From b670d322544f81891bee82c462534dcab2a7854a Mon Sep 17 00:00:00 2001
From: didihou <didi.hou@rwth-aachen.de>
Date: Fri, 8 Sep 2023 17:16:33 +0200
Subject: [PATCH] /
---
.../multi-area-model-checkpoint.ipynb | 1428 ++--------------
.../MAM2EBRAINS_LOAD_DATA-checkpoint.py | 154 ++
...> MAM2EBRAINS_VISUALIZATION-checkpoint.py} | 199 +--
figures/MAM2EBRAINS_LOAD_DATA.py | 154 ++
...BRAINS.py => MAM2EBRAINS_VISUALIZATION.py} | 199 +--
multi-area-model.ipynb | 1443 ++---------------
.../analysis_helpers-checkpoint.py | 814 ++++++++++
.../viscortex_processed_data.json | 2 +-
8 files changed, 1576 insertions(+), 2817 deletions(-)
create mode 100644 figures/.ipynb_checkpoints/MAM2EBRAINS_LOAD_DATA-checkpoint.py
rename figures/.ipynb_checkpoints/{MAM2EBRAINS-checkpoint.py => MAM2EBRAINS_VISUALIZATION-checkpoint.py} (67%)
create mode 100644 figures/MAM2EBRAINS_LOAD_DATA.py
rename figures/{MAM2EBRAINS.py => MAM2EBRAINS_VISUALIZATION.py} (67%)
create mode 100644 multiarea_model/.ipynb_checkpoints/analysis_helpers-checkpoint.py
diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index 821363d..02dfd2e 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -19,24 +19,20 @@
"source": [
"#### Notebook structure <a class=\"anchor\" id=\"toc\"></a>\n",
"* [S0. Configuration](#section_0)\n",
- "* [S1. Paramters specification](#section_1)\n",
+ "* [S1. Parameterization](#section_1)\n",
" * [1.1. Parameters to tune](#section_1_1)\n",
" * [1.2. Default parameters](#section_1_2)\n",
- "* [S2. Multi-area model instantiation and simulation](#section_2)\n",
+ "* [S2. Multi-Area Model Instantiation and Simulation](#section_2)\n",
" * [2.1. Insantiate a multi-area model](#section_2_1)\n",
" * [2.2. Predict firing rates from theory](#section_2_2)\n",
- " * [2.3. Extract interarea connectivity](#section_2_3)\n",
- " * [2.4. Run the simulation](#section_2_4)\n",
- "* [S3. Simulation results validation and connection extraction](#section_3)\n",
- "* [S4. Data loading and processing](#section_4)\n",
- "* [S5. Simulation results visualization](#section_5) \n",
+ " * [2.3. Extract interareal connectivity](#section_2_3)\n",
+ " * [2.4. Run a simulation](#section_2_4)\n",
+ "* [S3. SExtract Interneural Connectivity](#section_3)\n",
+ "* [S4. Data Loading and Processing](#section_4)\n",
+ "* [S5. Simulation Results Visualziation](#section_5) \n",
" * [5.1. Instantaneous and mean firing rate across all populations](#section_5_1)\n",
- " * [5.2. Raster plot of spiking activity for single area](#section_5_2)\n",
- " * [5.3. Population-averaged firing rate](#section_5_3)\n",
- " * [5.4 Time-averaged population rates](#section_5_4)\n",
- " * [5.5. Average pairwise correlation coefficients of spiking activity](#section_5_5)\n",
- " * [5.6. Irregularity of spiking activity](#section_5_6)\n",
- " * [5.7. Time series of population- and area-averaged firing rates](#section_5_7)"
+ " * [5.2 Resting state plots](#section_5_2)\n",
+ " * [5.3 Time-averaged population rates](#section_5_3)"
]
},
{
@@ -51,7 +47,6 @@
"cell_type": "markdown",
"id": "d782e527",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@@ -70,12 +65,13 @@
"# Create config file\n",
"with open('config.py', 'w') as fp:\n",
" fp.write(\n",
- "'''import os\n",
- "base_path = os.path.abspath(\".\")\n",
- "data_path = os.path.abspath(\"simulations\")\n",
- "jobscript_template = \"python {base_path}/run_simulation.py {label}\"\n",
- "submit_cmd = \"bash -c\"\n",
- "''')"
+ " '''\n",
+ " import os\n",
+ " base_path = os.path.abspath(\".\")\n",
+ " data_path = os.path.abspath(\"simulations\")\n",
+ " jobscript_template = \"python {base_path}/run_simulation.py {label}\"\n",
+ " submit_cmd = \"bash -c\"\n",
+ " ''')"
]
},
{
@@ -109,19 +105,17 @@
}
],
"source": [
- "# Import dependencies\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import nest\n",
- "from IPython.display import display, HTML\n",
"import json\n",
"\n",
- "# Import the MultiAreaModel class\n",
"from multiarea_model import MultiAreaModel\n",
"from multiarea_model import Analysis\n",
"from config import base_path, data_path\n",
+ "\n",
"import sys\n",
"sys.path.append('./figures')"
]
@@ -174,6 +168,7 @@
],
"source": [
"# Jupyter notebook display format setting\n",
+ "from IPython.display import display, HTML\n",
"style = \"\"\"\n",
"<style>\n",
"table {float:left}\n",
@@ -190,23 +185,14 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "565be233",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "df83f5ea-1c4b-44d3-9926-01786aa46e14",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
- "## S1. Paramters specification <a class=\"anchor\" id=\"section_1\"></a>"
+ "## S1. Parameterization <a class=\"anchor\" id=\"section_1\"></a>"
]
},
{
@@ -226,7 +212,7 @@
"|:----------------------------:|:-----------------------:|:--------------------------------------------------------------------:|:------------------:|:-----------:|\n",
"|scale_down_to |1. |(0, 1.] |0.005 |$^1$ |\n",
"|cc_weights_factor |1. |(0, 1.] |1. |$^2$ |\n",
- "|areas_simulated |complete_area_list |All sublists of complete_area_list |complete_area_list |$^3$ |\n",
+ "|areas_simulated |complete_area_list |Sublists of complete_area_list |complete_area_list |$^3$ |\n",
"|replace_non_simulated_areas |None |None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat' |'het_poisson_stat' |$^4$ |"
]
},
@@ -338,23 +324,14 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "c532a861-824f-4713-a311-590aef8b6134",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "de4a6703",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
- "## S2. Multi-area model instantiation and simulation <a class=\"anchor\" id=\"section_2\"></a>"
+ "## S2. Multi-Area Model Instantiation and Simulation <a class=\"anchor\" id=\"section_2\"></a>"
]
},
{
@@ -378,7 +355,7 @@
"output_type": "stream",
"text": [
"Initializing network from dictionary.\n",
- "RAND_DATA_LABEL 4682\n"
+ "RAND_DATA_LABEL 1680\n"
]
},
{
@@ -475,7 +452,7 @@
"id": "2062ddf3",
"metadata": {},
"source": [
- "### 2.3. Extract interarea connectivity <a class=\"anchor\" id=\"section_2_3\"></a>"
+ "### 2.3. Extract interareal connectivity <a class=\"anchor\" id=\"section_2_3\"></a>"
]
},
{
@@ -486,14 +463,6 @@
"The connectivity and neuron numbers are stored in the attributes of the model class. Neuron numbers are stored in `M.N` as a dictionary (and in `M.N_vec` as an array), indegrees in `M.K` as a dictionary (and in `M.K_matrix` as an array). Number of synapses can also be access via `M.synapses` (and in `M.syn_matrix` as an array). <br>"
]
},
- {
- "cell_type": "markdown",
- "id": "b7396606",
- "metadata": {},
- "source": [
- "#### 2.3.1 Node indegrees"
- ]
- },
{
"cell_type": "code",
"execution_count": 9,
@@ -501,19 +470,12 @@
"metadata": {},
"outputs": [],
"source": [
+ "# Indegrees\n",
"# Dictionary of nodes indegrees organized as:\n",
"# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: indegree_values}}}}\n",
"# M.K"
]
},
- {
- "cell_type": "markdown",
- "id": "253a2aba",
- "metadata": {},
- "source": [
- "#### 2.3.2 Synapses"
- ]
- },
{
"cell_type": "code",
"execution_count": 10,
@@ -521,6 +483,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "# Synapses\n",
"# Dictionary of synapses that target neurons receive, it is organized as:\n",
"# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: number_of_synapses}}}}\n",
"# M.synapses"
@@ -539,7 +502,7 @@
"id": "0c1cad59-81d0-4e24-ac33-13c4ca8c6dec",
"metadata": {},
"source": [
- "### 2.4. Run the simulation <a class=\"anchor\" id=\"section_2_4\"></a>"
+ "### 2.4. Run a simulation <a class=\"anchor\" id=\"section_2_4\"></a>"
]
},
{
@@ -554,72 +517,72 @@
"text": [
"Prepared simulation in 0.00 seconds.\n",
"Rank 0: created area V1 with 0 local nodes\n",
- "Memory after V1 : 1911.70 MB\n",
+ "Memory after V1 : 1911.46 MB\n",
"Rank 0: created area V2 with 0 local nodes\n",
- "Memory after V2 : 1938.31 MB\n",
+ "Memory after V2 : 1938.14 MB\n",
"Rank 0: created area VP with 0 local nodes\n",
- "Memory after VP : 1967.50 MB\n",
+ "Memory after VP : 1967.21 MB\n",
"Rank 0: created area V3 with 0 local nodes\n",
- "Memory after V3 : 1995.87 MB\n",
+ "Memory after V3 : 1995.58 MB\n",
"Rank 0: created area V3A with 0 local nodes\n",
- "Memory after V3A : 2015.66 MB\n",
+ "Memory after V3A : 2015.40 MB\n",
"Rank 0: created area MT with 0 local nodes\n",
- "Memory after MT : 2041.33 MB\n",
+ "Memory after MT : 2041.07 MB\n",
"Rank 0: created area V4t with 0 local nodes\n",
- "Memory after V4t : 2066.27 MB\n",
+ "Memory after V4t : 2066.01 MB\n",
"Rank 0: created area V4 with 0 local nodes\n",
- "Memory after V4 : 2093.21 MB\n",
+ "Memory after V4 : 2092.96 MB\n",
"Rank 0: created area VOT with 0 local nodes\n",
- "Memory after VOT : 2118.56 MB\n",
+ "Memory after VOT : 2118.31 MB\n",
"Rank 0: created area MSTd with 0 local nodes\n",
- "Memory after MSTd : 2140.07 MB\n",
+ "Memory after MSTd : 2139.78 MB\n",
"Rank 0: created area PIP with 0 local nodes\n",
- "Memory after PIP : 2161.43 MB\n",
+ "Memory after PIP : 2161.13 MB\n",
"Rank 0: created area PO with 0 local nodes\n",
- "Memory after PO : 2182.89 MB\n",
+ "Memory after PO : 2182.64 MB\n",
"Rank 0: created area DP with 0 local nodes\n",
- "Memory after DP : 2203.04 MB\n",
+ "Memory after DP : 2202.87 MB\n",
"Rank 0: created area MIP with 0 local nodes\n",
- "Memory after MIP : 2224.70 MB\n",
+ "Memory after MIP : 2224.36 MB\n",
"Rank 0: created area MDP with 0 local nodes\n",
- "Memory after MDP : 2246.17 MB\n",
+ "Memory after MDP : 2245.88 MB\n",
"Rank 0: created area VIP with 0 local nodes\n",
- "Memory after VIP : 2268.00 MB\n",
+ "Memory after VIP : 2267.81 MB\n",
"Rank 0: created area LIP with 0 local nodes\n",
- "Memory after LIP : 2292.05 MB\n",
+ "Memory after LIP : 2291.76 MB\n",
"Rank 0: created area PITv with 0 local nodes\n",
- "Memory after PITv : 2317.36 MB\n",
+ "Memory after PITv : 2317.05 MB\n",
"Rank 0: created area PITd with 0 local nodes\n",
- "Memory after PITd : 2342.57 MB\n",
+ "Memory after PITd : 2342.35 MB\n",
"Rank 0: created area MSTl with 0 local nodes\n",
- "Memory after MSTl : 2364.07 MB\n",
+ "Memory after MSTl : 2363.81 MB\n",
"Rank 0: created area CITv with 0 local nodes\n",
- "Memory after CITv : 2383.25 MB\n",
+ "Memory after CITv : 2382.88 MB\n",
"Rank 0: created area CITd with 0 local nodes\n",
- "Memory after CITd : 2402.54 MB\n",
+ "Memory after CITd : 2402.21 MB\n",
"Rank 0: created area FEF with 0 local nodes\n",
- "Memory after FEF : 2424.05 MB\n",
+ "Memory after FEF : 2423.68 MB\n",
"Rank 0: created area TF with 0 local nodes\n",
- "Memory after TF : 2439.70 MB\n",
+ "Memory after TF : 2439.32 MB\n",
"Rank 0: created area AITv with 0 local nodes\n",
- "Memory after AITv : 2462.29 MB\n",
+ "Memory after AITv : 2461.99 MB\n",
"Rank 0: created area FST with 0 local nodes\n",
- "Memory after FST : 2479.02 MB\n",
+ "Memory after FST : 2478.73 MB\n",
"Rank 0: created area 7a with 0 local nodes\n",
- "Memory after 7a : 2500.20 MB\n",
+ "Memory after 7a : 2499.90 MB\n",
"Rank 0: created area STPp with 0 local nodes\n",
- "Memory after STPp : 2518.91 MB\n",
+ "Memory after STPp : 2518.73 MB\n",
"Rank 0: created area STPa with 0 local nodes\n",
- "Memory after STPa : 2538.05 MB\n",
+ "Memory after STPa : 2537.76 MB\n",
"Rank 0: created area 46 with 0 local nodes\n",
- "Memory after 46 : 2553.50 MB\n",
+ "Memory after 46 : 2553.24 MB\n",
"Rank 0: created area AITd with 0 local nodes\n",
- "Memory after AITd : 2576.05 MB\n",
+ "Memory after AITd : 2575.80 MB\n",
"Rank 0: created area TH with 0 local nodes\n",
- "Memory after TH : 2588.77 MB\n",
- "Created areas and internal connections in 2.21 seconds.\n",
- "Created cortico-cortical connections in 22.96 seconds.\n",
- "Simulated network in 82.80 seconds.\n"
+ "Memory after TH : 2588.51 MB\n",
+ "Created areas and internal connections in 2.25 seconds.\n",
+ "Created cortico-cortical connections in 22.60 seconds.\n",
+ "Simulated network in 73.15 seconds.\n"
]
}
],
@@ -636,55 +599,14 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "4003c5a5-4a6f-49c5-be17-09f1bc68c411",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "28e071f8",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
- "## S3. Simulation results validation and connection extraction <a class=\"anchor\" id=\"section_3\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "89c7b7cf",
- "metadata": {},
- "source": [
- "### 3.1 Test if the correct number of synapses has been created"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "dc3b1820",
- "metadata": {},
- "outputs": [],
- "source": [
- "# # Uncomment the lines in this code cell below to test if the number of synapses created by NEST matches the expected values\n",
- "\n",
- "# print(\"Testing synapse numbers\")\n",
- "# for target_area_name in M.area_list:\n",
- "# target_area = M.simulation.areas[M.simulation.areas.index(target_area_name)]\n",
- "# for source_area_name in M.area_list:\n",
- "# source_area = M.simulation.areas[M.simulation.areas.index(source_area_name)]\n",
- "# for target_pop in M.structure[target_area.name]:\n",
- "# target_nodes = target_area.gids[target_pop]\n",
- "# for source_pop in M.structure[source_area.name]:\n",
- "# source_nodes = source_area.gids[source_pop]\n",
- "# created_syn = nest.GetConnections(source=source_nodes,\n",
- "# target=target_nodes)\n",
- "# syn = M.synapses[target_area.name][target_pop][source_area.name][source_pop]\n",
- "# assert(len(created_syn) == int(syn))"
+ "## S3. Extract Interneural Connectivity <a class=\"anchor\" id=\"section_3\"></a>"
]
},
{
@@ -692,7 +614,6 @@
"id": "57401110",
"metadata": {},
"source": [
- "### 3.2 Extract connections information\n",
"**Warning**: Memory explosion <br>\n",
"To obtain the connections information, you can extract the lists of connected sources and targets. Moreover, you can access additional synaptic details, such as synaptic weights and delays."
]
@@ -744,14 +665,6 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "529b1ade",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "57ff902c-d6ce-4f96-9e4f-8e3e7166ab66",
@@ -759,7 +672,7 @@
"tags": []
},
"source": [
- "## S4. Data loading and processing <a class=\"anchor\" id=\"section_4\"></a>"
+ "## S4. Data Loading and Processing <a class=\"anchor\" id=\"section_4\"></a>"
]
},
{
@@ -772,262 +685,10 @@
"outputs": [],
"source": [
"label_spikes = M.simulation.label\n",
- "label = M.simulation.label"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "6607a73d-1c74-4848-9603-081ad0e7cae8",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "loading spikes\n"
- ]
- }
- ],
- "source": [
- "\"\"\"\n",
- "Analysis class.\n",
- "An instance of the analysis class for the given network and simulation.\n",
- "Can be created as a member class of a multiarea_model instance or standalone.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "network : MultiAreaModel\n",
- " An instance of the multiarea_model class that specifies\n",
- " the network to be analyzed.\n",
- "simulation : Simulation\n",
- " An instance of the simulation class that specifies\n",
- " the simulation to be analyzed.\n",
- "data_list : list of strings {'spikes', vm'}, optional\n",
- " Specifies which type of data is to load. Defaults to ['spikes'].\n",
- "load_areas : list of strings with area names, optional\n",
- " Specifies the areas for which data is to be loaded.\n",
- " Default value is None and leads to loading of data for all\n",
- " simulated areas.\n",
- "\"\"\"\n",
- "# Instantiate an analysis class and load spike data\n",
- "A = Analysis(network=M, \n",
- " simulation=M.simulation, \n",
- " data_list=['spikes'],\n",
- " load_areas=None)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "1870cf34-ee62-4614-bc25-c36bc9a7377c",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data from file\n",
- "Computing population rates done\n"
- ]
- }
- ],
- "source": [
- "\"\"\"\n",
- "Calculate time-averaged population rates and store them in member pop_rates.\n",
- "If the rates had previously been stored with the same\n",
- "parameters, they are loaded from file.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "compute_stat : bool, optional\n",
- " If set to true, the mean and variance of the population rate\n",
- " is calculated. Defaults to False.\n",
- " Caution: Setting to True slows down the computation.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "\"\"\"\n",
- "A.create_pop_rates()\n",
- "print(\"Computing population rates done\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "50b7df89",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data from file\n",
- "Computing synchrony done\n"
- ]
- }
- ],
- "source": [
- "\"\"\"\n",
- "Calculate synchrony as the coefficient of variation of the population rate\n",
- "and store in member synchrony. Uses helper function synchrony.\n",
- "If the synchrony has previously been stored with the\n",
- "same parameters, they are loaded from file.\n",
- "\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "resolution : float, optional\n",
- " Resolution of the population rate. Defaults to 1 ms.\n",
- "\"\"\"\n",
- "A.create_synchrony()\n",
- "print(\"Computing synchrony done\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "d43b493c",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data from file\n",
- "Computing population LvR done\n"
- ]
- }
- ],
- "source": [
- "\"\"\"\n",
- "Calculate poulation-averaged LvR (see Shinomoto et al. 2009) and\n",
- "store as member pop_LvR. Uses helper function LvR.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "\"\"\"\n",
- "A.create_pop_LvR()\n",
- "print(\"Computing population LvR done\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "401ece2d-47c8-4775-80ae-92a8e432520c",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data from file\n",
- "Loading data from file\n",
- "Computing rate time series done\n"
- ]
- }
- ],
- "source": [
- "\"\"\"\n",
- "Calculate time series of population- and area-averaged firing rates.\n",
- "Uses ah.pop_rate_time_series.\n",
- "If the rates have previously been stored with the\n",
- "same parameters, they are loaded from file.\n",
+ "label = M.simulation.label\n",
"\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional\n",
- " Specifies the kernel to be convolved with the spike histogram.\n",
- " Defaults to 'binned', which corresponds to no convolution.\n",
- "resolution: float, optional\n",
- " Width of the convolution kernel. Specifically it correponds to:\n",
- " - 'binned' : bin width of the histogram\n",
- " - 'gauss_time_window' : sigma\n",
- " - 'alpha_time_window' : time constant of the alpha function\n",
- " - 'rect_time_window' : width of the moving rectangular function\n",
- "\"\"\"\n",
- "A.create_rate_time_series()\n",
- "print(\"Computing rate time series done\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "fa3ea20e-e456-4608-a711-e2c320bcaf91",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "pop_LvR\n",
- "pop_rates\n",
- "synchrony\n"
- ]
- }
- ],
- "source": [
- "A.save()"
+ "from MAM2EBRAINS_LOAD_DATA import load_data\n",
+ "tsteps, firing_rate = load_data(M, A)"
]
},
{
@@ -1038,14 +699,6 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "4d43d223-a62e-448a-a7ea-8379b8be8e86",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "bb71c922",
@@ -1053,7 +706,7 @@
"tags": []
},
"source": [
- "## S5. Simulation results visualziation <a class=\"anchor\" id=\"section_5\"></a>"
+ "## S5. Simulation Results Visualziation <a class=\"anchor\" id=\"section_5\"></a>"
]
},
{
@@ -1068,34 +721,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
- "id": "76ee6450-7d36-406e-aaa7-a7ca447e8da9",
- "metadata": {},
- "outputs": [],
- "source": [
- "import json\n",
- "import numpy as np\n",
- "import os\n",
- "\n",
- "import sys\n",
- "sys.path.append('./figures/Schmidt2018_dyn')\n",
- "\n",
- "from helpers import original_data_path, population_labels\n",
- "from multiarea_model import MultiAreaModel\n",
- "from plotcolors import myred, myblue\n",
- "\n",
- "import matplotlib.pyplot as pl\n",
- "from matplotlib import gridspec\n",
- "# from matplotlib import rc_file\n",
- "# rc_file('plotstyle.rc')\n",
- "\n",
- "icolor = myred\n",
- "ecolor = myblue"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
+ "execution_count": 23,
"id": "bea30fc8",
"metadata": {},
"outputs": [
@@ -1113,23 +739,8 @@
}
],
"source": [
- "# load spike data and calculate instantaneous and mean firing rates\n",
- "data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + \"-spikes-1-0.dat\", skiprows=3)\n",
- "tsteps, spikecount = np.unique(data[:,1], return_counts=True)\n",
- "rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)\n",
- "\n",
- "# ax = plt.subplot()\n",
- "# ax.plot(tsteps, rate)\n",
- "# ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')\n",
- "# ax.set_title('Instantaneous and mean firing rate across all populations')\n",
- "# ax.set_xlabel('time (ms)')\n",
- "# ax.set_ylabel('firing rate (spikes / s)')\n",
- "# ax.set_xlim(0, sim_params['t_sim'])\n",
- "# ax.set_ylim(0, 50)\n",
- "# ax.legend()\n",
- "\n",
- "from MAM2EBRAINS import plot_instan_mean_firing_rate\n",
- "plot_instan_mean_firing_rate(tsteps, rate, sim_params)"
+ "from MAM2EBRAINS_VISUALIZATION import plot_instan_mean_firing_rate\n",
+ "plot_instan_mean_firing_rate(tsteps, firing_rate, sim_params)"
]
},
{
@@ -1150,633 +761,79 @@
},
{
"cell_type": "code",
- "execution_count": 34,
- "id": "b7fd1f63-5927-4fb0-82f5-e8b0c173bd12",
- "metadata": {},
- "outputs": [],
- "source": [
- "def set_boxplot_props(d):\n",
- " for i in range(len(d['boxes'])):\n",
- " if i % 2 == 0:\n",
- " d['boxes'][i].set_facecolor(icolor)\n",
- " d['boxes'][i].set_color(icolor)\n",
- " else:\n",
- " d['boxes'][i].set_facecolor(ecolor)\n",
- " d['boxes'][i].set_color(ecolor)\n",
- " pl.setp(d['whiskers'], color='k')\n",
- " pl.setp(d['fliers'], color='k', markerfacecolor='k', marker='+')\n",
- " pl.setp(d['medians'], color='none')\n",
- " pl.setp(d['caps'], color='k')\n",
- " pl.setp(d['means'], marker='x', color='k',\n",
- " markerfacecolor='k', markeredgecolor='k', markersize=3.)\n",
- "\n",
- "def plot_resting_state(A, label_spikes, label): \n",
- " \"\"\"\n",
- " Figure layout\n",
- " \"\"\"\n",
- "\n",
- " nrows = 4\n",
- " ncols = 4\n",
- " width = 7.0866\n",
- " panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio\n",
- "\n",
- " height = width / panel_wh_ratio * float(nrows) / ncols\n",
- " pl.rcParams['figure.figsize'] = (width, height)\n",
- "\n",
- "\n",
- " fig = pl.figure()\n",
- " axes = {}\n",
- "\n",
- " gs1 = gridspec.GridSpec(1, 3)\n",
- " gs1.update(left=0.06, right=0.72, top=0.95, wspace=0.4, bottom=0.35)\n",
- " # axes['A'] = pl.subplot(gs1[:-1, :1])\n",
- " # axes['B'] = pl.subplot(gs1[:-1, 1:2])\n",
- " # axes['C'] = pl.subplot(gs1[:-1, 2:])\n",
- " axes['A'] = pl.subplot(gs1[:1, :1])\n",
- " axes['B'] = pl.subplot(gs1[:1, 1:2])\n",
- " axes['C'] = pl.subplot(gs1[:1, 2:])\n",
- "\n",
- " gs2 = gridspec.GridSpec(3, 1)\n",
- " gs2.update(left=0.78, right=0.95, top=0.95, bottom=0.35)\n",
- " axes['D'] = pl.subplot(gs2[:1, :1])\n",
- " axes['E'] = pl.subplot(gs2[1:2, :1])\n",
- " axes['F'] = pl.subplot(gs2[2:3, :1])\n",
- "\n",
- "\n",
- " gs3 = gridspec.GridSpec(1, 1)\n",
- " gs3.update(left=0.1, right=0.95, top=0.3, bottom=0.075)\n",
- " axes['G'] = pl.subplot(gs3[:1, :1])\n",
- "\n",
- " areas = ['V1', 'V2', 'FEF']\n",
- "\n",
- " labels = ['A', 'B', 'C']\n",
- " for area, label in zip(areas, labels):\n",
- " label_pos = [-0.2, 1.01]\n",
- " pl.text(label_pos[0], label_pos[1], r'\\bfseries{}' + label + ': ' + area,\n",
- " fontdict={'fontsize': 10, 'weight': 'bold',\n",
- " 'horizontalalignment': 'left', 'verticalalignment':\n",
- " 'bottom'}, transform=axes[label].transAxes)\n",
- "\n",
- " label = 'G'\n",
- " label_pos = [-0.1, 0.92]\n",
- " pl.text(label_pos[0], label_pos[1], r'\\bfseries{}' + label,\n",
- " fontdict={'fontsize': 10, 'weight': 'bold',\n",
- " 'horizontalalignment': 'left', 'verticalalignment':\n",
- " 'bottom'}, transform=axes[label].transAxes)\n",
- "\n",
- "\n",
- " labels = ['E', 'D', 'F']\n",
- " for label in labels:\n",
- " label_pos = [-0.2, 1.05]\n",
- " pl.text(label_pos[0], label_pos[1], r'\\bfseries{}' + label,\n",
- " fontdict={'fontsize': 10, 'weight': 'bold',\n",
- " 'horizontalalignment': 'left', 'verticalalignment':\n",
- " 'bottom'}, transform=axes[label].transAxes)\n",
- "\n",
- " labels = ['A', 'B', 'C', 'D', 'E', 'F']\n",
- "\n",
- " for label in labels:\n",
- " axes[label].spines['right'].set_color('none')\n",
- " axes[label].spines['top'].set_color('none')\n",
- " axes[label].yaxis.set_ticks_position(\"left\")\n",
- " axes[label].xaxis.set_ticks_position(\"bottom\")\n",
- "\n",
- " for label in ['A', 'B', 'C']:\n",
- " axes[label].yaxis.set_ticks_position('none')\n",
- "\n",
- "\n",
- " \"\"\"\n",
- " Load data\n",
- " \"\"\"\n",
- " LOAD_ORIGINAL_DATA = True\n",
- "\n",
- "\n",
- " if LOAD_ORIGINAL_DATA:\n",
- " # use T=10500 simulation for spike raster plots\n",
- " label_spikes = '3afaec94d650c637ef8419611c3f80b3cb3ff539'\n",
- " # and T=100500 simulation for all other panels\n",
- " label = '99c0024eacc275d13f719afd59357f7d12f02b77'\n",
- " data_path = original_data_path\n",
- " else:\n",
- " from network_simulations import init_models\n",
- " from config import data_path\n",
- " models = init_models('Fig5')\n",
- " label_spikes = models[0].simulation.label\n",
- " label = models[1].simulation.label\n",
- "\n",
- " \"\"\"\n",
- " Create MultiAreaModel instance to have access to data structures\n",
- " \"\"\"\n",
- " M = MultiAreaModel({})\n",
- "\n",
- " # spike data\n",
- " spike_data = {}\n",
- " for area in areas:\n",
- " spike_data[area] = {}\n",
- " for pop in M.structure[area]:\n",
- " spike_data[area][pop] = np.load(os.path.join(data_path,\n",
- " label_spikes,\n",
- " 'recordings',\n",
- " '{}-spikes-{}-{}.npy'.format(label_spikes,\n",
- " area, pop)))\n",
- " # stationary firing rates\n",
- " fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')\n",
- " with open(fn, 'r') as f:\n",
- " pop_rates = json.load(f)\n",
- "\n",
- " # time series of firing rates\n",
- " rate_time_series = {}\n",
- " for area in areas:\n",
- " fn = os.path.join(data_path, label,\n",
- " 'Analysis',\n",
- " 'rate_time_series_full',\n",
- " 'rate_time_series_full_{}.npy'.format(area))\n",
- " rate_time_series[area] = np.load(fn)\n",
- "\n",
- " # time series of firing rates convolved with a kernel\n",
- " rate_time_series_auto_kernel = {}\n",
- " for area in areas:\n",
- " fn = os.path.join(data_path, label,\n",
- " 'Analysis',\n",
- " 'rate_time_series_auto_kernel',\n",
- " 'rate_time_series_auto_kernel_{}.npy'.format(area))\n",
- " rate_time_series_auto_kernel[area] = np.load(fn)\n",
- "\n",
- " # local variance revised (LvR)\n",
- " fn = os.path.join(data_path, label, 'Analysis', 'pop_LvR.json')\n",
- " with open(fn, 'r') as f:\n",
- " pop_LvR = json.load(f)\n",
- "\n",
- " # correlation coefficients\n",
- " fn = os.path.join(data_path, label, 'Analysis', 'corrcoeff.json')\n",
- " with open(fn, 'r') as f:\n",
- " corrcoeff = json.load(f)\n",
- "\n",
- " \"\"\"\n",
- " Plotting\n",
- " \"\"\"\n",
- " print(\"Raster plots\")\n",
- "\n",
- " t_min = 3000.\n",
- " t_max = 3500.\n",
- "\n",
- " icolor = myred\n",
- " ecolor = myblue\n",
- "\n",
- " frac_neurons = 0.03\n",
- "\n",
- " for i, area in enumerate(areas):\n",
- " ax = axes[labels[i]]\n",
- "\n",
- " if area in spike_data:\n",
- " n_pops = len(spike_data[area])\n",
- " # Determine number of neurons that will be plotted for this area (for\n",
- " # vertical offset)\n",
- " offset = 0\n",
- " n_to_plot = {}\n",
- " for pop in M.structure[area]:\n",
- " n_to_plot[pop] = int(M.N[area][pop] * frac_neurons)\n",
- " offset = offset + n_to_plot[pop]\n",
- " y_max = offset + 1\n",
- " prev_pop = ''\n",
- " yticks = []\n",
- " yticklocs = []\n",
- " for jj, pop in enumerate(M.structure[area]):\n",
- " if pop[0:-1] != prev_pop:\n",
- " prev_pop = pop[0:-1]\n",
- " yticks.append('L' + population_labels[jj][0:-1])\n",
- " yticklocs.append(offset - 0.5 * n_to_plot[pop])\n",
- " ind = np.where(np.logical_and(\n",
- " spike_data[area][pop][:, 1] <= t_max, spike_data[area][pop][:, 1] >= t_min))\n",
- " pop_data = spike_data[area][pop][ind]\n",
- " pop_neurons = np.unique(pop_data[:, 0])\n",
- " neurons_to_ = np.arange(np.min(spike_data[area][pop][:, 0]), np.min(\n",
- " spike_data[area][pop][:, 0]) + n_to_plot[pop], 1)\n",
- "\n",
- " if pop.find('E') > (-1):\n",
- " pcolor = ecolor\n",
- " else:\n",
- " pcolor = icolor\n",
- "\n",
- " for kk in range(n_to_plot[pop]):\n",
- " spike_times = pop_data[pop_data[:, 0] == neurons_to_[kk], 1]\n",
- "\n",
- " _ = ax.plot(spike_times, np.zeros(len(spike_times)) +\n",
- " offset - kk, '.', color=pcolor, markersize=1)\n",
- " offset = offset - n_to_plot[pop]\n",
- " y_min = offset\n",
- " ax.set_xlim([t_min, t_max])\n",
- " ax.set_ylim([y_min, y_max])\n",
- " ax.set_yticklabels(yticks)\n",
- " ax.set_yticks(yticklocs)\n",
- " ax.set_xlabel('Time (s)', labelpad=-0.1)\n",
- " ax.set_xticks([t_min, t_min + 250., t_max])\n",
- " ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])\n",
- "\n",
- " print(\"plotting Population rates\")\n",
- "\n",
- " rates = np.zeros((len(M.area_list), 8))\n",
- " for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " rate = pop_rates[area][pop][0]\n",
- " if rate == 0.0:\n",
- " rate = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " rates[i][j + 2] = rate\n",
- " else:\n",
- " rates[i][j] = rate\n",
- "\n",
- "\n",
- " rates = np.transpose(rates)\n",
- " masked_rates = np.ma.masked_where(rates < 1e-4, rates)\n",
- "\n",
- " ax = axes['D']\n",
- " d = ax.boxplot(np.transpose(rates), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- " set_boxplot_props(d)\n",
- "\n",
- " ax.plot(np.mean(rates, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- " ax.set_yticklabels(population_labels[::-1], size=8)\n",
- " ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- " ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "\n",
- " x_max = 220.\n",
- " ax.set_xlim((-1., x_max))\n",
- " ax.set_xlabel(r'Rate (spikes/s)', labelpad=-0.1)\n",
- " ax.set_xticks([0., 50., 100.])\n",
- "\n",
- " print(\"plotting Synchrony\")\n",
- "\n",
- " syn = np.zeros((len(M.area_list), 8))\n",
- " for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " value = corrcoeff[area][pop]\n",
- " if value == 0.0:\n",
- " value = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " syn[i][j + 2] = value\n",
- " else:\n",
- " syn[i][j] = value\n",
- "\n",
- "\n",
- " syn = np.transpose(syn)\n",
- " masked_syn = np.ma.masked_where(syn < 1e-4, syn)\n",
- "\n",
- " ax = axes['E']\n",
- " d = ax.boxplot(np.transpose(syn), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- " set_boxplot_props(d)\n",
- "\n",
- " ax.plot(np.mean(syn, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "\n",
- " ax.set_yticklabels(population_labels[::-1], size=8)\n",
- " ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- " ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- " ax.set_xticks(np.arange(0.0, 0.601, 0.2))\n",
- " ax.set_xlabel('Correlation coefficient', labelpad=-0.1)\n",
- "\n",
- "\n",
- " print(\"plotting Irregularity\")\n",
- "\n",
- " LvR = np.zeros((len(M.area_list), 8))\n",
- " for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " value = pop_LvR[area][pop]\n",
- " if value == 0.0:\n",
- " value = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " LvR[i][j + 2] = value\n",
- " else:\n",
- " LvR[i][j] = value\n",
- "\n",
- " LvR = np.transpose(LvR)\n",
- " masked_LvR = np.ma.masked_where(LvR < 1e-4, LvR)\n",
- "\n",
- " ax = axes['F']\n",
- " d = ax.boxplot(np.transpose(LvR), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- " set_boxplot_props(d)\n",
- "\n",
- " ax.plot(np.mean(LvR, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- " ax.set_yticklabels(population_labels[::-1], size=8)\n",
- " ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- " ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "\n",
- "\n",
- " x_max = 2.9\n",
- " ax.set_xlim((0., x_max))\n",
- " ax.set_xlabel('Irregularity', labelpad=-0.1)\n",
- " ax.set_xticks([0., 1., 2.])\n",
- "\n",
- " axes['G'].spines['right'].set_color('none')\n",
- " axes['G'].spines['left'].set_color('none')\n",
- " axes['G'].spines['top'].set_color('none')\n",
- " axes['G'].spines['bottom'].set_color('none')\n",
- " axes['G'].yaxis.set_ticks_position(\"none\")\n",
- " axes['G'].xaxis.set_ticks_position(\"none\")\n",
- " axes['G'].set_xticks([])\n",
- " axes['G'].set_yticks([])\n",
- "\n",
- "\n",
- " print(\"Plotting rate time series\")\n",
- " pos = axes['G'].get_position()\n",
- " ax = []\n",
- " h = pos.y1 - pos.y0\n",
- " w = pos.x1 - pos.x0\n",
- " ax.append(pl.axes([pos.x0, pos.y0, w, 0.28 * h]))\n",
- " ax.append(pl.axes([pos.x0, pos.y0 + 0.33 * h, w, 0.28 * h]))\n",
- " ax.append(pl.axes([pos.x0, pos.y0 + 0.67 * h, w, 0.28 * h]))\n",
- "\n",
- " colors = ['0.5', '0.3', '0.0']\n",
- "\n",
- " t_min = 500.\n",
- " t_max = 10500.\n",
- " time = np.arange(500., t_max)\n",
- " for i, area in enumerate(areas[::-1]):\n",
- " ax[i].spines['right'].set_color('none')\n",
- " ax[i].spines['top'].set_color('none')\n",
- " ax[i].yaxis.set_ticks_position(\"left\")\n",
- " ax[i].xaxis.set_ticks_position(\"none\")\n",
- "\n",
- " binned_spikes = rate_time_series[area][np.where(\n",
- " np.logical_and(time >= t_min, time < t_max))]\n",
- " ax[i].plot(time, binned_spikes, color=colors[0], label=area)\n",
- " rate = rate_time_series_auto_kernel[area]\n",
- " ax[i].plot(time, rate, color=colors[2], label=area)\n",
- " ax[i].set_xlim((500., t_max))\n",
- "\n",
- " ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)\n",
- "\n",
- " if i > 0:\n",
- " ax[i].spines['bottom'].set_color('none')\n",
- " ax[i].set_xticks([])\n",
- " ax[i].set_yticks([0., 30.])\n",
- " else:\n",
- " ax[i].set_xticks([1000., 5000., 10000.])\n",
- " ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])\n",
- " ax[i].set_yticks([0., 5.])\n",
- " if i == 1:\n",
- " ax[i].set_ylabel(r'Rate (spikes/s)')\n",
- "\n",
- " ax[0].set_xlabel('Time (s)', labelpad=-0.05)\n",
- "\n",
- " fig.subplots_adjust(left=0.05, right=0.95, top=0.95,\n",
- " bottom=0.075, wspace=1., hspace=.5)\n",
- "\n",
- " # pl.savefig('Fig5_ground_state.eps')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
+ "execution_count": 29,
"id": "ae19bcc3",
"metadata": {
"tags": []
},
"outputs": [
{
- "ename": "IndexError",
- "evalue": "GridSpec slice would result in no space allocated for subplot",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
- "Input \u001b[0;32mIn [35]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mMAM2EBRAINS\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_resting_state\n\u001b[0;32m----> 2\u001b[0m \u001b[43mplot_resting_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel_spikes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS.py:90\u001b[0m, in \u001b[0;36mplot_resting_state\u001b[0;34m(A, label_spikes, label)\u001b[0m\n\u001b[1;32m 88\u001b[0m gs1 \u001b[38;5;241m=\u001b[39m gridspec\u001b[38;5;241m.\u001b[39mGridSpec(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m 89\u001b[0m gs1\u001b[38;5;241m.\u001b[39mupdate(left\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.06\u001b[39m, right\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.72\u001b[39m, top\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.95\u001b[39m, wspace\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.4\u001b[39m, bottom\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.35\u001b[39m)\n\u001b[0;32m---> 90\u001b[0m axes[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mA\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pl\u001b[38;5;241m.\u001b[39msubplot(\u001b[43mgs1\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 91\u001b[0m axes[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mB\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pl\u001b[38;5;241m.\u001b[39msubplot(gs1[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m 92\u001b[0m axes[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pl\u001b[38;5;241m.\u001b[39msubplot(gs1[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m:])\n",
- "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/gridspec.py:257\u001b[0m, in \u001b[0;36mGridSpecBase.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized subplot spec\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 256\u001b[0m num1, num2 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mravel_multi_index(\n\u001b[0;32m--> 257\u001b[0m [\u001b[43m_normalize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m, _normalize(k2, ncols, \u001b[38;5;241m1\u001b[39m)],\n\u001b[1;32m 258\u001b[0m (nrows, ncols))\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# Single key\u001b[39;00m\n\u001b[1;32m 260\u001b[0m num1, num2 \u001b[38;5;241m=\u001b[39m _normalize(key, nrows \u001b[38;5;241m*\u001b[39m ncols, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
- "File \u001b[0;32m/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/matplotlib/gridspec.py:237\u001b[0m, in \u001b[0;36mGridSpecBase.__getitem__.<locals>._normalize\u001b[0;34m(key, size, axis)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stop \u001b[38;5;241m>\u001b[39m start:\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m start, stop \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 237\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGridSpec slice would result in no space \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallocated for subplot\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
- "\u001b[0;31mIndexError\u001b[0m: GridSpec slice would result in no space allocated for subplot"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Initializing network from dictionary.\n",
+ "RAND_DATA_LABEL 4079\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3440: RuntimeWarning:Mean of empty slice.\n",
+ "/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
+ "Error in library(\"aod\") : there is no package called ‘aod’\n",
+ "Execution halted\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
+ "\n",
+ "\n",
+ "========================================\n",
+ "Customized parameters\n",
+ "--------------------\n",
+ "{}\n",
+ "========================================\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n",
+ "/tmp/ipykernel_14151/2679329638.py:237: UserWarning:FixedFormatter should only be used together with FixedLocator\n"
]
},
{
"data": {
+ "image/png": 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zpbqONfbzkGDT9HFz4GQRV2tsBHTwobpWY9veXGpterYhMUFIKUk+VkT1NQ2BBSEEZ3PKuVJj45vv9Bn5dbvS+Oa7XKqq63hwcAQWi4Xqmjp2Hsrn/U8PU1Zeja+PB6lZF5ka3w1hsaLZh53FAh4Cko8Xc61OUlsn+du6Y2zYk0EbLysIK1XVdSQf13df3X2kgPdWH2L1jrPU2iRtvKzEDwxl294scosrAYgO18fursMFAAOba/wIIUYIIZKFEAlCiL/Y07yEEKuFED8SQiQKIfYKId62HxsnhMgWQuyyP6bdbJ2OwtXde0XrosVZXiND/HhkZCSRIX6N5nP8Y5FSMmFkJEs2nCA80K8p09b1ymgszZXbYZFpzJLriKObgZSS1+fpFqsjZ0udrt9o48uzHzD/tG/mz7gxK7GBY3lNbX9rY+FHe5ES3vzZaCcRZbNpfLj+OF2C/eke0aFe32iaRm5xBS/NGub283D9rNwJRrg+tbt2Zzq/mzm00bKklPUs50YdGfmX2JiQQV2djeWbT9Il2P+mrJGOn39D48AQqiu2nOLVubpVOiFF/1MHGDs4op5F17HOZyb05qMNJ3l2Wl+6hrRze635pVVUXq3jyJlS06rqiGM7jRuO1mxhbQiLEDw0vCt7juQzZlAouw8X8rNH+9ElWP89TTlbytpd57BpNnYeyuUHQyN4fHxP1u9J5/kZfVm1/TRHz51n0uiu7EkpIq/kMpPjoujdtQMRQf7kFl9i/4kiHn0wmsCObXhtzjA2JGRyIr2M+IERHDhVxPMzBiClxkcbThA3IJTeXdrzXWoJUkKtTeLpITiZcYEZD/YgPNBPv1GUGp4eghkPRrM5MZOfP9afsvJqBvToTNGFauIHhjI0Npg6Wx2ff5umW1WtgsfH92JjQibD+wZz+GwZP3+0LxFB/qzblUbisSIeiA3l8BldFJZcrMHDKqizSTwsAgQcSzvPz2b0p7T8KoEdfEx3hpBO3nx7KJ9xQ0LZsS+X8UO7kHi0AE8r2DQbz03vixAWIoJ8iYnqSPzAMIbEBAP6b8DGxAzGDenKN9/lcK0O+nfvyPH0i/Ts0p4HYgLZlJTFtPgoyiuvkXS0gNiojqScK8Mq4Kkf9aJ/j4tsScpk4qiuxETq4z0qtB2DegaQcu48z03ryyc7znA2u7y5h1A28AMpZbVdsPYHOgPJwE4p5ZcAQoidQohA+zmrpJSvNbUC48bVkf1r3yCgq67By3KOwnvTb3iOomXS4iyvSccKWb39NEnHChvN52hNySqsYEtylj4F1AQash65ltsQjtOXd/qLYvzRJh4t4N0V32GxWLBarWx1uX5DmAA3tCTfKoYodvQdux9xHAemj5zUpwzd9YuUkjU703j/00MUlFW5/TwcPyvDwv7msv1uLatrdqaxclsqR86WNlpWt/D29abZHa2bUkoefbA7cybHomma27Y39QaoIcv/ii2nmDAqiqhQfwDiB4bxqycHExbg26D7izmWJVRcuYYQgu4RHepZXPccySO0sw8+3h5sTsqs11dGmww3jvzSykav6VZndFoCAR3a8N2pEh4c0oXkY8WA3vfGY1NiJgN7BurW0GobR8+dZ8f+HJ6f0Z+Csiuk5V6ijSek51eQV1LBIyOjSEgpICLIn4KyKj5Yf4pam2TNzgze+/QIGxIySTpaSL/uASQdK2DWpFjiB4YihIVnp/Ul6VgRH2w4RdLxYtbvzmBwr0Daelnp260zG/ekk1dSybVaje378qitkwgENdfqQMKQmGB6RXZm7OBwsgr12Q6LsODtZcXb24Nnp/UjqENbnpvWj/BAP6wW6BLcjoKyKhKPFeFhhZOZF3hsfA+8Pa14e1l44uGeTBrVFQ8PCxYhdEu+EGxOykIIwdGzZSzfdJxdhwvx8oDO7X14+pHezJ/Wl6nx3ai1wfZ9efxt7XHO5pbz7oqDdAluh9VqrTe+Hx4eyY9GRAGQmV9B3IAQzuVeYt3uDGrrNNbsSmdrcjZxg8LJKLiE1QI2CcUXaxjSO4g6m0bn9j78de1x3v8shfW70/Hw8ODtX8RRUn6NY2kXqK7VAH7UXONHSlkkpay2v60DbPbyv5JS1gIIIaxAEXDjqT49//NCiINCiIOlpaVGmvnY/P4MAroOJHXPclL3LKc067DT8bu9RkbRvLQ48Ro/MIwXnhpC/MCwJp8THdaO1+eNYPFzo5pkObmZ6XN3GF+UhqZpHWluFwN307Purr8hwdLcfN++bMl4e1l5bnp/rNbrXzPdp/IUFosFDw+Lky+1MQ4y8i+xOTGTpyfENGmcG1Pdr88b4fZzHNQrAB9vDwb1CjDT3I27xm42jHau3Z3B39Ye442l7oVyU2+A3I0Lw0d3x75s01WiKd8lo86IYH/a+XoREeRX7xoTjxbw/qeHOXruPF6eFuZN7etWjBpuEoZ7grubOqPcqFB/p3ytCb+2Xsye3IfvThUzbUx3rFYLH6w7zuIl+8gvrUKTkhMZF/Dz8WTWxBgeG9/ddE/5aMMJrlTXMSw2lG17s5k4OpqQTr5UXLnGtwfzWLrxJJ4eFibHReHTxgMfbyupmReZHB/NyYwL1NbZCAvwZe2udN7/9BBl5TV4elj4P4/355kJvXn8Bz04llbGgF5BJB8ros6mcf5SNbU2SdyAEPx8PBAWwZXqOv667jhvLN1nuo8ZN3lCCBY/N4rFz43CYrHw/mdH+Ovao+w8lE9VdR0FZVcQCISANl4ezJ/Wj6ExIfi19eTRB3uyJSmbPSkFgERYdH9XixBoUvLB+uN8sv0MfboFsG1vFgN6BvHpjnNsSMgg+XiR/ftopY2XlfHDItialGnesDnePBWUXcHDasFisfDs9H7MmhjLGz8bxch+oVgEjB8WgYfVgpenlZkTY/nhA10A8PKw4tvGg4ig6wvs1tv9hQHGDYkgOqwd3cLbM2ZQGJNHRxrrEbKbexwJIQYAAVLKU0AvKeVZe/rzwBngvJTSmN+f6eA2MNy1LCnlh1LKYVLKYYGBgUaa+Zjywnqn/LFj5zodb203mPc7Lc5twGKxmCuJm7oAy7DENBVXy+mt3LE11QLV3FOS7qZnjWtw11+u0/nNHc2hOfqyJWMIKQPjRsLxMzEElrF6HvTFbUNjgust1HLE8bPqFt6+QQFltVhp28YDq+W6j2pD466h9G7h7Xl+xgA+XH8MTcOt+LsZGvLzdbfQa+XWVLcL0lzpHtGBxc+NAq73jXEtxk1AaEBbtu7Nokuwf72x6NimbuHtzXa4ficcy3Vsr5SSab/eOGjTe9NTbrlj7iGMa4sK9SeoU1uWbTrBlLhuxA8MJSLIr54l3Ijo8LMZ/flo4wkeG9+dUQPCiB8YhpSS0zkXOXCyiPnT+hER5Ed0WDseHh7p9F2IiezE8s0nKSi7wo592TwzMZbHxnVnSEwQAA8O6YqUkpDOfozuH8LwPiEgJeFBvgR1aktYoC+ns8sZEhOMRVjZlJDO6P5hRIb4YbFYeH3ecPJLq4gK9Se7qNK8uS+5UMWGhAz2nSxi5sRYxgyy3zQKYUYqkFIyb2pf4gaEmv0xun8ESccKmBrfnfhBYUQE++tRBMquEDcghJH9QhndP4QRfUNBSlZuTWXmxFjatvFk7pS+hAW2JelYEUN6B5FVWMFby/YxcVQ0L80aRrfw9nQJ9jfH3b881AvQx7nFYiE0oC37T5Ywd0pfxg4OB+D5GQNYvvkkc6f0Nf/zjLSfTY6l9GI1O/Zn8/AI3R/cYrHw/KMD6NMtgD99cqhZF2wJIToB/w38xP76vHFMSvmhEGIpsEYIMdiefFNuA+4oyzlK7Ni55mtFK8b1zuReewwdOlS6kpZ7Uc5atF2m5V6sd+xGaJom0/PKpaZpjeYz4mem55XfdB03U09T830fbuZavu9130qZbvrgro+75ngMHTpUnsu5IJ9esFWey7ng9nqNvknLvWimaZom03IvyrPZ5+WuQ7nSZrPdsF8b62PH+ozXNpvN7bhrbDzabDb5z6/OyFmLtt3Sd+9WcNeextro2A83c92N4dq3DZWVnlcup7ywPl3eA2Pv+z5cf3c1TZO7D+c26bfB3edj9OHuw7mN9n1jfeuu7vS8cjlz4Tb59IKtMi33Yr3P/B9fnZZTX1gv//nVafN7NWvRdrnrUE69zzQt96I8l3NBpuVedDtOXMeW0R///PrMTfWLY9mO9Z7LuSD/8dVpM8bwjWjqd8Pd96CBz6DZxg+6YWwrMML+/ifAT+yvvR3yrQZigHHAW00t350ueGbhNjnlhfXm45mF227Yh4p7nobHWGMH74VHQ+J19uJbE69NFWc3Iyob+7FuTEwYP2K38od6MzT0o3mz4uX71t9QmW766q6Pu+Z4DB06VH77Xbb86Sub5dns82Zf7D6cK+e9uaOewHLsr92Hc+VTr2+R015s+I/M8XNNy71o/nk31v83GpcNlePY7t2Hc+vV2ZhQbGx8N3ReY9fTVKHuyLmcC/Kp151vIhrq03M5F5xuGmw2m9x9OFfW1dU5tSs9r9zsD6P9U15YP0jeA2Pv+z7c/e42Nj5uxM3ehDTlfCN916HcBv8PzuVckD99ZZMpbo3/jnM5F9yW5yiyXceYaxu+zw2RY5m7D+fKWYu2yZkLt8md32XLf3yVKs9mn7+tBo0GaLbxAzwJlAK77I8dQGf7sZ/b0xKBN+1p49DdFoz8Mxsr3934VLRKGhwDLc7nFXAbdknKpvmONnU6/2YWLzXkv9dQPY6Lqhyfb5ffnHEtWYUVTuHD3NXp7rqb2rc3qr+hvmytIYdqam18tOEEVdX66nYpZb1pcHd9k5F/iaUbT/LctH6N+ncb5xq+fEaaK46f9Y3G5ZvL9rv1ZzXaPXNiDFJixqo1Fom5hqZrbHw7jid35yWk5LPgw72mr6IrxjUYPoKapt0wnrEeZaCWvJJKt2PZaFNG/iUWfrSX91Yf0jdL4HpYsqRjhbyxdB8LPtxr+rxOGBXFii2nTN/Y1uIy4IqUkvS8cg6fLuHNpXv54ttz1NXVsftwLmm5F83PQNM0zuVc4POvz3A2+zznci6w+5Cex/jTSc8rJy33InuO5PP28v1mv9tsNvYcyaOurq7ec0JKPl2DfdlzJJ+03ItmXk3TiAjy47W5erSMtNyLpOeVo2kaabkXyS2+rIeksg+H6LB2zJ3SB9Aj1ySk5JvXcS73ghnWLn5gWL1Ft1JK8koqzLpt9nB3Rn3peeWk55U7tc2x/4z+Ma4/Lfciew7nsWTDCeZM7sPU+G4s3XSSdbvSeXPZAdLzys3+tdls7D6Uy86DOZzLvUBa7kXO5Vxg16FczuacZ/dhvT1puRfZfSiXczkXzHa5q9exnUY/T31xQ7P5dEkpP5NSBkopx0kpxwFLpZTn7cf+Zk+Pl1K+bk/bJaWMNPJLKVc1V1sUrZMW5/Nq/OEZO6C480NrzHfUcVHK9/HrNNrRUIipxkJDOf75Gj5lruF/bhXHdjXm0yelZNakWHNVd2PcjlBBru1sjSGIvD2tPDg4nG8P5rIlOYthfUJMH+Aboy8EiR8YSuLRAuIHhjXq/9rYMHb0O4aGQ1VFh7XjtbnXfQJdj70yZzi5xRX85bPD/OrJISyYP8I8BjgJypkTY4gbEEpEkL8Z1s4o0zFMG1wPSWWMyeCObdA0jTnT+je6AYixsGXmxBiWbTrV6O5tug+jBAlvLdvHnCl9nWLjGmL89XnDeW5aPz5Yf5ywAF/z+l6aNYy8kgomjYpi694spNQXgW3fm9Ukf9yWSHpeOYB5g7Two31UXq0lbkAoq7ef5sLlarYkZuLr48HPHxvAqm2nmTUplv/5PIUrNTa8vhJ4WAVXajTaeFlo4+XBtLHd2bgnnTqbhqeHhaljuiOl7u/98PAurN5+homjI9m+N5u9A0JJOlpovj85KpKtydm0baN/r7btzaFwQm++3J/DhFFRbErIwKZpCCGYNyWW//niONfqNISAmRNjzTHz4foTWC0wekAYW5KyiBsQQtKxItp6W2nj7cnCZ0diseiLKR13XluzM41PtqWSGhfN1qRM9g4IJfFoIW29LeZ302q1MG1MNz7dcQYpJV2C2xEV6k/i0QJWbDnFrEmxfLj+BCCR9plPi0WP6bxjfzbTxnRnc1IGU+K6kVdSyV8+O4xfW0/GDo5gc2ImAG29dd91TUqqr2lm2K7iCzFs2JNB5ZVrtG3jgbeXB/Om9jVvOv+29hi1dTYsFoHVYuHBIRFsTcoibqDez+hT+7dlhy0pZYveAEFx79HiLK9GeJo1O9N4a9n1wPs3Y71rbAV8Y1ZGdxajjPxLNy2EjT9fi8Xi9Nwci5kauzbX8GFGsPcbWVZvJvSXu7LcpbXmMEMGFVeusTU5i8fH92TB/BHmGDH+FBu69m7h7Zk/rR+rtqXaV1wfJvFoQYP13GgmwhhXbyzd7xTRwLVuo23LNp1s0CI/ZpAe7WPMoDCnMetoBV60ZB9LN54ku6iSbuHtyS6qNMca1A/TZlhDjVBZOw/lU3m1zklYuusno5zwQD9AkldSWc/SlJ5XbhcIFiKC/Fm2+RSPjIxk6caTZjg8o79sdktZl5B2eHt5ON0sHDlbyvufHWFDQgZT4rsBmBb0+IFhrS4cXE2tzcmyHh3WjkXPjuCZR2L4t58O5OkJMcybEsvMiTF4e3kQHujHy7MfICzAl58/1h8fbw/aeHkwbmgEbTwFVotganw3tu/NIn5gOF6eVqbEdWNzYqZ50xLU0QchIDaqI89MjOVU5kV8fTyJjeqIX1tPOrf3pq23BSkFOw/qQf8DO7QxI1TMn9aP56f3x8Nq4VzuJa7VacQPDOXFJ4fw+Pge5njy9LAwNb47+04U4eNtJeWsHnbpsfHdze8pOBsZElLy2ZKYzsCegcyZ1Ntsn5+PJ4//oCdenlbGDdEtvEEdffjVk4MJ7dyW1z9IZs+RPHOshAf64WEVzBjbneen98Pby4PnpvcnPNCXl2YNY2CvzozqF8LGhHRCO/swaXQU86f0IeloAXH9g2nrbWXc0HBzt7O4ASHU2STeHhYCOnjz3PS+/OqJwbzx/ChenzeC+IFhvDRrGCUXrmCz2aipldTWatTW6tbW0QNCOHq2FG8vC+ir/xWKFkGLE6+O4XQmjo5uMJzNjcq40ZR+YzsROU6/Ard1yv9maaqId8zXlHBW+aWVN7WzWXpeuTl11lhYpNYYZsjAy8OKv68XQ2KCnPYut9lsTBgZyYotp+rtagW6EIwfGMasSbE8+mC3mw4NB867ZqXnlZNXXAE475zl2O/OYrfhci0WC2MHR2BsIPD28vouBh5WC/On9XMrAhzjJxs7Zi3ffJLFS/aRV1LJzIkxJB4rwLeNlYggv0ZvchzDvU2J78aKLSfNm9o1O9NYvGSfm9i3EosQZl84lmW1CPLsMXhfnzfCadvjHfuymTkhlp/N6M+OfdkIcX1nM0d3nNaCt6eVeVP78uqcB8wbgYLSKjYmpLNhTybbkjPNG6pXZg8DILdYd1+JCPLnF4/1Z8zgcBKO5FOngU3TCOrow8yJMSQfL2TulD4Ed2qLlBopZ8tYseUU4YF+TBwdRVigH48+2I3JcVE8P6MvYQF+TI2PZs236Uwf0w2rRQ8TZRF6JI0xg8J5efYDRAT5MXpACKP6hzJrYi/GDArjV08MokuIPg7T88rRpMbcKX2Y8WA0k0dH88ZzIxg3NALfNhYswkLXYF+++PYcNpsNKSW5RZf5/JuzLN90gq4h7TlytpSNidk8Nq47k0fr7RvYM4CR/UJIOJJH32761rfFF65w5Gwpl6uucSb7Ir+bOdSMVTxpdCTrdqcTFujL3Cl9kVLyxtL95JVUsPDDfWxJzqb6Wh3fHsxjc1IW36WWcLW6hoOnS6mttfHtwTzi+ocC+nfr6R/1xMND8MG6E3y04QThQX4Unr9CZIgfiUcLyC2+zCfbTzN+aBe8Pa3UaaBhoa7OxtFzZWhSF/Q0Y5zXO8GiRYvu6fIUtxdxr1sLhg0bJg8ePOiU5ug6kFVY0WxhnRzLdlemu2ON5W8p3OgajOnZWZNi3W5D6q4sY3pZt9K5D23WSL0tsyNdGDZsmPznhm/M68vIv8TiJXupuWbD28vK/Gn9CAvw5c1lB+pNeaflXmThR/tY/NxIenTp2Gg9xufj6NZh9K2maSz4cC9CwPMz+jN2cASAU79LeX1nK+OGDJw36HD9rByn7A3Xl6RjhcQNCDUtrO58ed9evp/Zk/uY40jTNBJSCpBS8sn208ycGGuG+jHC/ySk5LNya2qDbitGWx4ZGcmOfdnm88yJsUQE+TlttGCU5TqWpZTsOZLHB+uOIwS88fxoc5rZ0ZfWcBdwdONw6ZtWMXb7DxgsB854mwE9AjieXmbfIlVSW1eHp4cHMx7sxuffnONanR5b9VhaGdeu1SERPD6+O2t2pnOtTtIlyJfckiosgJeXhcfH92BTYibPTuvH8s2nGNU/hL3Hi5g3tQ8lF6+yattpvD0tjB8Szvb9uXh7AMKCpmnU2mBQz84cTTvPr54YRNmlGmaMjWbviWJCO7flzWUH6BPdkaRjRfTv1pHjGReJGxDCmeyLTBodzbrd6UipoUnBg4PD2LY3h4E99PIGdO/EsfQLjO4fTPLxYuIHhjKibwjvfXoEgNH9g0nNvEjf7p158akh+s5vn6XgYdFddmpt4GkV1NqkffeqMtp6WwkP8uNc7iUmjY4kISWf2jqNOptGnQ2eergn6xMyqKvTsFoFPx7fk02JGUSFtuNMzkWQkuprEon+g+j4bLWAxV7vwB6dOZtznjrNgodFo3dUAClny5g4qivb9uYwaVRX9qTkM3ZwOLsP5xEe6MfZ3MvEDwjhaFoZ08d048LlGrbuza7b9N50z+YcR0KIEcBf0DcpOCil/JUQwgtYDqwAFgBW4Fsp5atCiHHAQ/IGIbOGDRsmDx061Ohsx4HZ86ktLzffe3bowPAVSxtra6uaPWklNPh72uIsr1B/2r05/y8as+C6O3YzFt97laYuqLqRcHUsy5hejh8Y1mD5raHvboTjAhVN05g7pS/eXlZ7vMww+1T99XzGNHducQWXq65x6HSx04IRd1PoUaH+pv+ykQeui0eLRXcHCA/0My29hs+46yIyQxwaLgYGuvDeZ6YZdYYH+vHOxwdYsvEk760+RNKxQrduEVJKNE3jEReLc2bBZVZtS6VLsD8vz36A8EBfXp83whSumQWXnTbccOwD43VUqD+vzBnO4+N7OD2PHRzutNOWEUf2dzOHUnz+itOCGiEEEUH+SKlRc82GzWYzF8K9tfyA2RajrwyB7rgQJyP/UrMuernbXKvTSDhaSPzACH42oz8eHvpufVdr6rBaLHh56L6XR8+VIaUAYeFanWTtrgxq6/TPPa+0CqsFNKD6msaGPbrfZln5VepsNvYcyafOZqP0YjVbkjLx9rRwrVZj12HdqhvbLYBrdRqeHh54e1jIKLjM04/0xiIsbEvOZNnmVN5bfYiUs2VoUnI8/QJtvKycy9Ot4MnHihjRL5QNCRkIAeOGdKG6po6ElALaeFk5lnaeNl5W0vMvI4T+nfH2tJJ0rJDSi1eZOFIP/J9ytpRam+RU5kWyiyo5k30R0IVkrQ28rIKHhnehrbeVjILLTB4dicUi9J3GvPTypAQhLNTZ9H/kS1eucbXGxrU6icDCluRs4gdFcCztPAILCIs5P+BpFXh5WEzhatMgOryD3v9p5+ka0pHaOo2uIR1JOVuGt6eVgA5tEEDS8SLGDdWF7LU6ybncy3h7WjiecQEpYVNSFon6jpW3Y+rA2CZ2DBAk9G1iR3N9m9h4KeUoYLS4vk3sTePuP6S2vJxf70vi7+nn+Hv6Of6/7VuadJ6iZdDiFmwp7jy3sqDKmF6+n6mptfHmsv3U2heNWC0WFswfwbypfVm26RRDYoJMf1Vj6lAIWDB/pH1qG7749hwhnXwZOyTCaaGT46YThv9yRJC+IMrRCtstvL0ZuB8w6zAWchii8JU5w83V16Gd29ZzHTB8Qh1X9q/cmsrLsx8w3T+emRhrCvJZk2JZuvEk4YF+dI/oQGbBZd5afgApYf6061Olr88bXs99xRDR7hYKOlqZXa8Vri9Ga2hRphCClHNlrNyWikQyLDbEzBMV6k/PLp04craUo+fO8+Mf9jQXpRl1vTz7ASe3HCNCQ22dZky9DgRSmmsM3S28vay8/fPRpJwt47Hx3QEouXiVft06sCeliBkPdmNgzwC+OZhL764d6BKs31gcPVdG/+6dOJZ+noB2bUDAmZyLJKQUMH2svtlAQekVVm1LZcbYHmxMzGBafDd27M9m3tR+hAW0tbtuaJzNKWfe1D4kHy9CIik5f4WNiZkEd/Jl5bZURvQL5cDJIqeNDIybmrySCkouXCG4ky9hQb4kHi3guen9GTMojJiozoQFtAUB+SWVhAf5gYSCsivEDwxlSO9gvj2Yx/Z92TwyoisCeGx8D4bGhACQW1zBnpQC2nhaGD8sgj1H9J22ko8X8fj4nmzdm8VDwyN5aHgkeSWVhAf6mmNQ0zRSzpYS1LEt8YPC6N2lI6dzLvKDYRF4WD2ICvUnJrIjYYG+INGv4+JVgjr6sGLLaSbHRdG/eyc2JGRyMuM8T/+oJxcraxg3OJzdKYXMmdSb5ZtP07GdN4N6BhLU0ddcDNzRz5sNCRlMfyiaQb0CKSy7QkhnH46lnWdgzwBe/M/EHzb3OJJSFjm8ddwm9mN5k9vECn1Xrudd0ty+Nvhl3/7898njjeZxV8bChQuVG8E9jhKvCsVtwtvTqq/eL6kkLNDXnCmA69EBHKejHVfvR4X6czr7AluSs/ho4wm6hFyPaiGldBJtrn7Ojq+FuL67nKZpzJval/BAX/N4VKi/2YY9R/J4/1MjksDIetEzPKwWCsp04eEo4hx3ozJ+/COC/J0EsLGzGGBOx7v+jzj6QRvlufpvN3atjjQUIUNKyaBeAUgZw+DegU55ko4VciytjDGDwnhsfHenvpNSmnU5/tm57pj2b3/Z3Wq29enRpSNWqxUhBGt2prFqWyrxg8JITCmgYztvQLI1OZvdh/N5+xdxdI/oQK/IzqTnlbMlKZvX5w3n8JkStibnMGl0JENjgvXZAAQzJ8YSZt/tbEhMEENjgwHMGbWFH+3FIgQPj4hCCMFH60+gacaCUcnMiTEs35zKvKl96t0k62P0NLMn9yEiyN+0sEcE6btsdQn2N2ceugS3M8eHxWIhq7ACIYTdLzcWicTH20JQx7bm575s00mklPzLD3vx5YEcfvH4QKSmsWTTSQb3DmRYnxDTpe3BIRH1XGd27M9h1qRYsgorKLtUzfa92fSJ7kyXYH1sGREKMgsu0zWkPQ8O6UJmwWVety8mSzxawKnMC9g0icViJSGlkISUAoSwEBPZkT0p+oLHzUlZvPH8aHMXv6COPkipEdxJ/y1auukkoweEsjUpC0nMbR1LwmGbWCGE6zaxvwW2SylrbuCS9iHwIdR3G3A35Z80/XH+nn7OfP/Lvv35rxPHXNvVaBmKexclXhWK28iRs6VsS87ktXkjnayD7sSha5inHz7Qld6RHZ2EoSF0XQWq61auUN9PVbfQnmL25D4IIUyRaOQ3FoUZ/pzGlLghUBfMH2kP66aHvnK0Pjr6zxrvHa/R9focLc6OAtJogyGqXS2ojn3QmG+6lJKXZg0zXztGLnhr2XfmqnLHcHFG3XEDQs2tQ12jKbjiel2b3pveav79jBuAWZNi2bEvm8nx0Xx3qpjJ8dFsScrCpmn4eFvx8KjvfSaEHlt3S1IWPt4eJB0rZP/JYuZN7cvSjScBybyp/cyFcY43G6Av+ps7pS8AyzadQgjBY+O6syEhg4+3pDJ3Sl+EMG6ShBnqzKZpeFgtTI6LZtmmU4BkSlw3rHbXGcdrWrLhBJqULHp2JAVlVSzZcAKLRZhlC2Hh480n7ULvFBYhmDe1L1Pio9mSlMmQmCAz/F1G/iU8rBbzBtWdfzdcv0FbuvGk2dZnJl53v5k1Kdb0ydZFsj5TYcxyGCG35k3tZ++bk1gsguljurMlOQuBwNvLg8fG9WBw7yDz+2P0jZSwfMsp5k7piyYl+04UmZ8nsAno0tzjSNz8NrHNxsHSEn7Zt7/5WtF6UOJVobhNVF65xurtp3l6QozbGMCO4tBVgGUWXObdFd8xe3IfJ79Nx/NvhKv10dGy6Tr9DfVdPRzPdxShgDnlX1BWxYotp3h1bn0RcqN4y+6EuKMLRGPlNVaP0XeGEKifRw+rBTjVZfi9ZhVW8O6K75o1rnFLxIhvm19SyW+fGUJh2RV+OKwLUaH+dPT3JqC9t/2GwuI0jqJC/ZkcF03cgBDCA33NmQdj7Lw+T48lbNxIuc4eSCmZO6WPOUNg5I8fGMrg3kHm6/BAX/JKKokK9aeuro7R/UMYMzCEhKPFzBgbzZDeQeSVVLJqWyrzpvY1b2SuxxWG5ZtPkl9axaptqcyf1o/wQF97G0cQGeJHyYUrDOzZGYvFQl5JJR9tOIGHVTB/Wj/zpiWz4DKRIX5MjosmMkT3K48M8WPCqCiWbzpB8YUrPDauuxmWMKSTD5NHRxLQoQ0Wi8Ue6g1emjXM6fm1ucPNG7jwQD/ySipZseUkE0ZFERagW4JfnTOMo+fO8+i4bgyJCTb96uMH6gsnDZ/weVP6oGkawiLM8R4R5GdaGn84rAu/+vc9U5t7DAkhPIBPgN9IKYuEED8BvrEf85ZS1kgpbUKIKuAq0OQv3MKFC83X7iymnh068OeRceb7mUPrx9d2PM+xPMW9jxKvCsVtwq+tl9OiNVcaE2CuU+i3IqJcp9iNBUuuU/xNOd/VMmZY1lZtS3UK0n8z8ZaNNjlem+v5DZXXWD2um4A45jFi6LoT8I4+xa1xx7ebRQhBQVkV7316mEG9AjlytpRZk2LJL61k9fYztG1jxWq1YLVY6Bpyffo96Vghq7efJrhTWyKC/Fm1/XQ9H2V9QZ5kyYYTSCkZOzjCHGdSSru11e7/bbGYi/ocXxeUVfGXzw5TcjGGNd+eo6q6jvzSKo6cLaVT+zY8Pr4HeSWVvDRrGEIIJ59y/UbFj9fnjbBb3qXpm2rcuCQfL2L1jtMEdx5ihofz9LDYxWGYGc3jzWUHmBwXZW5MsCUpi3lT+7B9bxax0Z35ZFsqAFuSMqmt0/3Gq67W0cbbiqeHvhmC1WJh3tQ+LNt0inlT+7Bq22mn7x7Aqm2pTBgVxYY9GazfnYbFcn1DhODOel+/tfw7u9UYJ8vt8i2nTL9so0+7R3Rw8iG/TbvD/QvwAPAH++9NDfCU/dhcIcQT6Dpkp5TytBAipKkF38gntbHIArdSnuLeokWGylK0elrFEtAbjd0bhSi7l8KwObYFuG2h6u4EDfVrM/V3y+mIRjDGrqZpfLThBNuSsxg9IJSzOeW6Nba0yrT+uUYN0TTNDCdmTNW7Gzc2m40FH+7F08PKIvuiwreX72fWpD6mkDSs8wkpBYQH+prbXBsW2sSjBcQNCCUj/xIpZ8uYPjaKDQlZPDauO0nHCnlv9SGemRjL4+N7mCLQnZvC4iX7EEK3uBrWTtewaI7jwzh/5sRYlm48zqTRUZRX1DBuaDhvLT/IgnnDOZp2nu17s5gwKsrJ8iqlJOVsKVuSsnQLc5Cf6XLz1vIDvDZ3OBaLxakeY5Gmpmm8tfwAk0dHsSU5i1fnPkBh2RWzr41oII6i1/GmwPWzutNh3oQQP5HNsNuW0gX3DQ2OSSVeFfcirUoAKO4rWt3YNcSoO1/gW+F6jOCmxfTV/Vn3ISUsfHZkk2chNE1jzc40duzLdrtwz1FUu4aXuxGOscaNxWwAMyfEsiU5k7lT+rJqW/14wu7qb0BIuk27nTHOaUFjV/223jco8apoUbSYH9HGUGP3vkSN3RvQmABrSMDdrLh0ret2zg5omkbCkXwkMGZQGNlFlS11VqLFNFT9tt43NDgmlc+rQqFQKO4Yjn7OrlZUd4sR3UXiuJW6bhcWi4UHh15fpN/QtSkUiuZDiVeFQqFQKBR3FCHEC8BjwDPAfiDVfuhTe0zXZmXmou2UV9Q0mqeDvzerFk1o7qoVtwElXhUKhUKhUNwxhBDe6LvRGXwlpXzmJsu4qU0FbiRcm5rn+7ZD0Two8apQKBQKheJO8iywAnjjbjVg/9o3COiq6+eynKOMeGzB3WqK4haovzWKQqFQKBQKxW1ACOEJPCil/NYh+WEhxC77Y1oD5z0vhDgohDhYWlpqpDX5sfn9GU7lBXQdSOqe5aTuWW6K2M3vz7ipMlvQYrxWhxKvCoVCoVAo7hQzgU9d0r6SUo6zPza6O0lK+aGUcpiUclhgYKCR1uTHlBfW37BhU15Yf1NlKneBu4dyG1AoFAqFQnGn6A0MEkL8HOgL3NK2tDcrHDv4ezv5tJblHCV27Fzzdfdh0+ng733b26FoHpR4VSgUCoVCcUeQUv7OeC2ESAQ2Aa8JIXbZk7dIKf/U3PXWiyLw3vTmrkJxB1HiVaFQKBQKxR1HShlvfxl8VxuiaHEon1eFQqFQKBQKRYtBiVeFQqFQKBQKRYtBiVeFQqFQKBQKRYtB+bwqFAqFQqG4a7hsFftWU3bbWrRokdNzUzkwez615eVuj3l26MDwFUtvqrxFixbddBsU3x9leVUoFAqFQnFXcLNVbJNYvHgxixcvbqjMBs9rSLje6FhD5TbUBsXtRVleFQqFQqFQ3C3uylaxv96XxLDAIAAOlpbw55Fxd7J6xfdEWV4VCoVCoVDccRrYKrahvOb2sIcOHXJMRwhRb+q+oe1c4zeuBWBYYBD/ffI4/33yuCli4zeubdJWsIsWLXJKa6gNituHsrwqFAqFQqG4G7jbKtYtUsoPgQ8Bhg0bJg0B29AOVw2lJ01/vME6Eqc9RtyGNQ0eN8Sqo5+rEELtsnUXUOJVoVAoFArF3aBZtop1pSli8mBpCb/s2998/UT3ns1SruLOcNPiVQhRKaX0c0l7Ad1vpQ4oBeZJKbMdjm8H5qP7tAwDBHAWmCOlrLz15isUCoVCoWiJNLBV7IimnLtw4cJbqtOzQwdqy8udfFwN4erZocNNl3er7VB8P8TN3kk0IF7HA/ullFeEEL8Axkkpf2o/5gPsllIOF0K0k1Jetqe/D5RIKX/fWH3Dhg2TBw8evKk2Klo8DS8VbUGosXtfosauoqXSYsauGp/3DQ2OyWZZsCWl3CmlvGJ/uw+IcDg8Dthlz2cIVwH4AMoGr1AoFAqFQqFoMrcj2sB8YJvD+4nAduONEGI5UATEAP91G+pXKBQKhUKhULRSmlW8CiGeQfdp/ZNDchyQaLyRUs4FwoBU4KfNWb9CoVAoFAqFonXTbNEGhBAPAa+ix2yrsad1A3KllNcc80opbUKIfwC/AZY3VxsUCoVCoVDc2wghooD96Easa8DzDu8BPrWHxmoWZi7aTnlFjdtjHfy9WbVoQnNVpbhDNIt4FUIMBj4AJkgpSxwOmS4Ddj/X7lLKNPvrqcDp5qhfoVAoFApFi+IrKeUzYIpZ831z05BwvdExxb3LrYjXtkKIPIf37wOTAD/gc3sQ3xwp5TRgAvCv9nwCWCGEaGd/fRT4xa02XKFQKBQKRYtlvBAiAVgLrPu+hTVls4D9a98goOtAAMpyjjLisQW3XJbi7nLT4lVK6c5P9n3XBCGENxAqpcyyn6eh+78qFAqFQqG4fykEegE1wAbgG+BhIcQu+/H3pZQbHU8QQjyP7l5A165d3RbquIWrK1NeWE9A14Gk7tE9FWPHzgVg8/szEPUUjOJe53ZEGwBASlkjpRx2u8pXKBQKhULR8rDrgyopZR2wGZiC7jYwzv7Y6OacD6WUw6SUwwIDAxsq1+1jygvrG2zLlBfW18uvuPe5beJVoVAoFAqFwhUhhL/D2zh0y+ttpyznKLFj5xI7di5lOUfvRJWK20SzRRtQKBQKhUKhaAJjhBBvorsNJALFOLsNbJFS/qmhk93RmMW0g7835RU1Tj6u3YdNN4/dTFmKewMlXhUKhUKhUNwxpJRbga0uycG3qz4VCqv1odwGFAqFQqFQKBQtBiVeFQqFQqFQKBQtBiVeFfcNQohKN2kvCCFOCSGOCSG+EUJEuhzfLoQIF0IsFUIctef7Qgjhd+darlAoFAqFwkCJV8X9zhFgmJRyAPAF8EfjgBDCB+gkpcwHfiWlHGjPlwP88q60VqFQKFoJQogoIUSxEGKXEOJL+/tP7na7FPc+asGW4r5GSrnT4e0+wHF7wnHALnu+y2Buc+wDqOWoCoVC8f1x3Sb2tnBg9nxqy8sbzePZoQPDVyy9XU1QNCNKvCoU15kPbHN4PxFYb7wRQixH3wr5FPDiHW2ZQqFQtE5ueZvYm9nG1VW4/npfEsMCgwA4WFrCn0fG3VDcft82KJoP5TagUABCiGeAYYBjbME49BiEAEgp5wJhQCrw0zvaQIVCoWh9GNvEjgceAto1lFEI8bwQ4qAQ4mBpaaljepMe8RvXOpU3LDCI/z55nP8+edwUsfEb1za5POOhuDso8aq47xFCPAS8CkyTUtbY07oBuVLKa455pZQ24B/A43e8oQqFQtGKaGCb2Ibyut0etqEtYV0fidMeu2F7Eqc91uTy1FaydxclXhX3NUKIwcAH6MK1xOHQRGC7PY8QQvQwXgNTgdN3uq2K+xcVKUPRGrlb28SC7irwy779+WXf/hwsLbnxCYp7CuXzqrifaCuEyHN4/z66D6sf8Ll9CihHSjkNmAD8qz2fAFYIIdrZXx8FfnHHWq1QuMeIlHFFCPEL9EgZPwXnSBlCiF85LDh8Hz1Sxu/vVqMVCgfcbRPbZG7G8unZoYOTT+ufR8aZr5/o3tPMc7Mo6+vdQYlXxX2DlNLdTMP7rglCCG8gVEqZZT9PQ7cKKBT3DCpShqKl08A2sc+4y/t9UVEEWhfKbUChcMHuhzXsbrdDobgJ3EXK2G68sUfKKAJigP+6s01TKBSK5kWJV4VCoWjBqEgZCoXifkOJV4VCoWihqEgZCoXifkSJV4VCoWiBqEgZipaKEGKWPULGLiFEnMMWsbuEEM/f7fYp7n3Ugi2FQqG491GRMhStAiFEOPCglPKH9vdROGwReyvMXLSd8ooaOvh7s2rRBAAWLVrk9KxoXSjxqlAoFPc4KlKGohXxCGAVQnyDvtX2v3/fAj9ZPJEpL6ynvKLGTFu8eDGgxGtrRYlXhUKhaCXY/V5VpAzFvUww4CWl/KEQ4g/AdOBhIcQu+/H3pZQbb7bQ/WvfIKDrQN5/P52vv/66GZuruBdR4lWhUCgUCsWd4hKw2/76W/SbrRu6Ddh9YZ8H6Nq1a73jAV0HkrpnOS/uqXceAAsXLlRW2FaEWrClUCgUCoXiTpEMDLC/HkQTN82QUn4opRwmpRwWGBjYaN733nvP8TyklEq4tjKUeFUoFAqFQnFHkFKmAFftbgIPAF9gdxuwP35zs2U+s3AbZTlHGfKj53jvvfeU28B9gHIbUCgUCoVCcceQUv7aJSn4+5S3atEEsEcZAHjhhReUpbWVo8SrQqFQKBSKVoUSr60b5TagUCgUCoVCoWgxKPGqUCgUCoVCoWgxKPGqUCgUCoVCoWgxKPGqUCgUCoXiriCEmCWE+MYeaSBOCPFJU89tql/rgdnzSZr+uNPjwOz5TW6j8p+991DiVaFQKBQKxR1HCBEOPCil/KGUchyQfzPnG1vAOpTnNl9teXmT0hoqx7Uexd1HRRtQKBQKhUJxN3gEsAohvgFOAf9+Oyv79b4khgUGAXCwtIS9t7MyxW1FWV4VCoVCoVDcDYIBLynlD4ErwPSGMgohnhdCHBRCHDx06JBpHRVCIIQwp/aN946P+I1rARgWGMR/nzzOf588zr6SYrd5jXIXLVrk9N61HsXdRYlXhUKhUCgUd4NLwG77628Bn4YyOm4PO3ToUKSURrrT9q/Ge8dH4rTH6pX3y7793eY1yl20aJHTe7XN7L2FchtQKBQKhUJxN0gGnrO/HgTI71OYITQb4mBpCb/s2998favlKO4+SrwqFAqFQqG440gpU4QQV4UQu4Ay4BVgQVPPX7hwYZPyeXboQG15OX8eGWemzRz6QJPb2dR6FHcOJV4VCoVCoVDcFaSUv3ZJeqap5zZ1Cn/4iqU30aJbr0dx51A+rwqFQqFQKBSKFkOLFa9SSiozMm+rb8qdqEOhUCgUCoVC0XRarHityszi9O//RFVm1k2ddzOCtCozi9R3/0hZYlKj+VubyG3K9TTXNbe2vlMoFAqFQnF7abHi1Tc6ipiXfoNvdFST8hsiqTIjs8mi1zc6iqhZT5O96tN6+R1FV1NFbnNwu8WelJKyxCRS3/1jg310ozw3e4NwKzchCoVCoVAo7k9alHh1FEVCCPy6RTe4HZwrlRmZnHrzHZCyyaJXCEHnuNFEznwKn8iuVGZkomlaPRHcmMhtbgyxZ7ThRgLxZq2oVZlZZK/6lKhZTzfYRzfK406QNtSOm70JUSgUCoVCcX/TosSrIUArMzJv+lwBCMFNi94rWdlkr/qUC8l7Of37P3E+KZnTv/8TAkzRJYQgID7ujogwQ+wJaNBi6SpGb2TZdMxjlB8QH9dgH90ojztB2lA7bvbzUCgUCoVCcX8j7nVfw2HDhsmDBw8CUJmeQerb7xL76sv4de92U+UYQs4Qmzd7XtuoSK5kZZvPN1tOc9PY9RhWYUNA3ui6b7Vvmqu9bmgVStZx7CruG9TYVbRUWszYFUKUAlXosWHvBAF3sK67Ud+9SpmUcoK7Ay1KvN4JkdUaaAX91CIb7YoSAPclauwqWiotauwKIQ5KKYe1trruRn0tkRa1SYExxaxoHNVPCoVCoVAoWistyudVoVAoFAqFQnF/o8SrQqFQKBSKlsaHrbSuu1Ffi0OJV4VCoVAoFC0KKeUdE3h3sq67UV9LRIlXhUKhUCgUCkWLQYlXhUKhUCgULQIhxAQhxBkhRJoQ4qXbVEeWEOK4ECJFCHHQntZJCPGVEOKc/bnj9yh/mRCiRAhxwiGtwfKFEC/br/eMEOKR73d1rQMlXhUKhUKhUNzzCCGswP8AE4E+wJNCiD63qbrxUspBDiGrXgK+kVL2BL6xv79VPgZc45e6Ld9+fU8Afe3n/K+9H+5rlHhVKBQKhULREhgOpEkpM6SU14C/A9PvUN3TgRX21yuAGbdakJRyD3ChieVPB/4upayRUmYCaej9cF+jxKtCoVAoFIqWQDiQ6/A+z57W3EjgSyHEISHE8/a0YCllIYD9OaiZ62yo/Dt1zS2KFrVJgUKhUCgUivsWd7uA3Y5tQuOklAVCiCDgKyHE6dtQR1O5U9fcolCWV4VCoVAoFC2BPKCLw/sIoKC5K5FSFtifS4B16NP0xUKIUAD7c0kzV9tQ+XfkmlsaSrwqFAqFQqFoCXwH9BRCRAshvNAXMm1szgqEEL5CCH/jNfAj4IS9ntn2bLOBDc1ZbyPlbwSeEEJ4CyGigZ7AgWauu8Wh3AYUCoVCoVDc80gp64QQvwR2AFZgmZTyZDNXEwysE0KArpE+lVJuF0J8B/xTCDEfyAH+5VYrEEJ8BowDAoQQecBC4PfuypdSnhRC/BM4BdQB/1dKabvVulsLSrwqFAqFQqFoEUgptwJbb2P5GcBAN+nngR82Ux1PNnDIbflSyreBt5uj7taCchtQKBQKhUKhULQYlHhVKBQKhUKhcEEIYbPvsnVCCLFJCNHhBvkHCSEm3UI9oUKIzbfYxiXGRg1CiMpbKcNNmduFEG7DcQkhfimEmNsc9XwflHhVKBQKhUKhqM9V+y5b/dA3Ffi/N8g/CLhp8Qq8AHx0C+chpXxWSnnqVs51hxDCB+gkpcxvIMsy4P81V323ihKvCoVCoVAoFI2zF/vmAEKI4UKIZCHEEftzb3v0gzeAn9qttT+1Ry5YJoT4zp63od3AHge228vuK4Q4YC/jmBCipxAiSghxWgixwp72hRCirT3/LiHEMMfChBABQoi9QojJQohAIcQaexu+E0LE2fM8aK8jxd42f/vp44Bd9jy/F0Kcstf5ZwAp5RUgSwhxV3f5Ugu2FAqFQqFQKBpACGFFX0y11J50Ghhrj37wEPCOlPJxIcQCYJiU8pf2894BvpVSzrO7HBwQQnwtpaxyKDsauCilrLEn/Rz4DynlarsgtqJHQOgNzJdSJgkhlgH/B/izm7YGo4fXek1K+ZUQ4lPgL1LKRCFEV/RIDbHAr9EjFyQJIfyAansRE4H1QohOwKNAjJRSurhMHATGcBdDdrU4y6uUkoz8S0gp3b5v6nmN5dE0rUll3kzZdxrXa2nsmm5X+2/2szKOT31xg7sdRRQKhUKhuJP4CCFSgPNAJ+Are3p74HMhxAngL0DfBs7/EfCSvYxdQBugq0ueUKDU4f1e4BUhxO+ASCnlVXt6rpQyyf76EyDeTX2ewDfAb6WURlsfAv7b3oaNQDu7lTUJeF8I8f+ADlLKOnv+OCARuIwuaJcIIR4DrjjUUwKENXDNd4QWJ14z8i+xeMk+0vPKyci/REb+Jd75+ACZBZcBXQCl55WTnlfuJJIyCy475XOHkSfxaAFvL99PQkp+k8SeY9kN1W+kp+VerHessbLdCT7HNHf50/PK2XMkj7eW7WPNzjTz+e3l++tdv5SShJR885imaew5koemaY1+Do51peeVo2kaabkX2XUol7Tci0gpySy4zNvL97PnSJ75eTX2GRj9iJswJQqFQqFQ3GGuSikHAZGAF9d9Xt8Edtp9Yaeii1J3COBxu9/sICllVyllqmsdjudLKT8FptnTdwghfmAccjnPnYioAw4BjzikWYBRDm0Il1JWSCl/DzwL+AD7hBAxQohu6CL5ml3MDgfWADOwuzXYaWNv312jxYlXACEgr6SSRR/tJbe4gpdnP0B0WDtAF7cLPtzLG0v3OQlaTdOYOTGWyBC/euLSEGGapvHy7AeIHxjG7Ml9WLk11akMQyQaIssQz1Gh/rwyZzjRYe1Izyvntb8lseijvU4iTW9XMq/+LdmpbQ2VbRx3fS+lZM+RfBZ9tJeM/EsOAjHfFMev/jWRv609xoRRUWzfm8WIfqFsS85kwqgookJ1txZDpKblXmTpxpPMmhRLdFg7ElLy+fMnh/ji23P1rLXuRPsbS/fzxtJ9fP7NWV7+3yTe+/Qwi5bo1xcd1o7Zk/uwbNMpFn2UzNcHcvjtM0NM0e1aZnRYO16ZMxzg6O0YNwqFQqFQ3CxSykvoi5R+LYTwRLe8Ggua5jhkrQD8Hd7vAP5V2Hc8EEIMdlP8WSDKeGMXkBlSyv9Et5QOsB/qKoQYZX/9JLp1tF5TgXlAjBDiJXval8AvHcofZH/uLqU8LqX8A7obQAy6y4Dhe+sHtLfH1f039MVoBr3Qdx27a7Qo8appGnkllbw2dzgRQX5oUrJ88ymEENjHBgCeHhbmTulritY9h/N49a9JLN14nKRjhbyxdD9vLtvvJBDfXLafN5cdMMsKD/RzEsWGSExIyTfFKsA7Hx8gq7CCqFB/9hzJ59DpYqqq65g2prt5rpSSvBI9goWHVTAlrhuRIX6k5V5k92HdKmkIVEPAGec6vjespMs2nUCziz9DIC7ffJI3lu4n5WwZV2psgGBI7yBmToxhz5F8HhkZyY592WQVViClZM3ONN5bfYiUs2UIAeGBfmQWXCYswJe2bTxYvzudL749x9vL95ORf8luzc13st5Ghfozb2pf5k7py4Y9GVRfsxE/MJSF80cQHdYOIQTxA8OYO6UP/boHsDkpk52H83l3xXem8He0xgoh6Bbenk3vTb93/C8UCoVCcd8jpTyCblh5Avgj8K4QIgndJ9VgJ9DHWLCFbqH1BI7ZXQzedFNuFZAuhOhhT/opcMI+zR8DrLSnpwKzhRDH0F0Y/tpAO232No4XQvwfdNE9zL7o6hS6Ty3Av9lDgB1Ft6JuAyZw3cLqD2y217cb+JVDNXHA14122G1G3Et+mu4YNmyYPHjwIAB7juTx3upDPDMxlsfH9zBFVLfw9qZ41TSNxKMFhAX48vuVB5k1KZYP1x/nctU1Zk6I5bHx3UlIKUAAYwaHY7FY0DSNhJQCwgN96RbensSjBazYcopX5+oiLLPgMlGh/iQeLWDl1lRemTOcbuHtTYttfmkVmmbj/c9S8G3jwWPje/DjH/TEYtHvDTLyL/H28v1MGBlFYEcfPtl+mlmTYvnb2mNUXq3lhSeH0CXYv961uJKeV84bS/czZ3IsFouF+IGhZBdVEhXqb/ZFZIgf63alM6hXID26dGTPkXze/+wwLzwxmC4h7czrWLHlFBNGRfHYuO5kF1WiaRpvLjvA6/OGk1dSyQfrjuHpYWX+tL6AYOnGE1gsgnlT+zJmUDhCCFN4vjz7AXKLLvO/a4/h4+3BgvkjAV2055dWmeeO6h/K/Kl9yCqs4MiZEjYmZLDw2ZFYLBZT7NppFT6vjmNXcd+gxq6ipdIqxm5LRAjxKDBUSvlaA8ejgM12N4Xb1QZvIElKOewG+QYDL0gpZ96utjSFFhVtIH5gGMUXrrAtOROAx8f3MEUU6MIvq7CClVtTeXn2A7wyZzhRof6EBfiSW3wZi7CQWXDZbq2FLiHtiA5rZxelulg1zp89uY8pXN/5+ACvzBnOmEHhRAT5m1ZRIQQFZVX85bPDPP1Ib/x8PLBaLQyNCTaFK1y3jn68+SQTR0Xzu5lDEULw/PR+LNl0krCAthw6XczmhAwWPjeK7hEdzOl047oMYScEWCwWVm1LRQicxHRmwWWyiyrZsT+HobEhCCEYMygMkIQF+GKz2fj8m7Os25XGo+O6M7hXIBaLhW7h7UnPK0cI/Zq6BPvj7eXB3Cl9iQjy490V3zFval/TkmpYgOMGhDpZiX28PZgS1w0pJW8tP0BtnYbVAqMHhNG7a0ceHBKBxWKhoKyKT3acoa234w2r4l5BSklVZha+0VEN3ki1lvrv9rUqFAqFlHKdEKLzXW5DDdCocLUTALx+m5tzQ1qU5RX0P5svvj3Hyq2pjBkUxqMPduet5d8BknlT+xIe6AfoIiw6rB1ZhRVOvpyTRkfRq2t7ysprePwHPcguquSdjw8wa1IsYwbpG0oY0/dCCNOSGz8wzBSkhm+qMZWfeLSAuAGh5pS8Mf3t+GcopeTzb87yybbTTIqL4rtTxabrweHTJazaloqvjydv/Xw03SM6mAvThIAF80fSLby92Za4AbrFNTLEj8SjBYQH+iGE4N0V3/HSrGEUlFURPzDMFPa5xZf5cP0JNE3jSo0NKaGNlxVPDwtv/mw03cLbOwllxz4A3XKcV1JpCvy8kgre//QwLzw1hLGDIwDd4r1mZxrb92aZ1yWl5MiZUlZtT6Wttwdv/yKO7hEdnCzdRrtfnv2A+ZmJVqIihg0bJnf983NTGDUklBpK1zSN80nJdI4b7XQz5HhO26hIrmRlNyq+bDYbBes3EDZjOlar1W05judXZmSS+u4fiZr1NAHxceb3oKG2uOJapnFup9GjuJqVjQT8ukW7ba+UkrLEJLJWrib25d/i1y36hvUYfdA2KpLKjExqCgroHB/ntp2ubavMyOT07/9EzEu/Mety7S939bh7tpfZasausrzed7SKsau4P2hRPq+gi9LH7FbDhJQCjpwpNf0ul206xZvL9nP4dDGvf5DMniN55sKqsABfJo6OZGtyFn9bd4LVO06TdKyQ6LB2vDz7AVP0OmII0xVbTpFVWGGmOy6iMvxjjWl7Q4y5LvQCGNI7CF8fT/YeL2L25D6mwN2+L4tJoyP52Yx+Tr6ur88bztwpfc1FVpkFl1m68SRZhRV0C29PdlElSzeeZNGSfUgpeWXOcIQQrNyaSlZhhenL+8G649TZNJ6f0Y8XnhzMMxN64+1lxWIRZrnvrvjOfA3O1t68kgqWbjzBrEmxpiX7V08OJm5AqLnYKquwgh37spk1KdY8v0eXjjz+gx7MnBBLG+/rRn6LxcKDQyJMC/PMiTFIKW8YDaKlodXUcPr3f6IqMwuAqswsTv/+T1SkZ1C6J4HLaemmMHLMB3aXi3XrOfv+f3A+Kble2cY555OSSX33j5QlJjUYGSNr2XJyVq7m3F/+A5vNRmVGJpqmUZmRaYo3x7p9o6OImvU02as+NdPPJyVz9v3/IH/devPchuorS0zi1Dt/MNt0PimZs3/5T3LXrOX4q69zYvFbZt2uZVRlZpG1cjXBP/ohVTm5VNj7yF09pQmJnHzjbcoSkzj9+z9RlpjEydcXcfa9fydv7Tq357n2tW90FDEv/Qbf6Kh6/ZW/bj0V6RmU7EmoV8+pN9+hLDGJ1Hf/SP669aS++0enPlQoFArF7aNFuQ0YFk8pJQVlVUyJj2ZwTCB/WHmIl2c/wIL5I8grqeSjDcepulqLQPDKnOGmP+fcybEkHSvk2Wl9EMJiWieFELy9fD+zJ/chLMCXN5buZ/7UvkgkyzadYv60vk6Lr6SUvDRrmClM31i6H5umYRGC+dP61VvoZbgddI/owFs/Hw1ct3BKKZk1KZblm0+x90QxXUPbm8LRcA/oEuxv5ne060SHtWP+tH4s33zStPYaItao//V5Izh8uphPdpzBavXgwSERSCkZFhtitsMQkHnFFazYeoo5U/qaVug9R/L4YN1xc1GXoz9wVmEFbyzdz4L5I+gW3p5X5gxHSsm7K74zXRmEEAyJCWJITJB5DQaZBZd5a/kBpIQF80c4tbs1oNXW0uu3L5oWwbZRkcS89Buu5uVx9r1/B09PIn7yOOGPPUrkzKdoGxVpillN0yjYtIXARx5G0zQ0TXOyJPpEdiX4kYfoOGqk/r1YtgLv0FAsFks9K2pp4l7adO3K+cQkfKOjKNr+FcGPPETh5m30ee1lYl76jdlG49yA+Dh8wsPN9E6jRxFRVETe2vXUlJ3nwneH6PPK7+pZRqsys8he9SkhEx4mY+nHSCnpHDeaXsDVomK0q9WE/GA8AupZPKWUICVdn3mSzA+XYKuswqN9O/osfB0hhJOl83xSMlnLV6DV1VJdXEKXp5+gTVgoFg8PRFsfirZso9OQIfh1i3aytvpGR9H7d79GahqV6Rn4dot2qr8sMYkLBw4SPHkC3oFBnHrzHdBsCAQ+4WHEvPQbpNQwotREznyKnE8+JWrW06YAVigUCsXtpcWIV2NaeltyJq/OHeG0At8I82T4b4YH+pJfWsWYQfpUv+HPGRHszxvPj3aa7o8fGGb6pK7cmsozE2KorbPxwfpjaBpYrYKIIH9TDBhidd5UPf+sSX14fZ4+TZ5fWsWqbammBfR6iK4YIkN066yjRTMj/5I5Zf76vBEA9USyoxCODmvHvKm6JTYj/xLRYe0YOzicLsH+5jU5TvcbrzWpIYH4gaGAbh02rJ7GzcCyTacAyegBYazYcoqIIH+klHy4/gRCwHPT+5NfWsXKraeYNSnWbJ8hph3F88uzHzCvPSElnyUbTvDs9P71xKtuXR5hWshcFm21eKqLiinduROAs398zxRqbaMi6bh3PxeT95K3+u9YrFaKtuvxpNuEhXHmD38m8pknsdXWUvb1t5Ru24HFYiFwzPWY1BeS95Kz+u+0CQqibXg4ILmUkkLRjq+dp9ulhJoaqnNy6DwmjtDp0/AODCTzo6VICdUFBQSMiacqM4vUd/9I5Myn8AkPN4VYVWYWZ/7wZ13gBgejVVVRvG07IZMn0jYq0ul69TGh0eXpJwDQ6mrJXLIMn/BwAsfEU1dXR82FC/j26oVPVCS9f/drMMeRoCI9g1OL3iR06mSEsBD+1BO0DQkBKUn9/Z8ImfAwRdu/Mq3C0XNnU11SQs7qv+Ph50ffRa8Tu/A1rubnA8Jsn2FtNfpfCMGpt95FCIh97RVTDHuFhJC+ZDkBo0fh36snOZ98SvS82fiE6Te5vvZzpZREz5tD9qpPifndr+n9u9+o+VaFQqG4g7QYt4HEowWs3n6aCaOiAEyxtmZnGh9vPmlO6wsh6NHl+uIg0K2Lr88bgRDCXJlvRC5ISNFDtYUH+vHSrGEIwMvTyoyx3fHwsPCcXXQZkQVyiysASXigH7Mn92HFlpMcOVtKt/D2jB0c7mQ9NCyLyzadIulYoVNsWCOuqSH0jHYa4s11Kt+Yml+5NZXEowUsXrKPjPxLTv61ju4MxuuElHwWL9nP5sRMsgor3G6uIKU0XS++O1VsLlYDPbTXjLE9iAjyY9U2fSFbRJA/73x8gPzSKl6fN6KeKM0vrTQ3e1i26RR1No3lm0/Wi21riHmLxeLkatFa8GzfjqLN2yj66mu6PP2kaVm9kpVNpxH6DU/HuNGETp9mCjKpaQQ/8hDtR47Av3t3ZG0t7QYOoNNoPbyflNK0hPb61f+j0+hRSCQhkydStEMXdoa1VNM0qgsKEN7edI6Po+LMWapzcmkbHo7F05PQaZPJ+GgZl9PSr1s8lywzp8RPvfkOUtNMq3CnuNFEPPUkQRMeoSxprzn1b7PZKE1IpCI9g9Nv/57Mj5aRuWQZgWPiEVarKeyqc3K5kJRM+r//JwXrNwCQap+Gl1JSU1CArbKCvDVr0Wpr8QkOImf1ZwAEP/IQBZu20vWZJ2kTFkrv3/2agDHxhD06g65PP0HMgldNcZn50TIyPlpiluvoGiClRGoaXWfPJHL2TNpGRVKWmMTZ9/+D8iNHkDXVFG/dRsYHSwh55CE6x41GWCz42PNpmmZapmNe+o1Z5+k//Fm5DSgUCsUdosVYXuMGhFJ8IYZBPQN45+MDzJ7cByklq7ef5qlHejtYAp0XwjhaIg2L6aptp5k5IQZ/Xy/CA/2crKmrtp9m/rR+hAX4snVvNl2C/c2FT28s3UedTfLcdH0nuLAAXyaMimL19tMEd2prTrUb9UaF+jtZVA1rpuFGYIjON5bud1qYZeQ3puGN/FGh/syaFEto57a4M1C6xog1XCYsAuIGhKFpGn9Ydcis28ivaRrLNp3k9XnDnc6XUjJpdBSbEjMY3DvQ6disSbEs23SSBfNHOPV5Rv4llm48ybypfYgfGEZ4oJ/ThgTGZ+ToTmFcl+Hb21qw+viAEJRu/5ILCUkIAT7h4Zz5w5/p/bsX6TrraYp3fEV1Ti6d43R3kuqCAnJW/52a8+e5fPQYCEFlVhZXs3Pw6xbtZEUMiI+jNCGRjI+WIqwedJs/h85xo/Up9ZWriZr1NFmrPiUgfhQXDhwkatYzpkU19rVXuJKXR11lpW6x3bqDqDkzsXh4EDlnFj7hYQgBNYWFZH/yGT7h+tgu3LINamqQHlaq8/PJXL6SzqNHULztS3r96v8R++rLVOXlk7V0GRf3HyB63hx87VbgtlGRRM+fR3VJMcU7vqLjoEGmaPcJD6dzfBxdS0rIX7sB29WrCIuFmJd+A1JStGU7wu6ac+YPuhVbCMHV7BwKt38FQuDfvRv2SOBQW0vW8hX4hOth3RwXaKW+/Xuw2cBqpW2XLviEh+Hp7493UDAWD0/axw3m0pGjFG7ZRpvgYLJXfUrwIw+R88lndC0pIeKxRxFC4NctGk3TuJqXR6/fvqjcBhQKheJOYQiKe/XRb8AgqWmaTM8rl/Pe3CF3HcqVuw7lyHlv7pA7D2bLf3x1Wp7NPi/nv/WlTM8rl1JKM/+5nAty5sJtctehHHk2+7x8esFWeSarTO4+nCvr6upkel651DRNpuVelLMXb5fnci6YaUZ6Wu5F8/2uQ7ly9uLtctehXDlr0XbznN2Hc6XNZpPpeeVy/ltfyt2Hc+X8t76UabkXzfIMbDabU/02m61ePY7nOL43yjfyG+c44pjfuIZ/fHVaTntxvfznV2fcnpOWe1HOWrRN7jyYYx5PzyuXsxdvl0+9vlU+vWCrTMu96FSH3gfbnNKNsmYv3u5UjmPbXT8jd8fu9phrrsfQoUNl8e49Musfn8t9M+fIA3OflRVp6bIiPcP8fIzXFekZ8rvnfiEvp6XJkj0JsvzsOZn8L0/KxGmPybQPPnIaD47nHJj7rNw3c44s3r3HqZySPQnSZrPJkj0J8rvnfi5L9iTU+9yN45fOpckDc5+Vl9PS6rXNZrM5peV8/oVMnPFjmfP5F/JyWprcP3Ou3D9nvsz5Yo202WxmGy+npcuKtHSnOo22NdQHxrmXzqXJkt173Jbn2B7jWM4Xa2Tio/9iXmPJ7j3ywJxnZcnuPbIiLV2vMz3DqazLaWnysr19RhuM69k3c47cN3OO2YaK9AxZV1cnc75YI7977udmWVJKWbInway7tY1dxX1Hs40f9B2jpMujvDnrUI/7+3HPh8oKDO8l9+0/AEBusb7aftqYbgR29OHjLalomrRbSttSUHaFMYPCyCqs4J2PDzBzYiwfrj+GpknGDgpnT0o+jz7Ygx37s3l17ginRVOO4bGMtISUfHNxktOmBCWVhAX6mj62judkFlwmMsSPpGOF5kYJhqUTMAP7z5oUy4otp5g9uY9psdVDUumuAcYGCY7T/HDdt9TRcuk4bW+Ub4Srenv5fp6Z0JsDp4o5k32R1+aNrHfdkSF+rN2Vzobdadg0yZs/ux6uy6jbqBdgz5F8lm48wfxpfRk7OKJeeCfDl9j4HAzrqmvIMQM3/d8qXAiNcENS6lP9AsxpZlekdA7hJKWkIi2dS0ePEvbojHohrsxzMjKdQk+5K+dGcUybksfAMWSWEKJe/Y1xM/XcDK5hvBzrAZpcp9mfUoLdsnqjkGZuQoi1qrGruK9otrFrD6qfCRxB340K4JqUcm1z1aG4v7nnxWv/gYPl+i07eXv5fkb0DeXbg9lcqdGYPDqKmKiOSAkfb0llclwUn+44wwtPDWHMoHBTZNo0jb+uPUb1NQ0vD4GXp5XHxvXg0XHdST5eVC9+qxHWCuDdFd8xc2IMEUH+pkjddSiH9z49wjOP9GJYn1CnWLJgF6DFFSy1T6m77h5lCDXHHbtenv0A+aWVLNlwHJsGz03vS0SQP/mllSzbdJI6m74wyiJg6pju5uYMroLbsfzosHZomsaSDSf45rscrl7TF479yw97me3MLb7Msk2nmDslliUbTxHXP4Tt+3N44ckhjBvaxSzLiNZguAjo7hMaix02VDDqNET1y7MfMNsD+mK2FVtOMmdKX1PYGm137Hd7PysBoGipqLGraKncDvH6JfC0PblWSnmpwZMUipvgnvd59fa00i28PRNGRekB/kdHAoJte7NIPFbAc9P7IwQM7h1ISGdfM/xVQVkVf/n7EZ58uBdCCLw9BZ4euvVqS3IWCMHq7acBfecuY0vZN5cdQAg9xJSjz6lhIRUIhID1ezLYti+HeVP7mrtcAWbYLHC2VkJ9C+OYQeGEB/qZGwBMievGxsQMQPDmsv12/9p+RATpMWiPnCnlk22pAPz4Bz3rLZRyLB9g7a50tiRn0cbLip+PB0N6B5n+u28u209tnYYQUHLhCperrtGxnRdtvT2QmkZ6XjmgC/hnJsRg0zSklObiN7geZsvRQu3qq/vIyEjW7UrDYhFMH9udlVv19rvuDObO71ehUCgULZofAaX217uBcXevKYrWxD0vXkFfFT+oZwBffOthj9Paj5iojqZFNCJIXxQUFuBrrmA3tjEtvlBF9TUbzzzSm6BObUFCeJAfCAjqOJj4gWEkpOTz/qeH+bcnBtt36fIlKtSfpGOFxA0INcNohQf6ERrQlkmjoxg/JBwPDw9zkVFUqL6wa8H8EU7T/FJeX0SWWXDZjCdruArkl+rC1Ygxu3VvFuGBvkyJ68bmpAy6BPvTPaIDoC/IOn/5KpsSMhjSO8hMN0Sro9AO7dyWtTvPMbp/CDPGdqP4QrWZ1whRpWkaBWVXsNnqEOibB3h4WPho40ksFsGiZ0eaC7o8rBbzGgwrtJTSyYXAEOWGqJ01KZYP159An4UVDO6l32DEDQglIsjfDPmlL2wbTn5pFZEhfkx9ccOgTe9NT7n9I0uhUCgUt5H9wGv21xfvZkMUrYt7PlRWTa2Ndz4+QOH5q7Rt48HU+G4s3XTSjFUKcPhMCYs+2strf0vi9Q+SSc8rJ6uwgoggf77cn8vMibEM7h3Eko0nWbrpJCnnyvj9ioNEBPmTVVhBWIAv/r5eWIRg1bZULBYLSccKef/TwyQdK2TMoHDTmrjgw31sTc6i+GI13SM6kF1Uae5oZbSpR5eObsM/OcaTNcJZrdyaaorZ7hEdeH3eCArKrrB9XxbzpupRDQwxnF1Uyb4TRWgurh7GVD1glp9ytozKq3UkHSviaNp5Plh/jFf/lmyPeasLzMLzV1i59RRWi5V2fl4MiQlm8XOj+NmM/nhYLWa+7hEdWDB/JEIIp12wMgsus3zzSbul+fr0vyHYI4L88fSw8PNH+9st5PruX9lFlXQLb2/6xGYVVpgbMiQdKwRYc1sHlUKhaBStpsb83TF8tl13VjPS3b13PKciPYPK9Ix6N/WV6RlU2NNdy3KHa/lGuQ3t+KZpGqUJidTV1VGakIjNZuNyWhq5X6yhtraW0oRENPuMkmu7Xa+hsbSG+sioX7PPxDVUj2M+47XrTnju6nZ3zLW8xq7hDrkMlkkpv7Y/Dt2JChX3B/e85dXb02ou+OkS7I/NZmPd7jSWbDgB6FPen2w/zaTR0SQc1WO2GpsFvDz7AV6aNYz80ipA9xnt1y2AbcmZzJmiC0PDP/ON50frdYS0IzqsnWlRNdwQuoW3Jz2vnDbeHjw+vifxA8MAnMI8OfpuOsZwNcScEMLpPCFEvbiwgBlPNTzQz9zEAPQfv9fm6hsgGIu5osPamVP1Rptfnv2A/bVkQ0IGQR19qLNJqmts5JVUmotaDOEcPzDMvG6jnrlT+jhthuC4e5ezxVTf1WzVtlSk1Phow0kWPzeSHl06EhXqz+S4aMKD/PjDKn0XNMfrdWx3el45j4zoyuj+IQCP35bBpFAomsTVomLKEpMIiI/Tw4u99S5Rc2eR88ln9Prti9QUFuIdGsqZP/yZqFlPExAfZ4Zx6/XbFylPSaFw8zai580ma/lKhIDer7xETUEBXqGh1BQWkrnsY2Sdjejn5iGEIGvlaqJnP0NAfByg7w6nL1LXQ52dT0om4+NVhE78EV4BgWQtWYbwsBI2dTKF274kevYzdI4bzZWsbDP/2b/8JyGnT1O07Uu6FBdT8MU6bFevUpWVzfnEZEJOnyZg3DjOvPMHYl97GSEEqe/+kahZT9MmLIzTv/8TIY88RN7GLYRPnYRXYBDZH68kcs5Mspd+TMyCV7FarWiaRupb7xI2ZSKF278icubTVJw5TfGW7WZ4NaMfY197GcB8XV1QwNm//Cc9paSmtNQMyea4E56RL3PZCqLmziL741Xm59H7d7+muqCArJWriX35t1zNz+fs+/9B15ISOgwaxJk//JmuzzxJ1vKVTtcYOfMp2oaHN7iItJkIE0I84fB+jZSy9nZVprh/uOfFK+i+lZqmkVusT417eliIGxDOUvv09tMTYhjcK4DeXTuAEMQPDDV3nUpIyee91YeYHBfN5LhoPt1xhklx0aYoNcSTYTm9vg2rYOzgCKd2RIe1Y+6UPoQFXndPMDYOiAjSheMbS/cDegSE8EA/Z39ZIZzyG7FWjSl/Q6i6xmqVUvLmsv1ICQuf1X1CjagChrCF6xsbvDJnOFarlR//sBdDY0Ow2Wx4eVr58fieRAT5mecZ9Thed0b+JXPLViEEH28+ycRR0QzqFWBGVzDcH2ZN6kNEkB9jBoXRJdifnMJLVFy5Rl5JJT26dCTpWCGrt58mqOMQp7oMkW+I4oz8Syxeup+KqlqCO/uiXAYUiruLV6dOZHy4FM/gYCqOHUNKzdwe92peHmf//b/o9W//asbpbRMWhgBdSOXnk7v673qcYyDm1ZewCKGLqr/8J1YfH6xtvAmbMon8DZvJ/GAJwsuTzqNHkrH0Y9qE6Tf4pxa9CYDw8NBF8MrVtI/tTe4nn2Ft2xaJJGBUHEXbvyR04o/IWrma6uJi8jdtNWMe9wI6jhpJu5gYvENDKdi4Bb8uXYj65f/Bo50/RVu349G+PVpdLVU5uQghCP7RD8lauZrev32RTg8MJW/9RrSaa+R+8hl4eICnB5VnzlJXUcHlo0cp/vIbuj79BLL2GvmbttChfz/S/vYhXLmC3/Bh5P1zDf79++NhtSLE9VVRQgBS0iYslJ7/9q/4hIeR88mnRDz1U7wCAun6zJNkf7wSYY8lnPnhUjSg8sxZpNRoExZK5MynQEqyVq4mZMLDtI2KxCeyKyGnT1OwdTtISa/fvoiUGtq1GqSm4dejO5EznyLzI13893n91XrbPDcjg4HPHN53BMpvV2WK+4cWIV6llHzx7TlWbTuNn4+VQb2CSDyaz7ypfbFYLIQH+vLW8gPU2TSsFgtdQ9qZYix+YBipWRfYkpjJxFFdGdU/hP0nCnl4eCTdwts7CUHXsFOO9Ttuo2rT9HoM/9aZE2PN7V/nTo6l+OIVlmw4wYL5I8xpfEex6mptfefjA7w0a5jTNrcGhsXTdftYx4VRxuYJi58bWc/aa1iMrRZBcGdfuoW3NwWvu+1YXbdsnTAqik92nGbtLitenh4sfHak6f6wbNNJJ0GtaRrtfL3MBWaG33F4oK9TXa5hvqLD2rHo2ZHklVQSFuDL1Bc3iE3vTb+3w2AoFK0YIaCuooLCDRu5kLSXiKefQEq4mp9H+xHD6TxqJNagQOpKSnVxpGmceuNtYhe+RodRI+m47wAe/n5kfLyKsEmP0HHwIDqOHkVEYRE1F8sJHDeW2pISoufPIXPZCnwjIylNTEbW2chfu56QaVPAaqHTyJF4duyAZ3AwQQ//kMKNm3RxFxhE1gcfUpqUTGB8HL59++KXkUXexs1w7RqZyz4GoK19kwoA3+goOseNpGTrDoo2bsK/d2/KdiXgFRiErbaW9P/6H33zCm8vuvzLj6kuKKBoyzaEtxeB48dTumMH2GxYPD25uO8AEU8/gUfnAIIe/gFeoSFgsdCubx/OJyTh06M7V9PSqS27gFZdTck33xLy8EMET5qAV5cIitZvIvCRR6jKzSV7xSdEzZ2F1CRdnn6KmpJi0v79P+n5b/9K5JzZICXVJcXYqqrw7dWLoi3b6DwmjrraOs7991+JfG4+7YcOJm/tBjw7dwYEJbsSaD+wHzmrPqWLBCk1bJVVFH/zra6eJWgCOvbpg3eXCCrS0jn24m9/GrdhzT+aY/xIKbNoJZE3FPcmLUK8ZhZcZktSFr5tPBjUM4DEo4W08dJ9MldtS+WlWcOYO6UvYQFtzdBUBhaLheem9wNgc2ImADMnxjhNkUspeXn2A05pjlEBHMM/GYLVsCAu/GgfVguUXLzCpoQMbJqGpkksFuEUUUBKic1mI/FoIeGBvmY9mqYxc2IsgJPINTDa4hhPFq5HMpBSMm9qP5ZvPmm6JrgK8eiwdkyJ78bKrafoEqxbiBcv2cf8af0YOzjcbblGBIGXZg1DahKEZEjv4HqREgAn94LnZ/Q3XRoM1wjD+mxcm6urhXF9BWVVxpa4A4GUZho+CoXiJhGengRPeoSypH1Y27TBKyCAU68txHb1Ku0GDeByyjHOJyWDlIQ/9VMswkLd5ctcOpLC+UOHuJiUDECn+NHkrvqUvH+uIeTR6RR+vgZsNqrS0rhyLo3ACT/CJ7ILl1KO0n7kCK7m5XM+MQnh25bOo0dSvGU7APmfrwGLBWpqsAgLAol2tZr2AwdSvGUbxTu+gro62vbty5XUVDqOiSf9gyWApN2A/pQn7yPsyXyk1H/rrp2/QMHaDfbfcQ1q63ThClBzjbx1G+j+s2fpOHoUF5P30q5PDH4xPSk/cAifqC54BwZy6dAR8j7RjYoBpWVIm41Lx06ApydXc3IA8O/VnZqMDKQUHH91AVRXcyHlKFXHT+r97O2NFIL0//kreHjqLl22OgJ+9EOuFpeQv34Dsq4OpASrlaqzZ2nTtSvnE5KoLi5BVleT9T//C5p+r5/+vx9gEQKtupqLB4+Apyd5a9eD1MDDSsm2HZR89Q2WNt50GDSQ84lJVKanU1NeDvAJ0CziVaG43dzzC7ZAF0fzpvbl548N4FRWOT7eHnh7eRAR5Mcrc4abItZqtdI9okM9a6IhYGdNjKG9n5cZMgquT7Ubws9Ic1yYZFg5DUut4+YEHlZB3IBwtiVnMjW+G89N64e3lwfPzxjg5BLwxtL9fP7NWf68+hALPkw2F2y9ueyAKTwdLbIGrm1xRReSYcyb2tf01XX1wc0suMz2vVlMGBlpOvFrUrJ880m35RpbvM6aFIvFYmFjYiaffnmOwvNXzONgxmQlI/8Siz7ay9qdaazYcoqkY4W8vXw/a+zvJ46OZtmmU7y5bD+ZBZdN14mswgrz+oyYt7Mn9wE4esuDRaFQfG+qC4s4n5BM6OQJ2ATkr1mP7epV2vboweVzabqQtP/M5v/jC/I2bLILpXVUZmQB4N29O/6DB4HVgqypofCfn5sC8cq5NLDoWydXHtPXL1w6fJhreXkA1JRfpvjrb/Vpeg8r1NbqAs7Li4KNm6gpLQUh8GzfAYSg0w/GITw9uZKaCprG+a++Qau9hrxWS3nyPgAK/vE5pdu2g4cHpbsTkFevIjWNitNnoK4OD7u7AkCHgQPI/Ggp5YcPY2nbFt8u4dSeP8/FvXsp+OyfZP7vB1ywC3SAsq++QZPQceAAhKcnXKulU9xo2sXEYmnblvN7dsO1awBUnUzF2N9b2my0GzgAWXMNn/BwgqdMRLtylbKvviVv9WfI6hqouQbXasG+8Ks6J4d2gwZwtbhEL8d63QblO2CAvkDMYtHze3hAXR2yzgYWq/6Z1dWhXa2mTZcuWDt2pKawCK/2HQAuNN8IUihuLy3C8ppVWGEuwHK0fDqGbHIn/ByxWCymD6hjPtdpfHdprj6hjpbN+dP6sXJrKhNHR7NjX7a9jSOdAvbPnBirbzJgEVgEPPpgD7PsBfOvx0x15zTvrn3u+sfR79bxtWExNqb5NydlsWD+CBY/N8os3x1CYFpKFz83kvzSKuIHhjlN+Rvlz5wYiyYlW5L1CAnGojTHBWFDegc51efq1xsV6k9EkO6nPHZwhHIZUCjuIt6dO4OANsHBcO0aNbm5AFxJS9PFpKYhvLyQ166BzUbQ2HhKd+1Bu3KFy4cOA1CTnk7OB0vAposu3359qTp2AotPG8A+61VdQ+CER2gX04tLp89Stn0HABWHD+uC1cuLDkOHUr53L0E//AFlCYnI2jo8OgdgbdMGv949KU9KwicggAt1dXQYNUIXq1Jf6GXx9MArMpKr587h378/V9MzwGKhc3wcxVu3YRECw4bjFxXJ5UvlYNPwjY7mUspRZHUN4U8/gUDgHRgEQhA0YQKeHduT/9nfQeo+ubKuDr+ICM4nJ9P1qSeRSAo3bubyqVMEjX+Qkl17dFcsu5g0qavTBaqHB1fOnsWzrY9ujb12DauPD/6DB1GevNf5PE8PqtIz6DBggC6ga2vx7tKFmtxcRE2NKZKFpyedhg7hfFKy/llJqa9/0zufgs+uG1mvFRcDvNfc40ihuF3c8ztsDRs2TH733Xdud5O6G7i6FDjumOW4a5RjXuOYsW2suy1Sm6tNQIOvXS2mTb3GG9UVFepvWnAdbyi+x2fWKnyl1C5F9yWtYuwO6d9ffvDASGJefYnKnFwy/+dvWLy9kDYbwmoFBFHPzaempJSizZuJXfAaV/MLyPpwiSkOzycl0XXubC5+d5iLycl4+PsTNn0K7QcNBARX8/LJXvYxsQtfRQgLp958G3mtlk5j4rm4/wD+fWK5fOy4fidtsxG7eAGXUlLIWf13QiY+QtG2HXR9+gmKtm6n6+xZZC9dTu/XX+FSylEKN20mdOoUirdtp+dLv+X87t10nTObmpxcJNA2KpKCdesp2rqdLrNmkvW3D7H4+BA1dxYCyP7kU4IffoiCTVuIfnaufVX/i1QXFNI5bjQAZYlJaJpGm7AwynbtpvzgIYIfeZjwR2dwJTOL1LffJWTSBIq//JquTz8FgFdoCJdTjlG4eTOBkyZyLTePynPniHjqScq/O8j5vXvp8uQTtAkKwic8DE1KUhe9SeSz8wBJbel5vIICyF62AmH3CT6/JwHh6Um7fn0ImTqFM2+/S+e4OPx69yLv088I/tFDeAYEkr10OVLTaD9oIBeSkrF4eyE8PAidNo3iLVupvXRpcNyGNSl3Z8QpFDdHixCvSgDcd7QKAaDG7n1Jqxm7u/75Ob7RUcD1sFVS6hcohMDXvkK9KjPLzFeVkYlEXxxlhKySUlKWmIRPWBh+3bs53dw7nluZkYkA2trPbRsVSVVmFka4LL9u0UgpOZ+UTMdRI7m4dx+dRo/ianYObaMizfqMNjmmubuBNuo36hHgdE3G+Tcqx/VajJv3htrget3Ga+PaOseNdtqy3LFcM82xnzOzkGBGDHBXtmP/+kRFcj4pWY8QIYT5WR391a8tcRvW3NuCQKGwo8Sr4l6k1QgANXbvO9TYVbRUWsXYVdwftIgFWwqFQqFQKBQAEyZMkOgmefVo3Y8GUeJVoVAoFApFi6GsrOxuN0Fxl2kR0QYUCoVCoVAobiczF22nvKKmXnoHf29WLZpww/MXLVrEokWLGnx/J7gbdd4NlOVVoVAoFApFsyGEGCGESBZCJAgh/mJP8xJCrBZC/EgIkSiE2CuEeNt+bJwQIlsIscv+mHY32u1OuDaW7srixYsbfX8nuBt13g2U5VWhUCgUCkVzkg38QEpZbRes/YHOQDKwU0r5JYAQYqcQItB+ziop5Wt3qoFGVIjG2L/2DQK6DgRg0qQP2Lp1602Xobg9KMurQqFQKBSKZkNKWSSlrLa/rQNswI+Ar6SUtQBCCCtQBLjfPtIFIcTzQoiDQoiDpaWlzdJOY2dN47H5/RlOxwO6DiR1z3JS9yxn27Zt9fKDPk3v+N71uPH6dk7lN9SG1uw+oMSrQqFQKBSKZkcIMQAIkFKeAnpJKc/a058HzgDnpZTGnPxMB7eB4a5lSSk/lFIOk1IOCwwMdD18S0gpnR5TXljfYN733nuvXn7QhaPje9fjxuvbLV7vdJ13GyVeFQqFQqFQNCtCiE7AfwPz7a/PG8eklB8CvYEIIcRge/IqKeU4++PAnW9xfcpyjhI7di6xY+fy9ddf3+3mKBxQPq8KhUKhUCiaDSGEB/AJ8BspZZEQ4ifAN/Zj3lLKGimlTQhRBVwF2t/pNrrzVe3g7+20OGvEYwvMdHfRBlzLWLhwYaPv7wR3o867gdphS3Ev0ip2elFj975EjV1FS6XZxq4Q4kngP4GT9qQa4Ckp5XkhxM+BJ9CNZzullK8LIcYBK4BMe/6lUspVDZWvxud9Q4NjUlleFQqFQqFQNBtSys+Az4z3QoifSCnP24/9DfibS/5dQOSdbKOiZaN8XhUKhUKhUNw2pJT/vNttULQulHhVKBQKhUKhULQYlHhVKG4TNbW2eg79mqax50gemqa5PUdKSVruRXYfbjxPRv4lNE0jI/+SWYdRts1mM4+n55WTnlfu1A7jfMe0htolpSQ9r5y03Iv1ymlKfe7qcneNjtdkHLPZbDfsi6a037GvXPvMXd8a1+DYFk3T3F5LQ9eoUChaHt83tJS7rVmN967PCmdutl/Ugi3FvUirWPTSOayXXLv5W8YMCiOzQI/DnVdSwfufHubpR3oT3NmP+IGhZBdVEhXqT1ZhBVJKXv8giYordcyc0Jsf/7AXWYUVRIe1M3dzSUjJZ8WWU8yaFMuyTad4be4DFJRVUXyhitXbz/DMxFi2783iRyO6snZXGgL4xeMDGTMo3KzjnY8PMHtyH8YMCkcIwZ4jebz/6WF+9eRgugS3IyrUn8yCy+QWV/DBumNICV6eVhY+OxIpJblFl9l/qoikY0XMnBjDl/tzmDUplo/WH6P6msY7vxhNz66dyMi/xNvL9zN7ch9G9w9h3e4MBvbozIIP93Glpo5fPz2UsYMjSM8rZ/GSfUyNj2bd7jQqrtQxOS6KPUfyqLhSx6yJMfz4h73MINyGAE45W8aj47qxbncGq7am8szEGIbGBNMtvD1CCDLyL/HOxwd4+pFe/G3dCR59sBtfHcjl1bkjiA5rZ4pOIQRRof6s3ZXOht1pSGDamG6s3ZXOleo6Zk2KZUjvIN5d8R2vzBlOt/D2SCnJLLiMlJK3l+9n4uhoVm5NtWx6b/q9/aPaBNTv7n1Js//uCiFGAH9B36TgoJTyV0IIL2A5+gKtBYAV+FZK+ap94dZDN9pp63aNz4Z2zDowez615eXme88OHRi+Yqnb88E5CoFRpuuzwpkG+kUt2FIo7jRSSv62NoWi85Vs2JOOlPCzGf158ke9+Pybs1TXSk5lRnIwtYTZk/uwcmsqL89+gGnx0az+8hxf7DxHYEcflmw8xeLnRtI9ooMpXGdP7kNo57bYNI3Dp0tYveMMvj5WRg8IZUD3jkhNY83ONK7U2PDx9mDZplMIIVi5NZXfzRzK8L4hfLz5JBFB/kSHtSO0sw8TRkUS2rkt73x8gJkTY/hw/XFsNomm2UBYmDulD5qm8cpfk6m+ZgP0qZvADm14efYDRIX6s+94IQlHCzhyphSr1UpUqD+zJ/dhxZZT7D1eQOLRQuIHhuLtZeXx8T2JGxBKWu5FDqUWU32tlnW70xgzMIxvD+Wx93ghjz7Yg8+/OcPn355jYM8APDw8iA5rR2bBZV7/YC9V1XWUXbpC4tFCHhnZhS++Ocuaned4++dxdI/ogJSSl2c/wKHUIq5U1/HPr8/yk4d6YbPV8c+vz7B+dzoWi8DTw8rEUZF8sv0M8QNCOZZexrpdadTVacT1D2H73iyG9A7i5dkPIKVE0zQSUgpYuvEEC+aPYOLoaFZvPw3wE+Afd3HYKRT3EreyTew9gyGoHIUrQG15OUII3nvvPQC+/vrrelvHKm4vym1AobhNaFJSc83Gul3pGLPZyzaf4lJlLdW1+h1m4tECZk6MQdM0npnQm+iwdlitVgBsNii5cIXKq7XklVSawnXmxBikJskrqcQiQFjA39eTBwdHkHi0kNc+2Me63WloUhLXP5g3nhvOvKl9iRsQyitzhlNQdoWtSZmM6BdK12BfvvjmHK/9LZmtSVkUnr/KK3OGEx7oB4AQMH5oF67W2Cgtv0pucYUpXAE8rIIP15/g8OkSMvIvcTLzApPjohgcE8g7Hx8gq7CCMYPCmTkxhsNnSrEIOHK6hDmTYxncO5DEowUs/Ggvn+w4Q22txtWaOnYeykUIGNUvlEfHdWf80K5crbGxdmcaby3bx57DeeQUXuK56X2YPDqK3l3bU3HlGhYhuHpN40q1jSNnSsnIv8S7K75DCMHgmCC8Pa3UabB+dwavf7CPT7afQUp4fnp/Xp3zAOcvXQHg0Olipo/tzoODw7lWJzl8ppjhfYKx2WzkFleweMk+9hzJ48P1x6iprSOvpJLHxnXnhaeGAKiFKQqFnebcJvZ2bA8LN95aVQhB/Ma1TucY71988UVefPFFc+tYh7bWK9P1ubVv39oUvs+2tsryqlDcRmKjO5Ged5nnp/fFYrFQUn6V7XuziB8YyuHUYqaP6QYI/vL3I/i28UAg6NzBm/7dOnA8o5zAjj68+NQQwgJ8+f3Kg8yaFEvxhSus2naaNl4WhsYEsSkhk+dn9Cc0oC3fHMzFahE8ODiCLcnZJB0vRlgsnMm+SJdgf7qFtycyxI/T2dEcOFlER39vVm07jdUCE0Z2IbSzj+k3+tz0/izffJJO7dvgZYXPvz7DuCHhAFgtYLEI/uWhHmzYncmq7amUlUdSXVNLz4j2FJRU8dKsYUSHtQOg5MJVrlTX4WEVVNXYKL1YzUcbTmDT4Ocz+nE65wJbk3PsvaZvbbglOYuYqI4I+y32gdRiHnqgCx9uOM7lqlp8vK14eQguVwXi28ZKbHQnEo8VMqBHABsT0gns6MPLsx+ga7Av63Zl8NbPRnA8/QIDewaQV1LB39ad4LlpfUAI8ksr2bY3F9BvOjYmZDB/ah92HylgcO9AtiRnsfNQHhYL1Nkkmk1D0yQCwfLNp+gS7E9EkP+dHFoKRYvBcZtYIYTrNrG/BbZLKWscBaAr9l25PgTdbaC52ubop+o6db148WKklCRNf9zpnMRpjzkJ2vfee48XXnhBuQ3cJI31/Y1Q4lWhuE209fYgI+8SV6ptlF2qYXNSJnU2GwN7BnL0XCnCIti6N5vX5g7nV08MpuRCFf+zJoWrNRoexpyIhLGDI9A0jVmTYgnp5MNf1xzD0woCQdKxIny8rYQF+FJQdgVPDwtSwg8e6EJHf28++/IsiUcLmTiqK1Gh/voP8bFCDpwsYtakWKSUeFqgVoOvDuSScLQQq9WCAKaN6c7cKX1YvvkUUliortXYnVKIp/X/Z++9w6O6roX9d5+ZUe99RkIV0UGigySaS4ypBjs3zTbFLfl9uSWO703sODZ22m1O7ne/3Ny4F2yn2ZheXCmSKMaAECAQaqg3UO8zZ//+ODODJCSKDQjwfp/nPDpnn13WPrNh1qyz9lrQ4zAU2Mkjo5gyysrhU3Vs2FNIR5eDP7x/lG675PHvpCKEoLy2hQ27CzBpYNbgrulxTEgO4a+f6HT16GgmE3dMjWP34Upmp9r4+PMyuh2ScQlBnCw5x9bsMwyPDqCgopmdX5Txf+6bSE1DO1syi4mzBrAnp4pZqTbSJ9g4VdpE5pFyunp0Xtt0nDWPzGTdzkLWbjtJ2vhIjuTXkzoiHE0z0dHl4OCpOrKPVvFP30olfUIUh07VMndSNFlHq4kO9+Pe25LZvreEB+aPZnxSMO9+mM/h/HrONndhMWvMHBdFSKA3DoeDX71xECAFODJUa06huNHolSb27wZKEyuEeBV4v1ea2JuG3m4Djz/++BBL8/VCKa8KxTWivctO6ohwcgrqiQjxZvXisbz4wVEyc6rw8tD45m3JTBwZgRCCmAg/Xt5wzO1eMCYxmKMFDdQ3GW/cSqpaeGPzcUbFhdDWacfLQ+Pe25KQEjZlFnP4VB079p9h2ZxktmQXIxA0tvbgcP6Q3XOkivkzEwF4a2seDy4YDRJe23SCO6YPY9veMuw6zJkYzbzJMWzYXczabXn8+HuTWblwDC9+kMu8SVGYNDPldc3kFjaiaQJN00iKCSIpJoiJI8Mpr22l+mwL7+w4zcmSc7yy8TgmTSMlOYI9OZWYNEF2bjWhgT5023VmpdjISLFSUtWCh8XEmIQwELA1u5T8siaOFTeSPiGKAF8PCiqamTw6ilkTDesvUrJ+dxHpE6I4daaBVzedYEtWCekTosgtPMuqRWOQUpI6Ioz3PjVz+FQ9Hd0OPj5Qyqj4YLw8NI4V1JOeYgNgb241Xp5msnNrAMg5fZbt+0rcG9v+9omhuGakWFk+bzgIwdvb8pASNKFxEaORQvG15GZIE9ub/qlVXZZAS1DQBRu2elsJXYrrQKlZXWX9/yr6cqXPRSmvCsU1QgjBsaJz3DNnOK9vzuPpVVN5bNl48orPsvNwBet3FxEZ6stbW/OYPyMes0mw9I5kNu4pZt6kGE4UN+CQDhwOI+TWXTPieHfHKdLHR5FTUM/mzBIWz0rE4dDZnFXMw0vHkT7BSmSoDxV1bWzJKmbC8BD8vCxMGxeJruskRgfy1Mpp6LrOmlf2IaVkdHwonx0sx+6Q7DpcQXCAF1lHK1mQHk9GipV1nxXS3mWnrUMnK9d4tW/S4N55w4m3+lNU0USCLYCkmCDnazOJj6eJXYcr0TRYlJ5A6ogw/H0t7MutZlFGIsvmJoKA7XtL2HOkEpAsmBnHq5uO8/TqqYQF+hISYOGPHxzncH49nhaNlOGh5Jyup7C8kcr6NtbtLKSt005SdBAzxll5af0xPMyCI/l1mEwamqbxmzc/58kVU/n1D9I4mFfJXz8uZNfhcnYfqcDDYiIjNYZte0uYNjqS73wjmXPNXYyKC0bTTLy9/WSfiAypI8L42ycmpo6JoLiymdTkUKQ+krAgb4ZF+TNpVAT/9LtdOUO66BSKG4tvAlOBf3O+Uu8Cvuu8t0oI0TtN7EkhRNTQiGkwmK/lQJEFLre9CpV1eVzpc1HKq0JxjZBSMnNcFBFB3kipcyS/nk2ZhXR1O7DbHdiFTlSItzvSwKpFY5C6ZElGAvmljdgd8O6OAjRhYktWCYvS4/H1NpMQHcDRwnrSJ9jYklWMyaSxOCORjBSb20J798x47k6LY2v2GQAOnqzBy8PMPXOGkzoinLKaFux2nUfvGYcmDEXP3uNASogM8cHf14M7psZSVNHEe5/mM39GLKPigsnKrWZEbCD5pU289+lpSiqbOFZ0jmcfnsHh/Do+2FmAxaxx25RYso5WkD4hmg27C/jbp6f5/5aPZ0xCKGu3nSR1RBhIuH/+SF7ZeIym1m68PDQ0TVBR28akURHouo4mJLpDMnNiNLsOl9PW6eCj/WfIPFqFySRYMDOOD3YVsnRWIkKAxWxCCMNfd1aqjWGR/sRF+bFuZyEf7CyixyGZPjacEyUNrF48jowUK6Pig0HCus8K6OiWfPp5KT/85kS+d9cIpC6x2+2s31VMaKAnZhP87/u5CCEwmwQgWDYniXd2nOJnq6ZzK4TJUiiuFl8yTezO6yii4iZFxXlV3IjcEi9gg6KGy3kP/hZvD43UkZHkFZ9l+lgrnx48Q49dYtfh/rtGEhXmhy3Mh8On6lhrhFvC0yKwOyQeZo17bxvOBzsL8bCYWTwrkc2ZxXT3OLCYNRalJ6BLnQ27i3hs2QRmT4zm/c8KeGf7Sf7pW6lkHS1j3/F6AFKTwzhyuh5fLxNSQkeXg+/dNZIt2SVMHxPOp19U8H/uncDcKYbSWlHXRnV9K2/vOIWHWfB3tyeDEHy4vxRrqDc5Befcst4xNY4t2SV4eZj45m3JfHiglDumxrDeGdc1+1gNHmbBD+9LQTOZ3P0uTIvntikxfPZFOTu/KKPbLjGZBN6eZlYvHsf//O0QHd0ST4vgtskxbNtXhq+XCZNJY1F6PCdKjFivft5m5k4axohhgby57SQ/Xz2dpJggAHcM2xljI8jKrcHfx8xjyyYQHe7n3mDxq9f3ExXqTW5hAybAZBZoArrskpThxnPz9tCYPCqSzKNV+HqZWT43iQ92FeJhMfHQknEuC+0tsXbV/7tfS26atavW59cGFedVobjeCGEooMEB3mQeqWRhRgJZORV02yUOHSYkBYOQ/PbdL7j/7tFsyS7B18tMSnII+47VokuYNDKCrdlnDGUtI4GU5FDCg7yJDjc2aL28/ijtXXZ67JL/fT+HuoZ2ls1NIiLYG6lLPj9hKK6RwZ6UVDXj7Wnm0XvGUtfYwbqdhUh0urp7+Pjzcuw6nCptZO6UWA6dquXtbSf50XdSmTkugr3Hanl7Rz6Pf2ci82fG894nhpJt1uDv7hjBlqwSzJqR6WrC8BAiQ33IKz5LW6edxJgA2jrt5BSc5Y8fHMXL08LC9Hi8PEzsO17NmMRQDpyoIXVEBJlHq/AUglWLxpKRYmVfbiSZR6vp6pG0dNjx8zaxbO5wwgK9+f17OfTYJanJYcRE+LE1u5gR357IgplxlNU0k2ALQNM0MlJs6LrOiWLjWSzJSAAEz72yDwk8+9B0po2NYktWCWDE8nHYJSnJoYxPDGNLdjGeZo25k6PJzKnG18vM95eNB4yICysXjnGHFlMoFArFtUfFeVUorhERQV5oAkprWlmQHs9Di8eQNt6Gw7kpq6C8iY17SvjuXSNJTQ5l1cLRLJ87nCP59ejOFyL7T1Rz98w4Hlk6nq3ZJTz3yn5eWp9LZX0bs1JtZKRE02OXRIV6o+sO1m47SdbRKmIi/HlpwzF3mKm2TjtLZiXh7WnibFM3W7LOoAmNTZkl6BL3ZqPMo5XsPlzOus8K8PY0MSwygKToIMD4zyKv+Cwbdxcy3GnVHJsYwr3zklm1aCwmk0a3XfLZFxX84f2j7D5SwcL0eFJHRJBf1gjAvCnDWLVoLB/uL+OH30zlZyunInXJ/fNHcjjfiN0ohMAW5sO6zwr44mQtHiYYERtEZk4VE0dGsmNfKXUNHfTYJbGRfnzvrmSyc6v53vxRaEKwdvspXnj3MO9/ehopJZqmIYRwh8I6UdLAG5uPsWRWEmaTRkVdGzu/KMOknf+Zrwljw1ZkqC8L0xNAwGcHy3E4dJbPG05MpD+vb8kDoK6xgzUv72X34QoW/3jDTWO9UigUipsVpbwqFNeIzm4dXYKnReOOqbGU1rSx91gVHmaBl4eJZXOTEAIiQ3z51RsHeWlDLu/vLGB2qg2zSSAAuwPe/6yAmAg/nl41jaWzEtF1nVc3Hqe4splR8cGYTYLqsx2MTjA2RUWH+yGlxOHQcaljy+cms3xeEosyEtmaXUTaeCuP3jOWhxePxWzS8LCY8fYQpAwPQ3fodHQ7uO+2ESTFBBEZ6guADmzbV0qP3UFMpBHTtLC8mZKqFmIi/PC0mJw+pEF0dtnRdWMzWFV9Bx1dDkbGBnHgeA3DIv15csVUosN9OXSqlhfePURdQycmk8DTomExa3x6sJy3tp2ko1vHGu5Pfmkjs1Jt5Jc2GpEShKHdl9a0sutwNSCZPCqSWROjuX/+SLw9TWzOKqaooomiiiasoT74eGrERvlxOL+e0QmhLJ+XxDMPzSA63Be7Axw6pE2IJCU5FF3CrBQr0eG+bNxTRFeP7lbyt2QVU1HXxs9XT+ORpePZsLuQzm47r206BkaoLIVCcQmEEI8LITKFEPFCiBohxE7n8eiX7fNKN/08sGY7i3+8gcU/3sADa7ZftC+10erGQimvCsU1oqW9G5MGj90zhoraVhwOB6sWjcXTw9hUFBnsiyagrqGdn62cytJZibR32vnkYAV2h2Tm+AjMpr7/SD9wpjNdvXgs5bWtvLU1j2/fmUzGBCPc1CNLxyGEQNd17A4du8PYxZ+SHMa6zwrZlm1k1tqSXcKLHxxDaBqeHsZr8MmjotiTU0VdYwc//u4k7r1tOLquU9PQxvjEIAAWpMXy/WUT2Hu0mtTkMCwWQzohBAjIOV1Hzbl27poRy7I5Sby19QTR4b7MSrVxuqyR+TPj0XWdL07W8MxL2XywswAPi4mUEaE8snQ8Xp5mlmQksvNQOd4eGiNigzhT3cKsVBs/+nYqd80wUthu3FOMl4fG3TOGsetwOQvS4tB1ncLyRjRhKMGL0g1F/7lX9lFR14auS0qrWxkeHcCxwrMUV55P6OPlYSJtXARH8s9yurSB1BFh5JWco7KujcXp8XiYBWZN8P1lE1i1aCyvbMjli7xqahva0HUdk6axMC0BQEUbUCgugRDCk74/9D6SUs51Hi992X6fe+65yx0fgMaWLneZ69x1r39fl9u34vqglFeF4hqhS8O3dfeRav7z3UM8/eJeAJZkJNLR5aDmXBszx1t5a9tJDufXuv/T9PQwcfeMYXyeV2dYBJ2bKivq2mjrtDNn4jCiw315ZUMuI+OC2bSnmMP5tXT3OADBb978nCP59XTbpTPZgeRnf8zmrW15zJ8Zz+1Th+HjqeHQdXTdwYoFo6k+18bnedUARIT4MHtiDFJKnntlP29vyye3qBGzCUL8vQDo6nGQc7qeRRmJJNgC0HWdWSnROBw67+w4zfZ9pWzMLOHBBaOprG8nr/gs6Sk2UoaH8vMX97J220l67DqpyWF09TiorGtHSondrgOS9i47t02Jpam1m0UZCfzo26m8uvE4b23N46P9Z9wb1sKCfGhp72HdZ4U8/ccsfvL7Pby17STpKdHs2H+Gyvp2pNQ5deYcDmfQ2/LaFhy6zqFTtax5eS/lta1IJIF+nrR12unuMSJDjIwL5qUNuazbWUi3XWLXdarPtVFzrpWOrh7e3pHP29tOMWfiMMxmjc1ZxQB/d/1W2I2HlJKiiqY+MTBdZbquX/TvQJuHL9V2oHFcZRe7HkjOwdoUljdSWN44aLv+MjocDnYfLsfhcFzWGP3vucbrP8fBnsXlynWxeQ8BDwNvDrUQAPvXPU/hwQ0UHtzAggULhlocxWWiNmwpFNeY/NIG7p4Ry56cSl7bdJzFGQkIYVhRJYaVcOOeYkyaxoN3jyZ1RBifHiyjx+4MkG02IYRgVqqN2oZ2duw7w6j4EHrsDrJyqliQFk/m0Uoj9SDw5Iqp6FJn3c4C7A6drXtL8bRo+HqZmDQygqSYIO67bSRvb8/j5Q3HkVLS1ukAwMMsEBhp+tbtLORwfh0jhgVSVtMKwDs78gnw8+DeecP5YFchE0eEU1LVwnOv7qelrccILwVEBHszLDIAIQxlesZ4G1uzign09cTTw8TcSdHsOlzB0cJz+Hlb0ITgpQ3H6Oiyo5k0Anw9uH3aMO6cHgdAdm41W/caYb92Hamkq1vnjqlxpIwI44NdhdwzJ5H1u4qwO3R8PM3cNiWGMQmhpE+wUtvQztvbT6I5Y9BOHh3JjHE23thyAl1KZznsPFwJwDdmxLL7cBVHC84ipeTe24azfpeRHe2dHfkA+HmbuX1qNFlHq9xylte28p/vfPFr4C/XZ2VdO7q6HRSWN5JgC3BbqF3nLsXH4XCw81AlqxaNYt/xWtInWMnMqeTVDcdYvWQsMRFGRrcj+fVszS7i7pnxfLCrkMUZcazbWcy9cxPZtreUhRkJbMksZtXiMQiM3ObpKVayc6uxhvrw3Cv7GD88lMMn61g2N4ktWSVGm6wSnnloujutZFlNMy+vP8Yj94xjWGQAFXWtvLnlBA8uGIMQAmuoN8+9eoBnH5rGkfx61u08zfK5ySybm8j63cUsm5NI1tFqXtmQy0NLxhId7ucO/2Y2CZbMGk5ooAevbDzBmoenU1nfjgCsYT489+oBHlo8mlc2niAjxcq2vaWcSI/n8xM1PLliKpX1baSNjyLraBW6rvPS+mM8tmw8wyIDSLAFuKN7WEO9eealfXhYNB5aMo5XNx5n9eKxxET4UV7bwmubTrB68Rhe23SChenxbM4sYmF6AiZNY2t2MasWG7Ges45WYQ314Zevf86qRaN5deNxlsxKJCU5jDWv7OfhJWMwmcykjY9k/e5ils9NwmQyXZe1JYSwAHOklP8jhHjeWXynEGKn8/y3UsqNA7R7FHgUIDY21l2+Zs2aPlZRlxHg2Wefveirfle90bNXkbf7dQDy+t3rHzzkcvtWXHtUqCzFjcgtseklJmG0nPW9F2jr7OGB+aPYkl3i3kWfmVNFzblW3tmRz/13jXRn2kqMDqS4splfv3GAqaMjGDEsiGFR5xMASCkprmwm3urPnsMVvLzxGM8+PB1NaJTXtrJ2Wx5PrZxGgi2A3YcreH3TcWaOj2JvbjWrFo9l9sRot1vBniOV2MJ8qKhr5ZWNx8mYYDXip2oazz48wx0fNTU5lF+9cZCVC0cDRjYwgF++foCfr55OYnQgheWNVNS1ER3uy7++dZCnVk4jMTrQLa+rrx37zvDggtHYwnz55esHWLVwDDGR/n2+wDNSrJypbnUrS79+4wA/fXAK5bWtgMQW5kvO6bPs2H+GB+4ezeubj/P0qmkAlNe2EhPh51aan1o5zXhWRypxOOy8+MFxvDzNPPPQdPfnlGALYM+RSl7fdJyF6Qksn5dESVUL5TUtvL7lBE+vmoYQgrKaZl7ZeJwlGQlMGhVJYnQgJVUtJNgC3J/Nkic2Ttz0wtIj13+1XV1CbcnyzlW/Y86kGHZ+UYYQGo8tG8crG08gpY6uSzq7HDgkDIvwpay2jfQJUeTk19Pj0NE0AdJwW+lxQOqIMArKGmjtcGD8hDDQBJhNRlgyhw49Tuv43TNj2b63lO/dNZK/fnKKbrtR39OiIZBIBEJIbpscy+7DFehSx+GQdNklXhYBQmDSBHMmxbDrUDntnQ7unhnH1r1nSBsXSfaxGvdcxyUEcay4kbtnxrLvWDVtHT1omvFvpNsOJgFJw4LIL23EpBlyxkb5UVpt/KBLHx9FVm41w2MCKChvxttD4NAFHmZIjg1lVkoU//23XNLGR5KdW4OnRaOrR8fLomGxmHhs2XheWp9LS1sP82fEsm1fKd+YGk17t84XeVXoUhgPzPDM4b7bktmwpwiHDna7g27nj9yMFCt5xWeJjQrkcH4d9981knW7CpiTauOTz8voduCeu1kDu447kkhKcig5p89el7UrhFgNnJNSrhdCZAL3A7+UUt5/uX0Mphe4/h1ehgxIKVn8Y8Pi6lJeX3jhBX784x8bhoB+fV1u34qryqC6gFJeFTcit4TyOmXKFPnnDz6ioq6VjBSbWyFz/XrXdZ3MnEoyUmxo2nkPHpfC17vuQPSvN9h1vNW/j5I1WD/xVn+3lS0xOtBddyB5BpPxYrL3vgd8qTkOJPNAcxtM5qKKpgvmd7lzvMzP5ZZYuyHW4TL9uy/0KUtNDqWwvIHE6CDySs7hcOBOP9yb3sppb8YnBpFX0khwgAd1jd197yUEkVvc2KtuILlFTaQkhdDd001eaStxkV6cqelkTJw/J860uOtqgGYy9vDpwJj4IHKLjL5MznvTxkQRE+HNXz4pRsOo15/kGD/iowL46GClM+zbwPVceJih237hfMclBnGsqBEfTxPtXQ78vU20dDjc9xNtvhRXtiEAocEvHpnK3z4r5mRJPZNGRZB1tNZdN8jXTGObvc+4Zg1unxLDJ1+UY3f0KjfBiJgATpxpJtHmS/KwYHbsLx9Q9lB/C2dbekiy+VFY2eoqbtz0wtLgi0z5qiCE+DcgFeOxTQeeAaZfT+XVxQNrtrPjzZ8RFpuCt5eZSFM5W7duHbAvpbwOCUp5VdxU3BIKgFq7X0tuibUbFDVczvreCxeUB/qYaGp3DNDiq2ESAyvCvfHz0mjtvJg6efUI9rPQ0NoDQHiQJ3WNXZdo8eXx9RS0dUl8PTXauq7P/AahfdMLS32v54C9LK/7Of/WfouU8j8u1m6w/1vXrFlz1V7n9+/ravatuGxUkgKFQqFQfDWuheIa4m/mXIv9kvW+quLq5aHR2X15fTS09hAeZFiI6xq7sJgEAT4mzl6GnFdKW5d0/v3qiqtZgP3L26O8v7IAV4iUMsN5Gnk1+ruayqUKlXVjo6INKBQKheKyCA3wGLA8xFke4HPl9pDLUVwvZc7297p4DX8fUx/FVbsM+3ij0/IqMCzDEcHXRrcz9foW9vX8an19BcUVoOOrja5QXD+U8qpQKBSKAemt5EUEe3KuuZtxzpi/LgJ9NM41Gz6sze12/LwG/loJ9B14N3tYgOWCsvGJwX3Gd+lk4UEDa3ctnQNrbYk2PzSM6AmuqQT7mdEl+Hsb8kSHeV3QTgA4o3cIAZ12SV5pywX1XPgMrNNfkshgL7w8NPd8O52uwJf6YtbEhQq96Hfuuo4Kvrhwvdop5VVx06CUV4VCoVD0QRNGBIBEm5+77FxTl1OZ7Pt6u6nduDYLYzNR2yCv9/29L1RSAc4291xQ5uNlKJZCnleuBFzge2q6xDdYUWUrOsbGKpd629BqWHpdm6gq6zsvaCeBHofxV78Ma2Z79+D3XPInWn3cSmWi1QeTgJqGTjq7dPJKGoxxnWMN9AT9vc+rmRoXboqTgKfl/HlSjJEFz9urrzU8xN+o5PoxIXH/SPj3wWehUNxYKOVVoVAoFH3w97GgSyiocO9Ex64bm6qOFTUP2MYuYcQw/wEjDQCUD6AkAkSHX/g6fv+JegAcnFfSBurXcZluouP7WYt7c722LBdVtSOd4xVVteNwKuYOaTxbk3bx6AYtHeclHcw9oKvX74CCcsNSXFzV3qfOuRajUlPbef9lp4L+L5c7l6tNv1Sxb3/Zfq61X+qBFQ+RtfTeC44DKx66JuMpP9vBUcqrQqFQKPrQ0t7XGhoeNPir5/DA8xbVstr2QesNRl1D51cK0XAp6yvgDp0FxpeeSTP+DuQyMBCD+cj6XIaP6sWUY5NJ4GmG2Eg/HLoR7sqF7yDuF1fCFfrQrv3KA34JBkgV+6W51ilcexobr6j8q6JS0g7ONVNehRCtF7l3nxBCCiGmXKvxFQqFQvHlEKKvUtg/Lmtv6pp63F8kLR0OvD0H9m0dTEHttssrtn5qAr4xzYanWcPLohEVevmbqXTOx3CtGMQabNLA29OQ2MdTDOo60D5ABK3IYE88LiNZ1bAIH+wOyaj4UEprWvEwa9w5LcZ9v+syIyNcjLZe8vn32kxnNg2okId/5QG/HDdMqtjB6B/X+Yl9Wfy58DR/LjzNE/uyLrud4upx3S2vQgh/4B8w4ropFAqF4gYjLMgbL4sJD7NG+gQjitGE4SF4exopRcGwFgKkjgg1/C09TIyIDaKrR3crky4FyXXtamM2Cbcy68rPkZIcChhKrsWs4e1pwmwSF1hWNWG85q5t6KLLrpM6IoKasx2kJofynW8kIwSkDA9FAB4WjbTxkfh6mZk/I5oRwwIBSI41/po0o45wzs+Q0Zdv3ZlMhzOElRAmZqVY8bQYGqnFrOHrlA3Aw3xeQJPJkEvrlWrV4qxnMQu8Pc34eZlZmB5Pc5sdIeC2KTHMSrHi521hdHwY3p4mvDxMmM0mBH2tsRazhq+XGS8PE3fPjOXumcMQwigHSBsfyfwZw/D1MpM+IZLv3JGEt6eZ5JgAWtoNX18Pi4aXhxkP53w8zBrO5ncMth6uFb1SxX56GXUfFUIcFEIcrKurc5evWbMGIUSflK5CiKv+yl0IQcbGdQBMCY/g98dz+f3xXKaER5CxcZ173N7Hl+F6zedmZyjivP4CwzH8iSEYW6FQKBSXwMNswmwxYbbAvfOSSYoJcacJvu/2EQwfFuJO9btsTiJZR6uIDvcjwRZA1tEqZo6LZP2uYsYnBZNb2MA9cxJYv7uYHfvOsCg9nj05lTjsOrqER+8Zy7nmbu6Zk0DW0SoAosONFL8Vta28vOEY3XaduZNshAR4sW1vKWMSgjlRfA4/bwszxkeRU1BPWW0bKxaOJSYigLTxUUZfEl7bfAKzWSMiOICPDlQyK9XGP30rhdc355GdW82qRaMRQmAL8+GZl/bR1ulg6mgr1hA/ahramTQygqSYIArKGvj0YBm3TRlGVX07r28+wcL0eFKSw6isawMBtjBfKuuNNMllNa3UN3WQMjyM6rMd2MJ93YpIgi2AO6bGulMqv/vhaR5aOg5bmC8+XhZWLRyDLdyXyro2rGE+VNa3ISXERPohEFTUtbF2Wx4/fXAKYxMjsIZ6U3W2g1mpNoorm/k8r45v3j6KxOhAZkwYhkN38MyLewHBY8vGERPhj5SSI/l1bMkqYWF6PG9tO3nhzrlrzwPAu5dTUUr5EvASGEkKXOW9kwdcyyxYUkqylt474L3MJctJ3/D+BeVfRoG9XvO52blmGbaEEK1SSr9+ZROBp6WU9wohdgJPSCkvmoJIZSn6WnJLvGtRa/dryS2zdv+y/mPgfCrdL5Petzf90xC78sf3T9Xbv03vlL6ucXunMk6wBQyY1rh/+3irP1lHq9zpmAdK/ztY+uDB5nIl87+cvlzz+7Jpkwe7N9jcen8mS/9508RNLyw98pUmc4V82VSxVys97JXiUl6f2JfFlPAIAA7W1fKfM9IHVF6/Kkp5vQEybAkhNOB3wMrrNaZCoVAovhxJMUF9rl2Kpove55dD7/b9+75Ym/51B+pjsP76t589MabPvd5zGGisi8l1pfO/3L4up9+LjT/QvcHm1rvu9VZcAaSUP+klSyawCUOJ/VI8++yzV0OsQbEEBdHT2Mh/zkh3l307KRlLUNA1Ge9az+dm5rpZXoUQgUAh4NrIFQWcA5ZczPqqrFdfS24Z65Vau1871NpV3KzcNGtXrc+vDUNveZVSNgFhruvLdRtQKBQKhUKhUChcXMtoAz5CiPJex+PXcCyFQqFQKBQKxdeAa2Z5lVJeVDGWUs69VmMrFAqFQqFQKG5NhiJUlkKhUCgUiq8pQoh4jFjveUA38Giva4B3naGxrjsPrNlOY0vf7BNB/p6sXTN/KMRRDIJSXhUKhUKhUFxvPnKFxHIqsx9dKkTW9aC/4jpYmWJoUcqrQqFQKBSK6808IcQeYB3wwVAIcLE4qvvXPU9YbAoA9aU58MLSL9WP4tqglFeFQqFQKBTXkypgBNAFbAA+Ae50RiEC+K2UcmPvBkKIRzHcC4iNjb1qggyUDGLR4+sJi00hb/frF62nGDquZbQBhUKhUCgUij5IKbuklG1SSjuwGViE4TYw13lsHKDNS1LKKVLKKeHh4VdTlj7HosfXX1Bn9OxVF9TrfSiuP8ryqlAoFAqF4rohhPCXUrY4L9OB/weMGUKRLqC+NIfRs1e5zxU3Fkp5VSgUCoVCcT2ZJYT4BYbbQCZQQ1+3gS1Syv+41kIMZDUN8veksaWL6cufcZdNnvd3V9yP4tqilFeFQqFQKBTXDSnlVmBrv+LIoZClPyok1s2B8nlVKPohhGi9yL37hBBSCDHlesqkUCgUCoXCQCmvCsVlIoTwB/4BI5i2QqFQKBSKIUAprwrF5fML4N+BzqEWRKFQKBSKrytKeVUoLgMhxERgmJRy81DLolAoFLcCQoh4IUSNEGKnEOJD5/XbQy3XzcaaNWuGWoRLcrVlVBu2FIpLIITQgN8BK4dYFIVCobjV6J8m9qbkwIqH6GlsvKy6lqAgpr356lUb+7nnnrvhFdirLaOyvCoUl8YfGAfsFEKUADOAjWrTluJGQ202VNyEzBNC7BFC/GioBbkUF8uydTHF9Yl9Wfy58DR/LjzNE/uyLlvJvdyxv44o5VWhuARSyiYpZZiUMl5KGQ/sA5ZIKQ8OsWgKxWWhNhsqblBcaWLnAXcAAYNVFEI8KoQ4KIQ4WFdXd73kG0iOAY+MjesGbTMlPILfH8/l98dzmRIeQcbGdYP2M9jRnzVr1vS55zq/kSyw11JGpbwqFBfiI4Qo73U8PtQCKRRfEbXZUHHDMUia2MHqXpP0sFfKYCliM5csv+w+Mpcsv2i62ctJQbtmzZo+91znN5ryeq1kVD6vCkU/pJQX/VEnpZx7nURRKL4yvTcbCiGeGGp5FAoXN0Oa2KvBwbpafjh2vPv820nJQyzRzY9SXhUKheIWRW02VNzgDJQm9oblYmlgLUFBg/qy/ueMdPf5t5OSsQQFXdWxn3322Svu73pztWUUN3pO3ilTpsiDB5Vr4deMW8IzXa3dryVDunaFEK1SSr9e14FAIeDayBUFnOMSPttq7X4tuWn+31Xr82vDoGtSWV4VCoXiFkVK2QSEua6FEDuBJ9RmQ4VCcTOjNmwpFArFrYPabKhQKG55lOVVoVAobhHUZkOFQvF1QFleFQqFQqFQXDeEEA8KIT5xpoVN75UidqcQ4tGhlk9x46MsrwqFQqFQKK4LQohoYI6U8nbndTy9UsTeLDywZjuNLV0XlAf5e7J2zfwhkOjrhbK8KhQKhUKhuF7cBZicltf/B5iGWqCLMVha1oEU14HKVVrXa4NSXhUKhUKhUFwvIgEPp+W1HVgK3NnLbWDJ0Ip3Zexf9zyFBzdQeHAD+9c9P9TifG1QbgMKhUKhUCiuF03ALuf5p8AULsNtwOkL+yhAbGzsNRVwgLEHLF/0+HrCYlPI2/06AKNnr2Lzb+9B/PZ6Svf1RFleFQqFQqFQXC+ygQnO81TgsjIlSSlfklJOkVJOCQ8Pv1ayDTb2Bceix9cPWHfR4+v71FNcG5TyqlAoFAqF4rogpTwCdDgTZkwF3qOv28A/D6V8/RlMAQ3y9wSgvjSH0bNXMXr2KupLc9zll2qv+GootwGFQqFQKBTXDSnlE/2KIodEkK+AO6LAC0uHVpCvKcryqlAoFAqFQqG4aVDKq0KhUCgUCoXipkEprwqFQqFQKBSKmwalvCoUCoVCoVAobhrUhi2FQqFQKBRDghDiQWAFRqatnwE/uNlSxfbmwIqH6GlsvGQ9S1AQ09589aqOvWbNGtasWXNV+7zaXC0ZleVVoVAoFArFdUcIEQ3MkVLeLqWcC1QMsUiXxcVSvl6O4nol9a5k7Oeee+5L9Xk9uVoyKsurQqFQKBSKoeAuwCSE+AQ4AfzX0IpzbXliXxZTwiMAOFhXy94hludmRlleFQqFQqFQDAWRgIeU8nagHRg0aKoQ4lEhxEEhxMG6urrrJuBF5BnwyNi4btA2U8Ij+P3xXH5/PJd9tTWD9nGxoz9r1qzpc891fiO5D1wLGZXyqlAoFAqFYihoAnY5zz8FvAerOJTpYQeRZ8Ajc8nyy2r/w7HjB+3jYkd/1qxZ0+ee6/xGU16vtozKbUChUCgUCsVQkA084jxPBW6KXKpfNuXrwbpafjh2vPv8eo59q6GUV4VCoVAoFNcdKeURIUSHEGInUA88BTwztFJ9NSxBQYNuxvrPGenu8wcmT73qYz/77LNXvc+rzdWSUSmvCoVCoVAohgQp5RP9im7aMFnAVQ9/dSXcSK4Cg3G1ZFQ+rwqFQqFQKBSKmwalvCoUCoVCoVAobhqU8qpQKBQKhUKhuGlQyqtCoVAoFAqF4qZBKa8KhUKhUCgUipsGpbwqFAqFQqFQKG4aVKgshUKhUCgUNw1ffPFFvRDizAC3wjDixV7LMsX1Y7uUcv5AN5TyqlAoFAqF4qZBSjlgflghxEEp5ZRrWaa4MVBuAwqFQqFQKBSKmwZleVUoFApFH/SuLpoLCumoqMA7OtpZKumsrCIkbSYNe/cRPHMG57L2oksd7+hoOisqQUBoehr1mdn01NVhXbaUjpIzdFRU4mmzYtI0fBLiaSsuQdcdNOccJXLJYqo3bMQjLBzvGBudlVXuMTUh8I6P42xWNp5WK52VlfjEROObkEBbUTEdlZWEpqfRXlxCe0UlXtYouqqq8HbVKS5BSp2Oiiq8bFF0VFShCQhOm0n1xk1Yly4x5HP201ZUTOORHAJTJqCZNHRd0l1VRUjvMWxRdFVV930O2XvxjrbhEx9PfWYWSPCOiQakMfeoKFqO5hKQOgFN05DSmJtPQjztJWfwiY9z/20rKqatvAJNCILTZlC1fgMeYeH4DIs5/zlUVGGJiqQ1NxfrPUvdcwhJm+l+9lH3LOFc9j4EkuC0mVSt34BneAShGec/n8ili6neuInAlBSOPfGT1PQN7x8ZguWmUFwxSnlVKBQKRR86Kqs49uTTyO5usFiMQinBbidw716asvcTseAuarfuMO6ZTOBwAFC/bz+N2fsAaC4uovGLw9DZBZoG3l5E37OEynUbkF1doOtU79xFd1mF0Y/ZDHY7eHmBlAiThm3ZUire+fP5ex4eRNw+j9rPdkJnFw3Hj1P/yU7o6QGLGXqMOiHTpnDuyFFEVxeypwfNywu9sxOAIKeMzQWFNB4+Ah2d1O3dR+PBL6C7h7K/vYfFy5uezk7o6iKqooLqDzZCV5dbjuB9+2nI2ktIRhrnMrMxeXsTcc9iqv70V2MurufW0wMmDRw6/PmvRnlPD3h6EDgimeaSUiLTZlCXtZeE1SsoevFl9LZ2ALzei6WztPR8f1Iah8MBQoCUNBcW0XjIeMahx45xdsfHADQVFtK0dz8A3pu20HG6AICzB/bTkGV8PrW799BZWkb5X/4GsAkYdjXX0RDw0nUoU9wAKLcBhUKhUPRBSons7sYjJsZQtHp6DMURaNp3AADvpOF4xcYCYLFa3W0bP//i/Pn+zw3FFUDXob2Dir+8h+zoMK7BUFwFYDa523mEh0NXF7K9g/aSM+cVN4Dubmq37XD3W7/9I0M+sxmfUaPcdc5lZmP29kb29OAZO4xhj6wGDwuYzUhvn/Py2Q2luzF7n/s8OGMWwWkz8ElOBqD50BFDcQWQOmgaDfs/B7OJc18cxiMmBkdHB61FJecfYq9n5hEfZyibdjuWiAijXGg0Hc5B8/Kiess2HK2ttFVWoXf3gCZAE3SWliKCgoz+7M7+nD8SQJ6fQ3cPAK2F58d3tLUbPyrArbgach80zoWgs7QMc0QE0mi/i5scKeUFyubVLlPcGCjlVaFQKBR9EAIQ0F1ejubhYRSanS/qdInJx5uuoiJDuTKb0RsajHaeHoaC5bI62h1uBdccGmpYUzXtfF1w1hVoFg9MznbdZWVoHh4ITw8a9u5HaMKt7Aanp51v24uIO++kq6gYAJ8RhtJpr6sDoKu0jK6iYjSTCex2mnbtNho5HIRMm+aetP/48QA07tlN7bYddBYWIjws9NSfJTA1BQDfpOGGLHY7OHSE3U53ebnzEZkNRRwweXsTnDbDmE9hMcJiPD+LS/aODkMEp4w47FR/sN5pqTUbuqnZjGxsNMTz8HB/Bl6xsS7d1fiwdB3v2Fi6CpxKqslE67FjiF4/CLxiY0HXEcIQUPPyQvPyxF5X6/qMv3XBQ1UoblCU24BCcY3Qu7pwOBycy95LaHoamvNLW9d1zmZlu8uklLQVl+CbEA9AS2ERXZWVhGakI4Rw33N96fRGSklrYZHbZ6/jTGmfug6Hg8r1G7DdsxSTyTRw+6JiBOCbmIAQwpCnqBgJ+DnLBpxfv3n07rO/zAOV9ZdjsDH7t+0vs5SSs1nZhKTNvGD+g/Ux2Gfg8j28mOwD1QVoKy4h50dPiPQN70tucrxsNob/6B+RUhr+rJWVeFqtNB3JwTM8HN/YGLzj4vAICSYgJQWB4WrgZY2iq7oaj6goOisr6a6rJ2rpYmo2bSZy8SKqN24iYMIEuqqq8LJa6aqqxtMWRWdlNd42KwhBW1kZPXX1BE5MBaCrqgqPKMPPFCRhGem0FRXTcPgwHuERCCRCMxGWkUbk7XNpyslx+9FawsJBSnrOnsW2bClh8+bSnJOD77hxtOYewyM8nLCMNM5Nn4JEEJI2g+oNG/EbP56Wo0cJTE1FCIEmBF5xsVRt2GjMw9m3EAIvWxRNh4+CANuypTTMmIau6/gOM55RZUICHmFheEXbaDl6lIjFiyh/cy3BszJoO34cv3HjaD56FM+ISLysUTQfzSUgZTzd1TXu59hVW0fQxBRA0Nxrfh7hEXhao2jNzSVq6RLOZe2ls7aOwJTxdNfU9PkcIpcsouzNtwibM4eu6mq8o22AoKOiAi+bjWNP/OT+IVtwV4AQYj7wfwET8IqU8l/7lwE24D4gBKgDsoAxgB9gdZb9wdn2H5xtq51/9wH/BVgwwmQtAd4GYjF0pv+UUr5+XSarGBQh5Y39/+yUKVPkwYMHh1oMxfVlYG3pJmN0aJjc8KtfUbftQ2If+C4x9y5HCEHdnkzyf/ffjPjRPxA+K4PWomJO/ut/MOqn/wzA8Wefx97SQvR3vkVHaSmNJ04yfPUKY2NKP8WqtaiY3GfWoLe0EnP/d6j76FNG/fSf8UtMAKD0vfcpW/suIRlpjPzxj9yKmkv5c+g6ec8+DxYzSQ+vNhSD4hLyfvlrpIRRP/spnZWVeNts+CUl9lFuzx0+TNk7f2bE4/9I+KwMd3l7RQUlb71Dwor7CctIR0pJxQfrqdr2IaOf/BeEEHjHxXIuKxtPmw3/pETaiks48YtfoffYSXz0IcIy0t0bWM5mZVPy1juM+uk/u8fP++VvAEnC6pU4HA4Kf/ffhM3/Bg1Zexmz5ucEDE9yfw66rlPxwXrKN28jYuZ0wubNpauqitP/9f8Y/o8/RNM0PK1WTvz63/AbFkNzUTHj1/wc/6REwPgxceIXvyZ+1YP4xsQggVP/9p/EPfBdzqx9lxH/8mOacnKo2vYhPXV1E2+FTS/q/92vJTf8/7tCCBOQD9wJlAOfA98DNvQqO+n8Gw38M/BTIBhYAawFfgn8APAEHsBQVvcDLwI/A7yB26SUpUKICOBhIFBK+RMhRDhwCoiSUnZfjzkrBkZZXhWKa4TUdep37kZYLFRu3krwpEn4JsTjabUS8+2/Q9d17HY7HeXljPiXH7steKOe+Rl1n35Gxd/eh54e/FPGU/TqG3TU1FC17UNsC+4ietk9aJqGb0I8tsWLKH/3z1hCw4h74Lv4xMcBhtKGBCxmzmVlUz99GsEzZ3DqV7+h6XQhZpOGdfFCHO3tBKfNoOCPL+NwOPAdFsOop35q+MRVVHD6hf8CDw8SfvAYZrMJT6uVvOd+gW63EzH/GwTPnEFLYRHt5RUUvfwKAoF16SKKXn3D8J2UktK33iEkIw3d4eDUb/6dyAV3Uf7OnxE+PgxbvpSACROIuns+Fes3UvTqG3TW1lK1eRtRC+6icsNmrEsW0njoMJXbPyThwe8Ru+IBeurqKH7zbYImTwSg/tOd0N1N05Ej+CbEczYrG2+bjY7KSkrf+TP+48ZQvWUb1R9+TNIPf0Dsd423pPm/+29G/NPfEzh6JOcys8GkUf3RxzR8EYJt+T20l5Wjd3RQ+OIrCE0Qs3QJI/7lx/glJuBls9Fw6BBl7/yZYfd/h7K17+Zc/5WmUHxtmAYUSCmLAIQQfwb+T7+yOqDTWbZOCPEb4G8YimoBsB5Dgf098AegBDgD2IFCIEBKWQogpawVQkjAXxgWAz/gnLOuYghRyqtCcY3wCAkmbO5c6rZtJyxtphEGx2lhdLS0gsNBU14edTs+Jvb+7+CXaIT26aqqoi5rr3sTS9jcuZS/8RZVm7cSOG4spW+9Q2tRCcFTJxM+KwPNZLyyb/7iEE0nTwEQlpFO3Z5Myt5+F/+U8bSeLsTLZqXgv/6bpsM5eA0bhr21Bc+ISIbd/x3KP9iIbG2l8H/+iMXPlzE//xl+iQl4x8Xis3Ez7acLKP7DH8HhYNh3vwV2B7Kjk9oPP8ISFETNth0IXUdIiRQCe0Mjek8PRS+9im3pIjRvL85lZiMQ6LoDgcDk50fAhPGUrn0X4f2B4dcoISxtBlWbt6J3dVP+V0OBr3j/A+juISR9JkUvvwYCTBYL1oV3U/7+egDCb5uHZ2gwFes30Xn2LLXbPsQc4M+YZ58m+Z/+nrbKalpyckHXKX7lNcweHoz62U8Z8aN/IDQ9DQ+rlYaDX+AZM8zYEAT0NDVSvycLvbuLyPnzqdu1i9K33yWyoQE/p8tCxbr1YLHgGT5g3HSFQnH1iAbKel2XA7P7lXkAAcCxXnU6gXhnvYeAbUAbMBpDmXX5VHkBYUKInYA/hhvB74GNQKWz7FtSSv2qzkpxxSjlVaG4RvQ0NdN04ACRC+dzdu8+Im6/Db/EBMLT06jesg0Aj+BQohbcRfX2D/GKiKD4tTcRAsLTZ1KzdTsmX1/844Yx5tmnaa+ooPTtdwmcmMK5zCzOZWbRc/YsNR9+TOisdLcPZ8lb7+AdHe12LWjJyUXz9UETGtalS2jY/zn2xkas9yym7J0/MeonT9BzroHqLdvQTBoJq1bgEx9Ha1Ex7eXltBcUAhB+x20Ejh6Fd3Q0NVt34DVsGG35+VSv30DiDx7Dy2alo6KKrtoayv70FyLv+gb1uzOp2rKNpP/v+7ScPEX19h1E3X0XtR99TPSyJVRt3orZz8/tw4eAc/v2k7B6JZ01NZS9/ScAYu69F80kqN6yHaEJEh5ajU9MND4J8XiEhVHy0qtE3Xk7XZWVOFpbObtrD8O+922CJ07ENyGBzsoqardvR3h6ILu6iV6ymJDJk/BNTMA/yXAx8E9KJPKO26jebHw2wWkzCZs7l7A5c2g+ehTrPUvxHzWSoj/8kZqt2wkYNRIB6B2dCA8LZ954CyAFOHIdl5lC8XXiclwbBOe3s7lwXVuBdCAD2IrhAtC7rgmIAGZiuA/sBQIx/k3fBiQBHwkh9kgpm7/cFBRXA6W8KhTXCG9rFKN/9iQSScO+AwhACEH8Q6uwBAUjkQRPmkj+v79A/IP3E5qehpfNZihEUnI2ay8JD63CL9HwNfVNTMAnJgav2GFUrd+AJSyM8FkZBE+ciHdcLL4J8dTs+Ij4B7+Hb0I8PvFxdNbUUrVpC/EPrcLX6Qeb/A//h5I33iJ4YiohTleG+PhVeIQEE5iSgv/wJNqKSzj5r//ByJ/8mOE/+ge6a+uo+ehjbN+4E9+EeEb//Ck8h8Vw5vU3CJ83jwBnm7J3/sTIn/wY76goPK1WGvYfIG7lg4TPyiAsI52A0aMISZtJ5O234xMfR1BqqvObRlK3YwdxKx/EOzoav8QEWouKqNq0FevihcTcuwwhBMETJ16wqSti9ix8hw3DNyEev8QEYmtrqd6ynZBJk/BLSqS1qJgza98lYfVKpISSV18nePJE/Jw+rS7aS87Q8PkXDLv/u3hFRuAdbePUv71A3APfpebDTwieNImI2RkIoPjV1/GOtuGbYIxXtWU7CaseJP+F/1JuAwrFtaOcvrFoYzBe+/f+x9wFtPSqF4OhiDYDi4FUKeVZIUQyhg40EXAAC5z9H5NStgFtQojdwErgn6SxQahACFEMjAIOXIsJKi4Tl0/ajXpMnjxZXgm6rsuWwiKp6/plld/s9J/XLTLPIV93V+Nwrd2LfSZXc70O1OZyyy7V17Vq81XlvFI5ruSzcF07HI6Lzqvf9ZCvu6txXOn/u4pbgiFfd5c6MJTNIiABwz0gB5jQr6wI2OP8uwxjU9cJoBSoGKTte8C3MTZ77XOO44PhevAXYI1z/EhnH2FD/Sy+7sdNG+dVSmPHtK7rtBYVuxa222LUVlzSp15LYRHHnvsl5e+vMzayXEbfrj77X1+JfL3b9C/TdZ26PZnoun5ZYwxUp/98B5x/YREthUUXlcVFb5kuNv7lPpOL9XcpWW4VhBCDhpwa7N7F2lzJOJdbdqm+rlWbryrnlcpxJZ+F61rTtIvO68vKqVAorgwppR34IbADyAP+KqU8CmzBUDrzgFeBo0Ao8FcMV4F2DH9VD4xoBc392i7ECI+1Fngfwzf2NEbYrR8BaUKIXOAT4CdSyvrrMF3FxRhq7flSx2AWgOaCQrl/5cOyZtdueeDh78va3XsGtKy46+3cJbO/c7/MXHqvrNm1e8A+XbQUFsnPH/mBbCksGvB6IPqP62rTmH9alv7tPdmUf1o2FxT2kbVm126Zec99snrnLlm7e4/c/9Bj7nuXI9dA4w4kx4FVD8v9Kx+WzQWF7nuDzal29x6Zueybsnb3nj79ORwOWbNrt9y34iHZXFDofq7NBYWDPo/mgkJZ+rf3ZObSe2Xpe+9fybyGfN1djUNZr76WDPm6uxqHWrtfS4Z83alDHZd73LSWV8N/ELyjbcQ/+D3OrH2X1qLiCwKhu+p52qyEz5mDyc/PGZx5cHwT4hn10392hy5yXbs2sUh5oYWwv8XTJz6OuAe+S9ORI5SufZfjP38WpHTL2lZcgne0DYu/P0IIzqx9F+vd36DkrXeoz8wacAzfhHhG/uQJcH54cN7qA0bMT+jrD+ibEM+op35K3MoH6CivIO83/+5+Ru45FRbRUlBIc0EhnlYryf/094Smp/WZ19msbEpef8vYUd7ruQ5ka5JSUp+ZxYlf/JrqrduJXHAXVZu30lJYNODz6/98s5beq0xYCoVCoVAoBuSm2bAlZd8sRBJJ3MoH8U1IwC8xEe/oaJDSucnkCWODS0I8vokJjH76KdrLyqjZtp3IBfPxTUgYtO/erxZduK57B5P3TYjvk+WnvwKm6zpFr75B3MoHiVww39iwIwRhGel4R0e75zFmzc/xiY/DJyYGn/g4PMPDKX7tTbycwdv701lZSclb7zD6yX/pI4MrcHrvubvmghCUvPI6wmwiYfVK99jgVE5/9Rt0ux0kaBYzo59+yp0xqfe8XJuJXFmN4letwKdXX67nKKV0bpBZgU90NA7dQf2uPbSXlXHmzbdJWL2C8FkZF7x2dT1f1I5thUKhUHwJhBBRGC4AUzE2b5VgbLjKv8rjxAObpZTjLlEnTUr5rvN6CvCglPIfrqYsXwUhxJ+AscDrGCHE/oyhUtwHrJVSpl2k7fPAbinlx19i3FTAJqXc+mXkvuEtr3pXF7quU5+Z5bYaGgrXv1Ly+lvua5eiOuqn/4wATv7rf/SxxHrHRGPy8eHsnswLLJttxSXk/ebfB7V4gtO9QteJvf87eMfFUp+ZxfHnf8WxNc9TtycTMCye7SVnOPmv/0FXZSVC1yl9cy1Rd9zO6J8/5U6/OaD/nVPx87bZBrVothWXUPLWO0TNv9Mtw4lf/Jq8X/0GAe655/3m36nbk0mr089VYCilCatWENYr5ejJf/0PkJK4FQ8Qv3oVo575GaN/9mQf5daFEAL/pER3lqX2kjOcWfsu7SVn+sh38l//wy1LWEY6AE1HcnC0tdNz9ixC192fW39cijKGI71CoVAoFJeNM5HAB8BOKWWSlHIM8BTGRqvLaW+62PWXIB74rutCSnnwBlNcozCU6wlSyt8B9wAbpJQTpZSFF1NcAaSUz3wZxdVJKkaEhy/FDZ8edkx4hNy1bh1n1r5L3APfNVJkOq17CGEoqv/2n31SYrrylntZrZz69xcMayTQVl7OmVffAJOJMT9/yl3f9Zr7zNp3+/TTm9aiYk784tcIAXErH6T4tTcJnTmd+swsTBYLo372JEIIdyB6kMZvF2dZ//z2rnFdlsqTv/oNUsKYp5+EXpbT3vSW05WaMvb+7+ATHd0nL319ZpY7Xuionz3ptsz2didwjY2UnPjlbxACRj/91AVzbyksIu+Xv2H000/2sQT3t1ZLaaQG1aVEc4Z1ai0q5sRzvwBNw7Z4oRFjNHuvOyXoRTa43BJuAyrF5tcStXYVNys3/doVQtyGERlg9gD3BPDvwN0YX4m/lFL+RQgxF3gWqMJQqP6/ftfjgX8F5mKklP0fKeWLvS2vzvO1gK9zuB9KKbOFEPswEiEUA28Ch4EnpJSLhBAhwGsYYb7agUellEeFEGuAWGd5LPBfUsr/HmA+84FfY8SmrZdS3n6RPn2B/+eci9n5jDYIIY4CyRjxbj/ASJvrAPKllPOEEK1SSj/neP+CkaVMB7ZJKX8qhHjD+QzeE0JMBn6LkYWsHlgppaxyJnzYD8wDgjCSROzHyHbmjRG94TdSyr9c8IFejKF2ur3UMXHcuD6havpv7hko9I27jnODUktB4flr52ajKw1N5NqAZLQvkPsfWCX3r3xI1uza7e7XJVd/GftvghpQzkHkGkiOgcL3DCZrb7kuNa+B+mopKJQHVj0sWwbZmNV/LrW795x/Ds62Nbt2X3ST2AAM+bq7Gofa9PK1ZMjX3dU4xo1PlQVlDdLhcMiCsgZZUNbQZ1NoYXljn3t2u13uOlQm7Xa7u8x1/3TpOXm69NyA9Xrf6x8mrfe4vccsLG/sU9b//sXkLSxvHFTW3nV69zPYmP1l7T2XgeY+UJ8uWRwOR59F1H9uA8nSv/5A4w1Wd5D5DPm6+6oH8A/A7wa5dy/wkVPZi8QInWXFUErbgARnvf7XjwJPO889gYMY4bbiMWLCghFWy8t5ngwc7NXX5l4yuK+dyuSzzvPbgCPO8zVAtnOsMOAsYOk3l3CMiAguGUMu0eevgfud50EYERd8e8+h19hP9Lpudf692ymTT7/x3sBwL7A474c7y78FvOY83wm84DxfAHzsPF8J/P7LftY3vM+r5unpDlUDF26m6u+f2r+OyyrY+3ogBuqn/32X5VFKyZhnn+5jzew9BtDnPDQ9jRHOvxeT83LoLedg8vaXtbcsF6s7EC6f4cHau+v18o3t7dPrauvyw72YLAqF4sagsr6VZ17K5pGl43hpfS52h+QHyycQE+nHobwaPthZyJzJ0ez8ogyHLpg7ycaO/eUcmxnL7sMV6FIyd1IMn35e6n5DJjSNuZOi2b63lPI7h/P+Z0UgdcxmE5oQLJs3nHvnDae4spnDp2r5YOdp7Do8/8h0dh6qZOehMu6ZncjGzBKef3QmAM++vI/nHpmBpmn86vX93DUjjvW7CpASHls2nuhwPw7n17JxdxFLZieycVch8dGB5Jw+y94UK4dP1WIyaTyydByvbTrBoox4NuwqZEJyOEdP12LXBcvnJrJpTzFLZiUipWTjniIyUqO5Y1osibZA3vs0n79+fBpNAyQITTB3Ugx7DlfQ45BYzAIhNO6Zk8j6XUUIdJJiQjhT1ciM8Ta2Zp+hsq4Za6g/1jAfKuvbkVLnpfXHeGzZeABe/CAXKXUmjowk93QdDy8dj9A00sZHsn53MROSQnj25f1I6cDuEHh7aiyZncT6XUWseXg6VWc7iArx4mjBOcYnBbPmlQM8/+gMqurbeXF9Ls89MoPH/++e1E0vLD0yZIvu2pMB/ElK6QBqhBC7MPxim4EDUsriXnV7X38DmCCEuM95HYihoPb2obUAv3f6cTqAEZcpz70AUspPhRChQohA570tUsouoEsIUYuhbJf3ajsDw9e02Nn+3CX6/AawRAjxhLOeF4ZVt+My5AS4A3hdStnebzwXI4FxGNnHwPiBUNXr/jrn3y8wFOavzA2vvPbnUkrmQHUup80Vy9BP4es/Ru9zTdMIn5VxSTmvBV91jMttP5hSfS0/B4VCce0QQiAQSAmd3Q7+uC4Hhy7o6nEAsDW7FE0Y2eA+PmB8r+46VIEuobNbZ9veUmdPEpMAh3TwyeeleHuaKK1ppduuY9KMbzmHLnln+0mEEGzYXUhLWw/jk0LIKTjL2m0nOVpofFe+91kh3T065bWt1Da009zWTVlNC8Mi/Zk/I46Ne4qwOww///9dl4vD4aCrR+LlobFhdxHtXQ5yTp/FpEFmThWeFg1Nk9Q1dODQdTbsLqKj20FmThXJwwI5XdbE3z7OR5ewdvspAEwabM0+w6cHy7l3XhLv7DB0GIvZ2LvQ0yPZtrcUD7NGt13njqlx7DxUzrrPCjGZBIm2YA7n15EcE8hnX5QB8LdPTtNtBw8zdNvBokGPDv/7/lG3caTHYchsMWv877oc2rt0UpPDOHK6nlkpNtq77IwYFsSp0kasYb68/2k+7V2Sj/afYfv+MjSM970TkkJo67Tz6cFy9uRU0NLewyefl4ER3zTpeqyta8hxDEvgQFzMQtR2kWsB/L2UckefzgxXARc/AmowNhtrQOdlyDpgsB7n365eZQ4u1NUGSoF7sT4FcK+U8lSfyn3ncDEGG6/3/eNSypmD3HfNZ6C5fClu+A1bCoVCobi+2MJ8WZyRgC51nntkBj/6ViqTRkXS1eMgNsoPAE2Ah1nDrBnnFhMsm5vI95eNxWIyvs0051fp2KRgwFDMFqfHIYShoN05dRiaEDy2bCzfu2skE5JCWJwez3e+kczYxBAArGE+mDVIGR6Mt6eZ++ePAiTrdxXgaRHUNbSz5uW9rNtZwMK0eMwmQWpyKHa7HYdd4mGGeZOjWZwej8Ws4WES3DsvgRGxgZiEJH28lfc+K+ChxWN5ePFYLCaNlKQQympaAJAITJqGyTmXaWPCsZgEmpA0tBg6yrBIX0xC4nCA2QQzx0XgYRZkTIgiOTYQXdfpsdsZnxjKt+9IwmwSVNa34DB+BzBldAS+XiZumxIDgC4NRTZ1RCi61JmdasUkICbcB4sJ5kyMxmISFJQ3siAtjnvmJPKdO5Mpq2nBGuLJmeoWrGGG+2XVOUMPiw73BmBUXCAWs8bs1EhmpdjwMMPOQ2VgbGy62fkU8BRCPOIqEEJMFULMAXYD3xJCmIQQ4cBsLi/F6w7gB0IIi7O/EU4f0t4EAlVSSh3DL9S10asFIznCQOwGvufscy6G32rzZcgDsBeYI4RIcLYPuUSfO4C/d/r9IoSYeJnjuPgQWC2E8Ok3notTQLgQYqbzvkUIMfYSfV7s2VySm87yqlAoFIprS49d550dp5ASHlwwmvAgL7KPVpEyPJSjBWcxmzAUNbOJO6bFsjmrBIB1nxVy320jcBpnsZg0NCEormzFrIFdh4MnaymsbGV8YjC7jlTS0eXgbFM363cX8ddPTtPVo+PjaULTBF4eGpk51dh1yCloYGF6PBEh3rz4wVG6ewzL7frdhSTaAskpOAtIOrsdZB+rdc9Fc8C2vWX4eJqQEhCS9z4pRsdQsD/cX4YO1J5rx2Qy0d7l4OSZBrrthqEp0N+DxqYudKfd6fCpeoQAh0MnM6eKjBQrmTlVCGGYpnQH7D9Wiw5kHq1mX241OoZCmpVbTWtHD3aHZOa4KI6crkOic+hUPWaTICzIBw+ToNsh0e2QnWvM49Mvyum2Q3ldOxaT4LNDlfQ4JB4SRsWFsOaV/YxPDKG9y0FHt/HwS6pamTkugr3HahFAWZ3xhri0ppUeu86rm0+RX9qIl4cx51sBKaUUQiwD/ksI8VMMC2gJ8E8Yit1MjGg2EvgXKWW1EGLUJbp9BeNV9yGn8leHsSu/N38A3hdCfBP4jPOW26OAXQiRg+EferhXmzXA685NU+3AiiuYZ50Q4lFgnRBCA2qBOy/S5y8wwocddc6hBFh0BeNtd7pEHBRCdANb6fVjR0rZ7XSr+G+nm4LZOd7xi3T7GfBTIcQRvsSGrRs+2oDa9fq15Kbf9Qpq7X5NuSXW7sgxE+Rzv/szDa3dHDhezYxxVsPndE4i739ayNzJMQyPDuDw6bMsnRXPRwfO8OGBSiwafGNmHElWfw6drmfyyHBe25zH7NRoAvzM/OnDQqaNCuXAybNMHRXM5ycbmDE2nMToIP72yWkEoCO4bbKVzKM1LJudCEKw7tPTjEwIpbiiCbtD0t1jp8cBKcmhnCxpoKtHxyQMq+je43UADI/xo7C8FQkkRfsxfayV9bsK6bbr2B1Oy7HFRKdT2fveXclsyTpDnNWfE0Vn3Qo49H1nOmNsOPuO12ESoGlw321JnChpJOf02QGfpeFaAZEhXtSc68RsgskjwzhR0sTi9Hje31lEV4+DWSk2jhWdpbOrm84eiZRG/7oOd88cxs4vKnHoOtPGRnLoZA3ddvjW7cOJCPbhd3/JwdMssDskDglBfmYaW+3MnxHDpwcrkVLHoRtypCSHcLzwHHYdMlKsLJ0Vz67DVWzOKtE2vbD0xlYIFAonSnlV3IjcEgqAWrtfS26JtRsUNVzetfq/MJkESTFBHMmvJ3VEGMUVTbR39dBjh+SYAE6XN6MJGB0XwPGSZsIDLdQ19ZA6Iowj+fWMGBZEYXkjDgnp4yM5dKqWHofEPohiGB3mRUV9p7vMpBmuCbqUdPVIMiZYOZxfQ3ePRNclugRvT42YyADySxsvmIdJA4d+/twkDGXQLiE63Isx8cF8crAKTcC0sZEcOF6DXTf86YQGo2IDOV7ShAkIDfKgtrGb9AkRZB2t7dM3gL8XtPTzdNSAqWPC+PxEPV6eAj9vD2obz7szjhgWQH5ZM8kxAZxt6mBMYhg93d3szzMUYYsJ5k2Kpq3LzqGTNQyLDCK/rJHQAE/ONnehCbhjSjQffl6BRYOEmEDyS5vcVu4Rw/zJLzPcH0xAgJ+FhtYeTBj+rx4WwcQRYewzFP5vb3ph6ZWFK1Iohgjl86pQKBSKPkSG+LB8XhI9dp2c/HosZkHO6XomDA/HYjLc+Srr2w1LqYTjJYar3rnWHjzMGqXVrYwYZihaLnU+K7eGeZNj8TQPHPddADUNhmIXE+GDJnBaCyXfvD0ZLw8TOQX1TBwRSY9DMj45DH9fC/fOS6a+sZPIEC/A8D9NGxcBwN/dPpzUEWGA0ZddB+mUp6Kuk48+r0KXxhyyc2vc93QACcdLmoy2QG1jNwA5p8+RPj4SXTfkdNFfcXX1s/9EPTrQY5d9FNewQC/yy4znVlDeTJw1iMycKr7INzanmTToccDHX1SQddSwtOaXNWIN9eFss9GPLuHTQ5Xu8/zSJqLDvLE7lWqX4uqaQ0Nrj/tc06CrR7oUV4C4AT8YheIGRCmvCoVCobiA9z8rRBOC++eP5Dc/SOMbU2PYe6ySCUnBeJhg+LAA7phq7dNGOowNQXdNjybY39hScfuUaKaPCcPDLEiK9qOnt7kSGOfczJUU7YvLBltW2+6+P2V0hBFzUjro6u7B39fMN6ZFk1/ayKL0eMKDvemx2/n2HcnEhPtQ39DB4fx6LCYoq22jrLqZjBQrHiYMX1UdkqP9CQuwuMfQJQT6mvGyaIx3yuOQxhekCfjG1GhmjAkjeVgASAe61NE0SBsX4d6UBoYCPi4hkOmjQxmXGASANcQDgEkjQ5k/PQZPsyA6zJvOrh5mjgvnrmnRzBwfwbduT2RCUgj3zoln5rgIpo4Ox6LB9DGRCGBsfDDTx4QxLjEYa7DRZ0SQGU1IZo6NYFSCEWVpTEIwpl7f7CbNcJuYMSaMaaNCGBsfAMCjS0aRPiGKO6fYSB8fBcamHIXipkC5DShuRG6JV6+utSulpLiymQRbgDvsTe9rF4OV978HDFpvoDbxVn+KKw0LT2J04EX7Hkg+XdfJzKkkI8WGpml9+i2parnieRRVNA0qy5XIdamxLtVn/+d4JX0NJts//nbnLeE3GGpLlunf+U98vc388vtGSul//u/ddDtNemYT2B3gYdHcG6cc+nkfzf54WjS6enQmjggzFEuzRo9dx2LW8LSYaO3ouaCNl4eJqaMjyTxaCfL8WBKYOCKcw/l1eHmY8PQwYbc70KWgo8uOALw8Tei6pKtHZ1aqjfzSRkYMC2JPTiUmp4xenmY6uuwXjPnN24fz9vZTmDTDh9RiFk4ZjbpxUf6cqW5xPgejDoDFJOhxhulyxSbqvxB8PM20O8c05Dwvg2t+9GprMWs4HIaibB9gT5XLn9aFABakx7IlywhTNiI2iMq6Vto67O7+epyf4cjYYE6XNaAbySqRkppNLyyNunAUheLGQ0UbUCiuEV3dDnZ9UYYE1m7L42erppMYHUhRRRNrXt7L4owEJo2KJCkmCCEERRVNPP/qfp556Hw9MJS84spmfv3GAZ5aOQ3AfZ4YHTioQlhc2cyvXt/P/JnxbM4sAgTPPjzD3calfBVVNPHcK/t4aMk4Zk+MpriymV++to+70xJYPjeJdTsLWbs1j5pz7dw7bziZOZW8ueUEKxaO4a2teTy5YipCCBJsAUgpef+zArZmFbFq8ThmpUa7ZUmwBVBY3sjTf8zGw6LxzEMzqKxvcyvFYCiFBeUNHD5Vx8QR4fzb2i94csVUAMpqWnh14zEeXjqe2RONfvccqeDNLSd4auU0twwXUzxdz/jnq6dRWd/mbgtQXtvKG5uPsSAtgYkjI9yfi0uu3spq78+j9+eDEefxyFVaQkOGLcyPx787iZgIPxKjDYver38wk/W7i5gyKpzocD92Hqpk3uRodwD8nYcrmZNq5bNDFYwcFsTJM+cICfQmKtQXW5gvRwvOsXR2POt3F5MyPJSc02dJHRGG0ATl1S3UNrQTHuwNEuqbO5k4IoLE6ECmj41CSokt3JeK+lbqznVwz5xENuwuISU5FE3TqKht5fUtJ7h3XiKRIb5Eh/shpSTn9FmWzU2ktKaN2EhfAv08yc6tZFF6AikjwqiobUNKnZpz7TS0dHLntDgSo4OIDPEjMtiTXYcrmTspGk3TKKtpJr+0iVWLRvHBrkIaWrqYNymG6rMdIIzwYpV1bUh3anDjWeq6Tn5pIyNjgxkW5e8cUyI0YcypppXaxnZSk8M5crqO8EBvhCaoPddO6ohwqs92EBnixWeHKhgRE8jJ0gZGxgZx8GQdSzLiOVbUQFiwF0gwaSbSU6yMig2ltrGd5XOHU1LVQllNM7UNHaQkh3L4VD1nqpr40Xcmsu9YLQ7dgaZpvPDu4flDt+IUiitDWV4VNyK3hOXVZb3y9jQxb8owHlk6DpPJxOnSc/z0fzLptkv8fcx8f3kK6ROsrPuskI17Cli9eBx1De389ZN8TCaNX30/ncToQLf106U8uRQpl/IpBDzz0Ay3siGlZNehMl5cl0vKiDCWzx1OcqwRnm/XoXJecmbWqahr44/rjuLpYeLZh2eSYAvgvU9P8/a2PObPjGPPkUpmjotg1+Eq7rttOB8dKGPFwjFkpNgoqWpBSslv3vycp1ZOo6ymmd++e4i70+L5/EQNKxaOITrcj9+8+TlPrpjKF3k1rN1+kgfmjyIixIff/ekQj393ErNSo8nMqSQqxJsn/5BNt11nYVo8d0433PDWvJxNR5cDpI7FYuYXj6UhhOD5V/ezerExhnE+lpgIP4QQbiW993PbfbiC1zYdY8WC0c4MRuOIjQpkzct7sTsMK922vaV4WgT33ZbM5NGRaMKwNv/itQM889B0kmKCbnnL6832/+7lWs2v1FL/NUM9EMVNg7K8KhTXiLAgb777jWTe+6yQLVklNLd18+PvTqKiro1uu8SkwawUG29uOUH12Tbe3naS9BQrL284Rku78zVqj4NDp2qRUvLiB7nYwnwZPiyYBFuA+9W9lJKfrz5veexNXUMnrZ12so5WE28NYERcKEUVTby8IZfW9h4On6pjc5aRBfHumXGUVht93jtvOAAf7Cygpb2HkqpWunp01n1WyDdvT8YWdj5Gd4ItgKdWTiPBFoDD4cDHy8y8ydGEBnq7LZtPrphKeW0rm7OK8fO2MGmU4cfo42XGFubLniMV/PbdQ9w1PZZuu44AsnMr3crrzHFWtu0rZXiMPwXlLZRWN1Pf1EmP3U50uBE0v7vHzv+uywEJnh4mHloyjtpz7by9/SQA0eF+vLbpOCsXjubzEzW0ddrJL21izqRhLJmVxNs7ThIa6IXZuZHlnR35bNhdjIfFxKpFY7A7HJRWN7vn7LJ4u6zdzmd/S1hebzZcP1auVj2FQnFjo5RXheIaUd/YSUNLN109OrGRfuw5UkmgnyfzJkXjadbosuvsyanisWXj0XUdT4sg53QdS2clUlTZRHZuDZqA9bsKQEJrRw/lta1uv9Nfv3GA+TPi2JRZzLMPzyApJgg4b13SdZ0NuwuICPaitqETkEhpHI8sGcfLG48REeLN6sVjeXlDLu99kk+XHRwOOxaLhWVzEsHpBlDb0M6I2CDunhHDKxtPsm5nAd9fPoG3tubxwN2jEEIjLsqPyvp2PCwmKmrbeP/T0zy2bBwJtgD2HKnk1Y3HWL14LNHhvpTXGK+JjUxD7djCfPHzsSCcG00kcPcMQ3H99RsHmDzK2DFeVt2Kt4fgYF4Ne3Kq8PUyUV7bamRz0gR2h5Gmc3FGEq9tOoEQcP/do0mfYASSdzjsfPZFBYfz6xgZG8z+41WMTghh2dxEEEbc0L9+XAC6TvKwAL6/fAKaMF5L67rO/7yXg9kkmDt5GFk5lQgBi2clce884/Ust0aKTYXiK+FMO1rcr7hJShl0/aVR3Ioo5VWhuGZIsnKr8PLQONfcyYjYIPYfq2JkbDBmswZCcs/sRKLD/fjFawcwmUzYHZKNmcVIaShhPXaJwyEJD/Hie3eNJDrcl1+9vp8HF4zhwQWjeX3zCXSnQlpY3oiUkvLaVl7bdJzVi8fS1eOgqc2w4hZXNlNQ1uD2I314yVj+uC6XNQ9PY3ZqDB/uM75rPj9Rw77jtdTMH8X2fWdYPm84Zxs72Lr3DBFB3rR32vHxMiF1yV0zYvnDezl0dOtUnx3JlqwSFmUkUN/YQVunnfrGLoorm3nxgxx67Dq6rnPoVA1vbzfywS/KSOCtrSd4cMEYHr1nPK9tOu7e/HPwVC2TR0fy4ILRvPjBUcDYWDJvcizb9p4hY4KVeKs/L63PxWwSzE6NYfeRchalxSPR+dnKKZhMJrdP6tpteYwfHk5mThUjYoM419zB9HFW3txyAoBt2cUg4ZElI/nDujzKa1oQCA7n17F5TwHR4QGcKm1AYGJrVgkL0uIBWLs1D8Blrb73+q0vheKG5zDw787z7qEURHFroZRXheIaYQvz45nV0/jsi3I+PVhKQVkT9989ipgIPzQNcAg2ZRYRGerHz1dPo7ymhVc2HeehRWOob+pk/e5CPC2SZXOSeGXDcXRdEhnsw4MLRrN2m7FR6uerp6PrOodP1bFxT2Gf3c3R4b7cO284f/ow3x3HMik6mKdWTiPe6s/HB0pp67Tz7oenOZJfx4RkYxPNtLFRpKXEkD7BSmSID69syKW1vRtNwNGCOh64ezQRwd68uuk4PXYHmibw9TIjBNgdDjbsLuLZh6chNI3lc5OcfrHQ1aPz4ge5bhkXpcfz8JKxjIwN4qX1uax5eDqrF4/j1Y25eHmYyS9t4tmX9/GLx9KYMzHGSEEqNEICPfHyEHxxqpYjp+vQNEH6hGiycyvRdcl7nxlZlITQ+ObtIwDjNf9PHpjMx5+fwWyC+oYOFs1KZMe+M27/3eqzbby9PY+k6EAkhuvAF3k1vPthPsNjAjlV2kDGhCiWzRvOkVP1bNhThEmDjFQb2/eWMHlUJJteWHrkui4yheLGpg742Hl+YUgJheJLouK8KhTXCE8PE0dO17E5q4TU5DDmz4xl2ZxEEqMDmZ0aQ1ePTrw1kDc2H6Oirs0Zr0bnwIka3v80n0Vp8cyZGENKcph7c8nrW04QE+Hv3oGfYAvgSH4db23LI32CjWdWT+PhxWN5ZMk4hBBs3FOELiHYz4yHCVJHhpEYHUhJVQv7j1cxYlgQxZUNpKdYKatuxc/bzLDIAGIi/AHQdcnMcVZ6HEZIngRbIBHB3qRNiGLmuCgcus6yOUk8tmw8W7LOcM/s4ZhNgqr6DpbPTSLraBVxUX58f9kEHv/2RL6/fDxmTZCRYmX14jGcqW5FSmhp76airo3ZE6NZvXgsLe3djE8KZnFGPF+crGH/8WoWzIxFCNicWcK8ybF0dDlYkhHP7NQY9h2rZEFaPHMnxeDjZWZRuhEpwYWu67y55QRbs0sxaRqrFo8ldUQY988f6fbfDQ/0wmIWJNicc5dQUt2El4dGRV0rHmbB1LGRmDQTk0ZFYDYZSvOpMw08uGA0UkoW/3iD2vSiUJznGxgKbB2wYYhlUdxCKMurQnGN6OoxrJAA2cdqARibGEZMhD97j1VhMWvknK5nQXo8L2/IRRPC/VobcFsQQwK8eGTpeGxhPmiaRoItwB2u6sEFo9mwpwgvDxN7j1UzOiGElzcex2wSPPPQDJbNGc5b207S0GrH02JCExpFFU3EW/25Oy2Bt7fl4eVp5njRORZlJBARbGQMeu6VfSxIi+Od7afw97XwvW8kc6z4HKfLmsgpOMSsEzb2HDEy+2zYU8xjyyYgBEwcGY7QBG9uOc7JM+fYmlVMzd2j2bHvDE+tnIaUEl1KsnKqiLcGsDX7DKsWjibA14OYCCO80cmSBlo77OQWNpBb2IAQGNbeIG+27y/jvnnJSCMHEqU1rWTnVrMgPYFNmcU0tXaTkWLltikx7vBbAOt2FpJTYKTcvO+2JIZF+vP8q/vo6na4N3e9sukEXT2SDw+Uu2N2ZufWsDAtgd1HypHAqxtPYNI0HloyjlWLxvD65jxWLx5DTIQ/v3nzc1AbthSK3uwHnnaeNwylIIpbC6W8KhTXCE+LiUeXjueP646CECybm+QO2bR68The33SchekJpI4MY9+xalYtGostzJsj+fXMTrWy73gtM8dHMXFUOP/21hfumKJFFU28tTWPFQvHYAvzxWzSeOS+ccREGpEHLGaNVYvGkhgdSIItgLAgL06WnOP2qbEIIdzxSe+dN5yIYG9s4b5U1rXz+ubjSAmrF49FCCNFaICfB48sMfretq8Mi1ljUXoCn+fVsDA9nsycSh5ZMo5ZqTaGOcffse8Md6clsGPfGe6/ezTL5yYxeVSkOxLCD5an8PLGY0SG+CAExET68/yjac6NXRVs23sGb08z985NJCLEB01ozHLGdRWawBbmyy9eO4CPl5kTJY3uMW6fMoxPPi9j694Sjhac5RePpbl3li+fm0RRRSPZR6uwhvqTGB1ofAabj7Nq0VgyUmzYwnz5+PMzbMs+w7fuTCYyxBcQzEq1MSo+xKi7cAwIwdpteTxw92hD/gijv6dWTuMff7szZ6jWm0JxA1Ivpfz40tUUiitDxXlV3IjcEq9ep0yZIj///PMBEwgMlunJdd47e1Xv+/0zQV3s3kBxLC+W3cslZ4ItgJKqlgtkuNj9/vMaLPvWperous6eI5VEh/v2SRLQv31RRZMR6N0Z+uhy2g+WKaz/+L3rDPTcBvqMeo1zy6xd9f/u146rtnZ7RRvYIqVcdLX6VShcKOVVcSOiFADFzYpau4qbFaW8Km4alNuAQqFQKBSKq4aUsoRb5Iec4sZERRtQKBQKhUKhUNw0KOVVoVAobhGEEK0DlK0UQtQJIY44j4eHQjaFQqG4Wii3AYVCobj1+YuU8odDLYRCoVBcDZTlVaFQKBQKhUJx06CUV4VCobj1uVcIcVQI8Z4QYthQC6NQKBRfhZtWee0d6/Fq1XXV03X9svseagab26Xm/GXbXWn/N9OzVChuUTYB8VLKCRh55t8cYnkUCoXiK3HTKa8upaiooolfv3HAfT6QctS77q9e38+eIxUD1tN1nd2Hyykoa+C5V/ax50gFv37jAMWVzX36KixvpLC8sY9C1r/cdT7QPVcb13h2u53dh8vRdf0CmR0OB7sOGTINpgBKKdlzpIJfvb7/All3H67gmRezeO+T09jtdndfrj6KK5sHnOPuw+WseXmvOyC9S9beMg7WfqDPZ8+RCp57ZZ9b/v5zc8mj67q7TOWHVyiuHlLKs1LKLufly8DkoZRHofiqzJ8/XwLquPWPQblpNmw5HA7e/6yAiGBv3txyggVpCdw/fyRSSn7z5uc8tXIaCbaAPplvdF3n+Vf3s3LhKIbHBPC/7+dgDfXBZDL1yYqTmVPJb989xHfuHEFXdw+6rvPkiqkk2ALcmXWklPzitf3oumTxrES27y3hZ6umA0YeeF1KHl4ylpc3HENKeP7RmVTUtfLiB0cBwWPLxrN220meWjmNsppmfvvuIY7PjGNr9hlOFJ/lkaXjKKlqoby2lbe2nmD+zHje3nYSf18Lj94znre25rnTg7ooqmji5fW5pKfYiI30paiiiQRbgFG+IZeuHgdv7zjJ2eYOtmSV4GkR/P03JzJ7UgwJtgCeXDEVh+5g5xelxET4I4TgtU0nsDscHDpZS1yUH69uOsGWzGIAZk+McY8db/XngbtHo+u6W1Hfc6QSKXXe2prHgwvG8MDdo7GGeuNw1imqaOL5V/ezMN2Ym4+Xie8vn+DODPW7Px/Bz9sCKj+8QnHVEEJYpZRVzsslQN5QyqNQfFXq6+uHWgTFEHPDK69d3YZydaL4LNv2luJp0Zg6JpK120/i520odg/cPZp4q79bOVq9eAxrt53kgbtHY3c4+P17R+mxG0r8up0F5BWfZWFGEvfOG46maWSk2ADD+tfe5eDVTSd47pGZbqX1N29+zpMrpvL0qmkcOlXLxt2FLJmVSFyUH8WVzSxIi2P9riKkbowhpaSspoWX1ufS1W3HYjFhC/N1K9i6ruPv68Ho+GA+PVjOlqwSKupaKSpvRGgajywdR0aKjfAgbwSQPsFKdLifW0kE3LLZHTrbss8QGujNjn1neGrlNAA0AbdNiSHYz4vwYC9MGnT1SP6wLgeJZFikkeZyzcv7aG7rwdfLxC+/n87Tq6bywc5C1m7L41xLJ1uzislItZE+wdrncymubOal9UeREn7xWBrltS288O4h/Lwt3DMniZfW5yIEPLR4LHa7TllNC8Mi/RECUpJDKam0cTi/hj+8n0tnl53HvzOR++8aReqIMB7/v3uGND+8EKJVSunXr2wl8B9AhbPo91LKV663bArFJfARQpT3uv4tEC6EWALYgXPAyqEQTKG4VqxZs4Y1a9Zcdv0H1mynsaXrgvIgf0/Wrpl/FSVTXCtueOW1oq6VF949jJ+3GU+LhqYJjhWeZcHMeIL8LPzvuqN4WjSGRfo7lTkHUpc8uWIq8VZ/TpacY3NWMbFRftScbSM7t5r0CVbe2X6SyBAfMlJs7lzmUkrqGjpISQ6jvLaVN7ccZ8XCsTy5YiqJ0YEUVzazJauYzm47H+wqAGDjniK6ehx0djkQQnDP7OFs3FNAfqnxut/uAE8PrU/udyklS2clkT7BRu25Dv70UT5H8uvJmGAlt+gs0eF+mEwmhkX68/yr+4mO8KOyvo03Nh9n5aKxRIf78Zs3P+enD07h+8smIIFZqTYmj4p0515fPCuJt7bm4WHWMJsEDh0sZoFA8NL6Y2iaYM3DM3hkyTj+sC4Xk0kghKC8toXMnEruTovjocVjCAnwYvveEjJzKomJ8Hfntdd1HV2XaJoxr+hwP/x9LNwzO4nUEeFs2F2IwyE5deYcbZ12Xt5wjF88lsYzD82gtLqZrKOVzJ8Rx56cCtJTrNjCfXl7xykmjYqAG9fyqsINKW5opJSDuYI9eV0FUXytEUJMB34HOICDUsofCSE8gNcxfK6fAUzAp1LKnwkh5jrLi51d/FZKufFyx3vuueeuSHkdSHG9WLnixuOGV16FgPvnj2DSyEjKa1r44/pcxiQEs/tIObouae90MHmkjWERPqzfVYyUktc2n+DhpeMA+DyvhlmpNrJyKpk/IxYJjIwNIi7Kn/QJVrfLgJQSIQTb9pYggc2ZxSzKSGDttjyeXDGV4spm4qL8WJSeyPrdBTgcOhv2FLE4PYFNmcUsn5NEXWMHW7OLGT88nC1ZJSxIiyM4wIuJI8Ipr23ltU3HeWjJWF78IJfWjh50qbMpsxhvDxMpyeFMGxvBiZJzABSWN1JW04KUOodP1bF9XwnzZ8bz6sbj/Hz1NJ5cMdVwMXDKl3W0ivQJVoorm0mwBbB8bhJF5Y1kHq3i9qmxBPt7snF3MY/eMw5N03h10zEOnawlIsQbb08TqxaNRUpJzdk2JNDc2oUQgvtuSyYi2JsXP8gFJMvmDGf7vjM8cPdoNE2wJCMRKSWJ0YE8es94Xtt0gpTkMGalRPPxgRJ27C/FwwRzJkYTb/XHZDKh6zq+3maEBna7g8ycKhJsgTy5YqrrY38fSLrui02hUCgUV4MzwG1Syk4hxDtCiPFAKJANfCal/BBACPGZECLc2WatlPLpayGMyzWtP/vXPU9YbAoA9aU5TF/+zBX3oRgabgLlVTBltJWkmCAq69vp6HKQnVuDr5eZ5fOGc66pi+37zhDo78nWrGIWpCUwIjbIreQ9tXIacVF+xFsDWPdZAW2ddrbtLSXA14OpY6xuiysSXt98jNEJoWzaU4QEJo2MYMroKKSU/PqNA9w/fyTrdxfy8JJxaELw2ubjaJrAZBKYTCbe3p5HeoqNkyXnSE+xsienEpOmERXqy0vrc9F1HVuYL4/dM54X1x+loaUTTcDSuclsziom70wDqxePpaKujdc2HQMEi2clsWPfGVYsHIMtzJet2SVuK+5rm46zevEYKura+O27X3CiOJ7so1U8+/AMhBAcLTT8grKOVnHP7OG0dzvQTCZmT4ymtrGDtVvz8PU28+g946hr6ODVjcfQpcRsFmQerSZxZyHfvH0EMRH+aJrA4ZBsyS5h9eKxWEN90HXJhj1FbNt3hp+vno6RylqSc/os2/aWYDJpOHSd8BBvNmeVMCo+hNkTYwwL9ZzhvLvjFHfPjGPnoXK2ZpcwZXSUy3J879CstktyrxBiNpAP/EhKWTbUAikUCsWNhpSyutelHcMC+w3gDSllD4AQwgRUA80X9nAhQohHgUcBYmNjWbNmDc8991zv+wA8++yzA1phXfddLHp8PWGxKeTtfh2A0bNXsfm39yB+e1lTVAwxN7zyag3zdW9SmpVqQ3c4OFnawKjYYN75MJ+fPDCZ0CBvls6KB+DA8WpGxYfQe51qmsbkUZFszS5h2dxEQDBpZIR709awyAB+/cYBZoy3sS27hO/dNYqJI8MRQrg3bT24YDS6Q6e1o8foVAhWLRrD29tPsXrxWGzhvkhG8eH+Uu5OS2Czc5PT4oxEbGG+aJpA00xomsbsSTHUNLTz9raTLEiPZ/m8JCaNikBKSUVdG29tPcHqxeOIDvcFDCXa9QyeeWiGe1OWsRlPkJFipbZhNJszi9F7+cU+smQcL288xpKMBMKCPPnRt1PJSDGss8vnJoE0lNH6xi7e3p7HgrQERsYG8caWPGaMjzLqAInRgax5eAYVta1ER/iRFBNEUUUTnh5mVi0cQ0ykPw6Hg/99P4fHlo1j9sQYwoO90HWdT78oJ7fgLN6eJqLDDR9hl8tDVKgv6ROs3DZlGEfy64mL8kMIwaYXlh65lmvqS7IJ+JOUsksI8X2MV1y3DbFMCoVCccMihJgAhEkpTwghRkgp853ljwL/Amx3/p8K8IAQIsPZ9F+klAd69yWlfAl4CWDKlCmyt5/r5VhFe99f/OMNA9ZZ9Ph6Nr2wdLC5XLR/xfXlhldePS0m96LRNA2T2cSOfaWMSQjjqZXTkFKyY98ZIkN8+PxEDSsWjiEjxeb2gf31Gwfcu/SfeWiGewPW1DFWd78JtgC3hXZ0fAgZKTZKqlrcbQHe2prHTx+cwo+/OwmpS373p0P86DuT3DK4NnVNGR1FvNWfSSMjKK9tZe22PCaNiuC5R2YChiIohKE8f7CzkL251XxjerxbIVy7LY8VC8cwKzXaHY7qqZXT3LK6lNjE6EAeWjKOt7bmMSzSn/tuS2bSyAj3c3PJ89iyCby0PpeWtm5+/L3JnKludfd53+0jmDw6irgoPxCwY98Z7pgWyzMPz+gTjUEIgaZprN1+0i2L63m66v3t43zaOu3UN3ZhMpmIjQrk128c4KcPTqGiro3ocF+SYoIA3BvXhg8LBqDqbDvv7DhJZKhPn4gGNxJSyrO9Ll8G/m2oZFEoFIobHSFECPB74O+c5+7/Q6WULwkhXgXeF0JMdBZfM7eBwagvzWH07FXu86QpAyuuihsPcaP7cEyZMkUePHjQfa3runuDlaZp7lBWrvBYvZUu171LlQ1E73pAnzaDydC/z4uN5QodBecV2v71LyXr5YwLhv9sRV0rs1KjEUJcsayXc9/hcLBuZyHL5yZhMpku+znDhZ8phv/BkDFItAF3uCEhxDLgJ1LKGRfrp//aVXwtuCXMM2rtfi25amtXCGEGNgLPSSn3CyH+DkBK+VchhKcr7rAQ4h3gF0AUcMflKq/916eKNnDLMuiavOmUV8XXgqFWXnWgslfRb4FwjBiZrnBDP5BSnrxYP2rtfi1RyqviZuVqKq/fAf4bOO4s6gK+K6U863S7+jbGm9/PpJQ/HyDawKtSyrWD9a/W59eGQdfkDe82oFBcb1S4IYVCofjySCn/BPzJdS2E+DuX65WU8o/AH/vV3wnEXU8ZFTc3N116WIVCoVAoFDcPUsq/DrUMilsLpbwqFAqFQqFQKG4alPKqUCgUCoVCobhpUD6vCoVCoVAorjpfMk3sZUcduBRXGoXgwIqH6Gls7FNmCQpi2puvXg1xFFcRZXlVKBQKhUJxLXCliZ0FRDjTxKZxPk1shpRyJpDWK03sVaN3Bi4XFwvd2F9xHazscvpSXFuU5VWhUCgUCsVV51qkib3WPLEviynhRsKfg3W1/OeM9CGWSDEQ18zyKoRoHaBspRCiTghxxHk8fK3GVygUCoVCMfT0ThML9E8Tewo460pccJE+HhVCHBRCHKyrqxu03po1axBC9MkQKYTo4z7gKut/ZGxcx5TwCH5/PJffH89lSngEGRvXDVpfMXQMhdvAX6SUqc7jlSEYX6FQKBQKxXWgV5rYhwZKEwuMBGJ6pYkdECnlS1LKKVLKKeHhg3sYrFmzBiklrgRMrvPeyqurrP+RuWT5Bf1lLlk+aH3F0KF8XhUKhUKhUFx1nGli3wb+2elCcAfwifOeJ4CU0gG0AR3XQ6aLKZ2WoCAO1tXyw7Hj+eHY8Rysq8USFPSl+lJcW4bC5/VeIcRsIB/4kZSybAhkUCgUCoVCcW35JjAV+Dfna/Yu4LvOe6uEEL3TxJ4UQkRdzcGfffbZK6o/7c1X2Xs1BVBcM6638roJ+JOUssuZ3/hN4LbrLINCoVAoFIprzJdME7vzao1/JWGyFDcX19VtQErZ2yn7ZWDy9RxfoVAoFArF0KDSxCquFtdVeRVCWHtdLgHyruf4CoVCoVAoFIqbm2vpNuAjhCjvdf1bIFwIsQQj3ts5YOU1HF+hUCgUXwK9qwu73U7lBxvwCAvDOyaarspKPKxWuqqqkBK8o610VVbjYY2iu6oKT5uNzooKdAmaAE+bjaYjR/CMiMA7OprOykq8bFY6KqoQAjytVnqqqwlOm8nZrGy6amux3rOUxn37CUmbSVtRMU1HcvAdP4623GP4jh/LuV17CJkzm9ajuVjCw/GOttGScxT/lAmYTBpSl3RUVuFtsyKBpiM5BKRMoKuyCgSEpqfRsHcfwTNncC57L142K1JCZ2UlAGEZ6XSUnEGXEiFA1yXdVVUEp83kXFY2unPe7eWV9NTXYb1nKQ179+FlsyFlr7rZe91lXVVVeFqtmDQN7/g4zmZl42m10llZiXd0NEhDZi9bFJ2V1XjbrAhNoDt0Go/k4BkejneMjc7KajytkbQczSVq6RIa9+7DHBlJa24ufuPHY6+pIWjmDKo3biJqyWIa9u5zj+Nls9FVWYUlKpLWo7n4p4ynu7oGj6go5+cpKfzdf6emb3j/yJAuPIXiMrlmyquUcjCr7pPXakyFQqFQfHU6KqvIffJp2vNPGwUeFujuAbMZ7HYAhKcnsqsLNA10/XwdFxYz9Nj7trdYoMdZx9lX2PHj1G//CICz+z+n/XQBEQvyqP34E6ONECCle5yardvPj2HSwKHDn/6CsFiQ9h6wO8DTwxhb1+HPf3XL3JSXR932jwhJn8m5zGyEhwcSoLvbef8kZ3ftRnfoCCGQDgf09OD/yae0HDnqnNf5OdTv/5yO0wXGXEwm6Ooi4JPPaD6SY5RJCQ6H0QZJ2O23Ub/9QzCbDDm9vMBhN2R19ethQTNb0Lu7jDq9x3Q+g3M5ubQezT3/7J1/fZKTaT99mnNHjhr3XZ9X7+cO8CeTIVevzxPYAUR+pYVzBQghHgeWA/cD+zn/JvZdZwitQal0/ti4mjywZjuNLReGmg3y92TtmvmX1ceVpqNVfHlUqCyFQqFQ9EVKQ3E1mUAIgqdOA00QPH0aCIHm7U30fcvRPD1B1wlMTcHk4QkC/FNSQBOE33EHmrcXmrcXJg9P46+nh/HXz8/oC9CEGZOfL8LDg/aCAjQvT85l7UXTTACYfHyIWnA3tm/fBwKEh4chU3oamsUDAP9x4wxF2u5AeHpi0kyGUicg/E5DDoTAMyQMs68vLSdOEpqRjuzpQTOZnHJ6c25PFnpHJ0JKZFcXQjO+IluO5hKYmgJCYPL0IDg9DYCOgkK3Ahg8ZQomb2+aXXXtdnA4CExNMfrpsXNu1x7nPQfC04OQKZOhx05AagomT2NemmZC0zQ0s8V4Pt5eBE+basxz/DgQgo7CIjQvT5AS79hY0HX8RoygvaDAkKuoCOHpCQ6Hs73xrIWHB5qnJ5qH8dxMXp4EpKa4PvV3r+2iOo8zTFZKr6KPpJRzncdFFVeAqqqqKx3vknUGUlwvVj5QnwOlo1VcG1R6WIVCoVD0wSvaRuz938VvgvE6OjhtJg17pxmv22dMw9tmwzcxgeBJqTQdycF6z1I6zpyho6KSkLSZNOzdR0jaTKx33m5YH53GU7cRVQi84mKpSkrAunQJUXfehpSSjooKvKNtCCHQdUlXZRXe0Tb8khKRUuJntWGJiqKnupqQ9DRai4ppzskhaukSGrL3oUuJT7QNhKC9vBxN0whNTyPqjtvoqqwkJD2NkMkTEYB3fBwh06fiZbMCAgFIoLOiAg+rle7qajytUXRUVKJpGiFOdwBvmw2fhHjOTp+KlOBhjaI1N9d4BiVn6Kg0nsHZzGy66uqwLVtKW3EJtZ9+RsRt8/BNiKdq/QYCUlKM88R4rEuXGG0rKvG0WdG0vvP3jo+jKinB7Q7gZbWCEHRWVhLc21Ugey+6BJ8YG1JCV1UVXjYbPvFxVCXG4T9hAiZNQwIdFRX4xETjHRdH1YaNlL71zuPXcYk9jBFt6PnrOOYl2b/uecJiDZ26vjSH6cufGWKJFIMyWOaIG+WYPHmyVHztGPJ1dzWOiePGyabTBbK5oKDP35aCQmm322XNzt2yeucu6XA4pMPhkNU7d8uaXtc1u3bLqs92yubTBdLhcMiWgkJ3e13Xpa7rsvl0gazZuVs2nS4YsMzhcMjmgkLZ3L9dQaFsLiiQzacLZHNBodF/YZG02+2ydvce6XA4pJTSXbfpdIGs2blLNp0ukF1dXTLvP16Q3d3d7jothUXnxyow+uzdl2s+zQUFfcaq2bVbNjtld+FwOGTt7j3SbrfLloJC2eyUu/9Yrnu9x7Hb7bL0b+/JptOnB2zTXFAga3btPt/3aedn43w+Ltl0XTf6eu992dPT4y7Tdd14Frv6PfOCQtlSUCgzlywXQ73ursah/t/9WnJd1hZgAf7qPM+E/5+98w6r4lj/+HcOYAMURZBiAey9V6xRo8aemBhjx56b/vPmxmgs0WhuEjXFmxu7scTEa0VUjIkloqJiwa6hiRSlidKknPP+/jjMunvOngJSdT7PM885uzs78+7s7O4777wzAy8AD6CfIus4gGEmzpsOICQ/EA8LFiywfGGy+OZC056TFf+HfLTXbHwiogULFqges0YugUVM1iNheRUIiomsuHhcm78ITKsFMQYbO303ILO1gXO3LrgfcEi/nd/9FLbiW+TvgE6nQ/jK7wEAttWqwmfaFERu2ARddg40FSug6bxP8SQuDmGr10KXlg5NlSposWQRGGO47OhqXgAA5XZJREFUvmgxtI/ToKlSBfVnTUfkhp9BuTlgdnbwmTIZFd3dcXPRkvy8AGZjC2+/iYj8eSuqNmmE5KDTaATApUd3ZERG4cbipdBmZuq7ZStVgn3dOsjI94VsMvsjfZyl/4Zzpw54cPwvaKDvnvQY+gqit/2KRvnl8ffK72Hn6AjvaX6I2rwN1Tu2x4MDh6BxdECDGdPg7NsNmVF3kR4djfBvf4BHXBwe+AcAtnZosWAe7L29kBR0ClGbt6HuuDGIXLMOZGML1x7dcP9AIOo+eIDslBQ8OBAIm937UH/WdNTs7ivJV6NTeyQcPQHKykLm/fu4v28/iDFomAZggMbWFl6TJyJq8zbUG/8WUm/cROKhw3iSlIRHFy6j8b9mIys2FuH/XQNdVhZsHR3RbOFneBIXh/B1G8FIB+i7Qi8Xc9USCMoz42HsonCEiMaZO4n07gRrAIAxRkQFW93KUvyh/7cP4SH7jPYP+Wgv9i8fbrSfv7flfq6MMYv5CIoGobwKBMUFY3Dt1RMPAgNhU8Ue3tP88kf9xuHu1l/g9sogJJ06hcqeHgAAGwcHfXempwdSL10GAFT37YY6r46AvY83QISojZvhNXkCGIC7W35B7eHDELvXH8zGBgyAvbcXfKZOQcSadWA2Nqjs6QFvv4m4u3Ez3AYPxN0tv6DeuDHQ2Nmi3qQJqOyh72K19/bCk4QERG/bDrfBA+Gc79Nn7+0Fb7+JiPhpLbQ5OdAAyElKRrW2rVH//XelOO6DXkb01u3QVKwAVqEivP0mwtm3Gyq5ukppNSRCZU8P2Ht7AwCiNvwMGwcHeA4fhrtb9N+yu1t+Qa2X+wKMQZv6GNrMLNQdOxz23l7IiIzC3S2/wGvCWBARtJlZcBs0AA/PX4Db4IG4fzAQRAS3wYPg0LgR7m75BZU9PWXy/QKbSpUABwdomEaf9ltvolrbNmDQf3iqeHvpZdv4M7RPsgHG4Ni4Cdz79weIcHfTZmhsbVF73BhUa9NGug+eQ1/BfflAIoFAYIrGANrkL1TUHMDQ4s7QWoUyKToUTXtOlv7X72CstBY0TUHxwMr6DejQoQOFhISUthiCksWyd305oF3LlnT+8mUknzqNyh56vz3eMs+IjEIVr3rIjLoL+3yFKT0iUq+A+nhDp9Mhbu8+eIwYDhsb/cAVfh6Pz9PIiIySzuPpy9OSx82MuqvIVz7oQKfTIfnUaTj7doNG83QsJxEhLTwCT+LipCl/eF6Kc4NOoYKJ44YQETIiIkHQK79yuSrXq4uU02dQvWsXPDwTLMkjv34iQvKp06jRrSuy7kbrz42MAgFwkF0zv0adToekoFOo5KH3p6ziVQ8pp88YXatcNp1Ohyfx8ajZ3fdp/vkyO8jKWl62oR/O1vju21W2X6pWIN67LyQl/t5ljAXBeLaBA0T0tbnzPDw8qKhnHBCzDZRJTNZJobwKyiLPhfIq6u4Liai7gvJKuam7on6+MJisk2KqLIFAIBAIBAJBuUEorwKBQCAQCASCcoNQXgUCgUBgESJCROwj6HQ6RMQ+Uh2wwuMYHjPcr9Pp8NelGOh0OqvzLczocsPz5PsMr0er1SpkUotblG525srTnOzmyl/t3OKQXSAobYTyKhAIBAIF2blaSUnivxGxj/DFxrPYdfRvLFx7BhGxjwDolaPwmFSEx6RKcU5ejlWc/9elWCxYcxp/XYoFEeHk5Vh8s/UCdh3926xCq9PpsPPo31iyIRjhManScZ1OhxMXYxB276FC6ZQrahGxj7Bw7Rn8dSlWihMekyrJJ13PsTAs2RCMtfuu4ZutF6R4Jy7GSNcZGfdYOo8rs/ya1ZRb+bb8uuQK6F+X9OmfvByLpZvOSWVsKLtOp5OufcmGYOw8egfzfjqFsHsPVZXZyLjH+fH+hk6nQ2TcYyxefwZr9l6FVqs1kq8swBj7iDEWxBjzYoxttRS/KAdrnZs4BaeGv2YUzk2cUqB0xGCtkkUorwKBQCBQcD8pA0GhcVi66Zz0CwATBzfDgdNR0MmUnsi4x/h8/Vks3nBWirP54E3F+RsDriNPq8PGgOuIjHsMTxcH2Fe2xYHTUYiMewwACAqNw4pfLiIo9KlicvJyLLYeuomBXb0Qm5ghHedxF64Lxu7j4dL+yLjHWLrpnJSmjggbA65LcWITMzBxcDP8fOAGYhLSMeGVpjgcfBcDu3rh5OU4VKlkCwaGxRvOYs3eK3iSkwedTgdvj6rSdUXGPdYrhBvO4vP1Z6Vteb7ybfl18f0nL8dizd5r0Op08HRxwKeT9Mu3qskeFBqHxRvOYsP+GxjUzRv+JyORnpWLy3eSFPeHn+ftURWDunljW+AtBIXGwdujKrq09EBAUCR2Hw83kq+0UVkq1iIFXR7WHLmpqQXab4pFixaJ5WFLEDHPq0BQTHDrFf9A+HhWA6D/cHh7VJWmWpJvyzF3rKDxLMXhlhgup5os5o7L4xARGGMm0zF3vXJrEGMM3h5VERWfpoivloY18lmbv2Eaavutza+84lbTHr6t3AEAvq3cUdvVEV7ujgCAuZM6Ii4pE17ujoiIfQQvd0d85tcJsYkZUpx/jW+P2MR0jBvYBF1b1MKD5Ey0alAD8clZ0Ol08PGshsUzuiEmIV2ySHZv7QEigkdN+6cWQQIcqtihbSMXMMbw4Zi26N5aPy+yTqdfzMjDxR4uTm3gUdMeXu6O+HRSJ0m2qcNaoLargyRX99bu0rRn6/ddxZDuPvhkQgfodDrsOR6GGSNbonsb/dzHD5LTsfXwbVy+k4QGdapL+dZzc0BUfBrmTdYrnEQEb4+qinzlcuh0OnzwplK+vDy9UjxteAupnhMRxg9qKinL04a1gI4I7jWrSGXevbU72jZyQUxCGjxdHdCuiat0bfXcHBAekwoAGNnLByCCW43KiIh9hClDm6G6Y0W0aVRTIW89Nwf8dSkG3Vt7GE0ZV4IUy1KxBV0wYHbwKXRwcQUAhCQm4JsuvsWSj6BoKNeWVz6fpaWKY228kparONIs6WsVmIZbrxatC8YCWfej3OIREfsIi9YFS4qQHGutI+bSACB1036x8axRWlwJC49Jxfw1Z/D5+mDV/LiladG6YKnrVJ4G79b8fH0wFqwNxuINxnnxdOTdr2p5zF9zBp+tPo1F64KNrEqmysXQEqaGvAvalGyfrw/G/DVnFGUpT5t37UbEPrKYX3mmop0N7t5Px+aDN3H3fjq83B2x8+jfWLT2DGIT07Fh/3Xp3kTFp4ExhvX+17D7WBi+2HgWRy/EYPkvl7B6zxXsPh6OLYdu4tjFWGzYfx2LN5xFUGgcGGPYGHADSzbq7yVjDIwxLPv5PCJiH+GvS7FYv/86po9oCcYYFm84h9qujtBoNFLctfuuYcmG8wCABWv1z4C3R1XJWrlu31X8GXIPEbGPsP9khGTV9ahpj8xsLbYE3kJsYjou/52E9Kw8JKZmISo+DVsO3US7JrUwdkBj7P0rHOExqYiKT8PPB25g97EwLFx7BrGJGQAg1QEfz2qIiH2Ez1afluQ4eTkOn68/C8YYvtwcgqj4NPh4VkNsYoY+v4dZ0vWevByHNXuv4PP1Z3HqSjw2BFzH6j1XsXj9WVz+Owk/H7iOoNB4MMZQp1ZV/HvzBTDGcPd+ul6u4+H4fH0wFq0Lxp7j4dj7VzgWrNW/e+7eT0eHpm749+YLUll7e1TF7mNh+GbrBZy8HFsq9YwxZgegFxEdtSLudMZYCGMsJH8bjDGzXfU8jrnQ3X83AKCDiytWXb+KVdevooOLK7r777bqfJ6PYZ7ChaB4KRfKq5pCptPpELN7D24s/TcyIqPMxs+IjMKtL79GekSktF8tTWsUP2viZERG4eayr5AUdEp14IKp800dIyIkBZ3CzWVfISMyqsDxdDqd2Wu1FC8tPALp4RFWK87ydApzvc8L3Ho1pLu3VBbeHlUxZ2JHyRcuJiEd+qWwlfD4n0zoIP03hznDX2TcY2w+eBMTBzeDt0dVo2NLN51DbGIG7Gw18BvaQhGHK7d661pnTBnWQuo65ce4Irfe/zqG+Ppg4dTO+Myvs1FegL5Lc8IrTbHe/7rCv49b3+ZN7oTpw1vAxkaDSYObgnT6MuCWKblVy3Df3EkdMcTXG/XcHFTLc9exMGw9dBODunkrzuVl6+XuiMHdvAE8LW9e9nMndYTfkGaIvv8YC9eeARHhM7/OmD9F/TqfB7h1jiuD2wJvIVdLYGBgDFJ3N79+HRECTkWicwt3nLt+H0N8vVHBzgaMAZUr2uCvS7EY0t0bkwY3w3r/6yAizJ/ytK7I6ykAbAy4Dh0Rars65isET2WLjHuMjQHXwRjDlGEtwMCQnpWL2MQMKR2/oS3Qo01tBARF4tiFWOiIsGH/DUTGPUZcUiZytToM8fWCp4sDDp6OQuWKNjh4OgoAMGdiRzDGUKuGvZQur7v7TkYgN0+LjQHXEZuYoZBLr5Q+lWNjwHVodTqA9GnystIwBg0DajlXkVwG5NfTvbUH/Ia2QKWKthji64OAoEgM6FIPa/ddxaJ1wQAglT13aTgcfBeThzTH0O4+CDgVCQDo3a42bDTM6H7yMjxwOgr2lW3h6WL8zJQQakvFqkJEa4ioAxF1yN8GEZlVEnkccyFo2Kuq5wYNe9Wq83k+hnkK5bWYsebmlGZo3749pYVH0PlpsygtPII4CX+dpKCRr1P0zl2k0+lIjmF8nU5HaeERlBYWLu1XS1NtnyHWxNHpdJTw10nVeKbO5+ecmzrT5DkJf52UrsVc2jze47BwOjtpKj048ZfZa5Wfpxbv7KSpdG7yVLPXbJi/uXK2oixLvd4VRWjRqg3duZtMb84NoDfn7qfjIdEUdu8hhd17SFOW/E5Hz0fR6E8D6Oj5KNLpdKTT6aTjPM7xC9E0YWEg/R2dQuExqaTVaqXjvN7Lz5M/Czqdjv6OTqFjIdH0d3SKlEd4TKpRfmrpEhGFx6TSlCW/U3hMKhERabVaOnHxHuXl5dGJi/fIb/FhCrv3kP6OTqEdR27T5M8D6cTFe0Zy8DyJSCqTO3eTpfTl13ksJJre+uwA/XLoOg35aC8dPR+lKgvf57f4MJ24eI+OhdylYf+3l45fiDaqa2H3HtKEhYH025FblJeXR+ExqRR276F0Lpdx/IJD9Mac/VI8fo0nLt6jiYsCacy8A/Tm3AD6OzrF1PWVer0ritC+fXtF+Wm1Wjp+QV+PtFqtog7J6+WxEP19PH4hWqorU5b8TjuO3Kax8w/SxEWBdPzCPZqw8BAdy38eDOuiNdt6ee5J8vwdnSLlKb8feXl5tOOP25SbmyvV07B7D6V7y+PLj/Ht8QsO0bHzd6V0iYj+jk6hsfMP0rHzdxXp8Dqlli4vk7B7DxXlyePxOmTqev+OTtGXW0g0TVwUqCg3wzrI3xvHQqJpx5HbivptiOF1D/loLyvpegbg3wAOAwgE8BDAuwC2WnGe0fUUlqBhr1LQsFepi2steqd5S3qneUvq4lqLgoa9WqB0oLdCFJlcAiIyUwfKhc+rvbcXmnzyT2lZTABw9u2GRvm/hv5mhvEZY3Dw0Vu/5PsN01TLxxpZDGGMoWZ3X2lddWvOl6/bbu4clr8OvVoahvH067UDlT09LF5rk0/+iSpe9Yxktvf2QtN5c/RLjZq5ZnmahumYKi9ryrI8E5+YgUu3E5GelYe2jVywdt8V5GmBxTO64tNJnRByIx4ZT/KQ+PAJAP3glHX7roFIh+6tPTF2QCN41LQHY3qLzpZDNyWrJUCYMqwFerTxlOr/5+vPYv6Uzqhf2wmA3rKycF0wHqXnYLBvPdx7oO8u3LD/BhZM7QLGGJZuOoeJg5vBx7MaiAgL1gZj0bQuaFCnOgC9NXLCK00lf7qYhHRsPngDACQrmY9nNUTGPUZgcBQ6NquFn3ZfgbtzFdjY2MDL3RFBoXH4+cANzJ3cGV7ujvjz/D2kZ+UhNjEDvdrVxqeTOkGr1UKbb4neGHADOp0OIbcTAAAJKZlGFleO3JI7eXBTONpXULUi6XQ6PMnJBUjv87rs5/OYMKgpBnSph437r+FBSiZG9vLB0O4+2Hn0NrYeugUA+P1sNCa80hQeNe0xb3InxCakY+OBGyAi/HUpBt1aumHPiQgcOh2JeX5dJL/m5wkiQlR8Gnq2rS3VNV5fTl6Oxc8HbuDTSZ3AGENtVwcwBql737eVO+4nZ6B1Q2fUrFYRiY+ewLeVG4h0+Gn3FVSw02DhtG4AgGU/n8eciR0ln2Rejyn/nhERFm84h/lTOgMANuy/jnmTO2LXsTCp/LnfprdHVcmq3q6xq+RqIM+HW3Q5cj9trVaLzCe5WOd/DZ/P8AVjTHIlsbXRoI6bXr6I2EfYfFC/qunmgzfx6aRO6Nm2tiQ3LxOAEJOQLqWv0WikeDxvfr0ApJkQBnXzxqu962Pe5E6ISUjDxFeagEE/sGvu5M5SfeOya7VavNy5LgBCYHAUJg5upnhHyOF5ht17iPlrzgD6QVOXC1lNCgUR/UsmTxCA/QA6WzrP3d29yGSwc3JCbmqqwsf1zfoNYefkVKB0FixYUGQyCSxTLpRXrnzK0Wg0cOnR3er4avsN45g6r6BxCiIDx1DxNHeOtWnb+3ij6bxPrUqT/1crE8f6PuYuVTVNc+Vs6TqeGxjQupEzrkW44PKdRHRv7YFTV+MRl5SJnm09EX3/EapUtIFr/qAKfVdnc5y7fh8Bp6JQJcQGb7/WGpOHNEP31u6oU0s/aMajpj0u3krAun3X4OniIH30DL9P3h5VMd+vE7YG3sKBU3dx4NRd2FeyBWP6D6uPZzVpBHVtV0fEJKTjcUYOLt5KkNLkiieA/K5eHXxbe6JbSzfUdnV82mWs02Fgl3rYcyIcGVm5uHQ7EQdOR2HykKbYGHATfkOb5fsAxuLQmbuoXNE2X9HRf3TDY1Jha6NBbVcHzJ/SGX+ci0bAqUjYaACX6pXxxcaz0ocYgORTqFcOHPUKUy1HfD69m5HbQ2TcY8QmZiArW4stgbfBmAbjBjbGT3uuwEbD0KqhC7Ye0isggcFR6NO+Do5fjEWbRjXRvkktxCSkY9nP5zF3cmf0bFcbddyq4t6DNKzcfhE3u3nh0OkojB3Y5Ll1H+CuJZ9O6iQprVyZ/PnADQzs6gUiwrKfz+OTCR3gN7S5VBanrsRjW+At7DluByIdMp9o4Vq9MhIfPkHGkzz0ae8NL3e9EvnppE4gIqO8uII84ZVmUh0nImh1Oly8lYBffr9tVP7cTzk3T5fvDtMcmw/exJyJHY3yAfQNP4DgN7Q5ars64vKdJGTn6tC/Uz3JpWHppnOYM7Ej5k/pIuUlH6glfx7k5TZnYkfJ3aZOLUerGjjyGQNq1agCAFix/RIq2tmgSiVbTBnWQhocJh98eOpKPLYeugX7yraY+Work4orL8PIuMeISUhHelYuADRGCSuvBvLwD/o4S3E9PDyKLN9OP68vknSEm0DJUi6U1xeB4lDknnvlsIzj7myPK3+nIPTvRAzu7o0pQ5uhy1V3dG/tgci4x9gaeBuvvdQQGwNu4jO/TtJHdcuhm+jR2gMdm7li44Eb0Gp1SHiYhdf6NIBGo4FGo0HAqUjk5ml5Fxp8PKspPqqA/v7HJ2ci9O8kDO5WD43rVQdjDJsO3JQGFfRo4wlPFwcQETxd7OFQ2Q4BpyLhVtMeni4OknW1e2sPeLo44OKtBGwNvInqjhXRvkktAFxROAfGgOnDWyAx9QlaN3LGwTNRAAG5eXl4kJIJIv2Icp2OwADFR9xQ/pc6eCImIQ2X/05C8qNsTHilqaRkA5CUWT5CfXK+n6ThLAJcgfhkQgd89GZb3Ip+iMDgKAzoXA/pWXnwbeWGa+HJGDuwiTRCe8+JcNjYMNhobADoLXyThzSV5tP08ayGem4OSHjYBCN6eqOpVw10b+3x3M04wFHzleRKGW/8tGvsKtVffp98PKs9nUHAxR6xCelY538dDAyHz97FkO7eOH/jAfp3rgcfz2qSsmqYl7wO1qnlKFlVbW00qOVcBY72FdCusaui/L09quIzv86S9dPbo6qkXPLR4fJ85k/pjJgE/UA0xoC5kzuCaRhe7V1fOp/HV7PWAjBSSuXn+HhWM1JuzcEYw2t9GqBWjSrSLAcPkjNx4FQkJg9tjh5tPI0aFQDQvbUHHiRn4MDpKCPLsiHyZ+P/3mqHr7de2GGVcAJBGYDxj19ZpUOHDhQSElLaYghKludCC2jZui11eG0Z6tSqis/8OsLOzk46RqQfLHTv/mOs9b+GRdO6okGd6gpL0/hBTQEQbkU9xKEzUfi/se3Rs23t/C7rWGwMuI7P/Doruht52rzrNTwmFQvXBWPh1C5S+vLprLg1dL3/dcyf0hmMMcQkpGPLoZvSoBX+wdbpdDh5KRb3UzJw8PRdMAbMn9IFXu6OOHk5Dp4u9gCAxRvO4TM/fTdyzIM0/Hd3KLKydfi/se3Qs21tRMQ+kpQfefoc/cwHp0EE9GzriZCbCfhkQgfEJWVISuLJy7HYfPCpG0WeVguA4fPpXRXlIS8L/rGe8EpTuNesgvmrz8DGRt/NvGhaVzDG8Pn6YORpdZg2vCV6tvWUBqNNHtIcG/ZfQ56W8Pn0rgCM3TTyeS7qrrn3rrxMgYJP/ebl7oio+DTptzBTvBmmZWk6OWvgzwZQNqc/MywLS2VTiCn2ytYFm0HoBS8MJuuksLwKBMVERTsbaXLwvX9F4vW+jaRj3PK5UWYF5ft51/h6f/1IZRuNBuMGNZUsMIwx9GzrKVmhDJFbZOrXdlJ0pfO8Fm84CyJgyrDm2LD/BnT5ymz92k7w8awmpS3/+AWFxmHlr5fw4Zh2kt8hVwq3HLopWd4Ye5rPlsBbmPVqa4AxSX5ukTLsIpbDR5f361gXL3f2MrLo9WjjKc096unigJgHadiY795gmA5P29CCuGSmr6TE8zif+emviysv3CLM59LcGHBdlrYVleA5RF5egNLiaHjM1HmmrJWW8jKX1rNi6Hda1jAsC2vKpiDpCQTlCWF5FZRFngu1oEOHDnT27FnsPh6OV3vXh42NjeK4OUuPoYW0IJYgS5YXeb5c+VSTwRCdToeg0DijCc1NWeLk/03JYcpypLYoQGHSKkrMWRxlPDd1V7x3XzhKrO4yxrwAnAVwE0AOgOmybQD4hYjWmDr/WeunpaVcxy8MRGpatrTt5FgRWxYONJmW/FdQpJisk+VCeT1//nyBukHUupLUPm7WdBNZ81E0TAco3Efb3DH5takpG1Z+WAt1fTqdTuoWrl/bqcCKSCGUCqEACMorou4KyislrbwuIaJxatuWeNb6yRvEphj6f/uM9u1fPtxkWgDMpicoNCbrZLlYpIB3g/IJ0eWVhCuOegUrVlq9Z+HaM/jrUgx0Oh3C7j3Ezj/vGK2sExH7SLGqEJF+paCwew8RHpMqKV487/CYVITHpEKn00n/8/LysGbvVSxce9ponWsur06nw9/RKfjfH7eh1WoRGfcYSzYEY+fRv6HVaqVrkq8AJM9PLgdfOcZwZR/5qkP8f3hMKk5cuIfjIdEIu/dQKjd+nTwsWHMaO/9UyiIv25OXY7Hil4tYsPYM/roUqygfTnhMKj5bfVo6xsuJ3xPD6xEIBALBC00fxthJxtiHpSmEKaPK2d2fIzxkH8JD9uHs7s+tOkdQcpQLn1e+KlFMQjp+PnAdk4Y0l6YAiYh9hM/Xn4XfUP2o14FdveBR0x46Iqz3v46Eh1nYeyIMjzNy0b21O7RardQVCwA2GqBrC3fk5eXplyP0vwYdEWw0GiyY2kXhn7d4w1lotTr4tnbH0fP3YGOjQa92tXHgVBR6tPbAq30aSNOXjB/URJo+ZsIrTfHfXaFIz8oDGMOolxpK06AQEQKCIuE3tDkSHmZiW+AtjB3YBPcepGH17isgEJbM9EX92k7SlCweNe0Rm5iBem4O0vyXRKRYweXTSZ1w70Ealv9yEQTAvpItlszsBo1GA51Oh4XrgqFhDJOHNENWdh42H7oJApelGerU0q9AxMv2wzFtwaD3+cvOyQMALJ7RDfVrOyEy7jGi7z+Wplk6eEY/mGfS4KZYvecqerX1hE6nw5KN56VBPtxC/Szd4wKBQCAol8QDaAQgG8A+AH8C6M8YO55/fAUR+ctPYIxNh969AHXr1i1whgsXLsSiRYvk6an+5zTtORk3/9poMo6p7QULFggXghKgXCiv3MK4+eANdG7hjk0B+qUFa7s65lvxCFqtFo3qOuHgqQi0a+yKRdO6SqOmpw1rgXM3HiAoNA5XwpIwY6R+/jtvj6ro1soDB05F4fjFe6hYwRZDu/ugdUNnxCdnSfMP+nhWg06nw+TBzXAjMhkHTt0FANhXYmhctxoepbvjdvRDaDQaRMWnSfMLTh/REnMmdoSXuyN0Oh0SUjKlqVde7V0fANCqQQ3sOR6GNXuvAGAY1M0LbRrWxKL156DVEZ7kahGbmIEGdapLU8TEJmbg5wPXkfAwE4FnoqTpavichbyb3svdEfeT0vC/P8MA6PDn+Xs4ez0eE15pBiLCkO4+YABytYQhvt5o06gmdh29g//svIJKFTQY1qMB8rRarPO/jkXTuuqne6nliIu3HmBL4C1cup0IxvRrkQ/oXBcaBrjWqIzJQ5qBSIcbEfr1wg+cvosa1SpLI9Dlg4zCY1Ix76fT0GiACna2CsVWIBAIBM8fRJQNveIKxlgAgCEAjphzG8j3gV0D6N0GCpqn3M9V7jag5kIw9P/0FldO056TcePEBmnb8Px8+QoqkuAZKPPKa3aOFgvXnoHf0OYYN7Ax1vnfwLAe3tiw/wb0k0q3wOQhzbDqf5eRnUsY1LWuZMXr0UY/L6BOp8M6/2to3agm+rT1xOaDN/UjlBPScfJyLCpXYOjVzhPVHSphf1AEXGtUxpZDt6QJpYkIQaFx2BBwHbl5OlSuaIuRvbxho9Fg7b7rIAJe7d0A9dwcEBWfhslDmmOd/zVs2H8dU4e3AABsDbyNTyZ0QFBoHDxd9JOzB+bPg2lnq4FvKw+cuBSDk5djUcOxEmxtGKYOa4mkVP2KNPrVjdKw3v86NBqGwb7eOBx8V5r/kI+8/utSDNbvu4Ypw/nqSxpk5+nQ1LsmDp6OxLhBTaXJ4Q+cjsJnfp3w4Ztt9OuVQ7/yS1ZWHjo2rYXA4CiM7NUQB07r18nmo3HruTkgIu4R/E+Go21jF3w6qRPquFZBSlo2PF0c8MWmEOTkavnE16hgw3DwdJQ0LyifK5NPy5SZnYdxAxqjfVO353aid4FAIBDoYYw5ElFa/qYvgB8ANCtFkYxIig5F056Tpf+CskWZV14BQEeEjQE3MLibF9Iyc1CrehVpUukth25i/KCmsLXRIDtXC8aAxRvOAmBYMFVvxQu79xBZ2Xm4fCcJnjUdMGdiRwDAhv3XoNXq0L6pGw6diUaVirawsWEAQdEFzyfK9hvaAqTTYa3/NdhobHDgdCR4Y+vA6SjUcrbH5oM38cmEDpg2vCV0Oh3W+1+XJqC/9yANy7ddhH1lWyye0Q0TXmmKDftvYEh3H/x+NhojezXE3r/CsO9kBKaPaIHaro7YGngbBMLuY2Gws7XBlGEtwBiDbys3xQTWMQlp0Ol02LD/BnK1hHX7ruFWVAqOXbiHinYa3L2fhnGDmuK1Pg3AGMOiaV0lJZ8xhpXb9VMgLZ7RDZduJ+LwWb1i3K2lG8AgTRVERNh9PBxBofGwr2QjWab/uhSDg6ciUcOxEj7z6wQd6bDneDhOhcZj9MuN0L6JG4gIC9cFgwGYNqJl/nRHDqhqXwHtmtQq09PUCAQCgaDI6MEYWwy99TUIwAMo3QYOENHXxZW5fClXNYupk2NFdH51vrTdvs8biuPyc8SysKVDmVdeK1awwaJp+knBdaSDf1AEPPMth76t3JDwMBPdWtZCQkpD7Dr2N85eT4Df0BbwdLGXBlYBQKUKtiDKw4lLMWjq7YwebTzgN7QF1u67iptRDzF+YFO0aVQTcUmZ0pyVvDtAPj9kROwjMMYQcCoSQ7v7oE2jmpICyFdx4askjRvYBHlaLWIT0tGzXW14uTsiIUW/+gljT5e1bNfYFe2b1AIRwaV6ZWw6cEOaz3Li4GZYs/cqMrO1GN+nEerUcsSyn/W+o5sP3oRHTXv8GXIPB4Ii8dFb7eA3tDncnSvj2IVYHDgdiUoVbFCxgg2GdvfBq73rS7Mw8HW5v9h4FuMHNsHYAY3RvbU7bGxsUL+2E9o1cQXwdHnHWjWqoGfb2oiMe4wDp6LgUNkOM0e2lLr4fVu542aUNwKDo9C+aS1ooMHf9x5h/CtNpZWh+BKgg7t5S9ZvrkjLfWCHzfZvs3/58MslW9MEAoFAUBIQ0UEABw121yqp/C35pJqaFqswaQmKh3IxVRafEkO+pvayn89jQJd60gCnw8F3MT6/S9zHsxoi4x5j0bpgMKafeDwmIR1r912FVqtDxQq20lKUhZlPMiL2kWT1VZtgnafBB0bZ2mgkX05TU1rJl1yUrzrEZwaITUyXBqnJpwPja6wP6uaFvh3q4MvNIdJSmgM611Mo5Hy/4brh6/2vGw2k4qsgyVc20mg0JqcX4/EnvNJUmmRfbWoyuez8PsrLMCL2Ed5fcTxi//Lh9Yu0IpUCYrqhF5LnYrShqLsvJOWm7or6+cJQvud5NaykXAmq5+aAU1fi4dvKHXfvp5udwxVAkY5qL8z8rwVcqs8q5BPHGyq2hssImpr/Vk3GgspTGPlNzb07bLZ/2+fB8ipesC8k5UYBMIeouy8k5abuivr5wvB8Ka+C555y8xI1h6i7LySlWncZY+lE5KCy/w0ACwEQgFAiestcOqLuvpCUm/euqJ8vDOV7kQKBoCRhjKWb2P8GY+wGY+w6Y+yXkpZLICgMjLGGAOYA8CWi5gA+KF2JBAI9jDEvxtgDxthxxtjv+dtbrT2/rPmbqslT1mR8XhCWV0FZpMxZr/IVgB0AXiKih4wxVyJKMJeOqLsvJGWx7n4F4A4RrbM2HVF3X0hKvO4WdplYXj8tLfNqDecmTkFuaqrqMTsnJ3T6eb3VaanJUxQyvsCYrJNlfrYBgaCMMA3Af4joIQBYUlwFgjJEIwBgjJ0CYANgIREFlq5IAoFEH8bYSQC7AewpyoStURy54jo7+BQ6uOhn2QlJTMA3XXxVlVqhjJYNhNuAQGAdjQA0YoydYowFM8asn0tFIChdbAE0BNAbwBgA6xhjTqUpkECQD18mtg+AfgBMrlLDGJvOGAthjIVcuHBBGuTLp6o01T3Pj5sK3f13AwA6uLhi1fWrWHX9qqTEdvffbRTfkIULFyqOqcW3JKOg4AjLq0BgHXIFoDaAk4yxFkSUWppCCQRWEAMgmIhyAUQyxm5DX5fPl65YghcdE8vEmoqrWB7WWrcBS8dPDX/N5LGgYa/Cd98uxT5DBdbUsrPy+MJSW/QIy6tAYB0xAPYRUS4RRQLgCoBAUNbZC71lC4yxmtBbuiJKUyCBANAvEyvb9AXwZ2nJEpKYgHeat8Q7zVsiJFF4hZV1is3yWlRTtggEZYS90He5bhIKgKAMU4UxFiPbXgFgJYCXGWM3AGgB/JOIkktFOoFAidoysVZjaWlWayyedk5OyE1NxTddfKV9b9ZvKB0rSJpq8ojlY4uHYpttQIzYFjwDpT1iWwcgTraLKwDLAQyEXgH4goh+NZeOqLsvJOVmrkxziLr7QlJu6q6ony8MZWa2ATFiW1DmISJT7jQf5QeBQCAQCASlREn7vIoR2wKBQCAQCASCQlPSllcxYlsgEAgEAoFAUGhK2vIqRmwLBAKBQPACwxibwBj7M39ZWF/ZErHHGWPTS1s+QdmnpC2veyFGbAsEAoFA8ELCGPME0IuI+uZvewE4YmlJ2KJg/MJApKZlqx5zcqyILQuFJ2N5oTgtr1UYYzGy8BGAwwCS86dsOQYxZYtAIBAIBC8SAwDY5Ftef4B+yeIiRW0lLAAmFVdzx0ylJShdis3yKkZsCwQCgUAgMKAWgApE1Jcx9m8AwwH0Z4wdzz++goj8i1uIs7s/R826rQEASdGh6Pzq/OLOUlCEiOVhBQKBQCAQlBSPAJzI/38UQAdY4TaQ7ws7HQDq1q1rMRNTFtMhH+0FANSs2xo3/9oIAGjaczICVowAW2GN+IKygFgeViAQCAQCQUlxGkCr/P9toF9t0yJEtIaIOhBRBxcXF2viGwWuuKox5KO9qucIyiZCeRUIBAKBQFAiENFlAFn5bgIdAexEvttAfvhnEeShut/JsaL0Pyk6FE17TkbTnpORFB2qOGZNWoLSRbgNCAQCgUAgKDGIaLbBrlolka9iNoHlw0siS0ExISyvAoFAIBAIBIJyg1BeBQKBQCAQCATlBqG8CgQCgUAgEAjKDUJ5FQgEAoFAIBCUG8SALYFAIBAIBKUCY2wCgInQr7Q1F8CsklgqFgDOTZyC3NRU1WN2Tk7o9PP6AqW3cOFCLFy48NkFE1hEWF4FAoFAIBCUOIwxTwC9iKgvEfUGEFuEaVuMY0pxtXTMVB6LFi2yeI6gaBCWV4FAIBAIBKXBAAA2jLE/AdwA8G1pCTI7+BQ6uLgCAEISE/BNF9/SEkVgBcLyKhAUM0SE9IhIo8muTe1/1nSLIu1npSTzL61rLe0yLk502dnQ6XTS9fFr1el0SAuPQFp4OB6HhSM9PEJxXPofHoG08AgpDXlaHB6Pp6PT6aTzDNPRarVIPBkErVarkCNddkyn0xndE7ncatdiLq6lc3Q6nZSv/L9aOlxWtTTl+fN4RuWkco5Op0PCXyeRFh5uVmZ53Mdh4U/vi6ysS5FaACoQUV8AmQBMTr7KGJvOGAthjIUkJiZalThjzGzo7r9bitvBxRWrrl/FqutX0cHFFd39d1s8H9C7Csi3+X/hPlC8CMurQFCMpIVHIDMmFlEbf0azzz6FY30fEBEyIqOg1Wpxc9ESeE/zg2vPHgCA9IhIMACVveohOegUCAwuPXyh0Wik8+y9vUBEiN29B7H7D6LZZ59Co9HA3ttLeoGmR0TixuKl8Jo8AVVqewKkX4NRwxjsfbzBGNOnl//BexIfj5rd9ZaGpKBTqOjuhidx91HF0wMO9X3y04xAVmwcanb3BWMMGZFRqOJVD5lRd1G5Xl2knD4DZ99u0Gg0SAuPwM1FS9B0wTw41veR5Ob5podHICsuDjW6dcXDM8HSefwaebqV6tZB/N59qNq6NRx8vJFy+gyqd+2Ch6fPoKKHBxzr++BxWDhuLvgcTRfNR9UG9ZEREQkCYO/thcyou7D39pLKRKfT4XFoKDxGjoBGo5HyyoiMAkDQ6Qg58fFwzr9G+TVrNE/b+jqdDjG79iB2rz+aL5oPBx9vJJ86jTvfrGS++3aVe202Ky4eCX+dRNTGzajRtTMq1qiB+MDfUbNzJyQcOw5oNGBaLVCxAjyHDkFenhbxe/ai2eKFyLl/H2E/rgZ0Ori/OgIP/APgNmIo4gMOoVYPX9SdPBEPzwRDq9UhYvUaQEewqVwJHkMGI2b3HoBp0GDmNFR0d8eN+Quh0+pQvWN7PAw6DZdBN5B84iTqTfND1Jr1IBBcevdC4qHDqH7mLCrXq4P7/gfgPc0P9nVqI+NeDCLXboBzD18knw6Gt98k5CQmIHb/QdT07YqEk6fg1rM7ak8cj8jv/4Pk0CuoM3wI4gIOwWPIK4jdfwAeQ14BQIg/EAi3wQMRvy8APtOngIgQvvJ7uFy/jrzHaXh46gw84+KRefcuHl6+As8RQ/Hg4GF4TZ6AsJ/WAnm58HjtVSQE/o4mcz9B6qXLiN29F94zpsKhTh2k34tBxI8/QVOhApovmg+dTof4vf5wbNcWMZs2o9G8OUgIOICH12/CZ9J4pN26gwcHD4FVrgyXPr2QdOoM3AcPQtyeffCaPhW5SUmI27cfzRfNR2ZMDMJXfg/Y2AB2dqjRoT0eXr4MYhrUaN0SHiOG49rsf5VG3X0E4ET+/6MAOpiKSERrAKwBgA4dOli7pKzZ46eGv2byWNCwV+G7b5fZ87mSyhVV/n4TFD/lQnmVf7St8WMp7DklJVtRnG/uPGvT5C1zBkgKzbNgmG9J3YOyii47GzeXLIMuNxeUlwvS6QAAGZFRuPXl16j1cl9oM9IRuW4DNBoNKnl44OaSZWAMcBs8CNGbtwEAGANce/ZARmQUbi77Cl4TxoKIEL3tV7BKFZF49BiSz4Wg2af/goOPt/4cANBpEbF2PVieFjobDTQEsAp28JkyGTW7++oV3EWLoc3NBWU9yc+L4e+V38OmYkVonzyBjaMDfKZNAQBErt2AvPR0MMZQ2dMTt778GnXHjUHUxs1wGzwQ9375DY0A1Ozui9RLl5D3+DGexMZCo9FIcvN8r81fCF1GJmrduoUHgUfQCIBLj+5S2dQb/xaiNm+DQ6MGSAk6Dc2uvag9aiSit/0Kt1u3cP/gYdg6OKD5ovl4HBoKbUYGko4dB2MMNz9fAmhs4DNlEu5u+QVNPtGvNnlj8VLoMjOhy84GGEP1tm2lvCI3/AxotdDl5UGbmYk6CQmo6OKKyNVroc3MBGMMNbv7Sspu7J69uLf1FwDAk9hYZMfH487K7wHgDQC/lUwNKz5Ip0NuUhKQnY3EQ4cBxuDcvRseBB4GdDo4d/dF8ukzcO7QHve2bQfyP9gP/APw6OpV4Im+PsXv2gPk5uL+7n1gRLh/4BByHz9G8qkzsKlYEch6AjAGl34vIX7/AVBWFmwqVUbUxs1wHzwQuqwnABEeng6Gcw9fpJw8BV1mJh5fuATKzyPl5CkwOzs8PHUaD0/p5Y9asx4aW1vo8vKgy8hAYuDvsKlUCXfXrkdeRgY0FSog4dBhMFtb3D9wCOlh4Ui/fQfMzg7x+w+AaXX635wcxPzyK0AEmyqVcX+vP3SZWYhavxEew4YAABIDfwcIqNa2NeL37IMuKwsAcH+vPzS2dnpLnDYPlJOL+N17YVOpIh6HhkrpRv20FpqKFUC5uUBODpitDbLj4hD2n/9C9yQbKWfPATod4rb/hkeXQ1GtbWtErduofxYrVoSGAQmHDsOmUiXE79kHysrC3Z/WQJeTAwDIjovT30sA0GoBBqQEnYKmciVUbax/vh5duAQArQFcLon6JeM0gGn5/9tA38YuEgqqRIYkJuCd5i2l/2/Wb1jkeQiKjnKhvMo/2tzqY805t778Gk0++af0QZcjV6x4fLmSZUrxMtxvKZ9nkdOc8pceEYmbS5ah6bw5cPDxVsQzOqZiieKyc2Wp6bxPpfwLq3QaXstT699EVKntCQe5xe8FUHI1FSui6bw5SL10CdHbfkV2fDzQsAHsvb3Q5JN/onK9uqjo6goQ9ErWv2aj6bw5kuUVOkLc/gOo7OkBQH//vCaMxd0tv6Dxv/4PjT58D1kPEnBv+6+oO3aMVJcBfWOk2YLPkHrxEqJ/+RVuAwcgJfgs3AYPxN0tv6CypycYAI2tLdyHDEF8gD4fe29vNCSSLK8MQNTGzWAM8J7mBwBw9u0GxhiafPJPEOnAGODUpg0q16oFZ99uyIiMwv2DgbB1cEDl2p4KuaV8bWyhcXCAS58+qNa0KZx9u0nX2OSTf6KKVz0AQOSGTbCpUhleM6bBpYcvKrm6onrXLqjauDEqenjA3tsLlevVRc7Dh3h4/gKqNmkMja0t6k2aAGffbqjs6SmVi7ffRERt3AzXfi/BY8RwaDQaKa9KHh7gltfHoaGIP3AI0OrAbG1Qd9wY6bq4svvg8BHUemUQkk+dyr9GbzQCcOeblTtKom4VN5VqucJj5AhUa9MaDy9eRqVarnD27YYanTqCiODs2w3OXTqhetcuqN6pI7RaLTL/DkM9v4l4cjcaGfdikJ2YiKqtWyL96jU4tmoFBuDxlStwGz4MNTp1REV3d2TFxkLDNHDu3g2uffsgMyYWlT09wJgGVbzqoaKrK7RaLWxsbKR7kHr5MtyHD0ONTh2gI6BKbQ/pvtnVrCk1BDUaBp2O8CQuDkRAFU8PgDFkxcaikoc7nsTdR0W3Wki7ehWuQ4cg5uctcO7VEzY2NvpCYADpCJmxcWAgVK5dG0SE7Lh4VPb0QBVvL1RwcdFb65MS4TlyBDIiI5Hw53E4NG6EKrU9YaPR6OO5uSH52Ak49+kJGxtbVPGqB7uaLshJTEDV1q1ho9FAR4TUy5dRvW1bOPh4w87NDfF7/VFr2GBoExJRrUtn3Pt5M+pOmoisu9F4EheHiu7uYPnXVNnDAwTCo8tXULV1SzyJuw8NA5y7++a7DugVLcdWLZFx7Rqqtm6NKl71cHfjz3Dp0xvXZv8rtKTrGRFdZoxlMcaOA0gC8CmA+SWVv52TkzQwS+7j+mb9hrBzcipwegsWLCgiyQQW4b4xZTW0b9+edDodJfx1ks5Pm0Vp4RFkDTqdjtLCI0in06keTwuPkNKT/1c7buo8a/J5FjlNyUBElBYWTucmT6W0sHCjeIbHzk2eSmcnTTUqQ51OR4/DwiktLFyRv7l8C3ItaWHhFDx+Ep0dP5nOTpr6VD5DeY3zK/V6VxShffv2RESk1Wop4a+TpNVqrSo3c/sN91lKmx/Py8ujtPAI0mq10vk8Lfk+NdnU6oglGQ3PkcezlKa1eavJYelarHlWeb6Pw8Losco18DxM5FXq9a4oAq+7gheKUq931gZRP18YTNYBRlS2zd4dOnSgkJAQEBWtdU6eHlB4y2txYi4vc/IbHTNheS1MvgWVPz0iEvqeIFYQy+tzYX7ldVfwQiHqrqC8Um7qrqifLwwm62S5cBsA9L54hemWtzY9w7RN5VfUcpjDXF7m5Dc6lj/gxjBeYfItCIwxOMryNpV+SZapQCAQCASC8o2YKksgEAgEAoFAUG4QyqtAIBAIBAKBoNwglFeBQCAQCAQCQblBKK8CgUAgEAgEgnJDmZ9tgDGWBuB2actRCtSEft67F5FKRNSitIV4VkTdfSERdbd8I+puOaAc1c/yUp/KqpxJRDRQ7UB5mG3gNhGZXDLueYUxFvIiXjegv/bSlqGIEHX3BUPU3fKNqLvlhnJRP8tLfSovcsoRbgMCgUAgEAgEgnKDUF4FAoFAIBAIBOWG8qC8riltAUqJF/W6gefn2p+X6ygoL+p1A8/PtT8v11FQXtTrBsrXtZcXWYWcxUSZH7AlEAgEAoFAIBBwyoPlVSAQCAQCgUAgACCUV4FAIBAIBOUAxthAxthtxlgYY+yT0pbHFIyxDYyxBMbYtdKWxRyMsTqMsWOMsZuMseuMsfdLWyZrEW4DAoFAIBAIyjSMMRsAdwD0BxAD4DyAMUR0o1QFU4Ex1hNAOoDNZXnuXMaYOwB3IrrIGHMEcAHAiLJYpoaUCcurNa0pxlgUY+wqY+xyOZuPzghz18sYa5x/jTw8Zox9IDv+XJSDNS3T8tDKFnVXcUzU3adxRN0tY4i6W+7rbicAYUQUQUQ5AH4FMLyUZVKFiP4CkFLacliCiOKJ6GL+/zQANwF4lq5UVkJEpRoA2AAIB+ADoAKAUADNVOJFAahZ2vKW1PXK4t4HUO85LIeeANoBuPas5VTW7+VzdM9E3SVRd8tjEHVXuo5yW3cBjAKwTrY9HsCq0pbLjLxepsq5LIZ8eaMBVC1tWawJZcHyWm5aU0VEQa63L4BwIrpbYtKVEGS5ZVoe6kV5kLEoEXUXou6WU0TdRbmvu0xln/B7LAIYYw4AdgH4gIgel7Y81lAWlFdPAPdk2zFQN1sTgN8ZYxcYY9NLRLLiwdrrBYA3AWw32Pe8lIMlClJOpYWou6LuqiHqbtlD1F3rKMt1NwZAHdl2bQBxpSTLcwNjzA56xXUbEe0ubXmsxba0BYD1rSlfIopjjLkCOMIYu5XfiixvWHW9jLEKAIYBmGNw6HkpB0uUh1a2qLui7qoh6m7ZQ9Rd6yjLdfc8gIaMMW8AsdA3Mt4qXZHKN4wxBmA9gJtEtKK05SkIZcHyalVrioji8n8TAOyBvnujPGJt63EQgItE9EC+8zkqB0uUh1a2qLui7qoh6m7ZQ9Rd6yizdZeI8gC8A+Aw9AOLdhDR9dKVSh3G2HYAZwA0ZozFMMamlLZMJvCF3nf4JdlgxVdKWyhrKAvKq9Saym/1vgnAXx6BMWbP9NM4gDFmD+BlAGV6/jQzWLzefMbAoOvqOSsHS1hbTqWJqLui7qoh6m7ZQ9Rd6yjTdZeIDhJRIyKqT0RflLY8piCiMUTkTkR2RFSbiNaXtkxqEFEQETEiakVEbfLDwdKWyxpKXXk115pijB1kjHkAqAUgiDEWCuAcgANEFFhaMj8L1lwvY6wK9HPZGfqfPDflYKplysugPLSyRd0VdVfU3fKBqLt6noe6KxAAYpECgUAgEAgEgkLBGEsnIocSznMSgA5E9E4BzukAYAIRvccY6w0gh4hOF4+ExU9ZGLAlEAgEAoFA8FzAGLMhIq2p7VKQx5aIQgDwBTZ6Q78CWLlVXkvdbUAgEAgEAoGgPMMY680YO8YY+wXAVZVtG8bY14yx84yxK4yxGfnnaRhjPzLGrjPGAvJdOEblH4tijNXM/9+BMXZcJd+hjLGzjLFLjLE/GGO18vcvZIytYYz9DmBzvjwBjDEvADMBfJg/QKsHYyyS6afMAmOsan6+diVQbIVGKK9FBGPMmT0drXefMRab/z+dMfZjMeX5AWNsgpnjQxhji4ojb8Hzg6i7gvKKqLuCMkYnAHOJqJnK9hQAj4ioI4COAKYx/bRfr0K/ulVLAFMBdC1gnkEAuhBRW+gXlfhYdqw9gOFEJE0pRkRRAH4CsDJ/gNZJAMcBDM6P8iaAXUSUW0A5ShThNlBEEFEygDaAvsUDIJ2Ivimu/BhjtgD8oF/qzxQHACxmjP2biDKLSxZB+UbUXUF5RdRdQRnjHBFFmth+GUArblUFUA1AQwDdAfyPiHQA7jPGjhUwz9oAfmOMuUO/pK88f38iyrIijXXQK717AUwGMK2AMpQ4wvJazHBTff7/hYyxnxljv+eb5V9ljH3FGLvKGAuUme3bM8ZOMP1qLofzK6UhL0E/H2Fe/jnvMcZu5HdH/AoApB+NdxzAkBK5WMFzhai7gvKKqLuCUiLDzDYD8K5sSipvIvod6gtDcPLwVE+rZCLODwBWEVFLADMM4hnKowoRnQLgxRjrBcCGiMr8VHBCeS156kNvnh8OYCuAY/mVLgvA4PwX6Q8ARhFRewAbAKjNZ+cL4IJs+xMAbYmoFfT+LJwQAD2K/CoELyKi7grKK6LuCkqbwwBmyRpLjZh+3uAgAK8xve9rLegHU3GioO/6B4DXTKRbDfoVxwBgopWypAFwNNi3Gfo5jjdamUapIpTXkudQvi/JVQA2APh8gVeh93tpDKAF9EsQXgYwD/puAUPcASTKtq8A2MYYGwd9a42TAMCjCOUXvLiIuisor4i6Kyht1gG4AeAiY+wagNXQu27ugn5lM77vLIBH+ecsAvAdY+wkAFOzFSwE8L/8OElWyrIfwMh8/3DeyNoGoDoMFukoqwif15InGwCISMcYy6WnE+3qoL8fDMB1IrLktJ0FZffAYAA9oV+X+zPGWPP8rq1K+XEFgmdF1F1BeUXUXUGxwOd4JaLj0LuLwMS2DsCn+UEBY2w2EaUzxpyhXwjjav45JwE0UslzE4BN+f/3AdinEmehwbYkDxHdAdDK4JTuAHYSUaqJSy1TCMtr2eM2ABfGWFcAYIzZMcaaq8S7CaBBfhwNgDpEdAx6p2snAHzS5EZ4fpcyFJQtRN0VlFdE3RWUJgH5Fv+TABYT0f2SzJwx9gOALwEsLsl8nwVheS1jEFFO/mjE7xlj1aC/R98CMFyi7xCALfn/bQBszY/PoJ8CIzX/WB8Ac4pbboFA1F1BeUXUXUFpQkS9Szn/d0sz/8IgloctxzDG9gD4mIj+NnG8FoBfiKhvyUomEJhH1F1BeUXUXYGg9BHKazmGMdYYQC0i+svE8Y4AconocokKJhBYQNRdQXlF1F2BoPQRyqtAIBAIBAKBoNwgBmwJBAKBQCAQCMoNYsCWMcIULRAIBAKBQFC6mFx9TFheBQKBQCAQCATlBquUV8aYPWNsOWPsLmMshzEWzxjbxxirW9wCCgQCgUAgEAgEHIvKK2OMATgA4CMAEQDeA/AdgHoAyoTyyhirxBg7xxgLZYxdZ4wtyt9fgzF2hDH2d/5v9dKWVSAQCAQCgUBQeCzONsAY6wvgD+hXFmlJRNr8/RoAFYmo1JfAy1ew7fOXV7MDEATgfQCvAkghoi8ZY58AqE5E/7KQnPB5FQgEAoFAIChdnsnntX3+7+9EpM23ctYEUMNcwiUJ6UnP37TLDwRgOICf8/f/DGBEyUsnEAgEAoFAICgqCjJgi1skZwJIzA8fF7lEhYQxZpO/NnACgCNEdBb6iaTjASD/17UURRQIBAKBQCAQPCPWKK8h+b9987vndwH4vPhEKhxEpCWiNgBqA+jEGGth7bmMsemMsRDGWEjz5s2LTUaBQCAQCAQCwbNhjfJ6DMBxAC0BHALwMgD3YpTpmSCiVOjlHQjgAWPMHQDyfxNMnLOGiDoQUYfKlSuXlKgCgUAgEAgEggJiUXkl/YiuoQC+BdACwH8BDAKwE/pZCEodxpgLY8wp/39lAP0A3ALgD2BifrSJAPaVioACgUAgEAgEgiLBqhW28gdDfZgfyiLuAH5mjNlAr5DvIKIAxtgZADsYY1MARAN4vTSFFAgEAoFAIBA8G8/F8rBEdAVAW5X9yQD6lrxEAoFAIBAIioPevXtjzpw5GDBggLTv22+/xZ07dxAREYHg4GB0794dAQEBpSiloDgRy8MKBAKBQCAoN4wZMwa//vqrYt+vv/6KMWPG4J///Ce2bNlSSpIJSgqhvAoEAoFAICg3jBo1CgEBAcjOzgYAREVFIS4uDt27d0ffvn3h6OhYyhIKihuhvAoEAoFAICg3ODs7o1OnTggMDASgt7qOHj0a+tk8BS8CQnkVCAQCgUBQrpC7DnCXAcGLg1BeBQKBQCAQlCtGjBiBP//8ExcvXkRWVhbatWtX2iIJShChvAoEAoFAIChXODg4oHfv3vDz8xNW1xcQobwKBAKBQCAod4wZMwahoaF48803pX09evTA66+/jj///BO1a9fG4cOHS1FCQXHB9AtoCTgdOnSgkJCQ0hZDIBAIBAKB4EXG5Ag8YXkVCAQCgUAgEJQbhPIqEAgEAoFAICg3COVVIBAIBAKBQFBuEMqrQCAQCAQCgaDcIJRXgUAgEAgEAkG5QSivAoFAIBAIBIJyg1BeBQKBQCAQCATlBqG8CgQCgUAgEAjKDUJ5FQgEgueImzdvon///sjIyChtUQQCgaBYEMprGWH16tXYsWMH/v7779IWRaDC2bNncfz48dIWQ1DK7Nq1C5GRkaUthkl+/fVXjBgxAn/88Qc2bdpU2uIIBAJBsWBb2gIIgL1792LmzJkAgOrVqyMlJaWUJRIY0qVLFwBAQZZT1mq1AAAbG5tikUlQsuh0OowaNQo1a9ZEYmJiaYtjRGRkJMaMGSNtZ2VllaI0gvKMTqcDEYl3VxkkNzcXNjY20GhebNvji331ZYS0tDTp/8OHD0tREoElcnNzrY7brFkz2NqK9uHzQmZmJgAgKSkJ//73v0tZGmN+++03xXZ2dnYpSSIo74wcOVK8u8ogkZGRqFChAubMmVPaopQ6QnktA3ALnaBsMmrUKOn/lStXrD7vzp07xSFOoZkzZ4546T0DSUlJ0v8dO3aUoiRKZs2ahW7duuGzzz5T7H/y5EkpSVT6TJo0CatWrSptMcot/v7+AICcnJwCn/v2229j2bJlRS2SAMDVq1cBlOz75/XXX8eWLVtKLD9rEcqrlTx48AAffvihUaX5/vvvn7mbPzU1VfpfsWLFZ0pLULQ8evQIu3btkrbDw8NLURrTZGVl4ZtvvoFOpzMZ58svv8SXX36Jb7/9Vlj4C8jly5excePG0hZDlZ9++glnzpxBXl6eYr+lAVtbtmwpcw2souDbb7/Fzz//jHfffbe0RSmTREVFYcOGDVbFTU9PL3D6//3vf/Hpp58W+LzSZMuWLbh+/Tr+97//SQaKkJAQ7N27t3QFM4AbukrKZUCn02Hnzp2YMGFCieRXIIhIBFlo3749qdG5c2cCQPoi03Ps2DECQL169VI9x1oWLVokpW1vb0+PHj16pvSsITs7m9LS0qTtlJSUYs+zPPLHH39I9wYAfffdd1afa1hfipPRo0cTAFq/fr1FeQBQjx49KDU1tURks5ZHjx5ReHj4M6eTnp5OWVlZRSDRU/r166cov3bt2hVp+s+CXC55ePXVV02eo9VqCQA5OTlRQkICZWVlUXp6eglKXTycPXtWUQYc/n7TarWUnJxcWuKVCerWrUsAKDMz02QcXn7R0dEFTr8k33uFRf79y87OJgDk6OhIAMjGxoaIzF9HcnIyabXaZ5Lh0aNHlJOTU6BzduzYQQCoQYMGz5S3KbRarUIXSExMtOp+FuZa1PJOTk6m5ORkuQwmdTVhebWSmJgY6f/jx48BAMnJyQCA6OjoZ0qbW16rV6+OjIwMtGvX7pnSs4bOnTvD0dERgN4iV6NGDZw6darY8y1vBAcHK7YTEhJKSRLz8BHw1lpUT548CScnp0J1CxYXHTp0QP369Z/Zuu3g4ABvb+8ikkoPf9bLE7t37zZ5jL9zUlNT4erqCgcHB1SvXr2EJCs+1KzNAQEBqFGjBtasWYOFCxfC2dkZly9fLnnhygixsbEArHtXPK/TrbVu3Vr6/nFLKx97YsmN7+bNm3B2dn5mF6xq1aph0KBBBTqHW8KLy/I6ZcoU1KhRQxqQau3A1GrVqmHAgAHPlPe0adPg7OwMZ2dn1KhRw2KvkFBerUQ+6jI8PBzvvfce5s2bpxr3jz/+QMeOHa0e7csfGj4YKDw8HIwxBAYGmjxn+PDhWL58ubXiG8Ff3owx6SE8ceIERo0ahaVLlxY63fJGVlYW2rZta1TWEydORLNmzYy6jQozytxwkNeGDRtUH/TAwEC0b9++UKPEeR5yt5OAgAC0bdsWT548MelOwD9kRU1gYCAYY3jllVcAAO+++y5mzJghHb916xZ8fHwUzxCfJu7GjRvSvl9//RW+vr4F9gu/f/++9P+dd95R5F0QIiMjUbVqVVy6dEmxn6yYdeK///0vWrRogbZt20r1ZsGCBRg3blyhZDFk165d6Nq1q9k4phonho0wrVaL3NxcMMZw6NChIpGvqFizZg369u1rVVxDF649e/bgzz//BADMmDFDGtR2/vz5ohWyhIiNjUWbNm1w8+ZNi3EfPHiAli1bmvTTb9u2rcVp39SU14cPH6JVq1Y4e/asYv+2bdtQr149i3KVNgEBAbh16xYAoH379jhz5kyBzg8KCgIA7N+/v9Ay8HLlddNauK6g0Wgwf/78Qr9LXnrpJaxdu9ZoP//e3b17F4B1xhr+jjl27BjOnTunGker1aJXr15GvrOffvopJkyYgM6dOxu5srRo0cJ8xubMsi9iUHMb+Prrr6lGjRqS+XzkyJGKrilvb2/KzMykTz/9lLKzs8nDw4MAUGBgoGVbOem7fG1sbKhixYqKdF1cXEyeA5kp//jx47R9+3ar8jI8Xx7mz58v/Y+IiKAxY8bQpk2bjM69efMmTZo0yWye165dox9//NGsDD/88ANdunSJtFotLVmyhOLj42np0qUUGRlZoGsh0ndvLF68mBYtWlQgF4jDhw8TAPLw8JD2rVy5skBdsfv27aOVK1fS6tWrpX1hYWHSOcnJyXT37l3y8/Oj5cuXS/vlbhtLly4le3t7AkBHjx41ymPz5s105swZ1WvgXcAAaPTo0TR58mQ6ffo0ubi4EADavXs3vfXWW6rXExQUZLGMNm/eTKdPnzYbR6vV0hdffEHR0dH09ddfk4ODg5THjz/+KP2fMmUKRUdH09KlS6V9M2bMoJSUFGl7xYoV9Nlnn9GhQ4ekff3796f58+eblWHbtm104sQJ6ZzExESaNGmSyW6vFStWUFhYmNk0TZUbADp//rzqOatWraIzZ84o4n711VdEVLRdqry+8FC1alWqU6eOYl98fDwdPXqUduzYoThXXk6GoU6dOqr5paam0rx58ygvL0/1+OnTp2ncuHH04YcfklarpbVr11qsN5aQ14u8vDzpPWvKLWT9+vWKa6lRowZ9+OGHRtdYu3Ztio2NVZz722+/0bFjxyzKtGLFCoqIiDB5/MaNGzRt2jSLdaswfPzxxwSA+vXrR0T6527RokWUmJhoFJe7o/Xs2VOKu2DBAkU5TJs2TTUffnzOnDn0zTff0N27d6Vj/Hlu06aN4pxOnTop0k5ISKDFixc/c/d6TEwMffnll4prKKzLk1q9f+WVV4z2nT59WvVZDQgIoD59+hAAMuViaA13796V0n/99del6yMiioyMpK+//lr1vCVLlhAAatasmXR+Tk4OzZs3z6zbz4oVK6TvalZWlnTuxYsXadq0aZJ7iJOTEwGggQMHUmZmJu3cuVOKu2HDBpo8ebLRcyN3LejcubNR3suXL5fqjLOzs+KYqXeQrOxN6mqlriyWtWBYISMjIy0WcK1atejVV18lALR8+XJydXWVlAbO/fv36eLFixQfH2+U/oABAxQfex5MKa95eXmKB6swH0S165C/5Js3by79N/TBrVmzpnSMv0RiYmIoOzubiPQvGBsbGwJAWq2WIiMjSavVUnR0tOQXExERQYBeady6davigXRyclIodnLu37+v6qvVo0cPSaZhw4aZvXb5i/jIkSMEgNzd3YmIKC0tzahc+vbtK11P7969zZZlXl4epaamUpUqVaR9kZGR9N577xmle+nSJSIiOnnypGL//v37TeYRHBxs5IdmShFxdnYmAFLdVAuGSo0a8muLiYlRjXPw4EHpRWzpeXn77beN4n300UfSf0MFTB5CQ0MV+aampkqNFbX7Jt+W15vk5GQCQG5ubqrXExERQVqtlgYMGGBSlooVK5JWq6W0tDRKTEyk9PR0unr1qmrcZcuWKWSU10FrMIyfmppqlEdgYKCiHAEo5JGzYcMGk9fVokULVV/Hl156iQDQb7/9piofr28A6IcffiAAVKVKlQJdp5z4+Hh6+eWXpTTj4+Npzpw5BID+8Y9/GDVyU1NTFeMHAL1C/8EHH6he5zvvvKM439J7NDo6mqKjownQGyxiY2Old54c7n8+c+ZMo2OxsbGKesjrjbXMnTuXAFDXrl2JiCTlolGjRkbvxe+//54AvX92SkoK7du3z6gMxo8fTwkJCUSkbyjw97lhvFatWknp8vd1w4YNiUh/n9LT06lq1aqKc/g7edmyZRYNCvw7wdPj1xITE0Nt2rQhABQSEkK7d++Wnm1TZGZm0v3794lIf8/kyrNaPZArgmqBy56ZmanYb8rvXavVGj2vOTk5dPr0aanhd+nSJaN8eGOnevXqBED1XfvJJ59Izyg/b/bs2QSAxo0bZ6RfPHr0iEJDQwl46icrN6x06NCBANDHH39M0dHRkvIK6MfyfPHFF0ZydunShYhI+h7Ivz+NGzdWKLd8XBAPTZo0ISK93hMfH2+23POfRaG8WhsMlVfDATuWwpdffilZaeWWR35crpDKb2ydOnUUCqNhXDkJCQmKB8vSS1cNNdnfeOMN6X/lypWl/+ZaS/369aP09HQCQEOGDCEios8++0w6fu3aNQJAgwcPJgA0atQoIiKF5Y0HOzs76b/cEmqYd8uWLY3216tXTzq3e/fuJq+bP2jcisdf/rVq1SIi5YPNw9KlS4lIP9DB0NpgWB6RkZGKwX2AXoFQU4J27txJRETvvvuuYv/atWsV6fPylQe5csGd+A0DVyYMLSLy8P3335ssK8PrGz9+vMmX6rhx4wiANBjkWYJcCTIMhoq9o6Mj2djYUE5OjlHcJk2aKLYbN24sncdf6GrPDbeKjBs3TvpwAvqXtmEeU6dOpdq1axMAql+/vkm533nnHanBJq8X1sAbWEuWLJH2dezY0SiPsLAwo+dK/mFRu6eAXvFRk5krAPKyVqsz/v7+Rud269bNZPlag7yBzkNoaChNmTKFgKfvJ/kARUMDQNWqVcnW1pZmzZpl8r4EBAQYlYkaXGnlChnvJevfv79RXP68GTaieQ9J27ZtFXl6eXlZXS5+fn5S3SYi+uqrryS5R4wYoYjLldfGjRsrGtNqgYhIo9GQvb29oix4qF27tpTu2rVrCdC/o3Nycqh69eo0YMAAYoyZTL9SpUpmr4sbTi5evEiAXtG6f/++Ig1/f3/pfW3q+0BE0nMfHBxMgF4xk5d3QYODgwMREfn6+ir2m+qh4NZxec/M+++/TwBo7ty5RER09OhR1bz4exR4atyQ849//IMAfWPC3L3k8N43+XUcP35ccV/k5xr25qgF3miZMGGCyTiPHj1S7cFs0KCBdI+tCSSU18Irr/KbL/9vKowZM0b6v2DBAiJSmukBvQXLUOFo06YNJScnGymwLVq0MGrd37x5Uzr+zTffSP/d3d0l5cHZ2Zn++OMPUmPYsGGqsptTcgC9tcHd3d1snDNnziiUt9WrV6u+xOTlZCrUq1eP3n77bUlueUvNEE9PT+kY71KT8/HHH5OHh4f0kmzWrJnRyOTatWtLLx554C3ili1bEgAaPnw4Eek/RoZKxJEjR4zqCX/hNmjQQFIA+L3jyr08aDQahexyRYuHPXv2SMf5R8pSOHLkCGk0GsW+Tz75hMLDw6lRo0YUEhJCAQEBVKtWLVqxYgURKa0NXKnkbg2TJ08mPz8/IiLJVaa4ww8//KAoG76fWyvkwVB5BUD79u0jIqLGjRtL+7p06UJ16tShSZMmGVnB5UHN2mlt0Gg0igYhoFf0Ob169VIop9u3b6c2bdpII6F5WL58OREZK2rvvfceERGtWbNGsV9uVSLSWwX5h5SHIUOG0B9//EHbt29X7LexsVFYJ/mHbfbs2Yp7oNYtz3sqAOus+/369aNPPvmEiPTd3YYNQF5/e/furdg3YcIEyepvGHx8fIz2rVmzRmF1fvvttyk7O1tRTmqzxwQFBZm8tw0bNqSAgADJGuvm5kaAvlu5TZs2tHnzZiIiunPnjnTO+vXrqX379tL2sGHDjMq1a9euBDx1PzPs3jZ8z3h4eFCjRo0oPDxc0d1rTZD3OMmfK/k1clasWFGoZ4DXzzZt2lBmZia1bduWNm/erOhC/+2338ym0bRpUwLMK8M8Lrf+A3o3OMO6U1DZ1Z5pfq8/+OADKX/+Da9QoYJk9eW9FoC+EWbN/alQoQLt379fejcRkZFroVqQW30Nj7355puFLgMeuPGI9zCrhWrVqhUozc6dO1NAQIBauZcf5RVABwAfAvgawOcA3gBQo6Tyr1q1KqWnp5NWq5VaOTwMGDCAvvvuO+mlYik0adKERo0aJVms1B4qHl5++WUiIkU3GQ8ff/wx5eTk0EcffUQHDx5UKJmGXaPy4ObmRrNnz6b4+Hjy9/enDRs2GHUryoNcAQTMW5LMBS8vL+m/YVcSoO9Ok3/czAV7e3vKycmh4OBghdJ9+PBhGjJkCP34449GXRNDhgyhhIQEGjt2LI0cOVJhUebKZoUKFahChQpmH6b9+/crPijylve0adMU6fLA743cCsEtP5MmTVJY8ho3bqx6vwGQr68vLVq0iLRaLfXq1cvouIeHhzTlz7x586wqSyK95Vneuu/evTt5e3sToP/YDhw4UDoWGBhoZG0A9I0YufWfK/UFDW3btlVst2zZ0qgxYPiCnD17Ni1dupRCQkLo0aNHZtNXU2gBKK6xIIFI3xOjZvUsbLh06ZKiW5v7mPN69M477xidk5mZSRqNRvGBuH37NhGRkQIqD6+88oqqb72vr69Ux+XPLqDvDSHS+/3zfXJXi40bNyqsrPIg74LkFs7s7Gz68MMPjaarkpexKfk7duxIDRo0UOxTc7fiQU15NcyvZs2aqpagr7/+mhYtWiT59VurDLZs2VL13fbyyy8bTbdmqo4RKX19GzZsaNTgMBe4dbYgYciQIRbvwcyZM+n9999XbeDzYM7CGxsbK/3nvv9VqlRRPOPWWP54eO2114ym1vvpp5+k4/IeQK5kmguTJ082eczwG6Pm2vTaa6/R22+/rWgIvfbaa6rlJXe9MxfkdclaxXPnzp20bds2GjFiRIHrgVowbCR17NjRyCBnTeDfGbXA3VUMnzOzumJJKYWWAoBJAC4C2AXgUwBTAbwD4HsAFwD8DKBuCchB69evV61wI0eOJCKi8PBwszfJzs6OvLy8zCpHhoH7Rx0+fJhsbGzou+++Uxw357doKVjy6VFTjhwcHMx+CIsidOzYUVIkzJXVnDlzrFZ2Af0HhPudFTbILZsccw+fYejTp4/UvcatBZcuXVIor4Zlobb/yy+/NJnHe++9R+np6ZJFxpyvKKD+YZSXq6Fl2FwwbNgB+q5ie3t7RVemqWBra0s//vgjVa9eXcrzpZdeMrJEc2uvvb092draSgqJh4dHsddPfk2tW7c26gKeP3++kRW7MMFQGQNAx48fN/s88Hr1xRdf0IwZMxS+dzExMeTk5GSVsrNkyRKqVKmSoq4buhBoNBojXz9A/4GUD2pRC/J3FnfZ4O/VmTNnklarpatXryqsy5bSBPTvV2ssUPJGGqBX3jny/WouTPIQGxsruStYG8wp1eYCkd5P0ZxMLVq0oDp16ijKwJwVTB7kPXWmgrxnz5ogb+Byf1i1IP+mWiuvpdC3b18KCQmhw4cP05EjRxTPTevWrQtc9oY+0zwYNoRHjx5dIEXbXJCPh6hSpQpNmzbNqvPk7w65S9PkyZOt1j1sbW0V22qND3nDBtD3GFmrSHO3hKZNm0r+2obBsGemXbt2NHDgQHJyciKzulpxK4MFUBr/AaCymeNtAPQtZNoDAdwGEAbgE3NxGWNUq1YtReFyC5F8BKChH6L8waxYsaIUT+3jpBYMu/jVfL4MgyU/JmsCt6zILS4bN26U5OAPM7eiqFmzeDeyfGS3WjC07MoH38hH4RdVsOTiYC6ojcLnfrvWhJkzZxp1g6SlpZlUXrk/nDWuKaYCkbovsWGdlM9OUNig1i3ER4DzwVA8cB/wUaNGKWTlcAvJ+PHjjWTj1vb+/fsr9letWtXIsvDxxx8bdZuPHTv2ma5z5cqVRvWAI7f8Tp8+3ar0uMJbs2ZNk5Zha8PBgwdNyqY2IMQwqPncGvr1MsYUrhTWflgB0KJFi6R3ac2aNYno6cDK6dOnSz0GhjMEWArciCDfxweWdOnSRapP27dvl/zJvb29FdcpP9fQVcswGPoFWntvCnpO/sfapFsXD9wtSu6fb41V96uvvjJyQ1FTcgryXfHx8TGqQ4Z1RM1nv6ChXbt2FsuFB8PeAx4sGR84hu498tCwYUMCQIcOHSIiKnD3uFqQ+xATqbuJyQNvGHGj0+DBg63+NnGXAj7gS6vVKur34MGDFTMrAU+fN8PnxlIw1Gn27NljFGfNmjUm32H5PTxlX3ktrgDABkA4AB8AFQCEAmhmKr7cEsW7Q+fNm6c65cu1a9ekVsnixYtp1apVBCgVhe7du5u8uWPHjqXQ0FAKDg5WvXkbN240WznkXSTySs1lsFS5+vbtKw2+kVvtuF8gkd5fd8eOHRQSEkKA3hJmOFKZTxuTk5ND+/fvVxyrUqUKBQUFUVBQkDRSmAf5lDtypWXPnj2qLbt+/fpRSEiIyY9J586dJSV72LBhdOPGDasftNDQUDp//rzkM3zt2jWj+5GWlqaqFJw+fZpCQ0MVVsPs7GzSarWK6ceIyKTfFZH+YyTvWjNs+JgamAXoW9C8HDdt2mR03HCQFd9vZ2dHrq6uii58tQE4cusAnyrOcICGWvrBwcFSw07uIiGH1zE+qwWPc/78ecrOzqZLly6pWv8Ava/4H3/8QTt27JBGKJ84cYIOHz5MO3fupLy8PMVzLB99P2jQILN1wpppxHjc9PR02rFjhzT1GqAfVHf27FnJMt65c2dJsRo0aNAzux/wUeKmMJyuSx7s7OxUp7yKjY2VRnQbhvfff5+ys7ONLDFqYfv27ZSdnU2xsbFGDQ9rgikLGKC3+hMpZ0zgCnDfvn1Jq9VK71Te3SsfrCe/b/JgrQLQsmVL1WeEBz7Dwu3bt416Q7p06UKnT582mpWCv7e4Na9Zs2Ymn/cxY8YQEUmDFG1sbOjRo0d0+PBhs136XOG6ceMG7d69mwICAhSDouTvXN6wkne3G773Q0NDVWcRyMrKkr4XgP5ZV7NEywf8GLqXvffee4rG4ZEjR1SntFIL8plnDO9bSEgIXb16VRq136lTJ7p586ZiAGx8fDwdOXKEpk6dapTe1q1bFd9rHhfQK5/c9aBt27Ymvz+GjW4+JSAfyGc4UA3QjyU5c+YMBQUFUXR0NPn7+0uzDEydOlUaEMwVdxcXF0pOTqaQkBBFDxEnJSVF+sbFxMRQREQEhYSEUFZWFiUkJNCRI0ekusQHWZt6buRlc+TIEQoNDVWdMUftnbRhwwbVeETEG5zlR3kF8BWAqgDsAPwJIAnAuGdIryuAw7LtOQDmmIkvFWxqaipNnz7d7DJ6KSkpNGPGDMrOzpYqq1x5PXr0qGJe2AULFlCnTp1o0KBBFpeB5Q7+FStWpMDAQOrSpQsNHjyYfvvtN5oxY4ZihPXIkSMpOTmZpk2bRh988AEFBwdLD6jaywGA4iE8c+YMdenShUaPHm3yehcsWEAnTpwgrVZLH3zwAQUEBEiDLORs2LCBNm3aRG+//bZiPsTMzEyaPn06TZs2jT777DOj80JDQ6X0srKyaPr06QofGD7Kefv27TRq1Ch6++23affu3TRjxgyaNm0aJSYmStZdPtOD/EP8448/0vvvvy899IDep/DTTz9VlPkHH3xgdm7ClStXKvwU5SxevJiOHDkibWu1Wpo1axYtWrSIiPQvu7ffflvxITAcqLF27Vravn27YoYCPofsqlWryN/fn1auXKmwGMnnhlWzrBqyfPlyOnLkCH399de0c+dOhUU4LS2N1q5dS6NHj6bx48fTiBEjKCsri1auXEl79uyRyrRRo0YUFBREvXv3Npoh4ccff5SmiuOuD126dKEdO3YYxTVk27Ztqi+1Q4cOUbdu3RRWTnlDyxSJiYk0a9YsaZo2+bPI/8+cOZP8/f1pwYIF9NVXX9Hhw4ctpsvL0XA+52PHjknzuhLp5z8dMWIE3bhxg9LT02natGmUkpJCYWFhig9j8+bNKSwsjPz8/BQzULz00kuKOtu4cWPFABFzzJ8/n6ZNm6ZQ1Pv27StN3WUKf39/RYOlb9++Uvndv3/faK7rvn370m+//UabN2+WBihxLl26ZJU1z97enpo3b05+fn6k1Wrp6NGj1L17dynNd955h0aMGCF9dOXTGObl5dHbb79tNFWQVqul2bNn08WLFxX7DfOeMGGCUcO0fv36NHnyZBo2bJjCH3Ly5MlEpPd/7t27N3300UfUq1cv6tOnD/Xq1Yvu3LmjKMdevXrRW2+9RdOmTVM0IuXKqeGARz4byvbt2+m9995TWEjlo9iXLFlCJ0+eVFzve++9pzqIztSyuPz48ePH6aOPPqJhw4ZJ70H5DBlarVYxZ7Ml5PFOnjxJXbt2VbizpKWl0ZQpU+jdd9+l4OBgaaDe66+/bvSs5uXlGc3WAejnjR47dix9+eWXNHPmTOrXrx/Fx8fTm2++aTTLRP369SXZsrOzacaMGWbni+WuUSNGjJBmFDH1vvn4448pNDSUMjMzFfeZz8sK6GdK+f7776Vv2759+2jp0qWk1Wrp3XffVQy0evvtt2nWrFnUs2dP+uKLL1TzTE9PpxkzZlB6ejqFh4fTO++8Q7dv36YRI0Yo3knXrl2jvn37mu1FUiM7O5tmzpypaCTv3LmTOnbsSO3ataNevXopXA6tndP55MmTNHv2bJo5c6bZJWXzG6nlSnm9nP87Eno/1xoAQp8hvVEA1sm2xwNYZRBnOoAQACHcx+61116z6kbIyc7Optq1axuNiCYiWrZsGdWuXbtAEzZnZ2eTp6enanqc7777jurVq2eyErz11ls0cOBACg0NJU9PT9q2bRs5OTmZnGC9LHLo0CFyd3c3mhzZVNy6desqPhK9evWiqVOnKuI1bdpUUigLy8svv0zjx48v9PnfffcdeXt7m5z0/dixY1S3bl2jQQlyhg8fThMmTDDa7+fnR/379ycPDw+FImUKrpCqTQVmCLdmmppI2xA+fyC3/BQFb7zxBr3xxhuFOnfWrFnk6+tL4eHh5OzsTCdOnCgyuQpDQEAAubi4KBSv7Oxsqlu3ruLZX7RokZEFsSCcP3+ePDw8jObKNcfcuXOpU6dOqsd496a9vb1JxYgjtzTWqFGDHBwcjKxx5ib+VyMvL4/q169P33zzTYHOIyIaMWIE9e/fn2bNmiUNls3JyVEMMjO8posXL1KtWrWsnuLMGn766Sdyc3Oj7du3U926dWnTpk3SrAVyQkJCyMnJqUD3btasWdSrVy+aP38+NWvWzGS8zZs3k4+Pj8m5tWfNmqWYfP6jjz6y6j3Rt29fo+9oZmYm1a9fX7Ggizn+8Y9/KOrf7NmzqVWrVtSsWTOaM2eOxfOHDx9OPXv2JDc3N2lqQmu5evUqOTg40JkzZ2jnzp3k6upqsZ6rMX78eKmOPY9s3ryZ6tata9EYV0hM6naM9MpbmYExdp2ImjPG1gLYRUSBjLFQImpdyPReBzCAiKbmb48H0ImI3lWLP3DgQDK3LKtAIBAIBAKBoNhhpg7YlqQUVrKfMXYLQBaAtxljLgCePEN6MQDqyLZrA4gzFVkorgKBQCAQCARllzJjeWWMuRNRfP7/6gAeE5GWMWYPwJGI7hcyXVsAdwD0BRAL4DyAt4jouolTykaBCAQCgUAgELy4lAvL64Z8pfU4gEAAQQBARBkAMgqbKBHlMcbeAXAY+pkHNphRXAUCgUAgEAgEZZgyY3kFAMZYJQC9AQwC4AsgGnpFNpCIoktIjLJTIAKBQCAQCAQvJiYtr2VKeTWEMeYNvSI7EIAbEXUqgWzLboEIBAKBQCAQvBiYVF41JSmFNTDG7BljXC476AdcvQagu5lzKjHGzjHGQhlj1xlji/L312CMHWGM/Z3/W734r0AgEAgEAoFAUFyUOcsrY+wCgB4AqgMIhn7+1QwiGmfmHAbAnojSGWN20PvLvg/gVQApRPQlY+wTANWJ6F8WRChbBSIQCAQCgUDw4lF+LK/QK9SZ0CuePxDRSAAtzJ2QP5ltev6mXX4gAMOhX+gA+b8jikVigUAgEAgEAkGJUCaVV8ZYVwBjARzI32djxUk2jLHLABIAHCGiswBq8em38n9dLaUzcODAwsotEAgEAsELRUpKCoKCgkpbDEERExcXh5CQkNIWwyRlaaoszgcA5gDYQ0TXGWM+AI5ZOomItADaMMacAOxhjJm11sphjE2HfolY1K1btzAyCwQCgUDwwjFw4ECcP38eaWlpcHBwKG1xBEVE69atkZSUhLLmWsopc5ZXIjpBRMMArMrfjiCi9wpwfir0c8UOBPCAMeYO6BdBgN4qq3bOGiLqQEQdXFxcnvEKBAKBoPj5xz/+gbVr15a2GIIXnDt37gAAxowZU8qSCIqSpKQkAMDLL7+MJ0+eZZHT4qHMKa+Msa6MsRsAbuZvt2aM/WjhHJd8iysYY5UB9ANwC4A/gIn50SYC2FdccgsEZZEnT57g0aNHpS1GuSMrKwuPHz8ubTFMkpWVhR9//BHTp08vbVEELzCJiYmoVq0aACAgIKCUpREUB0eOHMGuXbtKWwwjypzyCuBbAAMAJAMAEYUC6GnhHHcAxxhjV6Bf/vUIEQUA+BJAf8bY3wD6528LBC8Mbdq0gZOTU2mLUe5o3Lix9FEui3h5eZW2CIIXnCtXrsDV1RXR0SW1ftBTevfujcOHDyv2ffvtt3jllVfQtWtXNG/eHK1atcJvv/1W4rI9j9jb25e2CEaURZ9XENE9/exXEloL8a8AaKuyPxlA36KVTiAoP9y+fbu0RSiX3Lt3DwCg0+mg0ZS9Nn5CgqoHlEBQYly9elWxXZLPyZgxY/Drr79iwIAB0r5ff/0V//73v+Hh4YGGDRsiLi4O7du3x4ABA0QDvoBERUUptu3s7EpHEDOUvbcycI8x1g0AMcYqMMZmI9+FQCAQCEqS2NjY0hZBICiT6HQ6o+2srKwSyXvUqFEICAhAdnY2AL2yFRcXh549e6Jhw4YAAA8PD7i6uiIxMbFEZHqeGDJkiGJb+Lxax0wA/wDgCf3qWm0AvF2aAhUHzZo1A2MMX3zxRWmLIhAITHD58uXSFkEgKDeUlKLo7OyMTp06ITAwEIDe6jp69GjIe2zPnTuHnJwc1K9fv0Rkep4w7NkRyqt1dCSisURUi4hc81fWeqO0hSpqbt7UG5NXrVpVovn++uuvoiX6AqLVmvW8ERhQvbp+JekjR46UsiTqcH9cjUZjZAEraX799VfExcWVqgzm2LFjB+7fv1/aYpR5MjIy8PPPP1uOmI9avSvJbwt3HQD0dVA+20F8fDzGjx+PjRs3ljm3n127dpXp5wWAZNE2tV0WKFt3Vc9njLGX+AZj7GPoV8p6LmncuHGJ5ZWYmIgxY8agT58+JZanoGyQkZFR2iKUK7hyeOXKlVKWRB3eGNHpdMjMzCw1OVJSUjBmzBj4+vqWmgzmSEpKwujRo/Hyyy+XtihlnrfeeguTJk2SrJmWUJv/k0+vVBKMGDECf/75Jy5evIisrCy0a9cOAPD48WMMHjwYS5YsQZcuXUpMHmvIysrCqFGj0KtXr9IWxSy5ubmK7ZJyBykIZVF5HQZgKWOsB2PsCwCd8/eVST744AOMHz++wOdVqlQJAFChQoWiFskk6en6FXTFIJ7yQ1ZWFjw9PeHg4IAHDx4U6NzRo0dL/0tTwSmPcOXwxIkT+N///lfK0hiTk5MjdZEWZkqv48ePo3nz5s80Hdj8+fPRo0cPAMYDPMoKfOBdZGRkKUtS9uH3kJeZJdSscQ8fPixKkczi4OCA3r17w8/PT7K65uTkYOTIkZgwYQJef/11o3PmzZunur+k4EaEiIiIUpPBGgyVV+E2YAVElAS9svofAB4ARhFRrvmzip6MjAzs378fOTk5ZuN999132Lp1a4HSTkpKkipDSbZo0tLSAAB5eXkllqfg2Th9+jTi4uKQkZGBDRs2WH3e9evXsWPHDmlbKK8FQ/7y9vPzK0VJjNHpdMjJyUGVKlUAFM6q/vbbb+PGjRvYt6/wU18vXrwYN27cKPT5JQFXxMriaGlriYiIKBGlkJeRtYqV2jslOTm5SGXi3Lp1S/VbOWbMGISGhuLNN98EoHcR+euvv7Bp0ya0adMGbdq0Ufitf/HFF9i5c2exyGgN/BpK29XHEoY6gnAbMANjLI0x9pgxlgYgDEAjAK8DeMwYK/HZwj/44AMMGzYMX35Z9FPDNmrUSPpfksqrUGDKN7Vr17Yq3v3799GihXJ1ZG51F1iH3Ee4rC2PyBuhfCnOwiivNjY2AIwtLM9CWfSr5rNF2NqWyVkhraJ+/fpo29ZoJsgih5eRtd88NWtccSjZT548QdOmTRXTYnFGjhwJIkKTJk0AAOPGjUNubi4uX74shTZt2hS5TIWlLHa/W4OwvJqBiByJqKrstxIROfDtkpaHj7Yz51h97NixQqUtf8BLsjLLP3LWdg29iOzatQvz5s0rkrSWLl2Kbdu2Ffr8wnTrqk3vVF5fmqVFWe6d2Lt3LwCgalX9a7GgDZMdO3bg2rVrAIrWolIWB4Ly93d5tbzye3v37t1iz0teRtYoK2pximM1P14GwcHBRZbmpEmTSsX6KS+z999/H3/++adV53344Yf4/fffi0ssiwjLqxkYY03yf9uphdKSi1s51Ojfv/8zp1+SLRq55fWnn34qsXzLG6NGjSqyKczmzp2LcePGFfp8ufJqbV1JTU012ldWlNeyaJ1ToyxbXvmAmq5duwIouOV1/vz50v+itLyWxYUTyrvbAG9kFBadTmf1Mye3TltzL+XvI16n1N49zwr/bhksXPRM/Pzzz6Uy9kP+Df7+++/Rr18/i+fk5ubi22+/xaBBg6zOp6gVc6G8muej/N/lKuGbkhaGf+zNWTWK4kP8LMrrL7/8AsaY1a03+UcuKCio0Pk+C3PmzAFjrMxO/l65cuXSFkGBvPFk7QtEzQJWFmYbmDp1KmxtbctkF5Qhz/Js9+vXD4wx1KlTpwglekpWVhYqVaokzRpSUHcguR+/uca5OeSKRMWKFQGgxKajmjVrFhhjVvVKcItlUSrpJcmtW7cAQPJvLij16tWDj4+PVXHlCr4191L+HC9YsAC2trbFYnnldbQolVd5uiVJYd59MTExAKxXSG/evAkbGxv861//AmMMjDGrZ5AwhVBezUBE0/N/+6iElyydX1RkZWVhzZo1UiUzpbwarqtc2I/ds1QKPiDHmkEX6enpWLx4sbRdXI71crKysjBt2jTMmDFDGu27ZcsWAPqXsk6nw9KlS0t0hKolyppiVRjlVW26muPHj1s878KFC9L9KWoCAwOxfv16ACjW7q/c3FwsXLjwme/js1heeWMyJiYGX375ZZHX76ysLFSoUEFSaAqqvMqVgGeZbYDD3RdKym2Au+FYM1cmj1Ne/f153Sms5TgmJgbR0dG4cOGCxbjyPKy5l/L3kUajQaVKlZ5JIbx27RrWrVtntJ83vItaebXmuYyLi8Py5cuLLM/C9IBFR0cDeNpINMV//vMf3Lx5U1JUv/rqK+mYv79/gfOVY+235/r161izZs0z5WUtZUZ55TDGKjHGPmKM7WaM7WKMfcAYq1RS+d++fRszZsyQlFZTFquBAwcqtgs7IObx48eFtorxl401VoX//e9/0shgR0fHEml1vvPOO1i3bh3WrFlj9AIgIgQEBGDu3LmlOnWJOcpCF7e8Xj2L8vrNN5Y7Lzp06IAJEyZYL1wBkHd5BQQEFEsegL77ctGiRZg7d+4zpVNUbgNz5swp8vqdnZ0NOzu7Qs82UNTKK1/QoaQmXueDzayx8vHu77LWKLUWrnQ/60T7//73vy3GkbsNWKO8GpZp5cqVn6mHp0OHDpg2bZrR/qIqA0OsUV6HDBmC2bNnIyQkpEjyLEwjiltezVnfdTod3nnnHbRu3brI3XcYYxZnXeJ88cUXmDVrVom4qZU55RXAZgDNAfwAYBWAZgCKxxykAv9o8W7ts2fPWuV39I9//KPAeTVo0AC5ubmFNunzOWKtUV7lllYnJ6cCPUQeHh74/PPPCyxfSkqK9N9wnsWsrCxJhpIYjGCKpUuXws3NDTqdzuhlnJGRgQMHDqBOnTpWdaMNHjxY4cNkSvlt2rQp/vnPf1oln1x5nTt3rlUKtbzcDfHx8bE678Jy5coVVKlSBWfPnlU9vnbtWvzrX/8qlrx5A23FihXPZPGUl/OTJ08Uq/cUlKL+mHDLq729PQBgxowZVlnW1CiM8mrYfcndI5YsWVKsS3E2adIE7733nqTEWJoQPyMjQ/LBfPLkSZlojBYUrgwWVHHTarXSCHyg4L6o1iw2YKig2NnZPVNPIj/XsH69+uqrAJ7d8mqogE2aNAmvvfaa2XP43Nq9e/d+prw5hSmf+Ph4AOZd2ni6ubm5qrNFFKTsZsyYodi21tUrICAA27dvh06nw/Xr163Oq2fPnlbLJqcsKq+NiWgKER3LD9OhnzarRJF/cCZNmmQxvrUjyuXWgrVr1wJ46tdUUApieZXn6+zsbLXympubi/j4eCxYsAC//PILbt26hWvXruGPP/6w2LqSK6y89chJSUmRlBtLrbpnUfAtMXfuXDx48ACZmZlGCmp6ejpmzZqFmJgYyVr4+++/m7QuHDx4UOF/bOoBvnXrlkVL6IMHD3Dq1CmcPn1asd8aZcOwrIGn1rHIyEirrLCA/gNWGN/oH374AVlZWfjuu+8AQHVxhZMnTxY4XVM8fPgQJ06cAKC0GC1durRQCktSUpLRB5QvQ1kYuKWwKNBqtbh27RoqVqwoKa8A8OOPP1qdhvx9YU7BDwgIUPWzM+y1qV69Ouzt7fH48eNinXz99u3b+OGHHySZzDXSAODAgQMAAE9PT6viX7lyRXUxg8OHD5ea5VY+WMnf399qv8fY2FjFgCRr3huhoaHSf2sMCobvtwoVKhSJb2R2drbiPcvr6LMqr4Z1PTs7G7t37zaKp9PppPc9zzMjI8Ns2VvrmqFWjyzVLS43dxv4/fffjb69RTkVomG3f+XKla3q5ZAbuKxVXtesWYOTJ08WqhFdFpXXS4wxaU03xlhnAKdKKnO1Fu6FCxeKzG9t6NChAICXXnpJahkXdOUkDu/mscakL68cNWvWRFZWllUvQvlDMnbsWDRt2hQtW7ZE//79MWrUKJPnnTp1SvEyjI6OVuT3ySef4Ntvv7VK/lmzZmHQoEHSx6go4YpFUlKSkYUsIyND+lDHxMQgKioKAwYMwODBg61Ku3Xr1kb7rP0ItmvXDt27dzfqrrKmO0ZNebX2XODp/ejRowd69OhR4JGr/HzeM9C0aVPpWMWKFeHt7V2kL9t+/fqhd+/eyMjIUDyn33zzDT744IMCp/fSS8/mYs9Xz+MUZXfnpEmTkJmZifj4eIXyWpCBhnIFw5Tv+3//+18MHToUCxcuNDpm+C4cNGgQXFxcpO3iGBwlf264FdHSO3nZsmUAnj6HlqyJrVu3NhrcdPbsWQwcOBBjx44tqMhFAldek5KSMHz4cHz22WdWnWc4FaIlN7H//e9/ksJaqVIlq1ZMCw8PV2xXqFDB6u5lc1y9ehUDBw7EypUrFfuf9Tmy9hu+aNEiDB06FP/9738VeZqzXrdo0QIdOnSwmLaacm9pXl3+rrSxscGZM2cwYMAATJw4URGnON0Aq1atarHhBygbF3fu3ClQHn/99VeB5SqLymtnAKcZY1GMsSgAZwD0YoxdZYwV+0LjOp0OTk5O0vbkyZMVv+bo3bu3Rb8v3q2ZkpICV1dX2NjYFLpbkX8krHlhyCt3zZo1QURWDdoyZ6GVK6eG/P3334rt1NRUdOzYUXLHkFs5LcnPy+xZp41RgyuvycnJktWOT/Cfnp4uKSLfffed1ACwNN+gOaX+448/tkouU/XoyZMnSEpKQp8+fUx+YHg3k9p51sBflrxHYMqUKVi2bBn69OljlfuEXHn99NNPFR+NnJwcODg4FEh5ffDgAfr06SOVydixY9G3b18sWLAAwNNW/rZt24x6MeSr65jj+vXr6N27Nzp27IirV68CAPr27WtxOrzp06ejX79+GDBgAIYOHYrg4GCjBkpRWV4nTJggreaXnZ1dYOX1wYMH6N27t+JDZOqDfunSJQDqS0nLG8I9e/aEn58f3N3dpX3POuL84cOHeOmllxT1W+0daemDmpSUhHr16kmNEa7MylmxYoVZNxreELxyxfSnJysrC/369TMbB9DXMVdXV4wYMcJsPMO05ezduxf//Oc/LfaefPjhh4ptS89bWFiY9N/FxcVkA5jD3azeeOMN6dtSVMprcHAwiMjonj9+/Bg3b95Ev379CtVbyZXPt99+22Sc3NxcyYJ4584dhUJmzhrNy8BUQ1+r1WL48OGq346jR4+alZtboMPCwvDLL79I/+VYur8//vij1bMSGeLk5GSV4i9X9AvaA/PTTz/B3t4eDg4OmDlzplXnlEXldSAAbwC98oM3gFcADAEwtLgzr1SpksL83apVKwDWjeg/ceKEyflT4+LiEB0dLXVjpqSkQKPRwNHR0WqlwhCuSFjT6uJxhg0bBl9fXwCmWztZWVnSB8qctc7cQBY+QhIAxo8fDwC4ePGialxTL7zHjx8jOjpa6i65d++eVVbAlJQUPHnyBA8fPsSTJ0/w6NEjZGVlqVq4ufU6KSlJUpK5315qaqr0UkhNTZWUW0tdY7t27VJ9mSQlJeGHH36Qti197NTIzMzEsmXLcPz4cVWrok6nQ2JiIpycnBQO/tnZ2UaNlQcPHqh2qxvKvmnTJnz66ac4fvy4Vb6qcuXVUGEgIlStWtUoj5ycHOk5SE9PVyhAH3/8MY4fP4758+dDq9Xil19+wdGjR/H555/j/v37kvuMoa8WYOx3bKqXw8/PDydOnFBYuhMTE1GrVi2T16nT6bB27Vr8+eef+P333xEQECAN5JS/yE1ZjO7fv4+UlBTk5OQgLi4O9+7dQ25uLu7du2ekAOp0OqOZINzc3KT/htZeNdavX48TJ04oLKORkZGIjo42egb5c284SCQ3N1ehODg7OwMA6tatK+0ztMhptdoCveO+/PJLHDt2DH5+fpKsao0mc5Yw/hy0bNlSWolMbSaN//u//7PKjcbceycgIAB//vknhgwZotqzkp6ejocPH2LgwIFITEzEvn37rF7gxtB4EB8fj2+++cai3/r58+cB6O9L8+bN8fDhQ5Nds48fP1bUCTc3N4uNVP78Vq9eXSrfwiivDx8+xPXr16WFN4CnRgo1w0mXLl3w559/YuLEiXjw4AF0Oh0ePHggNWwNB0AnJiZK3wFeX1q0aGFyxTX585+VlaV4dq9du6baYJK/Y0wNdLt06RL8/f2xefNmAPrxEV27dkWrVq2MnhdD5NezatUqAHoDlBxr3ADfeOMNhQ5iDf7+/qhRo4bZbv3Hjx/jzp07ine6/PtvyL1794yOHzhwAJmZmcjIyMDq1autkq0sKq+2AO4T0V3oFdfhAB4R0d38fcVK8+bN8e677wIAatWqpegOU2PmzJmK5V5NfagaNmyIevXqSQ9Qw4YNAehH/hdm2qqIiAhpyiFrlVcHBwfs27dPcoA3nO5LLmu1atUAmH8oTCmvc+fOlaxigH4lEXOY6masVq0a6tWrJ03z9J///AezZ882mxag/6B26dIFNWrUQPv27SVFzs3NzejB4Faxjz76SLKK1qtXDwDw2muvKR7IgnSFGM5GodVqjeqS/IVtDg8PD+l/Zmam9PJRewnFxsYiOzsb48ePl8rdzs4OOp1OYU25d+8e3NzcVBU+cyOGrRl5zz9gpiyOVatWNapX/fv3h4uLC7RaLRo1aiQpRcBTSx73q5Tj7u5u9VzM//nPf+Dm5qY6bYzac+vj4yO5PqihZvnmDZsaNWqYTfvw4cNwd3eHs7MzBgwYgAYNGqBu3boYMWIE6tatq+j9AdQbkXKF1ZoPktp0S3l5eahXrx66dOmi2G9KeR00aBBGjx4tbfPy8fb2lvZ16dJFYWEaN24cXFxcrOp6BJ6W4bFjx6SZGtSUAnMf1ISEBGRnZ8PLy0vy9y4MXGk1V++58nbv3j1p7l05jo6OqFGjhuL5e+mll6xSYA2V4YK6r508eRL169dHWlqayfleq1Wrpnhf16lTB8nJyWYVdv4dk1v/C6O8urq6okWLFhg5cqS0j79ns7KyjOo1t4Tev38fbm5umDhxItzc3ODp6YkdO3agWrVq0nVeuHABrq6uqFGjBrp27SrJXK1aNcXzKUf+LVq9erXCUDFhwgTFe0m+n2PKOmv4jV+6dClOnz4NLy8vVR97OWrfYEM5rOnJSklJQb169VSX2VVj7NixGDp0KJydnc3qGNWqVUPjxo0VPaNqvX+cJk2awMfH55mt9GVRed0FQMsYawBgPfQK7C8lLcTZs2dx4cIFoxaOIS4uLooPnFr3lk6nM6qA3FG8SpUqVrWaFi5cKHXlAcqHxNL0JMuWLcPhw4elj52HhweqVq1q0rQvX0CgMJZXPp8nhyuDpsjOzrZqHlIA2Llzp9G+nJwcvPbaaxg4cCDee+89AE9dGrg1lWPYRcNb4PJ4/EPMPxT8RScvrw8++ACvv/66yZb2qVNKN201xddSdxGHW1EAy6Om+QukYcOG+OSTT/DTTz/Bz88PgFLZ4q399evXG734MjIyTA5Q2rVrl1lZr1+/Lg12MOX6UK1aNSOfa94L0LBhQ8THx0Or1eLDDz/EqlWrpIEQu3fvLvDHW57HoUOHACjnvI2MjMT7779vpNi5urpi69atZgeJqLmx8I+fXOlQU17lMzEcP35ces4OHjwo7Z8zZ47039KKeIbvkODgYCxZskSxT/5u6t27N06ePCnVf/m7BXj63BuWi2HXIz/f8GMqT4/XB2t9+7dv3y795wM11Z6z8PBw/POf/5Tu8bZt27BhwwZMnjxZ8tX19PTEG2+8gUqVKsHLywuAvmwMewRM+dPzd2tkZKTJj638+SnIEqbnzp1TbB87dgwrVqxQ7LPWT/3SpUvSNfP3Q5s2bVC3bl2p9zA5OVnR27Zs2TLVLnQfHx/k5uaqDl7j6fPZdbjiDugbUwXxd960aZPqMsx88KW8B5DDnyVel7kbDQAcOXJEcUze03f58mXJT7RatWqKBo28Lhha800pYbt27cKGDRsAKI0Qb775Js6cOWMU3zAdrvR7e3sjJydHUec5a9euxZ49e1S/74blLC8HQO/uY8rNzlr3AV73XF1dkZOTY/Tu5c+fGpGRkfjjjz8A6BvqvMdx7dq1kgHG3MwkVo2zIKIyFQBczP/9GMC7+f8vlVT+7du3JzmXLl0iAASAtFqttJ/vi4iIoLZt20rbr732GhmSmpoqHeeB065dO6pTp47ROXKysrIIADk4OEj7du7cKaXl4+Nj8tyUlBQpXrNmzaT9DRo0oCZNmqieI5fx5MmTRrLzUKtWLdXza9SooYin1WpNpqFWJoZyyEP16tWN4n3xxRdWpQ+A/Pz8FOe6uLgYxdm+fbti29PTU7q3hnFXrFhhVl4eduzYodjWaDRm7zuPZ29vr9gODAykt956iwDQwIEDjc774YcfCAAdOnRI2vf9998TABo/frzqNU6dOlWRx7Fjx8xeS2hoqEm5fXx8pHidOnUyOnf27Nn03nvvEQCKjo6WzmvcuLFV92/JkiVW32sA1KZNGymPIUOGEAD68MMPpX39+vUjAOTk5KQ475VXXiEioqlTp5qsn6tWrTKZr52dnfS/Z8+eRud+/fXXVskfFhamuDfyciQiGj16NAGgGTNmKNKvUqUKAaD09HRp3+TJk43Ol98jOS+99BIBoOnTpyv2G8rRu3dvIiJau3atYv/atWuNZDl9+rRRORhi+K6sWbMmERF99dVXJsvozJkzqrIBoM2bNxMRUceOHcnDw4OIiDQajXS9aufIkd/jZcuWqcq8ceNGxfkpKSlmy4yHL774QjWenK5du5o8X07VqlUJAGVmZtL9+/cJAL377rtERBQdHS2dw78h/JtiqswM76Gc9evXS3G//vpraf+AAQPI0dFR9Rw1unXrJqXj6upKfn5+CjkGDhxId+/eVezj7+uKFSsayS1/vxERrVmzRvX6goKCqEuXLqplaXgveeDly8tYfp6tra0i7htvvGF0rfJvlEajoczMTCIiCgwMVLxv5PD48nLiwTC+qbphTd0xlS9/XufPn0+A8Xvfmne2PD1z8hiG1NRUSRxToSxaXnMZY2MATADAZzMvtYWp5V298lawRqPBW2+9BW9vb8XKF7t27VK0UFxcXPDGG2+YTN/e3h737t3DrFmzpH1JSUlgjOHzzz8HYwwffaRfOZe38IcMGaIYFBQRESFNB2OI3FdX3kL08PCQLCEbN24EYwyrV69WWJr69u2rsOiMGDFCYW2V/5djabQ1r3yG8OXsPvvsM5MWr4cPH8LW1hb//e9/pX3muigM2bBhAxhjaNu2LTp27Khq0TG0bPLBMGqDGMLCwrBu3TqL07gY1gEHBwerLO6Glq+BAwdKTvtqVkheFnJrNx8gwn3+NBqNYt7SdevWYenSpdK2WtenHHNdSPJrMrQsdevWDV9//TUaNGgAQDmdijU+mwAwb948s8enTp2q2E5ISABjDF27dlVdHIGXr6HFxdHREYByBK2hlYE/j/JudI7cEvnXX3+hQYMGkuUPgKrFSQ01H8rt27fj66+/BgCpLsjjbdmyRboPbm5uqFmzJubNm4eNGzdKcfr27Ws2X36+KcsfLxf+rBh2zct9Cq2d2gownhGAX5fhMy5/LszN4FG7dm0A+vrFLadcHlOWVPl9lltVDev9lClTwBiTBvNyFwc+4M8Sc+fOlayicrcLOzs7TJ8+HZ6enqpWPDW4hXL+/PmSLzS/J3Xq1JFmS+AWNHMD/IYNGwYbGxuTK+HJ6y5/TjhpaWlWzwn+8OFDNGjQAESEBw8eSGMxOIGBgZIVlsPrndq4A7ml0ZzPrpOTk1FPAWMMERERqn7ULVq0wPTp06Vt+TumVatWRs/yjh07pGVZ+fMhf7+eO3dOKv8BAwbAwcEBBw8exPfff68qr+F0icCzL7ph+FwnJSWhWrVqUi/yq6++iq5duwJ46rZm6Kda1EsB//bbb9J/q3rYzGm2pRGgX5TgewBj8re9AXxSUvkbWl6JiBo1akQAKDY2llavXi21vCZNmkRERD179lS0Grgli1u8DAO3FBAR9erVS9Ey2bBhA/n7+5tsXZ45c0ayZCC/FSc/3xBumVmyZIli/8iRI6lChQpEROTq6iq1fg3zGzt2rPR/9OjRRPS09eTi4kKhoaH022+/SRYOIr01s1KlSvT999+Tv7+/4pylS5dK8QzzMmzBmgu1a9embdu2UXJyMjVo0MDq88yFefPm0fbt2ykvL49sbGyk/e3btycA5OHhYXSOm5tbofLiZWQKHs/V1VW1rHiQWy/3799Pzs7OBCgtbkQkWTsBUKVKlYzSkVsWLIU9e/YYyRsTE2NksTYMXbp0ISKiI0eOEKC32uXl5RGR9ZZXS2HZsmUW47Rv35527txJoaGhNHDgQNU4/BmePn26Yn94eLhUp7lF4sMPP1TE6dixI92+fVs1Xc6cOXOsuh5u/ZDvCwoKUpS9ra2t9GzKe2TkgTEm/f/yyy+lXiRDy+vmzZspOTmZmjZtSgDI29ubbt++TWvWrFHIUbt2bVq+fDlFRkYSEdEff/yhyO/dd9+l48ePExFRhQoVCABt3bpVIXdwcDAtWrSIQkNDpd+zZ88ayb59+3ZydHRU7Ktdu7b0PyAgwOQzwi3X/fv3p6pVq1JQUJB07OLFiybLPSwsjD777DNq1qyZtK9z5860efNm+vHHH1XzW7RoEQGgn376iYiIDh8+bPJ+GNaJwtT1BQsW0KNHjxTny8vp+++/l8qa98C1bNlSYaE0JU/jxo2pTp069NNPP9HWrVspMTFRSmv16tVS3G3btkn7+XvS2dnZ6P1gyL59+6RnhaP2/pC/h02FIUOGKL6JgN5K+NNPP6nGj42NpUmTJhntX7NmjZFFFtD3IJmy4loKx44do5UrV6rWSY782bxx4wYREV29elU1vaVLl5JGo6Fu3bpJ52dnZ0vH586dq+h1MyXX9u3bFTIYXt/48eOlY/v37yfgqbU3MDCQrl27JvVIGoYzZ85I9eDo0aPSflPvRB4OHTqkuH/8EkyFUldWy1pQU175h+bjjz8mAFJXIu+q69u3LwGQKr6TkxNt27ZN9QYZdqt07txZOnb48GEC9B8MczdZ3tUtV35zcnKMZG/WrBl5eXkZ7eddNFlZWdILwrDr1DBMnDiRiJ4+EDVr1jR6YRDpu7D69++vyI8rAWlpadK+AQMGKM6XP8TWBrWu6cIEQxcI+Qv+hx9+IEdHx0LJZyrwblmuvBnC43HXAlPp1KtXj4jIyDXDEHk3oYODg/S/UqVKUv21Nqh1J8rdBUyFzp07ExFRYmKitO+7774jIqK6desWSbnu2bOHKleubHX8jh07qu5/5513iIgUL1TgqZJPRDR79mwCjN1MYmNjiYho8ODBRuly3n77bavk4x8i+b6YmBhF2VeqVImGDRum6M40FXiDlSN//9y5c0cqE7lyyINc8atdu7YiHVOKIBFJLhSrVq1SreM8tGzZ0qi8TYWWLVtK/7lSrBYvOzubiIiGDRtm1Ggz1/CUPyNqwbA7G3j6/ubfBVPnGjaYLLlVqd0LHvjzo3Zs9+7divK2ppHP3zdyFxNA6X4jd+HYv3+/tJ9/tyy5wcmvt2XLltL+PXv2WHXvDYNWqzV6nk6fPk0rVqxQ7OPfiuzsbKkBLQ/169dXTX/btm2Kum8qVKtWzcjA8frrrxvFkzcE1O6dubqTmZlJderUoXbt2knnh4eHS8cNG7bc8KYW5HBlnsu/ePFi6VhoaKh0DncpAaD6zuaNkRYtWhgdUzOayANXegHQiRMnpOIxFUpdWZQEAfZDPxWWncoxHwCfA/ArbjnUlNfFixcToLcUyQv7vffeI6KnH6nBgwfTK6+8QnZ2dqpWU17B5bRp00Y69uOPPxKgtKZaCnLLaMOGDSkgIIDq1KlDP/74I/Xp00dRoeR89NFHBOhfwtbmN23aNH1tkl2L/LjcSjF8+HCjPA0VNW79efXVVxU+ggUJ1atXN9rXvXt3o31qHxseWrdubSQbV0xatWpFRGT0Yps1axYFBwcbpbVgwQKL15KTkyNZQj/++GOjcpKXcf369YmIKCAgwGR6q1atMioHc2ny8MEHHxAR0dKlSwtc5k2bNqW0tDRVfyxTQV4P5WXPlQhumb1582aB5Dlx4oT0X27VkFuITAVTH3ReNkTqL2K5b2ZycrJCBnNlzuu9tdf222+/GaVjWFe5osUtweZCxYoVFefKlVf5x0MtyJV0Q+U1MjJS9ZxPPvlE+u/o6EjLly+n5s2bK9571twPwyC3kNWpU4feffdd1fcY54033ihQnbIUeGPtnXfekfZFRkaSu7u7pJCZOpeIFBZS7qNqGHx9fSkhIcFsfZozZ47JhoNh70urVq0sXhfH0GJsZ2dHRHofcLkyLVMyJGWmQYMG1L17d9q8eTOtWrWK+vXrR0QklZW8l4e/34jMv+MsyWzY49K1a1eqV6+eYl9oaKhi3IpWq5V6OU0F/qw9evTIohxVq1Y1qr9qBiFDI5OpumXqWuvXr0/NmzcnIqKxY8fSiBEjpONHjx4lNdTSatWqFZ08eVL61lWpUoWysrKMGsfycTPmfPU3b94sXVthjEo3btyQ/leoUIEbusqF8uoG4CsA4QDOAzgI4CiACABHAAwvCTnUlFfegjNUEPigh5EjRxIAGjVqlJHTuWEwHHDEu+cApRXVUmjTpg3NmjVL8XEAQO3atSNA2X00YMAAo2viDuRqFmJTD8+ECROIyPRLWR7GjBljlKcpLH1033jjDZo0aZJkrZQHufX3hx9+oNGjR1NERITqC8NU+g0bNjSSaevWrQSAatSoQURE3333nRR/4MCBkkO5YVo7d+5UvJwNu714nZk1a5a0786dO4q85S1pPshO3v3CXzT8f48ePRTHDAelcQxl5a4kCQkJ1LdvX1q+fHmBXja8sWUpuLu7E6DvrueodZu/9NJLRESUnJxsMU0++AqAwjIg/zhZ02Xr5OSkahHg94lIb3kw/BDKB63k5OQoGkfmypyIJAun2vUbyrJ48WKKiYmRtrmCL6cgjV1DVxW58soH+5kKcmucofKalpZWoLpjKbz++uuqzzugd+8xfO/x4OjoSPv27aOPP/5Y0aU9bNgwo7jcTYrXz8IEuWtYSkoKDRs2jDQajaoVceXKlZL1+Y8//pDyN9UlPWXKFIvP8Msvv2zUEBg9ejTNmzfP6FzDLnH5QGMA1KdPHymumkJtmH+PHj0oKytLOoe7fKj1fMifUR769++vsBQaDmqVB1MDqbhccougqWDYECAiSk9PNzJKAXoXEMOudVMNLh4cHBwUBhxz8pq7p4Zh9uzZFBgYKL2TeG+qoWtBs2bNLPbkvfnmm4pzmjRpIv13c3NTPVd+vqlvxFtvvaXIW61M1cK+ffto/vz5tGPHDoqNjVUcW716NVF5UF4VQgFeALoCaAOgSknmraa8mrLg8BcEb9VPnDhRasUYBv6R4KNnOVOmTLHqJhuGWbNmEZHeR9ZS3HHjxhldkyl/IG4JVEuXj6KUf/BMBbVZF0yh9mKTB+6OkJeXJz2k/Bh3oTAc0a32wjCVvouLi5FMvKUtf6Fzny55y9kwrczMTGrdurW0LfcjlnfZyluwhm4d8u6Yf/zjH0REdPr0aWlfhw4dFD5bhv6i8g+KuTLhvntypk2bVqj6aFie8u3jx48T8HT0M5FeYTY8b8iQIURk3jLJGyvyBkp4eLjUsJQjt4aaC/KP+ssvv0yA3qJliLxL8JtvvlHUrfT0dCktc2WelZVFu3fvlrblbgurV69WbXjJFdqzZ89avK88qCkRVapUUZxr6n1lKbz55puKdApiTbYmnDlzRjHSv2PHjtLI+7y8PNUZXAClxVyO2qj9ihUrSm43csuspe5NeZA3kPLy8oz8G3ng4yPkWFK45OMDLN1rHtRGunP4zBo8GFod5b6SavfTMH81NzVTcqkZdQzhyu+0adPI3t5eisd7K02lTWTcuFcL8oatJbnVkPc8cCMR8HSsRpUqVRTGKHPyyuHfFQDUvHlzRdyVK1caxTfl6mToS6t2fUREo0aNkrblDbfGjRtbPN/U+8JaGQEo3KnkqLk9kRldrSzONgAiiiKiM0R0mYgsD8kuZvioOvlk8QCkWQb4iMLKlSsbTX587NgxXLp0CV988QUA45H3q1evNrvMqpxNmzahWbNmAJ7OPSofpWoKV1dXo32m5q/lk0UbTpIOPB3h+OeffyIoKAiMMZMrEFkzkp7TqlUrNGnSRPVYu3btpBG5NjY2CAsLQ1BQEJYvXw5AP/9jlSpVFPNjFhS1GQeqVq2K27dvKya0DwoKwrVr11Qnewf08wpWrlwZBw8eVF0GVj5a86OPPkJQUBA8PT2RmJiIdevWoU+fPvjhhx8U8fgIVPno4IMHD2LNmjV45ZVXAOjn9GvTpg3279+PGzdumBy5Hxsbi7CwMKk+qM0/+tNPPylmIigM8hWYwsPD0atXL4SGhuLbb7+V9qtN9s3lMbeG+blz53DixAlFvXd0dMTt27eNJgiXr0AFQJrlwJCXX35Z+s+fZbUVeOR13XDteHt7e1y9etVodLQh3333nTSaffv27Thy5Ii0OpVWq4W3t7fR/Iz8uRs9ejQ6depklKZaffzjjz9UZyAxnBXj3//+t+rk9f7+/tLIY3n5APrlNflKQRyNRoM7d+4YzavMkc8HKqd3796qE8Y7OTlJswF4e3vjyP+3d97hVRXpH/9OQiAFQui9N0lAuiCIgm1pgigsiAKCNBGkLGuPLKuoiyga+CFlaREMIEUwKyAgoEiN1EgMpNADCQkJCanc+/7+uMxkTrv3JhAIMJ/nuU9yz51zzpw5U95533fe2boVW7duRXR0NDw9PcUmKnqsjptFOsjJyRFlxzfU6NatG5YtWybStGzZElFRUabXBLRjgqenJ5o0aWKaziz+qX4jEz1WfaIVv/32myHep4z+Hejjb7vaFY7HNeVY9YNm6M812/zjkUcewdGjRzFv3jzEx8dj4cKFTq+5Z88eEQGGR5WwIjo62mm/4g5y5ImXXnoJwcHBAPL7CiISkSxef/110f5q1qxpubsk4Ijz3KJFCwDaSC979+413UWxTZs2ptdxRxYAtJEY5CgeVm1Uxp3d6ADnO2xlZ2cjISHBEL3Hx8cHUVFRbu1mCgB3Xcta3D5mmtezZ89S8+bNNRrCOnXq0G+//UZEjhlZ7dq1acOGDRpTqhyHk8fOrFatmuH6RPmxUfVmZnnhQFZWFs2ePZtq164tVvLm5ORQ+/btNZogQOuPKsfi4yQkJBhmOe3btxezU7NYn9x3idO3b1+aNm0aDRo0yBCp4IknnjB9Tiu6d+8uzq1bt67wg4yMjDRNz+PjAY4FbnqmTJkiZsFcG/HKK68Ynql79+5i0UNh4NfRa3z44o1KlSrRJ598Qn379jU9n+fJLN7sokWLRDpe92Sfafmdjxkzxu08Hzp0iFq0aEGXLl0y/Z37eOs/JUqUoOnTp1OnTp1cahZefvlljendDJ7+zTffpNq1a9OSJUvEb926daOmTZtSYGCgZSxSuV2YIWvn6tSpQ1988QWFhYUZNNWHDh2ikSNH0sSJE+nw4cPUpEkTSkhIMFxP9qXmrkL6POkZP368WG2Pm9qZUqVKUbNmzUS+uRZHXtBk5kJkVZ5mfnvnz5+ngwcPUlBQEAUGBtLGjRvpkUceoTVr1hjO1y+84O2cu7/oTbZmGnsZno5HvgBguShw0KBBNGXKFAoMDBT+gQ0aNKCcnBxKTk6m5s2bm2qbiUjEO3Ynb840c0T57XD48OFks9nEmgXup23mXlW7dm3KzMykkJAQeuqpp4hIu0pcXvfAo0Ho6dOnj2W+9O5ERI62+dBDDxlM/txq4Qy9yVeO//rwww+L2KP69+is3PRwd7SgoCBDVAN5wSuP2OGM3Nxcat++PW3YsIGISLP4SR/X+MaNG9SxY0d677333M6r1bPOnDnTNE1qaipVrFiRypUrR0lJSWKMrFChArVr147CwsLoiy++oDp16tC+ffs0vqhERMHBwfTCCy+YXptrxT/44APq378/TZs2zTKvZr7Brt7/1KlT6fnnnycia4uUszF7woQJlvVAjjTEcRZ5xlmccA7vM8mJrHbXhcU78QHQDUA0gBi4CLtlJrzKuGoMS5cuJSDfP5TDnerNApYT5ftk6c0O3Lw8efJkp/nSm9FkE6fs+yUj+5Tpg5GbmfKHDRtmeX85FBNg7qrgDN443BUkDx48KO4lb77gCrkDd+bj4y7cxKiHr07Vu4no4Qvn9B99JxcTE6MZTIm0Pp1mQklh4XVO7yPHVxyb+Q9HRES4PVBwCpLeLC2fMFnBTZ9mbiG8PJs1a+Z2fq1MYa7QDzZyGBqifJO1vDqcL5KQBUCrxX1EZOg3rAR6M/SLSnhEEH5vPhFzNljJAI4FPvLEe8SIEaZhj8z8OguCvv3s2LHD7bRyHzB06FAC8iOqcPO/3N7kKDBW9UZe2ENE9MYbb5i2Z7N86QV8Z2ZuIq3AZWbC16N3BdAH23d2ff3HTGFgBR+LeFnwxV8FRc6/FfK4JS+cdUVB2jOHh34y80MnyjeDW23mI8Pf/fvvv+8yrb7/dVVPzDB7p84EZqtznJXX+++/T4B2fYKnp6db+eN9DjmR1Yy2sWIAY8wHQG0iir4N1/IE8H8AngFwHsBBxthGIjK3b90igwcPRnZ2ttiSk9OqVSssX74cvXr1Mj1v5cqVWL58OWJiYjRmqsmTJ6NMmTKG4Ot6ypYti7CwMGHylfectjKpbNiwAatXrwZgDLQuBx1funQpbDYbBg0aZHl/2Qz08ssvY8GCBU7zq+fzzz9HYGCgy+fkyKZGOfi6KyZOnIgyZcqgTJkypibYgnLs2DHExMQYjnOTmqPNWyObE0ePHo2NGzciISHB4HbRoEEDhIWFaUy4ffv2xddffw1PT0+8+OKLt/IYGriLQm5uLg4dOoT09HScP39emDm9vLywdOlSvPrqq+Kc8uXL4/DhwwXavnXbtm2aTUCcsWPHDoN5+eDBg07NcR4eHtiwYYNwO5Fp0KABVqxYgS5duridX3e36tTTs2dP9OrVS2yS8Nlnn2l+X7ZsGbp166bZ371cuXJYvnw5nn32WWzfvh2xsbH4xz/+YXkPfdBydzd9APKD73/99deoWrWqMB/yNq038braR33Dhg1o1qwZmjZtqjn+448/4tSpU/D29kZAQABiYmLEds6FRd4gBoDpu9anLVeuHD744AOkpKSIfem524CzrZdXr16NLVu2oF69epabPPj7+2u+837A2T7un3/+ORo3bozr169j+/btqFevHkJCQlyauffs2YOLFy/Cw8PDLRO+fL01a9bAx8cHkZGRmu3AZQ4dOoQzZ85g5syZYrvradOmoWHDhujYsaPL+3F4uVetWhWbN292uV24s/yHhoY6fVZ504To6GisWLHC5YYcMu5u2Q0AjRs3xvLly9G9e3fT3318fBAeHo5GjRq5fU138PLywqZNm7Bjxw506tTplt0hAGDYsGEuN4BZvXo1hg8f7rL9c6ZNm4batWtj2LBhGDt2LP773/+6HA85nTt3dl2nnUm2d+MDR7isaADxN7+3BLDxFq73KIAt0vd3Abxrlf5WNa+3ypYtW4QDuKt4eWY0bNiQOnfurFlwxQOJF4ScnBzy9PR0qu2RkcO16ENtFAWZmZnEGDNdVVsc4JphVwG75e1309LSxHbE3FR2N+DRDhYvXuw0nWx+58HS72fkCAP8YxapwozffvuNSpYsWaAoHAVh1KhR5OnpSS1bttRsI+0OEydOJE9PT4MGh2sfL126pFlwExUV5dZ15YgS3MXqdiNv3+2qX+ZRTXhUCxluSt22bRsR5WvVXGmZzahcubIIFcj7AXfM5JGRkQTkh0crCtq3by/CLLkLj+vKF7cVFL7Alm/eUJTIMaQLwqhRo5xuGlPU8OgUhw8fviP342X01ltvkZ+fn1uae6L8DQv4x6wtmcEjpjDG3M7jzZBz1rKdsx/vxgfAHwDKAjgsHTt2C9frB+C/0vfBAOZYpb/bwuvthJu03a2YitsHH4gKYl6715D9MgtjulIobgfcF3n16tVO0/Fwd717975DObs/+PjjjwlwhC8rDNwk7moyfDvgu03dTUH0XuBOyzF8kxx9nHtn3AxtZinbFUe3gRtElOZqr/gCYHYhje6aMTYKwCgAYuWvFUuXLnVrVV5xYOfOndi+fXuBVoUqbg9BQUF49913xb7n9yOrVq3C8OHDb5vpSqEoDCtXrsTMmTNdus688cYbOHXqFKZNm3aHcnZ/MHHiRJw/fx7/+c9/CnV+WFgYpk2bJlw0ipKSJUti2rRpeO6554r8Xvcyu3fvxp49e+7Y/by9vQv8Xly5lDEi93wQ7hSMsUUAtgN4B8CLAN6EY9etMYW83qMA/kVEf7v5/V0AIKJPzdK3bduWIiIiCnMrhUKhUCgUCsXtwVKLWRzVJeMBBAHIAfAdgDQAE27hegcBNGKM1WOMlQQwEMBGq8RW8U8VCoVCoVAoFHef4qh57U9E37s6VsBr9gDwFQBPAIuJaLqT5MWrQBQKhUKhUCgePCw1r8VReD1ERK1dHStCileBKBQKhUKhUDx4WAqvxWbBFmOsO4AeAGowxkKkn/wB3Lg7uVIoFAqFQqFQFCeKjfAK4CKACAC94QiXxUkHMOmu5EihUCgUCoVCUawojm4DXkSU5zplkVG8CkShUCgUCoXiwaP4uw1I1GWMfQogEIDY45CI6t+9LCkUCoVCoVAoigPFMVTWEgDfwOHn2hVAKIBv79TN+f7txZmUlBRcuXLlbmdDoVAoFAqF4o5THIVXHyLaDodLwxki+heAJ+/Uze8FobBSpUoud59QKBQKhUKhuB8pjm4D2YwxDwCnGGPjAFwAUPku56lYYbfb73YWFAqFQqFQKO4KxVHzOhGALxzbwrYBMBhA0W+KrFDcp7z22muYPHny3c6GQqFQPJC88MILmD7d2d5IioJS7KIN6GGMlQAwgIhW3In7tW3bliIiIu7ErQoNY44FeMX93SmKB6q+KBSK+w1PT080b95cfP/hhx9w+vRp9OnTB/Xq1RPHZ86ciaeffto0fd26de9IXlUfXGgsow0UG80rY8yfMfYuY2wOY+xZ5mAcgBgAf7/b+XMXu92Ob775Bnl5dzPa153h0qVLWLly5R25V3x8PNavX39H7lVY9u/fj99///2O3GvXrl34448/XCcsIBkZGViyZEmBzwsPD8dff/112/NzJ7Db7ViwYAGysrLudlYUFixZsgQZGRlup9+8eTOioqKKMEeKu42Pjw+OHDkiPlwQ7dy5s+b4008/7TS94h6FiIrFB8AGAEsBjAawGsBWALsAtLyT+WjTpg3dCp988gkBoClTptzSdZwBRyzaIru+uwQGBhIAunTpUpHfq1KlSkX2zMnJyZSQkHDL17mT76Ug9ypI2l69ehEA2rRpU4Hzwxgr0DlFSVxcHGVmZmqOnThxgm7cuGFIO2/ePAJAo0aNuiN5O3HixB25z/3C1q1bCQB169bN7XMAkIeHRxHmSnG38fPzMxzbsWMH9ezZ0+30d4riMmbfg1jKasVG8wqgPhG9SkTzAbwEoC2AXkR05O5mq2BERkYCuDeiFtwqZ86cAQBkZmYW+b2SkpIAFM1itVq1aqFatWq3/br3IvHx8QAcWvWCQsXEJGa321G/fn0888wz4tipU6cQGBiIoUOHGtLv2bMHAAqk2SssS5cuRWBgIObMmVPk97pfSEhIAJDf37iLWth6f5OVlYWWLVuiZcuW6Nu3rzj+22+/ieMtW7ZEbGys0/SKe5PiJLwKOzsR2QDEE1H6XcxPoUhNTQUAlC1b1mm6qVOngjGGK1euoG/fvsInpiDYbLbCZPG2wfN8q4NEqVKl0KdPH/E9Ly8PjDGMGzfOkNZdQblFixaoVauWW2nvhPB9q0ycOBGMMeTm5hbpfTw8HF3CjRs33D5HzlNxEBi4ECq7cJw/fx4AsGLFCvztb3/TpD979iwAoGrVqkWet7179wJwDLAKIx988AEYY0hLSxPHeJ0qTB+psOa9994DYwzXrl2721kpFLIbgOxSpncbaNCggdP0Rc3dHqfvV4qT8NqCMXbt5icdwMP8f8bYPdO6uPDqSgs1b948AEB0dDR++OEHAECPHj3w5Zdfun2v69evFyqPtws+mLzyyiu3dJ3c3Fxs3LhRfOdl+H//93+GtO5ox5YsWYJjx44JgaW4smTJErRr184tgW/+/PkACqcRLQienp4ACtbhpqSkiP/vhPbSFXIeuF8w19wDwM8//6xJzwX1nJycIs8bL1+73Y6dO3eiefPmd014SEtLQ2BgIHbv3n1X7m/Gf/7zHwDAuXPnxDHel7orvBaHCdS9AB+D4uLi7nJO7m+UL33RUGyEVyLyJCL/m58yRFRC+t//bufPHex2u1gk4O4gLgsJmzZtwj/+8Q/TdLm5uRg6dCguX74sjqWnmyum8/Ly8Oqrr+LixYuW9508eTL27dtn+fvMmTPx/fffO807H0ycXccVZgK41XNZpdczfPjwQufnTjJ8+HBERES4peUsWbIkAPeE108//dRUs/Drr79anvPVV1/hu+++K5Q2/erVq+L/9PR0rFy58raGhVm9ejX+/e9/u51ebnv//Oc/AWiFVz38mZ1p4Hn7c3Ydd5AnB2PHjkVkZCS6d+9+S9csLGvXrkVUVBTGjh17V+5vBm8LfAIL5NdFbhUAHHn/9NNPTa9R1NaJ+wUvLy8A2rIuaj755BP8+OOPd+x+xYHbNaFfvHixUGIUhlmzZt22BdZr167F559/fluuVWicOcQ+iB+rBVtbt26liIgI0984ycnJwjG7f//+REQUExNDa9as0aRbuHAh+fv7EwDavHmzOAeSU/eNGzcoJCSEbDYbERH16NGDAIi/ACgqKso0H3PnziUA9NRTT4ljubm5NHv2bLLZbJSZmUkAyNfX1/JZ4IaDedmyZW/ZET0+Pt5wjcOHDxuO8e9Hjx51eU0vLy/D+aGhoXThwgXT9Lf6DIW9Dk+flpZGNpuNQkJCKDc3V/y+e/du2rVrFxERVaxYkQDQjz/+aLjXqlWr6OTJk0REtH//fsuyK1WqlMu8tGnThgBQSEiIy/zPnTuXMjMzac+ePeL8EydO3PbFCfx67i4MjIiIEOfwNjB16lRDG+N06NCBANDAgQMtrzl9+nQCQL179y78gxDRhAkTxHV4Wd/OstJz9uxZWrFihelvq1atIgDUsGHD23a/zMxMmjt3Ls2dO5dSU1Mt09lsNpozZw6lpaWJY0lJSaI8NmzYII4vWLCAANDDDz8sjjkrN/k6zti4ceN9sXguKyuL5syZI8YKd6levToBoFWrVpn+vmjRIkpJSbkdWRTczvputWDL39+fWrRoIT7ff/+9Zfo7QWxsrOa5V6xYQefPn3d53ubNm2nfvn3i+62W3e0s+6Lut+RbWX2Kjea1uPPMM8+gbdu2TtPI5j+uIWzXrh369esnjiclJWHkyJEirZXJMDg4GG+++SamTZsGm82Gn376CYDWBGGlhePmT9kcNGXKFIwfPx5ffPGFuCfXNNlsNhw7dszps5mhN+Pl5eWJBWuA41mdaX8BIDk52XDMmRnVHc1rqVKlNN+vXr2KIUOG4Mknne8ybLPZkJeXpwmxc+nSpSI31efk5GDWrFl48803NZsJPPbYY3jiiScA5GujExMTNeeePXsWAwYMwHPPPQcAaN++veZ3WYPqjlmca7eys7Odplu9ejXGjh2LIUOGaNwGuP9oYbh06ZLGsqBHb+63QtZ08Doq51EP19RlZmbi8uXLiI2Nxf/+9z+NBo+3lVs1AcpuA35+frd0LXfo1KkTXn75ZcTFxWk05EC+Nv92uksMGTIEY8eOxdixY9G1a1fLdHPmzMG4ceMwZcoUcWzbtm3ifzmvXBtr5jZg1h+4q+nq3bs3WrRoYThus9k0/didJDIyssBuD6+99hrGjRuHFSsKFgqda171fQrgsKa99tpreP755wt0zYIQFRV1S+3J7D136dIFBw8exO+//y78W/n4e7dcmmSLTlZWFl5++WU89thjTutYbm4uunXrhscee+xOZLFYkZeXhz///NNlOiW83kbkxsE7Vd4J84FQb6IxM5Hb7XYhMMXGxloKc1ZmMy6AxMfHC3+2U6dOAXBEQdBfb+bMmWjRogW+/fZbAO77O8qDSV5eHsaPH4/mzZvjwIEDAIBq1aqhRo0aTq9hFpXBmYCq/82s8+MCAoe/A1erlTMyMtC/f38EBgbiwoULABzP4E4kgluJ65udnS0WqJg12uDgYCFg6M3Wq1evBmDtaiELJrLZ1Qpedq4GFZ7f6OhozeIas4HQXapVq2a6aIrnSb6PM8yEV7ndcaGNw99dVlYW6tevj4YNG6JXr16YMWOGSHO7Fg2VKOHYkdtms2mE16Ja1MF9Rxs0aIBGjRppfuMTFFcTlYIgt7HDhw9bptuyZQsAbf2U65z8rvlxs7I3iy1cEIHIrN2OGTMGzZs3L5I4ys7YuHEjmjdvjmnTphXoPN63F9T870x45f3y6dOnC3RNd4mLi0NgYOBtd/HKzc1FkyZN0LNnz9t63VtBFl75WHT69Gk0b94cR44cMT2Ht4+CLJwtjhTG/3z06NFo1qyZZdlwlPBaCBhjGDhwoOG4PGjq/ee45kc/+zMTTNPS0uDj4wNAK9jwe/PB3Kpiy538yZMnAeR36D4+PoZ7cu3owoULATgP87Vs2TIwxnD48GGNMJSWloaDBw8CyPeBdWdAljUsPI/OfF7lch08eDB8fX0RHh6uSSMLJ9nZ2aLsycUiurS0NKH90eeBMYZZs2ZZniun79Wrl9P76MnOzhbRKcwENL6gD3C8G7lc+YCl1zZz5IHcSvCSNYw8jSsNt7e3t8i7nGdZuD5w4AAYY/jkk0/AGLMMD1W5cmXNzjcAMGzYMDDGYLfbxfPq38nIkSNFGhm5jvA6mpWVBS8vL/j6+qJx48aa9Pz5s7OzNecGBwdjx44dAAq+aEjm4sWLYIzh3//+t8iPzWYTbRy4/YsvGWOGcEB6KwcXWm9F81qhQgU88sgjBT6P94Pyc8tCtCyIORNeueAmY1aW1atXR6tWrcR3Z4Mqj1Jx4sQJyzRFAReWCxqJgpdbQTX5vM8w813kZV1UER6WLl0KAC4FFGd8++23Yizi8PFu//794ti5c+dEP3Q3kPsUff/eqlUrLFiwwHDO3drwJSsrC4wxvPnmm5ZpJkyY4PI6V65cEbJKQbX3PHShq/anhNcCwju9VatWAXC8SB47Uu409cIrHzj0A7CZKePixYsa4UAWNn/55RcxmFtpTOTKxWfXcgenvye/Fx8knJlueUPbtGmT5nhaWhrKly8PwOjOIC+2sdvteOyxx8R15AGVV3LZdO5MMDl69CiA/FWznOrVq4v/09PTTTUSaWlpaNGiBXbu3CmOXbt2TZST2aKPyZMna1ZB66/H+d///meaxoqcnBxRd8wmM7JmJDMzUyOQcsHDaiIj10krzas8WXGmebXZbKhfvz4YYxgyZAgAR72S37dsnucRJGbOnAlA+55iY2MRFBSEY8eOISkpSWNCe+ONN8TgJr8Hfb3laSIjIxEVFYUGDRogLi5O88yenp7Yt28f1qxZA5vNhho1ahgmVbLwqocvWjRbNOQO+/fvF1tVzp8/X7wnm83m9Nn07N69G0FBQQbTvxk8r/Kkxwz+jq9du+a0zXPGjx+PV199VXz/4IMPkJKSIiatgFbY4X2PGfw55PYvl7/cnvhxM0GKx/CUMRNeExISNIKSlcB+4cIF4TZU0BB68+fPR7t27Qp0jgxve2aT7JMnT6JRo0amz+usfADHhLJBgwb4+eef0bhxY6HVk8Pi3Y4IDc899xzef/99t9LyBaX+/oVfi/3FF18AgCZaRnR0NACtIM8tgYsWLXL72mvXrkWrVq1Mx4H58+ejQ4cObl/LyqLA0e9oaLPZ8NZbbwHIb0Py+9G/qw8//FC4jcn87W9/E9E7rLh27RoeeughbN68GUD+2L148WLT5wgMDERISIjm+KBBgwyLPuV6umHDBqd50MOVT64m9Ep4NeHy5ct4+eWXTQczfYcWEhKC0NBQrF+/3qnwygUo/XGz2WBiYqJG82qliTTL3/Xr1zWdX6lSpWC324U2NC8vz3A9fZ6craiWG448kKempqJixYoAjMLv1KlTxf+ffvopfv/9d4wePRqAdvDiPo3cZG+WN97Q//jjDxw/fhyAY6tUuZORB6b09HQxUObk5KBfv34ICQnBqFGjcOzYMU3Q+gkTJricGMybNw/Tp0/H6tWrERUVheHDh8NutxvK1G63Y9SoUW5pFmTh1UyI0ZtQ5c6Q/3/u3DnTkDfyc+Tl5Zn6pMqdp6yp1JOUlCQ2MZDzLr9vWbji74FPjv78808x6Vm1ahVOnDhhumnA3Llzxf/y+9eXDZ8s7du3D8HBwYiLi8PMmTMNAjufDNntdpQoUQI3btzAv//9b7z66qsIDg52KrwuW7YMQP7AV1BN1MSJE8X1iUiUid1u19zPVUf9xhtv4MSJE26tFnYmdD377LNigPr666/FcatIFNxH78KFC5gzZw6WLVsm+geziBJy+fD3bgbvD+X2r2+3HF5OvG7KbV3vCmSz2TBp0iTNMbOoKfoB2Oy41TtZu3YtPv74Y82xBQsWYMyYMYiIiCi0CxG3fJkJr1OnTkVMTIypqxgvN6v33qlTJ8TFxWHAgAE4deoUXn/9dQD55Xrjxg1DWMGCPMPevXsxYcIEhIeHO9VuymMHn6weOHAAo0ePxogRIzB48GDNfe12O0aPHm3Zh/J6KFvaeB9YunRpcYyXD3fZccXVq1fRr18/HDlyxNSNa8yYMdi/f7/b7jbONK+AcZInKxPsdjvsdrupGw3g0Fx/9NFHBuujzWbDzz//jHfeeQd//vmnpuzl9rN9+3ZER0cL4ZMrH8zK6sCBA4Ytl+12O8LCwvDNN98AAHbs2IFJkyZpXPf0bnyc9957z5BvIN8i4MpNTAmvJowaNQrfffcdPvvsM8yaNUvz4q38T1944QUhfPn6+hoqNh/U3fHZvHz5shgEzIRNjlnj0Ws97XY7vvrqK8399EIAb1z8nrLwqtfe8I716tWrmoHq2rVrohE6czv44IMPNN9d+UjqF9rwMpZ9mjIyMoQgC2gHwWvXrmkawdq1azFhwgThKypf/5dffhH/W3VMRIQPPvgAAwYMQK9evbBkyRLs3bvXIDhGRkZi4cKF6N27NwBHefEGric3N1e8k4yMDHz11VeaeiI/T3Z2tqbOyPkcNWqU4dr6+jVmzBhkZ2fjq6++EvVafif8/WZnZ2PlypX45z//Kequ2XvNy8vT1Jddu3YZ7i13hD169ACQ30G5Wgwn1z992+HCa1RUlCijUqVKaQaLy5cva+pGiRIlkJ6ejqlTp2LZsmX4+OOPxWTJbPDPzMzEkSNHhJClF17PnTuHZcuW4ezZs/jnP/9pEO7l95OQkCAGDrvdrnmv8rMdOHBAaEI4vr6+AKz7n5CQEOGz7qz9bd26FevXr0dSUpLQUgEOTcnp06cNWqD//Oc/+O677zBmzBhN/qyQyyc9PV0sBD1y5IgmRBJvk7GxsabWDv6cmzdvxnfffQcAiIiIgN1u15gT9ZaQTZs2GXxt//73v4v/eZ185513TPMv54HX++zsbM040K9fPwQHB2vO45Nx/tyAo+7997//NdwjJCQEkZGR4n0Bjn6IC5B2ux0ZGRn48ssvxT25UGemzeZ5NhtL7Ha7wSeWX0uuf3oztbta5+zsbHTs2NFyMgAAO3fuxLZt2yxDmC1YsACLFi3C8uXLNfG+4+PjsWDBArGpSFpamuhD4+LihLub3IdzS9qZM2eQnZ2NkJAQ8R7dFV5ly19cXBzsdju++OILLF26VOOmkpSUhAMHDmgEMLvdjpCQEMs+2qz96oU7uS+w2WxIT0/X9LHyhO+ll17SnLtz5078+uuvGl/lfv36ad61MysLvzYvq2vXrmH27NkAzF3T5OvOmjUL3bt3x1dffWUap1nGbrfj008/NdUY88mIq7CE7r3NBwyufZszZw6Sk5MtNST6xsiD6pcuXdog/PDO2p0Vj0lJSeLaMTExlhoAs87g0KFDmu85OTma2LFmmlzeUeXl5SEuLk5T8fv27aupfLxsuD+j/Hz8mc1WdmdnZyMmJkZzLC8vz+CHpy+flJQU00Vf+vI9deoU2rRpI67L0Quveqzeh5XwauZveunSJY3gy+8L5Jdtz549sXfvXrRp08bgI6jXvE6aNEkjcMmkp6dr3rGcT72mKzs72/AcpUqVwpgxY7Bs2TJcvnwZo0aNEtpIID9awMWLF0XHuHv3buzdu9c0MgQRaY7Lmgr+7HphKisrS2heXA0or732mvj/1KlTuH79Ovz8/GC328V9z507J4TGnJwc4TMFQGPSBhwDhZXAfP78eXh5eRm0Ttw9BTAKr127dkVsbCx69OiBn376CSkpKRrzpL6N8sE5ISFBCKSAtl/hESOICLm5udi7d6/43SzvV65cEa5CvXr1crlw5+LFiwYT6pkzZ/DKK6/g999/R/PmzUVkFT4pkweSw4cPW0bu0JdPy5YtYbfbhb8p70syMzMREBCA1NRU7N27F127dtUMhLxdDhgwQDPgL1q0SKNMkCe/hw8fNghw+gVHsbGxaNasmWneAW3fERMTgwsXLqBfv37Yt28fypcvb2op0MNdqFq2bIlLly7hsccew0MPPQTAIYDLbl39+/eHt7e3xiJit9sxefJkLFy4EDExMZg9e7Zon9evX8cPP/yAOnXqoFWrVjhy5IioE5mZmTh8+DA8PT3RuHFjeHt7myo+eJ3MyclBxYoVceXKFZw4cQLPPvusKEdni+1kzGIEZ2dna/oiHnXCnQVlsoDE88777xdeeAG//PILmjZtKpQC+uvKbbVDhw44evSoaGdcKDp9+jT8/PxQqVIl0zzIbTYmJgbh4eEiKob8XMnJyZq2CgChoaGYMGEC9u/fj88//xw2m01Tf83eR4kSJfDHH3+gVatW8PDwEHW/Xr16iI+Px+XLlzXtPjU1FZUqVRKCNcdut4uylgXqGzduaMa5K1euiN0n9e1Dr3kdPnw41q5diypVqphOaOT2Jgv9ch8s5zEiIgKtW7fWWEzi4uIQEBAAX19f/PXXX6IOuBJelebVBF54fHCUO0i5Epj5HwGOrWH1PlVWbgNmnDp1SjTYixcvWoabMvPbkrUMgFEIy8rK0gyUdrtd5Ck3NxcNGjTAu+++a5k3/hy//vqrZqCKj48X+THzy3v++ecNi3IuXbpkEIj0CzBSU1NNtSR8tspNifK7yM3N1fjNFGYHI/1CJDk/HF6O/fr105i6gfzZLc8fN7fwwVEfwsrKzURPeHi4ZiGOXB/LlSunSaufeAGOLVB5GX/22WeoX7++xl+WdyqyGTk6OhpZWVmmWnKbzYaUlBThMiLDy10vUHTr1k34q1qZlMz49ddfxTbCEydOFJ3bwYMHhdD8zTffOPX1dCYs5+TkmJpLZX9cvc8r15ZxjbPeLcNqEnvy5EmNOdRqgjpixAh06dJFTGbM+gJZa7Z27VqXfrGbNm0ytPEzZ84Y/PmB/HosD7rBwcGa7ZyB/HetF16ttC43btwQmnNe17nQULJkSVGv9W03MTFRlEWtWrVEvn799Ve0bt3aYM7n/sby8zgz98rvf+XKlahZs6ZwudI/i1W/kp6ejlmzZgmBQ06XkJBgeB7AuKMYf8/ffPMNhg0bJtrot99+i759+6J169aaCTsAhIWFoXXr1mjRooWYeJrVBf6Mubm5aNiwIQAIDfmBAwfQunVr4afuCrNdDPV1gyMLNFbI/ZleU8wnqFevXtXUR7mflsdEvniL96287derVw/169e3zIOs3Y6MjNT0zXLdkSflvJ1wK86FCxdQo0YN1K5dW/POrdZftG3bVvTr/H7c9SE9PV3T7qOjo9GjRw80a9ZMs+hTfnY5Uoa3t7emXOV+XBY4ARi01Px5/vrrLwwbNsyQd6t+Sz/5sdvt2LhxI9q1a4fg4GCN1adBgwZo0qQJXn/9dbRq1UoI1GbKEhklvJqgV4/LA1br1q3F/1YanICAAOTk5Gg6Dl653RFeZ8+erdlJw2r7PrmylitXzjQmnL7zys7O1lS4Xbt2CW2QO6uO5evJz/+///1PNOw///zTMIiZbUGZkJCA1NRUVKtWTfipcUGBh6fq2rWrJv5jmTJlAOQ3Lt7A5Zlcbm6ucNgPDw93a3cmeSYPOMwxAQEBhnTyylBn5cXDLPG6w8uc/5UF+W7duiEzM1Mzq9eHc7Jiw4YNorPVa5GTk5MN9c3MKmCGLNBeu3YNAQEBhokR4BhYrl+/jqpVq6JOnTqa39auXWt6bVkwdhW+TM+ePXtw+PBhYcoCzGPL1qtXT+P3xnFHWOaxdTl8EAQcnfA333wDxhgYY4b3e/nyZezfvx+MMXz77beGsrZq/5mZmVi5cqWh3fCV787Olyd8p0+fdqnh0vutValSBXFxcWLyI2s8+ECnX/mrtzRUqlQJLVu2dBnRgzEmhBC+WIcLd7ysSpcuLcpT/7645tzLywtVq1ZFRkYGqlatKt6ZVbxq/myXL1/WvE8AePvtt8X/znyP9Vr0smXLmq4Kv3btmsZFQn5nem0SL19ZCLTb7RphdteuXaYT6fPnz2smwbJA8MMPP2D27NkG4V1+jpycHFSoUAEVKlQQz6GPE33mzBlTv8QFCxaAMWY6BsquQ3L+3ImX/dJLL6F27doA8gU9u90utJCAQwMrw8umY8eOGoFSP2mWJ67OLKCy8Lpr1y7LtLKL0KBBgwDkW+ZkmeGjjz4S//P3Lys7uHWIj8O8DvL2cf36dc06kO+++05MBOTxWJ4k8bjwgCPCkFyveRmZRZnhmlcvLy8MGTJETNys/I6tFq7p3XmSk5PFtcLCwgy7Cl65ckVMSnm5uurHlPBqgt78+uWXX5qms/IdqVChAmw2m6aT5Cs8XS3MePzxxw3H5Iork5ubi+zsbLRs2RKpqamGgc4sj+fPn9f4OMqxLK18kj777DN06dIFgKOBmAkAycnJToU5s9WsiYmJuHbtGvz9/YUpnQszgYGBIp08y+ZCFc+Dp6cnfH19Rec/cuRIpKamCsHFSoB65JFHNOFAnnnmGc3vBV1prIf7BfJOjGsPuP+SXhjIyMiAj48P1q1bBwBiYY47Ybfy8vJQtmxZg0lqyJAhopPgmraZM2caXEtcoV8ZL8NdHkqXLi0GnaLEx8dHI7ha0aJFC1SoUMFw3B2/N25S48htKDw8XGMq1Qubx48fx1NPPQUAWLNmjcsOmNfjJUuWGPzX5EkYRxaGs7KyEBQUpPGdvHjxosuFDvq63bhxY5w9e1Zc++rVqzh79izq1q1rGa7GLGLD0aNHsXfvXqf3BvK1Qjw8HK+3vI6VKVNG5FE/icvNzUVycjJKly6NsmXLIiEhwa1ICd26dQPgGCQjIiI0v82YMUMoCJxprUePHm3oT5o2bSo0yJyMjAxNnrp27So0XHrrBRdmZO3cvn37NEL42bNnTQU/vRCunziYhWAC8vvQvLw8+Pj4oHLlyoiNjUWTJk1M0z/33HOoXbu2RlD/7LPPAMDUvUmuG/Lz6t3JzDaIAPIFH7ntOIs3y9O5qnvOol/IyGPVuXPnMHLkSNN08iQwMTERmzZtEouTrbZM565zTz/9tOX9ed2XhVf+/uvXr48dO3aIPMrCumx1kIVHb29vzW9co6mXK6KiooRsUKJECc2mF2bbjQPmliDAaGGIiIgQ1lH9ugCO/v04284cUMKrKVYxM/VYCZXchMobto+PDw4cOID169c7DdfRsmVLVK5c2XDcbLUs4OjId+/erfHz0aP3N9y+fbvmu7wwxCq497vvvotdu3Zh+vTpyMvLMzURZ2dnOxVeza6dmJiItLQ0lC1bVgxkvDFUqVJFpOONuVmzZsjKysIbb7whOmqbzYYKFSqIxsIXdA0ePBiAVvCVF1V8+OGHmhlxgwYN3O7cOPpBywy9mTkzM9PU3SQiIgL16tUz7OI2b948DB061HQxloxZ/F65Xui1ibeDypUrg4hw+fJl+Pn5Fbj8ZPz8/DQ7MtWsWVPzOxfiU1JSDIuKzGCMmca9tBJe5XanFwLkmJF65IGOT5j4BHXjxo0uJ0ENGjQQafVcuXJFtCnuM6k3DZ44cULEoi1ZsiQuXbqE0NBQy/vJ7Yrz9NNPIysrSwiVaWlpmDdvHs6cOWM5aXGFM79SXj7csrFmzRp8+umnQtjy9/fHiRMn0KBBAxGfl5OWloaUlBT4+/ubWkas4D66SUlJppO3qVOn4uOPP3apXJBDhXH0Am96erqhLc6aNQtLly7Fv/71L83xnTt3YsSIEabmdxku9ISGhoqQglzgtioHq/Z448YNsZugn58fKlasiMTERIMwLHPu3DmNK4GzjSBu3LiBs2fP4q+//tIsyNHvACZrp/Xjnt5X1Bl//PGHYUJiRl5enmV9zsvLw6BBg3Dy5Em3d+KS+6HIyEixGBWwLp8//vgDnp6eqFOnjub5ZfTtIyMjQ0wChg8fjrS0NDEBkS12chztlJQUjQJIdgWaMGECvvnmGwwYMEBzX9k6GhMTg7p165rmb8CAAYb+WY9ekdK/f39LCzLHSuC3QgmvJri7qwXXEsquBB4eHkJzw804fNegF154QZg49dodwKGFcWc3J05eXp7LXWD0wquzZ3O2ShnIX5Vulsfs7Gzk5uaiXLlyBt9LK5KSkpCRkYGAgABxDhdC5VitqampqFy5sijnuXPnCiHkv//9LypVqoQLFy4gJCQE165dw5NPPincAGThgcfOAxzvRI4xWLVqVY3/kDvo3RHkPHP0wmtGRoZpp3z9+nU88cQTBoGrRo0aWLp0qcaNxAxfX19hfpL94Dj6wPxWBAUFuS0UyIt2/P39CxwDVWbixIkawUw/yHzwwQd48cUXxfdRo0bhqaee0hzTYxZD0kx4nThxokZ4TE5O1mxb6gxZeDXbHUzGLFg3t2iYceXKFVy5cgWdO3dGVFQUqlevrikjeSW7l5cXKlasiKSkJKGl2rlzpxCOOWZlwjdc4RrbtLQ0jda6ZMmSaNiwocE6ARijhwAO7ZCsxdPHmuSTRn6PXbt24b333hO/80kAH+xefvll8VtSUhKOHTuGcuXKuTV5BBxtkFsgUlJSTLXJy5cvR3BwsKl7k4w7gs3GjRtNhdFhw4YZJvgzZszAokWL3JqQNW/eHIMHDxb9GH/P06ZNM7WGWUVysdlswkrn6+truXBJD+8fjx075tIFYMaMGXj77bc1QqXeD7JWrVpCA8mVF5z33nvP7R3DkpKSNO1IFoTlepybm6tx25DDzm3fvh1hYWHo06ePMLmbWW5keP7cKT/u0hcfH49q1aqhZMmSpla13bt3C8GX98OZmZmIiIhA5cqVRXxtGe63LJOdnY3OnTujRo0apoL02LFjDYtZ9eNSXFyc6bgxduxYvPHGGxZPqqVly5YAHOMbrwtmCrrCoIRXHTabzS3HciDfAV0WWipUqCA0k1xQHTFihPiNExMTI1Z3ckaNGuV0O1XZh69OnTrIy8tzOeN0tqe7K8wGJgCm5uGrV6/i+PHjCAgIsNRI67ly5QoyMzNRoUIF0VD57Et2qM/KykKZMmU0wuXVq1fRtm1b9O/fHzVr1kRSUhImTJgg4s2aDdKyUK0XNKpUqeI0LqUZ3BTJCQwMNFzj3LlzGk3rTz/9ZGnWrVGjhmYFekHgvsCAceEWYP7OZLjQ/PPPP2P37t1uaVHlhRn+/v5uh6Ixw9fXVzOw6wf5ihUrap6hZcuW2LZtm6VJ79VXXzWtA3rN2qpVqzBr1iy0b99e7DSUkpKCzz//3ODXaYYsZFsJr5UqVcIzzzxjanrjnbsZO3fuREpKiuhfvLy8RLls375dxKAFHIJF+fLlceHCBSQkJKBdu3Z44oknEBMTI/qdWbNmYfz48Yb76C0p6enpmnr8xhtv4NSpU8KqwWnTpo2pJtLLy0szSdYvuORmQzMLDmAUZLKzsxEUFAQAwkWiWrVqBh9rK6pUqYJq1aqhRIkSSExMdKoBuh3b9C5fvvyWr2EG7//44M/72XLlyhnanoeHh9MFL9wS4+fn57bC5PTp09i4caPBL1ZPxYoVsW7dOqd9CH/HvJ7pJ8wzZswQ+Tdrx3LscEDbrv/2t78J5UXXrl1F33bx4kWNdvmll14S6ym4gPfXX3+JCYqZoMiRN1xxpYWsXr26ZnLsTJHQpUsXoXDhk7NTp04hKioKzz77LGrUqGFwpXn44YdNr8XHIyvTvh5XC5Q5fn5+bu8IKLs0AUCjRo0MLgVmOHOr4CjhVUd8fLzb2wJyrafc+CtXriwaG5/5Dhw4EC1atBCNecyYMShZsqTwgwUcpspRo0Y57ZC58LplyxYxaOzZswdVq1Y1DV4NWAuvsj9Uhw4dDFqhdevWWW43qNfmdOnSBWlpacjLy0N8fLxLDSaPGclXOFeoUEEIXHxGrxfsy5Yta9hOk+ePD2ycSpUqmXZ48qDIBwDeEVepUsXwvFY+WUQEIjKYVQICAjQmZ65N4c78gMNn0krLYtYxuQtfvQ+Ya4BdLVRq164diAjVq1dHUFCQS3NxqVKlMHDgQNHBli1b1lR4lXdikxePvPLKKxphz9fXF506dRLf9UJdpUqV0LlzZ/GdLxTQCzqAwxWmd+/epkK83owvC/1cO8L9r+XfAPcGKT2BgYFITEwUgp++XgYFBVkKGTykErfSlCpVCjk5OTh27Jihc7927RoqVqyIhIQEZGdna9oKFxJ9fHxMhVd9GWZkZGg0jLJgIQ/+Q4cONZ1slyxZ0i0h0EpjpXdtSEtL00R8ABwmaGdhq+Rr161bFx4eHggICEBCQgISEhLQp08fy8lWmTJlCrTzU0EiZriD/vm5XzV/p/x37ldbvnx5g6Do4+Pj1sJMX19fpwoTmfDwcPTp08fpdW02G6pWrYqEhATL9QZffvmlYVtruY7xxbt8kmHW7pxNxu12u1gQO3z4cGRkZKBJkya4dOmSQeHwzDPPICoqylST7MwCNXz4cPF/o0aNLNMBjrFG7otcbZfKBWk+6VyzZg2ICCNGjICHh4fB4vDoo4+aXueVV17RCK/Lly/XxGuWSUtLMxVezVwBzXbplJHbjn5M9fPzM7XQ6eu8lY+tjBJedTjz5TGjZ8+emsbl5+cnKioflAICAlC+fHlNZ2OFXiCSBWneIEuWLCkEyIsXL6JevXoaVXxaWprwt9XHVuVwDV/p0qWxdetWg8BZq1YtS+FV79gv+wjzjtZqsQDg0FZ5eXkJX95q1aoZykS/SjYgIMCQRz7o6s3klStXNggegNaEzzv7HTt24OTJk/D09DQ0oF9//dUyHBrgGLTkd1++fHlhRg4LC8OQIUNQo0YNwzvgAp1+/+iaNWtq8ujMHKWfYMnRC+TYqO+//76lgzyQPyCaDcDOBjVuFuXCekBAgGmnJAuosm+gXmAqXbo0Zs2ahcjISJw4cUKjWenVqxf8/f3Rt29fHD16FMePHxdxQ+Xr8DrDfb30wqu3t7ehM5breIcOHXDixAmxqYdegJEtMmaDp9n70vvLxsfHa6IjVKpUCeXLl3e6hSK3QnDh1co3TK6/clvh5aA3C8bHxyM+Ph6enp6aSVNycrJGWyqXY6NGjRAZGYlDhw5h/Pjx8Pb2xokTJzT1sUmTJm5tN1q2bFnTOqNv5/y5ZPNouXLlUKNGDY27gYxc75o2bQrAIcTHxsYiNzcXdevWFW1XnwcicnsciI+Pd2nVqFevnmVfagYX/IODgxEbGyvOlfvsEiVKCA1WxYoVDWXm7v3KlClTqIWWvD3yfMXFxeHs2bPw8PBwKTTLWn0+dvD8BgQEiDGQRzng32WfTn4NM+0uEWHatGk4fvy4cBcx252NM2nSJEO8Wjk+rPzMHC8vL5EveVJthj6SjbyFcFhYmPi/dOnSaNCggSg/PumMjo7WRNSQx3q5vDidOnVCdHQ0ypcvr6kXHTt2NJ2UDRgwAMnJyaYbTWRnZxv6NT8/P6emf1k+0Ed8MYsAA+T3nRMnTsSZM2fcskAq4VWHbO6y8jOUK1/nzp01jd/Hx0cMqHywLlu2LCpWrCgGeitzGWDUavKOF3DMRhYsWIAuXbpoZnsPPfSQePlca2C2cpRXnHnz5omZU9u2bVG6dGlD3LuaNWuKTtTb21uzYEh/be6c3atXLzHAjxw5EosWLTLVYusX07z++uuaTmj06NGGAaVcuXKGCs0HVdlRHjCGSdq8ebPwt4qKihIr+gHH++JlqX8uf39/1K9fXzML1GuAZA1xQECAGLQbNmwIDw8PPPnkk5bab74Ih6PXMOgFH9lnrGnTpli1ahVWrVqFvXv3wtPTEytWrMC8efPw+OOPC61S7dq1TR3vf/zxR7z//vs4f/48QkNDTRf57NmzR2gj+/fvjzlz5uD48eMIDw8XdZgLg7Vq1dIIkpMmTcKiRYs0GlT5nZYoUUIj4HTv3h0eHh4ICgrS1Hl9OT388MOaxUDyPffu3YtFixYJTaXeZ+3o0aOGgVXv5tG0aVMhyOs7+lq1aolyNWtfZpo8fWddvnx5jb87L8fevXtjxowZphYU7p5x48YNxMfHaxYeysgWIDkKAr+fXltSt25dUTfkctD7xes1UEFBQWLyADjKrGnTpggLC0NwcDBCQ0PdcsGxWuSnf288rNDWrVvx1ltvaRaZfvTRR6YxSTdt2iSiN3ALSpUqVYSGrW7duti1axdWrVpleEdEJKKDTJ061XSf923btiEiIgJ169bFli1bsGTJEsNkdP78+Zg6dSp2796NiIgIfPHFF07L48SJEwgPDxeTjLZt26J+/fqiHsr9n7e3N65fvw5/f3+0bt3aILzyvtHLy0tsO8uRlQ1+fn6m/cO6deswdepU7Nixw9Q1TfZrZoyhXr16op7JealUqZKhb5H7AZ4XLy8vrFu3Dn/88Ydh0sz7xc6dO2Px4sUIDQ0V1zAThmw2Gzw9PTX9hL4MJk+ejHHjxgGAxgIKOCaxDz/8sMFKoy+H33//HcuXL9csNNVPGubMmWNwcZD7N7nPb9q0KdLT08XESRYQZYusvOivdu3a6NKli+adbt26Vbgm8P7Rz88P9erVM12MLkcc0lOqVCnDZLl06dJ4++23TdcbzJs3T6MJdld47d+/P2bPno2ZM2eidu3a8PDwcO26xk2g6kNcuCT+IccBw2fu3Lni/+XLl9PmzZvF92effZYiIiIIAJUuXZpKlChBREQzZ84UaUJDQ4kj34uIyGazkT4Ps2bNohUrVpBMYmKiSPP111/Tvn37CAD5+voars0/e/bsobFjx5LNZqOsrCwaMmQIJSQkEBHRwoULRbrq1asTEdH7779PAOipp56i0NBQ8fvRo0c11w0KCiIANH/+fDKjRo0amvQpKSlUvXp1AkCPPfaYIb/btm0z5P/ll1+mL774QnNs4sSJ4twxY8aI4/v27TMtW1ekpaVRt27dTM8LDQ2luXPnGs5JT08X6T/66CPy8PAgABQdHU1EJOqC2Ucu0zFjxpDNZnOZ788//5zWrFnj8llCQ0OpZ8+elJiYKI7x627dutXtMklMTKRXXnmFUlNTTX/39PQkALRz505KTU2lLl260JgxYzRp+H1/++03zburVKkSAaC+ffsarrt161aRNjMz0zJ/GRkZluW1adMm8Vu1atWIiKhq1aqad3Dw4EHLa8ttMSwsTPMsQ4YMMbzPCRMmaL4vXrzY8to8zY0bNwz3HDlypPj9ww8/FL+VK1dOc/0vv/ySVq9eTW3btqVVq1bR8ePHxW9PP/20OC8lJYUGDx5MGRkZREQUHh5OH330kea+/F2YfX766SfL57AiKSmJOnfuTK1atRLX+fvf/6657vr166lChQqG+2VmZlL37t0L1H711yBytOfBgweLuivff926deLcli1bas719fUV/+fk5BAR0ccff0xr1qxxmie5vvTv31+0Z05ycrJlGcvX5N95233ooYcIAA0ePFikqVy5MgGgxx9/nIiImjRpoinXhg0bEgBq1aqVoXxGjx6t6YMSEhIMeTl69Khp+bZt25YAUJs2bSgsLIwAUPny5TVpIyMjRfoOHToYxrRZs2aJtL179xb9H4ePZfwTGRmp6R+JiFatWkUAqFGjRjRixAhNX9q/f3/T9yNfNykpiYgc40aXLl3E8ZdeeonS0tKIyNG3Dx48mMLCwkR7MXv/NpuNatWqRQBEuQOgX375xbQMZQ4ePCiOd+3alQCIMjl06JD47ZlnntGcx99hv379iIioY8eOBICqVKmiSTdw4EACQE2aNCEiouDgYNN6V7t2bfF9w4YN9Omnn9KUKVNo9+7dmrzLbWLbtm2m10pISKAOHTrQyJEjiYho3bp1mj5Afz3e5vX4+fkROZPVnP34IH70L4L/7+PjI/7nDYc3gqysLPG9d+/eFBsbK76XLFmSiBydOT/GK4R8fZnevXtTUFAQDRo0yPBCOXKHsHPnToqPjycAVKpUKcO1mzVrRo8++qihM5VJT0+nOnXqkL+/vxBoExISqHnz5nT8+HGKiYnRVLTmzZuL77zib9y40fTa9evXN5RHo0aNCACNGDHCkN+TJ09qvvPGyztL/gkODhbn8ucHIAYrZwONFXJH7i7t27cnABrhmpchEdFLL71kOljx56lcubLmeoXJtzsUxXX5NS9dumSZJiQkRAhTPXv2JAA0efJkIbjs37/fcE5mZia1aNGCNm3a5PT+cjsw+61KlSoEgOrWrUtEJO75ySefUKtWrURHbMXUqVNp4MCBhud95513NO9y6NChBuHVGa7SmP3u5eUljo8fP970vA8//JAA0JNPPun0/nqqVatGgEMQ0ddT/SBcUDZs2ECPPvoo5eTk0BNPPCGuu3HjRtF3eHp6UmBgoChreVLoDmaDqJ5x48YZ+ggiokmTJmnO9fb2pnnz5lG3bt0s7+MqH3yiICPXVd4O+MfDw0OkmzdvHnXv3l18b926NQFaAY8Lr6NHjyai/ElZYGAgEZFQDvCJrnyvJUuWiP937NhBNptNI8DJfbD+uaZMmUIAqEKFCpSamkqtWrUyrR8dOnQQz6m//yeffCLS8YnD119/LY6lpKSYppVJT0+n5s2bi3ufPHlSnPPiiy+aniPnQ8+8efM0Ez4rxo8fb5icExF9+eWXBIBatmxJY8aMoXHjxhnSTJ8+3TCmy4I+f6f8Iyuo9AJ5bm4udezYkcLDw4mIaPny5QSAAgICDPkFQE888QQRWQuvH3/8sdO6PWnSJGratCl16tRJHDt8+LA45+uvv6YePXpYllvnzp0JAI0dO5aItPWhRo0apufcfBYlvLr70b9Us//j4uIIgEYTN2LECAJAAwcOpNTUVNOOlH/PysoSx2rVqkUPP/yw6ctzhVzJc3JyCNBqIytWrEh9+vQp1LX12Gw28vX1peHDhxvuP336dJEPM+RZnbe3NxERNW7c2NBpjR8/nry9vYVAUbNmTdEJh4WFaQRU3pFyzDRwZcqUoWeffbZAzym/O3fhHbCskZdnklzjGB4eTk2bNhVpoqOjCQBNmzZNc72C3t9diuK6vN67C9eoTZkyRQhKhw4duqU8OHuuFStWEJCveShdurTp4FzQe82ZM0f8zweGPXv2uBSgOD179tRYSfRUr16dunbtanpvAAbNKeejjz7S5MlduFDP25v8kSditwOunVq9erXQ+H7++eeGdIB2cusMnteSJUvSCy+8YJpm2rRpBIA6duyoOS6/S0CrALC6T2F/L1GihBDQ3b0nn+jL/UTJkiUJAM2bN4+IiMqWLSv6SSKizz77TNMn82sAoLNnz5r2U/Xq1RPHz58/r8lD8+bNycfHhxITE8nDw4Peffddy/wSEfXo0YMAx6SOiKhUqVLi2rLSISQkhABQfHy85vyC9lWZmZniHCslChHRk08+SVWrVnX7uu4ydepUAvI14e7CZQn+fvn/Xl5emskO17BasXPnTgIcSjaZUaNGEQDRJmRtbo0aNahx48ZERPT1118XuMzPnz/v9jncAjNjxgwiIgoMDBRWu5o1a5qec1Opo4RXdz9mwivXjHENohnc7L5gwQJT0z8RicH6dlFUQs7tvn/NmjVFWq7N4FoqbuIv6D3ljlr+jWseCgufBHTp0sXtc/jsdt++ffToo48SAEstt6zlyc3NNU1zLwmvBYUPqmvWrKG+ffveFuEIANWpU8f0t/379xOQr6nkbiHcNFiYewEO0xr/v3fv3uJ3eYJ1u+EmWwD02Wefmab55JNPCNC647gDd0no1auX2wJ4YRk7diwBoIiICGEitZr4uktgYKDL/PKykbVHRA43CsChteKCgxXyeGD1u7vl5u/vL9JzDaUZ3PR75swZw324eZ8Ls84mgvwcm81G5cqVI8aY5vfXX39dpElJSdH8ZrPZLPsrM4YOHUoAaMCAAeIYL//169e7PJ9PMu8VuGncql1aobf06dve5MmTCYBGE28GF4I9PT01x+fPn0+AcxcmIqLFixcToLUAuIKPle5Yebiyavny5eLYTz/9RACoVq1apufcPP5gC68ATgM4DuAIgAgXaTWV58KFC8LElJ6ebpiRynA/RyKtOYCTnJxMFy5csDy/oNxtYcTd+7/66qtiZsm1qjabjU6cOFHge549e5bS09MpKirK8FtsbKxT/0h3iY+P12jHXWGz2YQmLzMz06BF0Kfl5l8r7mfhlSi/neTm5lJsbOwtX+/8+fOUnp7u9H58MpGTk0NxcXGFvhcX8njHC2h9EWWT5+0mIyNDmJvlQUCGu6K88sorBbr2iy++KCaEUVFRRVpXbDabqAOZmZm3pQ5kZGTQ2bNnnabhfpFcG8jhFpA2bdoQ4PCjtEIeD8woSLmlpKRQQkICxcbGOnVfycrKopiYGM0xrinnPtNcm23mrsDhZmkix1ikH8tkpYsrdxpXcI2q3r1FHiOdkZycfNs1/kWN2ZjkirS0NKfCK1eKjRo1yul1cnNzLSfx7uSL+3Nztz53iYuLc2usfOqppwgA7d27VxzjPsh6CxOnYcOGREp4xWkAFd1MSyEhIRq/1MJS1MJCREQE7dy5s8iu74qtW7dSZGSky3S5ubk0d+5cw+KUB5X4+HjasGGD5e+7d+92upCosBQX4fVeJjY2ln788UfNIs133nlH/H7jxg0CjOa720VWVhYtWLDAaZqFCxcWaPJF5BDq586d69aiwXuZBQsWmJZNWFgYJSYm0po1a25JwXCn+uSTJ08Kf0cix6Te1ULOM2fOuNR6ytrZW8Fms9H8+fNvWQi+3+FCp5XwarPZaMGCBW6V47p16zTa+YKwZcuWIu23UlJSNAvVOaGhoZaTwZsuhZayGiOHwHZfwxg7DaAtETnf/xSAv78/HTt2zHJf34KwcOFCeHt7Y/Dgwbd8LYXiVuHhkx6ENl/U2O12DBs2DCkpKfj+++81oaHef/999O7d2+VORMWZ8PBw/PXXX25vk6u4P1B9xJ1HLvPvv/8ef//738X3O8X+/fvRoUMHlC5dWoS+LCYwyx8ehErKGIsHcBWOGc18IrKMoN+2bVtyteWqQnEvogYmhULhjPnz52P9+vWaWLqKokXfLw8dOhSBgYF4++2371gejhw5glatWqFcuXK3tKV8EfDAC6+DAEwFUBKAN4ABRPSr9PsoAKMAoHbt2m3OnDlzV/KpUBQlgYGBICJERUXd7awoFAqFAo4NJ55//nmsXLnyruXh6tWrKF++PKZNm4YPP/zwruXDhAdXeGWMeQI4CeAZAOcBnAUQSkRvmaVXmleFQqFQKBSKu46l8PogbA/7OIB4IooD4AUgB4D1/qwKhUKhUCgUimKLcTPu+4+mAFoxxo7C8bwHAGQ6P0WhUCgUCoVCURx5EITXJADriWgEADDGBgN4RE4g+7wCyGCMRd/ZLCoUCoVCoVAoJDYTUTezHx4E4fU8gFrS95oALsoJbkYfsIxAoFAoFAqFQqEoHjwIPq8HATRijNVjjJUEMBDAxrucJ4VCoVAoFApFIbjvNa9EdIMxNg7AFgCeABYT0Z93OVsKhUKhUCgUikJw34fKUigUCoVCoVDcPzwIbgMKhUKhUCgUivsEJbwqFAqFQqFQKO4ZlPCqUCgUCoVCobhnUMKrQqFQKBQKheKe4YESXhlj3Rhj0YyxGMbYOwVJwxg7zRg7zhg7whiLuHO5VigU9zLO+h3GWJObfQr/XGOMTZR+V/2OQqEoNjDGFjPGEhljkU7SFHm/9cBEG2CMeQI4CeAZODYuOAjgJSI64U4axthpAG2J6MqdzrtCobg3caff0aW9AKA9EZ25eew0VL+jUCiKCYyxxwFkAAglomYWaU6jiPutB0nz+giAGCKKI6JcACsB9ClEGoVCoXCXgvQpTwGI5YKrQqFQFDeI6FcAKXc7Hw+S8FoDwDnp+/mbx9xNQwB+Zoz9wRgbVWS5VCgU9xPu9DucgQDCdMdUv6NQKO41irzfuu932JJgJsf0PhPO0nQioouMscoAtjLG/ro5A1EoFAor3Ol3cHPr6t4A3tX9pPodhUJxr1Hk/daDpHk9D6CW9L0mgIvupiEi/jcRwHo4zIEKhULhDHf6HQDoDuAQEV2WD6p+R6FQ3GvciX7rQRJeDwJoxBird1PLMRDARnfSMMb8GGNlAIAx5gfgWQCWK+0UCoXiJu70OwDwEnQuA6rfUSgU9xp3qt96YIRXIroBYByALQCiAKwmoj8BgDH2E2OsupM0VQDsZowdBXAAwP+IaPPdeA6FQnHv4E6/wxjzhSMawTrd6arfUSgUxQrGWBiAvQCaMMbOM8Zeu3n8J8ZYddyhfuuBCZWlUCgUCoVCobj3eWA0rwqFQqFQKBSKex8lvCoUCoVCoVAo7hmU8KpQKBR3GcZYBWmL2EuMsQs3/89gjM0tontOZIwNcfJ7L8bYtKK4t0KhUNwKyudVoVAoihGMsX8ByCCimUV4jxIADgFofXNRmVkadjNNJyLKLKq8KBQKRUFRmleFQqEopjDGujDGwm/+/y/G2DLG2M+MsdOMsRcYYzMYY8cZY5sZY14307VhjO26ubvNFsZYNZNLPwlHXNkbN895kzF2gjF2jDG2EgDIodnYCaDXHXlYhUKhcBMlvCoUCsW9QwMAPQH0AbAcwA4iag4gC0DPmwLsbAD9iKgNgMUApptcpxOAP6Tv7wBoRUQPAxgjHY8A0Pm2P4VCoVDcAg/S9rAKhUJxr7OJiPIYY8cBeALg8ROPA6gLoAmAZnBsyYibaRJMrlMNjriznGMAVjDGfgDwg3Q8EUD125d9hUKhuHWU8KpQKBT3DjkAQER2xlge5S9asMPRnzMAfxLRoy6ukwXAW/reE8DjAHoDCGaMBd10KfC+mVahUCiKDcptQKFQKO4fogFUYow9CgCMMS/GWJBJuigADW+m8QBQi4h2AHgLQACA0jfTNYbaklahUBQzlPCqUCgU9wlElAugH4D/3Nye8QiAjiZJN8GhaQUcrgXLb7oiHAYwi4hSb/7WFcD/ijLPCoVCUVBUqCyFQqF4AGGMrQfwFhGdsvi9CoDviOipO5szhUKhcI4SXhUKheIBhDHWBEAVIvrV4vd2APKI6MgdzZhCoVC4QAmvCoVCoVAoFIp7BuXzqlAoFAqFQqG4Z1DCq0KhUCgUCoXinkEJrwqFQqFQKBSKewYlvCoUCoVCoVAo7hmU8KpQKBQKhUKhuGf4f6bDTjkgsM0WAAAAAElFTkSuQmCC\n",
"text/plain": [
- "<Figure size 510.235x450.49 with 0 Axes>"
+ "<Figure size 720x635.692 with 10 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
"source": [
- "from MAM2EBRAINS import plot_resting_state\n",
- "plot_resting_state(A, label_spikes, label)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3ef52a7c",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 5.2 Raster plot of spiking activity for single area <a class=\"anchor\" id=\"section_5_2\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1da18fee",
- "metadata": {},
- "outputs": [],
- "source": [
- "# from MAM2EBRAINS import plot_raster_plot\n",
- "# plot_raster_plot(A)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7d5a5e32-bd12-4e91-a65d-91d279edc450",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load spike data\n",
- "spike_data = A.spike_data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7947fea3-ba4c-4b1d-94fc-16614e4e4a11",
- "metadata": {},
- "outputs": [],
- "source": [
- "# plotting raster plot of spiking activity for single area\n",
- "from matplotlib import gridspec\n",
- "# axes = {}\n",
- "# gs1 = gridspec.GridSpec(1, 3)\n",
- "# # gs1.update(left=0.06, right=0.72, top=0.95, wspace=0.4, bottom=0.35)\n",
- "# axes['A'] = plt.subplot(gs1[:1, :1], figsize=(16,9), gridspec_kw={'height_ratios': [1, 2]})\n",
- "# axes['B'] = plt.subplot(gs1[:1, 1:2])\n",
- "# axes['C'] = plt.subplot(gs1[:1, 2:])\n",
- "f = plt.figure(figsize=(10,3))\n",
- "sub = [131, 132, 133]\n",
- "\n",
- "areas = ['V1', 'V2', 'FEF']\n",
- "labels = ['A', 'B', 'C']\n",
- "\n",
- "t_min = 0.\n",
- "t_max = 500.\n",
- "# t_min = 3000.\n",
- "# t_max = 3500.\n",
- "\n",
- "# icolor = myred\n",
- "# ecolor = myblue\n",
- "\n",
- "# frac_neurons = 0.03\n",
- "frac_neurons = 0.3\n",
- "\n",
- "for i, area in enumerate(areas):\n",
- " # ax = axes[labels[i]]\n",
- " # ax = plt.subplot()\n",
- " ax = f.add_subplot(sub[i])\n",
- "\n",
- " if area in spike_data:\n",
- " n_pops = len(spike_data[area])\n",
- " # Determine number of neurons that will be plotted for this area (for\n",
- " # vertical offset)\n",
- " offset = 0\n",
- " n_to_plot = {}\n",
- " for pop in M.structure[area]:\n",
- " n_to_plot[pop] = int(M.N[area][pop] * frac_neurons)\n",
- " offset = offset + n_to_plot[pop]\n",
- " y_max = offset + 1\n",
- " prev_pop = ''\n",
- " yticks = []\n",
- " yticklocs = []\n",
- " for jj, pop in enumerate(M.structure[area]):\n",
- " if pop[0:-1] != prev_pop:\n",
- " prev_pop = pop[0:-1]\n",
- " yticks.append('L' + population_labels[jj][0:-1])\n",
- " yticklocs.append(offset - 0.5 * n_to_plot[pop])\n",
- " ind = np.where(np.logical_and(\n",
- " spike_data[area][pop][:, 1] <= t_max, spike_data[area][pop][:, 1] >= t_min))\n",
- " pop_data = spike_data[area][pop][ind]\n",
- " pop_neurons = np.unique(pop_data[:, 0])\n",
- " neurons_to_ = np.arange(np.min(spike_data[area][pop][:, 0]), np.min(\n",
- " spike_data[area][pop][:, 0]) + n_to_plot[pop], 1)\n",
- "\n",
- " if pop.find('E') > (-1):\n",
- " pcolor = ecolor\n",
- " else:\n",
- " pcolor = icolor\n",
- "\n",
- " for kk in range(n_to_plot[pop]):\n",
- " spike_times = pop_data[pop_data[:, 0] == neurons_to_[kk], 1]\n",
- "\n",
- " _ = ax.plot(spike_times, np.zeros(len(spike_times)) +\n",
- " offset - kk, '.', color=pcolor, markersize=1)\n",
- " offset = offset - n_to_plot[pop]\n",
- " y_min = offset\n",
- " ax.set_title(areas[i])\n",
- " ax.set_xlim([t_min, t_max])\n",
- " ax.set_ylim([y_min, y_max])\n",
- " ax.set_yticklabels(yticks)\n",
- " ax.set_yticks(yticklocs)\n",
- " ax.set_xlabel('Time (s)', labelpad=-0.1)\n",
- " ax.set_xticks([t_min, t_min + 250., t_max])\n",
- " ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "019d805e",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 5.3 Population-averaged firing rates <a class=\"anchor\" id=\"section_5_3\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b069bc59-44ae-450a-b0a5-b073951e3604",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load data\n",
- "\n",
- "# stationary firing rates\n",
- "fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')\n",
- "with open(fn, 'r') as f:\n",
- " pop_rates = json.load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "96baac8f-3a0e-4d69-92ac-1d7cb4aff0d8",
- "metadata": {},
- "outputs": [],
- "source": [
- "def set_boxplot_props(d):\n",
- " for i in range(len(d['boxes'])):\n",
- " if i % 2 == 0:\n",
- " d['boxes'][i].set_facecolor(icolor)\n",
- " d['boxes'][i].set_color(icolor)\n",
- " else:\n",
- " d['boxes'][i].set_facecolor(ecolor)\n",
- " d['boxes'][i].set_color(ecolor)\n",
- " plt.setp(d['whiskers'], color='k')\n",
- " plt.setp(d['fliers'], color='k', markerfacecolor='k', marker='+')\n",
- " plt.setp(d['medians'], color='none')\n",
- " plt.setp(d['caps'], color='k')\n",
- " plt.setp(d['means'], marker='x', color='k',\n",
- " markerfacecolor='k', markeredgecolor='k', markersize=3.)\n",
- " \n",
- "# print(\"plotting Population rates\")\n",
- "\n",
- "rates = np.zeros((len(M.area_list), 8))\n",
- "for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " rate = pop_rates[area][pop][0]\n",
- " if rate == 0.0:\n",
- " rate = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " rates[i][j + 2] = rate\n",
- " else:\n",
- " rates[i][j] = rate\n",
- "\n",
- "\n",
- "rates = np.transpose(rates)\n",
- "masked_rates = np.ma.masked_where(rates < 1e-4, rates)\n",
- "\n",
- "# ax = axes['D']\n",
- "ax = plt.subplot()\n",
- "d = plt.boxplot(np.transpose(rates), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- "set_boxplot_props(d)\n",
- "\n",
- "ax.plot(np.mean(rates, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "\n",
- "ax.set_yticklabels(population_labels[::-1], size=8)\n",
- "ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- "ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "\n",
- "x_max = 100.\n",
- "ax.set_title(\"Population-averaged firing rates\")\n",
- "ax.set_xlim((-1., x_max))\n",
- "ax.set_xlabel(r'Rate (spikes/s)', labelpad=-0.1)\n",
- "ax.set_xticks([0., 50.])"
+ "from MAM2EBRAINS_VISUALIZATION import plot_resting_state\n",
+ "plot_resting_state(A, label_spikes)"
]
},
{
"cell_type": "markdown",
"id": "473d0882-8e45-4330-bfa2-2c7e1af0dac4",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
- "### 5.4 Time-averaged population rates <a class=\"anchor\" id=\"section_5_4\"></a>"
+ "### 5.3 Time-averaged population rates <a class=\"anchor\" id=\"section_5_3\"></a>\n",
+ "Plot overview over time-averaged population rates encoded in colors with areas along x-axis and populations along y-axis."
]
},
{
@@ -1786,254 +843,9 @@
"metadata": {},
"outputs": [],
"source": [
- "\"\"\"\n",
- "Plot overview over time-averaged population rates encoded in colors\n",
- "with areas along x-axis and populations along y-axis.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "area_list : list, optional\n",
- " Specifies with areas are plotted in which order.\n",
- " Default to None, leading to plotting of all areas ordered by architectural type.\n",
- "output : {'pdf', 'png', 'eps'}, optional\n",
- " If given, the function stores the plot to a file of the given format.\n",
- "\"\"\"\n",
- "A.show_rates()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "06a595de",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 5.5 Average pairwise correlation coefficients of spiking activity <a class=\"anchor\" id=\"section_5_5\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a8e77836-4c37-4b78-b7c4-5e11bc67b4fa",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load data\n",
- "\n",
- "# correlation coefficients\n",
- "# fn = os.path.join(data_path, label, 'Analysis', 'corrcoeff.json')\n",
- "fn = os.path.join(data_path, label, 'Analysis', 'synchrony.json')\n",
- "# synchrony.json\n",
- "with open(fn, 'r') as f:\n",
- " corrcoeff = json.load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "218367da-82ef-47b6-bf15-083ef3d43013",
- "metadata": {},
- "outputs": [],
- "source": [
- "# print(\"plotting Synchrony\")\n",
- "\n",
- "syn = np.zeros((len(M.area_list), 8))\n",
- "for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " value = corrcoeff[area][pop]\n",
- " if value == 0.0:\n",
- " value = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " syn[i][j + 2] = value\n",
- " else:\n",
- " syn[i][j] = value\n",
- "\n",
- "\n",
- "syn = np.transpose(syn)\n",
- "masked_syn = np.ma.masked_where(syn < 1e-4, syn)\n",
- "\n",
- "# ax = axes['E']\n",
- "ax = plt.subplot()\n",
- "d = ax.boxplot(np.transpose(syn), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- "set_boxplot_props(d)\n",
- "\n",
- "ax.plot(np.mean(syn, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "\n",
- "ax.set_yticklabels(population_labels[::-1], size=8)\n",
- "ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- "ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "# ax.set_xticks(np.arange(0.0, 0.601, 0.2))\n",
- "ax.set_xticks(np.arange(0.0, 10.0, 2.0))\n",
- "ax.set_xlabel('Correlation coefficient', labelpad=-0.1)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a3847e67",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 5.6 Irregularity of spiking activity <a class=\"anchor\" id=\"section_5_6\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "65377033-f3c0-4f90-be13-70594cfda292",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load data\n",
- "\n",
- "# local variance revised (LvR)\n",
- "fn = os.path.join(data_path, label, 'Analysis', 'pop_LvR.json')\n",
- "with open(fn, 'r') as f:\n",
- " pop_LvR = json.load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d7480a9b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# print(\"plotting Irregularity\")\n",
- "\n",
- "LvR = np.zeros((len(M.area_list), 8))\n",
- "for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " value = pop_LvR[area][pop]\n",
- " if value == 0.0:\n",
- " value = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " LvR[i][j + 2] = value\n",
- " else:\n",
- " LvR[i][j] = value\n",
- "\n",
- "LvR = np.transpose(LvR)\n",
- "masked_LvR = np.ma.masked_where(LvR < 1e-4, LvR)\n",
- "\n",
- "# ax = axes['F']\n",
- "ax = plt.subplot()\n",
- "d = ax.boxplot(np.transpose(LvR), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- "set_boxplot_props(d)\n",
- "\n",
- "ax.plot(np.mean(LvR, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "ax.set_yticklabels(population_labels[::-1], size=8)\n",
- "ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- "ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "\n",
- "\n",
- "x_max = 1.9\n",
- "ax.set_xlim((0., x_max))\n",
- "ax.set_xlabel('Irregularity', labelpad=-0.1)\n",
- "ax.set_xticks([0., 1., 2.])\n",
- "\n",
- "# axes['G'].spines['right'].set_color('none')\n",
- "# axes['G'].spines['left'].set_color('none')\n",
- "# axes['G'].spines['top'].set_color('none')\n",
- "# axes['G'].spines['bottom'].set_color('none')\n",
- "# axes['G'].yaxis.set_ticks_position(\"none\")\n",
- "# axes['G'].xaxis.set_ticks_position(\"none\")\n",
- "# axes['G'].set_xticks([])\n",
- "# axes['G'].set_yticks([])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "90ae8f4c",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 5.7 Time series of area-averaged firing rates <a class=\"anchor\" id=\"section_5_7\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0308d50a-1906-4860-9194-7f8664bd1f9d",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load data\n",
- "\n",
- "# time series of firing rates\n",
- "rate_time_series = {}\n",
- "for area in areas:\n",
- " fn = os.path.join(data_path, label,\n",
- " 'Analysis',\n",
- " 'rate_time_series_full',\n",
- " 'rate_time_series_full_{}.npy'.format(area))\n",
- " rate_time_series[area] = np.load(fn)\n",
- "\n",
- "# # time series of firing rates convolved with a kernel\n",
- "# rate_time_series_auto_kernel = {}\n",
- "# for area in areas:\n",
- "# fn = os.path.join(data_path, label,\n",
- "# 'Analysis',\n",
- "# 'rate_time_series_auto_kernel',\n",
- "# 'rate_time_series_auto_kernel_{}.npy'.format(area))\n",
- "# rate_time_series_auto_kernel[area] = np.load(fn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4460d823-543a-482b-8ef1-a049e5837af4",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(\"Plotting rate time series\")\n",
- "pos = axes['G'].get_position()\n",
- "ax = []\n",
- "h = pos.y1 - pos.y0\n",
- "w = pos.x1 - pos.x0\n",
- "ax.append(pl.axes([pos.x0, pos.y0, w, 0.28 * h]))\n",
- "ax.append(pl.axes([pos.x0, pos.y0 + 0.33 * h, w, 0.28 * h]))\n",
- "ax.append(pl.axes([pos.x0, pos.y0 + 0.67 * h, w, 0.28 * h]))\n",
- "\n",
- "colors = ['0.5', '0.3', '0.0']\n",
- "\n",
- "t_min = 500.\n",
- "t_max = 10500.\n",
- "time = np.arange(500., t_max)\n",
- "for i, area in enumerate(areas[::-1]):\n",
- " ax[i].spines['right'].set_color('none')\n",
- " ax[i].spines['top'].set_color('none')\n",
- " ax[i].yaxis.set_ticks_position(\"left\")\n",
- " ax[i].xaxis.set_ticks_position(\"none\")\n",
- "\n",
- " binned_spikes = rate_time_series[area][np.where(\n",
- " np.logical_and(time >= t_min, time < t_max))]\n",
- " ax[i].plot(time, binned_spikes, color=colors[0], label=area)\n",
- " rate = rate_time_series_auto_kernel[area]\n",
- " ax[i].plot(time, rate, color=colors[2], label=area)\n",
- " ax[i].set_xlim((500., t_max))\n",
- "\n",
- " ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)\n",
- "\n",
- " if i > 0:\n",
- " ax[i].spines['bottom'].set_color('none')\n",
- " ax[i].set_xticks([])\n",
- " ax[i].set_yticks([0., 30.])\n",
- " else:\n",
- " ax[i].set_xticks([1000., 5000., 10000.])\n",
- " ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])\n",
- " ax[i].set_yticks([0., 5.])\n",
- " if i == 1:\n",
- " ax[i].set_ylabel(r'Rate (spikes/s)')\n",
- "\n",
- "ax[0].set_xlabel('Time (s)', labelpad=-0.05)"
+ "# area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']\n",
+ "# output = {'pdf', 'png', 'eps'}, optional\n",
+ "A.show_rates(area_list)"
]
},
{
diff --git a/figures/.ipynb_checkpoints/MAM2EBRAINS_LOAD_DATA-checkpoint.py b/figures/.ipynb_checkpoints/MAM2EBRAINS_LOAD_DATA-checkpoint.py
new file mode 100644
index 0000000..1feb855
--- /dev/null
+++ b/figures/.ipynb_checkpoints/MAM2EBRAINS_LOAD_DATA-checkpoint.py
@@ -0,0 +1,154 @@
+def load_data(M, A):
+ # load spike data and calculate instantaneous and mean firing rates
+ data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
+ tsteps, spikecount = np.unique(data[:,1], return_counts=True)
+ firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
+
+
+ """
+ Analysis class.
+ An instance of the analysis class for the given network and simulation.
+ Can be created as a member class of a multiarea_model instance or standalone.
+
+ Parameters
+ ----------
+ network : MultiAreaModel
+ An instance of the multiarea_model class that specifies
+ the network to be analyzed.
+ simulation : Simulation
+ An instance of the simulation class that specifies
+ the simulation to be analyzed.
+ data_list : list of strings {'spikes', vm'}, optional
+ Specifies which type of data is to load. Defaults to ['spikes'].
+ load_areas : list of strings with area names, optional
+ Specifies the areas for which data is to be loaded.
+ Default value is None and leads to loading of data for all
+ simulated areas.
+ """
+ # Instantiate an analysis class and load spike data
+ A = Analysis(network=M,
+ simulation=M.simulation,
+ data_list=['spikes'],
+ load_areas=None)
+
+
+ """
+ Calculate time-averaged population rates and store them in member pop_rates.
+ If the rates had previously been stored with the same
+ parameters, they are loaded from file.
+
+ Parameters
+ ----------
+ t_min : float, optional
+ Minimal time in ms of the simulation to take into account
+ for the calculation. Defaults to 500 ms.
+ t_max : float, optional
+ Maximal time in ms of the simulation to take into account
+ for the calculation. Defaults to the simulation time.
+ compute_stat : bool, optional
+ If set to true, the mean and variance of the population rate
+ is calculated. Defaults to False.
+ Caution: Setting to True slows down the computation.
+ areas : list, optional
+ Which areas to include in the calculcation.
+ Defaults to all loaded areas.
+ pops : list or {'complete'}, optional
+ Which populations to include in the calculation.
+ If set to 'complete', all populations the respective areas
+ are included. Defaults to 'complete'.
+ """
+ A.create_pop_rates()
+ print("Computing population rates done")
+
+
+ """
+ Calculate synchrony as the coefficient of variation of the population rate
+ and store in member synchrony. Uses helper function synchrony.
+ If the synchrony has previously been stored with the
+ same parameters, they are loaded from file.
+
+
+ Parameters
+ ----------
+ t_min : float, optional
+ Minimal time in ms of the simulation to take into account
+ for the calculation. Defaults to 500 ms.
+ t_max : float, optional
+ Maximal time in ms of the simulation to take into account
+ for the calculation. Defaults to the simulation time.
+ areas : list, optional
+ Which areas to include in the calculcation.
+ Defaults to all loaded areas.
+ pops : list or {'complete'}, optional
+ Which populations to include in the calculation.
+ If set to 'complete', all populations the respective areas
+ are included. Defaults to 'complete'.
+ resolution : float, optional
+ Resolution of the population rate. Defaults to 1 ms.
+ """
+ A.create_synchrony()
+ print("Computing synchrony done")
+
+
+ """
+ Calculate poulation-averaged LvR (see Shinomoto et al. 2009) and
+ store as member pop_LvR. Uses helper function LvR.
+
+ Parameters
+ ----------
+ t_min : float, optional
+ Minimal time in ms of the simulation to take into account
+ for the calculation. Defaults to 500 ms.
+ t_max : float, optional
+ Maximal time in ms of the simulation to take into account
+ for the calculation. Defaults to the simulation time.
+ areas : list, optional
+ Which areas to include in the calculcation.
+ Defaults to all loaded areas.
+ pops : list or {'complete'}, optional
+ Which populations to include in the calculation.
+ If set to 'complete', all populations the respective areas
+ are included. Defaults to 'complete'.
+ """
+ A.create_pop_LvR()
+ print("Computing population LvR done")
+
+
+ """
+ Calculate time series of population- and area-averaged firing rates.
+ Uses ah.pop_rate_time_series.
+ If the rates have previously been stored with the
+ same parameters, they are loaded from file.
+
+
+ Parameters
+ ----------
+ t_min : float, optional
+ Minimal time in ms of the simulation to take into account
+ for the calculation. Defaults to 500 ms.
+ t_max : float, optional
+ Maximal time in ms of the simulation to take into account
+ for the calculation. Defaults to the simulation time.
+ areas : list, optional
+ Which areas to include in the calculcation.
+ Defaults to all loaded areas.
+ pops : list or {'complete'}, optional
+ Which populations to include in the calculation.
+ If set to 'complete', all populations the respective areas
+ are included. Defaults to 'complete'.
+ kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional
+ Specifies the kernel to be convolved with the spike histogram.
+ Defaults to 'binned', which corresponds to no convolution.
+ resolution: float, optional
+ Width of the convolution kernel. Specifically it correponds to:
+ - 'binned' : bin width of the histogram
+ - 'gauss_time_window' : sigma
+ - 'alpha_time_window' : time constant of the alpha function
+ - 'rect_time_window' : width of the moving rectangular function
+ """
+ A.create_rate_time_series()
+ print("Computing rate time series done")
+
+ A.save()
+
+ return tsteps, firing_rate
\ No newline at end of file
diff --git a/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py b/figures/.ipynb_checkpoints/MAM2EBRAINS_VISUALIZATION-checkpoint.py
similarity index 67%
rename from figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py
rename to figures/.ipynb_checkpoints/MAM2EBRAINS_VISUALIZATION-checkpoint.py
index 7cb4f44..f82cae6 100644
--- a/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py
+++ b/figures/.ipynb_checkpoints/MAM2EBRAINS_VISUALIZATION-checkpoint.py
@@ -3,7 +3,7 @@ import numpy as np
import os
import sys
-sys.path.append('/figures/Schmidt2018_dyn')
+sys.path.append('./figures/Schmidt2018_dyn')
from helpers import original_data_path, population_labels
from multiarea_model import MultiAreaModel
@@ -17,6 +17,7 @@ from matplotlib import gridspec
icolor = myred
ecolor = myblue
+
# Instantaneous and mean firing rate across all populations
def plot_instan_mean_firing_rate(tsteps, rate, sim_params):
ax = pl.subplot()
@@ -28,30 +29,6 @@ def plot_instan_mean_firing_rate(tsteps, rate, sim_params):
ax.set_xlim(0, sim_params['t_sim'])
ax.set_ylim(0, 50)
ax.legend()
-
-def plot_raster_plot(A):
- """
- Create raster display of a single area with populations stacked onto each other. Excitatory neurons in blue, inhibitory neurons in red.
-
- Parameters
- ----------
- area : string {area}
- Area to be plotted.
- frac_neurons : float, [0,1]
- Fraction of cells to be considered.
- t_min : float, optional
- Minimal time in ms of spikes to be shown. Defaults to 0 ms.
- t_max : float, optional
- Minimal time in ms of spikes to be shown. Defaults to simulation time.
- output : {'pdf', 'png', 'eps'}, optional
- If given, the function stores the plot to a file of the given format.
- """
- t_min = 0.
- t_max = 500.
- areas = ['V1', 'V2', 'FEF']
- frac_neurons = 1.
- for area in areas:
- A.single_dot_display(area, frac_neurons, t_min, t_max)
def set_boxplot_props(d):
for i in range(len(d['boxes'])):
@@ -68,14 +45,15 @@ def set_boxplot_props(d):
pl.setp(d['means'], marker='x', color='k',
markerfacecolor='k', markeredgecolor='k', markersize=3.)
-def plot_resting_state(A, label_spikes, label):
+def plot_resting_state(A, label_spikes):
"""
Figure layout
"""
nrows = 4
ncols = 4
- width = 7.0866
+ # width = 7.0866
+ width = 10
panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio
height = width / panel_wh_ratio * float(nrows) / ncols
@@ -87,9 +65,12 @@ def plot_resting_state(A, label_spikes, label):
gs1 = gridspec.GridSpec(1, 3)
gs1.update(left=0.06, right=0.72, top=0.95, wspace=0.4, bottom=0.35)
- axes['A'] = pl.subplot(gs1[:-1, :1])
- axes['B'] = pl.subplot(gs1[:-1, 1:2])
- axes['C'] = pl.subplot(gs1[:-1, 2:])
+ # axes['A'] = pl.subplot(gs1[:-1, :1])
+ # axes['B'] = pl.subplot(gs1[:-1, 1:2])
+ # axes['C'] = pl.subplot(gs1[:-1, 2:])
+ axes['A'] = pl.subplot(gs1[:1, :1])
+ axes['B'] = pl.subplot(gs1[:1, 1:2])
+ axes['C'] = pl.subplot(gs1[:1, 2:])
gs2 = gridspec.GridSpec(3, 1)
gs2.update(left=0.78, right=0.95, top=0.95, bottom=0.35)
@@ -100,6 +81,7 @@ def plot_resting_state(A, label_spikes, label):
gs3 = gridspec.GridSpec(1, 1)
gs3.update(left=0.1, right=0.95, top=0.3, bottom=0.075)
+ # gs3.update(left=0.1, right=0.95, top=0.25, bottom=0.075)
axes['G'] = pl.subplot(gs3[:1, :1])
areas = ['V1', 'V2', 'FEF']
@@ -107,26 +89,35 @@ def plot_resting_state(A, label_spikes, label):
labels = ['A', 'B', 'C']
for area, label in zip(areas, labels):
label_pos = [-0.2, 1.01]
- pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label + ': ' + area,
- fontdict={'fontsize': 10, 'weight': 'bold',
- 'horizontalalignment': 'left', 'verticalalignment':
- 'bottom'}, transform=axes[label].transAxes)
+ # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label + ': ' + area,
+ # fontdict={'fontsize': 10, 'weight': 'bold',
+ # 'horizontalalignment': 'left', 'verticalalignment':
+ # 'bottom'}, transform=axes[label].transAxes)
+ plt.text(label_pos[0], label_pos[1], label + ': ' + area,
+ fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left',
+ 'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
label = 'G'
label_pos = [-0.1, 0.92]
- pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
- fontdict={'fontsize': 10, 'weight': 'bold',
- 'horizontalalignment': 'left', 'verticalalignment':
- 'bottom'}, transform=axes[label].transAxes)
-
+ # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
+ # fontdict={'fontsize': 10, 'weight': 'bold',
+ # 'horizontalalignment': 'left', 'verticalalignment':
+ # 'bottom'}, transform=axes[label].transAxes)
+ plt.text(label_pos[0], label_pos[1], label,
+ fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left',
+ 'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
labels = ['E', 'D', 'F']
for label in labels:
label_pos = [-0.2, 1.05]
- pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
- fontdict={'fontsize': 10, 'weight': 'bold',
- 'horizontalalignment': 'left', 'verticalalignment':
- 'bottom'}, transform=axes[label].transAxes)
+ # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
+ # fontdict={'fontsize': 10, 'weight': 'bold',
+ # 'horizontalalignment': 'left', 'verticalalignment':
+ # 'bottom'}, transform=axes[label].transAxes)
+ plt.text(label_pos[0], label_pos[1], label,
+ fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left',
+ 'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
+
labels = ['A', 'B', 'C', 'D', 'E', 'F']
@@ -143,21 +134,25 @@ def plot_resting_state(A, label_spikes, label):
"""
Load data
"""
- LOAD_ORIGINAL_DATA = True
-
-
- if LOAD_ORIGINAL_DATA:
- # use T=10500 simulation for spike raster plots
- label_spikes = '3afaec94d650c637ef8419611c3f80b3cb3ff539'
- # and T=100500 simulation for all other panels
- label = '99c0024eacc275d13f719afd59357f7d12f02b77'
- data_path = original_data_path
- else:
- from network_simulations import init_models
- from config import data_path
- models = init_models('Fig5')
- label_spikes = models[0].simulation.label
- label = models[1].simulation.label
+# LOAD_ORIGINAL_DATA = True
+
+
+# if LOAD_ORIGINAL_DATA:
+# # use T=10500 simulation for spike raster plots
+# label_spikes = '3afaec94d650c637ef8419611c3f80b3cb3ff539'
+# # and T=100500 simulation for all other panels
+# label = '99c0024eacc275d13f719afd59357f7d12f02b77'
+# data_path = original_data_path
+# else:
+# from network_simulations import init_models
+# from config import data_path
+# models = init_models('Fig5')
+# label_spikes = models[0].simulation.label
+# label = models[1].simulation.label
+
+ # model = M
+ label_spikes = label_spikes
+ label = label_spikes
"""
Create MultiAreaModel instance to have access to data structures
@@ -165,15 +160,17 @@ def plot_resting_state(A, label_spikes, label):
M = MultiAreaModel({})
# spike data
- spike_data = {}
- for area in areas:
- spike_data[area] = {}
- for pop in M.structure[area]:
- spike_data[area][pop] = np.load(os.path.join(data_path,
- label_spikes,
- 'recordings',
- '{}-spikes-{}-{}.npy'.format(label_spikes,
- area, pop)))
+ # spike_data = {}
+ # for area in areas:
+ # spike_data[area] = {}
+ # for pop in M.structure[area]:
+ # spike_data[area][pop] = np.load(os.path.join(data_path,
+ # label_spikes,
+ # 'recordings',
+ # '{}-spikes-{}-{}.npy'.format(label_spikes,
+ # area, pop)))
+ spike_data = A.spike_data
+
# stationary firing rates
fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
with open(fn, 'r') as f:
@@ -182,20 +179,23 @@ def plot_resting_state(A, label_spikes, label):
# time series of firing rates
rate_time_series = {}
for area in areas:
+ # fn = os.path.join(data_path, label,
+ # 'Analysis',
+ # 'rate_time_series_full',
+ # 'rate_time_series_full_{}.npy'.format(area))
fn = os.path.join(data_path, label,
'Analysis',
- 'rate_time_series_full',
- 'rate_time_series_full_{}.npy'.format(area))
+ 'rate_time_series-{}.npy'.format(area))
rate_time_series[area] = np.load(fn)
# time series of firing rates convolved with a kernel
- rate_time_series_auto_kernel = {}
- for area in areas:
- fn = os.path.join(data_path, label,
- 'Analysis',
- 'rate_time_series_auto_kernel',
- 'rate_time_series_auto_kernel_{}.npy'.format(area))
- rate_time_series_auto_kernel[area] = np.load(fn)
+ # rate_time_series_auto_kernel = {}
+ # for area in areas:
+ # fn = os.path.join(data_path, label,
+ # 'Analysis',
+ # 'rate_time_series_auto_kernel',
+ # 'rate_time_series_auto_kernel_{}.npy'.format(area))
+ # rate_time_series_auto_kernel[area] = np.load(fn)
# local variance revised (LvR)
fn = os.path.join(data_path, label, 'Analysis', 'pop_LvR.json')
@@ -203,21 +203,25 @@ def plot_resting_state(A, label_spikes, label):
pop_LvR = json.load(f)
# correlation coefficients
- fn = os.path.join(data_path, label, 'Analysis', 'corrcoeff.json')
+ # fn = os.path.join(data_path, label, 'Analysis', 'corrcoeff.json')
+ fn = os.path.join(data_path, label, 'Analysis', 'synchrony.json')
with open(fn, 'r') as f:
corrcoeff = json.load(f)
"""
Plotting
"""
- print("Raster plots")
+ # print("Raster plots")
- t_min = 3000.
- t_max = 3500.
+ # t_min = 3000.
+ # t_max = 3500.
+ t_min = 500.
+ t_max = 1000.
icolor = myred
ecolor = myblue
+ # frac_neurons = 0.03
frac_neurons = 0.03
for i, area in enumerate(areas):
@@ -266,9 +270,10 @@ def plot_resting_state(A, label_spikes, label):
ax.set_yticks(yticklocs)
ax.set_xlabel('Time (s)', labelpad=-0.1)
ax.set_xticks([t_min, t_min + 250., t_max])
- ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])
+ # ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])
+ ax.set_xticklabels([r'$0.5$', r'$0.75$', r'$1.0$'])
- print("plotting Population rates")
+ # print("plotting Population rates")
rates = np.zeros((len(M.area_list), 8))
for i, area in enumerate(M.area_list):
@@ -301,7 +306,7 @@ def plot_resting_state(A, label_spikes, label):
ax.set_xlabel(r'Rate (spikes/s)', labelpad=-0.1)
ax.set_xticks([0., 50., 100.])
- print("plotting Synchrony")
+ # print("plotting Synchrony")
syn = np.zeros((len(M.area_list), 8))
for i, area in enumerate(M.area_list):
@@ -329,11 +334,12 @@ def plot_resting_state(A, label_spikes, label):
ax.set_yticklabels(population_labels[::-1], size=8)
ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))
ax.set_ylim((0., len(M.structure['V1']) + .5))
- ax.set_xticks(np.arange(0.0, 0.601, 0.2))
+ # ax.set_xticks(np.arange(0.0, 0.601, 0.2))
+ ax.set_xticks(np.arange(0.0, 10.0, 2.0))
ax.set_xlabel('Correlation coefficient', labelpad=-0.1)
- print("plotting Irregularity")
+ # print("plotting Irregularity")
LvR = np.zeros((len(M.area_list), 8))
for i, area in enumerate(M.area_list):
@@ -376,7 +382,7 @@ def plot_resting_state(A, label_spikes, label):
axes['G'].set_yticks([])
- print("Plotting rate time series")
+ # print("Plotting rate time series")
pos = axes['G'].get_position()
ax = []
h = pos.y1 - pos.y0
@@ -387,9 +393,12 @@ def plot_resting_state(A, label_spikes, label):
colors = ['0.5', '0.3', '0.0']
- t_min = 500.
- t_max = 10500.
- time = np.arange(500., t_max)
+ # t_min = 500.
+ # t_max = 10500.
+ t_min = 50.
+ t_max = 1550.
+ # time = np.arange(500., t_max)
+ time = np.arange(50., t_max)
for i, area in enumerate(areas[::-1]):
ax[i].spines['right'].set_color('none')
ax[i].spines['top'].set_color('none')
@@ -399,9 +408,11 @@ def plot_resting_state(A, label_spikes, label):
binned_spikes = rate_time_series[area][np.where(
np.logical_and(time >= t_min, time < t_max))]
ax[i].plot(time, binned_spikes, color=colors[0], label=area)
- rate = rate_time_series_auto_kernel[area]
+ # rate = rate_time_series_auto_kernel[area]
+ rate = rate_time_series[area]
ax[i].plot(time, rate, color=colors[2], label=area)
- ax[i].set_xlim((500., t_max))
+ # ax[i].set_xlim((500., t_max))
+ ax[i].set_xlim((50., t_max))
ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)
@@ -410,8 +421,10 @@ def plot_resting_state(A, label_spikes, label):
ax[i].set_xticks([])
ax[i].set_yticks([0., 30.])
else:
- ax[i].set_xticks([1000., 5000., 10000.])
- ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])
+ # ax[i].set_xticks([1000., 5000., 10000.])
+ ax[i].set_xticks([50., 750., 1500.])
+ # ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])
+ ax[i].set_xticklabels([r'$0.05$', r'$0.75$', r'$1.5$'])
ax[i].set_yticks([0., 5.])
if i == 1:
ax[i].set_ylabel(r'Rate (spikes/s)')
diff --git a/figures/MAM2EBRAINS_LOAD_DATA.py b/figures/MAM2EBRAINS_LOAD_DATA.py
new file mode 100644
index 0000000..1feb855
--- /dev/null
+++ b/figures/MAM2EBRAINS_LOAD_DATA.py
@@ -0,0 +1,154 @@
+def load_data(M, A):
+ # load spike data and calculate instantaneous and mean firing rates
+ data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
+ tsteps, spikecount = np.unique(data[:,1], return_counts=True)
+ firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
+
+
+ """
+ Analysis class.
+ An instance of the analysis class for the given network and simulation.
+ Can be created as a member class of a multiarea_model instance or standalone.
+
+ Parameters
+ ----------
+ network : MultiAreaModel
+ An instance of the multiarea_model class that specifies
+ the network to be analyzed.
+ simulation : Simulation
+ An instance of the simulation class that specifies
+ the simulation to be analyzed.
+ data_list : list of strings {'spikes', vm'}, optional
+ Specifies which type of data is to load. Defaults to ['spikes'].
+ load_areas : list of strings with area names, optional
+ Specifies the areas for which data is to be loaded.
+ Default value is None and leads to loading of data for all
+ simulated areas.
+ """
+ # Instantiate an analysis class and load spike data
+ A = Analysis(network=M,
+ simulation=M.simulation,
+ data_list=['spikes'],
+ load_areas=None)
+
+
+ """
+ Calculate time-averaged population rates and store them in member pop_rates.
+ If the rates had previously been stored with the same
+ parameters, they are loaded from file.
+
+ Parameters
+ ----------
+ t_min : float, optional
+ Minimal time in ms of the simulation to take into account
+ for the calculation. Defaults to 500 ms.
+ t_max : float, optional
+ Maximal time in ms of the simulation to take into account
+ for the calculation. Defaults to the simulation time.
+ compute_stat : bool, optional
+ If set to true, the mean and variance of the population rate
+ is calculated. Defaults to False.
+ Caution: Setting to True slows down the computation.
+ areas : list, optional
+ Which areas to include in the calculcation.
+ Defaults to all loaded areas.
+ pops : list or {'complete'}, optional
+ Which populations to include in the calculation.
+ If set to 'complete', all populations the respective areas
+ are included. Defaults to 'complete'.
+ """
+ A.create_pop_rates()
+ print("Computing population rates done")
+
+
+ """
+ Calculate synchrony as the coefficient of variation of the population rate
+ and store in member synchrony. Uses helper function synchrony.
+ If the synchrony has previously been stored with the
+ same parameters, they are loaded from file.
+
+
+ Parameters
+ ----------
+ t_min : float, optional
+ Minimal time in ms of the simulation to take into account
+ for the calculation. Defaults to 500 ms.
+ t_max : float, optional
+ Maximal time in ms of the simulation to take into account
+ for the calculation. Defaults to the simulation time.
+ areas : list, optional
+ Which areas to include in the calculcation.
+ Defaults to all loaded areas.
+ pops : list or {'complete'}, optional
+ Which populations to include in the calculation.
+ If set to 'complete', all populations the respective areas
+ are included. Defaults to 'complete'.
+ resolution : float, optional
+ Resolution of the population rate. Defaults to 1 ms.
+ """
+ A.create_synchrony()
+ print("Computing synchrony done")
+
+
+ """
+ Calculate poulation-averaged LvR (see Shinomoto et al. 2009) and
+ store as member pop_LvR. Uses helper function LvR.
+
+ Parameters
+ ----------
+ t_min : float, optional
+ Minimal time in ms of the simulation to take into account
+ for the calculation. Defaults to 500 ms.
+ t_max : float, optional
+ Maximal time in ms of the simulation to take into account
+ for the calculation. Defaults to the simulation time.
+ areas : list, optional
+ Which areas to include in the calculcation.
+ Defaults to all loaded areas.
+ pops : list or {'complete'}, optional
+ Which populations to include in the calculation.
+ If set to 'complete', all populations the respective areas
+ are included. Defaults to 'complete'.
+ """
+ A.create_pop_LvR()
+ print("Computing population LvR done")
+
+
+ """
+ Calculate time series of population- and area-averaged firing rates.
+ Uses ah.pop_rate_time_series.
+ If the rates have previously been stored with the
+ same parameters, they are loaded from file.
+
+
+ Parameters
+ ----------
+ t_min : float, optional
+ Minimal time in ms of the simulation to take into account
+ for the calculation. Defaults to 500 ms.
+ t_max : float, optional
+ Maximal time in ms of the simulation to take into account
+ for the calculation. Defaults to the simulation time.
+ areas : list, optional
+ Which areas to include in the calculcation.
+ Defaults to all loaded areas.
+ pops : list or {'complete'}, optional
+ Which populations to include in the calculation.
+ If set to 'complete', all populations the respective areas
+ are included. Defaults to 'complete'.
+ kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional
+ Specifies the kernel to be convolved with the spike histogram.
+ Defaults to 'binned', which corresponds to no convolution.
+ resolution: float, optional
+ Width of the convolution kernel. Specifically it correponds to:
+ - 'binned' : bin width of the histogram
+ - 'gauss_time_window' : sigma
+ - 'alpha_time_window' : time constant of the alpha function
+ - 'rect_time_window' : width of the moving rectangular function
+ """
+ A.create_rate_time_series()
+ print("Computing rate time series done")
+
+ A.save()
+
+ return tsteps, firing_rate
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS.py b/figures/MAM2EBRAINS_VISUALIZATION.py
similarity index 67%
rename from figures/MAM2EBRAINS.py
rename to figures/MAM2EBRAINS_VISUALIZATION.py
index 7cb4f44..f82cae6 100644
--- a/figures/MAM2EBRAINS.py
+++ b/figures/MAM2EBRAINS_VISUALIZATION.py
@@ -3,7 +3,7 @@ import numpy as np
import os
import sys
-sys.path.append('/figures/Schmidt2018_dyn')
+sys.path.append('./figures/Schmidt2018_dyn')
from helpers import original_data_path, population_labels
from multiarea_model import MultiAreaModel
@@ -17,6 +17,7 @@ from matplotlib import gridspec
icolor = myred
ecolor = myblue
+
# Instantaneous and mean firing rate across all populations
def plot_instan_mean_firing_rate(tsteps, rate, sim_params):
ax = pl.subplot()
@@ -28,30 +29,6 @@ def plot_instan_mean_firing_rate(tsteps, rate, sim_params):
ax.set_xlim(0, sim_params['t_sim'])
ax.set_ylim(0, 50)
ax.legend()
-
-def plot_raster_plot(A):
- """
- Create raster display of a single area with populations stacked onto each other. Excitatory neurons in blue, inhibitory neurons in red.
-
- Parameters
- ----------
- area : string {area}
- Area to be plotted.
- frac_neurons : float, [0,1]
- Fraction of cells to be considered.
- t_min : float, optional
- Minimal time in ms of spikes to be shown. Defaults to 0 ms.
- t_max : float, optional
- Minimal time in ms of spikes to be shown. Defaults to simulation time.
- output : {'pdf', 'png', 'eps'}, optional
- If given, the function stores the plot to a file of the given format.
- """
- t_min = 0.
- t_max = 500.
- areas = ['V1', 'V2', 'FEF']
- frac_neurons = 1.
- for area in areas:
- A.single_dot_display(area, frac_neurons, t_min, t_max)
def set_boxplot_props(d):
for i in range(len(d['boxes'])):
@@ -68,14 +45,15 @@ def set_boxplot_props(d):
pl.setp(d['means'], marker='x', color='k',
markerfacecolor='k', markeredgecolor='k', markersize=3.)
-def plot_resting_state(A, label_spikes, label):
+def plot_resting_state(A, label_spikes):
"""
Figure layout
"""
nrows = 4
ncols = 4
- width = 7.0866
+ # width = 7.0866
+ width = 10
panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio
height = width / panel_wh_ratio * float(nrows) / ncols
@@ -87,9 +65,12 @@ def plot_resting_state(A, label_spikes, label):
gs1 = gridspec.GridSpec(1, 3)
gs1.update(left=0.06, right=0.72, top=0.95, wspace=0.4, bottom=0.35)
- axes['A'] = pl.subplot(gs1[:-1, :1])
- axes['B'] = pl.subplot(gs1[:-1, 1:2])
- axes['C'] = pl.subplot(gs1[:-1, 2:])
+ # axes['A'] = pl.subplot(gs1[:-1, :1])
+ # axes['B'] = pl.subplot(gs1[:-1, 1:2])
+ # axes['C'] = pl.subplot(gs1[:-1, 2:])
+ axes['A'] = pl.subplot(gs1[:1, :1])
+ axes['B'] = pl.subplot(gs1[:1, 1:2])
+ axes['C'] = pl.subplot(gs1[:1, 2:])
gs2 = gridspec.GridSpec(3, 1)
gs2.update(left=0.78, right=0.95, top=0.95, bottom=0.35)
@@ -100,6 +81,7 @@ def plot_resting_state(A, label_spikes, label):
gs3 = gridspec.GridSpec(1, 1)
gs3.update(left=0.1, right=0.95, top=0.3, bottom=0.075)
+ # gs3.update(left=0.1, right=0.95, top=0.25, bottom=0.075)
axes['G'] = pl.subplot(gs3[:1, :1])
areas = ['V1', 'V2', 'FEF']
@@ -107,26 +89,35 @@ def plot_resting_state(A, label_spikes, label):
labels = ['A', 'B', 'C']
for area, label in zip(areas, labels):
label_pos = [-0.2, 1.01]
- pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label + ': ' + area,
- fontdict={'fontsize': 10, 'weight': 'bold',
- 'horizontalalignment': 'left', 'verticalalignment':
- 'bottom'}, transform=axes[label].transAxes)
+ # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label + ': ' + area,
+ # fontdict={'fontsize': 10, 'weight': 'bold',
+ # 'horizontalalignment': 'left', 'verticalalignment':
+ # 'bottom'}, transform=axes[label].transAxes)
+ plt.text(label_pos[0], label_pos[1], label + ': ' + area,
+ fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left',
+ 'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
label = 'G'
label_pos = [-0.1, 0.92]
- pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
- fontdict={'fontsize': 10, 'weight': 'bold',
- 'horizontalalignment': 'left', 'verticalalignment':
- 'bottom'}, transform=axes[label].transAxes)
-
+ # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
+ # fontdict={'fontsize': 10, 'weight': 'bold',
+ # 'horizontalalignment': 'left', 'verticalalignment':
+ # 'bottom'}, transform=axes[label].transAxes)
+ plt.text(label_pos[0], label_pos[1], label,
+ fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left',
+ 'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
labels = ['E', 'D', 'F']
for label in labels:
label_pos = [-0.2, 1.05]
- pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
- fontdict={'fontsize': 10, 'weight': 'bold',
- 'horizontalalignment': 'left', 'verticalalignment':
- 'bottom'}, transform=axes[label].transAxes)
+ # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
+ # fontdict={'fontsize': 10, 'weight': 'bold',
+ # 'horizontalalignment': 'left', 'verticalalignment':
+ # 'bottom'}, transform=axes[label].transAxes)
+ plt.text(label_pos[0], label_pos[1], label,
+ fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left',
+ 'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
+
labels = ['A', 'B', 'C', 'D', 'E', 'F']
@@ -143,21 +134,25 @@ def plot_resting_state(A, label_spikes, label):
"""
Load data
"""
- LOAD_ORIGINAL_DATA = True
-
-
- if LOAD_ORIGINAL_DATA:
- # use T=10500 simulation for spike raster plots
- label_spikes = '3afaec94d650c637ef8419611c3f80b3cb3ff539'
- # and T=100500 simulation for all other panels
- label = '99c0024eacc275d13f719afd59357f7d12f02b77'
- data_path = original_data_path
- else:
- from network_simulations import init_models
- from config import data_path
- models = init_models('Fig5')
- label_spikes = models[0].simulation.label
- label = models[1].simulation.label
+# LOAD_ORIGINAL_DATA = True
+
+
+# if LOAD_ORIGINAL_DATA:
+# # use T=10500 simulation for spike raster plots
+# label_spikes = '3afaec94d650c637ef8419611c3f80b3cb3ff539'
+# # and T=100500 simulation for all other panels
+# label = '99c0024eacc275d13f719afd59357f7d12f02b77'
+# data_path = original_data_path
+# else:
+# from network_simulations import init_models
+# from config import data_path
+# models = init_models('Fig5')
+# label_spikes = models[0].simulation.label
+# label = models[1].simulation.label
+
+ # model = M
+ label_spikes = label_spikes
+ label = label_spikes
"""
Create MultiAreaModel instance to have access to data structures
@@ -165,15 +160,17 @@ def plot_resting_state(A, label_spikes, label):
M = MultiAreaModel({})
# spike data
- spike_data = {}
- for area in areas:
- spike_data[area] = {}
- for pop in M.structure[area]:
- spike_data[area][pop] = np.load(os.path.join(data_path,
- label_spikes,
- 'recordings',
- '{}-spikes-{}-{}.npy'.format(label_spikes,
- area, pop)))
+ # spike_data = {}
+ # for area in areas:
+ # spike_data[area] = {}
+ # for pop in M.structure[area]:
+ # spike_data[area][pop] = np.load(os.path.join(data_path,
+ # label_spikes,
+ # 'recordings',
+ # '{}-spikes-{}-{}.npy'.format(label_spikes,
+ # area, pop)))
+ spike_data = A.spike_data
+
# stationary firing rates
fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
with open(fn, 'r') as f:
@@ -182,20 +179,23 @@ def plot_resting_state(A, label_spikes, label):
# time series of firing rates
rate_time_series = {}
for area in areas:
+ # fn = os.path.join(data_path, label,
+ # 'Analysis',
+ # 'rate_time_series_full',
+ # 'rate_time_series_full_{}.npy'.format(area))
fn = os.path.join(data_path, label,
'Analysis',
- 'rate_time_series_full',
- 'rate_time_series_full_{}.npy'.format(area))
+ 'rate_time_series-{}.npy'.format(area))
rate_time_series[area] = np.load(fn)
# time series of firing rates convolved with a kernel
- rate_time_series_auto_kernel = {}
- for area in areas:
- fn = os.path.join(data_path, label,
- 'Analysis',
- 'rate_time_series_auto_kernel',
- 'rate_time_series_auto_kernel_{}.npy'.format(area))
- rate_time_series_auto_kernel[area] = np.load(fn)
+ # rate_time_series_auto_kernel = {}
+ # for area in areas:
+ # fn = os.path.join(data_path, label,
+ # 'Analysis',
+ # 'rate_time_series_auto_kernel',
+ # 'rate_time_series_auto_kernel_{}.npy'.format(area))
+ # rate_time_series_auto_kernel[area] = np.load(fn)
# local variance revised (LvR)
fn = os.path.join(data_path, label, 'Analysis', 'pop_LvR.json')
@@ -203,21 +203,25 @@ def plot_resting_state(A, label_spikes, label):
pop_LvR = json.load(f)
# correlation coefficients
- fn = os.path.join(data_path, label, 'Analysis', 'corrcoeff.json')
+ # fn = os.path.join(data_path, label, 'Analysis', 'corrcoeff.json')
+ fn = os.path.join(data_path, label, 'Analysis', 'synchrony.json')
with open(fn, 'r') as f:
corrcoeff = json.load(f)
"""
Plotting
"""
- print("Raster plots")
+ # print("Raster plots")
- t_min = 3000.
- t_max = 3500.
+ # t_min = 3000.
+ # t_max = 3500.
+ t_min = 500.
+ t_max = 1000.
icolor = myred
ecolor = myblue
+ # frac_neurons = 0.03
frac_neurons = 0.03
for i, area in enumerate(areas):
@@ -266,9 +270,10 @@ def plot_resting_state(A, label_spikes, label):
ax.set_yticks(yticklocs)
ax.set_xlabel('Time (s)', labelpad=-0.1)
ax.set_xticks([t_min, t_min + 250., t_max])
- ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])
+ # ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])
+ ax.set_xticklabels([r'$0.5$', r'$0.75$', r'$1.0$'])
- print("plotting Population rates")
+ # print("plotting Population rates")
rates = np.zeros((len(M.area_list), 8))
for i, area in enumerate(M.area_list):
@@ -301,7 +306,7 @@ def plot_resting_state(A, label_spikes, label):
ax.set_xlabel(r'Rate (spikes/s)', labelpad=-0.1)
ax.set_xticks([0., 50., 100.])
- print("plotting Synchrony")
+ # print("plotting Synchrony")
syn = np.zeros((len(M.area_list), 8))
for i, area in enumerate(M.area_list):
@@ -329,11 +334,12 @@ def plot_resting_state(A, label_spikes, label):
ax.set_yticklabels(population_labels[::-1], size=8)
ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))
ax.set_ylim((0., len(M.structure['V1']) + .5))
- ax.set_xticks(np.arange(0.0, 0.601, 0.2))
+ # ax.set_xticks(np.arange(0.0, 0.601, 0.2))
+ ax.set_xticks(np.arange(0.0, 10.0, 2.0))
ax.set_xlabel('Correlation coefficient', labelpad=-0.1)
- print("plotting Irregularity")
+ # print("plotting Irregularity")
LvR = np.zeros((len(M.area_list), 8))
for i, area in enumerate(M.area_list):
@@ -376,7 +382,7 @@ def plot_resting_state(A, label_spikes, label):
axes['G'].set_yticks([])
- print("Plotting rate time series")
+ # print("Plotting rate time series")
pos = axes['G'].get_position()
ax = []
h = pos.y1 - pos.y0
@@ -387,9 +393,12 @@ def plot_resting_state(A, label_spikes, label):
colors = ['0.5', '0.3', '0.0']
- t_min = 500.
- t_max = 10500.
- time = np.arange(500., t_max)
+ # t_min = 500.
+ # t_max = 10500.
+ t_min = 50.
+ t_max = 1550.
+ # time = np.arange(500., t_max)
+ time = np.arange(50., t_max)
for i, area in enumerate(areas[::-1]):
ax[i].spines['right'].set_color('none')
ax[i].spines['top'].set_color('none')
@@ -399,9 +408,11 @@ def plot_resting_state(A, label_spikes, label):
binned_spikes = rate_time_series[area][np.where(
np.logical_and(time >= t_min, time < t_max))]
ax[i].plot(time, binned_spikes, color=colors[0], label=area)
- rate = rate_time_series_auto_kernel[area]
+ # rate = rate_time_series_auto_kernel[area]
+ rate = rate_time_series[area]
ax[i].plot(time, rate, color=colors[2], label=area)
- ax[i].set_xlim((500., t_max))
+ # ax[i].set_xlim((500., t_max))
+ ax[i].set_xlim((50., t_max))
ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)
@@ -410,8 +421,10 @@ def plot_resting_state(A, label_spikes, label):
ax[i].set_xticks([])
ax[i].set_yticks([0., 30.])
else:
- ax[i].set_xticks([1000., 5000., 10000.])
- ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])
+ # ax[i].set_xticks([1000., 5000., 10000.])
+ ax[i].set_xticks([50., 750., 1500.])
+ # ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])
+ ax[i].set_xticklabels([r'$0.05$', r'$0.75$', r'$1.5$'])
ax[i].set_yticks([0., 5.])
if i == 1:
ax[i].set_ylabel(r'Rate (spikes/s)')
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index c8979fc..02dfd2e 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -19,24 +19,20 @@
"source": [
"#### Notebook structure <a class=\"anchor\" id=\"toc\"></a>\n",
"* [S0. Configuration](#section_0)\n",
- "* [S1. Paramters specification](#section_1)\n",
+ "* [S1. Parameterization](#section_1)\n",
" * [1.1. Parameters to tune](#section_1_1)\n",
" * [1.2. Default parameters](#section_1_2)\n",
- "* [S2. Multi-area model instantiation and simulation](#section_2)\n",
+ "* [S2. Multi-Area Model Instantiation and Simulation](#section_2)\n",
" * [2.1. Insantiate a multi-area model](#section_2_1)\n",
" * [2.2. Predict firing rates from theory](#section_2_2)\n",
- " * [2.3. Extract interarea connectivity](#section_2_3)\n",
- " * [2.4. Run the simulation](#section_2_4)\n",
- "* [S3. Simulation results validation and connection extraction](#section_3)\n",
- "* [S4. Data loading and processing](#section_4)\n",
- "* [S5. Simulation results visualization](#section_5) \n",
+ " * [2.3. Extract interareal connectivity](#section_2_3)\n",
+ " * [2.4. Run a simulation](#section_2_4)\n",
+ "* [S3. SExtract Interneural Connectivity](#section_3)\n",
+ "* [S4. Data Loading and Processing](#section_4)\n",
+ "* [S5. Simulation Results Visualziation](#section_5) \n",
" * [5.1. Instantaneous and mean firing rate across all populations](#section_5_1)\n",
- " * [5.2. Raster plot of spiking activity for single area](#section_5_2)\n",
- " * [5.3. Population-averaged firing rate](#section_5_3)\n",
- " * [5.4 Time-averaged population rates](#section_5_4)\n",
- " * [5.5. Average pairwise correlation coefficients of spiking activity](#section_5_5)\n",
- " * [5.6. Irregularity of spiking activity](#section_5_6)\n",
- " * [5.7. Time series of population- and area-averaged firing rates](#section_5_7)"
+ " * [5.2 Resting state plots](#section_5_2)\n",
+ " * [5.3 Time-averaged population rates](#section_5_3)"
]
},
{
@@ -51,7 +47,6 @@
"cell_type": "markdown",
"id": "d782e527",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
@@ -70,12 +65,13 @@
"# Create config file\n",
"with open('config.py', 'w') as fp:\n",
" fp.write(\n",
- "'''import os\n",
- "base_path = os.path.abspath(\".\")\n",
- "data_path = os.path.abspath(\"simulations\")\n",
- "jobscript_template = \"python {base_path}/run_simulation.py {label}\"\n",
- "submit_cmd = \"bash -c\"\n",
- "''')"
+ " '''\n",
+ " import os\n",
+ " base_path = os.path.abspath(\".\")\n",
+ " data_path = os.path.abspath(\"simulations\")\n",
+ " jobscript_template = \"python {base_path}/run_simulation.py {label}\"\n",
+ " submit_cmd = \"bash -c\"\n",
+ " ''')"
]
},
{
@@ -109,19 +105,17 @@
}
],
"source": [
- "# Import dependencies\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import nest\n",
- "from IPython.display import display, HTML\n",
"import json\n",
"\n",
- "# Import the MultiAreaModel class\n",
"from multiarea_model import MultiAreaModel\n",
"from multiarea_model import Analysis\n",
"from config import base_path, data_path\n",
+ "\n",
"import sys\n",
"sys.path.append('./figures')"
]
@@ -174,6 +168,7 @@
],
"source": [
"# Jupyter notebook display format setting\n",
+ "from IPython.display import display, HTML\n",
"style = \"\"\"\n",
"<style>\n",
"table {float:left}\n",
@@ -190,23 +185,14 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "565be233",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "df83f5ea-1c4b-44d3-9926-01786aa46e14",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
- "## S1. Paramters specification <a class=\"anchor\" id=\"section_1\"></a>"
+ "## S1. Parameterization <a class=\"anchor\" id=\"section_1\"></a>"
]
},
{
@@ -226,7 +212,7 @@
"|:----------------------------:|:-----------------------:|:--------------------------------------------------------------------:|:------------------:|:-----------:|\n",
"|scale_down_to |1. |(0, 1.] |0.005 |$^1$ |\n",
"|cc_weights_factor |1. |(0, 1.] |1. |$^2$ |\n",
- "|areas_simulated |complete_area_list |All sublists of complete_area_list |complete_area_list |$^3$ |\n",
+ "|areas_simulated |complete_area_list |Sublists of complete_area_list |complete_area_list |$^3$ |\n",
"|replace_non_simulated_areas |None |None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat' |'het_poisson_stat' |$^4$ |"
]
},
@@ -338,23 +324,14 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "c532a861-824f-4713-a311-590aef8b6134",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "de4a6703",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
- "## S2. Multi-area model instantiation and simulation <a class=\"anchor\" id=\"section_2\"></a>"
+ "## S2. Multi-Area Model Instantiation and Simulation <a class=\"anchor\" id=\"section_2\"></a>"
]
},
{
@@ -378,7 +355,7 @@
"output_type": "stream",
"text": [
"Initializing network from dictionary.\n",
- "RAND_DATA_LABEL 9601\n"
+ "RAND_DATA_LABEL 1680\n"
]
},
{
@@ -475,7 +452,7 @@
"id": "2062ddf3",
"metadata": {},
"source": [
- "### 2.3. Extract interarea connectivity <a class=\"anchor\" id=\"section_2_3\"></a>"
+ "### 2.3. Extract interareal connectivity <a class=\"anchor\" id=\"section_2_3\"></a>"
]
},
{
@@ -486,14 +463,6 @@
"The connectivity and neuron numbers are stored in the attributes of the model class. Neuron numbers are stored in `M.N` as a dictionary (and in `M.N_vec` as an array), indegrees in `M.K` as a dictionary (and in `M.K_matrix` as an array). Number of synapses can also be access via `M.synapses` (and in `M.syn_matrix` as an array). <br>"
]
},
- {
- "cell_type": "markdown",
- "id": "b7396606",
- "metadata": {},
- "source": [
- "#### 2.3.1 Node indegrees"
- ]
- },
{
"cell_type": "code",
"execution_count": 9,
@@ -501,19 +470,12 @@
"metadata": {},
"outputs": [],
"source": [
+ "# Indegrees\n",
"# Dictionary of nodes indegrees organized as:\n",
"# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: indegree_values}}}}\n",
"# M.K"
]
},
- {
- "cell_type": "markdown",
- "id": "253a2aba",
- "metadata": {},
- "source": [
- "#### 2.3.2 Synapses"
- ]
- },
{
"cell_type": "code",
"execution_count": 10,
@@ -521,6 +483,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "# Synapses\n",
"# Dictionary of synapses that target neurons receive, it is organized as:\n",
"# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: number_of_synapses}}}}\n",
"# M.synapses"
@@ -539,7 +502,7 @@
"id": "0c1cad59-81d0-4e24-ac33-13c4ca8c6dec",
"metadata": {},
"source": [
- "### 2.4. Run the simulation <a class=\"anchor\" id=\"section_2_4\"></a>"
+ "### 2.4. Run a simulation <a class=\"anchor\" id=\"section_2_4\"></a>"
]
},
{
@@ -554,72 +517,72 @@
"text": [
"Prepared simulation in 0.00 seconds.\n",
"Rank 0: created area V1 with 0 local nodes\n",
- "Memory after V1 : 1911.96 MB\n",
+ "Memory after V1 : 1911.46 MB\n",
"Rank 0: created area V2 with 0 local nodes\n",
- "Memory after V2 : 1938.52 MB\n",
+ "Memory after V2 : 1938.14 MB\n",
"Rank 0: created area VP with 0 local nodes\n",
- "Memory after VP : 1967.71 MB\n",
+ "Memory after VP : 1967.21 MB\n",
"Rank 0: created area V3 with 0 local nodes\n",
- "Memory after V3 : 1996.12 MB\n",
+ "Memory after V3 : 1995.58 MB\n",
"Rank 0: created area V3A with 0 local nodes\n",
- "Memory after V3A : 2015.90 MB\n",
+ "Memory after V3A : 2015.40 MB\n",
"Rank 0: created area MT with 0 local nodes\n",
- "Memory after MT : 2041.57 MB\n",
+ "Memory after MT : 2041.07 MB\n",
"Rank 0: created area V4t with 0 local nodes\n",
- "Memory after V4t : 2066.47 MB\n",
+ "Memory after V4t : 2066.01 MB\n",
"Rank 0: created area V4 with 0 local nodes\n",
- "Memory after V4 : 2093.54 MB\n",
+ "Memory after V4 : 2092.96 MB\n",
"Rank 0: created area VOT with 0 local nodes\n",
- "Memory after VOT : 2118.73 MB\n",
+ "Memory after VOT : 2118.31 MB\n",
"Rank 0: created area MSTd with 0 local nodes\n",
- "Memory after MSTd : 2140.20 MB\n",
+ "Memory after MSTd : 2139.78 MB\n",
"Rank 0: created area PIP with 0 local nodes\n",
- "Memory after PIP : 2161.67 MB\n",
+ "Memory after PIP : 2161.13 MB\n",
"Rank 0: created area PO with 0 local nodes\n",
- "Memory after PO : 2183.14 MB\n",
+ "Memory after PO : 2182.64 MB\n",
"Rank 0: created area DP with 0 local nodes\n",
- "Memory after DP : 2203.41 MB\n",
+ "Memory after DP : 2202.87 MB\n",
"Rank 0: created area MIP with 0 local nodes\n",
- "Memory after MIP : 2224.90 MB\n",
+ "Memory after MIP : 2224.36 MB\n",
"Rank 0: created area MDP with 0 local nodes\n",
- "Memory after MDP : 2246.41 MB\n",
+ "Memory after MDP : 2245.88 MB\n",
"Rank 0: created area VIP with 0 local nodes\n",
- "Memory after VIP : 2268.36 MB\n",
+ "Memory after VIP : 2267.81 MB\n",
"Rank 0: created area LIP with 0 local nodes\n",
- "Memory after LIP : 2292.30 MB\n",
+ "Memory after LIP : 2291.76 MB\n",
"Rank 0: created area PITv with 0 local nodes\n",
- "Memory after PITv : 2317.61 MB\n",
+ "Memory after PITv : 2317.05 MB\n",
"Rank 0: created area PITd with 0 local nodes\n",
- "Memory after PITd : 2342.85 MB\n",
+ "Memory after PITd : 2342.35 MB\n",
"Rank 0: created area MSTl with 0 local nodes\n",
- "Memory after MSTl : 2364.31 MB\n",
+ "Memory after MSTl : 2363.81 MB\n",
"Rank 0: created area CITv with 0 local nodes\n",
- "Memory after CITv : 2383.38 MB\n",
+ "Memory after CITv : 2382.88 MB\n",
"Rank 0: created area CITd with 0 local nodes\n",
- "Memory after CITd : 2402.79 MB\n",
+ "Memory after CITd : 2402.21 MB\n",
"Rank 0: created area FEF with 0 local nodes\n",
- "Memory after FEF : 2424.29 MB\n",
+ "Memory after FEF : 2423.68 MB\n",
"Rank 0: created area TF with 0 local nodes\n",
- "Memory after TF : 2439.82 MB\n",
+ "Memory after TF : 2439.32 MB\n",
"Rank 0: created area AITv with 0 local nodes\n",
- "Memory after AITv : 2462.49 MB\n",
+ "Memory after AITv : 2461.99 MB\n",
"Rank 0: created area FST with 0 local nodes\n",
- "Memory after FST : 2479.19 MB\n",
+ "Memory after FST : 2478.73 MB\n",
"Rank 0: created area 7a with 0 local nodes\n",
- "Memory after 7a : 2500.52 MB\n",
+ "Memory after 7a : 2499.90 MB\n",
"Rank 0: created area STPp with 0 local nodes\n",
- "Memory after STPp : 2519.12 MB\n",
+ "Memory after STPp : 2518.73 MB\n",
"Rank 0: created area STPa with 0 local nodes\n",
- "Memory after STPa : 2538.26 MB\n",
+ "Memory after STPa : 2537.76 MB\n",
"Rank 0: created area 46 with 0 local nodes\n",
- "Memory after 46 : 2553.74 MB\n",
+ "Memory after 46 : 2553.24 MB\n",
"Rank 0: created area AITd with 0 local nodes\n",
- "Memory after AITd : 2576.39 MB\n",
+ "Memory after AITd : 2575.80 MB\n",
"Rank 0: created area TH with 0 local nodes\n",
- "Memory after TH : 2588.97 MB\n",
- "Created areas and internal connections in 2.26 seconds.\n",
- "Created cortico-cortical connections in 22.03 seconds.\n",
- "Simulated network in 64.38 seconds.\n"
+ "Memory after TH : 2588.51 MB\n",
+ "Created areas and internal connections in 2.25 seconds.\n",
+ "Created cortico-cortical connections in 22.60 seconds.\n",
+ "Simulated network in 73.15 seconds.\n"
]
}
],
@@ -636,55 +599,14 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "4003c5a5-4a6f-49c5-be17-09f1bc68c411",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "28e071f8",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
- "## S3. Simulation results validation and connection extraction <a class=\"anchor\" id=\"section_3\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "89c7b7cf",
- "metadata": {},
- "source": [
- "### 3.1 Test if the correct number of synapses has been created"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "dc3b1820",
- "metadata": {},
- "outputs": [],
- "source": [
- "# # Uncomment the lines in this code cell below to test if the number of synapses created by NEST matches the expected values\n",
- "\n",
- "# print(\"Testing synapse numbers\")\n",
- "# for target_area_name in M.area_list:\n",
- "# target_area = M.simulation.areas[M.simulation.areas.index(target_area_name)]\n",
- "# for source_area_name in M.area_list:\n",
- "# source_area = M.simulation.areas[M.simulation.areas.index(source_area_name)]\n",
- "# for target_pop in M.structure[target_area.name]:\n",
- "# target_nodes = target_area.gids[target_pop]\n",
- "# for source_pop in M.structure[source_area.name]:\n",
- "# source_nodes = source_area.gids[source_pop]\n",
- "# created_syn = nest.GetConnections(source=source_nodes,\n",
- "# target=target_nodes)\n",
- "# syn = M.synapses[target_area.name][target_pop][source_area.name][source_pop]\n",
- "# assert(len(created_syn) == int(syn))"
+ "## S3. Extract Interneural Connectivity <a class=\"anchor\" id=\"section_3\"></a>"
]
},
{
@@ -692,7 +614,6 @@
"id": "57401110",
"metadata": {},
"source": [
- "### 3.2 Extract connections information\n",
"**Warning**: Memory explosion <br>\n",
"To obtain the connections information, you can extract the lists of connected sources and targets. Moreover, you can access additional synaptic details, such as synaptic weights and delays."
]
@@ -744,14 +665,6 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "529b1ade",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "57ff902c-d6ce-4f96-9e4f-8e3e7166ab66",
@@ -759,7 +672,7 @@
"tags": []
},
"source": [
- "## S4. Data loading and processing <a class=\"anchor\" id=\"section_4\"></a>"
+ "## S4. Data Loading and Processing <a class=\"anchor\" id=\"section_4\"></a>"
]
},
{
@@ -772,262 +685,10 @@
"outputs": [],
"source": [
"label_spikes = M.simulation.label\n",
- "label = M.simulation.label"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "6607a73d-1c74-4848-9603-081ad0e7cae8",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "loading spikes\n"
- ]
- }
- ],
- "source": [
- "\"\"\"\n",
- "Analysis class.\n",
- "An instance of the analysis class for the given network and simulation.\n",
- "Can be created as a member class of a multiarea_model instance or standalone.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "network : MultiAreaModel\n",
- " An instance of the multiarea_model class that specifies\n",
- " the network to be analyzed.\n",
- "simulation : Simulation\n",
- " An instance of the simulation class that specifies\n",
- " the simulation to be analyzed.\n",
- "data_list : list of strings {'spikes', vm'}, optional\n",
- " Specifies which type of data is to load. Defaults to ['spikes'].\n",
- "load_areas : list of strings with area names, optional\n",
- " Specifies the areas for which data is to be loaded.\n",
- " Default value is None and leads to loading of data for all\n",
- " simulated areas.\n",
- "\"\"\"\n",
- "# Instantiate an analysis class and load spike data\n",
- "A = Analysis(network=M, \n",
- " simulation=M.simulation, \n",
- " data_list=['spikes'],\n",
- " load_areas=None)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "1870cf34-ee62-4614-bc25-c36bc9a7377c",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data from file\n",
- "Computing population rates done\n"
- ]
- }
- ],
- "source": [
- "\"\"\"\n",
- "Calculate time-averaged population rates and store them in member pop_rates.\n",
- "If the rates had previously been stored with the same\n",
- "parameters, they are loaded from file.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "compute_stat : bool, optional\n",
- " If set to true, the mean and variance of the population rate\n",
- " is calculated. Defaults to False.\n",
- " Caution: Setting to True slows down the computation.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "\"\"\"\n",
- "A.create_pop_rates()\n",
- "print(\"Computing population rates done\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "50b7df89",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data from file\n",
- "Computing synchrony done\n"
- ]
- }
- ],
- "source": [
- "\"\"\"\n",
- "Calculate synchrony as the coefficient of variation of the population rate\n",
- "and store in member synchrony. Uses helper function synchrony.\n",
- "If the synchrony has previously been stored with the\n",
- "same parameters, they are loaded from file.\n",
- "\n",
+ "label = M.simulation.label\n",
"\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "resolution : float, optional\n",
- " Resolution of the population rate. Defaults to 1 ms.\n",
- "\"\"\"\n",
- "A.create_synchrony()\n",
- "print(\"Computing synchrony done\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "d43b493c",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data from file\n",
- "Computing population LvR done\n"
- ]
- }
- ],
- "source": [
- "\"\"\"\n",
- "Calculate poulation-averaged LvR (see Shinomoto et al. 2009) and\n",
- "store as member pop_LvR. Uses helper function LvR.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "\"\"\"\n",
- "A.create_pop_LvR()\n",
- "print(\"Computing population LvR done\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "401ece2d-47c8-4775-80ae-92a8e432520c",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data from file\n",
- "Loading data from file\n",
- "Computing rate time series done\n"
- ]
- }
- ],
- "source": [
- "\"\"\"\n",
- "Calculate time series of population- and area-averaged firing rates.\n",
- "Uses ah.pop_rate_time_series.\n",
- "If the rates have previously been stored with the\n",
- "same parameters, they are loaded from file.\n",
- "\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional\n",
- " Specifies the kernel to be convolved with the spike histogram.\n",
- " Defaults to 'binned', which corresponds to no convolution.\n",
- "resolution: float, optional\n",
- " Width of the convolution kernel. Specifically it correponds to:\n",
- " - 'binned' : bin width of the histogram\n",
- " - 'gauss_time_window' : sigma\n",
- " - 'alpha_time_window' : time constant of the alpha function\n",
- " - 'rect_time_window' : width of the moving rectangular function\n",
- "\"\"\"\n",
- "A.create_rate_time_series()\n",
- "print(\"Computing rate time series done\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "fa3ea20e-e456-4608-a711-e2c320bcaf91",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "pop_LvR\n",
- "pop_rates\n",
- "synchrony\n"
- ]
- }
- ],
- "source": [
- "A.save()"
+ "from MAM2EBRAINS_LOAD_DATA import load_data\n",
+ "tsteps, firing_rate = load_data(M, A)"
]
},
{
@@ -1038,14 +699,6 @@
"Go back to [Notebook structure](#toc)"
]
},
- {
- "cell_type": "markdown",
- "id": "4d43d223-a62e-448a-a7ea-8379b8be8e86",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
{
"cell_type": "markdown",
"id": "bb71c922",
@@ -1053,7 +706,7 @@
"tags": []
},
"source": [
- "## S5. Simulation results visualziation <a class=\"anchor\" id=\"section_5\"></a>"
+ "## S5. Simulation Results Visualziation <a class=\"anchor\" id=\"section_5\"></a>"
]
},
{
@@ -1066,33 +719,6 @@
"### 5.1. Instantaneous and mean firing rate across all populations <a class=\"anchor\" id=\"section_5_1\"></a>"
]
},
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "76ee6450-7d36-406e-aaa7-a7ca447e8da9",
- "metadata": {},
- "outputs": [],
- "source": [
- "import json\n",
- "import numpy as np\n",
- "import os\n",
- "\n",
- "import sys\n",
- "sys.path.append('./figures/Schmidt2018_dyn')\n",
- "\n",
- "from helpers import original_data_path, population_labels\n",
- "from multiarea_model import MultiAreaModel\n",
- "from plotcolors import myred, myblue\n",
- "\n",
- "import matplotlib.pyplot as pl\n",
- "from matplotlib import gridspec\n",
- "# from matplotlib import rc_file\n",
- "# rc_file('plotstyle.rc')\n",
- "\n",
- "icolor = myred\n",
- "ecolor = myblue"
- ]
- },
{
"cell_type": "code",
"execution_count": 23,
@@ -1113,23 +739,8 @@
}
],
"source": [
- "# load spike data and calculate instantaneous and mean firing rates\n",
- "data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + \"-spikes-1-0.dat\", skiprows=3)\n",
- "tsteps, spikecount = np.unique(data[:,1], return_counts=True)\n",
- "rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)\n",
- "\n",
- "# ax = plt.subplot()\n",
- "# ax.plot(tsteps, rate)\n",
- "# ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')\n",
- "# ax.set_title('Instantaneous and mean firing rate across all populations')\n",
- "# ax.set_xlabel('time (ms)')\n",
- "# ax.set_ylabel('firing rate (spikes / s)')\n",
- "# ax.set_xlim(0, sim_params['t_sim'])\n",
- "# ax.set_ylim(0, 50)\n",
- "# ax.legend()\n",
- "\n",
- "from MAM2EBRAINS import plot_instan_mean_firing_rate\n",
- "plot_instan_mean_firing_rate(tsteps, rate, sim_params)"
+ "from MAM2EBRAINS_VISUALIZATION import plot_instan_mean_firing_rate\n",
+ "plot_instan_mean_firing_rate(tsteps, firing_rate, sim_params)"
]
},
{
@@ -1150,417 +761,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
- "id": "b7fd1f63-5927-4fb0-82f5-e8b0c173bd12",
- "metadata": {},
- "outputs": [],
- "source": [
- "def set_boxplot_props(d):\n",
- " for i in range(len(d['boxes'])):\n",
- " if i % 2 == 0:\n",
- " d['boxes'][i].set_facecolor(icolor)\n",
- " d['boxes'][i].set_color(icolor)\n",
- " else:\n",
- " d['boxes'][i].set_facecolor(ecolor)\n",
- " d['boxes'][i].set_color(ecolor)\n",
- " pl.setp(d['whiskers'], color='k')\n",
- " pl.setp(d['fliers'], color='k', markerfacecolor='k', marker='+')\n",
- " pl.setp(d['medians'], color='none')\n",
- " pl.setp(d['caps'], color='k')\n",
- " pl.setp(d['means'], marker='x', color='k',\n",
- " markerfacecolor='k', markeredgecolor='k', markersize=3.)\n",
- "\n",
- "def plot_resting_state(A, label_spikes): \n",
- " \"\"\"\n",
- " Figure layout\n",
- " \"\"\"\n",
- "\n",
- " nrows = 4\n",
- " ncols = 4\n",
- " # width = 7.0866\n",
- " width = 10\n",
- " panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio\n",
- "\n",
- " height = width / panel_wh_ratio * float(nrows) / ncols\n",
- " pl.rcParams['figure.figsize'] = (width, height)\n",
- "\n",
- "\n",
- " fig = pl.figure()\n",
- " axes = {}\n",
- "\n",
- " gs1 = gridspec.GridSpec(1, 3)\n",
- " gs1.update(left=0.06, right=0.72, top=0.95, wspace=0.4, bottom=0.35)\n",
- " # axes['A'] = pl.subplot(gs1[:-1, :1])\n",
- " # axes['B'] = pl.subplot(gs1[:-1, 1:2])\n",
- " # axes['C'] = pl.subplot(gs1[:-1, 2:])\n",
- " axes['A'] = pl.subplot(gs1[:1, :1])\n",
- " axes['B'] = pl.subplot(gs1[:1, 1:2])\n",
- " axes['C'] = pl.subplot(gs1[:1, 2:])\n",
- "\n",
- " gs2 = gridspec.GridSpec(3, 1)\n",
- " gs2.update(left=0.78, right=0.95, top=0.95, bottom=0.35)\n",
- " axes['D'] = pl.subplot(gs2[:1, :1])\n",
- " axes['E'] = pl.subplot(gs2[1:2, :1])\n",
- " axes['F'] = pl.subplot(gs2[2:3, :1])\n",
- "\n",
- "\n",
- " gs3 = gridspec.GridSpec(1, 1)\n",
- " # gs3.update(left=0.1, right=0.95, top=0.3, bottom=0.075)\n",
- " gs3.update(left=0.1, right=0.95, top=0.25, bottom=0.075)\n",
- " axes['G'] = pl.subplot(gs3[:1, :1])\n",
- "\n",
- " areas = ['V1', 'V2', 'FEF']\n",
- "\n",
- " labels = ['A', 'B', 'C']\n",
- " for area, label in zip(areas, labels):\n",
- " label_pos = [-0.2, 1.01]\n",
- " # pl.text(label_pos[0], label_pos[1], r'\\bfseries{}' + label + ': ' + area,\n",
- " # fontdict={'fontsize': 10, 'weight': 'bold',\n",
- " # 'horizontalalignment': 'left', 'verticalalignment':\n",
- " # 'bottom'}, transform=axes[label].transAxes)\n",
- " plt.text(label_pos[0], label_pos[1], label + ': ' + area,\n",
- " fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left', \n",
- " 'verticalalignment': 'bottom'}, transform=axes[label].transAxes)\n",
- "\n",
- " label = 'G'\n",
- " label_pos = [-0.1, 0.92]\n",
- " # pl.text(label_pos[0], label_pos[1], r'\\bfseries{}' + label,\n",
- " # fontdict={'fontsize': 10, 'weight': 'bold',\n",
- " # 'horizontalalignment': 'left', 'verticalalignment':\n",
- " # 'bottom'}, transform=axes[label].transAxes)\n",
- " plt.text(label_pos[0], label_pos[1], label,\n",
- " fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left', \n",
- " 'verticalalignment': 'bottom'}, transform=axes[label].transAxes)\n",
- "\n",
- " labels = ['E', 'D', 'F']\n",
- " for label in labels:\n",
- " label_pos = [-0.2, 1.05]\n",
- " # pl.text(label_pos[0], label_pos[1], r'\\bfseries{}' + label,\n",
- " # fontdict={'fontsize': 10, 'weight': 'bold',\n",
- " # 'horizontalalignment': 'left', 'verticalalignment':\n",
- " # 'bottom'}, transform=axes[label].transAxes)\n",
- " plt.text(label_pos[0], label_pos[1], label,\n",
- " fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left', \n",
- " 'verticalalignment': 'bottom'}, transform=axes[label].transAxes)\n",
- " \n",
- "\n",
- " labels = ['A', 'B', 'C', 'D', 'E', 'F']\n",
- "\n",
- " for label in labels:\n",
- " axes[label].spines['right'].set_color('none')\n",
- " axes[label].spines['top'].set_color('none')\n",
- " axes[label].yaxis.set_ticks_position(\"left\")\n",
- " axes[label].xaxis.set_ticks_position(\"bottom\")\n",
- "\n",
- " for label in ['A', 'B', 'C']:\n",
- " axes[label].yaxis.set_ticks_position('none')\n",
- "\n",
- "\n",
- " \"\"\"\n",
- " Load data\n",
- " \"\"\"\n",
- "# LOAD_ORIGINAL_DATA = True\n",
- "\n",
- "\n",
- "# if LOAD_ORIGINAL_DATA:\n",
- "# # use T=10500 simulation for spike raster plots\n",
- "# label_spikes = '3afaec94d650c637ef8419611c3f80b3cb3ff539'\n",
- "# # and T=100500 simulation for all other panels\n",
- "# label = '99c0024eacc275d13f719afd59357f7d12f02b77'\n",
- "# data_path = original_data_path\n",
- "# else:\n",
- "# from network_simulations import init_models\n",
- "# from config import data_path\n",
- "# models = init_models('Fig5')\n",
- "# label_spikes = models[0].simulation.label\n",
- "# label = models[1].simulation.label\n",
- " \n",
- " # model = M\n",
- " label_spikes = label_spikes\n",
- " label = label_spikes\n",
- "\n",
- " \"\"\"\n",
- " Create MultiAreaModel instance to have access to data structures\n",
- " \"\"\"\n",
- " M = MultiAreaModel({})\n",
- "\n",
- " # spike data\n",
- " # spike_data = {}\n",
- " # for area in areas:\n",
- " # spike_data[area] = {}\n",
- " # for pop in M.structure[area]:\n",
- " # spike_data[area][pop] = np.load(os.path.join(data_path,\n",
- " # label_spikes,\n",
- " # 'recordings',\n",
- " # '{}-spikes-{}-{}.npy'.format(label_spikes,\n",
- " # area, pop)))\n",
- " spike_data = A.spike_data\n",
- " \n",
- " # stationary firing rates\n",
- " fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')\n",
- " print(fn)\n",
- " with open(fn, 'r') as f:\n",
- " pop_rates = json.load(f)\n",
- "\n",
- " # time series of firing rates\n",
- " rate_time_series = {}\n",
- " for area in areas:\n",
- " # fn = os.path.join(data_path, label,\n",
- " # 'Analysis',\n",
- " # 'rate_time_series_full',\n",
- " # 'rate_time_series_full_{}.npy'.format(area))\n",
- " fn = os.path.join(data_path, label,\n",
- " 'Analysis',\n",
- " 'rate_time_series-{}.npy'.format(area))\n",
- " rate_time_series[area] = np.load(fn)\n",
- "\n",
- " # time series of firing rates convolved with a kernel\n",
- " # rate_time_series_auto_kernel = {}\n",
- " # for area in areas:\n",
- " # fn = os.path.join(data_path, label,\n",
- " # 'Analysis',\n",
- " # 'rate_time_series_auto_kernel',\n",
- " # 'rate_time_series_auto_kernel_{}.npy'.format(area))\n",
- " # rate_time_series_auto_kernel[area] = np.load(fn)\n",
- "\n",
- " # local variance revised (LvR)\n",
- " fn = os.path.join(data_path, label, 'Analysis', 'pop_LvR.json')\n",
- " with open(fn, 'r') as f:\n",
- " pop_LvR = json.load(f)\n",
- "\n",
- " # correlation coefficients\n",
- " # fn = os.path.join(data_path, label, 'Analysis', 'corrcoeff.json')\n",
- " fn = os.path.join(data_path, label, 'Analysis', 'synchrony.json')\n",
- " with open(fn, 'r') as f:\n",
- " corrcoeff = json.load(f)\n",
- "\n",
- " \"\"\"\n",
- " Plotting\n",
- " \"\"\"\n",
- " print(\"Raster plots\")\n",
- "\n",
- " # t_min = 3000.\n",
- " # t_max = 3500.\n",
- " t_min = 0.\n",
- " t_max = 2000.\n",
- "\n",
- " icolor = myred\n",
- " ecolor = myblue\n",
- "\n",
- " # frac_neurons = 0.03\n",
- " frac_neurons = 0.3\n",
- "\n",
- " for i, area in enumerate(areas):\n",
- " ax = axes[labels[i]]\n",
- "\n",
- " if area in spike_data:\n",
- " n_pops = len(spike_data[area])\n",
- " # Determine number of neurons that will be plotted for this area (for\n",
- " # vertical offset)\n",
- " offset = 0\n",
- " n_to_plot = {}\n",
- " for pop in M.structure[area]:\n",
- " n_to_plot[pop] = int(M.N[area][pop] * frac_neurons)\n",
- " offset = offset + n_to_plot[pop]\n",
- " y_max = offset + 1\n",
- " prev_pop = ''\n",
- " yticks = []\n",
- " yticklocs = []\n",
- " for jj, pop in enumerate(M.structure[area]):\n",
- " if pop[0:-1] != prev_pop:\n",
- " prev_pop = pop[0:-1]\n",
- " yticks.append('L' + population_labels[jj][0:-1])\n",
- " yticklocs.append(offset - 0.5 * n_to_plot[pop])\n",
- " ind = np.where(np.logical_and(\n",
- " spike_data[area][pop][:, 1] <= t_max, spike_data[area][pop][:, 1] >= t_min))\n",
- " pop_data = spike_data[area][pop][ind]\n",
- " pop_neurons = np.unique(pop_data[:, 0])\n",
- " neurons_to_ = np.arange(np.min(spike_data[area][pop][:, 0]), np.min(\n",
- " spike_data[area][pop][:, 0]) + n_to_plot[pop], 1)\n",
- "\n",
- " if pop.find('E') > (-1):\n",
- " pcolor = ecolor\n",
- " else:\n",
- " pcolor = icolor\n",
- "\n",
- " for kk in range(n_to_plot[pop]):\n",
- " spike_times = pop_data[pop_data[:, 0] == neurons_to_[kk], 1]\n",
- "\n",
- " _ = ax.plot(spike_times, np.zeros(len(spike_times)) +\n",
- " offset - kk, '.', color=pcolor, markersize=1)\n",
- " offset = offset - n_to_plot[pop]\n",
- " y_min = offset\n",
- " ax.set_xlim([t_min, t_max])\n",
- " ax.set_ylim([y_min, y_max])\n",
- " ax.set_yticklabels(yticks)\n",
- " ax.set_yticks(yticklocs)\n",
- " ax.set_xlabel('Time (s)', labelpad=-0.1)\n",
- " ax.set_xticks([t_min, t_min + 250., t_max])\n",
- " ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])\n",
- "\n",
- " print(\"plotting Population rates\")\n",
- "\n",
- " rates = np.zeros((len(M.area_list), 8))\n",
- " for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " rate = pop_rates[area][pop][0]\n",
- " if rate == 0.0:\n",
- " rate = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " rates[i][j + 2] = rate\n",
- " else:\n",
- " rates[i][j] = rate\n",
- "\n",
- "\n",
- " rates = np.transpose(rates)\n",
- " masked_rates = np.ma.masked_where(rates < 1e-4, rates)\n",
- "\n",
- " ax = axes['D']\n",
- " d = ax.boxplot(np.transpose(rates), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- " set_boxplot_props(d)\n",
- "\n",
- " ax.plot(np.mean(rates, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- " ax.set_yticklabels(population_labels[::-1], size=8)\n",
- " ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- " ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "\n",
- " x_max = 220.\n",
- " ax.set_xlim((-1., x_max))\n",
- " ax.set_xlabel(r'Rate (spikes/s)', labelpad=-0.1)\n",
- " ax.set_xticks([0., 50., 100.])\n",
- "\n",
- " print(\"plotting Synchrony\")\n",
- "\n",
- " syn = np.zeros((len(M.area_list), 8))\n",
- " for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " value = corrcoeff[area][pop]\n",
- " if value == 0.0:\n",
- " value = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " syn[i][j + 2] = value\n",
- " else:\n",
- " syn[i][j] = value\n",
- "\n",
- "\n",
- " syn = np.transpose(syn)\n",
- " masked_syn = np.ma.masked_where(syn < 1e-4, syn)\n",
- "\n",
- " ax = axes['E']\n",
- " d = ax.boxplot(np.transpose(syn), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- " set_boxplot_props(d)\n",
- "\n",
- " ax.plot(np.mean(syn, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "\n",
- " ax.set_yticklabels(population_labels[::-1], size=8)\n",
- " ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- " ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- " # ax.set_xticks(np.arange(0.0, 0.601, 0.2))\n",
- " ax.set_xticks(np.arange(0.0, 0.2, 0.4, 0.6))\n",
- " ax.set_xlabel('Correlation coefficient', labelpad=-0.1)\n",
- "\n",
- "\n",
- " print(\"plotting Irregularity\")\n",
- "\n",
- " LvR = np.zeros((len(M.area_list), 8))\n",
- " for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " value = pop_LvR[area][pop]\n",
- " if value == 0.0:\n",
- " value = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " LvR[i][j + 2] = value\n",
- " else:\n",
- " LvR[i][j] = value\n",
- "\n",
- " LvR = np.transpose(LvR)\n",
- " masked_LvR = np.ma.masked_where(LvR < 1e-4, LvR)\n",
- "\n",
- " ax = axes['F']\n",
- " d = ax.boxplot(np.transpose(LvR), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- " set_boxplot_props(d)\n",
- "\n",
- " ax.plot(np.mean(LvR, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- " ax.set_yticklabels(population_labels[::-1], size=8)\n",
- " ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- " ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "\n",
- "\n",
- " x_max = 2.9\n",
- " ax.set_xlim((0., x_max))\n",
- " ax.set_xlabel('Irregularity', labelpad=-0.1)\n",
- " ax.set_xticks([0., 1., 2.])\n",
- "\n",
- " axes['G'].spines['right'].set_color('none')\n",
- " axes['G'].spines['left'].set_color('none')\n",
- " axes['G'].spines['top'].set_color('none')\n",
- " axes['G'].spines['bottom'].set_color('none')\n",
- " axes['G'].yaxis.set_ticks_position(\"none\")\n",
- " axes['G'].xaxis.set_ticks_position(\"none\")\n",
- " axes['G'].set_xticks([])\n",
- " axes['G'].set_yticks([])\n",
- "\n",
- "\n",
- " print(\"Plotting rate time series\")\n",
- " pos = axes['G'].get_position()\n",
- " ax = []\n",
- " h = pos.y1 - pos.y0\n",
- " w = pos.x1 - pos.x0\n",
- " ax.append(pl.axes([pos.x0, pos.y0, w, 0.28 * h]))\n",
- " ax.append(pl.axes([pos.x0, pos.y0 + 0.33 * h, w, 0.28 * h]))\n",
- " ax.append(pl.axes([pos.x0, pos.y0 + 0.67 * h, w, 0.28 * h]))\n",
- "\n",
- " colors = ['0.5', '0.3', '0.0']\n",
- "\n",
- " # t_min = 500.\n",
- " # t_max = 10500.\n",
- " t_min = 500.\n",
- " t_max = 2000.\n",
- " time = np.arange(500., t_max)\n",
- " for i, area in enumerate(areas[::-1]):\n",
- " ax[i].spines['right'].set_color('none')\n",
- " ax[i].spines['top'].set_color('none')\n",
- " ax[i].yaxis.set_ticks_position(\"left\")\n",
- " ax[i].xaxis.set_ticks_position(\"none\")\n",
- "\n",
- " binned_spikes = rate_time_series[area][np.where(\n",
- " np.logical_and(time >= t_min, time < t_max))]\n",
- " ax[i].plot(time, binned_spikes, color=colors[0], label=area)\n",
- " # rate = rate_time_series_auto_kernel[area]\n",
- " rate = rate_time_series[area]\n",
- " ax[i].plot(time, rate, color=colors[2], label=area)\n",
- " ax[i].set_xlim((500., t_max))\n",
- "\n",
- " ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)\n",
- "\n",
- " if i > 0:\n",
- " ax[i].spines['bottom'].set_color('none')\n",
- " ax[i].set_xticks([])\n",
- " ax[i].set_yticks([0., 30.])\n",
- " else:\n",
- " ax[i].set_xticks([1000., 5000., 10000.])\n",
- " ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])\n",
- " ax[i].set_yticks([0., 5.])\n",
- " if i == 1:\n",
- " ax[i].set_ylabel(r'Rate (spikes/s)')\n",
- "\n",
- " ax[0].set_xlabel('Time (s)', labelpad=-0.05)\n",
- "\n",
- " fig.subplots_adjust(left=0.05, right=0.95, top=0.95,\n",
- " bottom=0.075, wspace=1., hspace=.5)\n",
- "\n",
- " # pl.savefig('Fig5_ground_state.eps')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
+ "execution_count": 29,
"id": "ae19bcc3",
"metadata": {
"tags": []
@@ -1571,225 +772,68 @@
"output_type": "stream",
"text": [
"Initializing network from dictionary.\n",
- "RAND_DATA_LABEL 6867\n"
+ "RAND_DATA_LABEL 4079\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3440: RuntimeWarning:Mean of empty slice.\n",
+ "/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
+ "Error in library(\"aod\") : there is no package called ‘aod’\n",
+ "Execution halted\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
+ "\n",
+ "\n",
+ "========================================\n",
+ "Customized parameters\n",
+ "--------------------\n",
+ "{}\n",
+ "========================================\n"
]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/srv/main-spack-instance-2302/spack/var/spack/environments/ebrains-23-02/.spack-env/view/lib/python3.8/site-packages/dicthash/dicthash.py:47: UserWarning:Float too small for safe conversion tointeger. Rounding down to zero.\n",
+ "/tmp/ipykernel_14151/2679329638.py:237: UserWarning:FixedFormatter should only be used together with FixedLocator\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x635.692 with 10 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "# from MAM2EBRAINS import plot_resting_state\n",
+ "from MAM2EBRAINS_VISUALIZATION import plot_resting_state\n",
"plot_resting_state(A, label_spikes)"
]
},
- {
- "cell_type": "markdown",
- "id": "3ef52a7c",
- "metadata": {
- "tags": []
- },
- "source": [
- "### 5.2 Raster plot of spiking activity for single area <a class=\"anchor\" id=\"section_5_2\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1da18fee",
- "metadata": {},
- "outputs": [],
- "source": [
- "# from MAM2EBRAINS import plot_raster_plot\n",
- "# plot_raster_plot(A)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7d5a5e32-bd12-4e91-a65d-91d279edc450",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load spike data\n",
- "spike_data = A.spike_data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7947fea3-ba4c-4b1d-94fc-16614e4e4a11",
- "metadata": {},
- "outputs": [],
- "source": [
- "# plotting raster plot of spiking activity for single area\n",
- "from matplotlib import gridspec\n",
- "# axes = {}\n",
- "# gs1 = gridspec.GridSpec(1, 3)\n",
- "# # gs1.update(left=0.06, right=0.72, top=0.95, wspace=0.4, bottom=0.35)\n",
- "# axes['A'] = plt.subplot(gs1[:1, :1], figsize=(16,9), gridspec_kw={'height_ratios': [1, 2]})\n",
- "# axes['B'] = plt.subplot(gs1[:1, 1:2])\n",
- "# axes['C'] = plt.subplot(gs1[:1, 2:])\n",
- "f = plt.figure(figsize=(10,3))\n",
- "sub = [131, 132, 133]\n",
- "\n",
- "areas = ['V1', 'V2', 'FEF']\n",
- "labels = ['A', 'B', 'C']\n",
- "\n",
- "t_min = 0.\n",
- "t_max = 500.\n",
- "# t_min = 3000.\n",
- "# t_max = 3500.\n",
- "\n",
- "# icolor = myred\n",
- "# ecolor = myblue\n",
- "\n",
- "# frac_neurons = 0.03\n",
- "frac_neurons = 0.3\n",
- "\n",
- "for i, area in enumerate(areas):\n",
- " # ax = axes[labels[i]]\n",
- " # ax = plt.subplot()\n",
- " ax = f.add_subplot(sub[i])\n",
- "\n",
- " if area in spike_data:\n",
- " n_pops = len(spike_data[area])\n",
- " # Determine number of neurons that will be plotted for this area (for\n",
- " # vertical offset)\n",
- " offset = 0\n",
- " n_to_plot = {}\n",
- " for pop in M.structure[area]:\n",
- " n_to_plot[pop] = int(M.N[area][pop] * frac_neurons)\n",
- " offset = offset + n_to_plot[pop]\n",
- " y_max = offset + 1\n",
- " prev_pop = ''\n",
- " yticks = []\n",
- " yticklocs = []\n",
- " for jj, pop in enumerate(M.structure[area]):\n",
- " if pop[0:-1] != prev_pop:\n",
- " prev_pop = pop[0:-1]\n",
- " yticks.append('L' + population_labels[jj][0:-1])\n",
- " yticklocs.append(offset - 0.5 * n_to_plot[pop])\n",
- " ind = np.where(np.logical_and(\n",
- " spike_data[area][pop][:, 1] <= t_max, spike_data[area][pop][:, 1] >= t_min))\n",
- " pop_data = spike_data[area][pop][ind]\n",
- " pop_neurons = np.unique(pop_data[:, 0])\n",
- " neurons_to_ = np.arange(np.min(spike_data[area][pop][:, 0]), np.min(\n",
- " spike_data[area][pop][:, 0]) + n_to_plot[pop], 1)\n",
- "\n",
- " if pop.find('E') > (-1):\n",
- " pcolor = ecolor\n",
- " else:\n",
- " pcolor = icolor\n",
- "\n",
- " for kk in range(n_to_plot[pop]):\n",
- " spike_times = pop_data[pop_data[:, 0] == neurons_to_[kk], 1]\n",
- "\n",
- " _ = ax.plot(spike_times, np.zeros(len(spike_times)) +\n",
- " offset - kk, '.', color=pcolor, markersize=1)\n",
- " offset = offset - n_to_plot[pop]\n",
- " y_min = offset\n",
- " ax.set_title(areas[i])\n",
- " ax.set_xlim([t_min, t_max])\n",
- " ax.set_ylim([y_min, y_max])\n",
- " ax.set_yticklabels(yticks)\n",
- " ax.set_yticks(yticklocs)\n",
- " ax.set_xlabel('Time (s)', labelpad=-0.1)\n",
- " ax.set_xticks([t_min, t_min + 250., t_max])\n",
- " ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "019d805e",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 5.3 Population-averaged firing rates <a class=\"anchor\" id=\"section_5_3\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b069bc59-44ae-450a-b0a5-b073951e3604",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load data\n",
- "\n",
- "# stationary firing rates\n",
- "fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')\n",
- "with open(fn, 'r') as f:\n",
- " pop_rates = json.load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "96baac8f-3a0e-4d69-92ac-1d7cb4aff0d8",
- "metadata": {},
- "outputs": [],
- "source": [
- "def set_boxplot_props(d):\n",
- " for i in range(len(d['boxes'])):\n",
- " if i % 2 == 0:\n",
- " d['boxes'][i].set_facecolor(icolor)\n",
- " d['boxes'][i].set_color(icolor)\n",
- " else:\n",
- " d['boxes'][i].set_facecolor(ecolor)\n",
- " d['boxes'][i].set_color(ecolor)\n",
- " plt.setp(d['whiskers'], color='k')\n",
- " plt.setp(d['fliers'], color='k', markerfacecolor='k', marker='+')\n",
- " plt.setp(d['medians'], color='none')\n",
- " plt.setp(d['caps'], color='k')\n",
- " plt.setp(d['means'], marker='x', color='k',\n",
- " markerfacecolor='k', markeredgecolor='k', markersize=3.)\n",
- " \n",
- "# print(\"plotting Population rates\")\n",
- "\n",
- "rates = np.zeros((len(M.area_list), 8))\n",
- "for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " rate = pop_rates[area][pop][0]\n",
- " if rate == 0.0:\n",
- " rate = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " rates[i][j + 2] = rate\n",
- " else:\n",
- " rates[i][j] = rate\n",
- "\n",
- "\n",
- "rates = np.transpose(rates)\n",
- "masked_rates = np.ma.masked_where(rates < 1e-4, rates)\n",
- "\n",
- "# ax = axes['D']\n",
- "ax = plt.subplot()\n",
- "d = plt.boxplot(np.transpose(rates), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- "set_boxplot_props(d)\n",
- "\n",
- "ax.plot(np.mean(rates, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "\n",
- "ax.set_yticklabels(population_labels[::-1], size=8)\n",
- "ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- "ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "\n",
- "x_max = 100.\n",
- "ax.set_title(\"Population-averaged firing rates\")\n",
- "ax.set_xlim((-1., x_max))\n",
- "ax.set_xlabel(r'Rate (spikes/s)', labelpad=-0.1)\n",
- "ax.set_xticks([0., 50.])"
- ]
- },
{
"cell_type": "markdown",
"id": "473d0882-8e45-4330-bfa2-2c7e1af0dac4",
"metadata": {
- "jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
- "### 5.4 Time-averaged population rates <a class=\"anchor\" id=\"section_5_4\"></a>"
+ "### 5.3 Time-averaged population rates <a class=\"anchor\" id=\"section_5_3\"></a>\n",
+ "Plot overview over time-averaged population rates encoded in colors with areas along x-axis and populations along y-axis."
]
},
{
@@ -1799,254 +843,9 @@
"metadata": {},
"outputs": [],
"source": [
- "\"\"\"\n",
- "Plot overview over time-averaged population rates encoded in colors\n",
- "with areas along x-axis and populations along y-axis.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "area_list : list, optional\n",
- " Specifies with areas are plotted in which order.\n",
- " Default to None, leading to plotting of all areas ordered by architectural type.\n",
- "output : {'pdf', 'png', 'eps'}, optional\n",
- " If given, the function stores the plot to a file of the given format.\n",
- "\"\"\"\n",
- "A.show_rates()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "06a595de",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 5.5 Average pairwise correlation coefficients of spiking activity <a class=\"anchor\" id=\"section_5_5\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a8e77836-4c37-4b78-b7c4-5e11bc67b4fa",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load data\n",
- "\n",
- "# correlation coefficients\n",
- "# fn = os.path.join(data_path, label, 'Analysis', 'corrcoeff.json')\n",
- "fn = os.path.join(data_path, label, 'Analysis', 'synchrony.json')\n",
- "# synchrony.json\n",
- "with open(fn, 'r') as f:\n",
- " corrcoeff = json.load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "218367da-82ef-47b6-bf15-083ef3d43013",
- "metadata": {},
- "outputs": [],
- "source": [
- "# print(\"plotting Synchrony\")\n",
- "\n",
- "syn = np.zeros((len(M.area_list), 8))\n",
- "for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " value = corrcoeff[area][pop]\n",
- " if value == 0.0:\n",
- " value = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " syn[i][j + 2] = value\n",
- " else:\n",
- " syn[i][j] = value\n",
- "\n",
- "\n",
- "syn = np.transpose(syn)\n",
- "masked_syn = np.ma.masked_where(syn < 1e-4, syn)\n",
- "\n",
- "# ax = axes['E']\n",
- "ax = plt.subplot()\n",
- "d = ax.boxplot(np.transpose(syn), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- "set_boxplot_props(d)\n",
- "\n",
- "ax.plot(np.mean(syn, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "\n",
- "ax.set_yticklabels(population_labels[::-1], size=8)\n",
- "ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- "ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "# ax.set_xticks(np.arange(0.0, 0.601, 0.2))\n",
- "ax.set_xticks(np.arange(0.0, 10.0, 2.0))\n",
- "ax.set_xlabel('Correlation coefficient', labelpad=-0.1)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a3847e67",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 5.6 Irregularity of spiking activity <a class=\"anchor\" id=\"section_5_6\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "65377033-f3c0-4f90-be13-70594cfda292",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load data\n",
- "\n",
- "# local variance revised (LvR)\n",
- "fn = os.path.join(data_path, label, 'Analysis', 'pop_LvR.json')\n",
- "with open(fn, 'r') as f:\n",
- " pop_LvR = json.load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d7480a9b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# print(\"plotting Irregularity\")\n",
- "\n",
- "LvR = np.zeros((len(M.area_list), 8))\n",
- "for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " value = pop_LvR[area][pop]\n",
- " if value == 0.0:\n",
- " value = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " LvR[i][j + 2] = value\n",
- " else:\n",
- " LvR[i][j] = value\n",
- "\n",
- "LvR = np.transpose(LvR)\n",
- "masked_LvR = np.ma.masked_where(LvR < 1e-4, LvR)\n",
- "\n",
- "# ax = axes['F']\n",
- "ax = plt.subplot()\n",
- "d = ax.boxplot(np.transpose(LvR), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- "set_boxplot_props(d)\n",
- "\n",
- "ax.plot(np.mean(LvR, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "ax.set_yticklabels(population_labels[::-1], size=8)\n",
- "ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- "ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "\n",
- "\n",
- "x_max = 1.9\n",
- "ax.set_xlim((0., x_max))\n",
- "ax.set_xlabel('Irregularity', labelpad=-0.1)\n",
- "ax.set_xticks([0., 1., 2.])\n",
- "\n",
- "# axes['G'].spines['right'].set_color('none')\n",
- "# axes['G'].spines['left'].set_color('none')\n",
- "# axes['G'].spines['top'].set_color('none')\n",
- "# axes['G'].spines['bottom'].set_color('none')\n",
- "# axes['G'].yaxis.set_ticks_position(\"none\")\n",
- "# axes['G'].xaxis.set_ticks_position(\"none\")\n",
- "# axes['G'].set_xticks([])\n",
- "# axes['G'].set_yticks([])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "90ae8f4c",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 5.7 Time series of area-averaged firing rates <a class=\"anchor\" id=\"section_5_7\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0308d50a-1906-4860-9194-7f8664bd1f9d",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load data\n",
- "\n",
- "# time series of firing rates\n",
- "rate_time_series = {}\n",
- "for area in areas:\n",
- " fn = os.path.join(data_path, label,\n",
- " 'Analysis',\n",
- " 'rate_time_series_full',\n",
- " 'rate_time_series_full_{}.npy'.format(area))\n",
- " rate_time_series[area] = np.load(fn)\n",
- "\n",
- "# # time series of firing rates convolved with a kernel\n",
- "# rate_time_series_auto_kernel = {}\n",
- "# for area in areas:\n",
- "# fn = os.path.join(data_path, label,\n",
- "# 'Analysis',\n",
- "# 'rate_time_series_auto_kernel',\n",
- "# 'rate_time_series_auto_kernel_{}.npy'.format(area))\n",
- "# rate_time_series_auto_kernel[area] = np.load(fn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4460d823-543a-482b-8ef1-a049e5837af4",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(\"Plotting rate time series\")\n",
- "pos = axes['G'].get_position()\n",
- "ax = []\n",
- "h = pos.y1 - pos.y0\n",
- "w = pos.x1 - pos.x0\n",
- "ax.append(pl.axes([pos.x0, pos.y0, w, 0.28 * h]))\n",
- "ax.append(pl.axes([pos.x0, pos.y0 + 0.33 * h, w, 0.28 * h]))\n",
- "ax.append(pl.axes([pos.x0, pos.y0 + 0.67 * h, w, 0.28 * h]))\n",
- "\n",
- "colors = ['0.5', '0.3', '0.0']\n",
- "\n",
- "t_min = 500.\n",
- "t_max = 10500.\n",
- "time = np.arange(500., t_max)\n",
- "for i, area in enumerate(areas[::-1]):\n",
- " ax[i].spines['right'].set_color('none')\n",
- " ax[i].spines['top'].set_color('none')\n",
- " ax[i].yaxis.set_ticks_position(\"left\")\n",
- " ax[i].xaxis.set_ticks_position(\"none\")\n",
- "\n",
- " binned_spikes = rate_time_series[area][np.where(\n",
- " np.logical_and(time >= t_min, time < t_max))]\n",
- " ax[i].plot(time, binned_spikes, color=colors[0], label=area)\n",
- " rate = rate_time_series_auto_kernel[area]\n",
- " ax[i].plot(time, rate, color=colors[2], label=area)\n",
- " ax[i].set_xlim((500., t_max))\n",
- "\n",
- " ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)\n",
- "\n",
- " if i > 0:\n",
- " ax[i].spines['bottom'].set_color('none')\n",
- " ax[i].set_xticks([])\n",
- " ax[i].set_yticks([0., 30.])\n",
- " else:\n",
- " ax[i].set_xticks([1000., 5000., 10000.])\n",
- " ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])\n",
- " ax[i].set_yticks([0., 5.])\n",
- " if i == 1:\n",
- " ax[i].set_ylabel(r'Rate (spikes/s)')\n",
- "\n",
- "ax[0].set_xlabel('Time (s)', labelpad=-0.05)"
+ "# area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']\n",
+ "# output = {'pdf', 'png', 'eps'}, optional\n",
+ "A.show_rates(area_list)"
]
},
{
diff --git a/multiarea_model/.ipynb_checkpoints/analysis_helpers-checkpoint.py b/multiarea_model/.ipynb_checkpoints/analysis_helpers-checkpoint.py
new file mode 100644
index 0000000..546ef80
--- /dev/null
+++ b/multiarea_model/.ipynb_checkpoints/analysis_helpers-checkpoint.py
@@ -0,0 +1,814 @@
+# -*- coding: utf-8 -*-
+
+"""
+analysis_helpers
+============
+
+Helper and analysis functions to support ana_vistools and
+the analysis of simulations of the multi-area model of
+macaque visual cortex (Schmidt et al. 2018).
+
+
+Functions
+--------
+_create_parameter_dict : Create parameter dict for functions
+ of data class.
+_check_stored_data : Check if stored data was computed
+ with the correct Parameters.
+online_hist : Compute spike histogram on a spike file line by line.
+pop_rate : Compute average firing rate.
+pop_rate_distribution : Compute distribution of single-cell firing rates.
+pop_rate_time_series : Compute time series of population rate.
+Regularity measures:
+ - pop_cv_isi : Compute population-averaged CV ISI.
+ - pop_LvR: Compute average LvR of neuronal population.
+
+Synchrony measures :
+ - synchrony : CV of population rate.
+ - synchrony_subthreshold : Synchrony measure on membrane potentials.
+ - spike_synchrony : Synchrony measure on population rate.
+
+spectrum : Compound power spectrum of a neuronal population.
+synaptic_output : Synaptic output of neuronal population.
+compare : Compare two simulations with each other.
+
+
+Authors
+--------
+Maximilian Schmidt
+Sacha van Albada
+
+"""
+
+from copy import copy
+from nested_dict import nested_dict
+import numpy as np
+import json
+from itertools import product
+from scipy.signal import welch
+
+
+area_list = ['V1', 'V2', 'VP', 'V3', 'PIP', 'V3A', 'MT', 'V4t', 'V4',
+ 'PO', 'VOT', 'DP', 'MIP', 'MDP', 'MSTd', 'VIP', 'LIP',
+ 'PITv', 'PITd', 'AITv', 'MSTl', 'FST', 'CITv', 'CITd',
+ '7a', 'STPp', 'STPa', 'FEF', '46', 'TF', 'TH', 'AITd']
+pop_list = ['23E', '23I', '4E', '4I', '5E', '5I', '6E', '6I']
+
+
+def model_iter(mode='single',
+ areas=None, pops='complete',
+ areas2=None, pops2='complete'):
+ """
+ Helper function to create a an iterator over all possible pairs of
+ populations in the model, possible restricted by specifying areas
+ or pops.
+
+ Parameters
+ ----------
+ mode : {'single', 'pairs'}, optional
+ If equal to 'single', loop over all populations of all areas.
+ If equal to 'pairs', loop over all pairs of
+ populations of all areas.
+ Defaults to 'single'.
+
+ areas, areas2 : list, optional
+ If specified, loop only over these areas as target and source
+ areas. Defaults to None, which corresponds to taking all areas
+ into account.
+ pops, pops2 : string or list, optional
+ If specified, loop only over these populations as target and
+ source populations. Defaults to 'complete', which corresponds
+ to taking all areas into account. If None, loop only over
+ areas.
+
+ Returns
+ -------
+ iterator : iterator
+ Cartesian product of 2 ('single' mode) or 4 ('double' mode) lists
+ """
+ if mode == 'single':
+ assert((areas2 is None) and (pops2 == 'complete'))
+ if pops is None or pops2 is None:
+ assert((pops is None) and (pops2 is None) or mode == 'single')
+ if pops == 'complete':
+ pops = pop_list
+ if areas is None:
+ areas = area_list
+ if pops2 == 'complete':
+ pops2 = pop_list
+ if areas2 is None:
+ areas2 = area_list
+ if mode == 'single':
+ if pops is None:
+ return product(areas)
+ else:
+ return product(areas, pops)
+ elif mode == 'pairs':
+ if pops is None:
+ return product(areas, areas2)
+ else:
+ return product(areas, pops, areas2, pops2)
+
+
+def area_spike_train(spike_data):
+ """
+ Helper function to create one spike train for an area from
+ the spike trains of the single populations.
+
+ Parameters
+ ----------
+ spike_data : dict
+ Dictionary containing the populations as keys
+ and their spike trains as values. Spike trains
+ are stored as 2D arrays with GIDs in the 1st column
+ and time stamps in the 2nd column.
+
+ Returns
+ -------
+ data_array : numpy.ndarray
+ """
+ data_array = np.array([])
+ for pop in spike_data:
+ data_array = np.append(data_array, spike_data[pop])
+ data_array = np.reshape(data_array, (-1, 2))
+ return data_array
+
+
+def centralize(data, time=False, units=False):
+ """
+ Code written by David Dahmen and Jakob Jordan,
+ available from https://github.com/INM-6/correlation-toolbox .
+
+ Set mean of the given data to zero by averaging either
+ across time or units.
+ """
+
+ assert(time is not False or units is not False)
+ res = copy(data)
+ if time is True:
+ res = np.array([x - np.mean(x) for x in res])
+ if units is True:
+ res = np.array(res - np.mean(res, axis=0))
+ return res
+
+
+def sort_gdf_by_id(data, idmin=None, idmax=None):
+ """
+ Code written by David Dahmen and Jakob Jordan,
+ available from https://github.com/INM-6/correlation-toolbox .
+
+ Sort gdf data [(id,time),...] by neuron id.
+
+ Parameters
+ ----------
+
+ data: numpy.array (dtype=object) with lists ['int', 'float']
+ The nest output loaded from gdf format. Each row contains a
+ global id
+ idmin, idmax : int, optional
+ The minimum/maximum neuron id to be considered.
+
+ Returns
+ -------
+ ids : list of ints
+ Neuron ids, e.g., [id1,id2,...]
+ srt : list of lists of floats
+ Spike trains corresponding to the neuron ids, e.g.,
+ [[t1,t2,...],...]
+ """
+
+ assert((idmin is None and idmax is None)
+ or (idmin is not None and idmax is not None))
+
+ if len(data) > 0:
+ # get neuron ids
+ if idmin is None and idmax is None:
+ ids = np.unique(data[:, 0])
+ else:
+ ids = np.arange(idmin, idmax+1)
+ srt = []
+ for i in ids:
+ srt.append(np.sort(data[np.where(data[:, 0] == i)[0], 1]))
+ return ids, srt
+ else:
+ print('CT warning(sort_spiketrains_by_id): empty gdf data!')
+ return None, None
+
+
+"""
+Helper functions for data loading
+"""
+
+
+def _create_parameter_dict(default_dict, T, **keywords):
+ """
+ Create the parameter dict for the members of the data class.
+
+ Parameters
+ ----------
+ default_dict : dict
+ Default dictionary of the function calling this function.
+ T : float
+ Maximal time of the simulation of the data class calling.
+
+ Returns
+ -------
+ d : dict
+ Parameter dictionary.
+ """
+ d = default_dict
+ if 't_min' not in keywords:
+ t_min = 500.
+ d.update({'t_min': t_min})
+ if 't_max' not in keywords:
+ t_max = T
+ d.update({'t_max': t_max})
+ d.update(keywords)
+ return d
+
+
+def _check_stored_data(fp, fn_iter, param_dict):
+ """
+ Check if a data member of the data class has already
+ been computed with the same parameters.
+
+ Parameters
+ ----------
+ fn : string
+ Filename of the file containing the data.
+ param_dict : dict
+ Parameters of the calculation to compare with
+ the parameters of the stored data.
+ """
+ if 'json' in fp:
+ try:
+ f = open(fp)
+ data = json.load(f)
+ f.close()
+ except IOError:
+ return None
+ param_dict2 = data['Parameters']
+ else:
+ try:
+ data = _load_npy_to_dict(fp, fn_iter)
+ except IOError:
+ return None
+ with open('-'.join((fp, 'parameters')), 'r') as f:
+ param_dict2 = json.load(f)
+ param_dict_copy = copy(param_dict)
+ param_dict2_copy = copy(param_dict2)
+ for k in param_dict:
+ if (isinstance(param_dict_copy[k], list) or
+ isinstance(param_dict_copy[k], np.ndarray)):
+ param_dict_copy[k] = set(param_dict_copy[k])
+ if (isinstance(param_dict2_copy[k], list) or
+ isinstance(param_dict2_copy[k], np.ndarray)):
+ param_dict2_copy[k] = set(param_dict2_copy[k])
+ if param_dict_copy == param_dict2_copy:
+ print("Loading data from file")
+ return data
+ else:
+ print("Stored data have been computed "
+ "with different parameters")
+ return None
+
+
+def _save_dict_to_npy(fp, data):
+ """
+ Save data dictionary to binary numpy files
+ by iteratively going through the dictionary.
+
+ Parameters
+ ----------
+ fp : str
+ File pattern to which the keys of the dictionary are attached.
+ data : dict
+ Dictionary containing the data
+ """
+ for key, val in data.items():
+ if key != 'Parameters':
+ fp_key = '-'.join((fp, key))
+ if isinstance(val, dict):
+ _save_dict_to_npy(fp_key, val)
+ else:
+ np.save(fp_key, val)
+ else:
+ fp_key = '-'.join((fp, 'parameters'))
+ with open(fp_key, 'w') as f:
+ json.dump(val, f)
+
+
+def _load_npy_to_dict(fp, fn_iter):
+ """
+ Load data stored in the files defined by fp
+ and fn_iter to a dictionary.
+
+ Parameters
+ ----------
+ fp : str
+ Base file pattern of the npy files
+ fn_iter : iterable
+ Iterable defining all the suffixes that are
+ appended to fp to form the file names.
+ """
+ data = nested_dict()
+ for it in fn_iter:
+ fp_it = (fp,) + it
+ fp_ = '{}.npy'.format('-'.join(fp_it))
+ if len(it) == 1:
+ data[it[0]] = np.load(fp_)
+ else:
+ data[it[0]][it[1]] = np.load(fp_)
+ return data
+
+
+"""
+Analysis functions
+"""
+
+
+def pop_rate(data_array, t_min, t_max, num_neur, return_stat=False):
+ """
+ Calculates firing rate of a given array of spikes.
+ Rates are calculated in spikes/s. Assumes spikes are sorted
+ according to time. First calculates rates for individual neurons
+ and then averages over neurons.
+
+ Parameters
+ ----------
+ data_array : numpy.ndarray
+ Array with spike data.
+ column 0: neuron_ids, column 1: spike times
+ tmin : float
+ Minimal time stamp to be considered in ms.
+ tmax : float
+ Maximal time stamp to be considered in ms.
+ num_neur : int
+ Number of recorded neurons. Needs to provided explicitly
+ to avoid corruption of results by silent neurons not
+ present in the given data.
+ Returns
+ -------
+ mean : float
+ Mean firing rate across neurons.
+ std : float
+ Standard deviation of firing rate distribution.
+ rates : list
+ List of single-cell firing rates.
+ """
+
+ indices = np.where(np.logical_and(data_array[:, 1] > t_min,
+ data_array[:, 1] < t_max))
+ data_array = data_array[indices]
+ if return_stat:
+ rates = []
+ for i in np.unique(data_array[:, 0]):
+ num_spikes = np.where(data_array[:, 0] == i)[0].size
+ rates.append(num_spikes / ((t_max - t_min) / 1000.))
+ while len(rates) < num_neur:
+ rates.append(0.0)
+ mean = np.mean(rates)
+ std = np.std(rates)
+ return mean, std, rates
+ else:
+ return data_array[:, 1].size / (num_neur * (t_max - t_min) / 1000.)
+
+
+def pop_rate_distribution(data_array, t_min, t_max, num_neur):
+ """
+ Calculates firing rate distribution over neurons in a given array
+ of spikes. Rates are calculated in spikes/s. Assumes spikes are
+ sorted according to time. First calculates rates for individual
+ neurons and then averages over neurons.
+
+ Parameters
+ ----------
+ data_array : numpy.ndarray
+ Array with spike data.
+ column 0: neuron_ids, column 1: spike times
+ tmin : float
+ Minimal time stamp to be considered in ms.
+ tmax : float
+ Maximal time stamp to be considered in ms.
+ num_neur: int
+ Number of recorded neurons. Needs to provided explicitly
+ to avoid corruption of results by silent neurons not
+ present in the given data.
+
+ Returns
+ -------
+ bins : numpy.ndarray
+ Left edges of the distribution bins
+ vals : numpy.ndarray
+ Values of the distribution
+ mean : float
+ Arithmetic mean of the distribution
+ std : float
+ Standard deviation of the distribution
+ """
+ indices = np.where(np.logical_and(data_array[:, 1] > t_min,
+ data_array[:, 1] < t_max))
+ neurons = data_array[:, 0][indices]
+ neurons = np.sort(neurons)
+ if len(neurons) > 0:
+ n = neurons[0]
+ else: # No spikes in [t_min, t_max]
+ n = None
+ rates = np.zeros(int(num_neur))
+ s = 0
+ for i in range(neurons.size):
+ if neurons[i] == n:
+ rates[s] += 1
+ else:
+ n = neurons[i]
+ s += 1
+ rates /= (t_max - t_min) / 1000.
+ vals, bins = np.histogram(rates, bins=100)
+ vals = vals / float(np.sum(vals))
+ if (num_neur > 0. and t_max != t_min
+ and len(data_array) > 0 and len(indices) > 0):
+ return bins[0:-1], vals, np.mean(rates), np.std(rates)
+ else:
+ return np.arange(0, 20., 20. / 100.), np.zeros(100), 0.0, 0.0
+
+
+def pop_rate_time_series(data_array, num_neur, t_min, t_max,
+ resolution=10., kernel='binned'):
+ """
+ Computes time series of the population-averaged rates of a group
+ of neurons.
+
+ Parameters
+ ----------
+ data_array : numpy.ndarray
+ Array with spike data.
+ column 0: neuron_ids, column 1: spike times
+ tmin : float
+ Minimal time for the calculation.
+ tmax : float
+ Maximal time for the calculation.
+ num_neur: int
+ Number of recorded neurons. Needs to provided explicitly
+ to avoid corruption of results by silent neurons not
+ present in the given data.
+ kernel : {'gauss_time_window', 'alpha_time_window',
+ 'rect_time_window'}, optional
+ Specifies the kernel to be
+ convolved with the spike histogram. Defaults to 'binned',
+ which corresponds to no convolution.
+ resolution: float, optional
+ Width of the convolution kernel. Specifically it correponds to:
+ - 'binned' : bin width of the histogram
+ - 'gauss_time_window' : sigma
+ - 'alpha_time_window' : time constant of the alpha function
+ - 'rect_time_window' : width of the moving rectangular function
+ Defaults to 1 ms.
+
+ Returns
+ -------
+ time_series : numpy.ndarray
+ Time series of the population rate
+ """
+ if kernel == 'binned':
+ rate, times = np.histogram(data_array[:, 1], bins=int((t_max - t_min) / (resolution)),
+ range=(t_min + resolution / 2., t_max + resolution / 2.))
+ rate = rate / (num_neur * resolution / 1000.0)
+ rates = np.array([])
+ last_time_step = times[0]
+
+ for i in range(1, times.size):
+ rates = np.append(
+ rates, rate[i - 1] * np.ones_like(np.arange(last_time_step, times[i], 1.0)))
+ last_time_step = times[i]
+
+ time_series = rates
+ else:
+ spikes = data_array[:, 1][data_array[:, 1] > t_min]
+ spikes = spikes[spikes < t_max]
+ binned_spikes = np.histogram(spikes, bins=int(
+ (t_max - t_min)), range=(t_min, t_max))[0]
+ if kernel == 'rect_time_window':
+ kernel = np.ones(int(resolution)) / resolution
+ if kernel == 'gauss_time_window':
+ sigma = resolution
+ time_range = np.arange(-0.5 * (t_max - t_min),
+ 0.5 * (t_max - t_min), 1.0)
+ kernel = 1 / (np.sqrt(2.0 * np.pi) * sigma) * \
+ np.exp(-(time_range ** 2 / (2 * sigma ** 2)))
+ if kernel == 'alpha_time_window':
+ alpha = 1 / resolution
+ time_range = np.arange(-0.5 * (t_max - t_min),
+ 0.5 * (t_max - t_min), 1.0)
+ time_range[time_range < 0] = 0.0
+ kernel = alpha * time_range * np.exp(-alpha * time_range)
+
+ rate = np.convolve(kernel, binned_spikes, mode='same')
+ rate = rate / (num_neur / 1000.0)
+ time_series = rate
+
+ return time_series
+
+
+def pop_cv_isi(data_array, t_min, t_max):
+ """
+ Calculate coefficient of variation of interspike intervals
+ between t_min and t_max for every single neuron in data_array
+ and average the result over neurons in data_array.
+ Assumes spikes are sorted according to time.
+
+ Parameters
+ ----------
+ data_array : numpy.ndarray
+ Array with spike data.
+ column 0: neuron_ids, column 1: spike times
+ tmin : float
+ Minimal time stamp to be considered in ms.
+ tmax : float
+ Maximal time stamp to be considered in ms.
+
+ Returns
+ -------
+ mean : float
+ Mean CV ISI value of the population
+ """
+ cv_isi = []
+ indices = np.where(np.logical_and(data_array[:, 1] > t_min,
+ data_array[:, 1] < t_max))[0]
+ if len(data_array) > 1 and len(indices) > 1:
+ for i in np.unique(data_array[:, 0]):
+ intervals = np.diff(data_array[indices][
+ np.where(data_array[indices, 0] == i), 1])
+ if intervals.size > 0:
+ cv_isi.append(np.std(intervals) / np.mean(intervals))
+ if len(cv_isi) > 0:
+ return np.mean(cv_isi)
+ else:
+ return 0.0
+ else:
+ print('cv_isi: no or only one spike in data_array, returning 0.0')
+ return 0.0
+
+
+def ISI_SCC(data_array, t_min, t_max):
+ """
+ Computes the serial correlation coefficient of
+ inter-spike intervals of the given spike data.
+
+ Parameters
+ ----------
+ data_array : numpy.ndarray
+ Arrays with spike data.
+ column 0: neuron_ids, column 1: spike times
+ t_min : float
+ Minimal time for the calculation.
+ t_max : float
+ Maximal time for the calculation.
+
+
+ Return
+ -------
+ bins : numpy.ndarray
+ ISI lags
+ values : numpy.ndarray
+ Serial correlation coefficient values
+ """
+ indices = np.where(np.logical_and(data_array[:, 1] > t_min,
+ data_array[:, 1] < t_max))
+ scc_averaged = np.zeros(max(1001, 2 * (t_max - t_min) + 1))
+ half = max(1000, 2 * (t_max - t_min)) / 2.0
+ if len(data_array) > 1 and len(indices) > 1:
+ for i in np.unique(data_array[:, 0]):
+ intervals = np.diff(data_array[indices][
+ np.where(data_array[indices, 0] == i), 1])
+
+ if intervals.size > 1:
+ mean = np.mean(intervals)
+ scc = (np.correlate(intervals, intervals, mode='full') - mean ** 2) / (
+ np.mean(intervals ** 2) - mean ** 2)
+ scc_averaged[half - scc.size /
+ 2:half + scc.size / 2 + 1] += scc
+
+ scc_averaged = scc_averaged / np.unique(data_array[:, 0]).size
+ return np.arange(-half, half + 1, 1), scc_averaged / np.sum(scc_averaged)
+ else:
+ print('cv_isi: no or only one spike in data_array, returning 0.0')
+ return 0.0
+
+
+def pop_LvR(data_array, t_ref, t_min, t_max, num_neur):
+ """
+ Compute the LvR value of the given data_array.
+ See Shinomoto et al. 2009 for details.
+
+ Parameters
+ ----------
+ data_array : numpy.ndarray
+ Arrays with spike data.
+ column 0: neuron_ids, column 1: spike times
+ t_ref : float
+ Refractory period of the neurons.
+ t_min : float
+ Minimal time for the calculation.
+ t_max : float
+ Maximal time for the calculation.
+ num_neur: int
+ Number of recorded neurons. Needs to provided explicitly
+ to avoid corruption of results by silent neurons not
+ present in the given data.
+
+ Returns
+ -------
+ mean : float
+ Population-averaged LvR.
+ LvR : numpy.ndarray
+ Single-cell LvR values
+ """
+ i_min = np.searchsorted(data_array[:, 1], t_min)
+ i_max = np.searchsorted(data_array[:, 1], t_max)
+ LvR = np.array([])
+ data_array = data_array[i_min:i_max]
+ for i in np.unique(data_array[:, 0]):
+ intervals = np.diff(data_array[
+ np.where(data_array[:, 0] == i)[0], 1])
+ if intervals.size > 1:
+ val = np.sum((1. - 4 * intervals[0:-1] * intervals[1:] / (intervals[0:-1] + intervals[
+ 1:]) ** 2) * (1 + 4 * t_ref / (intervals[0:-1] + intervals[1:])))
+ LvR = np.append(LvR, val * 3 / (intervals.size - 1.))
+ else:
+ LvR = np.append(LvR, 0.0)
+ if len(LvR) < num_neur:
+ LvR = np.append(LvR, np.zeros(num_neur - len(LvR)))
+ return np.mean(LvR), LvR
+
+
+def synchrony(data_array, num_neur, t_min, t_max, resolution=1.0):
+ """
+ Compute the synchrony of an array of spikes as the coefficient
+ of variation of the population rate.
+ Uses pop_rate_time_series().
+
+
+ Parameters
+ ----------
+ data_array : numpy.ndarray
+ Array with spike data.
+ column 0: neuron_ids, column 1: spike times
+ tmin : float
+ Minimal time for the calculation of the histogram in ms.
+ tmax : float
+ Maximal time for the calculation of the histogram in ms.
+ resolution : float, optional
+ Bin width of the histogram. Defaults to 1 ms.
+
+ Returns
+ -------
+ synchrony : float
+ Synchrony of the population.
+ """
+ spike_count_histogramm = pop_rate_time_series(
+ data_array, num_neur, t_min, t_max, resolution=resolution)
+ mean = np.mean(spike_count_histogramm)
+ std_dev = np.std(spike_count_histogramm)
+ try:
+ synchrony = std_dev / mean
+ except ZeroDivisionError:
+ synchrony = np.inf
+ return synchrony
+
+
+def spectrum(data_array, num_neur, t_min, t_max, resolution=1., kernel='binned', Df=None):
+ """
+ Compute compound power spectrum of a population of neurons.
+ Uses the powerspec function of the correlation toolbox.
+
+ Parameters
+ ----------
+ data_array : numpy.ndarray
+ Array with spike data.
+ column 0: neuron_ids, column 1: spike times
+ t_min : float
+ Minimal time for the calculation of the histogram in ms.
+ t_max : float
+ Maximal time for the calculation of the histogram in ms.
+ num_neur: int
+ Number of recorded neurons. Needs to provided explicitly
+ to avoid corruption of results by silent neurons not
+ present in the given data.
+ kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional
+ Specifies the kernel to be convolved with the spike histogram.
+ Defaults to 'binned', which corresponds to no convolution.
+ resolution: float, optional
+ Width of the convolution kernel. Specifically it correponds to:
+ - 'binned' : bin width of the histogram
+ - 'gauss_time_window' : sigma
+ - 'alpha_time_window' : time constant of the alpha function
+ - 'rect_time_window' : width of the moving rectangular function
+ Defaults to 1 ms.
+ Df : float, optional
+ Window width of sliding rectangular filter (smoothing) of the spectrum.
+ The default value is None and leads to no smoothing.
+
+ Returns
+ -------
+ power : numpy.ndarray
+ Values of the power spectrum.
+ freq : numpy.ndarray
+ Discrete frequency values
+ """
+ rate = pop_rate_time_series(
+ data_array, num_neur, t_min, t_max, kernel=kernel, resolution=resolution)
+ rate = centralize(rate, units=True)
+ freq, power = welch(rate, fs=1.e3,
+ noverlap=1000, nperseg=1024)
+ return power[0][freq > 0], freq[freq > 0]
+
+
+def synaptic_output(rate, tau_syn, t_min, t_max, resolution=1.):
+ """
+ Compute the synaptic output of a population of neurons.
+ Convolves the population spike histogram with an exponential
+ synaptic filter.
+
+ Parameters
+ ----------
+ rate : numpy.ndarray
+ Time series of the population rate.
+ tau_syn : float
+ Synaptic time constant of the single neurons
+ t_min : float
+ Minimal time for the calculation.
+ t_max : float
+ Maximal time for the calculation.
+ resolution : float, optional
+ Time resolution of the synaptic filtering kernel in ms.
+ Defaults to 1 ms.
+
+ """
+ t = np.arange(0., 20., resolution)
+ kernel = np.exp(-t / tau_syn)
+ syn_current = np.convolve(kernel, rate, mode='same')
+ return syn_current
+
+
+def compare(sim1, sim2):
+ """
+ Compares two simulations (2 instances of the ana_vistools data class)
+ in regards of their parameters.
+
+ Parameters
+ ----------
+ sim1, sim2 : ana_vistools.data
+ The two instances of the ana_vistools.data classe to be compared.
+
+ Returns
+ -------
+ None
+ """
+
+ template = "{0:30}{1:20}{2:25}{3:15}"
+ print(template.format("parameter", sim1.label[0:5], sim2.label[0:5], "equal?"))
+
+ info1 = sim1.sim_info
+ info2 = sim2.sim_info
+ compare_keys = []
+ for key in list(info1.keys()) + list(info2.keys()):
+ p = False
+ if key in list(info1.keys()):
+ value = info1[key]
+ else:
+ value = info2[key]
+
+ if isinstance(value, str):
+ # To exclude paths from the compared keys
+ if (value.find('_') == -1 and
+ value.find('/') == -1 and
+ value.find('.') == -1):
+ p = True
+ else:
+ p = True
+
+ if key in ['sim_label', 'K_stable_path']:
+ p = False
+ if p and key not in compare_keys:
+ compare_keys.append(key)
+ for key in compare_keys:
+ if key in info2 and key in info1:
+ out = (key, str(info1[key]), str(info2[
+ key]), info1[key] == info2[key])
+ elif key not in info1:
+ out = (key, '', str(info2[key]), 'false')
+ elif key not in info2:
+ out = (key, str(info1[key]), '', 'false')
+ print(template.format(*out))
+ # Compare sum of indegrees
+ s1 = 0.
+ s2 = 0.
+ for area1 in sim1.areas:
+ for pop1 in sim1.structure[area1]:
+ for area2 in sim1.areas:
+ for pop2 in sim1.structure[area2]:
+ s1 += sim1.indegree_data[area1][pop1][area2][pop2]
+ s2 += sim2.indegree_data[area1][pop1][area2][pop2]
+
+ out = ('indegrees', str(s1), str(s2), s1 == s2)
+ print(template.format(*out))
diff --git a/multiarea_model/data_multiarea/viscortex_processed_data.json b/multiarea_model/data_multiarea/viscortex_processed_data.json
index 230ba45..ffc7dfa 100644
--- a/multiarea_model/data_multiarea/viscortex_processed_data.json
+++ b/multiarea_model/data_multiarea/viscortex_processed_data.json
@@ -1 +1 @@
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\ No newline at end of file
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