diff --git a/_images/gctree.out.inference.abundance_rank.svg b/_images/gctree.out.inference.abundance_rank.svg index d0596fc..e865750 100644 --- a/_images/gctree.out.inference.abundance_rank.svg +++ b/_images/gctree.out.inference.abundance_rank.svg @@ -6,11 +6,11 @@ - 2024-01-22T22:15:51.489880 + 2024-03-06T21:50:57.368980 image/svg+xml - Matplotlib v3.8.2, https://matplotlib.org/ + Matplotlib v3.8.3, https://matplotlib.org/ @@ -43,7 +43,7 @@ L 80.046211 307.584 L 80.046211 54.144 L 73.832727 54.144 z -" clip-path="url(#pcc28759d24)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> +" clip-path="url(#pcebb3aa58c)"/> - - + @@ -418,7 +418,7 @@ z - + @@ -448,7 +448,7 @@ z - + @@ -488,7 +488,7 @@ z - + @@ -536,7 +536,7 @@ z - + @@ -751,12 +751,12 @@ z - - + @@ -769,7 +769,7 @@ L -3.5 0 - + @@ -782,7 +782,7 @@ L -3.5 0 - + @@ -795,7 +795,7 @@ L -3.5 0 - + @@ -840,7 +840,7 @@ z - + @@ -894,7 +894,7 @@ z - + @@ -908,7 +908,7 @@ z - + @@ -922,7 +922,7 @@ z - + @@ -936,7 +936,7 @@ z - + @@ -1115,7 +1115,7 @@ L 414.72 41.472 - + diff --git a/_images/newranking.tree_stats.pairplot.png b/_images/newranking.tree_stats.pairplot.png index 095a16e..e3ea9b7 100644 Binary files a/_images/newranking.tree_stats.pairplot.png and b/_images/newranking.tree_stats.pairplot.png differ diff --git a/_images/rendering-demo_20_0.png b/_images/rendering-demo_20_0.png index 8604e49..1075944 100644 Binary files a/_images/rendering-demo_20_0.png and b/_images/rendering-demo_20_0.png differ diff --git a/_modules/gctree/branching_processes.html b/_modules/gctree/branching_processes.html index 2bb3d84..60ec5e6 100644 --- a/_modules/gctree/branching_processes.html +++ b/_modules/gctree/branching_processes.html @@ -127,6 +127,7 @@

Source code for gctree.branching_processes

 import matplotlib.pyplot as plt
 from typing import Tuple, Dict, List, Union, Set, Callable, Mapping, Sequence, Optional
 from decimal import Decimal
+import math
 
 sequence_resolutions = hdag.parsimony_utils.standard_nt_ambiguity_map_gap_as_char.get_sequence_resolution_func(
     "sequence"
@@ -601,9 +602,11 @@ 
Source code for gctree.branching_processes
                     C = ete3.CircleFace(
                         radius=node_size2,
                         color=circle_color,
-                        label={"text": str(node.abundance), "color": text_color}
-                        if node.abundance > 0
-                        else None,
+                        label=(
+                            {"text": str(node.abundance), "color": text_color}
+                            if node.abundance > 0
+                            else None
+                        ),
                     )
                     C.rotation = -90
                     C.hz_align = 1
@@ -720,9 +723,11 @@ 
Source code for gctree.branching_processes
                                     node.add_face(
                                         T,
                                         0,
-                                        position="branch-bottom"
-                                        if start == 0
-                                        else "branch-top",
+                                        position=(
+                                            "branch-bottom"
+                                            if start == 0
+                                            else "branch-top"
+                                        ),
                                     )
                                 if "*" in aa:
                                     nstyle["hz_line_color"] = "red"
@@ -1020,9 +1025,9 @@ 
Source code for gctree.branching_processes
         for node in self.tree.traverse(strategy="postorder"):
             if node.is_leaf():
                 node.LB_down = {
-                    node: node.abundance * clone_contribution
-                    if node.abundance > 1
-                    else 0
+                    node: (
+                        node.abundance * clone_contribution if node.abundance > 1 else 0
+                    )
                 }
             else:
                 node.LB_down = {node: node.abundance * clone_contribution}
@@ -1241,9 +1246,11 @@ 
Source code for gctree.branching_processes
                     )
                 grad_l.append(grad_ls[i_prime, j] + res)
             # count_ls shouldn't have any zeros in it...
-            return (-np.log(count_ls.sum()) + scs.logsumexp(ls, b=count_ls)), np.array(
-                grad_l
-            )
+            # using math.log instead of np.log is essential because np.log
+            # doesn't work on large integers > 2**64 :eyeroll:
+            return (
+                -math.log(count_ls.sum()) + scs.logsumexp(ls, b=count_ls)
+            ), np.array(grad_l)
         else:
             return (ls * count_ls).sum(), np.array(
                 [(grad_ls[:, 0] * count_ls).sum(), (grad_ls[:, 1] * count_ls).sum()]
@@ -1933,9 +1940,7 @@ 
Source code for gctree.branching_processes
                 "original_ids": (
                     n.original_ids
                     if "original_ids" in n.features
-                    else {n.name}
-                    if n.is_leaf()
-                    else set()
+                    else {n.name} if n.is_leaf() else set()
                 ),
                 "isotype": frozendict(),
             },
diff --git a/_modules/gctree/isotyping.html b/_modules/gctree/isotyping.html
index bb7f180..69d4600 100644
--- a/_modules/gctree/isotyping.html
+++ b/_modules/gctree/isotyping.html
@@ -151,11 +151,11 @@ 
Source code for gctree.isotyping
         if weight_matrix is None:
             weight_matrix = [
                 [
-                    0.0
-                    if target == origin
-                    else 1.0
-                    if target > origin
-                    else float("inf")
+                    (
+                        0.0
+                        if target == origin
+                        else 1.0 if target > origin else float("inf")
+                    )
                     for target in range(n)
                 ]
                 for origin in range(n)
diff --git a/_modules/gctree/mutation_model.html b/_modules/gctree/mutation_model.html
index 369d7a1..5744bb6 100644
--- a/_modules/gctree/mutation_model.html
+++ b/_modules/gctree/mutation_model.html
@@ -343,9 +343,9 @@ 
Source code for gctree.mutation_model
                         + str(trials)
                         + " consecutive attempts"
                     )
-                sequence_list[
-                    mut_pos
-                ] = original_base  # <-- we only get here if we are retrying
+                sequence_list[mut_pos] = (
+                    original_base  # <-- we only get here if we are retrying
+                )
 
         return sequence

 
diff --git a/_modules/gctree/utils.html b/_modules/gctree/utils.html
index 0ecd6e6..63e7702 100644
--- a/_modules/gctree/utils.html
+++ b/_modules/gctree/utils.html
@@ -94,6 +94,7 @@
              
   
Source code for gctree.utils
 r"""Utility functions."""
+
 from functools import wraps, reduce
 import Bio.Data.IUPACData
 import operator
diff --git a/quickstart.html b/quickstart.html
index 320cd32..645c7d9 100644
--- a/quickstart.html
+++ b/quickstart.html
@@ -265,24 +265,6 @@ 
gctree<
 according to a linear combination of likelihood, isotype parsimony,
 mutabilities, and alleles:

 
$ gctree infer gctree.out.inference.parsimony_forest.p --frame 1 --idmap idmap.txt --isotype_mapfile ../example/isotypemap.txt --mutability ../HS5F_Mutability.csv --substitution ../HS5F_Substitution.csv --ranking_coeffs 1 0.1 0 --outbase newranking --summarize_forest --tree_stats --verbose
-/usr/share/miniconda/envs/gctree/lib/python3.9/site-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
-  data_subset = grouped_data.get_group(pd_key)
-/usr/share/miniconda/envs/gctree/lib/python3.9/site-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
-  data_subset = grouped_data.get_group(pd_key)
-/usr/share/miniconda/envs/gctree/lib/python3.9/site-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
-  data_subset = grouped_data.get_group(pd_key)
-/usr/share/miniconda/envs/gctree/lib/python3.9/site-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
-  data_subset = grouped_data.get_group(pd_key)
-/usr/share/miniconda/envs/gctree/lib/python3.9/site-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
-  data_subset = grouped_data.get_group(pd_key)
-/usr/share/miniconda/envs/gctree/lib/python3.9/site-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
-  data_subset = grouped_data.get_group(pd_key)
-/usr/share/miniconda/envs/gctree/lib/python3.9/site-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
-  data_subset = grouped_data.get_group(pd_key)
-/usr/share/miniconda/envs/gctree/lib/python3.9/site-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
-  data_subset = grouped_data.get_group(pd_key)
-/usr/share/miniconda/envs/gctree/lib/python3.9/site-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
-  data_subset = grouped_data.get_group(pd_key)
 Loading provided parsimony forest. If forest has fit parameters, parameter fitting will be skipped.
 Isotype parsimony will be used as a ranking criterion
 Mutation model parsimony will be used as a ranking criterion
@@ -309,7 +291,7 @@ 
gctree<
 
 Among trees with max Log Likelihood of: -78.00393661
 	Isotype Pars. range: 28 to 28
-	Mut. Pars. range: 66.45251202776497 to 68.93267315487162
+	Mut. Pars. range: 66.45251202776495 to 68.93267315487162
 	Alleles range: 48 to 48
 
 Among trees with min Isotype Pars. of: 23
@@ -335,7 +317,7 @@ 
gctree<
 Among trees with min Alleles of: 48
 	Log Likelihood range: -82.14344157 to -78.00393661
 	Isotype Pars. range: 26 to 28
-	Mut. Pars. range: 66.45251202776497 to 74.06179332885715
+	Mut. Pars. range: 66.45251202776495 to 74.06179332885715
 
 Among trees with max Alleles of: 53
 	Log Likelihood range: -92.15528744 to -90.28220403
diff --git a/rendering-demo.ipynb b/rendering-demo.ipynb
index 0710068..61cba09 100644
--- a/rendering-demo.ipynb
+++ b/rendering-demo.ipynb
@@ -27,10 +27,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-22T22:15:59.889325Z",
-     "iopub.status.busy": "2024-01-22T22:15:59.889151Z",
-     "iopub.status.idle": "2024-01-22T22:16:00.883068Z",
-     "shell.execute_reply": "2024-01-22T22:16:00.882467Z"
+     "iopub.execute_input": "2024-03-06T21:51:06.493483Z",
+     "iopub.status.busy": "2024-03-06T21:51:06.493287Z",
+     "iopub.status.idle": "2024-03-06T21:51:07.556452Z",
+     "shell.execute_reply": "2024-03-06T21:51:07.555786Z"
     },
     "id": "89WfPX7sgPqo"
    },
@@ -73,10 +73,10 @@
      }
     },
     "execution": {
-     "iopub.execute_input": "2024-01-22T22:16:00.885754Z",
-     "iopub.status.busy": "2024-01-22T22:16:00.885520Z",
-     "iopub.status.idle": "2024-01-22T22:16:00.888733Z",
-     "shell.execute_reply": "2024-01-22T22:16:00.888281Z"
+     "iopub.execute_input": "2024-03-06T21:51:07.559326Z",
+     "iopub.status.busy": "2024-03-06T21:51:07.559050Z",
+     "iopub.status.idle": "2024-03-06T21:51:07.562379Z",
+     "shell.execute_reply": "2024-03-06T21:51:07.561805Z"
     },
     "id": "Evd3oqJYgiNU",
     "outputId": "5e66a34c-fcb8-41ec-c4b0-bf2bf3b0e9e2"
@@ -104,10 +104,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-22T22:16:00.890698Z",
-     "iopub.status.busy": "2024-01-22T22:16:00.890527Z",
-     "iopub.status.idle": "2024-01-22T22:16:00.942420Z",
-     "shell.execute_reply": "2024-01-22T22:16:00.941828Z"
+     "iopub.execute_input": "2024-03-06T21:51:07.564613Z",
+     "iopub.status.busy": "2024-03-06T21:51:07.564274Z",
+     "iopub.status.idle": "2024-03-06T21:51:07.617609Z",
+     "shell.execute_reply": "2024-03-06T21:51:07.617031Z"
     },
     "id": "2vnHb87bjp1L"
    },
@@ -149,10 +149,10 @@
      "height": 680
     },
     "execution": {
-     "iopub.execute_input": "2024-01-22T22:16:00.970742Z",
-     "iopub.status.busy": "2024-01-22T22:16:00.970499Z",
-     "iopub.status.idle": "2024-01-22T22:16:01.010981Z",
-     "shell.execute_reply": "2024-01-22T22:16:01.010444Z"
+     "iopub.execute_input": "2024-03-06T21:51:07.648033Z",
+     "iopub.status.busy": "2024-03-06T21:51:07.647609Z",
+     "iopub.status.idle": "2024-03-06T21:51:07.689770Z",
+     "shell.execute_reply": "2024-03-06T21:51:07.689093Z"
     },
     "id": "DIfd348HjtFj",
     "outputId": "6775b36b-1ccc-4f68-a59c-02203c6bf847"
@@ -192,10 +192,10 @@
      "height": 680
     },
     "execution": {
-     "iopub.execute_input": "2024-01-22T22:16:01.013067Z",
-     "iopub.status.busy": "2024-01-22T22:16:01.012884Z",
-     "iopub.status.idle": "2024-01-22T22:16:01.043093Z",
-     "shell.execute_reply": "2024-01-22T22:16:01.042615Z"
+     "iopub.execute_input": "2024-03-06T21:51:07.692262Z",
+     "iopub.status.busy": "2024-03-06T21:51:07.691905Z",
+     "iopub.status.idle": "2024-03-06T21:51:07.724867Z",
+     "shell.execute_reply": "2024-03-06T21:51:07.724232Z"
     },
     "id": "Zarbo5ijkBct",
     "outputId": "d8297884-b57e-4ca9-b185-8faaf77878c6"
@@ -235,10 +235,10 @@
      "height": 279
     },
     "execution": {
-     "iopub.execute_input": "2024-01-22T22:16:01.045074Z",
-     "iopub.status.busy": "2024-01-22T22:16:01.044893Z",
-     "iopub.status.idle": "2024-01-22T22:16:01.072106Z",
-     "shell.execute_reply": "2024-01-22T22:16:01.071629Z"
+     "iopub.execute_input": "2024-03-06T21:51:07.727272Z",
+     "iopub.status.busy": "2024-03-06T21:51:07.726909Z",
+     "iopub.status.idle": "2024-03-06T21:51:07.755848Z",
+     "shell.execute_reply": "2024-03-06T21:51:07.755223Z"
     },
     "id": "PKCHvdJlkcr-",
     "outputId": "e2885de2-b6d0-46bb-da2c-965c9a9acfed"
@@ -278,10 +278,10 @@
      "height": 249
     },
     "execution": {
-     "iopub.execute_input": "2024-01-22T22:16:01.074137Z",
-     "iopub.status.busy": "2024-01-22T22:16:01.073957Z",
-     "iopub.status.idle": "2024-01-22T22:16:01.098025Z",
-     "shell.execute_reply": "2024-01-22T22:16:01.097554Z"
+     "iopub.execute_input": "2024-03-06T21:51:07.758394Z",
+     "iopub.status.busy": "2024-03-06T21:51:07.757970Z",
+     "iopub.status.idle": "2024-03-06T21:51:07.784628Z",
+     "shell.execute_reply": "2024-03-06T21:51:07.784001Z"
     },
     "id": "LVQvMp8DkiYq",
     "outputId": "7299a98e-7047-4b91-8c12-c5012e6c9334"
@@ -326,10 +326,10 @@
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    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-22T22:16:01.099992Z",
-     "iopub.status.busy": "2024-01-22T22:16:01.099813Z",
-     "iopub.status.idle": "2024-01-22T22:16:01.140510Z",
-     "shell.execute_reply": "2024-01-22T22:16:01.139923Z"
+     "iopub.execute_input": "2024-03-06T21:51:07.787142Z",
+     "iopub.status.busy": "2024-03-06T21:51:07.786737Z",
+     "iopub.status.idle": "2024-03-06T21:51:07.828498Z",
+     "shell.execute_reply": "2024-03-06T21:51:07.827907Z"
     }
    },
    "outputs": [
@@ -370,10 +370,10 @@
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    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-22T22:16:01.142656Z",
-     "iopub.status.busy": "2024-01-22T22:16:01.142470Z",
-     "iopub.status.idle": "2024-01-22T22:16:01.196832Z",
-     "shell.execute_reply": "2024-01-22T22:16:01.196299Z"
+     "iopub.execute_input": "2024-03-06T21:51:07.831106Z",
+     "iopub.status.busy": "2024-03-06T21:51:07.830718Z",
+     "iopub.status.idle": "2024-03-06T21:51:07.884180Z",
+     "shell.execute_reply": "2024-03-06T21:51:07.883451Z"
     }
    },
    "outputs": [
@@ -411,16 +411,16 @@
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    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-22T22:16:01.199093Z",
-     "iopub.status.busy": "2024-01-22T22:16:01.198899Z",
-     "iopub.status.idle": "2024-01-22T22:16:01.241724Z",
-     "shell.execute_reply": "2024-01-22T22:16:01.241132Z"
+     "iopub.execute_input": "2024-03-06T21:51:07.886719Z",
+     "iopub.status.busy": "2024-03-06T21:51:07.886364Z",
+     "iopub.status.idle": "2024-03-06T21:51:07.928847Z",
+     "shell.execute_reply": "2024-03-06T21:51:07.928163Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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EfmyezoiIgRge1l/vNIiIdMe/GEREPrp+wHSEGoL1TqNHM4ginh51i95pEBH1CCy0iYj88PDI+TBzrHaHpseMR2xQpN5pEBH1CCy0iYj8cHHfCUgMjtU7jR5JhIAnxyzUOw0ioh6DhTYRkZ9+O/JmRJvD9U6jRwkyWHDbkDkwClyunojoKBbaRER+Gh2RhCsTz4eFS7O36R8cg0WDZ+udBhFRj8JCm4goAHcMvRKjIwdD4JR/CDKY8faU3+qdBhFRj8NCm4goQK9MvB99zGF6p6Ero2DAkvOf1DsNIqIeiYU2EVEXLJ/27Fnbp20WjXh6zC2INXOWESKi9rDQJiLqAlEQ8faUR2ExmPVOpVtZRBNuGnQJpvcdr3cqREQ9FgttIqIuGhqWiCdH/RwhRoveqXQLo2DAzPiJuH3IFXqnQkTUowmKoih6J0FEdCY40HAEz6S9D4fk0jsVzVhEEy5PmIzfpNyodypERD0eC20iIhUdaszDIwffgqzIeqeiOrNowg0DZuCuYVfpnQoRUa/AQpuISGV2rxM373gedq9T71RUYxAMeHL0zzEr7hy9UyEi6jVYaBMRaeT+vX9Hvq0SXtmrdypdYjGY8N+pTyAuqI/eqRAR9SostImINPTa4c+wueYQmjw2vVPxm1k0YUBwLN6Z+qjeqRAR9UostImINLax6iBeObwcbtkDqZeM3TYKIm5OugR3DJmrdypERL0WC20iom7yfsFqLC1cp3canTIJBkyOHomnxixCiOHsmK6QiEgrLLSJiLrZi5kfY1NNKryypHcqbSyiCYNCEvDUmJ9jUEi83ukQEZ0RWGgTEemg0W3FX7M/RZ61AnXuRsg6fRRHmcMQZgzGb1NuxrioobrkQER0pmKhTUSko0JbJX6o3Iv1VfvR4LF2ywwlRsEAs2jEBbFjMCdxKs7tM0LzfRIRnY1YaBMR9RDVzgasrtiFTdUHUeaohUEQ4Vah8DYIIoyCAaHGYJwXMwrX9JuGlIiBKmRMRESd6RWFts3W+6bFIiLqCqfkxs7GLKQ1FSDDWohadxMAAaFiEFyKG7KitP4TZAgQYIAIQRBgFER4FAkuyYMYczgGBcdjYuQwXNBnNOItnAebiM4+QUFBMBgMuuzbqMte/bB27VrMmzdPtwY6E0lS6w1YZ3Obsg3O7Dbo6ccWaH6mqGBY4iNgDLfAEGyCIcQECAIEQYDkcEOyeyDZPbCXNcBZ0wKjUZuPeEVR4PV6YTKZGI/xGI/xeny822+/HW+++aYq8QJJoEd77rnnlGeffVbvNM4obFO2gaKc2W3Q049N6/w+/vhjZeHChZrF37lzp3LeeecxHuMxHuOddfH8JepT3hMRERERndlYaBMRERERaYCFNhERERGRBlhoExERERFpgIU2EREREZEGWGgTEREREWmAhTYRERERkQZYaBMRERERaYCFNhERERGRBlhoExERERFpgIU2EREREZEGWGgTEREREWmAhTYRERERkQZYaBMRERERaYCFNhERERGRBlhoExERERFpgIU2EREREZEGjHoncDKX5EVqTSWq7FZU2a1IDTPCAOB/+dlICA3HhNgEmA0GvdPsVdimgFv2Iqu5BLWuZtS4mlDQ3wNRAX6sSkVfSwRGRQyESexxbwfNuCUJ6aIZoySX3qmozi1JSIsSMbpR0juVUxRZ65DRWIHMvgpC3a0/J4XFqBK74HAFSvKqUV/djPQdVXCUh2H9qv2I6xeFsVOGqrIPm7cRFc5cFJq2I2lmECocuUgMHh5wvMbaFhw5VIKt6zJhscXhSFoJkscNVCVXNbikejS5MmAPXY9Jl3rR5MpEpGV0wPHq3TZkNZVivfMI5Mn9kdVUhlGR/VXMuGdpcFtxuKUUGz3ZMExNxOGWUqSED9A7rTZ1ThvS6iuxtrEIjnGDkV5fibHRCXqn1abZ04ICWyH2IxWRF0ajwFaEIaFJeqfVpr7FjqzCKqxPLYMcMwxZRVUYlRQfeDybHemlVfjxSBk8/Ycho6wKY/oHHq+n6BGVRbPLhS9zM/BlbiYO1VYh1GSCorQWiO5IM0RZQdaWHyAIgM3rxvjYRFw/fDSuTx6NMJNF7/R7pGa3C18eSceXeT+1qdkERVHgkiS4o0wQFQVZ276HIIiweVytbTpsNK5PHnPGtKnV68Ca8v1YU3UAh5tLEWywQIEMjyTBPdgLQVbwctbnEBQRdtmFUREDMCdhEuYknotQ45nRBu2RFQW3Lv0MqYYgFLhlPKcoEAVB77RUISsKfrZuKfbHGJFr8vaIY/PKEh7dtxLrKw5DEABFUeDpBwiKgv/b8BagKLgkcSRemTzf71wbalrwxbubsO6rfTAaRTjtbnjcEtwuDwQhDP/8/UoEhwWhucGGyRePxA13zsCYyUP8PoYN1Uuwv341PHLrFzPJ4sX4hWH4sPBReGUPBoaMwmUJ96BfcLJP8d5/6Wus/ng7XA4PBAHwur0IEYbgt9e+BrfbizFThuCeZ6/HiAmD/M5VDVl1r6Kw5XPIihOAADnEgytuB7aW3wpZcSM66ByMiXkUUZaxPsV74/AafFG8B07JBQgivLIXuHIk7tj5NtyyFxP7JGHxyLkYE9VzitCu+E/uaqwq2wGn7IGgAB5FhuGaYfjVnn/CrXgxLmowHkieh1ER+nyp+svBDfgkbz+cXi+A1vcoZo3D/HVL4JYkTO47AE+fcznGxyTqkt/y4s+xvnoT3LIbgAAvvIib3w8vZL4Ej+xFcvhwLEpagKGhg3XJ742VW/DF5jQ4XV5AALySBPSbjDteXgG3x4uJw/tj8U0zMGawb19a/rZmC1bsSYPD5YUgAh6vBIycjIVvr4Db68WkpP54fO4MjBvQc74E+UP3QvvF3RuxKi8LTS4XHF4PgNYi8XiyKKDFc+x3B6rLkV1fjTcP7sL1yaPxxJQZ3ZpzT/fino1YlZeJJrcbDo8bQOuXmePJgoAWt7vt59Y2rcGbqTtx/fCxvb5N/3VkNdZWHUCLxwGn1HqcVq/jhG0UUYDNe6xdMpqKkWetxIcFP+KKfpNx3/AruzXnQNlsNr+fYxEMMBtEjEtJgcNuD2i/oaGhAT2vI4EcR3uCBAMsBgPGJQd+bO0J5HiXF+7B86mroSjKKY8pggC31PqH/vuyDKwpz8BzE6/BjUnn+hT7zWdXYt3KvfC4JciSfGp8RYDd5oLd1voa37E2HVn7CzFgaBx+89KN6Dc49rT7qHWV4KOCJ+CUbZAV74kPioBXbv3MLrFn4ePCJzAq4kJc039xh/GKcyvx+I1vwtZkh8dz0hUHBXC7W/eRsacAj9/4Bi66agIeeXXRafNUS4u7ANsrbodXbj7leAURkJXWz5J65wFsr7gDiSGX45y4P3UYr8Bajbt3vwerxwGP/NPxKj+dK7H1ShsAHGwowj2738NlCWPxh/E3qH9g3aTIVoUH9/8bVo8DXuWk8ysKcP/UpmmNhXho378xM348nhp9c7fll9dUiwU/fowWrwtu6aT8BKHtd3trSrFg/UeYO3AUXjn/mm7Lr8xRjhczX4FdssPbzvvN89Pr5UhLLl7MfBlToifj3mF3dFt+BRX1uPtvK2C1u+DxnvSZIwBuz0+v59wy3PPK57js3GT84fY5HcbLr67Hbe+tQLPDBc/Rz7Djwrp/+iK0v6gMv3jvc8wem4yXbug4Xk+lW6G9Ki8LizethlEU275V+sPh9cLhbcEH6fvwbtpevDZrLq4eMlKDTHuPVXlZWLz5WxgFEU4pkDb1wOH14IPMvXg3fQ9em3lVr2vTdVUH8UL6pzCKIlwBtIFTcsMpubGieAuWF2/GM2N+hkvix2uQqTrWrl2LefPmweDv0B9BQPCEc/FZ6j58dt+pReDpSJKEVatWYfbs2X4/tz0BH0d7BAGGUUn4PD0fX4r3dD0eAjvev6avwSf5e9otstujKMCfUlejuKUej4y9vMPtaioa8djP30JdZTM8Hv9e4411VjTWWfHkbe/g1ofn4NLrJnW4bZE9HcsLfw+P4u5wm+N5ZBfSGjeizJGDe4e/BQEn9s6n787D0wvfgsvp8Sme0+HGjyv3IvtAMd5e/yQEUdsrE3XOfdhVcR8kxbfhVJLsRKntWzSUpmHWgK8gnHTL04H6Qjyw9wOfP4udkhuryw4gvbEEn01/SPcrMf462JCPR1PfhUvy8fzKbqyp3IfMxkJ8eMFjmh/vnpoi3LZhBZy+5uf14quCdBysK8MPc+/VPL/DzUfw8uFXf+rFPj2X7Mb2uh3Is+XjL+NfOOX9prYDR0rxwGtfwunjZ47T7cHqnVlIL6jEZ3+49ZT221dYirs++BIuH+s/h9uD/x3IwqGSSvzvoVPj9WS63Az5/M4N+MOO9fDKckBF9vGckhceWcLvt63Dn3ZtVCfBXuj5Xevxh10/trZpAAXm8Zzen9p0+zr8afdGdRLsBm8c+R9eO7wKkiIHVGQfzy23tsHfs7/CmznfqJSh+rZv347HH38cVqvVv38tLajZuhHWlhb/n2u14oknnsD27dv1P44Ojm3xVTfi8cceUydeAMf7Utp3+Dh/N1yyf69Dtyzhw/xd+Ev6mnYfrypvwD2z/4rKknq/i+zjVZfV499//Aqrl+1s9/FSexaWFz7rc5F9lAIZda5S/OfI/Sf8PnNvAZ5e9G+fi+y2eLKC0rwq3HPJi349z1/1zgPYVflLn4vsNooMm7sQG0uuPeHXBxuK8ODeJX5/FstQUGirwY1bXvMvD52lNxbisYPv+VxkH6UoCoodtbh15ysaZdZqX20Zbtuw3Oci+ygZCvKb6zF79dsaZdbqSEseXs72vcg+SlYUVDgq8UTqMxpl1upgXgUefN33IvsoWVFQWFmPG3+/5ITfHyiqwN1LfC+yj49XUFOPa15bcvqNe5BuL7Qf27IGnx5ORZ1TvUu6AFDndGBpdiqe2vqDqnF7g8e2fo9PDx9CnUPtNrVjafZBPLWt57fpn7M+x9elu9HoVmf4wVGNHiu+Lt+Fv2avVDUunblymiqxvGDvsaECfvLIXnySvxt5zdUn/L4gqxwPXP0qnA7//hh3xNrkwDsv/Q/L31p/ymPLip6Bx9+i8zj1ngpsrPqw7eenF70FVxfyLi+sxZKXtfvCu6vyfkiy4/QbdsDmLUF2/T/afn5wz3/hkAI/3mJbHf6Zszbg53e3R1LfhdPPIvF4pY5avJ33nYoZnegXG5Z1qQOqqLkBrxzaqF5CJ3k5+1W4/fxSe7wKVxU+K/lSxYxO9ODrK+FwB95+xdVN+OdX29p+vvu/K+HsQryiuia8vnbb6TfsIbq10L577Vf44kg6bB7/vlX6yuZxY3lOGu778StN4vdEd6/7El/kZMDmUeeP78l6Q5s+mboE31fshUOjGTTsXhe+LduDpw99pEl8OrPcteNjuAMsso/yyBLu3P5x289ej4QH/u81WJsDLwbb47S78cW7m7B9bXrb774pew3uLhTZAKAoEnbVfYkWbz3+vngpnPaufT7JkoyVb29EXVVTl+K052DNM10qsoHW481v+hBOqQbPHfoCDrlrf+NkKFhasA01ruYuxekOL2WtgNPbxfOryFhRvBm1Ghzvozu/gcPPnuyTSZDxXvYuVDmsKmV1zNt578PV5febgu8qf0CDu1GlrI557oM1cLi6+HpWZCxdux81jVY89cUaOLtYA8qKjP9u24/qZvXPhxa6rdD+296t2F5RDK986k07avLKMjaXFuP1/epd1u6p/rZvK7aXF51604nKvLKEzeWFPbJN3837AfsacrV/XSkSdtfn4IP83tPLRN3vx4osOLzqfOGzepzYVHUYAPDkrW9Dlv0fS++LliY7/vH053A63ZBkNzKbt7QOGO8ir+LB+ool2PS/gz6PU++M2+XBh39d3eU4x5MUN8qsa6Cg6/lJihuZdf/ADxWHVDlel+zBWznruhxHS27Zg/VVB1RpP7fsxXt536uQ1TEuScK3JVmQ1TgfkoRX0zapkNUxHtmDnfLYfaAAACAASURBVHW+38dxulifl6rbIeb2SPhhb446r2ePF29+tQ3fHcpR53x4vPjHuh1djtMduuVmyPXF+ViecwhWd/fM2WvzuLA0+yAmxffD9P6Du2Wf3W19ST6WHzkEq0Y92Sezud1YerhntemO2mx8W74HdpUKm9Oxe11YVbYbYyKTMDVmRLfsk3qX7TX5sHnb762p/3YXbPty4ThSCsnqxKDnbkHohI7nt7ZLbmypzIUl3YuC7AqtUgYAtDQ58K/ff4mbnxsPGe1/cc/ZUoMDX5Wg5FAjGiscmHnPcFz265RO4+Y3HoAitz+7SY2zCGW2TDS6K+GUWjAsYgqSIy7oNN7BbTm+HZCPml2ZOGGag+Ps29SCH79owOEDdtSUe3DTr+KwaHHnc/pW2XdAwYXt72tvHurWHoItuwzummYkLLgQ/W69uNN4u+vyfDoOveS0lKGj738NuwtQtSYdzVkVcFU3Y+DC8zH49os6jbevIVfV/NIbKiF30AnjOJiDls374cotgbe2EVHXzkSfmzq+CRkAtlUWqppfob2owy8p5TtKkfdNDmozamCrtGLsLyZi4n2dz0iU2Zylan5ZxdUdFtnNJVmoy9kDW3UR3NYGJJxzGfpNnttpvB0ZRVBC2o9nzctC06E9cJQVwdPcgNgLL0PfizuPtzOv2LcD0Vm39Gj/dvP3qLKrO3b2dKrsNizepG7vR0/y282rUWXr3ssmVXYrFm/uOW36YuZy1LjUv5TcmVpXE/6UsaJb90m9x4G6kg4fa/rxICSHC6ETh/ker6EUy9/aAFuLukNGTiZ7ZRzalY+0/AOQOriB8/CmKlTnWzF8WiwMJt/+dNjFBnjc7RfuNc4CWL31iA0aBFHwbbaZmvIGn7bzVZ1jX9uUfSfbs74FJUdcOGd6OIwm32Y48Mo1bVP2naxpdy6cxbWImDQUgtG34610dO/nm79SGwrappw7Wd3OPNiL6tBncpLPx1vlVHfow+7q4g6HcdkPZMNTVo3gccN9zq/cpu7QluzmnFOn8ftJ6dZiNBU2InFqf4g+vt/qXHVqpof9R0rh9rbffk3FmXA2VCFiQAoE0bf2q2uydxjPmpsJV20VQoemQPBx9qmKphafttOb5j3a/83YD1cXx6sFyubxYGn2QSwcOVGX/Wvlv5n7uzyrRqBsHnePaNMvSrb7PaODWhyyC1+V7sS1A87XZf/UMylQUNFJYTTkb/cAggBnbjmat2b4FLO0pR5Cnro3OXekvroJO77PhrmD6eOvfmpM2xR7h1aX+xbUbUToMBdseacuADUq6uK2Kckq7L71VIeEWZCfUYqhY9RZ2KXB3fF5uPfZfhB+qm82/8+3AlBSzEiwNKPSFXHKYwPvnw38NCVZ/Sbfzn+o0Yyc5gqMiNBn4ZTTyWruuEdx+IOXth1vzfpsn+KFGIOQ21KG4eHqrJZ5qL7j12nML65py8+6/ZBP8UJNZmQ1VGJUH3UWTsm3Fnb42JRHLmh7vxWu9e3KRpAhGEW2YiSFqrPQU2ZBVYePDZx2/bHXc+5+n+IZjSJErwS5ncozYfaxeM0ZvsULM5uRXVGNkYlxPm2vF817tD/KPAiru3uGN5zM5nHjg4wDuuxbSx9l7e+2ISMns3nc+CDTtzeBlr4s295tQ0ZOZve68EVJ77njmbqHAAFSZ2MPA5j31e3xwunono4Kj0fCkR86/lwJaB5rQYHibf95gcz7K3tlGEzq9Q91loEQyF9HQYasdBA1gPMvKwoMPvYW6kHorJECOl4JBlHN89tJDgGeD6OK50PoJIdA3m+yIsOoYvt1/gbxPz9F6WQ0fwDxJEWGUY21FzSmaaG9tawINU597wqtsrVgZ0XHl3N7m63lRajp5mE4J6uyWbGzQr+xUXvrj6DOqe8loxpXMw409Ozxk9T9kkKjVY0X0mSA1MGl1uO5JTty67dhe+lHWFvwOjYW/QeHqlfD6fXv89fTZEAXJ0w5kUmCvcisWjiXy4ukEeotwxwdpO6VOQMkVLvDVYvnlL0YFtZze+vGRiWpGs8tezEktPNx8P6YFKtOz/hRLsmD5Mi+qsVLDvd9GJkvPIoX/YP7qRZvwjB1r6TIsgJFxe8BLq8Xw+Ni1AuoEU0L7a/zs09ZTr27tbjd+DpP3RsE9PR1fhaaderNPqrF48TXeb5dCtTCusqDsHqduu0fAGxeB9ZVpeqaA/U858aoc8n2KHOeb73ZDc5yFDXtR3TQAIyJvRyDIs9Brb0AO8uWwuvH/MYeuwKlMTjQdE8RqsTCZFbvL2vCAHW/yEQHnQNRUO+LgFGMh1nFHsV+wX1Ui6WF8ZGDYVLxeBOC1D3eKXGDYFaxB3pAaJRqsQBgRHgyTIJ67dfXrO77Y+LwATD7OH7dF32jwlSN169PpGqxtKRpob23slSNWaK6RIGCXVVl+iahor1VpapMtdMVigLsqtLvKsGhxkJVppPqCgVAakO+rjlQzzMtbigiTEGqxAo3BsGQ7ltHRVRQIi5OuhcjY2ehX/hoDI2aionx8+D0tqDK5vtMHUajEd4GdfIHgBF9psIcZFIt3uSZI1WLBQAR5pEQBfWOt3/oDFgM6hVO0/omqxZLC8PD+sOiYqE9NbbzWWz8NToqDkEG9V5/Fyeq2wOdFDIQJoN6X/TGRY1VLRYApAyMhUXFL8ozxg+BxahevOnJg1WLpSXNboZ0eD0oavH/DmJHejZa1myGp6wSssMJQ3QkQqdMRMTcSyCYA3vD5DfWwyNLMPXgsW6+cHg9KGoOoE3TDqNlzSZ4yqqOtenUiYiYOyvwNm3Sp02dkgflTv/vrG7YXYCyz3bDXlgLyeaCuW8E+s4aiQE/Ow+iJbA2KLHXnBGvK1LPjPgRiDQHo9nT9Ssu0UGhCCu2w4rT92pbDKGn/K5PUOtlc7fk+4wlBqOAYcFTUIZ1UDqY9s5XomDA5Ym3o/Sm77Dq/U1dngfcYBRx+1P/16UYJxMFEwaFz0NB8zIoXVyPQIABY2IX45r+G/Bp0fYuzxVsEET8OmVOl2JozSQacGW/KVhZsrXz+xN8YBBE3D/sKpUya2USDbhh6Dh8mLMPktK117NREPHYxFkqZXY0phEzYqfhh8r1kLv6foMBCwbNVymzViajAddcMAafbjjQ9fevQcTD82dA+NGIpdsPdP31Iop4ZE7n00X2FJr1aFfarAgK4JuLt6oWhj6RiLj6UkTfOh/B40ah+fuNqP/w84BzCTIaUNnNU+FpodJuRVAAvSXe6loYoqMQcfUliL7tBgSPH4Xm7zai/sMvAs4lyGjUpU1rXU0wif4Xxs6yBlj6hmPgwgswbPEcRJ83FKXLdyH31cCXlzeLJtR28/SC1PN9cNFtMPk4XV1HjIIBSy66DUZT4HEaXa1zb4eb25/Huj1ul4Q5oxYi2NC1ccaiYMCsuF/AJAbhnmevQ0SfU78I+MNgMuD2J65BUIh6vX9HjYl5DCbx1FlC/CEKBoyKfhgGIRiPjJqLSFNIl+IZBAMeTJmNYBV7O7XyYPI8hJu6eH4FA+4dNhdBGhzvM5MuR6S5a1ctjIKIRyfMQohRvd7xoxYmLUCYsavtJ+LmpOthEU+d3aerHrn5YkSGdq39DKKIB6+7CMEWE56YezEiQ7p4PgwifjP7IgQH2FHY3TQrtKvsVogB3FUefulFiLnjZoRfciFCL5iEPgvmIfyyi2DfewiKN7Dp3AyCiCp77y+0A2/TCxFzx00Iv/Rom16D8MsuhH1P72vTWldzQG2QeN0kJD82F4nXTkLcZWMw5JeXoN9156J2UzZkT2A9WaIgarJkMPVu/YKjcE/K9IDH6ppFA3418mLEBUUgJj6wMYiKIiO7dgPCzLGICRns8/NkSUZ0XAQWDX4Rpi780U4IGobzY69r+/nPKx6EJeAhJAKGj+6PG+69JOB8Tmda4vswCIEfb4R5FIZF3db289vn3QVLAB0CQOtEDyMjEnHLkOkB59PdXp90XxeOV0ByeCIWJHW+eE9XLLt0YZeGkIyOjsfdo85TMaMTPTn6UZjFwL9kJIUMwtwE7a5+vP3IjQEPIREEYOSgONwy+9hiO/+960YEdWH2oFGJcbhjeueL9/QkmhXado9HtWlwDJERUGQ54GWBTQYRNp1vIFSD3e2G0aDOKTNE/dSmAV4OMokibJ7uv9HV7nV1ubfwKHNMGBSpC20AI+xS739dkfp+NXImfj3qkhOuQDlzy9G8PRO2tAIAgD2zGM3bM+HIKW3bJshgwuLRl+HelBkAgNGTkgKa5iujdi2snjpMiL/Kr2n0+vQNh9liQt+gJNw55DWIx92o1VjuQMYPFcj4oQKyrKCmwIqMHyqQvfHYXLuiYMDAkNG4fejfT4ibNCIB//j2URiPuxHKIbWg0pGLSkcuFMiwehpQ6chFtbOgbRuD0YDRk4fgtW8e8bsN/BFuHobpA5ZDxLFirLrMg23fNWHbd02QJQWleS5s+64Ju3889uVaEAzoE3QOpvf/5IR4Q8PisPTCX54wrMxd3YTGrdlo3JoNyAqcJXVo3JqNpl1H2rYxCCLG9xmED6fdr+HRqm9IaDzenfIQjMd9NruqmlG7OQe1m3OgyAocxfWo3ZyD+h3HZmsyCCLGRg3G21Me0jS/5Mi+WDXnDpjEY38/vbWNsO1Kh21XOiDL8JTXwLYrHfb9x270NwoGnNt3IL6afbum+Q0I7ofnxz59wlSOtkoritcXoHh9ARRJQXNRI4rXF6B067EZvwyCASPCh+MPY5/WNL+h/WKw9KmFMB03lZ7b2oDGglQ0FqQCigxnYzUaC1LRVHxsjniDQcT4Yf3w4VM/OyHe8LgYfPbLhSdMzedpakBzdiqas1OhKDJcddVozk6F9cixeEZRxDmD+mH5/SfG6+k0G6PdNyS0wxWjfKG43FAkCe7icrSs24Kwi6ZCMAX2jdTtlREX0rVLMz1Ba5sGPo7rhDZduxVh06cEPEbbLcmICwkLOJdAxVjC4enCWErJ6YHilWDLrUb5yn2Iv3I8REtgbwMPPIgxqzeVF51Z7kiehiFh0Xh4z2eQZAn1q3ejacOxmWpqV2wCAIRfMAqDHl8Ak2jA36fMx8yEYzeEzbzmHKxftR+2Ft/HfB+p34aylgycm3A9ws2+T0UmGkRcfNWEtp9jggbi4ZSPsCT/t2jy1CB/dylWPn0s/4y1lchYW4mQKDOe2no5jKIZU6Ln4ZL4X7Qbf9CIeHyy/wUsvu411JQ3oqy2FGkNa9ser3LkosqRC5MYhEv73QOTxYhr77gYdzw1z+dj6Ipw01BcPvhHbCu7FQ5vJdJ2NuD1x459Cdr+fRO2f9+E8CgDlu4bDVEwY2jkIoyKfrjdeEPC4vD9rCdw5863UeFsRF1qEYpe/abt8cZt2Wjclg1jRDDGf/obWEQjFgye1uPHZXckKSweX07/PX6175+odjag6mAxcv76fdvjtVtyULslB6aIYJy/8lcwi0bMHzgd9w3vfJlttSRHxmDntQ/hxrUfotzejNrMfNT8+9jwSdvuDNh2Z0AMC0HS27+D2WDA7SlT8PgE7a6kHK9/cD+8ec7f8XzGS6h116NyXwV2vLC57fHiDYUo3lAIS6QFN65ZBJNowuyES7FgoLrjsjsypF80vn/lbtz55xWoaGhGXXkuijYta3u8seAQGgsOwRgUivG3vACLyYAFl5yDX9/Q/pWZYXHR2PzE3Vj49gpUNjajqSgX5d8ci9eSfQgt2YdgCA7FiN+8ALPRgFumnYNH5vSeKz1HaVZox4eEwduForD0keehuFp7C0MmjUOfhdcGHMsrS7oUhWrrcpsufuFYm57bO9s01hIBbxduatlz4z8hOVtvLouZPgLDHrw84FgeRUKspWtjO+nMNitxJFLnPYMXUr/F94+Govmh62EURChQIECAV5ERaQ7CnP5j8Mz4U28EG33uYMTER/pcaBc3HUBeww6Mj5uLWD+GjABAdFw4Lrpy/Am/CzaE477k/2BP3f8QdtMqTLp2IIyCEQpaL/krkCEKBgwJnYg5ifcjwtT5ePDwPqF4Z+Pv8PX7m7Hy3XAMKBkNU5ABity6eIcsyzAaDThnegp++cf5iE1Udzq10zGLUZg18GvkNy2FZcHHuPSGPhAFc9s1AQUyBJjQN+Q8jIv5HYKMnc/5HGUOwRczHsanhdvxiTkMMZdP+Gm6OaX1eBUFBsGA82OH4rHR8xAf5N9QocbqJuTszcOW1bthqg7DkX35SD53aGAHD6DOZkd6eRXWFZbBNWgYMiqqMCbR93mtI00h+Pj8R/FF6VZ8dm044ueMg1k0/PR6aV2wRBQNmBo9Ag+nXIu+lu6dnq2PJRjrrr4XSw7vwXtBoQibMQkW0QhFaM1PlhUYDSIuih+KP0yejYQQ/zpS6lvsyCyqwoa0Msixw5BVXIVRg3xvvzBjKF6e8EesqVyHNddHYPhVI2AUjVCU1iEYsqLAKIgYEzkGtw3+OaLN/k2HWGe3I62iCj8WlcE1ZBjSK6swNsH3/KJCg/HFC7fh03UH8ElEKGJSprRN1yeg9f1rMIg4f3QSHvvZJYjv03mNEBUSjG8fvg0fbz+AD8NCETlhCiwGw0/HK0BWWuNNG5qEZ+ZdgvjI3lnHaVZoxwaHIMxsgSPAMcBxv7kbitsNd1EZmlevR937yxF7988DihVpCUJ0UNduTukJYoNDEWYyw+ENbKW4uMV3QXEdbdMNqHtvBWLvCewSTKRZnzbtYw5HqMEMV4BDNsb85SZITg+sRypRumwXjry8GiOeujqgWGHGYESae//rirT3zISr8MyEq2DzupDRWIGvv1+NV555HnVHihBq7Hxs8NWLpuGDl1fDYe98qFaFNRuZtT8iJeZi9Asf7XeO/Qf3RfLY9pc2nxJzDabEXAMAqHTmodKZjxBDOBKDkhFu8n/BiHl3zMC8O1qHx+SmlSI/qwzhUSEYMX4QYhL0nxt3aORCDI1cCABocmWhyX0YZjESUZYxCDL6v4DMgsHTsGDwNABAdnM5DjdXINIUgtGR/REX5P+X9XcfX4pv31kLp90FQRDgdXkQLPbBQxc9DY/Lg7EXjsT9f/8FRkzxbTq6V9Zvwad70+D0eCEIgMcrAWMmY8H7K+CWvJg0sD+emjMD4/r5tljQDQMuwg0DWmeEyGkpQ661HBHGEKREDOj24ro9t6VMwW0pUwAA6Q2VyGqoRpQ5CONiEpEQ7P9Vyn98uQVfbE2D09Xafm6vBAycjNv/ugJurxcTh/fHI/NnYEySb+03J+EyzEm4DABQaCtCsb0EYcYwDAlNQh8/i2sA+OuGLVh28Nj5dUsSMH4ybv7o2Pn93SUzMD7Rt/wWXHYOFlx2DgAgu7gah0tqEBkahNGD4xEX5X8xvGjaOVg0rTVeZnk1DlfUIDIkCGP6xyM+oncW18fTrNAWBQFxwWEBr2JoGda64lTQqGQY+kSi7t1lcM+5GOZB/q/0FB8aFsDtcz2PKAiICwlDjaOLbTo6GYboKNS9swzuOTNgTgqgTcP0aVNREBBriUC9O7AbMcNHt66aFTUpCZa+Ech56Rv0u3EKwpL9X40szhIZ0DLSavHIXuT38yCpXLO3ca9T2NiAtKpqpJnNCJVlFDY2YHBU4ItgFNU1IqO8CpmKGaGKjKK6RiTFBN7LGmq0YGrsYHyXV4MnbrvvtEU2AFyzaBo+eWNtp4V2k6sCadXfIdKSgGBjJCqtx+bODjX3Oe0QkqBgMx5+6UafjiEhaBgSgtSbT3j4uAEYPq79Ar8niLSMQqRllGrxRkb0w8iIwFbvK84qxSOznoO10Qav+6ROLBnwuFo7YdK3ZWPxrGcxff75ePy/D3QYL7+2HguXrECL0wWPdNKVQgFwS6372F9ShkVLPseVo5Lx52v9G9oyIrw/RoSru0Kjmsb2ScDYPoGtNlpQUY+7X12BFocLHu+pV1rdP3U0Hswtw91//xyXT0rGH27zr/0GhyZhcGhgK3Dm1dVj4Scr0OzL+f3kc1yRkoyXr/Yvv5GD4jBykHqrl47uF4fR/XruaqiB0PQv9PXDRyO3qQ6uAHu1jzpaCHpr6v0utC0GI24YPqZL++9Jrh8+BrmNdXBJKrVpbb3fhbbFaMQNw/Rr0zkJ56LQXt32IRGoo8W1q6LR70LbbDDhygT97nqWFQW/2vs2jiR5URrqhKwoEIUz4euk/7yyjN989x1+yMttHc6gyPAEWSAqCq748CMACmYPH4bXrrzKpzbySjIe/fw7/JiV13a51isFQVAUzHvzQ0ABLh01HK/cdGW3tfmL/70bv/35W7B3MISkxVULWZHQ5KrEwaqvT3gsKXISRsV2PM40JNSC6++8WPVVF0ldaZuz8cSVL8Dt8O1qnsvuwvqPN+Pw7ly8l/7qKTfV7i0qxR1Lv/T577PT48FXaVlILa/Et/ffetZ+3hy1/0gpfvXGl3B5fGw/twff7MpCekElPntW+/bbW1KK25d/CaeP59fh8eCrjCwcqqjE6rt4ftWk6cqQVwxOhkHwcxftjEF2ZrT2zhhj/e+ZMggC5gzu2atr+UO1Nk0/DAAwxvr/x9UAAXMGj/D7eWqZETcWRj/bQGlnZpGGva2zG1gCGAdqUARMj1d3Fa6T2Wy2Dv857HZYYIJRNGJM8kg47PZOt7fZArsK0tMtTU1Fyj9ex7c5h+GRJLglb9sNw7IgwC154ZYkfHM4B8mvvYpP0w51Gm/57lSM/8M/8H16Tms8rwTvTz1BiiDA7ZXgliR8l34YY3//GlbsTdP8GAFgyKh+uPd31yAktP0e8AER43DFsN+2+6+zIttoFDHuvGFY+OvA71Ug7WVsy8ZTc//oc5F9lCwrKMkuwx1jfnPC7/eXVuDOT3wvso9SFAX5tfW46q0lfj3vTHMwvwIPvul7kX2UoigoqKrH/Oe1bb99ZRW4c4XvRfZRiqIgr64eV75zdp9ftWnaoz0gPBJjY+Oxu8L35bqr/vpvmJMGwJQYBwgC3AXFsO3Yj+CxI2FO8v/y4oS+iUgMPXNmhhgQFomxsXHYXVl6+o1/UvXyf2BO6t/apqIAd35Ja5uOSwlo2IjebZoY3AfJ4f2Q2lDo83PSFy9D6Ih4hAyKAQQB1uwKVK/LQJ+pQwIaNjIycgDiNBxruHbtWsybNw8GQydTGQoCoi8fjf1rM/C+0v7MB0dJkoRVq1Zh9uzZKmeqnxc3b8LHqalQfJz2UwHwhw0bUdjYiCemzzjl8b98vxnLdvkX78VvNqCovhGPztb+TvjZ86eiT1wE/vLwJ7A1+77aY0csQWbMuGYCFr90kwrZkZaenPsnOE8zRr8z5bkVeP93y3DHn1rvybnr45Vw+lkkHq+orgl/37ANi2ddGHCM3uyBf6yE4+ShO34oqW7Cm6u24YH/06b97ly+EvYunN/Cxib8fdM2LL747Dy/atO0RxsAXrrwcoSZfJ+IPXjCaLiOFKDx82/R+Nk3cBeVIvL6KxH7q1v93neYyYyXLjpzCoujXrpwjn9tOnE0XLmFaPx8NRpXfNvapjdc0YU21X/6qUdHzvdpfOtR0RcMR0t6GQrf2YTCtzfCmlOFpDtnYORz153+yScJNljw+Chtp1Tavn07Hn/8cVit1o7/tbSgeOUuWFs62eanf0888QS2b9+uac7d6fmNG7DkwEG/e2xckhcfHDiAFzdtOuH3L63eiKU7D/jdw+eSJHy0/QD+8t2m02+sgikzRuLhP81HbIAL2RwVFR2GS6+bxCK7F/jrHf+E09q1NQtkScYXr/4PdeUNeOLrNXC4A7uh/ihJkfHfHftR3dL7F4Lz17MfqtB+soyP1+1HTZP67ff4t2vg8HQtP1mW8f6e/ai2nn3nVwua30U1PCoGswYOxeqCwz6tbR9xxUxEXDGzy/sVBQGXJQ3DkMjAb4TqqVrbdBhWF2T72KYXI+KKrq+6JYoiLhs0DEMi9G/TwaFxOD9mFDZUpULG6dug/81T0f/mqV3erwgB0/uOwYAQ35e1JnVl19RgWdoheOTA5lP3SBKWpB7ETWPHYnhMDHIqarBsT9qpNwv5EW/p7lTMnzQWw+L9n4HDXxddOR7DRvfHiw9+iPKSBthbfO/dDg4xQzQacP9z12LG3AmnfwLpyu30YOPy7T5fZTldrPeeWYbvhks+fWaejsvrxWsbd+DFa86eYUduj4Qf9uaocz68Xvxr1Q48e6t67eeSJKzOzIGsQn4urxevbt6Bl+aePedXK90yXcGbl1yDUUvyYO/ityx/hBhNeH1mYNO29QZvzroGo0pyu7dNDUa8PvOabtvf6fxh3M+xrTYTzm5cnTHIYMHvxy7otv3RqW5b+QVc3sAXLQJai+NbVn6BHXffg7s+XAlPF2/Y9ngl3LFkJTY9dneX4vgqMSkGb3z9G+zZmIUV/9mA7IPFCAoywdrOzZLmIBNEUUR4ZDCuv2MGrr299y34cLY6sj+/dQXfdtShEuVKIZpQDyfsGIJRGCZ0fpP63rWpkIe2v43tSBaaD+6Bo6QI3qYGRM+4DLGXdr6YzM6C4k4fP9NkFVd3WMQ2F2eh/vAe2KqK4LY2IGHSZUic2nn77T6sbvtlVFZ3+CXKfjgLzfv3wFlcBG9jA/rMugwxszvPb0fh2XV+tdJt84JtvuluXLT8HTgDnAPaHxaDEVsW3KP5fvS2+cZ7cNGKt7uvTW++V/P9+Gv5hY/jpq1/hkvWvg3MohErLnpc8/1Qx37Iy4Vdpdd7i8uFVzdth93VtSL7KKvLhU2HC3BxyhBV4vliysxRmDJzFBw2F3ZtyEJeRhkqS+rg9cqQJRkJA6MxOCUB50xLRsJA7XvbSV1pm7Pg6eD1WaNUwIZmxCAeFSjyKV5jWUPrHMrtsOVkwl1dhdDhKWg+uMeneOVNLT5td6bYn1vaOsd4O5qKMuFoqEL4wBTUH/at/Srr1W2/PSWlHZ/f7Ey4crYmQQAAIABJREFUq6oQkpyClv0+nt+Ws+v8aqXbCu2+waH47rpbcN3Xy9Do6vqNPB2JCgrGqmt+jmhLsGb76Cn6Bofiu2tvxXXffIJGp9ZturBHtmmMORwfnPcw7tv7TzR77JrtJ8Icgv+c+wCiTKGa7YNOb2tREWwdjI9s3rIF9qxsuIqKIDscSLjvXgSP6Hh2HLvHg7X5ubB52r8i0rBnC6y5WXCWFUFyOjBw4X0IHdJJPLcHm48UdmuhfVRwqAUzr56ImVdP7PZ9k3YO783t8LEUYWLbPP6Vim8TDojBJpgqbfAknPo5Fjf3+tblBwG0pO33KV6oxYzsymqMTDiz5j3uSEZhVYePDbzoWPs1HPGx/YLNyCmpxoiB6rRfannH+fWddyw/a6pv+YWZzciqqsao+LPj/GpF85shjzc0MgbLr7oZJrGTmRS6wCiK+Pyqn2Fw5NkzH+zQyBgsv3KBtm0692cYHNFz23RQaF+8ce69MAratcG/zr0fA0M5Lltv+8rLO3ysZfceKE4nglNSfI5X1NzY4WNNB3dDdjkROtT3eAeKO86PyG+dzGUcyGJZiiwDYgfPC2DeZFmSO58Z6QzTaRMF2n5G9dqvo1MLIKD8JFmG8Sw6v1rp1kIbAEZG90XqLQ9gcGQULCoVhxaDAUMi+yD91l8juc/Zd3l0ZHRfpC56EIMj+sCi0pvCYjC2tuktDyG5T88vMIeFJWL1zOcwMKQvzKI6F2rMohEDQ2Lx/cUvYHCo/1MAkroUABWdXMrsv/g3SPz1g4icNdPnmC7Ji47qlcF3LUbSL36N6Atm+RyvvLEZKtyHRAQAGHuByusVeGR44kJUC+fyepHc9+z5mzthaKKq8ZxeL4Ylqtd+k/qrm5/L60Vy7NlzfrXS7YU2AISazNh04924dewkmEQRhgBXIBIFESbRgF+MORcbb7wLwUaTypn2Hm1tOvpomwZ2ao+16SRsnH93r2rTEIMFy6Y9iusHToNJNPq9qM1RBkGEURAxf9CFWDbtMQQZek8bnMkEAFI7Cw8d2yCAHj4I6HAChkB6qBQ5kKcRtWvMRSNhtqj3+RM9MAZmFXso+0dpt5ZATzRx+ACYVeyB7hejbvudO3CAap1twNl3frWiS6F91NNTZ+LgLQ9iwcgJMIgiIs2+zYscYbbAKIpYOHI8Um95AE9N7frUdWeKp6fOwsFFv8aCkeNhEAX/2lQQsTBlPFIXPYinpszUNlENPfD/7d13mFTl2cfx75mZ7b1Qll06iLSlNxEQlWBv0Rh716gYTaKJJTGWvElMNcaGGntHFBUbCqgoSO9NysL23vuU8/6xsIDuLLM7Z9hZ+H2uiythzsy995x5XO55znOee+BZzJ96P2eljsdu2Ih2+La2PNoRgcOwc07qBD456SFuGXBmgDOVtuqT0PYunq2JtFt7m0rvpOBdYiWdz4CRfQmNsK7QnnT6KMJDrBvzUwf0sSxWZzAoLZkwC8/f5KF9LIsFMKRrMmEO6/Kb1q+PZbGOZW36RGbPnh2QJHoDvyOaXS4XWwgh2+amDpN6TOKxUY6HcAyiMEg17Qypt9PPDINNu3l10+6A5ARw000ds8uGFee56ZzGssvpYothkm1zHTinpo1yY985NQ1ScTCkzkE/Mxw2ZfDqpgz/3wTWn7+2npcY4FL6khtZS0a0g6LwehrsbhptHqKdIVSHuAj1GIS77XSpD6dPVTQ96iJhZyEvf/VCm/PrqPFyLBmbmsrGAu83/LRV//gEthcXWRZvbJ+2d1oV8cYR6uAnV01n3uOf4GnnPu/72R02bvzbldR88x0vr1yLp7WrQ77EM2zceeqJfsXobEIcds4+YShvLV7b+tU1H9jtNn55nrXnL8Ru54LhQ3l5jQWfr83GXScdW59voPhcaP/zn//knXfeYcQIa5ocbNiwAdM0fxQvbd+f/RocdsJ+sJ1OBbDWx3j+5FdVVcWdd95pSTxfWX2ewbdzumTDBr4O4vPn73mJpOnLx37rNm/EcHk47qB4Bfv+tEdHjZdjzZRevZm7eQuVDT/eL7qtYsJCmTFgALnZlVTV+9d5DyA6IowT+vfyO47IwW7+99UsfP0bKooq2h3DEWLn2v+7lPCoMO6ZOY0PNm2ltKb9O1U5bDZ+fcqJRIQce8vq7rxwGp+u2EppGxpF/ZDDZmPWuScSYeGyoP3uO3UaH27ZSkmtH/nZbfxm6rH5+QaCz4V2dXU1M2fO5IEHHrDkBz/44IOYphnU8ao7oP2o1efZV8F+/jrD+OuI8XKsOalvXxIiwi0ptJMjo7h18kTeX7HVkkI7OSqSKQP7+B1H5If+sfiPzBp3Nw117WvO1X9kXy6685zmv79y5UX89NnXqW9no6bBKV25btKYdr32aDD7VxdxxV9ep97Z9vNnAIN6duXKGYE7f69eehHnvfg6De34fA1gcNeuXD/h2P18rdaha7RFRNrq1QsvIsTm368uh83G6xf9DIAXr7mQELuf8ew2XrrmIr9iiHjTZ0hPnlz5NxyhB250q6eWQrIpJBsTkxoqKSSbIvKan2MPsTNs8vE8vvwvh8Qb0CWJuTddhuOgnb+c5WVUbVlP1Zb1mB4PjcWFVG1ZT832zc3PcdhsjO7Zg3euuySA7zb49U9J4rV7LiPkoBsPG6vKKN+9nvLd68H0UF9eSPnu9VTsPXD+7DYbI/r34JW7A3v+BiQnMe+aQ/NzlZdRvWk91ZuaPl9nUSHVm9ZTs/UHn29aD9696tj+fK2mQltEOpXUmBhumTCBsHbeyBhqt3P7pIl0i2pq2tEjPoYbp00grJ27CYQ57Nw6fRJdY9XMSAKn15BU3s55jp6DehAWGUYphWwwv2OD+R0e3BSSwwbzO7aYTV3/QsNDuPCOs/j3kodbjDcgKZFvf3MDfZMSiQhxUJexk7y3XiLvrZcwXS6qt2wg762XyJ/3JtA0zq+ZNJo3rrn4iL3nYNY3JZHPHrmB3t0SCQ91UJ27k4wFL5Gx4CU8bhfluzeQseAlMhfvO38hdq44dTTP33lkzt+ApES+nXUD/fZ9vrW7dpL/2kvkv7bv8920gfzXXqLwnX352e1cO240b16uz9dqR6wzpIiIVW6fOIlIRwj/XraM+n0t2RuysnCVluEqKQGgfncGnrp6HPFxhPVuWqEf7nBw1+TJXDP60Muis6ZPJCLEweMLlzVfTq/Py8JZXkpjeVO8uszdeOrrcMTGE5HaFC/M4eDXMyZz5Qmjj8j7ltbVu8sobdhKecRihp8MpQ3bSAw7vqPTskxMUjTPb/0P8x77mLn/+YjUjL6EhIdgekwMG3g8Jo4QO6NPSWfW49fRJa31PZDjIyL49NareGXFWl6MjiJ21DjCHHZMmhrieDweHHYbJ/Trzf2nn0z32Ogj80a9KKmpZVNuAQv35NDYqz+b8woYmtJxPQ7ioyJ474GreGPxWl6PjSLx+HFN2/+ZYBj7zp/Dxvjje3P3xSfTLeHInr+EiAg+u+EqXl61lhcjo4gd84PP1/TgsNmY3Lc3f5xxMt1jOvbztVppVS1bMgtYtCkHd5f+bM0sYHCvIz9eVGiLSKd0w9ix9E2IZ9ZHH+H2eKj85luqV65sPl6+YAEAUenpdL/2akING4+feRYn9+vXYrzrThxL36QE7njrI1ymh7KV31Cx4UC84iVN8WKOT6fnz64lxGbw6M/P5KRBLceTI2dd8ePsrHwPl1kPpoEn0sm0q+Hz7Otxm066RIxgdPIdJIUN6ehULXHeL8/gvF+eAcCONbvZtX4vsYnRHDe2P8mpbd9i8orxo7hi/CgANucVsi2/iPjIcIb16Ea3ICi+/rFwCW+t2ki904UBNLrdGEPGcsn/3qbR7WJ0r1TumTmV4T26d0h+l0wfxSXTm87ftsxCtmcXERcVzpDe3ega3/Hn78qxo7hy7L7PN7+QrYVFxEeEM7x7cHy+VvvP+0uYu2Qj9Y1NDclcbjf0HMtV/3wbp8vFqP6p/OanUxna+8iMFxXaIuKznMwSdmzNJed7k9BIk5zMElJ7dVznsFP7D2DbL2/n/oUL+ejacCovu5QQw4bHNLEZBk7TQ1xYGGcOOo4Hp59y2HgnD+7Phgd+yUPzF/Jp+LVUnncZDpuBaZgYpoHLYxIbEcbpwwbxh7N87xgpgVHRuIeFOTfT6KnCYzoPOWazgXvfY0V161mYfTM9o09mUrc/dkSqATNwdD8Gjrbuy97QlK4MTelqWTx/7C4u5fIX36aqvgHnD7Y3NA1odDddfVqTmcMVL77DaUMG8tfzZnZEqs2O79WV43sFx/lrydDuXRnaPXjz80dGXinX/+dtqmp/PF4wwLnvauXaXTlc/+g7zBg1kIeuDPx4UaEtIq1yudw8cu9cln65HcMA02PicpkYBtx00ZNgmkyaPph7/3Ihhq1j2iI+dMopPHTKKdQ0NrKxsICM0jL6JiYwvGs3okJD2xzv/rNO4f6zTqGmoZHNuYVkFJfSNzmRoT26EhXW9nhivaK6tSzKvR236duOMS6znj3Vn1Jcv4mzer+FoVuUgtqqvdlc9+p7Pu+cUe908sGGrWzIyWf+LVdiU4vWY8qandnc8sR7NDT6OF4anXy0YiubMvJ55/7Ajhf9phE5ipU25rKl8itsg3Ox9SqhtDG3Ta+fP2clZ49/mK8/34zL6cLZ6MK1bw920wRnowun083XCzZx2tgH+Pjd1YF4Gz6LCg1lYlpPLklPZ2Jaz3YV2YfECwtlfN80Lh6Xzvi+aSqyg0RR/XoW59zhc5G9n2l6qHJm8lHmzwOUmVhhTXYe17/me5G9n8c02V1cyllPvhSgzCQYrdudx6w2FNn7eUyTjMJSfvpwYMeLZrRFjjIe08X7OX9nR9UyDAxMTBzDXOA2+N+umzGBgTGTOC/tdxh4/xb/zL8+48O3V+AxfewwZsJTj3xEXlYp190+w5o3I9KCxbl34KL9e6lXObNZX/IUI5JutjArscoNr7zbrj2q99tTWsG/F33Lr06ebGFWEqxmPf4udW0ssg+WWVjB4x98y6xzAjNeNKMtchRZW/YRj2w9j22VS3CbLlymE7e57xeQ3dz3dyfbKr/mr1vOZF3ZJy3Geervn/D+m8tpbGjbL6/GRjfvvbaM2f/6zN+3ItKi7woewuVpf9c7ANN0s638depcxRZlJVa55/3PqHU6D//EVng8Hl5ctobCKjURO9r98ZXPqG30c7yYHl5ZuIaiisCMFxXaIkeJRQXP8kX+s4DnsM/d7/P8p1lc8Pwhj2XsyGf+OytxOd3tysPpdPPhm8vZu7uwXa8X8cZtNpJZ/QXg41WWw8TaUPq0/0mJZRrcbj7Z/D2mr1fRWovlcvGfxcssyEqCVaPTzWdrrBkvjS4XT84PzHjR0hGRo8AX+bNZU/7RgdlrH7lMJytK52Hi4eRu1wNw782v4GpsX5G9n9Pp5t6bX+G1z37jVxyRg5U1bMPj5Yvk+q8q+PrdInasq6Ykt5HzbunBRb9KazVefu3KVo/LkbUlrxC3p+WiqWbHVirWraQ+ey/OijKSpp5K8slntBpvWUZmINKUILE1qxCPl/FSmbmV0m0rqc3fS2N1Gd3GnErKhNbHy/LtgRkvmtEW6eQK6zNYU/Yxbk/71qh5TBerSj+guCGTpV9upa620ZK8qqvrWbFkuyWxRAAK69b+aBu//dYuLiN7Zx3pJ8bhCPFtB4Ead4GV6YmfVu3Nxulu+Ut+9fdbaCwqILL/IAy7b11c8yqqrExPgsyandk4XS2Pl8o9W6gvLSCm1yAMm2/jJb80MONFM9oindybmb9v3i+4vdymizf23gfLfk5dXcuFdmbRCoqrdlJRk43TXc+Y/peTFON9/9762kZWfruT8VMG+ZWbyH4lDVu8Hrvq/j4Y+6aOln5Y4lO8ECOKsoYdJIQNtCI98dOGHO9ffLqdcQHs24KtatMan+JFhYWyLb+Q44/SfaOPdZv3eh8vaVMOjJeyHT6Ol/BQtmcXMijN2vGiGW2RTuz7qmU4Pe3ffeFgjZ4a9tR5v5SeU7oOl7uBpJj+Psfcsl6XbsVK3v/JMtrxr5mJG5vh22yXBF6r2/C3Y59jt9uDw8fZbznKtGO8eDyBGS8qtEU6sT01a2n0sgPD0lf38uJNq3h40hfcO/QTdi5rfZav0VOPmZLt9fjEQTcwfuA19Ok6yef8CvIqLLlRRQQgOXy4pfHcnkbiQq3rqij+GZWWYmm8RpeLAV06rnOtBNbIftaOl3qni/4p1o8XFdoinVh2rfdL6avfy6a+2sXAyck+x4vqV+n1WGt7bnvj8ZgY6tAmFukaMQK7YV3ToOgQa/+hFv+M7p1GqIUziqnxcZbFkuAzsn8aoQ4Lx0tSYMaL1miLdFomFU7vW+jNmnMChs0gZ3MFGz7J8ylieJeGpjbrFk1Cp/bSbJJYJyH0OGxGKG7Tmht2UyJ9vzojgTe4WzLhIQ4avdwQ2VZTBvaxJI4Ep0FpyYSFOmj0ckNkW00a0seSOD/UaQttl6ecyoaVlNctobphA5POycbjMdhd6iE+YiqxYeNw2Hz/dlJVWceGDVmsWrGb77flkp3rwMDkP//6lHHj+5M+oifRMeEBfEewuTyHT44zmXgElrVWldewaekOVi/azPdr95CTU4BhGDxe/RpjThnK8EnHER0f6XO8alcNWyp3sq58K7uq95IzqQTDY/DM7jcZFT+EwbEDiHb4Hs9ynnJM5ypo+AacG7nhpzl4PAZmJRA2BSNkLLRhvASHpq6PXo+2uuCxZXaHzbIiG2D46N7WBZNjns0IoV/s2Xxf8Ram6ft+8S0xDDsjk261KDOxQojdzvkjh/Lq8rW4/fxFZDds3HnqiRZlJsEoxGHnnIlDeePLtV63+fOV3WbjjvMCM146ZaGdX/USuZXP0+guwmM23QgWnbj/2KsUVb+Dw55MatwNdI++4rDx3pu7krlzVlJWXE3DIR2GDD6ct5oFn6wnISGan10ykXPOHxOAdwRu08Mvl79NZQQs6WviNj3Y23N3jw/ef2YR8576nJKCChrrD92tYv7zX/L560uJ7xrLhbf9hLOvm37YeB/nf8n83IWUNVbS6NkXL7rpfz7L/5rFRcuId8RybuoMTus+1eq3c1hm7SuYNS+Apxj2jZfuyfuPvQ5172LakjCibsCIvPSI5+ePhJAU8t07LYsXQzeiY8KprvL/Bsuo6DBGT9D6V7HWmOQ72Fv1KfXusnbHsGEnPelWHLYICzMTK9wzcxofbtxKaU37u3/abTZ+c+qJRISEWJiZBKM7fzqNT1ZupbSq/ePFYbdx2zknEhEamPHSyQptD+vzzqHW+T1mK4053GY9blc2GSV/oqD6HUZ0nwctrC81PSY33/A8ezKKcLVy6aGhwUV+fjlPPr6ATz5ex5PPXNeeG1qZPXt2q8eHRhhURBtMro/nuWeePWy8m266qU0/3/SYzJr+MHu35bba9a+hvpGCzGJm3/sWC177lscW3tfiOlsTk9+u/yuZtbm4TO/xGt1OCt0lvJAxh0UF3/LIiLvbtd637TyYxedjunYCrWx/Z9aDOwez6k9Q+w5G8lxaGi/BqGfkMPLrrSu0j+82llVx1ZYU2glJ0YydrG3TxHqn9HiST3Ouxu1paMerDRLCBzEk/jLL8xJrvHzVRVz4zOvUu9reG8AAhqR05dpJgZkUk+DzzO0XcfnfXqe+sR29JAwYlNaVK08N3HjpNIW2aTpZk3MyDe4c31+Dk5qGjazOmcro1C8xOLBo3uV0c9WlT1FQUOFzPJfTw47t+Vx+8eO8/MYt2O2+zzj/85//5J133mHEiBGtPi891EZDYx5rDxNvw4YNVFVVceedd/r0812NLq4ddx+FWaU+Ztx0jnas28vVI+/h+dV/xu448H5dpotZax6gqKEN8Uw3u2qyuHn1H3hi9EMBm7EHwHTiKfoJeHwfL5guTNcmzKJTsHX5HAj+baH6Ro9iQ8XnNLhr/I4VbkTRN2oUjzzbn2vOeazdLdgBHA47f3v2ar9zEmlJXFg/Tkt7iU8yL8dD0z+uxTkN7N7Y9N+BxwO5u+pZ8WkpjlAbo0+OB8Bm2EkKG8aMtGc6LHc5vAFdknjnF5dx3pOv4vI0/R5ylpdRn9u0rtL0eGgsLqRqy3oMu4PoQUMBcNhspKd25/VrL+6w3OXI65+SxGu/u4yf//nV5oZHjVVl1BbuW4fr8dBQXkj5rvXY7A5i+xwYL8P6dOeF3wR2vHSaQntd3lltKrIP1uDKYX3eWYxM+aT5sZuue65NRfbBCvIr+MV1/+PZF2/w+TXV1dXMnDmTBx54oF0/84cefPBBqqurfX7+LdMealORfbCCrBJmnfQwT33zx+bHfrPuL20qsg9W1FDKXev/zL9G/r5dr/eFWXJu24rsg7mzMYvPw0j+0NqkAqB/9DgibbGWFNoRIfH0ix4D0fDza6fy9gtLaGzHDEFoqINLbphGUpdYv3MS8SYutC/n9/uYz7NuoNaVz5blxcz+3e7m4ys+K2XFZ6VExzuYvXI0diOU4+IvZlTSrA7MWnw1ICmRb+68gUv+9zZ5FZVU7NlJ/rw3mo9XbdlA1ZYN2COjGPDbhwlz2LliwijuPHVKB2YtHaVf90QW/OUGrv7n2+SXVVKas5PMRQfGS/muDZTv2oAjPIph1zaNl0umj+L28wI/XjpFoZ1d8RT1rgy/YtQ17iSn8hlSY2/kjdeWkp3lW+cwbzL3FvH2G9/xs0sm+hXnSHj70U/J3ulfq+G923N55/HPuHDWTN7NXkBevX/xsuvyeT/nc85NneFXnJaY1bMx3f6NF9O1E2r+hxF1nUVZBc6lff/K0zuuw93KcqrDsRl2Lu/zSPPfr/jFSURGhvDSU4toqG+KW1mbS11jOXWN5QCU12TictcTHhJLXFQaAGFhIVxz2ymcf5l2c2hJTl0RO6uzyOlVT1i9QU5dEakRXTo6rU4rzBbHWb3fZnv5W4T97E2mXpCM3QjFxMTAwMSDDQfdI8cztstdRDrUIbAziY+I4JNZV/HK8rW8FBNF3MhxhDrsmCbYDAOPx4PdbuOEfr25/4yT6R4b3dEpSweKj4pg3v1X8fritbweE0Xi4HGE2e37tgwwME0PDruNCcf35u6LT6Zb/JEZL0FfaDe6CsmvernVNdm+MHGTV/kCjsaZzJu7CrfbvztU3W6TuXNWMOO04SQkRPkVK5BK8st4/9lFeNz+3aHvcXt478kvGHPBUD7J/xK3n3f8u00P8/MWcVKXicSFxvgV6xCeQqh9FVpZM+4bN9S8ABHngS24t6iLdXRhUvLFfFf8Nq52tGJ32ByckHQp0Y7EQx7/6ZWT6dErif/73du4XR4yi1eSW7q++fiu/K8B6BY/mFExP8fhMLjvbxcxYaparh/MZbr5+7ZXWFq8AcMwME0TV383eODm1X8FEyYlD+fuwVcdoXsXjj6D4i9mUHzT5d/Shu2UNewgzB5LYthgIh36ItPZXTFhFFdMGAXAlrxCtuYXER8ZzrAe3egWo+JaDnXp9FFcOr1pvGzNKuT77CLiosIZ0qsbXY9QcX2woC+0y+oW4/S0b4nCDzndJWzaPpeK8vbcQPNjFeW1LF+2k9POaH3ddUdatWAz5cXem5C0RUVJNR+8/wUVI6usieesZnX5Rk7ueoIl8QDM+q8wPf5drWiOZZZB/ZcYkT+1JF4gTelyGSFGBEuKX8G17waxnM0VlOXUUZpVC8Ce1aXUVzmJ6xZOzxFNa1ZDjFCmdbmKcUnntxh30knHM3/5/fz3L/OJWRBBVdX5OPZtAWgY4HJ5iIkNZ9pPhnHr3We2Oe+cXQXsWJtBzndlhMY5yNlVQGr/bu08C8Hno7xveXLHHDz7t2E8+Pu9DZyepgmEr4vW8nXRWn553MWc3t26/x6ORYlhg0gM05e9o9WQlK4MSdGVCfHN4J5dGdyzY8dL8Bfa9YswvczSzXmhku++rGXzugaqKjw8+mp3xp3ofbsmExdVzi9xuSa0eDwrdxklpd9TUZWNy1XHqOHXkBjf32s8l8vNsqXBXWh/t2A9bi83te1t2EyxM4sKVxFOs4Gx0aeTFJLqNZbL6WLVgs24R7Qcb9ecjeR/l0np5gKcVQ2c+OjZdB2X5jWe23SzqmyTpYU2DYuBlq9+/Pe5cj5ZVMOKNfWUVXhY8FYqp0xtZW9v0wkNi6ATFNoAE5MvICmsB/Oy/4zb9LDstb2sef/AOvVFTzXtTjJsRncuf3QsNsPOeT3vZUD0+MPGvu2es7jtnrOoq2lgx9Y8sveWkNY7iYGDU4iICmtTni6nm0eufYql89c2N8dxOV0YNrhpwb0ATDp7NPe+cEu79gIPFs/smsf8vCUHimwfPLXzXXLririu77kBzExERI6UoC+0axu3ez320ZwqwiNsjJ8SwcL5vt0MFh6bDbRcaOflr8FmDyEpYQAFRRt9ipexM9+n53WUjC3ebwjMafgeu+EgKSSV/MbdXp93sPId1YR6uby956NtOMIddBvfk+yFvm05t7emnTcsemG6vI+XF9+sJDLSYMZJUbz9vm+z8qZre6e6mD8wZiJ3Df6Az/KeIOKRaC76czV2m6N5zarb4yLCHsPguCn8pPstbY4fERVG+tg+pI/t06785v9vEU/86uUWmwuYHnDuu/ny67kr+PrdFdz+2DWccc1J7fpZHenpXe/ycd43OD1tW8Lk9DiZl/01HtPDDf1avsogIiKdR5AX2marne+en5+KzQbbNjT4XGhjmBiGiWn+uHwaN/oWDAwqq3N8LrRNaL6MHmxM02y1l/ak2POa3q+72OdCuymm0eI20yc/fyGGzaBsW5HPhbZpms1FoP9anzlcuaAXNhusWl/vc6G97xOms+yrvd/MlFuZmXIrjZ468ut2UNyQQ3JYKt0jBhLaQU06nrnnTT58dqHvHbyqsKXNAAAgAElEQVRMeOquV8nbVcB1f+o823Vl1OTwce63ONt5n4DLdPFBzjfM7D6RXpEpFmcnIiJHUgA3MraCgUmj16O2dmRvs7tbLLKbflrbiymX0xOURTaAYRjNM4QtHm/H+zWdeK0523OZ34XbwhvADDCtHS9N8YL0A/ZBqC2CXlHpjE48nV5R6R1WZD/129d4f/bnNNZ7/3xa0ljv5L0nP2P2PW8c/slB4r6NT+H08+Ztl+ni3g1PWZSRiIh0lCAvtCEyZICl8eoqulsar1fvZEvjWa3XoB6Wxovua22hlhZh7edhOLyvqW9fPHU29FfGpkzmP7cYV3u6dgHORjcfzv6CvdusXWYUCMtK1tPgbtuXCW9q3fWsKN1sSSwREekYQV9ox4efZN26DMNGmOcEbBbdYGUzDCZMsraws9q4U4ZZdkOZYbORfvJAy2agDWyMSRhqSaxmYVOxbljbIGyaRbGOXfee+w9cjW3fdvBgzkYX9579N4syCpw1ZTuodbe8q9HeuetZddf7LDxrNp9Oe4yS1VmtxqpzN7CqdGsg0hQRkSMk6AvtuPDxhNqsmfUMs3Wjd4+TSUq2pmNdUnIMI0f3tiRWoKRPGURy9wRLYiV1j2PqKeNJDI23JF5iWBzDYo+zJNZ+RugEsFu0PZy9K0ZYyzfOim+WfriaumprttOsrqxjxafrLIkVKFsqvd/rkPPxFly1jSSP8/13xtYK/xoviYhIxwr6QjsqdDjx4ZMtiGQQHzGFwQOnMnpMb0vmZMdO6Ee/IN/zd0B6L0ZOG+z3RQEDg7GnDGXK2LGkxw+yZFZ7VPwQekd53/6vXUKGYoRa0ZXQwAidAo7jLYh17Fq9eDN1NfUtHst0bmVN/UIW177JgpqXKXHntRqrvqaBlQt8u0m5I5iYFNaXeT0+6dmfM/GJi+jz89E+x8xvKG31hnAREQluQV9oAwxI/hsOm3+z0A5bLP2T/grAXfecTWR02/b+/aGY2HB+89u2N+joCL954mqiYlvZL9oHUfGR3PGfqwCYNeBKIhzhfsWLdkRyc//L/IrhjRH3VzD87DZpi8WI+z9rEjqGbf1uh9djOa5duGgkye77fQRblnuP19GaWn57L4rbs4TLuh15RESkI3SKQhsgPeV9QmyJh39iCxz2RNJTPjjksaeeu57YuPbd2BcXF8FTz17frtd2lP8u+j2xSe1rPRqTFMXji39/yGN/T7+bmJB2xnNE8/f0u9v1Wl/Zkt8DWzuXzBjx2JLetTahY5BpmhRmee/SOTHiTMaHn06fEN/X6RdkFjdtMRmkeoRbe3N0aoTah4uIdGadptAOd/RmeMpc7MaBmdltGxpY/HENq5c2XZpev7KexR/XsHntgTWhNiOS9O7vEe7oeUi8Hj3i+e/T1xAeHtL8WGV1DoXFmykrb1pnWV6xh8LizVRUHbhpKSIilMdnX0v3lLiAvM9ASenbhUc/u5fwyNDmxyrdxRQ0ZlDizAWgzJVPQWMG5a7C5udERIXx2Oe/p/sPdlfpHt6Fvwy/izD7gSsDZduKyFm8i6LV2QCUrM8jZ/EuSjcXND8n3BbGI+m/pavFBcmP2HthS5oDxoEvU6vW1zN3fjWLv6kDYMnyOubOr+a71QctbbBFYUueC/aeP4wobWQYRqt7ZrdnptbjMTGCdT9NYGictTdHD4u1dtclERE5soK8Yc2hwh29mdBrE9uLbqWyYTXvvLSZT+ZWNx9/4T/lAJx0ehR/f2YYMWHjGdTlMa/xUlMTmL/gtzx8/7ts2pjNlu+/I69gbfPxjMzFAHRNHsq0KTeQnt6L3z/Qebu19ejXhXnZT/B/18xmy4qdbNz5FbmNBy7F76pveu/dQvtyctoFDD/hOO557kav8VLCu/D6hH/zj+3Psb1yN6vfWcTeTw50Ztz6wioAUk/qx+l//xmDYwbw60HXBejdtcDeC1u39Zjlv8RsXMsTz2/g5bcrmw8//K9SAC44M5o5z4/ACB2HEf/vI5ffMaBH/27sWGPdDX2pA4L7nojRCcfxecFyalx1fseKcoQzOlHbS4qIdGZHrNCurK5j/eZcVqzdzbZdBeTkRGAY8M+nP2fC6H6MGJJKTLRv634HdXmCyoYVPPnsF1Q8tpR6VyYOezwudznhjl7ER0wmMXIGMWFjfYr3h4cuYMO6TJYtHcLaVXvJzSslJjaSqspaeqQkMnpcHyZNHsjw9F7+nIKgcd8LN7Hh2x2c9Nl41n61lbyMImITo6gsrSGlbxdGnzSEiaeNYNgk3/6Rv3PQ9Wyp3MGUJ8eyoWIb+fXFRDsiqXbV0j08mfT44xmXmM7gmI6ZnTPiH8NoXMGLzy3mxSeWYnoywUgAswzD1gvCJmOEnQyhvo0XgMqaetbszGHZ1r1s2ZNPdnEUNgP+8uYiThjch1EDU4mN9O8+gKPBsEkDLS20h08O7ptTxyUOJTYkypJCOz40ljEJQyzISkREOsoRKbTnfrSGtz5cTWl5NQ0N+9sSN61amffZej5ZvJnE+CguOW8s558+yqeYsWHjiQ0bD/uW4TZ6igm1tX85QvrIXqSPPFBIl5ZWk5jYvjXInUH65IGkTz5QSJcWVZLYpf03nA6JHciQ2APxyhsriQ+1ZhtFS4SOxwgdDzH7+jx6iqGd4+WtL9fx6sLVFFfU0uDc34TFDsCcr9bzwbLNJMVEctWMsVw0bYQl6XdWY04ZxuevfUN1ea3fsaLiIhl9cvAXno8Mn8W1qx7G5WlfC3YAh83OI8NnWZiViIh0hIAW2h7T5PrfvEJGVjEul8fr8xoaXeQVVvDY818yf+Emnvv7FW3ejs6fIrsl/hbZlVV1bNiSw4o1GWzblU9udjgY8K+nPmf8mLbN4B8J/hTZLQmqIrsl7RgvHtPksr+8zq7cYlzu1sdzbkklf3/nK+Yt3cyrd19qWc+lzmbcT0YQmxhtSaGd0CWWsaemW5BVYHUJT+Dinj9hTtbnNHra3g0zxObgkl4zSQrrXPeBiIjIjwWs0Ha63Fx6y3PkF1X5/BqXy833uwr42S+e4c0nr8du7zT3ah5i7kermfP+GorLqmlsbju9bwb/03V8smgTCfFRXHLBOJ9n8KVjOV1uzn/gBXJL2jaet2YWcNbvn+ODh6/Fbuuc49lfj3x0N9eMuAtXox8zvCEO/vZJYHeqsdLlvU8jwh7GK3s+psHT1JK9YnshdXmV1OVVAFC2PgdnVQPhXaOJH9LUlCvUFsLVfc/k/NTpHZa7iIhYJ2D/8l/765fbVGQfLL+wkmt//YrFGQWexzS57lcv8cT/viK3oPygIvtQDY0u8gsrePzZRdzw65cJ4t3KZJ9L/vxam4rsg+WVVnHJn1+zOKPOo2vPJH5+5zmEhoUc/sktCA0L4bJ7ziUpxZoOp0fKT9Omc/fgK3HYHNgxyHx3Pev++DHbn/4WgJ0vrWDdHz8m4801OAw7obYQ7htytYpsEZGjSEBmtF99dwVZOaV+xdiTU8wb763ikvN9v0GtIzldbi79xXMUFFUe/sn7X+P2sH1XARff+AxvPN15Z/CPdi98tpK9Bd47/vkiI6+Elz9fzZUzxliUVedyxb3nERkdxksPv0tDXdMMb6WnhDpPNXVm085B5e4CXGYj4bZI4mxN+0eHhYdyzUMXcv4tMzssd39MTBrOhyf+kyd2ziH6/kiq7v0JDsOOxwSbAS7TTYwjiinJo7h14IUdna6IiFjM8kK7uKSadz9eg7uV/XN94XGbzJm/itOmDyEh3r+uhkfCdXe81KYi+2D5hRVc96uXefGxq61NSvxWVF7NW1+uw+3xvibbF26PyWuL1nDmhMEk+dmls7P66S9Pp0f/bvzfFU/g9phk1mwj17Wr+fgu5wYAujl6MypyOo4QO/e9eisTThvZUSlb5tYBF3HrgIuoczewoyqL7LpC0iK6MjCmJxF27U4jInK0srzQ/m7tbkor/N/aCqCsspZlq3dzxinDLIkXKK/NXU5Wjn8znnuzSnjjvZVccv44i7ISK3yzOYOyKovGc1Ut327K4JwTfO+EeLSZdOZo5pf+j//+6mVi3o2gqrwWR4gd0/RgGDZcTjcx8VFM++l4bv3XlR2druUi7GGkxw8gPV6NaEREjgWWF9pLV+7G7Wr5pqfcXd9QVriNqtJMXM46hk2+kfgu3vdqdrk8fLtqV1AX2sUl1cydv8aCGU8Pcz7YP4MfZVF24q8lGzNwulsez0Ubl1CZuZWawr24G+oYcNYviEk7zmssl9vDV5t2H9OF9n63/ftKbvv3ldRV17Nj7R6yd+aSNqAHA0f1ISKIduMRERHxh+WLgnftLfJ6rCBzJW5nA/FdvRcjP4q3x3u8YPDdmt2UV/q/dRlAeWUdy1bttiSWWGNHTrHXYyXbV+B21hObNsj3eNmtj+ec7FIWL9pC1l4bRYU2crL9u9ch2EVEh5M+5XjOuOZk0qccryJbRESOKpbOaB9u94xRJ90BhkF1eTbFOet9i4mJaRK0+xAvXbnL6x7hubu/obRwO1VlTTP4wyfdcJgZfDdLV+3ijFOHBypdaYPDjefjf/prMAxqi7Io27XO55g/HM8ul4c//2keS7/dARiYponLZQdMrrvmOcBk8uTj+P3952HYgvQ/BBEREfkRS2e0DQMavSwbaX5CGzmd7qAtsgF27/U+45mftQq3q56ENszg7wzyGfxjSUDGs/vQ8fzh+2s4/Sd/5asvt+F0unE6Xbiaf6aB0+nC6XTz5ZdbOfWUv/DRfN8KehEREel4lq/R7pOWRHFJtXXxeiZZFstqh5vxHD319uYZ/CIfZ/BpYcZTOk6/7okUlVs3nvulHBjPTz+5kPffX43Py/tNePyxBeRkl3LjL062LCcREREJDMvXaE8c1RebRZe3bTaDiaP7WRIrEAwDGp2ttFhuz4ynK7hn8I81k4f2wWbRB2IYBicO7QPAE//9nHnvraaxoW0tuhsbXbz7zkqeenKhJTmJiIhI4FheaI8c1pPkxGhLYiUnRjEmvZclsQKlT1qypfF6pyVaGk/8M2ZQGl3irRnPXeKiGXdcT3bvKuTDD9fgbO1LWiucLjfz3lvF3j3ely2JiIhIx7O80B7Uvxtj0nv7PStrAONH9aV/7y6W5BUoE0b3sWwG3zAMJo3tb0ksscbgnt0Yf3xP/P6EDZg8tDcD07rw27vexNnYytpvH7icbu668w1/sxIREZEACkjP73tvO42oCP+6nUVHh/O7TtB2eeTwXpbO4I8eHtwz+MeiB6+c6fd4jo0M5w+Xz+Dbb76nob7Rkrxqa+pZ/t2uwz9RREREOkRACm2A//3zCuJi2rcnblxMBP/7xxUWZxQYg/p3Y8yI3pbEmjC6L/37BPcM/rHq9XsuIyEqol2vjY+O4PW7LwNg1ard1Na2XGhn5yxj/caX+PrbP7Hoq/soLWu9iK6rc7Liu53tyklEREQCL2CFdo/u8cx+5DLCw0KaH6suz6Y4dwPlRU3FQWVJBsW5G6gq3dv8nIjwUJ7522WkdIsLVGqWu+eXpxMd5V+jjdjocH476zSLMhKrpXWJ48Xf/ZyIg8ZzbVEW5bvXU5WzA4DqvN2U715PTcGB8RwZHsIrv7uEHsmxAGzZlO31Z+Tmr8blqicpwff23Js2e48nIiIiHcvy7f0OlpqSwOdv3s79f/+Ajdty+X7NtxRmrmo+nrntcwCSewxn8sybGTkkjQfuPDuQKQXMc/+6gpvuepWKyro2vzYuNoJnOskM/rGsZ5d4vn10Fr99dj7rd+WyavE3lG5f2Xw8f/UCAOL7pTP+Z7MYPSCVv1x3RvNx04SCwkqv8ceNuRUDg6qqHAqKNvqUU0FBhbaDFBERCVJeC+2qqjo2bMxhxerdfP99Pjm5oQD8+78LGD+2HyOGpxHtY7vkh+46h3Wbs1g6dTCrNuwhN7+CmKgIqmrq6NE9jrHpfThxfH/SB6dZ8646QI/u8Tz9t8u45vaXqG9wAk0z+PW1ZdTXlgBQUZqBy1lPWEQcMQlNa7EjwkOZ/ffLO9UM/pFQ3lDHyqJsvsrbzaaSPLJ6OrBh4Fr5GSf16Me4Lj2JC+2Ydt1/u+EsVu/I4etxx7NiWxbZxeXERYVTUVNPWnI8Ewb3Yurw/owe2OOQ1xkGmG7vm68b7bjl0vSYKrJFRESCVIuF9rvvr+add1dRUlZ90D6/TatMPpi/ls8WbCQhMYqfXziec88e7dMPGjm0JyOH9gSmAVBaXktifKTfbyCYpKYksODtO7j/kQ/YuC2H79d+S0HW6ubjmdu/ACA5ZThTZt5M+pA0Hrirc87gB9KL21fxwtaVFNRX0+DeN/7Cmsbfa9+v4Z1dG+kaHsUNQ8ZzxXFjOiTHMQNTGTMwtfnvxZW1JMcefjynpiay/fs8y/JI1XaQIiIiQeuQQts0TW689UX27CnG5fberq6h0UV+fgWPP72Ijz/byNP/varNs2pHW5F9sId+dw7rNmWxdNpgVq3bS15+ObEx4VRW1ZPSPZ5xI/sweXx/0od03hn8QPCYJmd/8gI7Kopxerxvf9fgdpJVU87Dqxfy9q4NfHD6Nf5vv+cnX4psgKHpaZYW2ulBvs+8iIjIsay50Ha53Fx+7bMUFFT4/GKXy833O/K59MqnefXFG7HbA3ZvZaczclhPRg7r2fz30vJqEi1qfHI0cnrcTP9gNjk1vo8/p8fNptJ8psx7kq/O/QV2I/jH39ix/Vjw6Uaqq+v9jhUVFcbosX0tyEpEREQCobkyueHmF9tUZB8sv7CCG2950aqcjkoqslt3xsfPt6nIPlhOTQVnfvy8xRkFxoSJ/YmLbd82gT+UkBDF+PH9LIklIiIi1rMBvP7WMrKyS/0KtDerhLfmLLckKTm2PLV5GRmVJX7F2FlRzDNbOsf4+8ejl+Fw2P2K4bDb+Nd/LrcoIxEREQkEW0lJFe+9vxa3x/uabF+43R7eeW81ZWU1FqUmx4LC2ipe3r4at+l9Nw5fuE2T57evoLg++Mdf166xXHb5CYSEtm93zdBQO1dePYWkJF0lERERCWa25St3U15hTXFSXl7Ddyt3WxJLjg2L83ZR3FBrSayS+lq+zO0cLcmvvHoK1147lbDwAw1wqqpyKCzaRGl503uoqNhDYdEmKiuzmp8TFurg+htP5rIrJh/xnEVERKRtHEuX78Llank2OzvzW0qLt1NZkYXLWceIMdeTkOS9a53L7WHZdzs5/SfDA5WvHGUWZe/C5WWHkcrPllK3/nsadmXhqamj293XEjGslfHn8fBF9k4u7JceqHQt9bOfTyQ1LZGHH5yH2+MmO+c78grWNB/P2LsIgC5dhjJy2GXYHXb+8OAFTJrke+dIERER6TiOjIwirwfzc1Zjt4eQmDSQwvwNPgXctdt7PJEf2lbufbxUf70aIzSEiOEDqfnOt/G3razAqtSOiMknHsenn/+W/zz6GTHREVRXX4jdbsc0TQzDwO12Ex0TwUknDeaXd8zs6HRFRESkDRxmK2tjx0y8DcMwqKrM9rnQNvGoJbT4xKRpvHjT40+zwDBoyMjxudA29/3pbMPv9jtmcvsdM6mtbWTH93lkZZbQs1cSA49LITIytKPTExERkXZwOJ3eG4MY7aiWnU6PimzxiQE0ttIYqT0DqdHj7nRF9sEiI0MZMbI3I0b27uhURERExE+23r2SLQ3Yp3eSpfHk6DYw3trxMjDO2vEsIiIi0l62CeP6YVVDPcNmMHFcf2uCyTFhWkp/bBZdArEZBtN7aPyJiIhIcLCNTO9JclKMJcGSE6MZObKXJbHk2DChWy+6RVqzH3TXiGgmdtOSCxEREQkOtoEDuzN6VB+/17UawLhxfenft6sVeckxYnhidyZ383/8AUzr0Y/BCRp/IiIiEhxsAHf/5gwio8P8ChQdE85dd5xuSVJybPn7pLOICfFv/MWFhfPXCWdYlJGIiIiI/5pXZz/z+NXExUa2K0hsbATPPH61VTnJMeiDM64lIax94y8hPIIPT7vW4oxERERE/NNcaPdIieeJ/1xOxMEtoSuzKSrYSFnpvpbQ5RkUFWyksiKz+TkREaE8/diVdO8edwTTlqNN7+h43jvtSiIdB8ZfQ0YONSs2Ub+5afw1bN9DzYpNNOw80JI80hHK+zOvpme0xp+IiIgEF8NsoWPNA3+ax+YtOXzz1Qvk567+0Yu6dBvGtOm/YHh6T+6/55wjkqgc6sEHH8Q0TR544IGOTsVyt3zzHqsLs9ny6ItUL1nzo+OR44Yx/O6bGNe1J/898dwOyFBERETk8BwtPfjA789j/YYslk0fwuq1e8jNqyA2JozKqgZ6pMQxZlRfTpg0gPRhaUc6XzkGPHni+awozOSLPkP5Nn8PmdVlxIWGU9FYT6/oBE7s3pcZaQMY27VnR6cqIiIi4lWLhTbAiPSejEg/UMg8/PCfuf/++2itZbuIVcZ37cX4rge2iiyuryY53JptAEVERESOBJ9b1Xg8Tv74xz8GMhcRr1Rki4iISGdjUU9IERERERE5mAptEREREZEAUKEtIiIiIhIAKrRFRERERAJAhbaIiIiISACo0BYRERERCQAV2iIiIiIiAaBCW0REREQkAFRoi4iIiIgEgAptEREREZEAUKEtIiIiIhIAKrRFRERERAJAhbaIiIiISACo0BYRERERCQAV2iIiIiIiAaBCW0REREQkAFRoi4iIiIgEgAptEREREZEAUKEtIiIiIhIAjo5OQHxTXlfPytwcluzZw8b8ArIjozAAz8KFTO3Tl3GpqcSFh3V0miIiIiKyjwrtTuDlNev439rVFFfXUO9yNT1oa7oY8dq69czdtJnkyEhuHDeOy0aO6MBMRURERGQ/FdpBzGOanPvqa3xfUozL7fH6vHqXi+zKSh5a/CVvb9rIvMsvxziCeYqIiIjIj2mNdpByut2c9Oz/2FJY2GqRfTCXx82mgkKmPfscbo9vrxERERGRwFChHaTOfuVVcqoq2/XanMpKzn7lVYszEhEREZG2UKEdhJ5esZLdZaV+xdhZUsqzq1ZZlJGIiIiItJUK7SBTWF3Ny+vW4vaYfsVxmx6eX72G4tpaizITERERkbZQoR1kvszIoKTGmuK4rLaOL3fvtiSWiIiIiLSNdh0JMot3Z+DyciNjxTdLqNu2jfq9e/HU1ZFy001EDDzOayynx83C3RlcOGxYoNIVERERES80ox1kthUVeT1WtWIlnvp6IgcNakO8QivSEhEREZE20ox2EDH3/fEm7Ve/AsOgISuL6nXrfItpNsXUvtoiIiIiR5ZmtIOIATS2tme20fZy2el2q8gWERER6QAqtIPMgKQEa+MlJ1kaT0RERER8o0I7yEzr0xebRVPQBgbT+/a1JpiIiIiItIkK7SAzoWca3aJjLInVLSaaCT17WhJLRERERNpGhXaQGdatGyf06mXJuuqpffowuEsXCyKJiIiISFup0A5CfzttJtFhYX7FiAsP5y8/mWFRRiIiIiLSViq0g9T7V1xOQkR4u16bEBHO+5dfbnFGIiIiItIWhmmaLW7dXFVVx4aN2axcuZvvd+STnZOLgcn0k8Ywfnx/0oenER3dvkJQfLO3vJyzXn6FWqcTgIasLFxlZThLSij9aD4JM35CaEoKjrg4wnr3BiAyNJSPr7iCnvFxHZm6iIiIyDGvxUL7vXmrmPPuKspKamhodP7oRaGhDpISovjZzyZw7jmjj0iix7JZH37I6tw8Nj/3HFWrVv7oeFR6OsNvvpVxaak8dtaZHZChiIiIiPzQIYW2aZrcdMuL7NlTjMvlPuyLHQ4b/fp25aknrm5PLxVpgxVZ2SzcvZulmZnsLS8nPjyC8vo6esfHM7l3L07t14+xaWkdnaaIiIiI7NNcaLtcbq68ajb5hZVtDtKtayyvvvwL7HYt+T5SimtrSY6M7Og0RERERMSL5sr4xl+80K4iG6CgsJKbbn7BsqTk8FRki4iIiAQ3G8Abby4jM7vUr0B795bw1pzlliQlIiIiItLZ2UpKqnjv/TV43B6/Ark9Hua+u5KyshqLUhMRERER6bxsy1fuptyi4risrI7lK3ZZEktEREREpDNzfLdsJy4vs9nZWUspKd5OZWUWLmcdI0dfR0LiAK/B3G43S5ft5LSZ6YHKV0RERESkU7DtzijyejAvdxVuVwOJiQN9Drgro9CKvEREREREOjVHi20h9xk7/jYMw6CqMpvCgg2+RTTBNNG+2iIiIiJyTLM5nd4b0xjtqJZdTreKbBERERE55tl69UqyNGDv3smWxhMRERER6YxsE8b1w2azZgraZoMJ4/tbEktEREREpDOzjRzRi6TkGEuCJSXGMHJkL0tiiYiIiIh0ZraBA7szelRv/yMZMG5cX/r36+p/LBERERGRTs4G8Ls7zyQqOsyvQDHR4dz56zMsSUpEREREpLOz7f8/s5+8hrjYiHYFiYuNZPaT11iWlIiIiIhIZ9dcaPdIiefx/15JRHhI88GqymyKCjdRVrYbgPLyPRQVbqKyIrP5ORERoTz5+JV07x53BNMWEREREQluhmmaP+pZ88DD89i8KZtvlrxAft6aH72oS9dhTDvpJtLTe/KH+849IomKiIiIiHQmLRbaAOs3ZLHsu52sWbOH3LxyYmLDqaqsp0dKPGNG92HSpIGkD0870vmKiIiIiHQKxqpVq0zTNBk7dmyrTywtrSYxMfoIpdV2q1evxpf3ISIiIiJyJDjmz5/vU4EazEU2gK/vQ0RERETkSLAd/ikiIiIiItJWKrRFRERERAJAhbaIiIiISAA4cnNzyc3NZfbs2R2di19WrVpFjx49OjoNEREREREAHNXV1eTl5bF27dqOzsUv+fn5xMTEdHQaIiIiIiIAOI477jgGDhzIAw880NG5+OXBBx/Ey5bgIiIiIiJHnNZoi4iIiIgEgAptEREREZEAUKEtIiIiIhIAjgrkFywAAAPISURBVLFjxx4Va5uPlvchIiIiIkcHw1R1KiIiIiJiOS0dEREREREJgOZCe8uWLdxzzz2MGjWK2NhYUlJSOO2001i6dKlPgbZt24ZhGC3+efTRRwP2BkREREREgpFj//956KGHmDdvHjNmzOCMM86grKyMN998kxNPPJE33niDiy++2KeA5513HmPGjDnksRNOOMHarEVEREREglzzGu2PP/6Y8ePHk5yc3Hxw7969jBgxgoSEBDIyMloNtG3bNgYPHsyzzz7L9ddfH9isRURERESCXPPSkTPOOOOQIhugd+/eTJ8+nT179lBXV+dTQNM0ycnJoaioyNpMRUREREQ6kcPeDFlUVER8fDwRERE+BbzttttIS0uja9eu9O3bl+eff97vJEVEREREOhtHaweXLFnC0qVLue222w4byGazceKJJ3LmmWeSmprK3r17efrpp7nuuuuoqqri9ttvtyxpEREREZFg53Uf7by8PMaNG0doaChr164lLi6uzcHLysoYNmwYVVVV5OXlERUV5XfCIiIiIiKdQYtLR8rKypg5cyYNDQ18/PHH7SqyARISErj55pupqqpi5cqVfiUqIiIiItKZ/GjpSFVVFaeddhqZmZksWrSI448/3q8f0KtXLwBKS0v9iiMiIiIi0pkcUmjX1tZy5plnsmnTJhYsWMDo0aP9/gHbtm0DoFu3bn7HEhERERHpLJrXaDc0NHDWWWexZMkSPvzwQ2bMmOH1RQUFBWRkZHDccceRmJgIwPbt2xk4cCA224HVKNu3b2f8+PGEhYWRlZVFWFhYgN+OiIiIiEhwaJ7RnjVrFl988QVTp05l+fLlLF++/JAn3nbbbc1rtefMmcNtt93GnDlzuPDCCwG47777WLduHVOnTiUlJYXMzEzmzp1LQ0MDzz77rIpsERERETmmNBfa+zs/fv3113z99dc/euLll1/e6k2RF1xwAeXl5XzyySeUlJQQExPD9OnTufvuu5kyZUoAUhcRERERCV5et/cTEREREZH2O2xnSBERERERaTsV2iIiIiIiAaBCW0REREQkAFRoi4iIiIgEgAptEREREZEAUKEtIiIiIhIAKrRFRERERAJAhbaIiIiISACo0BYRERERCQAV2iIiIiIiAaBCW0REREQkAFRoi4iIiIgEgAptEREREZEAUKEtIiIiIhIAKrRFRERERAJAhbaIiIiISACo0BYRERERCQAV2iIiIiIiAaBCW0REREQkAFRoi4iIiIgEgAptEREREZEAUKEtIiIiIhIAKrRFRERERALg/wEGMn79P69KXQAAAABJRU5ErkJggg==",
+      "image/png": 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       "text/plain": [
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diff --git a/searchindex.js b/searchindex.js
index a94d0d9..69bdcee 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file