate impeding factors and temp repair that occur in parallel with temp repair scheudl
-% Temporary scaffolding for falling hazards
-prob_sim = rand(num_reals, 1);
-x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
-scaffold_impede_time = ceil(surge_factor*exp(x_vals_std_n * beta + log(impedance_options.scaffolding_lead_time))); % always round up
-
-prob_sim = rand(num_reals, 1); % repair time is not correlated to impedance time
-x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
-scaffold_repair_time = exp(x_vals_std_n * beta + log(impedance_options.scaffolding_erect_time)); 
-impeding_factors.temp_repair.scaffold_day = ceil(scaffold_impede_time + scaffold_repair_time); % round up (dont resolve issue on the same day repairs are complete)
-
-% Door Unjamming
-prob_sim = rand(num_reals, 1);
-x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
-impeding_factors.temp_repair.door_racking_repair_day = ceil(surge_factor*exp(x_vals_std_n * beta + log(impedance_options.door_racking_repair_day))); % always round up
-
-% Interior Flooding
-if include_flooding_impact
-	% Flooding Cleanup
-	prob_sim = rand(num_reals, 1);
-	x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
-	impeding_factors.temp_repair.flooding_cleanup_day = ...
-	    sys_repair_trigger.flooding .* ...
-	    ceil(surge_factor*exp(x_vals_std_n * beta + log(impedance_options.flooding_cleanup_day))); % always round up
-	
-	% Repair Flooding Damage
-	prob_sim = rand(num_reals, 1);
-	x_vals_std_n = icdf(trunc_pd, prob_sim);% Truncated lognormal distribution (via standard normal simulation)
-	impeding_factors.temp_repair.flooding_repair_day = ...
-	    sys_repair_trigger.flooding .* ...
-	    ceil(surge_factor*exp(x_vals_std_n * beta + log(impedance_options.flooding_repair_day))); % always round up
-else % zero out the flooding impedance (cleanup and repair)
-    impeding_factors.temp_repair.flooding_cleanup_day = zeros(num_reals, 1); % Flooding Cleanup
-    impeding_factors.temp_repair.flooding_repair_day = zeros(num_reals, 1); % Repair Flooding Damage
-end
-
-%% Format Impedance times for Gantt Charts
-% Full repair
-impeding_factors.breakdowns.full.inspection.start_day = max(start_day.inspection,[],2);
-impeding_factors.breakdowns.full.inspection.complete_day = max(complete_day.inspection,[],2);
-impeding_factors.breakdowns.full.financing.start_day = max(start_day.financing,[],2);
-impeding_factors.breakdowns.full.financing.complete_day = max(complete_day.financing,[],2);
-impeding_factors.breakdowns.full.contractor_mob.start_day = max(start_day.contractor_mob,[],2);
-impeding_factors.breakdowns.full.contractor_mob.complete_day = max(complete_day.contractor_mob,[],2);
-impeding_factors.breakdowns.full.eng_mob.start_day = max(start_day.eng_mob,[],2);
-impeding_factors.breakdowns.full.eng_mob.complete_day = max(complete_day.eng_mob,[],2);
-impeding_factors.breakdowns.full.design.start_day = max(start_day.design,[],2);
-impeding_factors.breakdowns.full.design.complete_day = max(complete_day.design,[],2);
-impeding_factors.breakdowns.full.permit_rapid.start_day = max(start_day.permit_rapid,[],2);
-impeding_factors.breakdowns.full.permit_rapid.complete_day = max(complete_day.permit_rapid,[],2);
-impeding_factors.breakdowns.full.permit_full.start_day = max(start_day.permit_full,[],2);
-impeding_factors.breakdowns.full.permit_full.complete_day = max(complete_day.permit_full,[],2);
-
-% Represent long lead times per system
-for s = 1:height(systems)
-    impeding_factors.breakdowns.long_lead.(systems.name{s}).start_day = start_day.long_lead(:,s);
-    impeding_factors.breakdowns.long_lead.(systems.name{s}).complete_day = complete_day.long_lead(:,s);
-end
-
-% Temporary Repairs
-for tmp = 1:height(tmp_repair_class)
-    impeding_factors.breakdowns.temp.(tmp_repair_class.name_short{tmp}).start_day = zeros(num_reals,1);
-    impeding_factors.breakdowns.temp.(tmp_repair_class.name_short{tmp}).complete_day = impeding_factors.temp_repair.time_sys(:,1);
-end
-
-
-end
diff --git a/+recovery/+repair_schedule/fn_allocate_workers_stories.m b/+recovery/+repair_schedule/fn_allocate_workers_stories.m
deleted file mode 100644
index 1dd2285..0000000
--- a/+recovery/+repair_schedule/fn_allocate_workers_stories.m
+++ /dev/null
@@ -1,107 +0,0 @@
-function [repair_start_day, repair_complete_day, max_workers_per_story] = fn_allocate_workers_stories(...
-    total_worker_days, required_workers_per_story, average_crew_size, max_crews_building, max_workers_per_building)
-% For a given building system, allocate crews to each story to
-% determine the system level repair time on each story; not considering
-% impeding factors or construction constriants with other systems.
-%
-%
-% Parameters
-% ----------
-% total_worker_days: [num reals x num stories]
-%   The total worker days needed to repair damage each story for this
-%   sequence
-% required_workers_per_story: array [num reals x num stories]
-%   Number of workers required to repair damage in each story
-% average_crew_size: array [num reals x num stories]
-%   Average crew size required to repair damage in each story
-% max_crews_building: logical array [num reals x 1]
-%   maximum number of crews allowed in the building for this system
-% max_workers_per_building: int
-%   maximum number of workers allowed in the buildings at a given time
-%
-% Returns
-% -------
-% repair_start_day: matrix [num reals x num stories]
-%   The number of days from the start of repair of this system until the repair of this system 
-%   starts on each story
-% repair_complete_day: matrix [num reals x num stories]
-%   The number of days from the start of repair of this system until each story is
-%   repaired for damage in this system
-% max_workers_per_story: matrix [num reals x num stories]
-%   number of workers required for the repair of this story and system as
-%
-% Notes
-% -----
-
-
-%% Initial Setup
-[num_reals, num_stories] = size(total_worker_days);
-repair_complete_day = zeros(num_reals,num_stories);
-repair_start_day = nan(num_reals,num_stories);
-max_workers_per_story = zeros(num_reals,num_stories);
-
-%% Allocate workers to each story
-% Loop through iterations of time reduce damage on each story based on
-% assigned workers
-iter = 0;
-while sum(sum(total_worker_days)) > 0.01
-    iter = iter + 1; 
-    if iter > 1000 % keep the while loop pandemic contained
-        error('PBEE_Recovery:RepairSchedule', 'Could not converge worker allocations for among stories in sequence');
-    end
-
-    % Determine the available workers in the building
-    available_workers_in_building = max_workers_per_building*ones(num_reals, 1);
-    assigned_workers_per_story = zeros(num_reals,num_stories); 
-    assigned_crews_per_story = zeros(num_reals,num_stories); 
-
-    % Define where needs repair
-    needs_repair = total_worker_days > 0;
-
-    % Defined Required Workers
-    required_workers_per_story = needs_repair .* required_workers_per_story;
-    
-    % Assign Workers to each story -- assumes that we wont drop the number
-    % of crews in order to meet worker per sqft limitations, and instead
-    % wait until more workers are made available
-    for s = 1:num_stories
-        % Are there enough workers to assign a crew
-        sufficient_workers = required_workers_per_story(:,s) <= available_workers_in_building;
-        
-        % Assign Workers to this story
-        assigned_workers_per_story(sufficient_workers,s) = required_workers_per_story(sufficient_workers,s);
-        assigned_crews_per_story(:,s) = assigned_workers_per_story(:,s) ./ average_crew_size(:,s);
-        assigned_crews_per_story(isnan(assigned_crews_per_story)) = 0;
-        num_crews_in_building = sum(assigned_crews_per_story,2);
-        exceeded_max_crews = num_crews_in_building > max_crews_building;
-        assigned_workers_per_story(exceeded_max_crews,s) = 0; % don't assign workers if we have exceeded the number of crews allowed in the building
-        
-        % Define Available Workers
-        available_workers_in_building = available_workers_in_building - assigned_workers_per_story(:,s);  
-    end
-    
-    % Define the start of repairs for each story
-    start_repair_filt = isnan(repair_start_day) & (assigned_workers_per_story > 0);
-    max_day_completed_so_far = max(repair_complete_day,[],2) .* ones(num_reals,num_stories);
-    repair_start_day(start_repair_filt) =  max_day_completed_so_far(start_repair_filt);
-    
-    % Calculate the time associated with this increment of the while loop
-    in_progress = assigned_workers_per_story > 0; % stories where work is being done
-    total_repair_days = inf(size(in_progress)); % pre-allocate with inf's becuase we take a min later
-    total_repair_days(in_progress) = total_worker_days(in_progress) ./ assigned_workers_per_story(in_progress);
-    delta_days = min(total_repair_days,[],2); % time increment is whatever in-progress story that finishes first
-    delta_days(isinf(delta_days)) = 0; % Replace infs from real that has no repair with zero
-    delta_worker_days = assigned_workers_per_story .* delta_days; % time increment is whatever in-progress story that finishes first
-    total_worker_days(in_progress) = max(total_worker_days(in_progress) - delta_worker_days(in_progress),0);
-    indx_neg_remaining = total_worker_days < 0.001; % find instances of remaining time that are excessively small and don't represent realistic amount of work
-    total_worker_days(indx_neg_remaining) = 0;  % zero remaining work that is negligible as defined above
-    
-    % Define Start and Stop of Repair for each story in each sequence
-    repair_complete_day = repair_complete_day + delta_days .* needs_repair;
-    
-    % Max Crew Size for use in later function
-    max_workers_per_story = max(max_workers_per_story,assigned_workers_per_story);
-end
-
-end
-
diff --git a/+recovery/+repair_schedule/fn_allocate_workers_systems.m b/+recovery/+repair_schedule/fn_allocate_workers_systems.m
deleted file mode 100644
index 3276cb8..0000000
--- a/+recovery/+repair_schedule/fn_allocate_workers_systems.m
+++ /dev/null
@@ -1,191 +0,0 @@
-function [repair_complete_day_per_system, worker_data] = ...
-    fn_allocate_workers_systems(systems, sys_repair_days, sys_crew_size, ...
-    max_workers_per_building, sys_idx_priority_matrix, ...
-    sys_constraint_matrix, condition_tag, sys_impeding_factors)
-% Stager repair to each system and allocate workers based on the repair
-% constraints, priorities, and repair times of each system
-%
-% Parameters
-% ----------
-% system_repair_days: [num reals x num systems]
-%   Number of days from the start of repair of each to the completion of
-%   the system (assuming all sequences start on day zero)
-% sys_crew_size: array [num reals x num systems]
-%   required crew size for each system
-% max_workers_per_building: int
-%   Maximum number of workers that can work in the building at the same time 
-% sys_idx_priority_matrix: matrix [num reals x systems]
-%   worker allocation order of system id's prioritized for each realiztion
-% sys_constraint_matrix: array [num reals x num_sys]
-%   array of system ids which define which systems (column index) are delayed by the
-%   system ids (array values)
-% condition_tag: logical array [num reals x 1]
-%   true/false if the building is red tagged
-% sys_impeding_factors: array [num_reals x num_sys]
-%   maximum impedance time (days) for each system. Pass in empty array when
-%   calculating repair times (i.e. not including impeding factors).
-%
-% Returns
-% -------
-% repair_complete_day_per_system: matrix [num reals x num systems]
-%   Number of days from the start of each sequence to the completion of the
-%   sequence considering the allocation of workers to each sequence (ie
-%   some sequences start before others)
-% worker_data.total_workers: array [num reals x varies]
-%   total number of workers in the building at each time step of the worker
-%   allocation algorthim. The number of columns varies with the number of
-%   increments of the worker allocation algorithm.
-% worker_data.day_vector: array [num reals x varies]
-%   Day of each time step of the worker allocation algorthim. The number of 
-%   columns varies with the number of increments of the worker allocation algorithm.
-%
-% Notes
-% -----
-
-%% Initial Setup
-% Initialize Variables
-[num_reals, num_sys] = size(sys_repair_days);
-priority_system_complete_day = zeros(num_reals,num_sys);
-day_vector = zeros(num_reals, 0);
-total_workers = zeros(num_reals, 0);
-
-% Re-order system variables based on priority
-[ priority_sys_workers_matrix ] = fitler_matrix_by_rows( sys_crew_size, sys_idx_priority_matrix );
-[ priority_sys_constraint_matrix ] = fitler_matrix_by_rows( sys_constraint_matrix, sys_idx_priority_matrix );
-[ priority_sys_repair_days ] = fitler_matrix_by_rows( sys_repair_days, sys_idx_priority_matrix );
-if isempty(sys_impeding_factors)
-    priority_sys_impeding_factors = zeros(num_reals, num_sys);
-else
-    [ priority_sys_impeding_factors ] = fitler_matrix_by_rows( sys_impeding_factors, sys_idx_priority_matrix );
-end
-
-% Round up days to the nearest day
-% Provides an implicit change of trade delay, as well as help to reduce the
-% number of delta increments in the following while loop
-priority_sys_repair_days = ceil(priority_sys_repair_days);
-priority_sys_impeding_factors = ceil(priority_sys_impeding_factors);
-
-%% Assign workers to each system based on repair constraints
-iter = 0;
-current_day = zeros(num_reals,1);
-priority_sys_waiting_days = priority_sys_impeding_factors;
-while sum(sum(priority_sys_repair_days)) > 0.01
-    iter = iter + 1; 
-    if iter > 1000 % keep the while loop pandemic contained
-        error('PBEE_Recovery:RepairSchedule', 'Could not converge worker allocations for among systems');
-    end
-    
-    % zero out assigned workers matrix
-    assigned_workers = zeros(num_reals,num_sys);
-    
-    % limit available workers to the max that can be on any one given story
-    % this ensures next system cannot start until completely unblocked on
-    % every story. Need to update for taller buildings which could start
-    % next sequence on lower story once previous sequence was far enough
-    % along
-%     available_workers = min(max(max_workers_per_story),max_workers_per_building)*ones(num_reals, 1);  % currently assumes all stories have the same crew size limitation (uniform sq ft for each story)
-    available_workers = max_workers_per_building*ones(num_reals, 1);
-    
-    % Define what systems are waiting to begin repairs
-    sys_blocked = zeros(num_reals,num_sys);
-    sys_incomplete = (priority_sys_repair_days > 0);
-%     constraining_systems = unique(priority_sys_constraint_matrix(priority_sys_constraint_matrix ~=0));
-    for s = 1:num_sys % Loop over each system that may constrain something
-        constrained_systems = priority_sys_constraint_matrix == s; % These systems are contrained by looped system
-        constraining_sys_filt = sys_idx_priority_matrix == s; % location in matrix of this system
-        is_constraining_sys_incomplete = max(sys_incomplete .* constraining_sys_filt,[],2); % Vec of relizations for looped system
-        sys_blocked = sys_blocked | (constrained_systems .* is_constraining_sys_incomplete); % System is constrained if blocked by an incomplete system
-    end
-    
-    % Need to wait for impeding factors or other repairs to finish
-    is_waiting = (current_day < priority_sys_impeding_factors) | sys_blocked; % assuming impeding factors are the only constraints
-    
-    % Define where needs repair
-    % System needs repair if there are still repairs days left and it is
-    % not waiting to be unblocked
-    needs_repair = (priority_sys_repair_days > 0) & ~is_waiting;
-
-    % Defined Required Workers
-    required_workers = needs_repair .* priority_sys_workers_matrix;
-    
-    % Assign Workers to each system
-    for s = 1:num_sys
-        % Assign Workers to this systems
-        enough_workers = required_workers(:,s) <= available_workers;
-        assigned_workers(enough_workers,s) = min(required_workers(enough_workers,s), available_workers(enough_workers));
-
-        % Define Available Workers
-        % when in series limit available workers to the workers in this system 
-        % (occurs for structural systems when the building is red tagged
-        is_sturctural = strcmp(systems.name{s},'structural');
-        in_series = condition_tag & is_sturctural;
-        available_workers(in_series & assigned_workers(:,s) > 0) = 0; 
-        % when not in series, calc the remaining workers
-        available_workers(~in_series) = available_workers(~in_series) - assigned_workers(~in_series,s); 
-    end
-    
-    % Calculate the time associated with this increment of the while loop
-    in_progress = assigned_workers > 0; % sequences where work is being done
-    total_repair_days = inf(size(in_progress)); % pre-allocate with inf's becuase we take a min later
-    total_repair_days(in_progress) = priority_sys_repair_days(in_progress);
-    total_waiting_days = priority_sys_waiting_days;
-    total_waiting_days(total_waiting_days == 0) = inf; % Convert zeros to inf such that zeros are not included in the min in the next step
-    total_time = min(total_repair_days,total_waiting_days); % combime repair time and waiting time
-    delta_days = min(total_time,[],2); % time increment is whatever in-progress story that finishes first
-    delta_days(isinf(delta_days)) = 0; % Replace infs from real that has no repair with zero
-    
-    % Reduce waiting time
-    priority_sys_waiting_days = max(priority_sys_waiting_days - delta_days,0);
-    
-    % Reduce time needed for repairs
-    delta_days_in_progress = delta_days .* in_progress; % change in repair time for all sequences being worked on
-    priority_sys_repair_days(in_progress) = max(priority_sys_repair_days(in_progress) - delta_days_in_progress(in_progress),0);
-    
-    % Define Start and Stop of Repair for each sequence
-    priority_system_complete_day = priority_system_complete_day + delta_days .* (needs_repair | is_waiting);
-    
-    % Define Cummulative day of repair
-    day_vector = [day_vector, current_day];
-    current_day = current_day + delta_days;
-    
-    % Save worker data data over time
-    total_workers = [total_workers, sum(assigned_workers,2), sum(assigned_workers,2)];
-    day_vector = [day_vector, current_day];
-end
-    
-% Untangle system_complete_day back into system table order
-[~, sys_idx_untangle_matrix] = sort(sys_idx_priority_matrix, 2);
-[ repair_complete_day_per_system ] = fitler_matrix_by_rows( priority_system_complete_day, sys_idx_untangle_matrix );
-
-% Save worker data matrices
-worker_data.total_workers = total_workers;
-worker_data.day_vector = day_vector;
-end
-
-
-function [ filtered_values ] = fitler_matrix_by_rows( values, filter )
-% Use a identiry matrix to filter values from another matrix by rows
-
-% Parameters
-% ----------
-% values: matrix [n x m]
-%   values to filter by row
-% filter: matrix [n x m]
-%   array indexes to grab from each row of values
-%
-% Returns
-% -------
-% filtered_values: matrix [n x m]
-%   values filtered by rows
-
-% Method
-x2t = values.';
-idx1 = filter.';
-y4         = zeros(size(idx1));
-for r = 1:size(values,1)
-    y4(:, r) = x2t(idx1(:, r), r);
-end
-filtered_values = y4.';
-
-
-end
\ No newline at end of file
diff --git a/+recovery/+repair_schedule/fn_calc_system_repair_time.m b/+recovery/+repair_schedule/fn_calc_system_repair_time.m
deleted file mode 100644
index 9f31c3e..0000000
--- a/+recovery/+repair_schedule/fn_calc_system_repair_time.m
+++ /dev/null
@@ -1,159 +0,0 @@
-function [schedule] = fn_calc_system_repair_time(damage, repair_type, systems, max_workers_per_building, max_workers_per_story)
-% Determine the repair time for each system if repaired in isolation 
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage and loss data for each component in the building
-% repair_type: string
-%   String identifier indicating whether the repair are temporary or full
-% systems: table
-%   data table containing information about each system's attributes
-% max_workers_per_building: int
-%   maximum number of workers allowed in the building at once
-% max_workers_per_story: array [1 x num_stories]
-%   maximum number of workers allowed in each story at once
-%
-% Returns
-% -------
-% schedule.per_system{sys}.repair_start_day [num reals x num stories]
-%   The number of days from the start of repair of a specifc system until
-%   the start of repair of each story for that given system (i.e. the day 
-%   of the start of repairs relative the to start of this system ... e.g.
-%   story 1 should always be zero)
-% schedule.per_system{sys}.repair_complete_day [num reals x num stories]
-%   The number of days from the start of repair of a specifc system until
-%   each story is fully repaired for that given system.
-% schedule.per_system{sys}.max_num_workers_per_story [num reals x num stories]
-%   The number of workers required to repair each story of this system
-% schedule.system_totals.repair_days [num reals x num systems]
-%   The number of days required to repair each system in isolation
-% schedule.system_totals.num_workers [num reals x num systems]
-%   The number of workers required for the repair of each system
-%
-% Notes
-% -----
-
-%% Initial Setup
-% Import Packages
-import recovery.repair_schedule.fn_allocate_workers_stories
-
-% General Varaible
-[num_reals, ~] = size(damage.tenant_units{1}.worker_days);
-schedule.system_totals.repair_days = zeros(num_reals,height(systems));
-schedule.system_totals.num_workers = zeros(num_reals,height(systems));
-
-%% Allocate workers to each story for each system
-% Repair finish times assumes all sequences start on day zero
-for sys = 1:height(systems)
-    % Define the Crew workers and total workers days for this sequence
-    % in arrays of [num reals by num stories]
-    [ total_worker_days, num_workers, average_crew_size, max_crews_building ] ...
-        = fn_repair_sequence_parameters( ...
-            damage, ...
-            repair_type, ...
-            systems.id(sys), ...
-            systems.num_du_per_crew(sys), ...
-            systems.max_crews_per_comp_type(sys), ...   
-            max_workers_per_story, ...
-            max_workers_per_building ...
-            );
-    
-    % Allocate workers to each story and determine the total days until
-    % repair is complete for each story and sequence
-    [ ... 
-        schedule.per_system{sys}.repair_start_day, ...
-        schedule.per_system{sys}.repair_complete_day, ...
-        schedule.per_system{sys}.max_num_workers_per_story ...
-    ] = fn_allocate_workers_stories( ...
-        total_worker_days, num_workers, average_crew_size, max_crews_building, max_workers_per_building);
-
-    % How many days does it take to complete each system in isloation
-    schedule.system_totals.repair_days(:,sys) = max(schedule.per_system{sys}.repair_complete_day,[],2);
-    schedule.system_totals.num_workers(:,sys) = max(schedule.per_system{sys}.max_num_workers_per_story,[],2);
-end 
-
-end
-
-
-function [total_worker_days, num_workers, average_crew_size, max_crews_building] = fn_repair_sequence_parameters( ...
-    damage, repair_type, sys, num_du_per_crew, max_crews_per_comp_type, max_workers_per_story, max_workers_per_building)
-% Define crew sizes, workers, and repair times for each story of a given
-% system. Based on worker limiations, and component worker days data from
-% the FEMA P-58 assessment.
-
-% Define Repair Type Variables (variable within the damage object)
-if strcmp(repair_type,'full')
-    repair_time_var = 'worker_days';
-    system_var = 'system';
-    crew_size_var = 'crew_size';
-elseif strcmp(repair_type,'temp')
-    repair_time_var = 'tmp_worker_day';
-    system_var = 'tmp_repair_class';
-    crew_size_var = 'tmp_crew_size';
-else
-    error('Unexpected Repair Type')
-end
-
-% Define Initial Parameters
-num_stories = length(damage.tenant_units);
-[num_reals, num_comps] = size(damage.tenant_units{1}.worker_days);
-sequence_filt = damage.comp_ds_table.(system_var)' == sys; % identifies which ds idices are in this seqeunce  
-comp_types = unique(damage.comp_ds_table.comp_idx(sequence_filt)); % Types of components in this system
-
-% Pre-allocatate variables
-total_worker_days = zeros(num_reals,num_stories);
-is_damaged_building = zeros(num_reals,num_comps);
-num_damaged_comp_types = zeros(num_reals,num_stories);
-num_damaged_units = zeros(num_reals,num_stories);
-average_crew_size = zeros(num_reals,num_stories);
-
-for s = 1:num_stories
-    % Define damage properties of this system at this story
-    num_damaged_units(:,s) = sum(sequence_filt .* damage.tenant_units{s}.qnt_damaged,2);
-    is_damaged = damage.tenant_units{s}.qnt_damaged > 0 & damage.tenant_units{s}.(repair_time_var) > 0;
-    is_damaged_building = is_damaged_building | is_damaged;
-    
-    for c = 1:length(comp_types)
-        num_damaged_comp_types(:,s) = num_damaged_comp_types(:,s) + any((damage.comp_ds_table.comp_idx' == comp_types(c)) .* is_damaged,2);
-    end
-
-    % Caluculate total worker days per story per sequeces
-    total_worker_days(:,s) = sum(damage.tenant_units{s}.(repair_time_var)(:,sequence_filt),2); % perhaps consider doing when we first set up this damage data structure
-    
-    % Determine the required crew size needed for these repairs
-    repair_time_per_comp = damage.tenant_units{s}.(repair_time_var) ./  damage.comp_ds_table.(crew_size_var)';
-    average_crew_size(:,s) = total_worker_days(:,s) ./ sum(repair_time_per_comp(:,sequence_filt),2);
-end
-    
-% Define the number of crews needed based on the extent of damage
-num_crews = ceil(num_damaged_units / num_du_per_crew);
-num_crews = min(num_crews,max_crews_per_comp_type .* num_damaged_comp_types);
-num_crews = min(num_crews,ceil(num_damaged_units)); % Safety check: num crews should never be greater than the number of damaged components
-
-% Round up total worker days to the nearest day to speed up the worker 
-% allocation loop and implicitly consider change of trade delays
-total_worker_days = ceil(total_worker_days);
-
-% Round crew sizes such that we have a realistic size (still implicitly
-% averaged based on type of damage)
-average_crew_size(isnan(average_crew_size)) = 0;
-average_crew_size = round(average_crew_size);
-
-% Limit the number of crews based on the space limitations at this story
-% and the assumed crew size
-worker_upper_lim = min(max_workers_per_story,max_workers_per_building);
-max_num_crews_per_story = max(floor(worker_upper_lim ./ average_crew_size),1);
-num_crews = min(num_crews,max_num_crews_per_story);
-
-% Calculate the total number of workers per story for this system
-num_workers = average_crew_size .* num_crews;
-
-% Repeat calc of number of uniquely damaged component types for the whole
-% building
-num_damaged_comp_types = zeros(num_reals,1);
-for c = 1:length(comp_types)
-    num_damaged_comp_types = num_damaged_comp_types + any((damage.comp_ds_table.comp_idx' == comp_types(c)) .* is_damaged_building,2);
-end
-max_crews_building = max_crews_per_comp_type .* num_damaged_comp_types;
-end
diff --git a/+recovery/+repair_schedule/fn_format_gantt_chart_data.m b/+recovery/+repair_schedule/fn_format_gantt_chart_data.m
deleted file mode 100644
index 7c0607c..0000000
--- a/+recovery/+repair_schedule/fn_format_gantt_chart_data.m
+++ /dev/null
@@ -1,111 +0,0 @@
-function [ repair_schedule ] = fn_format_gantt_chart_data( damage, systems, simulated_replacement_time )
-% Reformat data from the damage structure into data that is used for the
-% gantt charts
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage and loss data for each component in the building
-% systems: table
-%   data table containing information about each system's attributes
-% simulated_replacement_time: array [num_reals x 1]
-%   simulated time when the building needs to be replaced, and how long it
-%   will take (in days). NaN represents no replacement needed (ie
-%   building will be repaired)
-%
-% Returns
-% -------
-% repair_schedule: struct
-%   Contians reformated repair schedule data for gantt chart plots for
-%   bothy repair time and downtime calculations. Data is reformanted to
-%   show breakdowns by component, by story, by system, by story within each
-%   system, and by system within each story.
-%
-% Notes
-% -----
-% Since this all has to do with plotting gantt charts, a form of this
-% (whether the damage structure or a higher level repair schedule
-% structure) should be output and this reformatting logic moved outside the
-% functional recovery assessment and into the data visuallation logic
-
-%% Initial Setup
-num_stories = length(damage.tenant_units);
-[num_reals, ~] = size(damage.tenant_units{1}.recovery.repair_start_day);
-comps = unique(damage.comp_ds_table.comp_id);
-
-% Determine replacement cases
-replace_cases = ~isnan(simulated_replacement_time);
-
-%% Reformate repair schedule data into various breakdowns
-% Per component
-repair_schedule.repair_start_day.per_component = nan(num_reals,length(comps));
-repair_schedule.repair_complete_day.per_component = zeros(num_reals,length(comps));
-for c = 1:length(comps)
-    comps_filt = strcmp(damage.comp_ds_table.comp_id',comps{c});
-    for s = 1:num_stories
-        repair_schedule.repair_start_day.per_component(:,c) = min([repair_schedule.repair_start_day.per_component(:,c), damage.tenant_units{s}.recovery.repair_start_day(:,comps_filt)],[],2);
-        repair_schedule.repair_complete_day.per_component(:,c) = max([repair_schedule.repair_complete_day.per_component(:,c), damage.tenant_units{s}.recovery.repair_complete_day(:,comps_filt)],[],2);
-    end
-    repair_schedule.component_names{c} = comps{c};
-end
-
-% Per Story
-repair_schedule.repair_start_day.per_story = nan(num_reals,num_stories);
-repair_schedule.repair_complete_day.per_story = zeros(num_reals,num_stories);
-for s = 1:num_stories
-    repair_schedule.repair_start_day.per_story(:,s) = min([repair_schedule.repair_start_day.per_story(:,s), damage.tenant_units{s}.recovery.repair_start_day],[],2);
-    repair_schedule.repair_complete_day.per_story(:,s) = max([repair_schedule.repair_complete_day.per_story(:,s), damage.tenant_units{s}.recovery.repair_complete_day],[],2);
-end
-
-% Per Repair System
-repair_schedule.repair_start_day.per_system = nan(num_reals,height(systems));
-repair_schedule.repair_complete_day.per_system = zeros(num_reals,height(systems));
-for sys = 1:height(systems)
-    sys_filt = damage.comp_ds_table.system' == systems.id(sys); % identifies which ds idices are in this seqeunce  
-    for s = 1:num_stories
-        repair_schedule.repair_start_day.per_system(:,sys) = min([repair_schedule.repair_start_day.per_system(:,sys), damage.tenant_units{s}.recovery.repair_start_day(:,sys_filt)],[],2);
-        repair_schedule.repair_complete_day.per_system(:,sys) = max([repair_schedule.repair_complete_day.per_system(:,sys), damage.tenant_units{s}.recovery.repair_complete_day(:,sys_filt)],[],2);
-    end
-end
-repair_schedule.system_names = systems.name;
-
-% Per system per story
-num_sys_stories = num_stories * height(systems);
-repair_schedule.repair_start_day.per_system_story = nan(num_reals,num_sys_stories);
-repair_schedule.repair_complete_day.per_system_story = zeros(num_reals,num_sys_stories);
-id = 0;
-for s = 1:num_stories
-    for sys = 1:height(systems)
-        id = id + 1;
-        sys_filt = damage.comp_ds_table.system' == systems.id(sys); % identifies which ds idices are in this seqeunce  
-        repair_schedule.repair_start_day.per_system_story(:,id) = min([repair_schedule.repair_start_day.per_system_story(:,id), damage.tenant_units{s}.recovery.repair_start_day(:,sys_filt)],[],2);
-        repair_schedule.repair_complete_day.per_system_story(:,id) = max([repair_schedule.repair_complete_day.per_system_story(:,id), damage.tenant_units{s}.recovery.repair_complete_day(:,sys_filt)],[],2);
-    end
-end
-
-% Per story per system
-num_sys_stories = num_stories * height(systems);
-repair_schedule.repair_start_day.per_story_system = nan(num_reals,num_sys_stories);
-repair_schedule.repair_complete_day.per_story_system = zeros(num_reals,num_sys_stories);
-id = 0;
-for sys = 1:height(systems)
-    for s = 1:num_stories
-        id = id + 1;
-        sys_filt = damage.comp_ds_table.system' == systems.id(sys); % identifies which ds idices are in this seqeunce  
-        repair_schedule.repair_start_day.per_story_system(:,id) = min([repair_schedule.repair_start_day.per_story_system(:,id), damage.tenant_units{s}.recovery.repair_start_day(:,sys_filt)],[],2);
-        repair_schedule.repair_complete_day.per_story_system(:,id) = max([repair_schedule.repair_complete_day.per_story_system(:,id), damage.tenant_units{s}.recovery.repair_complete_day(:,sys_filt)],[],2);
-    end
-end
-
-
-%% Overwrite realization for demo and replace cases
-formats = fieldnames(repair_schedule.repair_start_day);
-for f = 1:length(formats)
-    repair_schedule.repair_start_day.(formats{f})(replace_cases,:) = 0;
-    
-    [~, format_width] = size(repair_schedule.repair_complete_day.(formats{f})); % apply replacement time to all comps / systems / stories / etc
-    repair_schedule.repair_complete_day.(formats{f})(replace_cases,:) = simulated_replacement_time(replace_cases)*ones(1,format_width);
-end
-
-end
-
diff --git a/+recovery/+repair_schedule/fn_prioritize_systems.m b/+recovery/+repair_schedule/fn_prioritize_systems.m
deleted file mode 100644
index 0efff24..0000000
--- a/+recovery/+repair_schedule/fn_prioritize_systems.m
+++ /dev/null
@@ -1,87 +0,0 @@
-function [ sys_idx_priority_matrix ] = fn_prioritize_systems( systems, repair_type, damage, tmp_repair_complete_day, impeding_factors )
-% Determine the priority of worker allocation for each system and realization
-% based on default table priorities, whether they have the potential to 
-% affect reoccupancy or function and whether they are resolved by 
-% temporary repairs.
-
-% Parameters
-% ----------
-% systems: table
-%   data table containing information about each system's attributes
-% damage: struct
-%   contains per damage state damage and loss data for each component in the building
-% tmp_repair_complete_day: array [num_reals x num_comp]
-%   contains the day (after the earthquake) the temporary repair time is 
-%   resolved per damage state damage and realization. Inf represents that
-%   there is not temporary repair time available for a given components
-%   damage.
-%
-% Returns
-% -------
-% sys_idx_priority_matrix: index array [num reals x num systems]
-%   row index to filter system matrices to be prioritized for each
-%   realiztion. Priority is left to right.
-% 
-% Notes
-% ------
-% The checks done here are only able to implicitly check for which systems
-% affect funtion the most (this prioritizes which have the potential to
-% affect function). To explicitly check which systems have the biggest
-% impact on function and prioritize those, this check would need to be
-% coupled with the function assessment
-
-%% Initial Setup
-% Define Repair Type Variables (variable within the damage object)
-if strcmp(repair_type,'full')
-    system_var = 'system';
-elseif strcmp(repair_type,'temp')
-    system_var = 'tmp_repair_class';
-else
-    error('Unexpected Repair Type')
-end
-
-% initialize variables
-num_sys = height(systems);
-[num_reals, ~] = size(damage.tenant_units{1}.qnt_damaged);
-
-% Find which components potentially affect reoccupancy accross any tenant unit
-affects_reoccupancy = zeros(num_reals, height(damage.comp_ds_table));
-for s = 1:length(damage.tenant_units)
-    affects_reoccupancy = affects_reoccupancy | (damage.fnc_filters.affects_reoccupancy & damage.tenant_units{s}.qnt_damaged);
-end
-
-% Find which components potentially affect function accross any tenant unit
-affects_function = zeros(num_reals, height(damage.comp_ds_table));
-for s = 1:length(damage.tenant_units)
-    affects_function = affects_function | (damage.fnc_filters.affects_function & damage.tenant_units{s}.qnt_damaged);
-end
-
-%% Define ranks for each system 
-sys_affects_reoccupancy = zeros(num_reals, num_sys); % only prioritize the systems that potentially affect function
-sys_affects_function = zeros(num_reals, num_sys); % only prioritize the systems that potentially affect function
-sys_tmp_repaired = zeros(num_reals, num_sys); % dont prioitize the systems that are completely resolved by temporary repairs
-for sys = 1:num_sys
-    sys_filter = damage.comp_ds_table.(system_var)' == sys;
-    if any(sys_filter) % Only if this system is present
-        sys_affects_reoccupancy(:,sys) = any(affects_reoccupancy(:,sys_filter),2); % any damage that potentially affects reoccupancy in this system
-        sys_affects_function(:,sys) = any(affects_function(:,sys_filter),2); % any damage that potentially affects function in this system
-        if ~isempty(tmp_repair_complete_day) % Only if temp repair data is passed in
-            all_sys_tmp_repaired = all(tmp_repair_complete_day(:,sys_filter)<inf | isnan(tmp_repair_complete_day(:,sys_filter)),2); % is every single damaged component resolved by temp repairs
-            tmp_repair_quick = max(tmp_repair_complete_day(:,sys_filter),[],2) < impeding_factors.time_sys(:,sys); % Are the temp repairs for this system resolved before impeding factors are complete
-            sys_tmp_repaired(:,sys) = all_sys_tmp_repaired & tmp_repair_quick; % damage is quickly resolved by temp repair
-        end
-    end
-end
-prioritize_system_reoccupancy = sys_affects_reoccupancy & ~sys_tmp_repaired;
-prioritize_system_function_only = sys_affects_function & ~prioritize_system_reoccupancy & ~sys_tmp_repaired;
-non_priorities = ~prioritize_system_reoccupancy & ~prioritize_system_function_only;
-
-sys_priority_matrix = prioritize_system_reoccupancy   .* (100 + systems.priority') + ... % First prioritize reoccpancy repairs (100 classifies first priority bank, important for sort function below)
-                      prioritize_system_function_only .* (200 + systems.priority') + ... % then priotize repairs that only affect function (200 classifies second priority bank)
-                      non_priorities                  .* (300 + systems.priority'); % finally, repair the systems that dont affect reoccupancy or function (300 classifies third priority bank)
-
-% use rank matrix to get row indeces of priorities
-[~, sys_idx_priority_matrix] = sort(sys_priority_matrix, 2);
-
-end
-
diff --git a/+recovery/+repair_schedule/fn_restructure_repair_schedule.m b/+recovery/+repair_schedule/fn_restructure_repair_schedule.m
deleted file mode 100644
index 205ff67..0000000
--- a/+recovery/+repair_schedule/fn_restructure_repair_schedule.m
+++ /dev/null
@@ -1,96 +0,0 @@
-function [ damage ] = fn_restructure_repair_schedule( damage, system_schedule, ...
-    repair_complete_day_per_system, systems, repair_type, simulated_red_tags)
-% Redistribute repair schedule data from the system and story level to the component level for use 
-% in the functionality assessment (ie, put repair schedule data into the
-% damage object)
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage and loss data for each component in the building
-% system_schedule: Structure
-%   repair time data for each system in isolation
-% repair_complete_day_per_system: matrix [num reals x num systems]
-%   Number of days from the start of each sequence to the completion of the
-%   sequence considering the allocation of workers to each sequence (ie
-%   some sequences start before others)
-% systems: table
-%   data table containing information about each system's attributes
-% tmp_repair_complete_day: array [num_reals x num_comp]
-%   contains the day (after the earthquake) the temporary repair time is 
-%   resolved per damage state damage and realization
-%
-% Returns
-% -------
-% damage: struct
-%   contains per damage state damage and loss data for each component in
-%   the building, including repair schedule data.
-%
-% Notes
-% -----
-% In the repair start day outputs:
-%    - Zero = Starts immediately
-%    - NaN = Repairs never started (because not damaged, has no temp repair, or controlled by red tag)
-% In the repair complete day outputs:
-%    - NaN = No damage (or controlled by red tag)
-%    - Inf = Is damaged, but has no System/RepairClass assignment (or no temp repair)
-% 
-
-%% Initialize Parameters
-num_sys = height(systems);
-num_units = length(damage.tenant_units);
-
-% Define Repair Type Variables (variable within the damage object)
-if strcmp(repair_type,'full')
-    repair_time_var = 'worker_days';
-    system_var = 'system';
-elseif strcmp(repair_type,'temp')
-    repair_time_var = 'tmp_worker_day';
-    system_var = 'tmp_repair_class';
-else
-    error('Unexpected Repair Type')
-end
-
-% Initialize recovery field
-for tu = 1:num_units
-    % Set to inf as a null repair time (will remain inf for components with
-    % no attributed system) - matters for temp repairs, shouldnt matter for
-    % full repair
-    damage.tenant_units{tu}.recovery.repair_start_day = nan(size(damage.tenant_units{tu}.qnt_damaged));
-    damage.tenant_units{tu}.recovery.repair_complete_day = inf(size(damage.tenant_units{tu}.qnt_damaged));
-    
-    % if not damaged, set repair complete time to NaN
-    is_damaged = damage.tenant_units{tu}.qnt_damaged > 0 & damage.tenant_units{tu}.(repair_time_var) > 0;
-    damage.tenant_units{tu}.recovery.repair_complete_day(~is_damaged) = NaN; 
-end
-
-%% Redistribute repair schedule data
-for sys = 1:num_sys
-    % Calculate system repair times on each story
-    system_duration = max(system_schedule.per_system{sys}.repair_complete_day,[],2); % total repair time spent in this system over all stories
-    start_day = repair_complete_day_per_system(:,sys) - system_duration;
-    story_start_day = start_day + system_schedule.per_system{sys}.repair_start_day;
-    story_complete_day = start_day + system_schedule.per_system{sys}.repair_complete_day;
-    
-    % Do not perform temporary repairs when building is red tagged
-    if strcmp(repair_type,'temp') && any(simulated_red_tags)
-        story_start_day(simulated_red_tags,:) = NaN;
-        story_complete_day(simulated_red_tags,:) = NaN;
-    end
-
-    % Re-distribute to each tenant unit
-    sys_filt = damage.comp_ds_table.(system_var)' == systems.id(sys); % identifies which ds idices are in this seqeunce  
-    for tu = 1:num_units
-        is_damaged = damage.tenant_units{tu}.qnt_damaged(:,sys_filt) > 0 & damage.tenant_units{tu}.(repair_time_var)(:,sys_filt) > 0;
-        is_damaged = is_damaged*1;
-        is_damaged(is_damaged == 0) = NaN;
-
-        % Re-distribute repair days to component damage states
-        damage.tenant_units{tu}.recovery.repair_start_day(:,sys_filt) = is_damaged .* story_start_day(:,tu);
-        damage.tenant_units{tu}.recovery.repair_complete_day(:,sys_filt) = is_damaged .* story_complete_day(:,tu);
-        
-    end
-end
-
-end
-
diff --git a/+recovery/+repair_schedule/fn_set_repair_constraints.m b/+recovery/+repair_schedule/fn_set_repair_constraints.m
deleted file mode 100644
index 1f76e42..0000000
--- a/+recovery/+repair_schedule/fn_set_repair_constraints.m
+++ /dev/null
@@ -1,58 +0,0 @@
-function [ sys_constraint_matrix ] = fn_set_repair_constraints( systems, repair_type, conditionTag )
-% Define a constraint matrix to be used by repair schedule
-%
-% Delelops a matrix of various constriants between each system (i.e. what
-% systems need to be repaired before others) for each realization
-
-% Parameters
-% ----------
-% systems: table
-%   data table containing information about each system's attributes
-% conditionTag: logical array (num_reals x 1)
-%   Is the building red tagged for each realization
-%
-% Returns
-% -------
-% sys_constraint_matrix: array [num reals x num_sys]
-%   array of system ids which define which systems (column index) are delayed by the
-%   system ids (array values)
-%
-% Notes
-% -----
-% Shortcoming: as implemented, each system can only be constrained by one
-%              other system
-% Example:
-%   [0 0 1 0 0 0 0 6 0]
-%   Interiors (column 3) are blocked by struture (value of 1 in the 3rd column)
-%   HVAC (column 8) is blocked by plumbing (value of 6 in the 8th column)
-
-%% Initial Setup
-num_sys = height(systems);
-num_reals = length(conditionTag);
-sys_constraint_matrix = zeros(num_reals, num_sys);
-
-%% Interior Constraints
-if strcmp(repair_type,'full') 
-    % Interiors are delayed by structural repairs
-    interiors_idx = find(strcmp(systems.name,'interior'));
-    structure_idx = find(strcmp(systems.name,'structural'));
-    sys_constraint_matrix(:,interiors_idx) = structure_idx;
-
-    % Red Tag Constraints
-    % All systems blocked by structural when red tagged
-    sys_constraint_matrix(logical(conditionTag),~strcmp(systems.name,'structural')) = structure_idx;
-elseif strcmp(repair_type,'temp')
-    % All temp repairs are blocked by shoring
-    shoring_id = 5;
-    shoring_filt = systems.id == shoring_id;
-    if any(shoring_filt) % If shoring is considred as a temp repair measure
-        shoring_idx = find(shoring_filt);
-        sys_constraint_matrix(:,~shoring_filt') = shoring_idx; % all classes that are not shoring idx are blocked by the shoring idx
-    end
-else
-    error('Unexpected Repair Type')
-end
-
-
-end
-
diff --git a/+recovery/+repair_schedule/main_repair_schedule.m b/+recovery/+repair_schedule/main_repair_schedule.m
deleted file mode 100644
index 9fbf9a2..0000000
--- a/+recovery/+repair_schedule/main_repair_schedule.m
+++ /dev/null
@@ -1,151 +0,0 @@
-function [damage, worker_data, building_repair_schedule ] = main_repair_schedule(...
-    damage, building_model, simulated_red_tags, repair_time_options, ...
-    systems, tmp_repair_class, impeding_factors, simulated_replacement_time)
-% Determine the repair time for a given damage simulation.
-%
-% Simulation of system and building level repair times based on a
-% simulation of impedance factor and repair scheduling algorithm.
-% Simulates the repair times for all realizations of a single ground motion
-% or intensity level.
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage and loss data for each component in the building
-% building_model: struct
-%   data structure containing general information about the building
-% simulated_red_tags: logical array [num_realization x 1]
-%   for each realization of the Monte Carlo simulation, is the structure
-%   expected to be red-tagged
-% repair_time_options: struct
-%   general repair time options such as mitigation factors
-% systems: table
-%   attributes of structural and nonstructural building systems; data 
-%   provided in static tables directory
-% tmp_repair_class: table
-%   data table containing information about each temporary repair class
-%   attributes. Attributes are similar to those in the systems table.
-% impeding_factors: struct
-%   simulated impedance times
-% simulated_replacement_time: array [num_reals x 1]
-%   simulated time when the building needs to be replaced, and how long it
-%   will take (in days). NaN represents no replacement needed (ie
-%   building will be repaired)
-%
-% Returns
-% -------
-% damage: struct
-%   contains per damage state damage and loss data for each component in the building
-% worker_data: struct
-%   simulated building-level worker allocations throught the repair process
-% building_repair_schedule: struct
-%   simulations of the building repair schedule, broken down by component,
-%   story, and system
-
-
-%% Initial Setup
-% Import Packages
-import recovery.repair_schedule.*
-
-% Define the maximum number of workers that can be on site, based on REDI
-max_workers_per_building = ...
-    min(max(floor(sum(building_model.area_per_story_sf) * ...
-    repair_time_options.max_workers_per_sqft_building + 10), ...
-    repair_time_options.max_workers_building_min), ...
-    repair_time_options.max_workers_building_max);
-                          
-%% Determine repair schedule per system for Temporary Repairs 
-% Define the maximum number of workers that can be on any given story
-max_workers_per_story = ...
-    ceil(building_model.area_per_story_sf * ...
-    repair_time_options.max_workers_per_sqft_story_temp_repair); 
-
-% Temporary Repairs
-repair_type = 'temp';
-[tmp_damage, tmp_worker_data] = fn_schedule_repairs(...
-    damage, repair_type, tmp_repair_class, ...
-    max_workers_per_building, max_workers_per_story, ...
-    impeding_factors.temp_repair, simulated_red_tags, []);
-
-% Calculate the max temp repair complete day for each component (anywhere
-% in building)
-tmp_repair_complete_day = nan(size(damage.tenant_units{1}.tmp_worker_day));
-% NaN = Never damaged
-% Inf  = Damage not resolved by temp repair
-for tu = 1:length(tmp_damage.tenant_units)
-    tmp_repair_complete_day = ...
-        max(tmp_repair_complete_day,...
-            tmp_damage.tenant_units{tu}.recovery.repair_complete_day);
-end
-
-%% Determine repair schedule per system for Full Repairs 
-% Define the maximum number of workers that can be on any given story
-max_workers_per_story = ...
-    ceil(building_model.area_per_story_sf * ...
-    repair_time_options.max_workers_per_sqft_story); 
-
-% Full Repairs
-repair_type = 'full';
-[damage, worker_data] = fn_schedule_repairs(...
-    damage, repair_type, systems, ...
-    max_workers_per_building, max_workers_per_story, impeding_factors, ...
-    simulated_red_tags, tmp_repair_complete_day);
-
-%% Combine temp and full repair schedules
-for tu = 1:length(tmp_damage.tenant_units)
-    % Repair time is the lesser of the full repair and temp repair times
-    damage.tenant_units{tu}.recovery.repair_complete_day_w_tmp = ...
-        min(damage.tenant_units{tu}.recovery.repair_complete_day,...
-            tmp_damage.tenant_units{tu}.recovery.repair_complete_day);
-    % Temporary Repair Times control if temporary repair times are less
-    % than the full repair time
-    tmp_day_controls = tmp_damage.tenant_units{tu}.recovery.repair_complete_day < ...
-        damage.tenant_units{tu}.recovery.repair_complete_day;
-    % Repair start day is set to the temp repair start day when temp
-    % repairs control
-    damage.tenant_units{tu}.recovery.repair_start_day_w_tmp = damage.tenant_units{tu}.recovery.repair_start_day;
-    damage.tenant_units{tu}.recovery.repair_start_day_w_tmp(tmp_day_controls) = ...
-        tmp_damage.tenant_units{tu}.recovery.repair_start_day(tmp_day_controls);
-end
-
-%% Format Outputs 
-% Format Start and Stop Time Data for Gantt Chart plots 
-% This is also the main data structure used for calculating full repair time
-% outputs
-[ building_repair_schedule.full ] = fn_format_gantt_chart_data( damage, systems, simulated_replacement_time );
-[ building_repair_schedule.temp ] = fn_format_gantt_chart_data( tmp_damage, systems, simulated_replacement_time );
-
-end
-
-
-
-function [damage, worker_data] = ...
-    fn_schedule_repairs(damage, repair_type, systems, max_workers_per_building, ...
-    max_workers_per_story, impeding_factors, simulated_red_tags, tmp_repair_complete_day)
-
-    %% Initial Setup
-    % Import Packages
-    import recovery.repair_schedule.*
-
-    %% Step 1 - Calculate the start and finish times for each system in isolation
-    % based on REDi repair sequencing and Yoo 2016 worker allocations
-    [ system_schedule ] = fn_calc_system_repair_time(damage, repair_type, systems, max_workers_per_building, max_workers_per_story);
-
-    %% Step 2 - Set system repair priority
-    [ sys_idx_priority_matrix ] = fn_prioritize_systems( systems, repair_type, damage, tmp_repair_complete_day, impeding_factors );
-
-    %% Step 3 - Define system repair constraints
-    [ sys_constraint_matrix ] = fn_set_repair_constraints( systems, repair_type, simulated_red_tags );
-
-    %% Step 4 - Allocate workers among systems and determine the total days until repair is completed for each sequence
-    [repair_complete_day_per_system, worker_data] = fn_allocate_workers_systems(...
-        systems, system_schedule.system_totals.repair_days, ...
-        system_schedule.system_totals.num_workers, max_workers_per_building, ...
-        sys_idx_priority_matrix, sys_constraint_matrix, simulated_red_tags, ...
-        impeding_factors.time_sys );
-    
-    %% Step 5 - Format outputs for Functionality calculations
-    [ damage ] = fn_restructure_repair_schedule( damage, system_schedule, ...
-                 repair_complete_day_per_system, systems, repair_type, simulated_red_tags);
-
-end
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 12d2f02..d601d32 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,36 @@
-outputs/*
-*.asv
\ No newline at end of file
+# Compiled python modules
+*.pyc
+
+# VSCode folders
+/.vscode
+
+# PyCharm folders
+/.idea
+/.cache
+
+# coverage
+.coverage
+
+# Python egg metadata, regenerated from source files by setuptools
+*.egg-info/
+.eggs/
+*.egg
+
+# Temporary jupyter files
+/.ipynb_checkpoints/
+*.ipynb_checkpoints
+
+# Output folder
+outputs/
+
+# Folder with original Matlab code
+/Matlab_code/
+
+.ai/
+Comparison_scripts/
+
+# All new files coming out of buildInputs
+inputs/example_inputs/*/optional_inputs.json
+inputs/example_inputs/*/optional_inputs.py
+inputs/example_inputs/*/simulated_inputs.json
+inputs/example_inputs/*/build_input.py
diff --git a/README.md b/README.md
index e6f778f..4ac0652 100644
--- a/README.md
+++ b/README.md
@@ -1,84 +1,122 @@
-# PBEE-Recovery
-Matlab codebase for quantifying building-specific functional recovery and reoccupancy based on a probabilistic performance-based earthquake engineering framework.
+# Functional-Recovery---Python
+This is translation of Matlab codebase into Python for quantifying building-specific functional recovery and reoccupancy based on a probabilistic performance-based earthquake engineering framework.
 
-### Method Description
-The method for quantifying building-specific functional recovery is based on the performance-based earthquake engineering framework. To quantify building function, the method maps component-level damage to system-level performance, and system-level performance to building function using a series of fault trees that describe the interdependencies between the functions of various building components. The method defines the recovery of function and occupancy at the tenant unit level, where a building can be made up of one-to-many tenant units, each with a possible unique set of requirements to regain building function; the recovery state of the building is defined as an aggregation of all the tenant units within the building. The method propagates uncertainty through the assessment using a Monte Carlo simulation. Details of the method are fully described in Cook, Liel, Haselton, and Koliou, 2022. "A Framework for Operationalizing the Assessment of Post Earthquake Functional Recovery of Buildings", Earthquake Spectra.
+## Requirements
 
-### Implementation Details
-The method is developed as part of the consequence module of the Performance-Based Earthquake Engineering framework and uses simulations of component damage from the FEMA P-58 method as an fundamental input. Therefore, this implementation will not perform a FEMA P-58 assessment, and instead, expects the simulations of component damage, from a FEMA P-58 assessment to be provided as inputs. Along with other information about the building, the buildings tenant units, and some analysis options, this implementation will perform the functional recovery assessment method, and provide simulated recovery times for each realization provided. The implementation runs an assessment for a single building at a single intensity level. The implementation of the method does not handle demo and replace conditions and predicts building function based on component damage simulation and recovery times assuming damage will be repaired in-kind. Building failure, demo, and replacement conditions can be handled as a post-process by either overwriting realizations where global failure occurs or only inputting realizations that are scheduled for repair.
+- **Python Version**: 3.9 or later (recommend 3.9)
+- **Package Manager**: pip (comes with Python)
 
-The method is built using Matlab v2017a; running this implementation using other versions Matlab may not perform as expected.
+### Installation
+
+The ATC-138 Functional Recovery Assessment tool is distributed as a Python package. Install it using pip:
+
+
+```bash
+# Create and activate a virtual environment (recommended)
+python -m venv .venv
+
+# Activate virtual environment
+# On Windows (PowerShell):
+.\.venv\Scripts\Activate.ps1
+# On macOS/Linux:
+source .venv/bin/activate
+
+# Install the package in editable mode
+pip install -e .
+```
+
+
+### Verify Installation
+
+After installation, verify that the CLI is available:
+
+```bash
+atc138 --help
+```
+
+You should see the command help output with available options.
 
 ## Running an Assessment
- - **Step 1**: Build the inputs matlab data file of simulated inputs. Title the file "simulated_inputs.mat" and place it in a directory of the model name within the "inputs" drirectory. This matlab data file can either be constructed manually following the inputs schema or using the build script as discussed in the _Building the Inputs File section_ below.
- - **Step 2**: Open the matlab file "driver_PBEErecovery.m" and set the "model_name", "model_dir", and "outputs_dir" variables.
- - **Step 3**: Run the script
- - **Step 4**: Simulated assessment outputs will be saved as a Matlab data file in a directory of your choice
+
+An assessment can be run directly from the command line, or as imported within a Python workflow. If `simulated_inputs.json` does not exist, it will be created using default inputs within `src/atc138/data`. Various assessment options can be overridden by placing them in file `optional_inputs.json` file within the input directory. This file can be customized for each assessment if desired and will be set as default values if not specified.
+
+### Running from the command line
+
+With the input directory containing the necessary inputs, perform an assessment by running:
+
+```bash
+python -m atc138.cli dir/to/inputs dir/to/outputs
+```
+
+ For example, the ICSB example case is run with:
+
+```bash
+python -m atc138.cli ./examples/ICSB ./examples/ICSB/output
+```
+
+### Imported via Python script
+
+Ensure that the `src/` directory is on the path of the main script. Then:
+
+```python
+from src.atc138 import driver
+
+example_dir = './examples/ICSB'
+output_dir = './examples/ICSB/output'
+
+driver.run_analysis(example_dir, output_dir, seed=985)
+```
 
 ## Example Inputs
-Several example inputs are provided to help illustrate both the construction of the inputs file and the implementation. These files are located in the inputs/example_inputs directory and can be run through the assessment by setting the variable names accordingly in **step 2** above.
+Four example inputs are provided to help illustrate both the construction of the inputs file and the implementation. These files are located in the `examples/` directory and can be run through the assessment by setting the variable names accordingly above.
 
 ## Definition of I/O
-A brief description of the various input and output variables are provided below. A detailed schema of all expected input and output subfields is provided in the PBEE-Recovery/schema directory.
+A brief description of the various input and output variables are provided below. A detailed schema of all expected input and output subfields is provided in the schema directory.
 
 ### Inputs
-  - **impedance_options**: [_struct_]
-   data structure containing optional method inputs for the assessment of impeding factors
- - **repair_time_options**: [_struct_]
-   data structure containing optional method inputs for the assessment of the repair schedule
- - **functionality_options**: [_struct_]
-   data structure containing optional method inputs for the assessment of building function, such as functionality limit state thresholds
- - **building_model**: [_struct_]
-   data structure containing general information about the building such as the number of stories and the building area
- - **damage**: [_struct_]
-   data structure containing simulated damage, simulated repair time, and component attribute data associated with each component's damages state in the building
- - **damage_consequences**: [_struct_]
-   data structure containing simulated building consequences, such as red tags and repair costs ratios
- - **functionality.utilities**: [_struct_]
-   data structure containing simulated utility downtimes
- - **tenant_units**: [_table_]
-   datatable that contains the attributes and functional requirements of each tenant unit in the building
+  - **impedance_options**: Python dictionary
+   Python dictionary containing optional method inputs for the assessment of impeding factors
+ - **repair_time_options**: Python dictionary
+   Python dictionary containing optional method inputs for the assessment of the repair schedule
+ - **functionality_options**: Python dictionary
+   Python dictionary containing optional method inputs for the assessment of building function, such as functionality limit state thresholds
+ - **building_model**: Python dictionary
+   Python dictionary containing general information about the building such as the number of stories and the building area
+ - **damage**: Python dictionary
+   Python dictionary containing simulated damage, simulated repair time, and component attribute data associated with each component's damages state in the building
+ - **damage_consequences**: Python dictionary
+   Python dictionary containing simulated building consequences, such as red tags and repair costs ratios
+ - **functionality['utilities']**: Python dictionary
+   Python dictionary containing simulated utility downtimes
+ - **tenant_units**: Python dictionary
+   Python dictionary that contains the attributes and functional requirements of each tenant unit in the building
 
 ### Outputs
- - **functionality.recovery**: [_struct_]
-   data structure containing the simulated tenant- and building-level functional recovery and reoccupancy outcomes
- - **functionality.building_repair_schedule**: [_struct_]
-   data structure containing the simulated building repair schedule
- - **functionality.worker_data**: [_struct_]
-   data structure containing the simulation of allocated workers throughout the repair process
- - **functionality.impeding_factors**: [_struct_]
-   data structure containing the simulated impeding factors delaying the start of system repair
-
-## Building the Inputs File
-Instead of manually defining the inputs matlab data file based on the inputs schema, the inputs file can be built from a simpler set of building inputs, taking advantage of default assessment assumptions and component, system, and tenant attributes contained within the _static_tables_ directory.
-
-### Instructions
- - **Step 1**: Copy the scripts build_inputs.m and optional_inputs.m from the _Inputs2Copy_ directory to the directory where you want to build the simulated_inputs.mat inputs file.
- - **Step 2**: Add the requried building specific input files listed below to the same directory
- - **Step 3**: Modify the optional_inputs.m file as needed.
- - **Step 4**: Make sure the variable _static_data_dir_ in build_inputs.m is correctly pointing to the location of the static data directory
- - **Step 5**: Run the build script
-
-#### Option for Customizing Static Data 
-If you would like to modify the static data tables listed below for a specifc model, simply copy the static data tables listed below to the build script directory, modify the files, and set the _static_data_dir_ vairable to the point to the location of the modified files (same directory as the build script).
+ - **functionality['recovery']**: Python dictionary
+   Python dictionary containing the simulated tenant- and building-level functional recovery and reoccupancy outcomes
+ - **functionality['building_repair_schedule']**: Python dictionary
+   Python dictionary containing the simulated building repair schedule
+ - **functionality['worker_data']**: Python dictionary
+   Python dictionary containing the simulation of allocated workers throughout the repair process
+ - **functionality['impeding_factors']**: Python dictionary
+   Python dictionary containing the simulated impeding factors delaying the start of system repair
+
+## Manually building the Inputs File
+By default, the inputs file are built from a simpler set of building inputs, taking advantage of default assessment assumptions and component, system, and tenant attributes contained within the _data_ directory. If you would like to manually modify the data tables listed below for a specific model, simply copy the files to the input directory and modify them.
 
 ### Required Building Specific Data
-Each file listed below contains data specific to the building performance model and simulated damage given for a specific level of shaking. Each file listed will need to be created for each unique assessment and saved in the root directory of the build script. Data are contained in either json structures or csv tables.
+Each file listed below contains data specific to the building performance model and simulated damage given for a specific level of shaking. Each file listed will need to be created for each unique assessment and saved in the root directory of the build script. Data are contained in either json  or csv format.
  - **building_model.json**: Basic properties of the building and performance model. Contains all variables within the _building_model_ structure defined in the inputs schema.
  - **tenant_unit_list.csv**: Table that lists each tenant unit within the building; one row per tenant unit. This table requires the following attributes:
      - id: [int or string] unique identifier for this tenant unit
      - story: [int] building story where this tenant unit is located (ground floor is listed at 1)
      - area: [number] total gross plan area of the tenant unit, in square feet
      - perim_area: [number] total exterior perimeter area (elevation) of the tenant unit, is square feet
-     - occupancy_id: [int] foreign key to the _occupancy_id_ attribute of the tenant_function_requirements.csv table in the _static_tables_ directory
+     - occupancy_id: [int] foreign key to the _occupancy_id_ attribute of the tenant_function_requirements.csv table in the _data_ directory
  - **comp_ds_list.csv**: Table that lists each component and damage state populated in the building performance model; one row per each component's damage state. This table requires the following attributes:
      - comp_id: [string] unique FEMA P-58 component identifier
      - ds_seq_id: [int] interger index of the sequential parent damage state (i.e., damage state 1, 2, 3, 4);
      - ds_sub_id: [int] interger index for the mutually exlusive of simeltaneous sub damage state; use 1 to indicate a sequential damage state with no sub damage state.
-  - **comp_ds_list.csv**: Table that lists the quantity and location of each component in the building performance model. This table requires the following attributes:
-     - story: [int] story location of the component
-     - dir: [int] direciton of the component (1, 2, or 3 for nondirectional);
-     - <1 attribute per comp>: [str] each component should be listed at its own column with string representation of the component id as the column header.
  - **damage_consequences.json**: Building-level and story-level simulated properties of building damage. Contains all variables within the _damage_consequences_ structure defined in the inputs schema.
  - **simulated_damage.json**: Component-level simulated damage properties. Contains all variables within the _damage.tenant_units_ structure defined in the inputs schema. Each variable containing realization of component damage should be defined uniquely for each tenant unit (shown as "tu" below). Each tenant_unit cell should contain the following variables:
      - tenant_unit{tu}.qnt_damaged: [array: simulations × damage states] The number of damaged components in each component damage state for each realization of the simulation.
@@ -90,40 +128,20 @@ Each file listed below contains data specific to the building performance model
      - tenant_unit{tu}.num_comps: [array: 1 × damage states] The total number of components associated with each damage state (should be uniform for damage state of the same component stack).
 
 ### Optional Building Specific Data
-The file(s) listed below contain data that is optional for the assessment. If the files do not exist, the method will make simplifying assumptions to account for the missing data (as noted below). Save in the root directory of the build script.
- - **utility_downtime.json**: Regional utility simulated downtimes for gas, water, and electrical power networks. Contains all variables within the _functionality.utilities_ structure defined in the inputs schema.
+The file(s) listed below contain data that is optional for the assessment. If the files do not exist, the method will make simplifying assumptions to account for the missing data (as noted below). Save in the input directory of your analysis.
+ - **utility_downtime.json**: Regional utility simulated downtimes for gas, water, and electrical power networks. Contains all variables within the _functionality['utilities']_ dictionary defined in the inputs schema.
 
 ### Default Optional Inputs
-The matlab file listed below defines additional assessment inputs based on set of default values. Copy the file from the _Inputs2Copy_ directory, place it in the root directory of the build script, and modify it as you see if (or build the script programmatically)
- - **optional_inputs.m**: Defines default variables for the impedance_options, repair_time_options, and functionality_options variables listed in the inputs schema.
+The Python file listed below defines additional assessment inputs based on set of default values. Place this file in the input directory of your analysis.
+ - **optional_inputs.json**: Defines default variables for the impedance_options, repair_time_options, functionality_options, and regional_impact variables listed in the inputs schema.
+
+<!-- How does a user modify the static tables (now data) without touching the original data files? -->
 
 ### Static Data
-The csv tables listed below contain default componet, damage state, system, and tenant function attributes that can be used to populate the required assessment inputs according to the methodology. Either point the _static_data_dir_ variable in build_inputs.m to the location of these tables in the _static_tables_ directory, or copy and modify them as you see fit and place them in the root directory of the build script.
+The csv tables listed below contain default component, damage state, system, and tenant function attributes that can be used to populate the required assessment inputs according to the methodology. These are located in the _data_ directory. To override the static data with custom versions, copy modified sheets and place them in the input directory.
  - **component_attributes.csv**: Attributes of components in the FEMA P-58 fragility database that are required for the functional recovery assessment.
  - **damage_state_attribute_mapping.csv**: Attributes of damage state in the FEMA P-58 fragility database and their affect on function and reoccupancy.
  - **subsystems.csv**: Attributes of each default subsystem considered in the method.
  - **tenant_function_requirements.csv**: Default tenant requirements for function for various occupancy classes.
-
-## Converting Inputs from PELICUN Outputs
-Convert outputs from SimCenter's PELICUN v3 tool into PBEE-Recovery inputs. Specifically; build the four input files: _comp_ds_list.csv_, _damage_consequences.json_, _building_model.json_, and _simulated_damage.json_ from PELICUN I/O
-
-### Instructions
- - **Step 1**: Create a model input directory in the _inputs_ directory
- - **Step 2**: Copy the PELICUN I/O files into a directory titled _pelicun_data_ within the model input directory.
- - **Step 3**: Create the files _tenant_unit_list.csv_ and _general_inputs.json_ and place them into the model input directory.
- - **Step 4**: Make sure the input variables at the begining of the file _driver_convert_PELICUN.m_ are all up to date.
- - **Step 5**: Run the file _driver_convert_PELICUN.m_
- - **Step 6**: Run the build script following the instructions above
-
-### PELICUN Files
-Place each of the following files in the _pelicun_results_ directory within the model input directory.
- - **DMG_sample.csv**: Realizations of damaged components for each damage state of each performance group.
- - **DV_bldg_repair_sample.csv**: Realizations of repair time (worker days) for each damage state of each performance group.
- - **DL_summary.csv**: Realizations of consequence data at the building level (including repair cost).
- - **CMP_QNT.csv**: Component population data.
- - **input.json**: Pelicun config file.
- 
- ### Required Additoinal Inputs
- Place each of the following files in the _treads_data_ directory within the model input directory.
- - **general_inputs.json**: Json containing other misc data needed for recovery assessment.
- - **tenant_unit_list.csv**: Table that lists each tenant unit within the building; see description above.
\ No newline at end of file
+ - **systems.csv**: Attributes of each default ssytem considered in the method.
+ - **temp_repair_class.csv**: Attributes of each temprary repair class considered in the method.
diff --git a/build_example_inputs.py b/build_example_inputs.py
deleted file mode 100644
index 30c64e8..0000000
--- a/build_example_inputs.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""
-Go through example inputs and rebuild simulated input file
-"""
-import os
-import shutil
-import subprocess
-import time
-
-def build_inputs(input_dir, build_file_dir):
-    """
-    Parameters
-    ----------
-    input_dir: str
-        location of the example input directories
-    build_file_dir: str
-        location of the build scripts to copy
-
-    Returns
-    -------
-    None, writes simulated_inputs.m in each example inputs directory
-    """
-
-    # Define files to copy
-    bld_script = build_file_dir + '/build_inputs.m'
-    opt_script = build_file_dir + '/optional_inputs.m'
-
-    # Go through each example input folder
-    dirs = os.listdir(input_dir)
-    for d in dirs:
-        # Copy files over to example input directory
-        shutil.copyfile(bld_script, input_dir + '/' + d + '/build_inputs.m')
-        shutil.copyfile(opt_script, input_dir + '/' + d + '/optional_inputs.m')
-
-        # Delete old simulated inputs file (if it exists)
-        file_exists = os.path.exists(input_dir + '/' + d + '/simulated_inputs.mat')
-        if file_exists:
-            os.remove(input_dir + '/' + d + '/simulated_inputs.mat')
-
-        # Execute Matlab build script
-        bld_path = os.path.abspath(input_dir + '/' + d + '/build_inputs.m')
-        exc_str = "run('" + bld_path + "'); exit;"
-        p = subprocess.run(["matlab", "-nosplash", "-nodesktop", "-r", exc_str])
-
-        time.sleep(60)  # low tech way of getting python to wait until matlab is finished
-
-        # Check if the input file has been successfully created
-        file_exists = os.path.exists(input_dir + '/' + d + '/simulated_inputs.mat')
-        if file_exists:
-            # Delete build scripts
-            os.remove(input_dir + '/' + d + '/build_inputs.m')
-            os.remove(input_dir + '/' + d + '/optional_inputs.m')
-        else:
-            raise Exception("Matlab process failed to build inputs")
-
-if __name__ == '__main__':
-
-    # Copy fragilities table over from original location to public repo (with some omissions)
-    example_input_dir = 'inputs/example_inputs'
-    build_file_dir = 'inputs/Inputs2Copy'
-    build_inputs(example_input_dir, build_file_dir)
diff --git a/demo.ipynb b/demo.ipynb
new file mode 100644
index 0000000..d2292da
--- /dev/null
+++ b/demo.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","execution_count":1,"id":"1fce86e9-d68d-489b-83df-4855fe002aed","metadata":{"id":"1fce86e9-d68d-489b-83df-4855fe002aed","executionInfo":{"status":"ok","timestamp":1674609674963,"user_tz":480,"elapsed":455,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}}},"outputs":[],"source":["import json, os, sys\n","import numpy as np\n","import pandas as pd\n","\n","pd.set_option('display.max_rows',100)"]},{"cell_type":"code","source":["# connect with Google Drive and change to the folder with ATC 138 scripts\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","main_dir = '/content/drive/MyDrive/SimCenter/FunctionalRecovery/ATC138/'\n","\n","os.chdir(main_dir)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EwswFIkCtCuu","executionInfo":{"status":"ok","timestamp":1674609722093,"user_tz":480,"elapsed":47132,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"2794626b-452a-4a04-fe6c-dbc8b314c508"},"id":"EwswFIkCtCuu","execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":3,"id":"a74c086e-9e47-4cc0-9600-330c1fdb5657","metadata":{"id":"a74c086e-9e47-4cc0-9600-330c1fdb5657","executionInfo":{"status":"ok","timestamp":1674609722093,"user_tz":480,"elapsed":2,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}}},"outputs":[],"source":["#model_name = 'RCSW_1story'\n","#model_name = 'haseltonRCMF_4story'\n","#model_name = 'haseltonRCMF_12story'\n","model_name = 'ICSB'\n","\n","model_dir = f'./inputs/{model_name}/'"]},{"cell_type":"markdown","id":"a17aa617-7a52-4310-a740-fe393878ab8c","metadata":{"id":"a17aa617-7a52-4310-a740-fe393878ab8c"},"source":["## Inputs\n","\n","Each file listed below contains data specific to the building performance model and simulated damage given **for a specific level of shaking**. Each file listed will need to be created for each unique assessment and saved in the root directory of the build script. \n","\n","Data are contained in either json or csv format."]},{"cell_type":"markdown","id":"212f99d5-cdbc-4751-8959-0945e9b5b95b","metadata":{"id":"212f99d5-cdbc-4751-8959-0945e9b5b95b"},"source":["---\n","### Building Model\n","\n","Provides general information about the building:\n","\n","- **building_value**: [number] [USD]\n","<br> Total replacement value of the building\n","- **num_stories**: [number]\n","<br> number of stories in the building, does not include basement levels\n","- **total_area_sf**: [number] [ft2]\n","<br> total interior area of the entire building\n","- **area_per_story_sf**: [array] [ft2]\n","<br> interior area for each story in the building, starting from the lowest level\n","- **ht_per_story_ft**: [array] [ft2]\n","<br> elevation height for each story in the building, starting from the lowest level\n","- **edge_lengths**: [array] [ft]\n","<br> external length of two orthogonal sides of the building, for each story in the building, starting from the lowest level\n","- **struct_bay_area_per_story**: [array] [ft]\n","<br> length of the typical structural bay, from centerline to centerline, for each story in the building, starting from the lowest level\n","- **num_entry_doors**: [number]\n","<br> number of external building exits/entrances\n","- **num_elevators**: [number]\n","<br> number of elevator shafts in the building\n","- **stairs_per_story**: [array]\n","<br> number of stairways on each story in the building, starting from the lowest level\n","- **occupants_per_story**: [array]\n","<br> total number of occuapnts (persons) that occupy each story of this building, starting from the lowest level"]},{"cell_type":"code","execution_count":4,"id":"d2398246-e9ef-44c7-a794-005d4af8148b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d2398246-e9ef-44c7-a794-005d4af8148b","executionInfo":{"status":"ok","timestamp":1674609723161,"user_tz":480,"elapsed":1070,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"54de60d7-7cb1-43ae-9222-5772e3f4d20a"},"outputs":[{"output_type":"stream","name":"stdout","text":["{\n","  \"building_value\": 15507252.123410186,\n","  \"num_stories\": 6,\n","  \"total_area_sf\": 56250,\n","  \"area_per_story_sf\": [\n","    9375,\n","    9375,\n","    9375,\n","    9375,\n","    9375,\n","    9375\n","  ],\n","  \"ht_per_story_ft\": [\n","    14.5,\n","    13.5,\n","    13.5,\n","    13.5,\n","    13.5,\n","    13.167\n","  ],\n","  \"edge_lengths\": [\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ],\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ],\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ],\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ],\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ],\n","    [\n","      96.82458365518542,\n","      96.82458365518542\n","    ]\n","  ],\n","  \"struct_bay_area_per_story\": [\n","    585.9375,\n","    585.9375,\n","    585.9375,\n","    585.9375,\n","    585.9375,\n","    585.9375\n","  ],\n","  \"num_entry_doors\": 2,\n","  \"num_elevators\": 2,\n","  \"stairs_per_story\": [\n","    2,\n","    2,\n","    2,\n","    2,\n","    2,\n","    2\n","  ],\n","  \"occupants_per_story\": [\n","    37.5,\n","    37.5,\n","    37.5,\n","    37.5,\n","    37.5,\n","    37.5\n","  ]\n","}\n"]}],"source":["with open(model_dir+'building_model.json') as f:\n","    building_model = json.load(f)\n","    \n","print(json.dumps(building_model, indent=2))"]},{"cell_type":"markdown","id":"647aa5af-b19b-4dbb-ad49-0d0d90e42d65","metadata":{"id":"647aa5af-b19b-4dbb-ad49-0d0d90e42d65"},"source":["---\n","### Tenant Units\n","\n","Table that lists each tenant unit within the building; one row per tenant unit. This table requires the following attributes:\n","\n","- **id**: [int or string] \n","<br> unique identifier for this tenant unit\n","- **story**: [int] \n","<br> building story where this tenant unit is located (ground floor is listed at 1)\n","- **area**: [float] [ft2]\n","<br> total gross plan area of the tenant unit\n","- **perim_area**: [float] [ft2] \n","<br>total exterior perimeter area (elevation) of the tenant unit\n","- **occupancy_id**: [int] \n","<br>foreign key to the occupancy_id attribute of the tenant_function_requirements.csv table in the static_tables directory\n","\n","Other attributes listed in the input schema - I assume these have defaults:\n","- **exterior**: [float]\n","<br> tolerance for damage to exterior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the perimeter area of severely damaged exterior components within the tenant-unit to the total perimeter area of the tenant unit (i.e., what percent of the exterior walls must be sufficiently damaged to cause tenants to not be able to function in this space).\n","- **interior**: [float]\n","<br> tolerance for damage to interior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the interior plan area of severely damaged interior components within the tenant-unit to the total plan area of the tenant unit (i.e., what percent of the interior space must be hindered to cause tenants to not be able to function in this space).\n","- **occ_per_elev**: [float]\n","<br> max number of occupants per elevator allowed for function\n","- **is_elevator_required**: [bool]\n","<br> is an operational elevator required for tenant function\n","- **is_electrical_required**: [bool] \n","<br> is an operational electrical system required for tenant function\n","- **is_water_required** [bool]\n","<br> is an operational plumbing system required for tenant function\n","- **is_hvac_required**: [bool] \n","<br> is an operational hvac system required for tenant function"]},{"cell_type":"code","execution_count":5,"id":"f48855f7-364b-49c6-8ca5-765d0b41ce8a","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"id":"f48855f7-364b-49c6-8ca5-765d0b41ce8a","executionInfo":{"status":"ok","timestamp":1674609723489,"user_tz":480,"elapsed":330,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"a8837fbd-901c-46f5-d01b-ac95123824ef"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    story  area  perim_area  occupancy_id\n","id                                       \n","1       1  9375        4800             0\n","2       2  9375        4800             1\n","3       3  9375        4800             1\n","4       4  9375        4800             1\n","5       5  9375        4800             1\n","6       6  9375        4800             1"],"text/html":["\n","  <div id=\"df-09671f02-6915-4375-8d02-63ba2a804035\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style 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<td>3</td>\n","      <td>9375</td>\n","      <td>4800</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>9375</td>\n","      <td>4800</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>9375</td>\n","      <td>4800</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>9375</td>\n","      <td>4800</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09671f02-6915-4375-8d02-63ba2a804035')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 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building performance model; one row per each component's damage state. \n","\n","This table requires the following attributes:\n","- **comp_id**: [string] \n","<br> unique FEMA P-58 component identifier\n","- **ds_seq_id**: [int] \n","<br> integer index of the sequential parent damage state (i.e., damage state 1, 2, 3, 4)\n","- **ds_sub_id**: [int] \n","<br> integer index for the mutually exlusive or simeltaneous sub damage state; use 1 to indicate a sequential damage state with no sub damage state."]},{"cell_type":"code","execution_count":6,"id":"0ca695c3-3b6d-4175-8917-5d706f838056","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"0ca695c3-3b6d-4175-8917-5d706f838056","executionInfo":{"status":"ok","timestamp":1674609723677,"user_tz":480,"elapsed":211,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"0cc60e66-dcb3-4c79-a07a-8976a0626fce"},"outputs":[{"output_type":"stream","name":"stdout","text":["(93, 2)\n"]},{"output_type":"execute_result","data":{"text/plain":["            ds_seq_id  ds_sub_id\n","comp_id                         \n","B1041.021a          1          1\n","B1041.021a          2          1\n","B1041.021a          3          1\n","B1041.021a          3          2\n","B1041.021b          1          1\n","B1041.021b          2          1\n","B1041.021b          3          1\n","B1041.021b          3          2\n","B1041.111a          1          1\n","B1041.111a          2          1\n","B1041.111a          3          1\n","B1041.111b          1          1\n","B1041.111b          2          1\n","B1041.111b          3          1\n","B1041.121b          1          1\n","B1041.121b          2          1\n","B1044.003           1          1\n","B1044.003           2          1\n","B1044.003           3          1\n","B1044.011           1          1\n","B1044.011           2          1\n","B1044.011           3          1\n","B2011.201a          1          1\n","B2011.201a          1          2\n","B2011.201b          1          1\n","C1011.001a          1          1\n","C1011.001a          2          1\n","C1011.001a          3          1\n","C2011.011b          1          1\n","C2011.011b          2          1\n","C2011.011b          3          1\n","C3011.001a          1          1\n","C3011.002a          1          1\n","C3011.002a          2          1\n","C3032.001a          1          1\n","C3032.001a          2          1\n","C3032.001a          3          1\n","C3032.001b          1          1\n","C3032.001b          2          1\n","C3032.001b          3          1\n","C3032.001c          1          1\n","C3032.001c          2          1\n","C3032.001c          3          1\n","C3032.001d          1          1\n","C3032.001d          2          1\n","C3032.001d          3          1\n","C3034.001           1          1\n","D1014.012           1          1\n","D1014.012           1          2\n","D1014.012           1          3\n","D1014.012      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<th>D3041.103c</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.103c</th>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>D3041.103c</th>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>D3052.013l</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D3052.013l</th>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>D3052.013l</th>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>D3052.013l</th>\n","      <td>1</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>D3067.011a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D4011.022a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D4011.022a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D4011.032a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D4011.032a</th>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D5011.011a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D5012.013a</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D5012.021d</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>D5012.031d</th>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83e64e1e-9515-4537-8b7b-3177f15cbc7d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":6}],"source":["damage_states = pd.read_csv(model_dir+'comp_ds_list.csv', header=0, index_col=0)\n","\n","print(damage_states.shape)\n","\n","damage_states"]},{"cell_type":"markdown","id":"12f4bc9c-a4da-495e-bb64-a0cd912a3b48","metadata":{"id":"12f4bc9c-a4da-495e-bb64-a0cd912a3b48"},"source":["---\n","### Consequences\n","\n","- **red_tag**: [array of bool]\n","<br> simulated logical of red tags, TRUE = building is red tagged\n","- **racked_stair_doors_per_story**: [array of int]\n","<br> simulated number of racked stairway doors per story\n","- **racked_entry_doors_side_1**: [array of int]\n","<br> simulated number of racked entry doors on one side of the building\n","- **racked_entry_doors_side_2**: [array of int]\n","<br> simulated number of racked entry doors on one side of the building\n","- **inpsection_trigger**: [array of bool]\n","<br> simulated logical of inspection triggers, TRUE = building requires inspection\n","- **repair_cost_ratio**: [array of float]\n","<br> simulated building repair cost normalized by the replacement value"]},{"cell_type":"code","execution_count":7,"id":"28c5c748-24e1-4fad-883c-b9eda5865bf0","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"28c5c748-24e1-4fad-883c-b9eda5865bf0","executionInfo":{"status":"ok","timestamp":1674609723979,"user_tz":480,"elapsed":322,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"30d62175-9b74-4a81-d515-3353ed508129"},"outputs":[{"output_type":"stream","name":"stdout","text":["red_tag 500\n","racked_stair_doors_per_story 500\n","racked_entry_doors_side_1 500\n","racked_entry_doors_side_2 500\n","inpsection_trigger 500\n","repair_cost_ratio 500\n"]}],"source":["with open(model_dir+'damage_consequences.json') as f:\n","    consequences = json.load(f)\n","    \n","for key, item in consequences.items():\n","    print(key, len(item))"]},{"cell_type":"code","execution_count":8,"id":"a43c7856-4906-4e76-9630-34504b7ccb49","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a43c7856-4906-4e76-9630-34504b7ccb49","executionInfo":{"status":"ok","timestamp":1674609723979,"user_tz":480,"elapsed":14,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"f6615088-3a47-41f4-d917-7e41490ec016"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.2769175506912296"]},"metadata":{},"execution_count":8}],"source":["np.array(consequences['repair_cost_ratio']).mean()"]},{"cell_type":"markdown","id":"2a521711-c700-49c7-9b10-e1dac5227e18","metadata":{"id":"2a521711-c700-49c7-9b10-e1dac5227e18"},"source":["---\n","### Simulated Damage\n","\n","Component-level simulated damage properties.\n","\n","Contains a list of dictionaries, each dictionary corresponds to a tenant unit (\"tu\"). Within each dictionary, the following variables need to be defined:\n","- **qnt_damaged**: [array: simulations × damage states] \n","<br>The number of damaged components in each component damage state for each realization of the simulation.\n","- **cost**: [array of float: sim x ds]\n","<br> simulated repair cost of each component's damage state\n","- **worker_days**: [array: simulations × damage states] \n","<br>The number of single worker days required to repair all damage to this damage state of this component at this story for each realization.\n","- **qnt_damaged_side_1**: [array: simulations × damage states] \n","<br>The number of damaged components in each component damage state assocaited with side 1 of the building; set to zero if not associated with a particular side. This is only for exterior cladding components.\n","- **qnt_damaged_side_2**: [array: simulations × damage states] \n","<br>The number of damaged components in each component damage state assocaited with side 2 of the building; set to zero if not associated with a particular side. This is only for exterior cladding components.\n","- **qnt_damaged_side_3**: [array: simulations × damage states] \n","<br>The number of damaged components in each component damage state assocaited with side 3 of the building; set to zero if not associated with a particular side. This is only for exterior cladding components.\n","- **qnt_damaged_side_4**: [array: simulations × damage states] \n","<br>The number of damaged components in each component damage state associated with side 4 of the building; set to zero if not associated with a particular side. This is only for exterior cladding components.\n","- **num_comps**: [array: 1 × damage states] \n","<br>The total number of components associated with each damage state (should be uniform for damage state of the same component stack)."]},{"cell_type":"code","execution_count":9,"id":"4b9133ea-2e31-494e-82e1-d69f52dff05d","metadata":{"tags":[],"colab":{"base_uri":"https://localhost:8080/"},"id":"4b9133ea-2e31-494e-82e1-d69f52dff05d","executionInfo":{"status":"ok","timestamp":1674609724742,"user_tz":480,"elapsed":775,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"007f84eb-e5cd-42b8-9aeb-80e893523cfc"},"outputs":[{"output_type":"stream","name":"stdout","text":["qnt_damaged (500, 93)\n","worker_days (500, 93)\n","qnt_damaged_side_1 (500, 93)\n","qnt_damaged_side_2 (500, 93)\n","qnt_damaged_side_3 (500, 93)\n","qnt_damaged_side_4 (500, 93)\n","num_comps (93,)\n"]}],"source":["with open(model_dir+'simulated_damage.json') as f:\n","    sim_dmg = json.load(f)\n","    \n","for key, item in sim_dmg[0].items():\n","    print(key, np.array(item).shape)"]},{"cell_type":"code","execution_count":10,"id":"72f316ea-2b48-43d6-aede-962e16dac0b5","metadata":{"id":"72f316ea-2b48-43d6-aede-962e16dac0b5","executionInfo":{"status":"ok","timestamp":1674609724742,"user_tz":480,"elapsed":4,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}}},"outputs":[],"source":["#pd.DataFrame(sim_dmg[0]['cost'])"]},{"cell_type":"markdown","id":"3ca062e5-f99d-4342-bc89-2b6f5834ac87","metadata":{"id":"3ca062e5-f99d-4342-bc89-2b6f5834ac87"},"source":["---\n","### Optional Inputs\n","\n","Details are described in the Inputs.json schema file"]},{"cell_type":"code","execution_count":11,"id":"9d08a5e9-d209-482d-864c-2dd239743bf7","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9d08a5e9-d209-482d-864c-2dd239743bf7","executionInfo":{"status":"ok","timestamp":1674609725078,"user_tz":480,"elapsed":339,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"d4476ffc-e908-4639-8dbb-8c1162035710"},"outputs":[{"output_type":"stream","name":"stdout","text":["{\n","  \"impedance_options\": {\n","    \"include_impedance\": {\n","      \"inspection\": true,\n","      \"financing\": true,\n","      \"permitting\": true,\n","      \"engineering\": true,\n","      \"contractor\": true\n","    },\n","    \"system_design_time\": {\n","      \"f\": 0.04,\n","      \"r\": 175,\n","      \"t\": 1.3,\n","      \"w\": 8\n","    },\n","    \"mitigation\": {\n","      \"is_essential_facility\": false,\n","      \"is_borp_equivalent\": false,\n","      \"is_engineer_on_retainer\": false,\n","      \"is_contractor_on_retainer\": false,\n","      \"funding_source\": \"private\",\n","      \"capital_available_ratio\": 0.1\n","    },\n","    \"impedance_beta\": 0.6,\n","    \"impedance_truncation\": 2\n","  },\n","  \"repair_time_options\": {\n","    \"temp_repair_beta\": 0.6,\n","    \"max_workers_per_sqft_story\": 0.001,\n","    \"max_workers_per_sqft_building\": 0.00025,\n","    \"max_workers_building_min\": 20,\n","    \"max_workers_building_max\": 260\n","  },\n","  \"functionality_options\": {\n","    \"door_racking_repair_day\": 3,\n","    \"egress_threshold\": 0.5,\n","    \"egress_threshold_wo_fs\": 0.75,\n","    \"required_ratio_operating_hvac_main\": 0.6667,\n","    \"required_ratio_operating_hvac_unit\": 0.6667,\n","    \"exterior_safety_threshold\": 0.1,\n","    \"interior_safety_threshold\": 0.25,\n","    \"door_access_width_ft\": 9\n","  },\n","  \"regional_impact\": {\n","    \"surge_factor\": 1\n","  }\n","}\n"]}],"source":["with open(model_dir+'optional_inputs.json') as f:\n","    options = json.load(f)\n","    \n","print(json.dumps(options, indent=2))"]},{"cell_type":"markdown","id":"89c23f41-64dc-4858-ba49-85ee845fc824","metadata":{"id":"89c23f41-64dc-4858-ba49-85ee845fc824"},"source":["## Analysis"]},{"cell_type":"code","execution_count":12,"id":"59046d7f-9e53-423d-8168-46f6d71e3ef8","metadata":{"id":"59046d7f-9e53-423d-8168-46f6d71e3ef8","executionInfo":{"status":"ok","timestamp":1674609725625,"user_tz":480,"elapsed":562,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}}},"outputs":[],"source":["from inputs.Inputs2Copy.build_input import build_input\n","from driver_PBEErecovery import run_analysis\n","os.chdir(main_dir)"]},{"cell_type":"code","execution_count":13,"id":"1187ae15-8fdc-4b1d-a037-5f18dcec8730","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1187ae15-8fdc-4b1d-a037-5f18dcec8730","executionInfo":{"status":"ok","timestamp":1674609731255,"user_tz":480,"elapsed":5632,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"b7b812dc-7ccf-4863-a609-f725bb068d77"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/SimCenter/FunctionalRecovery/ATC138/inputs/ICSB\n","Component ID B1041.021a does not belong to a subsystem\n","Component ID B1041.021a does not belong to a subsystem\n","Component ID B1041.021a does not belong to a subsystem\n","Component ID B1041.021a does not belong to a subsystem\n","Component ID B1041.021b does not belong to a subsystem\n","Component ID B1041.021b does not belong to a subsystem\n","Component ID B1041.021b does not belong to a subsystem\n","Component ID B1041.021b does not belong to a subsystem\n","Component ID B1041.111a does not belong to a subsystem\n","Component ID B1041.111a does not belong to a subsystem\n","Component ID B1041.111a does not belong to a subsystem\n","Component ID B1041.111b does not belong to a subsystem\n","Component ID B1041.111b does not belong to a subsystem\n","Component ID B1041.111b does not belong to a subsystem\n","Component ID B1041.121b does not belong to a subsystem\n","Component ID B1041.121b does not belong to a subsystem\n","Component ID B1044.003 does not belong to a subsystem\n","Component ID B1044.003 does not belong to a subsystem\n","Component ID B1044.003 does not belong to a subsystem\n","Component ID B1044.011 does not belong to a subsystem\n","Component ID B1044.011 does not belong to a subsystem\n","Component ID B1044.011 does not belong to a subsystem\n","Component ID B2011.201a does not belong to a subsystem\n","Component ID B2011.201a does not belong to a subsystem\n","Component ID B2011.201b does not belong to a subsystem\n","Component ID C1011.001a does not belong to a subsystem\n","Component ID C1011.001a does not belong to a subsystem\n","Component ID C1011.001a does not belong to a subsystem\n","Component ID C2011.011b does not belong to a subsystem\n","Component ID C2011.011b does not belong to a subsystem\n","Component ID C2011.011b does not belong to a subsystem\n","Component ID C3011.001a does not belong to a subsystem\n","Component ID C3011.002a does not belong to a subsystem\n","Component ID C3011.002a does not belong to a subsystem\n","Component ID C3032.001a does not belong to a subsystem\n","Component ID C3032.001a does not belong to a subsystem\n","Component ID C3032.001a does not belong to a subsystem\n","Component ID C3032.001b does not belong to a subsystem\n","Component ID C3032.001b does not belong to a subsystem\n","Component ID C3032.001b does not belong to a subsystem\n","Component ID C3032.001c does not belong to a subsystem\n","Component ID C3032.001c does not belong to a subsystem\n","Component ID C3032.001c does not belong to a subsystem\n","Component ID C3032.001d does not belong to a subsystem\n","Component ID C3032.001d does not belong to a subsystem\n","Component ID C3032.001d does not belong to a subsystem\n","Component ID C3034.001 does not belong to a subsystem\n","Component ID D4011.022a does not belong to a subsystem\n","Component ID D4011.022a does not belong to a subsystem\n"]}],"source":["%reload_ext autoreload\n","%autoreload 2\n","\n","os.chdir(model_dir)\n","build_input('simulated_inputs.json')\n","os.chdir(main_dir)"]},{"cell_type":"code","execution_count":14,"id":"71f870d8-bd26-4f66-a196-e3e9c06dfaff","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"71f870d8-bd26-4f66-a196-e3e9c06dfaff","executionInfo":{"status":"ok","timestamp":1674609740321,"user_tz":480,"elapsed":9082,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"05803b87-eb3a-46e7-fb0f-c4779f1a7a2b"},"outputs":[{"output_type":"stream","name":"stdout","text":["time to run 8.95s\n"]}],"source":["%reload_ext autoreload\n","%autoreload 2\n","\n","run_analysis(model_name)"]},{"cell_type":"markdown","id":"bdf2d49d-6936-43c7-83d3-a407ea309bcf","metadata":{"id":"bdf2d49d-6936-43c7-83d3-a407ea309bcf"},"source":["## Outputs"]},{"cell_type":"code","execution_count":15,"id":"a99fae91-4439-4d62-ac31-20c3384a6729","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a99fae91-4439-4d62-ac31-20c3384a6729","executionInfo":{"status":"ok","timestamp":1674609740321,"user_tz":480,"elapsed":18,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"b3f53ea8-d315-42e2-c130-34f814237401"},"outputs":[{"output_type":"stream","name":"stdout","text":["utilities ()\n","impeding_factors ()\n","worker_data ()\n","building_repair_schedule ()\n","recovery ()\n"]}],"source":["with open(f'./outputs/{model_name}/recovery_outputs.json') as f:\n","    outputs = json.load(f)\n","    \n","for key, item in outputs.items():\n","    print(key, np.array(item).shape)"]},{"cell_type":"code","execution_count":16,"id":"2491e900-a086-47b9-99ae-aa81db8aff37","metadata":{"id":"2491e900-a086-47b9-99ae-aa81db8aff37","executionInfo":{"status":"ok","timestamp":1674609740523,"user_tz":480,"elapsed":219,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}}},"outputs":[],"source":["from plotModel_PBEErecovery import plot_results"]},{"cell_type":"code","execution_count":17,"id":"b17eabe1-fc69-45b5-a9cd-f362b5087a1f","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"b17eabe1-fc69-45b5-a9cd-f362b5087a1f","executionInfo":{"status":"ok","timestamp":1674609758215,"user_tz":480,"elapsed":17693,"user":{"displayName":"Adam Zsarnoczay","userId":"08695679488835570500"}},"outputId":"ffdb3f20-15b8-45a6-d98e-6582e9898bda"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.font_manager:findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n","WARNING:matplotlib.font_manager:findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x576 with 2 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1HaZH+iHMzcE6zENCBf8bi2uvyZh2+R+mx7MV5nviy4eYnu7C9rIB9wWYG54/ORiIxZZS518wNy5zVLW0YWtHJaJgIqXD4XBUSkSkD/CWqgYOg1VZRGQo0EhVyzp0WykRkRRMsBAZMB+tPM6djAl+Gqnq9vI8d0UgxlblQ1UdWdF1cZQd15PlcDgqFXYYpa8d7mqKGab7sqLrdTSI8RvrYIe9umOGLKu0popEzBzRezB2MV4IsLph7CLK3BPoqBy4IMvhcFQ2BDMslY0ZLlyMGeKtytTBzMvahflH+TLmqTHHYWInmW/HPGl3RPPkqhIiMgZjxXKXHcJ3hAARGSXGSHhBMekixvR2hYjME5HOZSrXDRc6HA6Hw+HwMvahhp0YI+N2RaT3Be7A2OechDG0Pam0cl1PlsPhcDgcDk+jqj9hHuApjn6YAExV9TcgTkQal1auW3zyMBAR1+3ncDgcDs+hqmX1N6uuNMX/if31dt/Gkg4qU0+WiOSJWTg0TUTmiP+CoxPtY9rfBhzTQsxiwStE5BPxWRTUpl8qZnHQggVrG4hx5N4pIiNKqEt9MQvWLrd/69n9V9tx0vliFvvs6HNMiWOtlYHevXuzZMkSli9fzqBBh9oCJScnM3nyZNLS0pg6dSpNmx40Sc/NzSU1NZXU1FS+/rpqTvNw+p1+r+r3snYoXX9SUhJTpkxhzpw5pKWl0adPHwAGDBhQqD01NZW8vDw6dux4yPGVnZiYGFJSUkhJSaFevXqHpEdHR5OcnEybNm2oXdt/MY34+HiaN29O8+bND0mrykR3ul2D/RKziPksn9ffy0NLmeZkichOVa1t3/cGHlLVXnb7LIwZ482qeoHPMZ8CX6jqxyLyFpCmqm/atDoY35cawO2qOstOZuyEWYy1nareXkxdXsCYSj4vIoMx7uaDbOC3WFWz7SPfQwrGS0sbay0rBT1ZUac9eqRFFElYmDD/wzs5/+4xpGduZ/o7NzPwic9YsjqzMM8HT17O+F+X8cHEufTq3ILr+nbihqe/ACBz0sMk9H4mqHUqir3Tnwq6dnD6nf7Kr3/v9KcAb3/3AaIuCa57QFiYMH/EZZz/xETSs3Yx/YWLGDhsGkvWbyvMM+KWU0n7M4t3Ji2hbbM4vnrkXNre4m9zd0JyPT4dfDYn3PpZUOtXwN4vbyRtbfDnnOfl5XHn9Zfw6NA3qJ+QyIO3XcudDz9LUvODPr6bN21gz+5djP30P3Q9pScn9zwbgNm//cy4Lz7i4ede48D+Awy59+889uKbxNQKfrDVMblOufZkRXe6PeijRntSR5RlhY8UjHFvUXOy3gamqepHdnspZt3do+/JCqAu5qkfAFT1B4xRnG9lBLNK+ed21xjMquIFPIUx09vrU84uVZ3uu68Y+tny/MpV1V991pj6DZ8lLIobaxWRm0TkD9tD9z8xrs7lTrfjmrEyfSurN2ZzIDePz36YzwWntfXL0zalIT/OWQXAj3P+PCS9KuP0O/1e1e9l7QDdWiewcuN2Vmfs4EBuPp9NX8UF3f1XL1KgbowZCImNqcHGrbsPKefy01vy2fRV5VHloLJi6UIaNUkisUkzIiMjOfWMc5n1yzS/PA0bNaF5yzZImH+MsH7NnxzfoRPh4RFERUeT3LINc/840sUzKhkSFvzX0TMWuM4+ZdgDs4ZkiQEWlD3IirbDhUuwy7OUkr8BsM3HnK5g7BL72GOSqo4r47kDSfQRtgn/hTULuAGzHEJpfKGq3VS1I+Yx8bIutxFUmiTUYf3mnMLt9MztNI2v65dn/opN9Ot5PAD9eh5H3VpR1K9rDNujakQw/Z2b+fGtm7jw9Kr3A+z0O/1e1e9l7QBNGsSwPmtX4XZ61m6a1q/ll+eZT+ZwZc9WrHjnSr585FzuGTnjkHIuO7Uln1bBIGvrls00aHjwX1j9hESysjJLOOIgKa3aMPePGezbu4ftOdksnDuLrMyMUFW12iMiHwEzgGNFZL2I3CAit4jILTbLeMz6ryswqxfcWkxRfpR14vseVT3RVuRk4H0RaaeH6f9gDeSGYdZgOmpUVQMno4vImZhg6bSij/KjnYg8DcRhVn6fFIx6hYIH/zWJV+4+n2v6dOKXtNWkb84hL99IP7b/MDZs2UFK43pMHH49C1Zm8OeGohaOr7o4/U6/V/V7WTvA5ae14r9TlzN87AJOOqYh797Ziy53fUHBf59ubRLYvS+XRWurl+7S6Nj1ZFYsXcTDd/6NurH1OOb49oSFhVd0tYKDlP8ce1UtcakxG+/cdrjlHvbThao6Q8yCtgmYtZyKIgvzeGOE7c1qhlnjrQ5mztU0M6JII2CsiFykqrPKWIUMEWmsqhvt45OFdRCRDpietj52Da/SGA1crKppInI9cEZRmewEuZBNktuQuYNmDQ8uXdU0oS7pW/xNjDdm7eDKR8yazLWia3Bxr+PJ2WlGVjdsMaO1qzdm89Pc1Zx4TOMq9UPr9Dv9XtXvZe0AG7J206zBwZ6rpg1iSN+6yy/PwLOOod9T5v535rLNREWGE183iswc0wb9T6uavVgA9eMbkrX5YO/T1swMGjRIKPPxl159A5debQZgXn3mIRo3Sy7lCEd5c9gDlWJWUg/HBFJFYiO+qRxcrX0g8LWq5qhqvKqmqGoKZu7U4QRYYMZFC9b7Goh1TbbrWH0BXKuqy8pYVh1go4hEUsQiuz56/q2qXQ+jjofFrCXptG5Wn+aN44iMCKf/We0ZN91//eUGsTHYwJT7rzmdMeNTAYirHUWNyPDCPCe3S2bx6rJ1N1cWnH6n36v6vawdYNaKTFo3rkvzhrWJjAij/2ktGffHWr8867bs5IwOTQA4tmksUTXCCwMsEbj0lBZVcj4WQOtjj2dj+joyNqZz4MABfpn2HV1P6VWmY/Py8tiRYx4QWLNqOWv/XEHHroe7FnslpXLOyToiytqTFS0ic+17waxwnwcgIj8DbYHaIrIeuEFVJwGDMKvEP41ZGuPd0k4iIqsxE+triMjFwLmqukhERmIWiJ0FPA98KiI3AGuAy+3hj2Hmgr1hf5ByCwIjO9Z6BhBv6/i4qr6LWUV9Jmb19ZmYoKvcycvL5+5XxvHNy9cRHhbGmHFzWLw6k0dv+AtzlqQz7pel9OyUwpN/PwdFmZ62hruGGceMtikJvH7fReSrEibCSx/87PdkUlXA6Xf6varfy9oB8vKVu0fO4JvHziM8TBjzwzIWr9vGo1d2Zs7KLYz7Yy2DR//OG7eexh0XnoAq3PT6z4XHn3Z8I9Zn7WJ1RtVcbSY8PIIb7niAZwbfTn5+Hmee14+klFZ8PPpNWh1zPN1O6cWKJQt5cch97Nq5ndkzfubTMW/zyrufkZeXy6N33whATEwt7hj8FOHh1cT6sgKGC0OFW1bnMAiVhUNVIVSP8FcVnH7v6g+VhUNVIVQWDlWFUFk4VBXK3cKh2z3Bt3D4Y1iFRG4uyDoMnOO7w+FwOLxIuQZZ3e8LfpD1+0sVEmSVqW9RRPKA+ZihwjyMgeivNm0i0AOYHmBG2gL4GDOENxszV2q/T/qlGB+tbtaMtEHBNjC6BDPS+phV7FOA1cDl1oD0aswQpWB8u/6hqmn2mFHABcBmX5MxEZkG3HeYc8JCcjd7TvfWvHRnX8LDhNHfzuGlD372S09OjOWtBy8hPi6G7O17+NtT/yM900yQ3TltCAtWmcmT6zJy6P/gh0GvH4S2J8Ppd/ors/5Q9mRVdu0Q2p6sczo15aW/9SA8LIzRk5fy0pfz/NKT4mvxzh09ia1Vk/Aw4dH//sGkOeu5smcr7urXvjBf++b1Ofm+r5i3uqTl546MUPZkpf7+K++98RL5+Xmc1ediLrnqr37pi+bNYfQbL7Fm1QrueuTZQjNSgP++8xpzZk4H4NKrb+TUM88NSR07JpfzTJpqNFx4JBYOvYHngILZeS9iHd8DjhkKvOLj+H4D4Ov4fidmHlQBezFzpNrZV3EMBn7wcXwfjAmu/gR6+Ti+/xuzUjaYpwhHAO+XUW+5EhYmvHrPBX6uz9/+ssRvfsVzt/Xmg4lzC12fn7z57ELX5z37DtDjb29WVPWPGqff6feqfi9rB6v/plP8HN+//WOtn+P7oMtO5H+//nmI4/vHP63k459WAgcd30MRYIWSvLw83n39eT/H966n9PJzfI9v2IjbHniCsZ/+x+/Y2b/9zKrlS3jx7Q8LHd87dT8lJI7vjiPH047vlmut0eoCEeleyrlDguddn51+p9+j+r2sHZzju3N8L4Zq9HSh1x3fAWJsL92twKgjrNNR4XnXZ6ff6feofi9rB+f47hzfqz9ed3wH+MiW9ZOI1BWROFXd5psh1GakZcHrrs9Ov9PvVf1e1g7O8b04nON71cDrju9geqNL2kZV/w38O1RPF3re9dnpd/o9qt/L2sE5vjvH92KowOG9YON1x3eAK+zxp2FW1c4pJX/Q8bzrs9Pv9HtUv5e1g3N8d47v1R+vO74D7BWRVCAS+FsZ2yOoeN712el3+j2q38vawTm+O8f3YqhGw4XOjPQwcI7v3nX8Bqffy/qd47tzfHeO7+VoRnrqw8E3I/3lmcrr+F7JzEj7A0OA44DuBUONJR0vIs8A1wH1VPWITURC6fjeu3dvhg8fTnh4OCNHjmTo0KF+6cnJyYwaNYqEhAS2bt3KNddcQ3p6OgC5ubnMnz8fgLVr19KvX79QVTNkOP1Ov1f1e1k7lK4/KSmJMWPGEBcXR3h4OIMHD2bChAkMGDCA+++/vzBfhw4d6Ny5M2lpaeUt4aiIiYmhYcOGAOTk5JCd7T+nLjo6moSEBGrWrMnGjRvZuXNnYVp8fDy1apk5bVlZWX5pwaZcg6zTHg1+kDX9qUodZO0sCE6sGelDqtrLbp+FNSMNCLI+Bb7wMSNNU1VfM9JxQA1MwDZLRGoBnbBmpCUEWccB+cDb+Li1l3S8iPTADC0uD0aQFey72bAwYf6Hd/oZEg584jO/rv8Pnryc8b8uKzQkvK5vp0JDwsxJD5PQ+5mg1qkoQtWT4fQ7/ZVdf6h6sqqCdghdT1ZYmDB/xGV+ZqQDh03zMyMdccuppP2ZdYgZqS8FZqQn3PpZUOtXQKh6svLy8rjz+kv8zEjvfPhZPzPSzZs2sGf3LsZ++h+6ntKz0PF99m8/M+6Lj3j4udcKzUgfe/HNkJiRlntP1umPBT/I+vnJCgmyqpwZqaouVtWlRewv9nhV/c3HW8u3nheKyEwRSRWRySJSlOdWyPG8IaHT7/R7VL+XtYMzI3VmpNWfqmhGGkymAz1UtRNmaPOBiqiE5w0JnX6n36P6vawdnBmpMyMthmrk+F6lzUiDQDPgE+u3VQOz/mGlxOuGhE6/0+9V/V7WDs6MtDiqtRlpNaIqmpEGk9eBYao6VkTOwEyoP4RQO7573pDQ6Xf6Parfy9rBmZE6M9JicGakFWpGGkxiMcFfQR2LRFX/XeC5FQo8b0jo9Dv9HtXvZe3gzEidGWkxhEnwXxVElTMjFZFLMD1QCcA4EZmrqr1LOf4FYAAQY+s4UlWHYHquPhORbGAK0KKM7RFUPG9I6PQ7/R7V72Xt4MxInRlp9ceZkR4GzozUu2aU4PR7Wb8zI3VmpM6MtBwtHP7yTPAtHKY8XHl9shyGUJqROhwOh8NRWXFB1pFRpjlZIpJnLRzSRGSOiJzikzZRRLaJyLcBx7SwHlQrROQTEakRkH6piKiIFKwv2EBEporIThEZUUJd+ovIQhHJLzi2tONF5BkRWSciOwP2jxaRy6gE9O7dmyVLlrB8+XIGDRp0SHpycjKTJ08mLS2NqVOn0rRp08K03NxcUlNTSU1N5euvvy7PagcNp9/p96p+L2uH0vUnJSUxZcoU5syZQ1paGn369AFgwIABhdpTU1PJy8ujY8eO5V39oyYmJoaUlBRSUlKoV6/eIenR0dEkJyfTpk0batf2NxqNj4+nefPmNG/e/JC0Ko1I8F8VxJFYOPQGngMKZue9iHV8DzhmKPCKj+P7DYCv4/udwEyf/HuBR7GO7SXUZQHwfxjHd19KOv4bYASwvESVZSQUrs9vvOvr+nwXk9Kb+s2veOXJy/no12V8MPFHenVuwQvvflvo+rxnXy4n3/FFyOpXQCgdv51+p78y60N9LXYAACAASURBVA+l43tl1w6hdXx/w8/x/X4m7e7m5/j++C2n8uWyLN6ZNMc4vo/+hLa3fMoXe+CLJ2cDBY7vLVna8g6iWhZ3tiPHOb7XCXqZJeLlpwupRo7vlrNFZJaILBORC4rJE1I87/rs9Dv9HtXvZe3gHN+d43v1x+uO7wApQHfgfOAtEYkq7wp43vXZ6Xf6Parfy9rBOb47x/di8PhwYXVyfAf4VFXzgeUisgpjRzG3lGPKHa+7Pjv9Tr9X9XtZOzjH9+Ko1o7v1Wi40OuO72B6o0vado7vIcbpd/q9qt/L2sE5vjvH9+qP1x3fAfqLSJiItAJaAkXN93KO7yHE6Xf6varfy9rBOb47x/di8OBwYXV1fAdYC/xuj7tFVUuceB8KPO/67PQ7/R7V72Xt4BzfneN79ceZkR4GzvHdu47f4PR7Wb9zfHeO787xvRzNSM8bFnwz0on3VF7HdxHJA+ZjerHygNtV9VebNhHoAUxX1Qt8jmkBfIx50nA2cK2q7vdJvxRj8dDN9lA1KNgGRqvq7cXUpT9mzcHjgO4FQ40icg7wPFAD2A/cr6pTbNozwHVAPVWt7VPWaOBbVf2cMhBKx/fevXszfPhwwsPDGTlyJEOHDvVLT05OZtSoUSQkJLB161auueYa0tPN2ta5ubnMnz8fgLVr19KvX79QVTNkOP1Ov1f1e1k7lK4/KSmJMWPGEBcXR3h4OIMHD2bChAkMGDCA+++/vzBfhw4d6Ny5M2lpaeUt4aiIiYmhYcOGAOTk5JCd7T+nLjo6moSEBGrWrMnGjRvZufOgp3Z8fDy1apk5bVlZWX5pwaZcg6w+rwQ/yJpwd4UEWdXJjHQLcKGqbhCRdsAkrG0Ezow0KHjZjBKcfi/rd2akzozUmZE6joTqZEaaqqob7OZCzDyymjbNmZFWYpx+p9+r+r2sHZwZqTMjLQYJC/6rgqiuZqSXAnNUdV8Z8qbgzEgrFKff6feqfi9rB2dG6sxIqz/VzoxURE7A9JKdW8ZDnBlpFcDpd/q9qt/L2sGZkRZH9TYjrTjLhWBTrcxIRaQZ8CVwnaquLONhzoy0gnH6nX6v6veydnBmpM6MtBiqkeN7tTEjFZE4YBwwWFV/OYxDnRlpBeP0O/1e1e9l7eDMSJ0ZafWnOpmR3g60Bh4Tkcdskeeq6mZnRlq5cfqdfq/q97J2cGakzoy0GKpRT5YzIz0MnBmpd80owen3sn5nRurMSJ0ZaTn6ZF34RvB9sr65tfKakToMoTQjdTgcDoejslKuQdZFbwY/yBr7jwoJso66T05E8qy9Q5qIzBGRU3zSJorINhH5NuCYFiIyU0RWiMgnIlIjIP1SEVER6RqwP1lEdorIfcXUpchyRaSm3V5h01Ps/u627gX1v+Ro2+NI6d27N0uWLGH58uUMGjTokPTk5GQmT55MWloaU6dOpWnTpoVpubm5pKamkpqaytdff12e1Q4aTr/T71X9XtYOpetPSkpiypQpzJkzh7S0NPr06QPAgAEDCrWnpqaSl5dHx44dy7v6R01MTAwpKSmkpKRQr169Q9Kjo6NJTk6mTZs21K7tbzQaHx9P8+bNad68+SFpVZpq5JN11D1ZIrKzYKka6wb/kKr2sttnYd3gA5bc+RT4wscNPk1Vfd3gx2GWx7ndd1K8iHyOefpvpqq+VERdiixXRG4FOqjqLSJyJXCJql4hIjHAflXNFZHGQBrQxMffK7D8kAwXhoUJ8z/0dX2+mYFPfOY3v+KDJy9n/K/L+GDiXHp1bsF1fTsVuj5nTnqYhN7PBLVORRFKx2+n3+mvzPpD6fhe2bVDaB3f5/s5vl/EwGHT/BzfR9xyKml/ZvHOpCXG8f2Rc2l7y6d+5RjH97M54dbPglq/ApzjezkPF/Z7O/g9WV/fXDV7sgIIiRu8Pe5i4E+Mm/shlFJuP7uNTT9LRERVd/sEVFEUYd9QHnje9dnpd/o9qt/L2sE5vjvH92IQCf6rgghGkBVyN3gRqY15WvGJIynX/l0HYNNzbH5E5CQRWYhZAPuW4nqxQonnXZ+dfqffo/q9rB2c47tzfK/+BON5z/Jwgx+CWWx6pwQ5IlXVmcAJInIcMEZEJlSEjUNpeN312el3+r2q38vawTm+F0f1dnyvPhYOQTXVCJUbPHAScJn1u4oD8kVkr6qOKEO52L9JwHoRiQBiCTBTVdXFIrLT1sHPHNU5vocWp9/p96p+L2sH5/juHN+LoRotqxPUcDFUbvCqerrP/leBZwMCrGLLte/H2m1s+hRVVfs0YoSte3OMqerqIursHN9DiNPv9HtVv5e1g3N8d47v1Z9g9GSVixt8cYjIeOBGVd1QQrnvAv8RkRXAVuBKu/80YLCIHADygVtVdcuR1uVI8bzrs9Pv9HtUv5e1g3N8d47vRRPsaUEViTMjPQyc47t3Hb/B6feyfuf47hzfneN7+Vk41LrsvaAHJrs+/2vVdHwXkTzMk3kC5GG8rX61aROBHsD0AJ+sFsDHmCf8ZgPXqup+n/RLMVYL3QJ8spKBRcCQYnyyiixXRHpihhk7AFeq6uc+xxRZx2K0hiwi7d27N8OHDyc8PJyRI0cydOhQv/Tk5GRGjRpFQkICW7du5ZprriE93Uw5y83NZf78+QCsXbuWfv36haqaIcPpd/q9qt/L2qF0/UlJSYwZM4a4uDjCw8MZPHgwEyZMYMCAAdx///2F+Tp06EDnzp1JS0srbwlHRUxMDA0bNgQgJyeH7Gz/OXXR0dEkJCRQs2ZNNm7cyM6dOwvT4uPjqVXLzGnLysrySws2Lsg6MrxiRpqC8fC6DxgbEGQVWcditDozUo+aUYLT72X9zozUmZE6M9JyDLL6hyDI+qxigixPmJGq6mpVnYeZd+VHUXUsbzxvSOj0O/0e1e9l7eDMSJ0ZafXHK2aklRrPGxI6/U6/R/V7WTs4M1JnRlo0IhL0V0XheTPSqoLXDQmdfqffq/q9rB2cGWlxVGcz0ur0f94rZqRHhTMjDS1Ov9PvVf1e1g7OjNSZkVZ/vGJGelQ4M9LQ4vQ7/V7V72Xt4MxInRlp0bjhQn8qvRmpiHQDvgTqAReKyBOqekIpdSw3PG9I6PQ7/R7V72Xt4MxInRlp9ceZkR4GzozUu2aU4PR7Wb8zI3VmpM6MtPwsHGKv+k/QA5Ocj66tmj5ZXiKUZqQOh8PhcFRWyjXIGhCCIOvDigmyyjQnS0TyrE1DmojMEZFT7P4TRWSGiCwUkXkicoXPMS1EZKaIrBCRT0Skht3f05aRKyKXBZxnoohsE5FvS6jLPSKyyJ7vBzELOxekDRSR5fY10Gf/MyKyTkRCZ4d7lPTu3ZslS5awfPlyBg0adEj6sGHDSE1NJTU1laVLlxa6Anfs2JFff/2VBQsWkJaWxuWXX17eVQ8KR6ofYMKECWRnZ/PNN9+UZ5VDRmltAdC/f38WLlzIggUL+OCDD8q5hqElJiaGlJQUUlJSqFev3iHpsbGxNG/enOTkZJKSkqhRo0YF1DJ4lKZXRGjcuDEpKSkkJSUREWGGhMLCwmjWrBmtW7cudAyvjpTWPtUdr+uv6pSpJ0uKcXUXkWMwc86Xi0gTzFI2x6nqtlC5r4vImRjH990i8g/gDFW9QkTqA7OArhj/utlAF1XNFpEewBpgeYGOI6GgJ+v4h4I7ZStMYNzdp3HTe7PJ2L6XT/7Rg/s/mcfKzF1F5h/QI4njmtTl0S8W0rxBDAqszdpNQp2afHZbDy589Rd27M0t8tijYdGzvWn/6PdBLzdM4Js7T+HvY1LJ2L6Xj27uzqDPFrCqGP1XnZRE28Z1ePyrRQCc1LIeUZHhXNa1KXd8ELolNeY/dQ7nv/V7yMoH0xZvX9GeR8YtI2vXfl75v+N5YfJK1m076M3bpG5NBp3Tioe+Wcqu/XnERkWQE4LPO5Bxt3Rn3ILQ+vDk5+Xx7O0DuOXxV4hrkMArD9zEtfc8TqOkFoV59u7eRVSMeSJtwe/T+WXil9z82Mshrdf57YyX0a79h/gZHxV5eXlcfP55vPnOKBIbJXL1Ff157sWXadWqdWGeTz/+kGVLl/LI408wcfw4pv4wmaEvv8Ke3btZsmQxK5YvZ+WKZQx++LGg1s2XWjXM/fieA+XbmZ+Xl8dF5/fm7XfeIzExkQFXXMbzLw6jVevWpR8cRKIjpdy1Q+XSX549WXFX/zfojb3tg2sqb09WAIWu7qq6TFWX2/cbMLYNCaF0X1fVqapaYPn7G8aqAaA38L2qblXVbOB74Dx7zG+qujGwLBG50Pa2pYrIZBFJDMxTHrRvFsu6rbtZn72HA3nK+HmbOPO44u9M+3ZozPg0I2dN1m7WZpnmyNyxj60791OvVtW6s2/XLJa1W/eQnr2H3Dxl4vwMzmxb/GPMfdonMmH+psLtmauy2bUvrzyqGnKOaViLjdv3kbFjH7n5yk8rttIjxf/utfdxCYxbuJld+43m8giwyou1KxYT37gp8Y2aEBEZSafTzmLB79P98hQEWAD79+01j5hVURbMn0dScjLNkpKIjKxB7z59mTblB78806b8wIX9zKIYZ5/bm99nzkBViY6JoVPnLtSsWbW+74fDgvnzSEpqbtqnRg3O63s+06b+UPqB1QSv668OlPVRhIInCKOAxpgAyg8R6Y5Zb3Al5ee+fgMwwb5vCqzzSSvLOacDPVRVReRG4AHg3qDXshQS60axMedgT0XG9r10SIotMm/juCia1Y9m5qqth6S1b1aXiHBhXRHLTlRmEuvUJCNAf/tmxeiPjaJpvWh+L0J/daBBTA0ydxaulc6WXfs5tqG/A3aT2CgAXujXljARPpydzpx1/t5KVZVtWZnENTh4gxHbIIG1yxcfkm/6hC+YNvYT8nJzufWJV8uzikFl8+YMEhs1LtxOTGzEgvlpAXk208jmiYiIoHbtOmzbts0TQ0ebMzJo1LhR4XbDxETmz5tXgTUqX7yq34tmpCW6uotIY+A/GPuG/PJoIBG5BjM0WDZTkaJpBnxi618DszZipaZv+0Z8tyCD/IDO1Pg6NXjusvY89L8FVOdnGc5rn8j3Czcfot9LhIcJTWKjePCbpcTXiuT5i47j9s8WFPZseYHT+vwfp/X5P2b/9D3fff4+V//z4YquksPhCBLVKcg67OFCVZ0BFLi6IyJ1gXHAw6r6m81W6L5ut4Pmvm7PeTbwMMasdJ/dnQ4k+WQryzlfB0aoanvgZkxPXVHn+7uIzDq6WhdPxva9NI49eOrEulFk5OwrMm+fDo0YP89/5LNWzXDevK4zr32/gnnrcoo8rjKTsWMfiQH6N28vWv957Rv5DRVWN7J27yeh9sHhn/haNcjadcA/z679zFyzjbx8JWPHfjbk7C3s3arqxDVIYFvWwcUicrIyia0fX2x+M5z4c7HplZ2GDRPJ2HTw+5yRsYmEhokBeRqyyebJzc1l584dxMXFlWs9K4qGiYls2njw+745I4PExAqZ1VEheF1/deCwgyxfV3f7xOCXwPu+E9hD6b4uIp2AtzEBlu/SPZOAc0WknojUA861+0oiloOB2MDiMoXa8X1B+naSG8TQtF40keFC3w6NmLrk0FWJWsTHUDc6krlrDwZSkeHCa1efyNjUDXy3sGouDrowfTvN60fTNC6KiHDhvPaJTFtyqKliSnwMdaMiSKuCgWRZWbZ5F01ia5JYpwYRYULP1vWZucZ/mZQZq7Np37gOAHWjImgSG8Wm7XuLKq7KkdS6LZkb15OVsYHcAwdInf4DJ3Q7zS9P5oaDswIWzZ5BfONmgcVUGU5o1561a9eQvn49Bw7sZ9KE8Zxxpv9sjF5n/oVvvv4KgMnfTaLbST2q1Z1+SZj2Wc369es4sH8/E8ePo9eZh8xWqbZ4Vb8XHd+LdHUXkauAnkADEbnepl+vqnMJovu6iDwJzFLVscCLQG3gM9twa1X1IlXdKiJPAX/YejypqlttuS8AA4AYW+5IVR2CWXj6MxHJBqYABx9hKkfy8pVnvlnCv6/vTJgIX85JZ+XmXdx+VisWpm9nqg04+nRozIR5/r04vds1oktKPeJiIrm4s1l64uH/LWTJxqpjnJeXrzw7bilvXteJ8DDhqzkbWJm5i1v/0pJF6duZtnQLAH3aN2JiEU+3jb6hCynxtYipEc73957G418v4tcVVXPOVr7CW9PX8mTfYwkT+H7pFtZm7+Xqrk1Ynrmb39dsY8667XRuFssbl7cjP19577d17KgmE//DwyO49Ma7efvJe8nPz+eks86ncXILJnw0kqRWbWnX/TR+nvAFy+bNIjw8gpjadRhwR9UdKoyIiGDQQ49y6803kJ+XT79LLqVV6za8MeI1jj+hHWec+Rcu/r/LeOTBB7ioz7nUjY3l+ReHFR7f99y/sGvnLg4cOMDUKT/wxr/f9XsysaoTERHBgw8/xj/+fiP5+XlcfMmltG7dpqKrVW54XX91wJmRHgahsnCoKoTKwqGqUB4WDpWZ8rBwqKyEysKhqlBRFg6VhYqycKgslLeFQ4OBHwW9sbPGXFV5Hd9FJA+Yj+nFygNuV9VfReRE4E2MrUMe8IyqfmKPaQF8jHnScDZwraruF5GemEWeOwBXBvhkTQR6ANNL8Mm6B7gRyAUygb+p6hqbNhB4xGZ9WlXH2P3PANcB9Xx9skRkNPCtbx1KaYcK+5a9++67XHDBBWzevJn27dtXVDUqjOquv3fv3gwfPpzw8HBGjhzJ0KFD/dKTk5MZNWoUCQkJbN26lWuuuYb0dDPSnZSUxMiRI0lKSkJV6du3L2vWrKkIGUdMafqHDRvGmWeeCRhzxoYNGxY+XTd06FDOP/98wsLC+P7777nzzjvLvf5HQ4EegJycHD+jXYC4uDhiY83Ttnl5eWzatInc3FwiIiJo0sT0XosI27ZtIyen+g2ll9Y+1Z3Kor88g6z46z8O+v/aLaOvrNRBVrU0Iz3SICvqxNvKkj2onNq5Fbt272PkU9fRtf+z5X5+gL1z/1Uh2qES6T/5waCXGxYmzP/kHs6/cxTpm7cz/d1bGfj4JyxZfXBe3gdPX8X4X5bwwYRUenVpyXXnd+GGJz8DYNKIGxk6ZhpT/lhBrega5Ocre/YdKO50R8zeGc/R9B9fBr3cMIGfhpzDgNd+YeO2PYwbdAa3jZrF8k1FD3n/9YyWnNAslvv+m0qXlvV55JITuHSYmfz+5b09ef7rRcxYviWodUx/8xIAfl+1Lajl5uflcd+NlzH42RHUj2/IY/8cyG2Dn6Zp85aFeRalzaLVse2oGRXF5G8/Z/G8Odzx0LPkHjiAqhJZowZ79+xm8M1X8fgrI6nXoHiPuSOle0sz0d6ZkZYvlUm/C7KODE+bkVrOFpFZIrJMRIoM7CoDv8xZydacquV/FUyqs/5uxzdj5fosVm/I5kBuHp9NnscFpx/nl6dtSkN+nL0KgB9nrypMb5vSkIjwMKb8sQKAXXv2hyTACiUnptRjdeZO1mbt5kCe8vXs9ZzbsVGx+ft1bcrXs9YDoKrUjAynRkQYNSLCiQgXMndUnYcAVi5dSGLjZjRs3JSIyEh69DqX2TN+8stzfMeu1IwyT4+2btuerVtM8B0RGUmkXVLowIH9qFa/oUyvm3F6Vb+XJ75XNzNSgBSgO9AKmCoirVW16vxKO6o8TRJiWZ9xcJgnPTOH7scn+eWZv2IT/c44gX99+iv9ep1A3VpR1K8bTZvkBmzbuZePn72a5k3qMfWPFTzy5iTyq5CRWOO4aDZm7ync3pS9l04pRRttNq0fTVKDWvyy1DwMMufPbH5dlsns5/ogAqN/XMWKTZV2idJDyM7KpH7CwUfy68c3ZOXShcXm/3HSWDp2PblwOyszg5cevZuMjeu46oZ/hqQXqyLxqhlnAV7VX52eni1rT9YeVT1RVdtieofeF59W8DEj/auW0+2Ujxnpi0dZ1Keqmm975FZhnm50OCoVD44Yz+kntmDG6Ns5vVML0jfnkJevRISHc2rHFAaPGM9pN7xBiyb1ubZv54qubsjo16UZ41M3FJrRpiTUok2jOnR7eCJdH5rIqcck0L1Vg4qtZIiY/sMEVi1fzPmXXVu4r0FCIs+99SEvj/qCnyePIyc7qwJr6HBUbUTkPBFZKiIrRGRwEenJIjLVLsU3T0T6llam181IwczfKmk75GakDm+zITOHZokHlxFqmhBLeqb/Mjkbt+zgyoc+4OTrR/D4298BkLNzL+mbc5i3fCOrN2STl5fP2J8XceKxoeg0Dh0bt+2hcb3owu1G9aLYmLOnyLwXdW3KV3aoEOC8jo2Z82c2u/flsXtfHlMXZtClZdVZbqZegwS2Zh58YnPrls1F9kYtmPM7Yz9+j3uGvFQ4RBhYTrOUVixdMPeQtKqM1804PatfQvAq7ZQi4cC/gD7A8cBVInJ8QLZHMB0znYArgTdKK9frZqQA/UUkTERaAS2BpYEZQm1G6vA2sxan07pZPM0b1yMyIpz+Z3dg3HT/9foaxMYUdqHff10vxnw72x67ntjaUcTHmfUNz+jSiiV/HmpkW5lJW7ONFg1rk9QghshwoV+XZnw/71BX/1aJtYmNqcFsn3Ur07P30KNNA8LDhIgwoUebeJZXoeHClscez6YN69i8KZ3cAwf47cfv6NzjdL88q1csZdTrz3HPkJeIjatfuD8rM8MskA3s2rGdZQvn0rhZ83Ktf6jxqhlnAV7XX850B1ao6ipV3Y9xR+gXkEcx89LBmJlvKK1Qr5uRAqwFfrcNd0tlnY815rnrOb1LG+LjarNi4lM89dZ4xnw1o6KrVW5UZ/15efncPWws37zyV8LDhTHfzmbxn5t59MazmbNkPeOmL6Fn55Y8ecu5qML0uX9y18tjAcjPVx4cMYHxr/0NESF1STqjxv5RyhkrF3n5yqOfzOOD208hLEz4ZMYalm3cwX0XtCVtzTa+t8so9evajLE+vVgA4+akc+ox8Ux+5C+oKtMWbWZyFVp2KTw8goG33s8LD/+T/Px8ep17Ic1SWvH5+2/Tos1xdDm5Jx+NfI29e/bw2jPmydYGCY2494mX2bBuNR/+ezgioAp9L72GpBbVx4gUnBmnV/VX0JysouZ1nxSQZwjwnYjcAdQCzi6tUGdGehhUpIVDZaAiLRwqA6GycKgqhMrCoSoQKguHqkJFWThUFpwZaflaODS66fOgN3bGyP43A3/32fVvVf13wYaIXAacp6o32u1rgZNU9XafPPdg4qaXReRkTOdRu5Lmorsg6zCoSDNSh8PhcDgqiqoeZG1657IS62+DpiGq2ttuPwigqs/55FmICcTW2e1VQI+AqUt+lHlOlog0EpGPRWSliMwWkfEicoyITBSRbSLybUD+FiIy087S/8TO30JEeorIHBHJtZGj7zFFlhWQp6Ytb4UtP8Un7UG7f6k1TS3YP0pENovIgrLqrWzExMSQkpJCSkpKodO1l3D6nX6v6veydnD6vai/gnyy/gDa2NilBmZi+9iAPGuBs2wdj8PYWmWWVGiZ5mRZu4YvgTGqeqXd1xFIxMyRigFuDjhsKPCKj+P7DZgleNYC12Mc3wMprixfbgCyVbW1iFxpz3OFfQrgSuAEoAkwWUSOUdU8YDQwAni/LHpLw7kely9Ov9Nf0fqjI82PtJe/++Bt/V699uHg51+dUdVcEbkd88BcODBKVRcGzAm/F3hHRO7GTIK/XksZDixrT9aZwAFVfcunQmmq+nNRLu02KAuJ4ztmtv8Y+/5z4Cx7vn7Ax6q6T1X/BFZgnhZAVX8CtgYWJCI3icgfIpImIv8TkZhSzl0heNX1twCn3+n3qn4vawen36v6K6gnC1Udr6rHqGorVX3G7nvMBlio6iJVPVVVO1rv0O9KK7OsQVY7zFqAZSWUju+FTwDY8nPs+Y7E8f0LVe2mqh2BxZheskpHUa6/GRkZJRxRvXD6nX6v6veydnD6Pau/AnyyQkVZLRyqK+2sxUQcxhaiLL5aDofD4XA4HKVS1p6shUCXwyg3lI7vhc7utvxYe74jcXwfDdyuqu2BJzCT2A5BKtjx3bOuvxan3+n3qn4vawen36v6K2q4MBSUNciaAtQUkUKPCRHpICKnF5U5lI7vmNn+A+37y4Ap9nxjgSvt04ctgDYYk9GSqANsFJFI4OriMlW047vXXX+dfqffq/q9rB2cfq/rrw6UabhQVVVELgFeFZFBwF5gNXCXFOPSTugc398F/iMiKzCT2a+0dVwoIp8Ci4Bc4Db7ZCEi8hFwBhBvy31cVd8FHgVmYh7BnIkJuiodXnX9LcDpd/q9qt/L2sHp96r+iux5CjbOjPQwEGtG6lXnX+d67PR7VX9FWRhUFpx+7177UP6O70m3fR30xl73r34VErl5feL7EeEFz5Di8LJ2cPqdfqffq3hZu+PIKXOQJSKNgFeBbsA2IAOzWOJwzOLKecAzqvqJzd8Cs4p1A4z9w7Wqul9EetpyOgBXqurnPueYCPQApqvqBcXUoybGVLQLZsL7Faq6WkQaYHyzugGjA9Ybega4DqinqrV99o8GvvWtQ1k4+/XgL0zcNTmWW09PIUyECYs288mcQxf37tm6Ptd1b4YqrMrazXPfraBVfAz/PKMFMZHh5Kvy4awN/LgiK+j1A5h8x8kh0Q5Ov9NfufVPvuNkwNvffYDz3vgt6GV3SYrlltOaEybCxMWb+Sx14yF5Tm9Vn2u6NkNRVmXt5oXJKwF46vxjaZtYm4UbdzBkwrKg162Aibf2YPbqnJCUPfePX3n/zZfJz8/nzPP60e/K6/3Sx33+AVMnfk1YeDh1Y+O4+d7HSEhsDMBzD93BisULOLbdiTzw1CshqR9Al5TYkJVdJNUonj1ax/c44DpVXS4iTYDZIjJJVbdRzo7vmHlij2I8vdoFHPMNxvF9eVn0ljdhAnf0asGgrxezZed+Rlzejhl/ZrM2e09hnqaxU1ZlUwAAIABJREFUUVzVpSl3/W8hO/flERdtPrq9ufm88P1K0nP20qBWJP+6vD2z1m5j1/68ipJz2Dj9Tr9X9XtZOxj9t52ewkPfLGHLrv0Mv/QEZq7e5qe/SWxNrujUhHu/XMjO/XnERh/8t/W/uRupGRFGn+MbVkT1j5r8vDzeG/ECDz0/ggbxiTx8x0C6nNyTZs1bFuZJaX0sz4x4n5pRUXz/zed8OPI17nzYLKd3Yf9r2bd3Lz+M9+bC7VWBo3V8/1FVl9vtDcBmIKEiHN9VdZeqTscEW4Hl/qaqh94eGc4WkVkiskxEiuw9CzXHJtZmQ85eNm3fR26+Mm15Fqe09F+jqs8JDRk7fxM795kf0G17jM9r+ra9pOcYyVm7DrBtzwHioiPLV8BR4vQ7/V7V72XtAMc0tPp3GP0/rthKjxR//ecd15BvFmaw0waPOXtyC9Pmpm9n94GqE1QGsmLpQho1SSKxcTMiIiM5udc5zPr1R788J5zYlZpRxl2o9XHt2Zp5cC3idp26Ex1Tq1zrXB5UJwuHsg4Xlur4LiLdgRrASsrR8V1EChzftxxheSmY5XdaAVNFpLWqHhKohZL4WjXI3LG/cHvLzv20Taztl6dZnPmSvXrpCYQJvP/7emat9e++PrZhLSLDwtiQU67VP2qcfqffq/q9rB2s/l0++nft59iG/kFDU6v/pYuPJzxM+O8f65m9LjRDd+VN9pZMGiQc9L1qkJDIiiULis0/beLXdOx2SnlUrUKpTk8XlrUnq0REpDHwH+CvqnpID1Ul51NVzbc9cqswFhJ+VLQZKUB4mNA0Nop7v1zEs5NWcPeZLalVI7wwvX5MJIPOac1LP6ykOj4D4/Q7/V7V72XtAOFi9A8au5jnv1/BnWe08NPvFX6ePJ5VyxZzYf9rK7oqjsPgqB3fRaQuMA54WFULZkVWhOP7kRL4u3TI71SozUi37NpPQp0ahdvxtWuwxefuDswd7m+rs8nLVzbt2Ef6tr2Fd3gxkeE8fUFb3vttHYszdoaqmiHD6Xf6varfy9rB6q/lo79WDbJ2HTgkT4H+jAL9sUUuzlHlqBefQFbmwbUIszIzqNcg4ZB88+fM5KuP3uO+J14mskaNQ9KrG9VpuPBoHd97YSbEv+/7hF4FOb4fKf1FJExEWgEtgaVHW8HDZWnGTprGRtGoTk0iwoQz2jRgxp/Zfnl+WbWVDk3rAlA3KoKmcVFs3L6PiDBhSN9j+H5pJj+v3FreVQ8KTr/T71X9XtYOsGzzTprERZFo9fdqXZ/fVvvrn/FndpH6qwOtjj2eTelr2bwxndwDB5jx4/d0ObmnX54/Vyxl5PDnuO/Jl4mtV7+Calq+VKcg62gd338DegINROR6m/16VZ1LOTu+2+NXY+wkaojIxcC5qrpIRF4ABgAxttyRqjrEHrYWs/xOXeCW8p6PBZCvMOKn1TzXry1hIkxatJk1W/cwsHszlm3exYzV2cxam0OX5DhGDuhAvsI7v65lx95czjomnvZN6lA3KoLebc0d0Is/rGTllt3lLeOIcfqdfq/q97J2MPrf/Hk1T19wLOEifLckk7XZe7i2W1OWZe5i5uptzF6XQ+ekWN6+ogN5qrw7Yy079pnpvi9efBxJcdFERYbzn2s78cq0VcypQvO1wsMjuP72B3juoX+Sn5/HGb0vIimlFZ+NeYsWxxxH15N78eE7w9m7Zw/DnxoMQIOGjbj/yWEADLnnJjasW83ePXu4bcD5/P2eR+jY9eSKlOQIwDm+HwYFju+h8gqq7ITSJ6kq4PR7V38ofbKqAqH0yaoKhNInqyrQJSW2XB3fW9w9LuiByZ+vnF8h3VllDrKKMSMdCryCGXaMBF4vsHkQkS7AaCAaGA/caXvE+mNMTI8DuqvqLJu/WDPRgHrUBz7BPBW4GrhcVbOtbcRwoC+wG9OjNsceU6rJaRnboMIi0piYGBo2NF4wOTk5ZGdnl3JE9cLpd/q9qt/L2sHpryz6XZB1ZATDjPRkVd0nIrWBBSIy1npmvQnchFl4eTxwHjABWAD8H/B2wGlKMhP1ZTDwg6o+LyKD7fYgoA/Qxr5Osuc/yR5TFpPTMlPea1jl5eVx0fm9efud90hMTGTAFZfx/IvDaNW6dbnWo6LW73L6nf6K1l9Ra/dVBu3g9Hv52ofyX1LIixYOJZmRFsxArFlQnrV0qGtNQBWzDE6BGeliVT1kcnlJZqIB+JqRFpqc2v3vq+E3zNONjW3ZRZqcishjIvKHiCwQkX9LJf1kF8yfR1JSc5olJRFZowbn9T2faVN/qOhqlRtOv9PvVf1e1g5Ov1f1V6eJ72UNsoo1IxWRJBGZhzEIHWp7sZpiDEgLCKYZaaKPe/smoMDJrdCk9DDOOUJVu6lqO8ywZoU4vpfG5owMGjVuVLjdMDGRjIyMEo6oXjj9Tr9X9XtZOzj9XtdfHThqM1JVXaeqHYDWwEARSSztmGBhe8mOpg/3TBGZKSLzMcsAnRCcmjkcDofD4TgSRP6fvfMOj6rY//D73U0HkpCekIRI7yJFEQtNKYJi13tRr4oFUS92RARRfiqogIiCgqCoWBAE9dKU3kSBUEIJJPQUkpBA6AnZnd8f56RsCCHA7iabPe/z5IGcnZ2dz2FDZqe8Y/+vyuKKZaSF6CNY24Cb0ISh0SUetqeMNKNwGlD/s/AgpyJJaUVeU0R8gInAvUqplsAUoEzDXWUb38PCwzmcfrjo+8yMDMLDndaXrXSM/EZ+d83vztnByO/u+asDVyojvUlEfPXvawM3Arv06bzjItJBX+f0CI6RkZaUnP4GPCIaHYDccg6FhuIO1RF90f69FyroaOP7xWjeoiUHD+4nJeUQ5/LzWTh/Hp26dK2s5jgdI7+R313zu3N2MPK7a/7qtCbrSmWkc4HPdLWBAB8ppRL0pw2kWOGwQP9Cr2cCEArME5HNSqke+mP7KVsm+iXwua57GAXMFJH+wAHgfv315qPpG5LRFA6PFba/HMnpFLTRt8PA+grfNSfj4eHBkKHDeeapJ7BaLdx51z00aNCwspvlNIz8Rn53ze/O2cHI7675q+YWtMvDkJFeAoWerMrYylsVqKxtzFUFI7/75q8shUFVwcjvvu990PI705PV6LWFdr/Zuz/oWXU9WQa2ONsZUpVw5+xg5DfyG/ndFXfO7myqqE3psqjw7kIRiRCRH0Vkj4hsFJH5ItJJROJFZLOIbBeRASXKtxWRBBFJFpFPCh1UInKfXtYqIu1KlA8WkWUiclJEPi2nHUEi8qeIJOl/1tavi/46ySKyVUTa6Ndbi8hf+mtuFZEHLudGVTZ+fn7ExcURFxdH7dq1K7s5TsfIb+R31/zunB2M/O6e39VxB+P7aeARpVSSiEQBG0VkkVLqWEWyl4VhPXYuRn4jf2XnN4znRn53fe9DZRjfnfpyDqXaG9+VUruVUkn6a6ShKR9C9XYaxncXwMhv5HfX/O6cHYz87prfZBK7f1ValgqWqxbGdxG5FvAC9uiXDOO7C2DkN/K7a353zg5GfnfPXx1wG+O7Prr2LfCYUsqqXzaM7wYGBgYGBlUIw/heBlXZ+C4i/sA8YKg+lWgY310II7+R313zu3N2MPK7e/7qQLU3vouIF9qi/W+UUrNK1GMY310EI7+R313zu3N2MPK7a37D+O5axvf7gZuBYBF5VL/2qFJqs2F8dw2M/EZ+d83vztnByO+u+avmFrTLwzC+XwKG8d2wHhv53TO/YTw38rtrdnC+8b3lsD/tfrMTRt5atY3vIhIBfAy0B44BGcBoYBzatKMnMKFQ8yAibSkeyZoPDNJHxEai6RasaOupHlVKpYlIPzTflQAngGeUUlvKaMdVwI9AMNqOx4eVUvki4o2mimgLZAMPKKX2i0gwMEtv99dKqecu4f6UiSOcIT169GD8+PGYzWa+/PJLRo8ebfN4TEwM06dPJzAwELPZzOuvv86CBQsAaNmyJV988QX+/v5YrVbat29PXl5eWS9zxTjKl3K5+du3b8/kyZMBbYh5xIgRzJ071yFtBOf4Yi52L8aOHUuXLl0ATVQYFhbmNEmho/IX5gDIzc3l6NGjNo8HBAQQGBiIUgqlFBkZGeTn5+Pj41P0PBEhOzubkydPOqSN4Jj8F8suIkRERODt7Y3FYiE9PZ2CggL8/PwICQlBRFBKkZWVxZkzZ+zevpJUhvX8YvfHWVSW8b2q5HcmVdSmdFlUaCRLX1e1Fk1GWtiJKpSRrispIwU66p2mf4D/Uiwj/UQptUBE/JVSx/U6/gs0U0oNEJGOwE6l1FER6QWMUEpdV0ZbZgK/KKV+FJHPgS1KqUkiMhBopdf1IHCXUuoBEakBXIMuOb2STlbhSJbfPdMut4oyMZmELZ/cze3vLCI15zSrRt3Oox8vJzElt6jMhKc7smVfNl/+sYsm0QH88satNBs4C7NJWPvhHTzxyUoSDhwlqKY3x07nY7Xa/1PX6dmPU/uhGXav1yTC+g/7cPfopaTlnGHJOz148rM17Eo7XlRm3OPXsvVADl8tSaZxlD8/vdKZ1i/9hq+XmfwCKxarIjzAh5Xv3Uaz5+dgcUD+o9/1o9XwxXavtyQmgd/+ez1Pf7OJjON5fP9Ue16ftZ29WafKLP+v66JpElGLt37d6dB2AWx95xZmb06ze71Wq4Uxgx6m/5sf4R8cymdDBvDgoGGER8cVlTl7+hQ+fjUA2LFhDesWzeXxoR+Sn3cWs4cHZrMHx49m88mr/RnyxSzMZvueGHZP6ygATpy1XqTkpWGxWLjr9p5MnDyN8PBwHv7Xfbw3egz16hfLJmf++D3JSbt4Y9jbLFowj2VLFzPqw3Ek7txBcHAwoWHhJCft5rlnnmDh4pV2bV8htXy05buGjNS5VKX8zhzJuvqtJXa/2Vve7lYpPbfKkJEeL1FvDXQFg1JqrVKqsIu+Dtvdiej1CppqoXABe2kZaaGkdBbQTUSkPMmpiEwSkQ36kTtvV/Be2J12DULYe/gE+zNPcq7Ayqw1e+nTPtamjFLg7+cFaH+mH9U+sd5ydR22HThKwgHt1uWczHNIB8uRtK0fzL6MkxzIOsU5i5Vf1h2gV1vbf36lFLV8PAEt/+FjWv4z+ZaiDpW3lxlXn/5uUcefQzlnSD16lgKLYuG2DDo3Cblg+Z4twlmQ4NrenEPJiQRH1CEoPAoPD0+u7tiVnevX2JQp7GAB5J89W/RJ18vbp6hDVXAu3+U+AW/ftpWY2Fiio2Pw9PSie8/bzpNNrli+hD53aP/Ndbu1B//8/RdKKZo0bUZomLbTrH6DhuSdzSM/P9/pGRyJu8o4C3H3/NWBin7cK1dGiqZHaAC8qo9itaMcGamIvIu24zAXrQNXmv7oC+VLEQwcU0oVlFFvkYxUKVUgIrl6+SPl5BqqlMoRETOwRERaKaW2llPeIUQF+ZFypHikIjX7NO0ahtqUeW/mJn4b1oMBvZri5+1Bn3cWAdAgyh+lFL++2Z0Qfx9mrdnLuF+3ObX9V0pkbV9Sc4rzp+Wcpm19247F6F8SmD24K091b4yftwd3jSr+j6Zt/WAmPHEd0SE1eObzvxwyiuUswvx9OJxb/HkgMzePltH+ZZaNDPChTm1f/tmX46zmOYTjOVkEBBe/3/2DQzmUtOO8cn8tnMPqeT9jKTjHE8PHFV0/mLSD2ZM+4FjWYe5/fqjdR7EcibYlP7Lo+/DwCLYl2K6SyMrILCrj4eFBzZq1OHbsmM0U8ZI/F9GkaTO8vLyc03AnUZaMM2Gr0/+LrjTcNb+LfVYql0qRkSqlhiqlYoAZgM30nYh0QetkDb7StlWA+0UkHtiEJiJt5oTXvCzuu7Ee3y1PotHTM7n7vT/58vmbEQEPs4nrm4Tz+PgV3PLmPG6/ti6dW0ZevEIX457r4/hh1V5aDJrLAx8t5/MBHYt+EDfuyabjkPnc8tYiXri9Od6eV/y2dgl6tgxn8Y5MXLhPeUlc3/MuXp3wPT37Pc3S2d8WXY9t2IwXx37Ns+9/wfI5MziX75j1iFWVPclJfPLxGN4YXmmD8QYGBhegsmWkM4B7Cr8RkVbAl0BfpVR2GeWz0c4kLPyoWrLeIhmp/niAXr5M9AX0rwDd9E7iPCpJRpqWc5rokOLpkDrBfqTn2K7BeaRbQ2av3Q/AP7uz8PEyE1LLh9TsU6zZmUH2iTzO5FtYtCmF1lcFO6qpDiH96BnqBBXnjwryI/3oaZsyD3Wqx9y/DwKwPvkI3p5mgmt525TZnXacU3nnaBod6PhGO4jM42eJCCh+G4YFeJNxouxOQ3WYKgTwDwolNzur6Pvj2VkEBIVesHyrjl3ZsX71edfDouvi5eNLxqF9DmmnI9COSUkv+j4j43DRFGAhoeFhRWUKCgo4efIEgYHaezzj8GFeefE53nl3NDExtksMqgPuLuN01/zVyZPldBmpiJSUfPQFEvXrscAvaLsFd5fVCH191zKKxaGlZaSFktJ7gaWq/AU6/sApIFcffet1oYKOlpFuTD5C/Uh/6obVxNPDxL031GPe+kM2ZVKOnKKLPkLVuE4APp5mso6fZfHmVJrH1sbXy4zZJNzULIKdKccc1VSHEL83m3oRtYgNrYGn2cTdHeqyMN62T56SfZqbm2v/uTSK8sfb08SR43nEhtbArB/+GR3sR8NIfw5eYJG4K7A97QSxQX7UCfTBwyz0bBHOisTzZ7zjQvyo5ePBlkO5ZdTiWkTXb8yR9BRyMtMpKDjHlrVLadquo02ZI+nFqw92xa8jJFJbJZCTmY7Foq0eOJp1mKy0g9QOjcBVaNa8JYcOHCA1JYVz5/L5Y+F8OnW2lU126tyV//2m7Zhd8uci2l/bARHhxPHjDHruaZ4f9DKtr2lTGc13OO4q4yzEXfNXp2N1nC4jBUaJSGM0hcMBYIB+fTjaGqqJeq+zoLBjIyLzgSf00bLBwI8i8n9o03xT9edPBb4VkWQgB3iwsP0XkJxuEZFNaJ28Q4DtSlsnYrEqXv5yHb++2R2zSfhmaRI7U47x5gPXEL/nCPM3HGLI9H/4dMANPNenOUopnv5sFQDHTuUz4fdtrBx9OyhYFJ/CoviUi7xi1cJiVbz2zQZmvdoFs0mYsXIviam5DLm7JZv25bBwUyrDvo/n4/7X8UzPJigFz01eB0CHRqG80KcZ5ywKq1K8On0DOSddd7rIYlW8P38Xkx6+BpMJ5m5KZ0/WKQZ2qcf2tOOs2KV1uHq2CGfRNtcfxQIwmz244/FBTHv3VZTVSrsuvQiPuYo/f5pGnfqNadbuBv5aOIfkhI2YzWZ8a9bivmeHALA/MYEVc7/HbDYjJhN9+79ADX/XGcn08PDgtTeG8dwz/bFYrPS98x7qN2jIpM8+oVmzFnTq0pW+d93LsDdeo2/v7gQEBPDeB2MB+OnHGRw6eJApX0xkyhcTAfjs86kEBbvWSHZ5uKuMsxB3z18dMGSkl4CjFA6ugqMUDq6CMxQOVRlHKRxcAUcpHFyFylI4VBUMGalzFQ5tRy6z+83eOKxLpYxnGZ2sS6Cwk2VgYGBgYOBOGJ2sy6PC27BEJEJEfhSRPSKyUUTmi0gnEYkXkc26a2pAifJtRSRBRJJF5BN9bRYiMlJEturP+UNEovTr/fTrCSKyVpedltWOq0Tkb73en0Q7ABoR8da/T9Yfj9OvB4vIMhE5KSKflqrLcWroS6RHjx4kJiaSlJTE4MHnb6yMiYlh6dKlxMfHs2XLFnr10paQ1a1bl9OnT7Np0yY2bdrEpEmTnN10u3C5+W+55RY2bNjA1q1b2bBhQ5EJ3dW4WH6A++67j+3bt7Nt2zZmzCgeUYyJiWHRokXs2LGD7du3U7duXWc12yH4+fkRFxdHXFxcmSb7gIAA6tatS2xsLDExMS6vLbhYXhEhMjKSuLg4YmJi8PDQVnmYTCaio6Np0KBBkRG8OnKx+1Pdccf81WlNllsb30XkpFKqZoVvlj6S5f/gNxV9SoUwiRA/ri93vreY1OzTLHu3F/0nrGZXavGi5vFPdGDr/hymLt5N4zoB/Dy4K63+O4fYkBr89FpXrn/td7u2qSyO//gIQY98b/d6TSL880Fv7vlgGWk5Z1g8ojtPTVprY3wf+1h7Eg4c5aulmvH9x5c6cc0rv9MytjZZx89y+NgZmtQJYNarnWnxwq/lvNrlk/PNv+kwaoXd6zUJ/PRkewb9lEDmiTym/ecahv+WyP7s4h2W0bV9eLdvM577YSsn8gqo7efJ0dPnAPjsX634+q+DrN9/DF9PE1YFeQX2n9Za93onFmzLtHu9JbFaLPzfc/9i4FvjCAwOY8xrT/Cfl0YQEXNVUZmS9veEf1azeuEvPDN8rEPb1auF1ok5lWd/4/udfXoyafI0wiPC6ffgfbz/wRjqlzK+7969izeHv83CBfNYtmQxoz8ax5nTp0lM3ElychJ7knbz+tDhdm1bSWp4G8Z3w/juvJGs9u8ut/vNXj+0c5UeyaqWxne9znH6KNwSEbnwvnEH0rZBcLHx3WLll78O0LtdjE0ZpRS1fAuN554cLqU4cGXa1AuyMb7P+fsgvdqUNr5TZHyv5etZZHxPOHi06O+Jqbn4eJrx8nAtT1azyFqkHDtDWu5ZCqyKxTuzuLmh7eLlvldHMis+jRN5+k46vYMVF+yH2SSs36/tKD1zzuqQDpazOJC8k9DIaEIi6uDh6UmbG28h4R9bXYON/T3vjMtZ3kuyLUE3vsdoxvcevc43vi9ftoTbdeP7LSWM775+flzTpi3eLj6SVx7ubjx39/zVAXc3vtcANiilXhSR4cBblJKjOoOo2n6kZpc0vp+iXQNb4/n7s7cyZ0g3nurRmBreHvR9r3gBdt3Qmqx6vzfHz5zj/37azF+7HDvaYG8ia/uRmlPcadSM77adjA/mJDDr1S48eWsj/Lw9uPuDpefVc3u7GLYeOEq+i3UyQmt5k3m8eEdk5ok8mkfWsikTU9sXgC/6XY3JJExdfYB1+44SG+TLybMFvH9nM6ICfVi//ygTV+xzWUFpbnYWgcHFU1+BwaEcKMP+vmrBbJb99hOWggKefXu8M5toVzIzMwiPKGV837qlVJlMIiLKN75XV9zVeF6Iu+Z34c9N5+Huxncr8JP+9+/QPF9Vkns7xvH9yj00e+4X7v1gKV8MvAEROHzsDM2fn81NQ+Yx9NsNfPn8jUUjXtWJuzvU5YfV+2j54q88MGY5k5663uYHsXEdf9564Gpe+np95TXSgXiYhJjavgz8YSvDf0vk9Z6NqOmtudGujglgwrK9PD49nqhAH3q3dB1P1OVyU697GD5pJrc/PIA/Zk2/+BMMDAwMKgG3Nb5fKEZZFx1ufD96mjrBJY3vNYoOgC7k4S4NmPPXAQDWJx3Bx9NMcC0f8gusHD2pHQq7eV8O+zJO0KDUKEhVJ/3oaeoE+RV9rxnfbfM/1Kk+c//RjO8b9mRrxveamvE9qrYv3/z3JgZOXsf+zCqzl6HCZJ3II8y/2F4fVsubrJO2B/1mnshjVXI2FqsiPfcsh3JOE1Pbl8wTeSRlnCQt9ywWBSuTsmkcXuFlhlWOgOBQjmUXj8Qeu4j9XZtOXOWMpjmEsLBwMg6XMr6XMnqHhYVx+HDZxvfqjrsazwtx1/yG8Z1qYXwHLX9hXf8Gzj+rA8cb3+P3ZFM/ohZ1Q2tqxvPr6zJ/4/nG904ttBGKRlH+eHuZOXL8LMG1vDHpb6C4sJrUj/Bnf4ZrdTQ27cuhXngtYkM04/td18WyYJOtUDUl+xSdmunG90h/fDxNHDmRh7+fJz+81ImRM7fwT1J5M8NVl53pJ4ip7UtkgA8eJuGWpqGsSrb9fLAyKZs2Mdov1gBfD2KC/Eg9dpad6Seo6eNBoD562bZuIPuOuK7xPrZBE7LSD5GdkUbBuXPEr15Mi/Y32JTJTCv+2dixcS2hkect33QZmrdoycESxvdFC+bTuQzj+++68X1xCeO7O+CuxvNC3DV/ddpd6M7G9x1ox+pcKyJvApnAAxW5H/bGYlW88vU//DKkG2aT8N3yZBJTcnnj3qvZtC+bBRtTGPrdRj55sgMDb2uKUjBw0loAbmgazhv3Xc25AitKKV6c+jdHT+Vf5BWrFharYvC3G/j51c6YTcL3K/eyK/U4r9/Vks37NeP78B82Me7xaxnQozFKwbNf/g3Ak7c04qrwWrzStwWv9G0BwL0fLuPIBc77q4pYFIz5M5mP72+BSYT/JRxm35HTPHljXXYePsHq5BzW7TvKtVfV5vv+bbEq+HT5Xo6f1ZYmTli2lwkPtkRESDx8gl+3HL7IK1ZdzGYP7nniJSa98xJWq5UO3XoTGVuP+T98SUz9JrS89kZWLZjN7q0bMJs98K1Zi37PD63sZl82Hh4eDH5jGAMH9MdqsdL3Ls34PvHTT2jWvAWdu3Tlzrvv5c0hr3HHbd3xDwhg1AfFOylv69GVUydPce7cOZYtXcLEyVNtdia6Ou5uPHf3/NUBQ0Z6CThK4eAqOErh4Co4SuHgKjhD4VBVcZTCwVWoLIVDVcEwvjtX4XD96JV2v9l/Db656hvfRSQC+BhoDxwDMoDRwDi0qTdPYEIJl1Zbikez5gOD9FGxkWhThVa0EaRH9V2J/dBGqgQ4ATyjlLLdaqPVexXwI9rI10a0KcZ8EfFG00W0RVuP9YBSar+IBKNpHdoDX5d0ZV0K4kDje48ePRg/fjxms5kvv/yS0aNH2zweExPD9OnTCQwMxGw28/rrr7NgwQLat2/P5MmTC9vHiBEjmDt3rqOa6TAuln/s2LFFolE/Pz/CwsKKdleNGjWK3r17AzBy5Ehmzpzp3MbbgYvlB01GOmLECJRSbNmyhX79+gHaOp2EBG0A+eDBg/Tt29epbbc3hf++ALm5uRw9etTm8YCAAAIDA1FKoZQiIyOD/HzXGr0tycXyiggRERF4e3tjsVhIT0/HDuPwAAAgAElEQVSnoKAAk8lEVFQUPj4+HD9+nMzM6tkBvtj9qe5UlfxGJ+vyqHAnq7oKSS/pZumdLHuf32cSYf2Hfbh79FLScs6w5J0ePPnZGhsZ57jHr2XrgRy+WqLJOH96pTOtX/oNXy8z+QVWLFZFeIAPK9+7jWbPz8HigD38R7/rR+TTs+1er0lg9TvdeXD8atKPnmH+kC4MnLqepPQTZZZ/vHM9WsQE8tK38XRrEcGT3erTb8JavDxMzH7pZu7/eBUnzxaU+dwrIf2Le7jxI/svsjYJfP94W16ctY2sE/lM6deat+clsj+nePF/dKAPb9/ehEEzEziZZyHQ15NjZzRX1qLnr6fHhL/s3q7SrH7lJuY5+FBqq8XCe8/9mwFvjSMwOJRxrz3Jwy+9dUEZ6bZ/VrNm4RyeHj7Goe3q3UJbD3gq3wEy0t49mTRFl5E+cB/vf1iGjHTXLt58620WztdlpGNKyEiTktiT7GAZqZchIzVkpM7rZHX8wP6drLWvVU4n61IUDtVSSCoiPUU7GmiLiFSK5a1t/WAbGecv6w7Qq21pGacqknH6+3kVCTjP5FuKOlTeXmZccfr3mrgg9mee4uCR05yzKH5dn0KPVpEXLH9n+xjmbtAWxjeKrMW6JG3X3Zl8CztTc+nS3LV23zSNqEXqsbOk5+ZRYFUs2ZXFjQ1sPWG3t4pgzuZ0TuZZAIo6WNWNg8k7CYmsQ0hEFB6enlxzYze2lSsjPevSUp0yZaRLS8lIly7h9r66jLR7GTJSb0NGWl1x1/zVaXdhRWWkUA2FpLrhfQpws1Jqn4gElVXO0UTW9iU1p3hHmCbjtJWRjv4lgdmDu/JU98b4eXtw16jiH7S29YOZ8MR1RIfU4JnP/3LIKJYjiajtQ1oJZUP6sTO0uarsf4o6Qb7EhNRgdaI2NbIjJZeX+jTliz+T8PUy07FRKLvTj5f53KpKaE0vMkss1M86kUfTC8hIJz7YCpPAtL8O8o9ueffyMDGl39VYrIoZ61NYlZzjvMbbmWOlZKQBwaEcTNp5XrnVC35huS4jHfj2x85sol0pU0aaYMhIC3FXGWch7p6/OnApnawLopQ6BLQS7bDnuSIyqwLPGQoMFZEhaELStwofKyEkdbQctAOwUim1T29Tlf3tdM/1cfywai+fLUikfYMQPh/QkY5D5qEUbNyTTcch82kU5c9nT13P4q1p5J2rngt072wXw7z41CKj+YqdmVwdV5vfXutE9sk8Nu7LdrlOZkUwixAd6MvzMxMIq+nFhAdb8ej0eE7mWbhvynqOnMwnMsCb8fe1ZE/WadJyyzxFqtpwY6+7ubHX3Wxc+Sd/zPqGfv913R2GBgYGtrjw4PR5XMp0obsISc/D0TLS9KNnqBNUPAWiyThtzyZ8qFM95v6tyTjXJx/RZJy1vG3K7E47zqm8czSNdi1R4eGjZ4nSR2oAIgN9z5ORFtK3XTRz19s6xD5ZsItb313Kg+PXIAh7XUxImnUyn7AS/5ahtbw5UlpGejKPNXt0GenxPFJyzhAdqN2zwrLpuXlsPpRLo7AauCqBpWSkudlZBASFXLC8Np1YzWSkYYaMtBB3lXEW4q75q9N04aV0sqqjkHQdcLO+W5ELTRc6XEa6N5t6EbWIDdVknHd3qMvCeNv+aEr2aW7W1xo1ivLH29PEkeN5xIbWwGzS3kDRwX40jPTnYJZrySg3HzjKVWE1iQn2w9Ms9G0fzR9b088r1yC8JgE1PNmwt3jA0SRQu4a2JqVpHX+a1vFnxQ7X2mWVePgE0YG+RPp742ESujUOZfUe20HVVcnZtI4JADQZaXSQL2m5Z6npbcbTLEXXW9TxZ3+26x4eHtOgCVnpKUUy0k2rl9C8ve2AdpaNjPQvQlxdRnqwlIy0lGyyU5eu/P6rLiP9YxHtrzNkpO6Cu+evDlR4urC6Ckn1TuMvImJC00ncWtF7Yi8sVsVr32xg1qtdMJuEGSv3kpiay5C7W7JpnybjHPZ9PB/3v45nejZBKXhu8joAOjQK5YU+zThnUViV4tXpG8g56ToiTtDyD/1pM9//9wbMJuHHtQfYnX6CV29vypYDx4o6XH3bx/DrelsTvKfZxJxXbgbgxJkCnv9qg8tNF1oUjFu6hzH3tMBkgnnbMtiffZr+HWNJzDjJmj05/LP/GNfWrc23j7bBYlVMWrGP42cLaBFVi1duaYBS2hD7jH9SbHYluhqajPRFvnjnZaxWK9d1601k7FUs0GWkLa69kVULfimSkfrVrMW/q4OM9OkKyEh76TLSD0vISLsbMtLqjLvmr04fIgwZ6SXgKIWDq+AohYOr4CiFg6vgDIVDVcVRCgdXobIUDlUFQ0bqXIXDzWPX2P1mr3zphqrtyTJwrIzUwMDAwMCgquLMTlancfbvZK14sXI6WRVekyUiESLyo4jsEZGNIjJfRDrpjqnNIrJdRAaUKN9WRBJEJFlEPtHXZSEiI0Vkq/6cP/QdiYhIP/16goisFU10WlY7rhKRv/V6fxIRL/26t/59sv54nH49WESWichJEfm0nHyPlve4o+nRoweJiYkkJSUxePDg8x6PiYlh6dKlxMfHs2XLFnr16gWAp6cn06ZNY+vWrWzevJlOnTo5u+l24XLzA7Rs2ZK1a9eybds2tm7dire393nPr+pcLD9oxvft27ezbds2ZswoHk0dNWoUCQkJJCQkcP/99zuryXbFz8+PuLg44uLiylQTBAQEULduXWJjY4mJicHLS1uH5+PjQ2xsLLGxsdStW5eaNWs6u+lXzMWyiwiRkZHExcURExODh4e2ysNkMhEdHU2DBg2KjODVkYvdn+qOO+avTgvfK7QmS+8gzUGzvT+oXyu0vV9f0vYuIr/p66YmAU9SbHvvibYm60Ol1DC9jv+ircMaAOwDOpWwvU8GzrO9ox/jU8L23l9/rf7AUaVUA9Fs76PRDnw+CwxDt71f2u0pG5/bJ9qjmiJMJmHipH/Re/jvpGafYvWYV1h0ojWJh4qPT3jr2U7MSTzClAXraBJTm7lf/UiTJ2fw9G3NMceEce3QZYQG+DL3y5+58eVZOGKA8uzvA6lx31d2r9ckwsTxd3HH//1BavZpVr7/GovzriMxNbeozNtPXc+vyTl8+ecWmtQJYPbXM2n+3CztQOnRt/PEp6vYdmAXQTW98er7BR4OuAGnfn6Ma962vwjQJDDl2esZ+N0mMo7n8d0LQ0mKvZN9R4oXsMcE+fLOPS14+ttNnDhbQG2/GK55ewk3Ngym03UxPDZjC54ewpSxU0i7ZiCn8i12b+emt7rx8+Y0u9drtVoYN+hhHnvzI/yDQ5k0ZAAPDBpGWHRcUZmSlvedG9bw96K5PDr0Q/LzzmL28MBs9uD40Ww+fbU/g7+YhdlsFztNEfe1jgIM47thfHcuVSm/weVR0ZGs6mp7f0xEdot2/M8NFbwXdqd9wzD2pOeyP+ME5wqs/LwqmT7XxdmUUUrh76sZ3wP8vEjP0X4BN4kJYvlWbSdiVu4Zck/l0baBa32qbdcghL2HT7A/8yTnLFZmrd1H7/axNmWUglp+xcb7QsVFt6uj2HbwKNsOaG+dnJN5WF1sCrxFHX9Sjp4m9dhZCqyKRdsz6dw41KbM3W2imLkhhRP6cUFHT2vG93ohNYg/cAyLUpw9ZyUp8yQdS9niqzopyYkERdQhKDwKDw9PWnXsys71a2zK2FjezxZb3r28fYo6VAXn8l1OsGMY38vHXY3nhbhrfhH7f1UWFe1klWt7F5GtaKb10fooVh0uYnsXkUNAP7SRrNJcse0dzSR/wd82ekfwbbTO1Y1AswuVdTRRwTVIOVKsXUg9coo6wbauo3d/2MCDnRuRPO1h5rzVm5cmawuwE/Yfoc91cZhNQt3wWlxTP5ToENeaMokK8iMlu0T+7FNEBfnZlHn35808eFN9dk26j9lDbuGVaX8D0CAyAKVg7hu3snrU7bxwh10GK51KaC1vDucW7wjNPJ5n480CiA3yo26wH9Mea8P0x9vSsb5mG9mdcZKODYLw8TAR6OtJu7jahPu71nTp8ZwsAoKLO5X+waHk5mSdV27dwjmMef7fLJrxOX0e+2/R9UNJOxj/0qNMePkx+j75kt1HsRxJWcb3rMyMUmXKNr67A2UZzzMy3Gfzhbvnrw5ciierTJRSh5RSrdCO1PmPiFzUlKaUGqqUikETkdoc1izFtveyF6bYj+uA5UqpLKVUPvCTg1/virj/5gZ8t3QXDR7/lrvensfUF7shAtP/TCT1yEnWjL2XD5+4gXWJh7FYq98OqPtuuIrvlifT+Jmfuef9xXz5/E2IgIdZuL5JGP0nrOTW4fO5/dpYOre48LmHroqHSYgJ8uOp6ZsY8st23uzThJreHqzbm8OapGy+erwt793TnK0puS43kldROvS8i5cnfE+Pfk+zfPa3RddjGjZj0Niveeb9L1gxZwbn8l1LYWJgYGBLdVqTVdFOltva3vX6HGp8T8s+RXRI8chVnZAapGbbCkX/c2tTZq9OBuDvXRn4eHkQ4u+rObamrqXDCz9z/7sLCazhTVJaLq5EWs5pokuM3NUJrkFajq1Q8z9dG/LLX/sA+CcpC29PMyG1fEjLPs2anRlkn8jjTL6FPzalcPUFzj2sqmSdyCMioHj0Kczf2+YsQ4CM43ms3HWEAqsi7dhZDuacJjZYM75PXX2Af01ez8DvNiPAgWzX8mT5B4WSm108cnU8O4uAoNALlm/ZsSs71q8+73pYdF28fXzJOLTPIe10BIbxvXzc1XheiLvmd8fpwupoe/8b6KTvPvQE7rtQQUcb3zckZdIgKpC64bXw9DBx300NmPf3fpsyh7JO0rmV1m9tHB2Ij6eZrNwz+Hp54Oet9Tm7to6mwGq1WTDvCmzcc4T6kf7UDa2Jp9nEvR2vYv4G26NzDh05RecW2uLjxnUCtPzHz7J4SyrNY2rj62XGbBJubBpBYoprdTK3p54gJsiPqEAfPExCj+ZhrNhte6b58l1ZtI3TfrEG+noSG+RH6tEzmEQzvQM0DKtBw/CarNtTZY/gLJM69RuTnZ5CTmY6BQXn2Lp2KU3adbQpcyS9ePXBrvh1BEdqqwRyMtOxWPR1almHyUo7SO3QCFwFw/hePu5uPHf3/NWBCi1eqMa29xHAX8AxYHNF7oUjsFgVL36xit9H9MFsEqYvTmTnoaMM+3d74pOzmPfPfl6ftpaJz3Xi+b6tUAqeHL8UgNBAX34f0QerUqRln6L/WNdbFGmxKl6eto65Q2/FbBK+XZbMzpRjvHl/a+L3ZDN/4yHe+GY9E57uyHO9m6GApydqIxnHTuUzYd52Vr7fB6Vg0aYUFm1KKf8FqxgWpRi9YDef9WuNSYTfNqexN+sUAzpfxY60E6zcfYS1e3LoUD+IWc9ch8Wq+HhxMrlnCvAym5j6qDbIfCqvgDfn7MDiYtOFZrMHtz8+iK/ffRVltdKmSy/CY65i8U/TqFO/MU3b3cC6hXPYk7ARk9mMb81a3PvsEAAOJCawcu73mMxmxGTijv4vUMPfdUZ5DON7+bir8bwQd81vqkYfIgwZ6SWgdybtrnBwFRylcHAVHKVwcBUcpXBwBRylcHAVDOO7YXx3poz01k/X2f1m//lcB9c0votIBPAx0B5tRCgD3WWFNh3pCUwo1D+ISFuKR7jmA4P0kbKRaNOHVrQzBB/VR64KX6c92qjTg0qpQoVDyXZcqN4gtEXtcWijb/frLq4mwFdAG2CoUuqjCmR12E9Zjx49GD9+PGazmS+//JLRo0fbPB4bG8u0adMIDQ0lJyeHhx56iNRUbTnaqFGj6N27NwAjR45k5syZjmqmw7iS/KNHj6Z3796YTCb+/PNPBg0aVBkRroiL5Y+JiWH69OkEBgZiNpt5/fXXWbBggc3jO3bsYMSIEYwZM8bZzb9i/Pz8ioSaubm5HD1a9pR3zZo1iYqK4sCBA+Tl5VGrVi0bQaO3tzcHDx4kL891Fr9fLLuIEBERgbe3NxaLhfT0dAoKCvDz8yMkJAQRQSlFVlYWZ8641nq8ilDR90Z1parkd2Ynq/tn9u9k/fFs5XSyrmivs5MkpYiIGa3j9kc5zblQva8DS5RSo0Tkdf37wWhTiv+l2LNVYXyuef5Sn1IuJpMwccoweg/8jNSMY6z+7hUWJfmTuK94weO40Y/xw4rtzPjfr3Rq35APvphD/2Hf0vPGZrTr1JkOj32Ot6cHf0z5gGVpdTlx6jwt2BVzdtMEfNq/bPd6TSZh4peD6f3cZFIzc1k9fRCL9gWTuK94q/K49x/mh9U7mTFvAZ3aNeCDyb/Sf8QPdGhZl5t69OHa/tro4tIpz3Lr056sit9j93aeXT+GgH9/e/GCl4hJhElj7uDO95eQlnOaZSMHs8x6A7tKyFjf6X8dv+/LYdqS7TSuE8DP02fS6oW5RY9PGHQTi7dn43PNvwj4d2u7txEg9/uHGTJvl93rVVYLi94bwI0DRuIXGMzScS9x7cOv4h9h60o7d/Y0a6e8g9VSQNcXx1I71nbaJDdtP39Ne5eXPv6f3dv4fu/GAJzMs7+M9K4+PZk4WZORPvTgfbz/wRjqlZKRJu3exdDhb7NowTyWLlnM6I/GkbhzB8HBwYSGhZOctJtnBzzBoiUr7dq+Qmp6GzJSQ0bqPKrTmsMrVTg4XFKq8zwwG22E6zzKqxdbSWmRvFQplamUWg+cu6zkdqR9i7rsSclif2o25wos/Lwonj6dW9qUaVIvghXrtf0AK9Yn0aeT9njTehGsjt+DxWLl9Nl8EpLS6N6xqdMzXAntm8eyJyWb/Wk5Wv4/NtPn5uY2ZZpcFc6K9UkArNiQXPS4Ary9PPHyNOPt6YGHh5nMnBPOjnBFtK0fzN6MExzI0mSss9ft57a2ti5epaCWLqP19/Uk/WjxiEXvttEcyDzlcgv+C8k5mESNkEhqhkRg8vAk+pqbSdv293nldiyYQaOu92Dy8CyznkObVhJ9zU2Obq5d2ZawlejSMtJSssnly5bQ5w7tv7Nut/ZgvS4jbdK0WdFOxPoNGpJ3No/8/HynZ3Ak7irjLMTd8zsbEekpIrtEO57v9QuUuV9Edoh2lOD3F6vzSjtZDpeUikgd4C60kaoLUV694fpuR4DDQJXb/xoVGkjK4WK5YGrmMeqEBdiUSdidSt+u2nGOfbu2wr+mD0EBfmzdrXWqfH08CQ6sQad2DYkOd52FvwBRoQGkZJTKH1oqf1IafbtoHcu+nVsU5f874QArNyazb/5b7FswnMXrdrFrf5l98SpLZJAfqdnFyoq0nNNE1raVsY76ZSv333gV2yfcxc+vdeG16esBqOHtwaDbmzP6l61ObbM9OXMsG7/AkKLvfQOCOZNra185eiiZ08eyiGze/oL1pGxaRUwb1zq7Myszo0g0ChAWHkFmKdlkVgVkpEv+XESTps2KznSsLri7jNNd85vE/l8XQ58x+wzohSYn/5eINCtVpiEwBLhBKdUceOGiWS4jf4Wwo6T0Y2CwUuqKx+n1US6XXL04ZNxcbmrbgL++f42b2jQgNeMYFotiybpEFq7ZwbKvXmT6e//h7637sVhdMmK5DBn/P25qU5+/vn2Rm9rU1/NbqRcdTOO4cBr0GUn93iPp3K4BN7S+qrKba3fuvT6OH1bupfnzc7jvg2V8MbAjIvD6Pa2YuGAnp/IKLl6Ji6KsVrb+OpVWfftfsEzOgV2YvbwJiKzrxJZVDfYkJ/HJuDEMfevtym6KgYErcy2QrJTaqwvKf0SbCSvJk8BnhUcAKqUu+on+Ss+f2E6xs6pMlFJpIlIoKV1DxSWl84G3gHZoygaAEOA2ESlQSs0tUb48+WmGiEQqpdL1acVLHubQ/WBPXbTgZZKWdYzoiOLRpzphgaRm2k79pB85zoOvaLaKGr5e3NmtNbkntSmjD6b+wQdTteVqX7/7CEkHXGskJy0r12b0rU5YIKlZZeQfrM361vD14s4uLck9eZbH7+zAP9sOcOqMNk2yaO0urmtZlzWbXUdImZ5zmjrBxSNXUUF+RWczFvJQ5/rcO1rTdqxPPoKPp5ngWt60rR9C32tjeedfbQjw88KqFHnnLEz5s0zVXJXENzCY08eKvWBncrPxDSg+Easg7wzHDx9g5advAHD2xFHWTv0/OvZ/s2hd1qH4lcRcc7NzG24HQsPCi0SjAJkZhwkrJZsM1WWk4RER58lIMw4f5uUXnuOd90YTE2O7hq064K4yzkLcNX8lrckqOppPJwXtZJiSNAIQkTWAGRihlFpYXqVXOpLlcEmpUuoqpVScUioO7eDngaU6WJRXL7aS0pLy0grjcBnp9oM0iAmlblQQnh5m7uvRhnkrEmzKBAfWKHrjvfr4rUz/dR2gLRoPCtB+QbdoGEWLhlEsXpfoqKY6hA07DtEgJqQ4f/fWzFu13aZMcIBfcf5HuzL9d2267NDho9zUph5mswkPs4mb2tQjcZ9rdTLj92ZTP6IWdUNr4Gk2cU+HOBZstHV9pWSfolMLbdqgUZQ/3p5mjhzP47aRf9Dqhbm0emEukxYmMubXbS7VwQKoHdOQk1lpnMo+jLXgHCmbVhLV/Nqixz19a3D7/31Pr+FT6TV8KkF1G9t0sJTVSsqW1US7YCereYuWHDpgKyPt1LmUjLRzV/73m/Zf3pI/F9H+Wk1GeuL4cf777NM8/8LLtL6mTWU03+G4u4zTXfM7wvheeHJLia/LGTjxABoCnYF/AVNEpNz1OVc0kuUkSekFEZHNSqnCrVQXrBeYKSL99Xrv158bAWxAk5RaReQFoFmpBfhOwWKx8uLoWfz+2UDMJhPTf1vHzr2HGTbgNuJ3HGTeym3c3LYh7zyvCTdXx+/hhVE/A+DpYWbxVG1a+MSpszz+5rdYLK7l8rFYrLz44Rx+/+RJTcb6+3p27s1g2FM9iN95iHmrdnBz2wa8M7AXCli9aS8vfPALAL8s3Uqndg3Y8P3LKAV/rktk/uodlRvoErFYFa9+vZ7Zg7thNgnfrdhDYmoub9zTik37clgQn8KbM+IZ/8R1DOzZFIVi4Bd/VXaz7YbJbKb1PQNY/cVbKKuVuOtuwT+yLtsXfEftmIZEtSj9YdKWI3u34xcYSs0Q1zG9F1IoI312gCYjvUOXkU7SZaSddBnpsCGvccdt3QkICOD9DzQZ6U8/zODQoYNM+XwiUz7XdtdO/GIqQcHB5b2kS+GuMs5C3D2/PVFKTQYml1Ok6Gg+nbJm2lKAv5VS54B9IrIbrdO1/kKVGjLSS6BIRmpnhYOr4CiFg6vgKIWDq+AohYMr4CiFg6tQWQqHqoIhI3WujLTPF+vtfrP/93T7ctuvn3m8G+iG1rlaD/xbKbW9RJmewL+UUv8RkRC0U2daX+CsZe05Rier4jhSRmpgYGBgYFBVcWYn647J9u9k/fZU+Z0sABG5DW2znRmYppR6V0TeATYopX7TlyONQfNwWoB3lVI/llfnFa3JEhGLiGzWfRFbRORlESl0YgWLyDIROSkin5Z6XlsRSdBdFJ/oDUdE7tPrsorIeWugRCRWr++VC7TnKhH5W6/3JxHx0q/fLCLxIlIgIveWKN9aRP7SX3OriDxwJffDkfj5+REXF0dcXJyN4dpdMPIb+d01vztnByO/u+d3Jkqp+UqpRkqp+kqpd/Vrw5VSv+l/V0qpl5RSzZRSLS/WwYIr3114pnBNlIiEAd+jrXF6C2191jA0l1aLUs+7kJ19G3A38MUFXm8sxWutymI0ME4p9aOIfA7011/rIPAoULpzdhp4RCmVJCJRwEYRWaSUOkY5GNZj52LkN/JXdv5C47U7/+yDe+d31/c+GMb3K8FunizdF/EU8JyIiFLqlFJqNVpnq4iLWN93KqXKXPQhIncC+9C0EWU9LkBXtB2IYGt336+U2oq2qL5km3crpZL0v6eh6R1CLzm8g3F366+R38jvrvndOTsY+d09f3XArjJSpdRetLnMsHKKlWt9Lwv9/MPBQHm2vWDgmFKq0Mp40XpLvca1gBdg/0PvrhB3tf4WYuQ38rtrfnfODkZ+d83vCIVDZXGl04XOYgTaNOBJRwwj6qNr3wL/sYdZ3sDAwMDAwODyMFWj6UK7drJEpB7aivvybJDl2dkvxHXAvSLyARCI5rU6q5QquaA+GwgUEQ99NKsi9SIi/sA8YKhSat0FyjjU+H4x3NX6W4iR38jvrvndOTsY+d09f3XAbtOFIhIKfA58qsrxQlzEzn6h59xUwvr+MfBeqQ5W4bmEyyg+5ueidnd99+Ec4Bul1KwLlXO08f1iuKv1txAjv5HfXfO7c3Yw8rtrfmO6sBhfEdkMeAIFaFNuYwsfFJH9aLsNvfSF692VUju4gJ1dt8dPQFt8Pk83uvcorwEiMh94Ql+4PhjtnMP/Q5OETdXLtEfrTNUGbheRt/UTtO8HbgaCReRRvcpHlVKbr+Sm2Bt3t/4a+Y387prfnbODkd/d81cHDBnpJVAoI3VX869hPTbyu2v+ylIYVBWM/O773gfnG9/v/Sre7jd71mNtKmU8y1UWvlcpnO0MqUq4c3Yw8hv5jfzuijtndzbVaN37xTtZImIBEiieEvwGbaefVURuRTuA2QvIB15VSi3Vn9eW4inB+cAg/UDpD4Hb9fJ7gMeUUsdEJBjNcdUe+Fop9dwF2hME/ATEoR1Gfb9S6qiINAG+AtqgLWL/SC8fo7c5HFDAZKXU+Eu5SaXZfMDxZ0hvXr+WryZ+hNVqpVuvO7nzwUdtHv/frO9YsuBXzGYz/gG1eeaV4YSGRzq0Ta3r+rttdjDyu3P+1nX9tba5ef5tKSftXnf8P2uY9ulHWK0WbrntLu7+92M2j2/fspFpn43hwN4kXhr2Ph073VL02DdffMzGdauxKitXt+1A/+dedYjIskV0TY6cPGf3egHWrVnFxx+NwmKxcPtd9/DIYx5NkCsAACAASURBVE/aPJ6fn8/IYUNI3LmdgMBARo4aQ2SUZidK3r2L0e++zelTJxGTianf/oS3t7fd2xhS09PudboLFVn4fkYp1Vpfw3Qr0AvN6A5wBLhdKdUSbaF5ydNzC63uDfWvnvr1P4EWSqlWaIcxDtGvFxriyzwypwSvA0uUUg2BJfr3ADnAf4GPSpUvAF5WSjUDOgDPikizCuSuNKwWC1MnjOaN9z5h3Jc/s2bZIlIO7LUpE9egCaM++5aPJv9Ih5u78d2UTyqptfbFnbODkd/I7175LRYLU8aP5s1RExj/1WxWLV3Iof22eUPDI3l+8Ahu6tbT5nriti3s3LaFsV/+xMdTfyZ513a2b9nozOZfMRaLhY9Gv8uYCZ/z/ezfWLxwPvv2JtuU+X3ubGr5+/Pzbwt5oN8jTByvLXsuKCjg7Tdf57Whw5kx6zc+m/w1Hh7VY3LKJGL3r0rLcimFy7C6b9IXnINmYvcVEe+LWN3/KCEMXYeuc7iQIb4M+qLZ3MHW6p6plFoP2HzcUEqlK6Xi9b+fAHaiS0pF5EkRWa+fuzhbRPwu5X44iuRd24mIiiE8MhoPT086du7O+rUrbMq0aN0Obx8fABo2bUFOVvUQ1LlzdjDyG/ndK39y4jYi60QTERWNp6cnN3btwT9rl9uUCYuIIq5+I0wm219XInAuP4+CgnMUnMvHUlBAYO0g5zXeDuzYlkB0dAx1omPw9PTilh63sWr5Mpsyq5YvpVefvgB06dadDevXoZTin3Vrqd+wEQ0bNQEgIDAQs9ns9AwG5XPJCodyrO73APFKqTwqbnV/nPLPIiyLcF0DAXAYbRqwQohIHHAN2pmJAL8opdorpa5G63z1v8S2OIScI5kEhxbHCg4JI+fIhdVjSxf8SutrOzqjaQ7HnbODkd/I7175s49kERxWbDQPDgkjJ6s8zWIxjZtfTYvW7el/b3f639eD1u2vJ7puPUc11SFkZWUQHlE81RsaFk5WZkapMpmER2j3yMPDgxo1a5F77BiHDuxHRHhh4JM8+u97+e7rqU5tuyMRB3xVFnbxZIlIc7TDmZ++hOcMRZvKm3G5r6uPklVoF4J+NM9s4AWlVOHCihYiskpEEoB+QPMLPPcpEdlwue10JCsXz2fv7p3ccd8jld0Up+PO2cHIb+R37/zpqQdJObiPKTMXMmXmQhI2rWfH1vjKbpbTsFgsbN0cz4h3P+Dzqd+yYtkSNvxdpk/b5RARu39VFpfcySptdReRaDQH1SNKqcJz/8q1uutOqj5Av/LEpRcgQ5+OLDwO56Ife0TEE62DNUMp9UuJh74GntPXlL0N+JT1fGfLSINCwsguMQWQfSSToJDzj4PcGv83c76fxmvvjMXTy8tZzXMo7pwdjPxGfvfKHxwSSnZmsdE8+0gmQaHlHX1bzN+rltGoWUt8ff3w9fWjzbU3sGvHVkc11SGEhoaTcTi96PuszAxCw8JLlQkj47B2jwoKCjh18gQBgYGEhofTuk1bAmvXxsfXl4433sSuxB1Obb/BxbmkTlZpq7uIBKIdSfO6UmpNYbnyrO4i0hN4DbhDKXX6Mtr8G9oie6iY1V3QpKQ7lVJjSz1cC0jXO2H9LqMtDqF+42akpx4iMz2VgnPnWLv8D9pdf7NNmX3JiUz5+D1ee2csAS62DqE83Dk7GPmN/O6Vv0GT5qSnHiIjPZVz586xeuki2l/fqULPDQmPYMeWjVgsBRQUnGP7lo1Ex17l4Bbbl6bNW5By6CBpqSmcO5fP4kXzubFTF5syN3XqwoL/ab/mli35g7btr0NEuO76G9iTnMTZM2coKChg08YNxNWrXxkx7I5J7P9VWVxURlqGwuFbYKyucHgTbXdgUomndFdKZYpIO2yt7s/rHbNkwBvtrEGAdUqpAfpr7Uc3xAPH9Lp2iMiXwOdKqQ266mEmEAscQFM45IhIBLBBf74VOAk0A1oBq/QMhYc/v6GUmi8iz6B1+LLQ1mnVUko9Ws69UOCcbdzxf69m+qSxWK0WuvS4g7v79eenrz+nfqOmtOvYiZGvDeTgvmQCg0IACAkLZ/DIcQ5tk7O28FfF7GDkd+f8zlQ4VOX8jlA4bFy3mmkTP8JqsdKt1x3c+9AT/PDVJOo3asa1N3QiKXE7o4e/zKmTx/H08qZ27WDGfzULi8XC5PHvs2NrPCLCNe078tjAl+3ePnCswmHt6pWM/2gUFquVPnfcxaNPPM2USRNo0qw5N3XqSl5eHu8Me53diTvxDwjgnfc/ok50DAAL5/3Ot19NARE63nATz75wsc35l0dITU+nykj7fbvZ7jLSGQ+3rpSulmF8vwSc2cmqijjrl2xVxcjvvvmd2cmqijiyk+UKOLKT5Qo4u5P10Hdb7N4x+e6hq6tmJ6s6y0hFZDnwilKqQovaCztZlYGfnx9hYdpahdzcXI4ePVpZTakUjPxGfnfN787ZwchfVfI7s5P18Az7d7K+7Vc5nayKmMvOKKVaA4hIGPA92pTcWxTLSNNEpAWwiGJVQ6GM9G+0TlZPtGnDP4EhSqkCERmNNt04mGIZaQv960IUykhHicjr+veDKZaR3lmqfKGMNF5EagEbReRP/aDqy8LZZ1hZLBbu6N2DL6Z8RXh4OP9+4F5GfTiW+g0aOLUdlXV+l5HfyF/Z+Svr7L6qkB2M/O783gfjSKErwa1lpDoPi8hmEdkmItdeyv1wFtsSthITU5fomBg8vbzoeVtvli9bUtnNchpGfiO/u+Z35+xg5HfX/G6tcKhmMlIAP32kbiAw7RLb4hQyMzKIiCwW9oWFh5OR4bqW50vFyG/kd9f87pwdjPzumr867S60y0FHJWSk3S/hOXaRkVZ0ndQFZKQAP+h1rRQRfxEJVEodu9w2GRgYGBgYGBjAZXSy7Cwj7Xa5MlKlVLodZKRwvjH+vPaIyFNo06SVQlh4OIfTi4V9mRkZhIdXeADP5THyG/ndNb87Zwcjv7vmr8zpPXvj7jJSgAf0cjcCuUqp3NIFnG18L03zFi05eHA/KSmHOJefz8L58+jUpWtlNcfpGPmN/O6a352zg5Hf3fNXByoykuUrIpspJSPVH3sOaAAMF5Hh+rXu+gL5gdjKSAvXXn2KJiP9U++tlikjFZE7KUNGiqaMmCki/dFlpPpzbWSkIvICxTLSh4EEPQfoMlL972dFZJOe7/EK3A+n4+HhwZChw3nmqSewWi3cedc9NGjQsLKb5TSM/EZ+d83vztnByO+u+avPOJYhI70kCtd/VcZW3qpAZW1jrioY+d03f2UpDKoKRn73fe+Dlt+Znqwnftpm95v95QMtqqwny6AU7uwMcefsYOQ38hv53RV3zm5w+Vy0k+VCxvd+aFJSAU4AzyiltpRnfK/KXMzyGxgYSEBAAKAJ6w4fPkxBQcF59bgi7pwd3DP/xTL7+voSGhqKt7c36enpnDxZfLxLSEgINWrUACA7O9vmMVfgcrMXXi/Ey8uL9PR0Tp065bzG24Er+bf38PAgPDwcDw/tV1lqaqrL/SxcLL+IEBERgbe3NxaLhfT09KKMXl5ehIeHYzJpy6sPHjxIdZidqkbr3quV8X0f0EnvcPUCJgPX4QDj+yYHn19mtVh44bG7GTr6M4JDwhny3CMMeuNdouvWKyqzbfMGGjZpgbePD3/8PosdWzbywpvvO7Rd19T1d9vsoOXffNDx+Qc9ejdvjv6M4NBwhjz7CIOGlpP/t1ls37KRF4c5Pn/rWH+2HDxh93otFguDHr2LYaMnEhQazpBnH2bQ0PeIKZE583AaZ06f4reZ39Ku481cf/MtAGxct4p5v/zA0Pc/4Vz+OUa8/BTDP5yEX42adm3j1bG1ANieat8OnMVi4dlH7mLEhxMJDg3ntQEP8dKw94mJs81++tRJfv3pW9rf0ImOnW45r54Tx3MZ+FBfZi76C28fX7u2EaB5He1+7kq3bwfOYrEw4KE7GTlmEsGh4bz0dD9eHf4+sXH1i8pkpKdx+vRJ5vz4Ddfd0IkbOt9a9NiQQU9w/0NPcE37Dpw5fRoxCT4OyN84sgZZJ+x/dqHFYuHBu3rz8cQphIWH88TDDzDivQ+5ql6x0f2XmT+QnLyb1954i8WL5rNi2RJGjhpDQUEBj/e7j2Ej36dhoybkHjtGzVq1MJvNdm9naC1Pu9fpLlQn4/tapVThR4CS9V7Q+C4iT4rIehHZIiKzRcTvUu6Ho0jetZ3wqBjCI6Px8PSkY+furF+7wqZMi9bt8PbxAaBh0xZkZ1UPQZ07Zwctf0RUDOFRJfKvKT9/zhHXzl8ys6enJzd07s6GNcttyoRFRFG3XkOklFUw5cA+mrW6BrPZAx9fX2LrNWTz+rVObP2VkZS4jcioaCL07Dd27cE/ZWSPq98IMV34v+u/ViymzbU3OKSD5UiSdm4jsk5MUf6bu/bg79XLbcqER0ZxVRn5D+7fg8Vi4Zr2HQDw9fNzSAfLkezcnkB0TAx1omPw9PSiW/fbWLV8mU2ZVSuWclufvgB07tadjf+sQynFP+vWUr9hIxo2agJAQOD/s3fe8VFU6x9+3pCEECAEUpYWCAJSVVBARbpKERT5KeK9KBYUxYYFBUSxoFhQVCwoIIpevHa9KKAoHREE6ZDQEQghnRJpyeb8/pjZZDekuy075+tnPyY7Z2bfZ3ZJTmbOeU6kRzpYvog2vvu/8X14Ucctwvj+rVKqo1LqIozO1/By1uKRZKanEhVTgBUVHUtWevE6sCUL/ke7Tp29UZrHY2V2MPljnfhjYsnMKJ5/8U//o13Hys1fmLlOjI2MjLQy7RvftDkb1/7OmdOnOH4si20b11WqTndmehrRsQVG76iYWDJK+LwXlxVLfqbLlX3cWZpXkpGeSrTL591GRnrZ3vukgweoXqMmk556jFHDb2bWtDew2+2eKtUjSUtNIdZWL//7WJuNtEKf37S0VGJtxmckODiY6jVqcuzoUQ4e2I+I8Mj9d3PHv29kzuwPvVq7JyPi/oevEnDGdxHpidFZ6lLo+aKM721F5AUgEqiBcbuzUmXFr/PZszOBZ1+f7utSvB4rswMs/3U+e3ck8OwUa/IDXNThcnbv2M74UXcSUas257e+gKCgwPhrvqzJzEjjwN7dtO94ua9L8Wry7Lls37yBt2b+l5jYurzy3BgW/TSX3v0H+bo0r8Sea2fzxvXM/OQLwsLCeGjkcFq0akOHTpf5ujQdp5T7Spabje9DK2p8N4/jYnwXkQuBmcBApVSG0/PFGd8/Bh5QSl0APAeEFcM8QkTWlbPOCqdOdKzLX+MZ6anUji584RA2r1/Dt5/N4onnpxASGuqt8jwaK7ODyZ/qxJ+WSp2oIvj/XMN3n83iiYmVn78wc2ZaClFRMSXs4Zobhg7ntQ/+y4RX30MpRb2GjTxRpkdSJzqG9NQCo3dGWipRRXzeS8pvS37h0i49CQ6ufONmoqJjSXf5vKcQFV229z4qxkaTZudTt35DqgQHc1mXnuzZmeipUj2SmFgbqSnJ+d+npqQQE+N6cyYmJpbUFOMzkpuby9/ZJ6gVGUmszcZF7S8hsnZtwqpV4/IrurIjscJDjf0qQSJuf/iMpTyN/dn4LiKNgG+BW5VSO51qLsn4XhNINjthQ4t7QW8b35u2aM2RpIOkJieRm5PDqqUL6XB5N5c2+3YnMvPNSTzx/BRq1a7jrdI8Hiuzg8GfXJi/cyH+XYnMCCD+ZiZzSnISOTk5/LZ0IR06dy/Tvna7nRPHjKVG/9q7iwP7dnNRh8rzl3zzlm1c2Fcu/pmOZWR3ZOXin+h6ZV8PVejZNG/ZhsOHDnDE5F+++Gc6XdGjzPv+nX2CY0czAdi8fi2NnCYMVIa0bN2WQwcPcDjpEDk5Z1m0cD5duvd0adOle0/m/2gsbLJ00UIu6XgpIkKny69g7+5dnD51itzcXDauX0eTJk2LehkdH6ZUGWkRCodPgSmmwuEpjNmBu5x26a2UShWRDrga3x80O2a7MYzvjitNRRrfgaMUYXw3VQ9fAo0wje9KqUyzzQ3mcwC5SqkO5nI5K0yGPHPbk0qp+SIyEqPDl4YxTqumUur2Es6FAs/PLgTYsGYls6dNIS/PTo8+1/F/Q4fz5cfvc975rejQuTsTn7iPg/t2E1knGoDoWBtPTHzDozV5Y3Yh+Cc7eGd2IcD6NSuZ/Z7B37Ovwf/Fx+/T1MH/+H0c2LebyKgC/jFe4PfU7EIwmD9+73WTeSA3DB3O5x9Po+n5renYuTu7E7cx+dnR/J19nJCQqkTWieKND7/i7NkzPHGv8fdReHh17n74SZo0a+H2+jw1uxDgz9Ur+fDd18jLy+PKftcx+Ja7+GzWNJq1aE2nK7qzK3Ebrzz9GNnZxwkJrUrt2lFM/fhrwJh5OO7BO5jxxYL8afyeiKdmFwKsW72CGW8b/FddM5Aht97Ffz58j+YtW3PpFT3YmbCNSU8/SvaJ44SGGu/9e7O/AWDD2tXMem8KSimatmjFA6OfJiTE/Vf0PDW7EGDVyuVMff1l7PY8BgwcxG3D72HGtLdp2boNXbv34syZM0x8eiw7dyQQUasWz016jQYN4wD4ef4PfPLRDESEy6/oyv2jRnukxpiaIV6Vkd737Xa3eyje+7/WPrmcpY3v5Yg3O1n+GG91svw13upk+Ws82cny93iyk1UZ4slOVmWIJztZlSHe7mTd/12C2zsm7w5q5Z+dLC/KSIs9VqF6ipORtgQ+Ai4GxiulXjPbu01GWniQvTvTp08f3nrrLapUqcLMmTN55ZVXXLY3atSIWbNmERMTQ2ZmJrfccgtJScYwt9zcXLZs2QIYMrqBAwd6qkyPRfNrfqvyW5kdSuePi4tj9uzZRJqKgrFjx7JgwQIaN25MQkICO3bsAGD16tWMHDnSFwhuiz+LiHUnq2IpSycrWylVw/zaISP9TSn1jIi0B1KcZaRKKYeD6g/gIQpkpFOVUgtEpDew2ElGilJqTEnHKlTPq0Cmk4y0trl/LNAYw5uV5dTJqgfUc5aRAtdXREbq6GSFdXm6vLuWmKAgYctno+j/yGyS0o6zcsY93PbcVyTuL5jKPOf5m5i/aidzftpI94ubMOya9gx/wRjDn/bzeGL6vOjWmorK6ZUT3c4Oml/z+z//6ZUTAQjr+oxbjxsUJGyZ8yD9H/3UYJ9+N7c99w2JfzmxPzeY+b/vZM5Pmwz2fu0Y/uJ3AKT99CQxfSe5taaicnrFcwDUuOljtx43SIQNbw5i4IsLSco4ybKX+nPHW8vZkXQsv83Uuy9n0/5MPvxlBy0a1OKbsVfR9sFvaBRTna/GXMmlo+e6taaikv3l7Wz0goh51B2miLiMIuZtm/7kES+ImNs1jvBqJ+tBD3Sy3vZRJ8ufZKRFHquIMoqTkaYqpdYCLtd0K4OMtGOrhuxJymR/chY5uXa+WrSFAV1aurRpGR/LsvV7AVi2ft852ytzNL/mtyp/x1YNCrFvZUAX1zFlLeNjWLZ+HxBY7AAdmkWzN+U4+1OzybHn8c2qfQzoGOfSRqGIqGaMs6oVHsqRrIrMl/L/5Et5yyFizqxETjirxl9lpM7HKpyyyEiLjL/KSOvH1ORQasFfbklpx2kQHeHSZsvuIwzs1hqAgd1aEVE9jDoRht04LDSYlTPuYdn7d3Nt18r3A1jza36r8tePjuBQasEVkqS04zSIKcyewsBurQAHe1VX9ukjWDbtLq6thJ2venXCScooGOeVlHGSerWru7SZ9NUmhnQ9j8T3buTrsVcy+qM1+dsax9Rg5csDWPBMHzq3LJ/6wt9SlIg5swQx7eIAEzE7J5CM734nIy3PsYqSkZbwepVaRjru3Z9545H+3NKvPb9t2k9S6jHseQZ6i8FTOJx+gvh6tfnprdvZuieFfYezSjli5Yrm1/xW5R/33kLeeOQabunbjt82/0VS6vEC9pveKGB/8za27g0sdoDBVzRhzrLdvP3jdjo1j2HGA13pNPp/HMk6Rev7vyEz+wztmtThv6N70Wn0/zhxKvAHqC//dT57A1jEHORDQ7u7U+5OlptlpFc6y0iLOVbhpIhIPaVUcmEZaQk1lyQjvV4ptcmsqUcx+4/AuE3qkRxOO0HD2Fr53zeIiSAp3fX+f3LGCW5+6nMAqlcL5frurTmWbSzzeDjdmPG1PzmL5Rv30+78epXqB63m1/xW5T+cfpyGsQVXrhrERJCUVhT7F4DJ3q0E9uaVhx0gOfMkDaIKrlw1iAonOct1BuOwns0Z9NIvAPyxK42qIVWIqhlG+vHTZGYbNzs27stkX8oJmtWLYMPeDCpjihIx1ylGxPzdZ7N49vXplV5EbIX4jYy0uGMVkSJlpCXU7Pcy0nWJSTRrWIfG9SIJCa7C4CsvYN5KV3NxVK3w/Euej9/SldnzNwAQWSOM0JAq+W0ub9uIhP1lW/vLX6L5Nb9V+dclHqZZwygn9rbM+22HSxsX9qFdime/IK5SsQP8uSedpnUjaBxTg5AqQdzQuQnz1h1yaXMwPZsebY31/Vo0qEVYSBXSj58mumbVfJN3fGwNmtaLYH9K5VWMFCkiLkLEHEgi4uISJO5/+CpluZJVTUQ2UkhGam57AGgGTBCRCeZzvc0B8vfhKiN1jL16B0NG+ov5g8MhIy32WM4yUgzNw5ciMhxTRgogInWBdRgy0zwReRhoDVwI3ApsMTnAlJECT2OMz8qXkZbhfLg9dnsej7wxjx9eH0aVoCBmz1tPwv40nh7ei/WJScz7bQfd2sfz/IirUShWbvqLh6f8CBiDYt8efR15ShEkwmtzVrjMyqoM0fya36r8dnsej7w5nx9eu5UqQcLs+RsM9jt7sn7HYYO9XTzP33MlSmGwvzEPcLAPIC9PERQkvDZnpcusxMoQe55i9Kw1fP/kVQQFBfHp0l0kHjrK+MHt2LA3g/l/HuTJT9fxzj2dub9/a5SCe6cZf4N3bmXjqZvak2PPI08pHp7xO1l/n/UxUcVTpUowdz7wOC+Oe9CQ8va5jrj4pi4i4v9Mn8rpU6eYMnEs4D0RsbfjyzFU7o6WkZYjnlI4VJZ4agp/ZYnmty6/pxQOlSWeUjhUlnhD4eDP8bbC4bEfdri9Y/L6tS3805OlUxBPykh1dHR0dHT8Nd7sZD3+o/s7WZMH+KaTVeqYLBGxi8hGEdlm+qQeE5Egc9vVIvKniGwx/9/Lab9LzOd3i8hUc2wUIjJZRBJFZLOIfGeOxSrxWIXqqSMiv4jILvP/tc3nW4rI7yJyRkRGO7WPE5ElIrLdZBjltG2pGGss+n3Cw8OJj48nPj6e2rVr+7ocr0fza36r8luZHTS/1fkre8oyJuuUUqoduBjfI4BngHTgWmdLOwU+rGnA3RQY3/tijMv6BRjnZHwfB4wp5VjOGQsscjK+jzX3z8QwzF9fqH0u8Jiz8V1EfqmI8T3/hOR494KW3W7nuv59+GDGR9hsNv495EZenjyFps2aebWOaiHidXbQ/Jrf9/zVQow/gq38bx+szW/Vzz4UvP/eSgANybK28d3MreaVuq0i0qk858Nb2bplM3FxjWkYF0dIaCh9r+nP0iWLfF2W16L5Nb9V+a3MDprfqvxBIm5/+IylvDsEmPEdINy8UncfMKusx/JmUlNSqFuvbv73sTYbKSnWWU5B82t+q/JbmR00v9X5AyFWN74D/Nc81nIRiRCRSKXU0TJi6Ojo6Ojo6Lgx5b7648cpN4ubje9DK2p8N9v/U+M7QOFO2jmdNhEZISLrSnsdTyXWZuNI8pH871NTUrDZynwBr9JH82t+q/JbmR00v9X5AyFWN74DDDHbdQGOKaWOFW7gaeN7aWnT9gIOHNjPoUMHyTl7lp/mz6N7zyInXwZkNL/mtyq/ldlB81uVX8T9D1/F6sZ3gNMissHku7MM58PrCQ4OZtz4CYwccRd5eXauH3QDzZo193VZXovm1/xW5bcyO2h+q/L7cqC6u6NlpOWIY/yXL6by+kN8NY3ZX6L5rcvvK4WBv0TzW/ezDwa/N2WkT/+0y+0ne2Lf5j7puZV6JUtE7MAWCq5kfQK8oZTKE5GrMa4shQJngceVUovN/S6h4ErWfGCUeYtxMnCt2X4PcIdS6mhJxypUTx3gCyAe2A/cpJTKEpGhGL4sAU4AI5VSm0QkzqzZhjHearpS6q3ynijneMIZEh4eTmysMWHz2LFjZGVluWyPjIykVq1agOFOOXLkCLm5hgkjODgYm81GcLDxdiYlJeVvc3c85UvR/JrfqvyVhR38iz84OJj69esDxlp3R48e5dixc0Z7uC2++uyLCHXr1qVq1arY7XaSk5Pz32ObzUZYWBgAqampnDp1yiM1ejsBdCEroGSk+4DuZoerHzAduBQPyEiX78is6K5FJs9uZ/y9N/HoxKnUjorlhUfvYMTjE6nfqEl+m8TNf9Lk/DZUDQtjyfxv2LFlPfeOeRGAV8eNpP9Nt9Om/aWcPnUSkSCqmv/w3JluLeq4nR00v+b3f/5uLeoA1v63D/DHHvdOvM6z2xl9142MnfQOdaJjmTDqNu4f8wINGp+X32b7pnU0bdGWqmFh/Prj1yRsWc+D4yaRm5ODUoqQ0FBOnzrJ2Hv/xTNTZlI7KsatNQJ0ahrJ4aNFGYX+Wex2O7feOIDX3plOTGxd7r3tZp5+4VXiz2ua3+b7rz9nz66dPDZuAosWLmDl0kU8M+k1vvvqv+xI2MbYCS+QlZnBmIdH8v7HnxMU5P65efUji1JW6pQlgSQjXaWUcvwJ4HzcYmWkInK3iKw1lwv6RkTCy3M+3JV9u7YTW68hMXUbEBwSQqduV7NxzXKXNi0vvCT/h2fTFm3JyjAmVR4+sI88u5027S8FIKxauEd+yHoyml/zW5XfyuwAe3Zuw1a/IbH1DP7Luvfmz9Wu/K0v6pDP1azlBWSmG/zBISGEhIYCkJNzFqXyvFu8G5K4bQsNQxu1FgAAIABJREFUGjaifoM4QkJC6NW7H78tX+LS5rdlS+jb/zoAuve6mj/XrkEpxV/79nBxB+O9r10niho1ItiRsM3rDJ5IkLj/4TOW8u5QSWSkw4s6bhEy0m+VUh2VUhdhdL6GF3EsjycrI43a0QWns3ZULFkZacW2X/HLD1xwyeUApCQdILx6Td6dNIbnRg3jq1lvk2e3e7xmd0bza36r8luZHSArPY06MQU/wutEl8y/bOFcLupwef73GWkpjBv5b0YNu5YBg4d55CqWJ5OWlkqMrUA2GhNrIy0tpdg2wcHB1KhRg2PHjtK0eQt+W76E3NxckpMOsSNxO6kpRwiEWNr4XlScBKL3lGOf0mSkpR7LvErmMkBORHpidJbGFHq+KBlpWxFZISJbgKFAm7LW76v8vmQBf+1OoM//3QKAPc/Oru0buenOh3hqyizSjiTx26J5Pq7Sc9H8mt+q/FZmB1i5eAF7dybQ/4Zb85+LirHx0rTPeP3Db1nx6zyOZWX4sELvpt+1g4iJtXHPbTfzzhuv0PbCizxyq1Dnn6Xcxnc3y0ivrKiMVCmVXFhGKiIXAjOBfkqpDKfni5ORfgxcbw6Qvx3oUQzzCIzbpB5J7agYstILnKpZGalF/kW2feMfzPvyY554aRohIaHmvrHENTmfmLrGhcL2l3Vn746tnirVI9H8mt+q/FZmB6gdHUOm05WbzPSi+bdu+IO5n3/E+Fffz79F6HKcqBgaNm7Kjq0b6dT1So/W7M7ExMSS5nT1KS01hZgYW5FtYm11yc3NJTs7m1q1IhERHni04FrC/cNvIa5RvLdK92gCaeB7wMhIRaQR8C1wq1Jqp9NxS5KR1gSSzU7Y0OJe0NMy0vjmrUg5fJC0I4fJzcnhj+W/cFGnri5tDuzZwafvvsKDT08mIrJO/vNNmrfi5N8nOHHMGI6WsHkd9ZwGzVaGaH7Nb1V+K7MDnHd+a44cPkjqkSRyc3JYvWwhF1/myr9/9w5mTX2JR595jVpO/BlpKZw9cxqAv08cZ+f2jdRr2Nir9f/TtGjdlkMH/yI56RA5OTksXriAzl17uLTp3K0HP82bC8Cyxb9wcYdOiAinT5/i1CnjV+i6NauoUqWKy4B5Hf9IqZ6sIhQOnwJTTIXDUxizA3c57eIQiHbAVUb6oNkx240hI3VcaVqtlLq3lGPly0hFJAr4EmiEKSNVSmWabW4wnwPIVUp1ME3uK0wGx8jIJ5VS80VkJEaHLw1jnFZNpdTtJZwLBe6fYQSwed0qvpjxBnl5eVxx1QAGDLmD7/8znfjmLWl3aTdef+oBDv21h8ja0QDUibHx4NOvAbBtwxq+nDUVFDRu2oJhD4wjOCTE7TV6anYZaH7N79/8nppdCP7PDp6bXQiw8Y/f+M/0KeTZ8+je+1oG/utOvv7kA5qc34pLLuvGS+Pu5+D+PUTWiQIgKqYujz37OlvWr+GzGW8hAkrB1dcOptc1g9xeH3hudiHA6t+W886UV8nLs9Pv2kHceucIZn3wDi1ateGKbj05c+YMk54Zx66diURE1GLCi69Sv0EcyYeTeOKhe5EgITomlieeep669ep7pMb6kVW96sl6cdFut3uyxl/ZzCfXx7SMtBzxZCerMsSTv2QrQzS/dfk92cmqDPFkJ6syxJOdrMoQb3eyJi3a4/aOyZNXNtWdLH+Po5Olo6Ojo6NjpehOVsVSpoHvHrC+T8TwXeVhDFy/3ZSQtgQ+Ai4GxiulXiumnibA50AU8CfGOKyzIvIocJdZYxpwp1LqLxFphyFHjcAYtP+iUuqLcpwnn6c0K3CgR/NrfqvyW5kdNL8V+X3ptXJ3ynQlS0SylVI1zK8d1vfflFLPiEh7IMXZ1K6Ucsg+/wAeosD6PlUptUBEIhwaBRF5CGhtjsuKBRpjCEazSuhkfYnhuPpcRN4HNimlppn6hjVKqZPmeKseSqkhInI+hvFhl4jUx+iYtVJKlevat6/WLrTb7VzXvw8fzPgIm83Gv4fcyMuTp9C0WTOv1uGr9bs0v+b3Nb+v1u7zB3bQ/Fb+7IP31y58dYn7r2Q90dM3V7IqIiN1h/X9uNMhq2O6rpRSqUqptUBOca9vzhbsBXxtPuVsfV/iNGPR2fq+Uym1y/z6MMbVsxjzeBNM6/tWEZluHt+vsnXLZuLiGtMwLo6Q0FD6XtOfpUsW+bosr0Xza36r8luZHTS/1fkDIRUyl7nD+i4iL4rIQQx1woRyvHwUcNRpaZ7ibPLFWd87YdzadHi43jGt720xbmsOKEctXklqSgp16xVYgWNtNlJSUkrYI7Ci+TW/VfmtzA6a36r8IuL2h6/iNj1sea3vSqnxSqk4DOP7A+6qw6zlFqADMLnQ8/UwFBR3qIKFrnqKyBrT+t6LSmB919HR0dHR0fH/VKiT5Q7ru1PmYFwBK2sygEgRcQzaL2yTvwoYjyE7PeP0fASG7HS8Umq1+VwY8B5wo1LqAmAGcM4KqyIyQkTWlaNGtybWZuNIcoEVODUlBZutqCUbAzOaX/Nbld/K7KD5rcpv6QWi3WR9b+50yIFAYllf3xzftQS40XzK2freHvgAo4PlvNxOKEYn8BOl1NdOh3N0qNLNtQ1vpIh42vheWtq0vYADB/Zz6NBBcs6e5af58+jes5evyvF6NL/mtyq/ldlB81uVX8T9D1+lrGsXVhORjRSyvpvbHgCaARNExDG2qrfZybkPV+u7Y4zUyyLSAkPh8BdwL4CI1AXWYagW8kTkYYyZh8dFZD5wlzlwfQzwuYi8AGzAWDYHjNuDNYCvzHuwB5RS1wE3Ad2AKHONQjC0ERtFZAawFTgCrC3j+fBqgoODGTd+AiNH3EVenp3rB91As2bNS98xQKL5Nb9V+a3MDprf6vyBEC0jLUd8pXDwl/hqGrO/RPNbl99XCgN/iea37mcfvK9weHPFPref7Ie7NvFPGWkgi0hFZD/QQSmVXo5zlv8Dx5vxFyGdL9hB82t+6/L7CztYm9/qn30rRET6Am9h2BNmKqVeLqbdDRgaqY5KqRLHa5flduEppVQ788AOEWkE8AyQDlzrLCKlQKcwDbibAhFpX4zbhZOVUk+bx3sIQ99wL5CJIS69vpR6XsHo5DlEpMPN19qA0WFyiEhfBYYAJzEG5OeLSEXk5/KKSF1OiBbyeTWaX/P7ml/LODW/VT/74P0Opi8GqotIFeBd4GoMNdRaEZmrlNpeqF1NYBRG36bUlGvge6CJSM08ISJbROQPEfHuJ7eMsbqQTvNrfqvyW5kdNL9V+X008L0TsFsptVcpdRbjjtnAItpNxLjYc7osB62I8T2QRKQAx0x9wzvAm+WoxWuxqpDOEc2v+a3Kb2V20PxW5/dyGgAHnb4/p38hIhcDcUqpeWU9qFtkpJVYRArwX6f/X+7OWnR0dHR0dHTKlyDE7Q+H79LpMaI8NYlIEIZV4bHysZQzgSIidYoq5uv8aBmpb6P5Nb9V+a3MDprf6vzujMN36fSYXqhJEhDn9H3hfktNoC2w1Jw0dxkwV0RKdGiWq5MVYCJSR4Y4/f/3Yl5Xy0h9GM2v+a3Kb2V20PxW5ffRmKy1QHMRaWL2G24G5jo2KqWOKaWilVLxSql4jLHf17ljdmHAikjNr2uLyGbgDPCvMpwPr8fqQjrNr/mtym9ldtD8VuX3xexCpVSuiDyAYUmoAsxSSm0TkeeBdUqpuSUfoehoGWk5omWkWsin+a3Jr2Wcmt+q7OB9Gen7v+93+8m+9/J4/5SR6pwbX0np/CFWZgfNr/k1v1VjZXZvJ8iXiw26Ob4wvj+LISlNM1/iSaXUfBGJwjSoAh8rpYqcdSgidYAvgHhgP3CTUipLRIZi3EoU4AQwUim1SUTizJptGAPbpyul3irzGfJRSrP8VqtWjZiYGKpWrUpycjLZ2dm+KNMjsTI7aH7Nb11+K7KXxiwi1K1bl6pVq2K320lOTiY3Nzd/e3BwMPHx8WRkZGgbvB/GF8Z3MDpphZfNOQ08jTF6v20J9YwFFimlXhaRseb3Y4B9QHezw9UPmA5citExfEwptd40tf4pIr8UtriWJwczy+Qgq3Dsdju33XQtr06dTkysjfvu+BfjJ75CfJOm+W2OHE7i77//5qvPPubyrj3o3qu3R2sCiKsTZll20PxW5o+rEwZY+98+wMHMM6W0rHgM9gEme13uu+Nmxk98tQj2bL76bLaXP/tVyfw7t/SG5Yzdbuem669h6rSZxNps3DF0CBNfmkyTpgVe7K+//C97du5gzFPP8stP81m65FdefGVK/vZxox9GBNpccCFDh93p9hoB6lT37k2vALqQ5X3jewnH/lsptZLSLaoDMUzv4Gp8X6WUcnTjnY3vyUqp9ebXJ4AEzI6giNwtImtFZJOIfCMi4WU9F55M4vatNGjYiPoNGhISEkLPq/uyavkSlzZ16zegafPzMdQdgRMrs4Pm1/zW5U/cvsVkjzPZ+xXD3gIJkN/C27duoWFcIxo0jCMkJJSr+/Rj+dLFLm1WLF3MNdcavz57XtWbdX+sxjGWetmSX6nfoIFLpywQEiTi9ofPWMq7gzuM7xidtM0iMktEapezBJupiAA4gnEbsHCKM77HA+0pWHPoW6VUR6XURRidr+HlrMUjSU9LISa2ACsm1kZ6WmoJewROrMwOml/zW5c/PS2VmNgCu7nBHth287TUFGJtzkb3uqQVer/TUlOw1TXaBAcHU6NGTY4dPcrJk3/z6UcfMvye+7xas0754gvj+zSgKdAOSAZer+jrmlfJXGYhiEhPjM7SmELP1wC+AR52Wj+xrYisEJEtGEv8tKloLTo6Ojo6Ot7KzPff5eZbhhEeXt3Xpbg9PvJkeSTlvtH6T43vSqkUp2PNAH4sZwkpIlJPKZVs3pZ0Fo9eCMwE+imlMpyeD8HoYM1RSn3rdKyPgevNAfK3Az2KYR6BcZvUK4mOsZGWWvAXXFpqCtExhS8cBmaszA6aX/Nblz86Jpa01AK7ucEe2HbzmFgbqSnORvcjxBR6v2NibaQcOUKsrS65ublkZ5+gVmQk27ZuZvGvC3nnzdfJPnGCoCAhNLQqg28e6m0MtyeQboT7wvhez+mQg4Ct5ax5LobpHVyN742Ab4FblVI7nWoWDGFpglJqSqFj1QSSzU5YsZ9MbxvfW7ZqQ9LBv0g+fIicnByW/PITnbv28NbL+zRWZgfNr/mty9+yVdtC7AsCnr1Vm7YcPPAXh5MOkZNzll9+XkDXHj1d2nTt3pP5P3wPwJJfF9Kh46WICB/M+g/fz/+V7+f/ypCht3Lb8BEB0cEKtJQqIy1C4fApMMVUODwFjAN2Oe3SWymVaq7n8zEFxvcHzY7Zpxi3ChWGguEexxgrcz2gCAwlxFHzWNtFZCbwvlJqnal6+BJohGGMv0kplWm2ucF8DiBXKdVBRLoAK0wGx8LQDm3ESOAJDJ3EGqCmUur2Es6FAs/PMAJYs2oF777xKnl5dvoNuJ6hd4zgo+nv0qJlazp360ni9q08M+Zhsk8cJyS0KnWiopn13+88WpM3ZpeBf7KD5rcyv7dmF4K/83tudiHAmlXLndgHmezv0KJlGyf2UWSfOEFIaKjJ/r1HawLPzS4EWLViGW+89jJ5eXkMGDiIO+66l+nvvU3L1m3o1qMXZ86c4bmnxrBzRwIREZFMfPk1GjSMcznGjPffITw83KOzC70pI5297qDbZaS3dYjzyU1DbXwvR7zZyfLHeOuXrL9G81uX35udLH+MtzpZ/hpPdrIqQ3Qnq+LRnaxyxNHJ0tHR0dHRsVK82cn6xAOdrGE+6mRZ2vguIkuB0aWtol04vljD6rcVy3nl5RfJs+cx6IbBDL/ba+Pw8+PL9bs0v+b3Jb8v1+7zNTtofit/9sH7SwoF0rI6ZRn4fkop1U4p1Qa4GuiHYXuHAuP7BRiD0D912s9hfG9uPvo6bXvDPGY7pdR88zmH8X10KfU4jO/NgUXm91BgfL8AmIhhfIcC43tr4DLgfhFpXQZuv4ndbmfSi8/z3vsz+W7uPH6a/yN7du/2dVlei+bX/FbltzI7aH6r8wdCLG18N3OriGwUka0i0qnUk+CDbN2ymbi4xjSMiyMkNJS+1/Rn6ZJFvi7La9H8mt+q/FZmB81vVX7xwMNXsbrxHSDcXJvxPmBWOWvxSlJTUqhbz9kKbCMlJbBNyM7R/JrfqvxWZgfNb3X+QIjVje8A/zWPtRyIMN1fFNp3hIiUa9yWjo6Ojo6OTvkTSMb3cney3GF8V0rZlVJ5wAygvLfoUhxC0xKM7wPLaHyHQp20Ir73uoy0cGJtNo4kO1uBU7DZAtuE7BzNr/mtym9ldtD8VuUXEbc/fBWrG98BhpjtugDHlFLHylmPx9Om7QUcOLCfQ4cOknP2LD/Nn0f3nr18XZbXovk1v1X5rcwOmt/q/IGQsqxdWE1ENlLI+G5uewBoBkwQkQnmc73NAfL34Wp8d4yRelVEXIzvjhdyNr6LyPUUYXzHUEZ8KSLDMY3v5u4TgCjgPbPXmmtefboCuBXYYnKAqY0wvz4tIhtMPs/ocv9hgoODGTd+AiNH3EVenp3rB91As2bNfV2W16L5Nb9V+a3MDprfqvyBtHahlpGWIw4Zqa98Kb6OL10x/hDNb11+X3qi/CGa37qffTD4vSkj/WJDkttP9pD2DfxTRgqlCkk7UeCkEuBZpdR35n59gbcwZiPOVEq9bD7/IdDBbL8TuF0plS0i3YA3gQuBm5VSXxdTT3Gi08HAs0AroJNDMlqS6FREspVSNcpyHhzxtpgNIDw8nNhYY0LnsWPHyMrKKmUPz8QX7KD5Nb91+f2FHazNb/XPvjfjyzFU7k6ZOlmYQlIAEYkFPsO4rfcMxpiqDkqpXHO81SYR+QHjduC7GALTQ8BaEZmrlNoOPOKY4SciUzBuO74MHABup3QhqUN0ugajk9UX43bkVuD/gA8KtXeITtuaj38Ub/9FY7fbua5/Hz6Y8RE2m41/D7mRlydPoWmzZl6tw1d/zWl+ze9rfl9dyfEHdtD8Vv7sg/c7mIHTxaqYJ6uwkPSkUsqxcmYYBbPzOgG7lVJ7lVJngc8xRKI4dbAE42qUMp/fr5TaDOQV9/oliU6VUglKqR1F1Fyi6FRE3hCRbSKyyBzc71exqpDOEc2v+a3Kb2V20PxW5w+EVGh8WWEhqYhcKiLbMG4p3mt2uhoAB512cxGSishHGDLRlsDb5Xj50kSn5U11YJ25bNAyCpYM8ptYXUin+TW/VfmtzA6a36r8llU4FBel1Bqzk9IRGCciYWXY5w6gPsYyN0PcUUcFk4ex4DTAf4AuPqxFR0dHR0dHJ0BSoU5WYSGpI0qpBCAbY9xTEhDntDlfSOrU3o5xG/GGcrx8saJTN+WcG+++Nr5bVUjniObX/FbltzI7aH6r8gd54OGrVMT4XlhI2kREgs1tjTFu/+0H1gLNze2hwM3AXDHSzGwvwHVAYllfvyTRaQUTBNxofv1vYGURr+lT47vVhXSaX/Nbld/K7KD5rcofSLcLyzq7sCQhaRdgrIjkYNx6u08plQ4gIg8AP2OM35qllNomIkHAbBGJwJhEsAkYabbviLFET23gWhF5zrwNiYhsdMxwpBjRqYgMwhjfFQPMM/fpY27bTxGiU+BvoJOIPIVxZc6Xty6LjFWFdI5ofs1vVX4rs4Pmtzp/IETLSMsRLSPVQj7Nb01+LePU/FZlB+/LSL/ffMTtJ/v6C+v6r4xUxzW+ktL5Q6zMDppf82t+q8bK7N5OALlIyzYmS0TsIrLRdEltEpHHzNt+iEgnc9tGc9sgp/36isgOEdktImOdnv/QbLtZRL4WkRrm891EZL2I5IrIjedWkr//JSKyxTzuVHNsFiIyWUQSzeN+J8YC1ohIlIgsEZFsEXmn0LGyy3PCfJXw8HDi4+OJj4+ndu3avi7H69H8mt+q/FZmB81vdf7KnjLdLhSnpWekwPj+m1LqGREJB846G98x1AwKY8mcfOM78C9zweeIQsb3VKXUyyISjzFuajQwt4Rldf4AHqLA+D5VKbVARHoDi81aXgFQSo0RkepAe0zje0WX1fHV7UJ/sv5a3Xqs+a3Jr43nmt+qn33w/u3CH7akuP1kX3uBzSfXxwLN+L7QqZbVmKoHbXyv3NH8mt+q/FZmB81vdf5ASCAb3+/EnHVYSrTx3c+j+TW/VfmtzA6a36r8Iu5/+CoBaXwXkfEYqok5ZWiuje86Ojo6Ojp+EvHAf75KwBnfReR2YAAwVFXMT6GN734Wza/5rcpvZXbQ/FbnD4QElPFdRPoCTwDXKaVOlvGQ2vju59H8mt+q/FZmB81vVf5Aul0YUMZ34B2gKvCLaXVYrZS619x/P9r4Ximj+TW/VfmtzA6a3+r8gRBtfC9HtPFdW481vzX5tfFc81uVHbyvcPhpW5rbT3bfNjH+a3wXETvGzEHHlaxPgDeUUnki0gmY7mgKPKuU+s7cry/wFsaVrJlKqZcLHXcqcKeTg6sb8CZwIXBzCZ6sSyi4kjUfGGXeuqyDMYg9HuOW5U1KqSwRGQqMMes7AYxUSm0qC3tR8YX5Nzw8nNjYWACOHTtGVlaW12sA31mPNb/mtyq/v7CDtfmt/tn3ZgLJ+F7W24WnHLfqnGSkERi6g61AB2cZqYj8gDGA/F2cZKQiMte8RYeIdMC4LeicA8DtGDLSkjINuJsCGWlfjFuGY4FFpth0rPn9GGAf0N3scPXD6BReWkb2c0+GFvJ5NZpf8/uaX8s4Nb9VP/uglxT6J/GJjFREqgCTMQapOx/7H8lIzePPNr+eTYGkdJVSytH9z5eUmscbZi7Ds0lEPi3v+fBGrC6k0/ya36r8VmYHzW9V/kAa+O4rGekDGMvmJFfg5UuSkdqcjnkEKGqu63DMgfIi0gZ4CuillLoIGFWBejweqwrpHNH8mt+q/FZmB81vdf5ASFlvF5YYpdQaoI2ItMKYOVisaV1E6gODgR7ueO0SalKOgepOr90To5PlEI72Ar5yzIZUSmV6siYdHR0dHR2dkuNLeai74wsZaXugGbDb1CqEi8jucrx8STLSFPN2ouO2Yn59InIhMBMYqJTKKMfraRmpj6P5Nb9V+a3MDprfqvxB4v6Hz1jKu8M/lZEqpeYppeoqpeKVUvHASaVUmUfxlSQjBeYCt5lf30aBpLQR8C1wq1Jqp9PhFgODRSTKbFenmNfUMlIfRvNrfqvyW5kdNL/V+QMhXpeRlvQibpCRvgx8KSLDgb+Am8znJwBRwHumpDRXKdXBlKO+CCwzNRUbMGY3+lWsLqTT/JrfqvxWZgfNb1X+QLpdqGWk5YiWkWohn+a3Jr+WcWp+q7KD92WkixMz3H6ye7WM8l8ZqY5rrOwMsTI7aH7Nr/mtGiuzezuWk5FWIuP7YOBZoBXQSSm1zmwfBXwNdAQ+Vko9UBZub6Y0q29kZCS1atUCDEHdkSNHyM019GTR0dFUr14dgIyMDLKzs71YuXvyT/gBgoKCaNy4MX///TepqS7zMSpFSuOvVq0aMTExVK1aleTkZJf3uHnz5pw5cwaA3NxcDh8+7L3C3ZTS+EWEunXrUrVqVex2O8nJyeTm5hIcHEx8fDxnz54F4PTp05Xy/S8pVjR+O0fzW48/kG4XBprxfSvwf8AHhdqfBp7GmPXYtozMxebn7e79IZ5ntzPxvn9x/3NvEBkVy+TH7+L2x56lXlyT/DY7t6wn/vzWhFYNY8WC79i1dQN3Pv48W9etYukPXzJywmvk5uQw9akHeeD5t6gWXt2tNQL0aR3Lmj1H3X7cPLudx++6kTGT3qFOdCwTRt3G/WNeoEHj8/LbbN+0jqYt2lI1LIxff/yaxC3reWDcpPztn77/OieOZVG9Zi1uu+9xt9cIcGnTSHYc+dvtx7Xb7dw79HomTplGVIyNR0cM5fFnXqJRfNP8NinJhzl5MpvvPv+ES6/ozhU9rs7fNrhPZ35csdHtdRVOi7rVycjOLb1hOWO32xky6Breem8msTYbd94yhOdfmkyT8wrmw3zz5X/ZvWsHY8Y/yy8/z2fZ4l954ZUpJB9OYvSokcz5aq7b63JOVA3jR6WVjedgbX5tfNepSALK+K6USlBK7Sii5r+VUisxOluFj9dXRNabxnefqHT/2pVAdL2GRNdtQHBICJd0uYota1a6tDn/gosJrRoGQHyLNhzNSAPgyMH9NGvdjipVgqkaVo36jZuSsH611xn+Sfbs3IatfkNi6xn8l3XvzZ+rl7u0aX1RB6qGGfzNWl5AZnpBR3ffrgSOZWXS9uLLvFq3u7IrYSv1GsRRt35DQkJC6HZlH9asXOrSxlavPk2ano9Ihawrfp3tW7fQsGEjGjSMIyQklKv69GP50sUubVYsXcw1A4yFHXpe2Zt1a1djhfGkVjV+O6L5rclvaYUD+LXxvVwxdRQzgBtM4/vgihznn+ZoZhq1o2Pzv4+MiuFoZlqx7X//9UdaX2wsvdggvhnbN6zh7JnTZB8/yq6t68lKr1y3S7LS06gTU+B+qRMdS1ZG8fzLFs7lwg6XA5CXl8dnM97i33c95PE6PZWM9FSiYwv4o2JsZKQVz184Z8+e5ZG7/83oe4fx+4olnijRo0lLSyG2rpPVOrYuaYVu+aWlpWAz2wQHB1OjRk2OHTWuqh5OSmLYv/6PkXcNY+N6n+nsPBKrG781vzX5xQP/+SoBa3wvYy4Dliul9kHlML6vXfozB3cn8tCL7wDQqn0nDuxOYMqYe6lRK5ImLdoSFFTFx1V6Lr8tXsC+nQmMf/V9ABb9+DUXdezs0kmzWmZ9OZ+omFiOHD7E+IdHEH9eM+o1iCt9xwBIVHQM389fRK3ISBK3b2PMYw/y2VdzqV6jhq9L09HR0alYJ6sk47uIlMf4DqbxvRxC0pKM7x6JiIzAuEXqkUTWiXG5+nQ0I43IOjHntEv0QdkLAAAgAElEQVTctJafv/6EUS+8Q0hIaP7zfQbfRp/BhoP149efJbZ+5foFWzs6hsy0gr/OMtNTqR11Lv/WDX8w9/OPePLV9wkJNfh3JWxh57aNLPrxG06fPkluTi5hYdUYcqffzW0oNlHRsaSnFvBnpKUQFXMuf7H7xxhXQevWb0jbdh3YuyuxUnWyYmJspB5xslqnHiEmNvacNilHjhBrq0tubi7Z2SeoFRmJiBBqfhZatm5Dg4ZxHDiwn1at//HQS7+IVY3fjmh+a/IH0uzCQDO+lzergW4i0sSs3yfG90bNW5KWfJD0lMPk5uTw58pfuaDTFS5tDu7dyRfvTWbEky9TM7JgvkCe3c7fx48BkLR/N4f/2kPL9h09VapHct75rTly+CCpR5LIzclh9bKFXHxZV5c2+3fv4KOpL/HIM69RK7LgbbpvzETe/OQH3pj9P/511yi6XHVNpepgATRv2YbDhw5w5HASOTk5LF/0M52u6FGmfbNPHCfHnFl37GgWCVs2Ehd/Xil7+VdatWnLwYN/cTjpEDk5Z/n15wV07d7TpU2X7j2Z/+P3ACxZtJBLOl6KiJCVlYndbgcg6dBBDh74i/oNGp7zGpU1Vjd+a35r8wdCAsr4LiKDgLeBGGCeuU8fc9t+jBmRoSJyPdBbKbXdvEr1rRgjilMxZkN6NVWqBDP47kd577lHUfY8LruqP/Uance8z2bSqFlLLujUhe8/fpczp08x69WnAagdY+Oe8a9gt+fy5pP3AxAWHs6whydQpUrl0p9VqRLMsJGPM/mph8iz59Gt97U0bNyUbz75gCbnt+Liy7rx+YdTOX36FG9PGgdAVExdHn32dR9X7p5UCQ7m3ofH8Mzo+8jLy+OqawbSuElT/vPhezRv0ZpLu/RgZ8I2Jj31KNknjrN21XLmzHqf9z75hoP79/Luay8iQYLKU9w49A6XWYmVIcHBwTw2ZjwP3383eXl5DLhuEOc1bc70aW/TqnUbunbvxbXX38BzT4/hxuv6EFErkokvvQbAxvXrmDHtbYKDg5GgIJ548hlq1Yr0MZH7YlXjtyOa35r8AXQhSxvfyxOH8d3dCofKEk8pHCpLPKVwqCzxlMKhMsRXCgd/iTa+a+O7N43vv+8+6vaTfXmzSJ/03crUyQoAGenVGOsahgJngceVUouLOHRp58Fj/8oqKmMMCgqifv36hIWFcfz48YATMTpiRSGfczS/dfmtzA6a31/4dSerYrGKjDQduFYpdVhE2mLcwqyQ9gHg5Fn3vv92u52B/fvw/oyPsNW1MXTIjbw0eQpNmxYMVfvi8zns2rGDp555np/mz2Pxol949fU3OXXyJImJ29m9axe7d+9i3PgJbq3NOeGh4nb2sqQs58cbCQ/VQkKr8msZp+a36mcfvC8jDaTbhVaRkW5QSjnWGtmGMcasqnk8n8tIt27ZTFwjUzgXEkqffv1Zuti1lKWLF3PtwEEAXNW7D3+s+R2lFNXCw2l/cQdCq1b1ReleSVnOTyDHqkJCR6zMb2V20PxW5w+EWFFGegOwXil1xl9kpKmpKdR1kjHabDZSU1OKaFMPKJAxHj1qjcvmZTk/gRyrCgkdsTK/ldlB81uWXzzw8FEsJSMVkTbAK0Bv86lKJyPV0dHR0dEJ5ATSAtEVupJVkowUKI+MdD+mjLQcL18hGamINMTQQwxTSu0px+shIiNExGPrdcTG2jjiJGNMSUkhNtZWRBvjwp9DxhgZWXhIW2CmLOcnkGNVIaEjVua3MjtofqvzB0IsISMVkUhgHjBWKfWb0ya/kJE6hHNJhw6Sk3OWnxecK5zr3rMXP/zvOwB+XfgzHS+9DAkkLW4JKcv5CeRYXUhoZX4rs4Pmtyq/iPsfvopVZKQPYFw9myAijul3vZVSqf4gIw0ODmbskxMYec9d5NntDDSFc++98xat27SlR88rGfR/NzJ+3ONc2+9qImrV4pXJb+Tv3693L/7OziYnJ4cli39l2vRZXp9558kUd36sEqsKCR2xMr+V2UHzW50/EKJlpOWIw5PlC42BP8RXCgd/ia8UDv4SKwsZtYxT81uVHbwvI12795jbT3bH82r5tSdLxynhoda4TVdUrMwO3vfF+Fs0v+a3aqzM7vUE0Kku05gsEbGLyEYR2Wb6pB4zb68hIp3MbRvNbYOc9usrIjtEZLeIjC3iuFNFJNvp+26msypXRG4soZ5LRGSLedyp5tgsRGSiiGw2a1lozmRERIaaz28RkVUiclHZT5F30qdPHxITE9m1axdjxow5Z3ujRo349ddf2bRpE0uWLKFBA8Na0aNHDzZs2JD/OHXqFAMHDvR2+f84ml/zV4QfIC4ujp9//pnt27ezbds2Gjdu7M3S/3H+CXtubm7+e/+//5U4NNVvUxp/XFwcixcvZv369WzatIl+/foB0LhxY06ePJnPP23aNG+X7vaEh4cTHx9PfHw8tWufO7EpMjKSxo0b07hxYxo2bEhwsL5O4u8p67I62U5L3ziM778ppZ4RkXDgrLPxHaiPISXdiZPxHfhXIeP7KGCQ07HjMUzyozEGyRe3rM4fwEMUGN+nKqUWiEiEUuq42eYhoLVS6l4R6QwkKKWyRKQfxtI/l5b7ZJm3C8M6lSakL1+CgoQtX42h/4PTSUo9xsqPH+K2p+eQuK9g8uacSbcwf2UCc+b/SfdLmjLs2o4Mf/Zzl+PUjqjG1q/H0uzaFzh1JsetNQKc/uM1wi59ovSG5UxQkLDly8fp/9BMg/+jB7jt6f+SuN+J/8WhzP8tgTnz1xv8Azow/LkvXI5TO6IaW796gmbXTfIM/5pXCbvs3F8C/zRBQcKWL0bTf9SHBv+sB7htQmH+fzP/t8QC/v6XMPz5L12OY/A/TrPrXvIM/+pXCOv8pNuPGxQkbPn8Efo//BFJqcdZOXMktz37BYn70/LbzJl4M/NX7WDOgg10v/g8hvW/mOETjR8PP789nFc+WcritXuoXi2UvDzldv7TqyYBENblabceNyhI2PLZKPo/MpuktOOsnHEPtz33lSv78zcxf9VO5vy0ke4XN2HYNe0Z/sK3AKT9PJ6YPi+6taaicnrlRABq3PSxW48bJMKGNwcx8MWFJGWcZNlL/bnjreXsSDqW32bq3ZezaX8mH/6ygxYNavHN2Kto++A3NIqpzldjruTS0XPdWlNRyf7ydjb+ddyjr5FntzPqjv/jqVfeJSraxrgHhjHqyRdp2Pi8/DZbN66jecu2VA0LY+EPX7Nt05888tRLHq0LoF3jCK/eLly377jbbxd2aBLhk+tjgWZ8d/5XUN1Ri1JqlVLKYe5cjZMCQkSGmVe5NonIp+U9H+5Ix9aN2HMonf2HM8nJtfPVLxsZ0K2NS5uWTWwsW2eYLpb9ueec7QCDel3Iwt8TPfIL1pPp2DqOPYcynPg3MaBba5c2Br9h3jD4W59znEE9L2Dh6h2Vn//XIvjjy8j/eyXkb9WQPYcy2X84y+BftJkBXVu5tGnZJJZlf+4FYNn6vfnbW8bHEFwliMVrjXPz96mzlYq/Y6uG7EnKZH+yg30LA7q0dGnTMj6WZesd7PvO2V6Z06FZNHtTjrM/NZscex7frNrHgI5xLm0UiohqIQDUCg/lSNZJX5Tq8ezesY269eOw1WtIcEgInXv0Zu2qZS5t2rbrQNWwMACat2pLZlpgikkDaXZhwBnfReRFETkIDAWKWshvOAWzEdsATwG9TOP7qArU849TPzaCQylH879PSj1Gg5haLm227EpmYM8LABjYoy0R1cOoExHu0mbw1e34cuFGzxfs5tSPqcWh1NL4DzOwR1sABvZoUwz/RZWUP4JDqQV/uRv8ES5ttuxOLuDvXgz/VRfx5S+bPF+wm3Mu//Ei3v8jDOxudCwHdm9t8lejeVw0R7NP8/mkf/P7R/cz6f6+BAVVngEd9WNqurKnHadBdOH3/ggDzU71wG6t8tkBwkKDWTnjHpa9fzfXdq18na96dcJJyvg7//ukjJPUq13dpc2krzYxpOt5JL53I1+PvZLRH63J39Y4pgYrXx7Agmf60LllrNfq9kQy01OJiilwYEVFx5KZnlps+8UL/ke7Tp29UZrOP0iFOlmFo5RaY6oWOgLjRCSsuLZOxve33fHaRdQyXikVB8zB6Mw5v3ZPjE6W455PL+Arh3LCn43v46b+SNf25/H7Jw/T9eLzSEo9ij2v4IJf3aiatGlal19Wn7N0Y0Bk3Nvz6Hrxefw++yG6tj+PpNRjxfDv9GGVnsu4t+fRtX2TUvhtgcv/7gKD/6P76dquicmvCK4SxBUXxTP2nQV0uWsaTerX5tZrLvZ1uW7NuHd/pmu7eH7/cCRd28XnswO0GDyFLnd/wG3Pfc3kB/vRpH7gCYoHX9GEOct20/K+r7nx5UXMeKArInAk6xSt7/+GLmN/ZNwna/nwwW7UNK94BXqW/zqfvTsTuG7wMF+X4pEE0Ko6FZtdWJLx3RzIXlbjO5jG93IISctqfJ+DMV7rGbPmC4GZQD+lVEYZXwtz3xEYt0g9ksOpx2loi8z/vkFsLZLSjrm0SU4/zs1jPwGgerVQru95AceyT+dvv+Gqi5i7bCu59mLvtPptDqcdo2FsafwnuHmscTe3SP4rL2Tusm2VlP84DWMLrtwY/K7jP5LTT3DzuP8ADv62AcwfUfT7/+RngMnfow3Hsk+TlHaczbuS2X/YGA0wd3kCndrEMZs/vQfwD3I47YQre0wESemF3vuME9z8lDH+snq1UK7v3jr/vT+cfgKA/clZLN+4n3bn12Pf4cqzpmly5kkaRBVcuWoQFU5y1t8ubYb1bM6gl34B4I9daVQNqUJUzTDSj58mM/sMABv3ZbIv5QTN6kWwYW+5frz7TepEx5LhdPsvIz2VOtHnXp3bvH4N3302i2dfn05IaKg3S9SpQALK+C4izpa2gUCi+Xwj4FvgVqWU85/6i4HBIhJltvOJ8X1dwkGaxUXTuF5tQoKrMPjqdsxbvt2lTVSt8HzD++O39WL2D2tdtt/Uu3LeKgRYl3CIZnFRTvwXMW9FgksbV/6egc1/1UXMW1HC+z+sB7N/dF3l6aarK+etQoB1iUk0a+jEf+WFzFuZ6NLGhf/W7syeZ3Si1iUcolaNMKIjjVunPS45z2XCgL/HYK9D43qRJvsFJbPf0pXZ8zcAEFkjjNCQKvltLm/biASnAfOVIX/uSadp3Qgax9QgpEoQN3Ruwrx1h1zaHEzPpkfbegC0aFCLsJAqpB8/TXTNqgSZ5yU+tgZN60WwP+WE1xnclaYtWpOcdJDU5CRyc3JYtXQhHS7v5tJm3+5EZrw5iSeen0Kt2kX+ugqMBNClrIAyvgMvi0gLs46/gHvN5ycAUcB75g+rXKVUB6XUNhF5EVgmInZgA3B7Gc+J22K35/HIa9/zw9S7qRIUxOwf/iBhXwpPj+jN+oRDzFuxnW6XNOX5+/qhFKzcsJeHJ3+Xv3+jerVpGBvJCnNwbGWLwf8/fnhruMH/41qD/+6rWZ94iHkrEuh2cVOev68vSilWbtzHw5O/z9/f4K/Fig37fEhR8djteTzy+lx+ePNOk38dCftSDf6EQ8xbmUC3i8/j+ZEO/v08/JoTf93aNLRVcv43fuCHKbdTpYow+8f1Bv9dV7I+MYl5KxPp1r4Jz9/b2/j8b9rPw68bM8ry8hTj3l3A/LeGIwIbdhxm1lyPLTPq9hjs8/jh9WHGez9vPQn703h6eC+D/bcddGsfz/MjrkahWLnpLx6e8iNgDPp/e/R15ClFkAivzVnhMiuxMsSepxg9aw3fP3kVQUFBfLp0F4mHjjJ+cDs27M1g/p8HefLTdbxzT2fu798apeDeacbKaJ1b2Xjqpvbk2PPIU4qHZ/xO1t9nfUxU8VSpEsydDzzOi+MeJC/PTs8+1xEX35QvPn6fpue3okPn7vxn+lROnzrFlImGESk61saYiW+UcuTKl0BaIFob38sRTykcKks8pXCoLPGUwqGyxFMKh8oQTykcKks8pXCoLPGGwsGf422Fw4a/Tri9Y9K+cc1S6xeRvsBbGBeGZiqlXi60/VHgLoyLTWnAnUqpv0o8Zhk9WXaMmYOOK1mfAG8opfJEpBMw3dEUw0H1XRkLnmoW6fBkdQPeBC4Ebi7Bk/Uixm3C2o59zeermrVdAmQAQ5RS+0uqsTxxdLJ8kfDwcGJjjfvzx44dIyur8oy7cEc0v+a3Kr+V2UHz+wu/NztZGw+4v5PVrlHJnSxTLVWs29Ns0xNYo5Q6KSIjgR5KqSElHbestwtPOW7VOclIIzAGlW8FOjjLSEXkBwxH1bvOBYvI3EIy0sJTYQ5g3K4r7VLRD8A7wK5Czw8HspRSzUTkZuAVYEhxNTr5vcoVb69hZbfbua5/Hz6Y8RE2m41/D7mRlydPoWkz7y4C7av1uzS/5vc1v6/W7vMHdtD8Vv7sg2WWFMp3ewKIiMPtmd/JUkotcWq/GriltINWOhmp2W51MY6tgcBs8+uvgStLqdEvZKSlZeuWzcTFNaZhXBwhoaH0vaY/S5cs8nVZXovm1/xW5bcyO2h+q/L7aNx7SW7PopLv3CwplVFGWlLyX9Os4RjGgPcia/QXGWlpSU1JoW69uvnfx9pspKQEpum3qGh+zW9Vfiuzg+a3LL8HelkiMkJE1jk9KqxlEpFbgA4YF4tKjFtWl1RKrQHaiEgrYLaIFNu7c5KR9nDHa5c1xdRYaWSkOjo6Ojo6OhWLUmo6BWOzi0pxbk+XiMhVwHigu1LqTGmvW6ErWSXJSIHyyEj3Y8pIK1JHEcl/TdPdVQtjAHxxNZYpjh6wm2osd2JtNo4kH8n/PjUlBZvNVsIegRXNr/mtym9ldtD8VuUXD/xXhhTp9nSpS6Q98AFwnTl0qtRUOhlpKZkL3GZ+fSOwuJQa/UJGWlratL2AAwf2c+jQQXLOnuWn+fPo3rOXr8rxejS/5rcqv5XZQfNbnd+bMYcYOdyeCcCXpkvzeRG5zmw2GagBfCUiG0VkbjGHy0+llJGKyKvAvzGugh3C0EM8C3wIfGpeGcvE6NiVVGO6P8hIS0twcDDjxk9g5Ii7yMuzc/2gG2jWrHnpOwZINL/mtyq/ldlB81uVX3w0mVEpNR9jOT7n5yY4fX1VeY+pZaTliMOT5YupvP4QX01j9pdofuvy+0ph4C/R/Nb97IPB701P1tZD2W4/2W0b1vBJ180tA9+tFos4Q4qMldlB82t+zW/VWJldp+KpyJgsu3kvcpvplnpMRILMbZ3MbRvNbYOc9usrIjtEZLeIjHV6/mMR2ee0n+OW4FDTX7VFRFaJyP+3d95xcpXVG/8+yYaQJaQ3SmCBIKGjgChIVZDeq0hRinRQUEKLkaII2AClKgj+EAQBkd57MYQWQu8tDUgCgdTN+f1x3kkmy+5my8zc3bnnm89+MnPvO3fe504797znfd61m+jPCpKeSse9LtV/IWkTSc9Imitp96L2y6ftBQ2HFe17R9KA1p6TSlBbW0tdXR11dXX07dvQw7X6Cf2hP6/686wdQn8u9edwgehiSu7+Dvy8kSV03sanSE6RtA0+9XKDRvrzW3yJn2slXYwbhF1E0+7x44Fvm9ksST2BF1NfPmrxCQjX44oS+kN/1vrD8Tz05/W9D5HFaw9tsnAoUAr392aO/biZFRZpehK3gFgIScK9rgoB2t+BndPjG3WPN7PZRd4W3WnnOagEeXX9LRD6Q39e9edZO4T+vOrPyMKhLLQ7wCiB+zvAWWlo8A/yRZ4b0pR9fX9galFgtygbfFIfh0p6IfXpt63JYmVBbl1/E6E/9OdVf561Q+jPq36p9H9ZUfIsjpk9lWwX1gdOkrT4Ih5yEu5btT7QDzixeKd81euDGm5vZx/fN7O1cFPUAyRVv7tbEARBEAQVpd1BVjvd3zGz8ebMAq7AhxYLx14LuBzYycwWcm5PfAL0KRiN0oQNflOkDNaLwMaL0BiO7xkS+kN/XvXnWTuE/rzqr6K69/YFWe11f0/tlkr/C6+nejHdXw64EdjPzF5r7PnNTb4ewN3dwd3e/7OIPi8rqUe63Rc3Kn21uceE43u2hP7Qn1f9edYOoT+3+qsoymrL7MJSu7//XwrWBDwHFCwVRuI1V3/x+Iu5hUBH0u3AwSkTdSJwraQzccf2v6Y2TbnHrwr8LhmLCjjPzMa24TxUjLy6/hYI/aE/r/rzrB1Cf971VwPh+N4KwvE9XI9Dfz71h+N56M+rdqi84/sr478s+ckevlRtOL53FvLsGZJn7RD6Q3/ozyt51h60nRYFWWnx5LEsGCK8CjcAnSfpm7hRKPjw2ygzuyk9bmvgT/gQ4eVmdnbaLuBMYA+8aP4iMztf0s+BfYv6tiow0Mw+bdCfFXCvrf7AGLxua7akTYA/AmsBexcbnEq6E/gW8KiZbd/SE9QYWVzRPPbIw/z27LOYVz+PXXbbg4MOObTifcjyai70h/4s9WeZyclaO4T+PL/3ofIBZpaWC6WmpYXvM8xsnVTTtCWwDe7wDgtc3tcBtgYukVQjqSvu8r4NsBqwj6TV0mMOxGcbDjezVfGACTM7Nz3POri1w0MNA6xEweV9GDAFt3iABS7v1zTymHOB/Vqot0NRX1/Pr886nb9cfDk33XIbd95+K2++8UbW3aoYoT/051V/nrVD6M+r/iqqe2/97MISubwfDpxuZvOKjtmQfYB/NtzYFpf3tO8+4PNGjjdS0mhJL0q6NB2/Q5FX198CoT/051V/nrVD6M+7/mqgTRYOJXB5XwnYS9LTku6QtNB0CUm1eFbs3408fZtc3pvhQjNb38zWAHoA7RpKLAd5df0tEPpDf17151k7hP7c6q+iVFZJHN/b4PLeHZiZLBkuA/7WYP8OwGNNDBWWms0lPSVpLJ4hW70CzxkEQRAEQZXTpiCrvS7vePbpxnT7JrxQvZi9aWSoMNEul/diUjD4F2B3M1sTD/i+EiCG43u2hP7Qn1f9edYOoT+v+nO9QHQpXN6Bm4HN0+1NgdeKjt87bWvUub0tLu/NUAioPpbUs+iYDZ8zHN8zJPSH/rzqz7N2CP151V9NC0S31Cer1C7vZ+NO7z/FM18HFz3XLsDdZvZFcQfa6fKOpEfwALCnpA+Ag8zsLkmX4TMkJ+CBYYcj766/oT/051V/nrVD6M+7/mogHN9bQTi+h+tx6M+n/nA8D/151Q6Vd3x/c9KMkp/slQb16LiO782ZkRa1WQ54CTcjPS9ta8qM9CjgOHyW4cCizNdw4ArgG8ApheM00p+mzEh/hmfF5gKTgR+b2buS1gEuAnrhtWRnmdl1LTtFX6Ucxmy1tbUMGjQIgGnTpjFlypSF9vfu3Zs+ffpgZpgZEydOZPbs2dTU1FBXV8fs2bMBmDlzJpMmNeaIURrKZUoX+kN/XvV3Fu3QsfTX1tYyYMAAJGFmTJ48mRkzZpS8fwWyeu9LYsiQIXTv3p36+nrGjx/P3Lk+uX7w4MEsvrhXvUyaNKms+oO20dLhwhnJIBRJg3Czz14sMCQFHz68o3CnyIx0S7zQfbSkW8zsJeAx4FbgwQbP8ylwDMn3qhkKZqTXSroYNyO9CB86XM/MvpR0OHAOsBfwJbC/mb0uaWlgjKS7zGxqC/UvxP+NeX/RjVrBvHn1XPjTA9jv5HPo1X8gl51yBLsdfQoDl62b32bWl1/QvXYJAF59+nFG33MLPzzpbKZOnsA155zCEef+taR9aox91x1acu0Q+kN/x9e/77o+hyfPn32Am18YX9Ljzquv55xjfsghI39H734DuWDET/jBcSMZPLRufpuZX37B4kn/uNGP8cRdN3Pwqefy4Vuv0bNPP3r3G8CE997i8jN/zqmXNub60352XmspPpgyq+THra+vZ/89tufcCy5l4KAhHH7g3px6xjnUrbjS/Db/ueFa3nrjNX46YiT3330Hjz50HyPPOo+br/8nr748jhNHnsmUTz9hxHGHc9GV19KlS0lMAxZi2b7dS37MZulwbpVtp91mpACSdgbeBsYVNW3SjNTMnjWzdxo7tpmNBuY09fyLMCN9wMy+TNufxGceYmavmdnr6fZH+KzIgel4mZuRfvjGK/Qbsgx9By9N15purP7tzXnl6ccXalP4kgWYPWtmVS07EPpDf17151k7wPtvvMyAIcvQf/DS1HTrxtobbcG40Y8u1GbxhfTPmP/7u8yKX6N3vwEADB66AnNmz2LunNmV6npJeOWlsSyz7HIsvcxQunXrxhZbbsPjDz+wUJvHHn6ArbbbEYBNt9iSZ0Y/hZnx7ttv8vX1NgCgb7/+9FyyF6++PO4rz9EZqabZhW1aINrM3kqZqkGSvsAL0bcETihq1pgZ6QZt7WgRLTUjPYiizFqBtNbiYsCbadOFZnZ62nc1bkb63xL0s8V8PuVjevUfOP9+r/4D+fCNl7/S7n9338yTt91A/dy57H/qgpHUqZMncMmIn9C9Ry2b7/Ujlh/e0BGjYxP6Q39e9edZO8C0Tz+m94BB8+/37j+Q91//qv7H77iJh2/9F/Vz53DoqD9+Zf/YJx9imRW+Rk23xcra31Lz8aRJDBq8wGx0wKDBvDzuhYXbTJ7EoEHepmtNDUv07Mln06ay0sqr8PgjD/DdrbZh0qQJvPbKS0yeOIFVV1+zohqC5mlTkNWAUfjQ3fSOsiKNpB8C6+FWEMXbl8JnRh5QVE+2uaRfALVAPzwbV9Egq6V8c6ud+eZWOzP2sft45KZ/sPMRI+jZpx/HXXANtUv25qO3XuO6343kiHP/utDVb7UQ+kN/XvXnWTvAhtvswobb7MKzj9zD/TdcxV5Hnzx/34T33+b2f1zCIac1WsJbtWyzwy68+85bHHbg3gweshSrr7k2XbqWfqgwCzpIKFESSmFGugFwjqR38GL2k1Nhe3NmpO2hWTNSSd8DTgF2NLNZRdt7AbfhBVKlkEUAACAASURBVPVPpm0dwox0yb4D+OyTyfPvf/bJZJbsO6DJ9msUDSnUdFuM2iV7A7D0il+j7+Cl+WT8B+XqalkI/aE/r/rzrB2gd78BTPt4QbH+tE8m06tf0/rX3ui7Cw0nTv1kEledcyp7H30y/Ye0Z3W1bBgwaBCTJi4wG/140kQGDlzYbHTAwEFMmuRt6ufO5Yvp0+nVuw9da2o48qcnctk/buDM8y5g+vTPWbaoli3oGLTbjNTMNjazOjOrA/4I/NrMLqR5M9I205wZqaSvA5fgAdb8T256/puAq8zshqLDdQgz0mVWGs4nEz5kyqTx1M+dw7gnHmCVdTdcqE3xl+drzz5Jv/SF8sVnU5k3rx6AKRM/4tMJH9B38FLl6mpZCP2hP6/686wdYNlhw/l4/Ad8OnE8c+fM4fnH7me19TdaqM3kIv2vPPME/YcsC8CMLz7nil+PYJt9f0Ld8M45RDZ81TX48P13Gf/RB8yZM4f777mDb2+y2UJtNtx4M+6+zX86H7r/Hr6+3jeRxMyZM5gxw0uQn37qcbp27bpQwXxnpoqWLiyJGWmjmNncpsxIJR0D/AIYArwg6XYzO1jSEOBpfObiPEnHAauZ2WctMSMFzgV6Atenocv3zGxHYE9gE6C/pANT2wPN7LmOYEbapWtXtj3waP7xmxOxefNYZ7NtGDS0jgeuv4KlV1iFVdbbkP/dfTNvj32GLjU19FiiJzsffiIA7778Ag9efyVdamqQxHYHHUePnr2ykNFmQn/oz6v+PGsH6Nq1hp0OPo7LzzyBefPmsf4W2zJk6Arcde1fWXal4ay+/kY8fseNvPHCmPn69zr6JMDrtD6e8CH33vB37r3h7wAcctp59OzdN0tJraJrTQ1Hn3AyJx5zGPXz6tlmh11YYcVhXHHJhXxt1dXZaJPN2XbHXfn1qJP44W7bsmSv3px25jkATP30U35x7GF06SIGDBzESaN+k7GaElJFw4VhRtoKCmak5ZjG3hko1xT+zkLoz6/+clk4dBbKZeHQWSiXhUNnYdm+3StqRvrOJzNLHpjU9V88k9AtgqxWUAiygiAIgiBPVDLIeveTWSX/rV2+f/dMgqwW1WRJqpf0nKRxkp6XdLykLg3aLCdpuqQTirZtLelVSW9IGlG0/ai0zSQNKNo+XNITkmYVH6eR/qwg6al0jOtSzRWSDpM0NvX1UUmrpe1bShqT9o2R1OFW2KytraWuro66ujr69v1qulsSSy21FHV1dQwdOpSamoVHemtqahg2bFijj60GFnV+qp3Qn1/9edYOoT+P+qtpgegWZbIkTTeznul2wfH9MTP7ZVGbGwADnjKz85KP1msUOb4D+5jZS6lAfQru+L5e0bI6g4DlcXPRKc0sq/Mv4MYix/fnzewiSb3M7LPUZkd8seqt0/NNNLOPJK0B3GVmrZ6KUshkTfly7qKator6+nr22GlbLrj4cgYNHsyB++7FGb85lxVXGja/zQ3X/ZM3Xn+VEaeO4u47b+eh++/lrHMWlMWNOOE4BKy+5lr88IAfl7R/BfrW1mSyfld9fT07bvd9LrnsCgYPHswP9tqds8/9PSsNG7boB5eQrNYvC/3Z689q7b6OoB1Cf57f+1D5tQvf+7T0mazl+nXgTFYxHdzx/bOipkvgQV/h+T5K28fhhfzd0/EukvR0ytL9quVnonS89OJYlh26HMssO5Ru3RZjy+9vw8MP3r9Qm4cfvJ/tdvDVhrb43laM/t+TFALkh+6/l6WXXmahoKyaeHHsCwwdujzLDh1Kt8UWY+ttt+PBB+7LulsVI/TnV3+etUPoz6v+appd2CafLDN7C58xOChZH5wINAxQGnN8L4WRSbOO75KOlPQmvm7hMY08fjfgmSIPrVOSPcNawKaSKm6ZPGnSRAYPWeD6O2jwECY3WOh18qSJDEptampq6NlzSaZNncqXX37BVVf+lYMPO6Kifa4kkyZOZMhSxednMBMnTsywR5Ul9OdXf561Q+jPu/5qoBT2sKNIju8lOFa7MbM/m9lKeOB3avE+Savji0v/pGjznpKewa0gVgdWq1RfS8FlF/+Zffbdn9oqdHkOgiAI8kc11WS1aVmdRhzfd5d0DtAH97eaCYyhzI7vKZvV1HGvBS4q6vOyuCHp/mb2Ztq2Ar7e4vpmNkXSlTTh+I4PkZaFQYMGM3HCAtffSRMnMHDQoIXaDBw0mEkTJjB48BDmzp3L9Omf07tPH8aNfYEH7rmbC//4Oz7//HO6dBHdu3dnj733LVd3K86gwYOZML74/Exk8ODBzTyiugj9+dWfZ+0Q+vOrv3qMsqrN8X3loqbbAa+n7X3wJXVGmNljRW16AV8A0yQNBrZp4jnL6vi+6upr8P577/LRhx8wZ85s7rnrDjbZdPOF2my86ebc9t+bAbj/3rtZb/0NkMSlV/yDm++4l5vvuJe9992PAw46tKoCLIDV11iT9957hw8+eJ85s2dz5+23senmHW6CaNkI/fnVn2ftEPrzrr8aqDbH96PS2oVz8NmLBxS2A8OAkZJGpm1bmdnzkp4FXsHrx4oDsIpRU1PDCSNO4ZjDD2HevHnssNMurDhsZS75ywWsutrqbLLZFuy4y26MOuVEdtvh+/Tq1Yczf5ufxVBramo46ZSRHH7owcybV8/Ou+zGsGErL/qBVULoz6/+PGuH0J9X/dW0QHSYkbaCclk4dBaysnDoKGQ1jbujkGf9WVkYdBRCf37f+1B5C4cPp84u+cleps9iHdPxXVI9MJYFWayr8EL3eZLqgJeBV1PzJ83ssPS4dYErgR7A7cCxZmaS9sCL5VcFvmlmT6f2/XFbhvWBK83sqCb60w+4DqgD3gH2TPVUw4ErgG/gMwbPK3rM34DtgUlmtkbLTk2jz53Zp6y2tpZBqU5r2rRpTJkyJauuZELoD/151Z9n7RD6O4r+SgZZH5UhyFo6oyCrJcOFM8xsHVjIiLQXUDAifbOwvwEXAYcAT+FB1tbAHfhizLsClzRoPxM4DVgj/TXFCOA+Mzs7uciPwIcPP8UtG3Zu5DFXAhfiAWK7CUO+yhL6Q3/W+sOMM/Tn9b0PC17/SlFNw4WtKnxvzIi0MSQtBfQysydTofpVLDAMfdnMXm34GDP7wswexYOt5tgJNyCFhY1ImzQyNbOH8SCsYT8PkTQ6LRX0b0m1i3juTMirIV2B0B/686o/z9oh9OddfzXQFsf3+UakadMKkp6V9JCkjdO2ZXCT0AKlMiIFGGxmhaXgJwDtmc96o5mtb2Zr48OeB7W7d2Ug74Z0oT/051V/nrVD6M+rfpXhX1a0ySeriPHAcmb2SarBujkZflaEVOPVnhzuGmmGYh+gJz4TMgiCIAiCoN20xSdrvhGpmc0ys08AzGwM8CbwNdwcdNmih5XKiBRgYhqOLAxLTlpE++a4EjjKzNbElwX6ihFpep5DJT3djudpF/k1pHNCf+jPq/48a4fQn1v9VbR4YauCrIZGpJIGSuqa9q0IrAy8lYbzPpP0rVS7tT/JMLQE3MIC/6v5RqRtZElgvKRuQJMOnuU2I10UeTekC/2hP6/686wdQn9e9VdRjNWi4cLmjEg3AU6XNAeYBxxmZoUC8yNYYOFwR/pD0i7ABcBA4DZJz5nZ99O+d/CZi4tJ2hk3DH1J0uXAxcnu4WzgX5IOAt4F9kyPbc7I9J/AZsAASR8AvzSzv+KzGZ8CJqf/l2zNyasUeTWkKxD6Q39e9edZO4T+vOuvBsKMtBUU6r/yakoXhnyhP6/6w4wz9OdVO1TejHTS56U/2YOWrLAPRSKCrFaQpRlpEARBEGRFBFlto0U1WZLqJT0naVzylDpeUpe0r07SjLT/OUkXFz1uXUljJb0h6fyCt5akPdKx5klar6j9lpLGpMeMkdTo4LOkfpLukfR6+r9v2j5c0hOSZkk6oaj94pL+l/o+TtKv2na6sqO2tpa6ujrq6uro27dv1t2pOKE/9OdVf561Q+jPo/48WjhUyvX9Y2AHM/tI0hq4pUJj/lqtdX2fBWxhZtNTkfujku4wsydbqH8hwvW4soT+0J+1/nA8D/15fe9D5R3fM61ULzFtMSMtp+v7s2b2Ubo7Di+6797I4Vvl+m7O9HS3W/qz1M+RyfX9RUmXNqcpK/Lu+hv6Q39e9edZO4T+vOuvBlodZEHFXN93A54xs1mN7Gu167ukrmmW5CTgHjN7Ku26MLm+r4HPhNy+FX2sCHl1/S0Q+kN/XvXnWTuE/rzqz5uFw6Iouet7evxvga0W1balru9mVg+sI6kPcJOkNczsRWBzSb8AaoF+eAbtv+3pfxAEQRAEbaPjjSe1nTZlssrp+i5pWeAmYH8ze7OJZm12fTezqcADwNaSFgf+AuyeXN8voxHX93B8z5bQH/rzqj/P2iH0511/NdCWZXXK5vqesky3ASPM7LFmmrbK9T31sU+63QPYEniFBQHVx5J6Ars39vhwfM+W0B/686o/z9oh9OdVfx5nF1bK9f0oYBgwUtLIdIytzGxSe1zfgaWAv6dgsAvwLzO7NT3mMny24wRgdAvPR0XJu+tv6A/9edWfZ+0Q+vOuvxoIM9JWEI7v4Xoc+vOpPxzPQ39etUPlHd+nfFlf8pPdt7Zrx3R8l1QPjGVBFusq4A9mNk9SHfAyULBjeNLMDkuPW5cFWazbgWPT8OIewChgVeCbKTOFpC3xDNViwGzg52Z2fyP96QdcB9QB7wB7mtkUSfviXlkCPgcON7Pn02P+hs8anJRmETamsw64tan9qU1mn7La2loGDfLJnNOmTWPKlClZdSUTQn/oz6v+PGuH0N9R9EeQ1TZaMlzYWYxI3wY2TQHXNsClwAbpMVcCF+IBYrsJQ77KEvpDf9b6w4wz9Of1vQ8ZmJFWEa0qfO/gRqSPm1khxH+SopmNZvYw7gbfsJ/rpqV2ngeObEZ6puTdkC70h/686s+zdgj9edUvlf4vK9ri+N4ZjEgPIhXZL4IrgKPNbO1W9K3i5NWQrkDoD/151Z9n7RD6866/GmivGWmHMyKVtDkeZH1nEc/TB+iTslzgMya3aVOngyAIgiAoCVlaLpSatvhkdVgjUklrAZcDOxX6VQrCjDRbQn/oz6v+PGuH0J9X/bkdLuzIRqSSlgNuBPYzs9cWpSU5v0+VVMh47dtM2zAjzZDQH/rzqj/P2iH0511/NdCS4cJOYUQKjAT6A39JNflzC4GRpH8CmwEDJH0A/NLM/gr8CPhbGnK8u4XnrOLk3ZAu9If+vOrPs3YI/XnVXz2DhWFG2irCjDQM+UJ/PvWHGWfoz6t2qLwZ6ecz55X8ZC+5eJeOaUYaLCBLM9IgCIIgyIqKBlmzyhBkdc8myFpkTZakeknPSRqXPKWOl9Ql7auTNCPtf07SxUWPW1fSWElvSDq/4KslaY90rHmS1itqv6WkMekxYyQ1OvAsqZ+keyS9nv7vm7bvJOmF1I+ni2qtkHSnpKmSbm1G52bN7c+a2tpa6urqqKuro2/fvll3p+KE/tCfV/151g6hP4/687ZAdGdxfL8PuCUV5K8F/AsYnh5zLlAL/KQFehdJuB5XltAf+rPWH47noT+v730Ix/f2UE2O79NtwdjnEsD8T4SZ3YevZ9iwn1tLekXSM3jg1yHJq+tvgdAf+vOqP8/aIfTnVX9uLRygYzu+S9pF0iu4FcSPm3sCSYsDlwE7AOsCQ5prnyV5d/0N/aE/r/rzrB1Cf971VwOtDrIaUHB8/zrwM+AaSb3ac8Aix/dFDu2lzFVxxuomMxuOZ7fOWMTDhwNvm9nr6Tj/aHuvgyAIgiAoBSrDX1ZUleN7gbRUzoqSBrRYWPP9Csf3DAn9oT+v+vOsHUJ/bvVXUZRVTY7vw4pmMH4D6A40t7TOK0CdpJXS/X2aahiO79kS+kN/XvXnWTuE/rzrrzSpTvvV5IowopH93SVdl/Y/JaluUcesJsf33YD9U19mAHsVCuElPYIPD/ZMju8Hmdldkg5NffgSeARYsgXno+Lk1fW3QOgP/XnVn2ftEPrzqj8Ly4WUMPozsCVeRz5a0i1m9lJRs4OAKWY2TNLeeGnTXs0eN8xIW044vofrcejPp/5wPA/9edUOlXd8nzmXkp/sxWuaj9wkfRsYlZI+SDoJwMx+U9TmrtTmCUk1+OS7gdZMINWSTFbQgDx7huRZO4T+0B/680qeteeEZYD3i+5/AGzQVBszmytpGr5m8sdNHTSCrFZQyUi+KSQdamaXZt2PrMiz/jxrh9Af+vOrP2/aF5V1agupPOjQok2XVuKcttfCIag8hy66SVWTZ/151g6hP/TnlzxrLwmFCWxFfw0DrA+BoUX3G3NFmN8mDRf2pvkJdhFkBUEQBEGQe0YDK0taQdJiwN64m0Exxe4GuwP3N1ePBTFcGARBEARBzkk1Vkfh6yZ3Bf5mZuMknQ48bWa3AH8Frpb0BvApHog1SwRZnY/cjMs3QZ7151k7hP7Qn1/yrL1imNntwO0Nto0suj0T2KM1xwwLhyAIgiAIgjIQNVlBEARBEARlIIKsIAiCIOjkFJaVCzoWEWTlHElV9x4oWsMyl186Rfqr7rVtDcWvf5otlBvy/toXyMt3gCQVLSO3btb9CRYQH8ScIumbAGY2L+u+lJLCl42kLfF1Nbvk5YsWFtK/HXBJYQH3vNHgR2c/YKe8BFqSuhc+15JWl7R61n3Kggbvge0lfSvrPpWLIp0/Bs6U1CfjLgWJCLLyywmSzs26E6UmBRg7An8AHjazeUVfQFUfbCX92wO/AW4ws/o86G5I0Wt+JPAzfAr27Gx7VX4kDQcOk1Qj6QjgGuA2SSMX8dCqo+g9cAxwBjAp2x6VF0kb4TPfjjOzqXm9wOpoRJCVExoZPvgjUJ9FX8pJcuHdC9gNeEjSdyVdJmnpFIBUZcDRQNf3gIPxVeS3A64tZC6rVX+BBkOESwP7AbsC70vaTdIISd/IrIPlZw1gdeBE4PvAN4CNgW0lnZplx7Igve/3B75jZm9J2ljSXpK6Zd239lJcFpCytBsCSwG7SOqa1wusjkZYOFQ5kgbhq4SPk7Q5MAN4GugO3AlcbmZ/z7KPpSINBzwD/An4Or6Q59vAKkAPYFszm5tdD8tL0v8WcAzwHWAW8DiwPL6I6Z5mNiu7HlYOSWvhr/0IYAvgFaAbMBmYZWYjMuxeyZHUw8xmpNs/BNbHPwO7m9kkScOAq4AHzezkDLtaViR1KS6BkNQfOBtYAs9krQp8jp+HC7PpZftpMBTa38w+SRfSBwFrAg+Z2b8btg0qT5iRVjHpam1f4OuSzgYGAMcBTwL/A04CDpF0PTCjCj6IRwDvm9nhaYjgATMbK2l54HygJzA10x6WlwOAxc3sR5J2AV40s9eT/ivxQOujLDtYLiStYmavptvr4sNDewKXA+8Ad5nZe5IOAjZs+GPcmZHUE9hC0ofAcsBs4F789d5D0o1m9oakHwF/kTTAzD7OsMtlIdWizUq3Vwe6pM//tcDOuFv3y8BR+EVmp6XBcPj2kl4E/mdml6Vh4o3T+bimCr7XOzdmFn9V/AeshV/NXwQsgy9ouSHwIPAfYAqwSuEzm3V/W6lNDe6vB/ypwbY9gOeBXbLubwX0DwEuBoYUbdsdeAHYOev+lusc4BeLzwAXpm0DgKvxcoguRW0PTu3WyLrfJT4HSwK7AGPwTGaPtH0PvCzgKGCZtK1b1v0t0zkYDhyb3gtHpM/8u8DJDdrtn87Taln3uQSaDwYeAoYB/waeBY5M+44HfgssmXU/8/4XNVlVSqEGy8xeAG4CJgCnAsub2ePAlsAo4AbgXEmLWfp0dhbMzCRtluquFsODidXT1V2BDYBTzeymaqtPSPo3l7SzpOXNbAI+LLp/UbOVgZ+b2c3Vpr+A+RDwVsB6ks7Bf2gnA8Wz7IYDg4EDzOzFzDpbQgqvp5l9jg+FLY1nqL+Rtl+PX0ythWc7ugLVOlzeVC3ajpJOldQt1eLtBBxoZi9l19X2k7KX4MH1Nvhw6InA/pIOM7PfAWen90aQIVGTVYUUTeNfC5jHgiGiI/HCyL+a2ZjUdgl8Jt4x5usydSrS9Pyf4CuojwVeBI7Gr+g+K2pXlXUJknYGfgjMxLU/gteg7Gtm72XZt3LT8DWVNAC4Aw8k5uKZjM/wbNck4Fwzm55FX0tNg5qc3mY2TVJfYAdgU+AmM7tV0orAJsDtZlZ1s+taUYt2Lz7jtmtnfA809f0laVk8e72fmU2RdAt+kfEDM6vm0ohOQwRZVYqkrYHfA9fiU9jXBmqBHfFC8AvM7FlJ3wf+DGzYGb6EiwLI4fgq6JPx4ZKV8S/RmcDmwCZJX9XU3sBC+tcBvgA+Tl+uS+HB8lQ8k7Wrmd1ZmGWUZZ/LQYMgY0f8dX8Pr7/6N561OgoYCPTD6/OqIuhsoP0EYDv8s3ABPnx0FB5oCNf+IzP7NKPulo1CLRpQqEXrhk/s2Qt4ArjRzMZLWgUvl9jDzD7Jqr9tpVDYnm4fjQ8P9gHOwyd3/Af/jl8Lfy8cZVVYc9dZiSCrykhDCEPxmpQfAV/DZ9ttaD4DZRheDH+D+YzDVfDZVu9k1efWIveBGoVnbWrwzNxzad+a+Ayb4Xgd1oys+lku5LYMvwFuxH9M/2Rm96fXfiBwGG5b8J3OeNXeGiT9DL9wuB237TgMD7RuBx4xs19k17vyIunbeAnAqfhw2S+BQ9N7YXu8JuvcahkebYikJXG7klOBvsDqZjZD0h7ARsAbeEbvQ0ndzGxOht1tE+n7+cdmdqKko/AC/oOB64BnzCf5jMSHR5dLbZ/LrsdBQ6ImqwqQtJik2nR3CbyY/Ubgu8DpwPYpwNoJGI+P1Y9LV8SvdrIAaz08wNoBmIYPhZwgaUMAMxuLF/p/QBW+v9MQ8Cj8inUKUAf8UtK25kwys9Px+rS+mXW0AkgaCmxsZpsBvfD39ktmNgU/P99IGb6qQ27Hch4+o+xZM7sany18saQdzexWPINVdQFWzmrRBgPbJQ29gL3xC6iPgeNSpvp0PHu3aQRYHY+q+xHKG3LzzW8D30s1CWfgQwS7AL/CM1ivS1ofv+KrszTNuTPUKMmdq4uNA+cCh+JeMDvgdWZzgZMkfSe12QjYDB9G7NRIWlzSkpIKU84/xjN1KwEH4tmbJ4DfpgxXwYDxm0CHf31bQyOF+wLmSvo9/oO6t5nNkrQP7hG2pZmNr3Q/y0Ej2h/GZ0quL2m5dMF0Pf6ZP72oMLqqaKQW7TFgNTxz+eOUwQO/yHgcz2TVd4bvusYws4fxmZJL4cHkvfgs6h3T9/jhKcM1J4rcOybhk9XJMbO5kubixd7D8SUV3pX7AT2Cf+HOxtPMvzSzcRl2t1WkuqsRwNKSJuD+XpeYOxn/GjjezB6VtBUwBzcZBK9T2CLNtuu0SFoVD5r7AtMkvQmcZGYfSfo58Dtz/6P38R+UQr3Je8Dm1RJgwMImk5K+hhe1fwi8jl/Fb2RmM+Vrtx0J3G9mX2TW4RLSILDYGc9WTzCzoyWdjwdWv5L0rpn9n6RbqnGYuLFaNEmFWrSrcXuaXSXtRieuRZP0PbyA/zU8eByAZ+nOBB7Ds7X1ct+zI3B7lqqpO602IsjqxBR96TyBp82/BHpKGmpmb8odwHfFzQmPNbOHmpql0tFItQjX4maSTwPr4hm7dSQdBiwGnCHpdDybc4CZPZ/0vZVVv0tF0v8P4FLgATxztR++Dt12eKbmQEmG+wPtY2Zjkv5OHVw2RlGAdSywLfAqPlx6P/499l9Jd+PZzR+Y2cSs+lpqigKLY/B6yoeAPSXtYWaHSfoT7on0c+C9as1oFJ2Hb+OTW36G16JdjteiXVBUi3ZKZwywEjPw2sq1cZ+7lfGSj1skbQtcky406vBZlK9l1tNgkUTheyelECzJp/B+iAcd6+LT+V/FHc774EvqdKoPoaTlgEfxL8qr0zbhafJjgSfM7M+SrsCHvG82s5sy63CJSa/pGDxT94+i7QPx1/UjMzte0hm4L9ajZnZzNr0tL5LWBr5MQ97bAr/AZ5Tdgr/vDwcWx72CZgCvmtmbWfW3lMjtFz43s8lyq5Vr8YultyT1wK0JHsPfExcCZ1RT9rIxUi3amcDdZvartG0P4CzghBSIVM2MYkmD8SzWcfj33EWSFsdLAXpY2DR0eCKT1QkpfIlI2gYvbL8b98Mahf/g7Ipf3X0fr83qbKyGez7NvwJIAeUY4C48W/Fn8+VjFk/DRJ0iQ9dCeuNBVl1hQ3rNJ0u6EBiRCl5PK3ovVJP+Yn6Kn4fN8Nq7G/HZVd3wgGOepGGW1mmrFuQWHaOBsyRdkUoAeuPrUL6VZtFdjVuVzMOHjaqORt7XC9Wi4ctoXS83Iz5d0v14Rr9TowXWK5PN7A5JnwGjUnB9sZl9iWezgw5OFL53ItIHjPTD8h3gHNwTqQueVr4U/xK6AB9C/JGZjc6ou+3hIVzLhml4CJg/ZDQGWEm+8CuWDFSrLMB4GTgZWDEVdc8fLsPrzQbjS+jM315l+gsTOjCzA9P9E4E3gR8DB5nZ91NwfRTw03R1X01MxIOJZfGhwSXwz8QlKQADL4ReUT45ourc/BvWoknaF9jMzI7Glw/6FbB8avd/+EzT6dWQxUoBVuG7XqnA/yz8YmOxLPsWtI4IsjoJkvoBx8tdncFnzu2Dfwlvif/4LIVnsN4zs8vN7J7O9uWbvlBm4Bmre4FVJB1bpGNZPNCoOv8rmK9/Hj476nygj6TfFelfBTeerEr9AJK2BE6WL/QM/mM6DK9L+zfwP0kHpyL3H+ETADrdagWNUfQ6T8XfA0vj2ekj8SLoXwO3SvozcAy+ZNTMaguy4Su1aCfhNUrHSLrYzI7B3fx/i/sCFiwdOhUpePx1c21SFl9m9iCwVwwRdi4iyOoEpKv6L/BC6CUkbWRmd+C1V/vgtTtPsCDL8bXCYzvbl2/R8CnIxwAAEU5JREFUF8oMfImUe/FZk/umgtffAVemdHlVIJ+CX/D4sfR/caDVFzg1TWT4A/CXTlzU2xLWxou4fyNfNulxfPZoHfAX4D7cA24tqmstwi1wG4Jh6f0/Cvf+mo1nLg8BrsG1XwpsbZ18Db7GkLRiqj8sLPu1JT6x4xe4T1R/ScfhQ8mf4O+NTkf6zF8MHNGSQCvdrIqLiTwRNVkdnJS5Oh84L82e+zk+w64mzRZcAlgrXQGvic+yeSXLPrcGuQP9PvgQ2Utm9lJxoCXpDrze7Af4l+2+ZnZ7tdQgyWcR3gQ8Jl/Yem7REOA8SYVA6zTc52yXatJfTEGTmZ0naR4wHZ9RuRjwEj5c8pr5Ytf/wSfudPqhIZgfTPwG/ww/nyY1TMUL21/E69H2B04BLjezV7PqaznJWS1aX+AA/CLiofSdPn+FgsY+49X2mc8Dkcnq+CyGByAj5bONLsO/hH4oaQ38i/l7wEh8PcLOFGANx5eH6IvPDptfpJ8CrS5FQ4fXANuZ2e2F/Rl0uaSkAOtK/Id0aaBfoQaj0KYoo3U6XnNSNfqLkTv2nyF39AevuSk4XNfgV/DTgd9JWikFY1URYAGYe3odjtdTgs8o2xr3STsHfw/cgZ+LqsniNkJuatHM7D5gTBrm/D7wXUnnFTXpn03PglISFg6dAEmb4kMFtbg3zMf4Gm1fw2fZPS+pp5lN7ywZjlS4/k/gGjO7UtKueKbqXGCepaV+CnoaFMF2Co3NIang3nyWuYHk5XhQ8QMza3YJkGrQ35B0AXEm7n013szOlHQRPrtqZHq/jMSXy9nIqsgHq4B86ZR1cBuWxfAh0+PwH9vfm6/BV1tNQ+UFij7nPfDM7TL45J1ZeJ3pjvj74z+4R9bu1TJUqjSTUG7XcDted/gefvF8WLXUG+aVCLI6OJJ2wIdJbsZrVQz/8p2Au7wPw/1hOl2NjqRvmdmT6fYj+JDIS/iV6u/N7JEs+1duJG1qZg+l22viNSbnmNkrqiKvn0WhBTYUvXAvtKPwIONneBbnb2Z2S2o7wMw+zq635SUFWmvhwdU7ZvbLlK2pmqHRhqRatBWAh8xXMFgGz9y+hteYTsRrEVfAJwFMMbP3supvOdACywbkdg1f4stCjc22Z0F7iSCrAyOpC/Bn4D9mdqekOnz4ZF080PoU6G9mb2fWyTbQMICQG05umWpx+uCGo9PM7I+ZdbKMNBZApSv4/wPeNrPjs+lZ5SnKYDR8T1yMLx8zAM/cHm1mU6sxi9eQ9LlfC7+Imm1mh2fcpbKRhgPvJ9Wi4cOjU/HJLsW1aNPwWrT3M+pqSSi6oFgc6Ipb79xsZtPS/m8DNwBbWSdaAi1omqjJ6tgYbky5FUAaQnsKX2bhfHxR0E4VYMFCnk+F+8+b2Xnp9lTcnmBQFn2rBI0EWIXZlD8HNtKCha6rFklD0w/skrDQsjld0v3D8KDzfXwZnYJvVqcPsLSIaftFdXgX4QtgV/NnIW+1aD1SLeZVwO/xerPBRfsXx2svI8CqEmJ2YQei6Kp+ffzDNgGfTfQHSUea2Z9xb5gx+HBap/NKKk6Lp/s9inVI+iZek/LTLPpXbhrRX3Cs74IvevwssIakx6ohoGgM+dqLJ+PDQK9L+lthtly6yu9iZvNS9vYeYERnHA5vDC2Ytr+4JMzs5MbapfPwDDDWzKrS2bsoK/k8IHyIeCn8YmMmXos22MzukvRIZ69FS7OHlwNWxbNVNfisycmFNmb2QDa9C8pFDBd2MCTtiE/X/w++RtsleLB1IT7L8FvAkWb238w6WQLkhpPb4NnUn6bgcnO8yPVYM7s10w6Wmcb0p+27ApPM7NEs+1cu0mt8EW7b0R1fAuopM/t3ToYCv4v/uD6Or2xwrzUybb+az0XhwqLBtqquRUvZyJuBX+Jrbr6c7v/dzG7Msm9BeYnhwg6E3BPrMHxWyXg8m3VPKo7+Nl4Av7WZ/bczTl2WtIKkzSQ9gM8k/DpecyGYfxW3m5nd2hn1LYrm9Bf0mtmN1RpgJb6OW408myY9vA/smXGfKoa1cNp+FQdY3wWOlntfzSdld5/HC9yXlXSROZ0+wAIws0n4MOA9aVbkUDxz/WCmHQvKTgRZHYsaYBJwEHAg7mb9qaStgQGpdukl6HxfwpL2wLNz3wOuxa/oegIPFIaIAMzsufR/p9K3KBalv9r0NoWZ/R43Xy0wmrQQeMrg9MqkYxXEzD5Ow8aT8XqzzSWdLOmHwHmqvnUYgfnZ26uAZwuF3kX7ChMfqrkWrThg/CNQXy3D4EHTRE1WhhQNDQwxswlmNlnSB8AvgG3N7HVJm+HeUXtl2tn28wLuzvykmc2VtBs+VHIbfLUYvAppVn8eKLzfzeyjos1TgcISKvsBK0s608xmZ9LJCmHui9TV3O9r3QbT9qvKFyllaWuAnXC7mXvla7H2AGrN7PXiWrxqrUUrKgnoi2dwf5PuV+3QcBBBVqakAGtbYJSk8fhsk9vxK54/SboZz2qdaJ3ceC8VNhcvBbItbjaYC/KuH5rMTtYDUyUdhFt37FltAVYLp+1/TpVO20+v+xxJHwGzU4bqDnySx2YpqL7SipaTwk1IqxIzmyLpNJLGCLCqmyh8z4CiDFYffFmVs4DNcGuGO3Gbhk3wYZQPzeyRarrakfQD4Bgz+1bWfcmCvOlvLshIGY1X8GV0fmRmL2fZ13KQrCqWxW0JpuClAGua2Wtp/+bAu2b2VmadrACSjsJrSx/Bh8ouk7QR8C88c/98ph0MgjIQmawMSAHWd3F/lE/NbDQwOk3xLXgC3WxF1gbVEmAlHgS+AJAvitrsMjJVyIPkS38PSQ2DjCdww9lPJT0BjKzSACum7SfM7EJJ2+P1iPunbY/JF/uu9s9AkFMik5UBkr6FFz9fh//gXGZmp6Z9x+FLixxvVbg+W4GGflF5Iy/6mwgy9sQtHKZW2cXDQsS0/caRdBfu9/crYAN8+aStrZO7uQdBY0SQVWEkrQUcCTxsvjDwarjr731m9svUZjmrsrW5gvwRQcZX1qRbDjgBGJWHWWWNDBPvY2aXp31n4DVJG+LF8J265jQImiKGCyvPcPyq/gtJd5vZS5IOAf4pqZuZnRwBVlANmNkkSRs3CDLy5g3UcNr+u3kIsBJfGSaW9LiZvWRmp8FXV3wIgmojgqwyU1TkPhyYbGb/kjQBNx3dUtKdZvaypH2BqvcICnJHnoOM3E7bb6YWbXyDplVlVxEEDYnhwgqQTPj+gc8cnIn7YG0A7IevQH+LmX2SXQ+DoLykIGMUcFbKcFV1kNEYyWh1VrX5PzUkhomDYAGRySoTRRms3sAawC7Ae8BPcEfjw/E6hQOBu7LqZxBUgvAGAjP7LOs+VIIYJg6CBUQmq4yk5XB2BoYBx+Ou38sAhwJr4gFWNzP7OKs+BkEQlJriTKWkG/Fh4p9m3K0gqDixdmGJkVST/t8AGAk8jtda7Q70MbMPgMtwA8YVIsAKgqDaaK4WLct+BUGliUxWiZC0Am4sOi3dvhq43cx+LakOuABfRuKPyYCxe7XXZgRBEOSlFi0IGiMyWaVjJeDdtFTOe8DTwH6S1jGzd/DFgb8DnJBcvuMLJwiCqsfMPovvuyCvRCarhKQarAuA9VJG6xTcvX2UmT2fCkAHmdnTmXY0CIIgCIKyE0FWiZG0LXA+sC6+dMSJwBbAL8zsuSz7FgRBEARB5QgLhxJjZren2s6n8SzWb4FuuF1DEARBEAQ5ITJZZSINHV4BDDezaVn3JwiCIAiCyhJBVhmRtB3whZk9mHVfgiAIgiCoLBFkVYA8LiESBEEQBHkngqwgCIIgCIIyED5ZQRAEQRAEZSCCrCAIgiAIgjIQQVYQBEEQBEEZiCArCCqIpHpJz0l6UdJ/0zJMnQZJT6X+vydpcrr9nKQNJd1Qxue9WdKTDbbtIellSQ9I2kzShu18jjpJM4o0PSdp/1Ye40BJSxfdf0fSgPb0qwXPeaWk3cv5HEEQtI0wIw2CyjLDzNYBkPR34EjgrGy75DNg8Ykw85prZ2YbpPYH4stHHVW0+/Ey9a0PvoLCdEkrmtlbaddBwCFm9qikUcD01vQhrSE6t8HmNwuvTxv62RU4EHgR+KgtxwiCoLqITFYQZMcTwDIAklaSdKekMZIekTQ8bR8s6SZJz6e/DdP2n6Vs2IuSjkvbzpZ0ZOHgkkZJOiHd/rmk0ZJekPSrtK1O0quSrsIDg9Mk/bHo8YdI+kNLhKRjvZhuH5gyT/ekTM5Rqb/PSnpSUr/mNDfCrsB/gWuBvdNjR+ILrv9V0vXAYcBPU/ZpY0kDJf07aR4taaOic3K1pMeAq1uiLT3uIklPSxpXOH9p+zuSfivpGWAffJWH/0v96JGaHS3pGUlji17X/pLuTse7XNK7kgYUn8fU7oQUQBZej9HpffBvSbWN9POMlNnq2thrHgRBhTGz+Iu/+KvQHzA9/d8VuB7YOt2/D1g53d4AuD/dvg44rugxvfGszlhgCaAnMA74evp7qOi5XgKGAlsBlwLCL6xuBTYB6oB5wLdS+57Am0C3dP9xYM0mdBwIXFh0vw54sWjfG8CSwEBgGnBY2veHIj2Nam7kue4BNga+Bowt2v4gnk0DGAWcULTvGuA76fZywMtF7cYAPRp5njpgBvBc0d/GaV+/otfgQWCtdP8dfF3Sr/SpaP/R6fYRwOXp9vnAyHR7O8CAAcXnMe07AV9gHqB/0fYzi457JbA7cC5wcXqdG33Ns37/x1/85e0vhguDoLL0kPQcnsF6GbhHUk9gQ+B6H7UDoHv6fwtgfwAzqwemSfoOcJOZfQEg6UY8GDhf0qBUEzQQmGJm70s6Fv/RfTYdsyewMvAe8K6ZPZmOP13S/cD2kl7Gg62xbdT5gJl9DnwuaRqeiQIPDtdahOb5SBqc+vqomZmkOZLWMLMXG7ZtwPeA1YqO3Ss9J8AtZjajicc1NVy4p6RD8RKLpYDVgBfSvusW0Zcb0/9j8KwceJC7K4CZ3SZpyiKOAbCGpDOBPvhreFfRvtOAp8zsUABJW9H4a/5wC54nCIISEUFWEFSWGWa2ThrquQuvyboSmNrEj3truR7PagxhwY+/gN+Y2SXFDSXVAV80ePzlwMnAK/jam21lVtHteUX35+HfO11omeY9gb7A2ylg6oUPy52yiMd1wTN0M4s3pmM01NwsklbAM0rrm9kUSVcCixc1WdTxCtrrWfR37lwWLuMofp4rgZ3N7PlUE7dZ0b7RwLqS+pnZpzTxmgdBUFmiJisIMsDMvgSOAY4HvsSDiD3Ai9AlrZ2a3gccnrZ3ldQbeATYWVKtpCWAXdI28MBqbzzQuj5tuwv4cSGTI2kZSYOa6NdT+BDjD4B/llByw+f5jKY1F7MPPqRaZ2Z1+FDp3o20+xwfnixwN3B04Y6k9gSwvfBAalrKrG3TTNuG/WiKh/FzjKRt8EASYCIwKNVsdQe2L3rMksB4Sd2AfRsc707gbOA2SUvSitc8CILyEUFWEGSEmT2LDzntg/9oHiTpebzGaqfU7Fhgc0lj8eGm1czsGTyr8T/gKbzO59l0zHH4j/GHZjY+bbsbr1F6Ih3nBpoPBP4FPGZmLRnCag9NaQbmZ9qWB+ZbN5jZ23iws0GDY/0X2KVQ+I4HsOulou+X8ML4lrCSFrZwOMbMnseH3V7Bz+NjzTz+SuDiBoXvjfErYBNJ4/Bhw/eSvjnA6fhre096zgKn4a/3Yw22kx57PXAZcAsedLfmNQ+CoAzE2oVBECyEpFuBP5jZfVn3JS9IegcvmP84674EQVA6IpMVBAHgflSSXsPrxiLACoIgaCeRyQqCIAiCICgDkckKgiAIgiAoAxFkBUEQBEEQlIEIsoIgCIIgCMpABFlBEARBEARlIIKsIAiCIAiCMhBBVhAEQRAEQRn4f1vqJfsNTVVWAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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4z+tnWdw3S6r6Bc5z+bF7/4k4F6+kGY9zMclpJUKqmoTz+h2sqltweu6fwPnCsgXnQri0z+vsjtcMnOPyJU7PcE2c14m3j3FGBNKPl/sloDvOl7ANnEwOo3Jo83ycL1PLVDWnKQemEEmbmGuMMSaXROQy4B1VzTyEWWSJyHNARVXN7bB7oSQi1XESmPBM8xsL4rGr4iRkFVX1QEE+tj+I8xNLH6vqe/5ui8k96/EzxpgcuENg3dyhyso4Q6xf+7tdZ0Oc30Ns4g5ZtsYZbi7SMfmTOHOOH8D56ahgSPpa4fx0TK57TE3hYImfMcbkTHCGFPfiDPX+gTM8X5SVxpnndxjnw/slnKs1zWlyL6Q4gHOF6xnNuyxKRGQ8zs8y9XenX5h8ICLvi/Pj6yuz2S/i/FD4OhFZISLn5qpeG+o1xhhjjClc3At3DuH8+HujLPZ3A+7F+SmtNjg/At4mp3qtx88YY4wxppBR1Tk4F6ll50qcpFBVdQEQLSLxOdVrf2y5iBMR67I1xhgTdFQ1t7+/GKgqk/GXMra623b4upMlfsYYY4wxPkQ275fnnSzHlr95B87fW08zSlVH5fXjZGaJXwCI6DDI303wm2PzhgVt/MfmDQOC9/m3+C3+iKuC91dEjn19O79tDs7rKppWLe3vJuQJN8k7m0RvGxn/Qk0CufirUzbHzxhjjDHGFwnJ+9vZmwTc7F7d2xbnbyb7HOYF6/EzxhhjjCl0ROQToAtQXkS24vxUUDiAqr6D8xeEuuH8hZojOH/pJkeW+BljjDHG+CIFfx2Jqvr8M5bq/B7fPadbrw31GmOMMcYECevxM8YYY4zxJW/m5BUKlvgZY4wxxvjih6He/BI4KawxxhhjjPHJevyMMcYYY3yxoV5jjDHGmCBhQ73GGGOMMaaosR4/Y4wxxhhfAmioN3AiMcYYY4wxPlmPnzHGGGOMLwE0x88SP2OMMcYYXwJoqNcSP5Oti1rX4sX/diM0RBj37TJe/Ghuhv1V46J45/GrKB9dgr0HjnLrsC/ZlnQAgEOzh7Dy70QAtiTu55rHPy7w9p8tiz944w/m2MHiv6h5ZV68tS2hISGMm7GWF79ekWF/lfIlGX1vJ6JKFic0RBj04WKmLdvKdZ1q0v/KxunlGlcrR7uHJrJi456CDuGs/LroZ8a+9SKpqR4uuKwnV/37Pxn2r16xjHFvvcimv9fRf+DTtOt0Yfq+D0e/xrKF8wC4+obbOa/rxQXadpMzS/zyiIgMAQ6p6ovZ7O8N/KCq2wuyXWcqJER49YHuXH7/eLYlHWDe6Dv4dv4a1mxMSi/zzD2X8NHU5Xw0dTmdzz2HoXdcyG3DvwLg6D8ptL31bX81/6xZ/MEbfzDHDhZ/SIjwap/2XP7UVLYlH2be8z34dvFm1mzdl17m0V7N+PLnDYyetoZ6CdFMHHgx9e78nE/nrOfTOesBaFi1LJ8/dmGRS/o8Hg9jXn+WQc+9RbnYOB6/5yZatu9MlWo10suUr1CRex55ikmff5DhvksXzOXvv9bwwrsfk3I8hSEP9qV56/aUKFmqoMPIewE01Bs4fZf5RBx5cZx6A5XyoJ4C0ap+Auu37WHjjr2knPDwxczf6d6hXoYy9apX4KdlfwPw07INp+wvyiz+4I0/mGMHi79VrVjW7zjAxsSDpJxI5Yt5f9O9ddUMZRQoU6IYAFElirFjz5FT6vlXxxp8Me/vgmhynlq3dhUVK1UhrlIC4eHhnNflYpbMn52hTIWKlahWozYSkjEZ2rppAw2aNCc0NIyIyEiq1qjN8sU/F2DrTW5Y4pcFEakuImtF5H/ASmCQiCwWkRUi8pRXuQEi8qeIzAPq+qivF9AS+EhElotIpIgMdutcKSKjRJyvEyLSyn2c5SLygoiszOdws1QptjRbd+1PX9+WdIDK5ctkKPP7up1c2akBAFd2qk+ZkhGUKxMJQESxMOaNvoOf3unDFR2L3oeCxR+88Qdz7GDxV4opwdbkw+nr25KPULlcyQxlRny2jOs61WTd6Ov4euDFPPDeL6fU0+u8GnxeBBO/Pbt3EVMhLn29XGwcyclJPu5xUvWatVm++Bf+OXaUA/v3smr5EpKTEvOrqQVLQvL+5ic21Ju92sAtQBmgF9AaEGCSiHQCDgPXAc1wjuMyYGlWFanqBBHpBzykqksAROQNVR3qLn8AdAcmA2OBPqr6i4g8m4/xnbXH35zGK/dfzo2XNWf+bxvZtms/nlQFoO41L7N990Gqx5dl6sjerFyfyIbte/3c4rxl8Qdv/MEcO1j8/+pQkw9n/cXISStpU6cCY/7bmRb9v0KdQ0Cr2rEc+ecEqzcHVtw5adqyHevWrmbAf2+lTFRZ6jRoTEhIqL+blTcCaKjXEr/sbVLVBSLyInAx8Ku7vRROUlga+FpVjwCIyKTTrL+riDwClADKAatEZC5QWlXTvj5+jJMQnkJE+gJ9AU7sXEZYxXNP8+F92550kIQKUenrlWPLsG33gQxldiQf5LqBnwJQMrIYPTs3YP+hY879dx8EYOOOvcxZvpFmdeKL1Ju/xR+88Qdz7GDxb08+QkLMyR6+yjEl2LbncIYyt1xQhyuHTQNg4Z+7iAgPpXyZCJL2O8fgmg5Fs7cPoFz5CiTvOtlLtycpkZiY2Fzf/+obbuPqG24D4NURTxCfUDWHe5iCZkO92Ut7pQvwjKo2c2+1VHXM2VQsIhHAW0AvVW0MjAYiTqcOVR2lqi2BPE/6AJas2UathHJUi48mPCyUay5ozHfz1mQoExNVAneEmodv7Mj4KU5uHF0qgmLhoell2jWqyh8bczdUUFhY/MEbfzDHDhb/knVJ1IovQ7UKpQgPC+GaDjX4bvHmDGW27D5ElybOlO26laOIKBaanvSJwNXtzymS8/sAatVtwI5tW0jcsY2UlBTmz/6Blu075+q+Ho+Hg/udi2A2/f0Xmzeso2nLtvnZ3IJjQ71BZRowTEQ+UtVDIlIZSAHmAONE5Bmc43gF8K6Peg7i9BLCySRvt4iUwhlKnqCq+0TkoIi0UdWFOEPJfuHxpHL/K98x+aWbCQ0JYfx3y/hjYxKDbjufZWu28d38tXRqXp2hfS9CUeb9ton+L38LQL3qsbz+UA9SVQkR4cWP5ma4IrAosPiDN/5gjh0sfk+qcv97vzB58KWEhgjjZ/7JH1v2Mei6c1m2fjffLd7MY+MW8dbdHbj3ioaoQp/XT/7cTYcGFdmafJiNiQf9GMWZCw0N47Z7H2HEY/1ITfXQ9dIrqVK9Jp+Oe5uadRrQqn1n1q1ZxQtDHuLwoQMs/WUun49/l1fGfIHHc4JB998OQIkSJbn3sWGEhlqaUdiIpk1KMOlEpDrwrao2ctf/C9zu7j4E3Kiq60VkAM48wF3AZmCZj59zuRp4GjgKtAMGAP8GdgJ/4gwtDxGRNjg9gKnAT0BLVT3PR1s1osOgswu4CDs2bxjBGv+xecMALH6L388t8Y9j84YRcdV7/m6G3xz7+nZ+21w0k8uz1bSq04eiqgU28S6y89A8T5aO/jTYLxMHLRXPgqpuBBp5rY8ERmZRbgQwIpd1fgl86bVpoHvLbJWqNgEQkceAJbluuDHGGGPyXohd3GHyz+Ui8jjOc7MJ5/f/jDHGGGPOmiV+eUxE3gQyD82OVNWxubm/qn4GfJbnDTPGGGPMmbG/1Wuyo6r3+LsNxhhjjDFZscTPGGOMMcYX+wFnY4wxxpggEUBDvYETiTHGGGOM8cl6/IwxxhhjfLGhXmOMMcaYIGFDvcYYY4wxpqixHj9jjDHGGF8CaKjXevyMMcYYY4KE9fgZY4wxxvgSQHP8LPEzxhhjjPHFhnqNMcYYY0xRYz1+xhhjjDG+BNBQr6iqv9tgzoKI2BNojDEm6KhqgY2/RnYbmeeftUen/Ncv48fW42eMMcYY40sAzfGzxC8AHE0J3k6/yHAJ2vgjw503Iovf4g9Gwfzah+COP+3cL1ABNNQbOJEYY4wxxhifrMfPGGOMMcYX6/EzxhhjjDFFjfX4GWOMMcb4Yhd3GGOMMcYECRvqNcYYY4wxRY31+BljjDHG+BJAQ73W42eMMcYYEySsx88YY4wxxpcAmuNniZ8xxhhjjC821GuMMcYYY4oa6/EzxhhjjPFBAqjHzxI/Y4wxxhgfAinxs6FeY4wxxpggYYmfOWPz586hx+WX0P3SixgzepS/m1PgLP7gjT+YYweL3+IPwvglH25+kmPiJyLVRWRlQTQmi8eeLSIt/fHYuSUiPUWkwRncr4eIPJZDmd4i8saZty7/eDwenh4xlLfeeY+vJ33H1Cnfsn7dOn83q8BY/MEbfzDHDha/xR/c8QeCQtPjJyJFdb5hT+C0Ej8RCVPVSar6bD61Kd+t/H0FVapUI6FKFcKLFePSbpcze9ZMfzerwFj8wRt/MMcOFr/FH5zxi0ie3/wlt4lfqIiMFpFVIvKDiESKSB8RWSwiv4nIlyJSAkBElnvdjopIZxFpLSK/iMivIvKziNR1y/YWkUki8iMw0633UxH5Q0S+BiLTGiAi/xaR30VkpYg857X9kNdyLxEZ5y5f45b9TUTmZBeY24aJIjJdRDaKSD8RecBt6wIRKeeWOyVeEWkP9ABecOOt6d6mishSEZkrIvXc+48TkXdEZCHwvHdvnohcISIL3cecISJxuXxe/GZXYiIV4yumr1eIiyMxMdGPLSpYFn/wxh/MsYPFb/EHZ/zBmPjVBt5U1YbAPuBq4CtVbaWqTYE/gNsAVLWZqjYDBgFLgJ+BNUBHVW0ODAae9qr7XKCXqnYG7gKOqGp94EmgBYCIVAKeA84HmgGtRKRnDm0eDFzitq9HDmUbAf8HtAJGuG1oDvwC3OyWOSVeVf0ZmAQ87Ma9HhgF3KuqLYCHgLe8HicBaK+qD2R6/HlAW/cxPwUeyaG9xhhjjDGnLbfDqxtUdbm7vBSoDjQSkeFANFAKmJZWWERqAy8AXVU1RUQqAuPd7QqEe9U9XVX3uMudgNcAVHWFiKxwt7cCZqtqklv/R27ZiT7aPB8YJyKfA1/lEN8sVT0IHBSR/cBkd/vvQBN3Odt4veIuBbQHvvDK5ot7FflCVT1ZPH4C8JmIxAPFgA05tBcR6Qv0BRgzehS39emb013yVIW4OHbu2Jm+visxkbi4Qt9RmWcs/uCNP5hjB4vf4g/O+P3ZQ5fXctvj94/XsgcnYRwH9FPVxsBTQASkJz+fA31UdYd7n2E4yVUj4Iq0sq7DZ9x6h3otp9erqncCA4EqwFIRifFRh3d8qV7rqZxMjseRRbyZhAD70no93Vt9r/3Zxfo68IZb9x3Z1J2Bqo5S1ZZAgSd9AA0bNWbz5o1s3bqFlOPHmTrlOzp3Pb/A2+EvFn/wxh/MsYPFb/EHd/yB4GwuqCgN7BCRcOAGYJu7/X1grKrO9Sob5bW/t4865wDXAz+KSCNO9rYtAl4TkfLAXuDfOMkSQKKI1AfWAlcBBwFEpKaqLgQWishlOAlg8hnGCtnHe9Ddh6oeEJENInKNqn4hzleEJqr6Ww51ex+fW86ijQUmLCyMxwcM5q6+t5Oa6qHnVVdTq1ZtfzerwFj8wRt/MMcOFr/FH5zxB1KPn6iq7wIi1YFv3d46ROQhnKHORJy5aEnAQpzk50mcYcoVXlXcjjO0Ox6nx+s74EZVrS4ivYGWqtrPrTsSGAukzaOrDNyjqktE5N/AEzi/fvOdqj7q3qcXzvy/JJw5haVUtbeIfIUzN1GAmUB/zSLYLNqw0V3f7b1PRO7KHK/7OOcBo3F6CXvh9BK+DcS7cX+qqkPdi06+VdUJmR9XRK4EXsFJan8EWqlql8xty+b50aMpvp/DQBYZLgRr/JHhzhuRxW/xB6Ngfu1DcMefdu6raoFlY1HXf5DnB3v/xzf5JZvMMfEzhZslfvbmZ/Fb/MEomF/7ENzxW+J3dorqb+cZY4wxxhSIQBrqDZrET0QuwRkS9rZBVa/yR3uMMcYYYwpa0CR+qjqNLH6CxRhjjDHGF+vxM8YYY4wJEoGU+BWav9VrjDHGGGPyl/X4GWOMMcb4YD1+xhhjjDGmyLEeP2OMMcYYXwKnw88SP2OMMcYYX2yo1xhjjDHGFDnW42eMMcYY40Mg9fhZ4meMMcYY40MgJX421MUBa0EAACAASURBVGuMMcYYUwiJyKUislZE1onIY1nsryois0TkVxFZISLdcqrTEj9jjDHGGF8kH245PaRIKPAmcBnQAPi3iDTIVGwg8LmqNgeuA97KqV5L/IwxxhhjCp/WwDpV/VtVjwOfAldmKqNAGXc5CtieU6U2xy8ARIYHztyDM2HxW/zBLJjjD+bYweIvSH6a41cZ2OK1vhVok6nMEOAHEbkXKAlcmFOl1uNnjDHGGOODiOTHra+ILPG69T2Dpv0bGKeqCUA34AMR8ZnbWY9fAKjxwBR/N8Fv/n65G7PWJPu7GX7RtV4MAD+tDc74O9d14l+yYb+fW+IfLc+JAmD3oRQ/t8Q/ypcK52iK+rsZfhMZLkEbf6D0dKrqKGCUjyLbgCpe6wnuNm+3AZe69f0iIhFAeWBXdpVaj58xxhhjjA/50eOXC4uB2iJyjogUw7l4Y1KmMpuBC9w21gcigCRflVriZ4wxxhhTyKjqCaAfMA34A+fq3VUiMlREerjFHgT6iMhvwCdAb1X12RVsQ73GGGOMMT746wecVXUKMCXTtsFey6uB806nTkv8jDHGGGN8CYxphYAN9RpjjDHGBA3r8TPGGGOM8cH+Vq8xxhhjjClyrMfPGGOMMcaHQOrxs8TPGGOMMcaHQEr8bKjXGGOMMSZIWI+fMcYYY4wvgdPhZz1+xhhjjDHBwnr8jDHGGGN8CKQ5fpb4GWOMMcb4YImfCQqd6pZnUM/6hIYIny3cyrs//p1h/4Ae9WhbKwaAyGKhxJQqRvOBMwD484VLWbvjIADb9x3ljveXFWzj88DKpb/w+Xuvkurx0OHiHlza6+YM+6dP/IT50ycREhJKqahobrlvADEV4lm7YimfjxmZXm7n1k30eXgozdp2LugQzsrKpb/w6ehXSU310PGiHlx2Tcb4f5j4CfN+mERIaCily0TT+79O/GtWLOWz9zLG3/fhoTRvV3Ti/23xz/zvnZdI9aTS9bIr6XFt7wz7v/vyI2ZP/YaQ0FDKREXT94HBxMbFs3H9Wt5//TmOHj5ESGgoPa/7D+26XOyfIM7CgvlzefXFZ/F4PFxx1dXc/J8+GfYfP36cYYMeZ80fq4iKjmbYsy8RX6kyAOv+XMtzI57iyOFDSEgIYz74jOLFi/sjjHwzf+4cnnt2BKmeVK66+hpu69PX300qUMEef1FXYImfiPQE/nT/oHBe1lsd+FZVG+VlvcEuRGDI/zXklncXsXP/Mb7u356Zq3axLvFQepkRk9akL9/coRoNKpdJXz+W4uGKl+cXaJvzUqrHwyfvvkT/oSMpG1OBZx68lSatO1Kp6jnpZarWqEPnl8dSrHgEP035ii/HvUnfR4ZTt0kLBo38HwCHD+5n4B3X0KB5G3+FckZSPR4+fucl7h/mxD/igVtp2ubU+Ae8PJbiERHMnvIVE8a+yR2PDqdekxY8+drJ+J/oW7TiT/V4GPvm8zz+zBvElI9j4L23cG7bTiRUq5FepnrNugx//X8Uj4hg+uQJfPLea9w34BmKF4/groeHEF+5KnuTkxjQ7yaatGxHyVKl/RjR6fF4PLz43AhGvjWaCnFx3HbjtXTs3JVzatRKLzN54peULlOGLyZNZfq0Kbw18mWGPfcSJ06c4KmBjzF4+DPUrlOP/fv2ERYWWP0LHo+Hp0cM5d3RY4mLi+P6a3vRpev51KxVK+c7B4BgjT+QevwK8uKOnkCDrHaIiN/fGQpDGwqTplWj2ZR8mC17jpLiUb79dQcXNqyQbfkrmscz+dftBdjC/LXhr9VUiE8gtmJlwsLDadnxQn5bOCdDmbpNWlCseAQA59RtyL7du06pZ+n8WTRq0S69XFGx4a/VxHrF36rThSzPFH+9Ji0oHuHEVaNuQ/YmZx9/WrmiYN3aVcRVqkJcfAJh4eG063IRS3/5KUOZhs1apsdUu35j9rjPfXxCNeIrVwWgbEwsZaLKcWD/3oIN4CytXvk7CQlVqJxQhfDwYlx4STfmzp6Voczc2T9yWfcrAeh6wcUsWbwAVWXRgp+pWbsOtevUAyAqOprQ0NACjyE/rfx9BVWqVCOhShXCixXj0m6XM3vWTH83q8AEa/wikuc3fzmrxE9EbhSRRSKyXETeFZFQETkkIiNE5DcRWSAicSLSHugBvOCWrSkis0XkVRFZAgwQkQ0iEu7WW8Z7PYvHbeHW/xtwj9f2CBEZKyK/i8ivItI1h+29RWSSiPwIZHnmikgpEZkpIsvc+1/pte9mEVnhtuUDd1uciHyd1j439uyOVaiIjBORlW7d97tl7xOR1W7dn57Nc3Sm4qIi2LHvWPr6zv3HiIvK+sO7UtkIEspF8stfyenbioeFMLF/eybc146LGmWfMBZW+5KTKFv+ZLvLlq/AvuSkbMvPnz6Zhi3anbJ9ydwZtOp0Ub60MT/tS06inHf8Mb7jnzd9Mo2yiH/R3Bm0LmLx701OIiY2Ln29XPk49uzOPvZZU7+haav2p2xft2YVJ06kEBefkC/tzC9JSYnEVYxPX4+tEEfSrsRMZXYRV7EiAGFhYZQsVZr9+/axZdNGRIT+d/eh9/W9+HDcmAJte0HYlZhIxfiK6esV4uJITEz0cY/AEuzxB4Iz7uUSkfrAtcB5qpoiIm8BNwAlgQWqOkBEngf6qOpwEZmEMyQ7wb0/QDFVbemuVwcuByYC1wFfqWpKNg8/FuinqnNE5AWv7fcAqqqNRaQe8IOI1PGxHeBcoImq7snmsY4BV6nqAREpDyxwY2kADATaq+puESnnln8N+ElVrxKRUKCUj2O1CqicNkwtItFuHY8B56jqP17bCq3uzSoxdcVOUvXktk7DZ5N44B+qlIvkw7tas3bHITYnH/FfI/PRgllT2bRuDQ8+81aG7fv37GbbpvU0bN7WTy0rGAtmTWXjujU8nCn+fXt2s23jehqeG7jxz5s5hQ1//cGgF97NsH1v8m7efmEwdz40hJCQ4PnVLI/Hw4rlyxjzwWdERERw7523Ua9+Q1q2CdxzwASJwBnpPasevwuAFsBiEVnurtcAjgPfumWWAtV91PGZ1/J7wH/c5f/gJHencBOhaFVNG3f6wGt3B+BDAFVdA2wC6vjYDjDdR9IHztP9tIisAGYAlYE44HzgC1Xd7dabVsf5wNvuNo+q7if7Y/U3UENEXheRS4EDbh0rgI9E5EbgRDbHoa/bW8qBFd/7aP6ZSdx/jPjokz18FaMiSNx/LMuy3ZvHM/nXHRnvf+AfALbsOcrC9XsyzP8rCqJjYtnrNXS7d/cuomNiTyn3x/JFfP/FOO4e+Dzh4cUy7FsybybN2nYmtAjOcYqOiU0fvgTYm5x1/KuXL+K7z8fRL5v4m7frXOTmeJWNiSU56WQPxp7diZQrf2rsvy9byMRPxvLgUy8RXuxk7EcOH+KFwf35V++7qV2/cYG0OS/FxsaRuPPk6zlpVyKxFeIylalA4s6dAJw4cYLDhw4SFR1NbFwczc5tQXTZskRERtK+Q0fWrsnTad1+VyEujp07dqav70pMJC4uzsc9Akuwxx8IzibxE2C8qjZzb3VVdQiQoqppfT8efPcqHk5bUNX5QHUR6QKEqurKs2jb6Ticw/4bgFighao2AxKB052wlOWxUtW9QFNgNnAnTvILTs/nmzi9kYuzmn+oqqPSekvLNLnsNJuTsxVb9lO9fEkSykUSHip0bx7PzFWnzuGqUaEkUZFhLNu4L31bmcgwioU6p1bZkuG0qF42w0UhRUH12vXZtX0Lu3du50RKCkvmzqBpm44Zymxev5YP33qeuwe+QJnocqfUsXjO9CI3zJkmLf4kN/7Fc2bQtHUW8b/5PP0GZR3/oiIaf826Ddi5bTO7dm7jREoKv8yeTou2nTKU2bhuLWNee4YHn3qJKK/YT6Sk8MrQh+l4QTfadLygoJueJ+o3bMTWLZvZvm0rKSnHmTFtCh06d81QpmPnrnz/7TcAzJr5Ay1atUFEaNPuPNav+4tjR49y4sQJfl26hOo1avojjHzTsFFjNm/eyNatW0g5fpypU76jc9fz/d2sAhOs8QfSHL+z+So+E/hGRF5R1V3uUKevS9cO5rAf4H/Ax8Cw7Aqo6j4R2SciHVR1Hk5ilmauu/6jO5RbFVjrY/u5ObQHIArY5Q7RdgWqudt/BL4WkZdVNVlEyrm9fjOBu4BX04Z6yf5YHQaOq+qXIrIW+FBEQoAqqjpLRObhDHuXAvZRgDypylNfrWZc31aEiDBh0Vb+SjxE/0tq8/vW/elJYPdm8Xy7PGNvX624Ugzv1YhUVUJEeOfHv4tc4hcaGsZ1dzzIyCH9SU1N5bwLu1Opag0mfTSKarXq07RNR74c9wb/HD3CqOcGAFAuNo57BjozD3Yn7mDv7kRqN2ruzzDOWGhoGNff+SCvPtkfdeOvXK0G33w4imq169OsTUcmjH2DY8eO8M6zTvwxsXH0G+QVf1IidYpg/KGhYfS+5xGefeI+UlM9dLm4BwnVa/LF+HeoUac+Ldp15qPRIzl29CivDX8MgJgKFXnoqZdZMGc6a37/lUMH9jNnujPwccdDT1K9Zl1/hnRawsLCeODRAdx/T188qal073EVNWrWYvTbr1OvQUM6dj6f7j2vZuigx7imx6WUiYpi6DMvAlCmTBTX3XALt910LYjQ/ryOnNex6PyMT26EhYXx+IDB3NX3dlJTPfS86mpq1art72YVmGCN35+JWl6Tk51zZ3BnkWuBx3F6DlNw5tLNUNVS7v5eQHdV7S0i5wGjgX+AXsAY4CFVXeJVX0VgAxCvqtkmOiLSAngfUOAHoJuqNhKRCJxh1pY4Q6QPuAlUdtt7Ay1VtZ+PxyoPTMZJvpYAbYHLVHWjiNwCPIzTs/mrG2ccMApnKNcD3KWqv2RzrI7iDGmn9bw+jjOcPAsn4RTgQ1V91kf7tMYDU7LbHfD+frkbs9Yk51wwAHWt5/yG4k9rgzP+znWd+Jds2O/nlvhHy3OiANh9KLup0IGtfKlwjqac+edXURcZLkEbf2S4k4SpaoFlYzUf/D7PD/b6ly7zSzZ5VpNvVPUzMs7TAydBSts/AZjgLs8n48+5dMmiyg7ABF9Jn1vXUpwh0jSPuNuPcXKeoHf57LaPA8bl8Fi7gVMvV3T2jQfGZ9qWCFyZRdmsjhVk3evYwVebjDHGGFNwAqjDr/D85Q4ReR24DOjm77YYY4wxxgSiQpP4qeq9mbeJyJvAeZk2j1TVLK/4PRsi0piMVwgD/KOqRedPDhhjjDEmzwXSHL9Ck/hlRVXvyblUnj3W70Czgno8Y4wxxhQNAZT3FeifbDPGGGOMMX5UqHv8jDHGGGP8LZCGeq3HzxhjjDEmSFiPnzHGGGOMDwHU4WeJnzHGGGOMLyEhgZP52VCvMcYYY0yQsB4/Y4wxxhgfAmmo13r8jDHGGGOChPX4GWOMMcb4EEg/52KJnzHGGGOMDwGU99lQrzHGGGNMsLAeP2OMMcYYH2yo1xhjjDEmSARS4ieq6u82mLMgIvYEGmOMCTqqWmDZWNMnZ+b5Z+1vT13gl2zSevwCQIMnpvm7CX6z+ulLmLUm2d/N8Iuu9WIA+GltcMbfua4T/5IN+/3cEv9oeU4UALsPpfi5Jf5RvlQ4R1OC93tvZLgEbfyR4QWfLwVQh59d3GGMMcYYEyysx88YY4wxxodAmuNniZ8xxhhjjA8BlPfZUK8xxhhjTLCwHj9jjDHGGB8CaajXevyMMcYYY4KE9fgZY4wxxvgQQB1+lvgZY4wxxvhiQ73GGGOMMabIsR4/Y4wxxhgfAqjDz3r8jDHGGGOChfX4GWOMMcb4EEhz/CzxM8YYY4zxIYDyPhvqNcYYY4wJFtbjZ7LVoXYMj11ej9AQ4cslW3lvzsYM+x/tVpfWNcoCEBEeSrmSxWg3fBb14kszqEd9ShUPw6PKqNl/M/X3RD9EcHZWLv2Fz997lVSPhw4X9+DSXjdn2D994ifMnz6JkJBQSkVFc8t9A4ipEM/aFUv5fMzI9HI7t26iz8NDada2c0GHcFZWLv2FT0e/Smqqh44X9eCyazLG/8PET5j3wyRCQkMpXSaa3v914l+zYimfvZcx/r4PD6V5u6IT/2+Lf+Z/77xEqieVrpddSY9re2fY/92XHzF76jeEhIZSJiqavg8MJjYuno3r1/L+689x9PAhQkJD6Xndf2jX5WL/BHEWFsyfy6svPovH4+GKq67m5v/0ybD/+PHjDBv0OGv+WEVUdDTDnn2J+EqVAVj351qeG/EURw4fQkJCGPPBZxQvXtwfYeSb+XPn8NyzI0j1pHLV1ddwW5++/m5SgQrG+G2oN5+JyH3AXcAyVb0hi/1dgIdUtbuI9AZaqmo/EbkTOKKq/8vDthxS1VJ5Vd9pPvY44FtVnVDQjx0iMOCK+vQZu5TEA8f47K62zPojifVJh9PLPDdlbfry9W2rUL9SGQCOHvfw+ISVbE4+Qmzp4nxxT1vm/5XMwWMnCjqMM5bq8fDJuy/Rf+hIysZU4JkHb6VJ645UqnpOepmqNerQ+eWxFCsewU9TvuLLcW/S95Hh1G3SgkEjnVPw8MH9DLzjGho0b+OvUM5IqsfDx++8xP3DnPhHPHArTducGv+Al8dSPCKC2VO+YsLYN7nj0eHUa9KCJ187Gf8TfYtW/KkeD2PffJ7Hn3mDmPJxDLz3Fs5t24mEajXSy1SvWZfhr/+P4hERTJ88gU/ee437BjxD8eIR3PXwEOIrV2VvchID+t1Ek5btKFmqtB8jOj0ej4cXnxvByLdGUyEujttuvJaOnbtyTo1a6WUmT/yS0mXK8MWkqUyfNoW3Rr7MsOde4sSJEzw18DEGD3+G2nXqsX/fPsLCCuXHzBnzeDw8PWIo744eS1xcHNdf24suXc+nZq1aOd85AAR7/IGgsA713g1clFXS54uqvpOXSV9BEpFC9e7YOCGKLXuOsHXvUVI8ypQVO+lav0K25bs1iWfKbzsA2JR8hM3JRwBIOvgPew4dp2zJYgXS7ryy4a/VVIhPILZiZcLCw2nZ8UJ+WzgnQ5m6TVpQrHgEAOfUbci+3btOqWfp/Fk0atEuvVxRseGv1cR6xd+q04UszxR/vSYtKB7hxFWjbkP2Jmcff1q5omDd2lXEVapCXHwCYeHhtOtyEUt/+SlDmYbNWqbHVLt+Y/a4z318QjXiK1cFoGxMLGWiynFg/96CDeAsrV75OwkJVaicUIXw8GJceEk35s6elaHM3Nk/cln3KwHoesHFLFm8AFVl0YKfqVm7DrXr1AMgKjqa0NDQAo8hP638fQVVqlQjoUoVwosV49JulzN71kx/N6vABGv8Inl/85dCl/iJyDtADeB7EXlURH4RkV9F5GcRqZvDfYeIyEPu8mwReU5EFonInyLS0d1eQkQ+F5HVIvK1iCwUkZY51DtCRH4TkQUiEuduu8K9768iMsNr+xQRWe7e9ovILSJSXUTmisgy99beLdvF3T4JWC2ON0RkrYjMALLPtPJZXJkIduw/lr6eeOAYcVFZD9fER0eQUC6ShX/vOWVf44QyhIUKW/Ycybe25od9yUmULX/y8JctX4F9yUnZlp8/fTINW7Q7ZfuSuTNo1emifGljftqXnEQ57/hjfMc/b/pkGmUR/6K5M2hdxOLfm5xETGxc+nq58nHs2Z197LOmfkPTVu1P2b5uzSpOnEghLj4hX9qZX5KSEomrGJ++HlshjqRdiZnK7CKuYkUAwsLCKFmqNPv37WPLpo2ICP3v7kPv63vx4bgxBdr2grArMZGK8RXT1yvExZGYWPSmspypYI1fRPL85i+FLvFT1TuB7UBX4G2go6o2BwYDT59mdWGq2hroDzzpbrsb2KuqDYBBQIsc6igJLFDVpsAcIG2yyzygrdu2T4FH3PZ3U9VmwG3AJmAisAunB/Nc4FrgNa/6zwX+q6p1gKuAukAD4Gbg1E+TQqhb44r8sDKRVM24vXzpYjzTqzEDv1qFatb3DQQLZk1l07o1XPx/GTuo9+/ZzbZN62nYvK2fWlYwFsyaysZ1a7gkU/z79uxm28b1NDw3cOOfN3MKG/76g+69bsqwfW/ybt5+YTB3PDiYkJBC9zabbzweDyuWL2PIiOd5Z8wH/DRrJksWLvB3s4wxXgr7O1IU8IWIrAReARqe5v2/cv9fClR3lzvgJGqo6kpgRQ51HAe+zaKeBGCaiPwOPOzdNhEpD3wAXK+q+4FwYLRb9gucxC7NIlXd4C53Aj5RVY+qbgd+zK5RItJXRJYA7P11Sg4hnL7EA8eIjzo5PBdXJoLE/f9kWfayJhWZsmJHhm0li4fy9s3n8tr0dazYsj/P25ffomNi2es1dLt39y6iY2JPKffH8kV8/8U47h74POHhGYezl8ybSbO2nQktgnOcomNi04cvAfYmZx3/6uWL+O7zcfTLJv7m7ToXuTleZWNiSU462YOxZ3ci5cqfGvvvyxYy8ZOxPPjUS4QXOxn7kcOHeGFwf/7V+25q129cIG3OS7GxcSTuPPl6TtqVSGyFuExlKpC4cycAJ06c4PChg0RFRxMbF0ezc1sQXbYsEZGRtO/QkbVrVhdo+/Nbhbg4du7Ymb6+KzGRuLg4H/cILMEavw31FpxhwCxVbQRcAZzuRKG0TMXDmV/IkqKa3l/lXc/rwBuq2hi4I61tIhKKk1gOdRNLgPuBRKAp0BLw/oQ8zBlQ1VGq2hKgbPNuZ1KFTyu3HaBqTAkql40kPFTo1qQis9acOofrnPIlKBMZzvLNJ5O78FDhtRuaMenX7fywqmgOAVSvXZ9d27ewe+d2TqSksGTuDJq26ZihzOb1a/nwree5e+ALlIkud0odi+dML3LDnGnS4k9y4188ZwZNW2cR/5vP029Q1vEvKqLx16zbgJ3bNrNr5zZOpKTwy+zptGjbKUOZjevWMua1Z3jwqZeI8or9REoKrwx9mI4XdKNNxwsKuul5on7DRmzdspnt27aSknKcGdOm0KFz1wxlOnbuyvfffgPArJk/0KJVG0SENu3OY/26vzh29CgnTpzg16VLqF6jpj/CyDcNGzVm8+aNbN26hZTjx5k65Ts6dz3f380qMMEafyAN9Rb2r+JRwDZ3uXce1Tkf+BcwS0QaAGf6ldy7bbd4bX8WWKGqn2Yqu1VVU0XkFiC72c5zgDtEZDzO/L6uwMdn2L6z4klVRkxew6je5xIiwtfLtrF+12H6XVCTVdsOMGuNM+fpsibxfL9iZ4b7XtKoIi2qlyW6RDg9z60EwIAvV7Fmx8ECj+NMhYaGcd0dDzJySH9SU1M578LuVKpag0kfjaJarfo0bdORL8e9wT9HjzDquQEAlIuN456BLwCwO3EHe3cnUrtRc3+GccZCQ8O4/s4HefXJ/qgbf+VqNfjmw1FUq12fZm06MmHsGxw7doR3nnXij4mNo98gr/iTEqlTBOMPDQ2j9z2P8OwT95Ga6qHLxT1IqF6TL8a/Q4069WnRrjMfjR7JsaNHeW34YwDEVKjIQ0+9zII501nz+68cOrCfOdOdgYI7HnqS6jV9Tk8uVMLCwnjg0QHcf09fPKmpdO9xFTVq1mL0269Tr0FDOnY+n+49r2booMe4psellImKYugzLwJQpkwU191wC7fddC2I0P68jpzXsej8jE9uhIWF8fiAwdzV93ZSUz30vOpqatWq7e9mFZhgjz8QiBbCyVcishGnZ6w2MB6nV+w74EZVre7j51yGAIdU9UURme2WWeIOvS5x71vSrbMBsAbnQpJrVPWvbNqS/nMuItIL6K6qvUXkSpzh5704Q7KtVLWLiCiwCkj77ZLBwB/Al4ACU4F7VLWUdxxu/YLTk3gRsBlIAd739XMuIqINnpiW62MbaFY/fQmz1iT7uxl+0bVeDAA/rQ3O+DvXdeJfsqHoTSXICy3PiQJg96EUP7fEP8qXCudoSuH7/CookeEStPFHhju9ZapaYN1mnV6en+cHe84D5/ml269Q9vipanV3cTdQx2vXQHf/bGC2uzwOGOcuD/Gqo4vX8m5Ozs07hpNAHhORmsAMnIswsmtLKa/lCcAEd/kb4Jssymf3RDbxWn40cxzuugL9smuLMcYYY8zZKJSJXz4rgTPMGw4IcLeqHvdzm4wxxhhTSAXQH+4IvsRPVQ/iDCNnICILgcw/VHeTqv5eIA0zxhhjTKHkz4sx8lrQJX7ZUdWi8zeljDHGGGPOgCV+xhhjjDE+BFCHX6H/HT9jjDHGGJNHrMfPGGOMMcYHm+NnjDHGGBMkAijvs6FeY4wxxphgYT1+xhhjjDE+hARQl5/1+BljjDHGBAnr8TPGGGOM8SGAOvws8TPGGGOM8SWQruq1oV5jjDHGmEJIRC4VkbUisk5EHsumzL9EZLWIrBKRj3Oq03r8jDHGGGN8CPFDh5+IhAJvAhcBW4HFIjJJVVd7lakNPA6cp6p7RaRCTvVaj58xxhhjTOHTGlinqn+r6nHgU+DKTGX6AG+q6l4AVd2VU6WW+BljjDHG+CAieX7LhcrAFq/1re42b3WAOiIyX0QWiMilOVVqQ70BYPXTl/i7CX7VtV6Mv5vgV53rBnf8Lc+J8ncT/Kp8qXB/N8FvIsMDZ8L9mQj2+AtSflzbISJ9gb5em0ap6qjTrCYMqA10ARKAOSLSWFX3+bqDKeI6vDjX303wm3kPdeRoivq7GX6R9qZv8Vv8wSgyXII2dgju+AMl4XWTPF+J3jagitd6grvN21ZgoaqmABtE5E+cRHBxdpXaUK8xxhhjjA+SD/9yYTFQW0TOEZFiwHXApExlJuL09iEi5XGGfv/2Van1+BljjDHG+OCPq3pV9YSI9AOmAaHA+6q6SkSGAktUdZK772IRWQ14gIdVNdlXvZb4GWOMMcYUQqo6BZiSadtgr2UFHnBvuWKJnzHGGGOMD/aXO4wxxhhjTJFjPX7GGGOMMT4EUIefJX7GGGOMMb6EAo/WfwAAIABJREFUBFDmZ0O9xhhjjDFBwnr8jDHGGGN8CKAOP+vxM8YYY4wJFtbjZ4wxxhjjQyD9nIslfsYYY4wxPgRQ3mdDvcaY/2fvvuOrKNM2jv/uFEhoCQQSSuhBOtJEUGkqioqiq9j7KruuIlhW19e1YUfsqyJiQUXdtaMgoKiASJUiTZQmPQkQkJJASJ73j3OIOQgnIEmGnLm+fs7HzMwzc+7rpN15ZuYgIiJ+oRk/ERERkTD0di7iC50aJDLqmva8e20HLuuUesAxPY+pzltXt+fNq9px75nHFKz/9pYTee2Ktrx2RVsePbd5aZVcqqZOmcw5Z51On969ePWV4V6XU+r8nN/P2UH5ld/f+cu6EpvxM7NzgZ+dc4uL+bgNgM+dc62K87jFxczuB3Y454but34wMNk595UnhR2mKINbT2nMLR8sJHP7Hl65rC1Tl21m1ZbsgjGpiXFcfnwqN7w7nx2780iMjy3YtntvPte+Nc+L0ktFXl4ejzw8mJdfeZ2UlBQuvegCevQ8mcZpaV6XVir8nN/P2UH5ld+f+SNnvq9kZ/zOBVocaIOZeX6K+VBqKM46nXP3lpWmD6B5zcqs25rDhm272ZvvmLg0k5PSkkLGnN2mJh/P28CO3XkAbM3O9aJUTyxc8CN169YntW5dYsuVo/eZZ/HtNxO9LqvU+Dm/n7OD8iu/P/ObWbE/vHJYjZ+ZXW5mM81snpm9bGbRZrbDzB42s/lmNt3MUszsBOAc4Ing2MZm9q2ZPWNms4G7zWylmcUGj1ul8PIBnrdD8PjzgRsLrY8zs9fNbIGZzTWznkWsv9rMRpvZ18ABv1LNrIeZTTGz0cDi4LpPzOwHM1tkZv0Lje1tZnOCtf3heGZ2vZl9YWbxZvaGmV0QXL/KzB4I7rvAzJoF19cwsy+DzzPCzH41s+qH8zkqLjUqlSNj++6C5cztu6leqVzImLpV46lbNZ4XL27DsEva0KlBYsG2cjFRvHLZsQy7pA1d06qVWt2lJSM9nZq1ahYsJ6ekkJ6e7mFFpcvP+f2cHZRf+f2dPxIc8oyWmTUHLgJOdM7lmtmLwGVARWC6c+5uMxsCXO+ceyjYOH3unPsguD9AOedcx+ByA+As4BPgYuAj59zBpoxeB25yzk02sycKrb8RcM651sHmaYKZHRNmPUB7oI1zbkuYuO2BVs65lcHla51zW8wsHphlZh8SaJpfAbo551aaWUh3Y2Y3Ab2Ac51zuw/Q3W9yzrU3s38AtwPXAfcBXzvnHjWz3sBfw9TouWgzUhPjGfC/BSRXKsfzF7fh6pFz2LE7j36vzGLTjj3USijPs/1aszxzF+u35XhdsoiIyGGLiqBzvYcz43cK0IFA4zMvuNwI2AN8HhzzA9AgzDH+W+jjEcA1wY+vIdDc/YGZJQKJzrnJwVVvFdp8EvA2gHPuJ+BX4Jgw6wG+LKLpA5hZqOkDuDk42zgdqAs0AToTuGZvZfB5Ch/zSuAM4ALn3G4O7KPg/wu/ZicB7wWPNw7IOliBZtY/OHvKxumji4hz+DJ37CG5cvmC5RqVy7Npx56QMRk7djN1+Wby8h0bftvN2i3ZpCbGAxSM3bBtN/PWbOOY5IrFXqOXklNS2LhhY8FyRno6KSkpHlZUuvyc38/ZQfmV39/5I8HhNH4GjHTOtQ0+mjrn7gdynXMuOCaP8LOIO/d94JybCjQwsx5AtHNu4eGV/qftLHrI72OC9Z0KdHHOHQvMBeKK2H8BgWbuwLfCBuxrCIt6zQ7IOTd83+xpzc7nHO7uRfpp43ZSE+OpVaU8MVHGKU1r8N3y0H55yrLNtK2bAEBCfAyp1eJZvy2HSuWjiY22gvWt6lRh1eZdxV6jl1q2as3q1atYu3YNuXv2MG7sGLr3PNnrskqNn/P7OTsov/L7M38kXeN3OA3HROBTM3vaOZcRPLVZOcz47UVsB3gTeAd48GADnHNbzWyrmZ3knPuOwOnlfaYEl78OnsqtBywNs759EfUcSAKQ5ZzbFTxt3Dm4fjrwopk13Heqt9Cs31zgJWC0mZ3unFt/iM81FbgQeNzMTgOq/ol6i0Weg6e/Xs6T57ciKgrGLExn1eZd/PWEevyUvoOpy7cwc9VWOtWvyltXtycv3/HSpJX8lrOXVrUrc/upaTgXeLfzUTPXhtwNHAliYmK46+57uaH/deTn53HueeeTltbE67JKjZ/z+zk7KL/y+zN/BL2NH/b7ZN0hDDa7CLiLwExhLoFr6b5yzlUKbr8A6OOcu9rMTiRwDdxu4ALgVeB259zsQserCawEajnntoZ53g7Aa4ADJgBnOudamVkcgQarI7AXuNU5902Y9VcDHZ1zN4V5rh7BOvsEl8sTuA6xAYHmMRG43zn3rZmdATwSfD0ynHO9rNDbuZjZ6cBjBK71G0rwmkczWxWsY5OZdQSGOud6mFky8C6QAkwD+gANwpwuxszcSUOnHGxzxPvu9q5k5x7613AkiY8N/CRSfuX3o/hY82128Hf+fV/7zrlSa8euGDW/2F/sty471pN28rAav2J/8kCj2Nc5d4VnRRxFgk1mnnNur5l1AV5yzrUtYh81fj7/4af8yu9Hfm58wN/5vWj8rnznx2J/sd+8tI0njZ9n76dnZs8TuAHiTK9qOArVA/5nZlEEbpq53uN6REREfC+S7ur1rPFzzg3Yf52ZvQCcuN/qZ51zB7zj90iYWWtC7xAG2O2cO764n+tQOed+Adp59fwiIiIS2Tz/FzQKc87dWPSoYnuuBUDY06giIiIiXt6FW9xK8p9sExEREZGjyFE14yciIiJytImc+T41fiIiIiJhRelUr4iIiIiUNZrxExEREQkjgib8NOMnIiIi4hea8RMREREJI5LezkWNn4iIiEgYEdT36VSviIiIiF9oxk9EREQkDL2di4iIiIiUOZrxExEREQkjgib81PiJiIiIhKO7euWo8t3tXb0uwVPxsZHzDflnKL/y+5Wfs4Pyy5+jxi8CXPffBV6X4JkRF7Vm8tItXpfhiW5NqwEov/J7XIk3ujWtxvRlW70uwzOd0xLZvGOv12V4IqlS6bcukXRDRCRlEREREZEwNOMnIiIiEoau8RMRERHxiajI6ft0qldERETELzTjJyIiIhJGJM34qfETERERCSOSrvHTqV4RERERn9CMn4iIiEgYkXSqVzN+IiIiIj6hGT8RERGRMCLoEj81fiIiIiLhREVQ56dTvSIiIiI+oRk/ERERkTAiaZYskrKIiIiISBia8ZODalmzEhe3rUWUwZSVWYz7aVPI9hMaJHJBm5pszc4F4OtlW/huZRZ1E+O4rH1t4mOjyHcwZkkGs9f85kWEI7Lwh2m8+8rT5Ofn07XXOZzZ78qQ7RM+eYcpE0YTFR1N5SpVuWbg3SQl1wJgc8ZGRj7/CFs2pWNmDLzvKaqn1PYixp/m5/x+zg7K/+Psabz98pPk5+fT/fS+nH3hVSHbv/hoFJPGjyY6OprKCYlcN+geqqfUKtievWsH//rbxXTo0p0r//HP0i7/iE2bOoVnhj5KXl4e55x3AVdec33I9j179jD4nn/x05JFJCQm8tBjT1Grdh02rF/Hxef3oX79BgC0bH0sd959f+kHKAERdIlf5DV+ZnYzcANQE3jcOfeYxyWVSWZwafvaPD1pJVnZe7n71EbMX7+dDb/tDhk3a8023p27IWTdnr35vDZzLRk79pAQF8O/ezVm0cYdZOfml2aEI5Kfl8eoYUO59cHnqJqUzEO3XkPb47tSu17DgjH1GjXl30+9Qfm4OL4Z+yHvv/4f/n7nwwC8+vQDnHXh1bRsdzw52bswK1uT637O7+fsoPz5eXm8+eIQ7nj4P1Srnsx9g66ifeeu1KnXqGBM/cZNeeDZkZSPi2PimA9477XnuemuRwq2f/jmyzRt1daL8o9YXl4eTz7+EM++OILklBSuvfwiunbvScNGaQVjPvvkQypXqcIHo8fz5fixvPDskzz0+FMApKbW5c33Pvaq/BKjmzuObv8Aejnnqh6o6TOzUmt2LaBMvsYNq8WTuWM3m3bmkpfvmLV6G21rVz6kfdN37CFjxx4AtuXsZfvuvVQuX7b+xlj5y2KSa6VSo2YdYmJj6dStF/NmTA4Z06xNB8rHxQHQuGkrsjZnALB+9Ury8/Jo2e54AOLiKxSMKyv8nN/P2UH5l/+8iOTaqSTXCuTv3O005kwLzd/i2I6/52/WmqxNGQXbVv6yhG1bt9C6fedSrbu4LF64gNTUetRJrUtsbDlOPf0MJn/7dciYKd9+zZl9zgWg5ymnMXvWdJxzXpQrf0KZbEoOxsyGAY2AL8zsFjP7T3D9G2Y2zMxmAEPMrLGZjTOzH8xsipk1C3PMGmb2oZnNCj5OLLT+SzNbZGYjzOxXM6tuZg3MbKmZvQksBOqa2T+D+/5oZg8E969oZmPMbL6ZLTSzi4LrHzOzxcGxQ0v4JTuoxPhYtuzKLVjOyt5LYnzsH8a1T63Cfael8fcudal6gO0NqsUTE2VkBhvBsiJrcyZVqycXLFdNSiZrc+ZBx0/58jNad+gCQPq61VSoWJkXHrmTBwZeyfuvPU9+Xl6J11yc/Jzfz9lB+bM2Z5JUPaVguVr18Pknjx9Nm46B/Pn5+bw74lkuue7mEq+zpGRmppNcs2bBcnJyTTIzMv4wJiU4JiYmhkqVKrNt61YA1q9bx5WX/IUbrruSeXNml17hJcys+B9eiajGzzn3d2A90BPI2m9zKnCCc+5WYDgwwDnXAbgdeDHMYZ8FnnbOHQecD4wIrr8P+No51xL4AKhXaJ8mwIvBbU2Dy52AtkAHM+sG9AbWO+eOdc61AsaZWRJwHtDSOdcGeOjPvA6lZf767dw15mcemLCMxek7uLZTnZDtCXEx/LVTKm/MXEck/y047Zsv+HXZEk7/y+UA5OXn8cvieVx47c38+6nXyNy4jqkTx3hcZcnxc34/Zwfln/r1F6z8ZQlnXnAFABPHfMCxHU+gWqHG0U+Sqtfgk7ETefPdjxh4653cd/cd7Nyxw+uyZD9l6/zbkXnfOZdnZpWAE4D37feWu3yY/U4FWhQaWyV4jJMINGk458aZWeFG81fn3PTgx6cFH3ODy5UINIJTgCfN7HHgc+fclOBp6BzgVTP7HPj8YEWZWX+gP8BPX71Ps1P7FfkCHI6t2blUq/D7DF7V+JiCmzj22bnn97/kp6zM4vw2v/+VGBcTxYCu9fl4YTortmQXa22loWpSjZDTN1mbM6iaVOMP4xbPm8mY/73BHY++RGxsueC+ydRteAw1agYa4Xadu7Ni6cLSKbyY+Dm/n7OD8ldNqsHmTekFy1s2HTj/wrkzGf3f17n78WEF+ZctWcDSRfOYOOZDcnJ2sTd3L+Xj47nomptKrf4jVaNGChkbNxYsZ2RspEZy8h/GpG/cSHJKTfbu3cuOHdtJSEzEzChXLvBaNGvRkjqpdVm9ehXNW7Qq1QwlQf9Wb9m0M/j/KGCrc65toUfzMPtFAZ0Lja3jnCvqT5idhT424NFC+6c55151zv0MtAcWAA+Z2b3Oub0EZgY/APoA4w72BM654c65jkCxN30Aq7Zkk1ypPNUrxhIdZRxXL4H567eHjEmI+/3vhra1K7Nxe+DGj+go4x8n1mPaqq3MWVv27uYFaNCkOenr15C5cT17c3OZOflLju3UNWTM6uVLeeuFxxlwzxNUSaxWsL5hk+bs2rmd7dsCfwss+XE2tQpdGF8W+Dm/n7OD8jc6pkUw/zr25uYyffIE2nUOzb9q+VLeeP5Rbrl3aEj+G+54kGdGfsZTb3zKJX8dyEmnnFmmmj6A5i1bsWbNr6xft5bc3D18Nf4LunbvGTLmpO49Gfv5JwB8M3ECHY47HjMjK2sLecFT++vWrmHN6l+pXSe11DOUhCizYn94xU8zfgA4534zs5Vm1s85974FpvLaOOfmH2SXCcAA4AkAM2vrnJsHTAUuBB43s9OAqgfZfzzwoJmNcs7tMLM6QC6B136Lc+5tM9sKXBecSazgnBtrZlOBFcWV+3DlO3hnznoGdWuAmTF1ZRbrf9vNOS2T+TUrm/nrt3NykyTa1q5MnnPs3JPH6zPXAtAxtQpNalSkUrloTmyQCMDrs9axZmuOV3EOW3R0DJf+/XaeuW8g+fn5nHhqH+rUb8Qnbw+nQZNmtD2+G++//jw5ObsY9tjdAFSrkcKAe4YSFR1Nv2sHMPTfN4EL3AHY7bS+Hic6PH7O7+fsoPzR0TFcecM/GfLvm3H5+XQ77WxS6zfmw7depmGT5rTv3I33Xn2OnJxs/vPoXQAk1ajJLfc96XHlxSMmJobb7rybQTdeT35+Pn3OOY9GjZsw/KXnad6iJV27n8zZ557PA/fcyQXnnE6VhEQefDRwOfq8ObN55aXniYmJwaKiuOP/7iMhIdHjRLI/i7Q7ccxsFdCRwIxZR+fcTWb2BoHTqR8ExzQEXgJqAbHAe865wQc5XnXgBaA5gWZtsnPu72aWDLwLpADTgs/XIHjMz4PX7e07xkDguuDiDuByII1AM5lPoBG8AVgHfArEEZgpHOqcG1lEXnfdfxcc4qsTeUZc1JrJS7d4XYYnujUNzDQov/L7Ubem1Zi+bKvXZXimc1oim3fs9boMTyRVCsxZOedKbdrswa+WFXuzdM+paZ5M+0XcjJ9zrkHwwzeCD5xzV+83ZiWBmysO5XibgIsOsGkbcLpzbq+ZdQGOc87tBlYBIRc0OOeeJXCTSGHLCcwG7q/TodQlIiIicrgirvErRfWA/wXfp28PcH0R40VERKQMiqSbO9T4BZnZ3cD+d0m875x7+EDjnXO/AO1KvDARERHxlBE5nZ8av6Bgg3fAJk9EREQkEqjxExEREQlDp3pFREREfCKSGj8/vYGziIiIiK9pxk9EREQkDPPwX9oobprxExEREfEJzfiJiIiIhBFJ1/ip8RMREREJI4LO9OpUr4iIiIhfaMZPREREJIyoCJry04yfiIiIyFHIzHqb2VIzW2Zm/woz7nwzc2bWsahjasZPREREJAwvbu4ws2jgBaAXsBaYZWajnXOL9xtXGRgIzDiU42rGT0RERCQMs+J/HIJOwDLn3Arn3B7gPaDvAcY9CDwO5BzKQdX4iYiIiBx96gBrCi2vDa4rYGbtgbrOuTGHelCd6o0AIy5q7XUJnurWtJrXJXhK+ZXfrzqnJXpdgqeSKulXeGmJovjP9ZpZf6B/oVXDnXPDD2P/KOAp4OrDeV591USAU5+f5nUJnvlqQBff5v9qQBfAv59/5Vf+3i9O97oMz4z7R2d+WLXN6zI80aFBgtclFItgkxeu0VsH1C20nBpct09loBXwbfCflKsJjDazc5xzsw92UDV+IiIiImF49G4us4AmZtaQQMN3MXDpvo3OuW1A9X3LZvYtcHu4pg/U+ImIiIiE5cVdvc65vWZ2EzAeiAZec84tMrPBwGzn3Og/c1w1fiIiIiJHIefcWGDsfuvuPcjYHodyTDV+IiIiImHoX+4QERERkTJHM34iIiIiYUTQhJ8aPxEREZFwdKpXRERERMoczfiJiIiIhBFBE35q/ERERETCiaTTo5GURURERETC0IyfiIiISBgWQed6NeMnIiIi4hOa8RMREREJI3Lm+9T4iYiIiIQVSe/jp8avmJnZIGC4c25XEeNGAE855xaXTmWHr2O9BP7RtQFRZnyxOIP/zln/hzHd0qpxZadUnIMVm3fx6IRlNK5egZt7NKRCbDT5zvHO7PVMWrbZgwRHRvn9m9/P2UH5O9RN4O8n1SfKjHFLMnh/7oY/jOnauBqXd0zF4VixeRdDvloOwINnNaVZSiUWbdjO/V/8XNqlF4t5s77nzZeeJD8/n569+9L34qtDto/5YBTfjPuUqOhoqiQk8rfb7qVGSi0AHv2/ASxbspCmrdpyx4NPe1C9FEWNX/EbBLwNhG38nHPXHWi9mUU75/JKorDDEWUwoHtD7vx0CZt27OE/F7Zi2sosVmdlF4ypkxDHJR3qMOjDRezYnUdifODLKWdvPkO+XM66bTkkVYzlhQtbM3v1Vnbu8TzWIVN+/+b3c3ZQ/iiDG7s24P8++4lNO/fw7PktmbFqa0j+2gnluahdbW77eBE79uSREP/7r9IP522gfEwUZ7RI9qL8I5afl8fr/xnC/z32H5Kqp3D3gKvo0KUbqfUbFYxpkNaUh//zJuXj4vjysw94Z8RzDLz7UQDO7ncFu3NymDj2Y68ilIjIme/TzR1HxMwqmtkYM5tvZgvN7D6gNvCNmX0THPOSmc02s0Vm9kChfb81s47Bj3eY2ZNmNh/oYmaPmdliM/vRzIZ6ka1pSiXWb8th42+72Zvv+PaXzZzQqGrImDNaJjN6wUZ27A78UN+avReAdVtzWLctB4DNO3PZmp1LYnxs6QY4Qsrv3/x+zg7Kf0xyMP/2QP5Jy7bQuUFo/t7Nk/lsUTo7gg3ttmB+gHnrfmNXbtlpdPe3bOkiatauS0qtVGJiY+nSvRezv58UMqZl246Uj4sDIK15a7ZkZhRsa9WuE/EVKpZqzXJ4NON3ZHoD651zZwGYWQJwDdDTObcpOOZu59wWM4sGJppZG+fcj/sdpyIwwzl3m5klAa8CzZxzzswSSylLiOoVy5G5fU/B8qYde2iWUilkTGpi4Bv/mfNbEmXw5sy1zF69LWRM0+SKxEZFsT74y6CsUH7/5vdzdlD+6hXLkbmzUP6de2iaHNrI1AnmH3puC6KjjLdnreWHNaH5y6qsTZkk1UgpWE6qkcKynxYedPy34z7l2ONOKI3SPBVBl/hpxu8ILQB6mdnjZtbVOXeg7/wLzWwOMBdoCbQ4wJg84MPgx9uAHOBVM/sLBzllbGb9zWw2wNqpnxxhjD8nOsqokxDHbR8v5pHxy7ilZyMqlosu2F6tQix39kpj6MTlOE8qLFnK79/8fs4Oyh9tgfx3jl7CY18uY2CPhiH5/WLKV2NZ8fMSzu53hdellDgzK/aHV9T4HQHn3M9AewIN4ENmdm/h7WbWELgdOMU51wYYA8Qd4FA5+67rc87tBToBHwB9gHEHee7hzrmOAKknnls8gQrZtHMPNSqXK1iuXqkcmwr9FQyBmYDpq7LIy3ds3L6bdVtzCv4SrhAbzUN9mvH69DUsSd9R7PWVNOX3b34/Zwfl37RzDzUqFspfsRybd+b+Ycy+/On78icc6Ed72VO1eg02Z6YXLG/OTKdqUo0/jFswZwafvPs6tz/wJLHlyv1huxy91PgdATOrDexyzr0NPEGgCdwOVA4OqQLsBLaZWQpwxiEcsxKQ4JwbC9wCHFsStRdlafoO6iTEUbNyeWKijB5Nkpi2MitkzNQVW2hTpwoAVeJiqJMYx4bfdhMTZdx/5jF8uTSTKcu3eFH+EVN+/+b3c3ZQ/p8zdlA7MY6UYP7uadWYvio0/7SVWQfMHwkaN23BxnWrydiwjr25uUyb9CUdunQLGbNy2VJGPPsotw9+koSq1TyqtHRFlcDDK7rG78i0Bp4ws3wgF7gB6AKMM7P1zrmeZjYX+AlYA0w9hGNWBj41szgCNxLdWjKlh5fv4D+TV/Fo32ZEmTF+cQa/bsnmqk6p/Jyxk2mrspi9ehsd6iUy4tI25Dt45fvVbM/ZyynHVKd17cpUiYvh9GaBvxSfmLic5ZvC3uh8VFF+/+b3c3ZQ/nwHL01ZxUN9mhJtxoSfMlmdlc0Vx9Xh58ydzFi1lR/WbKN93QRevqgNec7x6rTVbN8duMHjiXObUzcxnrjYaN66oh1Pf7uCOWXo+r/o6BiuvukOHv2/m8nPz6PH6edQt0Fj3h85jIbHNKdjl+6888qz5GRn8+yD/wIgKbkm/xz8FAD333o969esIic7mxsvPYv+t/6bYzt28TKS7Meci8QrMPzDzNypz0/zugzPfDWgC37N/9WAwA9T5Vd+P/pqQBd6vzjd6zI8M+4fnflhVdlpKItThwYJADjnSu1Cuf/NW1/szdKFbWt7cqGfZvxEREREwoigm3p1jZ+IiIiIX2jGT0RERCQML99+pbhpxk9ERETEJzTjJyIiIhJGJM2SqfETERERCUOnekVERESkzNGMn4iIiEgYkTPfp8ZPREREJKwIOtOrU70iIiIifqEZPxEREZEwoiLoZK9m/ERERER8QjN+IiIiImFE0jV+avxEREREwjCd6hURERGRskYzfiIiIiJhRNKpXnPOeV2DHAEz0ydQRER8xzlXau3Y2EUZxf679syWyZ60k5rxiwBVLx/ldQmeyXr7MrJz/dn7xscGfmYov/L7UXys+TY7+Dv/vq/90hRJb+eixk9EREQkjEg61aubO0RERER8QjN+IiIiImFoxk9EREREyhzN+ImIiIiEEUlv4KzGT0RERCSMqMjp+3SqV0RERMQvNOMnIiIiEkYknerVjJ+IiIiIT2jGT0RERCSMSHo7FzV+IiIiImHoVK+IiIiIlDma8RMREREJI5LezkWNn4iIiEgYOtUrIiIiImVOxDd+ZjbIzCocwrgRZtbiTz5HDzP7/M/sezQ7pXUtZgzpw+yhZzOwzx9fmjpJFfj0rlP49sHeTHn4DE49tjYA7RslMemhM5j00BlMfvgMzuqQWtqll4qpUyZzzlmn06d3L159ZbjX5ZQ6P+f3c3ZQfuX3X36z4n94JeIbP2AQUGTj55y7zjm3eP/1ZhZdIlUdgJkdNafeo8wYclVHLnziG7rcOYbzu9Snae0qIWNu79uKT2b+So97xnHdC1MZelVHAJas3crJ946j+7+/oN+Qb3jq2k5ER9IFEkBeXh6PPDyYF4eN4OPRYxg39nOWL1vmdVmlxs/5/ZwdlF/5/Z0/EkRU42dmFc1sjJnNN7OFZnYfUBv4xsy+CY55ycxmm9kiM3ug0L7fmlnH4Mc7zOxJM5sPdDnIc/U2s5/MbA7wl0Lrq5kMXd+KAAAgAElEQVTZJ2b2o5lNN7M2Ray/38zeMrOpwFtm1tLMZprZvODYJiX0coXVoXESK9N38GvmTnLz8vlo+q+csd/MnXOOynGxAFSpUI6NW7MByN6TR16+A6B8uWicc6VbfClYuOBH6tatT2rdusSWK0fvM8/i228mel1WqfFzfj9nB+VXfn/mtxJ4eOWomWEqJr2B9c65swDMLAG4BujpnNsUHHO3c25LcCZvopm1cc79uN9xKgIznHO3HehJzCwOeAU4GVgG/LfQ5geAuc65c83sZOBNoG2Y9QAtgJOcc9lm9jzwrHNulJmVA0ptxrGwWlXjWbdlZ8Hy+i276NC4esiYxz9awId3nkz/05pSoXwM5z32+zd/h8ZJPH/d8aRWr8gNw6YVNIKRIiM9nZq1ahYsJ6eksODH/b+MIpef8/s5Oyi/8vszf1QEvYNzRM34AQuAXmb2uJl1dc5tO8CYC4OzdHOBlgSarv3lAR+GeZ5mwErn3C8uMJ31dqFtJwFvATjnvgaSzKxKmPUAo51z2cGPpwH/Z2Z3AvULrT/qnN+lAe9OWUGrgZ9w0dBvGfb3EwquW/hh+WZOuGssp943nkFnt6R8bKR9qYmIiJQ9EfXb2Dn3M9CeQAP4kJndW3i7mTUEbgdOcc61AcYAcQc4VI5zLq+k6y2kYGrNOfcOcA6QDYwNzg7+gZn1N7PZALt/+brYC9qQlU2dahULlmtXq8CGrF0hYy7v3ohPZqwGYNayTZSPjSapcvmQMT+v/42du3NpnppY7DV6KTklhY0bNhYsZ6Snk5KS4mFFpcvP+f2cHZRf+f2ZP5JO9UZU42dmtYFdzrm3gScINIHbgcrBIVUINFnbzCwFOONPPtVPQAMzaxxcvqTQtinAZcF6egCbnHO/hVm/f4ZGwArn3HPAp0CbAxXgnBvunOsIUL7JAXvDIzJnxWYa1axMvRoViY2O4i+d6zNuzrqQMWs376Jby8A3/DG1q1A+NopNv+2mXo2KBTdzpCZVoEmtKqzO3PmH5yjLWrZqzerVq1i7dg25e/YwbuwYuvcs/s/D0crP+f2cHZRf+f2dPxJE2jV+rYEnzCwfyAVuIHBzxjgzW++c62lmcwk0bmuAqX/mSZxzOWbWHxhjZrsINHX7msv7gdfM7EdgF3BVEev3dyFwhZnlAhuBR/5MjUcqL99xx5uz+eCfPYmOMkZNXsFP67Zx119aM3flFsbNXcc978zhmb8ezw29m+Ec3DR8OgCdj6nBoD4tyM1z5DvHP0fOZsuO3V7EKDExMTHcdfe93ND/OvLz8zj3vPNJS/PkPhxP+Dm/n7OD8iu/T/NHziV+WCTeceknZuaqXj7K6zI8k/X2ZWTn+vNrOD428JNI+ZXfj+JjzbfZwd/5933tO+dKrR2bsXxbsb/YxzdO8KSdjKhTvSIiIiJycJF2qrfYmdnHQMP9Vt/pnBvvRT0iIiJSuiLo3VzU+BXFOXee1zWIiIiIFAc1fiIiIiJhRNCEnxo/ERERkbAiqPPTzR0iIiIiPqEZPxEREZEwLIKm/NT4iYiIiIQRSXf16lSviIiIiE9oxk9EREQkjAia8NOMn4iIiIhfaMZPREREJJwImvJT4yciIiISRiTd1atTvSIiIiJHITPrbWZLzWyZmf3rANtvNbPFZvajmU00s/pFHVONn4iIiEgYZsX/KPo5LRp4ATgDaAFcYmYt9hs2F+jonGsDfAAMKeq4avxEREREjj6dgGXOuRXOuT3Ae0DfwgOcc98453YFF6cDqUUdVNf4RYCsty/zugRPxcdGzrUXf4byK79f+Tk7KH9p8uiVrgOsKbS8Fjg+zPi/Al8UdVA1fhEgru2NXpfgmZx5L/g2f868FwCI63ynx5V4I2f64wCkXPe+x5V4I31EPwBmrtjqcSXe6NQokexc53UZnomPNd/m96ThLYGnNLP+QP9Cq4Y754b/yWNdDnQEuhc1Vo2fiIiISCkLNnnhGr11QN1Cy6nBdSHM7FTgbqC7c253Uc+rxk9EREQkDI/ezmUW0MTMGhJo+C4GLg2py6wd8DLQ2zmXcSgH1c0dIiIiIkcZ59xe4CZgPLAE+J9zbpGZDTazc4LDngAqAe+b2TwzG13UcTXjJyIiIhLGobz9Sklwzo0Fxu637t5CH596uMdU4yciIiISRiTdP61TvSIiIiI+oRk/ERERkXAiaMpPM34iIiIiPqEZPxEREZEwPHo7lxKhxk9EREQkDK/u6i0JOtUrIiIi4hOa8RMREREJI4Im/NT4iYiIiIQVQZ2fTvWKiIiI+IRm/ERERETC0F29gpk1AE5wzr3zJ/b93jl3QrEXVcqG3XcZZ3RrReaW7XTs94jX5ZQqP2Tv1fkYhg46m+ho443Rsxj61qSQ7fVqJjLs7guonliRrN+yufb+91iX+Rvd2jdiyMA+BeOa1q/Blfe+y2eTF5d2hGLTs2UKD17clugoY9SUlfxn3NKQ7XWqxfPcNcdRpUIs0VHGwx8uZOLCjR5Ve+Tmz57GWy89SX5+Pj169+Wci64K2T72w1F8O3400VHRVE5MpP8t91A9pRab0jfw9OA7cC6fvL17Oa3vhZxy1vkepSg5U6dM5vHHHiY/L5/zzu/HX6/v73VJpcrv+cs6ner98xoAlx7ODmYWA3A4Td++fY5Gb302nb43vuB1GZ6I9OxRUcYzt/Wl762v0+6Sp+nXqy3NGiSHjHl0wJmM+mIOna54lkdem8jgG3oDMHnOCjpf9Rydr3qOMwa8wq7duXw14xcvYhSLKINHL23Hpc9+R7d7x3Nep7ocU6tyyJhBZzVn9Oy19HpwIn8fPoPHLmvnUbVHLj8vj5EvDOGOh55lyPD/Mv3b8az7dUXImAZpTXnwuZE8OuwdOp10Mu+++jwAidWqc//Tr/LIi6N44NnX+ey/b5K1OdOLGCUmLy+PRx4ezIvDRvDx6DGMG/s5y5ct87qsUuPX/GbF//CKGr/9mNnlZjbTzOaZ2ctmdryZ/WhmcWZW0cwWmVkr4DGga3DcLWYWbWZPmNms4Pi/BY/Xw8ymmNloYHFw3Y7g/y24z0IzW2BmFx1sn6PR1DnL2bJtl9dleCLSsx/Xoi7L125m1fot5O7N4/2v5tOnW4uQMc0apDBp9nIAJv2w/A/bAc7r2ZoJ05aSvTu3VOouCe0aVmNl5g5Wb9pJbp7jk1lrOL1t7ZAxzkHl+Fgg8P+NW3O8KLVYLF+6iJRaqSTXqkNMbCydu5/GD9Mmh4xpcWxHysfFAZDWrDVbNmUAEBMbS2y5cgDk5u7BufzSLb4ULFzwI3Xr1ie1bl1iy5Wj95ln8e03E70uq9T4Nb+VwMMravwKMbPmwEXAic65tkAe0BQYDTwEDAHeds4tBP4FTHHOtXXOPQ38FdjmnDsOOA643swaBg/dHhjonDtmv6f8C9AWOBY4FXjCzGoVsY9IiatdowprM7YVLK/L2EadGlVCxixYtoG+PVoB0Ld7S6pUjKNalQohY/qdeiz/+3J+yRdcgmolxrN+S3bB8oasbGolxoeMGfrZYs4/vh5zhpzJqJtP4u5355Z2mcUma3Mm1WqkFCxXq54cdtZu0vjRHNuxS8Hy5sx07vr7pQy84mz69LuSqkk1SrTe0paRnk7NWjULlpNTUkhPT/ewotLl9/yRQI1fqFOADsAsM5sXXG4EDAZ6AR0JNH8HchpwZXC/GUAS0CS4baZzbuUB9jkJeNc5l+ecSwcmEWgaw+0jclS46/kxdG3XkGkjb6Zru0asy9hGXv7vMzw1kyrTsnEKX07/2cMqS8d5nery3+9X0f6OsVz23Hf856+dIuqd/g/mu4lfsOKXJZx1wRUF65JqpPDosHd48rWPmPLVGLZlbfawQpFiEkFTfkft9WMeMWCkc+6ukJWBWbhKQCwQB+w8yL4DnHPj99u3x0HGFyXsPmbWH+gPsHfTImKqt/wTTyFyYOszfyM1OaFguU5yAusyfwsZs2HTdi6+620AKsaX49yerdi24/dTnOef0obRkxaxN69sn+7bsDWb2tV+n+GrVTWeDVuzQ8ZcelIDLnnmOwB+WLGF8rFRJFUqz6btu0u11uJQNakGWzJ/n8HZsinjgLN2C+fMZPR7r3P3E8MKTu/uf5zUBo1ZunAenbqeUqI1l6bklBQ2bvj9xp2M9HRSUlLC7BFZ/J4/EmjGL9RE4AIzSwYws2pmVh94GbgHGAU8Hhy7HSh8hfd44AYziw3ue4yZVSzi+aYAFwWvD6wBdANmHkqhzrnhzrmOgJo+KXazl6wlrW4S9WtVJTYmmn6nHsuYKaGXmyYlVMCC01r/vLIHIz+fHbL9wl5l/zQvwLxVWTRKrkS96hWIjTbOPa4uE+ZvCBmzbnM2XZsHbn5pUrMy5WOjy2TTB9CoaQs2rl9DxsZ17M3NZfqkCbTv3DVkzKplS3nt+Ue59f6hJCRWK1i/OTOdPbsDzf/O7b/x86J51EqtX6r1l7SWrVqzevUq1q5dQ+6ePYwbO4buPU/2uqxS49f8VgL/eUUzfoU45xab2b+BCWYWBeQCnwK5zrl3zCwa+N7MTibQtOWZ2XzgDeBZAnf6zrHAb8NM4NwinvJjoAswH3DAHc65jWbWrPjTFb+Rj15N1w5NqJ5YiWXjHuTBYWMZ+ck0r8sqFZGePS8vn1ueHM1nz1xLdFQUIz+fzZKVGdxzfS/mLFnLmO+W0K19Iwbf0BvnHN/NW8WgoZ8U7F+vZlVSUxKYMrfsX62Ql+/4v3fm8e6grkSb8e7UVSxd/xt3nNOCeb9mMWH+Bu5/fz5Dr+xA/1Ob4ICBr88u8rhHq+joGK76xz8ZcvfN5Ofn0/20s0lt0JgP3nyZhk2a06FLN94d8Rw52dk893Dg5EhSjZrc9sCTrF+zineGP4tZ4IaXM8+/nLoN0zxOVLxiYmK46+57uaH/deTn53HueeeTltak6B0jhF/zR9KlG+ac87oGOQJm5uLa3uh1GZ7JmfcCfs2fMy/wdjJxne/0uBJv5EwPTL6nXPe+x5V4I31EPwBmrtjqcSXe6NQokexc//7+io813+aPjw10Yc65UmvHlmVkF/uLnZYc70k7qRk/ERERkTAiaMJP1/iJiIiI+IVm/ERERETCiaApPzV+IiIiImF4eRducdOpXhERERGf0IyfiIiISBiR9HYumvETERER8QnN+ImIiIiEEUETfmr8RERERMKKoM5Pp3pFREREfEIzfiIiIiJhRNLbuajxExEREQlDd/WKiIiISJmjGT8RERGRMCJowk8zfiIiIiJ+oRk/ERERkTAi6Ro/c855XYMcATPTJ1BERHzHOVdq7djarD3F/rs2tWo5T9pJzfhFgLh2A7wuwTM5c5/3bf6cuc8DENdhkMeVeCPnh2cAiOt4i8eVeCNn9tMAxB13m8eVeCNn1pPE9X3Z6zI8k/Pp35j7629el+GJdvWreF1CmabGT0RERCSMSDrVq5s7RERERHxCM34iIiIiYUTQhJ8aPxEREZFwdKpXRERERMoczfiJiIiIhGERdLJXM34iIiIiPqEZPxEREZFwImfCT42fiIiISDgR1PfpVK+IiIiIX2jGT0RERCQMvZ2LiIiIiJQ5mvETERERCSOS3s5FjZ+IiIhIOJHT9+lUr4iIiIhfaMbvIMzse+fcCUWMGQQMd87tOsxjDwYmO+e+OpIaS1qvE5oz9Pa/EB0dxRsfT2PoG6Hl1qtVlWH3XUr1qpXI2raLa//9FusytgLw0M3n0PukFgA8NmI8H0yYW+r1Hynf5+/SjKG3n0d0lPHGJzMYOnJiyPZ6Nasy7N6LA/l/28W197zNuoxtADw0oE+h/BP44Mt5pV7/kejVpRlDbzuX6Kgo3vh0OkNHfh2yPZD9IqonBrPfO6og+8PB7FFmfD3jZ2578mMvIhyRXp2bMvS2vsH8Mxj65jch2+vVrMqwey6kemJFsn7L5tr73imU/yx6n9g8kH/mz9z25KdeRDgivdqlMvS6EwJf+1/+xNCP5odsr1u9Iq8M7ElCxXJERxn3vDWT8T+sISbaeOnG7rRtXJ2YKGPUt78w9MOy9bW/v3mzvueNF4eSn5/PyWecy7kXXx2y/fMP3ubrLz4lOjqaKglV+fvt91IjpZY3xZagCJrw04zfwRTV9AUNAiocznHNLNo5d+/hNH1mFn04z1EcoqKMZ+7sR98Bw2h3/iP0692BZg1rhox5dNC5jPp8Fp0uepxHXhnH4AFnA9D7pBa0bZbK8ZcModuVTzHoipOpXDGutCMcEeU3nrnzfPrePJx2/R6n3+ntaNYwJWTMo4POYdSY2XS65AkeeWU8g2/qA0DvE4P5Lx1Kt6ueYdDlPalcsbwXMf6UqCjjmTv+Qt+Bw2l34eP0O639H7MPPDuQ/dKhPDJiAoNvPAuAzm0a0OXYhhx3yRN0uHgIHVrUpWv7xl7E+NMC+c+j78ARtLvoiQN/7gf2YdTYH+h02VM88uqXDP7HmQB0bl2fLm0acNylT9LhkqFlN//fTqLv4C9oN+B9+nVNo1lqYsiYOy9sz4dTl9Pl1o+4cuhEnv3bSQCcf2IjysdGc9zADzjhto+47vTm1Euu5EWMYpGfl8drzz/OXY88x1Mj3mfqN+NZ++uKkDEN0prx6Atv8cTw9zi+2ymMeuU5j6otWWbF//CKGr+DMLMdwf/3MLNvzewDM/vJzEZZwM1AbeAbM/smOPY0M5tmZnPM7H0zqxRcv8rMHjezOUA/M3vDzC4IbjvFzOaa2QIze83Myh9on9LOf1yr+ixfm8mqdZvJ3ZvH++Pn0KdH65AxzRrVZNKsnwGYNOsX+nQPbG/eqCbfzVlOXl4+u3L2sOCX9Zx2QvPSjnBEfJ+/ZT2Wr9n0e/4Jc+nTvVXImGYNazJp9i8ATJq9jD7dAtubN0oJzb9sPad1KTv5f8++JZD9ywNkb1STSbOXAaHZnXOULxdDudgYysfGEBMTTcaW7aWe4Ugc17Iey9duZtX6YP4J8+jTrWXImGYNU5g0q/DnPrDdAeXLxVIuNrrs5m9Sg+UbtrEqfTu5e/N5/7vl9Dm+QcgY56BKfDkAEiqWY8OWnQXrK8TFEB1lxJePYU9uHtt35ZZ2hGKzbOkiUmrXJaVWKjGxsZzQ4zRmfT8pZEyrth0pHxf4w7ZJ81Zszkz3olQ5DGr8Dk07ArN7LYBGwInOueeA9UBP51xPM6sO/Bs41TnXHpgN3FroGJudc+2dc+/tW2FmccAbwEXOudYETr3fEG6f0lK7RiJrN24tWF6XsZU6yQkhYxb8vI6+Jx8LQN+T21ClUhzVEirw48+BRic+LpakxIp079iE1JTQv5iPdr7Pn5zI2vTC+bf9Mf8v6+jbsw0AfXu2/mP+8rEkJVSke4eylb92jYTQ7OlbqVNj/8/9evr2DDT6hbPPWPArk39Yxsov7mfluPv5avpPLF2VUar1H6k/5M84QP5fCuXv0eqP+cfex8ov7uWr6UvLXv5qFVm7aWfB8rrNO6lTrWLImIffm83FPZqwbMSlfHzPGdz6yvcAfPT9Cnbl7GXl65fz8yuX8synP5K1Y3ep1l+ctmzKIKnG77O9SdWTydp08M/nN198SttOh3KyrOyxEvjPK2r8Ds1M59xa51w+MA9ocIAxnQk0hlPNbB5wFVC/0Pb/HmCfpsBK59zPweWRQLci9jlq3PX0J3TtkMa0d+6ga/s01qVvJS/PMXH6T4ybuphvXr+FkY9cxYwfV5GX77wut9j5Pv8zo+navjHTRt1WKH8+E2csDeR/bSAjH7mCGQtWkZef73W5xequZ4PZ376Vru0bF2RvlFqdpg1SSDvrARqf+QA9OjbhxLYNvS632N317OeB/G/dsl/+pED+Pg/S+KwH6dExLSLzX9g1jbe/Xkrade9w3oNf8OqgnpjBcU2SycvPp9G1b9P8b+8ysG8bGqRU9rrcUjHlq7Es/3kJ5/S70utSpAi6uePQFP6TLY8Dv24GfOmcu+Qgx9h5kPXhHHQfM+sP9AfYu2khMdVbHWzon7I+cyupNX+fpamTnFhw8fY+Gzb9xsW3vwpAxfhynHtKW7btyAZgyKsTGPLqBADeePhKfvm1bP3V7/v8GVtDZunqJCccOP8drwPB/Ce3YduOHACGvPYVQ14LXMb6xkOX88vqzFKq/Mitz9wWmj0lkXWZB8r+BhDM3jOQ/dpzuzBz4a/szN4DwPhpP3F86wZMnbey1Oo/Un/In3yQ/HeOBPblbx3M3zk0//dLOb51/bKVf8tOUqv/PsNXJ6ki67aE/ii+6tSm9B38BQAzlmYQFxtN9SpxXNgtjQlz17I3z5G5LYdpS9LpkFaDVell63T3PtWqJ4ecut28KYOq1ZP/MO7HOTP46J3XuP/J4cSWK1eaJZYa/csdss92YN+fc9OBE80sDcDMKprZMUXsvxRosG8f4ApgUpjxBZxzw51zHYFib/oAZi9aTVrdGtSvXY3YmGj6nd6eMZMWhIxJSqyIBb8b/nltL0Z+Oh0IXBxdLSFwz0urJrVp1aQ2X03/qdhrLEm+z794TWj+09oxZvKikDFJCYXyX3MqI0fPAPbLn1YrmH9p6QY4ArMXryGtXqHsvdoxZvLCkDEh2a8+hZGfzQRgTXoWXds3Jjo6ipjoKLq2b8RPq8rWNU+Bz331Qp/7toyZsv/nvkKh/Ccz8rNZAKzZmEXX9o1C868sW3/0zP4lk7RaCdRPrkxsTBT9TmrMmJm/hoxZk7mDHm3qANA0NZG4ctFkbsthbeYOerSuDUCF8jF0aprM0rVb//AcZUXjpi3YuG4NGRvWsTc3l++/nUDHLt1Cxqxc9hMjnnmEOwY/RULVah5VKodDM35HZjgwzszWB6/zuxp4d98NGgSu+fv5YDs753LM7BrgfTOLAWYBw0q66EORl5fPLY9/wGcv/IPoqChGjp7OkhUbuefvZzJn8WrGTF5Itw5NGDygD87Bd3OWM+ix9wGIjYnmq1cHAbB9Zw7X/vst8vLK1qk+5c/nlic+5LPn/0Z0dBQjR88I5P9bb+YsWcOYyYvo1jGNwTeehXOO7+auYNDjHwDB/K8MAIL573m7TOXPy8vnliEf8dlz/YPZZ7JkRXpo9g6NQ7MP+RCAjybOp3vHJsx+95845/hy2k+MnbLY40SHJ/C5/5jPnrue6Chj5GezAvn7nx7IP2Ux3TqkMfgfZ+AgmP8jAD76+ke6d0xj9ju34Rx8Of0nxn5XxvLnO255ZSqf3XdG4PP/1VKWrMninks6MGfZJsbM+pV/vT6dF2/sxoCzW+NwXP/ctwAM+2IRwwf04IfnLsDMeGviUhb+usXbQEcgOjqGa2/6J4/cNYD8/Dx6nH4OdRs05n9vDKPRMc3peEJ33h7+HDnZ2Tz94L8AqJ6cwh0PPu1x5RKOORd51x75iZm5uHYDvC7DMzlzn8ev+XPmPg9AXIdBHlfijZwfngEgruMtHlfijZzZgV+uccfd5nEl3siZ9SRxfV/2ugzP5Hz6N+b++pvXZXiiXf0qADjnSu0E7NbsvGJvlhLjoz05gaxTvSIiIiI+oVO9IiIiImF4+fYrxU2Nn4iIiEgYuqtXRERERMoczfiJiIiIhBFBE36a8RMRERHxC834iYiIiIQTQVN+avxEREREwoiku3p1qldERETEJzTjJyIiIhKG3s5FRERERMoczfiJiIiIhBFBE35q/ERERETCiqDOT6d6RURERI5CZtbbzJaa2TIz+9cBtpc3s/8Gt88wswZFHVONn4iIiEgYVgL/FfmcZtHAC8AZQAvgEjNrsd+wvwJZzrk04Gng8aKOq8ZPREREJAyz4n8cgk7AMufcCufcHuA9oO9+Y/oCI4MffwCcYhb+6OacO7z0clQxM30CRUTEd5xzpXblXc5eiv13bVxM+Gk/M7sA6O2cuy64fAVwvHPupkJjFgbHrA0uLw+O2XSw4+rmjjKuNL/wD8bM+jvnhntdhxf8nB2UX/n9m9/P2cF/+Ytq0v4MM+sP9C+0anhpvKY61SvFoX/RQyKWn7OD8iu/f/k5Oyj/EXPODXfOdSz02L/pWwfULbScGlx3wDFmFgMkAJvDPa8aPxEREZGjzyygiZk1NLNywMXA6P3GjAauCn58AfC1K+IaPp3qFRERETnKOOf2mtlNwHggGnjNObfIzAYDs51zo4FXgbfMbBmwhUBzGJYaPykOvrnO4wD8nB2UX/n9y8/ZQflLhXNuLDB2v3X3Fvo4B+h3OMfUXb0iIiIiPqFr/ERERER8Qo2fiIiIFLui3khYvKHGT446ZhZxX5f7fgD69QdhofwR97k9VIU/98E79HzDz5/3wvz0/W9mtu/uUjPr4HU98jt9M8pRw8w6ATjn8r2upTjt+wFoZr2AwWYW5cdfAGZ2FvBy8N+f9JX9fgleAfT1S/NnZuX3fU+bWUsza+l1TV7Y72ugj/1/e+cdNkdZ7uH7BwQJvaMgEIqCSBU9aAANVQREugKCQdQTCFVAECmhiUpTEEHhHKMoovQivXcMLRJCUZCEQxEQElooIb/zx/NumHx8afB9mezsc1/XXjs7M7v7vDuzM8/7VOnzdcvUm1TG+m3gGEnz1yxSUkjFL5mZOEDS8XUL0dMUpWcLooH2LbYnVC6KjVcAy/g3B44Dzrf9bieMu0rleA8Gvk+UYni7Xql6H0krAoMkzSZpD+Ac4K+SDp/KWxtH5RzYGzgaeL5eiXofSWsTGaf72h7TiZO+mZFU/JLa6Mb983Pg3Tpk6U1KNfWvA9sAN0vaQNKZkhYvSlEjlaAu49oQ+A4wrFj+zm1ZeJs6fnife3dxYGdga+ApSdtIOljSZ2oTsPdZGfg0cBDwZeAzwLrAppIOrVOwOijn/C7AOrafkLSupK9L6lO3bD1BNaSjWLT7Ax8DtpI0aydO+mZGspxLMsORtCiwSClEuR4wDrgH+AhwFXCW7e3gWqYAABxeSURBVN/VKWNPUdw59wG/ANYAngL+BawA9AU2tT2+Pgl7lzL+J4C9gXWAt4A7gKWBhYDtbb9Vn4QzBkmrEsf9YGB94BGgD/AC8Jbtg2sUr8eR1Nf2uLL8TeBzxPm/re3nJS0P/B64yfYhNYraq0iapRq6Imkh4CfAXITF71PAq8Tv8Mt6pOwZuriyF7L9nzK53w1YBbjZ9gVd901mPFnAOZmhlJntTsAakn4CLAzsC9wF/A34IfBdSecB4xpwcdgDeMr27sXFc6PtByUtDZwCzA2MqVXC3uVbwBy2d5W0FTDC9j/K+IcSyt8zdQrYG0hawfajZXlNwrW3PXAW8CRwte3RknYD+ndVENoZSXMD60t6GlgKeBu4jjjW20m60PY/Je0K/ErSwrZfrFHkXqHENr5Vlj8NzFL+++cCWxIdFx4G9iQmvW1Nl3CGzSWNAP5m+8zi5l+3/CbnNOC63takqzeZodh+B7geGAnsRVh/NgUuAHYHDgS2AJZsRzdoN/K2lDtsn1Iu/NsR/RWH2m6U0tfN+I8E3pL0UdsXFaVvW+Ay4Be2G6X0KZgN+JOklgVnFNE0/Q1glO0zi9L3HWAwcHJTlL6CiPZSvwFOBC6zfRlwCfAJYBtJSxTFeJOGKn3dxTZeLukQ29fb3sv2g8COhPv/yjrl7SnKOb09cW1fFjhE0mDbvyK8HatJmqdOGZNU/JIZSCumz/bfgYuA54BDgaVt3wFsBAwBzgeOlzR7u80Mi7I6oMTxzQ78Hfh0mQW3WAs41PZF7abYTo0y/vUkbSlpadvPES7tXSq7fQI40PbFTRs/RH9NYGPgs5J+RnhWXgCq2a0rAosB37I9ojZhe5DWsbT9KuHGXJyw4n+mrD8PuAlYlbAIzQo0NcxhcrGNW0g6VFKfEtv5NWCg7ZH1idozFEsvwFbAVwh39kHALpIG2T4R+Ek5P5IayRi/ZIbQiukosU4TeM+9N5gI/v0f2/eWfeciMmD3Ln0I2wpFuY7/BoYBDwIjiBnwYNuvVPZrZJyLpC2BbwJvEmO/lYhr2sn26Dpl6026Hk9JCxOWnPHlMQp4hbCIPQ8cb/u1OmTtabrEd81ne6ykBYCvAl8CLrJ9uaRlgS8CV9huXFbrdMQ2Xkdkuc/arufA5K5fkj4OnAHsbPtlSZcSk58dm+bhaFdS8UtmGJI2AU4CziVKWqwGzEm4dlcATrV9v6QvA6cB/dvh5lBRalcEXiKsO/MQlq3jCAVoPeCLZXyNieeCSca/OvA68GK54H+MUODHEBa/rW1f1cruq1PmnqaL4rMFccxHE/F8FxDWvT2BRYAFiVjPRijBXcZ+ALAZ8T84FbiZGPcahMK7ILCr7ZdqErfXaMU2Aq3Yxj5E4trXgTuBC20/K2kF4HRgO9v/qUveD0MreaMs7wUsD8wPnEAkMV1CXONXJc6HPZvo0m9XUvFLep3iAloSOBvYFfgkkeXav2R+LU8kfJxfMn1XIDIdn6xL5ulFUaduCGHdmo2wYD5Qtq1CZLatCGzVsgg0CUWJluOAC4mb/C9s31CO/SLAIKKMyTrtauGYFiR9n5jIXEGU7xlEKH9XALfa/kF90vUukr5AhG4cSrg6jwC+V86DzYl6bsc3xbXdlRK7tiEx/gWAT9seV2J61wb+SVg+n5bUp8Q7tx3l+vxt2wdJ2pNIVPkO8GfgvpLIdjjh3l6q7PtAfRInXckYv6RXkDS7pDnLy7mAlwmlYAPgKGDzovR9DXiWiP14qFgPHm0zpe+zhNL3VWAs4co6QFJ/gBLEfTDwfzTwP1fc90OImf3LQD/gCEmbOnje9lFEvOMCtQnay0haEljX9gBgXuK8Hmn7ZeK3+UyxgjYORVmmE4gszvttn01k6J8haQvblxOWvsYpfR0Y27gYsFkZx7zAN4hJ3YvAvsWifxRh6fxSKn0zH427CSX1U7IavwBsWOJcjiZcPFsRWZ79S3bn54jZcT+XsgftEPNWMvWqBVfHA98jalV9lYhbHA/8UNI6ZZ+1gQGEC7itkTSHpHkktUpQvEhYNJcDBhKWrjuBnxZLYKtw7X8BM/3xnVa6SUwRMF7SScRN/hu235K0A1G/cCPbz85oOXuDbsZ+C1Gv8nOSlioTuPOI//tRlcD/RtFNbOPtwEqEhffbxdIJMem5g7D4vdsO17nJYfsWYDgRm704Ea/4WWCLch3fvVgC38lEjpmTrOOX9Di2x0saTyQ0rEi06xmlqFl2K3EjeJtwERxh+6EaxZ0uShzfwcDikp4j6g/+2lGR/sfA/rZvk7Qx8A5RnBUi7mX9kuXatkj6FKHILwCMlfQ48EPbz0g6EDjRUaPtKeJG14phGg2s1yDFZ2KcpqRPEokbTwP/ICwda9t+U9GndDBwg+3XaxO4B+mi7GxJWPSfs72XpFMIZe9ISaNs/1HSpU1073cX2yipFdt4NjAfsLWkbWjz2EZJGxKJKo8RSu3ChEXzGOB2wrL9rqI24x7Alk2KY24aqfglPUrlYngn4fZ4A5hb0pK2H1d0ctiaKOq6j+2bJ5cdNrNRYlvOJYrw3gOsSVg2V5c0CJgdOFrSUYTV61u2h5fxPVGX3D1FGf8fiPpsNxIWvp2J3qubEVatgZIM7APsYPveMv62Vni7UlH69iHqUD5KuLlvIK6rl0m6hrAA72j733XJ2tNUlJ29idjcm4HtJW1ne5CkXwA/JWpyjm6q1afyO3yBSN76PhHbeBYR23hqJbbxR+2q9BXGEbG6qwHbEolrm9u+VNKmwDllAtSPyGB+rDZJk6mSyR1Jj9FS4BTp/E8TitCaRGmPR4lixvMT7dra6sIgaSngNuICfnZZJ8LFsQ9wp+3TJP2WCKG42PZFtQncw5Rjei9h0fxDZf0ixHF9xvb+ko4m6vbdZvvieqTtPSStBrxRQhU2BX5AZHJeSpzzuwNzEHXMxgGP2n68Lnl7EkUplldtv6AouXQuMXl7QlJfokzJ7cT58Evg6KZYeCdHiW08BrjG9pFl3XbAscABRTFqWhb/YoS1b1/iOne6pDmIMI6+zpItMz1p8Ut6hNbFTdJXiOSNa4h6fUOIG+HWxEz4y0SsX7uxElGTbuJMqSi59wJXE5ad0xytyeYobr62sGROI/MRil+/1opyzF9QdKg4uAR1H1Y5F5o0/hb7Eb/BACKO80Iio7EPoQRNkLS8S0/SpqAo1TMMOFbSb0voxnxEz+UnSvbq2UTJogmEu69xdHNOTxLbSLRnPE9RvP0oSTcQXo+2R++VYXrB9pWSXgGGFKX/DNtvEFb/ZCYnkzuSD0X501NueOsAPyNqts1CuAR+Q1wcTyXcv7vaHlaTuB+Gm4mx9C/uPWCiy+9eYDlFA3Zcik43TOl5GDgEWLYkL0x0dxLxi4sBH62ub9L4FQlL2B5YXh8EPA58G9jN9peLsr8nsF+xgDSJfxMKzscJt+5cxP/h10UphAj0X1aR/NO4jixdYxsl7QQMsL0X8AQR27h02e+PRIb3a02x9hWlr3WtV0lkOZaYBM1ep2zJ9JGKX/KBkbQgsL+iQj9ExuoOxM1hI+Km+DHC0jfa9lm2r223m0K5yI0jLHvXAStI2qcyjo8Tyk/j6vPBxPFPIDITTwHml3RiZfwrEAV7mzr+jYieo2uWVUcSBWuXI4oz/03Sd0oix65EgkvbdZzpjsoxHkMc/8UJC/5gIsj/x0QP2tOAvYlWhG82Selv0SW28YdEvNveks6wvTfRleWnRM3SVnmXtqMotT+e0j7F2yHbNwFfT/due5GKX/KBKBaQ14lg/7kkrW37SiKWbwciFuxO3rMGfbL13na7KVQucuOIFlzXEdnKO5XA7hOBocXV0QgUJTladchcnqvK3wLAoSVZ52TgV20evD4lViMSFY5TtOO7g8jY7gf8CrieqE+5Ks3qvbs+UZJk+XLuDyFqE75NWHe/C5xDjP03wCZuQM/ZrkhatsSyttpJbkQkLv2AqGG3kKR9iTCA/xDnRltS/vNnAHtMi/JXFhsxyekkMsYvmW6Khe8U4ISStXogkdk6W8nSnQtYtVgLViEy3B6pU+bpQdFJZAfCvTnS9siq8ifpSiJ+cUfiJrCT7SuaEtOmyN69CLhd0mBgfMV9O0FSS/k7jKjDuFWTxt+iNR7bJ0iaALxGZDHPDowk3FyP2b5Y0iVEslwj3HrlP3wc8f8dXpJ2xhDJGyOI+MZdgB8BZ9l+tC5Ze5MOjG1cAPgWMbm5uVzTJ3ab6e4/3qT/fKeQFr/kgzA7oRQdrsj0O5O4OH5T0srEDWND4HCi/247KX0rEq2HFiAyMycmohTlb5aK2/ccYDPbV7S21yByj1KUvqHEDX5xYMFWTE9rn4rl7ygijqkx42+h6LpytKIrC0QMV6tLwWyEleM14ERJyxUFsRFKH4Cj5uDuRGwuRBbnJkQNx58Rx/9K4rdojKW7GzoqttH29cC9xU39ZWADSSdUdlmoHsmSniTLuSQfCElfIlw9cxL1q14k+pJ+kshuHS5pbtuvtYslqCRn/Ak4x/ZQSVsTFr3jgQkubeRa4+kS7N0WY5wSklpV+I91FN49i1B2drQ9xRZTTRh/lTKhOYaozfes7WMknU5kNB5ezpXDiVZsa7tBdfpaKFpyrU6UY5qdcHfvS9z8T3L0nJ2zSSEOLSr/8b6EdXsJIjntLSJmeQvi/LiEqOG3bZPc3CoZvIrSLVcQsayjiQn9oKbEsHYqqfgl042krxJurouJ+CcTN4XniG4dyxM1rNou5kvS523fVZZvJVxaI4lZ/Um2b61Tvt5G0pds31yWVyHiln5m+xE1rB7Z5NB75WjmJeo07kkoPt8nrF3/a/vSsu/Ctl+sT9repSh/qxIK35O2jyhWrca4tbtSYhuXAW52dKFZgrBuP0bEK/+biGtdhkh0edn26Lrk7S30XvkWFKVb3iDaDj5Yr2TJhyUVv2S6kDQLcBpwie2rJPUj3F9rEsrfS8BCtv9Vm5AfgK5KjaJQ70Ylvmt+okjzWNs/r03IXqQ7pa5YO/4I/Mv2/vVINmOpWHq6ng9nEK3JFias23vZHtM0S2d3lP/8qsSk7m3bu9csUq9RXLk3UGIbCdf2GCKZqxrbOJaIbXyqJlF7jMpEZw5gVqIM18W2x5btXwDOBzZ2G7XXTCZPxvgl04uJYr4bAxT3591EC59TiMbcbaX0wSQ16Vqvh9s+oSyPIUqVLFqHbDOCbpS+VhbzgcDaihqNjUXSkuWmPw9M0pJtlvJ6EKEEP0W0aGvV9Wt7pU9TKd9Riek8HRgvqcn/g06MbexbYnt/D5xExDAuVtk+BxHLm0pfQ8is3mSKVCwgnyMuAM8RmXwnSxps+zSiftW9hCu07Wq5VV0a5XXf6jgk/RcR57RfHfL1Nt2Mv9V5ZBZgFHA/sLKk25ug6HRF0Wf4EMKF9w9J/9vKUi2WkFlsTygW7muBg9sxjKE79F75jjkkYfuQ7vYrv8N9wIO2G9mdoWK9HQ6IcO9/jJj8vEnENi5m+2pJtzYhtrFk7S8FfIqw6s1GZCy/0NrH9o31SJf0FunqTaaKpC2I0h2XEH1Jf00ogL8ksns/Dwy2fVltQvYAikK9XyEs4fsVhXc9Iph7H9uX1ypgL9Pd+Mv6rYHnbd9Wp3y9QTm+pxPlez5CtBa82/YFHeLG3YC42d9BdKe5zt2U72jyb9Ga6HRZ1/jYxmK5vRg4gugz/XB5/TvbF9YpW9K7pKs3mSKKmn2DiGyuZwmr37UlAeALRJLHJrYva8dSBpKWkTRA0o1EBu8aRByPYOJsdxvbl7fj+KbGlMbfGq/tC5uo9BXWIEoO3V+Sep4Ctq9ZphmGp7F8R4OVvg2AvRS1+SZSLODDiSSOj0s63UEjlD4A288TLtxrS0bykoSF/6ZaBUt6nVT8kqkxG/A8sBswkOhM8JKkTYCFSyzcSGi/m4Ok7Qgr5obAucTMd27gxpaLD8D2A+W5rcY3NaY2/qaNtztsn0QUq24xjIhjbdVtnLcWwWYgtl8s7v4XiPjF9SQdIumbwAlqXt9hYKKF+/fA/a1Ehsq2VnJP02Mbq4rsz4F3mxLGkEyejPFLJqHi2vmo7edsvyDp/4AfAJva/oekAURtu6/XKuyH5+9Epf27bI+XtA3h6vorvD/hoYFMcfxNp3Wu236msnoM0GrPtTPwCUnH2H67FiFnEI6abbM66hGu2aV8R6NqthVL9mzA14iyU9cp+o73Bea0/Y9qbGeTYxsr4RwLENbu48rrxrr2k1T8ki4UpW9TYIikZ4ksryuImeEvJF1MWP8OcpsXLC0B/NVWU5sSRVo7ghx/tze2d4ExknYjSvhs3zSlbxrLd7xKQ8t3lOP+jqRngLeLJe9KIolpQFH0h7rSppAo3NxYbL8s6TDKOFPpazaZ3JEAk1j65idadh0LDCDKtFxFlGz5IuEGe9r2rU2aFUraEdjb9ufrlqUOOmn8U1J8iuXnEaJF2662H65T1t6glK35OFGi5GUihGMV24+V7esBo2w/UZuQMwBJexJxyrcSLs4zJa0N/IXwbgyvVcAk6SXS4pcAEy19GxD1m16yPQwYVtL9W3XLLnalzElTlL7CTcDrAIrG5FNsUdZAbqJzxt9XUlfF506iQPdLku4EDm+o0pflOwq2fylpcyK2dZey7nZJlxAJTknSSNLilwDRqowI8P8zcSM80/ahZdu+ROuq/d3AnqQtutaz6zQ6YfyTUXy2J8q5jGnYZGYSsnxH90i6mqhFeiSwFtGabxM3oCtHknRHKn4JklYFBgO32P6jpJWI6u3X2z6i7LOUG9iPMukcUvF5X//VpYADgCGdkMnZjYt/B9tnlW1HE/Ft/YmEj7aOX06SKZGu3gSiD+WngNclXWN7pKTvAn+S1Mf2Ian0Je2O7eclrdtF8em0umVdy3eM6gSlr/A+F7+kO2yPtH0YvL9rT5I0kVT8OpBKIseKwAu2/yLpOaJQ80aSrrL9sKSdgMbXMUs6ik5WfDq2fMcUYhuf7bJro0rXJEl3pKu3QynFS/9AZOy+SdTpWwvYGbgBuNT2f+qTMEl6j6L4DAGOLZbARis+3VGKU7/VxPp0VdLFnySTkha/DqJi6ZsPWBnYChgN/DdRnX53IvZlIHB1XXImSW+TdcvA9it1yzAjSBd/kkxKWvw6jNJqbUtgeWB/onvDEsD3gFUIpa+P7RfrkjFJkqQnqVp0JV1IuPj3q1msJKmF7NXbAUiarTyvBRwO3EHE7m0LzG/7/4AzicK1y6TSlyRJk5hSbGOdciVJHaTFr8FIWoYoxjy2LJ8NXGH7x5L6AacSbYp+XgrXfqTp8T5JknQ2nRLbmCSTIy1+zWY5YFRpwzYauAfYWdLqtp8E9gDWAQ4o3RryQpgkSaOx/Upe65JOJi1+DafE9J0KfLZY/n5EdOEYYnt4CXRe1PY9tQqaJEmSJEmvk4pfByBpU+AUYE2iNdFBwPrAD2w/UKdsSZIkSZLMOLKcSwdg+4oSw3wPYe37KdCHKN2SJEmSJEmHkBa/DqK4fX8LrGh7bN3yJEmSJEkyY0nFr8OQtBnwuu2b6pYlSZIkSZIZSyp+HUontqhKkiRJkk4nFb8kSZIkSZIOIev4JUmSJEmSdAip+CVJkiRJknQIqfglSZIkSZJ0CKn4JUkHI+ldSQ9IGiHpstLer22QdHeRf7SkF8ryA5L6Szq/F7/3Ykl3dVm3naSHJd0oaYCk/h/yO/pJGlcZ0wOSdpnOzxgoafHK6yclLfxh5JqG7xwqadve/I4kST44WcA5STqbcbZXB5D0O2AwcGy9IkXWOZF8NmFK+9leq+w/kGhLuGdl8x29JNv8RBec1yQta/uJsmk34Lu2b5M0BHhtemQo/bLHd1n9eOv4fAA5ZwUGAiOAZz7IZyRJ0jzS4pckSYs7gSUAJC0n6SpJ90q6VdKKZf1iki6SNLw8+pf13y9WwxGS9i3rfiJpcOvDJQ2RdEBZPlDSMEl/l3RkWddP0qOSfk8oK4dJ+nnl/d+VdPK0DKR81oiyPLBY6K4tFq89i7z3S7pL0oJTGnM3bA1cBpwLfKO893BgHeB/JJ0HDAL2K1a6dSUtIumCMuZhktau/CZnS7odOHtaxlbed7qkeyQ91Pr9yvonJf1U0n3ADkSnnj8WOfqW3faSdJ+kByvHdSFJ15TPO0vSKEkLV3/Hst8BRaltHY9h5Ty4QNKc3ch5dLEAztrdMU+SpAZs5yMf+ejQB/BaeZ4VOA/YpLy+HvhEWV4LuKEs/xnYt/Ke+Qjr14PAXMDcwEPAGuVxc+W7RgJLAhsDvwFETD4vB74I9AMmAJ8v+88NPA70Ka/vAFaZzDgGAr+svO4HjKhs+ycwD7AIMBYYVLadXBlPt2Pu5ruuBdYFPgk8WFl/E2F1BBgCHFDZdg6wTlleCni4st+9QN9uvqcfMA54oPJYt2xbsHIMbgJWLa+fJHpwv0+myva9yvIewFll+RTg8LK8GWBg4ervWLYdAAwpywtV1h9T+dyhwLbA8cAZ5Th3e8zrPv/zkY9OfKSrN0k6m76SHiAsfQ8D10qaG+gPnBceVwA+Up7XB3YBsP0uMFbSOsBFtl8HkHQhoaCcImnREmO2CPCy7ack7UMoAveXz5wb+AQwGhhl+67y+a9JugHYXNLDhAL44Acc5422XwVelTSWsNhBKKyrTmXME5G0WJH1NtuW9I6klW2P6LpvFzYEVqp89rzlOwEutT1uMu+bnKt3e0nfI8J1PgasBPy9bPvzVGS5sDzfS1gvIRTvrQFs/1XSy1P5DICVJR0DzE8cw6sr2w4D7rb9PQBJG9P9Mb9lGr4nSZIeJBW/JOlsxtlevbjpriZi/IYCYyajcEwv5xHWn4/ynkIi4Djbv67uKKkf8HqX958FHAI8QvSZ/qC8VVmeUHk9gbgOzsK0jXl7YAHgX0WJm5dwqf5oKu+bhbBkvlldWT6j65iniKRlCMvb52y/LGkoMEdll6l9Xmvs7zL1e8B4Jg0Jqn7PUGBL28NLjOWAyrZhwJqSFrT9EpM55kmSzHgyxi9JEmy/AewN7A+8QSg220EkWkharex6PbB7WT+rpPmAW4EtJc0paS5gq7IOQtn7BqH8nVfWXQ18u2XxkrSEpEUnI9fdhHt4R+BPPTjkrt/zCpMfc5UdCHd4P9v9CDf3N7rZ71XCtdziGmCv1gtJH0apnpdQ7sYWC+RXprBvVzkmxy3Eb4ykrxDKLcC/gUVLDOBHgM0r75kHeFZSH2CnLp93FfAT4K+S5mE6jnmSJL1LKn5JkgBg+37CXbgDcSPfTdJwImbva2W3fYD1JD1IuApXsn0fYf35G3A3ETd2f/nMhwgF4Wnbz5Z11xAxb3eWzzmfKSsnfwFutz0t7scPw+TGDEy0SC4NTCzjYvtfhAK2VpfPugzYqpXcQSjVny2JDSOJ5I9pYTlNWs5lb9vDCZfpI8TvePsU3j8UOKNLckd3HAl8UdJDhMt3dBnfO8BRxLG9tnxni8OI4317l/WU954HnAlcSkwEpueYJ0nSS2Sv3iRJZmokXQ6cbPv6umXpFCQ9SSSFvFi3LEmS9Cxp8UuSZKZE0vySHiPiEFPpS5Ik6QHS4pckSZIkSdIhpMUvSZIkSZKkQ0jFL0mSJEmSpENIxS9JkiRJkqRDSMUvSZIkSZKkQ0jFL0mSJEmSpENIxS9JkiRJkqRD+H9l0jbQSdfvWQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x576 with 2 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}}],"source":["%reload_ext autoreload\n","%autoreload 2\n","\n","plot_results(model_name)"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.0"},"colab":{"provenance":[],"toc_visible":true}},"nbformat":4,"nbformat_minor":5}
\ No newline at end of file
diff --git a/driver_PBEErecovery.m b/driver_PBEErecovery.m
deleted file mode 100644
index 187559c..0000000
--- a/driver_PBEErecovery.m
+++ /dev/null
@@ -1,48 +0,0 @@
-% This script facilitates the performance based functional recovery and
-% reoccupancy assessment of a single building for a single intensity level
-
-% Input data consists of building model info and simulated component-level
-% damage and conesequence data for a suite of realizations, likely assessed
-% as part of a FEMA P-58 analysis. Inputs are read in as matlab variables
-% direclty from matlab data files, as well as loaded from csvs in the
-% static_tables directory.
-
-% Output data is saved to a specified outputs directory and is saved into a
-% single matlab variable as at matlab data file.
-
-clear
-close all
-clc 
-rehash
-
-%% Define User Inputs
-% model_name = 'haseltonRCMF_4story'; % Name of the model;
-% model_name = 'haseltonRCMF_12story'; % Name of the model;
-% model_name = 'ICSB'; % Name of the model;
-% model_name = 'RCSW_1story'; % Name of the model;
-                     % inputs are expected to be in a directory with this name
-                     % outputs will save to a directory with this name
-model_dir = ['inputs' filesep 'example_inputs']; % Directory where the simulated inputs are located
-outputs_dir = ['outputs' filesep model_name]; % Directory where the assessment outputs are saved
-
-%% Load FEMA P-58 performance model data and simulated damage and loss
-load([model_dir filesep model_name filesep 'simulated_inputs.mat'])
-
-%% Load required static data
-systems = readtable(['static_tables' filesep 'systems.csv']);
-subsystems = readtable(['static_tables' filesep 'subsystems.csv']);
-impeding_factor_medians = readtable(['static_tables' filesep 'impeding_factors.csv']);
-tmp_repair_class = readtable(['static_tables' filesep 'temp_repair_class.csv']);
-
-%% Run Recovery Method
-[functionality, damage_consequences] = main_PBEErecovery(damage, damage_consequences, ...
-    building_model, tenant_units, systems, subsystems, tmp_repair_class, ...
-    impedance_options, impeding_factor_medians, repair_time_options, ...
-    functionality, functionality_options);
-
-%% Save Outputs
-if ~exist(outputs_dir,'dir')
-    mkdir(outputs_dir)
-end
-save([outputs_dir filesep 'recovery_outputs.mat'],'functionality')
-fprintf('Recovery assessment of model %s complete\n',model_name)
diff --git a/driver_convert_PELICUN.m b/driver_convert_PELICUN.m
deleted file mode 100644
index 3c3afc1..0000000
--- a/driver_convert_PELICUN.m
+++ /dev/null
@@ -1,298 +0,0 @@
-%% Script Converts PELICUN outputs into PBEE-Recovery tool inputs
-
-% Required inputs
-%    - comp_ds_list.csv
-%    - tenant_unit_list.csv
-%    - input_parameters.json
-%    - DL_model.json
-%    - IF_delays.csv
-%    - DV_rec_cost_agg.csv
-%    - DV_rec_time.csv
-%    - DMG.csv
-
-% Assumptions: (specific to 16 story treads model)
-%    - assume rugged entry doors
-%    - Stairs run the whole length of the building
-%    - Residential occupancy
-%    - uniform 10ft story ht
-%    - 33ft x 33ft structural bay area
-%    - square building (100ft sides)
-%    - 16 stories (4 basement, 12 above grade)
-%    - Stability IF longer than 10 days = red tag
-
-
-clear all
-close all
-clc
-rehash
-
-%% Define Inputs
-model_name = '001'; % Name of the model;
-                     % inputs are expected to be in a directory with this name
-                     % outputs will save to a directory with this name
-model_dir = ['inputs' filesep 'example_pelicun_inputs' filesep model_name]; % Directory where the simulated inputs are located
-
-%% Load Pelicun Outputs
-pelicun_dir = [model_dir filesep 'pelicun_data'];
-
-% Pull basic model info from Pelicun Inputs
-fileID = fopen([pelicun_dir filesep 'input.json'],'r');
-pelicun_inputs = jsondecode(fscanf(fileID,'%s'));
-fclose(fileID);
-num_stories = str2double(pelicun_inputs.DL.Asset.NumberOfStories);
-total_cost = str2double(pelicun_inputs.DL.Losses.BldgRepair.ReplacementCost.Median);
-plan_area = str2double(pelicun_inputs.DL.Asset.PlanArea);
-
-% Pull components from DL_model.json
-comps = readtable([pelicun_dir filesep 'CMP_QNT.csv']);
-
-% Pull repair cost realizations
-DV_rec_cost_agg = readtable([pelicun_dir filesep 'DL_summary.csv']);
-
-% Pull realizations of damaged components
-DMG = readtable([pelicun_dir filesep 'DMG_sample.csv']);
-frag_col_filt = ~cellfun(@isempty,regexp(DMG.Properties.VariableNames,'^B_')) | ...
-                ~cellfun(@isempty,regexp(DMG.Properties.VariableNames,'^C_')) | ...
-                ~cellfun(@isempty,regexp(DMG.Properties.VariableNames,'^D_')) | ...
-                ~cellfun(@isempty,regexp(DMG.Properties.VariableNames,'^E_')) | ...
-                ~cellfun(@isempty,regexp(DMG.Properties.VariableNames,'^F_'));
-DMG = DMG(:,frag_col_filt); % Filt to only component level damage
-DMG_ids = DMG.Properties.VariableNames;
-
-% Pull realization of repair time per componentDS
-DV = readtable([pelicun_dir filesep 'DV_bldg_repair_sample.csv']);
-rec_time_filt = ~cellfun(@isempty,regexp(DV.Properties.VariableNames,'^TIME'));
-DV_time = DV(:,rec_time_filt); % Filt to only component level damage
-repair_cost_filt = ~cellfun(@isempty,regexp(DV.Properties.VariableNames,'^COST'));
-DV_cost = DV(:,repair_cost_filt); % Filt to only component level damage
-
-%% Load ATC 138 model input data
-tenant_unit_list = readtable([model_dir filesep 'tenant_unit_list.csv']);
-ds_attributes = readtable(['static_tables' filesep 'damage_state_attribute_mapping.csv']);
-
-% general model inputs
-fileID = fopen([model_dir filesep 'general_inputs.json'],'r');
-general_inputs = jsondecode(fscanf(fileID,'%s'));
-fclose(fileID);
-
-%% Develop building_model.json
-% Count the number of stairs in the building
-stair_filt = contains(comps.ID,'C.20.11'); 
-if any(stair_filt)
-    num_stairs = min(comps.Theta_0(stair_filt)); % Assumes number of vertical egress routes is the min number of stairs on any story. This is faulty logic and wont hold true for all comp tables 
-else
-    num_stairs = 0;
-end
-% Count the number of elevator bays in the building
-elev_filt = contains(comps.ID,'D.10.14'); 
-if any(elev_filt)
-    num_elev_bays = max(max(comps.Theta_0(elev_filt)),0);  % Assumes the number of elevator bays is the max on any story
-else
-    num_elev_bays = 0;
-end
-
-% Set Variables
-building_model.building_value = total_cost; % num
-building_model.num_stories = num_stories; % int
-building_model.area_per_story_sf = plan_area*ones(num_stories,1); % num_stories x 1 array
-building_model.ht_per_story_ft = general_inputs.typ_story_ht_ft*ones(num_stories,1); % num_stories x 1 array
-building_model.edge_lengths = [general_inputs.length_side_1_ft; general_inputs.length_side_2_ft].*ones(1, num_stories); % 2 x num_stories array (json flips the others)
-building_model.struct_bay_area_per_story = general_inputs.typ_struct_bay_area_ft*ones(num_stories,1); % num_stories x 1 array
-building_model.num_entry_doors = general_inputs.num_entry_doors; % int
-building_model.num_elevators = num_elev_bays; % int
-building_model.stairs_per_story = num_stairs*ones(num_stories,1); % num_stories x 1 array
-building_model.occupants_per_story = general_inputs.peak_occ_rate*building_model.area_per_story_sf; % num_stories x 1 array
-
-% Write file
-fileID = fopen([model_dir filesep 'building_model.json'],'w');
-fprintf(fileID,'%s',jsonencode(building_model));
-fclose(fileID);
-
-%% Develop damage_consequences.json
-% Pull data from Pelicun structure 
-sim_repair_cost = DV_rec_cost_agg.repair_cost_;
-sim_replacement_time = NaN(height(DV_rec_cost_agg),1);
-sim_replacement_filt = DV_rec_cost_agg.collapse | DV_rec_cost_agg.irreparable;
-sim_replacement_time(sim_replacement_filt) = ...
-    mean([DV_rec_cost_agg.repair_time_parallel(sim_replacement_filt),...
-    DV_rec_cost_agg.repair_time_parallel(sim_replacement_filt)],2); % Take the average of parallel and series rep times (they should be the same)
-
-% Set Variables
-damage_consequences.repair_cost_ratio_total = sim_repair_cost / building_model.building_value;  % array, num real x 1
-damage_consequences.simulated_replacement_time = sim_replacement_time;
-
-% Write file
-fileID = fopen([model_dir filesep 'damage_consequences.json'],'w');
-fprintf(fileID,'%s',jsonencode(damage_consequences));
-fclose(fileID);
-
-%% Build comp_population.csv
-comp_population.story = zeros(num_stories*3,1);
-comp_population.dir = zeros(num_stories*3,1);
-for c = 1:height(comps)
-    idx = 1;
-    comp = comps(c,:);            
-    frag_id = comp.ID{1};
-    frag_id([2,5]) = []; % Remove extra periods
-    frag_id = strrep(frag_id,'.','_');
-    comp_population.(frag_id) = zeros(num_stories*3,1);
-    loc = comp.Location{1};
-    dir = comp.Direction{1};
-    for s = 1:num_stories
-        if  strcmp(loc,'all')
-            is_story = true;
-        elseif  strcmp(loc,'roof')
-            is_story = s == num_stories;
-        elseif  contains(loc,'--')
-            is_story = s >= str2double(loc(1)) & s<= str2double(loc(end));
-        else
-            loc_vec = str2double(strsplit(loc));
-            is_story = ismember(s,loc_vec);
-        end
-        
-        for d = 1:3
-            comp_population.story(idx) = s;
-            comp_population.dir(idx) = d;
-            dir_str = strrep(dir,'0','3'); % replace nondirection identifier
-            dir_vec = str2double(strsplit(dir_str,','));
-            is_dir = ismember(d,dir_vec);
-
-            if is_story && is_dir
-                % Assign to component population table (add duplicates)
-                comp_population.(frag_id)(idx) = comp_population.(frag_id)(idx) + comp.Theta_0;
-            end
-
-            idx = idx + 1;
-        end
-    end
-end
-
-% Convert to table and save
-comp_population = struct2table(comp_population);
-writetable(comp_population, [model_dir filesep 'comp_population.csv']);
-
-%% Build comp_ds_list.csv
-idx = 0;
-unique_comps = unique(comps.ID); % Only one entry per unique component id
-for c = 1:length(unique_comps)
-    frag_id = unique_comps{c};
-    frag_id([2,5]) = []; % Remove extra periods
-    ds_filt = ~cellfun(@isempty,regexp(frag_id,ds_attributes.fragility_id_regex));
-    ds_match = ds_attributes(ds_filt,:);
-    for ds = 1:height(ds_match)
-        idx = idx + 1;
-        comp_ds_list.comp_id{idx,1} = frag_id;
-        comp_ds_list.ds_seq_id(idx,1) = ds_match.ds_index(ds);
-        if strcmp(ds_match.sub_ds_index{ds},'NA')
-            comp_ds_list.ds_sub_id(idx,1) = 1;
-        else
-            comp_ds_list.ds_sub_id(idx,1) = str2double(ds_match.sub_ds_index{ds});
-        end
-    end
-end
-
-% Convert to table and save
-comp_ds_list = struct2table(comp_ds_list);
-writetable(comp_ds_list, [model_dir filesep 'comp_ds_list.csv']);
-
-%% Develop simulated damage.json
-[num_reals,~] = size(DMG(:,2:end));
-
-% Set Variables
-count = 0;
-for s = 1:num_stories
-    % Initialize variables
-    simulated_damage.story(s).qnt_damaged = zeros(num_reals,height(comp_ds_list));
-    simulated_damage.story(s).worker_days = zeros(num_reals,height(comp_ds_list));
-    simulated_damage.story(s).repair_cost = zeros(num_reals,height(comp_ds_list));
-    simulated_damage.story(s).qnt_damaged_dir_1 = zeros(num_reals,height(comp_ds_list));
-    simulated_damage.story(s).qnt_damaged_dir_2 = zeros(num_reals,height(comp_ds_list));
-    simulated_damage.story(s).qnt_damaged_dir_3 = zeros(num_reals,height(comp_ds_list));
-    simulated_damage.story(s).num_comps = zeros(height(comp_ds_list),1);
-       
-    % Go through each ds in PELICUN outputs and assign to simualated damage
-    % data structure
-    for c = 1:length(DMG_ids)
-        % Identify attributes of column IDS
-        DMG_id = strsplit(DMG_ids{c},'_');
-        frag_id = [DMG_id{1} DMG_id{2} DMG_id{3} '.' DMG_id{4}];
-        frag_filt = strcmp(comp_ds_list.comp_id,frag_id);
-        loc_id = str2double(DMG_id{end-2});
-        dir_id = str2double(DMG_id{end-1});
-        if dir_id == 0
-            dir_id = 3; % Change nondirection indexing
-        end
-        ds_id = str2double(DMG_id{end});
-        
-        % Find the associated column in the repair time table
-        frag_id_DV = [DMG_id{1} '_' DMG_id{2} '_' DMG_id{3} '_' DMG_id{4} '_' num2str(ds_id) '_' DMG_id{5} '_' DMG_id{6}];
-        DV_filt = contains(DV_time.Properties.VariableNames,frag_id_DV);
-        
-        % Assign simulated damage to new structure
-        if loc_id == s  && ds_id > 0 && any(frag_filt) % Only if there are components on this story
-            comp_ds = comp_ds_list(frag_filt,:);
-            seq_ds_filt = comp_ds_list.ds_seq_id == comp_ds.ds_seq_id(ds_id);
-            sub_ds_filt = comp_ds_list.ds_sub_id == comp_ds.ds_sub_id(ds_id);
-            sim_dmg_idx_filt = (frag_filt & seq_ds_filt & sub_ds_filt)';
-            if sum(sim_dmg_idx_filt) == 1 && sum(DV_filt) == 1 % if you find matching components
-                % Assign Damage data
-                dmg_data = DMG{:,c};
-                dmg_data(isnan(dmg_data)) = 0; % Change blank cases to no damage
-                simulated_damage.story(s).qnt_damaged(:,sim_dmg_idx_filt) = ...
-                    simulated_damage.story(s).qnt_damaged(:,sim_dmg_idx_filt) + dmg_data; % add number of damaged component amongst directions and multiple comps of the same frag id
-                
-                % Assign Repair time data
-                repair_time_data = DV_time{:,DV_filt};
-                repair_time_data(isnan(repair_time_data)) = 0; % Change blank cases to no damage
-                simulated_damage.story(s).worker_days(:,sim_dmg_idx_filt) = ...
-                    simulated_damage.story(s).worker_days(:,sim_dmg_idx_filt) + repair_time_data; % add the repair time amongst directions and multiple comps of the same frag id
-                
-                % Assign Repair Cost data
-                repair_cost_data = DV_cost{:,DV_filt};
-                repair_cost_data(isnan(repair_cost_data)) = 0; % Change blank cases to no damage
-                simulated_damage.story(s).repair_cost(:,sim_dmg_idx_filt) = ...
-                    simulated_damage.story(s).repair_cost(:,sim_dmg_idx_filt) + repair_cost_data; % add the repair time amongst directions and multiple comps of the same frag id
-                
-                % Assign Damage Data Per Direction
-                for dir = 1:3
-                    if dir_id == dir % only for the direction associated with this DMG column
-                        simulated_damage.story(s).(['qnt_damaged_dir_' num2str(dir)])(:,sim_dmg_idx_filt) = ...
-                        simulated_damage.story(s).(['qnt_damaged_dir_' num2str(dir)])(:,sim_dmg_idx_filt) + dmg_data; % add number of damaged component amongst directions and multiple comps of the same frag id
-                    end
-                end
-            else
-                error('Location in new damage state structure could not be found for this compoennt DS')
-            end
-        end
-    end
-    
-    % Assign component quantities
-    for c = 1:height(comps)
-        comp = comps(c,:);
-        loc = comp.Location{1};
-        if  strcmp(loc,'all')
-            is_story = true;
-        elseif  strcmp(loc,'roof')
-            is_story = s == num_stories;
-        elseif  contains(loc,'--')
-            is_story = s >= str2double(loc(1)) & s<= str2double(loc(end));
-        else
-            loc_vec = str2double(strsplit(loc));
-            is_story = ismember(s,loc_vec);
-        end
-        
-        if is_story
-            frag_id = comp.ID{1};
-            frag_id([2,5]) = []; % Remove extra periods
-            frag_filt = strcmp(comp_ds_list.comp_id,frag_id)';
-            simulated_damage.story(s).num_comps(frag_filt) = ...
-                simulated_damage.story(s).num_comps(frag_filt) + comp.Theta_0;
-        end
-    end
-end
-            
-% Write file
-fileID = fopen([model_dir filesep 'simulated_damage.json'],'w');
-fprintf(fileID,'%s',jsonencode(simulated_damage));
-fclose(fileID);
-
diff --git a/inputs/example_inputs/ICSB/building_model.json b/examples/ICSB/building_model.json
similarity index 100%
rename from inputs/example_inputs/ICSB/building_model.json
rename to examples/ICSB/building_model.json
diff --git a/inputs/example_inputs/ICSB/comp_ds_list.csv b/examples/ICSB/comp_ds_list.csv
similarity index 100%
rename from inputs/example_inputs/ICSB/comp_ds_list.csv
rename to examples/ICSB/comp_ds_list.csv
diff --git a/inputs/example_inputs/ICSB/comp_population.csv b/examples/ICSB/comp_population.csv
similarity index 100%
rename from inputs/example_inputs/ICSB/comp_population.csv
rename to examples/ICSB/comp_population.csv
diff --git a/inputs/example_inputs/ICSB/damage_consequences.json b/examples/ICSB/damage_consequences.json
similarity index 100%
rename from inputs/example_inputs/ICSB/damage_consequences.json
rename to examples/ICSB/damage_consequences.json
diff --git a/inputs/example_inputs/ICSB/simulated_damage.json b/examples/ICSB/simulated_damage.json
similarity index 100%
rename from inputs/example_inputs/ICSB/simulated_damage.json
rename to examples/ICSB/simulated_damage.json
diff --git a/inputs/example_inputs/ICSB/tenant_unit_list.csv b/examples/ICSB/tenant_unit_list.csv
similarity index 100%
rename from inputs/example_inputs/ICSB/tenant_unit_list.csv
rename to examples/ICSB/tenant_unit_list.csv
diff --git a/inputs/example_inputs/RCSW_1story/building_model.json b/examples/RCSW_1story/building_model.json
similarity index 100%
rename from inputs/example_inputs/RCSW_1story/building_model.json
rename to examples/RCSW_1story/building_model.json
diff --git a/inputs/example_inputs/RCSW_1story/comp_ds_list.csv b/examples/RCSW_1story/comp_ds_list.csv
similarity index 100%
rename from inputs/example_inputs/RCSW_1story/comp_ds_list.csv
rename to examples/RCSW_1story/comp_ds_list.csv
diff --git a/inputs/example_inputs/RCSW_1story/comp_population.csv b/examples/RCSW_1story/comp_population.csv
similarity index 100%
rename from inputs/example_inputs/RCSW_1story/comp_population.csv
rename to examples/RCSW_1story/comp_population.csv
diff --git a/inputs/example_inputs/RCSW_1story/damage_consequences.json b/examples/RCSW_1story/damage_consequences.json
similarity index 100%
rename from inputs/example_inputs/RCSW_1story/damage_consequences.json
rename to examples/RCSW_1story/damage_consequences.json
diff --git a/inputs/example_inputs/RCSW_1story/simulated_damage.json b/examples/RCSW_1story/simulated_damage.json
similarity index 100%
rename from inputs/example_inputs/RCSW_1story/simulated_damage.json
rename to examples/RCSW_1story/simulated_damage.json
diff --git a/inputs/example_inputs/RCSW_1story/tenant_unit_list.csv b/examples/RCSW_1story/tenant_unit_list.csv
similarity index 100%
rename from inputs/example_inputs/RCSW_1story/tenant_unit_list.csv
rename to examples/RCSW_1story/tenant_unit_list.csv
diff --git a/inputs/example_inputs/haseltonRCMF_12story/building_model.json b/examples/haseltonRCMF_12story/building_model.json
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_12story/building_model.json
rename to examples/haseltonRCMF_12story/building_model.json
diff --git a/inputs/example_inputs/haseltonRCMF_12story/comp_ds_list.csv b/examples/haseltonRCMF_12story/comp_ds_list.csv
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_12story/comp_ds_list.csv
rename to examples/haseltonRCMF_12story/comp_ds_list.csv
diff --git a/inputs/example_inputs/haseltonRCMF_12story/comp_population.csv b/examples/haseltonRCMF_12story/comp_population.csv
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_12story/comp_population.csv
rename to examples/haseltonRCMF_12story/comp_population.csv
diff --git a/inputs/example_inputs/haseltonRCMF_12story/damage_consequences.json b/examples/haseltonRCMF_12story/damage_consequences.json
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_12story/damage_consequences.json
rename to examples/haseltonRCMF_12story/damage_consequences.json
diff --git a/inputs/example_inputs/haseltonRCMF_12story/simulated_damage.json b/examples/haseltonRCMF_12story/simulated_damage.json
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_12story/simulated_damage.json
rename to examples/haseltonRCMF_12story/simulated_damage.json
diff --git a/inputs/example_inputs/haseltonRCMF_12story/tenant_unit_list.csv b/examples/haseltonRCMF_12story/tenant_unit_list.csv
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_12story/tenant_unit_list.csv
rename to examples/haseltonRCMF_12story/tenant_unit_list.csv
diff --git a/inputs/example_inputs/haseltonRCMF_4story/building_model.json b/examples/haseltonRCMF_4story/building_model.json
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_4story/building_model.json
rename to examples/haseltonRCMF_4story/building_model.json
diff --git a/inputs/example_inputs/haseltonRCMF_4story/comp_ds_list.csv b/examples/haseltonRCMF_4story/comp_ds_list.csv
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_4story/comp_ds_list.csv
rename to examples/haseltonRCMF_4story/comp_ds_list.csv
diff --git a/inputs/example_inputs/haseltonRCMF_4story/comp_population.csv b/examples/haseltonRCMF_4story/comp_population.csv
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_4story/comp_population.csv
rename to examples/haseltonRCMF_4story/comp_population.csv
diff --git a/inputs/example_inputs/haseltonRCMF_4story/damage_consequences.json b/examples/haseltonRCMF_4story/damage_consequences.json
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_4story/damage_consequences.json
rename to examples/haseltonRCMF_4story/damage_consequences.json
diff --git a/inputs/example_inputs/haseltonRCMF_4story/simulated_damage.json b/examples/haseltonRCMF_4story/simulated_damage.json
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_4story/simulated_damage.json
rename to examples/haseltonRCMF_4story/simulated_damage.json
diff --git a/inputs/example_inputs/haseltonRCMF_4story/tenant_unit_list.csv b/examples/haseltonRCMF_4story/tenant_unit_list.csv
similarity index 100%
rename from inputs/example_inputs/haseltonRCMF_4story/tenant_unit_list.csv
rename to examples/haseltonRCMF_4story/tenant_unit_list.csv
diff --git a/fn_red_tag.m b/fn_red_tag.m
deleted file mode 100644
index c029c0a..0000000
--- a/fn_red_tag.m
+++ /dev/null
@@ -1,140 +0,0 @@
-e function [ red_tag, red_tag_impact, inspection_tag ] = ...
-    fn_red_tag( calculate_red_tag, damage, comps, simulated_replacement_time )
-% Perform the ATC-138 functional recovery time assessement given similation
-% of component damage for a single shaking intensity
-%
-% Parameters
-% ----------
-% calculate_red_tag: logical
-%   flag to indicate whether on not to calculate red tags based on
-%   component damage. Typically assumed to be FALSE for small wood light
-%   frame type structures.
-% damage: struct
-%   contains per damage state damage and loss data for each component in the building
-% comps: struct
-%   data structure component population info
-% simulated_replacement_time: array [num_reals x 1]
-%   simulated time when the building needs to be replaced, and how long it
-%   will take (in days). NaN represents no replacement needed (ie
-%   building will be repaired)
-%
-% Returns
-% -------
-% red_tag: logical array [num_reals x 1]
-%   indicates the realizations that have a red tag
-% red_tag_impact: logical array [num_reals x num_comp_ds]
-%   indicates the realizations of various component damage states that
-%   contribute to the cause of red tag
-% inspection_tag: logical array [num_reals x 1]
-%   indicates the realizations that require inspection
-
-%% Initial Setup
-% Check to see if any components need the red tag check
-% if none of the components are assinged to structuctural systems, then
-% skip the red tag calc
-if ~any(comps.comp_table.structural_system)
-    calculate_red_tag = false;
-end
-
-%% Method
-if calculate_red_tag
-    % Simulate Red Tags
-    sc_ids = [1 2 3 4];
-    sc_thresholds = [0.5 0.25 0.1 0];
-    [ red_tag, red_tag_impact ] = simulate_tagging( damage, comps, sc_ids, sc_thresholds );
-
-    % Inspection is flagged for 50% of the red tag thresholds
-    [ inspection_tag, ~ ] = simulate_tagging( damage, comps, sc_ids, 0.5*sc_thresholds );
-else
-    % Do not calculate red tags based on component damage
-    [num_reals,num_comp_ds] = size(damage.tenant_units{1}.qnt_damaged);
-    red_tag = false(num_reals,1);
-    red_tag_impact = zeros(num_reals,num_comp_ds);
-    inspection_tag = false(num_reals,1);
-end
-
-% Account for global red tag cases
-replace_case = ~isnan(simulated_replacement_time);
-red_tag(replace_case) = 1;
-
-end
-
-function [ red_tag, red_tag_impact ] = simulate_tagging( damage, comps, sc_ids, sc_thresholds )
-
-% Simulate uncertainty in inspector threhsold
-[num_reals,~] = size(damage.story{1}.qnt_damaged_dir_1);
-% sc_beta = [0.5 0.5 0.5 0.5];
-% sc_mins = [0.05 0.05 0.05 0];
-% sc_max = [0.75 0.75 0.75 0.75];
-% p_inpsector = rand(num_reals,1); % Simulate inspector "conservatism"
-% sc_sim = max(min(logninv(p_inpsector,log(sc_thresholds),sc_beta),sc_max),sc_mins);
-
-red_tag_impact = zeros(size(damage.tenant_units{1}.qnt_damaged)); % num reals by num comp_ds
-
-% Go through each structural system and calc the realizations where the
-% building is red tagged
-for sc = 1:length(sc_ids)
-    for s = 1:length(damage.story)
-        sc_filt = damage.comp_ds_table.safety_class' >= sc_ids(sc);
-        
-        for dir = 1:3 % Fix assume there are three direction, where direction 3 = nondirectional
-            sc_dmg = damage.story{s}.(['qnt_damaged_dir_' num2str(dir)]) .* sc_filt;
-            num_comps = comps.story(s).(['qty_dir_' num2str(dir)]);
-
-            % For each structural system
-            structural_systems = unique([damage.comp_ds_table.structural_system; damage.comp_ds_table.structural_system_alt]);
-            structural_systems(structural_systems == 0) = []; % do not include components not assigned to a structural system
-            sys_tag = false(num_reals, length(structural_systems));
-
-            for sys = 1:length(structural_systems)
-                ss_filt_ds = damage.comp_ds_table.structural_system' == structural_systems(sys) | damage.comp_ds_table.structural_system_alt' == structural_systems(sys);
-                ss_filt_comp = comps.comp_table.structural_system' == structural_systems(sys) | comps.comp_table.structural_system_alt' == structural_systems(sys);
-
-                % Check damage among each series within this structural system
-                series = unique(damage.comp_ds_table.structural_series_id(ss_filt_ds));
-
-                ser_dmg = zeros(num_reals, length(series));
-                ser_qty = zeros(num_reals, length(series));
-
-                for ser = 1:length(series)
-                    ser_filt_ds = damage.comp_ds_table.structural_series_id' == series(ser); 
-                    ser_filt_comp = comps.comp_table.structural_series_id' == series(ser); 
-
-                    % Total damage within this series and system
-                    ser_dmg(:,ser) = sum(sc_dmg(:,ser_filt_ds & ss_filt_ds),2); 
-
-                    % Total number of components within this series and system
-                    ser_qty(:,ser) = sum(num_comps(:,ser_filt_comp & ss_filt_comp),2); 
-                end
-
-                % Check if this system is causing a red tag
-                sys_dmg = max(ser_dmg,[],2);
-                sys_qty = max(ser_qty,[],2);
-                sys_ratio = sys_dmg ./ sys_qty;
-                sys_tag(:,sys) = sys_ratio > sc_thresholds(sc);
-%                 sys_tag(:,sys) = sys_ratio > sc_sim(:,sc); % when using simulated safety class thresholds
-                
-                % Calculate the impact that each component has on red tag
-                % (boolean, 1 = affects red tag, 0 = does not affect)
-                % Take all damage that is part of this system at this story
-                % in this direction that is damaged to this safety class
-                % level, only where damage exceeds tagging threshold
-                red_tag_impact = max(red_tag_impact, sys_tag(:,sys) .* ss_filt_ds .* sc_filt .* (sc_dmg>0));
-            end
-            
-            % Combine across all systems in this direction
-            dir_tag(:,dir) = max(sys_tag,[],2);
-        end
-
-        % Combine across all directions at this story
-        story_tag(:,s) = max(dir_tag,[],2);
-    end
-    
-    % Combine across all stories for this safety class
-    sc_tag(:,sc) = max(story_tag,[],2);
-end
-
-% Combine all safety class checks into one simulated red tag
-red_tag =  max(sc_tag,[],2);
-
-end
\ No newline at end of file
diff --git a/inputs/Inputs2Copy/build_inputs.m b/inputs/Inputs2Copy/build_inputs.m
deleted file mode 100644
index d379548..0000000
--- a/inputs/Inputs2Copy/build_inputs.m
+++ /dev/null
@@ -1,355 +0,0 @@
-% This script builds the matlab datafile inputs required for the
-% reoccupancy and functional recovery assessment of a building given
-% simulated damage.
-
-% Currently, this script will build the inputs data file for an example
-% building model. Follow the instructions below to custumize this build
-% script for other assessments.
-
-% Instructions
-% ----------
-% Step 1: Place this script in the directory where you want the
-% simulated_inputs.mat assessment inputs file to be written
-% Step 2: Add the requried building specific input files listed below to
-% the same directory
-% Step 3: Copy the optional_inputs.m file from the example inputs directory
-% to the same directory and modify if needed.
-% Step 4: Make sure the variable "static_data_dir" is correctly pointing to
-% the location of the static data directory
-% Step 5: Run the build script
-
-% Option for customizing static data 
-% ----------
-% If you would like to modify the static data tables listed below for a 
-% specifc model, simply copy the subsequent static data tables to this 
-% input directory, modify them, and set the static_data_dir to this input 
-% directory.
-
-% Required building specific data (inputs directory)
-% ----------
-% building_model.json
-% tenant_unit_list.csv
-% comp_ds_list.csv
-% damage_consequences.json
-% simulated_damage.json
-
-% Optional building specific data (inputs directory)
-% ----------
-% utility_downtime.json
-
-% Static data (static data directory)
-% ----------
-% component_attributes.csv
-% damage_state_attribute_mapping.csv
-% subsystems.csv
-% tenant_function_requirements.csv
-
-% Static data (copy to this inputs directory)
-% ----------
-% optional_inputs.m
-
-clear
-close all
-clc 
-rehash
-
-%% DEFINE USER INPUTS
-% This is currently set to the default directory, relative to this example
-% inputs directory. If the location of this directory differs, updat the 
-% static_data_dir variable below. 
-static_data_dir = ['..' filesep '..' filesep '..' filesep 'static_tables'];
-
-%% PULL STATIC DATA
-% Load required data tables
-component_attributes = readtable([static_data_dir filesep 'component_attributes.csv']);
-damage_state_attribute_mapping = readtable([static_data_dir filesep 'damage_state_attribute_mapping.csv']);
-subsystems = readtable([static_data_dir filesep 'subsystems.csv']);
-tenant_function_requirements = readtable([static_data_dir filesep 'tenant_function_requirements.csv']);
-
-%% LOAD BUILDING DATA
-% This data is specific to the building model and will need to be created
-% for each assessment. Data is formated as json structures or csv tables
-
-% Building Model: Basic data about the building being assessed
-building_model = jsondecode(fileread('building_model.json'));
-
-% Transponse building model inputs to go from vertically oriented story
-% data to horizontally oriented
-names = fieldnames(building_model);
-for fn = 1:length(names)
-    building_model.(names{fn}) = building_model.(names{fn})';
-end
-
-% Translate edge length back to vertically oriented (2 sides) for single
-% story buildings
-% if building_model.num_stories == 1
-building_model.edge_lengths = building_model.edge_lengths';
-% end
-
-% List of tenant units within the building and their basic attributes
-tenant_unit_list = readtable('tenant_unit_list.csv');
-
-% List of component and damage states ids associated with the damage matrix
-comp_ds_list = readtable('comp_ds_list.csv');
-
-% List of component and damage states in the performance model
-comp_population = readtable('comp_population.csv');
-
-% Read header row unaltered by matlab
-fid = fopen('comp_population.csv');
-comp_header = strsplit(fgetl(fid), ',');
-fclose(fid);
-comp_list = strrep(comp_header(3:end),'_','.');
-building_model.comps.comp_list = comp_list;
-
-% Go through each story and assign component populations
-drs = unique(comp_population.dir);
-for s = 1:building_model.num_stories
-    for d = 1:length(drs)
-        filt = comp_population.story == s & comp_population.dir == drs(d);
-        building_model.comps.story{s}.(['qty_dir_' num2str(drs(d))]) = comp_population{filt,3:end};
-    end
-end
-
- % Set comp info table
-for c = 1:length(comp_list)
-    % Find the component attributes of this component
-    comp_attr_filt = strcmp(string(component_attributes.fragility_id),comp_list{c});
-    if sum(comp_attr_filt) ~= 1
-        error('Could not find component attrubutes')
-    else
-        comp_attr = component_attributes(comp_attr_filt,:);
-    end
-    
-    comp_info.comp_id{c,1} = comp_list{c};
-    comp_info.comp_idx(c,1) = c;
-    comp_info.structural_system(c,1)  = comp_attr.structural_system;
-    comp_info.structural_system_alt(c,1)  = comp_attr.structural_system_alt;
-    comp_info.structural_series_id(c,1)  = comp_attr.structural_series_id;
-end
-building_model.comps.comp_table = struct2table(comp_info);
-
-%% LOAD SIMULATED DATA
-% This data is specific to the building performance at the assessed hazard
-% intensity and will need to be created for each assessment. 
-% Data is formated as json structures.
-
-% Simulated damage consequences - various building and story level
-% consequences of simulated data, for each realization of the monte carlo
-% simulation.
-damage_consequences = jsondecode(fileread('damage_consequences.json'));
-
-% Simulated utility downtimes for electrical, water, and gas networks - 
-% for each realization of the monte carlo simulation.
-if exist('utility_downtime.json','file')
-    functionality = jsondecode(fileread('utility_downtime.json'));
-else
-    % If no data exist, assume there is no consequence of network downtime
-    num_reals = length(damage_consequences.repair_cost_ratio_total);
-    functionality.utilities.electrical = zeros(num_reals,1);
-    functionality.utilities.water = zeros(num_reals,1);
-    functionality.utilities.gas = zeros(num_reals,1);
-end
-
-% Simulated component damage per tenant unit for each realization of the
-% monte carlo simulation
-sim_damage = jsondecode(fileread('simulated_damage.json'));
-
-% Transform damage per story array to cell array to work with later code
-if isfield(sim_damage,'story')
-    for s = 1:length(sim_damage.story)
-        damage.story{s} = sim_damage.story(s);
-        if isfield(damage.story{s},'num_comps')
-            damage.story{s}.num_comps = damage.story{s}.num_comps'; 
-        end
-    end
-end
-
-% Transform damage per tenant unit array to cell array to work with later code
-if isfield(sim_damage,'tenant_units')
-    for tu = 1:length(sim_damage.tenant_units)
-        damage.tenant_units{tu} = sim_damage.tenant_units(tu);
-        if isfield(damage.tenant_units{tu},'num_comps')
-            damage.tenant_units{tu}.num_comps = damage.tenant_units{tu}.num_comps'; 
-        end
-    end
-end
-
-
-%% LOAD DEFAULT OPTIONAL INPUTS
-% various assessment otpions. Set to default options in the
-% optional_inputs.m file. This file is expected to be in this input
-% directory. This file can be customized for each assessment if desired.
-optional_inputs
-
-%% PULL ADDITIONAL ATTRIBUTES FROM THE STATIC DATA TABLES
-% Preallocate tenant unit table
-tenant_units = tenant_unit_list;
-tenant_units.exterior = zeros(height(tenant_units),1);
-tenant_units.interior = zeros(height(tenant_units),1);
-tenant_units.occ_per_elev = zeros(height(tenant_units),1);
-tenant_units.is_elevator_required = zeros(height(tenant_units),1);
-tenant_units.is_electrical_required = zeros(height(tenant_units),1);
-tenant_units.is_water_potable_required = zeros(height(tenant_units),1);
-tenant_units.is_water_sanitary_required = zeros(height(tenant_units),1);
-tenant_units.is_hvac_ventilation_required = zeros(height(tenant_units),1);
-tenant_units.is_hvac_heating_required = zeros(height(tenant_units),1);
-tenant_units.is_hvac_cooling_required = zeros(height(tenant_units),1);
-tenant_units.is_hvac_exhaust_required = zeros(height(tenant_units),1);
-tenant_units.is_data_required = zeros(height(tenant_units),1);
-
-% Pull default tenant unit attributes for each tenant unit listed in the
-% tenant_unit_list
-for tu = 1:height(tenant_unit_list)
-    fnc_requirements_filt = tenant_function_requirements.occupancy_id == tenant_units.occupancy_id(tu);
-    if sum(fnc_requirements_filt) ~= 1
-        error('Tenant Unit Requirements for This Occupancy Not Found')
-    end
-    tenant_units.exterior(tu) = tenant_function_requirements.exterior(fnc_requirements_filt);
-    tenant_units.interior(tu) = tenant_function_requirements.interior(fnc_requirements_filt);
-    tenant_units.occ_per_elev(tu) = tenant_function_requirements.occ_per_elev(fnc_requirements_filt);
-    if tenant_function_requirements.is_elevator_required(fnc_requirements_filt) && ...
-            tenant_function_requirements.max_walkable_story(fnc_requirements_filt) < tenant_units.story(tu)
-        tenant_units.is_elevator_required(tu) = 1;
-    else
-        tenant_units.is_elevator_required(tu) = 0;
-    end
-    tenant_units.is_electrical_required(tu) = tenant_function_requirements.is_electrical_required(fnc_requirements_filt);
-    tenant_units.is_water_potable_required(tu) = tenant_function_requirements.is_water_potable_required(fnc_requirements_filt);
-    tenant_units.is_water_sanitary_required(tu) = tenant_function_requirements.is_water_sanitary_required(fnc_requirements_filt);
-    tenant_units.is_hvac_ventilation_required(tu) = tenant_function_requirements.is_hvac_ventilation_required(fnc_requirements_filt);
-    tenant_units.is_hvac_heating_required(tu) = tenant_function_requirements.is_hvac_heating_required(fnc_requirements_filt);
-    tenant_units.is_hvac_cooling_required(tu) = tenant_function_requirements.is_hvac_cooling_required(fnc_requirements_filt);
-    tenant_units.is_hvac_exhaust_required(tu) = tenant_function_requirements.is_hvac_exhaust_required(fnc_requirements_filt);
-    tenant_units.is_data_required(tu) = tenant_function_requirements.is_data_required(fnc_requirements_filt);
-end
-
-
-% Pull default component and damage state attributes for each component 
-% in the comp_ds_list
-for c = 1:height(comp_ds_list)
-    
-    % Find the component attributes of this component
-    comp_attr_filt = strcmp(string(component_attributes.fragility_id),comp_ds_list.comp_id{c});
-    if sum(comp_attr_filt) ~= 1
-        error('Could not find component attrubutes')
-    else
-        comp_attr = component_attributes(comp_attr_filt,:);
-    end
-    
-    % Find the damage state attributes of this componnt
-    ds_comp_filt = ~cellfun(@isempty,regexp(comp_ds_list.comp_id{c},damage_state_attribute_mapping.fragility_id_regex));
-    ds_seq_filt = damage_state_attribute_mapping.ds_index == comp_ds_list.ds_seq_id(c);
-    if comp_ds_list.ds_sub_id(c) == 1
-        ds_sub_filt = ismember(damage_state_attribute_mapping.sub_ds_index,{'1','NA'});
-    else
-        ds_sub_filt = strcmp(damage_state_attribute_mapping.sub_ds_index,num2str(comp_ds_list.ds_sub_id(c)));
-    end
-    ds_filt = ds_comp_filt & ds_seq_filt & ds_sub_filt;
-    
-    if sum(ds_filt) ~= 1
-        error('Could not find damage state attrubutes')
-    else
-        ds_attr = damage_state_attribute_mapping(ds_filt,:);
-    end
-    
-    %% Populate data for each damage state
-    % Basic Component and DS identifiers
-    comp_ds_info.comp_id{c,1} = comp_ds_list.comp_id{c};
-    comp_ds_info.comp_type_id{c,1} = comp_ds_list.comp_id{c}(1:5); % first 5 characters indicate the type
-    comp_ds_info.comp_idx(c,1) = c;
-    comp_ds_info.ds_seq_id(c,1) = ds_attr.ds_index;
-    comp_ds_info.ds_sub_id(c,1) = str2double(strrep(ds_attr.sub_ds_index{1},'NA','1'));
-
-    % Set Component Attributes
-    comp_ds_info.system(c,1) = comp_attr.system_id;
-    comp_ds_info.subsystem_id(c,1) = comp_attr.subsystem_id;
-    comp_ds_info.structural_system(c,1)  = comp_attr.structural_system;
-    comp_ds_info.structural_system_alt(c,1)  = comp_attr.structural_system_alt;
-    comp_ds_info.structural_series_id(c,1)  = comp_attr.structural_series_id;
-    comp_ds_info.unit{c,1} = comp_attr.unit{1};
-    comp_ds_info.unit_qty(c,1) = comp_attr.unit_qty;
-    comp_ds_info.service_location{c,1} = comp_attr.service_location{1};
-
-    % Set Damage State Attributes
-    comp_ds_info.is_sim_ds(c,1) = ds_attr.is_sim_ds;
-    comp_ds_info.safety_class(c,1) = ds_attr.safety_class;
-    comp_ds_info.affects_envelope_safety(c,1) = ds_attr.affects_envelope_safety;
-    comp_ds_info.ext_falling_hazard(c,1) = ds_attr.exterior_falling_hazard;
-    comp_ds_info.int_falling_hazard(c,1) = ds_attr.interior_falling_hazard;
-    comp_ds_info.global_hazardous_material(c,1) = ds_attr.global_hazardous_material;
-    comp_ds_info.local_hazardous_material(c,1) = ds_attr.local_hazardous_material;
-    comp_ds_info.weakens_fire_break(c,1) = ds_attr.weakens_fire_break;
-    comp_ds_info.affects_access(c,1) = ds_attr.affects_access;
-    comp_ds_info.damages_envelope_seal(c,1) = ds_attr.damages_envelope_seal;
-    comp_ds_info.affects_roof_function(c,1) = ds_attr.affects_roof_function;
-    comp_ds_info.obstructs_interior_space(c,1) = ds_attr.obstructs_interior_space;
-    comp_ds_info.impairs_system_operation(c,1) = ds_attr.impairs_system_operation;
-    comp_ds_info.causes_flooding(c,1) = ds_attr.causes_flooding;
-    comp_ds_info.interior_area_factor(c,1) = ds_attr.interior_area_factor;
-    comp_ds_info.interior_area_conversion_type(c,1) = ds_attr.interior_area_conversion_type;
-    comp_ds_info.exterior_surface_area_factor(c,1) = ds_attr.exterior_surface_area_factor;
-    comp_ds_info.exterior_falling_length_factor(c,1) = ds_attr.exterior_falling_length_factor;
-    comp_ds_info.crew_size(c,1) = ds_attr.crew_size;
-    comp_ds_info.permit_type(c,1) = ds_attr.permit_type;
-    comp_ds_info.redesign(c,1) = ds_attr.redesign;
-    comp_ds_info.long_lead_time(c,1) = impedance_options.default_lead_time * ds_attr.long_lead;
-    comp_ds_info.requires_shoring(c,1) = ds_attr.requires_shoring;
-    comp_ds_info.resolved_by_scaffolding(c,1) = ds_attr.resolved_by_scaffolding;
-    comp_ds_info.tmp_repair_class(c,1) = ds_attr.tmp_repair_class;
-    comp_ds_info.tmp_repair_time_lower(c,1) = ds_attr.tmp_repair_time_lower;
-    comp_ds_info.tmp_repair_time_upper(c,1) = ds_attr.tmp_repair_time_upper;
-    if comp_ds_info.tmp_repair_class(c,1) > 0 % only grab values for components with temp repair times
-        time_lower_quantity = ds_attr.time_lower_quantity;
-        time_upper_quantity = ds_attr.time_upper_quantity;
-        if iscell(time_lower_quantity)
-            time_lower_quantity = str2double(time_lower_quantity);
-        end
-        if iscell(time_upper_quantity)
-            time_upper_quantity = str2double(time_upper_quantity);
-        end
-        comp_ds_info.tmp_repair_time_lower_qnty(c,1) = time_lower_quantity;
-        comp_ds_info.tmp_repair_time_upper_qnty(c,1) = time_upper_quantity;
-    else
-        comp_ds_info.tmp_repair_time_lower_qnty(c,1) = NaN;
-        comp_ds_info.tmp_repair_time_upper_qnty(c,1) = NaN;
-    end
-    comp_ds_info.tmp_crew_size(c,1) = ds_attr.tmp_crew_size;
-
-    % Subsystem attributes
-    subsystem_filt = subsystems.id == comp_attr.subsystem_id;
-    if comp_ds_info.subsystem_id(c,1) == 0
-        % No subsytem
-        comp_ds_info.n1_redundancy(c,1) = 0;
-        comp_ds_info.parallel_operation(c,1) = 0;
-        comp_ds_info.redundancy_threshold(c,1) = 0;
-    elseif sum(subsystem_filt) ~= 1
-        error('Could not find damage state attrubutes')
-    else
-        % Set Damage State Attributes
-        comp_ds_info.n1_redundancy(c,1) = subsystems.n1_redundancy(subsystem_filt);
-        comp_ds_info.parallel_operation(c,1) = subsystems.parallel_operation(subsystem_filt);
-        comp_ds_info.redundancy_threshold(c,1) = subsystems.redundancy_threshold(subsystem_filt);
-    end
-end
-damage.comp_ds_table = struct2table(comp_ds_info);
-
-%% Check missing data
-% Engineering Repair Cost Ratio - Assume is the sum of all component repair
-% costs that require redesign
-if ~isfield(damage_consequences,'repair_cost_ratio_engineering')
-    eng_filt = logical(damage.comp_ds_table.redesign');
-    damage_consequences.repair_cost_ratio_engineering = zeros(size(damage_consequences.repair_cost_ratio_total));
-    for s = 1:length(sim_damage.story)
-        damage_consequences.repair_cost_ratio_engineering = ...
-            damage_consequences.repair_cost_ratio_engineering + ...
-            sum(sim_damage.story(s).repair_cost(:,eng_filt),2);
-    end
-end
-
-%% SAVE INPUTS AS MATLAB DATAFILE
-save('simulated_inputs.mat',...
-    'building_model','damage','damage_consequences','functionality',...
-    'functionality_options','impedance_options',...
-    'repair_time_options','tenant_units')
-
diff --git a/inputs/Inputs2Copy/optional_inputs.m b/inputs/Inputs2Copy/optional_inputs.m
deleted file mode 100644
index 0d586e3..0000000
--- a/inputs/Inputs2Copy/optional_inputs.m
+++ /dev/null
@@ -1,67 +0,0 @@
-% Impedance Time Options
-impedance_options.include_impedance.inspection = true;
-impedance_options.include_impedance.financing = true;
-impedance_options.include_impedance.permitting = true;
-impedance_options.include_impedance.engineering = true;
-impedance_options.include_impedance.contractor = true;
-impedance_options.include_impedance.long_lead = false;
-impedance_options.system_design_time.f = 0.04;
-impedance_options.system_design_time.r = 200; 
-impedance_options.system_design_time.t = 1.3;
-impedance_options.system_design_time.w = 8;
-impedance_options.eng_design_min_days = 14;
-impedance_options.eng_design_max_days = 365;
-impedance_options.mitigation.is_essential_facility = false;
-impedance_options.mitigation.is_borp_equivalent = false;
-impedance_options.mitigation.is_engineer_on_retainer = false;
-impedance_options.mitigation.contractor_relationship = 'good';
-impedance_options.mitigation.contractor_retainer_time = 3;
-impedance_options.mitigation.funding_source = 'private';
-impedance_options.mitigation.capital_available_ratio = 0.02;
-impedance_options.impedance_beta = 0.6;
-impedance_options.impedance_truncation = 2;
-impedance_options.default_lead_time = 182;
-impedance_options.demand_surge.include_surge = 1;
-impedance_options.demand_surge.is_dense_urban_area = 1;
-impedance_options.demand_surge.site_pga = 1;
-impedance_options.demand_surge.pga_de = 1;
-impedance_options.scaffolding_lead_time = 5;
-impedance_options.scaffolding_erect_time = 2;
-impedance_options.door_racking_repair_day = 3;
-impedance_options.flooding_cleanup_day = 5;
-impedance_options.flooding_repair_day = 90;
-
-% Repair Schedule Options
-repair_time_options.max_workers_per_sqft_story = 0.001;
-repair_time_options.max_workers_per_sqft_story_temp_repair = 0.005;
-repair_time_options.max_workers_per_sqft_building = 0.00025;
-repair_time_options.max_workers_building_min = 20;
-repair_time_options.max_workers_building_max = 260;
-repair_time_options.allow_tmp_repairs = 1;
-repair_time_options.allow_shoring = 1;
-
-% Functionality Assessment Options
-functionality_options.calculate_red_tag = 1;
-functionality_options.red_tag_clear_time = 7;
-functionality_options.red_tag_clear_beta = 0.6;
-functionality_options.red_tag_options.tag_coupling_beams_over_height = true;
-functionality_options.red_tag_options.ignore_coupling_beam_for_red_tag = false;
-functionality_options.include_local_stability_impact = 1;
-functionality_options.include_flooding_impact = 1;
-functionality_options.egress_threshold = 0.5;
-functionality_options.fire_watch = true;
-functionality_options.local_fire_damamge_threshold = 0.25;
-functionality_options.min_egress_paths = 2;
-functionality_options.exterior_safety_threshold = 0.1;
-functionality_options.interior_safety_threshold = 0.25;
-functionality_options.door_access_width_ft = 9;
-functionality_options.habitability_requirements.electrical = 0;
-functionality_options.habitability_requirements.water_potable = 0;
-functionality_options.habitability_requirements.water_sanitary = 0;
-functionality_options.habitability_requirements.hvac_ventilation = 0;
-functionality_options.habitability_requirements.hvac_heating = 0;
-functionality_options.habitability_requirements.hvac_cooling = 0;
-functionality_options.habitability_requirements.hvac_exhaust = 0;
-functionality_options.water_pressure_max_story = 4;
-functionality_options.heat_utility = 'gas';
-
diff --git a/inputs/example_inputs/ICSB/simulated_inputs.mat b/inputs/example_inputs/ICSB/simulated_inputs.mat
deleted file mode 100644
index cbae7e90c73065f847633bbcc9542261d8738222..0000000000000000000000000000000000000000
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diff --git a/inputs/example_pelicun_inputs/001/general_inputs.json b/inputs/example_pelicun_inputs/001/general_inputs.json
deleted file mode 100644
index d21a422..0000000
--- a/inputs/example_pelicun_inputs/001/general_inputs.json
+++ /dev/null
@@ -1 +0,0 @@
-{"num_entry_doors":2,"typ_struct_bay_area_ft":1089,"typ_story_ht_ft":10,"length_side_1_ft":100,"length_side_2_ft":100,"peak_occ_rate":0.0031}
\ No newline at end of file
diff --git a/inputs/example_pelicun_inputs/001/pelicun_data/CMP_QNT.csv b/inputs/example_pelicun_inputs/001/pelicun_data/CMP_QNT.csv
deleted file mode 100644
index 5b81f48..0000000
--- a/inputs/example_pelicun_inputs/001/pelicun_data/CMP_QNT.csv
+++ /dev/null
@@ -1,34 +0,0 @@
-ID,Units,Location,Direction,Theta_0,Blocks,Family,Theta_1,Comment
-B.10.41.001a,ea,"3, 4","1,2",2,2,N/A,,"24x24 ACI 318 SMF, beam on one side"
-B.10.41.002a,ea,1,"1,2",2,2,,,"24x36 ACI 318 SMF, beam on one side"
-B.10.41.002a,ea,2,"1,2",1,,,,"24x36 ACI 318 SMF, beam on one side"
-B.10.41.002b,ea,2--4,"1,2",3,3,,,"24x36 ACI 318 SMF, beam on both sides"
-B.10.41.003a,ea,2,"1,2",1,,,,"36x36 ACI 318 SMF, beam on one side"
-B.10.41.003b,ea,1,"1,2",3,3,,,"36x36 ACI 318 SMF, beam on both sides"
-B.10.49.031,ea,all,0,15,15,,,Post-tensioned concrete flat slabs- columns with shear reinforcing 0<Vg/Vo<0.4
-B.20.22.031,ft2,1,1,5040,168,,,"Midrise stick-built curtain wall, Config: Monolithic, Lamination: Not laminated, Glass Type: Annealed, Details: 1/4 in. (6 mm) AN monolithic; glass-frame clearance = 0.43 in. (11 mm); aspect ratio = 6:5 sealant = dry"
-B.20.22.031,ft2,1,2,3360,112,,,"Midrise stick-built curtain wall, Config: Monolithic, Lamination: Not laminated, Glass Type: Annealed, Details: 1/4 in. (6 mm) AN monolithic; glass-frame clearance = 0.43 in. (11 mm); aspect ratio = 6:5 sealant = dry"
-B.20.22.031,ft2,2--4,1,4680,156,,,"Midrise stick-built curtain wall, Config: Monolithic, Lamination: Not laminated, Glass Type: Annealed, Details: 1/4 in. (6 mm) AN monolithic; glass-frame clearance = 0.43 in. (11 mm); aspect ratio = 6:5 sealant = dry"
-B.20.22.031,ft2,2--4,2,3120,104,,,"Midrise stick-built curtain wall, Config: Monolithic, Lamination: Not laminated, Glass Type: Annealed, Details: 1/4 in. (6 mm) AN monolithic; glass-frame clearance = 0.43 in. (11 mm); aspect ratio = 6:5 sealant = dry"
-B.30.11.011,ft2,roof,0,5832,59,,,"Concrete tile roof, tiles secured and compliant with UBC94"
-C.10.11.001a,ft,1,"1,2",660,7,,,"Wall Partition, Type: Gypsum with metal studs, Full Height, Fixed Below, Fixed Above"
-C.10.11.001a,ft,2--4,"1,2",891,9,,,"Wall Partition, Type: Gypsum with metal studs, Full Height, Fixed Below, Fixed Above"
-C.30.11.002c,ft,all,"1,2",113,2,,,"Wall Partition, Type: Gypsum + Ceramic Tile, Full Height, Fixed Below, Slip Track Above w/ returns (friction connection)"
-C.30.32.003b,ft2,all,0,21600,36,,,"Suspended Ceiling, SDC D,E (Ip=1.0), Area (A): 250 < A < 1000, Vert & Lat support"
-C.30.34.002,ea,all,0,648,24,,,Independent Pendant Lighting - seismically rated
-D.20.21.023a,ft,all,0,1230,2,,,"Cold or Hot Potable Water Piping (dia > 2.5 inches), SDC D,E,F, PIPING FRAGILITY"
-D.20.21.023b,ft,all,0,1230,2,,,"Cold or Hot Potable Water Piping (dia > 2.5 inches), SDC D,E,F, BRACING FRAGILITY"
-D.20.22.013a,ft,all,0,1920,2,,,"Heating hot Water Piping - Small Diameter Threaded Steel - (2.5 inches in diameter or less), SDC D, E, or F, PIPING FRAGILITY"
-D.20.22.023a,ft,all,0,760,,,,"Heating hot Water Piping - Large Diameter Welded Steel - (greater than 2.5 inches in diameter), SDC D, E, or F, PIPING FRAGILITY"
-D.20.22.023b,ft,all,0,760,,,,"Heating hot Water Piping - Large Diameter Welded Steel - (greater than 2.5 inches in diameter), SDC D, E, or F, BRACING FRAGILITY"
-D.20.31.013b,ft,all,0,1230,2,,,"Sanitary Waste Piping - Cast Iron w/flexible couplings, SDC D,E,F, BRACING FRAGILITY"
-D.20.61.013b,ft,all,0,1920,2,,,"Steam Piping - Small Diameter Threaded Steel - (2.5 inches in diameter or less), SDC D, E, or F, BRACING FRAGILITY"
-D.30.31.013i,ea,roof,0,1,,,,Chiller - Capacity: 350 to <750 Ton - Equipment that is either hard anchored or is vibration isolated with seismic snubbers/restraints - Combined anchorage/isolator & equipment fragility
-D.30.31.023i,ea,roof,0,1,,,,Cooling Tower - Capacity: 350 to <750 Ton - Equipment that is either hard anchored or is vibration isolated with seismic snubbers/restraints - Combined anchorage/isolator & equipment fragility
-D.30.41.021c,ft,all,0,1620,2,,,"HVAC Stainless Steel Ducting less than 6 sq. ft in cross sectional area, SDC D, E, or F"
-D.30.41.022c,ft,all,0,430,,,,"HVAC Stainless Steel Ducting -  6 sq. ft cross sectional area or greater, SDC D, E, or F"
-D.30.41.032c,ea,all,0,19.44,20,,,"HVAC Drops / Diffusers without ceilings - supported by ducting only - No independent safety wires, SDC D, E, or F"
-D.30.41.041b,ea,all,0,16,2,,,"Variable Air Volume (VAV) box with in-line coil, SDC C"
-D.30.52.013i,ea,roof,0,4,4,,,Air Handling Unit - Capacity: 10000 to <25000 CFM - Equipment that is either hard anchored or is vibration isolated with seismic snubbers/restraints - Combined anchorage/isolator & equipment fragility
-D.40.11.023a,ft,all,0,4320,5,,,"Fire Sprinkler Water Piping - Horizontal Mains and Branches - Old Style Victaulic - Thin Wall Steel - with designed bracing, SDC D, E, or F (OSHPD or sim), PIPING FRAGILITY"
-D.40.11.033a,ea,all,0,194,2,,,"Fire Sprinkler Drop Standard Threaded Steel - Dropping into unbraced lay-in tile SOFT ceiling - 6 ft. long drop maximum, SDC D, E, or F"
diff --git a/inputs/example_pelicun_inputs/001/pelicun_data/DL_summary.csv b/inputs/example_pelicun_inputs/001/pelicun_data/DL_summary.csv
deleted file mode 100644
index 1e1f399..0000000
--- a/inputs/example_pelicun_inputs/001/pelicun_data/DL_summary.csv
+++ /dev/null
@@ -1,501 +0,0 @@
-#,repair_cost-,repair_time-parallel,repair_time-sequential,collapse,irreparable
-0,12641648.444969147,13494.513696542552,13494.513696542552,0.0,1.0
-1,8847517.298934922,16051.418611689896,16051.418611689896,0.0,1.0
-2,18045498.745757017,17493.901589809582,17493.901589809582,0.0,1.0
-3,2163818.8196495427,579.7407747030634,1568.0878137380307,0.0,0.0
-4,13825441.850770922,16616.049826108814,16616.049826108814,1.0,0.0
-5,2706784.535966555,833.3909699936039,1641.9889025910168,0.0,0.0
-6,10621565.364381785,11219.390926360787,11219.390926360787,0.0,1.0
-7,11716704.9972336,10487.450526317214,10487.450526317214,1.0,0.0
-8,11010195.59193899,10555.092733057914,10555.092733057914,0.0,1.0
-9,11568471.383574812,12692.542374462746,12692.542374462746,0.0,1.0
-10,3698573.933667891,948.9341591160512,2983.959275336767,0.0,0.0
-11,10125421.97327695,22663.866038505785,22663.866038505785,0.0,1.0
-12,14750053.611632893,10941.123881930938,10941.123881930938,0.0,1.0
-13,2960740.466721861,948.2523571176471,2560.3434931992992,0.0,0.0
-14,3380055.7301829834,713.1319388374704,2244.346075399161,0.0,0.0
-15,3261937.444416523,992.1064655961244,2775.211912140815,0.0,0.0
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diff --git a/inputs/example_pelicun_inputs/001/pelicun_data/DMG_sample.csv b/inputs/example_pelicun_inputs/001/pelicun_data/DMG_sample.csv
deleted file mode 100644
index db9d7b7..0000000
--- a/inputs/example_pelicun_inputs/001/pelicun_data/DMG_sample.csv
+++ /dev/null
@@ -1,501 +0,0 @@
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diff --git a/inputs/example_pelicun_inputs/001/pelicun_data/DV_bldg_repair_sample.csv b/inputs/example_pelicun_inputs/001/pelicun_data/DV_bldg_repair_sample.csv
deleted file mode 100644
index f446fbf..0000000
--- a/inputs/example_pelicun_inputs/001/pelicun_data/DV_bldg_repair_sample.csv
+++ /dev/null
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diff --git a/inputs/example_pelicun_inputs/001/pelicun_data/input.json b/inputs/example_pelicun_inputs/001/pelicun_data/input.json
deleted file mode 100644
index 2e71395..0000000
--- a/inputs/example_pelicun_inputs/001/pelicun_data/input.json
+++ /dev/null
@@ -1,72 +0,0 @@
-{
-    "DL": {
-        "Asset": {
-            "ComponentAssignmentFile": "CMP_QNT.csv",
-            "ComponentDatabase": "FEMA P-58",
-            "NumberOfStories": "4",
-            "OccupancyType": "COM1",
-            "PlanArea": "15000"
-        },
-        "Damage": {
-            "CollapseFragility": {
-                "CapacityDistribution": "lognormal",
-                "CapacityMedian": "521.219",
-                "DemandType": "SA_1.13",
-                "Theta_1": "0.5"
-            },
-            "DamageProcess": "FEMA P-58",
-            "IrreparableDamage": {
-                "DriftCapacityLogStd": "0.3",
-                "DriftCapacityMedian": "0.015"
-            }
-        },
-        "Demands": {
-            "Calibration": {
-                "ALL": {
-                    "DistributionFamily": "lognormal"
-                },
-                "PID": {
-                    "DistributionFamily": "lognormal",
-                    "TruncateLower": "",
-                    "TruncateUpper": "0.06"
-                }
-            },
-            "CollapseLimits": {
-                "PID": "0.06"
-            },
-            "DemandFilePath": "demands_s4.csv",
-            "InferResidualDrift": {
-                "1": "0.0075",
-                "2": "0.0075",
-                "method": "FEMA P-58"
-            },
-            "SampleSize": "500"
-        },
-        "Losses": {
-            "BldgRepair": {
-                "ConsequenceDatabase": "FEMA P-58",
-                "MapApproach": "Automatic",
-                "ReplacementCost": {
-                    "Distribution": "normal",
-                    "Median": "12500000",
-                    "Theta_1": "0.3",
-                    "Unit": "USD_2011"
-                },
-                "ReplacementTime": {
-                    "Distribution": "lognormal",
-                    "Median": "15000",
-                    "Theta_1": "0.4",
-                    "Unit": "worker_day"
-                }
-            }
-        }
-    },
-    "GeneralInformation": {
-        "units": {
-            "force": "kips",
-            "length": "in",
-            "temperature": "C",
-            "time": "sec"
-        }
-    }
-}
diff --git a/inputs/example_pelicun_inputs/001/tenant_unit_list.csv b/inputs/example_pelicun_inputs/001/tenant_unit_list.csv
deleted file mode 100644
index 485c746..0000000
--- a/inputs/example_pelicun_inputs/001/tenant_unit_list.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-id,story,area,perim_area,occupancy_id
-1,1,206800,18200,0
-2,2,206800,18200,0
-3,3,206800,18200,0
-4,4,206800,18200,0
-5,5,206800,18200,7
-6,6,206800,18200,7
-7,7,206800,18200,7
-8,8,206800,18200,7
-9,9,206800,18200,7
-10,10,206800,18200,7
-11,11,206800,18200,7
-12,12,206800,18200,7
-13,13,206800,18200,7
-14,14,206800,18200,7
-15,15,206800,18200,7
-16,16,206800,18200,7
diff --git a/main_PBEErecovery.m b/main_PBEErecovery.m
deleted file mode 100644
index 5be3918..0000000
--- a/main_PBEErecovery.m
+++ /dev/null
@@ -1,79 +0,0 @@
-function [functionality, damage_consequences] = main_PBEErecovery(damage, damage_consequences, ...
-    building_model, tenant_units, systems, subsystems, tmp_repair_class, ...
-    impedance_options, impeding_factor_medians, repair_time_options, ...
-    functionality, functionality_options)
-% Perform the ATC-138 functional recovery time assessement given similation
-% of component damage for a single shaking intensity
-%
-% Parameters
-% ----------
-% damage: struct
-%   contains per damage state damage and loss data for each component in the building
-% damage_consequences: struct
-%   data structure containing simulated building consequences, such as red
-%   tags and repair costs ratios
-% building_model: struct
-%   general attributes of the building model
-% tenant_units: table
-%   attributes of each tenant unit within the building
-% systems: table
-%   data table containing information about each system's attributes
-% subsystems: table
-%   attributes of building subsystems; data provided in static tables
-%   directory
-% tmp_repair_class: table
-%   data table containing information about each temporary repair class
-%   attributes. Attributes are similar to those in the systems table.
-% impedance_options: struct
-%   general impedance assessment user inputs such as mitigation factors
-% impeding_factor_medians: table
-%   median delays for various impeding factors
-% repair_time_options: struct
-%   general repair time options such as mitigation factors
-% functionality.utilities
-%   data structure containing simulated utility downtimes
-% functionality_options: struct
-%   recovery time optional inputs such as various damage thresholds
-%
-%
-% Returns
-% -------
-% functionality: struct
-%   contains data on the recovery of tenant- and building-level function, 
-%   recovery trajectorires, and contributions from systems and components, 
-%   simulated repair schedule breakdowns and impeding times.
-
-%% Import Packages
-import preprocessing.main_preprocessing
-import recovery.impedance.main_impeding_factors
-import recovery.repair_schedule.main_repair_schedule
-import recovery.functionality.main_functionality
-
-%% Combine compoment attributes into recovery filters to expidite recovery assessment
-[damage, tmp_repair_class, damage_consequences] = main_preprocessing(...
-    damage.comp_ds_table, damage, repair_time_options, tmp_repair_class, ...
-    damage_consequences, building_model.num_stories);
-
-%% Calculate Red Tags
-[ damage_consequences.red_tag, damage_consequences.red_tag_impact, damage_consequences.inspection_trigger ] ...
-    = fn_red_tag( functionality_options.calculate_red_tag, damage, building_model.comps,...
-    damage_consequences.simulated_replacement_time, functionality_options.red_tag_options );
-
-%% Simulate ATC 138 Impeding Factors
-[functionality.impeding_factors] = main_impeding_factors(damage, impedance_options, ...
-    damage_consequences.repair_cost_ratio_total, damage_consequences.repair_cost_ratio_engineering, ...
-    damage_consequences.inspection_trigger, systems, tmp_repair_class, ...
-    building_model.building_value, impeding_factor_medians, functionality_options.include_flooding_impact);
-
-%% Construct the Building Repair Schedule
-[damage, functionality.worker_data, functionality.building_repair_schedule ] = ...
-    main_repair_schedule(damage, building_model, damage_consequences.red_tag, ...
-    repair_time_options, systems, tmp_repair_class, ...
-    functionality.impeding_factors, damage_consequences.simulated_replacement_time);
-
-%% Calculate the Recovery of Building Reoccupancy and Function
-[ functionality.recovery ] = main_functionality( damage, building_model, ...
-    damage_consequences, functionality.utilities, functionality_options, ...
-    tenant_units, subsystems, functionality.impeding_factors.temp_repair );
-
-end
diff --git a/plotModel_PBEErecovery.m b/plotModel_PBEErecovery.m
deleted file mode 100644
index 2c4067f..0000000
--- a/plotModel_PBEErecovery.m
+++ /dev/null
@@ -1,61 +0,0 @@
-% Plot Functional Recovery Plots For a Single Model and Single Intensity
-clear all
-close all
-clc
-rehash
-
-%% Define User inputs
-model_name = '16-story_RCSW_475yr_Example'; % Name of the model;
-                     % inputs are expected to be in a directory with this name
-                     % outputs will save to a directory with this name
-outputs_dir = ['outputs' filesep model_name]; % Directory where the assessment outputs are saved
-plot_dir = [outputs_dir filesep 'plots']; % Directory where the plots will be saved
-p_gantt = 50; % percentile of functional recovery time to plot for the gantt chart
-              % e.g., 50 = 50th percentile of functional recovery time
-
-%% Import Packages
-import plotters.main_plot_functionality
-
-%% Load Assessment Output Data
-load([outputs_dir filesep 'recovery_outputs.mat'])
-
-
-%% Create plots for UBC Study
-data_RO = functionality.recovery.reoccupancy;
-data_FR = functionality.recovery.functional;
-mkdir(plot_dir)
-
-% Functional Recovery Histogram
-histogram(data_FR.building_level.recovery_day,'Normalization','probability')
-ylabel('Percent of Relizations')
-xlabel('Days after earthquake')
-saveas(gcf,[plot_dir filesep 'FR_hist.png'])
-close
-
-% Recovery Trajectory
-hold on
-plot(data_FR.recovery_trajectory.recovery_day,data_FR.recovery_trajectory.percent_recovered,'color',[0.8 0.8 0.8],'linewidth',0.5)
-plot(median(data_FR.recovery_trajectory.recovery_day),data_FR.recovery_trajectory.percent_recovered,'-k','linewidth',1.5)
-plot(prctile(data_FR.recovery_trajectory.recovery_day,10),data_FR.recovery_trajectory.percent_recovered,'--k','linewidth',1.5)
-plot(prctile(data_FR.recovery_trajectory.recovery_day,90),data_FR.recovery_trajectory.percent_recovered,'--k','linewidth',1.5)
-box on
-ylabel('Percent of Functional Tenant Units')
-xlabel('Days after earthquake')
-saveas(gcf,[plot_dir filesep 'Recovery_trajectory.png'])
-close
-
-% Table
-outs = table;
-outs.Return_Period = 475;
-outs.Median_Downtime_FR = median(data_FR.building_level.recovery_day);
-outs.Robustness_RO = mean(data_RO.building_level.recovery_day == 0);
-outs.Robustness_FR = mean(data_FR.building_level.recovery_day == 0);
-outs.Rapidity_RO = mean(data_RO.building_level.recovery_day <= 120); % less than 4 months ~ 120 days
-outs.Rapidity_FR = mean(data_FR.building_level.recovery_day <= 120); % less than 4 months ~ 120 days
-writetable(outs, [plot_dir filesep 'summary_outs.csv'])
-
-% %% Create plot for single intensity assessment of PBEE Recovery
-% main_plot_functionality( functionality, plot_dir, p_gantt )
-
-
-
diff --git a/plotModel_PBEErecovery.py b/plotModel_PBEErecovery.py
new file mode 100644
index 0000000..a738f4c
--- /dev/null
+++ b/plotModel_PBEErecovery.py
@@ -0,0 +1,45 @@
+def plot_results(model_name):
+    """
+    Plot Functional Recovery Plots For a Single Model and Single Intensity
+    
+    Parameters
+    ----------
+    model_name: string
+        Name of the model. Inputs are expected to be in a directory with this 
+        name. Outputs will save to a directory with this name
+    
+    """
+    import os
+    import json
+    import pandas as pd
+    
+    # from plotters import main_plot_functionality
+    # Plot Functional Recovery Plots For a Single Model and Single Intensity
+    
+    ## Define User inputs
+    
+    # Load systems information
+    systems = pd.read_csv(os.path.join(os.path.dirname(__file__), 'static_tables', 'systems.csv'))
+    systems = systems['name']
+    
+    # outputs will save to a directory with this name
+    outputs_dir = os.path.join(os.path.dirname(__file__), 'outputs/'+model_name) # Directory where the assessment outputs are saved
+    plot_dir = outputs_dir +'/plots' # Directory where the plots will be saved
+    p_gantt = 50 # percentile of functional recovery time to plot for the gantt chart
+                  #e.g., 50 = 50th percentile of functional recovery time
+    
+    ## Import Packages
+    from plotters import main_plot_functionality
+    
+    ## Load Assessment Output Data
+    f = open(os.path.join(outputs_dir, 'recovery_outputs.json'))
+    functionality= json.load(f)
+    
+    ## Create plot for single intensity assessment of PBEE Recovery
+    main_plot_functionality.main_plot_functionality(functionality, plot_dir, p_gantt, systems)
+
+if __name__ == '__main__':
+
+    model_name = 'ICSB'
+
+    plot_results(model_name)
\ No newline at end of file
diff --git a/plotters/main_plot_functionality.py b/plotters/main_plot_functionality.py
new file mode 100644
index 0000000..b05aeb0
--- /dev/null
+++ b/plotters/main_plot_functionality.py
@@ -0,0 +1,65 @@
+def main_plot_functionality(functionality, save_dir, p_gantt, systems):
+    '''Plot function and occupancy loss and recovery at for a single model at a 
+    single intensity levels
+    
+    Parameters
+    ----------
+    functionality: dictionary
+     main output data strcuture of the functional recovery assessment. 
+     Loaded directly from the output mat file.
+    save_dir: str
+     Save directory for plots. Plots will save directly to this location as
+     png files.
+    p_gantt: int
+     percentile of functional recovery time to plot gantt chart
+    
+    Returns
+    -------'''
+     
+    import numpy as np
+    import os
+    
+    ## Initial Setup
+    # Import Packages
+    from plotters import other_plot_functions
+    
+    # Set plot variables to use
+    recovery = functionality['recovery']
+    impede = functionality['impeding_factors']['breakdowns']['full']
+    schedule = functionality['building_repair_schedule']
+    workers = functionality['worker_data']
+    full_repair_time = np.nanmax(np.array(schedule['full']['repair_complete_day']['per_story']), axis=1)
+    
+    if os.path.exists(save_dir) == False:
+        os.mkdir(save_dir)
+    ## Plot Performance Objective Grid for system and component breakdowns
+
+    plot_dir = os.path.join(save_dir,'breakdowns')
+    other_plot_functions.plt_heatmap_breakdowns(recovery, plot_dir)
+    
+    ## Plot Performance Target Distribution Across all Realizations
+    plot_dir = os.path.join(save_dir,'histograms')
+    other_plot_functions.plt_histograms(recovery,plot_dir)
+    
+    ## Plot Mean Recovery Trajectories
+    plot_dir = os.path.join(save_dir,'recovery_trajectories')
+    other_plot_functions.plt_recovery_trajectory( recovery, full_repair_time, plot_dir)
+    
+    # Plot Gantt Charts
+    plot_dir = os.path.join(save_dir,'gantt_charts')
+    fr_time = np.array(functionality['recovery']['functional']['building_level']['recovery_day'])
+    if len(np.where(fr_time == np.percentile(fr_time,p_gantt))[0])>0:
+        p_idx = np.where(fr_time == np.percentile(fr_time,p_gantt))[0][0] # Find the index of the first realization that matches the selected percentile
+        
+    else:
+        diff = abs(fr_time - np.percentile(fr_time,p_gantt))
+        p_idx = np.where(diff == min(diff))[0][0]
+        from scipy.stats import percentileofscore
+        p_gantt = percentileofscore(fr_time, fr_time[p_idx])
+    
+        
+    plot_name = 'prt_'+str(p_gantt)
+    other_plot_functions.plt_gantt_chart(p_idx, recovery, full_repair_time, workers, schedule, impede, plot_dir, plot_name, systems)
+    
+
+
diff --git a/plotters/other_plot_functions.py b/plotters/other_plot_functions.py
new file mode 100644
index 0000000..6280b4f
--- /dev/null
+++ b/plotters/other_plot_functions.py
@@ -0,0 +1,398 @@
+def plt_heatmap_breakdowns(recovery, plot_dir):
+    '''Plot the time and percent of realizations that each system and/or 
+    component is impeding function as a heatmap and as lineplots
+    
+    Parameters
+    ----------
+    recovery: dictionary
+     dictionary containing recovery breakdown data from the functional
+     recovery assessment
+     
+    plot_dir: str
+     Save directory for plots. Plots will save directly to this location as
+     png files.
+    
+    Returns
+    -------'''
+    
+    import seaborn as sb
+    import matplotlib.pyplot as plt
+    import os
+    import numpy as np
+    
+    
+    ## Initial Setup
+    if os.path.exists(plot_dir) == False:
+        os.mkdir(plot_dir)
+    plot_dir=plot_dir +'/'
+    
+    perform_targ_days = recovery['reoccupancy']['breakdowns']['perform_targ_days']
+    perform_targ_labs = ['Immediately', '>3 Days', '>7 Days', '>2 Weeks', '>1 Month', '>2 Months', '>3 Months', '>4 Months', '>6 Months', '>9 Months', '>1 Year', ]
+    
+    #FZ# Maximum day for recovery amongs all tenant units and all realizations
+    recovery_day_max = perform_targ_days[-1]
+   
+    if recovery_day_max <= 365:
+        perform_targ_labs = perform_targ_labs # Number of days for each performance target stripe
+    if recovery_day_max > 365:
+        num_years = (int(np.floor((recovery_day_max)/365)))
+        
+        # If number of complete years is more than 2
+        for yr in range(num_years-1):
+            quarters =[1,2,3]
+            for qtr in quarters:
+                if yr == 0:
+                    perform_targ_labs.append('>' + str(yr+1) +' Year & ' + str(qtr*3)+ ' months')
+                if yr > 0:
+                    perform_targ_labs.append('>' + str(yr+1) +' Years & ' + str(qtr*3)+ ' months')    
+            perform_targ_labs.append('>' + str(yr+2) +' Years')
+        
+        # For final incomplete year
+        num_quarters = int(np.floor((recovery_day_max - num_years*365)/90))
+        compl_yr = perform_targ_labs[-1]
+        for qtr in range(num_quarters):
+            perform_targ_labs.append(compl_yr + ' & '+str((qtr+1)*3) + ' Months')
+
+        perform_targ_labs.append(perform_targ_labs[-1] + ' & ' + str(int(perform_targ_days[-1] - perform_targ_days[-2])) + ' Days')    
+
+    
+    var = ['component_breakdowns', 'system_breakdowns']
+    labs = ['comp_names', 'system_names']
+    plt_ht = [10, 8]
+    
+    ## Plot Heatmaps
+    fnc_states = list(recovery.keys())
+    for fs in range(len(fnc_states)):
+        
+        perform_targ_days = recovery[fnc_states[fs]]['breakdowns']['perform_targ_days']
+        perform_targ_labs = ['Immediately', '>3 Days', '>7 Days', '>2 Weeks', '>1 Month', '>2 Months', '>3 Months', '>4 Months', '>6 Months', '>9 Months', '>1 Year', ]
+        
+        #FZ# Maximum day for recovery amongs all tenant units and all realizations
+        recovery_day_max = perform_targ_days[-1]
+       
+        if recovery_day_max <= 365:
+            perform_targ_labs = perform_targ_labs # Number of days for each performance target stripe
+        if recovery_day_max > 365:
+            num_years = (int(np.floor((recovery_day_max)/365)))
+            
+            # If number of complete years is more than 2
+            for yr in range(num_years-1):
+                quarters =[1,2,3]
+                for qtr in quarters:
+                    if yr == 0:
+                        perform_targ_labs.append('>' + str(yr+1) +' Year & ' + str(qtr*3)+ ' months')
+                    if yr > 0:
+                        perform_targ_labs.append('>' + str(yr+1) +' Years & ' + str(qtr*3)+ ' months')    
+                perform_targ_labs.append('>' + str(yr+2) +' Years')
+            
+            # For final incomplete year
+            num_quarters = int(np.floor((recovery_day_max - num_years*365)/90))
+            compl_yr = perform_targ_labs[-1]
+            for qtr in range(num_quarters):
+                perform_targ_labs.append(compl_yr + ' & '+str((qtr+1)*3) + ' Months')
+    
+            perform_targ_labs.append(perform_targ_labs[-1] + ' & ' + str(int(perform_targ_days[-1] - perform_targ_days[-2])) + ' Days')        
+        
+        # system breakdown for line plots
+        sys_bkdn = np.array(recovery[fnc_states[fs]]['breakdowns'][var[1]])
+        systs = recovery[fnc_states[fs]]['breakdowns'][labs[1]]
+        # no. of systems in one plot (total 3 plots)
+        num_systems = int(np.floor(len(systs)/3))
+        
+        # Plot 1
+        plt.figure(figsize=([6,6]))
+        for syst in range(num_systems):
+            plt.plot(perform_targ_days, sys_bkdn[syst], marker ='o', label=systs[syst])
+        plt.ylim([0,1])
+        plt.xlabel('Recovery Time After Earthquake (Days')
+        plt.ylabel('Fraction of Realizations Affecting Building')
+        plt.title(var[1] +' '+fnc_states[fs] + ' lineplot 1')
+        plt.legend()
+        plt.grid()
+        plt.savefig(plot_dir + var[1] +' '+fnc_states[fs] + ' lineplot 1.png', dpi=300)
+        
+        # Plot 2
+        plt.figure(figsize=([6,6]))
+        for syst in range(num_systems):
+            plt.plot(perform_targ_days, sys_bkdn[num_systems + syst], marker ='o', label=systs[num_systems + syst])
+        plt.ylim([0,1])
+        plt.xlabel('Recovery Time After Earthquake (Days')
+        plt.ylabel('Fraction of Realizations Affecting Building')
+        plt.title(var[1] +' '+fnc_states[fs] + ' lineplot 2')
+        plt.legend()
+        plt.grid()
+        plt.savefig(plot_dir + var[1] +' '+fnc_states[fs] + ' lineplot 2.png', dpi=300)        
+        
+        # Plot 3
+        plt.figure(figsize=([6,6]))
+        for syst in range(len(systs) - num_systems*2):
+            plt.plot(perform_targ_days, sys_bkdn[2*num_systems + syst], marker ='o', label=systs[2*num_systems + syst])
+        plt.ylim([0,1])
+        plt.xlabel('Recovery Time After Earthquake (Days')
+        plt.ylabel('Fraction of Realizations Affecting Building')
+        plt.title(var[1] +' '+fnc_states[fs] + ' lineplot 3')
+        plt.legend()
+        plt.grid()
+        plt.savefig(plot_dir + var[1] +' '+fnc_states[fs] + ' lineplot 3.png', dpi=300)
+        
+        
+        # Heat maps
+        for v in range(len(var)):
+            y_labs = recovery[fnc_states[fs]]['breakdowns'][labs[v]]
+            data = np.array(recovery[fnc_states[fs]]['breakdowns'][var[v]])
+            
+            plt.rcParams["font.family"] = "Times New Roman"
+            plt.figure(figsize=(plt_ht))
+            h = sb.heatmap(data, cmap='Blues', linewidths = 0.5, linecolor = 'black', annot = np.round(data,2), xticklabels = perform_targ_labs, yticklabels = y_labs, clip_on = False)
+            fnc_lab = fnc_states[fs][0].upper() + fnc_states[fs][1:len(fnc_states[fs])]+' Recovery'
+            title = 'Fraction of Realizations Affecting Building ' + fnc_lab
+            h.set(xlabel  = 'Recovery Time After Earthquake')
+            # h.set(ylabel = labs[v])
+            h.set_title(title, x=0.5, y=1.05)
+            plt.xticks(rotation=90)
+            plt.subplots_adjust(bottom=0.15)
+            
+            plt.savefig(plot_dir + var[v] +' '+fnc_states[fs] + '.png', dpi=300)
+
+    return
+
+
+def plt_histograms( recovery, plot_dir ):
+    
+    '''Plot all realizations of building level recovery as a histogram
+    
+    Parameters
+    ----------
+    recovery: dictionary
+      dictionary containing building-level recovery data from the 
+      functional recovery assessment
+     
+    plot_dir: str
+      Save directory for plots. Plots will save directly to this location as
+      png files.
+    
+    Returns
+    -------'''
+    
+    import seaborn as sb
+    import matplotlib.pyplot as plt
+    import os
+    
+    ## Initial Setup
+    if os.path.exists(plot_dir) == False:
+        os.mkdir(plot_dir)
+    plot_dir=plot_dir +'/'
+    
+    ## Plot Histograms
+    fnc_states = list(recovery.keys())
+    for fs in range(len(fnc_states)):
+        plt.rcParams["font.family"] = "Times New Roman"
+        plt.figure(figsize=(6,4))
+        sb.histplot(data = recovery[fnc_states[fs]]['building_level']['recovery_day'])
+        fnc_lab = fnc_states[fs][0].upper() + fnc_states[fs][1:len(fnc_states[fs])]+' Recovery'        
+        plt.xlabel(fnc_lab + ' Time (days)')
+        plt.ylabel('Number of Realizatons')
+        plt.savefig(plot_dir + fnc_states[fs] + '.png', dpi=300)
+        
+    return
+
+
+def plt_recovery_trajectory( recovery, full_repair_time, plot_dir):
+    '''Plot mean recovery trajectories
+    
+    Parameters
+    ----------
+    recovery: dictionary
+      dictionary containing recovery trajectory data from the functional
+      recovery assessment
+     
+    full_repair_time: array [num reals x 1] 
+      simulated realization of full repair time 
+    
+    plot_dir: str
+      Save directory for plots. Plots will save directly to this location as
+      png files.
+    
+    Returns
+    -------'''
+     
+    
+    import matplotlib.pyplot as plt
+    import os
+    import numpy as np
+    
+    ## Initial Setup
+    if os.path.exists(plot_dir) == False:
+        os.mkdir(plot_dir)
+    plot_dir=plot_dir +'/'
+    
+    
+
+    # Calculate mean recovery times
+    reoc = np.mean(np.array(recovery['reoccupancy']['recovery_trajectory']['recovery_day']), axis=0)
+    func = np.mean(np.array(recovery['functional']['recovery_trajectory']['recovery_day']), axis=0)
+    med = np.median(np.array(recovery['functional']['recovery_trajectory']['recovery_day']), axis=0)
+    per_10 = np.percentile(np.array(recovery['functional']['recovery_trajectory']['recovery_day']), 10, axis=0)
+    per_90 = np.percentile(np.array(recovery['functional']['recovery_trajectory']['recovery_day']), 90, axis=0)
+    full = np.mean(full_repair_time)
+    level_of_repair = recovery['functional']['recovery_trajectory']['percent_recovered']
+    
+    # Plot Recovery Trajectory
+    plt.rcParams["font.family"] = "Times New Roman"
+    plt.figure(figsize=(6,4)) 
+    plt.plot(reoc, level_of_repair,'r-', linewidth = 1.5, label = 'Re-Occupancy') 
+    plt.plot(func, level_of_repair,'b-', linewidth = 1.5, label= 'Functional') 
+    plt.plot([full, full], [0, 1],'k-', linewidth = 1.5, label= 'Fully Repaired') 
+    plt.xlim([0,np.ceil((full+1)/10)*10])
+    plt.xlabel('Days After Earthquake')
+    plt.ylabel('Fraction of Floor Area')
+    plt.legend(loc='upper left')
+    plt.grid()
+    plt.show()
+    
+    plt.savefig(plot_dir + 'recovery_trajectory.png', dpi=300)
+    
+    plt.figure(figsize=(6,4))
+    for i in range(len(recovery['functional']['recovery_trajectory']['recovery_day'])):
+        plt.plot(recovery['functional']['recovery_trajectory']['recovery_day'][i] , level_of_repair, color = 'lightgrey', linewidth = 0.5)
+    plt.plot(med, level_of_repair,'r-', linewidth = 1.5, label= 'Median')
+    plt.plot(per_10, level_of_repair,'b--', linewidth = 1.0, label= '10th percentile')
+    plt.plot(per_90, level_of_repair,'b--', linewidth = 1.0, label= '90th percentile')
+    plt.xlim([0,np.ceil((max(max(recovery['functional']['recovery_trajectory']['recovery_day'])))/10)*10])
+    plt.title('Functional Recovery Trajectories')
+    plt.xlabel('Days After Earthquake')
+    plt.ylabel('Fraction of Floor Area')
+    plt.legend(loc='upper left')
+    plt.grid()    
+
+    plt.savefig(plot_dir + 'functional_recovery_trajectories.png', dpi=300)
+    
+    return
+
+
+def plt_gantt_chart(p_idx, recovery, full_repair_time, workers, schedule, impede, plot_dir, plot_name, systems):
+    '''Plot gantt chart for a single realization of the functional recovery
+    assessment
+    
+    Parameters
+    ----------
+    p_idx: int
+      realization index of interest
+     
+    recovery: dictionary
+      dictionary containing recovery trajectory data from the functional
+      recovery assessment
+    
+    full_repair_time: array [num reals x 1] 
+      simulated realization of full repair time 
+    
+    workers: dictionary
+      data structure containing work allocation data from the functional
+      recovery assessment
+    
+    schedule: dictionary
+      dictionary containing repair schedule data from the functional
+      recovery assessment
+    
+    impede: dictionary
+      data structure containing impedance time data from the functional
+      recovery assessment
+    
+    plot_dir: str
+      Save directory for plots. Plots will save directly to this location as
+      png files.
+     
+    plot_name: str
+      name of file to save plot as (does not include the file type extension)
+    
+    Returns
+      -------'''
+
+    import matplotlib.pyplot as plt
+    import os
+    import numpy as np
+    
+    ## Initial Setup
+    if os.path.exists(plot_dir) == False:
+        os.mkdir(plot_dir)
+    plot_dir=plot_dir +'/'
+    
+    imps = list(impede.keys())
+    sys = list(systems)
+    
+    ## Format Gantt Chart Data
+    recovery_trajectory = {
+        'reoc' : np.array(recovery['reoccupancy']['recovery_trajectory']['recovery_day'])[p_idx,:],
+        'func' : np.array(recovery['functional']['recovery_trajectory']['recovery_day'])[p_idx,:],
+        'level_of_repair' : np.array(recovery['reoccupancy']['recovery_trajectory']['percent_recovered']),
+        'ful_rep' : np.ones(len(recovery['reoccupancy']['recovery_trajectory']['percent_recovered'])) * np.ceil(full_repair_time[p_idx])
+        }
+    
+    # Collect Worker Data
+    worker_data = {
+        'total_workers' : np.array(workers['total_workers'])[p_idx,:],
+        'day_vector' : np.array(workers['day_vector'])[p_idx,:]
+        }
+    
+    # Collect Impedance Times
+    sys_imp_times = []
+    labs = []
+    for i in range(len(imps)):
+        duration = impede[imps[i]]['complete_day'][p_idx] - impede[imps[i]]['start_day'][p_idx]
+        # if duration > 0:
+        sys_imp_times.append([impede[imps[i]]['start_day'][p_idx], duration])
+        labs.append(imps[i][0].upper() + imps[i][1:len(imps[i])].replace('_',' '))
+     
+    labs_imp = np.array(labs)
+    y_imp = np.array(sys_imp_times)
+
+    # Collect Repair Times 
+    sys_repair_times = []
+    labs = []
+    for s in range(len(sys)): 
+        duration = schedule['full']['repair_complete_day']['per_system'][p_idx][s] - schedule['full']['repair_start_day']['per_system'][p_idx][s]
+        if duration > 0:
+            sys_repair_times.append([schedule['full']['repair_start_day']['per_system'][p_idx][s], duration])
+            labs.append(sys[s][0].upper() + sys[s][1:len(sys[s])] + ' Repairs')
+
+    labs.reverse()    
+    labs_rep = labs
+    y_rep = np.flip(np.array(sys_repair_times), axis=0)
+    ## Plot Gantt
+    
+    plt.rcParams["font.family"] = "Times New Roman"
+    # Impedance Time
+    fig, (G1, G2, G3, G4) = plt.subplots(4, 1, sharex=True, figsize = (10,12), gridspec_kw={'height_ratios': [2, 1, 1, 1]})
+    G1.barh(labs_imp, y_imp[:,0], color = 'white')    
+    G1.barh(labs_imp, y_imp[:,1], left = y_imp[:,0], color='lightgrey')
+    G1.set_title('Impedance Time')
+    G1.grid(which = 'major', axis = 'x', alpha=0.5)
+    # G1.set_xlabel('Days After Earthquake')
+    
+    # Repair Time
+    G2.barh(labs_rep, y_rep[:,0], color = 'white')
+    G2.barh(labs_rep, y_rep[:,1], left = y_rep[:,0], color='grey')
+    G2.set_title('Repair Time') 
+    G2.grid(which = 'major', axis = 'x', alpha=0.5)    
+    
+    
+    # Workers
+    G3.plot(worker_data['day_vector'], worker_data['total_workers'])
+    G3.set_ylabel('Number of workers')
+    G3.set_title('Number of workers')
+    G3.grid(which = 'major', axis = 'x', alpha=0.5) 
+    
+    # Plot Recovery Trajectory
+    G4.plot(recovery_trajectory['reoc'], recovery_trajectory['level_of_repair'],'r-', linewidth = 1.5, label = 'Re-Occupancy')
+    G4.plot(recovery_trajectory['func'], recovery_trajectory['level_of_repair'],'b-', linewidth = 1.5, label = 'Functional')
+    G4.plot(recovery_trajectory['ful_rep'], recovery_trajectory['level_of_repair'],'k-', linewidth = 1.5, label = 'Fully Repaired')
+    G4.set_ylim([0,1])
+    G4.set_xlabel('Days After Earthquake')
+    G4.set_ylabel('Fraction of Floor Area')
+    G4.set_title('Building Recovery State')
+    G4.grid(which = 'major', axis = 'x', alpha=0.5)
+    G4.legend()
+    fig.subplots_adjust(left=0.20)
+    fig.savefig(plot_dir + plot_name + '.png', dpi=300)
+    
+    return
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..0fdd6fe
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,36 @@
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "atc138"
+version = "0.1.0"
+description = "Functional Recovery Assessment (ATC-138)"
+readme = "README.md"
+requires-python = ">=3.9"
+license = {file = "LICENSE"}
+authors = [
+  {name = "Dustin Cook", email = "dustin.cook@nist.gov"},
+]
+classifiers = [
+  "Programming Language :: Python :: 3",
+  "License :: OSI Approved :: BSD License",
+  "Operating System :: OS Independent",
+]
+dependencies = [
+  "numpy",
+  "pandas",
+  "scipy",
+  "matplotlib",
+  "seaborn",
+]
+
+[project.scripts]
+atc138 = "atc138.cli:main"
+
+[project.urls]
+"Homepage" = "https://github.com/NHERI-SimCenter/Functional-Recovery-Python"
+"Bug Tracker" = "https://github.com/NHERI-SimCenter/Functional-Recovery-Python/issues"
+
+[tool.setuptools.packages.find]
+where = ["src"]
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..af3fc8b
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+numpy
+pandas
+scipy
+matplotlib
+seaborn
\ No newline at end of file
diff --git a/schema/Inputs.json b/schema/Inputs.json
deleted file mode 100644
index c0d41c6..0000000
--- a/schema/Inputs.json
+++ /dev/null
@@ -1,541 +0,0 @@
-{
-	"definitions": {
-		"arrayOfNumbers": {
-			"type": "array",
-			"items": {
-				"type": "number"
-			}
-		},
-		"arrayOfArraysOfNumbers": {
-			"type": "array",
-			"items": {
-				"$ref": "#/definitions/arrayOfNumbers"
-			}
-		}
-	},
-	"properties": {
-		"impedance_options": {
-			"type": "object",
-			"description": "general impedance module inputs",
-			"properties": {
-				"include_impedance": {
-					"type": "object",
-					"description": "logical switches for defining which impedance factors to consider in the assessment",
-					"properties": {
-						"inspection": {
-							"type": "logical",
-							"description": "flag to include inpection delays"
-						},
-						"financing": {
-							"type": "logical",
-							"description": "flag to include financing delays"
-						},
-						"permitting": {
-							"type": "logical",
-							"description": "flag to include permitting delays"
-						},
-						"engineering": {
-							"type": "logical",
-							"description": "flag to include engineering mobilization and design delays"
-						},
-						"contractor": {
-							"type": "logical",
-							"description": "flag to include contractor mobilization delays"
-						},
-						"long_lead": {
-							"type": "logical",
-							"description": "flag to include long lead delays"
-						}
-					}
-				},
-				"system_design_time": {
-					"type": "object",
-					"description": "variables of formula to calculate median system design time",
-					"properties": {
-						"f": {
-							"type": "number",
-							"description": "design fee ratio: ratio of building value charged by engineering firm. Default to 0.04 for repair projects"
-						},
-						"r": {
-							"type": "number",
-							"description": "engineering rate: Average hourly rate of engineering work. Default to $175 per hour"
-						},
-						"t": {
-							"type": "number",
-							"description": "team factor: accounts for parallel time between multiple designers spent on project. Default to 1.3"
-						},
-						"w": {
-							"type": "number",
-							"description": "average workday: average number of hours per day spent on project: Default to 8 hours"
-						}
-					}
-				},
-				"eng_design_min_days":{
-					"type": "number",
-					"description": "Minumum number of days for the median engineering design time"
-				},
-				"eng_design_max_days":{
-					"type": "number",
-					"description": "Maximum number of days for the median engineering design time"
-				},
-				"mitigation": {
-					"type": "object",
-					"description": "general impedance assessment inputs such as mitigation factors",
-					"properties": {
-						"is_essential_facility": {
-							"type": "logical",
-							"description": "is the building deemed as essential by the local jurisdiction"
-						},
-						"is_borp_equivalent": {
-							"type": "logical",
-							"description": "are the BORP, or BORP equivalent, post-earthquake strategies in place to limit inspection delays"
-						},
-						"is_engineer_on_retainer": {
-							"type": "logical",
-							"description": "is an engineer contracted to be on retainer in case of post-earthquake damage"
-						},
-						"contractor_relationship": {
-							"type": "string",
-							"description": "string identifier that defines the state of existing relationships with contractors to perform repairs. Options include <'retainer', 'good', 'none'>"
-						},
-						"contractor_retainer_time": {
-							"type": "number",
-							"description": "time, in days, from onset of damage to when repairs start previosly agreed upon in retainer agreement. Only applies when 'retainer' contractor_relationship is selected"
-						},
-						"funding_source": {
-							"type": "string",
-							"description": "what is the source of funding for repair costs beyond that of the capital of the owner"
-						},
-						"capital_available_ratio": {
-							"type": "number",
-							"description": "ratio of the building value that the onwer has immediately available for repairs"
-						}
-					}
-				},
-				"impedance_beta": {
-					"type": "number",
-					"description": "lognormal uncetainty of impedance times",
-				},
-				"impedance_truncation": {
-					"type": "number",
-					"description": "Truncation bounds of lognormal distribution of impedance time, input as the number of standard deviations above and below the mean. Default to 2",
-				},
-				"default_lead_time": {
-					"type": "number",
-					"description": "Lead time, in days, to replace select components (equipment or other less readily available components). This default lead time will be applied to all components with the long_lead flag in the inputs builder. To add custom lead times per component, update the long_lead_time attribute in the damage.comp_ds_table field.",
-				},
-				"demand_surge": {
-					"type": "object",
-					"description": "general user input options for defining the demand surge",
-					"properties": {
-						"include_surge": {
-							"type": "logical",
-							"description": "include demand surge when simulating impedance times"
-						},
-						"is_dense_urban_area": {
-							"type": "logical",
-							"description": "is within a dense urban area, typically defined by a site within a metropolitan statistical area (MSA) with a population of over 1 million"
-						},
-						"site_pga": {
-							"type": "number",
-							"description": "peak ground acceleration (PGA) in g at the building site for the ground shaking intensity of interest"
-						},
-						"pga_de": {
-							"type": "number",
-							"description": "peak ground acceleration (PGA) in g associated with the design eartquake (DE). Method of combining ground motion pairs (e.g., max direction vs geomean) should match the field site_pga"
-						}
-					}
-				},
-				"scaffolding_lead_time": {
-					"type": "number",
-					"description": "The number of days it takes to get workers to the site to erect scaffolding",
-				},
-				"scaffolding_erect_time": {
-					"type": "number",
-					"description": "The number of days it takes for workers to errect scaffolding to protect doorways and walkways from falling hazards",
-				},
-				"door_racking_repair_day": {
-					"type": "number",
-					"description": "days after the earthquake required to unjam all racked doorways"
-				},
-				"flooding_cleanup_day": {
-					"type": "number",
-					"description": "days after the earthquake required to shut off leaks and dry out water"
-				},
-				"flooding_repair_day": {
-					"type": "number",
-					"description": "days after the earthquake required to repair all damage caused by interior flooding"
-				}
-			}
-		},
-		"repair_time_options": {
-			"type": "object",
-			"description": "general repair schedule module inputs",
-			"properties": {
-				"temp_repair_beta": {
-					"type": "number",
-					"description": "lognormal uncetainty of temporary repair times. Default to 0.6",
-				},
-				"max_workers_per_sqft_story": {
-					"type": "number",
-					"description": "Maximum density of workers on a given story. Default to 0.001 from FEMA P-58",
-				},
-				"max_workers_per_sqft_building": {
-					"type": "number",
-					"description": "Maximum density of workers for the entire building after the first 10. Default to 0.00025 from REDi",
-				},
-				"max_workers_building_min": {
-					"type": "number",
-					"description": "Lower limit on the maximum number of workers for the entire building (i.e., max number of workers is never less than this). Default to 20 from REDi",
-				},
-				"max_workers_building_max": {
-					"type": "number",
-					"description": "Upper limit on the maximum number of workers for the entire building (i.e., max number of workers is never more than this). Default to 260 from REDi",
-				},
-				"allow_tmp_repairs": {
-					"type": "logical",
-					"description": "Flag to allow temporay repair measures to be used to restore building reoccupancy and basic function",
-				},
-				"allow_shoring": {
-					"type": "logical",
-					"description": "Flag to allow shoring to be used as a temporary repair for mitigating local structural stability issues. Setting allow_tmp_repairs to zero overwrites this flag",
-				}
-			}
-		},
-		"functionality_options": {
-			"type": "object",
-			"description": "general functionality module inputs",
-			"properties": {
-				"calculate_red_tag": {
-					"type": "logical",
-					"description": "flag to indicate whether or not to include the assessment of red tags based on component damage"	
-				}, 
-				"red_tag_clear_time": {
-					"type": "number",
-					"description": "days after all red tag repairs are done before occupancy can be regained (i.e. time for permitting office to clear the tag)"
-				},
-				"red_tag_clear_beta": {
-					"type": "number",
-					"description": "lognormal dispersion defining the uncertaitny in the red tag clear time"
-				},
-				"include_local_stability_impact": {
-					"type": "logical",
-					"description": "flag to consider the effect of local structural stability issues (that would require shoring) on building occupancy"
-				},
-				"egress_threshold": {
-					"type": "number",
-					"description": "ratio of building egress paths required for building safety and access"
-				},
-				"fire_watch": {
-					"type": "logical",
-					"description": "will a fire watch be set if fire sprinkler systems are damaged"
-				},
-				"local_fire_damamge_threshold": {
-					"type": "number",
-					"description": "ratio of building fire sprinkler branches or drops that can be damaged with a given story without significantly disrupting the operation of the active fire supression system"
-				},
-				"min_egress_paths": {
-					"type": "number",
-					"description": "minimum number of egress pathways that must be maintained at each story to maintain occupancy"
-				},
-				"required_ratio_operating_hvac_main": {
-					"type": "number",
-					"description": "ratio of hvac units, which service the entire building, required to maintain an operational hvac system; not applied to components with n+1 redundancy"
-				},
-				"required_ratio_operating_hvac_unit": {
-					"type": "number",
-					"description": "ratio of hvac units, which service an individual tenant unit, required to maintain an operational hvac system; not applied to components with n+1 redundancy"
-				},
-				"exterior_safety_threshold": {
-					"type": "number",
-					"description": "damage theshold that triggers an exterior safety issue; threshold is defined in terms of the ratio of damaged area to perimeter area of a given tenant unit"
-				},
-				"interior_safety_threshold": {
-					"type": "number",
-					"description": "damage theshold that triggers an interior safety issue; threshold is defined in terms of the ratio of damaged area to interior area of a given tenant unit"
-				},
-				"door_access_width_ft": {
-					"type": "number",
-					"description": "width of door access zone, centerlined on the doorway, required for safe access. A safe door access zone must be free of falling hazards"
-				},
-				"heat_utility": {
-					"type": "string",
-					"description": "string identifier of the type of utility that is used to heat the building; string should match on of the fields in functionality.utilities"
-				},
-				"habitability_requirements": {
-					"type": "object",
-					"description": "flags to add additional system check on the reoccupancy time",
-					"properties": {
-						"electrical": {
-							"type": "logical",
-							"description": "electrical power required for reoccupancy/habitability"
-						},
-						"water_potable": {
-							"type": "logical",
-							"description": "potable water required for reoccupancy/habitability"
-						},
-						"water_sanitary": {
-							"type": "logical",
-							"description": "sanitary waste system required for reoccupancy/habitability"
-						},
-						"hvac_ventilation": {
-							"type": "logical",
-							"description": "active ventilation required for reoccupancy/habitability"
-						},
-						"hvac_heating": {
-							"type": "logical",
-							"description": "heating required for reoccupancy/habitability"
-						},
-						"hvac_cooling": {
-							"type": "logical",
-							"description": "cooling required for reoccupancy/habitability"
-						},
-						"hvac_exhaust": {
-							"type": "logical",
-							"description": "exhaust systems required for reoccupancy/habitability"
-						}
-					}
-				}
-			}
-		},
-		"building_model": {
-			"type": "object",
-			"description": "general information about the building such as the number of stories and the building area",
-			"properties": {
-				"building_value": {
-					"type": "number",
-					"description": "Total replacement value of the building, in USD"
-				},
-				"num_stories": {
-					"type": "number",
-					"description": "number of stories in the building, does not include basement levels"
-				},
-				"area_per_story_sf": {
-					"type": "#/definitions/arrayOfNumbers",
-					"description": "interior area, in square feet, for each story in the building, starting from the lowest level"
-				},
-				"ht_per_story_ft": {
-					"type": "#/definitions/arrayOfNumbers",
-					"description": "elevation height, in feet, for each story in the building, starting from the lowest level"
-				},
-				"edge_lengths": {
-					"type": "#/definitions/arrayOfArraysOfNumbers",
-					"description": "external length of two orthogonal sides of the building, in feet, for each story in the building, starting from the lowest level"
-				},
-				"struct_bay_area_per_story": {
-					"type": "#/definitions/arrayOfNumbers",
-					"description": "length of the typical structural bay, from centerline to centerline, in feet, for each story in the building, starting from the lowest level"
-				},
-				"num_entry_doors": {
-					"type": "number",
-					"description": "number of external building exits/entrances"
-				},
-				"num_elevators": {
-					"type": "number",
-					"description": "number of elevator shafts in the building"
-				},
-				"stairs_per_story": {
-					"type": "#/definitions/arrayOfNumbers",
-					"description": "number of stairways on each story in the building, starting from the lowest level"
-				},
-				"occupants_per_story": {
-					"type": "#/definitions/arrayOfNumbers",
-					"description": "total number of occuapnts (persons) that occupy each story of this building, starting from the lowest level"
-				},
-				"comps": {
-					"type": "object",
-					"description": "component population information",
-					"properties": {
-						"story": {
-							"type": "array",
-							"description": "component quantities at each story",
-							"items": {
-								"type": "object",
-								"properties": {
-									"qty_dir_1": {								
-										"description": "total number of component units for each component stack in direction 1 of this story",
-										"$ref": "#/definitions/arrayOfNumbers",
-									"qty_dir_2": {
-										"description": "total number of component units for each component stack in direction 2 of this story",
-										"$ref": "#/definitions/arrayOfNumbers"
-									},
-									"qty_dir_3": {
-										"description": "total number of component units for each component stack in direction 3 (nondirectional) of this story",
-										"$ref": "#/definitions/arrayOfNumbers"
-									}
-								}
-							}
-						},
-						"comp_list": {
-							"description": "fragility ID for each component stack",
-							"$ref": "#/definitions/arrayOfStrings"
-						},
-						"comp_table": {
-							"type": "table",
-							"description": "various component attributes. Can be populated from the component_attribites.csv database in the static_tables directory"
-						}
-					}
-				}
-			}
-		},
-		"damage": {
-			"type": "object",
-			"description": "simulated damage, simulated repair time, and compoennt attribute data associated with each component's damages state in the building",
-			"properties": {
-				"comp_ds_table": {
-					"type": "table",
-					"description": "various component attributes by damage state. Can be populated from the component_attribites.csv and damage_state_attribute_mapping.csv databases in the static_tables directory"
-				},
-				"story": {
-					"type": "array",
-					"description": "simulated component damage, broken down by each story in the building",
-					"items": {
-						"type": "object",
-						"properties": {
-							"qnt_damaged": {
-								"description": "simulation of the quantity of damaged components in each component damage state",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"cost": {
-								"description": "simulated repair cost of each component's damage state",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"worker_days": {
-								"description": "simulated worker days required to repair each component's damage state",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"qnt_damaged_dir_1": {
-								"description": "simulated number of damaged component damage states associated with damage in direction 1",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"qnt_damaged_dir_2": {
-								"description": "simulated number of damaged component damage states associated with damage in direction 2",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"qnt_damaged_dir_3": {
-								"description": "simulated number of damaged component damage states associated with nondirectional damage",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"num_comps": {
-								"description": "total number of components populated within each tenant unit associated with each damage state",
-								"$ref": "#/definitions/arrayOfNumbers"
-							}
-						}
-					}
-				}
-			}
-		},
-		"damage_consequences": {
-			"type": "object",
-			"description": "general impedance assessment inputs such as mitigation factors",
-			"properties": {
-				"simulated_replacement_time": {
-					"description": "simulated replacement time in days for all realizations where replacement is required, NaN = building does not need replaced and is scheduled for repairs. Note: to not consider building replacement, pass in an array of NaNs", 
-					"$ref": "#/definitions/arrayOfNumbers"
-				},
-				"repair_cost_ratio_total": {
-					"description": "simulated building repair cost of structural and nonstructural repairs (does not include tenant contents); normalized by the replacement value", 
-					"$ref": "#/definitions/arrayOfNumbers"
-				},
-				"repair_cost_ratio_engineering": {
-					"description": "simulated building repair cost of repairs that require engineering redesign; normalized by the replacement value", 
-					"$ref": "#/definitions/arrayOfNumbers"
-				}
-			}
-		},
-		"functionality": {
-			"type": "object",
-			"description": "simulated outputs of the performance based functional recovery methodology",
-			"properties": {
-				"utilities": {
-					"type": "object",
-					"description": "simulated utility downtimes at this building site",
-					"properties": {
-						"electrical": {
-							"description": "simulated electrical power network downtime", 
-							"$ref": "#/definitions/arrayOfNumbers"
-						},
-						"water": {
-							"description": "simulated water network downtime", 
-							"$ref": "#/definitions/arrayOfNumbers"
-						},
-						"gas": {
-							"description": "simulated natural gas network downtime", 
-							"$ref": "#/definitions/arrayOfNumbers"
-						}
-					}
-				}
-			}
-		},
-		"tenant_units": {
-			"type": "table",
-			"description": "attributes and functional requirements of each tenant unit in the building",
-			"properties": {
-				"id": {
-					"type": "number",
-					"description": "tenant unit unique id"
-				},
-				"story": {
-					"type": "number",
-					"description": "story where each tenant unit is located"
-				},
-				"area": {
-					"type": "number",
-					"description": "floor area, in square feet, of the tenant unit"
-				},
-				"perim_area": {
-					"type": "number",
-					"description": "perimeter area, in square feet, of the tenant unit"
-				},
-				"occupancy_id": {
-					"type": "number",
-					"description": "unique id key linking each tenant unit to its associated tenant function requirements"
-				},
-				"exterior": {
-					"type": "number",
-					"description": "tolerance for damage to exterior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the perimeter area of severely damaged exterior components within the tenant-unit to the total perimeter area of the tenant unit (i.e., what percent of the exterior walls must be sufficiently damaged to cause tenants to not be able to function in this space)."
-				},
-				"interior": {
-					"type": "number",
-					"description":  "tolerance for damage to interior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the interior plan area of severely damaged interior components within the tenant-unit to the total plan area of the tenant unit (i.e., what percent of the interior space must be hindered to cause tenants to not be able to function in this space)."
-				},
-				"occ_per_elev": {
-					"type": "number",
-					"description": "max number of occupants per elevator allowed for function"
-				},
-				"is_elevator_required": {
-					"type": "logical",
-					"description": "is an operational elevator required for tenant function"
-				},
-				"is_electrical_required": {
-					"type": "logical",
-					"description": "is an operational electrical system required for tenant function"
-				},
-				"is_water_potable_required": {
-					"type": "logical",
-					"description": "is an operational potable water plumbing system required for tenant function"
-				},
-				"is_water_sanitary_required": {
-					"type": "logical",
-					"description": "is an operational sanitary waste plumbing system required for tenant function"
-				},
-				"is_hvac_ventilation_required": {
-					"type": "logical",
-					"description": "is an operational hvac system required for tenant function"
-				},
-				"is_hvac_heating_required": {
-					"type": "logical",
-					"description": "is an operational hvac system required for tenant function"
-				},
-				"is_hvac_cooling_required": {
-					"type": "logical",
-					"description": "is an operational hvac system required for tenant function"
-				},
-				"is_hvac_exhaust_required": {
-					"type": "logical",
-					"description": "is an operational hvac system required for tenant function"
-				}
-			}
-		}
-	}
-}
\ No newline at end of file
diff --git a/schema/Optional Inputs.json b/schema/Optional Inputs.json
deleted file mode 100644
index 474ac5b..0000000
--- a/schema/Optional Inputs.json	
+++ /dev/null
@@ -1,515 +0,0 @@
-{
-	"definitions": {
-		"arrayOfNumbers": {
-			"type": "array",
-			"items": {
-				"type": "number"
-			}
-		},
-		"arrayOfArraysOfNumbers": {
-			"type": "array",
-			"items": {
-				"$ref": "#/definitions/arrayOfNumbers"
-			}
-		}
-	},
-	"properties": {
-		"impedance_options": {
-			"type": "object",
-			"description": "general impedance module inputs",
-			"properties": {
-				"include_impedance": {
-					"type": "object",
-					"description": "logical switches for defining which impedance factors to consider in the assessment",
-					"properties": {
-						"inspection": {
-							"type": "logical",
-							"description": "flag to include inpection delays"
-						},
-						"financing": {
-							"type": "logical",
-							"description": "flag to include financing delays"
-						},
-						"permitting": {
-							"type": "logical",
-							"description": "flag to include permitting delays"
-						},
-						"engineering": {
-							"type": "logical",
-							"description": "flag to include engineering mobilization and design delays"
-						},
-						"contractor": {
-							"type": "logical",
-							"description": "flag to include contractor mobilization delays"
-						},
-						"long_lead": {
-							"type": "logical",
-							"description": "flag to include long lead delays"
-						}
-					}
-				},
-				"system_design_time": {
-					"type": "object",
-					"description": "variables of formula to calculate median system design time",
-					"properties": {
-						"f": {
-							"type": "number",
-							"description": "design fee ratio: ratio of building value charged by engineering firm. Default to 0.04 for repair projects"
-						},
-						"r": {
-							"type": "number",
-							"description": "engineering rate: Average hourly rate of engineering work. Default to $175 per hour"
-						},
-						"t": {
-							"type": "number",
-							"description": "team factor: accounts for parallel time between multiple designers spent on project. Default to 1.3"
-						},
-						"w": {
-							"type": "number",
-							"description": "average workday: average number of hours per day spent on project: Default to 8 hours"
-						}
-					}
-				},
-				"eng_design_min_days":{
-					"type": "number",
-					"description": "Minumum number of days for the median engineering design time"
-				},
-				"eng_design_max_days":{
-					"type": "number",
-					"description": "Maximum number of days for the median engineering design time"
-				},
-				"mitigation": {
-					"type": "object",
-					"description": "general impedance assessment inputs such as mitigation factors",
-					"properties": {
-						"is_essential_facility": {
-							"type": "logical",
-							"description": "is the building deemed as essential by the local jurisdiction"
-						},
-						"is_borp_equivalent": {
-							"type": "logical",
-							"description": "are the BORP, or BORP equivalent, post-earthquake strategies in place to limit inspection delays"
-						},
-						"is_engineer_on_retainer": {
-							"type": "logical",
-							"description": "is an engineer contracted to be on retainer in case of post-earthquake damage"
-						},
-						"contractor_relationship": {
-							"type": "string",
-							"description": "string identifier that defines the state of existing relationships with contractors to perform repairs. Options include <'retainer', 'good', 'none'>"
-						},
-						"contractor_retainer_time": {
-							"type": "number",
-							"description": "time, in days, from onset of damage to when repairs start previosly agreed upon in retainer agreement. Only applies when 'retainer' contractor_relationship is selected"
-						},
-						"funding_source": {
-							"type": "string",
-							"description": "what is the source of funding for repair costs beyond that of the capital of the owner"
-						},
-						"capital_available_ratio": {
-							"type": "number",
-							"description": "ratio of the building value that the onwer has immediately available for repairs"
-						}
-					}
-				},
-				"impedance_beta": {
-					"type": "number",
-					"description": "lognormal uncetainty of impedance times",
-				},
-				"impedance_truncation": {
-					"type": "number",
-					"description": "Truncation bounds of lognormal distribution of impedance time, input as the number of standard deviations above and below the mean. Default to 2",
-				},
-				"default_lead_time": {
-					"type": "number",
-					"description": "Lead time, in days, to replace select components (equipment or other less readily available components). This default lead time will be applied to all components with the long_lead flag in the inputs builder. To add custom lead times per component, update the long_lead_time attribute in the damage.comp_ds_table field.",
-				},
-				"demand_surge": {
-					"type": "object",
-					"description": "general user input options for defining the demand surge",
-					"properties": {
-						"include_surge": {
-							"type": "logical",
-							"description": "include demand surge when simulating impedance times"
-						},
-						"is_dense_urban_area": {
-							"type": "logical",
-							"description": "is within a dense urban area, typically defined by a site within a metropolitan statistical area (MSA) with a population of over 1 million"
-						},
-						"site_pga": {
-							"type": "number",
-							"description": "peak ground acceleration (PGA) in g at the building site for the ground shaking intensity of interest"
-						},
-						"pga_de": {
-							"type": "number",
-							"description": "peak ground acceleration (PGA) in g associated with the design eartquake (DE). Method of combining ground motion pairs (e.g., max direction vs geomean) should match the field site_pga"
-						}
-					}
-				},
-				"scaffolding_lead_time": {
-					"type": "number",
-					"description": "The number of days it takes to get workers to the site to erect scaffolding",
-				},
-				"scaffolding_erect_time": {
-					"type": "number",
-					"description": "The number of days it takes for workers to errect scaffolding to protect doorways and walkways from falling hazards",
-				}
-			}
-		},
-		"repair_time_options": {
-			"type": "object",
-			"description": "general repair schedule module inputs",
-			"properties": {
-				"temp_repair_beta": {
-					"type": "number",
-					"description": "lognormal uncetainty of temporary repair times. Default to 0.6",
-				},
-				"max_workers_per_sqft_story": {
-					"type": "number",
-					"description": "Maximum density of workers on a given story. Default to 0.001 from FEMA P-58",
-				},
-				"max_workers_per_sqft_building": {
-					"type": "number",
-					"description": "Maximum density of workers for the entire building after the first 10. Default to 0.00025 from REDi",
-				},
-				"max_workers_building_min": {
-					"type": "number",
-					"description": "Lower limit on the maximum number of workers for the entire building (i.e., max number of workers is never less than this). Default to 20 from REDi",
-				},
-				"max_workers_building_max": {
-					"type": "number",
-					"description": "Upper limit on the maximum number of workers for the entire building (i.e., max number of workers is never more than this). Default to 260 from REDi",
-				},
-				"allow_tmp_repairs": {
-					"type": "logical",
-					"description": "Flag to allow temporay repair measures to be used to restore building reoccupancy and basic function",
-				},
-				"allow_shoring": {
-					"type": "logical",
-					"description": "Flag to allow shoring to be used as a temporary repair for mitigating local structural stability issues. Setting allow_tmp_repairs to zero overwrites this flag",
-				}
-			}
-		},
-		"functionality_options": {
-			"type": "object",
-			"description": "general functionality module inputs",
-			"properties": {
-				"calculate_red_tag": {
-					"type": "logical",
-					"description": "flag to indicate whether or not to include the assessment of red tags based on component damage"	
-				}, 
-				"red_tag_clear_time": {
-					"type": "number",
-					"description": "days after all red tag repairs are done before occupancy can be regained (i.e. time for permitting office to clear the tag)"
-				},
-				"red_tag_clear_beta": {
-					"type": "number",
-					"description": "lognormal dispersion defining the uncertaitny in the red tag clear time"
-				},
-				"include_local_stability_impact": {
-					"type": "logical",
-					"description": "flag to consider the effect of local structural stability issues (that would require shoring) on building occupancy"
-				},
-				"door_racking_repair_day": {
-					"type": "number",
-					"description": "days after the earthquake required to repaired all racked doorways"
-				},
-				"egress_threshold": {
-					"type": "number",
-					"description": "ratio of building egress paths required for building safety and access"
-				},
-				"fire_watch": {
-					"type": "logical",
-					"description": "will a fire watch be set if fire sprinkler systems are damaged"
-				},
-				"local_fire_damamge_threshold": {
-					"type": "number",
-					"description": "ratio of building fire sprinkler branches or drops that can be damaged with a given story without significantly disrupting the operation of the active fire supression system"
-				},
-				"min_egress_paths": {
-					"type": "number",
-					"description": "minimum number of egress pathways that must be maintained at each story to maintain occupancy"
-				},
-				"required_ratio_operating_hvac_main": {
-					"type": "number",
-					"description": "ratio of hvac units, which service the entire building, required to maintain an operational hvac system; not applied to components with n+1 redundancy"
-				},
-				"required_ratio_operating_hvac_unit": {
-					"type": "number",
-					"description": "ratio of hvac units, which service an individual tenant unit, required to maintain an operational hvac system; not applied to components with n+1 redundancy"
-				},
-				"exterior_safety_threshold": {
-					"type": "number",
-					"description": "damage theshold that triggers an exterior safety issue; threshold is defined in terms of the ratio of damaged area to perimeter area of a given tenant unit"
-				},
-				"interior_safety_threshold": {
-					"type": "number",
-					"description": "damage theshold that triggers an interior safety issue; threshold is defined in terms of the ratio of damaged area to interior area of a given tenant unit"
-				},
-				"door_access_width_ft": {
-					"type": "number",
-					"description": "width of door access zone, centerlined on the doorway, required for safe access. A safe door access zone must be free of falling hazards"
-				},
-				"heat_utility": {
-					"type": "string",
-					"description": "string identifier of the type of utility that is used to heat the building; string should match on of the fields in functionality.utilities"
-				}
-			}
-		},
-		"building_model": {
-			"type": "object",
-			"description": "general information about the building such as the number of stories and the building area",
-			"properties": {
-				"building_value": {
-					"type": "number",
-					"description": "Total replacement value of the building, in USD"
-				},
-				"num_stories": {
-					"type": "number",
-					"description": "number of stories in the building, does not include basement levels"
-				},
-				"area_per_story_sf": {
-					"type": "#/definitions/arrayOfNumbers",
-					"description": "interior area, in square feet, for each story in the building, starting from the lowest level"
-				},
-				"ht_per_story_ft": {
-					"type": "#/definitions/arrayOfNumbers",
-					"description": "elevation height, in feet, for each story in the building, starting from the lowest level"
-				},
-				"edge_lengths": {
-					"type": "#/definitions/arrayOfArraysOfNumbers",
-					"description": "external length of two orthogonal sides of the building, in feet, for each story in the building, starting from the lowest level"
-				},
-				"struct_bay_area_per_story": {
-					"type": "#/definitions/arrayOfNumbers",
-					"description": "length of the typical structural bay, from centerline to centerline, in feet, for each story in the building, starting from the lowest level"
-				},
-				"num_entry_doors": {
-					"type": "number",
-					"description": "number of external building exits/entrances"
-				},
-				"num_elevators": {
-					"type": "number",
-					"description": "number of elevator shafts in the building"
-				},
-				"stairs_per_story": {
-					"type": "#/definitions/arrayOfNumbers",
-					"description": "number of stairways on each story in the building, starting from the lowest level"
-				},
-				"occupants_per_story": {
-					"type": "#/definitions/arrayOfNumbers",
-					"description": "total number of occuapnts (persons) that occupy each story of this building, starting from the lowest level"
-				},
-				"comps": {
-					"type": "object",
-					"description": "component population information",
-					"properties": {
-						"story": {
-							"type": "array",
-							"description": "component quantities at each story",
-							"items": {
-								"type": "object",
-								"properties": {
-									"qty_dir_1": {								
-										"description": "total number of component units for each component stack in direction 1 of this story",
-										"$ref": "#/definitions/arrayOfNumbers",
-									"qty_dir_2": {
-										"description": "total number of component units for each component stack in direction 2 of this story",
-										"$ref": "#/definitions/arrayOfNumbers"
-									},
-									"qty_dir_3": {
-										"description": "total number of component units for each component stack in direction 3 (nondirectional) of this story",
-										"$ref": "#/definitions/arrayOfNumbers"
-									}
-								}
-							}
-						},
-						"comp_list": {
-							"description": "fragility ID for each component stack",
-							"$ref": "#/definitions/arrayOfStrings"
-						},
-						"comp_table": {
-							"type": "table",
-							"description": "various component attributes. Can be populated from the component_attribites.csv database in the static_tables directory"
-						}
-					}
-				}
-			}
-		},
-		"damage": {
-			"type": "object",
-			"description": "simulated damage, simulated repair time, and compoennt attribute data associated with each component's damages state in the building",
-			"properties": {
-				"comp_ds_table": {
-					"type": "table",
-					"description": "various component attributes by damage state. Can be populated from the component_attribites.csv and damage_state_attribute_mapping.csv databases in the static_tables directory"
-				},
-				"tenant_units": {
-					"type": "array",
-					"description": "simulated component damage, broken down by each tenant unit in the building",
-					"items": {
-						"type": "object",
-						"properties": {
-							"qnt_damaged": {
-								"description": "simulation of the quantity of damaged components in each component damage state",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"cost": {
-								"description": "simulated repair cost of each component's damage state",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"worker_days": {
-								"description": "simulated worker days required to repair each component's damage state",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"qnt_damaged_side_1": {
-								"description": "simulated number of damaged component damage states associated with side 1",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"qnt_damaged_side_2": {
-								"description": "simulated number of damaged component damage states associated with side 2",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"qnt_damaged_side_3": {
-								"description": "simulated number of damaged component damage states associated with side 3",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"qnt_damaged_side_4": {
-								"description": "simulated number of damaged component damage states associated with side 4",
-								"$ref": "#/definitions/arrayOfArraysOfNumbers"
-							},
-							"num_comps": {
-								"description": "total number of components populated within each tenant unit associated with each damage state",
-								"$ref": "#/definitions/arrayOfNumbers"
-							}
-						}
-					}
-				}
-			}
-		},
-		"damage_consequences": {
-			"type": "object",
-			"description": "general impedance assessment inputs such as mitigation factors",
-			"properties": {
-				"simulated_replacement": {
-					"description": "simulated replacement time in days for all realizations where replacement is required, NaN = building does not need replaced and is scheduled for repairs. Note: to not consider building replacement, pass in an array of NaNs", 
-					"$ref": "#/definitions/arrayOfNumbers"
-				},
-				"racked_stair_doors_per_story": {
-					"description": "simulated number of racked stairway doors per story", 
-					"$ref": "#/definitions/arrayOfArraysOfNumbers"
-				},
-				"racked_entry_doors_side_1": {
-					"description": "simulated number of racked entry doors on one side of the building", 
-					"$ref": "#/definitions/arrayOfNumbers"
-				},
-				"racked_entry_doors_side_2": {
-					"description": "simulated number of racked entry doors on one side of the building", 
-					"$ref": "#/definitions/arrayOfNumbers"
-				},
-				"repair_cost_ratio_total": {
-					"description": "simulated building repair cost of structural and nonstructural repairs (does not include tenant contents); normalized by the replacement value", 
-					"$ref": "#/definitions/arrayOfNumbers"
-				},
-				"repair_cost_ratio_engineering": {
-					"description": "simulated building repair cost of repairs that require engineering redesign; normalized by the replacement value", 
-					"$ref": "#/definitions/arrayOfNumbers"
-				}
-			}
-		},
-		"functionality": {
-			"type": "object",
-			"description": "simulated outputs of the performance based functional recovery methodology",
-			"properties": {
-				"utilities": {
-					"type": "object",
-					"description": "simulated utility downtimes at this building site",
-					"properties": {
-						"electrical": {
-							"description": "simulated electrical power network downtime", 
-							"$ref": "#/definitions/arrayOfNumbers"
-						},
-						"water": {
-							"description": "simulated water network downtime", 
-							"$ref": "#/definitions/arrayOfNumbers"
-						},
-						"gas": {
-							"description": "simulated natural gas network downtime", 
-							"$ref": "#/definitions/arrayOfNumbers"
-						}
-					}
-				}
-			}
-		},
-		"tenant_units": {
-			"type": "table",
-			"description": "attributes and functional requirements of each tenant unit in the building",
-			"properties": {
-				"id": {
-					"type": "number",
-					"description": "tenant unit unique id"
-				},
-				"story": {
-					"type": "number",
-					"description": "story where each tenant unit is located"
-				},
-				"area": {
-					"type": "number",
-					"description": "floor area, in square feet, of the tenant unit"
-				},
-				"perim_area": {
-					"type": "number",
-					"description": "perimeter area, in square feet, of the tenant unit"
-				},
-				"occupancy_id": {
-					"type": "number",
-					"description": "unique id key linking each tenant unit to its associated tenant function requirements"
-				},
-				"exterior": {
-					"type": "number",
-					"description": "tolerance for damage to exterior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the perimeter area of severely damaged exterior components within the tenant-unit to the total perimeter area of the tenant unit (i.e., what percent of the exterior walls must be sufficiently damaged to cause tenants to not be able to function in this space)."
-				},
-				"interior": {
-					"type": "number",
-					"description":  "tolerance for damage to interior systems required for tenant function (0 = any damage causes loss of function, 0.999 = everything must be damage for loss of tenant function). Ratio of the interior plan area of severely damaged interior components within the tenant-unit to the total plan area of the tenant unit (i.e., what percent of the interior space must be hindered to cause tenants to not be able to function in this space)."
-				},
-				"occ_per_elev": {
-					"type": "number",
-					"description": "max number of occupants per elevator allowed for function"
-				},
-				"is_elevator_required": {
-					"type": "logical",
-					"description": "is an operational elevator required for tenant function"
-				},
-				"is_electrical_required": {
-					"type": "logical",
-					"description": "is an operational electrical system required for tenant function"
-				},
-				"is_water_potable_required": {
-					"type": "logical",
-					"description": "is an operational potable water plumbing system required for tenant function"
-				},
-				"is_water_sanitary_required": {
-					"type": "logical",
-					"description": "is an operational sanitary waste plumbing system required for tenant function"
-				},
-				"is_hvac_ventilation_required": {
-					"type": "logical",
-					"description": "is an operational hvac system required for tenant function"
-				},
-				"is_hvac_heating_required": {
-					"type": "logical",
-					"description": "is an operational hvac system required for tenant function"
-				},
-				"is_hvac_cooling_required": {
-					"type": "logical",
-					"description": "is an operational hvac system required for tenant function"
-				},
-				"is_hvac_exhaust_required": {
-					"type": "logical",
-					"description": "is an operational hvac system required for tenant function"
-				}
-			}
-		}
-	}
-}
\ No newline at end of file
diff --git a/schema/Outputs.json b/schema/Outputs.json
deleted file mode 100644
index bef16e7..0000000
--- a/schema/Outputs.json
+++ /dev/null
@@ -1,385 +0,0 @@
-{
-	"definitions": {
-		"numberOrNaN": {
-		  "oneOf": [
-			{ 
-			  "type": "number"
-			},
-			{ 
-			  "type": "string",
-			  "pattern": "NaN"
-			}
-		  ]
-		},
-		"arrayOfNumbers": {
-		  "type": "array",
-		  "items": {
-			"type": "number"
-		  }
-		},
-		"arrayOfNumbersOrNaN": {
-		  "type": "array",
-		  "items": {
-			"$ref": "#/definitions/numberOrNaN"
-		  }
-		},
-		"arrayOfStrings": {
-		  "type": "array",
-		  "items": {
-			"type": "string"
-		  }
-		},
-		"arrayOfArraysOfNumbers": {
-		  "type": "array",
-		  "items": {
-			"$ref": "#/definitions/arrayOfNumbers"
-		  }
-		},
-		"arrayOfArraysOfNumbersOrNaN": {
-		  "type": "array",
-		  "items": {
-			"$ref": "#/definitions/arrayOfNumbersOrNaN"
-		  }
-		},
-		"summaryImpedingFactors": {
-		  "type": "object",
-		  "properties": {
-			"start_day": {
-			  "$ref": "#/definitions/arrayOfArraysOfNumbers"
-			},
-			"complete_day": {
-			  "$ref": "#/definitions/arrayOfArraysOfNumbers"
-			}
-		  }
-		},
-		"summaryImpedingFactorsSystem": {
-		  "type": "object",
-		  "properties": {
-			"structural": {
-			  "$ref": "#/definitions/summaryImpedingFactors"
-			},
-			"exterior": {
-			  "$ref": "#/definitions/summaryImpedingFactors"
-			},
-			"interior": {
-			  "$ref": "#/definitions/summaryImpedingFactors"
-			},
-			"stairs": {
-			  "$ref": "#/definitions/summaryImpedingFactors"
-			},
-			"elevators": {
-			  "$ref": "#/definitions/summaryImpedingFactors"
-			},
-			"plumbing": {
-			  "$ref": "#/definitions/summaryImpedingFactors"
-			},
-			"electrical": {
-			  "$ref": "#/definitions/summaryImpedingFactors"
-			},
-			"hvac": {
-			  "$ref": "#/definitions/summaryImpedingFactors"
-			},
-			"fire": {
-			  "$ref": "#/definitions/summaryImpedingFactors"
-			}
-		  },
-		  "required": [
-			"structural",
-			"exterior",
-			"stairs"
-		  ],
-		  "additionalProperties": false
-		}
-	},
-	"properties": {
-		"functionality": {
-			"type": "object",
-			"description": "simulated outputs of the performance based functional recovery methodology",
-			"properties": {
-				"recovery": {
-					"type": "object",
-					"description": "simulated tenant- and building-level functional recovery and reoccupancy outcomes",
-					"properties": {
-						"reoccupancy": {
-							"type": "object",
-							"description": "simulated tenant- and building-level reoccupancy outcomes",
-							"properties": {
-								"building_level" :{
-									"type": "object",
-									"description": "simulated building-level reoccupancy outcomes",
-									"properties": {
-										"recovery_day": {
-											"description": "simulated building reoccupancy time, in days",
-											"$ref": "#/definitions/arrayOfNumbers"
-										},
-										"initial_percent_affected": {
-											"description": "simulated portion of building that is non-occupiable, initially after the earthquake",
-											"$ref": "#/definitions/arrayOfNumbers"
-										}
-									}
-								},
-								"tenant_unit": {
-									"type": "object",
-									"description": "simulated tenant-unit-level reoccupancy outcomes",
-									"properties": {
-										"recovery_day": {
-											"description": "simulated reoccupancy time, in days, for each tenant-unit in the building",
-											"$ref": "#/definitions/arrayOfArraysOfNumbers"
-										}
-									}
-								},
-								"recovery_trajectory": {
-									"type": "object",
-									"description": "simulated recovery trajectory vectors; tracks the recovery of occupancy over time",
-									"properties": {
-										"recovery_day": {
-											"description": "simulated reoccupancy time, in days, for each step of the recovery trajectory (x-axis)",
-											"$ref": "#/definitions/arrayOfArraysOfNumbers"
-										},
-										"percent_recovered": {
-											"description": "each step of the recovery trajectory; step increment is equal to the inverse of the number of tenant-units in the building",
-											"$ref": "#/definitions/arrayOfNumbers"
-										}
-									}
-								},
-								"breakdowns": {
-									"type": "object",
-									"description": "deagregated reoccupancy times for various recovery targets (in days)",
-									"properties": {
-										"system_breakdowns": {
-											"description": "fraction of realization affected by loss of reoccupancy anywhere in the building, broken down by systems-level fault tree events",
-											"$ref": "#/definitions/arrayOfArraysOfNumbers"
-										},
-										"component_breakdowns": {
-											"description": "fraction of realization affected by loss of reoccupancy anywhere in the building, broken down by component",
-											"$ref": "#/definitions/arrayOfArraysOfNumbers"
-										},
-										"perform_targ_days": {
-											"description": "recovery targets of interest(in days)",
-											"$ref": "#/definitions/arrayOfNumbers"
-										},
-										"system_names": {
-											"description": "names of each of the systems in the systems_breakdowns field",
-											"$ref": "#/definitions/arrayOfStrings"
-										},
-										"comp_names": {
-											"description": "names of each of the components in the component_breakdowns field",
-											"$ref": "#/definitions/arrayOfStrings"
-										}
-									}
-								}
-							}
-						},
-						"functional": {
-							"type": "object",
-							"description": "simulated tenant- and building-level functional recovery outcomes",
-							"properties": {
-								"building_level": {
-									"type": "object",
-									"description": "simulated tenant- and building-level functional recovery outcomes",
-									"properties": {
-										"recovery_day": {
-											"description": "simulated building functional recovery time, in days",
-											"$ref": "#/definitions/arrayOfNumbers"
-										},
-										"initial_percent_affected": {
-											"description": "simulated portion of building that is non-functional, initially after the earthquake",
-											"$ref": "#/definitions/arrayOfNumbers"
-										}
-									}
-								},
-								"tenant_unit": {
-									"type": "object",
-									"description": "simulated tenant-unit-level functional recovery outcomes",
-									"properties": {
-										"recovery_day": {
-											"description": "simulated functional recovery time, in days, for each tenant-unit in the building",
-											"$ref": "#/definitions/arrayOfArraysOfNumbers"
-										}
-									}
-								},
-								"recovery_trajectory": {
-									"type": "object",
-									"description": "simulated recovery trajectory vectors; tracks the recovery of function over time",
-									"properties": {
-										"recovery_day": {
-											"description": "simulated functional recovery time, in days, for each step of the recovery trajectory (x-axis)",
-											"$ref": "#/definitions/arrayOfArraysOfNumbers"
-										},
-										"percent_recovered": {
-											"description": "each step of the recovery trajectory; step increment is equal to the inverse of the number of tenant-units in the building",
-											"$ref": "#/definitions/arrayOfNumbers"
-										}
-									}
-								},
-								"breakdowns": {
-									"type": "object",
-									"description": "deagregated functional recovery times for various recovery targets (in days)",
-									"properties": {
-										"system_breakdowns": {
-											"description": "fraction of realization affected by loss of function anywhere in the building, broken down by systems-level fault tree events",
-											"$ref": "#/definitions/arrayOfArraysOfNumbers"
-										},
-										"component_breakdowns": {
-											"description": "fraction of realization affected by loss of function, not including reoccupancy, anywhere in the building, broken down by component",
-											"$ref": "#/definitions/arrayOfArraysOfNumbers"
-										},
-										"perform_targ_days": {
-											"description": "recovery targets of interest(in days)",
-											"$ref": "#/definitions/arrayOfNumbers"
-										},
-										"system_names": {
-											"description": "names of each of the systems in the systems_breakdowns field",
-											"$ref": "#/definitions/arrayOfStrings"
-										},
-										"comp_names": {
-											"description": "names of each of the components in the component_breakdowns field",
-											"$ref": "#/definitions/arrayOfStrings"
-										},
-										"component_combined": {
-											"description": "fraction of realization affected by loss of function, including reocupancy, anywhere in the building, broken down by component",
-											"$ref": "#/definitions/arrayOfArraysOfNumbers"
-										}
-									}
-								}
-							}
-						}
-					}
-				},
-				"building_repair_schedule": {
-					"type": "object",
-					"description": "simulated building repair schedule",
-					"properties": {
-						"full": {
-							"type": "object",
-							"description": "full building repairs required to bring building back to pre-event conditions",
-							"properties": {
-								"repair_start_day": {
-									"type": "object",
-									"description": "the day after the earthquake that each repair starts",
-									"properties":{
-										"per_component":{
-											"description": "repair start day of each component",
-											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
-										},
-										"per_story":{
-											"description": "repair start day of each story",
-											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
-										},
-										"per_system":{
-											"description": "repair start day of each system",
-											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
-										},
-										"per_system_story":{
-											"description": "repair start day of each story within each system",
-											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
-										},
-										"per_story_system":{
-											"description": "repair start day of each system on each story",
-											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
-										}
-									}
-								},
-								"repair_complete_day": {
-									"type": "object",
-									"description": "the day after the earthquake that each repair is finished",
-									"properties":{
-										"per_component":{
-											"description": "repair complete day of each component",
-											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
-										},
-										"per_story":{
-											"description": "repair complete day of each story",
-											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
-										},
-										"per_system":{
-											"description": "repair complete day of each system",
-											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
-										},
-										"per_system_story":{
-											"description": "repair complete day of each story within each system",
-											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
-										},
-										"per_story_system":{
-											"description": "repair complete day of each system on each story",
-											"$ref": "#/definitions/arrayOfArraysOfNumbersOrNaN"
-										}
-									}
-								},
-								"component_names": {
-									"description": "names of each of the components in the per_component breakdown field",
-									"$ref": "#/definitions/arrayOfStrings"
-								},
-								"system_names": {
-									"description": "names of each of the systems in the per_system breakdown field",
-									"$ref": "#/definitions/arrayOfStrings"
-								}
-							}
-						},
-						"temp": {
-							"type": "object",
-							"description": "temporary building repairs only required to restore basic function",
-							"properties": {
-								"shares properties with full repair object"
-							}
-						}
-					}
-				},
-				"worker_data": {
-				    "type": "object",
-				    "description": "simulated worker allocations throughout the repair schedule",
-				    "properties": {
-						"total_workers": {
-							"description": "simulated worker allocated in the building for any repair at various time steps",
-							"$ref": "#/definitions/arrayOfArraysOfNumbers"
-						},
-						"day_vector": {
-							"description": "various time steps, in days after the earthquake, associated with the total_workers field",
-							"$ref": "#/definitions/arrayOfArraysOfNumbers"
-						}
-				    }
-				},
-				"impeding_factors": {
-				    "type": "object",
-				    "description": "simulated impeding factor times",
-				    "properties": {
-						"time_sys": {
-							"description": "simulated total impeding time for each system",
-							"$ref": "#/definitions/arrayOfArraysOfNumbers"
-						},
-						"breakdowns": {
-							"type": "object"
-							"description": "simulated impeding factor times broken down by factor",
-							"properties": {
-								"inspection": {
-									"description": "simulated inpsection time",
-									"$ref": "#/definitions/summaryImpedingFactors"
-								},
-								"financing": {
-									"description": "simulated financing time",
-									"$ref": "#/definitions/summaryImpedingFactors"
-								},
-								"contractor_mob": {
-									"description": "simulated contractor mobilization time",
-									"$ref": "#/definitions/summaryImpedingFactors"
-								},
-								"eng_mob": {
-									"description": "simulated engineering mobilization time for each system",
-									"$ref": "#/definitions/summaryImpedingFactorsSystem"
-								},
-								"design": {
-									"description": "simulated engineering design time for each system",
-									"$ref": "#/definitions/summaryImpedingFactorsSystem"
-								},
-								"permitting": {
-									"description": "simulated permitting time for each system",
-									"$ref": "#/definitions/summaryImpedingFactorsSystem"
-								}
-							}
-					    }
-				    }
-				}
-			}
-		}
-	}
-}
\ No newline at end of file
diff --git a/src/atc138/__init__.py b/src/atc138/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/atc138/cli.py b/src/atc138/cli.py
new file mode 100644
index 0000000..5934609
--- /dev/null
+++ b/src/atc138/cli.py
@@ -0,0 +1,26 @@
+import argparse
+import sys
+import os
+from .driver import run_analysis
+
+def main():
+    parser = argparse.ArgumentParser(description="Run ATC-138 Functional Recovery Assessment")
+    parser.add_argument("input_dir", help="Path to the directory containing input files (e.g., simulated_inputs.json)")
+    parser.add_argument("output_dir", help="Path to the directory where outputs will be saved")
+    parser.add_argument("--seed", type=int, help="Random seed for reproducibility", default=None)
+
+    args = parser.parse_args()
+
+    # Validate inputs
+    if not os.path.isdir(args.input_dir):
+        print(f"Error: Input directory '{args.input_dir}' does not exist.", file=sys.stderr)
+        sys.exit(1)
+
+    try:
+        run_analysis(args.input_dir, args.output_dir, seed=args.seed)
+    except Exception as e:
+        print(f"Error running analysis: {e}", file=sys.stderr)
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/static_tables/README.md b/src/atc138/data/README.md
similarity index 100%
rename from static_tables/README.md
rename to src/atc138/data/README.md
diff --git a/static_tables/component_attributes.csv b/src/atc138/data/component_attributes.csv
similarity index 100%
rename from static_tables/component_attributes.csv
rename to src/atc138/data/component_attributes.csv
diff --git a/static_tables/damage_state_attribute_mapping.csv b/src/atc138/data/damage_state_attribute_mapping.csv
similarity index 100%
rename from static_tables/damage_state_attribute_mapping.csv
rename to src/atc138/data/damage_state_attribute_mapping.csv
diff --git a/src/atc138/data/default_inputs.json b/src/atc138/data/default_inputs.json
new file mode 100644
index 0000000..1a7d4e6
--- /dev/null
+++ b/src/atc138/data/default_inputs.json
@@ -0,0 +1,81 @@
+{
+  "impedance_options": {
+    "include_impedance": {
+      "inspection": true,
+      "financing": true,
+      "permitting": true,
+      "engineering": true,
+      "contractor": true,
+      "long_lead": false
+    },
+    "system_design_time": {
+      "f": 0.04,
+      "r": 200,
+      "t": 1.3,
+      "w": 8
+    },
+    "eng_design_min_days": 14,
+    "eng_design_max_days": 365,
+    "mitigation": {
+      "is_essential_facility": false,
+      "is_borp_equivalent": false,
+      "is_engineer_on_retainer": false,
+      "contractor_relationship": "good",
+      "contractor_retainer_time": 3,
+      "funding_source": "private",
+      "capital_available_ratio": 0.02
+    },
+    "impedance_beta": 0.6,
+    "impedance_truncation": 2,
+    "default_lead_time": 182,
+    "demand_surge": {
+      "include_surge": 1,
+      "is_dense_urban_area": 1,
+      "site_pga": 1,
+      "pga_de": 1
+    },
+    "scaffolding_lead_time": 5,
+    "scaffolding_erect_time": 2,
+    "door_racking_repair_day": 3,
+    "flooding_cleanup_day": 5,
+    "flooding_repair_day": 90
+  },
+  "repair_time_options": {
+    "max_workers_per_sqft_story": 0.001,
+    "max_workers_per_sqft_story_temp_repair": 0.005,
+    "max_workers_per_sqft_building": 0.00025,
+    "max_workers_building_min": 20,
+    "max_workers_building_max": 260,
+    "allow_tmp_repairs": 1,
+    "allow_shoring": 1
+  },
+  "functionality_options": {
+    "calculate_red_tag": 1,
+    "red_tag_clear_time": 7,
+    "red_tag_clear_beta": 0.6,
+    "red_tag_options": {
+      "tag_coupling_beams_over_height": true,
+      "ignore_coupling_beam_for_red_tag": false
+    },
+    "include_local_stability_impact": 1,
+    "include_flooding_impact": 1,
+    "egress_threshold": 0.5,
+    "fire_watch": true,
+    "local_fire_damamge_threshold": 0.25,
+    "min_egress_paths": 2,
+    "exterior_safety_threshold": 0.1,
+    "interior_safety_threshold": 0.25,
+    "door_access_width_ft": 9,
+    "habitability_requirements": {
+      "electrical": 0,
+      "water_potable": 0,
+      "water_sanitary": 0,
+      "hvac_ventilation": 0,
+      "hvac_heating": 0,
+      "hvac_cooling": 0,
+      "hvac_exhaust": 0
+    },
+    "water_pressure_max_story": 4,
+    "heat_utility": "gas"
+  }
+}
\ No newline at end of file
diff --git a/static_tables/impeding_factors.csv b/src/atc138/data/impeding_factors.csv
similarity index 100%
rename from static_tables/impeding_factors.csv
rename to src/atc138/data/impeding_factors.csv
diff --git a/static_tables/subsystems.csv b/src/atc138/data/subsystems.csv
similarity index 100%
rename from static_tables/subsystems.csv
rename to src/atc138/data/subsystems.csv
diff --git a/static_tables/systems.csv b/src/atc138/data/systems.csv
similarity index 100%
rename from static_tables/systems.csv
rename to src/atc138/data/systems.csv
diff --git a/static_tables/temp_repair_class.csv b/src/atc138/data/temp_repair_class.csv
similarity index 100%
rename from static_tables/temp_repair_class.csv
rename to src/atc138/data/temp_repair_class.csv
diff --git a/static_tables/tenant_function_requirements.csv b/src/atc138/data/tenant_function_requirements.csv
similarity index 100%
rename from static_tables/tenant_function_requirements.csv
rename to src/atc138/data/tenant_function_requirements.csv
diff --git a/src/atc138/driver.py b/src/atc138/driver.py
new file mode 100644
index 0000000..3fbb0ec
--- /dev/null
+++ b/src/atc138/driver.py
@@ -0,0 +1,198 @@
+def run_analysis(input_dir, output_dir, seed=None):
+
+    '''This script facilitates the performance based functional recovery and
+    reoccupancy assessment of a single building for a single intensity level
+    
+    Input data consists of building model info and simulated component-level
+    damage and conesequence data for a suite of realizations, likely assessed
+    as part of a FEMA P-58 analysis. Inputs are read from json files, as well 
+    as loaded from csvs in the static_tables directory.
+    
+    Output data is saved to a specified outputs directory and is saved into a
+    single json output file.
+    
+        Main file for running the code
+    
+    Parameters
+    ----------
+    input_dir: string
+        Path to the directory containing the input files (simulated_inputs.json).
+    
+    output_dir: string
+        Path to the directory where the output file (recovery_outputs.json) will be saved.
+    
+    seed: int
+        Random seed to be passed to the Numpy random engine. Default behavior
+        is set as None and will not pass a random seed.
+    """'''
+    
+    import time
+    start_time = time.time() # For runtime calculation
+    
+    ''' ######################  Caution ########################'''
+    # Ignoring runtime warnings
+    import warnings
+    warnings.filterwarnings('ignore')
+    ''' ########################################################'''
+        
+    ## 1. Import Python modules to be used
+    import os
+    import json
+    import numpy as np
+    import math
+    import pandas as pd
+    from scipy.stats import truncnorm
+    from .input_builder import load_custom_static_tables
+    
+    ## 2. Define User Inputs
+    # Input/Output directories are passed as arguments
+    
+    ## 3. Load FEMA P-58 performance model data and simulated damage and loss
+    # Check if simulated_inputs.json exists, if not build it
+    sim_inputs_path = os.path.join(input_dir, 'simulated_inputs.json')
+    
+    if os.path.exists(sim_inputs_path):
+        f = open(sim_inputs_path)
+        simulated_inputs = json.load(f)
+    else:
+        print(f"simulated_inputs.json not found in {input_dir}. Building from raw inputs...")
+        from .input_builder import build_simulated_inputs
+        simulated_inputs = build_simulated_inputs(input_dir)
+        
+        # Save simulated inputs
+        with open(sim_inputs_path, 'w') as f:
+            json.dump(simulated_inputs, f)
+    
+    building_model = simulated_inputs['building_model']
+    damage = simulated_inputs['damage']
+    damage_consequences = simulated_inputs['damage_consequences']
+    functionality = simulated_inputs['functionality']
+    functionality_options = simulated_inputs['functionality_options']
+    impedance_options = simulated_inputs['impedance_options']
+    repair_time_options = simulated_inputs['repair_time_options']
+    tenant_units = simulated_inputs['tenant_units']
+    
+    # Change story indices in damage['tenant_units'], damage['story'] building_model['comps']['story'] to int from string
+    # (This ensures compatibility if JSON keys were strings)
+    damage_ten_units = []
+    if ('tenant_units' in damage.keys()) == True:
+        for tu in range(len(damage['tenant_units'])):
+            # Handle list vs dict if necessary, but assuming list structure from builder
+            if isinstance(damage['tenant_units'], list): # list
+                 damage_ten_units.append(damage['tenant_units'][tu])
+            elif str(tu) in damage['tenant_units']: # string key
+                 damage_ten_units.append(damage['tenant_units'][str(tu)])
+            elif tu in damage['tenant_units']: # integer key
+                 damage_ten_units.append(damage['tenant_units'][tu])
+            
+        damage['tenant_units'] = damage_ten_units  
+    
+    damage_story = []    
+    for s in range(len(damage['story'])):
+        if isinstance(damage['story'], list): # list
+            damage_story.append(damage['story'][s])
+        elif str(s) in damage['story']: # string key
+            damage_story.append(damage['story'][str(s)])
+        elif s in damage['story']: # integer key
+            damage_story.append(damage['story'][s])
+    
+    damage['story'] = damage_story 
+    
+    bldg_comps_story = []
+    for s in range(len(building_model['comps']['story'])):
+        if isinstance(building_model['comps']['story'], list): # list
+             bldg_comps_story.append(building_model['comps']['story'][s])
+        elif str(s) in building_model['comps']['story']: # string key
+             bldg_comps_story.append(building_model['comps']['story'][str(s)])
+        elif s in building_model['comps']['story']: # integer key
+             bldg_comps_story.append(building_model['comps']['story'][s])
+        
+    building_model['comps']['story'] = bldg_comps_story
+    
+    ## 4. Load required static data
+    # Static data is bundled with the package, so use __file__
+    # First, check if custom versions exist within input directory
+
+    pkg_dir = os.path.dirname(__file__)
+    static_data_dir = os.path.join(pkg_dir, 'data')
+
+    subsystems = load_custom_static_tables(
+        input_dir, static_data_dir, 'subsystems.csv')
+    systems = load_custom_static_tables(
+        input_dir, static_data_dir, 'systems.csv')
+    impeding_factor_medians = load_custom_static_tables(
+        input_dir, static_data_dir, 'impeding_factors.csv')
+    tmp_repair_class = load_custom_static_tables(
+        input_dir, static_data_dir, 'temp_repair_class.csv')
+
+    
+    ## 5. Run Recovery Method
+    from .engine import main_PBEE_recovery
+
+    # set a seed
+    # this seed propagates through the entire subfunctions
+    # so if subfunction requires a distinct random variate, a seed instance will have to be used instead
+    if seed is not None:
+        np.random.seed(seed)
+    
+    functionality, damage_consequences = main_PBEE_recovery(damage, 
+                                                            damage_consequences, 
+                                                            building_model, 
+                                                            tenant_units, 
+                                                            systems, 
+                                                            subsystems, 
+                                                            tmp_repair_class,
+                                                            impedance_options, 
+                                                            impeding_factor_medians, 
+                                                            repair_time_options,
+                                                            functionality, 
+                                                            functionality_options)
+           
+    # 6. Save Outputs
+    # Ensure output directory exists
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    
+    # Covert arrays to list for writing to json file   
+    fnc_keys_1 = list(functionality.keys())
+    for k_1 in fnc_keys_1:
+        if type(functionality[k_1]) == np.ndarray: 
+            functionality[k_1] = functionality[k_1].tolist() 
+        if type(functionality[k_1]) == dict:
+            fnc_keys_2 = list(functionality[k_1].keys())    
+       
+            for k_2 in fnc_keys_2:
+                if type(functionality[k_1][k_2]) == np.ndarray: 
+                    functionality[k_1][k_2] = functionality[k_1][k_2].tolist()
+                if type(functionality[k_1][k_2]) == dict:
+                    fnc_keys_3 = list(functionality[k_1][k_2].keys())    
+       
+                    for k_3 in fnc_keys_3:
+                        if type(functionality[k_1][k_2][k_3]) == np.ndarray: 
+                            functionality[k_1][k_2][k_3] = functionality[k_1][k_2][k_3].tolist()
+                        if type(functionality[k_1][k_2][k_3]) == dict:
+                            fnc_keys_4 = list(functionality[k_1][k_2][k_3].keys())
+     
+                            for k_4 in fnc_keys_4:
+                                if type(functionality[k_1][k_2][k_3][k_4]) == np.ndarray: 
+                                    functionality[k_1][k_2][k_3][k_4] = functionality[k_1][k_2][k_3][k_4].tolist()
+                                if type(functionality[k_1][k_2][k_3][k_4]) == dict:
+                                    fnc_keys_5 = list(functionality[k_1][k_2][k_3][k_4].keys())
+        
+                                    for k_5 in fnc_keys_5:
+                                        if type(functionality[k_1][k_2][k_3][k_4][k_5]) == np.ndarray: 
+                                            functionality[k_1][k_2][k_3][k_4][k_5] = functionality[k_1][k_2][k_3][k_4][k_5].tolist()
+                                        if type(functionality[k_1][k_2][k_3][k_4][k_5]) == dict:
+                                            fnc_keys_6 = list(functionality[k_1][k_2][k_3][k_4][k_5].keys())
+    
+    output_json_object = json.dumps(functionality)
+    
+    with open(os.path.join(output_dir, "recovery_outputs.json"), "w") as outfile:
+        outfile.write(output_json_object)
+    
+    end_time = time.time()
+    
+    # print('Recovery assessment of model ' + model_name + ' complete') # model_name no longer available
+    print('Recovery assessment complete')
+    print('time to run '+str(round(end_time - start_time,2))+'s')
+
diff --git a/src/atc138/engine.py b/src/atc138/engine.py
new file mode 100644
index 0000000..d2b455b
--- /dev/null
+++ b/src/atc138/engine.py
@@ -0,0 +1,103 @@
+def main_PBEE_recovery(damage, damage_consequences, building_model, 
+                      tenant_units, systems, subsystems, tmp_repair_class, 
+                      impedance_options, impeding_factor_medians, 
+                      repair_time_options, functionality, 
+                      functionality_options):
+    '''Perform the ATC-138 functional recovery time assessement given similation
+    of component damage for a single shaking intensity
+    
+    Parameters
+    ----------
+    damage: struct
+      contains per damage state damage and loss data for each component in the building
+    
+    damage_consequences: dictionary
+      dictionary containing simulated building consequences, such as red
+      tags and repair costs ratios
+    
+    building_model: dictionary
+      general attributes of the building model
+   
+    tenant_units: DataFrame
+      attributes of each tenant unit within the building
+    
+    systems: DataFrame
+      data table containing information about each system's attributes
+    
+    subsystems: DataFrame
+      attributes of building subsystems; data provided in static tables
+      directory
+    
+    tmp_repair_class: DataFrame
+      data table containing information about each temporary repair class
+      attributes. Attributes are similar to those in the systems table.
+    
+    impedance_options: dictionary
+      general impedance assessment user inputs such as mitigation factors
+    
+    impeding_factor_medians: DataFrame
+      median delays for various impeding factors
+    
+    repair_time_options: dictionary
+      general repair time options such as mitigation factors
+    
+    functionality['utilities']
+      dictionary containing simulated utility downtimes
+    
+    functionality_options: dictionary
+      recovery time optional inputs such as various damage thresholds
+    
+    
+    Returns
+    -------
+    functionality: dictionary
+      contains data on the recovery of tenant- and building-level function, 
+      recovery trajectorires, and contributions from systems and components, 
+      simulated repair schedule breakdowns and impeding times.'''
+    
+    ## Import Packages
+    import numpy as np
+    from .preprocessing import main_preprocessing
+    from .red_tag import fn_red_tag
+    from .impedance import main_impedance_function
+    from .repair_schedule import main_repair_schedule
+    from .functionality import main_functionality_function
+    
+    ## Combine compoment attributes into recovery filters to expidite recovery assessment
+    damage, tmp_repair_class, damage_consequences = main_preprocessing.main_preprocessing(damage['comp_ds_table'], 
+                                                damage , repair_time_options, tmp_repair_class, damage_consequences, 
+                                                building_model['num_stories'])
+    
+    ## Calculate Red Tags
+    RT, RTI, IT = fn_red_tag(functionality_options['calculate_red_tag'], 
+                                    damage, building_model['comps'],
+                                    np.array(damage_consequences['simulated_replacement_time']),
+                                    functionality_options['red_tag_options'])
+    
+    damage_consequences['red_tag'] = RT 
+    damage_consequences['red_tag_impact'] = RTI 
+    damage_consequences['inspection_trigger'] = IT
+    
+    ## Simulate ATC 138 Impeding Factors
+    functionality['impeding_factors'] = main_impedance_function.main_impeding_factors(damage, impedance_options, 
+                                          damage_consequences['repair_cost_ratio_total'],
+                                          damage_consequences['repair_cost_ratio_engineering'], 
+                                          damage_consequences['inspection_trigger'],
+                                          systems, tmp_repair_class, 
+                                          building_model['building_value'], 
+                                          impeding_factor_medians,
+                                          functionality_options['include_flooding_impact'])
+    
+    ## Construct the Building Repair Schedule
+    damage, functionality['worker_data'], functionality['building_repair_schedule'] = main_repair_schedule.main_repair_schedule(damage, building_model, damage_consequences['red_tag'], 
+        repair_time_options, systems, tmp_repair_class, functionality['impeding_factors'], 
+        damage_consequences['simulated_replacement_time'])
+    
+    ## Calculate the Recovery of Building Reoccupancy and Function
+    functionality['recovery'] = main_functionality_function.main_functionality(damage, building_model, 
+                                damage_consequences, functionality['utilities'], 
+                                functionality_options, tenant_units, subsystems, 
+                                functionality['impeding_factors']['temp_repair'])
+    
+    return functionality, damage_consequences
+
diff --git a/src/atc138/functionality/__init__.py b/src/atc138/functionality/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/atc138/functionality/fn_calculate_functionality.py b/src/atc138/functionality/fn_calculate_functionality.py
new file mode 100644
index 0000000..27d808d
--- /dev/null
+++ b/src/atc138/functionality/fn_calculate_functionality.py
@@ -0,0 +1,80 @@
+
+def fn_calculate_functionality(damage, damage_consequences, utilities, 
+                               building_model, subsystems, reoccupancy, 
+                               functionality_options, tenant_units, impeding_temp_repairs):
+    '''Calcualte the loss and recovery of building functionality based on global building
+    damage, local component damage, and extenernal factors
+
+    Parameters
+    ----------
+    damage: dictionary
+     contains per damage state damage, loss, and repair time data for each 
+     component in the building
+    damage_consequences: dictionary
+     data structure containing simulated building consequences, such as red
+     tags and repair costs ratios
+    utilities: dictionary
+     data structure containing simulated utility downtimes
+    building_model: dictionary
+     general attributes of the building model
+    subsystems: DataFrame
+     data table containing information about each subsystem's attributes
+    reoccupancy: dictionary
+     contains data on the recovery of tenant- and building-level function, 
+     recovery trajectorires, and contributions from systems and components 
+    functionality_options: dictionary
+     recovery time optional inputs such as various damage thresholds
+    tenant_units: DataFrame
+     attributes of each tenant unit within the building
+    impeding_temp_repairs: dictionary
+     contains simulated temporary repairs the impede occuapancy and function
+     but are calulated in parallel with the temp repair schedule
+    
+    Returns
+    -------
+    functionality: dictionary
+     contains data on the recovery of tenant- and building-level function, 
+     recovery trajectorires, and contributions from systems and components''' 
+    
+    import numpy as np
+    
+    ## Initial Set Up
+    # import packages
+    from . import other_functionality_functions
+    
+    ## Define the day each system becomes functionl - Building level
+    system_operation_day = other_functionality_functions.fn_building_level_system_operation(damage, 
+                                                              damage_consequences,
+                                                              building_model, 
+                                                              utilities, 
+                                                              functionality_options)
+    
+    ## Define the day each system becomes functionl - Tenant level
+    recovery_day = {}
+    comp_breakdowns = {}
+    recovery_day['tenant_function'], comp_breakdowns['tenant_function'] = other_functionality_functions.fn_tenant_function(damage,
+        building_model, system_operation_day, subsystems, tenant_units, impeding_temp_repairs, functionality_options)
+    
+    ## Combine Checks to determine per unit functionality
+    # Each tenant unit is functional only if it is occupiable
+    day_tenant_unit_functional = reoccupancy['tenant_unit']['recovery_day']
+    
+    # Go through each of the tenant function branches and combines checks
+    fault_tree_events = list(recovery_day['tenant_function'].keys())
+    for i in range(len(fault_tree_events)):
+        day_tenant_unit_functional = np.fmax(day_tenant_unit_functional, recovery_day['tenant_function'][fault_tree_events[i]])
+    
+    ## Reformat outputs into functionality data strucutre
+    functional = other_functionality_functions.fn_extract_recovery_metrics(day_tenant_unit_functional,
+        recovery_day, comp_breakdowns, damage['comp_ds_table']['comp_id'], 
+        damage_consequences['simulated_replacement_time'])
+    
+    ## get the combined component breakdown between reoccupancy and function
+    functional['breakdowns']['component_combined'] = other_functionality_functions.fn_combine_comp_breakdown(damage['comp_ds_table'], 
+    functional['breakdowns']['perform_targ_days'], # assumes names are consistent in both objects
+    functional['breakdowns']['comp_names'], # assumes names are consistent in both objects
+    reoccupancy['breakdowns']['component_breakdowns_all_reals'],
+    functional['breakdowns']['component_breakdowns_all_reals'])
+    
+    return functional, recovery_day, comp_breakdowns
+
diff --git a/src/atc138/functionality/fn_calculate_reoccupancy.py b/src/atc138/functionality/fn_calculate_reoccupancy.py
new file mode 100644
index 0000000..836ee00
--- /dev/null
+++ b/src/atc138/functionality/fn_calculate_reoccupancy.py
@@ -0,0 +1,97 @@
+def fn_calculate_reoccupancy(damage, damage_consequences, utilities, 
+                         building_model, functionality_options, 
+                         tenant_units, impeding_temp_repairs):
+    '''Calcualte the loss and recovery of building re-occupancy 
+    based on global building damage, local component damage, and extenernal factors
+
+    Parameters
+    ----------
+    damage: dictionary
+      contains per damage state damage, loss, and repair time data for each 
+      component in the building
+    damage_consequences: dictionary
+      data structure containing simulated building consequences, such as red
+      tags and repair costs ratios
+    utilities: dictionary
+      data structure containing simulated utility downtimes
+    building_model: dictionary
+      general attributes of the building model
+    subsystems: DataFrame
+      attributes of building subsystems; data provided in static tables
+      directory
+    functionality_options: dictionary
+      recovery time optional inputs such as various damage thresholds
+    tenant_units: DataFrame
+      attributes of each tenant unit within the building
+    impeding_temp_repairs: dictionary
+     contains simulated temporary repairs the impede occuapancy and function
+     but are calulated in parallel with the temp repair schedule
+    
+    Returns
+    -------
+    reoccupancy: dictionary
+     contains data on the recovery of tenant- and building-level reoccupancy, 
+     recovery trajectorires, and contributions from systems and components''' 
+    
+    ## Initial Set Up
+    import numpy as np
+    # Import packages
+    
+    from . import other_functionality_functions
+        
+    ## Stage 1: Quantify the effect that component damage has on the building safety
+    recovery_day={}
+    comp_breakdowns={}
+    
+    recovery_day['building_safety'], comp_breakdowns['building_safety'] = other_functionality_functions.fn_building_safety(damage, building_model, 
+                                                                                                                    damage_consequences, utilities, functionality_options
+                                                                                                                    ,impeding_temp_repairs)
+    
+    ## Stage 2: Quantify the accessibility of each story in the building
+    recovery_day['story_access'], comp_breakdowns['story_access'] = other_functionality_functions.fn_story_access( damage, 
+                                                                                    building_model, damage_consequences, 
+                                                                                    functionality_options, impeding_temp_repairs)
+    
+    # Delete added door column to damage ['comps'] and damage[qnt_damaged]
+    if len(damage['tenant_units']) !=1: #FZ# Story is accessible on day zero for 1 story building
+        for i in range(len(damage['tenant_units'])):
+            # damage['tenant_units'][i]['num_comps'].pop(-1) 
+            damage['tenant_units'][i]['recovery']['repair_complete_day'] = damage['tenant_units'][i]['recovery']['repair_complete_day'][:,0:len(damage['comp_ds_table']['comp_id'])]
+            for j in range(len(damage['tenant_units'][0]['qnt_damaged'])):
+                damage['tenant_units'][i]['qnt_damaged'][j].pop(-1)
+        
+        damage['fnc_filters']['stairs'] = damage['fnc_filters']['stairs'][0:len(damage['comp_ds_table']['comp_id'])]   
+        damage['fnc_filters']['stair_doors'] = damage['fnc_filters']['stair_doors'][0:len(damage['comp_ds_table']['comp_id'])]
+    
+    ## Stage 3: Quantify the effect that component damage has on the safety of each tenant unit
+    recovery_day['tenant_safety'], comp_breakdowns['tenant_safety'] = other_functionality_functions.fn_tenant_safety( damage, building_model, functionality_options, tenant_units)
+    
+    ## Combine Check to determine the day the each tenant unit is reoccupiable
+    # Go through each of the building safety checks and combine them to check the day the building is safe (max of all checks)
+    fault_tree_events_building_safety = list(recovery_day['building_safety'].keys())
+    day_building_safe = recovery_day['building_safety'][fault_tree_events_building_safety[0]]
+    for i in range(1, len(fault_tree_events_building_safety)):
+        day_building_safe = np.fmax(day_building_safe, recovery_day['building_safety'][fault_tree_events_building_safety[i]])
+
+    # Go through each of the story access checks and combine them to check the day each story is accessible (max of all checks)
+    fault_tree_events_story_access = list(recovery_day['story_access'].keys())
+    day_story_accessible = recovery_day['story_access'][fault_tree_events_story_access[0]]
+    for i in range(1, len(fault_tree_events_story_access)):
+        day_story_accessible = np.fmax(day_story_accessible, recovery_day['story_access'][fault_tree_events_story_access[i]])
+    
+    # Go through each of the tenant unit safety checks and combine them to check the day each tenant unit is safe (max of all checks)
+    fault_tree_events_tenant_unit_safe = list(recovery_day['tenant_safety'].keys())
+    day_tenant_unit_safe = recovery_day['tenant_safety'][fault_tree_events_tenant_unit_safe[0]]
+    for i in range(1, len(fault_tree_events_tenant_unit_safe)):
+        day_tenant_unit_safe = np.fmax(day_tenant_unit_safe, recovery_day['tenant_safety'][fault_tree_events_tenant_unit_safe[i]])
+
+    # Combine checks to determine when each tenant unit is re-occupiable
+    day_tenant_unit_reoccupiable = np.fmax(np.fmax(day_building_safe.reshape(len(day_building_safe),1), day_story_accessible), day_tenant_unit_safe)
+    
+    ## Reformat outputs into occupancy data strucutre
+    reoccupancy = other_functionality_functions.fn_extract_recovery_metrics(day_tenant_unit_reoccupiable, 
+                                              recovery_day, comp_breakdowns, 
+                                              damage['comp_ds_table']['comp_id'],
+                                              damage_consequences['simulated_replacement_time'])
+
+    return reoccupancy, recovery_day, comp_breakdowns
\ No newline at end of file
diff --git a/src/atc138/functionality/fn_check_habitability.py b/src/atc138/functionality/fn_check_habitability.py
new file mode 100644
index 0000000..17da486
--- /dev/null
+++ b/src/atc138/functionality/fn_check_habitability.py
@@ -0,0 +1,59 @@
+def fn_check_habitability( damage, damage_consequences, reoc_meta, func_meta, 
+                          habitability_requirements):
+    '''Overwrite reocuppancy with additional checks from the functionality check
+    
+    Parameters
+    ----------
+    damage: dictionary
+      contains per damage state damage, loss, and repair time data for each 
+      component in the building
+    damage_consequences: dictionary
+      data structure containing simulated building consequences, such as red
+      tags and repair costs ratios
+    reoc_meta: dictionary
+      meta data from reoccupancy assessment
+    func_meta: dictionary
+      meta data from functionality assessment
+    habitability_requirements: dictionary
+      basic requirements for habitability beyond basic reoccupancy.
+    
+    Returns
+    -------
+    reoccupancy: dictionary
+      contains data on the recovery of tenant- and building-level reoccupancy, 
+      recovery trajectorires, and contributions from systems and components''' 
+    
+    import numpy as np
+    from . import other_functionality_functions
+    
+    num_reals = len(damage_consequences['red_tag'])
+    # Functionality checks to adopt onto reoccupancy requirements for
+    # habitability:
+        # heating, cooling, vent, exhaust, potable water, sanitary, electrical
+    recovery_day = reoc_meta['recovery_day']
+    comp_breakdowns = reoc_meta['comp_breakdowns']
+    habitability_list = list(habitability_requirements.keys())
+    # habitability_list = {'electrical', 'water_potable', 'water_sanitary', 'hvac_ventilation', 'hvac_heating', 'hvac_cooling', 'hvac_exhaust'};
+    for i in range(len(habitability_list)):
+        if habitability_requirements[habitability_list[i]] ==1: # If this system is required for habitability
+            recovery_day['habitability'][habitability_list[i]] = func_meta['recovery_day']['tenant_function'][habitability_list[i]]
+            comp_breakdowns['habitability'][habitability_list[i]] = func_meta['comp_breakdowns']['tenant_function'][habitability_list[i]]
+   
+    # Go through each of the tenant function branches and combines checks
+    day_tenant_unit_reoccupiable = np.zeros([num_reals,1])
+    fault_tree_events_LV1 = list(recovery_day.keys())
+    for i in range(len(fault_tree_events_LV1)):
+        fault_tree_events_LV2 = list(recovery_day[fault_tree_events_LV1[i]].keys())
+        for j in range(len(fault_tree_events_LV2)):
+            day_tenant_unit_reoccupiable = np.fmax(day_tenant_unit_reoccupiable, 
+                                               recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]].reshape(num_reals, int(np.size(recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]])/num_reals)))
+
+    
+    # Reformat outputs into reoccupancy data strucutre
+    reoccupancy = other_functionality_functions.fn_extract_recovery_metrics(day_tenant_unit_reoccupiable, 
+                                              recovery_day, comp_breakdowns, 
+                                              damage['comp_ds_table']['comp_id'],
+                                              damage_consequences['simulated_replacement_time'])
+    
+    return reoccupancy
+
diff --git a/src/atc138/functionality/main_functionality_function.py b/src/atc138/functionality/main_functionality_function.py
new file mode 100644
index 0000000..ee492d0
--- /dev/null
+++ b/src/atc138/functionality/main_functionality_function.py
@@ -0,0 +1,65 @@
+def main_functionality(damage, building_model, damage_consequences, 
+                       utilities, functionality_options, tenant_units, 
+                       subsystems, impeding_temp_repairs):
+    '''Calculates building re-occupancy and function based on simulations of
+    building damage and calculates the recovery times of each recovery state
+    based on a given repair schedule
+    
+    Parameters
+    ----------
+    damage: dictionary
+     contains per damage state damage, loss, and repair time data for each 
+     component in the building
+    building_model: dictionary
+     general attributes of the building model
+    damage_consequences: dictionary
+     data structure containing simulated building consequences, such as red
+     tags and repair costs ratios
+    utilities: dictionary
+     data structure containing simulated utility downtimes
+    functionality_options: dictionary
+     recovery time optional inputs such as various damage thresholds
+    tenant_units: DataFrame
+     attributes of each tenant unit within the building
+    subsystems: DataFrame
+     attributes of building subsystems; data provided in static tables
+     directory
+    
+    Returns
+    -------
+    recovery['reoccupancy']: dictionary
+     contains data on the recovery of tenant- and building-level reoccupancy, 
+     recovery trajectorires, and contributions from systems and components 
+    recovery['functional']: dictionary
+     contains data on the recovery of tenant- and building-level function, 
+     recovery trajectorires, and contributions from systems and components''' 
+    
+    from . import fn_calculate_reoccupancy
+    from . import fn_calculate_functionality
+    from . import fn_check_habitability
+    ## Calaculate Building Functionality Restoration Curves
+    # Downtime including external delays
+    recovery = {}
+    reoc_meta = {}
+    recovery['reoccupancy'], reoc_meta['recovery_day'], reoc_meta['comp_breakdowns'] = fn_calculate_reoccupancy.fn_calculate_reoccupancy(damage, damage_consequences, utilities,
+        building_model, functionality_options, tenant_units, impeding_temp_repairs)
+    
+    func_meta = {}
+    recovery['functional'], func_meta['recovery_day'], func_meta['comp_breakdowns'] =  fn_calculate_functionality.fn_calculate_functionality(damage, damage_consequences, utilities,
+        building_model, subsystems, recovery['reoccupancy'], functionality_options, tenant_units, impeding_temp_repairs)
+    
+    # # delete all the extra per-realization data
+    del recovery['reoccupancy']['breakdowns']['component_breakdowns_all_reals']
+    del recovery['functional']['breakdowns']['component_breakdowns_all_reals']
+    
+    
+    ## Habitability Checks
+    # Overwrite reocuppancy with additional checks from the functionality check
+    if 'habitability_requirements' in functionality_options.keys():
+        recovery['reoccupancy'] = fn_check_habitability.fn_check_habitability(damage, 
+                                                        damage_consequences, 
+                                                        reoc_meta, func_meta, 
+                                                        functionality_options['habitability_requirements'])
+ 
+    return recovery
+
diff --git a/src/atc138/functionality/other_functionality_functions.py b/src/atc138/functionality/other_functionality_functions.py
new file mode 100644
index 0000000..103fede
--- /dev/null
+++ b/src/atc138/functionality/other_functionality_functions.py
@@ -0,0 +1,1688 @@
+'''
+Other functionality functions
+'''
+
+def fn_building_safety(damage, building_model, damage_consequences, utilities,
+                       functionality_options, impeding_temp_repairs):
+    '''Check damage that would cause the whole building to be shut down due to
+     issues of safety
+    
+    Parameters
+    ----------
+    damage: dictionary
+     contains per damage state damage, loss, and repair time data for each 
+     component in the building
+    
+    damage_consequences: dictionary
+     data structure containing simulated building consequences, such as red
+     tags and repair costs ratios
+     
+    building_model: dictionary
+     general attributes of the building model
+     
+    utilities: dictionary
+     data structure containing simulated utility downtimes
+     
+    functionality_options: dictionary
+     recovery time optional inputs such as various damage thresholds
+    
+    impeding_temp_repairs: dictionary
+     contains simulated temporary repairs the impede occuapancy and function
+      but are calulated in parallel with the temp repair schedule
+    
+    Returns
+    -------
+    recovery_day: dictionary
+     simulation of the number of days each fault tree event is affecting building
+     safety
+     
+    comp_breakdowns: dictionary
+     simulation of each components contributions to each of the fault tree events 
+     
+    system_operation_day: dictionary
+     simulation of recovery of operation for various systems in the building'''
+    
+    import numpy as np
+    ## Initial Setup
+    num_reals = len(damage_consequences['red_tag'])
+    num_units = len(damage['tenant_units'])
+    num_comps = len(damage['comp_ds_table']['comp_id'])
+    
+    ## Calculate effect of red tags and fire suppression system
+    # Initialize parameters
+    recovery_day = {'red_tag' : np.zeros(num_reals), 
+                    'shoring' : np.zeros(num_reals), 
+                    'hazardous_material' : np.zeros(num_reals), 
+                    'fire_suppression' : np.zeros(num_reals)}
+    
+    # check if building has fire supprsion system
+    fs_exists = any(damage['fnc_filters']['fire_building'])
+    
+ 
+    # Check damage throughout the building
+    comp_breakdowns = {'red_tag' : np.empty([num_reals,num_comps,num_units]),
+                       'shoring' : np.empty([num_reals,num_comps,num_units]),
+                       'fire_suppression' : np.empty([num_reals,num_comps,num_units])
+                       }
+    
+
+    for tu in range(num_units):
+        # Grab tenant and damage info for this tenant unit
+        repair_complete_day = damage['tenant_units'][tu]['recovery']['repair_complete_day'].copy()
+        repair_complete_day_w_tmp = damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp'].copy()
+        
+        is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0, np.array(damage['tenant_units'][tu]['worker_days']) > 0)
+        ## Red Tags
+        # The day the red tag is resolved is the day when all damage (anywhere in building) that has
+        # the potential to cause a red tag is fixed (ie max day)
+        if any(damage['fnc_filters']['red_tag']):
+            recovery_day['red_tag'] = np.fmax(recovery_day['red_tag'],
+                                          np.array(damage_consequences['red_tag'])*np.nanmax(repair_complete_day[:,damage['fnc_filters']['red_tag']], axis=1))
+   
+        # Component Breakdowns
+        
+        comp_breakdowns['red_tag'][:,:,tu] = recovery_day['red_tag'].reshape(num_reals,1) * damage_consequences['red_tag_impact']
+        
+        
+        ## Local Shoring
+        if np.logical_and(any(damage['fnc_filters']['requires_shoring']), functionality_options['include_local_stability_impact']):
+            #Any unresolved damao documentatige (temporary or otherwise) that requires shoring,
+            # blocks occupancy to the whole building
+            recovery_day['shoring'] = np.fmax(recovery_day['shoring'],
+                                      np.nanmax(repair_complete_day_w_tmp[:,damage['fnc_filters']['requires_shoring']], axis=1)) 
+        
+            # Componet Breakdowns (the time it takes to shore or fully repair each
+            # component is the time it blocks occupancy for)
+            comp_breakdowns['shoring'][:,:,tu] = damage['fnc_filters']['requires_shoring'] * repair_complete_day_w_tmp * is_damaged
+
+    
+    
+        ## Day the fire suppression system is operating again (for the whole building)
+        if np.logical_not(functionality_options['fire_watch']) and fs_exists:
+            # any major damage fails the system for the whole building so take the max
+            recovery_day['fire_suppression'] = np.fmax(recovery_day['fire_suppression'], np.amax(repair_complete_day[:,damage['fnc_filters']['fire_building']], axis=1))
+    
+            # Consider utilities (assume no backup water supply)
+            recovery_day['fire_suppression'] = np.fmax(recovery_day['building']['fire'], np.array(utilities['water'])) # Assumes building does not have backup water supply
+        
+            # Componet Breakdowns
+            comp_breakdowns['fire_suppression'][:,:,tu] = damage['fnc_filters']['fire_building'] * repair_complete_day
+
+
+        ## Hazardous Materials
+        # note: hazardous materials are accounted for in building functional
+        #assessment here, but are not currently quantified in the component
+        # breakdowns
+        if any(damage['fnc_filters']['global_hazardous_material']):
+            # Any global Nohazardous material shuts down the entire building
+            recovery_day['hazardous_material'] = np.fmax(recovery_day['hazardous_material'], np.amax(repair_complete_day[:,damage['fnc_filters']['global_hazardous_material']], axis=1))
+
+    
+    ## Building Egress
+    '''Calculate when falling hazards or racking of doors affects the building
+    safety due to limited entrance and exit door access'''
+    
+    '''Simulate a random location of the doors on two sides of the building for
+    each realization. Location is defined at the center of the door as a
+    fraction of the building width on that side
+    This is acting a random p value to determine if the unknown location of
+    the door is within the falling hazard zone'''
+    
+    door_location = np.random.rand(num_reals , building_model['num_entry_doors'])
+    
+    # Assign odd doors to side 1 and even doors to side two
+    door_numbers = np.linspace(1,building_model['num_entry_doors'], building_model['num_entry_doors'])
+    door_side = np.ones(building_model['num_entry_doors'])
+    door_side[np.remainder(door_numbers, 2) == 0] = 2
+    door_side = door_side.astype('int')
+    
+    # Determine the quantity of falling hazard damage and when it will be resolved
+    day_repair_fall_haz = np.zeros([num_reals,building_model['num_entry_doors']])
+    fall_haz_comps_day_rep = np.zeros([num_reals,num_comps,num_units,building_model['num_entry_doors']])
+    comp_affected_lf = np.zeros([num_reals,num_comps,num_units])
+    scaffold_filt = damage['comp_ds_table']['resolved_by_scaffolding'].astype(bool)
+    
+    repair_complete_day_w_tmp = np.empty([num_reals,num_comps,num_units])
+    for tu in range(num_units):
+        tmp_or_full_complete_day = damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp'].copy()
+
+        '''Effect of falling hazards on building safety are resolved either by
+        full repair, local temp repair, or erecting scaffolding. Whatever
+        occurs first'''
+        isdamaged = 1*(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0).astype('float')
+        isdamaged[isdamaged == 0] = np.nan # mark undamaged cases as NaN to help combine factors below
+        
+        scaffold_day = impeding_temp_repairs['scaffold_day'].reshape(num_reals,1) *  isdamaged[:,scaffold_filt]
+        complete_day_w_scaffold = tmp_or_full_complete_day
+        complete_day_w_scaffold[:,scaffold_filt] = np.fmin(tmp_or_full_complete_day[:,scaffold_filt], scaffold_day)
+        repair_complete_day_w_tmp[:,:,tu] = complete_day_w_scaffold
+
+    # Loop through component repair times to determine the day it stops affecting re-occupancy
+    num_repair_time_increments = np.sum(damage['fnc_filters']['ext_fall_haz_all'])*num_units # possible unique number of loop increments
+    edge_lengths = np.row_stack((np.array(building_model['edge_lengths']), np.array(building_model['edge_lengths'])))
+    
+    affected_ratio={}
+    for side in range(4):
+        affected_ratio['side_'+str(side+1)]=np.zeros([num_reals, num_units]) #FZ# Initiated with zeros. Check later if it works
+    for i in range(num_repair_time_increments):
+        # Calculate the falling hazards per side
+        for tu in range(num_units):
+            for side in range(4): # assumes there are 4 sides
+                lf_affected_direct_scale_all_comps = damage['comp_ds_table']['exterior_falling_length_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged_side_' + str(side+1)]  #FZ# +1 done to account for python indexing starting with 0.       
+                lf_affected_sf_all_comps = damage['comp_ds_table']['exterior_falling_length_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged_side_' + str(side+1)] / building_model['ht_per_story_ft'][tu]
+                
+                comp_affected_lf[:,damage['fnc_filters']['ext_fall_haz_lf'],tu] = lf_affected_direct_scale_all_comps[:,damage['fnc_filters']['ext_fall_haz_lf']]
+                comp_affected_lf[:,damage['fnc_filters']['ext_fall_haz_sf'],tu] = lf_affected_sf_all_comps[:,damage['fnc_filters']['ext_fall_haz_sf']]
+                comp_affected_lf[:,damage['fnc_filters']['ext_fall_haz_ea'],tu] = lf_affected_direct_scale_all_comps[:,damage['fnc_filters']['ext_fall_haz_ea']]    
+                
+                comp_affected_ft_this_story = comp_affected_lf[:,:,tu].copy()
+                affected_ft_this_story = np.fmin(np.sum(comp_affected_ft_this_story,axis = 1), edge_lengths[side,tu]) # Assumes cladding components do not occupy the same perimeter space
+                
+                affected_ratio['side_'+str(side+1)][:,tu] = np.fmin((affected_ft_this_story)/ edge_lengths[side,tu],1)
+
+
+        # Calculate the time increment for this loop
+        delta_day = np.nanmin(np.nanmin(repair_complete_day_w_tmp[:,damage['fnc_filters']['ext_fall_haz_all'],:],axis =2), axis=1)
+        delta_day[np.isnan(delta_day)] = 0
+        if sum(delta_day) == 0:
+            break # everything has been fixed
+        
+        
+        # Go through each door to determine which is affected by falling
+        # hazards
+        for d in range(building_model['num_entry_doors']):
+            # Combine affected areas of all stories above the first using SRSS
+            # HARDCODED ASSUMPTIONS: DOORS ONLY ON TWO SIDES
+            fall_haz_zone = np.fmin(np.sqrt(np.sum(affected_ratio['side_'+str(door_side[d])][:,1:num_units]**2, axis=1)),1)
+    
+            '''Augment the falling hazard zone with the door access zone
+            add the door access width to the width of falling hazards to account
+            for the width of the door (ie if any part of the door access zone is
+            under the falling hazard, its a problem)'''
+            door_access_zone = functionality_options['door_access_width_ft'] / building_model['edge_lengths'][door_side[d]-1][0] #FZ# -1 done to account for python indexing starting from zero
+            total_fall_haz_zone = fall_haz_zone + 2*door_access_zone # this is approximating the probability the door is within the falling hazard zone
+    
+            '''Determine if current damage affects occupancy
+            if the randonmly simulated door location is with falling hazard zone'''
+            affects_door = door_location[:,door_side[d]-1] < total_fall_haz_zone #FZ# -1 done to account for python indexing starting from zero
+    
+            # Add days in this increment to the tally
+            day_repair_fall_haz[:,d] = day_repair_fall_haz[:,d] + affects_door * delta_day
+    
+            # Add days to components that are affecting occupancy
+            # fall_haz_comps_day_rep[:,:,:,d] = fall_haz_comps_day_rep[:,:,:,d] + ((1 * comp_posing_falling_hazard).transpose(2,0,1) * (affects_door * delta_day).reshape(num_reals,1)).transpose(1,2,0) #FZ# Transpose and reshape done to align nd arrays
+            fall_haz_comps_day_rep[:,:,:,d] = fall_haz_comps_day_rep[:,:,:,d] + ((comp_affected_lf > 0 * damage['fnc_filters']['ext_fall_haz_all'].reshape(num_comps,1)).transpose(2,0,1) * (affects_door * delta_day).reshape(num_reals,1)).transpose(1,2,0) #FZ# Transpose and reshape done to align nd arrays      
+        
+        # Change the comps for the next increment
+        repair_complete_day_w_tmp = (repair_complete_day_w_tmp.transpose(2,0,1) - delta_day.reshape(num_reals,1)).transpose(1,2,0)
+        repair_complete_day_w_tmp[repair_complete_day_w_tmp <= 0] = np.nan
+
+    
+    # Determine when racked doors are resolved
+    day_repair_racked = np.zeros([num_reals, building_model['num_entry_doors']])
+    side_1_count = 0
+    side_2_count = 0
+    for d in range(building_model['num_entry_doors']):
+        if door_side[d] == 1:
+            side_1_count = side_1_count + 1
+            day_repair_racked[:,d] = impeding_temp_repairs['door_racking_repair_day'] * (np.array(damage_consequences['racked_entry_doors_side_1']) >= side_1_count)
+        else:
+            side_2_count = side_2_count + 1
+            day_repair_racked[:,d] = impeding_temp_repairs['door_racking_repair_day'] * (np.array(damage_consequences['racked_entry_doors_side_2']) >= side_2_count)
+
+    door_access_day = np.fmax(day_repair_racked, day_repair_fall_haz)
+    
+    '''Find the days until door egress is regained from resolution of both
+    falling hazards or door racking'''
+    cum_days = np.zeros(num_reals)
+    entry_door_access_day = np.zeros(num_reals)
+    door_access_day_nan = door_access_day.copy() #FZ# Be cautious! Numpy arrays are mutable
+    door_access_day_nan[door_access_day_nan == 0] = np.nan
+    num_repair_time_increments = np.array(building_model['num_entry_doors']) # possible unique number of loop increments
+    for i in range(num_repair_time_increments):
+        num_accessible_doors = np.sum(door_access_day <= cum_days.reshape(num_reals,1), axis=1)
+        entry_door_accessible = num_accessible_doors >= max(functionality_options['min_egress_paths'], functionality_options['egress_threshold'] * building_model['num_entry_doors'])
+        # Bean counting for this iteration
+        delta_day = np.nanmin(door_access_day_nan, axis=1);
+        delta_day[np.isnan(delta_day)] = 0
+        door_access_day_nan = door_access_day_nan - delta_day.reshape(num_reals,1)
+        cum_days = cum_days + delta_day
+        
+        # Save recovery time increments
+        entry_door_access_day = entry_door_access_day + delta_day * np.logical_not(entry_door_accessible)
+        
+    # Save recovery day values
+    recovery_day['entry_door_access'] = entry_door_access_day
+  
+    # Determine when Exterior Falling Hazards or doors actually contribute to re-occupancy
+    recovery_day['falling_hazard'] = np.fmin(recovery_day['entry_door_access'], np.nanmax(day_repair_fall_haz, axis=1))
+    recovery_day['entry_door_racking'] = np.fmin(recovery_day['entry_door_access'], np.nanmax(day_repair_racked,axis=1))
+    
+    # Component Breakdown
+    comp_breakdowns['falling_hazard'] = (np.fmin(recovery_day['entry_door_access'], (np.nanmax(fall_haz_comps_day_rep, axis=3)).transpose(2,1,0))).transpose(2,1,0) #FZ# Transpose done to align the nd array fopr operation
+    
+    
+    ## Fire Safety
+    if np.logical_not(functionality_options['fire_watch']) and fs_exists:
+        comp_breakdowns_local_fire = np.zeros([num_reals,num_comps,num_units])
+        fire_safety_day = np.zeros([num_reals,num_units]) # Day the local fire sprinkler system becomes operational
+        filt_fs_drop = damage['fnc_filters']['fire_drops']
+        filt_fs_branch = damage['fnc_filters']['fire_unit']
+        for tu in range(num_units):
+            repair_complete_day = damage['tenant_units'][tu]['recovery']['repair_complete_day'].copy()
+            repair_complete_day[repair_complete_day == 0] = np.nan # Make sure zero repair days are NaN
+            damaged_comps = damage['tenant_units'][tu]['qnt_damaged']
+            num_drops = max(np.array(damage['tenant_units'][tu]['num_comps'])* filt_fs_drop) # Assumes drops are all in one performance group
+            num_branches = max(np.array(damage['tenant_units'][tu]['num_comps']) * filt_fs_branch) # Assumes branches are all in one performance group
+            
+            if (num_drops + num_branches) > 0: # If there are any of these components on in this tenant unit
+                # Loop through component repair times to determine the day it stops affecting re-occupanc
+                num_repair_time_increments = sum(filt_fs_drop | filt_fs_branch) # possible unique number of loop increments
+                
+                for i in range(num_repair_time_increments):
+                    # Calculate the ratio of damaged drops and branches on this story
+                    ratio_damaged_drop = np.sum(damaged_comps * filt_fs_drop, axis=1) / num_drops # assumes comps are not simeltaneous
+                    ratio_damaged_branch = np.sum(damaged_comps * filt_fs_branch, axis=1) / num_branches # assumes comps are not simeltaneous
+    
+                    # Determine if fire drops and branches are adequately operating
+                    fire_drop_operational = functionality_options['local_fire_damamge_threshold'] >= ratio_damaged_drop
+                    fire_branch_operational = functionality_options['local_fire_damamge_threshold'] >= ratio_damaged_branch
+                    local_fire_operational = fire_drop_operational & fire_branch_operational
+                    
+                    # Add days in this increment to the tally
+                    delta_day = np.nanmin(repair_complete_day[:,filt_fs_drop], axis=1);
+                    delta_day[np.isnan(delta_day)] = 0
+                    fire_safety_day[:,tu] = fire_safety_day[:,tu] + np.logical_not(local_fire_operational) * delta_day
+    
+                    # Add days to components that are affecting occupancy
+                    contributing_drops = ((damaged_comps * filt_fs_drop) > 0)  * np.logical_not(fire_drop_operational).reshape(num_reals,1) #count all components that contributed to non operational fire drops
+                    contributing_branches = ((damaged_comps * filt_fs_drop) > 0)  * np.logical_not(fire_branch_operational).reshape(num_reals,1) # count all components that contributed to non operational fire drops
+                    contributing_comps = np.amax(contributing_drops, contributing_branches)
+                    comp_breakdowns_local_fire[:,:,tu] = comp_breakdowns_local_fire[:,:,tu] + contributing_comps * delta_day.reshape(num_reals,1)
+    
+                    # Change the comps for the next increment
+                    repair_complete_day = repair_complete_day - delta_day.reshape(num_reals,1)
+                    repair_complete_day[repair_complete_day <= 0] = np.nan
+                    fixed_comps_filt = np.isnan(repair_complete_day)
+                    np.array(damaged_comps)[fixed_comps_filt] = 0
+  
+        # Inoperable fire drips on any story shuts down entire building
+        fire_safety_day_building = np.nanmax(fire_safety_day, axis=1)
+        
+        # Combine parts of fire suppression system
+        recovery_day['fire_suppression'] = np.fmax(recovery_day['fire_suppression'], fire_safety_day_building)
+
+        comp_breakdowns['fire_suppression'] = np.fmax(comp_breakdowns['fire_suppression'],comp_breakdowns_local_fire)
+    
+    ## Delay Red Tag recovery by the time it takes to clear the tag
+    sim_red_tag_clear_time = np.ceil(np.random.lognormal(np.log(functionality_options['red_tag_clear_time']),
+                                                                functionality_options['red_tag_clear_beta'], num_reals))
+    recovery_day['red_tag'] = recovery_day['red_tag'] + sim_red_tag_clear_time * damage_consequences['red_tag']
+    comp_breakdowns['red_tag'] = comp_breakdowns['red_tag'] + (sim_red_tag_clear_time.reshape(num_reals,1) * (comp_breakdowns['red_tag'] > 0).transpose(2,0,1)).transpose(1,2,0)
+    
+    return recovery_day, comp_breakdowns
+
+
+def fn_story_access(damage, building_model, damage_consequences, 
+                    functionality_options, impeding_temp_repairs):
+    
+    '''Check each story for damage that would cause that story to be shut down due to
+    issues of access
+    
+    Parameters
+    ----------
+    damage: dictionary
+     contains per damage state damage, loss, and repair time data for each 
+     component in the building
+     
+    building_model: dictionary
+     general attributes of the building model
+     
+    damage_consequences: dictionary
+     data structure containing simulated building consequences, such as red
+     tags and repair costs ratios
+     
+    functionality_options: dictionary
+     recovery time optional inputs such as various damage thresholds
+    
+    impeding_temp_repairs: dictionary
+     contains simulated temporary repairs the impede occuapancy and function
+     but are calulated in parallel with the temp repair schedule
+    
+    Returns
+    -------
+    recovery_day: dictionary
+     simulation of the number of days each fault tree event is affecting access
+     in each story
+     
+    comp_breakdowns: dictionary
+     simulation of each components contributions to each of the fault tree events''' 
+    
+    import numpy as np
+    
+    ## Initial Setup
+    num_reals = len(damage_consequences['red_tag'])
+    num_units = len(damage['tenant_units'])
+    num_stories = len(damage['tenant_units'])
+    num_comps = len(damage['comp_ds_table']['comp_id'])
+    
+    # Pre-allocate data
+    recovery_day={}
+    comp_breakdowns={}
+    recovery_day['stairs'] = np.zeros([num_reals,num_units])
+    recovery_day['stair_doors'] = np.zeros([num_reals,num_units])
+    recovery_day['flooding'] = np.zeros([num_reals,num_units])
+    recovery_day['horizontal_egress'] = np.zeros([num_reals,num_units])
+    comp_breakdowns['stairs'] = np.zeros([num_reals,num_comps,num_units])
+    comp_breakdowns['flooding'] = np.zeros([num_reals,num_comps,num_units])
+    comp_breakdowns['horizontal_egress'] = np.zeros([num_reals,num_comps,num_units])
+    
+    
+    ## Horizontal Egress - Fire breaks
+    if any(damage['fnc_filters']['fire_break']):
+        for tu in range(num_stories):
+            # Grab tenant and damage info for this tenant unit
+            repair_complete_day_w_tmp = damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp'].copy()
+    
+            # Any significant damage to fire breaks in the story impairs the horizontal egress
+            recovery_day['horizontal_egress'][:,tu] = np.fmax(recovery_day['horizontal_egress'][:,tu], np.nanmax(repair_complete_day_w_tmp[:, damage['fnc_filters']['fire_break']], axis=1))
+    
+            # Componet Breakdowns
+            comp_breakdowns['horizontal_egress'][:,:,tu] = damage['fnc_filters']['fire_break'].reshape(1,num_comps) * repair_complete_day_w_tmp
+
+     
+    ## STORY FLOODING
+    for tu in reversed(range(num_stories)): # Go from top to bottom
+        is_damaged = np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0
+        flooding_this_story = np.any(is_damaged[:,damage['fnc_filters']['causes_flooding']], axis=1); # Any major piping damage causes interior flooding
+        flooding_cleanup_day = flooding_this_story * impeding_temp_repairs['flooding_cleanup_day']
+    
+        # Save clean up time per component causing flooding
+        comp_breakdowns['flooding'][:,:,tu] = damage['fnc_filters']['causes_flooding'].reshape(1,num_comps) * is_damaged * flooding_cleanup_day.reshape(num_reals,1)
+    
+        # This story is not accessible if any story above has flooding
+        recovery_day['flooding'][:,tu] = np.nanmax(np.column_stack((flooding_cleanup_day, recovery_day['flooding'][:,(tu+1):num_units])), axis=1)
+
+    # Go through each story and check if there is sufficient story access (stairs and stairdoors)
+    ## STAIRS AND STAIRDOORS
+    if num_stories == 1: 
+        return recovery_day, comp_breakdowns # Re-occupancy of one story buildigns is not affected by stairway access
+
+
+    # Augment damage filters with door data
+    damage['fnc_filters']['stairs'] = np.append(damage['fnc_filters']['stairs'], np.array([False]))
+    damage['fnc_filters']['stair_doors'] = np.append(np.zeros(num_comps), 1).astype(dtype=bool)
+    
+    ## Go through each story and check if there is sufficient story access (stairs and stairdoors)
+    # if stairs don't exist on a story, this will assume they are rugged (along with the stair doors)
+    for tu in range(num_stories):
+        # Augment damage matrix with door data
+        racked_stair_doors = np.fmin(np.array(damage_consequences['racked_stair_doors_per_story'])[:,tu], building_model['stairs_per_story'][tu])
+        damage['tenant_units'][tu]['qnt_damaged'] = (np.column_stack((np.array(damage['tenant_units'][tu]['qnt_damaged']), racked_stair_doors))).tolist() #FZ# Converted back to alist to keep it consistent with other objects in the dictionary damage['tenant_units'][tu]
+        door_repair_day = 1*(racked_stair_doors > 0) * impeding_temp_repairs['door_racking_repair_day']
+        damage['tenant_units'][tu]['recovery']['repair_complete_day'] = np.column_stack((damage['tenant_units'][tu]['recovery']['repair_complete_day'], door_repair_day))
+    
+        # Quantify damaged stairs on this story
+        repair_complete_day = damage['tenant_units'][tu]['recovery']['repair_complete_day'].copy()
+        damaged_comps = np.array(damage['tenant_units'][tu]['qnt_damaged']).copy()
+        
+        # Make sure zero repair days are NaN
+        repair_complete_day[repair_complete_day == 0] = np.nan
+    
+        '''Step through each unique component repair time and determine when
+        stairs stop affecting story access'''
+        stair_access_day = np.zeros(num_reals) # day story becomes accessible from repair of stairs
+        stairdoor_access_day = np.zeros(num_reals) # day story becomes accessible from repair of doors
+        filt_all = damage['fnc_filters']['stairs'] | damage['fnc_filters']['stair_doors']
+        num_repair_time_increments = sum(filt_all) # possible unique number of loop increments
+        for i in range(num_repair_time_increments):
+            # number of functioning stairs
+            num_dam_stairs = np.sum(damaged_comps * damage['fnc_filters']['stairs'], axis=1) # assumes comps are not simeltaneous
+            num_racked_doors = np.sum(damaged_comps * damage['fnc_filters']['stair_doors'], axis=1) # assumes comps are not simeltaneous
+            functioning_stairs = building_model['stairs_per_story'][tu] - num_dam_stairs
+            functioning_stairdoors = building_model['stairs_per_story'][tu] - num_racked_doors
+    
+            # Required egress with and without operational fire suppression system
+            required_stairs = max(functionality_options['min_egress_paths'] ,functionality_options['egress_threshold'] * building_model['stairs_per_story'][tu])
+    
+            # Determine Stair Access
+            sufficient_stair_access = functioning_stairs >= required_stairs
+
+            # Determine Stair Door Acces
+            sufficient_stairdoor_access = functioning_stairdoors >= required_stairs
+ 
+            # Add days in this increment to the tally
+            delta_day = np.nanmin(repair_complete_day[:,filt_all], axis=1)
+            delta_day[np.isnan(delta_day)] = 0
+            stair_access_day = stair_access_day + np.logical_not(sufficient_stair_access)* delta_day
+            stairdoor_access_day = stairdoor_access_day + np.logical_not(sufficient_stairdoor_access) * delta_day
+    
+            # Add days to components that are affecting occupancy
+            contributing_stairs = ((damaged_comps * damage['fnc_filters']['stairs'] > 0) * np.logical_not(sufficient_stair_access.reshape(len(sufficient_stair_access),1))) # Count any damaged stairs for realization that have loss of story access
+            contributing_stairs = np.delete(contributing_stairs, -1, 1) # remove added door column
+            comp_breakdowns['stairs'][:,:,tu] = comp_breakdowns['stairs'][:,:,tu] + contributing_stairs * delta_day.reshape(len(delta_day),1)
+    
+            # Change the comps for the next increment
+            repair_complete_day = repair_complete_day - delta_day.reshape(len(delta_day),1)
+            repair_complete_day[repair_complete_day <= 0] = np.nan
+            fixed_comps_filt = np.isnan(repair_complete_day)
+            damaged_comps[fixed_comps_filt] = 0
+           
+        ## This story is not accessible if this or any story below has insufficient stair egress
+        recovery_day['stairs'][:,tu] = np.nanmax(np.column_stack((stair_access_day, recovery_day['stairs'][:,0:(tu)])), axis=1)
+        
+        # For the third story and above, the story below is not accessible if
+        # there is insufficient stair egress at this story
+        if tu >= 2:
+            recovery_day['stairs'][:,(tu-1)] = np.nanmax(np.column_stack((stair_access_day, recovery_day['stairs'][:,(tu-1)])), axis=1)
+    
+        # Damage to doors only affects this story
+        recovery_day['stair_doors'][:,tu] = stairdoor_access_day
+   
+    return recovery_day, comp_breakdowns
+
+
+def fn_tenant_safety( damage, building_model, functionality_options, 
+                     tenant_units):
+    '''Check each tenant unit for damage that would cause that tenant unit 
+    to be shut down due to issues of locay safety
+    
+    Parameters
+    ----------
+    damage: dictionary
+     contains per damage state damage, loss, and repair time data for each 
+     component in the building
+     
+    building_model: dictionary
+     general attributes of the building model
+     
+    functionality_options: dictionary
+     recovery time optional inputs such as various damage thresholds
+     
+    tenant_units: DataFrame
+     attributes of each tenant unit within the building
+    
+    Returns
+    -------
+    recovery_day: dictionary
+     simulation of the number of days each fault tree event is affecting safety
+     in each tenant unit
+    
+    comp_breakdowns: dictionary
+     simulation of each components contributions to each of the fault tree events''' 
+    
+    import numpy as np
+    
+    ##Initial Setup
+    num_reals, num_comps = np.shape(damage['tenant_units'][0]['qnt_damaged'])
+    num_units = len(damage['tenant_units'])
+    comp_types_interior_check = np.unique(damage['comp_ds_table']['comp_type_id'][damage['fnc_filters']['int_fall_haz_all']])
+    
+    recovery_day={}
+    recovery_day['exterior'] = np.zeros([num_reals, num_units])
+    recovery_day['interior'] = np.zeros([num_reals, num_units])    
+    recovery_day['hazardous_material'] = np.zeros([num_reals, num_units])     
+    
+    comp_breakdowns = {}
+    comp_breakdowns['exterior'] = np.empty([num_reals,num_comps,num_units])
+    comp_breakdowns['interior'] = np.empty([num_reals,num_comps,num_units])
+    
+    # go through each tenant unit and quantify the affect that each system has on reoccpauncy
+    for tu in range(num_units):
+        # Grab tenant and damage info for this tenant unit
+        unit={}
+        for key in list(tenant_units.keys()):
+            unit[key] = tenant_units[key][tu]
+        repair_complete_day_w_tmp = tuple(map(tuple,(damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp']))) # day each component (and DS) is reparied of this TU
+        #FZ# Made tuple to bypass the issue of mutable numpy array
+        '''#Exterior Enclosure 
+           Calculated the affected perimeter area of exterior components
+           (assuming all exterior components have either lf or sf units)'''
+        area_affected_all_linear_comps = damage['comp_ds_table']['exterior_surface_area_factor'] * damage['comp_ds_table']['unit_qty'] * building_model['ht_per_story_ft'][tu] * damage['tenant_units'][tu]['qnt_damaged']
+        area_affected_all_direct_scale_comps = damage['comp_ds_table']['exterior_surface_area_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged']
+        
+        # construct a matrix of affected areas from the various damaged component types
+        comp_affected_area = np.zeros([num_reals,num_comps])
+        comp_affected_area[:,damage['fnc_filters']['exterior_safety_lf']] = area_affected_all_linear_comps[:,damage['fnc_filters']['exterior_safety_lf']]
+        comp_affected_area[:,damage['fnc_filters']['exterior_safety_sf']] = area_affected_all_direct_scale_comps[:,damage['fnc_filters']['exterior_safety_sf']]
+        
+        '''Go each possible unique repair time contributing to interior safety check
+           Find when enough repairs are complete such that interior damage no
+           longer affects tenant safety'''
+        comps_day_repaired = repair_complete_day_w_tmp # define as initial repair day considering tmp repairs
+        comps_day_repaired = np.array(comps_day_repaired)
+        ext_repair_day = np.zeros(num_reals)
+        all_comps_day_repaired = np.zeros([num_reals,num_comps])
+        num_repair_time_increments = np.sum(damage['fnc_filters']['exterior_safety_all']) # possible unique number of loop increments
+        for i in range(num_repair_time_increments):
+            
+            # Quantify Affected Area
+            area_affected = np.sum(comp_affected_area, axis=1) # Assumes cladding components do not occupy the same perimeter area
+            percent_area_affected = area_affected / unit['perim_area'] #FZ# Fraction area
+    
+            # Check if this is sufficent enough to cause as tenant safety issue
+            affects_occupancy = percent_area_affected > functionality_options['exterior_safety_threshold']
+    
+            # Determine step increment based on the component with the shortest repair time
+            delta_day = np.nanmin(comps_day_repaired[:,damage['fnc_filters']['exterior_safety_all']], axis=1)
+            delta_day[np.isnan(delta_day)] = 0
+            
+            # Add increment to the tally of days until the interior damage stops affecting occupancy
+            ext_repair_day = ext_repair_day + affects_occupancy * delta_day
+            
+            # Add days to components that are affecting occupancy
+            any_area_affected_all_comps = (damage['fnc_filters']['exterior_safety_all'] * comp_affected_area) > 0 # Count any component that contributes to the loss of occupancy regardless of by how much
+            all_comps_day_repaired = all_comps_day_repaired + any_area_affected_all_comps * affects_occupancy.reshape(num_reals,1) * delta_day.reshape(num_reals,1)
+            
+            # Reduce compent damaged for the next increment based on what was repaired in this increment
+            comps_day_repaired = comps_day_repaired - delta_day.reshape(num_reals,1)
+            comps_day_repaired[comps_day_repaired <= 0] = np.nan
+            fixed_comps_filt = np.isnan(comps_day_repaired)
+            comp_affected_area[fixed_comps_filt] = 0
+
+        
+        # Save exterior recovery day for this tenant unit
+        recovery_day['exterior'][:,tu] = ext_repair_day
+        comp_breakdowns['exterior'][:,:,tu] = all_comps_day_repaired
+        
+        ## Interior Falling Hazards
+        # Convert all component into affected areas
+        area_affected_all_linear_comps = damage['comp_ds_table']['interior_area_factor'] * damage['comp_ds_table']['unit_qty'] * building_model['ht_per_story_ft'][tu] * damage['tenant_units'][tu]['qnt_damaged']
+        area_affected_all_direct_scale_comps   = damage['comp_ds_table']['interior_area_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged']
+        area_affected_all_bay_comps    = damage['comp_ds_table']['interior_area_factor'] * building_model['struct_bay_area_per_story'][tu] * damage['tenant_units'][tu]['qnt_damaged']
+        area_affected_all_build_comps  = damage['comp_ds_table']['interior_area_factor'] * sum(building_model['area_per_story_sf']) * damage['tenant_units'][tu]['qnt_damaged']
+        
+        # Checking damage that affects components in story below
+        repair_complete_day_w_tmp_w_instabilities = repair_complete_day_w_tmp
+        repair_complete_day_w_tmp_w_instabilities = np.array(repair_complete_day_w_tmp_w_instabilities) #FZ# coverted to array from tuple
+        if tu > 0: #FZ# changed to zero to account for python indexing starting from 0.
+            area_affected_below = damage['comp_ds_table']['interior_area_factor'] * building_model['struct_bay_area_per_story'][tu-1] * damage['tenant_units'][tu-1]['qnt_damaged']
+            area_affected_all_bay_comps[:,damage['fnc_filters']['vert_instabilities']] = np.fmax(area_affected_below[:,damage['fnc_filters']['vert_instabilities']], area_affected_all_bay_comps[:,damage['fnc_filters']['vert_instabilities']])
+            repair_time_below = damage['tenant_units'][tu-1]['recovery']['repair_complete_day_w_tmp']
+            repair_complete_day_w_tmp_w_instabilities[:,damage['fnc_filters']['vert_instabilities']] = np.fmax(repair_time_below[:,damage['fnc_filters']['vert_instabilities']],np.array(repair_complete_day_w_tmp)[:,damage['fnc_filters']['vert_instabilities']])
+        
+    
+        # construct a matrix of affected areas from the various damaged component types
+        comp_affected_area = np.zeros([num_reals,num_comps])
+        comp_affected_area[:,damage['fnc_filters']['int_fall_haz_lf']] = area_affected_all_linear_comps[:,damage['fnc_filters']['int_fall_haz_lf']]
+        comp_affected_area[:,damage['fnc_filters']['int_fall_haz_sf']] = area_affected_all_direct_scale_comps[:,damage['fnc_filters']['int_fall_haz_sf']]
+        comp_affected_area[:,damage['fnc_filters']['int_fall_haz_ea']] = area_affected_all_direct_scale_comps[:,damage['fnc_filters']['int_fall_haz_ea']]        
+        comp_affected_area[:,damage['fnc_filters']['int_fall_haz_bay']] = area_affected_all_bay_comps[:,damage['fnc_filters']['int_fall_haz_bay']]
+        comp_affected_area[:,damage['fnc_filters']['int_fall_haz_build']] = area_affected_all_build_comps[:,damage['fnc_filters']['int_fall_haz_build']]
+        
+        '''Go each possible unique repair time contributing to interior safety check
+        Find when enough repairs are complete such that interior damage no
+        longer affects tenant safety'''
+        comps_day_repaired = repair_complete_day_w_tmp_w_instabilities # define as initial repair day considering tmp repairs
+        int_repair_day = np.zeros(num_reals)
+        all_comps_day_repaired = np.zeros([num_reals,num_comps])
+        num_repair_time_increments = np.sum(damage['fnc_filters']['int_fall_haz_all']) # possible unique number of loop increments
+        for i in range(num_repair_time_increments):
+            # Quantify Affected Area
+            diff_comp_areas = np.empty([num_reals, len(comp_types_interior_check)])
+            for cmp in range(len(comp_types_interior_check)):
+                filt = damage['comp_ds_table']['comp_type_id'] == comp_types_interior_check[cmp] # check to see if it matches the first part of the ID (ie the type of comp)
+                diff_comp_areas[:,cmp] = np.sum(comp_affected_area[:,filt], axis=1)
+            
+            area_affected = np.sqrt(np.sum(diff_comp_areas**2, axis=1)) # total area affected is the srss of the areas in the unit
+            
+            # Determine if current damage affects occupancy
+            percent_area_affected = np.fmin(area_affected / unit['area'], 1)
+            affects_occupancy = percent_area_affected > functionality_options['interior_safety_threshold']
+            
+            # Determine step increment based on the component with the shortest repair time
+            delta_day = np.nanmin(comps_day_repaired[:,damage['fnc_filters']['int_fall_haz_all']], axis=1)
+            delta_day[np.isnan(delta_day)] = 0
+            
+            # Add increment to the tally of days until the interior damage
+            # stops affecting occupancy
+            int_repair_day = int_repair_day + affects_occupancy * delta_day
+            
+            # Add days to components that are affecting occupancy
+            any_area_affected_all_comps = (damage['fnc_filters']['int_fall_haz_all'] * comp_affected_area) > 0 # Count any component that contributes to the loss of occupancy regardless of by how much
+            all_comps_day_repaired = all_comps_day_repaired + any_area_affected_all_comps * affects_occupancy.reshape(num_reals,1) * delta_day.reshape(num_reals,1)
+            
+            # Reduce compent damaged for the next increment based on what was repaired in this increment
+            comps_day_repaired = comps_day_repaired - delta_day.reshape(num_reals,1)
+            comps_day_repaired[comps_day_repaired <= 0] = np.nan
+            fixed_comps_filt = np.isnan(comps_day_repaired)
+            comp_affected_area[fixed_comps_filt] = 0
+        
+        
+        # Save interior recovery day for this tenant unit
+        recovery_day['interior'][:,tu] = int_repair_day
+        comp_breakdowns['interior'][:,:,tu] = all_comps_day_repaired
+        
+        ''' Hazardous Materials
+          note: hazardous materials are accounted for in building functional
+          assessment here, but are not currently quantified in the component
+          breakdowns'''
+        if any(damage['fnc_filters']['local_hazardous_material']):
+            # Any local hazardous material shuts down the entire tenant unit
+            recovery_day['hazardous_material'][:,tu] = np.nanmax(repair_complete_day_w_tmp[:,damage['fnc_filters']['local_hazardous_material']], axis=1) 
+        else:
+            recovery_day['hazardous_material'][:,tu] = np.zeros(num_reals)
+    
+    return recovery_day, comp_breakdowns
+
+    
+#########################functional recovery sub functions ########################################
+
+def fn_calc_subsystem_recovery(subsys_filt, damage,
+    repair_complete_day, total_num_comps, damaged_comps):
+    '''Check whether the components of a particular subsystem have redundancy
+    or not and calculate the day the subsystem recovers opertaions
+    
+    Parameters
+    ----------
+    subsys_filt: logical array [1 x num_comps_ds]
+     indentifies which rows in the damage.comp_ds_info are the components of interest
+    
+    damage: struct
+     contains per damage state damage, loss, and repair time data for each 
+     component in the building
+    
+    repair_complete_day: array [num_reals x num_comps_ds]
+     day reapairs are complete for each components damage state for a given
+     tenant unit
+    
+    total_num_comps: array [1 x num_comps_ds]
+     total number of each component damage state in this tenant unit
+    
+    damaged_comps: array [num_reals x num_comps_ds]
+     total number of damaged components in each damage state in this tenant unit
+    
+    Returns
+    -------
+    subsys_repair_day: array [num reals x 1]
+     The day this subsystem stops affecting functoin in a given tenant unit'''
+
+
+    import numpy as np    
+    ## Initial Setup
+    num_reals = len(repair_complete_day)
+    if len(damage['comp_ds_table']['parallel_operation'][subsys_filt])>0:
+        is_redundant = max(damage['comp_ds_table']['parallel_operation'][subsys_filt]) # should all be the same within a subsystem
+    else: 
+        is_redundant = 0
+    any_comps = any(subsys_filt)
+    
+    ## Check if the componet has redundancy
+    if any_comps:
+        if is_redundant == 1:
+            ## go through each component in this subsystem and find number of damaged units
+            comps = np.unique(damage['comp_ds_table']['comp_idx'][subsys_filt])
+            num_tot_comps = np.zeros(len(comps))
+            num_damaged_comps = np.zeros([num_reals,len(comps)])
+            for c in range(len(comps)):
+                this_comp = np.logical_and(subsys_filt, damage['comp_ds_table']['comp_idx'] == comps[c])
+                num_tot_comps[c] = max(total_num_comps * this_comp) # number of units across all ds should be the same
+                num_damaged_comps[:,c] = np.nanmax(damaged_comps * this_comp, axis=1)
+
+    
+            ## sum together multiple components in this subsystem
+            subsystem_num_comps = sum(num_tot_comps)
+            subsystem_num_damaged_comps = np.sum(num_damaged_comps, axis=1);
+            ratio_damaged = subsystem_num_damaged_comps / subsystem_num_comps
+    
+            ## Check failed component against the ratio of components required for system operation
+            # system fails when there is an insufficient number of operating components
+            n1_redundancy = max(damage['comp_ds_table']['n1_redundancy'][subsys_filt]) # should all be the same within a subsystem
+            if subsystem_num_comps == 0: # No components at this level
+                subsystem_failure = np.zeros([num_reals,1])
+            elif subsystem_num_comps == 1: # Not actually redundant
+                subsystem_failure = subsystem_num_damaged_comps == 1;
+            elif n1_redundancy ==1:
+                # These components are designed to have N+1 redundncy rates,
+                # meaning they are designed to lose one component and still operate at
+                # normal level
+                subsystem_failure = subsystem_num_damaged_comps > 1
+            else:
+                # Use a predefined ratio
+                redundancy_threshold = max(damage['comp_ds_table']['redundancy_threshold'][subsys_filt]) # should all be the same within a subsystem
+                subsystem_failure = ratio_damaged > redundancy_threshold
+    
+            ## Calculate recovery day and combine with other subsystems for this tenant unit
+            # assumes all quantities in each subsystem are repaired at
+            # once, which is true for our current repair schedule (ie
+            # system level at each story)
+            subsys_repair_day = np.nanmax(subsystem_failure.reshape(len(subsystem_failure),1) * subsys_filt.reshape(1, len(subsys_filt)) * repair_complete_day, axis=1) 
+        
+            '''else: This subsystem has no redundancy
+                any major damage to the components fails the subsystem at this
+                tenant unit'''
+        else: 
+            subsys_repair_day = np.nanmax(repair_complete_day * subsys_filt.reshape(1, len(subsys_filt)), axis=1)
+
+    else: # No components were populated in this subsystem
+        subsys_repair_day = np.zeros(num_reals)
+
+    return subsys_repair_day
+
+
+def fn_building_level_system_operation( damage, damage_consequences, 
+                                        building_model, utilities, 
+                                        functionality_options):
+    
+    '''Calculate the day certain systems recovery building-level opertaions
+    
+    Parameters
+    ----------
+    damage: dictionary
+      contains per damage state damage, loss, and repair time data for each 
+      component in the building
+     
+    damage_consequences: dictionary
+      dictionary containing simulated building consequences, such as red
+      tags and repair costs ratios
+     
+    building_model: dictionary
+      general attributes of the building model
+    
+    utilities: dictionary
+      dictionary containing simulated utility downtimes
+     
+    functionality_options: dictionary
+      recovery time optional inputs such as various damage thresholds
+    
+    Returns
+    -------
+    system_operation_day['building']: dictionary
+      simulation of the day operation is recovered for various systems at the
+      building level
+     
+    system_operation_day['comp']: dictionary
+      simulation number of days each component is affecting building system
+      operations'''
+    
+ 
+    # import packages
+    from . import other_functionality_functions
+    import numpy as np
+    
+    system_operation_day = {'building' : {}, 'comp' : {}}
+    ## Initial Setep
+    num_stories = building_model['num_stories']
+    num_reals = len(damage_consequences['red_tag'])
+    num_comps = len(damage['comp_ds_table']['comp_id'])
+    
+    system_operation_day['building']['hvac_main'] = np.zeros(num_reals)
+    
+    system_operation_day['comp']['elev_quant_damaged'] = np.zeros([num_reals,num_comps])
+    system_operation_day['comp']['elev_day_repaired'] = np.zeros([num_reals,num_comps])
+    system_operation_day['comp']['electrical_main'] = np.zeros([num_reals,num_comps])
+    system_operation_day['comp']['water_potable_main'] = np.zeros([num_reals,num_comps])
+    system_operation_day['comp']['water_sanitary_main'] = np.zeros([num_reals,num_comps])
+    system_operation_day['comp']['elevator_mcs'] = np.zeros([num_reals,num_comps])
+    system_operation_day['comp']['data_main'] = np.zeros([num_reals,num_comps])
+    
+    ## Loop through each story/TU and quantify the building-level performance of each system (e.g. equipment that severs the entire building)
+    for tu in range(num_stories):
+        damaged_comps = np.array(damage['tenant_units'][tu]['qnt_damaged'])
+        initial_damaged = damaged_comps > 0
+        total_num_comps = np.array(damage['tenant_units'][tu]['num_comps'])
+        repair_complete_day = damage['tenant_units'][tu]['recovery']['repair_complete_day']
+        
+        ## Elevators
+        # Assumed all components affect entire height of shaft
+        system_operation_day['comp']['elev_quant_damaged'] = np.fmax(
+            system_operation_day['comp']['elev_quant_damaged'], 
+            damage['tenant_units'][tu]['qnt_damaged'] * 
+            damage['fnc_filters']['elevators'])
+        
+        system_operation_day['comp']['elev_day_repaired'] = np.fmax(
+            system_operation_day['comp']['elev_day_repaired'], 
+            repair_complete_day * damage['fnc_filters']['elevators'])
+        
+        # Motor Control system - Elevators
+        system_operation_day['comp']['elevator_mcs'] = np.fmax(
+            system_operation_day['comp']['elevator_mcs'], 
+            repair_complete_day * damage['fnc_filters']['elevator_mcs'])
+        
+        ## Electrical
+        system_operation_day['comp']['electrical_main'] = np.fmax(
+            system_operation_day['comp']['electrical_main'], 
+            repair_complete_day * damage['fnc_filters']['electrical_main'])
+        
+
+        ## Water
+        system_operation_day['comp']['water_potable_main'] = np.fmax(
+            system_operation_day['comp']['water_potable_main'], 
+            repair_complete_day * damage['fnc_filters']['water_main'])
+        
+        system_operation_day['comp']['water_sanitary_main'] = np.fmax(
+            system_operation_day['comp']['water_sanitary_main'], 
+            repair_complete_day * damage['fnc_filters']['sewer_main'])        
+        
+        
+        ## HVAC
+        building_hvac_subsystems = list(damage['fnc_filters']['hvac']['building'].keys())
+        for s in range(len(building_hvac_subsystems)):
+            # Initialize variables
+            subsys_label = building_hvac_subsystems[s]
+
+            if ('building' in system_operation_day.keys()) == False or ('building' in system_operation_day.keys() and (subsys_label in system_operation_day['building']) ==  False):
+                # Initialize variables if not already initialized
+                system_operation_day['building'][subsys_label] = np.zeros(num_reals)
+                system_operation_day['comp'][subsys_label] = np.zeros([num_reals,num_comps])
+                
+            # go through each subsystem and calculate how long entire building
+            # operation is impaired
+            subs = list(damage['fnc_filters']['hvac']['building'][subsys_label].keys())
+                
+            for b in range(len(subs)):
+                filt = damage['fnc_filters']['hvac']['building'][subsys_label][subs[b]]
+                
+                repair_day = other_functionality_functions.fn_calc_subsystem_recovery( filt, damage,
+                     repair_complete_day, total_num_comps, damaged_comps)
+                
+                comps_breakdown = filt.reshape(1, num_comps) * initial_damaged * repair_day.reshape(num_reals,1)
+                
+                # combine with previous stories
+                system_operation_day['building'][subsys_label] = np.fmax(system_operation_day['building'][subsys_label], repair_day)
+                system_operation_day['comp'][subsys_label] = np.fmax(system_operation_day['comp'][subsys_label], comps_breakdown)
+                
+        ## Data
+        system_operation_day['comp']['data_main'] = np.fmax(system_operation_day['comp']['data_main'], repair_complete_day * damage['fnc_filters']['data_main'])
+        
+    ## Calculate building level consequences for systems where any major main damage leads to system failure
+    system_operation_day['building']['electrical_main'] = np.nanmax(system_operation_day['comp']['electrical_main'], axis=1)  # any major damage to the main equipment fails the system for the entire building
+    system_operation_day['building']['water_potable_main'] = np.nanmax(system_operation_day['comp']['water_potable_main'], axis=1)  # any major damage to the main pipes fails the system for the entire building
+    system_operation_day['building']['water_sanitary_main'] = np.nanmax(system_operation_day['comp']['water_sanitary_main'], axis=1) # any major damage fails the system for the whole building so take the max
+    system_operation_day['building']['elevator_mcs'] = np.nanmax(system_operation_day['comp']['elevator_mcs'], axis=1) # any major damage fails the system for the whole building so take the max
+    system_operation_day['building']['data_main'] = np.nanmax(system_operation_day['comp']['data_main'], axis=1)  # any major damage to the main equipment fails the system for the entire building
+   
+    ## Account for External Utilities impact on system Operation
+    # Electricity
+    system_operation_day['building']['electrical_main'] = np.fmax(system_operation_day['building']['electrical_main'] , utilities['electrical'])
+   
+    # Potable water
+    system_operation_day['building']['water_potable_main'] = np.fmax(system_operation_day['building']['water_potable_main'], utilities['water'])
+    
+    # Assume hvac control runs on electricity and heating system runs on gas
+    system_operation_day['building']['hvac_control'] = np.fmax(system_operation_day['building']['hvac_control'], system_operation_day['building']['electrical_main'])
+    system_operation_day['building']['hvac_heating'] = np.fmax(system_operation_day['building']['hvac_heating'], utilities[functionality_options['heat_utility']])
+
+    return system_operation_day
+
+
+def fn_tenant_function( damage, building_model, system_operation_day, 
+                        subsystems, tenant_units, impeding_temp_repairs, functionality_options):
+    
+    '''Check each tenant unit for damage that would cause that tenant unit 
+    to not be functional
+    
+    Parameters
+    ----------
+    damage: dictionary
+      contains per damage state damage, loss, and repair time data for each 
+      component in the building
+    
+    building_model: dictionary
+      general attributes of the building model
+      system_operation_day.building: struct
+      simulation of the day operation is recovered for various systems at the
+      building level
+     
+    system_operation_day.comp: dictionary
+      simulation number of days each component is affecting building system
+      operations
+    
+    subsystems: DataFrame
+      data table containing information about each subsystem's attributes
+    
+    tenant_units: DataFrame
+      attributes of each tenant unit within the building
+    
+    impeding_temp_repairs: dictionary
+      contains simulated temporary repairs the impede occuapancy and function
+      but are calulated in parallel with the temp repair schedule
+    
+    functionality_options: dictionary
+      recovery time optional inputs such as various damage thresholds
+      
+    Returns
+    -------
+    recovery_day: dictionary
+      simulation of the number of days each fault tree event is affecting
+      function in each tenant unit
+    
+    comp_breakdowns: dictionary
+      simulation of each components contributions to each of the 
+      fault tree events''' 
+
+    import numpy as np
+    import sys
+    
+    '''Subfunction'''
+    def subsystem_recovery(subsystem, damage, repair_complete_day, 
+                           total_num_comps, damaged_comps, 
+                           initial_damaged, dependancy):
+
+        # import packages
+        from . import other_functionality_functions
+        
+        # Set variables
+        recovery_day_all = dependancy['recovery_day'].copy()
+        comp_breakdowns_all = dependancy['comp_breakdown'].copy()
+        
+        # Go through each component group in this subsystem and determine recovery
+        # based on impact of system operation at the tenant unit level
+        subs = list(damage['fnc_filters']['hvac']['tenant'][subsystem].keys())
+        for b in range(len(subs)):
+            filt = damage['fnc_filters']['hvac']['tenant'][subsystem][subs[b]]
+            recovery_day = other_functionality_functions.fn_calc_subsystem_recovery(filt, damage, repair_complete_day, total_num_comps, damaged_comps)
+            comps_breakdown = filt * initial_damaged * recovery_day.reshape(len(recovery_day),1)
+            recovery_day_all = np.fmax(recovery_day_all, recovery_day) # combine with previous stories
+            comp_breakdowns_all = np.fmax(comp_breakdowns_all,comps_breakdown)
+    
+        return recovery_day_all, comp_breakdowns_all
+    '''Subfunction ends'''
+    
+    '''Subfunction'''
+    def check_roof_function(roof_sys_filter, damage_threshold, repair_complete_day_w_tmp, qnt_damaged, num_comps):
+
+        # Check the roof area for function (seal and function)
+        num_comp_damaged = roof_sys_filter * qnt_damaged
+        num_roof_comps = roof_sys_filter * num_comps
+        
+        comps_day_repaired = np.array(repair_complete_day_w_tmp)
+        roof_recovery_day = np.zeros(np.shape(comps_day_repaired)[0])
+        all_comps_day_roof = np.zeros(np.shape(comps_day_repaired))
+        num_repair_time_increments = sum(roof_sys_filter) # possible unique number of loop increments
+
+        # Loop through each unique repair time increment and determine when stops affecting function
+        for i in range(num_repair_time_increments):
+            # Determine the area of roof affected 
+            percent_area_affected = np.sum(num_comp_damaged, axis = 1) / sum(num_roof_comps) # Assumes roof components do not occupy the same area of roof            
+            # Determine if current damage affects function for this tenant unit
+            # if the area of exterior wall damage is greater than what is
+            # acceptable by the tenant 
+            affects_function = percent_area_affected >= damage_threshold
+
+            # Add days in this increment to the tally
+            delta_day = np.nanmin(comps_day_repaired[:, roof_sys_filter],axis = 1)
+            delta_day[np.isnan(delta_day)] = 0
+            roof_recovery_day = roof_recovery_day + affects_function * delta_day
+
+            # Add days to components that are affecting function
+            any_area_affected_all_comps = num_comp_damaged > 0 # Count any component that contributes to the loss of function regardless of by how much
+            all_comps_day_roof = all_comps_day_roof + any_area_affected_all_comps * (affects_function * delta_day).reshape(len(delta_day),1)
+
+            # Change the comps for the next increment
+            # reducing damage for what has been repaired in this time increment
+            comps_day_repaired = comps_day_repaired - delta_day.reshape(len(delta_day),1)
+            comps_day_repaired[comps_day_repaired <= 0] = np.nan
+            fixed_comps_filt = np.isnan(comps_day_repaired)
+            num_comp_damaged[fixed_comps_filt] = 0
+
+        return all_comps_day_roof, roof_recovery_day
+    '''Subfunction ends'''
+    
+    ## Initial Setup
+    num_units = len(damage['tenant_units'])
+    num_reals, num_comps = np.shape(np.array(damage['tenant_units'][0]['qnt_damaged']))
+    num_stories = building_model['num_stories']
+    
+    recovery_day = {
+        'elevators' : np.zeros([num_reals,num_units]),
+        'exterior' : np.zeros([num_reals,num_units]),
+        'roof': np.zeros([num_reals,num_units]),
+        'interior' : np.zeros([num_reals,num_units]),
+        'electrical' : np.zeros([num_reals,num_units]),
+        'flooding' : np.zeros([num_reals,num_units]),
+        'water_potable' : np.zeros([num_reals,num_units]),
+        'water_sanitary' : np.zeros([num_reals,num_units]),
+        'hvac_ventilation' : np.zeros([num_reals,num_units]),     
+        'hvac_cooling' : np.zeros([num_reals,num_units]),
+        'hvac_heating' : np.zeros([num_reals,num_units]),
+        'hvac_exhaust' : np.zeros([num_reals,num_units]),
+        'data' : np.zeros([num_reals,num_units]),
+        }
+    
+    comp_breakdowns = {
+        'elevators' : np.zeros([num_reals,num_comps,num_units]),
+        'electrical' : np.zeros([num_reals,num_comps,num_units]),
+        'exterior' : np.zeros([num_reals,num_comps,num_units]),
+        'roof' : np.zeros([num_reals,num_comps,num_units]),
+        'interior' : np.zeros([num_reals,num_comps,num_units]),
+        'flooding' : np.zeros([num_reals,num_comps,num_units]),
+        'water_potable' : np.zeros([num_reals,num_comps,num_units]),
+        'water_sanitary' : np.zeros([num_reals,num_comps,num_units]),
+        'hvac_ventilation' : np.zeros([num_reals,num_comps,num_units]),
+        'hvac_cooling' : np.zeros([num_reals,num_comps,num_units]),
+        'hvac_heating' : np.zeros([num_reals,num_comps,num_units]),
+        'hvac_exhaust' : np.zeros([num_reals,num_comps,num_units]),
+        'data' : np.zeros([num_reals,num_comps,num_units]),        
+        }
+    
+    ## Go through each tenant unit, define system level performacne and determine tenant unit recovery time
+    ## STORY FLOODING
+    for tu in reversed(range(num_stories)): # Go from top to bottom
+        is_damaged = np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0
+        flooding_this_story = np.any(is_damaged[:,damage['fnc_filters']['causes_flooding']], axis=1) # Any major piping damage causes interior flooding
+        flooding_recovery_day = flooding_this_story * impeding_temp_repairs['flooding_repair_day']
+    
+        # Save clean up time per component causing flooding
+        comp_breakdowns['flooding'][:,:,tu] = damage['fnc_filters']['causes_flooding'] * is_damaged * flooding_recovery_day.reshape(num_reals,1)
+    
+        # This story is not accessible if any story above has flooding
+        if tu < num_stories-1: #FZ# If this story id not top story
+            recovery_day['flooding'][:,tu] = np.nanmax(np.column_stack((flooding_recovery_day, recovery_day['flooding'][:,(tu+1):num_stories])), axis = 1)
+        else: #FZ# If this story is top story
+            recovery_day['flooding'][:,tu] = flooding_recovery_day                         
+    
+    ## SYSTEM SPECIFIC CONSEQUENCES    
+    for tu in range(num_units):
+        damaged_comps = np.array(damage['tenant_units'][tu]['qnt_damaged'])
+        initial_damaged = damaged_comps > 0
+        total_num_comps = np.array(damage['tenant_units'][tu]['num_comps'])
+        unit={}
+        for key in list(tenant_units.keys()):
+            unit[key] = tenant_units[key][tu]        
+        
+        repair_complete_day = tuple(map(tuple,damage['tenant_units'][tu]['recovery']['repair_complete_day'])) #FZ# Made tuple to bypass the issue of mutable numpy array
+        repair_complete_day_w_tmp = tuple(map(tuple, damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp'])) #FZ# Made tuple to bypass the issue of mutable numpy array
+        
+        ## Elevators
+        if unit['is_elevator_required'] == 1:
+            comps_day_repaired= tuple(map(tuple,system_operation_day['comp']['elev_day_repaired'])) #FZ# Made tuple to bypass the issue of mutable numpy array
+            comps_day_repaired = np.array(comps_day_repaired)
+            comps_day_repaired[comps_day_repaired == 0] = np.nan
+            comps_quant_damaged = tuple(map(tuple,system_operation_day['comp']['elev_quant_damaged'])) #FZ# Made tuple to bypass the issue of mutable numpy array
+            comps_quant_damaged = np.array(comps_quant_damaged)
+            elev_function_recovery_day = np.zeros(num_reals)
+            elev_comps_day_fnc = np.zeros([num_reals,num_comps])
+            num_repair_time_increments = sum(damage['fnc_filters']['elevators']) # possible unique number of loop increments
+            # Loop through each unique repair time increment and determine when
+            # stops affecting function
+            for i in range(num_repair_time_increments):
+                '''Take the max of component damage to determine the number of
+                shafts/cabs that are damaged/non_operational
+                This assumes that different elevator components are correlated'''
+                num_damaged_elevs = np.nanmax(comps_quant_damaged, axis=1) # this assumes elevators are in one performance group if simeltaneous
+                num_damaged_elevs = np.fmin(num_damaged_elevs, building_model['num_elevators']) # you can never have more elevators damaged than exist
+                
+                '''If elevators are in mutliple performance groups and those
+                elevators have simultaneous damage states, it is not possible
+                to count the number of damaged elevators without additional
+                information'''
+                num_elev_pgs = len(np.unique(damage['comp_ds_table']['comp_idx'][damage['fnc_filters']['elevators']]))
+                is_sim_ds = any(damage['comp_ds_table']['is_sim_ds'][damage['fnc_filters']['elevators']])
+                if (num_elev_pgs > 1) and is_sim_ds:
+                    sys.exit('Error! PBEE_Recovery:Function','Elevator Function check does not handle multiple performance groups with simultaneous damage states')
+                
+    
+                # quantifty the number of occupancy needing to use the elevators
+                # all occupants above the first floor will try to use the elevators
+                building_occ_per_elev = sum(building_model['occupants_per_story'][1:len(building_model['occupants_per_story'])]) / (building_model['num_elevators'] - num_damaged_elevs) 
+                
+                # elevator function check
+                # do tenants have sufficient elevator access need based on
+                # elevators that are still operational
+                # affects_function = building_occ_per_elev > max(unit['occ_per_elev'])
+                affects_function = building_occ_per_elev > unit['occ_per_elev'] #FZ# Check. Why max is done . Only a single value per unit
+                # Add days in this increment to the tally
+                delta_day = np.nanmin(comps_day_repaired[:,damage['fnc_filters']['elevators']], axis=1)
+                delta_day[np.isnan(delta_day)] = 0
+                elev_function_recovery_day = elev_function_recovery_day + affects_function * delta_day
+                
+                # Add days to components that are affecting occupancy
+                any_elev_damage = comps_quant_damaged > 0 # Count any component that contributes to the loss of function regardless of by how much 
+                elev_comps_day_fnc = elev_comps_day_fnc + any_elev_damage * affects_function.reshape(num_reals,1) * delta_day.reshape(num_reals,1)
+    
+                # Change the comps for the next increment
+                comps_day_repaired = comps_day_repaired - delta_day.reshape(num_reals,1)
+                comps_day_repaired[comps_day_repaired <= 0] = np.nan
+                fixed_comps_filt = np.isnan(comps_day_repaired)
+                comps_quant_damaged[fixed_comps_filt] = 0
+            
+            power_supply_recovery_day = np.fmax(system_operation_day['building']['elevator_mcs'], system_operation_day['building']['electrical_main'])
+            
+            recovery_day['elevators'][:,tu] = np.fmax(elev_function_recovery_day, power_supply_recovery_day) # electrical system and utility
+            power_supply_recovery_day_comp = np.fmax(system_operation_day['comp']['elevator_mcs'], system_operation_day['comp']['electrical_main'])
+            comp_breakdowns['elevators'][:,:,tu] = np.fmax(elev_comps_day_fnc, power_supply_recovery_day_comp)
+        
+        
+        ## Exterior Enclosure 
+        # Perimeter Cladding (assuming all exterior components have either lf or sf units)
+        area_affected_lf_all_comps = damage['comp_ds_table']['exterior_surface_area_factor'] * damage['comp_ds_table']['unit_qty'] * building_model['ht_per_story_ft'][tu] * damage['tenant_units'][tu]['qnt_damaged']
+        area_affected_direct_scale_all_comps = damage['comp_ds_table']['exterior_surface_area_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged']
+       
+        comp_affected_area = np.zeros([num_reals,num_comps])
+        comp_affected_area[:,damage['fnc_filters']['exterior_seal_lf']] = area_affected_lf_all_comps[:,damage['fnc_filters']['exterior_seal_lf']]
+        comp_affected_area[:,damage['fnc_filters']['exterior_seal_sf']] = area_affected_direct_scale_all_comps[:,damage['fnc_filters']['exterior_seal_sf']]
+        comp_affected_area[:,damage['fnc_filters']['exterior_seal_ea']] = area_affected_direct_scale_all_comps[:,damage['fnc_filters']['exterior_seal_ea']]
+        
+        comps_day_repaired = np.array(repair_complete_day)
+        ext_function_recovery_day = np.zeros(num_reals)
+        all_comps_day_ext = np.zeros([num_reals,num_comps])
+        num_repair_time_increments = sum(damage['fnc_filters']['exterior_seal_all']) # possible unique number of loop increments
+        # Loop through each unique repair time increment and determine when stops affecting function
+        for i in range(num_repair_time_increments):
+            # Determine the area of wall which has severe exterior encolusure damage 
+            area_affected = np.sum(comp_affected_area, axis=1) # Assumes cladding components do not occupy the same perimeter area
+            percent_area_affected = np.fmin(area_affected / unit['perim_area'], 1) # normalize it. #FZ# Should be fraction area?
+            
+            # Determine if current damage affects function for this tenant unit
+            # if the area of exterior wall damage is greater than what is
+            # acceptable by the tenant 
+            affects_function = percent_area_affected > unit['exterior'] 
+            
+            # Add days in this increment to the tally
+            delta_day = np.nanmin(comps_day_repaired[:,damage['fnc_filters']['exterior_seal_all']], axis=1)
+            delta_day[np.isnan(delta_day)] = 0
+            ext_function_recovery_day = ext_function_recovery_day + affects_function * delta_day
+            # Add days to components that are affecting occupancy
+            any_area_affected_all_comps = comp_affected_area > 0 # Count any component that contributes to the loss of occupance regardless of by how much
+            all_comps_day_ext = all_comps_day_ext + any_area_affected_all_comps * affects_function.reshape(num_reals,1) * delta_day.reshape(num_reals,1)
+            
+            # Change the comps for the next increment
+            # reducing damage for what has been repaired in this time increment
+            comps_day_repaired = comps_day_repaired - delta_day.reshape(num_reals,1)
+            comps_day_repaired[comps_day_repaired <= 0] = np.nan
+            fixed_comps_filt = np.isnan(comps_day_repaired)
+            comp_affected_area[fixed_comps_filt] = 0
+            
+        recovery_day['exterior'][:,tu] = ext_function_recovery_day
+        comp_breakdowns['exterior'][:,:,tu] = all_comps_day_ext        
+
+        if unit['story'] == num_stories: # If this is the top story, check the roof for function
+            #Roof structure check
+            all_comps_day_roof_struct, roof_structure_recovery_day = check_roof_function(damage['fnc_filters']['roof_structure'],
+                                                                                        np.array(subsystems['redundancy_threshold'][subsystems['id'] == 21])[0],
+                                                                                        repair_complete_day_w_tmp,
+                                                                                        damage['tenant_units'][tu]['qnt_damaged'],
+                                                                                        damage['tenant_units'][tu]['num_comps'])
+            
+            # Roof seatherproofing check
+            all_comps_day_roof_weather, roof_weather_recovery_day = check_roof_function(damage['fnc_filters']['roof_weatherproofing'],
+                                                                                        np.array(subsystems['redundancy_threshold'][subsystems['id'] == 22])[0],
+                                                                                        repair_complete_day_w_tmp,
+                                                                                        damage['tenant_units'][tu]['qnt_damaged'],
+                                                                                        damage['tenant_units'][tu]['num_comps'])           
+           
+            # Combine branches
+            recovery_day['roof'][:,tu] = np.fmax(roof_structure_recovery_day, roof_weather_recovery_day)
+            comp_breakdowns['roof'][:,:,tu] = np.fmax(all_comps_day_roof_struct, all_comps_day_roof_weather)
+        
+        ## Interior Area
+        area_affected_lf_all_comps    = damage['comp_ds_table']['interior_area_factor'] * damage['comp_ds_table']['unit_qty'] * building_model['ht_per_story_ft'][tu] * damage['tenant_units'][tu]['qnt_damaged']
+        area_affected_direct_scale_all_comps    = damage['comp_ds_table']['interior_area_factor'] * damage['comp_ds_table']['unit_qty'] * damage['tenant_units'][tu]['qnt_damaged']
+        area_affected_bay_all_comps   = damage['comp_ds_table']['interior_area_factor'] * building_model['struct_bay_area_per_story'][tu] * damage['tenant_units'][tu]['qnt_damaged']
+        area_affected_build_all_comps = damage['comp_ds_table']['interior_area_factor'] * sum(building_model['area_per_story_sf']) * damage['tenant_units'][tu]['qnt_damaged']
+        
+        repair_complete_day_w_tmp_w_instabilities = np.array(repair_complete_day_w_tmp)
+        if tu > 0: #FZ# changed to 0 to account for python index starting from 0.
+            area_affected_below = damage['comp_ds_table']['interior_area_factor'] * building_model['struct_bay_area_per_story'][tu-1] * damage['tenant_units'][tu-1]['qnt_damaged']
+            area_affected_bay_all_comps[:,damage['fnc_filters']['vert_instabilities']] = np.fmax(
+                area_affected_below[:,damage['fnc_filters']['vert_instabilities']],area_affected_bay_all_comps[:,damage['fnc_filters']['vert_instabilities']])
+            repair_time_below = damage['tenant_units'][tu-1]['recovery']['repair_complete_day_w_tmp']
+            repair_complete_day_w_tmp_w_instabilities[:,damage['fnc_filters']['vert_instabilities']] = np.fmax(
+                repair_time_below[:,damage['fnc_filters']['vert_instabilities']], np.array(repair_complete_day_w_tmp)[:,damage['fnc_filters']['vert_instabilities']])
+
+    
+        comp_affected_area = np.zeros([num_reals,num_comps])
+        comp_affected_area[:,damage['fnc_filters']['interior_function_lf']] = area_affected_lf_all_comps[:,damage['fnc_filters']['interior_function_lf']]
+        comp_affected_area[:,damage['fnc_filters']['interior_function_sf']] = area_affected_direct_scale_all_comps[:,damage['fnc_filters']['interior_function_sf']]
+        comp_affected_area[:,damage['fnc_filters']['interior_function_ea']] = area_affected_direct_scale_all_comps[:,damage['fnc_filters']['interior_function_ea']]        
+        comp_affected_area[:,damage['fnc_filters']['interior_function_bay']] = area_affected_bay_all_comps[:,damage['fnc_filters']['interior_function_bay']]
+        comp_affected_area[:,damage['fnc_filters']['interior_function_build']] = area_affected_build_all_comps[:,damage['fnc_filters']['interior_function_build']]
+    
+        frag_types_in_check = np.unique(damage['comp_ds_table']['comp_type_id'][damage['fnc_filters']['interior_function_all']])
+        comps_day_repaired = repair_complete_day_w_tmp_w_instabilities.copy()
+    
+        int_function_recovery_day = np.zeros(num_reals)
+        int_comps_day_repaired = np.zeros([num_reals,num_comps])
+        num_repair_time_increments = sum(damage['fnc_filters']['interior_function_all']) # possible unique number of loop increments
+        # Loop through each unique repair time increment and determine when stops affecting function
+        for i in range(num_repair_time_increments):
+            # Quantify the affected area (based on srss of differenct component
+            # types)
+            diff_comp_areas = np.empty([num_reals, len(frag_types_in_check)])
+            for cmp in range(len(frag_types_in_check)):
+                filt = damage['comp_ds_table']['comp_type_id'] == frag_types_in_check[cmp] # check to see if it matches the first part of the ID (ie the type of comp)
+                diff_comp_areas[:,cmp] = np.sum(comp_affected_area[:,filt], axis=1)
+            
+            area_affected = np.sqrt(np.sum(diff_comp_areas**2, axis=1)) # total area affected is the srss of the areas in the unit
+            percent_area_affected = np.fmin(area_affected / unit['area'], 1) # no greater than the total unit area
+        
+            # Determine if current damage affects function for this tenant unit
+            # affects function if the area of interior damage is greater than what is
+            # acceptable by the tenant 
+            affects_function = percent_area_affected > unit['interior'] 
+            
+            # Add days in this increment to the tally
+            delta_day = np.nanmin(comps_day_repaired[:,damage['fnc_filters']['interior_function_all']], axis=1)
+            delta_day[np.isnan(delta_day)] = 0
+            int_function_recovery_day = int_function_recovery_day + affects_function * delta_day
+            
+            # Add days to components that are affecting occupancy
+            any_area_affected_all_comps = comp_affected_area > 0 # Count any component that contributes to the loss of occupance regardless of by how much
+            int_comps_day_repaired = int_comps_day_repaired + any_area_affected_all_comps * affects_function.reshape(num_reals,1) * delta_day.reshape(num_reals,1)
+            
+            # Change the comps for the next increment
+            # reducing damage for what has been repaired in this time increment
+            comps_day_repaired = comps_day_repaired - delta_day.reshape(num_reals,1)
+            comps_day_repaired[comps_day_repaired <= 0] = np.nan
+            fixed_comps_filt = np.isnan(comps_day_repaired)
+            comp_affected_area[fixed_comps_filt] = 0
+            
+        recovery_day['interior'][:,tu] = int_function_recovery_day
+        comp_breakdowns['interior'][:,:,tu] = int_comps_day_repaired
+        
+        # Water and Plumbing System
+        # determine effect on funciton at this tenant unit
+        # any major damage to the branch pipes (small diameter) failes for this tenant unit
+        tenant_sys_recovery_day = np.nanmax(np.array(repair_complete_day) * damage['fnc_filters']['water_unit'], axis=1) 
+        recovery_day['water_potable'][:,tu] = np.fmax(system_operation_day['building']['water_potable_main'],tenant_sys_recovery_day)
+              
+        # distribute effect to the components
+        comp_breakdowns['water_potable'][:,:,tu] = np.fmax(system_operation_day['comp']['water_potable_main'], np.array(repair_complete_day) * damage['fnc_filters']['water_unit'])
+        
+        # In taller buildings, water needs to be pumped to reach upper stories
+        #and therefore requires electrical power
+        if unit['story'] > functionality_options['water_pressure_max_story']:
+            electrical_failure_controls = system_operation_day['building']['electrical_main'] > recovery_day['water_potable'][:,tu]
+            recovery_day['water_potable'][:,tu] = np.fmax(recovery_day['water_potable'][:,tu], system_operation_day['building']['electrical_main'])
+            comp_breakdowns['water_potable'][:,:,tu] = np.fmax(comp_breakdowns['water_potable'][:,:,tu] * np.logical_not(electrical_failure_controls).reshape(num_reals,1), 
+                                                               system_operation_day['comp']['electrical_main'] * electrical_failure_controls.reshape(num_reals,1))
+        
+        
+        ## Sanitary Waste System
+        # determine effect on funciton at this tenant unit
+        # any major damage to the branch pipes (small diameter) failes for this tenant unit
+        tenant_sys_recovery_day = np.nanmax(repair_complete_day * damage['fnc_filters']['sewer_unit'], axis=1) 
+        recovery_day['water_sanitary'][:,tu] = np.fmax(system_operation_day['building']['water_sanitary_main'], tenant_sys_recovery_day)
+    
+        # distribute effect to the components
+        comp_breakdowns['water_sanitary'][:,:,tu] = np.fmax(system_operation_day['comp']['water_sanitary_main'], np.array(repair_complete_day) * damage['fnc_filters']['water_unit'])
+    
+        # Sanitary waste operation at this tenant unit depends on the 
+        # operation of the potable water system at this tenant unit
+        recovery_day['water_sanitary'][:,tu] = np.fmax(recovery_day['water_sanitary'][:,tu],recovery_day['water_potable'][:,tu])
+        comp_breakdowns['water_sanitary'][:,:,tu] = np.fmax(comp_breakdowns['water_sanitary'][:,:,tu], comp_breakdowns['water_potable'][:,:,tu])
+  
+        ## Electrical Power System
+        # Does not consider effect of backup systems
+        if unit['is_electrical_required'] == 1:
+            # determine effect on funciton at this tenant unit
+            # any major damage to the unit level electrical equipment fails for this tenant unit
+            tenant_sys_recovery_day = np.nanmax(np.array(repair_complete_day) * damage['fnc_filters']['electrical_unit'], axis=1)
+            recovery_day['electrical'][:,tu] =np.fmax(system_operation_day['building']['electrical_main'], tenant_sys_recovery_day)
+                      
+            # distribute effect to the components
+            comp_breakdowns['electrical'][:,:,tu] = np.fmax(system_operation_day['comp']['electrical_main'], np.array(repair_complete_day) * damage['fnc_filters']['electrical_unit'])
+
+        ## HVAC System
+        # HVAC: Control System
+        recovery_day_hvac_control = system_operation_day['building']['hvac_control'] # Electrical power is counted in here
+        comp_breakdowns_hvac_control = system_operation_day['comp']['hvac_control']
+        
+        dependancy = {}
+        # HVAC: Ventilation
+        dependancy['recovery_day'] = recovery_day_hvac_control
+        dependancy['comp_breakdown'] = comp_breakdowns_hvac_control
+        recovery_day['hvac_ventilation'][:,tu], comp_breakdowns['hvac_ventilation'][:,:,tu] = subsystem_recovery('hvac_ventilation',
+                                                                         damage, 
+                                                                         repair_complete_day,
+                                                                         total_num_comps, 
+                                                                         damaged_comps, 
+                                                                         initial_damaged, 
+                                                                         dependancy)                               
+
+        #HVAC: Heating
+        dependancy['recovery_day'] = np.fmax(recovery_day['hvac_ventilation'][:,tu], system_operation_day['building']['hvac_heating'])
+        dependancy['comp_breakdown'] = np.fmax(comp_breakdowns['hvac_ventilation'][:,:,tu], system_operation_day['comp']['hvac_heating'])
+        recovery_day['hvac_heating'][:,tu], comp_breakdowns['hvac_heating'][:,:,tu] = subsystem_recovery('hvac_heating', 
+                                                                     damage, 
+                                                                     repair_complete_day, 
+                                                                     total_num_comps, 
+                                                                     damaged_comps, 
+                                                                     initial_damaged, 
+                                                                     dependancy)
+    
+        # HVAC: Cooling
+        dependancy['recovery_day'] = np.fmax(recovery_day['hvac_ventilation'][:,tu],system_operation_day['building']['hvac_cooling'])
+        dependancy['comp_breakdown'] = np.fmax(comp_breakdowns['hvac_ventilation'][:,:,tu], system_operation_day['comp']['hvac_cooling'])
+        recovery_day['hvac_cooling'][:,tu], comp_breakdowns['hvac_cooling'][:,:,tu] = subsystem_recovery('hvac_cooling', 
+                                                                     damage, 
+                                                                     repair_complete_day, 
+                                                                     total_num_comps, 
+                                                                     damaged_comps, 
+                                                                     initial_damaged, 
+                                                                     dependancy)
+    
+        # HVAC: Exhaust
+        dependancy['recovery_day'] = recovery_day_hvac_control
+        dependancy['comp_breakdown'] = comp_breakdowns_hvac_control
+        recovery_day['hvac_exhaust'][:,tu], comp_breakdowns['hvac_exhaust'][:,:,tu] = subsystem_recovery('hvac_exhaust', 
+                                                  damage, 
+                                                  repair_complete_day, 
+                                                  total_num_comps, 
+                                                  damaged_comps, 
+                                                  initial_damaged, 
+                                                  dependancy)    
+
+        ## Data
+        if unit['is_data_required'] == 1 and any(damage['fnc_filters']['data_unit'] | damage['fnc_filters']['data_main']):
+        # determine effect on funciton at this tenant unit
+        # any major damage to the unit level electrical equipment fails for this tenant unit
+            tenant_sys_recovery_day = np.nanmax(repair_complete_day * damage['fnc_filters']['data_unit'], axis=1)
+            recovery_day['data'][:,tu] = np.fmax(system_operation_day['building']['data_main'], tenant_sys_recovery_day)
+
+            # Consider effect of external power network
+                 
+            recovery_day['data'] = np.fmax(recovery_day['data'], system_operation_day['building']['electrical_main'].reshape(num_reals,1))
+    
+            # distribute effect to the components
+            comp_breakdowns['data'][:,:,tu] = np.fmax(system_operation_day['comp']['data_main'], np.array(repair_complete_day) * damage['fnc_filters']['data_unit'])
+
+        ## Post process for tenant-specific requirements 
+        # Zero out systems that are not required by the tenant
+        # Still need to calculate above due to dependancies between options
+        if unit['is_water_potable_required'] != 1:
+            recovery_day['water_potable'] = np.zeros([num_reals,num_units])
+            comp_breakdowns['water_potable'] = np.zeros([num_reals,num_comps,num_units])
+
+        if unit['is_water_sanitary_required'] != 1:
+            recovery_day['water_sanitary'] = np.zeros([num_reals,num_units])
+            comp_breakdowns['water_sanitary'] = np.zeros([num_reals,num_comps,num_units])
+
+        if unit['is_hvac_ventilation_required'] != 1:
+            recovery_day['hvac_ventilation'] = np.zeros([num_reals,num_units])
+            comp_breakdowns['hvac_ventilation'] = np.zeros([num_reals,num_comps,num_units])
+
+        if unit['is_hvac_heating_required'] != 1:
+            recovery_day['hvac_heating'] = np.zeros([num_reals,num_units])
+            comp_breakdowns['hvac_heating'] = np.zeros([num_reals,num_comps,num_units])
+
+        if unit['is_hvac_cooling_required'] != 1:
+            recovery_day['hvac_cooling'] = np.zeros([num_reals,num_units])
+            comp_breakdowns['hvac_cooling'] = np.zeros([num_reals,num_comps,num_units])
+
+        if unit['is_hvac_exhaust_required'] != 1:
+            recovery_day['hvac_exhaust'] = np.zeros([num_reals,num_units])
+            comp_breakdowns['hvac_exhaust'] = np.zeros([num_reals,num_comps,num_units])
+
+    return recovery_day, comp_breakdowns    
+
+
+def fn_combine_comp_breakdown(comp_ds_table, perform_targ_days, comp_names, reoccupancy, functional):
+    '''get the combined reoccupancy/functionality effect of components
+    
+    Parameters
+    ----------
+    comp_ds_table: table
+      contains basic component attributes
+    perform_targ_days: array
+      recovery time horizons to consider
+    comp_names: cell array
+      string ids of components to attribte recovery time to
+    reoccupancy: array [num reals x num comp_ds]
+      realizations of reoccupancy time broken down for each damage state of
+      each component
+    functional: array [num reals x num comp_ds]
+      realizations of functional recovery time broken down for each damage
+      state of each component
+    
+    Returns
+    -------
+    combined: array
+      Probability of recovering within time horizon for each component
+      considering both consequenses from reoccupancy and functional recovery'''
+    
+    import numpy as np
+    ## Method
+    combined = np.zeros([len(comp_names),len(perform_targ_days)])
+    max_reocc_func = np.fmax(reoccupancy, functional)
+    for c in range(len(comp_names)):
+        comp_filt = comp_ds_table['comp_id'] == comp_names[c] # find damage states associated with this component    
+        combined[c,:] = np.mean(np.nanmax(max_reocc_func[:,comp_filt], axis=1).reshape(len(max_reocc_func),1) > np.array(perform_targ_days).reshape(1, len(perform_targ_days)), axis=0)
+
+    return combined
+
+
+def fn_extract_recovery_metrics( tenant_unit_recovery_day, 
+                                 recovery_day, comp_breakdowns, comp_id, 
+                                 simulated_replacement_time):
+    '''Reformant tenant level recovery outcomes into outcomes at the building level, 
+    system level, and compoennt level
+    
+    Parameters
+    ----------
+    tenant_unit_recovery_day: array [num_reals x num_tenant_units]
+     simulated recovery day of each tenant unit
+     
+    recovery_day: dictionary
+     simulation of the number of days each fault tree event is affecting
+     recovery
+    comp_breakdowns: dictionary
+     simulation of each components contributions to each of the fault tree events 
+     
+    comp_id: cell array [1 x num comp damage states]
+     list of each fragility id associated with the per component damage
+     state structure of the damage object. With of array is the same as the
+     arrays in the comp_breakdowns structure
+     
+    simulated_replacement_time: array [num_reals x 1]
+     simulated time when the building needs to be replaced, and how long it
+     will take (in days). NaN represents no replacement needed (ie
+     building will be repaired)
+    Returns
+    -------
+    recovery['tenant_unit']['recovery_day']: array [num_reals x num_tenant_units]
+    simulated recovery day of each tenant unit
+    
+    recovery['building_level']['recovery_day']: array [num_reals x 1]
+    simulated recovery day of the building (all tenant units recovered)
+    
+    recovery['building_level']['initial_percent_affected']: array [num_reals x 1]
+    simulated fraction of the building with initial loss of
+    reoccupancy/function
+    
+    recovery['recovery_trajectory']['recovery_day']: array [num_reals x num recovery steps]
+    simulated recovery trajectory y-axis
+    
+    recovery['recovery_trajectory']['percent_recovered']: array [1 x num recovery steps]
+    recovery trajectory x-axis
+    
+    recovery['breakdowns']['system_breakdowns']: array [num fault tree events x target days]
+    fraction of realizations affected by various fault tree events beyond
+    specific target recovery days
+    
+    recovery['breakdowns']['component_breakdowns']: array [num components x target days]
+    fraction of realizations affected by various components beyond
+    specific target recovery days
+    
+    recovery['breakdowns']['perform_targ_days']: array [0 x target days]
+    specific target recovery days
+    
+    recovery['breakdowns']['system_names']: array [num fault tree events x 1]
+    name of each fault tree event
+    
+    recovery['breakdowns']['comp_names']: array [num components x 1]
+    fragility IDs of each component'''
+    
+    import numpy as np
+    
+    recovery={'tenant_unit' : {}, 'building_level' : {}, 'recovery_trajectory' : {}, 'breakdowns' : {}, 'partial' : {}}
+    
+    ## Initial Setup
+    num_units = np.size(tenant_unit_recovery_day,1)
+
+    # Define performance targets
+    perform_targ_days = [0, 3, 7, 14, 30, 60, 90, 120, 182, 270, 365]
+    #FZ# Maximum day for recovery amongs all tenant units and all realizations
+    recovery_day_max = max(np.nanmax(tenant_unit_recovery_day, axis=1))
+
+    #FZ# Append perform target days with more milestones if repair time exceeds 1 year  
+    if recovery_day_max <= 365:
+        perform_targ_days = perform_targ_days # Number of days for each performance target stripe
+    if recovery_day_max > 365:
+        num_years = (int(np.floor((recovery_day_max)/365)))
+        
+        # If number of complete years is more than 2
+        for yr in range(num_years-1):
+            quarters =[1,2,3]
+            for qtr in quarters:
+                perform_targ_days.append(365*(yr+1) + qtr * 90)
+            perform_targ_days.append(365*(yr+2))
+        
+        # For final incomplete year
+        num_quarters = int(np.floor((recovery_day_max - perform_targ_days[-1])/90))
+        for qtr in range(num_quarters):
+            perform_targ_days.append(perform_targ_days[-1] +  90)
+        perform_targ_days.append(recovery_day_max)
+        
+        
+    # Determine replacement cases
+    replace_cases = np.logical_not(np.isnan(simulated_replacement_time))
+    ''' Post process tenant-level recovery times
+      Overwrite NaNs in tenant_unit_day_functional
+      Only NaN where never had functional loss, therefore set to zero'''
+    tenant_unit_recovery_day[np.isnan(tenant_unit_recovery_day)] = 0
+    
+    # Overwrite building replacment cases to replacement time
+    tenant_unit_recovery_day[replace_cases,:] = np.array(simulated_replacement_time)[replace_cases].reshape(len(np.array(simulated_replacement_time)[replace_cases]),1)* np.ones([1,num_units])   
+   
+    ## Save building-level outputs to occupancy structure
+    # Tenant Unit level outputs
+    recovery['tenant_unit']['recovery_day'] = tenant_unit_recovery_day
+    
+    # Building level outputs
+    recovery['building_level']['recovery_day'] = np.nanmax(tenant_unit_recovery_day, axis=1)
+    recovery['building_level']['initial_percent_affected'] = np.mean(tenant_unit_recovery_day > 0, axis=1) # percent of building affected, not the percent of realizations
+    
+    ## Recovery Trajectory -- calcualte from the tenant breakdowns
+    recovery['recovery_trajectory']['recovery_day'] = np.sort(np.column_stack((tenant_unit_recovery_day, tenant_unit_recovery_day)), axis=1)
+    recovery['recovery_trajectory']['percent_recovered'] = np.sort(np.concatenate((np.arange(num_units), np.arange(1, num_units+1)))/num_units)
+    recovery['building_level']['perform_targ_days'] = perform_targ_days
+    recovery['building_level']['prob_of_target'] = np.mean(recovery['building_level']['recovery_day'].reshape(len(recovery['building_level']['recovery_day']),1) > np.array(perform_targ_days).reshape(1,len(perform_targ_days)), axis=0)
+    
+    
+    # Save specific breakdowns for red tags
+    if 'building_safety' in recovery_day.keys():
+        red_tag_day = recovery_day['building_safety']['red_tag']
+        red_tag_day[replace_cases] = np.array(simulated_replacement_time)[replace_cases]
+        recovery['building_level']['recovery_day_red_tag'] = red_tag_day
+
+    
+    ## Recovery Trajectory -- calcualte from the tenant breakdowns
+    recovery['recovery_trajectory']['recovery_day'] = np.sort(np.column_stack((tenant_unit_recovery_day, tenant_unit_recovery_day)), axis =1)
+    recovery['recovery_trajectory']['percent_recovered'] = np.sort(np.concatenate((np.arange(0, (num_units)), np.arange(1, (num_units+1))))) / num_units
+
+    #partial recovery
+    pct_recovered_targets = [0.1, 0.5, 0.75, 0.8, 1]
+    
+    # order the tennant recovery days so it's easier to see at what time the required numnber of units
+    # are required
+    ordered_tenant_repair_days = np.sort(tenant_unit_recovery_day, axis = 1)
+    
+    for i_pct in range(len(pct_recovered_targets)):
+        recovery['partial'][i_pct] = {}
+        target_recovery_ratio_units = pct_recovered_targets[i_pct]
+        recovery['partial'][i_pct]['target_recovery_ratio_units'] = target_recovery_ratio_units
+        recovery['partial'][i_pct]['target_recovery_day'] = perform_targ_days;
+        recovery['partial'][i_pct]['prob_of_target'] = {}
+        for i_targ_day in range(len(perform_targ_days)):
+            targ_day = perform_targ_days[i_targ_day]
+            # get the percentage of tenant units recovered at the given day
+            # pct_recovered_per_real = np.mean(np.sum(tenant_unit_recovery_day <= targ_day, axis=1) / num_units, axis=1);
+            pct_recovered_per_real = np.sum(tenant_unit_recovery_day <= targ_day, axis=1) / num_units
+            recovery['partial'][i_pct]['prob_of_target'][i_targ_day] = np.mean(pct_recovered_per_real < target_recovery_ratio_units)
+           
+            # how many units need to be repaired to meet the percent required 
+            reqd_units = int(np.ceil(num_units * target_recovery_ratio_units))
+            recovery['partial'][i_pct]['reqd_units'] = reqd_units
+            recovery['partial'][i_pct]['mean'] = np.mean(ordered_tenant_repair_days[:, reqd_units-1]) #FZ reqd_units - 1 because pytho indexing start from zero.
+            recovery['partial'][i_pct]['median'] = np.percentile(ordered_tenant_repair_days[:, reqd_units-1], 50)
+            recovery['partial'][i_pct]['fractile_75'] = np.percentile(ordered_tenant_repair_days[:, reqd_units-1], 75)
+            recovery['partial'][i_pct]['fractile_90'] = np.percentile(ordered_tenant_repair_days[:, reqd_units-1], 90)
+
+
+
+    ## Format and Save Component-level breakdowns
+    # Find the day each ds of each component stops affecting recovery for any story
+    
+    # Combine among all fault tree events
+    component_breakdowns_per_story = 0
+    fault_tree_events_LV1 = list(comp_breakdowns.keys())
+    for i in range(len(fault_tree_events_LV1)):
+        fault_tree_events_LV2 = list(comp_breakdowns[fault_tree_events_LV1[i]].keys())
+        for j in range(len(fault_tree_events_LV2)):
+            component_breakdowns_per_story = np.fmax(component_breakdowns_per_story, 
+                                                 comp_breakdowns[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]])
+
+    
+    # Combine among all stories
+    # aka time each component's DS affects recovery anywhere in the building
+    component_breakdowns = np.nanmax(component_breakdowns_per_story,axis=2)
+    
+    # Ignore repalcement cases
+    # component_breakdowns[replace_cases,:] = np.nan #FZ# conveted to nan
+    component_breakdowns = np.delete(component_breakdowns, replace_cases, 0)
+    ## Format and Save System-level breakdowns
+    # Find the day each system stops affecting recovery for any story
+    
+    # Combine among all fault tree events
+    system_breakdowns = {}
+    fault_tree_events_LV1 = list(recovery_day.keys())
+    for i in range(len(fault_tree_events_LV1)):
+        fault_tree_events_LV2 = list(recovery_day[fault_tree_events_LV1[i]].keys())
+        for j in range(len(fault_tree_events_LV2)):
+            # Combine among all stories or tenant units to represent the events
+            # effect anywhere in the building 
+            if len(np.shape(recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]]))>1:
+                building_recovery_day = np.nanmax(recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]], axis=1)
+            else:
+                building_recovery_day = recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]].reshape(len(recovery_day[fault_tree_events_LV1[i]][fault_tree_events_LV2[j]]),1)
+
+            # Ignore repalcement cases
+            if len(np.shape(building_recovery_day))>1:
+                # building_recovery_day[replace_cases,:] = np.nan #FZ# conveted to nan
+                building_recovery_day = np.delete(building_recovery_day, replace_cases, 0)
+            else:
+                # building_recovery_day[replace_cases] = np.nan #FZ# conveted to nan
+                building_recovery_day = np.delete(building_recovery_day, replace_cases)
+        
+            # Save per "system", which typically represents the fault tree level 2
+            if fault_tree_events_LV2[j] in system_breakdowns.keys():
+                # If this "system" has already been defined in another fault
+                # tree branch, combine togeter by taking the max (i.e., max
+                # days this system affects recovery anywhere in the building)
+                system_breakdowns[fault_tree_events_LV2[j]] =  np.fmax(
+                    system_breakdowns[fault_tree_events_LV2[j]],building_recovery_day.reshape(len(building_recovery_day),1))
+            else:
+                system_breakdowns[fault_tree_events_LV2[j]] = building_recovery_day.reshape(len(building_recovery_day),1)
+
+    
+    ## Format breakdowns as performance targets
+    system_names = list(system_breakdowns.keys())
+    
+    # pre-allocating variables
+    comps = np.unique(comp_id)
+    recovery['breakdowns']['system_breakdowns'] = np.zeros([len(system_names),len(perform_targ_days)])
+    recovery['breakdowns']['component_breakdowns'] = np.zeros([len(comps),len(perform_targ_days)])
+    
+    # Calculate fraction of realization each system affects recovery for each
+    # performance target time
+    for s in range(len(system_names)):
+        recovery['breakdowns']['system_breakdowns'][s,:] = np.mean(system_breakdowns[system_names[s]] > perform_targ_days, axis=0)
+
+    
+    # Calculate fraction of realization each component affects recovery for each
+    # performance target time
+    for c in range(len(comps)):
+        comp_filt = comp_id == comps[c] # find damage states associated with this component
+        recovery['breakdowns']['component_breakdowns'][c,:] = np.nanmean(np.max(component_breakdowns[:,comp_filt], axis=1).reshape(len(component_breakdowns),1) > perform_targ_days, axis=0)
+
+    # store this so we can properly overalap the reoccupancy and functionality
+    recovery['breakdowns']['component_breakdowns_all_reals'] = component_breakdowns
+    
+    # Save other variables
+    recovery['breakdowns']['perform_targ_days'] = perform_targ_days
+    recovery['breakdowns']['system_names'] = system_names
+    recovery['breakdowns']['comp_names'] = comps
+    
+    return recovery
\ No newline at end of file
diff --git a/src/atc138/impedance/__init__.py b/src/atc138/impedance/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/atc138/impedance/main_impedance_function.py b/src/atc138/impedance/main_impedance_function.py
new file mode 100644
index 0000000..0bb3334
--- /dev/null
+++ b/src/atc138/impedance/main_impedance_function.py
@@ -0,0 +1,317 @@
+def main_impeding_factors(damage, impedance_options, repair_cost_ratio_total, 
+                          repair_cost_ratio_engineering, inspection_trigger, 
+                          systems, tmp_repair_class, building_value, 
+                          impeding_factor_medians, include_flooding_impact):
+
+    '''Calculate ATC-138 impeding times for each system given simulation of damage
+    
+    Parameters (taken from Dustin's MATLAB file)
+    ----------
+    damage: dictionary
+      contains per damage state damage and loss data for each component in the building
+    
+    impedance_options: dictionary
+      general impedance assessment user inputs such as mitigation factors
+    
+    repair_cost_ratio: array [num_reals x 1]
+      total repair cost per realization normalized by building replacement
+      value
+    
+    inspection_trigger: logical array [num_reals x 1]
+      defines which realizations require inspection
+    
+    num_stories: int
+      Total number of building stories
+    
+    systems: DataFrame
+      data table containing information about each system's attributes
+    
+    building_value: number
+      The replacement value of the building, in USD
+   
+    impeding_factor_medians: table
+      median delays for various impeding factors
+   
+    surge_factor: number
+      amplification factor for imepding times due to materials and labor
+      impacts due to regional damage
+    
+    Returns
+    -------
+    impedingFactors['time_sys']: array [num_reals x num_sys]
+      Simulated total impeding time for each system
+    
+    impedingFactors['breakdown']: dictionary
+      feilds: 'inspection', 'financing', 'eng_mob', 'design', 'permitting', 'contractor_mob'
+      The simulated start day and complete day for each impeding factor,
+      broken down per system where applicable
+      
+    
+    Notes
+    -----
+    Correlation: Assuming factor-to-factor simulations are independent, but
+    within a given factor, simulated system impedance time is fully
+    correlated'''
+    
+    import numpy as np
+    from scipy.stats import truncnorm
+    
+    from . import other_impedance_functions
+
+    # Initialize parameters
+    num_reals = len(inspection_trigger)
+    num_sys = len(systems) # used height in MATLAB - Correlate with MATLAB at the time of running
+    
+    # Preallocate each impedance time
+    duration={
+    'inspection': np.zeros([num_reals, num_sys]),
+    'financing': np.zeros([num_reals, num_sys]),
+    'permit_rapid': np.zeros([num_reals, num_sys]),
+    'permit_full': np.zeros([num_reals, num_sys]),
+    'contractor_mob': np.zeros([num_reals, num_sys]),
+    'eng_mob': np.zeros([num_reals, num_sys]),
+    'design': np.zeros([num_reals, num_sys]),  
+    'long_lead': np.zeros([num_reals, num_sys]), 
+        }
+    
+    # System repair trigger
+    # are there any repairs needed for this system
+    # sys_repair_trigger = system_repair_time > 0
+    
+    # Create basic trucated standard normal distribution for later simulation
+    th_low = -impedance_options['impedance_truncation']
+    th_high = impedance_options['impedance_truncation']  
+    mu = 0
+    sigma = 1
+    trunc_pd = truncnorm((th_low - mu)/sigma, (th_high - mu)/sigma, loc=mu, scale=sigma)
+    beta = impedance_options['impedance_beta']
+
+    ## Calculate Demand Surge (if applicble)
+    if impedance_options['demand_surge']['include_surge'] ==1:
+        surge_factor = other_impedance_functions.fn_default_surge_factor(impedance_options['demand_surge']['is_dense_urban_area'], 
+                                               impedance_options['demand_surge']['site_pga'],
+                                               impedance_options['demand_surge']['pga_de'])
+    else:
+        surge_factor = 1
+
+
+    ## Parse through damage to determine which systems require repair
+    
+    sys_repair_trigger = {
+        'any': np.zeros([num_reals, num_sys]),
+        'rapid_permit' : np. zeros([num_reals, num_sys]),
+        'full_permit' : np.zeros([num_reals, num_sys]),
+        'redesign' : np. zeros([num_reals, num_sys]),
+        'flooding' : np. zeros(num_reals)
+        }
+    for sys in range(num_sys):
+        sys_filt = np.array(damage['comp_ds_table']['system']) == sys+1 #FZ +1 is done to coorrelate with python indexing starting from 0. 
+        for tu in range(len(damage['tenant_units'])): 
+            is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0, np.array(damage['tenant_units'][tu]['worker_days']) > 0) # There is damage that needs to be fixed
+            # Track if any damage exists that requires repair (assumes all
+            # damage requires repair)
+            sys_repair_trigger['any'][:,sys] = np.maximum(sys_repair_trigger['any'][:,sys] , np.amax((np.multiply( 1*(is_damaged) , 1*(sys_filt) )), axis=1))
+            
+            # Track if any damage exists that requires rapid permit per system
+            sys_repair_trigger['rapid_permit'][:,sys] = np.maximum(sys_repair_trigger['rapid_permit'][:,sys] , np.amax((np.multiply( 1*(is_damaged) , ( 1*(sys_filt) & 1*(damage['fnc_filters']['permit_rapid'])))), axis=1))
+            
+            # Track if any damage exists that requires full permit per system
+            sys_repair_trigger['full_permit'][:,sys] = np.maximum(sys_repair_trigger['full_permit'][:,sys] , np.amax((np.multiply( 1*(is_damaged) , ( 1*(sys_filt) & 1*(damage['fnc_filters']['permit_full'])))), axis=1))            
+            
+            # Track if any systems require redesign
+            sys_repair_trigger['redesign'][:,sys] = np.maximum(sys_repair_trigger['redesign'][:,sys] , np.amax((np.multiply( 1*(is_damaged) , ( 1*(sys_filt) & 1*(damage['fnc_filters']['redesign'])))), axis=1))         
+
+            # Track if any damage exists that triggers flooding
+            sys_repair_trigger['flooding'] = np.maximum(sys_repair_trigger['flooding'], np.amax((np.multiply( 1*(is_damaged) , ( 1*(sys_filt) & 1*(damage['fnc_filters']['causes_flooding'])))), axis=1)) 
+    
+    # other_impedance_functions.
+    # Simulate impedance time for each impedance factor 
+        
+    if impedance_options['include_impedance']['inspection'] == True:
+       
+        duration['inspection'] = other_impedance_functions.fn_inspection(impedance_options['mitigation']['is_essential_facility'],
+            impedance_options['mitigation']['is_borp_equivalent'], 
+            surge_factor, sys_repair_trigger['any'], inspection_trigger,
+            trunc_pd, beta, impeding_factor_medians)
+    
+    if impedance_options['include_impedance']['financing'] == True:
+       
+        duration['financing'] = other_impedance_functions.fn_financing(impedance_options['mitigation']['capital_available_ratio'],
+            impedance_options['mitigation']['funding_source'], 
+            surge_factor, sys_repair_trigger['any'], repair_cost_ratio_total,
+            trunc_pd, beta, impeding_factor_medians)
+        
+    if impedance_options['include_impedance']['permitting'] == True:
+       
+        duration['permit_rapid'], duration['permit_full'] = other_impedance_functions.fn_permitting(num_reals,
+            sys_repair_trigger, trunc_pd,
+            beta, impeding_factor_medians)
+    
+    
+    if impedance_options['include_impedance']['contractor'] == True:    
+        duration['contractor_mob'] = other_impedance_functions.fn_contractor(num_reals,
+            surge_factor, sys_repair_trigger['any'], trunc_pd, impedance_options['mitigation'])
+    
+    if impedance_options['include_impedance']['engineering'] == True:     
+        
+        duration['eng_mob'], duration['design'] = other_impedance_functions.fn_engineering( num_reals,
+            repair_cost_ratio_engineering, building_value, surge_factor,
+            sys_repair_trigger['redesign'],
+            impedance_options['mitigation']['is_engineer_on_retainer'],
+            impedance_options['system_design_time'], impedance_options['eng_design_min_days'],
+            impedance_options['eng_design_max_days'], trunc_pd, beta, impeding_factor_medians)
+
+    if impedance_options['include_impedance']['long_lead'] == True:
+        for sys in range(num_sys):
+            sys_filt = damage['comp_ds_table']['system'] == sys+1   #FZ +1 is done to coorrelate with python indexing starting from 0.
+            
+            # Simulate long lead times. Assume long lead times are correlated among
+            # all components within the system, but independant between systems
+            prob_sim = np.random.rand(num_reals, 1) # Truncated lognormal distribution (via standard normal simulation)
+            x_vals_std_n = trunc_pd.ppf(prob_sim) 
+            sim_long_lead = np.exp(x_vals_std_n * beta + np.log(np.array(damage['comp_ds_table']['long_lead_time'])))
+            
+            for tu in range(len(damage['tenant_units'])):
+                is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0, np.array(damage['tenant_units'][tu]['worker_days']) > 0)
+                
+                #Track if any damage exists that requires repair (assumes all
+                # damage requires repair). The long lead time for the system is
+                # the max long lead time for any component within the system
+                duration['long_lead'][:,sys] = np.maximum(duration['long_lead'][:,sys], 
+                                                np.nanmax(is_damaged * sys_filt * sim_long_lead, axis=1))
+
+
+
+    ## Aggregate experienced impedance time for each system/sequence and realization 
+    # Figure out when each impeding factor finishes
+    start_day = {'inspection' : np.zeros([num_reals,num_sys])}
+    complete_day = {'inspection' : duration['inspection']}
+    
+    start_day['financing'] = complete_day['inspection']
+    complete_day['financing'] = start_day['financing'] + duration['financing']
+    
+    start_day['eng_mob'] = np.maximum(complete_day['inspection'] ,start_day['financing'])
+    complete_day['eng_mob'] = start_day['eng_mob'] + duration['eng_mob']
+    
+    start_day['design'] = complete_day['eng_mob']
+    complete_day['design'] = start_day['design'] + duration['design']
+    
+    start_day['permit_rapid'] = complete_day['design']
+    complete_day['permit_rapid'] = start_day['permit_rapid'] + duration['permit_rapid']
+    
+    start_day['permit_full'] = complete_day['design'];
+    complete_day['permit_full'] = start_day['permit_full'] + duration['permit_full']
+    
+    start_day['contractor_mob'] = np.maximum(complete_day['inspection'], start_day['financing'])
+    complete_day['contractor_mob'] = start_day['contractor_mob'] + duration['contractor_mob']
+    
+    # if impedance_options['include_impedance']['long_lead'] == True:
+    start_day['long_lead'] = np.maximum(complete_day['inspection'] ,start_day['financing'])
+    complete_day['long_lead'] = start_day['long_lead'] + duration['long_lead']
+    
+    # Combine all impedance factors by system
+    impede_factors = np.array(list(complete_day.keys()));
+    impeding_factors = {'time_sys' : 0}
+    for i in range(len(impede_factors)):
+        impeding_factors['time_sys'] = np.maximum(impeding_factors['time_sys'],
+            complete_day[impede_factors[i]])
+    
+    ## Simulate Impeding Factors for Temporary Repairs
+    # Determine median times for each system
+    if impedance_options['mitigation']['contractor_relationship'] == 'retainer':
+        temp_impede_med = surge_factor * np.array(tmp_repair_class['impeding_time']) # days
+    elif impedance_options['mitigation']['contractor_relationship'] == 'good':
+        temp_impede_med = surge_factor * np.array(tmp_repair_class['impeding_time']) # days
+    elif impedance_options['mitigation']['contractor_relationship'] == 'none':
+        temp_impede_med = surge_factor * np.array(tmp_repair_class['impeding_time_no_contractor']) # days        
+    else:
+        sys.exit('error! PBEE_Recovery:RepairSchedule. Invalid contractor relationship type for impedance factor simulation')
+
+    # Find the which realization have damage that can be resolved by temp repairs
+    tmp_repair_class_trigger = np.zeros([num_reals, len(tmp_repair_class)])
+    for sys in range(len(tmp_repair_class)): 
+        sys_filt = np.array(damage['comp_ds_table']['tmp_repair_class']) == sys+1
+        for tu in range(len(damage['tenant_units'])):
+            is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0 , np.array( damage['tenant_units'][tu]['worker_days']) > 0)
+            # Track if any damage exists that requires repair (assumes all damage requires repair)
+            tmp_repair_class_trigger[:,sys] = np.maximum(tmp_repair_class_trigger[:,sys], np.nanmax(is_damaged * sys_filt, axis = 1))
+   
+    # Simulate Impedance Time
+    prob_sim = np.random.rand(num_reals, 1) # This assumes systems are correlated
+    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
+    tmp_impede_sys = np.exp(x_vals_std_n * beta + np.log(temp_impede_med))
+    
+    # Only use the simulated values for the realzation and system that
+    # trigger temporary repair damage
+    tmp_impede_sys = tmp_impede_sys * tmp_repair_class_trigger
+    
+    # Assume impedance always takes a full day
+    impeding_factors['temp_repair'] = {}
+    impeding_factors['temp_repair']['time_sys'] = np.ceil(tmp_impede_sys)
+    
+    ## Simulate impeding factors and temp repair that occur in parallel with temp repair schedule
+    # Temporary scaffolding for falling hazards
+    prob_sim = np.random.rand(num_reals)
+    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
+    scaffold_impede_time = np.ceil(surge_factor * np.exp(x_vals_std_n * beta + np.log(impedance_options['scaffolding_lead_time']))) # always round up
+    prob_sim = np.random.rand(num_reals) # repair time is not correlated to impedance time
+    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
+    scaffold_repair_time = np.exp(x_vals_std_n * beta + np.log(impedance_options['scaffolding_erect_time'])) 
+    impeding_factors['temp_repair']['scaffold_day'] = np.ceil(scaffold_impede_time + scaffold_repair_time) # round up (dont resolve issue on the same day repairs are complete)   
+    
+    # Door Unjamming
+    prob_sim = np.random.rand(num_reals)
+    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
+    impeding_factors['temp_repair']['door_racking_repair_day'] = np.ceil(surge_factor * np.exp(x_vals_std_n * beta + np.log(impedance_options['door_racking_repair_day']))) # always round up
+    
+    # Interior Flooding
+    
+    if include_flooding_impact == 1:
+        # Flooding Cleanup
+        prob_sim = np.random.rand(num_reals)
+        x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
+        impeding_factors['temp_repair']['flooding_cleanup_day'] = sys_repair_trigger['flooding'] * np.ceil(surge_factor * np.exp(x_vals_std_n * beta + np.log(impedance_options['flooding_cleanup_day']))) # always round up
+    
+        # Repair Flooding Damage
+        prob_sim = np.random.rand(num_reals)
+        x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
+        impeding_factors['temp_repair']['flooding_repair_day'] = sys_repair_trigger['flooding'] * np.ceil(surge_factor * np.exp(x_vals_std_n * beta + np.log(impedance_options['flooding_repair_day']))) # always round up
+
+
+    else: # zero out the flooding impedance (cleanup and repair)
+        impeding_factors['temp_repair']['flooding_cleanup_day'] = np.zeros(num_reals)
+        impeding_factors['temp_repair']['flooding_repair_day'] = np.zeros(num_reals)
+
+    ## Format Impedance times for Gantt Charts                                                     
+    # Full repair
+    impeding_factors['breakdowns'] = {}
+    
+    impeding_factors['breakdowns']['full'] = {'inspection':{'start_day': np.nanmax(start_day['inspection'],1), 
+                                                  'complete_day': np.nanmax(complete_day['inspection'],1)}} 
+    impeding_factors['breakdowns']['full']['financing'] = {'start_day': np.nanmax(start_day['financing'],1), 
+                                                  'complete_day': np.nanmax(complete_day['financing'],1)}                                    
+    impeding_factors['breakdowns']['full']['contractor_mob'] = {'start_day': np.nanmax(start_day['contractor_mob'],1), 
+                                                  'complete_day': np.nanmax(complete_day['contractor_mob'],1)}                                    
+    impeding_factors['breakdowns']['full']['eng_mob'] = {'start_day': np.nanmax(start_day['eng_mob'],1), 
+                                                  'complete_day': np.nanmax(complete_day['eng_mob'],1)}    
+    impeding_factors['breakdowns']['full']['design'] = {'start_day': np.nanmax(start_day['design'],1), 
+                                                  'complete_day': np.nanmax(complete_day['design'],1)}                                                        
+    impeding_factors['breakdowns']['full']['permit_rapid'] = {'start_day': np.nanmax(start_day['permit_rapid'],1), 
+                                                  'complete_day': np.nanmax(complete_day['permit_rapid'],1)}     
+    impeding_factors['breakdowns']['full']['permit_full'] = {'start_day': np.nanmax(start_day['permit_full'],1), 
+                                                  'complete_day': np.nanmax(complete_day['permit_full'],1)}    
+    
+    # Represent long lead times per system
+    # if impedance_options['include_impedance']['long_lead'] == True:
+    impeding_factors['breakdowns']['long_lead']= {}    
+    for s in range(len(systems)):
+        impeding_factors['breakdowns']['long_lead'][systems['name'][s]] = {'start_day' : start_day['long_lead'][:,s],
+                                                                           'complete_day' : complete_day['long_lead'][:,s]}
+    
+    # Temporary Repairs
+    impeding_factors['breakdowns']['temp'] = {}
+    for tmp in range(len(tmp_repair_class)):
+        impeding_factors['breakdowns']['temp'][tmp_repair_class['name_short'][tmp]] = {'start_day' : np.zeros(num_reals),
+                                                                                       'complete_day' : impeding_factors['temp_repair']['time_sys'][:,0]}
+        
+    return impeding_factors
\ No newline at end of file
diff --git a/src/atc138/impedance/other_impedance_functions.py b/src/atc138/impedance/other_impedance_functions.py
new file mode 100644
index 0000000..fe0ce59
--- /dev/null
+++ b/src/atc138/impedance/other_impedance_functions.py
@@ -0,0 +1,427 @@
+import numpy as np
+import sys
+
+def fn_inspection(is_essential_facility, is_borp_equivalent, surge_factor, 
+                  sys_repair_trigger, inspection_trigger, trunc_pd, beta, 
+                  impeding_factor_medians):
+    '''Simulutes inspection time
+   
+    Parameters
+    ----------
+    is_essential_facility: logical
+    is the building deemed essential by the local jurisdiction
+      
+    is_borp_equivalent: logical
+    does the building have an inspector on retainer (BORP equivalent)
+      
+    surge_factor: number
+    amplification factor for impedance time based on a post disaster surge
+    in demand for skilled trades and construction supplies
+      
+    sys_repair_trigger: logical array [num_reals x num_systems]
+      systems that require repair for each realization
+      
+    inpsection_trigger: logical array [num_reals x 1]
+    defines which realizations require inspection
+      
+    trunc_pd: python (scipy) normal distribution object
+    standard normal distrubtion, truncated at upper and lower bounds
+      
+    beta: number
+    lognormal standard deviation (dispersion)
+      
+    impeding_factor_medians: DataFrame
+    median delays for various impeding factors
+   
+    Returns
+    -------
+    inspection_imped: array [num_reals x num_sys]
+    Simulated inspection time for each system '''
+    
+    ## Define inspection distribtuion parameters
+    inspection_medians = impeding_factor_medians.loc[impeding_factor_medians['factor'] == 'inspection']
+        
+    if is_borp_equivalent == True:
+        filt = np.array(inspection_medians['category']) =='borp'
+        # BORP equivalent is not affected by surge
+        median = np.array(inspection_medians['time_days'])[filt] 
+    elif is_essential_facility == True:
+        filt = np.array(inspection_medians['category']) =='essential'
+        # BORP equivalent is not affected by surge
+        median = np.array(inspection_medians['time_days'])[filt] * surge_factor
+    else:
+        filt = np.array(inspection_medians['category']) =='default'
+        median = np.array(inspection_medians['time_days'])[filt] * surge_factor        
+        
+    ## Simulate 
+    # Truncated lognormal distribution
+    num_reals = len(inspection_trigger)
+    prob_sim = np.random.rand(num_reals, 1)
+    x_vals_std_n = trunc_pd.ppf(prob_sim)
+    inspection_time = np.exp(x_vals_std_n * beta + np.log(median))
+    
+    # Only use realizations that require inpsection
+    inspection_time[~inspection_trigger.astype(dtype=bool)] = 0
+    
+    # Affects all systems that need repair
+    # Assume impedance always takes a full day
+    inspection_imped = np.ceil(inspection_time * sys_repair_trigger) 
+    
+    return inspection_imped
+
+def fn_financing(capital_available_ratio, funding_source, surge_factor, 
+                 sys_repair_trigger, repair_cost_ratio, trunc_pd, beta, 
+                 impeding_factor_medians):
+    
+    '''Simulutes financing time
+     
+    Parameters
+    ----------
+    capital_available_ratio: number
+    liquidable funding on hand to make repairs immediately after the
+    damaging event. Normalized by building replacment value.
+    
+    funding_source: string
+    accepted values: {'sba', 'private', 'insurance'}
+    type of funding source for required funds greater than the capital on
+    hand
+    
+    surge_factor: number
+    amplification factor for impedance time based on a post disaster surge
+    sys_repair_trigger: logical array [num_reals x num_systems]
+    systems that require repair for each realization
+   
+    repair_cost_ratio: array [num_reals x 1]
+    simulated building repair cost; normalized by building replacemnt
+    value.
+    
+    trunc_pd: python (scipy) normal distribution object
+    standard normal distrubtion, truncated at upper and lower bounds
+    
+    beta: number
+    lognormal standard deviation (dispersion)
+    
+    impeding_factor_medians: DataFrame
+    median delays for various impeding factors
+   
+    Returns
+    -------
+    financing_imped: array [num_reals x num_sys]
+    Simulated financing time for each system '''
+    
+    ## Define financing distribution parameters
+    # Median financing times
+    finance_medians = impeding_factor_medians.loc[impeding_factor_medians['factor'] == 'financing']
+    
+    # Required Financing
+    financing_trigger = np.array(repair_cost_ratio) > capital_available_ratio
+    
+    if funding_source == 'sba': # SBA Backed Loans
+        filt = np.array(finance_medians['category']) =='sba'
+        # BORP equivalent is not affected by surge
+        median = np.array(finance_medians['time_days'])[filt] * surge_factor # days
+        
+    elif funding_source == 'private': # SBA Backed Loans
+        filt = np.array(finance_medians['category']) =='private'
+        # BORP equivalent is not affected by surge
+        median = np.array(finance_medians['time_days'])[filt] # days, not affected by surge
+
+    elif funding_source == 'insurance': # SBA Backed Loans
+        filt = np.array(finance_medians['category']) =='insurance'
+        # BORP equivalent is not affected by surge
+        median = np.array(finance_medians['time_days'])[filt] # days, not affected by surge        
+        
+    else: # Review this in relation to what is desired by MATLAB code
+        print('Error! PBEE_Recovery:RepairSchedule - Invalid financing type for impedance factor simulation-funding_source')
+
+    ## Simulate
+    # Truncated lognormal distribution (via standard normal simulation)
+    num_reals = len(repair_cost_ratio)
+    prob_sim = np.random.rand(num_reals, 1)
+    x_vals_std_n = trunc_pd.ppf(prob_sim)
+    financing_time = np.exp(x_vals_std_n * beta + np.log(median))    
+
+    
+    # Only use realizations that require financing
+    financing_time[~financing_trigger] = 0
+
+    # Affects all systems that need repair
+    # Assume impedance always takes a full day
+    financing_imped = np.ceil(financing_time * sys_repair_trigger)
+    
+    return financing_imped 
+
+
+def fn_permitting( num_reals, sys_repair_trigger, trunc_pd, 
+                  beta, impeding_factor_medians):
+    
+    '''Simulutes permitting time
+    
+    Parameters
+    ----------
+    num_reals: int
+    number of Monte Carlo simulations assessed
+    
+    surge_factor: number
+    amplification factor for impedance time based on a post disaster surge
+    in demand for skilled trades and construction supplies
+    
+    sys_repair_trigger: dictionary
+    contains simulation data indicate if rapid permits or full permits are
+    required for each system
+    
+    trunc_pd: python (scipy) normal distribution object
+    standard normal distrubtion, truncated at upper and lower bounds
+    
+    beta: number
+    lognormal standard deviation (dispersion)
+    
+    impeding_factor_medians: DataFrame
+    median delays for various impeding factors
+    
+    Returns
+    -------
+    permitting_imped: array [num_reals x num_sys]
+    Simulated permitting time for each system'''
+    
+    ## Define permitting distribution parameters
+    # Find the median permit time for each system
+    permit_medians = impeding_factor_medians.loc[impeding_factor_medians['factor'] == 'permitting']   
+       
+    # Rapid Permits
+    filt = np.array(permit_medians['category']) =='rapid'
+    rapid_permit_median = np.array(permit_medians['time_days'])[filt] # days
+    
+    # Full Permits
+    filt = np.array(permit_medians['category']) =='full'
+    full_permit_median = np.array(permit_medians['time_days'])[filt]  # days
+    
+    ## Simulate
+    # Rapid Permits
+    prob_sim = np.random.rand(num_reals, 1) # This assumes systems are correlated
+    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
+    rapid_permit_time = np.exp(x_vals_std_n * beta + np.log(rapid_permit_median))
+    permitting_rapid = np.ceil(rapid_permit_time * sys_repair_trigger['rapid_permit']) # Assume impedance always takes a full day
+    
+    # Full Permits - simulated times are independent of rapid permit times
+    prob_sim = np.random.rand(num_reals, 1); # This assumes systems are correlated
+    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
+    full_permit_time = np.exp(x_vals_std_n * beta + np.log(full_permit_median))
+    permitting_full = np.ceil(full_permit_time * sys_repair_trigger['full_permit'])
+       
+    return permitting_rapid, permitting_full
+
+def fn_contractor(num_reals, surge_factor, sys_repair_trigger, trunc_pd, contractor_options):
+
+    '''Simulutes contractor mobilization time
+    
+    Parameters
+    ----------
+    num_sys: int
+    number of building systems considered in the assessment
+    
+    num_reals: int
+    number of Monte Carlo simulations assessed
+    
+    surge_factor: number
+    amplification factor for impedance time based on a post disaster surge
+    in demand for skilled trades and construction supplies
+    sys_repair_trigger: logical array [num_reals x num_systems]
+    systems that require repair for each realization
+    
+    systems: DataFrame
+    data table containing information about each system's attributes
+    
+    contractor_options: 
+    various options that controll the contracting impedance time
+    
+    Returns
+    -------
+    contractor_mob_imped: array [num_reals x num_sys]
+    Simulated contractor mobilization time for each system'''
+    
+    ## Define contractor distribution parameters
+    if contractor_options['contractor_relationship'] == 'retainer':
+        med = surge_factor * contractor_options['contractor_retainer_time']
+        beta = 0.4
+    elif contractor_options['contractor_relationship'] == 'good':
+        med = (1 + 0.5*(surge_factor-1)) * 3
+        beta = 0.4
+    elif contractor_options['contractor_relationship'] == 'none':
+        med = surge_factor * 42
+        beta = 0.8
+    else:
+        sys.exit('error! PBEE_Recovery:RepairSchedule. Invalid contractor relationship type for impedance factor simulation')
+
+    ## Set median for each realization
+    contr_med = med * np.ones([num_reals,1])
+    
+    ## Simulate Impedance Time
+    prob_sim = np.random.rand(num_reals, 1) # This assumes systems are correlated
+    x_vals_std_n = trunc_pd.ppf(prob_sim) # Truncated lognormal distribution (via standard normal simulation)
+    contractor_mob_imped = np.exp(x_vals_std_n * beta + np.log(contr_med))
+    
+    # Only use the simulated values for the realzation and system that require permitting
+    contractor_mob_imped = contractor_mob_imped * sys_repair_trigger
+
+    # Amplify by the surge factor
+    # Assume impedance always takes a full day
+    contractor_mob_imped = np.ceil(contractor_mob_imped)
+    
+    return contractor_mob_imped
+
+
+def fn_engineering(num_reals, repair_cost_ratio, building_value, surge_factor, 
+                   redesign_trigger, is_engineer_on_retainer, user_options, 
+                   design_min, design_max, trunc_pd, beta, 
+                   impeding_factor_medians):
+       
+    '''Simulutes permitting time
+      
+    Parameters
+    ----------
+    num_reals: int
+    number of Monte Carlo simulations assessed
+    
+    repair_cost_ratio: array [num_reals x 1]
+    simulated building repair cost; normalized by building replacemnt
+    value.
+    
+    building_value: number
+    replacment value of building, in USD, non including land
+    
+    surge_factor: number
+    amplification factor for impedance time based on a post disaster surge
+    in demand for skilled trades and construction supplies
+    
+    redesign_trigger: logical array [num_reals x num_sys]
+    is redesign required for the given system
+    
+    is_engineer_on_retainer: logical
+    is there a pre-arranged agreement with an engineer for priorization of
+    redesign
+    
+    user_options: dictionary
+    contains paramters of system design time function, set by user
+    design_min: row vector [1 x n_systems]
+    lower bound on the median for each system
+    
+    design_max: row vector [1 x n_systems]
+    upper bound on the median for each system
+    
+    trunc_pd: python (scipy) normal distribution object
+    standard normal distrubtion, truncated at upper and lower bounds
+    
+    beta: number
+    lognormal standard deviation (dispersion)
+    
+    impeding_factor_medians: DataFrame
+    median delays for various impeding factors
+   
+    Returns
+    -------
+    eng_mob_imped: array [num_reals x num_sys]
+    Simulated enginering mobilization time for each system
+    
+    eng_design_imped: array [num_reals x num_sys]
+    Simulated enginering design time for each system
+ 
+    Notes
+    ------
+    assumes engineering mobilization and re-design time are independant, but
+    are correlated between each system. In other words, you will have the same 
+    designers for the structural, stairs, exterior, and whatever other systems 
+    need design time, but the time it takes to spin up an engineer is not
+    related to the time it takes for them to complete the re-design.'''
+    
+    
+    ## Engineering Mobilization Time
+    # Mobilization medians
+    eng_mob_medians = impeding_factor_medians.loc[impeding_factor_medians['factor'] == 'engineering mobilization']      
+    
+    if is_engineer_on_retainer == True:
+        
+        
+        filt = np.array(eng_mob_medians['category']) =='retainer'
+        
+    else:
+        filt = np.array(eng_mob_medians['category']) =='default'
+        
+    median_eng_mob = surge_factor * np.array(eng_mob_medians['time_days'])[filt] # days
+    
+    # Truncated lognormal distribution (via standard normal simulation)
+    prob_sim = np.random.rand(num_reals, 1) # This assumes systems are correlated
+    x_vals_std_n = trunc_pd.ppf(prob_sim)
+    eng_mob_time = np.exp(x_vals_std_n * beta + np.log(median_eng_mob))
+
+    # Assume impedance always takes a full day
+    eng_mob_imped = np.ceil(eng_mob_time * redesign_trigger)
+    
+    ## Engineering Design Time
+    ## Calculate System Design Time
+    RC_total = np.array(repair_cost_ratio) * building_value;
+    SDT = RC_total * user_options['f'] / (user_options['r'] * user_options['t'] * user_options['w'])     
+    
+    design_med=np.fmin(np.fmax(SDT, design_min), design_max).reshape(len(SDT),1)
+     
+    # Truncated lognormal distribution (via standard normal simulation)
+    # Assumes engineering design time is independant of mobilization time
+    prob_sim = np.random.rand(num_reals,1) # This assumes systems are correlated
+    x_vals_std_n = trunc_pd.ppf(prob_sim)
+    eng_design_time = np.exp(x_vals_std_n * beta + np.log(design_med))
+    # Assume impedance always takes a full day
+    eng_design_imped = np.ceil(eng_design_time * redesign_trigger)
+    
+    return eng_mob_imped, eng_design_imped  
+
+def fn_default_surge_factor(is_dense_urban_area, pga, pga_de):
+    '''This function determines the surge factor based on the intensity of shaking
+      
+       Parameters
+       ----------
+       is_dense_urban_area: bool
+         if the building is in a dense urban area or not
+       pga: number
+         peak ground acceleration control point 1 (below which surge factor is 1)
+       pga_de: number
+         peak ground acceleration at the design intensity
+      
+      
+       Returns
+       -------
+       surge_factor: number
+         surge factor which amplified impedance factor times
+      
+       Notes
+       -----
+      
+           |                                         _____________ 3.0
+           |                                       /              
+           |                                      /               
+           |                                     /                
+           |                                    /                 
+           |                                   /                  
+           |                                  /     ______________ 1.5
+           |                                 /    / |             
+           |                                /   /   |             
+           |                               /  /     |             
+           |                              / /       |             
+           |                             //         |             
+       1.0 |  __________________________/           |             
+           |                            |           |             
+           |                            |           |             
+           -----------------------------+-----------+-------------
+                                      pga_1       pga_2'''
+
+    if is_dense_urban_area:
+        max_surge = 3
+    else:
+        max_surge = 1.5
+    
+    pga_1 = max(0.2, pga_de);
+    pga_2 = pga_1 * 1.5;
+    surge_factor = np.interp(min(max(pga, pga_1), pga_2), [pga_1, pga_2], [1, max_surge])
+    
+    return surge_factor
+
+    
\ No newline at end of file
diff --git a/src/atc138/input_builder.py b/src/atc138/input_builder.py
new file mode 100644
index 0000000..9cb9c7c
--- /dev/null
+++ b/src/atc138/input_builder.py
@@ -0,0 +1,476 @@
+
+import numpy as np
+import json
+import pandas as pd
+import os
+import re
+import sys
+
+def clean_types(obj):
+    """
+    Recursively convert numpy types to native Python types for JSON serialization,
+    preserving NaN as float('nan') for Numpy compatibility in the engine.
+    """
+    if isinstance(obj, dict):
+        return {k: clean_types(v) for k, v in obj.items()}
+    elif isinstance(obj, list):
+        return [clean_types(i) for i in obj]
+    elif isinstance(obj, np.ndarray):
+        return clean_types(obj.tolist())
+    elif isinstance(obj, (np.int64, np.int32, int)):
+        return int(obj)
+    elif isinstance(obj, (np.float64, np.float32, float)):
+         # Preserving NaN for numpy compatibility in downstream engine
+         return float(obj)
+    elif pd.isna(obj): 
+        # Handle standalone pandas/numpy NaNs/NaTs
+        return float('nan') 
+    return obj
+
+def recursive_update(d, u):
+    """
+    Recursively update dictionary d with values from u.
+    """
+    for k, v in u.items():
+        if isinstance(v, dict) and k in d and isinstance(d[k], dict):
+            recursive_update(d[k], v)
+        else:
+            d[k] = v
+    return d
+
+def load_custom_static_tables(model_dir, static_dir, filename):
+    path = os.path.join(model_dir, filename)
+    if os.path.exists(path):
+        print(f"found custom {filename} in inputs. Overriding static tables...")
+    else:
+        path = os.path.join(static_dir, filename)
+    return pd.read_csv(path)
+
+
+def build_simulated_inputs(model_dir):
+    """
+    Generates simulated_inputs dictionary from raw input files in the model directory.
+    Based on the original build_inputs.py script.
+    
+    Parameters
+    ----------
+    model_dir: string
+        Path to the directory containing raw input files.
+        
+    Returns
+    -------
+    simulated_inputs: dict
+        The complete dictionary of inputs.
+    """
+        
+    ''' PULL STATIC DATA
+    If static data tables exist in the input directory, use those. Else, use
+    the defaults in src/atc138/data/
+    '''
+    
+    static_data_dir = os.path.join(os.path.dirname(__file__), 'data')
+
+    component_attributes = load_custom_static_tables(
+        model_dir, static_data_dir, 'component_attributes.csv')
+
+    damage_state_attribute_mapping = load_custom_static_tables(
+        model_dir, static_data_dir, 'damage_state_attribute_mapping.csv')
+
+    subsystems = load_custom_static_tables(
+        model_dir, static_data_dir, 'subsystems.csv')
+
+    tenant_function_requirements = load_custom_static_tables(
+        model_dir, static_data_dir, 'tenant_function_requirements.csv')
+    
+    
+    ''' LOAD BUILDING DATA
+    This data is specific to the building model and will need to be created
+    for each assessment. Data is formated as json structures or csv tables'''
+    
+    # 1. Building Model: Basic data about the building being assessed
+    with open(os.path.join(model_dir, 'building_model.json'), 'r') as f:
+        building_model = json.load(f)
+    
+    # If number of stories is 1, change individual values to lists in order to work with later code
+    if building_model['num_stories'] == 1:
+        for key in ['area_per_story_sf', 'ht_per_story_ft', 'occupants_per_story', 'stairs_per_story', 'struct_bay_area_per_story']:
+            if not isinstance(building_model[key], list):
+                building_model[key] = [building_model[key]]
+    if building_model['num_stories'] == 1:
+        for key in ['edge_lengths']:
+            if not isinstance(building_model[key][0], list):
+                building_model[key] = [[building_model[key][0]], [building_model[key][1]]]
+    
+    # 2. List of tenant units within the building and their basic attributes
+    tenant_unit_list = pd.read_csv(os.path.join(model_dir, 'tenant_unit_list.csv'))
+    
+    
+    # 3. List of component and damage states ids associated with the damage
+    comp_ds_list = pd.read_csv(os.path.join(model_dir, 'comp_ds_list.csv'))
+    
+    # 4. List of component and damage states in the performance model
+    comp_population = pd.read_csv(os.path.join(model_dir, 'comp_population.csv'))
+    comp_header = list(comp_population.columns)
+    comp_list = np.array(comp_header[2:len(comp_header)])
+    comp_list= np.char.replace(np.array(comp_list),'_','.')
+    comp_list = comp_list.tolist()
+    # Remove suffixes from repated entries
+    for i in range(len(comp_list)):
+        if len(comp_list[i]) > 10:
+            comp_list[i]=comp_list[i][0:10]
+    building_model['comps'] = {'comp_list' : comp_list} #FZ# Component list has been added to building model dictionary.
+    
+    # Go through each story and assign component populations
+    drs = np.unique(np.array(comp_population['dir']))
+    
+    building_model['comps']['story'] = {}
+    for s in range (building_model['num_stories']):
+        building_model['comps']['story'][s] = {}
+        for d in range(len(drs)):
+            # [FIX] Robust key generation and missing data handling
+            current_dir = drs[d]
+            filt = np.logical_and(np.array(comp_population['story']) == s+1, np.array(comp_population['dir']) == current_dir)
+            
+            # Format key identifier using integer representation of direction to ensure consistency (e.g. qty_dir_1 not qty_dir_1.0)
+            try:
+                dir_key_suffix = str(int(current_dir))
+            except:
+                dir_key_suffix = str(current_dir)
+            
+            qty_data = comp_population.to_numpy()[filt,2:len(comp_header)]
+            
+            if qty_data.shape[0] > 0:
+                building_model['comps']['story'][s]['qty_dir_' + dir_key_suffix] = qty_data.tolist()[0]
+            else:
+                # Missing data for this story/direction, fill with zeros to avoid crashes
+                num_comps = len(comp_header) - 2
+                building_model['comps']['story'][s]['qty_dir_' + dir_key_suffix] = [0] * num_comps
+    
+    
+    # Set comp info table
+    comp_info = {'comp_id': [], 'comp_idx': [], 'structural_system': [], 'structural_system_alt': [], 'structural_series_id': []}
+    for c in range(len(comp_list)):
+        # Find the component attributes of this component
+        comp_attr_filt = component_attributes['fragility_id'] == comp_list[c]
+        if np.logical_not(sum(comp_attr_filt) == 1):
+            sys.exit('error!.Could not find component attrubutes')
+        else:
+            comp_attr = component_attributes.to_numpy()[comp_attr_filt,:]
+        comp_info['comp_id'].append(comp_list[c])
+        comp_info['comp_idx'].append(c) 
+        
+        # [FIX] Scalar extraction: Use scalar indexing [0, col] instead of slicing [0, [col]] to avoid array-to-scalar conversion errors
+        comp_info['structural_system'].append(float(comp_attr[0, component_attributes.columns.get_loc('structural_system')]))
+        comp_info['structural_system_alt'].append(float(comp_attr[0, component_attributes.columns.get_loc('structural_system_alt')]))
+        comp_info['structural_series_id'].append(float(comp_attr[0, component_attributes.columns.get_loc('structural_series_id')]))
+    
+    building_model['comps']['comp_table'] = comp_info
+    
+    
+    ''' LOAD SIMULATED DATA
+    This data is specific to the building performance at the assessed hazard intensity 
+    and will need to be created for each assessment. 
+    Data is formated as json structures.'''
+    
+    # 1. Simulated damage consequences - various building and story level consequences of simulated data, for each realization of the monte carlo simulation.
+    with open(os.path.join(model_dir, 'damage_consequences.json'), 'r') as f:
+        damage_consequences = json.load(f)
+    
+    # 2. Simulated utility downtimes for electrical, water, and gas networks for each realization of the monte carlo simulation.
+    # If file exists load it 
+    utility_path = os.path.join(model_dir, 'utility_downtime.json')
+    if os.path.exists(utility_path):
+        with open(utility_path, 'r') as f:
+            functionality = json.load(f)
+    # else If no data exist, assume there is no consequence of network downtime
+    else:
+        num_reals = len(damage_consequences["repair_cost_ratio_total"])
+        functionality = {'utilities' : {'electrical':[], 'water':[], 'gas':[]} } 
+    
+        for real in range(num_reals):
+            functionality['utilities']['electrical'].append(0)
+            functionality['utilities']['water'].append(0)
+            functionality['utilities']['gas'].append(0)
+    
+    
+    # 3. Simulated component damage per tenant unit for each realization of the monte carlo simulation
+    # 3. Simulated component damage per tenant unit for each realization of the monte carlo simulation
+    with open(os.path.join(model_dir, 'simulated_damage.json'), 'r') as f:
+        sim_damage = json.load(f)
+    
+    # Write in individual dictionaries part of larger 'damage' dictionary 
+    damage = {'story' : {}, 'tenant_units' : {}}
+    
+    if 'story' in list(sim_damage.keys()):
+        for tu in range(len(sim_damage['tenant_units'])):
+            damage['tenant_units'][tu] = sim_damage['tenant_units'][tu]
+    
+    
+    if 'tenant_units' in list(sim_damage.keys()):
+        for s in range(len(sim_damage['story'])):
+            damage['story'][s] = sim_damage['story'][s]
+        
+    ''' OPTIONAL INPUTS
+    Various assessment otpions. Set to default options in the
+    optional_inputs.json file. This file is expected to be in this input
+    directory. This file can be customized for each assessment if desired.'''
+    
+    # Load defaults first, then merge user overrides
+    pkg_dir = os.path.dirname(__file__)
+    defaults_path = os.path.join(pkg_dir, 'data', 'default_inputs.json')
+    with open(defaults_path, 'r') as f:
+        options = json.load(f)
+
+    user_options_path = os.path.join(model_dir, 'optional_inputs.json')
+    if os.path.exists(user_options_path):
+        with open(user_options_path, 'r') as f:
+            user_options = json.load(f)
+        options = recursive_update(options, user_options)
+        
+    functionality_options = options['functionality_options']
+    impedance_options = options['impedance_options']
+    repair_time_options = options['repair_time_options']
+
+ 
+    
+    # Preallocate tenant unit table
+    tenant_units = tenant_unit_list.copy() # copy to avoid SettingWithCopy if passed dataframe
+    tenant_units['exterior'] = np.zeros(len(tenant_units))
+    tenant_units['interior'] = np.zeros(len(tenant_units))
+    tenant_units['occ_per_elev'] = np.zeros(len(tenant_units))
+    tenant_units['is_elevator_required'] = np.zeros(len(tenant_units))
+    tenant_units['is_electrical_required'] = np.zeros(len(tenant_units))
+    tenant_units['is_water_potable_required'] = np.zeros(len(tenant_units))
+    tenant_units['is_water_sanitary_required'] = np.zeros(len(tenant_units))
+    tenant_units['is_hvac_ventilation_required'] = np.zeros(len(tenant_units))
+    tenant_units['is_hvac_heating_required'] = np.zeros(len(tenant_units))
+    tenant_units['is_hvac_cooling_required'] = np.zeros(len(tenant_units))
+    tenant_units['is_hvac_exhaust_required'] = np.zeros(len(tenant_units))
+    tenant_units['is_data_required'] = np.zeros(len(tenant_units))  
+    '''Pull default tenant unit attributes for each tenant unit listed in the
+    tenant_unit_list'''
+    for tu in range(len(tenant_unit_list)):
+        occ_id = tenant_units.loc[tu, 'occupancy_id'] # Use .loc for pandas safety
+        fnc_requirements_filt = tenant_function_requirements['occupancy_id'] == occ_id
+        if sum(fnc_requirements_filt) != 1:
+            raise ValueError(f'error! Tenant Unit Requirements for Occupancy ID {occ_id} Not Found')
+        
+        # Accessing filtered rows. Original input builder used filtered Series assignment.
+        req_row = tenant_function_requirements[fnc_requirements_filt].iloc[0]
+        
+        tenant_units.loc[tu, 'exterior'] = req_row['exterior']
+        tenant_units.loc[tu, 'interior'] = req_row['interior']
+        tenant_units.loc[tu, 'occ_per_elev'] = req_row['occ_per_elev']
+        
+        story = tenant_units.loc[tu, 'story']
+        if req_row['is_elevator_required'] == 1 and req_row['max_walkable_story'] < story:
+            tenant_units.loc[tu, 'is_elevator_required'] = 1
+        else:
+            tenant_units.loc[tu, 'is_elevator_required'] = 0
+    
+        tenant_units.loc[tu, 'is_electrical_required'] = req_row['is_electrical_required']
+        tenant_units.loc[tu, 'is_water_potable_required'] = req_row['is_water_potable_required']
+        tenant_units.loc[tu, 'is_water_sanitary_required'] = req_row['is_water_sanitary_required']
+        tenant_units.loc[tu, 'is_hvac_ventilation_required'] = req_row['is_hvac_ventilation_required']
+        tenant_units.loc[tu, 'is_hvac_heating_required'] = req_row['is_hvac_heating_required']
+        tenant_units.loc[tu, 'is_hvac_cooling_required'] = req_row['is_hvac_cooling_required']
+        tenant_units.loc[tu, 'is_hvac_exhaust_required'] = req_row['is_hvac_exhaust_required']
+        tenant_units.loc[tu, 'is_data_required'] = req_row['is_data_required']    
+    '''Pull default component and damage state attributes for each component 
+    in the comp_ds_list'''
+    
+    ## Populate data for each damage state
+    comp_ds_info = {'comp_id' : [], 
+                    'comp_type_id' : [], 
+                    'comp_idx' : [], 
+                    'ds_seq_id' : [], 
+                    'ds_sub_id' : [],
+                    'system' : [],
+                    'subsystem_id' : [],
+                    'structural_system' : [],
+                    'structural_system_alt' : [],
+                    'structural_series_id' : [],
+                    'unit' : [],
+                    'unit_qty' : [],
+                    'service_location' : [],
+                    'is_sim_ds' : [],
+                    'safety_class' : [],
+                    'affects_envelope_safety' : [],
+                    'ext_falling_hazard' : [],
+                    'int_falling_hazard' : [],
+                    'global_hazardous_material' : [],
+                    'local_hazardous_material' : [],
+                    'weakens_fire_break' : [],
+                    'affects_access' : [],
+                    'damages_envelope_seal' : [],
+                    'affects_roof_function' : [],
+                    'obstructs_interior_space' : [],
+                    'impairs_system_operation' : [],
+                    'causes_flooding' : [],
+                    'interior_area_factor' : [],
+                    'interior_area_conversion_type' : [],
+                    'exterior_surface_area_factor' : [],
+                    'exterior_falling_length_factor' : [],
+                    'crew_size' : [],
+                    'permit_type' : [],
+                    'redesign' : [],
+                    'long_lead_time' : [],
+                    'requires_shoring' : [],
+                    'resolved_by_scaffolding' : [],
+                    'tmp_repair_class' : [],
+                    'tmp_repair_time_lower' : [],
+                    'tmp_repair_time_upper' : [],
+                    'tmp_repair_time_lower_qnty' : [],
+                    'tmp_repair_time_upper_qnty' : [],
+                    'tmp_crew_size' : [],
+                    'n1_redundancy' : [],
+                    'parallel_operation' :[],
+                    'redundancy_threshold' : []
+                    }
+    
+    for c in range(len(comp_ds_list)):
+        
+        # Find the component attributes of this component
+        comp_attr_filt = component_attributes['fragility_id'] == comp_ds_list['comp_id'][c]
+        if sum(comp_attr_filt) != 1:
+            raise ValueError('error! Could not find component attrubutes')
+        else:
+            comp_attr = component_attributes[comp_attr_filt].iloc[0] # Changed to Series access for robust scalar extraction
+                  
+        ds_comp_filt = []
+        for frag_reg in range(len(damage_state_attribute_mapping["fragility_id_regex"])):
+            regex_str = damage_state_attribute_mapping["fragility_id_regex"][frag_reg]
+            cid = comp_ds_list["comp_id"][c]
+            match = re.search(regex_str, cid)
+            
+
+            # Mapping components with attributes - Cjecks are based on mapping, comp_id, seq_id and sub_id
+
+            # Matching element ID using information contained in damage_state_attribute_mapping ["fragility_id_regex"]
+            if match and match.string == cid:
+                ds_comp_filt.append(True)
+            else:
+                ds_comp_filt.append(False)    
+        
+        ds_comp_filt = np.array(ds_comp_filt) # Convert to array for boolean indexing
+
+        ds_seq_filt = damage_state_attribute_mapping['ds_index'] == comp_ds_list['ds_seq_id'][c]
+        if comp_ds_list['ds_sub_id'][c] == 1:
+            ds_sub_filt = np.logical_or(damage_state_attribute_mapping['sub_ds_index'] ==1, damage_state_attribute_mapping['sub_ds_index'].isnull())
+        else:
+            ds_sub_filt = damage_state_attribute_mapping['sub_ds_index'] == comp_ds_list['ds_sub_id'][c]
+        
+        ds_filt = ds_comp_filt & ds_seq_filt & ds_sub_filt
+        
+        if sum(ds_filt) != 1:
+             raise ValueError('error!, Could not find damage state attrubutes')
+        else:
+            ds_attr = damage_state_attribute_mapping[ds_filt].iloc[0] # Series access
+        
+        ## Populate data for each damage state
+        # Basic Component and DS identifiers
+        comp_ds_info['comp_id'].append(comp_ds_list['comp_id'][c])
+        comp_ds_info['comp_type_id'].append(comp_ds_list['comp_id'][c][0:5]) # first 5 characters indicate the type
+        comp_ds_info['comp_idx'].append(c)
+        comp_ds_info['ds_seq_id'].append(ds_attr['ds_index'])
+        
+        sub_id = ds_attr['sub_ds_index']
+        if pd.isna(sub_id): sub_id = 1.0
+        comp_ds_info['ds_sub_id'].append(sub_id)
+            
+        # Set Component Attributes
+        comp_ds_info['system'].append(comp_attr['system_id'])
+        comp_ds_info['subsystem_id'].append(comp_attr['subsystem_id'])
+        comp_ds_info['structural_system'].append(comp_attr['structural_system'])
+        comp_ds_info['structural_system_alt'].append(comp_attr['structural_system_alt']) # component_attributes.csv does not have structural_system_alt field
+        comp_ds_info['structural_series_id'].append(comp_attr['structural_series_id'])
+        comp_ds_info['unit'].append(comp_attr['unit']) #FZ# Check w.r.t. matlab output
+        comp_ds_info['unit_qty'].append(comp_attr['unit_qty'])
+        comp_ds_info['service_location'].append(comp_attr['service_location']) #FZ# Check w.r.t. matlab output
+                   
+        # Set Damage State Attributes
+        # Map fields (legacy mapping logic preserved where simple)
+        comp_ds_info['is_sim_ds'].append(ds_attr['is_sim_ds'])
+        comp_ds_info['safety_class'].append(ds_attr['safety_class'])
+        comp_ds_info['affects_envelope_safety'].append(ds_attr['affects_envelope_safety'])
+        comp_ds_info['ext_falling_hazard'].append(ds_attr['exterior_falling_hazard'])
+        comp_ds_info['int_falling_hazard'].append(ds_attr['interior_falling_hazard'])
+        comp_ds_info['global_hazardous_material'].append(ds_attr['global_hazardous_material'])
+        comp_ds_info['local_hazardous_material'].append(ds_attr['local_hazardous_material'])
+        comp_ds_info['weakens_fire_break'].append(ds_attr['weakens_fire_break'])
+        comp_ds_info['affects_access'].append(ds_attr['affects_access'])
+        comp_ds_info['damages_envelope_seal'].append(ds_attr['damages_envelope_seal'])
+        comp_ds_info['affects_roof_function'].append(ds_attr['affects_roof_function'])
+        comp_ds_info['obstructs_interior_space'].append(ds_attr['obstructs_interior_space'])
+        comp_ds_info['impairs_system_operation'].append(ds_attr['impairs_system_operation'])
+        comp_ds_info['causes_flooding'].append(ds_attr['causes_flooding'])
+        comp_ds_info['interior_area_factor'].append(ds_attr['interior_area_factor'])
+        comp_ds_info['interior_area_conversion_type'].append(ds_attr['interior_area_conversion_type'])    
+        comp_ds_info['exterior_surface_area_factor'].append(ds_attr['exterior_surface_area_factor'])
+        comp_ds_info['exterior_falling_length_factor'].append(ds_attr['exterior_falling_length_factor'])                
+        comp_ds_info['crew_size'].append(ds_attr['crew_size'])
+        comp_ds_info['permit_type'].append(ds_attr['permit_type'])
+        comp_ds_info['redesign'].append(ds_attr['redesign'])
+        comp_ds_info['long_lead_time'].append(impedance_options['default_lead_time'] * ds_attr['long_lead'])
+        comp_ds_info['requires_shoring'].append(ds_attr['requires_shoring'])
+        comp_ds_info['resolved_by_scaffolding'].append(ds_attr['resolved_by_scaffolding'])
+        comp_ds_info['tmp_repair_class'].append(ds_attr['tmp_repair_class'])
+        comp_ds_info['tmp_repair_time_lower'].append(ds_attr['tmp_repair_time_lower'])
+        comp_ds_info['tmp_repair_time_upper'].append(ds_attr['tmp_repair_time_upper'])
+        
+        tmp_class = ds_attr['tmp_repair_class']
+        if tmp_class > 0:
+            comp_ds_info['tmp_repair_time_lower_qnty'].append(ds_attr['time_lower_quantity'])
+            comp_ds_info['tmp_repair_time_upper_qnty'].append(ds_attr['time_upper_quantity'])
+        else:
+            comp_ds_info['tmp_repair_time_lower_qnty'].append(np.nan)
+            comp_ds_info['tmp_repair_time_upper_qnty'].append(np.nan)
+    
+        comp_ds_info['tmp_crew_size'].append(ds_attr['tmp_crew_size'])
+    
+        # Subsystem attributes
+        sub_id = comp_attr['subsystem_id']
+        subsystem_filt = subsystems['id'] == sub_id
+        if sub_id == 0:
+            # No subsytem
+            comp_ds_info['n1_redundancy'].append(0)
+            comp_ds_info['parallel_operation'].append(0)
+            comp_ds_info['redundancy_threshold'].append(0)
+        elif sum(subsystem_filt) != 1:
+            sys.exit('error! Could not find damage state attrubutes')
+        else:
+            sub_row = subsystems[subsystem_filt].iloc[0]
+            comp_ds_info['n1_redundancy'].append(sub_row['n1_redundancy'])
+            comp_ds_info['parallel_operation'].append(sub_row['parallel_operation'])
+            comp_ds_info['redundancy_threshold'].append(sub_row['redundancy_threshold'])
+    
+    damage['comp_ds_table'] = comp_ds_info
+    
+    ## Check missing data
+    # Engineering Repair Cost Ratio - Assume is the sum of all component repair
+    # costs that require redesign
+    ## Check missing data
+    # Engineering Repair Cost Ratio - Assume is the sum of all component repair
+    # costs that require redesign
+    if 'repair_cost_ratio_engineering' not in damage_consequences:
+        eng_filt = np.array(damage['comp_ds_table']['redesign']).astype(bool)
+        # Re-calc using numpy arrays
+        costs = np.zeros(len(damage_consequences['repair_cost_ratio_total']))
+        if 'story' in sim_damage:
+             for s in range(len(sim_damage['story'])):
+                 story_costs = np.array(sim_damage['story'][s]['repair_cost'])
+                 costs += np.sum(story_costs[:, eng_filt], axis=1)
+        damage_consequences['repair_cost_ratio_engineering'] = costs.tolist()
+    
+
+    # Convert tenant_units dataframe to dictionary
+    tenant_units_dict = tenant_units.to_dict(orient='list')
+         
+    # Export output as simulated_inputs.json file 
+    
+    simulated_inputs = {'building_model' : building_model, 'damage' : damage, 'damage_consequences' : damage_consequences, 'functionality' : functionality, 'functionality_options' : functionality_options, 'impedance_options' : impedance_options, 'repair_time_options' : repair_time_options, 'tenant_units' : tenant_units_dict}
+    
+    # [FIX] Type cleaning using recursive helper (enables JSON serialization while preserving NaNs)
+    simulated_inputs = clean_types(simulated_inputs)
+    
+    return simulated_inputs
diff --git a/src/atc138/preprocessing/__init__.py b/src/atc138/preprocessing/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/atc138/preprocessing/main_preprocessing.py b/src/atc138/preprocessing/main_preprocessing.py
new file mode 100644
index 0000000..28ccaf5
--- /dev/null
+++ b/src/atc138/preprocessing/main_preprocessing.py
@@ -0,0 +1,64 @@
+def main_preprocessing(comp_ds_table, damage, repair_time_options, temp_repair_class, damage_consequences, num_stories):
+    '''Parameterize variables and simplifying assumptions to expedite the ATC138
+    recovery assessment
+
+    Parameters
+    ----------
+    comp_ds_table: DataFrame
+      various component attributes by damage state for each component 
+      within the performance model. Can be populated from the 
+      component_attribites.csv and damage_state_attribute_mapping.csv 
+      databases in the static_tables directory.  Each row of the table 
+      corresponds to each column of the simulated component damage arrays 
+      within damage['tenant_units'].
+    
+    damage: dictionary
+      contains simulated damage info and damage state attributes
+    
+    repair_time_options['allow_shoring']: logical
+      flag indicating whether or not shoring should be considered as a
+      temporary repair for local stability issues for structural components
+    
+    temp_repair_class: DataFrame
+      attributes of each temporary repair class to consider
+    
+    damage_consequences: dictionary
+      dictionary containing simulated building consequences, such as red
+    
+    num_stories: int
+      Integer number of stories in the building being assessed
+
+    Returns
+    -------
+    damage: dictionary
+      contains simulated damage info and damage state attributes
+    
+    temp_repair_class: DataFrame
+      attributes of each temporary repair class to consider
+    
+    damage_consequences: dictionary
+      dictionary containing simulated building consequences, such as red'''
+    
+    import numpy as np
+    from . import preprocessing_fns
+    
+    ## Define simulated damage in each tenant unit if not provided by the user
+    damage = preprocessing_fns.fn_populate_damage_per_tu(damage)
+    
+    ##Simulate damage per side, if not provided by the user
+    damage = preprocessing_fns.fn_simulate_damage_per_side(damage)
+    
+    ## Combine compoment attributes into recovery filters to expidite recovery assessment
+    damage['fnc_filters'] = preprocessing_fns.fn_create_fnc_filters(comp_ds_table)
+    
+    ## Simulate Temporary Repair Times for each component
+    damage, temp_repair_class = preprocessing_fns.fn_simulate_temp_worker_days(damage, temp_repair_class, repair_time_options)
+    
+    ## Set door racking damage if not provided by user
+    damage_consequences = preprocessing_fns.fn_define_door_racking(damage_consequences, num_stories)
+    
+    return damage, temp_repair_class, damage_consequences
+
+    
+  
+    
\ No newline at end of file
diff --git a/src/atc138/preprocessing/preprocessing_fns.py b/src/atc138/preprocessing/preprocessing_fns.py
new file mode 100644
index 0000000..ccf4d0d
--- /dev/null
+++ b/src/atc138/preprocessing/preprocessing_fns.py
@@ -0,0 +1,358 @@
+import numpy as np
+
+def fn_populate_damage_per_tu(damage):
+    
+    '''Check to see if the damage.tenant_units variable has been defined. If
+    not, assume its the same as damage.story. This assumption creates a 1:1
+    coupling between stories and tenant_units (one tenant unit per story)
+    
+    Parameters
+    ----------
+    damage['story']: dictionary
+    contains simulated per component and damage state damage info
+    disagregated by story
+    
+    Returns
+    -------
+    contains simulated per component and damage state damage info
+    disagregated by tenant unit'''
+
+# If tenant unit damage is not provided by the user, assume its the same as per story damage
+
+    if ('tenant_units' in damage.keys()) == False:
+        damage['tenant_units'] = damage['story']
+        
+    return damage
+
+def fn_simulate_damage_per_side(damage):
+    '''Simulate damage per side for the exterior falling hazard check, if not 
+    provided by the user. Component location within a story is typically not 
+    Provided in most PBEE assessments. Therefore, this script make the rough 
+    assumptions to distribute damage to 4 sides, randomly.
+    
+    Parameters
+    ----------
+    damage: dictionary
+      contains simulated damage info and damage state attributes
+    
+    Returns
+    -------
+    damage: dictionary
+      contains simulated damage info and damage state attributes'''
+    
+    
+    # Simulate damage per side, if not provided by the user
+    if ('qnt_damaged_side_1' in damage['tenant_units'][0].keys()) == False:
+        num_reals = len(damage['tenant_units'][0]['qnt_damaged'])
+        
+        # Randomly split damage between 4 sides
+        # (this will only matter for cladding components)
+        ratio_damage_per_side = np.random.rand(num_reals,4) # assumes square footprint
+        ratio_damage_per_side = ratio_damage_per_side / np.sum(ratio_damage_per_side, axis=1).reshape(num_reals,1) # force it to add to one
+    
+        # Assing damage
+        for tu in range(len(damage['tenant_units'])):
+            for s in range(4):
+                damage['tenant_units'][tu]['qnt_damaged_side_' +str(s+1)] = ratio_damage_per_side[:,s].reshape(num_reals, 1) * np.array(damage['tenant_units'][tu]['qnt_damaged'])
+                
+    return damage
+
+def fn_create_fnc_filters(comp_ds_table):
+    '''Define function filter arrays that allow rapid sampling of simulated
+    damage for use within the fault tree analysis
+
+    Parameters
+    ----------
+    comp_ds_table: DataFrame
+      various component attributes by damage state for each component 
+      within the performance model. Can be populated from the 
+      component_attribites.csv and damage_state_attribute_mapping.csv 
+      databases in the static_tables directory.  Each row of the table 
+      corresponds to each column of the simulated component damage arrays 
+      within damage.tenant_units.
+
+    Returns
+    -------
+    fnc_filters: dictionary
+      each filter is a 1 x num_comp_ds array that can be used to sample
+      select types of damage from the damage['tenant unit'] or damage['story']
+      simulated damage arrays.'''
+
+
+    # Convert damage['comp_ds_table'] lists to numpy arrays
+    import numpy as np    
+    
+    comp_ds_table_keys = list(comp_ds_table.keys())
+    
+    for key in comp_ds_table_keys:
+        comp_ds_table[key] = np.array(comp_ds_table[key])
+        
+    fnc_filters = {}  
+
+
+    ## Impedance filters
+    # fnc_filters['permit_rapid'] = strcmp(comp_ds_table.permit_type, 'rapid');
+    # fnc_filters.permit_full = strcmp(comp_ds_table.permit_type, 'full');
+    # fnc_filters.redesign = comp_ds_table.redesign == 1;
+    fnc_filters['permit_rapid'] = comp_ds_table['permit_type'] == 'rapid'
+    fnc_filters['permit_full'] = comp_ds_table['permit_type'] == 'full'
+    fnc_filters['redesign'] = comp_ds_table['redesign'] == 1   
+    
+    # Building level filters
+    '''combine all damage state filters that have the potential to affect
+    function or reoccupancy, other than structural safety damage (for repair
+    prioritization)'''
+    
+    fnc_filters['affects_reoccupancy'] = comp_ds_table['affects_envelope_safety'].astype('bool') | comp_ds_table['ext_falling_hazard'].astype('bool')  | comp_ds_table['int_falling_hazard'].astype('bool')  | comp_ds_table['global_hazardous_material'].astype('bool')  | comp_ds_table['local_hazardous_material'].astype('bool') | comp_ds_table['affects_access'].astype('bool')
+    fnc_filters['affects_function'] = fnc_filters['affects_reoccupancy'] | comp_ds_table['damages_envelope_seal'].astype('bool') | comp_ds_table['obstructs_interior_space'].astype('bool') | comp_ds_table['impairs_system_operation'].astype('bool') 
+    
+    # Define when building has resolved its red tag (when all repairs are complete that may affect red tags)
+    # get any components that have the potential to cause red tag
+    fnc_filters['red_tag'] = comp_ds_table['safety_class'] > 0
+    
+    # Define when the building requires shoring from external falling hazards
+    fnc_filters['requires_shoring'] = comp_ds_table['requires_shoring'].astype('bool')
+    
+    # Define when the building has issues with internal flooding
+    fnc_filters['causes_flooding'] = comp_ds_table['causes_flooding'].astype('bool')
+    
+    ## System dependent filters
+    # fire suppresion system damage that affects entire building
+    fnc_filters['fire_building'] = np.logical_and(comp_ds_table['system'] == 9, np.logical_and(comp_ds_table['service_location'] == 'building' , comp_ds_table['impairs_system_operation'] == 1))
+    
+    # fire suppresion damage that affects each tenant unit
+    fnc_filters['fire_unit'] = np.logical_and(comp_ds_table['system'] == 9, np.logical_and(comp_ds_table['subsystem_id'] == 23, np.logical_and(comp_ds_table['service_location'] == 'unit' , comp_ds_table['impairs_system_operation'] == 1))) # pipe and brace branches (not spinkler heads)
+    fnc_filters['fire_drops'] = np.logical_and(comp_ds_table['subsystem_id'] == 23, comp_ds_table['impairs_system_operation'] == 1)
+    
+    # Hazardous materials
+    fnc_filters['global_hazardous_material'] = comp_ds_table['global_hazardous_material'].astype('bool')
+    fnc_filters['local_hazardous_material'] = comp_ds_table['local_hazardous_material'].astype('bool')
+    
+    # Stairs
+    fnc_filters['stairs'] = np.logical_and(comp_ds_table['affects_access'] == 1, comp_ds_table['system'] == 4)
+    
+    # Exterior enclosure damage
+    fnc_filters['exterior_safety_lf'] = np.logical_and(comp_ds_table['unit'] == 'lf', comp_ds_table['affects_envelope_safety'] == 1) # Components with perimeter linear feet units
+    fnc_filters['exterior_safety_sf'] = np.logical_and(comp_ds_table['unit'] == 'sf' , comp_ds_table['affects_envelope_safety'] == 1) # Components with perimeter square feet units
+    fnc_filters['exterior_safety_all'] = fnc_filters['exterior_safety_lf'] | fnc_filters['exterior_safety_sf']
+    
+    # Exterior Falling hazards
+    fnc_filters['ext_fall_haz_lf'] = np.logical_and(comp_ds_table['unit'] =='lf', comp_ds_table['ext_falling_hazard'] == 1) # Components with perimeter linear feet units
+    fnc_filters['ext_fall_haz_sf'] = np.logical_and(comp_ds_table['unit'] == 'sf', comp_ds_table['ext_falling_hazard'] == 1) # Components with square feet units
+    fnc_filters['ext_fall_haz_ea'] = np.logical_and(comp_ds_table['unit'] == 'each', comp_ds_table['ext_falling_hazard'] == 1) # Components with "each" units
+    fnc_filters['ext_fall_haz_all'] = fnc_filters['ext_fall_haz_lf'] | fnc_filters['ext_fall_haz_sf'] | fnc_filters['ext_fall_haz_ea']
+    
+    # Exterior enclosure envelope seal damage
+    fnc_filters['exterior_seal_lf'] = np.logical_and(comp_ds_table['unit'] == 'lf', comp_ds_table['damages_envelope_seal'] == 1) # Components with perimeter linear feet units
+    fnc_filters['exterior_seal_sf'] = np.logical_and(comp_ds_table['unit'] == 'sf', comp_ds_table['damages_envelope_seal'] == 1) # Components with perimeter square feet units
+    fnc_filters['exterior_seal_ea'] = np.logical_and(comp_ds_table['unit'] == 'each', comp_ds_table['damages_envelope_seal'] == 1) # Components with perimeter each units    
+    fnc_filters['exterior_seal_all'] = fnc_filters['exterior_seal_lf'] | fnc_filters['exterior_seal_sf'] | fnc_filters['exterior_seal_ea']
+    
+    # Roofing components
+    fnc_filters['roof_structure'] = np.logical_and(comp_ds_table['subsystem_id'] == 21, comp_ds_table['affects_roof_function'] == 1)
+    fnc_filters['roof_weatherproofing'] = np.logical_and(comp_ds_table['subsystem_id'] == 22, comp_ds_table['affects_roof_function'] == 1)
+    
+    # Interior falling hazards
+    fnc_filters['int_fall_haz_lf'] = np.logical_and(comp_ds_table['int_falling_hazard'] == 1, np.logical_and(comp_ds_table['unit'] == 'lf', comp_ds_table['interior_area_conversion_type'] == 'component')) # Interior components with perimeter feet units
+    fnc_filters['int_fall_haz_sf'] = np.logical_and(comp_ds_table['int_falling_hazard'] == 1, np.logical_and(comp_ds_table['unit'] == 'sf', comp_ds_table['interior_area_conversion_type'] == 'component')) # Interior components with area feet units
+    fnc_filters['int_fall_haz_ea'] = np.logical_and(comp_ds_table['int_falling_hazard'] == 1, np.logical_and(comp_ds_table['unit'] == 'each', comp_ds_table['interior_area_conversion_type'] == 'component')) # Interior components with "each" for units (just scale directly)    
+    fnc_filters['int_fall_haz_bay'] = np.logical_and(comp_ds_table['int_falling_hazard'] == 1, comp_ds_table['interior_area_conversion_type'] == 'bay') # structural damage that does not cause red tags but affects function
+    fnc_filters['int_fall_haz_build'] = np.logical_and(comp_ds_table['int_falling_hazard'] == 1, comp_ds_table['interior_area_conversion_type'] == 'building') # structural damage that does not cause red tags but affects funciton (this one should only be tilt-ups)
+    fnc_filters['int_fall_haz_all'] = fnc_filters['int_fall_haz_lf'] | fnc_filters['int_fall_haz_sf'] | fnc_filters['int_fall_haz_ea'] | fnc_filters['int_fall_haz_bay'] | fnc_filters['int_fall_haz_build'] 
+    fnc_filters['vert_instabilities'] = np.logical_and(comp_ds_table['system'] == 1, comp_ds_table['int_falling_hazard'] == 1)
+    
+    # Interior function damage 
+    fnc_filters['interior_function_lf'] = np.logical_and(comp_ds_table['obstructs_interior_space'] == 1, np.logical_and(comp_ds_table['unit'] == 'lf', comp_ds_table['interior_area_conversion_type'] == 'component')) 
+    fnc_filters['interior_function_sf'] = np.logical_and(comp_ds_table['obstructs_interior_space'] == 1, np.logical_and(comp_ds_table['unit'] == 'sf', comp_ds_table['interior_area_conversion_type'] == 'component')) 
+    fnc_filters['interior_function_ea'] = np.logical_and(comp_ds_table['obstructs_interior_space'] == 1, np.logical_and(comp_ds_table['unit'] == 'each', comp_ds_table['interior_area_conversion_type'] == 'component'))     
+    fnc_filters['interior_function_bay'] = np.logical_and(comp_ds_table['obstructs_interior_space'] == 1, comp_ds_table['interior_area_conversion_type'] == 'bay')
+    fnc_filters['interior_function_build'] = np.logical_and(comp_ds_table['obstructs_interior_space'] == 1, comp_ds_table['interior_area_conversion_type'] == 'building')  
+    fnc_filters['interior_function_all'] = fnc_filters['interior_function_lf'] | fnc_filters['interior_function_sf'] | fnc_filters['interior_function_ea'] | fnc_filters['interior_function_bay'] | fnc_filters['interior_function_build'] 
+    
+    # Elevators
+    fnc_filters['elevators'] = np.logical_and(comp_ds_table['system'] == 5, np.logical_and(comp_ds_table['impairs_system_operation'], comp_ds_table['subsystem_id'] != 2))
+    fnc_filters['elevator_mcs'] = np.logical_and(comp_ds_table['system'] == 5, np.logical_and(comp_ds_table['impairs_system_operation'], comp_ds_table['subsystem_id'] == 2))
+    
+    # Electrical system
+    fnc_filters['electrical_main'] = np.logical_and(comp_ds_table['system'] == 7, np.logical_and(comp_ds_table['subsystem_id'] == 1, np.logical_and(comp_ds_table['service_location'] == 'building', comp_ds_table['impairs_system_operation'] == 1)))
+    fnc_filters['electrical_unit'] = np.logical_and(comp_ds_table['system'] == 7, np.logical_and(comp_ds_table['subsystem_id'] == 1, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1)))
+    
+    # Water and Plumbing
+    # Potable Water Plumping
+    fnc_filters['water_main'] = np.logical_and(comp_ds_table['system'] == 6, np.logical_and(comp_ds_table['subsystem_id'] == 8, np.logical_and(comp_ds_table['service_location'] == 'building', comp_ds_table['impairs_system_operation'] == 1)))
+    fnc_filters['water_unit'] = np.logical_and(comp_ds_table['system'] == 6, np.logical_and(comp_ds_table['subsystem_id'] == 8, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1)))
+    
+    # Sanitary Plumbing
+    fnc_filters['sewer_main'] = np.logical_and(comp_ds_table['system'] == 6 , np.logical_and(comp_ds_table['subsystem_id'] == 9, np.logical_and(comp_ds_table['service_location'] == 'building', comp_ds_table['impairs_system_operation'] ==1)))
+    fnc_filters['sewer_unit'] = np.logical_and(comp_ds_table['system'] == 6 , np.logical_and(comp_ds_table['subsystem_id'] == 9, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] ==1)))
+
+  
+    # HVAC
+    fnc_filters['hvac'] = {}
+    
+    fnc_filters['hvac'] = {'building': 
+                        {'hvac_control': {},
+                         'hvac_heating': {},
+                         'hvac_cooling': {}
+                         },
+                       'tenant':
+                        {'hvac_ventilation': {},
+                         'hvac_heating': {},
+                         'hvac_cooling': {},
+                         'hvac_exhaust': {}
+                         }
+                        }
+                                                
+    
+    # HVAC: Control System
+    
+    fnc_filters['hvac']['building']['hvac_control']['mcs'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 2, comp_ds_table['impairs_system_operation'] == 1))
+    fnc_filters['hvac']['building']['hvac_control']['control_panel'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 20, comp_ds_table['impairs_system_operation'] == 1))
+
+    # HVAC: Ventilation
+    fnc_filters['hvac']['tenant']['hvac_ventilation']['duct_mains'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 24, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1)))
+    fnc_filters['hvac']['tenant']['hvac_ventilation']['duct_braches'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 4, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1)))
+    fnc_filters['hvac']['tenant']['hvac_ventilation']['in_line_fan'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 5, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1))) 
+    fnc_filters['hvac']['tenant']['hvac_ventilation']['duct_drops'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 6, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] == 1)))
+    fnc_filters['hvac']['tenant']['hvac_ventilation']['ahu'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 19, comp_ds_table['impairs_system_operation'] == 1))
+    fnc_filters['hvac']['tenant']['hvac_ventilation']['rtu'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 17, comp_ds_table['impairs_system_operation'] == 1))
+
+    # HVAC: Heating
+    fnc_filters['hvac']['building']['hvac_heating']['piping'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 10, comp_ds_table['impairs_system_operation'] == 1))
+    fnc_filters['hvac']['tenant']['hvac_heating']['vav'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 7, comp_ds_table['impairs_system_operation'] == 1))
+
+    # HVAC: Cooling
+    fnc_filters['hvac']['building']['hvac_cooling']['piping'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 11, comp_ds_table['impairs_system_operation'] == 1))
+    fnc_filters['hvac']['building']['hvac_cooling']['chiller'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 15, comp_ds_table['impairs_system_operation'] == 1))
+    fnc_filters['hvac']['building']['hvac_cooling']['cooling_tower'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 16, comp_ds_table['impairs_system_operation'] == 1))
+    fnc_filters['hvac']['tenant']['hvac_cooling']['vav'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 7, comp_ds_table['impairs_system_operation'] == 1))
+
+    # HVAC: Exhaust
+    fnc_filters['hvac']['tenant']['hvac_exhaust']['exhaust_fan'] = np.logical_and(comp_ds_table['system'] == 8, np.logical_and(comp_ds_table['subsystem_id'] == 18, comp_ds_table['impairs_system_operation'] == 1))
+    
+    # Data system
+    fnc_filters['data_main'] = np.logical_and(comp_ds_table['system'] == 11, np.logical_and(comp_ds_table['service_location'] == 'building', comp_ds_table['impairs_system_operation'] == 1))
+    fnc_filters['data_unit'] = np.logical_and(comp_ds_table['system'] == 11, np.logical_and(comp_ds_table['service_location'] == 'unit', comp_ds_table['impairs_system_operation'] ==1))
+    
+    # Horizontal Egress: Fire break partitions
+    fnc_filters['fire_break'] = np.logical_and(comp_ds_table['system'] == 3, comp_ds_table['weakens_fire_break'] == 1) # only collect interior fire break partitions
+    
+    return fnc_filters
+
+    
+def fn_simulate_temp_worker_days(damage, temp_repair_class, repair_time_options):
+    '''Simulate Temporary Repair Times for each component, if not already
+        defined by the user. In a perfect system this should be done alongside 
+        the other full repair time simulation. However, most PBEE assessments do
+        not contain information on temp repair times per component when they are
+        simulating damage and consequences. Therefore, this is decoupled from the
+        rest of the assessment and simulated here (if not already provided by the
+        user)
+        
+        Parameters
+        ----------
+        damage: dictionary
+          contains simulated damage info and damage state attributes
+        repair_time_options['allow_shoring']: logical
+          flag indicating whether or not shoring should be considered as a
+          temporary repair for local stability issues for structural components
+        temp_repair_class: DataFrame
+          attributes of each temporary repair class to consider
+        
+        Returns
+        -------
+        damage: dictionary
+          contains simulated damage info and damage state attributes
+        temp_repair_class: DataFrame
+          attributes of each temporary repair class to consider'''
+        
+        
+    ## Define Temporary Repair Times Options
+    # Turn of temp repairs if specificied by the user
+    if ('allow_tmp_repairs' in repair_time_options.keys()) == False:
+        damage['comp_ds_table']['tmp_repair_class'] = np.zeros(np.shape(damage['comp_ds_table']['tmp_repair_class']))
+ 
+    # Set up temp_repair_class based on user inputs
+    if ('allow_shoring' in repair_time_options.keys()) == False:
+        temp_repair_class.drop(temp_repair_class.index[temp_repair_class['id'] == 5], inplace=True)
+
+    ## Simulate temp repair worker days per component
+    # if not already specified by the user
+    if ('tmp_worker_day' in damage['tenant_units'][0].keys()) == False:
+        # Find total number of damamged components
+        total_damaged = np.array(damage['tenant_units'][0]['qnt_damaged']).copy()
+        for tu in range(len(damage['tenant_units'])-1):
+            total_damaged = total_damaged + np.array(damage['tenant_units'][tu+1]['qnt_damaged'])
+    
+        # Aggregate the total number of damaged components accross each damage
+        # state in a component
+        num_reals = len(damage['tenant_units'][0]['qnt_damaged']) 
+        tmp_worker_days_per_unit = np.zeros([num_reals, len(damage['comp_ds_table']['comp_id'])])
+        for c in range(len(damage['comp_ds_table']['comp_id'])): # for each comp ds
+            # comp = comp_ds_table(c,:);
+            if damage['comp_ds_table']['tmp_repair_class'][c] > 0: # For damage that has temporary repair
+                filt = np.array(damage['comp_ds_table']['comp_id']) == damage['comp_ds_table']['comp_id'][c]
+                total_damaged_all_ds = np.sum(total_damaged[:,filt], axis=1)
+            
+                if float(damage['comp_ds_table']['tmp_repair_time_lower_qnty'][c]) == float(damage['comp_ds_table']['tmp_repair_time_upper_qnty'][c]):
+                    tmp_worker_days_per_unit[:,c] = damage['comp_ds_table']['tmp_repair_time_lower'][c]
+                else:
+                # Interpolate to get per unit temp repair times
+                    tmp_worker_days_per_unit[:,c] = np.interp(np.minimum(np.maximum(total_damaged_all_ds, float(damage['comp_ds_table']['tmp_repair_time_lower_qnty'][c])), float(damage['comp_ds_table']['tmp_repair_time_upper_qnty'][c])),
+                                np.array([float(damage['comp_ds_table']['tmp_repair_time_lower_qnty'][c]), float(damage['comp_ds_table']['tmp_repair_time_upper_qnty'][c])]),
+                                np.array([damage['comp_ds_table']['tmp_repair_time_lower'][c], damage['comp_ds_table']['tmp_repair_time_upper'][c]]),
+                                )
+            else:
+                tmp_worker_days_per_unit[:,c] = np.nan
+
+        '''Simulate uncertainty in per unit temp repair times
+        Assumes distribution is lognormal with beta = 0.4
+        Assumes time to repair all of a given component group is fully correlated, 
+        but independant between component groups''' 
+        
+        sim_tmp_worker_days_per_unit = np.random.lognormal(np.log(tmp_worker_days_per_unit), 0.4, np.shape(tmp_worker_days_per_unit))
+        
+        # Allocate per unit temp repair time among tenant units to calc worker days
+        # for each component
+        for tu in range(len(damage['tenant_units'])):
+            damage['tenant_units'][tu]['tmp_worker_day'] = np.array(damage['tenant_units'][tu]['qnt_damaged']) * sim_tmp_worker_days_per_unit
+
+    return damage, temp_repair_class
+
+def fn_define_door_racking(damage_consequences, num_stories):
+    '''Define building level damage consquences when not specificed by the user.
+    
+    Parameters
+    ----------
+    damage_consequences: dictionary
+      dictionary containing simulated building consequences, such as red
+      tags
+    num_stories: int
+      Integer number of stories in the building being assessed
+    
+    Returns
+    -------
+    damage_consequences['racked_stair_doors_per_story']: array, num real x num stories
+      simulated number of racked stairwell doors at each story
+    damage_consequences['racked_entry_doors_side_1']: array, num real x 1
+      simulated number of racked entry doors on one side of the building
+    damage_consequences['racked_entry_doors_side_2']: array, num real x 1
+      simulated number of racked entry doors on the other side of the building'''
+    
+    ## Set door racking damage if not provided by user
+    num_reals = len(damage_consequences['simulated_replacement_time'])
+    
+    # Assume there are no racked doors if not specified by the user
+    if ('racked_stair_doors_per_story' in damage_consequences.keys()) == False:
+        damage_consequences['racked_stair_doors_per_story'] = (np.zeros([num_reals,num_stories])).tolist() #num real x num stories
+
+    if ('racked_entry_doors_side_1' in damage_consequences.keys()) == False:
+        damage_consequences['racked_entry_doors_side_1'] = list(np.zeros(num_reals)) 
+
+    if ('racked_entry_doors_side_2' in damage_consequences.keys()) == False:
+        damage_consequences['racked_entry_doors_side_2'] = list(np.zeros(num_reals))
+    
+    return damage_consequences
+
+
+
diff --git a/src/atc138/red_tag.py b/src/atc138/red_tag.py
new file mode 100644
index 0000000..18603f5
--- /dev/null
+++ b/src/atc138/red_tag.py
@@ -0,0 +1,168 @@
+def fn_red_tag( calculate_red_tag, damage, comps, simulated_replacement_time, red_tag_options ):
+    '''Perform the ATC-138 functional recovery time assessement given similation
+    of component damage for a single shaking intensity
+    
+    Parameters
+    ----------
+    calculate_red_tag: logical
+      flag to indicate whether on not to calculate red tags based on
+      component damage. Typically assumed to be FALSE for small wood light
+      frame type structures.
+    damage: dictionary
+      contains per damage state damage and loss data for each component in the building
+    comps: dictionary
+      data structure component population info
+    simulated_replacement_time: array [num reals x 1]
+      Time 
+    
+    Returns
+    -------
+    red_tag: logical array [num_reals x 1]
+      indicates the realizations that have a red tag
+    red_tag_impact: logical array [num_reals x num_comp_ds]
+      indicates the realizations of various component damage states that
+      contribute to the cause of red tag
+    inspection_tag: logical array [num_reals x 1]
+      indicates the realizations that require inspection'''
+    
+    import numpy as np
+    
+    def simulate_tagging(damage, comps, sc_ids, sc_thresholds, red_tag_options ):
+        
+        red_tag_impact = np.zeros(np.shape(damage['tenant_units'][0]['qnt_damaged'])) # num reals by num comp_ds
+        red_tag_impact_cb = np.zeros(np.shape(damage['tenant_units'][0]['qnt_damaged'])) # num reals by num comp_ds, specific for counting red tags for coupling beams
+        
+        num_reals = len(red_tag_impact)
+        # Go through each structural system and calc the realizations where the
+        # building is red tagged
+        
+        sc_tag = np.zeros([num_reals,len(sc_ids)])
+        for sc in range(len(sc_ids)):
+            story_tag = np.zeros([num_reals,len(damage['story'])])
+
+            # resets the coupling beam counter for every safety class
+            coupling_beam_dmg = np.zeros([num_reals,3]) # three directions
+            coupling_beam_qty = np.zeros([1,3]) # three directions
+            red_tag_impact_cb_sc = np.zeros(np.shape(damage['tenant_units'][0]['qnt_damaged'])) # num reals by num comp_ds
+            
+            for s in range(len(damage['story'])):
+                sc_filt = np.array(damage['comp_ds_table']['safety_class']) >= sc_ids[sc]
+                
+                dir_tag = np.zeros([num_reals, 3])
+                for direc in [1,2,3]: # Fix assume there are three direction, where direction 3 = nondirectional
+                    sc_dmg = np.array(damage['story'][s]['qnt_damaged_dir_' + str(direc)]) * sc_filt
+                    num_comps = np.array(comps['story'][s]['qty_dir_' + str(direc)])
+        
+                    # For each structural system
+                    structural_systems = np.unique(np.array(damage['comp_ds_table']['structural_system'] + damage['comp_ds_table']['structural_system_alt']))
+                    structural_systems = np.delete(structural_systems, 0) # do not include components not assigned to a structural system
+                    
+                    sys_tag = np.zeros([num_reals, len(structural_systems)]).astype(bool)
+                    
+                    if red_tag_options['ignore_coupling_beam_for_red_tag']:
+                          # just ignore system 12 (coupling beams)
+                          structural_systems = structural_systems[structural_systems != 12]
+
+                    for sys in range(len(structural_systems)):
+                        ss_filt_ds = np.logical_or(np.array(damage['comp_ds_table']['structural_system']) == structural_systems[sys], np.array(damage['comp_ds_table']['structural_system_alt']) == structural_systems[sys])
+                        ss_filt_comp = np.logical_or(np.array(comps['comp_table']['structural_system']) == structural_systems[sys], np.array(comps['comp_table']['structural_system_alt']) == structural_systems[sys])
+        
+                        # Check damage among each series within this structural system
+                        series = np.unique(np.array(damage['comp_ds_table']['structural_series_id'])[ss_filt_ds])
+                        ser_dmg = np.zeros([num_reals,len(series)])
+                        ser_qty = np.zeros([1,len(series)])
+                        for ser in range(len(series)):
+                            ser_filt_ds = np.array(damage['comp_ds_table']['structural_series_id']) == series[ser] 
+                            ser_filt_comp = np.array(comps['comp_table']['structural_series_id']) == series[ser] 
+        
+                            # Total damage within this series and system
+                            ser_dmg[:,ser] = np.sum(sc_dmg[:,ser_filt_ds & ss_filt_ds], axis=1)
+        
+                            # Total number of components within this series and system
+                            ser_qty[:,ser] = np.sum(num_comps[ser_filt_comp & ss_filt_comp])
+        
+                        # Check if this system is causing a red tag
+                        if structural_systems[sys] == 12 and bool(red_tag_options.get('tag_coupling_beams_over_height', False)):
+                            # HARED CODED CHECK FOR COUPLING BEAMS
+                            # just do counts for now instead of adding to red tag per story check
+                            coupling_beam_dmg[:, direc-1] = coupling_beam_dmg[:,direc-1] + np.nanmax(ser_dmg, axis = 1)
+                            coupling_beam_qty[0, direc-1] = coupling_beam_qty[0,direc-1] + np.nanmax(ser_qty, axis = 1)
+                            red_tag_impact_cb_sc = np.fmax(red_tag_impact_cb_sc, ss_filt_ds.reshape(1,-1) * sc_filt.reshape(1,-1) * (sc_dmg>0))
+                        else:
+                            sys_dmg = np.nanmax(ser_dmg, axis = 1)
+                            sys_qty = np.nanmax(ser_qty, axis = 1)
+                            sys_ratio = sys_dmg / sys_qty
+                            sys_tag[:,sys] = sys_ratio > sc_thresholds[sc]
+                          
+                            '''Calculate the impact that each component has on red tag
+                            (boolean, 1 = affects red tag, 0 = does not affect)
+                            Take all damage that is part of this system at this story
+                            in this direction that is damaged to this safety class
+                            level, only where damage exceeds tagging threshold'''
+                            red_tag_impact = np.fmax(red_tag_impact, 1*sys_tag[:,sys].reshape(-1,1) * ss_filt_ds.reshape(1,-1) * sc_filt.reshape(1,-1) * (sc_dmg>0))
+                    
+                    
+                    # Combine across all systems in this direction
+                    dir_tag[:,direc-1] = np.nanmax(sys_tag, axis = 1) #FZ# -1 done to account for python ndexing starting from zero
+                
+        
+                # Combine across all directions at this story
+                story_tag[:,s] = np.nanmax(dir_tag, axis = 1)
+    
+            
+            # Combine across all stories for this safety class
+            sc_tag[:,sc] = np.nanmax(story_tag, axis = 1)
+
+            # Additional Hard Coded Check for Coupling Beams
+            if np.any(np.array(damage['comp_ds_table']['structural_system']) == 12):
+                CB_damage_ratio = coupling_beam_dmg / coupling_beam_qty
+                CB_tag_sc = np.nanmax(CB_damage_ratio, axis = 1) > sc_thresholds[sc]
+                sc_tag[:,sc] = np.fmax(sc_tag[:,sc], CB_tag_sc) # add it back to sum
+                red_tag_impact_cb = np.fmax(
+                    red_tag_impact_cb,
+                    red_tag_impact_cb_sc * sc_tag[:, sc].reshape(-1, 1)
+                )
+       
+        red_tag_impact = np.fmax(red_tag_impact, red_tag_impact_cb)
+    
+        # Combine all safety class checks into one simulated red tag
+        red_tag =  np.nanmax(sc_tag, axis = 1)
+        
+        return red_tag, red_tag_impact
+    
+    ##Initial Setup
+    '''Check to see if any components need the red tag check
+    if none of the components are assinged to structural systems, then
+    skip the red tag calc'''
+    if any(comps['comp_table']['structural_system']) == False:
+        calculate_red_tag = False
+    else:
+        calculate_red_tag = True
+    
+    ## Method
+    if calculate_red_tag == True:
+        # Simulate Red Tags
+        sc_ids = np.array([1, 2, 3, 4])
+        sc_thresholds = np.array([0.5, 0.25, 0.1, 0])
+        red_tag, red_tag_impact = simulate_tagging(damage, comps, sc_ids, sc_thresholds, red_tag_options )
+    
+        # Inspection is flagged for 50% of the red tag thresholds
+        inspection_thresholds = 0.5*sc_thresholds
+        inspection_tag, _ = simulate_tagging(damage, comps, sc_ids, inspection_thresholds, red_tag_options )
+    
+    else:
+        
+        # Do not calculate red tags based on component damage
+        num_reals, num_comp_ds = np.shape(np.array(damage['tenant_units'][0]['qnt_damaged']))
+        red_tag = np.zeros(num_reals)
+        red_tag_impact = np.zeros([num_reals,num_comp_ds])
+        inspection_tag = np.zeros(num_reals)
+    
+    
+    # Account for global red tag cases
+    replace_case = np.logical_not(np.isnan(np.array(simulated_replacement_time)))
+    red_tag[replace_case] = 1
+    
+    return red_tag, red_tag_impact, inspection_tag
+    
+    
diff --git a/src/atc138/repair_schedule/__init__.py b/src/atc138/repair_schedule/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/atc138/repair_schedule/main_repair_schedule.py b/src/atc138/repair_schedule/main_repair_schedule.py
new file mode 100644
index 0000000..1b788d2
--- /dev/null
+++ b/src/atc138/repair_schedule/main_repair_schedule.py
@@ -0,0 +1,160 @@
+def main_repair_schedule(damage, building_model, simulated_red_tags, 
+                         repair_time_options, systems, tmp_repair_class, 
+                         impeding_factors, simulated_replacement_time):
+    
+    '''Determine the repair time for a given damage simulation.
+      
+    Simulation of system and building level repair times based on a
+    simulation of impedance factor and repair scheduling algorithm.
+    Simulates the repair times for all realizations of a single ground motion
+    or intensity level.
+      
+    Parameters
+    ----------
+    damage: dictionary
+      contains per damage state damage and loss data for each component in the building
+      
+    building_model: dictionary
+      data structure containing general information about the building
+      
+    simulated_red_tags: logical array [num_realization x 1]
+      for each realization of the Monte Carlo simulation, is the structure
+      expected to be red-tagged
+      
+    repair_time_options: dictionary
+      general repair time options such as mitigation factors
+      
+    systems: DataFrame
+      attributes of structural and nonstructural building systems; data 
+      provided in static tables directory
+    
+    tmp_repair_class: DataFrame
+     data table containing information about each temporary repair class
+ 
+    impeding_factors: dictionary
+      simulated impedance times
+      
+    surge_factor: number
+      amplification factor for temporary repair times due to materials and
+      labor impacts due to regional damage
+      
+    Returns
+    -------
+    damage: dictionary
+      contains per damage state damage and loss data for each component in the building
+    
+    worker_data: dictionary
+      simulated building-level worker allocations throught the repair process
+    
+    building_repair_schedule: dictionary
+      simulations of the building repair schedule, broken down by component,
+      story, and system'''
+
+
+    ## Initial Setup
+    # Import Packages
+    import math
+    import numpy as np
+    
+    from . import other_repair_schedule_functions
+    
+    ## initial Setup
+    # Define the maximum number of workers that can be on site, based on REDI
+    max_workers_per_building = min(max(math.floor(sum(building_model['area_per_story_sf'])* repair_time_options['max_workers_per_sqft_building'] + 10), repair_time_options['max_workers_building_min'])
+           , repair_time_options['max_workers_building_max'])
+
+    def fn_schedule_repairs(damage, repair_type, systems, max_workers_per_building, 
+                            max_workers_per_story, impeding_factors, 
+                            simulated_red_tags, tmp_repair_complete_day):
+        
+        ## Step 1 - Calculate the start and finish times for each system in isolation
+        # based on REDi repair sequencing and Yoo 2016 worker allocations
+        system_schedule = other_repair_schedule_functions.fn_calc_system_repair_time(damage, repair_type, systems, max_workers_per_building, max_workers_per_story)
+                                  
+        ## Step 2 - Set system repair priority
+        sys_idx_priority_matrix = other_repair_schedule_functions.fn_prioritize_systems( systems, repair_type, damage, tmp_repair_complete_day, impeding_factors)
+                                  
+        ## Step 3 - Define system repair constraints
+        sys_constraint_matrix= other_repair_schedule_functions.fn_set_repair_constraints( systems, repair_type, simulated_red_tags)
+        
+        ## Step 4 - Allocate workers among systems and determine the total days until repair is completed for each sequence
+        repair_complete_day_per_system, worker_data = other_repair_schedule_functions.fn_allocate_workers_systems(systems, system_schedule['system_totals']['repair_days'], system_schedule['system_totals']['num_workers'],
+             max_workers_per_building, sys_idx_priority_matrix, sys_constraint_matrix,
+            simulated_red_tags, impeding_factors['time_sys'])
+                                  
+        ## Step 5 - Format outputs for Functionality calculations
+        damage_recovery = other_repair_schedule_functions.fn_restructure_repair_schedule( damage, system_schedule,
+                     repair_complete_day_per_system, systems, repair_type, simulated_red_tags)
+        
+        return damage_recovery, worker_data
+    
+    
+    ## Determine repair schedule per system for Temporary Repairs 
+    # Define the maximum number of workers that can be on any given story
+    max_workers_per_story = np.ceil(np.array(building_model['area_per_story_sf']) * repair_time_options['max_workers_per_sqft_story_temp_repair'])
+    
+    # Temporary Repairs
+    repair_type = 'temp'
+    tmp_damage, tmp_worker_data = fn_schedule_repairs(damage, repair_type, 
+                                                      tmp_repair_class, 
+                                                      max_workers_per_building, 
+                                                      max_workers_per_story,
+                                                      impeding_factors['temp_repair'], 
+                                                      simulated_red_tags, [])
+    
+    tmp_damage = tmp_damage.copy()
+    # Calculate the max temp repair complete day for each component (anywhere in building)
+    tmp_repair_complete_day = np.empty(np.shape(damage['tenant_units'][0]['tmp_worker_day']))
+    tmp_repair_complete_day[:] = np.nan
+    # NaN = Never damaged
+    # Inf  = Damage not resolved by temp repair
+
+    for tu in range(len(tmp_damage)):
+        tmp_repair_complete_day = np.fmax(tmp_repair_complete_day, tmp_damage[tu]['repair_complete_day'])
+    
+    ## Determine repair schedule per system for Full Repairs 
+    # Define the maximum number of workers that can be on any given story
+    max_workers_per_story = np. ceil(np.array(building_model['area_per_story_sf']) * repair_time_options['max_workers_per_sqft_story'])
+       
+    # Full Repairs   
+    repair_type = 'full'
+    full_damage, worker_data = fn_schedule_repairs(damage, repair_type, systems, 
+                                              max_workers_per_building, 
+                                              max_workers_per_story, 
+                                              impeding_factors, 
+                                              simulated_red_tags, 
+                                              tmp_repair_complete_day)
+
+
+    ## Combine temp and full repair schedules
+    for tu in range(len(full_damage)):
+        damage['tenant_units'][tu]['recovery'] = full_damage[tu]
+        # Repair time is the lesser of the full repair and temp repair times
+        damage['tenant_units'][tu]['recovery']['repair_complete_day_w_tmp'] = np.fmin(damage['tenant_units'][tu]['recovery']['repair_complete_day'],
+                                                                                  tmp_damage[tu]['repair_complete_day'])
+        
+        # Temporary Repair Times control if temporary repair times are less than the full repair time
+        tmp_day_controls = tmp_damage[tu]['repair_complete_day'] < damage['tenant_units'][tu]['recovery']['repair_complete_day']
+        # Repair start day is set to the temp repair start day when temp repairs control
+        damage['tenant_units'][tu]['recovery']['repair_start_day_w_tmp'] = damage['tenant_units'][tu]['recovery']['repair_start_day'].copy()
+        damage['tenant_units'][tu]['recovery']['repair_start_day_w_tmp'][tmp_day_controls] = tmp_damage[tu]['repair_start_day'][tmp_day_controls].copy()
+
+
+    
+    temporary_damage = { 'tenant_units': {} }
+    for tu in range(len(full_damage)):
+        temporary_damage['tenant_units'][tu]={}
+        temporary_damage['tenant_units'][tu]['recovery'] = tmp_damage[tu]
+    
+    temporary_damage['comp_ds_table'] = damage['comp_ds_table'].copy()
+    
+    ## Format Outputs 
+    # Format Start and Stop Time Data for Gantt Chart plots 
+    # This is also the main data structure used for calculating full repair time outputs
+    building_repair_schedule = {}
+    building_repair_schedule['full'] = other_repair_schedule_functions.fn_format_gantt_chart_data(damage, systems, simulated_replacement_time)
+    building_repair_schedule['temp'] = other_repair_schedule_functions.fn_format_gantt_chart_data(temporary_damage, systems, simulated_replacement_time)
+ 
+    return damage, worker_data, building_repair_schedule
+    
+
diff --git a/src/atc138/repair_schedule/other_repair_schedule_functions.py b/src/atc138/repair_schedule/other_repair_schedule_functions.py
new file mode 100644
index 0000000..4a1aa41
--- /dev/null
+++ b/src/atc138/repair_schedule/other_repair_schedule_functions.py
@@ -0,0 +1,900 @@
+
+"""
+Other repair schedule functions called by the main repair schedule function
+"""
+import numpy as np
+
+def fn_allocate_workers_stories(total_worker_days, required_workers_per_story, 
+                                average_crew_size, max_crews_building, 
+                                max_workers_per_building):
+
+    '''For a given building system, allocate crews to each story to
+    determine the system level repair time on each story; not considering
+    impeding factors or construction constraints with other systems.
+      
+      
+    Parameters
+    ----------
+    total_worker_days: [num reals x num stories]
+      The total worker days needed to repair damage each story for this
+      sequence
+      
+    required_workers_per_story: array [num reals x num stories]
+      Number of workers required to repair damage in each story
+      
+    average_crew_size: array [num reals x num stories]
+      Average crew size required to repair damage in each story
+      
+    max_crews_building: logical array [num reals x 1]
+      maximum number of crews allowed in the building for this system
+      
+    max_workers_per_building: int
+      maximum number of workers allowed in the buildings at a given time
+      
+    Returns
+    -------
+    repair_start_day: array [num reals x num stories]
+      The number of days from the start of repair of this system until the repair of this system 
+      starts on each story
+      
+    repair_complete_day: array [num reals x num stories]
+      The number of days from the start of repair of this system until each story is
+      repaired for damage in this system
+      
+    max_workers_per_story: array [num reals x num stories]
+      number of workers required for the repair of this story and system as
+      
+    Notes
+    -----'''
+    import sys
+    
+    ## Initial Setup
+    num_reals, num_stories = np.shape(total_worker_days)
+    repair_complete_day = np.zeros([num_reals,num_stories])
+    repair_start_day = np.empty([num_reals,num_stories])
+    repair_start_day[:] = np.nan
+    max_workers_per_story = np.zeros([num_reals,num_stories])
+    
+    ## Allocate workers to each story
+    # Loop through iterations of time reduce damage on each story based on assigned workers
+    iter = 0;
+    while sum(sum(total_worker_days)) > 0.01:
+        iter = iter + 1; 
+        if iter > 1000: # keep the while loop pandemic contained
+            # error('PBEE_Recovery:RepairSchedule', 'Could not converge worker allocations for among stories in sequence');
+            sys.exit('Error! could not converge worker allocations for among stories in sequence')
+    
+        # Determine the available workers in the building
+        available_workers_in_building = max_workers_per_building * np.ones([num_reals])
+        assigned_workers_per_story = np.zeros([num_reals,num_stories])
+        assigned_crews_per_story = np.zeros([num_reals,num_stories]) 
+    
+        # Define where needs repair
+        needs_repair = total_worker_days > 0
+    
+        # Defined Required Workers
+        required_workers_per_story = needs_repair * required_workers_per_story.copy()
+        
+        '''Assign Workers to each story -- assumes that we wont drop the number
+        of crews in order to meet worker per sqft limitations, and instead
+        wait until more workers are made available'''
+        
+        for s in range(num_stories):
+            # Are there enough workers to assign a crew
+            sufficient_workers = required_workers_per_story[:,s] <= available_workers_in_building
+            
+            # Assign Workers to this story
+            assigned_workers_per_story[sufficient_workers,s] = required_workers_per_story[sufficient_workers,s]
+            assigned_crews_per_story[:,s] = assigned_workers_per_story[:,s] / average_crew_size[:,s]
+            assigned_crews_per_story[np.isnan(assigned_crews_per_story)] = 0
+            num_crews_in_building = np.sum(assigned_crews_per_story, axis=1)
+            exceeded_max_crews = num_crews_in_building > max_crews_building
+            assigned_workers_per_story[exceeded_max_crews,s] = 0 # don't assign workers if we have exceeded the number of crews allowed in the building
+            
+            # Define Available Workers
+            available_workers_in_building = available_workers_in_building - assigned_workers_per_story[:,s]  
+        
+        
+        # Define the start of repairs for each story
+        start_repair_filt = np.isnan(repair_start_day) & (assigned_workers_per_story > 0)
+        
+        max_day_completed_so_far = np.transpose(np.multiply(np.amax(repair_complete_day, axis=1), np.ones([num_stories,num_reals]))) #FZ# transpose done to align the arrays for operation
+         
+        repair_start_day[start_repair_filt] =  max_day_completed_so_far[start_repair_filt]
+        
+        # Calculate the time associated with this increment of the while loop
+        in_progress = assigned_workers_per_story > 0 # stories where work is being done
+        total_repair_days = np.empty(np.shape(in_progress)) 
+        total_repair_days[:] = np.inf # pre-allocate with inf's becuase we take a min later
+        total_repair_days[in_progress] = total_worker_days[in_progress] / assigned_workers_per_story[in_progress]
+        delta_days = np.amin(total_repair_days,axis=1) # time increment is whatever in-progress story that finishes first 
+        delta_days[np.isinf(delta_days)] = 0 # Replace infs from real that has no repair with zero       
+        delta_worker_days = np.transpose(np.multiply(np.transpose(assigned_workers_per_story), delta_days)) # time increment is whatever in-progress story that finishes first #FZ# transpose done fto align the arrays for operation
+        total_worker_days[in_progress] = np.maximum(total_worker_days[in_progress] - delta_worker_days[in_progress], 0)
+        indx_neg_remaining = total_worker_days < 0.001 # find instances of remaining time that are excessively small and don't represent realistic amount of work
+        total_worker_days[indx_neg_remaining] = 0  # zero remaining work that is negligible as defined above
+        
+        # Define Start and Stop of Repair for each story in each sequence
+        repair_complete_day = repair_complete_day + np.transpose(np.multiply(delta_days, np.transpose(1*needs_repair))) #FZ# transpose done fto align the arrays for operation
+         
+        # Max Crew Size for use in later function
+        max_workers_per_story = np.maximum(max_workers_per_story , assigned_workers_per_story)
+    
+    return repair_start_day, repair_complete_day, max_workers_per_story
+    
+
+def fn_calc_system_repair_time(damage, repair_type, systems, max_workers_per_building, max_workers_per_story):
+    ''' From Dustin's work
+    Determine the repair time for each system if repaired in isolation 
+      
+       Parameters
+       ----------
+       damage: dictionary
+         contains per damage state damage and loss data for each component in the building
+       
+       repair_type: string
+         String identifier indicating whether the repair are temporary or full
+  
+       systems: DataFrame
+         data table containing information about each system's attributes
+         
+       max_workers_per_building: int
+         maximum number of workers allowed in the building at once
+         
+       max_workers_per_story: array [1 x num_stories]
+         maximum number of workers allowed in each story at once
+      
+       Returns
+       -------
+       schedule['per_system'][sys]['repair_start_day'] [num reals x num stories]
+         The number of days from the start of repair of a specifc system until
+         the start of repair of each story for that given system (i.e. the day 
+         of the start of repairs relative the to start of this system ... e.g.
+         story 1 should always be zero)
+         
+       schedule['per_system'][sys]['repair_complete_day'] [num reals x num stories]
+         The number of days from the start of repair of a specifc system until
+         each story is fully repaired for that given system.
+         
+       schedule['per_system'][sys]['max_num_workers_per_story'] [num reals x num stories]
+         The number of workers required to repair each story of this system
+         
+       schedule['system_totals']['repair_days'] [num reals x num systems]
+         The number of days required to repair each system in isolation
+         
+       schedule['system_totals']['num_workers'] [num reals x num systems]
+         The number of workers required for the repair of each system
+      
+       Notes
+       -----'''
+       
+    def fn_repair_sequence_parameters(damage, repair_type, sys, num_du_per_crew, 
+                                      max_crews_per_comp_type, 
+                                      max_workers_per_story, 
+                                      max_workers_per_building):
+        
+        '''Define crew sizes, workers, and repair times for each story of a given
+        system. Based on worker limiations, and component worker days data from
+        the FEMA P-58 assessment.'''
+        
+        # Define Repair Type Variables (variable within the damage object)
+        if repair_type == 'full':
+            repair_time_var = 'worker_days'
+            system_var = 'system'
+            crew_size_var = 'crew_size'
+        elif repair_type == 'temp':
+            repair_time_var = 'tmp_worker_day'
+            system_var = 'tmp_repair_class'
+            crew_size_var = 'tmp_crew_size'
+        else:
+            sys.exit('error!. Unexpected Repair Type')
+
+
+        # Define Initial Parameters
+        num_stories = len(damage['tenant_units']) 
+        num_reals, num_comps = np.size(damage['tenant_units'][0]['worker_days'],0), np.size(damage['tenant_units'][0][repair_time_var],1)
+        sequence_filt = np.array(damage['comp_ds_table'][system_var]) == sys # identifies which ds indices are in this seqeunce.
+        comp_types = np.unique(np.array(damage['comp_ds_table']['comp_idx'])[sequence_filt]) # Types of components in this system
+        
+        # Pre-allocate variables
+        total_worker_days = np.zeros([num_reals,num_stories])
+        is_damaged_building = np.zeros([num_reals,num_comps], dtype=bool)
+        num_damaged_comp_types = np.zeros([num_reals,num_stories])
+        num_damaged_units = np.zeros([num_reals,num_stories])
+        average_crew_size = np.zeros([num_reals,num_stories])
+        
+        for s in range(num_stories):
+            # Define damage properties of this system at this story
+            num_damaged_units[:,s] = np.sum((1*sequence_filt) * damage['tenant_units'][s]['qnt_damaged'], axis=1) #FZ# Total number of damaged components of one system in one story
+            is_damaged = np.logical_and(np.array(damage['tenant_units'][s]['qnt_damaged']) > 0, np.array(damage['tenant_units'][s][repair_time_var]) > 0) #FZ# No
+            is_damaged_building = is_damaged_building | is_damaged              #FZ# compiling for all stories. if there is damage at any story, building is damaged
+            
+            for c in range(len(comp_types)):
+                num_damaged_comp_types[:,s] = num_damaged_comp_types[:,s] + np.any((1* np.array(damage['comp_ds_table']['comp_idx']) == comp_types[c]) * (1*is_damaged), axis=1) #FZ# number of types of components damaged in a story in each realization
+            
+        
+            # Calculate total worker days per story per sequences
+            total_worker_days[:,s] = np.sum(np.array(damage['tenant_units'][s][repair_time_var])[:,sequence_filt], axis=1) # perhaps consider doing when we first set up this damage data structure
+            
+            # Determine the required crew size needed for  these repairs
+            repair_time_per_comp = np.array(damage['tenant_units'][s][repair_time_var]) / np.array(damage['comp_ds_table'][crew_size_var])
+            average_crew_size[:,s] = total_worker_days[:,s] / np.sum(repair_time_per_comp[:,sequence_filt], axis=1)
+        
+            
+        # Define the number of crews needed based on the extent of damage
+        num_crews = np.ceil(num_damaged_units / num_du_per_crew)
+        num_crews = np.fmin(num_crews, max_crews_per_comp_type * num_damaged_comp_types)
+        num_crews = np.fmin(num_crews, np.ceil(num_damaged_units)) # Safety check: num crews should never be greater than the number of damaged components
+        
+        # Round up total worker days to the nearest day to speed up the worker 
+        # allocation loop and implicitly consider change of trade delays
+        total_worker_days = np.ceil(total_worker_days)
+        
+        # Round crew sizes such that we have a realistic size (still implicitly
+        # averaged based on type of damage)
+        average_crew_size[np.isnan(average_crew_size)] = 0
+        average_crew_size = np.round(average_crew_size)
+        
+        # Limit the number of crews based on the space limitations at this story
+        # and the assumed crew size
+        worker_upper_lim = np.fmin(max_workers_per_story , max_workers_per_building)
+        max_num_crews_per_story = np.fmax(np.floor(worker_upper_lim / average_crew_size), 1)
+        num_crews = np.fmin(num_crews, max_num_crews_per_story) 
+        
+        # Calculate the total number of workers per story for this system
+        num_workers = average_crew_size* num_crews
+        
+        # Repeat calc of number of uniquely damaged component types for the whole building
+        num_damaged_comp_types = np.zeros([num_reals])
+        
+        for c in range(len(comp_types)):
+            num_damaged_comp_types = num_damaged_comp_types + np.any( (1* np.array(damage['comp_ds_table']['comp_idx']) == comp_types[c]) * (1*is_damaged_building), axis=1)                                                
+                                                                   
+        max_crews_building = max_crews_per_comp_type * num_damaged_comp_types
+            
+        return total_worker_days, num_workers, average_crew_size, max_crews_building
+    
+    # General Variable
+    num_reals = len(damage['tenant_units'][0]['worker_days'])
+    schedule = {'system_totals' : {'repair_days' : np.zeros([num_reals,len(systems)])}}
+    schedule['system_totals']['num_workers'] = np.zeros([num_reals,len(systems)])
+    
+    ## Allocate workers to each story for each system
+    # Repair finish times assumes all sequences start on day zero
+    schedule['per_system']={}    
+    for syst in range(len(systems)):
+        schedule['per_system'][syst]={}
+        # Define the Crew workers and total workers days for this sequence
+        # in arrays of [num reals by num stories]
+        total_worker_days, num_workers, average_crew_size, max_crews_building = fn_repair_sequence_parameters(damage, repair_type,
+            systems['id'][syst], 
+            systems['num_du_per_crew'][syst],
+            systems['max_crews_per_comp_type'][syst],
+            max_workers_per_story,
+            max_workers_per_building
+        )
+        # Allocate workers to each story and determine the total days until
+        # repair is complete for each story and sequence
+
+        # other_repair_schedule_functions.
+        AA,BB,CC = fn_allocate_workers_stories(total_worker_days, num_workers, 
+                                      average_crew_size, max_crews_building, 
+                                      max_workers_per_building)
+
+        schedule['per_system'][syst]['repair_start_day']=AA
+        schedule['per_system'][syst]['repair_complete_day']=BB
+        schedule['per_system'][syst]['max_num_workers_per_story']=CC
+    
+        # How many days does it take to complete each system in isloation
+        schedule['system_totals']['repair_days'][:,syst] = np.amax(schedule['per_system'][syst]['repair_complete_day'], axis=1)
+        schedule['system_totals']['num_workers'][:,syst] = np.amax(schedule['per_system'][syst]['max_num_workers_per_story'], axis=1)
+        
+    return schedule
+
+
+def fn_prioritize_systems( systems, repair_type, damage, tmp_repair_complete_day, impeding_factors):
+    '''Determine the priority of worker allocation for each system and realization
+    based on default table priorities, whether they have the potential to 
+    affect function and whether they are resolved by temporary repairs.
+       
+    Parameters
+    ----------
+    systems: DataFrame
+      data table containing information about each system's attributes
+    
+    repair_type: string
+      String identifier indicating whether the repair are temporary or full
+      
+    damage: dictionary
+      contains per damage state damage and loss data for each component in the building
+      
+    tmp_repair_complete_day: array [num_reals x num_comp]
+      contains the day (after the earthquake) the temporary repair time is 
+      resolved per damage state damage and realization. Inf represents that
+      there is not temporary repair time available for a given components
+      damage.
+    
+    iimpeding_factors: dictionary
+      simulated impedance times   
+    Returns
+    -------
+    sys_idx_priority_matrix: index array [num reals x num systems]
+      row index to filter system matrices to be prioritized for each
+      realiztion. Priority is left to right.
+    
+    Notes
+    ------
+    The checks done here are only able to implicitly check for which systems
+    affect funtion the most (this prioritizes which have the potential to
+    affect function). To explicitly check which systems have the biggest
+    impact on function and prioritize those, this check would need to be
+    coupled with the function assessment'''
+    
+    import sys
+    
+    ## Initial Setup
+    # Define Repair Type Variables (variable within the damage object)
+    if repair_type == 'full':
+        system_var = 'system'
+    elif repair_type == 'temp':
+        system_var = 'tmp_repair_class'
+    else:
+        sys.exit('error!. Unexpected Repair Type')
+
+
+    # initialize variables
+    num_sys = len(systems)
+    num_reals = np.size(damage['tenant_units'][0]['qnt_damaged'],0)    
+    
+    # Find which components potentially affect reoccupancy accross any tenant unit
+    affects_reoccupancy = np.zeros([num_reals, len(damage['comp_ds_table']['comp_id'])], dtype=bool)
+    for s in range(len(damage['tenant_units'])):
+        affects_reoccupancy = np.logical_or(affects_reoccupancy, np.logical_and(damage['fnc_filters']['affects_reoccupancy'], np.array(damage['tenant_units'][s]['qnt_damaged']) > 0))
+
+    
+    # Find which components potentially affect function accross any tenant unit 
+    for s in range(len(damage['tenant_units'])):
+        # affects_function = zeros(num_reals, len(damagecomp_ds_table));
+        affects_function = np.zeros([num_reals, len(damage['comp_ds_table']['comp_id'])], dtype=bool)
+        affects_function = np.logical_or(affects_function, np.logical_and(damage['fnc_filters']['affects_function'], np.array(damage['tenant_units'][s]['qnt_damaged']) > 0))
+
+       
+    ## Define ranks for each system 
+    sys_affects_reoccupancy = np.zeros([num_reals, num_sys]) # only prioritize the systems that potentially affect reoccupancy
+    sys_affects_function = np.zeros([num_reals, num_sys]) # only prioritize the systems that potentially affect function
+    sys_tmp_repaired = np.zeros([num_reals, num_sys]) # dont prioitize the systems that are completely resolved by temporary repairs
+    
+    for syst in range(num_sys):
+        sys_filter = damage['comp_ds_table'][system_var] == syst+1 #FZ# +1 done to account for python indexing starting from 0
+        if any(sys_filter): # Only if this system is present
+            sys_affects_reoccupancy[:,syst] = np.any(affects_reoccupancy[:,sys_filter], axis = 1); # any damage that potentially affects function in this system    
+            sys_affects_function[:,syst] = np.any(affects_function[:,sys_filter], axis = 1); # any damage that potentially affects function in this system
+            # check if the list (array) is non-empty
+            if list(tmp_repair_complete_day): # Only if temp repair data is passed in
+                all_sys_tmp_repaired = np.all(np.logical_or(tmp_repair_complete_day[:,sys_filter] < np.inf, np.isnan(tmp_repair_complete_day[:,sys_filter])), axis=1) # is every single damaged component resolved by temp repairs
+                tmp_repair_quick = np.nanmax(tmp_repair_complete_day[:,sys_filter], axis=1) < impeding_factors['time_sys'][:,syst] # Are the temp repairs for this system resolved before impeding factors are complete
+                sys_tmp_repaired[:,syst] = np.logical_and(all_sys_tmp_repaired, tmp_repair_quick) # damage is quickly resolved by temp repair
+
+    prioritize_system_reoccupancy = np.logical_and(sys_affects_reoccupancy == 1, sys_tmp_repaired == 0)
+    prioritize_system_function_only = np.logical_and(sys_affects_function == 1, np.logical_and(prioritize_system_reoccupancy, sys_tmp_repaired == 0))
+    non_priorities = np.logical_and(np.logical_not(prioritize_system_reoccupancy), np.logical_not(prioritize_system_function_only))
+    
+    '''First prioritize reoccpancy repairs (100 classifies first priority bank, 
+    important for sort function below), then priotize repairs that only 
+    affect function (200 classifies second priority bank). 
+    Finally, repair the systems that dont affect reoccupancy or 
+    function (300 classifies third priority bank)'''
+    
+    sys_priority_matrix = prioritize_system_reoccupancy * (100 + np.array(systems['priority'])) + prioritize_system_function_only * (200 + np.array(systems['priority'])) + non_priorities * (300 + np.array(systems['priority']))
+
+    
+    # use rank matrix to get row indices of priorities
+    sys_idx_priority_matrix = np.argsort(sys_priority_matrix, axis=1)
+    
+    return sys_idx_priority_matrix
+
+
+def fn_set_repair_constraints(systems, repair_type, conditionTag):
+    '''Define a constraint matrix to be used by repair schedule
+    
+    Develops a matrix of various constriants between each system (i.e. what
+    systems need to be repaired before others) for each realization
+    
+    Parameters
+    ----------
+    systems: DataFrame
+     data table containing information about each system's attributes
+
+    repair_type: string
+      String identifier indicating whether the repair are temporary or full
+      
+    conditionTag: logical array (num_reals x 1)
+     Is the building red tagged for each realization
+    
+    Returns
+    -------
+    sys_constraint_matrix: array [num reals x num_sys]
+     array of system ids which define which systems (column index) are delayed by the
+     system ids (array values)
+    
+    Notes
+    -----
+    Shortcoming: as implemented, each system can only be constrained by one
+                  other system
+    Example:
+    [0 0 1 0 0 0 0 6 0]
+    Interiors (column 3) are blocked by struture (value of 1 in the 3rd column)
+    HVAC (column 8) is blocked by plumbing (value of 6 in the 8th column)'''
+    
+    import sys
+    #Initial Setup
+    num_sys = len(systems)
+    num_reals = len(conditionTag)
+    sys_constraint_matrix = np.zeros([num_reals, num_sys])
+    
+    # Interior Constraints
+    if repair_type == 'full':
+        # Interiors are delayed by structural repairs
+        interiors_idx = np.where(np.array(systems['name']) == 'interior')[0][0] #FZ [0] is done to convert tuple to np array # [0][0] to get scalar
+        structure_idx = np.where(np.array(systems['name']) == 'structural')[0][0]
+ 
+        sys_constraint_matrix[:,interiors_idx] = structure_idx+1 #FZ# +1 is done to replace with the system id which starts with 1, but python indexing starts at 0. 
+        
+        # Red Tag Constraints
+        # All systems blocked by structural when red tagged
+        for rt in np.where(conditionTag)[0]:
+            for st in np.where(np.array(systems['name']) != 'structural')[0]:
+                sys_constraint_matrix[rt, st] = structure_idx+1
+    
+    elif repair_type == 'temp':
+        shoring_id = 5
+        shoring_filt = systems['id'] == shoring_id
+        if np.any(shoring_filt): # If shoring is considred as a temp repair measure
+            shoring_idx = np.where(shoring_filt)[0][0]
+            sys_constraint_matrix[:,np.logical_not(shoring_filt)] = shoring_idx + 1 # all classes that are not shoring idx are blocked by the shoring idx. #FZ# +1 is done to replace with the system id which starts with 1, but python indexing starts at 0. 
+
+    else:
+        sys.exit('error. Unexpected Repair Type')
+    
+    return sys_constraint_matrix
+
+
+def fn_allocate_workers_systems(systems, sys_repair_days, sys_crew_size, 
+                                max_workers_per_building, 
+                                sys_idx_priority_matrix, 
+                                sys_constraint_matrix, 
+                                condition_tag, 
+                                sys_impeding_factors):
+
+
+    '''Stager repair to each system and allocate workers based on the repair
+    constraints, priorities, and repair times of each system
+    
+    Parameters
+    ----------
+    system_repair_days: [num reals x num systems]
+     Number of days from the start of repair of each to the completion of
+     the system (assuming all sequences start on day zero)
+     
+    system_repair_days: [num reals x num systems]
+     Number of days from the start of repair of each to the completion of
+     the system (assuming all sequences start on day zero)
+    
+    sys_crew_size: array [num reals x num systems]
+     required crew size for each system
+     
+    max_workers_per_building: int
+     Maximum number of workers that can work in the building at the same time 
+     
+    sys_idx_priority_matrix: matrix [num reals x systems]
+     worker allocation order of system id's prioritized for each realiztion
+     
+    sys_constraint_matrix: array [num reals x num_sys]
+     array of system ids which define which systems (column index) are delayed by the
+     system ids (array values)
+     
+    condition_tag: logical array [num reals x 1]
+     true/false if the building is red tagged
+     
+    sys_impeding_factors: array [num_reals x num_sys]
+     maximum impedance time (days) for each system. Pass in empty array when
+     calculating repair times (i.e. not including impeding factors).
+    
+    Returns
+    -------
+    repair_complete_day_per_system: matrix [num reals x num systems]
+     Number of days from the start of each sequence to the completion of the
+     sequence considering the allocation of workers to each sequence (ie
+     some sequences start before others)
+     
+    worker_data['total_workers']: array [num reals x varies]
+     total number of workers in the building at each time step of the worker
+     allocation algorthim. The number of columns varies with the number of
+     increments of the worker allocation algorithm.
+     
+    worker_data['day_vector']: array [num reals x varies]
+     Day of each time step of the worker allocation algorthim. The number of 
+     columns varies with the number of increments of the worker allocation algorithm.
+    
+    Notes
+    -----'''
+    
+    import sys
+    
+    def fitler_matrix_by_rows( values, filter_by):
+    # Use a identiry matrix to filter values from another matrix by rows
+    
+        '''Parameters
+        ----------
+        values: matrix [n x m]
+          values to filter by row
+        filter: matrix [n x m]
+          array indexes to grab from each row of values
+        
+        Returns
+        -------
+        filtered_values: matrix [n x m]
+          values filtered by rows'''
+        
+        # Method
+        x2t = np.transpose(values)
+        idx1 = np.transpose(filter_by)
+        y4 = np.zeros(np.shape(idx1))
+        for r in range(np.size(values,0)):
+            y4[:, r] = x2t[idx1[:, r], r]
+        filtered_values = np.transpose(y4)
+ 
+        return filtered_values
+
+    
+    ## Initial Setup
+    # Initialize Variables
+    num_reals, num_sys = np.shape(sys_repair_days)
+    priority_system_complete_day = np.zeros([num_reals,num_sys])
+    day_vector = np.zeros([num_reals,0])
+    total_workers = np.zeros([num_reals,0])
+    
+    # Re-order system variables based on priority
+    priority_sys_workers_matrix = fitler_matrix_by_rows(sys_crew_size, sys_idx_priority_matrix)
+    priority_sys_constraint_matrix = fitler_matrix_by_rows(sys_constraint_matrix, sys_idx_priority_matrix)
+    priority_sys_repair_days = fitler_matrix_by_rows(sys_repair_days, sys_idx_priority_matrix)
+    if sys_impeding_factors.size == 0:
+        priority_sys_impeding_factors = np.zeros([num_reals, num_sys])
+    else:
+        priority_sys_impeding_factors = fitler_matrix_by_rows(sys_impeding_factors, sys_idx_priority_matrix)
+
+    
+    # Round up days to the nearest day
+    # Provides an implicit change of trade delay, as well as help to reduce the
+    # number of delta increments in the following while loop
+    priority_sys_repair_days = np.ceil(priority_sys_repair_days)
+    priority_sys_impeding_factors = np.ceil(priority_sys_impeding_factors) #FZ# Already rounded. Check requirement
+    
+    ## Assign workers to each system based on repair constraints
+    iter = 0
+    current_day = np.zeros(num_reals)
+    priority_sys_waiting_days = priority_sys_impeding_factors.copy()
+    while sum(sum(priority_sys_repair_days)) > 0.01:
+        iter = iter + 1; 
+        if iter > 1000: # keep the while loop pandemic contained
+            sys.exit('error (PBEE_Recovery:RepairSchedule, Could not converge worker allocations for among systems')
+
+        
+        # zero out assigned workers matrix
+        assigned_workers = np.zeros([num_reals,num_sys])
+        
+        # limit available workers to the max that can be on any one given story
+        # this ensures next system cannot start until completely unblocked on
+        # every story. Need to update for taller buildings which could start
+        # next sequence on lower story once previous sequence was far enough
+        # along
+        available_workers = max_workers_per_building * np.ones(num_reals)
+        
+        # Define what systems are waiting to begin repairs
+        sys_blocked = np.zeros([num_reals,num_sys]).astype(bool)
+        sys_incomplete = priority_sys_repair_days > 0
+        for s in range(num_sys): # Loop over each system that may constrain something
+            constrained_systems = priority_sys_constraint_matrix == s+1 # These systems are constrained by looped system #FZ# s+1 is done to correlate with +1 done in deriving constraint matrix
+            constraining_sys_filt = sys_idx_priority_matrix == s; # location in matrix of this system
+            is_constraining_sys_incomplete = np.amax(sys_incomplete * constraining_sys_filt, axis=1) # Vec of relizations for looped system
+            sys_blocked = sys_blocked | np.transpose(np.transpose(constrained_systems) * is_constraining_sys_incomplete) # System is constrained if blocked by an incomplete system. #FZ# transpose done to align arrays for the operation
+        
+        # Need to wait for impeding factors or other repairs to finish
+        is_waiting = np.transpose(current_day < np.transpose(priority_sys_impeding_factors)) | sys_blocked # assuming impeding factors are the only constraints. #FZ# transpose done to align arrays for the operation
+        
+        # Define where needs repair
+        # System needs repair if there are still repairs days left and it is
+        # not waiting to be unblocked
+        needs_repair = (priority_sys_repair_days>0) & np.logical_not(is_waiting)
+    
+        # Defined Required Workers
+        required_workers = needs_repair * priority_sys_workers_matrix
+        
+        # Assign Workers to each system
+        for s in range(num_sys):
+            # Assign Workers to this systems
+            enough_workers = required_workers[:,s] <= available_workers
+            assigned_workers[enough_workers,s] = np.fmin(required_workers[enough_workers,s], available_workers[enough_workers])
+    
+            # Define Available Workers
+            # when in series limit available workers to the workers in this system 
+            # (occurs for structural systems when the building is red tagged
+            is_structural = systems['name'][s] == 'structural'
+            in_series = np.logical_and(condition_tag, is_structural)
+            available_workers[np.logical_and(in_series, assigned_workers[:,s] > 0)] = 0 #FZ# Python index 0 is structural system
+            # when not in series, calc the remaining workers
+            available_workers[np.logical_not(in_series)] = available_workers[np.logical_not(in_series)] - assigned_workers[np.logical_not(in_series),s]
+
+        
+        # Calculate the time associated with this increment of the while loop
+        in_progress = assigned_workers > 0 # sequences where work is being done
+        total_repair_days = np.empty(np.shape(in_progress)) # pre-allocate with inf's becuase we take a min later
+        total_repair_days[:] = np.inf
+        total_repair_days[in_progress] = priority_sys_repair_days[in_progress]
+        total_waiting_days = priority_sys_waiting_days.copy()   #FZ# Check is.copy() worke here
+        
+        total_waiting_days[total_waiting_days == 0] = np.inf # Convert zeros to inf such that zeros are not included in the min in the next step
+        
+        total_time = np.fmin(total_repair_days,total_waiting_days) # combime repair time and waiting time
+        delta_days = np.amin(total_time,axis=1) # time increment is whatever in-progress story that finishes first
+        delta_days[np.isinf(delta_days)] = 0 # Replace infs from real that has no repair with zero
+                 
+        # Reduce waiting time
+        priority_sys_waiting_days = np.maximum(np.transpose(np.transpose(priority_sys_waiting_days) - delta_days), 0) #FZ# transpose done to align arrays for the operation
+        
+        # Reduce time needed for repairs
+        delta_days_in_progress = np.transpose(delta_days * np.transpose(in_progress)) # change in repair time for all sequences being worked on #FZ# transpose done to align arrays for the operation
+        priority_sys_repair_days[in_progress] = np.maximum(priority_sys_repair_days[in_progress] - delta_days_in_progress[in_progress], 0)
+        
+        # Define Start and Stop of Repair for each sequence
+        priority_system_complete_day = priority_system_complete_day + np.transpose(delta_days * np.transpose(needs_repair | is_waiting)) #FZ# transpose done to align arrays for the operation
+        
+        # Define Cummulative day of repair
+        day_vector = np.column_stack((day_vector, current_day))
+        current_day = current_day + delta_days
+        
+        # Save worker data data over time
+        # total_workers = [total_workers, np.sum(assigned_workers,axis=1), np.sum(assigned_workers,axis=1)] #FZ# why assigned workers stored twice - Review
+        total_workers = np.column_stack((total_workers, np.sum(assigned_workers,axis=1), np.sum(assigned_workers,axis=1)))
+        day_vector = np.column_stack((day_vector, current_day))
+
+        
+    # Untangle system_complete_day back into system table order
+    
+    sys_idx_untangle_matrix = np.argsort(sys_idx_priority_matrix, axis=1)
+    
+    repair_complete_day_per_system = fitler_matrix_by_rows(priority_system_complete_day, sys_idx_untangle_matrix)
+    
+    # Save worker data matrices
+    worker_data ={'total_workers' : total_workers, 'day_vector' : day_vector}
+
+    return repair_complete_day_per_system, worker_data 
+
+
+def fn_restructure_repair_schedule( damage, system_schedule,
+    repair_complete_day_per_system, systems, repair_type, simulated_red_tags):
+    
+    '''Redistribute repair schedule data from the system and story level to the component level for use 
+    in the functionality assessment (ie, put repair schedule data into the
+    damage object)
+    
+    Parameters
+    ----------
+    damage: dictionary
+     contains per damage state damage and loss data for each component in the building
+     
+    system_schedule: dictionary
+     repair time data for each system in isolation
+     
+    repair_complete_day_per_system: matrix [num reals x num systems]
+     Number of days from the start of each sequence to the completion of the
+     sequence considering the allocation of workers to each sequence (ie
+     some sequences start before others)
+     
+    systems: DataFrame
+     data table containing information about each system's attributes
+    
+    repair_type: string
+      String identifier indicating whether the repair are temporary or full
+      
+    simulated_red_tags: logical array [num_reals x 1]
+      indicates the realizations that have a red tag
+    
+    Returns
+    -------
+    damage: dictionary
+     contains per damage state damage and loss data for each component in
+     the building, including repair schedule data.
+    
+    % Notes
+    -----
+    In the repair start day outputs:
+       - Zero = Starts immediately
+       - NaN = Repairs never started (because not damaged, has no temp repair, or controlled by red tag)
+    In the repair complete day outputs:
+       - NaN = No damage (or controlled by red tag)
+       - Inf = Is damaged, but has no System/RepairClass assignment (or no temp repair)'''
+    
+    import sys
+    
+    ## Initialize Parameters
+    num_sys = len(systems)
+    num_units = len(damage['tenant_units'])
+    
+    # Define Repair Type Variables (variable within the damage object)
+    if repair_type == 'full':
+        repair_time_var = 'worker_days'
+        system_var = 'system'
+    elif repair_type == 'temp':
+        repair_time_var = 'tmp_worker_day'
+        system_var = 'tmp_repair_class'
+    else:
+        sys.exit('Unexpected Repair Type')
+
+    # Initialize recovery field
+    damage_recovery = {}
+    for tu in range(num_units):
+        # Set to inf as a null repair time (will remain inf for components with
+        # no attributed system) - matters for temp repairs, shouldnt matter for
+        # full repair
+        damage_recovery[tu]={}
+        damage_recovery[tu]['repair_start_day'] = np.empty(np.shape(np.array(damage['tenant_units'][tu]['qnt_damaged'])))
+        damage_recovery[tu]['repair_start_day'][:] = np.nan
+        damage_recovery[tu]['repair_complete_day'] = np.empty(np.shape(np.array(damage['tenant_units'][tu]['qnt_damaged'])))
+        damage_recovery[tu]['repair_complete_day'][:] = np.inf
+        
+        # if not damaged, set repair complete time to NaN
+        is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged']) > 0, np.array(damage['tenant_units'][tu][repair_time_var]) > 0)
+        damage_recovery[tu]['repair_complete_day'][np.logical_not(is_damaged)] = np.nan
+
+
+   ## Redistribute repair schedule data
+    for syst in range(num_sys):
+        # Calculate system repair times on each story
+        system_duration = np.nanmax(system_schedule['per_system'][syst]['repair_complete_day'], axis=1) # total repair time spent in this system over all stories
+        start_day = repair_complete_day_per_system[:,syst] - system_duration
+        story_start_day = start_day.reshape(len(start_day),1) + system_schedule['per_system'][syst]['repair_start_day']
+        story_complete_day = start_day.reshape(len(start_day),1) + system_schedule['per_system'][syst]['repair_complete_day']
+        
+        # Do not perform temporary repairs when building is red tagged
+        if np.logical_and(repair_type == 'temp', np.any(simulated_red_tags)):
+            story_start_day[simulated_red_tags.astype(bool),:] = np.nan
+            story_complete_day[simulated_red_tags.astype(bool),:] = np.nan
+   
+        # Re-distribute to each tenant unit
+        sys_filt = damage['comp_ds_table'][system_var] == systems['id'][syst] # identifies which ds idices are in this seqeunce  
+        for tu in range(num_units):
+            is_damaged = np.logical_and(np.array(damage['tenant_units'][tu]['qnt_damaged'])[:,sys_filt] > 0, np.array(damage['tenant_units'][tu][repair_time_var])[:,sys_filt] > 0)
+            is_damaged = (is_damaged * 1).astype(float)
+            is_damaged[is_damaged == 0] = np.nan
+    
+            # Re-distribute repair days to component damage states
+            damage_recovery[tu]['repair_start_day'][:,sys_filt] = is_damaged* story_start_day[:,tu].reshape(len(story_start_day[:,tu]),1)
+            damage_recovery[tu]['repair_complete_day'][:,sys_filt] = is_damaged * story_complete_day[:,tu].reshape(len(story_complete_day[:,tu]),1)
+            
+    return damage_recovery
+
+
+def fn_format_gantt_chart_data( damage, systems, simulated_replacement_time):
+    '''Reformat data from the damage structure into data that is used for the
+    gantt charts
+    
+    Parameters
+    ----------
+    damage: dictionary
+     contains per damage state damage and loss data for each component in the building
+    
+    systems: DataFrame
+     data table containing information about each system's attributes
+    
+    simulated_replacement_time: array [num_reals x 1]
+     simulated time when the building needs to be replaced, and how long it
+     will take (in days). NaN represents no replacement needed (ie
+     building will be repaired)
+    
+Returns
+    -------
+    repair_schedule: dictionary
+     Contians reformated repair schedule data for gantt chart plots for
+     both repair time and downtime calculations. Data is reformanted to
+     show breakdowns by component, by story, by system, by story within each
+     system, and by system within each story.
+    
+    Notes
+    -----
+    Since this all has to do with plotting gantt charts, a form of this
+    (whether the damage structure or a higher level repair schedule
+    structure) should be output and this reformatting logic moved outside the
+    functional recovery assessment and into the data visuallation logic'''
+    
+    ## Initial Setup
+    num_stories = len(damage['tenant_units'])
+    num_reals = np.size(damage['tenant_units'][0]['recovery']['repair_start_day'],0)
+    comps = np.unique(damage['comp_ds_table']['comp_id'])
+       
+    # Determine replacement cases
+    replace_cases = np.logical_not(np.isnan(simulated_replacement_time))
+
+    ## Reformat repair schedule data into various breakdowns
+    # Per component
+    repair_schedule = {'repair_start_day' : {}, 'repair_complete_day' : {}, 'component_names':[]}
+    repair_schedule['repair_start_day']['per_component'] = np.empty([num_reals, len(comps)])
+    repair_schedule['repair_start_day']['per_component'][:] = np.nan
+    repair_schedule['repair_complete_day']['per_component'] = np.zeros([num_reals,len(comps)])
+    for c in range(len(comps)):
+        comps_filt = damage['comp_ds_table']['comp_id'] == comps[c]
+        for s in range(num_stories):
+            repair_schedule['repair_start_day']['per_component'][:,c] = np.nanmin(np.column_stack((repair_schedule['repair_start_day']['per_component'][:,c], damage['tenant_units'][s]['recovery']['repair_start_day'][:,comps_filt])), axis=1)
+            repair_schedule['repair_complete_day']['per_component'][:,c] = np.nanmax(np.column_stack((repair_schedule['repair_complete_day']['per_component'][:,c], damage['tenant_units'][s]['recovery']['repair_complete_day'][:,comps_filt])), axis=1)
+
+        repair_schedule['component_names'].append(str(comps[c]))
+
+    
+    # Per Story
+    repair_schedule['repair_start_day']['per_story'] = np.empty([num_reals, num_stories])
+    repair_schedule['repair_start_day']['per_story'][:] = np.nan
+    repair_schedule['repair_complete_day']['per_story'] = np.zeros([num_reals,num_stories])
+    for s in range(num_stories):
+        repair_schedule['repair_start_day']['per_story'][:,s] = np.nanmin(np.column_stack((repair_schedule['repair_start_day']['per_story'][:,s], damage['tenant_units'][s]['recovery']['repair_start_day'])), axis=1)
+        repair_schedule['repair_complete_day']['per_story'][:,s] = np.nanmax(np.column_stack((repair_schedule['repair_complete_day']['per_story'][:,s], damage['tenant_units'][s]['recovery']['repair_complete_day'])), axis=1)
+    
+    
+    # Per Repair System
+    repair_schedule['repair_start_day']['per_system'] = np.empty([num_reals, len(systems)])
+    repair_schedule['repair_start_day']['per_system'][:] = np.nan
+    repair_schedule['repair_complete_day']['per_system'] = np.zeros([num_reals,len(systems)])
+    for sys in range(len(systems)):
+        sys_filt = damage['comp_ds_table']['system'] == systems['id'][sys] # identifies which ds idices are in this seqeunce  
+        for s in range(num_stories):
+            repair_schedule['repair_start_day']['per_system'][:,sys] = np.nanmin(np.column_stack((repair_schedule['repair_start_day']['per_system'][:,sys], damage['tenant_units'][s]['recovery']['repair_start_day'][:,sys_filt])), axis=1)
+            repair_schedule['repair_complete_day']['per_system'][:,sys] = np.nanmax(np.column_stack((repair_schedule['repair_complete_day']['per_system'][:,sys], damage['tenant_units'][s]['recovery']['repair_complete_day'][:,sys_filt])), axis=1)
+
+
+    repair_schedule['system_names'] = np.array(systems['name'])
+    
+    # Per system per story
+    num_sys_stories = num_stories * len(systems)
+    repair_schedule['repair_start_day']['per_system_story'] = np.empty([num_reals,num_sys_stories])
+    repair_schedule['repair_start_day']['per_system_story'][:] = np.nan
+    repair_schedule['repair_complete_day']['per_system_story'] = np.zeros([num_reals,num_sys_stories])
+    id = -1; #FZ# -1 done to account for python indexing starting from 0
+    for s in range(num_stories):
+        for sys in range(len(systems)):
+            id += 1
+            sys_filt = damage['comp_ds_table']['system'] == systems['id'][sys] # identifies which ds idices are in this seqeunce  
+            repair_schedule['repair_start_day']['per_system_story'][:,id] = np.nanmin(np.column_stack((repair_schedule['repair_start_day']['per_system_story'][:,id], damage['tenant_units'][s]['recovery']['repair_start_day'][:,sys_filt])), axis=1)
+            repair_schedule['repair_complete_day']['per_system_story'][:,id] = np.nanmax(np.column_stack((repair_schedule['repair_complete_day']['per_system_story'][:,id], damage['tenant_units'][s]['recovery']['repair_complete_day'][:,sys_filt])), axis=1)
+
+    
+    # Per story per system
+    num_sys_stories = num_stories * len(systems)
+    repair_schedule['repair_start_day']['per_story_system'] = np.empty([num_reals,num_sys_stories])
+    repair_schedule['repair_start_day']['per_story_system'][:] = np.nan
+    repair_schedule['repair_complete_day']['per_story_system'] = np.zeros([num_reals,num_sys_stories])
+    
+    id = -1; #FZ# -1 done to account for python indexing starting from 0
+    for sys in range(len(systems)):
+        for s in range(num_stories):
+            id += 1
+            sys_filt = damage['comp_ds_table']['system'] == systems['id'][sys] # identifies which ds idices are in this seqeunce 
+            repair_schedule['repair_start_day']['per_story_system'][:,id] = np.nanmin(np.column_stack((repair_schedule['repair_start_day']['per_story_system'][:,id], damage['tenant_units'][s]['recovery']['repair_start_day'][:,sys_filt])), axis=1)
+            repair_schedule['repair_complete_day']['per_story_system'][:,id] = np.nanmax(np.column_stack((repair_schedule['repair_complete_day']['per_story_system'][:,id], damage['tenant_units'][s]['recovery']['repair_complete_day'][:,sys_filt])), axis=1)
+    
+    # Overwrite realization for demo and replace cases
+    formats = list(repair_schedule['repair_start_day'].keys())
+    for f in range(len(formats)):
+        repair_schedule['repair_start_day'][formats[f]][replace_cases,:] = 0
+        
+        format_width = np.size(repair_schedule['repair_complete_day'][formats[f]], 1) # apply replacement time to all comps / systems / stories / etc
+        repair_schedule['repair_complete_day'][formats[f]][replace_cases,:] = np.array(simulated_replacement_time)[replace_cases].reshape(len(np.array(simulated_replacement_time)[replace_cases]),1) * np.ones([1,format_width])
+
+    return repair_schedule
+
+
+
+
+    
+
+    
+    
+    
\ No newline at end of file

From d6d7502244ed4d3a4878a228837cdb596c3b9c6a Mon Sep 17 00:00:00 2001
From: wangz539 <wangz539@berkeley.edu>
Date: Mon, 30 Mar 2026 13:25:29 -0700
Subject: [PATCH 3/4] Add testing markdown doc for standalone detailed
 inspection

---
 tests/detailed_inspection_guide.ipynb | 130 ++++++++++++++++++++++++++
 1 file changed, 130 insertions(+)
 create mode 100644 tests/detailed_inspection_guide.ipynb

diff --git a/tests/detailed_inspection_guide.ipynb b/tests/detailed_inspection_guide.ipynb
new file mode 100644
index 0000000..721697e
--- /dev/null
+++ b/tests/detailed_inspection_guide.ipynb
@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c60b39da",
+   "metadata": {},
+   "source": [
+    "### ATC-138 Python Numerical Validation Testing Against MATLAB\n",
+    "\n",
+    "#### 1. Overview\n",
+    "\n",
+    "This documentation provides instructions on how to validate ATC-138 Python implementation against the original MATLAB framework for numerical consistency and correct reproduction of calculations.\n",
+    "\n",
+    "The focus is on the standalone use of the comparison engine (`compare_runs.py`) for users who need to validate their own Python and MATLAB models.\n",
+    "\n",
+    "The goal is to:\n",
+    "- Explain the testing metrics used\n",
+    "- Outline the testing workflow\n",
+    "\n",
+    "This workflow is independent of the integration tests and can be applied to any model. Two example models are included here for demonstration purposes.\n",
+    "\n",
+    "#### 2. Example Models Used \n",
+    "\n",
+    "| Model | Description |\n",
+    "|------|-------------|\n",
+    "| ICSB | 6-story special moment frame |\n",
+    "| haseltonRCMF_4story | 4-story reinforced concrete moment frame building |\n",
+    "\n",
+    "\n",
+    "#### 3. Validation Workflow\n",
+    "\n",
+    "1. Run the MATLAB ATC-138 implementation using the provided example inputs.\n",
+    "2. Run the Python ATC-138 implementation using the same demand/data. \n",
+    "3. Extract both output files `recovery_outputs`. \n",
+    "4. Compare Python and MATLAB outputs using quantitative metrics using `compare_runs.py`.\n",
+    "5. Evaluate whether results fall within acceptable tolerance thresholds.\n",
+    "\n",
+    "#### 4. Comparison Metrics\n",
+    "\n",
+    "Four quantitative metrics are used to evaluate agreement between MATLAB and Python results:\n",
+    "\n",
+    "</br>\n",
+    "\n",
+    "**Metric 1 — Absolute difference of mean functional recovery time**\n",
+    "\n",
+    "```| mean_FR_python − mean_FR_matlab |```\n",
+    "\n",
+    "Units: ```days```\n",
+    "\n",
+    "Threshold: ```≤ 2 days```\n",
+    "\n",
+    "</br>\n",
+    "\n",
+    "**Metric 2 — Relative difference of mean functional recovery time**\n",
+    "\n",
+    "```| mean_FR_python − mean_FR_matlab | / mean_FR_matlab```\n",
+    "\n",
+    "Units: ```percent```\n",
+    "\n",
+    "Threshold: ```≤ 4 %```\n",
+    "\n",
+    "</br>\n",
+    "\n",
+    "**Metric 3 — Worst system mean absolute error (MAE)**\n",
+    "\n",
+    "System-level wise, the maximum MAE of the fraction of unresolved damage over time, across all functional-recovery and reoccupancy systems. \n",
+    "\n",
+    "Threshold: ```≤ 0.02``` (maximum mean difference of 2% unresolved fraction)\n",
+    "\n",
+    "</br>\n",
+    "\n",
+    "**Metric 4 — Worst system 95th percentile absolute error**\n",
+    "\n",
+    "System-level wise, the maximum 95th percentile aboslute error of the fraction of unresolved damage over time, across all functional-recovery and reoccupancy systems. \n",
+    "\n",
+    "Threshold: ```≤ 0.04``` (maximum mean difference of 4% unresolved fraction)\n",
+    "\n",
+    "</br>\n",
+    "\n",
+    "#### 5. Recovery Trajectory and System-Level Recovery Comparison\n",
+    "\n",
+    "For each model, plots of recovery trajectories and system-level recovery curves are saved: \n",
+    "\n",
+    "- `recovery_trajectory.jpg`\n",
+    "- `system_breakdowns_functional.jpg`\n",
+    "- `system_breakdowns_reoccupancy.jpg`\n",
+    "\n",
+    "\n",
+    "#### 6. Validation Results\n",
+    "\n",
+    "The comparison results for the two example models are summarized below. Twenty batch runs were carried out for each model, and the mean values are compared against the defined metrics.\n",
+    "\n",
+    "| Model | Absolute difference (days) | Relative difference (%) | Worst system MAE | Worst system P95 |\n",
+    "|------|------|------|------|------|\n",
+    "| ICSB | 1.38 | 0.241 | 0.015 | 0.030 |\n",
+    "| haseltonRCMF_4story | 1.74 | 1.61 | 0.007 | 0.027 |\n",
+    "\n",
+    "\n",
+    "All comparison metrics here fall within the defined tolerance thresholds."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d7cebb0",
+   "metadata": {},
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 7c9f06c8c0b8a14bdde24ef913fb1e3c21efba78 Mon Sep 17 00:00:00 2001
From: wangz539 <wangz539@berkeley.edu>
Date: Mon, 30 Mar 2026 13:25:29 -0700
Subject: [PATCH 4/4] Add testing markdown doc for standalone detailed
 inspection

---
 tests/detailed_inspection_guide.ipynb | 130 ++++++++++++++++++++++++++
 1 file changed, 130 insertions(+)
 create mode 100644 tests/detailed_inspection_guide.ipynb

diff --git a/tests/detailed_inspection_guide.ipynb b/tests/detailed_inspection_guide.ipynb
new file mode 100644
index 0000000..721697e
--- /dev/null
+++ b/tests/detailed_inspection_guide.ipynb
@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c60b39da",
+   "metadata": {},
+   "source": [
+    "### ATC-138 Python Numerical Validation Testing Against MATLAB\n",
+    "\n",
+    "#### 1. Overview\n",
+    "\n",
+    "This documentation provides instructions on how to validate ATC-138 Python implementation against the original MATLAB framework for numerical consistency and correct reproduction of calculations.\n",
+    "\n",
+    "The focus is on the standalone use of the comparison engine (`compare_runs.py`) for users who need to validate their own Python and MATLAB models.\n",
+    "\n",
+    "The goal is to:\n",
+    "- Explain the testing metrics used\n",
+    "- Outline the testing workflow\n",
+    "\n",
+    "This workflow is independent of the integration tests and can be applied to any model. Two example models are included here for demonstration purposes.\n",
+    "\n",
+    "#### 2. Example Models Used \n",
+    "\n",
+    "| Model | Description |\n",
+    "|------|-------------|\n",
+    "| ICSB | 6-story special moment frame |\n",
+    "| haseltonRCMF_4story | 4-story reinforced concrete moment frame building |\n",
+    "\n",
+    "\n",
+    "#### 3. Validation Workflow\n",
+    "\n",
+    "1. Run the MATLAB ATC-138 implementation using the provided example inputs.\n",
+    "2. Run the Python ATC-138 implementation using the same demand/data. \n",
+    "3. Extract both output files `recovery_outputs`. \n",
+    "4. Compare Python and MATLAB outputs using quantitative metrics using `compare_runs.py`.\n",
+    "5. Evaluate whether results fall within acceptable tolerance thresholds.\n",
+    "\n",
+    "#### 4. Comparison Metrics\n",
+    "\n",
+    "Four quantitative metrics are used to evaluate agreement between MATLAB and Python results:\n",
+    "\n",
+    "</br>\n",
+    "\n",
+    "**Metric 1 — Absolute difference of mean functional recovery time**\n",
+    "\n",
+    "```| mean_FR_python − mean_FR_matlab |```\n",
+    "\n",
+    "Units: ```days```\n",
+    "\n",
+    "Threshold: ```≤ 2 days```\n",
+    "\n",
+    "</br>\n",
+    "\n",
+    "**Metric 2 — Relative difference of mean functional recovery time**\n",
+    "\n",
+    "```| mean_FR_python − mean_FR_matlab | / mean_FR_matlab```\n",
+    "\n",
+    "Units: ```percent```\n",
+    "\n",
+    "Threshold: ```≤ 4 %```\n",
+    "\n",
+    "</br>\n",
+    "\n",
+    "**Metric 3 — Worst system mean absolute error (MAE)**\n",
+    "\n",
+    "System-level wise, the maximum MAE of the fraction of unresolved damage over time, across all functional-recovery and reoccupancy systems. \n",
+    "\n",
+    "Threshold: ```≤ 0.02``` (maximum mean difference of 2% unresolved fraction)\n",
+    "\n",
+    "</br>\n",
+    "\n",
+    "**Metric 4 — Worst system 95th percentile absolute error**\n",
+    "\n",
+    "System-level wise, the maximum 95th percentile aboslute error of the fraction of unresolved damage over time, across all functional-recovery and reoccupancy systems. \n",
+    "\n",
+    "Threshold: ```≤ 0.04``` (maximum mean difference of 4% unresolved fraction)\n",
+    "\n",
+    "</br>\n",
+    "\n",
+    "#### 5. Recovery Trajectory and System-Level Recovery Comparison\n",
+    "\n",
+    "For each model, plots of recovery trajectories and system-level recovery curves are saved: \n",
+    "\n",
+    "- `recovery_trajectory.jpg`\n",
+    "- `system_breakdowns_functional.jpg`\n",
+    "- `system_breakdowns_reoccupancy.jpg`\n",
+    "\n",
+    "\n",
+    "#### 6. Validation Results\n",
+    "\n",
+    "The comparison results for the two example models are summarized below. Twenty batch runs were carried out for each model, and the mean values are compared against the defined metrics.\n",
+    "\n",
+    "| Model | Absolute difference (days) | Relative difference (%) | Worst system MAE | Worst system P95 |\n",
+    "|------|------|------|------|------|\n",
+    "| ICSB | 1.38 | 0.241 | 0.015 | 0.030 |\n",
+    "| haseltonRCMF_4story | 1.74 | 1.61 | 0.007 | 0.027 |\n",
+    "\n",
+    "\n",
+    "All comparison metrics here fall within the defined tolerance thresholds."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d7cebb0",
+   "metadata": {},
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}