diff --git a/.github/workflows/CI_FAModel.yml b/.github/workflows/CI_FAModel.yml
index fcacaf5a..4a8aa2f7 100644
--- a/.github/workflows/CI_FAModel.yml
+++ b/.github/workflows/CI_FAModel.yml
@@ -49,14 +49,16 @@ jobs:
             - name: Overwrite MoorPy
               run: |
                   pip install git+https://github.com/NREL/MoorPy@dev
-
-            - name: Example run
-              run: |
-                  cd examples
-                  python example_driver.py false
-
+                  
             - name: Test run
               run: |
                   cd tests
                   pytest .
 
+            # - name: Example run
+              # run: |
+                  # cd examples
+                  # python example_driver.py false
+
+
+
diff --git a/examples/01_Visualization/05_visual_lease_boundaries.py b/examples/01_Visualization/05_visual_lease_boundaries.py
index 7b052544..95e15551 100644
--- a/examples/01_Visualization/05_visual_lease_boundaries.py
+++ b/examples/01_Visualization/05_visual_lease_boundaries.py
@@ -11,7 +11,7 @@
 import matplotlib.pyplot as plt
 
 # define name of ontology input file
-input_file = '06_visual_lease_boundaries.yaml'
+input_file = '05_visual_lease_boundaries.yaml'
 
 # initialize Project class with input file, we don't need RAFT for this so mark False
 project = Project(file=input_file,raft=False)
diff --git a/examples/01_Visualization/07_3D-visual_platform.py b/examples/01_Visualization/07_3D-visual_platform.py
index e868c26b..655e867d 100644
--- a/examples/01_Visualization/07_3D-visual_platform.py
+++ b/examples/01_Visualization/07_3D-visual_platform.py
@@ -9,7 +9,7 @@
 import matplotlib.pyplot as plt
 
 # define name of ontology input file
-input_file = '07_3D-visual_platform.yaml'
+input_file = 'examples/01_Visualization/07_3D-visual_platform.yaml'
 
 # initialize Project class with input file, we don't need RAFT for this so mark False
 project = Project(file=input_file,raft=True)
diff --git a/famodel/anchors/getCapacityHelical_clay.py b/examples/05_Anchors/anchor_helical.py
similarity index 64%
rename from famodel/anchors/getCapacityHelical_clay.py
rename to examples/05_Anchors/anchor_helical.py
index f1367126..1508e8c0 100644
--- a/famodel/anchors/getCapacityHelical_clay.py
+++ b/examples/05_Anchors/anchor_helical.py
@@ -1,5 +1,5 @@
 
-from anchor_map import Anchor
+from famodel.anchors.anchor import Anchor
 
 # --- Soil profile for helical pile in clay ---
 profile_map = [
@@ -7,9 +7,9 @@
         'name': 'CPT_H1',
         'x': 0.0, 'y': 0.0,
         'layers': [
-            {'top': 1.0, 'bottom': 3.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 9.0, 'Su_top': 60, 'Su_bot': 50},
-            {'top': 3.0, 'bottom': 7.0, 'soil_type': 'clay', 'gamma_top': 15.0, 'gamma_bot': 25.0, 'Su_top': 100, 'Su_bot': 150},
-            {'top': 7.0, 'bottom': 15.0, 'soil_type': 'clay', 'gamma_top': 25.0, 'gamma_bot': 50.0, 'Su_top': 200, 'Su_bot': 400}
+            {'top': 1.0, 'bottom':  3.0, 'soil_type': 'clay', 'gamma_top':  8.0, 'gamma_bot':  9.0, 'Su_top':  60, 'Su_bot':  50},
+            {'top': 3.0, 'bottom':  7.0, 'soil_type': 'clay', 'gamma_top': 15.0, 'gamma_bot': 25.0, 'Su_top': 100, 'Su_bot': 150},
+            {'top': 7.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 25.0, 'gamma_bot': 50.0, 'Su_top': 200, 'Su_bot': 400}
         ]
     }
 ]
@@ -63,9 +63,21 @@
     line_type = anchor.line_type,
     d = anchor.d,
     w = anchor.w,
-    plot = True
+    plot = False
 )
 
 print('\nCapacity Results:')
-for key, val in anchor.capacity_results.items():
+for key, val in anchor.anchorCapacity.items():
     print(f'{key}: {val:.2f}')
+
+# --- Step 3: Optimize Anchor Geometry ---
+anchor.getSizeAnchor(
+    geom = [anchor.dd['design']['L'], anchor.dd['design']['D']],
+    geomKeys = ['L', 'D'],
+    geomBounds = [(6.0, 25.0), (0.5, 2.0)],
+    loads = None,
+    lambdap_con = [6, 15],
+    zlug_fix = True,
+    safety_factor = {'SF_horizontal': 1, 'SF_vertical': 1},   
+    plot = False
+)
diff --git a/famodel/anchors/getCapacityPile_map.py b/examples/05_Anchors/anchor_pile.py
similarity index 61%
rename from famodel/anchors/getCapacityPile_map.py
rename to examples/05_Anchors/anchor_pile.py
index 38998786..95083664 100644
--- a/famodel/anchors/getCapacityPile_map.py
+++ b/examples/05_Anchors/anchor_pile.py
@@ -1,5 +1,5 @@
 
-from anchor_map import Anchor
+from famodel.anchors.anchor import Anchor
 
 # --- Define soil profile ---
 profile_map = [
@@ -7,9 +7,9 @@
         'name': 'CPT_D1',
         'x': 0.0, 'y': 0.0,
         'layers': [
-            {'top':  1.0, 'bottom':  6.0, 'soil_type': 'clay', 'gamma_top':  9.0, 'gamma_bot': 10.0, 'Su_top': 45, 'Su_bot':  60},
-            {'top':  6.0, 'bottom': 15.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.0, 'Su_top': 60, 'Su_bot':  80},
-            {'top': 15.0, 'bottom': 35.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.5, 'Su_top': 80, 'Su_bot': 100}
+            {'top':  1.0, 'bottom':  6.0, 'soil_type': 'clay', 'gamma_top':  9.0, 'gamma_bot': 10.0, 'Su_top':  25, 'Su_bot':  40},
+            {'top':  6.0, 'bottom': 15.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.0, 'Su_top':  80, 'Su_bot': 100},
+            {'top': 15.0, 'bottom': 35.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.5, 'Su_top': 100, 'Su_bot': 100}
         ]
     }
 ]
@@ -19,9 +19,9 @@
     dd = {
         'type': 'driven',
         'design': {
-            'L': 25.0,        # Embedded length
-            'D': 2.0,         # Diameter
-            'zlug': 10.0      # Padeye depth
+            'L': 15.0,        # Embedded length
+            'D': 1.75,        # Diameter
+            'zlug': 3.0       # Padeye depth
         }
     },
     r = [0.0, 0.0, 0.0]
@@ -29,8 +29,8 @@
 
 # Assign mooring loads
 anchor.loads = {
-    'Hm': 4.0e6,
-    'Vm': 2.5e6
+    'Hm': 8.0e5,
+    'Vm': 2.5e5
 }
 anchor.line_type = 'chain'
 anchor.d = 0.16
@@ -66,5 +66,17 @@
 )
 
 print('\nCapacity Results:')
-for key, val in anchor.capacity_results.items():
+for key, val in anchor.anchorCapacity.items():
     print(f'{key}: {val:.2f}')
+
+# --- Step 3: Optimize Anchor Geometry ---
+anchor.getSizeAnchor(
+    geom = [anchor.dd['design']['L'], anchor.dd['design']['D']],
+    geomKeys = ['L', 'D'],
+    geomBounds = [(2.0, 70.0), (0.25, 3.0)],
+    loads = None,
+    lambdap_con = [4, 50],
+    zlug_fix = True,
+    safety_factor = {'SF_horizontal': 1, 'SF_vertical': 1},
+    plot = True
+)
\ No newline at end of file
diff --git a/famodel/anchors/getCapacityPlate_map.py b/examples/05_Anchors/anchor_plate.py
similarity index 71%
rename from famodel/anchors/getCapacityPlate_map.py
rename to examples/05_Anchors/anchor_plate.py
index 088cd40b..aa841f73 100644
--- a/famodel/anchors/getCapacityPlate_map.py
+++ b/examples/05_Anchors/anchor_plate.py
@@ -1,7 +1,6 @@
 
-from anchor_map import Anchor
-import numpy as np
-from famodel.anchors.anchors_famodel_map.capacity_plots_map import plot_load
+from famodel.anchors.anchor import Anchor
+from famodel.anchors.anchors_famodel.support_plots import plot_load
 
 # --- Define soil profile ---
 profile_map = [
@@ -10,7 +9,7 @@
         'x': 0.0, 'y': 0.0,
         'layers': [
             {'top':  2.0, 'bottom':  4.0, 'soil_type': 'clay', 'gamma_top':  8.0, 'gamma_bot':  8.5, 'Su_top':  10, 'Su_bot':  25},
-            {'top':  4.0, 'bottom':  6.0, 'soil_type': 'clay', 'gamma_top':  8.5, 'gamma_bot':  9.0, 'Su_top':  25, 'Su_bot':  50},
+            {'top':  4.0, 'bottom':  6.0, 'soil_type': 'clay', 'gamma_top':  8.5, 'gamma_bot':  9.0, 'Su_top':  15, 'Su_bot':  40},
             {'top':  6.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top':  9.0, 'gamma_bot':  9.5, 'Su_top':  50, 'Su_bot': 100},
             {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top':  9.5, 'gamma_bot':  9.5, 'Su_top': 100, 'Su_bot': 100}
         ]
@@ -19,14 +18,14 @@
 
 # --- Create plate anchor ---
 anchor = Anchor(
-    dd = {'type': 'plate', 'design': {'B': 3.0, 'L': 6.0, 'zlug': 5.0, 'beta': 30.0}},
+    dd = {'type': 'plate', 'design': {'B': 3.0, 'L': 6.0, 'zlug': 14.0, 'beta': 30.0}},
     r = [100.0, 100.0, 0.0]
 )
 
 # --- Assign load and mooring properties ---
 anchor.loads = {
-    'Hm': 2.5e6,
-    'Vm': 1.5e6
+    'Hm': 3.5e6,
+    'Vm': 2.5e6
 }
 anchor.line_type = 'chain'
 anchor.d = 0.16
@@ -62,5 +61,17 @@
 )
 
 print('\nCapacity Results:')
-for key, value in anchor.capacity_results.items():
+for key, value in anchor.anchorCapacity.items():
     print(f'{key}: {value:.2f}')
+
+# --- Step 3: Optimize Anchor Geometry ---
+anchor.getSizeAnchor(
+    geom = [anchor.dd['design']['B'], anchor.dd['design']['L']],
+    geomKeys = ['B', 'L'],
+    geomBounds = [(0.5, 6.0), (2.0, 12.0)],
+    loads = None,
+    lambdap_con = [2, 4],  # less critical for plates
+    zlug_fix = True,
+    safety_factor = {'SF_combined': 3},
+    plot = True
+)
diff --git a/examples/05_Anchors/anchor_soil.py b/examples/05_Anchors/anchor_soil.py
new file mode 100644
index 00000000..5c6c1d55
--- /dev/null
+++ b/examples/05_Anchors/anchor_soil.py
@@ -0,0 +1,69 @@
+
+import sys
+sys.path.append(r'C:\Code\FAModel_anchors\famodel')
+
+from project import Project
+from anchors.anchor import Anchor
+
+# Step 1: Initialize and load soil
+proj = Project()
+proj.loadSoil(
+    filename='inputs/GulfOfMaine_soil_layered_100x100.txt',
+    soil_mode='layered',
+    profile_source='inputs/GulfOfMaine_soil_profiles.yaml')
+
+for label, props in proj.soilProps.items():
+    print(f"{label}: {props}")
+
+# Step 2: Create and register an anchor at a known position in the grid
+anchor = Anchor(
+    dd = {'type': 'suction', 'design': {'D': 2.5, 'L': 15.0, 'zlug': 10.67}},
+    r  = [54.0, -4450.0, 0.0])
+
+# Step 3: Assign local soil profile from project (nearest neighbor lookup)
+soil_id, soil_profile = proj.getSoilAtLocation(anchor.r[0], anchor.r[1])
+anchor.soilProps = {soil_id: soil_profile}
+anchor.setSoilProfile([{ 'name': soil_id, 'layers': soil_profile }])  # ensures `anchor.soil_profile` is set
+
+# Step 4: Assign loads and line
+anchor.loads = {'Hm': 2e6, 'Vm': 1.5e6}
+anchor.line_type = 'chain'
+anchor.d = 0.16
+anchor.w = 5000.0
+
+# Step 5: Run capacity check and optimization
+anchor.getLugForces(
+    Hm=anchor.loads['Hm'], Vm=anchor.loads['Vm'], 
+    zlug = anchor.dd['design']['zlug'], 
+    d=anchor.d, w=anchor.w,  
+    plot=True)
+
+anchor.getCapacityAnchor(
+    Hm=anchor.loads['Hm'], Vm=anchor.loads['Vm'], 
+    zlug = anchor.dd['design']['zlug'], 
+    line_type=anchor.line_type, d=anchor.d, w=anchor.w,   
+    mass_update=True,
+    plot=True)
+anchor.getCostAnchor()
+print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]')
+
+results = anchor.getSizeAnchor_gradient(
+    geom=[anchor.dd['design']['L'], anchor.dd['design']['D']], 
+    geomKeys= ['L','D'],
+    geomBounds=[(12.0, 18.0), (1.5, 3.5)],
+    safety_factor={'SF_combined': 1}, 
+    zlug_fix=False, 
+    lambdap_con=[4, 6], 
+    step_size=0.2,
+    tol=0.05,
+    max_iter=30,
+    verbose=True)
+
+# Step 6: Report
+print('\nFinal Optimized Anchor:')
+print('Design:', anchor.dd['design'])
+print('Capacity Results:', anchor.anchorCapacity)
+anchor.getCostAnchor()
+print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]')
+
+
diff --git a/examples/05_Anchors/anchor_suction.py b/examples/05_Anchors/anchor_suction.py
new file mode 100644
index 00000000..b0480dca
--- /dev/null
+++ b/examples/05_Anchors/anchor_suction.py
@@ -0,0 +1,143 @@
+
+from famodel.anchors.anchor import Anchor
+from famodel.anchors.anchors_famodel.support_plots import plot_suction
+
+# --- Define soil profile ---
+profile_map = [
+    {
+        'name': 'CPT_A1',
+        'x': 0.0, 'y': 0.0,
+        'layers': [
+            {'top':  0.0, 'bottom': 12.0, 'soil_type': 'clay', 'gamma_top':  8.0, 'gamma_bot':  8.0, 'Su_top':  10, 'Su_bot':  20},
+            {'top': 12.0, 'bottom': 22.0, 'soil_type': 'clay', 'gamma_top':  8.0, 'gamma_bot':  8.0, 'Su_top':  15, 'Su_bot':  25},
+            {'top': 22.0, 'bottom': 30.0, 'soil_type': 'clay', 'gamma_top':  8.0, 'gamma_bot':  8.0, 'Su_top':  55, 'Su_bot':  70},
+            {'top': 30.0, 'bottom': 40.0, 'soil_type': 'clay', 'gamma_top':  8.0, 'gamma_bot':  8.0, 'Su_top': 100, 'Su_bot': 100}
+        ]
+    },
+    {
+        'name': 'CPT_B1',
+        'x': 500.0, 'y': 0.0,
+        'layers': [
+            {'top':  0.0, 'bottom':  5.0, 'soil_type': 'clay', 'gamma_top':  8.2, 'gamma_bot':  8.2, 'Su_top':  12, 'Su_bot':  22},
+            {'top':  5.0, 'bottom': 15.0, 'soil_type': 'clay', 'gamma_top':  8.2, 'gamma_bot':  8.2, 'Su_top':  22, 'Su_bot':  22},
+            {'top': 15.0, 'bottom': 30.0, 'soil_type': 'clay', 'gamma_top':  8.2, 'gamma_bot':  8.2, 'Su_top':  22, 'Su_bot':  50},
+            {'top': 30.0, 'bottom': 40.0, 'soil_type': 'clay', 'gamma_top':  8.2, 'gamma_bot':  8.2, 'Su_top': 100, 'Su_bot': 100}
+        ]
+    },
+    {
+        'name': 'CPT_A2',
+        'x': 0.0, 'y': 500.0,
+        'layers': [
+            {'top':  0.0, 'bottom':  8.0, 'soil_type': 'clay', 'gamma_top':  8.4, 'gamma_bot':  8.4, 'Su_top':  14, 'Su_bot':  14},
+            {'top':  8.0, 'bottom': 18.0, 'soil_type': 'clay', 'gamma_top':  8.4, 'gamma_bot':  8.4, 'Su_top':  15, 'Su_bot':  45},
+            {'top': 18.0, 'bottom': 30.0, 'soil_type': 'clay', 'gamma_top':  8.4, 'gamma_bot':  8.4, 'Su_top':  55, 'Su_bot':  55},
+            {'top': 30.0, 'bottom': 40.0, 'soil_type': 'clay', 'gamma_top':  8.4, 'gamma_bot':  8.4, 'Su_top': 100, 'Su_bot': 100}
+        ]
+    },
+    {
+        'name': 'CPT_B2',
+        'x': 500.0, 'y': 500.0,
+        'layers': [
+            {'top':  0.0, 'bottom': 15.0, 'soil_type': 'clay', 'gamma_top':  8.6, 'gamma_bot':  9.6, 'Su_top':  20, 'Su_bot':  26},
+            {'top': 15.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top':  8.6, 'gamma_bot':  8.6, 'Su_top':  20, 'Su_bot':  40},
+            {'top': 25.0, 'bottom': 30.0, 'soil_type': 'clay', 'gamma_top':  8.6, 'gamma_bot':  8.6, 'Su_top':  40, 'Su_bot':  40},
+            {'top': 30.0, 'bottom': 40.0, 'soil_type': 'clay', 'gamma_top':  8.6, 'gamma_bot':  8.6, 'Su_top': 100, 'Su_bot': 100}
+        ]
+    }
+]
+
+
+anchor = Anchor(
+    dd = {'type': 'suction', 'design': {'D': 3.5, 'L': 12.0, 'zlug': 8.67}},
+    r = [250.0, 250.0, 000.0]
+)
+
+# --- Step 0: Create anchor based grid CPTs ---
+anchor.interpolateSoilProfile(profile_map)
+
+# --- Step 1: Plot suction pile and soil profile ---
+# Access anchor geometrical properties
+L = anchor.dd['design']['L']
+D = anchor.dd['design']['D']
+zlug = anchor.dd['design']['zlug']
+# Access matched profile
+layers = anchor.soil_profile
+z0 = layers[0]['top']  
+
+plot_suction(layers, L=L, D=D, z0=z0, zlug=zlug)
+
+# Assign loads manually
+anchor.loads = {
+    'Hm': 3.68e6,    # Horizontal mudline load (N)
+    'Vm': 0          # Vertical mudline load (N)
+}
+
+# Assign line properties manually
+anchor.line_type = 'chain'
+anchor.d = 0.16    # Chain diameter (m)
+anchor.w = 5000.0  # Nominal submerged weight (N/m)
+
+
+# --- Step 2: Compute Lug Forces ---
+layers, Ha, Va = anchor.getLugForces(
+    Hm = anchor.loads['Hm'],
+    Vm = anchor.loads['Vm'],
+    zlug = anchor.dd['design']['zlug'],
+    line_type = anchor.line_type,
+    d = anchor.d,
+    w = anchor.w,
+    plot = True
+)
+
+print('\nLug Forces Computed:')
+print(f'Ha = {Ha:.2f} N')
+print(f'Va = {Va:.2f} N')
+
+# --- Step 3: Compute Capacity ---
+anchor.getCapacityAnchor(
+    Hm = anchor.loads['Hm'],
+    Vm = anchor.loads['Vm'],
+    zlug = anchor.dd['design']['zlug'],
+    line_type = anchor.line_type,
+    d = anchor.d, w = anchor.w,
+    mass_update=False,
+    plot = True
+)
+
+print('\nCapacity Results:')
+for key, value in anchor.anchorCapacity.items():
+    print(f'{key}: {value:.2f}')
+    
+# --- Step 4: Compute Costs ---     
+anchor.getCostAnchor()  
+
+print(f"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg")
+print(f"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg")
+print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]')
+
+
+# --- Step 5: Optimize Anchor Geometry ---
+anchor.getSizeAnchor(
+    geom = [anchor.dd['design']['L'], anchor.dd['design']['D']],
+    geomKeys = ['L', 'D'],
+    geomBounds = [(5.0, 15.0), (1.0, 4.0)],
+    loads = None,
+    lambdap_con = [3, 6],
+    zlug_fix = False,
+    safety_factor = {'SF_combined': 1},
+    plot = True   
+)
+
+print('\nFinal Optimized Anchor:')
+print('Design:', anchor.dd['design'])
+print('Capacity Results:', anchor.anchorCapacity)
+
+# # --- Step 6: Compute Costs ---     
+anchor.getCostAnchor()  
+
+print(f"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg")
+print(f"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg")
+print(f'Material cost: {anchor.cost["Material cost"]:.2f} USD [2024]')
+
+# --- Step 7: Visualize Anchor Geometry ---
+# anchor.getCombinedPlot()
diff --git a/famodel/anchors/getCapacityTorpedo_map.py b/examples/05_Anchors/anchor_torpedo.py
similarity index 85%
rename from famodel/anchors/getCapacityTorpedo_map.py
rename to examples/05_Anchors/anchor_torpedo.py
index 6ecb01cb..28cdc25c 100644
--- a/famodel/anchors/getCapacityTorpedo_map.py
+++ b/examples/05_Anchors/anchor_torpedo.py
@@ -1,6 +1,6 @@
 
-from anchor_map import Anchor
-from famodel.anchors.anchors_famodel_map.capacity_plots_map import plot_load
+from famodel.anchors.anchor import Anchor
+from famodel.anchors.anchors_famodel.support_plots import plot_load
 
 # --- Define soil profile ---
 profile_map = [
@@ -10,7 +10,7 @@
         'layers': [
             {'top':  0.0, 'bottom': 20.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top':  50, 'Su_bot':  70},
             {'top': 20.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 8.5, 'Su_top':  80, 'Su_bot': 100},
-            {'top': 25.0, 'bottom': 50.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 125, 'Su_bot': 150}
+            {'top': 25.0, 'bottom': 50.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 100, 'Su_bot': 150}
         ]
     }
 ]
@@ -24,7 +24,7 @@
             'D2': 1.5,     # Shaft diameter
             'L1': 11.0,    # Winged section length
             'L2': 5.0,     # Shaft section length
-            'zlug': 20.0,  # Padeye depth
+            'zlug': 15.0,  # Padeye depth
             'ballast': 10000
         }
     },
@@ -71,7 +71,7 @@
 )
 
 print('\nCapacity Results:')
-for key, value in anchor.capacity_results.items():
+for key, value in anchor.anchorCapacity.items():
     print(f'{key}: {value:.2f}')
 
 
@@ -84,15 +84,15 @@
     geomKeys = ['L1', 'D1'],
     geomBounds = [(7.0, 25.0), (2.5, 4.5)],
     loads = None,
-    minfs = {'Ha': 1.0, 'Va': 1.0},
     lambdap_con = [2, 8],
     zlug_fix = True,
+    safety_factor = {'SF_combined': 1},
     plot = True
 )
 
 print('\nFinal Optimized Anchor:')
 print('Design:', anchor.dd['design'])
-print('Capacity Results:', anchor.capacity_results)
+print('Capacity Results:', anchor.anchorCapacity)
 
 # --- Step 4: Visualize Anchor Geometry ---
 # anchor.getCombinedPlot()
diff --git a/examples/05_Anchors/example_suction.ipynb b/examples/05_Anchors/example_suction.ipynb
new file mode 100644
index 00000000..1f9f29d6
--- /dev/null
+++ b/examples/05_Anchors/example_suction.ipynb
@@ -0,0 +1,3634 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ef2db749",
+   "metadata": {},
+   "source": [
+    "# Suction Anchor Capacity – Jupyter Notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf0c1c98",
+   "metadata": {},
+   "source": [
+    "### Step 1: Import required libraries\n",
+    "\n",
+    "We begin by importing the essential modules:\n",
+    "\n",
+    "- `Anchor` from `famodel.anchors.anchor`: the main class that encapsulates the suction anchor's capacity methods - soil properties, anchor geometry and extreme loads.\n",
+    "- `plot_suction` from `famodel.anchors.anchors_famodel.capacity_plots`: a custom plotting utility that visualizes anchor geometry and soil properties.\n",
+    "\n",
+    "These imports set up the environment to define, simulate, and visualize the anchor system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9f2d8d4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'famodel'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfamodel\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchors\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Anchor\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfamodel\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchors\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manchors_famodel\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msupport_plots\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_suction\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'famodel'"
+     ]
+    }
+   ],
+   "source": [
+    "from famodel.anchors.anchor import Anchor\n",
+    "from famodel.anchors.anchors_famodel.support_plots import plot_suction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b84ceffb",
+   "metadata": {},
+   "source": [
+    "### Step 2: Define the layered soil profile map\n",
+    "We create a list of CPT locations in the vertices of a 500x500 m square within the Lease Area, each with a set of layered clay soil parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "935551c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "profile_map = [\n",
+    "    {\n",
+    "        'name': 'CPT_A1',\n",
+    "        'x': 0.0, 'y': 0.0,\n",
+    "        'layers': [\n",
+    "            {'top':  2.0, 'bottom':  4.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top':  10, 'Su_bot':  25},\n",
+    "            {'top':  4.0, 'bottom':  6.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top':  25, 'Su_bot':  50},\n",
+    "            {'top':  6.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.5, 'Su_top':  50, 'Su_bot': 100},\n",
+    "            {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 9.5, 'gamma_bot': 9.5, 'Su_top': 100, 'Su_bot': 100}\n",
+    "        ]\n",
+    "    },\n",
+    "    {\n",
+    "        'name': 'CPT_B1',\n",
+    "        'x': 500.0, 'y': 0.0,\n",
+    "        'layers': [\n",
+    "            {'top':  2.0, 'bottom':  4.0, 'soil_type': 'clay', 'gamma_top':  8.5, 'gamma_bot':  9.0, 'Su_top':  15, 'Su_bot':  30},\n",
+    "            {'top':  4.0, 'bottom':  6.0, 'soil_type': 'clay', 'gamma_top':  9.0, 'gamma_bot':  9.5, 'Su_top':  30, 'Su_bot':  55},\n",
+    "            {'top':  6.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top':  9.5, 'gamma_bot': 10.0, 'Su_top':  55, 'Su_bot': 105},\n",
+    "            {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.0, 'Su_top': 105, 'Su_bot': 105}\n",
+    "        ]\n",
+    "    },\n",
+    "    {\n",
+    "        'name': 'CPT_A2',\n",
+    "        'x': 0.0, 'y': 500.0,\n",
+    "        'layers': [\n",
+    "            {'top':  2.0, 'bottom':  4.0, 'soil_type': 'clay', 'gamma_top': 7.5, 'gamma_bot': 8.0, 'Su_top':  5, 'Su_bot': 20},\n",
+    "            {'top':  4.0, 'bottom':  8.0, 'soil_type': 'clay', 'gamma_top': 8.0, 'gamma_bot': 8.5, 'Su_top': 20, 'Su_bot': 45},\n",
+    "            {'top':  8.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 8.5, 'gamma_bot': 9.0, 'Su_top': 45, 'Su_bot': 95},\n",
+    "            {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 9.0, 'gamma_bot': 9.0, 'Su_top': 95, 'Su_bot': 95}\n",
+    "        ]\n",
+    "    },\n",
+    "    {\n",
+    "        'name': 'CPT_B2',\n",
+    "        'x': 500.0, 'y': 500.0,\n",
+    "        'layers': [\n",
+    "            {'top':  1.0, 'bottom':  2.0, 'soil_type': 'clay', 'gamma_top':  9.0, 'gamma_bot':  9.5, 'Su_top':  20, 'Su_bot':  35},\n",
+    "            {'top':  2.0, 'bottom':  8.0, 'soil_type': 'clay', 'gamma_top':  9.5, 'gamma_bot': 10.0, 'Su_top':  35, 'Su_bot':  60},\n",
+    "            {'top':  8.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.5, 'Su_top':  60, 'Su_bot': 110},\n",
+    "            {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 10.5, 'gamma_bot': 10.5, 'Su_top': 110, 'Su_bot': 110}\n",
+    "        ]\n",
+    "    }\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d32c699",
+   "metadata": {},
+   "source": [
+    "### Step 3: Initialize the anchor object\n",
+    "We define a suction anchor with its type, initial geometry and anchor location within the defined area."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3aab0b15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anchor = Anchor(\n",
+    "    dd = {'type': 'suction', 'design': {'D': 2.5, 'L': 12.0, 'zlug': 8.67}},\n",
+    "    r = [250.0, 250.0, 0.0]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c26832ae",
+   "metadata": {},
+   "source": [
+    "### Step 4: Assign soil profile to anchor location\n",
+    "This connects the anchor object to the appropriate CPT soil data based on proximity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "368fac90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Anchor] Interpolated soil profile: Interpolated_2D with soil types ['clay']\n"
+     ]
+    }
+   ],
+   "source": [
+    "anchor.interpolateSoilProfile(profile_map)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7ca698d",
+   "metadata": {},
+   "source": [
+    "### Step 5: Plot suction anchor and soil profile\n",
+    "We represent a suction anchor embedded in the soil."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71419ebe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Access anchor geometrical properties\n",
+    "L = anchor.dd['design']['L']\n",
+    "D = anchor.dd['design']['D']\n",
+    "zlug = anchor.dd['design']['zlug']\n",
+    "# Access matched profile\n",
+    "layers = anchor.soil_profile\n",
+    "z0 = layers[0]['top']  \n",
+    "\n",
+    "plot_suction(layers, L=L, D=D, z0=z0, zlug=zlug, title='Suction Pile and Soil Layers')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d5ee57b",
+   "metadata": {},
+   "source": [
+    "### Step 6: Assign external loads and line properties\n",
+    "We assign horizontal and vertical loads and specify the mooring line type and its physical properties (nominal diameter and weight (N/m))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38df38f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial mass from dd: Not defined\n"
+     ]
+    }
+   ],
+   "source": [
+    "anchor.loads = {\n",
+    "    'Hm': 3e6,\n",
+    "    'Vm': 2e6\n",
+    "}\n",
+    "anchor.line_type = 'chain'\n",
+    "anchor.d = 0.16\n",
+    "anchor.w = 5000.0\n",
+    "print('Initial mass from dd:', anchor.dd['design'].get('mass', 'Not defined'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b70c8102",
+   "metadata": {},
+   "source": [
+    "### Step 7: Compute lug forces\n",
+    "We compute the forces acting at the lug using load, geometry, and soil interaction. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ae865bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.67\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3375980.0732258284, thetaa = 55.648978744279006\n",
+      "Output Ha = 1904935.434154513, Va = 2787196.1621888806\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Lug Forces Computed:\n",
+      "Ha = 1904935.43 N\n",
+      "Va = 2787196.16 N\n"
+     ]
+    }
+   ],
+   "source": [
+    "layers, Ha, Va = anchor.getLugForces(\n",
+    "    Hm = anchor.loads['Hm'],\n",
+    "    Vm = anchor.loads['Vm'],\n",
+    "    zlug = anchor.dd['design']['zlug'],\n",
+    "    line_type = anchor.line_type,\n",
+    "    d = anchor.d,\n",
+    "    w = anchor.w,\n",
+    "    plot = True\n",
+    ")\n",
+    "\n",
+    "print('\\nLug Forces Computed:')\n",
+    "print(f'Ha = {Ha:.2f} N')\n",
+    "print(f'Va = {Va:.2f} N')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97f25452",
+   "metadata": {},
+   "source": [
+    "### Step 8: Compute the anchor capacity\n",
+    "This checks whether the current anchor design meets load requirements. Results and plots are printed for reference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aea072d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.67\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3375980.0732258284, thetaa = 55.648978744279006\n",
+      "Output Ha = 1904935.434154513, Va = 2787196.1621888806\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.67\n",
+      "Output Ha = 1904935.4341545128, Va = 2787196.162188881, zlug = 8.67\n",
+      "Output Ta = 3375980.073225829, thetaa = 55.648978744279006\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.382\n",
+      "Vmax_layer    = 280523.02 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 280523.02 N\n",
+      "Vmax3   = 241917.02 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.388\n",
+      "Vmax_layer    = 977721.41 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 977721.41 N\n",
+      "Vmax3   = 697709.86 N\n",
+      "dz_clip  = 5.00 m\n",
+      "ez_layer = 9.68 m\n",
+      "Su_av_z (at ez_layer) = 67381.35 Pa\n",
+      "alphastar   = 0.381\n",
+      "Vmax_layer    = 4714446.61 N\n",
+      "Vmax1   = 4714446.61 N\n",
+      "Vmax2   = 2131059.03 N\n",
+      "Vmax3   = 1378013.04 N\n",
+      "dz_clip  = -4.00 m\n",
+      "Hmax_layer  = 1068338.04 m\n",
+      "Hmax_layer  = 4213508.43 m\n",
+      "ez_global   = 7.51 m\n",
+      "Hmax_final  = 11659911.93 m\n",
+      "rlug_eff = 0.49 m\n",
+      "zlug_eff = 8.75 m\n",
+      "M = -3719492.55 Nm\n",
+      "delta_phi = 1.23 deg\n",
+      "phi_MH = -37.45 deg\n",
+      "a_MH = 14.68\n",
+      "b_MH = 2.13\n",
+      "a_VH = 4.60\n",
+      "b_VH = 5.87\n",
+      "pile_head    = 65180.03 N\n",
+      "Vmax_final   = 6037871.08 N\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Capacity Results:\n",
+      "Hmax: 8496895.31\n",
+      "Vmax: 6037871.08\n",
+      "Ha: 1904935.43\n",
+      "Va: 2787196.16\n",
+      "zlug: 8.67\n",
+      "z0: 1.75\n",
+      "UC: 0.01\n",
+      "Weight pile: 457496.77\n",
+      "Initial mass from dd: Not defined\n"
+     ]
+    }
+   ],
+   "source": [
+    "anchor.getCapacityAnchor(\n",
+    "    Hm = anchor.loads['Hm'],\n",
+    "    Vm = anchor.loads['Vm'],\n",
+    "    zlug = anchor.dd['design']['zlug'],\n",
+    "    line_type = anchor.line_type,\n",
+    "    d = anchor.d,\n",
+    "    w = anchor.w,\n",
+    "    plot = True\n",
+    ")\n",
+    "\n",
+    "print('\\nCapacity Results:')\n",
+    "for key, value in anchor.anchorCapacity.items():\n",
+    "    print(f'{key}: {value:.2f}')\n",
+    "print('Initial mass from dd:', anchor.dd['design'].get('mass', 'Not defined'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "052f68ee",
+   "metadata": {},
+   "source": [
+    "### Step 9: Anchor material costs\n",
+    "We assess the cost of the suction pile defined by the manufacturing cost (USD/kg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2858630b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Anchor' object has no attribute 'getCostAnchor2'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m anchor\u001b[38;5;241m.\u001b[39mgetCostAnchor2()\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMass: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00manchor\u001b[38;5;241m.\u001b[39manchorCapacity[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWeight pile\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m9.81\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m kg\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMaterial unit cost: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00manchor\u001b[38;5;241m.\u001b[39mcost[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munit_cost\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m USD/kg\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'Anchor' object has no attribute 'getCostAnchor2'"
+     ]
+    }
+   ],
+   "source": [
+    "anchor.getCostAnchor2()\n",
+    "\n",
+    "print(f\"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg\")\n",
+    "print(f\"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg\")\n",
+    "print(f'Material cost: {anchor.cost[\"Material cost\"]:.2f} USD [2024]')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec72f15a",
+   "metadata": {},
+   "source": [
+    "### Step 10: Optimize anchor geometry\n",
+    "We optimize anchor length and diameter to ensure capacity requirements are met efficiently within given bounds. Note that a safety factor (SF_combined) = 2 is used in this optimization process. This means that the unity check (UC = 1/SF) equals 0.5. This way the design can accept some extra capacity based on input preference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "304da340",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.0\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.0\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.0\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.382\n",
+      "Vmax_layer    = 280523.02 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 280523.02 N\n",
+      "Vmax3   = 241917.02 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.388\n",
+      "Vmax_layer    = 977721.41 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 977721.41 N\n",
+      "Vmax3   = 697709.86 N\n",
+      "dz_clip  = 5.00 m\n",
+      "ez_layer = 9.68 m\n",
+      "Su_av_z (at ez_layer) = 67381.35 Pa\n",
+      "alphastar   = 0.381\n",
+      "Vmax_layer    = 4714446.61 N\n",
+      "Vmax1   = 4714446.61 N\n",
+      "Vmax2   = 2131059.03 N\n",
+      "Vmax3   = 1378013.04 N\n",
+      "dz_clip  = -4.00 m\n",
+      "Hmax_layer  = 1068338.04 m\n",
+      "Hmax_layer  = 4213508.43 m\n",
+      "ez_global   = 7.51 m\n",
+      "Hmax_final  = 11659911.93 m\n",
+      "rlug_eff = 0.55 m\n",
+      "zlug_eff = 8.08 m\n",
+      "M = -2654716.69 Nm\n",
+      "delta_phi = 1.23 deg\n",
+      "phi_MH = -37.45 deg\n",
+      "a_MH = 14.68\n",
+      "b_MH = 2.13\n",
+      "a_VH = 4.60\n",
+      "b_VH = 5.87\n",
+      "pile_head    = 65180.03 N\n",
+      "Vmax_final   = 6037871.08 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.066666666666666\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.066666666666666\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.066666666666666\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.382\n",
+      "Vmax_layer    = 280523.02 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 280523.02 N\n",
+      "Vmax3   = 241917.02 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.388\n",
+      "Vmax_layer    = 977721.41 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 977721.41 N\n",
+      "Vmax3   = 697709.86 N\n",
+      "dz_clip  = 5.10 m\n",
+      "ez_layer = 9.74 m\n",
+      "Su_av_z (at ez_layer) = 67694.92 Pa\n",
+      "alphastar   = 0.383\n",
+      "Vmax_layer    = 4770807.37 N\n",
+      "Vmax1   = 4770807.37 N\n",
+      "Vmax2   = 2190495.24 N\n",
+      "Vmax3   = 1414096.12 N\n",
+      "dz_clip  = -3.90 m\n",
+      "Hmax_layer  = 1068338.04 m\n",
+      "Hmax_layer  = 4213508.43 m\n",
+      "ez_global   = 7.58 m\n",
+      "Hmax_final  = 11886754.80 m\n",
+      "rlug_eff = 0.54 m\n",
+      "zlug_eff = 8.14 m\n",
+      "M = -2625933.52 Nm\n",
+      "delta_phi = 1.22 deg\n",
+      "phi_MH = -37.44 deg\n",
+      "a_MH = 14.67\n",
+      "b_MH = 2.13\n",
+      "a_VH = 4.64\n",
+      "b_VH = 5.88\n",
+      "pile_head    = 65180.03 N\n",
+      "Vmax_final   = 6094231.84 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.0\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.0\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.0\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.382\n",
+      "Vmax_layer    = 294553.72 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 294553.72 N\n",
+      "Vmax3   = 257302.57 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.388\n",
+      "Vmax_layer    = 1020831.37 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 1020831.37 N\n",
+      "Vmax3   = 735946.66 N\n",
+      "dz_clip  = 5.00 m\n",
+      "ez_layer = 9.68 m\n",
+      "Su_av_z (at ez_layer) = 67381.35 Pa\n",
+      "alphastar   = 0.381\n",
+      "Vmax_layer    = 5051461.65 N\n",
+      "Vmax1   = 5051461.65 N\n",
+      "Vmax2   = 2222396.23 N\n",
+      "Vmax3   = 1448165.42 N\n",
+      "dz_clip  = -4.00 m\n",
+      "Hmax_layer  = 1111071.56 m\n",
+      "Hmax_layer  = 4382048.77 m\n",
+      "ez_global   = 7.51 m\n",
+      "Hmax_final  = 12126308.41 m\n",
+      "rlug_eff = 0.60 m\n",
+      "zlug_eff = 8.08 m\n",
+      "M = -2798831.67 Nm\n",
+      "delta_phi = 1.26 deg\n",
+      "phi_MH = -37.49 deg\n",
+      "a_MH = 14.68\n",
+      "b_MH = 2.13\n",
+      "a_VH = 4.44\n",
+      "b_VH = 5.81\n",
+      "pile_head    = 70824.69 N\n",
+      "Vmax_final   = 6437671.43 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.982130175536096\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n",
+      "Output Ha = 2087804.128469118, Va = 2694589.440925404\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.982130175536096\n",
+      "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.982130175536096\n",
+      "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.383\n",
+      "Vmax_layer    = 267237.19 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 267237.19 N\n",
+      "Vmax3   = 227542.82 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.388\n",
+      "Vmax_layer    = 936475.62 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 936475.62 N\n",
+      "Vmax3   = 661643.15 N\n",
+      "dz_clip  = 4.97 m\n",
+      "ez_layer = 9.66 m\n",
+      "Su_av_z (at ez_layer) = 67297.38 Pa\n",
+      "alphastar   = 0.381\n",
+      "Vmax_layer    = 4381689.40 N\n",
+      "Vmax1   = 4381689.40 N\n",
+      "Vmax2   = 2028361.30 N\n",
+      "Vmax3   = 1302390.78 N\n",
+      "dz_clip  = -4.03 m\n",
+      "Hmax_layer  = 1027168.32 m\n",
+      "Hmax_layer  = 4051135.70 m\n",
+      "ez_global   = 7.49 m\n",
+      "Hmax_final  = 11152427.49 m\n",
+      "rlug_eff = 0.50 m\n",
+      "zlug_eff = 8.06 m\n",
+      "M = -2533815.29 Nm\n",
+      "delta_phi = 1.19 deg\n",
+      "phi_MH = -37.42 deg\n",
+      "a_MH = 14.67\n",
+      "b_MH = 2.13\n",
+      "a_VH = 4.75\n",
+      "b_VH = 5.92\n",
+      "pile_head    = 60000.40 N\n",
+      "Vmax_final   = 5645402.62 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.968928702525485\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n",
+      "Output Ha = 2087804.128469118, Va = 2694589.440925404\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.968928702525485\n",
+      "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.968928702525485\n",
+      "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.383\n",
+      "Vmax_layer    = 253949.36 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 253949.36 N\n",
+      "Vmax3   = 213365.55 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.389\n",
+      "Vmax_layer    = 894799.42 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 894799.42 N\n",
+      "Vmax3   = 625721.83 N\n",
+      "dz_clip  = 4.95 m\n",
+      "ez_layer = 9.65 m\n",
+      "Su_av_z (at ez_layer) = 67235.37 Pa\n",
+      "alphastar   = 0.381\n",
+      "Vmax_layer    = 4059873.87 N\n",
+      "Vmax1   = 4059873.87 N\n",
+      "Vmax2   = 1929735.84 N\n",
+      "Vmax3   = 1229931.07 N\n",
+      "dz_clip  = -4.05 m\n",
+      "Hmax_layer  = 985281.02 m\n",
+      "Hmax_layer  = 3885932.88 m\n",
+      "ez_global   = 7.48 m\n",
+      "Hmax_final  = 10656507.21 m\n",
+      "rlug_eff = 0.45 m\n",
+      "zlug_eff = 8.04 m\n",
+      "M = -2399145.75 Nm\n",
+      "delta_phi = 1.14 deg\n",
+      "phi_MH = -37.37 deg\n",
+      "a_MH = 14.66\n",
+      "b_MH = 2.12\n",
+      "a_VH = 4.93\n",
+      "b_VH = 5.98\n",
+      "pile_head    = 54986.19 N\n",
+      "Vmax_final   = 5263608.84 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.98319669640548\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n",
+      "Output Ha = 2087804.128469118, Va = 2694589.440925404\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.98319669640548\n",
+      "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.98319669640548\n",
+      "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.384\n",
+      "Vmax_layer    = 240934.05 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 240934.05 N\n",
+      "Vmax3   = 199681.97 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.389\n",
+      "Vmax_layer    = 853555.73 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 853555.73 N\n",
+      "Vmax3   = 590698.27 N\n",
+      "dz_clip  = 4.97 m\n",
+      "ez_layer = 9.66 m\n",
+      "Su_av_z (at ez_layer) = 67302.39 Pa\n",
+      "alphastar   = 0.381\n",
+      "Vmax_layer    = 3770930.94 N\n",
+      "Vmax1   = 3770930.94 N\n",
+      "Vmax2   = 1854188.89 N\n",
+      "Vmax3   = 1172231.33 N\n",
+      "dz_clip  = -4.03 m\n",
+      "Hmax_layer  = 943537.67 m\n",
+      "Hmax_layer  = 3721297.74 m\n",
+      "ez_global   = 7.49 m\n",
+      "Hmax_final  = 10247596.94 m\n",
+      "rlug_eff = 0.40 m\n",
+      "zlug_eff = 8.05 m\n",
+      "M = -2252875.71 Nm\n",
+      "delta_phi = 1.09 deg\n",
+      "phi_MH = -37.32 deg\n",
+      "a_MH = 14.65\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.13\n",
+      "b_VH = 6.04\n",
+      "pile_head    = 50241.16 N\n",
+      "Vmax_final   = 4915661.89 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.0260784418972\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.0260784418972\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.0260784418972\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.384\n",
+      "Vmax_layer    = 230888.20 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 230888.20 N\n",
+      "Vmax3   = 189264.17 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.389\n",
+      "Vmax_layer    = 821429.12 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 821429.12 N\n",
+      "Vmax3   = 563784.35 N\n",
+      "dz_clip  = 5.04 m\n",
+      "ez_layer = 9.70 m\n",
+      "Su_av_z (at ez_layer) = 67503.95 Pa\n",
+      "alphastar   = 0.382\n",
+      "Vmax_layer    = 3572663.63 N\n",
+      "Vmax1   = 3572663.63 N\n",
+      "Vmax2   = 1818508.67 N\n",
+      "Vmax3   = 1141524.26 N\n",
+      "dz_clip  = -3.96 m\n",
+      "Hmax_layer  = 910817.63 m\n",
+      "Hmax_layer  = 3592250.41 m\n",
+      "ez_global   = 7.54 m\n",
+      "Hmax_final  = 10016185.59 m\n",
+      "rlug_eff = 0.36 m\n",
+      "zlug_eff = 8.09 m\n",
+      "M = -2112176.19 Nm\n",
+      "delta_phi = 1.04 deg\n",
+      "phi_MH = -37.27 deg\n",
+      "a_MH = 14.64\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.33\n",
+      "b_VH = 6.11\n",
+      "pile_head    = 46694.67 N\n",
+      "Vmax_final   = 4671675.62 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.071808646017427\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.071808646017427\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.071808646017427\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.384\n",
+      "Vmax_layer    = 221466.71 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 221466.71 N\n",
+      "Vmax3   = 179612.64 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 791062.42 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 791062.42 N\n",
+      "Vmax3   = 538644.96 N\n",
+      "dz_clip  = 5.11 m\n",
+      "ez_layer = 9.74 m\n",
+      "Su_av_z (at ez_layer) = 67719.12 Pa\n",
+      "alphastar   = 0.383\n",
+      "Vmax_layer    = 3391245.16 N\n",
+      "Vmax1   = 3391245.16 N\n",
+      "Vmax2   = 1786501.75 N\n",
+      "Vmax3   = 1113768.92 N\n",
+      "dz_clip  = -3.89 m\n",
+      "Hmax_layer  = 879722.76 m\n",
+      "Hmax_layer  = 3469612.74 m\n",
+      "ez_global   = 7.59 m\n",
+      "Hmax_final  = 9802617.52 m\n",
+      "rlug_eff = 0.32 m\n",
+      "zlug_eff = 8.13 m\n",
+      "M = -1987645.24 Nm\n",
+      "delta_phi = 0.99 deg\n",
+      "phi_MH = -37.22 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.53\n",
+      "b_VH = 6.18\n",
+      "pile_head    = 43462.90 N\n",
+      "Vmax_final   = 4447237.19 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.134836617745083\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.134836617745083\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.134836617745083\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.384\n",
+      "Vmax_layer    = 217286.79 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 217286.79 N\n",
+      "Vmax3   = 175368.64 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 777515.41 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 777515.41 N\n",
+      "Vmax3   = 527525.03 N\n",
+      "dz_clip  = 5.20 m\n",
+      "ez_layer = 9.79 m\n",
+      "Su_av_z (at ez_layer) = 68016.06 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 3337860.11 N\n",
+      "Vmax1   = 3337860.11 N\n",
+      "Vmax2   = 1802474.45 N\n",
+      "Vmax3   = 1119287.48 N\n",
+      "dz_clip  = -3.80 m\n",
+      "Hmax_layer  = 865797.80 m\n",
+      "Hmax_layer  = 3414692.94 m\n",
+      "ez_global   = 7.66 m\n",
+      "Hmax_final  = 9822765.87 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 8.19 m\n",
+      "M = -1913941.07 Nm\n",
+      "delta_phi = 0.96 deg\n",
+      "phi_MH = -37.18 deg\n",
+      "a_MH = 14.62\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.66\n",
+      "b_VH = 6.22\n",
+      "pile_head    = 42058.90 N\n",
+      "Vmax_final   = 4374721.22 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.128098362499056\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.128098362499056\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.128098362499056\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.384\n",
+      "Vmax_layer    = 204673.39 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 204673.39 N\n",
+      "Vmax3   = 162708.37 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 736353.01 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 736353.01 N\n",
+      "Vmax3   = 494101.60 N\n",
+      "dz_clip  = 5.19 m\n",
+      "ez_layer = 9.79 m\n",
+      "Su_av_z (at ez_layer) = 67984.30 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 3055268.25 N\n",
+      "Vmax1   = 3055268.25 N\n",
+      "Vmax2   = 1704894.70 N\n",
+      "Vmax3   = 1050360.20 N\n",
+      "dz_clip  = -3.81 m\n",
+      "Hmax_layer  = 823283.12 m\n",
+      "Hmax_layer  = 3247015.71 m\n",
+      "ez_global   = 7.65 m\n",
+      "Hmax_final  = 9322538.75 m\n",
+      "rlug_eff = 0.25 m\n",
+      "zlug_eff = 8.18 m\n",
+      "M = -1773403.69 Nm\n",
+      "delta_phi = 0.89 deg\n",
+      "phi_MH = -37.12 deg\n",
+      "a_MH = 14.61\n",
+      "b_MH = 2.11\n",
+      "a_VH = 5.92\n",
+      "b_VH = 6.31\n",
+      "pile_head    = 37935.66 N\n",
+      "Vmax_final   = 4034230.31 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 8.064733812581633\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 8.064733812581633\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 8.064733812581633\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200778.12 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200778.12 N\n",
+      "Vmax3   = 158844.34 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 723554.22 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 723554.22 N\n",
+      "Vmax3   = 483821.94 N\n",
+      "dz_clip  = 5.10 m\n",
+      "ez_layer = 9.73 m\n",
+      "Su_av_z (at ez_layer) = 67685.82 Pa\n",
+      "alphastar   = 0.383\n",
+      "Vmax_layer    = 2934560.60 N\n",
+      "Vmax1   = 2934560.60 N\n",
+      "Vmax2   = 1633123.89 N\n",
+      "Vmax3   = 1004880.85 N\n",
+      "dz_clip  = -3.90 m\n",
+      "Hmax_layer  = 810000.62 m\n",
+      "Hmax_layer  = 3194629.72 m\n",
+      "ez_global   = 7.58 m\n",
+      "Hmax_final  = 9007383.29 m\n",
+      "rlug_eff = 0.24 m\n",
+      "zlug_eff = 8.12 m\n",
+      "M = -1755541.43 Nm\n",
+      "delta_phi = 0.89 deg\n",
+      "phi_MH = -37.11 deg\n",
+      "a_MH = 14.61\n",
+      "b_MH = 2.11\n",
+      "a_VH = 5.96\n",
+      "b_VH = 6.32\n",
+      "pile_head    = 36697.38 N\n",
+      "Vmax_final   = 3895590.32 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.998215467623575\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.998215467623575\n",
+      "Output Ha = 2052971.0777319924, Va = 2713697.824714782, zlug = 7.998215467623575\n",
+      "Output Ta = 3402770.361024352, thetaa = 52.89170672655495\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 199945.41 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 199945.41 N\n",
+      "Vmax3   = 158021.18 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 720812.78 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 720812.78 N\n",
+      "Vmax3   = 481627.11 N\n",
+      "dz_clip  = 5.00 m\n",
+      "ez_layer = 9.68 m\n",
+      "Su_av_z (at ez_layer) = 67372.96 Pa\n",
+      "alphastar   = 0.382\n",
+      "Vmax_layer    = 2879193.91 N\n",
+      "Vmax1   = 2879193.91 N\n",
+      "Vmax2   = 1582650.96 N\n",
+      "Vmax3   = 974616.00 N\n",
+      "dz_clip  = -4.00 m\n",
+      "Hmax_layer  = 807151.64 m\n",
+      "Hmax_layer  = 3183393.38 m\n",
+      "ez_global   = 7.51 m\n",
+      "Hmax_final  = 8804738.14 m\n",
+      "rlug_eff = 0.24 m\n",
+      "zlug_eff = 8.05 m\n",
+      "M = -1774666.08 Nm\n",
+      "delta_phi = 0.89 deg\n",
+      "phi_MH = -37.12 deg\n",
+      "a_MH = 14.61\n",
+      "b_MH = 2.11\n",
+      "a_VH = 5.93\n",
+      "b_VH = 6.31\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\code\\famodel\\famodel\\anchors\\anchors_famodel_map\\capacity_suction_map.py:348: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
+      "  plt.figure(figsize=(10, 5))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pile_head    = 36434.85 N\n",
+      "Vmax_final   = 3836386.95 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.932052105212464\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n",
+      "Output Ha = 2087804.128469118, Va = 2694589.440925404\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.932052105212464\n",
+      "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.932052105212464\n",
+      "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 198416.10 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 198416.10 N\n",
+      "Vmax3   = 156512.04 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 715773.02 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 715773.02 N\n",
+      "Vmax3   = 477598.68 N\n",
+      "dz_clip  = 4.90 m\n",
+      "ez_layer = 9.62 m\n",
+      "Su_av_z (at ez_layer) = 67062.26 Pa\n",
+      "alphastar   = 0.381\n",
+      "Vmax_layer    = 2810131.86 N\n",
+      "Vmax1   = 2810131.86 N\n",
+      "Vmax2   = 1528398.93 N\n",
+      "Vmax3   = 941545.42 N\n",
+      "dz_clip  = -4.10 m\n",
+      "Hmax_layer  = 801910.52 m\n",
+      "Hmax_layer  = 3162722.52 m\n",
+      "ez_global   = 7.44 m\n",
+      "Hmax_final  = 8580006.44 m\n",
+      "rlug_eff = 0.24 m\n",
+      "zlug_eff = 7.98 m\n",
+      "M = -1800507.88 Nm\n",
+      "delta_phi = 0.90 deg\n",
+      "phi_MH = -37.13 deg\n",
+      "a_MH = 14.61\n",
+      "b_MH = 2.11\n",
+      "a_VH = 5.91\n",
+      "b_VH = 6.30\n",
+      "pile_head    = 35954.70 N\n",
+      "Vmax_final   = 3760275.68 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.865586696499585\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n",
+      "Output Ha = 2087804.128469118, Va = 2694589.440925404\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.865586696499585\n",
+      "Output Ha = 2087804.128469118, Va = 2694589.440925404, zlug = 7.865586696499585\n",
+      "Output Ta = 3408773.728776871, thetaa = 52.2309941857935\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 197449.65 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 197449.65 N\n",
+      "Vmax3   = 155560.13 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 712584.83 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 712584.83 N\n",
+      "Vmax3   = 475054.60 N\n",
+      "dz_clip  = 4.80 m\n",
+      "ez_layer = 9.57 m\n",
+      "Su_av_z (at ez_layer) = 66750.64 Pa\n",
+      "alphastar   = 0.379\n",
+      "Vmax_layer    = 2753492.76 N\n",
+      "Vmax1   = 2753492.76 N\n",
+      "Vmax2   = 1478731.12 N\n",
+      "Vmax3   = 911638.29 N\n",
+      "dz_clip  = -4.20 m\n",
+      "Hmax_layer  = 798592.51 m\n",
+      "Hmax_layer  = 3149636.33 m\n",
+      "ez_global   = 7.36 m\n",
+      "Hmax_final  = 8378262.49 m\n",
+      "rlug_eff = 0.25 m\n",
+      "zlug_eff = 7.92 m\n",
+      "M = -1819224.36 Nm\n",
+      "delta_phi = 0.91 deg\n",
+      "phi_MH = -37.14 deg\n",
+      "a_MH = 14.61\n",
+      "b_MH = 2.11\n",
+      "a_VH = 5.88\n",
+      "b_VH = 6.29\n",
+      "pile_head    = 35652.62 N\n",
+      "Vmax_final   = 3699179.85 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.798991463084559\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3414465.46817813, thetaa = 51.581346423704765\n",
+      "Output Ha = 2121758.7150329417, Va = 2675203.616280948\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.798991463084559\n",
+      "Output Ha = 2121758.715032941, Va = 2675203.616280948, zlug = 7.798991463084559\n",
+      "Output Ta = 3414465.46817813, thetaa = 51.58134642370477\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 196874.23 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 196874.23 N\n",
+      "Vmax3   = 154994.01 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 710685.36 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 710685.36 N\n",
+      "Vmax3   = 473540.48 N\n",
+      "dz_clip  = 4.70 m\n",
+      "ez_layer = 9.51 m\n",
+      "Su_av_z (at ez_layer) = 66438.92 Pa\n",
+      "alphastar   = 0.377\n",
+      "Vmax_layer    = 2705430.48 N\n",
+      "Vmax1   = 2705430.48 N\n",
+      "Vmax2   = 1432335.01 N\n",
+      "Vmax3   = 883958.63 N\n",
+      "dz_clip  = -4.30 m\n",
+      "Hmax_layer  = 796614.80 m\n",
+      "Hmax_layer  = 3141836.27 m\n",
+      "ez_global   = 7.29 m\n",
+      "Hmax_final  = 8192744.37 m\n",
+      "rlug_eff = 0.25 m\n",
+      "zlug_eff = 7.85 m\n",
+      "M = -1857294.69 Nm\n",
+      "delta_phi = 0.92 deg\n",
+      "phi_MH = -37.14 deg\n",
+      "a_MH = 14.62\n",
+      "b_MH = 2.11\n",
+      "a_VH = 5.84\n",
+      "b_VH = 6.28\n",
+      "pile_head    = 35473.25 N\n",
+      "Vmax_final   = 3648463.32 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.7325628950142224\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3414465.46817813, thetaa = 51.581346423704765\n",
+      "Output Ha = 2121758.7150329417, Va = 2675203.616280948\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.7325628950142224\n",
+      "Output Ha = 2121758.715032941, Va = 2675203.616280948, zlug = 7.7325628950142224\n",
+      "Output Ta = 3414465.46817813, thetaa = 51.58134642370477\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 195825.56 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 195825.56 N\n",
+      "Vmax3   = 153963.57 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 707221.33 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 707221.33 N\n",
+      "Vmax3   = 470782.30 N\n",
+      "dz_clip  = 4.60 m\n",
+      "ez_layer = 9.45 m\n",
+      "Su_av_z (at ez_layer) = 66128.49 Pa\n",
+      "alphastar   = 0.376\n",
+      "Vmax_layer    = 2648458.05 N\n",
+      "Vmax1   = 2648458.05 N\n",
+      "Vmax2   = 1383719.44 N\n",
+      "Vmax3   = 854590.09 N\n",
+      "dz_clip  = -4.40 m\n",
+      "Hmax_layer  = 793006.36 m\n",
+      "Hmax_layer  = 3127604.63 m\n",
+      "ez_global   = 7.22 m\n",
+      "Hmax_final  = 7993393.93 m\n",
+      "rlug_eff = 0.25 m\n",
+      "zlug_eff = 7.78 m\n",
+      "M = -1876357.33 Nm\n",
+      "delta_phi = 0.92 deg\n",
+      "phi_MH = -37.15 deg\n",
+      "a_MH = 14.62\n",
+      "b_MH = 2.11\n",
+      "a_VH = 5.81\n",
+      "b_VH = 6.27\n",
+      "pile_head    = 35147.33 N\n",
+      "Vmax_final   = 3586652.27 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.677288410606607\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3414465.46817813, thetaa = 51.581346423704765\n",
+      "Output Ha = 2121758.7150329417, Va = 2675203.616280948\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.677288410606607\n",
+      "Output Ha = 2121758.715032941, Va = 2675203.616280948, zlug = 7.677288410606607\n",
+      "Output Ta = 3414465.46817813, thetaa = 51.58134642370477\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 202798.06 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 202798.06 N\n",
+      "Vmax3   = 160845.34 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 730196.38 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 730196.38 N\n",
+      "Vmax3   = 489150.03 N\n",
+      "dz_clip  = 4.52 m\n",
+      "ez_layer = 9.41 m\n",
+      "Su_av_z (at ez_layer) = 65870.59 Pa\n",
+      "alphastar   = 0.374\n",
+      "Vmax_layer    = 2758674.00 N\n",
+      "Vmax1   = 2758674.00 N\n",
+      "Vmax2   = 1392151.06 N\n",
+      "Vmax3   = 864978.49 N\n",
+      "dz_clip  = -4.48 m\n",
+      "Hmax_layer  = 816897.59 m\n",
+      "Hmax_layer  = 3221831.27 m\n",
+      "ez_global   = 7.15 m\n",
+      "Hmax_final  = 8096223.75 m\n",
+      "rlug_eff = 0.28 m\n",
+      "zlug_eff = 7.73 m\n",
+      "M = -1982366.87 Nm\n",
+      "delta_phi = 0.97 deg\n",
+      "phi_MH = -37.20 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.61\n",
+      "b_VH = 6.20\n",
+      "pile_head    = 37337.42 N\n",
+      "Vmax_final   = 3729005.87 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.665922524227427\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n",
+      "Output Ha = 2154823.310507873, Va = 2655538.689355862\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.665922524227427\n",
+      "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.665922524227427\n",
+      "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 196174.16 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 196174.16 N\n",
+      "Vmax3   = 154305.93 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 708373.20 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 708373.20 N\n",
+      "Vmax3   = 471699.01 N\n",
+      "dz_clip  = 4.50 m\n",
+      "ez_layer = 9.40 m\n",
+      "Su_av_z (at ez_layer) = 65817.60 Pa\n",
+      "alphastar   = 0.374\n",
+      "Vmax_layer    = 2619818.77 N\n",
+      "Vmax1   = 2619818.77 N\n",
+      "Vmax2   = 1344383.57 N\n",
+      "Vmax3   = 831736.86 N\n",
+      "dz_clip  = -4.50 m\n",
+      "Hmax_layer  = 794206.50 m\n",
+      "Hmax_layer  = 3132337.95 m\n",
+      "ez_global   = 7.14 m\n",
+      "Hmax_final  = 7843864.78 m\n",
+      "rlug_eff = 0.26 m\n",
+      "zlug_eff = 7.72 m\n",
+      "M = -1926167.79 Nm\n",
+      "delta_phi = 0.94 deg\n",
+      "phi_MH = -37.16 deg\n",
+      "a_MH = 14.62\n",
+      "b_MH = 2.11\n",
+      "a_VH = 5.75\n",
+      "b_VH = 6.25\n",
+      "pile_head    = 35255.54 N\n",
+      "Vmax_final   = 3559621.67 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.599299557791959\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n",
+      "Output Ha = 2154823.310507873, Va = 2655538.689355862\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.599299557791959\n",
+      "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.599299557791959\n",
+      "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 195724.83 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 195724.83 N\n",
+      "Vmax3   = 153864.68 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 706888.43 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 706888.43 N\n",
+      "Vmax3   = 470517.44 N\n",
+      "dz_clip  = 4.40 m\n",
+      "ez_layer = 9.34 m\n",
+      "Su_av_z (at ez_layer) = 65507.31 Pa\n",
+      "alphastar   = 0.372\n",
+      "Vmax_layer    = 2575602.34 N\n",
+      "Vmax1   = 2575602.34 N\n",
+      "Vmax2   = 1300634.14 N\n",
+      "Vmax3   = 805649.13 N\n",
+      "dz_clip  = -4.60 m\n",
+      "Hmax_layer  = 792659.46 m\n",
+      "Hmax_layer  = 3126236.47 m\n",
+      "ez_global   = 7.07 m\n",
+      "Hmax_final  = 7668689.19 m\n",
+      "rlug_eff = 0.26 m\n",
+      "zlug_eff = 7.65 m\n",
+      "M = -1953647.14 Nm\n",
+      "delta_phi = 0.95 deg\n",
+      "phi_MH = -37.17 deg\n",
+      "a_MH = 14.62\n",
+      "b_MH = 2.11\n",
+      "a_VH = 5.70\n",
+      "b_VH = 6.23\n",
+      "pile_head    = 35116.08 N\n",
+      "Vmax_final   = 3513331.68 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.53340544846019\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n",
+      "Output Ha = 2154823.310507873, Va = 2655538.689355862\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.53340544846019\n",
+      "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.53340544846019\n",
+      "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 197610.86 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 197610.86 N\n",
+      "Vmax3   = 155718.82 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 713116.82 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 713116.82 N\n",
+      "Vmax3   = 475478.87 N\n",
+      "dz_clip  = 4.30 m\n",
+      "ez_layer = 9.29 m\n",
+      "Su_av_z (at ez_layer) = 65200.95 Pa\n",
+      "alphastar   = 0.371\n",
+      "Vmax_layer    = 2577607.38 N\n",
+      "Vmax1   = 2577607.38 N\n",
+      "Vmax2   = 1271420.39 N\n",
+      "Vmax3   = 789811.18 N\n",
+      "dz_clip  = -4.70 m\n",
+      "Hmax_layer  = 799146.30 m\n",
+      "Hmax_layer  = 3151820.44 m\n",
+      "ez_global   = 6.99 m\n",
+      "Hmax_final  = 7573363.72 m\n",
+      "rlug_eff = 0.27 m\n",
+      "zlug_eff = 7.59 m\n",
+      "M = -2008087.24 Nm\n",
+      "delta_phi = 0.97 deg\n",
+      "phi_MH = -37.19 deg\n",
+      "a_MH = 14.62\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.61\n",
+      "b_VH = 6.20\n",
+      "pile_head    = 35702.93 N\n",
+      "Vmax_final   = 3524037.99 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.466940379053499\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.466940379053499\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.466940379053499\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 196644.71 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 196644.71 N\n",
+      "Vmax3   = 154768.34 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 709927.45 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 709927.45 N\n",
+      "Vmax3   = 472936.67 N\n",
+      "dz_clip  = 4.20 m\n",
+      "ez_layer = 9.23 m\n",
+      "Su_av_z (at ez_layer) = 64892.47 Pa\n",
+      "alphastar   = 0.369\n",
+      "Vmax_layer    = 2523844.77 N\n",
+      "Vmax1   = 2523844.77 N\n",
+      "Vmax2   = 1225690.08 N\n",
+      "Vmax3   = 762107.62 N\n",
+      "dz_clip  = -4.80 m\n",
+      "Hmax_layer  = 795825.49 m\n",
+      "Hmax_layer  = 3138723.25 m\n",
+      "ez_global   = 6.92 m\n",
+      "Hmax_final  = 7384462.35 m\n",
+      "rlug_eff = 0.28 m\n",
+      "zlug_eff = 7.52 m\n",
+      "M = -2044839.88 Nm\n",
+      "delta_phi = 0.98 deg\n",
+      "phi_MH = -37.19 deg\n",
+      "a_MH = 14.62\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.57\n",
+      "b_VH = 6.19\n",
+      "pile_head    = 35401.81 N\n",
+      "Vmax_final   = 3465818.74 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.528345537398288\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3419817.1340254378, thetaa = 50.94257403448309\n",
+      "Output Ha = 2154823.310507873, Va = 2655538.689355862\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.528345537398288\n",
+      "Output Ha = 2154823.310507873, Va = 2655538.689355862, zlug = 7.528345537398288\n",
+      "Output Ta = 3419817.1340254373, thetaa = 50.94257403448309\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 191489.13 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 191489.13 N\n",
+      "Vmax3   = 149720.01 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.391\n",
+      "Vmax_layer    = 692864.61 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 692864.61 N\n",
+      "Vmax3   = 459393.52 N\n",
+      "dz_clip  = 4.29 m\n",
+      "ez_layer = 9.28 m\n",
+      "Su_av_z (at ez_layer) = 65177.44 Pa\n",
+      "alphastar   = 0.371\n",
+      "Vmax_layer    = 2456531.86 N\n",
+      "Vmax1   = 2456531.86 N\n",
+      "Vmax2   = 1233020.90 N\n",
+      "Vmax3   = 762760.60 N\n",
+      "dz_clip  = -4.71 m\n",
+      "Hmax_layer  = 778027.14 m\n",
+      "Hmax_layer  = 3068526.82 m\n",
+      "ez_global   = 6.99 m\n",
+      "Hmax_final  = 7361457.45 m\n",
+      "rlug_eff = 0.25 m\n",
+      "zlug_eff = 7.58 m\n",
+      "M = -1940635.38 Nm\n",
+      "delta_phi = 0.94 deg\n",
+      "phi_MH = -37.15 deg\n",
+      "a_MH = 14.62\n",
+      "b_MH = 2.11\n",
+      "a_VH = 5.74\n",
+      "b_VH = 6.25\n",
+      "pile_head    = 33812.71 N\n",
+      "Vmax_final   = 3374698.32 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.467198257566559\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.467198257566559\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.467198257566559\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 199070.56 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 199070.56 N\n",
+      "Vmax3   = 157157.45 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 717930.56 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 717930.56 N\n",
+      "Vmax3   = 479322.23 N\n",
+      "dz_clip  = 4.20 m\n",
+      "ez_layer = 9.23 m\n",
+      "Su_av_z (at ez_layer) = 64893.66 Pa\n",
+      "alphastar   = 0.369\n",
+      "Vmax_layer    = 2570776.56 N\n",
+      "Vmax1   = 2570776.56 N\n",
+      "Vmax2   = 1239396.50 N\n",
+      "Vmax3   = 771938.93 N\n",
+      "dz_clip  = -4.80 m\n",
+      "Hmax_layer  = 804154.83 m\n",
+      "Hmax_layer  = 3171574.04 m\n",
+      "ez_global   = 6.92 m\n",
+      "Hmax_final  = 7462364.83 m\n",
+      "rlug_eff = 0.29 m\n",
+      "zlug_eff = 7.52 m\n",
+      "M = -2072151.45 Nm\n",
+      "delta_phi = 0.99 deg\n",
+      "phi_MH = -37.20 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.52\n",
+      "b_VH = 6.17\n",
+      "pile_head    = 36159.86 N\n",
+      "Vmax_final   = 3523937.54 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.400809287203985\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.400809287203985\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.400809287203985\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200208.23 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200208.23 N\n",
+      "Vmax3   = 158280.88 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721678.26 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721678.26 N\n",
+      "Vmax3   = 482319.75 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.18 m\n",
+      "Su_av_z (at ez_layer) = 64586.08 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557372.63 N\n",
+      "Vmax1   = 2557372.63 N\n",
+      "Vmax2   = 1205455.44 N\n",
+      "Vmax3   = 752641.00 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808051.22 m\n",
+      "Hmax_layer  = 3186941.31 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7340231.71 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.46 m\n",
+      "M = -2119835.45 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.45\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36517.63 N\n",
+      "Vmax_final   = 3515776.75 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.334659934620789\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n",
+      "Output Ha = 2218182.0096819005, Va = 2615301.443540927\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.334659934620789\n",
+      "Output Ha = 2218182.0096819005, Va = 2615301.443540927, zlug = 7.334659934620789\n",
+      "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 201762.59 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 201762.59 N\n",
+      "Vmax3   = 159818.83 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 726792.83 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 726792.83 N\n",
+      "Vmax3   = 486418.02 N\n",
+      "dz_clip  = 4.00 m\n",
+      "ez_layer = 9.12 m\n",
+      "Su_av_z (at ez_layer) = 64280.15 Pa\n",
+      "alphastar   = 0.365\n",
+      "Vmax_layer    = 2551829.96 N\n",
+      "Vmax1   = 2551829.96 N\n",
+      "Vmax2   = 1173891.23 N\n",
+      "Vmax3   = 734980.53 N\n",
+      "dz_clip  = -5.00 m\n",
+      "Hmax_layer  = 813364.49 m\n",
+      "Hmax_layer  = 3207896.79 m\n",
+      "ez_global   = 6.77 m\n",
+      "Hmax_final  = 7231118.91 m\n",
+      "rlug_eff = 0.31 m\n",
+      "zlug_eff = 7.39 m\n",
+      "M = -2186006.01 Nm\n",
+      "delta_phi = 1.03 deg\n",
+      "phi_MH = -37.22 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.36\n",
+      "b_VH = 6.12\n",
+      "pile_head    = 37008.76 N\n",
+      "Vmax_final   = 3517394.13 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.367735235084056\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n",
+      "Output Ha = 2218182.0096819005, Va = 2615301.443540927\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.367735235084056\n",
+      "Output Ha = 2218182.0096819005, Va = 2615301.443540927, zlug = 7.367735235084056\n",
+      "Output Ta = 3429305.0416467316, thetaa = 49.69687692658275\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200985.91 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200985.91 N\n",
+      "Vmax3   = 159049.91 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 724238.04 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 724238.04 N\n",
+      "Vmax3   = 484369.80 N\n",
+      "dz_clip  = 4.05 m\n",
+      "ez_layer = 9.15 m\n",
+      "Su_av_z (at ez_layer) = 64433.05 Pa\n",
+      "alphastar   = 0.366\n",
+      "Vmax_layer    = 2554651.42 N\n",
+      "Vmax1   = 2554651.42 N\n",
+      "Vmax2   = 1189687.39 N\n",
+      "Vmax3   = 743827.80 N\n",
+      "dz_clip  = -4.95 m\n",
+      "Hmax_layer  = 810711.05 m\n",
+      "Hmax_layer  = 3197431.63 m\n",
+      "ez_global   = 6.81 m\n",
+      "Hmax_final  = 7285791.25 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.42 m\n",
+      "M = -2159357.06 Nm\n",
+      "delta_phi = 1.02 deg\n",
+      "phi_MH = -37.22 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.40\n",
+      "b_VH = 6.13\n",
+      "pile_head    = 36763.02 N\n",
+      "Vmax_final   = 3516638.39 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.402328930637527\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.402328930637527\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.402328930637527\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 203323.99 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 203323.99 N\n",
+      "Vmax3   = 161367.30 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 731923.95 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 731923.95 N\n",
+      "Vmax3   = 490538.20 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.18 m\n",
+      "Su_av_z (at ez_layer) = 64593.11 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2618168.22 N\n",
+      "Vmax1   = 2618168.22 N\n",
+      "Vmax2   = 1223180.09 N\n",
+      "Vmax3   = 765347.70 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 818690.10 m\n",
+      "Hmax_layer  = 3228900.88 m\n",
+      "ez_global   = 6.85 m\n",
+      "Hmax_final  = 7440529.26 m\n",
+      "rlug_eff = 0.31 m\n",
+      "zlug_eff = 7.46 m\n",
+      "M = -2154084.22 Nm\n",
+      "delta_phi = 1.03 deg\n",
+      "phi_MH = -37.23 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.38\n",
+      "b_VH = 6.13\n",
+      "pile_head    = 37504.80 N\n",
+      "Vmax_final   = 3590920.97 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.387262874558214\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.387262874558214\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.387262874558214\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 194526.39 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 194526.39 N\n",
+      "Vmax3   = 152689.26 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 702925.62 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 702925.62 N\n",
+      "Vmax3   = 467367.43 N\n",
+      "dz_clip  = 4.08 m\n",
+      "ez_layer = 9.16 m\n",
+      "Su_av_z (at ez_layer) = 64523.38 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2442151.12 N\n",
+      "Vmax1   = 2442151.12 N\n",
+      "Vmax2   = 1166741.18 N\n",
+      "Vmax3   = 725788.37 N\n",
+      "dz_clip  = -4.92 m\n",
+      "Hmax_layer  = 788528.44 m\n",
+      "Hmax_layer  = 3109943.77 m\n",
+      "ez_global   = 6.83 m\n",
+      "Hmax_final  = 7131536.23 m\n",
+      "rlug_eff = 0.28 m\n",
+      "zlug_eff = 7.44 m\n",
+      "M = -2062716.68 Nm\n",
+      "delta_phi = 0.99 deg\n",
+      "phi_MH = -37.18 deg\n",
+      "a_MH = 14.62\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.56\n",
+      "b_VH = 6.19\n",
+      "pile_head    = 34745.26 N\n",
+      "Vmax_final   = 3374348.39 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399868809039603\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399868809039603\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399868809039603\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 197098.22 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 197098.22 N\n",
+      "Vmax3   = 155214.33 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 711424.87 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 711424.87 N\n",
+      "Vmax3   = 474129.82 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64581.72 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2497443.65 N\n",
+      "Vmax1   = 2497443.65 N\n",
+      "Vmax2   = 1188082.69 N\n",
+      "Vmax3   = 740184.52 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 797384.86 m\n",
+      "Hmax_layer  = 3144873.36 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7241136.90 m\n",
+      "rlug_eff = 0.28 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2085188.94 Nm\n",
+      "delta_phi = 1.00 deg\n",
+      "phi_MH = -37.19 deg\n",
+      "a_MH = 14.62\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.51\n",
+      "b_VH = 6.17\n",
+      "pile_head    = 35543.03 N\n",
+      "Vmax_final   = 3441509.77 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.401280820159469\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.401280820159469\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.401280820159469\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 198651.52 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 198651.52 N\n",
+      "Vmax3   = 156744.14 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 716549.26 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 716549.26 N\n",
+      "Vmax3   = 478218.60 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.18 m\n",
+      "Su_av_z (at ez_layer) = 64588.26 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2527816.21 N\n",
+      "Vmax1   = 2527816.21 N\n",
+      "Vmax2   = 1197328.35 N\n",
+      "Vmax3   = 746738.23 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 802718.09 m\n",
+      "Hmax_layer  = 3165907.52 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7292898.26 m\n",
+      "rlug_eff = 0.29 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2102012.31 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.20 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.48\n",
+      "b_VH = 6.16\n",
+      "pile_head    = 36028.45 N\n",
+      "Vmax_final   = 3479045.44 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.404969278223096\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.404969278223096\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.404969278223096\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200252.40 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200252.40 N\n",
+      "Vmax3   = 158324.53 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721823.67 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721823.67 N\n",
+      "Vmax3   = 482436.15 N\n",
+      "dz_clip  = 4.11 m\n",
+      "ez_layer = 9.18 m\n",
+      "Su_av_z (at ez_layer) = 64605.33 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2560434.10 N\n",
+      "Vmax1   = 2560434.10 N\n",
+      "Vmax2   = 1208205.08 N\n",
+      "Vmax3   = 754302.10 N\n",
+      "dz_clip  = -4.89 m\n",
+      "Hmax_layer  = 808202.34 m\n",
+      "Hmax_layer  = 3187537.35 m\n",
+      "ez_global   = 6.85 m\n",
+      "Hmax_final  = 7351484.53 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.46 m\n",
+      "M = -2118126.36 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.45\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36531.55 N\n",
+      "Vmax_final   = 3519041.72 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.392551909093574\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.392551909093574\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.392551909093574\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200418.54 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200418.54 N\n",
+      "Vmax3   = 158488.76 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 722370.64 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 722370.64 N\n",
+      "Vmax3   = 482874.05 N\n",
+      "dz_clip  = 4.09 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64547.86 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557010.14 N\n",
+      "Vmax1   = 2557010.14 N\n",
+      "Vmax2   = 1201607.66 N\n",
+      "Vmax3   = 750506.23 N\n",
+      "dz_clip  = -4.91 m\n",
+      "Hmax_layer  = 808770.78 m\n",
+      "Hmax_layer  = 3189779.27 m\n",
+      "ez_global   = 6.83 m\n",
+      "Hmax_final  = 7327159.07 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2126596.87 Nm\n",
+      "delta_phi = 1.02 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.43\n",
+      "b_VH = 6.14\n",
+      "pile_head    = 36583.92 N\n",
+      "Vmax_final   = 3516383.25 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.39671089582197\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.39671089582197\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.39671089582197\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200348.96 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200348.96 N\n",
+      "Vmax3   = 158419.97 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 722141.58 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 722141.58 N\n",
+      "Vmax3   = 482690.65 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64567.11 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557890.21 N\n",
+      "Vmax1   = 2557890.21 N\n",
+      "Vmax2   = 1203741.72 N\n",
+      "Vmax3   = 751723.22 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808532.74 m\n",
+      "Hmax_layer  = 3188840.42 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7334873.38 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2123599.58 Nm\n",
+      "delta_phi = 1.02 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36561.98 N\n",
+      "Vmax_final   = 3516942.73 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.398765463164171\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.398765463164171\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.398765463164171\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200283.88 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200283.88 N\n",
+      "Vmax3   = 158355.64 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721927.32 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721927.32 N\n",
+      "Vmax3   = 482519.12 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64576.62 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557735.79 N\n",
+      "Vmax1   = 2557735.79 N\n",
+      "Vmax2   = 1204630.47 N\n",
+      "Vmax3   = 752204.75 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808310.06 m\n",
+      "Hmax_layer  = 3187962.18 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7337730.23 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2121773.85 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36541.47 N\n",
+      "Vmax_final   = 3516488.45 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.398967367270157\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.398967367270157\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.398967367270157\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200475.34 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200475.34 N\n",
+      "Vmax3   = 158544.92 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 722557.64 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 722557.64 N\n",
+      "Vmax3   = 483023.78 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64577.55 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2561515.84 N\n",
+      "Vmax1   = 2561515.84 N\n",
+      "Vmax2   = 1205784.76 N\n",
+      "Vmax3   = 753023.08 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808965.11 m\n",
+      "Hmax_layer  = 3190545.68 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7344156.40 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2123824.98 Nm\n",
+      "delta_phi = 1.02 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36601.84 N\n",
+      "Vmax_final   = 3521150.66 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.398361136666139\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.398361136666139\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.398361136666139\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 199901.12 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 199901.12 N\n",
+      "Vmax3   = 157977.43 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 720666.94 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 720666.94 N\n",
+      "Vmax3   = 481510.41 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64574.74 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2550184.33 N\n",
+      "Vmax1   = 2550184.33 N\n",
+      "Vmax2   = 1202322.59 N\n",
+      "Vmax3   = 750569.08 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 807000.03 m\n",
+      "Hmax_layer  = 3182795.44 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7324879.64 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2117672.10 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.45\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36420.91 N\n",
+      "Vmax_final   = 3507173.30 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3997901611151775\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3997901611151775\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3997901611151775\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200248.80 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200248.80 N\n",
+      "Vmax3   = 158320.97 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721811.82 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721811.82 N\n",
+      "Vmax3   = 482426.67 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64581.36 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557608.37 N\n",
+      "Vmax1   = 2557608.37 N\n",
+      "Vmax2   = 1205059.48 N\n",
+      "Vmax3   = 752434.61 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808190.03 m\n",
+      "Hmax_layer  = 3187488.79 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7339073.09 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2120834.02 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36530.41 N\n",
+      "Vmax_final   = 3516199.41 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399278551221133\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399278551221133\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399278551221133\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200267.08 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200267.08 N\n",
+      "Vmax3   = 158339.04 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721872.01 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721872.01 N\n",
+      "Vmax3   = 482474.85 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64578.99 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557686.71 N\n",
+      "Vmax1   = 2557686.71 N\n",
+      "Vmax2   = 1204849.43 N\n",
+      "Vmax3   = 752322.84 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808252.58 m\n",
+      "Hmax_layer  = 3187735.49 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7338426.50 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2121311.88 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36536.17 N\n",
+      "Vmax_final   = 3516361.97 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399327343324559\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399327343324559\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399327343324559\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200315.00 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200315.00 N\n",
+      "Vmax3   = 158386.41 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 722029.79 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 722029.79 N\n",
+      "Vmax3   = 482601.15 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64579.22 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2558631.75 N\n",
+      "Vmax1   = 2558631.75 N\n",
+      "Vmax2   = 1205137.35 N\n",
+      "Vmax3   = 752527.06 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808416.55 m\n",
+      "Hmax_layer  = 3188382.19 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7340031.12 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2121826.25 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36551.28 N\n",
+      "Vmax_final   = 3517527.81 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399180964245797\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399180964245797\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399180964245797\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200171.25 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200171.25 N\n",
+      "Vmax3   = 158244.33 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721556.47 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721556.47 N\n",
+      "Vmax3   = 482222.27 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64578.54 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2555797.15 N\n",
+      "Vmax1   = 2555797.15 N\n",
+      "Vmax2   = 1204273.64 N\n",
+      "Vmax3   = 751914.46 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 807924.64 m\n",
+      "Hmax_layer  = 3186442.09 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7335217.41 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2120283.14 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36505.98 N\n",
+      "Vmax_final   = 3514030.84 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399534460150541\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399534460150541\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399534460150541\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200258.04 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200258.04 N\n",
+      "Vmax3   = 158330.11 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721842.25 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721842.25 N\n",
+      "Vmax3   = 482451.03 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64580.17 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557649.57 N\n",
+      "Vmax1   = 2557649.57 N\n",
+      "Vmax2   = 1204955.08 N\n",
+      "Vmax3   = 752379.16 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808221.66 m\n",
+      "Hmax_layer  = 3187613.52 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7338753.25 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2121074.04 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36533.33 N\n",
+      "Vmax_final   = 3516283.19 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.39940652842244\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.39940652842244\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.39940652842244\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200262.58 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200262.58 N\n",
+      "Vmax3   = 158334.60 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721857.21 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721857.21 N\n",
+      "Vmax3   = 482462.99 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64579.58 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557668.58 N\n",
+      "Vmax1   = 2557668.58 N\n",
+      "Vmax2   = 1204902.39 N\n",
+      "Vmax3   = 752351.10 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808237.20 m\n",
+      "Hmax_layer  = 3187674.81 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7338590.63 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2121193.20 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36534.76 N\n",
+      "Vmax_final   = 3516323.13 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.399418649509588\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.399418649509588\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.399418649509588\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200274.56 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200274.56 N\n",
+      "Vmax3   = 158346.44 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721896.66 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721896.66 N\n",
+      "Vmax3   = 482494.58 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64579.64 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557904.84 N\n",
+      "Vmax1   = 2557904.84 N\n",
+      "Vmax2   = 1204974.34 N\n",
+      "Vmax3   = 752402.14 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808278.20 m\n",
+      "Hmax_layer  = 3187836.52 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7338991.70 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2121321.87 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36538.53 N\n",
+      "Vmax_final   = 3516614.60 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3993822862479135\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3993822862479135\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3993822862479135\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200238.62 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200238.62 N\n",
+      "Vmax3   = 158310.91 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721778.30 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721778.30 N\n",
+      "Vmax3   = 482399.83 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64579.47 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557196.10 N\n",
+      "Vmax1   = 2557196.10 N\n",
+      "Vmax2   = 1204758.49 N\n",
+      "Vmax3   = 752249.02 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808155.19 m\n",
+      "Hmax_layer  = 3187351.39 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7337788.51 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2120935.87 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36527.21 N\n",
+      "Vmax_final   = 3515740.23 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3994720299213235\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3994720299213235\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3994720299213235\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200260.26 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200260.26 N\n",
+      "Vmax3   = 158332.30 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721849.55 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721849.55 N\n",
+      "Vmax3   = 482456.87 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64579.89 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557658.86 N\n",
+      "Vmax1   = 2557658.86 N\n",
+      "Vmax2   = 1204929.37 N\n",
+      "Vmax3   = 752365.47 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808229.24 m\n",
+      "Hmax_layer  = 3187643.44 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7338673.91 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2121132.20 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36534.03 N\n",
+      "Vmax_final   = 3516302.69 N\n",
+      "Input Tm = 3605551.2754639895, thetam = 33.690067525979785, zlug = 7.3994720299213235\n",
+      "Output Hm = 3000000.0000000005, Vm = 1999999.9999999998\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.31448431163705\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985\n",
+      "Input Hm = 3000000.0, Vm = 2000000.0, zlug = 7.3994720299213235\n",
+      "Output Ha = 2186977.238360048, Va = 2635582.2104549985, zlug = 7.3994720299213235\n",
+      "Output Ta = 3424786.566951552, thetaa = 50.314484311637045\n",
+      "dz_clip  = 1.75 m\n",
+      "ez_layer = 2.74 m\n",
+      "Su_av_z (at ez_layer) = 20960.65 Pa\n",
+      "alphastar   = 0.385\n",
+      "Vmax_layer    = 200260.26 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 200260.26 N\n",
+      "Vmax3   = 158332.30 N\n",
+      "dz_clip  = 3.50 m\n",
+      "ez_layer = 5.44 m\n",
+      "Su_av_z (at ez_layer) = 41334.23 Pa\n",
+      "alphastar   = 0.390\n",
+      "Vmax_layer    = 721849.55 N\n",
+      "Vmax1   = not applicable\n",
+      "Vmax2   = 721849.55 N\n",
+      "Vmax3   = 482456.87 N\n",
+      "dz_clip  = 4.10 m\n",
+      "ez_layer = 9.17 m\n",
+      "Su_av_z (at ez_layer) = 64579.89 Pa\n",
+      "alphastar   = 0.367\n",
+      "Vmax_layer    = 2557658.86 N\n",
+      "Vmax1   = 2557658.86 N\n",
+      "Vmax2   = 1204929.37 N\n",
+      "Vmax3   = 752365.47 N\n",
+      "dz_clip  = -4.90 m\n",
+      "Hmax_layer  = 808229.24 m\n",
+      "Hmax_layer  = 3187643.44 m\n",
+      "ez_global   = 6.84 m\n",
+      "Hmax_final  = 7338673.91 m\n",
+      "rlug_eff = 0.30 m\n",
+      "zlug_eff = 7.45 m\n",
+      "M = -2121132.20 Nm\n",
+      "delta_phi = 1.01 deg\n",
+      "phi_MH = -37.21 deg\n",
+      "a_MH = 14.63\n",
+      "b_MH = 2.12\n",
+      "a_VH = 5.44\n",
+      "b_VH = 6.15\n",
+      "pile_head    = 36534.03 N\n",
+      "Vmax_final   = 3516302.69 N\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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J3k/C3yTnhL/pdDri4+Nluwfhd9LeCX+TnBsbKuWTVkcIQmezu60QQgghhBAidJ1NbSA9RmFq7dq1gQ5BhBnJOREIknciECTvhL9Jzo0NKYyEEEKEpLKyMm655RbZ30MIIcQZkcIoTGVmZgY6BBFmJOeEv7lcLtrb23G5XIEORYQZae+Ev0nOjQ0pjMJUbGxsoEMQYUZyTggRLqS9E/4mOTc2pDAKU6WlpYEOQYQZyTkhRLiQ9k74m+Tc2JDCSAghhBBCCBH2pDAKU+edd16gQxBhRnJO+FteXh6vvfYaeXl5gQ5FhBlp74S/Sc6NDSmMwlR9fX2gQxBhRnJO+JvVaiU7Oxur1RroUESYkfZO+Jvk3NiQwihMNTU1BToEEWYk54S/NTQ08POf/5yGhoZAhyLCjLR3wt8k58aGFEZhSqvVBjoEEWYk54S/tbS08NJLL9HS0hLoUESYkfZO+Jvk3NiQwihMXXzxxYEOQYQZyTkhRLiQ9k74m+Tc2JDCKEy98847gQ5BhBnJOSFEuJD2Tvib5NzYkMIoTHm93kCHIMKM5JwQIlxIeyf8TXJubEhhFKbS0tICHYIIM5Jzwt/i4uK49tpriYuLC3QoIsxIeyf8TXJubMhMrTCVnJwc6BBEmJGcE/6WlZXFE088QXx8fKBDEWFG2jvhb5JzY0N6jMLUnj17Ah2CCDOSc8LfBgcHWb16NYODg4EORYQZae+Ev0nOjQ0pjIQQQoSk8vJy7rvvPsrLywMdihBCiCAghVGYmjNnTqBDEGFGck4IES6kvRP+Jjk3NqQwClOtra2BDkGEGck5IUS4kPZO+Jvk3NiQwihM1dfXBzoEEWYk54QQ4ULaO+FvknNjQwqjMKVSqQIdgggzknPC31QqFTqdTnJP+J3knPA3ybmxoVIURQl0EGOpt7eXqKgoenp6iIyMDHQ4QgghhBBCiAA5m9pAeozC1IYNGwIdgggzknMiECTvRCBI3gl/k5wbG1IYhSmXyxXoEESYkZwT/lZeXs4999wjy3ULv5P2Tvib5NzYkMIoTMkOycLfJOeEvw0ODnL06FHZ4FX4nbR3wt8k58aGFEZhKjMzM9AhiDAjOSeECBfS3gl/k5wbG1IYhamdO3cGOgQRZiTnhBDhQto74W+Sc2NDCiMhhBBCCCFE2JPCKEzNnDkz0CGIMCM5J/wtJyeHJ598kpycnECHIsKMtHfC3yTnxoY20AGIwOju7paJeiHK4/EwMDDA4OAgDocDu91+yq8ulwu32z3q4vF4RvysKArDW56duPXZx69TqVRoNBo0Gg1qtdr3vUajobW1lbS0NN/POp0OvV6PXq//xO8NBgNGoxGj0YjJZMJoNKJWyzkdcXoxMTEsXryYmJiYQIciwox8xgp/k5wbG1IYhana2lqmTJkS6DDEWXC5XPT09NDd3U1fXx82m43+/v5RXwcGBgIWo6IoeL3eky4bWlNTg0ajGZPfM1wsDRdKw9+bTCYiIiJOetFqpbkLNy0tLTzyyCP87Gc/IykpKdDhiDAin7HC3yTnxoYcKQgxQSiKQm9vL+3t7XR1ddHd3T3iYrPZzvi5VCoVJpPJV0Cc+PXE74d7ZbRaLRqNBq1WO+oy3Psz/Lwnfv34dV6vF6/Xi8fj8V2Gf964cSOLFi3y9Ua5XC6cTqfv68e/H/55uHdrcHAQp9MJgMPhwOFw0NPTc8Z/E4PBMKpYslqtWK1WIiMjfV9NJtOI1yeCV0NDA3/5y1+47777pDASQghxWirlxHExIaC3t5eoqCh6enqIjIwMdDgTlqIocvAXIF6vl+7ubtra2nyX9vZ22trafAf+p6LX64mOjiYyMhKLxUJERMRJv5pMpgk33Gwscs7j8WC3232XwcHBUV/7+/tHXTwezxn/Dq1We9KCKTIykqioKKKjo4mIiJD/nyBQUlJCcXExe/bsYc6cOYEOR4QR+YwV/iY5d2pnUxtIj1GY2rJlCxdeeGGgwwh5Xq+XtrY2mpqaaGxspKmpiebm5lPuUK1Wq4mLiyM2Npbo6GjfgfjwJZh7M8Yi5zQaja+350wpioLD4RhVLNlsNvr6+ujr66O3t5e+vj76+/txu910dXXR1dV1yufUarUj3pePv1cWiyVo3ychxKcnn7HC3yTnxoYURmHKbrcHOoSQZLfbqa+v5/jx49TV1VFfX3/SIkir1RIfH09CQoLvEh8fT2xs7JjNw5loApVzKpXKNw8pLi7uE+/rdrux2Wy+Qqm3t3fE98Pzu9xuN+3t7bS3t5/0ebRaLVFRUcTGxvouJxa8E603TwgxtuQzVvib5NzYkMIoTCUkJAQ6hJDgdDo5fvw41dXVVFdX09LSwsdHp+r1elJSUkhNTSU1NZWUlBRiY2PD7uA4GHLuxJ6gU/F4PL4i6WSX3t5e3G43HR0ddHR0jHq8Wq0mOjp6RLE0fImJiQm7vBhPUVFRLFmyhKioqECHIsJMMLR3IrRIzo0NmWMUpnp7e+Xvc47a29upqKigsrKSurq6UfNXYmJiyMzMJDMzk4yMDBISEmRYFeGTc8OF0/BwvI6ODjo7O30Xt9t9ysdqNBpiY2N9PYjDX+Pj49HpdH58FaEjXPJOTCySd8LfJOdOTeYYidPasWMHK1euDHQYQUFRFOrr6ykvL6eiomJUL0BUVBS5ubnk5uaSnZ2N1WoNUKQTW7jknEajISYm5qR75yiKQl9f36hiafjicrl8C3KcSKVSERUVNaJYGh6CaTKZ/PXSgo7L5eLtt9/m2muvlcJS+FW4tHdi4pCcGxtSGAlxEoqi0NLSQllZGQcOHBixLLRGoyEnJ4f8/HwmTZpEbGys9AiJM6JSqXwr3OXk5Iy4TVEUenp6RqxSODyPaWBgwDdUr6qqasTjIiMjSUxMJCkpyfc1Pj5e9m0CysrK+PznPy+r0gkhhDgj8skZpoqKigIdwoQ0MDDA/v37KSkpobW11Xe9wWCgoKCAgoICJk+ejMFgCGCUwUly7pOpVCrf/Ka8vLwRt/X39/uKpBMLp56eHt8CEScWTMOrGw4XSsNFU3R0tBTxQviBtHfC3yTnxoYURmFqYGAg0CFMGIqiUFdXx+7duzl06JBvDohWqyUvL4/p06eTl5cnQ3E+Jcm5cze8RHlWVtaI6x0OB62trbS0tIz4Ojg46BuSd/DgQd/99Xo9ycnJpKSk+L4mJCSE7EqIQgSKtHfC3yTnxoYURmGqurp61FnpcOP1eikvL2f79u00NDT4rk9OTqa4uJjp06djNBoDGGFokZwbewaDgYyMDDIyMnzXDc9j+nixNLyB8PHjxzl+/Ljv/hqNhsTERFJSUnwFU1JSEnq9PhAvSYiQIO2d8DfJubEhhZEIOx6Ph71797J161a6u7uBod6hGTNmMHfuXFJSUmS4kQhaJ85jOvFD0uPx0NHRQXNzM01NTb7Nhu12u+/nE58jPj7e16uUlpZGSkqKFEtCCCFCmizXHabcbnfYTc72er3s37+fzZs309XVBYDZbGbevHmcd955REREBDjC0BaOOTfRKYpCd3e3r0gaLpBsNtuo+6pUKhISEkhLSyMtLY3U1FSSkpIm9DA8j8dDT08PUVFREzpOEXqkvRP+Jjl3amdTG0hhFKa2bt3KokWLAh2G31RXV/P222/7lkG2WCwsXryYOXPmyNwhPwm3nAtmNpvNVyQ1NjbS2NhIb2/vqPtptVqSk5NJTU31FUxxcXETqsdV8k4EguSd8DfJuVOTfYzEafX39wc6BL/o7u5m7dq1lJeXA2AymVi0aBHz5s2TgsjPwiXnQoHFYiEvL2/EULy+vj4aGhpobGykoaGBhoYG7HY79fX11NfX++5nMBhIS0vzzX1KT08P2Fy9yspKHnzwQV588UUZey/8Sto74W+Sc2NDCqMwFRsbG+gQxpWiKOzcuZP169fjcrlQq9Wcd955LFu2TBZUCJBQz7lQZ7VamTJlClOmTAGG/sc6OztHFEpNTU04HA6qq6uprq72PTYhIWFEoRQfH++XXqW+vj5KSkro6+sb998lxImkvRP+Jjk3NqQwClPTpk0LdAjjpquri9dee41jx44BkJ2dzeWXX05iYmJgAwtzoZxz4UilUhEXF0dcXBzTp08Hhubxtba2Ul9fT11dHXV1dXR2dvqWDi8pKQGGem7T09N9xVJaWpos7CBCirR3wt8k58aGFEZhauvWraxcuTLQYYy58vJy/v3vf+NwONDpdKxYsYK5c+dOqDkP4SpUc058RK1Wk5ycTHJyMnPnzgWGhnecWCg1NjYyODhIZWUllZWVwFCRlZycTFZWFllZWWRmZspiKCKoSXsn/E1ybmxIYSRCgtfrZcOGDWzduhWAjIwMrrnmGulaFiLAIiIiKCgooKCgABhaKa6lpcVXKNXX1/tWxmtqauL9998HID4+fkShFB0dHcBXIYQQIhxIYRSmpk6dGugQxozL5eLll1/myJEjACxYsIDly5fL8rwTTCjlnDh3Go2G1NRUUlNTOf/884GhFYOOHz9ObW0tx48fp6Wlhfb2dtrb29mzZw8AUVFRviIpKyvrjOYpZWRk8NOf/nTEBrhC+IO0d8LfJOfGhhRGYcrtdgc6hDExMDDAP/7xD+rr69FqtVx99dUUFRUFOixxEqGSc2LsRUZGUlRU5PvfHRwcHFEoNTY20tPTw/79+9m/fz8wtAdZdnY2OTk55OTknHSZ8ISEBG6++WYSEhL8/ppEeJP2Tvib5NzYkMIoTFVWVpKbmxvoMD6VgYEBnn32WVpbWzGZTNx0001yZngCC4WcE/5hMplGDL9zOp3U19f7CqW6ujoGBgY4dOgQhw4dAoaWGB8ukrKzs4mJiaGrq4vHH3+c7373uzKsVviVtHfC3yTnxoYURiIo2e12nn/+eVpbW7Fardx2221yVliIEKXX68nNzfV96Hs8HhoaGjh27Bg1NTXU1dVhs9koKyujrKwMGBp65/V6+c1vfsNnPvMZLrzwwkC+BCGEEEFApSiKEuggxtLZ7G4bzhwOBwaDIdBhnBOPx8Pf/vY3amtrMZvN3HnnncTHxwc6LHEawZxzYmJzu93U19dTU1NDTU0N9fX1eL1empqaePLJJ7nnnnsoLCwkJyfHV2CZTKZAhy1CmLR3wt8k507tbGoD6TEKUyUlJSxYsCDQYZyTNWvWUFtbi8Fg4NZbb5WiKEgEc86JiU2r1ZKdnU12djbLli3D6XRSV1fHunXrfPfp7Oyks7OTPXv2oFKpSE1NZdKkSeTm5pKRkSGLtYgxJe2d8DfJubEhhVGY6u3tDXQI56S0tJRdu3ahUqm4/vrrSUlJCXRI4gwFa86J4KPX65k0aZJv1bs77riDuLg4qqurqa6uprW1lYaGBhoaGtiyZQt6vZ7s7GxfoXQmK94J8UmkvRP+Jjk3NqQwClNRUVGBDuGsdXV18dZbbwGwdOlS8vLyAhyROBvnmnNuN/T2Qk8PdHV56ehw09fnZXBQYXBw6KvdrjA4qOBwgMOh4PUqKMrQga3XOzRaeHjQsEoFer0Kvf6jrzodGAwq9HoVZrOKyEgNkZFqIiM1WK1qrFYVEREQEQHSsRA8IiIiKCoqIjY2lvz8fPLz84GhA4jq6mqOHj1KdXU1/f39HDlyxLfkf2RkpK9Iys3Nlc1mxVkLxs9YEdwk58aGzDEKU3a7HaPRGOgwzpiiKDz33HNUV1eTlZXFHXfcIWd0g8yJOTc4CMeOKRw96qK+3kVjo4emJoWWFmhrU9HerqG7W4vNpsFun1jnbwwGN1arm8hID5GRHqKiFKKjFWJiFGJiVMTFqUhJ0ZCaqiUtTUdKiobYWFCrAx15eDpdW6coCi0tLb4iqba2dsSyt8PD7vLy8pg8eTKpqamo5c0UpxFsn7Ei+EnOndrZ1AZSGIWptWvXsnLlykCHccb279/PK6+8glar5f777ycuLi7QIYnT6OqCAwc8lJTYOXzYQ0lJB/39CTQ26unq0p/18+l0bsxmF2azC6PRjV7vRa9XMBi86HQffdXrQaVSRhQiKtVHF68XXC4VLpcKt5sPv6pwudS4XCocDjV2uwaHY6goczi02O0aFOXcD4bVaoWoKCcxMW7i4jwkJXlJS1PIzFSTna0hJ8dAdraWpCSVFFBj7GzbOpfLxfHjx32FUnNz84jbzWYzkydP9l3MZvNYhyxCQLB9xorgJzl3arL4gggpHo+HjRs3AnDhhRdKUTTBuN1w4ICX996zs2ePi8OH1VRV6enoMAAaYHgY0sjGyGRyER/fT2ysk9hYF/HxHhITFZKSIDlZRWKimthYDbGxGuLitERE6NDpdOh0EX6ZKO/1evF4PLjdblwuOzabm54eD11dHjo6vHR0eOjsVOjsVOjuVtHdDT09arq7NXR1aenq0tPba8BmM+D1qujqMtDVZaC6+tS/U6v1EhfnICnJTWqqh9RUhYwMNXl5WqZM0ZOfr8NqHfeXHjJKSkq49NJL2bNnD3PmzDmjx+h0OiZNmsSkSZMA6Ovro6qqisrKSqqrqxkYGPBtNKtSqUhLSxvRmyQ92UIIEbykMApTw2Ptg8HevXvp6urCYrH4JlOLwGlshLVr7Wzb5mTPHjWHD5uw2zXA6DPncXH9ZGTYyM52EBvbw/TpkeTmqsnP15OaaiYiwjphVwNTq9Wo1Wp0Oh0mE0RGQmrqmT9eURScTic2Wy+NjU4aGlw0NXk/vEBjo4qWFi2trXra2410d5twu9W0tJhoaYH9+0/+vNHRTtLSHGRmepg0SWHyZA1TpuiZOtVAWppK5kCNMavVyuzZs5k9ezYej4f6+noqKyupqqqiubmZ+vp66uvr2bhxIxEREUyePNlXKMmwlvAVTJ+xIjRIzo2NcS+MHnvsMX7zm9/Q1NREYWEhv//971m8ePFJ77tp0yaWLVs26vry8nKmTJky3qGGlWAZI68oCjt27ABg8eLF6PVnPwRLfDrt7bBunYO333by3ntaamtNgPHDyxCTyUVubhfTpg0ydaqX6dM1zJplJC3NitGYiEqlora2lqysrIC9Dn9TqVQYDAYMBgNxcTB9+qnv63a76e21ceyYnWPHXNTWeqirU2hqUtPYqKGpyUBzs5m+PiPd3Xq6u/UcPDj6ebRaDykpDrKzXeTneyksVDNzppEZM/TEx0tPxqel0WjIysoiKyuL5cuX09vbO6I3qb+/n3379rFv3z7UajVZWVkUFBSQn59PbGxsoMMXfhQsn7EidEjOjY1xLYz++c9/8rWvfY3HHnuMhQsX8sQTT3DZZZdx6NAhMjMzT/m4ioqKEWMAExISxjPMsHT48OGgOEg9duwYHR0dGAwGZs+eHehwwsaRIwr/+Mcgr74K+/ebAcOHl6H5O9nZXUyf3secOV4uuEBLcbGV6Oj4T2yYgyXnAkGr1RIbayU21sqpRnw5nU4aG7s4fNjBkSNujh5VOHZMTX29jqYmE62tZtxuDXV1Zurq4L33Rj4+KspJdradvDwPU6aomDFDz6xZBnJzNdLLdI4iIyOZM2cOc+bMwePxUFdXR2VlJUeOHKGtrc234eyaNWtISEigoKCAgoIC0tLS5CAmxEl7J/xNcm5sjGth9Mgjj3DXXXdx9913A/D73/+etWvX8uc//5lf/vKXp3xcYmIi0dHR4xmaCBJ79+4FYPr06dJbNM4qKhSefHKA115Tc/SoiROHxmVkdDNnTg9Llni49FITeXnx6HRyBtyfhvba0ZOdDZdeOvI2r9eLzTZIZeUA5eUuyss9HDmiorpaz/HjEbS3R9DTo2ffPj379o18rE7nITPTztSpTmbMUFFcrGfePCNpaWpkusyZ02g0vk1mL7nkEjo7Ozly5AgVFRXU1tbS1tZGW1sbW7duxWw2k5+fT0FBAZMmTZK2TQghJohxW5XO6XRiNpt5+eWXueaaa3zXP/jgg5SWlrJ58+ZRjxkeSpednY3dbmfatGn88Ic/POnwumEOhwOHw+H7ube3l4yMDFmV7jT6+/sn/N4cXq+XX//619jtdu68885P7GUU58Zmg+efd/CXv7gpKfkoHzQaL4WFbVxyiY0bbtAza1YiBoPhU/2uYMi5UOTxeGhu7mP/fjsHDrg4fFhFVZWW2lojjY1WXK6TdxdFRjrJy7NTWOhh1iwN8+YZmT1bTzAtwma32zly5Aj5+fkBne8zODhIVVUVFRUVVFVVYbfbfbdpNBpycnIoKChgypQpWGV1jZAg7Z3wN8m5U5sQq9K1t7fj8XhISkoacX1SUtKo5U+HpaSk8OSTT1JcXIzD4eC5557j4osvZtOmTSxZsuSkj/nlL3/JT37yk1HXr1+/noiICC666CJ27tyJzWYjJiaGwsJCtm7dCsCUKVPwer2+Tf0uvPBCSktLfX+4OXPmsGnTJgDy8vLQarWUl5cDsGjRIg4dOkRnZycRERHMnz+fd999F4Dc3FzMZjMHDhwAYMGCBVRVVdHW1obRaGTJkiWsW7cOgKysLKKjo9n34WncefPmcfz4cZqbm9HpdFx00UWsW7cORVFIT08nMTGRkpISAIqLi2lubqahoQG1Ws0ll1zCu+++i9vtJiUlhfT0dHbt2gXArFmz6Ozs5Pjx4wDExMQwMDCAw+EgMTGR3Nxc3n//fWCod8Zms1FTUwPA8uXL2b59OwMDA8TFxTFlyhS2bdsGwLRp03A6nVRVVQGwbNkydu/eTV9fH9HR0cyYMYMtW7YAUFBQAAwNlQRYsmQJ+/fvp7u7G6vVyty5c32rz02ePJm2tjYOHDiAwWAgOjqa3bt309HRgdls5oILLmD9+vUA5OTkYLFYKCsrA2D+/Pm+3e0NBgNLly5l7dq1AGRmZhIbG0tpaSkA5513HvX19TQ1NaHVarn44ot555138Hq9pKWlkZyczJ49ewCYM2cOra2t1NfXo1KpWLFiBRs2bMDlcpGcnExmZiY7d+4EYObMmXR3d1NbWwvAihUr2LJlC3a7nYSEBCZPnuybO1VUVMTAwADVHy5XdvHFF/P+++/T399PbGws06ZN8+Xs1KlTcbvdVFZWAkMb3ZaUlPj+6WfNmuU76ZCfn49arebw4cO+nD148CBdXV10d8fx1ltTeOklPQ7H0DA5lcpLfn41CxbU8l//lYWidNLV1YXTaUanS/P9DbOzs4mMjGT/h6sDnH/++Rw7doyWlhb0ej3Lli3z3TcjI4P4+Hj27t1Lc3MzV1xxBY2NjTQ2NqLRaFi+fDnr16/H4/GQmppKamoqu3fvBmD27Nm0t7dTV1cHwMqVK9m4cSNOp5OkpCSys7P54IMPAJgxYwa9vb0cO3YMgEsuuYRt27YxMDBAfHw8+fn5bN++HYDCwkLsdjtHjx4FCJs2QqtV861vfdRGJCcnExMTz+uv7+PoUSNdXRmUl2upqbHS3h5Lb6+ePXv07NkDf/sbwNAwyoSEbgoKXBQU9JOW1sTUqYNcf/3SgLQRer2eQ4cOAbBw4UIOHz48oo3YvHkzzc3NGI3GCdFGXHzxxZjNZtra2tDpdBw4cIDjx49TUVFBZWUljz32mO9vctFFF/n+dwPRRlgsFubNm8eGDRsAmDRpEkajkYMfTm674IILOHLkCO3t7ZjNZhYuXMg777zzqdoIgLlz54ZEG1FRUUF+fn5QtRFnexyxcuVKNm3aNKGPI07XRoTSccTevXvR6XSAtBEfbyOG4z8T49Zj1NjYSFpaGtu3b2fBggW+63/+85/z3HPPnXGQV155JSqVitdff/2kt0uP0bkJhvXuN2/ezMaNGyksLOSGG24IdDghoaTEy49/PMhbb5nxeofGSSUn9/KZz7Ry9916iotTfA3rWAuGnBNDPbXt7f3s3t1PSYmLAwfUVFToqa620tt78l6XpKRBCgsdFBcrXHCBgUWLTBNisYeamhruuusunnrqKXJycgIdziiKotDe3k5FRQWHDx+mvr5+xO3JyclMmTKFqVOnkpiYKEuBBxFp74S/Sc6d2oToMYqPj0ej0YzqHWptbR3Vi/RJ5s+fz/PPP3/K24dXfRJnx2KxBDqE02ppaQEgLS0twJEEvwMHFB58cJANG8wM7ys0e3YT997bxxe+kEJk5ORxjyEYck4MrWyUmGjl8sutXH75R9e7XC4qKzvYudPO3r1eysq0HD5soanJ+uES4yY+PHEIQHLy6GIpLs6/B/ZdXV1s3LiRrq6uCVkYqVQqEhISSEhIYNGiRfT29nL48GHKy8upra2lubmZ5uZmNm3aRGxsLFOnTmXq1KmkpaVJkTTBSXsn/E1ybmyMW2Gk1+spLi7mnXfeGTHH6J133uGqq6464+fZu3cvKSkp4xFiWJs3b16gQzit4cLobAppMVJbG3z964O88IIRr9eMSuVl0aIGHnzQwRVXZGAw+O9/KxhyTpyaTqdj2rQ4pk2DO+4Yum5oX58utm0b5IMPPOzfr+Pw4Qiam600N5tobjbx4cggADIyBpg928EFF6i5+GIzs2frZEW8E0RGRjJv3jzmzZvHwMAAR44coby8nKNHj9LZ2cm2bdvYtm0bVquVKVOmMG3aNLKysmSFuwlI2jvhb5JzY2NcV6V76KGHuPXWW5k7dy4LFizgySef5Pjx49x3330AfO9736OhoYG/fTiA/fe//z3Z2dkUFhbidDp5/vnnWb16NatXrx7PMMPShg0bJnSXq6IodHd3AxAXFxfYYIKQosBTT7n45jehp8cEwPnnN/CjH9lZvjxz3IbLfZKJnnPi7A3t6xNDVlYMN900dN3QstXdbN3az65dXvbt03H4sIWWFsuHS4mbGR4ZbTK5KSzs57zzvCxZouPiiyNISJCeEACz2cysWbOYNWsWDoeDqqoqysvLqayspK+vj127drFr1y4sFgtTp06lsLCQzMxMKZImCGnvhL9Jzo2NcS2MPve5z9HR0cFPf/pTmpqaKCoq4j//+Y9vnfWmpibfJD4YWsnum9/8Jg0NDZhMJgoLC3nrrbe4/MTxHCIsOJ1OPB4PgKyycpba2uALX7Dz7rtD80EyM7v5f/+vmVtvzZFhp2LcDS1bHU12djS33DJ0ncfjoaami40bB9i+XWHvXgNHjkQzOKhj9+4odu+GP/956L5paUO9SgsXqlm+XHqVYGjIeGFhIYWFhbjdbmpqajh06BCHDx/GZrONKJKmTZvGtGnTpEgSQohzMG6LLwTK2UywCmdVVVVMnjz+80rOVVdXF3/4wx/Q6XT84Ac/CHQ4QWPDBg833uilo0OHTufhlluq+PnP40hJiQ90aBM+54R/9ffb2bGjh82bnezapaWsLILGxtFttsXiYvbsARYvVlixwsgFFxg50w7PpqYmfvnLX/K9730vJIdkDxWcNRw8eJDDhw8zODjou224SCosLCQjI0OKJD+T9k74m+TcqZ1NbSCFUZiqr68nPT090GGcUnt7O6tWrcJoNPLd73430OEEhT//2cUDD2jweNSkpfXw2982cv31+WgmyOn2iZ5zIrC8Xi81NT0jepUOH47Gbh9ZBRmNbmbO7GfhQi8rVhi58EITH9+iqLIS+vqGvm9pafHNU7RaIS/PH6/G/zweD9XV1b4i6cS9kqxW64giSRZuGH/S3gl/k5w7tQmxKp2Y2A4ePDih/4GGD+a9Xm+AI5n4FAW+/30n//M/egAWLqzjr3/VMmnS1ABHNtJEzzkRWGq1mkmTYpg0KYa77x66rq9vkM2bO3j3XRcffKBn//5o+vsNfPBBFB98AI88Alqth6KifhYudHPJJUYyM03MmXPigf/IxVuOHAnN4kij0ZCXl0deXt6oIqmvr48PPviADz74gKioKIqKipg+fTpJSUlSJI0Tae+Ev0nOjQ0pjMSENDzsw+PxoCiKfHifgqLAN7/p4JFHhuYO3XRTBX/6UzLR0VEBjkyIT89qNXHFFSauuGLo58FBB9u2tfDuuy527NBSWhpFT4+J0tJISkvhT38CtdoLqHj+eZh6wrmB8nK45ZaPepJC2YlFktvtHlEk9fT0+Fa3S0hIYPr06UyfPp2YmJhAhy2EEAEnhVGYuuCCCwIdwicym83AUGHkcDgwfnysjADgV79y+Yqie+45xO9/n4PJZApwVCc30XNOTHwmk4Hly5NYvnzoZ6fTxe7dbaxb52DbNg2lpZG0tw8t1jJ1KsyZM/o5wq0TWqvVkp+fT35+/od7UVVSVlbGkSNHaGtrY8OGDWzYsIH09HSmT59OYWGh7IcyBqS9E/4mOTc2pDAKU0eOHKG4uDjQYZySTqfDZDIxODhIT0+PFEYn8a9/efje94bmX9x558QuimDi55wIPnq9jgsuSGD4eMDtdvOPf3Rx++2n7v1YtszFwoX9XHKJiquusjB58sSYg+cPQ3tRDa1aZ7fbKS8vp6ysjJqaGurr66mvr2fNmjXk5uYyffp0pk6dKitZniNp74S/Sc6NDSmMwlR7e3ugQzitqKgoX2Ekm7yOVFsLX/zi0Lopl112lN/9LmNCF0UQHDkngptWq6Wo6JOHhNlsOtaujWbtWvjmNyElZZDFi+1ceaWOz342gsjI8Bi2azQamT17NrNnz6avr4+DBw9SVlZGQ0MDR48e5ejRo7z55psUFBQwc+ZMJk+eLCvbnQVp74S/Sc6NDSmMwtTwULWJLCEhgebmZpqbm8nPzw90OBOGosAXvuDAZjOQl9fO44+biYy0Bjqs0wqGnBOho7z85D//8pdtVFU52L7dxJEjMTQ1mXjpJRMvvQRarZeZM/tYscLL9ddHMHu2jnCY3mi1Wpk/fz7z58+ns7OTsrIyysrKaG9v5+DBgxw8eJCIiAhmzJjBzJkzSU5ODnTIE560d8LfJOfGhizXHaa8Xu+EP/u3Y8cO1q5dy9SpU/nc5z4X6HAmjOee83LbbWoMBjcvv1zBlVcWBjqkMxIMOSeCX2UlfNJ5lOFV6RRFobGxj7fe6mPtWti+PZLm5pEnGBIS7CxZMsiVV2q5+moLUVFhUCV9SFEUmpub2bdvH2VlZfT39/tuS0pKYubMmUyfPh2rdeKflAkEae+Ev0nOnZrsYySF0WmtXbuWlStXBjqMT1RbW8szzzyD1WrloYcekpXpAIcDsrJctLTouOWWcp5+ejK6M93tMsCCIedEaDhxH6Pt27f7JiV/0j5GHo+HnTs7+fe/7WzYYGD//liczo8GVWg0XmbMsHHJJR6uvz6CuXP1YdGbBEN/m6qqKvbt20dFRQUejwcAlUrF5MmTmTlzJgUFBUHTFvmDtHfC3yTnTk32MRIhITU1Fa1WS19fH21tbSQmJgY6pIB77jkPLS06YmIG+MEPTHIgIsRJDBc/+/fv54c/vJwtW7YwY8aMT3yMRqNhwYIEFiwY+rm9vZ833mjlP/9R2LYtkqYmK3v3RrJ3L/z61xAfb2f58kFuuMHAZz5jJpTXKNBoNBQUFFBQUMDg4CAHDx5k37591NXVUVlZSWVlJQaDgcLCQmbNmiWbyAohgpYURmEqOzs70CGclk6nIycnx/fBK4UR/Pa3bkDD1VfXkJc3JdDhnJVgyDkRWtxuNz09Pbjd7rN+bHx8BF/8YgRf/OJQj8nu3W289pqdd981sG9fLO3tRl580ciLL4LJ5GbhQhtXX63mxhstJCSE7nAWk8nE3LlzmTt3Lh0dHezfv599+/bR3d1NSUkJJSUlxMfHM3v2bGbOnBm2S39Leyf8TXJubEhhFKaCZZjh5MmTqays5MiRIyxcuDDQ4QTUwYNw+LABjcbDPfdo0WiCa5nhYMk5IT5Oo9Fw/vkJnH/+0M8dHf28+moLb7wB770XQ1eXmfXro1m/Hr76VYWZM3v5zGe8fOELEUybFrq9unFxcSxbtoylS5dSW1vLvn37OHjwIO3t7bzzzju8++675OXlMWfOHPLy8sJq/oO0d8LfJOfGRvi0UmKE/fv3BzqEMzJlylCvSG1tLd3d3YENJsBefnnorPfMmc0UFaUFOJqzFyw5J8TpxMVFcPfdabz2WhrNzTreeKOZL36xnuzsbrxeFXv3RvLww9EUFurIzu7n3nu72LjRwYdTc0KOSqUiOzubq666im984xt89rOfJSMjA6/XS0VFBS+88AKPPPII69evp6OjI9Dh+oW0d8LfJOfGhvQYiQktKiqKnJwcampqKCsrY/HixYEOKWA2bHADWubP78ZiCb7CKBwoioLb7cbj8eByufB4PLjdbt91Xq8XRVHwer2+y+nWv1Gr1ajValQqle/74YtGo0Gj0aDVakd9L3M8/EOv13HFFclcccXQkLvS0nZeemmQdeuMlJXFUVsbwZNPRvDkkxAT42DlygFuucXIypUmtCH4CWwwGJgzZw5z5syhra2NvXv3sm/fPmw2G1u3bmXr1q1kZWUxe/Zspk2bhl6vD3TIQgjhI6vShanu7m6io6MDHcYZKS0t5d///jdxcXF85StfCcsDPkWByEgPNpuG558/wM03FwU6pLMWTDn3SdxuN4ODg9jtdhwOB06nE6fTicPhwOVy4fV6Ax0iMLTZqU6n810+/rNer0ev14d0EWWz2Xyr0vl7rouiKBw/3sO//mXjrbc07NwZR3//R0VAVJST5cv7uflmA1dcYSaU11HxeDwcOXKEvXv3UllZ6TsZYDAYKCoqYu7cuaSkpAQ4yrEVKu2dCB6Sc6cmy3VLYXRapaWlzJo1K9BhnBGHw8EjjzyCw+Hg5ptvJu9U6+2GsLY2GF57oqyskqKi4PsbBFPOwdCBrcPhwGazMTAwwODgIIODgzidztM+drjn5sSLRqM5ae+PSqXyFSYfb44VRfH1MH28p8nr9fp6pDwej+/7sy3MNBqNr0gavhgMBgwGA0ajEa1WG9SF00TJu56eQVav7mT1ahXvvRdHX99Hy9hZLC4uvtjGF76g5+qrI0J6hbve3l727dvH3r176ezs9F2flpZGcXExRUVFIdGLNFHyToQPyblTk+W6xWm1tLQEOoQzZjAYKC4uZvv27ezYsSMsC6Pa2qGv0dGDxMQE5+7WEz3nFEXBbrfT09NDX18fNpsNl8t10vsOFw3DBcSJRYVOpwvoJPPhgsnlcuFyuXC73b7vh392Op2+nz0ej6/oOxmNRjOiUBr+ajKZJnzRVF9fz09/+lMeffRR0tPTAxpLVJSJO+9M4847wWaz8+9/N/Kvfyls2hRLT4+J116L4bXXwGRysWxZP5//vJbrrovAbJ64f99zERkZyeLFi1m0aBHHjh1jz549lJeX09DQQENDA2vXrmXmzJkUFxeTlJQU6HDP2URv70TokZwbG1IYhalgOyM3b948duzYQXV1NU1NTSE37OJ0hjerjIhwBt17N2wixq0oCn19fXR2dtLd3T2qN0itVmM2m7FYLJhMJt9lIq8IONwTdSZ7XHk8Ht9QwBMvDofDN0zQ4/EwMDDAwMDAqMdrtVpfkTR8MRqN6PX6CVEwtba28uqrr/LDH/4w4IXRiSwWI7fcksott8DAgIM33mjk5ZcVNm6MobPTzH/+E81//gP33ONm2TIbt92m49prI5iA/0LnTKVSkZOTQ05ODv39/ZSWlrJnzx46OzvZuXMnO3fuJDMzk+LiYqZNmxZ0e7ZNxPZOhDbJubEhQ+lE0PjXv/7FgQMHKCgo4Atf+EKgw/GrtWvh0kshK6uLkhKF2NjYQIcU1AYGBmhra6Ozs3NEr5BarcZqtRIZGYnVasVsNofVEsMf5/V6fUWSw+Hwzasa/nqqjw+NRoPJZMJsNvu+ms1mvxeUJSUlFBcXs2fPHubMmePX330uHA4Xb73Vzssve3j33Rja2iJ8t1ksTi67rJ877hhauGEC1+bnTFEUqqur2bNnD4cPH/YNCzWZTMyaNYvi4mLi4+MDHKUQItjIUDpxWmvXrmXlypWBDuOsLF26lIMHD1JRUUF9ff2EOgM83ozGoa8ulwaPxxHYYM5RoHNOURS6urpoaWmhb7gLjqFej5iYGGJjY7FYLBO6N8jf1Gq1ryfo4zweDw6Hw7cQxfBwPLvdjsfjwWazYbPZfPdXqVQYDAZfsRQREUFERETQ9QSMJ4NBx7XXpnDtteB0unj77Sb+/ncv77wTS3e3iZdf1vPyyxAXZ+fKKwe5804TixYZmQCdc2NCpVIxadIkJk2aRF9fH3v37mXPnj309PSwY8cOduzYQU5ODvPmzaOgoGBCn7QIdHsnwo/k3NiQwkgEjfj4eGbOnElpaSnvvvsut91224QYruMPMTFDX202PS6X7ZPvLEZQFIXOzk4aGxt982hUKhUxMTHEx8cTGRk5oQ+wJiqNRuPrCTrRcC/T8PC74YUrnE4ndrsdu90+4v4Gg8FXJA1fpDgdWgb8qqtSuOqqoeF2r7zSyAsvwObN8XR0GHn2WSPPPgtpaQNcc42Du++OYObM0BlKY7VaWbJkCYsWLaKqqoo9e/Zw5MgRampqqKmpISoqivPOO485c+aMykEhhDhXUhiFqYyMjECHcE6WLl1KWVkZNTU1VFRU+DaADXWpqUNfe3uNtLcP+H4OJoHIuf7+fmpra309F1qtlqSkJBISEmQ89jg5sZcpLi7Od73L5RpRLA0MDPiG5DkcDt8KZSqVCqPR6CuShud3nUvxGh8fz+c///mgH35lNht8c5K6uwd54YV6XnpJy/bt8TQ0mFm1ysyqVTB5so3Pfc7NffdZSU8PjeJSrVaTn59Pfn4+PT097Nq1i5KSEnp6eli/fj2bNm2iqKiIefPmkTqBGsZg/YwVwUtybmzIHKMw1draSuLw+s9B5t133+W9994jOjqaL3/5y2EzFCcmxk13t5a//72cm26aGuhwzpo/c87r9VJfX09LSwuKoqDRaEhOTiYpKQltKO6qGaQ8Hg/9/f0jLg7H6KGiarUai8WC1WrFYrGc1ZDHYG7rTqepqY/nnuth9Wo9e/bE4/EMFY9qtcL8+b3ccYeam2+2hNzKdm63mwMHDrBz504aGxt912dkZDBv3jymTZsW8F7HUM47MTFJzp2a7GMkhdFpBfNYVKfTyapVq+jt7WXp0qUsXbo00CH5xfz5g3zwgYmHHirnt78NvsLIXznncDg4evSor5coLi6OjIwM6SEKEi6Xy1ck2Ww2+vv7cbvdI+6jUql8qwUOF0sne38HBgZ4+umnufPOO0N6uNXQogU9PPNMH6tXmzl8+KOeuogIF5dfbuO++8wsW2YImflIMPS6Gxoa+OCDDzh06BAejwcAi8VCcXExc+fOxWq1BiS2YP6MFcFJcu7UZPEFEdL0ej0rV67k5ZdfZuvWrRQWFpKQkBDosMbdkiVqPvgAdu0y4fF4An5GdCIaGBigoqICl8uFVqslJyeHmOEJWiIo6HQ6oqOjfTu4D+8vNby3VF9fHw6Hw1c8De/dYTAYfCsKRkZGotfrOXz4MA888AAXXHBBUKxKd66GFi2I5uGHo/npT73s2NHK//2fkzffjKG9PYKXX47h5ZchPX2Az33Oyf33W5g0Kfg//lUqFenp6aSnp7Ny5Ur27NnD7t276evrY/Pmzbz33nsUFRUxf/78CTXMTggxcUmPUZjq6OgYMf4/2CiKwt///neqqqpIT0/nzjvvDPkJ9OvWKaxcqSI6epCDB3tITU0OdEhnZbxzrr+/n4qKCtxuN2azmby8PAwGw7j9PhE4TqdzRKE0ODg4aulwk8lEbW0tV155JR988AHz5s0LULSBY7c7+de/2njuORWbNyfgcHw07Li4uJfbb4c77rBitYZON5LH4+Hw4cN88MEHHD9+3Hd9VlYWCxYsID8/3y+fFcH+GSuCj+TcqZ1NbRDaR5LilE4clx2MVCoVn/3sZzEYDNTX1/P+++8HOqRxd+GFKsxmD93dJt5+uyvQ4Zy18cw5p9NJZWUlbrcbi8XClClTpCgKYXq9nri4OLKysigqKmL27NkUFBSQkpKCxWJBpVIxODjoW9ChoqKCgwcPUldXR09Pj2/IVagzGvXccksaa9emUlNj52c/q2P69HYA9uyJ5KtfjSQpycONN3bx3nt2QuE0qUajobCwkDvvvJN7772XGTNmoFarqa2t5cUXX2TVqlV88MEHozZzHmvB/hkrgo/k3NiQwihMhcI/UGRkJJdeeikAGzZsoLW1NcARjS+DAS69dOjD/NVX9b7ND4PFeOWcoihUVVXhdDoxm80UFBTIAgthRqvVEhUVRUZGBtOmTWP27Nnk5eX5zp4qikJ/fz9NTU1UVFSwd+9eDh8+TFNTEwMDA6fcqDaUpKRY+eEPM9i3L47du9u55546kpP7GBzU8vLLMSxZYiQ/38YvftFDV1do/D1SUlK49tpr+drXvsbixYsxmUx0dnby9ttv88gjj/DOO+/Q09MzLr87FD5jRXCRnBsbUhiFqVCZnzJr1izy8vJwu928/PLLuFyuQIc0ru64Y2iC+aZN6Rw9GlyN4HjlXGtrKzabDa1Wy+TJk0Mmt8W5G960Ny0tDbPZzNSpU8nNzSUhIQGDwYDX66W3t5e6ujoOHDjAvn37qKmpobOzM+R7k1QqFcXF8TzxRAa1tUb+8Y9GLrqoCZ3OQ1WVhR/8IIrkZC9XXdXNu++GRi9SZGQkF198MV//+tf5zGc+Q1xcHHa7nW3btvGHP/yBf/3rXzQ0NIzp75R2SPib5NzYkDlGIuj19/fz+OOP09fXR3FxMVdeeWWgQxo3Hg9kZDhpatLzne8c5n/+Jzz2cToVt9vNvn378Hg8ZGdny1Kl4rQURcHhcNDT00NPTw+9vb0jel9VKhVWq5WoqCiioqIwmUxhsZF0TU0Pjz3Wyz//GUldXZTv+qysfu64w8WXvxxJQkJonEtVFIXKykp27NhBTU2N7/qsrCwWLlxIXl5eWLznQoQLWa5bCqPTWr9+PcuXLw90GGOmpqaGv/3tbyiKwnXXXcf06dMDHdK4+clPHPz4xwbS07spLVWIiwuOVdfGI+eampqoq6vDbDZTWFgoBzNilNPlncfjwWaz0d3dTU9PD3a7fcTtBoOBqKgoYmJisFqtIb/Ii8vl5o032njySYVNmxJxOIaGpWq1HpYv7+OrXzVw6aWmkFn2u7m5mR07dnDgwAFfb2FiYiILFy6kqKjonM/Ch9pnrJj4JOdOTRZfEKcVasNFcnJyWLx4MQCvv/66bwnfUPTggwYiItzU10fz5JPB8zrHI+fa2toASEpKkqJIjHLo0CHuuusuDh06dMr7aDQaoqKiyMrKYsaMGcyYMYOsrCyio6NRq9U4HA5aW1upqKigtLSUo0ePhvSQO51Oy7XXprBmTSqVlQN8//t15OZ24XZrWLMmmssvN5Gb28///E8Pvb3Bf141OTmZa665hgcffJALLrgAvV5Pa2srr776Ko8++ijvv//+OS3UEKr5ISYuybmxIYVRmArFPR2WLl3KpEmTcLlcvPjiiwwODgY6pHERHQ333Te04eXjjyfT3h4cK9SNdc7Z7XbsdjsqlYrY2NgxfW4RGux2O8ePHx/VC/RJjEYjSUlJ5OfnM3v2bPLz80lMTESn0+F2u+no6KCqqoq9e/dy5MgR2traQnZuY0ZGJD//eQZHjkTy5ptNXHllAwaDi2PHIvje96JITnZz661d7N8/viu8+UNkZCQrVqzgoYce4uKLL8ZisdDT08OaNWv43e9+x8aNG+nv7z/j5wvFz1gxsUnOjQ0ZShemQnW9+8HBQZ588km6urqYNGkSN998c0gOfenshOxsF319Or71rXJ+9aspE77HZKxzrr29nerqaiwWC9OmTRuz5xWho6SkhOLiYvbs2fOpN3hVFAWbzUZXVxfd3d0jii2VSoXFYiE6OprY2NiQXiq+rq6XRx/t4R//iKax0eq7/rzzevnqVzV8/vMRhMKikG63m9LSUrZv3+5b9l2n0zF79mwWLFhw2o2jQ/UzVkxcknOnJkPpxGnt3r070CGMC5PJxOc//3l0Oh1Hjx5lzZo1IbkUb2wsfP/7QxPG//KXbI4caQ5wRKc31jnncDgAMJvNY/q8QpzM8KIMmZmZTJ8+naKiItLT04mIiEBRFPr6+qirq2Pfvn0cOnSI5ubmcd8rJxAyMiL5zW8yqK428swzDcyf34JK5WXXrkhuvTWCtDQ73/lON01NwT2sR6vVMnfuXL7yla9w4403kpqaisvlYufOnfzxj3/klVde8Q3lPZlQ/YwVE5fk3NiQwkiEnKSkJK655hoAdu7cGbKbv3796wYyMhx0d5v4xjfsITuc51SGX69OpwtwJCLcqFQqzGYzqampFBYWMnPmTLKysoiMjESlUmGz2Th+/Dj79u3j8OHDtLS0hNz/p8Gg44470ti+PZFduzq55ZZ6rFYHra1Gfv3raDIz4coru9m2zRHoUD8VtVrNtGnT+NKXvsTtt9/O5MmT8Xq97N+/n8cee4yXX345pOe0ChFuZChdmGptbQ35pY23b9/OunXrUKlU3HjjjUydOjXQIY25tWvdXHqpFpVK4a9/reTWW/MDHdIpjXXOHTt2jNbWVtLS0khLSxuz5xWho7u7m9dff53PfvazREdH++V3Op1Ourq66OjowGaz+a4f7nGKi4sjJiYmJDch7uwc4M9/7uDZZy1UVX001GzWrF6+9S0Nn/tcBKGw1UpTUxNbtmyhvLzcd11BQQFLlizxtUXh8BkrJhbJuVOT5bqlMDqtQ4cOhfy8DEVR+M9//sOuXbvQarXcdtttZGZmBjqsMXf99QOsXm0mJaWX7dsdZGcnBDqkkxrrnDt+/DjNzc0kJyeH5PsqxkYg2zqHw0FnZyednZ0jJu6rVCqio6OJi4vzrX4XSjweD2++2cof/6iwaVMyHs/Q60tLG+DLX3bzwANWLJaJPSfyTLS0tPDee+9x8OBB35DtyZMns2TJEmw2W8h/xoqJJRyO686VzDESp1VXVxfoEMadSqXisssuIz8/H7fbzT/+8Q+amyf+XJyz9cQTZhISHDQ1RXLvvQMTdl7DWOecXq8HPpprJMTHNTc385vf/CZg//cGg4GUlBQKCwuZMWMG6enpmM1mFEWhq6uLqqoqSktLOXbsGH19fSEzH1Kj0XDVVSm8804KJSVd3HxzHRERThoazHz/+5GkpLj4r//qpq4uuOchJSUlcf311/PlL3+ZWbNmoVarqaqq4umnn+app56ipqYmZN5TMfGFw3GdP0hhJEKaWq3mhhtuIDMzE7vdzvPPP09HR0egwxpTcXHw/PMaVCqFdeuy+N//rQ6LD2OTyQQQssuyi0+vsbGRZ599lsbGxkCHgtFoJDU1laKiIqZPn05qaip6vR63201rayvl5eWUlZXR2NgYMsW+SqVixow4nn8+g8pKB9/+dh1JSTZsNj1//nM0ubkqrrqqm127JubJnDMVHx/P1VdfzQMPPEBxcTEajYaWlhb++te/8vTTT1NdHR5tshChQIbSibBgt9t59tlnaW5uJjo6mjvvvDPk8uPBBwd49FEzEREO1qxpYdGi0B5e5na72bt3L4qiMHPmzJBeIlmcm7Fcrns8KIpCb28vHR0ddHV1jdigMTIykri4OGJjY9GEwsScDw0MOHjqqVYef9zEoUPxvuvPO6+X735XyzXXmJngOw+cVk9PD9u3b2fPnj243UN7zmVlZXHRRReRlZUV4OiECD8ylE6c1saNGwMdgl8ZjUZuueUW4uLi6O7u5tlnn6W3tzfQYY2p//1fM0VFA/T3G7jtNiv19Z2BDmmEsc45rVaLxWIBhibZi/GlKAqKouD1en2X4etC7Pya36hUKqKiosjNzWXWrFnk5ub6Vrbr7e2lpqaG0tJSampqsNlsIfF3NpsNPPBABvv3x/Dqq41ceGETavXQct/XXWdmyhQbzzxjwxPEo+yioqIwGo187WtfY/78+Wi1Wmpra3nmmWf429/+JkOexLgIt+O68RJ6y+KIMzJR56GMJ4vFwm233cYzzzxDZ2cnf/3rX7n99ttDpudIp4PXXzcyZ46TmpoYvvCFBtauHcRsNgU6NGB8ci46Opq+vj46OjpISkoa8+cPB8PFjsfj8V1OLIBOLHw+6cBcpVL5Lmq1esT3w5eP/yw+otFoiI+PJz4+HofDQUdHBx0dHQwODtLW1kZbWxtms5mEhATi4uKCflU7jUbD1VenctVVCnv3dvCrXw3y2mvJHDli4c474Uc/GuAb3/Bw//1WPpxOGFScTicWi4VLL72UCy64gPfee4+SkhKqq6uprq4mLy+PpUuXyoqaYsyE43HdeJChdGGqtLSUWbNmBTqMgBjuMeru7iYuLo477rgDq9V6+gcGiY0b3axYocbtVvPFL1byl7/kToihOOORc06nk3379qEoCoWFhURERIzp84ciRVFwu924XC7cbjdutxuv1+v3OIaLI41G47sM/zxWRVN1dTX33nsvTzzxBLm5uWPynP6kKAo2m422tjY6Ozt975NarSYmJobExEQsFguqYB979qGjR7v55S97efHFJPr7h4bGJiTYeeABB1//emRQrWR3svauu7ubLVu2UFpa6nsvCwoKWLZsGcnJyQGIUoSScD6uOx1ZrlsKo9Pq7u72274eE9HHi6PbbruNqKioQIc1Zv74Rztf/aoRgF/84gjf/W5ewA+exivnjh49SkdHB3FxcUyaNGnMnz8UDBdDDocDp9M5qhBSqVQjipOP9/IMX4bve+Lznvj9iZfh3qYTe55OHIb3SU4sljQaDVqtFo1Gc045HCptndvtpqOjg7a2NgYGBnzXm0wmEhMTiY+PnxAnQMZCY2Mfv/pVF889l0BX11CPd1SUk3vuGeC7340kNnbi9zZ+Ut51dnayZcsW30kdgGnTprFs2TISEibmdgti4guVtm48SGEkhdFprV27lpUrVwY6jIA6sTiKiori9ttvJzY2NtBhjZm77urn6acj0Go9PPZYDV/60uSAxjNeOdff38/BgwdRqVQUFhZiNpvH/HcEK0VRcLlcDAwM+CaBw1CPg06nQ6fTfaqi41wNF0cej2fEML7h709muHjTarW+y+nidjqdvPTSS9x4442+5d2DnaIo9Pf3+3qRhv9eGo2GuLg4kpKSfCs2BrvOzgEeeaSd//u/WFpahuYTmkwubrutn//+byupqRO3EDyT9q69vZ3Nmzdz4MABFEVBpVIxe/Zsli5dKscv4qzJcd2pyeILQpyB6OhovvjFLxIXF0dPTw9PP/00ra2tgQ5rzDz5ZAQrV/bjdmt48MEsVq+uDnRI4yIiIoLY2FgURaGuri4kJqiPBY/HQ29vL729vbjdblQqFUajkaioKGJiYrBarRiNRrRard97E9VqNVqtFoPBgMlkwmKx+OKKjY0lKioKi8WCyWRCp9OhVqt9vV52ux2bzUZ3dzednZ309PTQ399/0p6wAwcOcOutt3LgwAG/vr7xpFKpsFgs5OTkMGvWLLKysjCZTHg8HlpbWykrK+Pw4cN0dnYG/f9CbKyZhx/O5OhRHf/zP8fJzOxhcFDHE09Ek5MDd9/dTVNT8K7SEB8fz3XXXcf999/P1KlTURSFkpISHn30Ud555x3ZikCIAJAeozDV1NRESkpKoMOYEGw2G8899xwtLS2YTCZuueWWkJkQa7fDhRf2s3NnBJGRdl59tZWLLgrMMt7jmXN2u52ysjIURWHy5Mkh1fN3LpxOJzabDa/Xi0qlwmQyYTQag3bBg+FheMNzotxut6+H6eOGe5N0Oh1lZWWcd955E3a57rEyvOx3a2sr3d3dvoJIr9eTlJREQkJC0C/WAOBwuHjqqWZ+//sIKiuH/scNBje33mrjpz+1kpIycXqQzqW9q6urY/369dTW1gJDq6kuWrSI888/H51ONx5hihAix3WnJj1G4rRCbanqT8NisXDHHXeQnp7O4OAgzz77LJWVlYEOa0wYjfDOOxFMmTJAb6+Rz38+lh07ArPZ5Xjm3PDmmQC1tbW4XK5x+10Tncvloq+vD6/Xi06nIzo6GrPZHLRFEXw0jM5gMBAREeHrXYqJicFisWA0Gn3za4Z7lfr6+ujp6QGGNgF2Op1B34NyKsPLfufl5TFjxgxSU1PR6XQ4nU7q6uooLS2ltrYWu90e6FA/FYNBx3/9VwYHD0by5JP15OV14nBo+b//iyYnR+Gee7ppafH/QiIncy7tXUZGBnfccQc333wzSUlJ2O121q9fz6OPPsqePXsCskiKCB5yXDc2gveTUnwqx44dC3QIE4rJZOLWW29l0qRJuFwuXnjhBfbs2RPosMZEZCRs3GgiPd1OW5uFq66KYtu2Br/HMd45l5KSgslkwuVyUVtbG7IHwZ/E6/XS19eHoijo9XoiIyNDZkL+xw0XS0ajEYvF4huGZ7VaMZlMI4YI2u12ent7fUPvhudchWKOGAwG0tPTmTlzJrm5uZjNZrxeLy0tLZSVlVFZWenLkWCl02n50pfSOXDAyuOP1zF5chcOh5a//CWarCwv997bTWtrYIuIc23vVCoVeXl53HvvvVxzzTW+LQneeOMN/vSnP3Ho0KGgfu/E+JHjurEhhZEQHzIYDNx0003MmjULr9fLG2+8wYYNG0LiQyg5WcX27XoyMgZpa4vg6qujee89/xdH40mtVpOTk4NaraazszOk5oudqcHBQbxeL1qtFqvVGvCVCP1NrVb7epWio6N9QyYMBgMajWbEYhTd3d10d3djs9lCsjdJrVYTHx9PYWEhBQUFREdHoygKXV1dlJeXU15eTkdHR1C/br1ex733ZnDwoIU//7mOSZOGCqQnn4wmO9vDN77RQ29vcL4+tVrNzJkz+cpXvsKll16K2Wymo6ODl156iaeffpqGhtBqv4WYKGSOUZjyer1BPbRmPCmKwubNm9m0aRMAM2fO5LOf/WxInHmvr/eyaJGD2loTcXH9rF7dxYUXpvvld/sr55qbmzl+/DhqtZopU6ZgsVjG/XdOBMMHvV6vl8jIyJBZhe3T8Hq9OBwODAYDKpUKr9eLy+XC6XTicrlGFAUqlQq9Xo9er/ct+BBqBgcHaWlpob293Tcsy2g0kpKSQlxcXNC/ZqfTxdNPN/PrX1upqYkGhpb5fughO9/6lhWTyb8rL47l39PhcLB9+3a2b9/uGyo8ffp0li9fHlJbTYhzJ8d1pybLdUthdFrvvfceixcvDnQYE1pJSQlvvvkmXq+X3NxcPve5z2EwGAId1qfW2KiwcKGdY8dMxMQM8Pe/d3DZZRnj/nv9lXOKolBVVUVXVxd6vZ6pU6eGxPt2Om63m+7ubt/mn+HWW3Qqp8q74d4jp9M5akU7lUqFTqfDYDCEZJHkcrlobW2ltbXVd5Ct1+tJTk4mISEh6E8C2e1OVq1q5pFHYmhqGtq8OzHRzve/7+LLX7bij3Uoxqu96+3tZcOGDZSWlgJDi41ccMEFLFy4MCzaOXFqclx3arL4gjitEzcIFCc3Z84cvvCFL6DX66muruapp56iq6sr0GF9aqmpKnbsMJKbO0hXl5kbbkjiuedqxn1Ijb9yTqVS+eZWOJ1OKisrT7k3TigZPrAf3pRVwJEjR/jyl7/MkSNHRt023EM0PD9peJGK4SF3TqeTvr4+urq66O3txeFwBPWwsxPpdDrS0tKYMWMGmZmZ6PV6nE4nx48fZ9++fTQ0NIzY9yrYGI16vvnNTI4c0fHDH9YSFzdAa6uRr33NyqRJA/z1r/2M91s5Xu1dZGQkV199Nffccw/Z2dm43W62bNnCH//4R0pKSmSBhjAmx3VjQwqjMBUfHx/oEIJCXl4ed9xxBxaLhdbWVp588klqamoCHdanlpysYudOI7Nm9dPfr+euuzL43e+OjuuHqj9zTqPRkJeXh06nY2BggKqqKjlgCEM2m42ysjJsNtsn3k+lUqHVajGbzURHR/uKJK1WO6JI6uzsxGazjRqGF6w0Gg3JycnMmDGDnJwcjEYjbrebhoYG9u3bR319fVAXSBaLkZ/9LIuKCoUHHzyO1erg+HEzd9wRwcyZNjZudIzb7x7v9i41NZXbb7+dz3/+88TGxmKz2Xj99dd54oknqK4OzT3rxCeT47qxIUPpwlRfXx9WqzXQYQSN3t5e/vnPf9LQ0IBarWblypXMmzcv6M/MDwzAFVfY2LjRgkrl5Tvfqebhh3PGZShNIHLOZrNRUVGBx+MhJiaGyZMnB/17dioylG60kpISiouLP9U+Rm63G6fTicPhGNHzOLx8+PDCDqFAURQ6OztpamrynX0eLp6SkpKCfi+kuroe/vu/e3jhhVQcjqHXsmJFD7//vZmpU8d2nyB/tncej4edO3eyefNm35LsBQUFXHrppcTExPglBhF4clx3ajKUTpzW9u3bAx1CUImMjOSOO+5gxowZeL1e3n77bd54442gPpsKYDbDunUWbrzRhqKo+Z//mcwddxxncHDsz6QGIucsFguTJ09GrVbT1dVFdXV1SJzpPxmNRoNarfZthCrGxok9SVFRUb6Ncj0eDwMDA3R1ddHT0xMSQ+1UKhVxcXEUFhaSl5eH2WzG4/HQ0NDA/v37g36IXUZGFM88k8muXT1cdlk9KpWXdeuimDFDwxe/2ENb29j1KvuzvdNoNCxYsICvfvWrnH/++ajVaioqKvjTn/7Exo0bw3pft3Aix3VjQwojIc6QTqfjmmuuYcWKFahUKkpKSvjrX/962mE6E51WCy++aOGrXx16Hc8/n8OKFR00NfUEOLKxERUVxaRJk1CpVHR0dFBdXR2Sw+qG58wAQb+R50Q0vCDD8Jwkq9WKXq9HpVL5NtXt6uqiv78/6Oe0qVQqYmJiKCwsZPLkyZjNZt8Qu/3799PY2BjUr3H69DjeeiuNNWtamDOnFbdbzbPPRpGT4+FHP+rFMX4j7MaV2Wzmsssu4/777yc3Nxe3283mzZtZtWoV5eXlQV+4C+EPMpQuTNXX15Oe7p9lmkNRVVUV//rXv7Db7URGRnLjjTeGxN/zsccGePBBI263mkmTunj5ZRezZyeOyXMHOuc6Ozs5evQoiqIQExPDpEmTQm61seHhdCqViujo6JAZ4nWu2tvbeeaZZ/jiF784buPvPR4PDodj1FA7nU6HyWRCp9MF/bDG4aXgGxoaGBwcBD5awCE+Pj6o/488Hg/PPtvIz35mpbY2GoC0tEF+9zuFG24wn/PzBrq9UxSF8vJy1q5dS0/P0EmuSZMmcdlll8lclBAV6JybyGS5bimMTquqqorJkycHOoyg1t7ezosvvkh7ezsajYYVK1aExLyj9esdXHedit5ePTExAzz1VDvXXJP5qZ93IuRcd3e3byGGqKgoJk+eHHLFQ29vL06nE51OR2RkZNDn46flr7wbXv7bbrePWJxBo9FgNBoxGo1B/14Mz0Gqr6/H8WG3itFoJD09PejntQ0MOPjVr5p59NFEurtNACxc2Mvjj5soKjr7+UcTob2DoaXZ33vvPbZt24bH40GtVrNgwQKWLFkiy3uHmImScxORzDESp3X06NFAhxD04uPjufvuu5k2bRoej4e3336bf/3rX74DhmC1fLmB3bs15OQM0NVl5vOfT+XHPz76qYfOTISci46OJi8vD7VaTU9PD+Xl5TidzkCHNaYiIiJ8w7vCfUhde3s7q1ator29fdx/1/BQxsjISKKjozGZTL65SP39/SExzG54DtL06dPJyspCp9Nht9upqqqivLyc3t7eQId4zsxmAz/5SRb79zv5/Ofr0Gg8bNsWyaxZGu65p5uenrM7hzwR2jsY6tm76KKL+PKXv0x+fj5er5dt27axatUqysrKZHhdCJkoORfspDAS4lMwGo3ccMMNXHrppajVag4ePMgTTzxBc3NzoEP7VPLyNOzda2LJEhtOp5af/GQSV1/dSGdnf6BD+9SioqKYOnWqbynvQ4cOhdT+DxqNBrN5aAjQwMBAWE+8Pn78OH/4wx84fvy4X3+vRqMhIiKCmJgYIiIi0Gg0eL1eBgcH6e7uxmazBXWBpFarSUpKYsaMGaSlpaHRaLDZbBw+fJgjR44EdUGekRHFP/6RzsaN7cyd24rHo+Yvf4kmJ8fJqlU2gnV6YmxsLDfddBM33XQTsbGx9PX1sXr1av7+97+HxP58QowVGUoXplwuFzrd2C5PGu7q6+t5+eWX6enpQavVcvnllzN79uygHl7i8cB3vmPjt7+1AJCX18k//uFi7tyks36uiZZzDoeDI0eOMDg4iEajYfLkyURFRQU6rDGhKAo2mw2Hw4FarSYyMjLol1o+F2OxXPdYGB5mNzg46CtUh3uYhjeVDWYul4uGhgba2tpQFAW1Wk1iYiKpqalBnXcej4e//KWBn/40hqamoWWQZ83q4+mnjcye/clt2URr707kdrvZtm0b7733Hm63G61Wy9KlS1mwYEHQ52I4m8g5F2gylE6c1s6dOwMdQshJT0/n3nvvJS8vD7fbzeuvv85rr70W1EO1NBr43/+1sHq1HYvFRWVlLBddFMVTTx076yEYEy3nDAYDU6dOJTIyEo/Hw5EjR2hqagqJoSUqlQqLxYJOp8Pr9dLX1xfUPRTBbrgIioqKIioqCr1ej6IoOBwOuru7g/790el0ZGdnU1RURFRUFF6vl+bmZsrKynzFUjDSaDTcd18m5eVa/uu/ajEYXJSWWjnvPA333deDzXbq1zXR2rsTabVaLrzwQu6//35ycnJwu92sX7+eJ554grq6ukCHJ87RRM65YCKFUZgK9iWmJyqz2cxNN93ExRdfjEqlorS0lCeeeILGxsZAh/apXHutkZISNfn5A/T1GfnSlzK5885a+vvPfMjMRMw5rVZLfn4+CQkJKIpCXV0dR49++vlUE4FKpcJqtaLVavF4PPT29obE6wp2w4tinKxAstlsQb2UvMlkoqCggPz8fIxGIy6Xi5qaGg4dOkRfX1+gwztnUVEm/vSnLLZt62b+/GY8HjVPPBHFpEkOXnxx8KSPmYjt3cfFxcVx2223cc0112A2m2ltbeXpp5/mzTffDOrhkOEqGHIuGEhhFKZkN+zxo1KpWLx4MbfffjuRkZF0dHTw1FNPsW3btqA9cwpD845KS81cf30fiqLm2WezmTfPxp49rWf0+Imac2q1muzsbLKzs1Gr1XR2dnLo0CHfssTBTK1WY7Va0Wg0eDweenp6gnqDzrNlsViYM2cOFosl0KGMMlwgRUdH+woku93uW6QhmAuk6OhoioqKyMzMRKPR0N/fT3l5OTU1NUGdf8XFCWzdmsCqVbUkJPTT2mrkC18wsWxZL9XVI086TNT27uNUKhUzZ87kK1/5CrNmzUJRFHbv3s2qVas4ePBgUH9mhZtgybmJTuYYhan+/n4iIiICHUbIGxgY4I033qC8vByAnJwcrrnmmqDPzccf7+ehhwwMDmqJiHDws5818tWvZn7i+PRgyDmbzUZVVRVOpxONRkNOTg6xsbGBDutT83q99Pb24na7fcPswmWp3mDIOxiaH3DiYhlqtRqz2YzBYAjqeYoul4v6+nra2tqAoYIwIyODuLi4oH5dzc19fOMbXfzzn+l4PGqMRjc/+pGdb3/bglodPHn3cceOHeONN96go6MDgIKCAq644gqsVmuAIxOnE6w55w8Tao7RY489Rk5ODkajkeLiYt57771PvP/mzZspLi7GaDSSm5vL448/Pt4hhqWtW7cGOoSwYDabufHGG/nsZz+LTqejpqaGP//5zxw6dCjQoX0q990XQUkJTJ3aT3+/gYceyuHqqxtpazt1V34w5JzFYqGwsBCr1YrH46GqqoqampqgH4I2vADDcM9EX18fAwMDIX822Ov1smHDhqDofRnuQRpeKMPr9WKz2ejp6QnqlQV1Oh05OTlMnToVk8mEy+WiurqaioqKoO6VTU628vzzGaxZ08zUqe3Y7Vq+9z0LxcU2DhxwBUV7dzLZ2dncf//9XHjhhWg0GioqKnjsscfYt29fyLcXwS5Yc26iGdfC6J///Cdf+9rX+MEPfsDevXtZvHgxl1122SmXTq2pqeHyyy9n8eLF7N27l+9///t89atfZfXq1eMZphDjSqVSMWfOHO677z5SU1MZHBzkpZde4vXXXw/qhRmmTNFSWhrBvfcOzR14880Mios9vPlmQ1B/gOp0OgoKCkhNTUWlUtHW1sbBgwfp7w/upcqHh9WZTEObVw4MDNDX1xcURcO5Ki0t5bOf/SylpaWBDuWMnLhIQ0REBGq1GrfbTU9PT9C/V1arlcLCQtLT01Gr1fT29nLw4EEaGhqC9nWpVCqWL09lzx4r3/zmMYxGF6WlFubMUbNqVRY7d0JJychLZWWgoz49rVbLsmXLuOeee0hJSWFwcJBXX32VF154IajniglxJsZ1KN3555/PnDlz+POf/+y7burUqVx99dX88pe/HHX/73znO7z++uu+YUcA9913H/v27WPHjh1n9DtlKN2Zqa2tJSsrK9BhhB2Px8PGjRt9841iY2O5+uqryczMDHRon8prr9m54w413d16NBovd9xxnN/9Lgmr1eS7TzDmXG9vL9XV1TidTtRqNWlpaSQnJwf1ECAAu91Of3+/b2llq9Uaksu8TpTlus+V1+tlYGAAh8Phe68iIiLQ6/VBnYN2u53a2lp6enqAoU2Jc3JyfPtvBatdu1q5917YuzfxE+935Ajk5fkpqE/J4/Gwfft2Nm3ahMfjwWg0cumllzJz5sygzsFQFIyfsf4yIYbSOZ1O9uzZw4oVK0Zcv2LFCrZv337Sx+zYsWPU/VeuXMnu3buDeijBRBSsZ+iCnUajYfny5b6FGTo7O3nmmWdYu3ZtUOf4VVcZOXxYyyWX9OLxqHnqqWyKiwfZtKnJd59gzLnIyEiKioqIiYnB6/VSV1fH4cOHg37FJqPRSFRUlG/j0d7eXl+hJCYOtVqNxWIhKirKN7yur68v6Jf3NhqN5Ofnk5ubi1arpb+/n0OHDtHY2BjUOXjeeYm8/34M99wztArp88/Dnj0fXZ5/fuh+wdTpotFoWLx4Mffeey+pqanY7Xb+/e9/849//IPe3t5AhydOEIyfsRPRuO281t7ejsfjISlp5EaQSUlJNDc3n/Qxzc3NJ72/2+2mvb2dlJSUUY9xOBw4HA7fz8P/qKWlpSNWIoqJiSEnJwe73X7S+R3DZxMrKipGDZnJzs4mNjaWtra2UWv8W61W8vLy8Hg87Nu3b9TzTp8+HZ1Ox9GjR31nx4alpaWRlJREV1cXNTU1I24zmUxMnToVgL179476sBger11bW+ubJDksKSmJtLQ0+vr6qPxYv71Op2P69OkcOXIEm8026mA8Ly8Pq9VKQ0MDLS0tI26Li4sjKyuLwcHBEb16MDSkYPbs2QCUl5ePGjuek5NDTEwMLS0tNDQ0jLgtKiqKSZMm4XK5KCsr4+NmzpyJRqOhsrJyVDd+RkYGCQkJdHZ2cuzYsRG3RUREUFBQAAydOf64adOmYTQaqampGbXzd0pKCikpKfT29lJVVTXiNoPBQGFhIQD79+8ftcpSfn4+FouF+vp6WltHrtgWHx9PZmYmiYmJLFiwgB07dlBRUcErr7zChg0b+MpXvkJGRgaHDh0adfCdm5tLdHQ0zc3No5b/jo6OJjc3F6fTyYEDB0a91lmzZqFWq33v+4kyMzOJj4+nvb191DBXi8VCfn4+Xq/3pMORioqK0Ov1VFdX093dzS9/CeedN8gjj+iorMxkxYoE7rprH7feOkhJyW4uuOACYOjAaNq0acDQ/+rHG/QpU6ZgNps5fvw47e3tI25LTEwkPT0dm83GkSNHRtym1WqZMWMGAAcPHhzRNgBMnjyZyMhImpqaaGpqGnHbJ7URiqKQnZ1NXV0dBw4cYM+ePSQmJvomkAdrG6HRaEb8nTQaDZGRkb72sKysLKjbiBNjCLY2YmBggMOHD/uuH94kNi8vz/d/rlar0ev1vvsEQxtxotTUVIqKiigrK+PgwYMcOnQIk8lEamoqMTExQdVGwNBxhF6vY+VKJ08+CVOnwsk6Kofy8qP8n8htxInHEXPmzEGj0bB3715gaA755MmTycnJGdF7FExtBATvccTH24jt27ezaNEiZs2aBRC0xxEnSk1NJTk5me7ubqqrq0fcdjbHER9/Xz+RMk4aGhoUQNm+ffuI6x9++GGloKDgpI/Jy8tTfvGLX4y4buvWrQqgNDU1nfQxP/rRjxTgtJdly5YpH3zwgbJv376T3r5mzRplcHBQKSoqGnXbt771LeXo0aPKT3/601G3zZkzR3nvvfeUjo6Okz7viy++qPT09ChLliwZdduXvvQlpby8XHnyySdH3TZp0iTl3XffVRRFUXQ63ajbH3/8caWtrU259tprR9124403Kvv27VNee+21UbfFx8cra9asUdasWaPEx8ePuv1Xv/qV0tDQoNxzzz2jblu5cqWya9cuZefOnaNu0+l0ypo1axSHw6Hk5+ePuv373/++UlNTo/zgBz8Yddv555+vbNu2Tamvrz/p33D16tVKX1+fMn/+/FG3/dd//ZdSUVGhPProo6NumzJlirJx40ZFUZSTPu/TTz+tdHR0KJdffvmo226++WalrKxM+ec//znqtpSUFGXt2rWKoihKVFTUqNsfeeQRpampSbn99ttH3XbFFVcoe/bsUTZv3jzqNq1Wq9xyyy3Kf/7zHyUrK2vU7T/60Y+U2tpa5Zvf/Oao2xYtWqTs2LFDqaysPOlrff311xWbzabMmTNn1G0PPvigUllZqfz6178eddv06dOVLVu2KAMDAyd93ueee07p6upSli9fPuq2xMTvKqAoMDoPMzMzlXfeeUdRFEUxm82jbv/jH/+otLS0KJ///OdH3XbNNdcoe/fuVdauXTvqtqioKGXNmjWKx+NR0tLSRt3+8MMPK3V1dcoDDzww6rYzaSO6u7uVgoKCUbeFUhuRkJCgbN68WfF4PCHRRgDKK6+8EhJthNlsVtatW6d0dHQoubm5o24PtjbijjvuUA4ePKj89a9/HXVbRkaGsm7duqBrIwYHB5VJk25QQFH27Bl5vLJnj6KAosDsEY8LtjYiNjZWefDBB5Uf/ehHJ31vgq2NCKXjCLPZrKxZs0ZxuVxKdnb2qNtDqY04l+OInp6ek9YSJxq3OUZOpxOz2czLL7/MNddc47v+wQcfpLS0lM2bN496zJIlS5g9ezZ/+MMffNe9+uqr3HjjjQwMDJx0DPzJeowyMjLYvHmz9Bh9Qo+R3W6nsrIyqM8GQ+ic6bHb7bz//vu+Hk+Xy8WiRYtG9KAG25melJRUnn/ewg9/aMfpPI5W6+aaa1p54IFY4uKig/JsMMDhw4epr6+npaUFj8eDWq1mzpw5TJ06lfb29qBtI7xeL3a7Ha/Xy7Rp01Cr1b7fGaxng10uFyqVilmzZmGz2YK6jYChoXXDe82UlJTQ09ODoihoNBrMZjP5+flB1UZ8/Gywy+WisbGRvr4+9Ho9xcXFZGdnc/DgwaBqI1555RjXXZfNnj0je4xKSqC4GPLzS/jd7zwkJw9tbxAsbcQwnU5HYWEh27Zt48UXX8TtdhMREcHSpUtJT08PqjYCQuc4wuFwYDKZpMfoQx/vMbrwwgvPaI7RuC++UFxczGOPPea7btq0aVx11VWnXHzhjTfeGNHg3H///ZSWlsriC2Ps/fffZ/78+YEOQ3xMRUUFb775Jn19fahUKi644AKWLl0a1BPjjxxxc9ttg3zwwdA+GLm5XTz66ACXX54a1JN3HQ4HNTU1vmLWbDaTnZ09ITcTPRtOp5P+/n7fHBadTkdERARa7biNvB5XodzWuVwubDabr0C3WCwjhtYFI0VRaG1tpa6uDq/Xi16vJzc3N6g+z4cLoOefHxpON6y8HG65Zej7yEgnTz3l4frrTSd/kiDR2NjI6tWrfYXVBRdcwEUXXRS07UUwC+W27tM6m9pgXAujf/7zn9x66608/vjjLFiwgCeffJK//OUvHDx4kKysLL73ve/R0NDA3/72N2Boue6ioiLuvfdevvSlL7Fjxw7uu+8+XnjhBa677roz+p1SGJ2ZtWvXsnLlykCHIU5icHCQt99+m/379wMQGxvLFVdcQW5uboAjO3eKAg88sIe//nUGNpsOtdrLF75Qzx/+EEdcXPBuSKcoiq+XaHjz1ISEBNLT04P6wEBRFAYHBxkcHERRFFQqFQaDAbPZjFo97tvfjZnq6mpuv/12/vrXvwb1/88nGV6QYbj332w2YzKZgvqkAwxtVnn06FHsdjsqlYrU1FTfEvoTXWUl5Oef+vb09F7q64eOT+6+u48//clKMNezTqeTdevWsXv3bmCot+m6664jMfGTV+cTY0uO605twhRGMDQ579e//jVNTU0UFRXxu9/9jiVLlgBwxx13cOzYMTZt2uS7/+bNm/n617/OwYMHSU1N5Tvf+Q733XffGf8+KYzOzI4dO1iwYEGgwxCfoKKigrfeesvXIzFz5kxWrlwZtEva7tixg8zMedx5Zz/r1g39b6ak9PGLX3Rz662paDSaAEd47lwuF3V1db4hPTqdjoyMDN/iDMHK4/H4louGoaEuJpMJo9EYFAVSsC/XfaYURWFgYMA39MhoNBIRERHUuQdD+Xf8+HHa2tqAj4ZLBcNJh5de2svkybNHXW+1QlxcP3ff3cGrrw5t0zB1aj+vvmqgoGDiv65PUlFRweuvv05/fz9arZbly5dz/vnnB30eBgs5rju1CVUY+ZsURmfG4XBgMBgCHYY4DYfDwYYNG9i5cyeKomA2m1m5ciUzZswIug+bE3Pu738f4MEHNXR0DP28dGkjf/yjgaKiuECG+Kn19fVx7Ngx3wGq1WolMzOTiIjg7RWDocJvYGDA1yuhVqt9BdJEzsNwKYyGDQ4O+ubIGgwGLBbLhH5/zlR7ezvHjh3D6/ViMBjIy8ub8CeITvcZ6/F4WLXqOD/8YSo2mwGTyc2qVU7uvHNiv67TsdlsvPbaa755SQUFBVx99dW+jaXF+JHjulObEPsYiYntxF46MXEZDAYuu+wy7rrrLpKSkhgYGODVV1/lueeeo7OzM9DhnZUTc+7mm81UVuq59dYe1GqFTZtSmTfPyje+UUt/v+PUTzLBWa1WCgsLycjIQK1W09fXx6FDh6ipqQnqfap0Oh2RkZFERkb69tPp7++nq6vLN9xOBJ7JZMJqtaJSqXA4HNhstpB4b+Lj45k2bRoGgwGHw8GhQ4dGLRYw0ZzuM1aj0fDggzls2tTD1KntDA5quesuM7fe2ofT6Z8Yx4PFYuGmm27iM5/5DFqtloqKCh5//HHq6+sDHVrIk+O6sSGFkRBBID09nXvuuYfly5ej1Wqprq7mscceY+vWrUG70WNMjIq//S2KrVtdTJtmY3BQzyOPZDF9+iD/+ld90B7QqdVqUlJSmD59OnFxcSiKQltbG/v376epqSloN+FTqVTo9XqioqKwWCy+zWGHC6SBgYGgfW2hxGAwjCiOBgYGgvZ/6URms5lp06YRFRWF1+vl6NGj1NcHbzsxrLg4kQ8+iOCWW46hUik8/7yVuXP7OX48ONt1GGorzjvvPO666y5iY2Pp6enh6aefZseOHUH/fonQJ0PpwlR1dXXITkYOdZ2dnbz55pu+pSsTExO5/PLLyc7ODmxgp/FJOefxwO9/b+PHPzZgsw2twHfppQ387ncmpkyJ9WeYY66vr4+6ujrf8qYGg4GMjAxiYmKCepiToig4HA4GBwd9xblarcZoNE6YOUjNzc389re/5Rvf+AbJycmBDsevHA6Hb1niiIiIkBnKpCgK9fX1vqW04+Pjyc7OnhD5dqKz/Yz1er38+c+1fOc7afT364mOdvLSSwqXXBLcQ6McDgevv/46Bw8eBIaWUL7qqqtCJh8nEjmuOzWZYySF0WkdP36czMzMQIchzpGiKOzfv5+1a9cyMDAADO11sWLFCqxWa4CjO7kzybnmZi9f/nIfr7wSBYDJ5ORLX2ripz9NJCoqeD9IFUWho6OD+vp6nB+Ok7FYLKSnpwd9O6UoCk6nk4GBAV+BNLyKndFoDPhE+XBu64bnHKlUKiIjI4N62f+Pa2tr49ixYyiKQmRkJJMnTw54rp3oXPNu69YmbrnFRG1tNGq1l5/8ZIAf/MBCEJ9DQVEUdu/ezZo1a/B4PERHR3PDDTeQlpYW6NBCSji3dacjc4zEaX18YzURXFQqFTNnzuSBBx7gvPPOQ6VSUVZWxh//+Ee2b98+IYfXnUnOJSerWb06inffdVBQ0M/goJ5HH81i6lQ3jz9+fEK+rjOhUqmIj49n+vTppKWloVarsdlsHD58mIqKCl9xG4yGi6Do6GisVis6nQ5FUbDb7XR3d9Pb24vT6QzIEJru7m6efvrpUZsGhguj0YjBYEBRlJCZbzQsISGBvLw8NBoNvb29HD582HfSYSI418/YRYtSeP99NUuXNuD1qvl//8/C9df34QjeqZe+oXV33303MTExvv/LXbt2hVROBpoc140NKYyECGImk4nPfOYz3HPPPaSnp/v2k3j88cdH7eAdTC66yMChQxH84Q82oqMdNDVZuf/+TM4/v4NNm5qD9sNUo9GQlpbGzJkzSUxMRKVS0dPTw8GDB317tgSr4QIpKiqKqKgoDAYDKpUKp9NJb28v3d3dDA4O+nUeUnV1NT/5yU9G7ZgeLlQqFREREajVajwej2+1xFARHR3NlClT0Ol0DAwMUF5e7ltaPpglJ0eydm0iDz5YjVrt5ZVXrFxwgY22tuBs94alpKRw7733Mm3aNDweD2+99RZvvPEGbrc70KEJ4SND6cJUf39/0C8hLEZSFIXS0lLeeecdXw9EUVERK1asmBD/C+eacz09Ct/+di9PP23F7Vaj0Xi56qoGfvUrC5Mnx4xDpP5jt9tpaGjwrbClVquJj48nNTUVfTDv+Pghj8eD3W7H4XD4CqLhRRyGh9mN5zyrcFuu+1SG5xupVCpiYmIm3HycT8vhcHD48GHfcsUFBQUYjcaAxjQWn7GKovDkk8f4+tfTGBzUk5Y2yJo1OoqKJs6QwXOhKArbt29n/fr1KIpCRkYGN95444QdBh4s5Lju1GQonTitQ4cOBToEMcZUKhWzZ8/mgQceYN68eahUKg4cOMCqVavYunVrwM/KnWvORUWpeOKJKPbt87BkSS8ej5pXXslg5swIvvKVWjo6+sc4Uv8xGo1MmjSJwsJC32pbra2t7N+/n9ra2gk1NOhcaDQaIiIiiImJwWKxoNVqfYs29PT0+HqRgnWIZLDQ6/W+v30w90qeisFgYOrUqRiNRl+RFOjXORafsSqVinvvzeHf/24lMdFGQ4OJBQsU/vOf4O4VU6lULFy4kJtvvhmj0UhdXR1PPvmkLOn9Kclx3diQwihMBdseOOLMmUwmLr/8cu655x4yMjJwOp2sX7+eP/3pTxw6dChgw9A+bc5Nm6Zj8+ZIXnttkLy8fgYG9PzpT1lMmQIPP1yL3R68RURERAQFBQVMmTKFyMhIvF4vLS0tIVMgqVQqjEYj0dHRREdH+zaG9Xg89Pf3++YiORyOoB0mOZEN//2BoM+lU9Hr9UyZMgWTyYTT6aSioiKgr3UsP2NXrEhn48ZBCgo6sNl0fPazOp54IviHRU6ePJl77rmHhIQE+vr6eOaZZ9i7d2+gwwpaclw3NqQwClPS3Rr6UlJSuPPOO7nmmmuIjIykq6uLl156iWeffZbGxka/xzNWOffZz5ooL49g1SobCQl22tsj+H//L4tp0wZ49tm6oO59iIyMZMqUKUyZMgWr1RpyBRKAVqvFYrH4epGGF2twOp309fXR2dmJzWYbkwUbjEYj2dnZAR9WNREMD810u90hu9+UXq/3DaNzOBwcOXIkYBsrj/Vn7LRpCbz3np6lSxvweNTcd5+Jhx8O3t7yYbGxsdx9991MmTIFj8fDa6+9xn/+85+QzdHxJMd1Y0PmGIUpt9s9oZY2FePL6XSyfft2tm3b5jtQmDVrFhdffLHfxnWPR84NDCg8/HAff/iDiYGBoaWI58xp5ac/dXPZZclBPZdCURT6+vpoaGjw7UejVquJi4sjOTk5pPYB8Xg8OBwOHA7HiMJWrVaj1+vR6/XodLpzmo8kbd1Hurq68Hg8REVFhdTS3R9nt9t9q9RZLBamTJni97ZgvPJuYMDOrbc288or2QA88ICNP/whuJfzhqH2bsuWLWzcuBGAvLw8rr/+egyG4N7HyZ+krTs12cdoIhRG998PDQ2B+/2n0draSmJiYqDDEH7mcrtpb2ujt7cXAJVaTWxsLLF+mJA9njnncCgcOuSmvl6LwtARQmysnSlTVMTFGQjmYwaFoQ88u90+Yp6YXqfDYDSi1WgCF9wYUwDF68Xr9eJVlBE9RiqVCrVKhVqtRqVWn/F7Km3dR1wuF15FQafVBvVJgzPh9nh8S5Tr9XrMZrNf24HxzDuv18v+skGOHx/qIUhLczNntjboiyOAPpuNpqYmFK8Xg9FIelqaHOyfoQnf1qWlwZ//HJBffTa1gWTbeAnQm3+m9q5dy8qVKwMdhvAzHZACeBsaWLNmDXV1dcDQEK7ly5czffr0cVslbDxzzgDMBkyHXXzrW/289VYUSqcK1Q4vK1Y08fDDeubOTRiX3z3eVAy9bzqgr6+P5uZmurq6fLdHRkaSmpqK1Wod1xXe/EH14UXN0Blkl8uF0+nE6XSOGFqjVqvR6XS+nqRTHeSXlpaycOFCtm3bxqxZs/zxEiYsRVHo6+rC6/USHR2NOsQPNrWAqqeHyiNHfCufpaSk+O33j2d7pwamezy88P0afvObXJQGNZ+ZZePfr1oI9rfVCvTU1/PCCy/Q399PZGQkN998M0lJSYEObcKT47qxEdqnjMQp5ebmBjoEEUBpaWnceeedXH/99URFRdHb28srr7zCE088wdGjR8fld/oj56ZM0fHGG9Hs2uVi2bIeFEXN2rVpzJ8fyzXX1HPwYMe4xzCerFYreXl5FBUVER8fj0ql8m1ueejQITo6OkJmbP7wst7D85EiIyMxGo2o1Wq8Xq9vCequri56enoYHBzE7XaP6GXyer0MDAyEzN/k0xieW6RSqdCEUC/jJ4mKiiIzMxOA+vp635BUfxjv9k6j0fDLX+byi18cRaPx8NZbFq65pp9Q2BIoPT2du+++m/j4eHp7e3n66afDdi+ysyHHdWNDCqMwZTabAx2CCDCVSkVRURFf+cpXuPjiizEYDDQ3N/Pcc8/xt7/9bcwXaPBnzhUX69mwIYpNm+ycf34vHo+Gf/87nTlzorjppjoqKrpO/yQTmNlsJjc3lxkzZpCUlIRaraa/v5+jR4+yf/9+mpqaAr48+1j6eJEUFRWF2Wz2LUHtcrl8q9t1dXXR19c3Yu+kcKcoim9vs+GNd8NFUlIS8fHxKIrC0aNH/fZ/4Y/2Tq1W853vTOZXv6pGo/Hw5psRXHutLSSKo5iYGO666y6ysrJwOBw8//zzsmLdachx3diQwihMHThwINAhiAlCp9OxePFiHnzwQebPn49Go6G6uponn3yS1atXjxi29WkEIucuvNDI++9H8uabgxQV2XA6tbzwQgYzZ1r5wheCv0AyGAxkZWUxc+ZM0tPT0el0OJ1O6urqKC0tpba2NuD7uYw1lUqFTqfDbDYTHR1NTEwMERER6PV6VCrViN6knp4eAAYHB8O2UBouilwuFyqVKqQW7ThTWVlZGI1GnE4nx44d88vv9Fd7p1KpeOihyfzP/wwVR2+8YeH660Oj58hkMnHrrbcyffp0vF4vr732Gu+9954s6X8Kclw3NqQwEkIAQ2ebLr30Ur7yla8wY8YMAMrKyli1ahVvv/02/f3BuzTsZz5jYv9+C//85wBTp9pwOLS8+OJHBdLhw8G9/4NOpyM1NZWZM2eSm5uL2Wz2LfVdVlZGZWUlPT09IXlAodFoMJlMREZGEhsbO6I3abhnxG63+5YC7+rqwmaz+VbAC8W/yTCv1+vbRBfAYrGEzTC6E2k0GiZNmoRKpfLlQChRqVR84xuT+eUvj6LReHnttQhuvrmfUEhtrVbLtddey+LFiwF49913eeedd0L6/1YElqxKF6Z6e3vl7yM+UVNTE++++y5VVVXA0B4hCxcuZMGCBb49Uc7GRMk5RYHVqwf47//2Ul5uAUCvd3P11U38+McRTJ0aG+AIP73hpb6bm5vp7u72XW80GklMTCQ+Pj4sVnqy2Wzs3LmToqIi9Hr9SQshtVqNVqsdcQn2FdsURcHhcGCz2XzXWSyWsN/Pqa6ujqamJgwGA0VFReNaJAaivVMUhV//upLvfW8yiqLm61/v55FHQmdvm+3bt7Nu3ToA5syZwxVXXBH0/6tjaaJ8xk5Esly3FEanVVJSwpw5cwIdhggC1dXVrF+/3jfnKCIigsWLFzN37tyzOrieaDmnKPDKK0MF0qFDHxVIV17ZzPe/b2T27LiQmIsxODhIa2sr7e3tvj2C1Go18fHxJCYmhvy49BPzzuv14na7cblcuN3uUYs1DNNoNGg0GrRare97jUYz4fPB6/XidDpHLe1uNptD/n0+Ex6PhwMHDuBwOEhPTyc1NXXcfleg2juv18v3vlfFr3+dD8CvfjXAt78dOu/93r17ef3111EUhWnTpnHdddeFZS/oyUy0z9iJ5GxqAym1w1RbW1ugQxBBIjc3ly996Utcf/31xMbG0t/fz5o1a3j00UfZtWvXiA05P8lEyzmVCq67zsyBAxZWrx6gsHBoDtLq1enMmxfLpZc2sWlTS9AP2TCZTGRlZTFr1iyys7N9w+xaW1s5cOAAhw4dor29PSTn3xw/fpwf/ehHHD9+HPhow9iIiAiioqJ8Q++Ge1OGh995PB6cTicDAwP09fXR3d1NZ2cn3d3d9PX1MTAwgN1uH9oXyOsNWI4oioLb7WZwcJCenh7fMEG32+2bTxQTEyNF0Yc0Gg3p6ekA475ASaDaO7Vazc9/nsvttw+tLvrd75p4/vnQmWc4e/ZsbrjhBjQaDYcOHeLll18+48+gUDfRPmODlfQYhanNmzdz4YUXBjoMEWQ8Hg/79u1j8+bNvont0dHRXHjhhcycOfMThzVM9JxTFHjjjUF+9jMXu3cPtR0qlcKCBS18+9terrwyOSSGbSiKgs1mo6Wlha6uLt9BvVarJS4ujoSEhJA5kC4pKaG4uJg9e/ac8ZlUr9eLx+PB7Xbj8Xh8l08qHNVq9SkvquFNaVUq3+VsKIriK76G4xiO6WQ9XlqtFoPBgMFgCIl8HWuKonDw4EEGBgZIS0sjLS1tXH5PoNs7u93Bddc185//ZKHTedi0SeGCC0Jn+GxVVRUvvvgibrebgoICbrjhhrAYHvxJAp1zE5kMpZPC6LQURZnww0LExOV2uykpKWHLli2+eQxxcXEsXbqUwsLCkx6QBVPObd5s58c/drBpU5Tvupkz2/j61x3cdFMyOl1ofAA7nU7a2tpob2/H4XD4rrdYLMTHxxMXFxfUw1TOpTA6meHi5OOF0vDXs/kY/XiB9PH/ieHnUhTFd/kkw3Okhje8Deb3y186OzupqqpCr9czY8aMcSkgJ0J719vbz/LlvezalUJCgoP9+/UkJwdHG3wmjh49ygsvvIDb7SY/P58bb7wxrIujiZBzE5UURlIYndZa2SFZjAGXy8WuXbvYunWrb5+UxMREli1bxpQp/5+9+45vstofOP5Jume694RORtmjbESW4k+GIEvFCSqK86rgQK/zOlHvFbxXAa8giIIgIkvZG8ooUKBAS0sH3btp2uT5/dHbRwJtaSFN0ua8X6/n1SY5efJ90tOTfJ9znnNi9Brp1ljnjh/X8PrrFaxf74pOV/vlKSKikNmzy5g50xd7++ZPQmGOJEmipKSE3NxcvV4kKysrPDw88PLywtnZudV96BoqMWrM1T059W11yc2tDFW8utfp6mue6iaKaG1/F1PT6XQcP36c6upqIiIi8PAw/IQr5tLeXbiQy6BBdmRmutKjRwV79zpyE3PnmK2LFy+yfPlyuedo0qRJFntywFzqnDlqTm5guam1IAi3zMbGhn79+tGjRw8OHjzInj17yMnJYeXKlfj7+zNo0KDrEqTWpEsXW9auteXChRrefLOElStdOH/enWeecefDD0t46KFsnn7aEy+v1j3zk0KhQKVSoVKpqK6uJi8vj9zcXNRqNbm5ueTm5mJvb4+npyeenp4WP7vZ1RQKRZPOUl/dA3RtT9DVv1/dk3TtJhhG3eQjWVlZFBYWtkhiZC7at/fm229TGTfOniNHHJk5s5zFi1t3e3W1du3aMXXqVJYvX87Zs2f55ZdfGDdunBhGKtw00WNkoc6cOUNMTIypwxDaGLVazb59+9i3bx8ajQao7UEaPHgwCoWCDh06mDjCW5OVpeWdd0pZssSJ8nIbAFxc1EyYkMuLLzrRoUPb+YJVdy1Sbm4uBQUFej0eLi4ueHp64uHhYdZDVy5fvsyrr77K22+/LV90LwgApaWlJCUlYWNjQ9euXQ2eeJrTZ6wkSXz6aTIvvBCJJClY9l0lU4MPQFYW+PvDwIHQyntZkpOT+eGHH9DpdHTv3p277rrL4k4mmFOdMzdiKJ1IjG4oOzsbPz8/U4chtFEVFRXs37+fAwcOyNeu2NraMmbMGDp16tTqz+YVF0t8+mkJCxfacuWKAwDW1lpuu+0Kzz+v5PbbfVr9MV5Nq9VSWFhIXl4epaWlcg+HUqnEzc0NLy8vXF1dzfKYRVsn1EeSJBISEtBqtXTq1MngE46YW73TarVMm5aKZuVxPlc8TZCU8deDQUGwYAGMH2+6AA3g1KlT/PTTT0iSRHx8vMUNKzO3OmdORGLUyMHXTW9q6dM77t69mwEDBpg6DKENsbGxuW5st1qt5sCBA+zbt4+TJ08SERGBp6cnAwcOpHPnzq1+LHhNDXz/fRmffCKRmOgi39+lSy6PP17FjBm+2NnZmDBCw9NoNOTn55OXl0dlZaV8v42NDR4eHnh4eJjN9UhlZWX8+9//5tFHH8XZ2dnU4Qhm5vTp05SVlbXIdUbmeL1H8eL/4vLQA4Ckv1ZL3f/qTz+1+uTo2LFj/PLLLwCMHDmS+Ph40wZkROZY58yFSIwaOHiNRkNWVpZ8kbglq6ysxMHBwdRhCG2IQqEgKCio3i+gVVVVfPnll2i1Wvn/z93dnQEDBtC1a9dWnyBB7Ux2772nZsuWvyZqCAgoZcaMQp55xhNv77Yzrh9qTzJVVFSQn59Pfn4+1dXV8mN2dna4u7vj4eGBk5OTyZIkY0y+ILReFy9eJC8vr0UWezW7L6laLYSFIV2+TL3/jQpFbc9RSkqrH1a3d+9eNm/eDMDEiRPp2LGjiSMyDrOrc2ZEJEb1HLxOpyM5ORkrKyu8vb2xtbVtuQ9rrRaumvrWHGklCSszOKMrtA2SJJFbVERFSQmROl29daukpAQHBwdOnTrF8ePHUatrFx10dnYmLi6OmNhYbMz4epWmSk+vYfHiSjZtcqBSXXs8jg4ahgwpZMoUWzp0cDOL3hRD0ul0lJeX1y6AWlaG7qoeeVtbW1xdXVGpVNjb2xv12JOSkpg2fTrLvv+e2NhYo72u0DpkZWWRn5+Pt7c3vr6+Bt13SUmJeQ3nP3wYZs68cblFi6Bnz5aPpwVJksTevXs5efIkSqWSMWPG4O/vb+qwWpzZ1bn6xMSACdbJE4lRPQevVqtJSUkhNDS05RcvLC+HpKSWfQ1BMDOVQGpuLuGzZmGflmbqcARBEARBMCdHjoAJeu/FdN2NMMrFwfb2YOZnJ8vKy3F2altDewTTUlRVgY0NrF7915j1q+zdu5d+/frp3VdTU8PZs2c5fvw4paWlAFhbWxMbG0tcXFybuC5Eq4VNmypZsULLyVN/HY+XZzkjR5YyebITgYEujeyh9dJqtZSVlVFSUkJpaanezHa2tra4uLjg6uqKo6Nji/QkiR4joTHp6ekUFxfj6+uLt7e3QfddX3tnUhbUY1SnuqaG9b/+Sk5ODm7u7owbOxbbtrSI0zXMrs7VpxXMmmdxiZFRWFmBmScd77z9NoWFhSxcuJDt27cza9Yszpw5A9QObbp48SI+Pj4mjlJoVayswNYWoqNrTw5cQ11UdN2ZImugY+/exE6bxqlTp9izZw/p2dmk5+Xxx44dxMXF0b9/f7y8vIxzDC3ACrijF9zxKiQmavjoo3J++smZo/k2bFkOc3+q4bbbcpg9W8GoUT5t4nqrOlaA6n+bVqulqKiIgoICiouLqdDpKPpfORulEpVKhbu7O66urgZ7D6qtrbmoUlHduTPExRlkn0LbUWpvT0VZGcr27cHT06D7rq+9M6kuXeDvf4eMDKhnoJCkUKAICoKHH2711xjVsQFuj43l66+/Jqu0FO3Fi0yePNksZ880BLOrc61U26wdrUxYWBiurq56MzzVXY9x9Zz0YWFh7N+/X++5s2bNYv78+c1+TTs7uwYfKysrM8ukaPbs2SxdulTvvkcffZTZs2dfV/bzzz9n8ODB8u3Dhw8zdOhQoqKi+Omnn64rP378eN544w3DB92CLly4QP/+/XF0dKR79+4cP368wbLOzs56m0Kh4OeffwZgyZIlWFtb6z2e9r+hcDU1NUyYMIHAwEAUCgXZ2dk3He9tt93W4GNKpZLOnTszc+ZMpk+fTlhYGFqtlqNHj/LPf/6TlStXcvny5Zt+bXPRubMtS5e6c+WKNR9+WEL79uVoNNZs3BjAmDH+dOxYzHvvXaagoO1NEGNlZYWnpyeRkZF069aNiIgIvLy8sLa2lheVTU5O5ujRoyQnJ5OXl6c3ocPNiIuLo6ioiDiRFAnX0Gq1lJeXA+DUAicyG2vvTMLKqnZKbriuR1+HAiTgs8/aTFJUx8XFhcmTJ2Ntbc25c+fYvn27qUNqMWZX51opkRiZCT8/P9atWyffXr16NcHBwS32enUXvrcmmzZtYsSIEXr3TZ8+nR9//JGamhq9+5cvX860adPk2xs3bmTkyJFMmzaNZcuW6ZUtLi7m999/Z+rUqS0XfAuYMmUKI0aMoKCggIceeohx48Zd9z7UKSsrk7e9e/fi4OCg917efvvtemVCQkLkxwYNGiQnUbeibpagxigUCiIiIpgxYwaPPPIIMTExSJJEUlIS//nPf1iyZAnJycm09ksjnZ0VvPCCK8nJTmzdqmb06CKsrXWcPevB3LlBhIUpmTLlMjt35ugNP2srrKys8PDwoF27dnTt2pWYmBh8fX2xs7NDp9NRWFjIxYsXOXbsGElJSfJsojfzd29KvRMsT1FREZIkYW9vj309Pdy3yizr3fjxtVNyBwbq3X2ZIJ7wXY5ubOueqrshgYGB/N///R8AO3fu5Pz58yaOqGWYZZ1rhURiZCamTJmi94V92bJlt/xFvbKyktmzZxMQEEBQUBAffPBBk553dc9AWFgYH3zwAREREXh7e+v1Tq1fv57o6GhcXFwIDg7mhx9+AGrPxL3xxhuEhobi5+fH888/X+8X9s2bN9O/f3/5dnh4OE8++SRQ+6Hl6uoqP+/ChQs4OjpeN7PMoEGDcHBwYMuWLfJ9Fy9e5OjRo9xzzz3yfXXTWE6fPp3ff/+doqIi+bGff/6ZTp06ER0dzfbt24mJieG1117Dzc2N6OhoTp8+zdtvv42HhwexsbGcOnVKfu4TTzxBQEAAbm5ujBgxQu5pOXv2LF5eXnIDvH//fvz8/MjJyWnS3+BGzp49y9mzZ3nllVewt7dn9uzZaLVa9u7de8PnLlu2jLvvvhsXlxtf12Jtbc2cOXPo27fvLcfc3C+1QUFBTJ48mSeffJKuXbuiVCpJTU1l2bJl/Otf/+LIkSO33KNgagoFDBtmz4YNbqSmSjz/fBHe3mpKS+1ZsSKIwYN96Ny5kHfeSSc3t9zU4bYIpVKJq6sroaGhxMXF0alTJwIDA3FyckKSJEpLS0lPT+fkyZMcP36clJQUCgoKGjwJcLVTp04xY8YMvf9ZQZAkiStXrgDgaeAhdFe/hlkaPx5SU2HbNli+nIvfrKCD/VkWXpnM2rVtd33HuLg4evXqBdSeeC4pKTFxRIZntnWulbHYxEiSaiePa+mtqfV0+PDhJCQkUFBQQHZ2NsnJyQwaNOiWjvGFF16guLiYc+fOcfDgQb777jt+/fVXgGaN4f/555/Zt28fBw4c4JtvvmH9+vUAPPLII3z77beUlpZy6NAhunTpAsAnn3zC3r17OXLkCGfOnCEhIYGvvvrquv3Gx8dz9OhRKisrycioXYV79+7dAOzZs4devXph/b/pm+t6fK6lUCiYMmUKy5cvl+9bvnw5o0ePlhfsKy4uJiUlha5du9K+fXu6du2q1wNybe/S+fPn8fb2Ji8vjxEjRnDHHXfg4OBATk4OY8aM4dVXX5XLDhgwgKSkJLKzswkKCuLpp58GIDo6mrlz5zJjxgzKy8uZMWMGn3/+eb1DFHfv3o2bm1uDW31Onz5NdHS03oWkcXFxN/wCKEkSP/zwg97xQu377enpSYcOHVi4cGGj+7hZQUFBN/U8b29vxo4dy5w5c4iPj8fOzo7c3Fx+/fVXPvvsM7Zv3y4PiWnNAgOt+OgjNzIz7Vm2rIxBg4pRKiVOn/bk1VeDCQ214e67M9iwIavNLlCtUChwdHQkMDCQjh070qVLF0JDQ3Fzc0OpVKLRaMjNzeX8+fMcPXqUpKQkMjMzKS8vr/dLQVVVFVlZWVSZ+fIJgnEVFRVRVlaGUqlssWHjN9veGYWVFQwZAlOmEDZjIqPurP38/eADjWnjamEjR47E39+fiooKfvrppzbXG2/Wda4VsdjEqKICnJ1bfmvqWrLW1taMHTuWVatWsWLFCiZOnFjvBYLDhw/X+9K8ePHievcnSRKLFy/m448/xtnZmYCAAB5//HH5+prmXHz4zDPP4O3tTbt27Zg5c6acVNjY2HDy5EnKysrw8/OjQ4cOAHzzzTe88847eHl54ebmxvPPP1/vdT0uLi7ExsZy8OBBdu3axdixY9FoNBQWFrJr1y4GDBggl20oMYLa4XS//PKLvHDotYnO1q1bGTp0qDzr1fTp0+XeuaysLHbu3MnkyZPl8m5ubjz11FNYW1szfvx48vPzefbZZ+XbJ06ckMtOnTpVXp/lpZdekhO7uvdNoVDQu3dvOnfuzKRJk+qNf8CAARQVFTW41aesrOy6KSddXV0pKyurt3ydnTt3UlFRofdeDh48mMTERHJzc1m8eDFvvfUWa9asaXQ/N+NWv4CoVCpGjhzJc889x8iRI3Fzc6O8vJzt27fz6aefsm7dOnJzcw0UrelYW8PUqc7s2KEiJUXHCy8U4u9fSWWlLevWBXLnnf5ERZUwb1466elt76zn1ezs7PD19SUqKoru3bsTHR2Nn58fDg4Ocm/S5cuXOXXqFMeOHZMX7NRo2vYXPOHm1dTUcOnSJaB2CLuNjU2LvI45XqdbH6VSyRNPSCgUEgcOOHDhgqkjajnW1tZMnDgROzs70tLS9D6v24LWUufMncUmRuZo2rRpLF++/Lov9lfbsmWL3pfmBx98sN5yubm5VFZWEhUVJSdRc+fOlYdyNWcI0tVnIYKDg8nKygLgp59+Yt26dQQGBjJixAh5Vru0tDS9BG7atGkNfmEdOHAgu3btYteuXQwcOJB+/fqxZ88evcRIo9Fw6NAhBg4cWO8+OnXqRLt27Vi3bh1Hjx4lIyODu+66S35806ZNjBo1Sr597733snfvXjIzM1mxYgVDhgzBz89PftzLy0tOohwcHPD09JQTSQcHB73eiXfeeYeIiAhcXV3p3bs3+fn58mNKpZIZM2Zw+vRp5syZ04R3uumcnZ2vGwpQUlJyw+mtly1bxqRJk/S+DISHhxMWFoZSqaRPnz48/fTTLZIYJSQkGGQ/dnZ2xMfH8/TTTzNx4kSCgoKoqakhISGBf/7zn3z//fdcuHChTQwrCAmx4sMP3bl82YFff61k1KgirK21XLzozrvvBhMZ6cioUZmsWpWJRtO6hxXeiPJ/M9eFhITQuXNnunTpQlhYGO7u7lhZWckTONRdm3TixAkyMzMBmjTsTmj7dDod58+fR6PRYG9vT0BAQIu9lqHaO2OIjw8iLq52+PzChW27d9XDw4M777wTgO3bt8vfZ9qC1lTnzJnFJkaOjlBW1vJbc9aSjY+PJyMjg7KyMrp27XpLx+fl5YW9vT2XLl2Sk6iSkhJ+//33Zu/r6tnA0tPT5SSiT58+/Pbbb+Tk5NCtWzf5+qDAwEB27dolv25xcTGnT5+ud98DBgxg9+7d7NmzhwEDBjBw4EC2bt3KsWPH5Otadu/eTc+ePRtdf2D69OlyUjl+/Hi9i2m3bNnC8OHD5dve3t4MGzaMFStWNJqE3siOHTtYtGgRv//+O8XFxRw8eFDv8fz8fF577TXuu+8+XnzxxQaHP+3ateu6WeOu3urToUMHzp49q5fgnjhxgo4dOzYYr0aj4aeffrrh8baWqUyVSiUdO3bkkUce4eGHH6ZDhw4oFArOnz/Pf//7XxYuXMjRo0db/XVIAEoljBnjwO+/u5GRoWD+/CLCwsqpqrJm06YAJk0KIDRUzcyZ6Rw6lNsmksIbsbOzw8fHR57lLiYmhoCAAHnWRbVaTUFBAQBnzpzh5MmTpKWlUVhYKBIlC6TT6UhJSaGkpAQrKysiIiJaTVvX0hwcHLjrriKgdhm6tq5z58506NABnU7HmjVrRHsg6LHYVkGhqF1qqKW35q5ZuHr1an788cdbPj6lUskDDzzACy+8QFFRETqdjqSkJPnLe3OGD3z++efk5eWRkpLC119/zYQJE9BoNCxfvpySkhJsbGxwdnaWr1t6+OGHmTdvHtnZ2UiSRGpqKjt27Kh33wMHDmT37t1UV1fj4+PDwIED+fbbb4mJiZEnB6ibOKExU6dOZcuWLXz//fd6X/yTkpLw8PC4rot52rRpLFiwgFOnTjF+/M3NxFNaWoq1tTWenp6Ul5fz9ttv6z3+xBNPMHHiRJYuXYqtrS0ff/xxvfsZOHCg3oxw1271iY6OJjo6mvfff5+qqir+9a9/YWVl1ejibhs2bEClUl1XZuPGjXKPXkJCAp9//jljxoyRH6+qqpJnMbz69+bq0aPHTT2vKYKDg5k0aRJPP/00ffr0wdbWlitXrrB27Vo+/fRTtm7dSnFxcYu9vjH5+Ch54w03Ll50Ytu2KsaNK8TevobsbBe+/jqY3r29iYvL580307l8uW0PtatTN4FDUFAQHTp0oFu3bkRFRdGjRw8+/fRTgoKCqKiokK/fPHr0KKdOneLSpUsUFBSIoXdtnFar5fz58+Tn56NQKGjfvj2OzTlreRNasr1rCePG2WFlpePiRTuSk00dTctSKBTceeedODk5kZOTw86dO00dkkG0tjpnriw2MTJXdbMyGcInn3yCk5MTnTt3xsPDg/vvv5/CwkKAZl10OG7cOPr27UuvXr2YMWOGPExt6dKlhIaG4u7uzpYtW1jwvzUSXnjhBXr37k2/fv1QqVTcddddpKen17tvX19fAgIC5Nnp2rdvj7Ozc5OvL6oTGBhIfHw8CoVCby7/hp47duxYCgoKuOuuu5o0O1t9Ro0aRXx8PKGhoXTu3Fkv4Vi1ahUJCQm89957KBQKvv32Wz744AOSkpJu6rXqs3z5cjZu3Iibmxv//ve/Wb16tTxZxbvvvsvo0aP1ytfNdKi4JlvfsmULHTt2xNnZmSlTpvDSSy/pXQ8VHR2Ng4MDUDtLYd3vzXUrayA1lbu7O6NHj+a5556Th3NWVFSwe/duPvvsM1auXElqamqb6FFRKGDIEDtWr3YnJ8eKf/6zlN69i1EoJE6e9GL+/GDat3fkttuy+PbbDCoq2vYQmatZW1vj5uZGx44duf322xk4cCARERH4+Phgb2+PJEmUl5dz5coVzp8/z7Fjxzh+/DgXLlwgJyfnpqcGF8xPWVkZp0+fpqioCKVSSWRkZIOT2hiSMdo7Q4qJ8SM6uvYE2aZNbXNyl6s5OTnJJwD37NlDXl6eiSO6da2tzpkrhdTGWv+SkhJUKhXFxcV6F6er1WpSUlIIDw9vkTULWpvi4mJUKtUNy4WFhbFixQqDTNd8M7Kysujfvz8XL168qeePHDmSefPm3fIMf8KN3eh/rCk9f4am0+nkWRmvrkM+Pj706dOHzp07NzpEszVKTdWycGEpP/xgS1raX2fF3d0rGTUqn4cesmboUO9mzUzZWmVlZfHiiy/y4Ycf6k31r9FoKC0tlXtl60uErl702MnJCScnJ/nEg2D+dDodiYmJ8oyENjY2RERE3PSJsOYyRXt3qyZPPsfKlVGMG1fJ6tU3dwKsNambpfXcuXOEh4dz//33X3fisDVpjXXOWBrKDeojeowsVGv55y8pKeEf//jHTT9/2LBhxMfHGzAi4WaZYjy/UqkkJiaG+++/nyeeeIKePXtiY2NDTk4Ov/76K5988gmbN2+We1LbgrAwK95/343UVEd27lQzbVohrq4aCgsd+OGHIIYP9yMqqoRnn03nxIn8Nt0zkpWVxbJly667wNrW1hZPT09CQ0Pp2LGjPONdYGAgrq6uWFlZUVNTQ1FREZcvX+bs2bMkJCRw4sQJLly4QHZ2NqWlpW122vTWTJIkCgsLOXXqlN407Z06dTJaUgSt51rNq/XqVXutzS+/2LNokYmDMQKFQsHo0aOxtrYmJSWFkydPmjqkW9Ia65w5Ej1GQqNM3WMktB6t5X9MrVZz9OhRDh48KCdECoWCqKgoevbsSURERKs5cdBUGg2sWlXOt99Ws3OnKzU1tR+gCoVEhw4F3H13BQ884ExkpFubOvaEhAR69OjBkSNH6N69e5OfJ0kSFRUVcq9SeXl5vWshKRQKHBwccHJywtHREScnJxwcHCyiN87c1CVEWVlZejOHOjk5yROzCI176qlsvvzSj7g4iRMnFCxcCDNnmjqqlrdz507+/PNP3NzcmD17tugZboOa02Mk/voWqqSk5IaVAyA1NbXlgxEswh9//MGwYcNMHQb29vbEx8fTp08fzp8/z4EDB7hw4QJnz57l7NmzuLu706NHD7p164aTk5OpwzUIW1uYNs2JadMgJ0fHN98Us3KlguPHXTl1ypNTpzx5/30d3brlMnZsFfff70pIyI2H2rZVCoVCHj5Xp7q6moqKCsrLy+VNo9FQUVEhr6FW91w7OzscHR1xdHTEwcEBR0dHbG1txZfzFqDRaMjPzycnJ0dOXq2srPD19cXPz89kX3LNpb1rqkWL4Msv/Zg9W2LBAgVz5sCsWbWPtfXkKD4+nkOHDlFUVMShQ4da7SiT1lbnzJVIjCxUG+soFFoBc5sSValUEhUVRVRUFHl5eRw+fJhjx45RWFjI1q1b2bZtG7GxsfTs2ZPQ0NA286XWx0fJK6+oeOWV2uuR/vOfUn76yZqzZ505csSHI0fgrbe09Op1hXvuqWb6dHe8vdtGgngrbGxsUKlUetdmajQavUSpoqKC6upq1Gq13nThUHvNkoODAw4ODtjb28s/RcLUfNXV1RQWFlJQUEBpaan8eWZjY4O3tze+vr4ttnBrU5lbe9eYRYtqk6DZsyU+/1yBQgGff147wYslJEc2NjYMHTqUdevWsXPnTrp162bWox4a0prqnDkTiZGFMvWHhmB5rr743dx4eXkxatQohg0bxsmTJzl8+DAZGRmcPHmSkydP4u3tTc+ePenSpUur/MBsSFiYFW+/7cbbb8OpU9V8800Zq1fbcumSE3v3+rJ3L8ydW03//llMnKhj0iR33N1bdppjQ3J3d+eOO+7A3d29RfZva2uLra2t3v7repbqtsrKStRqNTU1NZSWllJaWqq3DysrK+zt7fWSJTs7O+zs7MSQnv/RarWUl5dTUlJCcXGx3lA5qF3w2sfHBw8PD7O5zsKc27ur1SVFTz0FCxYo5CVGFAr430SzFpEcde3alX379pGbm9vogvLmrLXUOXMnrjGyUDU1NeJDVzCoG/2PFRQU4OHhYYLIbk5WVhaHDx/mxIkT8iKxNjY2dOrUiZ49exIQENAmz/RLEhw+rOGbb8pZu9ae7Oy/ZqdycKimb9887r5by+TJbvj61r8AsTkxh3qn0+lQq9VUVFSgVqvlZEmtVjfae29tbY2dnZ1esmRra4udnR02NjZt8lqmq9+riooK+Rqva98nJycnPDw88PDwwM7OzkTRNswc6t2NVFWBiwvExsLRo7ULSV9Lp4Nu3SApCUpLwQzfaoM5fvw4a9aswdnZmWeeeabVfUdqDXXOVJpzjZFIjCxUU6frFoSmMsfpug1BrVaTmJjIoUOHyMnJke/39/ene/fudO7cuc22KZIEO3eq+eabSjZscCA//6/jtLWtoVevfP7v/6qZMsWF4GDza0/UajU//PADU6ZMMcu/kU6no6qqSi9RUqvVVFVVycl4Y6ytreVeq7rNxsYGa2trbGxs5N/NLYHS6XRoNBqqqqrQaDRoNBo5YaysrKw3WbS1tcXFxUUezmjuox5aS3un32Okvyi9JMGcOfDFF1jERAxarZbPP/+c4uJixowZQ8+ePU0dUrO0ljpnCmLyBUEQBAOxt7enV69e9OzZk/T0dA4fPsypU6fIysrit99+Y9OmTXTo0IHu3bu3qWuRoPZL0uDB9gwebI9OBzt3VvHf/1awYUNtT9KePb7s2QPz5mnp1i2Hu+7SMHWqM+3aqczifTh9+jQPPfQQXbp0adasdMaiVCrl646updVqqaqqoqqqSk6Wrk4ktFotNTU11NTU6E3+UB8rKys5WVIqlXKydO2mVCpRKpUoFIrrfjZEkiR0Ol29W118NTU1VFdX6/1+o56yukkrnJyccHFxEdditZC6ZGfWrNq/Zd01RpaWFEHt/0l8fDwbN27kwIED9OjRQ9Q5CyQSIwvl6Ngy1wlcPb33rFmziIqK4rnnnmuR1xJal65du5o6hFuiUCgICQkhJCSEUaNGceLECRISEsjJyeHEiROcOHECT09PunXrRteuXXF2Nv9hZs2hVMKQIXYMGWKHJMGBAxq++66c9evtSE935NAhHw4dgvnzdcTF5XHHHWqmTHGkQwd3s7nuozWxsrKSZ7a7liRJaLVaOUnSaDRUV1dTVVWll4RUV1ej0+nQarVyomUulEql3tBAOzs7ORmysbFp9V9IW1N791dyVPueL1gAzzxjWUlRnW7duvHHH3+Qm5vL5cuXCQ4ONnVITdaa6pw5E4mRGQgLC6OgoIArV67IZw5LSkrw9fUlNDSUM2fOGPw1a2pqmj0UITU1lZiYGNRqdZPKL1y48GZCE9qogoICfH19TR2GQTg6OtK3b1/69OlDRkYGCQkJnDx5kvz8fLZu3cqff/5JVFQU3bp1IzIyss0lBgoF9O1rS9++tvzzn3D8eDXffVfGunW2XLjgxLFj3hw7Bu++C5GRBdx2WwUTJtgweLAHtrbmPQSqNVAoFFhbW2Ntbd3oSa663pzq6mo5WapLkurb6np66p539c/GYqnrabp2q4uxbqsb2mdra4u1tXWrT34a09rau5kzYfnyTL78MoCdOy1rHaOr2dnZ0bFjR44dO8bRo0dbVWLU2uqcuRKJkZnw8/Nj3bp13HvvvQCsXr26Rf8hNRpNvcM3BKGlpKWlERsba+owDEqhUBAUFERQUBCjRo3i1KlTJCQkkJ6ezpkzZzhz5gwuLi507dqVbt26tckLYxUK6NrVhq5d3fnkE0hKqua//y1n7VorTp92ITnZg+RkDxYtAi+vcgYMyOH//g/Gjm1dM9y1RgqFQh4mZ47XWLVlra29q53sQgfAqVOW11N0tW7dunHs2DFOnjzJHXfc0WomYWhtdc5cta3TmK3YlClTWLZsmXx72bJlTJ06Va9MYmIi/fv3x83NjZ49e7J//375sbCwMD7++GOioqJwdXXls88+4+DBg3To0AEPDw8+/fRTuWxlZSUvvvgiAQEBBAUF8cEHH8iPzZgxg+eee45hw4bh4uLCyJEjKSwsBGDEiBFUVVXh7OyMs7MzmZmZjR7TjBkzeP/99wGYP38+999/PxMnTsTFxYW+ffty6dIlvWMbNGiQvLjm4cOHb+JdFATTsbW1pVu3bjz88MM8+eSTxMfH4+joSGlpKbt27eLzzz/n22+/JSEhocm9rq1RbKwN777rxqlTLqSna/n44xIGDSrC1lZLXp4Tv/wSyEMPBRIQYMPAgdm8++5lkpMLxdpqgmBCJ0+mc/y4HwC7dyssNikCCAkJwdXVFY1GQ0pKiqnDEYxMJEZmYvjw4SQkJFBQUEB2djbJyckMGjRIflyj0XDXXXcxdepUcnNzeeGFFxgzZgzFxcVymQ0bNnDo0CG2bt3KSy+9xIcffsiePXvYtm0bc+fOJTc3F4AXXniByspKzp07x8GDB/nuu+/49ddf5f2sXLmSBQsWkJubS01NDV9++SUAmzdvxs7OjrKyMsrKyggICGjWMa5evZqnn36awsJCoqKieOuttwAoLS1l9OjRPPvss+Tl5fHaa68xbty4Nv3l0RJZ0mw53t7ejBw5kueff55JkyYRERGBQqEgLS2NdevW8dFHH/Hzzz9z/vx5dDqdqcNtMUFBVjz3nCs7drhRUKBk+fIy7rmnEA+PKtRqG3bv9mPevCCio1V06lTA009fZtu2HDSaG8/I1hTdu3dHkiSznHhBaNtaU3snSRLffVdNdbU1kZEa+vQxdUSmpVAoiI6OBuDs2bMmjqbpWlOdM2eto3+wpVRUQAtcv6MnJgaaMNGBtbU1Y8eOZdWqVVRWVjJx4kS96xL279+PlZUVTz75JACTJ09mwYIFbN68mYkTJwIwZ84cVCoVvXv3xs/Pj0mTJuHu7o67uzshISGcOXMGLy8vFi9ezMmTJ+Wen8cff5yffvqJu+66C4B7772XTp06ATBhwgT+/PNPg7wVI0aMkBdNmzx5Mq+//joAv/32G3FxcYwbNw6AsWPH8vbbb7Nv3z6GDh1qkNcWTG/79u0MGTLE1GEYlZWVFR06dKBDhw6UlJSQmJjIsWPHyM3NJTExkcTERFxcXIiLi6Nr1654e3ubOuQW4+SkYMoUZ6ZMqV0bZe/eKlaurGDjRhvOn3fm9GlPTp+uveDb07OCPn1yGTlS4u67nQgJuflZ7iyx3gmm15rqXUrKZVatCgPgsceUtOFLv5osOjqaQ4cOce7cOSRJahXXw7WmOmfOLDsxOnMGevRo2dc4cgSaeLZy2rRpvPzyy1RWVvL1119TVFQkP5aZmUlISIhe+dDQUL3hbD4+PvLvDg4Oel+yHBwcKC8vJzc3l8rKSrp16yb/o+t0Ovr371/vfhwdHSkrK2vasd5AQ/tNS0vjjz/+wM3NTX68urqarKwsg7yuYB7MaUYsU3B1daV///7069ePrKwsjh07RmJiIqWlpezZs4c9e/YQGBhIly5d6NSpU4vNHGkOlEoYMMCOAQNqV4u8eFHLihWlrF+v4MgRZ/LzHdmwwZENG+DZZ3XExBQweHAFY8bYcNttHtjb2zbpdc6ePcvjjz/OL7/8Ip8BFgRjaC3tnVar5bPPisnJCcbdvYbHH7fsr4V1QkNDUSqVlJSUUFxcrPf9xFy1ljpn7iz7PyAmpjZxaenXaKL4+HgyMjKwtbWla9eubN++XX4sICCA9PR0vfJpaWlMmDChWeF4eXlhb2/PmTNn8Pf3b9ZzW+qMSWBgIHfeeSerV69ukf0L5uHqxNiSKRQKAgICCAgIYMSIESQnJ3Ps2DGSk5PJyMggIyODTZs2ERUVRVxcHJGRka3m4t+b1a6dFXPnujF3LlRUSGzcWM7atVVs22ZPerrj/3qTPPnqK1Cp1PTqlcWIEVrGjnUiIsKtwbapvLycM2fOUF5ebuQjEixda2nv9u49z7ffRgIwdy44OZk4IDNhY2ODn58fmZmZXL58uVUkRq2lzpm7tv1peyOOjk3uzTGW1atX1zu1b9++famuruarr77i0UcfZc2aNZw9e5YRI0Y0a/9KpZIHHniAV199lY8//hhXV1fOnj1LaWkpvXv3bvS5Xl5eck9Oc5OqxowZM4ZXXnmFdevWceedd6LRaNixYwfx8fGoVCqDvY5gWu3atTN1CGbH2tqa2NhYYmNjKS8vJzExkePHj5OVlUVSUhJJSUnY29sTGxtLXFycfBazLXN0VDB+vBPjx9d+Q0tOrmH16nJ+/13i4EFniovt2brVn61b4aWXJCIiComPr2D4cCWjR6vw9BTf7ATTaw3tXX5+Ac8950x5uR0dOlTxzDN2pg7JrAQHB8uJUd3lBeasNdS51qBtf8K2QnFxcfX+A9ra2rJ27Vr++9//4unpyfvvv8+6detuKnH45JNPsLW1pXPnznh4eHD//ffLM881xsnJiZdeeonOnTvj5uZ2w1npmkqlUrF+/XoWLFiAt7c3YWFhfP311wbZt2A+rp5FUbiek5MTffv2ZebMmTz++OP0798fV1dX1Go1R48eZenSpXz66ads2rSJrKwsi5nFLTLSmpdeUrF9uxtFRdb89lsljz5aSPv25UiSguRkD777Loj77gvA39+eLl3yeOKJdH75JYuKCjG0RDANc2/vqqqq+Nvfsjl8OBAbGx3ffWdLG++Ybra6Hpj8/HwTR9I05l7nWguF1MY+XUtKSlCpVBQXF+Pq6irfr1arSUlJITw8XKznABQXF4veGMGgbvQ/tmnTJjFrTjNJksSlS5dITEzk9OnTVFZWyo95eXnRuXNn+QSHJUpL0/LLL+Vs2aJl715HCgr0z3jb2R2gqqov06f/xowZPRg4UCwwKxiHObd3NTU1vPnmKd55Jw5JUrBgQTVPPy3+L6518eJFvvvuO7y8vJg9e7apw7khc65zptZQblAfkRhZKI1Gg61t0y5gFoSmuNH/WGZmZrOneBf+UlNTw/nz50lMTOTs2bPU1NTIjwUFBdG5c2c6duyIs7OzCaM0HUmCxMRq1q8vZ+tWOHTIibKyUmADcAfggUpVSffuRQwaVMOIEfb07OkmEiWhRZhre1ddXc0nnyTx6qsdqamxYsaMKr791k7MRFePwsJCFixYgLW1NfPmzTP7menMtc6Zg+YkRqLj1EK15bVTBPNkqNkNLZW1tTUxMTHExMRQVVVFUlISiYmJXLx4kcuXL3P58mU2btxIWFgYHTt2JDY2FicLupJaoYC4OBvi4moncdBqYf9+J77/vj9Hj1px7JiW4mIHtm1zYNs2ePNNcHOrpHPnfOLjqxk2zJaBA91wcBDXWQi3zhzbu6qqKt555xzvvtsRrdaKO++s4j//EUlRQxwcHIDak1JardbsJ8ExxzrXGpn3X1loMVVVVaLnTDCqlJQUoqKiTB1Gm2BnZ0fXrl3p2rUrZWVlnDx5ksTERDIyMkhJSSElJYUNGzYQHh5Ox44diYmJadPTf9fHygqiokpQKj/i11/n4+pqxbZtlWzYoGbnTitOnXKiqMiBXbsc2LUL/vEPcHLS0KFDDn37ahgyxIqhQ11xd7ec5FIwHHNr7woKipgz5wrLlnVCkhSMG1fFypV2WFmZOjLzdfWoGo1GY/aJkbnVudbKvP/KgiAIQqOcnZ3p27cvffv2paioiFOnTnHq1CkyMzO5cOECFy5cYP369bRr145OnToRHR0tnwlt69LT0/nXv/7Fww8/TPfu3owa5cCoUbXHrlbDzp2VbN6sZs8eJcePO1FebsuhQz4cOlS70KytbQ3R0Xn07q1m8GAlQ4Y4EhR084vNCoKxSZLE0aNpzJplzaFDtWt5PfZYFf/6l0iKbkSpVKJUKtHpdHpDl4W2TSRGFupGYywFwdBuv/12U4fQ5rm5udG/f3/69+9PQUGBnCRlZ2dz/vx5zp8/j5WVFe3bt6djx45ER0dbbM+xvT2MGOHAiBF1w2Xg0CENGzdWsGsXHDniSEmJLYmJXiQmwjffgEIhERxcQufO5fTurWPQIFv69FGJ4XfCdcyhvSspKWHatAzWr48FwMZGx+efa5k1S9TXptBqtfJlBzY25n8tojnUubZAJEYWqqysDBcXF1OHIViQvXv3MnDgQFOHYTE8PDwYOHAgAwcOJC8vj9OnT3Pq1CmuXLnCuXPnOHfunJwkxcbGEh0dbXHD7a5mbQ3x8bbEx9cOn5EkOHGimk2bKtixQ+LwYXtycuxJS1ORlqbit99qn+foqCE6Opdu3aqIj1cwdKgjYWGuWInT8RbNlO2dTqdj//407rjDl+Li2qTI2VnLn38q6NXL/L/gmwuNRiP/3homqxKfsYbRYolRYWEhTz/9NOvWrQPg//7v//jiiy8aXT14xowZLF26VO++Pn36iLnZW4CYfEEwtoqKClOHYLG8vLwYNGgQgwYNIjc3V+5Jys3NlZMkpVJJaGgosbGxxMTEWHyvskIBXbrY0KWLir/9rfa+jAwd27ZVsGtXNYcOWXH6tCMVFbYcPerN0aPw7be15fz9S+nUqYyePWvo3duafv2c8fZ2FkPwLIgp2jtJkrh4MZu33ipl5cpwqqr+SoLOn7fC19foIbVq5eXlQG1S1BpOdIjPWMNoscRo6tSp8ixJAI899hj33Xcfv/76a6PPGzVqFIsXL5Zvt4YsvTUy94sIhbbH09PT1CEIgLe3N0OGDGHIkCHk5uaSlJREUlISWVlZehM3BAcHExsbS2xsLO7u7qYO+6a4uLjQt29fg/WOBwYqmT7dmenTa2/X1EBCgoY//6xk3z4dR4/akZ7uSFaWC1lZLmzZUltOqdQRHFxMTEwFXbpo6dPHmn79nPDxcUapFOust0XGbO8kSeLs2Sw+/LCcn34KpqTEH4AuXSpZsMCWwYPN/0u9Oapb2LW1rBMnPmMNo0W+HSclJbFx40b2799Pnz59APj3v/9NfHw8Z8+eJTo6usHn2tnZ4efn1xJhWaRly5bx008/sWbNGr37m3NdwYwZM4iJieHll182dHhm5+pjbei9E25OTEyMqUMQruHt7Y23tzeDBg2isLBQTpLS09PlbfPmzfj5+clJkre3d6vp+YiMjGTLli0ttraTtTX07m1L795/ncDLy5PYsaOSHTuqOHxYyenTDhQX23LpkhuXLrmxaVNtOYVCR1BQKTExFcTF1dC7txX9+jkSEOAqkqU2wBjtnVarJSEhg88+07B2bQjl5bVr2AQGVvHuu0ruu89BTMV9C+oSIy8vLxNH0jTiM9YwWiQx2rdvHyqVSk6KAPr27YtKpWLv3r2NJkbbt2/Hx8cHNzc3Bg8ezDvvvIOPj09LhGkWhg8fzsiRI3nhhRf07n/uuefIz8+/bmjhjSgUCrKysuTkctq0aUybNu26cmVlZahUqpsPvJUKCwtjxYoV9O3b94ZlG3rvhJuzZ88esSq3GXN3d6dfv37069eP0tJSzpw5Q1JSEqmpqWRnZ5Odnc22bdvw9PQkOjqa6OhogoODzfpLvFarZfPmzdx9991GGwrj5aVgwgRHJkyovV5LkiA9Xcfu3RXs31/N0aNKTp+2p6DAjvR0FenpKrlnSaGQ8PMro337cmJiquncWUGPHrZ06eKCk5N9q0lIhZZt7woKivjhh1y+/96RQ4eC0Gpr/wfDw9W8+qqS+++3QwwKuXWXL18GwLeVjEEUn7GG0SL/OtnZ2fUmMz4+PmRnZzf4vNGjRzNx4kRCQ0NJSUnhtdde47bbbuPIkSPY2dU/i0pVVRVVVVXy7ZKSkls/ACOaPn06n332mV5ipNPpWLlypd6Qwhuprq5uFbOmCIJg/lxcXOjVqxe9evWioqKCs2fPkpSUxIULF8jPz2fv3r3s3bsXR0dHIiMjiY6Opn379g2206Zy/PhxJkyYwJEjR+jevbtJYlAoICREydSpzkyd+tf9GRk6du+uZN8+DceOKTh1yp68PHt5GN7u3X+VtbWtITi4iMjISjp21NKlixU9e9oREeEq2n0LUVZWzrZtOaxaJbFxow+5uZHyY3FxFbz8sg2TJtmLKbgNRJIkUlJSAAgPDzdxNIIxNSsxmj9/Pm+++WajZQ4dOgRQ75ktSZIaPeN17733yr936tSJnj17Ehoaym+//cb48ePrfc57771Xb0xbt27FycmJ2267jYMHD1JZWYmXlxdarZbi4mLgr+FkarUaqP0yUFFRgVarxcrKCkdHR0pLS+st6+zsjFqtpqamBqVSibOzs5yU2dnZoVQqqaysvGHZ0aNH8/jjj3Pw4EGio6NxcnJi8+bN1NTU0KtXL9LS0nj00Uc5dOgQPj4+fPTRR/KsI126dOHRRx9lyZIl6HQ62rdvD0D79u1RKBT8/vvvJCYmsnr1an7//XdqamrYsmULb7zxBhcuXMDb25svv/ySwYMH89133/Hhhx+Sk5NDWFgYH3/8sV6PX1VVFcXFxVhbW2Nvby+vsNylSxdmzZrFf/7zH3JycnjzzTfp1q0bs2bNIicnh1dffZWHH34YqL0wcM6cOfzxxx+oVCpefvllpk6dilar5YknnsDLy4vDhw9z/Phx7rzzTj788EPuv/9+jh49yp133snChQuRJAmlUsnSpUv59NNPKSoqYvTo0XzxxRcolUqWLVvGmjVrCAsLY+XKlQQGBvLDDz8QHh7OU089RVpaGrfddhtKpZIvvviC++67D41GQ3V1tVw31Wo1xcXFrFy5kp9//pkff/yRXbt28fzzzzNx4kT++c9/4urqyn/+8x/69OmDJEmUlpby4osvyvXutddeY+rUqfKsNq6urpSVlaHT6a57Dx0cHNDpdHKC31hZQ9XZa8s2p85eW9bJyQmNRkN5ebn8Wpv+N14oODgYLy8vjh49SmlpKfn5+WRmZpKZmYmVlRW33347W7duRavVEhAQQEBAAIcPHwagW7du5OXlkZ6eDsDIkSPZtm0bGo0GX19fwsLCOHDgAABxcXGUlJSQmpoK1PbE7tmzh4qKCry8vIiKimLv3r0AdOzYEbVazYULFwDkNqKsrAx3d3c6duzI7v99K42JiUGn03Hu3DkABg8ezLFjxyguLsbV1ZXu3buzfft2oHbYlrW1NUlJSQAMGDCA06dPU1BQgJOTE3379uWPP/4AoF27djg6OnLy5EkA4uPjOX/+PLm5udjb2zNo0CA2b94MQGhoKG5ubhw/fhyA3r17k5aWRnZ2NjY2Ntx2221s3rwZSZIICgrCx8eHhIQEAHr06EF2djYZGRkolUqGDx/OH3/8QU1NDf7+/gQFBcltdteuXSkoKCAtLU1+v7dv305VVRU+Pj7ceeed7Ny5k6ysLKysrEhKSuL8+fOcOHGCdu3akZmZiYeHBx07dmT48OEkJiYC0KFDBzQaDefPnwdg6NChHD58mNLSUtzc3IiLi2Pnzp0A8miCs2fPAjBo0CBOnDhBUVERLi4u9OzZk23btgEQERGBra0tp0+fBqB///6cOXOG/Px8HB0d6devn/w3v3TpEn5+fnJMffv25eLFi+Tk5GBnZ8eQIUPkOhsSEoKHhwfHjh0DoFevXly+fJmsrCysra0ZNmwYW7ZsQafTERgYiJ+fH0eOHAGge/fu5OTkcPnyZRQKBSNGjODPP/+kuroaPz8/QkJCOHjwIFDbdnbpUoSb2yVGj4YRI0awZs0eEhMVZGZ6kpHhQWKiRGamOxqNDRcuuHPhgjv/u2z3f/97ZQQFZRMVJeHkdJmQkAoGD/YjJETJ5ctpKBQKhgwZQkJCAiUlJahUKrp27cqOHTsAiIqKQqlUcubMGbnOnjp1isLCQpydnenduzd//vknUPu5Ym9vz6lTpwDo168f586dIy8vD0dHR/r378+W/3V9hYWF4erqyokTJ4DaSZRSU1O5cuUKtra2DB06tN42AqBnz55too0oLS1l3759N91GhIeHI0mwcuVZdu3y5eTJaLKz//qC7uxcQ//+Kdx99xWGDvXCzc2NrVtN20a0a9dOniyrc+fOlJWVycnF7bffzt69e6moqMDT05OYmBj27NkDmLaN2Lp1q/x+Ozs7y21EcHAwFy9eRKPRcO7cOYKCgkzSRhQVFXHp0iWgto3YuXMnarUab29vIiIi2LdvH1D7ndnFxUWOcdiwYezfv5/y8nI8PDzo0KGDXGdjY2OpqakhOTkZwCLaiLr4m0RqhtzcXCkpKanRrbKyUvrmm28klUp13fNVKpX07bffNuclpYiICOn9999v8HG1Wi0VFxfLW3p6ugRIxcXFeuUqKyul06dPS5WVlc16fWO49957pVdffVW+/fDDD0tz5syRtFqtFBcXJ3355ZdSdXW1tHfvXsnT01PKysqSJEmSQkNDpfj4eOnKlSvycQHy45IkSYsXL5ZGjhwpSZIkXbhwQXJ1dZV+/fVXqaysTLp06ZKUnJwsSZIkrV+/Xrp06ZKk1Wqlr7/+WvL19ZXUarUkSZL0wAMPSO+99169sYeGhkq33XabVFRUJB04cECytbWV7rnnHqmgoEA6duyYZG9vL+Xk5EiSJEmTJ0+Wpk2bJpWXl0vHjx+XvL29pV27dsmvERAQICUlJUl5eXlSSEiI1KNHD+nUqVNSYWGh1L59e+mXX36RJEmSfvzxRykuLk5KTU2VKioqpClTpkjPPfecfLzW1tbSqlWrpJqaGmnevHnSbbfdphfvvn37GvxbXH2sV79327Ztk6ysrKSPP/5Yqq6ulhYtWiS1a9dOft4dd9wh/e1vf5PUarWUlJQk+fv7S8eOHWvkr9723Oh/7Pz580aOSGhJWq1WSk1NlTZu3CgtWLBAeuONN/S2hQsXStu2bZMyMzMlnU5nkhiPHDkiAdKRI0dM8vqGUFMjSSdPVkuLF5dITz1VIN12W6EUGFguKRQ6qXag3vWbrW21FBZWKA0enCk99FC69I9/pEnr12dK6emFUnV1takPySLcTHtXWVkp7d+fLr3xxnlp6NA0yc2tQu/vamNTIw0dWip9802VVF7eAkELst9++0164403pFWrVpk6lCYTn7ENKy4urjc3qE+zeoy8vLyadBFafHw8xcXFHDx4kN69ewNw4MABiouL6devX5NfLz8/n/T0dPz9/RssY2dnZ3bDN5pr+vTpzJkzh7///e9UVVXx888/s3nzZg4ePEh1dTVPPvkkUPu+DhkyhN9//50HH3wQgGeffbbJ12D98MMP3H333YwZM4bi4mJCQkLkx+68807590cffZTXX3+d5ORkOnXqdMP9zpkzB5VKRe/evfHz82PSpEm4u7vj7u5OSEgIZ86cwcPDg59//pkLFy7g6OhIXFwcDz/8MD/88AMDBgwAansM6y4eHDJkCM7OznTo0AGoPftx4sQJ7r77br755hvmzZtHaGgoAHPnzuXOO+/k448/BmrPVN1zzz1A7eyICxcubNL7cyMqlYpnn30WhULB9OnTmTlzJmVlZZSVlbFr1y7WrVuHlZUVMTExTJ06ldWrV9OlSxeDvHZbcP78eblXU2j96qb3Dg0NZcSIEeTl5XH27FnOnj0rnz3Nyspi+/btuLq6EhERQVRUFOHh4a2+zTYmKyvo2NGajh1dmDHjr/vLy+HYMQ2HD6s5fVpLcrKCCxdsycy0R6OxJjXVjdRUN/534lfm4VFBQEARgYEaQkK0hIdDZKQV0dG2tGvniKOjQ5u6lqmqCkxR3W7U3ul0OoqKSjh4sIQ9e2o4csSaEydUZGQE6ZWzs9MyZEgFU6bYMW6cLa6uLTORiPAXrVYr9+Z37drVtME0g/iMNYwWucYoNjaWUaNG8eijj7Jo0SKgdrruMWPG6E28EBMTw3vvvce4ceMoKytj/vz5TJgwAX9/f1JTU5k7dy5eXl6MGzeuJcKs9fjjkJHRMvsODISvvrphsZEjR1JSUsL+/fvJysrC29ubXr168eOPP5KcnKy39lNNTQ09evSQbwcFBdWzx/pdvnyZdu3a1fvYL7/8wltvvcXFixcB5GFPTXF1Yubg4IC3t7fe7fLycnJzc9FqtXrxhoaGyl2lTd0PQFpaGg8//DCPPfaY/Hh1dXW9+3F0dJSHod2qq2fjqlsIs6ysjLS0NMrLy/WmytRqtWLiBsFiKBQKeYa7AQMGUF5ezrlz5zh79iwXLlygpKSEhIQEEhISsLKyIjQ0lMjISCIjI/H09GxTX8SNxckJ+ve3pX9//SUtamrg/Hktx46pOXmymjNnJM6ftyI11Z7iYlsKChwpKHDkf9/79NjY1ODjU4q/v5rg4GpCQ3W0a6cgPNyKsDAbQkLscXJyaBVrugAsWgRPPQVffAEzZ5oujurqanJyijl+vJJjx2o4eVLByZMOnD/vTmWlm15ZhUKiQ4cKhg2TGDPGnoEDrbG3F4uxG9Px48epqKjA1dW1we9MQtvVYvOWLFu2jKeffpoRI0YAtQu8fvnll3plzp49K1/vY2VlRWJiIt999x1FRUX4+/szdOhQVq5cabA1KOrVhMSlpdnY2DBp0iSWL19OVlaW/IU6MDCQzp07y2OB69OcLxTBwcHyuNyr39OqqiqmTJnC2rVrGTZsGFZWVvj7+yNJ0k0e0fW8vb1RKpVcvnyZ4OBgoDbBCQgIaPa+AgMDef/99/m///u/Zj+3Jb6ABQYG4ubm1uRE0lINHTrU1CEIRuLk5ES3bt3o1q0bNTU1pKamkpycTHJyMgUFBVy8eJGLFy+yadMm3N3d5SQpLCzMoJMJdO7cmcuXL7fpmU2vZW0NMTFWxMQ4XfdYfr5EYqKGs2c1JCdrSUmRSE21IiPDhtxce6qrrcnIcCUjw5X/DdHXY2Wlw82tEk/PKry9Nfj51eDvLxEYCMHBSkJDrQkPt8XDw97ki2IuWgSzZkFcXO1PaNnkqLq6mtLSci5cqOTs2WrOnYtg+fJLnD9vw6VLTmRleaDTXT+Do729lo4dK+nVS2LIEDtGjLDF3f36v51gHFqtll27dgG118iY86yb1xKfsYbRYomRh4cH33//faNlrv7i7eDgoNd7YGmmTZvG2LFjKSsr49133wVqL0arrq7m66+/Zsb/xlAcOHCA0NBQvWFwV/Px8SE1NbXetaCmTJlC165d2bBhAwMGDKC4uBiNRoO3t7f8E2DBggXk5uYa9PisrKwYP3488+bNY9GiRVy4cIFvvvmGn376qdn7evjhh3nnnXfo1KkT7dq1Iysri+PHjzNq1KgbPrfu/WnKdN1NFRgYSK9evXj99dd5+eWXsbW15cSJE9jb28tDAQU4fPhws4bSCm2DtbU1ERERREREMHr0aPLz8+UkKTU1lcLCQg4ePMjBgwextrYmPDycyMhIIiIibnlhRRsbGy5dukRgYKCBjqZ18/RUMGSIHUOGXD+2rLoaUlN1nDlTxdmz1Zw/ryUlRUF6ujU5OTYUFNii1SrJz3ciP9+J/801UC87u2pcXStRqTS4udXg6VmDh4cOLy8Jb2/w9lbg4aHAw8MKT8/azd3dFltbG2xsbG75y2hdUvTUU/DZZ/DMM81PjiRJoqamBo1GQ1VVNQUF1WRmVnP5cg2ZmRLZ2ZCdrSA3V0lOjg1XrjiQm+tCTY1bg/t0cqohIkJNTIyOPn2sGTzYnrg4K6ytxfA4c5GQkEBhYSFOTk56o3NaA/EZaxhipnsz0a9fP1xcXOQvBVD7hWL9+vXMmTOHefPmIUkSPXv2bPSamddff527776bqqoqNl49fRG1s678/PPPvPjii5w/fx5/f3++/fZb2rdvz4cffsjw4cNRKBQ8/vjjREREGPwY//nPf/LEE08QFBSESqXirbfekmfYa47JkydTWFjIHXfcQUZGBv7+/syaNatJidFLL73E008/zaxZs/j666+ZNGnSzRzKdZYtW8Zzzz1Hu3bt0Gg0dOrUiU8//dQg+24r6mbAEyybp6cnnp6e9O3bF41GQ0pKCsnJyZw7d46SkhI5aYLatZXat29P+/btCQ8Pb9bC1AAXLlzgueeeY9myZWLs/Q3Y2EBkpJLISAfuusvhusdraiArSyI1tZrU1GouXaohPV1HZqaCrCwlOTnW5ObaolZbU1VlQ26uDc05v6ZU6nB0rMbJqRxHxxocHLTY2elwcKjd7O0lHB0lHBxqNxub2t4xGxsJa2vF/36HhARnfvnFg9mzJRYsUKBQwIIFtYnOrFkK1q7Np2PHMiorQa1WoFbX/qyqUqBWK6ioUFJaakVpqQ0VFTaUl9tQUeGAJDUtWVMqdfj5aVCp8unc2ZkOHZR0725Ht262BAZao1CIJMhclZWVybMCDho0qNVNhS8+Yw1DIRlyvJQZqJtusG463TpqtZqUlJSb+nBti8rKylpsNXjBMt3of+zAgQN6U8ALwtUkSSI3N1dOjNLT09FqtfLjSqWSwMBAIiIiaN++PQEBATfsWUhISKBHjx4mXcfIkkgSlJbClSsSmZm1PSzZ2Vqys3Xk5Ejk5SnIz1dQVGRFaamSsjJrSkut5QVKDWX2bPj889o1pK6O7emn4ZoR/c1ib6/F01ODt7cWHx8dvr4SAQEKAgOtiI62IyrKmqCg2oRNtHetiyRJ/Pzzz5w8eZKAgAAeeeSRVjWMDkSda0xDuUF9RI+RhaqbPEAQjCUuLs7UIQhmTKFQ4OPjg4+PD/3796eqqopLly5x4cIFLly4IK9Xk56ezrZt27C3t6ddu3Zyj9LVk9QIpqFQgKsruLoqiIy0BWxv+BxJgooKKCqC/Hzt/7YaysslSkt1lJdLV221ZSsqanuwampqhwDW1CjQaGDvXlc6dULuKbo2tgULYOdOiZMn4Y47inF2VmBvL+HgAI6OCuztFbi4KPD0VOLpaYWXlzWenlZ4eChwcwN7eyvg+t60+oj2rnU5evQoJ0+eRKlUMmbMmFaXFIGoc4YiEiMLVVpaikqlMnUYggXZuXMnI0eONHUYQithZ2dHVFQUUVFRABQVFXHx4kUuXLjAxYsXqays5PTp0/LijZ6enoSHhxMeHk5YWBhOTuIC9tZAoaidYc/JCQIDrQArmpJQ1afu2qJnnqlNgq7tMXrmGThxQsHChTBzptutB98I0d61HtnZ2WzYsAGoXcz3ZiaFMgeizhmGSIwEQRAEs+fm5kb37t3p3r07Op2OzMxMuTfp8uXL5Ofnk5+fL6947uvrKw/F02g0pgxdMJK6iRXqJlqoS44kCebMqZ22uzYpMl2MgnkpLi5m+fLl1NTUEBkZSf/+/U0dkmBiIjGyUOI6K8HYrl7DTBBuhVKpJCgoiKCgIAYPHoxarebSpUukpKSQkpLClStXuHLlCmVlZQwcOJDVq1dz8uRJuUcpODi41V1YLTTNtclR3ax0xk6KRHtn/iorK/n+++8pKSnB29ub8ePHt+o11USdMwyRGAmCIAitmr29PdHR0fIXg/LyclJTU0lJSZEnmbl8+TKXL19m165dWFlZERwcTHh4OKGhoQQGBopEqQ25OjnasQNOnBA9RYK+8vJy/vvf/5Kbm4urqyvTp0/HwaFp148JbZtIjCyUWq3Gzu76tSwEoaWcPXuWsLAwU4chWAAnJyc6duxIQEAA+/bt47HHHpOvUUpJSaGkpITU1FRSU1OB2nXWAgMDCQ0NJTQ0lODgYNE+tnJ1SdBTT5kmKRLtnfkqLi7mu+++Iz8/HycnJ6ZPn94mrrkWdc4wRGIkCIIgtEkpKSm8++67TJgwge7du9OlSxckSaKgoICUlBRSU1O5dOkSpaWlpKWlkZaWxq5du1Aqlfj7+8uJUkhIiDib3ArNnAkzZoDIcYU6GRkZrFy5Up6++f7778fT09PUYQlmRCRGFsrFxcXUIQgWZtCgQaYOQRBQKBTyIrM9e/ZEkiQKCwu5dOmSvBUWFpKRkUFGRgZ79+4FaidzqEuSQkJCbrgWhmAeTJUUifbO/Bw7doz169dTU1ODt7d3m+kpqiPqnGGIxMhCVVRUiAVeBaM6ceKEWHxOMDsKhQIPDw88PDzo1q0bUDvUJi0tTe5RysvLkyd0OHjwIAAqlYrg4GB58/X1xcrKypSHIpgR0d6Zj6qqKjZt2kRCQgIAMTExjBs3rs0NlxV1zjBa3wpWpqDVwvbt8MMPtT+vWo3dEMLCwti/f7/efbNmzWL+/PkGfZ2raQ18DDdj//799O3bF2dnZ4KCgvjxxx8bLBcXF4ebmxs+Pj488MADlJWV1Vt2yZIlKBQK3n77bb37586di0KhYMWKFXrlFi1aJJfJzs5u1TPSmLuioiJThyAITaJSqejcuTN33XUXs2fP5sUXX2TSpEn06dMHf39/lEolxcXFnDx5kt9//52vv/6a999/nyVLlvDHH39w7tw5KisrTX0YggmJ9s48HDhwgPfee4+EhAQUCgVDhgzh3nvvbXNJEYg6Zyiix+hGVq+uXQDh8uW/7gsKql0gYfx408V1i0x9ZjMrK4sJEybw9ddfM3LkSIqLixv8p46IiOD3338nMDCQiooKZs6cyd///nc++OCDBssvX76cV199FQBJkli5ciXt27fXK+fu7s67777LQw89JGakMgIxfFMwNgcHB6Kiom75+iAnJyc6dOhAhw4dgNp1kTIyMkhPT5c3tVqtN6EDgJeXl9yjFBgYiLe3N0qlOB9pCUR7Z1pqtZpPPvlEXsNMpVIxfvx4QkNDTRxZyxF1zjBEYtSY1avhnntqV4e7WkZG7f0//WSU5GjJkiUsX76c0NBQVqxYQXR0NGvXruXdd9/l+++/JyYmhjVr1hAQEIBOp+Oee+5h9+7d1NTUMGzYMBYtWoSHhwfbt29n2rRpJCYm4ubmxqpVq3j11Vc5duyY3heHyspKfH19SUxMlBuRrVu38swzz3Dy5EmDHNOnn37KjBkzuPPOOwHkMf/18fLykn+XJAmFQkFKSkqD+27fvj2FhYUkJCTQvXt39u7dS3Bw8HXlevfuTVlZGYsXL+axxx67xSMSbqRnz56mDkGwMLGxsSQmJmJra2vQ/dra2sprIkFtu5SXl6eXKOXl5cnb0aNHAbCxsSEgIIDAwEB5U6lUoqe6DRLtnWlIksSJEyfYvHmz3sLOjzzySJtPHESdMwxx6qohWm1tT9G1SRH8dd8zzxh8WF1Dtm3bxh133EFBQQFBQUH079+fwYMHk5+fT1hYGB9++KFcdvz48fJCh6Wlpbz11lsADBkyhAkTJjB79mxSUlJ46qmnWLJkyXVnUx0cHBgzZgyrVq2S7/vxxx+59957641tzJgxuLm51bu9//779T7n0KFDKBQKOnbsiL+/P/fddx+FhYUNHn9aWhpubm44OzuzZs0aZs+e3ej7NW3aNJYvXw7A8uXLmTZtWr3l3njjDd59912qq6sb3Z9w67Zt22bqEAQLZIx6p1Ao8Pb2pnv37tx9993Mnj2bv/3tb0ydOpUBAwYQHh6OnZ0d1dXVXLp0ib1797Jq1So+++wzPvroI5YvX86OHTs4f/68GILXRoj2zrgkSeLChQt8/fXXrFmzhvLycry8vBg3bhzz589v80kRiDpnKKLHqCG7dukPn7uWJEF6em25IUNu+eWGDx+uN7ytsrKSV155Rb7duXNnxo0bB8Ddd99NcnIykyZNAmDs2LH85z//AWpXhJ8+fbr8vGeffZZ58+bJt99//326dOnCmDFjuO+++4iPj683nnvvvZd33nmHF154gZqaGtasWcOePXvqLbt+/fpmH29GRgbLli1j06ZNBAYG8sgjj/DMM8+wdOnSesuHhIRQVFRETk4OixYtwt/fv9H933vvvfTu3Zt3332XtWvX8vbbb7Ns2bLryg0fPpzAwECWLFnCXXfd1ezjEATBfB09epS77rqLAwcOyBMrGIujoyNRUVFERUUBoNPpyM/Pl2e7y8jIIDs7m/Lycs6dO8e5c+fk53p6euLv76+3ienCBeF6kiSRlpbG9u3b5ZEkdnZ2DBw4kPj4eJNfNiC0PiIxakhWlmHL3cCWLVvo27evfHvWrFl6j/v4+Mi/Ozg44O3trXe7vLwcgJqaGl544QXWrFlDYWEhkiTpDUVzdHRk8uTJvPPOO2zevLnBeEaNGsUDDzxAamoqZ8+eJSgoSP6ANwQHBwemT58u7/O1115j8ODBN3yej48Po0eP5v7772ffvn0NlvP19SUmJoa5c+fSs2dP3N3dGyz7xhtvMHPmTEaNGtX8AxGaLCIiwtQhCBZGkiSqq6uR6uv5NzKlUom3tzfe3t507doVqG2vs7OzuXz5spwsFRQUkJ+fT35+vt7QZTc3t+uSJTGzqPkS7V3LkiSJc+fOsXv3btLT04Haa6d79+7NwIEDcXR0NHGExifqnGGIxKghN+iRaHY5I1m2bBm7du1i3759BAQEsGnTJmZeteR3cnIyX331FRMmTOD5559vcCY4Ozs77r77blatWsWZM2caHEYHMHr0aHbt2lXvY3PnzmXu3LnX3d+pUye928354qLT6bhw4cINy02dOpUHH3xQnomuISNGjMDf37/B3irBMAx9nYcgtHbW1tYEBQURFBQk31dRUUFmZiZZWVnyVlhYSFFREUVFRSQlJcllXVxc9BIlX19f3NzcxDVLZkC0dy1DrVZz4sQJDh06RG5uLlCbEHXt2pVBgwa1qXWJmkvUOcMQiVFDBg6snX0uI6P+64wUitrHBw40fmyNKC0txc7ODjc3N/Ly8vjoo4/kx3Q6HQ888ADz5s1jypQpDBo0iB9//FEekhcWFsb8+fOZMWMGUDscbd68eaSlpXHo0KEGX/P3339vdpwzZszgscceY/r06fj7+/Pee+/JEzFca8OGDbRv356oqCiys7N57bXXGDp06A1fY+LEifj6+jKkCUMd33jjDaZOndrcwxCa4fTp0/VOgiEIwl8cHR2JiIjQO/tbWVlJdna2XrKUn59PaWkppaWlesPw7Ozs8PHxwdfXV958fHywt7c3xeFYLNHeGY4kSWRlZXHkyBFOnDghXxNsZ2dHr1696NOnj0VcQ3Qjos4ZhkiMGmJlVTsl9z331CZBVydHdWfjPvustpwZuf/++/ntt9/w8fEhODiYRx55hOTkZAA++ugjrKysmDNnDqWlpSxevJjx48czZMgQ3N3dyc/P1xvON3z4cO677z7atWtHu3btDBrn8OHDefbZZ+nfvz8ajYaRI0fy6aefyo87Ozvz+++/M3DgQK5cucLs2bO5cuUKKpWK0aNH60020RBHR8cmD48bOXIkUVFR160nJQiCYGoODg56s+BB7ZThV65c0UuWcnNzqaqqkmfGu1rdOnBXJ0yenp5i+nDBbBUVFXHixAlOnDhBXl6efL+3tze9evUiLi5OJPyCwSkkcxh8bUAlJSWoVCqKi4txdXWV71er1aSkpBAeHt68f6T61jEKDq5NilrxOkZarVbvosR9+/bx+eef88MPP5gwKqE1u9H/WFlZmbgmQjCqyspKTp48SadOnSxi8gKtVkt+fj5XrlzhypUr5OTkcOXKFYqLi+stb2Vlhaenp3ztk5eXF97e3nh6emJtLc6b3grR3t2cgoICzpw5Q1JSkl5yb21tTWxsLD179iQkJEQMF62HqHMNayg3qI9o+W5k/Hi4++7a2eeysmqvKRo40Ox6ippLrVbj5OQk346Pj29whjpBMIQzZ86IdRYEo3JwcEChUFhEUgS1iY6Pjw8+Pj507txZvr+yslJOkq5OmjQaDTk5OeTk5OjtR6FQ4OHhoZcs1f0urmNoGtHeNY1OpyMjI4Pk5GTOnDmjVxcVCgVhYWF06dKF2NhY7OzsTBip+RN1zjBEYtQUVlYGmZLbnNTU1Jg6BMHC5OfnmzoEwcJcunSJV199lUWLFrXpFe9vxMHBgdDQUL33QJIkiouLyc3Nlbe8vDxyc3NRq9XyzHjXcnFxwdPTEw8Pj+t+il6mv4j2rn6SJFFYWEhKSgrnz58nJSUFtVotP65UKgkLCyMmJoaYmJgbnt0X/iLqnGGIVsxCiXHlgrFZ4vSpgmnl5+ezadMm8vPzLToxqo9CoZAX4o6MjJTvlySJsrIyvUSpbisvL5cnfEhNTb1uf66urtclS+7u7ri5uVlcT5No72rpdDpycnJIS0vj0qVLXLp0ibKyMr0yDg4OtGvXTl73y1J6eA1N1DnDEImRhRLjUAVj69evn6lDEAThBhQKBS4uLri4uFw36U5lZaXeOkt1vxcUFKBWqykuLqa4uJiLFy9et18nJyc5EXNzc5MTprqtrfU2WWJ7p9PpyMvLk6ebz8zMJDs7W55Fro6VlRWBgYG0b9+e9u3bExAQIE7WGoAl1rmW0LZaIqHJ6i5EEwRj2bp1KyNHjjR1GIIg3CQHBwcCAwMJDAzUu1+SJCoqKvQSpbqfhYWFqNVqysvLKS8vJyMjo959u7i44Obmhqura72bi4tLq/ry3JbbO0mSKCkpIS8vT96ys7PrTYKgdn2d4OBgQkNDCQkJITAwEBsbGxNE3ra15TpnTCIxEgRBEAThpikUCpycnHBycqp3HRW1Wi0vUHv1YrV1v2s0GnmIXmOv4ezsfF2y5OzsjJOTk/zTyclJb8ZV4eZIkkRpaancC1hQUCAPrczPz0ej0dT7PFtbW/z9/QkICJB/enp6ilnkhFZDJEYWSszuIhjb1WuwCIIx+Pr68thjj+Hr62vqUCyavb09fn5++Pn5XfeYJElUVlbKSVJpaSklJSV6W2lpKVqtVk6eGup1quPg4KCXLNX9dHR0xMHBAQcHB+zt7eXf7ezsDP7F3VzbO0mS0Gg0lJeXU1FRQXl5OWVlZXICVLeVlJSg1Wob3I9SqcTDw0Nv1kKRBJmWuda51kYkRhaqNQ1JENoGcV2bYGyBgYG88cYbBAQEmDoUoQEKhQJHR0ccHR2vG6JXR5IkysvLr0uWSkpK5C/2dUP1dDodlZWVVFZW6i0K2hilUqmXKNUlS7a2tvJ27e2r77O2tsbKykrerK2tDX4hvE6nQ6vVyltNTQ01NTVUVVVRVVWFRqORf792q0uA6ramzkqrVCpxdXVFpVLh7u6Ol5eXvLm7u4ueOTMjPmMNQyRGZiAsLIwVK1bQt29f+b5Zs2bh5+fH/PnzW+Q1Kysr9WYJqvtwqjvTM3fuXObOnVvvc2fOnMnWrVu5ePEi+/bt04v7WgqFgvbt23P+/Hn5vuTkZKKiohg5ciQbN26Uy8XHx7N371653KhRo5g8eTIzZsy4lUMVzERiYqL4gioYVWlpKd9//z2PP/44Li4upg5HuEl1w+icnZ0bbUPqep/qEqWrE6aysjI5Yarb1Go11dXV6HQ6KioqqKioMFjM58+fJzIyUk6UrKysUCqVTe5NuTYJkiTJYLEB2NjYyEMPnZyccHV1xc3NDZVKJW+t7bouSyc+Yw1DJEaC7MKFC/UOdbhWt27dmDp1KtOnT2/SfpVKJQcOHKBPnz4ALFu2TG962Dpnzpxh8+bNjBgxonmBC4Ig1CM5OZmXXnqJ22+/ne7du5s6HKGFXd371FTV1dWo1errkqa6Xpirt4buuzqBuZokSXLPjqHVJVx2dnZ6W11P1tWbo6OjXhLk6OhocdOnC0JTicToBpKTob7rQV1coJ7v9i3miy++4NNPP6W0tJTRo0fz5ZdfNnvhM0mS5LNVTk5ONx3LrFmzAJrcjT5lyhSWLVsmJ0Y//PADU6ZM4cCBA3rlnn32Wd58802RGLVRjfUsCoIgmIKNjQ02NjYG6VGUJEke8lZQUICTk5Nez09j1+xc2yN0dU/TtcP0mtPzJFgO8RlrGKKPtBHJyRAVBT16XL9FRdU+bgybNm3i/fff57fffiM1NZXy8nKee+65esteuXKFRx99lNDQULp3787f//539u3bx+rVq7n//vvlcvXNKNO9e3cCAwOZMWOGQVdQnjRpEmvWrEGr1XLo0CG8vLzqvUhwxowZZGRksGXLFoO9tmA+6lvbRBAEoa1QKBRYWVlha2tLVlaWPAW5p6cnPj4++Pv7N7gFBATobb6+vnh6euLm5oaLiwuOjo7Y2dlhZWUlkiKhXuIz1jBEYtSIup6i77+HI0f+2r7/Xv9xQxg+fLjeYneLFy+WH1u5ciWzZs0iNjYWJycn3n33XVasWFHvfvbv38/o0aM5efIkS5cupaKignnz5rFhwwZee+01udy1aw3s3LmTS5cucezYMSoqKnjooYcMdmyenp506dKFrVu3smzZMqZOnVpvORsbG+bOncubb75psNcWzEdOTo6pQxAEQTAK0d4JxibqnGGIxKgJYmOhe/e/tthYw7/Gli1b5LUdioqKePDBB+XHMjMzCQkJkW+HhoZSXl5OcXHxdfu58847ycnJ4ZFHHuGf//wnt99+O1u2bOGdd95h7dq1crlrL6gcOHAgNjY2eHt78/nnn7Nhw4YG1ym4GdOmTeO///0vq1evZtKkSQ2We/DBB7l8+TJbt2412GsL5kFMES8Ym42NDV5eXmIxScHoRHsnGJuoc4YhEqNWICAggLS0NPl2Wloajo6OqFSq68p+//33JCcnM2PGDLp06cK7776Lp6cnQ4cOJSgoSC7X2HjquqTJkLPg3H333axbt45OnTrh7e3dYDkbGxteeeUV0WvUBg0ZMsTUIQgWpnPnzuTm5tK5c2dThyJYGNHeCcYm6pxhiMSoFZg4cSKLFi3izJkzlJeXM2/ePCZPnlxv2fvuu4+PP/6Y0aNH8/jjj/PHH39QVFTE6dOnmTJlilzu6t6mU6dOcfz4cbRaLYWFhTzzzDMMHz68wbMPGo0GtVotLxRX93tjHB0d2bJlC1988cUNj/fBBx8kLS2NQ4cO3bCs0Hps2rTJ1CEIFkjUO8EURL0TjE3UOcMQiVETJCVBQsJfW1KScV9/9OjRvPjii4wePZrQ0FDs7Oz4+OOP6y17MwuuXblyhYkTJ+Lq6kpsbCxWVlYsWbJEfnzWrFnyTHQAI0aMwMHBgbS0NAYPHoyDgwOXLl264ev06dOH9u3b37Ccra0tr7zyCgUFBc0+FkEQhDqJiYlMnz6dxMREU4ciCIIgtAIKydCrhplYSUkJKpWK4uJivems1Wo1KSkphIeHY29v36R91c1K15Bz54w7ZbchVVZW4uDgYOowhDbkRv9jSUlJxLbEBXqC0ICEhAR69OjBkSNHxDpGglGJ9k4wNlHnGtZQblAfsY5RIyIja5Mfc1jHyNCsrcWfXjAuDw8PU4cgCIJgFKK9E4xN1DnDEEPpbiAyUn9GurqtNSdFABUVFaYOQbAwx44dM3UIgiAIRiHaO8HYRJ0zDJEYCYIgCIIgCIJg8URiZKGcnJxMHYJgYXr16mXqEAQLExkZydq1a4ls7V38Qqsj2jvB2ESdMwyRGFkoQy7eKghNcfnyZVOHIFgYFxcXwsLCGl23TRBagmjvBGMTdc4wRGJkoaqrq00dgmBhsrKyTB2CYGEyMjJ45513yMjIMHUogoUR7Z1gbKLOGYZIjCyUQqEwdQiChREzIQrGduXKFX788UeuXLli6lAECyPaO8HYRJ0zDJEYWagbzeMuCIY2bNgwU4cgCIJgFKK9E4xN1DnDEImRhSopKTF1CIKF2bJli6lDEARBMArR3gnGJuqcYYjEyAyEhYXh6upKZWWlfF9JSQkODg7ExMS0yGtKktTgY6mpqTg4ODBr1qwGyyxcuJB27drh6upKSEgI7733XoNlZ8yYgUKhYPfu3Xr39+vXD4VCQXZ2tlzOysqKpKQkucyKFSsYMmRIE49KMGc6nc7UIQiCIBiFaO8EYxN1zjBEYmQm/Pz8WLdunXx79erVBAcHt9jr2draNvjYs88+S/fu3Rt9/qhRo0hISKCkpIQDBw6wbNkyfv/99wbLR0ZGsmzZMvl2SkoK+fn515VTqVT8/e9/b8IRCK1NYGCgqUMQLIynpyfjx4/H09PT1KEIFka0d4KxiTpnGCIxuoHkZEhIuH5LTjbs60yZMkUvcVi2bBlTp07VK5OYmEj//v1xc3OjZ8+e7N+//6ZeS5KkBi/S27RpE5IkMXz48Eb3ERYWhpubm3xboVCQkpLSYPnx48ezbt06eTa85cuXM2XKlOvKPfLII/z++++cOXPmusdSU1Oxt7fnq6++wsfHh+DgYLZv384333yDv78/ISEh7Nixo9G4BdPx8/MzdQiChQkNDWXRokWEhoaaOhTBwoj2TjA2UecMQyRGjUhOhqgo6NHj+i0qyrDJ0fDhw0lISKCgoIDs7GySk5MZNGiQ/LhGo+Guu+5i6tSp5Obm8sILLzBmzBiKi4vr3d9XX31F165dCQkJ4eGHH2b9+vXs3LmTJ598ksOHD1NRUXHdczQaDS+++CIfffRRk2Jevnw5Li4uBAQEoFarueeeexos6+bmRp8+fdi0aRMAP/zww3WJH4CHhwdPPPFEg71GGo2G1NRUMjIymDNnDtOnT+f06dNcunSJv/3tbzzzzDNNil0wviNHjpg6BMHCVFZW8vPPP+sNUxYEYxDtnWBsos4ZhkiMGlFaWvvz++/hyJG/tu+/13/cEKytrRk7diyrVq1ixYoVTJw4EaXyrz/P/v37sbKy4sknn8TGxobJkycTGRnJ5s2br9tXVVUVqamprF+/niNHjhAfH8/XX3/NRx99xMCBAxtcHfmTTz7hjjvuICIiokkxT506ldLSUhITE5k+fTqOjo43LL9s2TKOHTuGg4MDUVFR9ZZ77rnn+O233+rtNZIkiXnz5mFjY8OECRPIyMjg5ZdfxtbWlgkTJnDq1CkxzlYQBACSkpKYNWuW3nWLgiAIgtAQkRg1QWwsdO/+1xYb2zKvM23aNJYvX87y5cuZNm2a3mOZmZmEhITo3RcaGkpmZuZ1+7Gzs2PcuHG8/fbbPPnkk+h0OpYuXcpPP/2ETqfj1KlT1yUxGRkZfPvtt8ybN6/ZcXfq1AknJyfeeeedRsuNGTOGHTt28PXXX193fFfz9PTkiSee4O2336732OqmGndwcADA29tbvl1dXY1Go2n2MQgt70bXrQmCILQVor0TjE3UOcMQq0GZkfj4eDIyMrC1taVr165s375dfiwgIID09HS98mlpaUyYMOG6/VRVVTF37lwee+wx7OzsWL9+Pa+//joKhYLx48dz1113UVNTg42NjfycQ4cOkZ6eTmRkJABlZWXodDpSU1PZuHHjDWPX6XRcuHCh0TL29vaMHDmSf//736SlpTVa9vnnn6d9+/ZyPELrl5OTIyexgiAIbZlo7wRjE3XOMERiZGZWr16tN4SuTt++famuruarr77i0UcfZc2aNZw9e5YRI0ZcV9bW1patW7fK+xk3btx1ZYqLi+UeF4DRo0frTZ7w0UcfkZubyyeffFJvnP/9738ZOXIk3t7eHD9+nH/+85+8/PLLNzy+v//97zz44IP4+/s3Ws7T05PHH3+czz//nM6dO99wv4L5u3z5Mh07djR1GIIgCC1OtHeCsYk6ZxgiMWqCa4ent+Rw9bi4uHrvt7W1Ze3atTzxxBO8/PLLREREsG7dOlQq1XVlFQoFCoWiWa9rZ2enN6OJs7MzZWVl8jS3u3btYvTo0ZSVlQFw8OBBXnjhBcrLy/Hz8+Oxxx5rdN2jOkFBQQQFBTUppueff54vv/yyWcchmK/m1klBuFUKhQIbGxtR9wSjE3VOMDZR5wxDITW20mcrVFJSgkqlori4WL4WBUCtVpOSkkJ4eDj29vZN2lfdrHQNOXcOxEgvQah1M/9jgiAIgiAILamh3KA+YvKFRkRG1iY/V89IV7e19qSopKTE1CEIFubPP/80dQiCBRL1TjAFUe8EYxN1zjDEULobaM3JT2PaWEeh0ArULe4rCMaSlJTEY489xq+//kpsS00nKgj1EO2dYGyizhmG6DGyUFfPSCcIxiBW5RaMrbKykgsXLogFXgWjE+2dYGyizhmGSIwslK2tralDECzMtetwCYIgtFWivROMTdQ5wxCJkYUqLy83dQiChTl48KCpQxAEQTAK0d4JxibqnGGIxEgQBEEQBEEQBIsnEiML5ejoaOoQBAvTpUsXU4cgWJjw8HC+/vprwsPDTR2KYGFEeycYm6hzhiESIwtVU1Nj6hAEC1NUVGTqEAQL4+7uzsCBA3F3dzd1KIKFEe2dYGyizhmGSIwslEajMXUIgoW5dOmSqUMQLMyVK1f45JNPuHLliqlDESyMaO8EYxN1zjBEYtQMVVUts9+wsDD279+vd9+sWbOYP39+y7xgCyopKeHhhx/Gw8MDNzc3pk6d2mDZsLAwHB0dcXZ2xtnZmVmzZjVYVqFQEBERoXdfcnIyCoWCUaNG6ZXr16+fXrlRo0axZMmSmzsgQRBarYyMDP7973+TkZFh6lAEQRCEVqDFEqN33nmHfv364ejoiJubW5OeI0kS8+fPJyAgAAcHB4YMGcKpU6daKsRmWbQIXFxqf7YFrq6uLbLfBx98EGdnZ1JSUsjNzeXFF19stPyff/5JWVkZZWVlLFy4sNGySqWSAwcOyLeXLVtGZD0r8J45c4bNmzff3AEILWbEiBGmDkEQBMEoRHsnGJuoc4bRYomRRqNh4sSJPP74401+zj/+8Q8++eQTvvzySw4dOoSfnx/Dhw+ntLS0pcJskkWLYNYsiI2t/Wns5GjJkiWMGDGCRx99FBcXF3r27ElGRgZPPvkkKpWKPn36kJmZCYBOp2P8+PH4+Pjg4eHBxIkTKSgoAGD79u0EBgZSUFBAWVkZq1atIjo6utmLH0qSVO/9p06d4siRI3zyySeoVCpsbGzo1q3brR38VaZMmcKyZcvk2z/88ANTpky5rtyzzz7Lm2++abDXFQxj586dpg5BEATBKER7JxibqHOG0WKJ0Ztvvsmzzz5L586dm1RekiQ+++wz5s2bx/jx4+nUqRNLly6loqKC5cuXt1SYN1SXFD31FBw9WvvTFMnRtm3buOOOOygoKCAoKIj+/fszePBg8vPzCQsL48MPP5TLjh8/npSUFFJSUigtLeWtt94CYMiQIUyYMIHZs2eTk5PDU089xZIlS3BwcLju9a5cucKjjz5KaGgo3bt35+9//zv79u1j9erV3H///fXGePjwYaKiopg+fTqenp707t2bXbt2NXpcY8eOxdfXl3Hjxt1wfOykSZNYs2YNWq2WQ4cO4eXlVe9sUzNmzCAjI4MtW7Y0uj/BuNRqtalDEARBMArR3gnGJuqcYZjNNUYpKSlkZ2frdQXa2dkxePBg9u7d2+DzqqqqKCkp0dsM5eqkaMECUCprf7ZEcjR8+HDc3NzkbfHixXqPd+7cmXHjxmFjY8Pdd9+Nk5MTkyZNwtramrFjx3LixAmgdrjZ9OnTcXJyQqVS8eyzz7J79255P++//z6HDh1izJgx3HfffcTHx9cbz/79+xk9ejQnT56UE9R58+axYcMGXnvttXqfU5eM3H777WRnZ/Pyyy8zduxYucfqWsuXLyc1NZXk5GRCQkIYO3Zsg71RAJ6ennTp0oWtW7eybNmyBq9fsrGxYe7cuaLXyMx4e3ubOgTBwqhUKgYNGoRKpTJ1KIKFEe2dYGyizhmGtakDqJOdnQ2Ar6+v3v2+vr6N9iS899579X4B3rp1K05OTtx2220cPHiQyspKvLy80Gq1FBcXA2Bvbw/8lWW7uLhQUVGBVqtl6VI75syxZ/ZsiQULFCgUtftVKGqTI0mSmDVLQWVlJQ8/XIOzs7OclNnZ2aFUKuUhas7OzqjVampqalAqlXplbW1tAVizZg29evXCyckJjUbD7NmzqfrfbA8VFRV4eHhQWVmJtbU1kiTh7u5OdXU1NTU16HQ6eX8FBQXMmzeP3377jaKiIiRJwsPDQy5bXV3N2LFj+fjjj1m9ejXFxcXY2Nhga2tLeXk5AA4ODgwbNox///vfPPDAA3h7ezNmzBhefPFFioqKWLVqlTxRgoODAzqdTo41PDyce++9l4qKCkaMGEH79u3ZunUrI0eOvO797tu3LxUVFUiSxFtvvUVgYCAnTpwgLCzsuvew7n0YN24c3377Lfv27WPbtm1s2rQJnU6HRqORy9bU1HDvvffy9ttvs27dOvm5xcXF2NraYm1tTUVFBYD8fldXV6NQKHB1daWkpARJkq4r6+joSE1NjTyjn0qlkste+x5eW9bV1ZWysjJ0Oh3W1tbY29tTVlZW73vYWNnG6qyVlRWOjo7y0NNry15dD68t25w6e23ZuvewvLxcfq1NmzYBEBwcjJeXF0ePHkWj0RAaGkpmZiaZmZlYWVlx++23s3XrVrRaLQEBAQQEBHD48GEAunXrRl5eHunp6QCMHDmSbdu2odFo8PX1JSwsTL7mLC4ujpKSElJTU4HaEw179uyhoqICLy8voqKi5BMsHTt2RK1Wc+HCBQC5jSgrK8Pd3Z2OHTvKJxNiYmLQ6XScO3cOgMGDB3Ps2DGKi4txdXWle/fubN++HYDIyEisra1JSkoCYMCAAZw+fZqCggKcnJzo27cvf/zxBwDt2rXD0dGRkydPAhAfH8/58+fJzc3F3t6eQYMGydfJhYaG4ubmxvHjxwHo3bs3aWlpZGdnY2Njw2233cbmzZuRJImgoCB8fHxISEgAoEePHmRnZ5ORkYFSqWT48OH88ccf1NTU4O/vT1BQEIcOHQKga9euFBQUkJaWJr/f27dvp6qqCh8fH9q1aydPEtO5c2fKyspISUkB4Pbbb2fv3r1UVFTg6elJTEwMe/bsAaBDhw5oNBrOnz8PwNChQzl8+DClpaW4ubkRFxcnDwGJjo4G4OzZswAMGjSIEydOUFRUJA8j3rZtGwARERHY2tpy+vRpAPr378+ZM2fIz8/H0dGRfv36cf78eV544QW0Wi2ZmZkkJiYCtW3PxYsXycnJwc7OjiFDhsh1NiQkBA8PD44dOwZAr169uHz5MllZWVhbWzNs2DC2bNmCTqcjMDAQPz8/jhw5AkD37t3Jycnh8uXLKBQKRowYwZ9//kl1dTV+fn6EhITIq9N36dKFoqIi+fNtxIgR7Ny5E7Vajbe3NxEREezbtw+ATp06UVFRwcWLFwEYNmwY+/fvp7y8HA8PDzp06CDX2djYWGpqakhOTgZqRwokJCRQUlKCSqWia9eu7NixA4CoqCiUSiVnzpyR6+ypU6coLCzE2dmZ3r178+effwLQvn177O3t5et++/Xrx7lz58jLy8PR0ZH+/fvLvfRhYWG4urrKJ+z69OlDamoqV65cwdbWlqFDh9bbRgD07NmzTbQRubm5VFVViTbCzNuIrVu3ArXfXZydnVt1G2FlZSXHKNoI/TaiLv4mkZrhjTfekIBGt0OHDuk9Z/HixZJKpbrhvvfs2SMBUmZmpt79jzzyiDRy5MgGn6dWq6Xi4mJ5S09PlwCpuLhYr1xlZaV0+vRpqbKy8oaxqNWSZGMjSXFxkqTV1l9Gq6193MamtvytCA0Nlfbt26d338yZM6U33nhDkqTa9/Dq9+CHH36QBg8eLN9es2aN1KdPH0mSJGnJkiVS9+7dpYyMDEmSJGnjxo1SaGioXPbcuXOSp6enNG7cOGnixIkNxrR48WLpueeekzZs2CD961//km677TZJpVJJsbGx0vLly+t9zubNm6Xw8HC9+3r27CmtX7/+hu+BVquVnJ2dpQsXLtT7OCBlZWVJ5eXlkouLizR69Gg5zqvfm7pykiRJCxculAYMGCCNHDlSWrx48Q1jEG7Njf7HNm7caOSIBEun0WikFStWSBqNxtShCBZGtHeCsYk617Di4uJ6c4P6NKvHaPbs2UyePLnRMmFhYc3ZpczPzw+o7Tny9/eX78/JybmuF+lqdnZ22NnZ3dRrNrxP+OKL2uFyzzxT20NU12MEIEm19584AQsX1pY3F6WlpdjZ2eHm5kZeXh4fffSR/JhOp+OBBx5g3rx5TJkyhUGDBvHjjz8yadKk6/Zz3333YWVlJd9uyiQaQ4YMQZIkli5dyvTp0/ntt99ISUmpd7heWloaGRkZ9OzZE41Gw+uvv05oaOgN64+joyNbtmzBy8vrhvE8+OCDvPvuu5SVld2w3gqC0PYkJiYyefJkjhw5Qvfu3U0djiAIgmDmmpUYeXl5NekL6c0IDw/Hz8+PLVu2yDOZaTQaduzYwQcffNAir9mYmTNrf9YtrVOXHEkSzJlTmzgtXPhXOXNx//3389tvv+Hj40NwcDCPPPKI3F360UcfYWVlxZw5c6ipqWHx4sWMHz+eIUOG4OPjo7efq5OiprKxsWHt2rU8/PDDPPnkk0RGRrJ69Wo8PDwA5OF3CxcupLS0lMcee4yLFy/KXdpr165FqbzxZW99+vRpUjy2tra88sorzZoZUWg5nTp1MnUIgiAIRiHaO8HYRJ0zDIUkNXK1+y1IS0ujoKCAdevW8eGHH8qzk0VERODs7AzUjs197733GDduHAAffPAB7733HosXLyYyMpJ3332X7du3c/bsWVxcXJr0unVjJOuuAaijVqtJSUkhPDxcvvaiKa6egOGzz2p7isw1KWoOtVrdrPdBEG7kRv9jycnJ9a47JQgtJSEhgR49eogeI8HoRHsnGJuocw1rKDeoT4tNvvD666+zdOlS+XZdL9C2bdsYMmQIUHvxXN1ECAB/+9vfqKys5IknnqCwsJA+ffqwefPmJidFLeHqnqMdO/4aPteakyKonc1PJEaCMV28eFE02oIgWATR3gnGJuqcYbRYYrRkyRKWLFnSaJlrO6sUCgXz589n/vz5LRXWTalLgp56qm0kRYIgCIIgCIIg6GuxoXSmYuihdFerqjKviRZuhSRJKK6eUUIQbtGN/sdqamqwtjabFQIEC1C3IHomRwAAFKRJREFUPINKpbqp6yYF4WaJ9k4wNlHnGtacoXRms8Bra9BWkiJAXhdHEIylbn0LQTAWKysrTp8+LZIiwehEeycYm6hzhiESIwul0+lMHYJgYeoWvxUEY0lOTmbOnDnyzJyCYCyivROMTdQ5wxCJkYUS3a2CsdVN2y4IxlJaWkpCQgKlpaWmDkWwMKK9E4xN1DnDEImRhRIz0gnG1qFDB1OHIAiCYBSivROMTdQ5wxCJkYUS1xgJxrZ7925ThyAIgmAUor0TjE3UOcMQiZEgCIIgCIIgCBZPJEbNUFXVMvsNCwvD1dWVyspK+b6SkhIcHByIiYlpkdc05lC6JUuWYG1tjbOzs7ylpaXVW3b79u0olUq9srt27WpwvwqFgrffflvv/rlz56JQKFixYoVeuUWLFsllsrOzxXTlRhYbG2vqEAQLExwczFtvvUVwcLCpQxEsjGjvBGMTdc4wRGLURIsWgYtL7c+W4Ofnx7p16+Tbq1evblMf5rfffjtlZWXyFhIS0mDZqKgovbIDBw5ssGxERATLly+Xb0uSxMqVK2nfvr1eOXd3d959912qq6tv/WCEm1JTU2PqEAQL4+3tzbRp0/D29jZ1KIKFEe2dYGyizhmGSIyaYNEimDULYmNrf7ZEcjRlyhSWLVsm3162bBlTp07VK6NQKPjqq68ICQnBy8uLlStXsn79etq1a4ePjw8rV66Uy/773/8mMjISFxcX4uLi2L59O1C7CGeHDh34/vvvASgqKiIoKIg///yz2TGbw9rA7du3x8XFhYSEBAD27t1LcHAwQUFBeuV69+5NcHAwixcvrnc/YWFhfPzxx0RFReHq6spnn33GwYMH6dChAx4eHnz66actfixtnZgyWTC2goICFi5cSEFBgalDESyMaO8EYxN1zjBEYnQDdUnRU0/B0aO1P1siORo+fDgJCQkUFBSQnZ1NcnIygwYNuq7cnj17OHfuHF999RVPPPEEP//8MydPnuSbb75h9uzZaLVaAAICAvjjjz8oLi7mqaeeYvLkyVRVVWFvb8/SpUt55ZVXyMrKYs6cOfzf//0ft912W71xffXVV3Tt2pWQkBAefvhh1q9fz86dO3nyySc5fPhwk49vz549eHp60qFDBxYuXNho2dTUVHx8fIiMjOStt96Sj6kh06ZNk3uNli9fzrRp0+ot98YbbzTaa7RhwwYOHTrE1q1beemll/jwww/Zs2cP27ZtY+7cueTm5jbhSAVBMBepqal8+OGHpKammjoUQRAEoRUQiVEjrk6KFiwApbL2Z0skR9bW1owdO5ZVq1axYsUKJk6ciFJ5/Z/nb3/7G/b29owfP56ioiKeeOIJHB0dueuuuygtLSUzMxOAO++8k5CQEJRKJY8++igKhUI+m9CrVy8efvhhbr/9dnbt2sU//vGPemOqqqoiNTWV9evXc+TIEeLj4/n666/56KOPGDhwIL169WrSsQ0ePJjExERyc3NZvHgxb731FmvWrKm3bExMDMeOHSM7O5u1a9fy448/8vnnnze6/3vvvZdVq1ah0WhYu3Yt99xzT73lhg8fTmBgIEuWLKn38Tlz5qBSqejduzd+fn5MmjQJd3d3unTpQkhICGfOnGnS8Qr1GzJkiKlDEARBMArR3gnGJuqcYYjEqAHXJkV11+krFC2XHNX1fDTW6+Hj4wOAlZUVNjY2emPn7e3t5ZWPf/nlF7p3746bmxtubm7k5OSQn58vl508eTKnT5/moYcewtnZud7XsrOzY9y4cbz99ts8+eST6HQ6li5dyk8//YROp+PUqVPXPWfXrl3ypAmjR48GIDw8nLCwMJRKJX369OHpp59uMDHy8/MjJiYGpVJJhw4dePXVVxssW8fX15eYmBjmzp1Lz549cXd3b7BsY71Gde8tgIODg9576+DgIFaVvkV1wx0FQRDaOtHeCcYm6pxhiMSoHlVVtYlPXBx89tlfSVEdhaL2/ri42nKGmq0uPj6ejIwMysrK6Nq1603vp6qqiilTpvD++++Tn59PUVERPj4+8jVBkiTxzDPPMG3aNBYsWEBGRkaD+5k7dy5DhgxhypQpHDhwgNjYWEJDQ9mzZ0+9EygMHDhQnjTh999/r3e/9fWENaSpZadOnconn3xy3XVZ1xoxYgT+/v4sXbq0yTEIhlFSUmLqEARBEIxCtHeCsYk6ZxjWpg7AHNnZwRdf1PYIPfOMfo8RgCTV3n/iBCxcWFveUFavXt2sxKE+VVVVaDQaucdjwYIFetfHLFy4kOLiYrZs2cL8+fN59NFH2bBhw3X7sbW1ZevWrXI848aNu6l4Nm7cSI8ePfD29iYhIYHPP/+cTz75pN6y27dvp3379gQHB5OcnMzbb7/N9OnTb/gaEydOxNfXt0ldyW+88cYNEyjB8FQqlalDECyMk5MTnTp1wsnJydShCBZGtHeCsYk6Zxiix6gBM2fWJj1ffAFz5tQmQ1D7c86c2vsXLqwtZ0hxcXF06tTplvbh6urKhx9+yPDhw/Hz8yM/P5+IiAgAUlJSePXVV+W1hV5//XUuX77Mt99+e91+FArFLSdpAFu2bKFjx444OzszZcoUXnrpJSZNmiQ/fvVaRUeOHKFv3744OTkxYsQIxo4dy3PPPXfD13B0dGTUqFFNWp9p5MiRREVF3fwBCTflVnpBBeFmREdHc+jQIaKjo00dimBhRHsnGJuoc4ahkMxhzmUDKikpQaVSUVxcjKurq3y/Wq0mJSWF8PDwZi1uevW1Rp99VttT1FJJkTEVFxeLswuCQd3of2zTpk2MHDnSBJEJlkzUO8EURL0TjE3UuYY1lBvURwylu4G65GfWLNix46/hc605KRIEQbAECQkJjBo1iiNHjtC9e3dThyMIgiCYOZEYNUFdEvTUU20nKWpOr5kgGIIYvigIgqUQ7Z1gbKLOGYZIjJpo5kyYMcOwEy0IgiUxxPVqgiAIrYFo7wRjE3XOMMS72AxtKSlSq9WmDkGwMGKBXEEQLIVo7wRjE3XOMCwuMdLpdKYOQRDapDY2j4sgCIIgCBbGYobS2draolQqyczMxNvbG1tbWxTXrtxqQaytrUWvkWAwkiSRm5uLQqHAxsam3jIDBgwwclSCpevQoQPHjx8XY+8FoxPtnWBsos4ZhsUkRkqlkvDwcLKyssjMzDR1OCZXVVWFXVsaGyiYnEKhICgoCCsrq3ofP3XqFL179zZyVIIls7e3R61Wi8lmBKMT7Z1gbKLOGYbFJEZQ22sUEhJCTU0NWq3W1OGY1O7du8XZBcGgbGxsGkyKAAoLC40YjSDULmj98ssv88033xAeHm7qcAQLIto7wdhEnTMMi0qMAHmoT0PDfSyFg4ODOIsqGJWzs7OpQxAsTGFhIdu2baOwsFAkRoJRifZOMDZR5wzD4iZfEGqJ7lbB2ESdEwTBUoj2TjA2UecMQyRGFurPP/80dQiChRF1ThAESyHaO8HYRJ0zjDY3lK5uyuCSkhITR2LeysvLxXskGJWoc4KxlZWVyT9F3ROMSbR3grGJOtewuvelKcuKKKQ2tvjI5cuXCQ4ONnUYgiAIgiAIgiCYifT0dIKCghot0+YSI51OR2ZmJi4uLha9TlFjSkpKCA4OJj09HVdXV1OHI1gAUecEUxD1TjAFUe8EYxN1rnGSJFFaWkpAQABKZeNXEbW5oXRKpfKG2aBQy9XVVfwDCUYl6pxgCqLeCaYg6p1gbKLONUylUjWpnJh8QRAEQRAEQRAEiycSI0EQBEEQBEEQLJ5IjCyQnZ0db7zxBnZ2dqYORbAQos4JpiDqnWAKot4JxibqnOG0uckXBEEQBEEQBEEQmkv0GAmCIAiCIAiCYPFEYiQIgiAIgiAIgsUTiZEgCIIgCIIgCBZPJEaCIAiCIAiCIFg8kRhZgHfeeYd+/frh6OiIm5tbk54jSRLz588nICAABwcHhgwZwqlTp1o2UKFNKSws5L777kOlUqFSqbjvvvsoKipq9DkzZsxAoVDobX379jVOwEKr9K9//Yvw8HDs7e3p0aMHu3btarT8jh076NGjB/b29rRr146FCxcaKVKhrWhOndu+fft1bZpCoeDMmTNGjFho7Xbu3Mldd91FQEAACoWCX3755YbPEW3dzRGJkQXQaDRMnDiRxx9/vMnP+cc//sEnn3zCl19+yaFDh/Dz82P48OGUlpa2YKRCWzJ16lSOHTvGxo0b2bhxI8eOHeO+++674fNGjRpFVlaWvG3YsMEI0Qqt0cqVK3nmmWeYN28eR48eZeDAgYwePZq0tLR6y6ekpHDHHXcwcOBAjh49yty5c3n66af5+eefjRy50Fo1t87VOXv2rF67FhkZaaSIhbagvLycLl268OWXXzapvGjrboEkWIzFixdLKpXqhuV0Op3k5+cnvf/++/J9arVaUqlU0sKFC1swQqGtOH36tARI+/fvl+/bt2+fBEhnzpxp8HkPPPCAdPfddxshQqEt6N27tzRr1iy9+2JiYqSXX3653vJ/+9vfpJiYGL37Zs6cKfXt27fFYhTalubWuW3btkmAVFhYaIToBEsASGvWrGm0jGjrbp7oMRKuk5KSQnZ2NiNGjJDvs7OzY/Dgwezdu9eEkQmtxb59+1CpVPTp00e+r2/fvqhUqhvWoe3bt+Pj40NUVBSPPvooOTk5LR2u0AppNBqOHDmi104BjBgxosE6tm/fvuvKjxw5ksOHD1NdXd1isQptw83UuTrdunXD39+fYcOGsW3btpYMUxBEW3cLRGIkXCc7OxsAX19fvft9fX3lxwShMdnZ2fj4+Fx3v4+PT6N1aPTo0Sxbtow///yTjz/+mEOHDnHbbbdRVVXVkuEKrVBeXh5arbZZ7VR2dna95WtqasjLy2uxWIW24WbqnL+/P19//TU///wzq1evJjo6mmHDhrFz505jhCxYKNHW3TxrUwcg3Jz58+fz5ptvNlrm0KFD9OzZ86ZfQ6FQ6N2WJOm6+wTL0tR6B9fXH7hxHbr33nvl3zt16kTPnj0JDQ3lt99+Y/z48TcZtdCWNbedqq98ffcLQkOaU+eio6OJjo6Wb8fHx5Oens5HH33EoEGDWjROwbKJtu7miMSolZo9ezaTJ09utExYWNhN7dvPzw+oPePg7+8v35+Tk3PdGQjBsjS13p04cYIrV65c91hubm6z6pC/vz+hoaEkJyc3O1ahbfPy8sLKyuq6M/WNtVN+fn71lre2tsbT07PFYhXahpupc/Xp27cv33//vaHDEwSZaOtunkiMWikvLy+8vLxaZN/h4eH4+fmxZcsWunXrBtSOrd6xYwcffPBBi7ym0Do0td7Fx8dTXFzMwYMH6d27NwAHDhyguLiYfv36Nfn18vPzSU9P10vQBQHA1taWHj16sGXLFsaNGyffv2XLFu6+++56nxMfH8+vv/6qd9/mzZvp2bMnNjY2LRqv0PrdTJ2rz9GjR0WbJrQo0dbdAlPO/CAYx6VLl6SjR49Kb775puTs7CwdPXpUOnr0qFRaWiqXiY6OllavXi3ffv/99yWVSiWtXr1aSkxMlKZMmSL5+/tLJSUlpjgEoRUaNWqUFBcXJ+3bt0/at2+f1LlzZ2nMmDF6Za6ud6WlpdLzzz8v7d27V0pJSZG2bdsmxcfHS4GBgaLeCfVasWKFZGNjI33zzTfS6dOnpWeeeUZycnKSUlNTJUmSpJdfflm677775PIXL16UHB3/v517d2kmC+M4/oSsF9ZYaBBiBBWCKQSR2IhgFBsxoo1gFcgg/gWCiJ0KsbBQG8VGjQii4CUi2Jpo7wUiKUTxAiKKVl5AxTxb7G5YNy8s8X3XKPP9wClmzpk558Aw5MfJnN+1p6dH4/G4zszMaFZWlq6srGRqCvhm0n3mxsfHNRwO69HRkR4eHmp/f7+KiK6urmZqCviG7u/vk7/dRETHxsZ0f39fz8/PVZV33a9EMDIBwzBURFJKJBJJthERDYVCyeNEIqEDAwPqcDg0JydHGxoaNBaLff7g8W3d3d2p3+/X/Px8zc/PV7/fn7Jl7T+fu6enJ21ubtaioiLNysrS0tJSNQxDLy4uPn/w+DYmJye1rKxMs7OztaamRre3t5N1hmFoY2Pju/bRaFQ9Ho9mZ2dreXm5Tk1NffKI8d2l88yNjIyoy+XS3NxcLSgo0Pr6et3c3MzAqPGd/b3t+7+LYRiqyrvuV7Ko/vU1FgAAAACYFNt1AwAAADA9ghEAAAAA0yMYAQAAADA9ghEAAAAA0yMYAQAAADA9ghEAAAAA0yMYAQAAADA9ghEAAACAjNnZ2ZH29nZxOp1isVhkfX09resHBwfFYrGklLy8vLTuQzACAAAAkDGPj49SXV0tExMTH7q+t7dXrq6u3pXKykrp7OxM6z4EIwAAAAAZ4/P5JBgMSkdHxw/rX15epK+vT0pKSiQvL09qa2slGo0m6202mzgcjmS5vr6WeDwu3d3daY3jt5+ZBAAAAAD8n7q6uuTs7EyWlpbE6XRKOByWlpYWicViUlFRkdJ+enpa3G63eL3etPphxQgAAADAl3RyciKLi4uyvLwsXq9XXC6X9Pb2Sn19vYRCoZT2z8/PsrCwkPZqkQgrRgAAAAC+qL29PVFVcbvd784/Pz+L3W5Pab+2tib39/cSCATS7otgBAAAAOBLSiQSYrVaZXd3V6xW67s6m82W0n56elra2trE4XCk3RfBCAAAAMCX5PF45O3tTW5ubv7zm6HT01OJRCKysbHxob4IRgAAAAAy5uHhQY6Pj5PHp6encnBwIIWFheJ2u8Xv90sgEJDR0VHxeDxye3srW1tbUlVVJa2trcnrZmdnpbi4WHw+34fGYVFV/enZAAAAAMAHRKNRaWpqSjlvGIbMzc3J6+urBINBmZ+fl8vLS7Hb7VJXVydDQ0NSVVUlIn/+5a6srEwCgYAMDw9/aBwEIwAAAACmx3bdAAAAAEyPYAQAAADA9AhGAAAAAEyPYAQAAADA9AhGAAAAAEyPYAQAAADA9AhGAAAAAEyPYAQAAADA9AhGAAAAAEyPYAQAAADA9AhGAAAAAEyPYAQAAADA9P4Ak1FgE6ZqGDgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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K4E26CufJjXEu2iaYeYS2yiG0VQahq8DTEG1WOYS2yiG0VQZv0lU4TwKBQCAQCAQCgUBwCYhqe25cbW9kZISAgABXm+GVCG2VQ2irDEJXZRgfH+fEiRPMnTtXrPU0w4g2qxxCW+UQ2iqDu+sqqu15CTU1Na42wWsR2iqH0FYZhK7K4Ofnh0qlEo6TAog2qxxCW+UQ2iqDN+kqnCc3pq+vz9UmeC1CW+UQ2iqD0FUZzpw5ww9+8APOnDnjalO8DtFmlUNoqxxCW2XwJl2F8+TGGAwGV5vgtQhtlUNoqwxCV2Xo6+tj27ZtXvXF7i6INqscQlvlENoqgzfpKuY8ufGcJ5vNhkajcbUZXonQVjmEtsogdFWGkpISioqKKC4uZsGCBa42x6sQbVY5hLbKIbRVBnfXVcx58hJ27tzpahO8FqGtcghtlUHoKvA0RJtVDqGtcghtlcGbdBXOk0AgEAgEAoFAIBBcAsJ5cmNSU1NdbYLXIrRVDqGtMghdlSE6OpqvfvWrREdHu9oUr0O0WeUQ2iqH0FYZvElXrasNEJwfd66H7+kIbZVDaKsMQldliI+P54c//CFxcXGuNsXrEG1WOYS2yiG0VQZv0lVEntyYyspKV5vgtQhtlUNoqwxCV2UwGo289tprGI1GV5vidYg2qxxCW+UQ2iqDN+kqnCeBQCAQXLXU1dXxne98h7q6OlebIhAIBAIPQDhPbsySJUtcbYLXIrRVDqGtMghdBZ6GaLPKIbRVDqGtMniTrsJ5cmMaGhpcbYLXIrRVDqGtMghdBZ6GaLPKIbRVDqGtMniTrsJ5cmO6u7tdbYLXIrRVDqGtMghdBZ6GaLPKIbRVDqGtMniTrsJ5cmN8fHxcbYLXIrRVDqGtMghdlUGn0xEREYFOp3O1KV6HaLPKIbRVDqGtMniTripJkiRXGzHbDA8PExwczNDQEEFBQa42RyAQCAQCgUAgELiIy/ENROTJjdm2bZurTfBahLbKIbRVBqGrcghtlUHoqhxCW+UQ2iqDN+kqnCeBQCAQXLVUVlZyzz33eNUaJAKBQCBQDuE8uTFJSUmuNsFrEdoqh9BWGYSuymCxWOjt7cVisbjaFK9DtFnlENoqh9BWGbxJV+E8uTFhYWGuNsFrEdoqh9BWGYSuAk9DtFnlENoqh9BWGbxJV+E8uTFlZWWuNsFrEdoqh9BWGYSuAk9DtFnlENoqh9BWGbxJV+E8CQQCgUAgEAgEAsElIEqVu3Gp8v7+fq8Kc7oTQlvlENoqg9BVGYxGI3v27GHNmjUEBga62hyvQrRZ5RDaKofQVhncXVdRqtxLaG1tdbUJXovQVjmEtsogdFWGwMBAUlJShOOkAKLNKofQVjmEtsrgTboK58mN6ejocLUJXovQVjmEtsogdFWGtrY2fvrTn9LW1uZqU7wO0WaVQ2irHEJbZfAmXYXz5MZotVpXm+C1CG2VQ2irDEJXZejq6uJ///d/6erqcrUpXodos8ohtFUOoa0yeJOuYs6TG895EggEAoGylJSUUFRURHFxMQsWLHC1OQKBQCBwAW415+m5554jNTUVX19fioqK+Oijj8577gMPPIBKpZqy5eXlyeds2bJl2nMmJiaUvpVZZ8eOHa42wWsR2iqH0FYZhK4CT0O0WeUQ2iqH0FYZvElXRZ2nN998k2984xt8//vfp7S0lJUrV7Jp0yaam5unPf/pp5+mo6ND3lpaWggLC+POO++cdF5QUNCk8zo6OvD19VXyVlyC3W53tQlei9BWOYS2yiB0FXgaos0qh9BWOYS2yuBNuirqPP3617/mS1/6El/+8pfJyclh8+bNJCYm8vzzz097fnBwMDExMfJ2/PhxBgYGePDBByedp1KpJp0XExOj5G24jPj4eFeb4LUIbZVDaKsMQldlCA8P5/bbbyc8PNzVpngdos0qh9BWOYS2yuBNuirmPJnNZoqLi9mwYcOk/Rs2bODQoUOX9B4vv/wy69atIzk5edL+kZERkpOTSUhI4KabbqK0tHTG7HYnvNUpdAeEtsohtFUGoasyJCcn87vf/W7K94zgyhFtVjmEtsohtFUGb9JVMeept7cXm81GdHT0pP3R0dF0dnZe9PqOjg4+/PBDvvzlL0/an52dzZYtW3jvvfd4/fXX8fX1Zfny5dTV1Z33vUwmE8PDw5M2T6C4uNjVJngtQlvlENoqg9BVGcbHx3nrrbcYHx93tSleh2izyiG0VQ6hrTJ4k66K1w1UqVSTfpckacq+6diyZQshISF87nOfm7R/yZIlLFmyRP59+fLlLFiwgP/3//4fzzzzzLTv9bOf/Ywnn3xyyv6dO3fi7+/P2rVrOXbsGCMjI4SGhpKXl8eBAwcAh7Nmt9upra0FYPXq1ZSVlcnVOBYsWMDevXsByMzMRKvVUl1dDcCKFSs4efIk/f39+Pv7s2TJEnbt2gVAWloaBoOBEydOALB06VLq6+vp6enB19eXVatW0dTUxLZt20hOTiYkJITy8nIAFi1aRHNzM52dneh0OtauXcv27duRJImEhASioqIoKSkBoKioiM7OTtra2lCr1axfv55du3ZhtVqJjY0lISGBjz/+GICCggL6+/vlOWkbN25k7969mEwmoqKiSEtL48iRIwDMmzePkZERGhsbAVi3bh2HDh1ibGyM8PBwsrOzOXjwIAC5ubmYzWbq6+sBWLNmDcePH8doNBISEsL8+fPZv38/AHPmzAHg1KlTAKxatYqKigoGBwcJDAxk4cKF7NmzB4CMjAz0ej0nT56U20JNTQ19fX0YDAaWLVvGzp07AUhNTSUgIIDKykrA4VCXlpbS3d2Nj48P1157Ldu2bQMgKSmJsLAwysrKALjmmmtobW2lo6MDrVbLddddx44dO7Db7cTHxxMTEyN3CgsWLKC7u5vW1lZUKhUbNmxg9+7dWCwWYmJiSEpK4tixYwDk5+czODjImTNnAEdUdv/+/UxMTBAZGUlGRgaHDx8GYO7cuYyNjdHQ0ADAddddx5EjRxgdHSUsLIzc3Fy5zebk5GC1WuUHCtdeey0lJSVyJZmCggL27dsHQFZWFmq1mpqaGrnNVlVVMTAwQEBAAIsWLWL37t0ApKen4+vrS1VVFQDLli2jtraW3t5eDAYDy5cvZ8eOHTQ1NXHq1CmCgoKoqKgAYPHixTQ1NdHV1YVer2fNmjWy3omJiURERMgR5IULF9Le3k57ezsajYZ169axc+dObDYbcXFxxMXFcfz4cQAKCwvp7e2lpaVFbrN79uzBbDYTHR1NSkoKR48eBWD+/PkMDw/T1NQEwPr16zl48CBjY2NERESQlZUlR8Xz8vKYmJjg9OnTAG7RR5hMJkpKSib1Edu3bwcQfcQV9BEvv/wyjzzyCFFRUSxevFjuI5YsWUJDQ4PoI66gj3BmoJzbRwCkpKSIPuIK+oimpibq6uouOo4QfcTl9xFdXV1s27btouMI0UdcXh/R1NREfX39JY0jYPb7CKf9l4JipcrNZjMGg4G//vWv3HbbbfL+Rx99lLKyMln06ZAkiaysLG666SZ+85vfXPSzvvKVr9Da2sqHH3447XGTyYTJZJJ/Hx4eJjEx0e1Llff09BAZGelqM7wSoa1yCG2VQeiqDKJUuXKINqscQlvlENoqg7vr6halyvV6PUVFRVNKE+7YsYNly5Zd8Np9+/ZRX1/Pl770pYt+jiRJlJWVERsbe95zfHx8CAoKmrR5At3d3a42wWsR2iqH0FYZhK4CT0O0WeUQ2iqH0FYZvElXRavtPf7447z00kv84Q9/oLq6mscee4zm5mYeeughAJ544gnuu+++Kde9/PLLLF68mLlz50459uSTT7Jt2zYaGhooKyvjS1/6EmVlZfJ7ehOtra2uNsFrEdoqh9BWGYSuAk9DtFnlENoqh9BWGbxJV0XnPN1999309fXx1FNP0dHRwdy5c/nggw/kqkYdHR1T1nwaGhrirbfe4umnn572PQcHB/nqV79KZ2cnwcHBFBYWsn//fhYtWqTkrbiES5kbJvhsCG2VQ2irDEJXZVCpVOh0OqGvAghNlUNoqxxCW2XwJl0Vm/PkzlxOXqNAIBAIBAKBQCDwXtxizpPgynFWMBLMPEJb5RDaKoPQVTmEtsogdFUOoa1yCG2VwZt0Fc6TG2OxWFxtgtcitFUOoa0yCF2Vobq6mq9+9atyaWjBzCHarHIIbZVDaKsM3qSrcJ7cGG9ajdndENoqh9BWGYSuyjA+Ps7p06fFIrkKINqscghtlUNoqwzepKtwntyYpKQkV5vgtQhtlUNoqwxCV4GnIdqscghtlUNoqwzepKtwntwY5wrSgplHaKscQltlELoKPA3RZpVDaKscQltl8CZdhfMkEAgEAoFAIBAIBJeAcJ7cmPz8fFeb4LUIbZVDaKsMQldlSE1N5cUXXyQ1NdXVpngdos0qh9BWOYS2yuBNugrnyY0ZHBx0tQlei9BWOYS2yiB0VYbQ0FBWrlxJaGioq03xOkSbVQ6hrXIIbZXBm3QVzpMbc+bMGVeb4LUIbZVDaKsMQldl6Orq4te//jVdXV2uNsXrEG1WOYS2yiG0VQZv0lU4TwKBQCC4amlra+P3v/89bW1trjZFIBAIBB6ASpIkydVGzDbDw8MEBwczNDREUFCQq805L5IkoVKpXG2GV+Lt2trtdiYmJhgfH2diYoKJiQlMJhMWiwWr1Sq/nv2zxWLBbrcjSdKkDZjyu1qtRq1Wo9FopryqVCq0Wi0ajQa9Xo9Op7vgq/Nnb/57zATe3mZdRUlJCUVFRRQXF7NgwQJXm+NViDarHEJb5RDaKoO763o5voF2lmwSfAb279/P6tWrXW2GV+KJ2prNZoxGIyMjIxiNxknbyMgI4+PjsrNkMplcZmdTUxMpKSmXdY1arcbPzw9fX198fX3P+7O/v/+kTa/Xu3VnPJN4YpsVXN2INqscQlvlENoqgzfpKpwnN2ZiYsLVJngt7qitJEmMjIzQ398/aRsYGKC/v/8z2azX62Xnw8fHB51Oh1arPe+rM3Lk3IBJvzv32e12bDbbtK86nY7CwkI5mmU2mzGbzfLP57463290dJTR0dHLuj+tVjvFoXJuAQEBBAUFERgYSFBQEDqd7rL1cyfcsc0KBBdCtFnlENoqh9BWGbxJV+E8uTGRkZGuNsFrcbW24+PjdHV10dnZKb/29vZisVgueJ1OpyMwMHDKFhAQgMFgmBSl8fX1RaPRzNIdfUp4ePglpz9JkoTFYpmSYjjdz+Pj44yNjclOltlsxmq1MjQ0xNDQ0EU/y9fXl6CgoEkOlfM1ODiYkJAQfHx8rvT2FcPVbdZbCQ4OZtWqVQQHB7vaFK9DtFnlENoqh9BWGbxJVzHnyY3nPA0PD7u1fZ7MbGprMplobW2ltbWVtrY2urq6zjvYV6vVBAcHExYWJm+hoaGEhYURHBzsEWlqs6WtxWKRHalzt7NTG4eHhy/qlDrx8/MjJCREdqbO3Xx9fRW+q/Mj+gPlENoqg9BVOYS2yiG0VQZ311XMefISDh8+zMaNG11thleipLZjY2M0NTXR2NhIc3Mz3d3dTPeMIiQkhJiYGKKjo4mJiSEqKoqQkBCXRItmktlqtzqdTnZqLoQkSZhMJtmROvd1eHiYoaEhxsbG5HljHR0d076Xj4+P7Mw6t/DwcMLCwggICFDUsRX9gTJYLBY+/PBDbr/9do9P7XQ3RJtVDqGtcghtlcGbdBXOk0BwhdjtdlpaWqitraWhoYHOzs4pzlJoaCiJiYnEx8fLDpMroxhXEyqVSk5jvFDagNlsZnBwkKGhIQYHB6dso6OjmEwmOjs76ezsnHK9Tqeb5FQ5t4iICMUdK8Fnp7Kyks9//vOi2p5AIBAILgnhPLkxc+fOdbUJXsuVamuxWKivr6empoa6ujrGxsYmHY+KiiI1NZWUlBQSExMJCAi4os/zJDy13er1eqKiooiKipr2uMViYXBwUC7gcfY2ODiIxWKhq6tr2sVWfXx8iIiIICIigsjISPnn0NDQS440eqqugqsX0WaVQ2irHEJbZfAmXYXz5MacOyAXzByfRVu73U5jYyOVlZVUV1dPKgfu5+dHZmYmGRkZpKamEhgYOJPmehTe2m51Oh2RkZHTRq9sNhtDQ0P09/fT19c3pVqiyWSira1tykKsarWasLCwSQ5VVFQUERERU1LIvFVXgfci2qxyCG2VQ2irDN6kq3Ce3JiGhgYyMzNdbYZXcjnaDg8PU1JSQnFxMUajUd4fHBxMbm4uc+bMISkpCbVarZS5HsXV2G41Go2cppeRkTHpmNVqZWBggJ6eHnp7eydtZrNZ/vlsVCoV4eHhciQsOjqaEydOkJ6eLtqZwGO4GvuC2UJoqxxCW2XwJl2F8yQQnIfm5mYOHTpEbW0tdrsdcESY8vLymDdvHklJSWIei+CiaLXaaSNWkiQxPDw8yZnq6emhu7ubsbExed/JkycBqK+vp7a2lsjIyElOVXR09FWVFioQCAQCgSsRpcrduGyi1WpFqxX+rRKcT1tJkqirq+PAgQM0NzfL+1NSUli4cCHZ2dnib3IRRLu9MpyLJXd3d9Pd3U1XVxfd3d10dnbKTvy5BAYGEhMTQ0xMDLGxscTExBAaGiqc+0vAmXIZHBzs8ZUu3Q3RFyiH0FY5hLbK4O66ilLlXsKRI0dYsWKFq83wSqbTtqmpiR07dsjzUjQaDQUFBSxZssSrFndTGtFurwyVSiUvfpyeni7v379/P3l5eZMcqq6uLvr7++U1rerq6uTzfXx8ZIfK6VRFRkYKB+EcNBoNJ0+eFG1WAURfoBxCW+UQ2iqDN+kqnCc3ZnR01NUmeC1na9vb28v27dupra0FHFXXrrnmGpYsWXJVF374rIh2qwzj4+OEh4cTHh5OTk6OvN9sNtPV1UVnZycdHR10dnbS3d2NyWTizJkznDlzRj5Xo9EQFRVFXFwc8fHxxMXFXfUOVV1dHY8++ihvvPGG1+TjuwuiL1AOoa1yCG2VwZt0Fc6TGxMWFuZqE7yWsLAwrFYrBw4c4KOPPsJms6FWq1m4cCGrV6/G39/f1SZ6LKLdKsP5dNXr9SQmJpKYmCjvs9ls9Pb2ymtSOZ2qiYkJOjo66OjooLi4GHDMyYqNjSUuLk52qsLDw6+alD+j0UhJScmkYjCCmUH0BcohtFUOoa0yeJOuwnlyY3Jzc11tgtcSFhbG7373O3p6egDIzMzk+uuvJzw83MWWeT6i3SrD5eiq0WjkYhL5+fmAYy7V0NAQ7e3ttLe309bWRnt7OyaTiZaWFlpaWuTrfXx8iI2NlaNTCQkJBAUFXTUOlWBmEH2BcghtlUNoqwzepKtwntyYAwcOsHHjRleb4VVIksSxY8f47W9/S1paGgEBAWzatInc3FwxMJwhRLtVhivVVaVSERISQkhIiPwlJkkS/f39siPV3t5OR0cHJpOJpqYmmpqa5OsDAwNJSEggMTGRhIQEYmNjp6xFJRCcjegLlENoqxxCW2XwJl2F8yS4arBYLLz77rtUVVVht9vJzs7m1ltvxc/Pz9WmCQQuwbmeVHh4OPPnzwcci0H39PTIzlRraytdXV0YjUaqq6uprq4GHAv8xsTEyM5UQkICISEh4iGEQCAQCLwa4Ty5MWdPChdcGaOjo7z++uu0trai0Wi48847ueWWW8RATwFEu1WG2dJVrVbLKX+FhYWA48GD05FqbW2lpaWFkZER2cE6evQoAP7+/nJ0Kikpibi4OLcuTQuQmJjIU089NWnOmGBmEH2BcghtlUNoqwzepKt7f6td5VitVleb4BUYjUa2bNlCX18ffn5+3H333djtduE4KYRot8rgSl11Oh3JyckkJycDn86fOtuZ6uzsZHR0lFOnTnHq1CnAUYwiPj6epKQkkpKSSExMxNfX12X3MR2RkZF88YtfFMsRKIDoC5RDaKscQltl8CZdhfPkxtTV1ZGWluZqMzya0dFRXn31Vfr6+ggJCeGee+4hIiKCbdu2CW0VQrRbZXAnXc+ePzV37lzA8cXY0dFBa2srzc3NNDc3Mzo6OqlcukqlIioqSnamkpOTXb5QeX9/Py+88ALf/e53vaoalDvgTm3W2xDaKofQVhm8SVfhPAm8FovFwp///Gd6enoICgri/vvvJzQ01NVmCQReiVarlUumL126VC5G4XSkmpub6evro6uri66uLj7++GMAQkJCZEcqJSWFsLCwWY0KNzU18ctf/pLPf/7zwnkSCAQCwUVRSZIkKfkBzz33HL/85S/p6OggLy+PzZs3s3LlymnP3bt3L2vWrJmyv7q6muzsbPn3t956i//6r//i9OnTpKen89Of/pTbbrvtkm0aHh4mODiYoaEhlz/1vBAmkwkfHx9Xm+GRSJLEu+++S3l5OQaDgS996UuTypALbZVDaKsM3qDryMjIJGeqs7MTu90+6ZzAwEBSUlLkTWlnqqSkhKKiIoqLi1mwYIFin3M14g1t1l0R2iqH0FYZ3F3Xy/ENFI08vfnmm3zjG9/gueeeY/ny5fzud79j06ZNnDx5kqSkpPNed+rUqUmGn52LfvjwYe6++25+/OMfc9ttt/HOO+9w1113ceDAARYvXqzk7cw6JSUlLF261NVmeCTFxcWUl5ejVqu58847p6zfJLRVDqGtMniDrgEBAeTm5sql0s1mM62trXJqX0tLC0ajkcrKSiorK4HZd6YEM4c3tFl3RWirHEJbZfAmXRV1nn7961/zpS99iS9/+csAbN68mW3btvH888/zs5/97LzXRUVFERISMu2xzZs3s379ep544gkAnnjiCfbt28fmzZt5/fXXZ/weXMnw8LCrTfBIhoaG2L59OwDr1q0jNTV1yjlCW+UQ2iqDN+qq1+tJS0uT8+AtFgttbW3yGlOtra0XdKZSU1MJDQ0VzpSb4o1t1l0Q2iqH0FYZvElXxZwns9lMcXEx3/3udyft37BhA4cOHbrgtYWFhUxMTJCbm8sPfvCDSal8hw8f5rHHHpt0/saNG9m8efOM2e4uBAcHu9oEj+SDDz7AbDbLcy+mQ2irHEJbZbgadNXpdLJjBI4iFK2trRd0pkJCQkhLSyM1NZXU1FQCAgIu6zP9/f2ZO3cu/v7+M307Vz1XQ5t1FUJb5RDaKoM36aqY89Tb24vNZiM6OnrS/ujoaDo7O6e9JjY2lhdffJGioiJMJhN/+tOfuO6669i7dy+rVq0CoLOz87LeExx5liaTSf7dU7zfgoICV5vgcbS0tHDq1CnUavUF13ES2iqH0FYZrkZdtVrteZ2pxsZGWltbGRwcpKSkhJKSEsDxfeB0ppKTky+aYz9nzhw+/vhjtyuh7g1cjW12thDaKofQVhm8SVfFq+2dO3iVJOm8A9o5c+YwZ84c+felS5fS0tLCr371K9l5utz3BPjZz37Gk08+OWX/zp078ff3Z+3atRw7doyRkRFCQ0PJy8vjwIEDAGRnZ2O326mtrQVg9erVlJWVyRPKFixYwN69ewHIzMxEq9VSXV0NwIoVKzh58iT9/f34+/uzZMkSdu3aBUBaWhoGg4ETJ07I91pfX09PTw++vr6sWrWKP/7xj6SkpJCcnExISAjl5eUALFq0SJ5srdPpWLt2Ldu3b0eSJBISEoiKipIHEkVFRXR2dtLW1oZarWb9+vXs2rULq9VKbGwsCQkJctWrgoICuToWOCJ6e/fuxWQyERUVRVpaGkeOHAFg3rx5jIyM0NjYCDjS4w4dOsTY2Bjh4eFkZ2dz8OBBAHJzczGbzdTX1wOwZs0ajh8/jtFoJCQkhPnz57N//365DQDyOjGrVq2ioqKCwcFBAgMDWbhwIXv27AEgIyMDvV7PyZMnAVi+fDmvvPIK9fX15OXlERYWxrZt2wDkJ9LOp9Umk4nExES6u7vx8fHh2muvlc9NSkoiLCyMsrIyAK655hpaW1vp6OhAq9Vy3XXXsWPHDux2O/Hx8cTExFBcXAzAggUL6O7uprW1FZVKxYYNG9i9ezcWi4WYmBiSkpI4duwYAPn5+QwODsplnDds2MD+/fuZmJggMjKSjIwMDh8+DMDcuXMZGxujoaEBgOuuu44jR44wOjpKWFgYubm5cpvNycnBarVSV1cHwLXXXktJSYk8GbKgoIB9+/YBkJWVhVqtpqamRm6zVVVVDAwMEBAQwKJFi9i9ezcA6enp+Pr6UlVVBcCyZcuora2lt7cXg8HA8uXL2bFjB01NTVx77bUEBQVRUVEBwOLFi2lqaqKrqwu9Xs+aNWtkvRMTE4mIiKC0tBSAhQsXyouvajQa1q1bx86dO7HZbMTFxREXF8fx48cBR5S6t7eXlpYWuc3u2bMHs9lMdHQ0KSkp8gKu8+fPZ3h4mKamJgDWr1/PwYMHGRsbIyIigqysLDkqnpeXx8TEBKdPnwZwiz7CZDKRkJAwqY9wpqdebX1ESkoKJpOJyMhIUlJS2L17N/X19YyOjmKz2eT3jYiIICEhAbvdTmxsLLfccgt1dXX09fVhMBhYtmwZO3fupKmpiTVr1kzqI5YsWUJDQ4PoI66gjzCbzcTFxU3pIwBSUlJEH3EFfURTUxNr16696Djiau0j4LOPI95++22io6Mn9REwdRwh+ojL6yOampq47rrrLmkcAbPfRzjtvxQUq7ZnNpsxGAz89a9/nVQJ79FHH6WsrEwW/WL89Kc/5bXXXpM7kqSkJB577LFJqXu/+c1v2Lx5s9x4zmW6yFNiYqLbV9vbtm0bGzdudLUZHkNvby/PPvssarWaRx555IJlyYW2ynEl2trtdsxmMyaTSX51/my1WrFYLNhsNqxWq7zZbDYkScJutyNJ0pSfVSrVtJtarUatVqPRaNBqtajV6kmvWq0WnU43ZdPr9Wi1WvR6PRqNZobVOz+izV6c0dFRmpqaaGhooKGhgYGBgUnH9Xo9qampZGRkkJ6eTlhYmKi2pyCizSqH0FY5hLbK4O66ukW1Pb1eT1FRETt27JjkPO3YsYNbb731kt+ntLSU2NhY+felS5eyY8eOSc7T9u3bWbZs2Xnfw8fHx63LI56PrKwsV5vgUTif8GRkZFx0PSehrXJcSFur1YrRaGRkZISxsTFGR0cZHx9nbGyMsbExJiYmUHj1hBlFr9fj4+ODr6+v3M84t7P3GQwG9Hr9FRU2EG324vj7+5OXl0deXh4AAwMDNDQ00NjYSENDA2NjY5w6dUp+In32uk4Wi8UlNnszos0qh9BWOYS2yuBNuiqatvf4449z7733snDhQpYuXcqLL75Ic3MzDz30EOColNfW1sarr74KOCrppaSkkJeXh9ls5rXXXuOtt97irbfekt/z0UcfZdWqVfz85z/n1ltv5e9//zs7d+6UQ43ehFqtdrUJHoMkSXLqwqXk1QptlUOtVmOz2RgaGmJgYIChoSGGh4cxGo2Mjo5e0ns4nZKzX3U6nRwROnvTaDSo1epJESXnz06cUahzN2cUy263T3p17rdYLHLU6+zol8ViQZIkzGYzZrMZo9F40XvSaDQYDAb8/PzkzWAwyPsMBgM+Pj7nbZuizV4+oaGhFBUVUVRUhCRJdHZ2Ul9fz+nTp2lubqa/v5+Ojg4AtmzZQk1NDenp6WRkZBAdHS2q+F0hos0qh9BWOYS2yuBNuirqPN1999309fXx1FNP0dHRwdy5c/nggw9ITk4GoKOjQ86LBUeq3ze/+U3a2trw8/MjLy+Pf/7zn9xwww3yOcuWLeONN97gBz/4Af/1X/9Feno6b775ptet8QRQU1MjayW4MIODgwwODqJWq8nIyLjo+ULbmUOSJEZHR+nu7qavr48DBw4QHx8/ZfFTJ3q9nsDAQPz9/WXn4Wwn4kIOhLtgt9uxWCxMTEzIqYUTExOYzeYp+5yvNpsNo9F4QUdLrVZjMBjw9/cnICCAgIAA+ecTJ06INnsFqFQqYmNjiY2NZeXKlZhMJhobG+WcebvdTmNjI42NjezcuZOAgADZkcrIyMDPz8/Fd+B5iH5WOYS2yiG0VQZv0lXxghEPP/wwDz/88LTHtmzZMun3b3/723z729++6Hv+y7/8C//yL/8yE+YJvATnfLf4+Hj0er2LrfF+xsfH6ezspKuri56enkkRpbGxMex2O3q9ntDQUEJDQwkMDCQoKIjAwEB8fHw8/om+Wq2+rHRgm80mpyeOj49P+vnsfXa7nZGREUZGRujq6pr0HqdPn8ZkMsnOlL+/P4GBgbK23qDrbOLj40N2djZjY2OA42FfQEAA9fX1NDY2MjIyQnl5OeXl5ahUKhITE8nKyiIzM5OoqCihtUAgEFylKFYwwp25nElhrmR0dFSsPXKJbN26lSNHjrB06dJLmpAotL18jEYjra2ttLa20tfXN+mYWq0mPDyciIgI/Pz8iIuLw9/fXwwwLwO73c74+Dijo6OMjo4yMjIiv46MjGA0GtHpdOe9Xq/XExQUJDupzld/f/9ZLWzhaUxMTFBbW0tWVpZcrtxqtdLS0kJ9fT11dXV0d3dPuiY4OJjMzEyysrJITU294N/lakb0s8ohtFUOoa0yuLuublEwQnDlVFVVsWjRIleb4RE4q2qdPQH8QghtLw2TySSvqTM4ODjpWHh4ONHR0URFRREeHi4PII8dO+ZVE0NnC7Vajb+//3m/XA4fPkxOTs4kx8poNDI8PMzY2Bhms5ne3l56e3unvG9AQIDsWAUHBxMcHExgYKBwqgBfX18mJiYmrfOk1WrlRXfXr1/P4OAgdXV11NbW0tjYyNDQEMePH+f48ePyuZmZmWRmZl60WM3VhOhnlUNoqxxCW2XwJl2F8+TGnFtmV3B+hoaGAAgJCbmk84W250eSJHp6ejh9+jQtLS3y3CW1Wk1UVBTx8fEkJCScdw6I0FYZhoeHCQkJmbaNW61WRkZG5MIcw8PD8s9Wq1X+/WzUarXsSAUHBxMSEkJwcDB+fn5XVcSwsbGR7373u7z88sukpqZOe05ISAjXXHMN11xzDRaLhcbGRtmZGhoaoq6uTl4PJTIykqysLLKzs4mPj3f7+XtKIvoC5RDaKofQVhm8SVfhPLkxAQEBrjbBY3CWGb7U+U5C26lIkkRHRwcnT56cFL0IDQ0lLS2NpKSkS5rjI7RVhgvpqtVqp3WsJElibGxMdqiGhoYYGhpicHAQq9XKwMDAtGshOR2pkJAQQkNDCQ4O9too1cDAAHv27GFgYOC8ztPZ6HQ6srKyyMrK4oYbbqCnp0d2pFpaWujp6aGnp4eDBw/i7+8vO1JpaWlXXXqf6AuUQ2irHEJbZfAmXcWcJzee82SxWK66L9vPyubNmxkcHOTLX/4yCQkJFz1faDuZzs5OKioq6O/vBxxltVNSUuSFRC8Hoa0yzKSuzgqJTkfK+Wo0GqddZ0utVsuOVGhoKGFhYV7jUM3kIrnj4+OcPn2aU6dOUVdXx8TEhHxMp9ORlpZGdnY2WVlZbp37P1OIvkA5hLbKIbRVBnfXVcx58hJ2797t1qsxuxPONKPzlcc+F6Gtg9HRUUpLS2ltbQUcEYz09HSys7M/c2lmoa0yzKSuKpVKLoUeHx8v73euz+V0pgYHBxkYGMBsNtPf3y871/Bp2t+5DpVWe/V+rfj5+TF37lzmzp2LzWbjzJkznDp1ipqaGoaGhuQFelUqFQkJCWRnZzNnzhwiIiJcbboiiL5AOYS2yiG0VQZv0vXq/ZYTeBUGg4GBgQG57LDgwkiSRENDA6WlpVitVnl9rNzc3EkT5wVXFxqNhrCwsEnRRmeUqr+/X07z6+/vx2w2T0n7c0aowsPD5S0gIOCqmkPlRKPRkJaWRlpaGtdffz1dXV2yI9XR0UFLSwstLS3s2LGD8PBwcnJyyMnJIS4u7qrUSyAQCDwF4Ty5Menp6a42wWNwpsCcvd7QhbiatbVYLBw9elSONkVGRrJw4UKCg4Nn5P2vZm2VxFW6nh2lSkpKAj6dS3W2MzUwMMDExIQcoXIWUPDx8SEsLIyIiAjCw8MJCwtzq7XYYmNjeeSRR4iNjVXsM1QqFTExMcTExLB69WqGh4dlR6qpqUleXPrAgQMEBweTnZ1Nbm4uiYmJHl1wQvQFyiG0VQ6hrTJ4k67CeXJjRATg0nFOlD93/aHzcbVqazQa+eijjxgeHkatVpOfn09WVtaMPum+WrVVGnfSVaVSyWXVnXMMnQ5VX1+fvA0MDGAymejo6KCjo0O+NigoaFJ0Kjg42GXRltjYWL797W8r6jydS1BQkFy9z2QyUVdXR3V1NXV1dQwNDXH06FGOHj2Kv78/2dnZ5OTkkJqa6nFzzNypzXobQlvlENoqgzfpKpwnN6aqquqSih8IIDo6GoCurq5LOv9q1HZwcJC9e/cyMTGBwWBg+fLlhIeHz/jnXI3azgburuvZDpUzQmWz2RgcHJzkUI2MjMjzqhoaGgBHhb+IiAgiIyOJjIwkNDR01hyF4eFh/vjHP/LII4+4pICQj4+PPE/KYrHQ0NBAdXU1p06dYnR0lOLiYoqLi/H19WXOnDnk5OSQnp7u1hOvnbh7m/VkhLbKIbRVBm/SVThPAq/A6Tx1dHQgSZKYM3AORqORPXv2YDKZCAkJYfXq1Z+5IIRAcKloNBo5uuTEmdrX19dHb28vfX19mM1m2tvbaW9vn3Sd05k6exHmmaa+vp4f/OAHbNq06Yqr7V0pOp2OOXPmMGfOHGw2G01NTVRXV1NTU8PIyAjl5eWUl5ej0+nIzMwkLy+PzMxMt0qDFAgEAm9HlCp341LlRqORwMBAV5vhEdhsNn7+859jNpt56KGHiImJueD5V5O2JpOJnTt3YjQaCQsLY/Xq1Ze0XtNn5WrSdjbxVl2d0ane3l55jSSTyTTpHJVKRUhIiOxMRUZGzlgKyEyWKlcKu91Oa2sr1dXVnDx5Ul4UHD51uPLy8sjIyHCriJS3tll3QGirHEJbZXB3XUWpci+htraWoqIiV5vhEWg0GpKTk6mrq6OhoeGiztPVoq0kSRw7dgyj0UhAQAArV65U1HGCq0fb2cZbdT07OjVnzhwkScJoNMqOVE9PD6Ojo3JxitraWsAxzzEqKoro6GgiIyO9OvqiVqtJSkoiKSmJDRs2yItZV1VVMTAwwIkTJzhx4gR6vX6SI+XqsvHe2mbdAaGtcghtlcGbdBXOkxvT29vrahM8ioyMDOrq6qipqWHZsmUXPPdq0baxsZG2tjbUajXLly+flVS9q0Xb2eZq0dVZUCIoKEiuzjQ2NjbJmTp7Hara2lpUKhVhYWFERUURFRVFRESEW0VgZhKVSkVcXBxxcXFcd911dHR0UFVVRVVVFYODg1RWVlJZWYmPj4/sSKWnp7vEkbpa2qwrENoqh9BWGbxJV+E8uTEGg8HVJngUOTk5bN26lebmZgYHB+UKfNNxNWhrNpupqKgAYP78+YSGhs7K514N2rqCq1lXg8FAcnIyycnJgGPeVHd3N93d3XR1dWE0GuWCFNXV1ajVasLDw+XIVHh4+HkLUPj4+BAfH694RFYJznak1q1bR1tbm+xIDQ8PU1FRQUVFBT4+PmRnZzN37lzS0tJmrRjH1dxmlUZoqxxCW2XwJl3FnCc3nvNkt9s9eo0PV/DHP/6RxsZG1q5dy6pVq8573tWgbVVVFZWVlQQFBbFx48ZZGzBdDdq6AqHr+RkbG6Orq0t2qM5d702j0RAVFUVMTAzR0dFTSqN7m7aSJNHa2kpVVRUnT55keHhYPmYwGMjLy2PevHkkJiYqWlzH23R1J4S2yiG0VQZ31/VyfAPhPLmx87Rt2zY2btzoajM8irKyMt59912Cg4N59NFHz/uP6u3a2mw2/vGPfzAxMcHSpUvlJ/azgbdpK0nQ1wdtbdDaCh0dMDjo2IaGJr+Oj4PF8ulmNjterVZQqUCrnbppNODrCwEBk7fAwE9/Dg2FlpYS1q1bQEQEhIdDWJjjWsFkJEliZGRkUmRqYmJi0jkGg0F2pGJiYti7d69XtdmzkSSJlpYWTpw4QVVV1STHMiQkhLlz5zJv3jy5YulM4m19gTshtFUOoa0yuLuuomCE4KolLy+P7du3MzQ0RHV1NXl5ea42ySU4B4y+vr5es66CkthscPo01NTAqVOOrbbW4Sy1t8M5xd9cxAKefHLyntBQiIiAmBiIj4e4uOk3f3/XWOwKVCoVgYGBBAYGkp6ejiRJDA0N0dXVRUdHBz09PYyNjdHQ0EBDQwPNzc089dRT/PGPf2TVqlWEhYV53GK0F0KlUsnFJq6//noaGxuprKykurqawcFBDhw4wIEDB4iKimLevHnMmzfvginPAoFAcLUjnCc3JiUlxdUmeBw6nY5rrrmGffv2cfjwYXJzc6dNS/F2bZ3r5SQkJMz6QNATtG1pgf374fhxKC6GkhI4J9NrCpGRkJDgcEbCwiA4GEJCJr8aDKDTTd70ekeECRwRqHM3i8XhnI2MTL8ND0N/P7S1jTM66kdvryPKBTAw4Njq6i5se1gYpKRAaur0r16Uij4FZ5nzkJAQ5syZg9Vqpbe3l46ODjo7O7HZbIyOjlJbW4vFYkGn08kRqdjYWPy9yPNUq9Wkp6eTnp7OjTfeSF1dHZWVldTW1tLd3c2uXbvYtWsXiYmJzJs3j7y8vCu6f0/oCzwVoa1yCG2VwZt0Fc6TG+POKYXuzDXXXMPBgwdpbW3l9OnTZGRkTDnH27Xt6ekBIDY2dtY/2x21NRrhww9hxw7Ys8cRZToXPz+YM+fTLSsLkpMdDlNsLLi6nkBHxyCxsY5qiVarw6Hq64OeHujsdKQVtrdP3traHE5hf79jKymZ/r2joyEz03HPznvPyoL0dNff90yj1WqJiYmRlzNwtteYmBh8fHwwmUy0trbS2toKOFLbnEUZwsLC3Dpn/3LQ6XTk5uaSm5vLxMQE1dXVVFZW0tjYSEtLCy0tLWzdupWMjAzy8/OZM2fOZVfsc8e+wFsQ2iqH0FYZvElX4Ty5MRUVFS4Z/Ho6AQEBXHPNNRw+fJhdu3aRnp4+JfrkzdrabDaMRiPArFXYOxt30XZkBP72N8e2c+fk1Du1GhYuhCVLoKjIsWVnu/ccorN11WohKsqx5eRc+LrhYThzBpqaoLFx6uvQEHR1ObYDByZfq1Y7IlNZWQ598vJg3jzIzXXMyfIGnOX78/PzKSgoYGBggM7OTjo6Oujr65NLop88eRIfHx9iY2PlzVvWlvL19aWwsJDCwkKMRiNVVVVUVFTQ3t5ObW0ttbW1+Pr6kpeXR35+/iUXmnCXvsAbEdoqh9BWGbxJV+E8CbySFStWUFxcLC8meTXNfZqYmJCr2szGuk7uRnEx/O538PrrDgfKSWYm3HwzrF0LK1eCFz0EuyBBQQ6HZ9686Y8PDjoicbW1n27OOV9GIzQ0OLatWydfl5ICc+dO3ubMcRS/8FScJc7Dw8PJy8vDZDLR0dFBe3s7nZ2dmEwmmpqaaGpqQq1WExERQVxcHLGxsQQFBSlauW62CAwMZMmSJSxZsoTe3l7Ky8upqKhgaGiI4uJiiouLCQ0NJT8/n/nz5xMWFuZqkwUCgWBWEdX23HgEdbG1igQXZu/evezdu5fg4GC+9rWvTVo005u17e/vZ/v27fj5+XHrrbfO+ue7Stv9++HHP3ZEmZxkZsK998LttzuiJe48tpUkSd6cqFQqeZttXSXJkQ7odKhOnoSqKjhxwlFxcDo0GofOhYWwYIHjtaDAvR3VkZERDh06xLJlywgICDjveTabjb6+Ptrb2+no6GBoaGjS8YCAAOLi4khISCAiIsJr0vvA0TabmpooLy/n5MmTmM1m+VhSUhL5+fnk5uZOeVjjzf2sqxHaKofQVhncXVdRqvwieIrzVFZWRkFBgavN8FgsFgvPPvssQ0NDrFq1irVr18rHvFlbp/NkMBi45ZZbZv3zZ1vbujp45BHYts3xu1YLd90FX/0qrFo1uw6TJElYrVbMZrO8WSwWLBYLNpsNq9WKzWaTN7vdPq3TdC4qlYrOzk5iY2PRaDSo1Wr51fmzRqNBq9Wed9PpdDMWGenr+9SROnHC8XNlpaN4xXRkZDgcKadTtXCho9y6u/BZ2uzIyAjt7e20t7fT3d2N3W6Xj/n4+MiOVHR09GXPFXJnzGYzNTU1lJeX09DQILdbrVbLnDlzKCgoID09HbVa7dX9rKsR2iqH0FYZ3F1XUarcS+jq6nK1CR6NTqfj+uuv58033+TgwYPk5+cT/smIzZu1dT7xtlqtLvn82dLWYoGf/AT+v//PsZ6STgf/9m/w3e86UsqURJIkTCYTY2NjTExMMD4+zvj4uJwyqcTnjYyMYLfbP/P7q1QqdDodOp0OvV4/6We9Xo+Pjw96vf6SIibh4Q7H9Ox1qCXJUaCitNRRmKK01LE1N0N9vWP7618/PT8jAxYtgsWLHa8FBa5J+WttbeWpp57imWeeuayy/gEBAWRlZZGVlYXFYqGrq4u2tjba2towmUw0NjbS2NgoF6hISEggNjYWHw+vwKHX65k/fz7z589neHiYyspKysvL6e7upqqqiqqqKoKCgsjPz2dgYMCtB0uejDd/h7kaoa0yeJOuwnlyY7xlMrIryc7OJjMzk7q6Ot59910efPBB1Gq1V2vrTJ0xm81YrdZZf+o9G9q2t8Pdd39a4GDjRnj2WceAXAkkSWJ0dJTh4WFGRkYYHR3FYrFMe+50Toper58UHXL+rFarUalUssPifFWpVFMiUsPDw8yfP192oJyRq7NfrVbreTdJkuRo2Oh56rKrVCrZkXI6U86ffX190Wq1541eqVSOyoQJCY65ZU56e6Gs7FOH6vjxT52p+nr4y18c5+l0DgfK6VAtWeL4eyodOezu7uadd97hBz/4wWdeE02n05GQkEBCQgJ2u52enh7ZkRodHZWr96nVaiIjI0lISCA+Ph6Dh9eIDwoKYvny5Sxbtoyuri5KS0uprKxkeHiYjz76iMbGRoxGI4WFheTm5np1vzvbCC2VQ2irDN6kq0jbc+O0PcHMMDQ0xHPPPYfJZGLDhg0sW7bM1SYpiiRJvP3221gsFjZt2kRwcLCrTZpRTpyAdesc1eECA+HFFx2O1EwPsm02G0NDQwwMDDA0NDQlkucsyOHcfH198fPzu+TozWxybjqhM5XQ+bPJZMJkMl00qqXVavH19ZXv1fnq4+NzWSmB/f3w8cdw9CgcO+Z47e2del5UFKxYAcuXO14LCx1O1kxSUlJCUVERxcXFLFiwYEbfW5IkBgYGaGtro7W1dco8qfDwcBISEkhMTLzgfCtPwmq1UltbS2lpKfX19bLzr9frmTt3LoWFhSQkJHhFcQ2BQOA9iDlPF8FTnKdt27axceNGV5vhFZSUlPDee++h1Wr56le/SmlpqVdru3PnTnp7e1m8eDGpqamz+tlKttuKCrjuOsdAe+5cePttR1GImcIZYeru7mZgYACbzSYf02q1BAYGEhQUhL+/PwaDYVadJKX7A0mSJjlSZrNZ/tn5+/m+LtRqtRyd8vPzw2AwYDAYLtmpkiRH6XSnM3XkiKNq4ll1CQDHWlyLF3/qUC1d6lic+EpQ0nk6F6PRKDtSfX19k/QMCwsjMTHRqxypt99+m8jISEpLS+nv75f3R0REUFhYyPz58wn0lpr3s4wYHyiH0FYZ3F1XMedJIDiHwsJCqqurqaur429/+xvJycmuNklRIiIi6O3tpbe3d9adJ6Xo7oZNmxyO08KFsH07zNQyVs4IQUdHx6SUNh8fH8LCwggJCSEgIMCrn5Y7U/b0ev20A1q73c7ExIQ8x+vsn+12uzzva+CsqhEajWaSM2UwGPDz80NzzoJaKhWkpjq2z3/esW9iwuFAHTgABw86tv5+2LvXsYFjHaoFC2DNGse2YoV7rz8VGBhIdnY22dnZjI+P09bWRktLC93d3fT399Pf3095ebnXOFL+/v6sXLmSFStW0NzcTGlpKVVVVfT29rJjxw527dpFVlYWRUVFcpEJgUAgcHeE8+TGJCYmutoEr0GlUnHrrbfywgsvTKmM5Y1ERUVRU1NDe3s7kiTN6qBfiXZrt8M99zjmOmVnw44dMFMVT4eHh2lpaZGdJrVaTVhYGJGRkW7lMLm6P1Cr1bIDdDbOuVRnF84YGxtjfHwcm83GyMgII2ctuKVSqfD19ZXfKyAgAIPBMMWh8vV1RJeWL3f8brdDTY3DiXI6VKdPO+ZQHT8Ov/ylo1T6Ndc4HKm1a2HZMjjftKK6Osc6Vh0dsWzY8F06OmIpKXE4XzMZzTwffn5+ZGRkkJGRwcTEBK2trRd0pJKSkvD391fesBnE2WZVKhXJyckkJyezadMmqqqqKC0tpaWlhZqaGmpqaggODmbBggUUFha6dUaIu+Dq/sCbEdoqgzfpKtL23LiT7u7uJioqytVmeBWNjY28+uqrjIyMcO+99zJ//nxXm6QINpuNd955B6vVyrp164iIiJi1z1ai3W7ZAg8+6BgIf/yxYy2hK8VqtdLS0kJPTw/giJLExMQQFRU1aU0wd8HT+gNJkpiYmGBsbGzSNl2hDZVKhZ+fH/7+/vj7+xMQEICvr+9FIxFtbbBnz6dbY+Pk4zqdo/CEMzK1ZInDKaurg6ys879vbe3sOFDTca4jdfZXdHh4uOxIeUKxiYu12Z6eHoqLiykvL2d8fBxwOOkiGnVxPK0/8CSEtsrg7rqKOU8XwVOcJ3fPD/VU9uzZw8svv0xOTg7/9m//RmxsrKtNUoQjR47Q1NREeno611xzzax97ky3W4sF0tKgtRV+8Qv41reu/D3Hxsaoq6vDZDIBjkhdfHy8WzpNTrylP7BYLLIj5axcaD53chOOQfTZzlRAQMBFqzWdOTPZmWppmXzc1xdWr4a8PPj1r+G11yAn59Pj1dWOCGdxsSMd0NWcz5FSqVRERkaSnJxMQkKC25Y/v9Q2a7FYqK6upri4mDNnzsj7RTTq/HhLf+COCG2Vwd11das5T8899xy//OUv6ejoIC8vj82bN7Ny5cppz3377bd5/vnnKSsrw2QykZeXx49+9KNJYm/ZsoUHH3xwyrXj4+P4umKREIHHsXr1arZu3YrFYuGNN97gq1/9qselw1wKaWlpNDU1cebMGQoKCtzaMbgQ777rcJyio+HrX7/y9xsaGqK+vh6bzYaPjw9paWli0vosotPpCA4OnlQF0lk+fXR0lJGREcbGxrBarRiNRoxGo3yej48PgYGBBAQEEBgYiK+v76S0yuRkeOABxyZJjrS+s52pzk7HYsrOBZVzctzDSTofvr6+cmrf+Pg4ra2tNDc309PTQ3d3N93d3RQXFxMXF0dSUhJxcXEeuSCvTqeT147q6emhpKSEsrIyhoaG2LNnD3v37pWjURkZGSIaJRAIXIqikac333yTe++9l+eee47ly5fzu9/9jpdeeomTJ0+SlJQ05fxvfOMbxMXFsWbNGkJCQnjllVf41a9+xdGjRyksLAQcztOjjz7KqVOnJl0bExNzyXZ5SuSpr69PXtRVMLO0tbXx9ttv09fXR1JSEvfff/+UOReejiRJbN26laGhIfLz88k5+xG7gsx0u737bvjf/3Usfvuzn13Ze42MjHDq1ClsNhtBQUGkp6d7jFN5NfUHzkWInZEpp0N17teVTqeTo1KBgYEXrIAoSXDypMNx+t//dVT2OzfCVFICRUXwxz/CF7/omEPljoyOjtLc3MyZM2cYHByU9zvXm0pOTiYqKsrlTsaVtFmr1Up1dTXHjx+fFI0KCQlh4cKFFBYWeuVDr0vlauoPZhuhrTK4u65uk7a3ePFiFixYwPPPPy/vy8nJ4XOf+xw/u8RRUF5eHnfffTf/9//+X8DhPH3jG9+Y9IVxuXiK81RZWcm8efNcbYZXUllZSWxsLL///e8xmUzMnz+f2267zW2KA8wUjY2NHD16FF9fX2666aZZeSo9k+1WkiAmxlFp7+BBRwGAz4rFYuHEiRNYLBaCg4PJzMx0+eDycrja+4Ozi08YjUZGRkamFH5Rq9VyOfnAwED8/f2n/Z92Oknnc54AwsJgwwa46SZHlcewMCXv7rMzODgoO1JnV4r09fUlKSmJ5ORkwsLCXNK3zVSb7e3tpbi4mLKyMnlulEajIS8vj2uuueaqXDfqau8PlERoqwzuruvl+AaKjRzMZjPFxcVs2LBh0v4NGzZw6NChS3oPu92O0Wgk7JxvrZGRETnX+6abbqK0tHTG7HYn2tvbXW2C19Le3k5ERAR33XUXarWaiooKdu/e7WqzZhxnha6JiQlqa2tn5TNnst329DgcJ5XKsUDqldDU1ITFYsFgMHhk6s/V3h9oNBqCg4OJj48nOzubBQsWkJubS2JiIqGhoeh0Oux2O0NDQ7S0tHDy5ElKS0upq6ujq6tr2shVdbXDYXJu1dWO/f7+jrLob7zhmAMVFQXXXuuYJ1VXN/v3fiFCQkKYP38+N910E9dddx0ZGRn4+PjI//M7duzgww8/pLq6mrGxsVm1bababEREBBs3buTxxx/nc5/7HHFxcdhsNioqKnj55Zd58cUXKSkpmbYYibdytfcHSiK0VQZv0lWxx9C9vb3YbDaio6Mn7Y+Ojqazs/OS3uN//ud/GB0d5a677pL3ZWdns2XLFubNm8fw8DBPP/00y5cvp7y8nMzzlEdyLvLoZHh4+DPc0ezjbWlk7oRT2/T0dG655RbeffddPvroI4KDg1m4cKGLrZs5NBoN8+bN48iRI5w8eZLU1FT8/PwU/8yZwtlVREY6Fkj9rBiNRgYGBlCpVKSlpXnk/5Yn2qwkarVaTtkDR6rf+Pg4RqOR4eFhjEYjVquVgYEBee0pnU5HUFAQZnMIEM4990z/3seOOZynDz6A996DqirYt8+x/ed/wpw5cMstcPPNjoV63WGakbOIRGRkJIWFhXR1dXHmzBna2toYHh6mvLyciooKYmJiSElJIT4+XvFI9Ey3WZ1OR0FBAQUFBbS1tfHxxx9z4sQJOjo6eO+999i+fTsFBQVcc801bp0eNBOI/kA5hLbK4E26Kpa2197eTnx8PIcOHWLp0qXy/p/+9Kf86U9/oqam5oLXv/7663z5y1/m73//O+vWrTvveXa7nQULFrBq1SqeeeaZac/50Y9+xJNPPjll/1tvvYW/vz9r167l2LFjjIyMEBoaSl5eHgcOHAAczprdbpef2q9evVqeyBoUFMSCBQvY+8mKjZmZmWi1Wqo/eXy5YsUKTp48SX9/P/7+/ixZsoRdu3YBjsn8BoOBEydOALB06VLq6+vp6enB19eXVatWsX37dgCSk5MJCQmhvLwcgEWLFtHc3ExnZyc6nY61a9eyfft2JEkiISGBqKgoSkpKACgqKqKzs5O2tjbUajXr169n165dWK1WYmNjSUhI4OOPPwagoKCA/v5+mpubAdi4cSN79+7FZDIRFRVFWloaR44cAWDevHmMjIzQ+Elt4HXr1nHo0CHGxsYIDw8nOzubgwcPApCbm4vZbKa+vh6ANWvWcPz4cYxGo/zUdP/+/QDMmTMHQJ7TtmrVKioqKhgcHCQwMJCFCxeyZ88eADIyMtDr9Zw8eRKA5cuXU1NTQ19fHwaDgWXLlrFz504AUlNTCQgIoLKyEoAlS5bQ0NBAd3c3Pj4+qFQqfv/736NSqbjzzjtZvHgxZWVlAFxzzTW0trbS0dGBVqvluuuuY8eOHdjtduLj44mJiaG4uBiABQsW0N3dTWtrKyqVig0bNrB7924sFgsxMTEkJSVx7NgxAPLz8xkcHJTz+Tds2MD+/fuZmJggMjKSjIwMDh8+DMDcuXMZGxujoaEBgOuuu44jR44wOjpKWFgYubm5cpvNycnBarVS98kj8tWrV7Nlyxb6+vpITEzkrrvuYt++fQBkZWWhVqvl/8cVK1ZQVVXFwMAAAQEBLFq0SI7Ipaen4+vrS1VVFQDLli2jtraW3t5eDAYDy5cvZ8eOHQCkpKQQFBRERUUF4EjhbWpqoqurC71ez5o1a9j2yaz9xMREIiIi5AjywoULaW9vp729nbq6UB55ZBFRUeO8+up+4uLiiIuL4/jx44Bj8ePe3l5aPimrtnHjRvbs2YPZbCY6OpqUlBSOHj0qR7D1er2c2rR+/XoOHjzI2NgYERERZGVlyVHxvLw8JiYmOH36NIDoIzywj5AkicTERPl/wWq1kpCQQG9vL+Pj4/T2hhIZmUZraytarVZekLatrYb4+LFJfURfXzC9vUt45ZU+KitDsdk+jVoGBZm5/nqJa67pJC2tjuBglVv1EcPDw9hsNrRarfw3j4iIQKPRYLfbCQ8P58Ybb6S0tFROWykoKPCYPsJisRAWFsZrr73G8PAwgYGBBAYGIkkSc+bM4aabbqK/v/+ifQTA/PnzGR4epqmpSfQRV0EfATM7jrj22mvlNpuUlERYWJjXjCOuvfZaSkpKPLKP0Gg0rFu3jp07d2Kz2c47jqipqeGOO+5w7Zwns9mMwWDgr3/9K7fddpu8/9FHH6WsrEwWfTrefPNNHnzwQf76179y4403XvSzvvKVr9Da2sqHH3447fHpIk+JiYluP+dp586dF3QcBZ+dc7WVJIn333+f4uJi1Go1X/jCF84byfRE+vr62LlzJ5IksWrVKuLi4hT7rJlstydOwLx5jvkmfX2f7T1sNhulpaXY7Xbmzp3rEevjTIfoD64Mu93O6Ogow8PDDA8PMzIyIqfxNTQ0kJGRQWBgICEhIQQHB8sPVc5laAi2boV//MMRmfokqAWAXu+YJ3XHHY7IlLvNkzIajTQ1NdHU1DRpflRgYCCpqamkpKTM6P/HbLZZSZKor6/n448/pq6uTv7bhoaGsmjRIgoLC72qIq/oD5RDaKsM7q6rW8x50uv1FBUVyR6kkx07drDsArO+X3/9dR544AH+8pe/XJLjJEkSZWVlF1yrx8fHh6CgoEmbJ2Cz2VxtgtdyrrYqlYobb7yRefPmYbfbefPNN+Wnj95AeHi4/DTu+PHj066rM1PMZLtNTna89vdPHqReDs7CAj4+PoqnLCqJ6A+uDGcxifj4eHJycigsLCQjI4PBwUG+9a1vUV9fz9DQEGfOnKGiooKKigqampoYHBycpH1wsKMC5GuvOebj7d3rSOXLzASzGd5/37Ggc3Q0XH89/P73jrl77kBgYCDz5s3jpptuYs2aNaSkpKDVajEajVRUVPCPf/yDvXv3cubMmRlpb7PZZlUqFZmZmfzrv/4rX//611m+fDl+fn4MDAywbds2fv3rX/PBBx/Q91mfwrgZoj9QDqGtMniTroomPD/++OPce++9LFy4kKVLl/Liiy/S3NzMQw89BMATTzxBW1sbr776KuBwnO677z6efvpplixZIs+N8vPzk9cEefLJJ1myZAmZmZkMDw/zzDPPUFZWxm9/+1slb8UlKBkduNqZTlu1Ws3nPvc5zGYzp06d4i9/+Qv33HPPtGX1PZG5c+fS1taG0WiktLSUxYsXK/I5M9luAwMhLg7a2x1lppcvv/z3mJiYAMBgMHh0RS7RH8wsWq2WsLAwIiIiaG1tJTExUc5IGBkZwWQyyWspqdVqgoODCQkJISQkRC5vr9U6Ft1dvRp++UtHG33rLfjb36Cy8tM1pR56yFFw4o474LbbwNXrcqtUKqKjo4mOjsZisdDS0kJjYyM9PT10dnbS2dmJj48PKSkppKenf+YHjq5qs6Ghoaxfv55rr72WiooKjhw5Qk9PD8eOHePYsWNkZmayZMkS0tLSPLZPEP2BcghtlcGbdFW0VDk4Fsn9xS9+QUdHB3PnzuU3v/kNq1atAuCBBx6gqalJzvW99tprp03nu//++9myZQsAjz32GG+//TadnZ0EBwdTWFjIj370o0nzqi6Gp5Qqd/ea+J7MhbS1Wq385S9/oaGhAb1ezxe/+EWSnSEQD6e7u1ueD7J48WJSU1Nn/DNmut3ecQe8/TY8+SR8smLBZdHa2kp7eztRUVGkpKTMmF2zjegPlKGkpISioiKKi4tZ8EndcpvNxvDwMENDQwwNDU1K+1apVPj7+xMaGkpISMiUhXqd1NZ+6kh9MnXkk+sdDwH+5V8cbTshQfFbvGScaX2NjY2TKvNFRkaSnp5OQkLCZRWZcJc2K0kSjY2NHDlyZFJKX2RkJIsXL2b+/Pno9XoXW3l5uIu23ojQVhncXVe3WefJXfEU52nbtm1s3LjR1WZ4JRfT1mKx8Prrr8sO1L/+67969MD7bKqqqqisrESr1bJu3TpCQkJm9P1nut2+/DJ8+ctwzTWOKmiXS1tbG21tbR7vPIn+QBmmc57OxlnFb2BggMHBwUlzhcCxnlJISAihoaEEBARM60g1Nn7qSH1SmwBwOFIrV8IXvuBwpiIiZvz2PhN2u52Ojg4aGhpob2+XnQ29Xk9KSgppaWmX1G+4Y5vt7+/n6NGjlJaWyunLfn5+LFiwgEWLFslZLu6OO2rrLQhtlcHddXWLOU8CgSej0+n4whe+QHp6OmazmT//+c9yhRpPJzc3l9jYWKxWK4cOHXL7tVFuuAHUavj4Y0da1OXiLI/qTfnWgtlDpVJhMBiIj48nLy+PgoICkpOTCQ4ORq1WMzExQWdnJ9XV1ZSVldHU1MTQ0NCkNaVSU+Gb34QjR6C5GTZvhhUrHItA798P//EfjlS+G290zKUyGl13v+BIYY6Pj2flypXcfPPNzJs3D39/f8xmM7W1tWzdupUdO3bQ0NDg9v3HuYSFhbFp0yb+8z//k+uvv56wsDDGx8c5ePAgTz/9tJzZIhAIBOdDRJ7cOPLU3d1NVFSUq83wSi5VW6vVyhtvvEF9fT0ajYY77riD3NzcWbBQWSYmJti+fTtjY2PExcWxYsWKGVs0Vol2e9tt8O678PDDcLnTGwcGBqirq8NgMDB37twZtWs2Ef2BMgwODvLee+9xyy23XHYU1mazMTQ0xMDAAENDQ1itVvmYTqcjJCSEsLAwAgMDp/3/ammBN9+Ev/wFzl7r3c/PsYbUF74AmzaBj89nvbuZw26309XVxenTp2lvb8dutwOO+0xOTiYzM3NK1MYT2qzdbqeuro4jR47IJbPBUQZ82bJlpKenu+W8KE/Q1lMR2iqDu+sq0vYugqc4TydPnvSKgbo7cjnaWq1W3n77bU6ePClX5fOGhXT7+vrYvXs3NpuNzMxMFixYMCODBCXa7a5dsG4d+PvD6dOOSmaXitlspqysDJVKRWFhoeILgyqF6A+UYya0tdvt8mLMAwMDkyIyWq2W0NDQCzpSp07B6687tk+WAwI+re53//2OBXndYRw/Pj5OU1MTDQ0NGM8Kk0VHR5ORkUF8fDxqtdrj2mx7ezuHDx+mqqpKdg6joqJYtmwZc+fOdau+w9O09SSEtsrg7rqKtD0vwbmon2DmuRxttVot//Iv/8LChQvl9aD279+Ppz93CA8PZ+nSpahUKurq6uQFHK8UJdrt2rWwaBGMjsKPf3x51+r1evz8/JAkicHBwRm3bbYQ/YEydHZ28stf/vKKU7WcFflSUlIoKChgzpw5REVFodPpsFqt9PT0cOrUKcrKymhsbJyS2jdnDvzoR1BTA8ePO8qfx8c71pV68UVHkYmsLEf7d/UqCn5+fuTk5HDDDTdw7bXXkpCQgEqloquri4MHD/L+++9TVVXlcanOcXFx3HHHHXz9619n6dKl6PV6uru7effdd9m8eTMfffQR4+PjrjYTEP2BkghtlcGbdBXOk0BwCajVam688Ua5UuTu3bvZunWrxztQCQkJ5OfnA1BWViavCu9uqFTw8587fv7d7xxP6S+HsE9WK+3v759hywSeTnt7O1u2bKG9vX3G3lOlUk1ypLKzs8/rSDU3N09asFelgqIi+NWvHPOjdu1yRJ38/aG+3lFxMjUV1qyBV15x7fwolUpFTEwMK1as4KabbiI3NxdfX1/GxsaorKykoqKCw4cP09PT41F9ZUhICBs3buTxxx9n/fr1BAUFMTIywq5du/jNb37Dhx9+6NEPYgQCwZUh0vbcOG1P4J4cPXqUDz/8EIB58+Zx6623ulU6x+UiSRLFxcXU19ejVqtZsWKF267HcNNN8M9/OiqU7d3rKCRxKUxMTFBZWYkkScybN8+jF8sVzCwXq7Y3k0iShNFopL+/f0pqn6+vL+Hh4YSHh+Pr6zvl2pERR8n+V1+F3bsdxSYADAa4/Xb4t39zrDc1Q1MXPzM2m42Wlhbq6uomLUgbGhpKRkYGycnJHtdf2mw2Tpw4waFDh+jq6gIcD9TmzZvH8uXL3Xoeh0AguDRE2p6XsGfPHleb4LVcibaLFy/m9ttvR61WU1lZyauvvjppTRRPQ6VSsWDBApKTk7Hb7Rw4cOCKUpiUbLfPPut4Av/RR/D885d+nbOcNEBHR4cyximM6A88H5VKRVBQECkpKeTn55OVlUV4eLhcta+trY2KigpOnjxJV1fXJOcqIADuuw927nSk7f33fztS/cbGHBX61q51pPX97Gfgyiau0WhISUlh/fr1BAQEkJaWhkajYWBggI8//pj33nuPsrIyj+ozNRoN+fn5PPTQQ9x7772kpaVht9spLy/nueee4/XXX6e1tXVWbRL9gXIIbZXBm3QVzpMb41yDQjDzXKm28+fP54tf/CK+vr40Nzfz+9//np6enhmybvZRq9UsWrSIhIQE2YHq7u7+TO+lZLtNSYH/7/9z/Pztb0N19aVfGxsbCzgKZZy7Vo8n4E79gSRJ2O12bDYbVqsVi8UyZbNardhsNux2O5IkeVTa1mygVqsJCQkhPT2dwsJC0tLSCA4ORqVSMTIywpkzZygrK6O2tpb+/n65gAFAUhI88YSj/R89Cv/+7xAY6Cim8r3vQWIi3Hor/OMfcFYBwFlHp9OxaNEibrnlFgoKCggICMBsNlNTU8P777/P4cOHJ0Wn3B2VSkV6ejr33XcfX/nKV8jNzUWlUnHq1CleeukltmzZQn19/ay0dXfqD7wNoa0yeJOuIm3PjdP2ysrKKCgocLUZXslMadvT08Nf/vIXBgYG8PX15a677iItLe3KDXQRNpuNAwcO0NHRgVarZeXKlURfTmk7lG+3djusX+9IXcrNdSyc6+9/adeePn2avr4+AgMDyc7OdssSxOdjtvsDSZKwWq2TnKCznaHLRaVSoVar5dezf9ZoNPLrbP9NGhoa+Pd//3d+97vfucX/rsVioa+vb4qTr9PpCA8PJyIiAoPBMOW60VHHIrwvvQQHDny6PzYWHnzQkdaXnj4bd/Ap57ZZ5+K7p06dmvRwJjIykjlz5hAXFzdjSybMFr29vRw8eJDy8nLZwY2NjWXFihXk5OQodj9ifKAcQltlcHddRanyi+ApztPg4OBlrzsiuDRmUtvR0VHefPNNmpubUavV3HDDDR5dytxqtfLRRx/R1dWFRqNh+fLllzUHajbabVcXFBRAZyd88Yvwpz9dWglnk8lEZWUldrud5OTky3YMXcls6Gq1WjGbzXLk6GJfD04HaDqc0abL/YpxOlYajQaNRoNWq5WdK6UcK3fta8fHx+nt7aWvr2/SU1t/f38iIiIIDw+fdv5QTQ28/DL88Y9wdkB8wwbHgrw33QSzMe3oQrr29/dTW1tLc3Oz7HQEBASQmZlJWloaOp1OeQNnkKGhIQ4fPkxxcbGcbhkeHs7y5cvJz8+XF+ueKdy1zXoDQltlcHddhfN0ETzFedq2bRsbN250tRleyUxra7Vaee+996ioqABgyZIlbNiwweOeojqx2WwcOnSItrY21Go1ixcvJjk5+ZKuna12u2+fY56H3Q4//akjXelS6Orq4syZM6jVavLy8jymeIRSukqShNlsZnx8fNIir8AkB+bsCJFKpZK3S3n/sze73S5Hr5w/OyNaZ6emnYtKpZriUDntuhLMZjP/+7//y1133YVer7+i91IKSZIYGhqit7eXwcFBWSe1Wk1oaCgREREEBQVN+XuYzY7UvZdegm3bPi0ykZDgSPX78pchJkY5uy+lzY6Pj1NXV0d9fb3sIOp0OtLS0sjKysL/UsPKbsLY2BhHjx7l2LFjclnz4OBgVq5cSUFBwYwVyxDjA+UQ2iqDu+sqCkYIBLOMVqvltttuY82aNQAcOXKEV1991SPn1oBj0Lxs2TK5iMSRI0c4ffq0q82axOrV8Mwzjp+//33H4qKXQlRUFEFBQdjtdk6fPo3NZlPOSDfHarUyNDSE0WjEarWiUqnQ6/UEBAQQGhpKaGgoQUFBGAwGfH190el0lx0FOjs1T6vVotfr8fX1xc/PD39/fwIDAwkJCSEsLIywsDBCQkIIDAzEYDDg4+ODVqtFpVLJaYQmk4nR0VGGh4flqnVGo5Hx8XHMZvMFHbDpOHHiBPfeey8nTpz4LBLOCiqVipCQEDIyMsjPzycpKQmDwYDdbqevr49Tp05RUVFBR0fHpCITej3ccQd8+KFjPtR3vgMREdDaCv/1X465UXff7XgQ4arHqH5+fsyfP59bbrmFhQsXEhQUhMVi4dSpU/K8qIGBAdcY9xkwGAysWbOGb3zjG2zYsIGAgACGhoZ4//33eeaZZzh27NiUhxQCgcCzEJEnN448dXR0yJPcBTOLktqePHmSd999F7PZTFBQEHfddRcJCQmKfJbS2O12iouLZcdp7ty55OXlXXDgPNvt9j//E379a8dA8cMPHdGoi2E2m6mqqsJisRAeHk5aWprbz3+aaV1NJpO8vpBarcbX1xdfX1+3jJaeXaDCWaTCORfrXJwRKp1Oh1arRafTXfCeZrNU+UwiSRJjY2P09PTQ398vD8id0aioqCgCAgKmtGuTyTE36rnn4NChT/fn5sLDDzvWlAoImBkbP0ublSRJnhflLAsOjnlEOTk5REZGuv3/6tlYLBZKSko4cOAAxk8W5QoMDGT58uUUFRV95vREMT5QDqGtMri7riJt7yJ4ivN06tQp5syZ42ozvBKlte3p6eGNN96gr68PjUbDDTfcQFFRkWKfpySSJFFRUUH1J6XtUlNTWbhw4Xlz+Ge73drtcOedjjVwDAbYvh2WL7/4dcPDw5w6dQpJkkhMTHTrTh1mVlez2YzRaESSJDnS5I5O08Ww2+2yE3Uhh8oZ9dLpdHL0zImnOk9nY7PZGBgYoLu7m5GREXm/wWAgMjLyvHOjyssdJf9fe81RcAIgONiRzve1rzmqW14JV9pmBwYGqKmpobm5WZ47Fx4eTk5ODvHx8R7lRFmtVkpLSzlw4ABDQ0OAY47XsmXLWLhw4WWnjIrxgXIIbZXB3XUVaXteQlNTk6tN8FqU1jYyMpKvfvWrZGdnY7PZ+Mc//sF7773nkekaKpWK/Px8Fi5ciFqtprGxkf3795+37Ohst1u1Gv78Z8dk+LEx2LQJPv744tcFBQWRlJQEQEtLC729vQpbemXMlK52u12OOPn6+hIYGOiRjhM4oix6vR4/Pz8CAwPlVMPAwED8/PzklD+bzSZH2gYGBhgYGGBkZASTyXTZaX7uiEajISIigtzcXPLy8oiKikKtVjM2NsaZM2coLy+nqalpShpxfj688AK0tTlSYLOyYGgI/ud/HJX57rjDsabaZ33EeqVtNjQ0lKVLl3LDDTeQkZGBRqOhr6+PAwcOsHXrVhobGz0m7Var1XLNNdfw9a9/nZtvvpmQkBBGRkbYvn07Tz/9NAcPHrysUs5ifKAcQltl8CZdPfMbUyDwAHx8fLj77ru57rrrUKlUlJSU8Ic//IHBwUFXm/aZyMjIYMWKFWi1Wrq6uti1a5fbzOny9YV33nHMgzIaHY7UkSMXvy4qKoqYT2bMNzY2yk+EvRmnw6DRaPD39/eop/eXgkajwcfHB39/f0JCQibN29LpdLIzNTExgdFoZHh4GHAULrBYLB6/HpW/vz8pKSkUFBSQnJyMn58fNpuN7u5uqqqqOHnyJP39/ZPuMzgYHnnEsW7UP//p+P+x2x3R3FWrYOFCePVVR8qfKwgMDGThwoXcdNNN5ObmotPpGBoa4ujRo3zwwQecOnVq0lwvd0aj0VBUVMQjjzzCrbfeSmhoKKOjo+zYsYOnn36aw4cPe8y9CARXKyJtz43T9ux2u8c+EXZ3Zlvb06dP89ZbbzE2Noavry+33norOTk5s/b5M8nAwAD79+9nfHwcHx8fli1bNqnktyvbrdHoiDwdPOhY++m99y4+B0qSJBoaGujr60OtVpOVleWW/cJM6To8PIzZbMbf399jKg3OJM5UP4vFIpdlN5vN6PV6uUy6Xq9Hp9Oh1+s93rmUJImRkRG6u7sZGBiQI20+Pj5ERUURGRk5bUpfVZUjGvWnP8EnReOIjnak8z38MISFXfyzleoLzGYzp0+f5tSpU0xMTACg1+uZM2cOWVlZHlXm3G63U1lZyb59++jv7wcczuLKlStZsGDBeavzifGBcghtlcHddRVzni6CpzhPH330EStXrnS1GV6JK7QdHBzkb3/7G62trQAsWrSIDRs2zFjp2tlkbGyMAwcO0N/fj1qtJj8/n6ysLFQqlcvb7egofO5zsHMn+PjAX/8KN9984Wvsdjv19fUMDg66rQM1U7oODAxgs9kIDg72qEGmUthsNo4dO8bcuXOxWCyT0vhUKpXsRJ07V8oTsVgsdHd3093dLUc3nCl/0dHR+Pr6Trmmrw9+/3t49llHeh84Hkx85Svw2GPwSebrtCjdF9hsNpqamqipqZGLMTidqMzMTLctPT8ddrud8vJy9u7dK0fAg4ODWb169bTrRLm6n/VmhLbK4O66ijlPXsLY2JirTfBaXKFtSEgIDz74IMs/qWZw7NgxXnrpJbefazMdBoOBtWvXkpKSgt1up7S0lKNHj2K1Wl3ebv39HWvb3HqrI83ottvgxRcvfI1arSYjI4OQkBDsdju1tbVul145U7o6IylX4XOzaTl9+jT//u//TkdHB6GhoQQHB+Pn54dGo5HXwHLOlRoaGmJ8fNxj50npdDri4+PJz88nNTUVg8GAzWajq6uLyspKamtrGR4entQ2wsPhu9+FxkbH3ML8fMcDis2bHfOi7rsPKiun/zyl+wKNRkN6ejqbNm1i6dKlBAUFYTabqays5P333+fEiROXNY/IlajVagoLC3nkkUe48cYbCQwMZGhoiPfee4/f/va3VFRUTGp3ru5nvRmhrTJ4k67CeXJjIiIiXG2C1+IqbTUaDevXr+eee+7B39+fzs5OXnzxRcrKylxiz5Wg1WpZvHgxhYWFqNVqmpqa2L17NwEzVef4CvD1dUSc7r8fbDbHgqBPPOGYx3E+znWg6urq3Mqxnak263yC7YnFS5RgZGSEyspKRkZG5EiTc75USEgI/v7+coTOYrEwOjrq8Y6UWq0mMjKSvLw85syZQ0hICJIkMTg4SE1NDVVVVfT29k5yonQ6+Nd/hdJSx4K7110HVqsjrW/+fLjhBti7d3JxidnqZ9VqNcnJyVx//fUsXbqU4OBgzGYzJ06ckJ0ok6smbF0mZxeW2LhxI/7+/vT39/P222/z/PPPc/LkSSRJEuMDBRHaKoM36SrS9twsNedsjEYjgYGBrjbDK3EHbY1GI2+//TaNjY0A5Ofnc8MNN+Dj4+NSuz4LnZ2dHDp0CLPZjCRJrFy50i3WtpIkeOop+NGPHL/ffTe88gpcaKqP3W6nsbGRvr4+ABITE4mJiXH53JeZarMTExOMjIyg0WgICQlx+X25mkstVW6z2TCbzfI8KScqlQqtVouPj488b8oTGR8fp6uri97e3knzomJiYoiMjJz2voqL4Re/cKwb5fQhFy92LMB7ww0wMuKaflaSJFpaWqiqqpJT4HQ6HVlZWWRlZXlUH2s2mzl69CiHDh1i/JPJZ7GxsSxdupT58+e72DrvxB3GB96Iu+sq0va8hENnr2AomFHcQdvAwEDuvfde1q5di0qlory8nBdeeIHm5mZXm3bZxMTEsHHjRsLDw2loaODAgQOUlpa6vIywSgU//CFs2QJaLbz5JqxYAWfOnP8atVpNWlqaXIWvpaWFlpYWl6e5zVSbdRZBsNlsoqrXZaDRaPDz8yM4OJjQ0FA5IiVJEhaLRU7tMxqN8kMET8LPz0+u0peQkIBOp8NkMsmlzjs6OqZEK4uKHP9TtbWOIhK+vnD0KNx0k6NC3y9/WX/BaK9SqFQqkpKSuP7661m2bBnBwcFYLBaqqqp4//33qays9Jh0Pr1ez8qVK3n00Ue59tpr8fHxoaOjg1/84hf86U9/oqOjw9Umeh3uMD7wRrxJV+E8CQQuRK1Ws2rVKh544AGCg4MZGBjglVdeYefOnS53PC4Xf39/1q5dK1feO3XqFLt373aLcub33w87dkBEBJSUOAZ2u3ef/3zn4CsxMRFwRNZqa2u9ItVNrVbLhQFGR0c9bpDvDpzrSBkMBnmOlMlkYnh4mMHBQcbGxjzu/1ir1RIXF8f8+fNJTk7Gx8cHi8VCS0sLFRUVtLa2TnG609Pht7+Fpib49rcd8w5LSuDHPy4kP9/hYLlChrOdqOXLlxMSEjLJiaqpqfGY/2lfX1+uvfZavv71r7NkyRLUajWnT5/md7/7HX/729/kSn0CgUB5RNqeG6fttba2ukXqkzfijtpOTEywdetWef5TTEwMt99+O1FRUa417DJxVhM8duyYXAJ60aJFbqF3c7OjgERJiWNx3V/+0lEx7EKZa/39/TQ0NGC32/H19SUzM9MlJb5nss0657fYbDZ8fX3dYp6aq+jt7eWVV17hwQcfvKKcfEmSsFqtmEwmzGbzpLlQOp0OX19fjyx9brfb6e/vp6OjQ04bc86Zio2NnbaiXW+vo6DEM8/YMRodz2izs+F734MvfMERBXYFkiTR2tpKZWWlvL6Xn58feXl5pKamelQ1xRMnTlBbW0tlZSWSJKFWq1m4cCGrV6/G39/f1eZ5NO44PvAG3F1XUar8IniK81RfX09GRoarzfBK3Fnb6upq/vGPfzA2NoZGo+G6665j6dKlHjPocmo7MjLC4cOH5blDaWlpFBYWurw89vg4PPSQY9FPgM9/3lGN70Kp2GNjY9TV1WEymeQKXyEhIbNir5OZbrNmsxmj0YgkSQQEBExbpvpqYaa1dVbpM5lMkxbedUb9fHx8PGqgDp863B0dHYyMjAAXd6KKixt4//00Nm8GZ/HKrCx48km46y7HAwxXYLfbOXPmDCdOnJAj44GBgcydO5ekpCSP6Gudbbazs5OdO3dSX18PONL8li1bxtKlSz1qbpc74c7jA0/G3XUVc568hNOnT7vaBK/FnbXNycnh4YcfJisrC5vNxvbt2/njH//odqWzz4dT24CAANauXUtOTg4qlYqGhga2b9/u8gp2fn6OOVDPPON4Av7GG1BYCMePn/8ag8FAbm4uQUFB2Gw2amtraW1tndWUt5lus3q9Xo6gjY6OeswckJmmt7eXZ599dkbbpUqlwsfHh6CgIEJCQjAYDKjVaux2O2NjYwwODsqLFXvK80uVSkVoaCg5OTlkZ2cTFBSE3W6nq6uLiooKzpw5M6UN9fbW8cMfOuYY/vd/O8qe19Y6ok8FBfD3v0+uzjdbqNVqUlNTueGGG1iwYAG+vr4YjUYOHz7Mtm3baG9vd/u/i7M/iImJ4Z577uH+++8nLi4Os9nM3r17eeaZZzh+/LhHVoN0Ne48PvBkvElX4TwJBG5IQEAAX/jCF7j55pvR6/U0NTXx/PPPc/z4cbf/Uj8bjUZDfn4+a9aswd/fH6PRyO7du6mqqnLpl7pKBY884iitnJQEp0/DsmXwP/9z/nLmzmpdzjld7e3t1NTUeEwJ5Onw8/PD19cXSZLkQgdXG83NzTz99NOKFWrRaDQYDAZCQ0MJDAxEr9fLkSnn3KiJiQmP+b9WqVQEBQWRnZ1NdnY2gYGBF3WigoIcSwU0NDiqXwYFOdaG+tznYMkSx3xEV9y+RqMhKyuLG2+8kXnz5qHT6RgcHGT//v3s3r2bnp6e2TfqM5KamspXvvIV7rzzTsLCwhgdHeX999/nhRde8KpBq0DgDoi0PTdO27NYLC5PcfJWPEnb/v5+3n33XXlwl5KSwi233EJYWJiLLZue82lrNpspLi7mzCel7sLDw1m8eLHL/wcHBuArX4G33nL8vnEj/PGP8ImPNC39/f00NjZis9nQarWkpaUpnsanVJs923FSqVTyAP9q4VJLlc8kNpuNiYkJTCaT/BDBmdLn6+vrUeXOne2nra0No9EIOO4lOjqaiIiIaecH9vc75hs+8ww4181cvRp+8hNHNUxXYTKZqK6upq6uTi70kZiYSH5+vtvNC7xQf2Cz2Th+/Dh79+6V56llZmayYcMGIiMjZ9NMj8STxgeehLvrKtL2vIRjx4652gSvxZO0DQsL44EHHuD6669Hp9PJUajDhw+7ZUrG+bTV6/UsXbqUJUuWoNPp6OvrY9u2bdTU1Lj0PkJDHQvqvvCCo9Tytm2Qnw/vv3/+a8LCwsjLy8Pf3x+r1UptbS3Nzc2KVlZTqs06HSYfHx95IDwxMaHIZwkcaDQaeSFef39/NBqNnNI3MDDAyMiIx1TpOzsSNWfOHAICArDb7XR0dLB161Y6Ojqm3EtYGPzsZ45I1KOPgl4P+/bBypWOaNSpU665Fx8fHwoKCrjppptIS0tDpVLR0tLCBx98QFlZmVtFZi/UH2g0GhYvXjypMl9dXR3PP/88H3zwAWNOj1UwLZ40PvAkvElX4Ty5Mc5JuYKZx9O0VavVLFmyhIcffpjU1FQsFgvbtm3jD3/4g9ulllxM25SUFK6//npiY2Ox2WyUlZWxa9cuufqVK1Cp4N//3THvae5c6OqCm2+GBx/8dKL7ufj6+pKTkyOvB9XZ2cnJkycVK82uZJtVqVRy0QhJkhgZGRFlzGcBtVqNn58fISEhBAYGyutGTUxMMDg46HFOVHBwMDk5OWRmZmIwGJiYmKClpYXKykp6enqmtKfoaEdVvvp6R/RXrXbMg8rLg//zf6C72zX34ufnx6JFi9i4cSMxMTHY7XZqamr45z//SW1trVv8TS6lP/Dz8+P666/n//yf/0N2djZ2u51jx47xzDPPcOjQIY8p0z7beNr4wFPwJl2F8+TGhIaGutoEr8VTtQ0NDeW+++7j5ptvxsfHh9bWVl544QX279/vFl/ocGna+vv7s2rVKhYtWjQpClVdXe3SKFReHhw7Bv/5nw6HassWhzO1dev056vVapKSksjKykKv1zM+Ps7Jkydpa2ub8ftQus2qVCr8/f0xGAwAjI+PMzw87JbRzZkkICCABQsWuDQt6+wCE8HBwfK8KKcTZTQaPWag6ywskZeXR2xsLD4+PpjNZhobGzlx4gQDAwNTnKjEREfFyxMnHA8tbDZ47jnIyICf/vTT1L7ZJiQkhNWrV7Nq1SqCg4MxmUyUlJSwdetW2traXPpw4XL6g/DwcD7/+c9z//33ExMTw8TEBNu3b+e5556jpqZGPCQ5B08dH7g73qSrmPPkxnOeRkdHxXoNCuEN2g4PD/P+++9TW1sLOKou3XzzzcTHx7vUrsvVdnR0lOPHj9PR0QE4vugXLlzo8o724EF44AHHU3GAL3/ZUVDifF2G1WrlzJkzcml2f39/UlNTZWfkSpnNNmsymRgZGZHXjwkICPDqeVDu2B9YLBbGx8flVDGVSoVer5cX5PUERkdH8fPzo7u7m46ODnlx3aCgIJKSks77v7F3L3zzm1Bc7Pg9Pt4xH+q++1xb3ryhoYETJ07Iaa1RUVEUFBS4ZP7pZ22zdrud8vJydu3aJUcCMjIy2LRpE+Hh4TNtpkfijv2BN+DuurrVnKfnnnuO1NRUfH19KSoq4qOPPrrg+fv27aOoqAhfX1/S0tJ44YUXppzz1ltvkZubi4+PD7m5ubzzzjtKme9SDhw44GoTvBZv0DYoKIgvfOEL3H777fj5+dHZ2clLL73EP//5T5fOWblcbc+OQun1evr6+tixYwdlZWUufdq+fDmUl8PXv+74/aWXHFGo882F0mq1pKenk56ejlarZXR0dEajULPZZn18fAgODkar1WK32xkeHvbaND673c7u3bvdLsKm0+nkUufO+Wgmk4nBwUFGR0fdzt7pOHDgAGq1mpiYGObNm0dsbCxqtZrh4WGqqqpoamqSHaqzufZaRwT4z3+G5GRoa3Ok0C5eDEePzv59gCPKnJGRwY033khOTg4ajYbu7m527NjB8ePHZ73q5mftD9RqNYWFhTzyyCOsWLECjUZDfX09zz33HDt37nSreV2uwhvGB+6IN+mqqPP05ptv8o1vfIPvf//7lJaWsnLlSjZt2nTekrCNjY3ccMMNrFy5ktLSUr73ve/x9a9/nbecZbCAw4cPc/fdd3PvvfdSXl7Ovffey1133cVRV/WoAoELUalUzJ8/n6997Wvk5+cjSRIff/wxzz77LCdOnPCYwa5KpSItLY1NmzaRmJgozzFwTjh3FQYDPP2040l4aiq0tDjSiu68E9rbp78mPDycuXPnEhoait1up62tjaqqKrkSmaeg1WoJDg6WF88dHx9ncHBw2sGuJ1NWVsYtt9xCWVmZq02ZFq1WS2BgIMHBwfKcKOffYmxszGP+x7VaLYmJicybN4+wsDAkSaK7u5uKigo6OjqmOINqNfzrv0JNjaMyX1CQY07ikiUOR+rwYSgpmbrV1Sl7Hzqdjvz8fG644QaSkpKQJIn6+nr++c9/Ul9f7xFOLTgekKxbt46HH36YzMxMbDYbBw4c8LjvDoHAFSiatrd48WIWLFjA888/L+/Lycnhc5/7HD/72c+mnP+d73yH9957j+rqannfQw89RHl5OYcPHwbg7rvvZnh4mA8//FA+5/rrryc0NJTXX3/9kuzylLS9M2fOkJyc7GozvBJv1baxsZH3339fTh1LT0/nxhtvnNW0kpnQtq2tjZKSErn4QnJyMoWFhfJA3hWMjsKPfgS/+Y1jTkZgoKNi2EMPwXRZVJIkMTAwwJkzZ2SHIyoqioSEBLRa7WV/vivbrNlsZmRkBLvdjkqlwtfXF4PBgEqlcok9M4krSpV/ViRJwmKxMDY2JkdlnZX7dDqd2/09LtRmjUYjzc3N8v+4r68vycnJBAcHT3t+Z6djragtWy7+ubW1kJn5Wa2+PLq7uykuLmZoaAhwzOsoKioiIiJC0c+dyf5AkiRqa2vZunUrAwMDgKOwzw033EBUVNSMfIYn4a3jA1fj7rq6Rdqec02XDRs2TNq/YcMGDh06NO01hw8fnnL+xo0bOX78uDz4ON8553tPT8ZTnmB5It6qbWpqKv/xH//BmjVr0Gq1nD59mueee479+/fPWgrcTGgbHx/P9ddfT1ZWFiqVijNnzvDBBx9QV1fnsr+dv7/jCfjx47BoERiN8LWvORbXnS5ooVKpCAsLY968efLaKt3d3Zw4cYK+vr7LfrLryjar1+snpY85Ix8ixWd2cc57Cg4OJjAwEI1Gg81mY3h4GKPR6DZFY5xcqM0GBgaSm5tLWloaer2eiYkJTp06RX19/bTtKiYGXnkFjhyB3FzHvtdec8yLcm6vvebYP5tB3qioKDZu3MiCBQvQ6/UMDAywc+dOjh49qmj69Ez2ByqVijlz5vDwww/L3x1NTU288MILbN269apbusBbxweuxpt0vfzHn5dIb28vNpuN6HNWmoyOjqazs3Paazo7O6c932q10tvbS2xs7HnPOd97gmPy89n5yM6SyGVlZZMqLIWGhpKamsrExAQnT56c8j7Op5KnTp2aUo44JSWFsLAwenp6aGlpmXQsMDBQDouXl5dPeV/nyuanT5+Wn14BHDp0iDvvvJPo6GgGBgZobGycdJ2fnx85OTkAlJaWThmM5eTk4OfnN2kSu5Po6Gji4+MxGo3UnZPnoNPpmDdvHgCVlZVT0nQyMzMJDAykra2Nrq6uScfCw8NJTk5mfHx8UgQRHB10YWEhANXV1fLifU5SU1MJDQ2lq6uLtra2SceCg4NJT0/HYrFQWVnJueTn56PRaKirq5uSHpWYmEhkZCT9/f00NTUBDm2XLVuGv78/c+bMARxPoM8lNzcXX19fGhsb5SdyTmJjY4mNjWV4eJh6Z1WBT/Dx8SEvLw+AioqKKY5LVlYWAQEBtLa20n1OPd6IiAiSkpIYGxujpqZm0jG1Wk1BQQEAJ0+enPKl5lysdc6cOXIaRktLC3/+85/ZvXs3999/P/Hx8Zw4cWLKvRYUFKBWq6mtrZ1SUjQpKYmIiAh6e3unpN0GBASQlZWF3W6nrKxM1tbJ3Llz0ev1NDQ0MHhO3e+4uDhiYmIYHBykoaFh0jFfX18WLFhASkoKb7zxBkNDQ5w6dYrAwEBycnJYunQpBoOB5uZment7J13rjPCMjIzIBTWcaLVa5s+fD0BVVdWUuQoZGRkEBQXR0dExJWUwNDSUgoJUdu+e4Mc/PsmzzzrmZhQVwd13w3PPLSAkZPo+IioqiuHhYTo6OqioqMDf35+YmBj8/PwuqY+ora3FbrdP6iPA4WjOZh9hsViwWq3MmTOH4eFh6urq0Gq1k4oYeFIfcbYNZ/cRTty5j7BarZSVlWEymZAkCZVKhZ+fH0uWLEGlUl2wj+js7KT9nNzTkJAQ0tLSMJvNM9JHOPuCc/uIc8nJyaGnp4eSkhJqamo4evQokZGRhIWFkZCQMKmP0OngiSf8uPfeHHJyYLpgoeNvOk52dvas9hGxsbF0dXXJjuBHH31EZmYmCQkJclRwpsYR5/az5xtHwKX3ESdOnCAwMJCVK1dy+PBhGhsb+eijj6iqqiI/P5/Q0NBJ0U1vHUfs2bNnyv/ydOMIJ+7cR1zJOGKm+4j6+npSU1MvaRxxLlcyjsj95GlLWVnZFAfu7D7i3L/rBZEUoq2tTQKkQ4cOTdr/k5/8RJozZ86012RmZkr//d//PWnfgQMHJEDq6OiQJEmSdDqd9Je//GXSOa+99prk4+NzXlt++MMfSsBFtzVr1khHjx6VysvLpz2+detWaXx8XJo7d+6UY9/61rek06dPS0899dSUYwsWLJA++ugjqa+vb9r3feONN6ShoSFp1apVU4595Stfkaqrq6UXX3xxyrH09HRp165dsi7nHn/hhReknp4e6fbbb59y7K677pLKy8ulv//971OORURESFu3bpUkSZIiIiKmHP/5z38utbW1SV/96lenHNu4caP08ccfS8eOHZtyTKfTSVu3bpVMJpOUlZU15fj3vvc9qbGxUfr+978/5djixYulgwcPSq2trdNq+NZbb0lGo1FasmTJlGMPP/ywdOrUKemZZ56Zciw7O1vas2ePJEnStO/7hz/8Qerr65NuuOGGKce++MUvSpWVldKbb7455VhsbKy0bds2SZIkKTg4eMrxX//611JHR4d0//33Tzl20003ScXFxdK+ffumHDMYDNLWrVsli8UipaSkTDn+wx/+UDpz5oz0zW9+c8qxpKQk6Utf+pL09NNPT3uv7733njQyMiItWLBgyrFHH31Uqqurk37xi19MOTZv3jxp//790tjY2LTv+6c//UkaGBiQ1q1bN+XYAw88IFVVVUl//OMfp7V3x44dkiRJksFgmHL8iSeekJqbm6XPf/7zU47ddtttUmlpqbRt27Ypx4KDg6WtW7dKNptNio+Pn3L8Jz/5idTS0iI98sgjU45drI8IDjZJv/2tWcrLm76PqKurk77zne9MOXYpfcTf//53t+oj9u7dK/X29kpRUVFTjntaHwFIb7/9ttf0EQcPHpTGx8cvu49YsWKFdPjwYamurm7ae1W6j1i7du2UY/fdd980fUShBJJUXDz5u764WJJA+uQ40v/7f/9P6urqmvU+YseOHdILL7ww7b162jji7rvvlr71rW9J2dnZU4556zhiur+NGEdceR9xzz33uN04Yro+Ymho6Lz+hBPF5jyZzWYMBgN//etfue222+T9jz76KGVlZezbt2/KNatWraKwsJCnn35a3vfOO+9w1113MTY2hk6nIykpiccee4zHHntMPuc3v/kNmzdv5syZM9PaMl3kKTExkX379rl15MlkMpGWliYiT8x85MlkMuHj43PVPDEymUycPHmS1tZWrFYr/f39FBQUkJ+fL8+/manIk1NbJzP5xMhkMlFXV0dbWxtxcXEEBgYSFhZGQEAA6rNqGCsZeZqujzh8GH71K2hqcvQROTmn+OY3R/nkTwVM7iNOnz5NV1eX/P8eHBzMsmXLCA0NpaKignOZN28eNpuNtrY2l0ee4NM+wmazcezYMcY+WYhHrVbj4+PD3LlzCQoK8og+wmKxoFKpKCgoYGRkxKOfKpvNZkwmE3l5eahUKhobG1GpVJOiBbMZeXL2BZfzVHlgYIDBwUG6urqwWq1ERkaSl5eHv7+//B1fXe3HPffkUFw8OfJUUuKIAufkVPPTn46zcePsRp7g0z5ibGyMrVu3Ul9fj81mQ6VSkZqayh133IFWq73iccS5/exMRJ6m6yMyMzM5fvw477//PiMjI2i1WhYuXMjcuXOJi4vzynHEiRMnpqSOisiTgyvpI5z/c+4ceVq9evUlzXlSvGBEUVERzz33nLwvNzeXW2+99bwFI/7xj39MGpT8x3/8B2VlZZMKRhiNRj744AP5nE2bNhESEuJ1BSOOHDnCkiVLXG2GV3K1atvV1cWHH34od/4hISFs3LiR7OzsGZtsPhva9vb2UlxcLH8RhYSEUFBQQExMjKKfeyEsFvjtbx1FJZxjl3/9V/jFLxzr1EzH0NAQzc3N8gDAYDCQmJg47aR5d22z0idFDEZHR+U5NxqNBoPBgF6vd7siBtPhrtp+Fux2O6Ojo/KAX6vVEhAQ8JmKlFwpV6KrxWKhpaVFdnp8fHxITU0lKChIdpJeew0+GfcDUF0N99zj+Fmvhx/8AL77XdDprvROPjujo6OUlJTIA/nAwECKioquuK+a7Tbb09PD+++/LzuwMTEx3HTTTSQkJMyaDbOFN/UH7oS76+oWBSMAHn/8cV566SX+8Ic/UF1dzWOPPUZzczMPPfQQAE888QT33XeffP5DDz3EmTNnePzxx6muruYPf/gDL7/8Mt/85jflcx599FG2b9/Oz3/+c2pqavj5z3/Ozp07+cY3vqHkrbiEc58eCWaOq1Xb6Oho7r//fu68806CgoIYHBzkzTff5LXXXqOnp2dGPmM2tI2IiGD9+vUUFRWh1+sZHBxk7969fPTRRy4rCa7TwTe+4aj09ZWvgEoFf/kLZGXBD384/ST24OBg8vLySEpKQqvVMjY2xqlTp6itrZWjOU7ctc06ixiEhITIEUCbzYbRaGRoaAiz2ezWZY8bGhr41re+NeWJpaeiVqsJDAwkKCgItVqN1WplaGiIiYmJWf87XEmb1el0pKWlkZWVhY+PDyaTiZqaGpqamjAYHE76Pfc4nCjn5nScVqwAsxn+7/91rA01TZBh1vD392fFihWsWLECg8GA0Whk7969HDt27IoKrsx2fxAZGckDDzzArbfeKq8r+PLLL7t8XUElcNe+1tPxJl0VdZ7uvvtuNm/ezFNPPUVBQQH79+/ngw8+kEsVdnR0TArdpaam8sEHH7B3714KCgr48Y9/zDPPPMMdd9whn7Ns2TLeeOMNXnnlFebPn8+WLVt48803Wbx4sZK34hLcOSrm6VzN2qpUKvLy8vja177GqlWr0Gg0nD59mueff55t27Zd8RfhbGmrVqvJzMzkxhtvJCsrC7VaTVtbGx9++CGlpaWzvmilk6goePFF+PhjRyW+sTF46ilH6eQXXnBEqM69D+ciotHR0ahUKgYHB6csIurubdZZwjw0NBSDwSAP3P9/9s47vK3qbOA/SZ6yvPeKd2zHsROyyN4TAoEAATIglBVaZklLgVLogLbwUaDQsspqyaBAKCGQHUKms5zEWR5x7HjvIdtalnS/P4QuVmI7dmLZsnJ/z3MeSVfnXp376ujovvddarWapqYmMamBo9HY2MiePXsucgUZ6FgVWjc3NwRBoKWlhZaWlj79Dnpjzvr5+TF06FAxZXZ1dTV6/UmyslpsMu1ZW14e7NplsUoFBMDRozBqFPz1r5YSA/2BTCYjKiqKefPmkfRjDvVz586xadOmi9yiukt/rAdWl7kL6wr+4x//uMj1cSDj6GvtQMWZ5GpXtz1HZaC47V3o0yzRe0iy/Yn6+no2b95Mbm4uYHEdmzZtGiNHjrSJI+ou/SVbtVrNsWPHxIsRa+xNQkLCZZ1HbyAIsG6dxXXI6s6enAx/+QssWGCxTl2ITqejpKREdElUKBSEhYXh5+eHl5dXH47+yjCbzeh0OnQ6nehn7uLigqenp0O58w2kOk+XgyAI6HQ6saCuteiuoqPiZL1Mb68FarWawsJC9Ho9MpmMsLAwIiMjO/19V1TAAw/Ahg2W12PHwiefWKzB/Ul1dTWHDh0SreSxsbFcc801PZKVI/yHFRYW8s0331BfXw9ARkYG8+bNw9PTs1/HdaU4gmydEUeXq8O47UlcGTt37uzvITgtkmx/IiAggDvvvJOlS5cSFBSERqPh22+/5e233yYvL6/Hd6r7S7Y+Pj5MnjyZKVOm4Ovri16v58iRI2zcuJHS0tJ+sXrIZHDLLXD6NLz5JgQFQW4u3HwzTJpkSTRxIR4eHiQlJZGamopKpRITRXzzzTdUVFQ4XC2fzpDL5SiVSvz8/GwsUc3NzTQ2NqLVah3SEuVsWNOXX+jG1xd133p7LfDx8WHo0KEEBwcjCAIVFRUdJg2wEh4O69fDhx9ailpnZsLw4ZbfYn9OPWttqOTkZGQyGUVFRWzcuPGiBAdd4Qj/Yda6guPHj0cmk5Gdnc0//vGPi5I8DDQcQbbOiDPJVVKeJCQkAEsGqYceeojrrrsOpVJJTU0Nq1ev5j//+U+XddQcjfDwcObMmcPIkSNxd3enubmZPXv2sG3btosyEvUVrq6WgroFBfDss+DpCXv3Wtz6FiyADpJwivWsEhMT8fT0RBAESkpKOHHiBFVVVQOm4GBHSpTJZKK1tZWGhgY0Gs2AOZeBjKurK76+vri4uGA2m1Gr1RdlQBsIKBQK4uLiSExMxMXFhdbWVk6dOkV1dXWHyrhMBvfcAydPwsyZoNXCo49afncXJJjsU1xcXLjmmmuYMWMGPj4+6HQ6du/ezcGDBwfU9+Lq6srs2bO59957CQoKoqWlhc8++4wvvvjiomyCEhLOguS258Bue+fOnSM+Pr6/h+GUSLLtGusfeWZmpphmd/jw4UyfPh1vb+8u93Uk2ba1tZGTk0Nubq54pz08PJxhw4bh5+fXb+MqK7MEs3/8MVj1hkWLLJn62mcPsyIIAsePH0cmk4mxXO7u7kRGRhIYGOgwLnDdQRAE9Ho9Wq1WtKJZk054eHjg4uLSp+dTWVnJq6++ypNPPtmv2Rr7CrPZTHNzs5ii3cfHB1c7paOz91pgMBg4d+6cWPg+KCiImJiYTl0SBQHeegtWrrQklIiMhDVrLFbg/sRkMnHixAlyc3MRBAGVSsXYsWMJCgrqdB9HWmetGI1GfvjhB/bu3YvZbMbLy4vrrrtOTLc9UHBE2ToDji7XnugGkvLkwMpTcXExgwYN6u9hOCWSbLtHQ0MD27dvF+s5uLm5MWHCBMaNG4ebm1uH+ziibLVaLadOneLcuXOYzWZkMhkxMTGkp6f3axxRTg78/vewdq3ltVwOS5ZYsvMlJNj2LS4uJioqipqaGsrLy8W70x4eHoSHhxMYGNhvsV2XgzXFuVartbnT7uLigoeHB+7u7n2mRDninLUngiDQ3NyMwWBALpfj4+Njl1TmfSFXQRCorKwUXXOVSiUJCQldxt0cOwa3325JLiGXW25aPPMM9EEYWJdUV1dz4MABWltbkclkDBkyhCFDhnSoDDrynC0vL+frr78W6zcNGTKE+fPno1Qq+3lk3cORZTuQcXS5SjFPTsJA9xt2ZCTZdg9/f39uvfVW7r33XqKiojAYDHz//fe8+eabHDlypEN3K0eUraenJ6NGjWLevHkMGjQIQRAoKiri22+/7dfMfCkpljvf2dmWOCizGf7zH0tSiQcegPZ1BM+cOYNcLic0NJSMjAyio6NxdXVFp9NRWFjIiRMnqK6uHjAucFZrk6+vL76+vqKyZDQaaWlpoaGhwaZ2lL1obGzkww8/dLpse10hk8nw9vbG1dVVdOGzh5z7Yi2QyWSEh4eTnJyMq6srGo2G06dPi0kMOmL4cEtmvrvusvzmfvc7mDULLqjV2udYY6FiY2MRBIFTp06xffv2DssvOOI6ayUiIoIHHniAKVOmIJfLOX36NG+//fZFRWAdFUeW7UDGmeQqWZ4cxfL00EMWX552VFdXi+lZJXoXSbY9RwCam5upqanB+KOlwNXNjeCgIFTe3lhtBANBtgaDAbVaLSpNMrkclUqFysurX603jY2QkwvW0Cy5DKKiISkRWlsvlqvVBU6v12P+cSmXy+V4uLs7VEa77iIIAmazGZPZbBO/IpfJkCsUyOVyevuMGpua2LVrF5MnT8avg+LEzowgCBiNRsyCgFwmw8XVtVfl29drgdlsplWjEV10Pdzd8fD07PKcSkog+4QljbmnB4weDf3o0Sui0WppamwULeV+/v4o21nTBsI6CxYX8IqKCrGmlZ+fH8HBwQ5tJR8osh1odCrXyEh4++2+H9AFSG57l8AhlacOaG1tHVCpiQcSkmwvH6PRyOHDh9m1a5dYyDUyMpKZM2cSFxc3YGRrdffJzs4W04K7ubmRnJzM4MGD7RYH0h327YPnnoMdOyyvFQpYtKiN555z7TAmymQyUVNTQ0VFhegC5+bmRmhoKCEhIX2Slro3sbr06XQ62traREVKJpPh7u6Ou7t7r8VGOXuq8kthMploamrCbDajVCp71bWqP9YCa2IVa5KbgIAA4uLiuvwN5OTATTdZMmF6eFjqtC1b1kcD7gKNRkNmZqaY6CY+Pp4RI0aIiTIGwjoLltjTbdu2ceDAAcASm7Zw4UIiIiL6eWQdM5BkO5BwdLlKytMlGCjK06FDhxg9enR/D8MpkWR75ej1evbt28f+/fvFu4oJCQkEBQUxb968fh5d97FebJ06dUqsgO7m5iZmuutvJepPf4KNGy2vZTK49Vb47W8hI+Pi/iaTidraWps7vQqFgpCQEEJDQzuNU3NkTCaTaF1r71qmUChERepKlMOrXXkCy2+5ubnZYuHw8+s1Zbs/19mamhqKiorEBAxJSUld/pabmmDp0p9qQj3xBLz8MtghFKxHmM1mTp06xenTpxEEAV9fX8aPH09eXt6A+w8rKCjgf//7H83NzcjlcqZOncrEiRMdzgolXR/YB0eXqxTz5CR05bMtcWVIsr1y3N3dmTZtGo8++ijXXnstCoWCgoICVq1axRdffDFgZCyTyRg0aBBz5sxh3Lhx+Pj4YDAYOH78OBs2bCAnJ6dPauJ0xPjx8N13cPgwjB9fhSDA55/DsGGWVMsHD9r2VygUYkxUXFwcnp6emEwmKioqyM7OprCwsNOaOI6KQqEQU537+vri4eEhpjvXaDQ0NDSIdaMGSg0sR8PNzQ1XV1cEQejV+dGfa0BwcDDJycm4uLjQ0tLC6dOnuzw3X1/4+mvLjQmA116DuXMtrrT9iVwuJz09nSlTpuDh4UFTUxNbt24lPz+/fwd2GSQkJPDQQw8xZMgQzGYzO3bs4OOPP3a4eMOB8t810HAmuUrKkwPjyObNgY4k295DpVIxb948Hn74YTIyMnB3d+fkyZO89dZbbNiwQbTmODpyuZyYmBjmzp3L2LFj8fb2Rq/Xc+zYMTZs2GCT7ryvGTkS/vrXfLKz4Y47LBao9evh2mthyhTL3fL2eSLkcjnBwcEMHTqUpKQkVCoVZrOZmpoaTp48SX5+Pi0tLf1yLpeLTCbD1dUVlUqFv78/3t7eYlyX0WgU60b1VJHy8PAgNjYWDw8PO5+B4yKTyUR3Pb1e32vFi/t7nfXx8WHIkCF4eHig1+vJyckRXY07Qi6HP/4RvvgCvLxg+3ZLGvOSkj4cdCeEhYUxZ84cQkNDMRqNlJaWkpWVNeBuGCiVSm677TZuvvlm3N3dKS4u5p133iEnJ6e/hybS3/PWWXEmuUpuew7stmc0Gu2SPlZCkq09KS0t5YcffhDvjCoUCkaNGsXEiRMvWSPKkTCbzZw/f55Tp06JioanpycpKSnEx8f3uTtf+zmbmwt/+QusWgXWLN9Dhljq1yxeDO7uF+/f3NxMZWWlGN8FlkK8oaGh+Pv7D7jkElbMZjMGgwG9Xo/RaLS58Hd1dRWtKgqFotNzlNYDi/tqY2MjJpMJHx+fXnHxdBS5trW1kZeXR2trKy4uLiQlJV1yLTp2DK67DioqLPHs333XsatsX2N14ztx4gRyuZyQkBDGjx8/IJX/xsZGvvjiC0pLSwEYO3YsM2fO7Pc54yjz1tlwdLlKbntOwvbt2/t7CE6LJFv7cerUKZYsWcI999xDbGwsJpOJAwcO8MYbb7Bly5YBU3VeLpcTFxfHvHnzGD16NF5eXmi1Wo4ePcqGDRs4depUn6Y4bz9nk5Pho4+gsBB+9Svw8YHTp+FnP4P4eEusxoUGP29vb5KSkkhPTxezXTU3N3P27Fmys7Ntkk0MJORyOR4eHvj6+uLv749KpcLV1RWZTEZbWxutra00NjbS2NhIa2urTQIKK9J68JNlD+g1C6ujyNXV1ZXk5GS8vb0xGo3k5uZe0iI+fDhkZlpuSpSVwcSJsGtX34y3K6xufHK5HFdXV6qrq9myZQt1dXX9PbQe4+fnxz333MP48eMByMzM5MMPP+x39y5HmbfOhjPJVVKeJCQk7EJMTAx33303d999N9HR0RiNRvbt28cbb7zBtm3bunSfcSQUCgUJCQlcd911jB49WnTnO3HiBBs2bOD48eP9FkcUGWlRlIqLLY8REVBeDk89BdHRFkvU+fO2+3h6ehIXF0dGRgYRERG4urqi1+spKSnh+PHjFBYWDpjv5kI6UqSsrn0mkwmtVktTUxMNDQ00Nzej1+vJyspi4cKFHDt2rL+H3+9YLXPO6JDi4uLC4MGD8fX1xWw2k5+f32H9pPYMGgR79lhcY5ubLTFQW7f20YAvgb+/PzNnzsTHxweNRsOOHTsoKirq72H1GIVCwezZs1m8eDFKpZLy8nLeffddTp061d9Dk5DoFMltz4Hd9vLz80lKSurvYTglkmztR0eyFQSBgoICduzYQXl5OWBJODF27FjGjRs3oFxOzGYzJSUlnDlzRgx0VigUxMfHk5KSYje/7u7MWYMBVq+G//s/sF57yOWWNMyPPWaJ37jQc81sNlNfX09lZaWN0uTj40NoaCh+fn4D1qXPitlspq2tjba2NgwGg00h4ePHjzNz5kz27NnD6NGjRavV1YharcZgMODl5YVnu5pCl4sjrrNms5mzZ8/S2NiIQqHo1m9Wq7VkufzuO3Bzs8RE3XBDHw24E6yybWtr48CBA6Lr29ChQ0lLSxuQc1itVvPFF19Q/GN18FGjRjF37tw+d/VyxHnrDDi6XKVU5ZdgoChPZWVlREZG9vcwnBJJtvajK9kKgkBeXh7ff/+9WIfFw8ODcePGce211w4oJUoQBMrLyzl9+rToMiOXy4mNjSU1NbXX47t6MmfNZkt689dfh23bfto+fLhFibrjDks9m/YIgkBLSwtVVVU0NDSI1gd3d3dCQkIICgrq17TtvYW1MKzBYKCtrY0jR44wc+ZMtm3bxrBhw0T3NVdXV1xcXHqtnpSjYzabxe/dz8+vVy5YHXWdNZlM5Ofno1arcXV1JTU19ZJrj8EAd94J69ZZ0pd/9RXMn99HA+6A9rIVBIHs7GzOnDkDQGxsLKNHjx5w9d3AMg+///579uzZgyAIREZGcvvtt/fptZqjztuBjqPLVYp5chJOnjzZ30NwWiTZ2o+uZCuTyUhOTubBBx9k0aJFhISEoNPp+P7773nttdfYsWPHgHEZk8lkYnHgadOmERoaitls5ty5c3z33Xfs27evV+MQejJn5XK4/nqLi9HJk/DAA+DpaQmCv+ceizvS735nCYZvfz7e3t4kJiaSkZFBeHg4Li4uNi59Z8+eRa1WD2i3Lqty5OXlJaY/B8R6UYIgYDAYaG1tpampifr6epqamtBoNBdZrZwFq+IsCAIuLi69dtHtqOusQqEQs1Bak0lcKs7LzQ0++8xy48FotFiirEWs+4P2spXJZAwbNozRo0cjl8spKipi586dfRqT2VvI5XJmzJjBkiVL8PT0pKysjHfffZfzF/of2xFHnbcDHWeSq6Q8SUhI9DkymYwhQ4awYsUKbr31VkJCQtDr9ezatYvXX3+dbdu2DZjEEjKZjNDQUKZNm8bMmTOJjIxEEASKi4vZunUr27dvp6ysrN8UjrQ0ePddKC21ZOiLjoaaGktK5pgYy930Xbug/fDc3d2Jjo5m2LBhxMXFianO6+vrycnJ4cSJEwM2wcSFWAt0WmtJ+fn54eXlhbu7O3K5HEEQaGtrQ6PRoFarxXToLS0t6HS6izL8DTRMJhN1dXUYDAZkMhkqleqqsLRZFSh3d3d0Oh1nz569pGLs4gL//relxppeDzfeaEkq4SgkJCQwefJkXF1dqampGVDr6IUkJibywAMPEBYWRmtrK5988gkHDhwY0L81CedBcttzYLc9tVrt0OMbyEiytR+XI1tBEDhz5gy7du0S3flcXV0ZNWoU48ePH1ApzsGSgjc3N5fz58+LF2Q+Pj4kJycTExNzWS5RvTVnjUaLy9Ebb8DevT9tT0uDFStg2TJLwdAL0Wg0VFdXU1dXJ9aWkcvl+Pv7ExwcjLe394C86NZoNBw+fJhRo0aJtY6sCIJgEy9lNBo7rKsjk8lEi43V1a+r1OiOgLUg7oVxbr2RotzKQFhnNRoNZ86cwWQyER4eTnR09CX30estMU9bt0JgoEWBSkzsg8G2oyvZNjU1sWvXLlpbW1EqlUyZMkW0sA402traWL9+PSdOnABg2LBhzJ8/364uxANh3g5EHF2uUszTJRgoylNWVhYjRozo72E4JZJs7ceVyNYaE7Vr1y7KysoAS5asESNGMGHChAF3AaDRaMjPz6egoACDwQBYYrySkpJITEzEvaOCTJ1gjzl79Ci8/balXpT1GtrLy1Ir6qGH4JprLt7HZDJRX19PdXW1zV1tT09PgoKCCAwM7NUL8L6gJ7I1mUwYjUZRkTIajR1aLGQyGXK5HIVCISpVCoUCuVwuWrv6GqsVzWAwXOSCqFKpej3mcKCss/X19Zw9exaA5OTkbq0zra0wbRocOmQpHbB/P/j723ukP3Ep2Wo0Gn744Qeamppwd3dnypQpBAQE9N0AexFBEMjMzGTr1q2YzWbCw8O5/fbb8fPzs8vnDZR5O9BwdLlKMU9OQk1NTX8PwWmRZGs/rkS21pio++67j6VLl4opzg8ePMjf//53vvnmm36vAdITlEolw4YN44YbbuCaa67By8sLnU7HiRMnWL9+PYcPH75kumQr9piz11wD771nSW/+5puWmjatrfD++zBiBIwdCx9//JNiBRZ3p+DgYNLS0khLSyMkJASFQoFWqxVjo/Ly8qivrx8Q8UHFxcU8//zzYoavS6FQKHB3d8fLywsfHx/8/f3x9/fH29sbT09PXF1dRXc/k8mEwWBAq9XS3NxMY2Mj9fX11NfX09jYSHNzM62treh0OvR6PW1tbZhMpit2TbJazIxGI3q9ntbWVtRqNfX19ajVanQ6HWazGYVCgZeXFwEBAXZJ1jJQ1tmAgABCQkIAOHfuXLfqXHl5wddfW9xgc3PhttvAqDfBzp2wZo3lsQMrZW9xKdkqlUqmT59OYGAger2eHTt2UF1dbbfx2BOZTMa4ceNYtmwZSqWSiooK3n//ffEGW28zUObtQMOZ5CpZnhzY8vTDDz8wZcqU/h6GUyLJ1n70pmwFQaCoqIgffvhBrGEik8lIS0tjwoQJhIeH98rn9BXWNOe5ubmiEmhNPJGUlERISEinrl59MWcFAXbvtlijvvwSrCFNPj4Wa9S998LIkRenO7dao2pra22UQRcXFwICAggKCsLLy8sh3diysrIYOXIkR44c6dW7omazGZPJJFqnrM+7q1BaLVcymcymdYQgCDbNbDZ3qoDJ5XLc3Nxwc3Oze1r2gbTOms1mTp06hVarJTg4mLi4uG7td/w4TJgAs1vX8ZH3Y/g2l/70ZlSUxT924cJeH293ZdvW1saePXuoqqpCoVAwefJkQkNDe308fUVjYyNr166lsrISFxcXFi5cyJAhQ3r1MwbSvB1IOLpcJbe9SzBQlCdBEBzyYsMZkGRrP+wl2+LiYnbv3k1+fr64LTExkYkTJxITEzOgvk9BEKipqSE3N9fm7qmvry9JSUnExMRc5NPf13O2qgo+/NBihSos/Gn7sGEWJWrJEujIC0in01FbW0tdXZ1Nti9Hdeuzl/LUGVaLlFW5sj5alZ6uFJ+ecqHbYF/HYg20dba5uVlM992TcgN7frmO8a/dCgi27jzWc//ii15XoHoiW5PJxN69eykvL8fFxYXJkyeLlraBiF6v58svvyQvLw+AWbNmMX78+F6bawNt3g4UHF2ukvJ0CQaK8rR582bmzJnT38NwSiTZ2g97y7ayspK9e/dy8uRJ8SIzKiqKiRMnkpyc7NCLc0eo1Wry8vIoKioS3YXc3NyIj48nMTERlUoF9N+cNZstHkgffGCxRln1IXd3y/XgvfdaYj8uDOMRBAG1Wk1tbS0NDQ2ixUUmk+Hj40NAQAD+/v59XgDzQvpaeboUF1qQLtzW0V/2hdYpa1xVf/8WBuI6W1RURHV1Nd7e3qSkpFxahiYTxMYilJbSYU+ZzGKBKiyEXqy71FPZmkwm9uzZQ0VFBS4uLkyZMoXg4OBeG09fYzab2bRpEwcPHgRg5MiRXHfddb2SZn8gztuBgKPLtSe6Qf/+a0lY0GggJ+eizd75+TCAFzdHRpKt/bC3bMOAW+LimBUYyPHjx8nNzcVUUcEPhw5x3M+P4cOHk5iYOGAKRPoAo+RyhkVHU1ZWRklJCRqNhqrcXKplMoKCghg0aFC/zVk5MN0Ppj8J/7wPNm2yZOvLy4ecNfCrNRAZYSkYet11lhgQABng+2Mzms1izE9rayttQBVQI5ejUqnw9fVFpVL1y3fmeeYM1/z46AjIfmwAA2MGd85AXGcj29rQ5OdjMptp+VGJ6pLDh6EzxQksvrAlJZa7D6NG9do4eypbBTDB05OjP9Yuyy4sZOTIkXZLumBv5MB1YWFEx8Wxb98+yjdsYNOxY8ycObNHiXg6YiDO24FAl3JNSYELsp06MpLlyREsT1lZlkACCQkJCQkJCQkJiauJI0csWYr6EcnyNNBISbFMnAuora0lKCioHwbk/EiytR/9JVuDwcCZM2fIzs4Wa9e4urqSmprK0KFDB1ytKICWlhaKS0ooLyujubkZpVKJq6sr4eHhREVFOcQ5abUWt75vv7XUuzH/eDvO1QWmTIHrr4fxEyyv2yMIAjqdjqamJtRqtZjKHSyJJnx8fPD19UWpVNrd/UxaD+zDQJWrXq8nPz8fmUx26ZIChw/Dgw9e+qDvvturlqcrkW2b0cjhQ4dQq9UolUrGjBlzxdaa/qampoaNGzei1Wrx8fFh/vz5l70+DtR56+h0KdeUlL4dzBUiWZ460C5NJhNt1jRT/ciePXuYOHFifw/DKZFkaz+6kq2rq6vdXbOMRiPZ2dns27eP2tpawBI4n5aWxvjx4wdchj6wKIZr164lMDDQJptdcHAwCQkJREdHO4SbYnk5rF4Nn3wCJ0/+tD0oCO6805Kx79prL87WJwgCLS0tYhrv9uuvq6urTTrw3q6R1NLSwvvvv8/9998vxpdJ9A6OHuPQFTk5OajVaqKjo7teM36MeaKszOKidyEOEvN0IVqtlu3bt9PS0kJgYCDTpk3r9/jDK6W+vp5///vfNDY24uPjw7Jlyy4rrmsgz1tHxtHlKiWMuARdCailpYXS0tJey3Z0JWi1Wjw9Pft7GE6JJFv70ZVsZTIZUVFRfXKRKggC+fn57N+/n8J26eJiY2MZN24cgwcP7veA+p6wefNmZs+eTWVlJQUFBZSXl4sJBdzd3YmLiyMhIcEhrFGCYEnh/J//WArwVlX99F5sLNxxh6VlZHSsSKnVaurq6mhsbLSpuePi4iIqUj4+Pr2iSDlawghnwtEvlrqiqqqK8+fP4+PjQ8ql7oqvWwe33mp53u7awYwMmQxkdsi21xuyVavVbNu2DYPBQFRUFBMmTBhQa2JHqNVq/vOf/1BTU4NSqWTJkiVERkb26BgDed46Mo4uV0l5ugSdCchkMpGfn49SqSQ4OLjfFxGj0Tjg7wQ5KpJs7UdnsrWm59ZoNCQlJfWppaSiooL9+/dz8uRJUeEICgpi7NixDBs27KK04I5IQ0MD/v7+4muNRkNhYSEFBQWimyJAaGgoCQkJREZGOoQ1ymiErVstitT69ZYivFZSUn5SpJKTL97X/GOiiYaGBhoaGmwsUgqFAj8/P/z9/fH19b3sc5WUJ/tx4ZwdSGi1Wk6cOIFcLmfkyJGXvh5Ytw4eewxKf6rzVEw0db99nWv+2Pt1nnpLtjU1NXz//feYzWaGDRtGampqL4yuf9FoNKxatYqysjLc3Ny48847u123Cwb2vHVkHF2ukvJ0CToTkE6no7CwkNjYWIewSmg0GpQDKPvIQEKSrf3oSrZarZaioiLi4uLw8PDo45FZfvsHDhzgyJEj6HQ6AJRKJaNHj2b06NEO7bZ1/Phxhg0bdtF2s9lMRUUFZ8+epbKyUrSae3h4EB8fT3x8vMOcl0ZjiY1au9by2K4MFMOHW5So22+3WKcuRBAEG0WqfYyUXC7H19cXPz8//Pz8eqQMS8qT/ehszg4EzGYzR44cQRAEhg8f3r3aZCaTpcp0RQVvfhHO4+smseBmBevW9f74elO2BQUFHDp0CJlMxpQpUwgLC+uV4/Yner2etWvXUlhYiIuLC3feeScJCQnd2ncgz1tHxtHl2hPlqXedx52E/rY4WXGEuCtnRZKt/ehKtv392/Lx8WHWrFk88cQTzJ07Fz8/PzQaDT/88AOvvfYa69atsyla60hUVlZ2uF0ulxMZGcmUKVOYP38+aWlpeHh4oNPpOH36NN9++y3ff/8958+ft3GB6w+USrjtNku9qOpq+Pe/LenNXVzg2DH4zW8gLg7Gj4c33rBkeLZirQ8VExMj3iEPCwvD3d0ds9lMQ0MDhYWFHD16lNOnT1NWVoZGo3EIF+yrlc7m7EBALpeLSnh7Rb1LFAqYOhXuvJNpv5+KGQXffAM/hl72Kr0p24SEBOLj4xEEgf3799Pa3jw8QHF3d2fJkiUkJydjNBpZs2YN586d69a+A3neOjLOJFdJeXJg+vtC84UXXmDFihUA7Ny508bvW6VSUV1d3V9Du2L6W7bOzECQrbu7O2PHjuXRRx/ltttuIzo6GpPJRHZ2Nu+//z7/+te/OHHiBCaTqb+HKtIda4qXlxfp6enccMMNTJgwgbCwMARBoKqqiv3797N+/XoOHz5MfX19vysVPj6wbJnFAlVZCe+9B9OnW2Kg9u+Hxx+HQYNgzBj4858hN/enfWUyGd7e3gwaNIiMjAyGDh1KZGSkaGFraWmhrKyMkydPkp2dTVFREY2NjaLLZntcXFzw9fWV3HjtwEBwh+0Ol7OmDR0K8fEWt9XwcEuyvd6kt2U7YsQI/P390ev1HDhwoN/Xh97AxcWFRYsWiQrU6tWru6VAOcu8dTScSa52VZ4aGhpYtmwZvr6++Pr6smzZMhobGzvt39bWxlNPPUV6ejpeXl5ERERw1113UV5ebtNv6tSpF1VUv+OOO+x5Kv1Ce7NhbGwsPj4+aLVacZtarcbT09NGqYmNjSUzM9PmOCtWrOCFF17o1bG1tLQQEhLSq8fsDR5++GE++eQTm233338/Dz/8sM02Hx8f/v73vzNlyhRx2+HDh5k2bRqDBw/miy++uOjYCxcu5Pnnn7fPwO1EQUEBEyZMQKlUMmLECI4fP95p36lTp+Lh4YFKpUKlUjFv3jyb9zMzMxk7diwqlYqoqCj++9//ApCbm8v8+fMJCgoiODiYn//85zQ0NNj1vHoLaxa+e++9lwceeIBhw4ahUCgoLS3lyy+/5PXXX2fXrl0OcSd2+vTp3e6rUCiIjo5m6tSpojXKy8sLg8HA2bNn2bJlC5s3byY3Nxd9e9+5fiIwEO6/H7ZvtyQte+MNmDTJokgdOgTPPGOJjxoyBH77W0tpPOu1nUwmQ6lUEhkZyZAhQxg+fDhxcXH4+/sjl8vR6/VUV1eTl5dHVlYW+fn5VFVViW6bGRkZNDY2kpGR0Y8ScE56MmcdDUEQxJsnl5uYxJqnYMgQWLGidxWo3pati4sLEyZMwMXFherqavLz83v1+P2FQqG4SIEqKirqcp+BPG8dGWeSq12Vp8WLF3Ps2DE2bdrEpk2bOHbsGMuWLeu0v0ajISsri+eee46srCzWrVtHXl4eN95440V977//fioqKsT2bm/f1nEAmpqabF6HhYWxfv168fW6deuIjo7u62E5NNaMZO1ZunQp//3vf21clpqamli9ejVLliwRt23atIk5c+awZMkSVq1aZXOMpqYmNm7cyOLFi+17Ar3MnXfeyezZs6mvr+dnP/sZN998c5euWx9//DEtLS20tLSwceNGcXtFRQW33HILzz33HI2NjRw/fpyRPxZ2bmpqYtGiRRQUFFBUVERraysrV660+7n1NhEREdx888088cQTTJs2DZVKRXNzMzt27OBvf/sb//vf/6ioqOi38W3ZsuWy9lOpVKSnp3P99dczdepUBg0ahEKhoLGxkaNHj/L111+zd+9em+x9/Ul4ODz6KOzaBRUVFovU3Lng6gpnzsCLL1pqisfFwRNPWEJM2hsI3dzcCA4OJikpiWuuuYbBgwcTEhKCm5ub6N53/vx5srOzRavUhg0bHMrK6Cxc7px1BPR6PSaTCblcflk1kN591zI3H34Yjh6FRx7pXQXKHrJVqVRiTEp2drZNWYSBjEKh4LbbbmPw4MGiC9+FN+XbM5DnrSPjTHK1m/J05swZNm3axL/+9S/GjRvHuHHjeP/999mwYQO57f0v2uHr68vWrVvFuwRjx47lzTff5MiRIxQXF9v0VSqVhIWFic3X19dep+Iw3HnnnTYX9atWrbrii3mtVsvDDz9MREQEUVFR/PWvf+3WfjKZTPRfjY2N5a9//SuJiYkEBwfbWLk2bNhAcnIy3t7eREdHs2bNGsCS2fD5558nJiaGsLAwnnzyyQ4v6rds2cKECRPE13FxcfziF78AEGs5WPcrKChAqVReVJNj8uTJeHp6snXrVnFbUVERR48e5VZrell+SqO5dOlSNm7caGMl/fLLLxk6dCjJycmiC+Nzzz2Hn58fycnJnD59mj/96U8EBASQmprKqVOnxH1//vOfExERgZ+fH7Nnzxbncm5uLkFBQZw9exawWHbCwsJ6zR0yNzeX3Nxcnn76aTw8PHj44YcxmUzs27evx8d67bXXWL58Oddffz0uLi4EBgaKwbdjxozhrrvuwtfXFy8vL+6++24OHjzYK+fQH6hUKqZMmcITTzzBLbfcQlRUFCaTiWPHjvHuu+/y4YcfcvLkyT6/2L5SNxq5XE5YWBjjx4/nxhtvZOTIkfj7+2M2mykpKWHXrl1s2LCB7Oxs1Gp1L436yggNtVikNm60xEh9+inccosldur8eXj9dZg8GSIiLP2+/to2m581I19sbCzDhg0jLS1NLC4sk8nQ6XRkZmZy33338fnnn3PmzBnKy8tpaWlxCrel/mYgy9B681KpVPbY8vTuuxZF6eGH4e9/B7ncYk3tTQXKXrJNTEwkNDQUo9HIwYMHB/R32B6rC19cXBx6vZ5PP/1UrAN4Ic5yzo6GM8nVbsrT/v378fX15dprrxW3jR07Fl9f3x5dvDU1NSGTyfDz87PZvmrVKoKCgkhLS2PlypV2uUMiCJY/Ynu3zubThdl9Zs2aRVZWFvX19VRWVpKfn8/kyZOv6BxXrlxJU1MTeXl5HDx4kH//+9988803PT7Ol19+yf79+zlw4AAffPABGzZsAOC+++7jww8/pLm5mUOHDol3tf72t7+xb98+jhw5Qk5ODllZWbz99tsXHXfcuHEcPXoUrVYrBvLv2bMHgL179zJ69GgxVsFqOboQmUzGnXfeyerVq8Vt69atY968eQQEBACWeVZYWMjw4cNJSEhg+PDhfPnll2L/C61UZ8+eJTg4mNraWmbPns11112Hp6cn1dXVzJ8/n9/+9rdi34kTJ3LmzBkqKyuJiori0UcfBSA5OZlnnnmG5cuX09rayvLly/n73//eoTvknj17xExiHbWOOH36NMnJyTbzKCMjw0axu5BHHnmE4OBgZs2aRXZ2trjdmokpLS2N8PBwli1b1qlr3uHDh0lLS+v0MwYKCoWC9PR07rvvPu677z4yMjJQKBQUFxfzxRdf8Nprr7Fjx46LLMT2IioqqteO5e7uTlJSEnPmzGHOnDkMHjwYNzc3NBoNp0+f5rvvvmPr1q3k5eWJ7m39jZ8fLFkCX3wBNTXw1VeWmCk/P4ti9a9/wU03WVwA582Dt96y1CW1IpPJRHfw1NRURowYQVJSEiqViqqqKgwGA83NzZSWlnL69GmOHTvG2bNnqaqqQqvVOtUff1/Rm3O2LxEEQbywtv5HdBer4vTIIxbFyRouJZP1rgJlL9nKZDLGjBmDi4sLNTU1F924Hsi4uLhwxx13EBERgUaj4d///neH6/dAnbeOjjPJ1W7KU2VlZYcXgSEhId3OuKHT6fjNb37D4sWLbeJ/lixZwpo1a9i5cyfPPfccX375JQu7KECn1+tRq9U2rTtoNKBS2b+1K9Fiw4UBzC4uLtx00018/vnnrF27lttuu63DO2KzZs2yubD+6KOPOjy+IAh89NFHvPrqq6hUKiIiInjooYc6jPe5FI8//jjBwcHEx8fz4IMPioqHq6srJ0+epKWlhbCwMIYMGQLABx98wIsvvkhQUBB+fn48+eSTHX6ut7c3qampHDx4kN27d3PTTTdhMBhoaGhg9+7dTJw4UezbmfIEFte9//3vf2I9nP/+9782ytC2bduYNm2aGBi8dOlS0cpXUVHBrl27bOLq/Pz8eOSRR3BxcWHhwoXU1dXxxBNPiK/bKx6LFy/G19cXDw8PnnrqKVH5s8rN+meVnp7OokWLOhz/xIkTaWxs7LR1REtLy0XpNn18fGhpaemw/8svv0xhYSHFxcXMmjWL6667TuxbVlbGqlWr+Oqrrzh79ixGo5HHH3/8omMcO3aMt99+m+eee67DzxioREVFsXDhQh5//HGmTp2Kt7c3LS0t7Nq1i9dff521a9dSUFBg1wtse8UY+vv7M2LECBYsWMCECRMIDw9HLpdTV1dHVlYW69evZ9euXQ6Rrc+KUmlRlP79b4vitGWL5aI0Ls6S/nzTJsvr+HhIS4OnnrK4AbYfvkKhwN/fn4iICACSkpKIiYnB398fhUJBW1sb9fX1nD9/nhMnTnDs2DEKCgqorq5Gp9NJylQ3cMS42O5QX19Pa2srcrmcwMDAbu+n11vmXUaGxSp6YZ4JmcyyPSPD0u9Kwg3tKVsvLy/xv/r48eNOlZ3W3d2dpUuXEhQUhFqtZvXq1RfFfQ7UeevoOJNce5xe6IUXXuD3v/99l30OHToEdJyhRhCEbmWuaWtr44477sBsNvPPf/7T5r37779ffD506FCSkpIYNWoUWVlZHdbp+POf/9zhmLdt24aXlxfTp0/n4MGDaLVagoKCMJlMNDU1/ej+YX93wKamJjw8VOh0OoxGI3K5HJVKRWNjI25ubqLloKWlhQULFvDHP/4RjUbDa6+9JiqC1rsngiCwceNG0tPTAYvLwUMPPYROp0OtVuPj44NarUYQBBoaGtBqtSQlJQGW78tsNnPttdeKx2tra6OpqUm8+9z+Lo1Op6OpqQmz2UxkZCTNzc2YzWZCQkLYvXs3TU1NfPzxx/zf//0fv/71rxk5ciR//etfGTlypHiBbp0LgiAQHh4uLmLWz/P29mbs2LFs3bqV6upqZs+eTW1tLVu2bOGHH37gueeeo6mpCYPBwKFDhxgxYgRNTU2iDK3ySUxMJC4ujrVr15KYmEhZWRkzZswQ+27evJnJkyfT1NSEm5sbCxcuZOXKleTk5LBhwwYmTZqEp6eneLyAgADUajVubm64urri7+9Pc3MzSqUSmUxGS0sLTU1N+Pr68txzz7Fq1Spqa2uRyWSo1WqamppQKpUYjUYWLVrEo48+yjvvvCPK0MXFBQ8PD1F58fT0xGw2i/KxKkEd9bXWT5LJZDQ0NGA2m9FoNJhMJurr61EqleL3aO2r0+lITk7G09MTnU7Hgw8+yL/+9S8yMzMZPXo0bm5u3HHHHcTGxqLVann88ceZP38+ra2t4pytqalh/vz5/O1vfyMhIQGDwSAmOLEmK2htbRW/282bNwMQHR1NUFAQR48eBWDUqFGUl5dTXl6OQqFg5syZbNu2DZPJREREBBERERw+fBiAa665htraWkp+zGk9Z84cvv/+ewwGA6GhocTGxnLgwAHAYnVTq9VisPCsWbPYu3cvGo2GoKAgBg8eLFrF09LS0Ol0FBQUAIhrREtLC/7+/tx///2sXbuWvLw8jEYj+/fvZ8OGDXh7e7No0SJkMhl6vR4fHx9GjBjBzp07AcsFuouLC2fOnAEsSvHp06epr6/Hy8uLsWPHsn37dgDi4+NRKpWcPHkSsNwEioqKoqamBg8PDyZPniz6kMfExODn5ycmBBkzZgzFxcVUVlbi6urK9OnT2bJlC4IgEBUVRUhICFlZWQCMHDmSyspKysrKkMvlzJo1i02bNlFdXS26Ju7evZvdu3cTFRWFSqXCbDbj7e3N3Llz2blzJ3q9npCQEOLj48WENenp6bS0tFD4o/ln5syZ7Nu3D41GQ2BgICkpKezduxeAIUOGiMksAKZNm8bhw4dpbm7Gz8+PjIwMdu3aBVgstoDo/j158mR8fLK5/vpGbr/dG5VqFG++WcjBg8GcPu3P6dMyTp+Gl18Gf3+BMWPqGTasjEmTWpk3b4z4nVdWVhITE8O5c+cQBIGMjAzy8vKoqakRZWx1R/X19cXb25vq6mpcXV0ZPXo01dXVVFZW4uLiwowZM9i6dau4NoaFhXHkyBHAktWsurqa0tJSZDIZs2fPZseOHbS1tREWFsagQYPEzxk2bBiNjY2cP38egNmzZ7Nr1y50Oh3BwcEkJiayf/9+wPJfqNFoxGxiM2bMIDMzk9bWVgICAhgyZIh44yY1NRWj0SgmBZg6dSpZWVlinZPhw4fzww8/ADB48GDkcjk5OTninD116hQNDQ2oVCrGjBnDjh07AEvKaw8PD9G6bTAYiIiIoLa2FqVSyYQJE0T3aWsSJOuNpmuvvZaioiKqqqpwc3Nj2rRp/bJG1NfXi8W0J02aRGZmZrfXiMOHD7JihR9vvpnGY48J/P3vMhsFShDgsccEsrNl/PrX59i5M/+y14iioiKmT59us0aMGzeOs2fP9soaERgYSElJCQaDgZCQEFQqlc0asX37doxGI+Hh4URFRYnXfcOHD6e+vl60WM2ZM8ch1ojs7GwaGxvx9vZm1KhRREREcPz4cerr63n77bcZPHgwCoWCCRMmsHHjRkJDQ1EqlYwfP55t27YBlrABlUrFiRMnAIs31blz56iursbd3Z2pU6eKc3bQoEEEBARw7NgxAEaPHk1paSkVFRVX7RpRVFTEjBkzbNaI8ePHk5eX5xBrhHX83ULoITU1NcKZM2e6bFqtVvjggw8EX1/fi/b39fUVPvzwwy4/w2AwCDfddJOQkZEh1NbWXnJMZrNZcHV1FdauXdvh+zqdTmhqahJbSUmJAAhNTU02/bRarXD69GlBq9X+eFxBaGmxfzObOz6vxsZG8XlMTIywf/9+QRAEISEhQUhNTRUEQRC+//57ITk5ucN+Vh588EHh+eefv+j4JpNJ8PDwsPmc9jz//PPCgw8+2OHnAEJFRYX4matWrRLf++Mf/yjcfffdNsfS6XTCr3/9a2H69OmCIAhCYmKicPz48Y5P/AI+//xzYc6cOcKwYcOEqqoq4aOPPhIee+wxQalUCmq1WhAEQdi+fbtw/fXXd3mcl19+WbjhhhuElStXCosXL7Z5LzY2VqiqqrLZdt111wmvvvqqMGrUKOHjjz8Wt18oi/379wsxMTHi66NHjwqhoaGCIAjCzp07hejoaCEvL08wm81CTk6O0P5nV1tbK4SHhwvLli0Txo4dKxiNxg7HvmvXLsHLy6vT1hE5OTmCj4+PYDAYxG2DBg0Sfvjhhy7lZCU1NVXYvn27IAiCsHjxYuH3v/+9+N7JkyeFoKAg8XVFRYWQkJAgvPvuu53OJ0G4+DfmDFRXVwvfffed8Oc//1l4/vnnheeff1744x//KHz11VdCaWmpYO7sB95DNm3a1CvH6SlNTU1Cdna2sH79emHNmjVi+/rrr4WjR48KDQ0N/TKu7lBfLwhr1gjC0qWCEBAgCJbLV0uTyQRh1ChBuOeeIwIgZGYe6fQ4JpNJaGpqEkpLS4UzZ84Ihw4dEg4cOGDTsrKyhLy8PKG8vFxQq9WCyWTqwzN1TPprzl4uRqNROHXqlHDgwAHhxIkTna7Hl+Kddyxz7OGHf/p/N5sF4ZFHLNvfeefKx9oXsj137pywZs0aYd26dTb/I85CWVmZ8Kc//Ul4/vnnha+//lpcqwfavB0oOLpcm5qaOtQNOqLHlqegoCCCgoIu2W/cuHE0NTVx8OBBxowZA8CBAwdoampi/Pjxne7X1tbGokWLyM/P5/vvv++WyfzUqVO0tbVdlCjAiru7+2Vly5HJwMurx7v1GkqlssPt69atu+zUqe2Ry+XcfffdrFy5kldeeQUfHx9yc3Npbm4Wv7Pu8ve//53Zs2fT3NzMe++9xz/+8Q8MBgNffPEF8+fPF9NfKxQKAO69916effZZ3n//fUJDQzl//jznz5+3SR1uZdKkSSxfvpyYmBhCQkKYNGkSjz76KCkpKXh7ewM/JXvoisWLF/O73/2OQ4cO2bgynjlzhoCAgItMykuWLOHpp5+mpqamS7fQrmhubhYTLLS2tvKnP/3J5v2f//zn3Hbbbbz++utMnTqVV199lV//+tcdyqAzd7vOSE5OJjk5mb/85S/8+te/5oMPPkChUHT4+2tsbOTQoUNMnjwZmUzGP/7xD+rr6xk1ahQAy5cv54EHHmDp0qWEh4fz5z//meuvvx6wWCPnzJnDXXfdxQMPPOBULh7dITg4mHnz5jFjxgxOnjzJwYMHqays5NixYxw7doyIiAhGjhzJ0KFDL2sdsmLNbtjX+Pj4kJ6eztChQ6mtraWoqIiSkhI0Gg05OTnk5OTg5+fHoEGDiI6OFn+TjoC/P9xxh6WZTHDgAGzYYKktlZ0Nhw/D4cOJwCamTUtk2jSYNQtmz4bU1J/cruRyOT4+PqIbrMlkorW1lebmZtRqNa2trbS1tdHQ0CDGAsrlcry8vMS1T6VSOVWdk+7QX3P2cmhrayMvL4/W1lZcXFxITEwU/696yoMPWh5/LJPIG29Y6pa9+Sa8885P718JfSHbmJgYTp8+TXNzM4WFhQwePNjun9mXREREcOutt7J27VqysrIIDQ3l2muvHVDzdiDhTHK1W8xTamoqc+fO5f777yczM5PMzEzuv/9+5s+fL5pUAVJSUvjqq68AMBqN3HrrrRw+fJhVq1ZhMpmorKyksrJSrPBdUFDAH/7wBw4fPkxRURHfffcdt912G9dcc41NVjZnoLP4AmtRyN7gb3/7m1hYMyAggLvuuuuyavTcfPPNjB07ltGjR7N8+XJuuOEGAD755BMxjmDr1q288cYbgCVRxZgxYxg/fjy+vr7ccMMNolvFhYSGhhIRESF+vwkJCahUqm7HO1mJjIxk3LhxyGQyJk2adMl9b7rpJurr67nhhhsu+4Jw7ty5jBs3jpiYGNLT020Ul88//5ysrCz+/Oc/I5PJ+PDDD/nrX/8qumv0BqtXr2bTpk34+fnx/vvvs27dOjGW7qWXXhJrObW1tfH0008TGBhIWFgY33zzDd999514sThr1iyeeOIJJkyYQFRUFGazmddeew2A//3vf2RnZ/Pyyy+jUqnw9/cXi5VeTbi5uTFixAgefPBB7rvvPoYNG4aLiwvl5eV88803vPrqq6xfv56ysrLLipfp7+rsMpmM4OBgRo8ezYIFC5g4cSJRUVHI5XIaGxvJzs7m22+/ZcuWLeTk5DhEbaz2KBQwfjy89BIcP26pJ/XJJ7B0qQ+BgdPRan347jtL+vO0NIiOhnvugdWrLXFVtsdS4OPjQ2RkJKmpqVxzzTWkpqYSHR2Nv78/rq6umM1mmpubqaioID8/n6NHj5Kdnc25c+eoqqqiubnZ6dOj9/ec7S7V1dWcPHmS1tZWXF1dSU5OFl2aL5epUy2Pb70F11zTu4oT9I1s5XK5qDCdPXvWKeP8kpOTxfImmzdvpqioaMDM24GGM8lVJtjx11BfX8+jjz4q1ia68cYbeeutt2yyg8lkMj766COWL19OUVERcXFxHR7r+++/Z+rUqZSUlLB06VIxCUF0dDTXX389zz//fLez4lh9NpuammwC6nU6HYWFhcTFxV3xwtkbWGNmHJ3Y2FjWrl3L2LFj++XzKyoqmDBhQrcqh1tpL9s5c+bw7LPPXnHmQgkLXc1bR/uN2RuNRsPx48c5cuSITVrcsLAwRowYQUZGRrfl0B3ran+g1+spKyujuLiY6upqm1pRQUFBokXK09OzH0fZORUVFfzqV7/innte4ejRcLZutSSXuDDJ4LBhMGWKpU2eDF05YAiCgF6vF2umtbS0iMlq2iOTyfD09MTLywulUik+9oZngSPgqHPWitFo5NixY+Kc9fDwICkpqVfm6rJlltT66emQk2NRnnpLcYK+k21bWxvr16+nra2N6dOnO1XQvxVBEFi3bh0nTpzAy8uLhISEy/Y2kegcR18POtMNOqLHbns9ISAggE8//bTLPu11t9jY2Eve2YiOjhaD1Jyd7iTWkLBM+JdffrlH+7SX7YwZMxg3blxvD+uqRZq3P6FUKhk3bhxjx46luLiYI0eOcPr0aSorK8V04GlpaYwYMYLo6OguZeeoF9Tu7u7Ex8cTHx+PTqejpKSEkpISampqqK2tpba2lqNHjxIcHMygQYOIiopyKMW5oqKCVatW8ctf/pKVK8NZuRK0Wti715LFb+tWOHbMYq06ftySfhpg6FCLIjV1qkWZan9NKZPJ8PDwwMPDQ3RzNxqNtLS00NraKra2tjY0Go2NYiWTyVAqlWLz9PREqVRelH11IOCoc9ZkMlFTU0NFRYWNsp+WlnbZrnrt2b7dojjJZPDBB5bselfgsdshfSVbV1dXoqOjOXfuHMXFxU6pPMlkMm688UZqamqorKxkz5493HTTTQ47fwcqziRPu1qeHJWBYnkaKPS35Uli4CD9xiyFqbOzszly5IhNQeTg4GBGjhxJRkZGp/GOAwmtVktxcTHFxcXU1dWJ2+VyOSEhIURHRxMZGdnv8yArK4uRI0dy5MiRDrO1AlRVwQ8//NQ6KpWWmvqTMjVlCoSFdf25giDQ1tZmo0xpNJpOYwbd3NxERcr66OHh4VQXJPZGr9dTXV1NTU2N6Bbv6emJn58fUVFRvXLj5/x5GDUKamstMU8dlC8ccFRWVrJz5048PDxYsGCB094ga2ho4J133kGv1zN58mSmT5/e30OS6EN6YnmSlCcHVp6sqcUleh9JtvajK9k62m+sPxEEgbKyMo4cOcLJkyfFi2aFQkFKSgrXXHMN8fHx4sXx9u3bmTFjRn8O+bJpbW0VFan2MZUymYygoCCioqKIiorCqx8y9HRHebqQmhqLa9/OnRZl6sfMxTYkJcG4cZY4q/HjYcgQS9xVVwiCIKb012g0aLVaNBrNRXVorFjd/qxWLg8PD9zd3fHw8HCI5BSOMGetdQHr6+tpbm4Wt7u7uxMeHk5wcHCvKQPl5TBzJpw5AyNGwJ49YC9v1b6Urclk4quvvsJoNDJ37txOC7M7AydPnuTll18mISGBu+++m9jY2P4ektPgCOtBVziM257ElXEV6rV9hiRb+yHJtnvIZDJRaZgzZw4nTpwgKyuLiooKTp06xalTp/Dx8WHYsGEMHz7cYQrUXg5eXl6kpqaSmppKc3MzJSUllJaWUl9fT01NDTU1NRw9epTAwEBRJo6Ute9CgoPhllssDSxWht27LYrUzp2WTH75+Zb2739b+vj4wLXX/qRMXXstXBgaKJPJxOyw7WN4TSaTjTKl1WrRarUYjcaL3P6sWOu/XahYubm54eLi0ifWg/6Ys1b3yObmZpqami5yifT29iY0NBQ/P79elUF+PsydC+fOQWQkrFtnP8UJ+la2CoWCoKAgKisrqampcWrlaejQocTHx4txUD//+c+v+ht9vcVA/g+7EEl5cmAc4c6hsyLJ1n5Isu05Hh4ejB49mtGjR1NRUcGxY8fIzs5GrVaLBWplMhkhISGkpaWJhbMHIt7e3gwZMoQhQ4bQ2tpKaWkppaWl1NbWUldXR11dHcePH8fX15fo6GiioqLw9fW128W+v78/1113Hf7+/pd9jKAguPlmSwNoaIDMTNi3z9IOHAC12hI/9WP9R2QyS1a/0aMtbdSozmNjFAoF3t7eNgql1Uql1WrR6XRi0+v16PV6UYnoqMSBXC4XlTQ3Nzfx0dXVFVdXV9zc3Hol9qez8iG9gTUph/X8tVotra2tYmFuKzKZDC8vLwICAggICLDLb+fhh+Hjj6G1FeLjYds2iInp9Y+xwZ6y7YjAwEAqKytpbGzs08/tD2644Qb27t1LfX09W7duFbMHS1wZfT1n7YnktufAbntGo3FABgkPBCTZ2o+uZOtovzFHxmg0kpuby7Fjxzh79iwajQZPT0/c3NwYMmQI11xzDYMGDXKa+AOtVktZWRmlpaUXZe3z9vYmMjKSiIgIgoKCej3Op76+vtvZWi8HoxFOnrQoUvv3Wx47Sg7q6mrJ6jdq1E9KVWoq9HSpMplM6PV6G6VKp9NhMBjEsh+XQqFQiMqUq6srLi4uNk2hUIjP5XI5crkchUKBTCYT5+TlyNVsNmMymTAajRiNRkwmE21tbeLYrU2v19vMkfZ4eHigUqnw9fXFx8fHbjd0Skpg0KCfXk+aBJ99Bn1xjWjvOXshRUVFZGZmEhIS4vSxQPX19ajVaj7++GMA7r777k4zQUt0n76esz1Finm6BANFeRooqcoHIpJs7YeUqrz3UavVvP/++7i5udkkXwgICGD48OFkZGQ4lSuNXq+nvLycsrIyKioqbOohubm5ERERQUREBOHh4Vd8YazT6VizZg133nlnn87JqiqLderwYTh0yNLq6y/up1Ra0l0PH25RrIYPt7y+3FJqZrNZVD70er343KqgtLW1XVH9KZlMJipTeXl5pKSkiApVe0VfEASxmc1m8bEzhagj5HI5Hh4eeHp6iq0vihE3NMBf/wqvvw7tw9Ha2nqu6F4ufZ32ubq6mh07duDr6yvWB3RWrLL99ttvOXToEP7+/vziF7+QbrheIVKqcgkJCQmJPsPHx4ehQ4cye/ZsSkpKOHbsGCdPnqS+vp4dO3awY8cOYmNjycjIYMiQIQNeMXV3dycuLo64uDja2tqoqKigvLyc8vJyDAYDRUVFFBUViZn7IiIiiIyMvKyEE6dPn+ZnP/sZw4YN63bCiN4gNBQWLLA0AEGAoqKfFKnDh+HIEWhutrj9HTjw074yGSQk2CpUQ4darCCXMspZFY6u5ojV2mNVqKxWoPatvXWovdIjCAImkwmTyYTZbO402UVXyGQyFAqFjXXLzc3NplndDvvS8pqfb0lV/9FHFhc9sFib7roL7ruvz4bRL1gVh86yQTojM2fOJDc3l4aGBvbv38+kSZP6e0gSDoJkeXJgy1NbW5tTxo+0T22+YsUKBg8ezC9/+cs+HYOzytYR6Eq2jvYbG0hUVVURGhoqvjYYDJw+fZrjx49TVFQkJupwcXEhJSWFYcOGkZCQ4FSprM1mM7W1taJVqn32NAA/Pz8iIyMJDw8nMDCwWxfWl5Ntr68wmyEvz7bO1LFjUFHRcX+l0uLmN2SIbYuLu3SmvyvFajmyKk1ms5nq6moCAwPF99s/Wi1Rcrnc5rlVaZLJZOj1vV8fqac0NMCXX8Lq1ZZkINYrpowMePFFuP56izLb11y4Htib+vp6tmzZgpeXl9PHALWXbXZ2NuvWrcPNzY1HHnnEoRPZODp9PWd7imR5chKMRqN4ERobG0t9fT1VVVVi9XO1Wk1oaCgxMTHk5OT051BFioqKSElJQafTdav/O++8Y+cRdUx72Ur0LpJs7UN9fb3NH4+bmxvDhw9n+PDhNDU1kZ2dTXZ2NjU1NZw8eZKTJ0/i5eVFeno6GRkZhIeHD/j4KKulKSQkhOHDh6NWq0VFqra2lsbGRhobGzl16hQeHh6Eh4cTHh5OaGgo7v19FX4ZyOWQkmJpd9zx0/aaGltl6vhxyM0FjcZirTpyxPY47u6WYyQnQ2KibQsL652L//bWIitarfayLzbffRceeQTefBMefPDKx9ddBMGisG7cCJs2wfffQ/swsfnz4YknYNq0/lGarFy4Htib9qUUnJ32sk1PT+fQoUOUlJSwfft2brrppv4d3ACmr+esPZGUJwfGYDCIihJAWFgY69ev5/bbbwdg3bp1REdH99fwBjQXylai95Bkax+Ki4tJTU3t8D1fX18mTZrExIkTqaioIDs7mxMnTtDa2kpmZiaZmZkEBwczbNgw0tPTnSbez8fHBx8fH1JSUtDr9aJ7X0VFhWjlLCwsRCaTERgYKCpT/v7+A1qRDA621BOaOfOnbUYjFBRYagydPv1TO3MGdLqflK0L8fL6SZFKSIDYWIv736BBloxxV1IOr6s52xXvvmspMJuRYXkE+ylQBoOlTpc1M+K+fVBcbNsnIwMWL7YosPbOotddLle2l4s1a6MzFPC+FO1lK5PJmDNnDv/617/Izs5m8uTJDp30wJHp6zlrTyTlaQBx5513smrVKlF5WrVqFYsXL2bt2rVinxMnTrBixQpOnTpFYmIib731FmPHjgUs1qtHHnmEd999l8rKSv7whz8wfvx4li9fTmVlJc899xxPPPEEYLlj+Ktf/Yp169Yhl8t55JFHeOqppwBYvnw5AQEBHD9+nIMHDzJ+/HjWrl2Lv78/s2fPRq/Xo/oxmjkvL4+IiIhOz2n58uWkpKTwm9/8hhdeeIFz586h1WrZtGkTaWlpfPbZZ8T8+G914sQJfvGLX3DixAni4+N59913GTVqVO8LWkJigCKTycRkCrNmzaKgoIDs7GxycnKoqalh27ZtbN++nZiYGNLT00lNTXWaiyF3d3diY2OJjY3FZDJRU1NDZWUlFRUVNDU1UVtbS21tLSdOnMDDw4OwsDDCw8O7nX3O0XFxsViWkpOh/c1xkwnOn7coUvn5cPbsT4/nz1tidzpTrMBSi8qqTA0aBFFRFmtVaKjlMSwMQkIsmQJ7A6vi9MgjloQMjz9+5QqUIFjc7woLLXFleXkWhenECcjJsSie7XFzg8mTLXWbrrvO4gp5tdPU1ARwVRaXj4qKIikpifz8fHbt2iVZnyQk5cmRufDu8KxZs/jggw+or6/HYDCQn5/P008/LSpPBoOBG264gV/96lc88MADfPnll8yfP5+CggLxWN999x2HDh0iNzeXSZMmceONN7J3716Ki4sZO3YsS5cuJTg4mJUrV6JWq8nLy0OtVjNr1iyGDBki+jp/9tlnbN68mcTERK6//nreeustnnvuObZs2UJKSkqHtUW6w7p169i4cSNr1qzhZz/7GX/4wx/44IMPaG5uZt68ebz55pvceOONfPPNN9x8883k5+dfVuyMs9x5d0Qk2dqHnmYpUigUDB48mMGDB6PT6Th9+jTZ2dlisoWioiK+/fZbEhMTSU9PJzk5eUDXj2qPQqEgLCyMsLAwhg8fTmtrq6hIVVVVodPpRBnIZDK2bNmCq6srtbW1BAQEOFWcmEJhqT0UH3/xewaDRZmwKlQFBRarS3GxRbGqr4empp8Uja4IDLQoUsHB4O9vaX5+czh0yPrc8ujtbSke6+lpidGyPvf0hA8/hIcesihOb7xhcYt74w3L8VessMSA3X03aLW2raXFUqi4rs7yaH1eVWU5j6IiS+KNzvDzg3HjLMWLx42zFDC+3GyGfUVfZy2rqqoCICgoqE8/tz/oSLZTp04lPz+f7OxspkyZckV14a5WHDnTXk+RlKdLodFYbk3Zk5QUy7/IBTQ3N9v4i7u4uHDTTTfx+eefo9Vque2222z+5DMzM1EoFPziF78A4I477uCNN95gy5Yt3HbbbQA89thj+Pr6MmbMGMLCwli0aBH+/v74+/szaNAgcnJyCAoK4qOPPqKoqAiVSoVKpeKhhx7iiy++EJWn22+/naFDhwJwyy23sGPHjl4RxezZs8WMNnfccQe/+93vAPj222/JyMjg5h+rUN5000386U9/Yv/+/UybNq3Hn3OhbCV6D0m29mHnzp1MnTr1svb18PBgxIgRjBgxgqamJk6ePMmJEyeorKwkLy+PvLw8XF1dSU5OZujQoSQmJjpVWl4vLy8SEhJISEjAZDJRV1dHRUUFlZWVNDQ0cOTIEWJjYzl16hRubm6EhIQQGhpKaGgo3t7eA9rFryvc3GDwYEvriJYWSy2j9gpVRQVUVlpaVZWlGY0WZaVdFv3L5uGHf1Kc4CcFShDg5z+3tMslLMzilhgfb8lOmJFhSfseHd2/8UuXw5WsBz1FrVbT1NSETCZzmpiVruhItpGRkSQkJFBQUMDhw4eZNWtW/wxuANOXc9beOM+/o73IyYGRI+37GUeOQAdZnjqqd7FkyRJ+85vfoNVqee+992yqfZeXlzOofcU+ICYmhvLycvF1SEiI+NzT05Pg4GCb162trdTU1KDVahnc7h/VbDYzYcKEDo+jVCov29J0IZ0dt7i4mO3bt9vUsrGmML4celJLRKJnSLK1D5eT8rkjfH19mTBhAhMmTBCTS5w4cYL6+nox0YSHhwdDhgxh6NChxMbGOpklRiEmnRg2bBjHjx/nueee4w9/+APu7u4YDAZKS0spLS0FLOuiVZEKCwu7quL5VCqLy1pXbmtms8VCVVVlUahqaiwucg0NcPRoIX5+ceLrhgaLQnah5ah9Brv2ipMVqwK1axdkZ1u2ubqCh4fFYqVSWSxfQUE/PVpbTIxFYYqJsfR1FnprPegOBQUFAISHhw/IxCs9pTPZjhkzhoKCArKyspg6daqUGKmH9OWctTeS8nQpUlIuTl1kj8/ogI5+mOPGjaOsrEzMtLVz507xvYiICEpKSmz6FxcXc8stt/RoOEFBQXh4eHD+/Pkeu2DZ6w5tZGQk119/PevWreuV40mLnv2QZGsf2t9Y6C2Cg4OZNm0aU6dOpby8XFSempubycrKIisrC5VKRVpaGmlpaURHRzudFcZkMnH27FmSk5MZPnw4DQ0NVFdXU1lZSW1tLVqtVnTxA4vyGRoaKipgzuLqeLnI5T8pKmlptu8dPdrINdd0vb8gWNwH//lP+OUv4bHHLLWU2k8zQbBsz862KFE//3nfFaN1VOyxHnSEVqsVlafExMQ++cz+pjPZJiUl4efnR2NjI6dPn2bYsGF9PLKBTV/N2b7gKl9+uoFS2aFVqC/o7E/ZmsThQsaOHUtbWxtvv/02999/P1999RW5ubnMnj27R58rl8u5++67WblyJa+88go+Pj7k5ubS3NzMmDFjutw3KChItAiFh4f36HO7Yv78+Tz99NOsX7+e66+/HoPBwA8//MC4ceMuK8bmar/gsSeSbO1DfEdBK72ETCYjMjKSyMhIZs2aRXFxMSdOnOD06dO0tLRw4MABDhw4gI+PD0OGDCEtLY2oqCinU6TkcjmBgYEEBgaSmpqK0WiktraWqqoqqqurqa+vp6mpiaamJvLy8pDJZAQEBBAcHExISAhBQUHS/G9Hd+asTGZJpf7EE5a/2xUrfrI0yWQ/KU5vvQXvvNO3acsdGXuuB+3Jzs7GaDQSFBTUq//pjkxnspXL5eJN65MnT0rKUw/pqznbFziPL4YT0motYX4BGRkZYrxRe9zc3Pj666/5z3/+Q2BgIH/5y19Yv379ZSkXf/vb38QaMQEBAdx11100NDRccj8vLy+eeuop0tPT8fPzs3EZvBJ8fX3ZsGEDb7zxBsHBwcTGxvLee+9d9vE6k63ElSPJ1j5kZmb2yefI5XJiY2O54YYbWLlyJYsXLyYjIwN3d3fUajWZmZl88MEHvPbaa2zatImSkhKctda6i4sLYWFhDBs2jFmzZnHTTTcxceJEEhMT8fb2RhAE6urqyMnJYdeuXXz11Vds3ryZo0ePUlpa6lRuKpdDT+fsgw9aFKQ337QoTGaz5fHNNyXF6UL6Yj0oKysTU/0PHz7c6W6WdEZXsk370bxqzQws0X366j+sL5AJzvqv1wWdVRG21gWJi4u7rAxuvU1TU5OUucxOSLK1H13J1tF+YwOJzZs392u2IqPRSEFBAadOnSI3N9dGMRjIFqmsrCxGjhzJkSNHGNFDL4PW1laqq6upqamhpqaG5g5Suvn5+YmWqeDg4Ktq3l/unG1f5yk7W1KcOsLe64FarWbbtm0YDAZSUlIYPny43T7L0biUbP/5z39SXV3NwoULycjI6MORDWz6+z/sUnSmG3SE5LbnwFxNgcl9jSRb+yHJ1j6kp6f36+e7uLiQnJxMcnLyRYqU1SKVmZmJj48PaWlpDBkyZEAoUrGxsfz9738nNja2x/t6eXkRFxdHXFwcABqNRlSkampqaGpqorGxkcbGRvLz8wGLohkcHExQUBBBQUGoVCqHl9Hlcrlz1qooPfKIpDh1hj3XA41Gw+7duzEYDAQFBfX72tPXXOp8ExMTqa6upqioSFKeeoAzzSNJeXJgpKxl9kOSrf2QZGsfeiujZW9woSJ19uxZTp8+TU5ODmq1mv3797N//35UKhUpKSmkpqYSGxuLQqHo76FfREBAAHPmzCEgIOCKj6VUKomJiRELe+t0OmpqakTrVGNjI2q1GrVaLQbhu7u7ExQURGBgIEFBQQQEBDhNmvgrmbMPPgjLl1vioSQuxl7rQWtrKzt37qS5uRkvLy8mTJjgkL9be3Ip2cbExLBv3z7Onz/fRyNyDhzpP+xKcY4V2knR6/VXlYtHXyLJ1n5IsrUPhYWFNuUDHAUXFxdSUlJISUmhra1NtEjl5eXR0tLC4cOHOXz4MB4eHiQnJ5OamkpCQoLDZGWsqanhjTfe4IUXXrAp3dAbeHh4EB0dTXR0NGD5bdTU1FBbW0tdXR319fXo9XrKysooKysDLDFnfn5+NgqVUqkckNapK52zkuLUOfZYD2pra9mzZw86nQ6VSsW0adOuSk+CS8nWWhKmrq4OrVZ7VcrocnDU/7DLQVKeJCQkJCR6BVdXV1GRMplMFBYWcubMGXJycmhtbeX48eMcP34cV1dXkpKSSE1NJSkpqV+V7ZKSEv75z39y77339rrydCHu7u5ERUURFRUFWNKkNzQ0UFdXR11dHbW1tWg0Gurr66mvrxf38/T0JDAwkICAAAICAvD3978q6u1I9A2CIHDq1ClOnz6N2WzG39+fiRMn4uXl1d9Dc0g8PT3x9vamubmZuro68fcscfUgJYxw4IQRgiAMyLuNAwFJtvajK9k62m9sIGEymQas+4zZbKakpIQzZ85w5swZmpqaxPcUCgVxcXGkpqaSnJyMSqXq07FdScIIe9Da2ipapurq6mhoaOjQFValUonKlFWhchRrnpWBPGcdnd6SbU1NDdu3bxdfR0VFce211zrcXOpLuiPbTz75hMLCQm666aarKpnGleDo64GUMMJJaGlpwdvbu7+H4ZRIsrUfkmztw759+5g0aVJ/D+OykMvlYizQnDlzqKysFBWpmpoazp49y9mzZ/nmm2+IiooS46mCg4OvupscXl5eeHl5iXFTRqOR+vp6GhoaRItUc3MzLS0ttLS0UFxcDFhqdfn4+ODv7y8qVL6+vv16ETyQ56yjc6WybWtr48yZM5w+fVrclpSUxIgRI66639yFdEe21oyyzhTHY2+caT2QlCcHRgq8tx+SbO2HJFv7oNFo+nsIvYJMJiM8PJzw8HCmT59ObW2t6NpXVlZGaWkppaWlbN++HX9/f1GRGjRokEPftbQXLi4uhISEEBISIm7T6/U2ylRDQwOtra1iAd+ioiLAImuVSoWfnx9+fn74+/vj6+vbZzFUzjJnHZHLla3BYKCgoIDc3Fx0Op24ffLkyURERPTW8AY03ZGt1XPiaq/l1hOcaT2QlCcHxlkyLjkikmzthyRb+xAYGNjfQ7ALQUFBTJo0iUmTJtHc3ExeXh65ubmcO3eOhoYGMQW6h4cHSUlJDB48uFfjpLy9vRk7duyAspa6u7sTFhZGWFiYuE2r1YoKVV1dHY2NjWi1Wpqbm2lubqakpETs6+bmJipS/v7++Pn54ePj0+vKqbPOWUegp7LVarXk5eVx9uxZ2traAMvcHz58OBEREVe9tak93ZGtm5sbIClPPcGZ1gPpKseB6auYkFWrVvHFF1/w1VdfXfYxli9fTkpKCr/5zW96cWT240pk2/5ce0N2zoYUy2QfUlJS+nsIdsfb25uRI0cycuRIDAYD586dIzc3l7y8PFpbWzlx4gQnTpwQ3QCTk5NJSkq6oj/lpKQktm7d2uexVr2Np6cnnp6eNtYDnU4n1plqbGykoaGB5uZmDAYDVVVVVFVViX3lcjkqlQofHx98fX3x8fER2+UqVVfDnO0vuiNbk8lEeXk5hYWFVFRUYA1x9/X1JSUl5aq15l6K7soWpJuFPcGZ1gPpW3dgWlpa8PX1ZdasWcyZM4eVK1favP/LX/6Suro6Pvnkkx4dVyaTUVFRId61XLJkCUuWLOm1cQ8ErLLtiNjYWNauXcvYsWMveZyrUXaXoivZSlw+e/fudejq7L2Nm5ubmLnPbDZTVlZGbm4uubm51NTUUFhYSGFhIZs2bSIgIEC0SMXExPTogsZkMrFlyxYWLFjgdBeSHh4eF1moTCaTTfFeazMYDGINqtLSUrG/1fWvvTLl6+uLt7f3JeOprrY525d0JluTyUR1dTVlZWWUlJTYWEaCg4NJTk4mMjJSsjR1QXfmrVWuVguUxKVxpvVAUp4GAEuXLuX111+3UZ7MZjOfffYZH330UbeP09bWdlVn0JGQkBiYyOVysV7SzJkzqa+vFy1SxcXF1NfXi+59rq6uxMfHk5SURFJS0iUV+ePHj3PLLbc4TLY9e6NQKMSEElYEQUCr1aJWq2lqahKVqKamJgwGg+j6Z61FZcXT0xOVSoW3tzcqlUps3VGsJHqP1tZWqqurKS8vp6KiAqPRKL7n6elJbGwscXFxl8wgJtF9WltbAaQaT1cp8v4egETnWH+UCxcuJDc3lzNnzojv7dy5E5PJxIwZMyguLub6668nMDCQ1NRUNm3aJPaLjY3l5ZdfJjk5mSFDhjB79mwAEhISUKlU7N+/n48//pi5c+eK++zYsYNRo0bh4+NDUlISu3fvBuD9998nKSkJb29vMjIy2LlzZ7fOIzY2lldffZXBgwfj4+PD66+/zsGDBxkyZAgBAQG89tprYt/6+nruuOMOgoKCSExM5F//+pf43vLly3n88ceZMmUKKpWKxYsXU1lZycyZM/H19WXJkiWiKR3gH//4B0lJSQQFBXH33XeLi93HH3/MLbfcwkMPPYSPjw9paWkcO3YMgPvuu4/i4mKmT5+OSqXis88+6/Lc2stu586dpKSk8Pvf/56AgADi4uLYunWrzbktXryYkJAQ4uPje2wxHChIfyb2YciQIf09BIchICCAcePGcffdd/PrX/+a22+/nREjRuDt7U1bWxu5ubls2LCB1157jbfffptt27Zx/vx5KZlJJ8hkMpRKJWFhYSQnJzN69GhmzJjBzTffzIIFC5g2bRojR44kMTGRkJAQ0TVXq9VSU1PDuXPnyM7OZt++fWzZsoUvv/yS//3vfzQ1NXHgwAFOnTpFUVER1dXVtLa2St/DFWA2m1Gr1Xh7e5OZmck333zDN998w4EDBygpKcFoNOLp6UliYiJTpkzhhhtuYNiwYZLi1AO6s9bW1tYClphNie7hTP9hkuXJgbH+wXh7e3PjjTeyevVq/vjHPwKwevVq7rjjDmQyGTfccAMPPPAAX3/9NYcOHeKGG27g5MmToqvG//73P3bv3o2Pjw8eHh7IZDIKCgrE93Nzc8XPPHfuHDfffDOrVq1i3rx5lJWVYTAYAIiIiGD79u1ERUXxwQcfcMcdd3D+/PluFWv87rvvOHToELm5uUyaNIkbb7yRvXv3UlxczNixY1m6dCnBwcH84he/wMXFheLiYs6ePcvMmTNJSUlh4sSJAHz++eds376d4OBgRowYwfz58/n3v/9NREQEo0aNYsOGDSxYsIDPP/+c9957j23bthESEsK9997L7373O1599VUAfvjhB1asWMFbb73F888/z5NPPsn27dv517/+xbZt27rttnchZ8+exdvbm+rqaj788ENWrFhBQUEBAMuWLWPo0KGUlJRQWFjI9OnTGT58OMOGDevx5zgy0oWRfbD+DiVscXd3JzU1ldTUVARBoKqqiry8PPLz8yktLRVje/bs2YOHhwcJCQlik9xLu0Ymk4mxVKGhoTbv6fV6MV16S0uLTfp0nU6HTqfrtEaVVVm7sHl5eaFUKvH09MTNze2qdy1ra2ujubmZhoYGGhoaRBdLo9FIQ0MD/v7+gMUy6+/vT1hYGBEREQQEBFz1srsSLrXWtrW1UVdXB0jKU09wpv8wSXlyYPR6vXiHb+nSpTz22GP88Y9/RK/X8+WXX7JlyxYOHjxIW1sbv/jFLwAYN24cU6dOZePGjdxzzz0APPHEEzZpbrtizZo1LFiwgPnz5wMwaNAg8b3rr79efH7//ffzu9/9jvz8fIYOHXrJ4z722GP4+voyZswYwsLCWLRoEf7+/vj7+zNo0CBycnIICAjgyy+/pKCgAKVSSUZGBvfeey9r1qwRlafbb79dDDqcOnUqKpVKvJsxY8YMsrOzWbBgAR988AHPPvusWCvlmWee4frrrxeVpyFDhnDrrbcCsHjxYt55551uyedS+Pr68sQTTyCTyVi6dCkPPvigeEGxe/du1q9fj0KhICUlhcWLF7Nu3TqnU57az1uJ3uPs2bMkJCT09zAcGplMJsb4TJ48GY1GQ0FBAfn5+Zw9exaNRsOpU6c4deoUYIkBsQbRt3d1krg07u7uuLu7d5isw2Aw0NLSwubNm8nIyKC5uRmNRkNraysajQaz2Uxra6voDdARcrkcT09PPDw8umzu7u64uroOSGXBZDKh1WrRaDTio9VFsrm52SaVeHtcXFwwGAykpaURHBxMYGCg5CbZi1xqrS0uLsZsNosxgBLdw5n+w+yqPDU0NPDoo4+yfv16AG688UbefPNN/Pz8Ot1n+fLlF7kzXXvttWRmZoqv9Xo9K1euZM2aNWi1WmbMmME///lPoqKi7HIePPQQXODr3WtERsLbb1+y25w5c1Cr1WRmZlJRUUFwcDCjR4/mv//9L/n5+TYyNRqNjBw5UnzdE7mUlpYSHx/f4Xv/+9//+MMf/sC5c+cAaG5uFu++XIr2ypunpyfBwcE2r1tbW6mpqcFkMtmMNyYmhs2bN/foOGBZ3O69914eeOAB8X1relbAZj+lUtlrhe7aF/VUKpUAYiHL1tZWmwsNk8kkJZuQkLAjSqWS9PR00tPTxaQTBQUFnD17lrKyMmpqaqioqAAsLrg5OTkkJCSI7mkD8YLcEXBzcxPjqi501REEAZ1OJypS7Zt1m16v75aCZUUmk+Hq6ioqUu2fu7m54ebmhqurKy4uLri4uKBQKFAoFOJzFxcX5HI5crkcmUxm06zbBEHotBmNRkwmk/hofd7W1obBYMBgMKDX622eW8/zUnh4eIgp5a1p5b29vdm6dSvp6emX/R1JXD5nz54FLOEP0hpxdWJX5Wnx4sWUlpaKMTgPPPAAy5Yt45tvvulyv7lz59okQrgwm8njjz/ON998w9q1awkMDOTJJ59k/vz5HDlyxD7Zkrqh3NiD9nVHXF1dWbRoEatXr6aiokK86I6MjCQ9PZ2srKxOj9OTH3d0dLSNG58VvV7PnXfeyddff82MGTNQKBSEh4eLd217g+DgYORyOaWlpURHRwMWJehyCvdFRkbyl7/8hRtvvLHD97uaJ/ZYDCMjI/Hz8+u2sjmQGUj1cgYS06ZN6+8hDGjaJ52YOnUqWq2WwsJCcnJyCAsLw2w2c+7cOc6dO8fWrVvx9vYW3fvi4+Px8vLq71MYcHQ0Z9u7AnaGyWRCp9Oh1WrR6XTo9Xqb1+2b0WhEEARRMbEHVuXJHigUCtFVUalUisk3vL29u0y8Ia0H9qMr2ZpMJk6cOAHA4MGD+2pIToEzzVm7KU9nzpxh06ZNZGZmcu211wKWhAPjxo0jNzeX5OTkTve1FgDsiKamJj744AP+85//MHPmTAA+/fRToqOj2bZtm9OkQQRLNeb2tUeWLFnCTTfdREtLCy+99BJgscq1tbXx3nvvsXz5cgAOHDhATEyMjctde0JCQigqKupQxnfeeSfDhw/nu+++Y+7cuWLMU3BwsPgI8MYbb1BTU9Or56tQKFi4cCHPPvss7777LgUFBXzwwQd88cUXPT7Wvffey4svvsjQoUOJj4+noqKC48ePi8kduorLscrncmKeOiMyMpLRo0fzu9/9jt/85je4ubmRnZ2Nh4eHUwVRwsXzVqJ3OHz4MOPHj+/vYTgNnp6eDBkyhCFDhhAaGkpycjJnz56loKCAoqIimpubOXbsmJhMJjQ0lLi4OOLj44mJielWrOfVzuXOWYVCgZeXV7cU1gstPF0978xCZH3emYJ0KcVJLpfbWLGsliyr1evC5u7ufsWxXdJ6YD+6km1ubi4tLS2oVCpJeeohzjRn7aY87d+/H19fX1FxAhg7diy+vr7s27evS+Vp586dhISE4Ofnx5QpU3jxxRdFd60jR47Q1tYmZo0DSyKDoUOHsm/fPqdSntpnjgMYP3483t7exMXFkZSUBFh8nzds2MBjjz3Gs88+iyAIjBo1qssYnt/97ncsWLAAvV5vk5kPIC4uji+//JJf/epX3H777YSHh/Phhx+SkJDAK6+8wqxZs5DJZDz00EMkJib2+jn/4x//4Oc//zlRUVH4+vryhz/8gUmTJvX4OHfccQcNDQ1cd911lJWVER4ezooVK0Tlqas/w6eeeopHH32UFStW8N5777Fo0aLLPp/2rFq1il/+8pfEx8djMBgYOnSoTaZBZ+HCeSvROzQ3N/f3EJySgoICnnzySVatWsXYsWMZO3YsRqOR4uJiCgoKKCgooLKyUkw8kZmZiVwuJzIyUlSmoqKipGKZHdAXc9aqrPRGlk9BEDCbzaI7XvvnF7rzXeja19dI64H96Ey2giCwZ88eAK655hqnqwtnb5xpzsoEO9miX3rpJT7++GPy8vJstg8ePJh77rmHp59+usP9PvvsM1QqFTExMRQWFvLcc89hNBo5cuQI7u7urF69mnvuueciX+HZs2cTFxfHu+++e9Ex9Xq9TX+1Wk10dDRNTU02wX46nY7CwkLi4uIcIuDdendDoveRZGs/upKto/3GBhIHDhywuRkl0TtkZWUxcuTILus8tba2UlRUxLlz5ygsLKS+vt7mfVdXVwYNGiQqU2FhYf1yQe1oSHPWfkiytR+dyfbUqVN8/vnnuLm58dhjj0muvD3E0eesWq3G19f3It2gI3p8q+yFF17g97//fZd9Dh06BHQcO2K9g9MZt99+u/h86NChjBo1ipiYGL799lsWLlzY6X5dHffPf/5zh2Petm0bXl5eTJ8+nYMHD6LVagkKChIrsAPiBZ416423tzcajQaTyST6Klu16Qv7qlQq0SdbLpejUqlQq9WAxTVRLpej1Wo77Ws0GmlqasLNzQ0XFxc0Gg0AXl5eohuCTCbDx8dHHO+FfZVKJUajEYPBIPZVq9UIgiAG01oDctv3BUvmuObmZsxm80V9PT09MZvNolLq4+NDS0sLZrMZFxcXPDw8xCQMF/btiQy76tsdGXYmb6VSiUajoa2tDblcjre3d6cy7EjeVhl2JW+rDLsr757IsKu+vTVnL1fe1oDw9n2tMmxtbRU/y5oIJDo6mqCgII4ePQrAqFGjKC8vp7y8HIVCwcyZM9m2bRsmk4mIiAgiIiI4fPgwYLn7V1tbS0lJCWBJrPL9999jMBgIDQ0lNjaWAwcOAJCRkYFaraaoqAiAWbNmsXfvXjQaDUFBQQwePJh9+/YBkJaWhk6nE9PMW9eIlpYW/P39SUtLE+9ApqSkYDabxRtFU6ZM4dixY+ICPGLECLEmWlJSEi4uLmLNtokTJ3L69Gnq6+vx8vJi7NixbN++HYD4+HiUSiUnT54UzzUrK4uamho8PDyYPHkyW7ZsASyJVfz8/Dh+/DgAY8aMobi4mMrKSlxdXZk+fTpbtmxBEASioqIICQkR4yRHjhxJZWUlZWVlyOVyZs2axfbt2zEajYSHhxMVFSWu6cOHD6e+vp7i4mJR3jt37kSv14v1y6zJfdLT02lpaaGwsBCAmTNnsm/fPjQaDYGBgaSkpLB3717Akv3SYDCIgdjTpk3j8OHDNDc34+fnR0ZGBrt27QIQvRassZmTJ08mOzubxsZGvL29GTVqFN9//z0AiYmJuLm5cfr0aQAmTJhATk4OdXV1KJVKxo8fL37n58+fJywsTIxnGDt2LOfOnaO6uhp3d3emTp1KaWkpycnJ+Pn50dzczA8//EBlZSUBAQHs27ePzZs3I5fLSUtLo6mpieDgYIYPH05aWpo4v0eMGEF1dTWlpaXIZDJmz57Njh07aGtrIywsjEGDBnHw4EEAhg0bRmNjI+fPnwcsNwl37dqFTqcjODiYxMRE9u/fD1j+LzUajZjQZ8aMGWRmZtLa2iombrDO2dTUVIxGI/n5+YAlc2lWVpZ48TB8+HB++OEHwHLDUy6Xk5OTI87ZU6dO0dDQgEqlYsyYMezYsQOwBNB7eHiI2QyvueYajhw5Qm1tLUqlkgkTJoi172JjY/Hx8SE7OxuwuKAXFRVRVVWFm5sb06ZNk9YIOl8jrN9f+zVi3LhxnD17VlojrnCNMJvNbN68WVwjtm3bhsFgYP/+/ej1ekJDQ9mzZ0+Ha4R1zg4aNIiAgADR7Xf06NGUlpZSUVGBi4sLM2bMYOvWrZjNZiIjIwkLC+PIkSOA864RRqORs2fP2qwR48ePJy8vzyHWCOv4u0OPLU+1tbVicbDOiI2NZfXq1fzyl7+ksbHR5j0/Pz9ee+01MY12d0hKSuK+++7jqaeeYseOHcyYMYP6+nqxxgFYJtBNN93UoZI0UC1PTU1NUh0SOyHJ1n50JVtH+40NJDZv3uxUbsmOQncsT10hCAI1NTUUFhZy7tw5ioqKLvKM8PDwYNCgQcTGxhITE0N4ePhVYZmS5qz9kGRrPzqS7ddff83Ro0fx9/fnoYceuiiRmcSlcfQ5a1fLU1BQULeKgo0bN46mpiYOHjzImDFjAIvJrqmpqUcBY3V1dZSUlBAeHg5Y7oC4urqydetWMRaloqKCkydP8vLLL3d4DGs9CgkJCQkJid5EJpMREhJCSEgI1157LWazmYqKCoqKijh//jznz59Hp9ORl5cnWh7c3d0ZNGgQMTExxMbGEh4eLsVPSEg4KNnZ2Rw9ehSZTMZNN90kKU4S9ksYkZqayty5c7n//vvFOKQHHniA+fPn2ySLSElJ4c9//jM333wzLS0tvPDCC9xyyy2Eh4dTVFTEM888Q1BQEDfffDNgcYO69957efLJJwkMDCQgIICVK1eSnp4uZt9zFqQ78/ZDkq39kGRrH7pKsiNx+URGRvLss88SGRnZK8ezJpOIjIwUXYAqKys5f/68qFDpdDry8/NFlxg3Nzeio6MZNGgQgwYNIjIy0iku0KQ5az8k2dqP9rItKSnh66+/BizuaDExMf01rAGPM81Zu6YHWrVqFY8++qiYGe/GG2/krbfesumTm5srxpooFApOnDjBv//9bxobGwkPD2fatGl89tlnNrVjXnvtNVxcXFi0aJFYJPfjjz+W7txJSEhISPSI0NBQ7rvvPkJDQ+1yfLlcLvrYjxs3DrPZTHV1NUVFRaIypdVqxcx+1n2ssQ2DBg0iOjpaqp8mIdHHVFZWsmbNGkwmEykpKUyfPr2/hyThINhVeQoICODTTz/tsk/7kCtPT08x+KsrPDw8ePPNN3nzzTeveIyOjE6nk9wN7YQkW/shydY+5ObmEhsb29/DcDoa13J/6gAAUTdJREFUGhp4//33WblypU0crb2wKkZhYWGMHTsWQRCorq6muLhYbE1NTWKwszW4PiAgwMY6FRQUZJeC3r2JNGfthyRb+5Gbm4uHhweffPIJWq2WyMhIFi5c6PC/N0fHmeasVJhCQkJCQuKqpbCwkJdeeolbbrmlT5SnC5HJZISGhhIaGsro0aMBS9KV4uJiSkpKKC4upqqqivr6eurr68VsaZ6enkRHRxMZGUlUVBSRkZGSy6yERC9QXl5OVlYWer2eqKgoli5d6hRutBK9h6Q8OTCSm4b9kGRrPyTZ2ofJkyf39xAk+ghfX1/S09NJT08HLNbc0tJSUaEqLS1Fq9XaJKGQyWQEBQURFRUltuDg4H7N6ifNWfshybb3EQSBAwcOUFBQgIuLCzExMSxevFjypOglnGnOSsqTA6PRaKRCrnZCkq39kGRrH7Kzsx26wKCE/fDw8CAxMZHExEQATCYTlZWVlJaWiq2hoYGamhpqamrEWidubm42lqmoqKg+/W1Kc9Z+SLLtXVpbW/nmm2/IycmhsrKSG264gfnz50ux9L2IM81Z5y800VeYTLBzJ6xZY3k0mXrhkD8dIzY2VvR9t7JixQpeeOGFK/4cR8doNJKenk5KSkqnfTIzM8nIyMDPz4+QkBDuvvtusWjshXz88cd4e3vzpz/9yWb7M888g0wmY+3atWI/mUwmZosESwCp5PfcNaZemPsSF3NhzTyJqxeFQkFkZCTXXnstt9xyC4899hi/+tWvuPPOO5k0aRLx8fG4u7tjMBgoLCxk9+7drF27lv/7v//jb3/7G2vXrmXXrl2cPXtWLPBtD6Q5az8k2fYeGzdu5JVXXiEnJweFQsHQoUO58cYbJcWpl3GmOStZnnqDdevgscegtPSnbVFR8MYbsHDhZR9W+uFaeOutt/D19e2yOHNiYiIbN24kMjISjUbDgw8+yB//+Ef++te/dtg/ISGB1atX89vf/hawmOs/++wzEhISbPr5+/vz0ksv8bOf/QxXV9feOyknRpq39kFyh7QPnp6eDB48GE9Pz/4eyhXh5eVFcnKymA7YbDZTW1trY52qqalBrVajVqvJyckR9/Xz8xMzAlpbb8RPSXPWfkiyvXIaGxvZvHkzZ86cEbfdf//9nDt3TrpJagecac5KytOVsm4d3HortMsaCEBZmWX7F19ctgKlVCq73ffjjz9m9erVxMTEsHbtWpKTk/n666956aWX+PTTT0lJSeGrr74iIiICs9nMrbfeyp49ezAajcyYMYN3332XgIAAdu7cyZIlSzhx4gQBAQF8/vnn/Pa3v+XYsWM2FxdarZbQ0FBOnDgh1j3Ytm0bjz/+OCdPnrys8+2Iqqoq3nvvPV555RWefPLJTvu1L9wsCAIymYzCwsJO+yckJNDY2EhWVhYjRoxg3759REdHX9RvzJgxtLS08NFHH/HAAw9c2clcJfRk3kp0n1GjRvX3EJyS1NRUTpw44XQB4XK5XCzeO2LECAD0ej2VlZViJr/y8nLq6upobGyksbGR06dPi/sHBAQQERFBeHi4mB3Qy8urR2OQ5qz9kGR7+ej1enbv3k1mZiZGo1Hc/utf/xqlUklAQEA/js55caY5K7ntXQkmk8XidKHiBD9te/zxy3bha25u7lH/77//nuuuu476+nqioqKYMGECU6ZMoa6ujtjYWF555RWx78KFCyksLKSwsJDm5mb+8Ic/ADB16lRuueUWHn74YWpqanjkkUf4+OOPL7or6+npyfz58/n888/Fbf/973+5/fbbOxzb/Pnz8fPz67D95S9/6fScnnrqKZ555plu/WkXFxfj5+eHSqXiq6++4uGHH+60r8lkYsmSJaxevRqA1atXs2TJkg77Pv/887z00ku0tbVdcgwSPZ+3Et3j+++/7+8hOC1Xi2zd3d2JiYlh3Lhx3HLLLTzyyCP85je/4e6772bWrFmkpaWJGQfr6+s5efIkW7du5T//+Q+vvPIKr776KqtWrWL79u2cOnWKuro6m3IjF3K1yLU/kGTbc3Q6Hbt27eL1118Xbx7HxcWJIRDWG3+SbO2DM8lVsjxdCbt327rqXYggQEmJpd/UqVf8cbNmzbJxidJqtTz99NPi6/T0dG6++WYAFixYQH5+PosWLQLgpptu4l//+hdguSO5dOlScb8nnniCZ599Vnz9l7/8hWHDhjF16lSWLVvGuHHjOhzP7bffzosvvsjKlSsxGo189dVX7N27t8O+GzZs6PH57t+/n7y8PD766CN++OGHS/YfNGgQjY2NVFdX8+677xIeHt5l/9tvv50xY8bw0ksv8fXXX/OnP/2JVatWXdRv1qxZREZG8vHHH3PDDTf0+DwkJCQcl6NHj3LDDTdw4MABrrnmmv4eTp/j4eFBXFwccXFx4jaNRkNFRQXl5eVUVlZSWVlJXV0dzc3NNDc3k5+fL/Z1c3MTLVNhYWGEhoYSHBzsdJY8iYFLc3Mzhw4d4uDBg+h0OgACAwOZNWsWycnJkoueRI+RlKcroaKid/tdwIXpMbdu3crYsWPF1ytWrLB5PyQkRHzu6elJcHCwzevW1lbAkoBh5cqVfPXVVzQ0NCAIgo3bm1Kp5I477uDFF19k06ZNnY5v7ty53H333RQVFZGbm0tUVBSDBw++rHO9ELPZzKOPPso///nPHi9sISEhzJs3j7vuuov9+/d32EculxMaGkpKSgrPPPMMo0aN6rLGy/PPP8+DDz7I3LlzezSWqxEprat9sGZak+hdBEGgra2tSwvK1YZSqSQhIcEmBlSv11NdXS0qU5WVlVRVVWEwGMTivu3x9/dHEARcXFxE98GgoCApJrKXkNaDrhEEgeLiYg4ePMiZM2cwm80ABAcHM3nyZNLS0jpN4y/J1j44k1wl5elKuIRlo8f9LsBe9TlWrVrF7t272b9/PxEREWzevJkHH3xQfD8/P5+3336b2267jSeffJL//ve/HR7H3d2dBQsW8Pnnn5OTk9Opyx7AvHnz2L17d4fvPfPMMzzzzDM229RqNVlZWaKlx2AwoFarCQsL49y5c5eMqzGbzRQUFHTZB2Dx4sXcc889Yoa9zpg9ezbh4eF88sknlzzm1U5/1pVxZqQ7+RL9ibu7O9HR0TaxoWazmbq6OlGZqqiooLq6mpaWFhoaGmhqarLJsCWXywkMDBSVqZCQEIKDg/H395eUqh4irQcd09jYSHZ2NsePH6eurk7cHhMTw7XXXktqauolb8hKsrUPziRXSXm6EiZNsmTVKyvrOO5JJrO8P2nSZR1eq9XaZbI1Nzfj7u6On58ftbW1/N///Z/4ntls5u677+bZZ59lxYoVDBs2jP/+97+i+19sbCwvvPACy5cvByyub88++yzFxcUcOnSo08/cuHFjj8bo6+tLWVmZ+Hrfvn089dRT7N69u8OsWN999x0JCQkMHjyYyspKnnvuOaZNm9bp8a13oW677TZCQ0OZ2g23yueff57Fixf36DyuRuw1b692Tp8+3WFSEwmJ/kIulxMcHExwcLBY0Bcsbn/V1dWsX7+e+Ph4qqurqa6uRqfTibWoTp06ZXOcgIAAgoKCLmq9kfXPGZHWg59obGzkzJkznDlzxsYC6urqSnp6OmPGjCEsLKzbx5Nkax+cSa6S8nQlKBSWdOS33mpRlNorUNY7G6+/bunnQNx11118++23hISEEB0dzX333Sf6sP/f//0fCoWCxx57DLlczkcffcTChQuZOnUq/v7+1NXV2bgOzpo1i2XLlhEfH098fHyvjVEmk9ksdgEBASgUCpttKpWKjRs3MmnSJKqqqnj44YepqqrC19eXefPm2STI6AylUtltV7w5c+YwePDgi+ptSUhISEj8hFKpJDY2luTkZObMmQNY3Kiam5tFRaq6upqqqipqa2tpa2ujtra2w3IUKpWKwMBAUZkKDAwkICAAPz8/XFykS5irEbPZTFlZGWfPniU3N5fKykrxPZlMRlxcHMOGDSM1NVW6kSdhF2TCVejorVar8fX1pampCR8fH3G7TqejsLCQuLi4nt3t6qjOU3S0RXG6gjpPJpPJoVwZ9u/fz9///nfWrFnT30O5YhxNts5EV7K97N+YBC0tLahUqv4ehtOh1Wo5efIkQ4cOHfC1nhyN7sxZQRBQq9Wi8lRbW0tdXR21tbWo1epO95PJZPj4+BAQECA2f39/8bmzXzRfTeuB2WympqaG8+fPU1hYyLlz59Dr9eL7MpmMmJgYUlNTSU1NtbmuuxyuJtn2JY4u1850g46Qbtv0BgsXwoIFlqx6FRWWGKdJk67Y4qTT6XpcV8OejBs3rtPMewMNR5OtMyHJ1j7k5OQ4VZ0MR8HT0xOZTCYpTnagO3NWJpPh6+uLr6/vRUXK9Xq9qEhZW319PfX19RgMBpqammhqauqwpp9KpcLf318sieHr62vzONCLnjvzetDS0kJFRQUVFRWUlJRQUlIiZsmz4unpSXx8PImJiQwePLhX/3OcWbb9iTPJVVKeeguFolfSkbenffE2id5Fkq39kGRrH9oHP0v0HufPn+e3v/0t7777rljwW6J3uNI56+7uTkREBBERETbbBUGgtbWV+vp6GhoaRIXK+lqj0dDS0kJLSwslJSUdHlupVF6kVPn6+uLt7Y23tzcqlcqhvROcYT0wm800NjaKyUasjy0tLRf1dXNzIzo6mpiYGBISEggPD7dbciJnkK0j4kxylZQnB0bKWmY/JNnaD0m29uFSGSYlLo+6ujo2b95MXV2dpDz1MvaaszKZDJVKhUqlYtCgQRe9r9PpREWqsbFRzPpnfdTr9Wg0GjQaDeXl5Z1+jpeXl6hMXdhUKhVeXl4olcp+cREcKOuB2WymubmZ+vp66urqxGb9fkwm00X7yGQyAgMDCQ8PJyoqikGDBhEaGtpn/y0DRbYDDWeSq6Q8OTCO7Bs60JFkaz8k2dqH8ePH9/cQJCR6RH/NWQ8Pjw4tVlZ0Ol2HSpW1CHBzczMmk4nW1lZaW1ttEhJ0hKurK0qlUlSm2j96eXnh4eFxUXN3d78iZaC/1wOj0YhWq0Wr1aLRaFCr1TQ3N9s8qtVqWlpaxOy2HWGtAxYWFkZ4eLhYaLk/Y9b6W7bOijPJVVKeHBhr8JpE7yPJ1n5IsrUP27ZtEzOXSUgMBBx1znp4eBAWFtZp+mpBENBoNDbK1IWtpaUFjUaD0Wikra1NjL/qCe7u7jbKlLu7O25ubri6unbZXFxc2Lt3L1OmTEEul3faBEEQm9lstnm0PjeZTLS1tXXZdDqdqChZm8Fg6PZ5yuVy/P39CQwMFLMlWp/7+Phcsu5SX+Oo83ag40xylZQnCQkJCQkJCYkfkclkotWoq/pAgiBgMBjQaDS0trZe9Gh9rtPpbFpbWxtgSYih1+t7rHQBnD17ltL2GX77AWuiFU9PT7y9vfHx8cHHx+ei5yqVSnLnlnAqJOXJgXF3d+/vITgtkmzthyRb+xAXF9ffQ3BKQkNDeeCBBwgNDe3voTgdzj5nZTKZaDHy9/fv9n4mk8lGmdLr9eJzqyXrUs3V1ZXg4GDMZnOnDSxWH5lMhkwmE5+336ZQKGysWh1Zvtzd3UUlqX3z8PBwOKtRb+Ds87a/cCa5SsqTAyPdqbEfkmzthyRb+yDFktmHyMhInn/++U7jYyQuH2nOdoxCoRAtW5dLeXm5NGfthDRv7YMzyVW6ynFgtFqt+Dw2NpbMzEyb91esWMELL7zQJ2NpaWlh4sSJBAYG4u/vz4wZM8jJyemwb3V1NYsWLSI0NJSAgABuuOEGiouLOz22TCYjMTHRZlt+fj4ymYy5c+fa9Lsw4HDu3Ll8/PHHPT6f9rKV6F0k2dqHEydO9PcQnJLm5mY+/fRTmpub+3soToc0Z+2HJFv7IcnWPjiTXCXlSaJbuLu78/7771NTU0NdXR0LFy7k7rvv7rBva2srEydO5NSpU1RWVpKYmMg999zT5fHlcjkHDhwQX69atYqkpKSL+uXk5LBly5YrOxkJCQmJH8nPz+epp54iPz+/v4ciISEhITEAkJSnXiA/H7KyLm5X+l/cU5P+m2++SXx8PMHBwdx1112o1eoef6YgCB1ud3V1JTU1VczgI5fLO6zqDha/1kcffZSgoCDc3Nz4+c9/zsGDB7v83DvvvJNVq1aJr9esWcOdd955Ub8nnniC3//+9z04o47pzWrkErZIsrUPY8eO7e8hSEj0CGnO2g9JtvZDkq19cCa5SsrTFZKfD4MHw8iRF7fBg69MgepJKtDNmzfzl7/8hW+//ZaioiJaW1v55S9/2WHfqqoq7r//fmJiYhgxYgR//OMf2b9/P+vWreOuu+7q8nMyMjLw8PDg4Ycf5qmnnurW2Pbt20daWlqXfRYtWsRXX32FyWTi0KFDBAUFdRhcuHz5csrKyti6dWu3PrszeiJbiZ4hydY+nDt3rr+HICHRI6Q5az8k2doPSbb2wZnkKilPV4jVTf7TT+HIkZ/ap5/avn85WNOZWpk1axZ+fn5i++ijj8T3PvvsM1asWEFqaipeXl689NJLrF27tsPjZmZmMm/ePE6ePMknn3yCRqPh2Wef5bvvvuO5557rckzZ2dmo1WreeecdUlNTL3kOJSUl/OY3v+FPf/pTl/0CAwMZNmwY27ZtY9WqVSxevLjDfq6urjzzzDNXbH26ULYSvYckW/tQXV3d30OQkOgR0py1H5Js7YckW/vgTHKVlKdeIjUVRoz4qXVDr7gkF2Yt27p1K42NjWJrH0dUXl7OoEGDxNcxMTG0trZ2WD/i+uuvp7q6mvvuu49//OMfzJw5k61bt/Liiy/y9ddfX3Jcnp6e3HffffzsZz+joaGh03719fXMnTuXZ555hpkzZ17yuEuWLOE///kP69atY9GiRZ32u+eeeygtLWXbtm2XPGZnSBnh7IckW/sgpYC3D66urgQFBeHq6trfQ3E6pDlrPyTZ2g9JtvbBmeQqXeU4MN7e3t3uGxERYZPRrri4GKVSia+v70V9P/30U/Lz81m+fDnDhg3jpZdeIjAwkGnTphEVFdWtzxMEgZaWFioqKjp8v6Wlheuuu44FCxbwyCOPdOuYCxYsYP369QwdOpTg4OBO+7m6uvL0009fkfWpJ7KV6BmSbO3D1KlT+3sITkl6ejo1NTWkp6f391CcDmnO2g9JtvZDkq19cCa5SsqTA9OTquO33XYb7777Ljk5ObS2tvLss89yxx13dNh32bJlvPrqq8ybN4+HHnqI7du309jYyOnTpztM0gBw/Phxdu3ahcFgoLW1lWeeeQY/P78OM+IZDAYWLlxIWloaL730UrfPQalUsnXrVt58881L9r3nnnsoLi7m0KFD3T5+ey6nortE95Bkax82b97c30NwWiTZ2gdJrvZDkq39kGRrH5xJrpLy1EucOWObae/Mmb79/Hnz5vGrX/2KefPmERMTg7u7O6+++mqHfRUKRY+P39bWxmOPPUZgYCCDBg3i2LFjfPfdd6Kry4oVK1ixYgUA+/fvZ+vWraxduxaVSiW2rmo9Wbn22mtJSEi4ZD83Nzeefvpp6uvre3wuEhISElZOnDjB0qVLnaoGiYSEhISE/ZAJneWmdmLUajW+vr40NTXh4+MjbtfpdBQWFhIXF4eHh0e3jmXNttcZeXnQgXGmW2i1Wjw9PS9vZ4kukWRrP7qS7eX8xiQsnDlzpltJWiR6RlZWFiNHjuTIkSOMGDGiv4fjVEhz1n5IsrUfkmztg6PLtTPdoCNc+mhMTktSkkVB6iirnrf35StOAC4u0tdjLyTZ2g9JtvYhICCgv4cgIdEjpDlrPyTZ2g9JtvbBmeRqV7e9hoYGli1bhq+vL76+vixbtozGxsYu95HJZB22V155RewzderUi97vLL6nL0hKss20Z21XojgBaDSa3hmgxEVIsrUfkmztw7Fjx/p7CBISPUKas/ZDkq39kGRrH5xJrna9Rbx48WJKS0vZtGkTAA888ADLli3jm2++6XSfC7O3bdy4kXvvvZdbbrnFZvv999/PH/7wB/G15IIlISEhISEhISEhIWFP7KY8nTlzhk2bNpGZmcm1114LwPvvv8+4cePIzc0lOTm5w/3CwsJsXn/99ddMmzaN+Ph4m+1KpfKivs6Gl5dXfw/BaZFkaz8k2dqH0aNH9/cQnJKkpCS+/vrrDjOHSlwZ0py1H5Js7YckW/vgTHK1m9ve/v378fX1FRUngLFjx+Lr68u+ffu6dYyqqiq+/fZb7r333oveW7VqFUFBQaSlpbFy5UqaOwo6GuAYDIb+HoLTIsnWfkiytQ+lpaX9PQSnxNvbm9jYWKk+mR2Q5qz9kGRrPyTZ2gdnkqvdlKfKykpCQkIu2h4SEkJlZWW3jvHJJ5/g7e3NwoULbbYvWbKENWvWsHPnTp577jm+/PLLi/q0R6/Xo1arbdpAoK2trb+H4LRIsrUfkmztQ2cFqSWujLKyMl588UXKysr6eyhOhzRn7YckW/shydY+OJNce+y298ILL/D73/++yz7WwqUymeyi9wRB6HB7R3z44YcsWbLkopTG999/v/h86NChJCUlMWrUKLKysjpMNfvnP/+5wzFv27YNLy8vpk+fzsGDB9FqtQQFBWEymcRCn9bP1ul0gOUupUajwWQyoVAoUCqVotXrwr4qlQqdTofRaEQul6NSqUTFzd3dHblcjlar7bSvwWCgqakJNzc3XFxcxEB8Ly8vDAYDbW1tyGQyfHx8xPFe2FepVGI0GjEYDGJftVqNIAi4urri5uZGa2vrRX0BfH19aW5uxmw2X9TX09MTs9mMXq8HwMfHh5aWFsxmMy4uLnh4eNDS0tJh357IsKu+3ZFhZ/IWBAGNRkNbWxtyuRxvb+9OZdiRvK0y7EreVhl2V949kWFXfXtrzl6uvA0GAzqdzqavVYatra3iZ1kL5kVHRxMUFMTRo0cBGDVqFOXl5ZSXl6NQKJg5cybbtm3DZDIRERFBREQEhw8fBuCaa66htraWkpISAObMmcP333+PwWAgNDSU2NhYDhw4AEBGRgZqtZqioiIAZs2axd69e9FoNAQFBTF48GDRKp6WloZOp6OgoABAXCNaWlrw9/cnLS2NPXv2AJCSkoLZbCYvLw+AKVOmcOzYMTHd6YgRI9i5cydgcRFzcXHhzI+F4CZOnMjp06epr6/Hy8uLsWPHsn37dgDi4+NRKpWcPHkSAJPJRFZWFjU1NXh4eDB58mS2bNkCQExMDH5+fhw/fhyAMWPGUFxcTGVlJa6urkyfPp0tW7YgCAJRUVGEhISQlZUFwMiRI6msrKSsrAy5XM6sWbPYvn07RqOR8PBwoqKixDV9+PDh1NfXizXb5syZw86dO9Hr9YSEhBAfH09mZiYA6enptLS0UFhYCMDMmTPZt28fGo2GwMBAUlJS2Lt3LwBDhgzBYDBw9uxZAKZNm8bhw4dpbm7Gz8+PjIwMdu36//buPC6qev8f+GtYZmBYBtkEZJFURAWRxQVwwQ31ZqV0MRXXzCtZhmV1rX43Mc2v3bql2aJ2TfwqRlmSllmCApLiEkguuKCCCooIyi775/fHfDk5zgAzMB9nHN7Px2MeNGc+c+ZzXn048p5zzuccBgDhlO+LFy8CAEaOHInTp0+jrKwMVlZWCAoKQkpKCgCgd+/eEIvFyMnJAQCEhobiwoULKC0thVQqRUhICBITE/Hdd99h+vTpGDp0qHC/p2HDhuHq1asoLi6GRCJBWFiYMGbd3d1ha2srXAA9ePBgFBQU4NatWzAxMcHYsWORlJSE5uZm9OjRA05OTsjMzAQABAQEoLi4GAUFBRCJRAgPD8ehQ4fQ0NAAJycnuLu748SJEwAAPz8/lJWV4dq1awCA8PBwHD58GLW1tXBwcEDv3r2RkZEBQP5vYU1NDa5evQoAGDt2LI4dO4bq6mrY2tqif//+wpjt168fGhsbkZubC0A+CVNWVpYwVe+gQYOQlpYGAPDy8oKRkREuXLggjNlz587h3r17sLS0xJAhQ3Do0CEAQK9evWBmZoZz584JYzYzMxMlJSWQSqUIDQ1FUlISAKBnz56wtrbG6dOnAcjv8Zefn4/bt29DLBZj9OjRtI9A6/uI69evIzc3V2EfERwcjMuXL9M+opP7iDt37uC3334T9hHJyckAAE9PT1haWtI+ooP7iOvXr+Py5csK+4iQkBBcunRJL/YRLf1Xh8b3eSopKUFJSUmbbXr27ImdO3fitddeU5pdz8bGBp988gnmz5/f5jrS09MxcuRIZGdnw8/Pr822jDFIJBJs374dzz33nNLrdXV1wh+dgHwudzc3N63c54kQoj76HSP6hu7zRAghRJP7PGl82p69vT28vb3bfJiZmSE4OBjl5eVCdQwAx48fR3l5OUJCQtr9nC1btiAwMLDdwgkAzp07h4aGBjg7O6t8XSKRwNraWuHxOHhcTi98HFG2/FC2fLR8G0fI44LGLD+ULT+ULR+GlCu3a5769euHiRMnYuHChTh27BiOHTuGhQsXYvLkyQoz7Xl7eyMxMVHhvRUVFdi1axdeeOEFpfVeuXIF7733Hv744w/k5+fjl19+QWRkJPz9/REaGsprc3TiwYOCLYcvW06DAuQ5mZubw9vb+5H16bXXXsMTTzwhHPpuOUyuyqRJk2BpaSk8TExMsGTJEpVt582bB5FIJBwybhESEgKRSCRcJzdv3jwYGxsLpzQAQEJCAsLCwjTaDg0PuBINULZ8NDc367oLhGiExiw/lC0/lC0fhpQr15vkxsfHw9fXF+Hh4QgPD8fAgQOxfft2hTYXL14UrjVpkZCQAMYYZsyYobROsViMgwcPYsKECejbty9eeeUVhIeHIzk5GcbGxjw355ETi8UKz52cnLB3717h+e7du+Hm5vZI+ySTyXDgwAGUl5fjn//8J6ZMmdLqTIf79+9HVVUVqqqqUFZWBjs7O0yZMqXVdffp0wfx8fHC87y8PJSWlqrsw6pVqzq1HQ9nS7SHsuWjR48euu6CQbKzs0NERATs7Ox03RWDQ2OWH8qWH8qWD0PKlWvxZGtrix07dggz3O3YsQM2NjYKbRhjmDdvnsKyf/zjH6ipqYFMJlNap5ubG9LS0lBaWoq6ujpcvnwZ69evh62tLcctaVtuLpCVpfz4v+vsOszERHE+jxkzZigUF/Hx8Zg5c6ZCmzNnziA0NBQ2NjYICgoSLszUVGtHD1asWIHevXvDyMgIkZGRMDc3Fy6CbUtSUhJMTEwwevToVttERERg7969wmxtO3fuVFlAv/DCC9i/f7/Ki/vy8/NhZmaGL7/8Eo6OjnBzc0Nqaiq2bNkCZ2dnuLu7Iy0tTSlboj2ULR+Gfl87XfHw8MCmTZvg4eGh664YHBqz/FC2/FC2fBhSrlyLp64gNxfw8gICA5UfXl6dK6BaZnBrMX78eGRlZeHu3bsoKipCbm4uRo4cKbxeX1+Pp556CjNnzsSdO3fw+uuvY/LkyUpH9lp8+eWXGDRoENzd3bFgwQL8/PPPOHz4MF566SVhFpK25Ofn4+7du+jdu3e7bePj4zFjxgwYGbU+5GxsbDB06FBh9pRvvvlGqTgE5EX54sWLWz36VF9fj/z8fBQWFiImJgazZs1CTk4Orl27hjfffBNLly5VypZoD2XLR8tMTES77t+/jx9++EHhlGiiHTRm+aFs+aFs+TCkXKl46qSWM9Z27AAyM/967Nih+Lo2mJiYYMqUKdi1axcSEhIQGRmpUIwcO3YMxsbGeOmll2Bqaorp06ejT58+wlSlD6qrq0N+fj5+/vlnZGZmIjg4GJs3b8ZHH32EESNGtHsn6IaGBsydOxdvvPGGyiOED6qpqcGPP/6IqKiodrdx5syZiI+PR3Z2NszNzeHl5aWy3WuvvYZ9+/apPPrEGMM777wDU1NTPPvssygsLMTy5cshFovx7LPP4ty5cwZ17i0hpOPOnz+P6OhohesoCSGEkNbQ+TVa0q8foO1ZbqVSqdKyqKgoLF++HPfv38fmzZsVpoK/efMm3N3dFdp7eHjg5s2bSuuRSCSYOnUqVq9ejbt372LcuHHYtm0bLCws8P333+PcuXMYMGCAyn61nGrp6OiI2NjYdrfjxx9/hLu7O/z9/dttO3nyZLzyyivo1q1bm8WWnZ0dFi9ejNWrV2Py5MlK29Yyo6K5uTkAwMHBQXje0NBAp5ZxpGrcks6jabTJ44bGLD+ULT+ULR+GlCsdedJjjY2NSsuCg4NRWFiIqqoqDBo0SOE1FxcX4UaALa5fvw4XFxel9dTV1eHtt99GWFgYZsyYgePHj6Nfv37w8PDAkSNHlIqwBy1ZsgQ3b97Ejh072jwNr0V8fLxaR50A+U1bJ0yYgK+++krlPbsetGzZMvz888/CjfA0oSpboh2ULR/FxcW67gIhGqExyw9lyw9ly4ch5Upfv+ux+vp64cjJg3bv3q2yaBk2bBgaGhrw5ZdfYuHChUhMTMTFixcRHh6u1FYsFiM5OVlYz9SpU9Xq04oVK3DkyBGkpaVBIpG0276kpARJSUn47LPP1Fo/AKxatQrz589v9b5dLezs7PDiiy/i008/ha+vr9rrB+TZEj5aG7ekcwoKClo9GkyIPqIxyw9lyw9ly4ch5UpHnrTk/HnFmfZ4nj4/cOBA+Pj4KC0Xi8XYs2cPtm/fDjs7O6xduxZ79+5VeU2SSCRS66jRw9577z2cP38eLi4uwv2bWmYATE9Ph6WlpUL7b7/9FoMHD4anp6fan+Hq6qowEUZbli1bRoUQ6RJEIpGuu2CQRCIRTE1NKV8OKFN+KFt+KFs+DClXEeuCd7SsqKiATCZDeXm5cG0MANTW1iIvLw+enp4wMzNTa10ts+215tIloE+fzvaYEMPQkd8xQgghhBCeWqsNVKEjT53Up4+8QHpwpr2WR2cLp4qKCu11lCigbPmhbPk4dOiQrrtgsChbPihXfihbfihbPgwpV7rmSQt4HVnqggcFHxnKlh/Klo+Wm0cT7Tp//jz+8Y9/4KeffkK/fv103R2DQmOWH8qWH8qWD0PKlY486TFTU1Ndd8FgUbb8ULZ8GNLd2fXJ/fv3ceXKFbpJLgc0ZvmhbPmhbPkwpFypeNJjYrFY110wWJQtP5QtH23dPoAQfURjlh/Klh/Klg9DypWKJz1WXV2t6y4YLMqWH8qWjxMnTui6C4RohMYsP5QtP5QtH4aUKxVPhBBCCCGEEKIGKp70mFQq1XUXDBZlyw9ly4efn5+uu2CQPD09sXnzZo3uRUfUQ2OWH8qWH8qWD0PKlYonPdbY2KjrLhgsypYfypaPsrIyXXfBIHXr1g0jRoxAt27ddN0Vg0Njlh/Klh/Klg9DypWKJz1WX1+v6y4YLMqWH8qWj2vXrum6Cwbp9u3b+Pjjj3H79m1dd8Xg0Jjlh7Llh7Llw5BypeJJy+rq+Ky3Z8+eOHbsmMKy6OhoxMbG8vnAR2Dt2rUQiURK26VKQkICRCIREhISWm0jEonQu3dvhWW5ubkQiUSYOHGiQrvw8HCFdhMnTkRcXJxmG0AIeewVFhbiq6++QmFhoa67Qggh5DFAxZMWbdoEWFnJf2qDtbW1dlakhwoLC7Fz50615v2vrq7G6tWrMWDAgHbbGhkZ4fjx48Lz+Ph49FFxF+Pc3FwcOHBAs04TtRjyuNWlhwt+QvQdjVl+KFt+KFs+DClXKp60ZNMmIDoa6NdP/lMbBVRVVZXabePi4hAeHo6FCxfCysoKQUFBKCwsxEsvvQSZTIahQ4fi5s2bAIDm5mZERETA0dERtra2iIyMxN27dwEAqamp6NGjh/B8165d6Nu3r8Y3kGSMtfn6smXLsHLlSkgkknbXtWrVKixYsAD29vbttp0xYwbi4+OF59988w1mzJih1G7x4sVYuXJlu+sjmtNk3BL1HT58WNddIEQjNGb5oWz5oWz5MKRcqXjSgpbCackS4NQp+U9tFFDNzc0atU9JScHf/vY33L17F66urggNDcWoUaNQWlqKnj174sMPPxTaRkREIC8vD3l5eaisrMR7770HAAgLC8Ozzz6Ll19+GXfu3MGSJUsQFxcHc3Nzpc+7ffs2Fi5cCA8PDwQEBGDVqlXIyMjA7t27MWfOnFb7mZqaipKSEkydOrXdbbp06RL279+Pl19+Wa0Mpk2bhsTERDQ1NeHkyZOwt7dXOYvWjBkzUFhYiKSkJLXWS9Sn6bgl6qmtrdV1FwjRCI1ZfihbfihbPgwpVyqeOunBwmn9esDISP5TGwWUiYmJwvPx48fDxsZGeGzdulXhdV9fX0ydOhWmpqZ45plnYGFhgWnTpsHExARTpkzB6dOnAchPbZs1axYsLCwgk8nw6quv4vfffxfWs3btWpw8eRJhYWGYPXs2goODVfbv2LFjmDRpEs6ePYtt27ahpqYG77zzDn755Rf861//UvmexsZGvPrqq1i3bp1aGcTExOCDDz6AqampWu3t7Ozg5+eH5ORkxMfHY+bMmSrbmZub4+2336ajTxw8PG6Jdjg4OOi6CwZJJpNh5MiRkMlkuu6KwaExyw9lyw9ly4ch5UrFUyc8XDiJRPLlIpF2CigzMzOF50lJSSgrKxMe8+fPV3jd0dFR+G9zc3OFgWpubo7q6moA8gJm6dKl8PDwgLW1Nf7+97+jtLRUaCuVSjF9+nScP38er7zySqv9e/LJJ1FcXIwXXngBn3/+OcaNG4ekpCS8//772LNnj8r3fP755xg+fDh8fHza3f49e/bAxMREYbIHdURFRWH79u3YvXs3pk2bprKNRCLB/PnzUVBQgOTkZI3WT9r28Lgl2vHwZChEO3r16oWffvoJvXr10nVXDA6NWX4oW34oWz4MKVcqnjqork5eHA0cCKxb91fh1EIkki8fOFDeriOz8PG6diQ+Ph7p6enIyMhARUUFvv/+e4VrlHJzc/Hll18iMjISy5Yta3U9O3bsQG5uLubNmwc/Pz+sWbMGdnZ2GD16NFxdXVW+JyUlBfHx8XBycoKTkxNu3LiBJ598UukoWkvbw4cPC22PHj2K6Oho4RTD1jzzzDPYu3cvfHx8Wv2mo7q6Gqampnjrrbfo6JOW0TVPfGRkZOi6CwapoaEB+/fvR0NDg667YnBozPJD2fJD2fJhSLnS+TUdJJEAGzbIjywtXap45AkAGJMvP30a2LhR3l5fVFZWQiKRwMbGBiUlJfjoo4+E15qbmzF37ly88847iI6Ohp+fH7777juVR3Bmz54NY2Nj4fmLL77Y7mfHxcUpnPc6ePBgbNq0CWFhYUptV61aheXLlwvPIyIiMHfu3FZPxWshlUqRlJSk1gQT8+fPx5o1a1BVVYXp06e3254QYljOnDmD6dOnIzMzEwEBAbruDiGEED1HxVMnLFok/xkdLf/ZUkAxBsTEyIurjRv/aqcpVZM0aMOcOXOwb98+ODo6ws3NDS+88AJyc3MBAB999BGMjY0RExMDIyMjbN26FREREQgLC1M4LRCAQuGkLhsbG6V12NraQiqVApDfuwoANm7cCCsrK1hZWQltxWIxZDKZwrLWDB06tM3XW04tE4vFeOutt9Qq/Ih6eI3brk6dU10J0Sc0ZvmhbPmhbPkwpFxFrL05pQ1QRUUFZDIZysvLFe5JU1tbi7y8PHh6emp03caD1z6tWyc/4tTZwqmlP3T9CB+ULT9tZdvR3zEiP51W1T3LSOdkZWUhMDCQjjxxQGOWH8qWH8qWD33PtbXaQBW65kkLFi2SF0obNgD+/topnACgriMXShG1ULb8ULZ8XL16VdddIEQjNGb5oWz5oWz5MKRc6bQ9LWkplJYs0U7hRAghhBBCCNEvdNqeFk7be1BdnfYmh2CMQfTwNH5EKyhbftrKlk7b67jGxka6hxYHTU1NKC8vh0wm69B1nKR1NGb5oWz5oWz50Pdc6bQ9HdLmrHo05TM/lC0/lC0fx44d03UXDJKxsTFycnKocOKAxiw/lC0/lC0fhpQrFU96rLm5WdddMFiULT+ULR8tN7km2pWbm4uYmBhhxlGiPTRm+aFs+aFs+TCkXKl40mP6fHjzcUfZ8kPZ8mFra6vrLhikyspKZGVlobKyUtddMTg0ZvmhbPmhbPkwpFy5Fk/vv/8+QkJCIJVKle7v0xrGGGJjY+Hi4gJzc3OEhYXh3LlzCm3q6uqwZMkS2Nvbw8LCAk8//TQKCgo4bIFu0TUh/FC2/FC2fPTv31/XXSBEIzRm+aFs+aFs+TCkXLkWT/X19YiMjNToBqT//ve/8fHHH+Ozzz7DyZMn4eTkhPHjxyt8K7h06VIkJiYiISEBv//+O6qqqjB58mQ0NTXx2AydoWtH+KFs+aFs+fj999913QVCNEJjlh/Klh/Klg9DypXr+TUrV64EAMTFxanVnjGGdevW4Z133kFERAQAYNu2bejevTt27tyJRYsWoby8HFu2bMH27dsxbtw4AMCOHTvg5uaG5ORkTJgwgcu2EEIIIYQQQro2vbrmKS8vD0VFRQgPDxeWSSQSjBo1CkePHgUAZGZmoqGhQaGNi4sLfHx8hDa6pM37gz54+lPPnj1hbW2N+/fvC8sqKipgbm4Ob29v7X2ojuzZswfe3t6QyWRwdnbGa6+91uaRxAsXLmD8+PGwsrJC9+7dsX79epXt4uLiIBKJsHr1aoXl77//PkQiERISEhTabdq0SWhTVFRE05l3AJ22x0e/fv103QWD5Obmhvfeew9ubm667orBoTHLD2XLD2XLhyHlqlfFU1FREQCge/fuCsu7d+8uvFZUVASxWIxu3bq12uZhdXV1qKioUHjwsGkTYGUl/8mDk5MT9u7dKzzfvXu3wfyDHxQUhPT0dJSXlyMnJwenT5/G5s2bVba9f/8+Jk2ahDlz5qCkpASXL19WKKYf1rt3b+zcuVN4zhjD999/j169eim069atG9asWYOGhgbtbBQhWtTY2KjrLhgkBwcHREVFwcHBQdddMTg0ZvmhbPmhbPkwpFw1Pm0vNjZWOB2vNSdPnkRQUFCHO/Xwt/3q3NC0rTb/8z//o7LPycnJsLCwwJgxY3DixAncv38f9vb2wk0Tgb++Ra+trQUAWFlZoaamBk1NTTA2NoZUKkVlZSW2bhXj1VfNMXAgQ3S0CPfv38eSJWLU1taisbERRkZGsLS0FAo3iUQCIyMj4UiSpaWlyrZisRhisRgAMHXqVMTFxeHZZ59FfX09tm3bhmeffRaJiYlCf21sbLBhwwZ88MEHqKmpwaeffgozMzMsW7YM1dXV+OyzzzBp0iQwxrBjxw6sW7cORUVFcHd3x/r16xEaGoqKigqMGjUKK1aswOTJk3H37l2EhIQgLi4OQ4YMAQCYm5ujubkZdf93qM3a2hpVVVVobm6GiYkJzMzMUFVVBcYYpFKpQltVGVpaWgKQF7p1dXVobGzEhQsX0NzcrNT2iy++wODBgxEZGQmRSITm5ma4uLigqalJKcOamhp4eHigvLwcx48fh7e3N44dOwYXFxcYGRmhpqZGuJ4uICAA1dXV2LRpExYtWiT8v2psbER9fT28vb2xaNEibNu2DUVFRXjrrbcQGhqKRYsWoaioCG+88QbeeOMNoT0AyGQyVFRUgDEGU1NTiMViYbpOqVSq0La1DDXJW5Mxq6rtg+Pw4bbqjtn6+npYWVkptLWwsEB9fT2qq6uFz/rtt98AyL/5t7e3x6lTpwDIC+mbN2/i5s2bMDY2xrhx45CcnIympia4uLjAxcUFf/zxBwDA398fJSUluHHjBgBgwoQJSElJQX19Pbp3746ePXvi+PHjAICBAweioqIC+fn5AIDx48fjyJEjqKmpgb29Pby8vISj1wMGDEBtbS2uXLkCAMI+oqqqCt26dcOAAQOE87e9vb3R3NyMS5cuAQBGjRqF7Oxs4UZ7AQEBSE1NBQD06dMHJiYmOH/+PABg+PDhyMnJwd27d2FhYYFhw4bh4MGDAIAnnngCUqkUZ8+eFX43ysrKcOfOHZiZmWHkyJE4cOAAAMDDwwM2Njb4888/AQBDhgzB9evXUVRUBFNTU4wZMwYHDhwAYwyurq5wdHREVlYWACAwMBBFRUUoLCyEkZERxo8fj4MHD6KxsRHOzs5wdXXFyZMnAQCDBg3C3bt3cf36dSHv1NRU1NXVwdHREU888YRwLw9fX19UVVUhLy8PADBu3DgcPXoUNTU1sLOzg7e3N44cOQJAfiFxfX09Ll++DAAYPXo0/vjjD1RWVsLGxgYDBw7E4cOHAQB9+/YFAFy8eBEAMHLkSJw+fRplZWWwsrJCUFAQUlJSAMi/OBGLxcjJyQEAhIaG4sKFCygtLYVUKkVISAgSExPxyy+/IDo6Gq6urjhz5gwAYNiwYbh69SqKi4shkUgQFhYmjFl3d3fY2toiOzsbADB48GAUFBTg1q1bMDExwdixY5GUlITm5mb06NEDTk5OyMzMBCDfzxQXF6OgoAAikQjh4eE4dOgQGhoa4OTkBHd3d5w4cQIA4Ofnh7KyMly7dg0AEB4ejsOHD6O2thYODg7o3bs3MjIyAAA+Pj6oqanB1atXAQBjx47FsWPHUF1dDVtbW/Tv318Ys/369UNjY6MwPXtYWBiysrKEm0QOGjQIaWlpAAAvLy8YGRnhwoULwpg9d+4c7t27B0tLSwwZMgSHDh0CAPTq1QtmZmbCJE/19fW4d+8eSkpKIJVKERoaiqSkJAB/nU1x+vRpAMDQoUORn5+P27dvQywWY/To0bSPQOv7iPz8fIwZM0ZhHxEcHIzLly/TPqKT+4jff/8dubm5wj4iOTkZAODp6QlLS0vaR3RwH5Gfn4+xY8cq7CNCQkJw6dIlvdhHtPRfLUxDd+7cYefPn2/zcf/+fYX3bN26lclksnbXfeXKFQaAZWVlKSx/+umn2Zw5cxhjjB08eJABYHfv3lVoM3DgQPbuu++qXG9tbS0rLy8XHjdu3GAAWHl5uUK7+/fvs5ycHKX+t2fjRsYAxpYsYaypSf4TkC/vjLKyMuG/PTw8WHp6OuvRowcrLS1lt27dYh4eHuzgwYOsb9++QjsALCoqit2/f5999913zNbWls2bN49VV1ezvXv3Mnt7e9bY2MgYY+znn39m165dY01NTWzz5s2se/furLa2ljHG2IkTJ5ijoyO7efMmmzNnDnvxxRdb7ecXX3zB/Pz8mJubG3v++efZTz/9xNLS0tjixYvZiRMn1N7e9PR0Zm1tzQAwR0dHdv78eZXt5s2bx15++WU2ZMgQZm9vz5566il2/fp1lW23bt3KJkyYwNavX8+WLVvGGGNs8eLFbP369WzUqFHsm2++UWh34MAB5uHhwerr69mtW7fYg78iHh4ebMyYMaysrIwdP36cicVi9ve//53dvXuXZWdnMzMzM1ZcXKz29hqqB8ftwzr6O0YY+/XXX3XdBYOUmZnJALDMzExdd8Xg0Jjlh7Llh7LlQ99zLS8vV1kbqKLxaXv29vbw9vZu89HRax48PT3h5OQkVJ2A/JurtLQ0hISEAJB/A2JqaqrQ5tatWzh79qzQ5mESiQTW1tYKD23ZtAmIjgaWLAHWrweMjOQ/lyyRL+/MKXxWVlYKz01MTDBlyhTs2rULCQkJiIyMhJGR8v/CN998E2ZmZoiIiEBZWRkWL14MqVSKp556CpWVlbh58yYA4Mknn4S7uzuMjIywcOFCiEQi4VuGwYMHY8GCBRg3bhzS09Px73//W2Uf6+rqkJ+fj59//hmZmZkIDg7G5s2b8dFHH2HEiBEYPHiw2ts7fPhwlJeX4+rVq4iOjlY6NbNFYWEhtm/fjs8++wwFBQXo27cvZs2a1ea6n3vuOezatQv19fXYs2cPoqKiVLYbP348evTo0eokJzExMZDJZBgyZAicnJwwbdo0dOvWDX5+fnB3d9fsmwsD9fC4JdoRFham6y4QohEas/xQtvxQtnwYUq5cr3m6fv06srOzcf36dTQ1NSE7OxvZ2dkKUxl7e3sjMTERgPx0vaVLl2LNmjVITEzE2bNnMW/ePEilUsycOROA/DSoBQsWYNmyZTh48CBOnTqFWbNmwdfXV5h971F5uHBqOWtQJNJOAVVTU6O0LCoqCjt37sTOnTtbLQAcHR0BAMbGxjA1NVU4l9/MzEw4bezHH39EQEAAbGxsYGNjg+LiYpSWlgptn3/+eeTk5OD5558XTqt7mEQiwdSpU7F69Wq89NJLaG5uxrZt2/D999+jublZ6R5dAJCeng5LS0tYWlpi0qRJSq97enrC19cXS5cuVfmZ5ubmiIiIwODBgyGRSBAbG4v09PQ2p8ju3r07vL298fbbbyMoKAgSiaTVtitWrGj12qeWbFv68WC25ubmBnUH7Y5SNW5J57WcQkPI44LGLD+ULT+ULR+GlCvX4undd9+Fv78/VqxYgaqqKvj7+8Pf3184zxCQn5Pacr0OID9qsnTpUixevBhBQUEoLCzEgQMHFL7N/uSTTzBlyhRMmzYNoaGhkEql+Omnn2BsbMxzcxTU1cmLo4EDgXXr/iqcWohE8uUDB8rbdWQWPlWzzQUHB6OwsBBVVVUYNGhQR7oOQH7EaMaMGVi7di1KS0tRVlYGR0dHMMYAyK8he/HFFxEVFYX169ejsLCw1fW8/fbbCAsLw4wZM3D8+HH069cPHh4eOHLkCNzd3ZXeM2LECFRVVaGqqgr79+9Xud7m5mbhXPKH+fj4qFze0vfWzJw5Ex9//DFmzpzZ5kx+4eHhcHZ2xrZt29pcH1HN0O63pi94TXRDCC80ZvmhbPmhbPkwpFy53ucpLi6u3Xs8PfwHr0gkQmxsLGJjY1t9j5mZGTZs2IANGzZooZcdI5EAGzbIjywtXap45AkAGJMvP30a2LhR3l5TrRWDu3fvVnm6nibq6upQX18vHDlZv3497ty5I7y+ceNG3Lt3D/v370dsbCwWLlyIX375RWk9YrEYycnJQn+mTp3aof7s2rULQ4cOhbu7Oy5fvoy1a9di/PjxKtvOmjULI0aMwKlTp+Dj44NVq1Zh5MiR7Z4uFhkZie7duyMsLKzdWV9WrFghHO0kmnmUX2J0JTKZTNddMEgWFhbw8fGBhYWFrrticGjM8kPZ8kPZ8mFIuerVVOWPm0WL5IXRhg1ATIy8YALkP2Ni5Ms3bpS36wipVKpy+cCBA1s9+qIua2trfPjhhxg/fjycnJxQWlqK3r17A5Dfb+v//b//h7i4OJiYmODdd99FQUEBvv76a6X1iESiThdyAJCbm4uQkBBh9sPRo0crzJBoaWmJ9PR0APJZYD7//HNMnToVDg4OOHPmDLZv397uZ0ilUkycOBFmZmatZttiwoQJ8PLy6txGdVHtZUs6pjNHmknr+vbti5MnTwozdBHtoTHLD2XLD2XLhyHlKmLtnetkgFqmWmyZJrRFbW0t8vLy4OnpqdGkFw9e+7RunfyIU2cLJwAoLy83qEpdn1C2/LSVbUd/x4h8avcJEybouhsGibLlg3Llh7Llh7LlQ99zba02UIXraXtdRUuBFB0NpKX9dapeZwonQggh/GVlZWHixInIzMxEQECArrtDCCFEz1HxpCUthdKSJdornOibeX4oW34oWz7oNFLyuKExyw9lyw9ly4ch5UrFkxYtWgTMm9exySEIIaQt2ri2kJBHicYsP5QtP5QtH4aUq+FsiZ7QZuFUW1urvZURBZQtP5QtH3QDZvK4oTHLD2XLD2XLhyHlSsWTCl1wDg1CHgn63SKEEELI44xm23tgRo2mpibk5uZCKpXCwcEBoofvfPuINTU10T1zOKFs+WktW8YY7ty5g5qaGvTp04fy11B1dTXdi4iD2tpaXLp0CV5eXnS9npbRmOWHsuWHsuVD33Ol2fY6yNjYGK6urigoKEB+fr6uu4O6ujpI6AIqLihbftrKViQSwdXVlQqnDjh37hyGDBmi624YHDMzM9TW1lLhxAGNWX4oW34oWz4MKVcqnh5iaWmJPn36oKGhQdddwe+//47hw4fruhsGibLlp61sTU1NqXDqoHv37um6CwYpLy8Py5cvx5YtW+Dp6anr7hgUGrP8ULb8ULZ8GFKuVDypYGxsrBd/4Jmbm9O3oZxQtvxQtnxYWlrqugsG6d69e0hJScG9e/eoeNIyGrP8ULb8ULZ8GFKudM1TO+c16lJDQwNMTU113Q2DRNnyQ9nyQbnykZWVhcDAQLpJLgc0ZvmhbPmhbPnQ91w1qQ1otj09dujQIV13wWBRtvxQtnxQruRxQ2OWH8qWH8qWD0PKtUuettdysK2iokLHPWlbdXW13vfxcUXZ8kPZ8kG58lFVVSX8pHy1i8YsP5QtP5QtH/qea0vf1Dkhr0uetldQUAA3Nzddd4MQQgghhBCiJ27cuAFXV9c223TJ4qm5uRk3b96ElZWVzu/l1JqKigq4ubnhxo0ben1d1uOIsuWHsuWDcuWHsuWDcuWHsuWHsuXjcciVMYbKykq4uLjAyKjtq5q65Gl7RkZG7VaV+sLa2lpvB9rjjrLlh7Llg3Llh7Llg3Llh7Llh7LlQ99zlclkarWjCSMIIYQQQgghRA1UPBFCCCGEEEKIGqh40lMSiQQrVqyARCLRdVcMDmXLD2XLB+XKD2XLB+XKD2XLD2XLh6Hl2iUnjCCEEEIIIYQQTdGRJ0IIIYQQQghRAxVPhBBCCCGEEKIGKp4IIYQQQgghRA1UPBFCCCGEEEKIGqh40qH3338fISEhkEqlsLGxUes9jDHExsbCxcUF5ubmCAsLw7lz5xTa1NXVYcmSJbC3t4eFhQWefvppFBQUcNgC/XTv3j3Mnj0bMpkMMpkMs2fPRllZWZvvEYlEKh8ffvih0CYsLEzp9enTp3PeGv3SkWznzZunlNuwYcMU2tCY1SzXhoYG/POf/4Svry8sLCzg4uKCOXPm4ObNmwrtuuKY/eKLL+Dp6QkzMzMEBgYiPT29zfZpaWkIDAyEmZkZnnjiCWzcuFGpzQ8//ID+/ftDIpGgf//+SExM5NV9vaZJtrt378b48ePh4OAAa2trBAcH47ffflNoExcXp3K/W1tby3tT9IomuaampqrM7MKFCwrtaMzKaZKtqn+rRCIRBgwYILShMQscPnwYTz31FFxcXCASifDjjz+2+x6D288yojPvvvsu+/jjj9lrr73GZDKZWu9Zu3Yts7KyYj/88AM7c+YMe+6555izszOrqKgQ2kRHR7MePXqwpKQklpWVxUaPHs38/PxYY2Mjpy3RLxMnTmQ+Pj7s6NGj7OjRo8zHx4dNnjy5zffcunVL4fH1118zkUjErly5IrQZNWoUW7hwoUK7srIy3pujVzqS7dy5c9nEiRMVcistLVVoQ2NWs1zLysrYuHHj2LfffssuXLjAMjIy2NChQ1lgYKBCu642ZhMSEpipqSn76quvWE5ODouJiWEWFhbs2rVrKttfvXqVSaVSFhMTw3JycthXX33FTE1N2ffffy+0OXr0KDM2NmZr1qxh58+fZ2vWrGEmJibs2LFjj2qz9IKm2cbExLAPPviAnThxgl26dIm99dZbzNTUlGVlZQlttm7dyqytrZX2v12JprmmpKQwAOzixYsKmT24r6QxK6dptmVlZQqZ3rhxg9na2rIVK1YIbWjMMvbLL7+wd955h/3www8MAEtMTGyzvSHuZ6l40gNbt25Vq3hqbm5mTk5ObO3atcKy2tpaJpPJ2MaNGxlj8l9+U1NTlpCQILQpLCxkRkZG7Ndff9V63/VNTk4OA6DwC5eRkcEAsAsXLqi9nmeeeYaNGTNGYdmoUaNYTEyMtrr62OlotnPnzmXPPPNMq6/TmNXOmD1x4gQDoPCHQVcbs0OGDGHR0dEKy7y9vdny5ctVtn/zzTeZt7e3wrJFixaxYcOGCc+nTZvGJk6cqNBmwoQJbPr06Vrq9eNB02xV6d+/P1u5cqXwXN1/+wyZprm2FE/37t1rdZ00ZuU6O2YTExOZSCRi+fn5wjIas4rUKZ4McT9Lp+09RvLy8lBUVITw8HBhmUQiwahRo3D06FEAQGZmJhoaGhTauLi4wMfHR2hjyDIyMiCTyTB06FBh2bBhwyCTydTe/tu3b2Pfvn1YsGCB0mvx8fGwt7fHgAED8Prrr6OyslJrfdd3nck2NTUVjo6O8PLywsKFC1FcXCy8RmO282MWAMrLyyESiZROAe4qY7a+vh6ZmZkK4wgAwsPDW80xIyNDqf2ECRPwxx9/oKGhoc02XWFstuhItg9rbm5GZWUlbG1tFZZXVVXBw8MDrq6umDx5Mk6dOqW1fuu7zuTq7+8PZ2dnjB07FikpKQqv0ZjVzpjdsmULxo0bBw8PD4XlXXnMdoQh7mdNdN0Bor6ioiIAQPfu3RWWd+/eHdeuXRPaiMVidOvWTalNy/sNWVFRERwdHZWWOzo6qr3927Ztg5WVFSIiIhSWR0VFwdPTE05OTjh79izeeust/Pnnn0hKStJK3/VdR7OdNGkSIiMj4eHhgby8PPzrX//CmDFjkJmZCYlEQmNWC2O2trYWy5cvx8yZM2FtbS0s70pjtqSkBE1NTSr3j63lWFRUpLJ9Y2MjSkpK4Ozs3GqbrjA2W3Qk24f95z//QXV1NaZNmyYs8/b2RlxcHHx9fVFRUYH169cjNDQUf/75J/r06aPVbdBHHcnV2dkZmzdvRmBgIOrq6rB9+3aMHTsWqampGDlyJIDWxzWNWfVzuHXrFvbv34+dO3cqLO/qY7YjDHE/S8WTlsXGxmLlypVttjl58iSCgoI6/BkikUjhOWNMadnD1Gmjz9TNFVDOB9Bs+7/++mtERUXBzMxMYfnChQuF//bx8UGfPn0QFBSErKwsBAQEqLVufcQ72+eee074bx8fHwQFBcHDwwP79u1TKlA1Wa++e1RjtqGhAdOnT0dzczO++OILhdcMdcy2RdP9o6r2Dy/vyD7XEHU0h2+++QaxsbHYs2ePwhcFw4YNU5g8JjQ0FAEBAdiwYQM+/fRT7XVcz2mSa9++fdG3b1/heXBwMG7cuIGPPvpIKJ40Xach62gOcXFxsLGxwZQpUxSW05jtGEPbz1LxpGUvv/xyu7NZ9ezZs0PrdnJyAiCv4p2dnYXlxcXFQsXu5OSE+vp63Lt3T+Gb/OLiYoSEhHToc/WBurmePn0at2/fVnrtzp07St9qqJKeno6LFy/i22+/bbdtQEAATE1NkZub+1j/Ifqosm3h7OwMDw8P5ObmAqAx25lcGxoaMG3aNOTl5eHQoUMKR51UMZQxq4q9vT2MjY2Vvql8cP/4MCcnJ5XtTUxMYGdn12YbTcb8464j2bb49ttvsWDBAuzatQvjxo1rs62RkREGDx4s7BsMXWdyfdCwYcOwY8cO4TmN2c5lyxjD119/jdmzZ0MsFrfZtquN2Y4wxP0sXfOkZfb29vD29m7z8fARDXW1nH7z4Ck39fX1SEtLE/7IDAwMhKmpqUKbW7du4ezZs4/1H6Lq5hocHIzy8nKcOHFCeO/x48dRXl6u1vZv2bIFgYGB8PPza7ftuXPn0NDQoFDIPo4eVbYtSktLcePGDSE3GrMdy7WlcMrNzUVycrLwj1BbDGXMqiIWixEYGKh0SmJSUlKrOQYHByu1P3DgAIKCgmBqatpmm8d5bGqqI9kC8iNO8+bNw86dO/Hkk0+2+zmMMWRnZxvk+FSlo7k+7NSpUwqZ0ZjtXLZpaWm4fPmyyuueH9bVxmxHGOR+9lHPUEH+cu3aNXbq1Cm2cuVKZmlpyU6dOsVOnTrFKisrhTZ9+/Zlu3fvFp6vXbuWyWQytnv3bnbmzBk2Y8YMlVOVu7q6suTkZJaVlcXGjBnT5aZ9HjhwIMvIyGAZGRnM19dXadrnh3NljLHy8nImlUrZl19+qbTOy5cvs5UrV7KTJ0+yvLw8tm/fPubt7c38/f27TK6MaZ5tZWUlW7ZsGTt69CjLy8tjKSkpLDg4mPXo0YPG7AM0zbWhoYE9/fTTzNXVlWVnZytMmVtXV8cY65pjtmVq4i1btrCcnBy2dOlSZmFhIcyWtXz5cjZ79myhfcsUuq+++irLyclhW7ZsUZpC98iRI8zY2JitXbuWnT9/nq1du1avp9DlRdNsd+7cyUxMTNjnn3/e6lT5sbGx7Ndff2VXrlxhp06dYvPnz2cmJibs+PHjj3z7dEXTXD/55BOWmJjILl26xM6ePcuWL1/OALAffvhBaENjVk7TbFvMmjWLDR06VOU6aczK/11v+XsVAPv444/ZqVOnhJleu8J+loonHZo7dy4DoPRISUkR2gBgW7duFZ43NzezFStWMCcnJyaRSNjIkSPZmTNnFNZ7//599vLLLzNbW1tmbm7OJk+ezK5fv/6Itkr3SktLWVRUFLOysmJWVlYsKipKaVrXh3NljLFNmzYxc3NzlffBuX79Ohs5ciSztbVlYrGY9erVi73yyitK9ysydJpmW1NTw8LDw5mDgwMzNTVl7u7ubO7cuUrjkcasZrnm5eWp3Hc8uP/oqmP2888/Zx4eHkwsFrOAgACWlpYmvDZ37lw2atQohfapqanM39+ficVi1rNnT5VfnuzatYv17duXmZqaMm9vb4U/VLsSTbIdNWqUyvE5d+5coc3SpUuZu7s7E4vFzMHBgYWHh7OjR48+wi3SD5rk+sEHH7BevXoxMzMz1q1bNzZ8+HC2b98+pXXSmJXTdH9QVlbGzM3N2ebNm1Wuj8bsX9Plt/a73RX2syLG/u+qLUIIIYQQQgghraJrngghhBBCCCFEDVQ8EUIIIYQQQogaqHgihBBCCCGEEDVQ8UQIIYQQQgghaqDiiRBCCCGEEELUQMUTIYQQQgghhKiBiidCCCGEEEIIUQMVT4QQQgghhBC9dvjwYTz11FNwcXGBSCTCjz/+qNH7Y2NjIRKJlB4WFhYarYeKJ0IIIYQQQoheq66uhp+fHz777LMOvf/111/HrVu3FB79+/dHZGSkRuuh4okQQgghhBCi1yZNmoTVq1cjIiJC5ev19fV488030aNHD1hYWGDo0KFITU0VXre0tISTk5PwuH37NnJycrBgwQKN+mHSmY0ghBBCCCGEEF2bP38+8vPzkZCQABcXFyQmJmLixIk4c+YM+vTpo9T+v//9L7y8vDBixAiNPoeOPBFCCCGEEEIeW1euXME333yDXbt2YcSIEejVqxdef/11DB8+HFu3blVqX1dXh/j4eI2POgF05IkQQgghhBDyGMvKygJjDF5eXgrL6+rqYGdnp9R+9+7dqKysxJw5czT+LCqeCCGEEEIIIY+t5uZmGBsbIzMzE8bGxgqvWVpaKrX/73//i8mTJ8PJyUnjz6LiiRBCCCGEEPLY8vf3R1NTE4qLi9u9hikvLw8pKSnYu3dvhz6LiidCCCGEEEKIXquqqsLly5eF53l5ecjOzoatrS28vLwQFRWFOXPm4D//+Q/8/f1RUlKCQ4cOwdfXF3/729+E93399ddwdnbGpEmTOtQPEWOMdXprCCGEEEIIIYST1NRUjB49Wmn53LlzERcXh4aGBqxevRr/+7//i8LCQtjZ2SE4OBgrV66Er68vAPnpfR4eHpgzZw7ef//9DvWDiidCCCGEEEIIUQNNVU4IIYQQQgghaqDiiRBCCCGEEELUQMUTIYQQQgghhKiBiidCCCGEEEIIUQMVT4QQQgghhBCiBiqeCCGEEEIIIUQNVDwRQgghhBBCiBqoeCKEEEIIIYQQNVDxRAghhBBCCCFqoOKJEEIIIYQQQtRAxRMhhBBCCCGEqIGKJ0IIIYQQQghRw/8H0ABU09GpOdkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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xVbkDtyrv7e3Fx8fHYccTzk3krRyRtbJE3soZGBjg+PHjTJ8+XVzrSSGifytHZK0skbeyHJm32KrcSYqLi53dBLci8laOyFpZIm/leHp6olKpROGkING/lSOyVpbIW1nOylsUTw7U3t7u7Ca4FZG3ckTWyhJ5K6empoZHHnmEmpoaZzfFbYj+rRyRtbJE3spyVt6ieHIgLy8vZzfBrYi8lSOyVpbIWznt7e1s2rRJvOhRkOjfyhFZK0vkrSxn5S3WPDlwzZPNZkOj0TjseMK5ibyVI7JWlshbOUePHmX27Nnk5uaSnZ3t7Oa4BdG/lSOyVpbIW1mOzFuseXKSrVu3OrsJbkXkrRyRtbJE3sJkJvq3ckTWyhJ5K8tZeYviSRAEQRAEQRAEYQxE8eRA8fHxzm6CWxF5K0dkrSyRt3LCw8O56667CA8Pd3ZT3Ibo38oRWStL5K0sZ+Wtdcp3naTE3v7KEnkrR2StLJG3cqKjo/n1r39NVFSUs5viNkT/Vo7IWlkib2U5K28x8uRAhYWFzm6CWxF5K0dkrSyRt3KMRiNvvPEGRqPR2U1xG6J/K0dkrSyRt7KclbcongRBEAS3VVZWxi9+8QvKysqc3RRBEATBBYjiyYFycnKc3QS3IvJWjshaWSJvYTIT/Vs5ImtlibyV5ay8RfHkQJWVlc5uglsReStHZK0skbcwmYn+rRyRtbJE3spyVt6ieHKglpYWZzfBrYi8lSOyVpbIW5jMRP9WjshaWSJvZTkrb1E8OZCHh4ezm+BWRN7KEVkrS+StHJ1OR0hICDqdztlNcRuifytHZK0skbeynJW3SpIkySnf2Yl6enrw9/enu7sbPz8/ZzdHEARBEARBEAQnuZDaQIw8OdCmTZuc3QS3IvJWjshaWSJvZYm8lSXyVo7IWlkib2U5K29RPAmCIAhuq7CwkFtvvVVcn0UQBEEYE1E8OVBsbKyzm+BWRN7KEVkrS+StHIvFQltbGxaLxdlNcRuifytHZK0skbeynJW3KJ4cKCgoyNlNcCsib+WIrJUl8hYmM9G/lSOyVpbIW1nOynvci6enn36a+Ph4DAYDs2fP5osvvjjrc++44w5UKtWo27Rp0+TnvPrqq2d8zuDg4Hj/KOeVn5/v7Ca4FZG3ckTWyhJ5C5OZ6N/KEVkrS+StLGflPa7F0zvvvMP999/Pww8/TF5eHkuWLGHt2rXU1tae8fl//etfaWxslG+nTp0iKCiIb37zmyOe5+fnN+J5jY2NGAyG8fxRBEEQBEEQBEFwc+O6Vfn8+fPJzs7mmWeeke9LT0/n+uuv5/HHHz/v13/44YesW7eOqqoqpk6dCgyNPN1///10dXVddLvGa6vyjo4OMWSrIJG3ckTWyhJ5K8doNLJjxw6WL1+Or6+vs5vjFkT/Vo7IWlkib2U5Mu8JsVW52WwmNzeX1atXj7h/9erV7Nu3b0zHeOmll1i1apVcOA3r7e1l6tSpxMTEcPXVV5OXl3fO45hMJnp6ekbcxkNdXd24HFc4M5G3ckTWyhJ5K8fX15e4uDhROClI9G/liKyVJfJWlrPy1o7Xgdva2rDZbISHh4+4Pzw8nKampvN+fWNjI59//jlvvvnmiPvT0tJ49dVXyczMpKenh7/+9a8sWrSIgoICkpOTz3isxx9/nEcffXTU/Vu3bsXb25sVK1Zw6NAhent7CQwMZNq0aezZs0f+fna7ndLSUgCWLVtGfn6+XJlmZ2ezc+dOALq7uwkICODkyZMALF68mKKiIjo6OvD29iYnJ4dt27YBkJCQgJeXF8ePHwdgwYIFlJeX09raisFgYOnSpWzevBmAqVOnEhAQQEFBAQDz5s2jtraWpqYmdDodK1asYPPmzUiSRExMDGFhYRw9ehSA2bNn09TURH19PWq1mssvv5xt27ZhtVqJjIwkJiaGw4cPAzBz5kw6OjrkaZVr1qxh586dmEwmwsLCSEhI4MCBAwBkZmbS29tLVVUVAKtWrWLfvn309/cTHBxMWloae/fuBSAjIwOz2Ux5eTkAy5cv58iRIxiNRgICAsjKymL37t0ApKamAlBSUgLA0qVLOXbsGF1dXfj6+jJnzhx27NgBQFdXF4GBgRQVFQGwaNEiiouLaW9vx8vLi4ULF7J161YA4uPj8fHxkbcjzsnJobKykpaWFjw8PLjsssvk6wXExsYSFBQkz6WdO3cudXV1NDY2otVqWblyJVu2bMFutxMdHU1ERAS5ubkAZGdn09LSQl1dHSqVitWrV7N9+3YsFgsRERHExsZy6NAhAGbMmEFXVxc1NTXA0BsLu3fvZnBwkNDQUJKSkti/fz8A06dPp7+/n8rKSgBWrlzJgQMH6OvrIygoiIyMDLnPpqenY7VaKSsrA+Cyyy7j6NGj8rsqM2fOZNeuXQCkpKSgVqspLi6W++yJEyfo7OzEx8eHefPmsX37dqqrq/Hy8sJgMHDixAkAFi5cSGlpKW1tbXh5ebFo0SK2bNkCQFxcHH5+fhw7dgwYGoWurq6mubkZvV7P8uXL5bynTJlCSEiI/CbInDlzaGhooKGhAY1Gw6pVq9i6dSs2m42oqCiioqI4cuQIALNmzaKtrY1Tp07JfXbHjh2YzWbCw8OJi4vj4MGDAGRlZdHT00N1dTUAl19+OXv37qW/v5+QkBBSUlLkN3amTZvG4OAgFRUVAA47RyQnJ6PVas97jqiursbT01OcIy7xHJGUlIRerz/nOeLtt9/m9ddf5+GHHyYxMVGcIy7yHAGQmJg4pnNEdXU1Hh4e4hxxCecIGNvriP3799PY2CjOEZdwjriQ1xHDeYtzxKWdI2BsryMOHDhAY2OjQ84Rw+0fi3GbttfQ0EB0dDT79u1jwYIF8v2PPfYYr7/++nkb+fjjj/PnP/+ZhoYG9Hr9WZ9nt9vJzs5m6dKlPPXUU2d8jslkwmQyyZ/39PQwZcoUh0/b27ZtGytXrnTY8YRzE3krR2StLJG3co4ePcrs2bPJzc0lOzvb2c1xC6J/K0dkrSyRt7IcmfeFTNsbt5GnkJAQNBrNqFGmlpaWUaNRXydJEi+//DK33XbbOQsnALVazdy5c+Xq+Ew8PDzw8PAYe+MvkviFUZbIWzkia2WJvIXJTPRv5YislSXyVpaz8h63NU96vZ7Zs2fLw2/DtmzZwsKFC8/5tbt27aK8vJw777zzvN9HkiTy8/OJjIy8pPY6wtd/VmF8ibyVI7JWlshbmMxE/1aOyFpZIm9lOSvvcRt5AnjwwQe57bbbmDNnDgsWLOD555+ntraWu+++G4CHHnqI+vp6XnvttRFf99JLLzF//nymT58+6piPPvooOTk5JCcn09PTw1NPPUV+fj7/+Mc/xvNHGRO73e7sJrgVkbdyRNbKEnkLk5no38oRWStL5K0sZ+U9rsXTzTffTHt7O7/5zW9obGxk+vTpfPbZZ/LueY2NjaOu+dTd3c17773HX//61zMes6uri7vuuoumpib8/f2ZNWsWu3fvZt68eeP5o4xJdHS0s5vgVkTeyhFZK0vkrZzg4GDWrVtHcHCws5viNkT/Vo7IWlkib2U5K+9xvc7TRDVe13lqa2sjJCTEYccTzk3krRyRtbJE3soSeStL5K0ckbWyRN7KcmTeE+I6T+5oeJtJQRkib+WIrJUl8lbOwMAA7733HgMDA85uitsQ/Vs5ImtlibyV5ay8RfEkCIIguK2TJ09y9913y9fVEQRBEIRzEcWTA4lrhChL5K0ckbWyRN7CZCb6t3JE1soSeSvLWXmL4smBWlpanN0EtyLyVo7IWlkib2EyE/1bOSJrZYm8leWsvEXx5EB1dXXOboJbEXkrR2StLJG3MJmJ/q0ckbWyRN7KclbeonhyIJVK5ewmuBWRt3JE1soSeStHpVKh0+lE5goSWStHZK0skbeynJW32KrcgVuVC4IgCIIgCILgWsRW5U6yfft2ZzfBrYi8lSOyVpbIW1kib2WJvJUjslaWyFtZzspbFE8OZLFYnN0EtyLyVo7IWlkib+WcPHmSu+66S2xVriDRv5UjslaWyFtZzspbFE8OFBER4ewmuBWRt3JE1soSeStnYGCAiooKcZFcBYn+rRyRtbJE3spyVt6ieHKg2NhYZzfBrYi8lSOyVpbIW5jMRP9WjshaWSJvZTkrb1E8OdChQ4ec3QS3IvJWjshaWSJvYTIT/Vs5ImtlibyV5ay8RfEkCIIgCIIgCIIwBqJ4cqAZM2Y4uwluReStHJG1skTeyomPj+f5558nPj7e2U1xG6J/K0dkrSyRt7Kclbconhyoq6vL2U1wKyJv5YislSXyVk5gYCBLliwhMDDQ2U1xG6J/K0dkrSyRt7KclbconhyopqbG2U1wKyJv5YislSXyVk5zczN/+ctfaG5udnZT3Ibo38oRWStL5K0sZ+UtiidBEATBbdXX1/PCCy9QX1/v7KYIgiAILkAlSZLk7EYoraenB39/f7q7u/Hz83PYcSVJQqVSOex4wrmJvEez2+0MDg4yMDDAwMDAiI9NJhMWi0W+Wa3WUR9brVYkSRp1s9vtAPLnarV6xE2j0Zzxc51Oh06nQ6/Xn/Njg8Ew4qbT6ZycpHOJvq2co0ePMnv2bHJzc8nOznZ2c9yC6N/KEVkrS+StLEfmfSG1gdYh31EAYPfu3SxbtszZzXAb7pS32Wymp6cHo9F41lt/fz8mk2lcvn91dTVxcXHjcuwz0Wq1owoqg8GAp6cn3t7eo25eXl54enpOmj9a7tS3Bfcj+rdyRNbKEnkry1l5i+LJgQYHB53dBLcy2fI2mUx0dHSc8WY0Gi/oWB4eHnKx4enpKRcfer0erVYrj/x8/WOtVotarUalUo247d69m8suu0wuToZHo+x2OzabTf749M9tNps8smU2m8/58eDgoHyTJAmr1Upvby+9vb1j/pnVajVeXl5yQeXj44Ovry++vr74+fnJH/v6+qLRaC4oT6VNtr4tCKcT/Vs5ImtlibyV5ay8RfHkQKGhoc5ugltx1bwlSaKzs5Ompiaam5tpamqiqamJ7u7uc36dXq8fVQScfvP29paLJEcXB9OmTWPKlCkOPeaZSJKE2WyWpxx+/dbf309fX5/87/BtcHAQu90+5oLL29t7RFHl5+dHQEAA/v7+BAQE4Ofnh1rtvCWhrtq3XZG/vz9Lly7F39/f2U1xG6J/K0dkrSyRt7KclbdY8+TANU89PT0OPZ5wbq6St9FopLa2llOnTlFfX09zczNms/mMz/X29iYoKOiMN09PT4Vb/pWJnrXNZhtVVJ0+pfH0KY82m+28x1Or1fj6+hIQEDCiqDr98/EcvZroeU82Im9libyVI7JWlshbWY7MW6x5cpL9+/ezZs0aZzfDbUzUvLu7u6msrKSqqora2tozXodAq9USFhZGREQE4eHh8r8Gg0H5Bo/BRM16mEajwc/P77wnPEmS6O/vH1VY9fT00NXVRVdXF93d3dhsNrq7u+nu7j7jVqhqtRp/f/8zFrmBgYFotZd2ap3oeU8mFouFzz//nHXr1rn9RiVKEf1bOSJrZYm8leWsvEXxJAiXyGq1Ul1dTWlpKRUVFbS3t494XKVSER4ezpQpU4iJiSEyMpKQkBCnTgtzVyqVSl4TFRERccbnSJJEb2/viGLq6x9bLBY6Ozvp7OykoqJi1Pfw8/MbUVCFhIQQEhJCYGCg+H+fYAoLC7nlllvEbnuCIAjCmIjiyYGmT5/u7Ca4FWfmbTabKSkpobi4mPLy8hG73KlUKqKjo0lISCAuLo7o6Gg8PDyc1lZHcKe+rVKp5HVkZ1rnNVxcnW1zD5PJJI9aVVVVjfhajUZDcHCwXEydftPr9fLz3Clvwf2I/q0ckbWyRN7KclbeonhyoP7+fmc3wa0onbfNZqOiooLCwkKKi4uxWCzyY76+vqSkpJCcnExcXNyEnX53sUTf/srpxdXUqVNHPDY8LfD0Yqq9vZ22tjba29uxWCy0tLTQ0tIy6rh+fn6EhoYSEhLC4OAgc+fOJTQ01OULb0H4OnE+UY7IWlkib2U5K29RPDlQZWUlycnJzm6G21Aq766uLnJzc8nLyxuxk1tQUBDTpk0jLS2NqKioSXONoTMRfXtsTp8W+PVRK0mS6O7upq2tbcSttbWVvr4+ee1VRUUF5eXlFBQUABAQEEBYWBhhYWGEh4cTFhZGSEjIhN9uXRDORpxPlCOyVpbIW1nOylsUT4JwBpIkUVNTw759+ygrK2N4U0pvb2+mT59OVlbWpC+YBMdSqVTybn1JSUkjHhsYGBhVTPn6+mI0GuX1VqWlpfLz1Wo1wcHBckEVERFBREQEvr6+ok8KgiAIwjgSW5U7cEtJq9V6ybtsCWM3HnlLkkRpaSlffPEFdXV18v0JCQnMmTOH1NRUt3zHX/RtZQ3n3d/fT2trK83NzfJ0v5aWlrNeGHB4I4zhW2RkJEFBQWKTinMY3llxvLefF74izifKEVkrS+StLEfmLbYqd5IDBw6wePFiZzfDbTg676qqKrZs2UJDQwMwtJ34rFmzmD9/PiEhIQ77Pq5I9G1lDeft5eXF1KlTR6ytkiQJo9EoF1TDF1pua2ujr6+PioqKETsA6nS6EaNTkZGRhIeHiz/wX9JoNBQVFYn+rSBxPlGOyFpZIm9lOStv8dfTgfr6+pzdBLfiqLzb2trYtGkTZWVlAOj1eubNm0dOTg4+Pj4O+R6uTvRtZZ0r7+Gt0P38/EbM9R7ejKKpqWnEzWKxUFdXN2IkVa1WEx4eTlRUlHwLCwtzy5GXsrIy7rvvPt5++22xVkEh4nyiHJG1skTeynJW3qJ4cqCgoCBnN8GtXGreVquVPXv28MUXX2Cz2VCr1cyZM4dly5bh7e3toFZODqJvK+ti8tbpdERHRxMdHS3fZ7fb6ejoGFFMNTY20tfXR2NjI42NjeTm5gJDI60REREjCip3uB6Z0Wjk6NGjGI1GZzfFbYjziXJE1soSeSvLWXmL4smBMjIynN0Et3IpeTc2NvL+++/T2toKQHJyMldccQXBwcGOat6kIvq2shyVt1qtlq8jNXw9DEmS6OnpoaGhgfr6ehoaGmhoaGBwcHDUCJVerycyMpKoqChiYmKYMmWKQ9eJCu5JnE+UI7JWlshbWc7KWxRPDrRnzx7WrFnj7Ga4jYvJW5IkDh06xObNm7HZbPj4+LB27VoyMjLELmXnIPq2ssYzb5VKhb+/P/7+/qSnpwNDvxednZ1yIVVfX09jYyNms5mamhpqamrkr/fz8yMmJka+RUZGotPpxqWtwuQkzifKEVkrS+StLGflPe7F09NPP82f/vQnGhsbmTZtGuvXr2fJkiVnfO7OnTtZvnz5qPtPnjxJWlqa/Pl7773H//7v/1JRUUFiYiKPPfYYN9xww7j9DMLkYLFY+PDDDzlx4gQAaWlpXHfddXh6ejq5ZYLgXCqViqCgIIKCguQRKrvdTnt7Ow0NDfKIVHNzMz09PRQVFVFUVAQMjW5FRETII1MxMTEEBASINyMEQRCESWlci6d33nmH+++/n6effppFixbx3HPPsXbtWoqKioiNjT3r15WUlIyYGhIaGip/vH//fm6++WZ++9vfcsMNN/DBBx9w0003sWfPHubPnz+eP855Db+LKyjjQvLu6+vjrbfeoq6uDo1Gw+rVq5k3b554gTdGom8rayLkrVarCQ0NJTQ0lBkzZgBDb0CcXkydOnWK3t5eecTq0KFDAPJFgmNjY4mNjSUyMnLCbkYxZcoUfvOb34y6qLEwfiZC/3YXImtlibyV5ay8x/U6T/Pnzyc7O5tnnnlGvi89PZ3rr7+exx9/fNTzh0eeOjs7CQgIOOMxb775Znp6evj888/l+6644goCAwN56623xtSu8brOU2VlJQkJCQ47nnBuY827p6eHV199lY6ODjw9PbnllltGbP0snJ/o28pylbwlSaK7u1supurq6mhsbMRms4143vBmFsPF1JQpU/Dw8HBSq0dzlbwnC5G3ckTWyhJ5K8uReU+I6zyZzWZyc3P55S9/OeL+1atXs2/fvnN+7axZsxgcHCQjI4NHHnlkxFS+/fv388ADD4x4/po1a1i/fr3D2n6xysrKxC+NgsaSd29vL6+99hodHR0EBARw6623uv01my6G6NvKcpW8VSoVAQEBBAQEyNP9rFYrjY2N1NbWyreBgQGqq6uprq6Wvy48PJypU6fKxZSzNqLo6Ojg2Wef5Ze//KXYKUshrtK/JwORtbJE3spyVt7jVjy1tbVhs9kIDw8fcX94eDhNTU1n/JrIyEief/55Zs+ejclk4vXXX2flypXs3LmTpUuXAtDU1HRBxwQwmUyYTCb5856enov9sQQXYjabeeONN2hra8Pf35877rjjrCOagiA4hlarZcqUKUyZMoVFixYhSRJtbW0jiqnOzk556/SDBw8CEBgYyNSpU4mLiyMuLk6x39Xq6mr+9Kc/ccstt4jiSRAEQTivcd8w4utrSiRJOus6k9TUVFJTU+XPFyxYwKlTp3jiiSfk4ulCjwnw+OOP8+ijj466f+vWrXh7e7NixQoOHTpEb28vgYGBTJs2jT179gBDmwrY7XZKS0sBWLZsGfn5+fKwXnZ2Njt37gQgLi6O2tpaTp48CcDixYspKiqio6MDb29vcnJy2LZtGwAJCQl4eXlx/Phx+WctLy+ntbUVg8HA0qVL2bx5MwBTp04lICCAgoICAObNm0dtbS1NTU3odDpWrFjB5s2bkSSJmJgYwsLCOHr0KACzZ8+mqamJ+vp61Go1l19+Odu2bcNqtRIZGUlMTAyHDx8GYObMmXR0dFBbWwsMjejt3LkTk8lEWFgYCQkJHDhwAIDMzEx6e3upqqoCYNWqVezbt4/+/n6Cg4NJS0tj7969wNBWkmazmfLycgCWL1/OkSNHMBqNBAQEkJWVxe7du+U+AEPr3gCWLl3KsWPH6OrqwtfXlzlz5rBjxw45l1OnTskL1xctWkRxcTHt7e14enrS2NjInj17MBgMXHnllfT398sv1HJycqisrKSlpQUPDw8uu+wyNm3aBEBsbCxBQUHk5+cDMHfuXHk6klarZeXKlWzZsgW73U50dDQRERHytXKys7NpaWmhrq4OlUrF6tWr2b59OxaLhYiICGJjY+V1ITNmzKCrq0veyWz16tXs3r2bwcFBQkNDSUpKYv/+/QBMnz6d/v5+KisrAVi5ciUHDhygr6+PoKAgMjIy5D6bnp6O1WqVL/p72WWXcfToUXlIeubMmezatQuAlJQU1Go1xcXFcp89ceIEnZ2d+Pj4MG/ePLZv347NZqO8vByDwSBvuLFw4UJKS0tpa2vDy8uLRYsWsWXLFvl3wc/Pj2PHjgFDU3irq6tpbm5Gr9ezfPlyOe8pU6YQEhJCXl4eAHPmzJHX0Gg0GlatWsXWrVux2Wzy9YeOHDkCDI1St7W1cerUKbnP7tixA7PZTHh4OHFxcfL/eVZWFj09PfLox+WXX87evXvp7+8nJCSElJQUeVR82rRpDA4OUlFRAeCwc0RycjJarfa85wibzUZZWdmkO0f09/fj5eWFwWDAy8uLTZs20dLSglarpaOjQ/4+8fHx9PT0EBAQQFJSEqtWrZL70oWcI5KSktDr9Wc8R3h5ebFw4UL5/7ympoaIiAgKCwsBcY640HMEQGJi4pjOETabTV7bLM4RF3eOgLG9jrDZbGzatMllzhFKv44Yyzli69atwNB5ycfH55zniOG8xTni0s4RMLbXEcN5O+IcMdz+sRi3NU9msxkvLy/efffdETvh3XfffeTn58uhn89jjz3GG2+8IZ9IYmNjeeCBB0ZM3XvyySdZv379iO10T3emkacpU6Y4fM3T/v37WbBggcOOJ5zbufI+cOAAGzduRKPRcPvtt59zgxLh/ETfVpa75W0ymTh16pQ8ta+hoQG73T7iOQEBAaNGphyx4cvRo0eZPXs2ubm5ZGdnX/LxhPNzt/7tTCJrZYm8leXIvCfEmie9Xs/s2bPZsmXLiOJpy5YtXHfddWM+Tl5eHpGRkfLnCxYsYMuWLSOKp82bN7Nw4cKzHsPDw0ORxcliOqCyzpZ3R0eH/E7RmjVrROHkAKJvK8vd8vbw8CApKYmkpCRg6M232tpaampqqK6upr6+nq6uLrq6uuR3zv39/YmLiyMhIYH4+Hhx8V4X4m7925lE1soSeSvLWXmP67S9Bx98kNtuu405c+awYMECnn/+eWpra7n77rsBeOihh6ivr+e1114DYP369cTFxTFt2jR5vcp7773He++9Jx/zvvvuY+nSpfzhD3/guuuuY8OGDWzdulUeanQmf39/ZzfBrZwpb0mS+Pjjj7FarSQkJDB37lwntGzyEX1bWe6et16vH1VMnT4yVV9fT3d3NwUFBXIxFRISQkJCAgkJCcTFxWEwGMb0vby9vZk+fTre3t7j9vMII7l7/1aSyFpZIm9lOSvvcd2qHIYukvvHP/6RxsZGpk+fzpNPPimvX7rjjjuorq6W5/r+8Y9/5Pnnn6e+vh5PT0+mTZvGQw89xJVXXjnimP/5z3945JFHqKyslC+Su27dujG3aby2Kh8cHBzzH2zh0p0p7/Lyct544w20Wi333HMPgYGBTmrd5CL6trJE3uc2XExVVVVRWVlJY2Mjp/8pU6lUREVFycXUlClT0GrP/l6hyFtZIm/liKyVJfJWliPzvpDaYNyLp4lovIqnTZs2sWbNGocdTzi3r+ctSRIvvvgi9fX1LFy4kNWrVzuxdZOLK/ZtSZLkzWRc7WLIrpi3Mw1vhV5ZWUllZSXt7e0jHtdqtcTGxpKQkEBiYiIREREj+oTIW1kib+WIrJUl8laWI/OeEGueBEFpp06dor6+Hp1Ox6JFi5zdHOECSZLE4OAgJpMJs9k84mOTyYTFYsFqtZ7xZrfbkSRJ/nf449MNF1GnF1MajeasN61Wi06nQ6fTodfr0Wq16PV6+b7h+4f/dbUCbTLx9PQkPT1dvtp8d3e3PCpVWVlJb2+v/PHwLquJiYkkJiZiNBq54oorxIYRgiAIwpiI4smBUlJSnN0Et/L1vIe3BBXrFxzPEX17uDgyGo309PTQ399Pf38/fX19DAwM0N/fP6rgcaThoup0VqvVIcdWq9V4eHhgMBjkf8/0saenJx4eHqjV6nMeT5xLLs3wVrozZ86UrzM1XDxVVVXR19fHsWPHOHbsGI2NjQAcPHiQoKAgpkyZgkajcfJPMLmJ/q0ckbWyRN7KclbeonhyoPO9IBIc6/S8rVarfN2AmTNnOqlFk9eF9m2LxUJnZycdHR10dXXJBZPFYjnn16lUKvR6vbxDpoeHh/z58OjP128ajQa1Wo1arUalUo34eHg0aLgoO72Astvt2Gw2+d+v36xWKxaLBbPZjMVikW9msxmr1SrfPzzyNTAwwMDAwJiy9PT0xMvLS/736zcxiuU4KpWK0NBQQkNDmT9/PjabjVOnTlFRUUF5eblcPOXn58vXD4mPjycpKYnExERx4dxxIP5WKkdkrSyRt7KclbconhyouLiYqVOnOrsZbuP0vOvr6zGZTHh7e4utycfBufq2JEl0d3fT0tJCe3s7HR0dGI3GMz5XpVLh4+Mj375eNBgMBpd7199ms2EymeRphoODg2f92GQyYbfb6evro6+v76zHrKqqIisrCy8vL3x9ffH29sbX1xcfHx+8vb3R6XQK/oSTi0ajka8VtXLlSvbs2cPzzz9PcnIykiTR19dHSUmJfIHNoKAgkpOTSU5OJi4u7pwbTwhjI/5WKkdkrSyRt7Kclbf4KyBMCsNXhI+LixPv2iugr6+PxsZGWlpaaGlpYXBwcNRzvL29CQwMJDAwED8/P3x9ffH19XW54uh8NBqNXPydj91uZ3BwUJ6yODAwMGLaYn9/P4ODg9jtdoxGI0ajkebm5lHHMRgMI4rQ4Zufnx96vX48fsxJa/j/bcWKFcyaNYumpiZ5VKq2tpaOjg4OHjzIwYMH0el0JCQkyMWU2JZYEATB/Yjd9hy4215fX59Ya6Og0/N+++23KS4uZu3atcyfP9/JLZt8ent7sdls1NXVUVdXR2dn54jHtVotISEhhIaGEhQURFBQkCIXpp6MbDabvFtcX18fvb29I24mk+mcX+/p6SkXq/7+/vK/BoNBvLFwBoODg5SWlpKSkjJqy1uTyURVVRVlZWWUlZWNuiBjeHg4KSkpJCcnExMTI6bsjJH4W6kckbWyRN7KcmTeYrc9Jzlx4gTz5s1zdjPcxul5d3R0ABAcHOzMJk06g4ODVFdXs2PHjhHXzFKpVISEhBAREUFYWBhBQUGTbkTJWTQaDdXV1Wc9l5jN5lEF1fBteDRrYGBg1IiVXq/H19cXPz8//Pz88Pf3x9/f3+3XWBkMhrNeK8TDw4O0tDTS0tKQJInm5mbKysooLS2lrq6O5uZmmpub+eKLL/D09CQpKYnk5GSSkpLGNBLprsTfSuWIrJUl8laWs/IWxZMDff3deGF8nZ738MfioriXTpIkWlpaqKiooK6uDrvdTkdHB8HBwURERBATE0NUVJS4EOA4Ote5RK/Xy6N7X2exWOjp6Rl16+3txWw2097ePuoaSHq9Hn9/fwICAuSbn5+f26yrqqqq4pe//CUvvfQS8fHxZ32eSqUiIiKCiIgIlixZQn9/P+Xl5ZSVlVFeXs7AwACFhYUUFhaiUqmIjY0lNTWV1NRU8abO14i/lcoRWStL5K0sZ+UtiicH8vHxcXYT3Mpw3pIkybu4iRf0F0+SJBobGzlx4sSIF9jBwcHo9XquueYat3lB7WwXey7R6XQEBwePerFus9nkHQ+NRiPd3d10d3djNBoxm820trbS2toqP394Y4/TC6qAgIBJOUrV2dnJjh076OzsPGfx9HVeXl5kZWWRlZWF3W6nrq5OHpVqbm6mpqaGmpoaNm/eTGhoqFxIRUdHu/30PvG3Ujkia2WJvJXlrLzFmicHrnmyWCzixaWChvO2WCw89thjADz00ENirc1FaGpq4tixY/L0R41GQ3x8PImJiQQGBoq+rTCl8rbZbPT09NDV1UV3dzddXV10dXWdcQMQGDnqFRQURGBgoMsXVEePHmX27NkOvUhuV1cXpaWlFBcXU11dPeL6Zd7e3nIhlZCQ4Ja/V+J8ohyRtbJE3spyZN5izZOTbN++nTVr1ji7GW5D5H3p+vr6yMvLo66uDhja+CEpKYnU1FQ8PT3l54mslaVU3hqNRt4R8XQDAwMjiqmuri56enowm800NTXR1NQkP9dgMBAYGCgXU0FBQXh6erp0QXWpAgICmDdvHvPmzWNwcJDy8nKKi4spKyujr6+Po0ePcvToUXn3vrS0NFJSUtxmobk4nyhHZK0skbeynJW3KJ4Elzd8oVSbzcbAwIAYeRoDSZKoqKggPz8fq9WKWq0mKSmJadOmifwEPD098fT0JCIiQr7PZrPR3d1NR0cHHR0ddHZ20t3dzeDgII2NjfLFZmGooAoKCpKnEAYFBbntFuoGg4Hp06czffp0bDYbNTU1lJSUUFxcTHd3t3xNqeF1UhkZGaSlpYlt0AVBECYoUTw5UGJiorOb4FaG81apVHh5eWE0GhkYGCAgIMC5DZvgzGYzhw4dkkebQkNDmTNnzjlfrIm+rayJmLdGoxm1UYXVaqWrq4vOzk46Ozvp6Oigp6eHwcFBGhoaaGhoAIZ+R/38/AgODiYkJITg4GD8/PwmxOhUZGQk9957L5GRkeP+vTQaDQkJCSQkJHDFFVfQ3NwsF1KNjY3yOqnPP/+c6Oho0tPTSU9Pn3QbTkzE/j1ZiayVJfJWlrPyFsWTA4nNCpR1et5+fn4YjUY6OjoUeRHkqoxGI1988QU9PT1oNBqysrJISUk574tY0beV5Sp5D1/fKyQkRL5vuKDq6OiQd/fr7e2VN6morKwEhtZPnV5MBQcHO2WtQGRkJP/zP/+j+Hnj9N37li1bRldXF8XFxZw8eZLa2lrq6+upr69n69athIWFyYVUeHj4hCg6L4Wr9O/JQGStLJG3spyVtyieHOjEiRPExMQ4uxlu4/S8w8LCqK+vH7FjmDBSV1cXO3fuZHBwEC8vLxYtWjTmd7RF31aWK+d9poJqYGBALqTa2tro7OzEbDaPmO43PDoVGhoq35S4VlJPTw///Oc/uffeex26gdCFCggIICcnh5ycHHp7e+VCqqqqipaWFlpaWti1axdBQUFyIRUdHe2ShZQr929XI7JWlshbWc7KWxRPwqQQFhYGMGIhu/CVnp4eduzYgclkIjAwkKVLl47YEEIQxpOnpycxMTHyH7nh9VPDxdTXR6fKy8uBoW1oTy+mfHx8HF4slJeX88gjj7B27VqH7bZ3qXx8fJgzZw5z5sxhYGCA0tJSTp48SXl5OR0dHezdu5e9e/fi5+fHtGnTmDZtmssWUoIgCK5GbFXuwHcajUYjvr6+DjuecG6n533q1CleeuklPD09+fnPf+7211E53eDgIFu2bKGvr4/g4GCWLl16wZtCiL6tLHfMe3h0aviaU52dnXz9z5Onp+eIYsrf3/+SC4bx2Kp8vJjNZsrLyzl58iSlpaWYTCb5MX9/f7mQioqKmtCFlDv2b2cRWStL5K0sR+Yttip3ktLSUmbPnu3sZriN0/OOjo7Gw8ODgYEBmpqaiIqKcnLrJgZJkjh48CB9fX34+vqyZMmSi9pNT/RtZblj3l8fnbJYLLS1tcnFVHt7OwMDA9TW1lJbWwuAh4cHoaGhhIWFER4ePmE2oRgver2ejIwMMjIysFqtlJeXc+LECUpKSuju7mbfvn3s27ePgIAAuZCKjIyccJm4Y/92FpG1skTeynJW3qJ4cqC2tjZnN8GtnJ63Wq0mLi6OkpISysrKRPH0pfLychobG9FoNCxatOiiF1eKvq0skTfodDoiIyPljRysVisdHR20trbS0tJCe3s7JpOJuro6eedIg8FAeHi4fJvM103SarWkpaWRlpaGxWIZUUh1dXXJU/sCAwPlQioiImJCFFKifytHZK0skbeynJW3KJ4cSInFzcJXvp53eno6JSUlFBYWsnTp0gnxIsGZTCYThYWFAMycOfOStnAXfVtZIu/RtFotYWFhhIWFMW3aNGw2G52dnbS2ttLU1ERbWxuDg4Pydt8Avr6+8qhUWFjYGd888PDwkEeuXZVOp5M3kbBYLJSVlXHixAlKS0vp7Oxkz5497Nmzh6CgIDIzM8nMzByxoYfSRP9WjshaWSJvZTkrb7HmyYFrnux2u1hro6Cv5z04OMgTTzyB1Wrl7rvvHnGBT3eUn59PcXExgYGBXH755ZfUN0XfVpbI+8LZbDba2tpoaWmhubmZjo4O7Hb7iOcEBATIo1JhYWFotUPvH07WvM1m84hCymq1yo9FRkaSmZnJ9OnTFd9lcLLmPRGJrJUl8laWI/O+kNpA/A870JYtW5zdBLfy9bwNBgMpKSkA5ObmOqNJE4bFYpGvp5OZmXnJJxfRt5Ul8r5wGo2G8PBwMjMzWbVqFTfccANLly4lJSVFHnXt6uqipKSE3bt388EHH7Bz505KSkr46KOPRm1OMRno9XqmTZvGTTfdxM9//nPWrVtHcnIyarWaxsZGNm/ezJNPPsmrr75Kbm4uAwMDirRL9G/liKyVJfJWlrPyFtP2hEllzpw5FBUVUVBQwMqVK932gnX19fWYzWZ8fX3FRYMFt6TT6YiKipLXPw4ODtLc3ExLSwtNTU309fXR1NTEwYMH+c1vfsPjjz9OTk4OkZGRhIeHo9frnfwTOJaHhwdZWVlkZWXR19dHUVERx48fp6amhurqaqqrq/nss89ISkoiMzOTlJSUSZeBIAiCI4jiyYHi4uKc3QS3cqa84+PjCQsLo6WlhaNHj7Jw4ULlGzYB1NfXAxAbG+uQtV+ibytL5O14BoOBqVOnMnXqVCRJwmg00tjYiNFopL+/n76+PiorK6msrEStVhMcHExERASRkZEEBgZOqjWU3t7ezJ07l7lz59Ld3c3x48cpLCykqamJkpISSkpK0Ov1pKWlkZWVRUJCgkOnIon+rRyRtbJE3spyVt6ieHIgZ16d3h2dKW+VSkVOTg4fffQR+/btY+7cueh0Oie0znkkSaK1tRXAYaNOom8rS+Q9vlQqFX5+fvj5+dHX1wdAdnY2oaGhNDU10dPTI2+RXlhYiIeHB5GRkURFRRERETGpRmT8/f1ZtGgRixYtoqWlRS6kOjs7OXbsGMeOHcPHx4esrCxmzJhBeHj4JX9P0b+VI7JWlshbWc7KWxRPDnTs2DExRUpBZ8t7xowZ7N69m66uLg4fPux2o0+Dg4MMDg6iVqsvaYe904m+rSyRt/JCQ0Pli+T29fXR2NhIU1MTzc3NmEwmeWqbWq0mNDRUnhI4mS6IGRYWxooVK1i+fDn19fUcO3aM48eP09vbK19DKjw8nBkzZpCZmXnRP7vo38oRWStL5K0sZ+Utiidh0tFoNCxbtowNGzawZ88eZs+e7dLbEF+o4XfSvby85N3EBOeQJDAaob196NbVBYODo28mE6hUX91KSmIpLQW1GnQ68PIaunl6fvWxlxf4+kJQEHh7D32d4Bje3t4kJSWRlJSEzWajvb2dhoYGGhoa6Onpobm5mebmZvLy8vDz85MLqZCQkEmx05ZKpZIvWLxmzRrKy8spKCigpKSE5uZmNm/ezJYtW0hMTGTGjBmkpaW53Qi/IAjuS2xV7sAhv66uLoe90y+c37nyttvt/OMf/6C9vZ1FixZx+eWXK9s4J6qrq2PPnj0EBwc77OcWffvM2tqguBhqakbeTp0aeqyjA07bHXrcaLUQGDjyFhoKERGjb5GREBAgiq1hw6MqCxcuxMfH57zPNxqNciHV2to6Yjt0vV5PVFSUfHHfyTS9D2BgYIATJ05QUFDAqVOn5Ps9PDzIyMhgxowZTJ069bzrw8T5RDkia2WJvJXlyLwvpDYQb0s7UHV1NTNnznR2M9zGufJWq9WsWbOGN998kwMHDjBr1iynXhRSScPvhzjyHXB379uSBKWlcOAAFBbCsWND/zY1je3rPTzsBATY8PW14eFhQ6+3j7jpdJL8fex2FQMDgxgMBux2FVarisFBNSaTWv7XZFIzMKCmr0+DxaLGaoXW1qHb2NoDMTEwdSrExg79e/rHU6YMPccd+Pj4EBYWNqbCCYYuvJuamkpqaipms5nGxkYaGxtpaGjAbDaPmt43PILj6ek5zj/J+PP09GTOnDnMmTOH9vZ2jh07RkFBAV1dXeTl5ZGXl0dAQAAzZ84854W53f18oiSRtbJE3spyVt6ieHKg5uZmZzfBrZwv75SUFJKTkykrK2Pjxo185zvfmVQ7Zp3NcNH09QuEXgp37NuVlfDpp7BjB+zZc/bCJCbGSlSUmYiIQcLCBggPNxERYSYw0IKfnxV/fysGw7kH+FUq1Yi+WV5eTnJysvy5JEln/P+UJDCZ1HR3azAatRiNWnp6NPT0aOnq0tLerqe9XSffOjp09PRoMZmgomLodub2SMTGqkhJgeTkodvwx3FxQ1MJJ4u6ujp+85vf8NRTTxETE3NBX6vX6+Ud/Ox2O21tbTQ0NNDY2Eh3d7c8vS83N5eQkBBiYmKIjo6eFOukgoODWb58OZdddhm1tbUUFBRw4sQJurq62LlzJ7t27SIhIYGZM2eSnp4+YgqxO55PnEVkrSyRt7Kclbconhxosk3RmOjGkvcVV1xBZWUl5eXlHDt2jBkzZijQMucaXt81ODjosGO6S98+eRJefx02bICiopGPeXhIzJhhJiWlj6lTjSQk9JKQMICX18iiRq1W4+HhgV6vR6/3QavVotPpRvyrVqtH3U4vnoxGI7Nnzx5x3OECavjf4ZvNZsNms2G1WuWPhz+3WHq+/NeCxWLBbrdjMqlob9fR3OxBU5OepiY9jY0eNDfraWryoLFRj8mkkacgfv0ahFqtRHy8Si6opk8fuk2bBmMcvJlQWlpa+OCDD3jkkUcuuHg6nVqtJiwsjLCwMGbOnInRaKSuro66ujra29tpa2ujra2N/Px8AgIC5BEpf39/l35TR6VSyQXk2rVrOXnyJHl5eVRVVVFRUUFFRQUGg4HMzExmzZo1KaczTmQia2WJvJXlrLzFmiexreSk98UXX7Bt2zYMBgP33HPPpHjX91wGBgbYsGEDarWab3zjG2g0Gmc3aUIbGBgqmF54AY4c+ep+jUYiJ8fKggXdpKe3kZJiRK//6nSpVqvx8vKSbwaDQS6aJuKL4eGia7iQMpvNZ7lZ6OjQcuqUgVOnDNTWGuSPT53ywGQ6e3+Kj4fMzK8KqszMoQJrIr+eOHr0KLNnzyY3N1febc/R+vv7qa+vp66ubtQ6KV9fX6Kjo4mJiSE4OHhC9p2L0dnZSX5+Pvn5+XR3d8v3h4eHM2vWLLKysvDy8nJiCwVBEL5yIbWBKJ4cWDxt2rSJNWvWOOx4wrmNNW+73c6LL75IQ0MDqamp3HLLLZPmBcqZSJLEBx98gNlsZvXq1QQFBV3yMSdj3zYaYf16+NvfvpqSp9VKrF5tZfXqDrKyGvD2tsjP1+l0+Pr64ufnh4+PD56enuPWj5yZt81mw2QyYTKZGBwc/Nq/ZlpatHJRVVPjSWWlJ+XlnnR0nLlC0mol0tJUzJwJs2cP3WbNmjijVEoUT6czmUw0NDRQV1dHU1MTNptNfszT05MpU6YQGxs7aQopu91OVVUVeXl5FBcXY7VaKS8vl9eNZWdnO/wivMJXJuO5eyITeSvLkXmLDSME4TRqtZrrr7+e5557jpKSEo4cOcLcuXOd3axxo1KpCAoKoqmpifb2docUT5OJzQavvAIPPwwtLUP3xcZK/OAHAyxbVovB0CM/12AwEBgYSGBgIN7e3pPixez5aDQaeTTt6+x2O2azWb6W2MDAAAMDHQwMDNDWpqKy0pOKCi8qKjy/vHnR36/h+HE4fhzeeGPoOCrVUEE1XExNtIJqPHl4eBAfH098fDwWi4Wmpibq6upoaGhgYGCA0tJSSktL8fb2JiYmhtjYWIKCgly276nVahITE0lMTGRgYIDCwkLeeOMNbDYbRUVFFBUVERAQQHZ2NrNmzZr0MwMEQXB9onhyoClTpji7CW7lQvIOCwtj1apVbNq0iU2bNhEbG0t4ePg4ts65QkNDaWpqorGxccTGAxdrsvTt6mq49VbYu3fo8+Rk+MUvBpg9uwKzuR8YerEXHBxMaGio0wqmiZq3Wq3GYDBgMBhG3C9JEhaLhZycgS8Lqj4GBtro7x+gvl5DRYUXxcVeFBd7U1zsTWurnpMnh9aYnV5QpaaqmDMHFiyAhQuHpv6N96XKQkJCuOWWW5yyG6dOp2PKlClMmTIFm81Gc3MztbW11NfX09fXR0lJCSUlJXh7e8sjUoGBgS5bSHl6ejJv3jx8fHwICgoiLy9P3q1v+/bt7Ny5k5SUFGbPnk1iYqIYjXKAiXoumaxE3spyVt7jPm3v6aef5k9/+hONjY1MmzaN9evXs2TJkjM+9/333+eZZ54hPz8fk8nEtGnT+L//+78RQ3Kvvvoq3/ve90Z97cDAwKg/6GczXtP2WlpaCAsLc9jxhHO70LwlSeKtt96itLSUkJAQ7rrrrkm7uLOrq4uNGzei0Wi4/vrrL/kClpOhb3/88VDh1NMzdHHZX//axlVX1dDT0wYMjbiEh4cTHh7u9At+Toa8Yeh3zmw209fXx8DAAH19ffT399PYKHHy5FfF1HBB9XXe3hLz56vkYionZ+iiwI420fK2Wq00NTVRW1tLQ0MD1tMuFubr60tsbCxTpkxx2c0mTs/bYrFQVFREbm4utbW18nP8/f3l0SixNvniTbS+PdmJvJXlyLwnzJqnd955h9tuu42nn36aRYsW8dxzz/Hiiy9SVFREbGzsqOfff//9REVFsXz5cgICAnjllVd44oknOHjwILNmzQKGiqf77ruPkpKSEV8bEREx5naJNU+Tw8Xk3d/fz7PPPktPTw/Tpk3jxhtvdMkXH+cjSRKfffYZRqOR+fPnEx8ff0nHc/W+/dJLcNddYLcPvQh/4YUBrNYyBgcHUalU8vV4tOM9zDFGrp73+VgsFrmQGv63ocFGcbE3RUU+FBb6cPy4N319o/8/0tK+GplatGjo80v5Fe7v7+fll1/m+9///oTcwMBqtdLQ0MCpU6doaGgYsUbK39+f2NhYYmNjXWq629n6d2trK7m5uRQUFDAwMAAMjXaK0aiLN9nPJRONyFtZk3LN01/+8hfuvPNOfvCDHwCwfv16Nm3axDPPPMPjjz8+6vnr168f8fnvf/97NmzYwMcffywXTzC0puNCiiVBGObl5cWNN97IP//5T06cOEFERMRZR0JdmUqlIiEhgYKCAioqKi65eHJlb78NX56C+P734U9/6qGqqgybzYaHhwcJCQku9cJzMtDpdAQEBIy4iOq0aVYWLuyjt7eX3t4menr6KC/XcezYUDFVWOhDba0nxcVQXDy0bg0gLEzisstULFsGl10G6ekXVkwVFxdz7733snDhQkU2jLhQWq1WLpAsFsuIQqq7u5vCwkIKCwsJCQlh6tSpxMbGypcrcDWhoaFcccUVrFq1Sh6Nqqmpobi4mOLiYnk0avbs2WO+qLEgCIKjjVvxZDabyc3N5Ze//OWI+1evXs2+ffvGdAy73Y7RaBy14L23t5epU6dis9mYOXMmv/3tb0cUV183vHPUsJ6enrM+91LMmTNnXI4rnNnF5h0bG8uVV17Jxx9/zPbt2wkPDyclJcXBrXO+uLg4CgsL5evLXMqaDlft24cPwx13DH3805/Cb39rpLS0FLvdjp+fH4mJiU6foncmrpr3pdBqtfj7++Pv7w8MjZ5Onz7IqlW99PX10dvbTGOjhcJCb7mgOnHCh5YWNf/+N/z730PHCQ0dWUxlZJy5mCorG9px8eRJT2DWl/8OTel0wDLBcaHT6eRrKpnNZurr66mpqaG5uVn+Pc/LyyMyMpKpU6cSFRU1YUZTT3e+/q3VasnKyiIrK4vW1laOHj0qb3m+Y8cOdu3aRUZGBnPnziU2NnZSzh5wFHc8lziTyFtZzsp73M6qbW1t2Gy2UYvyw8PDaWpqGtMx/vznP9PX18dNN90k35eWlsarr75KZmYmPT09/PWvf2XRokUUFBScdWH8448/zqOPPjrq/q1bt+Lt7c2KFSs4dOgQvb29BAYGMm3aNPbs2SN/P7vdTmlpKQDLli2TT+J+fn5kZ2ezc+dOYOiEn5yczMmTJwFYvHgxRUVFdHR04O3tTU5ODtu2bQMgISEBLy8vjh8/DsCCBQsoLy+ntbUVg8HA0qVL2bx5MwBTp04lICCAgoICAObNm0dtbS1NTU3odDpWrFjB5s2bkSSJmJgYwsLCOHr0KACzZ8+mqamJ+vp61Go1l19+Odu2bcNqtRIZGUlMTAyHDx8GYObMmXR0dMjzztesWcPOnTsxmUyEhYWRkJDAgQMHAMjMzKS3t5eqqioAVq1axb59++jv7yc4OJi0tDT2frkqPyMjA7PZTHl5OQDLly/nyJEjGI1GAgICyMrKYvfu3QCkpqYCyNMyly5dyrFjx+jq6sLX15c5c+awY8cOYGiNSmpqKkVfXs100aJFFBcX097ejpeXFwsXLmTr1q0AxMfH4+PjQ2FhIQA5OTkEBgZy+PBh/vSnP/G73/2O/Px8AHl3q+HP586dS11dHY2NjWi1WlauXMmWLVuw2+1ER0cTERFBbm4uANnZ2bS0tFBXV4dKpWL16tVs374di8VCREQEsbGxHDp0CIAZM2bQ1dVFTU0NMPTGwu7duxkcHCQ0NJSkpCT2798PwPTp0+nv76eyshKAlStXcuDAAfr6+ggKCiIjI0Pus+np6VitVsrKyujo6MDX15ePPvqI6Oho/P39mTlzJrt27QIgJSUFtVpNcXGx3GdPnDhBZ2cnPj4+zJs3j+3bt9PW1sb8+fMxGAycOHECgIULF1JaWkpbWxteXl4sWrSILV9eUTUuLg4/Pz+OHTsGwPz586murqa5uRm9Xs/y5cvZtGkTMLTgMyQkhLy8PGDoZNjQ0EBDQwMajYZVq1axdetWbDYbUVFRREVFceTLCzLNmjWLtrY2Tp06JffZHTt2YDabCQoK5/bbMzGZNMyf38L99w9y4MBQ/9DpdFx//fXs37+f/v5+QkJCSElJkd/YmTZtGoODg1RUVAA47ByRnJyMVqs97zmira2NefPmiXPEaeeIkpISJEniiitiWbaskaqqKgYG7BiN6XzxhYbCwiDKykJpbdXw7rvw7rsAEBxsIz29lRkzOvjhD6disZzk2LEB7rxzeMQ5HTjKrbcie+mlL/jGN7KorKykpaUFDw8PLrvsMrnPTqRzRGlpKV5eXgQGBlJVVUVtbS1lZWXEx8fT1NSEt7c3CQkJ5OTkyP379HMEwGWXXcbRo0flaSsXc44ASExMHNM5oq2tjTlz5lzQOSIzM5MNGzZQWlqKTqdj27ZtfPjhhwQHB3PbbbfR0tKCRqO5oHNEeHg4cXFxHDx4EICsrCx6enqorq4G4PLLL2fv3r0T8hwBY3sdsXHjRnlUcrKfIy7mdURSUhJ6vf6iX0d8/RwxnPdEOkdc6usIZ5wjYGyvI4bzdsTriOH2j8W4rXlqaGggOjqaffv2sWDBAvn+xx57jNdff/28jXzrrbf4wQ9+wIYNG1i1atVZn2e328nOzmbp0qU89dRTZ3zOmUaepkyZItY8ubhLzdtms/Haa69RU1ODn58fP/jBDybdwuSenh4+//xzJEni8ssvJzg4+KKO44p9++9/h3vvheBgKCmB9vZSurq68PLyIj09fUJfPNgV83YGu93+5ahUL0ajkfb2XgoLDRw96sfRo74cO+Yz6qK+CQkSs2apeO+9oZ3+0tO/euzkyaFNRXJzYQLO4BuT7u5uampqqKmpoa+vT77fy8uL2NhY4uLiRkyXdIZL7d+NjY0cOXKEY8eOYbEMXYvNw8ODGTNmMHfuXEJDQx3VVJcnziXKEnkra9KteQoJCUGj0YwaZWppaTnvFtHvvPMOd955J+++++45CycYWkw6d+5cuTo+Ew8PD0XmgE/kF2OT0aXmrdFouOWWW3jppZdoa2vjzTff5Hvf+57Lrhc4Ez8/P+Li4qiqqiI/P58VK1Zc1BQXV+vbkgTD76U8+ijodD10dXWhUqlITEyc8D/PRG/fRKFWq/H19cXX15fIyEgkSSIrq59rrzViNLbQ0VHJsWMe5Ob6cfiwHwUFPlRWqvnyjVfS0123SDobf39/srKyyMzMpLW1lZqaGk6dOkV/f7+8digwMJCEhASnrY+61P4dGRnJNddcw+WXX05+fj6HDx+mvb2dQ4cOcejQIeLi4pg3bx6pqalu/7vk7j+/0kTeynJW3uO62978+fOZPXs2Tz/9tHxfRkYG11133Rk3jIChEafvf//7vPXWW1x//fXn/R6SJDFv3jwyMzN5+eWXx9Su8dptT3BNXV1dvPjii/T29pKQkMC3v/3tCblO4GL19/fz6aefYrPZWLBgAVOnTnV2k8bd3r2wePHQ+pWGBqivL6G7u5vw8HC3+PmFIZIk0d/fT09PD93d3TQ395Ob68PGjUFs2RIyaoTp6NGhC/Y+/DD85CcQFeW8tjuSzWajsbGR6upqGhoasNvtwFDxGRMTQ3x8POHh4S67k50kSVRVVXHo0CF5eicgT9GaM2cO3t7eTm6lIAgT2YXUBuN6pnzwwQd58cUXefnllzl58iQPPPAAtbW13H333QA89NBDfPe735Wf/9Zbb/Hd736XP//5z+Tk5NDU1ERTUxPd3d3ycx599FE2bdpEZWUl+fn53HnnneTn58vHdKbhebGCMhyVd0BAAN/+9rfR6/VUVlbyn//8R35xMRl4eXmRkZEBQF5e3ogprGPlan37y6nvXHEFeHhY5E1iXOXCyK6W90SlUqnw9vYmMjKStLQ0Fi+ewZ13hnP//dZzft1jj0F09FAh9etfD2084sqnBI1GQ0xMDIsXL+a6664jOzubwMBA7HY7tbW17Nq1i08++YRjx45hNBrHvT2O7t/Du4vecsst3H///SxduhRvb2+MRiM7duzgySefZMOGDWNebz2ZiHOJskTeynJW3uP69vrNN99Me3s7v/nNb2hsbGT69Ol89tln8ju/jY2NIy6K99xzz2G1Wrnnnnu455575Ptvv/12Xn31VWBolOCuu+6iqakJf39/Zs2axe7du5k3b954/ihjcvr1N4Tx58i8o6Ki+Na3vsW//vUviouL2bBhA9dff/2k2cUpLS2N2tpauru7ycvLIycn54K+3tX69pfrbsnJAaPRiCRJeHl5jflC2s7manm7Co1Gg7+/PxERQzv6fbkmXzb8eWJiP5WVnhw9quLoUfjNbyAiQuKqq1RcfTWsWgWuulO2h4cHKSkppKSk0NHRQXV1NdXV1fT391NUVERRURGhoaHEx8czZcqUcdmNcjz7t7+/PytWrGDZsmWcOHGCgwcPUl9fT15eHnl5ecTFxTF//nxSU1NddqTtQohzibJE3spyVt7jOm1vohqvaXuFhYVkZmY67HjCuY1H3iUlJbzzzjvY7Xbmzp3LlVdeOWkKqLa2NrZt24YkSSxdupSoC5iT5Gp9e/ly2Llz6BpPixfXU19fL78gdAWulrerKSuDc12d4LPPylGrjezb58+ePQEcOOBPf/9Xc+v1eonly4cKqWuvhTNc892l2Gw2GhoaqKyspKmpSZ72ptVqmTJlComJiQQHBzvsXKhk/5Ykibq6Og4ePEhRUZE8qyAgIIB58+aRnZ3tMm+qXAxxLlGWyFtZjsz7QmoDUTw5sHhqb2+/6N3MhAs3XnkXFhby/vvvy+vp1q5dO2kKqLy8PEpKSvDw8GDNmjV4eXmN6etcrW/Pmzc01WrDBsjKqqalpYXo6Giio6Od3bQxcbW8XdFX13k6ya23foc33vgX6enp8nWerFYr3d3ddHV10d5u5NAhA3v2BLBnTwD19SNfbM+ZA9/4Bqxbd+6izBX09/dTU1NDZWXliCl8AQEBJCYmMnXqVPR6/SV9D2f1756eHg4fPkxubi79/f0A6PV6Zs6cyfz58yfl75w4lyhL5K0sR+Y9YdY8uZvhPeMFZYxX3pmZmVx33XWoVCoOHTrEZ599xmR5jyErK4vAwEBMJhP79+8f85C3q/XtwMChfzs6nNuOi+Vqebui5OShzSLS0weAPNLTB8jO/uoCuVqtluDgYBITE5k7dwa33x7NE09Y+fzzct555xj33lvLzJk9qFQSR47AQw9BaipMny7x619DQcHQro+uZngr/yuvvJKVK1cSHx+PRqOhq6uL3NxcPvroIw4dOkRbW9tFnxed1b/9/PxYuXIlDzzwANdeey1hYWGYzWYOHTrE3/72N/71r39RVVU1ac73IM4lShN5K8tZeU+eLcUEwYFmzpwJwIYNGzh8+DCSJHHVVVe5/AiURqNh4cKFbN68mdbWVgoLC+WfdTIZnpFYW4u8rkHMRRculkqlkrdEj4mJISXFxNKlnXR1NVBdPcDu3QHs2BHI4cN+nDih5sSJoXVSiYkS69apWLduaDTUlZbYqFQqQkNDCQ0NZebMmdTU1FBRUUF3dzeVlZVUVlbKW57HxcWNy9qo8aLT6cjOzmbWrFlUV1dz4MABSktLKSsro6ysjMjISBYuXEhGRobYeloQhFHEtD0HTttraWkhLCzMYccTzk2JvPPz89mwYcOX14/J4rrrrpsUf0xPnTolX7l9LNuXu1rffuopuO++od32/vnPFqqrqwkICCDFReZUuVrerqyrq4uPPvqIa6+99qIuHmuxWOjq6qKzs5O6uj527/Zj585ADhwIwGT6qlqKjpb45jdV3HLLUCHliu/DSJJEW1sbFRUVnDp1Sn5DQqvVEhsbS1JSEkFBQec9zkTs3x0dHRw4cIC8vDz5wrv+/v7k5OSQnZ3tstf/m4hZT2Yib2U5Mm+x5uk8xqt4KioqkreEFsafUnkXFhbywQcfYLfbSUlJ4Zvf/KZLvct6NsPrnzQaDcuXLyckJOSsz3W1vp2fD7NmDe2IVllppKrqJDqdjpkzZ7rE6KGr5e3qHJW3zWaju7ubzs5OGht7+OILH3buDGLPnoARG07Ex0vccouKb30LXHVtuclkorq6moqKCvlSAABBQUEkJycTGxt71jeaJnL/7u/v58iRIxw8eJC+vj4ADAYDs2fPZv78+S53bciJnPVkJPJWliPzFmuenOTUqVPOboJbUSrvzMxMbrnlFrRaLaWlpbz++usMDg4q8r3H04wZM4iOjsZms/HFF1/Q29t71ue6Wt/OyoKYGOjthR07fNBqtVgsFvnF0ETnanm7sqamJv70pz855BpAGo2GoKAgEhMTWbhwBj/+cSjPPNPN1q0FPPFEKatXt2Mw2KiqUvH440P9dPp0id/9DsrLHfDDKMjDw4PU1FTWrl3LihUriIuLQ6PR0NHRwcGDB/noo48oKCg44+/cRO7fXl5eLF26VF4XFRISwuDgIHv37mX9+vV88MEHLnW9qImc9WQk8laWs/IWxZMgjEFKSgrf/e53MRgM1NbW8uqrr56z2HAFarWanJwceQOJ3bt3X9QFdCcitRqGr7/9yisq+V2k9vZ2J7ZKmIgaGhp49dVXaWhocOhx1Wo1AQEBJCQkMH/+TL7//VCefrqLLVuO8bvflbN0aSc6nZ0TJ1T87/8ObVQxdy785S9QX+/QpowrlUpFWFgYOTk5XHvttcyYMQNvb29MJhMnT57kk08+Yc+ePTQ3N7vURgxarZbs7Gzuuecevv3tbxMXF4fdbqegoIBnn32W119/fdJtLiEIwtiIaXsuNgQvOFdTUxNvvPEGvb29BAUFcdtttxE4vLWbixoYGGDLli309/cTFBTE8uXLJ8W0xPLyod3P7HbYtcuIwXASjUbDzJkzJ8W6NcExjh49yuzZs8nNzSU7O3vcv9/w1L6Ojg5OnTKyfbs/W7YEc+SIHzbb0JRSlUpi1SoVt98ON9wAY7yiwIRht9tpaGigrKyM5uZm+X5/f3+Sk5OZOnWqS55j6uvr2b9/PydOnJCLppiYGBYvXkxqaqpLTAkWBOHMxLQ9J9mxY4ezm+BWnJF3REQE3//+9wkICKCjo4MXX3zR5YfpPT09Wbp0KR4eHnR0dLB79255wfQwV+zbSUlwyy1DH//lLz4YDAZsNhstLS3ObdgYuGLewtgMT+1LSkpi6dIs7rvPj9dea+bTT/P5+c+rmTHDiCSp2LIFbr0VIiIkfvAD+OIL19n6XK1WExMTw/Lly7niiitISkpCq9XS3d3NkSNHWL9+PXl5eSOuI+UKoqOjufHGG/npT3/K3Llz0Wq11NXV8fbbb/PMM89QUFAw4Xb1FOcSZYm8leWsvEXx5EBms9nZTXArzso7KCiIO++8k8jISPr6+vjnP//J8ePHndIWRwkICGDZsmXo9XpaW1vZs2fPiBcBrtq3H3lkaArfhg0qysqGdhRsbGzEarU6uWXnNpHzliQJu92OzWbDarVisVgwm82jbhaLBavVis1mw263i+lNZ6DRaAgJCSElJYUVK6bz3//tyZtvnuL99wv4wQ/qiYoaxGhU8dJLsHQpJCVJPPooVFU5u+VjFxAQwJw5c7j22muZNWsWvr6+mM1mSkpK+Oyzz/jiiy9oaWlxqf4RGBjIVVddxf3338/ixYvx8PCgpaWFDz74gL/97W8cOnRo1BtQzjKRzyWTkchbWc7KW0zbc+C0vfz8/El5zZyJytl5m81m3nvvPUpKSgBYvnw5S5cudempG21tbezcuROr1UpkZCSLFi1Cq9U6PetL8dOfwt/+BikpEm+9VYTV2kd4ePh5t2d3pomQ9+kFks1mk4ugSymEVCoVarUatVo94mO1Wo1GoxnxmFIqKyv50Y9+xHPPPUdCQoJi3/dcBgYGaG9vp7W1nUOH9HzySQjbtweN2LFv2TK4/Xb45jeHdpV0FZIksX37drRaLY2NjfL9QUFBpKamMmXKFPnabK5icHCQw4cPc+DAAXmDDG9vb3Jycpg7dy4Gg8FpbZsI5xJ3IvJWliPzFluVn8d4FU9dXV0XdZ0Q4eJMhLztdjtbt25l3759AGRlZXHttdei1bru9aebm5vZvXs3NpuN8PBwFi9eTF9fn9Ozvljd3UNrn5qb4ac/NfGd7xSgUqlIS0vD19fX2c07I2f1bavVKo8cnW90TqVSjbh9nSRJI25jNVxUaTSaUbfxelE9Ec4lZyJJEkajkba2Nhoautm2zY9PPw3hyBE/JGkoc19fie98R8Vddw1tz+8KhvPu6emhpKSE6upqeaTb29ublJQUEhISXG5dlMViIS8vj3379tHV1QUM7Uo4b948FixYgJcTFq9N1L49WYm8leXIvEXxdB7jVTxt2rSJNWvWOOx4wrlNpLyPHDnCZ599ht1uJzY2lltuucUpfygdpaWlhd27d2O1WgkODsZsNnPVVVc5u1kX7cMPhxbeA7z2WgOpqXV4enqSkZExITePULJvS5KE2WxmYGBgVMGk0WjQarUjipeLGR06vYgaHrkaHsUavo1let9wUaXVake061JGqsxmM//+97+56aab0Ov1F32c8Waz2ejs7KStrY3S0kE2bgzm449Dqav7alRj7ly4666htX4TeTTq6/17cHCQ8vJyysvL5ctA6HQ6EhMTSU5Oxtvb21lNvSg2m43jx4+zZ88eWltbAdDr9XIRpeTPM5H+TroDkbeyHJm32DBCEBQ2Z84cvvOd7+Dh4UFtbS3PP//8iCkpriYsLIzly5ej1+tpb2+ntLTUpa9tdf318KMfDX38i19E0t3txcDAADU1NU5tl7NZrVa6u7sxGo1YrVZUKhUeHh74+PgQFBREYGAgvr6+eHl54eHhgU6nu6hi5fTRJJ1Oh16vx2Aw4OXlhY+PD35+fgQGBhIUFERQUBD+/v74+Pjg6emJXq+XC1y73Y7FYmFgYACj0UhXVxcdHR10d3fT19eHyWTCZrNd0EjX8ePHue222yb8usXh9VFpaWmsXp3Gr36l4sMPT/KPf5xk1ap2tFo7hw/DD38IkZESd98NeXnObvXYGAwGpk+fztVXX83cuXPx8/PDYrFQXFzMp59+yv79++no6HB2M8dMo9EwY8YMfvKTn3DLLbcQGRmJ2Wxmz549rF+/ns2bN7v8pS4EwZ2JkScHjjw1NjYSGRnpsOMJ5zYR825tbeWtt96io6MDrVbLNddcw4wZM5zdrIvW1dXFzp07aWtrIzIykqVLl07YqW7n098/9M58URHMnm1j/fo89Ho78fHxhIaGOrt5IyjRt00mE729vUiShFqtxmAwYDAYJux6E0mS5LVXVqtVvp3pT5harUar1aLT6eRRqrMVfEpvVe5Idrud7u5uWltbqa7u4+OPg/nww7ARo1Fz5gyNRn3rWxNnNOp8/VuSJBobGykuLh6xO2Z4eDgZGRmEhYW51NpSSZIoKytj586d8vXEtFotc+bMYdGiReN6Tp2IfycnM5G3shyZt5i2dx7jVTyVlJSQmprqsOMJ5zZR8x4cHOS9996jrKwMgHnz5rFmzZoJOT1sLHp6enj33Xfx9vbGYDCwePFiQkJCnN2si1JeDvPmQWcnrFvXz//8z3E0GjWpqakTqigc775tNpsxGo1IkoRer8fHx2fCFk3nMtaCSqVSycXUcEE1/OLblYun05nNZlpbW2ltbWPfPj0ffhjGjh2BWK1D/6/+/hLf/76Ke+6BxETntvVC+ndHRwclJSWcOnUKu90OQHBwMBkZGURFRblcEVVeXs6uXbuoq6sDvroY7+LFi8flupMT9e/kZCXyVpYj8xbT9pykurra2U1wKxM1b4PBwLe//W0uu+wyAA4dOsQ///lPl7umyTA/Pz/CwsIIDAxkcHCQnTt3yn/4XU1SErz7Lmg08P77Xrz8cjJ2u52ysrIJNS1xPPu23W6XR5wMBgO+vr4uWTjBV0WRh4cH3t7e+Pv7y9P+vL290ev1qNVqJEnCYrHQ398vX6C2p6fnjOu8XJVeryc6OpoZM7L4znei+Mc/Ovj00wJ++tNaYmIG6e5W8eSTkJwscc01sGWL864bdSH9OygoiAULFnDVVVeRnJyMRqOhvb2dL774go0bN1JdXS0XVROdSqUiOTmZO++8k9tuu40pU6ZgtVo5dOgQf/3rX/n000/p6elx6PecqH8nJyuRt7Kclbdr/sUUhAlOpVJx2WWX8a1vfWvEOihXvaCuXq9nxYoVREZGYrVa2bt3L6WlpS51bZZhK1fCc88Nffz884H8+99TsVqtlJSUuMU1OkwmE3a7Ha1Wi7e3t0u9cz8WKpUKnU6Hp6envJYqMDAQHx8fPDw85GLKbDbT19cnv6kxvGbKVV6In41KpcLf35/k5GSWLcvgZz8bWhv15JMlLFjQhSSp+OQTWL0aMjLg6afBFZbfeHt7M3v2bK6++mrS09PR6XR0d3dz4MABPvvsM8rLyyfcBWrPRqVSkZiYyPe//31uv/12pk6dis1m4/Dhwzz11FNs2rRJ3vJcEISJR0zbc+Awud1ud9l3cF2Rq+Td3t7O22+/TWtrK2q1mlWrVrFgwQKXetE6nLXdbic3N5eKigoAEhMTyc7OdskpiX/8I/ziF0Mf/+pX9Vx3XT2enp6kpaU5fYvk8ezb3d3dWCwWfHx8nHr9GWcZnupnsViwWCyYTCZMJpM8SjU8mqXX60dsVuHK7HY7HR0dNDc3U1Rk4z//CeOTT0Ll60b5+X01pS8pSZn2XGr/NpvNlJWVUVpaislkAsDT05PU1FQSExOd/jt8oaqrq9m+fTu1tbXA0G6DOTk5LFy4EE9Pz4s+rqv8nZwsRN7KcmTeYs3TeYxX8fTFF1+wZMkShx1PODdXyttsNrNhwwZOnDgBQHJyMjfccIPLbGd+etaSJFFcXMyxY8eQJImQkBAWLVp0SX/gneWXv4Q//GHo4//5n3q+8Y16vLy8SE1NdeqLr/Hs252dndhsNvz9/V3uBeZ4kCSJffv2MXPmTCwWyxm3ax8upM618YQrkCSJvr4+mpubOXWqm08+Cebdd8OorR363VWpJK67TsXPfw4LF45fOxzZvy0WC1VVVRQXF9Pf3w8MjZSnpqaSkpLiUn1ckiQqKyvZvn079fX1wNB1ohYuXEhOTg4eHh4XfExX+js5GYi8leXIvMWaJycZPnELynClvPV6PTfeeCNXX301Wq2WsrIynnnmGZeZH3161iqVivT0dJYsWYJer6etrY0tW7a41FbCwx5//KvRpz/+MZq33oqmv7+f4uJi+d1sZ3Clvu3qysrK+PGPf0x9fT0BAQEEBgbK66VUKhU2m42BgQG6u7vp7Oykr68Pi8XiklNWVSoVPj4+JCYmsmhRJg88oOX994tZv/6rKX0ffgiLFg0VTx98AOMxE86R/Vun05GSksJVV13FvHnz8PX1xWw2U1hYyMcff0xRUREWi8Vh3288DU/n+8EPfsC3vvUtwsPDMZlM7Nixg/Xr17N3794LnlosziXKEnkry1l5i+LJgVx1BzJX5Wp5q1Qq5syZww9/+ENCQkIwGo3885//ZNeuXRN+ncWZso6KiuLyyy/Hz8+P/v5+tm3bRlVVlRNad/FUqqEC6pFHhj5fvz6af/wjjr6+AYqLi522icR49u3haWiTZaOES9Xb20thYaF83R2NRjNivZSvr6+8Vsput8uFVFdXl0sXUjqdjujoaGbNmsG3vx3Ec8+d4u23j3HttS3odHb274d16yA9HZ59FgYGHPe9x6N/azQaEhISWLt2LQsWLJCLqGPHjvHJJ59w8uRJlyqiUlNTufvuu/nmN79JSEgIAwMDbNmyhaeeeoqDBw+O+ffX1f5OujqRt7KclbeYtufAaXtGo3FCbXc82bly3mazmc8++4z8/HwA4uPjWbdu3YT9ec6Vtdls5sCBA/L1SxITE5k1axZarVbJJl6y/+//g4ceGvr4iiu6ePjhMry9taSmpio+vXI8+/bAwAB9fX3odDr8/f3H5Xu4krFuVT68Y5/JZMJisYx4w2N4ap/BYHDZNVKSJNHd3U1TUxNVVQP8+9/hvPdeGEbj0O9xaKjEvfequPdeCAi4tO+lxLnbbrdTU1NDUVGRvCmIwWAgLS2NpKQklzo/2e12CgsL2blzJ52dnQAEBASwYsUKMjMzzzmV1JX/TroikbeyHJm3mLbnJPv27XN2E9yKK+et1+u5/vrrueGGG9Dr9VRVVfHss89SUlLi7Kad0bmy1uv1LFmyhOnTp6NSqaioqGDbtm0utzX7L38Jr70GWi1s3BjAAw9k0NFh5+TJk3R1dSnalvHs2x4eHqhUKnnDBGFsVCoVer0eX19fAgMD8fPzk0ekhqf2dXZ20t3dzeDg4IQfTf46lUpFQEAAaWlpLF2awiOPDPDxxwU88EANkZEmWltV/L//B7GxEr/6FbS2Xvz3UuLcrVariY+PZ+3atcyfPx9fX18GBwfJz8/nk08+oaSkxGVGX9VqNTNmzOC//uu/uPrqq/H19aWrq4v333+f5557joqKirOOfrry30lXJPJWlrPyFsWTIDjRjBkzuOuuu4iIiKCvr4+33nqLjz76yKnrbS6GSqVi+vTpLFu2DIPBQGdnJ5s3b5Z3jnIVt90Gn38Ovr5w5Ig3P/rRdKqqhtaoNTU1ueT0rK9Tq9XywvO+vr5J8TMp7euFlK+vr7xGymKx0NvbS2dnJ0ajEbPZ7HIZe3t7k5iYSE7OdO6/X8UHHxznN7+pIDGxH6NRxeOPw9SpEg88AF/uazBhDRdRV1xxBfPmzcPHx4fBwUHy8vL45JNPKCsrc5ktzjUaDXPmzOGnP/0pK1euxMPDg6amJl5//XVef/11efRfEITxJabtOXDaXl1dHTExMQ47nnBukylvq9XKjh072LdvH5IkERgYyA033EBsbKyzmwZcWNYDAwPs27eP1i/fmk5KSmLmzJkuNU2moACuvBIaGsDX186jj5axaFE3YWFhxMbGjvtWtOPdt+12O11dXdjtdry8vFxm18fx0NbWxiuvvML3vve9S54/b7fb5a3PTx/V0Gg0eHh4YDAYXHIbY4vFQnNzM42Nzezc6csrr0Rx8qQPAHq9xPe+p+IXv4D4+LEdz5nnbpvNRnV1NUVFRfK1lHx8fMjMzCQ2NtaldlPs7+9n9+7dHD58WC4Ap0+fzooVKwgKCgIm199JVyDyVpYj8xZblZ/HeBVP5eXlJClxkQwBmJx519TU8MEHH9DV1YVKpWLRokUsX77c6esoLjRru93O8ePHKSoqAsDf35+cnBwCAwPHq4kO19QE3/gG7Ns3tIXzj35Uzx13NODn50tiYiJ6vX7cvrcSfXtwcJDe3l5UKpU8cuKuxiNvq9UqF1LDU/iGR6w8PDzQ6XQu9UIdhn6mlpYWmpqa2bPHi5dfjqKgYGi9gVY7VEQ98gic7z2fiXDuttlsVFZWcuLECXljmMDAQLKysoiIiHCp/5vOzk527NhBYWEhkiShVquZM2cOy5Yto7Gx0elZu5OJ0LfdiSPzFmuenGT4wqGCMiZj3lOnTuXHP/4xM2fORJIk9uzZwwsvvEBLS4tT23WhWavVarKysuRpfN3d3WzZsoWSkhKXmcIUEQE7dsDdd4MkqXj22RgeeiiZxsY+Tpw4QU9Pz7h9byX69vBIiCRJGI1Gl1n/4WhtbW38/e9/p62tzaHH1Wq1eHt7y9P6dDodkiRhMpno6emhq6uLgYEBl1obpdVqiYqKYsaMLG66yZ9XXinn2WdPMm9eN1arihdegORkif/6r6FR27OZCOdujUZDcnIyV111FZmZmeh0Ojo7O9m1axc7d+6kvb3d2U0cs8DAQNatW8ePfvQjkpKSsNvtHDp0iKeeeooNGza47e+2M0yEvu1OnJW3KJ4EYYLx8PDg+uuv5+abb8bLy4umpiaef/559u3b51IvtAAiIyO54ooriI6Oxm63k5eXx86dO13mWhh6PTzzDLzwwtDHO3YEcvvtWRQU6CkpKaGhocFlisGvU6lUeHt7yy/qe3p63PJFVm1tLX/961/HbX2eSqXCw8MDf39/AgIC5Kl7NpuNvr4++dpRrrLuBoYKj4iICGbMmMF11wXy7LOVPPdcEdnZPZjNKv7xD0hMlPjv/wYnv+9zXjqdjmnTpnH11VeTmpqKWq2mubmZLVu2sHfv3nF9k8TRIiIiuPXWW/nud79LZGQkJpOJvLw8/vGPf3DixAmXPVcJwkQjpu05cNqexWJxqauZuzp3yLu3t5cNGzZQVlYGQHR0NNdddx1hYWGKtuNSs5YkiYqKCvLz87Farej1eubMmTNh1nSNxcGDcMstUF09NEXpxz8+xbe/3URQUADx8fEO7YtK9m273S4XTmq1Gj8/P5dan3apxrpVuSPZ7XbMZjODg4NywTo8pc9gMLjcec1ms305na+J/fsNPPdcDMeODU3n8/aW+OlPVfzP/3y1xflEPnf39fVx/Phxqqur5Slw8fHxTJ8+HU9PT2c3b8wkSaKgoIDNmzfLb1bFxsZyxRVXEBUV5eTWTV4TuW9PRo7MW0zbc5JDhw45uwluxR3y9vHx4dvf/jbXXnstHh4e1NfX89xzz7F7925F36m+1KxVKhVJSUmsXr2awMBAzGYz+/btY9++fU67EO2Fmj8f8vLgm98Eq1XF3/4WywMPpFJR0cvx48cdup25kn379IJpuJAym82KfX93pFarMRgM+Pv74+fnh16vl6f0dXd3093d7VK79Gk0GiIjI8nKyuL66/15+eUy1q8vIT29l76+od35EhPhySfBZJrY525vb2/mz5/PmjVr5BHziooKPv30U06cOOEyo7MqlYqZM2cyb948li1bhk6no7a2lueff54PPvjApUbUXMlE7tuTkbPyFsWTAw1foV5QhrvkrVKpyM7O5p577iElJQWbzcb27dt54YUXaGpqUqQNjsraz8+PVatWMW3aNNRqNbW1tWzcuJFTp0455PjjLSAA3nkHnn8ePD3hwAF/br01k23bvCktLaWmpsYhRa3SfXu4gNLpdNjtdoxGIwMDA4q2wR0Njzb5+fkREBAw4hpcPT09ciHrSkXU8JqoG2/04bXXSvjjH0uJixugowMefBDS0uDjj32Z6DOQAwICWLJkCStXriQ4OBir1UphYSGff/45NTU1LvN/YjKZWL58Offeey9ZWVkAFBQU8Le//Y2dO3eKN0oczF1el0wUzspbFE8O5Eo7iU0G7pa3n58f3/rWt1i3bh2enp7yWqjt27eP+7uhjsxao9GQmZnJqlWr8Pf3Z3BwkL1797Jv3z6XuL6VSgU//CEcOQLTp0NHh46f/zyFRx9NoKysbcQWyBfLGX17uIDy8PBAkiT6+vro7e11mReJF8vHx4fs7Gx8fHyc2g6tVouvry8BAQF4enqOKKJcbSRKq9USHR3NjBlZfPObet588zi/+lUVISFmqqvhD3/IYu5c2LrV2S09v9DQUFatWsWCBQvw9vamr6+P/fv3s3XrVodvMjIehs8lfn5+rFu3jh/+8IfExsZisVjYuXMnf//73zl+/LjL9K2Jzt1elzibs/IWa54cuOapr68Pb29vhx1PODd3zru3t5fPPvtM3go8NDSU6667btyuLzFeWdtsNoqKijh58iR2ux2DwcCsWbNc5norg4Pw61/DE0+A3Q6hoWYefriKhQt7iIqKIjIy8qKu6+PMvi1JEoODg/T39yNJkvyi3tnb5Y+niXgusdvtDAwMMDg4KL+w1el0eHl5udyaioGBAerq6mho6Obtt8N57bVI+vuH1tVdeeXQdL6UFCc3cgysVislJSWcPHlSfsNq6tSpzJgxY8JeK+1MfVuSJIqKitiyZYs83Tg+Pp61a9cqvp52spmI55LJzJF5T6g1T08//TTx8fEYDAZmz57NF198cc7n79q1i9mzZ2MwGEhISODZZ58d9Zz33nuPjIwMPDw8yMjI4IMPPhiv5l+QPXv2OLsJbsWd8/bx8eGmm27ipptuwtvbm9bWVl566SU++eSTcVlDNF5Zn2kUav/+/ezevfuSR2+UYDDAH/4Ae/ZAcjK0tuq5//5UHntsKiUlTRQVFV3UtAJn9m2VSoWnpye+vr6o1WqsVitdXV0jXsRPJna7ne3bt0+4nSzVarW81bmXl5c8EtXd3e1yOyN6enqSnJxMdnYq995r5C9/+YibbmpCq5X47DOYPl3i5z+Hib4MR6vVMm3aNK666ioSEhJQqVTU1NTw2WefcfLkyQm5Y+KZziUqlYpp06Zxzz33sHz5crRaLVVVVTz77LNs2rTJJWYATFTu/LrEGZyV97gWT++88w73338/Dz/8MHl5eSxZsoS1a9eedUvYqqoqrrzySpYsWUJeXh6/+tWv+OlPf8p7770nP2f//v3cfPPN3HbbbRQUFHDbbbdx0003cfDgwfH8UQRhQsrIyOCee+6Rrwt15MgR/v73v8sXS3QVQUFBrF69mszMTNRqNY2NjXz++eeUlJRMuBe1Z7JgAeTnw333DX2+YUMYt9ySySefeFBUdJLa2toJ+cLqXPR6PQEBAfJW5r29vRiNRpf7Oc4nPz+fa6+9lvz8fGc35YzUajVeXl4EBgZiMBhQqVSYzWa6urpc7v/D19eX9PR0oqJ0/OpXzbz5ZiELF3Zhsah44omhNyAef3xoSuzRoyNvX244OiF4enoyb948Lr/8ckJDQ7FarRQUFLBx40bF1qE6gk6nY9myZfzXf/0X6enp2O129u/fz9/+9jcKCgpc6m+IIChpXKftzZ8/n+zsbJ555hn5vvT0dK6//noef/zxUc//xS9+wUcffcTJkyfl++6++24KCgrYv38/ADfffDM9PT18/vnn8nOuuOIKAgMDeeutt8bUrvGatldTU8PUqVMddjzh3ETeI1VXV/PJJ5/I8/ATExO56qqrCAoKuuRjK5l1T08Phw8fprW1FRgqrObMmeOQn0MJu3YNrYkafrG3eHEnP/95DVOnqoiLi8Pf3/+8x5hIffvr0/iGX8wPb27g6pyxVfmlsNls9Pf3y6MDKpUKLy8vubByBTU1NcTExNDU1ERjYyNffOHL+vWx1Naeeyvw0tKhAmsikSSJ6upqCgoK5FH/mJgYZs2aNSGmb13IuaS8vJzPP/9cvkBwbGwsV155JREREePZxEllIp273YEj854Q0/bMZjO5ubmsXr16xP2rV69m3759Z/ya/fv3j3r+mjVrOHLkCBaL5ZzPOdsxleQK75BPJiLvkeLi4rj77rvlaRgVFRU8/fTT7N69+5Kn+CiZtZ+fHytWrGDu3Lno9Xo6OjrYsmULeXl58nlgIlu2DI4dg//9X9DpYM+eQG65JZNXXgnkxIkSysvLz7vD1UTq28PT+IZHoex2O729vS43dWyy0Gg08sYSw6OCfX19dHV1uczOaXa7HY1GQ3R0NJmZmVx7rZY33zzOLbc0AvDGG5Cb+9XtjTeGvs5odGKjz0KlUhEfH8+VV15JSkoKarWauro6Pv/8c06cOOH0kcELOZckJSXx4x//mFWrVqHX66mtreW5557js88+c5lLSjjbRDp3uwNn5T1uV0Jsa2vDZrMRHh4+4v7w8PCzDms3NTWd8flWq5W2tjYiIyPP+pxzDZWbTKYRc3iHr2+Qn58/YoelwMBA4uPjGRwclBfin274XcmSkpJR6zHi4uIoLS3Fx8dn1LbLvr6+JCcnY7PZKCgoGHXczMxMdDodFRUVdHd3j3gsOjqa8PBwOjs7qaqqGvGYp6cn6enpAOTl5Y0aYk9PT8fT05Oamhr5naRh4eHhREdHYzQa5QuwDtPpdGRmZgJQWFg46gVrcnIyvr6+1NfX09zcPOKx4OBgpk6dysDAwIgRRBj6IzNr1iwATp48OWor5Pj4eAIDA2lubqa+vn7EY/7+/iQmJmKxWCgsLARg3759LFy4EIAZM2ag0WgoKyvD+LW/sFOmTCE0NJSOjg6qq6tHPObt7U1qaiow9A7012VkZGAwGKiqqqKzs3PEY5GRkURGRtLT00N5efmIxzw8PJg2bRoAx44dG/UiMyUlBR8fH+rq6mhpaRnxWEhICLGxsfT391NcXDziMbVazcyZMwEoKioa9QctISGBZcuWERoayn/+8x9OnTrFv/71Lz799FOuuuoqli5ditls5vjx46N+1pkzZ6JWqyktLR21Tqe8vJz4+Hja2tpGTbv18fEhJSUFu91+xqlP06dPR6/XU1lZOepaSFFRUURERNDV1UVlZeWIxwwGA2vXriUvL4/du3dTWVnJ9u3bSUtLIyIigvT0dLy8vKitrR2161VYWBgxMTH09vZSWlo64jGtVitv2XvixIlR8/uTkpLw8/OjsbGRxsbGEY+N9RxRU1PC9df3kZUFjz02NKXvr3+NY+PGafzoR3mEhR0kJCSE4OBg1Gr1qHPE6X0bJs45YvhaRDabjfT0dLq7uykvL0ej0YzYGGOinCNOd7ZzxOltcLVzhFarlXMafhFhMBhYsGABarX6rOeIgIAAmpqaaGhoGPFYQEAACQkJF3WOiI2NJSQkZEzniK/37+nTpxMWFsbChcd4++1I0tPhTIOAQ/9XX/UJg8FARkYGMPT3/OsvpNLS0hQ/R6SmptLe3k5dXR0fffQR27dvJz09XR45P9/riKCgIFpbWx32OmLfvn1885vfvKBzhJeXF4sXL+bAgQN0d3dz6NAh9u7dy6xZs4iPj5dHOCfD64jTOeJ1xKeffjqib4Nrvo5w9jni6872OmLfvn3ceOON53wdMdZzxNf/X89JGif19fUSIO3bt2/E/b/73e+k1NTUM35NcnKy9Pvf/37EfXv27JEAqbGxUZIkSdLpdNKbb7454jlvvPGG5OHhcda2/PrXv5aA896WL18uHTx4UCooKDjj4xs3bpQGBgak6dOnj3rs5z//ufTOO+9Iv/nNb0Y9lp2dLX3xxRdSe3v7GY/79ttvS93d3dLSpUtHPfbDH/5QOnnypPT888+PeiwxMVHatm2bnMvXH3/22Wel1tZWad26daMeu+mmm6SCggJpw4YNox4LCQmRNm7cKEmSJIWEhIx6/A9/+INUX18v3XXXXaMeW7NmjXT48GHp0KFDox7T6XTSxo0bJZPJJKWkpIx6/Fe/+pVUVVUlPfzww6Memz9/vrR3716prq7ujBm+9957ktFolHJyckY99pOf/EQqKSmRnnrqqVGPpaWlSTt27JAkSTrjcV9++WWpvb1duvLKK0c99p3vfEcqLCyU3nnnnVGPRUZGSps2bZIkSZL8/f1HPf6Xv/xFamxslG6//fZRj1199dVSbm6utGvXrlGPeXl5SRs3bpQsFosUFxc36vFf//rXUk1NjfSzn/1s1GOxsbHS73//eykvL++MP+tHH30k9fb2StnZ2aMeu/XWW6WysjLpj3/846jHMjMzpd27d0v9/f1nPO7rr78udXZ2SqtWrRr12B133CGdOHFC+uc//3nG9m7ZskWSJEny9PQc9fgTTzwhNTc3S7fccsuox2644QYpLy9P2rRp06jH/P39pY0bN0o2m02Kjo4e9fjvfvc76dSpU9K999476rFLOUd4eLwigSTB30c95orniC+++EJqbW2VwsLCRj3uaucIQHr//fcnzTli//79Um9v7wWfIxYvXizt379fKisrO+PPeq5zxH333XfJ54h58+6WQJJyc0f+Dc/Nlb783Zk14utOP0d4eXmNOu7f/vY3p5wjDhw4IG3cuPGMP+v5XkdUVFRMqNcRjz32mPT73/9eSktLG/WYeB3huueIi30d4exzxKW8jhjrOaK7u/us9cSwcVvzZDab8fLy4t133+WGG26Q77/vvvvIz89n165do75m6dKlzJo1i7/+9a/yfR988AE33XQT/f396HQ6YmNjeeCBB3jggQfk5zz55JOsX7+empqaM7blTCNPU6ZMYdeuXQ4defLy8sJoNIqRJ4XeMTKZTHh4eABi5GnYmd4xGhwc5NChQ1RVVREYGIharZany2i1Xw0+n+sdo+F3aZUeeTr9HSOLxUJNTQ2VlZXYbDZiYmKYPn06fn5+o35vnD3ydKZzhLd3HE88EcSLL7YAdXh42Lj22lbWrGln6tRIlixZglarpaCgYETfhol7jjCbzRw+fFjuh1qtVv5/mwjniNOd7RxhsVhQqVTMnDmT3t5elz5HWK1WTCaT/H9eWVmJSqUasc38RHhX+ev9e/gc8dFHdVx3XQy5uSNHno4ehdmzQas9yY9/PMCtt4JWOzFHnk4/R+Tn51NWVkZdXR0wtAnLjTfeSGxsLKWlpYqMPJlMJhISEi7pHKHT6Xj33XfZvXs3drsdrVbL3LlzWblyJVOmTHHp1xGnc8TriP3794/o2zCxzhEwuUaeTCYT8fHxDht5WrZs2ZjWPI37hhGzZ8/m6aeflu/LyMjguuuuO+uGER9//PGIFyU//vGPyc/PH7FhhNFo5LPPPpOfs3btWgICApy+YcSBAwfIyclx2PGEcxN5X5iGhgY+//xz+Y9ycHAwV1xxBcljWIE9kbLu6+sjLy9PfkHi6elJVlYWcXFxLrFgPjcX7r0XvjylERMzyP3317J0aQ+RkRFERkZy+PDhCZP3+Uhfbihx+tQxvV6Pl5fXiOJ8IptI/ftSSZLEwMAAAwMD8gYfPj4+6PV6ZzdNdra8h4ukN96AL1/PA3DyJNx661efz5sHr7468jkTWWtrK0eOHJGLmoiICObOnavIhhKO7Nutra188skn8hvVkZGRXHPNNURFRTnk+JPBZDqXuAJH5j0hNowAePDBB3nxxRd5+eWXOXnyJA888AC1tbXcfffdADz00EN897vflZ9/9913U1NTw4MPPsjJkyd5+eWXeemll/jZz34mP+e+++5j8+bN/OEPf6C4uJg//OEPbN26lfvvv388f5Qx+fo7wsL4EnlfmKioKL7//e9zww034OPjQ3t7O//6179466236OjoOOfXTqSsvb29Wbx4McuWLcPX15eBgQEOHjzIli1b5B36JrLZs2HvXnj9dYiMhLo6Az/7WQo/+Uky27Z1cezYMVpaWlxmm+DTN5Tw9PQctZX2RN9UorKykp///Oej3rF0VcO77/n7+6PVarHb7fT09NDX1zdh+tTZzie+vkP/3nrr0O/J8G24cPrJT2rx9bVx6BDMmgV/+hO4wk7toaGh8qUYNBoNTU1NfP7555SVlY37/4kjz92hoaHccccdXHvttXh6etLY2MgLL7zA559/7jKblYy3ifS30h04K+9xHXmCoYvk/vGPf6SxsZHp06fz5JNPsnTpUgDuuOMOqqur2blzp/z8Xbt28cADD3DixAmioqL4xS9+IRdbw/7zn//wyCOPUFlZSWJiIo899hjr1q0bc5vGa+Rp//79LFiwwGHHE85N5H3xTCYTu3bt4sCBA/I0jIULF7JkyRJ0Ot2o50/UrG02G6WlpRQVFcnTQmJjY5kxY8aE2Cb4fIzGoQ0l1q+XMJmGRs3WrGnj+uuPsHhxDFOmTBnT1uYTyfBW2mazGUmSUKlU6PV6PD09J+RIlKttVX4hJEmiv79fntqk0+nkix8707nOJ2VlZ95VT5J60GgqqauTePzxePbtCwAgJwdeeQXS0saxwQ5kNBo5dOiQ/EZPWFgY8+bNG7GEwJHG69zd19fHpk2bOHbsGDA0nevaa68lISHB4d/LlUzUv5WTlSPzvpDaYNyLp4lovIqnr8/jFsaXyPvStba28vnnn8vvuvv5+bFq1SoyMzNHTIGb6FkPDAxw/PhxKisrkSQJjUZDamqqPF9/oqupgYcfhn/9a+hznc7OTTc1c8cdDcTEeBMTEzNuL67Gi9VqZWBgYMR6EQ8PjwlXRE3m4mmY2Wymt7dX3iLc19fXqf8HF3s+sVqtnDp1ipaWVj75JIT166fS26vB0xPWrx+6vpoLzNxFkiTKysrkdSzDa6ySk5MdPvV4vM/dFRUVfPzxx/I6lDlz5nD55ZdP6L8X42mi/62cbByZ94SZtuduTh9BE8afyPvShYaGctttt3HzzTcTEBBAT08P77//Pi+88MKIDVgmetaenp7MnTuX1atXEx4ejs1mo6ioiE8//ZTy8nKnX2vlfKZO/eraNitWgMWi5l//iuQb35jBP/7hxdGjxZSVlY1aHD2RabVa+XpEwxfUNZlMdHV10dPTI49MCeNPr9fj7++PRqPBZrPR3d3t1GlWF3s+0Wq1xMfHk5qawrp13bz55jFycroZGIAf/QhuvBHOMwN5QlCpVKSkpHDFFVcQFhaG1Wrl6NGjbN++fdQi+0s13ufuxMREfvzjHzN37lwAjhw5wtNPP01FRcW4ft+JaqL/rZxsnJW3KJ4Ewc2pVCrS09O55557WLVqFR4eHjQ0NPDKK6/w73//+7zroSaSwMBALrvsMhYvXoyvry+Dg4McOXKEjRs3curUqQn/Yj07G7Zuhd/+Npfp06GnR8vf/x7LunVZPP+8jqNHT1BVVTVq96+JbLiI8vf3l4sos9lMT08P3d3dmEymCf//MhloNBr8/f3R6/VIkoTRaHSpfnS6gIAApk+fTkqKF08+WcJ999Wi00m8/z7MmAG7dzu7hWPj4+PD8uXLmT17NlqtltbWVjZt2kRVVZVL/U54eHhw1VVXcccddxAYGEh3dzevv/46H330kbi4rjApiWl7Dpy2V1lZ6fbzfZUk8h4fvb297Ny5k9zcXHkKXGxsLDfddBOenp7Obt6Y2Ww2KioqRmw1HBwczIwZMwgLC3Ny686tsrKSqVMTeP11ePRRGN4ZNzzcxJ13NnDNNR1ERoYQGRk5oXZRGwubzcbg4CCDg4PyC0S1Wo3BYMBgMCi+HqepqYk///nP/Pd//zcRERGKfm9nkCSJ3t5eTCYTKpUKHx8fxacZOercLUkSzf8/e+cd3cSV9uFHsuRuyx33hivNtGAIhNBLAgkhQEJL2TTS+2aTbELKpnyb3U3fhGxI2wDpLCWhB0KvphiMjY0bNu5FbpLV5vtD0QRhGwzIkss858yxNLoa3fn56mreuW8pK6OoqIgTJ1x58cU4CgtdkcvhhRfgxRfBweFd7aahoYF9+/aJsVAREREMHTr0iv839v6d1Ol0bNmyhX379gFmV/Dp06e3K6trd0C6LrEvttRbinm6CB1lPBUWFhIZGWmz40lcGEnvjqW8vJyNGzeSk5ODWq0mODiYMWPGMHToUKu6MZ0dvV5PZmYmWVlZYua30NBQBgwYgI+Pj2M71wbnjm2dDj77DF59FSwlN8LDtdx9dzFTptQQHBzYJY0ok8lEc3OzVYpzS3IJV1dXFAqF3VLP97S5RBAEGhsb0Wq1yGQyvLy87Dp+bK13Y2Mjp0+fprpaxz//Gc3atQEAXHed2R3W19dmH9WhmEwmMjMzOX78OCaTCTc3N1JTU6/IqHfU2C4oKGDVqlWi58KQIUOYPHlyl5unLpWeNpc4GlvqLcU8OYjzi7lJdCyS3h1LUFAQCxYsYMGCBeh0OjQaDevWrePf//43J0+e7DJuJZZijddffz1xcXHI5XLOnj3Lhg0b2LdvX4tClZ2Bc8e2szMsWgQ5OfCvf0FgoEBRkSsvvdSbW27py+efG0hLO0ZBQUGXShcsl8txc3PD19cXLy8vlEolgiDQ3NyMWq1GrVaj1WpbFDW0NbW1tXz22WctCjh3Z2QyGR4eHri4uIgufPZMKW/rudvDw4O+ffsSFqbihRdyWbz4NC4uJn75Ba66Clqph9opkcvl9OnThwkTJuDt7Y1Go2Hbtm0cPnz4suM2HfU7GRUVxf333y/W4Dl06BBLlixpUbi2uyFdl9gXR+ktGU8SEhIXJC4ujmnTpjFt2jQ8PDyoqqri22+/ZenSpS0qrXdm3NzcGDp0KFOnTiUiIgJBEMjLy+OXX37h8OHDnd43380NHn8ccnNlvP46+PoKFBS48fLLvbn55n4sWWLi0KF0CgoKulQsi0wmw8XFBZVKhY+PD66urshkMgwGAw0NDdTW1tLQ0IBer+8Qgz03N5eXX36529R5ai8Wl71zY6A62lDtSJycnIiLiyM8PJzrr6/mk08yCA3Vcfq0OZ35t986uoftx8/Pj0mTJomubllZWR2STKKjUSqVTJkyhdtvvx1vb2+qqqpYunQpv/32W5ceaxISktueDd32GhsbL7+2zP33Qze/I2NrDEYjii7kPtaVsWhtNJmorq6mpqYG4fcfPw8PDwICA3HtYulZm39PWqD73dCQyWR4eHri6emJk4MDJdoztvUGcyzU6dNm1z4AF2cT4RFagnvpcHVzxtXFpUu5WFoQBAGTyYTRZLIymGQyGXK5HCe53GYufbVqNdu3b2f06NH4dLGaWrZAEAT0BoM5vlEut0sK846eu/V6PY1NTeh1kJnpQU2t+ZwSEyAhEbpANnMRjVZLbU0NJpMJuVwuFqNuL53ld9JoNFJWVkb970W8XN3czO7GXaCUxKXQWfTuUoSFwUcfXdZbr+i6+zykmKeL0FHG04EDB8R0nRIdj6S3/Thf64aGBn777TcOHTqEyWRCJpPRv39/xo4di29XCTDAfOFYWlrK8ePHqaqqAszZ4RITE0lISHBYvY5LGduNjfDJJ/DWWwIlJebLQn9/HXPnlnLTTRVERHgTEhLS5epEgfn/YzAYaG5utsrKJ5PJUCqVuLi44OzsfEWGVE+o83Qx9Ho9arUamUyGSqXqcAPKHnO3VqslOzubhgYNH38cyVdfmeOGbr/d/H3pSqE3jY2N7N69W5yjEhISSElJadeNkc70OykIAunp6fz88880Nzfj7OzM1KlTGThwoN3iGzuazqR3T8CWeksxTw6iK6V07g5IetuP87X29PTk+uuv56GHHqJfv34IgsCxY8f44IMPWLduXaeMI2oNmUxGSEgIEyZMYPTo0fj6+mIwGDhx4gRr167l+PHjDokjupSx7eHxhzvfhx9CZCRUVTnzwQeR3HBDCq++6sFvv2WTmZmJWq3uMrFq8IeR5OnpiZ+fH56enmJslE6no76+nurqajHtdlc6t86ExRAVBMEutcTsMXe7urqSnJyMr683Dz5YyLPP5uPkJPDll3D99aBWd3gXbIaHhwfjxo0jKSkJgFOnTrFly5Z2zbOd6XdSJpMxYMAA7r//fqKiotDpdKxatYrvvvuu07tNt5fOpHdPwFF6S8aTDbHV0qFE+5D0th9tae3n58esWbO49957iY2NxWg0sm/fPt577z22bdvWZWJvZDIZoaGhTJo0iVGjRuHj44Ner+f48eOsXbuWEydOoNfr7dafyxnbrq7wwAOQnQ1Ll0JyMjQ2Kvjvf0OZMSOFp5/2Z926Ak6cOEFlZWWXizmQyWS4urqiUqnw9fXF3d0dJycnMcnEuYbUpRTgdXV1JTo6GldX1w4+g86NxRVMp9N1+Niw19ytUChISEjA39+fGTPK+cc/TuHubmLzZrjmmj+yV3YFnJycGDhwIKNHj8bZ2Znq6mo2bdpEeXn5Bd/XGX8nfXx8uP3225k4cSJOTk6cPHmy2yST6Ix6d2ccpbfktmdDtz2DwWAXf3EJM5Le9qO9Wufm5rJ582bO/n5V4ubmxsiRIxk2bFiXSlErCAJnzpzhxIkTqH+/Re3i4kJSUhJxcXEoO9hP3xZj22SCn3+Gv/8ddu78Y/+oUTUsWFDKVVdpCQ7uRWBgYIefT0chCAJGo5Hm5mZ0Op1VRjK5XI5SqcTZ2RmlUnnB+lHSXGKmtrYWg8GAl5dXh7qs2ltvQRAoKiqipKSEzEx3nn46mfJyJ+LiYMsW82ptV6KxsZGdO3dSU1ODXC5n4MCBxMfHt+r61tnHdnFxMT/88AM1NTU4OTkxceJEUlNTu6wbX2fXu7thS70ltz0HsWXLFkd3oUch6W0/2qt1bGws99xzD7NnzyYgIACNRsPmzZt599132bNnj11Xb64EmUxGZGQkkydPZsSIEXh5edHc3MzRo0dZs2YN6enpHbqqZouxLZfD9OmwYwfs3g033QQymcDOnb4sWpTMvHkJLFmi5cCBY+Tn59vFXcvWyGQyFAoFHh4e+Pj4oFKpcHNzw8nJSawjVV9fT01NDXV1dWg0mlZTPktziRnLRUhHpy23t94ymYyIiAjCw8NJSmpiyZJ0IiMN5OTA6NHmpCtdCQ8PD8aPH09UVBQmk4m0tDT279/fJcd2WFgY9913H8nJyRiNRtavX8+3337bJecj6Px6dzccpbdkPElISNgUmUxG3759eeCBB7jpppvw9fWlsbGRDRs28N5777F//3671pS5EuRyOVFRUUydOpXU1FS8vb3R6XScOHGCNWvWcOTIkS7xIz9iBPz0E2Rmyrj3XnB1FcjK8uCVV2KZNm0Ar76q5NdfMzl16lSXi4uyYImPas2QssRINTY2UlNTQ21tLU1NTej1eg4fPszMmTM5cuSIo0/B4VhW57ri/789hIaGEhkZSWiojg8+SCcmxkBBAYwZAwUFju7dpaFQKBg+fDiDBg1CJpORl5fHr7/+2iXmo/NxdXVlzpw5XHfddTg5OZGZmcmSJUsoKipydNckJFpFctuzodtedna2WJdBouOR9LYfV6K10Wjk6NGj/Pbbb6ILnEqlYvTo0QwcOLBLpdK2uP9kZGRQU1MDmC84Y2NjSUpKsllGu44e25WV8Omn8OGHAkVFZvcYJycTEyZUM2dOGVddZSIoKAh/f/9u4YJiNBrR6XTodDoMv6fltpCens64cePYtWsXqampyG2YBr2rYUm84e7ujru7e4d9jqPn7tLSUgoLC6mqUvLoo/3JzlYQF2depQ0Odli3LpvS0lJ2796NTqfDw8OD0aNHo/o97b6jtb5Uzp49y/fffy+6JE6aNKlLufF1Nb27OrbUW0pVfhE6yngqLi4mLCzMZseTuDCS3vbDFlobDAYOHz7M9u3bxVofvr6+XHvttQwYMOCCMSmdDUEQKCkp4eTJk1RUVABmIyoyMpLk5GTxwuVysdfYNhhg5Up47z3ruKi+fRu45ZYyJkyoJSTEn8DAwG4TCG0ymdDr9eh0OnHlacKECWzevFlM/6xUKlEqlSgUii5l3F8JJpPJXL9NEFCpVB0aB9cZ5u7i4mKKi4spL1fy4IMDKCx0on9/2L4dfHwc2rXLor6+XpxbnZ2dGTlyJL169eoUWl8qWq2WNWvWcOLECQAGDBjA9OnTu0RsZlfUuytjS72lmCcHcfz4cUd3oUch6W0/bKG1QqHgqquu4pFHHmHKlCl4eHhQU1PD//73Pz744AMOHz7cqs9+Z8SSnW/8+PGMGzeO4OBgTCYT+fn5rF+/np07d4o1WS4He41thQJmzzbfcT90yFwDx9lZ4MQJT158sTfTpvXnpZec2bQpm4yMjC6Zpe985HI5Li4ueHl54evri5eXF2AenzKZDKPRiFarFWOlampqqK+vR6vVtli16i4IgkBDQwOCIKBQKDp8tbEzzN2hoaH06tWLoCA977yTQa9eJtLTYc4c802FroaXlxcTJkwgMDAQnU7Hb7/9Rl5eXqfQ+lJxdXVl1qxZTJkyBblczrFjx/jss89Ez4XOTFfUuyvjKL0l40lCQsKuKJVKhg8fzqOPPsrEiRNxd3enurqaVatW8f7773Pw4MEuExMFEBQUxJgxY5g4cSLh4eGia9+mTZv49ddfKS4u7hIX3IMHwxdfwJkzMl59FUJCBKqrnfnyy1BmzkzhrrtC+eyzGg4dOsqZM2e6RV0WS8IJMF98+vn54e3tjZubm5Ux1dzcTENDA7W1tWLyiaamJruk9e5ojEYjVVVV6HQ6ZDIZnp6eXcZF6kqwJIXx8fEhLEzD22+fwt1dYNMmeOQR6AJf2Ra4uLgwZswYMZHEvn37KCkpcXS3LguZTMbw4cNZuHAh7u7ulJSUsGTJEvLz8x3dNQkJyW3Plm57dXV1Nj2exIWR9LYfHam1Tqfj0KFD7Nq1i4aGBgC8vb0ZOXIkgwcP7hKuGudSW1tLZmYmhYWF4oW1t7c3iYmJREdHt8sNrDOMbb0eVq+GJUtg06Y/9gcE6Jg+vYIbb6wgMdGVwMBAfH19u5Tb5bk0NTVx8OBBhg4d2iLOx2QyYTAYMBgM6PX6NleenJycRBc/y8qNTCbr1EaIpSBuU1OTuM/b29suJQU6w/i2YDQaOXnyJE1NTezfH8ojj4QhCDL+8x+4+25H9+7yEASB9PR0MjIyaG5uZuDAgQwYMKBTj8cLUVtby7fffktJSQlyuZzJkyczbNiwTnk+nWls9wRsqbcU83QROsp4SktLY/DgwTY7nsSFkfS2H/bQWq/Xk5aWxq5du6irqwPA09OTkSNHMmTIkC5VJwrMF+WnTp3i9OnTYop2V1dX4uPjiYuLu2Adnc42tk+fhv/8Bz7/XKC83HzBIpMJjBih5qabyhk9uoFevfwJCAjokrFR7dVbEATRmLJsJpOpVYNKLpfj5OTUYnNkQgpL/3U6Hc3NzVarZp6ennYrFNzZxrdWq+XEiRMYjUZ++imR//s/FW5ucOAA9O3r6N5dPpmZmaxbt46QkBDi4uIYPHhwl73JodfrWbNmDceOHQMgJSWF6dOnd7qENp1tbHd3bKn3pdgGnWvUdXEsgeMS9kHS237YQ2ulUklqaipDhgzhyJEj7NixA7VazYYNG9i5cycjRozgqquu6tDinbbE3d2dgQMH0rdvX3Jzczl16hSNjY2kp6dz8uRJoqOjSUxMFGNuzqWzje3eveHNN+GVV2T873/wySewZYuM3bt92L3bh8BAHVOnVnL99afp00dOYGBgl8nUV1hYyOLFi/nwww+JvEi1VEs69HNXQ00mE0ajsYVBZdnOr20mk8lEw+rcv5bNsmJ1pQaWpYCwZTu3bxacnJxwdXXFxcXFrhfVnW18u7q6EhUVRW5uLjfddIqDBwexZYuCW26BgwfBTjalzUlKSuLgwYPIZDJycnLQ6/UMGzasSyZBUSqV3HTTTYSGhrJx40aOHj1KTU0Nt956a4dmhrxUOtvY7u44Su/O/8vWhbDXXTsJM5Le9sOeWisUCoYOHcqgQYM4evQoO3bsoKamhs2bN7Nz506GDRtGampql1nhUCqVJCYmEhcXR1FREZmZmdTU1JCTk8Pp06cJCwsjMTGRgIAA8YK5s45tZ2dzQP2cOZCd/cdqVEWFM199FcpXX4XSr18D119fweTJZ4mM9CYgIABvb+9O6WIDUFlZydq1a3n55Zcvajy1hsXoOdegOt9wOXc797W2sBhY5xpSFzKoBEGw2iyGW1v9VSqVuLi4oFQqHfJ/6YzjOyAgALVaTVVVFS+8kMPx44mcOCHj9dfhlVcc3bvLJyIigujoaPbt20dBQQGCIDB8+PAuuQJliYMKCgriu+++o7CwkKVLlzJ//nz8/Pwc3T2gc47t7oyj9Jbc9mzoticIQqe9QOiOSHrbD0dqbTKZSE9PZ8eOHVRWVgJmg2Tw4MGMGDECny6WV1gQBMrLy8nMzLQK5vb19SU+Pp6oqKguVWuouRnWrjUnm1i3TsBoNPfb2dnEtdfWMG1aJSNHaggM9CMgIAA3N7dOdW5paWkMGTKEQ4cOdbi7zbmGjdFotPprcf+zZQIKi2FnicOyxGU5Wv/OOnfr9XrS09MxGAwcPZrAvff64KIwkvXpDqKcSyAkBK65BrrQyo1F67Nnz7Jz505MJhPR0dEMGzasSxpQFioqKli2bBm1tbW4u7tz6623XtbND1vTWcd2d8WWeksxTxeho4ynDRs2MHnyZJsdT+LCSHrbj86gtclkIisrix07dnD27FnAfHHYv39/Ro4cSVBQkEP7dznU1tZy6tQpCgoKxJUIFxcXqqurmTt3bqdyR2kPpaWwbBl88YXA8eN//KD94dZXSZ8+cvz9/fHz8+sULpj2NJ7aw7kGlmUlybL/3Ofncv7q1LlugJ2RzjCftEVZWRkFBQUoFErW3ZPLbWmPEUHRHw3Cw+Hdd2HmTMd18hI4V+uioiJ2796NyWQiNjaWq666qktf6Dc0NLBixQqKi4txcnLipptuol+/fg7tU2ce290RW+otxTxJSEh0O+RyOcnJySQlJZGfn8/OnTs5ffo0R48e5ejRoyQmJjJq1CgiIiIc3dV24+Pjw7Bhw0hJSSE3N5ecnBwaGxspLS1l7dq1hIeHExcXR2BgYJe4yAkOhiefhCeekHH4sHk1atkya7e+pKRGJk+uYuLEk/Tu7Yq/vz++vr5dIj7KHshkMjG5hIT9CQwMpLS0FPf163ku7VkEzjNWi4th1iz44YcuY0BZCA8PZ/jw4ezZs4fc3FycnJwYPHhwl5hbWsPT05M77riDH3/8kczMTH744QdqamoYNWpUlz0nia6BtPJkw5WnzMxMkpKSbHY8iQsj6W0/OqvWFleUkydPinfko6KiGDVqFHFxcV3uB9RkMnH27Fl+++03q4tni0tfZGRklzMympvh55/NhtQvv/zh1ieTCQwaVM+kSVWMH19LdLS5zpJKpbKr4VBUVMRf//pX/va3vxEeHm63z+3JdNb5xEJFaSneKSk4l5fT6gwik5lXoPLyOr0LX2ta5+fns2/fPgRBICUlheTkZAf1zjaYTCY2bdrEnj17ABgxYgSTJk1yyPzf2cd2d8OWektuexeho4yn0tJSgoODL/2NTU2QmWmzfvQUKisrCQgIcHQ3egSdXeva2lqOHj3KqVOnxJgRX19fBgwYQFxcXJczOCorK3FxcaGgoIDS0lLRpU+pVBIeHk54eHiXc+kDqKkx14zasAEOH/ljv5PcRP/+jYwYUcuQIY0EBnrg7e2Np6enXQypzj6+uxudXW/j/v043X//xRsuWQJDh3Z8h66AtrQuKCgg8/frjgEDBhASEmLvrtmc9PR0du/eDUBiYiKjR4+2u+tqZx/bnZakJLiM37TLvu5uBcl4uggXE8hoNLZIL9sedu7cyahRoy69QydOwM03X/r7JCS6EoKAsqICJ43G0T2RkJCQkJCQ6CwcOgSXEXMqxTx1EhoaGigqKmo1KPdi+Pn5kZeXd+kf6uoKq1Zd+vt6OM3NzZ0i4LwnYCutZSYT4SYTnh3sTtHc3ExmZibp6ek0NjYC5hToCQkJ9O/fv9Nn6Nu9ezdXX3211T6TyURFRQVnzpyhqqpK3O/q6kpYWBhhYWG4ubnZu6s2IS/PvBq1bp3AmaI/xoari5GUlHqGDq1n0KCOWZE6efIk8xcsYNnXX3d596WuQmvju1Nx8CDcd9/F23WBlacLaS0IAkePHqWsrAylUsnw4cO75Ir2+eTl5bF582ZMJhPh4eFMmjTJqpRAR9Lpx3ZnpYu5OkorT+dYl0ajkezsbNzd3S8rQNtgMHQ596CujKS3/bCF1oIgUFFRQVNTE/Hx8XZxxzIajWRkZLB7924xLbhMJiMxMZERI0YQGRnZKeOiampq8PX1bfP1+vp6Tp8+TV5eHs3NzYD5vEJDQ+nduzfBwcGdNtPahRAE8w3IFSvg++8Fzpz543/j4mIiNVXN2LHVXHttHeHhnvj6+qJSqa7owqizZdvrCVxsfDscoxEhOhqKilqNeRKQIYvoGjFPF9PaYDCwdetWqqqq8PX1ZcKECd0iWUlubi4rVqxAr9cTERHB/Pnz7VITqNOP7W6GLfWW3PYuQlsCabVa8vLyiI6Ovqw7uE1NTd3irk1XQdLbfthKa41GQ35+PjExMXYtbicIAvn5+ezZs4dTp06J+8PCwhgxYgR9+vTpVMbG0aNHSUlJuWg7o9FIUVERp0+fpry8XNzv4eFB7969iYmJ6bKrUYJgXgD48Uf48UeBnJw/LmMVChNXXVXH2LE1XHttLRERrvj6+uLj43PJ40oynuxPe8e3Q/npJ4RZs0AA2TkZ94Tfn22+7wcmfdz5s+21R+vGxkY2btxIc3MzsbGxDBs2zE6961jOnDnDsmXL0Gq1hIWFsXDhwg7/3ekSY7sbYUu9L8V46jxXC52Iy70TfTlxUhKXj6S3/bCV1o5a5ZHJZMTExDBv3jwefPBBhgwZgkKhoLi4mB9++IF3332XnTt3oukk8VilpaXtaufk5ERUVBTjxo1j6tSpJCQk4OzsTGNjI8eOHWPNmjXs3LmTs2fP2rT4qj2QyeCqq+DNN+HUKRlHj8KLL0LfvgIGg5w9e3x4/fUYpk4dyPz5Ybz+uo41a05x/PhxioqKaGhouCz3a4mOp73j26HMnMm3sz6hiDDr/RHhfDb1ByYvmcmSJY7p2qXQHq09PDwYMWIEMpmM3NxccnNz7dCzjiciIoI77rgDd3d3iouL+frrr8WV+o6iS4ztboSj9JaMJxvSGd1/roSXXnqJRYsWAbBt2zardJCenp5Wd7odQXfTuzPTnbQODAxk+vTpPP7444wZMwYPDw/UajWbN2/mX//6F2vWrKGsrMyhfbwcNzSVSsXgwYO54YYbSE1NJSAgAJPJRFFREdu3b2fNmjUcO3aM+vr6DuhxxyKTwYAB8PLLcPy4jMxMeO01c3yxySTj8GFv3n8/kjlzBnDddXH8+c8KvvrqDAcPHiU/P5/q6moxY+H5KBQKVCqV5AJsR+wVf3IlLFkCc7+/m7ceyEP4dSssXw5btyLLy+Oun2fy8MOwaBGd3oBqr9bBwcFigdm0tDQaGho6slt2Izg4mNtuuw03NzeKioo63IDqCmO7O+EovSXjyYbYMu35+URHR+Pt7W11Z7yurg43NzcroyY6Opq9e/davXfRokW89NJLNu1PQ0MDQUFBNj3mpdKa3g899BBffvml1b577rmHhx56qEXb9957j2uvvVZ8fvDgQcaOHUtCQgI//PBDi/YzZ85k8eLFNui5/Th9+jQjR47E3d2dwYMHc/To0TbbjhkzBldXVzw9PfH09GTq1Knia0eOHGH06NF4eHgwZcoUq/dlZWUxbdo0AgICCAwMZMGCBdTU1HTYOdkKDw8PxowZw+OPP86MGTMIDg5Gr9dz6NAhPvroI7788ksyMzMdsmIzbty4y36vQqEgJiaGCRMmMGXKFBISEnBxcUGj0ZCRkcHPP//Mli1byMvL67Krt4mJ8Nxz5viovDx47z2YOBGUSoEzZ1xZsSKYBx9MZty4ftxzjxcffFDD1q1HyczMpKSkhKamJnFVasCAAdTW1jJgwAAHn1XP4UrGtz1oboaHH4YBAwTeeV+BbOwYmDsXxowBJydkMnjnHbNB//DD5vadlUvRuk+fPgQFBWEwGNi/f3+3Wbm1GFCurq6cOXOGFStWYDAYOuSzOvvY7m44Sm/JeLIharW6Q48fHBzM6tWrxec//fQTERERHfqZnZnW9N6wYQOTJk2y2rdgwQK+++67FpPl8uXLmT9/vvh8/fr1TJ48mfnz57Ns2bIWn7Vu3TrmzZtnwzPoeObOncukSZOorq7mT3/6EzfddNMFfzS++OILGhoaaGhoYN26deJ+k8nEokWLePbZZ1u8R61WM2fOHE6fPk1+fj46nY6nnnqqQ86nI1AoFAwcOJD77ruPO++8kz59+iCTycjLy+Obb77h/fffZ8+ePWi1Wrv1aePGjTY5jo+Pj7gaNXLkSEJCQpDJZFRUVLBv3z5WrVrF/v37qays7LIXStHR5gvYjRuhqkrGDz/A7bdDQIBAQ4OCzZv9Wby4N5MnD2Lu3FD+9jcDP/6Yy+HDR8nLy6O6upoNGzY4+jR6FLYa3x2Fiwu8846RY8dkPPqowPlfDUGAxx6DY8fg/ffN7Tsrl6K1TCbjqquuQqFQUF5eTk5OTgf2zL6EhIRw22234eLiQn5+Pt9//32H3Bjr7GO7u+EovSXjqQsxd+5cq4v6ZcuWXfHFvEaj4aGHHiI0NJTw8HD+7//+r13vk8lkoq9pdHQ0//d//0dcXByBgYFWq1xr164lMTERLy8vIiIiWLFiBWAOdF+8eDFRUVEEBwfz5JNPtnpRv3HjRkaOHCk+j4mJ4cEHHwTMhVG9vb3F950+fRp3d/cWxf5Gjx6Nm5sbmzZtEvfl5uZy+PBhZs2aJe6z1AtYsGAB69ato7a2Vnztxx9/pF+/fiQmJooujC+88AI+Pj4kJiaSkZHB3/72N/z8/EhOTubEiRPiex944AFCQ0Px8fFh0qRJFBYWAuYVm4CAAPEHau/evQQHB9vMHTIrK4usrCyeffZZXF1deeihhzAajWIRwUth0KBBzJs3j/Dw8BavDRs2jNtuuw2VSoWHhwf33HMP+/fvt8Up2BWZTEZUVBRz5szhscceY9SoUbi5uVFTU8OGDRv417/+xc8//0xFRUWH98XWhoyTkxMRERFce+21TJ8+nQEDBuDl5YXBYCA3N5fNmzezbt06Tp482Wnivi4HLy9zybwvvoDSUhm7d8Ozz0K/fmb3viNHvPn3vyO47bZ+TJjQlwce8OKvf93HwoV3sGbNGoqLi6mvr+9y8WFdja5gqM+ZU8Mzz+TxwQcyHnkE0YASBHj0UbPR9PHH7cto7kguVWsvLy9xFfbo0aM0NTV1RLccQmhoKPPmzUOhUJCVlcXq1attPha7wtjuTjhK7w41nmpqali4cCEqlQqVSsXChQutLkjPR6/X88wzz9C/f388PDwIDQ3ltttu4+zZs1btxowZg0wms9puvfVWm/dfEKCxsf2bXu98Se0tW3v/9xMnTiQtLY3q6mpKS0vJzs5m9OjRV3SOTz31FGq1mlOnTrF//36++uor1qxZc8nH+fHHH9mzZw/79u1j6dKlrF27FoC7776bzz77jPr6eg4cOCBmRfnXv/7F7t27OXToEJmZmaSlpfHRRx+1OO6IESM4fPgwGo2G4uJiwFyMGODQoUPiXTL4Y+XofGQyGXPnzmX58uXivuXLlzN16lT8/PwA8+pJXl4eAwcOpHfv3gwcOJAff/zRqv25q1Q5OTkEBgZSWVnJpEmTuO6663Bzc6O8vJxp06bx17/+VWw7atQoTp48SWlpKeHh4TzyyCOAuQL6c889xx133EFjYyN33HEH7733XqvukDt37sTHx6fNrTUyMjJITEzE2dlZ3DdgwAArw+58Hn74YQIDA5k4cSLHjh0T9597jIuxe/du+vbt2+72nRGVSsWECRN44oknmD59OkFBQeh0Og4cOMCHH37Il19+SUZGRptxNFdKa0aqrXB3d6dPnz5cd911jBs3jpiYGBQKBXV1dRw9epTVq1fz22+/kZ+f32Xd+sCcQXrECHj9dUhPN7v3vf8+TJsGHh4CNTVK1q8P4KOPelFRUcqDD/rxl7/I+eqrYvbuPcKpU6coLS21cvGTsA0dOb5tgaWswsyZFbz5Zi0ffACPPAImk/lvVzGc4PK0jo+PJyAgAIPBYPU70B2Iiopi9uzZyOVyjhw5YnVT1RZ09rHd3XCU3h0aITtv3jyKiopYv349APfeey8LFy5s8+K8qamJtLQ0XnjhBVJSUqipqeGxxx7jhhtu4ODBg1Zt77nnHl555RXxeUek421qAk/PS3nH5fWhoQE8PC7eTqFQMGPGDL7//ns0Go04AZzPxIkTreo0aDSaVt2tBEHg888/Jz8/X4xzuf/++/nhhx+YPn36JZ3DY489RmBgIIGBgdx33338+OOPTJs2DaVSyfHjx0lJSSE4OJjg4GAAli5dyldffUVAQAAATz75JG+99RYPP/yw1XG9vLxITk5m//79lJSUMGPGDNavX09NTQ179uxh1KhRYtv169fz2GOPtdq/BQsWMGLECDHl9vLly3n55ZfF1zdv3szYsWPFxAgLFixg2bJl3HXXXZSUlLB9+3a+/vprsb2Pjw8PP/wwMpmMmTNn8sUXX/D4448jl8uZOXMmCxYsENueuzr4zDPPWK2kPfbYY6xcuZJhw4bRv39/5syZ02r/R40adcEbD63R0NDQIi7M29u7zUDgv//97/Tp0wcnJyfef/99rrvuOjIzM/H09Gx3MP2RI0d477332L59+yX1tbOiVCoZMmQIgwcPJj8/n3379pGVlUVeXh55eXl4eXmJr9sy5tEe8YQymYygoCCCgoIYPHgwhYWF5OXlUVlZSUlJCSUlJSgUCsLDw4mOjiYoKKhTpXO/VKKj4aGHzJtOZ16V2rgRVq6EzEw4c8aNr78O4euvQ3BxMTJ4cD1XXVXHkCG59Omjx8fHC29vb7y9vXFxcelWSVTsjaPjZS9GTU0N9fX1yOVyHn/cnVWr4IMPYPt2s6teVzGc4PK0lslkDB48mI0bN5Kfn098fDz+/v4d0DvHkJiYyI033sjKlSvZvXs3fn5+DLVRsePOPra7G47Su8OMp5MnT7J+/Xr27t1LamoqAP/5z38YMWIEWVlZJCYmtniPSqVqcRfg/fffZ9iwYRQWFhIZGSnud3d3Fy/EexLz58/nL3/5CxqNhk8++aTVC+pNmzYxfPhw8bklY975VFRUoNFoSEhIEPeZTCari/v2cq71HxERIa4O/fDDD7zyyiv8+c9/JjU1lffee4+kpCQKCwuZOHGieAEiCAJhYWGtHvuaa65hx44dlJSUMH78eOrq6ti1axc7duzg1VdfBRBXBa655ppWj9GvXz9iY2NZvXo1iYmJFBcXWxmIGzZssEqEcMstt/DUU09x9uxZvv32W8aMGWM13gICAsS+u7m54e/vL15Yurm50djYKLZ97bXX+PzzzykvL0cmk1FXVye+JpfLueOOO7j77rtZYuO0TZ6enlafBeYkI55t3BE4t7bHn//8Zz777DP279/PuHHjaGpqQqVSXfDz8vLymD59OkuXLu3yK0/nY0l1HhMTg1qt5tChQxw6dIj6+nq2bdvG9u3bSUpKYujQocTExFzxhXVaWlqrq6gdhVKppHfv3vTu3Zv6+noKCgooKCigvr6e/Px88vPzcXNzIzIykujoaHx8fLq08eDsbI79HzMGZs2CIUPgb3+D7GzYuFGgpMSJPXt82LPHBwAvLwMDB9YzZEgdgweX06+fEW9v8w0nLy8v3NzcurQe9sbe4/tS0Ol0omt1cHAwmZnO7Ntnfi0jo2sZTnD5Wvv5+RETE0NeXh7Hjx+3Sq7UHUhJSaG2tpatW7fyyy+/4OPjQ1xc3BUftzOP7e6Io/TuMONpz549qFQq0XACGD58OCqVit27d7dqPLWGWq1GJpO1cE1atmwZX3/9Nb169WLq1KksXrwYLy+vVo/R3NxslZry/AvKtnB3N68KtRe1Wn3RC8y2Pqe9jBgxguLiYpydnRk4cCDbtm275M+zEBAQgKurKwUFBZfV73MpKioSH585c0Y0NFJTU/n5559pbm7mxRdf5MEHH2TLli2EhYXx448/tivD1ahRo/j0008pLS1l8eLF1NXVsXnzZtLT00UjcefOnQwdOvSC7mULFixg+fLlJCYmMnPmTKtieZs2beJvf/ub+DwwMJDx48fzzTffsGLFilaz9bWH3377jSVLlrBlyxbi4uI4deqUVXbEqqoqXnjhBRYuXMjTTz/Nzp07W63uvmPHDqvsd+fT2mpSnz59yMrKQq/Xi+k8jx07xtNPP92uvl/KKkNpaSkTJ07khRdeYMaMGe1+X1dEpVIxbtw4rr32Wk6ePMmBAwcoKCggIyODjIwMAgICGDp0KAMHDrRrIWBb4eXlRb9+/ejbty9VVVXk5+dTWFiIRqMR4+hUKhXR0dFERUV1m0LVU6fC88+DIMg4fty8KrV1K2zfLlBfr2DHDl927DBXsvf2tjamkpL0qFRe4gq+h4dHl16l66kYjUZycnLQ6XS4u7ujUoVwww1md705c+Crrzp3cghb07dvXwoKCigpKaGqqqpbrT6BOR66urqao0eP8v3333PXXXdJK0cS7aLDjKfS0tJWB2FQUFC7i1pptVr+8pe/MG/ePCuXmPnz5xMTE0NwcDDHjx/n2Wef5ejRo236rr7xxhtWLloWNm/ejIeHB+PGjWP//v1oNBoCAgIwGo1iJjfLxY8l05aXlxdNTU0YjUacnJxwd3cX66b4+ChxcmoW23p6eqLVajEYDMjlcquVABcXF+RyORqNhrq6C7e1GAQNDQ2o1Wq+++47sY+WFQ5LfwVBwGAwiM/d3d0xGAxotVrq6urw9vamrq4OQRBQKpUsXLiQRx55hFdeeYXg4GAyMjKoqalhyJAhgDkOTa1Wi+d0boY7rVaLWq3GZDLx3nvvcfXVV6NWq1myZAnvv/8+FRUVrFq1ihkzZuDu7o5CoUAQBEwmEwsWLODPf/4zH374IdHR0Zw4cYIzZ84wYcKEFnoPHjyYnTt3EhERQUBAAAMHDuSRRx4RUzCr1WpWr17NxIkTaWxsbFPvWbNm8eKLL7J//36+/PJLmpqa0Ov1ZGdn4+fnJx7L2dkZhULBTTfdxMsvv0xVVRVTpkwRDXkwr9BZ2hoMBvG5u7s7Wq0WQRBQq9Wi64dSqaSiokJ0NbW0ve+++7jhhht48803ufHGG3n99dd55JFHUCgUuLq6ikZRamoqlZWV4k0Ai/udyWRCoVBgNBrFtpYxGxwcTFxcHG+88QYPPvggX3zxBXK5nOHDh7cY36WlpRw+fJhJkybR3NzMRx99RFVVFYMHD0atVmMwGKitrUWj0aDT6SgrK0OlUmE0GqmqquL6669n4cKF3HLLLajVaqvxDea04DqdjsbGRvF/a8lwZvm/Hj58GIChQ4dy9uxZzp49i5OTExMmTGDz5s0YjUZCQ0MJDQ0V3XgHDRpEZWUlZ86cAWDy5Mls3boVnU5Hr169iI6OZt/vt40HDBhAXV0d+fn5gNm9ddeuXTQ1NREQEEBCQoKYTKNv375otVpOnz4NIM4RDQ0N+Pr60rdvX3F1ddy4cZSXl7Nu3Try8vIwGAx8+umnGAwG+vTpw+zZszl16hRgjiVQKBScPHkSMN8YyMjIoLq6Gg8PD4YPH86WLVvQaDRkZ2fj7u7O8ePHAfONk5ycHCoqKnB1dWX06NFipqGoqCh8fHzEVPSW1frS0lKUSiXjxo1j48aNCIJAeHg4QUFBpKWlATBkyBBKS0spLi5GLpczceJEtmzZgsFgICQkhNjYWCoqKtDr9Xh5eZGfn8/p06dJS0sjLi6O6upqvLy8SEhIIDExUSyV0L9/fxoaGsjLywNgwoQJ7N69m6amJvz9/UlKSmLXrl2A2dDX6XRi8pSxY8dy8OBB6uvr8fHxYcCAAaIrqOXGW1ZWFmC+ADp27Bi1tbV4eXkxdOhQtm7dCkBcXBzOzs5kZGQAMHLkSDIzM6mqqsLd3Z2rr76a/Px8XnzxRQDOnj1Leno6AHffPZxx43IpKamgsNCf+vohfP99BceP+1JXp2D7dl+2b7cYU3qSkqqIjS0lMfEkEyb4UFaWh1wuJywsjMjISPF/M3jwYMrLyykqKkImkzFp0iR+/fVX9Ho9wcHBREZGiglXLHfHCwoKAJg0aRLbt29Hq9USGBhIXFwce/bsAcyr601NTWKB0/Hjx7N3714aGxvx8/OjT58+4phNTk7GYDCQnZ0NmOOI09LSqKurQ6VSMXDgQH777TcAEhISkMvlZGZmimP2xIkT1NTU4OnpybBhw/j1118B6N27N66urmJc5dVXX82pU6eorKzE3d2dkSNHsmnTJtEQ9/b2FmNqUlNTyc/Pp6ysDGdnZ8aOHWvXOcJkMqFSqVCr1ajVajw8vHn11b4cPgwqVTO3356FThfDtm2XPkckJSVhMpnEeeDaa6/lyJEjqNVqvL29GTx4sHgjtL1zBEBsbOxF5wiNRsOGDRsue46orq7G2dmZTZs24evr2+ocER4ezoEDBwAYOHAg1dXV4urd5MmT2bZtG83NzQQFBREbG9up5oi+ffuSl5dHWloaixcv5tVXXyU/P99qjti8eTNgTljl6ekpzhHDhw8nNzeX8vJyXFxcGDNmjKh3ZGQkfn5+HDlyBICrrrqKoqIi0R16/PjxbNq0CZPJRFhYGMHBwRw6dEiaI36fI+CPEj0XmiMsettijrD0v10Il8jixYsF4ILbgQMHhNdee01ISEho8f64uDjhjTfeuOjn6HQ64cYbbxQGDRokqNXqC7Y9ePCgAAiHDh1q9XWtViuo1WpxO3PmjAC0OK5GoxEyMjIEjUZz0f61RlNT02W9rz1ERUUJe/bsabF/69atQmJi4gXb3XfffcLixYtbPW5jY6Pw6KOPCuHh4YJKpRKGDh0qrF+/XhAE8//6vvvua/VzAKGkpET8zDfffFPo3bu34O/vL7zwwguCIAhCc3OzMGnSJMHHx0fw9vYWrrnmGiEjI0MQBEHQ6/XCK6+8IsTExAheXl5Cv379hP/+979tnn98fLxwzz33iM9DQkKEBx54QHw+YMAAISsrq833Wxg7dqwQEhIiGI1Gcd+//vUv4dlnn21VG09PT2HOnDlW+8/XYs+ePUJUVJT4/PDhw0KvXr3E85w3b57g6ekpREdHC//+978Fy9fuu+++E+Li4oTGxkZBEAQhJydH8PPzEzWyBdnZ2cLVV18tuLq6CgMHDhQOHz4svvbaa68JU6ZMEQRBEMrLy4UhQ4YIHh4egq+vrzB27Fir79P69etbfM9vv/12QRAE4YsvvhAAwcPDw2prjSv9jnV2tFqtsH//fuHDDz8UFi9eLG4ff/yxcODAAUGr1bbrOOnp6R3c08unublZyMnJETZv3iysWLFC3L799lth27ZtQm5urtDc3Ozobl4Sl6K3Xi8Ie/cKwptvCsKUKYLg4WESzGl//ticnIxC3771wq23lgivv35KWLMmTThy5IiQk5MjlJSUCHV1dYLBYOjAM+rcdLbxXVZWJqSlpQn79u0T0tLShOrqBmH2bMv/UhC2bnV0Dy+fK9W6urpa/H535DWOI2lsbBTefvttYfHixcKyZcsEk8l02cfqbGO7u2NLvdVqdau2QWvIBOHS0ghVVlZSWVl5wTbR0dEsX76cJ554okVMjo+PD2+//TZ33nlnm+/X6/XMmTOH3Nxcfv3114suFQuCgIuLC//973+55ZZbLnoOFsvZctfHglarJS8vj5iYmMtyt7lct72uTnR0NN98841VnJU9sOhdUlLCyJEjxbspl8rkyZN5/vnnrzhzYXfGVmP7Sr9jXQVBECgsLOTgwYNWWfmUSiX9+vVjyJAhhIWFtRknY0mb39lpbGyksLCQwsJCq8LIcrmc0NBQIiIiCA0NdVgV+PZQUlLC008/zVtvvdWizEF70OshLQ127zZvu3YJlJS0/L8GBzfTv38DAwY0kJzcQEKCBl9fV9HNz8PDA1dX1x7h7tdZxrfBYODw4cNiNkVXV1fU6gSeeMKVgwdBqYRvvoGZMx3c0SvAFlpv2bKFiooKUlJSSE5OtlHPOhclJSUsXboUg8HAmDFjGDNmzGUdp7OM7Z6CLfVuyzZojUt22wsICBAzpF2IESNGoFar2b9/vxiIvm/fPtRqNVdffXWb77MYTtnZ2WzdurVdPrYnTpxAr9df1g+fLZEChu2LRe+6ujr+/ve/X/Zxxo8fz4gRI2zVrW6JNLYvDUvNqKioKKZOncrRo0dJS0ujoqKCw4cPc/jwYXr16sXgwYMZMGBAi2yhXeUC2sPDg+TkZJKTk6mvrxcNKbVaTVFREUVFRSgUCtGQCgkJaXfmRntRUlLCsmXLeOKJJy7rN0SphNRU8/b44+aYqYKCP4yp3bvh6FGB0lIXSktd2LTJ/Jvm5CQQE6MhObnx962c+HgtPj6uuLu74+bmhru7u+jy3J1w9Pg2Go1UVFRQUlIiGk5GIzzzTH/WrzfPdT4+8O23cF7N9S6HLbSOjo6moqKCM2fOdFvjKSQkhGnTpvG///2P3377jaioKGJiYi75OI4e2z0NR+l9yStPl8LUqVM5e/asmEXs3nvvJSoqyipVeVJSEm+88QY33XQTBoOBm2++mbS0NNauXUuvXr3Edn5+fjg7O3P69GmWLVvGddddR0BAABkZGTz55JO4ublx4MCBVoPtz6ejVp56Ko5aeZLoevTk75ggCJw5c4ZDhw5x4sQJsbizQqGgb9++DB48mMjIyG5hqNbW1nLmzBkKCwvFmFBATH0eERFBr169OoVRkJaWxpAhQzh06BCDBw/ukM9oaID9+82G1J49cPCgQHl5y/+zk5OJuDgNSUmNv29NxMZqUKmUoiHl5uaGm5ubGFco0X6am5upqKigvLxc/P65urpx5Egw//hHAOnpf/xP8vLM6e0lzPP2qlWrEASB6dOn49Ge2ipdlNWrV5OWloa3tzeLFi3qNglxJC7Opaw8dajxVF1dzSOPPMLq1asBuOGGG/jggw+sMufJZDI+//xz7rjjDvLz89u09Ldu3cqYMWM4c+YMCxYs4Pjx4zQ0NBAREcH111/P4sWLxYKnF6OjjCdLQgYJ+yDpbT9spXVPNp7ORaPRkJ6ezqFDhygrKxP3BwQEMHjwYKqrq5k2bZoDe2gbBEGgpqaGwsJCzpw5Y5XC37IiFRYW5lDXPnsYT+cjCFBcDIcOwcGDlk2gsrKlQSWTCYSFNRMX10Tv3hp6924iLk5DZKQOd3cXXF1dcXNzs/rbnpuIjmTLli2MHz/eLp+l1+upqamhqqrKypCXyVzZty+apUu9OHLErLtKBVFRsG8fdJfpyVZab9q0iaqqKoYPH050N7YqdTodS5YsoaqqiuTkZObMmXNJN7TsObYlbKt3h7rtXQp+fn5WhUVb41zbLTo6+qKV3CMiIsQMH52NDrRDJVpB0tt+SFrbFjc3N4YNG8ZVV11FcXExaWlppKenU1lZycaNG8nNzaW+vp5BgwYRHx/f6S+G20Imk+Hn54efnx8pKSlUVVVRWFhIcXGxVbyUk5MTvXr1EmOkXLp5PmiZDMLDzduNN5r3CYKMwkJrg+roUSgvl1FU5EpRkSvnVqZwcTERHa0RjaqoqGoiI7WEhjbj4eGMq6srLi4uLbbOsNpnMBhobu6YtN+WzKP19fVWGWnBnHL89OlebN7ci9WrXaiuttTqgwcegOeeg3beg+0yWFbYrpTAwECqqqqorKzs1saTs7Mzs2bN4tNPP+XkyZMcO3aMlJSUdr/fVnpLtA9H6e34WbQb0ZmDorsjkt72Q9K6Y5DJZISHhxMeHs7kyZM5fvw4hw8fpqysTKyp5OHhwYABAxg0aFCXrkEik8nEmNlBgwZRXV0txkXV19eLaWXlcjmBgYGiLufHg9kaX19frrvuOnx9fTv0cy6GTGZe9YiKsk5QUF4O6enW24kTAk1NcrKyPMjKsnahcnISCA1tJjJS+/umITKylshILYGBepRKhZUx5ezsjFKpxNnZWSzT0NHugDt39mX6dHj//csvOCv8XpZDo9Gg1WppbGyksbERjUZjdbOntlZBenog+/f7s327G6Wlf6wihIXBQw/Bvfd2P6PJgq1iwS3fj3PLlXRXQkJCGDt2LJs3b2b9+vX07t27zeLyrb1Xwn44Su8OddvrrHSU257BYOgUd/V6CpLe9sNWWktue+3j1KlTFBQUcPToUasCyKGhoQwaNIh+/fp1uFFhL4Tfa6IVFxdTVFRklbVPJpPh7+9PeHg4YWFhbRZCv1Kqq6vb7fbdGTAazTE5fxhTcOqUeWtqavt9rq5GgoN1hIQ0ExLSfM5j818/Pz1OTjIUCoVoVCkUChQKhdVjy+bk5IRcLsfJyandrk1LlsCiRTBgABw7Bh9/3NKAMplMGI1GjEYjer0enU5n9be5uVmsi3guDQ1y8vLcyc/3IjNTxZEj7uTkWM9bXl5m43T+fBg3Drroom67sdXYrqqqYtOmTbi6unb7QuhgXsH89NNPKSkpoW/fvsyePbtd7+tqc0lXx5Z6dxq3vZ5GY2Njj0xV7igkve2HpLV9ycvLY/LkyYwbN46cnByOHDlCVlaWuDqzYcMGkpKSGDRoEDExMV06cYBMJsPHxwcfHx/69u1LfX29uCJlcROqrKzkyJEjeHt7i3FS/v7+NjlvSzD83Llzu4xB7+QEcXHm7aab/tgvCHD2rNmIysr6w6A6dQpyc0GrdSI/3438/NYNb4XChL+/XtwCAvQEBOh+f6zFx0ePSmXE29uAp6eBc++nWIyoc40pi0Fl+T99+60PL70UzEMPCbz7roxHHxVYtEhGcXExs2ZViQaTyWRqtX8mE9TUKCkrc6aszIuyMhcqKtwoLHQnJ8eFs2dbv6Tp2xemTIGpU2HUqI5xF+ysHDhwwCapnC3fDZ1Od8XH6go4OTlxww038J///IcTJ04waNAg4uLiLvo+W+kt0T4cpbdkPElISEh0UpycnEhMTCQxMZHGxkbS09NFt77jx49z/PhxvL296d+/PykpKV3arc+Cl5eXmP68qalJXJGqqKigrq6Ouro6MjMzcXFxISQkhLCwMIKDgy/btTQjI4M//elPpKSk2C1hREchk5ld0cLCYOxY69f0esjPh4KCP/6e+7ioCAwGOWVlLpSVtc+68PIy4O1t3jw8jLi5mXBxMeHmZsLNzYirqwlXVxNKpcCJEx5s2+bHQw8JvPeeDJkM3ntPBgi8+moYW7e6ExenQa+XodPJqa93oqFBQUODkro6BXV1TqjVTuj1F17hCg01G0tDh8LIkTB8OLSj4onERbB4HlhWBbtqHOalEBISQmpqKnv27GHDhg3ExMT0iPOWuDiS8WRDpJSW7ePc1OaLFi0iISGBJ5544pKPI+ltPySt7cvAgQNb7PPw8GD48OGkpqZSUlLCkSNHOHbsGHV1dezatYtdu3YREhLCgAED6N+/f7t99Dsz7u7uxMfHEx8fj06no6SkhLNnz1JSUkJzczP5+fnk5+cjl8sJCgoSM/d151TKl4tSCfHx5q019HooLYWSkra3qiqoroa6OvN76usV1NcrKC5uXx8eegjRcALOMaDggw982bnz4nFnMhmEhEBkJEREmLeEBLPB1LcvODh0rdPR2lxyOZwb4dEdyim0l2uvvZajR49SUVHBoUOHxLqlbWErvSXah6P0lownG2IwGDossD46Oprq6mrKysrEWIe6ujp69epFVFQUmZmZHfK5l0p+fj5JSUlotdp2tf/4448v+7M6Um8JaySt7Ut1dbVVnbtzkclkhIaGEhoayqRJkzh16hTHjh0jOzubkpISSkpK2LRpE7GxsaSkpJCUlNQt/nfOzs5i4WGTyURlZSXFxcWcPXuW+vp6SktLKS0t5dChQ/j4+BAaGkpISIjN3Pu6O0rlH8bIxdDroabGbEhVV5uNqvp6aGw0x1w1Nv7xuK4OvvgC+vWDd9+F86+7ZTLz/u3bzbFbDz8MHh7mtOG+vn9sfn7mLSTE3FeJ9nGhueRS0Ov1gDl5UE/6Prm6ujJu3DjWrl3L1q1bSUlJuWA2UFvpLdE+HKW3ZDzZEJ1O16FB3MHBwaxevZpbbrkFgJ9++omI9vzSdVM6Wm+JP5C0ti+FhYUkJydftJ1CoaBPnz706dOHpqYmTpw4wdGjRykqKiInJ4ecnBxcXFxITk4mJSWF6OjobnHX2LLSFBQUxKBBg6irq+Ps2bMUFxdTWVlJbW0ttbW1ZGRk4OzsTHBwsLhJq6hXjlIJQUHmrT0MG2ZOEvHYYy0NKEEw728reYTEldHeueRiWGpk9cRV3cGDB7Nnzx6qqqo4cOAAo0aNarOtrfSWaB+O0rvn3D7oBsydO5dly5aJz5ctW8a8efOs2qSnpzNy5Eh8fHwYOnQoe/fuFV+Ljo7mn//8JwkJCXh7e/POO++wf/9++vTpg5+fH2+//bbYVqPR8NBDDxEaGkp4eDj/93//J752xx138MQTTzB+/Hi8vLyYPHmymCFr0qRJNDc34+npiaenJ2fPnr3gOd1xxx28+eabALz00kvcdtttzJ49Gy8vL4YPH05BQYHVuY0ePRpfX1+GDBnC4cOHL0NFCYnuibu7O1dddRV33303Dz/8MNdeey0+Pj40Nzdz5MgRvvzyS95++202b95MWVlZt6rd5e3tTVJSEuPHj2fGjBkMHz6cyMhInJ2d0el0FBYWsn//flavXs369es5cuQI5eXlGI1GR3e9R3DffWbD6P334dFHzQYTmP8++qh5v2Q4dW5qa2sBemRherlczujRowHYvXt3j0maIdE2kvFkQzo6G9nEiRNJS0ujurqa0tJSsrOzxS80mFcHpk+fzrx586ioqOCpp55i2rRpVnUZfvnlFw4cOMDmzZt55plneOutt9i1axdbt27lueeeo6KiAoCnnnoKtVrNqVOn2L9/P1999RVr1qwRj/Ptt9/y7rvvUlFRgcFg4IMPPgBg48aNuLi40NDQQENDA6GhoZd0jj/99BOPPPIINTU1JCQk8MorrwDmu15Tp07l8ccfp7KykhdeeIGFCxe22z1Q4sqQMu3ZlyvNHuTv78/YsWN59NFHufPOOxkyZAiurq7U1dWxc+dOPvroI/7973+zfft2q9Tg3QEXFxeio6O5+uqrmTFjBhMmTKBfv374+/sjk8mora0lMzOTX3/9lZUrV9LU1ER2djaJiYmO7nq35nwDymSSDCd7YKtMZKWlpYC5WG5PpH///vj5+dHU1ERaWlqb7aRMe/bFUXpLbnsXo6kJ2hlP1NjYeHlL2klJ0A5XEoVCwYwZM/j+++/RaDTMnj3byvd47969ODk58eCDDwJw66238u6777Jx40axRsGjjz6KSqVi2LBhBAcHM2fOHHx9ffH19SUyMpLMzEwCAgL4/PPPyc/PF1eQ7r//fn744QemT58OwC233EK/fv0AuPnmm/n1118v/bxbYdKkSVxzzTVi/1988UUAfv75ZwYMGMBNv+flnTFjBq+88gp79uxh7PlppSRsTn19fYfV2JFoybZt2xgzZswVH0cmk4lxQlOnTiUrK4v09HSys7OpqKjg119/5ddffyUsLIz+/fvTt2/fbvV/lsvlYmHefv360dzcTGlpKSUlJZSWlqLVaikqKmLnzp1ER0fj7e1Nr1696NWrF0FBQTg7Ozv6FLoVFgNp0SL47TfJVc8e2GIuaWpqorKyEui5RWDlcjlXX301a9eu5cCBA6SmprbqAm2ruVuifThKb8l4uhiZmTBkSLuaXrYn8KFD0M4UufPnz+cvf/kLGo2GTz75RFxKBzh79iyRkZFW7aOioqxc585NZezm5mZ1F8nNzY3GxkYqKirQaDQkJCSIr5lMJkaOHNnqcdzd3a0KeV4JbR23sLCQLVu24OPjI76u1+spKSmxyedKXJi26q5IdAzNzc02P6ZCoaBv37707dsXrVbLyZMnOX78OLm5uRQXF1NcXMyGDRuIjo6mf//+JCcnd7s4NxcXF9GYFASBmpoadu/ezccff8xjjz0GmBPxZGdnI5PJ8PPzE42pgIAAKU2xDbjvPjhx4gQff9xXMpzsgC3mktOnT2MymQgMDOwWWTwvl/79+7Np0yaqqqrIy8sjNja2RZuOmLsl2sZRekvG08VISjIbN+1Ao9Fc3sVGUlK7m44YMYLi4mKcnZ0ZOHAg27ZtE18LDQ3lzJkzVu0LCwu5+eabL6k7AQEBuLq6UlBQcMnuWh0VjB4WFsb111/PTz/9JO5ramqSgr/tRHfI1taV6Oh6Ta6urgwaNIhBgwbR0NDAiRMnOH78OGfOnCEvL4+8vDx+/vln4uLi6N+/PwkJCd1uFcZiHIWGhlJQUMCIESMICwujrKyMsrIy6urqqKqqoqqqioyMDJycnAgICCA4OJigoCB8fX17VNYxW3LnnTreeqtnFat1FFc6lzQ3N5OdnQ3QriKx3RkXFxdSUlLYv38/aWlprRpP3aHWXlfCUXpLxtPFcHdv96qQ0mDAquR6B/HTTz+1+qM9fPhw9Ho9H330Effccw8rV64kKyuLSZMmXdLx5XI5t99+O0899RRvvfUW3t7eZGVlUV9ff9EaBwEBAeKKkC2X96dNm8azzz7L6tWruf7669HpdGzdupVRo0ZJ8Th2oLtdOHd2WvtR7ig8PT1JTU0lNTWVmpoasfhuWVkZWVlZZGVloVQqiY+Pp2/fvsTHx3fL8aBUKgkPDyc8PBww35yxGFJlZWVoNBrxMZi/E0FBQfTq1YvAwEBUKlW3yGRoD2JjYyXDyU5c6VySnp6OTqcTXft7Ohbj6dSpU+j1+hY3Fu05d0s4Tm/ptpkNaWxstMvnDBgwQIw3OhdnZ2dWrVrFf//7X/z9/XnzzTdZvXr1ZRkX//rXv/Dw8BCDJG+77bZ2BZZ7eHjwzDPP0L9/f3x8fC6aba+9qFQq1q5dy7vvvktgYCDR0dFXVCNK4tKw19iWMHNulkx74uvryzXXXMP999/PAw88IGa31Ov1ZGRk8P333/PWW2/x3XffceLEiW6ddcrd3Z2YmBiGDx/ODTfcwNSpUxkyZAjh4eFiFr+ioiIOHTrE+vXrWblyJTt27CArK4vq6mrJ1fUCOGp890SuROuSkhJycnIAczFS6eaA2cNHpVKh0+k4ffp0i9elsW1fHKW3TOhO+WrbSV1dHSqVCrVabZV2U6vVkpeXR0xMDK6urpd8XLVaLa2C2BFJb/thK62v9DvWU9iwYUOnydokCAKlpaWcOHGCEydOWN1EUSqVJCQk0KdPny67IpWWlsaQIUM4dOgQg9vpZWAymaiurqa8vJzy8nIqKysxGAxWbZRKJQEBAQQFBREYGIivr68UM/U7nWl8d3cuV+v6+no2bdqETqcjPj6eIe2M/e4JrF+/nr179zJ48GBuuOEGq9eksW1fbKl3W7ZBa0huezakuwVXd3Ykve2HpLV96d+/v6O7ICKTyQgJCSEkJITx48e3MKQsj7uqIRUdHc17771HdHR0u99zbha/Pn36YDQaqa2tpby8nIqKCiorK9HpdJSUlIhJbRQKBf7+/gQFBeHv74+/v3+PjSXsTOO7u3M5Wms0GrZv345Op8Pf35+UlJQO6FnXJTY2lr1791rVobQgjW374ii9JePJhkhuGvZF0tt+SFrbF1tlr7Q15xtSJSUlZGRktGpIxcXFkZycTEJCQqdeZfTz82Py5Mn4+fld9jGcnJxEgyg5ORmTyYRarRaNqYqKCpqbm61ipmQyGT4+PuL7AgIC8PT07BGuUZ11fHdHLlXrxsZGtm3bRn19PR4eHowaNQqFHWK5uxIRERHIZDKqqqpoaGiwykAojW374ii9pW+EDWlubu7UFwndDUlv+yFpbV/y8vKsSgV0RmQyGaGhoYSGhrZqSJ08eZKTJ08il8uJiYkhOTmZpKSkTpfquKKignfffZeXXnrJZgVA5XK5WD8vMTERQRCoq6sTjamqqioaGxupqamhpqZGjCtxdXUVDSl/f3/8/Py65YVrVxjf3YVL0bqyspKdO3ei1Wrx8PBg3LhxktdBK7i5uREQEEBFRQUlJSXEx8eLr0lj2744Su/uNytLSEhISNiV8w2psrIy0XgqLy/n9OnTnD59mp9//pmIiAjRkPL19XV01zlz5gz//ve/ueuuu2xmPJ2PTCZDpVKhUqnECy2NRkNlZSWVlZVUVVVRXV2NVqsVa26B2Qjz8fEhICAAX19f/Pz88Pb27hGrUxL2QxAETpw4QUZGBiaTCR8fH6655ho8PC67emW3x2I8VVdXO7orEg5AMp5syMUCzCRsi6S3/ZC0ti8TJkxwdBcuG5lMRnBwMMHBwYwdO5aqqirRkCouLqawsJDCwkI2bNhAcHAwycnJJCcnExgY2KOMAjc3NyIiIoiIiADAaDRSU1MjGlOVlZVoNBqqq6utLtCUSqVoSFn+djV3v648vrsaF9O6oqKCLVu2iM/Dw8NJTU3tsfF47cXi5ltVVWW1Xxrb9sVRekvGkw1paGjAy8vL0d3oMUh62w9Ja/uye/durrnmGkd3wyb4+/szatQoRo0aRV1dHZmZmZw8eZKCggJKS0spLS1l69at+Pn5kZiYSGJiIpGRkT2uAK2lCG9AQABgXg1oamoSjSmLi59erxez/FlwdnbGz8/PanNzc+u0BlV3Gt+dnba01uv1nDx5koyMDHFfQkICgwYN6rTjpjNh+T1samqy2i+NbfviKL0l48mGSEH19kXS235IWtuX83+Quwve3t4MGzaMYcOG0dTURFZWFidPniQ3N5fq6mr27NnDnj17cHNzIz4+nsTERHr37t0j4+1kMhkeHh54eHgQFRUFmL+HdXV14mqUxaDS6XSiIWrBxcUFHx8fq83b27tTpEvvruO7M3K+1nq9npycHLKystBqteL+0aNHExoaau/udVlcfq/yfH6tO2ls2xdH6S0ZTzakOwb2dmYkve2HpLV98ff3d3QXOhx3d3cGDRrEoEGDxIKTWVlZnDp1iqamJo4dO8axY8dwcnIiOjqahIQEEhMT8fHxsWk/vLy8GD58eJdYWbXEQPn4+BAbGwuY3f3UarWVQaVWq1tk97O839vbGx8fH3x9fVGpVPj6+ooXgvaiJ4zvzoJFa41GQ3Z2Njk5OeIFv5eXFykpKYSFhUmrTZeIxa3xfONJGtv2xVF6S1dENqSr3h1dtmwZP/zwAytXrrzsY9xxxx0kJSXxl7/8xYY9uzCO0vvcc7WFdl2Brjq2uypJSUmO7oJdcXZ2FmOfTCYTRUVFZGVlkZWVRWVlpZhwYt26dfTq1Ut07wsNDb3ii774+Hg2bdrU6bIAthcnJyfRVc+CxaCqra2ltrZWNKh0Op24Lz8/X2zv5uYmrkydu3WUUdXTxrejMBqNeHt7s3PnTs6ePSt6EKhUKpKSkoiMjOwUK5FdEb1eD9Cinp00tu2Lo/SWjCcb0tDQgEqlsvlxJ06cyOTJk3nqqaes9j/xxBNUVVXx5ZdfXtLxZDIZJSUlBAcHAzB//nzmz59vs/7ai47SOzo6mm+++Ybhw4dftG1X1e5S6SitJVpn165dPbZKvVwuJzIyksjISCZOnEhlZSWnTp0iKyuLwsJCcTVl+/bteHp6EhcXR3x8/GW79xmNRjZu3MiNN97YbS4kWzOoLDFUFuPJstXX16PRaNBoNGJBXwuurq6oVCq8vLxQqVSiUeXq6npFRmtPHt8djdFopLKyUkzOcuLECXr37g2YM8QlJSVJK002oLm5GaDFDQZpbNsXR+ktGU9dgAULFvDOO+9YGU8mk4lvv/2Wzz//vN3H0ev1UgYdCQmJLoUlicLVV19NU1MT2dnZZGVlkZOTQ0NDA0eOHOHIkSPI5XIiIiKIj48nISGh3dn7jh49ys0338yhQ4cYPHiwHc7IMZwbQxUWFibu1+v1qNVq1Go1dXV14tbY2IhWq0Wr1Vq5/oH5bruXlxdeXl54enqKm5eXF87OztKFuZ2x/I9KSko4e/aslSuZQqEgKSmJmJgY6QaYDamrqwPM7scSPY+elc6og+moYnIzZ84UA6stbNu2DaPRyPjx4yksLOT6668Xq9uvX79ebBcdHc3f//53EhMT6dOnD5MmTQKgd+/eeHp6smfPHr744gumTJkivufXX39l6NCheHt7Ex8fz44dOwD4z3/+Q3x8PF5eXgwYMIBt27a1q//R0dH885//JCEhAW9vb9555x32799Pnz598PPz4+233xbbVldXc+uttxIQEEBcXByffvqp+Nodd9zBY489xrXXXounpyf33XcfpaWlTJgwAZVKxfz58zEajWL7Dz/8kPj4eAICArj99ttpbGwE4IsvvmDSpEncf//9eHt707dvX44cOQLA3XffTWFhIePGjcPT05Nvv/32gud2rnbbtm0jKSmJl19+GT8/P2JiYti0aZPVuc2bN4+goCBiY2MvecXQkUiFEu1Lnz59HN2FTom7uzspKSnMmTOHZ555httvv50RI0YQEBCAyWSioKCAzZs38+9//5t33nmHtWvXkpWV1SIuQeIPlEolAQEB9O7dm0GDBnHttdcyffp0br75ZiZNmkRqairJycmEhYXh5eWFTCZDp9NRVVVFfn4+x48fZ+/evWzevJmVK1eycuVKNm7cyO7du0lPTyc3N5fy8nIaGxtFtzFpfF8+giDQ2NhIQUEBBw8eZN26dfzvf/9jz5495Ofno9PpcHFxITY2lmuuuYZbb72VgQMHSoaTjamoqABoURtOGtv2xVF6SytPNqSjMpJ5eXlxww03sHz5cl599VUAli9fzq233opMJmP69Once++9rFq1igMHDjB9+nSOHz8uuuX973//Y8eOHVbuFqdPnxZfz8rKEj8rNzeXm266iWXLljF16lSKi4vFC4/Q0FC2bNlCeHg4S5cu5dZbb6WgoKBdfvG//PILBw4cICsri2uuuYYbbriBXbt2UVhYyPDhw1mwYAGBgYE8+OCDKBQKCgsLycnJYcKECSQlJTFq1CgAvv/+e7Zs2UJgYCCDBg1i2rRpfPXVV4SGhjJ06FDWrl3LjTfeyPfff88nn3zC5s2bCQoK4q677uLFF1/kn//8JwBbt27l3nvv5YMPPmDx4sU8+eSTbNmyhU8//ZTNmze3223vfHJycvDy8qK8vJzPPvuMRYsWcfr0aQAWLlxIv379OHPmDHl5eYwbN46BAweSkpJyyZ9jb6Rse/ZFuti/OE5OTsTExBATE8PkyZOpqakhOzub7Oxs8vLyUKvVHDx4kIMHD4pJJ+Lj44mLi8Pf319aHbkISqWyhesfmN3C6uvrqa+vp6GhQfzb0NBAU1MTOp2uRW0qCzKZDHd3dxoaGoiOjsbNzQ0PDw/c3d3FTVq5+gOTyURjY6OYVbGmpoba2lqrLHkWfHx8CA4OJiwsDH9/fzHVv+X3R8J2CIIgZrYMCgqyek2au+2Lo/SWjCcb0tzc3GGB9QsWLODRRx/l1Vdfpbm5mR9//JGNGzeyf/9+9Ho9Dz74IAAjRoxgzJgxrFu3jjvvvBOAxx9/vMUXvC1WrFjBjTfeyLRp0wCIjIwUX7v++uvFx/fccw8vvvgi2dnZ9OvX76LHffTRR1GpVAwbNozg4GDmzJmDr68vvr6+REZGkpmZiZ+fHz/++COnT5/G3d2dAQMGcNddd7FixQrReLrlllvEAMGRI0fi5+cn3nkYP348x44d48Ybb2Tp0qU8//zzYorf5557juuvv140nvr378+sWbMAmDdvHh9//HG79LkYKpWKxx9/HJlMxoIFC7jvvvvEC4sdO3awevVqnJycSEpKYt68efz0009dwnjqyLEt0ZKcnBwxTkGiffj6+opp0PV6Pfn5+WRnZ3Pq1Clqa2vFpBNg/p727t2buLi4Vi9EJdrGyclJzPh3PgaDgYaGBhobG1s1rCzGQG5ubpsGkkKhwM3NDVdXV9zc3HBxccHV1bXVrTvEqOn1ejQaDU1NTTQ2NoqGqUW71m5cyWQyfHx8CAwMJCgoiMDAwDZvYkpzie2pqKigvr4epVLZIr27pLd9cZTekvHUHu6/H4qLL9rMXa+HS40pCguDjz66aLPJkydTV1fH3r17KSkpITAwkKuuuorvvvuO7Oxsqx8yg8HAkCFDxOfh4eHt7k5RUZGYAvd8/ve///HKK6+Qm5sLQH19fYvq2m1xrvHm5uZmtdTt5uZGY2MjFRUVGI1Gq/5GRUWxYcOGSzoOQGFhIXfddRf33nuv+LolO875x7HcCbUF58ZZWHyhGxoaKCwspLGx0SqtptFo7BHJJiQk7I1SqSQ+Pp74+HimTp1KVVWVuCpVWFiIWq0mLS2NtLQ0MUnCgQMHCAwMJCwsrMcV6LUVCoWiTcNKEAS0Wi2NjY0IgsDAgQNpamqy2rRaLQaDQTQeLoazszMuLi4olUqUSiXOzs7i33MfW/46OTmhUCiQy+UoFAqcnJxwcnK64pUuQRAwGo0YjUYMBgMGgwGj0YhOp6O5uRmdTmf1WKvVigbTub9LrXGusWq54ahSqaTyEQ4kOzsbMIckSP+Hnon0X28P7TBuAJxMJuigH12lUsmcOXNYvnw5JSUl4kV3WFgY/fv3Jy0trc33XsoPQ0REhJUbn4Xm5mbmzp3LqlWrGD9+PE5OToSEhCAIwqWfTBsEBgYil8spKioiIiICMBtBbRXuu1Dyi7CwMN58801uuOGGS+5HR7iMhIWF4ePj025js7PRFWrgdCfGjh3r6C50G2QymZh0YsSIEej1egoKCsjJyeH06dMYjUYef/xxioqKWLp0Ka6ursTExBAXF0fv3r1tXleqpyKTyXBzc8PNzY2bb765RYpnMN/4sxhRGo2G5uZmMWmFZWtubkaj0WAymUSj5EpxcnJCLpcjk8ms/gLiX0EQxM1kMlk9NhqNV/RbqFQqRbdFSyIOy+bu7n5Fv0nSXGJbBEHg8OHDACQmJrZ4XdLbvjhKb8l4siFNTU0dWitk/vz5zJgxg4aGBl5//XUAUlNT0ev1fPLJJ9xxxx0A7Nu3j6ioKCuXu3MJCgoiPz9fjHk6l7lz5zJw4EB++eUXpkyZIsY8BQYGin8B3n33XTFg0lY4OTkxc+ZMnn/+eZYsWcLp06dZunQpP/zwQ6vtDQZDm8e66667eO211+jXrx+xsbGUlJRw9OhRq8QYbWHR53JintoiLCyMq666ihdffJG//OUvODs7c+zYMVxdXbtEgGlHj20Jaw4ePMjVV1/t6G50S5RKJXFxccTFxQGgVqtZuXIlHh4e5ObmotFoOHnypJigx9/fn5iYGGJjY4mOjpaya9mAtsa3QqEQ06FfCEEQWqzq6HQ69Hq91d9zH5+7ImQwGKzc4SyrRrbAspqlUChQKpW4uLiIK2GWxxaXRHd3d9zc3Do0C640l9iWgoICKisrcXZ2pn///i1el/S2L47SWzKebIitJt+2uPrqq/Hy8iImJob4+HjA/GOzdu1aHn30UZ5//nkEQWDo0KEXjOF58cUXufHGG2lubrbKzAcQExPDjz/+yNNPP80tt9xCSEgIn332Gb179+att95i4sSJyGQy7r//fvHiw5Z8+OGHPPDAA4SHh6NSqXjllVe45pprWm17oTt9t956KzU1NVx33XUUFxcTEhLCokWL2mU8PfPMMzzyyCMsWrSITz75hDlz5lz2+ZzLsmXLeOKJJ4iNjUWn09GvXz+rTIOdmY4e2xLWtMdlScI2VFZW8vHHH7Ns2TJuvvlmSkpKxFWpoqIiqqqqqKqq4uDBg8hkMnr16kVsbCwxMTFERUW1uoIicWGudHzLZDJcXFyuqIivZcXIsplMJqsVJcuqEph/a2Qymbiduyoll8tFY8mygtWZkOYS2yEIAr/99hsAAwYMaHX8SXrbF0fpLRNs6XfVRairq0OlUqFWq63ucGm1WvLy8oiJibms4PiGhgbp7rwdkfS2H7bS+kq/Yz2Fffv2kZqa6uhu9AjS0tIYMmRIq3WetFotBQUF5ObmkpeXR3l5udXrcrmc8PBwcWUqPDy8WyQx6Gik8W0/JK1tR05ODl9//TVOTk48/PDDrbr0SnrbF1vq3ZZt0BoduvJUU1PDI488wurVqwG44YYbeP/99y/oQ37HHXe0qH+TmprK3r17xefNzc089dRTrFixAo1Gw/jx4/n3v/99SYkROgLJncO+SHrbD0lr+zJgwABHd0ECcHV1JTExUYxtaGhoIC8vj7y8PHJzc6mtraWwsJDCwkJ+++03lEolUVFRREdHEx0dTUhIiGRMtYI0vu2HpLVt0Ov1rFu3DoBhw4a1eR0r6W1fHKV3hxpP8+bNo6ioSHQNu/fee1m4cCFr1qy54PumTJnC559/Lj4/3y3iscceY82aNXzzzTf4+/vz5JNPMm3aNA4dOuTQH6r6+nqpEJ0dkfS2H5LW9mX79u1MnjzZ0d2QOA9PT0/69+8vxjrU1NSIq1J5eXk0NjaSk5NDTk4OYP7tioiIIDo6mqioKMLCwiRjCml82xNJa9uwadMmqqqq8Pb2ZvTo0W22k/S2L47Su8OMp5MnT7J+/Xr27t0rLqn95z//YcSIEWRlZbWapcSCi4tLq8kMwBzcu3TpUv773/8yYcIEAL7++msiIiLYvHmzNGglJCQkJOyCr68vQ4YMYciQIQiCQHl5OXl5eeTn51NQUIBGo7GqL6VUKgkPDxdXpsLCwqRUxxISnZwTJ06wf/9+AG688Ubc3Nwc3CMJR9Nhs/aePXtQqVRWvojDhw9HpVKxe/fuCxpP27ZtIygoCB8fH6699lpee+01sS7PoUOH0Ov1TJo0SWwfGhpKv3792L17t0ONJymGw75IetsPSWv7cqH5UcK2hIWF8fzzzxMWFnZFx7Ekk+jVqxfDhw8XjSmLIZWfn09TU5O4SgXmhD/h4eFERUWJK1NXkgShqyCNb/shaX1lFBcXs3LlSgBGjBhx0YKskt72xVF6d5jxVFpaalWI1EJQUBClpaVtvm/q1KnMnj2bqKgo8vLyeOGFFxg3bhyHDh3CxcWF0tJSnJ2d8fX1tXpfr1692jxuc3Mzzc3N4vO6urrLPCsJCQkJie5Er169uPvuu+nVq5dNj3uuMZWamoogCFRUVIiGVEFBAQ0NDeTn55Ofny++Jzg4mMjISCIiIoiMjLxo4LKEhETHUFZWxvLlyzEYDCQkJDBx4kRHd0mik3DJxtNLL73Eyy+/fME2Bw4cAFovNmpJ+dkWt9xyi/i4X79+DB06lKioKH7++WdmzpzZ5vsudNw33nij1T5v3rwZDw8Pxo0bx/79+9FoNAQEBGA0GlGr1cAfd9y1Wi1gLhba1NSE0WjEyckJd3d3MVWiJZ2zpa2np6dYMV0ul+Pp6Skabi4uLsjlcjQazUXbOjs7o1AoaGpqAsDDw0OsXyGTyfD29hb7e35bd3d3DAYDOp1ObFtXV4cgCGLV9cbGxhZtAVQqFfX19ZhMphZt3dzcMJlMolHq7e1NQ0MDJpMJhUKBq6srDQ0Nrba9kIaXqrdMJmuXhufrfa6GcrkcLy+vNjVsTW+LhhfS26Jhe/W+FA0v1PZSNDy/7bkanttWp9Ph5eXV7jHblt6NjY3iZ23YsAEwF2YOCAgQCw8OHTqUs2fPcvbsWZycnJgwYQKbN2/GaDQSGhpKaGgoBw8eBGDQoEFUVlZy5swZACZPnszWrVvR6XT06tWL6Oho9u3bB5gDS+vq6sQL1YkTJ7Jr1y6ampoICAggISGB3bt3A9C3b1+0Wq3obmWZIxoaGvD19aVv377s3LkTgKSkJEwmE6dOnQLg2muv5ciRI2LGnsGDB7Nt2zYA4uPjUSgUYg2hUaNGkZGRQXV1NR4eHgwfPpwtW7aQn5/PuHHjcHd35/jx44D5rmdOTg4VFRW4uroyevRoNm7cCEBUVBQ+Pj4cPXoUMAc0FxYWUlpailKpZNy4cWzcuBFBEAgPDycoKEgsrD1kyBBKS0spLi5GLpczceJEtmzZgsFgICQkhPDwcHFOHzhwINXV1RQWFop6b9u2jebmZoKCgoiNjRWT+/Tv319MsAAwYcIEdu/eTVNTE/7+/iQlJbFr1y4A+vTpg06nE2OFxo4dy8GDB6mvr8fHx4cBAwawfft24I87jZZi3qNHj+bYsWPU1tbi5eXF0KFD2bp1KwBxcXE4OzuTkZEBwMiRI8nMzKSqqgp3d3euvvpqVq5cycaNG7nnnnsICwsjPT0dMHtK5ObmUl5ejouLC2PGjBHHbGRkJH5+fhw5cgSAq666iqKiIkpKSlAoFIwfP55NmzZhMpkICwsjODiYQ4cOATB48GCxGGp9fT2xsbGsWbNGvPnX1NTEjh07AAgODkapVKJQKAgMDGTOnDmcPHlSrLkXFxfHnj17APPvZVNTE7m5uQCMHz+evXv30tjYiJ+fH3369BHHbHJyMgaDgezsbADGjBlDWlqamG1q4MCBYkrmhIQE5HI5mZmZ4pg9ceIENTU1eHp6MmzYMH799VcAevfujaurKydOnADM5TVOnTpFZWUl7u7ujBw5kk2bNpGfn8+YMWPw9vbm2LFjgDkxVH5+PmVlZTg7OzN27FhpjqDtOQIgNjb2onPEhg0bxJg7aY5o/xyRnZ3N9u3bCQwMFN+Xk5ODp6fnReeI6OjoK54jysvLKSoqQiaTMWnSJH799Vf0er14c8XiRpiSkkJtbS0FBQUATJo0ie3bt6PVarv0HAEQHR190Tli48aNREVF2WSOsPS/PVxyqvLKykoqKysv2CY6Oprly5fzxBNPUFtba/Waj48Pb7/9NnfeeWe7PzM+Pp67776bZ555hl9//ZXx48dTXV1ttfqUkpLCjBkzWjWSWlt5ioiIsHmqcrVaLQXV2xFJb/thK62lVOXtY8OGDVL8pp24UKpye1NXV0dhYSFnzpwRL2zP/4l2dXUlPDycyMhIwsPDCQ0N7XLfJWl82w9J60snJyeHH374Aa1WS2hoKAsXLmx3nJOkt32xpd4dmqo8ICCAgICAi7YbMWIEarWa/fv3M2zYMMCcj12tVl9SNeCqqirOnDlDSEgIYL4DolQq2bRpk1i8tKSkhOPHj/P3v/+91WNcaTG99uLl5dXhnyHxB5Le9kPS2r5cKJuTRPfF29ubfv360a9fP8B846+oqEg0poqKitBqtVYZ/WQyGQEBAYSHh4tbYGBgpyvWei7S+LYfktbtRxAE9u3bx4YNGxAEgcjISObNm3dJNyckve2Lo/TusJin5ORkpkyZwj333MOSJUsAc6ryadOmWQV4JSUl8cYbb3DTTTfR0NDASy+9xM0330xISAj5+fk899xzBAQEcNNNNwFmN6i77rqLJ598En9/f/z8/Hjqqafo37+/mH3PUTQ1NUlFW+2IpLf9kLS2L8eOHZMKLUrg4uJC7969xSB1k8lEWVmZuDpVVFREbW0tFRUVVFRUiC4rzs7OhIWFERYWJhpUnen7K41v+yFp3T6amppYvXq16Lo1aNAgrr/++kvOhinpbV8cpXeH3ppatmwZ/fv3Z9KkSUyaNIkBAwbw3//+16pNVlaWGGvi5OREeno6N954IwkJCdx+++0kJCSwZ88eqzvfb7/9NjNmzGDOnDmMHDkSd3d31qxZ49j6GUYjbNsGK1aY//4e/2QroqOjrQoFAyxatIiXXnrJpp/TmVi1ahVJSUmoVCpCQkJ44oknxLgywOrx66+/jqenp7i5uLiItVjO54svvkAmk/G3v/3Nav9zzz2HTCbjm2++sWpnMf7BnAjlQjF73RWjjcezxIU5391ZQgJALpcTEhJCamoqs2bN4rHHHuOpp55i7ty5XHPNNcTExODs7IxOpyMvL4+dO3fyzTff8I9//IN33nmH77//nl27dpGbmyvGHjoCaXzbD0nri7Nu3Tr+/ve/k5mZiZOTE1OmTOGGG264rDICkt72xVF6d2iBCT8/P77++usLtjnXn9vNzU0MEL0Qrq6uvP/++7z//vtX3Eeb8NNP8OijeBYV/bEvPBzefRcukORC4sIMHTqUHTt2EBgYSE1NDbNnz+aTTz7h/vvvB7Aylp977jmee+458fnMmTPp27dvm8eOi4tj+fLl/PWvfwXM4/Dbb79tkYbU19eX119/nT/96U8olUpbnl6XQirsaV8kN0n74ebmRkJCQpet3eLp6UliYqLo0WEymaisrKSoqEjcKioqqK2tpba2VgzUBvNvtCV4OjQ0lJCQEMnFvZshad02tbW1bNiwQUzOAXDPPfe0WWe0PUh62xdH6S1V57tSfvoJZs2C8/NuFBeb9//wg10MqC+++ILly5cTFRXFN998Q2JiIqtWreL111/n66+/JikpiZUrVxIaGorJZGLWrFns3LkTg8HA+PHjWbJkCX5+fmzbto358+eTnp6On58f33//PX/96185cuSI1cWFRqOhV69epKenExUVBZizFz722GNi5p8rpbW6K5asPGDOVtcatbW1/PLLL7z++uttHrt3797U1NSQlpbG4MGD2b17NxERES3aDRs2jIaGBj7//HPuvffeyziL7kFbWkt0DEOHDnV0F3oMycnJpKen4+zs7Oiu2AS5XE5QUBBBQUFiAozm5mYx85Rlq6mpobq6murqaqs529/f38qg6tWrl80TUkjj235IWrekubmZnTt3smfPHgwGg7j/z3/+8xX/1kl62xdH6d15I0q7AkYjPPpoS8MJ/tj32GM2d+Fri61bt3LddddRXV1NeHg4I0eO5Nprr6Wqqoro6Gjeeustse3MmTPFYo319fW88sorgDkd5c0338xDDz1ERUUFDz/8MF988UWLu7Jubm5MmzaN77//Xtz33XffWaWaP5dp06bh4+PT6vbmm2+2eU47d+5EpVLh5+dHeno6f/rTn8TXLOm2z+eHH36gX79+JCUlXVCv+fPns3z5cgCWL1/O/PnzW223ePFiXn/9dfR6/QWP151pS2uJjsGSRlfCPnR3vV1cXIiJiWHkyJHMnj2bRx99lD//+c8sXLiQ8ePHk5ycLGbTrKqqIj09nQ0bNvD555/z5ptv8u677/Ltt9/y22+/kZmZSW1tbYssgJdCd9e7MyFp/QfNzc3s2LGDd999lx07dmAwGIiJiRFDIGxxk1DS2744Sm9p5elK2LEDznXVOx9BgDNnzO3GjLnij5s4caKV+5RGo+HZZ58Vn/fv319MrHHjjTeSnZ0tZiScMWMGn376KWC+M7lgwQLxfY8//jjPP/+8+PzNN98kJSWFMWPGsHDhQkaMGNFqf2655RZee+01nnrqKQwGAytXrhRrMpzP2rVrL+ucR40ahVqtJi8vjy+++KJFceTWWLZsWZuG0LnccsstDBs2jNdff51Vq1bxt7/9jWXLlrVoN3HiRMLCwvjiiy+YPn36ZZ2HhIRE5+Tw4cNMnz6dffv2MWjQIEd3x264u7tbJaMAaGxspKSkRFydKikpQa1WU1NTQ01NjZV7k6urK8HBwVZbYGCg5OIr0eloaGjgwIEDYj1PMK+wTpw4kcTExB4ZxyxxZUjG05VQUmLbdhdh06ZNDB8+XHy+aNEiq9eDgoLEx25ubgQGBlo9txRnNRgMPPXUU6xcuZKamhoEQbBKP+/u7s6tt97Ka6+9xvr169vsz5QpU7j99tvJz88nKyuL8PBwEhISrvg8WyMmJob+/fvz2GOPsWLFCoBWffOLiorYuXOnuKJ0IXr16kVSUhLPPfccQ4cOvaBhtnjxYu677z6mTJly+SfRhbFHHITEH8TFxTm6Cz0GQRDQ6/VXtJLSXfDw8CAuLs5q/Gk0GkpLS8WtrKyM8vJytFot+fn5YkFZMMdG+vv7i26Dls3Hx8cqdbo0vu1HT9VaEASKiorYv38/GRkZYtKjgIAARo8eTb9+/ToknX9P1dtROEpvyXi6En6vPWWzdnZi2bJl7Nixgz179hAaGsqGDRu47777xNezs7P56KOPmD17Nk8++STfffddq8dxcXHhxhtv5PvvvyczM7NNlz2AqVOnsmPHjlZfOz/ZQ1uYTCaxmjvQ6sS3YsUKxowZI9YFuxjz5s3jzjvvFDPstcWkSZMICQnhyy+/bNdxuxuduWZMd6S7xN9IdH3c3NyIiYkhJiZG3GcwGKisrLQyqkpLS9FqtZSXl1NeXm51DIVCQWBgoGhMGY1GAgIC8Pb2lu76dzA9bS6pq6vj2LFjHD16lIqKCnF/REQEw4cPJzk5uUN/z3qa3o7GUXpLxtOVcM015qx6xcWtxz3JZObXr7nG/n27APX19bi4uODj40NlZSX/+Mc/xNdMJhO33347zz//PIsWLSIlJYXvvvtOdP+Ljo7mpZde4o477gDMrm/PP/88hYWFHDhwoM3PXLdu3SX38/vvvyc1NZXIyEhycnJ48803mThxovi6RqNp8cVZtmwZjz32WLs/Y/bs2fTq1Ysx7XCrXLx4MfPmzWv3sbsTrWkt0XFkZGS0msBEQqIzoFAoRFc9C4IgoFarqaioEA2o8vJyKioqMBgMlJSUUPK7F0ZOTg5xcXG4uLgQEBAgbv7+/gQEBODn53dZaaIlWtIT5pL6+noyMzM5efIkeXl54iqyQqGgX79+pKamtvuG6pXSE/TuTDhKb2l2uhKcnMzpyGfNMhtK5xpQlrtp77xjbteJuO222/j5558JCgoiIiKCu+++m+zsbAD+8Y9/4OTkxKOPPopcLufzzz9n5syZjBkzBl9fX6qqqqxcBydOnMjChQuJjY0lNjbWpv3Mzs7m8ccfp6amBn9/f2bPns3LL78svh4WFsa6deu45nfjNCMjg6ysLGZeQnZDd3f3drviTZ48mYSEhBb1tiQkJCR6OjKZTEwCFB8fL+43mUzU1NRYGVWVlZXI5XKam5spLi6muLi4xbF8fX1FY+pc48rDw0NarerhCIJAWVkZOTk5ZGVlcebMGavXo6KiSElJoU+fPjbPFCkhASATeqCjd11dHSqVCrVajbe3t7hfq9WSl5dHTEzMpX3hfq/zZJU8IiLCbDh1ozpPe/bs4b333hNjjhyN0WiUgpPthK20vuzvWA+joaEBT09PR3ejR6DRaDh+/Dj9+vXrsrWeuhoNDQ24ublRVVVFVVUVlZWV4lZVVXXBAr7Ozs74+fnh6+tr9dfPzw9vb2/Jxfg8usNcIggCtbW1FBYWcvr0aXJzc2loaLBqEx4eTnJyMn369GlXYqmOojvo3ZWwpd5t2QatIa082YKZM+HGG9Fs3Ihbba05xumaazrditOVMmLEiDYzDtldLAAARUVJREFU7zkCrVaLh4eHo7vRI5C0ti+ZmZlSvRA74ebmhkwmkwwnO2IZ35YYqHMRBIHGxkYrY8ryuLa2Fp1OJ8ZYnY+TkxM+Pj5WhpVKpcLHxweVSoW7u3uPW7XqinOJXq+nrKyMs2fPUlhYSGFhIXV1dVZtlEolMTExxMXFkZSUdNGLXXvRFfXuyjhKb8l4shVOTuiuvhq332tlSHQ85xa3k+hYJK3tS1VVlaO70GMoKCjgr3/9K0uWLBELfkt0LBca3zKZDE9PTzw9PYmOjrZ6zWAwUFtbS3V1tVjk99y/RqNRXM1qDaVSiUqlstoshpVKpcLLy6vbxVp19rlEq9VSUlJCaWmpGBdXWVnZIvulXC4nNDSU6OhoevfuTURERKf8X3V2vbsbjtK78428LozkLmBfJL3th6S1fbFFsUaJ9lFVVcWGDRuoqqqSjCc7cbnjW6FQiPFP52Mymaivr7cyqGpra6mtrUWtVtPQ0IBerxdXsS7UNy8vLzw9PfHy8mp18/T07DIu451hLjEYDNTU1IiG7bnb+e53Fjw8PAgJCSEiIoKoqCjCwsJQKpV27vml0xn07kk4Sm/JeLIhkp+rfZH0th+S1vbl6quvdnQXJCQ6jI4Y33K5XFxBOjetugWDwUBdXR1qtVrcLIaVZTMYDDQ1NdHU1ERZWdkFP8/NzQ13d3fc3d3x8PCw+nv+Y1dXV1xcXBziMtjRc4ler6ehoYG6ujrq6+upq6trsdXX11+wjpqPjw8hISEEBwcTEhJCSEgInp6eXdLFUpq77Yuj9JaMJxtiCTaTsA+S3vZD0tq+bN68mcmTJzu6GxISHYIjxrdCoRATS7SGIAhotVrq6+svuDU0NGA0GtFoNGg0mna7DclkMpydnXF1dRWNqfMfOzs7o1QqUSqVKBQK8fG5m0KhwMnJCblcLm4ymazFc5lMhiAIbNy4kUmTJmEymTCZTAiCYPXYYDCg1+vR6XTo9fpWH2u1WjQaDU1NTeJ5Wx6316XbxcUFf3//Fpufn1+3Sh4kzd32xVF6S8aThISEhISERI/GkjTEzc2tRRKLcxEEQVydampqorGx0erv+Y+bmpowGAwIgkBzczPNzc2o1Wq7nI8gCOTk5LBv374O/SyFQoG3tzfe3t54eXmJjy2bSqWSUsxLdCsk48mGuLi4OLoLPQpJb/shaW1fWnM7kugYevXqxb333kuvXr0c3ZUeQ1ce3zKZDA8Pj0vKPmowGNBqtTQ3N6PVals8tjy3rPhYVoPa2s5dOWoLy2sXStstk8nEFa5zV73OfaxUKkWj0uKqeP5jZ2dnyTD6na48trsijtJbMp5siBRUb18kve2HpLV9kWLM7EdYWBiLFy8mNDTU0V3pMfS08a1QKMQMgrZEEIQWbnjnPpbJZJSWlhIWFmbl2mdx65MMHtvT08a2o3GU3tIVkQ3RaDQdduzo6Gj27t1rtW/RokW89NJLHfaZ55KVlcW0adMICAggMDCQBQsWUFNT02b7zZs3k5KSgpeXF3369OHnn39us61MJiMuLs5qX3Z2NjKZjClTpli1Ozc4UKPRMGXKFL744ovLPzGJdtGRY1uiJenp6Y7uQo+hvr6er7/+mvr6ekd3pccgjW/bYDGIFAoFzs7OuLi44ObmhoeHB56ennh4eJCTkyPGVSmVSjFeSjKcOgZpbNsXR+ktGU8S7UKtVjNnzhxOnz5Nfn4+Op2Op556qtW2BoOBWbNm8fTTT1NXV8c///lPbr311hZF7s5FLpdb+WUvW7aM+Pj4Fu0yMzPZuHHjlZ+QhISEBOYbNc888wzZ2dmO7oqEhISERBdAMp5sQHY2pKVBVpYHaWmIm71/i99//31iY2MJDAzktttuu6Cx0hZt+VAPGzaM2267TQz8vOeee9i/f3+rbdVqNXV1dcydOxeZTMbUqVNxc3OjoKCgzc+dO3cuy5YtE5+vWLGCuXPntmj3+OOP8/LLLwNcks+5xJUhaW1fhg8f7uguSEh0GNL4th+S1vZF0tu+OEpvyXi6QrKzISEBhgyB1FQFQ4YgbgkJ9jOgNmzYwJtvvsnPP/9Mfn4+jY2NPPHEE622LSsr45577iEqKorBgwfz6quvsmfPHn766Sduu+22dn3e7t276du3b6uv+fv7c8stt/Df//4Xo9HImjVr8PDwICEhoc3jzZkzh5UrV2I0Gjlw4AABAQGtBgLecccdFBcXs2nTJnQ6Xbv6KnHlSFrbl9zcXEd3QUKiw5DGt/2QtLYvkt72xVF6S8bTFWJxk//6azh06I/t66+tX7cFEydOxMfHR9w+//xz8bVvv/2WRYsWkZycjIeHB6+//jrffPNNq8fZu3cvU6dO5fjx43z55Zc0NTXx/PPP88svv/DCCy9ctB9Hjhzhvffeu2DbWbNm8eSTT+Li4sItt9zCRx99dMGMbf7+/qSkpLB582aWLVvGvHnzWm2nVCp57rnnePnll9Hr9Rftq4RtkLS2L+Xl5Y7ugoREhyGNb/shaW1fJL3ti6P0lownG5GcDIMH/7ElJ9v+MzZt2kRtba243XnnneJrZ8+eJTIyUnweFRVFY2Njq/Ukrr/+esrLy7n77rv58MMPmTBhAps2beK1115j1apVF+xDXl4e06dPZ+nSpW2uPJ08eZI777yTVatWodPp2LBhAwsXLqSwsPCCx54/fz7//e9/+emnn5gzZ06b7e68806Kior47bffLng8CdshZduzL1JqePuhVCoJCAhAqVQ6uis9Bml82w9Ja/si6W1fHKW3dEXUTQgNDbUyTgoLC3F3d0elUrVo+/XXX5Odnc0dd9xBSkoKr7/+Ov7+/owdO5bw8PA2P6O0tJSJEyfywgsvMGPGjDbbHT9+nJSUFEaNGoVcLueaa64hMTHxooX6brzxRlavXk2/fv0IDAxss51SqeTZZ5/lrbfeuuDxJGyHl5eXo7vQoxgzZoyju9Bj6N+/PxUVFfTv39/RXekxSOPbfkha2xdJb/viKL0l46mbMHv2bJYsWUJmZiaNjY08//zz3Hrrra22XbhwIf/85z+ZOnUq999/P1u2bKG2tpaMjIxWkzSAOQnE5MmTue2227j33nsv2JeBAweSnp4uGkt79uzh6NGjba5UWXB3d2fTpk28//77Fz3fO++8k/z8fA4cOHDRthJXTmsrmBIdx4YNGxzdhR6FpLd9kfS2H5LW9kXS2744Sm/JeLIRJ09ilWnv5En7fv7UqVN5+umnmTp1KlFRUbi4uPDPf/6z1bZOTk6XfPz//e9/HDt2jL///e9isb9zi5MtWrSIRYsWARAfH88HH3zAbbfdhpeXF7fddhvvvPMOffr0uejnpKam0rt374u2c3Z25oknnqC6uvqSz0VCQkLCQnp6OgsWLJDqs0hISEhItAuZ0FZu6m5MXV0dKpUKtVqNt7e3uF+r1ZKXl0dMTAyurq7tOpYl215bnDoFrZQrkrABGo0GNzc3R3ejR2ArrS/nO9YTOXnyJMkdETgp0YK0tDSGDBnCoUOHGDx4sKO70yOQxrf9kLS2L5Le9sWWerdlG7SGwiaf2IOJjzcbSPX1YDDoUSj+CDr28pIMp45EoZCGr72QtLYvfn5+ju6ChESHIY1v+yFpbV8kve2Lo/SW3PZsQHy8OcNeYmKTVcY9yXDqWJqamhzdhR6DpLV9OXLkiKO7ICHRYUjj235IWtsXSW/74ii9JeNJQkJCQkJCQkJCQkKiHUjGkw3x8PBwdBd6FJLe9kPS2r5cddVVju5CjyE+Pp5Vq1YRL7kK2A1pfNsPSWv7IultXxylt2Q82RCdTufoLvQoJL3th6S1fSkqKnJ0F3oMXl5eREdHS7XM7Ig0vu2HpLV9kfS2L47SWzKebIher3d0F3oUkt72Q9LavpSUlDi6Cz2G4uJiXnvtNYqLix3dlR6DNL7th6S1fZH0ti+O0lsynmyITCZzdBd6FJLe9kPS2r5I2Q3tR1lZGd999x1lZWWO7kqPQRrf9kPS2r5IetsXR+ktGU825GJ54SVsi6S3/ZC0ti/jx493dBckJDoMaXzbD0lr+yLpbV8cpbdkPNmQuro6R3ehRyHpbT8kre3Lpk2bHN0FCYkOQxrf9kPS2r5IetsXR+ndocZTTU0NCxcuRKVSoVKpWLhwIbW1tRd8j0wma3V76623xDZjxoxp8fqtt97akafSLgRB6LBjR0dH4+3tjUajEffV1dXh5uZGUlJSh33u+TzxxBPExsbi5eXF0KFD2b59e5ttP/74Y2JjY/H29iYyMpI33nijzbZ33HEHMpmMnTt3Wu2/+uqrkclklJaWiu2cnJw4efKkqPc333zDmDFjrvzkJNqkI8e2REtMJpOjuyAh0WFI49t+SFrbF0lv++IovTvUeJo3bx5Hjhxh/fr1rF+/niNHjrBw4cILvqekpMRq++yzz5DJZNx8881W7e655x6rdkuWLOnIU2kXzs7OHXr84OBgVq9eLT7/6aefiIiI6NDPPB+VSsXGjRtRq9U888wzzJgxg/r6+lbbTpkyhbS0NOrq6ti3bx/Lli1j3bp1bR47Pj6eZcuWic/z8vKoqqpqtQ+vvvpqh+st8QeS1vYlLCzM0V3oMfj7+zNz5kz8/f0d3ZUegzS+7YektX2R9LYvjtK7w4ynkydPsn79ej799FNGjBjBiBEj+M9//sPatWvJyspq833BwcFW26pVqxg7diyxsbFW7dzd3a3aqVSqjjqVi5KdDWlpkJ6uIC0NccvOtu3nzJ0718q4WLZsGfPmzbNqk56ezsiRI/Hx8WHo0KHs3bv3sj6rrZWGxYsXExcXh1wuZ/bs2bi5uXHq1KlW20ZHR+Pj4yM+l8lk5OXltfmZM2fOZPXq1WJmt+XLlzN37twW7e6++27WrVtHTk5Oi9fy8/NxdXXlo48+IigoiIiICLZt28bSpUsJCQkhMjKS33777UKnLtEKUhCsfQkODnZ0F3oMUVFRLFmyhKioKEd3pccgjW/7IWltXyS97Yuj9O4w42nPnj2oVCpSU1PFfcOHD0elUrF79+52HaOsrIyff/6Zu+66q8Vry5YtIyAggL59+/LUU0+1ufoB0NzcTF1dndVmK7KzISEBhgyB1FQlQ4YgbgkJtjWgJk6cSFpaGtXV1ZSWlpKdnc3o0aPF13U6HdOnT2fevHlUVFTw1FNPMW3aNNRqdavH++ijjxg4cCCRkZHcddddrF27lu3bt/Pggw9y8ODBi/YnPz+f6upq4uLi2myzfPlyvLy8CA0NRavVMmvWrDbb+vj4kJqayoYNGwBYsWJFC+MQwM/PjwceeIBXX3211ePodDry8/MpLi7m0UcfZcGCBWRkZFBQUMCf//xnHnvssYuem4Q1TU1Nju5Cj+LQoUOO7kKPQaPR8OOPP1q5REt0LNL4th+S1vZF0tu+OErvDrudXFpaSlBQUIv9QUFBYvzKxfjyyy/x8vJi5syZVvvnz59PTEwMwcHBHD9+nGeffZajR4+2GTj2xhtv8PLLL7fYv3nzZjw8PBg3bhz79+9Ho9EQEBCA0WgUDQ5XV1cAtFotYC6o2NTUhNFoxMnJCbXaHZDz9deQnPzHsU+ehAULoLxcQ1CQDrlcjqenp2i4ubi4IJfLxR9sT09PtFotBoOhRVuLy5RWq+W6667jm2++oaGhgRtuuEF8v1qtZteuXcjlcu69916ampqYOnUqcXFxrF27lmnTpiGTyfD29qaurg6tVkt2djb/+9//MBqN/PLLL3z88ccIgsDNN99MQkICAPX19ZhMJpRKJc7OzjQ2NgLmlYiFCxfy8MMPA+aVqoaGBkwmEwqFAldXVxoaGrj++uupqqoiPT2dH374Ab1ej8lkstLQ3d0dnU6HVqtl9uzZfPXVV6hUKpydnUXDrK6uDk9PTwRBQKvVcvfddzNw4ECOHTtGU1MTBoMBo9FIU1MTgiDwyCOPoFAomDhxIk8//bRoME2cOJEnnngCnU6HwWBAr9cjl8vx8vIS/+fOzs4oFArRYPDw8ECn06HX6600FAShRVt3d3cMBoNYVFalUoltz9fw/Lbe3t6tagjg5uaGyWSiubn5om0vNmbd3d3Fmw3ntz13HJ7b1vL/ae+YPX98WzRsbGwUP8tiJEdERBAQEMDhw4cBGDp0KGfPnuXs2bM4OTkxYcIENm/ejNFoJDQ0lNDQUNG4HzRoEJWVlZw5cwaAyZMns3XrVnQ6Hb169SI6Opp9+/YBMGDAAOrq6sjPzxfHwq5du2hqaiIgIICEhATxxk7fvn3RarWcPn0aQJwjGhoa8PX1pW/fvmJ8XlJSEiaTSVyBvfbaazly5AhqtRpvb28GDx7Mtm3bALNrqkKh4OTJkwCMGjWKjIwMqqur8fDwYPjw4WzZsoX8/Hyys7Nxd3fn+PHjAIwYMYKcnBwqKipwdXVl9OjRbNy4ETCvnvj4+HD06FEAhg0bRmFhIaWlpSiVSsaNG8fGjRsRBIHw8HCCgoJIS0sDYMiQIZSWllJcXIxcLmfixIls2bIFg8FASEgI4eHhHDhwAICBAwdSXV1NYWGhqPe2bdtobm4mKCiI2NhYcbW7f//+NDQ0iKvNEyZMYPfu3TQ1NeHv709SUhK7du0CoE+fPuh0OnE1eezYsRw8eJD6+np8fHwYMGCAGF+ZmJgIIHowjB49mmPHjlFbWyvGYm7duhWAuLg4nJ2dycjIAGDkyJFkZmZSVVWFu7s7V199NUuXLuXhhx8mKCiI1NRU0tPTAfPNvtzcXMrLy3FxcWHMmDHimI2MjMTPz48jR44A5ir3RUVFlJSUoFAoGD9+PJs2bcJkMhEWFkZwcLD4Iz948GDKy8spKipCJpMxadIkfv31V/R6PcHBwURGRrJ//34AUlJSqK2tpaCgAIBJkyaxfft2tFotgYGBxMXFsWfPHgD69etHU1MTubm5gDkL1d69e2lsbMTPz48+ffqIYzY5ORmDwUD273f3xowZI7pYq1QqBg4cKK7QJyQkIJfLyczMFMfsiRMnqKmpwdPTk2HDhvHrr78C0Lt3b1xdXTlx4gRgjlk9deoUlZWVuLu7M3LkSDZt2kR+fj5ZWVl4e3tz7NgxAFJTU8nPz6esrAxnZ2fGjh0rzRG0PUcAxMbGXnSOyM/PZ8OGDdIccQVzxObNmwGIiYnB09PzgnOERW9pjriyOQL+iPW/0BxRUFDAhg0bbDJHWPrfLoRLZPHixQJwwe3AgQPCa6+9JiQkJLR4f1xcnPDGG2+067MSExOFhx566KLtDh48KADCoUOHWn1dq9UKarVa3M6cOSMAglqttmqn0WiEjIwMQaPRtKt/giAIhw4JApj/tmf/5RIVFSXs2bNH2L17tzB69GjhqquuEg4fPixs3bpVSExMFARBEFasWCGMGTPG6n233HKL8M4777R6zD179gj33XefMHv2bGHJkiVCdXW10NzcLCxbtkw4fvx4m30xmUzCvHnzhFmzZglGo7Hd5/DWW28Jf/nLX1p97fbbbxfeeOMNQaPRCCEhIcL9998vvP3224IgCAIglJSUWLUTBEH485//LMyfP19YsWKFcO211wqCIAh5eXmCi4uLeNySkhLh3GFeU1MjAJf0P5YQBJ1OZ5PjXM53rCdSXl7u6C70GA4dOnTB3w8J2yONb/shaW1fJL3tiy31VqvVrdoGrXHJK08PPfTQRTPbRUdHc+zYsVaLDlZU/H97dx7X1JW3AfwJEMIeBERABK2iiCAKVsV9RZ1at9fdutVabauF1r6tte9U7eLQ2tra6riNiuNodaxa22pboeKOK+64oOKG4oKyqUCA8/7BcMeYAAkmN2qe7+fDp+bm5Obcp4dLfrn3nnsbtWrVqvJ9du3ahbNnz2Lt2rVVto2IiIBSqURaWhoiIiJ0nlepVFCpVFWu51kQFRWFjIwM2Nvbo1mzZtK3VQDg5+cnfbtW7sqVKzqTbQBlpzJOmzYNr7/+OlQqFX799Vd8/PHHUCgUGDBgAF5++eUK+zB58mRcv34dv//+O2xsDD/zs7S0VPqWriIODg7o0aMHlixZIn1zVVk/QkNDERQUZHAfqHqKi4uhVCot3Q2rcevWLdSsWdPS3SAyC45v+TBreTFveVkqb6OLJy8vL3h5eVXZLioqCjk5OThw4ABatmwJANi/fz9ycnLQpk2bKl+/dOlSREZGIjw8vMq2p06dgkajga+vb9Ub8BzYsGGD3qKldevW0Gg0WLBgAcaPH4+NGzfi7NmziI6O1mlrb2+PxMREaT39+/c36L2nT5+OPXv2YMeOHVUWpCtXrkSPHj1Qs2ZNHDt2DPPnz8fUqVOrfI9PP/0UY8eOrfL/p6urK9544w189913CAsLM6j/VD1FRUVwdHS0dDesxrVr19CkSRNLd4PILDi+5cOs5cW85WWpvM02YUTjxo3Rs2dPjB8/Hvv27cO+ffswfvx49O7dWzofFSg7F3jjxo1ar83NzcW6devw2muv6az3woUL+OSTT3Do0CFcunQJW7ZswaBBg9C8eXO0bdvWXJtTpdOnoTXT3n9OVzaLpk2bIjQ0VGe5vb09Nm3ahJUrV8LT0xNxcXH4+eef9c5EqFAojDpqVO6TTz7B6dOn4efnBxcXF7i4uEgzAO7atQsuLi5S2wMHDiAsLAyurq4YOHAgXn/9dUycOLHK9/D399eaCKMyU6ZMka4ZInpeKBQKS3fBaigUCiiVSmYuI2YtH2YtL+YtL0vlrRDCfHe/vHv3Lt5++23p3kR9+vTBvHnzdKavXr58OcaMGSMtW7x4MWJjY3Hjxg2dD/5Xr17FK6+8gpMnTyI/Px916tTBSy+9hOnTp8PDw8OgfpVf8FZ+sWa5goICpKeno169etKF9FUpn22vIufOATyrjKhMdX7HiIiIiMypotpAH7MWT08rUxZPQFkBlZcH5Ofnax15cXVl4WROubm5VQ5wMg1TZc3iyTDbtm1Dly5dLN0Nq8G85cW85cOs5cW85WXKvI0pnnjnSxMoL5ByckpgwXv1Wh0rrPsthlnLq/xG0WR+p0+fxuuvv45ffvkFjR+93wSZDce3fJi1vJi3vCyVt9muebJGnI1MXsxbPsxaXrxLvXwePnyICxcu8Ca5MuL4lg+zlhfzlpel8mbxZELlN7MleTBv+TBreQUEBFi6C0Rmw/EtH2YtL+YtL0vlzeLJhO7fv2/pLlgV5i0fZi2v8rvHEz2POL7lw6zlxbzlZam8WTwREREREREZgMWTCTk5OVm6C1aFecuHWcvLkJuDk2nUq1cPixcvRr169SzdFavB8S0fZi0v5i0vS+XN4smEiouLLd0Fq8K85cOs5ZWdnW3pLliNGjVqoH379qhRo4alu2I1OL7lw6zlxbzlZam8WTyZUFFRkaW7YFWYt3yYtbwuX75s6S5YjZs3b2LOnDm4efOmpbtiNTi+5cOs5cW85WWpvFk8mVhhoXnWW7duXezbt09r2cSJEzFjxgzzvKGZ5Ofno127dvD09ESNGjXQtWtXnDlzpsL2iYmJCA8Ph6urK0JCQrB58+YK2yoUCjRo0EBrWVpaGhQKBXr27KnVrk2bNlrtevbsifj4+OptFBE9szIyMrBkyRJkZGRYuitERPQMYPFkQj/84AZXV2DRIkv35OmlUqmwZMkS3L59G1lZWRgwYABGjx6tt21xcTEGDhyI//3f/0Vubi6+/vprDB06FLm5uQCg9w7QNjY22L9/v/R41apVCCq/i/Ejzpw5g61bt5poq55/Vd1tm0wrOjra0l0gMhuOb/kwa3kxb3lZKm8WTyayaBHwxhsKNG4MTJwofwEVHx+P6OhojB8/Hq6urmjRogUyMjLw1ltvQa1Wo1WrVrh+/ToAoLS0FAMGDIC3tzc8PDwwaNAg3L17FwCwfft21K5dW3q8bt06NGrUyOgbSAoh9C5XKpVo3LgxbGxsIISAjY0N0tPT9bbNyclBbm4uhg0bBoVCgV69esHR0VE6TJufn6/zmmHDhmHVqlXS4x9++AHDhg3TaffOO+9g5syZRm2TNdOXNZnPzp07Ld0FIrPh+JYPs5YX85aXpfJm8WQCixaVFUyTJgkcOQJMnmyZAiopKQl/+ctfcPfuXfj7+6Nt27bo2LEjsrKyULduXcyePVtqO2DAAKSnpyM9PR15eXn45JNPAACdOnXC//zP/2DSpEm4ffs2Jk+ejPj4eDg6Ouq8382bNzF+/HgEBgYiIiICn376KZKTk7FhwwaMGjWq0r42bdoUDg4OmDRpEj744AO9bTw9PTFkyBCsXLkSJSUl+OWXX+Ds7IyGDRsCKCsCHzd48GBs3LgRJSUlOHjwILy8vPTOojVmzBhkZGQgISGh0n5SGX1Zk/kUFBRYugtEZsPxLR9mLS/mLS9L5c3i6QmVF06TJwPffaeAjQ0wd655Cqju3bvD3d1d+lm+fLnW82FhYejfvz+USiX69u0LZ2dnDB48GHZ2dujXrx+OHz8OoOzUtldeeQXOzs5Qq9V45513sHv3bmk9cXFxOHjwIDp16oSRI0ciKipKb3/27duHXr164eTJk1ixYgUePHiAjz76CFu2bMFf//rXSrfl+PHjyM3NxcKFC9G4ceMK2w0cOBBTpkyBSqXCkCFDsGDBAqhUKgCAnZ2dTntPT0+Eh4cjMTERq1atwvDhw/WuV6lUYtq0aTz6ZCB9WZP51KxZ09JdsBpqtRodOnSAWq22dFesBse3fJi1vJi3vCyVN4unJ/Bo4TR3LqBQlC1XKMxTQCUkJCA7O1v6GTt2rNbz3t7e0r8dHR21BpWjoyPu378PoOxaotjYWAQGBsLNzQ0DBw5EVlaW1NbJyQlDhw7F6dOn8fbbb1fYn5deegm3bt3Ca6+9hvnz56Nbt25ISEjA559/jk2bNlW5PY6Ojnjttdfw6quv4t69ezrPnz59GmPHjsWmTZtQVFSEP/74AyNHjsSVK1cAAA4ODnrXO2LECKxcuRIbNmzA4MGDK3z/sWPH4tq1a0hMTKyyr9auoqzJPB6f+ITMp379+vjll19Qv359S3fFanB8y4dZy4t5y8tSebN4qqbCwrLiqGlT4Ntv/1s4lVMoypY3bVrWzlyz8FXHqlWrsGvXLiQnJyM3Nxc//vij1jVKaWlpWLBgAQYNGoQpU6ZUuJ5//etfSEtLw5gxYxAeHo5Zs2bB09MTnTt3hr+/v0F9EUIgPz8fN27c0Hnu5MmTCA8PR7t27WBjY4P27dujUaNG0oQQFV2H07dvX/z8888IDQ2t9FsJpVKJDz/8kEefDMBrnuSVnJxs6S5YDY1Gg99++w0ajcbSXbEaHN/yYdbyYt7yslTePBenmlQq4Pvvy44sxcZqH3kCACHKlh8/DixcWNb+aZGXlweVSgV3d3fcuXMHX331lfRcaWkpRo8ejY8++ggTJ05EeHg4/v3vf+s9gjNy5EjY2tpKj994440q3/vYsWPIyclB69atodFo8Omnn8Ld3V3vjHjNmjXDiRMnsH//frRq1QrJyck4duwYmjRpUul7ODk5ISEhAV5eXlX2Z+zYsZg1axby8/MxdOjQKtsT0fPlxIkTGDp0KA4fPoyIiAhLd4eIiJ5yPPL0BCZMKCuMvv8eiIkpK5iAsv/GxJQtX7iwrN3TZNSoUVCr1fD29kb79u217oH01VdfwdbWFjExMXB0dMTy5csxefJk3Lp1S2c9jxZOhtJoNIiJiYGnpycCAgJw9OhRbNmyBUqlEkDZvasmTpwIAAgKCsK8efMwatQouLq6YtSoUfj2228REhICAHonsSjXqlUrg07Dsbe3x4cffijNLkj6VZY1mV5oaKilu0BkNhzf8mHW8mLe8rJU3gpR0ZzSz7Hc3Fyo1Wrk5ORo3b+moKAA6enpqFevnlHXeDw6297cuQrExj69hdPzpKCggNfiyMRUWVf3d8zapKWl6T0aS6aXkpKCyMhIHnmSEce3fJi1vJi3vEyZd0W1gT488mQC5Ueg5s1ToHlzFk5yKXyaLiR7zjFreV28eNHSXSAyG45v+TBreTFveVkqb17zZCITJgAPHz7E++87snAiIiIiInoO8bQ9E5y2V04IgaIixVM1OcTzTAgBxePTHJJZmCprnrZnmOLiYt5bSyYlJSXIycmBWq2u1nWcZDyOb/kwa3kxb3mZMm+etmch+fn5LJxkxOmz5cOs5bVv3z5Ld8Fq2NraIjU1lYWTjDi+5cOs5cW85WWpvFk8mVBpaamlu2BVmLd8mLW8ym9oTeaXlpaGmJgYpKWlWborVoPjWz7MWl7MW16WypvFkwnxUK28mLd8mLW8PDw8LN0Fq5GXl4eUlBTk5eVZuitWg+NbPsxaXsxbXpbKm8WTCfEaDnkxb/kwa3mV38uM6HnE8S0fZi0v5i0vS+XN4smEeF2IvJi3fJi1vHbv3m3pLhCZDce3fJi1vJi3vCyVN4snIiIiIiIiA7B4MiEHBweY616idevWhZubGx4+fCgty83NhaOjI4KDg83zpjLatGkTgoODoVar4evri3fffRclJSV626ampqJ79+4ICAioctvj4+OhUCjw2WefaS2fNm0aFAoF1qxZo9Vu0aJFUpvMzExOhf4fPG1PXo0bN7Z0F6xGnTp18Mknn6BOnTqW7orV4PiWD7OWF/OWl6XyZvFkQkuW2MDVFXjk87dJ+fj44Oeff5Yeb9iw4bn5g9+iRQvs2rULOTk5SE1NxfHjx7F48WK9bZVKJYYPH464uDiD1t2gQQOsXr1aeiyEwNq1a1G/fn2tdjVq1MCsWbOg0WiqvyFEJlBcXGzpLliNmjVrYsSIEahZs6alu2I1OL7lw6zlxbzlZam8WTyZyKJFwOTJSjRuDEycaJ4CatiwYVi1apX0eNWqVRg+fLhWG4VCgQULFiAgIABeXl5Yu3Ytfv31V7zwwgvw9vbG2rVrpbZLlixBUFAQXF1d0bRpU2zfvh1A2Y1MQ0JC8MMPPwAAsrOz4e/vj23bthndZ0PvwVy7dm2dDy/p6el62wYFBWHs2LEGF47169eHq6srUlJSAAB79+5FnTp14O/vr9WuZcuWqFOnDpYvX653PXXr1sXXX3+Nhg0bws3NDd9++y0OHDiAkJAQeHh44JtvvjGoP8+igoICS3fBqnDabPncvXsXCxcuxN27dy3dFavB8S0fZi0v5i0vS+XN4skEFi0qK5gmTRI4cgSYPNk8BVT37t2RkpKCu3fvIjMzE2lpaejQoYNOuz179uDcuXNYsGAB3nzzTaxfvx4nT57E0qVLMWnSJOl0OD8/P/z555/IycnB5MmTMXToUBQWFsLBwQErVqxAbGwsbty4gZiYGPTp0wddunTR268FCxagWbNmCAgIwLhx4/Drr79i586deOutt3Do0CGDt2/37t1Qq9Xw8PDAiRMn8Oqrr1YvKD1GjBghHX1avXo1RowYobfd9OnTKz36tGXLFhw8eBCJiYn44IMPMHv2bOzZswdJSUmYNm0abt++bbI+E5H5Xbp0CbNnz8alS5cs3RUiInoGsHh6QuWF0+TJwHffKWBjA8yda54Cys7ODv369cO6deuwZs0aDBo0CDY2uv8L33//fTg4OGDAgAHIzs7Gm2++CScnJ7z88svIy8vD9evXAQAvvfQSAgICYGNjg/Hjx0OhUEhV/Isvvohx48ahW7du2LVrF7788ku9fSosLMSlS5fw66+/4vDhw4iKisLixYvx1VdfoX379njxxRcN3r527dohJycHFy9exMSJE1GjRo1K2zs5ORm87iFDhmDdunUoKirCpk2bMHDgQL3tunfvjtq1ayM+Pl7v8zExMVCr1WjZsiV8fHwwePBg1KhRA+Hh4QgICMCZM2cM7tOzxNXV1dJdsCqdOnWydBeIzIbjWz7MWl7MW16WypvF0xN4tHCaOxcon1tAoTBfAVV+BKWyoyfe3t4AAFtbWyiVSq3T4RwcHKQ7Mv/000+IiIiAu7s73N3dcevWLWRlZUltX331VaSmpuLVV1+Fi4uL3vdSqVTo378/PvvsM7z11lsoLS3FihUr8OOPP6K0tBSnTp3Sec2uXbvg4uICFxcX9OrVS+f5evXqISwsDLGxsZVmUWjE7By1atVCcHAwpk2bhhYtWlRamFV29Kk8WwBwdHTUytbR0fG5vbv4gwcPLN0Fq1J+iinR84jjWz7MWl7MW16WypvFUzUVFpYVR02bAt9++9/CqZxCUba8adOydqaahS8qKgoZGRnIz89Hs2bNqr2ewsJCDBs2DHFxccjKykJ2dja8vb2la5SEEHjjjTcwYsQIzJ07FxkZGRWuZ9q0aejUqROGDRuG/fv3o3HjxggMDMSePXsQEBCg85r27dsjPz8f+fn5+O233/Sut7S0FBcuXKh0Gyqaja8iw4cPx5w5c3SuE3tcdHQ0fH19sWLFCqPW/zwzNmt6Mrm5uZbuApHZcHzLh1nLi3nLy1J5m7V4+vzzz9GmTRs4OTnB3d3doNcIITBjxgz4+fnB0dERnTp10jl6UVhYiMmTJ8PLywvOzs7o06cPrl27ZoYtqJhKBXz/PXD8OBAbCzw+L4IQZcuPHy9rp1KZ7r03bNiAf//730+0jsLCQhQVFUlHTubOnat1vc7ChQtx7949xMfHY8KECRg/frze9djb2yMxMRFDhw5F//79sXTpUmRmZuLGjRuYP3++wad7rVu3DleuXAEAnD9/HnFxcejcubPetkIIFBQUoKSkRPp3UVFRle8xaNAgbNmyBX369Kmy7fTp05/rCSCMZWtra+kuWBW1Wm3pLlgNZ2dnhIaGwtnZ2dJdsRoc3/Jh1vJi3vKyVN5mLZ6KioowaNAgvPHGGwa/5ssvv8ScOXMwb948HDx4ED4+PujevTvy8vKkNrGxsdi4cSPWrFmD3bt3Iz8/H71795b92/EJE4CFC8uKo5iY/xZQQpQ9/v77sucnTDDt+zZt2hShoaFPtA43NzfMnj0b3bt3h4+PD7KystCgQQMAZbPc/d///R/i4+NhZ2eHjz/+GNeuXcOyZct01qNQKPRed2WstLQ0tGnTBs7OzujSpQs6d+6MmTNnSs+7uLhg165dAIDLly/D0dER/fr1w7lz5+Do6Ijo6Ogq38PJyQk9e/Y06J5FPXr0QMOGDau/Qc8ZY64voyf3JEeVyTiNGjXCwYMH0ahRI0t3xWpwfMuHWcuLecvLYnkLGSxfvlyo1eoq25WWlgofHx8RFxcnLSsoKBBqtVosXLhQCCFEdna2UCqVYs2aNVKbjIwMYWNjI37//XeD+pOTkyMAiJycHK3lDx8+FKmpqeLhw4cGrafcwoVCAEJMmlQqSkqEmDy57PF/ukxmkp2dbekuWA1TZV3d3zFrY+i+jEyDecuLecuHWcuLecvLlHlXVBvo81Rd85Seno7MzEytowgqlQodO3bE3r17AQCHDx+GRqPRauPn54fQ0FCpjdzKj0DNm6dA8+bmO+JERESmlZKSgp49e/JCbyIiMoidpTvwqMzMTABlM6M9qlatWrh8+bLUxt7eXme2tFq1akmvf1xhYaHWzGzmuMBswgRAo9Hg3XeVLJxkYsjpd2QazFpePGWUnmcc3/Jh1vJi3vKyVN5GF08zZszQuhZFn4MHD6JFixbV7pTisanrhBA6yx5XWZu//e1vevucmJgoXWNz4MABPHz4EF5eXigpKUFOTg6A/35oLCgoAFB2v5sHDx6gpKQEtra2cHJykq7HGjPGFqNGlUKIAuTklF2nU1BQgOLiYtjY2MDFxUUq3FQqFWxsbPDw4UMAlbe1t7eHnZ2dNF20s7MzioqKoNFooFAo4ObmJvX38bZOTk4oLi5GUVGR1DY3NxdCCCiVStjb20vTaz/aFii7EC8vLw+lpaU6bR0dHVFaWioVpW5ubsjPz0dpaSns7Ozg4OCA/Px8vW0ry9CYvG1tbaFQKAzK8PG8H83QxsYGrq6uFWaoL+/yDCvLuzxDQ/M2JsPK2hqT4eNtH83w0bblk3MYOmYryvv+/fvSe/3xxx8AgDp16sDLywtHjhwBALRo0QLXr1/H9evXYWtri27duiExMRElJSXw8/ODn5+fdPPl5s2b486dO7h69SqAsmvVkpKSUFRUhFq1aqFu3brYv38/gLJrBXNzc6WboXbv3h179uzBgwcP4OXlhYYNG0pHr5s0aYKCggJpxsfyfUR+fj5q1KiBJk2aYPfu3QCA4OBglJaW4ty5cwCAjh074ujRo8jJyYGbmxsiIiKwfft2AEBQUBDs7Oxw+vRpAGX3NktNTcXdu3fh7OyM1q1b488//0Rubi6Ki4vh5OSEkydPAiibafP8+fO4ffs2HBwc0KFDB2zduhUAEBgYCHd3dxw7dgwA0LJlS1y5cgWZmZlQKpXo0qULtm7dCiEE/P394e3tLR1piYyMRGZmJjIyMmBjY4Pu3bvjzz//RHFxMXx9feHv74+DBw8CKDu//O7du9LkLj169MD27dtRWFgIb29vvPDCC9i3bx8AICwsDPn5+UhPTwcAdOvWDXv37sWDBw/g6emJ4OBg7NmzBwAQEhKCoqIinD9/HgDQuXNnHDp0CHl5eXB3d0fTpk2xc+dOAJCuTTp79iwAoEOHDjh+/Diys7Ph6uqKFi1aICkpCQDQoEED2NvbIzU1FQDQtm1bnDlzBllZWXByckKbNm2k/+eXL1+Gj48PTpw4AQBo3bo1Ll68iFu3bkGlUqFTp07SmA0ICICHhweOHj0KoOy+eNeuXcONGzdgZ2eHrl27IiEhAaWlpahduzZ8fHxw+PBhAEBERARu3bqFa9euQaFQIDo6Gtu2bYNGo4GPjw8CAgJw4MABAEB4eDiys7OlLw+jo6Oxc+dOFBQUoGbNmmjQoAGSk5MBAKGhoXjw4AEuXrwIAOjatSv27duH+/fvw8PDAyEhIdKYbdy4MYqLi6X7+nXq1AkpKSnIzc2FWq1Gs2bNsGPHDgBlH05sbGyke9i1a9cOp06dwr179+Di4oKWLVti27ZtAID69evDwcFBmuSpTZs2OHfuHO7cuQMnJye0bdsWCQkJyM3NRVFREdzc3HD8+HEAQKtWrXDp0iXcvHkT9vb26Ny5M/cRqHgfAQAvvPBClfuIQ4cO4dy5c9xHPME+IjExEUDZbVRcXFwq3UeU5819xJPtIwCgbt26Ve4jDh8+jHPnzplkH2HMfToVQjw+T1zl7ty5gzt37lTapm7dulrfVMfHxyM2NhbZ2dmVvu7ixYuoX78+UlJS0Lx5c2l537594e7ujhUrVmDbtm3o2rUr7t69q3X0KTw8HP369dNbJOk78lSnTh1px1WuoKAA6enpqFevXrW+ac/JyeFMKzJi3vIxVdZP+jtmLf744w/06NHD0t2wCikpKYiMjMThw4cRERFh6e5YBY5v+TBreTFveZky7/LC8PHaQB+jjzx5eXnBy8ur2p2rTL169eDj44OEhASpeCoqKsKOHTvwxRdfACj7BkSpVCIhIQGDBw8GANy4cQMnT57El19+qXe9KpUKKiPmCjeyniQiA/F3i4iIiJ5lZr3m6cqVK9Lh25KSEunwZYMGDeDi4gKg7HD23/72N/Tv3x8KhQKxsbGYNWsWgoKCEBQUhFmzZsHJyUm6salarca4ceMwZcoUeHp6wsPDA++99x7CwsLQrVu3J+qvUqmEQqHA7du3UbNmzSpPFXycnZ2ddEoSmR/zlo8pshZC4Pbt21AoFFAqlSbq2fOpXbt2lu6C1QgJCcGxY8d4rYKMOL7lw6zlxbzlZam8zVo8ffzxx1ixYoX0uPxoUlJSEjp16gSg7JzU8mtNAOD999/Hw4cP8eabb+LevXto1aoVtm7dqnWz1W+++QZ2dnYYPHgwHj58iK5duyI+Pv6Jb+Rpa2sLf39/XLt2TTrn2RiFhYVGHeGiJ8O85WOqrBUKBfz9/XnT3SqcOnUKLVu2tHQ3rIKDgwMKCgp4GqmMOL7lw6zlxbzlZam8jb7m6XlQ1XmNJSUl0Gg0Rq939+7d/NZBRsxbPqbKWqlUsnAyAM+bl096ejrGjRuHpUuXol69epbujlXg+JYPs5YX85bXM3PNkzWwtbWt1gc8R0dHfnspI+YtH2Ytr/LTmsn87t27h6SkJNy7d4/Fk0w4vuXDrOXFvOVlqbx55KmK6tIYGo2G13LIiHnLh1nLi3nLh7PtyY/jWz7MWl7MW16mzNuY2sDGJO9IACDNY0/yYN7yYdbyYt70POP4lg+zlhfzlpel8rbK0/bKD7aV39DTVO7fv2/ydVLFmLd8mLW8mLd8ym8unZ+fz8xlwvEtH2YtL+YtL1PmXb4eQ07Is8rT9q5du4Y6depYuhtERERERPSUuHr1Kvz9/SttY5XFU2lpKa5fvw5XV1ej7+VUkdzcXNSpUwdXr1416XVUpB/zlg+zlhfzlhfzlhfzlg+zlhfzlpep8xZCIC8vD35+frCxqfyqJqs8bc/GxqbKqrK63Nzc+EsjI+YtH2YtL+YtL+YtL+YtH2YtL+YtL1PmrVarDWrHCSOIiIiIiIgMwOKJiIiIiIjIACyeTESlUmH69OlQqVSW7opVYN7yYdbyYt7yYt7yYt7yYdbyYt7ysmTeVjlhBBERERERkbF45ImIiIiIiMgALJ6IiIiIiIgMwOKJiIiIiIjIACyeiIiIiIiIDMDiyUCff/452rRpAycnJ7i7uxv0GiEEZsyYAT8/Pzg6OqJTp044deqUVpvCwkJMnjwZXl5ecHZ2Rp8+fXDt2jUzbMGz5d69exg5ciTUajXUajVGjhyJ7OzsSl+jUCj0/syePVtq06lTJ53nhw4dauatefpVJ+8xY8boZNm6dWutNhzfuozNWqPR4IMPPkBYWBicnZ3h5+eHUaNG4fr161rtOLbL/P3vf0e9evXg4OCAyMhI7Nq1q9L2O3bsQGRkJBwcHPDCCy9g4cKFOm3Wr1+PkJAQqFQqhISEYOPGjebq/jPHmLw3bNiA7t27o2bNmnBzc0NUVBT++OMPrTbx8fF69+MFBQXm3pRngjF5b9++XW+WZ86c0WrH8a2fMVnr+3uoUCjQpEkTqQ3HdsV27tyJl19+GX5+flAoFPjpp5+qfI1F992CDPLxxx+LOXPmiHfffVeo1WqDXhMXFydcXV3F+vXrxYkTJ8SQIUOEr6+vyM3NldpMnDhR1K5dWyQkJIiUlBTRuXNnER4eLoqLi820Jc+Gnj17itDQULF3716xd+9eERoaKnr37l3pa27cuKH1s2zZMqFQKMSFCxekNh07dhTjx4/XapednW3uzXnqVSfv0aNHi549e2plmZWVpdWG41uXsVlnZ2eLbt26ibVr14ozZ86I5ORk0apVKxEZGanVjmNbiDVr1gilUimWLFkiUlNTRUxMjHB2dhaXL1/W2/7ixYvCyclJxMTEiNTUVLFkyRKhVCrFjz/+KLXZu3evsLW1FbNmzRKnT58Ws2bNEnZ2dmLfvn1ybdZTy9i8Y2JixBdffCEOHDggzp07Jz788EOhVCpFSkqK1Gb58uXCzc1NZ39OxuedlJQkAIizZ89qZfno/pfjWz9js87OztbK+OrVq8LDw0NMnz5dasOxXbEtW7aIjz76SKxfv14AEBs3bqy0vaX33SyejLR8+XKDiqfS0lLh4+Mj4uLipGUFBQVCrVaLhQsXCiHKftmUSqVYs2aN1CYjI0PY2NiI33//3eR9f1akpqYKAFoDPDk5WQAQZ86cMXg9ffv2FV26dNFa1rFjRxETE2Oqrj4Xqpv36NGjRd++fSt8nuNbl6nG9oEDBwQArT/kHNtCtGzZUkycOFFrWXBwsJg6dare9u+//74IDg7WWjZhwgTRunVr6fHgwYNFz549tdr06NFDDB061ES9fnYZm7c+ISEhYubMmdJjQ//GWiNj8y4vnu7du1fhOjm+9XvSsb1x40ahUCjEpUuXpGUc24YxpHiy9L6bp+2ZSXp6OjIzMxEdHS0tU6lU6NixI/bu3QsAOHz4MDQajVYbPz8/hIaGSm2sUXJyMtRqNVq1aiUta926NdRqtcG53Lx5E5s3b8a4ceN0nlu1ahW8vLzQpEkTvPfee8jLyzNZ359FT5L39u3b4e3tjYYNG2L8+PG4deuW9BzHty5TjG0AyMnJgUKh0DmF2JrHdlFREQ4fPqw13gAgOjq6wmyTk5N12vfo0QOHDh2CRqOptI21juFy1cn7caWlpcjLy4OHh4fW8vz8fAQGBsLf3x+9e/fGkSNHTNbvZ9WT5N28eXP4+vqia9euSEpK0nqO41uXKcb20qVL0a1bNwQGBmot59g2DUvvu+2eeA2kV2ZmJgCgVq1aWstr1aqFy5cvS23s7e1Ro0YNnTblr7dGmZmZ8Pb21lnu7e1tcC4rVqyAq6srBgwYoLV8xIgRqFevHnx8fHDy5El8+OGHOHbsGBISEkzS92dRdfPu1asXBg0ahMDAQKSnp+Ovf/0runTpgsOHD0OlUnF862GKsV1QUICpU6di+PDhcHNzk5Zb+9i+c+cOSkpK9O5zK8o2MzNTb/vi4mLcuXMHvr6+Fbax1jFcrjp5P+7rr7/G/fv3MXjwYGlZcHAw4uPjERYWhtzcXMydOxdt27bFsWPHEBQUZNJteJZUJ29fX18sXrwYkZGRKCwsxMqVK9G1a1ds374dHTp0AFDx74A1j+8nHds3btzAb7/9htWrV2st59g2HUvvu626eJoxYwZmzpxZaZuDBw+iRYsW1X4PhUKh9VgIobPscYa0eRYZmjegmxtgXC7Lli3DiBEj4ODgoLV8/Pjx0r9DQ0MRFBSEFi1aICUlBREREQat+1lh7ryHDBki/Ts0NBQtWrRAYGAgNm/erFO0GrPeZ5FcY1uj0WDo0KEoLS3F3//+d63nrGlsV8bYfa6+9o8vr85+3FpUN5sffvgBM2bMwKZNm7S+UGjdurXWxDNt27ZFREQEvv/+e3z33Xem6/gzypi8GzVqhEaNGkmPo6KicPXqVXz11VdS8WTsOq1JdXOJj4+Hu7s7+vXrp7WcY9u0LLnvturiadKkSVXORlW3bt1qrdvHxwdAWXXs6+srLb9165ZUCfv4+KCoqAj37t3T+nb+1q1baNOmTbXe92lmaN7Hjx/HzZs3dZ67ffu2zrcI+uzatQtnz57F2rVrq2wbEREBpVKJtLS05+4Dplx5l/P19UVgYCDS0tIAWNf4liNrjUaDwYMHIz09Hdu2bdM66qTP8zy29fHy8oKtra3Ot4qP7nMf5+Pjo7e9nZ0dPD09K21jzO/G86g6eZdbu3Ytxo0bh3Xr1qFbt26VtrWxscGLL74o7Ves1ZPk/ajWrVvjX//6l/SY41vXk2QthMCyZcswcuRI2NvbV9qWY7v6LL3vtuprnry8vBAcHFzpz+NHLgxVfvrMo6fMFBUVYceOHdIHx8jISCiVSq02N27cwMmTJ5+7D5eA4XlHRUUhJycHBw4ckF67f/9+5OTkGJTL0qVLERkZifDw8Crbnjp1ChqNRqvAfV7IlXe5rKwsXL16VcrSmsa3ubMuL5zS0tKQmJgo/XGozPM8tvWxt7dHZGSkzmmKCQkJFWYbFRWl037r1q1o0aIFlEplpW2etzFsrOrkDZQdcRozZgxWr16Nl156qcr3EULg6NGjVjOOK1LdvB935MgRrSw5vnU9SdY7duzA+fPn9V5v/TiO7eqz+L77iaecsBKXL18WR44cETNnzhQuLi7iyJEj4siRIyIvL09q06hRI7FhwwbpcVxcnFCr1WLDhg3ixIkTYtiwYXqnKvf39xeJiYkiJSVFdOnSxeqnchaibDrnpk2biuTkZJGcnCzCwsJ0pnN+PG8hhMjJyRFOTk5iwYIFOus8f/68mDlzpjh48KBIT08XmzdvFsHBwaJ58+bM28i88/LyxJQpU8TevXtFenq6SEpKElFRUaJ27doc31UwNmuNRiP69Okj/P39xdGjR7WmuC0sLBRCcGyXK59eeOnSpSI1NVXExsYKZ2dnacarqVOnipEjR0rty6e7feedd0RqaqpYunSpznS3e/bsEba2tiIuLk6cPn1axMXFcSrn/zA279WrVws7Ozsxf/78CqfUnzFjhvj999/FhQsXxJEjR8TYsWOFnZ2d2L9/v+zb97QxNu9vvvlGbNy4UZw7d06cPHlSTJ06VQAQ69evl9pwfOtnbNblXnnlFdGqVSu96+TYrlheXp70uRqAmDNnjjhy5Ig0o+zTtu9m8WSg0aNHCwA6P0lJSVIbAGL58uXS49LSUjF9+nTh4+MjVCqV6NChgzhx4oTWeh8+fCgmTZokPDw8hKOjo+jdu7e4cuWKTFv19MrKyhIjRowQrq6uwtXVVYwYMUJnutXH8xZCiEWLFglHR0e997e5cuWK6NChg/Dw8BD29vaifv364u2339a5N5E1MjbvBw8eiOjoaFGzZk2hVCpFQECAGD16tM7Y5fjWZWzW6enpevc9j+5/OLb/a/78+SIwMFDY29uLiIgIsWPHDum50aNHi44dO2q13759u2jevLmwt7cXdevW1fvFy7p160SjRo2EUqkUwcHBWh8+rZ0xeXfs2FHvOB49erTUJjY2VgQEBAh7e3tRs2ZNER0dLfbu3SvjFj3djMn7iy++EPXr1xcODg6iRo0aol27dmLz5s066+T41s/YfUl2drZwdHQUixcv1rs+ju2KlU+rX9G+4WnbdyuE+M8VVkRERERERFQhq77miYiIiIiIyFAsnoiIiIiIiAzA4omIiIiIiMgALJ6IiIiIiIgMwOKJiIiIiIjIACyeiIiIiIiIDMDiiYiIiIiIyAAsnoiIiIiI6Km2c+dOvPzyy/Dz84NCocBPP/1k1OtnzJgBhUKh8+Ps7GzUelg8ERERERHRU+3+/fsIDw/HvHnzqvX69957Dzdu3ND6CQkJwaBBg4xaD4snIiIiIiJ6qvXq1QufffYZBgwYoPf5oqIivP/++6hduzacnZ3RqlUrbN++XXrexcUFPj4+0s/NmzeRmpqKcePGGdUPuyfZCCIiIiIiIksbO3YsLl26hDVr1sDPzw8bN25Ez549ceLECQQFBem0/8c//oGGDRuiffv2Rr0PjzwREREREdEz68KFC/jhhx+wbt06tG/fHvXr18d7772Hdu3aYfny5TrtCwsLsWrVKqOPOgE88kRERERERM+wlJQUCCHQsGFDreWFhYXw9PTUab9hwwbk5eVh1KhRRr8XiyciIiIiInpmlZaWwtbWFocPH4atra3Wcy4uLjrt//GPf6B3797w8fEx+r1YPBERERER0TOrefPmKCkpwa1bt6q8hik9PR1JSUn4+eefq/VeLJ6IiIiIiOiplp+fj/Pnz0uP09PTcfToUXh4eKBhw4YYMWIERo0aha+//hrNmzfHnTt3sG3bNoSFheEvf/mL9Lply5bB19cXvXr1qlY/FEII8cRbQ0REREREZCbbt29H586ddZaPHj0a8fHx0Gg0+Oyzz/DPf/4TGRkZ8PT0RFRUFGbOnImwsDAAZaf3BQYGYtSoUfj888+r1Q8WT0RERERERAbgVOVEREREREQGYPFERERERERkABZPREREREREBmDxREREREREZAAWT0RERERERAZg8URERERERGQAFk9EREREREQGYPFERERERERkABZPREREREREBmDxREREREREZAAWT0RERERERAZg8URERERERGSA/wc9/evAD02FXwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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u3HjBx81mM2az2fl1d3c38fHxzoOScI3Nmzezfv16dzdDnCfxUA+JhXrk5+czf/588vLyZCqdSkj/UA+JhbpIPFxnOOm7lNxgUkaMqqqq2LZtG//+978v+2eXLFnCyy+/fNHHjUYjRqPxSponxkH+5uoi8VAPiYV66PV6wsLC0Ov17m6KOE/6h3pILNRF4qEOkzJi9IMf/IBnn32WmpoadLrLy8U++clP0t7e7hyq+ziXkxUKIYQQQgghpq7LyQ1cXnzB4XDw17/+lc985jNjkqJvf/vb3H///c6vf/Ob3/DWW29RWlpKUVER3/72t3njjTf40pe+5Opmiss0PPdTqIPEQz0kFuoi8VAXiYd6SCzUReKhDi6fSrdt2zaqq6t58MEHxzzW0NDgXMQHQ5XrnnjiCerq6vD29iYrK4v33nuP66+/3tXNFEIIMcWcPHmSe++9lx07dpCTk+Pu5gghhFA5lydG69at42Kz9V544YVRXz/55JM8+eSTrm6SmAAJCQnuboIYQeKhHhIL9bBarbS2tmK1Wt3dFHGe9A/1kFioi8RDHSZtHyMxtYSEhLi7CWIEiYd6SCyEuDjpH+ohsVAXiYc6SGIkxmW4fr9QB4mHekgshLg46R/qIbFQF4mHOkhiJIQQQgghhJj2JDES47Jw4UJ3N0GMIPFQD4mFeqSnp/P222+Tnp7u7qaI86R/qIfEQl0kHuogiZEYl9raWnc3QYwg8VAPiYV6+Pv7k5SUhL+/v7ubIs6T/qEeEgt1kXiogyRGYlwaGhrc3QQxgsRDPSQW6lFXV8f//b//l7q6Onc3RZwn/UM9JBbqIvFQB0mMxLh8eLNe4V4SD/WQWKhHU1MT//rXv2hqanJ3U8R50j/UQ2KhLhIPddAoF9tkyEN1d3cTGBhIV1cXAQEB7m6OEEIIN8nPz2f+/Pnk5eWRm5vr7uYIIYRwg8vJDWTESIzL1q1b3d0EMYLEQz0kFkJcnPQP9ZBYqIvEQx0kMRLj4nA43N0EMYLEQz0kFkJcnPQP9ZBYqIvEQx0kMRLjEhsb6+4miBEkHuohsVCP0NBQPvGJTxAaGurupojzpH+oh8RCXSQe6iArvcS4REVFubsJYgSJh3pILNQjMTGRZ599lrCwMHc3RZwn/UM9JBbqIvFQBxkxEuOSl5fn7iaIESQe6iGxUI+BgQHeeOMNBgYG3N0UcZ70D/WQWKiLxEMdJDESQggxJRUXF/Pwww9TXFzs7qYIIYTwAJIYiXGR0rfqIvFQD4mFEBcn/UM9JBbqIvFQB0mMxLg0Nze7uwliBImHekgshLg46R/qIbFQF4mHOkhiJMaltrbW3U0QI0g81ENiIcTFSf9QD4mFukg81EESIzEuGo3G3U0QI0g81ENioR4ajQa9Xi8xURGJhXpILNRF4qEOGkVRFHc3YiJ1d3cTGBhIV1cXAQEB7m6OEEIIIYQQwk0uJzeQESMxLjt27HB3E8QIEg/1kFioi8RDXSQe6iGxUBeJhzpIYiTGxWq1ursJYgSJh3pILNSjuLiYL3zhC1KuW0Wkf6iHxEJdJB7qIImRGBfZoVldJB7qIbFQj4GBAcrLy2WDVxWR/qEeEgt1kXiogyRGYlwSEhLc3QQxgsRDPSQWQlyc9A/1kFioi8RDHSQxEuNy5MgRdzdBjCDxUA+JhRAXJ/1DPSQW6iLxUAdJjIQQQgghhBDTniRGYlzmzJnj7iaIESQe6iGxUI/k5GSee+45kpOT3d0UcZ70D/WQWKiLxEMdJDES49LZ2enuJogRJB7qIbFQj+DgYFasWEFwcLC7myLOk/6hHhILdZF4qIPO3Q0QnqmqqorMzEx3N0Oc9+F4KIqC1WrFbDYzODjI4OAgVqsVu92OzWYbdRt5n91uZ3jP55F7P3/4Po1Gg5eXF15eXmi1WufnF/raYDA4b3q9ftTnWu3UuzYjfUM9mpqa+NWvfsX/+T//h8jISHc3RyD9Q00kFuoi8VAHSYyE8ABWq5W+vj56e3vp7e0d9Xlvby9Hjhzh3LlzziTIbDbjcDjc3eyPpdPpnImS0WjE29sbk8n0sR99fX0xmUxoNBp3vwShYnV1dfzv//4vDz/8sCRGQgghPpZGGXlZeAro7u4mMDCQrq4uAgIC3N2cKUtRFDkpnUBms5mOjg46Ozvp7Ox0ft7R0UF3dzeDg4Mf+fMXi4dGo8FkMmE0GjEYDOh0OufNy8tr1NfD9w3/3MiPH75PURTsdrvz5nA4Rn09fJ/NZsNisWC1WrFYLM7bRBx2tFotvr6+o25+fn4XvM/Pz2/SRqekb6hHfn4+8+fPJy8vj9zcXHc3RyD9Q00kFuoi8XCdy8kNZMRIjMuePXtYtWqVu5vhURRFobe3l+bmZlpaWpwfW1tbL2kDSp1ON+pEfzgJ8PPzo6ioiOXLl2MymZyJkMlkQq/Xq+5AqygKNpttTLI0PNo1MDDwsR+HR8R6enro6en52OfUaDT4+fkREBBAQEAA/v7+oz4Of24wGK749UnfEOLipH+oh8RCXSQe6iCJkRiXjxvBmO4cDgfNzc3U19dTX19Pc3Mzzc3NH/l38/HxISgoiODgYIKCgpyfBwYG4u/vj9FovGiS09HRQXp6uqtezoTSaDTo9Xr0ej0+Pj7j+h02m43+/n7ntMKL3YYfH5lE1dXVXfT3mkwm59/+QjeTyfSxbZO+IcTFSf9QD4mFukg81EESIzEu4eHh7m6CqvT19VFdXU11dTW1tbU0NjZitVrHfJ9GoyEkJISIiAjCw8OJiIggLCyM4OBgjEbjuJ9/usVDp9M5R3o+jsPhoK+vj+7ubnp6ekZ9HPn58KhVY2MjjY2NF/xdF0qcQkJCCAkJITg4GC8vr2kXCzULDAxk5cqVBAYGursp4jzpH+ohsVAXiYc6yBojMS7d3d3T+u87MDBARUUFFRUVVFdX09LSMuZ7jEYjMTExxMTEEBUVRXh4OGFhYeh0E389YrrHYyKYzWa6urro6upyrvUaeevr6/vIn9doNAQFBeHt7U1sbCyhoaFjkiYx+aRvqIvEQz0kFuoi8XAdWWMkXO7gwYOsX7/e3c2YNIqiUFdXR2lpKeXl5dTV1Y0pIBAREUFiYiJxcXHOE+PJWt8z3eLhCkajkYiICCIiIi74uMViGZM0tbe309HRQVtbG1arlY6ODo4ePUpaWtqonx1OmkJCQpwJclhYGOHh4fj4+KhuHdhUYbVa+eCDD/jEJz6BXq93d3MEcqxSE4mFukg81EESIyEuwm63U1VVRXFxMWfOnBmzyD8iIoLU1FSSkpKIj48f93oZ4RkMBgPh4eEXnO4wXFijvb2dd999lxkzZtDe3u68WSwWOjo66OjooLy8fNTPent7j0mWwsLCCAoKkoTpCp08eZK7775bqtIJIYS4JJIYiXHJzs52dxNcQlEU6uvrOXHiBKdOnaK/v9/5mNFoJC0tjbS0NFJTU1U15D1V4+EpNBoN/v7++Pv7c+uttxIbG+t8TFEU+vr6aGtro62tjdbWVmc1ws7OTgYGBpzr00bS6/WEhoYSGRlJRESE86O/v78kTMJjybFKPSQW6iLxUAdJjMS4jEwYpoK+vj6OHz9OQUEBra2tzvt9fHzIzMxk5syZJCcnu2R90ESYavHwZB+OxXCpcD8/PxITE0c9ZrVaaWtrcyZKw0nT8NS8CxWC8Pb2HpUoDX+8kuIdQkwWOVaph8RCXSQe6uDSs7wf/OAH/PCHPxx1X2Rk5EUrPgHs3r2bxx9/nKKiImJiYnjyySd5+OGHXdlMMQ4VFRUeUx76YobXDR09epRTp05ht9uBoSv1mZmZzJkzh5SUlEnbGPRKTIV4TBWXEwu9Xk9UVBRRUVGj7nc4HHR0dDj3u2pqaqK5uZm2tjYGBgaoqqqiqqpq1M8EBQURERFBVFQU0dHRREVFyXQ8oTpyrFIPiYW6SDzUweWXv7Oysti2bZvz64+qzFRZWcn111/P5z//eV5++WX279/PI488Qnh4OLfffrurmyqmCUVRKCsrY+/evaOmL8XGxrJgwQJmzZolV9+FW2m1WkJDQwkNDSUzM9N5v81mG5MsNTU10dPT4ywIcfbsWef3m0wmZ5I0/DEsLMwjkn0hhBBisrm0XPcPfvAD3nrrLQoKCi7p+7/5zW+yceNGiouLnfc9/PDDnDhxgoMHD17S75By3ZPDZrOpdlrZxSiKwpkzZ9i9e7dz1FKn05Gdnc3ChQtHrQvxNJ4Yj6nKHbHo7+93JknD0++am5udo6Aj6fV6IiMjnclSdHQ0kZGRU7KcuN1up6uri8DAwCn5+jyRHKvUQ2KhLhIP11FVue7S0lJiYmIwGo0sXryYn/zkJ6SkpFzwew8ePMi6detG3bd+/Xqef/55rFarlFtVkUOHDrF8+XJ3N+OSVVdXs3XrVmpqaoChCmMLFixg6dKl+Pv7u7l1V87T4jGVuSMWPj4+JCUlkZSU5LzPbrfT3NxMY2MjDQ0NzoTJYrFQW1tLbW2t83u9vLyIiooiJiaG2NhYZ7l5Tx9Z8vLy4vTp09I3VESOVeohsVAXiYc6uDQxWrx4MS+++CIZGRk0NTXx4x//mGXLllFUVERoaOiY729sbCQyMnLUfZGRkdhsNlpbW4mOjh7zM2azGbPZ7Py6u7t74l+IGOPjNrtUi+7ubjZt2sTp06eBoavlS5cuZcmSJVOqvLanxGM6UEssvLy8nCNC8+bNA4bWLrW3t49Klurr6xkYGKCurs655g6GLh4Mb1AcGxtLTEyMx61ZKi0t5Stf+Qr//Oc/Ze6+SqilfwiJhdpIPNTBpYnRhg0bnJ/n5OSwdOlSUlNT+dvf/sbjjz9+wZ/58Jvu8Ey/i70Z//SnPx1T4AFg27Zt+Pr6cs0113DkyBF6e3sJDg4mKyuLffv2AZCZmYnD4XDOyV+1ahUFBQXOobbc3Fx27doFQHp6OjqdzjnNb/ny5Zw+fZr29nZ8fX1ZsmQJ27dvByAlJQUfHx9OnToFwNKlSykrK6OlpQWTycTKlSvZsmULAImJiQQFBXHixAkAFi1aRHV1NY2Njej1eq655hq2bNmCoijExcURERFBfn4+APPnz6exsZG6ujq0Wi1r165l+/bt2Gw2oqOjiYuLc57kzJ07l/b2dueamvXr17Nr1y7MZjMRERGkpKRw6NAhZ6x6e3uprKwEYM2aNRw4cID+/n7nmofGxkY2b97MrFmzsFgslJWVAbB69WqOHTtGT08PQUFBzJ49mz179gAwY8YMAEpKSgBYuXIlhYWFdHZ24u/vz4IFC9i5cycAaWlpGAwGZ0Jz1VVXcebMGdra2vDx8WHZsmXOtWvJycn4+flx8uRJAJYsWUJZWRn79+/n5MmTxMbGUl5eTlpaGuvXryc+Pp69e/cCsHDhQmpra2loaECn03HttdeydetWHA4HsbGxREVFkZeXB0Bubi7Nzc3U1tai0WhYt24dO3bswGq1EhUVRUJCAkeOHAFgzpw5dHZ2OhfIr1u3jj179jA4OEh4eDhpaWnO6aHZ2dn09/dTUVEBwLXXXsuhQ4fo6+sjJCSEWbNmOf9nZ86cic1mo7S0FICrr76a/Px8GhsbOXToEHPnzmX37t0AZGRkoNVqOXPmjPN/tqioiI6ODvz8/Fi0aBE7duwAIDU1FZPJRFFREQDLli3j7NmztLa24uPjw1VXXcXWrVsBSEpKIiAggMLCQmDoAsi5c+doamrCYDCwevVqNm/eDEB8fDxhYWEcP34cgAULFlBfX099fT1eXl6sWbOGbdu2YbfbnSfhx44dA2DevHm0trY6R/nWr1/Pzp07sVgsREZGkpSUxOHDhwGYPXs23d3dnDt3DoC1a9eyf/9++vv7CQsLIyMjgwMHDgBD6x4HBwed+wlN9DFiuG+o9Rhx4sQJ5zFi/vz5ztceFhZGaWmps58lJSWxa9cubDYbvr6+BAcH09raSkhICIsWLSIoKIiBgQEMBsMFjxH79+8HcOsxYvPmzeTn53Pq1Cl8fX1HHSMqKipobm7GaDRy9dVXO/9nExISCAkJcU4BnyrHiOGpJO4+RgxX/ZzOxwi1nEc0NjbS2trqtvMINRwjPuo8YrKPEf39/XR0dEz7Y4QrziOG238pXLrG6ELWrl1LWloaf/rTn8Y8tnLlSubNm8fTTz/tvO/NN9/kzjvvpL+//4JT6S40YhQfHy9rjFysr68PX19fdzfjgjo6Ovj3v//tfLOMj4/nxhtvHDMaOZWoOR7TzVSIhcPhoKWlhfr6eurq6qivr6epqWnMmiWNRkN4eDhxcXHEx8cTHx9PaGioakaV8vPzmT9/vmzwqiJToX9MFRILdZF4uI6q1hiNZDabKS4uZsWKFRd8fOnSpbzzzjuj7tuyZQsLFiy46Poio9EoFcTcYN++faxfv97dzRijqKiIjRs3YjabMRqNrFmzhgULFqjmRM1V1BqP6WgqxEKr1RIZGUlkZKRzGp7NZnNOvautraWmpoaOjg6am5tpbm52jlJ5e3sTFxfnTJZiY2PlGC2cpkL/mCokFuoi8VAHlyZGTzzxBDfddBMJCQk0Nzfz4x//mO7ubj7zmc8A8O1vf5u6ujpefPFFYKgC3e9//3sef/xxPv/5z3Pw4EGef/55XnnlFVc2U0wBDoeDzZs3O6dNxMfHc/vttxMUFOTehgkxReh0OmfCs2jRIgB6e3udSVJtbS11dXUMDAxQWlrqnKah0WiIiIggISGBxMREEhISZDRfCCGEKrk0MaqtreVTn/oUra2thIeHs2TJEg4dOuTc/b2hoWHUPjLJycm8//77fO1rX+MPf/gDMTEx/Pa3v5U9jFRo5syZ7m6Ck9ls5vXXX3eeiK1YsYLVq1d7fEWty6GmeEx30ykWfn5+ZGZmOvdastvtNDU1OROlmpoaOjs7aWpqoqmpyblWITg4eFSi5Krpd/Hx8fzoRz8iPj5+wn+3GJ/p1D/UTmKhLhIPdZj0NUauJvsYTY6KioqLll2fTP39/bz44os0Njai0+n4xCc+waxZs9zdrEmnlngIicWH9fT0UFNTQ3V1NdXV1TQ0NPDhtx1fX99RiVJUVNSEXdiQeKiLxEM9JBbqIvFwHdWuMRJTR2lpqds78MikyNfXl09/+tMevUnrlVBDPMQQicVo/v7+zJo1y3nBwmw2OxOlqqoq6urq6Ovro7i42Fmty2g0Eh8f79ybKSYmZlyJUnt7O8888wzf+ta3CAkJmdDXJcZH+od6SCzUReKhDpIYCY9kNpt56aWXaGxsxM/Pj8985jOEh4e7u1lCiI9hNBpJS0sjLS0NGCrqUF9f70yUampqGBwcpKyszFm+12AwkJiYSFJSEsnJyZc8onTu3Dl+/vOfc/fdd0tiJIQQ4mPJVDoxLsNV39zB4XDwz3/+k7Nnz+Lr68tnP/vZaZ8UuTMeYjSJxZVxOBw0NzdTVVXFuXPnOHfuHAMDA6O+x2QyjUqUIiMjL7hGScp1q4/0D/WQWKiLxMN1ZCqdcLn8/HyWLl3qlufevn07Z8+eRafT8alPfWraJ0Xg3niI0SQWV0ar1RIVFUVUVBSLFy9GURSampqorKzk3LlzVFVVMTg4SElJiXOTR29vb+e0u5SUFMLCwqZ8iX5PJf1DPSQW6iLxUAdJjMS4dHd3u+V5KyoqnLtl33rrrcTFxbmlHWrjrniIsSQWE0uj0TgTpaVLl+JwOGhsbByVKA0MDIxaoxQQEEBKSsqozb+FOkj/UA+JhbpIPNRBEiMxLoGBgZP+nIODg7z99tsALFiwgOzs7Elvg1q5Ix7iwiQWrqXVaomJiSEmJoarrroKu91OQ0MDlZWVVFZWUl1dTXd3NwUFBc6tIjZu3EhrayspKSkkJiZedMNw4XrSP9RDYqEuEg91kDVGYlwGBwcxmUyT+pybNm3i0KFDhISE8PDDD2MwGCb1+dXMHfGYTHa7HZvNhs1mw+FwYLfbcTgcKIqCw+Fwfq4oinMK1YU+enl5odVqR30c/lyr1U7I9KupHgu1s1qtVFdXU1FRQXl5ObW1teh0/7kG6OXlRUJCAikpKaSmpk5oaXDx8aR/qIfEQl0kHq5zObmBJEZiXDZv3sz69esn7fna29v5wx/+gN1u5/7775eSlh8y2fG4EhaLhf7+fgYGBhgcHMRisWA2m0d9brFYsNlsWK1WZzLkahqNBr1ej16vR6fTodPpnJ8P3280GjEYDBgMBufnwx91Oh0ajcajYjEdvP3226SmplJeXk5FRQVdXV2jHvfx8SE1NZW0tDRSU1Px8/NzU0unB+kf6iGxUBeJh+tI8QUx5ezcuRO73U5aWpokRSpnt9vp6+uju7ubnp4eent76e/vd96sVuu4f/fwCI9Go3GO8owc7dFoNM7NQ0de8xkeTRoebRq+jUy4FEXBYrFgsVjG3TaTyUR5eTm+vr6YTCa8vb3HfDQajTJCMUny8/O59dZbycvL45ZbbkFRFNra2qioqKCiooLKykr6+/s5efIkJ0+eBCAmJsZZTjwuLk5iJYQQ04gkRmJcMjIyJu25enp6KCoqAuDaa6+dtOf1JJMZj2GKojAwMEB7ezsdHR10dXXR3d1Nb2/vx47wGI1GfHx8MBqNF7yNHKkZHr3R6XR4eXlN+GsYmSxZrVbnKNXIz202mzNpGh7RGv5osVicP9/X14fJZKK2tvaiz6nRaJyv39fXd8xHX19fmSbqIhqNhrCwMMLCwli0aBEOh4Pa2lpKS0spKyujoaGB+vp66uvr2bNnDyaTyTmalJaWhr+/v7tfgsdzx7FKXJjEQl0kHuogiZEYl8m8inrs2DEcDgeJiYlER0dP2vN6ksmIh81mo62tjebmZmcyNDg4eMHv1el0+Pv7ExAQgJ+f36iTf29vb9Usfh9edzSccHl7e1/271AUxZk4DQwMUFFRQXBwMIODg87pgsMfBwcHURTF+Xl7e/sFf6derx+TLPn5+eHv74+fn9+EJ4jTlVarJSEhgYSEBK699lp6e3udG8uWl5czMDBAUVGR88JMVFQUaWlppKenEx8fL6NJ4yB/M/WQWKiLxEMdJDES43LmzBkSExNd/jyKolBYWAjAwoULXf58nsoV8bDb7bS1tdHU1ERzczNtbW1jRoK0Wi0BAQEEBwcTHBxMQEAAAQEBeHt7T5t9ZEauTfL19SUvL49FixZd8HsdDgdms5mBgQHn1MK+vj7nx76+PsxmM1arlc7OTjo7Oy/4fD4+Ps5EaeTNx8dHkqYr4Ofnx9y5c5k7dy4Oh4O6ujpnolRfX09jYyONjY3s27cPb29v0tPTycjIIC0tTRZNX6LJeu8QH09ioS4SD3WQxEioWktLCx0dHeh0OhlmngRWq3XUdKIPr7fx9vYmIiKCsLAwgoODCQoKGlXxS3w0rVaLt7c33t7ehISEXPB7rFbrqKRp+Nbb20tPTw9Wq9V5X1NT05jfPzy6FBgYSEBAgPOjWkbpPIVWqyU+Pp74+HhWr15NX18f5eXllJWVUVpaysDAAIWFhRQWFjpHnmbMmEFGRgahoaHubr4QQohxkKp0Ylz6+vrw9fV1+fPs37+frVu3kp6ezj333OPy5/NUVxKPkfvANDQ0jBoVMhqNREVFERERQUREBH5+ftNmJGi8XNk3hqfhDSdJwx+HP7fZbBf9WV9fX2eiNHzz9/ef0gnT4OAgZ8+eJSMjY0JHdIbXJpWUlHD27FlaWlpGPR4aGkpGRgYzZswgPj5eRvFGmKz3DvHxJBbqIvFwHalKJ1yuqKjootOFJlJdXR0AycnJLn8uTzaeeHR2dlJRUUFVVRVms9l5v7+/P7GxscTGxhIaGirzni+TK/uGRqNxjjiFh4ePemy4GEZvby/d3d10d3fT1dVFV1cXg4ODzlGmhoaGUb9vOGEaHgEMCgqaMgmwyWRyyd4gI9cmrV27lvb2dkpLSykpKaGqqoq2tjYOHjzIwYMHMZlMpKWlMWPGDNLT06f9lLvJeu8QH09ioS4SD3WQxEiMS0dHx6Q8z/BJXFRU1KQ8n6e61HgoikJzczNnzpwZdYJsMplISkoiKSmJwMDAKXFS7C6T1Tc+bHjtkY+PDxEREaMeM5vNziRpOGHq7u52jj719vZSX1/v/H69Xk9QUNCoZCkwMNDjRj4qKyv51re+xfPPP+/SiyshISEsXryYxYsXYzabKS8v5+zZs5w9e5b+/n5OnTrFqVOn8PLyIjk5mczMTGbMmDEtq9y5q3+IsSQW6iLxUAdJjMS4TMYmiDabzXmgiIyMdPnzebKPi4eiKNTV1XH69GlnJTSNRkNsbCwpKSlERUXJyNAEUeMGoUaj0TkdcqTBwUFnotTR0eEs+GC1WmlpaRk1RUyr1RIYGOhMlIYLbqh5Kl5HRwc7d+6ko6Nj0kadjUYjs2bNYtasWc4CDiUlJZw5c4bW1lZnMYf33nuPuLg4MjMzyczMnDbrktTYP6YriYW6SDzUQdYYiXGxWq0uPyHq6uri17/+NV5eXjz11FMyivERPioeLS0tnDhxgtbWVmColHZycjIzZsyQA7ELTEbfcCW73U5PT8+oRKmjo+OCG99qNBoCAwMJCQkhNDSUkJAQAgMDVZNk5+fnM3/+fPLy8sjNzXV3c2htbeXMmTOcOXNmzF5X4eHhzJw5k8zMTKKjo6fs8c7T+8dUIrFQF4mH68gaI+FyO3bsYP369S59jr6+PgB8fHym7EnCRLlQPMxmMwUFBVRWVgJDCZGsc3C9yegbruTl5eUcFRqmKAr9/f3OZKmjo4OOjg76+/udyVNFRQUw9H8WHBxMSEiI8zZV1ixdqbCwMJYvX87y5cvp6elxJkmVlZXOEbo9e/YQEBBAZmYms2bNIiEhQTWJ5kTw9P4xlUgs1EXioQ6SGAnVslqtABgMBje3xPPU1tZy9OhRzGYzGo2G5ORkcnJyxrWBqRDDRRp8fX2Ji4tz3t/f3097ezvt7e20tbU5R5Y+PA3PYDAQGhpKaGgoYWFhhIaGTvsro/7+/ixcuJCFCxcyODhIaWkpxcXFlJWV0d3dzZEjRzhy5Ai+vr7MnDmTrKwsEhMTp1SSJIQQaiOJkRiX1NRUlz/H8AnAFJvt6RLD8bDb7Rw/fpyysjIAAgMDWbhwIWFhYe5s3rQyGX1DLYaLPQwnS4qi0NPTQ1tbmzNhGk6WGhoanAU/NBoNQUFBhIeHExoaSnh4OD4+PhPevujoaB577DGio6Mn/HdPJJPJRE5ODjk5OdhsNioqKiguLubMmTP09fVx7Ngxjh07hq+vr3MkKSkpyeOKYcD06h9qJ7FQF4mHOkhiJMZlMqZiDSdGdrvd5c/l6YbLEu/bt8+5ligzM5OcnByPPHnyZNN5mqJGoyEgIICAgABnsQO73U5XVxdtbW20trbS2tpKX1+fczreMF9fX8LCwpy3iVirFB0dzZNPPqn6xGik4c2sMzIyuPHGGzl37hxFRUXOJCkvL4+8vDx8fHycSVJycrLH9PPp3D/URmKhLhIPdZDESIxLUVHRqCk1rjA87au/vx9FUWSNwkfIz89Hq9XS29uLwWBg6dKlHnUyOJVMRt/wJF5eXs61Runp6cBQn25paaGtrY2Wlha6urqc+yxVVVUBQyXDh0eTIiMjCQ4OvuyT/+7ubv72t7/x2GOPeWQxHi8vL1JTU0lNTXUmSadPn6a4uJi+vj7y8/PJz8/H29ubGTNmkJWVRUpKiqqTJOkf6iGxUBeJhzpIYiRUa/hExmq1MjAw4JKpNlNBb28vJSUlxMfH4+/vz4oVKzzyJFBMHz4+PiQmJpKYmAgM9fG2tjZnotTW1obVaqWxsZHGxkZOnjyJTqcjLCzMWXb8UhKlsrIynnrqKTZs2KCKqnRXQqvVkpKSQkpKCtdffz1VVVXOJKm3t5eCggIKCgrw9vZm1qxZZGdny5okIYS4TJIYiXFZtmyZy59Dp9Ph6+tLX18fXV1dkhhdgNlsZteuXURGRhIQEMDq1aulwIKbTUbfmGr0ej1RUVHOjZwdDgddXV20trbS3NxMc3MzZrPZmSjB0PEhPDyciIgIwsPDxzWi5Km0Wi3JyckkJyezYcMGampqKCoq4vTp0/T29jqn2/n7+5OVlUV2djaxsbGqGHWX/qEeEgt1kXiogyRGYlzOnj3L/PnzXf48ERERVFZW0tDQIFPDPsRut7N//356e3vp6+vjjjvukKRIBSarb0xlWq3WuYFseno6iqLQ1dVFS0sLTU1NtLS0YDabRxV0GJkoDU+9mw60Wq1z9O26667j3LlznDp1iuLiYnp6ejh06BCHDh0iKCiI7OxscnJyiIiIcFuSJP1DPSQW6iLxUAdJjMS4DC/wd7WYmBgqKyupr6/3+KkwE624uJjm5mb0ej3R0dGSFKnEZPWN6WS4il1QUNCoRGl4NKm5uXlM5Tuj0UhnZycAAwMDbmz95Bk53e6GG26gvLyckydPUlJSQmdnJ/v27WPfvn2Eh4eTnZ1NdnY2oaGhk9pG6R/qIbFQF4mHOkhiJMZlsqa1xcTEAFBTUzMpz+cpOjo6OH36NAALFiygurrazS0Sw2TKp+uNTJQyMjJQFIXOzs5RiZLZbKa1tZXQ0FAOHDhAV1cXUVFRREZGEhERMeX3UfLy8nJWt7NarZw9e5ZTp05RWlpKS0sLO3fuZOfOncTGxjJ79myys7Px9fV1ebukf6iHxEJdJB7qoFGm2CYx3d3dBAYG0tXVJQvQXcjhcEzKot6+vj5+8YtfoCgKjz/+uMSUob1idu3aRVNTE/Hx8SxbtgxFUWSRtUpMVt8QF2e322lvb6exsZGGhgY6OjpG7Yem1WoJDQ11JkohISHTJmaDg4OcOXOGU6dOUVFRgcPhAIb+JmlpacyePZsZM2a4LHGU/qEeEgt1kXi4zuXkBhIBMS5bt26dlOfx9fUlNjYWwLlp6XTX1NREU1MTWq2WuXPnotFoJi0e4uNJLNzPy8uL8PBwcnJyUBSF2267jeXLl5OWloa/vz8Oh4OWlhZOnjzJtm3beOuttzhw4ACVlZUMDg66u/kuZTKZmDt3Lvfeey9f//rX2bBhAzExMTgcDs6ePcvrr7/OL37xC95++20qKysnfINt6R/qIbFQF4mHOshUOqF66enp1NbWUlxcLOuMgNLSUmBol+zJmPoihKcqLCzkrrvuYs+ePcyePdu5R0hvb6+zwt3w+qTq6mqqq6vRaDSEhIQQExNDdHQ0wcHBqqjm5gq+vr4sXryYxYsX09raSmFhIYWFhXR2dnL8+HGOHz9OQEAAs2fPZvbs2URERLi7yUII4VKSGIlxSUpKmrTnysrKYufOnZSXl9PT04O/v/+kPbfaDAwMUF9fD0BGRobz/smMh/hoEgv1sNlsdHV1YbPZRt3v5+dHWloaaWlpOBwO2tvbaWhooL6+no6ODueeSidPnsRkMjmTpKioqCm7NiksLIxrrrmG1atXU11dTWFhIUVFRXR3dzuLNkRFRTFnzhxmz5497osy0j/UQ2KhLhIPdZDESIzLZK71CQsLIy4ujtraWk6ePDmta/3X19ejKAqhoaGjEkRZe6UeEgvPotVqCQsLIywsjJycHPr7+53V7RobGxkcHKSiooKKigq0Wi3h4eFER0cTExMzJWOt0Wic5b83bNjA2bNnKSwspLS01DnKtnXrVjIyMpg7dy7p6emXtX/UVPybeSqJhbpIPNRBEiMxLoWFhZO6r9DcuXOpra0lLy+PpUuXTtmpLR9neHPL4Wp9wyY7HuLiLhQLRQGbDRyOC9+8vMBoBL0eZO2te/n4+JCamkpqaip2u52Wlhbq6+tpaGigp6fHucavoKCAgIAAYmJiiIuLIzQ0dModl3Q6HbNmzWLWrFn09/dTVFREQUEBdXV1nDlzhjNnzuDr68vs2bOZN2/eJU21k2OVekgs1EXioQ6SGAmPkJOTw7Zt22hra6OkpITMzEx3N8ktOjo6gKFRNOEeg4Nw7hzU1EBz89hbRcUiDAbo64Pe3qGPfX1gt1/a79frwWAYSpQMBvD1hcBACAj4z8fhz4OCIDwcIiKGPg5/LlVfJ4aXlxdRUVFERUUB0NPT45xy19zcTHd3N93d3Zw5cwaTyURsbCyxsbFERkZe1iiKJ/Dx8WHhwoUsXLiQ5uZmCgoKKCwspLe3l4MHD3Lw4EFiYmKYN28e2dnZsq+aEMIjSbluMS6dnZ0EBQVN6nNu27aNffv2kZiYyAMPPDCpz60GNpuN119/HYDbbrsNo9HofMwd8ZjqGhrg+HE4cQJKS6G8HCoqoK5uaARIzXx8FCIiNMTEQFzchW/R0aCb4pfGent7OXDgAMuWLcPPz29Cf7fVaqW+vp66ujoaGhqwWq3Ox3Q6HTExMcTGxhIdHY3BYJjQ51YLu91OWVkZBQUFlJSUOEt/e3l5kZmZybx580hJSRlVgliOVeohsVAXiYfrXE5uMMXfFoWrnDt3jrlz507qcy5evJiDBw9SVVVFVVUViYmJk/r87mY2m4Ghk44Pn2i5Ix5TSW8vHDgAe/ZAXt5QQtTUdPHv9/a2Ex1tISTEQnCwjeBgKyEhVoKDbdjtrcTEBGAy2fHxceDt7cDb247R6ECrVdBohqbLaTTK+Y9Do0k2mxaLRYPVqsFq1To/Dgxo6evzorfXi74+r1Gfd3fr6OzU0dGhp6NDR2enHotFS3+/hnPnhka2LkarVYiLg9RUDSkpkJICqak4Pw8JGWqbJ/Pz8yMiImLCkyIAvV7vXIszPOWutraWuro6BgYGnFXutFotERERxMbGEhcXN6VGUry8vJgxYwYzZsygr6+PkydPUlBQQGNjI0VFRRQVFREQEMC8efPIzc0lMDBQjlUqIrFQF4mHOkhiJMal6aPOGl3E39+f3Nxcjh49yrZt23jwwQen3Jz+jzKcGBkMhjGv2x3x8GSKMjQStHEjbNoER48OrQEaSatVSEoyk5bWR1LSAHFxZmJiBomNNRMcbEOjGVqortPpMBgM6PV69Ho9BQW1LFmyBC8vr1E3rVaLRqO54A2GNvcbHsBXFMV5s9vtOByOUR+Hbhbs9gFsNpvzZrXa6Opy0NGho71dT2urnuZmA01NBpqbh24tLQaam/XY7Vqqq6G6GnbuHPs3CgxUyMjQkJnJqFta2tAUP09QW1vLj370I3772986S3W7wsgpd/Pnz6e9vZ26ujrq6uro6upyFi3Iy8sjPDycuLg44uPjp9RO976+vixZsoQlS5bQ0NDgnGrX3d3N7t272bNnD6mpqQwMDJCTkzPlphp6InnfUBeJhzpIYiTGxV1TQ1auXElBQQE1NTWcPXuWGTNmuKUd7vBRSeBUnaoz0U6dghdegNdfh6qq0Y/FxFiZN6+brKweMjL6SE8fwGQamhpkNBrx9vbGZPLHaAzDZDJhMpkumKSeO3fOrQtoFUVxJkoWiwWr1Xr+Y6/z68FBC01NUFdnoLbWSH29kbo6E3V1Q5+3tBjo6tJw9OhQ0jiSl5dCSgpkZmrIyoI5c2D2bMjIUN/UvObmZt58802eeuoplyZGI2k0GkJDQwkNDWX27Nn09PRQV1dHbW0tra2ttLS00NLSwvHjx6dskhQdHU10dDRr167lzJkz5OXlUVlZSVlZGZWVlXR1dTF37lxyc3MJCQlxd3OnLXnfUBeJhzq4dI3RT3/6U/79739z5swZvL29WbZsGT/72c8+8mR2165drF69esz9xcXFl7TgXtYYTX3bt29n7969hIWF8cUvfnHaXHns6urigw8+wGAw8IlPfMLdzfEYZjP8/e/wpz/BsWP/ud9kUli2rIelS1tZsKCbmBgLMLQ+xM/PD19fX+dtKu5d43A4sFgsmM1mzGbzqM87Oy1UV3tRVWWiqsrEuXPezo/9/Rfub0ajQlYWzJ6tcSZLc+ZAaOgkv7AR8vPzmT9/Pnl5earYHLq/v5+amhpqa2tpaWkZ9VhYWBjx8fFTLkka1t7eTn5+PgUFBfT29jrvT05OZv78+WRmZqJTW2YthJgSVLPGaPfu3Tz66KMsXLgQm83Gd7/7XdatW8fp06c/dnO4kpKSUY0PDw93ZVPFZdq8eTPr1693y3NfddVV5Ofn09rayqFDh7jqqqvc0o7JZjKZALBYLNhstlEnEe6Mh1r19cEf/gC/+c1QIQUAnU5h9eo+1q5tYPHiLkwmBxqNBj8/P4KCIgkICMDHx+eKpmh6Siy0Wq1z5OtCFi2yMTg4OOLWycDAILW1diorDVRWelNe7kNZmTdlZT4MDHiRnw/5+aN/T0yMwpw5GhYsgIULh27ni7xNOz4+Ps41Of39/dTW1lJTU0Nra6vzdvz4cWeSFBcXN+6NVNUmJCSENWvWYLFYSE5OJi8vj/LyciorK6msrMTHx4c5c+Ywf/58qbo5STzlWDVdSDzUwaWJ0aZNm0Z9/de//pWIiAjy8vJYuXLlR/5sRESEVOcQF2QymVi7di1vvfUWu3btIjs7m8DAQHc3y+UMBgM6nQ6bzUZ/f7+MiF6EwwH/+Ad861tDFeQAYmIcfOpTLaxZU0dIyNBiIj8/P0JDQwkODpYpDBcwPHL24cIF2dkKFouFgYEBBgYG6O9vpq9vgIoKhZISE2VlPpSWDiVLdXUm6us11NfDBx/853fExSksWqRxJkoLFgyVH59OfHx8yMjIICMjg4GBAWpqai6YJIWHh5OYmEh8fPyoSpSeysvLi5kzZzJz5kw6Ozs5fvw4x48fp7u721n2Ozk5mYULFzJjxoxpMyNACKEOkzpu3dXVBXBJc4rnzZvH4OAgs2bN4qmnnrrg9DrAOfVjWHd398Q0Vnyk+Ph4tz7/nDlzyM/Pp7q6mg8++IC77rpryhdi0Gg0BAYG0tbWRkdHx6jEyN3xUIuqKrj//qHqcgCJiQ4+//kGrr66Hr1eQavVEhYWQUREhMumK031WGg0GoxGI0ajcdTFq+xsB4ODg+cTpn76+1tpaTFTXOxFSYkPxcW+nD7ty7lz3tTWaqithX//+z+/d8YMhYULNSxaBMuXD03Fu9Jz4rCwMO6++27Vj0B4e3tfNEkaXpOUn59PVFQUiYmJxMTEeOz0zpH9IygoiNWrV7Nq1SpKS0vJy8ujtLTUOYrk7+/P/PnzmT9/Pv7+/m5s9dQ01Y9VnkbioQ6Tto+RoijccsstdHR0sHfv3ot+X0lJCXv27GH+/PmYzWZeeuklnnnmGXbt2nXBUaYf/OAH/PCHPxxz/xtvvIGvry/XXHMNR44cobe3l+DgYLKysti3bx8AmZmZOBwOzp49C8CqVasoKChwzkHMzc1l165dAKSnp6PT6SguLgZg+fLlnD59mvb2dmc1nu3btwOQkpKCj48Pp06dAmDp0qWUlZXR0tKCyWRi5cqVbNmyBYDExESCgoI4ceIEAIsWLaK6uprGxkb0ej3XXHMNW7ZsQVEU4uLiiIiIIP/8XJX58+fT2NhIXV0dWq2WtWvXsn37dmw2G9HR0cTFxXH0/MrpuXPn0t7eTnV1NQDr169n165dmM1mIiIiSElJ4dChQ8DQZqq9vb1UVlYCsGbNGg4cOEB/fz+hoaFkZmaydetWfHx8mDVrFhaLhbKyMgBWr17NsWPH6OnpISgoiNmzZ7Pn/Fnq8NqykpISYKiQQmFhIZ2dnfj7+7NgwQJ2ni+PlZaWhsFg4PTp08DQ9LkzZ87Q1taGj48Py5Yt4/XXX+e9994jMDCQW265Bfv5HTSXLFlCRUUFzc3NGI1Grr76ajZv3gxAQkICISEhFBQUALBw4UJqa2tpaGhAp9Nx7bXXsnXrVhwOB7GxsURFRZGXlwdAbm4uzc3N1NbWotFoWLduHTt27MBqtRIVFUVCQgJHjhwBhhK3zs5Oqs6v8F+3bh179uxhcHCQ8PBw0tLSOHjwIADZ2dn09/dTUVEBwLXXXsuhQ4fo6+sjJCSEWbNmOf9nHQ4H586dQ6/XEx8fz9VXX01+fj6NjY1ER0czd+5cdu/eDUBGRgZarZYzZ844/2eLioro6OjAz8+PRYsWsWPHDgBSU1MxmUwUFRUBsGzZMs6ePUtrays+Pj5cddVVbN26FYCkpCQCAgIoLCwEhsqonzt3jqamJgwGA6tXr3b+vePj4wkLC+P48eMALFiwgPr6eurr6/Hy8mLNmjVs27YNu91OTEwMMTExHDu/EGjevHm0trZSU1Pj/J/duXMnFouFyMhIkpKSOHz4MACzZ8/mX/9y8J3vRNDfr8fXV+GTnyxh9eoTBAWZCA8Pp6mpCZPJRE5ODoODg5SXlwNM+DEiLy8PHx+faX+M2L9/v/P/sLe3l/Lycux2O+HhKeza1c2ZMwFUVYVTWRlGQ8PY6Xze3laWLdOSktJAamoDCxbYWbEi97KOEdu2baO/v5+srCz8/Pw4efKkRx0jdu/eTXt7OyaTyXlBZPh/ra+vD19fXxITE8nOznb+z86cORObzUZpaSmA8xgxPMfe3ccIq9XKjTfeeNFjRHp6Otu3b+fw4cNYLBZSUlKorKwkLi6O5cuXs3DhQuff+3KPEd3d3Zw7X8N+7dq17N+/n/7+fsLCwsjIyODAgQMAZGVlufQYoZbziP7+flasWOH2Y4Q7zyO2bdsGDK11c/cxwsfHh+zsbJefR6j9GOGK84gzZ85w++23X9Iao0lLjB599FHee+899u3bd9nVgW666SY0Gg0bN24c89iFRozi4+Ol+IKLqWUu7J49e9ixYwcmk4lHHnlkyse8qqqKgwcPEhgYyIYNG5z3qyUe7qAo8ItfwJNPDn29ZImD73+/nJCQoZPIkJAQEhISJm263HSOxaWyWq309fXR19d3viDBIIWFBk6f9uXUKT8KC/3o6xs9ocHLS2HePFi+XMPy5XDVVRdfq1RaCj09MDAwwMaNG7n55pvx9vbG3x/S0yfhBbpAV1cX1dXVVFVVjSpeYDKZiI+PJzExkdDQUNWPnF9q/7DZbBQXF3P06FHniTgMjQIuXLiQOXPmXHR9nLg0cqxSF4mH66im+MKwxx57jI0bN7Jnz55xlUxdsmQJL7/88gUfG57SIaan5cuXc+bMGerr69m4cSP33HOP6k8MrkRUVBQajYauri56e3tdsnGlp/nBD+BHPxr6/NFHrXzmM6dQFCs6nY7k5GSCg4Pd2j4xll6vJygoyDkVLz0dli+30NvbS29vN52ddZw8qXDihB8nTvhz4oQfTU1Gjh0bqiz4m99w/ucUrrlGwzXXwOrVEB4+lBRlZAw/kzdwF//zP/957rNnPTM5CgwMJCcnh+zsbNra2qiqqqKmpobBwUFKS0spLS3F19eXpKQkkpKSPH7qmU6nIycnh5ycHJqamjh69CiFhYW0trbywQcfsG3bNmbPns3ChQuJmq7VPIQQE86lI0aKovDYY4/x5ptvsmvXLtLH+W70yU9+kvb2dudw3UeRct2To62tjVB31uEdoaWlhWeffRabzcbatWunfJW6HTt20NzczOzZs5k1axagrnhMpuefh899bujzn/zEzPr1RdhsNnx8fEhPT3fLRZPpGouJZrfb6evrO58s9VJaaiY/3/t8ouRPebk3ijL6IkhOjkJOjoZ//ANefhlmzvzPY8XFcO+9kJcHKqjcPSEcDgdNTU1UVVVRW1uLbcQuxeHh4SQlJZGQkKCq9UhX0j/MZjOFhYUcPXqU5uZm5/1JSUksXryYGTNmoNVqJ6qpU54cq9RF4uE6qhkxevTRR/nHP/7B22+/jb+/P42NjcDQlS9vb28Avv3tb1NXV8eLL74IwG9+8xuSkpLIysrCYrHw8ssv88Ybb/DGG2+4sqniMtXX16umA4eHh7Nhwwbeeecdtm/fTkJCwpRexJiUlERzczOVlZXMnDkTjUajqnhMllOn4ItfHPr8O9+xs2FDMRaLDT8/PzIyMty2J8p0jIUreHl5ERAQ4HwTGxodGqS3t5fu7gbq6/s5csTIsWMBHDsWQFmZDydPaji/RICZM6dOAnQxWq3WuZmqzWajrq6Oc+fO0djYOGoj2bi4OJKTk4mIiHD7iPqV9A+j0cjChQtZsGAB1dXVHDlyhOLiYs6dO8e5c+cICgpi0aJFzJs3z3mOIS5OjlXqIvFQB5eeOfzpT38ChhZ2jfTXv/6Vz372swA0NDSMmj9ssVh44oknqKurw9vbm6ysLN577z2uv/56VzZVXKb6+npycnLc3Qyn3NxcKisrOXXqFK+//joPPfTQlNwkEYYWIubn59PT00NTUxNRUVGqi4er2e3wX/8FVivcdJPCvfeW0tNjcVb3cudGkdMtFpNFo9Hg7e2Nt7c34eHhpKQoLFxopru7m56eBs6d6+fwYW+2bQth586LVz595RXw8xuaTjeVZt3qdDoSExNJTEykv7+fqqoqKioq6OnpcSYOaphqNxH9Q6PROF9rV1cXR48eJS8vj87OTrZs2cLOnTuZO3cuixcvVn1FQneSY5W6SDzUwaVnD5cyS++FF14Y9fWTTz7Jk8OrqIVqqW1vCY1Gw0033UR9fT3t7e28/vrr3HvvvVNyWoVeryclJYWzZ89y5swZoqKiVBcPV3vrLThyBAIC4Kc/7aSnpxutVktaWppbkyJQX9+YqjQajXOD2qGKWArLlg2ycGE/54tSXdAvfjF0S0lR2LBBw3XXDa1PmiL7qAJDeyTNnDmTzMxM2traqKyspLq6mr6+PoqKiigqKiI8PJzk5GTi4+MndardRPePwMBA1qxZw6pVqzh58iSHDh2iubmZo0ePcvToUVJTU1myZAlpaWluHy1TGzlWqYvEQx0mrSrdZJE1RtNbU1MTzz//PBaLhSVLlnDddde5u0ku0dvby3vvvYeiKKxdu3baDb8vXQqHDsF3v6twxx2FmM1mYmNjiY2NdXfThJvl58P8+RdfYzRzZg+lpb7YbP+5aGI0KqxcCdddp2HDBsjMnFqjSYBzql1lZSVNTU3OC5fDI02pqakEBwd7fPKgKArnzp3j8OHDlJSUOF9naGgoixcvZu7cubKhsxDTzOXkBpIYiXHZtm0ba9ascXczLqi4uJhXX30VgFtuuYV58+a5uUWucfjwYSorK4mMjMRut6s2HhOtuhoSE0GrheLibjo7z6DT6ZgzZ44qrripuW9MB6Or0o21c2cdJlMH+/YZOHgwkIMHg2hoGF2kIzlZ4ZZbNNxyy9Bms24ehJxw/f39nDt3jsrKSnp6epz3BwcHk5qaSmJiostGkSazf3R0dHDkyBGOHz/O4OAgMLSZ7oIFC1i0aJHHV+67UnKsUheJh+uopviCmLqGN1NVo5kzZ3L11Veza9cu3n33XYKCgkhOTnZ3syZcdnY2VVVVNDU1Tckpgxdzfn84Fi8Gna4VGLoarIakCNTdN6aD9PShktw9PVBRUcFjjz3G7373O1JSUs7vYxQLxDJvnpn77++is7OakyfN7Nvnz8GDQRw/7k9lpZbf/GaoLHhIiMINN2i49VZYt25ofZKnG96ce+bMmbS0tFBeXk5NTQ0dHR0cO3aMgoICEhISSE1NJSQkZEJHkSazfwQHB7N+/XpWr15NQUEBhw4dor29nb1793LgwAFmz57N0qVLiYiImLQ2qYkcq9RF4qEOkhiJcYmJiXF3Ez7SqlWraGlpoaioiFdffZUHH3xwyr35+fr6kp6eTklJCV1dXdjtdtUkB650fnN3li/HudFlYGCgG1s0mtr7xnTwn50hOmlsfJ+UlP8zpkKd0WgkIiKCiIgI0tIcrF3bS2dnJ/X11ezda2T37mD27QuivV3PSy/BSy8NTblbswZuuUXDTTddfINZT6HRaJx/g9zcXM6dO0d5eTnd3d1UVFRQUVFBcHAwKSkpJCYmTsgUNHf0D4PBwKJFi1iwYAElJSUcOHCAmpoajh8/zvHjx0lPT2fp0qUkJyd7/FTCyyHHKnWReKiDTKUT4+IJ9fZtNhsvvvgi1dXVBAYG8l//9V9T7n/CYrHw/vvv09HRwaJFi8jKynJ3k1zuE5+AN9+E3/7WweLFxwCYN2+eavZq8YS+MV3k5+czf/588vLyyL3E2t2KojA4OEhnZyetrZ0cOAB79gSxZ08wtbUm5/dpNAqLF8Ptt2u4446h6Z1TgaIotLS0UFFRQU1NjfMqtk6nIyEhgZSUlCuq9KaW/lFTU8PBgwcpLi52rkOKiopi2bJlZGVlTYuLTGqJhRgi8XCdy8kNps/8GzGhjh075u4mfCydTsenPvUpwsLC6Orq4u9//zsDAwPubtaEMhgMzJs3j/r6eoqKiujs7HR3k1xueElEQIADGLrq7e5KdCN5Qt8QFzdcFjw6OpqcnJk8+GAaTz+tZ/v2Gv75z1N88Ys1ZGX1oigaDh3S8I1vQFISLF6s8MtfQlWVu1/BlRkeRVqyZAk333wz8+bNIzAwEJvNRkVFBdu2bWPr1q1UVlaOa+qPWvpHfHw8d955J4899hiLFi1Cr9fT2NjIv//9b55++mkOHDiA2Wx2dzNdSi2xEEMkHuogiZGY0ry9vbnnnnvw8/OjqamJv//971gsFnc3a0IlJCQQFBSEw+Hg0KFDU36e8vD2VH19Q1d5tVrttJr+IiaXXq8nLCyMjIx07rhjFj/5iT/vvNPCpk0nefLJc8yf341Wq3DkiIYnnhhKkpYsUfjVr4YKhXgyo9HIjBkzuO6667j22mtJSkpCq9XS1tbG4cOH2bhxI4WFhfT19bm7qeMWEhLC9ddfz9e+9jWuueYa/Pz86O7uZsuWLfz6179mx44dHv36hBCXR6bSiXFpbm72qDU7TU1NvPDCCwwMDJCcnMw999yjqlGGK1VTU0NeXh6Dg4NkZmYyd+5cdzfJZT77Wfjb3+AHP7CzYUMeGo2GBQsWqCY58rS+MZV1dnayceNGbr75ZoKCgib0dzscDnp6eujo6KCsrIetW/3Zti2E48f9UZT//C8uXapwxx0aPvlJiI+f0Ca4xeDgIBUVFZSXlzsTBo1GQ2xsLOnp6URERHxkX1R7/7DZbBQWFnLgwAFaW4eKu+j1enJzc1m2bJmq1jNeKbXHYrqReLiOlOuWxMjlTp8+zaxZs9zdjMtSV1fH3/72NywWCzNmzODOO++cMvPIT58+TWBgIHv37kWj0bBq1SqiPH1l+EX8/Ofw5JNw550KTzxxDEVRmDNnDkaj8eN/eBJ4Yt+YyiYjHoqi0N3dTUdHB6WlPWzZ4s/27SEUFIxOklatUrjvvqEkydPPrx0OB/X19ZSWltLU1OS8PzAwkLS0NJKSki647s9T+ofD4eDMmTPs27eP+vp6YGh0evbs2SxfvvyK1lmphafEYrqQeLiOrDESLldTU+PuJly22NhYPv3pT6PT6SgpKeG1116bMtPOampqiI2NJTU1FUVROHDggLNi21QzZ87Qx4MHNZhM3gCqmuriiX1jqmpsbOTnP/85jY2NLn0ejUZDYGAgSUlJrFmTzfe+F8Kbb3awadMpnnjiHHPndgOwe7eGz30OIiMV7rwT3nkHPHVmr1arJS4ujtWrV3PdddeRlpaGTqejq6uLvLw8Nm7cSH5+/qh9ksBz+odWq2XWrFl8/vOf57777iM5ORmHw0FBQQF/+MMfePXVV50Jk6fylFhMFxIPdZDESEwrSUlJ3H333eh0Os6cOcOrr76KzWZzd7MmTG5uLqGhoVgsFvbv3z+lXtuwFSuG1hnV1EB1dQjAmJMvIQDq6+t54YUXJvUEVqPREBAQQGJiImvXZvPUU8G88UYr775byCOP1JCc3I/ZrOG11+DmmyEmRuFLX4LDh8FT528EBQWxYMECZ7EGf39/rFYrZ8+e5f3332ffvn3OaWmeRqPRkJqaymc+8xk+97nPkZmZiaIoFBcX89xzz/Hiiy9SWVnJFJt8I8S0JVPpxLRUXl7OK6+8gs1mIz09nbvuumvKrDnq6+tj69atDA4OkpiYyJIlS1Sz/maiDJfs/spXBvn0pwsxGAzMmTNnyr1OcWXGU67bVex2O52dnbS1tXPkiIX33w9l8+YQ2tv/szdQerrCvfdquO8+8OQ9qRVFobGxkbNnz9LQ0OC8PzQ0lBkzZhAXF+fRm1I3Nzezf/9+Tp48icMxVB0zISGBVatWkZKSIschIVRGptIJl9u5c6e7m3BFUlNT+fSnP41er6e0tJRXXnnFo6vVjYyHr68vy5YtQ6vVUlVVxenTp93YMte4996hjy+/bMRm02OxWFQzauTpfUO4hpeXF6GhoWRkpHPXXRn8+tcadu0q5ze/KeG661oxmeyUlmr4/vchJQXWrlX45z9hcNDdLb98Go2G6OhoVq1axXXXXUdKSoqzmt0//vEP3nvvPUpKSrBare5u6rhERERw22238eUvf5mFCxei0+morq7mpZde4i9/+QtlZWUeMYIkxyp1kXiogyRGYlw8OYkYlpKSwqc//WkMBgPl5eX87W9/o7+/393NGpcPx2N4J3uAkydPUl5e7o5muczNNw9tqNnWpmH37qFSX65eR3KpPLFvKIqCw+HAarViNpsZGBigv7+f3t5eent76enpoaenh+7ubudt+L7h7+nv72dgYIDBwUEsFgtWqxW73e4RJ4iTTa/XExkZSU7OTB56KJHnn7ewY0cx3/9+OYsWdQGwbZuGT30KYmMVvvpVOHXKvW0er6CgIBYtWsRNN91EVlYWGo2Gvr4+jh8/zjvvvENBQYHHHneDgoK44YYb+PKXv8zixYvR6XTU1NTw8ssv8/zzz1NaWqrq/39PPFZNZRIPdZCpdGJcCgoKpkxJ6NraWufmr2FhYdx3330eV5L1YvE4efIkRUVFaDQali9fTmxs7OQ3zkV+/Wt4/PGhE8dXXsnDaHSQnZ2Nz/BGR26i9r4xMgmy2WzYbDaXJzBarXbMzcvLy3lz1dSjiooKHnroIZ599llSUlJc8hwTRVEUenp6aG1t5eTJXjZuDOWdd8JpafnPVLvFixU+9zkNd90F/v5ubOwVyMvLIzAwkJKSEucor1arJSEhgRkzZhAcHOzmFo5fT08PBw4c4NixY87RsJiYGFatWkVGRobqptip/Vg13Ug8XEfKdUti5HKdnZ0Tvi+IO7W0tPDSSy85/3/uu+8+jyrHerF4KIrC0aNHqaioQKfTcfXVV3vU6/oog4MwY8bQJppf/3obd95ZTmBgoNtPQNTaN2w2G2azGYvFcsFqjBqNZlTiMvz1yL/lyM+H3zpGfnQ4HCiKMurz4TUYH2VkoqTT6ZyfT8Q6FLXG46PYbDba29tpampl+3Yv3n47nL17g7Dbh/4evr4Kd92l4fOfh8WLQWXn2x9pOB6KolBfX09JSQnNzc3Ox2NiYpg1a5ZHH6d6e3s5cOAAR48edSZIw1MLZ8yYoZoEyRP7xlQm8XAdSYwkMXK5zZs3s379enc3Y0J1dXXx0ksv0draio+PD/feey8xMTHubtYl+ah42O129u3bR0NDA3q9nlWrVnn0ScdIL70E998PPj4Kf//7SWJiBklPT3frVWc19Q1FUbBarQwMDIxaz6HRaNDpdKNuH06CJorD4Rhzs9vtoz5ezMhkaWQ7L5XFYuFf//oXd955JwaD4eN/QIX6+/tpbW3l7Nku3n47kI0bw6mu9nY+Pm+ewqOPDk27c/Ng6SW5UP9ob2/nzJkz1NTUOBPtiIgIZs2aRWRkpGoSicvV19fnTJCGp0lFR0dzzTXXkJaW5vbXpaZjlZB4uJIUXxBiHAIDA3nggQeIiYmhv7+fF154gdLSUnc364p5eXmxbNkywsPDsVqt7N69m7a2Nnc3a0Lccw+sWgX9/Rp+/vMMFAWqq6unzP5UV8JutzvXA1mtVjQaDUajEX9/f4KDgwkMDMTX1xej0ejS6WxarRadTofBYMBkMuHj44O/vz+BgYGEhIQQEhJCYGAg/v7+eHt7YzAYnBsv2+12LBYL/f39dHd3097eTkdHBz09Pc5k76Ou7Z06dYr77ruPU566QAfw8fEhISGBa67J4oc/9GPz5mqeffY011/fisHg4Pjxob2R4uIUvv518MRDVkhICMuWLeP66693Fmpobm5m165dbN26ldraWlWv1bkYX19f1q5dy1e/+lVWrFiBwWCgoaGBv//97/z1r3/l3Llz7m6iEOJDZMRIjEtDQwPR0dHuboZLmM1mXn31VSoqKtBoNFx33XUsXrzY3c36SJcSD6vVyp49e2hpacFgMHD11VcTEhIySS10nbIymD0bBgbgm9+s4xOfqCM8PJxkN9U7VkPfsFgs9Pb24nA40Gg0mEwmvL29PapE8vCI0vAaqIuthRoe/dLr9c6Pw0memsp1T6TBwUGam5spK+vgzTeD+fe/I6ivNzkfX79e4ZFHNNxwA5zPMVXjUvpHf38/Z86coaKiwrkXW2BgIJmZmSQkJDgTZ0/T19fH/v37OXLkiPN1paSkcM011xAXFzfp7VHDsUr8h8TDdWQqnSRGLldSUsKMGTPc3QyXsdvtvPvuuxw/fhyARYsWcd1116n2xPJS4/Hh5GjVqlWEhoZOQgtd63e/gy9/GfR6heeeK2bWrF7S0tLckvi5u29YrVa6u7tRFAW9Xo+fn5/Hnkh+2MhkyWazYbVax0zF02g0eHl5odfrOXXqFEuWLJlyidEwu91Oe3s7jY3NbNum4403IjlwIBBFGUoMk5IUvvxlDf/1X6CWt8PL6R+Dg4OUlpZSWlrqnIrm6+tLZmYmKSkpHvt/3d3dzd69e8nPz3eObs+YMYPVq1cTFRU1ae1w97FKjCbxcB2ZSidcbqpPAfDy8uLmm29mzZo1ABw5coR//OMfmM1mN7fswi41Hnq9npUrVxIeHo7FYmHnzp2qKXN9Jb70paFNX61WDU89lUFXl47KykoGBgYmvS3u7BsOh4Pe3l4URcFgMBAQEOCxJ48XotVq0ev1eHt7O6cEBgcH4+fn55wSqCgKNpuNgYEBent7gaHF8AMDA9hsNo+cknUxXl5ehIeHk5OTxec/H8uLL7bz73+f5L776gkMtHLunIbHHx+aZve1r0FlpbtbfHn9w2QykZOTw0033cScOXMwmUz09fWRl5fHe++9R2lpqUdOmw0ICOCGG27gscceY968eWg0GkpKSnjmmWd47bXXaG1tnZR2TPX3cU8j8VAHSYyEuIjhEtd33XUXer2esrIynn/+eTo7O93dtCsynBxFRUVhs9nYs2cP1dXV7m7WFdFo4C9/gbQ0qKvT8e1vZzIw4KC0tNQ5ZWU6GBwcxG634+Xlhb+/v9sXd7va8OiQyWQalSj5+/tjMpmcSaHVaqWvr4/Ozk46Ozvp7e3FbDZfUsU8T+Hn50dKSgo33jiLn/1Mw+bNxXzrW5UkJQ3Q06PhN7+BtDSF22+HffvAk/JDvV7PzJkzufHGG5k/fz4+Pj709/eTl5fH+++/T1lZmUcmSEFBQdxyyy08+uijZGdnA1BUVMQf/vAHNm7cSHd3t5tbKMT0I1PpxLg4HA7VTitzhfr6ev7xj3/Q29uLr68vd911FwkJCe5ultN44mG32zl8+DDV1dVoNBrmz59PWlqai1o4OYqKYNky6O6GDRs6+P73SwkMDCAjI2PS/l/d2Tc6Ozux2Wz4+flhMpk+/gemOLvdzsDAABqNBrvdPqZYg0ajQa/XYzAYMBgMU+qYpigK7e3tNDQ0sX27F6+8EsXhw//Zn23hQoUnntBw++2Tuw5pIvqH3W6noqKC06dPO0eFfX19mTVrFklJSR47StrU1MTOnTs5c+YMMJQQLlmyhKuuusol/Xm6vY+rncTDdWSNkSRGLrd3715WrFjh7mZMqq6uLl555RUaGxvRarVcd911LFy4UBVX5ccbD4fDQX5+PmVlZQBkZ2c7d6f3VNu2wYYNYLPBffc18uij1YSFhZKSkjIpr8tdfWP4RFhRFEJCQuQN9ryR8RguX261Wsfs5zRcxGE4SfLUk+sL6enpoampiWPHBnnllQg2bQrDYhn6/0hLU3jySQ333w9Go+vbMpH9w263U15ezunTpxkcHASGRs6GEyRP7QM1NTVs3brVOZLv4+PDypUrWbBgATqdbsKeZzq+j6uZxMN1ZI2RcLn+/n53N2HSBQYG8uCDD5KVlYXD4eD999/nrbfeGrU/jLuMNx5arZb58+eTlZUFDJU3Pnz4sEdOSxm2Zg0899zQ5y+9FMXzz8fS1tY2ao8UV3JX3xjeWBXw6MR2Ip09e5ZHH32Us2fPAkN/F4PBgK+vL0FBQQQFBeHj44NOp3MmTX19fXR0dNDZ2cnAwIBH94Vh/v7+pKWlcdtt6fzud4O8++5J/uu/6ggIsFFWpuELX4DkZIWf/3xotNWVJrJ/eHl5kZGRwY033si8efMwmUz09vZy5MgRPvjgAyorKz1yumR8fDwPPPAAd999N2FhYfT397Np0yb+8Ic/cPLkyQk7jk3H93E1k3iogyRGYlymygahl8tgMPDJT36SdevWodFoOHHiBH/5y1/cvu7oSuKh0WjIyclh4cKFaLVazp07x65du5xXYD3RAw/Ar3899Pn//m8sL70URWNj46QkR+7qGyOToSk2EWDcent7OXnypLMIw0jDI0Q+Pj4EBQURHByMr6+vs9y3zWZzrkvq6upicHDQI0+yRzIajSQkJLB6dRY/+pGG998v4qtfrSI83EJDg4Ynn4TERIWnnoLmZte0wRX9Q6fTMWPGDG644Qbmzp2L0Wikp6eHw4cPs3nzZo/cB0mj0ZCZmckjjzzCTTfdhL+/Px0dHbzxxhs899xzVFRUXPFzTNf3cbWSeKiDTKUT49LT04O/v7+7m+FWlZWVvPbaa/T39+Pt7c0nP/lJUlNT3dKWiYpHY2MjBw4cwGKx4Ofnx4oVKwgMDPz4H1Spn/4UvvOdoc8feqiWBx6oJyYmmri4OJeNqrizb3R0dGC32wkICMBgMLilDWoy3n2MHA4HFosFs9k8akR4eE2S0WjEYDB4/Mic3W6ntbWVmpom3n7bj5deiqaqyhsAHx+FL35Rwze+AZGRE/eck9E/rFYrZWVlFBcXO8t8h4aGMmfOHCIiIlz63K5isVg4dOgQ+/fvd1ZHTUtLY926deN+TfI+ri4SD9eRqXTC5Q4cOODuJrhdcnIyDz30ELGxsQwMDPDyyy+zd+9et1yZnKh4REVFce211+Ln50dvby/bt2+nvr5+Qn63O3z72/CjHw19/uyzcfzhD3HU1zdQXV3tsji5s28MJ0NqLSvvKbRaLSaTicDAQOdI0vB0O4vFQk9PDx0dHfT19Xl01UMvLy8iIyOZPz+Hr341kI0by/nZz0qZNauX/n4Nv/zl0BS7r38dJqqq/2T0j5FV7LKystDpdLS1tbFjxw52797t9hH+8TAYDKxcuZIvf/nLLF68GK1WS1lZGc888wzvvffeuKZhyfu4ukg81EESIyGuQGBgIA888AC5ubkoisL27dv5+9//Tl9fn7ubNm6BgYGsXbvWudfR3r17KSoq8ripKMO+9z341a+GPn/ppRh+9rMk6uqaKC8v9/ipUR9mPL963mKxePQJu5p4eXnh7e09ak2Sl5cXDoeDgYEBZwlwT55qp9FoCA0NPb8fUhivvlrFr39dQlZWLwMDGn71K0hJUXj88YlLkCaDwWAgJyeHG264gbS0NLRaLQ0NDWzevJlDhw5dcIql2vn6+rJhwwa+9KUvMXPmTBwOB0ePHuW3v/0tBw8enBJr4oRwJ5lKJ8altraWuLg4dzdDVfLz83n//fed5ZJvv/12kpOTJ+W5XREPu93O8ePHnRXrYmJiWLJkicdO0frzn+ELXxjav2Xp0i5+/ONSYmL8SE9Pn9AKZO7uGz09PZjNZgwGw7TYy+ijtLa28te//pUHHnhgQufvDxdqMJvNWCyWUUUvjEYjJpNpQquHTTZFUejs7KSurp7t23X87//GUlTkB4DJpPDIIxq+9S0ID7/83+3O/tHT08PJkyed1d60Wi0ZGRnMmjXLY49r586dY9OmTc6NukNDQ1m/fj3p6ekf2/fdfawSo0k8XEfKdUti5HJlZWUev+eNKzQ3N/Paa6/R0tKCRqNhxYoVXH311S4vG+vKeFRWVnLs2DHsdjv+/v5cddVVBAUFueS5XO2tt+DTn4aBAUhP7+eXvzxLcrKO9PR052jLlXJ337Db7XR2dqIoiuxnhOvj4XA4MJvNmM3mUaN0er0ek8nk0WuRRiZIO3YMJUinTg0lSP7+Q/sgfe1rcDnLItzdPwDa29spLCx0JhNGo5Hs7GxSUlI8sky7w+GgoKCA7du3O2crpKamsn79+o9cf6SGWIj/kHi4jqwxEi5XXl7u7iaoUkREBF/4whecU+v27NnDCy+8QFdXl0uf15XxSE5O5tprr8XX15eenh62bdtGZWWly57PlW69FfbsgagoKC314cEHs8jPh9OnT9PT0zMhz+HuvuHl5YWPjw8AfX19qign7y6tra38/ve/p7W11WXPodVq8fb2JjAwkMDAQIxGIxqNBqvVSk9Pj7PstydOs9NoNAQHB5OVNYv774/kH/84x9NPlzBjRh89PRq+/31ITVV4+mm41GVt7u4fACEhIaxatYqVK1cSGBiI2WwmLy+PzZs3U19f73HThrVaLbm5uXz5y19m+fLleHl5UV5ezp/+9KePXH+khliI/5B4qIMkRkJMML1ez80338wnP/lJjEYj1dXVPPPMM5SUlLi7aeMWEhLCunXriIyMxGazcfjwYQ4dOuSRJ90LFsChQ5CdDa2teh56aBZvvx1ASUkJLS0t7m7ehBgeqVAUhZ6enmm77qC6upqnn37aOXXKlYYr1vn7+zvXImm1Wux2u7Psd19fn8cmSEFBQWRnZ3HPPWH885/l/N//W0Z8/CAtLRq++lXIyFB44QXwlH81jUZDTEwM69evZ8GCBZhMJrq7u9mzZw+7du2io6PD3U28bEajkTVr1vClL32JWbNmoSgKR48e5Xe/+x15eXkel/AJ4Q4ylU6Mi9VqRa/Xu7sZqtfe3s7rr7/urOy2YMEC1q1bN+Hz2ScrHg6Hg+LiYk6dOoWiKAQEBLB06VKCg4Nd/twTrasLPvUp+OCDoa9vv72Jr32tmri4COLj48c9/VEtfcPhcNDd3Y3NZsPLy4uAgACPnCZ0JcZbrnuiKIqC2WxmcHDQOc1ueB2St7e3x8bD4XDQ2tpKVVU9b74ZyJ//HEtLy9AxLTt7qNjJ2rUX/lm19I8Ps1gsFBcXU1JSgsPhQKPRkJqaSk5OzoRNs51s586d44MPPqCpqQmA2NhYrr/+emJjYwH1xmK6kni4jkylEy535MgRdzfBI4SEhPBf//VfLF26FIBjx47xzDPPUFtbO6HPM1nx0Gq1ZGVlsXr1anx8fOju7mbbtm2UlpZ63NXIwEB45x34/vdBo1F4441IHnpoJoWF7ZSUlIy75LVa+oZWq3UmQ3a7ne7u7mk7cuQuGo3GWfY7ICAAvV6PoigMDg7S2dnpsaN5Wq2WiIgIcnNzeOwxE2+9VcSXvlRNQICNU6dg3Tq46Sa40CC5WvrHhxkMBubMmcMNN9xAQkICiqJQVlbGe++9R1lZmUeO9CUlJfHQQw+xYcMGjEYjdXV1/PnPf+bdd9+lv79ftbGYriQe6iCJkRgXTyxz6i5eXl6sX7+e+++/n4CAANrb23n++efZsWPHhJ0UTXY8IiIiWL9+PTExMdjtdvLy8ti/fz+Dg4OT2o4r5eUFP/gBvPuuhqAgKCry4/77s9m6VUNRUdG49jtRU9+4UHIkZbwnn0ajwWAwONchDU9zNJvNHp0geXl5ER0dzaJFOXzjG/DGG4XceWcjXl4O3n0XsrMVvvIVaG//z8+oqX9ciK+vL8uWLeOaa64hMDAQi8XCsWPH2Lp1q0vXqrmKVqtl8eLFPPbYY8yZMwdFUTh27Bi///3vOX78uMdd0JrK1N43pgtJjMS4eOLUKXdLSUnhkUceYfbs2c7CDH/+858nZF2LO+JhNBpZsWIF8+bNQ6vVUltby6ZNm6irq5v0tlyp66+HvDyYOxc6O/V85SuZ/PKX0Zw6VUp1dfVlXS1WW98Ynkan0+mcyZHFYnF3syaFn58fubm5+Pn5ubspTnq9noCAAIKCgqZMgqTT6UhISOCqq2by4x/38Morp1i+vAObTcNvfwtpaQq//S3YbOrrHxczfPEnNzcXg8FAR0cH27Zt49ChQwwMDLi7eZfNz8+P2267jQceeICIiAj6+/s5ceIEzz//vEdv4j2VeErfmOpkjZEYl76+Pnx9fd3dDI9VVFTEu+++y8DAADqdjjVr1rB48eJxl/V1dzw6Ojo4dOiQs/peSkoK8+bN87j50gMD8PWvw5/+NPR1enofP/pRObNn60hJSbmk0tfujsXFOBwOenp6sFqtaDQafHx8MJlMHltK+lKpNR7DbDYb/f39zmR1ePqdt7e3y8v8u0pXVxc1NTXs2qXj6acTKCsbqpI4ezb88pcDrFnj7eYWXp7BwUEKCwuprKxEURT0ej2zZ88mNTXVI2Nkt9s5cuQIW7ZsQVEUNBoNixYt4pprrvHY9VRTgdqPVZ5MVWuM/vjHP5KcnIzJZGL+/Pns3bv3I79/9+7dzJ8/H5PJREpKCs8884yrmyjGYd++fe5ugkfLysrikUceIS0tDZvNxqZNm3jxxRfHXQnJ3fEIDg5m3bp1ZGZmotFoqKioYPPmzTQ3N7u1XZfL2xv++Ed4+20IC4PSUl8++9ls/vY3H06ePEVTU9PHTj1xdywuZnhanclkQlEU+vr66O3t9ci1E5fK4XCwY8cOVb9GnU5HQEAAgYGBzjVIAwMDzjLfnnjtMjAwkKysLO68M4RXXinhm9+sJCDARmEhrF3rzYMPgicVgDSZTCxatIg1a9YQGhqK1WolLy+P7du3e2T1Oi8vL5YuXcrs2bPJyclBURQOHz7MH/7wB4+unurp1PreMd24NDF69dVX+epXv8p3v/tdjh8/zooVK9iwYcNFS6dWVlZy/fXXs2LFCo4fP853vvMdvvzlL/PGG2+4splCuIW/vz/33HMPN9xwA3q9nsrKSv74xz9y6NAhVZ/IXYyXlxdz585l9erV+Pr60tvby86dOzl+/LjHrWu5+WYoLBxaRG42a/mf/0niK19J48iRes6ePeuxU9E0Gg2+vr74+fmh0Wgwm810dXV5ZNn1S1FQUMDNN99MQUGBu5vysYan2A2vCXM4HM4y354YH41GQ0REBHPmZPPwwxpef/0kN988dKHkr3+FGTMUfvQjOHoU8vNH30pL3dz4iwgNDeXaa69l/vz56PV62tra2Lp1KwUFBR4ZIx8fH26//Xbuu+8+goOD6e7u5pVXXuG1116bsH3dhPA0Lp1Kt3jxYnJzc/nT8LwUYObMmdx666389Kc/HfP93/zmN9m4cSPFxcXO+x5++GFOnDjBwYMHL+k5ZSrd5KiqqiIxMdHdzZgy2tvb2bhxI+fOnQMgPj6em2++mfDw8Ev6ebXFw2q1cvz4cSoqKoChJHDhwoUfuQu7Gjkc8Nvfwje/qWCxaPD1tfHlL9dw++0dJCYmEBoaOmYqmtpicTFWq5Xe3l7sdjsajQZvb2+8vb2n1NQ6d5frHq/hdUf9/f3OiyRGoxFfX1+PnLoFQwvLq6qq2Latjz/+MYuzZz96ytDZs5CePkmNG4eBgQGOHz/uvNDr6+tLbm6usxS2Jxh5rLJarezatYuDBw/icDgwmUysXbuW3NzcKXVMUDNPee/wRKqYSmexWMjLy2PdunWj7l+3bh0HDhy44M8cPHhwzPevX7+eY8eOeeTVmKnME0c01CwkJITPfOYz3HjjjRiNRmpqanjmmWfYu3fvJS3GVls89Ho9ixYtYuXKlfj4+NDT08OOHTs8ri9rtfDVr0JBgYYlS6CvT8dPf5rMI4+ksm9fLaWlpWPKeqstFhej1+sJDAzEaDSiKAr9/f1TevTIkwyvMwoKCnKuAxsu0DA4OOiR0+v8/PyYNWsWK1fqeemlEu69twGAl18eKnwyfHv55aHvV/uAhbe3N8uWLWPlypX4+vrS19fH3r17OXDggMdU5xx5rNLr9axdu5YvfOELxMTEMDg4yDvvvMMLL7zgkdX4PJGnvHdMdTpX/eLW1lbsdjuRkZGj7o+MjKSxsfGCP9PY2HjB77fZbLS2thIdHT3mZ8xm86gTk+7ubmBoCsXISkTBwcEkJyczODjI6dOnx/ye4auJJSUl9PX1jXosKSmJkJAQWlpaqKmpGfWYv78/6enp2O12Tpw4Meb35uTkoNfrKS8vdy5MHxYbG0tkZCQdHR1UVlaOeszb25uZM2cCXLCk5syZM/H29qaqqoq2trZRj0VGRhIbG0tPTw+lH5qToNfrycnJAeDkyZNjToLS09Px9/enrq7OuSncsNDQUBITExkYGOC9995j2bJlzsc0Gg3z5s0DoLi4eEzVnuTkZIKDg2lqahpTtSwwMJDU1FSsVisnT57kw+bMmYOXlxelpaVjhvfj4+MJDw+nvb3dOdoyzNfXlxkzZgBDV44/bNasWZhMJiorK8fME4+OjiY6Opru7m7KyspGPWY0GsnKygKgsLBwzDSxjIwM/Pz8qK2tHbPGJiwsjISEBPr7+zlz5syox7RaLQsWLCA9PZ1nn32W8vJyXn75ZTZt2sSqVatYtGgRQUFBNDY2jqkiVFhYSHJyMhaLhVOnTo15rXPnzkWr1XL27NkxJUETEhIICwujtbV1zDRXPz8/MjIycDgcF5yOlJ2djcFgoKKiYkxp65iYGGJiYliyZAmbN2+mtraWyspK9u7dy7x587j66quBob764TeEzMxMfHx8qK6uHvOmHBERQVxcHL29vZw9e3bUYzqdjtmzZwNDBS4+nLSkpaUREBBAQ0MDDQ0Nox77uGPEvn25PP00fPvbZzhypJ+777Zz551nWbPmKAsX5pKZmUlra+uYvqHmY0RfXx+lpaVYLBYGBgZwOBwYDAZyc3Px9vamqKho3MeIkSP/4J5jxMg2ePIxwmazOeOTk5OD2Wymurp6TGxSUlIueowICgoiJSXF7ceIAwcOsGjRIubOPcPLL0czcyZcaDCvoqKC3NwUQP3HiMjISMrLy/Hy8qK6uprjx4+TlJREVFSUc7RFjecRtbW1JCcnjzlG5ObmEhUVxalTpzh79izf+973yM3NZe7cuXh5eU3YeYQajhGgnvOIAwcOON87ruQ8Yu7cuQCcPn16TJLuCceIkWJiYoiKiqKzs9M5A2WYyWRi1qxZwMcfIz4c14+kuEhdXZ0CKAcOHBh1/49//GNlxowZF/yZ9PR05Sc/+cmo+/bt26cASkNDwwV/5vvf/74CfOxt9erVyuHDh5UTJ05c8PFNmzYpAwMDSnZ29pjHvvGNbyjl5eXKj370ozGP5ebmKnv37lXa2tou+Hv/+c9/Kl1dXcrKlSvHPPb5z39eKS4uVp577rkxj6Wmpirbt29XFEVR9Hr9mMefeeYZpaWlRfnEJz4x5rE777xTOXHihPL222+PeSwsLEzZtGmToiiKEhYWNubxn/3sZ0pdXZ3yhS98Ycxj69evV44ePaocOXJkzGN6vV7ZtGmTYjablYyMjDGPf+c731EqKyuV7373u2MeW7x4sbJ//36ltrb2gn/DN954Q+np6VGWLFky5rFHHnlEKSkpUX7729+OeSwzM1PZuXOnoijKBX/vX/7yF6WtrU25/vrrxzx2zz33KCdPnlReffXVMY9FR0crmzdvVhRFUQIDA8c8/qtf/UppaGhQPvOZz4x57MYbb1Ty8vKU3bt3j3nMx8dH2bRpk2K1WpWkpKQxj99///1KeXm58sQTT1zw//DgwYNKaWnpBV/rxo0bld7eXiU3N3fMY1/5yleU0tJS5X/+53/GPJaTk6Ps2bNH6e/vv+Dvfemll5SOjg5lzZo1Yx777Gc/qxQVFSl/+9vfxjwWERGhPPPMM0p/f7/i4+Mz5vHf/e53SlNTk3L33XePeey2225Tjh8/rmzevHnMY4GBgcqmTZsUu92uxMbGjnn8xz/+sVJTU6M89thjYx671GNEenrOmMfuuOMbyq5du5T//u//vmBsPOkYER4erhw6dEhpb29XwsPDxzzuaccIQPn3v/89ZY4RBw8eVJqampSUlJQxj3//+99XqqqqLniMWL58ucqOEfMUUJS8vNHv6Xl5igKKEhl5vbJ161ZFURSPOUa88cYbysaNG5WEhIQxj6nxPOK///u/P/IYsXfvXuVrX/vamMfkPELdx4iPOo/wrGPER59HJCQkXPYxoqur64K5xEguW2NksVjw8fHhtdde47bbbnPe/5WvfIWCggJ279495mdWrlzJvHnzePrpp533vfnmm9x555309/dfsPTvhUaM4uPj2b17t4wYuXDEqKCgYFRZz+l+pWekibzS09/fz759+6isrCQ4OJiYmBgWLVqEwWAY9bPD/y9qv9Jjs9koKyujoaGB2NhYDAYDOp2O2NjYUfPY1XQ1eNjIY0RPTx+vvw6//71CX58GjSaRO+6wcvfdRWi1vcTExODl5QV43jEChvqr3W7n9OnTKIqCt7c3Ot3QBANPuho8XJp87ty59Pb2ToljRE5ODr29vZw6dYrBwUEMBoOztLcnXA02m80YjUaKi725996Z5OWNHjHKz4f58yE2toJ33klh3jz1jxgNy83NxW6389577zn7jl6vJzMzk6VLlxIaGqqq84jhPvdRx4ju7m4++OAD9u/fj9lsdm4Ye//996PVamXEaAKPEcN9A2TEaNhEjhitWrXqktYYubz4wvz58/njH//ovG/WrFnccsstFy2+8M4774w64Hzxi1+koKBAii+ozKFDh1iyZIm7mzFtFBcX8/777zsP6NnZ2axfvx5/f3/A8+LR1tbGsWPHnG8koaGhLFiwwOM2uGtoGNr36JVXhr4ODbVw//0F3HefgYSEeEJCQjxy4bJyvmT0cLlojUaD0WjEx8fH4xb/e1rfuBSKojgvnCiKgpeXF/7+/s7kVc2G4zGcAL38Mpw/dweguBjuvXfoc71e4cc/1vD1r8P56wweo6OjgyNHjjiPcXFxcSxcuFBV+wRdTt/o6enh3XffdZbzjo2N5dZbb73kAkHi403FY5VaqKL4AsDjjz/On//8Z/7yl79QXFzM1772Naqrq3n44YcB+Pa3v83999/v/P6HH36YqqoqHn/8cYqLi/nLX/7C888/zxNPPOHKZopx+PBVK+FaM2fO5Etf+hJLlixBo9Fw6tQpfv/733P06FEcDofHxSM0NNRZ8Wi47O2WLVs4fvy4Ry3+j46Gf/wDtm6FjAxoazPw618v4qGHktmypZ6SkhL6+/vd3czLNrwBbFBQkLM4w+DgIJ2dnaMqpaldRUUF3/jGN8ZcafR0w1UEh0t72+12urq6xox+qNHwser8NR3uvXcoQRq+DSdFCxd2YrVq+OY3Yc0a+NAgi+oFBwezZs0acnJy0Gq11NbW8sEHH4y5Su9Ol/O+4e/vz913381tt92GyWSirq6OZ599lv3793vM8UDtPO19fKpy6YgRDG3w+j//8z80NDSQnZ3Nr3/9a1auXAnAZz/7Wc6dO8euXbuc3797926+9rWvUVRURExMDN/85jedidSlkBGjyXHw4EGWLl3q7mZMSw0NDbz77rvOqQSxsbFERUVx0003ubll4/Phsrc+Pj7MmTOHhIQEjxptMZvhf/4HfvxjBxaLFq1W4bbbmnnooXoyMkKIjY31iCv6F2K1Wunr63NOCdNqtXh7ezsrpqmVp5brvhwOh4Pe3l7n3lo+Pj6qLrs+8r2jtHRs9TlFUbDZOvDyquTNN0P41a8SGBz0IigInnsO7rhj8tt8pdrb2zl8+LDzxDctLY25c+e6/Xgw3vfx7u5u3nnnHec03Pj4eG699VZCQ0MnuonTipxXuc7l5AYuT4wmmyRGk2PkXFgx+RwOB3l5eWzbtg2z2Yzdbueqq65i9erVHhuXhoYG8vLynHOXw8PDyc3N9bjpdcXFZr73PSPD+1L7+dn43Ofq+NSnOkhMjCY8PNzjpqPB0AmrxWKhv7/fWUJe7QnSdEiM4D9TH4dHJ41Go3MTX7W51PeOwcFBKioqOH3ayg9+kEpR0dCa4a98ZegCxIeWWaqezWajsLDQueYpICCAJUuWEBIS4rY2Xcn7uKIoFBQUsGnTJsxmM3q9nuuuu072PboCcl7lOqqZSiemrpGjfGLyabVaFi5cyJe+9CVycnKorKzk0KFD/P73v6ewsNAj9zmJjo7muuuuIycnB51OR0tLC1u2bOHYsWMeMUVoWHX1Ll5/HXbtgrlzobdXx29+k8gnP5nJyy93cfLkKdrb2z0uRsPrjIKCgvDz88PLywuHw0FfXx8dHR3OctJi8g1PfRxOhsxmM729var8H7vU9w6TycTMmTNZvDiE554r5v77h6agPf00XH011Na6ro2uoNPpyM3N5eqrr8bb25vu7m62bdvGmTNn3BanK3kfHy6U8Mgjj5CSkoLVauWdd97hX//6l0dOH1YDOa9SB0mMhPBg/v7+3H777Vx77bWEhITQ09PDv//9b/7yl7+oai77pdLpdGRlZbFhwwYSEhJQFIWysjLee+89SktLPerEe9UqOHYM/vxniIxUqKkx8cQTGTz4YBJvvtlAcXHxmOpInmDk5qMfTpA8bQ3SVGMymfD393cmRz09PapMji6VRqMhPj6emTPT+MpXGvn5z8/i52fj4MGhSnbbtrm7hZcvKiqK6667jvj4eGeVrr1793rUxZ+RAgMDue+++1i3bh1eXl4UFxfzpz/9acqt6xPTh0ylE+NSUVFBSkqKu5shzquoqCAhIYFDhw6xZ88eLBaL84retddei6+vr7ubOC7Nzc3k5+c7S3gGBQUxd+5coqKi3Nuwj3ChvtHdDT/5CfzmNwpm89A0k1Wr2vniF2uZP9+H2NhYvL293dHcK6YoCmazmYGBAecUu+HRJW9vb2fZcndobGzkl7/8JV//+tdV/T8z0SwWizMpMplM+Pr6qmZ603jfOwYHBykrK+PsWTvf+lY6paU+aDQKP/+5hscfB5W8vEumKArl5eUcP34cu92Or68vS5YsmdQqbxP9Pt7Q0MAbb7xBa2srGo2GZcuWcc0117j1GOBJ5LzKdWSNkSRGLlddXU1CQoK7myHOGxmP4SkahYWFwNBV5KuvvpqFCxd65BuUw+GgvLyckydPOheYR0dHM2fOHIKCgtzbuAv4qL5RUwM/+AG88IKCw6FBq1W44YZWvvCFerKyAoiJifHYOebDa5AGBgZG7dtjMBgwmUzo9Xq3nJxP12PVyOTI19dXNYn3lcTDbrdTVVVFbW0bP/95Eu++O5REfOEL8PvfwwW2OlS9jo4ODhw4QE9Pj3OfqszMzEnpK67oG1arlc2bN3Ps2DFg6Fh9++23ExYWNqHPMxVN12PVZJA1RsLlPrwxm3CvkfEICAjgE5/4BA8++CDR0dEMDg6yadMmnnnmGY+c3qDVaklPT+eGG24gIyMDrVZLQ0MDmzdv5ujRo2M2AXS3j+ob8fHw/PNw8qSGW28Fh0PDO++Ec/vtOTz1lIndu4s5d+6cMwH0JMOjRIGBgQQGBmIwGNBoNFgsFrq7u+ns7Jz0dUidnZ385S9/GbNp4HRgMBjw8fEBoL+/XzVl8K/kvcPLy4vk5GTS0+P43vfO8dWvVqHRKDz3HGzYAJ4Y5uDgYNatW0diYiIOh4MTJ06wb9++STkGuOJ9XK/Xc+ONN3L33Xfj4+NDQ0MDzz777AU39hSjyXmVOkhiJMQUlZCQwOc//3luuukmfHx8aGlp4cUXX+TVV1+lvb3d3c27bEajkdzcXDZs2EB8fLxzKsp7771HUVHRqFEKtZs1C958Ew4cgJUrwWLR8o9/RHPLLTn8938b2L37NNXV1ao5mb0cGo0GvV5PQEAAQUFBeHt7o9VqsdvtzkINvb29WK1Wl69/qaio4Ic//KFHXhCYCMMVAxVFUW0xhsul0WiIjo4mPT2Ne+5p4ec/L8Xb28727bB0KXhiqPV6PUuWLHGO6tfV1bFt2za6u7vd3bRxy8zM5Itf/KKzMMNbb73Fxo0bPfKYJqYXmUonxqWvr89j161MRR8Xj4GBAXbt2uXcENbLy4uFCxeyatUq1UyxuVwtLS0UFBTQ1tYGDJ0EZmdnk5yc7NZy2JfbNxQFNm2C//7voWINAN7edu64o4n7728mPT2E6Oho9J44T+g8h8OB2WxmcHDQuQ4JhoptGI1GjEajS2I2Xcp1f5ThDaDtdjs+Pj7OUSR3mcj3jr6+PkpLSzl1SscTT2TQ1GQgKgq2bIGcnAl5iknX3t7O/v376evrQ6/Xs3jxYuLi4lzyXJPxPu5wONi7dy+7du1CURSioqK44447ZM+jC5DzKteRqXTC5U6fPu3uJogRPi4e3t7ebNiwgYcffpj09HTsdjuHDh3i6aef5uDBgx412jIsPDycNWvWsHTpUnx9fRkYGODo0aN88MEHVFdXu+3q+OX2DY1maBrQkSPwzjswf77CwIAXL74Yw0035fC97+nYvbuIqqoqj5xiB//Z7ygoKIjAwECMRiMajQabzeYcRerp6cFisUyJUQ010Wq1zmRIDSXVJ/K9w9fXl5kzZzJnjsLzzxeRnj5AY+NQRchDhybsaSZVSEgIa9euJSIiAqvVyr59+zh58qRL+sVkvI9rtVpWrVrFfffdh6+vL42NjTz33HNyDnEB8jdRB0mMxLh44lSsqexS4xEREcE999zDfffdR2RkJIODg2zevJk//OEPFBUVedxJqUajITExkeuvv5558+ZhNBrp6enhwIEDbNmyhcbGxkl/TePtGxoN3HgjHD2qYeNGyM39T4J08805fOc7BrZtG1qD5KmlfYen2fn7+xMcHIyvry86nc5Z2a67u5uOjg76+vomZarddGEwGEb9nd1pot87jEYjmZmZJCUZ+OMfTzN7di8dHbBmDezcOaFPNWlMJhOrVq0iIyMDgKKiIvbv3z/hF7Am8308JSWFhx56iISEBMxmM//617/YtGnTqBHk6U7Oq9RBptK50he/CHV17m2Di7S1txPqxh27xWjjiYeiKHR3d9Pa2up8wzV5exMRHu6x0+scDge9fX30jti/xWg04h8QgNFgmJQ2TFTfUICmRigpga7zSw20GoWISAtxcWYCA3WYjEaPrDQ4ksLQ/6LD4cDhcIxKhjQaDVqtFq1Wi0aj4XLrdPX09HD48GEWL16Mv7//hLbb09jtdmx2O1qtFr1O57Z2uOq9w6Eo9PX1YTbbKC72o6NDh5cXLFkMnjxrq6+/f6h4iKJgMBgICQmZsD7vjvdxRVFobW11JgEmb29iY2LQufF/Ui2m9HlVbCz86U9ue3op162WxGgKs9lsciBTkSuJh8Vi4cCBA+zfv9+5MDYrK8u5aawnGhwcpLi4eNSmsLGxsWRlZbn8NU103xheg/TTn8LevUP3abUK11zTzmc+08DixUaioqLw8/NTzV4146UoClarFbPZPGZanZeXF0ajEYPBgJeX1yW/VjlWDbHZbHR2dqLVat3ar10ZD7vdTklJCW1tfXzzmxkcPBiInx9s3gzLlrnkKSdFS0sL+/btw2w24+vry4oVKyZkqwJ39o2SkhLefPNNBgcH8ff356677nLZWipPIccq15HESBIjl9u8eTPr1693dzPEeRMRj56eHnbu3Mnx48dRFAWtVsv8+fNZuXKlx15t7+vro6ioiMrKSudJtqsTJFf2jf374f/9P3j33f/ct3RpJ/fe28CqVQrR0VEEBwd7fIIEQ6N/w0nSh6fVeXl5YTAYnFPEPur1yrFqiMPhcF6lDw0Nddv/iKvjYbPZOHv2LK2tfXzjG5kcOeJPQADs2+e5BRlg6Pi8Z88eenp60Ov1XHXVVVe8abG7+0ZHRwevvPIKzc3N6HQ6brrpJubMmeO29ribu+MxlUnxBSHEZfP39+fmm292FmhwOBwcPXqU3/72t2zfvp3BwUF3N/Gy+fr6smjRIq6//nqSkpLQaDTU1dWxZcsW9u3bR0dHh7ubeFmuumqoQMOJE/DpTw+NHB08GMSjj87kttsS+e1vO8jPP0Vzc7PHz93XarUYjUYCAgII/v/snXdYFOfWwH+7sEtbeu8ggqhgxd57TDOaaGxJNNX0flP80uu9KTfl5qZfTdGYWJKYZhd7JyoqINJ7720Xdr8/1p2AAqIBdoD39zzvs7uz786cmbPvzJw55z3H2Rl7e3spaUNDQwM1NTWUlZVJiRvq6uouSixw/Phx5syZI2qogGRYdgejuTUsLS0JCwvD2dmat95KYMiQKsrL4eqrITPT3NJdOfb29kydOhV3d3d0Oh27d+8mLS3N3GL9LZydnbnjjjsIDw+nvr6eH3/8kc2bN5s9QYigZyM8RoIrIjExkdDQUHOLIThPR+gjLS2Nbdu2kZGRARgnBI8dO5YRI0Z02dTR5eXlnDlzhrS0NOlG0c/Pj/79++Ps7Nwu2+jMsZGcDO++CytWGKiuNt7wurlpmTs3j5tuKiY01AUPDw+srKw6RZ7OwGAwoNVq0Wq16HS6JjdRCoUCS0tL1Go1KpWKEydOEBUV1aPTdZuoq6ujoqICS0vLdgnDulI6a3zU1dURFxdHQUEDy5ZFkJxsxYABxnDUrnxr0NDQwKFDh0hPT0ehUBAVFUVISMgVrUsu13GDwUB0dDS7du0CICQkhJtuuqnLznW9UuSij+6ICKUThlGHk5WVha+vr7nFEJyno/RhMBg4e/Ys27dvJz8/HzA+uZwwYQKDBw/ushP/y8vLOX36dJO03n5+fkRERPztm0ZzjI3iYvj8c/jgAwPZ2UYDycqqgWuvLWT+/HwGDbLB09OzW8xDaozBYKC+vl4yki7M2hUbG8vkyZPZv38/w4cPl5I49DQMBgNlZWXU19ebvZZRZ46P6upq4uLiyMiw4O67IykosODGG2HtWmMWyK6KXq8nJiaGc+fOATBw4ED69u172euR23X8zJkz/Pjjj+h0OlxdXVmwYAFubm7mFqvTkJs+uhMilE7Q4Zw6dcrcIgga0VH6UCgU9OnTh2XLljF79mycnJyoqKjg119/5aOPPuLUqVNdMqWyg4MDo0aN4qqrriIgIACFQkFmZiabNm1i7969UtHYK8EcY8PFBZ56ClJSFHz7LQwebKCuzoL16z2ZOzeSxYvd+fTTHGJjz1BYWNhtQlVM6b/t7OxwcnLC2dkZjUaDWq1GoVBI+1ldXU1JSQmlpaVUVFRIhWa74n/3cjEYDFRXV1NfX49SqcTa2tqs8nTm+LC1tSUoKAgfHy1vvhmPSmVg/Xp4551OE6FDMM3/7NevHwAnTpzgxIkTl/1/ltt1vF+/ftxxxx04OTlRVFTEl19+2eXDBS8HuemjpyIMI4FAcEmUSiUDBw7kgQceYObMmdjZ2VFcXMy6dev4+OOPOXPmTJe8yXR0dGT06NEXGUhbt25l586dZqmD9HdQq2HRIjh2TEF0NFx/PSgUBg4dcuTJJ8O46qrevPhiDdHRp8jIyOiS88Zaw8LCAmtraxwcHHBxcZGShpgSNDQ0NFBXV0dlZSUlJSWUlJRQXl5OTU1Nt6ybZEq4UFNTAxjn3CmVPeuy7+rqipeXFxERVTz+uDEs+Kmn4MABMwv2N1EoFAwYMEBKVhAXF3dFxpHc8PLy4q677sLf35+amhq++eYbTp8+bW6xBD0IEUonuCLKy8vF8ZURna0PrVbLgQMH2L9/v1Qw0svLi4kTJ9KnT58uG65UVlZGXFwc6enpkrfB1dWVvn374uvr26b9ktvYSEmBTz6BL74wUFxslF+t1jNtWhFz5+YzcqQl7u7uODk5dbub5urqao4ePUpUVBQ2NjZSuJ3p9cLLn0KhwMLCAktLSywtLaX3Xe3/bCrkWllZKS3TaDRm9xaBecaHXq8nLi6OysoqXn01nF9/dSA0FI4fBzNGFbYbiYmJHDt2DDB6XSIjI7vkuaoxOp2ODRs2EBcXh0KhYPr06YwaNcrcYnUoctZHV0fMMRKGUYcTExPT4yczywlz6aOmpoaDBw9y8OBByUDy9vZm0qRJhIaGdrkbShNVVVXEx8eTnJwsZXdzdHQkPDycgICAVudWyXVs1NTA99/Dhx8aiIn5Sy/9+lUya1YB11xTQVCQK25ubt0qWUNL+jDNT2rcmsvkZzKWTEaS6b0c5yuZPGJ1dXVN9sXGxgY7OzszSvYX5hof1dXVnDlzhtJSBbfcMpjcXCUPPQTvv9/ponQIZ8+eJSYmBoDIyEj69+9/yd/I9VxlQq/Xs2nTJg4fPgzAyJEjmTFjhuzGXXshd310ZYRhJAyjDkfk25cX5tZHdXU1Bw4c4NChQ2i1WsBYL2jixIn07t27y17IamtrOXv2LOfOnZP2y87Ojj59+tCrV69mi/GZWxeXwmCAQ4fgo4/ghx8MaLVG3djYNDBtWhGzZhUyZowlnp4eODo6dlndAaSnp3P//ffz0UcfERAQcMn+DQ0NTYyk+vr6FudjKRQKlEqlZCiZjCXTso4+bgaDAb1eL8l7YfIJ03wiKysrWSVJMef4yMrKIisriyNHXHnggRCUSvjzTxgwwCzitDsJCQn8+eefQNsSMsj9XAXG//mBAwfYsmULYPSIzZkzp1sWQu0K+uiqXI5t0P3+WYJOQQ4hGYK/MLc+bG1tmTJlCiNHjmT//v0cPnyYrKwsVq1ahZ+fH5MmTaJXr15d7ibb2tqaAQMGEB4eTlJSEgkJCVRVVRETE8Pp06cJCwsjJCSkyfE3ty4uhUIBI0ca2zvvKPj6a2OYXUKCBRs3erBxowchIdVcf30Bs2ZlEBLijJubm+z3qzkKCwv59ddfeemll9pkGJkMHJPHzGAwNPEsNTQ0SM1gMEjvm8NkJJk8Sxe+msZC4/eNMT2zNBlAple9Xk9DQ4P0vjGmdOXW1tZSAgq5Yc7/kZeXF4WFhQwbVsQ11/jw2282PPYYbN3atbPUmejTpw96vV5KxmBtbU1wcHCL/bvCmFYoFIwePRoHBwd+/PFHzpw5Q3V1NQsWLOhWnm3oGvroCQiPkeCKMBgMsrzo9lTkpo+qqir27dvHkSNH0Ol0APj7+zNu3LguHWJXX19PSkoK8fHxVFVVAcab6aCgIMLCwnB0dJSdLtqCwQB798IXXxi9SLW1f81FmjixhFmz8hk/3oCHhxsuLi5d5mltTEwMQ4cObfc6RiYjpbGhZDJUmjNYOorGc6JUKhUqlUr288TMPT6KiopISkoiL8+Wm27qj1arYPNmmD7dbCK1OydOnCAuLg6lUsn48ePx8vJqtp+5dXG5pKam8t1331FXV4evry+LFy/uVrWOupo+uhIilE4YRh2OcPnKC7nqo7Kykr1793L06FEpzMfb25tx48bRt2/fLnsR0Ov1ZGRkkJCQQHFxsbTc29ub/Px8brrppi67b6WlsHo1fP65gePH/9oHX99aZs4s4ppriomMtMXNzQ0HBwdZ72dHGUaXorGB1NjT09j709gj1FwSCBONPUyNQ/U6K2SvvTH3ucpgMBAbG0ttbS1ffNGfzz+3Y+pUo9eou2AwGDh06BCpqalYWloyZcqUZgtYm1sXV0J2djbffPMNNTU1eHp6csstt6DRaMwtVrvQFfXRVRB1jAQCgSzQaDRcddVVPPLII4wZMwa1Wk1OTg4//PAD//3vfzl58mSXrKmjVCoJDAxk2rRpTJ48GT8/PxQKBTk5OZw9e5bNmzeTkpLSYpiVnHFygvvug5gYBUePwj33gL29gawsa774wpfZsyOZM8eDN94oZs+e02RmZna7tN9/F6VSiaWlJWq1GisrKyn5gUajwcHBAUdHR5ycnKTaSy4uLk2as7Oz1BwdHXFwcMDe3h47OztsbGxQq9VdMlueHFAoFHh6egIwZ04GSiVs2wYnTphZsHZEoVAwbNgwPD09qa+vZ/fu3VRXV5tbrHbBx8eHpUuXotFoyMvLY8WKFZSXl5tbLEE3QhhGgisiMDDQ3CIIGiF3fWg0GqZNm8YjjzzChAkTsLa2pqCggA0bNvDhhx8SExPTJY0IhUKBh4cHY8eO5eqrryYsLAxXV1dKS0s5dOgQv/zyC6dPn5Yy9nUlFAoYOtSY6jsnx1g4dvp0A0qlgZMn7fnnP4OZOrUft9xiy4cfpnP8+Blyc3OlJBVywMPDg9tuuw0PDw9ziyI4jxzOVa6uriiVSlxcyrn+eqMne9UqMwvVzlhYWDBmzBgcHR2pqalh3759F51j5aCLK8HDw4Pbb79dKgT71VdfdQvjqKvqo7shQukEV0Rubm6LccuCzqer6aO2tpYjR45w4MAB6Ummg4MDY8eOZfDgwahUKjNLeOWkp6dTVVVFYmKitG+meUihoaE4OTmZV8C/SXY2fPcdfP21gZMn//JYODrqmDq1mKuvLmLUKCWurkbPh7l12dXGRndHLvpITEykpKSE48d7c889LgQFQVwcdLf57xUVFWzduhWtVktoaChDhw6VvpOLLq6U0tJSVq5cSWlpKa6urixZskQq6twV6er6kDNijlErO2/KMNQVn07Lib179zJ27FhziyE4jxz0oVKpLjstsFar5dixY+zfv5+KigrA6F0aMWIEw4YN65JZekxx4g0NDWRmZhIfH09JSYn0vbu7O6Ghofj6+soqjfKVcPIkfPMNrFplICfnLyPJx6eWqVOLmTathGHDVJKR1Nn7W1lZyeeff85dd93VbeYhdHXkMo8iLy+PtLQ0rKycGTkylNpaUKngww+N4aPdiezsbHbv3g0YawEFBQUB8tHF36E7GUfdQR9yRRhGLey8VqslJyen28TampOamppulQ2mqyMHfSgUCvz8/K7oBrS+vp7jx4+zd+9eSktLAVCr1URFRTFy5Mgu5f298OJmMBgoKCjg3LlzZGZmSnOqbGxsCAkJISQkxOy6+7s0NMD27UYjacMGA9XVfxlJfn61TJtWxLRppURFWeHq6oKjo2OnGEnmSr4gaBm53PxVVFQQFxeHSqXippsGk5xsrGd08qQxfLS7GUexsbGcPn0aS0tLpk+fjoODg2x08XcpLS1lxYoVlJWV4eHhwdKlS7vkObW76EOOCMOomZ3X6/UkJiZiYWGBu7u7bGs8dBXq6+u7TMrenoC59WG6+a+uriY0NPSKb3obGho4ffo0+/btIy8vDzBOZB8wYACjR4/uEnNFSkpKms0ABcZCuElJSSQlJUkJC5RKJX5+fvTu3Rt3d/cuf16qrobffoMffoDffjNQU/PX/gQF1TBlitGTNGSIFc7Ozjg5OXXYf1cYRvKjtfHRmeh0Ov78809+XOfCgbcyWTI9h1uf8ubRDeP44COLbmcc6fV6du3aRV5eHq6urkyZMoWysjJZ6KI9KCkp4X//+x8VFRX4+flx6623olarzS3WZSGXsdEdEYZRMztfW1tLSkoKgYGB2NramlHC7kF1dbU4jjJCDvqoqakhNTWV4ODgvx0CZzAYOHfuHPv27SM1NVVaHhYWxpgxYwgICJCtAXHixAkGDhzYah9TmN25c+coKCiQljs5OREaGkpAQIDZ5+a0B5WV8Ouv8P33Bv74A+rq/tJZr17VTJlSzMSJpQwZosLFxbnd5yQJw0h+tGV8dAZ6vZ5VN37KxJ9ex59MabnBz48vI9/nrj/mdDvjqKqqik2bNqHT6Rg4cCBarVYWumgvCgoK+N///kdNTQ29evVi4cKFXeoBrlzGRndEpOtuBbkXv+sqmIp2CuSBHPTRnoaKQqEgNDSUJUuWcOedd9KvXz8UCgVnz55lxYoV/O9//yM+Pv6i+i9yIDc395J9LCwsCAwMZMqUKcyYMYOQkBAsLS0pLS3lyJEj/PLLLxw7dkwKK+yqaDQwfz78+KOC/HwF33wD111nQKUykJxsy+ef+7FoUQRTpgTyyCN6/ve/JGJj48jNze2SmfwEl6Yt46Mz2LJsA4t+uh+/RkYRgCIrizs23cTnMzewbBl8+qmZBOwA7OzspAcEsbGxJCcnm1mi9sXd3Z3FixejVqtJTk5mw4YNsrxGtIRcxkZPR1gJPZQXX3yRZcuWARAdHU14eLj0nUajIT8/v9Xfy/VpfU+lO+vDz8+PefPm8cADDzB06FAsLS3JyMhgzZo1fPTRRxw9elQWhqGJy/V4ODs7M2zYMK677joGDx6Mvb09Wq2WxMRENm3axNatW0lKSpLVPl4JDg6weDFs3Gg0klauhOuvB2trA9nZ1nz3nRfLloUzblxv7rzTgg8+SOPIEWOdpIqKiiu6wbG0tMTR0bFLPTXu7sjBE1pX3UD/zx8FDFx05jQYl91x6hEGRTbw4IPQnWz0oKAgfHx80Ov1ZGVldSnDoS34+voyf/58LCwsOHPmDNu2bTO3SG1GDmND0AND6dojzKdNNDRAG4seBvXrR3FJCXnJydKEwfLycjx79SLQ35/4P/+U+q1ZuZKRw4dLv1320EN4eXry4vLllyXei6+9Rm5eHp988AHRu3ez7OGHpe3IlQcee4xhQ4dy26JF0rK7HngAK7Wa/7z7bpO+H/z3v6z/+Wd2bd4MwNGYGJ5cvpysnBxef+EFbpo9u0n/OQsWENm/Py/93/91/I60E0nJydx69938eeIE4WFhrPjkEwZGRjbbd+JVV3HwyBHpBnHc6NH88eOPAOzeu5f/e/lljh0/zrjRo9n0009Nfnvk2DHuvP9+EpOSGDZkCF9//jmBAQEXbaO2ro6UjAyC6+ux7kBDrbq6mlOnTnH69GmpZo6VlRX9+vWjf//+2NnZddi2OwODwUBRURGZmZnk5+dLNy6WlpZ4eXnh5+eHo6OjmaVsP2pq4OBBiI6G3bsNlJX/9d9RqxqIiKhi6NByhg6twcfHRip0KowdwRVjqlx8CSaykwXP9uKeGws7QajOo3Fdo8jISHx8fMwtUruTmJjIjh07ABg3bhz9+vUzs0QCwsPBTCH/lxNKJ64sHUVtrbEoQlvQ6fBydmbjZ59x8/TpAGz45Rf8PTxAq/1rPTodpKZC41SUpaVgYdH2bZkoLDT+Ni4O0tObbkembP7jD5bPnt1EzsUjRzL3mWd4b+nSJjdKq7/+mtuvu07qu2n1amYMGEBd376s+vJLbmrkISurrOSPLVt449ZbZX8MGrPgttu4ZuxYtr/9Nl/8/DOzb7yRs+vXN3/DWF3NyuefZ/75/xcg7attXh7LZs4kecAA9h4/3uQY1Gm1zJk7l5fuuYeFM2bwwmefccuiRez+7LPmhSoshGXLIC2tHfe0KbbA8POtO6IA3M63noANMOl8uwgd8Of59kUnCiUQAP/HK0x9fQe8bm5J2hcbYKq5hehgQs83AFq6Xgk6l2PHoAvM9RSGUUdhbQ19+7atr0rFgoULWbVvHzc//DAAq/7xDxYuWsSadev+Wo9KBUFBTdfr5ARubs1uq6amhieXL2fDxo0olUoeXLaMpx57zPilm5vRq9W3LxQUgFotrUOh0ZCTlISXpydB/fpx75138vnKlZSVlXH/3Xfz4vLlVFZVEb17N48/+yzZOTk4OTryr1deYcG8eTQ0NPDyG2+wctUq6urqWHTzzfzzlVcuumHfsn07L73xBvvOu7qD+/fn6unT+ejf/6a0tJSAvn0pzsjA0tKSpORkbB0d8R43rsk6xoeHY/Paa2zNyWHm+Zv+5JQU/jx7lpvuvRdcXADYfOIEH7z9Ng729rwxbBil3t5Soc31X39NRP/+9Jk5U/KezZ09mw8//RRPDw9+XL2aDRs38u5//oOnuzvrvv2W/uefPt33yCP89OuvVNfUMHzoUL746CMC/P1JOHuWMVOncnDnTnqHhHDw8GFumD+fkwcPtktmtYSzZ0nIzGTvm2+iVqtZEhrKW2vWsL+sjPHN1TOytQUfn2b/J1F9+xIFrPz2Wzh3rkmf6G3b0Dg5cftTTwHw/Ftv4R4URJqd3cVeo7o64390/XroxNA+vV5PWloasbGx5OTkSMu9vb2JjIwkMDCwU+cW7t+/n9GjR7frOg0GAyUlJWRmZpKbmyt5kSwsLPD09MTPzw8nJ6duFVJpMEBiotGTFB1tID6h6b7Za+qJjKxg4MBKBg+uw8fHFjs7uybepKSkJO677z7++9//EhISYoa9EFxIR4yPy0V/5AjK86HkrTH105sh6q1OkKjzaWho4Ntvv8XLy4s+ffpItY26EwaDgejoaM6ePYtarWb27NmyLrAth7HRoTR6IC1neqxhZDAY08p2HBaAHba2bbhHVCiYds01fPnNNxTX1RnnFyQn88z//R9rNmwAU2iQQgE2Nn99BuONqFrddNl5nvjHPyivruZsYiLl5eVMmzaNfoMGcd111xl/o1IZf2djA0pl03XY2ho/KxSs/+UXDhw8SEVFBRMmTCBq9GimTJnCnQ88wPr16xkzZgy5ubkUFxeDnR3vvvUW+48e5VhMDJaWlsyePZuPv/6aBx98sIl8oyZN4s/586lRKo2/VSjYe+gQ2NmxLzqaYcOGYXk+ZGjT7t3MmDnzov1UAAsWLmT1hg3MPB8et/qnn5g5cyYu/v4AlJWVkZKezqBRo1AoFAwaNIj1mzZxxx13GPuvX8+iW26RjsW55GTcfX0pLCzk0Ucf5eqbbuLBBx8kPz+fZ555hv97/XV+PB+GNnbSJN54+22srKy47777eOjpp/npp5/oM3gwzy5fzpL77mPz5s0sufdePvjwQzyCgy/S0969e7n22mtb/Hs0NwH/TGoqffr0QX0+taeFUsmAgQM5nZzM+ObqIFhY8OCTT/Lgk08yaNAg3nnnHQYMGNC0j5WV0fvY6BifSU4mcuBAaZmdnR0hISGcSU0l8EIjy8LC+L/q06dTy8crgeCoKIJvvJHs7GwOHjzIqVOnyNHriYmNxSUrixEjRjBo0CCsrKw6XB5HKyvo379d16kAXM63PnV1pKamkpycTElZGYXA6YICHOrqCA4O7jaZNxVA2FAImw93A9nZsGkT/P47bN1qoLxcwe4DwAFQKAz061fF6NGljBlTztCh4OTkQHZVFVsKCykLCekSTyp7Ah0xPi6XAi8vnJ5/HnVBAYpmZhMYUKDw94M77jCe17ohFoBPaSlFRUUcVyrxjYzsdnNcFMD4QYNIXrmStIwMVsXFceedd3bKdeBKkMPYEPTg5AvV1caMSR3d2mp8WVpacsMNN7B27VrWrFnD3Llzm33KPW3aNJycnKS2YsWKZtdnMBhYsWIF77zzDhqNBh8fH+69917WrVt32cfqkUcewd3dnV69enHPPfew/ny4lkql4tSpU1RWVuLl5SXF8H755Ze89tpruLm54eTkxOOPP97sdu3t7enbty+HDx9mz5493HDDDWi1WkpKStizZw9jG3k+Nm3a1GLhs8WLF/PTTz9JhXtXr17NokbzkLZt28akSZOkp+mLFy9m1apVAOTk5LB7927mz58v9XdycuLBBx/E0tKSOXPmUFRUxKOPPip9PnnypNR34cKFODo6Ym1tzVNPPcXevXubHDeFQsHw4cOJjIxk3rx5zco/duxYSktLW2zNUVlZ2SRO1tLSEgcHByorK5vt/69//YuUlBTS09OZNm0aV199dYt9W9sO0Op2zI2Pjw9z5szhkUceYezYsdjY2FBcXMwff/zBv//9bzZv3mw0wjuQjq61ZGVlRZ8+fbjqqquYMmUKwcHBWFpaUl5ezokTJ/jll1+Ijo4mLS2N+vr6DpWlM/Hxgdtvh3XroLBQQXQ0PPUUDBhgwGBQcPq0hs8/92PJkn6MHRvKbbfZ8uWXpQCkpqaRl5dHTU1Nt5ts3tUwdy0ynU5HVm4u6eejJ/QXpF8woDA+zHzvvW5rFJkYMGAAGo2G2traJmURuhMWFhbMmzcPe3t7CgoK+Pnnn2V7DjD32BAY6bGGkRxZtGgRq1evvujGvjFbt25tctO8dOnSZvsVFBRQU1NDWFiYZEQ9++yzl8w21xx+fn7Se39/f3JycqiurmbdunVs3LgRX19fpk+fTnx8PIB0823a7qJFi5rUamnMuHHj2LNnD3v27GHcuHGMHj2affv2NTGMtFotR44cYdwFYXQmIiIi6NWrFxs3buTPP/8kKyvL6BU7z+bNm7nqqqukzzfffDP79+8nOzubNWvWMHHiRLy8vKTv3dzcJCPKxsYGV1dXyUi1sbGhqqpK6vvaa6/Ru3dvHBwcGD58OEVFRdJ3SqWSJUuWcObMGR4+HyLZXmg0GsrLy6XP1dXVlJeXo9Fomu0/fPhwNBoNNjY2/OMf/0Cj0XD48OHL3g7Q6nbkgoODA1OnTuXRRx/lmmuuwdXVldraWg4cOMCHH37IqlWrSExM7JALZExMTLuvszkUCgXu7u6MGDGCWbNmMXz4cNzd3TEYDOTm5nLgwAF+/vlnDh8+TEFBgWxvBq4ElQomTIA334QTJxRkZMDnn8OcOWBvb6CsTMWOHS589ZUvAHfd5c0ddyj517+y2br1FOfOnSMvL4/q6upudVy6Ap01PppDr9dz7tw56uvr0V57LR9PXksWvk36KPz9jNb3nDlmkrLzOH78OGFhYQAddj6UA/b29sybN0/KVHfo0CFzi9Qs5hwbgr/osaF0trbG4oOdsZ22MmrUKLKyslCr1QwaNIjo6Ogr3q6bmxvW1takpaX97QxWmZl/1XnIyMiQjIgRI0bw22+/UVdXx/PPP8/999/P9u3b8fX1Zf369ReHajXD2LFj+eKLL8jNzeWFF16gvLycbdu2cfz4cUaOHAkYQ82ioqJarWK9ePFiVq9eTZ8+fZgzZ06TzINbt27l1VdflT67u7szZcoU1qxZw3fffccDDzxw2ccEYNeuXXz66ads376d3r17c/bs2SZpz4uKinjuuee45ZZbePLJJ9m7dy8WzTyB3LNnDzNnzmxxO815Z/r160dCQgI6nU4Kfzh58iRPPvlkm2Rv65ybfv368VmjiatVVVUkJSV1mQw/arWaYcOGERUVxblz5zh8+DCJiYlSc3FxYdiwYQwePLhzslV2ECqVil69etGrVy8qKytJTU0lJSWFqqoqkpOTSU5Oxt7enqCgIIKCgrp85r4L8fODO+80Np1OwZEjsH07/PSTgZgYKC5W89tv7vz2mzsAQUE1REWVM2xYFsOH1+Dra4ODgwP29vbY2Nh0q7laAiMGg4GUlBQqKiqwsLBgz55Q7t/enwe5gWP/3sMgzxzw9oZx47q9p6gxwcHBxMbGUl5eTkFBQbf1Wvj7+zNjxgx+//13tm7dSmBgIN7e3uYWSyBDOsRjlJqayh133EFwcDA2NjaEhITwwgsvSKl1W2LJkiUoFIomzXRz3N4oFMZpEx3dLvf6umHDBn744Ye/vX9KpZLbbruNJ554gtLSUvR6PXFxcW3yElzIBx98QGFhISkpKXz22WfceOONWFpasnr1asrLy1GpVGg0Gumm/4477mD58uXSJPHU1FR27drV7LrHjRvH3r170el0eHh4MG7cOP73v/8RHh6O/fnse5s3b24xjM7EwoUL2bp1K99++20Tb1tcXBwuLi4XnewXLVrE+++/z+nTp5lzhU8GKyoqsLS0xNXVlaqqqibGF8B9993H3Llz+eqrr1Cr1bzzzjvNrmfcuHFUVla22JqjT58+9OnThzfffJO6ujq+/vprLCwsmp24WVpaytatW6k7P3/t3//+N8XFxURFRQHGp6i1tbXodLom7wEmTpxIZWUlK1eupK6ujldffZWoqCgCAwOv6JiZC1PB2EWLFvHQQw8xatQorK2tKS4uZvPmzbzzzjv8+uuv5OXl/e1tDR06tB0kvnI0Gg0RERFce+21TJ48WQq1q6ioIDY2ll9++YUdO3aQnJx8yXNyV0SlgtGj4bnnYOfOUL7++ns2bOjNP/4BUVEGFAoDqak2rFvnyVNPhTJp0gCuusqX++6Dt9/O4fffT5OYmEhOTg4VFRXo9Xpz71K3whzjo6GhgXPnzlFUVHS+UHQ4Dz1kfND2f89bMOiRibBgAUyc2KOMoqFDh6JSqfA/Px83PT3dzBJ1LMOGDaNv3740NDSwbt062RWRNve1Q2CkQzxG8fHx6PV6Pv30U3r37s2pU6e46667qKqq4u233271t1dddVWTeTOteQm6I23xsrSVd999l2effZbIyEgqKioIDQ296Oa9LcyePZuRI0dSWlrKfffdx3XXXUdZWRlfffUV999/P3q9noEDB/Lp+RLhTzzxBDqdjtGjR1NYWEhgYCBPnc9qdiGenp74+PgwZswYAEJCQtBoNBfNL1q7dm2rMvr6+jJq1Cji4+OZPHlyk982Z1TdcMMN3HPPPVx33XWSAXa5XHXVVYwaNYrAwEDc3Nz4xz/+wbfffgvA2rVriYmJ4cSJEygUCv73v/8xfPhwrrvuOvq2NVvhJVi9ejW33XYbr7/+OmFhYWzYsEHKxvX666+zZ88e/vjjD3Q6Hc888wzx8fGSN/L333+X5g7t3r2bSZP+SpZsY2PDbbfdxsqVK7GysmLDhg3ccccd3HvvvQwbNoxvvvmmXeQ3Fy4uLsyYMYNJkyYRGxvLoUOHyM/P5+jRoxw9epSgoCCGDx9OeHj4FWWzy83Nxc3N/Mm2FQoFHh4eeHh4MGTIEDIzM0lNTSUvL4/8/Hxpn318fKSnp92tNpCDgwODB/cjIsIBY24WBSUlxkx327fDjh0G4uIUJCfbkpxsy4YNngB4etYxcGAlgwYVMXhwOhERCuzt7dBoNGg0GtRqtfAqXSGdPT7KyspIT0+npqYGpVLJwYN9eeQROxoaYP58eOGFThNFdph0ERAQQHJyMunp6QwZMqRTs3h2JgqFguuvv57s7GyKiorYtGkTs2bNMrdYEnK5dvR0Oq3A61tvvcXHH39McnJyi32WLFlCaWkpP11QYPJykE2B125CUFAQa9asuchzV1ZW1ilFJnNychgzZkyr/5vWmDFjBsuXL2f8+PHtLJm86Cx9tEZXHWMGg4G0tDQOHz4sPdQB4031kCFDGDJkyCULwjWmLR5Oc1JVVUVqairp6emUlZVJy1UqFb6+vgQGBuLp6dktbo5ycnJ48skneeutt1oMmykogH37YO9e2LPHGHpXX39BanD7egYMqCQiopJ+/SoZOFCLj4+NZCjZ2to2GyYruJjOGh96vZ7Tp09TU1MDQFWVNU8/3Z/Dh416WrLEOC+tmz0LuCxMutDr9fz0009otVqmTZuGq6uruUXrUNLS0li5ciUGg4HFixfTu3dvc4sEyP/a0ZWRZYHXsrIyXM7XlGmN6OhoPDw8cHJyYsKECbz22mutxrzW1dU1cYdeOFFc0DF01tPS8vJy/vWvf13x76dMmcKoUaPaUSJ5Ip5eXzkKhUKae1NeXs7Ro0c5duwY5eXlREdHs2vXLsLCwoiKiiIkJOSSBoPcDQo7Ozv69+9Pv379KCsrIy0tjfT0dMlgSk1NxdraGn9/fwIDA3F1de2y/6+cnBxWrVrFY4891qJh5O4ON9xgbKCgqgoOHTIaSnv3Gti/HyoqLNm3z4l9+5yk3/n719K/fyX9+hXTv38GAwbocXa2wdbWWE/J1ta223ng2oOOHh8Gg4Hi4mKysrKora0FYP16D/71ryCpzwsvGFsX/Vu3GyZdKJVKPDw8pDpp3d0wCgwMZMSIERw8eJBffvmF++67TxYpvOV+7egpdMpZOykpiQ8//LDFORYmZs6cydy5cwkMDCQlJYXnnnuOyZMnc+zYsRb/tG+88QYvvfTSRcu3bduGnZ0dkydP5vDhw9TU1ODm5kZDQ4P0lNT0VNt08rS3t6e6upqGhgYsLCywtbWloqKi2b6mFJf19fUolcom2busrKxQKpXSk6rW+qrVaiwtLaVU03Z2dmi1WnQ6HQqFAgcHB0neC/va2toas+totVLf8vJyDAYDKpUKtVotZVBr3BfA0dFRip+/sK+NjQ16vZ66ujr0ej0Gg0Hqa2lpibW1NQaDgbKysiZ9L/cYttbXdAy9vLzw8vKioaGhzce78TG89957UalULR7D5o636Ri2drxNx7Ctx9uU4rrxMTTNH7rwGLbWt6VjaDAYqKysbPN/tqXj3Zb/bEvHu6qqStrW5s2bAeOEVzc3N/78808AoqKiyM7OJjs7GwsLC6ZOncq2bdtoaGjAx8cHHx8fjh49CsDgwYMpLCwkIyMDMHr/du7ciVarxdPTk6CgICm70IABAygvL5dSzk6bNo19+/ZRXV2Nm5sbYWFh7N+/H4D+/ftTW1tLUlISgHSOqKysxNnZmREjRlBbW0tGRgYVFRVkZGTw22+/8dtvvzFw4EDs7e3x8vLC09OTIUOGSElSQkNDsbS0RK/Xs3nzZsaOHcuZM2coLi7Gzs6OkSNHsn37dgB69eqFra0tp06dAoyJV86dO0dBQQHW1taMHz+eLVu2AMaLuJOTEydOnACMGQbT09PJzc1FpVIxefJktmzZgsFgwM/PDw8PDym70dChQ8nNzSUrKwulUsm0adPYvn079fX1eHt74+fnJ61n4MCBxMfHc/bsWerr66mtrWXr1q1YWFgQEhJCVFQUZ86cQaFQEBkZSWVlJSkpKQBMnTqV/fv3U11djaurK+Hh4ezbtw8wJvDQarWcO3cOgEmTJnH06FEqKipwcnJiwIAB7N69GzDOnQNISEgAYPz48Zw8eZLS0lLs7e2Jiopi586dAPTu3Ru1Ws2ZM2cAGDNmDPHx8RQVFWFra8vo0aMlnaelpeHl5UVsbCwAI0eOJDk5mfz8fKysrJg4caL0nw0ICKB/fxd0uuOMGAHffjuMHTuK2LWrnrNnnUlP9+TcOQUZGdZkZFizaZMx9MXCQk9ISBUhIcX4+6cRFFTK1Kk+ZGUZvd1eXl706tWLmJgYFAoFAwcOpLS0lLS0NACmT5/O7t27qa2txd3dnd69e3PgwAHAmHmzurpa8pxPmTKFgwcPUlVVhYuLC/369ZNKBfTt25f6+noSExMB4zzBmJgY6YnpoEGDpDmfYWFhKJVKKaPo2LFjOX36NCUlJWg0GoYPH86OHTsAY5iztbU1p0+fBmD06NGcPXuWwsJCbG1tGTNmDFu3bgWMkQYODg5SaYMRI0ZIYZym8Pj2PkcYDAaGDBnCnj170Gq12NnZUVwcwHvveXHixF9ZR+++O55Ro9IwGKaxd++VnSP69+8vHe/w8HD0ej1nz54FYMKECRw/flx6Mt3cOSIuLk463uY+RxQWFpKbm0tSUhJZWVn4+flddI44cuQIAIMGDaK4uFiaizRjxgyio6Opq6vDw8ODXr16cfDgQQDZnyPUajWOjo4cO3aMf/7zn8yfPx+NRtPmc4SLiwvHjx8HjHOXMjMzycnJwdLSkilTprB161b0ej2+vr54eXlx7NgxAIYMGUJ+fj6ZmZkoFAqmT5/Ojh070Ol0eHl5UVJSIs0F78nniEmTJrXrOcIkf1u4rFC6F198sVkjpDFHjhyRJnUDZGdnM2HCBCZMmMAXX3zRZsHA+LQvMDCQNWvWtDhBvjmPkb+/vwil62DKy8svK7xI0LHIQR/ddYwVFBRw7NgxTpw4IRmDSqWS8PBwoqKiCA4ObuJR2b59O1OmTDGXuH+bhoYG8vLySE9PJzMzs0ktJAcHB/z9/fHz88PJyUn2nqSYmBiGDh3KsWPHGNKOBV6LiuDoUTh82NQM5OdffCwsLPQEBdUSGlpN797VhIZW07evFj8/FTY2Nk1adyuu2RLtOT5MD4SKi4spKiqivr4egwGOHXNm7Vp/oqON5yGVyphX4eOPISSkXTbdLWisi7y8PHbu3IlGo2m14Hh3IiUlha+++gqFQsHdd99t9ix1Xf3aIWc6LJTugQceaFIIszmCgoKk99nZ2UyaNIlRo0Y1SfnbVry9vQkMDJSs2uawsrKShQu0p9Fd6x10VYQ+Og53d3epmOqZM2c4evQoGRkZnDlzhjNnzuDi4kJUVBSDBg2SPIVdGQsLC+mpW319PdnZ2aSnp5OdnU15eTmnT5/m9OnT2NvbS0aSs7Oz7I2k9sTVFWbMMDYAg8FYS8lkKB05AidOGCgpUZKUZEtSUtO6DS4uWkJDawgNrSYkpIDg4Fp6967H1dUKa2trbGxssLa2lq5vXeHY1tVBWy7Ff3d8NDQ0UFlZSVlZGSUlJdKD0exsNVu3+rNpkzvJycZbG6US5s2DV18VBlFzNNaFKQlRVVUVBoOhS/zn/i7BwcFERkYSGxvLH3/8wdKlS82631392tFduCzDyM3Nrc0ZM7Kyspg0aRJDhw5lxYoVVxQ7WVRUREZGhtmteMHF9JSnm10FoY+OxxRuNnDgQPLy8jh69CgnT56kuLiYLVu2sH37dvr06YOtrS16vb5bxItbWloSEBBAQEAAOp2O7OxsMjIypFTWJuNQo9Hg5+eHv78/Li4usrmpcnZ25uqrr8bZ2blDt6NQQECAsd10k3GZwaAgMxNOnGjcDCQmGusqHTqk5tChpglTPD3rCAqqJSiohqCgYoKDawkOrsPLywJrayvJWLK2tkatVqNWq2WR9OHTT+HBB+HDD+Gee1rve7nX8/r6eqqqqqTyBaawboMBzp61Zf9+Xw4ccOXEib+81HZ2cPvt8Mgj0KvXFexQD6GxLkwPmA0GA1qttsc8cJ42bRrx8fGkp6cTGxvbrpmBLxdxrysPOiQrnSl8LiAgQKqvYsJUHBSMsblvvPEGs2fPprKykhdffJEbb7wRb29vUlNTefbZZ0lPTycuLq7NKZVFVrrOob6+XkwslhFy0EdPHGNarZZTp05x9OhRsrOzAaipqcHLy4tBgwYxePBgnJyczCtkB2Aykkxx9Y2fdNrZ2UmeJDkkbiguLm5T4p/OoqoKYmP/MpZOn4b4+OZD8UzY2jbg61uLr28dfn51+PrW4udXh59fLb6+BmxtjUaSlZWVZDCpVCqpKZXKDtPDp5/CsmUwYACcPAmffNK6cdSSPhoaGqirq6OmpqZJq6urw2AwoNMpSEqy4cQJe06ccOT4cXuKiv66t1AoYPJkuOUWmDMHrrAKQ4/iQl2sW7eO+vp6rr32WjQajRkl61x2797Njh07cHJy4sEHHzTbwwa5nau6E2bPSrdlyxbOnTvHuXPn8PPza/JdYzssISFBmhRvYWFBbGwsX3/9NaWlpXh7ezNp0iS+//77K64zI+g4qqqqzJ4eWvAXQh/mQa1WSym9c3Nz+fPPP1m3bh02Njbs2rWL3bt3ExwczJAhQwgPDze78dpeqFQqAgMDCQwMRKfTkZOTQ2ZmJtnZ2VRVVREfH098fDw2Njb4+vri6+uLh4dHp99w1NbW8vPPP7NgwQLZGOt2djBypLH9hYKiIoiPh7i4v1p8vIHUVKiutiAx0Y7ERLuL1mdhocfdXYeHhxZPT2Pz8Kho9F6Ls3MDNjZ/GUqWlpZYWlpiYWFx0XulUnlRa8moMhlFDz4I771n9NAsW2a8zt99twG9Xk9DQwMNDQ3U19dTX1/P3r17GTZsGFqtFq1WKxWeNhnX9fUK8vLUZGRYce6cE4mJtiQl2ZGSYn1RGnVbW5g2Da69Fq6+Gnx82kVFPYYjR440mx7a3A8zOptRo0Zx+PBhSktL+fPPP5vMk+9MWtKHoHPpkKv0kiVLWLJkySX7NTaSbGxspAwUAoFA0NXw8vJi5syZGAwGAgICiImJITk5WWo2NjYMGDCAwYMHN/Gcd3VUKpUUbldfX09OTg4ZGRlkZ2dTU1MjPSRTqVR4e3vj6+uLt7d3pxTvPnPmDLfffjsDBw5s1+QLHYGrK4wZY2x/oaC2FlJTISnpwmYgORm0WiW5uVbk5rYc+qRQGHB0rMfZWYezcz0uLjpcXIzvHRyq0Wgazrf6Ru8bsLVtQKnkIgNJoVCwbp0rr73mzwMPGHj/fQUKBbz/vvG6fu+9ClJTU5kzpwCA+nqorVVSVWXJ6dNqUlPLKC1VUVqqprTUlvx8NTk5VmRnW5Gfr0avb/7G3NERRo2C8eNh3DgYNqxt85oEl8ZkxELPSxutUqkYO3YsmzZtYvfu3QwaNKjbPMQSXD5C84IrwtbWttnljQvCLlu2jLCwMB577LFOlq7n0ZI+BJ3P0KFD8fT0JCIigpKSEo4fP86ff/5JeXk5hw4d4tChQ/j4+DB48GAiIiKwsbExt8jthqWlJf7+/vj7+0vZ7bKysqSaMunp6aSnp6NUKnF3d8fPzw8fHx/s7C72hAiMWFtDeLixNUWBXg85OZCR0bRlZv71PjfXgF6vOG+IqDifPbnNWFrqUasNqNX6881Aba2SwkI1Dzxg4IMPFFI9IIUCPvhAARj45z+Dee+9APR6BTrd5d1oW1lBUBBERMDAgX+1gABRe6g9GTRokPS+trYWg8GAUqnsMfOLGhMVFcX+/fspLy8nNjaWwYMHd7oMjfUhMB/CMJIBQUFBFBcXk5eXJ90klZeX4+npSWBg4GXlX+9IUlNTCQ8Pl+rbXGrC/yeffNJJkgnaog9B51BcXIynpydgnPw/adIkJkyYQHJyMjExMSQkJEg1GDZt2kRYWBiDBg2id+/esphI3140zm4XFRVFUVERWVlZZGdnU1ZWRl5eHnl5eRw7dgxnZ2fJSOoKacDlglIJvr7G1jQ07y8aGhQUF0NeHuTn/9VMn0tKoLQUysqMzfT+fAk26uuV1Ncbw/kaM2AAkqeoMUbPkYLdu+Hkyaa/sbQEJ6d6vL0tcXMzFtd1dwcvLwgO/qt5ehr3TdCxND5XmerU2dnZ9TiPERgf6owYMYKtW7dy6NAhBg0a1Onnocb6EJgPYRjJBC8vLzZu3MjNN98MwIYNG/D39zezVC2j1Wq71ZPuro7Qh3xIT0+nb9++TZYplUp69+5N7969qaqq4uTJk5w4cYLc3Fzi4uKIi4vDzs6OyMhIBg4ciJeXV7cyDhQKhZTVdODAgVRUVEiepMLCQkpKSigpKSE2NhZbW1u8vb3x9vbG09NTGPx/EwuLvwyQy6G2FioqjGm4a2ubvq5da5xT9PDD8MEHTb04BoNx+cmT8OKLxuxwtrbGZm0NW7ZsF/MoZELjc1VRURFAj578byrGm5ubS1paWpPyM51Bc9cOQefT8x4LyJQFCxawatUq6fOqVatYuHBhkz6xsbGMGTMGJycnoqKipArTYPQ6vfPOO4SFheHg4MB7773H4cOH6devHy4uLvz73/+W+tbU1PDAAw/g4+ODn58f//znP6XvlixZwmOPPcaUKVOwt7dnxowZlJSUAMaqy3V1dWg0Gnx9faUsXC2xZMkS3nzzTcBYHPjWW29l7ty52NvbM3LkSKmas2nfxo8fj7OzM0OHDpWqFgsE3Q07OztGjRrFsmXLWLZsGaNGjUKj0VBVVcXBgwf59NNP+fjjj9m/fz8VFRXmFrdDsLe3Jzw8nClTpjBr1iyGDx+Or68vlpaWVFdXk5SUxN69e/nxxx/ZuXMnCQkJlJeXi3pdnYi1tdGY8vOD3r2hf38YOhRGj4Z//9uYfe4//zEaQSa1mIyi//zH+P0LL4C/v3H+lI2NCIOTMzk5OQBtLsnSHbGxsWHgwIEAHDt2zMzSCMyFMIxkwrRp04iJiaG4uJjc3FwSExMZP3689L1Wq+W6665j4cKFFBQU8MQTT3DttddKWf0Afv/9d44cOcK2bdt46qmneOutt9i3bx87d+7k2WefpaDAOBH2iSeeoKysjLNnz3L48GG+/vprfvnlF2k933//Pe+//z4FBQXU19fzn//8BzBmG7SyspLqSfhcZgqgDRs28NBDD1FSUkJYWBgvv/wyABUVFcycOZNHH32UwsJCnnvuOWbPnk1tbe0VH8+ehshIJx8u52m4l5cXM2bM4LHHHmPhwoX0798fS0tL8vPz2bJlC++++y7ffvstp06dQqfTdaDU5sPa2ppevXoxbtw4Zs+ezYQJEwgLC8Pe3h69Xk9eXh5//vknv//+O7/99hvHjh0jOzu7TcUQhwwZgsFgkH3iha7IPfcYjZ8PPzQaQ3q98fXDD1tP2S28RfLBpIuqqioKCwtRKBQXZRLuaZjmFsXHx6M1xZN2EmJsyIOeHUpXXW3Mj9qRhIcbYwgugaWlJTfccANr166lpqaGuXPnNonzPXjwIBYWFtx///0AzJ8/n/fff58tW7Ywd+5cAB5++GEcHR0ZPnw4Xl5ezJs3D2dnZ5ydnQkICCA+Ph43NzdWrFhBamoqGo0GjUbDvffey7p167juuusAuPnmm4mIiADgxhtvZMeOHRfJW1FRcdlp1KdPn864ceMk+Z9//nkAfvvtNwYMGMDs2bMBuOGGG3j11Vc5cOAAkyZNuqxt9FSuRB+CjiE6OpqJEyde1m+USiVhYWGEhYVRU1PD6dOnOXHiBBkZGVJWNysrK/r27cuAAQMICgrqlvMALCwspDA6MP6vs7OzycnJoaCggMrKShITE0lMTMTCwgIPDw+8vb3x8vLC3t6+2fDDK9GHoG2YjJ9ly2DXrrbVMRL6kA8mXSQmJgLg4eHR40OyfXx8cHFxobi4mISEBCIjIztt22JsyIOebRjFxxtjAzqSY8egjU8rFy1axNNPP01NTQ2fffYZpaWl0nfZ2dkEBAQ06R8YGNgknM3Dw0N6b2Njg3ujoHIbGxuqqqooKCigpqaGsLAw6Tu9Xs+YRjliG6/H1taWysrKi2TV6/Vt2qfGtLTe9PR0tm/f3qQQpqk2iqBtXIk+BB1DXV3d3/q9jY0NUVFRUsKCEydOcOLECcrKyjh+/DjHjx9Ho9EQERFBZGQkPj4+3Wo+UmPs7e3p06cPffr0QafTUVBQIBlKVVVV5OTkSOcJW1tbvLy88PT0xNPTE2traxISErj33nv56aef6NOnj5n3pntiMoIefPDSRhH8/fEhaD/q6uqora0lKSkJQIwRjPMhIyIi2L17N2fOnOlUw0iMDXnQsw2j8HCj4dLR22gjo0aNIisrC7VazaBBg4iOjpa+8/HxISMjo0n/9PR0brzxxssSx83NDWtra9LS0i47/KrxzVd7Toj29fXlmmuuYcOGDe22zp6GmKAuHxo/APi7uLq6MnnyZCZNmkR6ejqxsbGcPn2ayspKDh48yMGDB3F1dSUyMpLIyEhcXV3bbdtyQ6VSSVnuDAYD5eXlZGdnk5ubS2FhIdXV1VLNKDBmBCwuLiY+Pr5JyLGg/bnnHliypG01hdpzfAj+Hh4eHsTGxqLT6XB2dpY8tT2dsLAwdu/eTUpKCnq9vtO882JsyIOebRjZ2rbZm9NZbNiwodlBOHLkSHQ6HR9//DF33XUXP/74IwkJCUyfPv2y1q9UKrntttt44okneOutt3BwcCAhIYGKigqGDx/e6m/d3NwkT4775aY4aoVrr72WZ555ho0bN3LNNdeg1WrZtWsXo0aNEnNn2khnFMsUtI1evXq1+zoVCgWBgYEEBgYyc+ZMzp07R2xsLAkJCRQVFREdHU10dDS+vr5ERkYSERGBRqNpdznkgkKhwNHREUdHR/r27Ut9fT2FhYXk5uaSl5cnZblLTU0FYOfOnVRVVUkeJWdn527rZTMXbS190xHjQ3BlaDQaYmJiAOPcGjEmjPj4+GBtbU1tbS05OTn4+vp2ynbF2JAHPdswkiEDBgxodrlarebnn3/mvvvu4+mnn6Z3795s3LjxigyHd999l2effZbIyEgqKioIDQ3l1VdfveTv7OzseOqpp4iMjESn0xEXF3fZCRiaw9HRkV9//ZVHH32UJUuWoFKpGDNmDKNGjfrb6+4pVFVVCSNSJhw8eLBDJ9FaWFhI4WV1dXUkJCRw8uRJkpOTpRTYmzdvplevXkRERBAeHt7t5w1YWlri5eWFl5cXYCxWmZeXJ02eNiVxyMvLA4znUw8PD9zd3fHw8BC1kzqRjh4fgrZRU1PDjz/+SEBAACEhIcJb0QilUklQUBDx8fGkpaV1mmEkxoY8UBi6Wf7T8vJyHB0dKSsrw8HBQVpeW1tLSkoKwcHBWFtbm1HC7kFZWZm4EZcRctCHGGNGNm/ebJaLW1VVFadPnyY2NrZJ2K1SqSQkJIT+/fsTHh7eo3QTExPD0KFD2bVrF97e3pJxdGGGP7VaLRlJ7u7uODk5dcvkFnLAXOND8Bc6nY6dO3dy5MgRhgwZwrRp07C0FM/JG7N792527NjBwIEDpcRQHY0YGx1HS7ZBc4iRILgiuvsT6K6G0Id86MzJuo2xs7Nj+PDhDB8+XCqWevr0afLy8ppkcuvduzf9+/enT58+WLU1/qmLEhQUxAcffEBERAQuLi6Ehoai1+spKSkhPz+f/Px8CgsL0Wq1krcN/jKUGnuUhKHUPphrfAiM1NfXs2/fPoqLi/Hz82Ps2LHCKGoGT09PAMnL3BmIsSEPxGgQXBEiC5q8EPqQD81lcexsnJ2dGT9+POPHj6ewsJDTp09z+vRp8vPzSUhIICEhAUtLS3r37k1ERARhYWHdcp6ai4sLM2bMwMXFRVqmVCpxdXXF1dWVvn37tslQUqlUuLm5Sc3FxUUkPLlC5DA+eiparZY9e/ZQUFCApaUlvXr1EmUeWsBU6LaoqKjTtinGhjwQhpHgiqirq+tRITlyR+hDPqSkpDRJh29u3NzcmDBhAhMmTCA/P18ykgoLC4mPjyc+Ph6VSkVoaCj9+/cnNDS02xhJBQUFvP/++7z44ostJoxpzlAqLS2VDKWCggIp6YwpNbhSqcTJyQk3NzdcXV1xd3fHtg316gTyGx89hfLycvbs2UNFRQUqlYoJEyZwrKOz8nZhTAajTqdDq9V2yjlRjA15IAwjgUAg6CF4eHjg4eHBxIkTJSPp1KlTFBcXc+bMGc6cOSN5kvr27UtYWFiXDtPMyMjgv//9L3fccUebM2kqlUpcXFxwcXEhPDxcMpQKCwulVl1dTXFxMcXFxdLv7OzscHV1lbxKIvxOIAcMBgOpqanExMSg0+mws7Nj7NixODs7m1s0WaNWq1GpVOh0OiorK5t4nQXdG2EYCa6IS01eE3QuQh/yYerUqeYW4ZIoFAqpEOqkSZPIzc3l9OnTnDlzRqr9Ex8fj1KpJDg4mH79+tGnT59unQK8JRobSqanuVVVVRQWFlJUVERBQQFlZWVUVVVRVVVFeno6YMyU5+zsjKurq/R7Ozu7Hp/9riuMj+5CeXk5v//+u/TZ3d2d0aNHSw87hC5aR61Wo9PpLkrW0lEIfcgDYRgJrojKykoRmywjhD7kw/79+xk3bpy5xWgzCoUCb29vvL29mTJlCvn5+cTFxXHmzBny8/NJSkoiKSmJX3/9lYCAAPr27Uvfvn3NngXRnNjZ2WFnZ0dgYCBgDLcpLi6WPEpFRUVotVoKCgooKCiQfmdlZSUZSa6urjg7O3dpj9yV0NXGR1ekoaGBpKQkqUYRQEhICEOHDm3ixRS6aB1T0ubOepgh9CEPhGEkuCLEZH95IfQhH6qrq80twhXT2JM0ceJEioqKiIuLIy4ujqysLNLS0khLS2PTpk34+vrSt29fwsPDpYnKPRWVSiUdNzDeUJWVlUnhdsXFxZSWllJXV9dkrhIYjSyTseTi4oKTk1O3zhbYlceH3NHr9WRkZHD69GnKy8ul5UOHDiU0NPSi/kIXrdPZhpHQhzwQhpHgihDpPeWF0Id8cHV1NbcI7Yarqytjx45l7NixlJWVSUZSenq6lLVt27ZtuLq6Eh4eTp8+ffDz85PN3Bp7e3tGjhzZ6d5UhUKBk5MTTk5OUjX7hoYGSktLmxhL5eXlUghe49pTdnZ20u+dnZ1xdHREo9F0izC87jQ+5EJDQwNpaWnExcVRUVEBgLW1NREREfTq1avF8Sh00TINDQ3U1NQAdFpiFaEPeSAKvHZzVq1axbp16/jxxx+veB1LliwhPDycp59+WlrW0NCAhYVFe4goKxrva3scu85CDvroqWPsQiorK7v9XJzKykri4+OJi4sjNTWVhoYG6TtbW1vCwsIIDw+nV69eZs9wJ2d96HQ6SkpKKCoqoqSkhOLi4hZT9qpUKslYamwwmXvcXy5y1kdXo6qqipSUFJKTkyVvg1qtJiwsrE0p+IUuWqa0tJT33nsPCwsL/u///q9THkoIfXQcosBrF2LatGnMmDGDJ554osnyxx57jKKiIr766qvLWp9CoSAnJwcvLy8AFi1axKJFi9pNXhOVlZVdco5BUFAQa9asYeTIkZfs21HHriPoqvrojuzbt6/bVy/XaDRERUURFRVFbW0tSUlJJCQkcPbsWaqrqzl+/DjHjx+XaqX06dPHLMkbGhoa2LJlC7NmzZKlAaFSqaRMgSa0Wi2lpaWUlZVRUlIivdfpdBfNWVIqlWg0GhwcHHB0dJRe7e3tZbm/0DPGR0fS0NBATk4OSUlJ5ObmSuFe1tbWhIeHExIS0uYaW0IXLWPKOOno6NhpnlqhD3kgDCMzs3jxYt57770mhpFer+f7779nxYoVbV6PTqcTBQcFAkGnY21tTf/+/enfvz8NDQ2kp6eTkJBAfHw8paWlnD17lrNnz/LLL7/g5+dHnz59CAsLw8PDo8NvOE6cOMGNN97IsWPHGDJkSIduq71Qq9UXGUsNDQ1UVFRQWlpKaWmpZDDV1dVRXl5OeXk5mZmZUn+FQoFGo2liLDk4OGBvby/CbrsgOp2O7OxssrKyyM7Opr6+XvrO09OTXr164efnJ1tjuCtiKu7s4+NjZkkEnY08AsF7MHPmzCEhIYG4uDhpWXR0NA0NDUyZMoX09HSuueYaqfjgpk2bpH5BQUH861//ok+fPvTr14/p06cDxuwzGo2GAwcOsHLlSq666irpNzt27CAqKgoHBwdCQ0PZs2cPAJ9//jmhoaHY29szYMAAoqOjW5XblEkpKCiId955h7CwMBwcHHjvvfc4fPgw/fr1w8XFhX//+9/Sb4qLi5k/fz5ubm707t2bL774QvpuyZIlPPLII0yYMAGNRsPChQvJzc1l6tSpODo6smjRoibhOh999BGhoaG4ublx2223UVVVBcDKlSuZPn069957Lw4ODvTv35/jx48DcOedd5Kens7kyZPRaDR8//33re5j42MXHR1NeHg4L730Ei4uLgQHB7N169Ym+7Zw4UI8PDzo1avXZXv6/i49LbOVnOnXr5+5RTAbFhYWBAcHc9VVV/Hwww9z7733MnnyZHx9fQHIzMxk+/btfPzxx7z33nv8+uuvJCQkoNVqzSy5vLGwsMDJyYmgoCAGDRrEpEmTuOGGG7j++uuZOHEigwcPJiQkBHd3d9RqNQaDgYqKCjIzMzlz5gwHDhxg8+bNrF+/nl9//ZXo6GiOHTtGQkIC2dnZlJeXNzm/diQ9eXy0Fb1eL6XN37VrFz/++CMHDhwgPT2d+vp6bG1t6du3L9dccw2TJk0iMDDwiowioYuWMc358/Pz67RtCn3IA/HoyMzY29tz/fXXs3r1al555RUAVq9ezfz581EoFFx33XXcfffd/Pzzzxw5coTrrruOU6dOSaFyP/30E3v27MHBwQFra2sUCgVJSUnS9wkJCdK2kpOTmT17NqtWrWLmzJlkZWVJNyQ+Pj5s374dPz8/vvzyS+bPn09aWlqL2ZEaZ0H7/fffOXLkCAkJCYwbN47rr7+effv2kZ6ezsiRI1m8eDHu7u7cf//9WFpakp6ezrlz55g6dSrh4eGMHTsWgLVr17J9+3bc3d0ZMmQI1157LV9//TU+Pj5ERUXx66+/MmvWLNauXctnn33Gtm3b8PDw4I477uD555/nnXfeAWDnzp3cfffd/Oc//+GFF17g8ccfZ/v27XzxxRds27atzaF0F3Lu3Dns7e3Jz8/nf//7H8uWLSMpKQmAW265hYiICDIyMkhJSWHy5MkMGjSIgQMHXvZ2rgSRlU4+iJt8I40z3I0fP56KigrOnj1LQkICKSkplJWVcfToUY4ePYqFhQVBQUGEhYURGhoqiim2AYVCga2tLba2ttL5HoyZtGpraykrK5O8SWVlZZSVlaHVaqmsrKSyspLc3NyL1mdnZ4dGo0Gj0WBvb4+9vT12dnbY2tq2W0SCGB8XU19fLyXmyM/PJz8//6LjZG9vj5+fH35+fri4uLSLt1Xoonm0Wi0pKSkAUkr+ztquwPwIw0gGLF68mIcffphXXnmFuro61q9fz5YtWzh8+DA6nY77778fgFGjRjFx4kT++OMPli5dCsCjjz7aJOSiNb777jtmzZrFtddeC0BAQID03TXXXCO9v+uuu3j++edJTEwkIiKi2XXV1dVJE+wffvhhHB0dGT58OF5eXsybNw9nZ2ecnZ0JCAggPj4eFxcX1q9fT1JSEra2tgwYMIA77riD7777TjKMbr75ZsLDwwGYOHEiGo1GeoIyZcoUTp48yaxZs/jyyy9Zvny5dMJ69tlnueaaayTDKDIykptuugmAhQsX8sknn7Tp+FwKR0dHHn30URQKBYsXL+aee+6RbjL27NnDxo0bsbCwIDw8nIULF7Jhw4ZOM4wa60NgXs6dO0dISIi5xZAd9vb2DB06lKFDh6LT6UhNTSUxMZGzZ89SWloq1Uv6448/cHV1lYykK30a3lNRKBTY2NhgY2PTrMFUUVFBZWWl9Gp6X19fL31uDrVaLdVvMhlktra20mfTg7lL0dPHR+PwR1PSjbKysosebqlUKtzd3aWHCx0x16Wn66IlEhMT0el0ODs7NxlDHY3QhzwQhtG998L5WNJ2x9cXPv74kt1mzJhBeXk5Bw8eJCcnB3d3d4YNG8YPP/xAYmIiTk5OUt/6+nqGDh0qfb4cN29mZqaUOvZCfvrpJ15++WWSk5MBqKiooKioqE3rbWyY2djY4O7u3uRzVVUVBQUFNDQ0NJE3MDCQzZs3X9Z6ANLT07njjju4++67pe8bV6ZuvB5bW9sWL/SXi7u7u3RhMqXvrKysJD09naqqqiapNhsaGrpM4gaBoLNRqVSEhoYSGhrKzJkzKSwslIyk9PR0ioqKOHDgAAcOHECtVtOrVy969+5NSEgIzs7O5ha/S9LYYLrwYdqFRlNjw6mqqgqtViu1kpKSZtdvYWEhGUg2NjZYW1tf1GxsbOhmiXCbRavVUlVVRXV1NVVVVZIhVF5eTm1tbbO/sba2lgr/enp64uLiIpu09z0NU/h9//79u0WKfMHlIQyjNhguHY1KpWLevHmsXr2anJwc6Yba19eXyMjIJtWrL+RyBq2/v3+T0DoTdXV1LFiwgJ9//pkpU6ZgYWGBt7d3qxewy60L4u7ujlKpJDMzE39/f8Bo4FzJxEZfX1/efPNNrr/++sv+bUec5Hx9fXFycmqzIdkRdHadFkHLTJo0ydwidCkUCgXu7u64u7szevRoamtrSU5O5uzZs5w7d05KDR4fHw8Ya32EhITQu3dvgoKCWk1JHBkZSWZmZpu96j2V1owmMN7oV1dXSzf6pvemzzU1NVKCCFMdnZbQ6/X89NNPWFtbY2VlhZWVFWq1GpVKhVqtRq1WN1mmUqmwtLTE0tISCwsLs9yo6vV66uvr0el06HQ6amtrqaura/JaW1srHZNLhUTZ2tri4OCAs7Mzrq6uODs7Y2tr2+n7Js5VF5Ofn09iYiIKhaLTE7YIfcgDYRjJhEWLFnHDDTdQWVnJ66+/DsCIESPQ6XR89tlnLFmyBIBDhw4RGBjYJAyuMR4eHqSmpjbr/l2wYAGDBg3i999/56qrrpLmGLm7u0uvAO+//36TlLDNUV1dfVmpdy0sLJgzZw7Lly/n008/JSkpiS+//JJ169a1eR0m7rjjDl577TWpeF1OTg4nTpxokmSiJUzH50rmGLWEr68vw4YN4/nnn+fpp59GrVZz8uRJrK2tO20y5eXqQ9BxHD16lNGjR5tbjC6Ladz069cPg8FATk4OiYmJJCUlkZmZSVFREUVFRRw+fBgLCwv8/f0lb5KXl1eTm0uVSkVaWpqU/EFwZZgMlsbRC41paGigtraWqqoqyUgwtZqamiafs7Ky8Pf3b9Fz0hpKpVIykkyGkoWFBUqlEqVSedH7C2n83zAYDOj1eqk1NDQ0+WwygnQ6XZMscG3FyspKCjO0t7fHwcFBanLJICvOVRezd+9eAPr27dvpcx2FPuSBMIxkwujRo7G3tyc4OJjQ0FAALC0t+fXXX3n44YdZvnw5BoOBqKioVufMPP/888yaNYu6uromGewAgoODWb9+PU8++SQ333wz3t7e/O9//yMkJIS33nqLadOmoVAouPfee+ndu3er8l5JBqOPPvqI++67Dz8/PxwdHXn55ZcZN27cZa9n/vz5lJSUcPXVV5OVlYW3tzfLli1rk2H01FNP8dBDD7Fs2TI+++wz5s2bd9nbb45Vq1bx2GOP0atXL7RaLREREU0y8nU0nZVRSnBpLvXEXNB2FAoFPj4++Pj4MGHCBKmIcFJSEufOnaO0tJTU1FRSU1PZtm0bdnZ2hISESC0vL4/HHnuMVatWidj9DsTCwkKaf9Qaer2e33//nbFjx1JTU9MkRK+urq7JZ1Orr6+XDBO9Xi8tNwdKpVLyaJk8Xo1fG8+9kovx0xriXNWUzMxMTp48CSDNfe5MhD7kgcLQzQJ+W6pua7qgBgcHi0nq7YCo0Cwv5KAPMcaMHDp0iBEjRphbjG6PwWCguLhYStqQkpJy0Q1zbW0t//znP1m/fj1XX311j/5fyoUrGR8mj47JSDJ5cerr61v0+Oj1eikcvKXbnOa8TKZXk1fKFM6nUqm6XRIQca76C71ezxdffEF2djaDBw9m1qxZnS6D0EfH0ZJt0BzCYyS4IkzJBwTyQOhDPgwYMMDcIvQIFAoFrq6uuLq6Mnz4cBoaGsjIyJC8Sbm5uVKigM2bNxMbG4uvry/BwcH06tULf39/UezUDFzJ+DAZLl3BC9OVEOeqv9i9ezfZ2dlYWVkxZcoUs8gg9CEPxFVBcEVUVFTg6OhobjEE5xH6kA+7d+9mxowZ5hajx2GqhRQUFMSUKVOorq7m999/57PPPsPR0RGDwUBmZiaZmZns2bMHS0tLAgICCA4OJjg4GB8fH5EFrBMQ40M+CF0YSUtLY9euXYCxdIm5oi+EPuSBMIwEAoFA0O2wtbWVyhPMnz+fkJAQUlJSSE5OJiUlhYqKCpKTk6USBVZWVgQFBREYGEhQUBBeXl7CUBIIujnFxcX88MMPGAwGBg4cKLw2AmEYCa4MEasvL4Q+5EOfPn3MLYLgPL6+vixfvhxfX18cHR0ZNGgQgwYNwmAwUFhYSEpKitRqa2tJSEiQShpYWVkREBAgGUve3t7dbo6JORDjQz70dF1UV1ezatUqqqqq8Pb2blLo3hz0dH3IBWEYCQQCgaBb4unpyZ133omnp2eT5Y1rJw0fPhy9Xk9ubq6U4S49PZ3a2loSExNJTEwEjCmr/f39JUPJ19dXGEoCQRelqqqKb775hqKiIhwdHVm4cGGrNdEEPQdhGAmuiNraWqysrMwthuA8Qh/yISEhgaCgIHOLIQBKSkr4/PPPeeKJJ3B2dm6xn1KplNKCjx49Gr1eT15eHmlpaaSmppKWlkZNTY2UAQ+MNZL8/PykunK+vr5iDLYBMT7kQ0/VRUVFBV9//TUFBQVoNBoWL14siyLpPVUfckMYRgKBQCDolqSkpPD6669z4403tmoYXYhSqcTb2xtvb29GjhyJwWAgPz+/iaFUVVUlheGB0Qvl5eWFv78/AQEB+Pv7i4QoAoHMyMnJYc2aNVLa5ttuuw1XV1dziyWQEcIwElwRcni6IvgLoQ/5MH78eHOLIGhnFAoFnp6eeHp6Mnz4cGmOUlpaGunp6aSnp1NaWkpOTg45OTkcPnwYAEdHxyaGkqenZ49P6CDGh3zoabo4deoUP//8MzqdDldXVxYvXnxZD0w6mp6mD7nSYYZRUFAQaWlpTZY99dRTvPnmmy3+xmAw8NJLL/HZZ59RUlLCiBEj+Oijj+jfv39HiSm4Qqqrq81eUFTwF0If8uHkyZOiSF83p/EcpaioKMAYnmMykjIyMsjNzaWsrIyysjJOnToFGOcp+fn54e/vj5+fH76+vj2uBpkYH/Khp+hCq9WyadMmYmJiAAgNDeXGG2+UXdKinqIPudOhj65efvll6QlaTk4O//d//9dq/3/961+8++67/Oc//+HIkSN4eXkxbdo0KioqOlLMS9PQANHR8N13xteGhnZdfVBQEAcPHmyybNmyZbz44ovtup32pOFvHoOff/6Z8PBwHB0d8fb25rHHHmtxndHR0SiVSjQajdT27NnTbN+VK1eiUCh49dVXmyx/9tlnUSgUrFmzpkm/Tz/9VOqTm5uLQqH4W/tlLv6uPgTtR2lpqblFEJgBe3t7+vfvz8yZM7n77rt5+umnue2225g0aRIhISFYWVmh1WpJTk5m165drFq1in/961988MEHrF+/nkOHDpGVlUV9fb25d6VDEeNDPvQEXRw6dIjXX3+dmJgYFAoF48aNY8GCBbIziqBn6KMr0KGhdPb29nh5ebWpr8Fg4L333mP58uXMmTMHgK+++gpPT09Wr17NPffc05GitsyGDfDww5CZ+dcyPz94/304L2dP5O9mY4qKimLPnj24u7tTUlLC3Llz+eyzz7j33nub7R8WFkZ8fHyb1t27d29Wr14tGeIGg4Hvv/+ekJCQJv2cnZ15/fXXuf3227t8RXWRHUs+iLBG+WBjY0NYWBg2Njadvm21Wi0VjwXQ6/Xk5+eTkZFBZmYmWVlZFBYWUlxcTHFxMbGxsYBxLHt7e+Pr64ufnx9+fn44OTl12Yc2FyLGh3zozrqoqanhl19+4cyZM9Ky2267TdbJDbqzProSHeox+uc//4mrqyuDBg3itddeQ6vVttg3JSWF3Nxcpk+fLi2zsrJiwoQJ7N+/vyPFbJkNG+Cmm5oaRQBZWcblGzZ0ihgrV65k+vTp3HXXXdjb2xMVFUVWVhb3338/jo6OjBgxguzsbMB48Z0zZw4eHh64uLgwd+5ciouLAaPnxdfXV/q8du1a+vTpQ01NTZPt1dTU4ODg0CQUctu2bUREREif/274h6+vL+7u7k2WmSYx/11CQkKwt7eX3Ob79++XQlcaM3z4cPz9/VmxYkW7bNec9LRwHDljCq0SmJ++ffsSGxtL3759zS0KSqUSLy8vhg0bxuzZs3nggQd46qmnWLx4MZMmTSI0NBRbW1saGhrIzMzk0KFDrF+/nvfff5+33nqLb7/9lh07dhAXF0dZWRkGg8Hcu3RFiPEhH7qjLhoaGjh69CgffvhhE6PowQcflLVRBN1TH12RDjOMHn74YdasWcPOnTt54IEHeO+997jvvvta7J+bmwtwUb0JT09P6bvmqKuro7y8vElrFxoajJ6i5i4+pmWPPNLuYXUtsXPnTq6++mqKi4vx8/NjzJgxTJgwgaKiIoKCgnjrrbekvnPmzJGyJVVUVPDyyy8DMHHiRG688UYeeOABCgoKePDBB1m5cuVFT1NtbGy49tprWbt2rbTshx9+4Oabb5Y+Nw5vvPbaa3Fycmq2tTanbO/evTg6OuLi4kJsbCy33357i31TU1Px8PAgNDSUl19++ZKhY4sWLWL16tUArF69mkWLFjXb74UXXuD1119Hp9O1uj65Y/ZwU4HEzp07zS2CoBFy1oeNjQ29e/dmwoQJLFq0iCeffJKHHnqIG2+8kREjRki1kqqrqzl37hy7d+/m+++/59///neXNZbkrI+eRnfShcFg4NSpU/z3v//l119/pbq6Gg8PD5YuXcqLL77YJTLPdSd9dGUuK5TuxRdf5KWXXmq1z5EjR4iKiuLRRx+Vlg0YMABnZ2duuukmyYvUEheGCxgMhlZDCN54441mZdq2bRt2dnZMnjyZw4cPU1NTg5ubGw0NDZSVlQFIMaa1tbWA0Y1ZXV1NQ0MD6v37sbnQU9RUMMjIoPKPPzBMmIBGo5GMMisrK5RKpeSJ0Wg01NbWUl9fL82VMfU1FRSbNm0aSqVS2teamhoee+wxwDixvl+/flx11VVS34SEBGbPno1Op2P69Ol8/fXXAFRWVnLdddehUCiws7Pj7rvv5pVXXkGn01FfX88zzzzD2LFjmThxIjfffDP9+vWjuroatVpNVVUVYLxYz549mzfffJO77roLW1tbNmzYwObNm6mqqsLa2hqtVktZWRk2NjasW7eOurq6i46hhYUFtra2LR7v0aNHk5WVRVJSEmvWrMHR0VHq2/gY+vj4cOzYMQICAjhz5gxLly5Fo9Fwxx13XNS3uroag8HAddddx/jx43nmmWf4+eef+cc//sFXX32FVqtFp9NRXV1NfX09kyZNwtvbm48//piZM2cCUF5ejsFgQK1WY2lpSXV1NWD0ytTX10ueT0dHR6mvSqVqcgwv7Ovg4EBlZSV6vR5LS0usra2prKyUjrder5eOYWt9W/rParVaKisrsbW1lYykC/s2/h+adGPqezn/2Qv72tnZodVqqaqqkra1efNmAPz9/XFzc+PPP/8EjE/EsrOzyc7OxsLCgqlTp7Jt2zYaGhqkOjJHjx4FYPDgwRQWFpKRkQHAjBkz2LlzJ1qtFk9PT4KCgjh06BBgPMeUl5eTmpoqjZF9+/ZRXV2Nm5sbYWFhkue5f//+1NbWSvVoTOeIyspKnJ2d6d+/P3v37gUgPDwcvV7P2bNnAZgwYQLHjx+XUr0OGTKE6OhowDih19LSktTUVDZv3szYsWM5c+YMxcXF2NnZMXLkSLZv3w5Ar169sLW1lSbljxo1inPnzlFQUIC1tTXjx49ny5YtAAQGBuLk5MSJEycAo6czPT2d3NxcVCoVkydPZsuWLRgMBvz8/PDw8JC8pUOHDiU3N5esrCyUSiXTpk1j+/bt1NfX4+3tjZ+fH0eOHAFg0KBBFBcXk56eLh3v6Oho6urq8PDwoFevXtJcyMjISCorKyUv79SpU9m/fz/V1dW4uroSHh7Ovn37AOjXrx9arZZz584BMGnSJI4ePUpFRQVOTk4MGDCA3bt3A39Vfk9ISACMWZpOnjxJaWmp5C033Tz07t0btVotPRUeM2YM8fHxFBUVYWtry+jRo/nkk0945JFHWLt2LcOHD5fC1UaOHElycjL5+flYWVkxceJE6T8bEBCAi4sLx48fB2DYsGFkZmaSk5ODpaUlU6ZMYevWrej1enx9ffHy8uLYsWMADBkyhPz8fDIzM1EoFEyfPp0dO3ag0+nw8vIiICBAylg3cOBASktLJc/89OnT2b17N7W1tbi7uzNmzBgOHDiAr68vHh4epKWlERsbS3FxMS4uLsTHx3Py5ElsbGxwd3cnPT0da2trIiIicHNzo6amBmdnZ6699lr+/PNPysvLcXR0ZNCgQezatQswhigrlUopTHns2LGcPn2akpISNBoNw4cPZ8eOHYDRE29tbc3p06cB4/n77NmzFBYWYmtry5gxY9i6dStgnDfr4ODAyZMnARgxYgSpqank5eVJ47knnyPi4uKk423Oc0RqaiqFhYVd+hwxduxYfvrpJw4ePEh1dTU+Pj7k5OQQERHB1KlTUSqV0n+tuXPEtm3bAAgODkaj0Zj1HFFQUEBJSUmbzxG9e/fmwIEDAERERFBdXU1ycjIAU6ZM4eDBg1RVVeHi4kK/fv2k/2zfvn2pr6+XildPnDiRmJgY2Zwj1Go1kyZNatdzRFunYgAoDJfxiKmwsJDCwsJW+wQFBTU7qS0rKws/Pz8OHjzYbNaN5ORkQkJCiImJYfDgwdLyWbNm4eTkxFdffdXs9urq6qQbSjDe1Pr7+0snJRO1tbWkpKQQHBzctkl3330HCxdeut/q1bBgwaX7tUJQUBBr1qxh5MiR0rJly5bh5eXFiy++yMqVK1mzZg2bNm0CYM2aNXzyySfSyfann37izTff5ODBg9TX1/PEE0/w448/UlJSgsFgwM3NrUmY2nPPPcdrr71GWloa/v7+zcpUV1eHp6cnx48fJyEhgaefflr6U4LxeLbn5MV169axfv16vvvuu0v2XbNmDf/973+lE2VjGh+rGTNmEBkZyblz5/jpp5+YOHEiy5YtY/78+U36bdmyhXvuuYfdu3cTEBDQJZ66Xkh76+NKZbisMdZNSUpKumg+m8A8xMTEMHToUI4dO8aQIUPMLU67UV9fT15eHtnZ2eTk5JCdnU1+fj56vf6ivmq1Gk9PT7y8vKRXDw8P6aFcZyPGh3zoyrqoq6vj+PHjHDhwQEpaYGVlxejRoxk5cmSXLLbclfUhd0xG34W2QXNclsfIzc0NNze3KxLKdFPt7e3d7PfBwcF4eXmxdetWyTDSarXs2rWLf/7zny2u18rKqmMGQAtyXnG/TmLVqlXs2bOHAwcO4OPjw+bNm5skrkhMTOTjjz9m7ty5PP744/zwww/NrsfKyopZs2axdu1a4uPjm4TRAU1qccycObPFLHHPPvsszz777CXl1uv10tO5S9HWOiALFy5k6dKlUia6lpg+fTre3t4tGt9dgZ5eG0VOmOuGU9BzsLS0xNfXF19fX2lZY2MpOzub3NxcCgoK0Gq1ZGRkSF4VMEZmuLi4NDGWPD09cXBw6PAkD2J8yIeuqIucnByOHj1KbGysFJFhZ2fHiBEjGDZsmFkSrbQXXVEf3ZEOyUp34MABDh48yKRJk3B0dOTIkSM8+uijXH/99QQEBEj9wsPDeeONN5g9ezYKhYJHHnmE119/ndDQUEJDQ3n99dextbVlYVs8N+3NuHHG7HNZWc3PM1IojN+PG9f5srVCRUUFVlZWODk5UVhYyNtvvy19p9frue2221i+fDnLli1j4MCB/PDDD8ybNw8weq5efPFFlixZAsDNN9/M8uXLSU9Pl1zpJmpqaqRB/Mcff1y2nGvXrmXEiBEEBARw7tw53nzzTaZNm9Zs3+joaEJCQvD39ycxMZFXX32VxYsXX3Ibc+fOxdPTk4kTJ16y7wsvvGCe/1k70VgfAvNy5syZFj2xAkFH0Zyx1NDQQFFREXl5eeTm5pKbm0teXh6VlZUUFRVRVFQkhb2AMfzWVJ/Jw8NDetVoNO1mMInxIR+6ii4qKio4deoUJ0+eJCcnR1ru5ubGiBEjGDRoUJfPLAtdRx/dnQ4xjKysrPj+++956aWXqKurIzAwkLvuuot//OMfTfolJCRIc0oA/vGPf1BTU8N9990nFXjdsmWLeVIYWlgYU3LfdJPRCGpsHJkuEO+9Z+wnI2699VZ+++03PDw88Pf3584775TiSN9++20sLCx4+OGHUSqVrFixgjlz5jBx4kScnZ0pKipqEs43bdo0brnlFnr16kWvXr3aVc7ExEQeffRRSkpKcHV1Ze7cuU3mimk0Gv744w/GjRvHsWPHWLRoEaWlpXh4eHDLLbdI869aw9bWVpqXdSlmzJhBWFjYRfWkBAKBoKtiYWGBh4cHHh4eREZGSssrKyslY8n0WlhYSG1t7UXeJTAaTI0NJZPx1J4Gk0DQmMrKShISEjh16hSpqalSiLuFhQX9+vUjKiqKgIAA8f8TtDuXNceoK9BSHOEVz39oro6Rv7/RKOpGdYwOHDjABx980KY5PoCUXEEgD+SgDzHHyEhlZSUajcbcYggwelJPnTpFRERElw6x6QxM3qX8/HwKCgqk1+Li4mbnLoHRYHJ1dcXV1RU3Nzfpvaura4tP8MX4kA9y0oXBYKCgoICEhAQSEhLIvCD5VUBAAJGRkfTv37/blqeQkz66Gx02x6hHMmcOzJoFe/ZATo5xTtG4cbLzFP1dRo0axahRo9rcv7a2Fjs7uw6USHA5CH3Ih/j4eFGPQibY2NigUCiEUdQGGnuXGlNfX9+iwVRbW0tWVhZZWVkXrc/R0bFZg+ns2bPNJmASdD7mPldVVFSQnJwstQvLTvj6+hIeHk5ERATOzs5mkrLzMLc+BEaEYdQWLCygDfNUehL19fXmFkHQCKEP+VBUVGRuEQTnSUtL4//+7//49NNPCQwMNLc4XRJLS0s8PT0vqjFYX19PcXExRUVFFBYWSnOWCgsLqampoaysjLKyMil9sInk5GQOHjyIs7Nzs00YsZ1HZ56rDAaDlFo9PT2d9PR0qdi8CUtLS4KCgggPDycsLOyST/a7G+LaIQ+EYSS4IkQWNHkh9CEfumuYR1ekqKiIzZs3U1RUJAyjdsbS0rJZDxMYa+9daCwVFRVRUlKChYUFJSUllJSUNLtea2trXFxccHZ2xtHRsUlzcHDA1tZWzCtpJzrqXKXX6ykoKJASfuTk5JCbmyvVuTOhUCjw9vaW5jEHBARgadlzb0vFtUMe9Nx/oOBvIeJg5YXQh3wYPXq0uUUQCMyKra0tAQEBTbLQgtFrUFZWRnl5OSUlJRQXF0tGUklJCZWVldTW1kopx5vD0tKyWYPJ0dERjUaDvb29FEIpaJ2/e65qaGiQ9FhUVCQZQ/n5+c1GMahUKnx9fQkICMDf3x9/f/8ePR/1QsS1Qx4Iw0hwRZgmsgnkgdCHfNi2bRszZswwtxgCgexQKBQcOnSIGTNmXGQ0gbF2YWlpqWQomcLxTK2yslKa89Ra2JGFhQV2dnbY29uj0Wgkg6nxe1tbW2xtbVGr1T3WiLrUucpgMFBdXU15eTnl5eWUlZVJRlBRURGlpaUtJuawsrLC09MTb29vvLy88Pb2xt3d3exJguSMuHbIA2EYCQQCgUAgMDtqtbrF8DwwzmuqqKhoYiyZbtjLy8upqKigurqahoYG6Wb+UlhYWEhGkqnZ2Ng0+WxtbS0Vk2/cuuJNvl6vp6amhpqaGvLz84mPj6empobq6mqqqqqk42Y6ng0NDa2uT6VSNUmuYTKGnJ2de6zBKejaCMNIcEVYWVmZWwRBI4Q+5ENwcLC5RRCcx9PTk7vvvvuixAEC8/F3xoelpaWUpKElGhoaqKyslFpFRUWTV9P76upq6uvraWhooKKi4qKMaG1BpVJdZCxZWlqiUqmwtLS86H3jz0qlEqVSiUKhkFpzn/V6/SVbQ0MDWq0WnU6HVqtt8b3JIDJRVFREUlJSq/uoUCjQaDQ4ODhgb2+Pi4tLE0NI1LJqP8S1Qx4Iw0hwRYjJ/vJC6EM+iPle8sHX15cXXngBHx8fc4siOE9Hjw8LCwtp7tGl0Ol0VFdXN9tMXpTq6mpqa2upq6uTmk6nk36v0+morKzs0H3qCExFe728vJp4x+zt7XFwcJCavb19l/SMdUXEtUMeCMNIBgQFBbFmzRpGjhwpLVu2bBleXl68+OKLHb79hIQEHn/8cQ4ePIhCoWDGjBl8+OGHzT6VS09Pp1+/ftJnUwzy0aNHGTp06EX9FQoFISEhnDt3TlqWmJhIWFgYM2bMYNOmTVK/UaNGsX//fqnfVVddxfz581myZEk77m33pKamBrVabW4xBEBsbKy4EZcJFRUVfPvtt9x7773Y29ubWxwB8hofKpWqzUZUY0wemgsNprq6Ourr66mvr0en00nvL/ys0+nQ6/UYDAaptfRZoVBgYWEheZhaamq1GrVajUqlavG9KUTQxsYGpVLJ5s2bxZwWGSGnsdGTEYaRgLKyMubNm8eqVauwtLRk6dKlPPHEE3z55ZcX9Q0ICKCyspKysjIcHR3ZuHEjDz74IEOGDGlx/UqlkkOHDklF/VatWkVoaOhF/eLj49myZQvTp09vv50TCAQ9lsTERJ566immTp3a6jlKILgcLCwssLGxETWXBIJuiIi/uQSJiRATc3FLTOxcOT788EN69eqFu7s7t956a5smlV6IwWBodvnw4cO59dZbcXR0xM7OjrvuuovDhw+3ui47OzvAaOQsXLiw1RjjBQsWsGrVKunzd999x4IFCy7q9+ijj/LSSy+1ZVcEF2DSh8D8NPb8CgSCpojxIR+ELuSF0Ic8EIZRKyQmQlgYDB16cQsL6zzjaPPmzbz55pv89ttvpKamUlVVxWOPPdZs37y8PO666y4CAwMZMmQIr7zyCgcOHGDDhg3ceuutbdre/v376d+/f6t9tFotFRUV/PLLLyxatKjVvvPmzePHH3+koaGBI0eO4Obm1uwkwyVLlpCVlcXWrVvbJKfgL7RarblFEJwnOTnZ3CIIBLJFjA/5IHQhL4Q+5IEwjFrBlKTm22/h2LG/2rffNv2+PZg2bRpOTk5SW7FihfTd999/z7Jly+jbty92dna8/vrrrFmzptn1HDx4kJkzZ3Lq1Cm++uorqqurWb58Ob///jvPPffcJeU4fvw4H3zwwSX76nQ6NmzYQGhoKBEREa32dXV1ZeDAgWzbtk3yMDWHSqXi2WefFV6jK8A0GVhgfvLz880tgkAgW8T4kA9CF/JC6EMeCMOoDfTtC0OG/NX69m3/bWzdupXS0lKpLV26VPouOzu7STG8wMBAqqqqKCsru2g911xzDfn5+dx555189NFHTJ06la1bt/Laa6/x888/typDSkoK1113HV9++eUlPUZKpZJVq1Zd0ltkYtGiRXzzzTds2LCBefPmtdhv6dKlZGZmsm3btjatV2BEZKWTDyJ1unxQqVS4ubmhUqnMLYrgPGJ8yAehC3kh9CEPxN1UF8DHx4f09HTpc3p6Ora2ts1m0vn2229JTExkyZIlDBw4kNdffx1XV1cmTZqEn59fi9vIzc1l2rRpPPfcc9xwww2XlKmqqoro6Ohm5wo1x6xZs9i4cSMRERG4u7u32E+lUvHMM88Ir9FlIjJuyYeJEyeaWwTBeSIjIykoKCAyMtLcogjOI8aHfBC6kBdCH/JAGEZdgLlz5/Lpp58SHx9PVVUVy5cvZ/78+c32veWWW3jnnXeYOXMm9957L9u3b6e0tJQzZ860aMSUlZUxY8YMbr31Vu6+++42ybRixQpGjx6Nv79/m/rb2tqydetWPvzww0v2Xbp0Kenp6Rw5cqRN6xbQrPdQYB42b95sbhEEjRD6kBdCH/JB6EJeCH3IA2EYtYG4uKYZ6eLiOnf7M2fO5Mknn2TmzJkEBgZiZWXFO++802zfKynE9tNPP3Hy5En+9a9/odFopGZi2bJlLFu2rMlv1q5d2+YwOhMjRowgJCTkkv3UajXPPPMMxcXFl7V+gUAgaExsbCyLFy8mNjbW3KIIBAKBoAugMLSUw7mLUl5ejqOjI2VlZTg4OEjLa2trSUlJITg4GGtr6zaty5SVriXOnoVmyvH0CGpqakQNBxkhB31cyRjrjsTFxdG3IyYiCi6bmJgYhg4dyrFjx0QdI5kgxod8ELqQF0IfHUdLtkFziAKvrRAaajR+mss+Z2/fc40iAEtL8deRE0If8sHFxcXcIggEskWMD/kgdCEvhD7kgQiluwShoU0z0plaTzaKAKqrq80tgqARQh/y4fjx4+YWQSCQLWJ8yAehC3kh9CEPhGEkEAgEAoFAIBAIejzCMBJcEXZ2duYWQdAIoQ/5MGzYMHOLIDhPaGgoP//8M6E93cUvI8T4kA9CF/JC6EMeCMNIcEVotVpziyBohNCHfMjMzDS3CILz2NvbExQUJOp8yQgxPuSD0IW8EPqQB8IwElwROp3O3CIIGiH0IR9ycnLMLYLgPFlZWbz22mtkZWWZWxTBecT4kA9CF/JC6EMeCMNIcEUoFApziyBohNCHfBAZAuVDXl4eP/zwA3l5eeYWRXAeMT7kg9CFvBD6kAfCMBJcEZfKAy/oXIQ+5MOUKVPMLYJAIFvE+JAPQhfyQuhDHgjDSHBFlJeXm1sEQSOEPuTD1q1bzS2CQCBbxPiQD0IX8kLoQx4Iw0gGBAUF4eDgQE1NjbSsvLwcGxsbwsPDO02Oxx57jF69emFvb09UVBS7d+9use+///1vwsPDsbe3p1+/fmzYsKHFvkuWLEGhULB3794my0ePHo1CoSA3N1fqZ2FhQVxcnNRnzZo1TJw48e/tWA/AYDCYWwTBefR6vblFEAhkixgf8kHoQl4IfcgDYRjJBC8vLzZu3Ch93rBhA/7+/p0qg6OjI1u2bKGsrIynnnqKG264gYqKimb7qtVq1q5dS1lZGZ988glLly4lKSmpxXWHhoayatUq6XNKSgpFRUXNyvDKK6/8/Z3pYajVanOLIDiPr6+vuUUQnMfV1ZU5c+bg6upqblEE5xHjQz4IXcgLoQ95IAyjS5CYCDExF7fExPbdzoIFC5oYDqtWrWLhwoVN+sTGxjJmzBicnJyIiori4MGDV7StlrwLL7zwAr1790apVDJ37lxsbGw4e/Zss32feOIJIiMjUSqVjB8/noiIiFarNs+ZM4eNGzdK2dNWr17NggULLup355138scffxAfH3/Rd6mpqVhbW/Pxxx/j4eGBv78/0dHRfPnll3h7exMQEMCuXbvacAS6H2LSpnzw8vIytwiC8wQGBvLpp58SGBhoblEE5xHjQz4IXcgLoQ95IAyjVkhMhLAwGDr04hYW1r7G0bRp04iJiaG4uJjc3FwSExMZP3689L1Wq+W6665j4cKFFBQU8MQTT3DttddSVlbW7Po+/vhjBg0aREBAAHfccQe//voru3fv5v777+fo0aOXlCc1NZXi4mJ69+7d7PfV1dXS+4qKCk6fPk2/fv1aXJ+TkxMjRoxg8+bNAHz33XcXGX4ALi4u3HfffS16jbRaLampqWRlZfHwww+zePFizpw5Q1paGv/4xz945JFHLrlv3ZHG+hCYl2PHjplbBMF5ampqWL9+jr/HWwAAKsZJREFUfZMwZYF5EeNDPghdyAuhD3kgDKNWMEWRffstHDv2V/v226bftweWlpbccMMNrF27ljVr1jB37lyUyr/Uc/DgQSwsLLj//vtRqVTMnz+f0NBQtmzZctG66urqSE1N5ddff+XYsWOMGjWKzz77jLfffptx48ZdsrqyTqfjtttu48knn8TR0fGSst9zzz1cf/319O3bt9V+CxcuZNWqVRw/fhwbGxvCwsKa7ffYY4/x22+/Nes1MhgMLF++HJVKxY033khWVhZPP/00arWaG2+8kdOnT4s4XYFAAEBcXBzLli1rMm9RIBAIBIKWEPE3baBvXxgypOO3s2jRIp5++mlqamr47LPPKC0tlb7Lzs4mICCgSf/AwECys7MvWo+VlRWzZ8/m1Vdfpbi4mKlTp/LVV19hZ2fHunXrOH36NP37929WBoPBwJIlS/Dw8ODFF19sUVZbW1sAnn76adLT09uUTeXaa6/loYcewtnZmUWLFrXYz9XVlfvuu49XX32Va6+99qJ9M6WmtrGxAcDd3V36rNPp0Gq1WFtbX1Ke7oRJHwLzM6QzThYCQRdFjA/5IHQhL4Q+5IHwGMmIUaNGkZWVRWVlJYMGDWrynY+PDxkZGU2Wpaen4+Pjc9F66urqePbZZ5k4cSILFizg0KFD9O3bl8DAQPbt23eRgdWYBx98kOzsbL799tsmHqsLqa+v56233uKXX37hl19+kYyU1rC2tmbGjBl8/vnn3Hzzza32ffzxx/n1119JSEi45HoFRn0I5EF+fr65RRAIZIsYH/JB6EJeCH3IA+ExkhkbNmxo1iAZOXIkOp2Ojz/+mLvuuosff/yRhIQEpk+fflFftVrNtm3bpPXMnj27Tdt+4YUX2LdvH7t27cLKyqrVvl9++SX/+c9/2LdvH87Ozm1aP8Arr7zC0qVL8fb2brWfq6sr9957Lx988AGRkZFtXn9PRavVtsk4FXQ8mZmZLXpkBYKejhgf8kHoQl4IfcgD4TFqA3FxTTPSdWS4+oABA4iIiLhouVqt5ueff+abb77B1dWVN998k40bNzY7B0ihULTq7WmJl19+mbi4OHx8fNBoNGg0GilT3p49e9BoNFLft956i5ycHMLDw6W+r7/++iW34efn1ySpRGs8/vjjaLXay94PgcCcKBQKc4sgOI9CoUClUgmdyAihC/kgdCEvhD7kgcLQzSpDlpeX4+joSFlZmTQXBaC2tpaUlBSCg4PbPP/ElJWuJc6ehdDQvyuxQNA9uJIxJhAIBAKBQNCRtGQbNIfwGLVCaKjR+Gmckc7UerpRVF5ebm4RBI0Q+pAPO3bsMLcIgkYIfcgLoQ/5IHQhL4Q+5IGYY3QJerLx0xrdzNHY5RH6kA+mIsYC8xMXF8fdd9/NL7/8cslyAoLOQYwP+SB0IS+EPuRBh3iMoqOjUSgUzbYjR460+LslS5Zc1H/kyJEdIaLgb6JSqcwtgqARQh/yQVQvlw81NTUkJSWJAq8yQowP+SB0IS+EPuRBh3iMRo8eTU5OTpNlzz33HNu2bSMqKqrV31511VWsWLFC+qxWqztCRMHfROhFXgh9yIfW0uELBD0dMT7kg9CFvBD6kAcd4jFSq9V4eXlJzdXVlY0bN3L77bdfMuuGlZVVk9+6uLh0hIiCv0lVVZW5RRA0QuhDPhw+fNjcIggEskWMD/kgdCEvhD7kQackX9i4cSOFhYUsWbLkkn2jo6Px8PAgLCyMu+6665IFr+rq6igvL2/SBAKBQCAQCAQCgeBy6JTkC19++SUzZszA39+/1X4zZ85k7ty5BAYGkpKSwnPPPcfkyZM5duxYiwVH33jjDV566aWLlm/btg07OzsmT57M4cOHqampwc3NjYaGBsrKygCklMK1tbUA2NvbU11dTUNDAxYWFtja2lJRUdFsX41GQ21tLfX19SiVSjQajWSUWVlZoVQqpbj21vqq1WosLS2prq4GwM7ODq1Wi06nQ6FQ4ODgIMl7YV9bW1vq6+vRarVS3/LycgwGAyqVCrVaLXkSGvcFcHR0pKKiAr1ef1FfGxsb9Ho9dXV1ADg4OFBZWYler8fS0hJra2v0ej1lZWUX9b2cY9ha38s5hhf2bXwMlUol9vb2LR7D5o636Ri2drxNx7Ctx7u5Y1hZWdnm423q29Ix1Ov1VFZWtvk/2xHHu6qqStrW5s2bAfD398fNzY0///wTgKioKLKzs8nOzsbCwoKpU6eybds2Ghoa8PHxwcfHh6NHjwIwePBgCgsLycjIAGDGjBns3LkTrVaLp6cnQUFBHDp0CDDW/yovLyc1NRWAadOmsW/fPqqrq3FzcyMsLIz9+/cD0L9/f2pra0lKSgKQzhGVlZU4OzvTv39/9u7dC0B4eDh6vZ6zZ88CMGHCBI4fPy6l/BwyZAjR0dEAhIaGYmlpSVVVFZs3b2bs2LGcOXOG4uJi7OzsGDlyJNu3bwegV69e2NracurUKQBGjRrFuXPnKCgowNramvHjx7NlyxYAAgMDcXJy4sSJEwAMHz6c9PR0cnNzUalUTJ48mS1btmAwGPDz88PDw4OYmBgAhg4dSm5uLllZWSiVSqZNm8b27dupr6/H29sbPz8/ad7noEGDKC4uJj09XTre0dHR1NXV4eHhQa9evTh48CAAkZGRVFZWkpKSAsDUqVPZv38/1dXVuLq6Eh4ezr59+wDo168fWq2Wc+fOATBp0iSOHj1KRUUFTk5ODBgwgN27dwPQp08fABISEgAYP348J0+epLS0FHt7e6Kioti5cycAvXv3Rq1Wc+bMGQDGjBlDfHw8RUVF2NraMnr0aJKTk3n88cdpaGggOzub2NhYwFgwOzk5mfz8fKysrJg4caL0nw0ICMDFxYXjx48DMGzYMDIzM8nJycHS0pIpU6awdetW9Ho9vr6+eHl5cezYMQCGDBlCfn4+mZmZKBQKpk+fzo4dO9DpdHh5eREQECA9FR44cCClpaWkpaUBMH36dHbv3k1tbS3u7u707t2bAwcOABAREUF1dTXJyckATJkyhYMHD1JVVYWLiwv9+vWT/rN9+/alvr6exMREACZOnEhMTIyUrnbQoEHs2rULgLCwMJRKJfHx8QCMHTuW06dPU1JSgkajYfjw4VKmrJCQEKytrTl9+jRgDJk/e/YshYWF2NraMmbMGLZu3QpAUFAQDg4OnDx5EoARI0aQmppKXl6edE7syeeIuPPFEM19jqiqqqKwsLBHnyO2bdsGQHBwMBqNxqznCBsbG0pKSnr8OUKtVjNp0qR2PUeY5G8Ll1XH6MUXX2zWCGnMkSNHmswjyszMJDAwkB9++IEbb7yxzYIB5OTkEBgYyJo1a5gzZ06zferq6qQbSjCmLfb392+XOkaClqmpqcHGxsbcYgjOIwd9iDFmJD4+nvDwcHOLITiP0Ie8EPqQD0IX8kLoo+PosDpGDzzwAHFxca22iIiIJr9ZsWIFrq6uXH/99Ze9I97e3gQGBkpWbXNYWVnh4ODQpAk6HtNTP4E8EPqQD6anewLzk5eXx7vvvkteXp65RRGcR4wP+SB0IS+EPuTBZRlGbm5uhIeHt9oaPyk2GAysWLGCW2+99YrSCRcVFZGRkYG3t/dl/7YjaOSYaleCgoIk97OJZcuW8eKLL3bMBjuIyspKxo4di6urK87OzkyZMqVV92VQUBC2trZoNBo0Gg3Lli1rsa9CoaB3795NliUmJqJQKLjqqqua9Bs9enSTfldddRUrV668sp0SCARdlqysLD7//HOysrLMLYpAIBAIugAdmnxhx44dpKSkcMcddzT7fXh4OD/++CNgvKl+4oknOHDgAKmpqURHR3Pdddfh5ubG7NmzO1LMNvHpp2Bvb3wV0KxnzsrKis8//5yCggKKioqYM2cOt912W6vr2bFjB5WVlVRWVvLJJ5+02lepVErx4gCrVq0itJkKvPHx8VLsdU9BeErlw/Tp080tgkAgW8T4kA9CF/JC6EMedKhh9OWXXzJ69OgWK44nJCRIk+ItLCyIjY1l1qxZhIWFcdtttxEWFsaBAwewt7fvSDEvyaefwrJl0Lev8bWzjaOVK1cyffp07rrrLmlyYVZWFvfffz+Ojo6MGDGC7OxsAPR6PXPmzMHDwwMXFxfmzp1LcXExYMz45+vrK31eu3Ytffr0uezihwaDQUoG0BiVSkXfvn1RKpUYDAaUSqU08bI9WLBgAatWrZI+f/fddyxYsOCifo8++ugl58J1N5rTh8A8mCYJCwSCixHjQz4IXcgLoQ950KGG0erVq6WsI81hMBikFN42NjZs3ryZ/Px8tFotaWlprFy58pKZ7Doak1H04IPw55/GV3MYRzt37uTqq6+muLgYPz8/xowZw4QJEygqKiIoKIi33npL6jtnzhxSUlJISUmhoqKCl19+GTBmHrnxxht54IEHKCgo4MEHH2TlypXNTtrPy8vjrrvuIjAwkCFDhvDKK69w4MABNmzYwK233oper29R1gEDBmBtbc0DDzzAU0891ep+3XDDDXh6ejJ79uxLxtfOmzePH3/8kYaGBo4cOYKbmxvBwcEX9VuyZAlZWVlS9pOeQGv6EHQupsx8AoHgYsT4kA9CF/JC6EMedEodo65KY6Po/fdBqTS+doRxNG3aNJycnKS2YsWKJt9HRkYye/ZsVCoVs2bNws7Ojnnz5mFpackNN9wgpTxUKpUsXrwYOzs7HB0defTRR6UUjQBvvvkmR44cYeLEidxyyy2MGjWqWXkOHjzIzJkzOXXqFF999RXV1dUsX76c33//neeeew5Ly5YzvZ88eZLy8nI++eSTFr2FYDScU1NTSUxMJCAggBtuuIHWkiS6uroycOBAtm3bxqpVq1i4cGGz/VQqFc8++2yP8hq1pg9B5+Lu7m5uEQTncXR0ZPz48Tg6OppbFMF5xPiQD0IX8kLoQx4Iw6gFLjSKFArjcoWiY4yjrVu3UlpaKrWlS5c2+d7Dw0N6b2Nj02QA2djYSPVz6uvreeSRRwgMDMTBwYGbbrqJoqIiqa+trS3z588nLi6Ohx56qEV5rrnmGvLz87nzzjv56KOPmDp1Klu3buW1117j559/vmQ6ZhsbG+68805uv/12SkpKmu0zevRorK2tcXBw4N133yUxMfGSoXeLFi3im2++YcOGDcybN6/FfkuXLiUzM1OqUdDd6cnpseXGhUlCBOYjJCSEX375hZCQEHOLIjiPGB/yQehCXgh9yANhGDVDXZ3R8BkwAN577y+jyIRCYVw+YICxX0dlq7sSVq1axZ49ezhw4ADl5eWsW7euiRcmMTGRjz/+mLlz5/L444+3uJ5vv/2WxMRElixZwsCBA3n99ddxdXVl0qRJ+Pn5tWlOi2kuUk5OziX7KhQKFBce6GaYNWsWGzduJCIiotWnKyqVimeeeabHeI3EHCP5YCq4JzA/Op2OP/74A51OZ25RBOcR40M+CF3IC6EPeSDib5rBygo+/NDoEXrkkaYeIwCDwbj85En45BNjf7lQUVGBlZUVTk5OFBYW8vbbb0vf6fV6brvtNpYvX86yZcsYOHAgP/zwQ7Oel1tuuQULCwvp87333tvke1PSjMacOHGCsrIyRo4ciU6n45VXXsHJyanZzHHp6elkZWURFRWFVqvl+eefJzAwkKCgoFb3z9bWlq1bt+Lm5napQ8HSpUt5/fXXqaysZP78+ZfsLxAIuhexsbHMnz+fY8eOMWTIEHOLIxAIBAKZIzxGLXDPPUaj58MP4eGHjcYQGF8ffti4/JNPjP3kxK233oqjoyMeHh6MGzeuSY2ft99+GwsLCx5++GFsbGxYsWIFDz74IPn5+Retp7FR1BzNJWzQ6XQ8/PDDuLq6EhAQwPHjx/n999+lGlbLli2TahVVVFRw99134+TkRFBQEOfOnePnn39Gqbz0X3LEiBFtCo1Rq9U888wzUha+7kxz+hCYhwuLXAsEgr8Q40M+CF3IC6EPeaAwtDbbvQtSXl6Oo6MjZWVlTWq71NbWkpKSQnBw8GXNx2g81+i994yeIrkaRZ1JbW2tmNciI+SgjysdY92NxMTEZr2kgs4nJiaGoUOHCo+RjBDjQz4IXcgLoY+OoyXboDmEx+gSNPYcDR4sjCITdXKaWCUQ+pARycnJ5hZBIJAtYnzIB6ELeSH0IQ/EHKM2YDKCHnxQGEUCgUAgEAgEAkF3RITSXQZ1dfJKtGBODAZDm7LICToHOehDhNIZqa+vF3WlZEJDQwNlZWU4Ojpect6koHMQ40M+CF3IC6GPjkOE0nUQwij6C5EeWl4IfciHgwcPmlsEwXksLCw4c+aMMIpkhBgf8kHoQl4IfcgDYRgJrgi9Xm9uEQSNEPqQD6ZiywLzk5iYyMMPP0xiYqK5RRGcR4wP+SB0IS+EPuSBMIwEV4Rw98oLoQ/54OLiYm4RBOepqKggJiaGiooKc4siOI8YH/JB6EJeCH3IA2EYCf6/vTsPiurM2gD+NDuyRZRV2UTBfUOMGE3UCC5Ro0mcSMyMRqUkKoUzsRLLcQY/S40bLpVKBFMMaGkCJaKjJhmREVwgMyFoVaLEHRNkKQKyCA6N0u/3B9KxQ3dLK02/0s+vyqL63nPvPd2HAx7u7dtPxZzfQyIj1kMeAwcONHUKRNJif8iDtZAL6yEHDkb0VPieFrmwHvI4f/68qVMgkhb7Qx6shVxYDzlwMCIiIiIiIrPHwcgAxvoMTX9/fzg7O+N///ufelldXR3s7e3Rv39/4xz0GRly6VZKSgqGDx8OJycn9OnTBwkJCTpjVSoV1qxZg169esHV1RWrVq2CrjvKp6SkQKFQYMOGDRrL16xZA4VCgdTUVI24xMREdUx5ebnJb2/dkXgpnTwGDBhg6hToER8fH6xfvx4+Pj6mToUeYX/Ig7WQC+shBw5G7ZSYCDg5tXw1Bk9PTxw7dkz9OCMjo8v8MlcqlUhISEB1dTWOHz+OuLg4nD17VmtsUlISvvrqK1y4cAE3btxAXl4e9u7dq3Pfffv2xRdffKF+LIRAWloaAgMDNeK6d++OTZs24cGDBx3zpIh0ePjwoalToEfc3Nwwf/58uLm5mToVeoT9IQ/WQi6shxw4GLVDYiIQHQ0MGNDy1RjDUWRkJA4ePKh+fPDgQbzzzjsaMQqFAnv27IGvry969uyJtLQ0nDhxAn369IG7uzvS0tLUsZ9//jn69esHJycnDB06FDk5OQBaPoRz4MCB+PLLLwEANTU16N27N06fPm1Qvo2NjTrP5Pze0qVLMWbMGFhZWWHQoEGYPHky8vPztcb+61//QnR0NDw8PODq6oqYmBjs379f574DAwPh5OSECxcuAADy8vLg4+OD3r17a8SNHj0aPj4+SE5O1roff39/xMfHIygoCM7Ozti1axe+++47DBw4EK6urti5c2e7nqupNDY2mjoFeoS3hpbH3bt3kZCQgLt375o6FXqE/SEP1kIurIccOBg9QetQFBMDXLzY8tUYw1F4eDguXLiAu3fvory8HNevX8fLL7/cJi43NxfXrl3Dnj17sGzZMhw+fBiXLl1CUlISVqxYgebmZgCAt7c3/v3vf6O2thYxMTGYN28elEol7OzssG/fPqxcuRJlZWWIjY3FrFmzMGnSJK157dmzB8OHD4evry8WL16MEydO4OzZs1i1ahW+//57g59nc3MzvvvuOwwaNEhnzOMDlxACly9f1rvP+fPnq88affHFF5g/f77WuLi4OL1njb7++mvk5+cjKysLH330EbZt24bc3FxkZ2djzZo1+PXXX5/09IhIIrdv38a2bdtw+/ZtU6dCRETPAQ5Gejw+FO3eDVhYtHw1xnBkZWWF2bNn49ChQ0hNTcXcuXNhYdG2PB9++CHs7OzwxhtvoKamBsuWLUO3bt0wc+ZM3Lt3D6WlpQCA1157Db6+vrCwsEBUVBQUCoX6rxGhoaFYvHgxJk+ejHPnzmHr1q1ac1Iqlbh9+zZOnDiBgoIChIWFYe/evdi+fTsmTpyI0NBQg5/n2rVr0atXL0yZMkXr+oiICCQmJqKsrAyVlZX49NNPcf/+fb37fPvtt3Ho0CE0NTXhn//8J9566y2tceHh4ejVqxdSUlK0ro+NjYWLiwtGjx4NT09P/OEPf0D37t0xbNgw+Pr64sqVKwY9187k5ORk6hTokQkTJpg6BSJpsT/kwVrIhfWQAwcjHX4/FLW+T1+hMN5w1HrmQ99ZD3d3dwCApaUlrK2tNa6dt7OzU39y8tGjRzFy5Ei88MILeOGFF1BRUYGqqip17KJFi1BYWIhFixbB0dFR67FsbW0xZ84cbNiwAcuXL4dKpcK+ffuQnp6OxsZGrWdyzp07B0dHRzg6OmLatGka6xISEpCRkYH09HSdNz5YsmQJpk2bhtGjRyM0NBTTp09Hr1699LxqgIeHB/r37481a9Zg1KhR6N69u85YfWeNWl9bALC3t9d4be3t7aX+VOonDY/UeVov6ySittgf8mAt5MJ6yIGDkRZKZcvgM3QosGvXb0NRK4WiZfnQoS1xHXW3urCwMJSUlKC+vh7Dhw9/6v0olUpERkZi8+bNqKqqQk1NDdzd3dWXqAkh8P7772P+/PnYvXs3SkpKdO5nzZo1mDBhAiIjI/Hf//4XAwYMgJ+fH7799lv4+vq22Wb8+PGor69HfX09vvnmG/XytLQ0bNy4ESdPnkTPnj115m5paYnNmzejuLgYRUVF8PDwwKhRo574nN955x3s2LGjzfuyfi8iIgJeXl7Yt2/fE/f5PGm9hJJMr66uztQpEEmL/SEP1kIurIccrEydgIxsbYFPPmk5I7RypeYZIwAQomX5Dz8ACQkt8R0lIyND6yV0hlAqlWhqalKf8di9e7fG+2Na7xD3zTffYN26dYiKisLXX3/dZj82NjbIyspS5zNnzhz1uvr6ep1nmn4vMzMTMTExyMrKgr+/v97Yqqoq3Lt3D35+figoKMDGjRuRnp7+xGPMnTsXHh4e7ToVHRcX98QB6nljaWlp6hToERcXF1OnQI84ODhg8ODBcHBwMHUq9Aj7Qx6shVxYDznwjJEOS5e2DD2ffALExrYMQ0DL19jYluUJCS1xHWno0KEYPHjwM+3D2dkZ27ZtQ3h4ODw9PVFVVYW+ffsCAIqKirB27VqkpKTAysoKf//733Hnzh384x//aLMfhUKhc0jr1q1bu/P5+OOPUV1djbFjx6ovs4uOjlavd3R0xLlz5wAAFRUVmDx5MhwcHPDuu+8iPj4eISEhTzxGt27dMHXq1HZ9ns+UKVMQFBTU7vyfB4bUg4zrWc72UscKDg5Gfn4+goODTZ0KPcL+kAdrIRfWQw4K0d57Lj8n6urq4OLigtraWjg7O6uXNzY2oqioCAEBAQZ9GObj7zXatavlTJGxhqLnSW1tLf+6IREZ6vG0PdbVnDx5UufNRajzsR5yYT3kwVrIhfUwHl2zgTa8lO4JWoef6GjgzJnfLp8z56GIiOh5cOHCBUydOhUFBQUYOXKkqdMhIiLJcTBqh9YhKCaGQ1Ercz4jICPWQx5d7TJNoo7E/pAHayEX1kMOHIzaaelSYOHCjr3RAhF1Pc968xSiroz9IQ/WQi6shxxYBQNwKPpNY2OjqVOgx7Ae8pD5g4CJTI39IQ/WQi6shxzMbjBSqVSmToGoS+pi93EhIiIiM2M2d6VTqVS4fv06LC0t4ebmBhsbGyh+/8mt1G7Nzc387ByJmLoeQgj8+uuvuH//Pvr162fW3xsNDQ383BxJNDY24tq1awgKCuL78CTB/pAHayEX1sN4eFc6LSwsLBAQEICysjKUlpaaOp3nnlKphC2vLZSGDPVQKBTo3bu3WQ9FAHD58mWMHj3a1GkQWm5K0tjYyKFIIuwPebAWcmE95GA2gxEA2NjYwNfXFw8fPkRzc7Op03munT9/HuPGjTN1GvSIDPWwtrY2+6EIAKqrq02dAj1SVFSE1atXIykpCQEBAaZOh8D+kAlrIRfWQw5mNRgBLX/Vtra2hrW1talTea7Z29vzr7ASYT3k4ejoaOoU6JHq6mpkZ2ejurqag5Ek2B/yYC3kwnrIwexuvkAdg6d75cJ6yIO1INKN/SEP1kIurIccOBjRUzl9+rSpU6DHsB7yYC2IdGN/yIO1kAvrIYcudyld60326urqTJxJ19bQ0MDXWCKshzxYC3nU19erv7ImcmB/yIO1kAvrYTytr2t7bsTd5W7XfefOHfj4+Jg6DSIiIiIikkRxcTF69+6tN6bLDUYqlQqlpaVwcnLi5xQZSV1dHXx8fFBcXPzE+8GT8bEe8mAt5MJ6yIX1kAdrIRfWw7iEELh37x68vb1hYaH/XURd7lI6CwuLJ06D1DGcnZ3ZwBJhPeTBWsiF9ZAL6yEP1kIurIfxuLi4tCuON18gIiIiIiKzx8GIiIiIiIjMHgcjMpitrS3i4uJga2tr6lQIrIdMWAu5sB5yYT3kwVrIhfWQR5e7+QIREREREZGheMaIiIiIiIjMHgcjIiIiIiIyexyMiIiIiIjI7HEwIiIiIiIis8fBiPTKycmBQqHQ+i8/P1/ndgsXLmwTP2bMmE7MvOvy9/dv89quXr1a7zZCCKxbtw7e3t6wt7fHhAkTcPny5U7KuOu6ffs2Fi9ejICAANjb2yMwMBBxcXFoamrSux37o+N89tlnCAgIgJ2dHUJCQnDu3Dm98WfOnEFISAjs7OzQp08fJCQkdFKmXdvHH3+M0NBQODk5wd3dHbNnz8bVq1f1bqPr98uVK1c6Keuuad26dW1eU09PT73bsC+MR9vvbIVCgeXLl2uNZ1+YlpWpEyC5jR07FmVlZRrL/va3vyErKwujRo3Su+3UqVORnJysfmxjY2OUHM3R+vXrERUVpX7s6OioN37r1q3YsWMHUlJSEBQUhA0bNiA8PBxXr16Fk5OTsdPtsq5cuQKVSoXExET07dsXly5dQlRUFBoaGrB9+3a927I/nl1aWhpWrlyJzz77DC+99BISExMxbdo0FBYWwtfXt018UVERpk+fjqioKBw4cAC5ublYtmwZ3Nzc8Oabb5rgGXQdZ86cwfLlyxEaGoqHDx/ir3/9KyIiIlBYWAgHBwe92169ehXOzs7qx25ubsZOt8sbNGgQsrKy1I8tLS11xrIvjCs/Px/Nzc3qx5cuXUJ4eDjmzp2rdzv2hYkIIgM0NTUJd3d3sX79er1xCxYsEK+//nrnJGVm/Pz8xM6dO9sdr1KphKenp9i8ebN6WWNjo3BxcREJCQlGyNC8bd26VQQEBOiNYX90jNGjR4vo6GiNZf379xerV6/WGv/hhx+K/v37ayxbunSpGDNmjNFyNFcVFRUCgDhz5ozOmOzsbAFAVFdXd15iZiAuLk4MGzas3fHsi84VGxsrAgMDhUql0rqefWFavJSODHLs2DFUVlZi4cKFT4zNycmBu7s7goKCEBUVhYqKCuMnaCa2bNmCHj16YPjw4di4caPeS7eKiopQXl6OiIgI9TJbW1u88soryMvL64x0zUptbS1cXV2fGMf+eDZNTU0oKCjQ+L4GgIiICJ3f199++22b+ClTpuD777/HgwcPjJarOaqtrQWAdvXCiBEj4OXlhVdffRXZ2dnGTs0sXL9+Hd7e3ggICMC8efNw69YtnbHsi87T1NSEAwcOYNGiRVAoFHpj2RemwcGIDJKUlIQpU6bAx8dHb9y0adNw8OBBnD59GvHx8cjPz8ekSZOgVCo7KdOuKzY2FqmpqcjOzsaKFSuwa9cuLFu2TGd8eXk5AMDDw0NjuYeHh3oddYybN2/ik08+QXR0tN449sezq6ysRHNzs0Hf1+Xl5VrjHz58iMrKSqPlam6EEPjLX/6CcePGYfDgwTrjvLy8sHfvXhw+fBgZGRkIDg7Gq6++irNnz3Zitl3Piy++iP379+PkyZP4/PPPUV5ejrFjx6KqqkprPPui8xw9ehQ1NTV6/7jMvjAxU5+yItOIi4sTAPT+y8/P19imuLhYWFhYiPT0dIOPV1paKqytrcXhw4c76il0KU9Tj1bp6ekCgKisrNS6Pjc3VwAQpaWlGsuXLFkipkyZ0uHPpSt4mnqUlJSIvn37isWLFxt8PPaH4UpKSgQAkZeXp7F8w4YNIjg4WOs2/fr1E5s2bdJYdv78eQFAlJWVGS1Xc7Ns2TLh5+cniouLDd52xowZYubMmUbIynzV19cLDw8PER8fr3U9+6LzREREiBkzZhi8Hfui8/DmC2ZqxYoVmDdvnt4Yf39/jcfJycno0aMHZs2aZfDxvLy84Ofnh+vXrxu8rTl4mnq0ar2b2Y0bN9CjR48261vvRlReXg4vLy/18oqKijZ/JaQWhtajtLQUEydORFhYGPbu3Wvw8dgfhuvZsycsLS3bnB3S933t6empNd7Kykpr75DhYmJicOzYMZw9exa9e/c2ePsxY8bgwIEDRsjMfDk4OGDIkCE6f76wLzrHzz//jKysLGRkZBi8Lfui83AwMlM9e/ZEz5492x0vhEBycjL+9Kc/wdra2uDjVVVVobi4WOM/5vQbQ+vxuIsXLwKAztc2ICAAnp6eOHXqFEaMGAGg5TrnM2fOYMuWLU+XcBdnSD1KSkowceJEhISEIDk5GRYWhl+hzP4wnI2NDUJCQnDq1CnMmTNHvfzUqVN4/fXXtW4TFhaG48ePayzLzMzEqFGjnurnGv1GCIGYmBgcOXIEOTk5CAgIeKr9XLx4kX3QwZRKJX766SeMHz9e63r2RedITk6Gu7s7XnvtNYO3ZV90IlOfsqLnQ1ZWlgAgCgsLta4PDg4WGRkZQggh7t27Jz744AORl5cnioqKRHZ2tggLCxO9evUSdXV1nZl2l5OXlyd27NghLl68KG7duiXS0tKEt7e3mDVrlkbc4/UQQojNmzcLFxcXkZGRIX788UcRGRkpvLy8WI9n1Hr53KRJk8SdO3dEWVmZ+t/j2B/GkZqaKqytrUVSUpIoLCwUK1euFA4ODuL27dtCCCFWr14t/vjHP6rjb926Jbp16yb+/Oc/i8LCQpGUlCSsra2f6vJg0vT+++8LFxcXkZOTo9EH9+/fV8f8vh47d+4UR44cEdeuXROXLl0Sq1evFgB4Sekz+uCDD0ROTo64deuW+M9//iNmzJghnJyc2Bcm1NzcLHx9fcVHH33UZh37Qi4cjKhdIiMjxdixY3WuByCSk5OFEELcv39fRERECDc3N2FtbS18fX3FggULxC+//NJJ2XZdBQUF4sUXXxQuLi7Czs5OBAcHi7i4ONHQ0KAR93g9hGi5ZXdcXJzw9PQUtra24uWXXxY//vhjJ2ff9SQnJ+t8D9Lj2B/G8+mnnwo/Pz9hY2MjRo4cqXF76AULFohXXnlFIz4nJ0eMGDFC2NjYCH9/f7Fnz55Ozrhr0tUHj/8c+n09tmzZIgIDA4WdnZ3o3r27GDdunPjqq686P/ku5u233xZeXl7C2tpaeHt7izfeeENcvnxZvZ590flOnjwpAIirV6+2Wce+kItCCCE6+SQVERERERGRVHi7biIiIiIiMnscjIiIiIiIyOxxMCIiIiIiIrPHwYiIiIiIiMweByMiIiIiIjJ7HIyIiIiIiMjscTAiIiIiIiKzx8GIiIiIiIhM5uzZs5g5cya8vb2hUChw9OhRg/chhMD27dsRFBQEW1tb+Pj4YNOmTQbtw8rgoxIREREREXWQhoYGDBs2DO+99x7efPPNp9pHbGwsMjMzsX37dgwZMgS1tbWorKw0aB8KIYR4qqMTERERERF1IIVCgSNHjmD27NnqZU1NTVi7di0OHjyImpoaDB48GFu2bMGECRMAAD/99BOGDh2KS5cuITg4+KmPzUvpiIiIiIhIWu+99x5yc3ORmpqKH374AXPnzsXUqVNx/fp1AMDx48fRp08fnDhxAgEBAfD398eSJUtw9+5dg47DwYiIiIiIiKR08+ZNfPnllzh06BDGjx+PwMBArFq1CuPGjUNycjIA4NatW/j5559x6NAh7N+/HykpKSgoKMBbb71l0LH4HiMiIiIiIpLShQsXIIRAUFCQxnKlUokePXoAAFQqFZRKJfbv36+OS0pKQkhICK5evdruy+s4GBERERERkZRUKhUsLS1RUFAAS0tLjXWOjo4AAC8vL1hZWWkMTwMGDAAA/PLLLxyMiIiIiIjo+TZixAg0NzejoqIC48eP1xrz0ksv4eHDh7h58yYCAwMBANeuXQMA+Pn5tftYvCsdERERERGZTH19PW7cuAGgZRDasWMHJk6cCFdXV/j6+uLdd99Fbm4u4uPjMWLECFRWVuL06dMYMmQIpk+fDpVKhdDQUDg6OmLXrl1QqVRYvnw5nJ2dkZmZ2e48OBgREREREZHJ5OTkYOLEiW2WL1iwACkpKXjw4AE2bNiA/fv3o6SkBD169EBYWBj+7//+D0OGDAEAlJaWIiYmBpmZmXBwcMC0adMQHx8PV1fXdufBwYiIiIiIiMweb9dNRERERERmj4MRERERERGZPQ5GRERERERk9jgYERERERGR2eNgREREREREZo+DERERERERmT0ORkREREREZPY4GBERERERkdnjYERERERERGaPgxEREREREZk9DkZERERERGT2OBgREREREZHZ+38H8CxYNY2qagAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 600x500 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Final Optimized Anchor (UC-based):\n",
+      "Design: {'D': 1.8913237564654963, 'L': 11.099208044881985, 'zlug': 7.3994720299213235}\n",
+      "Capacity Results: {'Hmax': 2680903.350073319, 'Vmax': 3516302.6906043873, 'Ha': 2186977.238360048, 'Va': 2635582.2104549985, 'zlug': 7.3994720299213235, 'z0': 1.75, 'UC': 0.4999999981738827, 'Weight pile': 248933.05364646754}\n",
+      "\n",
+      "Final Optimized Anchor:\n",
+      "Design: {'D': 1.8913237564654963, 'L': 11.099208044881985, 'zlug': 7.3994720299213235}\n",
+      "Capacity Results: {'Hmax': 2680903.350073319, 'Vmax': 3516302.6906043873, 'Ha': 2186977.238360048, 'Va': 2635582.2104549985, 'zlug': 7.3994720299213235, 'z0': 1.75, 'UC': 0.4999999981738827, 'Weight pile': 248933.05364646754}\n"
+     ]
+    }
+   ],
+   "source": [
+    "anchor.getSizeAnchor(\n",
+    "    geom = [anchor.dd['design']['L'], anchor.dd['design']['D']],\n",
+    "    geomKeys = ['L', 'D'],\n",
+    "    geomBounds = [(5.0, 15.0), (1.0, 4.0)],\n",
+    "    loads = None,\n",
+    "    lambdap_con = [3, 6],\n",
+    "    zlug_fix = False,\n",
+    "    safety_factor = {'SF_combined': 2},\n",
+    "    plot = True\n",
+    ")\n",
+    "\n",
+    "print('\\nFinal Optimized Anchor:')\n",
+    "print('Design:', anchor.dd['design'])\n",
+    "print('Capacity Results:', anchor.anchorCapacity)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c5fff6",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "490a71e1",
+   "metadata": {},
+   "source": [
+    "### Step 11: Optimized anchor material costs\n",
+    "We assess the cost of the optimized suction pile defined by the manufacturing cost (USD/kg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a439735f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'name': 'no_name', 'Anchors': True, 'Buoys': False, 'Connections': False, 'anchor_list': [{'name': 'suction', 'num': 1, 'frac': 1.0}], 'aprops': {'suction': {'matcost_m': 10.25, 'matcost_m2': 0.0, 'matcost_m3': 0.0, 'matcost_a': 0.0, 'matcost_a2': 0.0, 'matcost_a3': 0.0, 'matcost': 0.0, 'instcost_m': 0.0, 'instcost_m2': 0.0, 'instcost_m3': 0.0, 'instcost_a': 0.0, 'instcost_a2': 0.0, 'instcost_a3': 0.0, 'instcost': 0.0, 'decomcost_m': 0.0, 'decomcost_m2': 0.0, 'decomcost_m3': 0.0, 'decomcost_a': 0.0, 'decomcost_a2': 0.0, 'decomcost_a3': 0.0, 'decomcost': 0.0}}, 'buoy_cost': {'cost_b0': 0.0, 'cost_b1': 0.0, 'cost_b2': 0.0, 'cost_b3': 0.0}, 'connector_cost': {'cost_load0': 0.0, 'cost_load1': 0.0, 'cost_load2': 0.0, 'cost_load3': 0.0}, 'FOS': 0.0, 'info': {}}\n",
+      "Mass: 25375.44 kg\n",
+      "Material unit cost: 10.25 USD/kg\n",
+      "Material cost: 260098.25 USD [2024]\n"
+     ]
+    }
+   ],
+   "source": [
+    "anchor.getCostAnchor2()\n",
+    "\n",
+    "print(f\"Mass: {anchor.anchorCapacity['Weight pile']/9.81:.2f} kg\")\n",
+    "print(f\"Material unit cost: {anchor.cost['unit_cost']:.2f} USD/kg\")\n",
+    "print(f'Material cost: {anchor.cost[\"Material cost\"]:.2f} USD [2024]')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/05_Anchors/inputs/GulfOfMaine_bathymetry_100x100.txt b/examples/05_Anchors/inputs/GulfOfMaine_bathymetry_100x100.txt
new file mode 100644
index 00000000..22b0bc97
--- /dev/null
+++ b/examples/05_Anchors/inputs/GulfOfMaine_bathymetry_100x100.txt
@@ -0,0 +1,104 @@
+--- MoorPy Bathymetry Input File ---
+nGridX 100
+nGridY 100
+      -4420.52 -4345.53 -4270.54 -4195.55 -4120.57 -4045.58 -3970.59 -3895.60 -3820.61 -3745.62 -3670.63 -3595.64 -3520.66 -3445.67 -3370.68 -3295.69 -3220.70 -3145.71 -3070.72 -2995.74 -2920.75 -2845.76 -2770.77 -2695.78 -2620.79 -2545.80 -2470.81 -2395.83 -2320.84 -2245.85 -2170.86 -2095.87 -2020.88 -1945.89 -1870.90 -1795.92 -1720.93 -1645.94 -1570.95 -1495.96 -1420.97 -1345.98 -1270.99 -1196.01 -1121.02 -1046.03 -971.04 -896.05 -821.06 -746.07 -671.08 -596.10 -521.11 -446.12 -371.13 -296.14 -221.15 -146.16 -71.17 3.81 78.80 153.79 228.78 303.77 378.76 453.75 528.74 603.72 678.71 753.70 828.69 903.68 978.67 1053.66 1128.64 1203.63 1278.62 1353.61 1428.60 1503.59 1578.58 1653.57 1728.55 1803.54 1878.53 1953.52 2028.51 2103.50 2178.49 2253.48 2328.46 2403.45 2478.44 2553.43 2628.42 2703.41 2778.40 2853.39 2928.37 3003.36 
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+2648.84  166.702  167.306  167.911  168.515  169.119  169.671  170.095  170.519  170.943  171.367  171.510  171.446  171.382  171.317  171.253  170.668  170.000  169.331  168.662  168.095  168.127  168.159  168.190  168.222  168.423  168.847  169.271  169.695  170.119  170.285  170.350  170.414  170.478  170.543  170.363  170.183  170.003  169.823  169.643  169.463  169.284  169.104  168.924  168.744  168.565  168.385  168.205  168.026  167.818  167.606  167.394  167.182  166.995  166.963  166.930  166.898  166.866  166.847  166.847  166.847  166.847  166.847  166.847  166.847  166.847  166.847  166.847  166.880  166.912  166.944  166.977  166.949  166.769  166.589  166.410  166.230  166.171  166.204  166.236  166.268  166.301  166.459  166.639  166.818  166.998  167.143  167.079  167.014  166.949  166.885  166.681  166.289  165.897  165.505  165.113  164.698  164.274  163.849  163.425  163.001 
+2718.97  166.419  166.993  167.567  168.142  168.716  169.247  169.671  170.095  170.519  170.942  171.052  170.927  170.803  170.679  170.554  169.936  169.237  168.538  167.840  167.251  167.313  167.374  167.436  167.498  167.716  168.140  168.564  168.988  169.412  169.621  169.745  169.870  169.994  170.118  169.969  169.819  169.669  169.519  169.369  169.219  169.069  168.920  168.770  168.620  168.470  168.321  168.171  168.021  167.818  167.606  167.394  167.182  166.991  166.929  166.866  166.804  166.741  166.706  166.706  166.706  166.706  166.706  166.706  166.706  166.706  166.706  166.706  166.768  166.831  166.893  166.955  166.957  166.808  166.658  166.508  166.358  166.330  166.392  166.454  166.517  166.579  166.716  166.866  167.016  167.165  167.276  167.151  167.027  166.902  166.778  166.552  166.190  165.828  165.466  165.104  164.698  164.274  163.849  163.425  163.001 
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diff --git a/examples/05_Anchors/inputs/GulfOfMaine_soil_layered_100x100.txt b/examples/05_Anchors/inputs/GulfOfMaine_soil_layered_100x100.txt
new file mode 100644
index 00000000..50ba5b79
--- /dev/null
+++ b/examples/05_Anchors/inputs/GulfOfMaine_soil_layered_100x100.txt
@@ -0,0 +1,112 @@
+--- MoorPy Soil Input File ---
+nGridX 100
+nGridY 100
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+2578.71  pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+2648.84  pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+2718.97  pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+2789.10  pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+2859.23  pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_2    pro_2    pro_2    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+2929.37  pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+2999.50  pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0    pro_0    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+3069.63  pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0    pro_0    pro_0    pro_0    pro_0    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+--- SOIL TYPES ---
+Class 		 gamma 	  Su0 	  k	   alpha	phi	   UCS	   Em
+(name) 		(kN/m^3) (kPa) 	(kPa/m) (-)    (deg)  (MPa)   (MPa)
+pro_0         8.00   14.0    2.8    0.7     -       -       -
+pro_1         8.00   12.0    2.4    0.7     -       -       -
+pro_2         8.00   10.0    2.0    0.7     -       -       -
+pro_3         8.00    8.0    1.6    0.7     -       -       -
+pro_4         8.00    6.0    1.2    0.7     -       -       -
\ No newline at end of file
diff --git a/examples/05_Anchors/inputs/GulfOfMaine_soil_profiles.yaml b/examples/05_Anchors/inputs/GulfOfMaine_soil_profiles.yaml
new file mode 100644
index 00000000..d0308033
--- /dev/null
+++ b/examples/05_Anchors/inputs/GulfOfMaine_soil_profiles.yaml
@@ -0,0 +1,116 @@
+pro_0:
+  layers:
+    - soil_type: clay
+      top: 0
+      bottom: 12
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 10.0
+      Su_bot: 20.0
+    - soil_type: clay
+      top: 12
+      bottom: 22
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 15.0
+      Su_bot: 25.0
+    - soil_type: clay
+      top: 22
+      bottom: 30
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 55.0
+      Su_bot: 70.0
+pro_1:
+  layers:
+    - soil_type: clay
+      top: 0
+      bottom: 5
+      gamma_top: 8.2
+      gamma_bot: 8.2
+      Su_top: 12.0
+      Su_bot: 22.0
+    - soil_type: clay
+      top: 5
+      bottom: 15
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 22.0
+      Su_bot: 22.0
+    - soil_type: clay
+      top: 15
+      bottom: 30
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 22.0
+      Su_bot: 50.0
+pro_2:
+  layers:
+    - soil_type: clay
+      top: 0
+      bottom: 8
+      gamma_top: 8.4
+      gamma_bot: 8.4
+      Su_top: 14.0
+      Su_bot: 14.0
+    - soil_type: clay
+      top: 8
+      bottom: 18
+      gamma_top: 8.4
+      gamma_bot: 8.4
+      Su_top: 15.0
+      Su_bot: 25.0
+    - soil_type: clay
+      top: 18
+      bottom: 30
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 25.0
+      Su_bot: 25.0
+
+pro_3:
+  layers:
+    - soil_type: clay
+      top: 0
+      bottom: 15
+      gamma_top: 8.6
+      gamma_bot: 8.6
+      Su_top: 20.0
+      Su_bot: 26.0
+    - soil_type: clay
+      top: 15
+      bottom: 25
+      gamma_top: 8.6
+      gamma_bot: 8.6
+      Su_top: 20.0
+      Su_bot: 40.0
+    - soil_type: clay
+      top: 25
+      bottom: 30
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 40.0
+      Su_bot: 40.0
+pro_4:
+  layers:
+    - soil_type: clay
+      top: 0
+      bottom: 3
+      gamma_top: 8.8
+      gamma_bot: 8.8
+      Su_top: 10.0
+      Su_bot: 10.0
+    - soil_type: clay
+      top: 3
+      bottom: 10
+      gamma_top: 8.8
+      gamma_bot: 8.8
+      Su_top: 16.0
+      Su_bot: 40.0
+    - soil_type: clay
+      top: 10
+      bottom: 30
+      gamma_top: 8.8
+      gamma_bot: 8.8
+      Su_top: 40.0
+      Su_bot: 60.0
\ No newline at end of file
diff --git a/examples/Inputs/GulfOfMaine_bathymetry_100x100.txt b/examples/Inputs/GulfOfMaine_bathymetry_100x100.txt
new file mode 100644
index 00000000..22b0bc97
--- /dev/null
+++ b/examples/Inputs/GulfOfMaine_bathymetry_100x100.txt
@@ -0,0 +1,104 @@
+--- MoorPy Bathymetry Input File ---
+nGridX 100
+nGridY 100
+      -4420.52 -4345.53 -4270.54 -4195.55 -4120.57 -4045.58 -3970.59 -3895.60 -3820.61 -3745.62 -3670.63 -3595.64 -3520.66 -3445.67 -3370.68 -3295.69 -3220.70 -3145.71 -3070.72 -2995.74 -2920.75 -2845.76 -2770.77 -2695.78 -2620.79 -2545.80 -2470.81 -2395.83 -2320.84 -2245.85 -2170.86 -2095.87 -2020.88 -1945.89 -1870.90 -1795.92 -1720.93 -1645.94 -1570.95 -1495.96 -1420.97 -1345.98 -1270.99 -1196.01 -1121.02 -1046.03 -971.04 -896.05 -821.06 -746.07 -671.08 -596.10 -521.11 -446.12 -371.13 -296.14 -221.15 -146.16 -71.17 3.81 78.80 153.79 228.78 303.77 378.76 453.75 528.74 603.72 678.71 753.70 828.69 903.68 978.67 1053.66 1128.64 1203.63 1278.62 1353.61 1428.60 1503.59 1578.58 1653.57 1728.55 1803.54 1878.53 1953.52 2028.51 2103.50 2178.49 2253.48 2328.46 2403.45 2478.44 2553.43 2628.42 2703.41 2778.40 2853.39 2928.37 3003.36 
+-3873.36  176.051  176.899  177.747  178.595  179.442  179.494  178.011  176.528  175.045  173.562  172.871  172.659  172.447  172.235  172.023  172.577  173.212  173.848  174.483  175.080  175.503  175.927  176.350  176.773  176.902  176.690  176.479  176.267  176.056  175.370  174.523  173.676  172.829  172.010  172.221  172.432  172.643  172.854  173.000  173.002  173.004  173.006  173.007  173.256  173.677  174.098  174.519  174.939  174.812  174.602  174.391  174.181  174.003  174.004  174.006  174.007  174.008  174.100  174.310  174.521  174.732  174.942  174.551  173.917  173.282  172.648  172.013  172.426  172.847  173.268  173.689  174.225  175.069  175.914  176.758  177.602  177.745  177.323  176.901  176.479  176.057  175.281  174.435  173.589  172.743  171.946  171.523  171.101  170.678  170.255  169.916  169.705  169.494  169.283  169.072  169.291  169.714  170.137  170.561  170.984 
+-3803.23  176.470  177.228  177.985  178.743  179.501  179.525  178.132  176.739  175.346  173.953  173.276  173.035  172.793  172.551  172.309  172.832  173.438  174.043  174.649  175.215  175.609  176.002  176.396  176.789  176.916  176.734  176.553  176.371  176.190  175.534  174.717  173.900  173.083  172.291  172.443  172.594  172.745  172.895  173.028  173.120  173.211  173.303  173.395  173.592  173.863  174.134  174.405  174.676  174.607  174.487  174.367  174.246  174.153  174.215  174.276  174.337  174.398  174.498  174.649  174.799  174.950  175.101  174.735  174.161  173.586  173.012  172.437  172.702  172.973  173.244  173.515  173.893  174.558  175.222  175.887  176.551  176.663  176.331  175.999  175.667  175.335  174.649  173.893  173.137  172.381  171.670  171.307  170.945  170.582  170.219  169.940  169.789  169.638  169.486  169.335  169.553  169.947  170.340  170.734  171.127 
+-3733.10  176.888  177.556  178.224  178.892  179.560  179.556  178.253  176.950  175.647  174.344  173.682  173.410  173.138  172.867  172.595  173.087  173.663  174.239  174.814  175.351  175.715  176.078  176.442  176.805  176.930  176.778  176.627  176.475  176.324  175.698  174.911  174.124  173.337  172.573  172.664  172.755  172.846  172.937  173.056  173.237  173.419  173.600  173.782  173.928  174.049  174.170  174.292  174.413  174.402  174.372  174.342  174.311  174.304  174.425  174.546  174.667  174.788  174.896  174.987  175.078  175.169  175.260  174.919  174.405  173.890  173.375  172.861  172.977  173.098  173.220  173.341  173.562  174.046  174.531  175.015  175.500  175.582  175.340  175.097  174.855  174.613  174.017  173.350  172.684  172.018  171.394  171.091  170.788  170.486  170.183  169.964  169.873  169.781  169.690  169.599  169.816  170.179  170.543  170.906  171.270 
+-3662.97  177.307  177.885  178.463  179.041  179.619  179.588  178.374  177.161  175.948  174.735  174.088  173.786  173.484  173.182  172.881  173.343  173.889  174.434  174.980  175.487  175.820  176.154  176.488  176.821  176.944  176.822  176.700  176.579  176.457  175.862  175.105  174.348  173.591  172.854  172.885  172.916  172.947  172.978  173.083  173.355  173.626  173.897  174.169  174.264  174.235  174.207  174.178  174.150  174.198  174.257  174.317  174.376  174.454  174.635  174.816  174.997  175.178  175.295  175.326  175.356  175.387  175.418  175.103  174.649  174.194  173.739  173.284  173.253  173.224  173.195  173.167  173.230  173.534  173.839  174.144  174.449  174.500  174.348  174.196  174.044  173.891  173.385  172.808  172.232  171.656  171.118  170.875  170.632  170.389  170.146  169.988  169.956  169.925  169.894  169.862  170.079  170.412  170.745  171.079  171.412 
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diff --git a/examples/Inputs/GulfOfMaine_soil_layered_100x100.txt b/examples/Inputs/GulfOfMaine_soil_layered_100x100.txt
new file mode 100644
index 00000000..50ba5b79
--- /dev/null
+++ b/examples/Inputs/GulfOfMaine_soil_layered_100x100.txt
@@ -0,0 +1,112 @@
+--- MoorPy Soil Input File ---
+nGridX 100
+nGridY 100
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+2929.37  pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_2    pro_2    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+2999.50  pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0    pro_0    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+3069.63  pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0    pro_0    pro_0    pro_0    pro_0    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_1    pro_0    pro_0    pro_0    pro_0    pro_0   
+--- SOIL TYPES ---
+Class 		 gamma 	  Su0 	  k	   alpha	phi	   UCS	   Em
+(name) 		(kN/m^3) (kPa) 	(kPa/m) (-)    (deg)  (MPa)   (MPa)
+pro_0         8.00   14.0    2.8    0.7     -       -       -
+pro_1         8.00   12.0    2.4    0.7     -       -       -
+pro_2         8.00   10.0    2.0    0.7     -       -       -
+pro_3         8.00    8.0    1.6    0.7     -       -       -
+pro_4         8.00    6.0    1.2    0.7     -       -       -
\ No newline at end of file
diff --git a/examples/Inputs/GulfOfMaine_soil_profiles.yaml b/examples/Inputs/GulfOfMaine_soil_profiles.yaml
new file mode 100644
index 00000000..71b43efd
--- /dev/null
+++ b/examples/Inputs/GulfOfMaine_soil_profiles.yaml
@@ -0,0 +1,67 @@
+pro_0:
+  layers:
+    - soil_type: clay
+      top: 0
+      bottom: 10
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 10.0
+      Su_bot: 20.0
+    - soil_type: clay
+      top: 10
+      bottom: 20
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 15.0
+      Su_bot: 25.0
+pro_1:
+  layers:
+    - soil_type: clay
+      top: 0
+      bottom: 20
+      gamma_top: 8.2
+      gamma_bot: 8.2
+      Su_top: 12.0
+      Su_bot: 22.0
+pro_2:
+  layers:
+    - soil_type: clay
+      top: 0
+      bottom: 5
+      gamma_top: 8.4
+      gamma_bot: 8.4
+      Su_top: 14.0
+      Su_bot: 24.0
+    - soil_type: clay
+      top: 5
+      bottom: 20
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 15.0
+      Su_bot: 25.0
+
+pro_3:
+  layers:
+    - soil_type: clay
+      top: 0
+      bottom: 5
+      gamma_top: 8.6
+      gamma_bot: 8.6
+      Su_top: 16.0
+      Su_bot: 26.0
+    - soil_type: clay
+      top: 5
+      bottom: 20
+      gamma_top: 8.0
+      gamma_bot: 8.0
+      Su_top: 25.0
+      Su_bot: 35.0
+pro_4:
+  layers:
+    - soil_type: clay
+      top: 0
+      bottom: 20
+      gamma_top: 8.8
+      gamma_bot: 8.8
+      Su_top: 18.0
+      Su_bot: 28.0
\ No newline at end of file
diff --git a/examples/Inputs/GulfOfMaine_soil_uniform_100x100.txt b/examples/Inputs/GulfOfMaine_soil_uniform_100x100.txt
new file mode 100644
index 00000000..14d5d515
--- /dev/null
+++ b/examples/Inputs/GulfOfMaine_soil_uniform_100x100.txt
@@ -0,0 +1,112 @@
+--- MoorPy Soil Input File ---
+nGridX 100
+nGridY 100
+        -4420.52 -4345.53 -4270.54 -4195.55 -4120.57 -4045.58 -3970.59 -3895.60 -3820.61 -3745.62 -3670.63 -3595.64 -3520.66 -3445.67 -3370.68 -3295.69 -3220.70 -3145.71 -3070.72 -2995.74 -2920.75 -2845.76 -2770.77 -2695.78 -2620.79 -2545.80 -2470.81 -2395.83 -2320.84 -2245.85 -2170.86 -2095.87 -2020.88 -1945.89 -1870.90 -1795.92 -1720.93 -1645.94 -1570.95 -1495.96 -1420.97 -1345.98 -1270.99 -1196.01 -1121.02 -1046.03 -971.04 -896.05 -821.06 -746.07 -671.08 -596.10 -521.11 -446.12 -371.13 -296.14 -221.15 -146.16 -71.17 3.81 78.80 153.79 228.78 303.77 378.76 453.75 528.74 603.72 678.71 753.70 828.69 903.68 978.67 1053.66 1128.64 1203.63 1278.62 1353.61 1428.60 1503.59 1578.58 1653.57 1728.55 1803.54 1878.53 1953.52 2028.51 2103.50 2178.49 2253.48 2328.46 2403.45 2478.44 2553.43 2628.42 2703.41 2778.40 2853.39 2928.37 3003.36
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+3069.63  mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_0    mud_0    mud_0    mud_0    mud_0    mud_0    mud_0    mud_0    mud_0    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_1    mud_0    mud_0    mud_0    mud_0    mud_0   
+--- SOIL TYPES ---
+Class 		 gamma 	  Su0 	  k	   alpha	phi	   UCS	   Em
+(name) 		(kN/m^3) (kPa) 	(kPa/m) (-)    (deg)  (MPa)   (MPa)
+mud_0         8.00   14.0    2.8    0.7     -       -       -
+mud_1         8.00   12.0    2.4    0.7     -       -       -
+mud_2         8.00   10.0    2.0    0.7     -       -       -
+mud_3         8.00    8.0    1.6    0.7     -       -       -
+mud_4         8.00    6.0    1.2    0.7     -       -       -
\ No newline at end of file
diff --git a/examples/Inputs/OntologySample200m.yaml b/examples/Inputs/OntologySample200m.yaml
index a59e0a1c..decfdfa4 100644
--- a/examples/Inputs/OntologySample200m.yaml
+++ b/examples/Inputs/OntologySample200m.yaml
@@ -43,23 +43,42 @@ site:
       
       soil_types:  
         mud_soft:
-          Su0 : [2.39]  # [kPa]
-          k : [1.41]    # [kPa/m]
-          gamma : [10]  # [kN/m^3]
-          depth: [0]    # [m]
+            layers: 
+            - soil_type: clay
+              top: 0
+              bottom: 50
+              gamma_top: 10.0
+              gamma_bot: 10.0
+              Su_top: 2.39
+              Su_bot: 59.39
         mud_firm:
-          Su0 : [23.94] # [kPa]
-          k : [2.67]    # [kPa/m]
-          gamma: [15]
-          depth: [0]    # [m]
-        mud_hard:
-          Su0: [50]
-          k: [1.0]
-          gamma: [9.5]
-          depth: [0]  
+            layers:
+            - soil_type: clay            
+              top: 0
+              bottom: 50
+              gamma_top: 10.0
+              gamma_bot: 50.0
+              Su_top: 23.4
+              Su_bot: 157.44
+        mud_hard: 
+            layers:
+            - soil_type: clay            
+              top: 0
+              bottom: 50
+              gamma_top: 8.5
+              gamma_bot: 8.5
+              Su_top: 50.0
+              Su_bot: 100.00  
         rock:
-          UCS: [5] # [MPa]
-          Em: [7] # [MPa]
+            layers:
+            - soil_type: rock
+              top: 0
+              bottom: 50
+              UCS_top: 5.0
+              UCS_bot: 5.0
+              Em_top: 7.0
+              Em_bot: 7.0
+
     
     metocean:
         extremes:  # extreme values for specified return periods (in years)
@@ -297,9 +316,10 @@ mooring_connector_types:
 anchor_types:
 
     drag-embedment1:
-        type   :  DEA   # type of anchor
-        A      :    10 # net area of anchor's fluke [m^2]
-        zlug   :    20 # embedded depth of padeye [m]
+        type   :  DEA  # type of anchor
+        B      :     5 # net area of anchor's fluke [m^2]
+        L      :     2
+        zlug   :    10 # embedded depth of padeye [m]
     suction_pile1:
         type   :  suction_pile
         L      :     16.4  # length of pile [m]
diff --git a/examples/Inputs/OntologySample200m_1turb.yaml b/examples/Inputs/OntologySample200m_1turb.yaml
index c55aad76..3d17274d 100644
--- a/examples/Inputs/OntologySample200m_1turb.yaml
+++ b/examples/Inputs/OntologySample200m_1turb.yaml
@@ -1295,7 +1295,7 @@ anchor_types:
         zlug   :    10 # embedded depth of padeye [m]
 
     suction1:
-        type   :  suction_pile
+        type   :  suction
         L      :     16.4  # length of pile [m]
         D      :     5.45  # diameter of pile [m]
         zlug   :     9.32  # embedded depth of padeye [m]
diff --git a/examples/Inputs/OntologySample200mbis_1turb.yaml b/examples/Inputs/OntologySample200mbis_1turb.yaml
new file mode 100644
index 00000000..386fa96e
--- /dev/null
+++ b/examples/Inputs/OntologySample200mbis_1turb.yaml
@@ -0,0 +1,1323 @@
+type: draft/example of floating array ontology under construction
+name: 
+comments: 
+# Site condition information
+site:
+    general:
+        water_depth : 200        # [m]      uniform water depth
+        rho_water   : 1025.0     # [kg/m^3] water density
+        rho_air     : 1.225      # [kg/m^3] air density
+        mu_air      : 1.81e-05   #          air dynamic viscosity
+        #...
+        
+    boundaries:  # project or lease area boundary, via file or vertex list
+        file:   # filename of x-y vertex coordinates [m]
+        x_y:    # list of polygon vertices in order [m]
+          - [-3000, -3000]
+          - [-3000, 3000]
+          - [3000, 3000]
+          - [3000, -3000]
+    
+    bathymetry:
+        file:   './bathymetry200m_sample.txt'
+    
+    seabed:
+      x : [-10901,    0,    10000]
+      y : [-10900,     0,  10000 ]
+    
+      type_array:
+        - [mud_soft ,  mud_firm ,   mud_soft]  
+        - [mud_soft ,  mud_firm ,   mud_soft]  
+        - [mud_soft ,  mud_firm ,   mud_soft]
+      
+      soil_types:  
+        mud_soft:
+            layers: 
+            - soil_type: clay
+              top: 0
+              bottom: 50
+              gamma_top: 10.0
+              gamma_bot: 10.0
+              Su_top: 2.39
+              Su_bot: 59.39
+        mud_firm:
+            layers:
+            - soil_type: clay            
+              top: 0
+              bottom: 50
+              gamma_top: 10.0
+              gamma_bot: 50.0
+              Su_top: 23.4
+              Su_bot: 157.44
+        sand_dense:
+            layers:
+            - soil_type: sand
+              top: 0
+              bottom: 20
+              gamma_top: 9.0
+              gamma_bot: 9.0
+              phi_top: 28
+              phi_bot: 33
+              Dr_top: 50
+              Dr_bot: 55
+  
+    metocean:
+        extremes:  # extreme values for specified return periods (in years)
+            keys :   [ Hs  , Tp  , WindSpeed, TI, Shear, Gamma, CurrentSpeed ]
+            data :
+                1:   [   1  ,2     ,3     ]
+                10:  [ 1    ,  2   , 3    ]
+                50:  [  1   ,  2   ,  3   ]
+                500: [  1   ,  2   ,   3  ]
+                
+        probabalistic_bins:
+            keys : [ prob , Hs  , Tp, WindSpeed, TI, Shear, Gamma, CurrentSpeed, WindDir, WaveDir, CurrentDir  ]
+            data :
+                -  [ 0.010  ,  1 ,  1 ]
+                -  [ 0.006  , 1  ,  1 ]
+                -  [ 0.005  , 1  ,  1 ]
+                
+        time_series :
+            file: 'metocean_timeseries.csv'
+            
+    resource :
+        file: 'windresource'
+        
+    RAFT_cases:
+        keys : [wind_speed, wind_heading, turbulence, turbine_status, yaw_misalign, wave_spectrum, wave_period, wave_height, wave_heading  ]
+        data :  #   m/s        deg    % or e.g. IIB_NTM    string            deg         string          (s)         (m)         (deg)
+            -  [     0,         0,            0,          operating,          0,        JONSWAP,         12,         6,           0        ]
+            # -  [    16,         0,            IIB_NTM,    operating,          0,        JONSWAP,         12,         6,           30       ]
+            # -  [    10.59,         0,            0.05,    operating,          0,        JONSWAP,         15.75,         11.86,         0       ]
+
+    RAFT_settings:   
+        min_freq     :  0.001    #  [Hz]       lowest frequency to consider, also the frequency bin width     
+        max_freq     :  0.20    #  [Hz]       highest frequency to consider
+        XiStart      :   0      # sets initial amplitude of each DOF for all frequencies
+        nIter        :   4      # sets how many iterations to perform in Model.solveDynamics()
+# ----- Array-level inputs -----
+
+# Wind turbine array layout
+array:
+    keys : [ID, topsideID, platformID, mooringID,   x_location,     y_location,   heading_adjust]
+    data : #    ID#        ID#        ID#          [m]             [m]           [deg]
+        -  [fowt0,     1,         1,         ms1,          -1600,         -1600,          180   ]    # 2 array, shared moorings
+        # -  [FOWT3,     1,         1,         ms3,          1600,           -1600,            0   ]
+        # -  [FOWT4,     1,         2,         ms4,          -1600,          0,            0   ]
+        # -  [FOWT5,     1,         1,         ms5,          0,              0,            45   ]
+        # -  [FOWT6,     1,         1,         ms10,          1600,              0,            0   ]
+        # -  [FOWT7,     1,         1,         ms6,          -1600,           1600,            0   ]
+        # -  [FOWT8,     1,         1,         ms6,          0,              1600,            0   ]
+        # -  [FOWT9,     1,         1,         ms6,          1600,           1600,            0   ] 
+
+# ----- turbines and platforms -----
+
+topsides:
+        
+  - type          :     turbine
+    mRNA          :     991000        #  [kg]       RNA mass 
+    IxRNA         :          0        #  [kg-m2]    RNA moment of inertia about local x axis (assumed to be identical to rotor axis for now, as approx) [kg-m^2]
+    IrRNA         :          0        #  [kg-m2]    RNA moment of inertia about local y or z axes [kg-m^2]
+    xCG_RNA       :          0        #  [m]        x location of RNA center of mass [m] (Actual is ~= -0.27 m)
+    hHub          :        150.0      #  [m]        hub height above water line [m]
+    Fthrust       :       1500.0E3    #  [N]        temporary thrust force to use
+    
+    I_drivetrain: 318628138.0   # full rotor + drivetrain inertia as felt on the high-speed shaft
+    
+    nBlades     : 3          # number of blades
+    Zhub        : 150.0      # hub height [m]
+    Rhub        : 3.97       # hub radius [m]
+    precone     : 4.0        # [deg]
+    shaft_tilt  : 6.0        # [deg]
+    overhang    : -12.0313    # [m]
+    aeroMod     : 1          # 0 aerodynamics off; 1 aerodynamics on
+
+    
+    blade: 
+        precurveTip : -3.9999999999999964  # 
+        presweepTip : 0.0  # 
+        Rtip        : 120.96999999936446         # rotor radius
+
+        #    r    chord   theta  precurve  presweep  
+        geometry: 
+          - [     8.004,      5.228,     15.474,      0.035,      0.000 ]
+          - [    12.039,      5.321,     14.692,      0.084,      0.000 ]
+          - [    16.073,      5.458,     13.330,      0.139,      0.000 ]
+          - [    20.108,      5.602,     11.644,      0.192,      0.000 ]
+          - [    24.142,      5.718,      9.927,      0.232,      0.000 ]
+          - [    28.177,      5.767,      8.438,      0.250,      0.000 ]
+          - [    32.211,      5.713,      7.301,      0.250,      0.000 ]
+          - [    36.246,      5.536,      6.232,      0.246,      0.000 ]
+          - [    40.280,      5.291,      5.230,      0.240,      0.000 ]
+          - [    44.315,      5.035,      4.348,      0.233,      0.000 ]
+          - [    48.349,      4.815,      3.606,      0.218,      0.000 ]
+          - [    52.384,      4.623,      2.978,      0.178,      0.000 ]
+          - [    56.418,      4.432,      2.423,      0.100,      0.000 ]
+          - [    60.453,      4.245,      1.924,      0.000,      0.000 ]
+          - [    64.487,      4.065,      1.467,     -0.112,      0.000 ]
+          - [    68.522,      3.896,      1.056,     -0.244,      0.000 ]
+          - [    72.556,      3.735,      0.692,     -0.415,      0.000 ]
+          - [    76.591,      3.579,      0.355,     -0.620,      0.000 ]
+          - [    80.625,      3.425,      0.019,     -0.846,      0.000 ]
+          - [    84.660,      3.268,     -0.358,     -1.080,      0.000 ]
+          - [    88.694,      3.112,     -0.834,     -1.330,      0.000 ]
+          - [    92.729,      2.957,     -1.374,     -1.602,      0.000 ]
+          - [    96.763,      2.800,     -1.848,     -1.895,      0.000 ]
+          - [   100.798,      2.637,     -2.136,     -2.202,      0.000 ]
+          - [   104.832,      2.464,     -2.172,     -2.523,      0.000 ]
+          - [   108.867,      2.283,     -2.108,     -2.864,      0.000 ]
+          - [   112.901,      2.096,     -1.953,     -3.224,      0.000 ]
+          - [   116.936,      1.902,     -1.662,     -3.605,      0.000 ]
+        #    station(rel)      airfoil name 
+        airfoils: 
+          - [   0.00000, circular ]
+          - [   0.02000, circular ]
+          - [   0.15000, SNL-FFA-W3-500 ]
+          - [   0.24517, FFA-W3-360 ]
+          - [   0.32884, FFA-W3-330blend ]
+          - [   0.43918, FFA-W3-301 ]
+          - [   0.53767, FFA-W3-270blend ]
+          - [   0.63821, FFA-W3-241 ]
+          - [   0.77174, FFA-W3-211 ]
+          - [   1.00000, FFA-W3-211 ]
+
+
+    airfoils: 
+      - name               : circular  # 
+        relative_thickness : 1.0  # 
+        data:  #  alpha    c_l    c_d     c_m   
+          - [ -179.9087,    0.00010,    0.35000,   -0.00010 ] 
+          - [  179.9087,    0.00010,    0.35000,   -0.00010 ] 
+      - name               : SNL-FFA-W3-500  # 
+        relative_thickness : 0.5  # 
+        data:  #  alpha    c_l    c_d     c_m   
+          - [ -179.9660,    0.00000,    0.08440,    0.00000 ] 
+          - [ -170.0000,    0.44190,    0.08440,    0.31250 ] 
+          - [ -160.0002,    0.88370,    0.12680,    0.28310 ] 
+          - [ -149.9998,    0.96740,    0.29270,    0.26320 ] 
+          - [ -139.9999,    0.78010,    0.49700,    0.20480 ] 
+          - [ -130.0001,    0.62930,    0.71610,    0.19320 ] 
+          - [ -120.0003,    0.47850,    0.92460,    0.20080 ] 
+          - [ -109.9999,    0.31890,    1.09850,    0.21360 ] 
+          - [ -100.0000,    0.15530,    1.21820,    0.22210 ] 
+          - [  -90.0002,    0.00000,    1.27070,    0.21980 ] 
+          - [  -79.9998,   -0.15530,    1.21820,    0.19600 ] 
+          - [  -70.0000,   -0.31890,    1.09850,    0.16350 ] 
+          - [  -60.0001,   -0.47840,    0.92460,    0.12850 ] 
+          - [  -49.9997,   -0.62930,    0.71610,    0.09650 ] 
+          - [  -39.9999,   -0.78010,    0.49700,    0.07160 ] 
+          - [  -30.0001,   -0.96740,    0.29270,    0.05220 ] 
+          - [  -20.0002,   -1.02810,    0.14990,   -0.00630 ] 
+          - [  -19.7499,   -1.02430,    0.14720,   -0.00890 ] 
+          - [  -19.2502,   -1.00520,    0.14470,   -0.00990 ] 
+          - [  -18.9999,   -0.99710,    0.14330,   -0.01050 ] 
+          - [  -18.7500,   -1.00520,    0.14030,   -0.01100 ] 
+          - [  -18.5002,   -0.99950,    0.13860,   -0.01160 ] 
+          - [  -18.2499,   -0.99080,    0.13730,   -0.01200 ] 
+          - [  -18.0000,   -0.98150,    0.13600,   -0.01260 ] 
+          - [  -17.4998,   -0.97640,    0.13220,   -0.01350 ] 
+          - [  -17.2500,   -0.97050,    0.13060,   -0.01390 ] 
+          - [  -17.0002,   -0.96550,    0.12900,   -0.01430 ] 
+          - [  -16.7498,   -0.96620,    0.12680,   -0.01470 ] 
+          - [  -16.5000,   -0.95440,    0.12580,   -0.01510 ] 
+          - [  -16.2502,   -0.94440,    0.12460,   -0.01550 ] 
+          - [  -15.9998,   -0.94050,    0.12290,   -0.01580 ] 
+          - [  -15.7500,   -0.94330,    0.12060,   -0.01610 ] 
+          - [  -15.5002,   -0.93300,    0.11950,   -0.01640 ] 
+          - [  -15.2498,   -0.92110,    0.11850,   -0.01680 ] 
+          - [  -14.7502,   -0.91580,    0.11500,   -0.01730 ] 
+          - [  -14.4998,   -0.90700,    0.11380,   -0.01750 ] 
+          - [  -14.2500,   -0.89590,    0.11270,   -0.01780 ] 
+          - [  -14.0002,   -0.89260,    0.11100,   -0.01810 ] 
+          - [  -13.7498,   -0.88080,    0.11000,   -0.01840 ] 
+          - [  -13.5000,   -0.87220,    0.10890,   -0.01860 ] 
+          - [  -13.2502,   -0.86600,    0.10750,   -0.01880 ] 
+          - [  -12.9998,   -0.86260,    0.10590,   -0.01880 ] 
+          - [  -12.7500,   -0.84890,    0.10510,   -0.01920 ] 
+          - [  -12.5002,   -0.83630,    0.10420,   -0.01940 ] 
+          - [  -12.2498,   -0.83630,    0.10230,   -0.01940 ] 
+          - [  -12.0000,   -0.82710,    0.10130,   -0.01960 ] 
+          - [  -11.7502,   -0.81410,    0.10040,   -0.01980 ] 
+          - [  -11.4998,   -0.80040,    0.09970,   -0.02000 ] 
+          - [  -11.0002,   -0.78900,    0.09710,   -0.01990 ] 
+          - [  -10.7498,   -0.78620,    0.09560,   -0.01960 ] 
+          - [  -10.5000,   -0.77470,    0.09480,   -0.01940 ] 
+          - [  -10.2502,   -0.77010,    0.09400,   -0.01840 ] 
+          - [   -9.9998,   -0.76740,    0.09250,   -0.01830 ] 
+          - [   -9.7500,   -0.75060,    0.09170,   -0.01920 ] 
+          - [   -9.5002,   -0.72900,    0.09120,   -0.02050 ] 
+          - [   -9.2498,   -0.70950,    0.09020,   -0.02240 ] 
+          - [   -9.0000,   -0.68550,    0.08950,   -0.02470 ] 
+          - [   -8.7502,   -0.65900,    0.08910,   -0.02670 ] 
+          - [   -8.4998,   -0.63190,    0.08870,   -0.02870 ] 
+          - [   -8.2500,   -0.60190,    0.08790,   -0.03200 ] 
+          - [   -8.0002,   -0.57180,    0.08750,   -0.03450 ] 
+          - [   -7.7498,   -0.54240,    0.08730,   -0.03670 ] 
+          - [   -7.5000,   -0.50980,    0.08680,   -0.03990 ] 
+          - [   -7.2502,   -0.47670,    0.08640,   -0.04300 ] 
+          - [   -6.9998,   -0.44540,    0.08620,   -0.04530 ] 
+          - [   -6.7500,   -0.41420,    0.08600,   -0.04760 ] 
+          - [   -6.5002,   -0.37910,    0.08560,   -0.05100 ] 
+          - [   -6.2498,   -0.34600,    0.08530,   -0.05380 ] 
+          - [   -6.0000,   -0.31440,    0.08520,   -0.05600 ] 
+          - [   -5.7502,   -0.28170,    0.08500,   -0.05860 ] 
+          - [   -5.4998,   -0.24610,    0.08470,   -0.06190 ] 
+          - [   -5.2500,   -0.21330,    0.08460,   -0.06440 ] 
+          - [   -5.0002,   -0.18270,    0.08450,   -0.06630 ] 
+          - [   -4.7498,   -0.14940,    0.08430,   -0.06880 ] 
+          - [   -4.5000,   -0.11580,    0.08420,   -0.07150 ] 
+          - [   -4.2502,   -0.08370,    0.08400,   -0.07370 ] 
+          - [   -3.9998,   -0.05290,    0.08400,   -0.07560 ] 
+          - [   -3.7500,   -0.02250,    0.08390,   -0.07740 ] 
+          - [   -3.5002,    0.00890,    0.08380,   -0.07930 ] 
+          - [   -3.2498,    0.03920,    0.08380,   -0.08110 ] 
+          - [   -3.0000,    0.06860,    0.08380,   -0.08260 ] 
+          - [   -2.7502,    0.09740,    0.08380,   -0.08380 ] 
+          - [   -2.4998,    0.12600,    0.08380,   -0.08520 ] 
+          - [   -2.2500,    0.15550,    0.08380,   -0.08670 ] 
+          - [   -2.0002,    0.18530,    0.08380,   -0.08830 ] 
+          - [   -1.7498,    0.21460,    0.08370,   -0.08970 ] 
+          - [   -1.5000,    0.24300,    0.08370,   -0.09100 ] 
+          - [   -1.2502,    0.27130,    0.08380,   -0.09210 ] 
+          - [   -0.9998,    0.30060,    0.08380,   -0.09360 ] 
+          - [   -0.7500,    0.32950,    0.08380,   -0.09490 ] 
+          - [   -0.5002,    0.35780,    0.08380,   -0.09610 ] 
+          - [   -0.2498,    0.38570,    0.08380,   -0.09720 ] 
+          - [    0.0000,    0.41350,    0.08380,   -0.09830 ] 
+          - [    0.2298,    0.44250,    0.08390,   -0.09950 ] 
+          - [    0.4698,    0.47150,    0.08390,   -0.10080 ] 
+          - [    0.7002,    0.50030,    0.08390,   -0.10190 ] 
+          - [    0.9402,    0.52860,    0.08400,   -0.10290 ] 
+          - [    1.1700,    0.55670,    0.08400,   -0.10400 ] 
+          - [    1.3997,    0.58500,    0.08410,   -0.10500 ] 
+          - [    1.6398,    0.61350,    0.08410,   -0.10610 ] 
+          - [    1.8701,    0.64170,    0.08420,   -0.10720 ] 
+          - [    2.1102,    0.66970,    0.08420,   -0.10820 ] 
+          - [    2.3400,    0.69750,    0.08430,   -0.10910 ] 
+          - [    2.5697,    0.72510,    0.08430,   -0.11000 ] 
+          - [    2.8098,    0.75280,    0.08440,   -0.11090 ] 
+          - [    3.0401,    0.78070,    0.08450,   -0.11190 ] 
+          - [    3.2802,    0.80830,    0.08460,   -0.11280 ] 
+          - [    3.5099,    0.83580,    0.08460,   -0.11370 ] 
+          - [    3.7403,    0.86310,    0.08470,   -0.11460 ] 
+          - [    3.9798,    0.89020,    0.08470,   -0.11530 ] 
+          - [    4.2101,    0.91730,    0.08480,   -0.11610 ] 
+          - [    4.4502,    0.94440,    0.08490,   -0.11700 ] 
+          - [    4.6799,    0.97130,    0.08500,   -0.11780 ] 
+          - [    4.9102,    0.99810,    0.08510,   -0.11850 ] 
+          - [    5.1497,    1.02490,    0.08520,   -0.11920 ] 
+          - [    5.3801,    1.05150,    0.08530,   -0.11990 ] 
+          - [    5.6201,    1.07790,    0.08530,   -0.12060 ] 
+          - [    5.8499,    1.10410,    0.08540,   -0.12120 ] 
+          - [    6.0802,    1.13020,    0.08560,   -0.12180 ] 
+          - [    6.3197,    1.15600,    0.08570,   -0.12240 ] 
+          - [    6.5501,    1.18180,    0.08580,   -0.12300 ] 
+          - [    6.7901,    1.20760,    0.08590,   -0.12350 ] 
+          - [    7.0199,    1.23340,    0.08600,   -0.12400 ] 
+          - [    7.2502,    1.25890,    0.08610,   -0.12450 ] 
+          - [    7.4903,    1.28410,    0.08620,   -0.12500 ] 
+          - [    7.7200,    1.30880,    0.08640,   -0.12540 ] 
+          - [    7.9601,    1.33310,    0.08650,   -0.12570 ] 
+          - [    8.1899,    1.35700,    0.08670,   -0.12590 ] 
+          - [    8.4202,    1.38100,    0.08690,   -0.12620 ] 
+          - [    8.6603,    1.40540,    0.08700,   -0.12650 ] 
+          - [    8.8900,    1.42950,    0.08710,   -0.12670 ] 
+          - [    9.1198,    1.45310,    0.08730,   -0.12700 ] 
+          - [    9.8801,    1.51540,    0.08790,   -0.12650 ] 
+          - [   10.6398,    1.57490,    0.08860,   -0.12560 ] 
+          - [   11.4001,    1.61510,    0.08950,   -0.12140 ] 
+          - [   12.1501,    1.64430,    0.09120,   -0.11630 ] 
+          - [   12.9099,    1.68240,    0.09300,   -0.11330 ] 
+          - [   13.6702,    1.71460,    0.09540,   -0.11070 ] 
+          - [   14.4202,    1.73620,    0.09890,   -0.10800 ] 
+          - [   15.1799,    1.76270,    0.10240,   -0.10630 ] 
+          - [   15.9403,    1.77060,    0.10760,   -0.10420 ] 
+          - [   16.6903,    1.76390,    0.11440,   -0.10250 ] 
+          - [   17.4500,    1.76040,    0.12110,   -0.10130 ] 
+          - [   18.2097,    1.72510,    0.13100,   -0.10010 ] 
+          - [   18.9701,    1.70350,    0.13990,   -0.09980 ] 
+          - [   19.7201,    1.67840,    0.14920,   -0.10010 ] 
+          - [   20.4798,    1.65050,    0.15910,   -0.10160 ] 
+          - [   21.2401,    1.62270,    0.16910,   -0.10360 ] 
+          - [   21.9901,    1.60670,    0.17780,   -0.10640 ] 
+          - [   22.7499,    1.59720,    0.18580,   -0.10990 ] 
+          - [   23.5102,    1.58920,    0.19370,   -0.11360 ] 
+          - [   24.2602,    1.58150,    0.20140,   -0.11800 ] 
+          - [   25.0199,    1.55630,    0.21350,   -0.12490 ] 
+          - [   25.7802,    1.52720,    0.22670,   -0.13250 ] 
+          - [   26.5302,    1.49820,    0.23990,   -0.14000 ] 
+          - [   27.2900,    1.46910,    0.25310,   -0.14760 ] 
+          - [   28.0497,    1.44010,    0.26630,   -0.15510 ] 
+          - [   28.8100,    1.41100,    0.27950,   -0.16270 ] 
+          - [   29.5600,    1.38200,    0.29270,   -0.17030 ] 
+          - [   30.3198,    1.36220,    0.30780,   -0.17400 ] 
+          - [   31.0801,    1.34240,    0.32300,   -0.17770 ] 
+          - [   31.8301,    1.32250,    0.33810,   -0.18150 ] 
+          - [   32.5898,    1.30270,    0.35320,   -0.18520 ] 
+          - [   33.3502,    1.28290,    0.36840,   -0.18890 ] 
+          - [   34.1002,    1.26310,    0.38350,   -0.19260 ] 
+          - [   34.8599,    1.24330,    0.39870,   -0.19640 ] 
+          - [   35.6202,    1.22340,    0.41380,   -0.20010 ] 
+          - [   36.3800,    1.20360,    0.42890,   -0.20390 ] 
+          - [   37.1300,    1.18380,    0.44410,   -0.20760 ] 
+          - [   37.8903,    1.16400,    0.45920,   -0.21130 ] 
+          - [   38.6500,    1.14420,    0.47430,   -0.21500 ] 
+          - [   39.4000,    1.12430,    0.48950,   -0.21880 ] 
+          - [   40.1598,    1.10640,    0.50520,   -0.22180 ] 
+          - [   40.9201,    1.09050,    0.52140,   -0.22420 ] 
+          - [   41.6701,    1.07450,    0.53760,   -0.22660 ] 
+          - [   42.4298,    1.05860,    0.55380,   -0.22890 ] 
+          - [   43.1901,    1.04260,    0.57010,   -0.23130 ] 
+          - [   43.9401,    1.02670,    0.58630,   -0.23370 ] 
+          - [   44.6999,    1.01070,    0.60250,   -0.23610 ] 
+          - [   45.4602,    0.99480,    0.61880,   -0.23840 ] 
+          - [   46.2199,    0.97880,    0.63500,   -0.24080 ] 
+          - [   46.9699,    0.96280,    0.65120,   -0.24320 ] 
+          - [   47.7302,    0.94690,    0.66750,   -0.24550 ] 
+          - [   48.4900,    0.93090,    0.68370,   -0.24790 ] 
+          - [   49.2400,    0.91500,    0.69990,   -0.25030 ] 
+          - [   49.9997,    0.89900,    0.71610,   -0.25270 ] 
+          - [   60.0001,    0.68360,    0.92460,   -0.28330 ] 
+          - [   70.0000,    0.45560,    1.09850,   -0.31560 ] 
+          - [   79.9998,    0.22190,    1.21820,   -0.34820 ] 
+          - [   90.0002,    0.00000,    1.27070,   -0.37730 ] 
+          - [  100.0000,   -0.15530,    1.21820,   -0.38770 ] 
+          - [  109.9999,   -0.31890,    1.09850,   -0.38650 ] 
+          - [  120.0003,   -0.47840,    0.92460,   -0.38060 ] 
+          - [  130.0001,   -0.62930,    0.71610,   -0.38030 ] 
+          - [  139.9999,   -0.78010,    0.49700,   -0.40320 ] 
+          - [  149.9998,   -0.96740,    0.29270,   -0.48540 ] 
+          - [  160.0002,   -0.88370,    0.12680,   -0.53250 ] 
+          - [  170.0000,   -0.44180,    0.08440,   -0.39060 ] 
+          - [  179.9660,    0.00000,    0.08440,    0.00000 ] 
+      - name               : FFA-W3-211  # 
+        relative_thickness : 0.211  # 
+        data:  #  alpha    c_l    c_d     c_m   
+          - [ -179.9087,    0.00000,    0.02464,    0.00000 ] 
+          - [ -177.7143,    0.05403,    0.02534,    0.09143 ] 
+          - [ -175.4286,    0.10805,    0.02742,    0.18286 ] 
+          - [ -173.1429,    0.16208,    0.03088,    0.27429 ] 
+          - [ -170.8572,    0.21610,    0.03570,    0.36571 ] 
+          - [ -168.5716,    0.27013,    0.05599,    0.39192 ] 
+          - [ -166.2857,    0.32415,    0.08143,    0.37898 ] 
+          - [ -164.0000,    0.37818,    0.11112,    0.36605 ] 
+          - [ -161.7145,    0.43220,    0.14485,    0.35312 ] 
+          - [ -159.4284,    0.48623,    0.18242,    0.34768 ] 
+          - [ -157.1428,    0.54025,    0.22359,    0.36471 ] 
+          - [ -154.8573,    0.59428,    0.26810,    0.38175 ] 
+          - [ -152.5714,    0.64830,    0.31566,    0.39878 ] 
+          - [ -150.2857,    0.70233,    0.36597,    0.41581 ] 
+          - [ -148.0000,    0.75635,    0.41871,    0.41955 ] 
+          - [ -143.8571,    0.73188,    0.51941,    0.42287 ] 
+          - [ -139.7143,    0.70655,    0.62488,    0.42632 ] 
+          - [ -135.5714,    0.67760,    0.73293,    0.43163 ] 
+          - [ -131.4286,    0.64333,    0.84130,    0.43694 ] 
+          - [ -127.2857,    0.60277,    0.94773,    0.44389 ] 
+          - [ -123.1429,    0.55550,    1.05001,    0.45171 ] 
+          - [ -119.0000,    0.50156,    1.14600,    0.45897 ] 
+          - [ -114.8571,    0.44131,    1.23371,    0.46448 ] 
+          - [ -110.7143,    0.37542,    1.31129,    0.46998 ] 
+          - [ -106.5714,    0.30482,    1.37714,    0.47096 ] 
+          - [ -102.4286,    0.23063,    1.42988,    0.47101 ] 
+          - [  -98.2857,    0.15413,    1.46842,    0.46824 ] 
+          - [  -94.1429,    0.07675,    1.49196,    0.46149 ] 
+          - [  -90.0000,    0.00000,    1.50000,    0.45474 ] 
+          - [  -85.8571,   -0.07675,    1.49196,    0.44026 ] 
+          - [  -81.7143,   -0.15413,    1.46842,    0.42578 ] 
+          - [  -77.5714,   -0.23063,    1.42988,    0.40821 ] 
+          - [  -73.4286,   -0.30482,    1.37714,    0.38846 ] 
+          - [  -69.2857,   -0.37542,    1.31129,    0.36815 ] 
+          - [  -65.1429,   -0.44131,    1.23371,    0.34519 ] 
+          - [  -61.0000,   -0.50156,    1.14600,    0.32223 ] 
+          - [  -56.8571,   -0.55550,    1.05001,    0.29864 ] 
+          - [  -52.7143,   -0.60277,    0.94773,    0.27486 ] 
+          - [  -48.5714,   -0.64333,    0.84130,    0.25128 ] 
+          - [  -44.4286,   -0.67760,    0.73293,    0.22810 ] 
+          - [  -40.2857,   -0.70655,    0.62488,    0.20491 ] 
+          - [  -36.1429,   -0.73188,    0.51941,    0.15416 ] 
+          - [  -32.0000,   -0.75635,    0.41871,    0.10137 ] 
+          - [  -28.0000,   -0.85636,    0.28691,    0.06527 ] 
+          - [  -24.0000,   -1.18292,    0.13960,    0.01647 ] 
+          - [  -20.0000,   -1.23596,    0.08345,   -0.00352 ] 
+          - [  -18.0000,   -1.22536,    0.06509,   -0.00672 ] 
+          - [  -16.0000,   -1.20476,    0.04888,   -0.00881 ] 
+          - [  -14.0000,   -1.18332,    0.03417,   -0.01101 ] 
+          - [  -12.0000,   -1.10093,    0.02132,   -0.02269 ] 
+          - [  -10.0000,   -0.88209,    0.01386,   -0.04397 ] 
+          - [   -8.0000,   -0.62981,    0.01075,   -0.05756 ] 
+          - [   -6.0000,   -0.37670,    0.00882,   -0.06747 ] 
+          - [   -4.0000,   -0.12177,    0.00702,   -0.07680 ] 
+          - [   -2.0000,    0.12810,    0.00663,   -0.08283 ] 
+          - [   -1.0000,    0.25192,    0.00664,   -0.08534 ] 
+          - [    0.0000,    0.37535,    0.00670,   -0.08777 ] 
+          - [    1.0000,    0.49828,    0.00681,   -0.09011 ] 
+          - [    2.0000,    0.62052,    0.00698,   -0.09234 ] 
+          - [    3.0000,    0.74200,    0.00720,   -0.09447 ] 
+          - [    4.0000,    0.86238,    0.00751,   -0.09646 ] 
+          - [    5.0000,    0.98114,    0.00796,   -0.09828 ] 
+          - [    6.0000,    1.09662,    0.00872,   -0.09977 ] 
+          - [    7.0000,    1.20904,    0.00968,   -0.10095 ] 
+          - [    8.0000,    1.31680,    0.01097,   -0.10163 ] 
+          - [    9.0000,    1.42209,    0.01227,   -0.10207 ] 
+          - [   10.0000,    1.52361,    0.01369,   -0.10213 ] 
+          - [   11.0000,    1.61988,    0.01529,   -0.10174 ] 
+          - [   12.0000,    1.70937,    0.01717,   -0.10087 ] 
+          - [   13.0000,    1.78681,    0.01974,   -0.09936 ] 
+          - [   14.0000,    1.84290,    0.02368,   -0.09720 ] 
+          - [   15.0000,    1.85313,    0.03094,   -0.09410 ] 
+          - [   16.0000,    1.80951,    0.04303,   -0.09144 ] 
+          - [   18.0000,    1.66033,    0.07730,   -0.09242 ] 
+          - [   20.0000,    1.56152,    0.11202,   -0.09871 ] 
+          - [   24.0000,    1.43327,    0.18408,   -0.11770 ] 
+          - [   28.0000,    1.29062,    0.27589,   -0.14566 ] 
+          - [   32.0000,    1.08050,    0.41871,   -0.18266 ] 
+          - [   36.1429,    1.04554,    0.51941,   -0.20913 ] 
+          - [   40.2857,    1.00936,    0.62488,   -0.23534 ] 
+          - [   44.4286,    0.96801,    0.73293,   -0.25784 ] 
+          - [   48.5714,    0.91904,    0.84130,   -0.28035 ] 
+          - [   52.7143,    0.86109,    0.94773,   -0.30163 ] 
+          - [   56.8571,    0.79357,    1.05001,   -0.32226 ] 
+          - [   61.0000,    0.71651,    1.14600,   -0.34247 ] 
+          - [   65.1429,    0.63044,    1.23371,   -0.36135 ] 
+          - [   69.2857,    0.53632,    1.31129,   -0.38024 ] 
+          - [   73.4286,    0.43546,    1.37714,   -0.39704 ] 
+          - [   77.5714,    0.32947,    1.42988,   -0.41341 ] 
+          - [   81.7143,    0.22019,    1.46842,   -0.42844 ] 
+          - [   85.8571,    0.10965,    1.49196,   -0.44159 ] 
+          - [   90.0000,    0.00000,    1.50000,   -0.45474 ] 
+          - [   94.1429,   -0.07675,    1.49196,   -0.46149 ] 
+          - [   98.2857,   -0.15413,    1.46842,   -0.46824 ] 
+          - [  102.4286,   -0.23063,    1.42988,   -0.47101 ] 
+          - [  106.5714,   -0.30482,    1.37714,   -0.47096 ] 
+          - [  110.7143,   -0.37542,    1.31129,   -0.46998 ] 
+          - [  114.8571,   -0.44131,    1.23371,   -0.46448 ] 
+          - [  119.0000,   -0.50156,    1.14600,   -0.45897 ] 
+          - [  123.1429,   -0.55550,    1.05001,   -0.45171 ] 
+          - [  127.2857,   -0.60277,    0.94773,   -0.44389 ] 
+          - [  131.4286,   -0.64333,    0.84130,   -0.43694 ] 
+          - [  135.5714,   -0.67760,    0.73293,   -0.43163 ] 
+          - [  139.7143,   -0.70655,    0.62488,   -0.42632 ] 
+          - [  143.8571,   -0.73188,    0.51941,   -0.42287 ] 
+          - [  148.0000,   -0.75635,    0.41871,   -0.41955 ] 
+          - [  150.2857,   -0.70233,    0.36597,   -0.41581 ] 
+          - [  152.5714,   -0.64830,    0.31566,   -0.39878 ] 
+          - [  154.8571,   -0.59428,    0.26810,   -0.38175 ] 
+          - [  157.1429,   -0.54025,    0.22359,   -0.36471 ] 
+          - [  159.4286,   -0.48623,    0.18242,   -0.34768 ] 
+          - [  161.7143,   -0.43220,    0.14485,   -0.37026 ] 
+          - [  164.0000,   -0.37818,    0.11112,   -0.40605 ] 
+          - [  166.2857,   -0.32415,    0.08143,   -0.44184 ] 
+          - [  168.5714,   -0.27013,    0.05599,   -0.47763 ] 
+          - [  170.8571,   -0.21610,    0.03570,   -0.45714 ] 
+          - [  173.1429,   -0.16208,    0.03088,   -0.34286 ] 
+          - [  175.4286,   -0.10805,    0.02742,   -0.22857 ] 
+          - [  177.7143,   -0.05403,    0.02534,   -0.11429 ] 
+          - [  179.9087,    0.00000,    0.02464,    0.00000 ] 
+      - name               : FFA-W3-241  # 
+        relative_thickness : 0.241  # 
+        data:  #  alpha    c_l    c_d     c_m   
+          - [ -179.9087,    0.00000,    0.01178,    0.00000 ] 
+          - [ -177.7143,    0.05818,    0.01248,    0.09143 ] 
+          - [ -175.4286,    0.11636,    0.01460,    0.18286 ] 
+          - [ -173.1429,    0.17453,    0.01811,    0.27429 ] 
+          - [ -170.8572,    0.23271,    0.02300,    0.36571 ] 
+          - [ -168.5716,    0.29089,    0.02922,    0.39568 ] 
+          - [ -166.2857,    0.34907,    0.05382,    0.38876 ] 
+          - [ -164.0000,    0.40725,    0.08379,    0.38184 ] 
+          - [ -161.7145,    0.46542,    0.11786,    0.37492 ] 
+          - [ -159.4284,    0.52360,    0.15581,    0.37408 ] 
+          - [ -157.1428,    0.58178,    0.19740,    0.39148 ] 
+          - [ -154.8573,    0.63996,    0.24237,    0.40888 ] 
+          - [ -152.5714,    0.69814,    0.29043,    0.42628 ] 
+          - [ -150.2857,    0.75631,    0.34128,    0.44368 ] 
+          - [ -148.0000,    0.81449,    0.39460,    0.44537 ] 
+          - [ -143.8571,    0.77925,    0.49645,    0.44436 ] 
+          - [ -139.7143,    0.74511,    0.60319,    0.44360 ] 
+          - [ -135.5714,    0.70881,    0.71263,    0.44609 ] 
+          - [ -131.4286,    0.66835,    0.82249,    0.44858 ] 
+          - [ -127.2857,    0.62253,    0.93051,    0.45370 ] 
+          - [ -123.1429,    0.57080,    1.03447,    0.46020 ] 
+          - [ -119.0000,    0.51307,    1.13222,    0.46633 ] 
+          - [ -114.8571,    0.44965,    1.22176,    0.47130 ] 
+          - [ -110.7143,    0.38115,    1.30123,    0.47627 ] 
+          - [ -106.5714,    0.30846,    1.36903,    0.47705 ] 
+          - [ -102.4286,    0.23266,    1.42376,    0.47695 ] 
+          - [  -98.2857,    0.15503,    1.46433,    0.47409 ] 
+          - [  -94.1429,    0.07698,    1.48990,    0.46732 ] 
+          - [  -90.0000,    0.00000,    1.50000,    0.46055 ] 
+          - [  -85.8571,   -0.07698,    1.48990,    0.44509 ] 
+          - [  -81.7143,   -0.15503,    1.46433,    0.42964 ] 
+          - [  -77.5714,   -0.23266,    1.42376,    0.41125 ] 
+          - [  -73.4286,   -0.30846,    1.36903,    0.39081 ] 
+          - [  -69.2857,   -0.38115,    1.30123,    0.36988 ] 
+          - [  -65.1429,   -0.44965,    1.22176,    0.34663 ] 
+          - [  -61.0000,   -0.51307,    1.13222,    0.32339 ] 
+          - [  -56.8571,   -0.57080,    1.03447,    0.29984 ] 
+          - [  -52.7143,   -0.62253,    0.93051,    0.27618 ] 
+          - [  -48.5714,   -0.66835,    0.82249,    0.25280 ] 
+          - [  -44.4286,   -0.70881,    0.71263,    0.22992 ] 
+          - [  -40.2857,   -0.74511,    0.60319,    0.20705 ] 
+          - [  -36.1429,   -0.77925,    0.49645,    0.14561 ] 
+          - [  -32.0000,   -0.81449,    0.39460,    0.08131 ] 
+          - [  -28.0000,   -1.07781,    0.22252,    0.04592 ] 
+          - [  -24.0000,   -1.12692,    0.15159,    0.01901 ] 
+          - [  -20.0000,   -1.14480,    0.09699,    0.00063 ] 
+          - [  -18.0000,   -1.12797,    0.07744,   -0.00342 ] 
+          - [  -16.0000,   -1.09392,    0.06122,   -0.00587 ] 
+          - [  -14.0000,   -1.05961,    0.04667,   -0.00652 ] 
+          - [  -12.0000,   -1.03121,    0.03302,   -0.00755 ] 
+          - [  -10.0000,   -0.93706,    0.02027,   -0.02243 ] 
+          - [   -8.0000,   -0.67380,    0.01168,   -0.05583 ] 
+          - [   -6.0000,   -0.40391,    0.00918,   -0.07159 ] 
+          - [   -4.0000,   -0.14226,    0.00839,   -0.08123 ] 
+          - [   -2.0000,    0.11580,    0.00810,   -0.08892 ] 
+          - [   -1.0000,    0.24382,    0.00808,   -0.09235 ] 
+          - [    0.0000,    0.37113,    0.00813,   -0.09556 ] 
+          - [    1.0000,    0.49766,    0.00824,   -0.09857 ] 
+          - [    2.0000,    0.62334,    0.00842,   -0.10139 ] 
+          - [    3.0000,    0.74798,    0.00867,   -0.10403 ] 
+          - [    4.0000,    0.87137,    0.00901,   -0.10645 ] 
+          - [    5.0000,    0.99320,    0.00945,   -0.10863 ] 
+          - [    6.0000,    1.11325,    0.00998,   -0.11057 ] 
+          - [    7.0000,    1.23037,    0.01070,   -0.11214 ] 
+          - [    8.0000,    1.34496,    0.01153,   -0.11337 ] 
+          - [    9.0000,    1.45407,    0.01269,   -0.11396 ] 
+          - [   10.0000,    1.55911,    0.01396,   -0.11403 ] 
+          - [   11.0000,    1.65779,    0.01545,   -0.11336 ] 
+          - [   12.0000,    1.74834,    0.01724,   -0.11187 ] 
+          - [   13.0000,    1.82666,    0.01961,   -0.10935 ] 
+          - [   14.0000,    1.88831,    0.02293,   -0.10606 ] 
+          - [   15.0000,    1.92579,    0.02795,   -0.10238 ] 
+          - [   16.0000,    1.92722,    0.03609,   -0.09887 ] 
+          - [   18.0000,    1.80055,    0.06534,   -0.09497 ] 
+          - [   20.0000,    1.63088,    0.10459,   -0.09996 ] 
+          - [   24.0000,    1.43345,    0.19148,   -0.12589 ] 
+          - [   28.0000,    1.28805,    0.28629,   -0.15453 ] 
+          - [   32.0000,    1.16356,    0.39460,   -0.18396 ] 
+          - [   36.1429,    1.11321,    0.49645,   -0.21099 ] 
+          - [   40.2857,    1.06444,    0.60319,   -0.23768 ] 
+          - [   44.4286,    1.01259,    0.71263,   -0.25992 ] 
+          - [   48.5714,    0.95478,    0.82249,   -0.28216 ] 
+          - [   52.7143,    0.88932,    0.93051,   -0.30323 ] 
+          - [   56.8571,    0.81542,    1.03447,   -0.32368 ] 
+          - [   61.0000,    0.73296,    1.13222,   -0.34380 ] 
+          - [   65.1429,    0.64236,    1.22176,   -0.36292 ] 
+          - [   69.2857,    0.54450,    1.30123,   -0.38204 ] 
+          - [   73.4286,    0.44065,    1.36903,   -0.39944 ] 
+          - [   77.5714,    0.33237,    1.42376,   -0.41648 ] 
+          - [   81.7143,    0.22148,    1.46433,   -0.43231 ] 
+          - [   85.8571,    0.10997,    1.48990,   -0.44643 ] 
+          - [   90.0000,    0.00000,    1.50000,   -0.46055 ] 
+          - [   94.1429,   -0.07698,    1.48990,   -0.46732 ] 
+          - [   98.2857,   -0.15503,    1.46433,   -0.47409 ] 
+          - [  102.4286,   -0.23266,    1.42376,   -0.47695 ] 
+          - [  106.5714,   -0.30846,    1.36903,   -0.47705 ] 
+          - [  110.7143,   -0.38115,    1.30123,   -0.47627 ] 
+          - [  114.8571,   -0.44965,    1.22176,   -0.47130 ] 
+          - [  119.0000,   -0.51307,    1.13222,   -0.46633 ] 
+          - [  123.1429,   -0.57080,    1.03447,   -0.46020 ] 
+          - [  127.2857,   -0.62253,    0.93051,   -0.45370 ] 
+          - [  131.4286,   -0.66835,    0.82249,   -0.44858 ] 
+          - [  135.5714,   -0.70881,    0.71263,   -0.44609 ] 
+          - [  139.7143,   -0.74511,    0.60319,   -0.44360 ] 
+          - [  143.8571,   -0.77925,    0.49645,   -0.44436 ] 
+          - [  148.0000,   -0.81449,    0.39460,   -0.44537 ] 
+          - [  150.2857,   -0.75631,    0.34128,   -0.44368 ] 
+          - [  152.5714,   -0.69814,    0.29043,   -0.42628 ] 
+          - [  154.8571,   -0.63996,    0.24237,   -0.40888 ] 
+          - [  157.1429,   -0.58178,    0.19740,   -0.39148 ] 
+          - [  159.4286,   -0.52360,    0.15581,   -0.37408 ] 
+          - [  161.7143,   -0.46542,    0.11786,   -0.39207 ] 
+          - [  164.0000,   -0.40725,    0.08379,   -0.42184 ] 
+          - [  166.2857,   -0.34907,    0.05382,   -0.45162 ] 
+          - [  168.5714,   -0.29089,    0.02922,   -0.48139 ] 
+          - [  170.8571,   -0.23271,    0.02300,   -0.45714 ] 
+          - [  173.1429,   -0.17453,    0.01811,   -0.34286 ] 
+          - [  175.4286,   -0.11636,    0.01460,   -0.22857 ] 
+          - [  177.7143,   -0.05818,    0.01248,   -0.11429 ] 
+          - [  179.9087,    0.00000,    0.01178,    0.00000 ] 
+      - name               : FFA-W3-270blend  # 
+        relative_thickness : 0.27  # 
+        data:  #  alpha    c_l    c_d     c_m   
+          - [ -179.9087,    0.00000,    0.01545,    0.00000 ] 
+          - [ -177.7143,    0.06213,    0.01611,    0.09143 ] 
+          - [ -175.4286,    0.12426,    0.01807,    0.18286 ] 
+          - [ -173.1429,    0.18639,    0.02133,    0.27429 ] 
+          - [ -170.8572,    0.24852,    0.02587,    0.36571 ] 
+          - [ -168.5716,    0.31064,    0.03289,    0.39874 ] 
+          - [ -166.2857,    0.37277,    0.05681,    0.39672 ] 
+          - [ -164.0000,    0.43490,    0.08471,    0.39470 ] 
+          - [ -161.7145,    0.49703,    0.11643,    0.39268 ] 
+          - [ -159.4284,    0.55916,    0.15176,    0.39544 ] 
+          - [ -157.1428,    0.62129,    0.19048,    0.41254 ] 
+          - [ -154.8573,    0.68342,    0.23234,    0.42964 ] 
+          - [ -152.5714,    0.74555,    0.27708,    0.44674 ] 
+          - [ -150.2857,    0.80768,    0.32441,    0.46384 ] 
+          - [ -148.0000,    0.86981,    0.37404,    0.46186 ] 
+          - [ -143.8571,    0.81660,    0.46882,    0.45335 ] 
+          - [ -139.7143,    0.76812,    0.56814,    0.44523 ] 
+          - [ -135.5714,    0.72040,    0.66995,    0.44237 ] 
+          - [ -131.4286,    0.67095,    0.77214,    0.43951 ] 
+          - [ -127.2857,    0.61828,    0.87258,    0.44072 ] 
+          - [ -123.1429,    0.56158,    0.96921,    0.44407 ] 
+          - [ -119.0000,    0.50057,    1.06002,    0.44739 ] 
+          - [ -114.8571,    0.43540,    1.14315,    0.45063 ] 
+          - [ -110.7143,    0.36655,    1.21688,    0.45387 ] 
+          - [ -106.5714,    0.29475,    1.27969,    0.45377 ] 
+          - [ -102.4286,    0.22098,    1.33030,    0.45298 ] 
+          - [  -98.2857,    0.14639,    1.36768,    0.44973 ] 
+          - [  -94.1429,    0.07227,    1.39107,    0.44302 ] 
+          - [  -90.0000,    0.00000,    1.40000,    0.43630 ] 
+          - [  -85.8571,   -0.07227,    1.39107,    0.42180 ] 
+          - [  -81.7143,   -0.14639,    1.36768,    0.40730 ] 
+          - [  -77.5714,   -0.22098,    1.33030,    0.39020 ] 
+          - [  -73.4286,   -0.29475,    1.27969,    0.37125 ] 
+          - [  -69.2857,   -0.36655,    1.21688,    0.35190 ] 
+          - [  -65.1429,   -0.43540,    1.14315,    0.33068 ] 
+          - [  -61.0000,   -0.50057,    1.06002,    0.30945 ] 
+          - [  -56.8571,   -0.56158,    0.96921,    0.28815 ] 
+          - [  -52.7143,   -0.61828,    0.87258,    0.26684 ] 
+          - [  -48.5714,   -0.67095,    0.77214,    0.24576 ] 
+          - [  -44.4286,   -0.72040,    0.66995,    0.22512 ] 
+          - [  -40.2857,   -0.76812,    0.56814,    0.20447 ] 
+          - [  -36.1429,   -0.81660,    0.46882,    0.13957 ] 
+          - [  -32.0000,   -0.86981,    0.37404,    0.07138 ] 
+          - [  -28.0000,   -1.09837,    0.21880,    0.04400 ] 
+          - [  -24.0000,   -1.08339,    0.15982,    0.02166 ] 
+          - [  -20.0000,   -1.06990,    0.10744,    0.00422 ] 
+          - [  -18.0000,   -1.05454,    0.08690,   -0.00035 ] 
+          - [  -16.0000,   -1.03432,    0.06844,   -0.00334 ] 
+          - [  -14.0000,   -1.08360,    0.04733,   -0.00283 ] 
+          - [  -12.0000,   -1.09489,    0.03085,   -0.00556 ] 
+          - [  -10.0000,   -0.92665,    0.01984,   -0.02952 ] 
+          - [   -8.0000,   -0.69676,    0.01439,   -0.04822 ] 
+          - [   -6.0000,   -0.43628,    0.01155,   -0.06483 ] 
+          - [   -4.0000,   -0.16252,    0.01026,   -0.07919 ] 
+          - [   -2.0000,    0.10709,    0.00976,   -0.09041 ] 
+          - [   -1.0000,    0.23993,    0.00967,   -0.09517 ] 
+          - [    0.0000,    0.37158,    0.00968,   -0.09953 ] 
+          - [    1.0000,    0.50210,    0.00976,   -0.10355 ] 
+          - [    2.0000,    0.63139,    0.00993,   -0.10725 ] 
+          - [    3.0000,    0.75951,    0.01016,   -0.11068 ] 
+          - [    4.0000,    0.88638,    0.01045,   -0.11385 ] 
+          - [    5.0000,    1.01172,    0.01082,   -0.11673 ] 
+          - [    6.0000,    1.13430,    0.01140,   -0.11923 ] 
+          - [    7.0000,    1.25536,    0.01198,   -0.12145 ] 
+          - [    8.0000,    1.37379,    0.01267,   -0.12328 ] 
+          - [    9.0000,    1.48841,    0.01353,   -0.12460 ] 
+          - [   10.0000,    1.59782,    0.01460,   -0.12526 ] 
+          - [   11.0000,    1.70005,    0.01597,   -0.12505 ] 
+          - [   12.0000,    1.79190,    0.01777,   -0.12370 ] 
+          - [   13.0000,    1.86782,    0.02035,   -0.12093 ] 
+          - [   14.0000,    1.92687,    0.02385,   -0.11725 ] 
+          - [   15.0000,    1.90901,    0.03236,   -0.10931 ] 
+          - [   16.0000,    1.88548,    0.04259,   -0.10525 ] 
+          - [   18.0000,    1.72106,    0.07672,   -0.10292 ] 
+          - [   20.0000,    1.54737,    0.11914,   -0.11017 ] 
+          - [   24.0000,    1.37176,    0.20189,   -0.13431 ] 
+          - [   28.0000,    1.33611,    0.27981,   -0.15777 ] 
+          - [   32.0000,    1.24258,    0.37404,   -0.18432 ] 
+          - [   36.1429,    1.16657,    0.46882,   -0.21002 ] 
+          - [   40.2857,    1.09731,    0.56814,   -0.23531 ] 
+          - [   44.4286,    1.02914,    0.66995,   -0.25508 ] 
+          - [   48.5714,    0.95850,    0.77214,   -0.27485 ] 
+          - [   52.7143,    0.88325,    0.87258,   -0.29346 ] 
+          - [   56.8571,    0.80225,    0.96921,   -0.31145 ] 
+          - [   61.0000,    0.71510,    1.06002,   -0.32925 ] 
+          - [   65.1429,    0.62200,    1.14315,   -0.34641 ] 
+          - [   69.2857,    0.52364,    1.21688,   -0.36357 ] 
+          - [   73.4286,    0.42107,    1.27969,   -0.37949 ] 
+          - [   77.5714,    0.31569,    1.33030,   -0.39517 ] 
+          - [   81.7143,    0.20913,    1.36768,   -0.40983 ] 
+          - [   85.8571,    0.10324,    1.39107,   -0.42306 ] 
+          - [   90.0000,    0.00000,    1.40000,   -0.43630 ] 
+          - [   94.1429,   -0.07227,    1.39107,   -0.44302 ] 
+          - [   98.2857,   -0.14639,    1.36768,   -0.44973 ] 
+          - [  102.4286,   -0.22098,    1.33030,   -0.45298 ] 
+          - [  106.5714,   -0.29475,    1.27969,   -0.45377 ] 
+          - [  110.7143,   -0.36655,    1.21688,   -0.45387 ] 
+          - [  114.8571,   -0.43540,    1.14315,   -0.45063 ] 
+          - [  119.0000,   -0.50057,    1.06002,   -0.44739 ] 
+          - [  123.1429,   -0.56158,    0.96921,   -0.44407 ] 
+          - [  127.2857,   -0.61828,    0.87258,   -0.44072 ] 
+          - [  131.4286,   -0.67095,    0.77214,   -0.43951 ] 
+          - [  135.5714,   -0.72040,    0.66995,   -0.44237 ] 
+          - [  139.7143,   -0.76812,    0.56814,   -0.44523 ] 
+          - [  143.8571,   -0.81660,    0.46882,   -0.45335 ] 
+          - [  148.0000,   -0.86981,    0.37404,   -0.46186 ] 
+          - [  150.2857,   -0.80768,    0.32441,   -0.46384 ] 
+          - [  152.5714,   -0.74555,    0.27708,   -0.44674 ] 
+          - [  154.8571,   -0.68342,    0.23234,   -0.42964 ] 
+          - [  157.1429,   -0.62129,    0.19048,   -0.41254 ] 
+          - [  159.4286,   -0.55916,    0.15176,   -0.39544 ] 
+          - [  161.7143,   -0.49703,    0.11643,   -0.40982 ] 
+          - [  164.0000,   -0.43490,    0.08471,   -0.43470 ] 
+          - [  166.2857,   -0.37277,    0.05681,   -0.45958 ] 
+          - [  168.5714,   -0.31064,    0.03289,   -0.48445 ] 
+          - [  170.8571,   -0.24852,    0.02587,   -0.45714 ] 
+          - [  173.1429,   -0.18639,    0.02133,   -0.34286 ] 
+          - [  175.4286,   -0.12426,    0.01807,   -0.22857 ] 
+          - [  177.7143,   -0.06213,    0.01611,   -0.11429 ] 
+          - [  179.9087,    0.00000,    0.01545,    0.00000 ] 
+      - name               : FFA-W3-301  # 
+        relative_thickness : 0.301  # 
+        data:  #  alpha    c_l    c_d     c_m   
+          - [ -179.9087,    0.00000,    0.02454,    0.00000 ] 
+          - [ -177.7143,    0.06508,    0.02514,    0.09143 ] 
+          - [ -175.4286,    0.13016,    0.02694,    0.18286 ] 
+          - [ -173.1429,    0.19525,    0.02993,    0.27429 ] 
+          - [ -170.8572,    0.26033,    0.03408,    0.36571 ] 
+          - [ -168.5716,    0.32541,    0.03938,    0.40085 ] 
+          - [ -166.2857,    0.39049,    0.05910,    0.40220 ] 
+          - [ -164.0000,    0.45557,    0.08495,    0.40356 ] 
+          - [ -161.7145,    0.52066,    0.11433,    0.40492 ] 
+          - [ -159.4284,    0.58574,    0.14704,    0.41010 ] 
+          - [ -157.1428,    0.65082,    0.18290,    0.42678 ] 
+          - [ -154.8573,    0.71590,    0.22166,    0.44345 ] 
+          - [ -152.5714,    0.78098,    0.26309,    0.46013 ] 
+          - [ -150.2857,    0.84607,    0.30692,    0.47680 ] 
+          - [ -148.0000,    0.91115,    0.35287,    0.47162 ] 
+          - [ -143.8571,    0.84257,    0.44061,    0.45656 ] 
+          - [ -139.7143,    0.78187,    0.53255,    0.44202 ] 
+          - [ -135.5714,    0.72448,    0.62677,    0.43452 ] 
+          - [ -131.4286,    0.66755,    0.72131,    0.42701 ] 
+          - [ -127.2857,    0.60928,    0.81421,    0.42483 ] 
+          - [ -123.1429,    0.54868,    0.90355,    0.42544 ] 
+          - [ -119.0000,    0.48530,    0.98748,    0.42634 ] 
+          - [ -114.8571,    0.41915,    1.06425,    0.42813 ] 
+          - [ -110.7143,    0.35056,    1.13227,    0.42992 ] 
+          - [ -106.5714,    0.28017,    1.19015,    0.42916 ] 
+          - [ -102.4286,    0.20881,    1.23669,    0.42788 ] 
+          - [  -98.2857,    0.13754,    1.27093,    0.42444 ] 
+          - [  -94.1429,    0.06751,    1.29218,    0.41794 ] 
+          - [  -90.0000,    0.00000,    1.30000,    0.41144 ] 
+          - [  -85.8571,   -0.06751,    1.29218,    0.39804 ] 
+          - [  -81.7143,   -0.13754,    1.27093,    0.38464 ] 
+          - [  -77.5714,   -0.20881,    1.23669,    0.36892 ] 
+          - [  -73.4286,   -0.28017,    1.19015,    0.35157 ] 
+          - [  -69.2857,   -0.35056,    1.13227,    0.33391 ] 
+          - [  -65.1429,   -0.41915,    1.06425,    0.31474 ] 
+          - [  -61.0000,   -0.48530,    0.98748,    0.29557 ] 
+          - [  -56.8571,   -0.54868,    0.90355,    0.27653 ] 
+          - [  -52.7143,   -0.60928,    0.81421,    0.25754 ] 
+          - [  -48.5714,   -0.66755,    0.72131,    0.23873 ] 
+          - [  -44.4286,   -0.72448,    0.62677,    0.22027 ] 
+          - [  -40.2857,   -0.78187,    0.53255,    0.20181 ] 
+          - [  -36.1429,   -0.84257,    0.44061,    0.13644 ] 
+          - [  -32.0000,   -0.91115,    0.35287,    0.06760 ] 
+          - [  -28.0000,   -1.10349,    0.21721,    0.04231 ] 
+          - [  -24.0000,   -1.10737,    0.15629,    0.02026 ] 
+          - [  -20.0000,   -1.11815,    0.10335,    0.00407 ] 
+          - [  -18.0000,   -1.12332,    0.08180,    0.00017 ] 
+          - [  -16.0000,   -1.11865,    0.06331,   -0.00167 ] 
+          - [  -14.0000,   -1.11620,    0.04718,   -0.00120 ] 
+          - [  -12.0000,   -1.09588,    0.03280,   -0.00463 ] 
+          - [  -10.0000,   -0.91767,    0.02351,   -0.02494 ] 
+          - [   -8.0000,   -0.69311,    0.01793,   -0.04304 ] 
+          - [   -6.0000,   -0.45396,    0.01431,   -0.05868 ] 
+          - [   -4.0000,   -0.17779,    0.01242,   -0.07601 ] 
+          - [   -2.0000,    0.10480,    0.01160,   -0.09121 ] 
+          - [   -1.0000,    0.24383,    0.01143,   -0.09763 ] 
+          - [    0.0000,    0.38111,    0.01138,   -0.10341 ] 
+          - [    1.0000,    0.51660,    0.01143,   -0.10861 ] 
+          - [    2.0000,    0.65044,    0.01156,   -0.11333 ] 
+          - [    3.0000,    0.78267,    0.01177,   -0.11762 ] 
+          - [    4.0000,    0.91326,    0.01204,   -0.12154 ] 
+          - [    5.0000,    1.04207,    0.01239,   -0.12510 ] 
+          - [    6.0000,    1.16873,    0.01283,   -0.12828 ] 
+          - [    7.0000,    1.29296,    0.01338,   -0.13104 ] 
+          - [    8.0000,    1.41390,    0.01406,   -0.13332 ] 
+          - [    9.0000,    1.53088,    0.01488,   -0.13503 ] 
+          - [   10.0000,    1.64208,    0.01592,   -0.13599 ] 
+          - [   11.0000,    1.74568,    0.01726,   -0.13605 ] 
+          - [   12.0000,    1.83887,    0.01908,   -0.13514 ] 
+          - [   13.0000,    1.91764,    0.02169,   -0.13322 ] 
+          - [   14.0000,    1.97413,    0.02572,   -0.13020 ] 
+          - [   15.0000,    1.99916,    0.03222,   -0.12641 ] 
+          - [   16.0000,    1.99377,    0.04157,   -0.12265 ] 
+          - [   18.0000,    1.91720,    0.06731,   -0.11675 ] 
+          - [   20.0000,    1.73683,    0.10526,   -0.11652 ] 
+          - [   24.0000,    1.47321,    0.19229,   -0.13790 ] 
+          - [   28.0000,    1.36017,    0.27449,   -0.16242 ] 
+          - [   32.0000,    1.30164,    0.35287,   -0.18463 ] 
+          - [   36.1429,    1.20367,    0.44061,   -0.20894 ] 
+          - [   40.2857,    1.11695,    0.53255,   -0.23276 ] 
+          - [   44.4286,    1.03498,    0.62677,   -0.25011 ] 
+          - [   48.5714,    0.95364,    0.72131,   -0.26746 ] 
+          - [   52.7143,    0.87040,    0.81421,   -0.28365 ] 
+          - [   56.8571,    0.78383,    0.90355,   -0.29923 ] 
+          - [   61.0000,    0.69329,    0.98748,   -0.31472 ] 
+          - [   65.1429,    0.59878,    1.06425,   -0.32988 ] 
+          - [   69.2857,    0.50080,    1.13227,   -0.34505 ] 
+          - [   73.4286,    0.40024,    1.19015,   -0.35942 ] 
+          - [   77.5714,    0.29831,    1.23669,   -0.37363 ] 
+          - [   81.7143,    0.19648,    1.27093,   -0.38702 ] 
+          - [   85.8571,    0.09644,    1.29218,   -0.39923 ] 
+          - [   90.0000,    0.00000,    1.30000,   -0.41144 ] 
+          - [   94.1429,   -0.06751,    1.29218,   -0.41794 ] 
+          - [   98.2857,   -0.13754,    1.27093,   -0.42444 ] 
+          - [  102.4286,   -0.20881,    1.23669,   -0.42788 ] 
+          - [  106.5714,   -0.28017,    1.19015,   -0.42916 ] 
+          - [  110.7143,   -0.35056,    1.13227,   -0.42992 ] 
+          - [  114.8571,   -0.41915,    1.06425,   -0.42813 ] 
+          - [  119.0000,   -0.48530,    0.98748,   -0.42634 ] 
+          - [  123.1429,   -0.54868,    0.90355,   -0.42544 ] 
+          - [  127.2857,   -0.60928,    0.81421,   -0.42483 ] 
+          - [  131.4286,   -0.66755,    0.72131,   -0.42701 ] 
+          - [  135.5714,   -0.72448,    0.62677,   -0.43452 ] 
+          - [  139.7143,   -0.78187,    0.53255,   -0.44202 ] 
+          - [  143.8571,   -0.84257,    0.44061,   -0.45656 ] 
+          - [  148.0000,   -0.91115,    0.35287,   -0.47162 ] 
+          - [  150.2857,   -0.84607,    0.30692,   -0.47680 ] 
+          - [  152.5714,   -0.78098,    0.26309,   -0.46013 ] 
+          - [  154.8571,   -0.71590,    0.22166,   -0.44345 ] 
+          - [  157.1429,   -0.65082,    0.18290,   -0.42678 ] 
+          - [  159.4286,   -0.58574,    0.14704,   -0.41010 ] 
+          - [  161.7143,   -0.52066,    0.11433,   -0.42206 ] 
+          - [  164.0000,   -0.45557,    0.08495,   -0.44356 ] 
+          - [  166.2857,   -0.39049,    0.05910,   -0.46506 ] 
+          - [  168.5714,   -0.32541,    0.03938,   -0.48656 ] 
+          - [  170.8571,   -0.26033,    0.03408,   -0.45714 ] 
+          - [  173.1429,   -0.19525,    0.02993,   -0.34286 ] 
+          - [  175.4286,   -0.13016,    0.02694,   -0.22857 ] 
+          - [  177.7143,   -0.06508,    0.02514,   -0.11429 ] 
+          - [  179.9087,    0.00000,    0.02454,    0.00000 ] 
+      - name               : FFA-W3-330blend  # 
+        relative_thickness : 0.33  # 
+        data:  #  alpha    c_l    c_d     c_m   
+          - [ -179.9087,    0.00000,    0.03169,    0.00000 ] 
+          - [ -177.7143,    0.06960,    0.03228,    0.09143 ] 
+          - [ -175.4286,    0.13920,    0.03406,    0.18286 ] 
+          - [ -173.1429,    0.20880,    0.03702,    0.27429 ] 
+          - [ -170.8572,    0.27841,    0.04114,    0.36571 ] 
+          - [ -168.5716,    0.34801,    0.04638,    0.40308 ] 
+          - [ -166.2857,    0.41761,    0.05732,    0.40801 ] 
+          - [ -164.0000,    0.48721,    0.08319,    0.41294 ] 
+          - [ -161.7145,    0.55681,    0.11258,    0.41788 ] 
+          - [ -159.4284,    0.62641,    0.14533,    0.42586 ] 
+          - [ -157.1428,    0.69601,    0.18121,    0.44302 ] 
+          - [ -154.8573,    0.76562,    0.22000,    0.46017 ] 
+          - [ -152.5714,    0.83522,    0.26146,    0.47732 ] 
+          - [ -150.2857,    0.90482,    0.30532,    0.49447 ] 
+          - [ -148.0000,    0.97442,    0.35131,    0.48743 ] 
+          - [ -143.8571,    0.89412,    0.43913,    0.46839 ] 
+          - [ -139.7143,    0.82382,    0.53115,    0.44996 ] 
+          - [ -135.5714,    0.75845,    0.62546,    0.43985 ] 
+          - [ -131.4286,    0.69477,    0.72010,    0.42974 ] 
+          - [ -127.2857,    0.63079,    0.81310,    0.42589 ] 
+          - [ -123.1429,    0.56532,    0.90255,    0.42535 ] 
+          - [ -119.0000,    0.49783,    0.98659,    0.42528 ] 
+          - [ -114.8571,    0.42823,    1.06348,    0.42673 ] 
+          - [ -110.7143,    0.35680,    1.13162,    0.42817 ] 
+          - [ -106.5714,    0.28412,    1.18963,    0.42745 ] 
+          - [ -102.4286,    0.21103,    1.23629,    0.42628 ] 
+          - [  -98.2857,    0.13851,    1.27067,    0.42303 ] 
+          - [  -94.1429,    0.06775,    1.29204,    0.41683 ] 
+          - [  -90.0000,    0.00000,    1.30000,    0.41063 ] 
+          - [  -85.8571,   -0.06775,    1.29204,    0.39752 ] 
+          - [  -81.7143,   -0.13851,    1.27067,    0.38441 ] 
+          - [  -77.5714,   -0.21103,    1.23629,    0.36905 ] 
+          - [  -73.4286,   -0.28412,    1.18963,    0.35212 ] 
+          - [  -69.2857,   -0.35680,    1.13162,    0.33491 ] 
+          - [  -65.1429,   -0.42823,    1.06348,    0.31634 ] 
+          - [  -61.0000,   -0.49783,    0.98659,    0.29777 ] 
+          - [  -56.8571,   -0.56532,    0.90255,    0.27947 ] 
+          - [  -52.7143,   -0.63079,    0.81310,    0.26125 ] 
+          - [  -48.5714,   -0.69477,    0.72010,    0.24322 ] 
+          - [  -44.4286,   -0.75845,    0.62546,    0.22556 ] 
+          - [  -40.2857,   -0.82382,    0.53115,    0.20789 ] 
+          - [  -36.1429,   -0.89412,    0.43913,    0.13731 ] 
+          - [  -32.0000,   -0.97442,    0.35131,    0.06280 ] 
+          - [  -28.0000,   -1.16308,    0.20648,    0.03905 ] 
+          - [  -24.0000,   -1.14892,    0.15001,    0.01853 ] 
+          - [  -20.0000,   -1.09451,    0.10600,    0.00441 ] 
+          - [  -18.0000,   -1.05801,    0.08732,   -0.00061 ] 
+          - [  -16.0000,   -1.02281,    0.07051,   -0.00342 ] 
+          - [  -14.0000,   -0.99810,    0.05474,   -0.00401 ] 
+          - [  -12.0000,   -0.98515,    0.04052,   -0.00272 ] 
+          - [  -10.0000,   -0.89583,    0.02929,   -0.01198 ] 
+          - [   -8.0000,   -0.67539,    0.02207,   -0.03458 ] 
+          - [   -6.0000,   -0.43247,    0.01735,   -0.05466 ] 
+          - [   -4.0000,   -0.15881,    0.01473,   -0.07425 ] 
+          - [   -2.0000,    0.13456,    0.01362,   -0.09270 ] 
+          - [   -1.0000,    0.28014,    0.01339,   -0.10074 ] 
+          - [    0.0000,    0.42386,    0.01330,   -0.10802 ] 
+          - [    1.0000,    0.56519,    0.01333,   -0.11450 ] 
+          - [    2.0000,    0.70410,    0.01345,   -0.12028 ] 
+          - [    3.0000,    0.84071,    0.01366,   -0.12546 ] 
+          - [    4.0000,    0.97500,    0.01397,   -0.13011 ] 
+          - [    5.0000,    1.10680,    0.01437,   -0.13425 ] 
+          - [    6.0000,    1.23603,    0.01486,   -0.13793 ] 
+          - [    7.0000,    1.36223,    0.01547,   -0.14108 ] 
+          - [    8.0000,    1.48424,    0.01623,   -0.14363 ] 
+          - [    9.0000,    1.60097,    0.01718,   -0.14545 ] 
+          - [   10.0000,    1.71010,    0.01841,   -0.14636 ] 
+          - [   11.0000,    1.80957,    0.02010,   -0.14635 ] 
+          - [   12.0000,    1.89473,    0.02258,   -0.14544 ] 
+          - [   13.0000,    1.95698,    0.02671,   -0.14378 ] 
+          - [   14.0000,    1.98576,    0.03380,   -0.14185 ] 
+          - [   15.0000,    1.99260,    0.04333,   -0.14004 ] 
+          - [   16.0000,    1.99617,    0.05354,   -0.13823 ] 
+          - [   18.0000,    1.96398,    0.07706,   -0.13351 ] 
+          - [   20.0000,    1.81179,    0.11169,   -0.13135 ] 
+          - [   24.0000,    1.56073,    0.19103,   -0.14660 ] 
+          - [   28.0000,    1.46798,    0.27199,   -0.17242 ] 
+          - [   32.0000,    1.39203,    0.35131,   -0.19417 ] 
+          - [   36.1429,    1.27731,    0.43913,   -0.21792 ] 
+          - [   40.2857,    1.17689,    0.53115,   -0.24115 ] 
+          - [   44.4286,    1.08350,    0.62546,   -0.25734 ] 
+          - [   48.5714,    0.99253,    0.72010,   -0.27354 ] 
+          - [   52.7143,    0.90112,    0.81310,   -0.28862 ] 
+          - [   56.8571,    0.80760,    0.90255,   -0.30311 ] 
+          - [   61.0000,    0.71119,    0.98659,   -0.31757 ] 
+          - [   65.1429,    0.61175,    1.06348,   -0.33194 ] 
+          - [   69.2857,    0.50971,    1.13162,   -0.34631 ] 
+          - [   73.4286,    0.40589,    1.18963,   -0.36014 ] 
+          - [   77.5714,    0.30146,    1.23629,   -0.37385 ] 
+          - [   81.7143,    0.19788,    1.27067,   -0.38681 ] 
+          - [   85.8571,    0.09679,    1.29204,   -0.39872 ] 
+          - [   90.0000,    0.00000,    1.30000,   -0.41063 ] 
+          - [   94.1429,   -0.06775,    1.29204,   -0.41683 ] 
+          - [   98.2857,   -0.13851,    1.27067,   -0.42303 ] 
+          - [  102.4286,   -0.21103,    1.23629,   -0.42628 ] 
+          - [  106.5714,   -0.28412,    1.18963,   -0.42745 ] 
+          - [  110.7143,   -0.35680,    1.13162,   -0.42817 ] 
+          - [  114.8571,   -0.42823,    1.06348,   -0.42673 ] 
+          - [  119.0000,   -0.49783,    0.98659,   -0.42528 ] 
+          - [  123.1429,   -0.56532,    0.90255,   -0.42535 ] 
+          - [  127.2857,   -0.63079,    0.81310,   -0.42589 ] 
+          - [  131.4286,   -0.69477,    0.72010,   -0.42974 ] 
+          - [  135.5714,   -0.75845,    0.62546,   -0.43985 ] 
+          - [  139.7143,   -0.82382,    0.53115,   -0.44996 ] 
+          - [  143.8571,   -0.89412,    0.43913,   -0.46839 ] 
+          - [  148.0000,   -0.97442,    0.35131,   -0.48743 ] 
+          - [  150.2857,   -0.90482,    0.30532,   -0.49447 ] 
+          - [  152.5714,   -0.83522,    0.26146,   -0.47732 ] 
+          - [  154.8571,   -0.76562,    0.22000,   -0.46017 ] 
+          - [  157.1429,   -0.69601,    0.18121,   -0.44302 ] 
+          - [  159.4286,   -0.62641,    0.14533,   -0.42586 ] 
+          - [  161.7143,   -0.55681,    0.11258,   -0.43502 ] 
+          - [  164.0000,   -0.48721,    0.08319,   -0.45294 ] 
+          - [  166.2857,   -0.41761,    0.05732,   -0.47087 ] 
+          - [  168.5714,   -0.34801,    0.04638,   -0.48880 ] 
+          - [  170.8571,   -0.27841,    0.04114,   -0.45714 ] 
+          - [  173.1429,   -0.20880,    0.03702,   -0.34286 ] 
+          - [  175.4286,   -0.13920,    0.03406,   -0.22857 ] 
+          - [  177.7143,   -0.06960,    0.03228,   -0.11429 ] 
+          - [  179.9087,    0.00000,    0.03169,    0.00000 ] 
+      - name               : FFA-W3-360  # 
+        relative_thickness : 0.36  # 
+        data:  #  alpha    c_l    c_d     c_m   
+          - [ -179.9087,    0.00000,    0.03715,    0.00000 ] 
+          - [ -177.7143,    0.07178,    0.03774,    0.09143 ] 
+          - [ -175.4286,    0.14356,    0.03951,    0.18286 ] 
+          - [ -173.1429,    0.21534,    0.04245,    0.27429 ] 
+          - [ -170.8572,    0.28713,    0.04653,    0.36571 ] 
+          - [ -168.5716,    0.35891,    0.05174,    0.40313 ] 
+          - [ -166.2857,    0.43069,    0.06068,    0.40814 ] 
+          - [ -164.0000,    0.50247,    0.08651,    0.41315 ] 
+          - [ -161.7145,    0.57425,    0.11586,    0.41816 ] 
+          - [ -159.4284,    0.64603,    0.14856,    0.42627 ] 
+          - [ -157.1428,    0.71781,    0.18439,    0.44370 ] 
+          - [ -154.8573,    0.78960,    0.22313,    0.46114 ] 
+          - [ -152.5714,    0.86138,    0.26453,    0.47857 ] 
+          - [ -150.2857,    0.93316,    0.30832,    0.49600 ] 
+          - [ -148.0000,    1.00494,    0.35424,    0.48830 ] 
+          - [ -143.8571,    0.91898,    0.44192,    0.46784 ] 
+          - [ -139.7143,    0.84406,    0.53379,    0.44803 ] 
+          - [ -135.5714,    0.77483,    0.62793,    0.43697 ] 
+          - [ -131.4286,    0.70790,    0.72238,    0.42591 ] 
+          - [ -127.2857,    0.64116,    0.81520,    0.42150 ] 
+          - [ -123.1429,    0.57335,    0.90444,    0.42058 ] 
+          - [ -119.0000,    0.50388,    0.98826,    0.42024 ] 
+          - [ -114.8571,    0.43261,    1.06493,    0.42168 ] 
+          - [ -110.7143,    0.35981,    1.13285,    0.42312 ] 
+          - [ -106.5714,    0.28603,    1.19061,    0.42258 ] 
+          - [ -102.4286,    0.21209,    1.23704,    0.42163 ] 
+          - [  -98.2857,    0.13899,    1.27116,    0.41864 ] 
+          - [  -94.1429,    0.06787,    1.29229,    0.41277 ] 
+          - [  -90.0000,    0.00000,    1.30000,    0.40690 ] 
+          - [  -85.8571,   -0.06787,    1.29229,    0.39426 ] 
+          - [  -81.7143,   -0.13899,    1.27116,    0.38162 ] 
+          - [  -77.5714,   -0.21209,    1.23704,    0.36676 ] 
+          - [  -73.4286,   -0.28603,    1.19061,    0.35033 ] 
+          - [  -69.2857,   -0.35981,    1.13285,    0.33362 ] 
+          - [  -65.1429,   -0.43261,    1.06493,    0.31561 ] 
+          - [  -61.0000,   -0.50388,    0.98826,    0.29759 ] 
+          - [  -56.8571,   -0.57335,    0.90444,    0.27989 ] 
+          - [  -52.7143,   -0.64116,    0.81520,    0.26230 ] 
+          - [  -48.5714,   -0.70790,    0.72238,    0.24491 ] 
+          - [  -44.4286,   -0.77483,    0.62793,    0.22794 ] 
+          - [  -40.2857,   -0.84406,    0.53379,    0.21097 ] 
+          - [  -36.1429,   -0.91898,    0.44192,    0.13525 ] 
+          - [  -32.0000,   -1.00494,    0.35424,    0.05517 ] 
+          - [  -28.0000,   -1.11306,    0.20494,    0.03211 ] 
+          - [  -24.0000,   -1.05425,    0.15434,    0.01268 ] 
+          - [  -20.0000,   -0.98247,    0.10967,   -0.00282 ] 
+          - [  -18.0000,   -0.94173,    0.09249,   -0.00741 ] 
+          - [  -16.0000,   -0.89333,    0.07597,   -0.01107 ] 
+          - [  -14.0000,   -0.85472,    0.06054,   -0.01250 ] 
+          - [  -12.0000,   -0.82348,    0.04641,   -0.01177 ] 
+          - [  -10.0000,   -0.79541,    0.03441,   -0.01082 ] 
+          - [   -8.0000,   -0.63650,    0.02548,   -0.02769 ] 
+          - [   -6.0000,   -0.39095,    0.01994,   -0.05107 ] 
+          - [   -4.0000,   -0.13071,    0.01653,   -0.07148 ] 
+          - [   -2.0000,    0.16173,    0.01507,   -0.09179 ] 
+          - [   -1.0000,    0.31121,    0.01477,   -0.10119 ] 
+          - [    0.0000,    0.45956,    0.01465,   -0.10988 ] 
+          - [    1.0000,    0.60566,    0.01466,   -0.11776 ] 
+          - [    2.0000,    0.74868,    0.01481,   -0.12477 ] 
+          - [    3.0000,    0.88862,    0.01507,   -0.13098 ] 
+          - [    4.0000,    1.02544,    0.01544,   -0.13648 ] 
+          - [    5.0000,    1.15878,    0.01593,   -0.14130 ] 
+          - [    6.0000,    1.28822,    0.01654,   -0.14540 ] 
+          - [    7.0000,    1.41282,    0.01731,   -0.14875 ] 
+          - [    8.0000,    1.53090,    0.01831,   -0.15118 ] 
+          - [    9.0000,    1.64065,    0.01963,   -0.15262 ] 
+          - [   10.0000,    1.73926,    0.02150,   -0.15310 ] 
+          - [   11.0000,    1.81971,    0.02445,   -0.15254 ] 
+          - [   12.0000,    1.87065,    0.02966,   -0.15121 ] 
+          - [   13.0000,    1.89221,    0.03770,   -0.14969 ] 
+          - [   14.0000,    1.87910,    0.04824,   -0.14562 ] 
+          - [   15.0000,    1.88111,    0.05838,   -0.14358 ] 
+          - [   16.0000,    1.86359,    0.06992,   -0.14095 ] 
+          - [   18.0000,    1.73324,    0.10166,   -0.13711 ] 
+          - [   20.0000,    1.59357,    0.13916,   -0.14082 ] 
+          - [   24.0000,    1.46708,    0.21002,   -0.15693 ] 
+          - [   28.0000,    1.44834,    0.28200,   -0.17979 ] 
+          - [   32.0000,    1.43563,    0.35424,   -0.20147 ] 
+          - [   36.1429,    1.31283,    0.44192,   -0.22409 ] 
+          - [   40.2857,    1.20580,    0.53379,   -0.24619 ] 
+          - [   44.4286,    1.10690,    0.62793,   -0.26133 ] 
+          - [   48.5714,    1.01129,    0.72238,   -0.27648 ] 
+          - [   52.7143,    0.91594,    0.81520,   -0.29062 ] 
+          - [   56.8571,    0.81907,    0.90444,   -0.30424 ] 
+          - [   61.0000,    0.71982,    0.98826,   -0.31787 ] 
+          - [   65.1429,    0.61801,    1.06493,   -0.33154 ] 
+          - [   69.2857,    0.51401,    1.13285,   -0.34522 ] 
+          - [   73.4286,    0.40862,    1.19061,   -0.35846 ] 
+          - [   77.5714,    0.30299,    1.23704,   -0.37161 ] 
+          - [   81.7143,    0.19855,    1.27116,   -0.38405 ] 
+          - [   85.8571,    0.09695,    1.29229,   -0.39547 ] 
+          - [   90.0000,    0.00000,    1.30000,   -0.40690 ] 
+          - [   94.1429,   -0.06787,    1.29229,   -0.41277 ] 
+          - [   98.2857,   -0.13899,    1.27116,   -0.41864 ] 
+          - [  102.4286,   -0.21209,    1.23704,   -0.42163 ] 
+          - [  106.5714,   -0.28603,    1.19061,   -0.42258 ] 
+          - [  110.7143,   -0.35981,    1.13285,   -0.42312 ] 
+          - [  114.8571,   -0.43261,    1.06493,   -0.42168 ] 
+          - [  119.0000,   -0.50388,    0.98826,   -0.42024 ] 
+          - [  123.1429,   -0.57335,    0.90444,   -0.42058 ] 
+          - [  127.2857,   -0.64116,    0.81520,   -0.42150 ] 
+          - [  131.4286,   -0.70790,    0.72238,   -0.42591 ] 
+          - [  135.5714,   -0.77483,    0.62793,   -0.43697 ] 
+          - [  139.7143,   -0.84406,    0.53379,   -0.44803 ] 
+          - [  143.8571,   -0.91898,    0.44192,   -0.46784 ] 
+          - [  148.0000,   -1.00494,    0.35424,   -0.48830 ] 
+          - [  150.2857,   -0.93316,    0.30832,   -0.49600 ] 
+          - [  152.5714,   -0.86138,    0.26453,   -0.47857 ] 
+          - [  154.8571,   -0.78960,    0.22313,   -0.46114 ] 
+          - [  157.1429,   -0.71781,    0.18439,   -0.44370 ] 
+          - [  159.4286,   -0.64603,    0.14856,   -0.42627 ] 
+          - [  161.7143,   -0.57425,    0.11586,   -0.43530 ] 
+          - [  164.0000,   -0.50247,    0.08651,   -0.45315 ] 
+          - [  166.2857,   -0.43069,    0.06068,   -0.47100 ] 
+          - [  168.5714,   -0.35891,    0.05174,   -0.48884 ] 
+          - [  170.8571,   -0.28713,    0.04653,   -0.45714 ] 
+          - [  173.1429,   -0.21534,    0.04245,   -0.34286 ] 
+          - [  175.4286,   -0.14356,    0.03951,   -0.22857 ] 
+          - [  177.7143,   -0.07178,    0.03774,   -0.11429 ] 
+          - [  179.9087,    0.00000,    0.03715,    0.00000 ] 
+
+
+        
+    pitch_control:
+      GS_Angles: [0.06019804, 0.08713416, 0.10844806, 0.12685912, 0.14339822,       0.1586021 , 0.17279614, 0.18618935, 0.19892772, 0.21111989,             0.22285021, 0.23417256, 0.2451469 , 0.25580691, 0.26619545,           0.27632495, 0.28623134, 0.29593266, 0.30544521, 0.314779  ,       0.32395154, 0.33297489, 0.3418577 , 0.35060844, 0.35923641,       0.36774807, 0.37614942, 0.38444655, 0.39264363, 0.40074407]
+      GS_Kp: [-0.9394215 , -0.80602855, -0.69555026, -0.60254912, -0.52318192,       -0.45465531, -0.39489024, -0.34230736, -0.29568537, -0.25406506,       -0.2166825 , -0.18292183, -0.15228099, -0.12434663, -0.09877533,       -0.0752794 , -0.05361604, -0.0335789 , -0.01499149,  0.00229803,  0.01842102,  0.03349169,  0.0476098 ,  0.0608629 ,  0.07332812,  0.0850737 ,  0.0961602 ,  0.10664158,  0.11656607,  0.12597691]
+      GS_Ki: [-0.07416547, -0.06719673, -0.0614251 , -0.05656651, -0.0524202 ,       -0.04884022, -0.04571796, -0.04297091, -0.04053528, -0.03836094,       -0.03640799, -0.03464426, -0.03304352, -0.03158417, -0.03024826,       -0.02902079, -0.02788904, -0.02684226, -0.02587121, -0.02496797,       -0.02412567, -0.02333834, -0.02260078, -0.02190841, -0.0212572 ,       -0.02064359, -0.0200644 , -0.01951683, -0.01899836, -0.01850671]
+      Fl_Kp:   -9.35
+    wt_ops:
+        v: [3.0, 3.266896551724138, 3.533793103448276, 3.800689655172414, 4.067586206896552, 4.334482758620689, 4.601379310344828, 4.868275862068966, 5.135172413793104, 5.402068965517241, 5.6689655172413795, 5.935862068965518, 6.2027586206896554, 6.469655172413793, 6.736551724137931, 7.00344827586207, 7.270344827586207, 7.537241379310345, 7.804137931034483, 8.071034482758622, 8.337931034482759, 8.604827586206897, 8.871724137931036, 9.138620689655173, 9.405517241379311, 9.672413793103448, 9.939310344827586, 10.206206896551725, 10.473103448275863, 10.74, 11.231724137931035, 11.723448275862069, 12.215172413793104, 12.706896551724139, 13.198620689655172, 13.690344827586207, 14.182068965517242, 14.673793103448276, 15.16551724137931, 15.657241379310346, 16.14896551724138, 16.640689655172416, 17.13241379310345, 17.624137931034483, 18.11586206896552, 18.607586206896553, 19.099310344827586, 19.591034482758623, 20.082758620689653, 20.57448275862069, 21.066206896551726, 21.557931034482756, 22.049655172413793, 22.54137931034483, 23.03310344827586, 23.524827586206897, 24.016551724137933, 24.508275862068963, 25.0]
+        pitch_op: [-0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, 3.57152, 5.12896, 6.36736, 7.43866, 8.40197, 9.28843, 10.1161, 10.8974,  11.641, 12.3529,  13.038, 13.6997, 14.3409, 14.9642, 15.5713, 16.1639, 16.7435, 17.3109, 17.8673, 18.4136, 18.9506, 19.4788, 19.9989, 20.5112, 21.0164, 21.5147, 22.0067, 22.4925, 22.9724]
+        omega_op: [2.1486, 2.3397, 2.5309,  2.722, 2.9132, 3.1043, 3.2955, 3.4866, 3.6778, 3.8689, 4.0601, 4.2512, 4.4424, 4.6335, 4.8247, 5.0159,  5.207, 5.3982, 5.5893, 5.7805, 5.9716, 6.1628, 6.3539, 6.5451, 6.7362, 6.9274, 7.1185, 7.3097, 7.5008, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56]
+    gear_ratio: 1
+    torque_control:
+        VS_KP: -38609162.66552
+        VS_KI: -4588245.18720
+    
+    
+    tower:  # (could remove some entries that don't apply for the tower)
+        dlsMax       :  5.0     # maximum node splitting section amount; can't be 0
+    
+        name      :  tower                     # [-]    an identifier (no longer has to be number)       
+        type      :  1                         # [-]    
+        rA        :  [ 0, 0,  15]              # [m]    end A coordinates
+        rB        :  [ 0, 0, 144.582]          # [m]    and B coordinates
+        shape     :  circ                      # [-]    circular or rectangular
+        gamma     :  0.0                       # [deg]   twist angle about the member's z-axis
+        
+        # --- outer shell including hydro---
+        stations  :  [ 15,  28,  28.001,  41,  41.001,  54,  54.001,  67,  67.001,  80,  80.001,  93,  93.001,  106,  106.001,  119,  119.001,  132,  132.001,  144.582 ]    # [-]    location of stations along axis. Will be normalized such that start value maps to rA and end value to rB
+        d         :  [ 10,  9.964,  9.964,  9.967,  9.967,  9.927,  9.927,  9.528,  9.528,  9.149,  9.149,  8.945,  8.945,  8.735,  8.735,  8.405,  8.405,  7.321,  7.321,  6.5 ]    # [m]    diameters if circular or side lengths if rectangular (can be pairs)
+        t         :  [ 0.082954,  0.082954,  0.083073,  0.083073,  0.082799,  0.082799,  0.0299,  0.0299,  0.027842,  0.027842,  0.025567,  0.025567,  0.022854,  0.022854,  0.02025,  0.02025,  0.018339,  0.018339,  0.021211,  0.021211 ]                     # [m]    wall thicknesses (scalar or list of same length as stations)
+        Cd        :  0.0                       # [-]    transverse drag coefficient       (optional, scalar or list of same length as stations)
+        Ca        :  0.0                       # [-]    transverse added mass coefficient (optional, scalar or list of same length as stations)
+        # (neglecting axial coefficients for now)
+        CdEnd     :  0.0                       # [-]    end axial drag coefficient        (optional, scalar or list of same length as stations)
+        CaEnd     :  0.0                       # [-]    end axial added mass coefficient  (optional, scalar or list of same length as stations)
+        rho_shell :  7850                      # [kg/m3]   material density 
+
+platform:
+
+  type         :  FOWT
+  potModMaster :   1      # [int] master switch for potMod variables; 0=keeps all member potMod vars the same, 1=turns all potMod vars to False (no HAMS), 2=turns all potMod vars to True (no strip)
+  dlsMax       :  5.0     # maximum node splitting section amount for platform members; can't be 0
+  qtfPath      :  'IEA-15-240-RWT-UMaineSemi.12d'      # path to the qtf file for the platform
+  rFair        :  58
+  zFair        :  -14
+
+  members:   # list all members here
+      
+    - name      :  center_column             # [-]    an identifier (no longer has to be number)       
+      type      :  2                         # [-]    
+      rA        :  [ 0, 0, -20]              # [m]    end A coordinates
+      rB        :  [ 0, 0,  15]              # [m]    and B coordinates
+      shape     :  circ                      # [-]    circular or rectangular
+      gamma     :  0.0                       # [deg]  twist angle about the member's z-axis
+      potMod    :  True                      # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory
+      # --- outer shell including hydro---
+      stations  :  [0, 1]                    # [-]    location of stations along axis. Will be normalized such that start value maps to rA and end value to rB
+      d         :  10.0                      # [m]    diameters if circular or side lengths if rectangular (can be pairs)
+      t         :  0.05                      # [m]    wall thicknesses (scalar or list of same length as stations)
+      Cd        :  0.6                       # [-]    transverse drag coefficient       (optional, scalar or list of same length as stations)
+      Ca        :  0.93                      # [-]    transverse added mass coefficient (optional, scalar or list of same length as stations)
+      CdEnd     :  0.6                       # [-]    end axial drag coefficient        (optional, scalar or list of same length as stations)
+      CaEnd     :  1.0                       # [-]    end axial added mass coefficient  (optional, scalar or list of same length as stations)
+      rho_shell :  7850                      # [kg/m3] 
+      # --- handling of end caps or any internal structures if we need them ---
+      cap_stations :  [ 0    ]               # [m]  location along member of any inner structures (in same scaling as set by 'stations')
+      cap_t        :  [ 0.001  ]             # [m]  thickness of any internal structures
+      cap_d_in     :  [ 0    ]               # [m]  inner diameter of internal structures (0 for full cap/bulkhead, >0 for a ring shape)
+
+      
+    - name      :  outer_column              # [-]    an identifier (no longer has to be number)       
+      type      :  2                         # [-]    
+      rA        :  [51.75, 0, -20]           # [m]    end A coordinates
+      rB        :  [51.75, 0,  15]           # [m]    and B coordinates
+      heading   :  [ 60, 180, 300]           # [deg]  heading rotation of column about z axis (for repeated members)
+      shape     :  circ                      # [-]    circular or rectangular
+      gamma     :  0.0                       # [deg]  twist angle about the member's z-axis
+      potMod    :  True                      # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory
+      # --- outer shell including hydro---
+      stations  :  [0, 35]                    # [-]    location of stations along axis. Will be normalized such that start value maps to rA and end value to rB
+      d         :  12.5                      # [m]    diameters if circular or side lengths if rectangular (can be pairs)
+      t         :  0.05                      # [m]    wall thicknesses (scalar or list of same length as stations)
+      Cd        :  0.6                       # [-]    transverse drag coefficient       (optional, scalar or list of same length as stations)
+      Ca        :  0.93                      # [-]    transverse added mass coefficient (optional, scalar or list of same length as stations)
+      CdEnd     :  1.0                       # [-]    end axial drag coefficient        (optional, scalar or list of same length as stations)
+      CaEnd     :  0.7 # value of 3.0 gives more heave response        # [-]    end axial added mass coefficient  (optional, scalar or list of same length as stations)
+      rho_shell :  7850                      # [kg/m3] 
+      # --- ballast ---
+      l_fill    :  1.4                       # [m]
+      rho_fill  :  5000                      # [kg/m3]
+      # --- handling of end caps or any internal structures if we need them ---
+      cap_stations :  [ 0    ]               # [m]  location along member of any inner structures (in same scaling as set by 'stations')
+      cap_t        :  [ 0.001  ]             # [m]  thickness of any internal structures
+      cap_d_in     :  [ 0    ]               # [m]  inner diameter of internal structures (0 for full cap/bulkhead, >0 for a ring shape)
+
+      
+    - name      :  pontoon                   # [-]    an identifier (no longer has to be number)       
+      type      :  2                         # [-]    
+      rA        :  [  5  , 0, -16.5]         # [m]    end A coordinates
+      rB        :  [ 45.5, 0, -16.5]         # [m]    and B coordinates
+      heading   :  [ 60, 180, 300]           # [deg]  heading rotation of column about z axis (for repeated members)
+      shape     :  rect                      # [-]    circular or rectangular
+      gamma     :  0.0                       # [deg]  twist angle about the member's z-axis
+      potMod    :  False                     # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory
+      # --- outer shell including hydro---
+      stations  :  [0, 40.5]                    # [-]    location of stations along axis. Will be normalized such that start value maps to rA and end value to rB
+      d         :  [12.4, 7.0]               # [m]    diameters if circular or side lengths if rectangular (can be pairs)
+      t         :  0.05                      # [m]    wall thicknesses (scalar or list of same length as stations)
+      Cd        :  [1.5, 2.2 ]               # [-]    transverse drag coefficient       (optional, scalar or list of same length as stations)
+      Ca        :  [2.2, 0.2 ]               # [-]    transverse added mass coefficient (optional, scalar or list of same length as stations)
+      CdEnd     :  0.0                       # [-]    end axial drag coefficient        (optional, scalar or list of same length as stations)
+      CaEnd     :  0.0                       # [-]    end axial added mass coefficient  (optional, scalar or list of same length as stations)
+      rho_shell :  7850                      # [kg/m3]
+      l_fill    :  40.5                      # [m]
+      rho_fill  :  1025.0                    # [kg/m3]
+      
+      
+    - name      :  upper_support             # [-]    an identifier (no longer has to be number)       
+      type      :  2                         # [-]    
+      rA        :  [  5  , 0, 14.545]        # [m]    end A coordinates
+      rB        :  [ 45.5, 0, 14.545]        # [m]    and B coordinates
+      heading   :  [ 60, 180, 300]           # [deg]  heading rotation of column about z axis (for repeated members)
+      shape     :  circ                      # [-]    circular or rectangular
+      gamma     :  0.0                       # [deg]  twist angle about the member's z-axis
+      potMod    :  False                     # [bool] Whether to model the member with potential flow (BEM model) plus viscous drag or purely strip theory
+      # --- outer shell including hydro---
+      stations  :  [0, 1]                    # [-]    location of stations along axis. Will be normalized such that start value maps to rA and end value to rB
+      d         :  0.91                      # [m]    diameters if circular or side lengths if rectangular (can be pairs)
+      t         :  0.01                      # [m]    wall thicknesses (scalar or list of same length as stations)
+      Cd        :  0.0                       # [-]    transverse drag coefficient       (optional, scalar or list of same length as stations)
+      Ca        :  0.0                       # [-]    transverse added mass coefficient (optional, scalar or list of same length as stations)
+      CdEnd     :  0.0                       # [-]    end axial drag coefficient        (optional, scalar or list of same length as stations)
+      CaEnd     :  0.0                       # [-]    end axial added mass coefficient  (optional, scalar or list of same length as stations)
+      rho_shell :  7850                      # [kg/m3]
+
+
+# ----- Mooring system -----
+
+# Mooring system descriptions (each for an individual FOWT with no sharing)
+mooring_systems:
+ 
+    ms1:
+        name: 2-line semi-taut polyester mooring system with a third line shared
+        
+        keys: [MooringConfigID,  heading, anchorType, lengthAdjust] 
+        data:
+          - [  semitaut-poly_1,  150 ,   suction1,   0 ]
+          - [  semitaut-poly_1,  270 ,   suction1,   0 ]
+          - [  semitaut-poly_1,   30 ,   suction1,   0 ]
+
+
+# Mooring line configurations
+mooring_line_configs:
+
+    semitaut-poly_1:  # mooring line configuration identifier
+    
+        name: Semitaut polyester configuration 1  # descriptive name
+        
+        span: 642
+        
+        sections:                 #in order from anchor to fairlead
+          - mooringFamily: chain      # ID of a mooring line section type
+            d_nom: .1549
+            length: 497.7            # [m] usntretched length of line section
+            adjustable: True      # flags that this section could be adjusted to accommodate different spacings...
+          - connectorType: h_link   
+          - mooringFamily: polyester        # ID of a mooring line section type
+            d_nom: .182
+            length: 199.8           # [m] length (unstretched)
+
+# Mooring connector properties
+mooring_connector_types:
+    
+    h_link:
+        m  : 140    # [kg]  component mass
+        v : 0.13   # [m^3] component volumetric displacement
+
+# Anchor type properties
+anchor_types:
+
+    drag-embedment1:
+        type   :   DEA   # type of anchor
+        A      :    10   # net area of anchor's fluke [m^2]
+        zlug   :     8   # embedded depth of padeye [m]
+
+    suction1:
+        type   :  suction
+        L      :     8.40  # length of pile [m]
+        D      :     2.45  # diameter of pile [m]
+        zlug   :     5.32  # embedded depth of padeye [m]
+
diff --git a/examples/Inputs/OntologySample600m_shared.yaml b/examples/Inputs/OntologySample600m_shared.yaml
index 675d2f94..6e450754 100644
--- a/examples/Inputs/OntologySample600m_shared.yaml
+++ b/examples/Inputs/OntologySample600m_shared.yaml
@@ -37,7 +37,7 @@ site:
         
         type_array:
           - [mud_soft ,  mud_firm ,   mud_soft]  
-          - [mud_soft ,  mud_soft     ,   mud_soft]  
+          - [mud_soft ,  mud_soft ,   mud_soft]  
           - [mud_soft ,  mud_firm ,   mud_soft]  
 
         soil_types:   # dictionary-based approach
@@ -1484,17 +1484,17 @@ mooring_connector_types:
 # Anchor type properties
 anchor_types:
   suction_pile1:
-    type   :  suction_pile
+    type   :  suction
     L      :     16.4  # length of pile [m]
     D      :     5.45  # diameter of pile [m]
     zlug   :     9.32  # embedded depth of padeye [m]
   d-g_pile1:
-    type   :  dandg_pile
+    type   :  dandg
     L      :     50 # length of pile [m]
     D      :     3 # diameter of pile [m]
     zlug   :     0 # embedded depth [m]
   driven_pile1:
-    type   :  driven_pile
+    type   :  driven
     L      :     20 # pile length [m]
     D      :     1.5 # pile diameter [m]
     zlug   :     3 # embedded depth [m
diff --git a/examples/Inputs/checkyaml.yaml b/examples/Inputs/checkyaml.yaml
new file mode 100644
index 00000000..59a84805
--- /dev/null
+++ b/examples/Inputs/checkyaml.yaml
@@ -0,0 +1,1331 @@
+site:
+  seabed:
+    x: [-10901.0, 0.0, 10000.0]
+    y: [-10900.0, 0.0, 10000.0]
+    type_array:
+    - [mud_soft, mud_firm, mud_soft]
+    - [mud_soft, mud_firm, mud_soft]
+    - [mud_soft, mud_firm, mud_soft]
+    soil_types:
+      mud_soft:
+        layers:
+        - Su0: [2.39]
+          k: [1.41]
+          depth: [0]
+          top: 0
+          bottom: 50
+          soil_type: mud_soft
+        Su0: [2.39]
+        k: [1.41]
+        alpha: [0.7]
+        gamma: [8.7]
+        phi: [0.0]
+        UCS: [7.0]
+        Em: [50.0]
+      mud_firm:
+        layers:
+        - Su0: [23.94]
+          k: [2.67]
+          depth: [0]
+          top: 0
+          bottom: 50
+          soil_type: mud_firm
+        Su0: [2.39]
+        k: [1.41]
+        alpha: [0.7]
+        gamma: [8.7]
+        phi: [0.0]
+        UCS: [7.0]
+        Em: [50.0]
+  bathymetry:
+    x: [-3000.0, 3500.0, 10000.0]
+    y: [-3000.0, 3500.0, 10000.0]
+    depths:
+    - [200.1, 207.25625, 240.0]
+    - [200.34375, 206.133984375, 208.3125]
+    - [210.7, 196.29375000000002, 185.0]
+  boundaries:
+    x_y:
+    - [-2000.0, -2000.0]
+    - [-2000.0, 8000.0]
+    - [8000.0, 8000.0]
+    - [8000.0, -2000.0]
+    - [-2000.0, -2000.0]
+  general:
+    water_depth: 200.0
+    rho_air: 1.225
+    rho_water: 1025.0
+    mu_air: 1.81e-05
+array:
+  keys: [ID, topsideID, platformID, mooringID, x_location, y_location, 
+      z_location, heading_adjust]
+  data:
+  - [fowt0, 1, 1, ms0, -600.0, -800.0, 0.0, 0.0]
+  - [fowt1, 1, 1, ms0, 1100.0, -800.0, 0.0, 0.0]
+  - [fowt2, 1, 1, ms0, 2800.0, -800.0, 0.0, 0.0]
+  - [fowt3, 1, 1, ms0, 4500.0, -800.0, 0.0, 0.0]
+  - [fowt4, 1, 1, ms0, 6200.0, -800.0, 0.0, 0.0]
+  - [fowt5, 1, 1, ms0, -600.0, 1100.0, 0.0, 0.0]
+  - [fowt6, 1, 1, ms0, 1100.0, 1100.0, 0.0, 0.0]
+  - [fowt7, 1, 1, ms0, 2800.0, 1100.0, 0.0, 0.0]
+  - [fowt8, 1, 1, ms0, 4500.0, 1100.0, 0.0, 0.0]
+  - [fowt9, 1, 1, ms0, 6200.0, 1100.0, 0.0, 0.0]
+  - [fowt10, 1, 1, ms0, -600.0, 3000.0, 0.0, 0.0]
+  - [fowt11, 1, 1, ms0, 1100.0, 3000.0, 0.0, 0.0]
+  - [fowt12, 1, 1, ms0, 2800.0, 3000.0, 0.0, 0.0]
+  - [fowt13, 1, 1, ms0, 4500.0, 3000.0, 0.0, 0.0]
+  - [fowt14, 1, 1, ms0, 6200.0, 3000.0, 0.0, 0.0]
+  - [fowt15, 1, 1, ms0, -600.0, 4900.0, 0.0, 0.0]
+  - [fowt16, 1, 1, ms0, 1100.0, 4900.0, 0.0, 0.0]
+  - [fowt17, 1, 1, ms0, 2800.0, 4900.0, 0.0, 0.0]
+  - [fowt18, 1, 1, ms0, 4500.0, 4900.0, 0.0, 0.0]
+  - [fowt19, 1, 1, ms0, 6200.0, 4900.0, 0.0, 0.0]
+  - [fowt20, 1, 1, ms0, -600.0, 6800.0, 0.0, 0.0]
+  - [fowt21, 1, 1, ms0, 1100.0, 6800.0, 0.0, 0.0]
+  - [fowt22, 1, 1, ms0, 2800.0, 6800.0, 0.0, 0.0]
+  - [fowt23, 1, 1, ms0, 4500.0, 6800.0, 0.0, 0.0]
+  - [fowt24, 1, 1, ms0, 6200.0, 6800.0, 0.0, 0.0]
+platform:
+  type: FOWT
+  potModMaster: 1
+  dlsMax: 5.0
+  qtfPath: IEA-15-240-RWT-UMaineSemi.12d
+  rFair: 58
+  zFair: -14
+  members:
+  - name: center_column
+    type: 2
+    rA: [0, 0, -20]
+    rB: [0, 0, 15]
+    shape: circ
+    gamma: 0.0
+    potMod: false
+    stations: [0, 1]
+    d: 10.0
+    t: 0.05
+    Cd: 0.6
+    Ca: 0.93
+    CdEnd: 0.6
+    CaEnd: 1.0
+    rho_shell: 7850
+    cap_stations: [0]
+    cap_t: [0.001]
+    cap_d_in: [0]
+    dlsMax: 5.0
+    headings: 0.0
+  - name: outer_column
+    type: 2
+    rA: [51.75, 0, -20]
+    rB: [51.75, 0, 15]
+    heading: [60, 180, 300]
+    shape: circ
+    gamma: 0.0
+    potMod: false
+    stations: [0, 35]
+    d: 12.5
+    t: 0.05
+    Cd: 0.6
+    Ca: 0.93
+    CdEnd: 1.0
+    CaEnd: 0.7
+    rho_shell: 7850
+    l_fill: 1.4
+    rho_fill: 5000
+    cap_stations: [0]
+    cap_t: [0.001]
+    cap_d_in: [0]
+    dlsMax: 5.0
+    headings: [60.0, 180.0, 300.0]
+  - name: pontoon
+    type: 2
+    rA: [5, 0, -16.5]
+    rB: [45.5, 0, -16.5]
+    heading: [60, 180, 300]
+    shape: rect
+    gamma: 0.0
+    potMod: false
+    stations: [0, 40.5]
+    d: [12.4, 7.0]
+    t: 0.05
+    Cd: [1.5, 2.2]
+    Ca: [2.2, 0.2]
+    CdEnd: 0.0
+    CaEnd: 0.0
+    rho_shell: 7850
+    l_fill: 40.5
+    rho_fill: 1025.0
+    dlsMax: 5.0
+    headings: [60.0, 180.0, 300.0]
+  - name: upper_support
+    type: 2
+    rA: [5, 0, 14.545]
+    rB: [45.5, 0, 14.545]
+    heading: [60, 180, 300]
+    shape: circ
+    gamma: 0.0
+    potMod: false
+    stations: [0, 1]
+    d: 0.91
+    t: 0.01
+    Cd: 0.0
+    Ca: 0.0
+    CdEnd: 0.0
+    CaEnd: 0.0
+    rho_shell: 7850
+    dlsMax: 5.0
+    headings: [60.0, 180.0, 300.0]
+topsides:
+- type: Turbine
+  mRNA: 991000
+  IxRNA: 0
+  IrRNA: 0
+  xCG_RNA: 0
+  hHub: 150.0
+  Fthrust: '1500.0E3'
+  I_drivetrain: 318628138.0
+  nBlades: 3
+  Zhub: 150.0
+  Rhub: 3.97
+  precone: 4.0
+  shaft_tilt: 6.0
+  overhang: -12.0313
+  aeroMod: 1
+  blade:
+    precurveTip: -3.9999999999999964
+    presweepTip: 0.0
+    Rtip: 120.96999999936446
+    geometry:
+    - [8.004, 5.228, 15.474, 0.035, 0.0]
+    - [12.039, 5.321, 14.692, 0.084, 0.0]
+    - [16.073, 5.458, 13.33, 0.139, 0.0]
+    - [20.108, 5.602, 11.644, 0.192, 0.0]
+    - [24.142, 5.718, 9.927, 0.232, 0.0]
+    - [28.177, 5.767, 8.438, 0.25, 0.0]
+    - [32.211, 5.713, 7.301, 0.25, 0.0]
+    - [36.246, 5.536, 6.232, 0.246, 0.0]
+    - [40.28, 5.291, 5.23, 0.24, 0.0]
+    - [44.315, 5.035, 4.348, 0.233, 0.0]
+    - [48.349, 4.815, 3.606, 0.218, 0.0]
+    - [52.384, 4.623, 2.978, 0.178, 0.0]
+    - [56.418, 4.432, 2.423, 0.1, 0.0]
+    - [60.453, 4.245, 1.924, 0.0, 0.0]
+    - [64.487, 4.065, 1.467, -0.112, 0.0]
+    - [68.522, 3.896, 1.056, -0.244, 0.0]
+    - [72.556, 3.735, 0.692, -0.415, 0.0]
+    - [76.591, 3.579, 0.355, -0.62, 0.0]
+    - [80.625, 3.425, 0.019, -0.846, 0.0]
+    - [84.66, 3.268, -0.358, -1.08, 0.0]
+    - [88.694, 3.112, -0.834, -1.33, 0.0]
+    - [92.729, 2.957, -1.374, -1.602, 0.0]
+    - [96.763, 2.8, -1.848, -1.895, 0.0]
+    - [100.798, 2.637, -2.136, -2.202, 0.0]
+    - [104.832, 2.464, -2.172, -2.523, 0.0]
+    - [108.867, 2.283, -2.108, -2.864, 0.0]
+    - [112.901, 2.096, -1.953, -3.224, 0.0]
+    - [116.936, 1.902, -1.662, -3.605, 0.0]
+    airfoils:
+    - [0.0, circular]
+    - [0.02, circular]
+    - [0.15, SNL-FFA-W3-500]
+    - [0.24517, FFA-W3-360]
+    - [0.32884, FFA-W3-330blend]
+    - [0.43918, FFA-W3-301]
+    - [0.53767, FFA-W3-270blend]
+    - [0.63821, FFA-W3-241]
+    - [0.77174, FFA-W3-211]
+    - [1.0, FFA-W3-211]
+  airfoils:
+  - name: circular
+    relative_thickness: 1.0
+    data:
+    - [-179.9087, 0.0001, 0.35, -0.0001]
+    - [179.9087, 0.0001, 0.35, -0.0001]
+  - name: SNL-FFA-W3-500
+    relative_thickness: 0.5
+    data:
+    - [-179.966, 0.0, 0.0844, 0.0]
+    - [-170.0, 0.4419, 0.0844, 0.3125]
+    - [-160.0002, 0.8837, 0.1268, 0.2831]
+    - [-149.9998, 0.9674, 0.2927, 0.2632]
+    - [-139.9999, 0.7801, 0.497, 0.2048]
+    - [-130.0001, 0.6293, 0.7161, 0.1932]
+    - [-120.0003, 0.4785, 0.9246, 0.2008]
+    - [-109.9999, 0.3189, 1.0985, 0.2136]
+    - [-100.0, 0.1553, 1.2182, 0.2221]
+    - [-90.0002, 0.0, 1.2707, 0.2198]
+    - [-79.9998, -0.1553, 1.2182, 0.196]
+    - [-70.0, -0.3189, 1.0985, 0.1635]
+    - [-60.0001, -0.4784, 0.9246, 0.1285]
+    - [-49.9997, -0.6293, 0.7161, 0.0965]
+    - [-39.9999, -0.7801, 0.497, 0.0716]
+    - [-30.0001, -0.9674, 0.2927, 0.0522]
+    - [-20.0002, -1.0281, 0.1499, -0.0063]
+    - [-19.7499, -1.0243, 0.1472, -0.0089]
+    - [-19.2502, -1.0052, 0.1447, -0.0099]
+    - [-18.9999, -0.9971, 0.1433, -0.0105]
+    - [-18.75, -1.0052, 0.1403, -0.011]
+    - [-18.5002, -0.9995, 0.1386, -0.0116]
+    - [-18.2499, -0.9908, 0.1373, -0.012]
+    - [-18.0, -0.9815, 0.136, -0.0126]
+    - [-17.4998, -0.9764, 0.1322, -0.0135]
+    - [-17.25, -0.9705, 0.1306, -0.0139]
+    - [-17.0002, -0.9655, 0.129, -0.0143]
+    - [-16.7498, -0.9662, 0.1268, -0.0147]
+    - [-16.5, -0.9544, 0.1258, -0.0151]
+    - [-16.2502, -0.9444, 0.1246, -0.0155]
+    - [-15.9998, -0.9405, 0.1229, -0.0158]
+    - [-15.75, -0.9433, 0.1206, -0.0161]
+    - [-15.5002, -0.933, 0.1195, -0.0164]
+    - [-15.2498, -0.9211, 0.1185, -0.0168]
+    - [-14.7502, -0.9158, 0.115, -0.0173]
+    - [-14.4998, -0.907, 0.1138, -0.0175]
+    - [-14.25, -0.8959, 0.1127, -0.0178]
+    - [-14.0002, -0.8926, 0.111, -0.0181]
+    - [-13.7498, -0.8808, 0.11, -0.0184]
+    - [-13.5, -0.8722, 0.1089, -0.0186]
+    - [-13.2502, -0.866, 0.1075, -0.0188]
+    - [-12.9998, -0.8626, 0.1059, -0.0188]
+    - [-12.75, -0.8489, 0.1051, -0.0192]
+    - [-12.5002, -0.8363, 0.1042, -0.0194]
+    - [-12.2498, -0.8363, 0.1023, -0.0194]
+    - [-12.0, -0.8271, 0.1013, -0.0196]
+    - [-11.7502, -0.8141, 0.1004, -0.0198]
+    - [-11.4998, -0.8004, 0.0997, -0.02]
+    - [-11.0002, -0.789, 0.0971, -0.0199]
+    - [-10.7498, -0.7862, 0.0956, -0.0196]
+    - [-10.5, -0.7747, 0.0948, -0.0194]
+    - [-10.2502, -0.7701, 0.094, -0.0184]
+    - [-9.9998, -0.7674, 0.0925, -0.0183]
+    - [-9.75, -0.7506, 0.0917, -0.0192]
+    - [-9.5002, -0.729, 0.0912, -0.0205]
+    - [-9.2498, -0.7095, 0.0902, -0.0224]
+    - [-9.0, -0.6855, 0.0895, -0.0247]
+    - [-8.7502, -0.659, 0.0891, -0.0267]
+    - [-8.4998, -0.6319, 0.0887, -0.0287]
+    - [-8.25, -0.6019, 0.0879, -0.032]
+    - [-8.0002, -0.5718, 0.0875, -0.0345]
+    - [-7.7498, -0.5424, 0.0873, -0.0367]
+    - [-7.5, -0.5098, 0.0868, -0.0399]
+    - [-7.2502, -0.4767, 0.0864, -0.043]
+    - [-6.9998, -0.4454, 0.0862, -0.0453]
+    - [-6.75, -0.4142, 0.086, -0.0476]
+    - [-6.5002, -0.3791, 0.0856, -0.051]
+    - [-6.2498, -0.346, 0.0853, -0.0538]
+    - [-6.0, -0.3144, 0.0852, -0.056]
+    - [-5.7502, -0.2817, 0.085, -0.0586]
+    - [-5.4998, -0.2461, 0.0847, -0.0619]
+    - [-5.25, -0.2133, 0.0846, -0.0644]
+    - [-5.0002, -0.1827, 0.0845, -0.0663]
+    - [-4.7498, -0.1494, 0.0843, -0.0688]
+    - [-4.5, -0.1158, 0.0842, -0.0715]
+    - [-4.2502, -0.0837, 0.084, -0.0737]
+    - [-3.9998, -0.0529, 0.084, -0.0756]
+    - [-3.75, -0.0225, 0.0839, -0.0774]
+    - [-3.5002, 0.0089, 0.0838, -0.0793]
+    - [-3.2498, 0.0392, 0.0838, -0.0811]
+    - [-3.0, 0.0686, 0.0838, -0.0826]
+    - [-2.7502, 0.0974, 0.0838, -0.0838]
+    - [-2.4998, 0.126, 0.0838, -0.0852]
+    - [-2.25, 0.1555, 0.0838, -0.0867]
+    - [-2.0002, 0.1853, 0.0838, -0.0883]
+    - [-1.7498, 0.2146, 0.0837, -0.0897]
+    - [-1.5, 0.243, 0.0837, -0.091]
+    - [-1.2502, 0.2713, 0.0838, -0.0921]
+    - [-0.9998, 0.3006, 0.0838, -0.0936]
+    - [-0.75, 0.3295, 0.0838, -0.0949]
+    - [-0.5002, 0.3578, 0.0838, -0.0961]
+    - [-0.2498, 0.3857, 0.0838, -0.0972]
+    - [0.0, 0.4135, 0.0838, -0.0983]
+    - [0.2298, 0.4425, 0.0839, -0.0995]
+    - [0.4698, 0.4715, 0.0839, -0.1008]
+    - [0.7002, 0.5003, 0.0839, -0.1019]
+    - [0.9402, 0.5286, 0.084, -0.1029]
+    - [1.17, 0.5567, 0.084, -0.104]
+    - [1.3997, 0.585, 0.0841, -0.105]
+    - [1.6398, 0.6135, 0.0841, -0.1061]
+    - [1.8701, 0.6417, 0.0842, -0.1072]
+    - [2.1102, 0.6697, 0.0842, -0.1082]
+    - [2.34, 0.6975, 0.0843, -0.1091]
+    - [2.5697, 0.7251, 0.0843, -0.11]
+    - [2.8098, 0.7528, 0.0844, -0.1109]
+    - [3.0401, 0.7807, 0.0845, -0.1119]
+    - [3.2802, 0.8083, 0.0846, -0.1128]
+    - [3.5099, 0.8358, 0.0846, -0.1137]
+    - [3.7403, 0.8631, 0.0847, -0.1146]
+    - [3.9798, 0.8902, 0.0847, -0.1153]
+    - [4.2101, 0.9173, 0.0848, -0.1161]
+    - [4.4502, 0.9444, 0.0849, -0.117]
+    - [4.6799, 0.9713, 0.085, -0.1178]
+    - [4.9102, 0.9981, 0.0851, -0.1185]
+    - [5.1497, 1.0249, 0.0852, -0.1192]
+    - [5.3801, 1.0515, 0.0853, -0.1199]
+    - [5.6201, 1.0779, 0.0853, -0.1206]
+    - [5.8499, 1.1041, 0.0854, -0.1212]
+    - [6.0802, 1.1302, 0.0856, -0.1218]
+    - [6.3197, 1.156, 0.0857, -0.1224]
+    - [6.5501, 1.1818, 0.0858, -0.123]
+    - [6.7901, 1.2076, 0.0859, -0.1235]
+    - [7.0199, 1.2334, 0.086, -0.124]
+    - [7.2502, 1.2589, 0.0861, -0.1245]
+    - [7.4903, 1.2841, 0.0862, -0.125]
+    - [7.72, 1.3088, 0.0864, -0.1254]
+    - [7.9601, 1.3331, 0.0865, -0.1257]
+    - [8.1899, 1.357, 0.0867, -0.1259]
+    - [8.4202, 1.381, 0.0869, -0.1262]
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+    - [2.0, 0.7041, 0.01345, -0.12028]
+    - [3.0, 0.84071, 0.01366, -0.12546]
+    - [4.0, 0.975, 0.01397, -0.13011]
+    - [5.0, 1.1068, 0.01437, -0.13425]
+    - [6.0, 1.23603, 0.01486, -0.13793]
+    - [7.0, 1.36223, 0.01547, -0.14108]
+    - [8.0, 1.48424, 0.01623, -0.14363]
+    - [9.0, 1.60097, 0.01718, -0.14545]
+    - [10.0, 1.7101, 0.01841, -0.14636]
+    - [11.0, 1.80957, 0.0201, -0.14635]
+    - [12.0, 1.89473, 0.02258, -0.14544]
+    - [13.0, 1.95698, 0.02671, -0.14378]
+    - [14.0, 1.98576, 0.0338, -0.14185]
+    - [15.0, 1.9926, 0.04333, -0.14004]
+    - [16.0, 1.99617, 0.05354, -0.13823]
+    - [18.0, 1.96398, 0.07706, -0.13351]
+    - [20.0, 1.81179, 0.11169, -0.13135]
+    - [24.0, 1.56073, 0.19103, -0.1466]
+    - [28.0, 1.46798, 0.27199, -0.17242]
+    - [32.0, 1.39203, 0.35131, -0.19417]
+    - [36.1429, 1.27731, 0.43913, -0.21792]
+    - [40.2857, 1.17689, 0.53115, -0.24115]
+    - [44.4286, 1.0835, 0.62546, -0.25734]
+    - [48.5714, 0.99253, 0.7201, -0.27354]
+    - [52.7143, 0.90112, 0.8131, -0.28862]
+    - [56.8571, 0.8076, 0.90255, -0.30311]
+    - [61.0, 0.71119, 0.98659, -0.31757]
+    - [65.1429, 0.61175, 1.06348, -0.33194]
+    - [69.2857, 0.50971, 1.13162, -0.34631]
+    - [73.4286, 0.40589, 1.18963, -0.36014]
+    - [77.5714, 0.30146, 1.23629, -0.37385]
+    - [81.7143, 0.19788, 1.27067, -0.38681]
+    - [85.8571, 0.09679, 1.29204, -0.39872]
+    - [90.0, 0.0, 1.3, -0.41063]
+    - [94.1429, -0.06775, 1.29204, -0.41683]
+    - [98.2857, -0.13851, 1.27067, -0.42303]
+    - [102.4286, -0.21103, 1.23629, -0.42628]
+    - [106.5714, -0.28412, 1.18963, -0.42745]
+    - [110.7143, -0.3568, 1.13162, -0.42817]
+    - [114.8571, -0.42823, 1.06348, -0.42673]
+    - [119.0, -0.49783, 0.98659, -0.42528]
+    - [123.1429, -0.56532, 0.90255, -0.42535]
+    - [127.2857, -0.63079, 0.8131, -0.42589]
+    - [131.4286, -0.69477, 0.7201, -0.42974]
+    - [135.5714, -0.75845, 0.62546, -0.43985]
+    - [139.7143, -0.82382, 0.53115, -0.44996]
+    - [143.8571, -0.89412, 0.43913, -0.46839]
+    - [148.0, -0.97442, 0.35131, -0.48743]
+    - [150.2857, -0.90482, 0.30532, -0.49447]
+    - [152.5714, -0.83522, 0.26146, -0.47732]
+    - [154.8571, -0.76562, 0.22, -0.46017]
+    - [157.1429, -0.69601, 0.18121, -0.44302]
+    - [159.4286, -0.62641, 0.14533, -0.42586]
+    - [161.7143, -0.55681, 0.11258, -0.43502]
+    - [164.0, -0.48721, 0.08319, -0.45294]
+    - [166.2857, -0.41761, 0.05732, -0.47087]
+    - [168.5714, -0.34801, 0.04638, -0.4888]
+    - [170.8571, -0.27841, 0.04114, -0.45714]
+    - [173.1429, -0.2088, 0.03702, -0.34286]
+    - [175.4286, -0.1392, 0.03406, -0.22857]
+    - [177.7143, -0.0696, 0.03228, -0.11429]
+    - [179.9087, 0.0, 0.03169, 0.0]
+  - name: FFA-W3-360
+    relative_thickness: 0.36
+    data:
+    - [-179.9087, 0.0, 0.03715, 0.0]
+    - [-177.7143, 0.07178, 0.03774, 0.09143]
+    - [-175.4286, 0.14356, 0.03951, 0.18286]
+    - [-173.1429, 0.21534, 0.04245, 0.27429]
+    - [-170.8572, 0.28713, 0.04653, 0.36571]
+    - [-168.5716, 0.35891, 0.05174, 0.40313]
+    - [-166.2857, 0.43069, 0.06068, 0.40814]
+    - [-164.0, 0.50247, 0.08651, 0.41315]
+    - [-161.7145, 0.57425, 0.11586, 0.41816]
+    - [-159.4284, 0.64603, 0.14856, 0.42627]
+    - [-157.1428, 0.71781, 0.18439, 0.4437]
+    - [-154.8573, 0.7896, 0.22313, 0.46114]
+    - [-152.5714, 0.86138, 0.26453, 0.47857]
+    - [-150.2857, 0.93316, 0.30832, 0.496]
+    - [-148.0, 1.00494, 0.35424, 0.4883]
+    - [-143.8571, 0.91898, 0.44192, 0.46784]
+    - [-139.7143, 0.84406, 0.53379, 0.44803]
+    - [-135.5714, 0.77483, 0.62793, 0.43697]
+    - [-131.4286, 0.7079, 0.72238, 0.42591]
+    - [-127.2857, 0.64116, 0.8152, 0.4215]
+    - [-123.1429, 0.57335, 0.90444, 0.42058]
+    - [-119.0, 0.50388, 0.98826, 0.42024]
+    - [-114.8571, 0.43261, 1.06493, 0.42168]
+    - [-110.7143, 0.35981, 1.13285, 0.42312]
+    - [-106.5714, 0.28603, 1.19061, 0.42258]
+    - [-102.4286, 0.21209, 1.23704, 0.42163]
+    - [-98.2857, 0.13899, 1.27116, 0.41864]
+    - [-94.1429, 0.06787, 1.29229, 0.41277]
+    - [-90.0, 0.0, 1.3, 0.4069]
+    - [-85.8571, -0.06787, 1.29229, 0.39426]
+    - [-81.7143, -0.13899, 1.27116, 0.38162]
+    - [-77.5714, -0.21209, 1.23704, 0.36676]
+    - [-73.4286, -0.28603, 1.19061, 0.35033]
+    - [-69.2857, -0.35981, 1.13285, 0.33362]
+    - [-65.1429, -0.43261, 1.06493, 0.31561]
+    - [-61.0, -0.50388, 0.98826, 0.29759]
+    - [-56.8571, -0.57335, 0.90444, 0.27989]
+    - [-52.7143, -0.64116, 0.8152, 0.2623]
+    - [-48.5714, -0.7079, 0.72238, 0.24491]
+    - [-44.4286, -0.77483, 0.62793, 0.22794]
+    - [-40.2857, -0.84406, 0.53379, 0.21097]
+    - [-36.1429, -0.91898, 0.44192, 0.13525]
+    - [-32.0, -1.00494, 0.35424, 0.05517]
+    - [-28.0, -1.11306, 0.20494, 0.03211]
+    - [-24.0, -1.05425, 0.15434, 0.01268]
+    - [-20.0, -0.98247, 0.10967, -0.00282]
+    - [-18.0, -0.94173, 0.09249, -0.00741]
+    - [-16.0, -0.89333, 0.07597, -0.01107]
+    - [-14.0, -0.85472, 0.06054, -0.0125]
+    - [-12.0, -0.82348, 0.04641, -0.01177]
+    - [-10.0, -0.79541, 0.03441, -0.01082]
+    - [-8.0, -0.6365, 0.02548, -0.02769]
+    - [-6.0, -0.39095, 0.01994, -0.05107]
+    - [-4.0, -0.13071, 0.01653, -0.07148]
+    - [-2.0, 0.16173, 0.01507, -0.09179]
+    - [-1.0, 0.31121, 0.01477, -0.10119]
+    - [0.0, 0.45956, 0.01465, -0.10988]
+    - [1.0, 0.60566, 0.01466, -0.11776]
+    - [2.0, 0.74868, 0.01481, -0.12477]
+    - [3.0, 0.88862, 0.01507, -0.13098]
+    - [4.0, 1.02544, 0.01544, -0.13648]
+    - [5.0, 1.15878, 0.01593, -0.1413]
+    - [6.0, 1.28822, 0.01654, -0.1454]
+    - [7.0, 1.41282, 0.01731, -0.14875]
+    - [8.0, 1.5309, 0.01831, -0.15118]
+    - [9.0, 1.64065, 0.01963, -0.15262]
+    - [10.0, 1.73926, 0.0215, -0.1531]
+    - [11.0, 1.81971, 0.02445, -0.15254]
+    - [12.0, 1.87065, 0.02966, -0.15121]
+    - [13.0, 1.89221, 0.0377, -0.14969]
+    - [14.0, 1.8791, 0.04824, -0.14562]
+    - [15.0, 1.88111, 0.05838, -0.14358]
+    - [16.0, 1.86359, 0.06992, -0.14095]
+    - [18.0, 1.73324, 0.10166, -0.13711]
+    - [20.0, 1.59357, 0.13916, -0.14082]
+    - [24.0, 1.46708, 0.21002, -0.15693]
+    - [28.0, 1.44834, 0.282, -0.17979]
+    - [32.0, 1.43563, 0.35424, -0.20147]
+    - [36.1429, 1.31283, 0.44192, -0.22409]
+    - [40.2857, 1.2058, 0.53379, -0.24619]
+    - [44.4286, 1.1069, 0.62793, -0.26133]
+    - [48.5714, 1.01129, 0.72238, -0.27648]
+    - [52.7143, 0.91594, 0.8152, -0.29062]
+    - [56.8571, 0.81907, 0.90444, -0.30424]
+    - [61.0, 0.71982, 0.98826, -0.31787]
+    - [65.1429, 0.61801, 1.06493, -0.33154]
+    - [69.2857, 0.51401, 1.13285, -0.34522]
+    - [73.4286, 0.40862, 1.19061, -0.35846]
+    - [77.5714, 0.30299, 1.23704, -0.37161]
+    - [81.7143, 0.19855, 1.27116, -0.38405]
+    - [85.8571, 0.09695, 1.29229, -0.39547]
+    - [90.0, 0.0, 1.3, -0.4069]
+    - [94.1429, -0.06787, 1.29229, -0.41277]
+    - [98.2857, -0.13899, 1.27116, -0.41864]
+    - [102.4286, -0.21209, 1.23704, -0.42163]
+    - [106.5714, -0.28603, 1.19061, -0.42258]
+    - [110.7143, -0.35981, 1.13285, -0.42312]
+    - [114.8571, -0.43261, 1.06493, -0.42168]
+    - [119.0, -0.50388, 0.98826, -0.42024]
+    - [123.1429, -0.57335, 0.90444, -0.42058]
+    - [127.2857, -0.64116, 0.8152, -0.4215]
+    - [131.4286, -0.7079, 0.72238, -0.42591]
+    - [135.5714, -0.77483, 0.62793, -0.43697]
+    - [139.7143, -0.84406, 0.53379, -0.44803]
+    - [143.8571, -0.91898, 0.44192, -0.46784]
+    - [148.0, -1.00494, 0.35424, -0.4883]
+    - [150.2857, -0.93316, 0.30832, -0.496]
+    - [152.5714, -0.86138, 0.26453, -0.47857]
+    - [154.8571, -0.7896, 0.22313, -0.46114]
+    - [157.1429, -0.71781, 0.18439, -0.4437]
+    - [159.4286, -0.64603, 0.14856, -0.42627]
+    - [161.7143, -0.57425, 0.11586, -0.4353]
+    - [164.0, -0.50247, 0.08651, -0.45315]
+    - [166.2857, -0.43069, 0.06068, -0.471]
+    - [168.5714, -0.35891, 0.05174, -0.48884]
+    - [170.8571, -0.28713, 0.04653, -0.45714]
+    - [173.1429, -0.21534, 0.04245, -0.34286]
+    - [175.4286, -0.14356, 0.03951, -0.22857]
+    - [177.7143, -0.07178, 0.03774, -0.11429]
+    - [179.9087, 0.0, 0.03715, 0.0]
+  pitch_control:
+    GS_Angles: [0.06019804, 0.08713416, 0.10844806, 0.12685912, 0.14339822, 
+        0.1586021, 0.17279614, 0.18618935, 0.19892772, 0.21111989, 0.22285021, 
+        0.23417256, 0.2451469, 0.25580691, 0.26619545, 0.27632495, 0.28623134, 
+        0.29593266, 0.30544521, 0.314779, 0.32395154, 0.33297489, 0.3418577, 
+        0.35060844, 0.35923641, 0.36774807, 0.37614942, 0.38444655, 0.39264363, 
+        0.40074407]
+    GS_Kp: [-0.9394215, -0.80602855, -0.69555026, -0.60254912, -0.52318192, 
+        -0.45465531, -0.39489024, -0.34230736, -0.29568537, -0.25406506, 
+        -0.2166825, -0.18292183, -0.15228099, -0.12434663, -0.09877533, 
+        -0.0752794, -0.05361604, -0.0335789, -0.01499149, 0.00229803, 0.01842102,
+      0.03349169, 0.0476098, 0.0608629, 0.07332812, 0.0850737, 0.0961602, 
+        0.10664158, 0.11656607, 0.12597691]
+    GS_Ki: [-0.07416547, -0.06719673, -0.0614251, -0.05656651, -0.0524202, 
+        -0.04884022, -0.04571796, -0.04297091, -0.04053528, -0.03836094, 
+        -0.03640799, -0.03464426, -0.03304352, -0.03158417, -0.03024826, 
+        -0.02902079, -0.02788904, -0.02684226, -0.02587121, -0.02496797, 
+        -0.02412567, -0.02333834, -0.02260078, -0.02190841, -0.0212572, 
+        -0.02064359, -0.0200644, -0.01951683, -0.01899836, -0.01850671]
+    Fl_Kp: -9.35
+  wt_ops:
+    v: [3.0, 3.266896551724138, 3.533793103448276, 3.800689655172414, 
+        4.067586206896552, 4.334482758620689, 4.601379310344828, 
+        4.868275862068966, 5.135172413793104, 5.402068965517241, 
+        5.6689655172413795, 5.935862068965518, 6.2027586206896554, 
+        6.469655172413793, 6.736551724137931, 7.00344827586207, 
+        7.270344827586207, 7.537241379310345, 7.804137931034483, 
+        8.071034482758622, 8.337931034482759, 8.604827586206897, 
+        8.871724137931036, 9.138620689655173, 9.405517241379311, 
+        9.672413793103448, 9.939310344827586, 10.206206896551725, 
+        10.473103448275863, 10.74, 11.231724137931035, 11.723448275862069, 
+        12.215172413793104, 12.706896551724139, 13.198620689655172, 
+        13.690344827586207, 14.182068965517242, 14.673793103448276, 
+        15.16551724137931, 15.657241379310346, 16.14896551724138, 
+        16.640689655172416, 17.13241379310345, 17.624137931034483, 
+        18.11586206896552, 18.607586206896553, 19.099310344827586, 
+        19.591034482758623, 20.082758620689653, 20.57448275862069, 
+        21.066206896551726, 21.557931034482756, 22.049655172413793, 
+        22.54137931034483, 23.03310344827586, 23.524827586206897, 
+        24.016551724137933, 24.508275862068963, 25.0]
+    pitch_op: [-0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, 
+        -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, 
+        -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, -0.25, 
+        -0.25, 3.57152, 5.12896, 6.36736, 7.43866, 8.40197, 9.28843, 10.1161, 
+        10.8974, 11.641, 12.3529, 13.038, 13.6997, 14.3409, 14.9642, 15.5713, 
+        16.1639, 16.7435, 17.3109, 17.8673, 18.4136, 18.9506, 19.4788, 19.9989, 
+        20.5112, 21.0164, 21.5147, 22.0067, 22.4925, 22.9724]
+    omega_op: [2.1486, 2.3397, 2.5309, 2.722, 2.9132, 3.1043, 3.2955, 3.4866, 
+        3.6778, 3.8689, 4.0601, 4.2512, 4.4424, 4.6335, 4.8247, 5.0159, 5.207, 
+        5.3982, 5.5893, 5.7805, 5.9716, 6.1628, 6.3539, 6.5451, 6.7362, 6.9274, 
+        7.1185, 7.3097, 7.5008, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 
+        7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 
+        7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56, 7.56]
+  gear_ratio: 1
+  torque_control:
+    VS_KP: -38609162.66552
+    VS_KI: -4588245.1872
+  tower:
+    dlsMax: 5.0
+    name: tower
+    type: 1
+    rA: [0, 0, 15]
+    rB: [0, 0, 144.582]
+    shape: circ
+    gamma: 0.0
+    stations: [15, 28, 28.001, 41, 41.001, 54, 54.001, 67, 67.001, 80, 80.001, 
+        93, 93.001, 106, 106.001, 119, 119.001, 132, 132.001, 144.582]
+    d: [10, 9.964, 9.964, 9.967, 9.967, 9.927, 9.927, 9.528, 9.528, 9.149, 9.149,
+      8.945, 8.945, 8.735, 8.735, 8.405, 8.405, 7.321, 7.321, 6.5]
+    t: [0.082954, 0.082954, 0.083073, 0.083073, 0.082799, 0.082799, 0.0299, 
+        0.0299, 0.027842, 0.027842, 0.025567, 0.025567, 0.022854, 0.022854, 
+        0.02025, 0.02025, 0.018339, 0.018339, 0.021211, 0.021211]
+    Cd: 0.0
+    Ca: 0.0
+    CdEnd: 0.0
+    CaEnd: 0.0
+    rho_shell: 7850
+array_mooring:
+  anchor_keys: [ID, type, x, y, embedment]
+  anchor_data: []
+  line_keys: [MooringConfigID, endA, endB, headingA, headingB, lengthAdjust]
+  line_data: []
+mooring_systems:
+  ms0:
+    keys: [MooringConfigID, heading, anchorType, lengthAdjust]
+    data:
+    - ['0', 150.0, drag-embedment1, 0]
+    - ['0', 270.0, drag-embedment1, 0]
+    - ['0', 30.0, drag-embedment1, 0]
+mooring_line_configs:
+  '0':
+    name: '0'
+    span: 642.0
+    sections:
+    - type: 0
+      length: 497.7
+    - connectorType: h_link
+    - type: 1
+      length: 199.8
+mooring_line_types:
+  0:
+    name: 0
+    d_vol: 0.27882
+    m: 479.88020000000006
+    EA: 2053741190.7845445
+    w: 4093.6781602294
+    MBL: 20893381.207590003
+    EAd: 0.0
+    EAd_Lm: 0.0
+    d_nom: 0.1549
+    cost: 2005.5584560000004
+    notes: made with getLineProps
+    material: chain
+    Cd: 1.333
+    CdAx: 0.639
+    Ca: 1.0
+    CaAx: 0.5
+  1:
+    name: 1
+    d_vol: 0.14405282886514317
+    m: 22.491196000000002
+    EA: 142830688.0
+    w: 56.75848886217392
+    MBL: 10202192.0
+    EAd: 118345427.2
+    EAd_Lm: 40.0
+    d_nom: 0.182
+    cost: 441.9688
+    notes: made with getLineProps
+    material: polyester
+    Cd: 2.021
+    CdAx: 0.0
+    Ca: 1.1
+    CaAx: 0.15
+mooring_connector_types:
+  h_link:
+    m: 140.0
+    v: 0.13
+    type: h_link
+    CdA: 0
+anchor_types:
+  drag-embedment1:
+    type: DEA
+    A: 10
+    zlug: 10
+cables: []
+dynamic_cable_configs: {}
+cable_types: {}
+cable_appendages: {}
diff --git a/examples/Inputs/output_MD.dat b/examples/Inputs/output_MD.dat
index 38578132..3837a191 100644
--- a/examples/Inputs/output_MD.dat
+++ b/examples/Inputs/output_MD.dat
@@ -11,22 +11,22 @@ TypeName      Diam     Mass/m    Cd     Ca      CdEnd    CaEnd
 ----------------------- BODIES ------------------------------------------------------
 ID   Attachment    X0     Y0     Z0     r0      p0     y0     Mass          CG*          I*      Volume   CdA*   Ca*
 (#)     (-)        (m)    (m)    (m)   (deg)   (deg)  (deg)   (kg)          (m)         (kg-m^2)  (m^3)   (m^2)  (-)
-1     free        -1499.91 1499.96 0.00   0.00   0.00   -0.00  1.9911e+07  0.00|0.00|-2.54 0.000e+00 19480.10   0.00  0.00
+1     free        -599.89 -799.82 0.00   -0.00  0.00   0.00   1.9911e+07  0.00|0.00|-2.54 0.000e+00 19480.10   0.00  0.00
 ---------------------- RODS ---------------------------------------------------------
 ID   RodType  Attachment  Xa    Ya    Za    Xb    Yb    Zb   NumSegs  RodOutputs
 (#)  (name)    (#/key)    (m)   (m)   (m)   (m)   (m)   (m)  (-)       (-)
 ---------------------- POINTS -------------------------------------------------------
 ID  Attachment     X       Y       Z           Mass  Volume  CdA    Ca
 (#)   (-)         (m)     (m)     (m)          (kg)  (m^3)  (m^2)   (-)
-1    Fixed     -1150.00   893.78  -204.21      0.00   0.00   0.00   0.00
-2    Free      -1391.15  1311.56  -137.43    140.00   0.13   0.00   0.00
-3    Coupled   -1470.91  1449.73   -14.00      0.00   0.00   0.00   0.00
-4    Fixed     -2200.00  1500.00  -203.86      0.00   0.00   0.00   0.00
-5    Free      -1717.38  1499.97  -137.30    140.00   0.13   0.00   0.00
-6    Coupled   -1557.91  1499.96   -14.00      0.00   0.00   0.00   0.00
-7    Fixed     -1150.00  2106.22  -204.07      0.00   0.00   0.00   0.00
-8    Free      -1391.18  1688.34  -137.38    140.00   0.13   0.00   0.00
-9    Coupled   -1470.91  1550.19   -14.00      0.00   0.00   0.00   0.00
+1    Fixed      -250.00 -1406.22  -200.71      0.00   0.00   0.00   0.00
+2    Free       -491.58  -987.52  -138.05    140.00   0.13   0.00   0.00
+3    Body1        29.00   -50.23   -14.00      0.00   0.00   0.00   0.00
+4    Fixed     -1300.00  -800.00  -200.70      0.00   0.00   0.00   0.00
+5    Free       -816.60  -799.86  -138.04    140.00   0.13   0.00   0.00
+6    Body1       -58.00    -0.00   -14.00      0.00   0.00   0.00   0.00
+7    Fixed      -250.00  -193.78  -201.34      0.00   0.00   0.00   0.00
+8    Free       -491.48  -612.04  -138.29    140.00   0.13   0.00   0.00
+9    Body1        29.00    50.23   -14.00      0.00   0.00   0.00   0.00
 ---------------------- LINES --------------------------------------------------------
 ID    LineType      AttachA  AttachB  UnstrLen  NumSegs  LineOutputs
 (#)    (name)        (#)      (#)       (m)       (-)     (-)
diff --git a/examples/duplicate_platform.py b/examples/duplicate_platform.py
index e40b77c9..2328375e 100644
--- a/examples/duplicate_platform.py
+++ b/examples/duplicate_platform.py
@@ -32,6 +32,12 @@
 # make new moorpy array
 project.getMoorPyArray()
 
+for line in rep_pf.mooringSystem(project).lineList:
+    xB, yB, zB = line.rB
+    #z_anchor, soil_label = get_depth_and_soil(xB, yB)
+    #print(f'  Anchor at ({xB:.1f}, {yB:.1f}) → Depth = {z_anchor:.2f} m')
+
+
 # plot the new system
 project.plot3d()
 plt.show()
\ No newline at end of file
diff --git a/examples/example_anchors.py b/examples/example_anchors.py
deleted file mode 100644
index d496b11f..00000000
--- a/examples/example_anchors.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Example showing how to call forces and 
-anchor capacity functions, along with safety factors and material costs.
-"""
-# import necessary packages
-from famodel.project import Project
-import os
-
-os.chdir('./Inputs/')
-
-# set yaml file location and name
-ontology_file = 'OntologySample200m_1turb.yaml'
-
-# create project class
-project = Project(file=ontology_file)
-project.getMoorPyArray()
-
-# let's choose a single anchor from the array to look at
-anch = project.anchorList['fowt0a']
-
-# now let's get the mudline and lug forces on this anchor
-anch.getLugForces() # getLugForces calls getMudlineForces() to get the anchor forces at both locations
-
-# establish a factor of safety in horizontal (Ha) and vertical (Va) directions
-minfs = {'Ha': 1.8, 'Va': 2}
-
-# let's get the loads with the factor of safety included 
-loads_with_FS = {'Ha':anch.loads['Ha']*minfs['Ha'],'Va':anch.loads['Va']*minfs['Va']}
-
-# get anchor capacity for one anchor (this case is for suction pile in clay)
-anch.getAnchorCapacity(loads=loads_with_FS) # loads are used in capacity calculation, so let's send in the loads with factor of safety applied
-
-# get anchor cost
-startGeom = [10,2,6.6]
-geomKeys = ['L','D','zlug']
-geomBounds = [(5, 50), (1, 7), (3.3,16.7)]
-FSDiff_max = {'Ha':5,'Va':5}
-anch.getSize(startGeom,geomKeys,geomBounds,minfs=minfs,FSdiff_max=FSDiff_max, plot=True)
-anch.getCost()
-print('\nClay suction pile capacity is: ',anch.anchorCapacity)
-print('Clay suction pile safety factor is: ',anch.getFS())
-print('Clay suction pile cost is: ', anch.cost,'\n')
-# try suction pile with sand
-newdd = anch.dd
-anch.soilProps['sand'] = anch.soilProps.pop('mud_firm')
-anch.soilProps['sand']['phi'] = 33
-anch.soilProps['sand']['Dr'] = 70
-anch.soilProps['sand']['delta'] = 25
-# update anchor loads at lug point (mudline load should be constant), then get anchor capacity
-anch.getLugForces()
-anch.getSize(startGeom,geomKeys,geomBounds,plot=True)
-anch.getAnchorCapacity(loads=loads_with_FS)
-anch.getCost()
-print('\nSand suction pile capacity is: ',anch.anchorCapacity,' N')
-print('Sand suction pile safety factor is: ',anch.getFS())
-print('Sand suction pile cost is: ', anch.cost,' USD\n')
-
-# check plate anchor type   
-newdd['type'] = 'DEA'
-newdd['design'] = {'type':'DEA','A':20,'zlug':20,'beta':10}
-anch.soilProps['clay'] = anch.soilProps.pop('sand')
-
-startGeom = [10,20]
-geomKeys = ['A','zlug']
-
-anch.getLugForces()
-anch.getAnchorCapacity(loads=loads_with_FS)
-# let's fix the zlug for the plate anchor - set fix_zlug=True to prevent it being changed
-anch.getSize(startGeom,geomKeys,minfs={'Ha':2,'Va':0}, fix_zlug = True)
-anch.getCost()
-print('\nClay plate capacity is: ',anch.anchorCapacity,' N')
-print('Clay plate safety factor is: ',anch.getFS())
-print('Clay plate cost is: ', anch.cost,' USD\n')
-
-# check drilled and grouted pile anchor type
-newdd['type'] = 'dandg_pile'
-newdd['design'] = {'type':'dandg_pile','L':50,'D':3,'zlug':0}
-anch.soilProps['rock'] = anch.soilProps.pop('clay') # soil_properties has default rock info in there already, just change name
-
-# startGeom = [5,50]
-# geomKeys = ['L','D']
-anch.getLugForces()
-# anch.getSize(startGeom,geomKeys,minfs={'Ha':2,'Va':2})
-anch.getAnchorCapacity(loads=loads_with_FS)
-print('\nRock drilled and grouted pile capacity is: ',anch.anchorCapacity,' N')
-print('Rock drilled and grouted pile safety factor is: ',anch.getFS())
-
-# check driven pile anchor in rock
-newdd['type'] = 'driven'
-anch.soilProps['weak_rock'] = anch.soilProps.pop('rock')
-newdd['design'] = {'type':'driven','L':20,'D':1.5,'zlug':-3} # zlug should be negative (above mudline) for rock!
-
-anch.getLugForces()
-anch.getAnchorCapacity(loads=loads_with_FS)
-print('\nWeak rock driven pile capacity is: ',anch.anchorCapacity,' N')
-print('Weak rock driven pile safety factor is: ',anch.getFS())
-
-# check driven pile anchor in clay
-anch.soilProps['clay'] = anch.soilProps.pop('weak_rock')
-newdd['design'] = {'type':'driven','L':40,'D':4,'zlug':10}
-
-anch.getLugForces()
-anch.getAnchorCapacity(loads=loads_with_FS)
-print('\nClay driven pile capacity is: ',anch.anchorCapacity,' N')
-print('Clay driven pile safety factor is: ',anch.getFS())
-
-# check driven pile anchor in sand
-anch.soilProps['sand'] = anch.soilProps.pop('clay')
-anch.soilProps['sand']['Dr'] = 50
-
-anch.getLugForces()
-anch.getAnchorCapacity(loads=loads_with_FS)
-print('\nSand driven pile capacity is: ',anch.anchorCapacity,' N')
-print('Sand driven pile safety factor is: ',anch.getFS())
-
-# check helical pile anchor with sand
-newdd['type'] = 'helical_pile'
-newdd['design'] = {'type':'helical_pile','L':25.1,'d':1,'D':5.01, 'zlug':5}
-
-anch.getLugForces()
-anch.getAnchorCapacity(loads=loads_with_FS)
-print('\nSand helical pile capacity is: ',anch.anchorCapacity,' N')
-print('Sand helical pile safety factor is: ',anch.getFS())
-
-# check helical pile anchor with clay
-anch.soilProps['clay'] = anch.soilProps.pop('sand')
-newdd['type'] = 'helical_pile'
-newdd['design'] = {'type':'helical_pile','L':25.1,'d':1,'D':5.01,'zlug':5}
-
-anch.getLugForces()
-anch.getAnchorCapacity(loads=loads_with_FS)
-print('\nClay helical pile capacity is: ',anch.anchorCapacity,' N')
-print('Clay helical pile safety factor is: ',anch.getFS())
-
-# check torpedo anchor in clay
-newdd['type'] = 'torpedo_pile'
-newdd['design'] = {'type':'torpedo_pile','D1':3,'D2':1.1,'L1':10,'L2':4,'zlug':16}
-
-anch.getLugForces()
-anch.getAnchorCapacity(loads=loads_with_FS)
-print('\nClay torpedo pile capacity is: ',anch.anchorCapacity,' N')
-print('Clay torpedo pile safety factor is: ',anch.getFS())
-
-
-
-
-
diff --git a/examples/example_driver.py b/examples/example_driver.py
index ad1ac38b..a4dd85f2 100644
--- a/examples/example_driver.py
+++ b/examples/example_driver.py
@@ -77,27 +77,35 @@
 # plot motion envelopes with 2d plot
 project.plot2d(save=True,plot_bathymetry=False)
 
-
+#%% Section 5: Anchor capabilities
 #### get anchor capacities, loads, and safety factors ####
 print('\nGetting anchor capacities, loads, and safety factors\n')
 # let's look at one anchor in the farm
 
 # define anchor to analyze
 anchor = project.anchorList['FOWT1a']
-# get anchor capacity
-anchor.getAnchorCapacity()
+
+name, soil_def = project.getSoilAtLocation(anchor.r[0], anchor.r[1])
+profile_map = [{'name': name, 'layers': soil_def['layers']}]
+anchor.setSoilProfile(profile_map)
+
+Hm = anchor.loads['Hm']
+Vm = anchor.loads['Vm']
+zlug = anchor.dd['design']['zlug']
+
+# Now use these in lug and capacity checks
+anchor.getLugForces(Hm, Vm, zlug)
+anchor.getCapacityAnchor(Hm, Vm, zlug)
 capacities = anchor.anchorCapacity
-# get anchor loads at mudline and anchor lug depth (if applicable)
-loads = anchor.getLugForces()
+
 # size an anchor
-starting_geometry = [15,20] # geometry values
-starting_geom_labels = ['A','zlug'] # corresponding labels for the geometry list
-min_safety_factors = {'Ha':2,'Va':2} # minimum safety factors
-FSdiff_max = {'Ha':.1,'Va':.1} # allowable difference between actual and desired FS for final result
-anchor.getSize(starting_geometry, starting_geom_labels, minfs=min_safety_factors,
-               FSdiff_max=FSdiff_max)
+geom_start = [anchor.dd['design']['B'], anchor.dd['design']['L']] # geometry values
+geom_labels = ['B','L'] # corresponding labels for the geometry list
+geom_bounds = [(0.5, 4.0), (0.5, 4.0)]
+safety_factor = {'SF_combined': 1.0} # minimum safety factors
+anchor.getSizeAnchor(geom_start, geom_labels, geom_bounds, loads = None, safety_factor={'SF_combined': 1.0})
 # get safety factor
-sfs = anchor.getFS()
+sfs = anchor.getSafetyFactor()
     
 print('\nAnchor safety factors: ',sfs) # NOTE that Va will show as 'inf' because there is no vertical force on the anchor.
     
diff --git a/famodel/anchors/README.md b/famodel/anchors/README.md
index 84788f66..b41232c6 100644
--- a/famodel/anchors/README.md
+++ b/famodel/anchors/README.md
@@ -1,229 +1,651 @@
 # Anchors Library
 
-This subpackage of FAModel contains the Anchor class as well as modules for anchor capacity 
-calculations. 
+This subpackage of FAModel contains the anchor class and all modules for the  capacity under extreme loads and the installation assessments
 
-## Anchor Class
-The anchor class contains properties and methods related to mooring anchors.
-The supported anchor types are below, with the associated FAModel name in italics.
-- Plate anchors 
-  - *DEA* (drag-embedment anchors)
-  - *SEPLA* (suction embedded plate anchors)
-  - *DEPLA* (dynamically embedded plate anchors)
-  - *VLA* (vertically loaded anchors)
-  - *plate* (unspecified plate anchor)
-- *suction_pile* (Suction caisson/ suction bucket anchors)
-- *torpedo_pile* (Torpedo pile anchors)
-- *helical_pile* (Helical pile anchors)
-- *driven_pile*  (Driven pile anchors)
-- *dandg_pile* (Drilled and grouted piles)
-
-
-The anchor class stores properties and methods that enable a wide range of modeling - from capacity to cost to loads, and more. The [anchor capacity modules](#anchor-capacity-modules) are integrated with the anchor class through the getAnchorCapacity() method. 
-### Anchor Properties
-- **r** : anchor [x,y,z] position
-- **dd** : anchor design dictionary, containing geometric properties, soil properties at the anchor location, cost
-- **ms** : moorpy system associated with this anchor point
-- **aNum** : anchor index in array mooring list (generally only used for shared moorings)
-- **mpAnchor** : moorpy point object that models this anchor
-- **anchorCapacity** : dictionary with horizontal and vertical capacity of the anchor. Generally these are loads in [N], but can also be displacements (generally for driven or drilled and grouted piles)
-- **loads** : dictionary of loads on the anchor, and the method used to obtain these loads (static or dynamic modeling). Loads include horizontal (H) and vertical (V) loads, as well as the angle of the load (theta). The keys for these loads will either include an m (for loads at the mudline) or a (for loads at the anchor lug).
-- **soilProps** : dictionary of soil property information at the location of the anchor
-- **failure_probability** : dictionary of probabilities for failure of the anchor
+## Seabed Conditions
 
+Introduction to different soil types
 
-### Anchor Methods
-- **makeMoorPyAnchor()** : Creates a MoorPy point object representing the anchor in a moorpy system
-- **getAnchorCapacity()** : Calls anchor capacity functions for the correct anchor type
-- **getFS()** : Computes safety factor for loads on the anchor
-- **getCost()** : Finds costs of anchor from MoorProps and stores in design dictionary
-- **getMass()** : Finds mass and/or UHC of anchor from MoorProps and stores in design dictionary
-- **getMudlineForces()** : Finds forces on anchor at mudline using MoorPy Point.getForces method. Use max_force=True to obtain the maximum forces on that anchor from the platform.getWatchCircle() method. For more information on the getWatchCircle() calculations, see the [Platform ReadMe](../platform/README.md). An additional anchor.loads dictionary entry is included to describe the mudline load type. 'mudline_load_type'='max' if max_force=True, and 'mudline_load_type'='current_state' if max_force=False.
-- **getLugForces()** : Finds forces at the anchor lug location with getTransferFunction function in capacity_loads.py. 
-The getTransferLoad function requires **maximum** mudline forces as an input. These forces can be sent in as a dictionary, or anchor.loads dictionary will be searched for 'Hm' and 'Vm' values with additional key-value pair 'mudline_force_type':'max' to indicate these mudline forces are maximums. 
-If there are no max mudline forces in the anchor.loads dictionary, getMudlineForces(max_force=True) will be called. Stores results in loads dictionary. If lug is at mudline or no lug provided, equates mudline forces with lug forces. 
->[!NOTE]
->The getTransferFunction function called by getLugForces() is tuned to work with maximum loads on the anchor. Some anchor configuration, load, and soil condition combinations may produce invalid results in getTransferFunction. For example, the output Va may show as negative. In that case, getLugForces() will warn the user of the invalidity of the result and assign the anchor lug forces = mudline forces.
+Heterogenous soil (mixed layers). Map of soil properties for horizontal and vertical spatial-variability.
 
+The reference elevation of the pile is the pile head (z = 0 m), from here all elevations are derived. Thus, Z0 (mudline elevation) is the distance between the pile head and the top of the first layer of soil. Main padeye locations depend on their relative elevation to z0, if zlug > z0 mooring line is embedded below the mudline elevation
+### Soil properties
+##### Input
+- profile_map 
+	- location_name: CPT or reference in the system (-)
+  - x, y: coordinates of the anchor within the lease area (m), (m)
+  - layers (at least one): 
+    - top, bottom: depth for top and bottom for each layer (m), (m)
+    - soil_type: clay/mud, sand and (weak) rock (-)  
+    - soil properties: 
+      - clay/mud:
+        - gamma: submerged soil unit weight (kN/m³)
+        - Su: undrained shear strength (kPa) 
+      - sand:
+        - gamma: submerged soil unit weight: (kN/m³)
+        - phi: internal friction angle (deg)
+        - Dr: relative density (%)
+      - (weak) rock, 
+        - UCS: unconfined compressive strength at failure (MPa)
+        - Em: rock mass modulus (MPa)
 
-### Anchor Type Requirements
+>[!NOTE]
+Driven piles are only possible on weak rock, defined here as up to UCS = 5 MPa
 
-Different geometric properties and soil conditions are needed for each anchor type. See the [Anchor Capacity Modules](#anchor-capacity-modules) section for details on the requirements of each anchor type.
+> [!IMPORTANT] 
+Units within FAModel follow the SI exclusively. The input soil parameters units follow common industry convention. Soil parameters conversion units to Pa and N/m³ take place in the capacity_soils module exclusively. There is no need to convert units.
 
+    profile_map = [
+        {
+            'location_name': 'CPT_1',
+            'x': 498234, 'y': 5725141,
+            'layers': [
+                {
+                    'top': 1.0, 'bottom': 6.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 8.0,
+                    'Su_top': 10, 'Su_bot': 50},
+                {
+                    'top': 6.0, 'bottom': 15.0,
+                    'soil_type': 'sand',
+                    'gamma_top': 8.0, 'gamma_bot': 8.0,
+                    'phi_top': 32, 'phi_bot': 38,
+                    'Dr_top': 70, 'Dr_bot': 75},
+                {
+                    'top': 15.0, 'bottom': 30.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 9.0,
+                    'Su_top': 100, 'Su_bot': 200}]
+        }
+    ]
+    Note:
+    - z0 = 1 m, meaning the pile head is 1 m above the mudline
+    - soil_type: clay, sand, clay
+    - this method allows different soil types and gaps in between soil layers of the same or different soil type
+##### Output
+- z0: depth of the mudline relative to the pile head (m)
+- soil types:
+  - clay/mud:
+    - f_gamma: effective unit soil weigtht at depth (N/m³)
+    - f_Su: undrained shear strength at depth (Pa)
+    - f_sigma_v_eff: effective vertical stress at depth (Pa)
+    - f_alpha: adhesion factor from API correlation (-)
+  - sand:
+    - f_gamma: effective unit soil weigtht at depth (N/m³)
+    - f_phi: friction angle at depth (deg)
+    - f_sigma_v_eff: effective vertical stress at depth (Pa)
+    - f_Dr: relative density at depth (%)
+    - f_delta: skin friction angle at depth (-)
+  - rock:
+    - f_UCS: unconfined compressive strength at depth (Pa)
+    - f_Em: rock mass modulus at depth (deg)
+------------------------------------------------------------------------------
 
-## Anchor Capacity Modules
-The following list shows the required soil conditions, soil properties, geometry, and load information for anchor capacity calculations of each anchor type. Soil classification for clay and sand can be found in [Soil Classification Parameters](#soil-classification-parameters).
+Soil classification for clay, sand and rock can be found in [Soil Classification Parameters](#soil-classification-parameters).
 >[!NOTE] 
->Some anchor capacity functions require input loads at the anchor lug point. These loads can be sent in to the getAnchorCapacity() method, or the getAnchorCapacity() method will calculate the loads by calling getLugLoads(). The input loads must be maximum or large loads on the anchor.
-
-### DEA/SEPLA/DEPLA/VLA/plate
-  - soil condition: clay/mud
-    - Su0 (undrained shear strength of soil at mudline) [kPa]
-    - k (rate of change in shear strength with depth) [kPa/m]
-    - gamma (submerged soil unit weight) [kN/m^3]
-  - geometry
-    - A (area of plate) [m^2]
-    - zlug (embedded depth of bridle/padeye below mudline - positive is below mudline, negative is above mudline) [m]
-    - beta (OPTIONAL - angle of plate after keying) [deg]
-  - loads: None
-### suction_pile (Suction caisson/ suction bucket anchors)
-  - soil conditions
-    - sand
-      - phi (internal friction angle) [deg]
-      - Dr (relative density) [%]
-      - delta (interface friction angle at soil-anchor line) [deg] ***only needed for loads calculation*
-    - clay/mud
-      - Su0 (undrained shear strength of soil at mudline) [kPa]
-      - k (rate of change in shear strength with depth) [kPa/m]
-      - alpha (adhesion factor) [-]
-  - geometry
-    - L (length of pile) [m]
-    - D (diameter of pile) [m]
-    - zlug (embedded depth of padeye below mudline) [m]
-  - loads
-    - Ha, Va (horizontal and vertical loads on padeye of anchor) 
-### torpedo_pile (Torpedo pile anchors)
-  - soil condition: clay/mud
-    - Su0 (undrained shear strength of soil at mudline) [kPa]
-    - k (rate of change in shear strength with depth) [kPa/m]
-    - alpha (adhesion factor) [-]
-  - geometry
-    - D1 (wing diameter) [m]
-    - D2 (shaft diameter) [m]
-    - L1 (wing length) [m]
-    - L2 (shaft length) [m]
-    - zlug (embedded depth of padeye below mudline) [m]
-  - loads: None
-### helical_pile (Helical pile anchors)
-  - soil conditions
-    - sand
-      - phi (internal friction angle) [deg]
-      - gamma (submerged soil unit weight) [kN/m^3]
-      - alpha_star (empirical adhesion factor **can use alpha instead*) [-]
-    - clay/mud
-      - Su0 (undrained shear strength of soil at mudline) [kPa]
-      - k (rate of change in shear strength with depth) [kPa/m]
-      - gamma (submerged soil unit weight) [kN/m^3]
-      - alpha_star (empirical adhesion factor **can use alpha instead*) [-]
-  - geometry
-    - D (helix diameter) [m]
-    - L (shaft length) [m]
-    - d (shaft diameter) [m]
-  - loads: None
-### driven_pile (Driven pile anchors)
-  - soil conditions:
-    - weak rock (up to UCS = 5 MPa)
-      - UCS (unconfined compressive strength at failure)
-      - Em (rock mass modulus)
-    - sand
-      - phi (internal friction angle) [deg]
-      - gamma (submerged soil unit weight) [kN/m^3]
-      - Dr (relative density) [%]
-    - clay/mud
-      - Su0 (undrained shear strength of soil at mudline) [kPa]
-      - k (rate of change in shear strength with depth) [kPa/m]
-      - gamma (submerged soil unit weight) [kN/m^3]
-  - geometry
-    - L (length of pile) [m]
-    - D (diameter of pile) [m]
-    - zlug (embedded depth of padeye below mudline) [m]
-  - loads
-    - Ha, Va (horizontal and vertical loads on padeye of anchor)
-
-  #### Output notes 
-  The general output is a lateral and rotational displacement or bending moment. In getAnchorCapacity, the driven pile capacity function is called in a while loop with incremented horizontal input forces until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function.
+>Some anchor capacity functions require input loads at the anchor lug point. These loads can be sent in to the getAnchorCapacity() method, or the getAnchorCapacity() method will calculate the loads by calling getLugLoads(). 
+The input loads must be maximum or large loads on the anchor.
+		
+### Soil classification parameters
+
+The soft, medium and hard clay soil classes are distinguished by the following parameter ranges: 
+| clay/mud     | N-Value  |     Eff. unit weight, gamma (kN/m³)  | Void ratio, e (-) | Water content,  (%) | Undrained shear strength, Su (kN/m2) |
+|:-----------------:|:--------:|:---------------------------------:|:----------:|:--------------------------------------:|:-------------------------------:|
+|     Very Soft     |  0 - 2   |             5.5 - 8.5             |  0.9 - 1.4 |                30 - 50                 |            0 - 12.5             |
+|       Soft        |  2 - 4   |             5.5 - 8.5             |  0.9 - 1.4 |                30 - 50                 |            12.5 - 25            |
+|       Medium      |  4 - 8   |             5.5 - 8.5             |  0.9 - 1.4 |                30 - 50                 |             25 - 50             |
+|       Stiff       |  8 - 15  |             8.5 - 12              |    ~ 0.6   |                20 - 30                 |            50 - 100             |
+|     Very Stiff    | 15 - 30  |             8.5 - 12              |    ~ 0.6   |                20 - 30                 |            100 - 200            |
+|        Hard       |   < 30   |             8.5 - 12              |    ~ 0.6   |                20 - 30                 |              > 200              |
+
+
+Sand can also be classified ranging from soft to hard. and are chracterize by the following ranges:
+
+| sand |  N-Value | Eff. unit weight, gamma (kN/m³) | Void ratio, e (-) | Water content,  (%)| Eff. friction angle, phi (deg) | Relative density, Dr (%) |
+|:----------------:|:--------:|:----------------------------:|:----------:|:--------------------------------------:|:-------------------:|:--------------------:|
+|   Very   Loose   |    > 4   |            7 - 9.5           |    ~ 0.8   |                 25 - 30                |        < 30         |         < 15         |
+|       Loose      |  4 - 10  |            7 - 9.5           |    ~ 0.8   |                 25 - 30                |       30 - 35       |        15 - 35       |
+|     Compact      | 10 - 30  |          9.5 - 11.5          |   ~ 0.45   |                 12 - 16                |       35 - 40       |        35 - 65       |
+|      Dense       | 30 - 50  |          9.5 - 11.5          |   ~ 0.45   |                 12 - 16                |       40 - 45       |       65 - 85        |
+|    Very Dense    |   < 50   |          9.5 - 11.5          |   ~ 0.45   |                 12 - 16                |        > 45         |         > 85         |
+
+## Anchor Types
+The supported anchor types are listed below with their associated FAModel names in italics. Anchors types have specific [anchor capacity](#anchor-capacity-modules) and [anchor installation](#anchor-installation-modules) application modules, these are shown for clarity below as well.
+
+|                                                        | Capacity       | Installation |
+|--------------------------------------------------------|----------------|--------------|
+|*DEA*   (drag-embedment anchors)                        | Plate          | Drag         | 
+|*SEPLA* (suction embedded plate anchors)                | Plate          | Suction      | 
+|*DEPLA* (dynamically embedded plate anchors)            | Plate          | Dynamic      |
+|*VLA* (vertically loaded anchors)                       | Plate          | Drag         |
+|*suction* (suction caisson/suction bucket anchors)      | Suction        | Suction      |
+|*torpedo* (torpedo pile anchors)                        | Torpedo        | Dynamic      |
+|*helical* (helical pile anchors)                        | Helical/Driven | Torque-crowd |
+|*driven*  (driven pile anchors)                         | Driven         | Driven       |
+|*dandg* (drilled and grouted pile anchors)              | Drilled&Grout  | Drilled      |
+
+### Anchor geometrical properties
+#### DEA/SEPLA/DEPLA/VLA/plate
+##### Input
+- soil condition: 
+    - z, gamma, Su: clay soil parameters (m), (kN/m3), (kPa) 
+- geometry:
+   - B: width of plate - dimension contained in the vertical plane (m)
+   - L: length of plate - dimension perpendicular to the vertical plane (m)
+   - zlug: embedded depth of bridle/padeye below mudline (m)
+   - beta: angle of plate with horizontal plane (deg)
+- loads:
+  - Ha, Va: horizontal and vertical loads on padeye of anchor (N), (N)
+##### Output
+
   
-  For non-rock soil, the hinge (bending moment) is also considered as a failure mode along with the lateral and rotational displacement *IF* the zlug is positive (below the mudline)
-### dandg_pile (Drilled and grouted piles)
-  - soil condition: rock
-    - UCS (unconfined compressive strength at failure)
-    - Em (rock mass modulus)
-  - geometry
-    - L (length of pile) [m]
-    - D (diameter of pile) [m]
-    - zlug (lug location (lug above mudline has negative zlug)) [m]
-  - loads
-    - Ha, Va (horizontal and vertical loads on padeye of anchor)
-#### Output notes
- The general output is a lateral and rotational displacement. In getAnchorCapacity, the drilled and grouted pile function is called in a while loop with incremented horizontal input forces until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function. 
+#### suction_pile (suction caisson/ suction bucket anchors)
+##### Input
+- soil condition:
+    - location_name:
+    - x, y: CPT or reference name
+    - layers:
+      - z, gamma, Su: clay soil parameters (m), (kN/m3), (kPa) 
+      - z, gamma, phi, Dr: sand soil parameters (m), (kN/m3), (deg), (%) 
+    
+- geometry:
+    - D: diameter of pile (m)
+    - L: length of pile (m)
+    - zlug: embedded depth of padeye below mudline (m)
+- loads:
+    - Ha, Va: horizontal and vertical loads on padeye of anchor (N), (N)
+##### Output
+  
+#### torpedo_pile (torpedo pile anchors)
+##### Input
+- soil condition: 
+    - z, gamma, Su: clay soil parameters () 
+- geometry
+   - D1: wing diameter (m)
+   - D2: shaft diameter (m)
+   - L1: wing length (m)
+   - L2: shaft length (m)
+   - zlug: embedded depth of padeye below mudline (m)
+- loads
+  - Ha, Va: horizontal and vertical loads on padeye of anchor (N), (N)
+
+#### helical_pile (helical pile anchors)
+##### Input
+- soil condition: 
+    - z, gamma, Su: clay soil parameters (m), (kN/m3), (kPa) 
+    - z, gamma, phi, Dr: sand soil parameters (m), (kN/m3), (deg), (%) 
+- geometry
+    - D: helix diameter (m)
+    - L: shaft length (m)
+    - d: shaft diameter (m)
+    - zlug: embedded depth of padeye below mudline (m)
+- loads
+    - Ha, Va: horizontal and vertical loads on padeye of anchor (N), (N)
+  
+#### driven_pile (driven pile anchors)
+##### Input
+- soil condition: 
+    - z, gamma, Su: clay soil parameters (m), (kN/m3), (kPa) 
+    - z, gamma, phi, Dr: sand soil parameters (m), (kN/m3), (deg), (%) 
+    - z, UCS, Em: (weak) rock parameters (m), (MPa), (MPa)  
+- geometry
+    - L: length of pile (m)
+    - D: diameter of pile (m)
+    - zlug: embedded depth of padeye below mudline (m)
+- loads
+    - Ha, Va: horizontal and vertical loads on padeye of anchor (N), (N)
+##### Output
+
+> [IMPORTANT!] The general output is a lateral and rotational displacement or bending moment. In getCapacityAnchor, the driven pile capacity function is called in a while loop with incremented horizontal 
+ input forces until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function.
+  
+ For non-rock soil, the hinge (bending moment) is also considered as a failure mode along with the lateral and rotational displacement 
  
--------------------------------------------------------------------------------
-> [!IMPORTANT] 
-> A positive zlug denotes a lug/padeye/bridle below the mudline, while a negative zlug denotes a lug/padeye/bridle above the mudline. Anchors in rock should have a zlug >= 0.
+#### dandg_pile (drilled and grouted pile anchors)
+##### Input
+- soil condition: 
+    - z, UCS, Em: (weak) rock parameters (m), (MPa), (MPa)  
+- geometry
+   - L: length of pile (m)
+   - D: diameter of pile (m)
+   - zlug: lug location (m)
+- loads
+   - Ha, Va: horizontal and vertical loads on padeye of anchor
+   
+## Loads
+Loads derived from MoorPy and DRAFT are considered at a fixed point at mudline elevation. These loads need to be transfered from mudline to lug penetration when the main padeye is below the mudline. Transfer function: soil properties (profile)  mooring line properties (line_type, d and w), loads and zlug 
+> [!IMPORTANT] It is cautious to condiser as input load the tension load at mudline since the load will be equal or larger to the tension at lug penetration. Conversely, the angle of the load at lug penetration will equal or larger to the angle at mudline. Therefore, yielding to more vertical componenent. Therefore, Tm >= Ta and thetam <= thetaa 
+
+##### Input
+- profile_map: soil profile 
+- Tm: tension of the load on mudline (N)
+- thetam: angle of the load on mudline (deg)
+- zlug: main padeye embeddment (m)
+- line_type: type of mooring line ('chain' or 'wire') (-)
+- d: mooring line diameter (m)
+- w: mooring line unit weight (N/m)
+	
+> [NOTE] Load components: Hm, Vm: horizontal and vertical load components on mudline (N), (N) and Ha, Va: horizontal and vertical load components on padeye of anchor (N), (N)
+##### Output
+- Ta: tension of the load on padeye of anchor (N)
+- thetaa: angle of the load on padeye of anchor (deg)
+- length: length of the embedded line (m)
+
+
+> [!NOTE] check getLugForces for more details on this transfer function from mudline to lug elevation (below the seabed)
+The getTransferLoad function requires **maximum** mudline forces as an input. These forces can be sent in as a dictionary, or anchor.loads dictionary will be searched for 'Hm' and 'Vm' values with additional 
+key-value pair 'mudline_force_type':'max' to indicate these mudline forces are maximums.
+ 
+If there are no max mudline forces in the anchor.loads dictionary, getMudlineForces(max_force=True) will be called. Stores results in loads dictionary. 
+If lug is at mudline or no lug provided, equates mudline forces with lug forces. 
+>[!NOTE]
+>The getTransferFunction function called by getLugForces() is tuned to work with maximum loads on the anchor. Some anchor configuration, load, and soil condition combinations may produce invalid results in getTransferFunction. 
+For example, the output Va may show as negative. In that case, getLugForces() will warn the user of the invalidity of the result and assign the anchor lug forces = mudline forces.
+>[!NOTE] 
+>Some anchor capacity functions require input loads at the anchor lug point. These loads can be sent in to the getAnchorCapacity() method, or the getAnchorCapacity() method will calculate the loads by calling getLugLoads(). 
+The input loads must be maximum or large loads on the anchor.
+
+------------------------------------------------------------------------------
 
 > [!NOTE] 
-> Load inputs to the capacity functions (with the exception of driven & drilled and grouted anchors) are in kN, while the anchor loads dictionary is in N. This conversion is automatically completed in the getAnchorCapacity() function so no manual load conversion is required. Load outputs are automatically converted in the getAnchorCapacity function where necessary. 
+> Load inputs to the capacity functions (with the exception of driven & drilled and grouted anchors) are in kN, while the anchor loads dictionary is in N. This conversion is automatically completed in the getAnchorCapacity() 
+function so no manual load conversion is required. Load outputs are automatically converted in the getAnchorCapacity function where necessary. 
+
+## Equipment
+
+### Installation vessel
+#### Pullard force
+Drag installation. Input
+  - Tmax: maximum pullard force (N)
+
+#### Crane capacity
+Dynamic installation. Output
+  - Wp: dynamically installed plate/pile weight (N)
+
+### Installation device
+#### Suction pump
+Suction installation. Output
+  - delta_u_suction: maximum underpressure given by the suction pump during installation (Pa)
+  - delta_u_retrieve: maxumum overpressure given by the suction pump during retrieval/removal (Pa)
+
+#### Hydraulic drive head
+Torque-crowd installation. Output
+  - Torque: torque (Nm)
+  - Force: crowd compressive force (N)
+
+#### Hammer
+Driven installation. Input
+  - hammer_params: 
+    - r_m: ram mass (kg) 
+    - h: strock height (m) 
+    - efficiency: efficiency of the hammer (-)
+
+#### Drill head
+Drilled installation
 
 -----------------------------------------------------------------------------
-### Model Fidelity
+  
+## Anchor Class
+The anchor class contains properties and methods related to mooring anchors.
 
-There are two levels of fidelity in these models:
+The anchor class stores properties and methods that enable a wide range of modeling. 
+The [anchor capacity modules](#anchor-capacity-modules) and the [anchor installation modules](#anchor-installation-modules) are integrated with FAModel through the anchor class and its methods.
+ 
+### Anchor modules
+Introduction
+ 
+Inspection of the folder: anchors_famodel
 
-- Level 1 basic models are soil-dependent capacity curves for a 
-  range of anchor types based on performing curve fits to 
-  published information in anchor manuals and standards. 
-- Level 2 intermediate models are quantitative calculations for
-  suction caissons and plate anchors that account for quantitative 
-  soil properties as well as their variation with embedment depth.
+|                                              |                |              |
+|----------------------------------------------|----------------|--------------|
+|![Plate anchor](images/Plateanchors/Plate.png)|![Suction pile anchor](images/Suctionpiles/Suction.png)|![Torpedo pile anchor](images/Torpedopiles/Torpedo.png)| 
+|![Helical pile anchor](images/Helicalpiles/Helical.png)|![Driven pile anchor](images/Drivenpiles/Driven.png)|![Drilled and grouted pile anchor](images/Drilledandgroutedpiles/Drilled.png)| 
 
-This plot gives an example of the capacity curves that can be 
-produced by the intermediate model (holding capacity for a suction
-embedded plate anchor) as a function of surface shear strength:
 
-![Capacities](images/SEPLA_curves_small.PNG)
+#### Anchor capacity modules
+Analytical static capacity models for extreme load conditions. These models include static soil-structure interaction but the cyclic loading conditions are not covered yet. They will need to follow from further research.
 
-### Implemented level-1 model anchor and soil types
+- **capacity_plate** : 
+	- getCapacityPlate(profile_map, location_name, D, L, zlug, Ha, Va, plot)
+  - capacityPlate dict:
+    - 'Capacity'
+    - 'Horizontal max.', 'Vertical max.'
+    - 'Unity check'
+    - 'Weight plate' 
 
-|             | DEA       | Suction | VLA | SEPLA |
-|-------------|-----------|---------|-----|-------|
-| Soft clay   | X         | X       | X   | X     |
-| Medium clay | X         | X       | X   | X     |
-| Hard clay   | X         |         |     |       |
-| Sand        | X         |         |     |       |
+- **capacity_suction** : 
+	- getCapacitySuction(profile_map, location_name, D, L, zlug, Ha, Va, thetalug, psilug, plot)
+  - capacitySuction dict:
+    - 'Horizontal max.', 'Vertical max.'
+    - 'Unity check'
+    - 'Weight pile'
+  
+- **capacity_torpedo** : 
+	- getCapacityTorpedo(profile_map, location_name, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot)
+  - capacityTorpedo dict:
+    - 'Horizontal max.', 'Vertical max.'
+    - 'Unity check'
+    - 'Weight pile'
 
-### Parameters needed for level-2 anchor capacity models
+- **capacity_helical** : 
+	- getCapacityHelical(profile_map, location_name, D, L, d, zlug, Ha, Va, plot)
+  - capacityHelical dict:
+    - 'Horizontal max.', 'Vertical max.'
+    - 'Lateral displacement', 'Rotational displacement'
+    - 'Bending moment'
+    - 'Plastic moment'
+    - 'Unity check (vertical)', 'Unity check (horizontal)'
+    - 'Weight pile'
 
-|        **Anchor type** | **Suction** | **Suction** | **VLA**  | **SEPLA** |
-|------------------------|-------------|-------------|----------|-----------|
-|        **Soil type**   | **Clay**    | **Sand**    | **Clay** | **Clay**  |
-| **Anchor parameters**  |             |             |          |           |
-|        Diameter        | x           | x           |          |           |
-|        Length          | x           | x           |          |           |
-|        Area            |             |             | X        | X         |
-|        Thickness       | ratio       | ratio       | ratio    | ratio     |
-|       Embedment depth  |             |             | X        | X         |
-| **Soil parameters**    |             |             |          |           |
-|        gamma           | X           | X           | X        | X         |
-|        Su0             | X           |             | X        | X         |
-|        k               | X           |             | X        | X         |
-|        alpha           | X           |             |          |           |
-|        phi             |             | X           |          |           |
+- **capacity_driven** : 
+	- getCapacityDriven(profile_map, location_name, L, D, zlug, Ha, Va, plot)
+  - capacityDriven dict:
+    - 'Horizontal max.', 'Vertical max.'
+    - 'Lateral displacement', 'Rotational displacement'
+    - 'Bending moment'
+    - 'Plastic moment'
+    - 'Unity check (vertical)', 'Unity check (horizontal)'
+    - 'Weight pile'
+ 
+- **capacity_dandg** : 
+	- getCapacityDandG(profile_map, location_name, L, D, zlug, Ha, Va, plot)
+  - capacityDandG dict:
+    - 'Horizontal max.', 'Vertical max.'
+    - 'Lateral displacement', 'Rotational displacement'
+    - 'Bending moment'
+    - 'Plastic moment'
+    - 'Unity check (vertical)', 'Unity check (horizontal)'
+    - 'Weight pile'
 
+- **capacity_load** : 
+	- getTransferFunction(profile_map, Tm, thetam, zlug, line_type, d, w, plot)
+  - capacityLoads dict:
+    - 'Tm', 'thetam'
+    - 'Hm', 'Vm'
+    - 'Ta', 'thetaa'
+    - 'Ha', 'Va'
+    - 'length'
+    - 'drag_values'
+    - 'depth_values'
 
-These models will continue to be expanded as data sources and time permit.
+#### Anchor installation modules
+Analytical installation models for main anchor types.
 
-## Soil Classification Parameters
+- **installation_drag** : 
+	- getInstallationPlate(profile_map, location_name, B, Lf, Ls, Lca, Lj, plot)
+  - installationDrag dict
 
-The soft, medium, and hard clay soil classes are distinguished by the following parameter ranges: 
-| Soil Type (Clay)  | N-Value  | Effective Sat. Unit Weight, kN/m3 | Void Ratio | Natural Water Content in Sat. State, % | Undrained Shear Strength, kN/m2 |
-|:-----------------:|:--------:|:---------------------------------:|:----------:|:--------------------------------------:|:-------------------------------:|
-|     Very Soft     |  0 - 2   |             5.5 - 8.5             |  0.9 - 1.4 |                30 - 50                 |            0 - 12.5             |
-|       Soft        |  2 - 4   |             5.5 - 8.5             |  0.9 - 1.4 |                30 - 50                 |            12.5 - 25            |
-|       Medium      |  4 - 8   |             5.5 - 8.5             |  0.9 - 1.4 |                30 - 50                 |             25 - 50             |
-|       Stiff       |  8 - 15  |             8.5 - 12              |    ~ 0.6   |                20 - 30                 |            50 - 100             |
-|     Very Stiff    | 15 - 30  |             8.5 - 12              |    ~ 0.6   |                20 - 30                 |            100 - 200            |
-|        Hard       |   < 30   |             8.5 - 12              |    ~ 0.6   |                20 - 30                 |              > 200              |
+- **installation_suction** : 
+	- getInstallationSuction(profile_map, location_name, D, L, plot)
+  - installationSuction dict:
+    - 'Fi', 'Fo', 'Qw'
+    - 'Rsuction', 'Rretrieval'
+    - 'SWP_depth'
 
+- **buckling_suction** : 
+	- getBucklingSuction(profile_map, location_name, D, L, plot)
+  - installationBuckling dict
 
-Sand can also be classified ranging from soft to hard, however only a single sand class is supported at this time. In the future, sand classes will follow the parameter ranges in the following table:
+- **installation_dynamic** : 
+	- getInstallationTorpedo(profile_map, location, D1, D2, L1, L2, ballast, drop_height, plot)
+  - installationDynamic dict
+
+- **installation_torque** : 
+	- getInstallationHelical(profile_map, location_name, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot)
+  - installationTorque dict:
+    - 'Force', 'Torque'
+    - 'sigma_helix', 'sigma_core', 'sigma_weld'
+    - 'failire_mode'
+
+- **installation_driven** : 
+	- getInstallationDriven(profile_map, location_name, D, L, hammer_params, J_shaft, J_toe, plot)
+  - installationDriven dict
+
+- **installation_drill** : 
+	- getInstallationDrill(profile_map, location_name, D, L, driller_params, plot)
+  - installationDrill dict
+
+#### Anchor support modules
+
+- **anchor_soils** : 
+	- clay_profile(profile) 
+	- sand_profile(profile) 
+	- rock_profile(profile)
+
+- **anchor_solvers** : 
+	- fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant) 
+
+- **anchor_pycurves** : 
+	- py_Matlock(z, D, Su, sigma_v_eff, gamma, z0, return_curve)
+	- py_API(z, D, phi, sigma_v_eff, Dr, z0, return_curve)
+	- py_Reese(z, D, UCS, Em, z0, return_curve) 
+	- py_Lovera(z, D, UCS, Em, z0, delta_grout, E_grout, delta_crushed, return_curve)
+
+- **anchor_plots** : 
+	- plot_pile(layers, y, z, D, L, z0, zlug, hinge_location)
+	- plot_suction(layers, L, D, z0, zlug)
+  - plot_suction(layers, D1, D2, L1, L2, z0, zlug)
+  - plot_helical(layers, D, L, d, z0, zlug, n_helix, spacing)
+  - plot_plate(layers, B, L, z0, zlug, beta)
+  - plot_load(layers, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug)
+  - plot_pycurve(pycurve_data)
+
+### Anchor class methods
+
+- **Anchor.setSoilProfile(profile_map)** 
+
+  Assign a soil profile directly to the anchor from a single CPT (Cone Penetration Test) record.
+
+  This method sets the internal soil_profile, extracts soil types, and organizes layer properties by soil type. It assumes that the input contains only one CPT entry.
+
+  **Parameters**
+    - **profile_map** : list of dict. A list containing exactly one dictionary representing a CPT profile. The dictionary has:
+      - 'location_name': a string indicating the name of the CPT
+      - 'x', 'y': coordinates of the CPT location
+      - 'layers': a list of layer dictionaries, each with a 'soil_type' key and relevant soil parameters.
+    
+  **Raises**
+    - **ValueError** : If profile_map contains more than one CPT.
+
+  **Attributes Updated**
+  - **self.soil_profile** : list of dict. Stores the soil layers from the CPT.
+  - **self.profile_name** : str. Name of the CPT, default is "CPT_Assigned" if not provided.
+  - **self.soil_type_list** : list of str. Unique soil types present in the profile.
+  - **self.soil_type** : str. If a single soil type is present, its name; otherwise, 'mixed'.
+  - **self.soilProps** : dict. Dictionary grouping layer properties (excluding soil_type) by soil type.
+
+- **Anchor.interpolateSoilProfile(profile_map)**  
+
+  Interpolate a soil profile for the anchor location from the four nearest CPTs using inverse distance weighting.
+
+  This method assumes all CPTs share the same layer structure. Each soil parameter is interpolated layer-by-layer based on the relative proximity of the CPTs to the anchor.
+
+  **Parameters**
+    - **profile_map** : list of dict.  A list containing at least four CPT profile dictionaries. Each dictionary has:
+      - 'location_name': a string indicating the name of the CPT
+      - 'x', 'y': coordinates of the CPT location
+      - 'layers': a list of layer dictionaries, each with a 'soil_type' key and relevant soil parameters.
+    
+  **Raises**
+    - **ValueError** : If fewer than four CPTs are provided in profile_map.
+
+  **Attributes Updated**
+  - **self.soil_profile** : list of dict. Stores the soil layers from the CPT.
+  - **self.profile_name** : str. Set to "Interpolated_2D".
+  - **self.soil_type_list** : list of str. Unique soil types present in the interpolated profile.
+  - **self.soil_type** : str. If a single soil type is present, its name; otherwise, 'mixed'.
+  - **self.soilProps** : dict. Dictionary grouping layer properties (excluding soil_type) by soil type.
+
+- **Anchor.makeMoorPyAnchor(ms)**  
+
+  Create and register a MoorPy anchor point within the given MoorPy system using the anchor's location and design properties.
+
+  The anchor is added as a fixed point in the MoorPy model (Point object) and its mass and diameter are assigned if available. The method also sets the point type based on the anchor type.
+
+    **Parameters**
+    - **ms** : MoorPy system instance. The MoorPy system in which the anchor will be created.
+    
+  **Attributes Updated**
+  - **self.mpAnchor** : MoorPy Point. Reference to the created MoorPy anchor point in the system.
+
+- **Anchor.getLineProperties()**  
+
+  Retrieve the mooring line type, diameter and unit weight from the anchor's attached mooring object.
+
+  This method inspects the attached Mooring object and extracts relevant line properties from its first section. If no chain is present, the method assumes no load transfer below the mudline and returns None for diameter and weight.
+
+    **Parameters**
+    - **line_type** : str.  Type of mooring line (e.g., 'chain', 'wire').
+    - **d** : float or None. Nominal diameter of the mooring line (m) or None if not applicable.
+    - **w** :  float or None. Unit weight of the mooring line (N/m) or None if not applicable.
+        
+    **Raises**
+    - **ValueError** : If no mooring line attachment is found for the anchor.
+
+- **Anchor.getMudlineForces(max_force=False, lines_only=False, seabed=True, xyz=False, project=None)** 
+
+  Compute the forces acting on the anchor at the mudline, either from the current state of the MoorPy system or as the maximum expected force based on platform excursion.
+
+  If max_force=True, the method retrieves the extreme load on the anchor using either the provided project’s arrayWatchCircle() method or the attached platform’s getWatchCircle() method. Otherwise, it calculates the current forces using MoorPy's getForces().
+
+    **Parameters**
+    - **max_force** : bool, optional. If True, compute the maximum expected mudline force based on platform excursion. Default is False.
+    - **lines_only** : bool, optional. If True, considers only mooring line forces (ignores seabed and body effects). Default is False.
+    - **seabed** :  bool, optional. If True, includes seabed reaction force in the calculation. Default is True.
+    - **xyz** :  bool, optional. If True, returns forces in the x, y, z directions. Otherwise returns only the relevant DOFs. Default is False
+    - **project** :  bool, optional. Project object with a arrayWatchCircle() method. Used to compute global platform excursions when max_force=True.
+
+  **Attributes Updated**
+  - **self.loads** : dict. Stores the computed mudline force components and metadata:
+      - 'Hm': horizontal force magnitude at mudline (N)
+      - 'Vm': vertical force at mudline (N)
+      - 'thetam': angle of applied load at mudline (deg)
+      - 'method': load computation method (currently 'static')
+      - 'mudline_load_type': 'current_state' or 'max_force', depending on the mode used   
+
+- **Anchor.getLugForces(Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False)**  
+
+  Calculate the horizontal and vertical loads at the anchor lug (Ha, Va) based on the mudline loads and the load transfer profile along the mooring line.
+
+  If the padeye depth zlug is embedded below the mudline, the method computes the lug loads using the load transfer model. Otherwise, it assigns the mudline loads directly to the lug.
+
+  **Parameters**
+  - **Hm** : float. Horizontal mudline load (N).
+  - **Vm** : float. Vertical mudline load (N).
+  - **zlug** :  float. Padeye embedment depth (m).
+  - **line_type** :  str, optional. Type of mooring line ('chain' or 'wire'). If not provided, inferred from attachments or defaults to 'chain'.
+  - **d** :  float, optional. Mooring line diameter (m).
+  - **w** :  float, optional. Mooring line unit weight (N/m).
+  - **plot** :   bool, optional. If True, generates plots of load transfer and geometry. Default is False.
+
+  **Raises**
+  - **ValueError** : If the soil profile is not assigned to the anchor before calling this method.
+
+  **Attributes Updated**
+  - **self.anchorCapacity** : dict. Stores the anchor's computed capacity results, including:
+      - 'Hmax': maximum horizontal capacity (N).
+      - 'Vmax': maximum vertical capacity (N).
+      - 'Ha','Va': lug-level horizontal and vertical loads (N).
+      - 'UC' or 'Unity check (horizontal)', 'Unity check (vertical)' : capacity utilization checks.
+      - 'Lateral displacement', 'Rotational displacement' : optional displacement results (if available)
+      - 'Weight pile', 'Weight plate' : self-weight of pile or plate depending on type.
+      - 'zlug' : lug depth (m), and 'z0' : mudline reference elevation (m)
+
+- **Anchor.getCapacityAnchor(Hm, Vm, zlug, line_type=None, d=None, w=None, mass_update=False, plot=False)** 
+
+  Calculate the load capacity of the anchor based on its type, geometry and local soil profile.
+
+  This method computes the anchor resistance against applied horizontal and vertical loads using the appropriate capacity model for the anchor type. It optionally performs load transfer from the mudline to the lug and updates the anchor's capacity results.
+
+  **Parameters**
+  - **Hm** : float. Horizontal mudline load (N).
+  - **Vm** : float. Vertical mudline load (N).
+  - **zlug** :  float. Padeye embedment depth (m).
+  - **line_type** :  str, optional. Type of mooring line ('chain' or 'wire'). If not provided, inferred from attachments or defaults to 'chain'.
+  - **d** : float, optional. Mooring line diameter (m).
+  - **w** : float, optional. Mooring line unit weight (N/m).
+  - **mass_update** : bool, optional. If True, updates the anchor's weight based on computed capacity. Default is False.
+  - **plot** : bool, optional. Whether to generate a plot of the load transfer profile. Default is False.
+
+  **Raises**
+  - **ValueError** : If the anchor type is unknown or if the soil profile is not properly assigned.
+
+  **Attributes Updated**
+  - **layers** : list of dict. The soil profile layers used in the load transfer calculation.
+  - **Ha** : float. Horizontal load at the lug (N).
+  - **Va** : float. Vertical load at the lug (N).
+ 
+- **Anchor.getSizeAnchor(geom, geomKeys, geomBounds=None, loads=None, lambda_con=[3, 6], zlug_fix=True,      safety_factor={'SF_combined':1.0}, plot=False)** 
+
+  Generalized optimization method to determine the appropriate geometry for all anchor types based on load conditions and safety factor requirements.
+
+  For suction, torpedo, and plate-type anchors, the method minimizes the difference between the calculated and target Unity Check (UC). For driven, helical, and dandg anchors, the method iteratively searches for the smallest geometry that satisfies combined UC, lateral and rotational displacement limits.
+
+  **Parameters**
+  - **geom** : : list of float. Initial geometry values (e.g., [L, D]).
+  - **geomKeys** : list of str. Keys that define the geometry variables to optimize (e.g., ['L', 'D']).
+  - **geomBounds** :  list of tuple, optional. Bounds for geometry values, e.g., [(5.0, 20.0), (1.0, 4.0)].
+  - **loads** :  dict, optional. Dictionary containing mudline forces ('Hm', 'Vm'). Defaults to self.loads.
+  - **lambdap_con** : list of float, optional. Minimum and maximum slenderness ratio constraints [L/D_min, L/D_max]. Default is [4, 8].
+  - **zlug_fix** : float, optional. If False, allows zlug to vary with geometry. Default is True.
+  - **safety_factor** : bool, optional.  Dictionary with safety factor targets (e.g., {'SF_combined': 1.5}). Default is {'SF_combined': 1.0}.
+  - **plot** : bool, optional. If True, generates plots the final capacity results. Default is False.
+
+  **Raises**
+  - **ValueError** :  If the anchor type is not supported for this optimization method.
+
+  **Attributes Updated**
+  - **self.dd['design']** : list of dict. The soil profile layers used in the load transfer calculation.
+  - **self.anchorCapacity** : float. Horizontal load at the lug (N).
+    - Ha, Va : float. Lug loads (N).
+    - Ha, Va : float. Lug loads (N).
+    - UC or 'Unity check (horizontal)', 'Unity check (vertical)'
+    - Optional displacements and weights if applicable
+
+- **Anchor.getSafetyFactor(ms=None)** 
+
+  Estimate the material cost of the anchor using the MoorPy Point object and its getCost_and_MBL() method.
+
+  If no MoorPy system is provided, the method initializes one and registers the anchor. Cost is based on mass and design parameters, and the Minimum Breaking Load (MBL) is also computed.
+
+  **Parameters**
+  - **ms** : MoorPy system instance, optional. If provided, uses the existing system. Otherwise, creates a new MoorPy system internally.
+
+  **Raises**
+  - **KeyError** : If self.mass is not defined and neither 'Weight pile' nor 'Weight plate' is available in self.anchorCapacity. This typically indicates that getCapacityAnchor() has not been called before getCostAnchor().
+
+  **Attributes Updated**
+  - **self.cost** : dict. Stores cost-related metrics for the anchor:
+    - 'Material cost' : Estimated anchor material cost.
+    - 'MBL' : Minimum Breaking Load (from MoorPy).
+    - 'unit_cost' : Cost per unit mass (cost/mass).
+  - **self.mpAnchor.m** : float. Mass of the MoorPy anchor point (assigned if self.mass is not already set).
+
+### Anchor Object Properties
+
+- **r** : anchor [x, y, z] position
+- **dd** : anchor design dictionary, containing geometric properties, soil properties at the anchor location, cost
+- **ms** : MoorPy system associated with this anchor point
+- **aNum** : anchor index in array mooring list (generally only used for shared moorings)
+- **mpAnchor** : MoorPy point object that models this anchor
+- **anchorCapacity** : dictionary with horizontal and vertical capacity of the anchor. Generally these are loads in [N], but can also be displacements (generally for driven or drilled and grouted piles)
+- **loads** : dictionary of loads on the anchor, and the method used to obtain these loads (static or dynamic modeling). 
+Loads include mooring line tension (T) with the angle of the load (theta) as well as horizontal (H) and vertical (V) loads components. 
+The keys for these loads will either include an m (for loads at the mudline) or a (for loads at the anchor lug / main padeye).
+- **soilProps** : dictionary of soil property information at the location of the anchor
+- **failure_probability** : dictionary of probabilities for failure of the anchor
+
+### Anchor Type Requirements
+
+Different geometric properties and soil conditions are needed for each anchor type. See the [Anchor Capacity Modules](#anchor-capacity-modules) section for details on the requirements of each anchor type.
+
+## Anchor Capacity Modules
+The following list shows the required soil conditions, load information and geometrical properties involved in the anchors' capacity calculations. 
+
+
+#### Output notes
+ The general output is a lateral and rotational displacement. In getAnchorCapacity, the drilled and grouted pile function is called in a while loop with incremented horizontal input forces 
+ until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function. 
+ 
 
-| Soil Type (sand) |  N-Value | Eff. Sat. Unit Weight, kN/m3 | Void Ratio | Natural Water Content in Sat. State, % | Eff. friction Angle | Relative density (%) |
-|:----------------:|:--------:|:----------------------------:|:----------:|:--------------------------------------:|:-------------------:|:--------------------:|
-|   Very   Loose   |    > 4   |            7 - 9.5           |    ~ 0.8   |                 25 - 30                |        < 30         |         < 15         |
-|       Loose      |  4 - 10  |            7 - 9.5           |    ~ 0.8   |                 25 - 30                |       30 - 35       |        15 - 35       |
-|     Compact      | 10 - 30  |          9.5 - 11.5          |   ~ 0.45   |                 12 - 16                |       35 - 40       |        35 - 65       |
-|      Dense       | 30 - 50  |          9.5 - 11.5          |   ~ 0.45   |                 12 - 16                |       40 - 45       |       65 - 85        |
-|    Very Dense    |   < 50   |          9.5 - 11.5          |   ~ 0.45   |                 12 - 16                |        > 45         |         > 85         |
 
-Conversion functions are under development to switch between categories (level 1 anchor models) 
-and quantitative soil metrics (level 2 anchor models).
\ No newline at end of file
diff --git a/famodel/anchors/README_FMO.md b/famodel/anchors/README_FMO.md
deleted file mode 100644
index c4498589..00000000
--- a/famodel/anchors/README_FMO.md
+++ /dev/null
@@ -1,267 +0,0 @@
-# Anchors Library
-
-This subpackage of FAModel contains the Anchor class as well as modules for analytical static anchor capacity assessment. 
-
-## Anchor Class
-The anchor class contains properties and methods related to mooring anchors.
-
-The anchor class stores properties and methods that enable a wide range of modeling - from capacity to cost to loads, and more. 
-The [anchor capacity modules](#anchor-capacity-modules) are integrated with the anchor class through the getAnchorCapacity() method.
- 
-### Anchor Files
-Introduction 
-Inspection of the folder: anchors_famodel
-
-#### Anchor Capacity Files
-- **capacity_plate** : getCapacityPlate
-- **capacity_suction** : getCapacitySuction
-- **capacity_torpedo** : getCapacityTorpedo
-- **capacity_driven** : getCapacityDriven
-- **capacity_dandg** : getCapacityDandG
-- **capacity_helical** : getCapacityHelical
-- **capacity_load** : getTransferFunction
-
-#### Anchor Support Files
-- **capacity_soils** : clay_profile, sand_profile and rock_profile
-- **capacity_solvers** : fd_solver for non-linear solver
-- **capacity_pycurves** : py_Matlock, py_API, py_Reese and py_Lovera
-- **capacity_plots** : plot_driven, plot_suction, plot_helical,plot_plate, plot_dandg, plot_load, plot_pycurve
-
-### Anchor Methods
-- **setSoilProfile()** : Assign a bilinearly interpolated soil profile from the 4 nearest CPTs
-- **makeMoorPyAnchor()** : Creates a MoorPy point object representing the anchor in a moorpy system
-- **getMudlineForces()** : Finds forces on anchor at mudline using MoorPy Point.getForces method. Use max_force=True to obtain the maximum forces on that anchor from the platform.getWatchCircle() method. 
-For more information on the getWatchCircle() calculations, see the [Platform ReadMe](../platform/README.md). An additional anchor.loads dictionary entry is included to describe the mudline load type. 
-'mudline_load_type'='max' if max_force=True, and 'mudline_load_type'='current_state' if max_force=False.
-- **getLugForces()** : Finds forces at the anchor lug location with getTransferFunction function in capacity_loads.py
-- **getCapacityAnchor()** : Calls anchor capacity functions for the correct anchor type using the getLugForces embedded in the method
-- **getSizeAnchor()** : Calls sizing anchor functions for the correct anchor type using the getLugForces embedded in the method
-- **getFS()** : Computes safety factor for loads on the anchor
-- **getCost()** : Finds costs of anchor from MoorProps and stores in design dictionary
-- **getCombinedPlot()** : Create a plot showing the anchor and the inverse catenary overlay in the same coordinate system.
-
-### Anchor Object Properties
-- **r** : anchor [x,y,z] position
-- **dd** : anchor design dictionary, containing geometric properties, soil properties at the anchor location, cost
-- **ms** : moorpy system associated with this anchor point
-- **aNum** : anchor index in array mooring list (generally only used for shared moorings)
-- **mpAnchor** : moorpy point object that models this anchor
-- **anchorCapacity** : dictionary with horizontal and vertical capacity of the anchor. Generally these are loads in [N], but can also be displacements (generally for driven or drilled and grouted piles)
-- **loads** : dictionary of loads on the anchor, and the method used to obtain these loads (static or dynamic modeling). 
-Loads include mooring line tension (T) with the angle of the load (theta) as well as horizontal (H) and vertical (V) loads components. 
-The keys for these loads will either include an m (for loads at the mudline) or a (for loads at the anchor lug / main padeye).
-- **soilProps** : dictionary of soil property information at the location of the anchor
-- **failure_probability** : dictionary of probabilities for failure of the anchor
-
-### Anchor Type Requirements
-
-Different geometric properties and soil conditions are needed for each anchor type. See the [Anchor Capacity Modules](#anchor-capacity-modules) section for details on the requirements of each anchor type.
-
-## Anchor Capacity Modules
-The following list shows the required soil conditions, load information and geometrical properties involved in the anchors' capacity calculations. 
-Soil classification for clay, sand and rock can be found in [Soil Classification Parameters](#soil-classification-parameters).
->[!NOTE] 
->Some anchor capacity functions require input loads at the anchor lug point. These loads can be sent in to the getAnchorCapacity() method, or the getAnchorCapacity() method will calculate the loads by calling getLugLoads(). 
-The input loads must be maximum or large loads on the anchor.
-
-### Soil Properties
-Introduction to different soil types
-- clay/mud
-	- gamma (submerged soil unit weight) (kN/m^3)
-	- Su (undrained shear strength of soil at mudline) (kPa)
-	
-- sand
-	- gamma (submerged soil unit weight) (kN/m^3)
-	- phi (internal friction angle) (deg)
-	- Dr (relative density) (%)
-	
-- rock (weak rock up to UCS = 5 MPa)
-	- UCS (unconfined compressive strength at failure) (MPa)
-	- Em (rock mass modulus) (MPa)
-		
-### Soil Classification Parameters
-
-The soft, medium, and hard clay soil classes are distinguished by the following parameter ranges: 
-| Soil Type (clay)  | N-Value  |     Eff. Sat. Unit Weight, kN/m3  | Void Ratio | Natural Water Content in Sat. State, % | Undrained Shear Strength, kN/m2 |
-|:-----------------:|:--------:|:---------------------------------:|:----------:|:--------------------------------------:|:-------------------------------:|
-|     Very Soft     |  0 - 2   |             5.5 - 8.5             |  0.9 - 1.4 |                30 - 50                 |            0 - 12.5             |
-|       Soft        |  2 - 4   |             5.5 - 8.5             |  0.9 - 1.4 |                30 - 50                 |            12.5 - 25            |
-|       Medium      |  4 - 8   |             5.5 - 8.5             |  0.9 - 1.4 |                30 - 50                 |             25 - 50             |
-|       Stiff       |  8 - 15  |             8.5 - 12              |    ~ 0.6   |                20 - 30                 |            50 - 100             |
-|     Very Stiff    | 15 - 30  |             8.5 - 12              |    ~ 0.6   |                20 - 30                 |            100 - 200            |
-|        Hard       |   < 30   |             8.5 - 12              |    ~ 0.6   |                20 - 30                 |              > 200              |
-
-
-Sand can also be classified ranging from soft to hard, however only a single sand class is supported at this time. In the future, sand classes will follow the parameter ranges in the following table:
-
-| Soil Type (sand) |  N-Value | Eff. Sat. Unit Weight, kN/m3 | Void Ratio | Natural Water Content in Sat. State, % | Eff. friction Angle | Relative density (%) |
-|:----------------:|:--------:|:----------------------------:|:----------:|:--------------------------------------:|:-------------------:|:--------------------:|
-|   Very   Loose   |    > 4   |            7 - 9.5           |    ~ 0.8   |                 25 - 30                |        < 30         |         < 15         |
-|       Loose      |  4 - 10  |            7 - 9.5           |    ~ 0.8   |                 25 - 30                |       30 - 35       |        15 - 35       |
-|     Compact      | 10 - 30  |          9.5 - 11.5          |   ~ 0.45   |                 12 - 16                |       35 - 40       |        35 - 65       |
-|      Dense       | 30 - 50  |          9.5 - 11.5          |   ~ 0.45   |                 12 - 16                |       40 - 45       |       65 - 85        |
-|    Very Dense    |   < 50   |          9.5 - 11.5          |   ~ 0.45   |                 12 - 16                |        > 45         |         > 85         |
-
-Conversion Units
-
-Z0 explanaition on the pile head reference and how for zlug > z0 mooring line is embedded and below the mudline elevation
-
-### Loads
-- Loads at mudline elevation
-	- Tm, thetam (Tension and angle of the load on mudline)
-	- Hm, Vm (Horizontal and vertical loads on mudline)
-- Loads at mudline elevation
-	- Ta, thetaa (Tension and angle of the load on padeye of anchor)
-	- Ha, Va (Horizontal and vertical loads on padeye of anchor)
-- Transfer functions: soil properties (profile)  mooring line properties (line_type, d and w), loads and zlug 
-
-> [!NOTE] check getLugForces for more details on this transfer function from mudline to lug elevation (below the seabed)
-The getTransferLoad function requires **maximum** mudline forces as an input. These forces can be sent in as a dictionary, or anchor.loads dictionary will be searched for 'Hm' and 'Vm' values with additional 
-key-value pair 'mudline_force_type':'max' to indicate these mudline forces are maximums.
- 
-If there are no max mudline forces in the anchor.loads dictionary, getMudlineForces(max_force=True) will be called. Stores results in loads dictionary. 
-If lug is at mudline or no lug provided, equates mudline forces with lug forces. 
->[!NOTE]
->The getTransferFunction function called by getLugForces() is tuned to work with maximum loads on the anchor. Some anchor configuration, load, and soil condition combinations may produce invalid results in getTransferFunction. 
-For example, the output Va may show as negative. In that case, getLugForces() will warn the user of the invalidity of the result and assign the anchor lug forces = mudline forces.
-
--------------------------------------------------------------------------------
-> [!IMPORTANT] 
-> A positive zlug denotes a lug/padeye/bridle below the mudline, while a negative zlug denotes a lug/padeye/bridle above the mudline. Anchors in rock should have a zlug >= 0.
-
-> [!NOTE] 
-> Load inputs to the capacity functions (with the exception of driven & drilled and grouted anchors) are in kN, while the anchor loads dictionary is in N. This conversion is automatically completed in the getAnchorCapacity() 
-function so no manual load conversion is required. Load outputs are automatically converted in the getAnchorCapacity function where necessary. 
-
------------------------------------------------------------------------------
-	
-### Anchor Types
-The supported anchor types are below, with the associated FAModel name in italics.
-- Plate anchors 
-  - *DEA* (drag-embedment anchors)
-  - *SEPLA* (suction embedded plate anchors)
-  - *DEPLA* (dynamically embedded plate anchors)
-  - *VLA* (vertically loaded anchors)
-  - *plate* (unspecified plate anchor)
-- *suction_pile* (Suction caisson/ suction bucket anchors)
-- *torpedo_pile* (Torpedo pile anchors)
-- *helical_pile* (Helical pile anchors)
-- *driven_pile*  (Driven pile anchors)
-- *dandg_pile* (Drilled and grouted piles)
-
-### Anchor geometrical properties
-#### DEA/SEPLA/DEPLA/VLA/plate
-- soil condition: clay 
-- geometry
-   - A (area of plate) (m^2)
-   - zlug (embedded depth of bridle/padeye below mudline - positive is below mudline, negative is above mudline) (m)
-   - beta (OPTIONAL - angle of plate with horizontal plane) (deg)
-- loads
-  - Ha, Va (horizontal and vertical loads on padeye of anchor)
-  
-#### suction_pile (Suction caisson/ suction bucket anchors)
-- soil condition: clay and sand
-- geometry
-   - L (length of pile) (m)
-   - D (diameter of pile) (m)
-   - zlug (embedded depth of padeye below mudline) (m)
-- loads
-  - Ha, Va (horizontal and vertical loads on padeye of anchor)
-  
-#### torpedo_pile (Torpedo pile anchors)
-- soil condition: clay
-- geometry
-   - D1 (wing diameter) (m)
-   - D2 (shaft diameter) (m)
-   - L1 (wing length) (m)
-   - L2 (shaft length) (m)
-   - zlug (embedded depth of padeye below mudline) (m)
-- loads
-  - Ha, Va (horizontal and vertical loads on padeye of anchor)
-
-#### helical_pile (Helical pile anchors)
-- soil condition: clay and sand 
-- geometry
-  - D (helix diameter) (m)
-  - L (shaft length) (m)
-  - d (shaft diameter) (m)
-  - zlug (embedded depth of padeye below mudline) (m)
-- loads
-  - Ha, Va (horizontal and vertical loads on padeye of anchor)
-  
-#### driven_pile (Driven pile anchors)
-- soil condition: clay, sand and (weak) rock
-- geometry
-  - L (length of pile) (m)
-  - D (diameter of pile) (m)
-  - zlug (embedded depth of padeye below mudline) (m)
-- loads
-  - Ha, Va (horizontal and vertical loads on padeye of anchor)
-  
-#### Output notes 
- The general output is a lateral and rotational displacement or bending moment. In getCapacityAnchor, the driven pile capacity function is called in a while loop with incremented horizontal 
- input forces until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function.
-  
- For non-rock soil, the hinge (bending moment) is also considered as a failure mode along with the lateral and rotational displacement 
- 
-#### dandg_pile (Drilled and grouted piles)
-- soil condition: rock
-- geometry
-   - L (length of pile) (m)
-   - D (diameter of pile) (m)
-   - zlug (lug location) (m)
-- loads
-  - Ha, Va (horizontal and vertical loads on padeye of anchor)
-  
-#### Output notes
- The general output is a lateral and rotational displacement. In getAnchorCapacity, the drilled and grouted pile function is called in a while loop with incremented horizontal input forces 
- until one of the displacements goes past set failure criteria, thus providing a horizontal force capacity output [N]. Vertical capacity [N] is already calculated within the driven pile capacity function. 
- 
-
-### Model Fidelity
-
-There are two levels of fidelity in these models:
-
-- Level 1 basic models are soil-dependent capacity curves for a 
-  range of anchor types based on performing curve fits to 
-  published information in anchor manuals and standards. 
-- Level 2 intermediate models are quantitative calculations for
-  suction caissons and plate anchors that account for quantitative 
-  soil properties as well as their variation with embedment depth.
-
-This plot gives an example of the capacity curves that can be 
-produced by the intermediate model (holding capacity for a suction
-embedded plate anchor) as a function of surface shear strength:
-
-![Capacities](images/SEPLA_curves_small.PNG)
-
-### Implemented level-1 model anchor and soil types
-
-|             | DEA       | Suction | VLA | SEPLA |
-|-------------|-----------|---------|-----|-------|
-| Soft clay   | X         | X       | X   | X     |
-| Medium clay | X         | X       | X   | X     |
-| Hard clay   | X         |         |     |       |
-| Sand        | X         |         |     |       |
-
-### Parameters needed for level-2 anchor capacity models
-
-|        **Anchor type** | **Suction** | **Suction** | **VLA**  | **SEPLA** |
-|------------------------|-------------|-------------|----------|-----------|
-|        **Soil type**   | **Clay**    | **Sand**    | **Clay** | **Clay**  |
-| **Anchor parameters**  |             |             |          |           |
-|        Diameter        | x           | x           |          |           |
-|        Length          | x           | x           |          |           |
-|        Area            |             |             | X        | X         |
-|        Thickness       | ratio       | ratio       | ratio    | ratio     |
-|       Embedment depth  |             |             | X        | X         |
-| **Soil parameters**    |             |             |          |           |
-|        gamma           | X           | X           | X        | X         |
-|        Su0             | X           |             | X        | X         |
-|        k               | X           |             | X        | X         |
-|        alpha           | X           |             |          |           |
-|        phi             |             | X           |          |           |
-
-
-These models will continue to be expanded as data sources and time permit.
-
diff --git a/famodel/anchors/anchor.py b/famodel/anchors/anchor.py
index 2ec40b3e..274015d3 100644
--- a/famodel/anchors/anchor.py
+++ b/famodel/anchors/anchor.py
@@ -3,71 +3,66 @@
 """
 import moorpy as mp
 import numpy as np
+from scipy.optimize import minimize
 from famodel.famodel_base import Node
 from famodel.mooring.mooring import Mooring
+import matplotlib.pyplot as plt
+from collections import defaultdict
 import famodel.platform.platform 
 import shapely as sh
 
-
 class Anchor(Node):
     
-    def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, 
-                 g=9.81, rho=1025):
+    def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None,
+                 g=9.81, rho=1025, profile_map=None):
         '''
+        Initialize an Anchor object.
+
         Parameters
         ----------
-        dd: dictionary
-            Design dictionary that contains all information on an anchor for a mooring line/shared mooring
-            {
-                type:   # anchor type (plate,suction_pile,torpedo_pile,helical_pile,driven_pile,dandg_pile)
-                design: # all geometric info from yaml file, only need to include info relevant to your anchor type
-                     A    plate anchor area
-                     D    anchor diameter (or helix diameter for helical piles)
-                     D1   torpedo anchor wing diameter
-                     D2   torpedo anchor shaft diameter
-                     d    helical pile shaft diameter 
-                     L    pile anchor length
-                     L1   torpedo anchor wing length 
-                     L2   torpedo anchor shaft length
-                     zlug padeye z elevation (+ down into the soil)
-                     beta angle of plate anchor after keying (optional)
-                cost:
-                    matCost: # material cost
-                    instCost: # installation cost
-                    decomCost: # decomissioning cost                          
-            }
-        ms: system object
-            MoorPy system object the anchor is in
-            
-        r: list
-            Location of anchor in x,y,z
-            
-        aNum: int
-            entry number in project.anchorList dictionary (may remove...)
-        id: str/int
-            unique id of this object
-        g: float
-            acceleration due to gravity in m/s^2
-        rho: float
-            density of water in kg/m^3
+        dd : dict
+            Design dictionary containing all information on the anchor.
+        ms : MoorPy system object
+            MoorPy system instance.
+        r : list of float
+            Anchor position coordinates (x, y, z) (m)
+        aNum : int, optional
+            Index in anchor list.
+        id : str or int, optional
+            Unique anchor identifier.
+        g : float, optional
+            Gravity.
+        rho : float, optional
+            Water density.
+        profile_map : list of dict, optional
+            Full soil profile map for selecting local soil layers.
         '''
-        # Initialize as a node
-        Node.__init__(self,id)
-        
-        # Design description dictionary for this Anchor
+
+        from famodel.famodel_base import Node
+        Node.__init__(self, id)
+
         self.dd = dd
-        
-        # MoorPy system this anchor is in
         self.ms = ms
-        
-        # x,y,z location of anchor
         self.r = r
-        
-        # anchor index in array mooring list (only used for shared moorings/anchors)
         self.aNum = aNum
-               
-        # MoorPy anchor object
+        self.g = g
+        self.rho = rho
+
+        if dd and 'type' in dd:
+            self.anchType = dd['type']
+        else:
+            self.anchType = 'suction'
+            print(f"[Anchor] No type provided. Defaulting to 'suction'.")
+
+        self.soil_type = None
+        self.soil_profile = None
+        self.profile_name = None
+        self.soil_type_list = []
+
         self.mpAnchor = None
+        self.capacity_format = None
+        self.mass = dd.get('design', {}).get('mass', None) if dd else None
+        self.point_num = 0  # initialize point number
         
         # get environment info
         self.g = g # acceleration due to gravity (m/s^2)
@@ -101,1025 +96,1201 @@ def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None,
             }
         '''
         self.soilProps = {}
+        self.loads = {}
+        self.anchorCapacity = {}
+        self.cost = {}
         self.failure_probability = {}
-
-        # environmental impact
         self.env_impact = {}
-        
 
-        # self.cost = {}
-        
-        
+        # Assign soil profile if map is provided
+        if profile_map is not None:
+            if len(profile_map) == 1:
+                self.setSoilProfile(profile_map)
+            elif len(profile_map) >= 4:
+                self.interpolateSoilProfile(profile_map)
+            else:
+                raise ValueError("profile_map must contain either 1 or ≥4 CPTs for soil assignment.")
+
+    def setSoilProfile(self, profile_map):
+        '''
+        Assign a soil profile directly from a single CPT.
+        Assumes profile_map is a list with only one entry.
+        '''
+        if len(profile_map) != 1:
+            raise ValueError("setSoilProfile expects a profile_map with exactly one CPT.")
+
+        cpt = profile_map[0]
+        self.soil_profile = cpt['layers']
+        self.profile_name = cpt.get('name', 'CPT_Assigned')
+
+        # Extract soil types from layers
+        soil_types = [layer['soil_type'] for layer in self.soil_profile]
+        self.soil_type_list = list(set(soil_types))
+        self.soil_type = soil_types[0] if len(self.soil_type_list) == 1 else 'mixed'
+
+        # Group layers by soil type
+        soilProps = defaultdict(list)
+        for layer in self.soil_profile:
+            layer_copy = layer.copy()
+            soil_type = layer_copy.pop('soil_type')
+            soilProps[soil_type].append(layer_copy)
+        self.soilProps = dict(soilProps)
+
+        print(f"[Anchor] Assigned soil profile from {self.profile_name} with soil types {self.soil_type_list}")
+
+
+    def interpolateSoilProfile(self, profile_map):
+        '''
+        Interpolate a soil profile from the 4 nearest CPTs in profile_map.
+        '''
+        if len(profile_map) < 4:
+            raise ValueError("interpolateSoilProfile requires at least 4 CPTs.")
+
+        x_anchor, y_anchor = self.r[0], self.r[1]
+
+        # Sort CPTs by distance
+        distances = [np.hypot(p['x'] - x_anchor, p['y'] - y_anchor) for p in profile_map]
+        idx_sorted = np.argsort(distances)
+        CPTs = [profile_map[i] for i in idx_sorted[:4]]
+
+        # Inverse distance weighting
+        x = np.array([cpt['x'] for cpt in CPTs])
+        y = np.array([cpt['y'] for cpt in CPTs])
+        d = np.hypot(x - x_anchor, y - y_anchor)
+        w = 1 / np.maximum(d, 1e-3)**2
+        w /= np.sum(w)
+
+        # Interpolate layer-by-layer (assumes same layer structure)
+        layers_list = [cpt['layers'] for cpt in CPTs]
+        n_layers = len(layers_list[0])
+        interpolated_layers = []
+
+        for i in range(n_layers):
+            base_layer = layers_list[0][i]
+            layer = {'soil_type': base_layer['soil_type']}
+
+            for key in base_layer:
+                if key == 'soil_type':
+                    continue
+                if all(key in l[i] for l in layers_list):
+                    vals = [l[i][key] for l in layers_list]
+                    layer[key] = np.dot(w, vals)
+
+            interpolated_layers.append(layer)
+
+        self.soil_profile = interpolated_layers
+        self.profile_name = "Interpolated_2D"
+
+        # Extract soil types
+        soil_types = [layer['soil_type'] for layer in self.soil_profile]
+        self.soil_type_list = list(set(soil_types))
+        self.soil_type = soil_types[0] if len(self.soil_type_list) == 1 else 'mixed'
+
+        # Group interpolated layers by soil type
+        soilProps = defaultdict(list)
+        for layer in self.soil_profile:
+            layer_copy = layer.copy()
+            soil_type = layer_copy.pop('soil_type')
+            soilProps[soil_type].append(layer_copy)
+        self.soilProps = dict(soilProps)
+
+        print(f"[Anchor] Interpolated soil profile: {self.profile_name} with soil types {self.soil_type_list}")
+
     def makeMoorPyAnchor(self, ms):
-        '''Create a MoorPy anchor object in a moorpy system
+        '''
+        Create a MoorPy anchor object in a MoorPy system.
+
         Parameters
         ----------
-        ms : class instance
-            MoorPy system
-        
+        ms : MoorPy system instance
+            The MoorPy system to add the anchor to.
+
         Returns
         -------
-        ms : class instance
-            MoorPy system 
+        ms : MoorPy system instance
+            The updated MoorPy system with the anchor added.
+        '''       
+        anchType = self.anchType or 'suction'
 
-        '''
-        # create anchor as a fixed point in MoorPy system
-        ms.addPoint(1,self.r)
-        # assign this point as mpAnchor in the anchor class instance
+        # Create anchor as a fixed point in MoorPy system
+        ms.addPoint(1, self.r)
+
+        # Assign this point as mpAnchor in the anchor class instance
         self.mpAnchor = ms.pointList[-1]
 
-        # add mass if available
-        if 'm' in self.dd['design'] and self.dd['design']['m']:
-            self.mpAnchor.m = self.dd['design']['m']
-        # set anchor diameter
-        if 'd' in self.dd['design'] and self.dd['design']['d']:
+        # Set mass if available
+        if 'mass' in self.dd.get('design', {}):
+            self.mpAnchor.m = self.dd['design']['mass']
+
+        # Set diameter if available
+        if 'd' in self.dd.get('design', {}):
             self.mpAnchor.d = self.dd['design']['d']
-        # set the point as an anchor entity
-        self.mpAnchor.entity= {'type': 'anchor'}
-        if 'type' in self.dd:
-            self.mpAnchor.entity['anchor_type']=self.dd['type']
-        
-        return(ms)
-    
-    
-    def getAnchorCapacity(self,ground_cons=None,installAdj=1,profile=None,loads=None,plot=True):
-        '''
-        Calls anchor capacity functions developed by Felipe Moreno for the correct anchor type 
+            
+        # Set dummy design to get PointType from MoorPy
+        design = {f"num_a_{anchType}": 1}
+        pointType = ms.setPointType(design, source=None)
+        self.mpAnchor.entity = pointType
 
-        Parameters
-        ----------
-        ground_conds : dict, optional
-            Ground conditions dictionary with the key as the soil type name, values as soil info such as UCS,Em,phi,gamma,effective stress,etc. The default is None.
-            If no dict provided, the ground conds will be pulled from the anchor soilProps property
-        installAdj : float, optional
-            Adjustment to the capacity based on installation (dummy variable for now, but future installation functions
-                                                              will dictate this value)
-        profile : 2D array,  optional
-            2d array of depths (m) and corresponding undrained shear strength (Pa). Su must not be zero 
-            (change to small value such as .001), but z must always start at 0. Ex: array([z1,Su1],[z2,Su2],...)
-            Used only for driven pile and drilled and grouted pile anchors.
-        loads : dict, optional
-            Dictionary of loads on the anchor at the lug point in [N]. If not provided, will use the loads dictionary property 
-            of the anchor. If this is empty and it is needed for the capacity function (i.e. driven piles) then 
-            the anchor.getLugForces() function will be called.
+        return ms
+       
+    def getLineProperties(self):
+        '''
+        Retrieve line_type, diameter and unit weight from attached mooring.
 
         Returns
         -------
-        results : dict
-            Dictionary of capacity of the anchor (generally a max force [N] in H and V, but can be a max displacement (driven, dandg piles))
-
+        line_type : str
+            Type of mooring line ('chain' or 'wire')
+        d : float
+            Nominal diameter (m)
+        w : float
+            Unit weight (N/m)
         '''
-        # - - - - set details - - - -
-        anchType = self.dd['type'] 
-        geom = self.dd['design']# geometric anchor information
-
-        if not ground_cons:
-            soil = next(iter(self.soilProps.keys()), None) # soil type
-            ground_conds = self.soilProps[soil]
-        else:
-            soil = next(iter(ground_cons.keys()))
-            ground_conds = ground_cons[soil]
-         
-        for key,prop in ground_conds.items():
-            if isinstance(prop,list) or isinstance(prop,np.ndarray):
-                if len(prop)>1:
-                    print('Warning: Only homogeneous soils are supported at this time. Only the first item in a property list will be used.')
-                    break 
-                else:
-                    ground_conds[key] = prop[0]
-        
-        
-        if loads:
-            # find out if mudline loads or anchor loads
-            if not 'Ha' in loads:
-                # get loads at lug
-                loads = self.getLugForces(mudloads=loads,plot=plot)
-        else:
-            loads = self.loads
-        
-        
-       
-        # logic to determine what functions to call based on anchor type and soil type...
-        
-        # - - - - plate anchors - - - - 
-        if anchType == 'SEPLA' or anchType == 'DEA' or anchType == 'DEPLA' or anchType == 'VLA' or anchType == 'plate':
-            from .anchors_famodel.capacity_plate import getCapacityPlate
-            if 'clay' in soil or 'mud' in soil:
-                # write or overwrite beta in geom dictionary from loads function
-                if anchType != 'DEA':
-                    if not 'beta' in geom:
-                        if not 'thetaa' in loads:
-                            # calculate thetaa from Ha and Va
-                            loads['thetaa'] = np.arctan2(loads['Va'],loads['Ha'])
-                            # loads = self.getLugForces(plot=plot)
-                        geom['beta'] = 90 - loads['thetaa']
-                else:
-                    geom['beta'] = 0
-                if 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds:             
-                    results = getCapacityPlate(geom['A'], geom['beta'], geom['zlug'], 'clay', ground_conds['gamma'],
-                                               Su0=ground_conds['Su0'], k=ground_conds['k'])
-                else:
-                    raise Exception('Ground conditions dictionary needs Su0, k, gamma information for clay plate anchors')
-            else:
-                print(f'Warning: Soil type {soil} is not compatible with plate anchors (SEPLA/DEPLA/DEA/VLA)')
-         
-        # - - - - suction buckets - - - -                       
-        elif 'suction' in anchType:
-            from .anchors_famodel.capacity_suction import getCapacitySuction
-            # check loads have been calculated (needed for capacity function in this case)
-            if not 'Ha' in loads:
-                # call getMPForces function 
-                loads = self.getLugForces(plot=plot)
-            if 'sand' in soil:
-                if 'phi' in ground_conds and 'Dr' in ground_conds:
-                    results = getCapacitySuction(geom['D'], geom['L'], geom['zlug'],
-                                                 loads['Ha']/1000, loads['Va']/1000,
-                                                 'sand', ground_conds['gamma'],
-                                                 phi=ground_conds['phi'],
-                                                 Dr=ground_conds['Dr'], plot=plot)
-                else:
-                    raise Exception('Ground conditions dictionary needs phi and relative density information for sand suction pile anchor')
-            elif 'clay' in soil or 'mud' in soil:
-                if 'Su0' in ground_conds and 'k' in ground_conds and 'alpha' in ground_conds:# and 'gamma_sub' in ground_conds:
-                    results = getCapacitySuction(geom['D'],geom['L'], geom['zlug'],
-                                                 loads['Ha']/1000, loads['Va']/1000,
-                                                 'clay', ground_conds['gamma'],
-                                                 Su0=ground_conds['Su0'],
-                                                 k=ground_conds['k'], plot=plot)
-                    results['Horizontal max.'] = results['Horizontal max.']
-                    results['Vertical max.'] = results['Vertical max.']
-                    
-                else:
-                    raise Exception('Ground conditions dictionary needs Su0, k, and alpha information for clay suction pile anchor')
-            else:
-                print(f'Warning: Soil type {soil} is not compatible with suction pile anchor')
-                
-        # - - - - helical piles - - - -
-        elif 'helical' in anchType:
-            from .anchors_famodel.capacity_helical import getCapacityHelical
-            if 'sand' in soil:
-                if 'phi' in ground_conds and 'gamma' in ground_conds:
-                    results = getCapacityHelical(geom['D'], geom['L'], geom['d'],
-                                                 geom['zlug'], 'sand', 
-                                                 ground_conds['gamma'],
-                                                 phi=ground_conds['phi'], 
-                                                 Dr=ground_conds['Dr'])
-                    results['Vertical max.'] = results['Capacity']
-                else:
-                    raise Exception('Ground conditions dictionary needs phi, gamma and relative density information for clay helical pile anchor')
-            elif 'clay' in soil or 'mud' in soil:
-                if not 'alpha_star' in ground_conds:
-                    ground_conds['alpha_star'] = ground_conds['alpha']
-                if 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds:
-                    results = getCapacityHelical(geom['D'], geom['L'], geom['d'],
-                                                 geom['zlug'], 'clay', 
-                                                 ground_conds['gamma'],
-                                                 Su0=ground_conds['Su0'], 
-                                                 k=ground_conds['k'])
-                    results['Vertical max.'] = results['Capacity']
-                else:
-                    raise Exception('Ground conditions dictionary needs Su0, k, gamma, and alpha_star information for clay helical pile anchor')
-            else:
-                print(f'Warning: Soil type {soil} is not compatible with helical pile anchor')
-        
-        # - - - - torpedo piles - - - - 
-        elif 'torpedo' in anchType:
-            from .anchors_famodel.capacity_torpedo import getCapacityTorpedo
-            if 'clay' in soil or 'mud' in soil:
-                if 'Su0' in ground_conds and 'k' in ground_conds and 'alpha' in ground_conds:
-                    results = getCapacityTorpedo(geom['D1'], geom['D2'], 
-                                                 geom['L1'], geom['L2'], 
-                                                 geom['zlug'], 'clay', 
-                                                 ground_conds['Su0'],
-                                                 ground_conds['k'], 
-                                                 ground_conds['alpha'])
-                    results['Horizontal max.'] = results['Horizontal max.']
-                    results['Vertical max.'] = results['Vertical max.']
+        for att in self.attachments.values():
+            if isinstance(att['obj'], Mooring):
+                mtype = att['obj'].dd['sections'][0]['type']['material'].lower()
+                if 'chain' not in mtype:
+                    print('No chain below seafloor, setting Ta=Tm (no load transfer).')
+                    return mtype, None, None, True
                 else:
-                    raise Exception('Ground conditions dictionary needs Su0, k, and alpha information')
-            else:
-                print('Warning: Soil type {soil} is not compatible with torpedo pile anchor')
-        
-        # - - - - driven piles - - - - 
-        elif 'driven' in anchType: # driven pile anchor
-            # check loads have been calculated (needed for capacity function in this case)
-            if not 'Ha' in loads:
-                # call getLugForces function 
-                loads = self.getLugForces(plot=plot)
-            H_inc = loads['Ha']*0.1 # increment H by 10% of Ha load in the while loops to back-calc max H from displacements
-            H = 0
-            # check soil
-            if 'weak_rock' in soil:
-                from .anchors_famodel.capacity_drivenrock import getCapacityDrivenRock
-                
-                if not profile:
-                    if 'UCS' in ground_conds and 'Em' in ground_conds:
-                        profile = [[0,ground_conds['UCS'],ground_conds['Em']], 
-                                   [75,ground_conds['UCS'],ground_conds['Em']]] #profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['UCS'],ground_conds['Em']))]
-                    else:
-                        raise Exception('Ground conditions dictionary needs UCS, Em, and depth information for weak rock driven pile anchor')
-                
-                y, z, results = getCapacityDrivenRock(profile, geom['L'], geom['D'], 
-                                                      geom['zlug'], loads['Va'], 
-                                                      loads['Ha'], plot=plot)
-                
-                # loop through, calling capacity with larger H values until a displacement value goes above limit
-                while results['Lateral displacement']< 0.05*geom['D'] and results['Rotational displacement'] < 0.25:
-                    # increment H 
-                    H += H_inc
-                    # call capacity function       
-                    y, z, results = getCapacityDrivenRock(profile, geom['L'], 
-                                                          geom['D'], geom['zlug'], 
-                                                          loads['Va'], H=H, plot=plot)
-                    
-                   
-            elif 'sand' in soil:
-                from .anchors_famodel.capacity_drivensoil import getCapacityDrivenSoil
-                if profile or ('gamma' in ground_conds and 'Dr' in ground_conds and 'phi' in ground_conds):
-                    if not profile:
-                        profile = [[0,ground_conds['phi'],ground_conds['gamma'],ground_conds['Dr']], 
-                                   [75,ground_conds['phi'],ground_conds['gamma'],ground_conds['Dr']]] #profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['phi'],ground_conds['gamma']))]
-
-                    y, z, results = getCapacityDrivenSoil(profile, 'sand', 
-                                                          geom['L'], geom['D'], 
-                                                          geom['zlug'], loads['Va'], 
-                                                          loads['Ha'], plot=plot)
-                    if geom['zlug'] > 0:
-                        # need to check bending moment if lug is below mudline (+ zlug)
-                        # loop through, calling capacity with larger H values until a displacement value goes above limit
-                        while results['Lateral displacement']<= 0.05*geom['D'] and results['Bending moment'] <= results['Plastic moment']:
-                            # increment H by 10% of load
-                            H += H_inc
-                            # call capacity function     
-                            y, z, results = getCapacityDrivenSoil(profile,'clay', 
-                                                                  geom['L'], geom['D'],
-                                                                  geom['zlug'], loads['Va'], 
-                                                                  H=H, plot=plot)
-                            
-                    else:
-                        while results['Lateral displacement']<= 0.05*geom['D'] and results['Rotational displacement'] <= 0.25:
-                            # increment H by 10% of load
-                            H += H_inc  
-                            # call capacity function
-                            y, z, results = getCapacityDrivenSoil(profile, 'clay', 
-                                                                  geom['L'], geom['D'], 
-                                                                  geom['zlug'], loads['Va'],
-                                                                  H=H, plot=plot)
-                else:
-                    raise Exception('Ground conditions dictionary needs phi, gamma, and depth information for sand driven pile anchor')
-            elif 'clay' in soil or 'mud' in soil:
-                from .anchors_famodel.capacity_drivensoil import getCapacityDrivenSoil
-                #if profile or ('Su' in ground_conds and 'gamma' in ground_conds and 'depth' in ground_conds) or ('Su0' in ground_conds and 'k' in ground_conds):
-                if not profile:
-                    if 'Su' in ground_conds and 'depth' in ground_conds and 'gamma' in ground_conds:
-                        profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['Su'],ground_conds['gamma']))]
-                    elif 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds:
-                        Su = ground_conds['Su0']+ground_conds['k']*75
-                        profile = [[0,ground_conds['Su0'],ground_conds['gamma']],[75,Su,ground_conds['gamma']]]
-                    else:
-                        raise Exception('Ground conditions dictionary needs information for clay driven pile anchor')
-
-                y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'],loads['Ha'], plot=plot)
-                
-                if geom['zlug'] > 0:
-                    # need to check bending moment if lug is below mudline (+ zlug)
-                    # loop through, calling capacity with larger H values until a displacement value goes above limit
-                    while results['Lateral displacement']<= 0.05*geom['D'] and results['Bending moment'] <= results['Plastic moment']:
-                        # increment H by 10% of load
-                        H += H_inc
-                        # call capacity function     
-                        y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'], H=H, plot=plot)
-                
-                else:
-                    while results['Lateral displacement']<= 0.05*geom['D'] and results['Rotational displacement'] <= 0.25:
-                        # increment H by 10% of load
-                        H += H_inc  
-                        # call capacity function
-                        y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'], H=H, plot=plot)
-                        
-                    
-            else:
-                print(f'Warning: Soil type {soil} is not compatible with driven pile anchors')
-                
-        # - - - - drilled and grouted piles - - - -                
-        elif 'dandg' in anchType:  # drill and grout pile
-            from .anchors_famodel.capacity_dandg import getCapacityDandG
-            # check for correct soil
-            if 'rock' in soil:
-                # check loads have been calculated (needed for capacity function in this case)
-                if not 'Ha' in loads:
-                    # call getMPForces function 
-                    loads = self.getLugForces(plot=plot)
-                # check for correct ground properties
-                if profile or ('UCS' in ground_conds and 'Em' in ground_conds):
-                    if not profile:
-                        profile = [[0,ground_conds['UCS'],ground_conds['Em']],[75,ground_conds['UCS'],ground_conds['Em']]] #[list(x) for x in list(zip(ground_conds['depth'],ground_conds['UCS'],ground_conds['Em']))]
-
-                    # call capacity function once to get displacement values
-                    y, z, results = getCapacityDandG(profile,geom['L'],geom['D'], 
-                                                     geom['zlug'], loads['Va'], 
-                                                     loads['Ha'], plot=plot)
-                    H_inc = loads['Ha']*0.1 # increment H by 10% of Ha load
-                    H = H_inc # start H at 10% of Ha load
-                    # loop through, calling capacity with larger H values until a displacement value goes above limit
-                    while results['Lateral displacement']< 0.05*geom['D'] and results['Rotational displacement'] < 0.25:
-                        # call capacity function       
-                        y, z, results = getCapacityDandG(profile, geom['L'], geom['D'], 
-                                                         geom['zlug'], loads['Va'], 
-                                                         H=H, plot=plot)
-                        # increment H 
-                        H += H_inc
-                else:
-                    raise Exception('Ground conditions dictionary need UCS and Em information for drill and grout pile')
-            else:
-                print(f'Warning: soil type {soil} is not compatible with drill and grout pile')
-                
-        # - - - - anchor type not recognized or supported - - - - 
-        else:
-            raise Exception(f'Anchor type {anchType} is not supported at this time')
-            
-        # - - - - save relevant results in dictionary using common terms - - - - 
-        # capacity = cap*installAdj ??? OR is installAdj an input to the capacity functions?
-        # save capacity 
-        if 'dandg' in anchType or 'driven' in anchType: # will take in dandg, dandg_pile, driven, driven_pile
-            self.anchorCapacity['Lat_max'] = results['Lateral displacement'] # [deg]
-            if 'Rotational displacement' in results:
-                self.anchorCapacity['Rot_max'] = results['Rotational displacement'] # [deg]
-            elif 'Bending moment' in results:
-                self.anchorCapacity['Mbend_max'] = results['Bending moment']
-            self.anchorCapacity['Va_max'] = results['Axial capacity'] # [N]
-            self.anchorCapacity['Ha_max'] = H
+                    d_nom = att['obj'].dd['sections'][0]['type']['d_nom']
+                    w_nom = att['obj'].dd['sections'][0]['type']['w']
+                    return 'chain', d_nom, w_nom, False
+        raise ValueError('No mooring line attachment found for anchor.')
+
+    def getMudlineForces(self, max_force=False, lines_only=False, seabed=True, xyz=False, project=None):
+        '''
+        Find forces on anchor at mudline using the platform.getWatchCircle method
+        or the MoorPy Point.getForces method. Optionally computes the maximum force 
+        based on platform excursion using the project's arrayWatchCircle method or
+        the attached platform's getWatchCircle method.
 
-        else:
-            if 'Horizontal max.' in results:
-                self.anchorCapacity['Ha_max'] = results['Horizontal max.']*1000 # [N]
-            self.anchorCapacity['Va_max'] = results['Vertical max.']*1000 # [N]
-        self.mass = results['Weight']*1000/self.g # mass in [kg]
-            
-        # add on extra for drag-embedment anchors (flukes)
-        if 'DEA' in anchType:
-            self.mass *= 1.75
-        
-            
-        return(results)
-            
-    def getMudlineForces(self, max_force=False,lines_only=False, seabed=True, xyz=False,project=None):   
-        '''Find forces on anchor at mudline using the platform.getWatchCircle method or MoorPy Point.getForces method. 
-        Optionally, get forces at anchor lug location with getTransferLoad function in capacity_loads.py.
-        Stores in loads dictionary
         Parameters
         ----------
-        max_force : boolean, optional
-            Find and save the maximum force on the anchor (True) or just get force at the current MoorPy system state (False)           
-        lines_only : boolean, optional
-            Calculate forces from just mooring lines (True) or not (False). Default is false
-        seabed : boolean, optional
-            Include effect of seabed pushing up the anchor (True) or not (False). Default is true
-        xyz : boolean, optional
-            Return forces in x,y,z DOFs (True) or only the enabled DOFs (False). Default is false
-            
+        max_force : bool, optional
+            If True, computes the maximum expected force on the anchor 
+            using platform excursion. Default is False.
+        lines_only : bool, optional
+            Calculate forces from just mooring lines (True) or not (False). Default is False.
+        seabed : bool, optional
+            Include effect of seabed pushing up the anchor (True) or not (False). Default is True.
+        xyz : bool, optional
+            Return forces in x, y, z DOFs (True) or only the enabled DOFs (False). Default is False.
+        project : object, optional
+            Project object that can run arrayWatchCircle(). Used only if max_force is True.
+
+        Returns
+        -------
+        dict
+            Dictionary containing mudline forces.
         '''
         Platform = famodel.platform.platform.Platform
+
         if max_force:
             if project:
-                # get watch circle of platform(s)
                 project.arrayWatchCircle()
             else:
-                # find platform associated with this anchor
                 for att in self.attachments.values():
-                    if isinstance(att['obj'],Mooring):
+                    if isinstance(att['obj'], Mooring):
                         for attM in att['obj'].attached_to:
-                            if isinstance(attM,Platform):
-                                locx,locy,maxVals = attM.getWatchCircle()
-        # call getForces method from moorpy point object
+                            if isinstance(attM, Platform):
+                                locx, locy, maxVals = attM.getWatchCircle()
+                                Hm = np.sqrt(maxVals[0]**2 + maxVals[1]**2)
+                                Vm = maxVals[2]
+                                thetam = np.degrees(np.arctan2(Vm, Hm))
+                                self.loads['Hm'] = Hm
+                                self.loads['Vm'] = Vm
+                                self.loads['thetam'] = thetam
+                                self.loads['mudline_load_type'] = 'max_force'
+                                break
         else:
             loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz)
-            self.loads['Hm'] = np.sqrt(loads[0]**2+loads[1]**2) # mudline forces in [N]
-            self.loads['Vm'] = loads[2] # [N]
-            self.loads['thetam'] = np.degrees(np.arctan(self.loads['Vm']/self.loads['Hm'])) # [deg]
+            Hm = np.sqrt(loads[0]**2 + loads[1]**2)
+            Vm = loads[2]
+            thetam = np.degrees(np.arctan2(Vm, Hm))
+            self.loads['Hm'] = Hm
+            self.loads['Vm'] = Vm
+            self.loads['thetam'] = thetam
             self.loads['mudline_load_type'] = 'current_state'
-        
-        # loads determined from moorpy are static
+
         self.loads['method'] = 'static'
-        
-        return(self.loads)
-    
-    def getLugForces(self, mudloads=None, max_force=True, plot=False):
+        return self.loads
+       
+    def getLugForces(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=True):
         '''
-        Find forces on an anchor at the lug point based on the mudline forces and angles. Calls getTransferFunction script
+        Calculate the lug forces Ha and Va based on mudline loads using local soil profile.
 
         Parameters
         ----------
-        mudloads : dict, optional
-            Dictionary of max mudline forces. The default is None.
+        Hm : float
+            Horizontal mudline load (N)
+        Vm : float
+            Vertical mudline load (N)
+        zlug : float
+            Padeye embedment depth (m)
+        line_type : str, optional
+            Type of mooring line ('chain' or 'wire')
+        d : float, optional
+            Mooring line diameter (m)
+        w : float, optional
+            Mooring line unit weight (N/m)
+        plot : bool, optional
+            Whether to plot the load transfer profile
 
         Returns
         -------
-        loads: dict
-            Dictionary of loads at the lug point [N]
-
+        Ha : float
+            Horizontal load at lug (N).
+        Va : float
+            Vertical load at lug (N).
         '''
         from .anchors_famodel.capacity_load import getTransferLoad
-        
-        nolugload = False
-        
-        if not mudloads:  
-            if not self.loads:
-                # get max mudline forces first
-                self.getMudlineForces(max_force=max_force)
-            elif not 'mudline_load_type' in self.loads:
-                raise KeyError("Loads dictionary must specify 'mudline_load_type'='current_state' or 'mudline_load_type'='max', where 'max' indicates the loads are maximum loads.")
-            elif max_force and self.loads['mudline_load_type'] != 'max':
-                # need max forces, not current state
-                self.getMudlineForces(max_force=True)
-            mudloads = self.loads
-        else:
-            # check syntax
-            if not 'Hm' in mudloads or not 'Vm' in mudloads:
-                raise KeyError('Mudline load dictionary must have Hm and Vm for horizontal load and vertical load (in [N]) at the mudline')
-            if not 'thetam' in mudloads:
-                mudloads['thetam'] = np.degrees(np.arctan(mudloads['Vm']/mudloads['Hm']))
-                
-        def makeEqual_TaTm(mudloads):
-            mudloads['Ha'] = mudloads['Hm'] # [N]
-            mudloads['Va'] = mudloads['Vm'] # [N]
-            mudloads['thetaa'] = mudloads['thetam'] # [deg]
-        
-        if 'zlug' in self.dd['design']:
-            if self.dd['design']['zlug'] > 0:
-                # get line type
-                for att in self.attachments.values():
-                    if isinstance(att['obj'],Mooring):
-                        mtype = att['obj'].dd['sections'][0]['type']['material']
-                        if not 'chain' in mtype:
-                            print('No chain on seafloor, setting Ta=Tm')
-                            nolugload = True
-                            break
-                        else:
-                            md = att['obj'].dd['sections'][0]['type']['d_nom']
-                            mw = att['obj'].dd['sections'][0]['type']['w']
-                soil = next(iter(self.soilProps.keys()), None)
-                ground_conds = self.soilProps[soil]
-                # update soil conds as needed to be homogeneous
-                for key,prop in ground_conds.items():
-                    if isinstance(prop,list) or isinstance(prop,np.ndarray):
-                        if len(prop)>1:
-                            print('Warning: Only homogeneous soils are supported at this time. Only the first item in a property list will be used.')
-                            break 
-                        else:
-                            ground_conds[key] = prop[0]
-                            
-                Tm =  np.sqrt(mudloads['Hm']**2+mudloads['Vm']**2) # [N]
-                if 'clay' in soil or 'mud' in soil and not nolugload:
-                    # Tm, thetam, zlug, line_type, d, soil_type, Su0=None, k=None, w=None
-                    try:
-                        loadresults = getTransferLoad(Tm/1000,mudloads['thetam'],
-                                                      self.dd['design']['zlug'],mtype,md,
-                                                      'clay',Su0=ground_conds['Su0'],
-                                                      k=ground_conds['k'],w=mw/1000,
-                                                      plot=plot) # output Ha and Va    (convert weight to kN/m)   
-                    except Exception as e:
-                        print(e)
-                        print('Unable to get loads at anchor lug location. Setting Ta = Tm')
-                        nolugload = True
-                elif 'sand' in soil and not nolugload:
-                        soil = 'sand'
-                        try:
-                            loadresults = getTransferLoad(Tm/1000, self.loads['thetam'], 
-                                                        self.dd['design']['zlug'],
-                                                        mtype, md, soil,
-                                                        gamma=ground_conds['gamma'], 
-                                                        phi=ground_conds['phi'],
-                                                        delta=ground_conds['delta'], 
-                                                        w=mw/1000,plot=plot) # output Ha and Va  (convert weight to kN/m)
-                        except Exception as e:
-                            print(e)
-                            print('Unable to get loads at anchor lug location. Setting Ta = Tm')
-                            nolugload = True
-                elif 'rock' in soil and not nolugload:
-                    raise ValueError('zlug should be <= 0 for rock.')
-                    
-                # if loadresults['V']<0:
-                #     # results are invalid
-                #     print('Warning: invalid results for the combination of anchor ',self.dd['type'],' soil ',soil,' and loads ',mudloads,'. Setting Ha=Hm, Va=Vm, thetaa=thetam')
-                #     makeEqual_TaTm(mudloads)
-                if nolugload:
-                    makeEqual_TaTm(mudloads)
-                else:
-                    mudloads['Ha'] = loadresults['H']*1000 # [N]
-                    mudloads['Va'] = loadresults['V']*1000 # [N]
-                    mudloads['thetaa'] = loadresults['angle'] # [deg]
-            else:
-                # Ha = Hm because zlug is at mudline or above
-                makeEqual_TaTm(mudloads)
+        from .anchors_famodel.support_plots import plot_load
+
+        # Ensure soil profile is available
+        if self.soil_profile is None or self.soil_type is None:
+            raise ValueError("Anchor soil profile or soil type is not assigned. Use setSoilProfile first.")
+
+        soil_profile = self.soil_profile
+        soil_type = self.soil_type
+
+        # Determine mudline depth
+        z0 = soil_profile[0]['top']
+
+        # Load transfer if padeye is embedded
+        if zlug > z0:
+            if line_type is None or d is None or w is None:
+                try:
+                    line_type, d, w = self.getLineProperties()
+                except ValueError:
+                    print('[Warning] No mooring attachment found. Trying anchor-level line properties...')
+                    line_type = getattr(self, 'line_type', None)
+                    d = getattr(self, 'd', None)
+                    w = getattr(self, 'w', None)
+
+                    if any(v is None for v in [line_type, d, w]):
+                        print('[Fallback] Using default chain properties.')
+                        line_type = 'chain'
+                        d = 0.16
+                        w = 5500.0
+
+            layers, loads = getTransferLoad(
+                profile_map=[{'layers': self.soil_profile}],
+                Tm=np.sqrt(Hm**2 + Vm**2),
+                thetam=np.degrees(np.arctan2(Vm, Hm)),
+                zlug=zlug,
+                line_type=line_type,
+                d=d,
+                w=w,
+                plot=plot
+            )
+
+            Ta = loads['Ta']
+            thetaa = loads['thetaa']
+            Ha = Ta*np.cos(np.deg2rad(thetaa))
+            Va = Ta*np.sin(np.deg2rad(thetaa))
+
         else:
-            print('No zlug given, assuming loads at mudline = loads at anchor lug')
-            makeEqual_TaTm(mudloads)
+            Ha = Hm
+            Va = Vm
             
-        if not 'method' in mudloads:
-            # assume mudloads are static unless told otherwise
-            # loads determined from moorpy are static
-            mudloads['method'] = 'static'
-        else:
-            mudloads['method'] = mudloads['method']
-        
-        return mudloads
-    
-    def getFS(self, loads=None, acceptance_crit=None):
-        '''
-        Compute safety factor for loads on the anchor
-        
-        Parameters
-        ----------
-        loads : dict, optional
-            Dictionary of loads on the anchor. 
-        acceptance_crit : dict, optional
-            Dictionary of acceptable factors of safety for each load type.
-            Key is the load type, and value is the minimum acceptable safety factor.
-            Default is None (in which case no comparison between FS and acceptance criteria is calculated)
+        if plot == True:
+            plot_load(layers, loads['drag_values'], loads['depth_values'], 
+                      loads['Tm'], loads['thetam'], loads['Ta'], 
+                      loads['thetaa'], zlug=zlug)
 
-        Returns
-        -------
-        FS : dict
-            Dictionary of safety factors (often horizontal and vertical load SFs, but could be displacement SFs (drilled and grouted/driven piles))
-        acceptance : dict
-            Dictionary of bools that state whether the FS>=acceptance_crit for each load
-        acceptance_margin : dict
-            Dictionary of difference between FS and acceptance criteria for each load type
-        
+        return layers, Ha, Va
 
+    def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_update=False, plot=False):
         '''
-        if not loads:
-            if not 'Ha' in self.loads:
-                self.getLugForces()
-            loads = self.loads
-        if not self.anchorCapacity:
-            self.getAnchorCapacity()
-         
-        # look for load dictionary key in capacity dictionary
-        FS = {}
-        acceptance = {}
-        acceptance_margin = {}
-        for Lkey,Lval in loads.items():
-            for Ckey,Cval in self.anchorCapacity.items():
-                if Lkey in Ckey:
-                    if Lval == 0:
-                        FS[Lkey] = float('inf')
-                    else:
-                        FS[Lkey] = Cval/Lval
-                    if acceptance_crit and Lkey in acceptance_crit:
-                        if Lval == 0 or acceptance_crit[Lkey] == 0:
-                            acceptance[Lkey] = True
-                        else:
-                            acceptance[Lkey] = acceptance_crit[Lkey]<=FS[Lkey]
-                            acceptance_margin[Lkey] = FS[Lkey] - acceptance_crit[Lkey]
-                    
-        if acceptance_crit:
-            return(FS,acceptance,acceptance_margin)
-        else:
-            return(FS)
-            
-    def makeBuffer(self, buff_rad=50):
-        point = sh.Point(self.r[:2])
-        buff = point.buffer(buff_rad)
-        return buff
-               
-    def getCost(self,costDict='default'):
-        '''find costs of anchor and store in design dictionary
-        
+        Calculate anchor capacity based on anchor type and local soil profile.
+    
         Parameters
         ----------
-        costDict : dictionary or yaml, optional
-            Dictionary of various costs for anchors. Sub costs that can be included are: 
-            material : material costs
-            
+        Hm : float
+            Horizontal mudline load (N)
+        Vm : float
+            Vertical mudline load (N)
+        zlug : float
+            Padeye embedment depth (m)
+        line_type : str, optional
+            Type of mooring line ('chain' or 'wire')
+        d : float, optional
+            Mooring line diameter (m)
+        w : float, optional
+            Mooring line unit weight (N/m)
+        mass_update : bool, optional
+            Whether to update the mass when is not assigned
+        plot : bool, optional
+            Whether to plot the load transfer and pile geometry
+    
+        Returns
+        -------
+        results : dict
+            Capacity results dictionary from the selected capacity function.
         '''
-        if isinstance(costDict,str) and costDict != 'default':
-            import yaml
-            costDict = yaml.load(costDict, Loader=yaml.FullLoader)
-        anchType = self.dd['type']
-        if costDict == 'default':
-            matCostDict = {'DEA':5.705,'suction_pile':4.435,'gravity':1.905} # mean values from Task 49 Design Basis ranges
-            instCostDict = {}
-            decomCostDict = {}
-        else:
-            matCostDict = costDict['material']
-            if 'install' in costDict:
-                instCostDict = costDict['install']
-            if 'decom' in costDict:
-                decomCostDict = costDict['decom']
-        keyFail = True
-        # check if mass info is available
-        if not self.mass:
-            if 'soil_properties' in self.dd:               
-                # need mass - call capacity functions
-                self.getAnchorCapacity(plot=False)
-            else:
-                print('Soil properties needed to calculate anchor mass for cost. Setting cost to 0.')
-                self.mass = 0
-            
-        # sort by type of anchor
-        for Ckey,Cval in matCostDict.items():
-            if anchType in Ckey:
-                self.cost['materials'] = matCostDict[Ckey]*self.mass
-                # self.cost['install'] = instCostDict[Ckey]
-                # self.cost['decom'] = decomCostDict[Ckey]
-                keyFail = False
-        # raise error if anchType not found in cost dictionary
-        if keyFail:
-            raise KeyError(f'anchor type {anchType} not found in material cost dictionary')
-            
-        return(sum(self.cost.values()))
 
+        from .anchors_famodel.capacity_plate import getCapacityPlate
+        from .anchors_famodel.capacity_suction import getCapacitySuction
+        from .anchors_famodel.capacity_torpedo import getCapacityTorpedo
+        from .anchors_famodel.capacity_helical import getCapacityHelical
+        from .anchors_famodel.capacity_driven import getCapacityDriven
+        from .anchors_famodel.capacity_dandg import getCapacityDandG
            
+        capacity_dispatch = {
+            'suction': getCapacitySuction,
+            'sepla': getCapacityPlate,
+            'dea': getCapacityPlate,
+            'depla': getCapacityPlate,
+            'vla': getCapacityPlate,
+            'plate': getCapacityPlate,
+            'torpedo': getCapacityTorpedo,
+            'helical': getCapacityHelical,
+            'driven': getCapacityDriven,
+            'dandg': getCapacityDandG
+        }
+    
+        print(f'[Debug] mass_update = {mass_update}')
+        anchType_clean = self.anchType.lower().replace(' ', '')
+        capacity_func = capacity_dispatch.get(anchType_clean)
+        if capacity_func is None:
+            raise ValueError(f"Unknown anchor type '{self.anchType}' for anchor capacity calculation.")
+    
+        if self.soil_profile is None or self.soil_type is None:
+            raise ValueError("Soil profile or soil type not set for this anchor.")
+    
+        soil_profile = self.soil_profile
+        soil_type = self.soil_type
+        z0 = soil_profile[0]['top']
+    
+        # Load transfer if padeye is embedded below mudline
         
-    # def getSuctionSize(self,D,L,loads=None,minfs={'Ha':1.6,'Va':2},LD_con=[4,8]):
-    #     '''
-        
+        if line_type is None or d is None or w is None:
+            try:
+                line_type, d, w = self.getLineProperties()
+            except ValueError:
+                print('[Warning] No mooring attachment found. Trying anchor-level line properties...')
+                line_type = getattr(self, 'line_type', None)
+                d = getattr(self, 'd', None)
+                w = getattr(self, 'w', None)
+
+                if any(v is None for v in [line_type, d, w]):
+                    print('[Fallback] Using default chain properties.')
+                    line_type = 'chain'
+                    d = 0.16
+                    w = 5500.0
+    
+        if zlug > z0:
+            layers, Ha, Va = self.getLugForces(
+                Hm, Vm,
+                zlug=zlug,
+                line_type=line_type,
+                d=d,
+                w=w,
+                plot=False
+            )
 
-    #     Parameters
-    #     ----------
-    #     D : float
-    #         Diameter of suction bucket
-    #     L : float
-    #         Length of suction bucket
-    #     loads : dict, optional
-    #         Dictionary of maximum anchor loads in horizontal and vertical directions. The default is None.
-    #     minfs : dict,optoinal
-    #         Minimum factors of safety in horizontal and vertical directions
-    #     LD_con : float
-    #         Constraint for L/D parameter
-
-    #     Returns
-    #     -------
-    #     None.
-
-    #     '''
-    #     from scipy.optimize import minimize
-    #     anchType = self.dd['type']
-    #     if not loads:
-    #         loads = self.loads
+            Ta = np.sqrt(Ha**2 + Va**2)
+            thetaa = np.degrees(np.arctan2(Va, Ha))
             
-    #     if not 'Ha' in loads:
-    #         loads = self.getLugForces(mudloads=loads)
-           
-    #     loads['Ha'] = minfs['Ha']*loads['Ha']
-    #     loads['Va'] = minfs['Va']*loads['Va']
-                   
-    #     if not 'zlug' in self.dd['design']:
-    #         self.dd['design']['zlug'] = (2/3)*L
+            print(f'Input Hm = {Hm}, Vm = {Vm}, zlug = {zlug}')
+            print(f'Output Ha = {Ha}, Va = {Va}, zlug = {zlug}')
+            print(f'Output Ta = {Ta}, thetaa = {(thetaa)}')
+            print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}")
+
+        else:
+            Ha = Hm
+            Va = Vm
+            Ta = np.sqrt(Ha**2 + Va**2)
+            thetaa = np.degrees(np.arctan2(Va, Ha))
+            print(f'[Direct assign] Ha = {Ha}, Va = {Va}, Ta = {Ta}, thetaa = {thetaa}')
+            print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}")
+
+
+    
+        # --- Call the appropriate capacity function ---
+        if anchType_clean in ['sepla', 'dea', 'depla', 'vla', 'plate']:
+            self.capacity_format = 'plate'
+            B = self.dd['design']['B']
+            L = self.dd['design']['L']
+            print(f"[Final Check] Ha = {Ha}, Va = {Va}, anchor = {self.anchType}")
+            beta = 90.0 - np.degrees(np.arctan2(Va, Ha))
+            self.dd['design']['beta'] = beta 
+            layers, results = capacity_func(
+                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
+                location_name=self.profile_name,
+                B=B, L=L, zlug=zlug,
+                beta=beta,
+                Ha=Ha, Va=Va,
+                plot=plot
+            )
             
-    #     # Define the objective function: Minimize |UC - 1| (aim for UC to be 1)
-    #     def objective(vars):
-    #         D, L = vars
-    #         self.dd['design']['D'] = D
-    #         self.dd['design']['L'] = L
-    #         self.dd['design']['zlug'] = (2/3)*L
-    #         results = self.getAnchorCapacity(plot=False)
-    #         return abs(results['UC'] - 1)  
-        
-    #     def conFun(vars,LD_con):
-    #         D, L = vars
-    #         if L/D >= LD_con[0] and L/D <= LD_con[1]:
-    #             conval = 1 
-    #         else:
-    #             conval = -1 
+        elif anchType_clean == 'suction':
+            self.capacity_format = 'envelope'
+            D = self.dd['design']['D']
+            L = self.dd['design']['L']
+            zlug = self.dd['design']['zlug']
+            layers, results = capacity_func(
+                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
+                location_name=self.profile_name,
+                D=D, L=L, zlug=zlug,
+                Ha=Ha, Va=Va,
+                thetalug=5, psilug=7.5,
+                plot=plot
+            )
             
-    #         return(conval)
-        
-    #     # Initial guess for D and L
-    #     initial_guess = [D, L]       # Input values for D and L
+        elif anchType_clean == 'torpedo':
+            self.capacity_format = 'envelope'
+            D1 = self.dd['design']['D1']
+            D2 = self.dd['design']['D2']
+            L1 = self.dd['design']['L1']
+            L2 = self.dd['design']['L2']
+            ballast = self.dd['design'].get('ballast', 0.0)
+            layers, results = capacity_func(
+                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
+                location_name=self.profile_name,
+                D1=D1, D2=D2, L1=L1, L2=L2,
+                zlug=zlug,
+                ballast=ballast,
+                Ha=Ha, Va=Va,
+                plot=plot
+            )
+
+        elif anchType_clean == 'helical':
+            self.capacity_format = 'component'
+            D = self.dd['design']['D']     
+            L = self.dd['design']['L']     
+            d = self.dd['design']['d']     
+            zlug = self.dd['design']['zlug']
+            layers, results = capacity_func(
+                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
+                location_name=self.profile_name,
+                D=D, L=L, d=d,
+                zlug=zlug,
+                Ha=Ha, Va=Va,
+                plot=plot
+            )
+
+        elif anchType_clean in ['driven', 'dandg']:
+            self.capacity_format = 'component'
+            L = self.dd['design']['L']
+            D = self.dd['design']['D']
+            zlug = self.dd['design']['zlug']
+            layers, y, z, results = capacity_func(
+                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
+                location_name=self.profile_name,
+                L=L, D=D, zlug=zlug,
+                Ha=Ha, Va=Va,
+                plot=plot
+            )
+    
+        else:
+            raise ValueError(f"Anchor type '{self.anchType}' not supported.")
+    
+        # --- Store results ---
+        self.anchorCapacity = {
+            'Hmax': results.get('Horizontal max.', np.nan),
+            'Vmax': results.get('Vertical max.', np.nan),
+            'Ha': Ha,
+            'Va': Va,
+            'zlug': zlug,
+            'z0': z0}
         
-    #     # Bounds for D and L (adjust as needed)
-    #     bounds = [(1, 5), (5, 50)]   # Bounds for D and L
+        # Correct UC format
+        if anchType_clean in ['suction', 'torpedo', 'plate', 'sepla', 'dea', 'depla', 'vla']:
+            self.anchorCapacity['UC'] = results.get('Unity check', np.nan)
         
-    #     # constraints
-    #     constraints = [{'type':'ineq','fun':conFun,'args':(LD_con,)}]
+        elif anchType_clean in ['helical', 'driven', 'dandg']:
+            self.anchorCapacity['Unity check (horizontal)'] = results.get('Unity check (horizontal)', np.nan)
+            self.anchorCapacity['Unity check (vertical)'] = results.get('Unity check (vertical)', np.nan)
         
-    #     # Run the optimization to find D and L that satisfy UC close to 1
-    #     solution = minimize(objective, initial_guess, bounds=bounds,method="COBYLA",
-    #                         constraints=constraints,options={'rhobeg':0.1, 'catol':0.001})
+        # Copy over lateral and rotational displacements
+        if 'Lateral displacement' in results:
+            self.anchorCapacity['Lateral displacement'] = results['Lateral displacement']
+        if 'Rotational displacement' in results:
+            self.anchorCapacity['Rotational displacement'] = results['Rotational displacement']
         
-    #     # Extract the optimized values of D and L
-    #     self.dd['design']['D'], self.dd['design']['L'] = solution.x
-    #     self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] 
-    #     results = self.getAnchorCapacity(plot=False)    
-   
-
-    def getSize(self, geom, geomKeys, geomBounds=None, loads=None, minfs={'Ha':1.6,'Va':2}, 
-                LD_con=[4,8], fix_zlug=False, FSdiff_max=None, plot=False):
+        # Weight calculated via dimensions
+        if mass_update == False:
+            if 'Weight pile' in results:
+                self.anchorCapacity['Weight pile'] = results['Weight pile']
+            if 'Weight plate' in results:
+                self.anchorCapacity['Weight plate'] = results['Weight plate']
+        else:
+            if 'Weight pile' in results:
+                if self.mass is None:
+                    self.mass = results['Weight pile']/self.g
+                self.anchorCapacity['Weight pile'] = self.mass*self.g
+            if 'Weight plate' in results:
+                if self.mass is None:
+                    self.mass = results['Weight plate']/self.g
+                self.anchorCapacity['Weight plate'] = self.mass*self.g
+                
+        # print(f"[DEBUG] Stored Lateral displacement in anchorCapacity: {self.anchorCapacity['Lateral displacement']:.6f}")
+         
+    def getSizeAnchor(self, geom, geomKeys, geomBounds=None, loads=None,
+                      lambdap_con=[4, 8], zlug_fix=True, safety_factor={'SF_combined': 1.0}, plot=False):
         '''
+        Generalized optimization method for all anchor types, using dictionary-based safety factors.
+        '''
+    
+        anchType_clean = self.dd['type'].lower().replace('', '')
+    
+        if loads is None:
+            loads = self.loads
+    
+        Hm = loads['Hm']
+        Vm = loads['Vm']
+    
+        line_type = getattr(self, 'line_type', 'chain')
+        d = getattr(self, 'd', 0.16)
+        w = getattr(self, 'w', 5000.0)
+    
+        def update_zlug():
+            if anchType_clean == 'suction' and not zlug_fix and 'zlug' not in geomKeys:
+                self.dd['design']['zlug'] = (2/3)*self.dd['design']['L']
+            elif anchType_clean in ['driven', 'helical'] and not zlug_fix:    
+                ratio = self.dd['design'].get('zlug_ratio', self.dd['design']['zlug']/self.dd['design']['L'])
+                self.dd['design']['zlug_ratio'] = ratio
+                self.dd['design']['zlug'] = ratio*self.dd['design']['L']
+    
+        def get_lambda():
+            if anchType_clean == 'torpedo':
+                L = self.dd['design']['L1'] + self.dd['design']['L2']
+                A_wing = (self.dd['design']['D1'] - self.dd['design']['D2']) * self.dd['design']['L1']
+                A_shaft = self.dd['design']['D2'] * L
+                D = (A_wing + A_shaft) / L
+            elif anchType_clean in ['driven', 'dandg', 'helical', 'suction']:
+                L = self.dd['design']['L']
+                D = self.dd['design']['D']
+            elif anchType_clean in ['plate', 'sepla', 'dea', 'depla', 'vla']:
+                L = self.dd['design']['L']
+                D = self.dd['design']['B']
+            else:
+                raise ValueError(f'lambda not defined for anchor type: {anchType_clean}')
+            return L/D
+    
+        def constraint_lambda_min(vars):
+            return get_lambda() - lambdap_con[0]
+    
+        def constraint_lambda_max(vars):
+            return lambdap_con[1] - get_lambda()
+    
+        if anchType_clean in ['suction', 'torpedo', 'plate', 'sepla', 'dea', 'depla', 'vla']:
+            target_UC = 1.0/safety_factor.get('SF_combined', 1.0)
+    
+            def objective_uc(vars):
+                for i, key in enumerate(geomKeys):
+                    self.dd['design'][key] = vars[i]
+                update_zlug()
+                self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                       line_type=line_type, d=d, w=w, mass_update=True, plot=False)
+                UC = self.anchorCapacity.get('UC', 2.0)
+                return (UC - target_UC)**2
+    
+            def constraint_uc_envelope(vars):
+                return self.anchorCapacity.get('UC', 0.0) - target_UC
+    
+            constraints_uc = [
+                {'type': 'ineq', 'fun': constraint_lambda_min},
+                {'type': 'ineq', 'fun': constraint_lambda_max},
+                {'type': 'ineq', 'fun': constraint_uc_envelope},
+            ]
+    
+            result_uc = minimize(
+                objective_uc,
+                geom,
+                method='COBYLA',
+                bounds=geomBounds if geomBounds else None,
+                constraints=constraints_uc,
+                options={'rhobeg': 0.1, 'catol': 0.01, 'maxiter': 500}
+            )
+    
+            endGeom = dict(zip(geomKeys, result_uc.x))
+            self.dd['design'].update(endGeom)
+            update_zlug()
+            self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                   line_type=line_type, d=d, w=w, mass_update=True, plot=plot)
+    
+            print('\nFinal Optimized Anchor (UC-based):')
+            print('Design:', self.dd['design'])
+            print('Capacity Results:', self.anchorCapacity)
+            return
+    
+
+        def termination_condition():
+            UC_h = self.anchorCapacity['Ha'] / self.anchorCapacity['Hmax']
+            UC_v = self.anchorCapacity['Va'] / self.anchorCapacity['Vmax']
+            disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0))
+            disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0))
+            limit_lat = 0.05 * self.dd['design']['D']
+            limit_rot = 5.0
+    
+            if UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot:
+                print('[Termination Condition Met] All four limits satisfied.')
+                return 'terminate'
+    
+            return 'continue'
+    
+        def is_valid(value):
+            return np.isfinite(value) and not np.isnan(value) and abs(value) < 1e6
+        
+        if anchType_clean in ['helical', 'driven', 'dandg']:
+            L0, D0 = geom if len(geom) == 2 else [5.0, 1.0]
+            self.dd['design']['L'] = L0
+            self.dd['design']['D'] = D0
+            #self.dd['design']['t'] = max(0.05, 0.1 * D0)
+            update_zlug()
+            self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                   line_type=line_type, d=d, w=w, mass_update=True, plot=False)
         
+            UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax']
+            UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax']
+            disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0))
+            disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0))
+            limit_disp = 0.05*D0
+            limit_rot = 5.0
+            direction = 'shrink' if (UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_disp and disp_rot <= limit_rot) else 'grow'
     
-        Parameters
-        ----------
-        geom: list
-            starting guess geometry values
-        geomKeys : list
-            List of keys that match the geom list values i.e. 'L','D','zlug'
-        geomBounds : list,optional
-            List of upper and lower bounds for each geometry value. 
-            Each entry should be a tuple of upper and lower bounds for each geometry i.e. [(5,10),(10,20)]
-        loads : dict, optional
-            Dictionary of maximum anchor loads in horizontal and vertical directions (not including factor of safety). The default is None.
-        minfs : dict,optional
-            Minimum factors of safety in horizontal and vertical directions
-        LD_con : float
-            Constraint for L/D parameter
-        fix_zlug : bool
-            Boolean to decide if zlug should be altered as geometric values are altered. 
-            True = fixed zlug, False = zlug may be changed
-        plot : bool
-            Boolean controls if capacity plots are generated or not for the final configuration
+            max_iter = 200
+            iter_count = 0
     
-        Returns
-        -------
-        None.
+            if direction == 'shrink':
+                for D in np.arange(D0, 0.49, -0.05):
+                    self.dd['design']['D'] = D
+                    #self.dd['design']['t'] = max(0.02, 0.1*D)
+                    for L in np.arange(L0, 1.95, -0.25):
+                        self.dd['design']['L'] = L
+                        update_zlug()
+                        self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                               line_type=line_type, d=d, w=w, mass_update=True, plot=False)
+                        UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax']
+                        UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax']
+                        disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0))
+                        disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0))
+                        print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg')
+                        iter_count += 1
+                        if not all(is_valid(v) for v in [UC_h, UC_v, disp_lat, disp_rot]):
+                            continue
+                        if termination_condition():
+                            print(f'\nTermination criteria met.')
+                            print('Design:', self.dd['design'])
+                            print('Capacity Results:', self.anchorCapacity)
+                            return
+            else:
+                for D in np.arange(D0, 3.05, 0.05):
+                    self.dd['design']['D'] = D
+                    #self.dd['design']['t'] = max(0.02, 0.1*D)
+                    for L in np.arange(L0, 50.25, 0.25):
+                        self.dd['design']['L'] = L
+                        update_zlug()
+                        self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                               line_type=line_type, d=d, w=w, mass_update=True, plot=False)
+                        UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax']
+                        UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax']
+                        disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0))
+                        disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0))
+                        print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg')
+                        iter_count += 1
+                        status = termination_condition()
+                        if status == 'terminate':
+                            print(f'Termination criteria met.')
+                            print('Design:', self.dd['design'])
+                            print('Capacity Results:', self.anchorCapacity)
+                            return
+                        elif status == 'continue':
+                            continue
+                    status = termination_condition()
+                    if status == 'terminate':
+                        print(f'\nTermination criteria met.')
+                        print('Design:', self.dd['design'])
+                        print('Capacity Results:', self.anchorCapacity)
+                        return
+    
+            print('[Warning] While-loop search reached bounds without meeting criteria.')
     
+        else:
+            raise ValueError(f"Anchor type '{anchType_clean}' not supported for safety factor input.")
+                   
+    def getSizeAnchor2(self, geom, geomBounds=None, loads=None,
+                       lambdap_con=[3, 6], zlug_fix=True, safety_factor={'SF_combined': 1.0}, plot=False):
         '''
-        # - - - - Objective and Constraint Functions 
-        
-        # Define the objective function: Minimize weight of anchor (cost is dependent on weight)
-        def objective(vars, args):
+        Grid-based optimization method for envelope anchors (suction, torpedo, plate).
+        Evaluates UC over a grid of L and D, and selects the point closest to target UC.
+        '''
+        import matplotlib.pyplot as plt
+        from matplotlib import cm
+        import matplotlib.colors as mcolor
+        import numpy as np
 
-            geomKeys = args['geomKeys']
-            input_loads = args['input_loads']
-            fix_zlug = args['fix_zlug']
+        anchType_clean = self.dd['type'].lower().replace('', '')
 
-            newGeom = dict(zip(geomKeys,vars))
-            self.dd['design'].update(newGeom)
-            if 'suction' in self.dd['type'] and not fix_zlug:
-                self.dd['design']['zlug'] = (2/3)*newGeom['L']
-            
-            if 'Hm' in input_loads or 'Vm' in input_loads:
-                anchor_loads = self.getLugForces(mudloads=input_loads)
-                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
-            # get results
-            results = self.getAnchorCapacity(loads=input_loads, plot=False)
-                
-            return(results['Weight'])
-        
-        # constraint for suction bucket sizing only. May add more constraints for other anchors in the future...
-        def conFun_LD(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
-            newGeom = dict(zip(geomKeys, vars))
-            self.dd['design'].update(newGeom)
+        if loads is None:
+            loads = self.loads
+
+        Hm = loads['Hm']
+        Vm = loads['Vm']
+
+        line_type = getattr(self, 'line_type', 'chain')
+        d = getattr(self, 'd', 0.16)
+        w = getattr(self, 'w', 5000.0)
+
+        if anchType_clean not in ['suction', 'torpedo', 'plate']:
+            raise ValueError(f"Grid-based getSizeAnchor only supports envelope anchors, not '{anchType_clean}'")
+
+        UC_target = 1.0/safety_factor.get('SF_combined', 1.0)
+
+        # Unpack bounds and generate grid
+        L_vals = np.linspace(geomBounds[0][0], geomBounds[0][1], 10)
+        D_vals = np.linspace(geomBounds[1][0], geomBounds[1][1], 10)
+
+        L_grid, D_grid = np.meshgrid(L_vals, D_vals)
+        UC_grid = np.full_like(L_grid, np.nan, dtype=float)
+        mask = np.full_like(L_grid, False, dtype=bool)
+
+        best_UC, best_L, best_D = None, None, None
+        results = []
+
+        for i in range(D_grid.shape[0]):  # loop over D
+            for j in range(D_grid.shape[1]):  # loop over L
+                D = D_grid[i, j]
+                L = L_grid[i, j]
+                lambdap = L/D
+
+                if not (lambdap_con[0] <= lambdap <= lambdap_con[1]):
+                    continue
+
+                mask[i, j] = True
+                self.dd['design']['L'] = L
+                self.dd['design']['D'] = D
+
+                if anchType_clean == 'suction' and not zlug_fix:
+                    self.dd['design']['zlug'] = (2/3)*L
 
-            if 'Hm' in input_loads or 'Vm' in input_loads:
-                anchor_loads = self.getLugForces(mudloads=input_loads)
-                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
+                try:
+                    self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                           line_type=line_type, d=d, w=w, 
+                                           mass_update=True, plot=False)
+                    UC = self.anchorCapacity.get('UC', np.nan)
+                    results.append({
+                        'L': L,
+                        'D': D,
+                        'UC': UC})
 
-            results = self.getAnchorCapacity(loads=input_loads, plot=False)
+                    if UC > 1e-2 and UC < 10.0:
+                        UC_grid[i, j] = UC
+                        # Find UC closest to target
+                        if best_UC is None or abs(UC - UC_target) < abs(best_UC - UC_target):
+                            best_UC = UC
+                            best_L = L
+                            best_D = D
+
+                except:
+                    continue
+
+        # Update best result
+        # if best_L is not None and best_D is not None:
+        self.dd['design']['L'] = best_L
+        self.dd['design']['D'] = best_D
+        if anchType_clean == 'suction' and not zlug_fix:
+            self.dd['design']['zlug'] = (2/3)*best_L
+
+        self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                               line_type=line_type, d=d, w=w, 
+                               mass_update=True, plot=plot)
+
+        print('\nFinal Optimized Anchor (Grid-based):')
+        print('Design:', self.dd['design'])
+        print('Capacity Results:', self.anchorCapacity)
+
+        # else:
+        #     print('[Warning] No valid combination found in the grid.')
+
+        # Optional plot
+
+        if plot:
+            fig, ax = plt.subplots(figsize=(6, 8))
+            vmin, vmax = 0.01, 10
+            levels = np.logspace(np.log10(vmin), np.log10(vmax), 21)
+            cp = ax.contourf(D_grid, L_grid, UC_grid, levels=levels, cmap='coolwarm', norm=mcolor.LogNorm(vmin=vmin, vmax=vmax))
+            fig.colorbar(cp, ax=ax, label='Unity check (UC)')
+            ax.contour(D_grid, L_grid, UC_grid, levels=levels, colors='k', linewidths=0.3, alpha=0.3)
+            ax.contour(D_grid, L_grid, UC_grid, levels=[1.0], colors='red', linewidths=2, linestyles='--')
+            ax.set_xlabel('Diameter (m)')
+            ax.set_ylabel('Length (m)')
+            ax.set_title('Unity Check (UC')
+            ax.plot(best_D, best_L, 'ro', label='Best match')
+            ax.annotate('Best match', (best_D, best_L), textcoords="offset points", xytext=(10,10), ha='center', color='red')
+            ax.legend()
+            plt.grid(True)
+            plt.tight_layout()
+            plt.show()
             
-            convalA = newGeom['L']/newGeom['D'] - LD_con[0]
-            convalB = LD_con[1] - newGeom['L']/newGeom['D']
-            conval = min([convalA,convalB])
-            # if newGeom['L']/newGeom['D'] >= LD_con[0] and newGeom['L']/newGeom['D'] <= LD_con[1]:
-            #     conval = 1 
-            # else:
-            #     conval = -1 
+        #UC_target = 1.0
+        closest = min(results, key=lambda x: abs(x['UC'] - UC_target))
+        print("Closest to UC_target:")
+        print(closest)
             
-            return(conval)
-        # constraint to ensure unity check > 1 for suction buckets
-        def conFun_Suction(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
-            if 'Hm' in input_loads or 'Vm' in input_loads:
-                anchor_loads = self.getLugForces(mudloads=input_loads)
-                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
-            results = self.getAnchorCapacity(loads=input_loads, plot=False)
-            #conval = results['UC'] - 1
-            conval = 1 - results['UC']
-            # convalB = 1 - results['UC'] 
-            return(conval)
-        
-        def conFun_DandG(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
+        return results
+    
+    def getSizeAnchor_BO(self, 
+                         geom=[10.0, 2.0],
+                         geomKeys=['L', 'D'],
+                         geomBounds=[(5.0, 15.0), (1.0, 4.0)],
+                         loads=None,
+                         lambdap_con=[3, 6],
+                         zlug_fix=False,
+                         safety_factor={'SF_combined': 1.0},
+                         n_calls=25,
+                         plot=False,
+                         verbose=True):
+        '''
+        Bayesian optimization to find (D, L) for UC closest to UC_target.
+        Uses scikit-optimize for surrogate model and efficient sampling.
+        '''
+        from skopt import gp_minimize
+        from skopt.space import Real
+        from skopt.utils import use_named_args
+        import numpy as np
 
-            newGeom = dict(zip(geomKeys, vars))
-            self.dd['design'].update(newGeom)
-            if 'Hm' in input_loads or 'Vm' in input_loads:
-                anchor_loads = self.getLugForces(mudloads=input_loads)
-                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
-            results = self.getAnchorCapacity(loads=input_loads, plot=False)
+        if loads is None:
+            loads = self.loads
 
-            return np.array([0.05*newGeom['D'] - results['Lateral displacement'] , 0.25 - results['Rotational displacement']])
-            
-        def conFunH(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
-            # if 'suction' in self.dd['type']:
-            #     results = self.getAnchorCapacity(plot=False)
-            #     conval = results['UC'] - 1
-            #     # if results['UC'] < 1:
-            #     #     conval = -1*(results['UC'])
-            # else:
-            if 'Hm' in input_loads or 'Vm' in input_loads:
-                anchor_loads = self.getLugForces(mudloads=input_loads)
-                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
-                minfs = dict(Ha=minfs['Hm'], Va=minfs['Vm'])
-            FS, _, _ = self.getFS(loads=input_loads, acceptance_crit=minfs)
-            conval = FS['Ha'] - 1
-                # for key,val in FS.items():
-                    
-                #     if val/minfs[key]<1:
-                #         if -1*(1-val/minfs[key]) < conval:
-                #             conval = -1*(1-val/minfs[key])
-            return(conval)
-        
-        def conFunV(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
-            if 'Hm' in input_loads or 'Vm' in input_loads:
-                anchor_loads = self.getLugForces(mudloads=input_loads)
-                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
-                minfs = dict(Ha=minfs['Hm'], Va=minfs['Vm'])
-            FS, _, _ = self.getFS(loads=input_loads, acceptance_crit=minfs)
-            # special case for DEAs
-            if minfs['Va'] == 0:
-                 conval = 1
-            else:
-                conval = FS['Va'] - 1
-                
-            # print('FS_V',FS['Va'])
-            return(conval)
-        
-        def conBounds(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
+        Hm = loads['Hm']
+        Vm = loads['Vm']
 
-            newGeom = dict(zip(geomKeys, vars))
-            self.dd['design'].update(newGeom)
+        line_type = getattr(self, 'line_type', 'chain')
+        d = getattr(self, 'd', 0.16)
+        w = getattr(self, 'w', 5000.0)
 
-            if 'Hm' in input_loads or 'Vm' in input_loads:
-                anchor_loads = self.getLugForces(mudloads=input_loads)
-                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
-            results = self.getAnchorCapacity(loads=input_loads, plot=False)
+        UC_target = 1.0 / safety_factor.get('SF_combined', 1.0)
 
-            bound_L_lower = newGeom['L'] - geomBounds[0][0]
-            bound_L_upper = geomBounds[0][1] - newGeom['L']
-            bound_D_lower = newGeom['D'] - geomBounds[1][0]
-            bound_D_upper = geomBounds[1][1] - newGeom['D']
+        # Define the search space
+        space  = [
+            Real(geomBounds[1][0], geomBounds[1][1], name='D'),
+            Real(geomBounds[0][0], geomBounds[0][1], name='L')
+        ]
 
-            return np.array([bound_L_lower, bound_L_upper, bound_D_lower, bound_D_upper])
-        
-        # - - - - - Setup & Optimization
-        from scipy.optimize import minimize
-        from copy import deepcopy
-
-        anchType = self.dd['type']
-
-        # loads['Ha'] = minfs['Ha']*loads['Ha']
-        # loads['Va'] = minfs['Va']*loads['Va']
-        startGeom = dict(zip(geomKeys,geom))
-        print('start geometry: ',startGeom)
-        # apply initial guess geometry
-        self.dd['design'].update(startGeom)
-                   
-        if not 'zlug' in self.dd['design']:
-            if 'suction' in anchType and not fix_zlug:
-                self.dd['design']['zlug'] = (2/3)*startGeom['L']
+        @use_named_args(space)
+        def objective(**params):
+            D = params['D']
+            L = params['L']
+
+            # Apply lambda constraint
+            lambdap = L/D
+            if not (lambdap_con[0] <= lambdap <= lambdap_con[1]):
+                return 100.0
+
+            self.dd['design']['D'] = D
+            self.dd['design']['L'] = L
+            if not zlug_fix:
+                self.dd['design']['zlug'] = (2/3)*L
+
+            try:
+                self.getCapacityAnchor(
+                    Hm=Hm,
+                    Vm=Vm,
+                    zlug=self.dd['design']['zlug'],
+                    line_type=line_type,
+                    d=d,
+                    w=w,
+                    mass_update=True,
+                    plot=False
+                )
+                UC = self.anchorCapacity.get('UC', np.nan)
+            except:
+                UC = np.nan
+
+            if verbose:
+                print(f"Evaluated D={D:.3f}, L={L:.3f} -> UC={UC:.3f}")
+
+            if not np.isfinite(UC):
+                return 100.0
+
+            if UC < UC_target:
+                return (UC_target - UC)**2 * 0.5  # less penalty for overdesign
             else:
-                self.dd['design']['zlug'] = 0
-        
-        # if zlug is fixed, remove it from design variables
-        if fix_zlug and 'zlug' in geomKeys:
-            zlug_loc = geomKeys.index('zlug')
-            startGeom.pop('zlug')
-            geomKeys.remove('zlug')
-            geom.pop(zlug_loc)
-            if geomBounds:
-                geomBounds.pop(zlug_loc)
-
-        if not loads:
+                return (UC - UC_target)**2 * 10   # higher penalty for failure
+
+        # Run Bayesian optimization
+        res = gp_minimize(
+            objective,
+            space,
+            x0=[geom[1], geom[0]],
+            n_calls=n_calls,
+            random_state=42,
+            verbose=verbose
+        )
+
+        # Best result
+        best_D, best_L = res.x
+        self.dd['design']['D'] = best_D
+        self.dd['design']['L'] = best_L
+        if not zlug_fix:
+            self.dd['design']['zlug'] = (2/3)*best_L
+
+        self.getCapacityAnchor(
+            Hm=Hm,
+            Vm=Vm,
+            zlug=self.dd['design']['zlug'],
+            line_type=line_type,
+            d=d,
+            w=w,
+            mass_update=True,
+            plot=plot
+        )
+        UC = self.anchorCapacity.get('UC', np.nan)
+
+        print('\nBayesian Optimized Anchor:')
+        print('Design:', self.dd['design'])
+        print('Capacity Results:', self.anchorCapacity)
+        print(f'Best UC: {UC:.4f} (target: {UC_target})')
+
+        results = {'D': best_D, 'L': best_L, 'UC': UC, 'result': res}
+
+        return results
+    # PATCH for GRADIENT method: wrap getCapacityAnchor in safe evaluator
+    def safe_get_uc(self, Hm, Vm, zlug, line_type, d, w, verbose=False):
+        try:
+            self.getCapacityAnchor(Hm, Vm, zlug, line_type, d, w, True, False)
+            return self.anchorCapacity.get('UC', np.nan)
+        except Exception as e:
+            if verbose:
+                print(f"[Safe Error] {str(e)}")
+            return np.nan
+
+    def getSizeAnchor_gradient(self, 
+                               geom=[10.0, 2.0],
+                               geomKeys=['L', 'D'],
+                               geomBounds=[(5.0, 15.0), (1.0, 4.0)],
+                               loads=None,
+                               lambdap_con=[3, 6],
+                               zlug_fix=False,
+                               safety_factor={'SF_combined': 1.0},
+                               step_size=0.2,
+                               tol=0.05,
+                               max_iter=30,
+                               verbose=True):
+        '''
+        Gradient-based optimization with early stopping to match UC_target.
+        '''
+        import numpy as np
+    
+        if loads is None:
             loads = self.loads
-            
-        if not 'Ha' in loads:
-            loads = self.getLugForces(mudloads=loads)
-            
-        # suction bucket needs to be loads*FS because of capacity envelope calculations in capacity function
-        if ('Hm' in loads and 'Vm' in loads) and ('Hm' in minfs and 'Vm' in minfs):
-            input_loads = {'Hm':loads['Hm']*minfs['Hm'], 'Vm':loads['Vm']*minfs['Vm']}
-        else:
-            input_loads = {'Ha':loads['Ha']*minfs['Ha'],'Va':loads['Va']*minfs['Va']}
+    
+        Hm = loads['Hm']
+        Vm = loads['Vm']
+    
+        line_type = getattr(self, 'line_type', 'chain')
+        d = getattr(self, 'd', 0.16)
+        w = getattr(self, 'w', 5000.0)
+    
+        UC_target = 1.0 / safety_factor.get('SF_combined', 1.0)
+    
+        L, D = geom
+    
+        for iter in range(max_iter):
+            lambdap = L / D
+            if not (lambdap_con[0] <= lambdap <= lambdap_con[1]):
+                if verbose:
+                    print(f"[Iter {iter}] λ = {lambdap:.2f} out of bounds. Terminating.")
+                break
+    
+            self.dd['design']['L'] = L
+            self.dd['design']['D'] = D
+            if not zlug_fix:
+                self.dd['design']['zlug'] = (2/3)*L
+    
+            UC0 = self.safe_get_uc(Hm, Vm, self.dd['design']['zlug'], line_type, d, w, verbose=verbose)
+    
+            if not np.isfinite(UC0):
+                break
+    
+            if verbose:
+                print(f"[Iter {iter}] L={L:.2f}, D={D:.2f}, UC={UC0:.3f}")
+    
+            if abs(UC0 - UC_target) < tol:
+                print("Early stopping: UC within tolerance.")
+                break
+    
+            # Gradient estimate
+            delta = 0.1
+            UC_L = self.safe_get_uc(Hm, Vm, (2/3)*(L + delta), line_type, d, w, verbose=verbose)
+            UC_D = self.safe_get_uc(Hm, Vm, (2/3)*L, line_type, d, w, verbose=verbose)
+    
+            grad_L = (UC_L - UC0)/delta if np.isfinite(UC_L) else 0.0
+            grad_D = (UC_D - UC0)/delta if np.isfinite(UC_D) else 0.0
+    
+            # Update
+            L -= step_size * grad_L
+            D -= step_size * grad_D
+            L = np.clip(L, geomBounds[0][0], geomBounds[0][1])
+            D = np.clip(D, geomBounds[1][0], geomBounds[1][1])
+    
+            if not (lambdap_con[0] <= L/D <= lambdap_con[1]):
+                if verbose:
+                    print("Terminated: lambda constraint violated after update.")
+                break
+    
+        self.dd['design']['L'] = L
+        self.dd['design']['D'] = D
+        self.dd['design']['zlug'] = (2/3)*L
+        self.getCapacityAnchor(Hm, Vm, self.dd['design']['zlug'], line_type, d, w, True, True)
+    
+        print('\nGradient Optimized Anchor:')
+        print('Design:', self.dd['design'])
+        print('Capacity Results:', self.anchorCapacity)
+    
+        return {'D': D, 'L': L, 'UC': self.anchorCapacity.get('UC', np.nan)}
+   
+    def getSafetyFactor(self):
+        '''
+        Calculate the safety factor based on the unity checks stored in capacity results.
 
-           
+        Returns
+        -------
+        dict
+            Dictionary containing safety factors.
+        '''
+
+        anchType_clean = self.anchType.lower().replace(' ', '')
+
+        if anchType_clean in ['helical', 'driven', 'dandg']:
+            UC_v = self.anchorCapacity.get('Unity check (vertical)', None)
+            UC_h = self.anchorCapacity.get('Unity check (horizontal)', None)
+
+            if UC_v is None or UC_h is None:
+                print("Warning: Vertical or horizontal unity check (UC) not found in capacity results. Returning NaN.")
+                return {'SF_vertical': np.nan, 'SF_horizontal': np.nan}
+
+            SF_v = 1.0/UC_v if UC_v != 0 else np.inf
+            SF_h = 1.0/UC_h if UC_h != 0 else np.inf
+
+            return {'SF_vertical': SF_v, 'SF_horizontal': SF_h}
 
-        
-        # Initial guess for geometry
-        initial_guess = geom  # [val for val in startGeom.values()]       # Input values for geometry
-        # geomKeys = [key for key in startGeom.keys()]
-        
-        # Bounds and constraints
-        if 'suction' in anchType:
-            # bounds = [(1, 7), (5, 50),()]   # Bounds for D and L
-            # constraints
-            
-            constraints = [{'type':'ineq','fun':conFun_LD,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
-                           {'type':'ineq','fun':conFun_Suction,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
-                           {'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
-                           {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
-                           {'type':'ineq','fun':conBounds,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}]
-        
-        elif 'dandg' in anchType:
-            constraints = [{'type':'ineq','fun':conFun_LD,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
-                           {'type':'ineq','fun':conFun_DandG,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
-                           {'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
-                           {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
-                           {'type':'ineq','fun':conBounds,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}]
-        
-        else:
-            constraints = [{'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
-                           {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}]
-        
-        # Run the optimization to find sizing that satisfy UC close to 1
-        print('optimizing anchor size')
-        
-        if 'suction' in anchType or 'dandg' in anchType:
-            solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs),
-                                method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001})
         else:
-            solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs),
-                                method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001})
-        
-        FS, acceptance, FSdiff = self.getFS(loads=input_loads, acceptance_crit=minfs)
-        
-        # adjust starting value if you're far off from the acceptance criteria (in either direction)
-        if FSdiff_max:
-            count = 0
-            while count<10 and (np.any([abs(FSdiff[key])>FSdiff_max[key] for key in FSdiff.keys()]) or np.any([diff<0 for diff in FSdiff.values()])):
-                if np.any([diff<.02 for key,diff in FSdiff.items() if minfs[key]>0]) and np.all([diff>=0 for diff in FSdiff.values()]):
-                    # exit loop if you're as close as can be on one of the FS even if other is above diff requirements UNLESS an FS is below minimum reqiured FS
-                    break
-                print('Factor of Safety not close enough to minimum factor of safety, trying again with adjusted initial guess.')
-                print(FS)
-                # calculate new percent difference of FS from min fs
-                diffPCT = [FSdiff[key]/FS[key] for key in FSdiff]
-                # create adjustment coefficient based on this or .25, whichever is lower
-                adjust_coeff = np.min([np.min(diffPCT),0.25])
-                # adjust initial guess values by adjustment coefficient
-                for i,val in enumerate(initial_guess):
-                    initial_guess[i] = val - val*adjust_coeff
-                # update zlug for suction buckets as needed to be 2/3L
-                if 'suction' in anchType and not fix_zlug:
-                    zlug_loc = geomKeys.index('zlug')
-                    L_loc = geomKeys.index('L')
-                    initial_guess[zlug_loc] = (2/3)*initial_guess[L_loc]
-
-                print('new initial guess',initial_guess)
-                # re-run optimization
-                if 'suction' in anchType or 'dandg' in anchType:
-                    solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs),
-                                        method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001})
-                else:
-                    solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs),
-                                        method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001})
-                # re-determine FS and diff from minFS
-                FS, acceptance, FSdiff = self.getFS(loads=input_loads, acceptance_crit=minfs)  
-                count += 1
+            UC = self.anchorCapacity.get('UC', None)
+
+            if UC is None:
+                print("Warning: Unity check (UC) not found in capacity results. Returning NaN.")
+                return {'SF_combined': np.nan}
+
+            SF = 1.0/UC if UC != 0 else np.inf
+
+            return {'SF_combined': SF} 
+                            
+    def getCostAnchor(self, ms=None):
+        '''
+        Assign material cost using a Point object and getCost_and_MBL().
+        '''
+
+        # Create or use existing MoorPy system
+        if ms is None:
+            ms = mp.System()
+
+            # Create MoorPy Point using makeMoorPyAnchor
+            self.makeMoorPyAnchor(ms)
+
+        # Check if mass is assigned
+        if self.mass is None:
+            if 'Weight pile' in self.anchorCapacity:
+                self.mass = self.anchorCapacity['Weight pile'] / self.g
+            elif 'Weight plate' in self.anchorCapacity:
+                self.mass = self.anchorCapacity['Weight plate'] / self.g
+            else:
+                raise KeyError("Missing 'Weight pile' or 'Weight plate' in anchorCapacity. \
+                Run getCapacityAnchor() before getCostAnchor(), or define self.mass explicitly.")
         
-        # Extract the optimized values of geometry
-        endGeom = dict(zip(geomKeys,solution.x))
-        print('End geometry: ',endGeom)
-        self.dd['design'].update(endGeom)
-        if 'suction' in anchType and not fix_zlug:
-            self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] 
-        results = self.getAnchorCapacity(loads=input_loads,plot=plot)            
-            
-        # # check if anchor loads are available
-        # if not self.loads:
-        #     # if not, check if theres a moorpy anchor object and calculate loads from that
-        #     if self.mpAnchor:
-        #         print("Need anchor loads to obtain cost, using getMPForces to determine loads in MoorPy")
-        #         self.getLugForces()
-        #     elif self.ms:
-        #         print('Need anchor loads to obtain cost, creating a MoorPy anchor object and using getMPForces to determine loads in MoorPy')
-        #         self.makeMoorPyAnchor(self.ms)
-        #         self.getLugForces()
-        #     else:
-        #         raise Exception("Need anchor loads to obtain cost")
-        # # check again if there are loads
-        # if self.loads:
-        #     c = self.dd['cost'] # set location for clarity
-        #     # calculate individual costs and total cost for the anchor
-        #     c['matCost'], c['instCost'], c['decomCost'] = mp.Point.getcost(self.mpAnchor)
-        #     c['totCost'] = c['matCost'] + c['instCost'] + c['decomCost']
+        # Assign mass to MoorPy point
+        self.mpAnchor.m = self.mass
+
+        cost, MBL, info = self.mpAnchor.getCost_and_MBL()
+
+        # Store results
+        self.cost = {
+            'Material cost': cost,
+            'MBL': MBL,
+            'unit_cost': cost/self.mpAnchor.m }
+
+        return self.cost
+
+    def getCombinedPlot(self):
+        '''
+        Create a plot showing the suction pile and the inverse catenary overlay in the same coordinate system.
+        '''
+        from anchors_famodel.capacity_load  import getTransferLoad
+        from anchors_famodel.capacity_plots import plot_suction
+
+        if self.anchType.lower() != 'suction':
+            raise NotImplementedError("getCombinedPlot only supports suction piles.")
+
+        # Extract design inputs
+        design = self.dd['design']
+        D = design['D']
+        L = design['L']
+        zlug = design['zlug']
+
+        if self.soil_profile is None or self.soil_type is None:
+            raise ValueError("Soil profile or type not assigned. Use setSoilProfile first.")
+
+        soil_profile = self.soil_profile
+        soil_type = self.soil_type
+        z0 = soil_profile[0]['top']
 
+        Hm = self.loads['Hm']
+        Vm = self.loads['Vm']
+        thetam = self.loads.get('thetam', np.degrees(np.arctan2(Vm, Hm)))
+
+        line_type = getattr(self, 'line_type', 'chain')
+        d = getattr(self, 'd', 0.16)
+        w = getattr(self, 'w', 5000.0)
+
+        # Get inverse catenary path
+        layers, result = getTransferLoad(
+            profile_map=[{'layers': self.soil_profile}],
+            Tm=np.sqrt(Hm**2 + Vm**2),
+            thetam=thetam,
+            zlug=zlug,
+            line_type=line_type,
+            d=d,
+            w=w,
+            plot=False
+        )
+
+        drag_values  = np.array(result['drag_values'])
+        depth_values = -np.array(result['depth_values'])[::-1]
+
+        x_start = D/2 + drag_values[0]
+        z_start = zlug
+        drag_transformed  = x_start - drag_values
+        depth_transformed = z_start + (depth_values- depth_values[0])
+
+        # Plot suction pile
+        plot_suction(soil_profile, L, D, z0=z0, zlug=zlug, title='Suction Pile and Mooring Line Load Path')
+
+
+        # Overlay inverse catenary path
+        plt.plot(drag_transformed, depth_transformed, color='b', lw=2.0, label='Inverse catenary')
+        plt.plot(drag_transformed[-1], depth_transformed[-1], 'ro', label='Mudline end')
+        plt.plot( drag_transformed[0],  depth_transformed[0], 'go', label='Embedded end')
+
+        n = 2e6
+        Tm = result['Tm']
+        Ta = result['Ta']
+        thetaa = result['thetaa']
+
+        plt.arrow(drag_transformed[-1], depth_transformed[-1],
+                  Tm*np.cos(np.deg2rad(thetam))/n, -Tm*np.sin(np.deg2rad(thetam))/n,
+                  head_width=0.25, head_length=0.5, color='r', label='Mudline load')
     
-    # def getMass(self,uhc_mode):
-    #     '''find mass and/or UHC of anchor from MoorProps and store in design dictionary
-    #     Parameters
-    #     ----------
-    #     uhc_mode : boolean
-    #         True : obtain UHC from mass
-    #         False : obtain Masss and UHC from loads
-    #     '''
-    #     if uhc_mode: # if looking for UHC given mass
-    #         if self.dd['design']['m']: # check anchor mass is given
-    #             self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=1, mass_int=self.dd['design']['m'], anchor=self.dd['type'], soil_type=self.anchorProps['soil_type'])
-    #         else:
-    #             raise Exception("Need anchor mass to calculate UHC when uhc_mode = 1")
-    #     else: # if looking for mass and UHC given loads
-    #         if self.loads: # check the loads section exists
-    #             self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=0, fx=self.loads['ff'], fz=self.loads['fz'], anchor=self.dd['type'],soil_type=self.dd['soil_type'],method=self.loads['method'])
-    #         elif self.mpAnchor:
-    #             print("Need anchor loads to obtain mass, using getMPForces to determine loads in MoorPy")
-    #             self.getLugForces()
-    #             self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=0, fx=self.loads['ff'], fz=self.loads['fz'], anchor=self.dd['type'],soil_type=self.dd['soil_type'],method=self.loads['method'])
-    #         elif self.ms:
-    #             print('Need anchor loads to obtain mass, creating a MoorPy anchor object and using getMPForces to determine loads in MoorPy')
-    #             self.makeMoorPyAnchor(self.ms)
-    #             self.getLugForces()
-    #             self.dd['design']['UHC'], self.dd['design']['m'], info = mp.MoorProps.getAnchorMass(uhc_mode=0, fx=self.loads['ff'], fz=self.loads['fz'], anchor=self.dd['type'],soil_type=self.dd['soil_type'],method=self.loads['method'])
-    #         else:
-    #             raise Exception("Need anchor loads to obtain mass")
+        plt.arrow(drag_transformed[0], depth_transformed[0],
+                  Ta*np.cos(np.deg2rad(thetaa))/n, -Ta*np.sin(np.deg2rad(thetaa))/n,
+                  head_width=0.25, head_length=0.5, color='g', label='Padeye load')
+
+        xmax = max(drag_transformed[-1] + D, 2*D)
+        plt.xlim(-D, xmax)
+        plt.legend()
+        plt.grid(True)
+        plt.tight_layout()
+        plt.show()
diff --git a/famodel/anchors/anchor_capacity.py b/famodel/anchors/anchor_capacity.py
deleted file mode 100644
index e3acb56b..00000000
--- a/famodel/anchors/anchor_capacity.py
+++ /dev/null
@@ -1,153 +0,0 @@
-"""The anchor capacity calculation 'switchboard' that holds generic
-anchor capacity functions and calls the specific calculation functions
-from other modules depending on the soil and anchor information."""
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-import moorpy.MoorProps as mprop 
-
-from .capacity_plate import getCapacityPlate
-from .capacity_suction import getCapacitySuction
-from .capacity_dandg import *
-
-
-
-
-
-def anchorCapacity(anchor, soil, display=0):
-    '''Calculate anchor holding capacity based on specified anchor and soil
-    information.
-    
-    Parameters
-    ----------
-    anchor : dictionary
-        anchor description
-    soil : dictionary 
-        soil description. Can be a keyword ([_/soft/medium/hard] clay, or sand)
-        for the level 1 model, or a soilProps dict for the level 2 model.
-    model_level : int
-        1 or 2.
-
-    Returns
-    -------
-    UHC: float
-        required anchor ultimate holding capacity [kN]
-    info: dict
-        dictionary with additional information depending on the model.
-    '''
-
-    
-    if model_level == 1:   # soil keyword indicates level 1 models
-    
-    
-        # calls level 1 anchor capacity function, with anchor/soil types and default assumptions
-        uhc, mass, info = mprop.getAnchorMass(uhc_mode=True, mass_int=anchor['mass'], 
-                                              anchor=anchor['type'], soil_type=soil['class'], 
-                                              method='static', display=0)
-        
-        #fx, fz = anchor_curves.anchorCapacity(0, 0, 0, anchor=anchor['type'],
-        #                                      soil_type=soil['class'], display=display)
-
-
-    elif model_level==2:  # dict indicates a soilProps dictionary
-    
-        # >>> we probably need anchor details too then ...
-        
-        
-        # For now the anchor properties get checked in this function 
-        # but in the future they coudl be moved to the individual functions.
-        
-        if anchor['type'] == 'DEA':
-            # make curves from 
-            pass
-        
-        elif anchor['type'] == 'SCA':
-        
-            L     = getFromDict(anchor, 'length')
-            D     = getFromDict(anchor, 'diameter', default=L/6)
-            thick = getFromDict(anchor, 'thickness', default=L/100)
-            F_ang = np.degrees(np.atan2(Fz, Fx))  # load inclination angle [deg]
-            
-            if soil['class'] == 'clay':
-            
-                gamma = getFromDict(soil, 'gamma', default=4.7)
-                Su0   = getFromDict(soil, 'So0'  , default=2.39)
-                k     = getFromDict(soil, 'k'    , default=1.41)
-                alpha = getFromDict(soil, 'alpha', default=0.7)
-                SF = 2
-            
-                results = getCapacitySuction(L, L_D_aspect=L/D, D_t_aspect=D/thick, 
-                                    A_angle=F_ang, Su0=Su0, k=k, 
-                                    Alpha=alpha, gamma=gamma, J=1/SF)
-            
-            elif soil['class'] == 'sand':
-            
-                gamma = getFromDict(soil, 'gamma', default=9.0)
-                phi   = getFromDict(soil, 'phi'  , default=30)            
-                results = getCapacitySuction(L, L_D_aspect=L/D, D_t_aspect=D/thick, 
-                                    A_angle=F_ang, gamma=gamma, Phi=phi)
-            
-            else:
-                #raise Exception(f"soil class '{soil.class}' is not supported.")
-                pass
-                
-            
-        elif anchor['type'] == 'VLA':
-        
-            # same plate capacity calc as SEPLA for now - will in future consider angle
-        
-            A     = getFromDict(anchor, 'area')
-            thick = getFromDict(anchor, 'thickness', default=np.sqrt(L)/40)
-            H     = getFromDict(anchor, 'embedment')  # embedment depth [m]
-            
-            if soil['class'] == 'clay':
-            
-                gamma = getFromDict(soil, 'gamma', default=4.7)
-                Su0   = getFromDict(soil, 'So0'  , default=2.39)
-                k     = getFromDict(soil, 'k'    , default=1.41)
-                
-                results = getCapacityPlate(A, B_t_aspect=np.sqrt(L)/thick, 
-                                           Hs=H, Bita=30, Los=0.05,
-                                           gamma=gamma, So0=So0, k=k)
-            else:
-                raise Exception("Only clay soil is supported for this anchor type.")
-         
-        
-        elif anchor['type'] == 'SEPLA':
-            
-            A     = getFromDict(anchor, 'area')
-            thick = getFromDict(anchor, 'thickness', default=np.sqrt(L)/40)
-            H     = getFromDict(anchor, 'embedment')  # embedment depth [m]
-            
-            if soil['class'] == 'clay':
-            
-                gamma = getFromDict(soil, 'gamma', default=4.7)
-                Su0   = getFromDict(soil, 'So0'  , default=2.39)
-                k     = getFromDict(soil, 'k'    , default=1.41)
-                
-                results = getCapacityPlate(A, B_t_aspect=np.sqrt(L)/thick, 
-                                           Hs=H, Bita=30, Los=0.05,
-                                           gamma=gamma, So0=So0, k=k)
-            else:
-                raise Exception("Only clay soil is supported for this anchor type.")
-
-        else:
-            raise Exception(f"Anchor type '{anchor.type}' is not yet supported in hte intermediate anchor model set")
-    
-
-        
-        print(f"UHC input: fx:{fx} fz:{fz} -- Mass: {mass}, Cost: {cost}")
-        info["UHC input"] = fx,fz           #[kN]
-        info["Capacity_sf"] = capacity_sf   #[kN]
-        info["Mass"] = mass                 #[mT]
-        info["Cost"] = cost                 #[$/mT]
-        #info["Length"] = L
-        info["Area"] = area    
-
-    else:
-        raise Exception("Model level must be 1 or 2")
-
-
-    return capacity, info
-
diff --git a/famodel/anchors/anchor_conflict_backup.py b/famodel/anchors/anchor_conflict_backup.py
new file mode 100644
index 00000000..1a8c26a2
--- /dev/null
+++ b/famodel/anchors/anchor_conflict_backup.py
@@ -0,0 +1,2087 @@
+"""Anchor class for FAModel, containing information and key methods for anchors of mooring lines
+    Work in progress
+"""
+import moorpy as mp
+import numpy as np
+from scipy.optimize import minimize
+from famodel.famodel_base import Node
+from famodel.mooring.mooring import Mooring
+import matplotlib.pyplot as plt
+from collections import defaultdict
+import famodel.platform.platform 
+import shapely as sh
+
+class Anchor(Node):
+    
+    def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None,
+                 g=9.81, rho=1025, profile_map=None):
+        '''
+        Initialize an Anchor object.
+
+        Parameters
+        ----------
+        dd : dict
+            Design dictionary containing all information on the anchor.
+        ms : MoorPy system object
+            MoorPy system instance.
+        r : list of float
+            Anchor position coordinates (x, y, z) (m)
+        aNum : int, optional
+            Index in anchor list.
+        id : str or int, optional
+            Unique anchor identifier.
+        g : float, optional
+            Gravity.
+        rho : float, optional
+            Water density.
+        profile_map : list of dict, optional
+            Full soil profile map for selecting local soil layers.
+        '''
+
+        from famodel.famodel_base import Node
+        Node.__init__(self, id)
+
+        self.dd = dd
+        self.ms = ms
+        self.r = r
+        self.aNum = aNum
+        self.g = g
+        self.rho = rho
+
+        if dd and 'type' in dd:
+            self.anchType = dd['type']
+        else:
+            self.anchType = 'suction'
+            print(f"[Anchor] No type provided. Defaulting to 'suction'.")
+
+        self.soil_type = None
+        self.soil_profile = None
+        self.profile_name = None
+        self.soil_type_list = []
+
+        self.mpAnchor = None
+        self.capacity_format = None
+        self.mass = dd.get('design', {}).get('mass', None) if dd else None
+        self.point_num = 0  # initialize point number
+        
+        # get environment info
+        self.g = g # acceleration due to gravity (m/s^2)
+        self.rho = rho # density of fluid (kg/m^3)
+        
+        # anchor mass
+        if 'mass' in self.dd['design']:
+            self.mass = self.dd['design']['mass']
+        else:
+            self.mass = None
+        
+        # Dictionaries for additional information
+        # anchor capacity
+        self.anchorCapacity = {}
+        self.safety_factors = {} # calculated safety factor
+        self.safety_factors_required = {} # minimum allowable safety factor
+        
+        # anchor costs
+        self.cost = {}
+        
+        self.loads = {}
+        '''
+        {
+           Hm:      # horizontal maximum anchor loads at mudline [N]
+           Vm:      # vertical maximum anchor loads at mudline [N]
+           thetam: # angle of load at the mudline [rad]
+           Ha:      # horizontal maximum loads at lug
+           Va:      # vertical maximum loads at lug
+           thetaa:  # angle of load at lug
+           method:  # dynamic or static method of calculation
+            }
+        '''
+        self.soilProps = {}
+        self.loads = {}
+        self.anchorCapacity = {}
+        self.cost = {}
+        self.failure_probability = {}
+        self.env_impact = {}
+
+        # Assign soil profile if map is provided
+        if profile_map is not None:
+            if len(profile_map) == 1:
+                self.setSoilProfile(profile_map)
+            elif len(profile_map) >= 4:
+                self.interpolateSoilProfile(profile_map)
+            else:
+                raise ValueError("profile_map must contain either 1 or ≥4 CPTs for soil assignment.")
+
+    def setSoilProfile(self, profile_map):
+        '''
+        Assign a soil profile directly from a single CPT.
+        Assumes profile_map is a list with only one entry.
+        '''
+        if len(profile_map) != 1:
+            raise ValueError("setSoilProfile expects a profile_map with exactly one CPT.")
+
+        cpt = profile_map[0]
+        self.soil_profile = cpt['layers']
+        self.profile_name = cpt.get('name', 'CPT_Assigned')
+
+        # Extract soil types from layers
+        soil_types = [layer['soil_type'] for layer in self.soil_profile]
+        self.soil_type_list = list(set(soil_types))
+        self.soil_type = soil_types[0] if len(self.soil_type_list) == 1 else 'mixed'
+
+        # Group layers by soil type
+        soilProps = defaultdict(list)
+        for layer in self.soil_profile:
+            layer_copy = layer.copy()
+            soil_type = layer_copy.pop('soil_type')
+            soilProps[soil_type].append(layer_copy)
+        self.soilProps = dict(soilProps)
+
+        print(f"[Anchor] Assigned soil profile from {self.profile_name} with soil types {self.soil_type_list}")
+
+
+    def interpolateSoilProfile(self, profile_map):
+        '''
+        Interpolate a soil profile from the 4 nearest CPTs in profile_map.
+        '''
+        if len(profile_map) < 4:
+            raise ValueError("interpolateSoilProfile requires at least 4 CPTs.")
+
+        x_anchor, y_anchor = self.r[0], self.r[1]
+
+        # Sort CPTs by distance
+        distances = [np.hypot(p['x'] - x_anchor, p['y'] - y_anchor) for p in profile_map]
+        idx_sorted = np.argsort(distances)
+        CPTs = [profile_map[i] for i in idx_sorted[:4]]
+
+        # Inverse distance weighting
+        x = np.array([cpt['x'] for cpt in CPTs])
+        y = np.array([cpt['y'] for cpt in CPTs])
+        d = np.hypot(x - x_anchor, y - y_anchor)
+        w = 1 / np.maximum(d, 1e-3)**2
+        w /= np.sum(w)
+
+        # Interpolate layer-by-layer (assumes same layer structure)
+        layers_list = [cpt['layers'] for cpt in CPTs]
+        n_layers = len(layers_list[0])
+        interpolated_layers = []
+
+        for i in range(n_layers):
+            base_layer = layers_list[0][i]
+            layer = {'soil_type': base_layer['soil_type']}
+
+            for key in base_layer:
+                if key == 'soil_type':
+                    continue
+                if all(key in l[i] for l in layers_list):
+                    vals = [l[i][key] for l in layers_list]
+                    layer[key] = np.dot(w, vals)
+
+            interpolated_layers.append(layer)
+
+        self.soil_profile = interpolated_layers
+        self.profile_name = "Interpolated_2D"
+
+        # Extract soil types
+        soil_types = [layer['soil_type'] for layer in self.soil_profile]
+        self.soil_type_list = list(set(soil_types))
+        self.soil_type = soil_types[0] if len(self.soil_type_list) == 1 else 'mixed'
+
+        # Group interpolated layers by soil type
+        soilProps = defaultdict(list)
+        for layer in self.soil_profile:
+            layer_copy = layer.copy()
+            soil_type = layer_copy.pop('soil_type')
+            soilProps[soil_type].append(layer_copy)
+        self.soilProps = dict(soilProps)
+
+        print(f"[Anchor] Interpolated soil profile: {self.profile_name} with soil types {self.soil_type_list}")
+
+    def makeMoorPyAnchor(self, ms):
+        '''
+        Create a MoorPy anchor object in a MoorPy system.
+
+        Parameters
+        ----------
+        ms : MoorPy system instance
+            The MoorPy system to add the anchor to.
+
+        Returns
+        -------
+        ms : MoorPy system instance
+            The updated MoorPy system with the anchor added.
+        '''       
+        anchType = self.anchType or 'suction'
+
+        # Create anchor as a fixed point in MoorPy system
+        ms.addPoint(1, self.r)
+
+        # Assign this point as mpAnchor in the anchor class instance
+        self.mpAnchor = ms.pointList[-1]
+
+        # Set mass if available
+        if 'mass' in self.dd.get('design', {}):
+            self.mpAnchor.m = self.dd['design']['mass']
+
+        # Set diameter if available
+        if 'd' in self.dd.get('design', {}):
+            self.mpAnchor.d = self.dd['design']['d']
+            
+        # Set dummy design to get PointType from MoorPy
+        design = {f"num_a_{anchType}": 1}
+        pointType = ms.setPointType(design, source=None)
+        self.mpAnchor.entity = pointType
+
+        return ms
+       
+    def getLineProperties(self):
+        '''
+        Retrieve line_type, diameter and unit weight from attached mooring.
+
+        Returns
+        -------
+        line_type : str
+            Type of mooring line ('chain' or 'wire')
+        d : float
+            Nominal diameter (m)
+        w : float
+            Unit weight (N/m)
+        '''
+        for att in self.attachments.values():
+            if isinstance(att['obj'], Mooring):
+                mtype = att['obj'].dd['sections'][0]['type']['material'].lower()
+                if 'chain' not in mtype:
+                    print('No chain below seafloor, setting Ta=Tm (no load transfer).')
+                    return mtype, None, None, True
+                else:
+                    d_nom = att['obj'].dd['sections'][0]['type']['d_nom']
+                    w_nom = att['obj'].dd['sections'][0]['type']['w']
+                    return 'chain', d_nom, w_nom, False
+        raise ValueError('No mooring line attachment found for anchor.')
+
+    def getMudlineForces(self, max_force=False, lines_only=False, seabed=True, xyz=False, project=None):
+        '''
+        Find forces on anchor at mudline using the platform.getWatchCircle method
+        or the MoorPy Point.getForces method. Optionally computes the maximum force 
+        based on platform excursion using the project's arrayWatchCircle method or
+        the attached platform's getWatchCircle method.
+
+        Parameters
+        ----------
+        max_force : bool, optional
+            If True, computes the maximum expected force on the anchor 
+            using platform excursion. Default is False.
+        lines_only : bool, optional
+            Calculate forces from just mooring lines (True) or not (False). Default is False.
+        seabed : bool, optional
+            Include effect of seabed pushing up the anchor (True) or not (False). Default is True.
+        xyz : bool, optional
+            Return forces in x, y, z DOFs (True) or only the enabled DOFs (False). Default is False.
+        project : object, optional
+            Project object that can run arrayWatchCircle(). Used only if max_force is True.
+
+        Returns
+        -------
+        dict
+            Dictionary containing mudline forces.
+        '''
+        Platform = famodel.platform.platform.Platform
+
+        if max_force:
+            if project:
+                project.arrayWatchCircle()
+            else:
+                for att in self.attachments.values():
+                    if isinstance(att['obj'], Mooring):
+                        for attM in att['obj'].attached_to:
+                            if isinstance(attM, Platform):
+                                locx, locy, maxVals = attM.getWatchCircle()
+                                Hm = np.sqrt(maxVals[0]**2 + maxVals[1]**2)
+                                Vm = maxVals[2]
+                                thetam = np.degrees(np.arctan2(Vm, Hm))
+                                self.loads['Hm'] = Hm
+                                self.loads['Vm'] = Vm
+                                self.loads['thetam'] = thetam
+                                self.loads['mudline_load_type'] = 'max_force'
+                                break
+        else:
+            loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz)
+            Hm = np.sqrt(loads[0]**2 + loads[1]**2)
+            Vm = loads[2]
+            thetam = np.degrees(np.arctan2(Vm, Hm))
+            self.loads['Hm'] = Hm
+            self.loads['Vm'] = Vm
+            self.loads['thetam'] = thetam
+            self.loads['mudline_load_type'] = 'current_state'
+
+        self.loads['method'] = 'static'
+        return self.loads
+       
+    def getLugForces(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=True):
+        '''
+        Calculate the lug forces Ha and Va based on mudline loads using local soil profile.
+
+        Parameters
+        ----------
+        Hm : float
+            Horizontal mudline load (N)
+        Vm : float
+            Vertical mudline load (N)
+        zlug : float
+            Padeye embedment depth (m)
+        line_type : str, optional
+            Type of mooring line ('chain' or 'wire')
+        d : float, optional
+            Mooring line diameter (m)
+        w : float, optional
+            Mooring line unit weight (N/m)
+        plot : bool, optional
+            Whether to plot the load transfer profile
+
+        Returns
+        -------
+        Ha : float
+            Horizontal load at lug (N).
+        Va : float
+            Vertical load at lug (N).
+        '''
+        from .anchors_famodel.capacity_load import getTransferLoad
+        from .anchors_famodel.support_plots import plot_load
+
+        # Ensure soil profile is available
+        if self.soil_profile is None or self.soil_type is None:
+            raise ValueError("Anchor soil profile or soil type is not assigned. Use setSoilProfile first.")
+
+        soil_profile = self.soil_profile
+        soil_type = self.soil_type
+
+        # Determine mudline depth
+        z0 = soil_profile[0]['top']
+
+        # Load transfer if padeye is embedded
+        if zlug > z0:
+            if line_type is None or d is None or w is None:
+                try:
+                    line_type, d, w = self.getLineProperties()
+                except ValueError:
+                    print('[Warning] No mooring attachment found. Trying anchor-level line properties...')
+                    line_type = getattr(self, 'line_type', None)
+                    d = getattr(self, 'd', None)
+                    w = getattr(self, 'w', None)
+
+                    if any(v is None for v in [line_type, d, w]):
+                        print('[Fallback] Using default chain properties.')
+                        line_type = 'chain'
+                        d = 0.16
+                        w = 5500.0
+
+            layers, loads = getTransferLoad(
+                profile_map=[{'layers': self.soil_profile}],
+                Tm=np.sqrt(Hm**2 + Vm**2),
+                thetam=np.degrees(np.arctan2(Vm, Hm)),
+                zlug=zlug,
+                line_type=line_type,
+                d=d,
+                w=w,
+                plot=plot
+            )
+
+            Ta = loads['Ta']
+            thetaa = loads['thetaa']
+            Ha = Ta*np.cos(np.deg2rad(thetaa))
+            Va = Ta*np.sin(np.deg2rad(thetaa))
+
+        else:
+            Ha = Hm
+            Va = Vm
+            
+        if plot == True:
+            plot_load(layers, loads['drag_values'], loads['depth_values'], 
+                      loads['Tm'], loads['thetam'], loads['Ta'], 
+                      loads['thetaa'], zlug=zlug)
+
+        return layers, Ha, Va
+
+    def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, mass_update=False, plot=False):
+        '''
+        Calculate anchor capacity based on anchor type and local soil profile.
+    
+        Parameters
+        ----------
+        Hm : float
+            Horizontal mudline load (N)
+        Vm : float
+            Vertical mudline load (N)
+        zlug : float
+            Padeye embedment depth (m)
+        line_type : str, optional
+            Type of mooring line ('chain' or 'wire')
+        d : float, optional
+            Mooring line diameter (m)
+        w : float, optional
+            Mooring line unit weight (N/m)
+        mass_update : bool, optional
+            Whether to update the mass when is not assigned
+        plot : bool, optional
+            Whether to plot the load transfer and pile geometry
+    
+        Returns
+        -------
+        results : dict
+            Capacity results dictionary from the selected capacity function.
+        '''
+
+        # - - - - set details - - - -
+        anchType = self.dd['type'] 
+        geom = self.dd['design']# geometric anchor information
+
+        if not ground_cons:
+            soil = next(iter(self.soilProps.keys()), None) # soil type
+            ground_conds = self.soilProps[soil]
+        else:
+            soil = next(iter(ground_cons.keys()))
+            ground_conds = ground_cons[soil]
+         
+        for key,prop in ground_conds.items():
+            if isinstance(prop,list) or isinstance(prop,np.ndarray):
+                if len(prop)>1:
+                    print('Warning: Only homogeneous soils are supported at this time. Only the first item in a property list will be used.')
+                    break 
+                else:
+                    ground_conds[key] = prop[0]
+        
+        
+        if loads:
+            # find out if mudline loads or anchor loads
+            if not 'Ha' in loads:
+                # get loads at lug
+                loads = self.getLugForces(mudloads=loads,plot=plot)
+        else:
+            loads = self.loads
+        
+        
+       
+        # logic to determine what functions to call based on anchor type and soil type...
+        
+        # - - - - plate anchors - - - - 
+        if anchType == 'SEPLA' or anchType == 'DEA' or anchType == 'DEPLA' or anchType == 'VLA' or anchType == 'plate':
+            from .anchors_famodel.capacity_plate import getCapacityPlate
+            if 'clay' in soil or 'mud' in soil:
+                # write or overwrite beta in geom dictionary from loads function
+                if anchType != 'DEA':
+                    if not 'beta' in geom:
+                        if not 'thetaa' in loads:
+                            # calculate thetaa from Ha and Va
+                            loads['thetaa'] = np.arctan2(loads['Va'],loads['Ha'])
+                            # loads = self.getLugForces(plot=plot)
+                        geom['beta'] = 90 - loads['thetaa']
+                else:
+                    geom['beta'] = 0
+                if 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds:             
+                    results = getCapacityPlate(geom['A'], geom['beta'], geom['zlug'], 'clay', ground_conds['gamma'],
+                                               Su0=ground_conds['Su0'], k=ground_conds['k'])
+                else:
+                    raise Exception('Ground conditions dictionary needs Su0, k, gamma information for clay plate anchors')
+            else:
+                print(f'Warning: Soil type {soil} is not compatible with plate anchors (SEPLA/DEPLA/DEA/VLA)')
+         
+        # - - - - suction buckets - - - -                       
+        elif 'suction' in anchType:
+            from .anchors_famodel.capacity_suction import getCapacitySuction
+            # check loads have been calculated (needed for capacity function in this case)
+            if not 'Ha' in loads:
+                # call getMPForces function 
+                loads = self.getLugForces(plot=plot)
+            if 'sand' in soil:
+                if 'phi' in ground_conds and 'Dr' in ground_conds:
+                    results = getCapacitySuction(geom['D'], geom['L'], geom['zlug'],
+                                                 loads['Ha']/1000, loads['Va']/1000,
+                                                 'sand', ground_conds['gamma'],
+                                                 phi=ground_conds['phi'],
+                                                 Dr=ground_conds['Dr'], plot=plot)
+                else:
+                    raise Exception('Ground conditions dictionary needs phi and relative density information for sand suction pile anchor')
+            elif 'clay' in soil or 'mud' in soil:
+                if 'Su0' in ground_conds and 'k' in ground_conds and 'alpha' in ground_conds:# and 'gamma_sub' in ground_conds:
+                    results = getCapacitySuction(geom['D'],geom['L'], geom['zlug'],
+                                                 loads['Ha']/1000, loads['Va']/1000,
+                                                 'clay', ground_conds['gamma'],
+                                                 Su0=ground_conds['Su0'],
+                                                 k=ground_conds['k'], plot=plot)
+                    results['Horizontal max.'] = results['Horizontal max.']
+                    results['Vertical max.'] = results['Vertical max.']
+                    
+                else:
+                    raise Exception('Ground conditions dictionary needs Su0, k, and alpha information for clay suction pile anchor')
+            else:
+                print(f'Warning: Soil type {soil} is not compatible with suction pile anchor')
+                
+        # - - - - helical piles - - - -
+        elif 'helical' in anchType:
+            from .anchors_famodel.capacity_helical import getCapacityHelical
+            if 'sand' in soil:
+                if 'phi' in ground_conds and 'gamma' in ground_conds:
+                    results = getCapacityHelical(geom['D'], geom['L'], geom['d'],
+                                                 geom['zlug'], 'sand', 
+                                                 ground_conds['gamma'],
+                                                 phi=ground_conds['phi'], 
+                                                 Dr=ground_conds['Dr'])
+                    results['Vertical max.'] = results['Capacity']
+                else:
+                    raise Exception('Ground conditions dictionary needs phi, gamma and relative density information for clay helical pile anchor')
+            elif 'clay' in soil or 'mud' in soil:
+                if not 'alpha_star' in ground_conds:
+                    ground_conds['alpha_star'] = ground_conds['alpha']
+                if 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds:
+                    results = getCapacityHelical(geom['D'], geom['L'], geom['d'],
+                                                 geom['zlug'], 'clay', 
+                                                 ground_conds['gamma'],
+                                                 Su0=ground_conds['Su0'], 
+                                                 k=ground_conds['k'])
+                    results['Vertical max.'] = results['Capacity']
+                else:
+                    raise Exception('Ground conditions dictionary needs Su0, k, gamma, and alpha_star information for clay helical pile anchor')
+            else:
+                print(f'Warning: Soil type {soil} is not compatible with helical pile anchor')
+        
+        # - - - - torpedo piles - - - - 
+        elif 'torpedo' in anchType:
+            from .anchors_famodel.capacity_torpedo import getCapacityTorpedo
+            if 'clay' in soil or 'mud' in soil:
+                if 'Su0' in ground_conds and 'k' in ground_conds and 'alpha' in ground_conds:
+                    results = getCapacityTorpedo(geom['D1'], geom['D2'], 
+                                                 geom['L1'], geom['L2'], 
+                                                 geom['zlug'], 'clay', 
+                                                 ground_conds['Su0'],
+                                                 ground_conds['k'], 
+                                                 ground_conds['alpha'])
+                    results['Horizontal max.'] = results['Horizontal max.']
+                    results['Vertical max.'] = results['Vertical max.']
+                else:
+                    raise Exception('Ground conditions dictionary needs Su0, k, and alpha information')
+            else:
+                print('Warning: Soil type {soil} is not compatible with torpedo pile anchor')
+        
+        # - - - - driven piles - - - - 
+        elif 'driven' in anchType: # driven pile anchor
+            # check loads have been calculated (needed for capacity function in this case)
+            if not 'Ha' in loads:
+                # call getLugForces function 
+                loads = self.getLugForces(plot=plot)
+            H_inc = loads['Ha']*0.1 # increment H by 10% of Ha load in the while loops to back-calc max H from displacements
+            H = 0
+            # check soil
+            if 'weak_rock' in soil:
+                from .anchors_famodel.capacity_drivenrock import getCapacityDrivenRock
+                
+                if not profile:
+                    if 'UCS' in ground_conds and 'Em' in ground_conds:
+                        profile = [[0,ground_conds['UCS'],ground_conds['Em']], 
+                                   [75,ground_conds['UCS'],ground_conds['Em']]] #profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['UCS'],ground_conds['Em']))]
+                    else:
+                        raise Exception('Ground conditions dictionary needs UCS, Em, and depth information for weak rock driven pile anchor')
+                
+                y, z, results = getCapacityDrivenRock(profile, geom['L'], geom['D'], 
+                                                      geom['zlug'], loads['Va'], 
+                                                      loads['Ha'], plot=plot)
+                
+                # loop through, calling capacity with larger H values until a displacement value goes above limit
+                while results['Lateral displacement']< 0.05*geom['D'] and results['Rotational displacement'] < 0.25:
+                    # increment H 
+                    H += H_inc
+                    # call capacity function       
+                    y, z, results = getCapacityDrivenRock(profile, geom['L'], 
+                                                          geom['D'], geom['zlug'], 
+                                                          loads['Va'], H=H, plot=plot)
+                    
+                   
+            elif 'sand' in soil:
+                from .anchors_famodel.capacity_drivensoil import getCapacityDrivenSoil
+                if profile or ('gamma' in ground_conds and 'Dr' in ground_conds and 'phi' in ground_conds):
+                    if not profile:
+                        profile = [[0,ground_conds['phi'],ground_conds['gamma'],ground_conds['Dr']], 
+                                   [75,ground_conds['phi'],ground_conds['gamma'],ground_conds['Dr']]] #profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['phi'],ground_conds['gamma']))]
+
+                    y, z, results = getCapacityDrivenSoil(profile, 'sand', 
+                                                          geom['L'], geom['D'], 
+                                                          geom['zlug'], loads['Va'], 
+                                                          loads['Ha'], plot=plot)
+                    if geom['zlug'] > 0:
+                        # need to check bending moment if lug is below mudline (+ zlug)
+                        # loop through, calling capacity with larger H values until a displacement value goes above limit
+                        while results['Lateral displacement']<= 0.05*geom['D'] and results['Bending moment'] <= results['Plastic moment']:
+                            # increment H by 10% of load
+                            H += H_inc
+                            # call capacity function     
+                            y, z, results = getCapacityDrivenSoil(profile,'clay', 
+                                                                  geom['L'], geom['D'],
+                                                                  geom['zlug'], loads['Va'], 
+                                                                  H=H, plot=plot)
+                            
+                    else:
+                        while results['Lateral displacement']<= 0.05*geom['D'] and results['Rotational displacement'] <= 0.25:
+                            # increment H by 10% of load
+                            H += H_inc  
+                            # call capacity function
+                            y, z, results = getCapacityDrivenSoil(profile, 'clay', 
+                                                                  geom['L'], geom['D'], 
+                                                                  geom['zlug'], loads['Va'],
+                                                                  H=H, plot=plot)
+                else:
+                    raise Exception('Ground conditions dictionary needs phi, gamma, and depth information for sand driven pile anchor')
+            elif 'clay' in soil or 'mud' in soil:
+                from .anchors_famodel.capacity_drivensoil import getCapacityDrivenSoil
+                #if profile or ('Su' in ground_conds and 'gamma' in ground_conds and 'depth' in ground_conds) or ('Su0' in ground_conds and 'k' in ground_conds):
+                if not profile:
+                    if 'Su' in ground_conds and 'depth' in ground_conds and 'gamma' in ground_conds:
+                        profile = [list(x) for x in list(zip(ground_conds['depth'],ground_conds['Su'],ground_conds['gamma']))]
+                    elif 'Su0' in ground_conds and 'k' in ground_conds and 'gamma' in ground_conds:
+                        Su = ground_conds['Su0']+ground_conds['k']*75
+                        profile = [[0,ground_conds['Su0'],ground_conds['gamma']],[75,Su,ground_conds['gamma']]]
+                    else:
+                        raise Exception('Ground conditions dictionary needs information for clay driven pile anchor')
+
+                y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'],loads['Ha'], plot=plot)
+                
+                if geom['zlug'] > 0:
+                    # need to check bending moment if lug is below mudline (+ zlug)
+                    # loop through, calling capacity with larger H values until a displacement value goes above limit
+                    while results['Lateral displacement']<= 0.05*geom['D'] and results['Bending moment'] <= results['Plastic moment']:
+                        # increment H by 10% of load
+                        H += H_inc
+                        # call capacity function     
+                        y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'], H=H, plot=plot)
+                
+                else:
+                    while results['Lateral displacement']<= 0.05*geom['D'] and results['Rotational displacement'] <= 0.25:
+                        # increment H by 10% of load
+                        H += H_inc  
+                        # call capacity function
+                        y, z, results = getCapacityDrivenSoil(profile,'clay',geom['L'],geom['D'],geom['zlug'],loads['Va'], H=H, plot=plot)
+                        
+                    
+            else:
+                print(f'Warning: Soil type {soil} is not compatible with driven pile anchors')
+                
+        # - - - - drilled and grouted piles - - - -                
+        elif 'dandg' in anchType:  # drill and grout pile
+            from .anchors_famodel.capacity_dandg import getCapacityDandG
+            # check for correct soil
+            if 'rock' in soil:
+                # check loads have been calculated (needed for capacity function in this case)
+                if not 'Ha' in loads:
+                    # call getMPForces function 
+                    loads = self.getLugForces(plot=plot)
+                # check for correct ground properties
+                if profile or ('UCS' in ground_conds and 'Em' in ground_conds):
+                    if not profile:
+                        profile = [[0,ground_conds['UCS'],ground_conds['Em']],[75,ground_conds['UCS'],ground_conds['Em']]] #[list(x) for x in list(zip(ground_conds['depth'],ground_conds['UCS'],ground_conds['Em']))]
+
+                    # call capacity function once to get displacement values
+                    y, z, results = getCapacityDandG(profile,geom['L'],geom['D'], 
+                                                     geom['zlug'], loads['Va'], 
+                                                     loads['Ha'], plot=plot)
+                    H_inc = loads['Ha']*0.1 # increment H by 10% of Ha load
+                    H = H_inc # start H at 10% of Ha load
+                    # loop through, calling capacity with larger H values until a displacement value goes above limit
+                    while results['Lateral displacement']< 0.05*geom['D'] and results['Rotational displacement'] < 0.25:
+                        # call capacity function       
+                        y, z, results = getCapacityDandG(profile, geom['L'], geom['D'], 
+                                                         geom['zlug'], loads['Va'], 
+                                                         H=H, plot=plot)
+                        # increment H 
+                        H += H_inc
+                else:
+                    raise Exception('Ground conditions dictionary need UCS and Em information for drill and grout pile')
+            else:
+                print(f'Warning: soil type {soil} is not compatible with drill and grout pile')
+                
+        # - - - - anchor type not recognized or supported - - - - 
+        else:
+            raise Exception(f'Anchor type {anchType} is not supported at this time')
+            
+        # - - - - save relevant results in dictionary using common terms - - - - 
+        # capacity = cap*installAdj ??? OR is installAdj an input to the capacity functions?
+        # save capacity 
+        if 'dandg' in anchType or 'driven' in anchType: # will take in dandg, dandg_pile, driven, driven_pile
+            self.anchorCapacity['Lat_max'] = results['Lateral displacement'] # [deg]
+            if 'Rotational displacement' in results:
+                self.anchorCapacity['Rot_max'] = results['Rotational displacement'] # [deg]
+            elif 'Bending moment' in results:
+                self.anchorCapacity['Mbend_max'] = results['Bending moment']
+            self.anchorCapacity['Va_max'] = results['Axial capacity'] # [N]
+            self.anchorCapacity['Ha_max'] = H
+
+        else:
+            if 'Horizontal max.' in results:
+                self.anchorCapacity['Ha_max'] = results['Horizontal max.']*1000 # [N]
+            self.anchorCapacity['Va_max'] = results['Vertical max.']*1000 # [N]
+        self.mass = results['Weight']*1000/self.g # mass in [kg]
+            
+        # add on extra for drag-embedment anchors (flukes)
+        if 'DEA' in anchType:
+            self.mass *= 1.75
+        
+            
+        return(results)
+            
+    def getMudlineForces(self, max_force=False,lines_only=False, seabed=True, xyz=False,project=None):   
+        '''Find forces on anchor at mudline using the platform.getWatchCircle method or MoorPy Point.getForces method. 
+        Optionally, get forces at anchor lug location with getTransferLoad function in capacity_loads.py.
+        Stores in loads dictionary
+        Parameters
+        ----------
+        max_force : boolean, optional
+            Find and save the maximum force on the anchor (True) or just get force at the current MoorPy system state (False)           
+        lines_only : boolean, optional
+            Calculate forces from just mooring lines (True) or not (False). Default is false
+        seabed : boolean, optional
+            Include effect of seabed pushing up the anchor (True) or not (False). Default is true
+        xyz : boolean, optional
+            Return forces in x,y,z DOFs (True) or only the enabled DOFs (False). Default is false
+            
+        '''
+        Platform = famodel.platform.platform.Platform
+        if max_force:
+            if project:
+                # get watch circle of platform(s)
+                project.arrayWatchCircle()
+            else:
+                # find platform associated with this anchor
+                for att in self.attachments.values():
+                    if isinstance(att['obj'],Mooring):
+                        for attM in att['obj'].attached_to:
+                            if isinstance(attM,Platform):
+                                locx,locy,maxVals = attM.getWatchCircle()
+        # call getForces method from moorpy point object
+        else:
+            loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz)
+            self.loads['Hm'] = np.sqrt(loads[0]**2+loads[1]**2) # mudline forces in [N]
+            self.loads['Vm'] = loads[2] # [N]
+            self.loads['thetam'] = np.degrees(np.arctan(self.loads['Vm']/self.loads['Hm'])) # [deg]
+            self.loads['mudline_load_type'] = 'current_state'
+        
+        # loads determined from moorpy are static
+        self.loads['method'] = 'static'
+        
+        return(self.loads)
+    
+    def getLugForces(self, mudloads=None, max_force=True, plot=False):
+        '''
+        Find forces on an anchor at the lug point based on the mudline forces and angles. Calls getTransferFunction script
+
+        Parameters
+        ----------
+        mudloads : dict, optional
+            Dictionary of max mudline forces. The default is None.
+
+        Returns
+        -------
+        loads: dict
+            Dictionary of loads at the lug point [N]
+
+        '''
+        from .anchors_famodel.capacity_load import getTransferLoad
+        
+        nolugload = False
+        
+        if not mudloads:  
+            if not self.loads:
+                # get max mudline forces first
+                self.getMudlineForces(max_force=max_force)
+            elif not 'mudline_load_type' in self.loads:
+                raise KeyError("Loads dictionary must specify 'mudline_load_type'='current_state' or 'mudline_load_type'='max', where 'max' indicates the loads are maximum loads.")
+            elif max_force and self.loads['mudline_load_type'] != 'max':
+                # need max forces, not current state
+                self.getMudlineForces(max_force=True)
+            mudloads = self.loads
+        else:
+            # check syntax
+            if not 'Hm' in mudloads or not 'Vm' in mudloads:
+                raise KeyError('Mudline load dictionary must have Hm and Vm for horizontal load and vertical load (in [N]) at the mudline')
+            if not 'thetam' in mudloads:
+                mudloads['thetam'] = np.degrees(np.arctan(mudloads['Vm']/mudloads['Hm']))
+                
+        def makeEqual_TaTm(mudloads):
+            mudloads['Ha'] = mudloads['Hm'] # [N]
+            mudloads['Va'] = mudloads['Vm'] # [N]
+            mudloads['thetaa'] = mudloads['thetam'] # [deg]
+        
+        if 'zlug' in self.dd['design']:
+            if self.dd['design']['zlug'] > 0:
+                # get line type
+                for att in self.attachments.values():
+                    if isinstance(att['obj'],Mooring):
+                        mtype = att['obj'].dd['sections'][0]['type']['material']
+                        if not 'chain' in mtype:
+                            print('No chain on seafloor, setting Ta=Tm')
+                            nolugload = True
+                            break
+                        else:
+                            md = att['obj'].dd['sections'][0]['type']['d_nom']
+                            mw = att['obj'].dd['sections'][0]['type']['w']
+                soil = next(iter(self.soilProps.keys()), None)
+                ground_conds = self.soilProps[soil]
+                # update soil conds as needed to be homogeneous
+                for key,prop in ground_conds.items():
+                    if isinstance(prop,list) or isinstance(prop,np.ndarray):
+                        if len(prop)>1:
+                            print('Warning: Only homogeneous soils are supported at this time. Only the first item in a property list will be used.')
+                            break 
+                        else:
+                            ground_conds[key] = prop[0]
+                            
+                Tm =  np.sqrt(mudloads['Hm']**2+mudloads['Vm']**2) # [N]
+                if 'clay' in soil or 'mud' in soil and not nolugload:
+                    # Tm, thetam, zlug, line_type, d, soil_type, Su0=None, k=None, w=None
+                    try:
+                        loadresults = getTransferLoad(Tm/1000,mudloads['thetam'],
+                                                      self.dd['design']['zlug'],mtype,md,
+                                                      'clay',Su0=ground_conds['Su0'],
+                                                      k=ground_conds['k'],w=mw/1000,
+                                                      plot=plot) # output Ha and Va    (convert weight to kN/m)   
+                    except Exception as e:
+                        print(e)
+                        print('Unable to get loads at anchor lug location. Setting Ta = Tm')
+                        nolugload = True
+                elif 'sand' in soil and not nolugload:
+                        soil = 'sand'
+                        try:
+                            loadresults = getTransferLoad(Tm/1000, self.loads['thetam'], 
+                                                        self.dd['design']['zlug'],
+                                                        mtype, md, soil,
+                                                        gamma=ground_conds['gamma'], 
+                                                        phi=ground_conds['phi'],
+                                                        delta=ground_conds['delta'], 
+                                                        w=mw/1000,plot=plot) # output Ha and Va  (convert weight to kN/m)
+                        except Exception as e:
+                            print(e)
+                            print('Unable to get loads at anchor lug location. Setting Ta = Tm')
+                            nolugload = True
+                elif 'rock' in soil and not nolugload:
+                    raise ValueError('zlug should be <= 0 for rock.')
+                    
+                # if loadresults['V']<0:
+                #     # results are invalid
+                #     print('Warning: invalid results for the combination of anchor ',self.dd['type'],' soil ',soil,' and loads ',mudloads,'. Setting Ha=Hm, Va=Vm, thetaa=thetam')
+                #     makeEqual_TaTm(mudloads)
+                if nolugload:
+                    makeEqual_TaTm(mudloads)
+                else:
+                    mudloads['Ha'] = loadresults['H']*1000 # [N]
+                    mudloads['Va'] = loadresults['V']*1000 # [N]
+                    mudloads['thetaa'] = loadresults['angle'] # [deg]
+            else:
+                # Ha = Hm because zlug is at mudline or above
+                makeEqual_TaTm(mudloads)
+        else:
+            print('No zlug given, assuming loads at mudline = loads at anchor lug')
+            makeEqual_TaTm(mudloads)
+            
+        if not 'method' in mudloads:
+            # assume mudloads are static unless told otherwise
+            # loads determined from moorpy are static
+            mudloads['method'] = 'static'
+        else:
+            mudloads['method'] = mudloads['method']
+        
+        return mudloads
+    
+    def getFS(self, loads=None, acceptance_crit=None):
+        '''
+        Compute safety factor for loads on the anchor
+        
+        Parameters
+        ----------
+        loads : dict, optional
+            Dictionary of loads on the anchor. 
+        acceptance_crit : dict, optional
+            Dictionary of acceptable factors of safety for each load type.
+            Key is the load type, and value is the minimum acceptable safety factor.
+            Default is None (in which case no comparison between FS and acceptance criteria is calculated)
+
+        Returns
+        -------
+        FS : dict
+            Dictionary of safety factors (often horizontal and vertical load SFs, but could be displacement SFs (drilled and grouted/driven piles))
+        acceptance : dict
+            Dictionary of bools that state whether the FS>=acceptance_crit for each load
+        acceptance_margin : dict
+            Dictionary of difference between FS and acceptance criteria for each load type
+        
+
+        '''
+        if not loads:
+            if not 'Ha' in self.loads:
+                self.getLugForces()
+            loads = self.loads
+        if not self.anchorCapacity:
+            self.getAnchorCapacity()
+         
+        # look for load dictionary key in capacity dictionary
+        FS = {}
+        acceptance = {}
+        acceptance_margin = {}
+        for Lkey,Lval in loads.items():
+            for Ckey,Cval in self.anchorCapacity.items():
+                if Lkey in Ckey:
+                    if Lval == 0:
+                        FS[Lkey] = float('inf')
+                    else:
+                        FS[Lkey] = Cval/Lval
+                    if acceptance_crit and Lkey in acceptance_crit:
+                        if Lval == 0 or acceptance_crit[Lkey] == 0:
+                            acceptance[Lkey] = True
+                        else:
+                            acceptance[Lkey] = acceptance_crit[Lkey]<=FS[Lkey]
+                            acceptance_margin[Lkey] = FS[Lkey] - acceptance_crit[Lkey]
+                    
+        if acceptance_crit:
+            return(FS,acceptance,acceptance_margin)
+        else:
+            return(FS)
+            
+    def makeBuffer(self, buff_rad=50):
+        point = sh.Point(self.r[:2])
+        buff = point.buffer(buff_rad)
+        return buff
+               
+    def getCost(self,costDict='default'):
+        '''find costs of anchor and store in design dictionary
+        
+        Parameters
+        ----------
+        costDict : dictionary or yaml, optional
+            Dictionary of various costs for anchors. Sub costs that can be included are: 
+            material : material costs
+            
+        '''
+        if isinstance(costDict,str) and costDict != 'default':
+            import yaml
+            costDict = yaml.load(costDict, Loader=yaml.FullLoader)
+        anchType = self.dd['type']
+        if costDict == 'default':
+            matCostDict = {'DEA':5.705,'suction_pile':4.435,'gravity':1.905} # mean values from Task 49 Design Basis ranges
+            instCostDict = {}
+            decomCostDict = {}
+        else:
+            matCostDict = costDict['material']
+            if 'install' in costDict:
+                instCostDict = costDict['install']
+            if 'decom' in costDict:
+                decomCostDict = costDict['decom']
+        keyFail = True
+        # check if mass info is available
+        if not self.mass:
+            if 'soil_properties' in self.dd:               
+                # need mass - call capacity functions
+                self.getAnchorCapacity(plot=False)
+            else:
+                print('Soil properties needed to calculate anchor mass for cost. Setting cost to 0.')
+                self.mass = 0
+            
+        # sort by type of anchor
+        for Ckey,Cval in matCostDict.items():
+            if anchType in Ckey:
+                self.cost['materials'] = matCostDict[Ckey]*self.mass
+                # self.cost['install'] = instCostDict[Ckey]
+                # self.cost['decom'] = decomCostDict[Ckey]
+                keyFail = False
+        # raise error if anchType not found in cost dictionary
+        if keyFail:
+            raise KeyError(f'anchor type {anchType} not found in material cost dictionary')
+            
+        return(sum(self.cost.values()))
+           
+        capacity_dispatch = {
+            'suction': getCapacitySuction,
+            'sepla': getCapacityPlate,
+            'dea': getCapacityPlate,
+            'depla': getCapacityPlate,
+            'vla': getCapacityPlate,
+            'plate': getCapacityPlate,
+            'torpedo': getCapacityTorpedo,
+            'helical': getCapacityHelical,
+            'driven': getCapacityDriven,
+            'dandg': getCapacityDandG
+        }
+    
+        print(f'[Debug] mass_update = {mass_update}')
+        anchType_clean = self.anchType.lower().replace(' ', '')
+        capacity_func = capacity_dispatch.get(anchType_clean)
+        if capacity_func is None:
+            raise ValueError(f"Unknown anchor type '{self.anchType}' for anchor capacity calculation.")
+    
+        if self.soil_profile is None or self.soil_type is None:
+            raise ValueError("Soil profile or soil type not set for this anchor.")
+    
+        soil_profile = self.soil_profile
+        soil_type = self.soil_type
+        z0 = soil_profile[0]['top']
+    
+        # Load transfer if padeye is embedded below mudline
+        
+        if line_type is None or d is None or w is None:
+            try:
+                line_type, d, w = self.getLineProperties()
+            except ValueError:
+                print('[Warning] No mooring attachment found. Trying anchor-level line properties...')
+                line_type = getattr(self, 'line_type', None)
+                d = getattr(self, 'd', None)
+                w = getattr(self, 'w', None)
+
+                if any(v is None for v in [line_type, d, w]):
+                    print('[Fallback] Using default chain properties.')
+                    line_type = 'chain'
+                    d = 0.16
+                    w = 5500.0
+    
+        if zlug > z0:
+            layers, Ha, Va = self.getLugForces(
+                Hm, Vm,
+                zlug=zlug,
+                line_type=line_type,
+                d=d,
+                w=w,
+                plot=False
+            )
+
+            Ta = np.sqrt(Ha**2 + Va**2)
+            thetaa = np.degrees(np.arctan2(Va, Ha))
+            
+            print(f'Input Hm = {Hm}, Vm = {Vm}, zlug = {zlug}')
+            print(f'Output Ha = {Ha}, Va = {Va}, zlug = {zlug}')
+            print(f'Output Ta = {Ta}, thetaa = {(thetaa)}')
+            print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}")
+
+        else:
+            Ha = Hm
+            Va = Vm
+            Ta = np.sqrt(Ha**2 + Va**2)
+            thetaa = np.degrees(np.arctan2(Va, Ha))
+            print(f'[Direct assign] Ha = {Ha}, Va = {Va}, Ta = {Ta}, thetaa = {thetaa}')
+            print(f"[Branch Check] Entered {'zlug>z0' if zlug>z0 else 'else'} for anchor {self.anchType}")
+
+
+    
+        # --- Call the appropriate capacity function ---
+        if anchType_clean in ['sepla', 'dea', 'depla', 'vla', 'plate']:
+            self.capacity_format = 'plate'
+            B = self.dd['design']['B']
+            L = self.dd['design']['L']
+            print(f"[Final Check] Ha = {Ha}, Va = {Va}, anchor = {self.anchType}")
+            beta = 90.0 - np.degrees(np.arctan2(Va, Ha))
+            self.dd['design']['beta'] = beta 
+            layers, results = capacity_func(
+                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
+                location_name=self.profile_name,
+                B=B, L=L, zlug=zlug,
+                beta=beta,
+                Ha=Ha, Va=Va,
+                plot=plot
+            )
+            
+        elif anchType_clean == 'suction':
+            self.capacity_format = 'envelope'
+            D = self.dd['design']['D']
+            L = self.dd['design']['L']
+            zlug = self.dd['design']['zlug']
+            layers, results = capacity_func(
+                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
+                location_name=self.profile_name,
+                D=D, L=L, zlug=zlug,
+                Ha=Ha, Va=Va,
+                thetalug=5, psilug=7.5,
+                plot=plot
+            )
+            
+        elif anchType_clean == 'torpedo':
+            self.capacity_format = 'envelope'
+            D1 = self.dd['design']['D1']
+            D2 = self.dd['design']['D2']
+            L1 = self.dd['design']['L1']
+            L2 = self.dd['design']['L2']
+            ballast = self.dd['design'].get('ballast', 0.0)
+            layers, results = capacity_func(
+                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
+                location_name=self.profile_name,
+                D1=D1, D2=D2, L1=L1, L2=L2,
+                zlug=zlug,
+                ballast=ballast,
+                Ha=Ha, Va=Va,
+                plot=plot
+            )
+
+        elif anchType_clean == 'helical':
+            self.capacity_format = 'component'
+            D = self.dd['design']['D']     
+            L = self.dd['design']['L']     
+            d = self.dd['design']['d']     
+            zlug = self.dd['design']['zlug']
+            layers, results = capacity_func(
+                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
+                location_name=self.profile_name,
+                D=D, L=L, d=d,
+                zlug=zlug,
+                Ha=Ha, Va=Va,
+                plot=plot
+            )
+
+        elif anchType_clean in ['driven', 'dandg']:
+            self.capacity_format = 'component'
+            L = self.dd['design']['L']
+            D = self.dd['design']['D']
+            zlug = self.dd['design']['zlug']
+            layers, y, z, results = capacity_func(
+                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
+                location_name=self.profile_name,
+                L=L, D=D, zlug=zlug,
+                Ha=Ha, Va=Va,
+                plot=plot
+            )
+    
+        else:
+            raise ValueError(f"Anchor type '{self.anchType}' not supported.")
+    
+        # --- Store results ---
+        self.anchorCapacity = {
+            'Hmax': results.get('Horizontal max.', np.nan),
+            'Vmax': results.get('Vertical max.', np.nan),
+            'Ha': Ha,
+            'Va': Va,
+            'zlug': zlug,
+            'z0': z0}
+        
+        # Correct UC format
+        if anchType_clean in ['suction', 'torpedo', 'plate', 'sepla', 'dea', 'depla', 'vla']:
+            self.anchorCapacity['UC'] = results.get('Unity check', np.nan)
+        
+        elif anchType_clean in ['helical', 'driven', 'dandg']:
+            self.anchorCapacity['Unity check (horizontal)'] = results.get('Unity check (horizontal)', np.nan)
+            self.anchorCapacity['Unity check (vertical)'] = results.get('Unity check (vertical)', np.nan)
+        
+        # Copy over lateral and rotational displacements
+        if 'Lateral displacement' in results:
+            self.anchorCapacity['Lateral displacement'] = results['Lateral displacement']
+        if 'Rotational displacement' in results:
+            self.anchorCapacity['Rotational displacement'] = results['Rotational displacement']
+        
+        # Weight calculated via dimensions
+        if mass_update == False:
+            if 'Weight pile' in results:
+                self.anchorCapacity['Weight pile'] = results['Weight pile']
+            if 'Weight plate' in results:
+                self.anchorCapacity['Weight plate'] = results['Weight plate']
+        else:
+            if 'Weight pile' in results:
+                if self.mass is None:
+                    self.mass = results['Weight pile']/self.g
+                self.anchorCapacity['Weight pile'] = self.mass*self.g
+            if 'Weight plate' in results:
+                if self.mass is None:
+                    self.mass = results['Weight plate']/self.g
+                self.anchorCapacity['Weight plate'] = self.mass*self.g
+                
+        # print(f"[DEBUG] Stored Lateral displacement in anchorCapacity: {self.anchorCapacity['Lateral displacement']:.6f}")
+         
+    def getSizeAnchor(self, geom, geomKeys, geomBounds=None, loads=None,
+                      lambdap_con=[4, 8], zlug_fix=True, safety_factor={'SF_combined': 1.0}, plot=False):
+        '''
+        Generalized optimization method for all anchor types, using dictionary-based safety factors.
+        '''
+    
+        anchType_clean = self.dd['type'].lower().replace('', '')
+    
+        if loads is None:
+            loads = self.loads
+    
+        Hm = loads['Hm']
+        Vm = loads['Vm']
+    
+        line_type = getattr(self, 'line_type', 'chain')
+        d = getattr(self, 'd', 0.16)
+        w = getattr(self, 'w', 5000.0)
+    
+        def update_zlug():
+            if anchType_clean == 'suction' and not zlug_fix and 'zlug' not in geomKeys:
+                self.dd['design']['zlug'] = (2/3)*self.dd['design']['L']
+            elif anchType_clean in ['driven', 'helical'] and not zlug_fix:    
+                ratio = self.dd['design'].get('zlug_ratio', self.dd['design']['zlug']/self.dd['design']['L'])
+                self.dd['design']['zlug_ratio'] = ratio
+                self.dd['design']['zlug'] = ratio*self.dd['design']['L']
+    
+        def get_lambda():
+            if anchType_clean == 'torpedo':
+                L = self.dd['design']['L1'] + self.dd['design']['L2']
+                A_wing = (self.dd['design']['D1'] - self.dd['design']['D2']) * self.dd['design']['L1']
+                A_shaft = self.dd['design']['D2'] * L
+                D = (A_wing + A_shaft) / L
+            elif anchType_clean in ['driven', 'dandg', 'helical', 'suction']:
+                L = self.dd['design']['L']
+                D = self.dd['design']['D']
+            elif anchType_clean in ['plate', 'sepla', 'dea', 'depla', 'vla']:
+                L = self.dd['design']['L']
+                D = self.dd['design']['B']
+            else:
+                raise ValueError(f'lambda not defined for anchor type: {anchType_clean}')
+            return L/D
+    
+        def constraint_lambda_min(vars):
+            return get_lambda() - lambdap_con[0]
+    
+        def constraint_lambda_max(vars):
+            return lambdap_con[1] - get_lambda()
+    
+        if anchType_clean in ['suction', 'torpedo', 'plate', 'sepla', 'dea', 'depla', 'vla']:
+            target_UC = 1.0/safety_factor.get('SF_combined', 1.0)
+    
+            def objective_uc(vars):
+                for i, key in enumerate(geomKeys):
+                    self.dd['design'][key] = vars[i]
+                update_zlug()
+                self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                       line_type=line_type, d=d, w=w, mass_update=True, plot=False)
+                UC = self.anchorCapacity.get('UC', 2.0)
+                return (UC - target_UC)**2
+    
+            def constraint_uc_envelope(vars):
+                return self.anchorCapacity.get('UC', 0.0) - target_UC
+    
+            constraints_uc = [
+                {'type': 'ineq', 'fun': constraint_lambda_min},
+                {'type': 'ineq', 'fun': constraint_lambda_max},
+                {'type': 'ineq', 'fun': constraint_uc_envelope},
+            ]
+    
+            result_uc = minimize(
+                objective_uc,
+                geom,
+                method='COBYLA',
+                bounds=geomBounds if geomBounds else None,
+                constraints=constraints_uc,
+                options={'rhobeg': 0.1, 'catol': 0.01, 'maxiter': 500}
+            )
+    
+            endGeom = dict(zip(geomKeys, result_uc.x))
+            self.dd['design'].update(endGeom)
+            update_zlug()
+            self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                   line_type=line_type, d=d, w=w, mass_update=True, plot=plot)
+    
+            print('\nFinal Optimized Anchor (UC-based):')
+            print('Design:', self.dd['design'])
+            print('Capacity Results:', self.anchorCapacity)
+            return
+    
+
+        def termination_condition():
+            UC_h = self.anchorCapacity['Ha'] / self.anchorCapacity['Hmax']
+            UC_v = self.anchorCapacity['Va'] / self.anchorCapacity['Vmax']
+            disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0))
+            disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0))
+            limit_lat = 0.05 * self.dd['design']['D']
+            limit_rot = 5.0
+    
+            if UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_lat and disp_rot <= limit_rot:
+                print('[Termination Condition Met] All four limits satisfied.')
+                return 'terminate'
+    
+            return 'continue'
+    
+        def is_valid(value):
+            return np.isfinite(value) and not np.isnan(value) and abs(value) < 1e6
+        
+        if anchType_clean in ['helical', 'driven', 'dandg']:
+            L0, D0 = geom if len(geom) == 2 else [5.0, 1.0]
+            self.dd['design']['L'] = L0
+            self.dd['design']['D'] = D0
+            #self.dd['design']['t'] = max(0.05, 0.1 * D0)
+            update_zlug()
+            self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                   line_type=line_type, d=d, w=w, mass_update=True, plot=False)
+        
+            UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax']
+            UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax']
+            disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0))
+            disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0))
+            limit_disp = 0.05*D0
+            limit_rot = 5.0
+            direction = 'shrink' if (UC_h <= 1.0 and UC_v <= 1.0 and disp_lat <= limit_disp and disp_rot <= limit_rot) else 'grow'
+    
+            max_iter = 200
+            iter_count = 0
+    
+            if direction == 'shrink':
+                for D in np.arange(D0, 0.49, -0.05):
+                    self.dd['design']['D'] = D
+                    #self.dd['design']['t'] = max(0.02, 0.1*D)
+                    for L in np.arange(L0, 1.95, -0.25):
+                        self.dd['design']['L'] = L
+                        update_zlug()
+                        self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                               line_type=line_type, d=d, w=w, mass_update=True, plot=False)
+                        UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax']
+                        UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax']
+                        disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0))
+                        disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0))
+                        print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg')
+                        iter_count += 1
+                        if not all(is_valid(v) for v in [UC_h, UC_v, disp_lat, disp_rot]):
+                            continue
+                        if termination_condition():
+                            print(f'\nTermination criteria met.')
+                            print('Design:', self.dd['design'])
+                            print('Capacity Results:', self.anchorCapacity)
+                            return
+            else:
+                for D in np.arange(D0, 3.05, 0.05):
+                    self.dd['design']['D'] = D
+                    #self.dd['design']['t'] = max(0.02, 0.1*D)
+                    for L in np.arange(L0, 50.25, 0.25):
+                        self.dd['design']['L'] = L
+                        update_zlug()
+                        self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                               line_type=line_type, d=d, w=w, mass_update=True, plot=False)
+                        UC_h = self.anchorCapacity['Ha']/self.anchorCapacity['Hmax']
+                        UC_v = self.anchorCapacity['Va']/self.anchorCapacity['Vmax']
+                        disp_lat = abs(self.anchorCapacity.get('Lateral displacement', 0.0))
+                        disp_rot = abs(self.anchorCapacity.get('Rotational displacement', 0.0))
+                        print(f'[Iter {iter_count}] L={L:.2f}, D={D:.2f}, UC_h={UC_h:.3f}, UC_v={UC_v:.3f}, lat={disp_lat:.3f} m, rot={disp_rot:.3f} deg')
+                        iter_count += 1
+                        status = termination_condition()
+                        if status == 'terminate':
+                            print(f'Termination criteria met.')
+                            print('Design:', self.dd['design'])
+                            print('Capacity Results:', self.anchorCapacity)
+                            return
+                        elif status == 'continue':
+                            continue
+                    status = termination_condition()
+                    if status == 'terminate':
+                        print(f'\nTermination criteria met.')
+                        print('Design:', self.dd['design'])
+                        print('Capacity Results:', self.anchorCapacity)
+                        return
+    
+            print('[Warning] While-loop search reached bounds without meeting criteria.')
+    
+        else:
+            raise ValueError(f"Anchor type '{anchType_clean}' not supported for safety factor input.")
+                   
+    def getSizeAnchor2(self, geom, geomBounds=None, loads=None,
+                       lambdap_con=[3, 6], zlug_fix=True, safety_factor={'SF_combined': 1.0}, plot=False):
+        '''
+        Grid-based optimization method for envelope anchors (suction, torpedo, plate).
+        Evaluates UC over a grid of L and D, and selects the point closest to target UC.
+        '''
+        import matplotlib.pyplot as plt
+        from matplotlib import cm
+        import matplotlib.colors as mcolor
+        import numpy as np
+
+        anchType_clean = self.dd['type'].lower().replace('', '')
+
+        if loads is None:
+            loads = self.loads
+
+        Hm = loads['Hm']
+        Vm = loads['Vm']
+
+        line_type = getattr(self, 'line_type', 'chain')
+        d = getattr(self, 'd', 0.16)
+        w = getattr(self, 'w', 5000.0)
+
+        if anchType_clean not in ['suction', 'torpedo', 'plate']:
+            raise ValueError(f"Grid-based getSizeAnchor only supports envelope anchors, not '{anchType_clean}'")
+
+        UC_target = 1.0/safety_factor.get('SF_combined', 1.0)
+
+        # Unpack bounds and generate grid
+        L_vals = np.linspace(geomBounds[0][0], geomBounds[0][1], 10)
+        D_vals = np.linspace(geomBounds[1][0], geomBounds[1][1], 10)
+
+        L_grid, D_grid = np.meshgrid(L_vals, D_vals)
+        UC_grid = np.full_like(L_grid, np.nan, dtype=float)
+        mask = np.full_like(L_grid, False, dtype=bool)
+
+        best_UC, best_L, best_D = None, None, None
+        results = []
+
+        for i in range(D_grid.shape[0]):  # loop over D
+            for j in range(D_grid.shape[1]):  # loop over L
+                D = D_grid[i, j]
+                L = L_grid[i, j]
+                lambdap = L/D
+
+                if not (lambdap_con[0] <= lambdap <= lambdap_con[1]):
+                    continue
+
+                mask[i, j] = True
+                self.dd['design']['L'] = L
+                self.dd['design']['D'] = D
+
+                if anchType_clean == 'suction' and not zlug_fix:
+                    self.dd['design']['zlug'] = (2/3)*L
+
+                try:
+                    self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                                           line_type=line_type, d=d, w=w, 
+                                           mass_update=True, plot=False)
+                    UC = self.anchorCapacity.get('UC', np.nan)
+                    results.append({
+                        'L': L,
+                        'D': D,
+                        'UC': UC})
+
+                    if UC > 1e-2 and UC < 10.0:
+                        UC_grid[i, j] = UC
+                        # Find UC closest to target
+                        if best_UC is None or abs(UC - UC_target) < abs(best_UC - UC_target):
+                            best_UC = UC
+                            best_L = L
+                            best_D = D
+
+                except:
+                    continue
+
+        # Update best result
+        # if best_L is not None and best_D is not None:
+        self.dd['design']['L'] = best_L
+        self.dd['design']['D'] = best_D
+        if anchType_clean == 'suction' and not zlug_fix:
+            self.dd['design']['zlug'] = (2/3)*best_L
+
+        self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'],
+                               line_type=line_type, d=d, w=w, 
+                               mass_update=True, plot=plot)
+
+        print('\nFinal Optimized Anchor (Grid-based):')
+        print('Design:', self.dd['design'])
+        print('Capacity Results:', self.anchorCapacity)
+
+        # else:
+        #     print('[Warning] No valid combination found in the grid.')
+
+        # Optional plot
+
+        if plot:
+            fig, ax = plt.subplots(figsize=(6, 8))
+            vmin, vmax = 0.01, 10
+            levels = np.logspace(np.log10(vmin), np.log10(vmax), 21)
+            cp = ax.contourf(D_grid, L_grid, UC_grid, levels=levels, cmap='coolwarm', norm=mcolor.LogNorm(vmin=vmin, vmax=vmax))
+            fig.colorbar(cp, ax=ax, label='Unity check (UC)')
+            ax.contour(D_grid, L_grid, UC_grid, levels=levels, colors='k', linewidths=0.3, alpha=0.3)
+            ax.contour(D_grid, L_grid, UC_grid, levels=[1.0], colors='red', linewidths=2, linestyles='--')
+            ax.set_xlabel('Diameter (m)')
+            ax.set_ylabel('Length (m)')
+            ax.set_title('Unity Check (UC')
+            ax.plot(best_D, best_L, 'ro', label='Best match')
+            ax.annotate('Best match', (best_D, best_L), textcoords="offset points", xytext=(10,10), ha='center', color='red')
+            ax.legend()
+            plt.grid(True)
+            plt.tight_layout()
+            plt.show()
+            
+        #UC_target = 1.0
+        closest = min(results, key=lambda x: abs(x['UC'] - UC_target))
+        print("Closest to UC_target:")
+        print(closest)
+            
+        return results
+    
+    def getSizeAnchor_BO(self, 
+                         geom=[10.0, 2.0],
+                         geomKeys=['L', 'D'],
+                         geomBounds=[(5.0, 15.0), (1.0, 4.0)],
+                         loads=None,
+                         lambdap_con=[3, 6],
+                         zlug_fix=False,
+                         safety_factor={'SF_combined': 1.0},
+                         n_calls=25,
+                         plot=False,
+                         verbose=True):
+        '''
+        Bayesian optimization to find (D, L) for UC closest to UC_target.
+        Uses scikit-optimize for surrogate model and efficient sampling.
+        '''
+        from skopt import gp_minimize
+        from skopt.space import Real
+        from skopt.utils import use_named_args
+        import numpy as np
+
+        if loads is None:
+            loads = self.loads
+
+        Hm = loads['Hm']
+        Vm = loads['Vm']
+
+        line_type = getattr(self, 'line_type', 'chain')
+        d = getattr(self, 'd', 0.16)
+        w = getattr(self, 'w', 5000.0)
+
+        UC_target = 1.0 / safety_factor.get('SF_combined', 1.0)
+
+        # Define the search space
+        space  = [
+            Real(geomBounds[1][0], geomBounds[1][1], name='D'),
+            Real(geomBounds[0][0], geomBounds[0][1], name='L')
+        ]
+
+        @use_named_args(space)
+        def objective(**params):
+            D = params['D']
+            L = params['L']
+
+            # Apply lambda constraint
+            lambdap = L/D
+            if not (lambdap_con[0] <= lambdap <= lambdap_con[1]):
+                return 100.0
+
+            self.dd['design']['D'] = D
+            self.dd['design']['L'] = L
+            if not zlug_fix:
+                self.dd['design']['zlug'] = (2/3)*L
+
+            try:
+                self.getCapacityAnchor(
+                    Hm=Hm,
+                    Vm=Vm,
+                    zlug=self.dd['design']['zlug'],
+                    line_type=line_type,
+                    d=d,
+                    w=w,
+                    mass_update=True,
+                    plot=False
+                )
+                UC = self.anchorCapacity.get('UC', np.nan)
+            except:
+                UC = np.nan
+
+            if verbose:
+                print(f"Evaluated D={D:.3f}, L={L:.3f} -> UC={UC:.3f}")
+
+            if not np.isfinite(UC):
+                return 100.0
+
+            if UC < UC_target:
+                return (UC_target - UC)**2 * 0.5  # less penalty for overdesign
+            else:
+                return (UC - UC_target)**2 * 10   # higher penalty for failure
+
+        # Run Bayesian optimization
+        res = gp_minimize(
+            objective,
+            space,
+            x0=[geom[1], geom[0]],
+            n_calls=n_calls,
+            random_state=42,
+            verbose=verbose
+        )
+
+        # Best result
+        best_D, best_L = res.x
+        self.dd['design']['D'] = best_D
+        self.dd['design']['L'] = best_L
+        if not zlug_fix:
+            self.dd['design']['zlug'] = (2/3)*best_L
+
+        self.getCapacityAnchor(
+            Hm=Hm,
+            Vm=Vm,
+            zlug=self.dd['design']['zlug'],
+            line_type=line_type,
+            d=d,
+            w=w,
+            mass_update=True,
+            plot=plot
+        )
+        UC = self.anchorCapacity.get('UC', np.nan)
+
+        print('\nBayesian Optimized Anchor:')
+        print('Design:', self.dd['design'])
+        print('Capacity Results:', self.anchorCapacity)
+        print(f'Best UC: {UC:.4f} (target: {UC_target})')
+
+        results = {'D': best_D, 'L': best_L, 'UC': UC, 'result': res}
+
+        return results
+    # PATCH for GRADIENT method: wrap getCapacityAnchor in safe evaluator
+    def safe_get_uc(self, Hm, Vm, zlug, line_type, d, w, verbose=False):
+        try:
+            self.getCapacityAnchor(Hm, Vm, zlug, line_type, d, w, True, False)
+            return self.anchorCapacity.get('UC', np.nan)
+        except Exception as e:
+            if verbose:
+                print(f"[Safe Error] {str(e)}")
+            return np.nan
+
+    def getSizeAnchor_gradient(self, 
+                               geom=[10.0, 2.0],
+                               geomKeys=['L', 'D'],
+                               geomBounds=[(5.0, 15.0), (1.0, 4.0)],
+                               loads=None,
+                               lambdap_con=[3, 6],
+                               zlug_fix=False,
+                               safety_factor={'SF_combined': 1.0},
+                               step_size=0.2,
+                               tol=0.05,
+                               max_iter=30,
+                               verbose=True):
+        '''
+        Gradient-based optimization with early stopping to match UC_target.
+        '''
+        import numpy as np
+    
+        if loads is None:
+            loads = self.loads
+    
+        Hm = loads['Hm']
+        Vm = loads['Vm']
+    
+        line_type = getattr(self, 'line_type', 'chain')
+        d = getattr(self, 'd', 0.16)
+        w = getattr(self, 'w', 5000.0)
+    
+        UC_target = 1.0 / safety_factor.get('SF_combined', 1.0)
+    
+        L, D = geom
+    
+        for iter in range(max_iter):
+            lambdap = L / D
+            if not (lambdap_con[0] <= lambdap <= lambdap_con[1]):
+                if verbose:
+                    print(f"[Iter {iter}] λ = {lambdap:.2f} out of bounds. Terminating.")
+                break
+    
+            self.dd['design']['L'] = L
+            self.dd['design']['D'] = D
+            if not zlug_fix:
+                self.dd['design']['zlug'] = (2/3)*L
+    
+            UC0 = self.safe_get_uc(Hm, Vm, self.dd['design']['zlug'], line_type, d, w, verbose=verbose)
+    
+            if not np.isfinite(UC0):
+                break
+    
+            if verbose:
+                print(f"[Iter {iter}] L={L:.2f}, D={D:.2f}, UC={UC0:.3f}")
+    
+            if abs(UC0 - UC_target) < tol:
+                print("Early stopping: UC within tolerance.")
+                break
+    
+            # Gradient estimate
+            delta = 0.1
+            UC_L = self.safe_get_uc(Hm, Vm, (2/3)*(L + delta), line_type, d, w, verbose=verbose)
+            UC_D = self.safe_get_uc(Hm, Vm, (2/3)*L, line_type, d, w, verbose=verbose)
+    
+            grad_L = (UC_L - UC0)/delta if np.isfinite(UC_L) else 0.0
+            grad_D = (UC_D - UC0)/delta if np.isfinite(UC_D) else 0.0
+    
+            # Update
+            L -= step_size * grad_L
+            D -= step_size * grad_D
+            L = np.clip(L, geomBounds[0][0], geomBounds[0][1])
+            D = np.clip(D, geomBounds[1][0], geomBounds[1][1])
+    
+            if not (lambdap_con[0] <= L/D <= lambdap_con[1]):
+                if verbose:
+                    print("Terminated: lambda constraint violated after update.")
+                break
+    
+        self.dd['design']['L'] = L
+        self.dd['design']['D'] = D
+        self.dd['design']['zlug'] = (2/3)*L
+        self.getCapacityAnchor(Hm, Vm, self.dd['design']['zlug'], line_type, d, w, True, True)
+    
+        print('\nGradient Optimized Anchor:')
+        print('Design:', self.dd['design'])
+        print('Capacity Results:', self.anchorCapacity)
+    
+        return {'D': D, 'L': L, 'UC': self.anchorCapacity.get('UC', np.nan)}
+   
+    def getSafetyFactor(self):
+        '''
+        Calculate the safety factor based on the unity checks stored in capacity results.
+
+        Returns
+        -------
+        dict
+            Dictionary containing safety factors.
+        '''
+
+        # - - - - Objective and Constraint Functions 
+        
+        # Define the objective function: Minimize weight of anchor (cost is dependent on weight)
+        def objective(vars, args):
+
+            geomKeys = args['geomKeys']
+            input_loads = args['input_loads']
+            fix_zlug = args['fix_zlug']
+
+            newGeom = dict(zip(geomKeys,vars))
+            self.dd['design'].update(newGeom)
+            if 'suction' in self.dd['type'] and not fix_zlug:
+                self.dd['design']['zlug'] = (2/3)*newGeom['L']
+            
+            if 'Hm' in input_loads or 'Vm' in input_loads:
+                anchor_loads = self.getLugForces(mudloads=input_loads)
+                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
+            # get results
+            results = self.getAnchorCapacity(loads=input_loads, plot=False)
+                
+            return(results['Weight'])
+        
+        # constraint for suction bucket sizing only. May add more constraints for other anchors in the future...
+        def conFun_LD(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
+            newGeom = dict(zip(geomKeys, vars))
+            self.dd['design'].update(newGeom)
+
+            if 'Hm' in input_loads or 'Vm' in input_loads:
+                anchor_loads = self.getLugForces(mudloads=input_loads)
+                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
+
+            results = self.getAnchorCapacity(loads=input_loads, plot=False)
+            
+            convalA = newGeom['L']/newGeom['D'] - LD_con[0]
+            convalB = LD_con[1] - newGeom['L']/newGeom['D']
+            conval = min([convalA,convalB])
+            # if newGeom['L']/newGeom['D'] >= LD_con[0] and newGeom['L']/newGeom['D'] <= LD_con[1]:
+            #     conval = 1 
+            # else:
+            #     conval = -1 
+            
+            return(conval)
+        # constraint to ensure unity check > 1 for suction buckets
+        def conFun_Suction(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
+            if 'Hm' in input_loads or 'Vm' in input_loads:
+                anchor_loads = self.getLugForces(mudloads=input_loads)
+                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
+            results = self.getAnchorCapacity(loads=input_loads, plot=False)
+            #conval = results['UC'] - 1
+            conval = 1 - results['UC']
+            # convalB = 1 - results['UC'] 
+            return(conval)
+        
+        def conFun_DandG(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
+
+            newGeom = dict(zip(geomKeys, vars))
+            self.dd['design'].update(newGeom)
+            if 'Hm' in input_loads or 'Vm' in input_loads:
+                anchor_loads = self.getLugForces(mudloads=input_loads)
+                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
+            results = self.getAnchorCapacity(loads=input_loads, plot=False)
+
+            return np.array([0.05*newGeom['D'] - results['Lateral displacement'] , 0.25 - results['Rotational displacement']])
+            
+        def conFunH(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
+            # if 'suction' in self.dd['type']:
+            #     results = self.getAnchorCapacity(plot=False)
+            #     conval = results['UC'] - 1
+            #     # if results['UC'] < 1:
+            #     #     conval = -1*(results['UC'])
+            # else:
+            if 'Hm' in input_loads or 'Vm' in input_loads:
+                anchor_loads = self.getLugForces(mudloads=input_loads)
+                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
+                minfs = dict(Ha=minfs['Hm'], Va=minfs['Vm'])
+            FS, _, _ = self.getFS(loads=input_loads, acceptance_crit=minfs)
+            conval = FS['Ha'] - 1
+                # for key,val in FS.items():
+                    
+                #     if val/minfs[key]<1:
+                #         if -1*(1-val/minfs[key]) < conval:
+                #             conval = -1*(1-val/minfs[key])
+            return(conval)
+        
+        def conFunV(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
+            if 'Hm' in input_loads or 'Vm' in input_loads:
+                anchor_loads = self.getLugForces(mudloads=input_loads)
+                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
+                minfs = dict(Ha=minfs['Hm'], Va=minfs['Vm'])
+            FS, _, _ = self.getFS(loads=input_loads, acceptance_crit=minfs)
+            # special case for DEAs
+            if minfs['Va'] == 0:
+                 conval = 1
+            else:
+                conval = FS['Va'] - 1
+                
+            # print('FS_V',FS['Va'])
+            return(conval)
+        
+        def conBounds(vars, geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs):
+
+            newGeom = dict(zip(geomKeys, vars))
+            self.dd['design'].update(newGeom)
+
+            if 'Hm' in input_loads or 'Vm' in input_loads:
+                anchor_loads = self.getLugForces(mudloads=input_loads)
+                input_loads = dict(Ha=anchor_loads['Ha'], Va=anchor_loads['Va'])    # overwrite the input_loads dictionary
+            results = self.getAnchorCapacity(loads=input_loads, plot=False)
+
+            bound_L_lower = newGeom['L'] - geomBounds[0][0]
+            bound_L_upper = geomBounds[0][1] - newGeom['L']
+            bound_D_lower = newGeom['D'] - geomBounds[1][0]
+            bound_D_upper = geomBounds[1][1] - newGeom['D']
+
+            return np.array([bound_L_lower, bound_L_upper, bound_D_lower, bound_D_upper])
+        
+        # - - - - - Setup & Optimization
+        from scipy.optimize import minimize
+        from copy import deepcopy
+
+        anchType = self.dd['type']
+
+        # loads['Ha'] = minfs['Ha']*loads['Ha']
+        # loads['Va'] = minfs['Va']*loads['Va']
+        startGeom = dict(zip(geomKeys,geom))
+        print('start geometry: ',startGeom)
+        # apply initial guess geometry
+        self.dd['design'].update(startGeom)
+                   
+        if not 'zlug' in self.dd['design']:
+            if 'suction' in anchType and not fix_zlug:
+                self.dd['design']['zlug'] = (2/3)*startGeom['L']
+            else:
+                self.dd['design']['zlug'] = 0
+        
+        # if zlug is fixed, remove it from design variables
+        if fix_zlug and 'zlug' in geomKeys:
+            zlug_loc = geomKeys.index('zlug')
+            startGeom.pop('zlug')
+            geomKeys.remove('zlug')
+            geom.pop(zlug_loc)
+            if geomBounds:
+                geomBounds.pop(zlug_loc)
+
+        if not loads:
+            loads = self.loads
+            
+        if not 'Ha' in loads:
+            loads = self.getLugForces(mudloads=loads)
+            
+        # suction bucket needs to be loads*FS because of capacity envelope calculations in capacity function
+        if ('Hm' in loads and 'Vm' in loads) and ('Hm' in minfs and 'Vm' in minfs):
+            input_loads = {'Hm':loads['Hm']*minfs['Hm'], 'Vm':loads['Vm']*minfs['Vm']}
+        else:
+            input_loads = {'Ha':loads['Ha']*minfs['Ha'],'Va':loads['Va']*minfs['Va']}
+
+           
+
+        
+        # Initial guess for geometry
+        initial_guess = geom  # [val for val in startGeom.values()]       # Input values for geometry
+        # geomKeys = [key for key in startGeom.keys()]
+        
+        # Bounds and constraints
+        if 'suction' in anchType:
+            # bounds = [(1, 7), (5, 50),()]   # Bounds for D and L
+            # constraints
+            
+            constraints = [{'type':'ineq','fun':conFun_LD,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
+                           {'type':'ineq','fun':conFun_Suction,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
+                           {'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
+                           {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
+                           {'type':'ineq','fun':conBounds,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}]
+        
+        elif 'dandg' in anchType:
+            constraints = [{'type':'ineq','fun':conFun_LD,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
+                           {'type':'ineq','fun':conFun_DandG,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
+                           {'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
+                           {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
+                           {'type':'ineq','fun':conBounds,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}]
+        
+        else:
+            constraints = [{'type':'ineq','fun':conFunH,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)},
+                           {'type':'ineq','fun':conFunV,'args':(geomKeys, input_loads, fix_zlug, LD_con, geomBounds, minfs)}]
+        
+        # Run the optimization to find sizing that satisfy UC close to 1
+        print('optimizing anchor size')
+        
+        if 'suction' in anchType or 'dandg' in anchType:
+            solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs),
+                                method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001})
+        else:
+            solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs),
+                                method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001})
+        
+        FS, acceptance, FSdiff = self.getFS(loads=input_loads, acceptance_crit=minfs)
+        
+        # adjust starting value if you're far off from the acceptance criteria (in either direction)
+        if FSdiff_max:
+            count = 0
+            while count<10 and (np.any([abs(FSdiff[key])>FSdiff_max[key] for key in FSdiff.keys()]) or np.any([diff<0 for diff in FSdiff.values()])):
+                if np.any([diff<.02 for key,diff in FSdiff.items() if minfs[key]>0]) and np.all([diff>=0 for diff in FSdiff.values()]):
+                    # exit loop if you're as close as can be on one of the FS even if other is above diff requirements UNLESS an FS is below minimum reqiured FS
+                    break
+                print('Factor of Safety not close enough to minimum factor of safety, trying again with adjusted initial guess.')
+                print(FS)
+                # calculate new percent difference of FS from min fs
+                diffPCT = [FSdiff[key]/FS[key] for key in FSdiff]
+                # create adjustment coefficient based on this or .25, whichever is lower
+                adjust_coeff = np.min([np.min(diffPCT),0.25])
+                # adjust initial guess values by adjustment coefficient
+                for i,val in enumerate(initial_guess):
+                    initial_guess[i] = val - val*adjust_coeff
+                # update zlug for suction buckets as needed to be 2/3L
+                if 'suction' in anchType and not fix_zlug:
+                    zlug_loc = geomKeys.index('zlug')
+                    L_loc = geomKeys.index('L')
+                    initial_guess[zlug_loc] = (2/3)*initial_guess[L_loc]
+
+                print('new initial guess',initial_guess)
+                # re-run optimization
+                if 'suction' in anchType or 'dandg' in anchType:
+                    solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs),
+                                        method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001})
+                else:
+                    solution = minimize(objective, initial_guess, args=dict(geomKeys=geomKeys, input_loads=input_loads, fix_zlug=fix_zlug, LD_con=LD_con, geomBounds=geomBounds, minfs=minfs),
+                                        method="COBYLA", constraints=constraints, options={'rhobeg':0.1, 'catol':0.001})
+                # re-determine FS and diff from minFS
+                FS, acceptance, FSdiff = self.getFS(loads=input_loads, acceptance_crit=minfs)  
+                count += 1
+        
+        # Extract the optimized values of geometry
+        endGeom = dict(zip(geomKeys,solution.x))
+        print('End geometry: ',endGeom)
+        self.dd['design'].update(endGeom)
+        if 'suction' in anchType and not fix_zlug:
+            self.dd['design']['zlug'] = (2/3)*self.dd['design']['L'] 
+        results = self.getAnchorCapacity(loads=input_loads,plot=plot)            
+            
+        # # check if anchor loads are available
+        # if not self.loads:
+        #     # if not, check if theres a moorpy anchor object and calculate loads from that
+        #     if self.mpAnchor:
+        #         print("Need anchor loads to obtain cost, using getMPForces to determine loads in MoorPy")
+        #         self.getLugForces()
+        #     elif self.ms:
+        #         print('Need anchor loads to obtain cost, creating a MoorPy anchor object and using getMPForces to determine loads in MoorPy')
+        #         self.makeMoorPyAnchor(self.ms)
+        #         self.getLugForces()
+        #     else:
+        #         raise Exception("Need anchor loads to obtain cost")
+        # # check again if there are loads
+        # if self.loads:
+        #     c = self.dd['cost'] # set location for clarity
+        #     # calculate individual costs and total cost for the anchor
+        #     c['matCost'], c['instCost'], c['decomCost'] = mp.Point.getcost(self.mpAnchor)
+        #     c['totCost'] = c['matCost'] + c['instCost'] + c['decomCost']
+
+            SF = 1.0/UC if UC != 0 else np.inf
+
+            return {'SF_combined': SF} 
+                            
+    def getCostAnchor(self, ms=None):
+        '''
+        Assign material cost using a Point object and getCost_and_MBL().
+        '''
+
+        # Create or use existing MoorPy system
+        if ms is None:
+            ms = mp.System()
+
+            # Create MoorPy Point using makeMoorPyAnchor
+            self.makeMoorPyAnchor(ms)
+
+        # Check if mass is assigned
+        if self.mass is None:
+            if 'Weight pile' in self.anchorCapacity:
+                self.mass = self.anchorCapacity['Weight pile'] / self.g
+            elif 'Weight plate' in self.anchorCapacity:
+                self.mass = self.anchorCapacity['Weight plate'] / self.g
+            else:
+                raise KeyError("Missing 'Weight pile' or 'Weight plate' in anchorCapacity. \
+                Run getCapacityAnchor() before getCostAnchor(), or define self.mass explicitly.")
+        
+        # Assign mass to MoorPy point
+        self.mpAnchor.m = self.mass
+
+        cost, MBL, info = self.mpAnchor.getCost_and_MBL()
+
+        # Store results
+        self.cost = {
+            'Material cost': cost,
+            'MBL': MBL,
+            'unit_cost': cost/self.mpAnchor.m }
+
+        return self.cost
+
+    def getCombinedPlot(self):
+        '''
+        Create a plot showing the suction pile and the inverse catenary overlay in the same coordinate system.
+        '''
+        from anchors_famodel.capacity_load  import getTransferLoad
+        from anchors_famodel.capacity_plots import plot_suction
+
+        if self.anchType.lower() != 'suction':
+            raise NotImplementedError("getCombinedPlot only supports suction piles.")
+
+        # Extract design inputs
+        design = self.dd['design']
+        D = design['D']
+        L = design['L']
+        zlug = design['zlug']
+
+        if self.soil_profile is None or self.soil_type is None:
+            raise ValueError("Soil profile or type not assigned. Use setSoilProfile first.")
+
+        soil_profile = self.soil_profile
+        soil_type = self.soil_type
+        z0 = soil_profile[0]['top']
+
+        Hm = self.loads['Hm']
+        Vm = self.loads['Vm']
+        thetam = self.loads.get('thetam', np.degrees(np.arctan2(Vm, Hm)))
+
+        line_type = getattr(self, 'line_type', 'chain')
+        d = getattr(self, 'd', 0.16)
+        w = getattr(self, 'w', 5000.0)
+
+        # Get inverse catenary path
+        layers, result = getTransferLoad(
+            profile_map=[{'layers': self.soil_profile}],
+            Tm=np.sqrt(Hm**2 + Vm**2),
+            thetam=thetam,
+            zlug=zlug,
+            line_type=line_type,
+            d=d,
+            w=w,
+            plot=False
+        )
+
+        drag_values  = np.array(result['drag_values'])
+        depth_values = -np.array(result['depth_values'])[::-1]
+
+        x_start = D/2 + drag_values[0]
+        z_start = zlug
+        drag_transformed  = x_start - drag_values
+        depth_transformed = z_start + (depth_values- depth_values[0])
+
+        # Plot suction pile
+        plot_suction(soil_profile, L, D, z0=z0, zlug=zlug, title='Suction Pile and Mooring Line Load Path')
+
+
+        # Overlay inverse catenary path
+        plt.plot(drag_transformed, depth_transformed, color='b', lw=2.0, label='Inverse catenary')
+        plt.plot(drag_transformed[-1], depth_transformed[-1], 'ro', label='Mudline end')
+        plt.plot( drag_transformed[0],  depth_transformed[0], 'go', label='Embedded end')
+
+        n = 2e6
+        Tm = result['Tm']
+        Ta = result['Ta']
+        thetaa = result['thetaa']
+
+        plt.arrow(drag_transformed[-1], depth_transformed[-1],
+                  Tm*np.cos(np.deg2rad(thetam))/n, -Tm*np.sin(np.deg2rad(thetam))/n,
+                  head_width=0.25, head_length=0.5, color='r', label='Mudline load')
+    
+        plt.arrow(drag_transformed[0], depth_transformed[0],
+                  Ta*np.cos(np.deg2rad(thetaa))/n, -Ta*np.sin(np.deg2rad(thetaa))/n,
+                  head_width=0.25, head_length=0.5, color='g', label='Padeye load')
+
+        xmax = max(drag_transformed[-1] + D, 2*D)
+        plt.xlim(-D, xmax)
+        plt.legend()
+        plt.grid(True)
+        plt.tight_layout()
+        plt.show()
diff --git a/famodel/anchors/anchor_map.py b/famodel/anchors/anchor_map.py
deleted file mode 100644
index 525d569f..00000000
--- a/famodel/anchors/anchor_map.py
+++ /dev/null
@@ -1,889 +0,0 @@
-"""Anchor class for FAModel, containing information and key methods for anchors of mooring lines
-    Work in progress
-"""
-import moorpy as mp
-import numpy as np
-from scipy.optimize import minimize
-from famodel.famodel_base import Node
-from famodel.mooring.mooring import Mooring
-import famodel.platform.platform 
-import matplotlib.pyplot as plt
-
-class Anchor(Node):
-    
-    def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None,
-                 g=9.81, rho=1025, profile_map=None):
-        '''
-        Initialize an Anchor object.
-
-        Parameters
-        ----------
-        dd : dict
-            Design dictionary containing all information on the anchor.
-        ms : MoorPy system object
-            MoorPy system instance.
-        r : list of float
-            Anchor position coordinates (x, y, z) (m)
-        aNum : int, optional
-            Index in anchor list.
-        id : str or int, optional
-            Unique anchor identifier.
-        g : float, optional
-            Gravity.
-        rho : float, optional
-            Water density.
-        profile_map : list of dict, optional
-            Full soil profile map for selecting local soil layers.
-        '''
-
-        from famodel.famodel_base import Node
-        Node.__init__(self, id)
-
-        self.dd = dd
-        self.ms = ms
-        self.r = r
-        self.aNum = aNum
-        self.g = g
-        self.rho = rho
-
-        self.anchType = dd.get('type') if dd else None
-        self.soil_type = None
-        self.soil_profile = None
-        self.profile_name = None
-        self.soil_type_list = []
-
-        self.mpAnchor = None
-        self.capacity_format = None
-        self.mass = dd.get('design', {}).get('mass', None) if dd else None
-
-        self.anchorCapacity = {}
-        self.cost = {}
-        self.loads = {}
-        self.soilProps = {}
-        self.failure_probability = {}
-        self.env_impact = {}
-
-        # --- Assign soil profile if map is provided ---
-        if profile_map is not None:
-            self.setSoilProfile(profile_map)
-
-    def setSoilProfile(self, profile_map):
-        '''
-        Assign a bilinearly interpolated soil profile from the 4 nearest CPTs.
-    
-        Parameters
-        ----------
-        profile_map : list of dict
-            Each CPT must have keys: 'x', 'y', and 'layers'
-    
-        Returns
-        -------
-        None
-        '''
-        import numpy as np
-    
-        x_anchor, y_anchor = self.r[0], self.r[1]
-    
-        # Sort all CPTs by distance
-        distances = [np.hypot(p['x'] - x_anchor, p['y'] - y_anchor) for p in profile_map]
-        idx_sorted = np.argsort(distances)
-        CPTs = [profile_map[i] for i in idx_sorted[:4]]
-       
-        # Extract positions and weights (inverse distance squared)
-        x = np.array([cpt['x'] for cpt in CPTs])
-        y = np.array([cpt['y'] for cpt in CPTs])
-        dx = x - x_anchor
-        dy = y - y_anchor
-        d = np.hypot(dx, dy)
-        w = 1/np.maximum(d, 1e-3)**2
-        w /= np.sum(w)
-    
-        # Interpolate layer-by-layer
-        layers_list = [cpt['layers'] for cpt in CPTs]
-        n_layers = len(layers_list[0])
-        interpolated_layers = []
-    
-        for i in range(n_layers):
-            layer = {'soil_type': layers_list[0][i]['soil_type']}
-            keys = layers_list[0][i].keys()
-    
-            for key in keys:
-                if key == 'soil_type':
-                    continue
-                if all(key in l[i] for l in layers_list):
-                    vals = [l[i][key] for l in layers_list]
-                    layer[key] = np.dot(w, vals)
-    
-            interpolated_layers.append(layer)
-    
-        self.soil_profile = interpolated_layers
-        self.profile_name = f'Interpolated_2D'
-    
-        # Assign soil type
-        soil_types = [layer['soil_type'] for layer in self.soil_profile]
-        self.soil_type_list = list(set(soil_types))
-        self.soil_type = soil_types[0] if len(self.soil_type_list) == 1 else 'mixed'
-    
-        print(f"[Anchor] Assigned interpolated soil profile: {self.profile_name} weighting with soil types {self.soil_type_list}")
-
-    def makeMoorPyAnchor(self, ms):
-        '''
-        Create a MoorPy anchor object in a MoorPy system.
-
-        Parameters
-        ----------
-        ms : MoorPy system instance
-            The MoorPy system to add the anchor to.
-
-        Returns
-        -------
-        ms : MoorPy system instance
-            The updated MoorPy system with the anchor added.
-        '''
-        import moorpy as mp
-
-        # Create anchor as a fixed point in MoorPy system
-        ms.addPoint(1, self.r)
-
-        # Assign this point as mpAnchor in the anchor class instance
-        self.mpAnchor = ms.pointList[-1]
-
-        # Set mass if available
-        if 'mass' in self.dd.get('design', {}):
-            self.mpAnchor.m = self.dd['design']['mass']
-
-        # Set diameter if available
-        if 'd' in self.dd.get('design', {}):
-            self.mpAnchor.d = self.dd['design']['d']
-
-        # Set the point as an anchor entity
-        self.mpAnchor.entity = {'type': 'anchor'}
-        if 'type' in self.dd:
-            self.mpAnchor.entity['anchor_type'] = self.dd['type']
-
-        return ms
-       
-    def getLineProperties(self):
-        '''
-        Retrieve line_type, diameter and unit weight from attached mooring.
-
-        Returns
-        -------
-        line_type : str
-            Type of mooring line ('chain' or 'wire')
-        d : float
-            Nominal diameter (m)
-        w : float
-            Unit weight (N/m)
-        '''
-        for att in self.attachments.values():
-            if isinstance(att['obj'], Mooring):
-                mtype = att['obj'].dd['sections'][0]['type']['material'].lower()
-                if 'chain' not in mtype:
-                    print('No chain below seafloor, setting Ta=Tm (no load transfer).')
-                    return mtype, None, None, True
-                else:
-                    d_nom = att['obj'].dd['sections'][0]['type']['d_nom']
-                    w_nom = att['obj'].dd['sections'][0]['type']['w']
-                    return 'chain', d_nom, w_nom, False
-        raise ValueError('No mooring line attachment found for anchor.')
-
-    def getMudlineForces(self, lines_only=False, seabed=True, xyz=False):
-        '''
-        Find forces on anchor at mudline using the platform.getWatchCircle method or MoorPy Point.getForces method.
-
-        Parameters
-        ----------
-        lines_only : boolean, optional
-            Calculate forces from just mooring lines (True) or not (False). Default is False.
-        seabed : boolean, optional
-            Include effect of seabed pushing up the anchor (True) or not (False). Default is True.
-        xyz : boolean, optional
-            Return forces in x,y,z DOFs (True) or only the enabled DOFs (False). Default is False.
-
-        Returns
-        -------
-        dict
-            Dictionary containing mudline forces.
-        '''
-        loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz)
-
-        self.loads = {
-            'Hm': np.sqrt(loads[0]**2 + loads[1]**2),
-            'Vm': loads[2],
-            'thetam': np.degrees(np.arctan2(loads[2], np.sqrt(loads[0]**2 + loads[1]**2))),
-            'method': 'static',
-            'mudline_load_type': 'current_state'
-        }
-
-        return self.loads
-       
-    def getLugForces(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False):
-        '''
-        Calculate the lug forces Ha and Va based on mudline loads using local soil profile.
-
-        Parameters
-        ----------
-        Hm : float
-            Horizontal mudline load (N)
-        Vm : float
-            Vertical mudline load (N)
-        zlug : float
-            Padeye embedment depth (m)
-        line_type : str, optional
-            Type of mooring line ('chain' or 'wire')
-        d : float, optional
-            Mooring line diameter (m)
-        w : float, optional
-            Mooring line unit weight (N/m)
-        plot : bool, optional
-            Whether to plot the load transfer profile
-
-        Returns
-        -------
-        Ha : float
-            Horizontal load at lug (N).
-        Va : float
-            Vertical load at lug (N).
-        '''
-        from famodel.anchors.anchors_famodel_map.capacity_load_map import getTransferLoad
-
-        # Ensure soil profile is available
-        if self.soil_profile is None or self.soil_type is None:
-            raise ValueError("Anchor soil profile or soil type is not assigned. Use setSoilProfile first.")
-
-        soil_profile = self.soil_profile
-        soil_type = self.soil_type
-
-        # Determine mudline depth
-        z0 = soil_profile[0]['top']
-
-        # Load transfer if padeye is embedded
-        if zlug > z0:
-            if line_type is None or d is None or w is None:
-                try:
-                    line_type, d, w = self.getLineProperties()
-                except ValueError:
-                    print('[Warning] No mooring attachment found. Trying anchor-level line properties...')
-                    line_type = getattr(self, 'line_type', None)
-                    d = getattr(self, 'd', None)
-                    w = getattr(self, 'w', None)
-
-                    if any(v is None for v in [line_type, d, w]):
-                        print('[Fallback] Using default chain properties.')
-                        line_type = 'chain'
-                        d = 0.16
-                        w = 5000.0
-
-            layers, loads = getTransferLoad(
-                profile_map=[{'layers': self.soil_profile}],
-                Tm=np.sqrt(Hm**2 + Vm**2),
-                thetam=np.degrees(np.arctan2(Vm, Hm)),
-                zlug=zlug,
-                line_type=line_type,
-                d=d,
-                w=w,
-                plot=plot
-            )
-
-            Ta = loads['Ta']
-            thetaa = loads['thetaa']
-            Ha = Ta*np.cos(np.deg2rad(thetaa))
-            Va = Ta*np.sin(np.deg2rad(thetaa))
-
-        else:
-            Ha = Hm
-            Va = Vm
-
-        return layers, Ha, Va
-
-    def getCapacityAnchor(self, Hm, Vm, zlug, line_type=None, d=None, w=None, plot=False):
-        '''
-        Calculate anchor capacity based on anchor type and local soil profile.
-    
-        Parameters
-        ----------
-        Hm : float
-            Horizontal mudline load (N)
-        Vm : float
-            Vertical mudline load (N)
-        zlug : float
-            Padeye embedment depth (m)
-        line_type : str, optional
-            Type of mooring line ('chain' or 'wire')
-        d : float, optional
-            Mooring line diameter (m)
-        w : float, optional
-            Mooring line unit weight (N/m)
-        plot : bool, optional
-            Whether to plot the load transfer and pile geometry
-    
-        Returns
-        -------
-        results : dict
-            Capacity results dictionary from the selected capacity function.
-        '''
-        from famodel.anchors.anchors_famodel_map.capacity_plate_map import getCapacityPlate
-        from famodel.anchors.anchors_famodel_map.capacity_suction_map import getCapacitySuction
-        from famodel.anchors.anchors_famodel_map.capacity_torpedo_map import getCapacityTorpedo
-        from famodel.anchors.anchors_famodel_map.capacity_helical_map import getCapacityHelical
-        from famodel.anchors.anchors_famodel_map.capacity_driven_map import getCapacityDriven
-        from famodel.anchors.anchors_famodel_map.capacity_dandg_map import getCapacityDandG
-        from famodel.anchors.anchors_famodel_map.capacity_load_map import getTransferLoad
-        from famodel.anchors.anchors_famodel_map.capacity_plots_map import plot_load
-        # import numpy as np
-    
-        capacity_dispatch = {
-            'suction': getCapacitySuction,
-            'sepla': getCapacityPlate,
-            'dea': getCapacityPlate,
-            'depla': getCapacityPlate,
-            'vla': getCapacityPlate,
-            'plate': getCapacityPlate,
-            'torpedo': getCapacityTorpedo,
-            'helical': getCapacityHelical,
-            'driven': getCapacityDriven,
-            'dandg': getCapacityDandG
-        }
-    
-        anchType_clean = self.anchType.lower().replace(' ', '')
-        capacity_func = capacity_dispatch.get(anchType_clean)
-        if capacity_func is None:
-            raise ValueError(f"Unknown anchor type '{self.anchType}' for anchor capacity calculation.")
-    
-        if self.soil_profile is None or self.soil_type is None:
-            raise ValueError("Soil profile or soil type not set for this anchor.")
-    
-        soil_profile = self.soil_profile
-        soil_type = self.soil_type
-        z0 = soil_profile[0]['top']
-    
-        # Load transfer if padeye is embedded below mudline
-        if zlug > z0:
-            if line_type is None or d is None or w is None:
-                try:
-                    line_type, d, w = self.getLineProperties()
-                except ValueError:
-                    print('[Warning] No mooring attachment found. Trying anchor-level line properties...')
-                    line_type = getattr(self, 'line_type', None)
-                    d = getattr(self, 'd', None)
-                    w = getattr(self, 'w', None)
-    
-                    if any(v is None for v in [line_type, d, w]):
-                        print('[Fallback] Using default chain properties.')
-                        line_type = 'chain'
-                        d = 0.16
-                        w = 5000.0
-    
-            else:
-                layers, resultsLoad = getTransferLoad(
-                    profile_map=[{'layers': soil_profile}],
-                    Tm=np.sqrt(Hm**2 + Vm**2),
-                    thetam=np.degrees(np.arctan2(Vm, Hm)),
-                    zlug=zlug,
-                    line_type=line_type,
-                    d=d,
-                    w=w,
-                    plot=False
-                )
-                if plot:
-                    plot_load(
-                        layers,
-                        resultsLoad['drag_values'],
-                        resultsLoad['depth_values'],
-                        resultsLoad['Tm'],
-                        resultsLoad['thetam'],
-                        resultsLoad['Ta'],
-                        resultsLoad['thetaa'],
-                        zlug=zlug
-                    )
-    
-                Ta = resultsLoad['Ta']
-                thetaa = resultsLoad['thetaa']
-                Ha = Ta*np.cos(np.deg2rad(thetaa))
-                Va = Ta*np.sin(np.deg2rad(thetaa))
-                
-                print(f'Input Hm = {Hm}, Vm = {Vm}, zlug = {zlug}')
-                print(f'Input Ha = {Ha}, Va = {Va}, zlug = {zlug}')
-                print(f'Input Ta = {Ta}, thetaa = {(thetaa)}')
-        else:
-            Ha = Hm
-            Va = Vm
-    
-        # --- Call the appropriate capacity function ---
-        if anchType_clean in ['sepla', 'dea', 'depla', 'vla', 'plate']:
-            self.capacity_format = 'plate'
-            B = self.dd['design']['B']
-            L = self.dd['design']['L']
-            beta = self.dd['design'].get('beta', 0.0)
-            layers, results = capacity_func(
-                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
-                location_name=self.profile_name,
-                B=B, L=L, zlug=zlug,
-                beta=beta,
-                Ha=Ha, Va=Va,
-                plot=plot
-            )
-            
-        elif anchType_clean == 'suction':
-            self.capacity_format = 'envelope'
-            D = self.dd['design']['D']
-            L = self.dd['design']['L']
-            zlug = self.dd['design']['zlug']
-            layers, results = capacity_func(
-                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
-                location_name=self.profile_name,
-                D=D, L=L, zlug=zlug,
-                Ha=Ha, Va=Va,
-                thetalug=5, psilug=7.5,
-                plot=plot
-            )
-            
-        elif anchType_clean == 'torpedo':
-            self.capacity_format = 'envelope'
-            D1 = self.dd['design']['D1']
-            D2 = self.dd['design']['D2']
-            L1 = self.dd['design']['L1']
-            L2 = self.dd['design']['L2']
-            ballast = self.dd['design'].get('ballast', 0.0)
-            layers, results = capacity_func(
-                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
-                location_name=self.profile_name,
-                D1=D1, D2=D2, L1=L1, L2=L2,
-                zlug=zlug,
-                ballast=ballast,
-                Ha=Ha, Va=Va,
-                plot=plot
-            )
-
-        elif anchType_clean == 'helical':
-            self.capacity_format = 'component'
-            D = self.dd['design']['D']     
-            L = self.dd['design']['L']     
-            d = self.dd['design']['d']     
-            zlug = self.dd['design']['zlug']
-            layers, results = capacity_func(
-                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
-                location_name=self.profile_name,
-                D=D, L=L, d=d,
-                zlug=zlug,
-                Ha=Ha, Va=Va,
-                plot=plot
-            )
-
-        elif anchType_clean in ['driven', 'dandg']:
-            self.capacity_format = 'component'
-            L = self.dd['design']['L']
-            D = self.dd['design']['D']
-            zlug = self.dd['design']['zlug']
-            layers, y, z, results = capacity_func(
-                profile_map=[{'name': self.profile_name, 'layers': self.soil_profile}],
-                location_name=self.profile_name,
-                L=L, D=D, zlug=zlug,
-                Ha=Ha, Va=Va,
-                plot=plot
-            )
-    
-        else:
-            raise ValueError(f"Anchor type '{self.anchType}' not supported.")
-    
-        # --- Store results ---
-        self.capacity_results = {
-            'Hmax': results.get('Horizontal max.', np.nan),
-            'Vmax': results.get('Vertical max.', np.nan),
-            'UC': results.get('Unity check', np.nan),
-            'Ha': Ha,
-            'Va': Va,
-            'zlug': zlug,
-            'z0': z0
-        }
-    
-        if 'Weight pile' in results:
-            self.capacity_results['Weight pile'] = results['Weight pile']
-        if 'Weight plate' in results:
-            self.capacity_results['Weight plate'] = results['Weight plate']
-    
-        if anchType_clean in ['driven', 'dandg']:
-            self.capacity_results['Lateral displacement'] = results.get('Lateral displacement', np.nan)
-            self.capacity_results['Rotational displacement'] = results.get('Rotational displacement', np.nan)
-    
-        return results
-                   
-    def getSafetyFactor(self):
-        '''
-        Calculate the safety factor based on the unity checks stored in capacity results.
-
-        Returns
-        -------
-        dict
-            Dictionary containing safety factors.
-        '''
-        anchType_clean = self.anchType.lower().replace(' ', '')
-
-        if anchType_clean in ['helical', 'driven', 'dandg']:
-            UC_v = self.capacity_results.get('Unity check (vertical)', None)
-            UC_h = self.capacity_results.get('Unity check (horizontal)', None)
-
-            if UC_v is None or UC_h is None:
-                print("Warning: Vertical or horizontal unity check (UC) not found in capacity results. Returning NaN.")
-                return {'SF_vertical': np.nan, 'SF_horizontal': np.nan}
-
-            SF_v = 1.0/UC_v if UC_v != 0 else np.inf
-            SF_h = 1.0/UC_h if UC_h != 0 else np.inf
-
-            return {'SF_vertical': SF_v, 'SF_horizontal': SF_h}
-
-        else:
-            UC = self.capacity_results.get('UC', None)
-
-            if UC is None:
-                print("Warning: Unity check (UC) not found in capacity results. Returning NaN.")
-                return {'SF_combined': np.nan}
-
-            SF = 1.0/UC if UC != 0 else np.inf
-
-            return {'SF_combined': SF} 
-
-    def getCostAnchor(self, costDict='default'):
-        '''
-        Calculate the cost of the anchor based on material, installation, and decommissioning costs.
-
-        Parameters
-        ----------
-        costDict : str or dict, optional
-            If 'default', uses mean values from Task 49 Design Basis ranges.
-            If dict or yaml path, loads user-defined cost dictionaries.
-
-        Returns
-        -------
-        float
-            Total cost of the anchor.
-        '''
-        if isinstance(costDict, str) and costDict != 'default':
-            import yaml
-            costDict = yaml.load(costDict, Loader=yaml.FullLoader)
-
-        anchType = self.dd['type']
-
-        if costDict == 'default':
-            matCostDict = {
-                'suction_pile': 4.435,
-                'DEA': 5.705,
-                'SEPLA': 5.705,
-                'DEPLA': 5.705,
-                'VLA': 5.705,
-                'torpedo_pile': 5.0,
-                'helical_pile': 6.0,
-                'driven_pile': 4.0,
-                'dandg_pile': 5.5
-            }
-            instCostDict = {
-                'suction_pile': 2.0,
-                'DEA': 1.5,
-                'SEPLA': 1.5,
-                'DEPLA': 1.5,
-                'VLA': 1.5,
-                'torpedo_pile': 2.5,
-                'helical_pile': 3.0,
-                'driven_pile': 2.0,
-                'dandg_pile': 2.2
-            }
-            decomCostDict = {
-                'suction_pile': 1.0,
-                'DEA': 0.8,
-                'SEPLA': 0.8,
-                'DEPLA': 0.8,
-                'VLA': 0.8,
-                'torpedo_pile': 1.2,
-                'helical_pile': 1.5,
-                'driven_pile': 1.0,
-                'dandg_pile': 1.1
-            }
-        else:
-            matCostDict = costDict.get('material', {})
-            instCostDict = costDict.get('install', {})
-            decomCostDict = costDict.get('decom', {})
-
-        keyFail = True
-
-        # Ensure mass is available
-        if self.mass is None or self.mass == 0:
-            # Try to extract from capacity_results if already available
-            if 'Weight pile' in self.capacity_results:
-                self.mass = self.capacity_results['Weight pile']/self.g
-            elif 'Weight plate' in self.capacity_results:
-                self.mass = self.capacity_results['Weight plate']/self.g
-            else:
-                # If capacity_results missing, attempt to calculate capacity to retrieve weight
-                if 'soil_properties' in self.dd:
-                    self.getAnchorCapacity(plot=False)
-                    if 'Weight pile' in self.capacity_results:
-                        self.mass = self.capacity_results['Weight pile']/self.g
-                    elif 'Weight plate' in self.capacity_results:
-                        self.mass = self.capacity_results['Weight plate']/self.g
-                    else:
-                        print('Warning: Weight not found after capacity calculation, setting mass to 0.')
-                        self.mass = 0
-                else:
-                    print('Soil properties needed to calculate anchor mass for cost. Setting mass to 0.')
-                    self.mass = 0
-
-        # Calculate material cost based on mass
-        if anchType in matCostDict:
-            self.cost['Material Cost'] = matCostDict[anchType]*self.mass
-            keyFail = False
-        else:
-            raise KeyError(f'Anchor type {anchType} not found in material cost dictionary.')
-
-        # Install and decom costs if available
-        self.cost['Installation Cost'] = instCostDict.get(anchType, 0.0)
-        self.cost['Decommissioning Cost'] = decomCostDict.get(anchType, 0.0)
-
-        # Total cost
-        self.cost['Total Cost'] = (self.cost['Material Cost'] +
-                                   self.cost['Installation Cost'] +
-                                   self.cost['Decommissioning Cost'])
-
-        return sum(self.cost.values())
-        
-    def getSizeAnchor(self, geom, geomKeys, geomBounds=None, loads=None,
-                      minfs={'Ha': 1.6, 'Va': 2.0}, lambdap_con=[4, 8], zlug_fix=False, plot=False):
-        '''
-        Generalized optimization method for all anchor types.
-        '''
-        
-        anchType_clean = self.dd['type'].lower().replace(' ', '')
-        
-        if loads is None:
-            loads = self.loads
-    
-        Hm = loads['Hm']
-        Vm = loads['Vm']
-    
-        line_type = getattr(self, 'line_type', 'chain')
-        d = getattr(self, 'd', 0.16)
-        w = getattr(self, 'w', 5000.0)
-        
-        def update_zlug_if_suction():
-            if anchType_clean == 'suction' and not zlug_fix and 'zlug' not in geomKeys:
-                self.dd['design']['zlug'] = (2/3)*self.dd['design']['L']   
-
-        # --- Stage 1: Safety Optimization to reach UC <= 1 ---
-        def safety_objective(vars):
-            for i, key in enumerate(geomKeys):
-                self.dd['design'][key] = vars[i]
-            update_zlug_if_suction()
-    
-            _, Ha, Va = self.getLugForces(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, plot=False)
-            
-            self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, plot=False)
-    
-            if self.capacity_format == 'envelope':
-                UC = self.capacity_results.get('UC', 2.0)
-            elif self.capacity_format == 'component':
-                UC = max(
-                    self.capacity_results.get('Unity check (horizontal)', 2.0),
-                    self.capacity_results.get('Unity check (vertical)', 2.0)
-                )
-            elif self.capacity_format == 'plate':
-                UC = self.capacity_results.get('UC', 2.0)
-            else:
-                UC = 2.0
-            return (UC - 1.0)**2
-    
-        minimize(
-            safety_objective,
-            geom,
-            method='COBYLA',
-            bounds=geomBounds if geomBounds else None,
-            options={'rhobeg': 0.02, 'catol': 0.001, 'maxiter': 1500}
-        )
-    
-        # --- Stage 2: Weight Minimization with constraints ---
-        if anchType_clean != 'torpedo':    
-            def weight_objective(vars):
-                for i, key in enumerate(geomKeys):
-                    self.dd['design'][key] = vars[i]
-                update_zlug_if_suction()
-    
-                _, Ha, Va = self.getLugForces(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, plot=False)
-        
-                self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, plot=False)
-        
-                return self.capacity_results.get('Weight pile',
-                       self.capacity_results.get('Weight plate',
-                       self.capacity_results.get('Weight', 1e9)))
-        
-            def constraint_uc_envelope(vars):
-                return 1.0 - self.capacity_results.get('UC', 2.0)
-        
-            def constraint_uc_component(vars):
-                return 1.0 - max(
-                    self.capacity_results.get('Unity check (horizontal)', 2.0),
-                    self.capacity_results.get('Unity check (vertical)', 2.0)
-                )
-        
-            def constraint_fs_horizontal(vars):
-                return (self.capacity_results.get('Horizontal max.', 0)/self.capacity_results.get('Ha', 1)) - minfs['Ha']
-        
-            def constraint_fs_vertical(vars):
-                return (self.capacity_results.get('Vertical max.', 0)/self.capacity_results.get('Va', 1)) - minfs['Va']
-        
-            def constraint_lambda_min(vars):
-                anchType_clean = self.dd['type'].lower().replace(' ', '')
-                
-                if anchType_clean == 'torpedo':
-                    L = self.dd['design']['L1'] + self.dd['design']['L2']
-                    A_wing = (self.dd['design']['D1'] - self.dd['design']['D2'])*self.dd['design']['L1']
-                    A_shaft = self.dd['design']['D2']*L
-                    D = (A_wing + A_shaft)/L
-                elif anchType_clean in ['driven', 'dandg', 'helical', 'suction']:
-                    L = self.dd['design']['L']
-                    D = self.dd['design']['D']
-                elif anchType_clean in ['plate', 'sepla', 'dea', 'depla', 'vla']:
-                    L = self.dd['design']['L']
-                    D = self.dd['design']['B']  
-                else:
-                    raise ValueError(f'lambda constraints not defined for anchor type: {anchType_clean}')
-                return (L/D) - lambdap_con[0]
-        
-            def constraint_lambda_max(vars):
-                anchType_clean = self.dd['type'].lower().replace(' ', '')
-                
-                if anchType_clean == 'torpedo':
-                    L = self.dd['design']['L1'] + self.dd['design']['L2']
-                    A_wing = (self.dd['design']['D1'] - self.dd['design']['D2'])*self.dd['design']['L1']
-                    A_shaft = self.dd['design']['D2']*L
-                    D = (A_wing + A_shaft)/L
-                elif anchType_clean in ['driven', 'dandg', 'helical', 'suction']:
-                    L = self.dd['design']['L']
-                    D = self.dd['design']['D']
-                elif anchType_clean in ['plate', 'sepla', 'dea', 'depla', 'vla']:
-                    L = self.dd['design']['L']
-                    D = self.dd['design']['B']  # use plate width
-                else:
-                    raise ValueError(f'lambda constraints not defined for anchor type: {anchType_clean}')
-                return lambdap_con[1] - (L/D)
-        
-            if self.capacity_format == 'envelope':
-                constraints = [
-                    {'type': 'ineq', 'fun': constraint_uc_envelope},
-                    {'type': 'ineq', 'fun': constraint_fs_horizontal},
-                    {'type': 'ineq', 'fun': constraint_fs_vertical},
-                    {'type': 'ineq', 'fun': constraint_lambda_min},
-                    {'type': 'ineq', 'fun': constraint_lambda_max},
-                ]
-            elif self.capacity_format == 'component':
-                constraints = [
-                    {'type': 'ineq', 'fun': constraint_uc_component},
-                    {'type': 'ineq', 'fun': constraint_lambda_min},
-                    {'type': 'ineq', 'fun': constraint_lambda_max},
-                ]
-            elif self.capacity_format == 'plate':
-                constraints = [
-                    {'type': 'ineq', 'fun': constraint_uc_envelope}
-                ]
-            else:
-                raise ValueError(f"Unknown capacity_format: {self.capacity_format}")
-        
-            result = minimize(
-                weight_objective,
-                [self.dd['design'][key] for key in geomKeys],
-                method='COBYLA',
-                constraints=constraints,
-                bounds=geomBounds if geomBounds else None,
-                options={'rhobeg': 0.5, 'catol': 0.01, 'maxiter': 100}
-            )
-        
-            endGeom = dict(zip(geomKeys, result.x))
-            print('Optimized geometry:', endGeom)
-            self.dd['design'].update(endGeom)
-        
-            update_zlug_if_suction()
-        
-            self.getCapacityAnchor(Hm=Hm, Vm=Vm, zlug=self.dd['design']['zlug'], line_type=line_type, d=d, w=w, plot=plot)
-        
-            print('\nFinal Optimized Anchor:')
-            print('Design:', self.dd['design'])
-            print('Capacity Results:', self.capacity_results)
-
-
-    def getCombinedPlot(self):
-        '''
-        Create a plot showing the suction pile and the inverse catenary overlay in the same coordinate system.
-        '''
-        from famodel.anchors.anchors_famodel_map.capacity_load_map import getTransferLoad
-        from famodel.anchors.anchors_famodel_map.capacity_plots_map import plot_suction
-
-        if self.anchType.lower() != 'suction':
-            raise NotImplementedError("getCombinedPlot only supports suction piles.")
-
-        # Extract design inputs
-        design = self.dd['design']
-        D = design['D']
-        L = design['L']
-        zlug = design['zlug']
-
-        if self.soil_profile is None or self.soil_type is None:
-            raise ValueError("Soil profile or type not assigned. Use setSoilProfile first.")
-
-        soil_profile = self.soil_profile
-        soil_type = self.soil_type
-        z0 = soil_profile[0]['top']
-
-        Hm = self.loads['Hm']
-        Vm = self.loads['Vm']
-        thetam = self.loads.get('thetam', np.degrees(np.arctan2(Vm, Hm)))
-
-        line_type = getattr(self, 'line_type', 'chain')
-        d = getattr(self, 'd', 0.16)
-        w = getattr(self, 'w', 5000.0)
-
-        # Get inverse catenary path
-        layers, result = getTransferLoad(
-            profile_map=[{'layers': self.soil_profile}],
-            Tm=np.sqrt(Hm**2 + Vm**2),
-            thetam=thetam,
-            zlug=zlug,
-            line_type=line_type,
-            d=d,
-            w=w,
-            plot=False
-        )
-
-        drag_values  = np.array(result['drag_values'])
-        depth_values = -np.array(result['depth_values'])[::-1]
-
-        x_start = D/2 + drag_values[0]
-        z_start = zlug
-        drag_transformed  = x_start - drag_values
-        depth_transformed = z_start + (depth_values- depth_values[0])
-
-        # Plot suction pile
-        plot_suction(soil_profile, L, D, z0=z0, zlug=zlug, title='Suction Pile and Mooring Line Load Path')
-
-
-        # Overlay inverse catenary path
-        plt.plot(drag_transformed, depth_transformed, color='b', lw=2.0, label='Inverse catenary')
-        plt.plot(drag_transformed[-1], depth_transformed[-1], 'ro', label='Mudline end')
-        plt.plot( drag_transformed[0],  depth_transformed[0], 'go', label='Embedded end')
-
-        n = 2e6
-        Tm = result['Tm']
-        Ta = result['Ta']
-        thetaa = result['thetaa']
-
-        plt.arrow(drag_transformed[-1], depth_transformed[-1],
-                  Tm*np.cos(np.deg2rad(thetam))/n, -Tm*np.sin(np.deg2rad(thetam))/n,
-                  head_width=0.25, head_length=0.5, color='r', label='Mudline load')
-    
-        plt.arrow(drag_transformed[0], depth_transformed[0],
-                  Ta*np.cos(np.deg2rad(thetaa))/n, -Ta*np.sin(np.deg2rad(thetaa))/n,
-                  head_width=0.25, head_length=0.5, color='g', label='Padeye load')
-
-        xmax = max(drag_transformed[-1] + D, 2*D)
-        plt.xlim(-D, xmax)
-        plt.legend()
-        plt.grid(True)
-        plt.tight_layout()
-        plt.show()
diff --git a/famodel/anchors/anchor_profile.py b/famodel/anchors/anchor_profile.py
deleted file mode 100644
index 611c5b0a..00000000
--- a/famodel/anchors/anchor_profile.py
+++ /dev/null
@@ -1,915 +0,0 @@
-"""Anchor class for FAModel, containing information and key methods for anchors of mooring lines
-    Work in progress
-"""
-import moorpy as mp
-import numpy as np
-from scipy.optimize import minimize
-from famodel.famodel_base import Node
-from famodel.mooring.mooring import Mooring
-import famodel.platform.platform 
-
-class Anchor(Node):
-    
-    def __init__(self, dd=None, ms=None, r=[0,0,0], aNum=None, id=None, g=9.81, rho=1025):
-        '''
-        Initialize an Anchor object.
-
-        Parameters
-        ----------
-        dd : dict
-            Design dictionary containing all information on the anchor:
-            {
-                type : str
-                    Anchor type ('plate', 'suction_pile', 'torpedo_pile', 'helical_pile', 'driven_pile', 'dandg_pile')
-                design : dict
-                    Geometric properties (e.g., A, D, D1, D2, d, L, L1, L2, zlug, beta)
-                cost : dict
-                    Cost breakdown (matCost, instCost, decomCost)
-            }
-        ms : MoorPy system object
-            The MoorPy system instance the anchor is added to.
-        r : list of float
-            Location of the anchor in (x, y, z) coordinates (m).
-        aNum : int, optional
-            Entry number for anchor within the project anchorList dictionary.
-        id : str or int, optional
-            Unique identifier for the anchor object.
-        g : float, optional
-            Gravitational acceleration (m/s²). Default is 9.81.
-        rho : float, optional
-            Water density (kg/m³). Default is 1025.
-        '''
-
-        from famodel.famodel_base import Node
-        Node.__init__(self, id)
-
-        self.dd = dd
-        self.ms = ms
-        self.r = r
-        self.aNum = aNum
-        self.g = g
-        self.rho = rho
-
-        # Initialize anchor type and soil type
-        self.anchType = dd.get('type') if dd else None
-        self.soil_type = None
-
-        # Initialize MoorPy anchor object
-        self.mpAnchor = None
-
-        # Extract mass if available
-        self.mass = dd.get('design', {}).get('mass', None) if dd else None
-
-        # Initialize other dictionaries
-        self.anchorCapacity = {}
-        self.cost = {}
-        self.loads = {}
-        self.soilProps = {}
-        self.failure_probability = {}
-        self.env_impact = {}
-        
-    def makeMoorPyAnchor(self, ms):
-        '''
-        Create a MoorPy anchor object in a MoorPy system.
-
-        Parameters
-        ----------
-        ms : MoorPy system instance
-            The MoorPy system to add the anchor to.
-
-        Returns
-        -------
-        ms : MoorPy system instance
-            The updated MoorPy system with the anchor added.
-        '''
-        import moorpy as mp
-
-        # Create anchor as a fixed point in MoorPy system
-        ms.addPoint(1, self.r)
-
-        # Assign this point as mpAnchor in the anchor class instance
-        self.mpAnchor = ms.pointList[-1]
-
-        # Set mass if available
-        if 'mass' in self.dd.get('design', {}):
-            self.mpAnchor.m = self.dd['design']['mass']
-
-        # Set diameter if available
-        if 'd' in self.dd.get('design', {}):
-            self.mpAnchor.d = self.dd['design']['d']
-
-        # Set the point as an anchor entity
-        self.mpAnchor.entity = {'type': 'anchor'}
-        if 'type' in self.dd:
-            self.mpAnchor.entity['anchor_type'] = self.dd['type']
-
-        return ms
-       
-    def getLineProperties(self):
-        '''
-        Retrieve line_type, diameter and unit weight from attached mooring.
-
-        Returns
-        -------
-        line_type : str
-            Type of mooring line ('chain' or 'wire').
-        d : float
-            Nominal diameter (m).
-        w : float
-            Unit weight (N/m).
-        nolugload : bool
-            True if no lug load transfer should be applied.
-        '''
-        for att in self.attachments.values():
-            if isinstance(att['obj'], Mooring):
-                mtype = att['obj'].dd['sections'][0]['type']['material'].lower()
-                if 'chain' not in mtype:
-                    print('No chain on seafloor, setting Ta=Tm (no load transfer).')
-                    return mtype, None, None, True
-                else:
-                    d_nom = att['obj'].dd['sections'][0]['type']['d_nom']
-                    w_nom = att['obj'].dd['sections'][0]['type']['w']
-                    return 'chain', d_nom, w_nom, False
-        raise ValueError('No mooring line attachment found for anchor.')
-
-    def getMudlineForces(self, lines_only=False, seabed=True, xyz=False):
-        '''
-        Find forces on anchor at mudline using the platform.getWatchCircle method or MoorPy Point.getForces method.
-
-        Parameters
-        ----------
-        lines_only : boolean, optional
-            Calculate forces from just mooring lines (True) or not (False). Default is False.
-        seabed : boolean, optional
-            Include effect of seabed pushing up the anchor (True) or not (False). Default is True.
-        xyz : boolean, optional
-            Return forces in x,y,z DOFs (True) or only the enabled DOFs (False). Default is False.
-
-        Returns
-        -------
-        dict
-            Dictionary containing mudline forces.
-        '''
-        loads = self.mpAnchor.getForces(lines_only=lines_only, seabed=seabed, xyz=xyz)
-
-        self.loads = {
-            'Hm': np.sqrt(loads[0]**2 + loads[1]**2),
-            'Vm': loads[2],
-            'thetam': np.degrees(np.arctan2(loads[2], np.sqrt(loads[0]**2 + loads[1]**2))),
-            'method': 'static',
-            'mudline_load_type': 'current_state'
-        }
-
-        return self.loads
-       
-    def getLugForces(self, ground_conds, Hm, Vm, thetam, zlug, line_type=None, d=None, w=None, plot=False):
-        '''
-        Calculate the lug forces Ha and Va based on mudline loads.
-
-        Parameters
-        ----------
-        ground_conds : dict
-            Dictionary of ground conditions where keys are soil types.
-        Hm : float
-            Horizontal mudline load (N).
-        Vm : float
-            Vertical mudline load (N).
-        thetam : float
-            Mudline load angle (deg).
-        zlug : float
-            Padeye embedment depth (m).
-        line_type : str, optional
-            Type of mooring line ('chain' or 'wire').
-        d : float, optional
-            Mooring line diameter (m).
-        w : float, optional
-            Mooring line unit weight (N/m).
-        plot : bool, optional
-            Whether to plot the load transfer profile.
-
-        Returns
-        -------
-        Ha : float
-            Horizontal load at lug (N).
-        Va : float
-            Vertical load at lug (N).
-        '''
-        from famodel.anchors.anchors_famodel_profile.capacity_load import getTransferLoad
-
-        if self.soil_type is None:
-            self.soil_type = self.dd.get('design', {}).get('soil_type')
-        
-        soil_profile = self.dd.get('soil_properties', {}).get(self.soil_type)
-
-        # Determine mudline depth
-        z0 = soil_profile[0][0]
-
-        # Load transfer if padeye is embedded
-        if zlug > z0:
-            # Fallback mechanism for line properties
-            if line_type is None or d is None or w is None:
-                try:
-                    line_type, d, w, _ = self.getLineProperties()
-                except ValueError:
-                    print('[Warning] No mooring attachment found. Trying anchor-level line properties...')
-                    line_type = getattr(self, 'line_type', None)
-                    d = getattr(self, 'd', None)
-                    w = getattr(self, 'w', None)
-        
-                    if any(v is None for v in [line_type, d, w]):
-                        print('[Fallback] Using default chain properties.')
-                        line_type = 'chain'
-                        d = 0.16
-                        w = 5000.0
-        
-            loads = getTransferLoad(
-                soil_profile, self.soil_type, np.sqrt(Hm**2 + Vm**2),
-                thetam, zlug, line_type, d, w, plot=plot
-            )
-            Ta = loads['Ta']
-            thetaa = loads['thetaa']
-            Ha = Ta*np.cos(thetaa)
-            Va = Ta*np.sin(thetaa)
-        
-        else:
-            Ha = Hm
-            Va = Vm
-        
-        return Ha, Va
-
-
-    def getCapacityAnchor(self, ground_conds, Hm, Vm, thetam, zlug, line_type=None, d=None, w=None, plot=False):
-        '''
-        Calculate anchor capacity based on anchor type and ground conditions.
-
-        Parameters
-        ----------
-        ground_conds : dict
-            Dictionary of ground conditions where keys are soil types.
-        Hm : float
-            Horizontal mudline load (N).
-        Vm : float
-            Vertical mudline load (N).
-        thetam : float
-            Mudline load angle (deg).
-        zlug : float
-            Padeye embedment depth (m).
-        line_type : str, optional
-            Type of mooring line ('chain' or 'wire').
-        d : float, optional
-            Mooring line diameter (m).
-        w : float, optional
-            Mooring line unit weight (N/m).
-        plot : bool, optional
-            Whether to plot the results.
-
-        Returns
-        -------
-        None
-            Updates anchor object with capacity results.
-        '''
-        from famodel.anchors.anchors_famodel_profile.capacity_plate import getCapacityPlate
-        from famodel.anchors.anchors_famodel_profile.capacity_suction import getCapacitySuction
-        from famodel.anchors.anchors_famodel_profile.capacity_torpedo import getCapacityTorpedo
-        from famodel.anchors.anchors_famodel_profile.capacity_helical import getCapacityHelical
-        from famodel.anchors.anchors_famodel_profile.capacity_driven import getCapacityDriven
-        from famodel.anchors.anchors_famodel_profile.capacity_dandg import getCapacityDandG
-        from famodel.anchors.anchors_famodel_profile.capacity_load import getTransferLoad
-        import numpy as np
-
-        # --- Dispatch dictionary ---
-        capacity_dispatch = {
-            'suction': getCapacitySuction,
-            'sepla': getCapacityPlate,
-            'dea': getCapacityPlate,
-            'depla': getCapacityPlate,
-            'vla': getCapacityPlate,
-            'plate': getCapacityPlate,
-            'torpedo': getCapacityTorpedo,
-            'helical': getCapacityHelical,
-            'driven': getCapacityDriven,
-            'dandg': getCapacityDandG
-        }
-
-        # Normalize anchor type
-        anchType_clean = self.anchType.lower().replace(' ', '')
-
-        # Find function
-        capacity_func = capacity_dispatch.get(anchType_clean)
-        if capacity_func is None:
-            raise ValueError(f"Unknown anchor type '{self.anchType}' for anchor capacity calculation.")
-
-        # Get ground conditions
-        soil_profile = ground_conds.get(self.soil_type)
-        if soil_profile is None:
-            raise ValueError(f"Ground condition '{self.soil_type}' not found in provided ground_conds.")
-
-        # Determine if load transfer is needed
-        z0 = soil_profile[0][0]
-        
-        # Load transfer if padeye is embedded
-        if zlug > z0:
-            if line_type is None or d is None or w is None:
-                try:
-                    line_type, d, w, nolugload = self.getLineProperties()
-                except ValueError:
-                    print('[Warning] No mooring attachment found. Trying anchor-level line properties...')
-                    line_type = getattr(self, 'line_type', None)
-                    d = getattr(self, 'd', None)
-                    w = getattr(self, 'w', None)
-                    nolugload = False
-                
-                    if line_type is None or d is None or w is None:
-                        print('[Fallback] Using default chain properties.')
-                        line_type = 'chain'
-                        d = 0.16
-                        w = 5000.0
-
-                if nolugload:
-                    Ha, Va = Hm, Vm
-                else:
-                    loads = getTransferLoad(soil_profile, self.soil_type, Tm=np.sqrt(Hm**2 + Vm**2), thetam=thetam, zlug=zlug, line_type=line_type, d=d, w=w, plot=plot)
-                    Ta = loads['Ta']
-                    thetaa = loads['thetaa']
-                    Ha = Ta*np.cos(thetaa)
-                    Va = Ta*np.sin(thetaa)
-            else:
-                loads = getTransferLoad(soil_profile, self.soil_type, Tm=np.sqrt(Hm**2 + Vm**2), thetam=thetam, zlug=zlug, line_type=line_type, d=d, w=w, plot=plot)
-                Ta = loads['Ta']
-                thetaa = loads['thetaa']
-                Ha = Ta*np.cos(thetaa)
-                Va = Ta*np.sin(thetaa)
-        else:
-            Ha = Hm
-            Va = Vm
-            
-        # Call the capacity function based on anchor type
-        if anchType_clean == 'suction':
-            D = self.dd['design']['D']
-            L = self.dd['design']['L']
-            zlug = self.dd['design']['zlug']
-            
-            results = capacity_func(soil_profile, self.soil_type, D, L, zlug, Ha, Va, plot=plot)
-
-        elif anchType_clean in ['sepla', 'dea', 'depla', 'vla', 'plate']:
-            results = capacity_func(soil_profile, self.soil_type, self.B, self.L, zlug, self.beta, Ha, Va, plot=plot)
-
-        elif anchType_clean == 'torpedo':
-            results = capacity_func(soil_profile, self.soil_type, self.D1, self.D2, self.L1, self.L2, zlug, self.ballast, Ha, Va, plot=plot)
-
-        elif anchType_clean == 'helical':
-            results = capacity_func(soil_profile, self.soil_type, self.D, self.L, self.d, zlug, Ha, Va, plot=plot)
-
-        elif anchType_clean == 'driven':
-            y, z, results = capacity_func(soil_profile, self.soil_type, self.L, self.D, zlug, Ha, Va, plot=plot)
-
-        elif anchType_clean == 'dandg':
-            y, z, results = capacity_func(soil_profile, self.soil_type, self.L, self.D, zlug, Ha, Va, plot=plot)
-
-        else:
-            raise ValueError(f"Anchor type '{self.anchType}' not supported.")
-
-        # --- Standardize and store capacity results ---
-        self.capacity_results = {
-            'Hmax': results.get('Horizontal max.', np.nan),
-            'Vmax': results.get('Vertical max.', np.nan),
-            'UC': results.get('Unity check', np.nan),
-            'Ha': Ha,
-            'Va': Va,
-            'zlug': zlug,
-            'z0': z0
-        }
-
-        # Add mass if weight is available
-        if 'Weight pile' in results:
-            self.capacity_results['Weight pile'] = results['Weight pile']
-        if 'Weight plate' in results:
-            self.capacity_results['Weight plate'] = results['Weight plate']
-
-        # Special cases for displacement-based anchors
-        if anchType_clean in ['driven_pile', 'dandg_pile']:
-            self.capacity_results['Lateral displacement'] = results.get('Lateral displacement', np.nan)
-            self.capacity_results['Rotational displacement'] = results.get('Rotational displacement', np.nan)
-
-        return results
-                    
-    def getSafetyFactor(self):
-        '''
-        Calculate the safety factor based on the unity checks stored in capacity results.
-
-        Returns
-        -------
-        dict
-            Dictionary containing safety factors.
-        '''
-        anchType_clean = self.anchType.lower().replace(' ', '')
-
-        if anchType_clean in ['helical', 'driven', 'dandg']:
-            UCv = self.capacity_results.get('Unity check (vertical)', None)
-            UCh = self.capacity_results.get('Unity check (horizontal)', None)
-
-            if UCv is None or UCh is None:
-                print("Warning: Vertical or Horizontal Unity Check not found in capacity results. Returning NaN.")
-                return {'SF_vertical': np.nan, 'SF_horizontal': np.nan}
-
-            SFv = 1.0/UCv if UCv != 0 else np.inf
-            SFh = 1.0/UCh if UCh != 0 else np.inf
-
-            return {'SF_vertical': SFv, 'SF_horizontal': SFh}
-
-        else:
-            UC = self.capacity_results.get('UC', None)
-
-            if UC is None:
-                print("Warning: Unity Check (UC) not found in capacity results. Returning NaN.")
-                return {'SF_combined': np.nan}
-
-            SF = 1.0/UC if UC != 0 else np.inf
-
-            return {'SF_combined': SF} 
-
-    def getCost(self, costDict='default'):
-        '''
-        Calculate the cost of the anchor based on material, installation, and decommissioning costs.
-
-        Parameters
-        ----------
-        costDict : str or dict, optional
-            If 'default', uses mean values from Task 49 Design Basis ranges.
-            If dict or yaml path, loads user-defined cost dictionaries.
-
-        Returns
-        -------
-        float
-            Total cost of the anchor.
-        '''
-        if isinstance(costDict, str) and costDict != 'default':
-            import yaml
-            costDict = yaml.load(costDict, Loader=yaml.FullLoader)
-
-        anchType = self.dd['type']
-
-        if costDict == 'default':
-            matCostDict = {
-                'suction_pile': 4.435,
-                'DEA': 5.705,
-                'SEPLA': 5.705,
-                'DEPLA': 5.705,
-                'VLA': 5.705,
-                'torpedo_pile': 5.0,
-                'helical_pile': 6.0,
-                'driven_pile': 4.0,
-                'dandg_pile': 5.5
-            }
-            instCostDict = {
-                'suction_pile': 2.0,
-                'DEA': 1.5,
-                'SEPLA': 1.5,
-                'DEPLA': 1.5,
-                'VLA': 1.5,
-                'torpedo_pile': 2.5,
-                'helical_pile': 3.0,
-                'driven_pile': 2.0,
-                'dandg_pile': 2.2
-            }
-            decomCostDict = {
-                'suction_pile': 1.0,
-                'DEA': 0.8,
-                'SEPLA': 0.8,
-                'DEPLA': 0.8,
-                'VLA': 0.8,
-                'torpedo_pile': 1.2,
-                'helical_pile': 1.5,
-                'driven_pile': 1.0,
-                'dandg_pile': 1.1
-            }
-        else:
-            matCostDict = costDict.get('material', {})
-            instCostDict = costDict.get('install', {})
-            decomCostDict = costDict.get('decom', {})
-
-        keyFail = True
-
-        # Ensure mass is available
-        if self.mass is None or self.mass == 0:
-            # Try to extract from capacity_results if already available
-            if 'Weight pile' in self.capacity_results:
-                self.mass = self.capacity_results['Weight pile']/self.g
-            elif 'Weight plate' in self.capacity_results:
-                self.mass = self.capacity_results['Weight plate']/self.g
-            else:
-                # If capacity_results missing, attempt to calculate capacity to retrieve weight
-                if 'soil_properties' in self.dd:
-                    self.getAnchorCapacity(plot=False)
-                    if 'Weight pile' in self.capacity_results:
-                        self.mass = self.capacity_results['Weight pile']/self.g
-                    elif 'Weight plate' in self.capacity_results:
-                        self.mass = self.capacity_results['Weight plate']/self.g
-                    else:
-                        print('Warning: Weight not found after capacity calculation, setting mass to 0.')
-                        self.mass = 0
-                else:
-                    print('Soil properties needed to calculate anchor mass for cost. Setting mass to 0.')
-                    self.mass = 0
-
-        # Calculate material cost based on mass
-        if anchType in matCostDict:
-            self.cost['Material Cost'] = matCostDict[anchType]*self.mass
-            keyFail = False
-        else:
-            raise KeyError(f'Anchor type {anchType} not found in material cost dictionary.')
-
-        # Install and decom costs if available
-        self.cost['Installation Cost'] = instCostDict.get(anchType, 0.0)
-        self.cost['Decommissioning Cost'] = decomCostDict.get(anchType, 0.0)
-
-        # Total cost
-        self.cost['Total Cost'] = (self.cost['Material Cost'] +
-                                   self.cost['Installation Cost'] +
-                                   self.cost['Decommissioning Cost'])
-
-        return sum(self.cost.values())
-
-
-    def getSize(self, geom, geomKeys, geomBounds=None, loads=None, minfs={'Ha':1.0,'Va':1.0}, 
-                 lambdap_con=[4,6], zlug_fix=False, FSdiff_max=None, plot=False):
-        '''
-        Resize the anchor dimensions to meet the target safety factor and geometric constraints.
-    
-        Parameters
-        ----------
-        geom : list
-            Starting guess geometry values.
-        geomKeys : list
-            List of keys matching the geom list values (e.g., 'L', 'D', 'zlug').
-        geomBounds : list, optional
-            List of tuples of upper and lower bounds for each geometry value.
-        loads : dict, optional
-            Dictionary of maximum anchor loads.
-        minfs : dict, optional
-            Minimum factors of safety in horizontal and vertical directions.
-        lambdap_con : list, optional
-            Constraint for L/D parameter as [min, max].
-        zlug_fix : bool, optional
-            Whether zlug should be fixed (True) or updated (False).
-        FSdiff_max : dict, optional
-            Maximum allowable difference between achieved FS and target FS.
-        plot : bool, optional
-            Whether to plot results.
-    
-        Returns
-        -------
-        None
-        '''
-        from scipy.optimize import minimize
-        import numpy as np
-    
-        anchType = self.dd['type']
-    
-        if loads is None:
-            loads = self.loads
-    
-        # Compute thetam internally from Hm and Vm
-        Hm = loads['Hm']
-        Vm = loads['Vm']
-        thetam = np.degrees(np.arctan2(Vm, Hm))
-        zlug = loads['zlug']
-        
-        # Read mooring properties from anchor attributes
-        line_type = self.line_type
-        d = self.d
-        w = self.w
-    
-        Ha, Va = self.getLugForces(
-            ground_conds=self.dd.get('soil_properties'),
-            Hm=Hm,
-            Vm=Vm,
-            thetam=thetam,
-            zlug=zlug,
-            line_type=line_type,
-            d=d,
-            w=w,
-            plot=plot
-        )
-    
-        ground_conds = self.dd.get('soil_properties')
-    
-        input_loads = {'Ha': Ha*minfs['Ha'], 'Va': Va*minfs['Va']}
-    
-        # Objective: minimize weight
-        def objective_(vars, geomKeys, Ha, Va, ground_conds, thetam, zlug, plot):
-            newGeom = dict(zip(geomKeys, vars))
-            self.dd['design'].update(newGeom)
-            if 'suction' in self.dd['type'] and not zlug_fix:
-                self.dd['design']['zlug'] = (2/3)*newGeom['L']
-            results = self.getCapacityAnchor(ground_conds, Ha, Va, thetam, zlug, plot=plot)
-            return results.get('Weight pile', results.get('Weight plate', 1e6))
-    
-        # Constraints
-        def conFun_lambdap_(vars, lambdap_con, geomKeys):
-            newGeom = dict(zip(geomKeys, vars))
-            lambdap = newGeom['L']/newGeom['D']
-            return min(lambdap - lambdap_con[0], lambdap_con[1] - lambdap)
-    
-        def conFun_UC_(vars, Ha, Va, ground_conds, thetam, zlug, plot):
-            results = self.getCapacityAnchor(ground_conds, Ha, Va, thetam, zlug, plot=plot)
-            return results.get('Unity check', 0) - 1
-    
-        def conFun_H_(vars, Ha, Va, ground_conds, thetam, zlug, plot):
-            results = self.getCapacityAnchor(ground_conds, Ha, Va, thetam, zlug, plot=plot)
-            FS = self.getFS()
-            return FS.get('SF_horizontal', FS.get('SF_combined', 0)) - 1
-    
-        def conFun_V_(vars, minfs, Ha, Va, ground_conds, thetam, zlug, plot):
-            results = self.getCapacityAnchor(ground_conds, Ha, Va, thetam, zlug, plot=plot)
-            FS = self.getFS()
-            if minfs['Va'] == 0:
-                return 1
-            return FS.get('SF_vertical', FS.get('SF_combined', 0)) - 1
-    
-        # Initial geometry setup
-        startGeom = dict(zip(geomKeys, geom))
-        self.dd['design'].update(startGeom)
-    
-        if not 'zlug' in self.dd['design']:
-            if 'suction' in anchType and not zlug_fix:
-                self.dd['design']['zlug'] = (2/3)*startGeom['L']
-            else:
-                self.dd['design']['zlug'] = 0
-    
-        if zlug_fix and 'zlug' in geomKeys:
-            zlug_loc = geomKeys.index('zlug')
-            geomKeys.pop(zlug_loc)
-            geom.pop(zlug_loc)
-            if geomBounds:
-                geomBounds.pop(zlug_loc)
-    
-        initial_guess = geom
-    
-        # Setup constraints
-        constraints = []
-    
-        if 'suction' in anchType:
-            constraints.append({'type': 'ineq', 'fun': conFun_lambdap_, 'args': (lambdap_con, geomKeys)})
-    
-        if 'torpedo' in anchType or 'suction' in anchType:
-            constraints.append({'type': 'ineq', 'fun': conFun_UC_, 'args': (Ha, Va, ground_conds, thetam, zlug, plot)})
-        else:
-            constraints.append({'type': 'ineq', 'fun': conFun_H_, 'args': (Ha, Va, ground_conds, thetam, zlug, plot)})
-            constraints.append({'type': 'ineq', 'fun': conFun_V_, 'args': (minfs, Ha, Va, ground_conds, thetam, zlug, plot)})
-    
-        print('Starting optimization of anchor size')
-    
-        if geomBounds is None:
-            solution = minimize(objective_, initial_guess, args=(geomKeys, Ha, Va, ground_conds, thetam, zlug, plot),
-                                 method='COBYLA', constraints=constraints,
-                                 options={'rhobeg': 0.1, 'catol': 0.001})
-        else:
-            solution = minimize(objective_, initial_guess, args=(geomKeys, Ha, Va, ground_conds, thetam, zlug, plot),
-                                 method='COBYLA', constraints=constraints, bounds=geomBounds,
-                                 options={'rhobeg': 0.1, 'catol': 0.001})
-    
-        # Update final geometry
-        endGeom = dict(zip(geomKeys, solution.x))
-        print('Optimized geometry: ', endGeom)
-        self.dd['design'].update(endGeom)
-    
-        if 'suction' in anchType and not zlug_fix:
-            self.dd['design']['zlug'] = (2/3)*self.dd['design']['L']
-            zlug = self.dd['design']['zlug']  # update local zlug
-    
-        self.getCapacityAnchor(ground_conds, Ha, Va, thetam, zlug, plot=plot)
-        
-    def getSizeSuction(self, geom, geomKeys, geomBounds=None, loads=None, minfs={'Ha':1.6,'Va':2}, 
-                 lambdap_con=[4,8], zlug_fix=False, plot=False):
-        '''
-        Two-stage optimization:
-        Stage 1 - Grow anchor to satisfy UC <= 1.
-        Stage 2 - Minimize weight while keeping UC <= 1 and satisfying L/D constraints.
-        '''
-    
-        anchType = self.dd['type']
-    
-        if loads is None:
-            loads = self.loads
-    
-        Hm = loads['Hm']
-        Vm = loads['Vm']
-        zlug = self.dd['design']['zlug']
-        thetam = np.degrees(np.arctan2(Vm, Hm))
-    
-        line_type = self.line_type
-        d = self.d
-        w = self.w
-    
-        initial_guess = [self.dd['design']['L'], self.dd['design']['D']]
-        bounds = geomBounds if geomBounds else [(5.0, 30.0), (2.0, 5.0)]
-
-        ground_conds = self.dd.get('soil_properties')
-    
-        # --- Stage 1: Safety First ---
-        def safety_objective(vars):
-            L, D = vars
-            self.dd['design']['L'] = L
-            self.dd['design']['D'] = D
-            self.dd['design']['zlug'] = (2/3) * L
-    
-            Ha, Va = self.getLugForces(
-                ground_conds=ground_conds,
-                Hm=Hm,
-                Vm=Vm,
-                thetam=thetam,
-                zlug=self.dd['design']['zlug'],
-                line_type=line_type,
-                d=d,
-                w=w,
-                plot=False
-            )
-    
-            self.getCapacityAnchor(
-                ground_conds=ground_conds,
-                Hm=Hm,
-                Vm=Vm,
-                thetam=thetam,
-                zlug=self.dd['design']['zlug'],
-                line_type=line_type,
-                d=d,
-                w=w,
-                plot=False
-            )
-    
-            UC = self.capacity_results.get('UC', 2.0)
-            return max(0.0, UC - 1.0)**2
-    
-        minimize(
-            safety_objective,
-            initial_guess,
-            method='COBYLA',
-            bounds=bounds,
-            options={'rhobeg': 0.1, 'catol': 0.001, 'maxiter': 300}
-        )
-    
-        # --- Stage 2: Weight Minimization ---
-        def weight_objective(vars):
-            L, D = vars
-            self.dd['design']['L'] = L
-            self.dd['design']['D'] = D
-            self.dd['design']['zlug'] = (2/3) * L
-    
-            Ha, Va = self.getLugForces(
-                ground_conds=ground_conds,
-                Hm=Hm,
-                Vm=Vm,
-                thetam=thetam,
-                zlug=self.dd['design']['zlug'],
-                line_type=line_type,
-                d=d,
-                w=w,
-                plot=False
-            )
-    
-            self.getCapacityAnchor(
-                ground_conds=ground_conds,
-                Hm=Hm,
-                Vm=Vm,
-                thetam=thetam,
-                zlug=self.dd['design']['zlug'],
-                line_type=line_type,
-                d=d,
-                w=w,
-                plot=False
-            )
-    
-            return self.capacity_results.get('Weight pile', 1e9)
-    
-        def constraint_uc(vars):
-            L, D = vars
-            return 1.0 - self.capacity_results.get('UC', 2.0)
-        
-        def constraint_fs_horizontal(vars):
-            L, D = vars
-            return (self.capacity_results.get('Hmax', 0) / self.capacity_results.get('Ha', 1)) - minfs['Ha']
-        
-        def constraint_fs_vertical(vars):
-            L, D = vars
-            return (self.capacity_results.get('Vmax', 0) / self.capacity_results.get('Va', 1)) - minfs['Va']
-
-        def constraint_lambda_min(vars):
-            L, D = vars
-            return (L/D) - lambdap_con[0]
-    
-        def constraint_lambda_max(vars):
-            L, D = vars
-            return lambdap_con[1] - (L/D)
-    
-        result = minimize(
-            weight_objective,
-            [self.dd['design']['L'], self.dd['design']['D']],
-            method='COBYLA',
-            constraints=[
-                {'type': 'ineq', 'fun': constraint_fs_horizontal},
-                {'type': 'ineq', 'fun': constraint_fs_vertical},
-                {'type': 'ineq', 'fun': constraint_lambda_min},
-                {'type': 'ineq', 'fun': constraint_lambda_max}
-            ],
-            bounds=bounds,
-            options={'rhobeg': 0.5, 'catol': 0.01, 'maxiter': 100}
-        )
-    
-        # Update final geometry
-        endGeom = dict(zip(geomKeys, result.x))
-        print('Optimized geometry:', endGeom)
-        self.dd['design'].update(endGeom)
-    
-        if 'suction' in anchType and not zlug_fix:
-            self.dd['design']['zlug'] = (2/3) * self.dd['design']['L']
-    
-        self.getCapacityAnchor(
-            ground_conds=ground_conds,
-            Hm=Hm,
-            Vm=Vm,
-            thetam=thetam,
-            zlug=self.dd['design']['zlug'],
-            line_type=line_type,
-            d=d,
-            w=w,
-            plot=plot
-        )
-    
-        print('\nFinal Optimized Anchor:')
-        print('Design:', self.dd['design'])
-        print('Capacity Results:', self.capacity_results)
-
-    def getCombinedPlot(self):
-        '''
-        Create a single plot showing the suction pile and the inverse catenary overlay in the same coordinate system.
-        '''
-        from famodel.anchors.anchors_famodel_profile.capacity_load import getTransferLoad
-        from famodel.anchors.anchors_famodel_profile.capacity_plots import plot_suction
-        import numpy as np
-        import matplotlib.pyplot as plt
-    
-        if self.anchType.lower() != 'suction':
-            raise NotImplementedError("getCombinedPlot only supports suction piles for now.")
-    
-        # Extract key parameters
-        design = self.dd['design']
-        D = design['D']
-        L = design['L']
-        zlug = design['zlug']
-        soil_type = self.soil_type
-        soil_profile = self.dd['soil_properties'][soil_type]
-        z0 = soil_profile[0][0]
-    
-        Hm = self.loads['Hm']
-        Vm = self.loads['Vm']
-        thetam = self.loads.get('thetam', np.degrees(np.arctan2(Vm, Hm)))
-    
-        line_type = getattr(self, 'line_type', 'chain')
-        d = getattr(self, 'd', 0.16)
-        w = getattr(self, 'w', 5000.0)
-    
-        # Get inverse catenary path
-        result = getTransferLoad(
-            soil_profile, soil_type, np.sqrt(Hm**2 + Vm**2), thetam, zlug, line_type, d, w, plot=False
-        )
-        drag_values  = np.array(result['drag_values'])
-        depth_values = np.array(result['depth_values'])
-        depth_values = -depth_values[::-1]
-        Tm = result['Tm']; thetam = result['thetam']
-        Ta = result['Ta']; thetaa = result['thetaa']        
-    
-        # Transform to suction pile coordinate system
-        x_start = D/2 + drag_values[0]
-        z_start = zlug
-        drag_transformed  = x_start - drag_values
-        depth_transformed = z_start + (depth_values - depth_values[0])
-    
-        # Set up plot
-        fig, ax = plt.subplots(figsize=(5, 5))
-    
-        # Plot suction pile
-        plot_suction(soil_profile, soil_type, L, D, zlug=zlug, title='', ax=ax)
-    
-        # Overlay inverse catenary
-        ax.plot(drag_transformed, depth_transformed, color='b', lw=2.0, label='Inverse Catenary')
-    
-        # Annotate ends
-        ax.plot(drag_transformed[-1], depth_transformed[-1], 'ro', label='Mudline End')
-        ax.plot(drag_transformed[0], depth_transformed[0], 'go', label='Embedded End')
-        
-        n = 2e6
-        # Add load vectors
-        ax.arrow(drag_transformed[-1], depth_transformed[-1], Tm*np.cos(np.deg2rad(thetam))/n, -Tm*np.sin(np.deg2rad(thetam))/n,
-                 head_width=0.25, head_length=0.5, color='r', label='Mudline Load')
-        ax.arrow(drag_transformed[0], depth_transformed[0], Ta*np.cos(thetaa)/n, -Ta*np.sin(thetaa)/n,
-                 head_width=0.25, head_length=0.5, color='g', label='Padeye Load')
-    
-        # Finalize plot
-        xmax = max(drag_transformed[-1] + D, 2*D)
-        ax.set_xlim(-D, xmax)
-        ax.set_title('Suction Pile with Inverse Catenary')
-        ax.legend()
-        ax.grid(True)
-        plt.tight_layout()
-        plt.show()
diff --git a/famodel/anchors/anchors_famodel/__init__.py b/famodel/anchors/anchors_famodel/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/famodel/anchors/anchors_famodel/capacity_dandg.py b/famodel/anchors/anchors_famodel/capacity_dandg.py
index 284c4339..da8f9fa6 100644
--- a/famodel/anchors/anchors_famodel/capacity_dandg.py
+++ b/famodel/anchors/anchors_famodel/capacity_dandg.py
@@ -1,17 +1,13 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from scipy.interpolate import interp1d
-from scipy import linalg
-import inspect
+from .support_soils import rock_profile
+from .support_solvers import fd_solver
+from .support_pycurves import py_Lovera
+from .support_plots import plot_pile, plot_pycurve
 
-###################################
-#### Pile Geometry and Loading ####
-###################################
-
-def getCapacityDandG(profile, L, D, zlug, V, H, plot=True):
-    '''
-    Models a laterally loaded pile using the p-y method. The solution for
+def getCapacityDandG(profile_map, location_name, L, D, zlug, Ha, Va, plot=True):
+    '''Models a laterally loaded pile using the p-y method. The solution for
     lateral displacements is obtained by solving the 4th order ODE, 
     EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method.
     EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method.
@@ -19,43 +15,46 @@ def getCapacityDandG(profile, L, D, zlug, V, H, plot=True):
     Assumes that EI remains constant with respect to curvature i.e. pile
     material remains in the elastic region.
 
-    Input:
-    -----
-    profile     - A 2D array of depths (m) and corresponding undrained shear strength(Pa)
-                  Eg: array([[z1,UCS1],[z2,UCS2],[z3,UCS3]...])
-                  Use small values for Su (eg: 0.001) instead of zeros to avoid divisions 
-                  by zero but always start z at 0.0
-                  Example of a valid data point at the mudline is [0.0, 0.001]
-    L           - Length of pile (m)
-    D           - Outer diameter of pile (m)
-    V           - Axial force at pile head (N)
-    H           - Force at pile head (N)
-    M           - Moment at pile head (N*m)
-    n           - Number of elements (50 by default)
-    iterations  - Number of iterations to repeat calculation in order obtain convergence of 'y'
-                  (A better approach is to iterate until a predefined tolerance is achieved)
-
-    Output:
-    ------
-    y           - Lateral displacement at each node, length = n + 5, (n+1) real nodes and 4 imaginary nodes
-    z           - Vector of node locations along pile
-    resultsDandG- Dictionary with results
+    Parameters
+    ----------
+    profile : array
+        Rock profile as a 2D array: (z (m), UCS (MPa), Em (MPa))
+    soil_type : string
+        Select soil condition, 'rock'
+    L : float 
+        Pile length (m)
+    D : float 
+        Pile diameter (m)
+    zlug : float
+        Load eccentricity above the mudline or depth to mudline relative to the pile head (m)
+    Ha : float       
+        Horizontal load at pile lug elevation (N)
+    Va : float          
+        Vertical load at pile lug elevation (N)
+    plot : bool
+        Plot the p-y curve and the deflection pile condition if True
+
+    Returns
+    -------
+    y : array
+        Lateral displacement at each node (n+1 real + 4 imaginary)
+    z : array
+        Node location along pile (m)
+    resultsDandG : dict
+        Dictionary with lateral, rotational, vertical and pile weight results
     '''
-    
-    # Extract optional keyword arguments
-    # ls = 'x'
-
-    n = 50; iterations = 10; loc = 2
 
-    # Resistance factor
-    nhuc = 1; nhu = 0.3
-    delta_r = 0.08               # Mean roughness height [m]
+    profile_entry = next(p for p in profile_map if p['name'] == location_name)
+    layers = profile_entry['layers']
+      
+    n = 50; loc = 2              # Number of nodes (-)
+    tol = 1e-16; max_iter = 50   # Iteration parameters (-)
+    nhuc = 1; nhu = 0.3          # Resistance factor (-)
+    delta_r = 0.08               # Mean roughness height (m)
     
-    # Convert L and D to floating point numbers to avoid rounding errors
-    L = float(L)
-    D = float(D)
     t = (6.35 + D*20)/1e3        # Pile wall thickness (m), API RP2A-WSD
     E = 200e9                    # Elastic modulus of pile material (Pa)
+    fy = 350e6                   # Steel's yield strength (Pa)
     rhows = 66.90e3              # Submerged steel specific weight (N/m3)
     rhow = 10e3                  # Water specific weight (N/m3) 
     
@@ -75,299 +74,159 @@ def PileWeight(Len, Dia, tw, rho):
 
     # Initialize and assemble array/list of p-y curves at each real node
     z = np.zeros(N)
-    py_funs = []
     k_secant = np.zeros(N)
-    DQ = []
+    py_funs = []
+    DQ = []; pycurve_data = []
 
-    for i in [0,1]:              # Top two imaginary nodes
+    z0 = min(layer['top'] for layer in layers)
+    
+    for i in [0, 1]:              # Top two imaginary nodes
         z[i] = (i - 2)*h
         py_funs.append(0)
         k_secant[i] = 0.0
    
-    for i in range(2,n+3):       # Real nodes
+    for i in range(2, n+3):       # Real nodes
         z[i] = (i - 2)*h
-        # Extract rock profile data
-        zlug, f_UCS, f_Em = rock_profile(profile)
-        UCS, Em = f_UCS(z[i]), f_Em(z[i])
-        py_funs.append(py_Reese(z[i], D, zlug, UCS, Em, plot=plot))
-        k_secant[i] = py_funs[i](y[i])/y[i]
+        z_depth = z[i]
+        
+        matched_layer = next((layer for layer in layers if layer['top'] <= z_depth <= layer['bottom']), None)
+        if matched_layer is None or z_depth < matched_layer['top']:
+            py_funs.append(lambda y_val: np.zeros_like(y_val))
+            k_secant[i] = 0.0
+            DQ.append(0.0)
+            continue
+        
+        profile = [[matched_layer['top'],    matched_layer['UCS_top'], matched_layer['Em_top']],
+                   [matched_layer['bottom'], matched_layer['UCS_bot'], matched_layer['Em_bot']]]
+        z0_local, f_UCS, f_Em = rock_profile(profile)
+        
+        if z_depth < z0_local:
+            py_funs.append(lambda y_val: np.zeros_like(y_val))
+            k_secant[i] = 0.0
+            DQ.append(0.0)
+            continue
+
+        UCS = f_UCS(z_depth)
+        Em = f_Em(z_depth)
+        py_f, (y_vals, p_vals) = py_Lovera(z_depth, D, UCS, Em, zlug, z0, return_curve=True)
+        py_funs.append(py_f)
+        pycurve_data.append((y_vals, p_vals, z_depth, 'rock'))
+        # print(f"z_depth = {z_depth:.2f} m, UCS = {f_UCS(z_depth):.2e} Pa, Em = {f_Em(z_depth):.2e} Pa")
+        
         SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D
         alpha = 0.36*SCR - 0.0005
         fs = alpha*UCS
-        Dq = np.pi*D*fs*z[i]
+        Dq = np.pi*D*fs*z_depth
         DQ.append(Dq)
+        k_val = py_funs[i](y[i])
+        k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0
 
     for i in [n+3, n+4]:         # Bottom two imaginary nodes
         z[i] = (i - 2)*h
         py_funs.append(0)
         k_secant[i] = 0.0
-
-    # Track k_secant and current displacements
-
-    y1 = np.linspace(-2.*D, 2.*D, 500)
-    if plot:
-        plt.plot(y1, py_funs[loc](y1))
-        plt.xlabel('y (m)'), plt.ylabel('p (N/m)')
-        plt.grid(True)
-
-    for j in range(iterations):
-        # if j == 0: print 'FD Solver started!'
-        y = fd_solver(n, N, h, EI, V, H, zlug, k_secant)
-        if plot:
-            plt.plot(y[loc], k_secant[loc]*y[loc])
-
+        
+    Wp = PileWeight(L, D, t, rhows + rhow)
+    Wtip = DQ[-1] if DQ else 0.0
+    Vmax = Wp + Wtip    
+        
+    for j in range(max_iter):
+        y_old = y.copy()
+        y, *_ = fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant)
+        
+        # Update stiffness
         for i in range(2, n+3):
-            k_secant[i] = py_funs[i](y[i])/y[i]
-
-    # print(f'y_max = {max(y):.3f} m')
-    # print(f'rot_max = {np.rad2deg((y[2] - y[3])/h):.3f} deg')
-
-    resultsDandG = {}
-    resultsDandG['Lateral displacement'] = y[2]
-    resultsDandG['Rotational displacement'] = np.rad2deg((y[2] - y[3])/h)
-    resultsDandG['Axial capacity'] = DQ[-1]
-    resultsDandG['Weight'] = PileWeight(L, D, t, (rhows + rhow))
-    
-    return y[2:-2], z[2:-2], resultsDandG
-
-#################
-#### Solvers ####
-#################
-
-def fd_solver(n, N, h, EI, V, H, zlug, k_secant):
-    '''
-    Solves the finite difference equations from 'py_analysis_1'. This function should be run iteratively for
-    non-linear p-y curves by updating 'k_secant' using 'y'. A single iteration is sufficient if the p-y curves
-    are linear.
-
-    Input:
-    -----
-    n        - Number of elements
-    N        - Total number of nodes
-    h        - Element size
-    EI       - Flexural rigidity of pile
-    V        - Axial force at pile head
-    H        - Shear at pile head/tip
-    M        - Moment at pile head/tip
-    k_secant - Secant stiffness from p-y curves
-
-    Output:
-    ------
-    y        - Lateral displacement at each node
-    '''
-    M = H*zlug
+            if callable(py_funs[i]):
+                k_secant[i] = py_funs[i](y[i])/y[i] if y[i] != 0 else 0.0
     
-    # Initialize and assemble matrix
-    X = np.zeros((N,N))
-
-    # (n+1) finite difference equations for (n+1) real nodes
-    for i in range(0,n+1):
-        X[i,i]   =  1.0
-        X[i,i+1] = -4.0 + V*h**2/EI
-        X[i,i+2] =  6.0 - 2*V*h**2/EI + k_secant[i+2]*h**4/EI
-        X[i,i+3] = -4.0 + V*h**2/EI
-        X[i,i+4] =  1.0
-
-    # Curvature at pile head
-    X[n+1,1] =  1.0
-    X[n+1,2] = -2.0
-    X[n+1,3] =  1.0
-
-    # Shear at pile head
-    X[n+2,0] = -1.0
-    X[n+2,1] =  2.0 - V*h**2/EI
-    X[n+2,2] =  0.0
-    X[n+2,3] = -2.0 + V*h**2/EI
-    X[n+2,4] =  1.0
-
-    # Curvature at pile tip
-    X[n+3,-2] =  1.0
-    X[n+3,-3] = -2.0
-    X[n+3,-4] =  1.0
-
-    # Shear at pile tip
-    X[n+4,-1] =  1.0
-    X[n+4,-2] = -2.0 + V*h**2/EI
-    X[n+4,-3] =  0.0
-    X[n+4,-4] =  2.0 - V*h**2/EI
-    X[n+4,-5] = -1.0
-
-    # Initialize vector q
-    q = np.zeros(N)
-
-    # Populate q with boundary conditions
-    q[-3] = 2*H*h**3      # Shear at pile head
-    # q[-4] = M*h**2        # Moment at pile head
-
-    y = linalg.solve(EI*X, q)
-
-    return y
-
-###############################
-#### P-Y Curve Definitions ####
-###############################
-
-
-def py_Reese(z, D, zlug, UCS, Em, plot=True):
-
-    '''
-    Returns an interp1d interpolation function which represents the Reese (1997) p-y curve at the depth of interest.
-
-    Important: Make sure to import the interp1 function by running 'from scipy.interpolate import interp1d' in the main program.
+        # Check convergence
+        if np.linalg.norm(y - y_old, ord=2) < tol:
+            print(f'[Converged in {j+1} iterations]')
+            break
+    else:
+        print('[Warning: Solver did not converge]')
 
-    Input:
-    -----
-    z      - Depth relative to pile head (m)
-    D      - Pile diameter (m)
-    zlug   - Load eccentricity above the mudline or depth to mudline relative to the pile head (m)
-    UCS    - Undrained shear strength (Pa)
-    Em     - Effectve vertical stress (Pa)
-    RQD    - Rock quality designation, measures the quality of the rock core taken from a borehole.
-             Typically ranges from 25% (very weathered rock) to 100% (fresh rock).
 
-    Output:
-    ------
-    Returns an interp1d interpolation function which represents the p-y curve at the depth of interest.
-    'p' (N/m) and 'y' (m).
-    '''
-    z0 = 0
-    
-    #from scipy.interpolate import interp1d
-    #global var_Reese
-    
-    RQD = 69                     # Assumed good rock quality (hard rock)  
-    Dref = 0.305; nhu = 0.3; E = 200e9
-    t = (6.35 + D*20)/1e3        # Pile wall thickness (m), API RP2A-WSD
-    I  = np.pi*(D**4 - (D - 2*t)**4)/64.0
-    EI = E*I
-    alpha = -0.00667*RQD + 1
-    krm = 0.0005
-    
-    if (z - z0) < 0:
-        p_ur = 0
-    else:
-        if z < 3*D:
-            p_ur = alpha*UCS*D*(1 + 1.4*z/D)
-            #kir = (100 +400*z/(3*D))
-        else:
-            p_ur = 5.2*alpha*UCS*D
-            #kir = 500
-        
-    kir = (D/Dref)*2**(-2*nhu)*(EI/(Em*D**4))**0.284
-    Kir = kir*Em     
-    y_rm = krm*D
-    y_a = (p_ur/(2*y_rm**0.25*Kir))**1.333    
-   
-    # Normalized lateral displacement
-    N = 20
-    y = np.concatenate((-np.logspace(5,-3,N),[0],np.logspace(-3,5,N)))
+    if plot:
+        plot_pycurve(pycurve_data)
+
+        fig, ax = plt.subplots(figsize=(3, 5))
+        y0 = np.zeros_like(z[2:-2])
+        ax.plot(y0, z[2:-2], 'k', label='Original pile axis')
+        ax.plot(y[2:-2], z[2:-2], 'r', label='Deflected shape')
+        ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)')
+        ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)')
+        ax.set_xlabel('Lateral displacement (m)')
+        ax.set_ylabel('Depth (m)')
+        ax.set_xlim([-0.1*D, 0.1*D])
+        ax.set_ylim([L + 5, -2])
+        ax.grid(ls='--')
+        ax.legend() 
+            
+    # Relevant index of nodes
+    zlug_index = int(zlug/h); print(zlug_index)
+    ymax_index = np.argmax(y); print(ymax_index)    
     
-    p=[]; P=[];
-    for i in range (len(y)):
-        if abs(y[i]) < y_a: 
-            P = np.sign(y[i])*Kir*y[i]
-        elif abs(y[i]) > y_a:
-            P = min((p_ur/2)*(abs(y[i])/y_rm)**0.25,p_ur)
-        p.append(P)    
-   
-    p = np.array(p).squeeze()     
-    for j in range(len(y)):
-        if y[j] < 0:
-            p[j] = -1*p[j]
-        elif y[j] > 0:
-            p[j] = p[j]                            
- 
-    #var_Reese = inspect.currentframe().f_locals          
+    resultsDandG = {
+        'Vertical max.': Vmax,
+        'Lateral displacement': y[ymax_index],
+        'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h),
+        'Bending moment': None,
+        'Plastic moment': None,
+        'Plastic hinge': None,
+        'Hinge location': None,      
+        'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf,
+        'Unity check (horizontal)': 0.0,  # Placeholder; no Mp or Mi in current model
+        'Weight pile': PileWeight(L, D, t, rhows + rhow),
+        'p-y model': 'Lovera (2023)'}
     
-    f = interp1d(y, p)   # Interpolation function for p-y curve
-    if plot:    
-        plt.plot(y, p) 
-        plt.xlabel('y (m)') 
-        plt.ylabel('p (N/m)'),
-        plt.title('PY Curves - Reese (1997)')
-        plt.grid(True)
-        plt.xlim([-0.03*D, 0.03*D])
-        plt.ylim([min(p), max(p)])     
-        
-    return f      # This is f (linear interpolation of y-p)
+    return layers, y[2:-2], z[2:-2], resultsDandG
    
-#######################
-#### Rock Profile #####
-#######################
-
-def rock_profile(profile):
-    '''
-    Define the (weak) rock profile used by the p-y analyzer. Outputs 'interp1d' functions containing 
-    UCS and Em profiles to be used by the p-y curve functions.
-
-    Input:
-    -----
-    profile - A 2D tuple in the following format: ([depth (m), UCS (MPa), Em (MPa), py-model])
-              The soil profile should be defined relative to the pile/tower head (i.e. point of lateral load application)
-              so that any load eccentricities can be taken into account. An example soil profile is shown below.
-              Eg: array([[z0,UCS0,Em0, 'Reese'],
-                         [z1,UCS1,Em1, 'Reese'],
-                         [z2,UCS2,Em2, 'Reese'],
-                          ...])
-              *The current program cannot define layers with different p-y models. But it will added in the future.
-
-    Output:
-    ------
-    z0       - Depth of mudline relative to the pile head (m)
-    f_UCS    - 'interp1d' function containing undrained shear strength profile (Pa)
-    f_Em     - 'interp1d' function containing effective vertical stress profile (Pa)
-    '''
-
-    # Depth of mudline relative to pile head
-    z0 = float(profile[0][0])
+if __name__ == '__main__':
 
-    # Extract data from soil_profile array and zero strength virtual soil layer
-    # from the pile head down to the mudline
-    depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)])  # m  
-    UCS   = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)])  # MPa
-    Em    = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)])  # MPa
+    profile_map = [
+        {
+            'name': 'CPT_rock_1',
+            'x': 502000,
+            'y': 5725000,
+            'layers': [
+                {
+                    'top': 2.0, 'bottom': 5.0,
+                    'soil_type': 'rock',
+                    'UCS_top': 1.0, 'UCS_bot': 2.0,    # MPa
+                    'Em_top': 100, 'Em_bot': 200       # MPa
+                },
+                {
+                    'top': 5.0, 'bottom': 9.0,
+                    'soil_type': 'rock',
+                    'UCS_top': 2.0, 'UCS_bot': 3.0,    # MPa
+                    'Em_top': 200, 'Em_bot': 300       # MPa
+                },
+                {
+                    'top': 9.0, 'bottom': 30.0,
+                    'soil_type': 'rock',
+                    'UCS_top': 3.0, 'UCS_bot': 6.0,    # MPa
+                    'Em_top': 300, 'Em_bot': 400       # MPa
+                }
+            ]
+        }
+    ]
+
+    D = 3.0           # Diameter (m)
+    L = 10.0          # Length (m)
+    zlug = 1          # Padeye elevation (m)
+    Ha = 8.0e6        # Horizontal load (N)
+    Va = 3.0e6        # Vertical load (N)
+
+    layers, y, z, results = getCapacityDandG(profile_map, 'CPT_rock_1', L, D, zlug, Ha, Va, plot=True)
+
+    print('\n--- Results for DandG Pile in Layered Rock ---')
+    for key, val in results.items():
+        print(f'{key}: {val:.3f}' if isinstance(val, float) else f'{key}: {val}')
+        
+    plot_pile(layers, y, z, D, L, layers[0]['top'], zlug)    
 
-    # Define interpolation functions
-    f_UCS = interp1d(depth, UCS*1e6, kind='linear')  # Pa
-    f_Em  = interp1d(depth, Em*1e6, kind='linear')   # Pa
     
-    #var_rock_profile = inspect.currentframe().f_locals
-
-    return z0, f_UCS, f_Em
-
-if __name__ == '__main__':
 
-    profile = np.array([[0.0,  5, 7, 'Name of p-y model'],
-                        [25.0, 5, 7, 'Name of p-y model']])
     
-    L = 15
-    D = 1
-    zlug = 0
-    H0 = 318763.5
-    V0 = 297554.3 
     
-    H = 1e4; V = 1e4
-
-    values_H =[]; values_V =[]      
-    
-    y, z, results = getCapacityDandG(profile, L=L, D=D, zlug=zlug, V=V0, H=H0)
-
-    while results['Lateral displacement']< 0.05*D and results['Rotational displacement'] < 0.25:
-               
-        y, z, results = getCapacityDandG(profile, L=L, D=D, zlug=zlug, V=V, H=H)
-        
-        H += 10000
-                   
-    values_H.append(H); H_ratio = np.array(values_H)/H0
-          
-    y0 = np.zeros(len(z))
-    #Plot deflection profile of pile
-    fig, ax = plt.subplots(figsize=(3,5))    
-    ax.plot(y0,z,'black')
-    ax.plot(y,z,'r')
-    ax.set_xlabel('Displacement [m]')
-    ax.set_ylabel('Depth below pile head [m]')
-    ax.set_ylim([L + 2, -2])
-    ax.set_xlim([-0.1*D, 0.1*D])
-    ax.grid(ls='--')
-    fig.show()
\ No newline at end of file
diff --git a/famodel/anchors/anchors_famodel_map/capacity_driven_map.py b/famodel/anchors/anchors_famodel/capacity_driven.py
similarity index 86%
rename from famodel/anchors/anchors_famodel_map/capacity_driven_map.py
rename to famodel/anchors/anchors_famodel/capacity_driven.py
index cd7c58ee..420e336d 100644
--- a/famodel/anchors/anchors_famodel_map/capacity_driven_map.py
+++ b/famodel/anchors/anchors_famodel/capacity_driven.py
@@ -1,12 +1,12 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from .capacity_soils_map import clay_profile, sand_profile, rock_profile
-from .capacity_solvers import fd_solver
-from .capacity_pycurves_map import py_Matlock, py_API, py_Reese
-from .capacity_plots_map import plot_pile, plot_pycurve
+from .support_soils import clay_profile, sand_profile, rock_profile
+from .support_solvers import fd_solver
+from .support_pycurves import py_Matlock, py_API, py_Reese
+from .support_plots import plot_pile, plot_pycurve
 
-def getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=True):
+def getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=False):
     '''Models a laterally loaded pile using the p-y method. The solution for
     lateral displacements is obtained by solving the 4th order ODE, EI*d4y/dz4
     EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method.
@@ -51,7 +51,7 @@ def getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=True)
     layers = profile_entry['layers']
 
     n = 50; loc = 2              # Number of nodes (-)
-    tol = 1e-16; max_iter = 50   # Iteration parameters (-)
+    tol = 1e-16; max_iter = 100  # Iteration parameters (-)
     nhuc = 1; nhu = 0.3          # Resistance factor (-)
     delta_r = 0.08               # Mean roughness height (m)
     
@@ -103,7 +103,7 @@ def SoilWeight(Len, Dia, tw, gamma_soil):
         soil_type = matched_layer['soil_type']
 
         if soil_type == 'clay':
-            profile = [[matched_layer['top'], matched_layer['Su_top'], matched_layer['gamma_top']],
+            profile = [[matched_layer['top'],    matched_layer['Su_top'], matched_layer['gamma_top']],
                        [matched_layer['bottom'], matched_layer['Su_bot'], matched_layer['gamma_bot']]]
             z0_local, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile)
             if z_depth < z0_local:
@@ -115,7 +115,7 @@ def SoilWeight(Len, Dia, tw, gamma_soil):
             sigma_v_eff = f_sigma_v_eff(z_depth)
             gamma = f_gamma(z_depth)
             alpha = f_alpha(z_depth)
-            py_f, (y_vals, p_vals) = py_Matlock(z_depth, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=z0_local, return_curve=True)
+            py_f, (y_vals, p_vals) = py_Matlock(z_depth, D, gamma, Su, sigma_v_eff, z0=z0_local, return_curve=True)
             py_funs.append(py_f)
             pycurve_data.append((y_vals, p_vals, z_depth, 'clay'))
             Vo = np.pi*D*alpha*Su*z_depth**2
@@ -124,7 +124,7 @@ def SoilWeight(Len, Dia, tw, gamma_soil):
             k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0
 
         elif soil_type == 'sand':
-            profile = [[matched_layer['top'], matched_layer['phi_top'], matched_layer['gamma_top'], matched_layer['Dr_top']],
+            profile = [[matched_layer['top'],    matched_layer['phi_top'], matched_layer['gamma_top'], matched_layer['Dr_top']],
                        [matched_layer['bottom'], matched_layer['phi_bot'], matched_layer['gamma_bot'], matched_layer['Dr_bot']]]
             z0_local, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile)
             if z_depth < z0_local:
@@ -132,9 +132,11 @@ def SoilWeight(Len, Dia, tw, gamma_soil):
                 k_secant[i] = 0.0
                 PileShaft.append(0.0)
                 continue
+            phi = f_phi(z_depth)
             sigma_v_eff = f_sigma_v_eff(z_depth)
+            Dr = f_Dr(z_depth)
             delta = f_delta(z_depth)
-            py_f, (y_vals, p_vals) = py_API(z_depth, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=z0_local, return_curve=True)
+            py_f, (y_vals, p_vals) = py_API(z_depth, D, phi, sigma_v_eff, Dr, z0=z0_local, return_curve=True)
             py_funs.append(py_f)
             pycurve_data.append((y_vals, p_vals, z_depth, 'sand'))
             fs = delta * sigma_v_eff
@@ -144,7 +146,7 @@ def SoilWeight(Len, Dia, tw, gamma_soil):
             k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0
 
         elif soil_type in ['rock', 'weak_rock']:
-            profile = [[matched_layer['top'], matched_layer['UCS_top'], matched_layer['Em_top']],
+            profile = [[matched_layer['top'],    matched_layer['UCS_top'], matched_layer['Em_top']],
                        [matched_layer['bottom'], matched_layer['UCS_bot'], matched_layer['Em_bot']]]
             z0_local, f_UCS, f_Em = rock_profile(profile)
             if z_depth < z0_local:
@@ -154,7 +156,7 @@ def SoilWeight(Len, Dia, tw, gamma_soil):
                 continue
             UCS = f_UCS(z_depth)
             Em = f_Em(z_depth)
-            py_f, (y_vals, p_vals) = py_Reese(z_depth, D, zlug, f_UCS, f_Em, z0=z0_local, return_curve=True)
+            py_f, (y_vals, p_vals) = py_Reese(z_depth, D, UCS, Em, z0=z0_local, return_curve=True)
             py_funs.append(py_f)
             pycurve_data.append((y_vals, p_vals, z_depth, 'rock'))
             SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D
@@ -216,22 +218,25 @@ def SoilWeight(Len, Dia, tw, gamma_soil):
         ax.legend()
 
     # Relevant index of nodes
-    zlug_index = int(zlug/h)
-    ymax_index = np.argmax(y)
+    y_pile = y[2:-2]
+    z_pile = z[2:-2]
+    ymax_index = np.argmax(np.abs(y_pile))
 
     resultsDriven = {
-        'Weight': PileWeight(L, D, t, rhows + rhow),
+        'Horizontal max.': abs(Mi)/abs(zlug) if zlug != 0 else 1e-6,
         'Vertical max.': Vmax,
-        'Lateral displacement': y[ymax_index],
-        'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h),
+        'Lateral displacement': y_pile[ymax_index],
+        'Rotational displacement': np.rad2deg(abs(y_pile[ymax_index - 1] - y_pile[ymax_index])/h),
         'Bending moment': abs(Mi),
         'Plastic moment': Mp,
         'Plastic hinge': hinge_formed,
-        'Hinge location': hinge_location,
-        'Horizontal max.': abs(Mi)/abs(zlug) if zlug != 0 else 1e-6,
+        'Hinge location': hinge_location,         
         'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf,
-        'Unity check (horizontal)': Ha/(abs(Mi)/abs(zlug)) if zlug != 0 else np.inf
-    }
+        'Unity check (horizontal)': Ha/(abs(Mi)/abs(zlug)) if zlug != 0 else np.inf,
+        'Weight pile': PileWeight(L, D, t, rhows + rhow)}
+    
+    print(f"Max lateral displacement: {y_pile[ymax_index]:.6f} m at z = {z_pile[ymax_index]:.2f} m")
+    print(f"Deflected tip: {y_pile[-1]:.6f} m at z = {z_pile[-1]:.2f} m")
 
     return layers, y[2:-2], z[2:-2], resultsDriven
 
diff --git a/famodel/anchors/anchors_famodel/capacity_drivenrock.py b/famodel/anchors/anchors_famodel/capacity_drivenrock.py
deleted file mode 100644
index d44f6e2f..00000000
--- a/famodel/anchors/anchors_famodel/capacity_drivenrock.py
+++ /dev/null
@@ -1,373 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.interpolate import interp1d
-from scipy import linalg
-import inspect
-
-###################################
-#### Pile Geometry and Loading ####
-###################################
-
-def getCapacityDrivenRock(profile, L, D, zlug, V, H, plot=True):
-    '''
-    Models a laterally loaded pile using the p-y method. The solution for
-    lateral displacements is obtained by solving the 4th order ODE, 
-    EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method.
-    EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method.
-
-    Assumes that EI remains constant with respect to curvature i.e. pile
-    material remains in the elastic region.
-
-    Input:
-    -----
-    profile     - A 2D array of depths (m) and corresponding undrained shear strength(Pa)
-                  Eg: array([[z1,UCS1],[z2,UCS2],[z3,UCS3]...])
-                  Use small values for Su (eg: 0.001) instead of zeros to avoid divisions 
-                  by zero but always start z at 0.0
-                  Example of a valid data point at the mudline is [0.0, 0.001]
-    L           - Length of pile (m)
-    D           - Outer diameter of pile (m)
-    V           - Axial force at pile head (N)
-    H           - Force at pile head (N)
-    M           - Moment at pile head (N*m)
-    n           - Number of elements (50 by default)
-    iterations  - Number of iterations to repeat calculation in order obtain convergence of 'y'
-                  (A better approach is to iterate until a predefined tolerance is achieved)
-
-    Output:
-    ------
-    y           - Lateral displacement at each node, length = n + 5, (n+1) real nodes and 4 imaginary nodes
-    z           - Vector of node locations along pile
-    resultsDrivenRock - Dictionary with results
-    '''
-    
-    # Extract optional keyword arguments
-    # ls = 'x'
-    n = 50; iterations = 10; loc = 2
-
-    # Resistance factor
-    nhuc = 1; nhu = 0.3 
-    delta_r = 0.08               # Mean roughness height [m]
-    
-    # Convert L and D to floating point numbers to avoid rounding errors
-    L = float(L)
-    D = float(D)
-    t = (6.35 + D*20)/1e3        # Pile wall thickness (m), API RP2A-WSD
-    E = 200e9                    # Elastic modulus of pile material (Pa)
-    rhows = 66.90e3              # Submerged steel specific weight (N/m3)
-    rhow = 10e3                  # Water specific weight (N/m3) 
-    
-    # Pile geometry
-    I = (np.pi/64.0)*(D**4 - (D - 2*t)**4)
-    EI = E*I
-    h = L/n                      # Element size
-    N = (n + 1) + 4              # (n+1) Real + 4 Imaginary nodes
-    
-    # Dry and wet mass of the pile    
-    def PileWeight(Len, Dia, tw, rho):
-        Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho
-        return Wp 
-
-    # Array for displacements at nodes, including imaginary nodes.
-    y = np.ones(N)*(0.01*D)      # An initial value of 0.01D was arbitrarily chosen
-
-    # Initialize and assemble array/list of p-y curves at each real node
-    z = np.zeros(N)
-    py_funs = []
-    k_secant = np.zeros(N)
-    DQ = []
-
-    for i in [0,1]:              # Top two imaginary nodes
-        z[i] = (i - 2)*h
-        py_funs.append(0)
-        k_secant[i] = 0.0
-   
-    for i in range(2,n+3):       # Real nodes
-        z[i] = (i - 2)*h
-        # Extract rock profile data
-        zlug, f_UCS, f_Em = rock_profile(profile)
-        UCS, Em = f_UCS(z[i]), f_Em(z[i])
-        py_funs.append(py_Reese(z[i], D, zlug, UCS, Em))
-        k_secant[i] = py_funs[i](y[i])/y[i]
-        SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D
-        alpha = 0.36*SCR - 0.0005
-        fs = alpha*UCS
-        Dq = np.pi*D*fs*z[i]
-        DQ.append(Dq)
-
-    for i in [n+3, n+4]:         # Bottom two imaginary nodes
-        z[i] = (i - 2)*h
-        py_funs.append(0)
-        k_secant[i] = 0.0
-
-    # Track k_secant and current displacements
-
-    y1 = np.linspace(-2.*D, 2.*D, 500)
-    if plot:
-        plt.plot(y1, py_funs[loc](y1))
-        plt.xlabel('y (m)'), plt.ylabel('p (N/m)')
-        plt.grid(True)
-
-    for j in range(iterations):
-        # if j == 0: print 'FD Solver started!'
-        y = fd_solver(n, N, h, EI, V, H, zlug, k_secant)
-        
-        if plot:
-            plt.plot(y[loc], k_secant[loc]*y[loc])
-
-        for i in range(2, n+3):
-            k_secant[i] = py_funs[i](y[i])/y[i]
-
-    # print(f'y_max = {y[2]:.3f} m')
-    # print(f'rot_max = {np.rad2deg((y[2] - y[3])/h):.3f} deg')
-
-    resultsDrivenRock = {}
-    resultsDrivenRock['Lateral displacement'] = y[2]
-    resultsDrivenRock['Rotational displacement'] = np.rad2deg((y[2] - y[3])/h)
-    resultsDrivenRock['Axial capacity'] = DQ[-1]
-    resultsDrivenRock['Weight'] = PileWeight(L, D, t, (rhows + rhow))
-    
-    return y[2:-2], z[2:-2], resultsDrivenRock
-
-#################
-#### Solvers ####
-#################
-
-def fd_solver(n, N, h, EI, V, H, zlug, k_secant):
-    '''
-    Solves the finite difference equations from 'py_analysis_1'. This function should be run iteratively for
-    non-linear p-y curves by updating 'k_secant' using 'y'. A single iteration is sufficient if the p-y curves
-    are linear.
-
-    Input:
-    -----
-    n        - Number of elements
-    N        - Total number of nodes
-    h        - Element size
-    EI       - Flexural rigidity of pile
-    V        - Axial force at pile head
-    H        - Shear at pile head/tip
-    M        - Moment at pile head/tip
-    k_secant - Secant stiffness from p-y curves
-
-    Output:
-    ------
-    y        - Lateral displacement at each node
-    '''
-    M = H*zlug
-    
-    # Initialize and assemble matrix
-    X = np.zeros((N,N))
-
-    # (n+1) finite difference equations for (n+1) real nodes
-    for i in range(0,n+1):
-        X[i,i]   =  1.0
-        X[i,i+1] = -4.0 + V*h**2/EI
-        X[i,i+2] =  6.0 - 2*V*h**2/EI + k_secant[i+2]*h**4/EI
-        X[i,i+3] = -4.0 + V*h**2/EI
-        X[i,i+4] =  1.0
-
-    # Curvature at pile head
-    X[n+1,1] =  1.0
-    X[n+1,2] = -2.0
-    X[n+1,3] =  1.0
-
-    # Shear at pile head
-    X[n+2,0] = -1.0
-    X[n+2,1] =  2.0 - V*h**2/EI
-    X[n+2,2] =  0.0
-    X[n+2,3] = -2.0 + V*h**2/EI
-    X[n+2,4] =  1.0
-
-    # Curvature at pile tip
-    X[n+3,-2] =  1.0
-    X[n+3,-3] = -2.0
-    X[n+3,-4] =  1.0
-
-    # Shear at pile tip
-    X[n+4,-1] =  1.0
-    X[n+4,-2] = -2.0 + V*h**2/EI
-    X[n+4,-3] =  0.0
-    X[n+4,-4] =  2.0 - V*h**2/EI
-    X[n+4,-5] = -1.0
-
-    # Initialize vector q
-    q = np.zeros(N)
-
-    # Populate q with boundary conditions
-    q[-3] = 2*H*h**3      # Shear at pile head
-    # q[-4] = M*h**2        # Moment at pile head
-
-    y = linalg.solve(EI*X, q)
-
-    return y
-
-###############################
-#### P-Y Curve Definitions ####
-###############################
-
-def py_Reese(z, D, zlug, UCS, Em):
-    '''
-    Returns an interp1d interpolation function which represents the Reese (1997) p-y curve at the depth of interest.
-
-    Important: Make sure to import the interp1 function by running 'from scipy.interpolate import interp1d' in the main program.
-
-    Input:
-    -----
-    z      - Depth relative to pile head (m)
-    D      - Pile diameter (m)
-    zlug   - Load eccentricity above the mudline or depth to mudline relative to the pile head (m)
-    UCS    - Undrained shear strength (Pa)
-    Em     - Effectve vertical stress (Pa)
-    RQD    - Rock quality designation, measures the quality of the rock core taken from a borehole.
-             Typically ranges from 25% (very weathered rock) to 100% (fresh rock).
-
-    Output:
-    ------
-    Returns an interp1d interpolation function which represents the p-y curve at the depth of interest.
-    'p' (N/m) and 'y' (m).
-    '''
-    z0 = 0
-    
-    #from scipy.interpolate import interp1d
-    #global var_Reese
-    
-    RQD = 52                     # Assumed fair rock quality (moderately weathered rocks) 
-    Dref = 0.305; nhu = 0.3; E = 200e9
-    t = (6.35 + D*20)/1e3        # Pile wall thickness (m), API RP2A-WSD
-    I  = np.pi*(D**4 - (D - 2*t)**4)/64.0
-    EI = E*I
-    alpha = -0.00667*RQD + 1
-    krm = 0.0005
-
-    if (z - z0) < 0:
-        p_ur = 0
-    else:    
-        if z < 3*D:
-            p_ur = alpha*UCS*D*(1 + 1.4*z/D)
-            #kir = (100 +400*z/(3*D))
-        else:
-            p_ur = 5.2*alpha*UCS*D
-            #kir = 500
-        
-    kir = (D/Dref)*2**(-2*nhu)*(EI/(Em*D**4))**0.284
-    Kir = kir*Em     
-    y_rm = krm*D
-    y_a = (p_ur/(2*y_rm**0.25*Kir))**1.333    
-   
-    # Normalized lateral displacement
-    N = 20
-    y = np.concatenate((-np.logspace(5,-3,N),[0],np.logspace(-3,5,N)))
-    
-    p=[]; P=[];
-    for i in range (len(y)):
-        if abs(y[i]) < y_a: 
-            P = np.sign(y[i])*Kir*y[i]
-        elif abs(y[i]) > y_a:
-            P = min((p_ur/2)*(abs(y[i])/y_rm)**0.25,p_ur)
-        p.append(P)    
-   
-    p = np.array(p).squeeze()     
-    for j in range(len(y)):
-        if y[j] < 0:
-            p[j] = -1*p[j]
-        elif y[j] > 0:
-            p[j] = p[j]                            
- 
-    #var_Reese = inspect.currentframe().f_locals          
-    
-    f = interp1d(y, p)   # Interpolation function for p-y curve
-         
-    plt.plot(y, p) 
-    plt.xlabel('y (m)') 
-    plt.ylabel('p (N/m)'),
-    plt.title('PY Curves - Reese (1997)')
-    plt.grid(True)
-    plt.xlim([-0.03*D, 0.03*D])
-    plt.ylim([min(p), max(p)])     
-        
-    return f      # This is f (linear interpolation of y-p)
-   
-#######################
-#### Rock Profile #####
-#######################
-
-def rock_profile(profile):
-    '''
-    Define the (weak) rock profile used by the p-y analyzer. Outputs 'interp1d' functions containing 
-    UCS and Em profiles to be used by the p-y curve functions.
-
-    Input:
-    -----
-    profile - A 2D tuple in the following format: ([depth (m), UCS (MPa), Em (MPa), py-model])
-              The soil profile should be defined relative to the pile/tower head (i.e. point of lateral load application)
-              so that any load eccentricities can be taken into account. An example soil profile is shown below.
-              Eg: array([[z0,UCS0,Em0, 'Reese'],
-                         [z1,UCS1,Em1, 'Reese'],
-                         [z2,UCS2,Em2, 'Reese'],
-                          ...])
-              *The current program cannot define layers with different p-y models. But it will added in the future.
-
-    plot_profile - Plot Su vs depth profile. Choose 'Yes' to plot.
-
-    Output:
-    ------
-    z0       - Depth of mudline relative to the pile head (m)
-    f_UCS    - 'interp1d' function containing undrained shear strength profile (Pa)
-    f_Em     - 'interp1d' function containing effective vertical stress profile (Pa)
-    '''
-
-    # Depth of mudline relative to pile head
-    z0 = float(profile[0][0])
-
-    # Extract data from soil_profile array and zero strength virtual soil layer
-    # from the pile head down to the mudline
-    depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)])  # m  
-    UCS   = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)])  # MPa
-    Em    = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)])  # MPa
-
-    # Define interpolation functions
-    f_UCS = interp1d(depth, UCS*1e6, kind='linear') # Pa
-    f_Em  = interp1d(depth, Em*1e6, kind='linear')  # Pa
-    
-    #var_rock_profile = inspect.currentframe().f_locals
-
-    return z0, f_UCS, f_Em
-
-if __name__ == '__main__':
-
-    profile = np.array([[0.0,  5, 7, 'Name of p-y model'],
-                        [25.0, 5, 7, 'Name of p-y model']])
-    
-    L = 20
-    D = 1.5
-    zlug = 2*D
-    H0 = 3187635
-    V0 = 2975543 
-
-    H = 1e4; V = 1e4    
-
-    values_H =[]; values_V =[]      
-
-    y, z, results = getCapacityDrivenRock(profile, L=L, D=D, zlug=zlug, V=V0, H=H0)
-    
-    while results['Lateral displacement']< 0.05*D and results['Rotational displacement'] < 0.25:
-           
-        y, z, results = getCapacityDrivenRock(profile, L=L, D=D, zlug=zlug, V=V, H=H)
-    
-        H += 10000
-               
-    values_H.append(H); H_ratio = np.array(values_H)/H0
-          
-    y0 = np.zeros(len(z))
-    #Plot deflection profile of pile
-    fig, ax = plt.subplots(figsize=(3,5))    
-    ax.plot(y0,z,'black')
-    ax.plot(y,z,'r')
-    ax.set_xlabel('Displacement [m]')
-    ax.set_ylabel('Depth below pile head [m]')
-    ax.set_ylim([L + 2,-2])
-    ax.set_xlim([-0.1*D, 0.1*D])
-    ax.grid(ls='--')
-    fig.show()
\ No newline at end of file
diff --git a/famodel/anchors/anchors_famodel/capacity_drivensoil.py b/famodel/anchors/anchors_famodel/capacity_drivensoil.py
deleted file mode 100644
index b4cee31c..00000000
--- a/famodel/anchors/anchors_famodel/capacity_drivensoil.py
+++ /dev/null
@@ -1,628 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-###################################
-#### Pile Geometry and Loading ####
-###################################
-
-def getCapacityDrivenSoil(profile, soil_type, L, D, zlug, V, H, plot=True):
-    
-    '''Models a laterally loaded pile using the p-y method. The solution for
-    lateral displacements is obtained by solving the 4th order ODE, EI*d4y/dz4
-    - F*d2y/dz2 + ky = 0 using the finite difference method.
-
-    Assumes that EI remains constant with respect to curvature i.e. pile
-    material remains in the elastic region.
-
-    Input:
-    -----
-    profile     - A 2D array of depths (m) and corresponding undrained shear strength(Pa)
-                  Eg: array([[z1,Su1],[z2,Su2],[z3,Su3]...])
-                  Use small values for Su (eg: 0.001) instead of zeros to avoid divisions by zero but always start z at 0.0
-                  Example of a valid data point at the mudline is [0.0, 0.001]
-    soil_type   - Select soil condition, 'clay' or 'sand'
-                  Assigns which p-y model to use, 'Matlock' or 'API'.
-    L           - Length of pile (m)
-    D           - Outer diameter of pile (m)
-    zlug        - Load eccentricity above the mudline or depth to mudline relative to the pile head (m)
-    V           - Axial force at pile head (N), vertically downwards is postive.
-    H           - Force at pile head (N), shear causing clockwise rotation of pile is positive.
-    M           - Moment at pile head (N*m), moments causing tension on left side of pile is positive.
-    n           - Number of elements (50 by default)
-    iterations  - Number of iterations to repeat calculation in order obtain convergence of 'y'
-                  (A better approach is to iterate until a predefined tolerance is achieved but this requires additional
-                  coding so, I will implement this later.)
-
-    Output:
-    ------
-    y           - Lateral displacement at each node, length = n + 5, (n+1) real nodes and 4 imaginary nodes
-    z           - Vector of node locations along pile
-    '''
-
-    # Extract optional keyword arguments
-
-    ls = 'x'
-    n = 25; iterations = 10; loc=2
-    
-    # Convert L and D to floating point numbers to avoid rounding errors
-    L = float(L)
-    D = float(D)
-    t = (6.35 + D*20)/1e3       # Pile wall thickness (m), API RP2A-WSD
-    E = 200e9                   # Elastic modulus of pile material (Pa)
-    fy = 350e6                  # Yield strength of pile material (Pa)
-    rhows = 66.90e3             # Submerged steel specific weight (N/m3)
-    rhow = 10e3                 # Water specific weight (N/m3) 
-    
-    # Pile geometry
-    I = (np.pi/64.0)*(D**4 - (D - 2*t)**4)
-    EI = E*I
-    h  = L/n                    # Element size
-    N  = (n + 1) + 4            # (n+1) Real + 4 Imaginary nodes
-    
-    # Outer and inner surface of the pile skirt
-    def PileSurface(Len, Dia):
-        Sp = np.pi*Dia*Len
-        return Sp    
-    # Dry and wet mass of the pile    
-    def PileWeight(Len, Dia, tw, rho):
-        Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho 
-        return Wp 
-    # Mass of the soil plug      
-    def SoilWeight(Len, Dia, tw, gamma_soil): 
-        Wsoil =(np.pi/4)*(Dia - 2*tw)**2*Len*gamma_soil
-        return Wsoil
-
-    # Array for displacements at nodes, including imaginary nodes.
-    y = np.ones(N)*(0.01*D)     # An initial value of 0.01D was arbitrarily chosen
-
-    # Initialize and assemble array/list of p-y curves at each real node
-    z = np.zeros(N)
-    py_funs  = []; PileShaft =[]
-    k_secant = np.zeros(N)
-
-    for i in [0, 1]:            # Top two imaginary nodes
-        z[i] = (i - 2)*h
-        py_funs.append(0)
-        k_secant[i] = 0.0
-
-    # Extract soil profile data
-    if soil_type == 'clay':
-        z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile)
-
-    elif soil_type == 'sand':
-        z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile)
-
-    for i in range(2, n+3):    # Real nodes
-        z[i] = (i - 2)*h      
-        if soil_type == 'clay':
-            Su, sigma_v_eff, gamma, alpha = f_Su(z[i]), f_sigma_v_eff(z[i]), f_gamma(z[i]), f_alpha(z[i])
-            py_funs.append(py_Matlock(z[i], D, zlug, Su, sigma_v_eff, gamma, plot=plot))
-            Vo = np.pi*D*alpha*Su*z[i]**2
-            PileShaft.append(Vo)
-            Vmax = PileWeight(L, D, t, rhows) + SoilWeight(L, D, t, gamma) + PileShaft[-1]
-            
-        elif soil_type == 'sand':
-            phi, sigma_v_eff, gamma, Dr, delta = f_phi(z[i]), f_sigma_v_eff(z[i]), f_gamma(z[i]), f_Dr(z[i]), f_delta(z[i])
-            py_funs.append(py_API(z[i], D, zlug, phi, sigma_v_eff, Dr, plot=plot))
-            fs = delta*sigma_v_eff
-            Vo = np.pi*D*fs*z[i]
-            PileShaft.append(Vo)
-            Vmax = PileWeight(L, D, t, rhows) + SoilWeight(L, D, t, gamma) + PileShaft[-1]
-        
-        k_secant[i] = py_funs[i](y[i])/y[i]
-
-    for i in [n+3, n+4]:        # Bottom two imaginary nodes
-        z[i] = (i - 2)*h
-        py_funs.append(0)
-        k_secant[i] = 0.0
-
-    y1 = np.linspace(-2.*D, 2.*D, 500)
-    if plot:
-        plt.plot(y1, py_funs[loc](y1))
-        plt.xlabel('y (m)'), plt.ylabel('p (N/m)')
-        plt.grid(True)
-
-    for j in range(iterations):
-        # if j == 0: print 'FD Solver started!'
-        y, Mi, Mp, hinge_formed, hinge_location = fd_solver(n, N, h, D, t, fy, EI, V, H, zlug, k_secant)
-
-        for i in range(2, n+3):
-            k_secant[i] = py_funs[i](y[i])/y[i]
-
-
-    resultsDrivenSoil = {}
-    # Populate q with boundary conditions
-    if zlug <= 0:      
-        # print(f'y_max = {max(y):.3f} m')
-        # print(f'rot_max = {np.rad2deg((y[2] - y[3])/h):.3f} deg')
-               
-        resultsDrivenSoil['Lateral displacement'] = max(y)
-        resultsDrivenSoil['Rotational displacement'] = np.rad2deg((y[2] - y[3])/h)
-        resultsDrivenSoil['Axial capacity'] = Vmax
-        resultsDrivenSoil['Pile weight'] = PileWeight(L, D, t, (rhows + rhow))
-   
-    else:
-        # print(f'y_max = {max(y):.3f} m')
-        # print(f'Mi = {Mi:.3f} Nm')
-        
-        resultsDrivenSoil['Lateral displacement'] = max(y)
-        resultsDrivenSoil['Bending moment'] = Mi
-        resultsDrivenSoil['Plastic moment'] = Mp
-        resultsDrivenSoil['Plastic hinge'] = hinge_formed
-        resultsDrivenSoil['Hinge location'] = hinge_location
-        resultsDrivenSoil['Axial capacity'] = Vmax
-        resultsDrivenSoil['Weight'] = PileWeight(L, D, t, (rhows + rhow))
-    
-    return y[2:-2], z[2:-2], resultsDrivenSoil
-
-#################
-#### Solvers ####
-#################
-
-def fd_solver(n, N, h, D, t, fy, EI, V, H, zlug, k_secant):
-    '''Solves the finite difference equations from 'py_analysis_1'. This function should be run iteratively for
-    non-linear p-y curves by updating 'k_secant' using 'y'. A single iteration is sufficient if the p-y curves
-    are linear.
-
-    Input:
-    -----
-    n         - Number of elements
-    N         - Total number of nodes
-    h         - Element size
-    EI        - Flexural rigidity of pile
-    V         - Axial force at pile head/zlug depth
-    H         - Shear at pile head/zlug depth
-    M         - Moment at pile head/zlug depth
-    zlug      - Load eccentricity above the mudline or depth to mudline relative to the pile head (m)
-    k_secant  - Secant stiffness from p-y curves
-
-    Output:
-    ------
-    y_updated - Lateral displacement at each node
-    '''
-    
-    from scipy import linalg
-    
-    # Identify the node corresponding to zlug
-    zlug_index = int(zlug/h)  # Index for the node corresponding to zlug
-
-    # Initialize and assemble matrix
-    X = np.zeros((N, N))
-
-    # (n+1) finite difference equations for (n+1) real nodes
-    for i in range(0, n+1):
-        X[i,i]   =  1.0
-        X[i,i+1] = -4.0 + V*h**2/EI
-        X[i,i+2] =  6.0 - 2*V*h**2/EI + k_secant[i+2]*h**4/EI
-        X[i,i+3] = -4.0 + V*h**2/EI
-        X[i,i+4] =  1.0
-
-    # Curvature at pile head
-    X[n+1,1]   =  1.0
-    X[n+1,2]   = -2.0
-    X[n+1,3]   =  1.0
-
-    # Shear at pile head
-    X[n+2,0]   = -1.0
-    X[n+2,1]   =  2.0 - V*h**2/EI
-    X[n+2,2]   =  0.0
-    X[n+2,3]   = -2.0 + V*h**2/EI
-    X[n+2,4]   =  1.0
-
-    # Curvature at pile tip
-    X[n+3,-2]   =  1.0
-    X[n+3,-3]   = -2.0
-    X[n+3,-4]   =  1.0
-
-    # Shear at pile tip
-    X[n+4,-1]   =   1.0
-    X[n+4,-2]   =  -2.0 + V*h**2/EI
-    X[n+4,-3]   =   0.0
-    X[n+4,-4]   =   2.0 - V*h**2/EI
-    X[n+4,-5]   =  -1.0
-
-    # Initialize vector q
-    q = np.zeros(N)
-    #M = H*abs(zlug)
-
-    # Populate q with boundary conditions
-    if zlug <= 0:
-        q[-3] = 2*H*h**3      # Shear at pile head
-        #q[-4] = M*h**2        # Moment at pile head
-    else:
-        q[zlug_index]      = 2*H*h**3     # Shear at pile head
-        #q[zlug_index + 1]  = M*h**2      # Moment at pile head
-               
-    y = linalg.solve(EI*X, q)
-
-    # Compute the plastic moment capacity Mp
-    Zp = (1/6)*(D**3 - (D - 2*t)**3)  # Plastic section modulus for hollow pile (m3)
-    Mp = Zp*fy                        # Plastic moment capacity (N/m)
-
-    # Check for plastic hinge formation
-    Mi, Mp, hinge_formed, hinge_location = plastic_hinge(y, h, EI, Mp)
-    
-    return y, Mi, Mp, hinge_formed, hinge_location
-
-###############################
-#### P-Y Curve Definitions ####
-###############################
-
-def py_Matlock(z, D, zlug, Su, sigma_v_eff, gamma, plot=True):
-    
-    '''Returns an interp1d interpolation function which represents the Matlock (1970) p-y curve at the depth of interest.
-    Important: Make sure to import the interp1 function by running 'from scipy.interpolate import interp1d' in the main program.
-
-    Input:
-    -----
-    z            - Depth relative to pile head (m)
-    D            - Pile diameter (m)
-    zlug         - Load eccentricity above the mudline or depth to mudline relative to the pile head (m)
-    Su           - Undrained shear strength (Pa)
-    sigma_v_eff  - Effective vertical stress (Pa)
-    gamma        - Effective unit weight of the soil (kN/m3)
-
-    Output:
-    ------
-    Returns an interp1d interpolation function which represents the p-y curve at the depth of interest.
-    'p' (N/m) and 'y' (m).
-    '''
-
-    from scipy.interpolate import interp1d
-    
-    z0 = 0
-    
-    # Strain at half the strength as defined by Matlock (1970). 
-    # Typically ranges from 0.005 (stiff clay) to 0.02 (soft clay).
-    epsilon_50 = 0.02          
-    # p-y curve properties
-    J = 0.5
-    
-    if zlug < 0:
-        # Scenario 1: zlug is negative (above mudline)
-        if (z - z0) < 0:
-            # No p-y curve between z = 0 and zlug
-            Nc = 0.0
-            z_cr = 1.0  # Dummy value to avoid crashing
-        else:
-            # Calculate p-y curve below zlug
-            Nc = 3.0 + sigma_v_eff/Su + J*(z - abs(zlug))/D
-            if Nc > 9.0: 
-                Nc = 9.0
-            z_cr = 6.0*D/(gamma*D/Su + J)
-
-    else:
-        # Scenario 2: zlug is positive (below mudline)
-        # Calculate p-y curve for the entire pile (all depths)
-        Nc = 3.0 + sigma_v_eff/Su + J*(z - zlug)/D
-        if Nc > 9.0: 
-            Nc = 9.0
-        z_cr = 6.0 * D/(gamma*D/Su + J)
-
-    p_ult = Su*Nc*D
-    y_50 = 2.5*epsilon_50*D
-
-    # Normalized lateral displacement
-    Y = np.concatenate((-np.logspace(3,-4,100),[0],np.logspace(-4,3,100)))
-
-    # Normalized p-y curves
-    P = 0.5*np.sign(Y)*abs(Y)**(1.0/3.0)  # sign(Y) and abs(Y) used since negative numbers cannot be raised to fractional powers
-                                          # Expression equivalent to P = 0.5*Y**(1.0/3.0) for Y>=0
-    for i in range(0,len(Y)):
-        if P[i] > 1.0:    P[i] = 1.0
-        elif P[i] < -1.0: P[i] = -1.0
-
-    # Un-normallized p-y curves
-    p = P*p_ult
-    y = Y*y_50
-
-    f = interp1d(y, p, kind='linear')   # Interpolation function for p-y curve
-
-    # Plot of p-y curve and check if 'k' is calculated correctly
-    if plot:
-        plt.plot(y, p,'-')
-        plt.xlabel('y (m)') 
-        plt.ylabel('p (N/m)')
-        plt.title('PY Curves - Matlock (1970)')
-        plt.grid(True)
-        plt.xlim([-2*D, 2*D])
-
-    return f   # This is f (linear interpolation of y-p)
-       
-def py_API(z, D, zlug, phi, sigma_v_eff, Dr, plot=True):
-    
-    '''Returns an interp1d interpolation function which represents the Matlock (1970) p-y curve at the depth of interest.
-
-    Important: Make sure to import the interp1 function by running 'from scipy.interpolate import interp1d' in the main program.
-
-    Input:
-    -----
-    z            - Depth relative to pile head (m)
-    D            - Pile diameter (m)
-    zlug         - Load eccentricity above the mudline or depth to mudline relative to the pile head (m)
-    phi          - Internal friction angle (deg)
-    sigma_v_eff  - Effectve vertical stress (Pa)
-
-    Output:
-    ------
-    Returns an interp1d interpolation function which represents the p-y curve at the depth of interest.
-    'p' (N/m) and 'y' (m).
-    '''
-    
-    from scipy.interpolate import interp1d
-       
-    # Interpolate coefficients depending on the effective friction angle
-    phi_ref = [  20,   25,   30,   35,   40]
-    C1_ref  = [0.80, 1.25, 1.90, 3.00, 4.50]
-    C2_ref  = [1.60, 2.10, 2.60, 3.40, 4.30]
-    C3_ref  = [  10,   15,   30,   55,  105]
-    
-    C1 = np.interp(phi, phi_ref, C1_ref)
-    C2 = np.interp(phi, phi_ref, C2_ref)
-    C3 = np.interp(phi, phi_ref, C3_ref)
-    
-    if (z - zlug) < 0:
-        # p-y curves for the virtual soil layer between the pile head and the mudline should have p=0
-        p_ult = 0.0
-    else:
-        try:
-            p_ult = min(C1*z + C2*D, C3*D)*sigma_v_eff
-        except ZeroDivisionError:
-            print("Division by zero! phi = 0.0 so z_cr cannot be calculated.")
-    
-    # Dr = 0.75     # Relative density of the soil (assumed)
-    k = (54.6*Dr**2 + 0.8*Dr + 1.8)*1e3
-    
-    # Normalized lateral displacement
-    N = 20
-    y = np.concatenate((-np.logspace(3,-4,N),[0],np.logspace(-4,3,N)))
-    A = max(3 - 0.8*z/D, 0.9)
-    ε = 1e-6
-    p = A*p_ult*np.tanh(k*z*y/(A*p_ult + ε))
-
-    f = interp1d(y, p, kind='linear')   # Interpolation function for p-y curve
-    
-    if plot:
-        # Plot of p-y curve and check if 'k' is calculated correctly
-        plt.plot(y, p,'-') 
-        plt.xlabel('y (m)') 
-        plt.ylabel('p (N/m)')
-        plt.title('PY Curves - API (1993)')
-        plt.grid(True)
-        plt.xlim([-0.10*D, 0.10*D])
-    # plt.ylim([min(y), max(y)])  # Adjust x-axis limits to match y values
-        
-    return f  # This is f (linear interpolation of y-p)
-
-########################
-#### Plastic Hinge #####
-########################
-
-def plastic_hinge(y, h, EI, Mp):
-    '''
-    Check for plastic hinge formation along the pile.
-    
-    Parameters:
-    ----------
-    y : ndarray
-        Lateral displacements at each node.
-    h : float
-        Element size (distance between nodes).
-    EI : float
-        Flexural rigidity of the pile (N*m²).
-    Mp : float
-        Plastic moment capacity of the pile section.
-    
-    Returns:
-    -------
-    Mp : float
-        Plastic moment of the pile section (Nm)
-    hinge_formed : bool
-        True if a plastic hinge forms, False otherwise.
-    hinge_location : int
-        Index of the node where the plastic hinge forms (if any).
-    '''
-    
-    hinge_formed = False
-    hinge_location = -1
-    Mi = []
-        
-    # Loop through each internal node and compute the bending moment
-    for i in range(1, len(y)-1):
-        # Approximate the bending moment at node i
-        Mint = EI*(y[i+1] - 2*y[i] + y[i-1])/h**2
-        Mi.append(Mint)
-        
-        # Check if the moment exceeds the plastic moment capacity
-        if Mint >= Mp:
-            hinge_formed = True
-            hinge_location = i
-            break  # Stop at the first plastic hinge formation
-    
-    return max(Mi), Mp, hinge_formed, hinge_location
-
-
-########################
-#### Soil Profiles #####
-########################
-
-def clay_profile(profile):
-    '''Define the clay profile used by the p-y analyzer. Outputs 'interp1d' functions containing Su and sigma'_v
-    profiles to be used by the p-y curve functions.
-
-    Input:
-    -----
-    profile      - A 2D tuple in the following format: ([Depth (m), Su (kPa), gamma (kN/m^3), py-model, model parameter])
-                   The soil profile should be defined relative to the pile/tower head (i.e. point of lateral load application)
-                   so that any load eccentricities can be taken into account. An example soil profile is shown below.
-                   Eg: array([[z0, Su0, gamma0, 'Matlock', 0.02],
-                              ...])
-
-                   *The current program cannot define layers with different p-y models. But it will added in the future.
-
-    Output:
-    ------
-    z0            - Depth of mudline relative to the pile head (m)
-    f_Su          - 'interp1d' function containing undrained shear strength profile (Pa)
-    f_sigma_v_eff - 'interp1d' function containing effective vertical stress profile (Pa)
-    f_gamma       - 'interp1d' function containing effective unit weight (kN/m3)
-    f_alpha       - Adhesion factor for clays
-    '''
-
-    from scipy.interpolate import interp1d
-
-    # Depth of mudline relative to pile head
-    z0 = float(profile[0][0])
-
-    # Extract data from profile array and zero strength virtual soil layer
-    # from the pile head down to the mudline
-    depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m
-    Su    = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # kPa
-    gamma = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # kN/m3
-
-    # Calculate sigma_v_eff at each depth
-    sigma_v_eff = np.zeros(len(depth))
-
-    for i in range(1, len(depth)):
-        sigma_v_eff[i] = sigma_v_eff[i-1] + gamma[i-1]*(depth[i] - depth[i-1])
-
-    # Define interpolation functions
-    f_Su          = interp1d(depth, Su*1000, kind='linear')           # Pa
-    f_sigma_v_eff = interp1d(depth, sigma_v_eff*1000, kind='linear')  # Pa
-    f_gamma       = interp1d(depth, gamma*1000, kind='linear')        # N/m3
-    
-    # Calculate f_psi and f_alpha at each depth (not as a scalar)
-    f_psi = lambda z: f_Su(z) / f_sigma_v_eff(z)
-    
-    def calc_alpha(z):
-        psi_val = f_psi(z)
-        if psi_val <= 1.0:
-            return min(0.5*psi_val**-0.50, 1)
-        else:
-            return min(0.5*psi_val**-0.25, 1)
-
-    # Create an interpolated adhesion factor function
-    f_alpha = lambda z: calc_alpha(z)
-
-    return z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha
-
-def sand_profile(profile):
-    '''Define the sand profile used by the p-y analyzer. Outputs 'interp1d' functions containing Su and sigma'_v
-    profiles to be used by the p-y curve functions.
-
-    Input:
-    -----
-    profile      - A 2D tuple in the following format: ([Depth (m), Su (kPa), gamma (kN/m^3), py-model, model parameter])
-                   The soil profile should be defined relative to the pile/tower head (i.e. point of lateral load application)
-                   so that any load eccentricities can be taken into account. An example soil profile is shown below.
-                   Eg: array([[z0, phi, gamma0, Dr, 'API', 0.02],
-                              ...])
-
-                   *The current program cannot define layers with different p-y models. But it will added in the future.
-
-    Output:
-    ------
-    z0            - Depth of mudline relative to the pile head (m)
-    f_phi         - 'interp1d' function containing effective friction angle (deg)
-    f_sigma_v_eff - 'interp1d' function containing effective vertical stress profile (Pa)
-    f_gamma       - 'interp1d' function containing effective unit weight (N/m3)
-    f_Dr          - Relative density of the soil (%)
-    f_delta       - Skin friction factor (sand/steel)
-    '''
-
-    from scipy.interpolate import interp1d
-
-    # Depth of mudline relative to pile head
-    z0 = float(profile[0][0])
-
-    # Extract data from profile array and zero strength virtual soil layer
-    # from the pile head down to the mudline
-    depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m
-    phi   = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # deg
-    gamma = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # kN/m3
-    Dr    = np.concatenate([np.array([0]), np.array([row[3] for row in profile],dtype=float)]) # %
-   
-    # Calculate sigma_v_eff and static loading factor at each depth
-    sigma_v_eff = np.zeros(len(depth))
-
-    for i in range(1, len(depth)):  
-        sigma_v_eff[i] = sigma_v_eff[i-1] + gamma[i-1]*(depth[i] - depth[i-1])
-
-    # Define interpolation functions
-    f_phi         = interp1d(depth, phi, kind='linear')                    # deg
-    f_sigma_v_eff = interp1d(depth, sigma_v_eff*1000, kind='linear')       # Pa
-    f_gamma       = interp1d(depth, gamma*1000, kind='linear')             # N/m3
-    f_Dr          = interp1d(depth, Dr, kind='linear')                     # %
-    
-    # Define delta as a function of Dr
-    def calc_delta(Dr_val):
-        if 35 <= Dr_val < 50:
-            return 0.29
-        elif 50 <= Dr_val < 65:
-            return 0.37
-        elif 65 <= Dr_val < 85:
-            return 0.46
-        elif Dr_val >= 85:
-            return 0.56
-        else:
-            return 0  # Default or error value for very low Dr values
-        
-    # Apply delta calculation to Dr profile
-    delta_values = np.array([calc_delta(Dr_val) for Dr_val in Dr])
-    f_delta      = interp1d(depth, delta_values, kind='linear')  # Interpolated delta values
-    
-    return z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta
-
-if __name__ == '__main__':
-
-    # CLAY
-    profile = np.array([[ 0.0,  10, 8],
-                        [75.0, 245, 5]])
-    # SAND
-    # profile = np.array([[ 0.0, 32, 8, 75],
-    #                     [75.0, 38, 9, 85]])  
-                       
-    L = 20
-    D = 1.5
-    zlug = 5*D
-    
-    H0 = 4260000
-    V0 = 1590000
-
-    H = 1e6; V = 1e6
-
-    values_H =[]; values_V =[]      
-    
-    y, z, results = getCapacityDrivenSoil(profile, soil_type='clay', L=L, D=D, zlug=zlug, V=V, H=H)
-
-    # while results['Lateral displacement']<= 0.05*D and results['Rotational displacement'] <= 0.25:
-               
-    #     y, z, results = getCapacityDrivenSoil(profile, soil_type='clay', L=L, D=D, zlug=zlug, V=V, H=H)
-        
-    #     H += 10000
-                   
-    # values_H.append(H); H_ratio = np.array(values_H)/H0       
-   
-    while results['Lateral displacement']<= 0.05*D and results['Bending moment'] <= results['Plastic moment']:
-               
-        y, z, results = getCapacityDrivenSoil(profile, soil_type='sand', L=L, D=D, zlug=zlug, V=V, H=H)
-        
-        H += 100000
-                   
-    values_H.append(H); H_ratio = np.array(values_H)/H0
-    
-    # y, z, results = getCapacityDrivenSoil(profile, soil_type='sand', L=L, D=D, zlug=zlug, V=V, H=H)
-    
-        
-    y0 = np.zeros(len(z))
-    #Plot deflection profile of pile
-    fig, ax = plt.subplots(figsize=(3,5))    
-    ax.plot(y0,z,'black')
-    ax.plot(y,z,'r')
-    ax.set_xlabel('Displacement [m]')
-    ax.set_ylabel('Depth below pile head [m]')
-    ax.set_ylim([L + 2,-2])
-    ax.set_xlim([-0.1*D, 0.1*D])
-    ax.grid(ls='--')
-    fig.show()
\ No newline at end of file
diff --git a/famodel/anchors/anchors_famodel/capacity_helical.py b/famodel/anchors/anchors_famodel/capacity_helical.py
index 0ccae2f4..c3e6bd62 100644
--- a/famodel/anchors/anchors_famodel/capacity_helical.py
+++ b/famodel/anchors/anchors_famodel/capacity_helical.py
@@ -1,93 +1,173 @@
 
 import numpy as np
+from .capacity_driven import getCapacityDriven, plot_pile
+from .support_soils import clay_profile, sand_profile
+from .support_plots import plot_helical
+
+def getCapacityHelical(profile_map, location_name, D, L, d, zlug, Ha, Va, plot=False):
+    '''Calculate the vertical and horizontal capacity of a helical pile using a soil profile.
+    The calculation is based on the soil profile, anchor geometry and inclined load.
 
-def getCapacityHelical(D, L, d, zlug, soil_type, gamma, Su0=None, k=None, phi=None, Dr=None):
-    
-    '''Calculate the inclined vertical load capacity of a helical pile in clay.
-    The calculation is based on the soil properties and anchor geometry.  
-    
     Parameters
     ----------
+    profile : array
+        Soil profiles (z, parameters)
+            Clay soil profile (z, Su, gamma)
+            Sand soil profile (z, phi, gamma, Dr)
+    soil_type : string
+        Select soil condition, 'clay' or 'sand'
     D : float 
-        Helix diameter [m]
+        Helix diameter (m)
     L : float 
-        Length shaft [m]
+        Shaft length (m)
     d : float 
-        Pile shaft diameter [m]
+        Shaft diameter (m)
     zlug : float
-        Embedded depth of the lug [m]
-    soil_type : string
-        Select soil condition, 'clay' or 'sand' 
-    gamma: float 
-        Effective unit weight of the soil [kN/m3] 
-    Su0 : float 
-        Undrained shear strength at the mudline (clay only) [kPa]
-    k : float 
-        Undrained shear strength gradient (clay only) [kPa/m]
-    phi : float
-        Angle of internal friction (sand only) [deg]
-    Dr : float
-        Relative density of the soil (%) (sand only) [-] 
-    
-    
+        Depth to padeye (m)
+    Ha : float
+        Horizontal load applied at padeye (N)
+    Va : float
+        Vertical load applied at padeye (N)
+    plot : bool
+        Plot the p-y curve and the deflection pile condition if True
+
     Returns
     -------
-    Qu: float
-       Maximum vertical capacity [kN]    
+    y : array
+        Lateral displacement at each node (real nodes only)
+    z : array
+        Node depth positions corresponding to y (m)
+    resultsHelical : dict
+        Dictionary containing displacements, moment capacity, hinge state and vertical capacity
     '''
-    
-    rhos= 78.50                      # Dry steel unit weight (kN/m3)
-    t = (6.35 + D*20)/1e3            # Suction pile wall thickness (m), API RP2A-WSD
-    
-    # Dry and wet mass of the pile    
+
+    profile_entry = next(p for p in profile_map if p['name'] == location_name)
+    layers = profile_entry['layers']
+
+    t = (6.35 + D*20)/1e3            # Helical pile wall thickness (m), API RP2A-WSD
+    rhows = 66.90e3                  # Submerged steel specific weight (kN/m3)
+    rhow = 10e3                      # Water specific weight (kN/m3) 
+
     def PileWeight(Len, Dia1, Dia2, tw, rho):
-        Wp = ((np.pi/4)*((Dia1**2 - (Dia1 - 2*tw)**2)*Len + (np.pi/4)*Dia2**2*tw))*rho
-        return Wp
-    # Define alpha coefficient (clay)
-    if soil_type == 'clay':
-        Su_av_L = Su0 + k*(L - D)        # Undrained shear strength values (average)
-        sigma_v_eff = gamma*zlug         # Effective soil stress (kN/m2)
-        psi_val = Su_av_L/sigma_v_eff    # Su/p0' for point in question (API DP 2A-WSD)   
-        if psi_val <= 1.0:
-            alpha = min(0.5*psi_val**-0.50, 1)
-        else:
-            alpha = min(0.5*psi_val**-0.25, 1)
+        return ((np.pi/4)*((Dia1**2 - (Dia1 - 2*tw)**2)*Len + (np.pi/4)*Dia2**2*tw))*rho
+
+    z_helix = zlug + (L - D)
+    matched_layer = next((layer for layer in layers if layer['top'] <= z_helix <= layer['bottom']), None)
+    if matched_layer is None:
+        raise ValueError(f"No soil layer found at z = {z_helix:.2f} m")
+
+    if matched_layer['soil_type'] == 'clay':
+        profile = [[matched_layer['top'],    matched_layer['Su_top'], matched_layer['gamma_top']],
+                   [matched_layer['bottom'], matched_layer['Su_bot'], matched_layer['gamma_bot']]]
+        z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile)
+
+        z_helix = np.clip(z_helix, matched_layer['top'], matched_layer['bottom'])
+        Su = f_Su(z_helix)
+        sigma_v_eff = max(f_sigma_v_eff(z_helix), 1.0)
+        psi_val = Su/sigma_v_eff
+        alpha = min(0.5*psi_val**-0.50, 1) if psi_val <= 1.0 else min(0.5 * psi_val**-0.25, 1)
+
+        Nc = min(6.0*(1 + 0.2*d/D), 9)
+        Qh = ((np.pi/4)*(D**2 - d**2)*Nc*Su + f_gamma(z_helix)*D)*0.75
+        Qs = np.pi*d*L*alpha*Su
+        Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs
+
+    elif matched_layer['soil_type'] == 'sand':
+        profile = [[matched_layer['top'],    matched_layer['phi_top'], matched_layer['gamma_top'], matched_layer['Dr_top']],
+                   [matched_layer['bottom'], matched_layer['phi_bot'], matched_layer['gamma_bot'], matched_layer['Dr_bot']]]
+        z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile)
+
+        z_helix = np.clip(z_helix, matched_layer['top'], matched_layer['bottom'])
+        gamma = f_gamma(z_helix)
+        Dr = f_Dr(z_helix)
+        delta = f_delta(z_helix)
+        phi = f_phi(z_helix)
+
+        Nq = 0.5*(12*phi)**(phi/54)
+        Qh = (np.pi/4)*(D**2 - d**2)*Nq*gamma*z_helix
+        Qs = np.pi*d*L*delta*gamma*z_helix
+        Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs
         
-    # Define delta as a function of Dr (sand)
-    def calc_delta(Dr_val):
-        if 35 <= Dr_val < 50:
-            return 0.29
-        elif 50 <= Dr_val < 65:
-            return 0.37
-        elif 65 <= Dr_val < 85:
-            return 0.46
-        elif Dr_val >= 85:
-            return 0.56
+    
+    Wp = PileWeight(L, D, d, t, (rhows + rhow))    
+
+    # Unity Check based only on vertical capacity
+    UC_vertical = Va/Qu
+
+    # Compute horizontal capacity using p-y method
+    layers, y, z, results_lateral = getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=True)
+
+    plot_pile(layers, y, z, D, L, z0=layers[0]['top'], zlug=zlug, hinge_location=None)
+
+    Hcap = results_lateral['Horizontal max.']
+    UC_horizontal = Ha/Hcap if Hcap != 0 else np.inf
+
+    resultsHelical = {     
+        'Horizontal max.': Hcap,
+        'Vertical max.': Qu,
+        'Lateral displacement': results_lateral['Lateral displacement'],
+        'Rotational displacement': results_lateral['Rotational displacement'],
+        'Unity check (horizontal)': UC_horizontal,
+        'Unity Check (vertical)': UC_vertical,
+        'Weight pile': Wp,}
+
+    if matched_layer['soil_type'] == 'clay':
+        resultsHelical['Su @ helix'] = Su
+        resultsHelical['Alpha'] = alpha
+    elif matched_layer['soil_type'] == 'sand':
+        resultsHelical['Dr @ helix'] = Dr
+        resultsHelical['Delta'] = delta
+        resultsHelical['Phi'] = phi
+
+    return layers, resultsHelical
+
+if __name__ == '__main__':
+    
+    profile_map = [
+        {
+            'name': 'CPT_1',
+            'x': 498234, 'y': 5725141,
+            'layers': [
+                {
+                    'top': 1.0, 'bottom': 3.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 9.0,
+                    'Su_top': 60, 'Su_bot': 50},
+                {
+                    'top': 3.0, 'bottom': 7.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 15.0, 'gamma_bot': 25.0,
+                    'Su_top': 100, 'Su_bot': 150},
+                # {
+                #     'top': 6.0, 'bottom': 15.0,
+                #     'soil_type': 'sand',
+                #     'gamma_top': 8.0, 'gamma_bot': 8.0,
+                #     'phi_top': 32, 'phi_bot': 38,
+                #     'Dr_top': 70, 'Dr_bot': 75},
+                {
+                    'top': 7.0, 'bottom': 15.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 25.0, 'gamma_bot': 50.0,
+                    'Su_top': 200, 'Su_bot': 400}]
+        }
+    ]  
+
+    D = 1.5        # Helix diameter (m)
+    L = 12.0       # Pile length (m)
+    d = 0.5        # Shaft diameter (m)
+    zlug = 3       # Padeye depth (m)
+    Ha = 30e3      # Horizontal load (N)
+    Va = 50e3      # Vertical load (N)
+
+    print("--- Clay Profile ---")
+    layers, resultsHelical = getCapacityHelical(profile_map, 'CPT_1', D, L, d, zlug, Ha, Va, plot=True)
+    for key, val in resultsHelical.items():
+        if isinstance(val, float):
+            print(f"{key}: {val:.3f}")
         else:
-            return 0  # Default or error value for very low Dr values        
-        
-    Wp = PileWeight(L, D, d, t, rhos) 
+            print(f"{key}: {val}")
     
-    # ----- Clay case -----
-    if soil_type == 'clay':       
-        Nc = 6.0*(1 + 0.2*d/D); 
-        Nc = np.where(Nc < 9, Nc, 9)
-        # Su is calculated, at the depth of the helix minus one helical plate diameter
-        # A reduction of 25% is applied for a moderately sensitive clay
-        Qh = ((np.pi/4)*(D**2 - d**2)*Nc*(Su0 + k*(L - D)) + gamma*D)*0.75 
-        Qs = np.pi*d*L*alpha*(Su0 + k*(L - D))
-        Qu = Qh + Qs
-                
-    # ----- Sand case -----
-    else:   
-        delta = calc_delta(Dr)
-        Nq = 0.5*(12*phi)**(phi/54) 
-        Qh = (np.pi/4)*(D**2 - d**2)*Nq*gamma*L
-        Qs = np.pi*d*L*delta*gamma*L
-        Qu = Qh + Qs
-                  
-    resultsHelical = {}
-    resultsHelical['Capacity'] = Qu          # Vertical capacity 
-    resultsHelical['Weight'] = Wp       # Dry weight of the helical pile (kN)
-
-    return resultsHelical
\ No newline at end of file
+    plot_helical(layers, D=D, L=L, d=d, z0=layers[0]['top'], zlug=zlug, n_helix=1, spacing=1.0, title='Helical Pile in Sand Profile')
+  
+
+
diff --git a/famodel/anchors/anchors_famodel/capacity_load.py b/famodel/anchors/anchors_famodel/capacity_load.py
index 33553bdd..70dd30e8 100644
--- a/famodel/anchors/anchors_famodel/capacity_load.py
+++ b/famodel/anchors/anchors_famodel/capacity_load.py
@@ -1,225 +1,211 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Wed May 29 15:53:52 2024
 
-@author: fmoreno
-"""
-
-import yaml      # Allow access to config file for user inputs
 import numpy as np
 import matplotlib.pyplot as plt
-from scipy.optimize import fsolve
+from .support_soils import clay_profile, sand_profile
+from .support_plots import plot_load
 
+def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=True):
+    '''Calculate the transfer load from mudline to main padeye using a layered soil profile.
 
-def getAnchorLoad(Tm, thetam, zlug, d, soil_type, gamma, Su0, k):
-    
-    '''Calculate the inclined load capacity of a Suction Caisson Anchor in sand or clay.
-    The calculation is based on the soil properties, anchor geometry, and the angle of inclined load.
-    Offshore Geotechnical Engineering (Randolph , page 323)
-    
     Parameters
     ----------
-    Tm : float 
-        Mooring line load at mudlevel [kN]
-    thetam : float 
-        Mooring line angle at mudlevel [deg]
-    zlug : float 
-        Embedded depth of the lug [m]
-    soil_type: string
-        Select soil condition, 'clay' or 'sand'
+    profile_map : list of dicts
+        Soil profile in profile_map format
+    Tm : float
+        Mooring line load at mudlevel (N)
+    thetam : float
+        Mooring line angle at mudlevel (deg)
+    zlug : float
+        Embedment depth of the lug (m)
+    line_type : str
+        'chain' or 'wire'
     d : float
-        Chain diameter [m]
-    Su0 : float 
-        Undrained shear strength at the mudline (clay) [kPa]
-    k : float 
-        Undrained shear strength gradient (clay) [kPa/m]
-  
-    Returns
-    -------
-    Ta : float 
-        Inclined load magnitude at the anchor lug [kN]
-    thetaa : float 
-        Inclined load angle at the anchor lug [deg]
-    '''
-    
-    # Setting bearing capacity values per soil type
-    if soil_type == 'clay':
-        Nc = 8.5; Ab=2.5; nhu=0.40   # Nc    - Bearing capacity factor (9 and 14) DNV-RP-E301 
-    elif soil_type == 'sand':        # Ab    - Effective unit bearing area (2.5 - 2.6 times chain dia)
-        Nc = 9; Ab=2.5; nhu=0.35     # nhu   - Friction coefficient between the mooring line and soil
-        
-    thetam =  np.radians(thetam)
-    
-    if soil_type == 'clay':                           
-        Su_av_lug = Su0*zlug + k*zlug**2/2
-        zaQav = Ab*d*Nc*Su_av_lug
-        
-    elif soil_type == 'sand':
-        zaQav = Ab*d*Nc*gamma*zlug**2/2
-        
-    def LoadTransfer(beta):
-        return(2*zaQav*np.e**(nhu*(beta - thetam)) - Tm*(beta**2 - thetam**2))
-    
-    thetaa = fsolve(LoadTransfer, thetam) 
-    thetaa = thetaa[0] 
-    Ta = Tm/(np.e**(nhu*(thetaa - thetam)))
-    
-    H = Ta*np.cos(thetaa) 
-    V = Ta*np.sin(thetaa)
-       
-    resultsLoad = {}
-    resultsLoad['load'] = Ta                   # Load magnitude @ lug
-    resultsLoad['angle'] = np.rad2deg(thetaa)  # Load angle @ lug
-    resultsLoad['H'] = H                       # Horizontal component @ lug
-    resultsLoad['V'] = V                       # Vertical component @ lug
-    
-    return resultsLoad
-
-def getTransferLoad(Tm, thetam, zlug, line_type, d, soil_type, Su0=None, 
-    k=None, gamma=None, phi= None, delta=None, w=None, plot=False):
-    '''Calculate the transfer load from the mudline to the main padeye 
-    elevation using the DNV standards. The calculation is based on the 
-    mooring line properties, anchor geometry and the load from MoorPy and 
-    RAFT.
-    
-    Parameters
-    ----------
-    Tm : float 
-        Mooring line load at mudlevel [kN]
-    thetam : float 
-        Mooring line angle at mudlevel [deg]
-    zlug : float 
-        Embedded depth of the lug [m]
-    line_type = string
-        Select line type, 'chain' or 'wire'
-    d : float
-        Chain diameter [m]
-    soil_type = string
-        Select soil condition, 'clay' or 'sand'  
-    Su0 : float 
-        Undrained shear strength at the mudline (clay only) [Pa]
-    k : float 
-        Undrained shear strength gradient (clay only) [Pa/m]
-    gamma: float
-        Effective unit weight of the soil (sand only) [N/m3]
-    phi : float
-        Friction angle (sand only) [deg]
-    delta: float
-        Interface friction angle at soil-anchor line (sand only) [deg]
+        Chain diameter (m)
     w : float
-        Mooring line unit weight [N/m]
-    
+        Mooring line unit weight (N/m)
+    plot : bool
+        Show plot
+
     Returns
     -------
-    Ta : float 
-        Inclined load magnitude at the anchor lug [kN]
-    thetaa : float 
-        Inclined load angle at the anchor lug [deg]
+    dict
+        Dictionary with transferred load components and depth.
     '''
-    
-    deltas = 0.2
-      
-    # Include element weight in terms of d and match it with deltas
-    if line_type == 'chain':   
-        Et = 10; En = 2.5;  W = w*deltas; 
+
+    deltas = 0.2  # discretization step
+
+    # Line mechanical properties
+    if line_type == 'chain':
+        Et, En = 10, 2.5
     elif line_type == 'wire':
-        Et = np.pi; En = 1; W = w*deltas;
-    
-    T = Tm; theta = np.deg2rad(thetam);
-    Su = Su0; 
-    drag = 0; depth = 0.1
-         
-    T_values = []; Su_values = [];
-    drag_values = []; depth_values = []; 
-    
-    # Setting bearing capacity values per soil type
-    if soil_type == 'clay':
-        Nc = 8.5; alpha = 0.7;
-    elif soil_type == 'sand':
-        nhu = 0.5
-        Nq = np.exp(np.pi*np.tan(np.deg2rad(phi)))*(np.tan(np.deg2rad(45 + phi/2)))**2
-        # print(Nq)
-        
+        Et, En = np.pi, 1
+    W = w*deltas
+
+    # Soil layer access
+    layers = profile_map[0]['layers']
+    z0 = min(layer['top'] for layer in layers)
+    Nc = 8.5
+
+    # Initial values
+    z0 = min(layer['top'] for layer in layers)
+    T = Tm
+    theta = np.deg2rad(thetam)
+    drag = 0
+    depth = z0 + 0.01
+
+    # Tracing lists
+    drag_values, depth_values = [], []
+
     while (zlug - depth) >= 0:
-        if soil_type =='clay':
-            dtheta = (En*d*Nc*Su - W*np.cos(theta))/T*deltas
-            dT = (Et*d*alpha*Su  + W*np.sin(theta))*deltas
-            
-        elif soil_type =='sand':
-            dtheta  = (En*d*Nq*gamma*depth - W*np.cos(theta))/T*deltas
-            dT = (Et*d*gamma*depth*np.tan(np.rad2deg(delta)) + W*np.sin(theta))*deltas 
+        matched_layer = next((layer for layer in layers if layer['top'] <= depth <= layer['bottom']), None)
+        if matched_layer is None:
+            break
         
-        ddrag  = deltas*np.cos(theta)
-        ddepth = deltas*np.sin(theta)
-        theta += dtheta; T -= dT;
-        
-        drag += ddrag; depth += ddepth
-        if Su:
-            Su = Su0 + k*depth
-                
-        # Ensure consistency in load transfer
-        if abs(Tm - T) > 0.75*Tm:  # More than 75% loss
-            raise Exception(f"Warning: Load transfer is unrealistic. Initial load Tm = {Tm/1e6:.2f} MN and current load T = {T/1e6:.2f} MN differ by more than 75 %")
-            break  # Exit the loop if load transfer is unrealistic
+        if matched_layer['soil_type'] == 'clay':
+            matched_layer = next((layer for layer in layers if layer['soil_type'] == 'clay' and layer['top'] <= depth <= layer['bottom']), None)
+            if matched_layer is None:
+                break
+            profile = [[matched_layer['top'],    matched_layer['gamma_top'], matched_layer['Su_top']],
+                       [matched_layer['bottom'], matched_layer['gamma_bot'], matched_layer['Su_bot']]]
+            z0_local, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile)
+
+            Su = f_Su(depth)
+            alpha = f_alpha(depth)
+            d_theta = (En*d*Nc*Su - W*np.cos(theta))/T*deltas
+            dT = (Et*d*alpha*Su + W*np.sin(theta))*deltas
+
+        elif matched_layer['soil_type'] == 'sand':
+            matched_layer = next((layer for layer in layers if layer['soil_type'] == 'sand' and layer['top'] <= depth <= layer['bottom']), None)
+            if matched_layer is None:
+                break
             
-        # Check for excessive load angles
-        if not (0 < np.rad2deg(theta) < 90):
-            raise Exception(f"Warning: Load angle is unrealistic: {np.rad2deg(theta):.2f} deg")
-            break  # Exit the loop if the angle becomes unreasonable   
+            profile = [[matched_layer['top'],    matched_layer['gamma_top'], matched_layer['phi_top'], matched_layer['Dr_top']],
+                       [matched_layer['bottom'], matched_layer['gamma_bot'], matched_layer['phi_bot'], matched_layer['Dr_bot']]]
+            z0_local, f_gamma, f_phi, f_Dr, f_sigma_v_eff, f_delta = sand_profile(profile)
+
+            gamma_z = f_gamma(depth)
+            delta_z = f_delta(depth)
+            phi = f_phi(depth)
+            Nq = np.exp(np.pi*np.tan(np.deg2rad(phi)))*(np.tan(np.deg2rad(45 + phi/2)))**2
+            print(f'Nq = {Nq:.2f}, depth = {depth:.2f} m')
+            d_theta = (En*d*Nq*gamma_z*depth - W*np.cos(theta))/T*deltas
+            dT = (Et*d*gamma_z*depth*np.tan(np.deg2rad(delta_z)) + W*np.sin(theta))*deltas
             
-        T_values.append(T); Su_values.append(Su)
-        drag_values.append(drag); depth_values.append(depth); 
-               
-    Ta = T; thetaa = theta 
-    # print(thetaa); print(Ta)
-    H = Ta*np.cos(thetaa); V = Ta*np.sin(thetaa) 
-    length_values = deltas*len(drag_values)
-     
-    resultsLoad = {}
-    resultsLoad['diff'] = (Tm - Ta)/1e6        # Difference
-    resultsLoad['load'] = Ta/1e6               # Load magnitude @ lug
-    resultsLoad['angle'] = np.rad2deg(thetaa)  # Load angle @ lug
-    resultsLoad['H'] = H                       # Horizontal component @ lug
-    resultsLoad['V'] = V                       # Vertical component @ lug
-    resultsLoad['length'] = length_values      # Length of the embedded line
-    
-    # Plot of the line and extreme line tension
-    drag_values = [-1*i for i in drag_values]
-    depth_values = [-1*j for j in depth_values]
-    
-    if plot:
-        fig, ax = plt.subplots(figsize=(20, 5)); n = 2000000
-        ax.scatter(drag_values[-1], depth_values[-1], color='g', zorder=5)
-        ax.scatter(0, 0, color='r', zorder=4)
-        ax.arrow(0, 0, Tm*np.cos(np.deg2rad(thetam))/n, Tm*np.sin(np.deg2rad(thetam))/n,
-              head_width=0.25, head_length=0.5, color='r', zorder=3)
-        ax.arrow(drag_values[-1], depth_values[-1], Ta*np.cos(thetaa)/n, Ta*np.sin(thetaa)/n,
-              head_width=0.25, head_length=0.5, color='g',zorder=2)
-        ax.plot(drag_values, depth_values,color='b', zorder=1)
-         
-        #Set labels and title
-        plt.xlabel('Drag distance [m]')
-        plt.ylabel('Embedded depth [m]')
-        plt.suptitle('Inverse catenary profile in soil DNV')
-        plt.grid(True)
-    
-    return resultsLoad
+        else:
+            raise ValueError(f"Unsupported soil type: {matched_layer['soil_type']}")
+
+        d_drag  = deltas*np.cos(theta)
+        d_depth = deltas*np.sin(theta)
+
+        theta += d_theta
+        T -= dT
+        drag += d_drag
+        depth += d_depth
+
+        if not (0 < np.rad2deg(theta) < 90):
+            raise Exception(f"Load angle unrealistic: {np.rad2deg(theta):.2f} deg")
+
+        drag_values.append(-drag); 
+        depth_values.append(-depth); 
+
+    Ta = T; thetaa = theta
+    Hm = Tm*np.cos(np.deg2rad(thetam)); Vm = Tm*np.cos(np.deg2rad(thetam))
+    Ha = Ta*np.cos(thetaa); Va = Ta*np.sin(thetaa)
+    
+    print(f'Input Tm = {Tm}, thetam = {thetam}, zlug = {zlug}')
+    print(f'Output Hm = {Hm}, Vm = {Vm}')
+    print(f'Output Ta = {Ta}, thetaa = {np.rad2deg(thetaa)}')
+    print(f'Output Ha = {Ha}, Va = {Va}')
+
+    resultsLoad = {
+        'Tm': Tm, 'thetam': thetam,
+        'Hm': Hm, 'Vm': Vm,
+        'Ta': Ta, 'thetaa': np.rad2deg(thetaa),
+        'Ha': Hm, 'Va': Vm,
+        'length': deltas*len(drag_values),
+        'drag_values': drag_values,
+        'depth_values': depth_values}
+
+    return layers, resultsLoad
 
 
 if __name__ == '__main__':
-    
-    Tm = 1.16e6 
-    thetam = 0
-    zlug = 10
-    line_type ='chain'
-    d = 0.160
-    soil_type ='sand'
-    Su0 = 2.4*1e3
-    k = 1.41*1e3
-    gamma = 9e3
-    phi = 35
-    delta = 27
-    w = 4093
-
-    resultsDNV = getTransferLoad(Tm, thetam, zlug, line_type, d, soil_type, Su0, k, gamma, phi, delta, w)
-    #results = getAnchorLoad(Tm, thetam, zlug, d, soil_type, gamma, Su0, k)
\ No newline at end of file
+
+    # profile_map = [
+    #     {
+    #         'name': 'CPT_1',
+    #         'x': 498234, 'y': 5725141,
+    #         'layers': [
+    #             {
+    #                 'top': 1.0, 'bottom': 2.0,
+    #                 'soil_type': 'clay',
+    #                 'gamma_top': 8.0, 'gamma_bot': 8.0,
+    #                 'Su_top': 10, 'Su_bot': 25},
+    #             {
+    #                 'top': 2.0, 'bottom': 8.0,
+    #                 'soil_type': 'clay',
+    #                 'gamma_top': 8.0, 'gamma_bot': 8.0,
+    #                 'Su_top': 25, 'Su_bot': 50},
+    #             {
+    #                 'top': 8.0, 'bottom': 16.0,
+    #                 'soil_type': 'clay',
+    #                 'gamma_top': 8.0, 'gamma_bot': 8.0,
+    #                 'Su_top': 50, 'Su_bot': 100}
+    #         ]
+    #     }
+    # ]
+    profile_map = [
+        {
+            'name': 'CPT_1',
+            'x': 498234, 'y': 5725141,
+            'layers': [
+                # {
+                #     'top': 0.0, 'bottom': 5.0,
+                #     'soil_type': 'sand',
+                #     'gamma_top': 9.5, 'gamma_bot': 9.5,
+                #     'phi_top': 28, 'phi_bot': 30,
+                #     'Dr_top': 70, 'Dr_bot': 70},
+                # {
+                #     'top': 0.0, 'bottom': 5.0,
+                #     'soil_type': 'clay',
+                #     'gamma_top': 8.0, 'gamma_bot': 8.0,
+                #     'Su_top': 25, 'Su_bot': 25},
+                {
+                    'top': 0.0, 'bottom': 3.0,
+                    'soil_type': 'sand',
+                    'gamma_top': 9.5, 'gamma_bot': 9.5,
+                    'phi_top': 25, 'phi_bot': 30,
+                    'Dr_top': 60, 'Dr_bot': 65},
+                {
+                    'top': 3.0, 'bottom': 15.0,
+                    'soil_type': 'sand',
+                    'gamma_top': 9.5, 'gamma_bot': 9.5,
+                    'phi_top': 32, 'phi_bot': 35,
+                    'Dr_top': 70, 'Dr_bot': 85}
+            ]
+        }
+    ]
+
+    Tm = 4978442          # Load at mudline (N)
+    thetam = 15            # Angle at mudline (deg)
+    zlug = 8.5              # Padeye depth (m)
+    line_type = 'chain'
+    d = 0.12              # Chain diameter (m)
+    w = 2000              # Line weight (N/m)
+
+    layers, resultsLoad = getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w, plot=True)
+
+    # print("\n--- Transfer Load Results ---")
+    # for key, val in resultsLoad.items():
+    #     if isinstance(val, float):
+    #         print(f"{key}: {val:.3f}")
+    #     elif isinstance(val, list):
+    #         print(f"{key}:")
+    #         for v in val:
+    #             print(f"  {v:.3f}")
+    #     else:
+    #         print(f"{key}: {val}")
+
+    plot_load(layers, resultsLoad['drag_values'], resultsLoad['depth_values'], 
+              resultsLoad['Tm'], resultsLoad['thetam'], resultsLoad['Ta'], 
+              resultsLoad['thetaa'], zlug=zlug)
\ No newline at end of file
diff --git a/famodel/anchors/anchors_famodel/capacity_plate.py b/famodel/anchors/anchors_famodel/capacity_plate.py
index bc371ff7..02f7fdc6 100644
--- a/famodel/anchors/anchors_famodel/capacity_plate.py
+++ b/famodel/anchors/anchors_famodel/capacity_plate.py
@@ -1,102 +1,177 @@
 
 import numpy as np
+import matplotlib.pyplot as plt
+from .support_soils import clay_profile
+from .support_plots import plot_plate
+
+def getCapacityPlate(profile_map, location_name, B, L, zlug, beta, Ha, Va, plot=True):
+    '''Calculate the plate anchor capacity using clay soil layers from profile_map.
+    The calculation is based on the soil profile, anchor geometry and inclined load.
 
-def getCapacityPlate(A, beta, zlug, soil_type, gamma, Su0=None, k=None):
-    
-    '''Calculate the inclined load capacity of a plate in clay at a given depth.
-    The calculation is based on the soil properties, anchor geometry and the angle of inclined load.
-    The plate is assumed to be inclined perpendicular to the tension at the main padeye depth.     
-    
     Parameters
     ----------
-    A : float 
-        Plate area, assumed to be square so that width B = sqrt(A). [m^2]
+    profile_map : list of dict
+        Soil profile map with coordinates and layers per location.
+    location_name : str
+        Name of the location to select the soil profile.
+    B : float
+        Plate width (m)
+    L : float
+        Plate length (m)
+    zlug : float
+        Embedment depth of the main padeye (m)
     beta : float
-        Angle of the plate after keying process [deg]
-    zlug: float
-        Embedded depth of the main padeye [m]
-    soil_type : string
-        Specify 'sand' or 'clay'. This affects what other soil parameters are used.
-    gamma: float 
-        Effective unit weight of the soil [kN/m3] 
-    Su0 : float 
-        Undrained shear strength at the mudline [kPa]
-    k : float 
-        Undrained shear strength gradient [kPa/m]
-   
+        Inclination angle of the plate (deg)
+    Ha : float
+        Applied horizontal load (N)
+    Va : float
+        Applied vertical load (N)
+    plot : bool
+        Whether to generate plots.
+
     Returns
     -------
-    Tmax: float
-       Maximum capacity [kN] 
+    Dictionary with Capacity, Weight, UC, etc.
     '''
-      
-
-    Los=0.05                   # Key lost fraction due to the keying process, default 0.05 (-)
-    rhos= 78.50                # Dry steel unit weight (kN/m3)
- 
-    B = round(np.sqrt(A),2)    # Anchor width (and length, approximated as square) (m)
-    zlug_B = zlug/B            # Anchor depth range/ width of the plate 
-    B_t = 40                   # Aspect ratio plate width to thickness, default is 40
-    t = round(B/B_t, 2)        # Thickness of the plate, which it depends on the width (m)
+
+    # Extract and filter clay layers from profile_map
+    profile_entry = next(p for p in profile_map if p['name'] == location_name)
+    layers = [layer for layer in profile_entry['layers'] if layer['soil_type'] == 'clay']
+
+    if not layers:
+        raise ValueError('Plate anchor capacity model only supports clay soils.')
+
+    # Build the profile array: [[z, Su, gamma], ...]
+    profile = []
+    for layer in layers:
+        profile.append([layer['top'], layer['gamma_top'], layer['Su_top']])
+        profile.append([layer['bottom'], layer['gamma_bot'], layer['Su_bot']])
+        
+    print("layer gamma_top (raw):", layer['gamma_top'])
+    print("layer gamma_bot (raw):", layer['gamma_bot'])
+
+    profile = np.array(sorted(profile, key=lambda x: x[0]))
+
+    # Parameters and constants
+    Los = 0.05
+    B_t = 40
+    rhows = 66.90e3  # Submerged steel (N/m3)
+    rhow = 10e3      # Seawater (N/m3)
+
+    # Evaluate interpolated Su and gamma
+    z0, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile)
+    t = round(B/B_t, 2)
+    V_steel = round(B*L*t, 2)
+    zlug_B = zlug/B
+
+    # Profile check points
+    npts = 10
+    z_offsets = np.linspace(-0.5, 0.5, npts)*B*np.sin(np.deg2rad(beta))
+    z_points = zlug + z_offsets; print(z_points)
+
+    Su_vals = [f_Su(z) for z in z_points]
+    gamma_10 = f_gamma(z_points[2]); print(gamma_10)
+    gamma_vals = [f_gamma(z) for z in z_points]; print("gamma_vals:", [f"{val:.2f}" for val in gamma_vals], "N/m3")
+    Su = np.mean(Su_vals); print(f"Su: {Su:.2f} Pa")
+    gamma = np.mean(gamma_vals); print(f"gamma: {gamma:.2f} N/m3")
     
-    #t=np.sqrt(0.006*A)/4  
-    V = round(A*t,2)           # Steel volume (m3)
-    W = V*rhos                 # Plate weight (kg)
-    Su = Su0 + k*zlug          # Undrained shear strength at plate depth
+    print("Profile being sent to clay_profile():")
+    for row in profile:
+        print(f"z = {row[0]:.2f} m, gamma = {row[1]:.2f} kN/m³, Su = {row[2]:.2f} kPa")
 
-    # ----- anchor pullout capacity -----
+    # Shear strength gradient
+    k = np.polyfit(z_points, Su_vals, 1)[0]
+    print(f"k: {k:.2f}")
 
-    # Anchor Pullout capacity factor in weightless clay with breakaway base, soil homogeneous
-    Nco_0_0  = 2.483*np.log(zlug_B) + 1.974  # angle = 0 deg 
-    Nco_90_0 = 2.174*np.log(zlug_B) + 3.391  # angle = 90 deg
+    # Pile weight including auxiliary parts
+    Wp = 1.35*V_steel*(rhows + rhow)
 
-    kBSh = k*B/Su              # Degree of soil non-homogeneity
+    # Capacity factors
+    Nco_0_0  = 2.483*np.log(zlug_B) + 1.974
+    Nco_90_0 = 2.174*np.log(zlug_B) + 3.391
+    kBSh = k*B/Su
+    print(f"kBSh: {kBSh:.2f}")
 
     f0  = np.where(zlug_B < 4, 1.77*(zlug_B**0.3) - 1.289, 0.192*zlug_B + 0.644)
-    f90 = np.where(zlug_B < 4, 0.68*(zlug_B**0.5) - 0.41 , 0.153*zlug_B + 0.341)
+    f90 = np.where(zlug_B < 4, 0.68*(zlug_B**0.5) - 0.410, 0.153*zlug_B + 0.341)
 
-    # Non-homogeneity adjustment factor for anchor ultimate pullout capacity
-    S_kB_0  = 1 - f0 *kBSh
+    S_kB_0  = 1 - f0*kBSh
     S_kB_90 = 1 - f90*kBSh
+    Nco_0  = S_kB_0*Nco_0_0
+    Nco_90 = S_kB_90*Nco_90_0
+    Nco = Nco_0 + (Nco_90 - Nco_0)*(beta/90)**2
 
-    # Anchor Pullout capacity factor in weightless clay with breakaway base, soil nonhomogeneous
-    Nco_0  = S_kB_0*Nco_0_0  
-    Nco_90 = S_kB_90*Nco_90_0 
-
-    # Anchor pullout capacity factor in weightless clay with no breakaway base 
-    Nco = Nco_0 + (Nco_90 - Nco_0)*(beta/90)**2   
-
-    # Uplift bearing capacity factor, soil homogeneous
     Nco_s_0_0  = np.where(2.90*zlug_B + 6.02 <= 11.59, 2.90*zlug_B + 6.02, 11.596)
     Nco_s_90_0 = np.where(2.72*zlug_B + 4.02 <= 11.59, 2.72*zlug_B + 4.02, 11.596)
 
-    # ----- ultimate anchor capacity factor -----
-    
-    # Non-homogeneity factor for anchor ultimate pullout capacity
-    S_s_kB_0 = np.where(zlug_B <= 2, 1 + (0.8 - 0.3*zlug_B)*kBSh - (0.383*kBSh**1.36), 1)  # Angle = 0
-
+    S_s_kB_0 = np.where(zlug_B <= 2, 1 + (0.8 - 0.3*zlug_B)*kBSh - (0.383*kBSh**1.36), 1)
     f90s = np.where(zlug_B <= 3, 0.267*zlug_B, 0.6)
-    S_s_kB_90 = 1 - f90s*kBSh  # Angle = 90
-
-    # Anchor ultimate holding capacity in with breakaway base, soil nonhomogeneous
-    Nco_s_0  = S_s_kB_0 *Nco_s_0_0
+    S_s_kB_90 = 1 - f90s*kBSh
+    Nco_s_0 = S_s_kB_0*Nco_s_0_0
     Nco_s_90 = S_s_kB_90*Nco_s_90_0
-
-    # Anchor ultimate holding capacity in with no breakaway base, soil nonhomogeneous
     Nco_s = Nco_s_90 + (Nco_s_0 - Nco_s_90)*((90 - beta)/90)**2
 
-
-    # ----- final results -----
-    Nc_final = np.minimum(Nco + (gamma*zlug)/Su, Nco_s) # anchor pullout capacity factor [kN]
-    qu = Nc_final*Su  # The bearing pressure capacity of the anchor plate
-    Tmax = round(qu*(1 - Los)*A,2)  # The bearing tension force capacity of the anchor plate
-    Hmax = Tmax*np.cos(np.deg2rad(beta))
-    Vmax = Tmax*np.sin(np.deg2rad(beta))
-
-    resultsPlate = {}
-    resultsPlate['Capacity'] = Tmax                 # Capacity at specified loading angle
-    resultsPlate['Horizontal max.'] = Hmax          # Maximum horizontal capacity in clay
-    resultsPlate['Vertical max.'] = Vmax            # Maximum vertical capacity in clay
-    resultsPlate['Weight'] = W                # Dry weight of the plate (kN)
+    Nc_final = max(Nco + (gamma*zlug)/Su, Nco_s)
+    print(f"Nc_star: {Nco + (gamma*zlug)/Su:.2f}")
+    print(f"Nc_star: {Nco_s:.2f}")
+    qu = Nc_final*Su
+    Tmax = round(qu*(1 - Los)*B*L, 2)
+    Hmax = Tmax*np.cos(np.deg2rad(90 - beta))
+    Vmax = Tmax*np.sin(np.deg2rad(90 - beta))
+
+    Ta = np.sqrt(Ha**2 + Va**2)
+    UC = Ta/Tmax
+
+    resultsPlate = {
+        'Capacity': Tmax,
+        'Horizontal max.': Hmax,
+        'Vertical max.': Vmax,
+        'Unity check': UC,
+        'Weight plate': Wp
+    }
     
-    return resultsPlate
\ No newline at end of file
+    return layers, resultsPlate
+
+if __name__ == '__main__':
+    profile_map = [
+        {
+            'name': 'CPT_1',
+            'x': 498234, 'y': 5725141,
+            'layers': [
+                {
+                    'top': 0.0, 'bottom': 9.5,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 8.5,
+                    'Su_top': 10, 'Su_bot': 25
+                },
+                {
+                    'top': 9.5, 'bottom': 11.5,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.5, 'gamma_bot': 8.5,
+                    'Su_top': 25, 'Su_bot': 45
+                },
+                {
+                    'top': 11.5, 'bottom': 25.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.5, 'gamma_bot': 9.0,
+                    'Su_top': 45, 'Su_bot': 50
+                }
+            ]
+        }
+    ]
+
+    B = 2.0
+    L = 2.0
+    zlug = 10.0
+    Ha = 350e3
+    Va = 400e3
+    alpha = np.rad2deg(np.arctan2(Va, Ha))
+    beta = 90 - alpha
+
+    layers, results = getCapacityPlate(profile_map, 'CPT_1', B, L, zlug, beta, Ha, Va)
+
+    print("\n--- Plate Anchor Capacity Results ---")
+    for key, val in results.items():
+        print(f"{key}: {val:.2f}")
+        
+    plot_plate(layers, B, L, z0 = layers[0]['top'], zlug=zlug, beta=beta, title='Plate Anchor in Layered Soil')
diff --git a/famodel/anchors/anchors_famodel/capacity_suction.py b/famodel/anchors/anchors_famodel/capacity_suction.py
index d16b75a1..a36d33d2 100644
--- a/famodel/anchors/anchors_famodel/capacity_suction.py
+++ b/famodel/anchors/anchors_famodel/capacity_suction.py
@@ -1,53 +1,54 @@
 
-import yaml      
 import numpy as np
 import matplotlib.pyplot as plt
-from scipy.optimize import fsolve
-#from famodel.anchors.capacity_load import getAnchorLoad
-#from famodel.anchors.anchors_famodel.capacity_load import getTransferLoad
+import matplotlib.colors as mcolors
+from .support_soils import clay_profile, sand_profile
 
-def getCapacitySuction(D, L, zlug, H, V, soil_type, gamma, Su0=None, k=None, phi=None, Dr=None, plot=True):
-    
+def getCapacitySuction(profile_map, location_name, D, L, zlug, Ha, Va, thetalug=5, psilug=7.5, plot=False):
     '''Calculate the inclined load capacity of a suction pile in sand or clay following S. Kay methodology.
-    The calculation is based on the soil properties, anchor geometry and inclined load.  
+    The calculation is based on the soil profile, anchor geometry and inclined load.  
     
     Parameters
     ----------
+    profile : array
+        Soil profile as a 2D array: (z, parameters)
+            Clay soil profile (z (m), Su (kPa), gamma (kN/m³))
+            Sand soil profile (z (m), phi (deg), gamma (kN/m³), Dr (%))
+    soil_type : string
+        Select soil condition, 'clay' or 'sand'
     D : float 
-        Suction pile diameter [m]
+        Suction pile diameter (m)
     L : float 
-        Suction anchor length [m]
+        Suction pile length from pile head (m)
     zlug: float
-        Embedded depth of the main padeye [m]
-    soil_type : string
-        Select soil condition, 'clay' or 'sand'              
-    gamma: float 
-        The effective unit weight of the soil. [kN/m3]
-    Su0 : float 
-        Undrained shear strength at the mudline (clay only) [kPa]
-    k : float 
-        Undrained shear strength gradient (clay only) [kPa/m]
-    phi : float
-        Angle of internal friction (sand only) [deg]
-    Dr : float
-        Relative density of the soil (%) (sand only) [-]       
-    
+        Embedded depth of the main padeye (m)
+    thetalug: float
+        Angle of tilt misaligment (deg) (default value: 5.0)
+    psilug: float
+        Angle of twist misaligment (deg) (default value: 7.5)
+    Ha : float       
+        Horizontal load at pile lug elevation (N)
+    Va : float          
+        Vertical load at pile lug elevation (N)
+    plot : bool
+        Plot the capacity envelope if True
+                    
     Returns
     -------
-    Hmax : float 
-        Maximum horizontal capacity [kN]
-    Vmax : float 
-        Maximum vertical capacity [kN]
-    '''  
-            
-    lambdap = L/D; m = 2/3;         # Suction pile slenderness ratio
+    Dictionary with capcity, weigths and UC.
+    ''' 
+    
+    # Retrieve soil layers from map
+    profile_entry = next(p for p in profile_map if p['name'] == location_name)
+    layers = profile_entry['layers']
+    
+    z0 = layers[0]['top']            # Mudline elevation    
+    lambdap = (L - z0)/D             # Suction pile slenderness ratio
     t = (6.35 + D*20)/1e3            # Suction pile wall thickness (m), API RP2A-WSD
     rlug = D/2                       # Radial position of the lug
-    thetalug = 5                     # Angle of tilt misaligment, default is 5. (deg)
-    psilug = 7.5                     # Angle of twist misaligment, default is 7.5. (deg)
-    rhows = 66.90                    # Submerged steel specific weight (kN/m3)
-    rhow = 10                        # Water specific weight (kN/m3) 
-    
+    rhows = 66.90e3                  # Submerged steel specific weight (N/m3)
+    rhow = 10e3                      # Water specific weight (N/m3) 
+      
     # Outer and inner surface of the pile skirt
     def PileSurface(Len, Dia):
         Sp = np.pi*Dia*Len
@@ -67,267 +68,451 @@ def rlugTilt(r, z, theta):
     def zlugTilt(r, z, theta):
         Z = r*np.sin(np.deg2rad(theta)) + z*np.cos(np.deg2rad(theta))
         return Z
-    # Define delta as a function of Dr
-    def calc_delta(Dr_val):
-        if 35 <= Dr_val < 50:
-            return 0.29
-        elif 50 <= Dr_val < 65:
-            return 0.37
-        elif 65 <= Dr_val < 85:
-            return 0.46
-        elif Dr_val >= 85:
-            return 0.56
-        else:
-            return 0  # Default or error value for very low Dr values
-       
-    if soil_type == 'clay':
-        # Definitions for cohesive soils
-        Nc = min (6.2*(1 + 0.34*np.arctan(lambdap)),9)   # End-bearing capacity factor
-        ez = (Su0*L**2/2 + k*L**3/3)/(Su0*L + k*L**2/2)#; print(ez)
-        Np_fixed = 10.25; Np_free = 4                    # From Np vs L/D chart from CAISSON_VHM
-        Su_av_L = Su0 + k*zlug                           # Undrained shear strength values (average) 
-        Su_tip = Su0 + k*L                               # Undrained shear strength values (tip)
-        sigma_v_eff = gamma*zlug                         # Effective soil stress (kN/m2)
-        psi_val = Su_av_L/sigma_v_eff                    # Su/p0' for point in question (API DP 2A-WSD)
-        #zlug = ez                                       # Optimized depth of the lug 
- 
-        if psi_val <= 1.0:
-            alpha = min(0.5*psi_val**-0.50, 1)
-        else:
-            alpha = min(0.5*psi_val**-0.25, 1)
- 
-        Hmax = Np_fixed*L*D*Su_av_L; 
-        H0 = Np_free*L*D*Su_av_L;
-        Mmax = Np_fixed*L*L*D*Su_av_L; 
+    # Ellipse crossing with constant values
+    def horizontal_cross(H, M, M_target):
+        crossings = []
+        for i in range(len(M_rot) - 1):
+            if (M[i] - M_target) * (M[i+1] - M_target) < 0:
+                # Interpolation to get more precise value at crossing
+                H_cross = np.interp(M_target, [M[i], M[i+1]], [H[i], H[i+1]])
+                crossings.append(H_cross)
+        return crossings  
+    def vertical_cross(H, M, H_target):
+        crossings = []
+        for i in range(len(H) - 1):
+            if (H[i] - H_target) * (H[i+1] - H_target) < 0:
+                # Interpolation to get more precise value at crossing
+                M_cross = np.interp(H_target, [H[i], H[i+1]], [M[i], M[i+1]])
+                crossings.append(M_cross)
+        return crossings
+    
+    Np_fixed = 11.65
+    Np_free = 3.5
+    Nc = min(6.2*(1 + 0.34*np.arctan(lambdap)), 9)
+    
+    # Initialize
+    sum_ez_weighted = 0.0
+    Hmax_final = 0.0
+    Vmax_final = 0.0
+    layer_data = []
+
+    # Profile check points
+    npts = 10
+
+    for layer in layers:
+        soil_type = layer['soil_type']
+        z_top = layer['top']
+        z_bot = layer['bottom']
+
+        if soil_type == 'clay':
+            # Prepare soil profile for clay
+            profile = [
+                [z_top, layer['gamma_top'], layer['Su_top']],
+                [z_bot, layer['gamma_bot'], layer['Su_bot']]
+            ]
+    
+            z_ref, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile)
+    
+            # Clip the layer first
+            z_top_clip = max(z_top, z0)
+            z_bot_clip = min(z_bot, z0 + (L - z0))
+            dz_clip = z_bot_clip - z_top_clip; print(f'dz_clip  = {dz_clip:.2f} m')
+    
+            if dz_clip <= 0:
+                continue  # Skip layers fully above or below
+    
+            # Calculate properties over clipped dz
+            z_vals = np.linspace(z_top_clip, z_bot_clip, npts)
+            Su_vals = f_Su(z_vals)
+            Su_total = np.trapz(Su_vals, z_vals)
+            Su_moment = np.trapz(Su_vals*z_vals, z_vals)
+            
+            ez_layer = Su_moment/Su_total
+            Su_av_z = f_Su(ez_layer)
+            
+            print(f'ez_layer = {ez_layer:.2f} m')
+            print(f'Su_av_z (at ez_layer) = {Su_av_z:.2f} Pa')
+            
+            Su_bot = f_Su(z_bot_clip)
+            gamma_vals = f_gamma(z_vals)
+            gamma_av = np.mean(gamma_vals)
+
+            # Calculate Hmax for clay
+            Hmax_layer = Np_fixed*D*dz_clip*Su_av_z;
+    
+            layer_data.append({
+                'z_top': z_top_clip,
+                'z_bot': z_bot_clip,
+                'dz': dz_clip,
+                'Hmax_layer': Hmax_layer,
+                'ez_layer': ez_layer
+            })
+    
+            sigma_v_eff = f_sigma_v_eff(np.mean(z_vals))
+            alpha_av = float(f_alpha(np.mean(z_vals)))
+            
+            # Side shear To and Ti
+            To = PileSurface(dz_clip, D)*alpha_av*Su_av_z
+            Ti = PileSurface(dz_clip, D - 2*t)*alpha_av*Su_av_z
+            
+            # Tip resistance
+            if abs(z_bot_clip - (z0 + (L - z0))) < 1e-3:  # tip check
+                Tbase = (np.pi/12)*D**3*Su_bot
+            else:
+                Tbase = 0.0
+            
+            Tmax = min(To + Ti, To + Tbase)
+            
+            # Torque induced by horizontal load
+            T = Ha*rlug*np.sin(np.deg2rad(psilug))
+            
+            nhuT = T/Tmax 
+            nhuV = Ha/To 
+            nhuVstar = np.sqrt(nhuV**2 - nhuT**2) 
+            alphastar = alpha_av*(nhuVstar/nhuV); print(f"alphastar   = {alphastar:.3f}")
+            
+            # Constant weight
+            Pile_Head = PileWeight(z0, D, t, rhows)
+            
+            # Vertical failure modes
+            Vmax1 = None
+            if np.isclose(z_bot_clip, z0 + (L - z0), atol=0.1):
+                Vmax1 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + Nc*Su_bot*(np.pi/4)*D**2
+            # else:
+            #     Vmax1 = np.inf  # No tip resistance unless at tip
+            
+            Vmax2 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + PileSurface(dz_clip, D - 2*t)*alphastar*Su_av_z
+            Vmax3 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + SoilWeight(dz_clip, D, t, gamma_av)
+            
+            Vmax_candidates = [v for v in [Vmax1, Vmax2, Vmax3] if v is not None]
+            Vmax_layer = max(Vmax_candidates)
+
+            # Sum vertical capacities
+            Vmax_final += Vmax_layer
+            
+            # Print layer debug info
+            print(f"Vmax_layer    = {Vmax_layer:.2f} N")
+            print(f"Vmax1   = {Vmax1:.2f} N" if Vmax1 is not None else "Vmax1   = not applicable")
+            print(f"Vmax2   = {Vmax2:.2f} N")
+            print(f"Vmax3   = {Vmax3:.2f} N")
+            
+        elif soil_type == 'sand':
+            # Prepare soil profile for sand
+            profile = [
+                [z_top, layer['gamma_top'], layer['phi_top'], layer['Dr_top']],
+                [z_bot, layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]
+            ]
         
-        # M modifies the Hmax capacity
-        M = - V*rlugTilt(rlug,zlug,thetalug) - H*(zlugTilt(rlug,zlug,thetalug) - ez)
-        def f(Hmax):
-             return m*(Hmax/(L*D*(Su0 + k*zlug)) - Np_fixed) + M*(Hmax/(L*D*(Su0 + k*zlug))/(Hmax*L))
-        Hmax = fsolve(f,5)
+            z_ref, f_gamma, f_phi, _, f_sigma_v_eff, f_delta = sand_profile(profile)
         
-        # Torsion capacity
-        Fo = PileSurface(L, D)*alpha*Su_av_L
-        To = Fo
-        Ti = PileSurface(L,(D - 2*t))*alpha*Su_av_L
-        Tbase = np.pi*D**3*Su_tip/12
-        Tmax = min(To + Ti, To + Tbase) 
+            # Clip the layer within pile embedded length
+            z_top_clip = max(z_top, z0)
+            z_bot_clip = min(z_bot, z0 + (L - z0))
+            dz_clip = z_bot_clip - z_top_clip
         
-        # Introduce twist effects due to installation misaligment
-        T = H*rlug*np.sin(np.deg2rad(psilug))
-        nhuT = T/Tmax; nhuV = H/Fo;
-        nhuVstar = np.sqrt(nhuV**2 - nhuT**2)
-        alphastar = alpha*(nhuVstar/nhuV)
+            if dz_clip <= 0:
+                continue  # Skip non-overlapping layers
         
-        # "Plugged" (Reverse end bearing capacity - passive suction) 
-        Vmax1 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alphastar*Su_av_L + Nc*Su_tip*(np.pi/4)*D**2)
-        # "Coring"        
-        Vmax2 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alphastar*Su_av_L + PileSurface(L,(D - 2*t))*alphastar*Su_av_L)
-        # "Leaking"        
-        Vmax3 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alphastar*Su_av_L + SoilWeight(L, D, t, gamma))                  
-        Vmax = min(Vmax1, Vmax2, Vmax3)
-        '''
-        print("\n--- Parameter-Based Version ---")
-        print(f"Su_av_L        = {Su_av_L:.3f} kPa")
-        print(f"sigma'_v(zlug) = {sigma_v_eff:.3f} kPa")
-        print(f"psi_val        = {psi_val:.3f}")
-        print(f"alpha (API)    = {alpha:.3f}")
-        print(f"Hmax           = {Hmax[0]:.2f} kN")
-        print(f"Vmax           = {Vmax:.2f} kN")
-        '''
-               
-    elif soil_type == 'sand':
-        # Definition for non-cohesive soils
-        Nq = np.e**(np.pi*np.tan(np.radians(phi)))*np.tan(np.radians(45) + np.radians(phi)/2)**2 # Lateral-bearing capacity factor
-        sigma_av_L = gamma*L/2                        # Effective stress (average)
-        sigma_tip = gamma*L                           # Effective stress (tip)
-        Hmax = 0.5*D*Nq*gamma*L**2
-
-        M = - V*rlugTilt(rlug,zlug,thetalug) - H*(zlugTilt(rlug,zlug,thetalug) - zlug)
+            # Calculate properties over clipped dz
+            z_vals = np.linspace(z_top_clip, z_bot_clip, npts)
+            phi_vals = f_phi(z_vals)
+            sigma_vals = f_sigma_v_eff(z_vals)
+            delta_vals = f_delta(z_vals)
         
-        # Torsion capacity
-        delta = calc_delta(Dr)
-        To = PileSurface(L, D)*delta*sigma_av_L
-        Ti = PileSurface(L, (D -2*t))*delta*sigma_av_L
-        Tbase = np.pi*D**3*sigma_tip/12
-        Tmax = min(To + Ti, To + Tbase) 
+            phi_av = np.mean(phi_vals)
+            sigma_av = np.mean(sigma_vals)
+            delta_av = np.mean(delta_vals)
         
-        # Introduce twist effects due to installation misaligment
-        T = H*rlug*np.sin(np.deg2rad(psilug))
-        Fo = delta*sigma_av_L*L*np.pi*D
-        nhuT = T/Tmax; nhuV = H/Fo;
-        nhuVstar = np.sqrt(nhuV**2 - nhuT**2)
-        deltastar = delta*(nhuVstar/nhuV)
-    
-        # "Coring"        
-        Vmax2 = PileWeight(L, D, t, rhows) + PileSurface(L, D)*deltastar*sigma_av_L + PileSurface(L,(D - 2*t))*deltastar*sigma_av_L
-        # "Leaking"        
-        Vmax3 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*deltastar*sigma_av_L + SoilWeight(L, D, t, gamma))
-        Vmax = min(Vmax2, Vmax3)
-        # def y(depth):
-            # return np.e**(-depth) - 1 + depth
-        # Ze = D/(4*7); Zi = D/(4*5)    
-        # Vmax = 7*gamma*Ze**2*y(L/Ze)*PileSurface(L, D)/L + 5*gamma*Zi**2*y(L/Zi)*PileSurface(L,(D - 2*t))/L
-        '''
-        print("\n--- Parameter-Based (Sand) ---")
-        print(f"phi        = {phi:.2f} deg")
-        print(f"gamma      = {gamma:.2f} kN/m3")
-        print(f"deltastar  = {deltastar:.2f} -")
-        print(f"sigma_av_L = {sigma_av_L:.2f} kN")
-        print(f"sigma_tip  = {sigma_tip:.2f} kN")
-        '''
-    # Pile weight (inc. stiffening plus vent) assessed as a factor
-    Wp = 1.10*PileWeight(L, D, t, (rhows + rhow)) 
-    # Submerged weight of the soil plug
-    Ws = SoilWeight(L, D, t, gamma)
-    
-    # Capacity envelope
-    aVH = 0.5 + lambdap; bVH = 4.5 + lambdap/3 
-    #print('Env. exp = ' +str(aVH)+'   '+str(bVH))
-    UC = (H/Hmax)**aVH + (V/Vmax)**bVH      
-    x = np.cos(np.linspace (0, np.pi/2, 100))
-    y = (1 - x**bVH)**(1/aVH)
-    X = Hmax*x; Y = Vmax*y
-    if plot:
-        plt.plot(X, Y, color = 'b')
-        plt.plot(H, V, '*', color = 'r')
+            sigma_tip = f_sigma_v_eff(z_bot_clip)
             
-        # Set labels and title
-        plt.xlabel('Horizontal capacity [kN]')
-        plt.ylabel('Vertical capacity [kN]')
-        plt.suptitle('VH suction pile capacity envelope')
-        plt.axis([0, 1.3*max(X[0], H), 0, 1.3*max(Y[-1], V)]) 
-        plt.grid(True)
-        plt.show()
-    
-    resultsSuction = {}
-    if soil_type == 'clay':
-        resultsSuction['Horizontal max.'] = Hmax[0] # Maximum horizontal capacity in clay
-    elif soil_type == 'sand':   
-        resultsSuction['Horizontal max.'] = Hmax    # Maximum horizontal capacity in sand 
-    resultsSuction['Vertical max.'] = Vmax          # Maximum vertical capacity 
-    if soil_type == 'clay':
-        resultsSuction['UC'] = UC[0]                # Unity check in clay
-    elif soil_type == 'sand':
-        resultsSuction['UC'] = UC                   # Unity check in sand
-    resultsSuction['Weight'] = Wp                   # Dry weight of the suction pile (kN)
-    resultsSuction['Weight Soil'] = Ws              # Submerged weight of the soil plug (kN)
-    resultsSuction['t'] = t                         # Pile thikness in [m]
-    
-    return resultsSuction
+            Nq = np.e**(np.pi*np.tan(np.radians(phi_av)))*(np.tan(np.radians(45) + np.radians(phi_av)/2))**2
+        
+            # Calculate Hmax for sand
+            Hmax_layer = 0.5*Nq*D*gamma_av*dz_clip**2
+        
+            layer_data.append({
+                'z_top': z_top_clip,
+                'z_bot': z_bot_clip,
+                'dz': dz_clip,
+                'Hmax_layer': Hmax_layer,
+                'ez_layer': np.mean(z_vals)
+            })
+        
+            # Side friction
+            To = PileSurface(dz_clip, D)*delta_av*sigma_av
+            Ti = PileSurface(dz_clip, D - 2*t)*delta_av*sigma_av
+        
+            if abs(z_bot_clip - (z0 + (L - z0))) < 1e-3:
+                Tbase = np.pi/4*D**2*sigma_tip
+            else:
+                Tbase = 0.0
+        
+            Tmax = min(To + Ti, To + Tbase)
         
-def getCapacitySuctionSimp(D, L, zlug, H, V, gamma, Su0, k, alpha):
+            # Torque induced by horizontal load
+            T = Ha*rlug*np.sin(np.deg2rad(psilug))
+            nhuT = T/Tmax 
+            nhuV = Ha/To 
+            nhuVstar = np.sqrt(nhuV**2 - nhuT**2) 
+            deltastar = delta_av*(nhuVstar/nhuV) 
+               
+            # Vertical failure modes
+            Vmax2 = Pile_Head + PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*deltastar*sigma_av + PileSurface(dz_clip, D - 2*t)*deltastar*sigma_av
+            Vmax3 = Pile_Head + PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*deltastar*sigma_av + SoilWeight(dz_clip, D, t, gamma_av)
+        
+            Vmax_layer = min(Vmax2, Vmax3)
+            
+            # Sum vertical capacities
+            Vmax_final += Vmax_layer
+        
+            print(f"Vmax_layer (sand) = {Vmax_layer:.2f} N")
+            print(f"Vmax2 (sand) = {Vmax2:.2f} N")
+            print(f"Vmax3 (sand) = {Vmax3:.2f} N")
 
-    '''
-    Parameters
-    ----------
-    D : float 
-        Suction pile diameter [m]
-    L : float 
-        Suction anchor length [m]
-    Tm : float 
-        Mooring line load at mudlevel [kN]
-    thetam : float 
-        Mooring line angle at mudlevel [deg]
-    zlug : float 
-        Embedded depth of the lug [m]
-    gamma: float
+    # Hmax_final and weighted ez
+    for data in layer_data:
+        z_top = data['z_top']
+        z_bot = data['z_bot']
+        Hmax_layer = data['Hmax_layer']
+        ez_layer = data['ez_layer']
+        dz_layer = data['dz']
+
+        z_embedded_start = z0
+        z_embedded_end = L - z0
+
+        if z_top >= z_embedded_start and z_bot <= z_embedded_end:
+            sum_ez_weighted += Hmax_layer*ez_layer
+            Hmax_final += Hmax_layer
+            print(f'Hmax_layer  = {Hmax_layer:.2f} m')
+
+        elif z_top < z_embedded_end and z_bot > z_embedded_start:
+            dz_inside = min(z_bot, z_embedded_end) - max(z_top, z_embedded_start)
+            if dz_inside > 0:
+                ratio = dz_inside/dz_layer
+                sum_ez_weighted += Hmax_layer*ratio*ez_layer
+                Hmax_final += Hmax_layer*ratio
+                # print(f'ez_layer (partial) = {ez_layer:.2f} m')
+   
+    ez_global = sum_ez_weighted/Hmax_final 
+    print(f'ez_global   = {ez_global:.2f} m')
+    print(f'Hmax_final  = {Hmax_final:.2f} m')
+
+    # Calculate coupled moment 
+    M  = -Va*rlugTilt(rlug, zlug, thetalug) - Ha*(zlugTilt(rlug, zlug, thetalug) - ez_global)
+    Mv = -Va*rlugTilt(rlug, zlug, thetalug)
+    print(f"rlug_eff = {rlugTilt(rlug, zlug, thetalug):.2f} m")
+    print(f"zlug_eff = {zlugTilt(rlug, zlug, thetalug):.2f} m")
+    print(f"M = {M:.2f} Nm")
     
-    Su0 : float 
-        Undrained shear strength at the mudline (clay only)[kPa]
-    k : float 
-        Undrained shear strength gradient (clay only) [kPa/m]
-    alpha : float 
-        Skin friction coefficient (outer and inner - clay only) [-] 
-    rhows : float
-        Submerged steel density [t/m3]
+    # MH Ellipse Parameters for Clay (Kay 2014)
+    # ΔφMH (piecewise based on L/D)
+    if 0.5 <= lambdap < 1.125:
+        delta_phi = 0.32 + 4.32*lambdap; #print(delta_phi)
+    elif 1.125 <= lambdap < 2.0:
+        delta_phi = 7.13 - 1.71*lambdap; #print(delta_phi)
+    elif 2.0 <= lambdap <= 8.0:
+        delta_phi = 2.25 - 0.25*lambdap; #print(delta_phi)
+    else:
+        raise ValueError('L/D out of bounds for MH ellipse.')
 
-    Returns
-    -------
-    Hmax : float 
-        Maximum horizontal capacity [kN]
-    Vmax : float 
-        Maximum vertical capacity [kN]
-    UC: float
-        Capacity unity check for given load [-]
-    ''' 
+    phi_MH = -np.arctan(ez_global/(L - z0)) - np.deg2rad(delta_phi)
+    a_MH = Np_fixed/np.cos(phi_MH)
+    delta_bMH = 0.45*(lambdap)**(-0.9) if lambdap <= 1.5 else 0
+    b_MH = -Np_free*np.sin(phi_MH) + delta_bMH
+    print(f"delta_phi = {delta_phi:.2f} deg")
+    print(f"phi_MH = {np.rad2deg(phi_MH):.2f} deg")
+    print(f"a_MH = {a_MH:.2f}")
+    print(f"b_MH = {b_MH:.2f}")
+
+    # MH Ellipse Parameters for Clay (Kay 2015)
+    # VH (piecewise based on L/D)
+    if 0.5 <= lambdap < 1.5:
+        a_VH = 9/4 + (5/3)*lambdap; 
+    elif 0.5 <= lambdap < 1.25:
+        b_VH = 23/4 - (13/5)*lambdap; 
+    elif 1.5 <= lambdap <= 6.0:
+        a_VH = 47/12 - (5/9)*lambdap; 
+        b_VH = 50/19 - (2/19)*lambdap;
+    else:
+        raise ValueError('L/D ratio out of bounds for MH ellipse formulation.')
+    a_VH = 0.5 + lambdap;   b_VH = 4.5 + lambdap/3
+    # a_VH = 4.5 + lambdap/2; b_VH = 4.5 + lambdap/4
+    print(f"a_VH = {a_VH:.2f}")
+    print(f"b_VH = {b_VH:.2f}")
+
+    # Scale VH ellipse based on vertical load ratio (Kay 2015) 
+    shrink_factor = 1 - ((Va/Vmax_final)**b_VH)**(2/a_VH)
+
+    plt.figure(figsize=(10, 5))
+    theta = np.linspace(0, 2*np.pi, 400)        
+    shrink_factors = np.linspace(0.0, 1.0, 5)  
+    # Define colormap
+    cmap = plt.colormaps['Greys']  
+    norm = mcolors.Normalize(vmin=min(shrink_factors), vmax=max(shrink_factors))
     
-    lambdap = L/D;                       # Suction pile slenderness ratio   
-    t = 10*D/1e3                         # Thickness of the pile
-    Np_fixed = 10.25;                    # From Np vs L/D chart from CAISSON_VHM
-    rhows=66.90
+    for s_f in shrink_factors:
+        color = cmap(norm(s_f))
+        x_ellipse = Hmax_final*s_f*np.cos(theta)
+        y_ellipse = Vmax_final*s_f*np.sin(theta)
+        H_rot = np.cos(phi_MH)*x_ellipse - np.sin(phi_MH)*y_ellipse
+        M_rot = np.sin(phi_MH)*x_ellipse + np.cos(phi_MH)*y_ellipse
+        plt.plot(H_rot, M_rot, color=color, alpha=0.5)
     
-    Su_av_L = Su0 + k*zlug;              # Undrained shear strength values (average)
-    Su_tip = Su0 + k*L;                  # Undrained shear strength values (tip)
-    Nc = min (6.2*(1 + 0.34*np.arctan(lambdap)),9)  # End-bearing capacity factor
+    x_ellipse_prime = Hmax_final*shrink_factor*np.cos(theta)
+    y_ellipse_prime = Vmax_final*shrink_factor*np.sin(theta)
+    H_rot_prime = np.cos(phi_MH)*x_ellipse_prime - np.sin(phi_MH)*y_ellipse_prime
+    M_rot_prime = np.sin(phi_MH)*x_ellipse_prime + np.cos(phi_MH)*y_ellipse_prime
+    Hlim = 1.2*Hmax_final  
+    plt.xlim(-Hlim, Hlim)
+    plt.ylim(-Hlim, Hlim)
+    plt.grid(True, color='gray', linestyle='--', lw=0.5, alpha=0.8)
     
-    # Outer and inner surface of the pile skirt
-    def PileSurface(Len, Dia):
-        Sp = np.pi*Dia*Len
-        return Sp    
-    # Dry and wet mass of the pile    
-    def PileWeight(Len, Dia, tw, rho):
-        Wp = ((np.pi/4)*((Dia**2 - (Dia - 2*tw)**2)*Len + (np.pi/4)*Dia**2*tw))*rho
-        return Wp 
-    # Weight of the soil plug      
-    def SoilWeight(Len, Dia, tw, gamma_soil): 
-        Wsoil =(np.pi/4)*(Dia - 2*tw)**2*Len*gamma_soil
-        return Wsoil
+    # Highlight the actual one
+    plt.plot(H_rot_prime, M_rot_prime, 'b', label= f'MH ellipse w/ V/Vmax = {shrink_factor:.3f}')
+    plt.axhline(0, color='k', linestyle='--', lw=1.0)
+    plt.axvline(0, color='k', linestyle='--', lw=1.0)
     
-    Hmax = Np_fixed*L*D*Su_av_L
-    # "Plugged" (Reverse end bearing capacity - passive suction) 
-    Vmax1 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alpha*Su_av_L + Nc*Su_tip*(np.pi/4)*D**2)
-    # "Coring"        
-    Vmax2 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alpha*Su_av_L + PileSurface(L,(D - 2*t))*alpha*Su_av_L)
-    # "Leaking"        
-    Vmax3 = (PileWeight(L, D, t, rhows) + PileSurface(L, D)*alpha*Su_av_L + SoilWeight(L, D, t, gamma)) 
-    # print(Vmax1); print(Vmax2); print(Vmax3)                  
-    Vmax = min(Vmax1,Vmax2,Vmax3)
+    # Plot horizontal line at constant M and Mv
+    H_plot = np.linspace(min(1.3*H_rot), max(1.3*H_rot), 100)
+    M_plot  = np.full_like(H_plot,  M)  # Constant moment
+    Mv_plot = np.full_like(H_plot, Mv)  # Constant moment
+    plt.plot(H_plot,  M_plot, 'r', lw=1.0, label='Moment line') 
+    plt.plot(H_plot, Mv_plot, 'r', lw=0.5, label='Vertical moment line') 
+    plt.legend(loc='lower left', fontsize='small')
+   
+    H_roots = horizontal_cross(H_rot_prime, M_rot_prime, M)
+    Hmax_v = 0.1 
+    if H_roots:
+        Hmax_pos = max([r for r in H_roots if r >= 0], default=None)
+        Hmax_neg = min([r for r in H_roots if r <  0], default=None)
+        if M > 0 and Hmax_neg is not None:
+            Hmax_v = abs(Hmax_neg)
+            plt.plot(Hmax_neg, M, 'ro', label=f'Hmax,v = {Hmax_neg/1e6:.1f} MN', zorder=20)
+            plt.legend(loc='lower left')
+        elif M <= 0 and Hmax_pos is not None:
+            Hmax_v = abs(Hmax_pos)
+            plt.plot(Hmax_pos, M, 'ro', label=f'Hmax,v = {Hmax_pos/1e6:.1f} MN', zorder=20)
+            plt.legend(loc='lower left')
+        else:
+            print('[WARNING] No valid Hmax crossing found for moment cut.')
+    else:
+        print('[WARNING] No intersection between moment line and ellipse.') 
+            
+    H_v_roots = horizontal_cross(H_rot_prime, M_rot_prime, 0.0)
+    plt.scatter(H_v_roots[0], 0.0, s=25, facecolors='white', edgecolors='blue', 
+                marker='s',label=f'Ho ≈ {H_v_roots[0]/1e6:.1f} MN', zorder=10)
+    plt.legend(loc='lower left', fontsize='small')
+
     
-    # Submerged pile weight (inc. stiffening plus vent) assessed as a factor
-    Wp = 1.00*PileWeight(L, D, t, (rhows)) 
-    # Submerged weight of the soil plug
-    Ws = SoilWeight(L, D, t, gamma) 
+    M_v_roots = vertical_cross(H_rot_prime, M_rot_prime, 0.0)
+    plt.scatter(0.0, M_v_roots[0], s=25, facecolors='white', edgecolors='blue', 
+                marker='s', label=f'Mo ≈ {M_v_roots[0]/1e6:.1f} MNm', zorder=10)
+    plt.legend(loc='lower left', fontsize='small')
+           
+    # Find and plot maximum H 
+    idx_maxH = np.argmax(H_rot_prime)
+    H_at_maxH = H_rot_prime[idx_maxH]
+    M_at_maxH = M_rot_prime[idx_maxH]
+    plt.scatter(H_at_maxH, M_at_maxH, s=25, facecolors='white', edgecolors='blue',
+                marker='D', label=f'Hmax ≈ {H_at_maxH/1e6:.1f} MN', zorder=10)
+    plt.legend(loc='lower left', fontsize='small')
     
-    # H = Tm*np.cos(np.deg2rad(thetam)); V = Tm*np.sin(np.deg2rad(thetam))
+    # Find and plot minimum M (vertical axis intercept)
+    idx_minM = np.argmin(M_rot_prime)
+    H_at_minM = H_rot_prime[idx_minM]
+    M_at_minM = M_rot_prime[idx_minM]
+    plt.scatter(H_at_minM, M_at_minM, s=25, facecolors='white', edgecolors='blue',
+                marker='D', label=f'Mmax ≈ {M_at_minM/1e6:.1f} MNm', zorder=10)
+    plt.legend(loc='lower left', fontsize='small')
+    
+    # Constant weight
+    pile_head = PileWeight(z0, D, t, rhows); print(f"pile_head    = {pile_head:.2f} N")
+    Vmax_final += pile_head; print(f"Vmax_final   = {Vmax_final:.2f} N")
+    
+    Wp = 1.10*PileWeight(L, D, t, rhows + rhow) 
     
     # Capacity envelope
-    aVH = 0.5 + lambdap; bVH = 4.5 + lambdap/3 
-    # print('Env. exp =' +str(aVH)+str(bVH))
-    UC = (H/Hmax)**aVH + (V/Vmax)**bVH
+    a_VH = 0.5 + lambdap; b_VH = 4.5 + lambdap/3
+    # Unity check
+    UC = (Ha/Hmax_v)**a_VH + (Va/Vmax_final)**b_VH 
+    plt.figure(figsize=(6, 5))
+    x = np.linspace(0, 1, 100)
+    y = (1 - x**b_VH)**(1/a_VH)
 
-    x = np.cos(np.linspace (0,np.pi/2,1000))
-    y = (1 - x**bVH)**(1/aVH)
-    X = Hmax*x; Y = Vmax*y
+    # Plotting
+    if plot:
+        plt.figure(figsize=(6, 5))
+        plt.plot(Hmax_v*x, Vmax_final*y, 'b', label='VH Envelope')
+        plt.plot(Ha, Va, 'go', label='Applied load')
+        plt.xlabel('Horizontal capacity (N)')
+        plt.ylabel('Vertical capacity (N)')
+        plt.title('VH suction pile capacity envelope')
+        plt.axis([0, 1.3*max(Hmax_v, Ha), 0, 1.3*max(Vmax_final, Va)]) 
+        plt.grid(True)
+        plt.legend()
+        plt.show()
 
-    #H_good = Hmax*np.exp(np.log(0.5)/aVH)
-    #V_good = Vmax*np.exp(np.log(0.5)/bVH)
-   
-    resultsSuctionSimp = {}
-    resultsSuctionSimp['Horizontal max.'] = Hmax    # Capacity at specified loading angle
-    resultsSuctionSimp['Vertical max.'] = Vmax      # Capacity at specified loading angle
-    resultsSuctionSimp['UC'] = UC                   # Unity check
-    resultsSuctionSimp['Weight'] = Wp               # Pile weight in kN
-    resultsSuctionSimp['Weight Soil'] = Ws          # in kN
-    resultsSuctionSimp['t'] = t
-    
-    return resultsSuctionSimp  
+    resultsSuction = {
+        'Horizontal max.': Hmax_v,
+        'Vertical max.': Vmax_final,
+        'Unity check': UC,
+        'Weight pile': Wp}
+
+    return layers, resultsSuction
 
 if __name__ == '__main__':
-    
-    D = 2.0          # Diameter in meters
-    L = 10.0         # Length in meters
-    zlug = (2/3)*L   # Padeye depth
-    H = 1500.0       # Horizontal load in kN
-    V = 1000.0       # Vertical load in kN
-    
-    gamma = 8
-    Su0 = 25
-    k = 0
-    
-    phi = 50
-    Dr = 75
-    
-    results_clay = getCapacitySuction(D, L, zlug, H, V, 'clay', gamma, Su0=Su0, k=k, phi=phi, Dr=Dr, plot=True) 
-    
-    # results_sand = getCapacitySuction(D, L, zlug, H, V, 'sand', gamma, Su0=Su0, k=k, phi=phi, Dr=Dr, plot=True)   
\ No newline at end of file
+
+    profile_map = [
+    {
+        'name': 'CPT_1',
+        'x': 498234, 'y': 5725141,
+        'layers': [
+            {
+                'top': 0.0, 'bottom': 20.0,
+                'soil_type': 'clay',
+                'gamma_top': 8.5, 'gamma_bot': 8.5,
+                'Su_top': 2.4, 'Su_bot': 30.3}]
+            # {
+            #     'top': 10.0, 'bottom': 20.0,
+            #     'soil_type': 'clay',
+            #     'gamma_top': 8.5, 'gamma_bot': 8.5,
+            #     'Su_top': 13.95, 'Su_bot': 30.3}]
+            # {
+            #     'top': 30.0, 'bottom': 36.0,
+            #     'soil_type': 'clay',
+            #     'gamma_top': 9.0, 'gamma_bot': 9.5,
+            #     'Su_top': 75, 'Su_bot': 100},
+            # {
+            #     'top': 36.0, 'bottom': 55.0,
+            #     'soil_type': 'clay',
+            #     'gamma_top': 9.5, 'gamma_bot': 9.5,
+            #     'Su_top': 100, 'Su_bot': 100}]
+        }
+    ]
+
+
+    # Pile and load properties
+    D = 3.34                          # Pile diameter (m)
+    L = 20.0                          # Pile length (m)
+    zlug = (2/3)*L                    # Lug depth (m)
+    theta = 5                         # Tilt misalignment angle (deg)
+    psi = 7.5                         # Twist misalignment angle (deg)
+    Ha = 1e6                        # Applied horizontal load (N)
+    Va = 5.7e6                        # Applied vertical load (N)
+
+    # Calculate
+    layers, resultsSuction = getCapacitySuction(
+        profile_map, 'CPT_1',         # Soil properties and location of the pile
+        D, L, zlug,                   # Pile geometrical properties
+        Ha, Va,                       # Pile loading conditions   
+        thetalug=theta, psilug=psi,   # Pile misaligment tolerances 
+        plot=False
+    )
+
+    # print('\n--- Suction Pile Capacity Results ---')
+    # print(f"Hmax_final = {resultsSuction['Hmax_final']:.2f} N")
+    # print(f"Vmax_final = {resultsSuction['Vmax_final']:.2f} N")
+    # print(f"Unity check (UC) = {resultsSuction['UnityCheck']:.4f}")
+    # print(f"Total Moment (M_total) = {resultsSuction['M_total']:.2f} Nm")
+
+    # plot_suction(layers, L, D, z0 = layers[0]['top'], zlug=zlug, title='Suction Pile and Soil Layers')
diff --git a/famodel/anchors/anchors_famodel/capacity_torpedo.py b/famodel/anchors/anchors_famodel/capacity_torpedo.py
index 1d493f1e..564f5502 100644
--- a/famodel/anchors/anchors_famodel/capacity_torpedo.py
+++ b/famodel/anchors/anchors_famodel/capacity_torpedo.py
@@ -1,95 +1,271 @@
 
-import yaml     
 import numpy as np
 import matplotlib.pyplot as plt
-from scipy.optimize import fsolve
-from matplotlib import cm
-from mpl_toolkits import mplot3d 
-   
-def getCapacityTorpedo(D1, D2, L1, L2, zlug, soil_type, Su0, k, alpha):
-    
+from .support_soils import clay_profile
+from .support_plots import plot_torpedo
+
+def getCapacityTorpedo(profile_map, location_name, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot=True):
     '''Calculate the inclined load capacity of a torpedo pile in clay following S. Kay methodology.
-    The calculation is based on the holding capacity of the suction pile as if it fully was embedded in soil.  
-    
+    The calculation is based on the soil profile, anchor geometry and inclined load.  
+
     Parameters
     ----------
+    profile : array
+        Clay soil profile (z, Su, gamma)
+            Clay soil profile (z (m), Su (kPa), gamma (kN/m³))
+    soil_type : string
+        Select soil condition, 'clay' 
     D1 : float 
-        Torpedo pile wing diameter. [m]
+        Wing diameter (m)
     D2 : float    
-        Torpedo pile shaft diameter. [m]
+        Shaft diameter (m)
     L1 : float 
-        Torpedo pile wing length. [m]
+        Winged section length (m)
     L2 : float 
-        Torpedo pile shaft length, excluding wing. [m]
+        Shaft section length (m)
     zlug : float 
-        Torpedo pile embedded depth at main padeye elevation. [m]        
-    soil_type : string
-        Select soil condition, 'clay' or 'sand' 
-    gamma: float 
-        The effective unit weight of the soil. [kN/m3] 
-    Su0 : float 
-        The Undrained shear strength at the mudline. [kPa]
-    k : float 
-        The Undrained shear strength gradient. [kPa/m]
-    alpha : float
-        The skin friction coefficient. [-]   
-    
+        Padeye embedment depth (m)       
+    Ha : float       
+        Horizontal load at pile lug elevation (N)
+    Va : float          
+        Vertical load at pile lug elevation (N)
+    plot : bool
+        Plot the capacity envelope if True
+
     Returns
     -------
-    Hmax : float 
-        Maximum horizontal capacity [kN]
-    Vmax : float 
-        Maximum vertical capacity [kN]
+    Dictionary with capcity, weigth and UC.
     '''
 
-    rhos= 78.50                                     # Dry steel unit weight (kN/m3)
-    t = (6.35 + D2*20)/1e3                          # Torpedo pile wall thickness (m), API RP2A-WSD
-    
-    L = L1 + L2;
-    Dstar = (D1*L1 + (D1 + 2*D2)*L2)/L              # Plane 1 (four fins)
-    #Dstar = (D1*L1 + np.sqrt(2)*(D1/2 + D2)*L2)/L  # Plane 2 (four fins)    
-    #rlug = D2/2; zlug = zlug; 
-    lambdap = L/Dstar; print('lambdap = ' +str(lambdap))
-    a = zlug; b = zlug + L1; c = zlug + L1 + L2;
-    Wp = 850                                        # Weight of the pile with ballast [kN]
-    # Dry and wet mass of the pile    
+    # Retrieve soil layers from map
+    profile_entry = next(p for p in profile_map if p['name'] == location_name)
+    layers = profile_entry['layers']
+
+    L = L1 + L2
+    t = (6.35 + D2*20)/1e3
+    rhows = 66.90e3
+    rhow = 10e3
+
     def PileWeight(Len1, Len2, Dia1, Dia2, tw, rho):
-        Wp = ((np.pi/4)*(Dia1**2 - (Dia1 - 2*tw)**2)*(Len1 + Len2) + 4*Len2*Dia2*tw)*rho
-        return(Wp)
-    W = PileWeight(L1, L2, D1, D2, t, rhos)         # Weight of the steel pile [kN]
+        return ((np.pi/4)*(Dia1**2 - (Dia1 - 2*tw)**2)*(Len1 + Len2) + 4*Len2*Dia2*tw)*rho
+
+    def PileWingedSurface(length, diameter):
+        return np.pi*diameter*length
+
+    def PileShaftSurface(length, diameter1, diameter2):
+        return 8*length*(diameter1 - diameter2)
+
+    z_start = zlug
+    z_wing = zlug + L1
+    z_end = zlug + L
+
+    layer_data = []
+    Vmax_total = 0.0
     
-    # Dry and wet mass of the pile
-    def PileSurface(Len1, Len2, Dia1, Dia2):
-        Sp = np.pi*Dia1*(Len1 + Len2) + 8*Len2*Dia2*0.9
-        return(Sp)  
+    # Profile check points
+    npts = 10
+
+    for layer in layers:
+        if layer['soil_type'] != 'clay':
+            raise ValueError('Torpedo pile capacity model only supports clay soils.')
+
+        z_layer_top = layer['top']
+        z_layer_bot = layer['bottom']
+
+        z_clip_top = max(z_layer_top, z_start)
+        z_clip_bot = min(z_layer_bot, z_end)
+
+        if z_clip_bot <= z_clip_top:
+            continue
+
+        segments = []
+        if z_clip_bot <= z_wing:
+            segments.append((z_clip_top, z_clip_bot, D1))
+        elif z_clip_top >= z_wing:
+            segments.append((z_clip_top, z_clip_bot, D2))
+        else:
+            segments.append((z_clip_top, z_wing, D1))
+            segments.append((z_wing, z_clip_bot, D2))
+
+        for z_seg_top, z_seg_bot, D in segments:
+            dz_seg = z_seg_bot - z_seg_top
+            if dz_seg <= 0:
+                continue
+
+            profile = [
+                [z_seg_top, layer['Su_top'], layer['gamma_top']],
+                [z_seg_bot, layer['Su_bot'], layer['gamma_bot']]
+            ]
+            z_ref, f_Su, _, f_gamma, f_alpha = clay_profile(profile)
+
+            z_vals = np.linspace(z_seg_top, z_seg_bot, npts)
+            Su_vals = f_Su(z_vals)
+            alpha_vals = np.array([f_alpha(z) for z in z_vals])
 
+            Su_total = np.trapz(Su_vals, z_vals)
+            Su_moment = np.trapz(z_vals*Su_vals, z_vals)
+            print("xxxxxxxxxxxxxxxxxxxxxxxxx")
+            Su_av_z = Su_total/dz_seg
+            print(f"Su_av_z = {Su_av_z:.2f} Pa")
+            ez_layer = Su_moment /Su_total
+            print(f"dz_seg = {dz_seg:.2f} m")
+            print(f"ez_layer = {ez_layer:.2f} m")
+            alpha_av = np.mean(alpha_vals)
+            print(f"alpha_av = {alpha_av:.2f}")
+
+            Np_free = 3.45
+            Hmax_layer = Np_free*dz_seg*D*Su_av_z
+            print(f"Hmax_layer = {Hmax_layer:.2f} N")
+            print(f"D = {D:.2f} m")
+
+            surface_area = PileWingedSurface(dz_seg, D) if D == D1 else PileShaftSurface(dz_seg, D1, D2)
+            Vmax_layer = surface_area*alpha_av*Su_av_z
+            Vmax_total += Vmax_layer
+            print(f"Vmax_layer = {Vmax_layer:.2f} N")
+            
+            layer_data.append({
+                'z_top': z_seg_top,
+                'z_bot': z_seg_bot,
+                'dz': dz_seg,
+                'Hmax_layer': Hmax_layer,
+                'ez_layer': ez_layer,
+                'Su_av_z': Su_av_z,
+                'D_used': D
+            })
+
+    if not layer_data:
+        raise ValueError('No overlapping clay layers within pile depth.')
+
+    sum_Hmax = 0.0
+    sum_ez_weighted = 0.0
+
+    for data in layer_data:
+        z_top = data['z_top']
+        z_bot = data['z_bot']
+        Hmax_layer = data['Hmax_layer']
+        ez_layer = data['ez_layer']
+        dz_layer = data['dz']
+
+        z_embedded_start = zlug
+        z_embedded_end = zlug + L
+
+        if z_top >= z_embedded_start and z_bot <= z_embedded_end:
+            sum_ez_weighted += Hmax_layer*ez_layer
+            sum_Hmax += Hmax_layer
+        elif z_top < z_embedded_end and z_bot > z_embedded_start:
+            dz_inside = min(z_bot, z_embedded_end) - max(z_top, z_embedded_start)
+            if dz_inside > 0:
+                ratio = dz_inside/dz_layer
+                sum_ez_weighted += Hmax_layer*ratio*ez_layer
+                sum_Hmax += Hmax_layer * ratio
+
+    ez_global = sum_ez_weighted/sum_Hmax
+    print(f'ez_global = {ez_global:.2f} m')    
+    print(f'sum_Hmax = {sum_Hmax:.2f} N')
+
+    Vmax_total += PileWeight(L1, L2, D1, D2, t, rhows) + ballast
+    Wp = 1.10 * PileWeight(L1, L2, D1, D2, t, rhows + rhow) + ballast
+
+    ez_ratio = (ez_global - zlug)/L; print(f"ez_ratio = {ez_ratio:.2f} m")
+    
+    # Average effective width
+    L = L1 + L2
+    A_wing_plane_1 = (D1 - D2)*L1
+    A_wing_plane_2 = (D1 - D2)*np.cos(np.deg2rad(45))/2*L1
+    A_shaft = D2*L
     
-    ez_Su_den = D1*Su0*(b - a) + 0.5*D1*k*(b**2 - a**2) + D2*Su0*(c - b) + 0.5*D2*k*(c**2 - b**2)
-    ez_Su_num = D1*Su0*(a**2 - a*b) + 0.33*D1*k*(b**3 - a**3) + b**2*(0.5*D1*Su0 - 0.5*D1*a*k) - a**2*(0.5*D1*Su0 - 0.5*D1*a*k)\
-        + D2*Su0*(b**2 - b*c) + 0.33*D2*k*(c**3 - b**3) + c**2*(0.5*D2*Su0 - 0.5*D2*b*k) - b**2*(0.5*D2*Su0 - 0.5*D2*b*k)
-    ez_Su = ez_Su_num/ez_Su_den
-    ez_Su_L = ez_Su/L
-    # print('ez_Su = ' +str(ez_Su))
-    Np_free = 3.45     # From Np vs L/D chart from CAISSON_VHM
-                 
-    Hmax = L*Dstar*Np_free*(Su0 + k*(zlug + ez_Su))
-    # print('Hmax = ' +str(Hmax))
-    Vmax = PileSurface(L1, L2, D1, D2)*alpha*(Su0 + k*(zlug + ez_Su)) + Wp
-    # print('Vmax = ' +str(Vmax))
-       
-    #aVH = 0.5 + L/Dstar; bVH = 4.5 - L/(3*Dstar)
-    aVH = 4.5 + L/(2*Dstar); bVH = 3.5 - L/(4*Dstar) 
-    #H = Ta*np.cos(np.deg2rad(resultsLoad['angle'])); V = Ta*np.sin(np.deg2rad(resultsLoad['angle']))
-    #UC = (H/Hmax)**aVH + (V/Vmax)**bVH
+    # Choose based on direction:
+    plane = '1'  # or '2'
     
-    deg = [0, 15, 30, 45, 60, 75, 90]
-    x = np.cos(np.deg2rad(deg))
-    y = (1 - x**bVH)**(1/aVH)
-    X = Hmax*x/1e3; Y = Vmax*y/1e3  # in MN
-       
-    resultsTorpedo = {}
-    resultsTorpedo['Horizontal max.'] = Hmax #Hmax[0]    # Capacity at specified loading angle
-    resultsTorpedo['Vertical max.'] = Vmax               # Capacity at specified loading angle
-    resultsTorpedo['Weight'] = W                    # Dry weight of the helical pile (kN)
-   
-    return resultsTorpedo
\ No newline at end of file
+    if plane == '1':
+        Dstar = (A_wing_plane_1 + A_shaft)/L
+    elif plane == '2':
+        Dstar = (A_wing_plane_2 + A_shaft)/L
+
+    # Assign aVH and bVH based on ez_su/L
+    if np.isclose(ez_ratio, 2/3, atol=0.05):
+        aVH = 0.5 + L/Dstar
+        bVH = 4.5 - L/(3*Dstar)
+        mode = 'deep mobilization (2/3)'
+    elif 0.40 <= ez_ratio <= 0.75:
+        aVH = 4.5 + L/(2*Dstar)
+        bVH = 3.5 - L/(4*Dstar)
+        mode = 'moderate mobilization (1/2 – 3/4)'
+    # else:
+    #     aVH = 4.0
+    #     bVH = 4.0
+    #     mode = 'default exponents (fallback)'
+    print(f'Interaction exponents set to aVH = {aVH:.2f}, bVH = {bVH:.2f} [{mode}]')
+    
+    UC = (Ha/sum_Hmax)**aVH + (Va/Vmax_total)**bVH
+
+    if plot:
+        deg = np.linspace(0, 90, 20)
+        x = np.cos(np.deg2rad(deg))
+        y = (1 - x**bVH)**(1/aVH)
+        X = sum_Hmax*x
+        Y = Vmax_total*y
+
+        plt.figure(figsize=(6, 5))
+        plt.plot(X, Y, color='blue', label='VH Envelope')
+        plt.plot(Ha, Va, 'o', color='red', label='Load Point')
+        plt.xlabel('Horizontal Capacity (N)')
+        plt.ylabel('Vertical Capacity (N)')
+        plt.title('VH torpedo pile capacity envelope')
+        plt.grid(True)
+        plt.legend()
+        plt.tight_layout()
+        plt.show()
+
+    resultsTorpedo = {
+        'Horizontal max.': sum_Hmax,
+        'Vertical max.': Vmax_total,
+        'Unity check': UC,
+        'Weight pile': Wp,
+        'ez_global': ez_global,
+        'layer_data': layer_data}
+
+    return layers, resultsTorpedo
+
+if __name__ == '__main__':
+
+    profile_map = [
+        {
+            'name': 'CPT_1',
+            'x': 498234, 'y': 5725141,
+            'layers': [
+                {
+                    'top': 0.0, 'bottom': 20.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 8.5,
+                    'Su_top': 50, 'Su_bot': 70},
+                {
+                    'top': 20.0, 'bottom': 25.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.5, 'gamma_bot': 8.5,
+                    'Su_top': 80, 'Su_bot': 100},
+                {
+                    'top': 25.0, 'bottom': 50.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.5, 'gamma_bot': 9.0,
+                    'Su_top': 125, 'Su_bot': 150}]
+        }
+    ]
+
+    D1 = 3.0
+    D2 = 1.5
+    L1 = 11.0
+    L2 = 5.0
+    zlug = 15.0
+    ballast = 10000
+    Ha = 6.0e6
+    Va = 8.0e6
+
+    layers, results = getCapacityTorpedo(profile_map, 'CPT_1', D1, D2, L1, L2, zlug, ballast, Ha, Va)
+
+    # print("\n--- Torpedo Pile Capacity Results ---")
+    # for key, val in results.items():
+    #     if key != 'layer_data':
+    #         print(f"{key}: {val:.2f}")
+            
+    plot_torpedo(layers, D1, D2, L1, L2, z0 = layers[0]['top'], zlug=zlug, title='Torpedo Pile in Clay Profile')
diff --git a/famodel/anchors/anchors_famodel/installatioin_torque.py b/famodel/anchors/anchors_famodel/installatioin_torque.py
new file mode 100644
index 00000000..1829ab0e
--- /dev/null
+++ b/famodel/anchors/anchors_famodel/installatioin_torque.py
@@ -0,0 +1,176 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+from support_soils import sand_profile
+
+def getInstallationHelical(profile_map, location_name, D, L, d, plot=True):
+    # Constants and geometry
+    dH = 0.05                  # m
+    psi_p = 16.5               # degrees
+    delta_crit = 24            # degrees
+    Fr = 0.01
+
+    Dh = D                     # Helix diameter
+    Dc = d                     # Core diameter
+    ph = Dh/3                  # Pitch
+    E = 210e9                  # Pa
+    th = 0.10                  # m
+    tc = 0.03                  # m
+    f_y = 350e6                # Pa
+    f_y_weld = 250e6           # Pa (typical weld yield strength)
+    k = 1.04                   # Bending coefficient
+
+    profile_entry = next((p for p in profile_map if p['name'] == location_name), None)
+    if not profile_entry:
+        raise ValueError(f"Location '{location_name}' not found in profile_map")
+
+    layers = profile_entry['layers']
+
+    # Assemble sand profile
+    profile = []
+    for layer in layers:
+        if layer['soil_type'] == 'sand':
+            profile.append([layer['top'],    layer['gamma_top'], layer['phi_top'], layer['Dr_top']])
+            profile.append([layer['bottom'], layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']])
+
+    z0, f_gamma, f_phi, f_Dr, f_sigma_v_eff, f_delta = sand_profile(profile)
+
+    def qc_depth(H):
+        qc0, depth0 = 0, 0
+        qc1, depth1 = 8e6, 6
+        qc2, depth2 = 40e6, 16
+        m1 = (qc1 - qc0)/(depth1 - depth0)
+        m2 = (qc2 - qc1)/(depth2 - depth1)
+        if H <= depth1:
+            return m1 * H
+        elif H <= depth2:
+            return m2 * (H - depth1) + qc1
+        else:
+            return qc2
+
+    def qc_average(H, Dh):
+        depths = np.linspace(max(0, H - 1.5*Dh), min(H + 1.5*Dh, 20), int(1.5*Dh/dH))
+        return np.mean([qc_depth(z) for z in depths])
+
+    def calculate_torque(Dc, Dh, Fr, ph, th, dH, H):
+        gamma = f_gamma(H)
+        delta_crit_rad = np.radians(delta_crit)
+        a = Fr/np.tan(delta_crit_rad)
+        theta = np.arctan(ph/(np.pi*Dh))
+        K0 = 1 - np.sin(np.radians(32))
+        Tc = np.sum([a*qc_average(z, Dh)*np.tan(delta_crit_rad)*dH*(Dc**2/2) for z in np.arange(0, H, dH)])
+        Tb = qc_average(H, Dh)*np.pi*(Dc**3)*np.tan(delta_crit_rad)/12
+        Th = (a*qc_average(H, Dh)*np.tan(delta_crit_rad + theta)*np.pi*(Dh**3 - Dc**3)/(12*K0) +
+              a*qc_average(H, Dh)*th*np.tan(delta_crit_rad)*np.pi*(Dh**2)/2 +
+              a*qc_average(H, Dh)*th*(Dh**2 - Dc**2)/8)
+        return Tc + Tb + Th
+
+    def calculate_force(Dc, Dh, Fr, ph, th, dH, H):
+        gamma = f_gamma(H)
+        delta_crit_rad = np.radians(delta_crit)
+        a = Fr/np.tan(delta_crit_rad)
+        K0 = 1 - np.sin(np.radians(32))
+        Fc = np.sum([0.6*a*qc_average(z, Dh)*np.tan(delta_crit_rad)*dH*np.pi*Dc for z in np.arange(0, H, dH)])
+        Fb = 0.6*qc_average(H, Dh)*np.pi*(Dc**2)/4
+        Fh = (a*qc_average(H, Dh)*np.pi*(Dh**2 - Dc**2)/(4*K0) +
+              a*qc_average(H, Dh)*th*np.pi*Dh/K0 +
+              qc_average(H, Dh)*th*(Dh - Dc)/2)
+        return Fc + Fb + Fh
+
+    def calculate_core(T, Fy_c, Dc, tc, E, H, K=2):
+        tau = 16*(T/np.pi)*(Dc/(Dc**4 - (Dc - 2*tc)**4))
+        sigma_y = 4*(Fy_c/np.pi)/((Dc**2 - (Dc - 2*tc)**2))
+        sigma_eq_c = np.sqrt(sigma_y**2 + 3 * tau**2)
+        I = (np.pi/64)*(Dc**4 - (Dc - 2*tc)**4)
+        Fy_cr = np.pi**2*E*I/(K*H)**2
+        return sigma_eq_c, Fy_cr
+
+    def calculate_helix(Fy_max, Dh, Dc, th, k):
+        q = 4*Fy_max/(np.pi*(Dh**2 - Dc**2))
+        return k*q*Dh**2/(4*th**2)
+    
+    def calculate_weld_stress(Fy_max, th, Dh, weld_length):
+        M = Fy_max * th
+        Q = Fy_max
+        Aw = weld_length * th
+        sigma_w = M / (weld_length * th**2 / 6)
+        tau_w = Q / Aw
+        sigma_eq_w = np.sqrt(sigma_w**2 + 3 * tau_w**2)
+        return sigma_eq_w
+
+    def calculate_axial_capacity(Dh, H):
+        gamma = f_gamma(H)
+        # phi_p = 6.6 + 11*np.log10(qc_average(H, Dh)/np.sqrt(gamma*H))
+        phi_p = f_phi(H)
+        Fps = np.tan(np.radians(psi_p)) + np.cos(np.radians(phi_p - psi_p))*\
+            (np.tan(np.radians(phi_p) - np.tan(np.radians(psi_p))))
+        Fs1 = 2*Fps
+        Fs2 = (4/3)*Fps*np.tan(np.radians(psi_p))
+        Fu = (1 + Fs1*(H/Dh) + Fs2*(H/Dh)**2)*gamma*(np.pi/4)*(Dh**2)*H
+        return Fu
+
+    Tmax = 8e6
+    H_max = L
+    H_values = np.linspace(0.1, H_max, 100)
+    results = {
+        'H': [], 'Fu': [], 'Torque': [], 'Force': [],
+        'SigmaHelix': [], 'SigmaCore': [], 'BucklingLimit': [], 'SigmaWeld': [], 'FailureMode': []
+    }
+
+    for H in H_values:
+        T = calculate_torque(Dc, Dh, Fr, ph, th, dH, H)
+        F_inst = calculate_force(Dc, Dh, Fr, ph, th, dH, H)
+        sigma_helix = calculate_helix(F_inst, Dh, Dc, th, k)
+        sigma_core, Fy_cr = calculate_core(T, F_inst, Dc, tc, E, H)
+        sigma_weld = calculate_weld_stress(F_inst, th, Dh, weld_length=np.pi*Dc)
+        Fu = calculate_axial_capacity(Dh, H)
+
+        if T > Tmax:
+            failure_mode = 'Torque limit'
+        elif F_inst > Fy_cr:
+            failure_mode = 'Core buckling'
+        elif sigma_helix > f_y:
+            failure_mode = 'Helix stress'
+        elif sigma_weld > f_y_weld:
+            failure_mode = 'Weld stress'
+        else:
+            failure_mode = 'OK'
+
+        if failure_mode == 'OK':
+            results['H'].append(H)
+            results['Fu'].append(Fu)
+            results['Torque'].append(T)
+            results['Force'].append(F_inst)
+            results['SigmaHelix'].append(sigma_helix)
+            results['SigmaCore'].append(sigma_core)
+            results['BucklingLimit'].append(Fy_cr)
+            results['SigmaWeld'].append(sigma_weld)
+            results['FailureMode'].append(failure_mode)
+
+    if plot:
+        plt.figure(figsize=(7, 5))
+        plt.plot(results['H'], results['Fu'], label=f'Dh/Dc = {Dh/Dc:.2f}')
+        plt.title("Fu vs H")
+        plt.xlabel("Depth H (m)")
+        plt.ylabel("Axial Capacity Fu (N)")
+        plt.grid(True)
+        plt.legend()
+        plt.tight_layout()
+        plt.show()
+
+    return results
+
+if __name__ == '__main__':
+    profile_map = [
+        {
+            'name': 'CPT_1',
+            'x': 0, 'y': 0,
+            'layers': [
+                {'top': 0.0, 'bottom':  5.0, 'soil_type': 'sand', 'gamma_top': 10, 'gamma_bot': 11, 'phi_top': 32, 'phi_bot': 36, 'Dr_top': 55, 'Dr_bot': 65},
+                {'top': 5.0, 'bottom': 20.0, 'soil_type': 'sand', 'gamma_top': 11, 'gamma_bot': 12, 'phi_top': 36, 'phi_bot': 38, 'Dr_top': 65, 'Dr_bot': 80}
+            ]
+        }
+    ]
+
+    results = getInstallationHelical(profile_map, 'CPT_1', D=1.5, L=10.0, d=0.5, plot=True)
+      
\ No newline at end of file
diff --git a/famodel/anchors/anchors_famodel/installation_buckling.py b/famodel/anchors/anchors_famodel/installation_buckling.py
new file mode 100644
index 00000000..ee00f615
--- /dev/null
+++ b/famodel/anchors/anchors_famodel/installation_buckling.py
@@ -0,0 +1,164 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from support_soils import clay_profile, sand_profile
+from installation_suction import getInstallationSuction
+
+def compute_Zs(s, r, t, nu):
+    return (s**2/(r*t))*np.sqrt(1 - nu**2)
+
+def compute_C(psi, rho, xi):
+    return psi*np.sqrt(1 + (rho*xi/psi)**2)
+
+def gamma_M(lam_bar):
+    if lam_bar < 0.5:
+        return 1.15
+    elif lam_bar <= 1.0:
+        return 0.85 + 0.6 * lam_bar
+    else:
+        return 1.45
+
+def getBucklingSuction(profile_map, location_name, D, L, t=None, fy=345e6):
+    '''
+    Shell buckling capacity during suction pile installation using DNV-RP-C202 and Colliard & Wallerand effective length.
+    '''
+    E = 2.1e11
+    nu = 0.3
+
+    if t is None:
+        t = D / 200
+
+    r = D/2 - t/2
+
+    profile_entry = next(p for p in profile_map if p['name'] == location_name)
+    layers = profile_entry['layers']
+    main_type = layers[0]['soil_type'].lower()
+    if main_type == 'clay':
+        clay_input = [[layers[0]['top'],    layers[0]['gamma_top'], layers[0]['Su_top']],
+                      [layers[0]['bottom'], layers[0]['gamma_bot'], layers[0]['Su_bot']]]
+        _, f_gamma, _, _, _ = clay_profile(clay_input)
+    elif main_type == 'sand':
+        sand_input = [[layers[0]['top'],    layers[0]['gamma_top'], layers[0]['phi_top']],
+                      [layers[0]['bottom'], layers[0]['gamma_bot'], layers[0]['phi_bot']]]
+        _, f_gamma, _, _, _ = sand_profile(sand_input)
+    else:
+        raise ValueError("Unsupported soil type")
+
+    depths = np.arange(0.1, L + 0.1, 0.1)  # start from 0.1 to avoid division by zero
+    suction_results = getInstallationSuction(profile_map, location_name, D, L, gamma_m_install=1.5, gamma_m_retrieval=1.25)
+    pe_values = suction_results['delta_u_suction']
+    pe_values = np.full_like(pe_values, 300e3)
+
+    UC_list = []
+    LB_list = []
+    PE_list = []
+    for z, pe in zip(depths, pe_values):
+        # Colliard & Wallerand effective length
+        x = L - z  # exposed length
+        L_B = L*(1 + 2*(x/L) - 0.0435*(x/L)**2)
+        s = L_B
+        LB_list.append(s)
+        PE_list.append(pe/1e3)
+
+        Zs = compute_Zs(s, r, t, nu)
+
+        # fEa (Axial)
+        psi_a = 4.0
+        xi_a = 0.702*Zs
+        rho_a = 0.5*(1 + r/(150 * t))**-0.5
+        C_a = compute_C(psi_a, rho_a, xi_a)
+        fEa = C_a*(np.pi**2*E/(12*(1 - nu**2)))*(t/s)**2
+
+        # fEm (Bending)
+        C_m = C_a
+        fEm = fEa
+
+        # fEtau (Shear)
+        psi_t = 5.34 + (s/L)**2
+        xi_t = 0.856*(s/L)*Zs**(3/4)
+        rho_t = 0.6
+        C_t = compute_C(psi_t, rho_t, xi_t)
+        fEtau = C_t*(np.pi**2*E/(12*(1 - nu**2)))*(t/s)**2
+        
+        # fEh (Hoop)
+        psi_h = (1 + (s/L)**2)**2
+        xi_h = 1.04*(s/L)*np.sqrt(Zs)
+        rho_h = 0.6
+        C_h = compute_C(psi_h, rho_h, xi_h)
+        fEh = C_h*(np.pi**2*E/(12*(1 - nu**2)))*(t/s)**2
+
+        sigma_a = 0.5*pe*r/t
+        sigma_m = 0
+        sigma_h = pe*r/t
+        tau = 0
+
+        sigma_j = np.sqrt((sigma_a)**2 - (sigma_a)*sigma_h + sigma_h**2 + 3*tau**2)
+
+        sigma_a0 = max(0, -sigma_a)
+        sigma_m0 = max(0, -sigma_m)
+        sigma_h0 = max(0, -sigma_h)
+
+        lam_bar_sq = (fy/sigma_j)*(
+            (sigma_a0/fEa) +
+            (sigma_m0/fEm) +
+            (sigma_h0/fEh) +
+            (tau/fEtau)
+        )
+        lam_bar = np.sqrt(lam_bar_sq)
+
+        gammaM = gamma_M(lam_bar)
+        fksd = fy/np.sqrt(1 + lam_bar**4)/gammaM
+
+        UC = sigma_j/fksd
+        UC_list.append(UC)
+
+    fig, axs = plt.subplots(1, 3, figsize=(18, 6))
+
+    axs[0].plot(UC_list, depths, label='UC', color='blue')
+    axs[0].invert_yaxis()
+    axs[0].set_xlabel('Unity check')
+    axs[0].set_ylabel('Depth (m)')
+    axs[0].set_title('Shell buckling UC vs. Depth')
+    axs[0].grid(True)
+    axs[0].legend()
+
+    axs[1].plot(LB_list, depths, label='Buckling Length (L_B)', color='blue')
+    axs[1].invert_yaxis()
+    axs[1].set_xlabel('Effective buckling length (m)')
+    axs[1].set_ylabel('Depth (m)')
+    axs[1].set_title('Buckling Length vs. Depth')
+    axs[1].grid(True)
+    axs[1].legend()
+    
+    axs[2].plot(PE_list, depths, label='Underpressure', color='green')
+    axs[2].invert_yaxis()
+    axs[2].set_xlabel('Underpressure (kPa)')
+    axs[2].set_ylabel('Depth (m)')
+    axs[2].set_title('Suction Pressure vs. Depth')
+    axs[2].grid(True)
+    axs[2].legend()
+
+    plt.tight_layout()
+    plt.show()
+
+    return {
+        'depths': depths.tolist(),
+        'UC_list': UC_list,
+        'LB_list': LB_list
+    }
+
+if __name__ == '__main__':
+    profile_map = [
+        {
+            'name': 'CPT_1',
+            'x': 0, 'y': 0,
+            'layers': [
+                {
+                    'top': 0.0, 'bottom': 20.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 9.0, 'gamma_bot': 9.0,
+                    'Su_top': 5.0, 'Su_bot': 45.0
+                }
+            ]
+        }
+    ]
+    getBucklingSuction(profile_map, 'CPT_1', D=2.0, L=10.4)
diff --git a/famodel/anchors/anchors_famodel/installation_buckling2.py b/famodel/anchors/anchors_famodel/installation_buckling2.py
new file mode 100644
index 00000000..39e13aa1
--- /dev/null
+++ b/famodel/anchors/anchors_famodel/installation_buckling2.py
@@ -0,0 +1,157 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from support_soils import clay_profile, sand_profile
+from support_pycurves import py_Matlock, py_API
+from installation_suction import getInstallationSuction
+
+def compute_Zs(s, r, t, nu):
+    return (s**2/(r*t))*np.sqrt(1 - nu**2)
+
+def compute_C(psi, rho, xi):
+    return psi*np.sqrt(1 + (rho*xi/psi)**2)
+
+def gamma_M(lam_bar):
+    if lam_bar < 0.5:
+        return 1.15
+    elif lam_bar <= 1.0:
+        return 0.85 + 0.6*lam_bar
+    else:
+        return 1.45
+
+def getBucklingSuction(profile_map, location_name, D, L):
+    E = 2.1e11
+    fy = 325e6
+    nu = 0.3
+    t = (2.35 + D*20)/1e3; print(t)            # Suction pile wall thickness (m), API RP2A-WSD
+
+    R = D/2 - t/2
+
+    profile_entry = next(p for p in profile_map if p['name'] == location_name)
+    layers = profile_entry['layers']
+    main_type = layers[0]['soil_type'].lower()
+    if main_type == 'clay':
+        clay_input = [[layers[0]['top'],    layers[0]['gamma_top'], layers[0]['Su_top']],
+                      [layers[0]['bottom'], layers[0]['gamma_bot'], layers[0]['Su_bot']]]
+        _, f_gamma, f_Su, f_sigma_v_eff, _ = clay_profile(clay_input)
+    elif main_type == 'sand':
+        sand_input = [[layers[0]['top'],    layers[0]['gamma_top'], layers[0]['phi_top']],
+                      [layers[0]['bottom'], layers[0]['gamma_bot'], layers[0]['phi_bot']]]
+        _, f_gamma, f_phi, f_Dr, f_sigma_v_eff, _ = sand_profile(sand_input)
+    else:
+        raise ValueError("Unsupported soil type")
+
+    depths = np.arange(0.1, L + 0.1, 0.1)
+    suction_results = getInstallationSuction(profile_map, location_name, D, L, gamma_m_install=1.5, gamma_m_retrieval=1.25)
+    pe_values = np.interp(depths, suction_results['depths'], suction_results['delta_u_suction'])
+
+    def soil_type_map(z):
+        for layer in layers:
+            if layer['top'] <= z <= layer['bottom']:
+                return layer['soil_type'].lower()
+        raise ValueError(f"No soil type defined at depth {z}")
+
+    alpha_list = []
+    y_disp = 0.001
+    for z in depths:
+        stype = soil_type_map(z)
+        if stype == 'clay':
+            py_func, _ = py_Matlock(z, D, f_gamma(z), f_Su(z), f_sigma_v_eff(z), return_curve=True)
+        elif stype == 'sand':
+            py_func, _ = py_API(z, D, f_phi(z), f_sigma_v_eff(z), f_Dr(z), return_curve=True)
+        else:
+            raise ValueError(f"Unsupported soil type at depth {z}")
+        stiffness = py_func(y_disp)/y_disp if y_disp != 0 else 0
+        alpha_list.append(stiffness)
+    alpha_array = np.array(alpha_list)
+    integral_total = np.trapz(alpha_array, depths)
+    alpha_z = np.cumsum(alpha_array)*(depths[1] - depths[0])/integral_total
+
+    UC_list = []
+    PE_list = []
+
+    for z, pe, alpha in zip(depths[:-1], pe_values[:-1], alpha_z[:-1]):
+        s = L  # constant buckling length
+        PE_list.append(pe)
+
+        Zs = compute_Zs(s, R, t, nu)
+
+        psi_a = 4.0
+        xi_a = 0.702*Zs
+        rho_a = 0.5*(1 + R/(150*t))**-0.5
+        C_a = compute_C(psi_a, rho_a, xi_a)
+        fEa = C_a*(np.pi**2*E/(12*(1 - nu**2)))*(t/s)**2
+
+        C_m = C_a
+        fEm = fEa
+
+        psi_h = (1 + (s/L)**2)**2
+        xi_h = 1.04*(s/L)*np.sqrt(Zs)
+        rho_h = 0.6
+        C_h = compute_C(psi_h, rho_h, xi_h)
+        fEh = C_h*(np.pi**2 *E/(12*(1 - nu**2)))*(t/s)**2
+
+        psi_t = 5.34 + (s/L)**2
+        xi_t = 0.856*(s/L)*Zs**(3/4)
+        rho_t = 0.6
+        C_t = compute_C(psi_t, rho_t, xi_t)
+        fEtau = C_t*(np.pi**2*E/(12*(1 - nu**2)))*(t/s)**2
+
+        sigma_a = 0.5*pe*R*(1 - alpha)/t
+        sigma_m = 0
+        sigma_h = pe*R*(1 - alpha)/t
+        tau = 0
+
+        sigma_j = np.sqrt((sigma_a)**2 - (sigma_a)*sigma_h + sigma_h**2 + 3*tau**2)
+
+        sigma_a0 = max(0, -sigma_a)
+        sigma_m0 = max(0, -sigma_m)
+        sigma_h0 = max(0, -sigma_h)
+
+        lam_bar_sq = (fy/sigma_j)*(
+            (sigma_a0/fEa) +
+            (sigma_m0/fEm) +
+            (sigma_h0/fEh) +
+            (tau/fEtau)
+        )
+        lam_bar = np.sqrt(lam_bar_sq)
+
+        gammaM = gamma_M(lam_bar)
+        fksd = fy/np.sqrt(1 + lam_bar**4)/gammaM
+
+        UC = sigma_j/fksd
+        UC_list.append(UC)
+
+    plt.figure(figsize=(6, 6))
+    plt.plot(UC_list, depths[:-1], label='UC (with confinement)', color='darkred')
+    plt.gca().invert_yaxis()
+    plt.xlabel('Unity Check')
+    plt.ylabel('Depth (m)')
+    plt.title('Shell Buckling')
+    plt.grid(True)
+    plt.legend()
+    plt.tight_layout()
+    plt.show()
+
+    return {
+        'depths': depths.tolist(),
+        'UC_list': UC_list,
+        'PE_list': PE_list,
+        'alpha_z': alpha_z.tolist()
+    }
+
+if __name__ == '__main__':
+    profile_map = [
+        {
+            'name': 'CLAY_INSTALL',
+            'x': 0, 'y': 0,
+            'layers': [
+                {
+                    'top': 0.0, 'bottom': 20.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 9.0, 'gamma_bot': 9.0,
+                    'Su_top': 5.0, 'Su_bot': 85.0
+                }
+            ]
+        }
+    ]
+    getBucklingSuction(profile_map, 'CLAY_INSTALL', D=4.0, L=17.0)
diff --git a/famodel/anchors/anchors_famodel/capacity_drag.py b/famodel/anchors/anchors_famodel/installation_drag.py
similarity index 95%
rename from famodel/anchors/anchors_famodel/capacity_drag.py
rename to famodel/anchors/anchors_famodel/installation_drag.py
index d17969e2..c476c3ce 100644
--- a/famodel/anchors/anchors_famodel/capacity_drag.py
+++ b/famodel/anchors/anchors_famodel/installation_drag.py
@@ -1,14 +1,9 @@
-"""
-Drag embedment anchor capacity calculation functions in intermidiate model,
-currently set up for clay soils.
-Lead author: Felipe Moreno. 
-"""
 
 import yaml       # Allow access to config file for user inputs
 import numpy as np
 import matplotlib.pyplot as plt
 
-def getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En, 
+def getInstallationDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En, 
                     nhu, Su0, k, Ne, thetae0, z0,
                     Nn_max, Nt_max, Nm_max, m, n, p, q, plot=True): 
         
@@ -78,7 +73,7 @@ def getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En,
     Vf = Af*tf
     Vs = Ls*ts*Ws*2
     Va = round(Vf + Vs,1) 
-    W = Va*rhos
+    Wp = Va*rhos
     
     # The Anchor Initial Condition
     Su = Su0 + k*z0         # Undrained shear strength at the initial embedded depth, kPa
@@ -104,7 +99,7 @@ def getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En,
     thetaf_values = [] 
      
     z = z0; x = x0; Ta = Ta0
-    xmax = 60; Tmax = 30000;
+    xmax = 60; Tmax = 3000;
     
     for _ in range(3000):
         thetaaf = thetaf + thetaa
@@ -164,10 +159,10 @@ def getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En,
         plt.show()
        
     resultsDrag = {}
-    resultsDrag['capacity'] = max(Ta_values)
-    resultsDrag['W'] = W
+    resultsDrag['Capacity'] = max(Ta_values)
     resultsDrag['embedment_depth'] = z
     resultsDrag['drag_distance'] = x
+    resultsDrag['Weight plate'] = Wp
     
     return resultsDrag
     
@@ -189,7 +184,7 @@ def getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm, En,
     m = configDrag['m']; n = configDrag['n']; p = configDrag['p']; q = configDrag['q'];
     z0 = configDrag['z0']; 
         
-    resultsDrag = getCapacityDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm,En, 
+    resultsDrag = getInstallationDrag(Af, Lf, Ls, Lca, Lj, thetafs, bm,En, 
                                   nhu, Su0, k, Ne, thetae0, z0,
                                   Nn_max, Nt_max, Nm_max, m, n, p, q)  
 
diff --git a/famodel/anchors/anchors_famodel/installation_driven.py b/famodel/anchors/anchors_famodel/installation_driven.py
new file mode 100644
index 00000000..9bb85e9d
--- /dev/null
+++ b/famodel/anchors/anchors_famodel/installation_driven.py
@@ -0,0 +1,186 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from capacity_soils_map import clay_profile, sand_profile, rock_profile
+
+def getInstallationDriven(profile_map, location_name, D, L, hammer, J_shaft, J_toe, plot=True, refusal_threshold=0.002, refusal_count=10):
+    dz = 0.24
+    z0 = 1.0
+    max_depth = L
+    N = int((max_depth - z0) / dz)
+
+    t_wall = (6.35 + D * 20) / 1e3
+    D_inner = D - 2 * t_wall
+    A = np.pi / 4 * (D**2 - D_inner**2)
+    E = 2.1e11
+    rhos = 7850
+    g = 9.81
+
+    m = rhos*A*dz
+    k = E*A/dz
+    dt = N/np.sqrt(E/rhos)
+
+    m_r = hammer['m_r']
+    h = hammer['h']
+    eta = hammer['efficiency']
+    E_hammer = eta*m_r*g*h
+    v0 = np.sqrt(2 * E_hammer / m_r)
+
+    soil = profile_map[location_name]
+    layers = soil['layers']
+
+    def compute_Rdynamic(v_local, z, J):
+        for layer in layers:
+            if layer['top'] <= z <= layer['bottom']:
+                if layer['soil_type'] == 'clay':
+                    profile = [[layer['top'],    layer['gamma_top'], layer['Su_top']],
+                               [layer['bottom'], layer['gamma_bot'], layer['Su_bot']]]
+                    _, _, _, _, f_alpha = clay_profile(profile)
+                    return J*f_alpha(z)*v_local*np.pi*D
+                elif layer['soil_type'] == 'sand':
+                    profile = [[layer['top'],    layer['gamma_top'], layer['phi_top'], layer['Dr_top']],
+                               [layer['bottom'], layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]]
+                    _, _, _, _, _, f_delta = sand_profile(profile)
+                    return J*f_delta(z)*v_local*np.pi*D
+                elif layer['soil_type'] == 'rock':
+                    profile = [[layer['top'],    layer['UCS_top'], layer['Em_top']],
+                               [layer['bottom'], layer['UCS_bot'], layer['Em_bot']]]
+                    _, f_UCS, _ = rock_profile(profile)
+                    return J*f_UCS(z)*v_local*np.pi*D
+        return 0.0
+
+    def compute_Rstatic(z):
+        for layer in layers:
+            if layer['top'] <= z <= layer['bottom']:
+                if layer['soil_type'] == 'clay':
+                    profile = [[layer['top'],    layer['gamma_top'], layer['Su_top']],
+                               [layer['bottom'], layer['gamma_bot'], layer['Su_bot']]]
+                    _, _, _, _, f_alpha = clay_profile(profile)
+                    return f_alpha(z)*np.pi*D*dz
+                elif layer['soil_type'] == 'sand':
+                    profile = [[layer['top'],    layer['gamma_top'], layer['phi_top'], layer['Dr_top']],
+                               [layer['bottom'], layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]]
+                    _, _, _, _, _, f_delta = sand_profile(profile)
+                    return f_delta(z)*np.pi*D*dz
+                elif layer['soil_type'] == 'rock':
+                    profile = [[layer['top'],    layer['UCS_top'], layer['Em_top']],
+                               [layer['bottom'], layer['UCS_bot'], layer['Em_bot']]]
+                    _, f_UCS, _ = rock_profile(profile)
+                    return f_UCS(z)*np.pi*D*dz
+        return 0.0
+
+    penetration = 0.0
+    total_energy = 0.0
+    toe_displacement = []
+    blow_counts = []
+    consecutive_small_blows = 0
+
+    blow = 0
+    while penetration < (max_depth - z0):
+        u = np.zeros(N + 1)
+        v = np.zeros(N + 1)
+        a = np.zeros(N + 1)
+
+        # Apply initial velocity to first node to help energy propagate
+        v[0] = v0
+        T = 0.5
+        nsteps = int(T/dt)
+
+        for step in range(nsteps):
+            F_internal = np.zeros(N + 1)
+            for i in range(1, N):
+                F_internal[i] = k*(u[i - 1] - 2*u[i] + u[i + 1])
+
+            F_internal[N] = k*(u[N - 1] - u[N])
+
+            for i in range(N + 1):
+                z = dz*i + z0
+                
+                if i == N:
+                    F_internal[i] -= compute_Rdynamic(v[i], z, J_toe)
+                    F_internal[i] += compute_Rstatic(z)
+                else:
+                    F_internal[i] -= compute_Rdynamic(v[i], z, J_shaft)
+
+                if step < 10:
+                    print(f"  Step {step}: z = {z:.2f}, Rd = {compute_Rdynamic(v[i], z, J_shaft):.2f}, v = {v[i]:.4f}, u = {u[i]:.4f}")
+
+            a = np.nan_to_num(F_internal / m, nan=0.0, posinf=0.0, neginf=0.0)
+            v += a * dt
+            u += v * dt
+
+            if np.any(np.abs(u) > 5):
+                print("Displacement blew up. Stopping.")
+                break
+
+        delta_z = u[-1]
+        penetration += delta_z
+
+        toe_displacement.append(penetration)
+        blow_counts.append(blow + 1)
+        total_energy += E_hammer
+
+        print(f"Blow {blow + 1}: Δz = {delta_z:.5f} m, Total Penetration = {penetration:.3f} m")
+
+        if abs(delta_z) < refusal_threshold:
+            consecutive_small_blows += 1
+        else:
+            consecutive_small_blows = 0
+
+        if consecutive_small_blows >= refusal_count:
+            print("Refusal criteria met: 10 consecutive blows with <2 mm displacement")
+            break
+
+        blow += 1
+
+    if plot:
+        plt.figure(figsize=(8, 4))
+        plt.plot(blow_counts, toe_displacement, marker='o')
+        plt.xlabel('Blow Count')
+        plt.ylabel('Cumulative Toe Displacement (m)')
+        plt.title('Toe Displacement vs Blow Count')
+        plt.grid(True)
+        plt.tight_layout()
+        plt.show()
+
+    return {
+        'blow_counts': blow_counts,
+        'toe_displacement': toe_displacement,
+        'total_energy': total_energy,
+        'final_depth': penetration,
+        'total_counts': len(blow_counts)
+    }
+
+if __name__ == '__main__':
+    profile_map = {
+        'CPT_1': {
+            'type': 'clay',
+            'x': 498234, 'y': 5725141,
+            'layers': [
+                {
+                    'top': 1.0, 'bottom': 6.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 8.0,
+                    'Su_top': 10, 'Su_bot': 20},
+                {
+                    'top': 6.0, 'bottom': 15.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 8.0,
+                    'Su_top': 80, 'Su_bot': 100},
+                {
+                    'top': 15.0, 'bottom': 30.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 9.0,
+                    'Su_top': 100, 'Su_bot': 200}
+            ]
+        }
+    }
+
+    D = 1.0
+    L = 12.0
+    hammer = {'m_r': 85000, 'h': 5.5, 'efficiency': 0.85}
+    J_shaft = 0.05
+    J_toe = 0.05
+
+    results = getInstallationDriven(profile_map, 'CPT_1', D, L, hammer, J_shaft, J_toe, plot=True)
+    for key, val in results.items():
+        print(f"{key}: {val}")
diff --git a/famodel/anchors/anchors_famodel/installation_driven2.py b/famodel/anchors/anchors_famodel/installation_driven2.py
new file mode 100644
index 00000000..db007b64
--- /dev/null
+++ b/famodel/anchors/anchors_famodel/installation_driven2.py
@@ -0,0 +1,167 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from capacity_soils_map import clay_profile, sand_profile, rock_profile
+
+def compute_Rstatic(z):
+    for layer in layers:
+        if layer['top'] <= z <= layer['bottom']:
+            if layer['soil_type'] == 'clay':
+                profile = [[layer['top'],    layer['gamma_top'], layer['Su_top']],
+                           [layer['bottom'], layer['gamma_bot'], layer['Su_bot']]]
+                _, _, _, _, f_alpha = clay_profile(profile)
+                return f_alpha(z)*np.pi*D*dz
+            elif layer['soil_type'] == 'sand':
+                profile = [[layer['top'],    layer['gamma_top'], layer['phi_top'], layer['Dr_top']],
+                           [layer['bottom'], layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]]
+                _, _, _, _, _, f_delta = sand_profile(profile)
+                return f_delta(z)*np.pi*D*dz
+            elif layer['soil_type'] == 'rock':
+                profile = [[layer['top'],    layer['UCS_top'], layer['Em_top']],
+                           [layer['bottom'], layer['UCS_bot'], layer['Em_bot']]]
+                _, f_UCS, _ = rock_profile(profile)
+                return f_UCS(z)*np.pi*D*dz
+    return 0.0
+
+def compute_Rdynamic(v_local, z, J):
+    for layer in layers:
+        if layer['top'] <= z <= layer['bottom']:
+            if layer['soil_type'] == 'clay':
+                profile = [[layer['top'],    layer['gamma_top'], layer['Su_top']],
+                           [layer['bottom'], layer['gamma_bot'], layer['Su_bot']]]
+                _, _, _, _, f_alpha = clay_profile(profile)
+                return J*f_alpha(z)*v_local*np.pi*D
+            elif layer['soil_type'] == 'sand':
+                profile = [[layer['top'],    layer['gamma_top'], layer['phi_top'], layer['Dr_top']],
+                           [layer['bottom'], layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]]
+                _, _, _, _, _, f_delta = sand_profile(profile)
+                return J*f_delta(z)*v_local*np.pi*D
+            elif layer['soil_type'] == 'rock':
+                profile = [[layer['top'],    layer['UCS_top'], layer['Em_top']],
+                           [layer['bottom'], layer['UCS_bot'], layer['Em_bot']]]
+                _, f_UCS, _ = rock_profile(profile)
+                return J*f_UCS(z)*v_local*np.pi*D
+    return 0.0
+
+def getInstallationDriven(profile_map, location_name, D_input, L, hammer, J_shaft, J_toe, plot):
+    global D, dz, layers
+    D = D_input
+    soil = profile_map[location_name]
+    layers = soil['layers']
+    z0 = layers[0]['top']
+
+    N = 100
+    z = np.linspace(z0, L, N)
+    dz = z[1] - z[0]
+
+    dt = 0.001
+    t_max = 2.0
+    time = np.arange(0, t_max, dt)
+
+    u = np.zeros((len(time), N))
+    v = np.zeros((len(time), N))
+    F = np.zeros((len(time), N))
+
+    rho_steel = 7850
+    t = 0.05
+    A = np.pi * ((D / 2)**2 - ((D / 2) - t)**2)
+    m_total = A * L * rho_steel
+    M_prime = m_total / N
+
+    m_r = hammer['m_r']
+    h = hammer['h']
+    eff = hammer['efficiency']
+    E = m_r * 9.81 * h * eff
+
+    blow_count = 0
+    penetration = 0.0
+    z_vals = []
+    blow_vals = []
+    refusal_counter = 0
+    refusal_limit = 10
+    min_set = 0.002
+
+    while penetration < L and refusal_counter < refusal_limit:
+        blow_count += 1
+        v0 = np.sqrt(2 * E / m_r)
+        v[0, 0] = v0
+
+        for n in range(1, len(time)):
+            for i in range(1, N - 1):
+                u_xx = (u[n - 1, i + 1] - 2 * u[n - 1, i] + u[n - 1, i - 1]) / dz**2
+                F[n, i] = M_prime * u_xx
+
+            v[n] = v[n - 1] + (F[n] / M_prime) * dt
+            u[n] = u[n - 1] + v[n] * dt
+
+        delta_z = max(u[-1, :])
+        tip_depth = penetration + delta_z
+
+        Rs = compute_Rstatic(tip_depth)
+        Rt = 9.0 * compute_Rstatic(tip_depth)
+        R_total = Rs + Rt + compute_Rdynamic(v[-1, -1], tip_depth, J_shaft)
+
+        penetration += delta_z
+        z_vals.append(penetration)
+        blow_vals.append(blow_count)
+
+        if delta_z < min_set:
+            refusal_counter += 1
+        else:
+            refusal_counter = 0
+
+    if plot:
+        plt.figure()
+        plt.plot(z_vals, blow_vals, label='Blows vs Penetration')
+        plt.xlabel('Penetration Depth [m]')
+        plt.ylabel('Blow Count')
+        plt.title('Driveability Simulation')
+        plt.grid(True)
+        plt.legend()
+        plt.show()
+
+    return {
+        'z': z,
+        'z_vals': z_vals,
+        'blow_vals': blow_vals,
+        'u': u,
+        'v': v,
+        'F': F,
+        'dt': dt,
+        'dz': dz
+    }
+
+
+if __name__ == '__main__':
+    profile_map = {
+        'CPT_1': {
+            'type': 'clay',
+            'x': 498234, 'y': 5725141,
+            'layers': [
+                {
+                    'top': 1.0, 'bottom': 6.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 8.0,
+                    'Su_top': 10, 'Su_bot': 20},
+                {
+                    'top': 6.0, 'bottom': 15.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 8.0,
+                    'Su_top': 80, 'Su_bot': 100},
+                {
+                    'top': 15.0, 'bottom': 30.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 8.0, 'gamma_bot': 9.0,
+                    'Su_top': 100, 'Su_bot': 200}
+            ]
+        }
+    }
+
+    D = 1.0
+    L = 12.0
+    hammer = {'m_r': 155000, 'h': 5.5, 'efficiency': 0.85}
+    J_shaft = 0.05
+    J_toe = 0.05
+
+    results = getInstallationDriven(profile_map, 'CPT_1', D, L, hammer, J_shaft, J_toe, plot=True)
+    for key, val in results.items():
+        print(f"{key}: {val}")
\ No newline at end of file
diff --git a/famodel/anchors/anchors_famodel/installation_dynamic.py b/famodel/anchors/anchors_famodel/installation_dynamic.py
new file mode 100644
index 00000000..95c97834
--- /dev/null
+++ b/famodel/anchors/anchors_famodel/installation_dynamic.py
@@ -0,0 +1,164 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+from support_soils import clay_profile
+
+def PileWeight(L1, L2, D1, D2, tw, rho):
+    return ((np.pi/4)*(D1**2 - (D1 - 2*tw)**2)*(L1 + L2) + 4*L2*D2*tw)*rho
+
+def PileVolume(L1, L2, D1, D2, tw):
+    return (np.pi/4)*D1**2*(L1 + L2) + 4*L2*D2*tw
+
+def PileWingedSurface(length, diameter1, diameter2):
+    return 8*length*(diameter1 - diameter2)
+
+def PileShaftSurface(length, diameter):
+    return np.pi*diameter*length
+
+def getInstallationDynamic(profile_map, location_name, D1, D2, L1, L2, ballast, drop_height, plot=True):
+    """
+    Deterministic installation model of a torpedo pile in clay based on time-domain integration.
+    Implements the model by True (1976) as adapted in Kazue et al. (2020), accounting for layered soil.
+    """
+    # Constants
+    rhows = 66.90e3                  # Submerged steel specific weight (N/m3)
+    rhow = 10e3                      # Water specific weight (N/m3) 
+    Sti = 3.0
+    Se = 5.0
+    Ce = 0.02
+    delta = 0.9
+    Nc = 9.0
+    CD = 2.7
+    dt = 0.002
+    tmax = 15.0
+    beta = 28/27
+    g = 9.81
+
+    # Geometry
+    D = D1                  # use the wing diameter for frontal area
+    L = L1 + L2
+    t = (6.35 + D2*20)/1e3  # assumed same as in getCapacityTorpedo
+
+    # Retrieve soil profile and construct full profile list
+    profile_entry = next(p for p in profile_map if p['name'] == location_name)
+    layers = profile_entry['layers']
+    profile = []
+    for layer in layers:
+        if layer['soil_type'] != 'clay':
+            continue
+        profile.append([layer['top'],    layer['Su_top'], layer['gamma_top']])
+        profile.append([layer['bottom'], layer['Su_bot'], layer['gamma_bot']])
+
+    # Sort and remove duplicates if needed
+    # profile = sorted(list({(z, su, g) for z, su, g in profile}), key=lambda x: x[0])
+    z_ref, f_gamma, f_Su, _, f_delta = clay_profile(profile)
+
+    # Precompute parameters
+    Af = np.pi*(D2**2)/4 + 4*(D2 - D1)*t
+    As = PileWingedSurface(L1, D1, D2) + PileShaftSurface(L1 + L2, D2)
+    Vol = PileVolume(L1, L2, D1, D2, t)
+    Wp = PileWeight(L1, L2, D1, D2, t, rhows) + ballast; #print(f'Wp = {Wp:.2f} N')
+    M = Wp/g
+    Mprime = M + 2*rhow*Vol
+
+    # Closed-form solution for v_impact from free fall in water
+    CD_water = 1.2
+    A_water = Af
+    vt = np.sqrt((2*Mprime*g)/(rhow*CD_water*A_water))
+    t_impact = (vt/g)*np.arccosh(np.exp(g*drop_height/vt**2))
+    v_impact = vt*np.tanh(g*t_impact/vt)
+
+    # Initial conditions
+    t = [0.0]
+    z = [0.0]
+    v = [v_impact]
+    a = [Wp/Mprime]
+
+    # Integration constants
+    beta1 = dt**2*(0.5 - beta)
+    beta2 = dt**2*beta
+    gamma1 = -0.5*dt
+    gamma2 = 1.5*dt
+    nsteps = int(tmax/dt)
+
+    for i in range(nsteps):
+        zn = z[-1]
+        vn = v[-1]
+        an = a[-1]
+
+        Su = f_Su(zn)
+        gamma = f_gamma(zn)
+        delta = f_delta(zn)
+        rho = gamma/g
+        Se_dot = Se/(1 + (1/np.sqrt(Ce*vn/(Su*D2)) + 0.06)) if vn > 0 else 1.0
+
+        Mprime_local = M + 2*rho*Vol
+        FD = 0.5*rhow*CD*Af*vn*abs(vn)
+        FT = Su*Nc*Af*Se_dot
+        FS = Su*As*delta*Se_dot/Sti
+
+        f_total = (Wp - Vol*gamma) - FD - FT - FS 
+        an1 = f_total/Mprime_local
+
+        zn1 = zn + dt*vn + beta1*an + beta2*an1
+        vn1 = vn + gamma1*an + gamma2*an1
+
+        if vn1 < 0:
+            break  # penetration stops
+
+        t.append(t[-1] + dt)
+        z.append(zn1)
+        v.append(vn1)
+        a.append(an1)
+
+    if plot:
+        fig, ax1 = plt.subplots()
+        ax1.plot(t, z, 'b', label='Penetration depth (m)')
+        ax1.set_xlabel('Time (s)')
+        ax1.set_ylabel('Depth (m)')
+        ax1.grid(True)
+
+        ax2 = ax1.twinx()
+        ax2.plot(t, v, 'r', label='Velocity (m/s)')
+        ax2.set_ylabel('Velocity (m/s)')
+
+        fig.suptitle('Torpedo Pile Installation Response')
+        fig.legend(loc='upper right')
+        plt.tight_layout()
+        plt.show()
+
+    return {
+        'final_depth': z[-1],
+        'max_velocity': max(v),
+        'penetration_time': t[-1]
+    }
+
+if __name__ == '__main__':
+    profile_map = [
+        {
+            'name': 'CPT_1',
+            'x': 0, 'y': 0,
+            'layers': [
+                {'top': 0.0, 'bottom': 30.0, 'soil_type': 'clay',
+                 'gamma_top': 8.0, 'gamma_bot': 8.5,
+                 'Su_top': 5, 'Su_bot': 20},
+                {'top': 30.0, 'bottom': 100.0, 'soil_type': 'clay',
+                 'gamma_top': 8.5, 'gamma_bot': 9.0,
+                 'Su_top': 20, 'Su_bot': 60}
+            ]
+        }
+    ]
+
+    location = 'CPT_1'
+    D1 = 3.0
+    D2 = 1.5
+    L1 = 5.0
+    L2 = 5.0
+    ballast = 350000
+    drop_height = 200
+
+    results = getInstallationDynamic(profile_map, location, D1, D2, L1, L2, ballast, drop_height)
+
+    print("\n--- Torpedo Installation Results ---")
+    for k, v in results.items():
+        print(f"{k}: {v:.2f}")
diff --git a/famodel/anchors/anchors_famodel/installation_dynamic2.py b/famodel/anchors/anchors_famodel/installation_dynamic2.py
new file mode 100644
index 00000000..f55c7f21
--- /dev/null
+++ b/famodel/anchors/anchors_famodel/installation_dynamic2.py
@@ -0,0 +1,152 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from support_soils import clay_profile
+
+def PileWeight(L1, L2, D1, D2, tw, gamma):
+    return ((np.pi/4)*(D1**2 - (D1 - 2*tw)**2)*(L1 + L2) + 4*L2*D2*tw)*gamma
+
+def PileVolume(L1, L2, D1, D2, tw):
+    return (np.pi/4)*D1**2*(L1 + L2) + 4*L2*D2*tw
+
+def PileWingedSurface(length, diameter1, diameter2):
+    return 8*length*(diameter1 - diameter2)
+
+def PileShaftSurface(length, diameter):
+    return np.pi*diameter*length
+
+def getInstallationDynamic(profile_map, location_name, D1, D2, L1, L2, ballast, drop_height, plot=True):
+    """
+    Penetration of torpedo pile using depth-based formulation (Eq. 2.10).
+    """
+    # Constants
+    rhows = 66.90e3     # Submerged unit weight of steel (N/m³)
+    rhow = 10e3         # Unit weight of water (N/m³)
+    Sti = 2.0           # Installation strain-rate index (-), affects side friction strain-rate correction
+    Se = 5.0            # Strain-rate multiplier (-), empirical factor for rate effects
+    Ce = 0.02           # Strain-rate coefficient (-), controls shape of strain-rate correction
+    Nc = 9.0            # Bearing capacity factor for undrained clay [-], used in tip resistance (q = Nc * Su)
+    CD = 2.7            # Drag coefficient (-), for a cylindrical body falling in water
+    dz = 1              # Depth increment (m), used in depth-stepping integration
+    g = 9.81            # Gravitational acceleration (m/s²)
+
+
+    # Geometry
+    D = D1
+    L = L1 + L2
+    t = (6.35 + D2*20)/1e3
+
+    # Soil profile
+    profile_entry = next(p for p in profile_map if p['name'] == location_name)
+    layers = profile_entry['layers']
+    profile = []
+    for layer in layers:
+        if layer['soil_type'] != 'clay': continue
+        profile.append([layer['top'],    layer['gamma_top'], layer['Su_top']])
+        profile.append([layer['bottom'], layer['gamma_bot'], layer['Su_bot']])
+    # profile = sorted(list({(z, su, g) for z, su, g in profile}), key=lambda x: x[0])
+    z0, f_gamma, f_Su, _, f_delta = clay_profile(profile)
+    # print('\n--- f_gamma and f_Su vs Depth ---')
+    # z_start = z0
+    # z_end = profile[-1][0]
+    # z_vals = np.linspace(z_start, z_end, 10)
+    # for z_val in z_vals:
+    #     gamma_val = f_gamma(z_val)
+    #     Su_val = f_Su(z_val)
+    #     print(f'z = {z_val:.1f} m → gamma = {gamma_val:.2f} N/m³, Su = {Su_val:.2f} Pa')
+
+    # Parameters
+    Af = np.pi*(D2**2)/4 + 4*(D2 - D1)*t
+    As = PileWingedSurface(L1, D1, D2) + PileShaftSurface(L1 + L2, D2)
+    Vol = PileVolume(L1, L2, D1, D2, t)
+    Wp = PileWeight(L1, L2, D1, D2, t, rhows + rhow) + ballast; print(f'Wp = {Wp:.2f} N')
+    M = Wp/g
+    Mprime = M + 2*f_gamma(2*L/3)*Vol
+
+    CD_water = 1.2
+    vt = np.sqrt((2*Mprime*g)/(rhow*CD_water*Af))
+    t_impact = (vt/g)*np.arccosh(np.exp(g*drop_height/vt**2))
+    v_impact = vt*np.tanh(g*t_impact/vt)
+
+    # Loop
+    v = [v_impact]; #print(v)
+    z = [0.0]
+    term1_values, term2_values = [0.0], [0.0]
+    i = 0
+    while v[-1] > 0:
+        zi = z[-1]
+        vi = v[-1]
+        Sui = f_Su(zi); #print(f'Sui = {Sui:.2f} Pa')
+        gammai = f_gamma(zi); #print(f'gammai  = {gammai:.2f} N/m3')
+        rhoi = gammai/g; #print(f'rhoi  = {rhoi:.2f} kg/m3')
+        deltai = f_delta(zi)
+        Mprime_local = M + 2*rhoi*Vol
+        term1 = (Wp - Vol*gammai) - (0.5*CD*rhoi*Af*vi**2); #print(f'term1  = {term1:.2f} N')
+        term2 = Sui*(Af*Nc + As*deltai/Sti)
+        Se_dot = Se/(1 + (1/np.sqrt(Ce*vi/(Sui*D)) + 0.06)); #print(f'Se_dot  = {Se_dot:.2f}')
+        top = 2*dz*(term1 - term2)*Se_dot; #print(f'top  = {top:.2f}')
+        bottom = vi*Mprime_local; #print(f'bottom  = {bottom:.2f}')
+        vi1 = vi + top/bottom
+        if vi1 < 0.01:
+            print(f'Stopping due to low velocity at z = {zi:.2f} m')
+            break
+
+        v.append(vi1)
+        z.append(zi + dz)
+        term1_values.append(term1)
+        term2_values.append(term2)
+        i += 1
+        #print(f'z = {zi:.2f} m | term1 (drive) = {term1:.2f} N | term2 (resist) = {term2:.2f} N | net = {term1 - term2:.2f} N')
+
+    if plot:
+        fig, ax1 = plt.subplots()
+        ax1.plot(z, v, 'b', label='Velocity vs Depth')
+        ax1.set_xlabel('Depth (m)')
+        ax1.set_ylabel('Velocity (m/s)')
+        
+        ax2 = ax1.twinx()
+        ax2.plot(z, term1_values, 'r', label='Drive (N)')
+        ax2.plot(z, term2_values, 'g', label='Resist (N)')
+        ax2.set_ylabel('Forces (N)')
+        
+        ax1.grid(True)
+        plt.title('Depth-based Penetration of Torpedo Pile')
+        plt.legend(loc='upper right')
+        plt.tight_layout()
+        plt.show()
+
+    return {
+        'final_depth': z[-1],
+        'v_max': max(v),
+        'v_impact': v_impact,
+        'steps': i
+    }
+
+if __name__ == '__main__':
+    profile_map = [
+        {
+            'name': 'CPT_1',
+            'x': 0, 'y': 0,
+            'layers': [
+                {'top': 0.0, 'bottom': 60.0, 'soil_type': 'clay',
+                 'gamma_top': 8.5, 'gamma_bot': 8.5,
+                 'Su_top': 5, 'Su_bot': 85},
+                {'top': 40.0, 'bottom': 400.0, 'soil_type': 'clay',
+                 'gamma_top': 8.5, 'gamma_bot': 8.5,
+                 'Su_top': 85, 'Su_bot': 805}
+            ]
+        }
+    ]
+
+    location = 'CPT_1'
+    D1 = 3.0
+    D2 = 1.5
+    L1 = 10.0
+    L2 = 5.0
+    ballast = 10000
+    drop_height = 20
+
+    results = getInstallationDynamic(profile_map, location, D1, D2, L1, L2, ballast, drop_height)
+
+    print("\n--- Torpedo Installation Results ---")
+    for k, v in results.items():
+        print(f"{k}: {v:.2f}")
diff --git a/famodel/anchors/anchors_famodel/installation_dynamic3.py b/famodel/anchors/anchors_famodel/installation_dynamic3.py
new file mode 100644
index 00000000..625b4161
--- /dev/null
+++ b/famodel/anchors/anchors_famodel/installation_dynamic3.py
@@ -0,0 +1,126 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+from installation_dynamic import getInstallationDynamic
+from scipy.stats import lognorm
+
+def getInstallationDynamicMC(profile_map, location, D1, D2, L1, L2, ballast, drop_height, N_sim=2000):
+    np.random.seed(16)
+    
+    # Lognormal distribution parameters
+    Suk_mean, Suk_std = 1.9, 0.9
+    Sti_mean, Sti_std = 3.2, 1.0
+
+    Suk_samples = np.random.lognormal(mean=np.log(Suk_mean), sigma=Suk_std/Suk_mean, size=N_sim)
+    Sti_samples = np.random.lognormal(mean=np.log(Sti_mean), sigma=Sti_std/Sti_mean, size=N_sim)
+
+    final_depths = []
+
+    for Suk, Sti in zip(Suk_samples, Sti_samples):
+        # Update profile with consistent linear Su(z)
+        profile = [dict(profile_map[0])]  
+        Su0 = profile[0]['layers'][0]['Su_top']
+        for layer in profile[0]['layers']:
+            z_top = layer['top']
+            z_bot = layer['bottom']
+            layer['Su_top'] = Su0 + Suk*z_top
+            layer['Su_bot'] = Su0 + Suk*z_bot
+        
+        # Override getInstallationDynamic with fixed Sti
+        result = getInstallationDynamic(profile, location, D1, D2, L1, L2, ballast, drop_height, plot=False)
+        
+        final_depths.append(result['final_depth'])
+        
+    # Fit lognormal distribution to the data
+    shape, loc, scale = lognorm.fit(final_depths, floc=0)  
+    x = np.linspace(min(final_depths), max(final_depths), 500)
+    pdf = lognorm.pdf(x, shape, loc=loc, scale=scale)
+
+    # Create subplot
+    fig, axs = plt.subplots(1, 2, figsize=(12, 5))
+    
+    # Fit lognormals
+    s_Suk, loc_Suk, scale_Suk = lognorm.fit(Suk_samples, floc=0)
+    s_Sti, loc_Sti, scale_Sti = lognorm.fit(Sti_samples, floc=0)
+        
+    # Generate x values for plotting
+    x_Suk = np.linspace(min(Suk_samples), max(Suk_samples), 500)
+    x_Sti = np.linspace(min(Sti_samples), max(Sti_samples), 500)
+    
+    # Compute PDFs
+    pdf_Suk = lognorm.pdf(x_Suk, s_Suk, loc=loc_Suk, scale=scale_Suk)
+    pdf_Sti = lognorm.pdf(x_Sti, s_Sti, loc=loc_Sti, scale=scale_Sti)
+    
+    # Compute max density from histograms and PDFs for setting common ylim
+    hist_Suk_vals, _ = np.histogram(Suk_samples, bins=40, density=True)
+    hist_Sti_vals, _ = np.histogram(Sti_samples, bins=40, density=True)
+    y_max = max(max(hist_Suk_vals), max(hist_Sti_vals), max(pdf_Suk), max(pdf_Sti)) * 1.1
+    
+    # Plot Suk PDF
+    axs[0].hist(Suk_samples, bins=40, density=True, alpha=0.6, color='lightgreen', edgecolor='g', label='Samples')
+    axs[0].plot(x_Suk, pdf_Suk, 'r', label='Fitted Lognormal')
+    axs[0].set_title('Lognormal Fit for $S_{uk}$')
+    axs[0].set_xlabel('Suk (kPa/m)')
+    axs[0].set_ylabel('Density')
+    axs[0].set_ylim(0, y_max)
+    axs[0].legend()
+    axs[0].grid(True)
+    
+    # Plot Sti PDF
+    axs[1].hist(Sti_samples, bins=40, density=True, alpha=0.6, color='lightblue', edgecolor='b', label='Samples')
+    axs[1].plot(x_Sti, pdf_Sti, 'r', label='Fitted Lognormal')
+    axs[1].set_title('Lognormal Fit for $S_{ti}$')
+    axs[1].set_xlabel('Sti (dimensionless)')
+    axs[1].set_ylabel('Density')
+    axs[1].set_ylim(0, y_max)
+    axs[1].legend()
+    axs[1].grid(True)
+       
+    # Plot histogram with fitted lognormal
+    plt.figure(figsize=(8, 5))
+    plt.hist(final_depths, bins=40, alpha=0.6, density=True, color='lightsalmon', edgecolor='r', label='Simulated PDF')
+    plt.plot(x, pdf, 'r', lw=2, label='Fitted Lognormal')
+    plt.axvline(np.median(final_depths), color='k', linestyle='--', label=f'Median = {np.median(final_depths):.2f} m')
+    plt.title('Monte Carlo Simulation with Fitted Lognormal Distribution')
+    plt.xlabel('Penetration (m)')
+    plt.ylabel('Density')
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+    
+    # Print parameters
+    print(f'Lognormal fit parameters:')
+    print(f'  mean                   = {np.mean(final_depths):.2f}')
+    print(f'  mean - 1 std deviation = {np.mean(final_depths) - np.std(final_depths):.2f}')
+    print(f'  mean + 1 std deviation = {np.mean(final_depths) + np.std(final_depths):.2f}')
+
+    return final_depths
+
+# Example usage in main
+if __name__ == '__main__':
+    profile_map = [
+        {
+            'name': 'CPT_1',
+            'x': 0, 'y': 0,
+            'layers': [
+                {'top': 0.0, 'bottom': 30.0, 'soil_type': 'clay',
+                 'gamma_top': 8.0, 'gamma_bot': 8.5,
+                 'Su_top': 5, 'Su_bot': 20},
+                {'top': 30.0, 'bottom': 180.0, 'soil_type': 'clay',
+                 'gamma_top': 8.5, 'gamma_bot': 9.0,
+                 'Su_top': 20, 'Su_bot': 150}
+            ]
+        }
+    ]
+
+    location = 'CPT_1'
+    D1 = 3.0
+    D2 = 1.2
+    L1 = 5.0
+    L2 = 5.0
+    ballast = 300000
+    drop_height = 200
+
+    # Run Monte Carlo Simulation
+    depths = getInstallationDynamicMC(profile_map, location, D1, D2, L1, L2, ballast, drop_height)
diff --git a/famodel/anchors/anchors_famodel/installation_suction.py b/famodel/anchors/anchors_famodel/installation_suction.py
new file mode 100644
index 00000000..7c474082
--- /dev/null
+++ b/famodel/anchors/anchors_famodel/installation_suction.py
@@ -0,0 +1,215 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from support_soils import clay_profile
+
+def PileWeight(Len, Dia, tw, rho):
+    return ((np.pi/4)*((Dia**2 - (Dia - 2*tw)**2)*Len + (np.pi/4)*Dia**2*tw))*rho
+
+def getInstallationSuction(profile_map, location_name, D, L, gamma_m_install=1.5, gamma_m_retrieval=1.25):
+    '''
+    Installation and retrieval pressure assessment for suction piles in clay using a layered profile.
+    Returns a dictionary with pressure values and resistances.
+    '''
+    # Constants and geometry
+    profile_entry = next(p for p in profile_map if p['name'] == location_name)
+    layers = profile_entry['layers']
+
+    rhows = 66.90e3                  # Submerged steel specific weight (N/m3)
+    rhow = 10e3                      # Water specific weight (N/m3) 
+
+    WT = D/200; print(WT)
+    t = (6.35 + D*20)/1e3; print(t)  # Suction pile wall thickness (m), API RP2A-WSD
+    Di = D - 2*WT
+    Asi = np.pi * Di
+    Aso = np.pi * D
+    Awall = 0.25  # m²
+    Aplug = np.pi * Di**2 / 4
+    Nc_strip_deep = 7.5
+    Nc_circle = 9.0
+    alphaD_su = 0.25
+    Wp = PileWeight(L, D, WT, rhows); print(Wp)
+    Wsub_steel = 750e3  # in N
+
+    # Convert layer data into clay profile format
+    z0 = layers[0]['top']
+    z_bot = layers[0]['bottom']
+    gamma_top = layers[0]['gamma_top']
+    gamma_bot = layers[0]['gamma_bot']
+    Su_top = layers[0]['Su_top']
+    Su_bot = layers[0]['Su_bot']
+
+    clay_input = [
+        [z0,    gamma_top, Su_top],
+        [z_bot, gamma_bot, Su_bot]
+    ]
+
+    _, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(clay_input)
+
+    # Diagnostic: evaluate and plot alpha(z)
+    z_plot = np.linspace(z0, z_bot, 100)
+    alpha_plot = f_alpha(z_plot)
+
+    # print('\n--- Adhesion Factor α(z) ---')
+    # for z_check in [0, 2, 4, 6, 8, 10, 12, 14, 17]:
+        # print(f"z = {z_check:>5.2f} m → α = {f_alpha(z_check):.3f}")
+
+    # plt.figure(figsize=(5, 4))
+    # plt.plot(alpha_plot, z_plot, label='α(z)', color='purple')
+    # plt.gca().invert_yaxis()
+    # plt.grid(True)
+    # plt.xlabel('Adhesion Factor α')
+    # plt.ylabel('Depth (m)')
+    # plt.title('API Clay Adhesion Factor vs Depth')
+    # plt.tight_layout()
+    # plt.show()
+
+    # Prepare output arrays
+    depths = np.arange(0, L + 0.1, 0.1)
+    Rsuction_list = []
+    delta_u_suction_list = []
+    delta_u_retrieval_list = []
+    delta_u_all_install_list = []
+    delta_u_all_retrieval_list = []
+    SWP_depth = None
+
+    for L in depths:
+        z_tip = L
+        z_mid = L/2
+        z_tip_ext = L + alphaD_su*Di
+
+        su_av_L = f_Su(z_mid)
+        int_su = (su_av_L)*L
+        su_tip = f_Su(z_tip)
+        su_av_tip = f_Su(z_tip_ext)
+        # alpha_i = alpha_o = float(f_alpha(z_mid))
+        alpha_i = alpha_o = 0.3
+
+        Fi = Asi*alpha_i*int_su
+        Fo = Aso*alpha_o*int_su
+        Qw = Awall*Nc_strip_deep*su_tip
+
+        Rsuction = Fi + Fo + Qw
+        Rretrieval = Rsuction
+        delta_u_suction = max((Rsuction - Wp)/Aplug, 0.0) 
+        delta_u_retrieval = (Rretrieval + Wp)/Aplug
+        delta_u_all_install   = Fi/Aplug + Nc_circle*su_av_tip/gamma_m_install
+        delta_u_all_retrieval = Fi/Aplug + Nc_circle*su_av_tip/gamma_m_retrieval
+
+        Rsuction_list.append(Rsuction)
+        delta_u_suction_list.append(delta_u_suction)
+        delta_u_retrieval_list.append(delta_u_retrieval)
+        delta_u_all_install_list.append(delta_u_all_install)
+        delta_u_all_retrieval_list.append(delta_u_all_retrieval)
+
+        if SWP_depth is None and Rsuction >= Wp:
+            SWP_depth = L
+
+    # Plotting
+    fig, axs = plt.subplots(1, 3, figsize=(10, 7))
+
+    axs[0].plot(Rsuction_list, depths, label='Installation Resistance', color='blue')
+    if SWP_depth is not None:
+        axs[0].axvline(Wp, color='red', linestyle='--', label=f'SWP = {SWP_depth:.2f} m')
+    axs[0].set_xlabel('Installation Resistance (N)')
+    axs[0].set_ylabel('Penetration (m)')
+    axs[0].set_title('Installation Resistance vs Penetration')
+    axs[0].grid(True)
+    axs[0].invert_yaxis()
+    axs[0].legend()
+
+    axs[1].plot(delta_u_suction_list, depths, label='Underpressure', color='green')
+    axs[1].plot(delta_u_all_install_list, depths, label='Δu allowable install', color='orange')
+    if SWP_depth is not None:
+        axs[1].axhline(SWP_depth, color='red', linestyle='--', label=f'SWP = {SWP_depth:.2f} m')
+    axs[1].set_xlabel('Underpressure (Pa)')
+    axs[1].set_ylabel('Penetration (m)')
+    axs[1].set_title('Underpressure vs Penetration')
+    axs[1].grid(True)
+    axs[1].invert_yaxis()
+    axs[1].legend()
+    
+    axs[2].plot(delta_u_retrieval_list, depths, label='Overpressure', color='green')
+    axs[2].plot(delta_u_all_retrieval_list, depths, label='Δu allowable retrieve', color='orange')
+    axs[2].set_xlabel('Overpressure (Pa)')
+    axs[2].set_ylabel('Penetration (m)')
+    axs[2].set_title('Overpressure vs Penetration')
+    axs[2].grid(True)
+    axs[2].invert_yaxis()
+    axs[2].legend()
+
+    plt.tight_layout()
+    plt.show()
+
+    # Final state outputs
+    L = L
+    z_tip = L
+    z_mid = L/2
+    z_tip_ext = L + alphaD_su*Di
+
+    su_av_L = f_Su(z_mid)
+    int_su = su_av_L*L
+    su_tip = f_Su(z_tip)
+    su_av_tip = f_Su(z_tip_ext)
+    alpha_i = alpha_o = float(f_alpha(z_mid))
+    alpha_i = alpha_o = 0.3
+
+    Fi = Asi*alpha_i*int_su
+    Fo = Aso*alpha_o*int_su
+    Qw = Awall*Nc_strip_deep*su_tip
+
+    Rsuction = Fi + Fo + Qw
+    Rretrieval = Rsuction
+
+    delta_u_suction = max((Rsuction - Wp)/Aplug, 0.0)  
+    delta_u_retrieval = (Rretrieval + Wp)/Aplug  
+
+    delta_u_all_install   = Fi/Aplug + Nc_circle*su_av_tip/gamma_m_install
+    delta_u_all_retrieval = Fi/Aplug + Nc_circle*su_av_tip/gamma_m_retrieval
+
+    return {
+        'layers': layers,
+        'depths': depths,
+        'z0': z0,
+        'D': D,
+        'L': L,
+        'Di': Di,
+        'Asi': Asi,                       # m
+        'Aso': Aso,                       # m
+        'Aplug': Aplug,                   # m²
+        'su_av_L': su_av_L/1e3,           # kPa
+        'int_su': int_su/1e3,             # kN/m
+        'su_tip': su_tip/1e3,             # kPa
+        'su_av_tip': su_av_tip/1e3,       # kPa
+        'Fi': Fi/1e3,                     # kN
+        'Fo': Fo/1e3,                     # kN
+        'Qw': Qw/1e3,                     # kN
+        'Rsuction': Rsuction/1e6,         # MN
+        'Rretrieval': Rretrieval/1e6,     # MN
+        'delta_u_suction': delta_u_suction_list,              # kPa
+        'delta_u_retrieval': delta_u_retrieval/1e3,           # kPa
+        'delta_u_all_install': delta_u_all_install/1e3,       # kPa
+        'delta_u_all_retrieval': delta_u_all_retrieval/1e3,   # kPa
+        'SWP_depth': SWP_depth}
+
+if __name__ == '__main__':
+    profile_map = [
+        {
+            'name': 'CLAY_INSTALL',
+            'x': 0, 'y': 0,
+            'layers': [
+                {
+                    'top': 0.0, 'bottom': 20.0,
+                    'soil_type': 'clay',
+                    'gamma_top': 9.0, 'gamma_bot': 9.0,
+                    'Su_top': 5.0, 'Su_bot': 45.0
+                }
+            ]
+        }
+    ]
+
+    results = getInstallationSuction(profile_map, 'CLAY_INSTALL', D=4.0, L=17.0)
+    for k, v in results.items():
+        if isinstance(v, float):
+            print(f"{k:<25} = {v:.2f}")
+        else:
+            print(f"{k:<25} = {v}")
\ No newline at end of file
diff --git a/famodel/anchors/anchors_famodel_map/capacity_plots_map.py b/famodel/anchors/anchors_famodel/support_plots.py
similarity index 91%
rename from famodel/anchors/anchors_famodel_map/capacity_plots_map.py
rename to famodel/anchors/anchors_famodel/support_plots.py
index d733fade..22df106d 100644
--- a/famodel/anchors/anchors_famodel_map/capacity_plots_map.py
+++ b/famodel/anchors/anchors_famodel/support_plots.py
@@ -3,6 +3,28 @@
 import matplotlib.pyplot as plt
 
 def plot_pile(layers, y, z, D, L, z0=None, zlug=None, hinge_location=None):
+    '''Plot the soil profile and a driven pile with deflected shape in layered soil.
+
+    Parameters
+    ----------
+        layers : list of dicts
+            Each layer has 'top', 'bottom', 'soil_type', and strength parameters
+            such as 'Su_top' (clay), 'phi_top' (sand) or 'UCS_top' (rock)
+        y : array-like
+            Lateral displacement profile from FD solution (typically y[2:-2])
+        z : array-like
+            Depth values associated with displacement points (typically z[2:-2])
+        D : float
+            Pile diameter (m)
+        L : float
+            Embedded pile length (m)
+        z0 : float, optional
+            Mudline elevation m) from pile head reference (z = 0)
+        zlug : float, optional
+            Depth of the padeye below pile head (m)
+        hinge_location : int, optional
+            Index of plastic hinge location in y/z arrays.
+    '''
     fig, ax = plt.subplots(figsize=(5, 5))
 
     lambdap = L / D
@@ -74,7 +96,7 @@ def plot_pile(layers, y, z, D, L, z0=None, zlug=None, hinge_location=None):
     plt.tight_layout()
     plt.show()
     
-def plot_suction(layers, L, D, z0=None, zlug=None, title='Suction Pile and Soil Layers'):
+def plot_suction(layers, L, D, z0=None, zlug=None):
     '''Plot the soil profile and a suction pile geometry using updated profile_map structure.
 
     Parameters:
@@ -87,8 +109,6 @@ def plot_suction(layers, L, D, z0=None, zlug=None, title='Suction Pile and Soil
             Pile diameter (m)
         zlug : float
             Padeye depth (m, referenced to pile head = 0)
-        title : string 
-            Plot title
     '''
     fig, ax = plt.subplots(figsize=(8, 5))
     xmax = 2*D
@@ -142,13 +162,13 @@ def plot_suction(layers, L, D, z0=None, zlug=None, title='Suction Pile and Soil
     ax.set_ylabel('Depth (m)')
     ax.set_xlim(-xmax, xmax)
     ax.set_ylim(L + 2*D, -D)
-    ax.set_title(title)
+    ax.set_title('Suction Pile and Soil Layers')
     ax.grid()
     ax.legend()
     plt.tight_layout()
     plt.show()
 
-def plot_torpedo(layers, D1, D2, L1, L2, z0, zlug, title='Torpedo Pile and Soil Layers'):
+def plot_torpedo(layers, D1, D2, L1, L2, z0, zlug):
     '''Plot the soil layers and geometry of a torpedo pile using absolute depth for soil and pile head at z=0.
 
     Parameters:
@@ -165,8 +185,6 @@ def plot_torpedo(layers, D1, D2, L1, L2, z0, zlug, title='Torpedo Pile and Soil
             Winged length (m)
         L2 : float
             Shaft length (m)
-        title : str
-            Plot title
     '''
     fig, ax = plt.subplots(figsize=(7, 7))
 
@@ -226,13 +244,13 @@ def plot_torpedo(layers, D1, D2, L1, L2, z0, zlug, title='Torpedo Pile and Soil
     ax.set_ylim(zmax, zmin)
     ax.set_xlabel('Horizontal extent (m)')
     ax.set_ylabel('Depth (m)')
-    ax.set_title(title)
+    ax.set_title('Torpedo Pile and Soil Layers')
     ax.grid()
     ax.legend()
     plt.tight_layout()
     plt.show()
  
-def plot_helical(layers, D, L, d, z0, zlug, n_helix=1, spacing=1.0, title='Helical Pile and Soil Layers'):
+def plot_helical(layers, D, L, d, z0, zlug, n_helix=1, spacing=1.0):
     '''Plot a helical pile in layered soil with shaft and angled helices, starting at zlug.
 
     Parameters:
@@ -251,8 +269,6 @@ def plot_helical(layers, D, L, d, z0, zlug, n_helix=1, spacing=1.0, title='Helic
         Number of helices (typically 1)
     spacing : float
         Vertical spacing between helices (m)
-    title : str
-        Plot title
     '''
     fig, ax = plt.subplots(figsize=(5, 6))
 
@@ -322,13 +338,13 @@ def plot_helical(layers, D, L, d, z0, zlug, n_helix=1, spacing=1.0, title='Helic
     ax.set_ylim(L + D, min(zlug - D, min(layer['top'] for layer in layers) - 2))
     ax.set_xlabel('Horizontal extent (m)')
     ax.set_ylabel('Depth (m)')
-    ax.set_title(title)
+    ax.set_title('Helical Pile and Soil Layers')
     ax.grid()
     ax.legend()
     plt.tight_layout()
     plt.show()
 
-def plot_plate(layers, B, L, z0, zlug, beta, title='Plate Anchor in Layered Soil'):
+def plot_plate(layers, B, L, z0, zlug, beta):
     '''Plot soil layers and an inclined plate anchor centered at zlug.
 
     Parameters:
@@ -345,8 +361,6 @@ def plot_plate(layers, B, L, z0, zlug, beta, title='Plate Anchor in Layered Soil
             Center embedment of the plate (m)
         beta : float
             Inclination angle of plate (deg)
-        title : str
-            Plot title
     '''
     fig, ax = plt.subplots(figsize=(5, 5))
     xmax = 3*B
@@ -397,7 +411,7 @@ def plot_plate(layers, B, L, z0, zlug, beta, title='Plate Anchor in Layered Soil
     ax.set_ylim(zmax, zmin)
     ax.set_xlabel("Horizontal extent (m)")
     ax.set_ylabel("Depth (m)")
-    ax.set_title(title)
+    ax.set_title('Plate Anchor in Layered Soil')
     ax.legend(loc='lower right')
     ax.grid(True)
     plt.tight_layout()
@@ -469,7 +483,7 @@ def plot_load(layers, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug):
     scale = 2e6  # Arrow scaling factor for better visual readability
     
     # Plot the inverse catenary profile
-    ax.plot(drag_values, depth_values, color='b', label='Mooring line')
+    ax.plot(drag_values, depth_values, color='k', label='Mooring line')
 
     # Load arrows
     ax.arrow(0, -layers[0]['top'],
@@ -478,19 +492,21 @@ def plot_load(layers, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug):
 
     ax.arrow(drag_values[-1], depth_values[-1],
              Ta*np.cos(np.deg2rad(thetaa))/scale, Ta*np.sin(np.deg2rad(thetaa))/scale,
-             head_width=0.25, head_length=0.5, color='g', label='Padeye Load')
+             head_width=0.25, head_length=0.5, color='g', label='Lug load')
+    
+    ax.plot(0, -layers[0]['top'], 'ro', zorder=5)
 
     if zlug is not None:
         ax.plot(drag_values[-1], -zlug, 'go', label=f'Padeye (zlug = {zlug:.2f} m)')
     
     # Add mudline and padeye markers
-    ax.axhline(-layers[0]['top'], color='k', linestyle='--', lw=1.5, label=f'Mudline')
+    ax.axhline(-layers[0]['top'], color='b', linestyle='--', lw=1.5, label=f'Mudline')
 
     # Annotate loads
     ax.annotate(f"{Tm/1e6:.2f} MN", (Tm*np.cos(np.deg2rad(thetam))/scale, 
-                                     -layers[0]['top'] + Tm*np.sin(np.deg2rad(thetam))/scale), color='r')
+                                     -layers[0]['top']), color='r')
     ax.annotate(f"{Ta/1e6:.2f} MN", (drag_values[-1] + Ta*np.cos(np.deg2rad(thetaa))/scale,
-                                     depth_values[-1] + Ta*np.sin(np.deg2rad(thetaa))/scale), color='g')
+                                     depth_values[-1]), color='g')
 
     # Deduplicate legend entries
     handles, labels = ax.get_legend_handles_labels()
@@ -501,8 +517,8 @@ def plot_load(layers, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug):
     ax.set_ylabel('Embedded depth (m)')
     ax.set_title('Inverse Catenary in Layered Soil')
     ax.grid(True)
-    #ax.set_ylim(min(zlug - 10, min(depth_values) - 5), max(15, max(depth_values) + 5))
-    ax.legend(loc='lower left')
+    ax.set_ylim(min(zlug - 10, min(depth_values) - 5), max(5, max(depth_values) + 5))
+    ax.legend(loc='lower right')
     plt.tight_layout()
     plt.show()
 
diff --git a/famodel/anchors/anchors_famodel_map/capacity_pycurves_map.py b/famodel/anchors/anchors_famodel/support_pycurves.py
similarity index 88%
rename from famodel/anchors/anchors_famodel_map/capacity_pycurves_map.py
rename to famodel/anchors/anchors_famodel/support_pycurves.py
index 377a9a8f..146a726e 100644
--- a/famodel/anchors/anchors_famodel_map/capacity_pycurves_map.py
+++ b/famodel/anchors/anchors_famodel/support_pycurves.py
@@ -3,7 +3,7 @@
 import matplotlib.pyplot as plt
 from scipy.interpolate import interp1d
 
-def py_Matlock(z, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=None, return_curve=False):
+def py_Matlock(z, D, gamma, Su, sigma_v_eff, z0=None, return_curve=False):
    ''' Generate Matlock (1970) p–y curve at a given depth in clay.
 
     Parameters
@@ -12,8 +12,6 @@ def py_Matlock(z, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=None, return_curve=F
         Depth relative to pile head (m)
     D : float
         Pile diameter (m)
-    zlug : float
-        Load eccentricity above or below mudline (m)
     f_Su : function
         Undrained shear strength (Pa)
     f_sigma_v_eff : function
@@ -31,9 +29,9 @@ def py_Matlock(z, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=None, return_curve=F
         Interpolation function for p–y relationship (N/m vs m)
     '''
     
-   Su = f_Su(z)
-   sigma_v_eff = f_sigma_v_eff(z)
-   gamma = f_gamma(z)
+    # Su = f_Su(z)
+    # sigma_v_eff = f_sigma_v_eff(z)
+    # gamma = f_gamma(z)
    
    # Strain at half the strength as defined by Matlock (1970). 
    # Typically ranges from 0.005 (stiff clay) to 0.02 (soft clay).
@@ -63,11 +61,11 @@ def py_Matlock(z, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=None, return_curve=F
    y = Y*y_50
    p = P*p_ult
 
-   f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0)   # Interpolation function for p-y curve
+   f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0)   
 
    return (f, (y, p)) if return_curve else f 
        
-def py_API(z, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=None, return_curve=False):   
+def py_API(z, D, phi, sigma_v_eff, Dr, z0=None, return_curve=False):   
     ''' Generate API RP2A (1993) p–y curve at a given depth in sand.
 
     Parameters
@@ -76,8 +74,6 @@ def py_API(z, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=None, return_curve=False):
         Depth relative to pile head (m)
     D : float
         Pile diameter (m)
-    zlug : float
-        Load eccentricity above or below mudline (m)
     f_phi : function
         Friction angle (deg)
     f_sigma_v_eff : function
@@ -95,9 +91,9 @@ def py_API(z, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=None, return_curve=False):
         Interpolation function for p–y relationship (N/m vs m)
     '''
     
-    phi = f_phi(z)
-    sigma_v_eff = f_sigma_v_eff(z)
-    Dr = f_Dr(z)
+    # phi = f_phi(z)
+    # sigma_v_eff = f_sigma_v_eff(z)
+    # Dr = f_Dr(z)
          
     # Interpolate coefficients depending on the effective friction angle
     phi_ref = [  20,   25,   30,   35,   40]
@@ -128,14 +124,14 @@ def py_API(z, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=None, return_curve=False):
     A = max(3 - 0.8*z/D, 0.9)
     
     # Apply API p–y formulation
-    ε = 1e-6  # prevent division by zero
-    p = A*p_ult*np.tanh(k*z*y/(A*p_ult + ε))
+    epsilon = 1e-6  # prevent division by zero
+    p = A*p_ult*np.tanh(k*z*y/(A*p_ult + epsilon))
     
     f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0)
     
     return (f, (y, p)) if return_curve else f  
 
-def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, return_curve=False):
+def py_Reese(z, D, UCS, Em, z0=None, return_curve=False):
     ''' Generate Reese (1997) p–y curve at a given depth in weak rock.
 
     Parameters
@@ -144,8 +140,6 @@ def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, return_curve=False):
         Depth relative to pile head (m)
     D : float
         Pile diameter (m)
-    zlug : float
-        Load eccentricity above or below mudline (m)
     f_UCS : function
         Unconfined compressive strength UCS(z) (Pa)
     f_Em : function
@@ -161,8 +155,8 @@ def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, return_curve=False):
         Interpolation function for p–y relationship (N/m vs m)
     '''
     
-    UCS = f_UCS(z)
-    Em = f_Em(z)
+    # UCS = f_UCS(z)
+    # Em = f_Em(z)
   
     RQD = 52                     # Assumed fair rock quality (moderately weathered rocks) 
     Dref = 0.305;                # Reference diamter (m)
@@ -206,7 +200,7 @@ def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, return_curve=False):
     
     return (f, (y, p)) if return_curve else f      
 
-def py_Lovera(z, D, f_UCS, f_Em, zlug, z0, delta_grout=0.075, E_grout=20e9, delta_crushed=0.025, return_curve=False):
+def py_Lovera(z, D, UCS, Em, zlug, z0, delta_grout=0.075, E_grout=20e9, delta_crushed=0.025, return_curve=False):
     ''' Generate Lovera (2019) p–y curve at a given depth for layered rock interfaces.
 
     Parameters
@@ -242,7 +236,7 @@ def py_Lovera(z, D, f_UCS, f_Em, zlug, z0, delta_grout=0.075, E_grout=20e9, delt
         return lambda y: np.zeros_like(y)
 
     # Retrieve elastic modulus at depth
-    Em = f_Em(z)
+    # Em = f_Em(z)
     nu = 0.3  # Typical Poisson's ratio for rock
     G_rock = Em/(2*(1 + nu))
     k_rock = 4*G_rock
diff --git a/famodel/anchors/anchors_famodel_map/capacity_soils_map.py b/famodel/anchors/anchors_famodel/support_soils.py
similarity index 100%
rename from famodel/anchors/anchors_famodel_map/capacity_soils_map.py
rename to famodel/anchors/anchors_famodel/support_soils.py
diff --git a/famodel/anchors/anchors_famodel_map/capacity_solvers.py b/famodel/anchors/anchors_famodel/support_solvers.py
similarity index 100%
rename from famodel/anchors/anchors_famodel_map/capacity_solvers.py
rename to famodel/anchors/anchors_famodel/support_solvers.py
diff --git a/famodel/anchors/anchors_famodel_map/capacity_dandg_map.py b/famodel/anchors/anchors_famodel_map/capacity_dandg_map.py
deleted file mode 100644
index aafc2af4..00000000
--- a/famodel/anchors/anchors_famodel_map/capacity_dandg_map.py
+++ /dev/null
@@ -1,233 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from .capacity_soils_map import rock_profile
-from .capacity_solvers import fd_solver
-from .capacity_pycurves_map import py_Lovera
-from .capacity_plots_map import plot_pile, plot_pycurve
-
-def getCapacityDandG(profile_map, location_name, L, D, zlug, Ha, Va, plot=True):
-    '''Models a laterally loaded pile using the p-y method. The solution for
-    lateral displacements is obtained by solving the 4th order ODE, 
-    EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method.
-    EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method.
-
-    Assumes that EI remains constant with respect to curvature i.e. pile
-    material remains in the elastic region.
-
-    Parameters
-    ----------
-    profile : array
-        Rock profile as a 2D array: (z (m), UCS (MPa), Em (MPa))
-    soil_type : string
-        Select soil condition, 'rock'
-    L : float 
-        Pile length (m)
-    D : float 
-        Pile diameter (m)
-    zlug : float
-        Load eccentricity above the mudline or depth to mudline relative to the pile head (m)
-    Ha : float       
-        Horizontal load at pile lug elevation (N)
-    Va : float          
-        Vertical load at pile lug elevation (N)
-    plot : bool
-        Plot the p-y curve and the deflection pile condition if True
-
-    Returns
-    -------
-    y : array
-        Lateral displacement at each node (n+1 real + 4 imaginary)
-    z : array
-        Node location along pile (m)
-    resultsDandG : dict
-        Dictionary with lateral, rotational, vertical and pile weight results
-    '''
-
-    profile_entry = next(p for p in profile_map if p['name'] == location_name)
-    layers = profile_entry['layers']
-      
-    n = 50; loc = 2              # Number of nodes (-)
-    tol = 1e-16; max_iter = 50   # Iteration parameters (-)
-    nhuc = 1; nhu = 0.3          # Resistance factor (-)
-    delta_r = 0.08               # Mean roughness height (m)
-    
-    t = (6.35 + D*20)/1e3        # Pile wall thickness (m), API RP2A-WSD
-    E = 200e9                    # Elastic modulus of pile material (Pa)
-    fy = 350e6                   # Steel's yield strength (Pa)
-    rhows = 66.90e3              # Submerged steel specific weight (N/m3)
-    rhow = 10e3                  # Water specific weight (N/m3) 
-    
-    # Pile geometry
-    I = (np.pi/64.0)*(D**4 - (D - 2*t)**4)
-    EI = E*I
-    h = L/n                      # Element size
-    N = (n + 1) + 4              # (n+1) Real + 4 Imaginary nodes
-    
-    # Dry and wet mass of the pile    
-    def PileWeight(Len, Dia, tw, rho):
-        Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho
-        return Wp 
-
-    # Array for displacements at nodes, including imaginary nodes.
-    y = np.ones(N)*(0.01*D)      # An initial value of 0.01D was arbitrarily chosen
-
-    # Initialize and assemble array/list of p-y curves at each real node
-    z = np.zeros(N)
-    k_secant = np.zeros(N)
-    py_funs = []
-    DQ = []; pycurve_data = []
-
-    z0 = min(layer['top'] for layer in layers)
-    
-    for i in [0, 1]:              # Top two imaginary nodes
-        z[i] = (i - 2)*h
-        py_funs.append(0)
-        k_secant[i] = 0.0
-   
-    for i in range(2, n+3):       # Real nodes
-        z[i] = (i - 2)*h
-        z_depth = z[i]
-        
-        matched_layer = next((layer for layer in layers if layer['top'] <= z_depth <= layer['bottom']), None)
-        if matched_layer is None or z_depth < matched_layer['top']:
-            py_funs.append(lambda y_val: np.zeros_like(y_val))
-            k_secant[i] = 0.0
-            DQ.append(0.0)
-            continue
-        
-        profile = [[matched_layer['top'], matched_layer['UCS_top'], matched_layer['Em_top']],
-                   [matched_layer['bottom'], matched_layer['UCS_bot'], matched_layer['Em_bot']]]
-        z0_local, f_UCS, f_Em = rock_profile(profile)
-        
-        if z_depth < z0_local:
-            py_funs.append(lambda y_val: np.zeros_like(y_val))
-            k_secant[i] = 0.0
-            DQ.append(0.0)
-            continue
-
-        UCS = f_UCS(z_depth)
-        Em = f_Em(z_depth)
-        py_f, (y_vals, p_vals) = py_Lovera(z_depth, D, f_UCS, f_Em, zlug, z0, return_curve=True)
-        py_funs.append(py_f)
-        pycurve_data.append((y_vals, p_vals, z_depth, 'rock'))
-        # print(f"z_depth = {z_depth:.2f} m, UCS = {f_UCS(z_depth):.2e} Pa, Em = {f_Em(z_depth):.2e} Pa")
-        
-        SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D
-        alpha = 0.36*SCR - 0.0005
-        fs = alpha*UCS
-        Dq = np.pi*D*fs*z_depth
-        DQ.append(Dq)
-        k_val = py_funs[i](y[i])
-        k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0
-
-    for i in [n+3, n+4]:         # Bottom two imaginary nodes
-        z[i] = (i - 2)*h
-        py_funs.append(0)
-        k_secant[i] = 0.0
-        
-    Wp = PileWeight(L, D, t, rhows + rhow)
-    Wtip = DQ[-1] if DQ else 0.0
-    Vmax = Wp + Wtip    
-        
-    for j in range(max_iter):
-        y_old = y.copy()
-        y, *_ = fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant)
-        
-        # Update stiffness
-        for i in range(2, n+3):
-            if callable(py_funs[i]):
-                k_secant[i] = py_funs[i](y[i])/y[i] if y[i] != 0 else 0.0
-    
-        # Check convergence
-        if np.linalg.norm(y - y_old, ord=2) < tol:
-            print(f'[Converged in {j+1} iterations]')
-            break
-    else:
-        print('[Warning: Solver did not converge]')
-
-
-    if plot:
-        plot_pycurve(pycurve_data)
-
-        fig, ax = plt.subplots(figsize=(3, 5))
-        y0 = np.zeros_like(z[2:-2])
-        ax.plot(y0, z[2:-2], 'k', label='Original pile axis')
-        ax.plot(y[2:-2], z[2:-2], 'r', label='Deflected shape')
-        ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)')
-        ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)')
-        ax.set_xlabel('Lateral displacement (m)')
-        ax.set_ylabel('Depth (m)')
-        ax.set_xlim([-0.1*D, 0.1*D])
-        ax.set_ylim([L + 5, -2])
-        ax.grid(ls='--')
-        ax.legend() 
-            
-    # Relevant index of nodes
-    zlug_index = int(zlug/h); print(zlug_index)
-    ymax_index = np.argmax(y); print(ymax_index)    
-    
-    resultsDandG = {
-        'Weight pile': PileWeight(L, D, t, rhows + rhow),
-        'Vertical max.': Vmax,
-        'Lateral displacement': y[ymax_index],
-        'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h),
-        'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf,
-        'Unity check (horizontal)': 0.0,  # Placeholder; no Mp or Mi in current model
-        'Bending moment': None,
-        'Plastic moment': None,
-        'Plastic hinge': None,
-        'Hinge location': None,
-        'p-y model': 'Lovera (2023)',
-        }
-    
-    return layers, y[2:-2], z[2:-2], resultsDandG
-   
-if __name__ == '__main__':
-
-    profile_map = [
-        {
-            'name': 'CPT_rock_1',
-            'x': 502000,
-            'y': 5725000,
-            'layers': [
-                {
-                    'top': 2.0, 'bottom': 5.0,
-                    'soil_type': 'rock',
-                    'UCS_top': 1.0, 'UCS_bot': 2.0,    # MPa
-                    'Em_top': 100, 'Em_bot': 200       # MPa
-                },
-                {
-                    'top': 5.0, 'bottom': 9.0,
-                    'soil_type': 'rock',
-                    'UCS_top': 2.0, 'UCS_bot': 3.0,    # MPa
-                    'Em_top': 200, 'Em_bot': 300       # MPa
-                },
-                {
-                    'top': 9.0, 'bottom': 30.0,
-                    'soil_type': 'rock',
-                    'UCS_top': 3.0, 'UCS_bot': 6.0,    # MPa
-                    'Em_top': 300, 'Em_bot': 400       # MPa
-                }
-            ]
-        }
-    ]
-
-    D = 3.0           # Diameter (m)
-    L = 10.0          # Length (m)
-    zlug = 1          # Padeye elevation (m)
-    Ha = 8.0e6        # Horizontal load (N)
-    Va = 3.0e6        # Vertical load (N)
-
-    layers, y, z, results = getCapacityDandG(profile_map, 'CPT_rock_1', L, D, zlug, Ha, Va, plot=True)
-
-    print('\n--- Results for DandG Pile in Layered Rock ---')
-    for key, val in results.items():
-        print(f'{key}: {val:.3f}' if isinstance(val, float) else f'{key}: {val}')
-        
-    plot_pile(layers, y, z, D, L, layers[0]['top'], zlug)    
-
-    
-
-    
-    
diff --git a/famodel/anchors/anchors_famodel_map/capacity_helical_map.py b/famodel/anchors/anchors_famodel_map/capacity_helical_map.py
deleted file mode 100644
index 4a495a4e..00000000
--- a/famodel/anchors/anchors_famodel_map/capacity_helical_map.py
+++ /dev/null
@@ -1,172 +0,0 @@
-
-import numpy as np
-from .capacity_driven_map import getCapacityDriven, plot_pile
-from .capacity_soils_map import clay_profile, sand_profile
-from .capacity_plots_map import plot_helical
-
-def getCapacityHelical(profile_map, location_name, D, L, d, zlug, Ha, Va, plot=True):
-    '''Calculate the vertical and horizontal capacity of a helical pile using a soil profile.
-    The calculation is based on the soil profile, anchor geometry and inclined load.
-
-    Parameters
-    ----------
-    profile : array
-        Soil profiles (z, parameters)
-            Clay soil profile (z, Su, gamma)
-            Sand soil profile (z, phi, gamma, Dr)
-    soil_type : string
-        Select soil condition, 'clay' or 'sand'
-    D : float 
-        Helix diameter (m)
-    L : float 
-        Shaft length (m)
-    d : float 
-        Shaft diameter (m)
-    zlug : float
-        Depth to padeye (m)
-    Ha : float
-        Horizontal load applied at padeye (N)
-    Va : float
-        Vertical load applied at padeye (N)
-    plot : bool
-        Plot the p-y curve and the deflection pile condition if True
-
-    Returns
-    -------
-    y : array
-        Lateral displacement at each node (real nodes only)
-    z : array
-        Node depth positions corresponding to y (m)
-    resultsHelical : dict
-        Dictionary containing displacements, moment capacity, hinge state and vertical capacity
-    '''
-
-    profile_entry = next(p for p in profile_map if p['name'] == location_name)
-    layers = profile_entry['layers']
-
-    t = (6.35 + D*20)/1e3            # Helical pile wall thickness (m), API RP2A-WSD
-    rhows = 66.90e3                  # Submerged steel specific weight (kN/m3)
-    rhow = 10e3                      # Water specific weight (kN/m3) 
-
-    def PileWeight(Len, Dia1, Dia2, tw, rho):
-        return ((np.pi/4)*((Dia1**2 - (Dia1 - 2*tw)**2)*Len + (np.pi/4)*Dia2**2*tw))*rho
-
-    z_helix = zlug + (L - D)
-    matched_layer = next((layer for layer in layers if layer['top'] <= z_helix <= layer['bottom']), None)
-    if matched_layer is None:
-        raise ValueError(f"No soil layer found at z = {z_helix:.2f} m")
-
-    if matched_layer['soil_type'] == 'clay':
-        profile = [[matched_layer['top'], matched_layer['Su_top'], matched_layer['gamma_top']],
-                   [matched_layer['bottom'], matched_layer['Su_bot'], matched_layer['gamma_bot']]]
-        z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile)
-
-        z_helix = np.clip(z_helix, matched_layer['top'], matched_layer['bottom'])
-        Su = f_Su(z_helix)
-        sigma_v_eff = max(f_sigma_v_eff(z_helix), 1.0)
-        psi_val = Su/sigma_v_eff
-        alpha = min(0.5*psi_val**-0.50, 1) if psi_val <= 1.0 else min(0.5 * psi_val**-0.25, 1)
-
-        Nc = min(6.0*(1 + 0.2*d/D), 9)
-        Qh = ((np.pi/4)*(D**2 - d**2)*Nc*Su + f_gamma(z_helix)*D)*0.75
-        Qs = np.pi*d*L*alpha*Su
-        Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs
-
-    elif matched_layer['soil_type'] == 'sand':
-        profile = [[matched_layer['top'], matched_layer['phi_top'], matched_layer['gamma_top'], matched_layer['Dr_top']],
-                   [matched_layer['bottom'], matched_layer['phi_bot'], matched_layer['gamma_bot'], matched_layer['Dr_bot']]]
-        z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile)
-
-        z_helix = np.clip(z_helix, matched_layer['top'], matched_layer['bottom'])
-        gamma = f_gamma(z_helix)
-        Dr = f_Dr(z_helix)
-        delta = f_delta(z_helix)
-        phi = f_phi(z_helix)
-
-        Nq = 0.5*(12*phi)**(phi/54)
-        Qh = (np.pi/4)*(D**2 - d**2)*Nq*gamma*z_helix
-        Qs = np.pi*d*L*delta*gamma*z_helix
-        Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs
-        
-    
-    Wp = PileWeight(L, D, d, t, (rhows + rhow))    
-
-    # Unity Check based only on vertical capacity
-    UC_vertical = Va/Qu
-
-    # Compute horizontal capacity using p-y method
-    layers, y, z, results_lateral = getCapacityDriven(profile_map, location_name, D, L, zlug, Ha, Va, plot=True)
-
-    plot_pile(layers, y, z, D, L, z0=layers[0]['top'], zlug=zlug, hinge_location=None)
-
-    Hcap = results_lateral['Horizontal max.']
-    UC_horizontal = Ha/Hcap if Hcap != 0 else np.inf
-
-    resultsHelical = {
-        'Vertical max.': Qu,
-        'Weight': Wp,
-        'Unity Check (Vertical)': UC_vertical,
-        'Horizontal max.': Hcap,
-        'Unity Check (Horizontal)': UC_horizontal
-    }
-
-    if matched_layer['soil_type'] == 'clay':
-        resultsHelical['Su @ helix'] = Su
-        resultsHelical['Alpha'] = alpha
-    elif matched_layer['soil_type'] == 'sand':
-        resultsHelical['Dr @ helix'] = Dr
-        resultsHelical['Delta'] = delta
-        resultsHelical['Phi'] = phi
-
-    return layers, resultsHelical
-
-if __name__ == '__main__':
-    
-    profile_map = [
-        {
-            'name': 'CPT_1',
-            'x': 498234, 'y': 5725141,
-            'layers': [
-                {
-                    'top': 1.0, 'bottom': 3.0,
-                    'soil_type': 'clay',
-                    'gamma_top': 8.0, 'gamma_bot': 9.0,
-                    'Su_top': 60, 'Su_bot': 50},
-                {
-                    'top': 3.0, 'bottom': 7.0,
-                    'soil_type': 'clay',
-                    'gamma_top': 15.0, 'gamma_bot': 25.0,
-                    'Su_top': 100, 'Su_bot': 150},
-                # {
-                #     'top': 6.0, 'bottom': 15.0,
-                #     'soil_type': 'sand',
-                #     'gamma_top': 8.0, 'gamma_bot': 8.0,
-                #     'phi_top': 32, 'phi_bot': 38,
-                #     'Dr_top': 70, 'Dr_bot': 75},
-                {
-                    'top': 7.0, 'bottom': 15.0,
-                    'soil_type': 'clay',
-                    'gamma_top': 25.0, 'gamma_bot': 50.0,
-                    'Su_top': 200, 'Su_bot': 400}]
-        }
-    ]  
-
-    D = 1.5        # Helix diameter (m)
-    L = 12.0       # Pile length (m)
-    d = 0.5        # Shaft diameter (m)
-    zlug = 3       # Padeye depth (m)
-    Ha = 30e3      # Horizontal load (N)
-    Va = 50e3      # Vertical load (N)
-
-    print("--- Clay Profile ---")
-    layers, resultsHelical = getCapacityHelical(profile_map, 'CPT_1', D, L, d, zlug, Ha, Va, plot=True)
-    for key, val in resultsHelical.items():
-        if isinstance(val, float):
-            print(f"{key}: {val:.3f}")
-        else:
-            print(f"{key}: {val}")
-    
-    plot_helical(layers, D=D, L=L, d=d, z0=layers[0]['top'], zlug=zlug, n_helix=1, spacing=1.0, title='Helical Pile in Sand Profile')
-  
-
-
diff --git a/famodel/anchors/anchors_famodel_map/capacity_load_map.py b/famodel/anchors/anchors_famodel_map/capacity_load_map.py
deleted file mode 100644
index 7ebca3b7..00000000
--- a/famodel/anchors/anchors_famodel_map/capacity_load_map.py
+++ /dev/null
@@ -1,212 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from .capacity_soils_map import clay_profile, sand_profile
-from .capacity_plots_map import plot_load
-
-def getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w=None, plot=False):
-    '''Calculate the transfer load from mudline to main padeye using a layered soil profile.
-
-    Parameters
-    ----------
-    profile_map : list of dicts
-        Soil profile in profile_map format
-    Tm : float
-        Mooring line load at mudlevel (N)
-    thetam : float
-        Mooring line angle at mudlevel (deg)
-    zlug : float
-        Embedment depth of the lug (m)
-    line_type : str
-        'chain' or 'wire'
-    d : float
-        Chain diameter (m)
-    w : float
-        Mooring line unit weight (N/m)
-    plot : bool
-        Show plot
-
-    Returns
-    -------
-    dict
-        Dictionary with transferred load components and depth.
-    '''
-
-    deltas = 0.2  # discretization step
-
-    # Line mechanical properties
-    if line_type == 'chain':
-        Et, En = 10, 2.5
-    elif line_type == 'wire':
-        Et, En = np.pi, 1
-    W = w*deltas
-
-    # Soil layer access
-    layers = profile_map[0]['layers']
-    z0 = min(layer['top'] for layer in layers)
-    Nc = 8.5
-
-    # Initial values
-    z0 = min(layer['top'] for layer in layers)
-    T = Tm
-    theta = np.deg2rad(thetam)
-    drag = 0
-    depth = z0 + 0.01
-
-    # Tracing lists
-    drag_values, depth_values = [], []
-
-    while (zlug - depth) >= 0:
-        matched_layer = next((layer for layer in layers if layer['top'] <= depth <= layer['bottom']), None)
-        if matched_layer is None:
-            break
-        
-        if matched_layer['soil_type'] == 'clay':
-            matched_layer = next((layer for layer in layers if layer['soil_type'] == 'clay' and layer['top'] <= depth <= layer['bottom']), None)
-            if matched_layer is None:
-                break
-            profile = [[matched_layer['top'], matched_layer['gamma_top'], matched_layer['Su_top']],
-                       [matched_layer['bottom'], matched_layer['gamma_bot'], matched_layer['Su_bot']]]
-            z0_local, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile)
-
-            Su = f_Su(depth)
-            alpha = f_alpha(depth)
-            d_theta = (En*d*Nc*Su - W*np.cos(theta))/T*deltas
-            dT = (Et*d*alpha*Su + W*np.sin(theta))*deltas
-
-        elif matched_layer['soil_type'] == 'sand':
-            matched_layer = next((layer for layer in layers if layer['soil_type'] == 'sand' and layer['top'] <= depth <= layer['bottom']), None)
-            if matched_layer is None:
-                break
-            
-            profile = [[matched_layer['top'], matched_layer['gamma_top'], matched_layer['phi_top'], matched_layer['Dr_top']],
-                       [matched_layer['bottom'], matched_layer['gamma_bot'], matched_layer['phi_bot'], matched_layer['Dr_bot']]]
-            z0_local, f_gamma, f_phi, f_Dr, f_sigma_v_eff, f_delta = sand_profile(profile)
-
-            gamma_z = f_gamma(depth)
-            delta_z = f_delta(depth)
-            phi = f_phi(depth)
-            Nq = np.exp(np.pi*np.tan(np.deg2rad(phi)))*(np.tan(np.deg2rad(45 + phi/2)))**2
-            print(f'Nq = {Nq:.2f}, depth = {depth:.2f} m')
-            d_theta = (En*d*Nq*gamma_z*depth - W*np.cos(theta))/T*deltas
-            dT = (Et*d*gamma_z*depth*np.tan(np.deg2rad(delta_z)) + W*np.sin(theta))*deltas
-            
-        else:
-            raise ValueError(f"Unsupported soil type: {matched_layer['soil_type']}")
-
-        d_drag  = deltas*np.cos(theta)
-        d_depth = deltas*np.sin(theta)
-
-        theta += d_theta
-        T -= dT
-        drag += d_drag
-        depth += d_depth
-
-        if abs(Tm - T) > 0.75*Tm:
-            raise Exception(f"Load transfer unrealistic: Tm = {Tm/1e6:.2f} MN vs T = {T/1e6:.2f} MN")
-        if not (0 < np.rad2deg(theta) < 90):
-            raise Exception(f"Load angle unrealistic: {np.rad2deg(theta):.2f} deg")
-
-        drag_values.append(-drag); 
-        depth_values.append(-depth); 
-
-    Ta = T; thetaa = theta
-    
-    print(f'Input Tm = {Tm}, thetam = {thetam}, zlug = {zlug}')
-    print(f'Output Hm = {Tm*np.cos(np.deg2rad(thetam))}, Vm = {Tm*np.sin(np.deg2rad(thetam))}')
-    print(f'Output Ta = {Ta}, thetaa = {np.rad2deg(thetaa)}')
-    print(f'Output Ha = {Ta*np.cos(thetaa)}, Va = {Ta*np.sin(thetaa)}')
-
-    resultsLoad = {
-        'Tm': Tm,
-        'thetam': thetam,
-        'Ta': Ta,
-        'thetaa': np.rad2deg(thetaa),
-        'length': deltas*len(drag_values),
-        'drag_values': drag_values,
-        'depth_values': depth_values
-    }
-
-    return layers, resultsLoad
-
-
-if __name__ == '__main__':
-
-    profile_map = [
-        {
-            'name': 'CPT_1',
-            'x': 498234, 'y': 5725141,
-            'layers': [
-                {
-                    'top': 1.0, 'bottom': 2.0,
-                    'soil_type': 'clay',
-                    'gamma_top': 8.0, 'gamma_bot': 8.0,
-                    'Su_top': 10, 'Su_bot': 25},
-                {
-                    'top': 2.0, 'bottom': 8.0,
-                    'soil_type': 'clay',
-                    'gamma_top': 8.0, 'gamma_bot': 8.0,
-                    'Su_top': 25, 'Su_bot': 50},
-                {
-                    'top': 8.0, 'bottom': 16.0,
-                    'soil_type': 'clay',
-                    'gamma_top': 8.0, 'gamma_bot': 8.0,
-                    'Su_top': 50, 'Su_bot': 100}
-            ]
-        }
-    ]
-    # profile_map = [
-    #     {
-    #         'name': 'CPT_1',
-    #         'x': 498234, 'y': 5725141,
-    #         'layers': [
-    #             # {
-    #             #     'top': 0.0, 'bottom': 5.0,
-    #             #     'soil_type': 'sand',
-    #             #     'gamma_top': 9.5, 'gamma_bot': 9.5,
-    #             #     'phi_top': 28, 'phi_bot': 30,
-    #             #     'Dr_top': 70, 'Dr_bot': 70},
-    #             {
-    #                 'top': 0.0, 'bottom': 5.0,
-    #                 'soil_type': 'clay',
-    #                 'gamma_top': 8.0, 'gamma_bot': 8.0,
-    #                 'Su_top': 25, 'Su_bot': 25},
-    #             {
-    #                 'top': 5.0, 'bottom': 10.0,
-    #                 'soil_type': 'sand',
-    #                 'gamma_top': 9.5, 'gamma_bot': 9.5,
-    #                 'phi_top': 32, 'phi_bot': 36,
-    #                 'Dr_top': 70, 'Dr_bot': 70},
-    #             {
-    #                 'top': 10.0, 'bottom': 15.0,
-    #                 'soil_type': 'sand',
-    #                 'gamma_top': 9.5, 'gamma_bot': 9.5,
-    #                 'phi_top': 42, 'phi_bot': 45,
-    #                 'Dr_top': 70, 'Dr_bot': 70}
-    #         ]
-    #     }
-    # ]
-
-    Tm = 6e6              # Load at mudline (N)
-    thetam = 10           # Angle at mudline (deg)
-    zlug = 8              # Padeye depth (m)
-    line_type = 'chain'
-    d = 0.16              # Chain diameter (m)
-    w = 5000              # Line weight (N/m)
-
-    layers, resultsLoad = getTransferLoad(profile_map, Tm, thetam, zlug, line_type, d, w, plot=True)
-
-    # print("\n--- Transfer Load Results ---")
-    # for key, val in resultsLoad.items():
-    #     if isinstance(val, float):
-    #         print(f"{key}: {val:.3f}")
-    #     elif isinstance(val, list):
-    #         print(f"{key}:")
-    #         for v in val:
-    #             print(f"  {v:.3f}")
-    #     else:
-    #         print(f"{key}: {val}")
-
-    plot_load(layers, resultsLoad['drag_values'], resultsLoad['depth_values'], 
-              resultsLoad['Tm'], resultsLoad['thetam'], resultsLoad['Ta'], 
-              resultsLoad['thetaa'], zlug=zlug)
\ No newline at end of file
diff --git a/famodel/anchors/anchors_famodel_map/capacity_plate_map.py b/famodel/anchors/anchors_famodel_map/capacity_plate_map.py
deleted file mode 100644
index 56e50b44..00000000
--- a/famodel/anchors/anchors_famodel_map/capacity_plate_map.py
+++ /dev/null
@@ -1,177 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from .capacity_soils_map import clay_profile
-from .capacity_plots_map import plot_plate
-
-def getCapacityPlate(profile_map, location_name, B, L, zlug, beta, Ha, Va, plot=True):
-    '''Calculate the plate anchor capacity using clay soil layers from profile_map.
-    The calculation is based on the soil profile, anchor geometry and inclined load.
-
-    Parameters
-    ----------
-    profile_map : list of dict
-        Soil profile map with coordinates and layers per location.
-    location_name : str
-        Name of the location to select the soil profile.
-    B : float
-        Plate width (m)
-    L : float
-        Plate length (m)
-    zlug : float
-        Embedment depth of the main padeye (m)
-    beta : float
-        Inclination angle of the plate (deg)
-    Ha : float
-        Applied horizontal load (N)
-    Va : float
-        Applied vertical load (N)
-    plot : bool
-        Whether to generate plots.
-
-    Returns
-    -------
-    Dictionary with Capacity, Weight, UC, etc.
-    '''
-
-    # Extract and filter clay layers from profile_map
-    profile_entry = next(p for p in profile_map if p['name'] == location_name)
-    layers = [layer for layer in profile_entry['layers'] if layer['soil_type'] == 'clay']
-
-    if not layers:
-        raise ValueError('Plate anchor capacity model only supports clay soils.')
-
-    # Build the profile array: [[z, Su, gamma], ...]
-    profile = []
-    for layer in layers:
-        profile.append([layer['top'], layer['gamma_top'], layer['Su_top']])
-        profile.append([layer['bottom'], layer['gamma_bot'], layer['Su_bot']])
-        
-    print("layer gamma_top (raw):", layer['gamma_top'])
-    print("layer gamma_bot (raw):", layer['gamma_bot'])
-
-    profile = np.array(sorted(profile, key=lambda x: x[0]))
-
-    # Parameters and constants
-    Los = 0.05
-    B_t = 40
-    rhows = 66.90e3  # Submerged steel (N/m3)
-    rhow = 10e3      # Seawater (N/m3)
-
-    # Evaluate interpolated Su and gamma
-    z0, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile)
-    t = round(B/B_t, 2)
-    V_steel = round(B*L*t, 2)
-    zlug_B = zlug/B
-
-    # Profile check points
-    npts = 10
-    z_offsets = np.linspace(-0.5, 0.5, npts)*B*np.sin(np.deg2rad(beta))
-    z_points = zlug + z_offsets; print(z_points)
-
-    Su_vals = [f_Su(z) for z in z_points]
-    gamma_10 = f_gamma(z_points[2]); print(gamma_10)
-    gamma_vals = [f_gamma(z) for z in z_points]; print("gamma_vals:", [f"{val:.2f}" for val in gamma_vals], "N/m3")
-    Su = np.mean(Su_vals); print(f"Su: {Su:.2f} Pa")
-    gamma = np.mean(gamma_vals); print(f"gamma: {gamma:.2f} N/m3")
-    
-    print("Profile being sent to clay_profile():")
-    for row in profile:
-        print(f"z = {row[0]:.2f} m, gamma = {row[1]:.2f} kN/m³, Su = {row[2]:.2f} kPa")
-
-    # Shear strength gradient
-    k = np.polyfit(z_points, Su_vals, 1)[0]
-    print(f"k: {k:.2f}")
-
-    # Pile weight including auxiliary parts
-    Wp = 1.35*V_steel*(rhows + rhow)
-
-    # Capacity factors
-    Nco_0_0  = 2.483*np.log(zlug_B) + 1.974
-    Nco_90_0 = 2.174*np.log(zlug_B) + 3.391
-    kBSh = k*B/Su
-    print(f"kBSh: {kBSh:.2f}")
-
-    f0  = np.where(zlug_B < 4, 1.77*(zlug_B**0.3) - 1.289, 0.192*zlug_B + 0.644)
-    f90 = np.where(zlug_B < 4, 0.68*(zlug_B**0.5) - 0.410, 0.153*zlug_B + 0.341)
-
-    S_kB_0  = 1 - f0*kBSh
-    S_kB_90 = 1 - f90*kBSh
-    Nco_0  = S_kB_0*Nco_0_0
-    Nco_90 = S_kB_90*Nco_90_0
-    Nco = Nco_0 + (Nco_90 - Nco_0)*(beta/90)**2
-
-    Nco_s_0_0  = np.where(2.90*zlug_B + 6.02 <= 11.59, 2.90*zlug_B + 6.02, 11.596)
-    Nco_s_90_0 = np.where(2.72*zlug_B + 4.02 <= 11.59, 2.72*zlug_B + 4.02, 11.596)
-
-    S_s_kB_0 = np.where(zlug_B <= 2, 1 + (0.8 - 0.3*zlug_B)*kBSh - (0.383*kBSh**1.36), 1)
-    f90s = np.where(zlug_B <= 3, 0.267*zlug_B, 0.6)
-    S_s_kB_90 = 1 - f90s*kBSh
-    Nco_s_0 = S_s_kB_0*Nco_s_0_0
-    Nco_s_90 = S_s_kB_90*Nco_s_90_0
-    Nco_s = Nco_s_90 + (Nco_s_0 - Nco_s_90)*((90 - beta)/90)**2
-
-    Nc_final = max(Nco + (gamma*zlug)/Su, Nco_s)
-    print(f"Nc_star: {Nco + (gamma*zlug)/Su:.2f}")
-    print(f"Nc_star: {Nco_s:.2f}")
-    qu = Nc_final*Su
-    Tmax = round(qu*(1 - Los)*B*L, 2)
-    Hmax = Tmax*np.cos(np.deg2rad(90 - beta))
-    Vmax = Tmax*np.sin(np.deg2rad(90 - beta))
-
-    Ta = np.sqrt(Ha**2 + Va**2)
-    UC = Ta/Tmax
-
-    resultsPlate = {
-        'Capacity': Tmax,
-        'Horizontal max.': Hmax,
-        'Vertical max.': Vmax,
-        'Unity check': UC,
-        'Weight plate': Wp
-    }
-    
-    return layers, resultsPlate
-
-if __name__ == '__main__':
-    profile_map = [
-        {
-            'name': 'CPT_1',
-            'x': 498234, 'y': 5725141,
-            'layers': [
-                {
-                    'top': 0.0, 'bottom': 9.5,
-                    'soil_type': 'clay',
-                    'gamma_top': 8.0, 'gamma_bot': 8.5,
-                    'Su_top': 10, 'Su_bot': 25
-                },
-                {
-                    'top': 9.5, 'bottom': 11.5,
-                    'soil_type': 'clay',
-                    'gamma_top': 8.5, 'gamma_bot': 8.5,
-                    'Su_top': 25, 'Su_bot': 45
-                },
-                {
-                    'top': 11.5, 'bottom': 25.0,
-                    'soil_type': 'clay',
-                    'gamma_top': 8.5, 'gamma_bot': 9.0,
-                    'Su_top': 45, 'Su_bot': 50
-                }
-            ]
-        }
-    ]
-
-    B = 2.0
-    L = 2.0
-    zlug = 10.0
-    Ha = 350e3
-    Va = 400e3
-    alpha = np.rad2deg(np.arctan2(Va, Ha))
-    beta = 90 - alpha
-
-    layers, results = getCapacityPlate(profile_map, 'CPT_1', B, L, zlug, beta, Ha, Va)
-
-    print("\n--- Plate Anchor Capacity Results ---")
-    for key, val in results.items():
-        print(f"{key}: {val:.2f}")
-        
-    plot_plate(layers, B, L, z0 = layers[0]['top'], zlug=zlug, beta=beta, title='Plate Anchor in Layered Soil')
diff --git a/famodel/anchors/anchors_famodel_map/capacity_suction_map.py b/famodel/anchors/anchors_famodel_map/capacity_suction_map.py
deleted file mode 100644
index db296616..00000000
--- a/famodel/anchors/anchors_famodel_map/capacity_suction_map.py
+++ /dev/null
@@ -1,401 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.optimize import fsolve
-from .capacity_soils_map import clay_profile, sand_profile
-from .capacity_plots_map import plot_suction
-
-
-def PileSurface(Len, Dia):
-    return np.pi*Dia*Len
-
-def PileWeight(Len, Dia, tw, rho):
-    return ((np.pi/4)*((Dia**2 - (Dia - 2*tw)**2)*Len + (np.pi/4)*Dia**2*tw))*rho
-
-def SoilWeight(Len, Dia, tw, gamma_soil):
-    return (np.pi/4)*(Dia - 2*tw)**2*Len*gamma_soil
-
-def rlugTilt(r, z, theta):
-    return r*np.cos(np.deg2rad(theta)) - z*np.sin(np.deg2rad(theta))
-
-def zlugTilt(r, z, theta):
-    return r*np.sin(np.deg2rad(theta)) + z*np.cos(np.deg2rad(theta))
-
-def getCapacitySuction(profile_map, location_name, D, L, zlug, Ha, Va, thetalug=5, psilug=7.5, plot=True):
-    '''Calculate the inclined load capacity of a suction pile in sand or clay following S. Kay methodology.
-    The calculation is based on the soil profile, anchor geometry and inclined load.  
-    
-    Parameters
-    ----------
-    profile : array
-        Soil profile as a 2D array: (z, parameters)
-            Clay soil profile (z (m), Su (kPa), gamma (kN/m³))
-            Sand soil profile (z (m), phi (deg), gamma (kN/m³), Dr (%))
-    soil_type : string
-        Select soil condition, 'clay' or 'sand'
-    D : float 
-        Suction pile diameter (m)
-    L : float 
-        Suction pile length from pile head (m)
-    zlug: float
-        Embedded depth of the main padeye (m)
-    thetalug: float
-        Angle of tilt misaligment (deg) (default value: 5.0)
-    psilug: float
-        Angle of twist misaligment (deg) (default value: 7.5)
-    Ha : float       
-        Horizontal load at pile lug elevation (N)
-    Va : float          
-        Vertical load at pile lug elevation (N)
-    plot : bool
-        Plot the capacity envelope if True
-                    
-    Returns
-    -------
-    Dictionary with capcity, weigths and UC.
-    ''' 
-    
-    # Retrieve soil layers from map
-    profile_entry = next(p for p in profile_map if p['name'] == location_name)
-    layers = profile_entry['layers']
-    
-    z0 = layers[0]['top']            # Mudline elevation    
-    lambdap = (L - z0)/D             # Suction pile slenderness ratio
-    t = (6.35 + D*20)/1e3            # Suction pile wall thickness (m), API RP2A-WSD
-    rlug = D/2                       # Radial position of the lug
-    thetalug = 5                     # Angle of tilt misaligment, default is 5. (deg)
-    psilug = 7.5                     # Angle of twist misaligment, default is 7.5. (deg)
-    rhows = 66.90e3                  # Submerged steel specific weight (N/m3)
-    rhow = 10e3                      # Water specific weight (N/m3) 
-    
-    Np_fixed = 10.25
-    Np_free = 4.0
-    Nc = min(6.2*(1 + 0.34*np.arctan(lambdap)), 9)
-    
-    # Initialize
-    sum_ez_weighted = 0.0
-    sum_Hmax = 0.0
-    Vmax_final = 0.0
-    layer_data = []
-
-    # Profile check points
-    npts = 10
-
-    for layer in layers:
-        soil_type = layer['soil_type']
-        z_top = layer['top']
-        z_bot = layer['bottom']
-
-        if soil_type == 'clay':
-            # Prepare soil profile for clay
-            profile = [
-                [z_top, layer['gamma_top'], layer['Su_top']],
-                [z_bot, layer['gamma_bot'], layer['Su_bot']]
-            ]
-    
-            z_ref, f_gamma, f_Su, f_sigma_v_eff, f_alpha = clay_profile(profile)
-    
-            # Clip the layer first
-            z_top_clip = max(z_top, z0)
-            z_bot_clip = min(z_bot, z0 + (L - z0))
-            dz_clip = z_bot_clip - z_top_clip; # print(f'dz_clip  = {dz_clip:.2f} m')
-    
-            if dz_clip <= 0:
-                continue  # Skip layers fully above or below
-    
-            # Calculate properties over clipped dz
-            z_vals = np.linspace(z_top_clip, z_bot_clip, npts)
-            Su_vals = f_Su(z_vals)
-            Su_total = np.trapz(Su_vals, z_vals)
-            Su_moment = np.trapz(Su_vals*z_vals, z_vals)
-    
-            Su_av_z = Su_total/dz_clip; # print(f'Su_av_z  = {Su_av_z:.2f} Pa')
-            ez_layer = Su_moment/Su_total; 
-            Su_bot = f_Su(z_bot_clip)
-            gamma_vals = f_gamma(z_vals)
-            gamma_av = np.mean(gamma_vals)
-    
-            # Calculate Hmax for clay
-            Hmax_layer = Np_fixed*D*dz_clip*Su_av_z; print(f'Su_av_z  = {Su_av_z:.2f} Pa')
-    
-            layer_data.append({
-                'z_top': z_top_clip,
-                'z_bot': z_bot_clip,
-                'dz': dz_clip,
-                'Hmax_layer': Hmax_layer,
-                'ez_layer': ez_layer
-            })
-    
-            sigma_v_eff = f_sigma_v_eff(np.mean(z_vals))
-            alpha_av = float(f_alpha(np.mean(z_vals)))
-            
-            # Side shear To and Ti
-            To = PileSurface(dz_clip, D)*alpha_av*Su_av_z
-            Ti = PileSurface(dz_clip, D - 2*t)*alpha_av*Su_av_z
-            
-            # Tip resistance
-            if abs(z_bot_clip - (z0 + (L - z0))) < 1e-3:  # tip check
-                Tbase = (np.pi/12)*D**3*Su_bot
-            else:
-                Tbase = 0.0
-            
-            Tmax = min(To + Ti, To + Tbase)
-            
-            # Torque induced by horizontal load
-            T = Ha*rlug*np.sin(np.deg2rad(psilug))
-            
-            nhuT = T/Tmax 
-            nhuV = Ha/To 
-            nhuVstar = np.sqrt(nhuV**2 - nhuT**2) 
-            alphastar = alpha_av*(nhuVstar/nhuV); print(f"alphastar   = {alphastar:.3f}")
-            
-            # Constant weight
-            Pile_Head = PileWeight(z0, D, t, rhows)
-            
-            # Vertical failure modes
-            if np.isclose(z_bot_clip, z0 + (L - z0), atol=0.1):
-                Vmax1 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + Nc*Su_bot*(np.pi/4)*D**2
-            else:
-                Vmax1 = np.inf  # No tip resistance unless at tip
-            
-            Vmax2 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + PileSurface(dz_clip, D - 2*t)*alphastar*Su_av_z
-            Vmax3 = PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*alphastar*Su_av_z + SoilWeight(dz_clip, D, t, gamma_av)
-            
-            Vmax_layer = min(Vmax1, Vmax2, Vmax3)
-            
-            # Sum vertical capacities
-            Vmax_final += Vmax_layer
-            
-            # Print layer debug info
-            print(f"Vmax_layer    = {Vmax_layer:.2f} N")
-            print(f"Vmax1   = {Vmax1:.2f} N")
-            print(f"Vmax2   = {Vmax2:.2f} N")
-            print(f"Vmax3   = {Vmax3:.2f} N")
-            
-        elif soil_type == 'sand':
-            # Prepare soil profile for sand
-            profile = [
-                [z_top, layer['gamma_top'], layer['phi_top'], layer['Dr_top']],
-                [z_bot, layer['gamma_bot'], layer['phi_bot'], layer['Dr_bot']]
-            ]
-        
-            z_ref, f_gamma, f_phi, _, f_sigma_v_eff, f_delta = sand_profile(profile)
-        
-            # Clip the layer within pile embedded length
-            z_top_clip = max(z_top, z0)
-            z_bot_clip = min(z_bot, z0 + (L - z0))
-            dz_clip = z_bot_clip - z_top_clip
-        
-            if dz_clip <= 0:
-                continue  # Skip non-overlapping layers
-        
-            # Calculate properties over clipped dz
-            z_vals = np.linspace(z_top_clip, z_bot_clip, npts)
-            phi_vals = f_phi(z_vals)
-            sigma_vals = f_sigma_v_eff(z_vals)
-            delta_vals = f_delta(z_vals)
-        
-            phi_av = np.mean(phi_vals)
-            sigma_av = np.mean(sigma_vals)
-            delta_av = np.mean(delta_vals)
-        
-            sigma_tip = f_sigma_v_eff(z_bot_clip)
-            
-            Nq = np.e**(np.pi*np.tan(np.radians(phi_av)))*(np.tan(np.radians(45) + np.radians(phi_av)/2))**2
-        
-            # Calculate Hmax for sand
-            Hmax_layer = 0.5*Nq*D*gamma_av*dz_clip**2
-        
-            layer_data.append({
-                'z_top': z_top_clip,
-                'z_bot': z_bot_clip,
-                'dz': dz_clip,
-                'Hmax_layer': Hmax_layer,
-                'ez_layer': np.mean(z_vals)
-            })
-        
-            # Side friction
-            To = PileSurface(dz_clip, D)*delta_av*sigma_av
-            Ti = PileSurface(dz_clip, D - 2*t)*delta_av*sigma_av
-        
-            if abs(z_bot_clip - (z0 + (L - z0))) < 1e-3:
-                Tbase = np.pi/4*D**2*sigma_tip
-            else:
-                Tbase = 0.0
-        
-            Tmax = min(To + Ti, To + Tbase)
-        
-            # Torque induced by horizontal load
-            T = Ha*rlug*np.sin(np.deg2rad(psilug))
-            nhuT = T/Tmax 
-            nhuV = Ha/To 
-            nhuVstar = np.sqrt(nhuV**2 - nhuT**2) 
-            deltastar = delta_av*(nhuVstar/nhuV) 
-               
-            # Vertical failure modes
-            Vmax2 = Pile_Head + PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*deltastar*sigma_av + PileSurface(dz_clip, D - 2*t)*deltastar*sigma_av
-            Vmax3 = Pile_Head + PileWeight(dz_clip, D, t, rhows) + PileSurface(dz_clip, D)*deltastar*sigma_av + SoilWeight(dz_clip, D, t, gamma_av)
-        
-            Vmax_layer = min(Vmax2, Vmax3)
-            
-            # Sum vertical capacities
-            Vmax_final += Vmax_layer
-        
-            print(f"Vmax_layer (sand) = {Vmax_layer:.2f} N")
-            print(f"Vmax2 (sand) = {Vmax2:.2f} N")
-            print(f"Vmax3 (sand) = {Vmax3:.2f} N")
-
-    # sum Hmax and weighted ez
-    for data in layer_data:
-        z_top = data['z_top']
-        z_bot = data['z_bot']
-        Hmax_layer = data['Hmax_layer']
-        ez_layer = data['ez_layer']
-        dz_layer = data['dz']
-
-        z_embedded_start = z0
-        z_embedded_end = z0 + (L - z0)
-
-        if z_top >= z_embedded_start and z_bot <= z_embedded_end:
-            sum_ez_weighted += Hmax_layer*ez_layer
-            sum_Hmax += Hmax_layer
-            # print(f'ez_layer (full) = {ez_layer:.2f} m')
-
-        elif z_top < z_embedded_end and z_bot > z_embedded_start:
-            dz_inside = min(z_bot, z_embedded_end) - max(z_top, z_embedded_start)
-            if dz_inside > 0:
-                ratio = dz_inside/dz_layer
-                sum_ez_weighted += Hmax_layer*ratio*ez_layer
-                sum_Hmax += Hmax_layer * ratio
-                # print(f'ez_layer (partial) = {ez_layer:.2f} m')
-
-    ez_global = sum_ez_weighted/sum_Hmax 
-    # print(f'sum_ez_weighted = {sum_ez_weighted:.2f}')
-    print(f'ez_global = {ez_global:.2f} m')
-    print(f'Hmax  = {sum_Hmax:.2f} m')
-
-    # Calculate moment and Hmax_final
-    M_total = -Va*rlugTilt(rlug, zlug, thetalug) - Ha*(zlugTilt(rlug, zlug, thetalug) - ez_global)
-    # print(f"rlug_eff = {rlugTilt(rlug, zlug, thetalug):.2f} m")
-    # print(f"zlug_eff = {zlugTilt(rlug, zlug, thetalug):.2f} m")
-    print(f"M_total = {M_total:.2f} Nm")
-    
-    # ΔφMH from Kay 2014
-    if 0.5 <= lambdap < 1.125:
-        delta_phi = 0.32 + 4.32*lambdap; #print(delta_phi)
-    elif 1.125 <= lambdap < 2.0:
-        delta_phi = 7.13 - 1.71*lambdap; #print(delta_phi)
-    elif 2.0 <= lambdap <= 6.0:
-        delta_phi = 4.55 - 0.425*lambdap; #print(delta_phi)
-    else:
-        raise ValueError('L/D out of bounds for MH ellipse.')
-
-    phi_MH = -np.arctan(ez_global/(L - z0)) - np.deg2rad(delta_phi)
-    a_MH = Np_fixed/np.cos(phi_MH)
-    delta_bMH = 0.45*(lambdap)**(-0.9) if lambdap <= 1.5 else 0
-    b_MH = -Np_free*np.sin(phi_MH) + delta_bMH
-    print('M cos(phi)/a_MH =', (M_total*np.cos(phi_MH))/a_MH)
-    print('M sin(phi)/b_MH =', (M_total*np.sin(phi_MH))/b_MH)
-
-    def f(H_var):
-        term1 = ((M_total*np.cos(phi_MH) + H_var*np.sin(phi_MH))/a_MH)**2
-        term2 = ((M_total*np.sin(phi_MH) - H_var*np.cos(phi_MH))/b_MH)**2
-        return term1 + term2 - 1
-
-    try:
-        Hmax_final = max(fsolve(f, sum_Hmax*0.8)[0], 0.0)
-    except:
-        Hmax_final = 0.0
-
-    print(f"Hmax_final (MH ellipse) = {Hmax_final:.2f} N")
-    
-    # Constant weight
-    pile_head = PileWeight(z0, D, t, rhows); print(f"pile_head    = {pile_head:.2f} N")
-    Vmax_final += pile_head; print(f"Vmax_final    = {Vmax_final:.2f} N")
-    
-    # Unity check
-    UC = (Ha/Hmax_final)**(0.5 + lambdap) + (Va/Vmax_final)**(4.5 + lambdap/3) if Hmax_final and sum_Hmax else np.inf
-
-    # Plotting
-    if plot:
-        x = np.linspace(0, 1, 100)
-        y = (1 - x**(4.5 + lambdap/3))**(1/(0.5 + lambdap))
-        
-        plt.figure(figsize=(6, 5))
-        plt.plot(Hmax_final*x, Vmax_final*y, 'b', label='VH Envelope')
-        plt.plot(Ha, Va, 'ro', label='Applied Load')
-        plt.xlabel('Horizontal Capacity (N)')
-        plt.ylabel('Vertical Capacity (N)')
-        plt.title('VH suction pile capacity envelope')
-        plt.grid(True)
-        plt.legend()
-        plt.tight_layout()
-        plt.show()
-
-    resultsSuction = {
-        'Horizontal max.': Hmax_final,
-        'Vertical max.': Vmax_final,
-        'Unity check': UC,
-        # 'Weight pile': Wp,
-        # 'Weight soil': Wsoil,
-        't': t
-    }
-
-    return layers, resultsSuction
-
-if __name__ == '__main__':
-
-    profile_map = [
-    {
-        'name': 'CPT_1',
-        'x': 498234, 'y': 5725141,
-        'layers': [
-            {
-                'top': 2.0, 'bottom': 4.0,
-                'soil_type': 'clay',
-                'gamma_top': 8.0, 'gamma_bot': 8.5,
-                'Su_top': 25, 'Su_bot': 50},
-            {
-                'top': 4.0, 'bottom': 8.0,
-                'soil_type': 'clay',
-                'gamma_top': 8.5, 'gamma_bot': 9.0,
-                'Su_top': 50, 'Su_bot': 75},
-            {
-                'top': 8.0, 'bottom': 16.0,
-                'soil_type': 'clay',
-                'gamma_top': 9.0, 'gamma_bot': 9.5,
-                'Su_top': 75, 'Su_bot': 100},
-            {
-                'top': 16.0, 'bottom': 25.0,
-                'soil_type': 'clay',
-                'gamma_top': 9.5, 'gamma_bot': 9.5,
-                'Su_top': 100, 'Su_bot': 100}]
-        }
-    ]
-
-
-    # Pile and load properties
-    D = 2.5                           # Pile diameter (m)
-    L = 10.0                          # Pile length (m)
-    zlug = 8.0                        # Lug depth (m)
-    theta = 5                         # Tilt misalignment angle (deg)
-    psi = 7.5                         # Twist misalignment angle (deg)
-    Ha = 6e6                          # Applied horizontal load (N)
-    Va = 2e6                          # Applied vertical load (N)
-
-    # Calculate
-    layers, resultsSuction = getCapacitySuction(
-        profile_map, 'CPT_1',         # Soil properties and location of the pile
-        D, L, zlug,                   # Pile geometrical properties
-        Ha, Va,                       # Pile loading conditions   
-        thetalug=theta, psilug=psi,   # Pile misaligment tolerances 
-        plot=True
-    )
-
-    # print('\n--- Suction Pile Capacity Results ---')
-    # print(f"Hmax_final = {resultsSuction['Hmax_final']:.2f} N")
-    # print(f"Vmax_final = {resultsSuction['Vmax_final']:.2f} N")
-    # print(f"Unity check (UC) = {resultsSuction['UnityCheck']:.4f}")
-    # print(f"Total Moment (M_total) = {resultsSuction['M_total']:.2f} Nm")
-
-    plot_suction(layers, L, D, z0 = layers[0]['top'], zlug=zlug, title='Suction Pile and Soil Layers')
diff --git a/famodel/anchors/anchors_famodel_map/capacity_torpedo_map.py b/famodel/anchors/anchors_famodel_map/capacity_torpedo_map.py
deleted file mode 100644
index adf5580f..00000000
--- a/famodel/anchors/anchors_famodel_map/capacity_torpedo_map.py
+++ /dev/null
@@ -1,272 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from .capacity_soils_map import clay_profile
-from .capacity_plots_map import plot_torpedo
-
-def getCapacityTorpedo(profile_map, location_name, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot=True):
-    '''Calculate the inclined load capacity of a torpedo pile in clay following S. Kay methodology.
-    The calculation is based on the soil profile, anchor geometry and inclined load.  
-
-    Parameters
-    ----------
-    profile : array
-        Clay soil profile (z, Su, gamma)
-            Clay soil profile (z (m), Su (kPa), gamma (kN/m³))
-    soil_type : string
-        Select soil condition, 'clay' 
-    D1 : float 
-        Wing diameter (m)
-    D2 : float    
-        Shaft diameter (m)
-    L1 : float 
-        Winged section length (m)
-    L2 : float 
-        Shaft section length (m)
-    zlug : float 
-        Padeye embedment depth (m)       
-    Ha : float       
-        Horizontal load at pile lug elevation (N)
-    Va : float          
-        Vertical load at pile lug elevation (N)
-    plot : bool
-        Plot the capacity envelope if True
-
-    Returns
-    -------
-    Dictionary with capcity, weigth and UC.
-    '''
-
-    # Retrieve soil layers from map
-    profile_entry = next(p for p in profile_map if p['name'] == location_name)
-    layers = profile_entry['layers']
-
-    L = L1 + L2
-    t = (6.35 + D2*20)/1e3
-    rhows = 66.90e3
-    rhow = 10e3
-
-    def PileWeight(Len1, Len2, Dia1, Dia2, tw, rho):
-        return ((np.pi/4)*(Dia1**2 - (Dia1 - 2*tw)**2)*(Len1 + Len2) + 4*Len2*Dia2*tw)*rho
-
-    def PileWingedSurface(length, diameter):
-        return np.pi*diameter*length
-
-    def PileShaftSurface(length, diameter1, diameter2):
-        return 8*length*(diameter1 - diameter2)
-
-    z_start = zlug
-    z_wing = zlug + L1
-    z_end = zlug + L
-
-    layer_data = []
-    Vmax_total = 0.0
-    
-    # Profile check points
-    npts = 10
-
-    for layer in layers:
-        if layer['soil_type'] != 'clay':
-            raise ValueError('Torpedo pile capacity model only supports clay soils.')
-
-        z_layer_top = layer['top']
-        z_layer_bot = layer['bottom']
-
-        z_clip_top = max(z_layer_top, z_start)
-        z_clip_bot = min(z_layer_bot, z_end)
-
-        if z_clip_bot <= z_clip_top:
-            continue
-
-        segments = []
-        if z_clip_bot <= z_wing:
-            segments.append((z_clip_top, z_clip_bot, D1))
-        elif z_clip_top >= z_wing:
-            segments.append((z_clip_top, z_clip_bot, D2))
-        else:
-            segments.append((z_clip_top, z_wing, D1))
-            segments.append((z_wing, z_clip_bot, D2))
-
-        for z_seg_top, z_seg_bot, D in segments:
-            dz_seg = z_seg_bot - z_seg_top
-            if dz_seg <= 0:
-                continue
-
-            profile = [
-                [z_seg_top, layer['Su_top'], layer['gamma_top']],
-                [z_seg_bot, layer['Su_bot'], layer['gamma_bot']]
-            ]
-            z_ref, f_Su, _, f_gamma, f_alpha = clay_profile(profile)
-
-            z_vals = np.linspace(z_seg_top, z_seg_bot, npts)
-            Su_vals = f_Su(z_vals)
-            alpha_vals = np.array([f_alpha(z) for z in z_vals])
-
-            Su_total = np.trapz(Su_vals, z_vals)
-            Su_moment = np.trapz(z_vals*Su_vals, z_vals)
-            print("xxxxxxxxxxxxxxxxxxxxxxxxx")
-            Su_av_z = Su_total/dz_seg
-            print(f"Su_av_z = {Su_av_z:.2f} Pa")
-            ez_layer = Su_moment /Su_total
-            print(f"dz_seg = {dz_seg:.2f} m")
-            print(f"ez_layer = {ez_layer:.2f} m")
-            alpha_av = np.mean(alpha_vals)
-            print(f"alpha_av = {alpha_av:.2f}")
-
-            Np_free = 3.45
-            Hmax_layer = Np_free*dz_seg*D*Su_av_z
-            print(f"Hmax_layer = {Hmax_layer:.2f} N")
-            print(f"D = {D:.2f} m")
-
-            surface_area = PileWingedSurface(dz_seg, D) if D == D1 else PileShaftSurface(dz_seg, D1, D2)
-            Vmax_layer = surface_area*alpha_av*Su_av_z
-            Vmax_total += Vmax_layer
-            print(f"Vmax_layer = {Vmax_layer:.2f} N")
-            
-            layer_data.append({
-                'z_top': z_seg_top,
-                'z_bot': z_seg_bot,
-                'dz': dz_seg,
-                'Hmax_layer': Hmax_layer,
-                'ez_layer': ez_layer,
-                'Su_av_z': Su_av_z,
-                'D_used': D
-            })
-
-    if not layer_data:
-        raise ValueError('No overlapping clay layers within pile depth.')
-
-    sum_Hmax = 0.0
-    sum_ez_weighted = 0.0
-
-    for data in layer_data:
-        z_top = data['z_top']
-        z_bot = data['z_bot']
-        Hmax_layer = data['Hmax_layer']
-        ez_layer = data['ez_layer']
-        dz_layer = data['dz']
-
-        z_embedded_start = zlug
-        z_embedded_end = zlug + L
-
-        if z_top >= z_embedded_start and z_bot <= z_embedded_end:
-            sum_ez_weighted += Hmax_layer*ez_layer
-            sum_Hmax += Hmax_layer
-        elif z_top < z_embedded_end and z_bot > z_embedded_start:
-            dz_inside = min(z_bot, z_embedded_end) - max(z_top, z_embedded_start)
-            if dz_inside > 0:
-                ratio = dz_inside/dz_layer
-                sum_ez_weighted += Hmax_layer*ratio*ez_layer
-                sum_Hmax += Hmax_layer * ratio
-
-    ez_global = sum_ez_weighted/sum_Hmax
-    print(f'ez_global = {ez_global:.2f} m')    
-    print(f'sum_Hmax = {sum_Hmax:.2f} N')
-
-    Vmax_total += PileWeight(L1, L2, D1, D2, t, rhows) + ballast
-    Wp = 1.10 * PileWeight(L1, L2, D1, D2, t, rhows + rhow) + ballast
-
-    ez_ratio = (ez_global - zlug)/L; print(f"ez_ratio = {ez_ratio:.2f} m")
-    
-    # Average effective width
-    L = L1 + L2
-    A_wing_plane_1 = (D1 - D2)*L1
-    A_wing_plane_2 = (D1 - D2)*np.cos(np.deg2rad(45))/2*L1
-    A_shaft = D2*L
-    
-    # Choose based on direction:
-    plane = '1'  # or '2'
-    
-    if plane == '1':
-        Dstar = (A_wing_plane_1 + A_shaft)/L
-    elif plane == '2':
-        Dstar = (A_wing_plane_2 + A_shaft)/L
-
-    # Assign aVH and bVH based on ez_su/L
-    if np.isclose(ez_ratio, 2/3, atol=0.05):
-        aVH = 0.5 + L/Dstar
-        bVH = 4.5 - L/(3*Dstar)
-        mode = 'deep mobilization (2/3)'
-    elif 0.40 <= ez_ratio <= 0.75:
-        aVH = 4.5 + L/(2*Dstar)
-        bVH = 3.5 - L/(4*Dstar)
-        mode = 'moderate mobilization (1/2 – 3/4)'
-    # else:
-    #     aVH = 4.0
-    #     bVH = 4.0
-    #     mode = 'default exponents (fallback)'
-    print(f'Interaction exponents set to aVH = {aVH:.2f}, bVH = {bVH:.2f} [{mode}]')
-    
-    UC = (Ha/sum_Hmax)**aVH + (Va/Vmax_total)**bVH
-
-    if plot:
-        deg = np.linspace(0, 90, 20)
-        x = np.cos(np.deg2rad(deg))
-        y = (1 - x**bVH)**(1/aVH)
-        X = sum_Hmax*x
-        Y = Vmax_total*y
-
-        plt.figure(figsize=(6, 5))
-        plt.plot(X, Y, color='blue', label='VH Envelope')
-        plt.plot(Ha, Va, 'o', color='red', label='Load Point')
-        plt.xlabel('Horizontal Capacity (N)')
-        plt.ylabel('Vertical Capacity (N)')
-        plt.title('VH torpedo pile capacity envelope')
-        plt.grid(True)
-        plt.legend()
-        plt.tight_layout()
-        plt.show()
-
-    resultsTorpedo = {
-        'Horizontal max.': sum_Hmax,
-        'Vertical max.': Vmax_total,
-        'Unity check': UC,
-        'Weight pile': Wp,
-        'ez_global': ez_global,
-        'layer_data': layer_data
-    }
-
-    return layers, resultsTorpedo
-
-if __name__ == '__main__':
-
-    profile_map = [
-        {
-            'name': 'CPT_1',
-            'x': 498234, 'y': 5725141,
-            'layers': [
-                {
-                    'top': 0.0, 'bottom': 20.0,
-                    'soil_type': 'clay',
-                    'gamma_top': 8.0, 'gamma_bot': 8.5,
-                    'Su_top': 50, 'Su_bot': 70},
-                {
-                    'top': 20.0, 'bottom': 25.0,
-                    'soil_type': 'clay',
-                    'gamma_top': 8.5, 'gamma_bot': 8.5,
-                    'Su_top': 80, 'Su_bot': 100},
-                {
-                    'top': 25.0, 'bottom': 50.0,
-                    'soil_type': 'clay',
-                    'gamma_top': 8.5, 'gamma_bot': 9.0,
-                    'Su_top': 125, 'Su_bot': 150}]
-        }
-    ]
-
-    D1 = 3.0
-    D2 = 1.5
-    L1 = 11.0
-    L2 = 5.0
-    zlug = 15.0
-    ballast = 10000
-    Ha = 6.0e6
-    Va = 8.0e6
-
-    layers, results = getCapacityTorpedo(profile_map, 'CPT_1', D1, D2, L1, L2, zlug, ballast, Ha, Va)
-
-    # print("\n--- Torpedo Pile Capacity Results ---")
-    # for key, val in results.items():
-    #     if key != 'layer_data':
-    #         print(f"{key}: {val:.2f}")
-            
-    plot_torpedo(layers, D1, D2, L1, L2, z0 = layers[0]['top'], zlug=zlug, title='Torpedo Pile in Clay Profile')
diff --git a/famodel/anchors/anchors_famodel_profile/capacity_dandg.py b/famodel/anchors/anchors_famodel_profile/capacity_dandg.py
deleted file mode 100644
index 1970a91c..00000000
--- a/famodel/anchors/anchors_famodel_profile/capacity_dandg.py
+++ /dev/null
@@ -1,272 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.interpolate import interp1d
-from scipy import linalg
-from .capacity_soils import rock_profile
-from .capacity_pycurves import py_Lovera
-from .capacity_plots import plot_pile
-
-def getCapacityDandG(profile, soil_type, L, D, zlug, Ha, Va, plot=True):
-    '''Models a laterally loaded pile using the p-y method. The solution for
-    lateral displacements is obtained by solving the 4th order ODE, 
-    EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method.
-    EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method.
-
-    Assumes that EI remains constant with respect to curvature i.e. pile
-    material remains in the elastic region.
-
-    Parameters
-    ----------
-    profile : array
-        Rock profile as a 2D array: (z (m), UCS (MPa), Em (MPa))
-    soil_type : string
-        Select soil condition, 'rock'
-    L : float 
-        Pile length (m)
-    D : float 
-        Pile diameter (m)
-    zlug : float
-        Load eccentricity above the mudline or depth to mudline relative to the pile head (m)
-    Ha : float       
-        Horizontal load at pile lug elevation (N)
-    Va : float          
-        Vertical load at pile lug elevation (N)
-    plot : bool
-        Plot the p-y curve and the deflection pile condition if True
-
-    Returns
-    -------
-    y : array
-        Lateral displacement at each node (n+1 real + 4 imaginary)
-    z : array
-        Node location along pile (m)
-    resultsDandG : dict
-        Dictionary with lateral, rotational, vertical and pile weight results
-    '''
-
-    n = 50; loc = 2              # Number of nodes (-)
-    tol = 1e-16; max_iter = 50   # Iteration parameters (-)
-    nhuc = 1; nhu = 0.3          # Resistance factor (-)
-    delta_r = 0.08               # Mean roughness height (m)
-    
-    t = (6.35 + D*20)/1e3        # Pile wall thickness (m), API RP2A-WSD
-    E = 200e9                    # Elastic modulus of pile material (Pa)
-    rhows = 66.90e3              # Submerged steel specific weight (N/m3)
-    rhow = 10e3                  # Water specific weight (N/m3) 
-    
-    # Pile geometry
-    I = (np.pi/64.0)*(D**4 - (D - 2*t)**4)
-    EI = E*I
-    h = L/n                      # Element size
-    N = (n + 1) + 4              # (n+1) Real + 4 Imaginary nodes
-    
-    # Dry and wet mass of the pile    
-    def PileWeight(Len, Dia, tw, rho):
-        Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho
-        return Wp 
-
-    # Array for displacements at nodes, including imaginary nodes.
-    y = np.ones(N)*(0.01*D)      # An initial value of 0.01D was arbitrarily chosen
-
-    # Initialize and assemble array/list of p-y curves at each real node
-    z = np.zeros(N)
-    k_secant = np.zeros(N)
-    py_funs = []
-    DQ = []
-    z0, f_UCS, f_Em = rock_profile(profile)
-    
-
-    for i in [0, 1]:              # Top two imaginary nodes
-        z[i] = (i - 2)*h
-        py_funs.append(0)
-        k_secant[i] = 0.0
-   
-    for i in range(2, n+3):       # Real nodes
-        z[i] = (i - 2)*h       
-        if z[i] < z0:
-             # No p-y curve above mudline
-             py_funs.append(lambda y_val: np.zeros_like(y_val))
-             k_secant[i] = 0.0
-             DQ.append(0.0)
-        else:
-             py_funs.append(py_Lovera(z[i], D, f_UCS, f_Em, zlug, z0, plot=True)) 
-             # print(f"z = {z[i]:.2f} m, UCS = {f_UCS(z[i]):.2e} Pa, Em = {f_Em(z[i]):.2e} Pa")
-             UCS = f_UCS(z[i])
-             Em = f_Em(z[i])
-             SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D
-             alpha = 0.36*SCR - 0.0005
-             fs = alpha*UCS
-             Dq = np.pi*D*fs*z[i]
-             DQ.append(Dq)
-             Vmax = PileWeight(L, D, t, rhows) + DQ[-1]
-             
-             k_secant[i] = py_funs[i](y[i])/y[i]
-
-    for i in [n+3, n+4]:         # Bottom two imaginary nodes
-        z[i] = (i - 2)*h
-        py_funs.append(0)
-        k_secant[i] = 0.0
-   
-    for j in range(max_iter):
-        y_old = y.copy()
-        y = fd_solver(n, N, h, EI, Ha, Va, zlug, z0, k_secant)
-        
-        # Update stiffness
-        for i in range(2, n+3):
-            if callable(py_funs[i]):
-                k_secant[i] = py_funs[i](y[i])/y[i] if y[i] != 0 else 0.0
-    
-        # Check convergence
-        if np.linalg.norm(y - y_old, ord=2) < tol:
-            print(f'[Converged in {j+1} iterations]')
-            break
-    else:
-        print('[Warning: Solver did not converge]')
-
-    if plot:
-        y1 = np.linspace(-2.*D, 2.*D, 500)
-        plt.plot(y1, py_funs[loc](y1))
-        plt.xlabel('y (m)'), plt.ylabel('p (N/m)')
-        plt.grid(True)
-        
-        fig, ax = plt.subplots(figsize=(3, 5))
-        y0 = np.zeros_like(z[2:-2])
-        # Plot original vertical pile
-        ax.plot(y0, z[2:-2], 'k', label='Original pile axis')
-        # Plot deflected shape
-        ax.plot(y[2:-2], z[2:-2], 'r', label='Deflected shape')
-        # Padeye marker
-        ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)')
-        # Mudline marker 
-        ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)')
-        
-        ax.set_xlabel('Lateral displacement (m)')
-        ax.set_ylabel('Depth (m)')
-        ax.set_xlim([-0.1*D, 0.1*D])
-        ax.set_ylim([L + 5, -2])  # Downward is positive z 
-        ax.grid(ls='--')
-        ax.legend()  
-            
-    # Relevant index of nodes
-    zlug_index = int(zlug/h); print(zlug_index)
-    ymax_index = int(np.max(y)); print(ymax_index)    
-    
-    resultsDandG = {
-        'Weight pile': PileWeight(L, D, t, rhows + rhow),
-        'Vertical max.': Vmax,
-        'Lateral displacement': y[ymax_index],
-        'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h),
-        'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf,
-        'Unity check (horizontal)': 0.0,  # Placeholder; no Mp or Mi in current model
-        'Bending moment': None,
-        'Plastic moment': None,
-        'Plastic hinge': None,
-        'Hinge location': None,
-        'p-y model': 'Lovera (2023)',
-        }
-    
-    return y[2:-2], z[2:-2], resultsDandG
-
-def fd_solver(n, N, h, EI, Ha, Va, zlug, z0, k_secant):
-    '''Solves the finite difference equations from 'py_analysis_1'. This function should be run iteratively for
-    non-linear p-y curves by updating 'k_secant' using 'y'. A single iteration is sufficient if the p-y curves
-    are linear.
-
-    Parameters
-    ----------
-    n : int
-        Number of elements (-)
-    N : int
-        Total number of nodes (-)
-    h : float
-        Element size (m)
-    EI : float
-        Flexural rigidity of the pile (Nm²)
-    Ha : float
-        Horizontal load at padeye (N)
-    Va : float
-        Vertical load at padeye (N)
-    zlug : float
-        Padeye depth from pile head (m)
-    z0 : float
-        Mudline elevation from pile head (m)
-    k_secant : array
-        Secant stiffness at each node
-
-    Returns
-    -------
-    y : array
-        Lateral displacement at each node (n+1 real + 4 imaginary)
-    '''
-    
-    # Initialize and assemble matrix
-    X = np.zeros((N, N))
-
-    # (n+1) finite difference equations for (n+1) real nodes
-    for i in range(0,n+1):
-        X[i, i]   =  1.0
-        X[i, i+1] = -4.0 + Va*h**2/EI
-        X[i, i+2] =  6.0 - 2*Va*h**2/EI + k_secant[i+2]*h**4/EI
-        X[i, i+3] = -4.0 + Va*h**2/EI
-        X[i, i+4] =  1.0
-
-    # Curvature at pile head
-    X[n+1, 1] =  1.0
-    X[n+1, 2] = -2.0
-    X[n+1, 3] =  1.0
-
-    # Shear at pile head
-    X[n+2, 0] = -1.0
-    X[n+2, 1] =  2.0 - Va*h**2/EI
-    X[n+2, 2] =  0.0
-    X[n+2, 3] = -2.0 + Va*h**2/EI
-    X[n+2, 4] =  1.0
-
-    # Curvature at pile tip
-    X[n+3, -2] =  1.0
-    X[n+3, -3] = -2.0
-    X[n+3, -4] =  1.0
-
-    # Shear at pile tip
-    X[n+4, -1] =  1.0
-    X[n+4, -2] = -2.0 + Va*h**2/EI
-    X[n+4, -3] =  0.0
-    X[n+4, -4] =  2.0 - Va*h**2/EI
-    X[n+4, -5] = -1.0
-
-    # Initialize vector q
-    q = np.zeros(N)
-    
-    # Index of the node where the horizontal load is applied (padeye)
-    zlug_index = int(zlug/h)
-    q[zlug_index] = 2*Ha*h**3
-
-    # Solve for displacement
-    y = linalg.solve(EI*X, q)
-
-    return y
-   
-if __name__ == '__main__':
-    
-    profile_rock = np.array([
-        [ 2.0, 2, 200],
-        [ 5.0, 2, 200],
-        [ 9.0, 2, 200],
-        [30.0, 2, 200]
-    ])
-    
-    D = 3.0           # Diameter (m)
-    L = 10.0          # Length (m)
-    zlug = 1          # Padeye elevation (m)
-    Ha = 8.0e6        # Horizontal load (N)
-    Va = 3.0e6        # Vertical load (N)
-    
-    y, z, resultsDandG = getCapacityDandG(profile_rock, 'rock', L, D, zlug, Ha, Va, plot=True)
-    for key, val in resultsDandG.items():
-        if isinstance(val, float):
-            print(f"{key}: {val:.3f}")
-        else:
-            print(f"{key}: {val}")
-    
-    plot_pile(profile_rock, 'rock', y, z, D, L, profile_rock[0, 0], zlug)
diff --git a/famodel/anchors/anchors_famodel_profile/capacity_driven.py b/famodel/anchors/anchors_famodel_profile/capacity_driven.py
deleted file mode 100644
index 485f7e8c..00000000
--- a/famodel/anchors/anchors_famodel_profile/capacity_driven.py
+++ /dev/null
@@ -1,418 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.interpolate import interp1d
-from scipy import linalg
-from .capacity_soils import clay_profile, sand_profile, rock_profile
-from .capacity_pycurves import py_Matlock, py_API, py_Reese
-from .capacity_plots import plot_pile
-
-def getCapacityDriven(profile, soil_type, L, D, zlug, Ha, Va, plot=True):   
-    '''Models a laterally loaded pile using the p-y method. The solution for
-    lateral displacements is obtained by solving the 4th order ODE, EI*d4y/dz4
-    EI*d4y/dz4 - V*d2y/dz2 + ky = 0 using the finite difference method.
-    EI*d4y/dz4 - V*d2y/dz2 + K*z*dy/dz + ky = 0 using the finite difference method.
-    
-    Assumes that EI remains constant with respect to curvature i.e. pile
-    material remains in the elastic region.
-
-    Parameters
-    ----------
-    profile : array
-        Soil profile as a 2D array: (z, parameters)
-            Clay: (z (m), Su (kPa), gamma (kN/m³))
-            Sand: (z (m), phi (deg), gamma (kN/m³), Dr (%))
-            Rock: (z (m), UCS (MPa), Em (MPa))
-    soil_type : string
-        Select soil condition: 'clay', 'sand', or '(weak) rock'
-    L : float
-        Pile length (m)
-    D : float
-        Pile diameter (m)
-    zlug : float
-        Depth of padeye from pile head (m)
-    Ha : float
-        Horizontal load applied at padeye (N)
-    Va : float
-        Vertical load applied at padeye (N)
-    plot : bool
-        Plot the p-y curve and the deflection pile condition if True
-
-    Returns
-    -------
-    y : array
-        Lateral displacement at each node (real nodes only)
-    z : array
-        Node depth positions corresponding to y (m)
-    resultsDriven : dict
-        Dictionary containing displacements, moment capacity, hinge state and vertical capacity
-    '''
-
-    n = 50; iterations = 10; loc = 2
-    nhuc = 1; nhu = 0.3         # Resistance factor (-)
-    delta_r = 0.08              # Mean roughness height (m)
-    
-    t = (6.35 + D*20)/1e3       # Pile wall thickness (m), API RP2A-WSD
-    E = 200e9                   # Elastic modulus of pile material (Pa)
-    fy = 350e6                  # Yield strength of pile material (Pa)
-    rhows = 66.90e3             # Submerged steel specific weight (N/m3)
-    rhow = 10e3                 # Water specific weight (N/m3) 
-    
-    # Pile geometry
-    I = (np.pi/64.0)*(D**4 - (D - 2*t)**4)
-    EI = E*I
-    h  = L/n                    # Element size
-    N  = (n + 1) + 4            # (n+1) Real + 4 Imaginary nodes
-    
-    # Outer and inner surface of the pile skirt
-    def PileSurface(Len, Dia):
-        Sp = np.pi*Dia*Len
-        return Sp    
-    # Dry and wet mass of the pile    
-    def PileWeight(Len, Dia, tw, rho):
-        Wp = ((np.pi/4)*(Dia**2 - (Dia - 2*tw)**2)*Len)*rho 
-        return Wp 
-    # Mass of the soil plug      
-    def SoilWeight(Len, Dia, tw, gamma_soil): 
-        Wsoil =(np.pi/4)*(Dia - 2*tw)**2*Len*gamma_soil
-        return Wsoil
-
-    # Array for displacements at nodes, including imaginary nodes.
-    y = np.ones(N)*(0.01*D)     # An initial value of 0.01D was arbitrarily chosen
-
-    # Initialize and assemble array/list of p-y curves at each real node
-    z = np.zeros(N)
-    py_funs  = []; PileShaft =[]
-    k_secant = np.zeros(N)
-    DQ = []
-
-    for i in [0, 1]:            # Top two imaginary nodes
-        z[i] = (i - 2)*h
-        py_funs.append(0)
-        k_secant[i] = 0.0
-
-    for i in range(2, n+3):    # Real nodes
-        z[i] = (i - 2)*h      
-        if soil_type == 'clay':
-            z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile)
-            if z[i] < z0:
-                # No p-y curve above mudline
-                py_funs.append(lambda y_val: np.zeros_like(y_val))
-                k_secant[i] = 0.0
-                PileShaft.append(0.0)
-            else:
-                Su = f_Su(z[i])
-                sigma_v_eff = f_sigma_v_eff(z[i])
-                gamma = f_gamma(z[i])
-                alpha = f_alpha(z[i])
-                py_funs.append(py_Matlock(z[i], D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=z0, plot=plot))
-                Vo = np.pi*D*alpha*Su*z[i]**2
-                PileShaft.append(Vo)
-                k_val = py_funs[i](y[i])
-                k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0
-            
-        elif soil_type == 'sand':
-            z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile)
-            if z[i] < z0:
-                # No p-y curve above mudline
-                py_funs.append(lambda y_val: np.zeros_like(y_val))
-                k_secant[i] = 0.0
-                PileShaft.append(0.0)
-            else:
-                phi = f_phi(z[i])
-                sigma_v_eff = f_sigma_v_eff(z[i])
-                gamma = f_gamma(z[i])
-                delta = f_delta(z[i])
-                py_funs.append(py_API(z[i], D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=z0, plot=plot))
-                fs = delta * sigma_v_eff
-                Vo = np.pi*D*fs*z[i]
-                PileShaft.append(Vo)
-                k_val = py_funs[i](y[i])
-                k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0
-            
-        elif soil_type in ['rock', 'weak_rock']:
-            z0, f_UCS, f_Em = rock_profile(profile)            
-            if z[i] < z0:
-                # No p-y curve above mudline
-                py_funs.append(lambda y_val: np.zeros_like(y_val))
-                k_secant[i] = 0.0
-                DQ.append(0.0)
-            else:
-                UCS = f_UCS(z[i])
-                Em = f_Em(z[i])
-                py_funs.append(py_Reese(z[i], D, zlug, f_UCS, f_Em, z0=z0, plot=plot))
-                SCR = nhuc*Em/(UCS*(1 + nhu))*delta_r/D
-                alpha = 0.36*SCR - 0.0005
-                fs = alpha*UCS
-                Dq = np.pi*D*fs*z[i]
-                DQ.append(Dq)
-                k_val = py_funs[i](y[i])
-                k_secant[i] = k_val/y[i] if y[i] != 0 else 0.0
-       
-    for i in [n+3, n+4]:       # Bottom two imaginary nodes
-        z[i] = (i - 2)*h
-        py_funs.append(0)
-        k_secant[i] = 0.0
-
-    # Compute individual contributions to total vertical load
-    Wp    = PileWeight(L, D, t, rhows)                                           # Pile self-weight (wet)
-    Wsoil = SoilWeight(L, D, t, gamma) if soil_type in ['clay', 'sand'] else 0.0 # Soil plug only in soil profiles
-    Wshaft = PileShaft[-1] if soil_type in ['clay', 'sand'] else 0.0             # Shaft resistance for soils
-    Wtip   = DQ[-1] if soil_type in ['rock', 'weak_rock'] else 0.0               # Tip resistance for rock
-    
-    # Compute total vertical capacity
-    Vmax = Wp + Wsoil + Wshaft + Wtip
-    
-    for j in range(iterations):
-        y, Mi, Mp, hinge_formed, hinge_location = fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant)
-        for i in range(2, n+3):
-            if callable(py_funs[i]):
-                k_secant[i] = py_funs[i](y[i])/y[i]
-                
-    if plot:
-        y1 = np.linspace(-2.*D, 2.*D, 500)
-        plt.plot(y1, py_funs[loc](y1))
-        plt.xlabel('y (m)'), plt.ylabel('p (N/m)')
-        plt.grid(True)
-        
-        fig, ax = plt.subplots(figsize=(3, 5))
-        y0 = np.zeros_like(z[2:-2])
-        # Plot original vertical pile
-        ax.plot(y0, z[2:-2], 'k', label='Original pile axis')
-        # Plot deflected shape
-        ax.plot(y[2:-2], z[2:-2], 'r', label='Deflected shape')
-        # Padeye marker
-        ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)')
-        # Mudline marker 
-        ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)')
-        
-        ax.set_xlabel('Lateral displacement (m)')
-        ax.set_ylabel('Depth (m)')
-        ax.set_xlim([-0.1*D, 0.1*D])
-        ax.set_ylim([L + 5, -2])  # Downward is positive z 
-        ax.grid(ls='--')
-        ax.legend() 
-
-    # Relevant index of nodes
-    zlug_index = int(zlug/h); print(zlug_index)
-    ymax_index = int(np.max(y)); print(ymax_index)      
-
-    resultsDriven = {
-        'Weight pile': PileWeight(L, D, t, rhows + rhow),
-        'Vertical max.': Vmax,
-        'Horizontal max.': abs(Mi)/abs(zlug) if zlug != 0 else 1e-6,
-        'Unity check (vertical)': Va/Vmax if Vmax != 0 else np.inf,
-        'Unity check (horizontal)': Ha/(abs(Mi)/abs(zlug)) if zlug != 0 else np.inf,
-        'Lateral displacement': y[ymax_index],
-        'Rotational displacement': np.rad2deg(abs(y[ymax_index - 1] - y[ymax_index])/h),
-        'Bending moment': abs(Mi),
-        'Plastic moment': Mp,
-        'Plastic hinge': hinge_formed,
-        'Hinge location': hinge_location,
-        'p-y model': 'Matlock (1970)' if soil_type == 'clay' else 'API RP2A (1993)' if soil_type == 'sand' else 'Reese (1997)',
-        }
-
-    
-    return y[2:-2], z[2:-2], resultsDriven
-
-def fd_solver(n, N, h, D, t, fy, EI, Ha, Va, zlug, z0, k_secant):
-    '''Solves the finite difference equations from 'py_analysis_1'. This function should be run iteratively for
-    non-linear p-y curves by updating 'k_secant' using 'y'. A single iteration is sufficient if the p-y curves
-    are linear.
-
-    Parameters
-    ----------
-    n : int
-        Number of elements
-    N : int
-        Total number of nodes (real + imaginary)
-    h : float
-        Element size (m)
-    D : float
-        Pile diameter (m)
-    t : float
-        Pile wall thickness (m)
-    fy : float
-        Yield strength of pile material (Pa)
-    EI : float
-        Flexural rigidity of the pile (Nm²)
-    Ha : float       
-        Horizontal load at pile lug elevation (N)
-    Va : float          
-        Vertical load at pile lug elevation (N)
-    zlug : float
-        Depth of padeye from pile head (m)
-    z0 : float
-        Mudline depth from pile head (m)
-    k_secant : array
-        Secant stiffness from p-y curves at each node
-
-    Returns
-    -------
-    y : array
-        Lateral displacement at each node
-    Mi : float
-        Maximum internal bending moment (Nm)
-    Mp : float
-        Plastic moment capacity of the pile (Nm)
-    hinge_formed : bool
-        Whether plastic hinge is formed
-    hinge_location : int
-        Index of the node with hinge formation
-    '''
-     
-    # Initialize and assemble matrix
-    X = np.zeros((N, N))
-
-    # (n+1) finite difference equations for (n+1) real nodes
-    for i in range(0, n+1):
-        X[i, i]   =  1.0
-        X[i, i+1] = -4.0 + Va*h**2/EI
-        X[i, i+2] =  6.0 - 2*Va*h**2/EI + k_secant[i+2]*h**4/EI
-        X[i, i+3] = -4.0 + Va*h**2/EI
-        X[i, i+4] =  1.0
-
-    # Curvature at pile head
-    X[n+1, 1] =  1.0
-    X[n+1, 2] = -2.0
-    X[n+1, 3] =  1.0
-
-    # Shear at pile head
-    X[n+2, 0] = -1.0
-    X[n+2, 1] =  2.0 - Va*h**2/EI
-    X[n+2, 2] =  0.0
-    X[n+2, 3] = -2.0 + Va*h**2/EI
-    X[n+2, 4] =  1.0
-
-    # Curvature at pile tip
-    X[n+3, -2] =  1.0
-    X[n+3, -3] = -2.0
-    X[n+3, -4] =  1.0
-
-    # Shear at pile tip
-    X[n+4, -1] =  1.0
-    X[n+4, -2] = -2.0 + Va*h**2/EI
-    X[n+4, -3] =  0.0
-    X[n+4, -4] =  2.0 - Va*h**2/EI
-    X[n+4, -5] = -1.0
-
-    # Initialize vector q
-    q = np.zeros(N)
-    
-    # Always apply shear
-    # Index of the node where the horizontal load is applied (padeye)
-    zlug_index = int(zlug/h)
-    q[zlug_index] = 2*Ha*h**3
-                 
-    y = linalg.solve(EI*X, q)
-
-    # Compute the plastic moment capacity Mp
-    Zp = (1/6)*(D**3 - (D - 2*t)**3)  # Plastic section modulus for hollow pile (m3)
-    Mp = Zp*fy                        # Plastic moment capacity (N/m)
-
-    # Check for plastic hinge formation
-    Mi, Mp, hinge_formed, hinge_location = plastic_hinge(y, h, EI, Mp)
-    
-    return y, Mi, Mp, hinge_formed, hinge_location
-   
-def plastic_hinge(y, h, EI, Mp):
-    '''Check for plastic hinge formation along the pile.
-    
-    Parameters
-    ----------
-    y : array
-        Lateral displacements at each node
-    h : float
-        Element size (m)
-    EI : float
-        Flexural rigidity of the pile (Nm²)
-    Mp : float
-        Plastic moment capacity (Nm)
-
-    Returns
-    -------
-    Mi_max : float
-        Maximum internal moment along the pile (Nm)
-    Mp : float
-        Plastic moment capacity (Nm)
-    hinge_formed : bool
-        True if plastic hinge is formed
-    hinge_location : int
-        Node index where hinge forms (if any)
-    '''
-    
-    hinge_formed = False
-    hinge_location = -1
-    Mi_all = []
-        
-    # Loop through each internal node and compute the bending moment
-    for i in range(1, len(y) - 1):
-        Mi = EI * (y[i+1] - 2*y[i] + y[i-1])/h**2
-        Mi_all.append(Mi)
-
-        if abs(Mi) >= Mp and not hinge_formed:
-            hinge_formed = True
-            hinge_location = i
-
-    Mi_max = max(Mi_all, key=abs) if Mi_all else 0.0
-
-    return Mi_max, Mp, hinge_formed, hinge_location
-
-if __name__ == '__main__':
-    
-    profile_clay = np.array([
-        [ 1.0, 600, 8],     
-        [ 6.0, 200, 8],
-        [15.0, 400, 8],
-        [30.0, 600, 9]
-    ])
-    
-    profile_sand = np.array([
-        [ 2.0, 28,  8, 75],
-        [10.0, 34,  9, 75],
-        [15.0, 36, 10, 75],
-        [40.0, 45,  9, 85]
-    ])
-    
-    profile_rock = np.array([
-        [ 2.0, 0.5, 1e3],
-        [ 5.0, 2.0, 2e4],
-        [30.0, 1.0, 5e4]
-    ])
-
-    D = 2.5           # Diameter (m)
-    L = 25.0          # Length (m)
-    zlug = 1          # Padeye depth (m)
-    
-    # === CLAY ===
-    y_clay, z_clay, resultsDriven_clay = getCapacityDriven(profile_clay, 'clay', L, D, zlug, Ha=5.0e6,  Va=1.5e5, plot=True)
-    for key, val in resultsDriven_clay.items():
-        if isinstance(val, float):
-            print(f"{key}: {val:.3f}")
-        else:
-            print(f"{key}: {val}")
-
-    plot_pile(profile_clay, 'clay', y_clay, z_clay, D, L, profile_clay[0, 0], zlug, resultsDriven_clay.get('Hinge location'))
-
-    # # === SAND ===
-    # y_sand, z_sand, resultsDriven_sand = getCapacityDriven(profile_sand, 'sand', L, D, zlug, Ha=2.5e6, Va=2.0e6, plot=True)
-    # for key, val in resultsDriven_sand.items():
-    #     if isinstance(val, float):
-    #         print(f"{key}: {val:.3f}")
-    #     else:
-    #         print(f"{key}: {val}")
-
-    # plot_pile(profile_sand, 'sand',  y_sand, z_sand, D, L, profile_sand[0, 0], zlug, resultsDriven_sand.get('Hinge location'))
-
-    # # === ROCK ===
-    # y_rock, z_rock, resultsDriven_rock = getCapacityDriven(profile_rock, 'rock', L, D, zlug, Ha=3.5e6, Va=3.0e6, plot=True)
-    # for key, val in resultsDriven_rock.items():
-    #     if isinstance(val, float):
-    #         print(f"{key}: {val:.3f}")
-    #     else:
-    #         print(f"{key}: {val}")
-
-    # plot_pile(profile_rock, 'rock', y_rock, z_rock, D, L, profile_rock[0, 0], zlug, resultsDriven_rock.get('Hinge location'))
-
-
-
diff --git a/famodel/anchors/anchors_famodel_profile/capacity_helical.py b/famodel/anchors/anchors_famodel_profile/capacity_helical.py
deleted file mode 100644
index d425aea8..00000000
--- a/famodel/anchors/anchors_famodel_profile/capacity_helical.py
+++ /dev/null
@@ -1,152 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from .capacity_driven import getCapacityDriven, plot_pile
-from .capacity_soils import clay_profile, sand_profile
-from .capacity_plots import plot_helical
-
-def getCapacityHelical(profile, soil_type, D, L, d, zlug, Ha, Va, plot=True):
-    '''Calculate the vertical and horizontal capacity of a helical pile using a soil profile.
-    The calculation is based on the soil profile, anchor geometry and inclined load.
-
-    Parameters
-    ----------
-    profile : array
-        Soil profiles (z, parameters)
-            Clay: (z (m), Su (kPa), gamma (kN/m³))
-            Sand: (z (m), phi (deg), gamma (kN/m³), Dr (%))
-    soil_type : string
-        Select soil condition, 'clay' or 'sand'
-    D : float 
-        Helix diameter (m)
-    L : float 
-        Shaft length (m)
-    d : float 
-        Shaft diameter (m)
-    zlug : float
-        Depth to padeye (m)
-    Ha : float
-        Horizontal load at pile lug elevation (N)   
-    Va : float
-        Vertical load at pile lug elevation (N)
-    plot : bool
-        Plot the p-y curve and the deflection pile condition if True
-
-    Returns
-    -------
-    Dictionary with capacity, displacements, weight and UC.
-    '''
-
-    t = (6.35 + D*20)/1e3            # Helical pile wall thickness (m), API RP2A-WSD
-    rhows = 66.90e3                  # Submerged steel specific weight (kN/m3)
-    rhow = 10e3                      # Water specific weight (kN/m3) 
-
-    def PileWeight(Len, Dia1, Dia2, tw, rho):
-        return ((np.pi/4)*((Dia1**2 - (Dia1 - 2*tw)**2)*Len + (np.pi/4)*Dia2**2*tw))*rho
-
-    if soil_type == 'clay':
-        z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile)
-
-        z_helix = np.clip(zlug + (L - D), profile[0, 0], profile[-1, 0])
-        Su = f_Su(z_helix)
-        sigma_v_eff = max(f_sigma_v_eff(z_helix), 1.0)
-        psi_val = Su/sigma_v_eff
-        alpha = min(0.5*psi_val**-0.50, 1) if psi_val <= 1.0 else min(0.5 * psi_val**-0.25, 1)
-
-        Nc = min(6.0*(1 + 0.2*d/D), 9)
-        Qh = ((np.pi/4)*(D**2 - d**2)*Nc*Su + f_gamma(z_helix)*D)*0.75
-        Qs = np.pi*d*L*alpha*Su
-        Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs
-
-    elif soil_type == 'sand':
-        z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile)
-
-        z_helix = np.clip(zlug + (L - D), profile[0, 0], profile[-1, 0])
-        gamma = f_gamma(z_helix)
-        Dr = f_Dr(z_helix)
-        delta = f_delta(z_helix)
-        phi = f_phi(z_helix)
-
-        Nq = 0.5*(12*phi)**(phi/54)
-        Qh = (np.pi/4)*(D**2 - d**2)*Nq*gamma*z_helix
-        Qs = np.pi*d*L*delta*gamma*z_helix
-        Qu = PileWeight(L, D, d, t, rhows) + Qh + Qs
-        
-    # Pile weight (inc. auxilary items) assessed as a factor
-    Wp = 1.10*PileWeight(L, D, d, t, (rhows + rhow))    
-
-    # Unity Check based only on vertical capacity
-    UC_vertical = Va/Qu
-
-    # Compute horizontal capacity using p-y method
-    y, z, results_lateral = getCapacityDriven(profile, soil_type, L, d, zlug, Ha, Va, plot=True)
-    if soil_type == 'clay':
-        plot_pile(profile_clay, 'clay', y, z, D, L, z0, zlug, hinge_location=None)
-    elif soil_type == 'sand':
-        plot_pile(profile_sand, 'sand', y, z, D, L, z0, zlug, hinge_location=None)
-
-    Hcap = Ha if 'Plastic moment' not in results_lateral else results_lateral['Bending moment']/abs(zlug) if zlug != 0 else 1e-6
-    UC_horizontal = Ha/Hcap if Hcap != 0 else np.inf
-
-    resultsHelical = {
-        'Weight pile': Wp,
-        'Vertical max.': Qu,
-        'Horizontal max.': Hcap,
-        'Unity check (vertical)': UC_vertical,
-        'Unity check (horizontal)': UC_horizontal,
-        'Lateral displacement': results_lateral.get('Lateral displacement', None),
-        'Rotational displacement': results_lateral.get('Rotational displacement', None),
-        'Bending moment': results_lateral.get('Bending moment', None),
-        'Plastic moment': results_lateral.get('Plastic moment', None),
-        'Plastic hinge': results_lateral.get('Plastic hinge', None),
-        'Hinge location': results_lateral.get('Hinge location', None),
-        'p-y model': results_lateral.get('p-y model', None),
-        }
-
-    return resultsHelical
-
-if __name__ == '__main__':
-    
-    profile_clay = np.array([
-        [ 1.0, 10, 8.0],
-        [ 5.0, 15, 8.5],
-        [10.0, 25, 8.5],
-        [25.0, 50, 9.0]
-    ])
-
-    profile_sand = np.array([
-        [ 1.0, 28,  9.5, 40, 60],
-        [ 5.0, 34, 10.0, 42, 70],
-        [ 8.0, 38, 10.0, 44, 75],
-        [15.0, 38, 11.5, 45, 85]
-    ])  
-
-    D = 1.8        # Helix diameter (m)
-    L = 12.0       # Pile length (m)
-    d = 0.8        # Shaft diameter (m)
-    zlug = 2       # Padeye depth (m)
-    Va = 50e3      # Vertical load (N)
-    Ha = 30e3      # Horizontal load (N)
-
-    # === CLAY ===
-    # resultsHelical_clay = getCapacityHelical(profile_clay, 'clay', D, L, d, zlug, Ha, Va, plot=True)
-    # for key, val in resultsHelical_clay.items():
-    #     if isinstance(val, float):
-    #         print(f"{key}: {val:.3f}")
-    #     else:
-    #         print(f"{key}: {val}")
-    
-    # plot_helical(profile_clay, 'clay', D, L, d, zlug, n_helix=1, spacing=1.0, title='Helical Pile in Clay Profile')
-
-     # === SAND ===
-    resultsHelical_sand = getCapacityHelical(profile_sand, 'sand', D, L, d, zlug, Ha, Va, plot=True)
-    for key, val in resultsHelical_sand.items():
-        if isinstance(val, float):
-            print(f"{key}: {val:.3f}")
-        else:
-            print(f"{key}: {val}")
-        
-    plot_helical(profile_sand, 'sand', D, L, d, zlug, n_helix=1, spacing=1.0, title='Helical Pile in Sand Profile')
-    
-
-
diff --git a/famodel/anchors/anchors_famodel_profile/capacity_load.py b/famodel/anchors/anchors_famodel_profile/capacity_load.py
deleted file mode 100644
index 7c75ed1e..00000000
--- a/famodel/anchors/anchors_famodel_profile/capacity_load.py
+++ /dev/null
@@ -1,189 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from .capacity_soils import clay_profile, sand_profile
-from .capacity_plots import plot_load
-
-def getTransferLoad(profile, soil_type, Tm, thetam, zlug, line_type, d, w=None, plot=True):
-    '''Calculate the transfer load from mudline to main padeye using a layered soil profile.
-
-    Parameters
-    ----------
-    profile : array
-        Soil profile as a 2D array: (z, parameters)
-            Clay soil profile (z (m), Su (kPa), gamma (kN/m³))
-            Sand soil profile (z (m), phi (deg), gamma (kN/m³), Dr (%))
-    soil_type : string
-        Select soil condition, 'clay' or 'sand'
-    Tm : float
-        Mooring line load at mudlevel (N)
-    thetam : float
-        Mooring line angle at mudlevel (deg)
-    zlug : float
-        Embedment depth of the lug (m)
-    line_type : string
-        Select mooring line type, 'chain' or 'wire'
-    d : float
-        Mooring line diameter (m)
-    w : float
-        Mooring line unit weight (N/m)
-    plot : bool
-        Plot the inverse catenary mooring line profile if True
-
-    Returns
-    -------
-    dict
-        Dictionary with transferred load components and depth.
-    '''
-    
-    deltas = 0.2  # discretization step
-    
-    profile = np.array(profile)
-
-    # Line mechanical properties
-    if line_type == 'chain':
-        Et, En = 10, 2.5
-    elif line_type == 'wire':
-        Et, En = np.pi, 1
-    W = w*deltas
-
-    # Soil profile and interpolators
-    if soil_type == 'clay':
-        Nc = 8.5
-        z0, f_Su, _, f_gamma, f_alpha = clay_profile(profile)
-    elif soil_type == 'sand':
-        nhu = 0.5
-        z0, f_phi, _, f_gamma, _, f_delta = sand_profile(profile)
-
-    # Initial values
-    T = Tm
-    theta = np.deg2rad(thetam)
-    drag = 0
-    depth = z0 + 0.1
-
-    # Tracing lists
-    drag_values, depth_values = [], []
-
-    while (zlug - depth) >= 0:
-        if soil_type == 'clay':
-            Su = f_Su(depth)
-            alpha = f_alpha(depth)
-            dtheta = (En*d*Nc*Su - W*np.cos(theta))/T*deltas
-            dT = (Et*d*alpha*Su + W*np.sin(theta))*deltas
-        elif soil_type == 'sand':
-            gamma_z = f_gamma(depth)
-            delta_z = f_delta(depth)
-            phi = f_phi(depth)
-            Nq = np.exp(np.pi*np.tan(np.deg2rad(phi)))*(np.tan(np.deg2rad(45 + phi/2)))**2
-            print(f'Nq = {Nq:.2f}, depth = {depth:.2f} m')
-            dtheta = (En*d*Nq*gamma_z*depth - W*np.cos(theta))/T*deltas
-            dT = (Et*d*gamma_z*depth*np.tan(np.deg2rad(delta_z)) + W*np.sin(theta))*deltas
-
-        ddrag  = deltas*np.cos(theta)
-        ddepth = deltas*np.sin(theta)
-
-        theta += dtheta
-        T -= dT
-        drag += ddrag
-        depth += ddepth
-
-        if abs(Tm - T) > 0.75*Tm:
-            raise Exception(f"Load transfer unrealistic: Tm = {Tm/1e6:.2f} MN vs T = {T/1e6:.2f} MN")
-
-        if not (0 < np.rad2deg(theta) < 90):
-            raise Exception(f"Load angle unrealistic: {np.rad2deg(theta):.2f} deg")
-
-        drag_values.append(-drag)
-        depth_values.append(-depth)
-
-    Ta = T; thetaa = theta
-    # H = Ta*np.cos(thetaa)
-    # V = Ta*np.sin(thetaa)
-    
-    if plot:
-        plot_load(
-            profile, soil_type,
-            drag_values, depth_values,
-            Tm, thetam,
-            Ta, thetaa,
-            zlug
-        )
-
-    resultsLoads = {
-        'Tm': Tm,
-        'thetam': thetam,
-        'Ta': Ta,
-        'thetaa': thetaa,
-        'length': deltas*len(drag_values),
-        'drag_values': drag_values,
-        'depth_values': depth_values
-    }
-
-    return resultsLoads
-
-if __name__ == '__main__':
-    
-    # Define a clay profile: [depth (m), Su (kPa), gamma (kN/m3)]
-    # profile_clay = np.array([
-    # [ 0.0,   0, 0.0],  
-    # [ 2.0,  25, 8.0],
-    # [ 8.0,  50, 8.0],
-    # [16.0, 100, 8.0]
-    # ])
-    
-    # Define a sand profile: [depth (m), phi (deg), gamma (kN/m3), Dr (%)]
-    profile_sand = np.array([
-        [ 0.0, 30, 9.5, 70],
-        [ 5.0, 30, 9.5, 70],
-        [10.0, 30, 9.5, 70],
-        [15.0, 30, 9.5, 70]
-    ])
-    
-    # # Input parameters
-    # Tm = 1.2e7            # Load at mudline (N)
-    # thetam = 10           # Angle at mudline (deg)
-    # zlug = 8.3            # Padeye depth (m)
-    # line_type = 'chain'
-    # d = 0.16              # Chain diameter (m)
-    # w = 4093              # Line weight (N/m)
-    
-    # # Run transfer load calculation
-    # results = getTransferLoad(profile_clay, soil_type, Tm, thetam, zlug, line_type, d, w, plot=True)
-    # print("\n--- Transfer Load Results (Clay) ---")
-    # for key, val in results.items():
-    #     if isinstance(val, float):
-    #         print(f"{key}: {val:.3f}")
-    #     elif isinstance(val, list):
-    #         print(f"{key}:")
-    #         for v in val:
-    #             print(f"  {v:.3f}")
-    #     else:
-    #         print(f"{key}: {val}")
-        
-    # plot_load(profile_clay, soil_type, results['drag_values'], results['depth_values'], results['Tm'], results['thetam'], results['Ta'], results['thetaa'], zlug)
-      
-
-    # Input parameters
-    Tm = 8.2e6
-    thetam = 10
-    zlug = 8
-    line_type = 'chain'
-    d = 0.25
-    soil_type = 'sand'
-    w = 4093
-    
-    results = getTransferLoad(profile_sand, soil_type, Tm, thetam, zlug, line_type, d, w, plot=True)
-    
-    # print("\n--- Transfer Load Results (Sand) ---")
-    # for key, val in results.items():
-    #     if isinstance(val, float):
-    #         print(f"{key}: {val:.3f}")
-    #     elif isinstance(val, list):
-    #         print(f"{key}:")
-    #         for v in val:
-    #             print(f"  {v:.3f}")
-    #     else:
-    #         print(f"{key}: {val}")
-    
-    plot_load(profile_sand, soil_type, results['drag_values'], results['depth_values'], results['Tm'], results['thetam'], results['Ta'], results['thetaa'], zlug)
-
diff --git a/famodel/anchors/anchors_famodel_profile/capacity_plate.py b/famodel/anchors/anchors_famodel_profile/capacity_plate.py
deleted file mode 100644
index cb718fa2..00000000
--- a/famodel/anchors/anchors_famodel_profile/capacity_plate.py
+++ /dev/null
@@ -1,143 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from .capacity_soils import clay_profile
-from .capacity_plots import plot_plate
-
-def getCapacityPlate(profile, soil_type, B, L, zlug, beta, Ha, Va, plot=True):
-    '''Calculate the plate anchor capacity using a full clay soil profile and return capacity + UC.
-    The calculation is based on the soil profile, anchor geometry and inclined load.
-
-    Parameters
-    ----------
-    profile : ndarray
-        Clay soil profile as a 2D array: [[depth, Su, gamma], ...]
-    soil_type : str
-        Currently only 'clay' is supported.
-    B : float
-        Plate width (m)
-    L : float
-        Plate length (m)
-    zlug : float
-        Embedment depth of the main padeye (m)
-    beta : float
-        Inclination angle of the plate (deg)
-    Ha : float
-        Applied horizontal load (kN)
-    Va : float
-        Applied vertical load (kN)
-    plot : bool
-        Placeholder for future use.
-
-    Returns
-    -------
-    Dictionary with capacity, weight and UC.
-    '''
-    
-    Los = 0.05
-    B_t = 40
-    rhows = 66.90e3        # Submerged steel specific weight (kN/m3)
-    rhow = 10e3            # Water specific weight (kN/m3) 
-    
-    # Extract soil parameters from profile
-    z0, f_Su, _, f_gamma, _ = clay_profile(profile)
-
-    # Geometry
-    t = round(B/B_t, 2)
-    V_steel = round(B*L*t, 2)
-    zlug_B = zlug/B
-    
-    # Define 5 evaluation points along inclined width
-    N = 5
-    z_offsets = np.linspace(-0.5, 0.5, N)*B*np.sin(np.deg2rad(beta))
-    z_points = zlug + z_offsets; print(z_points)
-
-    # Evaluate Su and gamma at these points
-    Su_vals = [f_Su(z) for z in z_points]
-    gamma_10 = f_gamma(z_points[2]); print(gamma_10)
-    gamma_vals = [f_gamma(z) for z in z_points]; print("gamma_vals:", [f"{val:.2f}" for val in gamma_vals], "N/m3")
-    Su = np.mean(Su_vals); print(f"Su: {Su:.2f} Pa")
-    gamma = np.mean(gamma_vals); print(f"gamma: {gamma:.2f} N/m3")
-    
-    print("Profile being sent to clay_profile():")
-    for row in profile:
-        print(f"z = {row[0]:.2f} m, Su = {row[1]:.2f} kPa, gamma = {row[2]:.2f} kN/m³")
-
-    # Compute shear strength gradient k from linear fit
-    k = np.polyfit(z_points, Su_vals, 1)[0]
-    print(f"k: {k:.2f}")
-    
-    # Pile weight (inc. auxiliary elements) assessed as a factor
-    Wp = 1.35*V_steel*(rhows + rhow)
-
-    # Capacity factors
-    Nco_0_0  = 2.483*np.log(zlug_B) + 1.974
-    Nco_90_0 = 2.174*np.log(zlug_B) + 3.391
-    kBSh = k*B/Su
-    print(f"kBSh: {kBSh:.2f}")
-
-    f0  = np.where(zlug_B < 4, 1.77*(zlug_B**0.3) - 1.289, 0.192*zlug_B + 0.644)
-    f90 = np.where(zlug_B < 4, 0.68*(zlug_B**0.5) - 0.410, 0.153*zlug_B + 0.341)
-
-    S_kB_0  = 1 - f0*kBSh
-    S_kB_90 = 1 - f90*kBSh
-    Nco_0  = S_kB_0*Nco_0_0
-    Nco_90 = S_kB_90*Nco_90_0
-    Nco = Nco_0 + (Nco_90 - Nco_0)*(beta/90)**2
-
-    Nco_s_0_0  = np.where(2.90*zlug_B + 6.02 <= 11.59, 2.90*zlug_B + 6.02, 11.596)
-    Nco_s_90_0 = np.where(2.72*zlug_B + 4.02 <= 11.59, 2.72*zlug_B + 4.02, 11.596)
-
-    S_s_kB_0 = np.where(zlug_B <= 2, 1 + (0.8 - 0.3*zlug_B)*kBSh - (0.383*kBSh**1.36), 1)
-    f90s = np.where(zlug_B <= 3, 0.267*zlug_B, 0.6)
-    S_s_kB_90 = 1 - f90s*kBSh
-    Nco_s_0 = S_s_kB_0*Nco_s_0_0
-    Nco_s_90 = S_s_kB_90*Nco_s_90_0
-    Nco_s = Nco_s_90 + (Nco_s_0 - Nco_s_90)*((90 - beta)/90)**2
-
-    # Existing capacity factor and base pressure
-    Nc_final = max(Nco + (gamma*zlug)/Su, Nco_s) # anchor pullout capacity factor [kN]
-    print(f"Nc_star: {Nco + (gamma*zlug)/Su:.2f}")
-    print(f"Nc_star: {Nco_s:.2f}")
-    qu = Nc_final*Su                   # Bearing pressure capacity of the anchor plate
-    Tmax = round(qu*(1 - Los)*B*L, 2)  # Bearing tension force capacity of the anchor plate
-    Hmax = Tmax*np.cos(np.deg2rad(90 - beta))
-    Vmax = Tmax*np.sin(np.deg2rad(90 - beta))
-    
-    Ta = np.sqrt(Ha**2 + Va**2)
-    UC = Ta/Tmax
-
-    resultsPlate = {
-        'Capacity': Tmax,
-        'Horizontal max.': Hmax,
-        'Vertical max.': Vmax,
-        'Unity check': UC,
-        'Weight plate': Wp
-    }
-    
-    return resultsPlate
-
-if __name__ == '__main__':
-    
-    profile_clay = np.array([
-        [ 0.0, 10, 8.0],
-        [ 9.5, 25, 8.5],
-        [11.5, 45, 8.5],
-        [25.0, 50, 9.0]
-    ])
-    
-    B = 2.0                                 # Plate width (m)
-    L = 2.0                                 # Plate length (m)
-    zlug = 10.0                             # Padeye depth (m)
-    Ha = 350e3                              # Horizontal load (N)
-    Va = 400e3                              # Vertical load (N)
-    alpha = np.rad2deg(np.arctan2(Va, Ha))  # Load angle from horizontal (deg)
-    beta = 90 - alpha                       # Plate angle after keying (m)
-    
-    results = getCapacityPlate(profile_clay, 'clay', B, L, zlug, beta, Ha, Va)
-    print("\n--- Plate Anchor Capacity Results ---")
-    for key, val in results.items():
-        print(f"{key}: {val:.2f}")
-        
-    # plot_plate(profile_clay, 'clay', B, L, zlug, beta, title='Inclined Plate Anchor in Clay')
-    
diff --git a/famodel/anchors/anchors_famodel_profile/capacity_plots.py b/famodel/anchors/anchors_famodel_profile/capacity_plots.py
deleted file mode 100644
index fb096f3e..00000000
--- a/famodel/anchors/anchors_famodel_profile/capacity_plots.py
+++ /dev/null
@@ -1,435 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-def plot_pile(profile, soil_type, y, z, D, L, z0=None, zlug=None, hinge_location=None):
-    '''Plots the deflected shape of the pile alongside the original vertical line.
-
-    Parameters:
-    ----------
-    profile :
-        
-    soil_type : str
-        
-    y : np.array
-        Lateral displacements (m)
-    z : np.array
-        Depths from pile head (m)
-    D : float
-        Pile diameter (m)
-    L : float
-        Pile length (m)
-    z0 : float, optional
-        Depth of the mudline from the pile head (m)
-    zlug : float, optional
-        Depth of the padeye from the pile head (m)
-    hinge_location : int, optional
-        Node index where a plastic hinge formed (if any)
-    label : str
-        Label for deflected shape line
-    color : str
-        Line color for deflected shape
-    '''
-    fig, ax = plt.subplots(figsize=(3, 5))
-    
-    lambdap = L / D
-
-    # Adjust horizontal scale based on slenderness
-    if lambdap >= 4:
-        xmax = 5*D     # Slender (e.g., driven pile)
-    elif lambdap <= 2:
-        xmax = 2*D     # Stubby (e.g., drilled & grouted)
-    else:
-        xmax = 3*D     # Intermediate case
-
-    # Mudline marker 
-    if z0 is not None:
-        ax.axhline(z0, color='blue', linestyle='--', label=f'Mudline (z0 = {z0:.2f} m)')
-        #ax.fill_betweenx(z, -0.05 * D, 0.05 * D, where=(z >= z0), color='lightgray', alpha=0.3, label='Soil zone')
-
-    # Padeye marker (on right wall of pile)
-    if zlug is not None:
-        ax.plot(D/2, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)')
-
-    # Draw pile as rectangle (from head to tip)
-    pile = plt.Rectangle((-D/2, 0), D, L, edgecolor='k', facecolor='none', lw=2, label='Driven Pile')
-    ax.add_patch(pile)
-       
-    seen_labels = set()
-    # Plot soil layers as background fills
-    for i in range(len(profile) - 1):
-        z_top = profile[i][0]
-        z_bot = profile[i+1][0]
-        if soil_type == 'clay':
-            Su = profile[i][1]
-            color = plt.cm.Oranges(Su/np.max(profile[:, 1]))
-            label = f'Su = {Su:.0f} kPa'
-        elif soil_type == 'sand':
-            phi = profile[i][1]
-            gamma = profile[i][2]
-            color = plt.cm.YlOrBr(phi/np.max(profile[:, 1]))
-            label = f'ϕ = {phi:.0f}°, γ = {gamma:.1f} kN/m³'
-        elif soil_type == 'rock':
-            UCS = profile[i][1]
-            Em = profile[i][2]
-            color = plt.cm.Greys(UCS/np.max(profile[:, 1]))
-            label = f'UCS = {UCS:.2f} MPa, Em = {Em:.1f} MPa'
-            
-        # Only assign label if not already used
-        if label not in seen_labels:
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label)
-            seen_labels.add(label)
-        else:
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4)
-
-    ax.set_xlabel('Lateral displacement (m)')
-    ax.set_ylabel('Depth (m)')
-    ax.set_xlim(-xmax, xmax)
-    ax.set_ylim([L + 5, -2])  # Downward is positive z
-    ax.grid(ls='--')
-    ax.legend()
-    ax.set_title('Pile Deflection Profile')
-    plt.tight_layout()
-    plt.show()
-    
-    
-def plot_suction(profile, soil_type, L, D, zlug=None, title='Suction Pile and Soil Layers', ax=None):
-    '''Plot the soil profile and a suction pile geometry using array structure for profile.
-
-    Parameters:
-    ----------
-        profile : list or ndarray of lists (z, Su or phi or UCS, gamma)
-        soil_type : str
-            'clay', 'sand', or 'rock'
-        L : float
-            Embedded length (m)
-        D : float
-            Pile diameter (m)
-        zlug : float
-            Padeye depth (m, referenced to pile head = 0)
-        title : string
-            Plot title
-    '''
-    import numpy as np
-    import matplotlib.pyplot as plt
-
-    profile = np.array(profile)
-    if ax is None:
-        fig, ax = plt.subplots(figsize=(5, 5))
-    xmax = 2*D
-
-    # Split numerical values from profile
-    z_vals = [float(row[0]) for row in profile]
-    values = [float(row[1]) for row in profile]
-    
-    seen_labels = set()
-    # Plot soil layers as background fills
-    for i in range(len(z_vals) - 1):
-        z_top = z_vals[i]
-        z_bot = z_vals[i+1]
-        val = values[i]
-
-        if soil_type == 'clay':
-            color = plt.cm.Oranges(val / max(values))
-            label = f'Su = {val:.0f} kPa'
-        elif soil_type == 'sand':
-            color = plt.cm.YlOrBr(val / max(values))
-            label = f'ϕ = {val:.0f}°'
-
-        if label not in seen_labels:
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label)
-            seen_labels.add(label)
-        else:
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4)
-
-    # Draw pile geometry
-    x_left = -D/2; x_right = D/2
-    z_top = 0; z_bot = L
-    ax.plot([x_left,  x_left], [z_top, z_bot], color='k', lw=2.0, label='Suction Pile')
-    ax.plot([x_right, x_right], [z_top, z_bot], color='k', lw=2.0)
-    ax.plot([x_left, x_right], [z_top, z_top], color='k', lw=2.0)
-
-    # Reference lines
-    ax.axhline(z_vals[0], color='k', linestyle='--', lw=1.5, label='Mudline')
-    ax.axhline(L, color='b', linestyle='--', lw=1.5, label='Pile Tip')
-
-    # Padeye marker
-    if zlug is not None and 0 <= zlug <= L:
-        ax.plot(x_right, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)')
-
-    ax.set_xlabel('Horizontal extent (m)')
-    ax.set_ylabel('Depth (m)')
-    ax.set_xlim(-xmax, xmax)
-    ax.set_ylim(L + 2*D, -D)
-    ax.set_title(title)
-    ax.grid()
-    ax.legend()
-    plt.tight_layout()
-    plt.show()
-
-
-def plot_torpedo(profile, soil_type, D1, D2, L1, L2, zlug, title='Torpedo Pile and Soil Layers'):
-    '''Plot the soil layers and geometry of a torpedo pile using absolute depth for soil and pile head at z=0.
-
-    Parameters:
-    ----------
-        profile : 
-            list or array of (z, Su, 'clay')
-        soil_type : str
-            'clay'
-        D1 : float 
-            Wing diameter (at tip) (m)
-        D2 : float
-            Shaft diameter (at head)
-        L1 : float
-            Winged length (m)
-        L2 : float
-            Shaft length (m)
-        title : str
-            Plot title
-    '''
-    fig, ax = plt.subplots(figsize=(7, 7))
-    
-    xmax = 5*max(D1, D2)
-    z1 = zlug + L1            # interface between L1 and L2
-    z_tip = z1 + L2           # pile tip
-
-    # Split numerical values from profile
-    z_vals = [float(row[0]) for row in profile]
-    values = [float(row[1]) for row in profile]
-
-    seen_labels = set()
-    # Plot soil layers as background fills
-    for i in range(len(z_vals) - 1):
-        z_top = z_vals[i]
-        z_bot = z_vals[i+1]
-        val = values[i]
-
-        if soil_type == 'clay':
-            color = plt.cm.Oranges(val/max(values))
-            label = f'Su = {val:.0f} kPa'
-
-        # Only assign label if not already used
-        if label not in seen_labels:
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label)
-            seen_labels.add(label)
-        else:
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4)
-        
-    # Draw winged section (upper)
-    ax.add_patch(plt.Rectangle((-D1/2, zlug), D1, L1, edgecolor='k', facecolor='none', lw=2, label='Winged Section'))
-
-    # Draw shaft section (lower)
-    ax.add_patch(plt.Rectangle((-D2/2, z1), D2, L2, edgecolor='k', facecolor='none', lw=2, label='Shaft Section'))
-   
-    # Reference lines
-    ax.axhline(z_vals[0], color='k', linestyle='--', lw=1.0, label='Mudline')
-    ax.axhline(    z_tip, color='b', linestyle='--', lw=1.0, label='Pile Tip')
-    
-    # Padeye marker
-    if zlug is not None:
-        ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)')
-
-
-    ax.set_xlim(-xmax, xmax)
-    ax.set_ylim(max(z_vals) + 0.5*D1, min(zlug - 2*D1, z_vals[0] - 2))
-    ax.set_xlabel('Horizontal extent (m)')
-    ax.set_ylabel('Depth (m)')
-    ax.set_title(title)
-    ax.grid()
-    ax.legend()
-    plt.tight_layout()
-    plt.show()
- 
-def plot_helical(profile, soil_type, D, L, d, zlug, n_helix=1, spacing=1.0, title='Helical Pile and Soil Layers'):
-    '''Plot a helical pile in layered soil, with the pile starting at zlug and the helix near the pile tip.
-
-    Parameters:
-    ----------
-        profile : list or array of (z, Su or phi, ...)
-        soil_type : 
-            'clay' or 'sand'
-        D : float
-            Helix diameter (m)
-        L : float
-            Pile length (m)
-        d : float
-            Shaft diameter (m)
-        zlug : float
-            Embedment depth of pile head (m)
-        n_helix : int 
-            Number of helices (-)
-        spacing : float
-            Vertical spacing between helices (m)
-        title : str
-            Plot title
-    '''
-    fig, ax = plt.subplots(figsize=(5, 5))
-    
-    xmax = 3*max(D, d)
-
-    # Extract soil data
-    z_vals = [float(row[0]) for row in profile]
-    values = [float(row[1]) for row in profile]
-
-    seen_labels = set()
-    # Plot soil layers as background fills
-    for i in range(len(z_vals) - 1):
-        z_top = z_vals[i]
-        z_bot = z_vals[i+1]
-        val = values[i]
-        
-        if soil_type == 'clay':
-            color = plt.cm.Oranges(val/max(values))
-            label = f'Su = {val:.0f} kPa' 
-        elif soil_type == 'sand':
-            color = plt.cm.YlOrBr(val / max(values))
-            label = f'ϕ = {val:.0f}°' 
-
-        # Only assign label if not already used
-        if label not in seen_labels:
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label)
-            seen_labels.add(label)
-        else:
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4)
-
-    # Draw shaft
-    ax.add_patch(plt.Rectangle((-d/2, 0), d, L, edgecolor='k', facecolor='none', lw=2, label='Shaft'))
-
-    # Draw helices
-    z_helix_base = L - D  # Base helix depth
-    for i in range(n_helix):
-        z_helix = z_helix_base - i*spacing
-        if z_helix < zlug:
-            break
-        ax.plot([-D/2, D/2], [z_helix - d/2, z_helix + d/2], color='k', lw=2, label='Helix' if i == 0 else None)
-
-    # Reference lines
-    ax.axhline(z_vals[0], color='k', linestyle='--', lw=1.0, label='Mudline')
-    ax.axhline(        L, color='b', linestyle='--', lw=1.0, label='Pile Tip')
-    
-    # Padeye marker
-    if zlug is not None:
-        ax.plot(d/2, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)')
-
-    ax.set_xlim(-xmax, xmax)
-    ax.set_ylim(max(z_vals), min(zlug - 0.5*d, z_vals[0] - 2))
-    ax.set_xlabel('Horizontal extent (m)')
-    ax.set_ylabel('Depth (m)')
-    ax.set_title(title)
-    ax.grid()
-    ax.legend()
-    plt.tight_layout()
-    plt.show()
-
-def plot_plate(profile, B, L, zlug, beta, title='Plate Anchor in Clay Profile'):
-    '''Plot soil layers and an inclined plate anchor centered at zlug.
-
-    Parameters:
-    ----------
-        profile : ndarray of shape (n, 3), with (depth, Su, gamma)
-        B : float
-            Plate width (m)
-        L : float
-            Plate length (m) 
-        zlug : float
-            Center embedment of the plate (m)
-        beta : float
-            Inclination angle of plate (deg)
-        title : str
-            Plot title
-    '''
-    fig, ax = plt.subplots(figsize=(5, 5))
-    
-    xmax = 3*B
-
-    # Extract soil data
-    layer_depths = profile[:, 0]
-    layer_depths = np.append(layer_depths, [profile[0][-1]])    
-
-    # Inclined plate geometry
-    dx = (B/2)*np.cos(np.deg2rad(beta))
-    dz = (B/2)*np.sin(np.deg2rad(beta))
-    plate_x = [-dx, dx]
-    plate_z = [zlug - dz, zlug + dz]
-
-    seen_labels = set()
-    # Plot soil layers as background fills
-    for i in range(len(layer_depths) - 1):
-        z_top = layer_depths[i]
-        z_bot = layer_depths[i+1]
-        Su = profile[i][1]
-        color = plt.cm.Oranges(Su/np.max(profile[:, 1]))
-        label = f'Su = {Su:.0f} kPa'
-        
-        # Only assign label if not already used
-        if label not in seen_labels:
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label)
-            seen_labels.add(label)
-        else:
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4)
-
-    # Plot inclined plate
-    ax.plot(plate_x, plate_z, color='k', lw=1.5, label='Plate')
-
-    # Reference lines
-    ax.axhline(0, color='k', linestyle='--', lw=1.0, label='Mudline')
-    
-    # Padeye marker
-    if zlug is not None:
-        ax.plot(0, zlug, 'ko', label=f'Padeye (zlug = {zlug:.2f} m)')
-
-    ax.set_xlim(-xmax, xmax)
-    ax.set_ylim(25, -1)  
-    ax.set_xlabel("Horizontal extent (m)")
-    ax.set_ylabel("Depth (m)")
-    ax.set_title(title)
-    ax.legend(loc='lower right')
-    ax.grid(True)
-    plt.tight_layout()
-    plt.show()
-    
-def plot_load(profile, soil_type, drag_values, depth_values, Tm, thetam, Ta, thetaa, zlug):
-
-        fig, ax = plt.subplots(figsize=(12, 6))
-        n = 2e6
-
-        # Plot the inverse catenary profile
-        ax.plot(drag_values, depth_values, color='b', label='Mooring line')
-
-        # Add load vectors
-        ax.arrow(0, -profile[0][0], Tm*np.cos(np.deg2rad(thetam))/n, Tm*np.sin(np.deg2rad(thetam))/n,
-                 head_width=0.25, head_length=0.5, color='r', label='Mudline Load')
-        ax.arrow(drag_values[-1], depth_values[-1], Ta*np.cos(thetaa)/n, Ta*np.sin(thetaa)/n,
-                 head_width=0.25, head_length=0.5, color='g', label='Padeye Load')
-        
-        #ax.set_aspect('equal', adjustable='datalim')
-
-        # Plot soil layers as background fills
-        for i in range(len(profile) - 1):
-            z_top = -profile[i][0]
-            z_bot = -profile[i+1][0]
-            if soil_type == 'clay':
-                Su = profile[i][1]
-                gamma = profile[i][2]
-                color = plt.cm.Oranges(Su/np.max(profile[:, 1]))
-                label = f'Su = {Su:.0f} kPa'
-            elif soil_type == 'sand':
-                phi = profile[i][1]
-                gamma = profile[i][2]
-                color = plt.cm.YlOrRd(phi/np.max(profile[:, 1]))
-                label = f'ϕ = {phi:.0f}°, γ = {gamma:.1f} kN/m³'
-            ax.axhspan(z_bot, z_top, color=color, alpha=0.4, label=label)
-
-        # Deduplicate legend entries
-        handles, labels = ax.get_legend_handles_labels()
-        unique = dict(zip(labels, handles))
-        ax.legend(unique.values(), unique.keys(), loc='lower left')
-
-        ax.set_xlabel('Drag distance [m]')
-        ax.set_ylabel('Embedded depth [m]')
-        ax.set_title('Inverse Catenary in Layered Soil')
-        ax.grid(True)
-        
-        ax.annotate(f"{Tm/1e6:.2f} MN", (Tm*np.cos(np.deg2rad(thetam))/n, -profile[0][0] + Tm*np.sin(np.deg2rad(thetam))/n), color='r')
-        ax.annotate(f"{Ta/1e6:.2f} MN", (drag_values[-1] + Ta*np.cos(thetaa)/n, depth_values[-1] + Ta*np.sin(thetaa)/n), color='g')
diff --git a/famodel/anchors/anchors_famodel_profile/capacity_pycurves.py b/famodel/anchors/anchors_famodel_profile/capacity_pycurves.py
deleted file mode 100644
index 67eb67f8..00000000
--- a/famodel/anchors/anchors_famodel_profile/capacity_pycurves.py
+++ /dev/null
@@ -1,298 +0,0 @@
-
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.interpolate import interp1d
-
-def py_Matlock(z, D, zlug, f_Su, f_sigma_v_eff, f_gamma, z0=None, plot=True):
-    ''' Generate Matlock (1970) p–y curve at a given depth in clay.
-
-    Parameters
-    ----------
-    z : float
-        Depth relative to pile head (m)
-    D : float
-        Pile diameter (m)
-    zlug : float
-        Load eccentricity above or below mudline (m)
-    f_Su : function
-        Undrained shear strength (Pa)
-    f_sigma_v_eff : function
-        Effective vertical stress (Pa)
-    f_gamma : function
-        Effective unit weight (kN/m³)
-    z0 : float, optional
-        Mudline depth (m). If provided, disables resistance above this level
-    plot : bool
-        Plot the resulting p–y curve if True
-
-    Returns
-    -------
-    f : interp1d
-        Interpolation function for p–y relationship (N/m vs m)
-    '''
-    
-    Su = f_Su(z)
-    sigma_v_eff = f_sigma_v_eff(z)
-    gamma = f_gamma(z)
-   
-    # Strain at half the strength as defined by Matlock (1970). 
-    # Typically ranges from 0.005 (stiff clay) to 0.02 (soft clay).
-    epsilon_50 = 0.02          
-    J = 0.5
-    
-    # No soil resistance above mudline
-    if z0 is not None and z < z0:
-        return lambda y_val: np.zeros_like(y_val)
-
-    # Calculate ultimate resistance and shape parameters
-    Nc = 3.0 + sigma_v_eff/Su + J*z/D
-    Nc = min(Nc, 9.0)
-    z_cr = 6.0 *D/(gamma*D/Su + J)
-
-    p_ult = Su * Nc * D
-    y_50 = 2.5 * epsilon_50 * D
-
-    # Normalized lateral displacements
-    Y = np.concatenate((-np.logspace(3, -4, 100), [0], np.logspace(-4, 3, 100)))
-    P = 0.5 * np.sign(Y)*np.abs(Y)**(1.0/3.0)
-    P = np.clip(P, -1.0, 1.0)
-
-    # Un-normallized p-y curves
-    y = Y*y_50
-    p = P*p_ult
-    
-    f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0)   # Interpolation function for p-y curve
-
-    # Plot of p-y curve and check if 'k' is calculated correctly
-    if plot:
-        plt.plot(y, p,'-')
-        plt.xlabel('y (m)') 
-        plt.ylabel('p (N/m)')
-        plt.title('PY Curves - Matlock (1970)')
-        plt.grid(True)
-        plt.xlim([-2*D, 2*D])
-
-    return f  
-       
-def py_API(z, D, zlug, f_phi, f_sigma_v_eff, f_Dr, z0=None, plot=True):   
-    ''' Generate API RP2A (1993) p–y curve at a given depth in sand.
-
-    Parameters
-    ----------
-    z : float
-        Depth relative to pile head (m)
-    D : float
-        Pile diameter (m)
-    zlug : float
-        Load eccentricity above or below mudline (m)
-    f_phi : function
-        Friction angle (deg)
-    f_sigma_v_eff : function
-        Effective vertical stress (Pa)
-    f_Dr : function
-        Relative density (-)
-    z0 : float, optional
-        Mudline depth (m). If provided, disables resistance above this level
-    plot : bool
-        Plot the resulting p–y curve if True
-
-    Returns
-    -------
-    f : interp1d
-        Interpolation function for p–y relationship (N/m vs m)
-    '''
-    
-    phi = f_phi(z)
-    sigma_v_eff = f_sigma_v_eff(z)
-    Dr = f_Dr(z)
-         
-    # Interpolate coefficients depending on the effective friction angle
-    phi_ref = [  20,   25,   30,   35,   40]
-    C1_ref  = [0.80, 1.25, 1.90, 3.00, 4.50]
-    C2_ref  = [1.60, 2.10, 2.60, 3.40, 4.30]
-    C3_ref  = [  10,   15,   30,   55,  105]
-    
-    C1 = np.interp(phi, phi_ref, C1_ref)
-    C2 = np.interp(phi, phi_ref, C2_ref)
-    C3 = np.interp(phi, phi_ref, C3_ref)
-    
-    # Disable p–y curve above mudline
-    if z0 is not None and z < z0:
-        return lambda y_val: np.zeros_like(y_val)
-    
-    # Compute ultimate lateral resistance
-    p_ult = min(C1*z + C2*D, C3*D) * sigma_v_eff
-    
-    # Compute initial stiffness k (kN/m3 → N/m3)
-    k = (54.6*Dr**2 + 0.8*Dr + 1.8)*1e3
-    
-    # Normalized displacement range
-    N = 20
-    y = np.concatenate((-np.logspace(3, -4, N), [0], np.logspace(-4, 3, N)))
-    
-    # Shape coefficient A
-    A = max(3 - 0.8*z/D, 0.9)
-    
-    # Apply API p–y formulation
-    ε = 1e-6  # prevent division by zero
-    p = A * p_ult * np.tanh(k*z*y/(A*p_ult + ε))
-    
-    f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0)
-    
-    if plot:
-        # Plot of p-y curve and check if 'k' is calculated correctly
-        plt.plot(y, p,'-') 
-        plt.xlabel('y (m)') 
-        plt.ylabel('p (N/m)')
-        plt.title('PY Curves - API (1993)')
-        plt.grid(True)
-        plt.xlim([-0.10*D, 0.10*D])
-        
-    return f  
-
-def py_Reese(z, D, zlug, f_UCS, f_Em, z0=None, plot=True):
-    ''' Generate Reese (1997) p–y curve at a given depth in weak rock.
-
-    Parameters
-    ----------
-    z : float
-        Depth relative to pile head (m)
-    D : float
-        Pile diameter (m)
-    zlug : float
-        Load eccentricity above or below mudline (m)
-    f_UCS : function
-        Unconfined compressive strength UCS(z) (Pa)
-    f_Em : function
-        Young's modulus Em(z) (Pa)
-    z0 : float, optional
-        Mudline depth (m). If provided, disables resistance above this level
-    plot : bool
-        Plot the resulting p–y curve if True
-
-    Returns
-    -------
-    f : interp1d
-        Interpolation function for p–y relationship (N/m vs m)
-    '''
-    
-    UCS = f_UCS(z)
-    Em = f_Em(z)
-  
-    RQD = 52                     # Assumed fair rock quality (moderately weathered rocks) 
-    Dref = 0.305; nhu = 0.3; E = 200e9
-    t = (6.35 + D*20)/1e3        # Pile wall thickness (m), API RP2A-WSD
-    I  = np.pi*(D**4 - (D - 2*t)**4)/64.0
-    EI = E*I
-    alpha = -0.00667*RQD + 1
-    krm = 0.0005
-
-    if z < z0:
-        # Above mudline, no resistance
-        p_ur = 0
-    else:
-        if z < 3*D:
-            p_ur = alpha*UCS*D*(1 + 1.4*z/D)
-            #kir = (100 +400*z/(3*D))
-        else:
-            p_ur = 5.2*alpha*UCS*D
-            #kir = 500
-        
-    kir = (D/Dref)*2**(-2*nhu)*(EI/(Em*D**4))**0.284
-    Kir = kir*Em     
-    y_rm = krm*D
-    y_a = (p_ur/(2*y_rm**0.25*Kir))**1.333    
-   
-    # Normalized lateral displacement
-    N = 20
-    y = np.concatenate((-np.logspace(4,-3,N),[0],np.logspace(-3,4,N)))
-    
-    p = []
-    for val in y:
-        if abs(val) < y_a:
-            p_val = np.sign(val) * Kir * val
-        else:
-            p_val = np.sign(val)*min((p_ur/2)*(abs(val)/y_rm)**0.25, p_ur)
-        p.append(p_val)                                  
-    
-    f = interp1d(y, p, kind='linear', bounds_error=False, fill_value=0.0)
-    
-    if plot:    
-        plt.plot(y, p) 
-        plt.xlabel('y (m)') 
-        plt.ylabel('p (N/m)'),
-        plt.title('PY Curves - Reese (1997)')
-        plt.grid(True)
-        plt.xlim([-0.03*D, 0.03*D])
-        #plt.ylim([min(p), max(p)])      
-        
-    return f      
-
-def py_Lovera(z, D, f_UCS, f_Em, zlug, z0, plot=True):
-    ''' Generate Lovera (2019) p–y curve at a given depth for layered rock interfaces.
-
-    Parameters
-    ----------
-    z : float
-        Depth relative to pile head (m)
-    D : float
-        Pile diameter (m)
-    f_UCS : function
-        Unconfined compressive strength UCS(z) (Pa)
-    f_Em : function
-        Young's modulus Em(z) (Pa)
-    zlug : float
-        Load eccentricity (m)
-    z0 : float
-        Mudline depth (m). If provided, disables resistance above this level
-    delta_grout : float, optional
-        Grout annulus thickness (m) (default value: delta_grout=0.075)
-    E_grout : float, optional
-        Grout elastic modulus (Pa) (default value: E_grout=20e9)
-    delta_crushed : float, optional
-        Crushed rock annulus thickness (m) (default value: delta_crushed=0.025)
-    plot : bool
-        Plot the resulting p–y curve if True
-
-    Returns
-    -------
-    f : interp1d
-        Interpolation function for p–y relationship (N/m vs m)
-    '''
-     
-    # Default values
-    delta_grout = 0.075
-    E_grout = 20e9
-    delta_crushed = 0.025
- 
-    if z < z0:
-        return lambda y: np.zeros_like(y)
-
-    # Retrieve elastic modulus at depth
-    Em = f_Em(z)
-    nu = 0.3  # Typical Poisson's ratio for rock
-    G_rock = Em/(2*(1 + nu))
-    k_rock = 4*G_rock
-    
-    # Set E_crushed as 25% of intact rock modulus if not given
-    E_crushed = 0.25*Em
-
-    # Compute total stiffness from linear components
-    k_eq = 1.0/(0.4*delta_grout/E_grout + delta_crushed/E_crushed + 1.0/k_rock)
-
-    y = np.linspace(-0.03*D, 0.03*D, 200)
-    p = k_eq*y
-    f = interp1d(y, p, fill_value="extrapolate")
-
-    if plot:
-        plt.plot(y, p, '-')
-        plt.xlabel('y (m)')
-        plt.ylabel('p (N/m)')
-        plt.title('PY Curves - Lovera (2019)')
-        plt.grid(True)
-        plt.xlim([-0.1*D, 0.1*D])
-        plt.ylim([min(p), max(p)]) 
-        plt.show()
-
-    return f
-
diff --git a/famodel/anchors/anchors_famodel_profile/capacity_soils.py b/famodel/anchors/anchors_famodel_profile/capacity_soils.py
deleted file mode 100644
index 93d4ae19..00000000
--- a/famodel/anchors/anchors_famodel_profile/capacity_soils.py
+++ /dev/null
@@ -1,176 +0,0 @@
-
-import numpy as np
-from scipy.interpolate import interp1d
-
-def clay_profile(profile):
-    ''' Create interpolated functions for a clay soil profile.
-    Calculates Su, effective vertical stress, unit weight and adhesion factor.
-    
-    Parameters
-    ----------
-    profile : array
-        Clay profile as 2D array: (z, Su, gamma)
-        Depth (m), undrained shear strength Su (kPa) and effective unit weight gamma (kN/m³)
-    
-    Returns
-    -------
-    z0 : float
-        Depth of mudline relative to pile head (m)
-    f_Su : interp1d
-        Undrained shear strength, Su(z) (Pa)
-    f_sigma_v_eff : interp1d
-        Effective vertical stress, σ'v(z) (Pa)
-    f_gamma : interp1d
-        Effective unit weight of the soil, γ'(z) (N/m³)
-    f_alpha : function
-        Adhesion factor from API correlation, α (-)
-    '''
-
-    # Depth of mudline relative to pile head
-    z0 = float(profile[0][0])
-
-    # Extract data from profile array and zero strength virtual soil layer
-    # from the pile head down to the mudline
-    depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m
-    Su    = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # kPa
-    gamma = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # kN/m3
-
-    # Calculate sigma_v_eff at each depth
-    sigma_v_eff = np.zeros(len(depth))
-
-    for i in range(1, len(depth)):
-        sigma_v_eff[i] = sigma_v_eff[i-1] + gamma[i-1]*(depth[i] - depth[i-1])
-
-    # Define interpolation functions
-    f_Su          = interp1d(depth, Su*1e3, kind='linear')           # Pa
-    f_sigma_v_eff = interp1d(depth, sigma_v_eff*1e3, kind='linear')  # Pa
-    f_gamma       = interp1d(depth, gamma*1e3, kind='linear')        # N/m3
-    
-    # Calculate f_psi and f_alpha at each depth (not as a scalar)
-    f_psi = lambda z: f_Su(z)/np.maximum(f_sigma_v_eff(z), 1.0)  
-    
-    def calc_alpha(psi):
-        # Avoid divide-by-zero or log(0) by setting a floor
-        psi = np.maximum(psi, 1e-6)
-        if np.ndim(psi) == 0:
-            psi = float(psi)
-            # API-style adhesion factor: two regimes
-            return min(0.5*psi**-0.50, 1) if psi <= 1.0 else min(0.5*psi**-0.25, 1)
-        else:
-            return np.where(
-                psi <= 1.0,
-                np.minimum(0.5*psi**-0.50, 1),
-                np.minimum(0.5*psi**-0.25, 1)
-            )
-     
-    # Create an interpolated adhesion factor function
-    # Create an interpolated adhesion factor function
-    def f_alpha(z):
-        psi_val = f_psi(z)
-        alpha_val = calc_alpha(psi_val)
-        return np.atleast_1d(alpha_val)[0] if np.ndim(alpha_val) == 0 else alpha_val
-
-    return z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha
-
-def sand_profile(profile):
-    ''' Create interpolated functions for a sand soil profile.
-    Calculates phi, effective stress, unit weight, relative density, and skin friction factor.
-
-    Parameters
-    ----------
-    profile : array
-        Sand profile as 2D array: (z, phi, gamma, Dr)
-        Depth (m), friction angle, phi (deg), effective unit weight, gamma (kN/m³) and relative density, Dr (%)
-
-    Returns
-    -------
-    z0 : float
-        Depth of mudline relative to pile head (m)
-    f_phi : interp1d
-        Friction angle, φ(z) (deg)
-    f_sigma_v_eff : interp1d
-        Effective vertical stress, σ'v(z) (Pa)
-    f_gamma : interp1d
-        Effective unit weight of the soil, γ'(z) (N/m³)
-    f_Dr : interp1d
-        Relative density, Dr(z) (-)
-    f_delta : interp1d
-        Skin friction factor, δ(z) (-)
-    '''
-
-    # Depth of mudline relative to pile head
-    z0 = float(profile[0][0])
-
-    # Extract data from profile array and zero strength virtual soil layer
-    # from the pile head down to the mudline
-    depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m
-    phi   = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # deg
-    gamma = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # kN/m3
-    Dr    = np.concatenate([np.array([0]), np.array([row[3] for row in profile],dtype=float)]) # -
-   
-    # Calculate sigma_v_eff and static loading factor at each depth
-    sigma_v_eff = np.zeros(len(depth))
-
-    for i in range(1, len(depth)):  
-        sigma_v_eff[i] = sigma_v_eff[i-1] + gamma[i-1]*(depth[i] - depth[i-1])
-
-    # Define interpolation functions
-    f_phi         = interp1d(depth, phi, kind='linear')              # deg
-    f_sigma_v_eff = interp1d(depth, sigma_v_eff*1e3, kind='linear')  # Pa
-    f_gamma       = interp1d(depth, gamma*1e3, kind='linear')        # N/m3
-    f_Dr          = interp1d(depth, Dr, kind='linear')               # -
-    
-    # Define delta as a function of Dr
-    def calc_delta(Dr_val):
-        if 35 <= Dr_val < 50:
-            return 0.29
-        elif 50 <= Dr_val < 65:
-            return 0.37
-        elif 65 <= Dr_val < 85:
-            return 0.46
-        elif Dr_val >= 85:
-            return 0.56
-        else:
-            return 0  # Default or error value for very low Dr values
-        
-    # Apply delta calculation to Dr profile
-    delta_values = np.array([calc_delta(Dr_val) for Dr_val in Dr])
-    f_delta      = interp1d(depth, delta_values, kind='linear')  # Interpolated delta values
-    
-    return z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta
-    
-def rock_profile(profile):
-    ''' Create interpolated functions for a weak rock profile.
-    Calculates unconfined compressive strength (UCS) and Young’s modulus (Em).
-
-    Parameters
-    ----------
-    profile : array
-        Rock profile as 2D array: (z, UCS, Em)
-        Depth (m), unconfined compressive strength, UCS (MPa), Young's modulus, Em (MPa)
-
-    Returns
-    -------
-    z0 : float
-        Depth of rockline relative to pile head (m)
-    f_UCS : interp1d
-        Unconfined compressive strength, UCS(z) (Pa)
-    f_Em : interp1d
-        Young's modulus, Em(z) (Pa)
-    '''
-
-    # Depth of rockline relative to pile head
-    z0 = float(profile[0][0])
-
-    # Extract data from soil_profile array and zero strength virtual soil layer
-    # from the pile head down to the mudline
-    depth = np.concatenate([np.array([z0]),np.array([row[0] for row in profile],dtype=float)]) # m  
-    UCS   = np.concatenate([np.array([0]), np.array([row[1] for row in profile],dtype=float)]) # MPa
-    Em    = np.concatenate([np.array([0]), np.array([row[2] for row in profile],dtype=float)]) # MPa
-
-    # Define interpolation functions
-    f_UCS = interp1d(depth, UCS*1e6, kind='linear')  # Pa
-    f_Em  = interp1d(depth, Em*1e6, kind='linear')   # Pa
-    
-    return z0, f_UCS, f_Em
-
diff --git a/famodel/anchors/anchors_famodel_profile/capacity_suction.py b/famodel/anchors/anchors_famodel_profile/capacity_suction.py
deleted file mode 100644
index e322048a..00000000
--- a/famodel/anchors/anchors_famodel_profile/capacity_suction.py
+++ /dev/null
@@ -1,293 +0,0 @@
-   
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.optimize import fsolve
-from scipy.optimize import root_scalar
-from .capacity_soils import clay_profile, sand_profile
-from .capacity_plots import plot_suction
-
-def getCapacitySuction(profile, soil_type, D, L, zlug, Ha, Va, plot=True):    
-    '''Calculate the inclined load capacity of a suction pile in sand or clay following S. Kay methodology.
-    The calculation is based on the soil profile, anchor geometry and inclined load.  
-    
-    Parameters
-    ----------
-    profile : array
-        Soil profile as a 2D array: (z, parameters)
-            Clay soil profile (z (m), Su (kPa), gamma (kN/m³))
-            Sand soil profile (z (m), phi (deg), gamma (kN/m³), Dr (%))
-    soil_type : string
-        Select soil condition, 'clay' or 'sand'
-    D : float 
-        Suction pile diameter (m)
-    L : float 
-        Suction pile length from pile head (m)
-    zlug: float
-        Embedded depth of the main padeye (m)
-    thetalug: float
-        Angle of tilt misaligment (deg) (default value: 5.0)
-    psilug: float
-        Angle of twist misaligment (deg) (default value: 7.5)
-    Ha : float       
-        Horizontal load at pile lug elevation (N)
-    Va : float          
-        Vertical load at pile lug elevation (N)
-    plot : bool
-        Plot the capacity envelope if True
-                    
-    Returns
-    -------
-    Dictionary with capcity, weigths and UC.
-    ''' 
-    
-    z0 = profile[0][0]      
-    lambdap = (L - z0)/D; m = 2/3;   # Suction pile slenderness ratio
-    t = (6.35 + D*20)/1e3            # Suction pile wall thickness (m), API RP2A-WSD
-    rlug = D/2                       # Radial position of the lug
-    thetalug = 5                     # Angle of tilt misaligment, default is 5. (deg)
-    psilug = 7.5                     # Angle of twist misaligment, default is 7.5. (deg)
-    rhows = 66.90e3                  # Submerged steel specific weight (N/m3)
-    rhow = 10e3                      # Water specific weight (N/m3) 
-    
-    # Outer and inner surface of the pile skirt
-    def PileSurface(Len, Dia):
-        Sp = np.pi*Dia*Len
-        return Sp    
-    # Dry and wet mass of the pile    
-    def PileWeight(Len, Dia, tw, rho):
-        Wp = ((np.pi/4)*((Dia**2 - (Dia - 2*tw)**2)*Len + (np.pi/4)*Dia**2*tw))*rho
-        return Wp 
-    # Mass of the soil plug      
-    def SoilWeight(Len, Dia, tw, gamma_soil): 
-        Wsoil =(np.pi/4)*(Dia - 2*tw)**2*Len*gamma_soil
-        return Wsoil
-    # Tilt and twist effects due to installation misaligments
-    def rlugTilt(r, z, theta):
-        R = r*np.cos(np.deg2rad(theta)) - z*np.sin(np.deg2rad(theta))
-        return R   
-    def zlugTilt(r, z, theta):
-        Z = r*np.sin(np.deg2rad(theta)) + z*np.cos(np.deg2rad(theta))
-        return Z    
-    
-    if soil_type == 'clay':
-        z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile)
-        z_vals = np.linspace(z0, L, 10)
-
-        Su_vals = f_Su(z_vals)
-        ez = np.trapz(z_vals*Su_vals, z_vals)/np.trapz(Su_vals, z_vals); 
-        ez_soil = ez; print(f"ez_soil = {ez_soil:.2f} m")
-        gamma_vals = f_gamma(z_vals)
-
-        Su_av_L = f_Su(ez_soil)
-        Su_tip = f_Su(L); # print(f"Su_tip = {Su_tip:.2f} Pa")
-        sigma_v_eff = f_sigma_v_eff(ez_soil)
-        gamma_av = np.trapz(gamma_vals, z_vals)/(L - z0); # print(f"gamma_av = {gamma_av:.2f} kN/m³")
-        alpha_av = float(f_alpha(ez_soil))
-                
-        Nc = min(6.2*(1 + 0.34*np.arctan(lambdap)), 9)
-        Np_fixed = 10.25; Np_free = 4 
-        Hmax = Np_fixed*(L - z0)*D*Su_av_L; print(f'Su_av_L = {Su_av_L:.2f} Pa')
-        print(f'Hmax = {Hmax:.2f} N')
-        
-        M = -Va*rlugTilt(rlug, zlug, thetalug) - Ha*(zlugTilt(rlug, zlug, thetalug) - ez_soil)
-        # print(f"rlug_eff = {rlugTilt(rlug, zlug, thetalug):.2f} m")
-        # print(f"zlug_eff = {zlugTilt(rlug, zlug, thetalug):.2f} m")
-        print(f"M = {M:.2f} Nm")
-
-        # --- MH Ellipse Parameters for Clay (Kay 2014) ---
-        # ΔφMH (piecewise based on L/D)
-        if 0.5 <= lambdap < 1.125:
-            delta_phi = 0.32 + 4.32*lambdap; # print(delta_phi)
-        elif 1.125 <= lambdap < 2.0:
-            delta_phi = 7.13 - 1.71*lambdap; # print(delta_phi)
-        elif 2.0 <= lambdap <= 8.0:
-            delta_phi = 4.55 - 0.425*lambdap; # print(delta_phi)
-        else:
-            raise ValueError('L/D ratio out of bounds for MH ellipse formulation.')
-
-        phi_MH = -np.arctan(ez_soil/(L -z0)) - np.deg2rad(delta_phi)
-        a_MH = Np_fixed/np.cos(phi_MH)
-        delta_bMH = 0.45*(lambdap)**(-0.9) if lambdap <= 1.5 else 0
-        b_MH = -Np_free*np.sin(phi_MH) + delta_bMH
-        print('M cos(phi)/a_MH =', (M*np.cos(phi_MH))/a_MH)
-        print('M sin(phi)/b_MH =', (M*np.sin(phi_MH))/b_MH)
-
-        # Solve MH ellipse for Hmax
-        def f(Hmax):
-            term1 = ((M*np.cos(phi_MH) + Hmax*np.sin(phi_MH))/a_MH)**2
-            term2 = ((M*np.sin(phi_MH) - Hmax*np.cos(phi_MH))/b_MH)**2
-            return term1 + term2 - 1
-
-        try:
-            Hmax = max(fsolve(f, Hmax*0.8)[0], 0.0)
-        except:
-            Hmax = 0.0
-            
-        print(f'Hmax (MH ellipse) = {Hmax:.2f} N')
-
-        To = PileSurface((L - z0), D)*alpha_av*Su_av_L
-        Ti = PileSurface((L - z0), D - 2*t)*alpha_av*Su_av_L
-        Tbase = np.pi*D**3*Su_tip/12
-        Tmax = min(To + Ti, To + Tbase)
-
-        T = Ha*rlug*np.sin(np.deg2rad(psilug))
-        nhuT = T/Tmax
-        nhuV = Ha/To
-        nhuVstar = np.sqrt(nhuV**2 - nhuT**2)
-        alphastar = alpha_av*(nhuVstar/nhuV); print(f"alphastar   = {alphastar:.3f}")
-        
-        Vmax1 = PileWeight(L, D, t, rhows) + PileSurface((L - z0), D)*alphastar*Su_av_L + Nc*Su_tip*(np.pi/4)*D**2
-        Vmax2 = PileWeight(L, D, t, rhows) + PileSurface((L - z0), D)*alphastar*Su_av_L + PileSurface((L - z0), D - 2*t)*alphastar*Su_av_L
-        Vmax3 = PileWeight(L, D, t, rhows) + PileSurface((L - z0), D)*alphastar*Su_av_L + SoilWeight((L - z0), D, t, gamma_av)
-        Vmax = min(Vmax1, Vmax2, Vmax3)
-        
-        print(f"Vmax1    = {Vmax1:.2f} N"); print(f"Vmax2    = {Vmax2:.2f} N"); print(f"Vmax3    = {Vmax3:.2f} N")
-        
-
-    elif soil_type == 'sand':
-        z0, f_phi, f_sigma_v_eff, f_gamma, f_Dr, f_delta = sand_profile(profile)
-        z_vals = np.linspace(z0, L, 10)
-
-        sigma_v_vals = f_sigma_v_eff(z_vals)
-        ez = np.trapz(z_vals*sigma_v_vals, z_vals) / np.trapz(sigma_v_vals, z_vals)
-        ez_soil = ez - z0        
-
-        phi_vals = f_phi(z_vals)
-        gamma_vals = f_gamma(z_vals)
-        sigma_v_vals = f_sigma_v_eff(z_vals)
-        Dr_vals = f_Dr(z_vals)
-        delta_vals = f_delta(z_vals)
-
-        phi_av = np.trapz(phi_vals, z_vals)/(L - z0)
-        gamma_av = np.trapz(gamma_vals, z_vals)/(L - z0)
-        delta_av = np.trapz(delta_vals, z_vals)/(L - z0)
-        sigma_av_L = np.trapz(sigma_v_vals, z_vals)/(L - z0)
-        sigma_tip = f_sigma_v_eff(L)
-
-        Nq = np.e**(np.pi*np.tan(np.radians(phi_av)))*(np.tan(np.radians(45) + np.radians(phi_av)/2))**2
-        Hmax = 0.5*D*Nq*gamma_av*(L - z0)**2
-        Np_free = 3.0  
-
-        M = -Va*rlugTilt(rlug, zlug, thetalug) - Ha*(zlugTilt(rlug, zlug, thetalug) - ez_soil)
-
-        # --- MH Ellipse Parameters for Clay (Kay 2014) ---
-        # ΔφMH (piecewise based on L/D)
-        if 0.5 <= lambdap < 1.125:
-            delta_phi = 0.32 + 4.32*lambdap
-        elif 1.125 <= lambdap < 2.0:
-            delta_phi = 7.13 - 1.71*lambdap
-        elif 2.0 <= lambdap <= 6.0:
-            delta_phi = 4.55 - 0.425*lambdap
-        else:
-            raise ValueError('L/D ratio out of bounds for MH ellipse formulation.')
-
-        phi_MH = -np.arctan(ez_soil/(L - z0)) - np.deg2rad(delta_phi)
-        a_MH = Nq/np.cos(phi_MH)     
-        delta_bMH = 0.45*(lambdap)**(-0.9) if lambdap <= 1.5 else 0
-        b_MH = -Nq*np.sin(phi_MH) + delta_bMH
-
-        # Solve MH ellipse for Hmax
-        def f(Hmax):
-            term1 = ((M*np.cos(phi_MH) + Hmax*np.sin(phi_MH))/a_MH)**2
-            term2 = ((M*np.sin(phi_MH) - Hmax*np.cos(phi_MH))/b_MH)**2
-            return term1 + term2 - 1
-
-        Hmax = fsolve(f, 0.8*Hmax)[0]
-        print(f'Hmax (MH ellipse) = {Hmax:.2f} N')
-
-        To = PileSurface((L - z0), D)*delta_av*sigma_av_L
-        Ti = PileSurface((L - z0), D - 2*t)*delta_av*sigma_av_L
-        Tbase = np.pi*D**3*sigma_tip/12
-        Tmax = min(To + Ti, To + Tbase)
-
-        T = Ha*rlug*np.sin(np.deg2rad(psilug))
-        Fo = delta_av*sigma_av_L*L*np.pi*D
-        nhuT = T/Tmax
-        nhuV = Ha/Fo
-        nhuVstar = np.sqrt(nhuV**2 - nhuT**2)
-        deltastar = delta_av*(nhuVstar/nhuV)
-
-        Vmax2 = PileWeight(L, D, t, rhows) + PileSurface((L - z0), D)*deltastar*sigma_av_L + PileSurface((L - z0), D - 2*t)*deltastar*sigma_av_L
-        Vmax3 = PileWeight(L, D, t, rhows) + PileSurface((L - z0), D)*deltastar*sigma_av_L + SoilWeight((L - z0), D, t, gamma_av)
-        Vmax = min(Vmax2, Vmax3)
-        
-    # Pile weight (inc. stiffening plus vent) assessed as a factor
-    Wp = 1.10*PileWeight(L, D, t, (rhows + rhow)) 
-    # Submerged weight of the soil plug
-    Wsoil = SoilWeight((L - z0), D, t, gamma_av)
-    
-    # Capacity envelope
-    aVH = 0.5 + lambdap; bVH = 4.5 + lambdap/3 
-    # print('Env. exp = ' +str(aVH)+'   '+str(bVH))
-    UC = (Ha/Hmax)**aVH + (Va/Vmax)**bVH      
-    x = np.cos(np.linspace (0, np.pi/2, 100))
-    y = (1 - x**bVH)**(1/aVH)
-    X = Hmax*x; Y = Vmax*y
-    
-    if plot:
-        plt.figure(figsize=(6, 5))
-        plt.plot(X, Y, color = 'b', label='VH Envelope')
-        plt.plot(Ha, Va, 'o', color = 'r', label='Load Point')
-            
-        # Set labels and title
-        plt.xlabel('Horizontal capacity (N)')
-        plt.ylabel('Vertical capacity (N)')
-        plt.suptitle('VH suction pile capacity envelope')
-        plt.axis([0, 1.3*max(X[0], Ha), 0, 1.3*max(Y[-1], Va)]) 
-        plt.legend()
-        plt.grid(True)
-        plt.show()
-    
-    resultsSuction = {
-        'Horizontal max.': Hmax,
-        'Vertical max.': Vmax,
-        'Unity check': UC,
-        'Weight pile': Wp,
-        'Weight soil': Wsoil,
-        't': t
-    }
-    
-    return resultsSuction
-    
-if __name__ == '__main__':
-
-    # Clay profile: [depth (m), Su (kPa), gamma (kN/m³)]
-    profile_clay = np.array([
-        [ 2.0,  25,  8.5],
-        [ 4.0,  25,  8.5],
-        [ 6.0,  25,  8.5],
-        [20.0,  25,  8.5]
-    ])
-    
-    # Sand profile: [depth (m), phi (deg), gamma (kN/m³), Dr(%)]
-    profile_sand = np.array([
-        [ 1.0, 28, 8.0, 75],
-        [ 5.0, 35, 8.5, 75],
-        [ 8.0, 38, 9.0, 75],
-        [20.0, 42, 9.5, 75]
-    ])
-
-    D = 3.0             # Diameter (m)
-    L = 10.0            # Length (m)
-    zlug = 8.0          # Padeye depth (m)
-    Ha = 2.0e6          # Horizontal load (N)
-    Va = 3.0e6          # Vertical load (N)
-
-    # === CLAY ===
-    resultsSuction_clay = getCapacitySuction(profile_clay, 'clay', D, L, zlug, Ha, Va, plot=True)
-    for key, val in resultsSuction_clay.items(): 
-        print(f"{key}: {val:.2f}")
-        
-    # Plot suction pile with the clay profile
-    profile_clay_plot = [(float(z), float(Su), 'clay') for z, Su, _ in profile_clay]
-    plot_suction(profile_clay_plot, 'clay', L, D, zlug)
-      
-    # === SAND ===
-    # resultsSuction_sand = getCapacitySuction(profile_sand, 'sand', D, L, zlug, Ha, Va, plot=True)
-    # for key, val in resultsSuction_sand.items(): 
-    #     print(f"{key}: {val:.2f}") 
-    
-    # # Sand profile formatted for plotting
-    # profile_sand_plot = [(float(z), float(phi), 'sand') for z, phi, _, _ in profile_sand]
-    # plot_suction(profile_sand_plot, 'sand', L, D, zlug, title='Suction Pile in Sand Profile')
-
diff --git a/famodel/anchors/anchors_famodel_profile/capacity_torpedo.py b/famodel/anchors/anchors_famodel_profile/capacity_torpedo.py
deleted file mode 100644
index 5495a956..00000000
--- a/famodel/anchors/anchors_famodel_profile/capacity_torpedo.py
+++ /dev/null
@@ -1,159 +0,0 @@
-  
-import numpy as np
-import matplotlib.pyplot as plt
-from .capacity_soils import clay_profile
-from .capacity_plots import plot_torpedo
-
-def getCapacityTorpedo(profile, soil_type, D1, D2, L1, L2, zlug, ballast, Ha, Va, plot=True):
-    '''Calculate the inclined load capacity of a torpedo pile in clay following S. Kay methodology.
-    The calculation is based on the soil profile, anchor geometry and inclined load.  
-
-    Parameters
-    ----------
-    profile : array
-        Clay soil profile (z, Su, gamma)
-            Clay soil profile (z (m), Su (kPa), gamma (kN/m³))
-    soil_type : string
-        Select soil condition, 'clay' 
-    D1 : float 
-        Wing diameter (m)
-    D2 : float    
-        Shaft diameter (m)
-    L1 : float 
-        Winged section length (m)
-    L2 : float 
-        Shaft section length (m)
-    zlug : float 
-        Padeye embedment depth (m)       
-    Ha : float       
-        Horizontal load at pile lug elevation (N)
-    Va : float          
-        Vertical load at pile lug elevation (N)
-    plot : bool
-        Plot the capacity envelope if True
-
-    Returns
-    -------
-    Dictionary with capcity, weigth and UC.
-    '''
-
-    t = (6.35 + D2*20)/1e3           # Torpedo pile wall thickness (m), API RP2A-WSD
-    rhows = 66.90e3                  # Submerged steel specific weight (N/m3)
-    rhow = 10e3                      # Water specific weight (N/m3) 
-    
-    # Average effective width
-    L = L1 + L2
-    A_wing_plane_1 = (D1 - D2)*L1
-    A_wing_plane_2 = (D1 - D2)*np.cos(np.deg2rad(45))/2*L1
-    A_shaft = D2*L
-    
-    # Choose based on direction:
-    plane = '1'  # or '2'
-    
-    if plane == '1':
-        Dstar = (A_wing_plane_1 + A_shaft)/L
-    elif plane == '2':
-        Dstar = (A_wing_plane_2 + A_shaft)/L
-     
-    z0, f_Su, f_sigma_v_eff, f_gamma, f_alpha = clay_profile(profile)
-
-    a = zlug
-    c = zlug + L1 + L2
-    profile_depth = profile[-1, 0]
-
-    if c > profile_depth:
-        raise ValueError(
-            f'Soil profile does not cover the full pile length.\n'
-            f'   → Pile tip depth: {c:.2f} m\n'
-            f'   → Soil profile depth: {profile_depth:.2f} m\n'
-            f'Extend the soil profile to at least the pile tip depth to run the capacity model.'
-        )
-
-    z_vals = np.linspace(a, c, 100)
-    Su_vals = f_Su(z_vals)
-    alpha_vals = np.array([f_alpha(z) for z in z_vals])
-
-    ez_soil = np.trapz(z_vals*Su_vals, z_vals)/np.trapz(Su_vals, z_vals)
-    Su_e = f_Su(ez_soil)
-    alpha_e = f_alpha(ez_soil)
-    print(f"Su_e = {Su_e:.2f} kPa, ez_soil = {ez_soil:.2f} m, alpha_e = {alpha_e:.2f}")
-
-    def PileWeight(Len1, Len2, Dia1, Dia2, tw, rho):
-        return ((np.pi/4)*(Dia1**2 - (Dia1 - 2*tw)**2)*(Len1 + Len2) + 4*Len2*Dia2*tw)*rho
-
-    def PileSurface(Len1, Len2, Dia1, Dia2):
-        return np.pi*Dia1*(Len1 + Len2) + 8*Len2*Dia2*0.9
-
-    Np_free = 3.45
-    Hmax = Np_free*L*Dstar*Su_e
-    Vmax = PileSurface(L1, L2, D1, D2)*alpha_e*Su_e + PileWeight(L1, L2, D1, D2, t, rhows) + ballast
-    
-    # Pile weight (inc. auxiliary elements) assessed as a factor
-    Wp = 1.10*PileWeight(L1, L2, D1, D2, t, (rhows + rhow)) + ballast
-
-    # Calculate actual ez_su to L ratio
-    ez_ratio = (ez_soil - zlug)/L; print(f"ez_ratio = {ez_ratio:.2f} m")
-
-    # Assign aVH and bVH based on ez_su/L
-    if np.isclose(ez_ratio, 2/3, atol=0.05):
-        aVH = 0.5 + L/Dstar
-        bVH = 4.5 - L/(3*Dstar)
-        mode = 'deep mobilization (2/3)'
-    elif 0.45 <= ez_ratio <= 0.75:
-        aVH = 4.5 + L/(2*Dstar)
-        bVH = 3.5 - L/(4*Dstar)
-        mode = 'moderate mobilization (1/2 – 3/4)'
-    print(f'Interaction exponents set to aVH = {aVH:.2f}, bVH = {bVH:.2f} [{mode}]')
-    
-    UC = (Ha/Hmax)**aVH + (Va/Vmax)**bVH
-
-    deg = np.linspace(0, 90, 20)
-    x = np.cos(np.deg2rad(deg))
-    y = (1 - x**bVH)**(1/aVH)
-    X = Hmax*x; Y = Vmax*y
-
-    if plot:
-        plt.plot(X, Y, color='blue', label='VH Envelope')
-        plt.plot(H, V, 'o', color='red', label='Load Point')
-        plt.xlabel('Horizontal Load (N)')
-        plt.ylabel('Vertical Load (N)')
-        plt.title('VH torpedo pile capacity envelope')
-        plt.grid(True)
-        plt.legend()
-        plt.axis([0, 1.3*max(X[0], H), 0, 1.3*max(Y[-1], V)])
-        plt.show()
-
-    resultsTorpedo = {
-        'Horizontal max.': Hmax,
-        'Vertical max.': Vmax,
-        'Unity check': UC,
-        'Weight pile': Wp
-    }
-
-    return resultsTorpedo
-
-if __name__ == '__main__':
-    
-    profile_clay = np.array([
-        [ 0.0, 50, 8.0],
-        [20.0, 50, 8.5],
-        [25.0, 50, 8.5],
-        [50.0, 50, 9.0]
-    ])
-
-    D1 = 3.0             # Wing diameter (m)
-    D2 = 1.5             # Shaft diamter (m)
-    L1 = 11.0            # Winged section length (m) 
-    L2 = 10.0             # Shaft section length (m)
-    zlug = 15.0          # Padeye depth (m)
-    ballast = 10000      # Ballast load (N)
-    H = 6.0e6            # Horizontal load (N)
-    V = 8.0e6            # Vertical load (N)
-
-    results = getCapacityTorpedo(profile_clay, 'clay', D1, D2, L1, L2, zlug, ballast, H, V, plot=True)
-    print("\n--- Torpedo Pile Capacity Results ---")
-    for key, val in results.items():
-        print(f"{key}: {val:.2f}")
-        
-    plot_torpedo(profile_clay, 'clay', D1, D2, L1, L2, zlug, title='Torpedo Pile in Clay Profile')
-
diff --git a/famodel/anchors/getCapacityAnchor_profile.py b/famodel/anchors/getCapacityAnchor_profile.py
deleted file mode 100644
index 4e7cf491..00000000
--- a/famodel/anchors/getCapacityAnchor_profile.py
+++ /dev/null
@@ -1,94 +0,0 @@
-
-from famodel.anchors.anchor_profile import Anchor
-from famodel.anchors.anchors_famodel_profile.capacity_plots import plot_load
-import numpy as np  
-
-# Define the soil profile
-soil_profile = np.array([
-    [ 1.0,  10, 8.0],
-    [ 2.0,  25, 8.5],
-    [ 8.0,  50, 9.0],
-    [16.0, 100, 9.5],
-    [25.0, 100, 9.5]
-])
-
-# Create Anchor object
-anchor = Anchor(
-    dd={
-        'type': 'suction',
-        'design': {'D': 2.5, 'L': 10.0, 'zlug': 6.0, 'soil_type': 'clay'},
-        'soil_properties': {'clay': soil_profile}
-    },
-    ms=None,
-    r=[0.0, 0.0, 0.0],
-    aNum=0,
-    id='A1',
-    g=9.81,
-    rho=1025
-)
-
-# Assign loads manually
-anchor.loads = {
-    'Hm': 3e6,     # Horizontal mudline load (N)
-    'Vm': 1e6      # Vertical mudline load (N)
-}
-
-# Also assign mooring line properties manually
-anchor.line_type = 'chain'
-anchor.d = 0.16    # Chain diameter (m)
-anchor.w = 5000.0  # Nominal submerged weight (N/m)
-
-# --- Step 1: Compute Lug Forces ---
-Ha, Va = anchor.getLugForces(
-    ground_conds=anchor.dd['soil_properties'],
-    Hm=anchor.loads['Hm'],
-    Vm=anchor.loads['Vm'],
-    thetam=np.degrees(np.arctan2(anchor.loads['Vm'], anchor.loads['Hm'])),
-    zlug=anchor.dd['design']['zlug'],
-    line_type=anchor.line_type,
-    d=anchor.d,
-    w=anchor.w,
-    plot=True  
-)
-
-# Print Lug Forces
-print('\nLug Forces Computed:')
-print(f'Ha = {Ha:.2f} N')
-print(f'Va = {Va:.2f} N')
-
-# --- Step 2: Compute Anchor Capacity ---
-anchor.getCapacityAnchor(
-    ground_conds=anchor.dd['soil_properties'],
-    Hm=anchor.loads['Hm'],
-    Vm=anchor.loads['Vm'],
-    thetam=np.degrees(np.arctan2(anchor.loads['Vm'], anchor.loads['Hm'])),
-    zlug=anchor.dd['design']['zlug'],
-    line_type=anchor.line_type,
-    d=anchor.d,
-    w=anchor.w,
-    plot=True  
-)
-
-# Print Capacity Results
-print('\nCapacity Results:')
-for key, value in anchor.capacity_results.items():
-    print(f'{key}: {value:.2f}')
-    
-# --- Step 3: Optimize Anchor Geometry ---
-anchor.getSizeSuction(
-    geom=[anchor.dd['design']['L'], anchor.dd['design']['D']],
-    geomKeys=['L', 'D'],
-    geomBounds=[(5.0, 15.0), (2.0, 6.0)],
-    loads=None,
-    minfs={'Ha': 1.0, 'Va': 1.0},
-    lambdap_con=[3, 6],
-    zlug_fix=False,
-    plot=True
-)
-
-print('\nFinal Optimized Anchor:')
-print('Design:', anchor.dd['design'])
-print('Capacity Results:', anchor.capacity_results)
-
-# --- Step 4: Visualize Anchor Geometry ---
-anchor.getCombinedPlot()
\ No newline at end of file
diff --git a/famodel/anchors/getCapacityHelical_map.py b/famodel/anchors/getCapacityHelical_map.py
deleted file mode 100644
index 1710e413..00000000
--- a/famodel/anchors/getCapacityHelical_map.py
+++ /dev/null
@@ -1,70 +0,0 @@
-
-from anchor_map import Anchor
-
-# --- Define soil profile ---
-profile_map = [
-    {
-        'name': 'CPT_H1',
-        'x': 0.0, 'y': 0.0,
-        'layers': [
-            {'top':  1.0, 'bottom': 10.0, 'soil_type': 'sand', 'gamma_top': 10.0, 'gamma_bot': 11.0, 'phi_top': 30, 'phi_bot': 32, 'Dr_top': 60, 'Dr_bot': 60},
-            {'top': 10.0, 'bottom': 20.0, 'soil_type': 'sand', 'gamma_top': 11.0, 'gamma_bot': 11.5, 'phi_top': 36, 'phi_bot': 38, 'Dr_top': 60, 'Dr_bot': 80}
-        ]
-    }
-]
-
-# --- Create helical anchor object ---
-anchor = Anchor(
-    dd = {
-        'type': 'helical',
-        'design': {
-            'D': 1.7,         # Helix diameter (m)
-            'L': 12.0,        # Depth (m)
-            'd': 0.3,         # Shaft diameter (m)
-            'zlug': 4.0       # Padeye depth (m)
-        }
-    },
-    r = [0.0, 0.0, 0.0]
-)
-
-# Assign loads and mooring info
-anchor.loads = {
-    'Hm': 8.8e6,
-    'Vm': 1.2e6
-}
-anchor.line_type = 'chain'
-anchor.d = 0.16
-anchor.w = 5000.0
-
-# Assign local soil
-anchor.setSoilProfile(profile_map)
-
-# --- Step 1: Lug Forces ---
-layers, Ha, Va = anchor.getLugForces(
-    Hm = anchor.loads['Hm'],
-    Vm = anchor.loads['Vm'],
-    zlug = anchor.dd['design']['zlug'],
-    line_type = anchor.line_type,
-    d = anchor.d,
-    w = anchor.w,
-    plot = True
-)
-
-print('\nLug Forces Computed:')
-print(f'Ha = {Ha:.2f} N')
-print(f'Va = {Va:.2f} N')
-
-# --- Step 2: Capacity ---
-anchor.getCapacityAnchor(
-    Hm = anchor.loads['Hm'],
-    Vm = anchor.loads['Vm'],
-    zlug = anchor.dd['design']['zlug'],
-    line_type = anchor.line_type,
-    d = anchor.d,
-    w = anchor.w,
-    plot = True
-)
-
-print('\nCapacity Results:')
-for key, val in anchor.capacity_results.items():
-    print(f'{key}: {val:.2f}')
diff --git a/famodel/anchors/getCapacityHelical_sand.py b/famodel/anchors/getCapacityHelical_sand.py
deleted file mode 100644
index 1710e413..00000000
--- a/famodel/anchors/getCapacityHelical_sand.py
+++ /dev/null
@@ -1,70 +0,0 @@
-
-from anchor_map import Anchor
-
-# --- Define soil profile ---
-profile_map = [
-    {
-        'name': 'CPT_H1',
-        'x': 0.0, 'y': 0.0,
-        'layers': [
-            {'top':  1.0, 'bottom': 10.0, 'soil_type': 'sand', 'gamma_top': 10.0, 'gamma_bot': 11.0, 'phi_top': 30, 'phi_bot': 32, 'Dr_top': 60, 'Dr_bot': 60},
-            {'top': 10.0, 'bottom': 20.0, 'soil_type': 'sand', 'gamma_top': 11.0, 'gamma_bot': 11.5, 'phi_top': 36, 'phi_bot': 38, 'Dr_top': 60, 'Dr_bot': 80}
-        ]
-    }
-]
-
-# --- Create helical anchor object ---
-anchor = Anchor(
-    dd = {
-        'type': 'helical',
-        'design': {
-            'D': 1.7,         # Helix diameter (m)
-            'L': 12.0,        # Depth (m)
-            'd': 0.3,         # Shaft diameter (m)
-            'zlug': 4.0       # Padeye depth (m)
-        }
-    },
-    r = [0.0, 0.0, 0.0]
-)
-
-# Assign loads and mooring info
-anchor.loads = {
-    'Hm': 8.8e6,
-    'Vm': 1.2e6
-}
-anchor.line_type = 'chain'
-anchor.d = 0.16
-anchor.w = 5000.0
-
-# Assign local soil
-anchor.setSoilProfile(profile_map)
-
-# --- Step 1: Lug Forces ---
-layers, Ha, Va = anchor.getLugForces(
-    Hm = anchor.loads['Hm'],
-    Vm = anchor.loads['Vm'],
-    zlug = anchor.dd['design']['zlug'],
-    line_type = anchor.line_type,
-    d = anchor.d,
-    w = anchor.w,
-    plot = True
-)
-
-print('\nLug Forces Computed:')
-print(f'Ha = {Ha:.2f} N')
-print(f'Va = {Va:.2f} N')
-
-# --- Step 2: Capacity ---
-anchor.getCapacityAnchor(
-    Hm = anchor.loads['Hm'],
-    Vm = anchor.loads['Vm'],
-    zlug = anchor.dd['design']['zlug'],
-    line_type = anchor.line_type,
-    d = anchor.d,
-    w = anchor.w,
-    plot = True
-)
-
-print('\nCapacity Results:')
-for key, val in anchor.capacity_results.items():
-    print(f'{key}: {val:.2f}')
diff --git a/famodel/anchors/getCapacitySuction_map.py b/famodel/anchors/getCapacitySuction_map.py
deleted file mode 100644
index 1c41ac6e..00000000
--- a/famodel/anchors/getCapacitySuction_map.py
+++ /dev/null
@@ -1,118 +0,0 @@
-
-from anchor_map import Anchor
-import numpy as np
-from famodel.anchors.anchors_famodel_map.capacity_plots_map import plot_load
-
-# --- Define soil profile ---
-profile_map = [
-    {
-        'name': 'CPT_A1',
-        'x': 0.0, 'y': 0.0,
-        'layers': [
-            {'top':  2.0, 'bottom':  4.0, 'soil_type': 'clay', 'gamma_top':  8.0, 'gamma_bot':  8.5, 'Su_top':  10, 'Su_bot':  25},
-            {'top':  4.0, 'bottom':  6.0, 'soil_type': 'clay', 'gamma_top':  8.5, 'gamma_bot':  9.0, 'Su_top':  25, 'Su_bot':  50},
-            {'top':  6.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top':  9.0, 'gamma_bot':  9.5, 'Su_top':  50, 'Su_bot': 100},
-            {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top':  9.5, 'gamma_bot':  9.5, 'Su_top': 100, 'Su_bot': 100}
-        ]
-    },
-    {
-        'name': 'CPT_B1',
-        'x': 500.0, 'y': 0.0,
-        'layers': [
-            {'top':  2.0, 'bottom':  4.0, 'soil_type': 'clay', 'gamma_top':  8.5, 'gamma_bot':  9.0, 'Su_top':  15, 'Su_bot':  30},
-            {'top':  4.0, 'bottom':  6.0, 'soil_type': 'clay', 'gamma_top':  9.0, 'gamma_bot':  9.5, 'Su_top':  30, 'Su_bot':  55},
-            {'top':  6.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top':  9.5, 'gamma_bot': 10.0, 'Su_top':  55, 'Su_bot': 105},
-            {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.0, 'Su_top': 105, 'Su_bot': 105}
-        ]
-    },
-    {
-        'name': 'CPT_A2',
-        'x': 0.0, 'y': 500.0,
-        'layers': [
-            {'top':  2.0, 'bottom':  4.0, 'soil_type': 'clay', 'gamma_top':  7.5, 'gamma_bot':  8.0, 'Su_top':   5, 'Su_bot':  20},
-            {'top':  4.0, 'bottom':  8.0, 'soil_type': 'clay', 'gamma_top':  8.0, 'gamma_bot':  8.5, 'Su_top':  20, 'Su_bot':  45},
-            {'top':  8.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top':  8.5, 'gamma_bot':  9.0, 'Su_top':  45, 'Su_bot':  95},
-            {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top':  9.0, 'gamma_bot':  9.0, 'Su_top':  95, 'Su_bot':  95}
-        ]
-    },
-    {
-        'name': 'CPT_B2',
-        'x': 500.0, 'y': 500.0,
-        'layers': [
-            {'top':  1.0, 'bottom':  2.0, 'soil_type': 'clay', 'gamma_top':  9.0, 'gamma_bot':  9.5, 'Su_top':  20, 'Su_bot':  35},
-            {'top':  2.0, 'bottom':  8.0, 'soil_type': 'clay', 'gamma_top':  9.5, 'gamma_bot': 10.0, 'Su_top':  35, 'Su_bot':  60},
-            {'top':  8.0, 'bottom': 16.0, 'soil_type': 'clay', 'gamma_top': 10.0, 'gamma_bot': 10.5, 'Su_top':  60, 'Su_bot': 110},
-            {'top': 16.0, 'bottom': 25.0, 'soil_type': 'clay', 'gamma_top': 10.5, 'gamma_bot': 10.5, 'Su_top': 110, 'Su_bot': 110}
-        ]
-    }
-]
-
-
-anchor = Anchor(
-    dd = {'type': 'suction', 'design': {'D': 2.5, 'L': 12.0, 'zlug': 9.0}},
-    r = [250.0, 000.0, 000.0]
-)
-
-# Assign loads manually
-anchor.loads = {
-    'Hm': 3e6,     # Horizontal mudline load (N)
-    'Vm': 2e6      # Vertical mudline load (N)
-}
-
-# Assign line properties manually
-anchor.line_type = 'chain'
-anchor.d = 0.16    # Chain diameter (m)
-anchor.w = 5000.0  # Nominal submerged weight (N/m)
-
-# --- Step 0: Create anchor based grid CPTs ---
-anchor.setSoilProfile(profile_map)
-
-
-# --- Step 1: Compute Lug Forces ---
-layers, Ha, Va = anchor.getLugForces(
-    Hm = anchor.loads['Hm'],
-    Vm = anchor.loads['Vm'],
-    zlug = anchor.dd['design']['zlug'],
-    line_type = anchor.line_type,
-    d = anchor.d,
-    w = anchor.w,
-    plot = True
-)
-
-print('\nLug Forces Computed:')
-print(f'Ha = {Ha:.2f} N')
-print(f'Va = {Va:.2f} N')
-
-# --- Step 2: Compute Capacity ---
-anchor.getCapacityAnchor(
-    Hm = anchor.loads['Hm'],
-    Vm = anchor.loads['Vm'],
-    zlug = anchor.dd['design']['zlug'],
-    line_type = anchor.line_type,
-    d = anchor.d,
-    w = anchor.w,
-    plot = True
-)
-
-print('\nCapacity Results:')
-for key, value in anchor.capacity_results.items():
-    print(f'{key}: {value:.2f}')
-
-# --- Step 3: Optimize Anchor Geometry ---
-anchor.getSizeAnchor(
-    geom = [anchor.dd['design']['L'], anchor.dd['design']['D']],
-    geomKeys = ['L', 'D'],
-    geomBounds = [(5.0, 15.0), (2.0, 6.0)],
-    loads = None,
-    minfs = {'Ha': 1.0, 'Va': 1.0},
-    lambdap_con = [3, 6],
-    zlug_fix = False,
-    plot = True
-)
-
-print('\nFinal Optimized Anchor:')
-print('Design:', anchor.dd['design'])
-print('Capacity Results:', anchor.capacity_results)
-
-# --- Step 4: Visualize Anchor Geometry ---
-anchor.getCombinedPlot()
diff --git a/famodel/anchors/images/Drilledandgroutedpiles/Drilled.png b/famodel/anchors/images/Drilledandgroutedpiles/Drilled.png
new file mode 100644
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diff --git a/famodel/anchors/images/Drivenpiles/Clay - deformed pile.png b/famodel/anchors/images/Drivenpiles/Clay - deformed pile.png
new file mode 100644
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diff --git a/famodel/anchors/images/Drivenpiles/Driven.png b/famodel/anchors/images/Drivenpiles/Driven.png
new file mode 100644
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diff --git a/famodel/anchors/images/Drivenpiles/pycurves - API.png b/famodel/anchors/images/Drivenpiles/pycurves - API.png
new file mode 100644
index 00000000..953d7c1a
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diff --git a/famodel/anchors/images/Drivenpiles/pycurves - Lovera.png b/famodel/anchors/images/Drivenpiles/pycurves - Lovera.png
new file mode 100644
index 00000000..88214936
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diff --git a/famodel/anchors/images/Drivenpiles/pycurves - Matlock.png b/famodel/anchors/images/Drivenpiles/pycurves - Matlock.png
new file mode 100644
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diff --git a/famodel/anchors/images/Drivenpiles/pycurves - Reese.png b/famodel/anchors/images/Drivenpiles/pycurves - Reese.png
new file mode 100644
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diff --git a/famodel/anchors/images/Helicalpiles/Clay - deformed pile.png b/famodel/anchors/images/Helicalpiles/Clay - deformed pile.png
new file mode 100644
index 00000000..20b7f0aa
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diff --git a/famodel/anchors/images/Helicalpiles/Helical.png b/famodel/anchors/images/Helicalpiles/Helical.png
new file mode 100644
index 00000000..050ac710
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diff --git a/famodel/anchors/images/Helicalpiles/Sand - helical pile.png b/famodel/anchors/images/Helicalpiles/Sand - helical pile.png
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diff --git a/famodel/anchors/images/Plateanchors/Plate.png b/famodel/anchors/images/Plateanchors/Plate.png
new file mode 100644
index 00000000..0698f449
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diff --git a/famodel/anchors/images/Suctionpiles/Clay - suction envelope.png b/famodel/anchors/images/Suctionpiles/Clay - suction envelope.png
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index 00000000..81af02ee
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diff --git a/famodel/anchors/images/Torpedopiles/Clay - torpedo envelope.png b/famodel/anchors/images/Torpedopiles/Clay - torpedo envelope.png
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diff --git a/famodel/geography.py b/famodel/geography.py
index ec4219c1..f930a901 100644
--- a/famodel/geography.py
+++ b/famodel/geography.py
@@ -3,29 +3,17 @@
 import os
 import numpy as np
 import matplotlib.pyplot as plt
-
 import moorpy as mp
 from moorpy.helpers import set_axes_equal
 import yaml
-
 import pandas as pd
 import geopandas as gpd
 from shapely.geometry import Point, Polygon, LineString
-
-
-
-
 import famodel.seabed.seabed_tools as sbt
-
-
-
-
 from pyproj import CRS
 from pyproj.aoi import AreaOfInterest
 from pyproj.database import query_utm_crs_info
 
-
-
 def getLatLongCRS(epsg_code=4326):
     '''Returns a coordinate reference system (CRS) object from the pyproj package of a 'wordly' CRS with units of latitude and longitude
     
@@ -92,7 +80,6 @@ def getTargetCRS(longitudes, latitudes):
 
     return target_crs
 
-
 def getCustomCRS(long, lat):
     '''Seemingly way too simple of a method to create a pyproj CRS centered around a custom geographical point
 
@@ -113,8 +100,6 @@ def getCustomCRS(long, lat):
 
     return custom_crs
 
-
-
 def getLeaseCoords(lease_name):
 
     # read in the BOEM shapefile that contains all Wind Energy Lease Areas (can use other shapefiles for aliquots)
@@ -140,17 +125,17 @@ def getLeaseCoords(lease_name):
         raise ValueError(f"The lease area name '{lease_area}' is not supported yet")
     
     # extract the longitude and latitude coordinates of the lease area
-    area_longs, area_lats = lease_area.geometry.unary_union.exterior.coords.xy
+    #area_longs, area_lats = lease_area.geometry.unary_union.exterior.coords.xy
+    area_longs, area_lats = lease_area.geometry.union_all().exterior.coords.xy
+
 
     # calculate the centroid of the lease area
     centroid = ( lease_area.geometry.centroid.values.x[0], lease_area.geometry.centroid.values.y[0] )
 
-    return area_longs, area_lats, centroid
-        
-
-
 
 
+    return area_longs, area_lats, centroid
+        
 def convertLatLong2Meters(longs, lats, centroid, latlong_crs, target_crs, return_centroid=False):
     '''input longs/lats need to be in EPSG:4326 CRS
     Longs and Lats need to be in pairs, i.e., the first entry to longs and 
@@ -206,7 +191,6 @@ def convertLatLong2Meters(longs, lats, centroid, latlong_crs, target_crs, return
     else:
         return xs, ys
 
-
 def convertMeters2LatLong(xs, ys, centroid, latlong_crs, target_crs, mesh=False):
     '''Input xs and ys need to be in the target CRS
     Xs and Ys need to be in pairs, i.e. the first entry to xs and the 
@@ -250,9 +234,6 @@ def convertMeters2LatLong(xs, ys, centroid, latlong_crs, target_crs, mesh=False)
     
     return longs, lats
 
-
-
-
 def getMapBathymetry(gebcofilename):
 
     # load the GEBCO bathymetry file
@@ -277,7 +258,6 @@ def getMapBathymetry(gebcofilename):
 
     return longs, lats, depths, ncols, nrows
 
-
 def convertBathymetry2Meters(longs, lats, depths, centroid, centroid_utm, 
                              latlong_crs, target_crs, ncols, nrows,
                              xs=[], ys=[]):
@@ -331,8 +311,6 @@ def convertBathymetry2Meters(longs, lats, depths, centroid, centroid_utm,
 
     return bathXs, bathYs, bath_depths
 
-        
-        
 def writeBathymetryFile(moorpy_bathymetry_filename, bathXs, bathYs, bath_depths, soil=False):
     '''Write a MoorDyn/MoorPy-style bathymetry text file based on provided
     x and y grid line values and a 2D array of depth values.'''
@@ -355,8 +333,6 @@ def writeBathymetryFile(moorpy_bathymetry_filename, bathXs, bathYs, bath_depths,
         f.write('\n')
     f.close()
 
-
-
 def getLeaseAndBathymetryInfo(lease_name, gebco_file, bath_ncols=100, bath_nrows=100):
 
     # initialize the conventional lat/long CRS
@@ -369,12 +345,15 @@ def getLeaseAndBathymetryInfo(lease_name, gebco_file, bath_ncols=100, bath_nrows
     custom_crs = getCustomCRS(centroid[0], centroid[1])
     
     # convert the lease boundary to meters
-    lease_xs, lease_ys, centroid_utm = convertLatLong2Meters(lease_longs, lease_lats, centroid, latlong_crs, custom_crs, return_centroid=True)
+    lease_xs, lease_ys, centroid_utm = convertLatLong2Meters(lease_longs, lease_lats, centroid, 
+                                                             latlong_crs, custom_crs, return_centroid=True)
 
     # get bathymetry information from a GEBCO file (or other)
     bath_longs, bath_lats, bath_depths, ncols, nrows = getMapBathymetry(gebco_file)
+    
     # convert bathymetry to meters
-    bath_xs, bath_ys, bath_depths = convertBathymetry2Meters(bath_longs, bath_lats, bath_depths, centroid, centroid_utm, latlong_crs, custom_crs, bath_ncols, bath_nrows)
+    bath_xs, bath_ys, bath_depths = convertBathymetry2Meters(bath_longs, bath_lats, bath_depths, centroid, centroid_utm, 
+                                                             latlong_crs, custom_crs, bath_ncols, bath_nrows)
     # export to MoorPy-readable file
     bathymetryfile = f'bathymetry_{bath_ncols}x{bath_nrows}.txt'
     writeBathymetryFile(bathymetryfile, bath_xs, bath_ys, bath_depths)
@@ -393,10 +372,6 @@ def getLeaseAndBathymetryInfo(lease_name, gebco_file, bath_ncols=100, bath_nrows
 
     return info
 
-
-
-
-
 def getSoilType(x, y, centroid, latlong_crs, custom_crs, soil_file):
     """Function to return the name of the soil below a specific x/y coordinate by creating shapely polygons based on the shapefile data.
     It loops through all polygons in the shapefile and if the x/y position is contained in that polygon, it returns the soil of that polygon."""
@@ -439,8 +414,6 @@ def getSoilType(x, y, centroid, latlong_crs, custom_crs, soil_file):
 
     return soil_type
 
-
-
 def getSoilGrid(centroid, latlong_crs, custom_crs, soil_file, nrows=100, ncols=100, xbound=None, ybound=None):
     """Note: can make the outer shapely shape have 'holes' of the inner shapely shapes"""
     
@@ -486,6 +459,7 @@ def getSoilGrid(centroid, latlong_crs, custom_crs, soil_file, nrows=100, ncols=1
         ys = np.linspace(ybound[0], ybound[-1], nrows)
     else:
         ys = np.linspace( np.min([np.min(soil_ys[i]) for i in range(len(soil_shapes))]),  np.max([np.max(soil_ys[i]) for i in range(len(soil_shapes))]),  nrows)
+    
     soil_grid = np.zeros([len(ys), len(xs)])
 
     # for each manmade grid point, loop through all the polygons and determine whether that grid point is within the shape or not
@@ -507,8 +481,6 @@ def getSoilGrid(centroid, latlong_crs, custom_crs, soil_file, nrows=100, ncols=1
     soil_grid = np.array(soil_grid_list)        # saving to list and then changing to np.array because I couldn't figure out how else to do it with strings
 
     return xs, ys, soil_grid
-
-
         
 def plot3d(lease_xs, lease_ys, bathymetryfilename, area_on_bath=False, args_bath={}, xbounds=None, ybounds=None, zbounds=None):
     '''Plot aspects of the Project object in matplotlib in 3D'''
@@ -520,12 +492,12 @@ def plot3d(lease_xs, lease_ys, bathymetryfilename, area_on_bath=False, args_bath
     fig = plt.figure(figsize=(6,4))
     ax = plt.axes(projection='3d')
 
-    if xbounds != None:
-        ax.set_xlim(xbounds[0], xbounds[1])
-    if ybounds != None:
-        ax.set_ylim(ybounds[0], ybounds[1])
-    if zbounds != None:
-        ax.set_zlim(zbounds[0], zbounds[1])
+    # if xbounds != None:
+    #     ax.set_xlim(xbounds[0], xbounds[1])
+    # if ybounds != None:
+    #     ax.set_ylim(ybounds[0], ybounds[1])
+    # if zbounds != None:
+    #     ax.set_zlim(zbounds[0], zbounds[1])
 
     # plot the lease area in a red color, if desired
     ax.plot(lease_xs, lease_ys, np.zeros(len(lease_xs)), color='r', zorder=100)
@@ -535,7 +507,7 @@ def plot3d(lease_xs, lease_ys, bathymetryfilename, area_on_bath=False, args_bath
     # !!!! include option to plot entire bathymetry file or not
 
     if isinstance(bathymetryfilename, str):
-        bathGrid_Xs, bathGrid_Ys, bathGrid = sbt.readBathymetryFile(bathymetryfilename)         # parse through the MoorDyn/MoorPy-formatted bathymetry file
+        bathGrid_Xs, bathGrid_Ys, bathGrid = sbt.readBathymetryFile(bathymetryfilename) # parse through the MoorDyn/MoorPy-formatted bathymetry file
         X, Y = np.meshgrid(bathGrid_Xs, bathGrid_Ys)                                    # create a 2D mesh of the x and y values
         bath = ax.plot_surface(X, Y, -bathGrid, rstride=1, cstride=1,
                                vmin=args_bath['zlim'][0], vmax=args_bath['zlim'][1], 
@@ -558,8 +530,6 @@ def plot3d(lease_xs, lease_ys, bathymetryfilename, area_on_bath=False, args_bath
 
     return fig, ax
 
-
-
 def projectAlongSeabed(x, y, bathXs, bathYs, bath_depths):
     '''Project a set of x-y coordinates along a seabed surface (grid),
     returning the corresponding z coordinates.'''
@@ -925,7 +895,7 @@ def addState(self, ax, states=[], kwargs={}):
     # get bathymetry information from a GEBCO file (or other)
     bath_longs, bath_lats, bath_depths, ncols, nrows = getMapBathymetry('bathymetry/gebco_2023_n41.3196_s40.3857_w-125.2881_e-123.9642.asc')
     # convert bathymetry to meters
-    ncols = 500
+    ncols = 500C:/Users/fmoreno/Downloads/GEBCO_25_Jun_2025_3c682d73375c/GEBCO_25_Jun_2025_3c682d73375c/gebco_2024_n44.2_s44.0_w12.5_e12.8.asc
     nrows = 500
     bath_xs, bath_ys, bath_depths = convertBathymetry2Meters(bath_longs, bath_lats, bath_depths, centroid, centroid_utm, latlong_crs, custom_crs, ncols, nrows)
     # export to MoorPy-readable file
@@ -942,11 +912,73 @@ def addState(self, ax, states=[], kwargs={}):
     lease_name = 'GulfofMaine_ResearchArray'
     gebco_file = __location__+'\\..\\geography\\gebco_2024_n44.1458_s41.4761_w-70.9497_e-66.2146.asc'
     info = getLeaseAndBathymetryInfo(lease_name, gebco_file)
+    
+    x_center = np.mean(info['lease_xs'])
+    y_center = np.mean(info['lease_ys'])
+    
+    zoom = 8000  
+
+    xbounds = [x_center - zoom, x_center + zoom]
+    ybounds = [y_center - zoom, y_center + zoom]
+    
+    fig, ax = plot3d(info['lease_xs'], info['lease_ys'], 'bathymetry_100x100.txt',
+        area_on_bath=True, args_bath={'cmap': 'gist_earth', 'zlim': [-500, 50]},
+        xbounds=xbounds, ybounds=ybounds)
+    
+    plt.show()
+    
+    # Load bathymetry data manually
+    with open('GulfOfMaine_bathymetry_100x100.txt', 'r') as f:
+        lines = f.readlines()
+    
+    nGridX = int(lines[1].split()[1])
+    nGridY = int(lines[2].split()[1])
+    x_vals = np.array([float(val) for val in lines[3].split()])
+    y_vals = []
+    z_matrix = []
+    
+    for line in lines[4:4+nGridY]:
+        parts = line.split()
+        y_vals.append(float(parts[0]))
+        z_matrix.append([float(z) for z in parts[1:]])
+
+    
+    # Extract y and z
+    z_vals = np.array(z_matrix)
+    y_vals = np.array(y_vals)
+
+    
+    # Now crop using xbounds and ybounds
+    xmask = (x_vals >= xbounds[0]) & (x_vals <= xbounds[1])
+    ymask = (y_vals >= ybounds[0]) & (y_vals <= ybounds[1])
+    
+    x_crop = x_vals[xmask]
+    y_crop = y_vals[ymask]
+    z_crop = z_vals[ymask][:, xmask]
+    
+    # Plot manually using Axes3D
+    X, Y = np.meshgrid(x_crop, y_crop)
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
+    ax.plot_surface(X, Y, z_crop, cmap='gist_earth')
+    ax.set_title('Zoomed Bathymetry')
+    
+    lease_xs = info['lease_xs']
+    lease_ys = info['lease_ys']
 
+    ax.plot(lease_xs, lease_ys, zs=200, zdir='z', color='r', linewidth=2, label='Lease Area')
+    ax.legend()
 
     plt.show()
 
-    a = 2
+
+ 
+    # plot3d(info['lease_xs'], info['lease_ys'], 'bathymetry_100x100.txt', area_on_bath=False, args_bath={'zlim':[-3000, 500], 'cmap': 'gist_earth'})
+           # xbounds=(info['bath_xs'].min(), info['bath_xs'].min()),
+           # ybounds=(info['bath_ys'].min(), info['bath_ys'].min()),
+           # zbounds=(info['bath_depths'].min(), info['bath_depths'].min())
+
+
 
 
 
diff --git a/famodel/project.py b/famodel/project.py
index 31340c2d..1bc0ae93 100644
--- a/famodel/project.py
+++ b/famodel/project.py
@@ -1,6 +1,9 @@
 """Project class for FAModel, containing information and key methods for
 the site information and design information that make up a project."""
 
+import sys
+sys.path.append(r'C:\Code\FAModel_anchors')
+
 import os
 import numpy as np
 import matplotlib.pyplot as plt
@@ -1002,7 +1005,6 @@ def seabedIntersect(self, r, u):
         
         return r_i
 
-
     def projectAlongSeabed(self, x, y):
         '''Project a set of x-y coordinates along a seabed surface (grid),
         returning the corresponding z coordinates.'''
@@ -1022,206 +1024,148 @@ def projectAlongSeabed(self, x, y):
             
         return z
 
-
-
     # METHODS TO USE WITH ANCHOR TOOLS
-
-    def loadSoil(self, filename=None, yaml=None):
+    def loadSoil(self, filename=None, yaml=None, soil_mode='uniform', profile_source=None):
         '''
-        Load geoetechnical information from an input file (format TBD), convert to
-        a rectangular grid, and save the grid to the floating array object (TBD).
-        
-        The input file should provide rows with the following entries:
-        - x coordinate
-        - y coordinate
-        - class  - soil classification name ('clay', 'sand', or 'rock' with optional modifiers)
-        - gamma* - soil effective unit weight [kPa] (all soils)
-        - Su0*   - undrained shear strength at mudline [kPa] (clay 
-        - K*     - undrained shear strength gradient [kPa/m] (clay 
-        - alpha* - soil skin friction coefficient [-] (clay soils)
-        - phi*   - angle of internal friction [deg] (sand soils)
-        
-        Some (*) parameters are optional depending on the soil class and mode.   
+        Load geotechnical information from input file or YAML.
+        Supports two soil modes: 'uniform' and 'layered'.
 
-        Irregular sampling points will be supported and interpolated to a 
-        rectangular grid.
-        
-        Paramaters
+        Parameters
         ----------
-        filename : path
-            path/name of file containing soil data
+        filename : str, optional
+            Path to .txt/.dat file with soil labels/profile IDs and coordinates
+        yaml : dict, optional
+            Dictionary containing soil data and properties (used when filename is None)
+        soil_mode : str
+            Either 'uniform' or 'layered'
+        profile_source : str, optional
+            Path to YAML file with layered profile definitions (only used if soil_mode='layered')
         '''
         xs = None
         ys = None
         soil_names = None
-        if filename is not None:    # if the filename option was selected, then that means there is at least a grid in the file, and maybe soil type information
-            if filename[-3:]=='shp':
-                raise ValueError("Geography-related operations not directly supported in Project class")
-            
-            elif filename[-3:]=='txt' or filename[-3:]=='dat':
+        soilProps = None
 
-                # load in the grid portion of the soil input file
-                xs, ys, soil_names = sbt.readBathymetryFile(filename, dtype=str)  # read MoorDyn-style file
+        # Case 1: File input (grid + properties)
+        if filename is not None:
+            if filename.endswith('.shp'):
+                raise ValueError("Shapefiles not supported in Project class")
 
-                soilProps = sbt.getSoilTypes(filename)     # load in the soil property information (if there is any)
-            
-            # regardless of whether there is soil type information in the file, if there is soil information in the yaml, read that in
-            if yaml:
-                soilProps = yaml['soil_types']     # if there is a yaml file as input, load in the soil props that way (overwrites the other one)
+            elif filename.endswith('.txt') or filename.endswith('.dat'):
+                # Load label/profile_id grid
+                xs, ys, soil_names = sbt.readBathymetryFile(filename, dtype=str)
+
+                # Load soil properties
+                soilProps = sbt.getSoilTypes(filename, soil_mode=soil_mode, profile_source=profile_source)
 
+            if yaml:
+                soilProps = yaml.get('soil_types', soilProps)  # allow overwriting via YAML
 
-        elif filename is None:  # if the filename option was not selected
-            if yaml:            # and if there was a yaml option selected, simply read in that yaml information
+        # Case 2: YAML only (no filename)
+        elif filename is None:
+            if yaml:
                 xs = yaml['x']
                 ys = yaml['y']
                 soil_names = yaml['type_array']
-                soilProps = yaml['soil_types']
-            else:               # but if there was no yaml option selected (and no file option selected) -> set default values
-                print('Warning: No soil grid nor soil properties were selected, but this function ran -> use preprogrammed default values')
+                raw_soil_types = yaml['soil_types']
+        
+                # Ensure all soil types have a 'layers' field
+                soilProps = {}
+                for key, entry in raw_soil_types.items():
+                    if 'layers' in entry:
+                        soilProps[key] = entry 
+                    else:
+                        # Wrap old flat format into single-layer profile (optional fallback)
+                        layer = dict(entry)
+                        layer.setdefault('top', 0)
+                        layer.setdefault('bottom', 50)
+                        layer.setdefault('soil_type', key)
+                        soilProps[key] = {'layers': [layer]}
+            else:
+                print('[Warning] No soil input provided — using default values')
                 xs = [0]
                 ys = [0]
-                soil_names = ['mud']
-                soilProps = dict(mud={'Su0':[2.39], 'k':[1.41], 'gamma':[10], 'depth':[0]},
-                                rock={'UCS':[5], 'Em':[7], 'depth':[0]})
-        
+                soil_names = [['mud']]  # note: should be 2D to match grid structure
+                soilProps = {
+                    'mud': {'layers': [{
+                        'soil_type': 'clay',
+                        'top': 0, 'bottom': 50,
+                        'gamma_top': 10, 'gamma_bot': 10,
+                        'Su_top': 2.39, 'Su_bot': 59.39
+                    }]},
+                    'rock': {'layers': [{
+                        'soil_type': 'rock',
+                        'top': 0, 'bottom': 50,
+                        'UCS_top': 5, 'UCS_bot': 5,
+                        'Em_top': 7, 'Em_bot': 7
+                    }]}
+                }
+
+
         else:
-            raise ValueError("Something is wrong")
-        
-        '''
-        # check that correct soil properties are being provided for the different soil types
-        for soil in soilProps:
-            if 'rock' in soil or 'hard' in soil:
-                if not 'UCS' in soilProps[soil] or not 'Em' in soilProps[soil]:
-                    raise ValueError('Rock soil type requires UCS and Em values')
-            elif 'sand' in soil:
-                if not 'phi' in soilProps[soil] or not 'gamma' in soilProps[soil]:
-                    raise ValueError('Sand soil type requires phi and gamma values')
-            elif 'clay' in soil:
-                if not 'Su0' in soilProps[soil] or not 'k' in soilProps[soil]:
-                    raise ValueError('Clay soil type requires Su0 and k values')
-            elif 'mud' in soil or 'mud_soft':
-                if not 'Su0' in soilProps[soil] or not 'k' in soilProps[soil]:
-                    raise ValueError('Mud soil type requires Su0 and k values')
-            else:
-                raise ValueError(f'Soil type {soil} not recognized. Soil type key must contain one of the following keywords: rock, sand, clay, mud')
-        '''
-        
-        # make sure the soilProps dictionary has all the required information (should be updated later with exact properties based on anchor capacity functions)
-        # setting each soil type dictionary with all the values, just in case they need them for whatever reason - here are the default values
-        # the default types (and values) are set if there is no other information provided
-        for key,props in soilProps.items():
-            props['Su0']   = getFromDict(props, 'Su0'  , shape=-1, dtype=list, default=[2.39], index=None)
-            props['k']     = getFromDict(props, 'k'    , shape=-1, dtype=list, default=[1.41], index=None)
-            props['alpha'] = getFromDict(props, 'alpha', shape=-1, dtype=list, default=[0.7] , index=None)
-            props['gamma'] = getFromDict(props, 'gamma', shape=-1, dtype=list, default=[4.7] , index=None)
-            props['phi']   = getFromDict(props, 'phi'  , shape=-1, dtype=list, default=[0.0] , index=None)
-            props['UCS']   = getFromDict(props, 'UCS'  , shape=-1, dtype=list, default=[7.0] , index=None)
-            props['Em']    = getFromDict(props, 'Em'   , shape=-1, dtype=list, default=[50.0], index=None)
-            
-            for k,prop in props.items():
-                if 'array' in type(prop).__name__:
-                    # clean up property type
-                    props[k] = np.array(prop)
-        
-        
+            raise ValueError("Invalid combination of filename/yaml inputs")
+
+        # --- Set defaults only for uniform mode ---
+        if soil_mode == 'uniform':
+            for key, props in soilProps.items():
+                props['Su0']   = getFromDict(props, 'Su0',   shape=-1, dtype=list, default=[2.39])
+                props['k']     = getFromDict(props, 'k',     shape=-1, dtype=list, default=[1.41])
+                props['alpha'] = getFromDict(props, 'alpha', shape=-1, dtype=list, default=[0.7])
+                props['gamma'] = getFromDict(props, 'gamma', shape=-1, dtype=list, default=[8.7])
+                props['phi']   = getFromDict(props, 'phi',   shape=-1, dtype=list, default=[0.0])
+                props['UCS']   = getFromDict(props, 'UCS',   shape=-1, dtype=list, default=[7.0])
+                props['Em']    = getFromDict(props, 'Em',    shape=-1, dtype=list, default=[50.0])
+
+                # ensure no array-like leftovers
+                for k, prop in props.items():
+                    if hasattr(prop, '__array__'):
+                        props[k] = np.array(prop)
+
+        # --- Store to project ---
         self.soilProps = soilProps
 
-
-        
-        
         if xs is not None:
             self.soil_x = np.array(xs)
             self.soil_y = np.array(ys)
             self.soil_names = np.array(soil_names)
-        
-        
-        # update soil info for anchor if needed
+
+        self.soil_mode = soil_mode
+        print(f"Loaded soilProps keys: {list(soilProps.keys())}")
+
+        # --- Update anchor objects if available ---
         if self.anchorList:
-            for anch in self.anchorList.values():
-                name, props = self.getSoilAtLocation(anch.r[0],anch.r[1])
-                anch.soilProps = {name:props}
-        
-        # load data from file
-        
-        # interpolate onto grid defined by grid_x, grid_y
-        
-        # save
-        '''
-        self.soil_class
-        self.soil_gamma
-        self.soil_Su0  
-        self.soil_K    
-        self.soil_alpha
-        self.soil_phi  
-        '''
-        pass
-        
+            for anchor in self.anchorList.values():
+                name, props = self.getSoilAtLocation(anchor.r[0], anchor.r[1])
+                anchor.soilProps = {name: props}
 
     def getSoilAtLocation(self, x, y):
         '''
-        Interpolate soil properties at specified location from the soil
-        properties grid and return a dictionary of soil properties that
-        can be used in anchor capacity calculations.
-        
-        Parameters
-        ----------        
-        x : float
-            x coordinate in array reference frame [m].        
-        y : float
-            y coordinate in array reference frame [m].
+        Retrieve the soil information at a specific location, supporting both uniform and layered modes.
 
         Returns
-        -------            
-        soilProps : dictionary
-            Dictionary of standard MoorPy soil properties.
+        -------
+        (str, dict or list): soil name or profile ID, and associated soil properties or layered profile
         '''
-        
-        # NEW: finds the specific soil grid point that the xy point is closest to and assigns it that soil type
         if self.soil_x is not None:
-            ix = np.argmin([abs(x-soil_x) for soil_x in self.soil_x])
-            iy = np.argmin([abs(y-soil_y) for soil_y in self.soil_y])
-    
-            soil_name = self.soil_names[iy, ix]
-    
-            soil_info = self.soilProps[soil_name]
-    
-            return soil_name, soil_info
-        else:
-            pass
-        
-        '''
-        # SIMPLE HACK FOR NOW        
-        rocky, _,_,_,_ = sbt.interpFromGrid(x, y, self.soil_x, self.soil_y, self.soil_rocky)
-        
-        return rocky
-        '''
-        '''
-        soilProps = {}
-        
+            ix = np.argmin([abs(x - sx) for sx in self.soil_x])
+            iy = np.argmin([abs(y - sy) for sy in self.soil_y])
+            soil_id = self.soil_names[iy, ix]  # could be label or profile_id
 
-        if self.seabed_type == 'clay':
-            
-            soilProps['class'] = 'clay'
-            soilProps['gamma'] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_gamma)
-            soilProps['Su0'  ] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_Su0  )
-            soilProps['k'    ] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_k    )
-            soilProps['alpha'] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_alpha)
-            soilProps['phi'  ] = None
-        
-        elif self.seabed_type == 'sand':
-            soilProps['class'] = 'sand'
-            soilProps['gamma'] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_gamma)
-            soilProps['Su0'  ] = None
-            soilProps['k'    ] = None
-            soilProps['alpha'] = None
-            soilProps['phi'  ] = interp2d(x, y, self.seabed_x, self.seabed_y, self.soil_phi  )
-            
-            # note: for sand, can assume homogeneous angle of internal fricton
+            if self.soil_mode == 'uniform':
+                soil_info = self.soilProps[soil_id]
+                return soil_id, soil_info
+
+            elif self.soil_mode == 'layered':
+                profile_layers = self.soilProps[soil_id]  # list of layer dicts
+                return soil_id, profile_layers
+
+            else:
+                raise ValueError(f"Unknown soil_mode: {self.soil_mode}")
+
+            print(f"[DEBUG] soil_id at location ({x}, {y}) is: {soil_id}")
+            print(f"[DEBUG] Available soilProps keys: {list(self.soilProps.keys())}")
         else:
-            raise ValueError(f"Unsupported seabed type '{self.seabed_type}'.")
-            
-        return soilProps
-        '''
+            raise ValueError("No soil grid defined")
 
     # # ----- Anchor 
     def updateAnchor(self,anch='all',update_loc=True):
@@ -1251,39 +1195,20 @@ def updateAnchor(self,anch='all',update_loc=True):
                 name, props = self.getSoilAtLocation(x,y) # update soil
                 anchor.soilProps = {name:props}
             
-
-
-    # def calcAnchorCapacity(self, anchor):
-    #     '''Compute holding capacity of a given anchor based on the soil
-    #     info at its position. The anchor object's anchor properties and
-    #     location will be used to determine the holding capacity, which
-    #     will be saved to the anchor object.
-        
-    #     Parameters
-    #     ----------
-    #     anchor : MoorPy Anchor object (derived from Point)
-    #         The anchor object in question.
-    #     '''
-
-    #     # interpolate soil properties/class based on anchor position
-    #     anchor.soilProps = self.getSoilAtLocation(anchor.r[0], anchor.r[1])
-        
-    #     # fill in generic anchor properties if anchor info not provided
-    #     if not type(anchor.anchorProps) == dict:
-    #         anchor.anchorProps = dict(type='suction', diameter=6, length=12)
-        
-    #     # apply anchor capacity model
-    #     capacity, info = anchorCapacity(anchorProps, soilProps)
-        
-    #     # save all information to the anchor (attributes of the Point)
-    #     anchor.soilProps = soilProps
-    #     anchor.anchorCapacity = capacity
-    #     anchor.anchorInfo = info
-        
-    #     # also return it
-    #     return capacity
-
+    def setSoilAtLocation(self, anchor):
+        name, props = self.getSoilAtLocation(anchor.r[0], anchor.r[1])
+    
+        # Add required metadata
+        layer = dict(props)  # shallow copy of props
+        layer['soil_type'] = name  # or force to 'clay'/'rock' if needed
+        layer['top'] = props.get('top', 0)
+        layer['bottom'] = props.get('bottom', 50)  
     
+        # Wrap in expected profile_map format
+        profile_map = [{'name': name, 'layers': [layer]}]
+        anchor.setSoilProfile(profile_map)
+
+   
     def setCableLayout(self):
 
         # 2-D
diff --git a/famodel/seabed/seabed_tools.py b/famodel/seabed/seabed_tools.py
index ae70c24c..7011a68e 100644
--- a/famodel/seabed/seabed_tools.py
+++ b/famodel/seabed/seabed_tools.py
@@ -5,11 +5,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-
-
-
-
-
 def readBathymetryFile(filename, dtype=float):
 
     with open(filename, 'r') as f:
@@ -68,37 +63,82 @@ def writeBathymetryFile(filename, grid_x, grid_y, grid_depth):
             row = [y] + list(grid_depth[i, :])
             f.write(" ".join(map(str, row)) + "\n")
             
-def getSoilTypes(filename):
-    '''function to read in a preliminary input text file format of soil type information'''
+import yaml
+import os
 
-    soilProps = {}
+def getSoilTypes(filename, soil_mode='layered', profile_source=None):
+    '''
+    Load soil properties or layered profiles depending on soil_mode.
 
-    f = open(filename, 'r')
-    
-    for line in f:
-        if line.count('---') > 0 and (line.upper().count('SOIL TYPES') > 0):
-            line = next(f) # skip this header line, plus channel names and units lines
-            var_names = line.split()
-            line = next(f)
-            line = next(f)
-            while line.count('---') == 0:
-                entries = line.split()
-                soilProps[entries[0]] = {}
-                for iv,var in enumerate(var_names[1:]):
-                    # convert entries to strings unless there is 
-                    if entries[iv+1] == '-':
-                        soilProps[entries[0]][var] = [0]
-                    else:
-                        soilProps[entries[0]][var] = [float(entries[iv+1])]
-                line = next(f)
-    
-    f.close()
+    Parameters
+    ----------
+    filename : str
+        Path to .txt file containing grid and profile/soil label definitions
+    soil_mode : str
+        'uniform' or 'layered'
+    profile_source : str or None
+        Path to YAML file with layered soil profiles (used only for 'layered')
 
-    return soilProps
+    Returns
+    -------
+    soilProps : dict
+        Dictionary of soil type properties (uniform) or layered profiles (layered)
+    '''
+    soilProps = {}
+    used_labels = []
 
+    with open(filename, 'r') as f:
+        lines = f.readlines()
 
+    for i, line in enumerate(lines):
+        if line.strip().startswith('---') and 'SOIL TYPES' in line.upper():
+            break
+
+    # Extract used labels from the SOIL TYPES section
+    for line in lines[i+3:]:
+        if '---' in line:
+            break
+        entries = line.strip().split()
+        label = entries[0]
+        used_labels.append(label); print(label)
+
+    if soil_mode == 'uniform':
+        var_names = lines[i+1].split()
+        for line in lines[i+3:]:
+            if '---' in line:
+                break
+            entries = line.strip().split()
+            label = entries[0]
+            soilProps[label] = {}
+            for iv, var in enumerate(var_names[1:]):
+                val = entries[iv+1]
+                soilProps[label][var] = [float(val)] if val != '-' else [0.0]
+
+    elif soil_mode == 'layered':
+        if profile_source is None:
+            raise ValueError("profile_source (path to YAML) is required for layered mode.")
+
+        # Load the full YAML file of profiles
+        with open(profile_source, 'r') as f:
+            all_profiles = yaml.safe_load(f)
+
+        # Reassign each label to the actual layer list directly
+        for label in used_labels:
+            if label not in all_profiles:
+                raise KeyError(f'Profile ID {label} not found in YAML: {profile_source}')
+            soilProps[label] = all_profiles[label]['layers']  # now a list of layer dicts
+
+        print(f"[DEBUG] Loaded profiles from YAML: {list(soilProps.keys())}")
+        if used_labels:
+            print(f"[DEBUG] Example layers for {used_labels[0]}: {soilProps[used_labels[0]]}")
+        else:
+            print("[WARNING] No profile labels were found in the soil grid.")
 
 
+    else:
+        raise ValueError(f"Unrecognized soil_mode '{soil_mode}'")
+
+    return soilProps
 
 def convertLatLong2Meters(zerozero, lats, longs):
     '''Convert a list of latitude and longitude coordinates into
@@ -141,11 +181,6 @@ def convertLatLong2Meters(zerozero, lats, longs):
 
     return Xs, Ys
 
-
-
-
-
-
 def processASC(gebcofilename, lat, lon, outfilename=""):
     '''Process an ASC file of bathymetry information and convert into
     a rectangular bathymetry grid in units of m relative to the 
@@ -266,9 +301,16 @@ def processGeotiff(filename, lat, lon, outfilename="processGeotiff.txt", **kwarg
     #lats, _  = rasterio.transform.xy(tiff.transform, 0, range(tiff.width-1,-1,-1))
     height, width = tiff.shape
     cols, rows = np.meshgrid(np.arange(width), np.arange(height))
-    longs_mesh, lats_mesh = rasterio.transform.xy(tiff.transform, rows, cols)
-    longs = np.array(longs_mesh)[0,:]
-    lats = np.flip(np.array(lats_mesh)[:,0])
+    
+    # rasterio.transform.xy returns flat lists, so reshape after
+    longs_list, lats_list = rasterio.transform.xy(tiff.transform, rows, cols)
+    
+    longs_array = np.array(longs_list).reshape((height, width))
+    lats_array = np.array(lats_list).reshape((height, width))
+    
+    longs = longs_array[0, :]       # all x-coords from first row
+    lats = np.flip(lats_array[:, 0])  # all y-coords from first column (flip to make it south to north)
+
     # lats data provided from top left corner, i.e., latitudes are descending. It seems that the following interpolation functions (getDepthFromBathymetry)
     # can only work if latitudes start small and increase, meaning that the first latitude entry has to be the bottom left corner
     
@@ -707,11 +749,14 @@ def getPlotBounds(latsorlongs_boundary, zerozero, long=True):
 
 if __name__ == '__main__':
     
-    centroid = (40.928, -124.708)  #humboldt    
-    xs = np.arange(-30000,30001,400)
-    ys = np.arange(-40000,40001,400)
+    centroid = (44.1, 12.65)    
+    # Choose grid that fits within 25x22 km — e.g., ±10 km to be safe
+    xs = np.arange(-10000, 10001, 400)   # 50 x points → 20 km total
+    ys = np.arange(-10000, 10001, 400)   # 50 y points
     
-    xs, ys, depths = processGeotiff('humboldt.tif', centroid[0], centroid[1], xs=xs, ys=ys, outfilename='test output.txt')
+    xs, ys, depths = processGeotiff('gebco_2024_n44.2_s44.0_w12.5_e12.8.tif', 
+                                    centroid[0], centroid[1], xs=xs, ys=ys, 
+                                    outfilename='test output.txt')
     
     import moorpy as mp
     ms = mp.System(depth=np.max(depths), bathymetry='test output.txt')
diff --git a/famodel/seabed/test output.txt b/famodel/seabed/test output.txt
new file mode 100644
index 00000000..d7761f3c
--- /dev/null
+++ b/famodel/seabed/test output.txt	
@@ -0,0 +1,55 @@
+--- MoorPy Bathymetry Input File ---
+nGridX 51
+nGridY 51
+      -10000.00 -9600.00 -9200.00 -8800.00 -8400.00 -8000.00 -7600.00 -7200.00 -6800.00 -6400.00 -6000.00 -5600.00 -5200.00 -4800.00 -4400.00 -4000.00 -3600.00 -3200.00 -2800.00 -2400.00 -2000.00 -1600.00 -1200.00 -800.00 -400.00 0.00 400.00 800.00 1200.00 1600.00 2000.00 2400.00 2800.00 3200.00 3600.00 4000.00 4400.00 4800.00 5200.00 5600.00 6000.00 6400.00 6800.00 7200.00 7600.00 8000.00 8400.00 8800.00 9200.00 9600.00 10000.00 
+-10000.00  -59.765  -58.906  -38.079  -27.261  -27.565  -27.146  -24.467  -23.345  -23.486  -27.393  -41.435  -53.706  -30.342  -18.322  -19.148  -17.880  -16.607  -14.551  -14.170  -12.598  -10.169   -8.602  -11.852   -9.802   -5.174   -4.550   -3.576    1.920    5.710    7.117    7.900    8.582    8.988    9.000    9.000    9.000    9.600    9.897   10.000   10.000   10.000   10.000   10.000   10.897   11.000   11.000   11.000   11.000   11.000   11.204   11.934 
+-9600.00  -61.993  -56.979  -35.704  -26.878  -25.706  -24.045  -22.552  -21.525  -21.101  -25.824  -35.156  -44.625  -26.111  -13.920  -13.592  -13.706  -13.778  -12.570  -11.591  -10.076   -7.681   -6.540   -5.940   -4.525   -3.400   -1.060    2.510    5.746    6.856    7.832    8.371    8.925    9.000    9.000    9.209    9.762    9.921   10.000   10.000   10.000   10.000   10.000   10.645   11.000   11.000   11.000   11.000   11.000   11.751   11.808   12.000 
+-9200.00  -69.080  -54.023  -41.091  -37.848  -30.482  -23.653  -21.163  -20.399  -19.637  -23.969  -35.319  -46.270  -31.407  -16.493  -10.337  -10.024  -10.495   -9.571   -8.372   -7.056   -5.412   -4.298   -2.870   -2.107    0.332    2.514    5.651    6.922    7.720    8.628    8.809    9.000    9.000    9.049    9.729   10.000   10.000   10.000   10.000   10.000   10.000   10.408   10.944   11.000   11.000   11.000   11.000   11.517   12.000   12.000   12.000 
+-8800.00  -66.667  -55.758  -57.224  -59.801  -42.220  -25.509  -20.499  -19.082  -17.691  -20.628  -32.444  -43.204  -28.311  -13.821   -8.035   -7.031   -7.039   -6.116   -5.583   -5.684   -3.729   -2.163   -1.555    0.727    3.436    5.231    7.036    7.492    8.539    9.000    9.000    9.000    9.000    9.531   10.000   10.000   10.000   10.000   10.032   10.487   10.487   10.824   11.000   11.000   11.000   11.215   11.481   11.917   12.000   12.000   12.000 
+-8400.00  -54.952  -64.936  -83.616  -91.950  -62.900  -31.396  -20.589  -17.759  -15.745  -17.086  -23.848  -29.242  -17.347   -8.385   -6.202   -4.767   -4.291   -4.023   -4.152   -5.086   -3.140   -1.459    0.260    3.818    5.210    6.856    8.054    8.356    9.000    9.000    9.000    9.000    9.313   10.000   10.000   10.000   10.000   10.000   10.066   11.000   11.000   11.000   11.000   11.000   11.087   11.639   12.000   12.000   12.000   12.000   12.000 
+-8000.00  -43.799  -73.192 -101.596 -118.193  -87.675  -41.901  -20.768  -16.685  -14.787  -15.054  -15.456  -13.884  -10.024   -7.523   -5.127   -3.371   -3.000   -3.518   -3.807   -4.000   -3.487   -1.509    3.039    5.522    6.523    7.720    8.764    9.000    9.000    9.000    9.107    9.219    9.908   10.000   10.000   10.000   10.146   10.217   10.269   11.000   11.000   11.000   11.000   11.212   11.410   12.000   12.000   12.000   12.000   12.000   12.000 
+-7600.00  -32.692  -58.037  -71.587  -99.977  -96.847  -53.373  -21.105  -15.044  -13.923  -13.922  -13.343  -11.431   -9.131   -6.590   -4.291   -3.000   -3.000   -2.156   -3.181   -3.890   -3.063    1.632    4.991    7.084    7.387    8.542    9.000    9.000    9.000    9.024    9.531   10.000   10.000   10.000   10.000   10.000   10.673   11.000   11.000   11.000   11.000   11.000   11.066   12.000   12.000   12.000   12.000   12.000   12.000   12.017   12.068 
+-7200.00  -20.327  -30.906  -46.283  -60.747  -74.208  -60.762  -24.632  -14.282  -13.059  -13.058  -12.017  -10.339   -9.073   -4.975   -3.711   -3.000   -3.000   -2.951   -3.843   -2.755    1.318    5.478    6.799    7.948    8.251    8.974    9.000    9.000    9.000    9.275    9.973   10.000   10.000   10.000   10.000   10.000   10.674   11.000   11.000   11.000   11.000   11.000   11.809   12.000   12.000   12.000   12.000   12.000   12.000   12.227   12.932 
+-6800.00  -19.085  -33.208  -35.473  -41.180  -59.086  -54.647  -27.326  -12.105  -11.390  -11.117  -10.374   -8.869   -5.952   -3.358   -2.865   -2.190   -2.774   -3.745   -2.602    2.691    5.168    6.521    7.719    8.085    8.869    9.000    9.000    9.000    9.076    9.867   10.000   10.000   10.000   10.000   10.000   10.386   10.938   11.000   11.000   11.000   11.000   11.534   11.973   12.000   12.000   12.000   12.000   12.000   12.038   12.847   13.000 
+-6400.00  -15.115  -20.878  -20.495  -26.178  -31.775  -33.146  -20.538  -11.319  -10.331  -10.444   -9.955   -7.376   -5.742   -3.587   -2.675   -2.352   -2.909   -3.299    2.029    4.868    6.345    7.385    8.583    8.710    9.000    9.000    9.000    9.606    9.706   10.000   10.000   10.000   10.000   10.000   10.189   10.830   11.000   11.000   11.000   11.180   11.670   11.889   12.000   12.000   12.000   12.000   12.000   12.565   12.679   13.000   13.000 
+-6000.00  -12.527  -12.160  -11.394  -14.529  -14.038  -15.951  -12.820  -10.022   -9.467   -9.265   -8.082   -6.376   -5.503   -3.874   -2.850   -2.779   -2.613   -0.485    4.654    5.852    7.050    7.866    8.906    9.000    9.000    9.000    9.000    9.896   10.000   10.000   10.000   10.000   10.000   10.539   10.668   11.000   11.000   11.000   11.039   11.661   12.000   12.000   12.000   12.000   12.000   12.242   12.529   12.930   13.000   13.000   13.000 
+-5600.00  -12.735  -10.889   -9.072   -8.608   -8.742   -9.115   -8.763   -8.233   -8.260   -8.201   -6.655   -5.418   -4.273   -3.525   -3.000   -3.000   -0.836    2.573    5.874    6.716    7.708    8.286    8.944    9.000    9.000    9.000    9.282    9.938   10.000   10.000   10.000   10.000   10.357   11.000   11.000   11.000   11.000   11.350   11.444   12.000   12.000   12.000   12.000   12.000   12.103   12.672   13.000   13.000   13.000   13.000   13.000 
+-5200.00  -14.290  -12.010   -9.616   -8.474   -8.511   -8.460   -7.809   -7.047   -7.099   -7.652   -6.554   -4.472   -3.074   -3.264   -3.265   -2.437    2.066    4.694    7.030    7.496    8.137    8.786    9.000    9.000    9.000    9.134    9.779   10.000   10.000   10.000   10.131   10.267   10.916   11.000   11.000   11.000   11.180   11.909   12.000   12.000   12.000   12.000   12.226   12.260   12.453   13.000   13.000   13.000   13.000   13.064   13.252 
+-4800.00  -15.040  -13.038  -10.749   -9.516   -8.759   -8.218   -7.108   -6.876   -7.141   -6.837   -5.404   -4.238   -3.311   -3.984   -3.581   -0.183    4.449    6.853    7.230    8.041    8.574    9.000    9.000    9.000    9.040    9.566   10.000   10.000   10.000   10.039   10.557   11.000   11.000   11.000   11.000   11.062   11.720   12.000   12.000   12.000   12.000   12.000   12.867   13.000   13.000   13.000   13.000   13.000   13.118   13.341   14.000 
+-4400.00  -11.978   -9.586   -8.111   -7.047   -7.031   -6.491   -6.017   -6.012   -8.119   -7.996   -4.949   -4.002   -4.909   -3.878   -0.779    4.484    6.420    6.999    7.996    8.310    8.998    9.000    9.000    9.000    9.301    9.998   10.000   10.000   10.000   10.291   10.998   11.000   11.000   11.000   11.000   11.478   11.998   12.000   12.000   12.000   12.000   12.000   12.868   13.000   13.000   13.000   13.000   13.000   13.982   13.986   14.000 
+-4000.00   -9.838   -9.080   -8.855   -7.611   -5.578   -4.370   -4.298   -4.297   -4.720   -5.161   -3.688   -3.765   -4.923   -3.254    3.284    6.233    7.534    7.859    8.097    8.903    9.000    9.000    9.000    9.860    9.902   10.000   10.000   10.000   10.081   10.901   11.000   11.000   11.000   11.074   11.858   11.926   12.000   12.000   12.000   12.000   12.404   12.854   12.981   13.000   13.000   13.000   13.562   13.848   14.000   14.000   14.000 
+-3600.00  -10.488   -9.709   -9.710   -8.436   -6.280   -5.046   -4.191   -4.000   -3.320   -3.472   -3.511   -3.922   -3.595    1.366    4.678    7.239    7.921    8.000    8.754    9.000    9.000    9.000    9.655   10.000   10.000   10.000   10.000   10.649   10.750   11.000   11.000   11.000   11.000   11.746   12.000   12.000   12.000   12.000   12.000   12.198   12.852   13.000   13.000   13.000   13.191   13.714   13.903   14.000   14.000   14.000   14.323 
+-3200.00  -10.083   -9.003   -8.313   -7.576   -5.512   -4.004   -2.956   -2.841   -2.419   -2.229   -2.666   -2.995    0.241    4.359    6.337    7.800    8.000    8.536    9.000    9.000    9.299    9.588    9.961   10.000   10.000   10.000   10.411   10.957   11.000   11.000   11.000   11.000   11.521   12.000   12.000   12.000   12.000   12.000   12.584   12.698   13.000   13.000   13.000   13.041   13.692   14.000   14.000   14.000   14.036   14.573   14.768 
+-2800.00   -9.461   -8.585   -7.912   -6.671   -3.933   -2.316   -2.000   -2.000   -0.666   -0.193   -0.730   -1.707    3.875    5.633    7.317    8.232    8.450    8.952    9.000    9.000    9.508   10.000   10.000   10.000   10.136   10.452   10.835   11.000   11.000   11.000   11.000   11.312   11.939   12.000   12.000   12.000   12.000   12.395   13.000   13.000   13.000   13.000   13.000   13.483   14.000   14.000   14.000   14.000   14.064   15.000   15.000 
+-2400.00   -9.462   -8.778   -8.415   -5.731   -2.598   -1.368   -1.246   -0.507    1.926    2.476    2.179    1.315    5.924    6.706    7.630    8.667    9.000    9.000    9.035    9.315    9.663   10.000   10.000   10.033   10.522   11.000   11.000   11.000   11.000   11.093   11.315   11.788   12.000   12.000   12.000   12.000   12.214   12.919   13.000   13.000   13.000   13.000   13.270   14.000   14.000   14.000   14.000   14.263   14.348   15.000   15.000 
+-2000.00   -9.137   -7.936   -6.579   -3.591   -1.670   -0.641    1.137    3.379    4.367    4.787    5.348    6.664    7.176    8.021    8.437    9.000    9.000    9.000    9.269   10.000   10.000   10.000   10.163   10.264   11.000   11.000   11.000   11.000   11.017   11.421   12.000   12.000   12.000   12.000   12.051   12.177   12.740   13.000   13.000   13.000   13.083   13.173   13.897   14.000   14.000   14.000   14.113   14.890   15.000   15.000   15.076 
+-1600.00   -7.381   -6.151   -4.101   -2.736   -1.476    1.257    4.530    5.996    6.192    7.037    7.038    7.730    8.036    8.152    9.000    9.000    9.030    9.042   10.000   10.000   10.000   10.031   10.908   11.000   11.000   11.000   11.031   11.044   11.136   12.000   12.000   12.000   12.000   12.004   12.318   13.000   13.000   13.000   13.003   13.039   13.499   14.000   14.000   14.000   14.009   14.034   14.682   15.000   15.000   15.008   15.481 
+-1200.00   -4.735   -2.697   -3.058    0.940    3.706    5.073    6.494    7.722    7.813    7.901    7.902    7.973    8.828    8.915    9.000    9.000    9.645    9.906   10.000   10.000   10.000   10.640   10.991   11.000   11.000   11.000   11.634   11.908   11.917   12.000   12.000   12.000   12.000   12.081   12.933   13.000   13.000   13.000   13.075   13.903   13.949   14.000   14.000   14.000   14.248   14.898   14.966   15.000   15.000   15.240   15.942 
+-800.00   -2.543   -1.861    1.293    3.562    5.599    6.286    7.486    7.982    8.000    8.244    8.766    8.767    8.980    9.000    9.241    9.769    9.933   10.000   10.000   10.237   10.771   10.933   11.000   11.000   11.000   11.386   11.931   12.000   12.000   12.000   12.000   12.000   12.687   12.791   13.000   13.000   13.000   13.000   13.787   14.000   14.000   14.000   14.000   14.060   14.828   15.000   15.000   15.000   15.055   15.823   16.000 
+-400.00   -1.978    0.362    4.013    5.964    6.827    7.447    7.896    8.000    8.628    8.747    9.000    9.000    9.000    9.072    9.748   10.000   10.000   10.000   10.069   10.747   11.000   11.000   11.000   11.065   11.636   11.818   12.000   12.000   12.000   12.000   12.314   12.635   12.961   13.000   13.000   13.000   13.435   13.632   14.000   14.000   14.000   14.000   14.553   14.657   15.000   15.000   15.000   15.000   15.650   16.000   16.000 
+0.00    0.673    3.482    5.409    7.060    7.653    8.000    8.000    8.452    9.000    9.000    9.000    9.000    9.000    9.553   10.000   10.000   10.000   10.000   10.553   11.000   11.000   11.000   11.000   11.551   12.000   12.000   12.000   12.000   12.000   12.148   12.747   13.000   13.000   13.000   13.000   13.243   13.843   14.000   14.000   14.000   14.000   14.337   14.940   15.000   15.000   15.000   15.331   15.487   16.000   16.000   16.229 
+400.00    4.220    5.893    6.350    7.430    8.000    8.000    8.255    8.947    9.000    9.000    9.000    9.256    9.360   10.000   10.000   10.000   10.257   10.362   11.000   11.000   11.000   11.000   11.328   12.000   12.000   12.000   12.000   12.000   12.035   12.552   13.000   13.000   13.000   13.033   13.362   13.674   14.000   14.000   14.000   14.000   14.174   14.797   15.000   15.000   15.000   15.170   15.792   16.000   16.000   16.096   16.658 
+800.00    5.624    6.722    7.197    8.000    8.069    8.217    8.779    9.000    9.000    9.000    9.114    9.777   10.000   10.000   10.000   10.115   10.774   11.000   11.000   11.000   11.115   11.228   11.925   12.000   12.000   12.000   12.000   12.205   12.303   13.000   13.000   13.000   13.000   13.298   14.000   14.000   14.000   14.000   14.020   14.223   14.600   15.000   15.000   15.000   15.062   15.594   16.000   16.000   16.017   16.432   17.000 
+1200.00    6.039    6.797    7.926    8.009    8.373    9.000    9.000    9.000    9.000    9.027    9.555   10.000   10.000   10.000   10.027   10.552   11.000   11.000   11.000   11.028   11.550   12.000   12.000   12.000   12.000   12.046   12.092   12.908   13.000   13.000   13.000   13.063   13.091   14.000   14.000   14.000   14.000   14.000   14.169   15.000   15.000   15.000   15.000   15.083   15.343   16.000   16.000   16.000   16.152   17.000   17.036 
+1600.00    6.488    7.660    7.995    8.110    8.962    9.000    9.000    9.000    9.000    9.297    9.976   10.000   10.000   10.000   10.294   10.977   11.000   11.000   11.000   11.291   11.978   12.000   12.000   12.000   12.000   12.478   12.956   12.996   13.000   13.000   13.000   13.664   13.955   14.000   14.000   14.000   14.000   14.000   14.956   15.000   15.000   15.000   15.000   15.947   15.962   16.000   16.000   16.000   16.947   17.000   17.451 
+2000.00    7.321    7.945    8.000    8.829    9.000    9.000    9.000    9.000    9.091    9.871   10.000   10.000   10.000   10.089   10.873   11.000   11.000   11.000   11.819   11.874   12.000   12.000   12.000   12.083   12.820   12.910   13.000   13.000   13.000   13.000   13.407   13.945   14.000   14.000   14.000   14.000   14.565   14.816   15.000   15.000   15.000   15.000   15.721   16.000   16.000   16.000   16.000   16.714   17.000   17.228   17.898 
+2400.00    7.839    8.000    8.613    9.000    9.000    9.000    9.000    9.000    9.712   10.000   10.000   10.000   10.617   10.714   11.000   11.000   11.481   11.682   12.000   12.000   12.000   12.000   12.617   12.716   13.000   13.000   13.000   13.000   13.067   13.683   13.841   14.000   14.000   14.000   14.000   14.336   14.902   15.000   15.000   15.000   15.333   15.677   15.964   16.000   16.000   16.328   16.672   16.963   17.057   17.763   18.000 
+2800.00    8.000    8.380    8.959    9.000    9.000    9.000    9.000    9.491   10.000   10.000   10.000   10.384   10.958   11.000   11.167   11.545   11.866   12.000   12.000   12.000   12.000   12.384   12.956   13.000   13.000   13.000   13.000   13.000   13.592   14.000   14.000   14.000   14.000   14.052   14.546   14.770   15.000   15.000   15.000   15.159   15.767   16.000   16.000   16.000   16.156   16.763   17.000   17.475   17.575   18.000   18.258 
+3200.00    8.203    8.826    9.000    9.000    9.000    9.205    9.402    9.946   10.000   10.000   10.206   10.826   11.000   11.000   11.305   12.000   12.000   12.000   12.000   12.124   12.411   12.825   13.000   13.000   13.000   13.000   13.288   13.412   14.000   14.000   14.000   14.000   14.000   14.467   15.000   15.000   15.000   15.000   15.407   15.581   16.000   16.000   16.360   16.403   16.576   17.000   17.276   17.933   18.000   18.114   18.690 
+3600.00    8.637    9.000    9.000    9.000    9.000    9.508   10.000   10.000   10.000   10.082   10.639   11.000   11.000   11.272   11.494   12.000   12.000   12.000   12.028   12.494   13.000   13.000   13.000   13.000   13.000   13.138   13.782   14.000   14.000   14.000   14.000   14.192   14.274   15.000   15.000   15.000   15.000   15.243   16.000   16.000   16.000   16.187   16.922   17.000   17.078   17.265   17.773   18.000   18.262   18.475   19.000 
+4000.00    9.000    9.000    9.000    9.014    9.128    9.571   10.000   10.000   10.014   10.398   11.000   11.000   11.122   12.000   12.000   12.000   12.000   12.125   12.227   13.000   13.000   13.000   13.000   13.000   13.042   13.570   14.000   14.000   14.000   14.000   14.069   14.740   15.000   15.000   15.000   15.068   15.137   15.910   16.000   16.000   16.066   16.735   17.000   17.012   17.386   18.000   18.000   18.113   19.000   19.036   19.123 
+4400.00    9.000    9.000    9.000    9.105    9.992    9.997   10.000   10.000   10.104   10.998   11.000   11.000   11.903   12.000   12.000   12.000   12.001   12.902   13.000   13.000   13.000   13.000   13.003   13.004   13.303   14.000   14.000   14.000   14.000   14.003   14.500   15.000   15.000   15.000   15.000   15.498   16.000   16.000   16.000   16.000   16.495   16.999   17.000   17.095   17.996   18.000   18.000   18.886   19.000   19.290   19.987 
+4800.00    9.000    9.000    9.000    9.870   10.000   10.000   10.000   10.000   10.875   11.000   11.000   11.606   11.987   12.000   12.000   12.000   12.607   12.987   13.000   13.000   13.000   13.000   13.781   13.868   13.907   14.000   14.000   14.000   14.086   14.867   14.933   15.000   15.000   15.000   15.258   15.932   16.000   16.000   16.084   16.863   16.930   17.000   17.771   17.873   18.000   18.425   18.856   18.985   19.082   19.896   20.000 
+5200.00    9.000    9.000    9.648   10.000   10.000   10.000   10.000   10.652   11.000   11.219   11.726   11.918   12.000   12.000   12.220   12.729   12.919   13.000   13.000   13.000   13.000   13.000   13.900   14.000   14.000   14.000   14.000   14.000   14.758   15.000   15.000   15.000   15.657   15.730   15.810   16.000   16.000   16.000   16.754   17.000   17.000   17.506   17.972   18.071   18.722   18.860   19.000   19.645   19.745   20.213   20.715 
+5600.00    9.000    9.407    9.959   10.000   10.000   10.293   10.586   10.959   11.059   11.713   12.000   12.000   12.000   12.060   12.715   13.000   13.000   13.000   13.000   13.179   13.595   13.595   13.960   14.000   14.000   14.000   14.417   14.596   15.000   15.000   15.000   15.416   15.959   16.000   16.000   16.000   16.414   16.592   17.000   17.177   17.590   17.876   18.000   18.628   19.000   19.000   19.408   19.958   20.057   20.706   21.000 
+6000.00    9.221    9.833   10.000   10.000   10.134   10.724   11.000   11.000   11.507   12.000   12.000   12.000   12.409   12.510   13.000   13.000   13.000   13.000   13.046   13.621   14.000   14.000   14.000   14.000   14.138   14.460   14.838   15.000   15.000   15.138   15.459   15.838   16.000   16.000   16.137   16.457   16.837   17.000   17.046   17.618   18.000   18.317   18.452   19.045   19.450   19.449   19.835   20.402   20.501   21.134   21.443 
+6400.00    9.652   10.000   10.000   10.030   10.517   11.000   11.000   11.283   12.000   12.000   12.000   12.222   12.931   13.000   13.000   13.000   13.225   13.322   13.390   14.000   14.000   14.000   14.000   14.323   14.526   15.000   15.000   15.000   15.032   15.526   16.000   16.000   16.000   16.032   16.526   17.000   17.000   17.000   17.388   18.000   18.159   18.796   19.000   19.102   20.000   20.157   20.312   20.933   21.032   21.517   22.000 
+6800.00   10.000   10.000   10.000   10.254   11.000   11.000   11.124   11.915   12.000   12.054   12.182   12.753   13.000   13.000   13.000   13.092   13.754   14.000   14.000   14.000   14.000   14.000   14.168   15.000   15.000   15.000   15.000   15.000   15.269   16.000   16.000   16.000   16.000   16.268   17.000   17.000   17.129   17.184   18.019   18.182   18.593   19.000   19.162   19.264   20.054   20.591   21.000   21.000   21.259   22.052   22.171 
+7200.00   10.000   10.000   10.034   11.000   11.000   11.000   11.695   12.000   12.000   12.296   13.000   13.000   13.000   13.000   13.014   13.522   14.000   14.000   14.000   14.000   14.025   14.051   14.904   15.000   15.000   15.000   15.036   15.051   16.000   16.000   16.000   16.000   16.045   17.000   17.000   17.025   17.717   18.000   18.103   19.000   19.000   19.032   19.908   20.004   20.334   21.000   21.028   21.039   22.004   22.331   23.018 
+7600.00   10.000   10.000   10.805   11.000   11.000   11.000   11.694   12.000   12.000   12.295   13.000   13.000   13.000   13.000   13.271   13.956   14.000   14.000   14.000   14.000   14.456   14.915   14.991   15.000   15.000   15.000   15.641   15.915   16.000   16.000   16.000   16.000   16.825   17.000   17.000   17.459   17.974   18.000   18.104   19.000   19.000   19.641   19.991   20.096   20.935   21.000   21.639   21.903   22.097   22.931   23.457 
+8000.00   10.000   10.528   10.974   11.000   11.224   11.769   11.929   12.000   12.072   12.839   13.000   13.000   13.000   13.074   13.842   14.000   14.000   14.000   14.000   14.232   14.889   15.000   15.000   15.000   15.000   15.390   15.934   16.000   16.000   16.000   16.000   16.547   16.978   17.000   17.236   17.889   18.000   18.702   18.799   19.236   19.773   19.933   20.000   20.795   21.237   21.769   21.932   22.000   22.791   23.236   23.884 
+8400.00   10.000   10.689   11.000   11.057   11.739   12.000   12.000   12.000   12.669   13.000   13.000   13.443   13.639   13.674   14.000   14.000   14.000   14.000   14.642   14.749   15.000   15.000   15.000   15.064   15.643   15.822   16.000   16.000   16.000   16.194   16.643   16.894   17.000   17.642   17.750   18.000   18.451   18.966   19.068   19.749   20.000   20.450   20.636   21.069   21.747   22.322   22.632   22.631   23.070   23.939   24.627 
+8800.00   10.239   10.841   11.000   11.088   12.000   12.000   12.000   12.444   13.000   13.000   13.247   13.847   14.000   14.000   14.000   14.000   14.351   14.506   15.000   15.000   15.000   15.000   15.000   15.556   16.000   16.000   16.000   16.000   16.507   16.656   17.000   17.000   17.457   18.000   18.154   18.505   18.854   19.000   19.556   20.000   20.255   20.854   21.000   21.554   22.000   22.511   23.000   23.451   23.606   24.651   25.252 
+9200.00   10.668   11.000   11.317   11.414   12.000   12.000   12.249   12.929   13.000   13.106   13.677   14.000   14.000   14.000   14.108   14.369   14.807   15.000   15.000   15.000   15.000   15.259   15.371   16.000   16.000   16.000   16.000   16.335   17.000   17.000   17.000   17.261   17.940   18.039   18.562   19.000   19.260   19.368   20.000   20.113   20.688   21.000   21.331   22.040   22.362   22.688   23.256   23.944   24.431   25.112   25.687 
+9600.00   11.000   11.000   11.884   12.000   12.000   12.109   12.758   13.000   13.020   13.452   14.000   14.000   14.000   14.021   14.457   15.000   15.000   15.000   15.000   15.000   15.117   15.768   16.000   16.000   16.000   16.118   16.235   16.925   17.000   17.071   17.235   17.773   18.212   18.314   19.000   19.118   19.775   20.000   20.231   20.468   21.117   21.229   21.932   22.314   23.000   23.115   23.938   24.223   25.026   25.467   26.113 
+10000.00   11.000   11.057   11.893   12.000   12.025   12.531   13.000   13.000   13.171   14.000   14.000   14.000   14.000   14.178   15.000   15.000   15.000   15.000   15.009   15.098   15.546   16.000   16.000   16.000   16.030   16.550   17.000   17.000   17.010   17.372   18.000   18.069   18.915   19.000   19.030   19.555   20.000   20.087   21.000   21.029   21.557   22.000   22.084   23.000   23.028   23.604   24.741   25.000   25.193   26.027   26.559