https://github.com/MDverse/mdda
Revision 2a34d2207b68372c91391a9afeb5c8750ad85dba authored by Pierre Poulain on 05 February 2023, 17:14:47 UTC, committed by Pierre Poulain on 05 February 2023, 17:14:47 UTC
1 parent 25d400e
Tip revision: 2a34d2207b68372c91391a9afeb5c8750ad85dba authored by Pierre Poulain on 05 February 2023, 17:14:47 UTC
Add new sankey graph with plotly
Add new sankey graph with plotly
Tip revision: 2a34d22
paper_numbers_figures.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "e8c316ba-206f-4a5b-8dc3-de6d2ae365f9",
"metadata": {},
"source": [
"# Paper numbers & figures\n",
"\n",
"## Import libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ceeb9885-6af0-4e65-99be-b0f4f4b2b721",
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.lines import Line2D\n",
"import matplotlib.patches as mpatches\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"from pysankey import sankey # https://github.com/Pierre-Sassoulas/pySankey\n",
"from upsetplot import UpSet # https://upsetplot.readthedocs.io/en/stable/index.html\n",
"from upsetplot import plot as UPplot\n",
"import yaml\n",
"\n",
"pd.set_option(\"display.max_colwidth\", None)"
]
},
{
"cell_type": "markdown",
"id": "5a501f81-e984-47b1-89e6-bda91d17e44e",
"metadata": {},
"source": [
"## Load data\n",
"\n",
"All data files should be located in `../data/`"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "467a243a-6ff4-46e0-bf5e-038841fbe403",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"zenodo: found 996 datasets.\n",
"zenodo: found 154375 files.\n",
"zenodo: merged dataframe has 154375 entries.\n",
"\n",
"figshare: found 906 datasets.\n",
"figshare: found 79075 files.\n",
"figshare: merged dataframe has 79075 entries.\n",
"\n",
"osf: found 67 datasets.\n",
"osf: found 6148 files.\n",
"osf: merged dataframe has 6148 entries.\n",
"\n",
"Dimensions of the final dataframe: 239598 lines (files) x 18 columns\n"
]
}
],
"source": [
"df = pd.DataFrame()\n",
"for repository in [\"zenodo\", \"figshare\", \"osf\"]:\n",
" datasets = pd.read_csv(\n",
" f\"../data/{repository}_datasets.tsv\",\n",
" sep=\"\\t\",\n",
" dtype={\"dataset_id\": str}\n",
" )\n",
" print(f\"{repository}: found {datasets.shape[0]} datasets.\")\n",
" files = pd.read_csv(\n",
" f\"../data/{repository}_files.tsv\",\n",
" sep=\"\\t\",\n",
" dtype={\"dataset_id\": str, \"file_type\": str,\n",
" \"file_md5\": str, \"file_url\": str}\n",
" )\n",
" print(f\"{repository}: found {files.shape[0]} files.\")\n",
" tab = pd.merge(files, datasets, how=\"left\", on=[\"dataset_id\", \"dataset_origin\"], validate=\"many_to_one\")\n",
" print(f\"{repository}: merged dataframe has {tab.shape[0]} entries.\\n\")\n",
" df = pd.concat([df, tab], ignore_index=True)\n",
"\n",
"print(f\"Dimensions of the final dataframe: {len(df)} lines (files) x {df.shape[1]} columns\")"
]
},
{
"cell_type": "markdown",
"id": "9fd651dc-6f32-4da0-b07d-af9da62ff8ec",
"metadata": {},
"source": [
"## Assign year"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "75d37f50-65ac-4867-b46d-659e88725964",
"metadata": {},
"outputs": [],
"source": [
"df[\"year\"] = df[\"date_creation\"].apply(lambda x: int(x[:4]))"
]
},
{
"cell_type": "markdown",
"id": "47780c1b-9f42-40c0-817e-9fd4ae59e6e6",
"metadata": {},
"source": [
"## Number of datasets and files by data repository"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ed9e8778-27c0-469b-b82c-ff0e7e5c276c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_39404\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_39404_level0_col0\" class=\"col_heading level0 col0\" >number_of_datasets</th>\n",
" <th id=\"T_39404_level0_col1\" class=\"col_heading level0 col1\" >date_first_dataset</th>\n",
" <th id=\"T_39404_level0_col2\" class=\"col_heading level0 col2\" >date_last_dataset</th>\n",
" <th id=\"T_39404_level0_col3\" class=\"col_heading level0 col3\" >files_from_datasets</th>\n",
" <th id=\"T_39404_level0_col4\" class=\"col_heading level0 col4\" >total_size_in_GB</th>\n",
" <th id=\"T_39404_level0_col5\" class=\"col_heading level0 col5\" >zip_files</th>\n",
" <th id=\"T_39404_level0_col6\" class=\"col_heading level0 col6\" >files_from_zip_files</th>\n",
" <th id=\"T_39404_level0_col7\" class=\"col_heading level0 col7\" >total_files</th>\n",
" </tr>\n",
" <tr>\n",
" <th class=\"index_name level0\" >dataset_origin</th>\n",
" <th class=\"blank col0\" > </th>\n",
" <th class=\"blank col1\" > </th>\n",
" <th class=\"blank col2\" > </th>\n",
" <th class=\"blank col3\" > </th>\n",
" <th class=\"blank col4\" > </th>\n",
" <th class=\"blank col5\" > </th>\n",
" <th class=\"blank col6\" > </th>\n",
" <th class=\"blank col7\" > </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_39404_level0_row0\" class=\"row_heading level0 row0\" >figshare</th>\n",
" <td id=\"T_39404_row0_col0\" class=\"data row0 col0\" >906</td>\n",
" <td id=\"T_39404_row0_col1\" class=\"data row0 col1\" >2012-08-20</td>\n",
" <td id=\"T_39404_row0_col2\" class=\"data row0 col2\" >2023-02-03</td>\n",
" <td id=\"T_39404_row0_col3\" class=\"data row0 col3\" >3,235</td>\n",
" <td id=\"T_39404_row0_col4\" class=\"data row0 col4\" >745</td>\n",
" <td id=\"T_39404_row0_col5\" class=\"data row0 col5\" >589</td>\n",
" <td id=\"T_39404_row0_col6\" class=\"data row0 col6\" >75,840</td>\n",
" <td id=\"T_39404_row0_col7\" class=\"data row0 col7\" >79,075</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_39404_level0_row1\" class=\"row_heading level0 row1\" >osf</th>\n",
" <td id=\"T_39404_row1_col0\" class=\"data row1 col0\" >60</td>\n",
" <td id=\"T_39404_row1_col1\" class=\"data row1 col1\" >2017-05-24</td>\n",
" <td id=\"T_39404_row1_col2\" class=\"data row1 col2\" >2023-01-17</td>\n",
" <td id=\"T_39404_row1_col3\" class=\"data row1 col3\" >6,148</td>\n",
" <td id=\"T_39404_row1_col4\" class=\"data row1 col4\" >489</td>\n",
" <td id=\"T_39404_row1_col5\" class=\"data row1 col5\" >14</td>\n",
" <td id=\"T_39404_row1_col6\" class=\"data row1 col6\" >0</td>\n",
" <td id=\"T_39404_row1_col7\" class=\"data row1 col7\" >6,148</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_39404_level0_row2\" class=\"row_heading level0 row2\" >zenodo</th>\n",
" <td id=\"T_39404_row2_col0\" class=\"data row2 col0\" >996</td>\n",
" <td id=\"T_39404_row2_col1\" class=\"data row2 col1\" >2014-11-19</td>\n",
" <td id=\"T_39404_row2_col2\" class=\"data row2 col2\" >2023-02-03</td>\n",
" <td id=\"T_39404_row2_col3\" class=\"data row2 col3\" >19,697</td>\n",
" <td id=\"T_39404_row2_col4\" class=\"data row2 col4\" >12,562</td>\n",
" <td id=\"T_39404_row2_col5\" class=\"data row2 col5\" >1,598</td>\n",
" <td id=\"T_39404_row2_col6\" class=\"data row2 col6\" >134,678</td>\n",
" <td id=\"T_39404_row2_col7\" class=\"data row2 col7\" >154,375</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_39404_level0_row3\" class=\"row_heading level0 row3\" >total</th>\n",
" <td id=\"T_39404_row3_col0\" class=\"data row3 col0\" >1,962</td>\n",
" <td id=\"T_39404_row3_col1\" class=\"data row3 col1\" >nan</td>\n",
" <td id=\"T_39404_row3_col2\" class=\"data row3 col2\" >nan</td>\n",
" <td id=\"T_39404_row3_col3\" class=\"data row3 col3\" >29,080</td>\n",
" <td id=\"T_39404_row3_col4\" class=\"data row3 col4\" >13,796</td>\n",
" <td id=\"T_39404_row3_col5\" class=\"data row3 col5\" >2,201</td>\n",
" <td id=\"T_39404_row3_col6\" class=\"data row3 col6\" >210,518</td>\n",
" <td id=\"T_39404_row3_col7\" class=\"data row3 col7\" >239,598</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f7534fc1fa0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Add columns for easier aggregation\n",
"df[\"_is_zip_file\"] = np.where((df[\"file_type\"] == \"zip\"), True, False)\n",
"df[\"_is_not_from_zip_file\"] = ~ df[\"from_zip_file\"]\n",
"df[\"_size_not_from_zip_file\"] = np.where((df[\"from_zip_file\"] == False), df[\"file_size\"]/1E9, 0.0)\n",
"dataset_agg = (df\n",
" .groupby(\"dataset_origin\")\n",
" .agg(\n",
" number_of_datasets=(\"dataset_id\", \"nunique\"),\n",
" date_first_dataset=(\"date_creation\", \"min\"),\n",
" date_last_dataset=(\"date_creation\", \"max\"),\n",
" files_from_datasets=(\"_is_not_from_zip_file\", \"sum\"),\n",
" total_size_in_GB=(\"_size_not_from_zip_file\", \"sum\"),\n",
" zip_files=(\"_is_zip_file\", \"sum\"),\n",
" files_from_zip_files=(\"from_zip_file\", \"sum\"),\n",
" total_files=(\"dataset_id\", \"count\")\n",
" )\n",
")\n",
"dataset_agg.loc[\"total\"] = dataset_agg.sum(numeric_only=True)\n",
"dataset_agg.style.format(thousands=\",\", precision=0)"
]
},
{
"cell_type": "markdown",
"id": "70981cdc-2a18-4944-aa39-69f6941fd837",
"metadata": {},
"source": [
"<div class=\"alert alert-info\">\n",
"Files found in zip files are almost 8 times more numerous than files found directly in datasets.\n",
"\n",
"In the following, we will considere all files (found directly in datasets and extracted from zip files).\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "964d8aac-92c6-4be3-b289-a591118a448f",
"metadata": {},
"source": [
"### Assign MD engine"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e8bc44ea-d57b-4fa6-a996-1268f257fc5d",
"metadata": {},
"outputs": [],
"source": [
"file_types_list = []\n",
"with open(\"../params/file_types.yml\", \"r\") as param_file:\n",
" data_loaded = yaml.safe_load(param_file)\n",
" file_types_list = data_loaded[\"file_types\"]\n",
"\n",
" ext_gromacs = []\n",
"ext_namd = []\n",
"ext_amber = []\n",
"ext_charmm = []\n",
"ext_desmond = []\n",
"\n",
"list_coordinate = []\n",
"list_trajectory = []\n",
"list_topology = []\n",
"\n",
"for type_dict in file_types_list:\n",
" # assign MD engine\n",
" if type_dict[\"engine\"] == \"gromacs\":\n",
" ext_gromacs.append(type_dict[\"type\"])\n",
" elif type_dict[\"engine\"] == \"namd\":\n",
" ext_namd.append(type_dict[\"type\"])\n",
" elif type_dict[\"engine\"] == \"amber\":\n",
" ext_amber.append(type_dict[\"type\"])\n",
" elif type_dict[\"engine\"] == \"charmm\":\n",
" ext_charmm.append(type_dict[\"type\"])\n",
" elif type_dict[\"engine\"] == \"desmond\":\n",
" ext_desmond.append(type_dict[\"type\"])\n",
" # assign file category\n",
" if type_dict[\"category\"] == \"coordinate\":\n",
" list_coordinate.append(type_dict[\"type\"])\n",
" elif type_dict[\"category\"] == \"topology\":\n",
" list_topology.append(type_dict[\"type\"])\n",
" elif type_dict[\"category\"] == \"trajectory\":\n",
" list_trajectory.append(type_dict[\"type\"])\n",
"\n",
"ext_all = ext_gromacs + ext_namd + ext_amber + ext_charmm\n",
" \n",
"# The filetype \"top\" is included in both Gromacs and Amber. \n",
"# And, the filetype \"prm\" is included in Namd and Amber.\n",
"# But, to simplify the analyses, I consider that \"top\" is only a filetype of Gromacs and prm a filetype of Namd.\n",
"\n",
"def assign_md_engine(file_type):\n",
" \"\"\"Assign MD engine based on file type.\n",
" \n",
" Parameters\n",
" ----------\n",
" file_type : str\n",
" File extension.\n",
" \n",
" Returns\n",
" -------\n",
" str\n",
" Name of MD engine.\n",
" \"\"\"\n",
" if file_type in ext_gromacs:\n",
" return \"GROMACS\"\n",
" elif file_type in (ext_namd + ext_charmm):\n",
" return \"NAMD/CHARMM\"\n",
" elif file_type in ext_amber:\n",
" return \"AMBER\"\n",
" elif file_type in ext_desmond:\n",
" return \"DESMOND\"\n",
" else:\n",
" return \"Unknown\"\n",
"\n",
"df[\"MD_engine\"] = df[\"file_type\"].apply(assign_md_engine)"
]
},
{
"cell_type": "markdown",
"id": "6c399da1-9b75-45ab-9a90-8cfe26ca02c8",
"metadata": {},
"source": [
"### Number of datasets and files per MD engine"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5ca41d39-b55b-44e5-8ba4-63a5c97c5f77",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_340cf\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th id=\"T_340cf_level0_col0\" class=\"col_heading level0 col0\" >datasets</th>\n",
" <th id=\"T_340cf_level0_col1\" class=\"col_heading level0 col1\" >files</th>\n",
" </tr>\n",
" <tr>\n",
" <th class=\"index_name level0\" >MD_engine</th>\n",
" <th class=\"blank col0\" > </th>\n",
" <th class=\"blank col1\" > </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_340cf_level0_row0\" class=\"row_heading level0 row0\" >AMBER</th>\n",
" <td id=\"T_340cf_row0_col0\" class=\"data row0 col0\" >229</td>\n",
" <td id=\"T_340cf_row0_col1\" class=\"data row0 col1\" >9,826</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_340cf_level0_row1\" class=\"row_heading level0 row1\" >DESMOND</th>\n",
" <td id=\"T_340cf_row1_col0\" class=\"data row1 col0\" >48</td>\n",
" <td id=\"T_340cf_row1_col1\" class=\"data row1 col1\" >1,855</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_340cf_level0_row2\" class=\"row_heading level0 row2\" >GROMACS</th>\n",
" <td id=\"T_340cf_row2_col0\" class=\"data row2 col0\" >1,242</td>\n",
" <td id=\"T_340cf_row2_col1\" class=\"data row2 col1\" >83,731</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_340cf_level0_row3\" class=\"row_heading level0 row3\" >NAMD/CHARMM</th>\n",
" <td id=\"T_340cf_row3_col0\" class=\"data row3 col0\" >664</td>\n",
" <td id=\"T_340cf_row3_col1\" class=\"data row3 col1\" >12,312</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_340cf_level0_row4\" class=\"row_heading level0 row4\" >Unknown</th>\n",
" <td id=\"T_340cf_row4_col0\" class=\"data row4 col0\" >1,151</td>\n",
" <td id=\"T_340cf_row4_col1\" class=\"data row4 col1\" >131,874</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f75338ef040>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df\n",
" .groupby(\"MD_engine\")\n",
" .agg(\n",
" datasets=(\"dataset_id\", \"nunique\"),\n",
" files=(\"dataset_id\", \"count\")\n",
" ).style.format(thousands=\",\", precision=0)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "12bc8a8d-61b3-4543-b776-d34d99751610",
"metadata": {},
"source": [
"### Assign category"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "93da40a9-bc82-4f7b-9be7-a6063b1c5ba9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"none 167966\n",
"trajectory 35355\n",
"topology 29553\n",
"coordinate 6724\n",
"Name: category, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def assign_file_category(file_type):\n",
" \"\"\"Assign file category based on file type.\n",
" \n",
" Parameters\n",
" ----------\n",
" file_type : str\n",
" File extension.\n",
" \n",
" Returns\n",
" -------\n",
" str\n",
" Category.\n",
" \"\"\"\n",
" if file_type in list_coordinate:\n",
" return \"coordinate\"\n",
" elif file_type in list_topology:\n",
" return \"topology\"\n",
" elif file_type in list_trajectory:\n",
" return \"trajectory\"\n",
" else:\n",
" return \"none\"\n",
"\n",
"df[\"category\"] = df[\"file_type\"].apply(assign_file_category)\n",
"df[\"category\"].value_counts()"
]
},
{
"cell_type": "markdown",
"id": "1356561e-fcee-4ef8-9146-178b901a0840",
"metadata": {},
"source": [
"## Figure 1\n",
"\n",
"Evolution of number of “molecular dynamics” papers in PubMed vs time\n",
"\n",
"See notebook `search_MD_in_pubmed.ipynb`."
]
},
{
"cell_type": "markdown",
"id": "47d9a199-d394-40ef-bc7f-a5815cb41afd",
"metadata": {},
"source": [
"## Figure 2\n",
"\n",
"### 2.B. Evolution of the number MD files deposited in data repositories"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5eb8e167-98a8-47c7-a9dd-9c7c252f4325",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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r2dvbW20DAAAAAEB5UeIg4vnnn1d8fLw2bdpUmuMpwLS8wtrXyJEjJUlz5syR0WhUbGysJMlgMKhfv36SpMWLFxd45p49exQTEyNHR0f16dPH4l7//v0lSUuWLFFeXp7Fvfj4eP3888+SpAEDBlitW716tdLT0y3upaenKyQkRJI0cOBAq3Vbt241HwFqkpeXp6VLl1qtAwAAAACgPLqrIGLixIl69tln9emnn+ry5culOa67NnXqVDk5OWnLli2aP3++ea+IuLg4jRkzRpI0btw488wJk4kTJ6p69eqKjo7W5MmTlZOTI0lKTk7W0KFDlZubq169eqlVq1YWdQMGDFBgYKCSk5M1evRoZWRkSJKuXbum0aNHKzk5Wc2aNVPfvn0t6tq2bavu3bsrNzdXw4YNU3JysiQpJydHkyZNUnR0tGrWrKnx48eX+jsCAAAAAMDWDMbCdnO8gwYNGkiSEhISlJ+fL0mqXr263NzcrHdkMOjUqVMlHKZ1o0aN0tKlSzVnzhy9+eabBe5//fXXGj16tPLz8+Xj46OaNWvqyJEjysnJUatWrbRjxw6r4/3ll1/Uu3dvZWVlqUaNGvLz81N0dLQyMjLk7++vvXv3FggwJOnIkSPq0qWLrly5Ik9PTzVs2FAnT55UamqqqlWrpl27dikoKKhA3dmzZ9WhQwfFxcWpUqVKatKkieLj45WUlCQXFxf9+OOP6tq1a7HeTVpamjw9PZWamsrGlQAAAAAK1Xxp87IeQpFEjows6yEUSXl5n1Lpv9Oifg4t8YyI2NhYxcbGKi8vz7xUIikpyXzd2petjRgxQrt27VLv3r2VmZmpqKgoNWjQQLNmzVJoaGihoUm3bt108OBBDRkyRAaDQZGRkfL29tbkyZMVHh5uNYSQpGbNmikiIkLjxo1T5cqVFRkZqcqVK+v5559XRESE1RBCkurUqaNDhw5p0qRJ8vb2VmRkpAwGg4YMGaKwsLBihxAAAAAAANyvSjwjIi4urtg1nPpgO8yIAAAAAFAU5eVf8JkRUfrKakaEQ0k7IFQAAAAAAADFVeKlGQAAAAAAAMVV4hkRJtevX1dISIh27NihxMREZWVl6ZdffjHf37t3r9LT09WtWzfZ29vfbXcAAAAAAKAcu6sgYt++fRo8eLD++OMP8/GYBoPBos3GjRs1f/58/fDDD+rRo8fddAcAAAAAAMq5Ei/NOH36tHr27KmEhAT1799fS5cuVdOmTQu0e+6552Q0GrV27dq7GigAAAAAACj/ShxEzJ07V2lpaXr33XcVEhKi4cOHq0qVKgXaNWvWTNWqVdOBAwfuZpwAAAAAAKACKPHSjK1bt8rT01PTpk27Y1t/f/8SHfcJAAAAADf7Mx+NCFQUJZ4RkZSUpICAgAJ7Qlhjb2+vq1evlrQrAAAAAABQQZQ4iKhSpYoSExOL1PbUqVPy9vYuaVcAAAAAAKCCKHEQ0bZtW128eFG7du26bbsNGzbo8uXL6ty5c0m7AgAAAAAAFUSJg4iXXnpJRqNRY8aM0e+//261zc6dOzV+/HgZDAa99NJLJR4kAAAAAACoGEocRPTo0UOvvPKKTp06pdatW6tdu3Y6fvy4JGnEiBFq2bKlHnvsMV26dEnTpk1Tu3btSm3QAAAAAACgfCrxqRmS9Mknn6hJkyaaNWuWfvvtN/P1ZcuWSZKqV6+u2bNna+LEiXc3SgAAAAAAUCHcVRAhSRMmTNCYMWO0d+9eRUZGKjU1VZUrV1ZQUJA6d+4sZ2fn0hgnAAAAAACoAO46iJAkR0dHdenSRV26dCmNxwEAAAAAgAqqxHtEAAAAAAAAFNddBxEXLlzQO++8ow4dOqh69epydnZW9erV1aFDB73zzjs6f/58aYwTAAAAAABUAHe1NGPt2rUaN26c0tLSZDQazdcvX76sffv2af/+/fr444+1aNEiBQcH3/VgAQAAAABA+VbiICI0NFSDBw9Wfn6+WrZsqRdffFFNmjSRt7e3Ll68qOjoaH3++ecKDw/X0KFDVadOHXXs2LE0xw4AAAAAAMqZEi/NmD17toxGo15//XUdPHhQY8aMUfv27dWgQQO1a9dOo0eP1sGDBzVt2jTl5eXpnXfeKc1xAwAAAACAcqjEQcT+/fvl5eWld99997bt5syZo+rVq2v//v0l7QoAAAAAAFQQJQ4iDAaD6tevLzu72z/C3t5e9evXl8FgKGlXAAAAAACggihxEPHQQw/p9OnTysvLu2273NxcnT59Wg8//HBJuwIAAAAAABVEiYOI6dOn6/Lly5o+ffpt282YMUOXL1/WG2+8UdKuAAAAAABABVGkUzPi4+MLXGvSpInee+89vfXWW/rll1/0wgsvqEmTJqpZs6aSkpIUHR2tL774QkeOHNG8efPUuHHjUh88AAAAAAAoX4oURPj7+xe6x4PRaNThw4c1YcKEQuunT5+uN954Q7m5uSUbJQAAAAAAqBCKFET4+fmx2SQAAAAAALhrRQoiYmNj7/EwAAAAAADAn0GJN6sEAAAAAAAoLoIIAAAAAABgM0VamnEnKSkpOnPmjK5evSqj0Vhouy5dupRGdwAAAAAAoJy6qyDi119/1YwZM/Tbb7/dsa3BYODUDAAAAAAA/uRKHET88MMP6tu3r3Jzc+Xi4qL69eurRo0anK4BAAAAAAAKVeIg4u2331ZeXp4mTJig999/X56enqU5LgAAAAAAUAGVOIiIiopS9erV9eWXX5bmeAAAAAAAQAVW4iCiatWq8vHxKc2xAAAAAACACq7Ex3c++eSTio6O1rVr10pzPAAAAAAAoAIrcRDxzjvvyM3NTRMmTFBWVlZpjgkAAAAAAFRQJV6a4efnp507d2rEiBFq2LChnn32WQUEBKhSpUqF1owYMaKk3QEAAAAAgAqgxEGEJP32229KSEjQuXPn9NFHH92xPUEEAAAAAAB/biUOIlatWmUOFurWravmzZurRo0aMhgMpTY4AAAAAABQsZQ4iJg3b54MBoPmzZunKVOmyM6uxNtNAAAAAACAP4kSBxHHjx+Xj4+PXnvttdIcDwAAAAAAqMBKPI3By8tL3t7epTkWAAAAAABQwZU4iPjLX/6iI0eOKDk5uTTHAwAAAAAAKrASBxFz586Vr6+vhgwZogsXLpTmmAAAAAAAQAVV4j0i/vWvf+mpp57Sl19+qYCAAPXq1UsBAQGqVKmS1fYGg0FvvfVWiQcKAAAAAADKvxIHEbNmzZLBYJDRaFROTo7Wrl1rtZ2pDUEEAAAAAAAocRAxc+bM0hwHAAAAAAD4EyCIAAAAAAAANlPizSoBAAAAAACKq1wEERs2bNCECRPUqlUr1a5dW05OTqpSpYo6dOigTz/9VNevXy+0du/evXrmmWdUo0YNubq6KigoSHPmzFFWVtZt+4yOjtawYcNUu3Ztubi4KCAgQFOmTFFKSspt6xITEzV+/Hj5+vrK2dlZfn5+mjBhghITE29bl5KSoilTpiggIEAuLi6qXbu2hg0bpujo6NvWAQAAAABQnhiMRqOxJIWzZ88uXkd3sVllp06dtHv3bjk7O6tOnTry8vLSuXPnzB/uW7VqpZ9//llVqlSxqFu+fLlGjhypvLw8+fj4qGbNmjpy5IhycnLUpk0bbd++3eopH9u2bdPTTz+tzMxM1ahRQ76+voqJiVFGRoYaNGigPXv2yNvbu0BdVFSUOnfurMuXL8vT01MBAQE6deqUUlNT5eXlpdDQUAUGBhaoO3/+vNq3b6/Y2FhVqlRJgYGBSkhIUFJSklxdXfXTTz+pS5cuxXpnaWlp8vT0VGpqqjw8PIpVCwAAANyvmi9tXtZDKLLIkZFlPYQiKS/vlPdZ+kr7nRb1c2iJgwg7OzvziRgFHmowWHxvOjUjLy+vJF1pyZIl8vf3V8eOHeXo6Gi+vm/fPgUHB+uPP/7Qiy++qM8//9x8LzY2VoGBgcrOztYHH3ygKVOmyGAwKC4uTj169NCxY8f00ksv6V//+pdFX+np6QoICFBSUpJeeeUV/eMf/5Cjo6OSk5P1zDPPaPfu3Xr66af1v//9z6IuLy9PLVq0UFRUlAYMGKCvv/5alSpV0rVr1zRixAitW7dOLVq00KFDh2RnZzkRpWfPntq8ebM6deqkDRs2yMvLSzk5Ofq///s/ffbZZ/L29tapU6fk5uZW5HdGEAEAAICK6M/8Ie9eKS/vlPdZ+spdEPHOO+8Ueu/atWs6efKkNm/erLy8PL344ovy9PS8JxtchoSEaNCgQapTp47F8oeXXnpJX3zxhZ588klt3rzZombPnj3mUCMhIcFidsP8+fP12muvqUmTJoqMjJS9vb35Xnx8vAICApSbm6uwsDC1bNmywDi8vLx05swZubu7m++lp6erfv36Sk5O1rp169SvXz/zvYMHD6pNmzZycHDQqVOn5OfnZ76Xl5en5s2bKzo6Wh999JEmTZpU5PdCEAEAAICK6M/8Ie9eKS/vlPdZ+soqiLinp2b88ccfGjZsmDZv3qy9e/eWtKvbMi11yMjIMF8zGo1av369JGns2LEFajp06KDAwEDFxMRo48aNGj9+vPneunXrJEmjRo2yCCEkyc/PT0888YR++uknrVmzxiKIMNUNGjTIIoSQJHd3dwUHB2vBggUKCQmxCCLWrl0rSerevbtFCCFJ9vb2GjlypKZNm6aQkJBiBREAAAAAANyP7ulmlXXr1tXKlSt14sSJEu8PcSemgOPmUCA+Pl7nzp2TJHXs2NFqnen6/v37zddMMx2KWyfdWCZyL+vCwsJKvLQFAAAAAID7RYlnRBRV7dq11axZM61fv16ffvppqTwzLy9P586d03fffadp06bJzc1N8+bNM98/ceKEJJk3t7SmQYMGFm2lG/tK5OTkWNwvSt3169cVHx9fpDpTH6a9LkzPuVPd9evXFRcXV2i77OxsZWdnm79PS0uz2g4AAAC2VV6maZeXae8Ayj+bHN+ZmpqqpKSku37OJ598IoPBIAcHB/n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\n",
"text/plain": [
"<Figure size 1200x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams.update({\"font.size\": 16})\n",
"(df\n",
" .query(\"year != 2023\")\n",
" .groupby([\"year\", \"dataset_origin\"])\n",
" .size().unstack()\n",
" .plot(kind=\"bar\", stacked=True, figsize=(12,7))\n",
")\n",
"plt.xlabel(\"Years\")\n",
"plt.xticks(rotation=0)\n",
"plt.ylabel(\"Number of files\")\n",
"plt.legend(title=\"Data repositories\")\n",
"filename = \"fig/number_of_files_per_years_per_data_repositories\"\n",
"plt.savefig(f\"{filename}.png\", dpi=300, bbox_inches=\"tight\")\n",
"plt.savefig(f\"{filename}.svg\", bbox_inches=\"tight\")"
]
},
{
"cell_type": "markdown",
"id": "12712e8b-abd9-4a66-8281-49cb18e9890c",
"metadata": {},
"source": [
"### 2.B. MD engines distribution"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3faba910-d405-4227-8d38-e8e7ed4fd079",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1200x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams.update({'font.size': 16})\n",
"(df\n",
" .loc[:, \"MD_engine\"]\n",
" .value_counts()\n",
" .sort_index()\n",
" .plot(kind=\"bar\", color=[\"tab:blue\", \"tab:orange\", \"tab:green\", \"tab:purple\", \"lightgrey\"], figsize=(12,7))\n",
")\n",
"plt.xlabel(\"MD engine\")\n",
"plt.xticks(rotation=0)\n",
"plt.ylabel(\"Number of files\")\n",
"filename = \"fig/number_of_files_per_MD_engine\"\n",
"plt.savefig(f\"{filename}.png\", dpi=300, bbox_inches=\"tight\")\n",
"plt.savefig(f\"{filename}.svg\", bbox_inches=\"tight\")"
]
},
{
"cell_type": "markdown",
"id": "2301fbea-c9e4-458b-aff9-a1275b721115",
"metadata": {},
"source": [
"### 2.C. Type of files not associated to any MD engine"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "65445844-8799-46c4-b08b-61391ee65711",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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8tSNGjNDhw4d1yy23KC4uzlH3r3/9S6mpqRo4cKDOnTvn3sEDAAAAAKAUKRNBxFNPPaWvv/5aTz/9tLp27apKlSpdsmb27NlKT0/XHXfcoUmTJslms0mS6tatqwULFkiS3nnnHZ04ccKp7q233tKpU6fUpEkTvfzyy/L29pYkBQcH68MPP5SXl5e+/fZbbdu2zalu2bJl2rNnj4KDg/Xee+85ZlpUrFhRCxcuVHBwsHbt2qUvv/zSqe7XX3/VihUr5OXlpcWLFys4OFiS5O3trVdeeUVNmjTRiRMn9M4777hx5AAAAAAAKF3KRBBRVMYYff7555LkcvZC27Zt1bhxY2VmZuYJBpYtWybpwiwFT09Pp3VhYWG67bbbJEmfffaZy7qBAwcqICDAaV1AQIAGDBggSVqyZInTuqVLl0qSbr/9doWFhTmt8/T01PDhw13WAQAAAABQFpXLICIuLk5//fWXJKldu3Yu29iXb9682bEsKytLW7duLXKdJP38889XtG7r1q3Kzs522QYAAAAAgLKiXAYR+/btkyT5+vqqdu3aLttEREQ4tZUuXFciMzPTaX1h6jIyMhQXF1eoutx95N7OpeoyMjJ05MgRl20kKT09XcnJyU4PAAAAAABKm3IZRJw+fVqSVLlyZce1IS5WpUoVp7YXf21fX5i6pKQkx0UoL1WXk5PjFBLYt3Opuov7vNisWbMUFBTkeISGhubbFgAAAACAklIug4i0tDRJko+PT75tfH19JUmpqal56gqqvdy6/GqLWnexKVOmKCkpyfE4evRovm0BAAAAACgpXiU9gCvBz89P0oXTGfKTnp4uSfL3989TZ6/N/X1R6grqz1Xt+fPni1x3MV9fX6fQAgAAAACA0qhczoiwn85w5swZGWNctnF1SkRhToNwVRcUFCQPD49C1Xl4eCgwMDBPn5equ7hPAAAAAADKonIZRDRo0EDShdkEx44dc9nm4MGDTm0lKTw8XN7e3k7rC1Pn4+PjuPXmpepy95F7O5eq8/HxUd26dV22AQAAAACgrCiXQURYWJhq1qwpSYqOjnbZxr78xhtvdCzz8vJSy5Yti1yX+/srVdeqVSt5enq6bAMAAAAAQFlRLoMIm82mPn36SJLmz5+fZ/3GjRu1d+9eeXt7q2fPnk7r+vbtK0lauHChsrOzndbFxcXphx9+kCT169fPZd2nn36qlJQUp3UpKSlasmSJJKl///4u61atWuW4Bahddna23n//fZd1AAAAAACUReUyiJCkSZMmycfHRytXrtTs2bMd14o4cuSIRo0aJUkaM2aMY+aE3fjx4xUSEqLY2FhNmDBBmZmZkqSEhATdfffdysrKUrdu3dSqVSunun79+qlx48ZKSEjQyJEjdf78eUnSuXPnNHLkSCUkJCgqKkq9e/d2qrvhhht0++23KysrS0OHDlVCQoIkKTMzU48++qhiY2NVvXp1jR07ttiPEQAAAAAAVrOZ/K7mWIpER0erV69eju/Pnj2r9PR0VahQwelOEtu3b1doaKjj+0WLFmnkyJHKyclRnTp1VL16dcXExCgzM1OtWrXS2rVrVbFixTz9/fjjj+rRo4fS0tJUrVo1hYWFKTY2VufPn1d4eLg2bdqUJ8CQpJiYGHXo0EGnT59WUFCQ6tevr/379yspKUlVq1bV+vXr1bRp0zx1x44dU9u2bXXkyBFVqFBBTZo0UVxcnE6dOiU/Pz8tX75cnTp1KtIxS05OVlBQkJKSkpwujllcYmJiin2bVomKiirpIbiFYw4AAACgNCvs59AyMSMiMzNTCQkJjof9lpbnz593Wn7xqRTDhg3T+vXr1aNHD6WmpmrPnj2KiIjQtGnTtGHDBpchhCR16dJFv/76qwYPHiybzabdu3erRo0amjBhgrZt2+YyhJAufNjauXOnxowZo0qVKmn37t2qVKmS7rvvPu3cudNlCCFJtWvX1vbt2/Xoo4+qRo0a2r17t2w2mwYPHqytW7cWOYQAAAAAAKC0KhMzIlB0zIjIX1n96zzHHAAAAEBpVq5mRAAAAAAAgPKBIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFiGIAIAAAAAAFjmigQR2dnZ2rx5sz7//HMdPnz4SnRRaCdPntTEiRPVrFkzVahQQX5+foqMjNTYsWO1f//+fOs2bdqkXr16qVq1avL391fTpk01Y8YMpaWlFdhfbGyshg4dqlq1ajn6mjhxos6cOVNgXXx8vMaOHavQ0FD5+voqLCxM48aNU3x8vDu7DQAAAABAqWQzxhh3ClesWKG3335bAwcO1ODBgx3Ljx07pp49e2r79u0XOrDZ9PTTT+uZZ54pnhEXwe+//64OHTro5MmT8vb2VkREhLy9vbV//36lpaWpQoUK+u6779SxY0enusWLF2v48OHKzs5WnTp1VL16dcXExCgzM1Nt2rTRmjVrVKFChTz9rV69Wt27d1dqaqqqVaum0NBQ7d27V+fPn1dERIQ2btyoGjVq5Knbs2eP2rdvr8TERAUFBSkyMlIHDhxQUlKSgoODtWHDBjVu3LhI+56cnKygoCAlJSUpMDCwaAeuEGJiYop9m1aJiooq6SG4hWMOAAAAoDQr7OdQt2dELFq0SF9++aUaNmzotPzRRx/Vtm3bFBgYqGuvvVY2m03Tp09XdHS0u1257cEHH9TJkyfVrl07HTx4UHv37tXu3bv1559/qmfPnjp//rxGjhyp3FnM4cOHNXr0aGVnZ+vFF1/U0aNHtW3bNu3bt0+NGjXSli1bNHny5Dx9paSkaNCgQUpNTdVDDz2k+Ph4bd26VXFxcY7+R48enacuOztbAwYMUGJiovr166djx45p69atio+PV9++fZWQkKBBgwYpJyfnih4rAAAAAACs4HYQsWXLFgUFBally5aOZYmJifr8889VrVo1/fHHH9q2bZs++ugjGWM0Z86c4hhvoZ0/f16rV6+WJL355pu65pprHOuCg4O1cOFC2Ww2HTp0SHv37nWsmz17ttLT03XHHXdo0qRJstlskqS6detqwYIFkqR33nlHJ06ccOrvrbfe0qlTp9SkSRO9/PLL8vb2dvT14YcfysvLS99++622bdvmVLds2TLt2bNHwcHBeu+99xwzLSpWrKiFCxcqODhYu3bt0pdfflnMRwgAAAAAAOu5HUScOnVKoaGhTstWr16trKwsDRkyRNWqVZMk9e/fX7Vq1dLOnTsvb6RFlJGR4ZhFEBERkWd9lSpVVLVqVUlSVlaWJMkYo88//1ySXM5eaNu2rRo3bqzMzMw8wcCyZcskSSNGjJCnp6fTurCwMN12222SpM8++8xl3cCBAxUQEOC0LiAgQAMGDJAkLVmy5FK7DAAAAABAqed2EHH+/Pk8H7g3bNggm82mLl26OC2/5ppr9Oeff7rblVsqV67sCEo2btyYZ/3vv/+uhIQEVa5cWQ0aNJAkxcXF6a+//pIktWvXzuV27cs3b97sWJaVlaWtW7cWuU6Sfv75Z7fqAAAAAAAoi9wOImrUqKHDhw87ZhNIFy5g6eHhofbt2zu1TU1NVcWKFd0fpZtmzpwpSRo1apSWLl2qhIQEJSUlacWKFerdu7dsNptefPFF+fn5SZL27dsnSfL19VXt2rVdbtM+u8LeVrpwXYnMzEyn9YWpy8jIUFxcXKHqcvcBAAAAAEBZ5XYQ0b59eyUlJWn69Ok6e/as5s+fr7179+qmm25S5cqVHe0yMzO1b9++fD/YX0nDhg3T0qVLFRISov79+yskJESVK1dW165d5ePjo++++0733Xefo/3p06clXZhNYb82xMWqVKni1Pbir+3rC1OXlJTkOH3kUnU5OTlKTk7Od1/T09OVnJzs9AAAAAAAoLRxO4h44okn5Ofnp+eee05BQUEaO3asJOnJJ590ardq1Sqlp6erbdu2lzdSNxhjdPDgQSUkJMjT01P169dX06ZN5ePjo5iYGL3zzjtKTEx0tE9LS5Mk+fj45LtNX19fSRdmeVxcV1Dt5dZdXHuxWbNmKSgoyPG4+PodAAAAAACUBm4HEc2aNdPq1at11113qWHDhurSpYu++eYbde3a1and//t//09BQUG66667LnuwRTV+/HhNmjRJoaGh2r9/v/bt26fffvtNR48e1V133aXPP/9cnTt3VnZ2tiQ5TtHIyMjId5vp6emSJH9/f8cye11BtZdbd3HtxaZMmaKkpCTH4+jRo/m2BQAAAACgpHhdTvENN9ygr7/+usA2H3300eV04badO3dq3rx58vb21scff+w0Q6B69epavHixIiMjtWvXLn366acaMmSI4zSIM2fOyBjj8vQM+6kVuU+lyP316dOnVatWrULVBQUFycPDQzk5OU6nbLiq8/DwUGBgYL776+vr6zR7AgAAAACA0uiygojSLDo6WsYYNWzY0OVpCoGBgbrhhhv03Xff6ddff9WQIUMcd89IT0/XsWPHVKdOnTx1Bw8elCRHW0kKDw+Xt7e3MjMzdfDgQZdBhKs6Hx8fhYWF6fDhwzp48KBuvvnmfOvsfQCwTkxMTEkPwW1RUVElPQQAAADAJbdPzbDLyMjQ4sWLNXbsWHXv3j3PrTs3bdqklStXOk5/sEpKSsol2xhjJP3vWg1hYWGqWbOmpAtBhiv25TfeeKNjmZeXl1q2bFnkutzfF7UOAAAAAICy6LKCiJ9//lkNGjTQsGHD9O6772r58uVas2aNU5svv/xS3bp10w8//HA5XRWZfebBH3/84fJ6CcnJydqyZYskqWHDhpIkm82mPn36SJLmz5+fp2bjxo3au3evvL291bNnT6d1ffv2lSQtXLgwT+gSFxfn2P9+/fq5rPv000/zhCcpKSlasmSJJKl///6X2mUAAAAAAEo9t4OIgwcPqmvXrjp69Kj69u2r999/X82aNcvT7p577pExRkuXLr2sgRbVHXfcoZCQEGVmZmrw4ME6fPiwY93Jkyc1dOhQ/f333/Lz83P6kD9p0iT5+Pho5cqVmj17tmPWxJEjRzRq1ChJ0pgxYxwzJ+zGjx+vkJAQxcbGasKECcrMzJQkJSQk6O6771ZWVpa6deumVq1aOdX169dPjRs3VkJCgkaOHKnz589Lks6dO6eRI0cqISFBUVFR6t27d3EfIgAAAAAALGcz9k/aRTRq1CgtXLhQzz33nKZMmSJJat++vTZu3JhnRkBISIhCQ0O1ffv2yx9xESxfvlx9+/ZVWlqaPD09FRERIW9vb+3fv18ZGRny8vLSu+++q+HDhzvVLVq0SCNHjlROTo7q1Kmj6tWrKyYmRpmZmWrVqpXWrl2rihUr5unvxx9/VI8ePZSWlqZq1aopLCxMsbGxOn/+vMLDw7Vp06Y8AYZ04Tz0Dh066PTp0woKClL9+vW1f/9+JSUlqWrVqlq/fr2aNm1apH1PTk5WUFCQkpKSCrzIpbs4d956HHPrccwBAACAwivs51C3Z0SsWrVKQUFBevzxxy/ZNjw8XH/++ae7XbmtW7du2rlzp8aOHat69eopLi5O+/fvV61atXTvvfdq8+bNeUIISRo2bJjWr1+vHj16KDU1VXv27FFERISmTZumDRs2uAwhJKlLly769ddfNXjwYNlsNu3evVs1atTQhAkTtG3bNpchhHThA8POnTs1ZswYVapUSbt371alSpV03333aefOnUUOIQAAAAAAKK3cvmvGqVOnFBUV5fIWlxfz9PTU2bNn3e3qsjRs2FBvv/12kevatm17yVuTutKsWTO3blkaGhqqefPmFbkOAAAAAICyxO0ZEZUrV1Z8fHyh2h44cEA1atRwtysAAAAAAFBOuB1E3HDDDTp58qTWr19fYLsvvvhCiYmJat++vbtdAQAAAACAcsLtIOLBBx+UMUajRo3Srl27XLZZt26dxo4dK5vNpgcffNDtQQIAAAAAgPLB7SDizjvv1EMPPaQDBw6odevWuummm/THH39IunCxx5YtW6pz5876+++/9fjjj+umm24qtkEDAAAAAICyye2LVUrSnDlz1KRJE02bNk2//PKLY/kHH3wg6cJtO6dPn67x48df3igBAAAAAEC5cFlBhCSNGzdOo0aN0qZNm7R7924lJSWpUqVKatq0qdq3by9fX9/iGCcAAAAAACgHLjuIkCRvb2916NBBHTp0KI7NAQAAAACAcsrta0QAAAAAAAAUVaFmRMTFxRVLZ2FhYcWyHQAAAAAAUDYVKogIDw+XzWa7rI5sNpuysrIuaxsAAAAAAKBsK1QQERYWdtlBBAAAAAAAQKGCiMOHD1/hYQAAAAAAgKtBsdw1AwCA4hATE1PSQ3BbVFRUSQ8BAACgTOCuGQAAAAAAwDIEEQAAAAAAwDKFOjUjIiJCklS/fn2tXLnSaVlh2Ww2HThwoIjDAwAAAAAA5UmRLlbp5+eXZ1lhcdcNAAAAAABQqCDi0KFDkiRvb+88ywAAAAAAAAqrUEGEzWaTn5+fqlev7lhWt27dKzYoAAAAAABQPhXqYpXh4eEaMGCA07JRo0bp+eefvyKDAgAAAAAA5VOh75phjHH6fuHChfruu++KfUAAAAAAAKD8KlQQERAQoGPHjl3psQAAAAAAgHKuUNeIaNmypdatW6dHH31UXbt2lb+/vyQpKSlJ69atK3RnHTp0cG+UAAAAAACgXChUEPHMM8+oa9eumjt3rubOnetYHhMTo86dOxeqI5vNpqysLPdGCQAAAAAAyoVCBRGdOnXStm3bNH/+fO3du1epqalas2aNAgMDdd11113hIQIAAAAAgPKiUEGEJDVt2lQvvfSS43sPDw81b95cq1evviIDAwAAAAAA5U+hg4iLDR8+XI0aNSrOsQAAAAAAgHLO7SDivffeK85xAAAAAACAq0Chbt8JAAAAAABQHAgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQoVRMTFxenkyZNXeiwAAAAAAKCcK9TtO8PDw9W+fXutXbvWsWzUqFFq2LChHn/88Ss2OAAAcGXFxMSU9BDcFhUVVdJDAAAAbij0qRnGGKfvFy5cqO+++67YBwQAAAAAAMqvQs2ICAgI0LFjx670WAAAAMo9ZqEAAK52hQoiWrZsqXXr1unRRx9V165d5e/vL0lKSkrSunXrCt1Zhw4d3BslAAAAAAAoFwoVRDzzzDPq2rWr5s6dq7lz5zqWx8TEqHPnzoXqyGazKSsry71RAgAAAACAcqFQQUSnTp20bds2zZ8/X3v37lVqaqrWrFmjwMBAXXfddVd4iAAAAAAAoLwoVBAhSU2bNtVLL73k+N7Dw0PNmzfX6tWrr8jAAAAAAABA+VPoIOJiw4cPV6NGjYpzLAAAAECx4wKhAFC6uB1EvPfee8U5DgAAAAAAcBVwO4jI7dChQ1q1apX++OMPpaSkKCAgQA0bNtTtt9+uevXqFUcXAAAAAACgHLisIOL06dN64IEHtGTJEhljJEnGGNlsNkkX7pQxaNAgvfbaa6pSpcrljxYAAAAAAJRpbgcRqamp6tKli3bu3CljjG6++WY1a9ZMNWrU0IkTJ/Tbb79p06ZN+vjjj7V3715FR0fLz8+vOMcOAAAAAADKGLeDiFdeeUU7duxQ48aNtWjRIrVu3TpPm19//VXDhw/Xjh07NGfOHD3++OOXNVgAAAAAAFC2ebhb+Omnn8rT01PffPONyxBCklq3bq2vvvpKHh4e+vjjj90eJAAAAAAAKB/cDiL279+vqKgoRUREFNguMjJSUVFR2r9/v7tdAQAAAACAcsLtIMLT01OZmZmFapuZmSkPD7e7AgAAAAAA5YTb6UCjRo0UGxurnTt3Fthux44d2rNnj5o0aeJuVwAAAAAAoJxwO4i49957ZYxRjx499PXXX7ts89VXX6lnz56y2Wy699573R4kAAAAAAAoH9y+a8b999+vL774QqtXr1bv3r0VFhamxo0bq3r16jp58qRiY2N19OhRGWN066236v777y/OcQMAAAAAgDLI7SDCy8tL3377rZ566im99dZbOnLkiI4cOeLUpkKFCrr//vs1Y8YMeXp6XvZgAQAAAABA2eZ2ECFJfn5++s9//qNnnnlGGzZs0B9//KGzZ8+qUqVKatiwoW655RYFBAQU11gBAAAAAEAZd1lBhF1AQIC6deumbt26FcfmAAAAAABAOcU9NQEAAAAAgGUIIgAAAAAAgGUIIgAAAAAAgGUIIgAAAAAAgGUIIgAAAAAAgGUIIgAAAAAAgGUIIgAAAAAAgGXcDiI8PDwUEhKi9PT04hwPAAAAAAAox9wOIipVqqTIyEj5+voW53gAAAAAAEA55nYQ0bhxY504caI4xwIAAAAAAMo5L3cL77vvPo0bN07ffvutunfvXpxjAgAAAFCGxcTElPQQ3BYVFVXSQwDKPbdnRNx3330aP368hgwZoldffVWJiYnFOS4AAAAAAFAOuR1ERERE6Pvvv1dqaqomTJigatWqqUaNGoqIiHD5iIyMLM5xF0l2drbmzZunjh07KiQkRH5+fqpbt6569+6tL7/80mXNpk2b1KtXL1WrVk3+/v5q2rSpZsyYobS0tAL7io2N1dChQ1WrVi35+fkpMjJSEydO1JkzZwqsi4+P19ixYxUaGipfX1+FhYVp3Lhxio+Pd3e3AQAAAAAoddw+NePw4cN5lp06dUqnTp1y2d5ms7nb1WU5ffq07rrrLv3888+y2Wxq2LChwsPDdezYMX355Zfy8vJSr169nGoWL16s4cOHKzs7W3Xq1FFoaKhiYmI0depUff3111qzZo0qVKiQp6/Vq1ere/fuSk1NVbVq1dSsWTPt3btXL730kj7//HNt3LhRNWrUyFO3Z88etW/fXomJiQoKClJUVJQOHDigd955R0uXLtWGDRvUuHHjK3aMAAAAAACwittBxKFDh4pzHFdETk6OevbsqZ9//ll9+/bVq6++qmuuucax/s8//9TBgwedag4fPqzRo0crOztbL774oiZOnCibzaYjR47ozjvv1JYtWzR58mS99tprTnUpKSkaNGiQUlNT9dBDD+k///mPvL29lZCQoF69eik6OlqjR4/WN99841SXnZ2tAQMGKDExUf369dOiRYtUoUIFnTt3TsOGDdOyZcs0aNAgbd++XR4ebk9gAQAAAACgVHA7iKhbt25xjuOKeOedd7RhwwZ17txZS5YsyfNB/pprrnEKJiRp9uzZSk9P1x133KFJkyY5ltetW1cLFixQu3bt9M477+jpp592mt3w1ltv6dSpU2rSpIlefvlleXp6SpKCg4P14YcfKjIyUt9++622bdumli1bOuqWLVumPXv2KDg4WO+9955jpkXFihW1cOFCrV27Vrt27dKXX36pPn36FPsxAgAAAADASuX6T+yvvvqqJGnGjBmFmk1gjNHnn38uSRo9enSe9W3btlXjxo2VmZmZ59oSy5YtkySNGDHCEULYhYWF6bbbbpMkffbZZy7rBg4cqICAAKd1AQEBGjBggCRpyZIllxw/AAAAAACl3WUHERkZGVq8eLHGjh2r7t27q0uXLk7rN23apJUrVyo7O/tyuyqSffv2ae/evapataratm2rL7/8Uvfcc4+6dOmiwYMH691331V6erpTTVxcnP766y9JUrt27Vxu17588+bNjmVZWVnaunVrkesk6eeff3arDgAAAACAssjtUzOkCx+iBw0apD///FPGGEl5L0r55Zdfavbs2fruu+905513Xk53RWIPBho3bqx7771Xixcvdlr/ySef6KWXXtL333/vOM1k3759kiRfX1/Vrl3b5XYjIiKc2koXriuRmZnptL4wdRkZGYqLiytUnb0Pb2/v/HYZAAAAAIBSz+0ZEQcPHlTXrl119OhR9e3bV++//76aNWuWp90999wjY4yWLl16WQMtKvvMhi1btmjx4sUaM2aMDh8+rLS0NP3www+KiIjQ3r171a9fP+Xk5Ei6cIcNSapcuXK+d/moUqWKU9uLv7avL0xdUlKSo+9L1eXk5Cg5OTnf/U1PT1dycrLTAwAAAACA0sbtIGLmzJlKTk7Wc889pyVLlujee+9V5cqV87SLiopS1apVtWXLlssZZ5GdO3dOkpSZman27dtr3rx5qlu3rnx9fdWlSxctW7ZMNptNW7du1bfffitJSktLkyT5+Pjku11fX19JUmpqqmOZva6g2sutu7j2YrNmzVJQUJDjERoamm9bAAAAAABKitunZqxatUpBQUF6/PHHL9k2PDxcR44ccbcrt/j5+Tm+fvjhh/Osv/baa9W5c2f99NNP+v777/WPf/zDUZORkZHvdu3XlfD393fZV0ZGhtP3RakrqL+Lay82ZcoUTZgwwfF9cnIyYQQAAABwlYiJiSnpIbgtKiqqpIcAi7k9I+LUqVOKjIzM9xSG3Dw9PXX27Fl3u3JL7lMdGjdu7LJNkyZNJF24/kLumjNnzjiueXEx+6kVubef++vcp15cqi4oKMhxN49L1Xl4eCgwMNBlG+nCzInAwECnBwAAAAAApY3bQUTlypUVHx9fqLYHDhxQjRo13O3KLY0aNXJ8nfv0htzsy+139GjQoIGkC7MQjh075rLm4MGDTm2lCzM+7BeRtK8vTJ2Pj4/CwsIKVZe7DwAAAAAAyiq3T8244YYb9O2332r9+vVq3759vu2++OILJSYmqlu3bu525Zbrr79efn5+SktL08GDB1W/fv08bewf8uvUqSNJCgsLU82aNXX8+HFFR0dr4MCBeWqio6MlSTfeeKNjmZeXl1q2bKnNmzcrOjra5a04XdXZvz98+LCio6M1dOjQQtcBAAAAAEoOp8O4z+0ZEQ8++KCMMRo1apR27drlss26des0duxY2Ww2Pfjgg24P0h0VK1bUXXfdJUl6//3386w/fvy4VqxYIUm69dZbJV249WifPn0kSfPnz89Ts3HjRu3du1fe3t7q2bOn07q+fftKkhYuXOiYYWEXFxenH374QZLUr18/l3WffvqpUlJSnNalpKRoyZIlkqT+/ftfapcBAAAAACj13A4i7rzzTj300EM6cOCAWrdurZtuukl//PGHJGnYsGFq2bKlOnfurL///luPP/64brrppmIbdGFNnTpVnp6e+vjjj53CiDNnzmjEiBFKTU1VRESEBgwY4Fg3adIk+fj4aOXKlZo9e7bjWhFHjhzRqFGjJEljxoxRzZo1nfoaP368QkJCFBsbqwkTJigzM1OSlJCQoLvvvltZWVnq1q2bWrVq5VTXr18/NW7cWAkJCRo5cqTOnz8v6cJdP0aOHKmEhARFRUWpd+/exX58AAAAAACwmttBhCTNmTNHb775poKDg/XLL7/o1KlTMsbogw8+0I4dOxQcHKw33nhDzz33XHGNt0iuvfZavfbaazLGaMSIEapbt67atGmjOnXqaMWKFQoJCdHSpUudbp1Zr149zZs3Tx4eHpo8ebJCQ0PVsmVLNWjQQL///rtatWql2bNn5+krMDBQH3/8sfz8/DR37lzVqVNHrVu3VlhYmKKjoxUeHq4FCxbkqfP09NSSJUtUpUoVLV26VLVr11br1q1Vp04dLV26VFWrVtUnn3ziuKglAAAAAABl2WV/uh03bpzi4uK0Zs0a/fe//9XMmTM1Z84crVy5UkePHtX48eOLY5xuGz9+vNauXat//OMfOn/+vHbt2qXq1avrwQcf1I4dO3TdddflqRk2bJjWr1+vHj16KDU1VXv27FFERISmTZumDRs2qGLFii776tKli3799VcNHjxYNptNu3fvVo0aNTRhwgRt27YtzywKu6ioKO3cuVNjxoxRpUqVtHv3blWqVEn33Xefdu7cqaZNmxbnIQEAAAAAoMS4fbHK3Ly9vdWhQwd16NChODZX7Nq3b1/gBTVdadu2rb7++usi99WsWTN99NFHRa4LDQ3VvHnzilwHAAAAAEBZwnx/AAAAAABgmcsOIk6cOKFnn31Wbdu2VUhIiHx9fRUSEqK2bdvq2Wef1fHjx4tjnAAAAAAAoBy4rFMzli5dqjFjxig5OdlxdwlJSkxM1M8//6zNmzfrlVde0bx585zuTAEAAAAAAK5ObgcRGzZs0KBBg5STk6OWLVvqgQceUJMmTVSjRg2dPHlSsbGxev3117Vt2zbdfffdql27ttq1a1ecYwcAAAAAAGWM26dmTJ8+XcYYPfbYY/r11181atQo3XzzzYqIiNBNN92kkSNH6tdff9Xjjz+u7OxsPfvss8U5bgAAAAAAUAa5HURs3rxZwcHBeu655wpsN2PGDIWEhGjz5s3udgUAAAAAAMoJt4MIm82mevXqycOj4E14enqqXr16stls7nYFAAAAAADKCbeDiOuuu04HDx5UdnZ2ge2ysrJ08OBBXX/99e52BQAAAAAAygm3g4gpU6YoMTFRU6ZMKbDdk08+qcTERD3xxBPudgUAAAAAAMqJQt01Iy4uLs+yJk2a6N///reefvpp/fjjj7r//vvVpEkTVa9eXadOnVJsbKzeeOMNxcTEaNasWWrUqFGxDx4AAAAAAJQthQoiwsPD873GgzFGO3bs0Lhx4/KtnzJlip544gllZWW5N0oAAAAAAFAuFCqICAsL42KTAAAAAADgshUqiDh8+PAVHgYAAAAAALgauH2xSgAAAAAAgKIiiAAAAAAAAJYp1KkZl3LmzBkdOnRIZ8+elTEm33YdOnQoju4AAAAAAEAZdVlBxE8//aQnn3xSv/zyyyXb2mw27poBAAAAAMBVzu0g4rvvvlPv3r2VlZUlPz8/1atXT9WqVePuGgAAAAAAIF9uBxFTp05Vdna2xo0bp+eff15BQUHFOS4AAAAAAFAOuR1E7NmzRyEhIXrzzTeLczwAAAAAAKAcczuIqFKliurUqVOcYwEAAAAAAOWc27fvvOOOOxQbG6tz584V53gAAAAAAEA55nYQ8eyzz6pixYoaN26c0tLSinNMAAAAAACgnHL71IywsDCtW7dOw4YNU/369TVkyBBFRkaqQoUK+dYMGzbM3e4AAAAAAEA54HYQIUm//PKLjh49qr/++ksvv/zyJdsTRAAAAAAAcHVzO4j45JNPHMHCNddco+bNm6tatWqy2WzFNjgAAAAAAFC+uB1EzJo1SzabTbNmzdLEiRPl4eH25SYAAAAAAMBVwu0g4o8//lCdOnU0efLk4hwPAAAAAAAox9yexhAcHKwaNWoU51gAAAAAAEA553YQ8Y9//EMxMTFKSEgozvEAAAAAAIByzO0gYubMmQoNDdXgwYN14sSJ4hwTAAAAAAAop9y+RsRrr72mu+66S2+++aYiIyPVrVs3RUZGqkKFCi7b22w2Pf30024PFAAAAAAAlH1uBxHTpk2TzWaTMUaZmZlaunSpy3b2NgQRAAAAAADA7SDimWeeKc5xAAAAAACAqwBBBAAAAAAAsIzbF6sEAAAAAAAoKoIIAAAAAABgGbdPzZg+fXqR2nOxSgAAAAAAUCx3zbiYzWZz+p67ZgAAAAAAAOkKXazy3Llz2r9/v1asWKHs7Gw98MADCgoKcrcrAAAAAABQTlzRu2b8+eefGjp0qFasWKFNmza52xUAAAAAACgnrujFKq+55hp9/PHH2rdvH6dlAAAAAACAK3/XjFq1aikqKkqff/75le4KAAAAAACUcpbcvjMpKUmnTp2yoisAAAAAAFCKXfEgYvny5Tp06JBCQ0OvdFcAAAAAAKCUc/tilYsWLcp3nTFGp06d0pYtW/TFF1/IZrNp+PDh7nYFAAAAAADKCbeDiBEjRshmsxXYxhgjSRo9erSmTJniblcAAAAAAKCccDuIGDZsWL5BhM1mU8WKFRUREaFu3bqpcePGbg8QAAAAAACUH24HEQsXLizGYQAAAAAAgKuBJXfNAAAAAAAAkAgiAAAAAACAhQp9akZBd8korGHDhl32NgAAAAAAQNlV6CCiMHfJuBSCCAAAAAAArm6FDiJuvfXWIgcRmZmZio6OVnZ29mWHGAAAAAAAoOwrdBDxww8/FHqj2dnZWrhwoWbOnKmcnBxJ0nXXXVfkwQEAAAAAgPKlWC9WmZOTo/fff1+NGjXS2LFjdeTIEUVFRemzzz7T1q1bi7MrAAAAAABQBhV6RkRBjDFavHixZsyYof3798sYo6ZNm+qZZ57RgAEDiqMLAAAAAABQDlx2EPHxxx9r+vTp+v3332WMUaNGjTR16lQNHjyY60IAAAAAAAAnbgcRS5Ys0bPPPqvY2FgZY9SgQQM9/fTTuvvuu+XhUaxnfAAAAAAAgHKiyEHEsmXL9OyzzyomJkbGGEVEROjpp5/WvffeSwABAAAAAAAKVOgg4quvvtK0adO0c+dOGWMUHh6uJ598UiNGjJCnp+eVHCMAAAAAACgnCh1E9O7dWzabTZ6enhoyZIhGjRolb29vbd68udCdtW3b1q1BAgAAAACA8qHIp2ZkZ2frgw8+0AcffFCkOpvNpqysrKJ2BwAAAAAAypFCBxFhYWHcBQMAAAAAAFyWQgcRhw8fvoLDAAAAAAAAVwNucwEAAAAAACxDEAEAAAAAACxDEAEAAAAAACxz1QURTz31lGw2m2w2m2bOnJlvu02bNqlXr16qVq2a/P391bRpU82YMUNpaWkFbj82NlZDhw5VrVq15Ofnp8jISE2cOFFnzpwpsC4+Pl5jx45VaGiofH19FRYWpnHjxik+Pt6d3QQAAAAAoFS6qoKI2NhYzZ49+5LtFi9erPbt2+urr76Sr6+vmjRpov3792vq1Knq0KGDzp8/77Ju9erVatWqlT788ENlZ2erWbNmOn78uF566SW1atVKJ06ccFm3Z88etWjRQvPmzVNKSoqioqKUnJysd955R9dee6327t17WfsNAAAAAEBpcdUEEcYYjRs3Tt7e3rr11lvzbXf48GGNHj1a2dnZevHFF3X06FFt27ZN+/btU6NGjbRlyxZNnjw5T11KSooGDRqk1NRUPfTQQ4qPj9fWrVsVFxendu3a6eDBgxo9enSeuuzsbA0YMECJiYnq16+fjh07pq1btyo+Pl59+/ZVQkKCBg0apJycnGI9HgAAAAAAlISrJoiYP3++1q9fr6lTpyo0NDTfdrNnz1Z6erruuOMOTZo0STabTZJUt25dLViwQJL0zjvv5Jnd8NZbb+nUqVNq0qSJXn75ZXl7e0uSgoOD9eGHH8rLy0vffvuttm3b5lS3bNky7dmzR8HBwXrvvfdUoUIFSVLFihW1cOFCBQcHa9euXfryyy+L7VgAAAAAAFBSroog4tSpU3rsscfUtGlTPfroo/m2M8bo888/lySXsxfatm2rxo0bKzMzM08wsGzZMknSiBEj5Onp6bQuLCxMt912myTps88+c1k3cOBABQQEOK0LCAjQgAEDJElLliy55H4CAAAAAFDaXRVBxKOPPqrExES98cYbjpkKrsTFxemvv/6SJLVr185lG/vyzZs3O5ZlZWVp69atRa6TpJ9//tmtOgAAAAAAyqJyH0T8+OOPWrx4se655x517NixwLb79u2TJPn6+qp27dou20RERDi1lS5cVyIzM9NpfWHqMjIyFBcXV6i63H0AAAAAAFBWeZX0AK6ktLQ0jR8/XkFBQfrPf/5zyfanT5+WJFWuXNlxbYiLValSxantxV/b1xemLikpyXERykvV5eTkKDk5WcHBwS7bpaenKz093fF9cnKyy3YAAAAAAJSkcj0jYubMmdq/f7+ee+451ahR45Lt09LSJEk+Pj75tvH19ZUkpaam5qkrqPZy6y6uvdisWbMUFBTkeBR0QU4AAAAAAEpKuQ0iYmNjNXv2bLVs2VL3339/oWr8/PwkXThlIj/2WQf+/v556gqqvdy6i2svNmXKFCUlJTkeR48ezbctAAAAAAAlpdyemvHAAw8oKytLb775pjw8Cpe32E+DOHPmjIwxLk/PsJ9akftUitxfnz59WrVq1SpUXVBQkDw8PJSTk+N0yoarOg8PDwUGBuY7dl9fX6fZEwAAAAAAlEbldkbE9u3bZbPZ1LNnT9WsWdPp8cknn0iSXnjhBdWsWVNt2rSRJDVo0EDShVkIx44dc7ndgwcPOrWVpPDwcMfdOOzrC1Pn4+OjsLCwQtXl7gMAAAAAgLKq3AYRkpSdna0TJ07kedivzXD27FmdOHFCp06dkiSFhYWpZs2akqTo6GiX27Qvv/HGGx3LvLy81LJlyyLX5f6+qHUAAAAAAJRF5TaIsJ9e4eoxfPhwSdKMGTNkjNHhw4clSTabTX369JEkzZ8/P882N27cqL1798rb21s9e/Z0Wte3b19J0sKFC5Wdne20Li4uTj/88IMkqV+/fi7rPv30U6WkpDitS0lJ0ZIlSyRJ/fv3L/IxAAAAAACgtCm3QYS7Jk2aJB8fH61cuVKzZ8+WMUaSdOTIEY0aNUqSNGbMGMfMCbvx48crJCREsbGxmjBhgjIzMyVJCQkJuvvuu5WVlaVu3bqpVatWTnX9+vVT48aNlZCQoJEjR+r8+fOSpHPnzmnkyJFKSEhQVFSUevfufYX3HAAAAACAK48g4iL16tXTvHnz5OHhocmTJys0NFQtW7ZUgwYN9Pvvv6tVq1aaPXt2nrrAwEB9/PHH8vPz09y5c1WnTh21bt1aYWFhio6OVnh4uBYsWJCnztPTU0uWLFGVKlW0dOlS1a5dW61bt1adOnW0dOlSVa1aVZ988kmhL7gJAAAAAEBpxqdbF4YNG6b169erR48eSk1N1Z49exQREaFp06Zpw4YNqlixosu6Ll266Ndff9XgwYNls9m0e/du1ahRQxMmTNC2bdvyzKKwi4qK0s6dOzVmzBhVqlRJu3fvVqVKlXTfffdp586datq06ZXcXQAAAAAALFNub99ZkIULF2rhwoUFtmnbtq2+/vrrIm+7WbNm+uijj4pcFxoaqnnz5hW5DgAAAACAsoQZEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDIEEQAAAAAAwDLlNogwxmjDhg2aNGmSbrrpJlWuXFk+Pj6qXbu2+vXrp9WrVxdYv2nTJvXq1UvVqlWTv7+/mjZtqhkzZigtLa3AutjYWA0dOlS1atWSn5+fIiMjNXHiRJ05c6bAuvj4eI0dO1ahoaHy9fVVWFiYxo0bp/j4+KLuOgAAAAAApVa5DSJ++ukntW/fXv/5z3+0ZcsW1ahRQ1FRUUpJSdGyZct066236umnn3ZZu3jxYrVv315fffWVfH191aRJE+3fv19Tp05Vhw4ddP78eZd1q1evVqtWrfThhx8qOztbzZo10/Hjx/XSSy+pVatWOnHihMu6PXv2qEWLFpo3b55SUlIUFRWl5ORkvfPOO7r22mu1d+/eYjsuAAAAAACUpHIbRBhjVL9+fb3xxhv6+++/9fvvv2vbtm1KSEjQlClTJEkzZ87UN99841R3+PBhjR49WtnZ2XrxxRd19OhRbdu2Tfv27VOjRo20ZcsWTZ48OU9/KSkpGjRokFJTU/XQQw8pPj5eW7duVVxcnNq1a6eDBw9q9OjReeqys7M1YMAAJSYmql+/fjp27Ji2bt2q+Ph49e3bVwkJCRo0aJBycnKuzIECAAAAAMBC5TaIuOGGGxQbG6v7779fVapUcSz38fHRv//9b3Xr1k2SNG/ePKe62bNnKz09XXfccYcmTZokm80mSapbt64WLFggSXrnnXfyzG546623dOrUKTVp0kQvv/yyvL29JUnBwcH68MMP5eXlpW+//Vbbtm1zqlu2bJn27Nmj4OBgvffee6pQoYIkqWLFilq4cKGCg4O1a9cuffnll8V4dAAAAAAAKBnlNogIDAyUl5dXvutvv/12SdIff/zhWGaM0eeffy5JLmcvtG3bVo0bN1ZmZmaeYGDZsmWSpBEjRsjT09NpXVhYmG677TZJ0meffeaybuDAgQoICHBaFxAQoAEDBkiSlixZku++AAAAAABQVpTbIOJS7Bed9Pf3dyyLi4vTX3/9JUlq166dyzr78s2bNzuWZWVlaevWrUWuk6Sff/7ZrToAAAAAAMqi/KcMlGPGGMcMg9wBwL59+yRJvr6+ql27tsvaiIgIp7bShetKZGZmOq0vTF1GRobi4uIKVWfvw37Kx8XS09OVnp7u+D45OdllOwAAAAAAStJVOSNi3rx52r59u3x8fPTII484lp8+fVqSVLlyZce1IS5mv96Eve3FX+e+HsWl6pKSkhwXobxUXU5OToHhwqxZsxQUFOR4hIaG5tsWAAAAAICSctUFEdu2bdPDDz8s6cJdMyIjIx3r7Kdr+Pj45Fvv6+srSUpNTc1TV1Dt5dZdXHuxKVOmKCkpyfE4evRovm0BAAAAACgpV9WpGYcOHVKPHj2Ulpamu+++WxMnTnRa7+fnJ+nCKRP5sZ/+kPvaEvY6e23u74tSV1B/F9dezNfX1ym0AAAAAACgNLpqZkQcP35ct99+u/766y91795dCxcuzHP6hf00iDNnzsgY43I79lMrcp9Kkfvr3KdeXKouKChIHh4eharz8PBQYGBg/jsIAAAAAEAZcFUEEYmJibr99tt14MABdezYUUuWLHF50ccGDRpIujAL4dixYy63dfDgQae2khQeHu7Ynn19Yep8fHwUFhZWqLrcfQAAAAAAUFaV+yDi7NmzuuuuuxQTE6M2bdro66+/zvcUh7CwMNWsWVOSFB0d7bKNffmNN97oWObl5aWWLVsWuS7390WtAwAAAACgLCrXQUR6erp69eqlzZs3q1mzZvr+++8VEBCQb3ubzaY+ffpIkubPn59n/caNG7V37155e3urZ8+eTuv69u0rSVq4cKGys7Od1sXFxemHH36QJPXr189l3aeffqqUlBSndSkpKY7bjPbv3/+S+wsAAAAAQGlXboOI7OxsDR48WD/99JMiIyO1atUqVa1a9ZJ1kyZNko+Pj1auXKnZs2c7rhVx5MgRjRo1SpI0ZswYx8wJu/HjxyskJESxsbGaMGGCMjMzJUkJCQm6++67lZWVpW7duqlVq1ZOdf369VPjxo2VkJCgkSNH6vz585Kkc+fOaeTIkUpISFBUVJR69+59uYcEAAAAAIASV27vmvHpp5/qiy++kHThQo8DBgxw2a5WrVqOWQeSVK9ePc2bN08jR47U5MmT9eqrr6p69eqKiYlRZmamWrVqpdmzZ+fZTmBgoD7++GP16NFDc+fO1UcffaSwsDDFxsbq/PnzCg8P14IFC/LUeXp6asmSJerQoYOWLl2qH374QfXr19f+/fuVlJSkqlWr6pNPPnFc1BIAAAAAgLKs3H66zX3by3379ik6OtrlY8uWLXlqhw0bpvXr16tHjx5KTU3Vnj17FBERoWnTpmnDhg2qWLGiyz67dOmiX3/9VYMHD5bNZtPu3btVo0YNTZgwQdu2bcszi8IuKipKO3fu1JgxY1SpUiXt3r1blSpV0n333aedO3eqadOmxXNQAAAAAAAoYeV2RsSIESM0YsQIt+vbtm2rr7/+ush1zZo100cffVTkutDQUM2bN6/IdQAAAAAAlCXldkYEAAAAAAAofQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiAAAAAACAZQgiSpnvvvtOt912m6pWraqKFSuqZcuW+u9//6ucnJySHhoAAAAAAJeNIKIUef7559W9e3f9+OOPqlKliurXr6+dO3fqoYceUp8+fQgjAAAAAABlHkFEKbFp0yY98cQT8vDw0IcffqgDBw5o586d2rZtm2rUqKGvvvpKL7/8ckkPEwAAAACAy0IQUUrMnDlTxhiNGTNGQ4YMcSy/9tprHQHE888/r8zMzJIaIgAAAAAAl40gohRITk7WDz/8IEkaPXp0nvUDBgxQYGCgEhIStHr1aquHBwAAAABAsSGIKAW2b9+ujIwM+fn5qWXLlnnWe3t7q02bNpKkzZs3Wz08AAAAAACKDUFEKbBv3z5JUlhYmLy8vFy2iYiIcGoLAAAAAEBZ5PpTLyx1+vRpSVKVKlXybWNfZ297sfT0dKWnpzu+T0pKknThtI8r4ezZs1dku1a4UsfkSuOYW49jbj2OufU45tbjmFuPY249jrn1OObW45jnv11jTIHtCCJKgbS0NEmSj49Pvm18fX0lSampqS7Xz5o1S88++2ye5aGhocUwQgAAAAAACiclJUVBQUH5rieIKAX8/PwkSRkZGfm2sc928Pf3d7l+ypQpmjBhguP7nJwcJSYmKjg4WDabrRhHe2UlJycrNDRUR48eVWBgYEkP56rAMbcex9x6HHPrccytxzG3Hsfcehxz63HMrVeWj7kxRikpKapdu3aB7QgiSoFLnXaRe11+p2/4+vo6Zk3YVa5cuXgGWAICAwPL3JOurOOYW49jbj2OufU45tbjmFuPY249jrn1OObWK6vHvKCZEHZcrLIUaNCggSQpLi5OWVlZLtscPHjQqS0AAAAAAGURQUQpcP3118vb21tpaWnatm1bnvWZmZnasmWLJOnGG2+0engAAAAAABQbgohSIDAwULfddpskaf78+XnWL1myRMnJyQoODlanTp0sHp21fH199cwzz+Q5zQRXDsfcehxz63HMrccxtx7H3Hocc+txzK3HMbfe1XDMbeZS99WAJaKjo9W+fXvZbDZ98MEHGjJkiCRp586duvPOO3XixAm98MILmjx5cgmPFAAAAAAA9xFElCLPPfecnnrqKUlSRESEKlWqpJiYGOXk5Kh79+768ssv5enpWcKjBAAAAADAfQQRpcw333yjV155RVu3blVmZqYaNGigkSNH6p///CchBAAAAACgzCOIAAAAAAAAluFilQCcdOrUSTabTWvWrCnUcuBKstlsstlsJT0MFEF4eLhsNpsOHz5c0kMplzi+uNocPnxYNptN4eHhLtfv3LlTPXr0UNWqVeXh4ZHnvcqaNWvUuXNnBQYGOn6n8PwBSp5XSQ8AAMqDOXPm6MyZM3rkkUdUuXLlYtnmtGnTnP4FgOJy5swZzZkzR5UrV9YjjzxS0sPBVc7d36EnT55U586ddfr0adWpU0dNmjSRzWZTUFCQJOm3337TnXfeqYyMDNWrV08tWrSQJPn5+V2J3YAuBEcLFy5UeHi4RowYUdLDQSlGEAEAxWDOnDk6cuSIRowYUWxBxLPPPiuJIAJA8Ttz5oyeffZZ1a1blyACJa6g36He3t5q1KiR6tSpk6fu448/1unTp9WrVy8tW7ZMHh7Ok73nz5+vjIwM/etf/9LcuXOv5C7g/3f48GE9++yz6tixI0EECkQQAQAAAKBUqlOnjvbu3etynX35nXfemSeEyL2+W7duV26AANzCNSIAAAAAlDmpqamSJH9/f7fWAyg5BBEoktwXjlu+fLk6dOiggIAABQUFqVu3btq+fXu+tQkJCZo8ebIaNWokf39/ValSRZ06ddLixYvl6uYtCxculM1m04gRI5Senq5p06apfv368vPzU2hoqCZMmKBz587l29+ff/6phx56SA0bNpS/v78qV66szp0767PPPrv8A1EK5P5ZfPjhh7rhhhtUqVIlVa1aVb1791ZMTEy+tX///bceeOAB1alTR35+fmrUqJFmzJihzMzMQvX9yy+/qHv37qpataoqVqyotm3b6osvviiO3Spz7P9Pjxw5IkmqV6+e42djs9m0YsUKNW/eXDabTTNmzMhTb4xR586dZbPZNHbsWEkXTsXIfYHG3Nu7mi+yVZTXHHefH7kvBLhixQp16tRJQUFBCgwM1O23367169dfsf0rS44cOaJ77rlH1atXV4UKFdSiRQu9/vrrLl/LZ86cKZvNpqioKKWlpeVZv2DBAtlsNtWuXVsJCQlWDL/UK8rxlaSDBw/qhRdeUKdOnRQaGipfX19Vq1ZNXbt21bfffpun/YgRI1SvXj1HXxe/xlxNxowZI5vNpttvv93l8Z06dapsNpuaN2+uEydOyN/fX97e3jpx4kS+2+zRo4dsNptef/11Sf97Tb/U42p0qd+ha9ascXmxSvsxXbhwoSRp5MiRjppOnTppxIgRThettP+etb+vvFoV5b24/Rjnd3po7vfpdp06dVLnzp0lSWvXrnX6WeZ3sdHyqKivK76+vvLy8irwdaVfv36y2Wx5TjFKSUnR5MmTFR4eLj8/P9WrV0+PPfaYzp0753ge2J8npY4BikCSkWTefPNNY7PZTK1atUzLli1NxYoVjSRTqVIlExsbm6du3759JjQ01EgyPj4+pmXLliYiIsKxvWHDhpmcnBynmvfee89IMnfffbfp0KGDsdlsplmzZqZRo0bGw8PDSDK33367y3GuWbPGBAUFGUnG39/fNG/e3NG/JPN///d/V+T4WMm+Ly+88IKRZGrWrGlat25tAgICHPu9fv36PHV//fWX49h7eXmZ6667zjRo0MBIMj169DAdOnQwkszq1aud6jp27GgkmenTpxsfHx9TqVIl07p1a1OrVi3HWF566SWL9r70+O6770y7du2Mr6+vkWRat25t2rVr53hs27bNbN++3fj4+BgvLy/zyy+/ONXPnj3bSDKRkZEmJSXFGGPM/PnzTbt27RzHNff22rVrZ/7666+S2NUS4e5rjrvPj7p16xpJZtasWcZms5mqVaua1q1bm+DgYCPJeHh4mE8//dSKXS+19uzZ4zgefn5+plWrViYsLMxIMg888IDjGB46dMgYY0xWVpa5+eabjSTz8MMPO23r0KFDjp/Jd999Z/3OlEJFPb7GGDN69GjH86Fhw4Z5Xpuff/55pz6ee+4507p1ayPJ+Pr65nmNuZqkpKQ4fie+8sorTut+/vln4+npaXx8fMyOHTuMMcYMGTKkwN93x48fN15eXsbHx8ckJCQYY/73mu7q0ahRI8fP6WpUmN+hhw4dMpJM3bp1HXX2Y1q9enUjyTRo0MBR889//tM899xzpl27diYwMNBIMlFRUY71zz33XMntcAkq6nvxZ555xkgyzzzzjMvt2d+nDx8+3LHsn//8p4mKijKSTGBgoNPPsn///ld4D0uPor6u/OMf/zCSzNy5c11uLykpyfj5+RlPT0+n94BJSUnm+uuvd7w/ad68uWnWrJmx2WymTZs2jter995770rt6mW5Ol/14Db7i1WFChWc/lMnJyebLl26GElm0KBBTjU5OTmONzwdO3Y0x48fd6xbvny54wPFG2+84VRnf4Hz9vY2TZs2Nb///rtj3aZNmxy/XJYvX+5UFx8fb6pWrWpsNpv597//bdLS0hzroqOjTZ06dYwk8/XXXxfHISkx9p+Ft7e3eemll0x2drYxxphz586ZoUOHOn5pnz9/3qmuT58+RpJp2bKliYuLcyz/8ccfTUBAgPH29i4wiPDy8jKDBw82Z8+eNcZc+PnOnTvXsc7+onq1cfXhILdZs2YZSaZhw4bm3Llzxhhjdu/ebXx9fY2np6fZuHFjnpqr+c2pnTuvObnrivr8sP8cvby8zIQJE0xGRoYxxpjMzEwzefJkx5urY8eOXbmdLsVycnJMy5YtjSRz5513Oj5oGWPMRx99ZLy9vY2Xl1ee58L+/ftNxYoVjc1mM6tWrTLGGJOdnW3at29vJJn777/f6l0pldw9vt999535+eef8wT669atM7Vq1TKenp5m//79Tutcfbi7WkVHRxtPT0/j5+dnYmJijDEXXivsIf0LL7zgaLtq1SojybRo0cLltl566SUjqVAfus6dO+f4EDF+/Pji2ZkyqqDfoQX9Xx0+fHiBH7Ts710ufk9ztXHnvbg7QYQxxqxevdrRz9WsKK8rH330kZFkbr75ZpfbWrhwoZFkbrvtNqflDz74oJFkIiIizJ49exzLY2JiTN26dR3v6QkiUC7Y39z/61//yrNu165dRpIJCgpyWm7/pe3r6+vyL7kvvvii4xdM7jdR9hc4m81mtmzZkqduwoQJRpJ56KGHXC5/9NFHXe7D119/bSSZW2+9tTC7XGrZfxY9e/bMsy49Pd3UrFnTSDILFixwLN+3b5+x2WxGkuNFMbeXX37Zsd38gojq1aub1NTUPLV9+/Z1JOpXo0sFEdnZ2eaWW25xfOhKT0831157rZFknnrqKZc1BBHuvebkrivK88OY//0cr732WpfjsX9InDp1qlv7U9b98MMPjhklp06dyrP+oYcechz7i58Lb7/9tpFk6tSpYxITE12Gc1e7yzm++Xn33XeNpDx/BSaIcDZlyhQjyVx33XUmPT3djBs3zkgyHTp0cASZxlz4QBceHm4kme3bt+fZTosWLYwk880331yyz8GDBxtJpn379o7Q82pFEHFlufNenCDi8hX2deXcuXOmUqVK+T4H7rzzTiPJzJ8/37HszJkzxs/Pz0gyGzZsyFNj/zmU5iCCa0TALWPGjMmzrHnz5vLz81NSUpLTeb4rV66UJA0YMEA1a9bMUzd+/Hj5+vrqyJEj+v333/Osv+6669S6des8y9u0aSPpwrmxuS1btizfMUpS165d5ePjo40bNyorKyu/XSwzHnzwwTzLfHx8HPu/YsUKx/KVK1fKGKMOHTqoWbNmeerGjBkjHx+fAvsbPXq0y/tvP/DAA3n6w/94eHho0aJFCggI0Jtvvqnu3btr586datWqlaZOnVrSwyv1ivKak1tRnh+52f8/57f8av1/bt/vAQMGKCQkJM/6/I6bJI0dO1Y9evRQfHy8+vTpo2eeeUZeXl764IMPVKFChSs25rLkco7vqVOn9Oqrr+ruu+/WbbfdpltuuUW33HKL5syZI0nauXPnFRlzefHss8/q+uuv144dO9SjRw+9/fbbCgwM1KJFi5zuxmCz2TR8+HBJ0vvvv++0jR07dmjXrl2qWbOmunbtWmB/s2bN0scff6ywsDAtXbpU3t7exb9TwP/vct+Lwz2FfV2pUKGCevXqJenCLWlzO3XqlH788Uf5+vqqX79+juXr169XWlqaGjRooHbt2uXpu1OnTo5rAZVWBBFwS2RkpMvl1apVkySdPXvWseyPP/6QJDVt2tRlTUBAgEJDQ53aFqav6tWr5+nr7Nmzjgv5jR071vFGLPejU6dOkqS0tLRycWG0Jk2aFLg89zG1f51fTUBAgMv7dBelvxMnTig5ObngQV+l6tWr5/hQ8MMPP8jf318ffPABb0ALoSivObkV5flRHHXl3aVeQxo0aCAvr/zvDP7uu++qWrVqWrt2rTIyMvTUU085QmW4f3xXrlypBg0a6JFHHtFHH32kH3/8UdHR0YqOjnZcmDUxMfHKDbwc8Pb21gcffCA/Pz+tWrVKkjR37lzVrVs3T1v7hRE//PBDpz9o2IOJe+65R56envn29e233+qpp55ShQoV9OWXXzpex4Ar5XLfi8M9RXldufvuuyVJH330kdPyJUuWKCsrS926dVNQUJBj+b59+yRJLVq0yLf/5s2bX/Y+XEkEEXBLxYoVXS63p3sm1xVi7R8Q7MGBKzVq1JB04cqvl9NXUlKS42v7mzBXj4yMDEn/u61TWZbfcXV1TO0/i4Le9Njritpf7uWufo64oEOHDo4PEjfffLMaN25cwiMqG4ryOpBbUZ4fxVFX3l3qNcTDw8PlX/LtatSo4ZiN5eHhcVVfvd4Vd47vmTNnNHjwYCUlJWnYsGH6+eefdfr0aWVnZ8sY43jzW9i7Il3N6tevr7CwMElSUFCQ018fc6tbt65uvfVWnTx5UsuXL5ckZWVl6cMPP5SkAv9f//777xo6dKhycnK0YMECXXfddcW6D4Arl/teHO4r7OvKHXfcoZCQEO3atUt79uxxLLcHE/agws5+58CAgIB8+y5oXWlAEIErrlKlSpKkkydP5tvGfruay33C2PuSpIyMDJkL10HJ91EebiV06tQpl8vtxzv3MbUfn/xqctcVtb/cy0v7C19Jyc7O1rBhw5SVlSUPDw/99NNPWrx4cUkPq1wryvOjOOrKu0u9huTk5BQ40+z111/XmjVr5OHhoZycHN133335hkhXI3eO7/Lly3X69GndfPPNWrhwoW688UZVrlzZEdIdPXr0yg66HHnyySf1xx9/yMPDQ0lJSXr00UfzbTtq1ChJ/5sFsXz5cp08eVKtW7d2eeqjdOGPJb169VJSUpKeeOIJDRo0qPh3AnDBnffi9lvK5vcabf8gjIIV9nXFy8tL/fv3l/S/8OHo0aOKjo5WQECAevTo4dTe/gea/GaESqU/VCKIwBXXsGFDSXJK93JLSUlxvFGyt3VXUFCQateuLUn67bffLmtbZUVsbGyBy3MfU/vXe/fudVlz9uxZ/fnnn5fVX40aNRQYGFjwoMuhwtwD/t///rc2bdqkZs2a6ZNPPpEk/fOf/+SDwhVUlOdHcdSVd5d6Ddm/f3++f3n/448/NHnyZHl4eOirr75SvXr1tGrVKr322mtXbLxljTvH13464s033+zydSi/a0MU5jXrarJu3Tq9/PLLqlChglatWqXKlSvr3Xff1ddff+2yfd++fVW5cmV9/fXXSkxM1MKFCyXlPxsiJydHd999t37//Xf16NFDM2bMuEJ7Ujbx//HKcue9uP2Dbn7B6P79+10u52f5P0V9Xbn49IyPPvpIxhj17t1b/v7+Tm3tP6ddu3bl2//u3buLYzeuGIIIXHF33nmnpAvnOB0/fjzP+rffflvp6emqW7euGjVqdNn99e3bV5Ic5+KXd2+88UaeZRkZGZo/f76kC1O97Oxfr1u3zuUvo3fffddx2kp+5s+fr/T09HzHkbu/q4n9F0R+p/ts3bpVM2bMcJwv2L9/f9133306c+aMRowY4fIvDpfaJi6tKM+PS9XlXn61/j+37/eSJUtcznzI77hlZWXp3nvv1fnz5/V///d/6t69u+NiXY899hgXR/v/uXN87a8T9r9m5paQkOD4v55fHa8vUnJysoYPH66cnBzNnj1bt956q15//XVJFy6U6+qDmJ+fn4YMGaKMjAy99tpr+uabb+Tj46MhQ4a47GPKlCn67rvv1LhxYy1evNjpQnXg/+OV5s578YiICEnSli1b8rQ/d+5cnosq2vGzvMCd15VbbrlFYWFhOnDggH755RdHIOHqdeWWW26Rn5+f/vjjD23atCnP+nXr1unQoUPFvFfFzPL7dKBM0yVuJ+jq9ks5OTmmTZs2RpLp1KmTOXHihGPdihUrHLerefPNN522ld9tgezyuz3Q0aNHTdWqVR238Dx9+rTT+oSEBDN//nwzY8aMQu1zaWX/WXh7e5s5c+Y4brd0/vx5M2zYMCPJhIaG5rktXq9evYwk07p1a3P06FHH8tWrV5ugoCDHPYfzu32nl5eXGTp0qDl79qwx5sLP9/XXXzc2m814enq6vJ3Z1aB79+4u/x8bc+Fn0qRJEyPJ/Pvf/3YsT0lJMZGRkUaSeeWVV/LUNWvWzEgyy5cvv5JDL9Xcec3JXVfU54d9e15eXmbSpEmOW+plZmY6bsMVEBBg4uPji3dHy4icnBxz/fXXG0mmW7duJjEx0bHuk08+MT4+PsbLyyvPz8R+G7jmzZubtLQ0x/LJkyc7Xo8yMzOt3JVSyZ3j++uvvzr+r69atcrR/tixY6Zjx46O27td/LsyJyfHBAQEGElO95+/GtlvAXnHHXc4LR80aJCRZHr37u2ybsuWLY7XC0mmf//+Ltt99NFHRpKpXLmy+eOPP4p9/OVBQb9DuX3n5XPnvXhiYqLj9ePtt992LD99+rTp37+/4/3ixe/TT548aSSZSpUqmZMnT17xfSut3H1dsf9e7Natm5FkQkJC8v39+OCDDxpJJjIy0uzdu9ex/LfffjPh4eGOn1FpvX0nQQSKxN0PBfv27TPXXHON4x7GLVu2NPXr13ds795773V8ULBzN4gwxpgNGzaYkJAQx5uz5s2bmxtvvNFEREQYm81mJJlBgwYVdfdLFfuxe+GFF4wkU7NmTdOmTRsTGBhoJBk/Pz+zdu3aPHXx8fGOe6B7e3ub66+/3jRs2NBIMt27dzcdOnQoMIiYPn268fHxMQEBAaZ169amdu3ajrG8+OKLFu196bNo0SLHcYiKijIdO3Y0HTt2NNu3bzf/+te/jCTTtm1bk5WV5VQXHR1tPD09jZ+fX54PA9OnTzeSjKenp7n++usd23R1D/Dy6nKDiKI+P+zbmzVrlrHZbCY4ONi0adPG8Xri4eFhPvroo+LezTIlJibGEfb6+/ub1q1bO47bAw88kOdnsnnzZuPl5WV8fHzMjh07nLaVnp5urr32WiPJTJ06tQT2pvQp6vE1xpj+/fs7/s/Xr1/fXHfddcbLy8sEBASYOXPm5Pu7ctSoUY7nQ+vWrR2vMVeTZcuWGUmmSpUqeQLGxMREx++4BQsWuKxv0aKF49h/8803LtvYf3/WqlXLtGvXLt/H1ayg36EEEcXDnffiM2bMcKyvU6eOadWqlfH39zc1atQw06ZNy/d9+q233uoI7m+88UbTsWPHMv++uygu53Vlx44djmMuydx///359pOUlGSuu+46x/uTFi1amObNmxubzWZat25tBg8ebCSZRYsWFfs+FgeCCBSJux8KjDHm1KlTZuLEiaZBgwbG19fXBAYGmg4dOpj/9//+X54XPmMuL4gw5kIi++STT5prr73WVKpUyfj7+5v69eubbt26mTfeeMMcP368MLtcauX+WSxevNi0adPGVKhQwQQFBZmePXuanTt35lt78uRJM378eFOrVi3j6+trGjRoYKZPn24yMjLy/aWde/nmzZtNt27dTOXKlY2/v7+56aabzLJly67k7pYJr776qmnRooXx9/d3/HxWrVplbDabqVixotm/f7/LuieeeMJIMi1btnT8Bd4YYzIyMswzzzxjGjVqZHx9fR3bdPX8Kq8uN4gwpmjPj9zb+/77702HDh1MQECAqVSpkrn11ltdhhdXo4MHD5q7777bBAcHGz8/P9O8eXPz3//+1+Tk5Dgdw3PnzjmCzlmzZrnc1u7du42vr6/x8vIymzdvtnhPSqfCHl+79PR08/TTTzv+AlazZk0zePBgs3fv3gJ/V6akpJiHH37Y6S9nBT3fypvjx487Qsb8AsYVK1YYm81mAgICXL72vvzyy46w8+Kg2c7++/NSj6udq9+hq1evJogoRkV9L26MMa+//rpp2rSp8fHxMdWrVzf33nuvOXr0aIHv048fP25GjBhh6tSp45gx5OrnVx4Vx+tK06ZNHc+B9evXF9hfcnKymThxogkLCzM+Pj6mbt26ZsKECSYlJcURUn/++efFsGfFz2YMl6sGyqJLXc0YuJq5+/wIDw/XkSNHdOjQoXJxVx0AV9bjjz+uF154QRMnTtTs2bNLejgA4NC8eXPFxMRo+/btpfJWwVwpBwAAACiizMxMLVq0SJI0cuTIEh4NAPzPli1bFBMTo8qVK+d7S+GSRhABAAAAFNHcuXP1119/qWPHjmratGlJDwfAVeiJJ55QfHy807JffvlFAwcOlCSNGjVK3t7eJTG0S+LUDKCM4tQMIH+cmgHgSjh+/LgGDx6shIQExcTEyMPDQ+vWrVO7du1KemgArkL29zs1a9ZUaGioTp48qSNHjkiSWrdurdWrV6tSpUolOcR8MSMCAAAAKIS0tDStXbtWv//+u5o1a6ZPP/2UEAJAiXnhhRfUsWNHSdLOnTuVkJCgVq1a6YUXXtDatWtLbQghMSMCAAAAAABYiBkRAAAAAADAMgQRAAAAAADAMgQRAAAAAADAMgQRAAAAAADAMgQRAADAcp06dZLNZtOaNWuclk+bNk02m03Tpk0rkXEBAIArjyACAAAUi/DwcNlstgIfc+bMKelhOqxZs0bTpk3LE4YAAIAry6ukBwAAAMqXBg0aqHr16i7X1alTR5IUFhamRo0aqUKFClYOzcmaNWv07LPPSrowQwMAAFiDIAIAABSrJ554QiNGjCiwzaJFi6wZDAAAKHU4NQMAAAAAAFiGIAIAAFguv4tVXsqff/6phx56SA0bNpS/v78qV66szp0767PPPivSdmw2m+O0jGeffdbpOhYjRozQmTNn5O/vL29vb504cSLf7fTo0UM2m02vv/66y3375Zdf1L17d1WtWlUVK1ZU27Zt9cUXXxQ4tl9++UWDBw9WnTp15OPjoxo1amjAgAHavn27y/ZHjhzRuHHjFBERIV9fXwUEBCgiIkJ9+vTRxx9/XKTjAgCAFQgiAABAmbB27VpFRUXpv//9r/788081aNBAgYGBWrNmjQYMGKCJEycWelvt2rVTaGioJCk0NFTt2rVzPBo2bKjKlSurT58+ysrK0uLFi11u48SJE1qxYoV8fHw0ZMiQPOvXr1+v9u3ba926dYqMjFRQUJA2bdqkPn366OWXX3a5zVdeeUU33XSTPvnkE6WlpSkqKkrZ2dn67LPPdOONN2rZsmVO7Q8fPqzWrVvrnXfe0YkTJ9SoUSPVr19fSUlJ+uKLL/T8888X+pgAAGAVgggAAFDqHTt2TH379lVycrL+/e9/6/Tp09q1a5fi4uIUHR2tOnXq6KWXXtI333xTqO1t2LBBo0aNkiSNGjVKGzZscDyeeOIJx3JJev/9911uY/HixcrKylLPnj1VtWrVPOunT5+uvn376vjx49qyZYvi4+M1d+5cSdJjjz2mnTt3OrX//vvv9X//938KDg7W0qVLlZCQoG3btunvv//Wu+++K2OMRowYob/++stR89JLL+nvv//W8OHDdeLECe3atUvbt29XQkKCYmNj9cADDxTqeAAAYCWCCAAAUKxGjhzp8tadl3NnipdeekmJiYl65JFHNGXKFPn6+jrWtW3bVm+99ZakCzMKikuXLl0UHh6uXbt2aceOHXnW2wOK/C7MWbVqVb333nuqWLGipAung/zrX/9S3759lZWVlWdWxJNPPiljjObPn6++ffs6rRs9erQefvhhpaSk6N1333Us37dvnyRpwoQJqlSpklNN48aNNXbs2CLtMwAAViCIAAAAxapBgwZOpzrYH82bN3d7m/ZTEsaMGeNyfdeuXeXj46ONGzcqKyvL7X5ys9lsGj58uKS8syJ27NihXbt2qWbNmuratavL+tGjR8vPzy/PcvsshRUrVjiWHTlyRNu2bVP16tXVs2dPl9uzL1+7dq1jmf30ks8++0zGmMLuGgAAJYrbdwIAgGJVmNt3FsXZs2d1+PBhSbrkX/jT0tKUkJCgGjVqFEvfI0eO1PTp0/Xhhx9q9uzZ8vK68NbJHkzcc8898vT0dFnbpEmTApefOHFCycnJCgwM1O7dux3jv+WWW1zWpaWlSZLi4+Mdyx588EG9//77mjFjhhYtWqSuXbuqffv26ty5s2rXru3GHgMAcOURRAAAgFItKSnJ8XV0dPQl26emphZb33Xr1tWtt96qH3/8UcuXL9c//vEPZWVl6cMPP5SU/2kZklS9evVLLk9JSVFgYKBjH5OTky+5j7n377rrrtO6dev0zDPP6KefftLbb7+tt99+WzabTbfffrvmzJmTbyACAEBJ4dQMAABQquW+9kFGRoaMMQU+wsPDi7X/iy9auXz5cp08eVKtW7dWs2bN8q07derUJZcHBARI+t8+tmvX7pL7Z58dYnfTTTdpxYoVOn36tL7//ns99thjuuaaa7Ry5UrdfvvtOnPmjLu7DgDAFUEQAQAASrWgoCDHaQa//fZbsW3XZrMVql3fvn1VuXJlff3110pMTNTChQslFTwbQpJiY2MLXF6jRg0FBgZKkpo2bepYl5OTU6hxXaxSpUq688479fzzz2vv3r2KjIxUfHy8li9f7tb2AAC4UggiAABAqWe/i8ScOXOKbZv+/v6SLn0qh5+fn4YMGaKMjAy99tpr+uabb+Tj46MhQ4YUWDd//nylp6fnWf7GG29Iku644w7HsgYNGigqKkqJiYlatGhRUXcljwoVKjguDnrs2LHL3h4AAMWJIAIAAJR6jz32mKpWrar3339fEyZMyHO6QWJiohYsWKCZM2cWepsRERGSVKg7bdhPz5gxY4YyMjLUs2dPVa1atcCahIQEjR49WufOnZMkGWP0xhtvaNmyZfL09NSECROc2r/wwguy2Wx68MEH9e677+YZ08GDB/Xcc8857iAiSffff78++eQTnT9/3qntunXr9OOPP0qSWrZsWeA4AQCwGkEEAAAo9a655hp99dVXCgkJ0SuvvKLq1aurRYsWuummmxQZGamQkBCNHj1aMTExhd7mHXfcoSpVqmjDhg0KCwvTLbfcok6dOun555/P07Z169Zq0aKFIxwozF1Bpk6dqiVLlqhWrVpq06aNrrnmGj344IMyxmjWrFm67rrrnNrfdddd+u9//6v09HTdd999qlq1qlq3bq02bdqoZs2aioyM1FNPPaWTJ086ajZt2qTBgwcrKChITZs21Y033qjw8HB17NhRKSkpuueee9S5c+dCHxMAAKxAEAEAAMqEdu3aac+ePXryySfVtGlTHTp0SLt27ZKHh4e6du2qN954Q6+++mqhtxcYGKiVK1eqW7duSk9P16ZNm7R27Vrt3bvXZXt7+FCzZk117dr1kttv37691q9fr1tuuUX79+/X6dOnddNNN2nZsmWaNGmSy5oHH3xQO3bs0JgxY1StWjX99ttv2rdvn0JCQjRkyBAtWbJEw4YNc7R/5ZVX9PDDD6tFixb6+++/tWPHDknSnXfeqa+++qpYTvMAAKC42YwxpqQHAQAAUNo9/vjjeuGFFzRx4kTNnj0733adOnXS2rVrtXr1anXq1Mm6AQIAUEYwIwIAAOASMjMzHbMLRo4cWcKjAQCgbCOIAAAAuIS5c+fqr7/+UseOHR232gQAAO7xKukBAAAAlEbHjx/X4MGDlZCQoJiYGHl4eOi5554r6WEBAFDmMSMCAADAhbS0NK1du1a///67mjVrpk8//VTt2rUr6WEBAFDmcbFKAAAAAABgGWZEAAAAAAAAyxBEAAAAAAAAyxBEAAAAAAAAyxBEAAAAAAAAyxBEAAAAAAAAyxBEAAAAAAAAyxBEAAAAAAAAyxBEAAAAAAAAyxBEAAAAAAAAy/x/Oarq0/rcxbAAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1200x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams.update({\"font.size\": 16})\n",
"(df\n",
" .query(\"MD_engine == 'Unknown'\")\n",
" .loc[:, \"file_type\"]\n",
" .value_counts()\n",
" .head(n=10)\n",
" .plot(kind=\"bar\", color=\"lightgrey\", figsize=(12,7))\n",
")\n",
"plt.xlabel(\"File types\")\n",
"plt.xticks(rotation=0)\n",
"plt.ylabel(\"Number of files\")\n",
"filename = \"fig/number_of_files_per_filetype_no_MD_engine\"\n",
"plt.savefig(f\"{filename}.png\", dpi=300, bbox_inches=\"tight\")\n",
"plt.savefig(f\"{filename}.svg\", bbox_inches=\"tight\")"
]
},
{
"cell_type": "markdown",
"id": "b3412192-1309-4e90-a811-5328265829cb",
"metadata": {},
"source": [
"Most files with no file type are simulation snapshots, log or README files."
]
},
{
"cell_type": "markdown",
"id": "bdddf576-d1c1-4dc2-a33a-8defe4f89d1d",
"metadata": {},
"source": [
"## Figure 3\n",
"\n",
"### 3.A. Gromacs file types by categories\n",
"\n",
"10 most frequent Gromacs file types"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "99aa48bf-2b81-44a2-b070-3a87eceb36f4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1200x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colors={\n",
" \"trajectory\":\"red\",\n",
" \"topology\":\"green\",\n",
" \"none\":\"grey\"\n",
"}\n",
"gromacs_file_types = (df\n",
" .query(\"MD_engine == 'GROMACS'\")\n",
" .groupby(\"file_type\").agg({\"category\": \"first\", \"file_name\": \"count\"})\n",
" .sort_values(by=\"file_name\", ascending=False)\n",
")\n",
"plt.rcParams.update({'font.size': 16})\n",
"gromacs_file_types[\"color\"] = gromacs_file_types[\"category\"].map(colors)\n",
"gromacs_file_types.plot(kind=\"bar\", y=\"file_name\", figsize=(12,7), color=gromacs_file_types[\"color\"])\n",
"plt.xlabel(\"File type\")\n",
"plt.ylabel(\"Number of files\")\n",
"# Custom legend.\n",
"traj_patch = mpatches.Patch(color=\"red\", label=\"Trajectory\")\n",
"topol_patch = mpatches.Patch(color=\"green\", label=\"Topology\")\n",
"misc_patch = mpatches.Patch(color=\"grey\", label=\"Misc.\")\n",
"plt.legend(handles=[traj_patch, topol_patch, misc_patch])\n",
"filename = \"fig/number_of_files_by_Gromacs_filetype\"\n",
"plt.savefig(f\"{filename}.png\", dpi=300, bbox_inches=\"tight\")\n",
"plt.savefig(f\"{filename}.svg\", bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d5a79c7e-eb0d-42de-b560-ee4a49aff4d1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total of 83731 Gromacs files from 1242 datasets.\n"
]
}
],
"source": [
"gromacs_files = df.query(\"MD_engine == 'GROMACS'\")\n",
"print(\n",
" f\"Total of {gromacs_files.shape[0]} Gromacs files \"\n",
" f\"from {gromacs_files['dataset_id'].nunique()} datasets.\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "45e0c150-5c64-4413-9846-d71550ae7b79",
"metadata": {},
"source": [
"### 3.B. Cumulated distribution of gro files"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7a2805b1-543a-4043-a16b-d46066ebf931",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset has 8671 gro files\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_origin</th>\n",
" <th>dataset_id</th>\n",
" <th>file_name</th>\n",
" <th>atom_number</th>\n",
" <th>has_protein</th>\n",
" <th>has_nucleic</th>\n",
" <th>has_lipid</th>\n",
" <th>has_glucid</th>\n",
" <th>has_water_ion</th>\n",
" <th>dataset_url</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>zenodo</td>\n",
" <td>1043926</td>\n",
" <td>DPPC-298K-42-CA.gro</td>\n",
" <td>229788.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>https://zenodo.org/record/1043926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>zenodo</td>\n",
" <td>1043926</td>\n",
" <td>DPPC-298K-48-CA.gro</td>\n",
" <td>229788.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>https://zenodo.org/record/1043926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>zenodo</td>\n",
" <td>1043926</td>\n",
" <td>DPPC-298K-54-CA.gro</td>\n",
" <td>229788.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>https://zenodo.org/record/1043926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>zenodo</td>\n",
" <td>1043926</td>\n",
" <td>DPPC-298K-60-CA.gro</td>\n",
" <td>229788.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>https://zenodo.org/record/1043926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>zenodo</td>\n",
" <td>1043926</td>\n",
" <td>DPPC-298K-66-CA.gro</td>\n",
" <td>229788.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>https://zenodo.org/record/1043926</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_origin dataset_id file_name atom_number has_protein \\\n",
"0 zenodo 1043926 DPPC-298K-42-CA.gro 229788.0 False \n",
"1 zenodo 1043926 DPPC-298K-48-CA.gro 229788.0 False \n",
"2 zenodo 1043926 DPPC-298K-54-CA.gro 229788.0 False \n",
"3 zenodo 1043926 DPPC-298K-60-CA.gro 229788.0 False \n",
"4 zenodo 1043926 DPPC-298K-66-CA.gro 229788.0 False \n",
"\n",
" has_nucleic has_lipid has_glucid has_water_ion \\\n",
"0 False True False True \n",
"1 False True False True \n",
"2 False True False True \n",
"3 False True False True \n",
"4 False True False True \n",
"\n",
" dataset_url \n",
"0 https://zenodo.org/record/1043926 \n",
"1 https://zenodo.org/record/1043926 \n",
"2 https://zenodo.org/record/1043926 \n",
"3 https://zenodo.org/record/1043926 \n",
"4 https://zenodo.org/record/1043926 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gro_df = pd.read_csv(\"../data/gromacs_gro_files_info.tsv\", sep=\"\\t\")\n",
"gro_df = pd.merge(\n",
" gro_df,\n",
" df[[\"dataset_origin\", \"dataset_id\", \"dataset_url\"]].drop_duplicates(),\n",
" on=[\"dataset_origin\", \"dataset_id\"],\n",
" how=\"left\"\n",
")\n",
"print(f\"Dataset has {len(gro_df)} gro files\")\n",
"gro_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a7a29173-f637-4045-ac16-81156154e07d",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_system_size_distribution(df):\n",
" \"\"\"Plot system size distribution\"\"\"\n",
" # Convert system size to k-particules (1000) \n",
" df[\"k_particles\"] = gro_df[\"atom_number\"] / 1000\n",
"\n",
" logbins = np.geomspace(df[\"k_particles\"].min()+1, df[\"k_particles\"].max(), 1_000)\n",
"\n",
" plt.rcParams.update({\"font.size\": 16})\n",
" fig, ax = plt.subplots(figsize=(10,7))\n",
" values, bins, _ = ax.hist(\n",
" df[\"k_particles\"], \n",
" bins=logbins,\n",
" color=\"tab:orange\", linewidth=3,\n",
" histtype=\"step\", cumulative=True, density=True\n",
" )\n",
" ax.set_xlabel(r\"System size ($\\times$ 1000 particles)\")\n",
" ax.set_ylabel(\"Cumulated distribution of files (%)\")\n",
" ax.set_yticks(np.arange(0, 1.1, 0.2), [f\"{value:.0f}\" for value in np.arange(0, 1.1, 0.2)*100])\n",
" ax.set_xscale(\"log\")\n",
" ax.set_xlim(1, df[\"k_particles\"].max() - df[\"k_particles\"].max()*0.1)\n",
" filename = \"fig/gromacs_gro_system_size_cumulated_distribution\"\n",
" plt.savefig(f\"{filename}.png\", dpi=300, bbox_inches=\"tight\")\n",
" plt.savefig(f\"{filename}.svg\", bbox_inches=\"tight\")\n",
"\n",
"plot_system_size_distribution(gro_df)"
]
},
{
"cell_type": "markdown",
"id": "00f14551-a8d0-4235-b7a5-38d96e6ae708",
"metadata": {},
"source": [
"Smallest systems:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "d9750dbe-a955-4641-8bd5-d24453ffa516",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_origin</th>\n",
" <th>dataset_id</th>\n",
" <th>file_name</th>\n",
" <th>atom_number</th>\n",
" <th>has_protein</th>\n",
" <th>has_nucleic</th>\n",
" <th>has_lipid</th>\n",
" <th>has_glucid</th>\n",
" <th>has_water_ion</th>\n",
" <th>dataset_url</th>\n",
" <th>k_particles</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8115</th>\n",
" <td>figshare</td>\n",
" <td>8846045</td>\n",
" <td>Tutorial/gro/cell.gro</td>\n",
" <td>2.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://acs.figshare.com/articles/dataset/Nucleation_Mechanisms_of_Self-Assembled_Physisorbed_Monolayers_on_Graphite/8846045</td>\n",
" <td>0.002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6841</th>\n",
" <td>figshare</td>\n",
" <td>5190502</td>\n",
" <td>H2O_v2.gro</td>\n",
" <td>3.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://tandf.figshare.com/articles/dataset/Organic_and_Third_Phase_in_HNO_sub_3_sub_TBP_i_n_i_-Dodecane_System_No_Reverse_Micelles/5190502</td>\n",
" <td>0.003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6860</th>\n",
" <td>figshare</td>\n",
" <td>12517490</td>\n",
" <td>topolpgy_backmapping_files/topologies/Benzene/benzene_cg.gro</td>\n",
" <td>3.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://acs.figshare.com/articles/dataset/Capturing_Protein_Ligand_Recognition_Pathways_in_Coarse-Grained_Simulation/12517490</td>\n",
" <td>0.003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7920</th>\n",
" <td>figshare</td>\n",
" <td>19534516</td>\n",
" <td>setup_files/2_packmol/CO2.gro</td>\n",
" <td>3.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://figshare.com/articles/dataset/All-atom_molecular_simulation_study_of_cellulose_acetate_amorphous_structure_and_the_dissolution_of_small_molecule/19534516</td>\n",
" <td>0.003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7921</th>\n",
" <td>figshare</td>\n",
" <td>19534516</td>\n",
" <td>setup_files/2_packmol/H2O.gro</td>\n",
" <td>3.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>https://figshare.com/articles/dataset/All-atom_molecular_simulation_study_of_cellulose_acetate_amorphous_structure_and_the_dissolution_of_small_molecule/19534516</td>\n",
" <td>0.003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7845</th>\n",
" <td>figshare</td>\n",
" <td>11702442</td>\n",
" <td>ct9b01139_si_001/Topology_Files/AMMONIA/AMMONIA_GMX.gro</td>\n",
" <td>4.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://acs.figshare.com/articles/dataset/Alchemical_Hydration_Free-Energy_Calculations_Using_Molecular_Dynamics_with_Explicit_Polarization_and_Induced_Polarity_Decoupling_An_On_the_Fly_Polarization_Approach/11702442</td>\n",
" <td>0.004</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_origin dataset_id \\\n",
"8115 figshare 8846045 \n",
"6841 figshare 5190502 \n",
"6860 figshare 12517490 \n",
"7920 figshare 19534516 \n",
"7921 figshare 19534516 \n",
"7845 figshare 11702442 \n",
"\n",
" file_name \\\n",
"8115 Tutorial/gro/cell.gro \n",
"6841 H2O_v2.gro \n",
"6860 topolpgy_backmapping_files/topologies/Benzene/benzene_cg.gro \n",
"7920 setup_files/2_packmol/CO2.gro \n",
"7921 setup_files/2_packmol/H2O.gro \n",
"7845 ct9b01139_si_001/Topology_Files/AMMONIA/AMMONIA_GMX.gro \n",
"\n",
" atom_number has_protein has_nucleic has_lipid has_glucid \\\n",
"8115 2.0 False False False False \n",
"6841 3.0 False False False False \n",
"6860 3.0 False False False False \n",
"7920 3.0 False False False False \n",
"7921 3.0 False False False False \n",
"7845 4.0 False False False False \n",
"\n",
" has_water_ion \\\n",
"8115 False \n",
"6841 False \n",
"6860 False \n",
"7920 False \n",
"7921 True \n",
"7845 False \n",
"\n",
" dataset_url \\\n",
"8115 https://acs.figshare.com/articles/dataset/Nucleation_Mechanisms_of_Self-Assembled_Physisorbed_Monolayers_on_Graphite/8846045 \n",
"6841 https://tandf.figshare.com/articles/dataset/Organic_and_Third_Phase_in_HNO_sub_3_sub_TBP_i_n_i_-Dodecane_System_No_Reverse_Micelles/5190502 \n",
"6860 https://acs.figshare.com/articles/dataset/Capturing_Protein_Ligand_Recognition_Pathways_in_Coarse-Grained_Simulation/12517490 \n",
"7920 https://figshare.com/articles/dataset/All-atom_molecular_simulation_study_of_cellulose_acetate_amorphous_structure_and_the_dissolution_of_small_molecule/19534516 \n",
"7921 https://figshare.com/articles/dataset/All-atom_molecular_simulation_study_of_cellulose_acetate_amorphous_structure_and_the_dissolution_of_small_molecule/19534516 \n",
"7845 https://acs.figshare.com/articles/dataset/Alchemical_Hydration_Free-Energy_Calculations_Using_Molecular_Dynamics_with_Explicit_Polarization_and_Induced_Polarity_Decoupling_An_On_the_Fly_Polarization_Approach/11702442 \n",
"\n",
" k_particles \n",
"8115 0.002 \n",
"6841 0.003 \n",
"6860 0.003 \n",
"7920 0.003 \n",
"7921 0.003 \n",
"7845 0.004 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(gro_df\n",
" .query(\"atom_number < 5\")\n",
" .sort_values(by=\"atom_number\", ascending=True)\n",
" #.to_csv(\"system_size_below_5_particles.tsv\", sep=\"\\t\", index=False)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "1588cf01-4fb0-48bf-b29a-24a5b61b19b9",
"metadata": {},
"source": [
"Systems with about 1 k particles:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "8f7e2992-4510-485c-9e59-6152ce5cefd7",
"metadata": {},
"outputs": [
{
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" dataset_origin dataset_id \\\n",
"4294 zenodo 4371296 \n",
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"2494 zenodo 7061501 \n",
"2404 zenodo 7061501 \n",
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"\n",
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"4294 8443.0 True False True False \n",
"4295 8443.0 True False True False \n",
"4296 8443.0 True False True False \n",
"4297 8443.0 True False True False \n",
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"... ... ... ... ... ... \n",
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"4296 True https://zenodo.org/record/4371296 8.443 \n",
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},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(gro_df\n",
" .query(\"atom_number > 8_400\")\n",
" .query(\"atom_number < 8_500\")\n",
" .sort_values(by=\"atom_number\", ascending=False)\n",
" #.to_csv(\"system_size_between_8400_and_8500_particles.tsv\", sep=\"\\t\", index=False)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "59f6ce21-30c8-4fff-b0e5-b529d50ee2f0",
"metadata": {},
"source": [
"Largest systems:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "9f54aad3-d906-4b0e-be36-9e4c97a44ab3",
"metadata": {},
"outputs": [
{
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"text/plain": [
" dataset_origin dataset_id \\\n",
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"3305 simulations/protein_simulations/prepore/33mer_largemembrane/prepore_33mer_largemembrane_2200ns.gro \n",
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"3287 simulations/protein_simulations/pore/27mer/27mer_equilibrated.gro \n",
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"3292 simulations/protein_simulations/pore/33mer/33mer_equilibrated.gro \n",
"\n",
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"3286 2125.422 \n",
"3287 2125.422 \n",
"3290 2119.785 \n",
"3291 2119.785 \n",
"3292 2119.785 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(gro_df\n",
" .query(\"atom_number > 2_000_000\")\n",
" .sort_values(by=\"atom_number\", ascending=False)\n",
" #.to_csv(\"system_size_above_5_000_000_particles.tsv\", sep=\"\\t\", index=False)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "70ef1c6a-9777-49ef-8f52-324f24a00d95",
"metadata": {},
"source": [
"### 3.C. System composition"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "c06a3b22-a217-412f-8d13-789c4dd58737",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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xMTE2rwcOHKi77rpLL7zwgsaNG6eQkBCHxV9VjBw5UoMHD9Y///lP/fbbb5o7d64k6ZVXXpEkxcXFadiwYYqMjFSNGjW0f/9+LV68WH/88Ye1TFZWlgYOHKjIyEgtWbJEYWFhOnXqlL766qtyrSDyn//8R5MnT9bAgQO1cOFCZWRkaNGiRXafZ2HevHl69tln9be//U0PPfSQAgICdPDgQSUkJJR6jMVi0YgRI7Rnzx498cQTuv766/Xdd99pyJAhdo2tPOz5RcVisejcuXMutUy5Pdvval/6zp07Z9cEXW5urvz9/e1Wn6PZ8zMhLy9PhYWFLjVxtz3b72rvfXvHm5ubW20/91xtDiOLxVJt25+SkmL33nh//PGHXetztOp8zS+6TtmLK733Jfv+vfLz85Wfn+9SixvZs/3OHsrrOr91ON3ChQv13HPPac6cOfrXv/5VYpnU1FRFRUXpxIkTWr9+vZo0aVKmuu+++25t3rxZ+/btU69evewZdpX0z3/+0zp5X/fu3XXkyBGtXr1ay5cvl8Fg0MKFC61lCwsLdfPNNysoKEjjx4/X/PnzFRgYqD/++EMpKSlavny5+vfvby0/ePDgMsdRWFioBQsWqGPHjnrrrbes22+66Sa1a9fObhNpx8fHa8mSJbrrrru0cuVK6/bbb7/9ksdt27ZNX375pZ555hmNGzfOekyNGjX01FNP2SU2Z3O1iZvtmfCozm13RH2ORvvtF291brsj6nO06vy3tzdXar+9erU5uk5HcrXrtD3xuUf7q2JdV8J1Hq3BqRYuXKiFCxdq+vTppQ7lSk1N1cCBAxUfH69PPvmkXEtPFv1DcKWnvRXRr18/m9etWrWS2Wy2To72yy+/6O6771ZkZKTq1KmjoKAg/etf/1JBQYH++usvSefnu6lTp47mzJmjmJgY/f777+WO488//9SpU6eKJYkaNmyozp07X2HrituxY4cKCgp0//33l+u4r776SpJ011132WwfNmyY3WIrL3svK+pKTzsk+7a/OrfdEfU5mj3jNRgMLvd5b8/2V+e/vSPqc7Tq/Le3979VV2p/QECA3ZdTrlOnjl3rczR7Xqer82e+I+pzNHvfo7la+93pfte1/uXBKZ555hktXLhQjz/+uKZPn15imQuTPevWrSv3xLvvvfeevL29Sx0m5m6CgoJsXhd1bc/JydGxY8fUp08fnTx5Us8884y2bNminTt3asmSJZL+1y2wdu3a2rRpk1q3bq25c+eqU6dOuuaaazR//vwyP0EqWi2lpNXYStp2pYpWJ6hfv365jktJSZGXl1exVeDq1q1rt9jKy8fHx251eXl5ydvb2271VQZfX98qWVdl8Pb2tutF29Xab8/3vtFotPsXKUezZ/td7W/v6+tr17+Xq7Xfnn97e9ZVWapr+/38/NS4cWO71efp6WldwdZVVOdrvqenp2rUqGG3+lyt/fa+5rtaws+d3vuu9XgVlW7ZsmWaP3++7rjjDvXu3Vvff/+9zf5OnTopJydHgwYN0i+//KJFixYpPz/fplxISIiuuuoqSdLSpUv1+++/q2vXrqpfv77OnDmjVatW6YsvvtD06dNLXN69ulm/fr2ysrK0evVqNWrUyLp9//79xcped911io2NlcVi0YEDB/T222/rmWeeka+vr6ZMmXLZcxUlnkpadvH06dOXPb7oYnDxfAwXL+FZNC9TYmKiGjRocNl6L4wvPz9fycnJNu+NU6dOlbkOe/Px8VGNGjXsMhbbleZvKeLv72+35UVdtf32WG3C29vbpb74SFLNmjXl4eFhly7+tWrVskNElcvf398uS7QW1eVKDAaD/Pz87LJii6+vr8s96bXn+9XV/vbS+fbbYwJTg8Hgcu3v3r27jh07ZpcVlgoLC3XrrbfaIarK4+/vX6b7wbLW5Wr8/f2tD0crwsvLy+Wu+X5+fvL09LTLiqKu+re316qKzm6/a6XaUOk2bdok6fxcKnfccUexH+l8YmDfvn2yWCx64oknipV59tlnrfVdc801OnLkiGbMmKGBAwfqoYcektls1r///e9Sew9VN0VPUS9MoBRNgH2pY1q3bq1FixapTp06+uWXX8p0rquvvlp169bVRx99ZLP92LFj2rNnz2WPL0pIHThwwGZ70fumyO233y5PT89iE3Zfzm233Sbp/OpeF3L2sqaBgYF2qefinl6uwN/f3y6TrRqNRqdfAK+Evf5mrvi39/DwsMtwBIPBYLd/Q5WpTp06dklU1KxZ06UmLC5Snd/73t7edkn6eHh4uOR7PzAw0C49vGrXru1yyb5Ro0bZbTlto9Ho1CHpV8LPz88uiYoaNWqUuKprVWevzyt7/RuqTPa8Vrvi535AQIBdeuH7+vo6PdlHDx9c0saNGy9bJjIyUhkZGWWqr2/fvurbt29Fw3JrRZMSjxkzRpMnT1Zubq5WrlxZrFfBpk2btHLlSg0YMECNGzeWxWLRZ599prS0NHXv3r1M5/Lw8NCMGTM0efJk/eMf/9DIkSOVnp6uRYsWqV69epftftmrVy8FBgZq4sSJmjlzpjw9PfXOO+8UWxY+MjJSjz76qJ599lmZzWYNHTpUAQEB+v3335WcnKyZM2eWWH+PHj10yy23aM6cOcrOzlb79u313Xff6b333itT+xwlODhYqampFerlU7t2bfn5+dkxqspTr149HT16tMJ1uCI/Pz/Vrl27Qj09atSo4bK9GcPCwpSenl6hJ37BwcF27SZfWQwGg+rWrasTJ05UqA5Xfe/Xrl1bycnJFerp4evr63JzmBSpV6+ezp49W6HJN+vWretywxqk870TQkNDK9TTw9PT06nDsa9Uu3bt1LNnT33xxRcV+twzGAyaOHGiateubcfoKke9evUUHx9foTpc8W8vne/VXadOnQr17PX29nbZFYhDQ0OVlpZWoaRnUFCQSz7kMBgMCgsLK/adprzstQBORbjeVQdwc9dcc43eeustpaWlaeTIkXr88cfVpk0bm55SktSsWTPVrl1bL774ooYPH657771XP//8s1599VWNGjWqzOcbPXq0XnrpJe3fv19///vftWjRIk2ZMkVt2rS57I1JQECAPvroI/n7++v+++/XI488omuvvbbEib1nzZql119/XQkJCRo7dqxGjBiht99++5Lj4z08PPTee+/prrvu0tKlS61LtH/44Ydlbp8jeHh4qH79+lf8tMbb27tKXACuVK1atSr0tCYoKMglh/QUiYiIuOKEhcFgUP369V3yS590/otfRETEFR9vNBpd9sZfqvh7NywszOlj+SuiQYMGV9xDw9PTs0Kfm85mNBorlKyrWbOmyyZ6pfNf/Cry3q1Xr57LzVlX5KWXXpKPj88Vf257enqqadOmeuKJJ+wcWeXw9/evUMKiTp06LpnoKhIeHl7ha76r9Wwr4unpWeFrvqs+5JDO98yqyHs3LCysSjzcNVicvU6Yg2RmZlbaufLz8/Xll1+qS5cuTp+F291c2P3T19fXqXO3VCdpaWm6/vrrNWDAAL300kvODqfKysjI0PHjx8s1p0mNGjXUuHFjl+zhcLHjx4+X+6lXnTp1XPpLX5Fz584pPj6+XL28DAaDGjZs6JLd2i+WkpKikydPlqu3g9FoVJMmTVz+OllYWKiEhASdPXu2XMeFhIS49I1vkZycHB09erRcT3w9PT0VGRlZJW58K+r06dPl7ulSs2ZNRUZGumyit0hBQYHi4+OVk5NTruPCw8NdOtklSTt37tTQoUOVn59frmu+p6enAgMD9fnnn6tp06YOjNDxTpw4Ue75bGrXrq0GDRq4/DU/Ly9P8fHxys3NLfMxBoNBDRo0cOlkV5HU1FSdOHGi3Nf8xo0bu2yit4jFYlFCQkK5cwvBwcGV8nC3LA+hSPjYAQkfxyHh43inTp3Sc889py5duigoKEgJCQl65ZVX9Mcff2jXrl269tprnR1ilZabm6vjx4+X6Qa4Tp06Cg8Pd9knPSVJTU1VUlLSZbu6e3p6ql69ei45f0VpCgoKdPLkyTIlvXx9fdWgQQOX7NZcmuzsbCUmJpbpBjgkJER169Z1+Zv+C5lMJp0+ffqyX/6KevS5Q6KvSH5+vhITE8t0r+Xv76/69eu7/E3/hTIzM3XixInLrohZNCQgJCTEbd77FotFp0+flslkuuyXvxo1aqh+/fqqWbNmJUXnWN9++63uvfdenTlzpkzDuwwGg6677jqtXr3auniJq0tLS9PJkycv234PDw/Vq1fPJeduKU1hYaFOnjxZpol8fXx81KBBA6fP3WJPOTk5On78eJmu+cHBwS47hLU0ycnJOnXq1GWv+V5eXgoPD6+0RB8Jn0pCwsdxSPhcucLCwjJ9KKWmpupf//qX9u3bp9TUVPn5+aljx46aMWOGbrjhhkqK1vWdPXtWqampys7OtvkSUDQ5cWBgoFtd+C9UUFCgtLQ0paeny2w2W993Hh4e8vHxUe3ate024W1VZDablZqaqrNnz9rcCHl7e8vPz0+BgYEuOUF1WVgsFmVmZio1NVU5OTnWXh8Gg0FGo1G1atVSYGCgW/RoK0l+fr71vZ+bm2t973t6elrnfqhdu7Zb3fReKDs72/q5d+F7v0aNGvLz81NQUJBb9OopicVisf7tc3JyrF+ADQaDfHx8FBAQoMDAQLe9L8zLy1NKSooyMzOVm5trTf54eXlZ52oKCAhwm0RXkfT0dM2fP1+xsbEym83y9va2ueYXvQ4MDNTkyZP10EMPuVWyUzp/f5mamqqMjAzl5ORUu2t+bm6uUlJSdPbsWZ07d8763vf29pavr68CAwNdetj65WRkZCgtLU05OTnW977BYFCNGjWsQ/7d9ZpfUFCg1NTUUq/5Re/9yrzmk/CpJCR8HIeEz5V78MEH9c4771yyTFkn20b5FBQUyGKxyMPDw22/6F1K0Zf+6vh5aLFYVFBQIIPB4LY3u5dS9N739PR0uy96l1Pd//ZFDxmq6+ce7/3q9d5PT0/X+vXr9cMPP2jVqlXKy8tTjRo19MADD+jGG29U37593fZL78W45lev9/6F+Nxz/t+ehE8lIeHjOCR8rtzRo0eVnJx8yTLXX399JUUDAADgflq0aKETJ04oIiJCv//+u7PDAVCNlCXhQ3YCcFORkZGKjIx0dhgAAAAAACcg4YMq7cIhR+VZEQQAAAAAgOqs+g2yBgAAAAAAcHMkfAAAAAAAANwMCR8AAAAAAAA3Q8IHAAAAAADAzZDwAQAAAAAAcDMkfAAAAAAAANyM2y7LXqtWrUo7V15eniTJ399f3t7elXbe6iYvL4/fLwAAAKoMg8Fg/W9lfv8AgLKghw8AAAAAAICbIeEDAAAAAADgZkj4AAAAAAAAuBkSPgAAAAAAAG6GhA8AAAAAAICbIeEDAAAAAADgZkj4AAAAAAAAuBkSPgAAAAAAAG6GhA8AAAAAAICbIeEDAAAAAADgZkj4AAAAAAAAuBkSPgAAAAAAAG6GhA8AAAAAAICbIeEDAAAAAADgZkj4AAAAAAAAuBkSPgAAAAAAAG6GhA8AAAAAAICb8XJ2AHBPBoPB7nX6+voqOzvb7vUCAAAAAOBuSPgAAABUgMViUVxcnJKSkuTh4aHGjRurXr16zg4LAOAgeXl5+uOPP5SWliaj0ahrrrlGAQEBzg6r0qSlpenPP//UuXPnFBgYqGuuuUZeXtUjtWCxWBQfH6+TJ0/KYDCocePGCg8Pd3ZYpaoefxUAqAR5eXnKzs6W2WxWYWGhPD095evrKz8/P3l6ejo7PIfLyclRdna28vLyJEne3t7y8/OTr6+vkyNzvIKCAmVnZysnJ0cFBQXy8PCQj4+P/Pz85O3t7ezwHO7cuXPW977FYpGXl5f1ve/h4Z6jxy0Wi3bu3KlXXnlFW7ZsUWZmps3+sLAwDRs2TOPGjdN1113npCgdLz8/3/q3L3rvF/3tq8PNv9lsVk5OjnJzc2WxWOTt7W1tvyN6O1clFotFWVlZysnJUX5+vgwGg4xGo/z8/GQ0Gp0dnsMVXfMLCgoknb8OZGZmVqtrfk5Ojs6dOydJNu99d3Xu3Dl9+OGHeu211/Tdd99Z216kadOmGjVqlMaOHeuWSf/ExES98cYbWrVqleLi4mz2GY1G3XzzzRo3bpyioqLc7t7HYrHoq6++0quvvqoNGzYUu+aHhoZq8ODBGj9+vNq0aeOkKEtmsFgsFmcH4ery8vK0ceNG9evXz+3e3FeKIV2oTrKysnT69OliH/5FPDw8VKdOHYWFhalGjRqVHJ1jWSwWJScny2QyFbvxKVKjRg2FhIQoODjY7b4A5eXl6dSpU0pLS1NhYWGJZWrVqqWwsDDVrFmzkqNzvIyMDJ05c0ZZWVkl7vf09FRgYKDCwsLc6sv/0aNHdd9992n79u3y8vJSfn5+ieWK9j344IN67rnn5O/vX8mROo7ZbNbp06eVnp6ukm4lDQaDAgICVLduXfn4+DghQsdKS0vTmTNnlJOTU+J+Ly8vBQUFKSwszO2SngUFBTp9+rRSUlKsyY6L+fn5KTQ0VLVr167k6BwvOztbp0+fVkZGhiSpZ8+eOn36tMLCwvT555/LYDCoTp06qlu3rlte81NSUmQymZSbm1timRo1aig4OFghISFudc3/9ttv9Y9//EOHDx+Wp6dnqe99Dw8PeXt765lnntFDDz3kFv/+CwoKtGTJEs2aNUsFBQWl3u8U/V6aN2+u1atXq2PHjpUcqWMkJiZq7Nix2rx5c5mu+ffdd5+WLFlSZT7/SPjYAQmf4kj4oDqwWCw6efKkTCZTmcp7eHgoIiJCQUFBDo6scuTm5iohIaHULzwX8/X1VaNGjdzmyW9qaqoSExNLvfG5WHBwsCIiItziBriwsFDHjx9XWlpamcp7eXmpQYMGbtHd/auvvlK/fv1kNptLvem7WNEwrx07dqhRo0YOjtDxzpw5o6SkpBITPRczGAwKCwtT3bp1KyEyx8vPz9exY8dKTfBfrEaNGmrYsKHbJHwzMzN1/Phxa0/Oy6ldu7YaNGjgFj1eLBaLkpKSdObMGZvtFyd8inh4eCg8PFzBwcGVHapDnDt3TgkJCWW+F/fx8VGjRo3cIuH76quvasKECfLw8Cg10VOSvn376sMPP3Tpns5ZWVn629/+pi+++KLMx3h6espisWjlypUaPXq0A6NzvD179qh3797Kysoq8zXf09NT9evX144dO3TVVVc5OMLLc/2UYzWTmZmpqVOnqlevXgoNDZXBYFB0dLRNmYKCAj3//PPq06ePGjRoID8/P1177bWaNm1aqTfny5YtU4sWLWQ0GtWkSRPNnTu3xIv5li1bdMstt8jX11e1a9fWnXfeqd9++80BLQWqNovFoqNHj5Y52SP970vy6dOnHRhZ5TCbzTp8+HCZkz3S+e7ff/31l8xmswMjqxxnzpzRsWPHypzskaTk5GQdPXq0TF+Sq7LCwkIdOXKkzMke6fyX5Pj4eKWmpjousErw008/qXfv3srOzi7zjZ90/neWkJCgrl27KiUlxYEROt6JEyd08uTJMr+PLRaLTp06pePHjzs4MsfLz8/X4cOHy5zskc5/ST5y5Ei5jqmq0tPTFR8fX+ZkT9ExR44cKdeX5KrIYrEoISGhWLLnUgoLC5WYmKhTp045MLLKkZubq7/++qtcD16v5D6hKnrzzTc1fvx4WSyWcr+Pt2zZomHDhpXrXqEqKSgo0N/+9jft2rWr3McVFhZqzJgx+uCDDxwUneP99ttv6tGjh86ePVuua35BQYFOnDihLl26VIl7fhI+LiY5OVkrVqxQbm6uoqKiSiyTk5Oj6OhoRUZG6sUXX9TGjRt1//33a8WKFbrllluKffDOnz9fkydP1uDBg7VlyxaNHz9eCxYs0IQJE2zKffLJJ+rbt6/CwsKs41f//PNP3XbbbTp8+LCjmgxUSUlJSdbu3JV5bFVQWFio+Pj4cl38ihQUFCg+Pt5lb36k84n3kydPXtGxGRkZSkpKsnNElev48eNX3Nvy+PHjLnvzbzabNXz4cJ07d+6K3r9FPUMeeughB0RXOYqGclT2sVXF0aNHSx3GcilFDwjKkyipasxmsxISEq4oYZ2Tk6Njx445IKrKUzR88UqcOnXqio+tCoomqK3INd9VE35xcXF68MEHr/j4wsJCbdiwQa+99podo6o8L7zwgr744osK/f3GjBnjkgn/vLw83XPPPdb56corPz9fSUlJ+te//uX0B30kfFxMZGSkUlNTtWvXLi1cuLDEMr6+voqLi9Prr7+uoUOHqlu3bpoyZYpWrFihgwcP6sMPP7SWTU5O1rx583T//fdrwYIF6tatmx5//HE9+eSTWrlypQ4ePGgt+8QTT6h169b66KOP1K9fP91zzz3asmWLsrKyNGfOHIe3vaqLjo6WwWBw+Rtae4iNjZXBYFB8fHylHltZcnJyyvWUryTlGQpU1SQlJZU6X09ZnDt3zmWTHkW9tCrizJkzLjs8NSMjo1w9ey5msVhc9ovf0qVL9eeff1boxregoEDvvPOOduzYYcfIKkdeXt4VJzqLVPSzw5mSk5NLnauqLOzx2eFMx48fr9CXlop+djhT0XxVFZGYmOiySY9Tp05dUaKziD0+O5xl0qRJV5Touthjjz2m5ORkO0RUeU6ePKmZM2dWuB6z2axHH33UDhFVrldeeUUHDhyo8DV/3bp12rx5sx0jKz8SPi7GYDBcdv4HT0/PEscLd+rUSZJsbrY3b94ss9lcbHzl6NGjZbFYtG7dOknnb3QOHTqkvn372pw/MjJSrVq10rp161z2QoaqpX///vr222+r9PKGFU32SOdvgFxxeEtBQYFdhqRcarLPqiwtLc0uT+nt8R5yBnt0TTabzS7Xw62goEBLly61y1M6Ly8vvfTSS3aIqnLZ499sYWGhy33pKWKPf7OZmZkuOaT17NmzdklSu+rn3pkzZyr8bz8/P98lr/mFhYV2eZCZmppql8RJZYqLi9OGDRvsEndubq5iY2MrHlQlWrlypV3aXlBQoA8//FAnTpywQ1SVo7CwUC+++KJd6vL09HT6NZ+ETzVSNNnWhcvDHjhwQJLUunVrm7Lh4eEKCQmx7i96IlfSZKtGo1HZ2dkM64JdhIaGqnPnzlV2Yt+CggK7dc12xZu/S61GVR6FhYUu+bTXXvOvZGRkuFzCKzc31249k1ztvf/NN9/Y7Ql1fn6+Pv300wr1FnEGe733XXEOo7Nnz9qtZ5Irtt9e/16LlvF2JRaLxW7XfFf826enp9vlmm+xWFzuc/+DDz6w2wpbhYWFWrVqlV3qqiyrVq2yW090i8WitWvX2qWuyrBv3z7Fx8fb5SFPQUGBtmzZ4tT3PwmfaiIxMVHTpk1Tx44dNWDAAOv25ORkGY3GElePCAoKsj6Jq1u3roKCgvT111/blElLS7MmhVz1qZ29nTp1Svfcc49q166tunXrasyYMTY3C8uXL1eXLl2syzS3bt1azz77bLFeAz/99JMGDBigsLAwGY1GRUREqH///uXqEj5q1Cj5+/vrr7/+Ur9+/eTv76+GDRvq0Ucftemeu3PnThkMBu3cudPm+Pj4eBkMhmJPJfbs2aM777xTwcHB8vHxUdOmTfXwww9fNp5t27apR48eCgj4v/buOzqKsm0D+DXb0pNND4EQCARRehGR3iK9SBFEBBGkiSAi5UVFiiCgFAtFBUGkCAiIIEVAQFDUUEQs9EBCAiGkkJ5sme+PnN2PkASy2dndzOb6nZOj7M48e8/O7Dwz9zzFG+7u7mjZsiUOHz5caJmSunTt378fHTt2hI+Pj3kg8pK6NdpSTk6OZH1xpSzLXqTsiiS3bk2iKEp2syJlWfZSkfd9dHS0pFPrGo1GnDt3TrLybE2n00k2/ozBYLCqe4gjVORjH6jY25+TkyPZTW9ubq7sunJX5H3/xx9/SFrev//+K5tzX0ZGhqQP8hUKBU6dOiVZebYWHR0t6YyqoijizJkzkpVnKSZ8KoCUlBR069YNoihiy5YtRS5aH3ZAm95TKBR49dVXcfjwYcydOxd37tzBlStXMGTIEPMJXMqLYTnr168fatWqhe3bt2P69OnYtGkTJk2aZH7/6tWrGDx4ML7++mvs2bMHI0aMwAcffIDRo0ebl8nKykJUVBQSExOxfPlyHDx4EMuWLUPVqlUtnulDp9OhV69e6NixI3bt2oWXX34ZS5cuxcKFC8u0fQcOHEDr1q0RGxuLJUuWYN++fXj77bcfOQvFhg0b8Mwzz8Db2xtfffUVtm7dCj8/P3Tu3LlI0udBa9asQbdu3WA0GrFq1Srs3r0bEyZMcMh4CFJW1qIoym48Cym3Xy4XPib5+fmSJujktv1SxqvT6WR143Pp0iXJp5W+ePGipOXZktTHakU+9uW27aIoVujt57Ffcff9v//+K2lLXL1ej5iYGMnKs6UrV65Ier2j1+tlNavzxYsXoVKpJCtPEASH1vnSbQmVS6mpqYiKikJ8fDx++uknREREFHrf398fubm5yM7Ohru7e6H3UlJS0KRJE/O/Z86ciczMTLz33nvmQZq7d++O4cOHY/Xq1ahcubLtN0gGRowYgSlTpgAAOnXqhCtXruDLL7/EmjVrIAgClixZYl7WaDSidevW8Pf3x/Dhw7F48WL4+vriwoULSE5Oxpo1a9C7d2/z8s8995zF8eTn52P27NkYMGAAAKBjx444deoUNm3aVKbBtl999VVUrVoVv//+O1xdXc2vPzgO1P2ys7MxceJE9OjRAzt37jS/3q1bNzRu3BgzZszA77//Xuy6mZmZeOONN9CyZUv89NNP5iRkx44dLY5dClK3yJHTTS8g7fZX5G0HuP1yat2m0+kkj1dOyd6KvO8BaeOV27ZLTW7bX9GPfSnrKbltuy3O0XKZqc8WccqpzpN6+wVBcOi+Z5MMJ5aamopOnTohJiYGBw8eRP369YssYxq75/z584Vev337Nu7evYu6deuaX1OpVFiyZAmSk5Px119/ISEhAXv27EFsbCyqV6+OKlWq2HaDZKJXr16F/l2/fv1CMzycPXsWvXr1gr+/P5RKJdRqNYYOHQqDwYBLly4BAGrWrAlfX19MmzYNq1atKjRbmqUEQUDPnj2LxHTjxg2Ly7p06RKuXr2KESNGFEr2PMqvv/6KlJQUDBs2DHq93vxnNBrRpUsXREdHlziexa+//or09HSMGzdO0uaVZSVlxt8W5dmalK0cKvK2AxV7+wVBkFWrUF9fX8nPP35+fpKWZ0tSH/tSl2drUsYrt22X+rcqt+2v6Od9KeOV274vbgIca8nlvG+LOAMCAiQv01b8/Pwkf8DpyH0vn6stsogp2XPt2jX8+OOPaNSoUbHLdenSBa6urkXGaDGNo9KnT58i63h6eqJevXqoVKkSzpw5g8OHD2PixIk22Ap5erCCMA0+nJOTg9jYWLRu3Rrx8fH46KOPcPz4cURHR2P58uXmZQDAx8cHx44dQ8OGDTFjxgzUqVMHoaGhePfddy3OELu7uxdJzri4uJRpphDTDBuWJvdM3b369+8PtVpd6G/hwoUQRbHEwQzL+pm2Ykmi61FUKhXUarVk5dmDm5tbuSzLHtRqtaQXv3LbfinjdXV1LRcJ3NJq1KiR5E/nSqqXyyM3NzdJ91dFPvbltu1Axd5+KeNVKpXQaDSSlWcPFXnfP/nkk5Jeo/n6+iI0NFSy8mwpIiKiSM8Pa6jVajRt2lSy8mytYcOGks8q58g6X15pZgIA7Nu3D1lZWeaxXP7991/zyOfdunWDIAjo3Lkzzp49i2XLlkGv1+O3334zrx8YGIgaNWoAKMhgvv3223jnnXfg5+eHZ555BtHR0Zg1axZGjhyJJ554wrze0aNHER0djfr160MURfzxxx9YuHAhunTpgvHjx9vxG5Cv7777DllZWdixYwfCw8PNr//5559Flq1Xrx6++eYbiKKIv/76C+vWrcOcOXPg5uaG6dOnSxqXKYnxYP/qB6fiDAwMBACLx84xZfU/+eQTNG/evNhlgoODi329rJ9pK66urtBoNJI0TfXy8pIgIvvy8vKSbGpduW6/FDMtqNVqSZOH9uDh4QGFQiFJE3+57fvWrVtDEATJnvgFBweb62E5EAQBHh4eyMzMtLosd3d32T3pl/J4lduxDxTELMWscoIgwNPTU4KI7Eej0cDFxUWS8Wfktu1Awb5/1BiNlpQlJ23btsWKFSskKUulUqFdu3ayedChUCjQpk0bHDx4UJJxjHQ6Hdq0aSNBZPbRsmVLya53gIJkX+3atSUpqyyY8JGhsWPHFuqOs23bNmzbtg0AzIOBRUdHA0CxLW+GDRtWqEXPW2+9BS8vLyxfvhwffvghQkJCMH36dLz11luF1tNoNNi+fTvee+895OXlITIyEnPmzMGECRNkd/HmKKYT/f1TjouiiC+++OKh6zRo0ABLly7FunXrbDLKe7Vq1QAAf/31Fzp37mx+/fvvvy+0XK1atVCjRg18+eWXeOONN0o9dXrLli2h1Wrx77//WpwcbNGiBXx8fLBq1SoMGjSoXFSWfn5+uH37ttXl2KK5sK15enpKcvHr4uIiy4tff39/SRI+ctz3CoUCvr6+Vs/IKAiCbJq1m1StWhWdO3eW5OJXoVCUmy6qlvD395ck4SPHY1+tVsPb2xvp6elWlWP6DcmNn58fEhMTrU54arVaWV4v+vv7IyEhQZJy5Mbd3R1ubm5Wzyqp0Wjg7e0tUVT20bt3b/j5+ZXYAt0Ser0e48aNkyAq+xk7diz2798vSVmVKlVC165dJSnLHkJCQtCrVy/s3r3b6jpfqVRizJgxDu3OyYSPDD04XXVxLK2UJ0yYgAkTJjx0mRYtWhRqKUSWi4qKgkajwfPPP4+pU6ciNzcXK1euLHIDuWfPHqxYsQJ9+vRBREQERFHEjh07kJaWhqioKMnjCgkJQadOnfD+++/D19cX4eHhOHz4MHbs2FFk2eXLl6Nnz55o3rw5Jk2ahKpVqyI2NhYHDhzAxo0biy3f09MTn3zyCYYNG4aUlBT0798fQUFBSEpKwrlz55CUlISVK1eWuO7ixYsxcuRIdOrUCa+88gqCg4Nx5coVnDt3Dp9++qmk30VpBAQEICUlxapWPlqtVtLmsvYUGhpq9UwTcmnW/CB3d3dotVqkpaWVuQyNRiOrvuz3Cw4ORlpamlUXQAEBAbLr1gAAb7/9Ng4cOGBVGYIgwNvbG2PHjpUoKvvx8fGBh4eHVS09TL8fOapUqRIyMjKsSnqEhITIauwqE5VKhaCgIKtaeiiVSoSEhEgYlf34+/sjOTnZqgcd3t7esnzIARQc+9euXbO6DLlxcXHBW2+9hcmTJ1tVjkqlQuPGjdGhQweJIrOP7t27o169epLMVvbOO+/Ibvyqt99+G7t27bK6HDc3N4f3hJFfrUMkY7Vr18b27duRmpqKvn374rXXXkPDhg3x8ccfF1ouMjISWq0WixYtQq9evTBgwACcOXMG69atwyuvvGKT2L7++mt07NgR06ZNw4ABAxAfH4/NmzcXWa5z5874+eefUalSJUyYMAFdunTBnDlzSuySZTJkyBAcOXIEmZmZGD16NDp16oSJEyfizJkzj5xxa8SIEdi7dy8MBgNGjhyJHj16mKepdwSFQoGwsLAyP6FXq9WyTXgABc2yrXlS6e/vL7um3ferXLlymRMWgiAgLCxMljd9QMGFqzXjabm6usr2pq9ly5aYOHGiVftOFEWsXLnS3FVVbsLCwsrcQkOpVKJKlSqya9lk4uLiYtVNq6enp2wTvQAQFBRk1UOK0NBQ2Y1ZZ2LteVutVpebcQjLwtPT06pzlq+vL3x8fCSMyH4mTpyIZs2alTlZYRr0fP369bKr95VKJTZs2GDVOVulUqFNmzYYPXq0hJHZR5MmTTBt2jSr66yPP/7Y4df8gii3OfLKIZ1Oh71796Jbt26yrcykZosLOjc3N2RnZ0teLlFZpaenIzY21qI+vhqNBhEREbJs4fCguLg4i7s3+fr6yvqmzyQ/Px/Xrl2zqJWXIAgIDw+XXbP24qSkpCA+Pt6i1g6urq6IiIiQ3VO+++Xl5aFnz544fPhwmfr2/+9//8P8+fNtEJn95OTkICYmxqIBLZVKJapXry7bVo33S0xMtLili6enJ6pVqya7G74HGQwGxMTEWHwtFhoaKutkl0lGRgZu3LhR5LcfFRWFO3fuICgoCAcPHiz0nlqtRkRERKm7wJdn8fHxFnfp1Wq1Vj0gKw/i4+PRokULJCQkWHTeEwQBgiBg27Zt6Nu3rw0jtK3NmzfjhRdeAGBZDxKVSoXw8HD8+uuvCAoKslV4NqXT6dCvXz/s2bOnTK07X3/9dSxZssThx7+8ax4iIgfy9vZGZGRkqW9ifH19ERkZ6RTJHqDgaX9pn/grlUrz8o6u+KSg0WgQGRlZ6vE43N3dUatWLadI9gAFY3rUqFGj1ANPBwYGIjIyUtbJHqCglcfu3bvx0ksvAUCpbuCVSiVUKhUWL16MefPm2ThC23Nzc7PoWPby8kKtWrWcItkDFHRrrF69eqke8AmCgJCQEFSvXl32yR6g4FiuUaMGgoKCSnUe12g0qFGjhlMke4D/P5Y9PDxKtbxWq0WtWrWcItkDFLRurVq1aqnqfIVCgSpVqqBq1aqyr/MrV66MkydPolmzZqVeR6lUQqvV4ocffpB1sgcAnn/+eXz33Xfw9va2qIVny5YtZZ3sAQoSttu3bze3UCptna9UKjF//vxykewB2MJHEmzhUxRb+NiW0Wh85NNlud9YyU1mZiZSUlKQlZVVaPpmV1dXeHp6ws/PT3azMpWWwWBAWloa0tLSkJOTYz42FQoF3NzcoNVqZTtYZ2nk5uYiJSUFmZmZyM3NNb+uVqvh4eEBPz8/2Y7d8CiiKCIjIwMpKSnIzs42P/0UBAGurq7w8vKCn5+f0yQ577d3715MmzYNf//9N5RKJURRLHReNs3wERUVhSVLlqBu3boOjNY2srOzzee9+8c30Wg08PDwgL+/v9Mkeh4kiqL5vJednW0e40IQBLi5ucHb2xt+fn5OWxfrdDokJycjIyMDubm55qffKpUK7u7u8PX1hbe3d7m42bEFU52fnZ2Ntm3bmlv4/Pzzz/D09IS/v7/T1vlGoxGpqam4d+8esrOzi9T5Pj4+8PX1dbo632g0YsWKFZg3bx5u374NtVpd6HoPKPj9q1QqvPjii1i4cKHTJDuBgtaNU6dOxaZNm2AwGKBUKgu1eDJ9H5UrV8Y777yDV155xSkS3SaHDh3Cm2++iXPnzhWp8+//d/v27bF06VI0aNDAwRH/PyZ8JMCET1FM+NjWSy+9hK+++uqhy/Cn7TgGgwGiKEKhUDhVZVdapgsAZ73ReRhRFGEwGCAIgtNd7JaG6dhXKpVOe6N3P1EU8fvvv+PHH3/E6dOnsW/fPuh0Ori5uWHu3Lno3bs3atas6egw7cL0IKKinvcq2rF/v4p+3qtcuTISEhIQGhqK+Ph4R4djdxWtztfr9di/fz+OHz+OM2fO4NixY9DpdHB3d8eyZcvQt29fWc7IVlpJSUnYvn07Tp06hfXr10On08HFxQWTJk1CmzZt8Mwzzzj1eSA6OhoHDhzAqVOncPPmTQiCgJo1a6JJkybo2bMnHnvsMUeHWAQTPhJgwqcoJnxs6/r167h79+5Dl2natKmdoiEiIgCoUqUK4uPjUblyZdy8edPR4RCRHfB3X7FV5P1fkbddTipGKpbIyVSrVg3VqlVzdBhERERERERUTjHhQzZhi4ZjD/aTJSIiIiIiIqLiVbxO1kRERERERERETo4JHyIiIiIiIiIiJ8OEDxERERERERGRk2HCh4iIiIiIiIjIyTDhQ0RERERERETkZJjwISIiIiIiIiJyMkz4EBERERERERE5GSZ8iIiIiIiIiIicDBM+REREREREREROhgkfIiIiIiIiIiInw4QPEREREREREZGTYcKHiIiIiIiIiMjJMOFDRERERERERORkmPAhIiIiIiIiInIyTPgQERERERERETkZJnyIiIiIiIiIiJyMytEB2EpGRobdPkuv1wMAMjMzoVI57VfqcHq9nt8vERGVW6Iomv9rz+sQInIc/u4rtoq8/yvytpcXXl5ej1yGLXyIiIiIiIiIiJwMEz5ERERERERERE6GCR8iIiIiIiIiIifDhA8RERERERERkZNhwoeIiIiIiIiIyMkw4UNERERERERE5GSY8CEiIiIiIiIicjIqRwdAD+ft7e3oEMoNNzc3JCYmOjoMIiIiIiIionKPCR8iIpKEKIqIj49HfHw8ACA0NBRVqlSBIAgOjsw+dDodLl++jPT0dGg0GkRGRsLLy8vRYRGRjSUlJSEmJgaiKCIgIADVq1eHQsFG9BVBZmYm8vPzARTUAfn5+dBoNA6Oyj5EUURCQgJu3rwJAKhUqRLCwsIqVJ1/5coV5OXlAQCMRqODI7KvtLS0Qse+Xq+HSsXUQnnEvUJEJBGdTofs7Gzk5eXBaDRCqVTC1dUV7u7uUCqVjg7PZk6fPo3Vq1fjhx9+QFpaWqH3fHx80L17d4wcORJNmzZ1TIA2lJeXh507d2LNmjU4c+YMdDqd+T1BEBAREYEhQ4Zg6NChCAwMdGCktpWfn28+9kVRhEqlMh/7zn7jK4oicnJykJOTA4PBAKDgwj83Nxeurq4Ojs729Hq9ed8bDAYoFArzvnfmi//Lly9jzZo12L59e5HWx+7u7mjfvj1GjhyJ9u3bO+1vQBRFZGVlITc3F3q9HoIgQKPRwN3dHS4uLo4Oz2aSk5OxceNGrF+/HpcvX4YoigAKEn8hISFo2LAhXn75ZfTr1w9ubm4OjlZ6586dwxdffIE9e/YgJSWl0Hve3t7o0qULXnnlFTRr1szpkj/5+fnYtWsXvvzyS0RHR5sTHgBw+/ZtNGzYEIMHD8awYcMQHBzswEhtIyEhAevWrcM333yD69evm183HfvNmzfHiBEj0KNHD6jVascFakP31/l6vR4AoFar4e7uXm7rfEE0naWcTEZGht0+S6/X4+eff0abNm0kv7hhl67/xy5dVF5lZWUhKSkJmZmZxb6vUCjg4+ODwMBAp3ryl5ycjMmTJ2PHjh1QqVTmiu9Bpvf69OmDJUuWICAgwM6R2sYvv/yCUaNGIS4uDgqFosSnewqFAhqNBvPmzcOIESOc6uYvPT0dd+/eRXZ2drHvK5VKaLVaBAYGOt3Nv9FoxN27d5GammpO9PXu3RtJSUkIDAzErl274OLigoCAAPj6+jo4Wunl5uYiKSkJ6enpKO5SUhAEeHl5ISgoqNxeBJdFdnY25syZg5UrV0KhUJiTfA9SKpUwGAx4+umnsXLlSkRERNg5UtsxGAxISkpCampqidvv5uaGgIAA+Pj42Dk62xFFERs2bMCUKVOQk5NT7HEPwFwfVKpUCatWrUL79u3tHKltpKamYurUqdiyZUup6vxu3brho48+cprEx++//45Ro0YhJibG/PsujkKhgFqtxuzZszFmzBinqPMNBgM++eQTzJ07FwaDocTrHdP3EhkZiS+++AKNGze2c6S2I4oi7t69i5SUlEIP9+7n4uICf39/+Pn52S2u0rQkZ8JHAkz42AcTPlTeiKKI27dvIzk5uVTLKxQKhISE2LUisJX//vsPPXr0QEpKSokXPQ8y3fzv2bMHderUsXGEtvXpp59ixowZD73oK06PHj2wdu1a2T/9NhqNiI+Px71790q1vEqlQmhoqNPUadnZ2bh582ahp7tA0YSPiYeHB6pUqeI0Tzzv3r2LxMTEEm947ycIAgIDAxEUFGSHyGzr9u3b6NmzJy5fvlzq7htKpRJqtRpbtmxxihv/jIwMJCQklHjD8yBvb29UrlxZ9q1cdTodxowZg23btpV6HVPi55133sGUKVNsGJ3tXb58GT169MCdO3csqvO9vb2xa9cuNGzY0LYB2tjq1asxefLkhyZ5ixMVFYUNGzbIuqVXVlYWBg0ahGPHjpV6HaVSCVEU8emnn2LIkCE2jM4+cnJycPPmTXP3vUdxd3dHWFiYXer80iR85J9yLKO//voL/fv3xxNPPIGgoCBUrVoVHTt2xDfffFNoOW9v7xL/mjRpYl7u8OHD8PPzK3HZJUuWmJc9cuQIevfujVq1aiEgIAARERHo0aMHDhw4YLftJyLriKKI2NjYUid7gIKb5ISEBCQlJdkwMtuLjY1F165dLUr2AAVPiNLS0tCtW7dCTYHlZu3atZgxYwYAWLT9ALB3716MHDmyVDfK5ZXRaERMTEypkz1AwYOR2NhYpKam2jAy+8jKysL169eLJHsetU5MTEypb5LLs1u3buH27dulPoZFUcSdO3fMY3vJVXp6Orp3744rV65YNFaHwWBAfn4+BgwYgN9//92GEdrevXv3EBsba9FxnJ6ejpiYGIvPleWJKIoYP348vv32W4vWMx0nc+fOxaeffmqL0OwiISEBXbt2tSjZAxQc++np6ejRowcuX75swwhta+PGjXjjjTcgiqLFx/Hhw4cxdOhQ2Y7vYzAYMGjQIBw/ftzi9YxGI8aNG4cdO3bYKDr7yM7ORkxMTKmTPaZ1rl27ZtF1gi1V2ITPvXv3UKVKFcycORPffvstPvvsM1StWhWjRo3CokWLzMsdOnSoyN+CBQsAAD179jQv17RpUxw4cKDIsqanOfcvm5KSgtq1a+P999/Hd999h48++ggqlQoDBgwoknAiovIpMTGxzC0JExMTkZ6eLnFE9mE0GjF69Gjcu3evTBfwpgvA0aNHy/IC6PLly1Y9qTUajdi1axc2bNggYVT2FR8fj5ycnDKtm5CQUOZ1ywNT4qosx25+fj5u3Lgh62RfSkqKRUnu+6WmppZ53fLgrbfewtWrV8t03jMajdDr9Xj55ZeRlZVlg+hsLzc3Fzdv3izT8WtaV662b9+OzZs3W/Xbfeedd/D3339LGJV9iKKIV199FcnJyWWu87OysjBq1ChZJv2uX7+OSZMmlXl9o9GIAwcOYM2aNRJGZT/Lly/HsWPHrLpeGzdunGwT/tbU+TqdrtzU+ezS9YAOHTrg9u3b+Pfff0tcZuzYsdi0aRPOnDmDGjVqlNilKysrC5GRkahXr94jW+/odDrUq1cP1apVw/79+82vO0vzdymU9y5d8+fPx4IFCxATEwN/f39Hh2N3Fy5cwI4dO/DCCy8gPDy8TGVs3LgRY8eOxfnz58tchj3k5OTg6tWrVpWhVqsRGRkpu77dW7duxciRIyUp67PPPsPzzz8vSVn20rNnT5w4ccLqC1cPDw9cvHhRduf49PR0xMbGWlWGi4sLIiMjJYrIvmJjYx+arC2pS9f9QkJCZDmOlWlGGmuOfYVCgZo1a8puLLNTp06hQ4cOVpejUCjw5ptv4u2335YgKvu6evWq1cnaKlWqQKvVShOQneTk5KB27dpIS0uz6sZNqVSicePGOHz4sITR2d73338vWZecjz/+GC+99JIkZdnLc889h4MHD1pd57u6uuK///6T1f3B7du3UbduXatbqSiVSvTq1QtfffWVRJHZz82bN4tMRmKpoKAgm3ZpZpeuMvD393/oODwZGRn47rvv0KpVK9SoUeOhZe3YsQOZmZkYOnToIz9XrVbDx8fH6Qa1pIrjwoULWLBggVU3g507d8ahQ4cQEhIiYWTSu3v3rtVl6HQ6WXZvWbFihSRJKoVCgRUrVkgQkf1cunQJx44dk+QpZXZ2NrZs2SJBVPYlRXfEvLw8WbZwkyru5OTkcvHEz1IPG6C3tIxGY5FZfeTgs88+k+T6zGg04osvvig3zfxLKzMzU5KWeVLUnfa2Y8cOpKamWv2bNRgMiI6Oxrlz5ySKzD5WrFghyfhLgiBg+fLlsjr3Xb9+HQcOHJCkzs/Ly8OmTZskiMp+vvrqqxIH5raEwWDA999/j1u3bkkQlf3odDqLuq6XpDzU+RU+4WNqZnv37l188cUXOHz4MF5//fUSl9++fTuysrJKlcRZv349vL298eyzzz70s2/duoV58+bhypUrGD9+fFk3hUhSpmkH7SkgIADNmjUr1wPamrokScHapwb2dv36dZw5c0aSrlhGoxHnzp3DtWvXJIjMPrZv3y7pwKObN2+WrCx7yMvLk+ycILdjH5AuZp1OZ9eJJaQiVYJabolunU6HnTt3SnLjAxRsvyWDn5YHUh37ubm5suvSuWXLFsla4qpUKosGfXa027dv49dff5Uk4SGKIi5evIj//vtPgsjsY+fOnZJNKy+Kouzq/M2bN0vW9d5oNOK7776TpCx7sbZVn4mU9w1lVeETPm+88Qb8/PwQERGB//3vf1i0aBFefvnlEpdfv349tFotevfu/dByL126hN9//x39+/eHu7t7scv069cPfn5+eOyxx7By5UqsW7cOXbp0sWp7yPHu3LmD4cOHo0qVKqhRowbGjRtXKEP8+eefo0uXLoiIiEBISAiaN2+OZcuWFRkE8dy5cxgwYAAiIiIQEBCAWrVqoX///hb1gx0zZgwqVaqE//77Dz179kRISAiqV6+OyZMnF5lG2dvbG5MnT8aaNWvQtGlTBAQEmJ9GnDx5Ej179kRoaCiCg4PRqVOnQl0PN27caE6Cdu/e3TxY+caNG83LHDlyBD179kTlypURHByMqKgoHD16tFAMGzduhLe3N27cuGF+rVu3bnjqqadw+vRpdO7cGcHBwahfvz6WLFnikDFgHjYVq6Vyc3MdnvW3xNmzZ2VRpq2cPn1asmNOFEWcP39esptIeyhp6vWykNtNH1Cxt1+n00k24LTBYLBo8EtH+++//yRtkaNUKmV13gOkPV6l/B3ZmiiKkp739Xo9Tp06JUlZ9sA6/7RkCR+g4Fwil3NfRkYGYmJiJCtPjuc9Z6rzK3zCZ/LkyTh69Ci2bduGF198EW+++SY+/vjjYpf977//cOrUKQwYMACurq4PLXf9+vUA8NCWQB988AGOHDmCb775Bh07dsRLL70kq8w/FW/IkCGoWbMmvv76a0yaNAnbtm3D//73P/P7MTExGDBgAD7//HNs3boVL774Ij766CNMnDjRvExWVhZ69+6NO3fuYPHixdi1axcWLFiAsLAwZGZmWhSPTqdD//790bZtW2zatAmjRo3C2rVri+1H/cMPP2DNmjWYNm0adu7ciRYtWuDEiRPo0aMH0tPT8emnn+LLL7+Ep6cnBg4ciO3btwMo6Ir17rvvAgAWL15sHrS8c+fOAIBvvvkGffr0gZeXF1atWoV169bB19cXzz77bJGkT3ESExMxcuRIDBw4EN988w06deqEWbNmOWSQcykra1EUZdW0/8qVK5J2O1WpVLKauePChQuSJujy8vKQkJAgWXm2JuWxqtPpZDdot5TbL5eLfhOp45XT9l+5ckXyMuV03hNFUdL9Jac6LyUlRfLWeJcuXZK0PFu6cuWKpK1a1Wq1rI79CxcuSDrQtMFgKPRAszy7du2apNc7er1eVq27AGnrqdzcXMnKKosKP2BMWFgYwsLCAMB8czpr1iwMHjy4yKCKpiTOsGHDHlqmTqfD5s2bUa9ePTRu3LjE5WrWrGn+/27duqFv376YPHky+vXrJ7uBXOn/DR061Jy8ad++Pa5du4YNGzZg+fLlEAQB77//vnlZo9GIFi1awM/PD+PGjcO8efPg6+uLS5cuISUlBcuXL0f37t3Ny/ft29fiePLz8zF+/HiMHTsWQMHA5Gq1GnPmzMFvv/2G5s2bm5fNysrCyZMn4evra36tY8eO0Gq1+OGHH+Dp6QkA6NKlC1q2bIm3334bffv2RUBAgHlMq9q1a6NZs2bm9bOzszFt2jR06dKlUP/lzp07o3Xr1pg9ezbatWv30G1ISUnBt99+i6ZNmwIo+F5PnDiBbdu2YfDgwRZ/J+WJnG56dTqdpE+7BEGQ1cW/LabUltP2S90aTU6t2wBp463I226L8mxJ6t+9KIo2OZfIRUXe97Yq01akrvNNZcpFRd7/FXnbTZypzmdW4QFNmjSBXq8v0owtPz8f33zzDRo1aoT69es/tIz9+/cjKSmpVOP8PPjZaWlpshzUjv5ft27dCv27bt26yM3NNQ92eu7cOQwcOBDh4eHQarXw8/PD6NGjYTAYzE8SIyIioNVqMXPmTKxZswYXLlywKqbnnnuu0L8HDBgAAPj5558Lvd6mTZtCyZ6srCycOnUKvXv3Nid7gIKmmYMGDUJ8fPwjn9b8/vvvSE1NxeDBg6HX681/RqMRnTp1wpkzZx45TW1wcLA52WNSp04dxMXFPXQ9W5DyaRcAWQ3UrtVqJX/a5efnJ1l5tnb/b6M8l2krUh77giDI7sGGlNsv9XnE1qSOV07bL/WsUkqlUla/e6l/q3La997e3pInPOQ0S5nUdb7RaJTVsV+R63xbxCmnGcoAac9Vjr7Wl9fVlh38/PPPUCgUqF69eqHX9+7di+TkZLz44ouPLGP9+vVwdXUtcpP9MKIo4pdffjEnAEi+Htx/pgGIc3JyEBcXhy5duuDWrVtYuHAhDhw4gKNHj2Lx4sUA/r/Jn4+PD/bt24d69eph9uzZaNasGWrVqoV58+ZZnCFXqVRFTrLBwcEAUGS2FNPrJqYBy4qbNcv02qNmXLlz5w4A4MUXX4Sfn1+hv6VLl0IUxUcO4lncb8LFxcUhTSQf1Z3TEiqVCmq1WrLybK1+/fqStkgyGo2PTKCXJ40bN5a00g4ODpbVBZCUx76Li4vkN1K2JuX2u7m5SVaWPbi5uUm6v+S0/Q0aNJC0PL1eL6vzHiDtsS9lWbbm7u6OatWqSVaeUqlEkyZNJCvP1urXry9pywSDwSCrY1/qOl+r1aJSpUqSlWdL1atXl/Q8rVKp0KhRI8nKswcpt9/RdZ58Hi1LbMKECfDy8kKTJk0QFBSE5ORk7Ny5Ezt27MDEiROL7c7l5uZmbhlRklu3buHQoUPo27dvidnRQYMGoW7duqhfvz78/Pxw69YtbNy4ESdOnMDixYsdngUk29mzZw+ysrKwYcMGVK1a1fz6+fPniyxbp04drFu3DqIo4u+//8bGjRuxcOFCuLm54Y033ij1Z+r1eiQnJxe6sUxMTARQNJHy4AW9VquFQqHA7du3i5Rreu1RN6ym9z/44AM8+eSTxS4TFBT0iK0oP1xdXaHRaCTpinN/qyk5aNSoEVxdXSVLtLm4uMjqAqBVq1bYsGGDJGWpVCq0bdtWkrLsxcPDAwqFQpKkn5eXlwQR2Zenp6ckU7SaypITQRDg7u7+yNaYpeHm5iarVh4hISEIDw+XbOwNURTRsmVLScqyFy8vL0kGMBUEQXbHfvv27REXFyfJAPtGoxGtWrWSICr7qFu3Ljw8PCT53QMF9d6DrbXLs1atWmH16tWSlKVUKtG6dWvZPOhQKBRo2bIljhw5IkkrL71eL7vznqenp2SzSjr6vFdhW/g0a9YMp0+fxuTJk9GrVy+89tpruHPnDj7//HPMnTu30LI3b97ETz/9hD59+sDHx+eh5W7cuBEGg+Gh4/w0b94chw4dwvjx49GzZ09MmTIFgiBg69ateOWVVyTZPiqfTCf6+6cdF0UR69ate+g69erVw4IFC6DVanHu3DmLP3fr1q2F/m0aHLx169YPXc/DwwNNmzbF7t27C40wbzQasWXLFlSuXNk8FtX9LZnu17x5c2i1Wly4cAGNGzcu9k+j0Vi8TY4kVVNXubXm8/DwwODBgyW5WVOpVBg0aJCsbvxNA49LQa/XY+TIkZKUZS8KhUKS7giCIMimWfv9tFqtJMe+h4dHoTpALqQ6X8ntvAcAo0aNkuRGTaFQ4Omnn0atWrUkiMp+fH19Jdl+Hx8fWSX7AOCll16SbDZFFxeXRz44Lk9cXV0xdOhQSfaZUqlE3759ZfX77969u2R1lcFgkF2dP3LkSMm69Jlm55UTb29vSVrhu7m5ObxlY4VtSjJkyBAMGTKkVMtWqVIFaWlppVr2zTffxJtvvvnQZV5//XW8/vrrpSqPnEuHDh2g0Wjw8ssvY+LEicjLy8Pq1auLHF/79u3D6tWr0aNHD1SrVg2iKGL37t1IS0tD+/btLfpMjUaDTz/9FFlZWWjcuDF+//13fPDBB4iKisLTTz/9yPVnzZqF3r17o3v37pgwYQI0Gg2++OIL/Pvvv/jyyy/NF4GPP/44AGDdunXw8vKCi4sLwsPD4e/vj0WLFmHMmDFITU1Fnz59EBgYiLt37+L8+fNITk7G0qVLLdomR/P390dqaqpVrXx8fHzg7u4uYVT2MX78eGzYsMHqiwBBEDBhwgSJorIPd3d3TJo0CXPnzrWqmbtKpUKzZs3w1FNPSRidfQQFBeHevXtW7X9/f3/ZJXmBgmM2ODjYqpnVBEEotousHPj4+CA5Odmqlh5ubm6yGsPE5MUXX8SSJUuQmppqVQs3o9GI6dOnSxiZfahUKgQGBpq7aJeFUqks0m1cDho2bIioqCj89NNPVp33BEHA+PHjH/nguLx59dVXsXbtWklu/C1pnV4euLi4YMqUKZgxY4ZV5SiVSjRs2FB2rXo7d+6MOnXqSDJb2bRp02TXg0UQBAQFBSE+Pt6qcspDN74K28KHyBFq1aqFr7/+GmlpaRgyZAimTJmC+vXrY9GiRYWWq1mzJnx8fLBs2TIMGjQIw4YNw59//omVK1cWO536w6jVamzduhVHjhzB888/j1WrVmHYsGHmWecepVWrVtizZw88PDwwduxYDB8+HOnp6fjmm2/Qr18/83LVqlXDggUL8Pfff6Nbt25o164d9u/fD6CgG+MPP/yArKwsTJw4Eb169cK0adNw7tw52VWAQMFT2sqVK5f5iadarS4XFUBZ1KxZE7NmzbK6nJkzZyIyMtL6gOxs4sSJqFOnTpmfeAqCAKVSiZUrV8qmaff9VCoVQkNDy7y+i4uLLG/6TPz8/Kxq5RUUFOTwvvzWqFKlSpmPfaVSadV505G0Wi2WL19uVbJHoVBg2LBhFj+0KS8CAwOtOnZDQkJkNWbd/T7++GO4urqWefBqpVKJGjVqYNq0aRJHZntVq1bFvHnzrCpDEARMnz4dTzzxhERR2c/YsWPRpEkTq+v8zz//XJYTFXzxxRdWl9GyZUu8/PLLEkVlX76+vlYlaYOCgsrFw11BdPQ8YTaSkZFht8/S6/X4+eef0aZNG8mzl97e3pKWJ2dubm7msWeodMaMGYNdu3bh1q1bjg7FKaWnp+PmzZsW3QRoNBpUq1ZNli0cTIxGI8aNG4dNmzZZvK4gCBg4cCBWrVolu4sfk7i4OHTq1Al37tyx6KmXQqGAIAjYvHkzunTpYsMIbS8lJQW3bt2yqKWTi4sLqlevLrunfA8yGo2IjY1FZmZmkfd69+6NpKQkBAYGYteuXYXeCwgIkG3rnvvl5OTgxo0bFnVzUSqVCA8PLxcXvtb44IMPinT7Lw2FQoHmzZtj586dsk74GQwGXL9+vUjX7UepVKmSrAaoL87Ro0fRv39/8yyjpWWale3gwYOoUaOGDSO0HVEU8frrr2Pt2rUWrysIAnr37o21a9fKrjufSUJCAqKiopCQkGBRnS8IAgRBwPr169GrVy8bRmhb27ZtM3dHs6TOVyqVqFq1Kg4dOoTAwEBbhWdzoigiNjbW4tyCv7+/XR7uluYhlDyvtomIygFvb2/UqFGj1BfwWq0WNWrUkHWyByi4eVmxYgXeeOMN89OrR1EqlRAEARMnTsTKlStlm+wBgLCwMBw6dMii2XtMF/07d+6UfbIHKGjpUr169VKPRRMQEICaNWvKPtkDFBz/1apVQ0hISKmOY7VajapVqzpFsgcoePhSs2bNUrd08vT0RM2aNWWf7AGAKVOmYPHixdBoNKU675laM/Xr1w87duyQdbIHKDiPRUREIDAwsFQttTQaDapXry77ZA8AtGvXDrt370ZQUFCpExeCIODxxx/HoUOHZJvsAQq2Y+nSpZg+fToUCkWptt90bhw7diy+/PJL2SZ7ACA0NBSHDh2yaMBppVIJrVaLbdu2yTrZAwADBgzA5s2b4eXlZdF+fPrpp2Wf7AEKjv/w8HBUqlSpVHW+SqVCWFhYuWrJL98rbqIKzGg0Qq/XP/SP7MPFxQU1atRAtWrV4OPjU6TJuouLC/z9/VGzZk2rukOUNwqFArNmzcKPP/5onmZWqVQWqQxN29u4cWMcOHAAc+bMcYrvICwsDIcPH8b8+fPNszqWlMzQaDQYNmwYTp8+jXbt2tkxSttyd3dHzZo1UbVqVXh5eRXafkEQ4OrqisDAQNSqVQshISGy7MrzMAEBAeZtc3NzK3Lse3h4oHLlyoiMjHS61roqlQrh4eGIiIiAr69vkcSfRqOBVqtFREQEqlWrJtuuPMV55ZVXcPLkSXTq1AmCIBS5Ab4/CR4ZGYlNmzZhzZo1TpHwAv5/LKtatWohMDAQrq6uhX7bKpUKXl5eCAsLQ2RkJDw8PBwYrbSefvppREdH45VXXjEPwvrgsW36t6+vL959910cO3YMERERdo9VagqFAjNmzMBPP/1kHn9OqVQWqc9N/27QoAH27t2LBQsWOEWiv1KlSjhw4AA++OADc7fk4rZLEASo1WoMHjwYZ86ckd1AxSXp1q0bzpw5g+eeew4qlQqCIBSp803/rlSpEpYtW4Y9e/bIPtlzP39/fzz22GPF1vlKpRIeHh4IDQ1FrVq1yt1YXezSJQF26bIPdun6f2PGjHlkd5r09HQ7RUMPMhgMEEURCoVC1i1ZLPHPP//gwIEDOHv2LPbu3QudTgdXV1dMmzYNnTt3Rt26dR0dos3odDr8+OOPOHnyJM6dO4dffvkFer0e7u7uWLRoEXr27CnLWanKwnTsm1p0VSSiKKJ27dq4desWQkNDceHCBUeHZFdGoxFGo7FCnfdu3LiBPXv24M8//8Tly5dhNBoREhKChg0bon379mjevHmF+B2IogiDwVDqFp/O4N69e9izZw9OnTqF9evXQ6fTQaPRYNSoUXjqqafQtWtX2bfmfZgLFy5g//79OHv2LPbs2WOu86dOnYqoqCiLWsDKjV6vx8GDB3Hy5En8+eefOHHihLnOX7BgAXr27OkUrdpKcvfuXezatQtnz57Ff//9h/z8fPj5+aFRo0Zo0aIFOnbsWCHOA+XlvFea1rZM+EiACR/7YMLn/924cQPJyckPXaZx48Z2ioaosNq1ayMhIaFC3vQC3P6KjPueqOKp6L97bn/F3n5yrNIkfOTfxo6oAgoPD0d4eLijwyAiIiIiIqJyigmfco7dcv4fx6UhIiIiIiIiKp2K0cmaiIiIiIiIiKgCYcKHiIiIiIiIiMjJMOFDRERERERERORkmPAhIiIiIiIiInIyTPgQERERERERETkZJnyIiIiIiIiIiJyM007L7uXlZbfP0ul0AABPT0+o1Wq7fW5Fo9Pp+P0SyYAgCOb/2vNcXF5U9O2vyLjviSqeiv675/ZX7O2n8o8tfIiIiIiIiIiInAwTPkREREREREREToYJHyIiIiIiIiIiJ8OEDxERERERERGRk2HCh4iIiIiIiIjIyTDhQ0RERERERETkZJjwISIiIiIiIiJyMkz4EBERERERERE5GSZ8iIiIiIiIiIicDBM+REREREREREROhgkfIiIiIiIiIiInw4QPEREREREREZGTYcKHiIiIiIiIiMjJMOFDRERERERERORkmPAhIiIiIiIiInIyTPgQERERERERETkZJnyIiIiIiIiIiJyMytEBUFGCIDg6hHLJzc0N2dnZjg6DiIiIiIiIqNxjwoeISEIZGRm4ePEi8vLy4O3tjcceewwajcbRYdmFKIq4efMm8vLyAAB6vR6iKDKJTUTkpAwGA65cuYK7d+9CpVKhZs2a8Pf3d3RYZAeiKCI+Pp51PlE5x4QPEZGVkpKS8OWXX2Lt2rW4dOkSRFE0v6dSqfDkk09i7NixGDBgAFxdXR0YqW1ER0djxYoV2LVrF1JTU82vJyYmws/PD71798a4cePQrFkzB0ZpWwaDAdnZ2cjJyYHBYAAAGI1G6HQ6qNVqB0dne/n5+cjOzkZubi5EUYRKpYKbmxvc3d2hUDh373FRFJGTk4Ps7OxC+z43N9cpf+8P0uv15n1vMBigUCjM+16lcv7LzNzcXOTk5CAvLw+iKEKtVpu331lvfA0GA/bu3YuVK1fiyJEjyM3NLfR+lSpV8MILL2D06NGoXr26g6K0PZ1OV+h3bzAYkJGRAXd3dyiVSgdHZztnz57F8uXL8d133yE5Odn8emJiInx9fdGjRw+MGzcOTz/9tNP+BoxGI7KyssznPdNrFa3Oz8vLg9ForJB1fk5ODnQ6HQBAo9HA3d293Nb5gnj/nQmViU6nw969e9GtWzdJfuTOenK0Frt0UXljNBrx+eef44033jBXesVRKBQwGo0ICwvD+vXr0a5dO/sGaiN3797F+PHjsWXLFqhUKuj1+mKXM703YMAALF++HIGBgXaO1HZ0Oh0SExORlpZm3v9RUVG4c+cOgoKCcPDgQXh5eSEoKAgeHh4OjlZ66enpSEpKQlZWVrHvK5VK+Pr6IigoyOlu/o1GI5KSkpCSkmK+6Htw37u6uiIwMBC+vr4OjlZ6ubm5uHPnDu7du4fiLiUFQYC3tzeCg4PL7UWwNdLS0pCUlIScnJxi31epVPDz80NQUJBT3QD9888/GDp0KM6cOQOlUmm+2X2QUqmEKIqYMWMG3nnnHadq6ZqdnY07d+4gPT0dQNHfvSAI0Gq1CA4OdqrtTk1NxYQJE7Bhw4ZS1fm9evXCZ599hpCQEDtHajs6nQ537txBampqiXW+p6cngoKC4Onp6eBopZeRkYGkpCRkZmYW+75SqTQf+85W54uiiKSkJCQnJ5vr/Ae5uroiICAAfn5+do7u4ZynBiIisqP8/HwMGDAAY8eORU5OTonJHgDm9xISEtC+fXssXrzYXmHazD///IM6derg22+/BYASL/zuf2/Hjh144okncP78ebvEaGupqam4ePEiUlJSHrr/MzIycPXqVcTHxxd7YyxHRqMRsbGxuH79eonJHqDgiffdu3dx6dIl882RM8jOzsalS5eQmJhY4oUfUJAUiYuLw7Vr1x66nNwkJSXh8uXLSEtLK/GYFkUR9+7dw+XLl5GYmGjnCG1Hr9cjJiYGsbGxJSZ7TMvduXMHly5deuhvRE527NiBRo0a4dy5cwBQYrLH9J7RaMS8efPQsmVLpKSk2CtMmxFFEbdu3cKVK1ceej4TRRGpqam4dOlSoRYwcnbx4kXUrVsXmzdvBlC6Ov+HH35AnTp1cObMGbvEaGtpaWnmffqwOj8zMxPXrl3DzZs3narOj4uLQ0xMTInJHqDgd5+cnIyLFy/i3r17dozQtnJycnDp0iXcvn37kXX+zZs3cfXq1XJV51eohM+ff/6J7t27o2rVqnBzc4Ofnx+efvppbNiwodByoiji448/Ru3ateHi4oJKlSph7NixhboqAEBWVhYGDRqEOnXqYNCgQdBqtahTpw7ee++9IpX7oUOHEBUVhdDQULi4uCAoKAgdOnTA3r17bb7dRCQtURTx4osv4rvvvrNoPdPF8ZtvvomVK1faIDL7uHHjBtq2bYvk5OSHXvA/yGAwIDU1Fe3atUNMTIwNI7S9pKQkxMXFPfSi70HJycm4ceOG7C8AjUYjrl27hrS0tFKvo9frcf369SL1qBxlZWXh2rVryM/PL/U6mZmZ5e4CsKwSEhJw69atUh/HoigiMTERN2/etHFktqfX63H16lVkZGSUep38/Hxcu3bNonXKox9//BHPPfcc9Hq9Red9URRx9uxZPPPMM0W6fsmJKIqIjY1FUlJSqdcxGo2Ij4+XfcIzPj4ebdu2xZ07dyyu8+/du4cOHTrg0qVLNozQ9u7evYvY2FiLtj8lJQUxMTFOUefHxMRYVH8bDAbcuHHDKRK92dnZuHr1qnmsqtLIysrClStXLLpOsKUKlfBJS0tDWFgY5s+fj71792L9+vWoVq0aXnzxRbz33nvm5d58801MmjQJvXv3xp49ezB9+nRs2rQJUVFRhS7WdDodRFHE66+/jmnTpmH79u3o168f5syZg969exf67OTkZNSpUwdLly7Fjz/+iM8++wxqtRrdu3cvknAiovLtq6++wtatWy262X/QxIkT8d9//0kYlX0YjUYMHToU9+7ds+jCx8RgMCA9PR1Dhw616vtzpIyMDNy6datM66anp+P27dsSR2RfN2/eLHP32ps3bz60VUR5Z0pcleXYzc/Px/Xr12V98Z+SkoK7d+/afd3y4saNGxZd9JuIoogbN27INuGXkpKCF154AUajsUzHr8FgwNmzZzF79mwbRGcfpu6LZZGYmCjb1g6iKGLEiBFITk5+aKuekhgMBmRmZuLFF18s0zVDeZCZmYmEhAS7r1texMfHl7mVojXXC+WBNXW+TqcrN3V+hUr4tGvXDqtWrcKQIUPQvn179OjRA5s3b8ZTTz2Fzz//HEDBQf3RRx/h1VdfxcKFCxEVFYWJEydi1apVOH36NNatW2cuT6vVYsuWLXjllVfQqFEjdOzYEXPmzMGkSZNw+PBhXLt2zbzswIEDsWzZMgwcOBBt27bFs88+iz179qBy5crmzyZ6mFmzZkEQhHJ1wWyK6X7t2rUr8xg169atgyAIuH79+iOXteZzrJGRkYEJEyZYXY7RaMS4ceMkiMi+vvnmG/z8889luvAz0ev1OHHiBDZu3ChhZPZhNBqtbqmQlJQk2wug9PR0i1r2PEgURcTFxUkXkJ3Fx8dbddOSk5NTrs7hltDpdGVOdJrcvn273DzxtFRycrJVXbOkOHc4ysyZM5GammrVjYvRaMTChQtx8eJFCSOzD9N4Vdaw9tzhKDt37sSBAwesqvMNBgP++OMPfPnllxJGZh+m2UetYe25w5EyMzOtbpkbFxdXLpIeZXHr1i2rjn0pzh1SqFAJn5IEBASYB5b67bffYDAY0K1bt0LL9OjRAwCwffv2R5ZnGpD0UYNVqdVqaLVapxvUiiq2FStWYMWKFWVat3v37jh58iQqVaokcVTS2bhx40P7L5eWwWDA0aNHZdfKZ9myZZIMQKpQKLBs2TLrA7KztLQ0SZ7SW9ItoDyR4sIlNzdXluP55OXlSfKU/u7du7K8+E1JSbH6htVoNMp2TBMpfrMZGRmy69aUnp6ONWvWSJKsUCqVsuzOnJSUZPVvVq/Xy7JL67JlyySZcUwQBCxdulR25760tDRJktQVuc7Py8uTZZ2v0+msesBlUh7q/AqZ8DEajdDr9UhKSsKKFStw4MABTJs2DQDMP2oXF5dC66jVagiCgL/++qtIeaIomrsp7N+/H4sXL8bzzz+PqlWrlvjZCQkJePfdd3Hp0iVMnjzZBltJ5BhPPPEEnnjiiTKtGxgYiObNmxf5/ZUnUnbBVCqV5gEQ5SAmJgbR0dGSdMUyGo04c+YMrl69KkFk9iNVf/T09HTZPe3Ny8uTrGWSHG98pIpZp9PJcjwXqY59OY7pkJmZKVnLJLlt/549eyRLUun1enz99dcOv/mxhGnwcSnIbd/funULx48fl6SuEkUR//33H/755x8JIrMfqc776enpVrUUcQSdTifJA05AvnW+FOcqU47AkSpkwmfcuHFQq9UICgrCpEmT8PHHH2P06NEAYL5R/eWXXwqt8+uvv0IUxWKfTG3duhX9+vVDQEAAunbtiq5du2L9+vXFfrZp6vbKlStj2bJl2LJlC7p37y7xFpIzS0xMxPPPPw8fHx8EBwfj5ZdfLnQxsnz5crRp08Y8DXS9evWwaNGiIq0Szp49ix49eiAoKAguLi4IDQ1F9+7drW66+mBXq+vXr0MQBCxatAjz5s1D1apV4erqiqZNm+Lw4cOF1i2uS5coili0aBHCw8Ph6uqKxo0bY9++fVbFWFYGgwGnT5+W7GLVaDTijz/+kKQsezh16pQsyrQVURQlG39GyrLsRcpuaHLs0laRt1+n00k2/ozBYCjTODiOVJH3fXR0NNRqtWTlpaSkyKpr26Nm4bREbm6urMauq+h1PlCxf/sVedsB59r+CtmXaMaMGRg5ciTu3LmD3bt3Y/z48cjKysKbb76JBg0aoE2bNvjggw/w2GOPISoqCv/++y/GjBkDpVJZbFeGZ555Bh9++CHq16+P6OhoLFy4EMnJydi5c2eR5T/55BOkpaXh1q1b2LBhAwYOHIivvvoKzz//vL02n2SuX79+GDhwIEaMGIHz58/jf//7HwCY+0ZfvXoVgwcPRvXq1aHRaHDu3DnMmzcPFy5cMC+TlZWFqKgoVK9eHcuXL0dwcDBu376NI0eO2OzJ86efforw8HAsW7YMRqMRixYtQteuXXHs2DE8/fTTJa43e/ZszJ49GyNGjED//v0RFxeHV155BQaDAY899phNYi3JrVu3JG2OL4oi/v33X8nKs7VLly5BpVJJ9pRKpVLJajyH/Px8SZ9M5+XlwdPTU7LybE3Km3SdTgej0ShJ90B7kXL75ZbwkDrevLy8ct2S80EVed9fvHhR8sGmL168iLCwMEnLtBVbHPtubm6Slmkrly5dglKplKw1qlqtllWdb6qnpCK3376U17t6vR56vV5Ww5hIuf2O7sorn29dQlWrVjV3tzKN1fO///0Pw4YNQ2BgILZt24aXXnoJzz33HABAo9Fg0qRJOHToULF9+Xx9fVGzZk20a9cOUVFRqFGjBgYNGoRdu3bh2WefLbRsZGSk+f979eqFrl274tVXX8XAgQNldeFLjjNixAhMmTIFANCpUydcuXIFX375JdasWQNBELBkyRLzskajEa1bt4a/vz+GDx+OxYsXw9fXFxcuXEBycjLWrFlTaEY50zFvCwaDAQcPHoSrqysAoHPnzqhWrRpmzpyJgwcPFrtOWloaFi5ciGeffRarV682v16nTh20bNnS7gkfW8ywIqcBTHU6XZFBuq0hCIKstl/qbghyetILSL/9curWAUgbb0XedluUZ2sVed/b4hxdkc/7ctr/Utf5pjLloiLve4Db70znfWYYADRr1gx6vd48q1ZQUBD27t2LxMREnDt3Dnfu3MGcOXNw6dIltGnTplTlAQWZ8dIsm5qaKtvBvMj+evXqVejf9evXLzQK/NmzZ9GrVy/4+/tDqVRCrVZj6NChMBgM5mOyZs2a8PX1xbRp07Bq1Sq7tDLp27evOdkDAF5eXujZsyd+/vnnEp8enTx5Erm5uXjhhRcKvd6iRQuEh4fbNN7iaLVaycv08/OTvExb8fX1lXTcGYPBIKvtl2LgyvvJ6UkXIO32C4Igu4ccUm6/1MeSrUkdb0Xefrltu7+/v+S/VZ735UHqOt9oNHLfy4jU8crt3Cfl9jt638vrastGjhw5AoVCgYiIiEKvBwUFoX79+vDx8cGqVauQlZWF8ePHl6o8oOCm+mFEUcSxY8eg1Wrh7+9f9g2gCuXBY8XULD4nJwexsbFo3bo14uPj8dFHH+H48eOIjo7G8uXLzcsAgI+PD44dO4aGDRtixowZqFOnDkJDQ/Huu+/a7OlLSEhIsa/l5+eXOCicacyskta1N19fX4SGhkpWnkqlMieI5aBhw4aStkoxGo1o1KiRZOXZmlqtlrTSlkuzfhMp43V1dZX8ybGtSbn9ctz3Uu4vOW5/eSzLHho1aiTpvlcoFKhfv75k5dmalPtLqVRCo9FIVp6tNWrUSNKWCQaDQVZ1vtT7S26/fanrfLk95HGm8768Uo1WGjVqFLy9vdGsWTMEBwfj7t272LZtG7Zs2YIpU6aYp1P/4osvAAA1atRAWloa9u3bhzVr1mD+/Plo3LixubzPPvsMx48fR4cOHZCQkABRFHHy5El88sknaNGiRaGuMr1790aDBg3QsGFD+Pv7IyEhAevWrcOxY8ewfPlyh2f+yDl89913yMrKwo4dOwq1gPnzzz+LLFuvXj188803EEURf/31F9atW4c5c+bAzc0N06dPlzy227dvF/uaRqMpcRwTU3KrpHWrVasmaYyl0alTJ2zatEmScWz0ej3atm0rQVT20bRpU7i6ukrWF9k0eLeceHl5STLbhFqtLtTiTQ48PDygUCgkSfp5eXlJEJF9eXl5STJFq6ksOREEAR4eHpLM2OLu7i67J71S7i+57fu2bdtK1spDoVCgUaNGcHd3l6Q8e9BoNHBxcZFk/BU5jdkGFLQg9/T0lGymJpVKhaeeekqSsuzFy8ur2Al7LKVSqWRX55vO1VL8/uV27AMF+16qmfUcfd6XV6rNSk8//TT++OMPvPrqq+jUqRNGjhyJ27dv4+uvv8aiRYvMy4miiKVLl6Jnz54YNmwYLl68iJ07dxa5Ca5Xrx7u3buHd955B7Nnz8YLL7yAAwcOYMaMGTh48GChJE7Lli2xf/9+jBw5Eh07dsRrr70GQRCwZ88ejBs3zm7fATk301O4+wfDFEXRnMQsaZ0GDRpg6dKl0Gq1OHPmjE1i27FjR6FEQUZGBnbv3o3WrVuXePHfvHlzuLq6YuPGjYVe//XXX3Hjxg2bxPkoY8aMkWzQYk9PTwwcOFCSsuzBw8MDw4YNkyRBrVKpMGTIEIdXgpaSqjWmHFt1KhQK+Pr6Wl2OIAiyatZvotVqJUlUeHp6ymrAYpOKfOyr1Wp4e3tbXY5UvyF7at68OR5//HFJWvkYjUa8+uqrEkRlXxX12Hd1dcWIESMkOe+pVCoMHDhQdud+qfaZn5+f7Fq1SllXy+3YBwBvb29JZih0d3d3eLKvQjUrGT58OIYPH/7I5UaNGoVRo0Y9crkWLVpg9+7d0Ol02Lt3r3nK9eJMnToVU6dOtThmIktERUVBo9Hg+eefx9SpU5Gbm4uVK1cWaZGwZ88erFixAn369EFERAREUcSOHTuQlpaGqKgom8SmVCoRFRWFN954A0ajEQsXLkR6ejpmz55d4jq+vr5488038d5772HkyJEYMGAA4uLiMGvWLId06QIKLn7btWuHEydOWJX4EQQBU6dOldWTTgCYPHky1q5da3U5giDgzTfflCAi+3J3d4dWq7WqpYdGo0FAQIB0QdlRcHAw0tLSrHriFxAQIKtuDSaCIKBSpUpWTSltKkOOfHx84OHhgaysrDKXYfr9yFGlSpWQkZFhVReXkJAQ2XVrEAQBs2fPtnpSB6VSiSpVqmDQoEESRWY//v7+SE5OtqqVj7e3tyxbOUyaNAmfffaZJK08bNF63NZcXV3h6+trVctetVpt7kUiN0FBQUhNTbXqetff31+WDzkEQUBISAji4uKsKkfKoSDKSl61DhE9VO3atbF9+3akpqaib9++eO2119CwYUN8/PHHhZaLjIyEVqvFokWL0KtXLwwYMABnzpzBunXr8Morr9gktvHjxyMqKgoTJkzA4MGDodfr8cMPP6Bly5YPXW/OnDl4//338eOPP6JXr1745JNPsGrVKrvP0GUiCALWrl0LtVpd5qc1KpUKderUkeXFT2RkJN5//32ry5k3b57D9qG1KleuXOaEhSAICAsLk91Nn4lKpUKVKlXKvL6rq6vDkrVS8PPzs6qlR3BwsMP78lsjLCyszE/7TTf8cnvKbeLi4mJVss7T01O2id7+/fvj2Weftaqlh9FoxNdffy3L49/a87ZarbbqvOlI4eHhWLx4sVVlCIKAmTNnom7duhJFZV+hoaFW1/ly68ZqYjpvl5Wrq6tsH3IABQ+erXlIERwcXC4e7Aqio+cJcwKlaeFjCbleDNmam5sbsrOzHR0GWej69euoXr06PvjgA1m26CjJvn370KtXLxiNRovGNFGpVAgICMDJkycdMgaRFIxGI15++WV89dVXFq8rCAKGDBmCdevWyTbpARRMK3zt2rVipxeOiorCnTt3EBQUhIMHD5pfFwQB4eHhknQNcbSUlBTEx8db1NrB1dUVERERsh+zzmg04saNG8jIyCjyXkn7HgACAwNlfeFrkpOTg5iYGIue+CqVSlSvXr1cXPhaKzExEYmJiRat4+npiWrVqsn6nHfv3j20bdsWf//9d5laeyxfvlz2QxhkZGTgxo0bRer8h/3u1Wo1IiIiZNnCwUQURYwZMwaff/65xesKgoD+/ftj8+bNsk16AAX3eteuXSu2ldfD6vyqVavCx8fHnqHaRGpqKm7evGlxnV+9enVJ7o0dSRRF3LhxA+np6RatFxAQUC5a9wBs4UNEVCZdu3bFjz/+CH9//1JfxJjGS/rtt99km+wBCsah+PLLLzF9+nQIglCq7VcqlRAEAVOmTMHatWtlfeMDFHTLioyMLPV4HO7u7qhVq5ZTJHuAgpYuNWrUKHW/9MDAQERGRso+2QMUHP/Vq1dHpUqVSnUcq9VqVKtWzSmSPUDBwxdLjmUvLy/UqlXLKZI9QMET29LexJi6BFSvXl325zzT7J7dunUDULqHkyqVCm5ubvj6669ln+wB/v9Y9vDwKNXyWq0WtWrVknWyByjY1ytXrsS7774LhUJRqjrfdLxPnDgRmzZtknWyByg4j0dGRpZ6TBs3NzdERkY6RbIHKGjpUrNmzVLX+QEBAahZs6bskz1AwfFfrVo1hIaGluo8rlKpEB4eXm6SPUAFG8OHiEqnNK1WnOHGzVrt27fHxYsXMWPGDKxduxb5+flQqVSFprZXq9XQ6XQIDAzEtGnTMHHiRKf47hQKBd5//3306tULkydPxsmTJ6FUKiGKYqFjxzTDw5NPPokPP/zwkV345ESpVCIsLAyBgYFISUlBZmZmkRnMtFot/Pz8ZDl2w6O4u7sjMjISGRkZSElJQXZ2trnVhyAIcHV1hZeXF/z8/GQ5Zs+jBAYGmsd2uHfvXpF97+npCV9fX/j4+Mj+Zv9BKpUK1apVQ3Z2NlJSUpCVlVXoybdGo4GHhwf8/f2dJtFzPy8vL9SuXRtpaWlIS0tDdna2udWLIAhwc3ODt7c3/Pz8nOJ8b+Lj44Ndu3Zh06ZNeOeddxATEwOVSgWDwWB+8q9Sqcx1QJ8+ffDhhx8WmjVU7jQaDWrUqIHMzEzzee9+Li4u8PT0hL+/v8MHapWSQqHArFmz0KNHD7zxxhs4fvy4OYljOvYVCgUEQYDBYEDjxo3x4Ycfymom0kdRKBSoUqUKAgMDkZycjMzMzCItfnx8fODn5ye7CSlKw5TsT09PR2pqKrKzs83Xu4IgwMXFBV5eXvD393fKOj8gIAC+vr5ISUkx1/mmc51SqYSbmxu0Wi20Wm25q/PZpUsC7NJlH+zSZT8vvfTSI7vr8NRRWFpaGrZv347o6Ghz8kej0WDChAlo2bIlunfv7hRPOkpy/vx5/PDDDzh16hS+//576HQ6uLq6YubMmejevTvq16/v6BDtQhRFVKlSBQkJCahcubJVA/zKkenGz9SiqyKp6Pve9KBAoVCUu4tde6hox77RaMSRI0dw9OhRnDp1ComJiVCr1Xj88cfRpEkT9O3bF5UrV3Z0mHZRuXJlJCQkIDQ0FPHx8Y4Oxy7+/fdf7NmzB6dPn8a1a9cgiiKqVq2Kpk2bomvXrmjUqJGjQ7SLin7er2jnvfuJogiDwVDqlu6OxISPBJjwsQ8mfOzn+vXruHv37kOXadq0qZ2ikZ8qVaogPj6+Qlb+ALe/om9/RcZ9T1Tx8HdfsXH/U3nnPO1MiUgy1apVk/UYM0RERERERBUdEz7lEBtdFe/+cVGIiIiIiIiIqGQVr5M1EREREREREZGTY8KHiIiIiIiIiMjJMOFDRERERERERORkmPAhIiIiIiIiInIyTPgQERERERERETkZJnyIiIiIiIiIiJwMEz5ERERERERERE6GCR8iIiIiIiIiIifDhA8RERERERERkZNhwoeIiIiIiIiIyMkw4UNERERERERE5GSY8CEiIiIiIiIicjJM+BARERERERERORkmfIiIiIiIiIiInAwTPkREREREREREToYJHyIiIiIiIiIiJ6NydAC2kpGRYbfP0uv1AIDMzEyoVE77lTqcXq/n90uyIIqi+b/2PBeVF9z+ir39FRn3PVHFw999xcb9T47k5eX1yGXYwoeIiIiIiIiIyMkw4UNERERERERE5GSY8CEiIiIiIiIicjJM+BARERERERERORkmfIiIiIiIiIiInAwTPkREREREREREToYJHyIiIiIiIiIiJ6NydAC24u3tbbfPcnNzw+bNm1G5cmXk5OTY7XMrGjc3NyQmJjo6DCIiIiIiIqJyz2kTPkREjpCRkYH8/HwAgE6nQ35+PjQajYOjsg9RFBEfH2/efr1eD1EUIQiCgyOzD51Oh8uXLyMvLw8AYDQaHRwR2Ysoirh+/br52DcYDA6OyL5yc3Nx6dIlZGdnw83NDZGRkXB3d3d0WEQ2l5mZWWHr/IpOp9PhypUrrPOp3GPCh4jISnfv3sXXX3+NDRs24MqVKxBFEQCQlJSEkJAQNG7cGCNGjMCzzz4LV1dXB0crvdOnT2P16tX44YcfkJaWZn79zp07qFq1Krp3746RI0eiadOmjgvSRvLy8rBz506sWbMGZ86cgU6nM793+/ZtNGrUCEOGDMHQoUMRGBjowEhtKz8/H9nZ2cjLy4MoilCpVHB1dYW7uzsUCufsPS6KIo4fP44vvvgChw8fRmZmpvm9xMRERERE4Nlnn8XIkSPx+OOPOzBS28jIyMDWrVuxdu1a/PPPP4WSXAqFAo8//jiGDRuG559/Hj4+Pg6M1LZyc3ORk5OD/Px8iKIItVptPvadPdktiiKysrKQm5sLvV4PQRCg0Wjg7u4OFxcXR4dnM8nJydi4cSPWr1+Py5cvF6nzGzZsiJdffhn9+vWDm5ubg6O1nZycHOTk5JjrPZVKBTc3N6dO9ubn52PXrl348ssvER0dbU72AQV1fsOGDTF48GAMGzYMwcHBDozUtkx1fn5+PoxGY4Wo801EUTQf+3q9HgCgVqvh7u5ebq/xBdF0lnIy9qxkTV26nn/+eXbpsiF26aLyxmg0Yu3atZgxYwby8vJKfLqjUChgNBpRpUoVfPbZZ2jdurWdI7WN5ORkTJ48GTt27IBKpTJXfA8yvdenTx8sWbIEAQEBdo7UNn755ReMGjUKcXFx5n1cHIVCAY1Gg3nz5mHEiBFOdTGUnp6Ou3fvIjs7u9j3lUoltFotAgMDoVI5zzOm2NhYvPrqqzh27Fipjv0RI0Zg7ty58PT0tHOktrFnzx6MHz8eKSkpEAQBxV1Kmq7DvL29sWzZMvTt29epEiBpaWm4e/cucnNzi31fpVLB19cXgYGBTvWbBwpasCUlJSE1NbXE1mxubm4ICAhwqmSfKIrYsGEDpkyZgpycnGKPe+D/6/xKlSph1apVaN++vZ0jtR1RFJGSkoKUlBRzy5YHaTQa+Pn5wd/f36l+87///jtGjRqFmJgYKJXKEo99hUIBtVqN2bNnY8yYMU71+8/IyMDdu3eRlZVV7PtKpRI+Pj4ICgpyqjofKDj27969i5SUlEIP9+7n4uICf39/+Pn52S0uLy+vRy7DhI8EmPCxDyZ8qDzJz8/Hyy+/jO+//77U65guEObNm4fXXnvNhtHZ3n///YcePXogJSWl1N1XTDf/e/bsQZ06dWwcoW19+umnmDFjxkMv+orTo0cPrF27VvZPv41GI+Lj43Hv3r1SLa9SqRAaGmrX8fVs5ddff0W/fv2Qm5tb6n2vUChQtWpV/PDDDwgLC7NxhLYjiiLeeecdfPzxxw9Nct7PlBAaOXIkPvzwQ9nf/Oj1ety8ebNQi66H0Wg0qFy5Mjw8PGwcmX1kZGQgISGhxBueB3l7e6Ny5cpQKpU2jsy2dDodxowZg23btpV6HdNv5J133sGUKVNsGJ195OfnIy4urtT3Oi4uLggLCyu3rR4ssXr1akyePBkKhcKiOj8qKgobNmyQfUsvo9GIhISEQq24H0apVCI0NNRpEr45OTm4efNmiUnOB7m7uyMsLAxqtdrGkZUu4VOmWvfEiRPo1q0bfH19zX21586da37/pZdegiAIRf5q165dpKzbt29j/PjxiIiIgJubG8LDwzFixAjExsYWWm7dunXFlikIAm7fvl2WzSAiKhNRFDFq1Cjs3r3bovVMFwlvvfUWVq9ebYvQ7CI2NhZdu3a1KNkDFGx/WloaunXrhuvXr9suQBszteoCLB+rZe/evRg5cmSJT4blwGg0IiYmptTJHqDgJjk2Nhapqak2jMz2zp07hz59+iAnJ8eifW80GhEXF2f+3cjVe++9h48//hhA6cerMB3rq1evxltvvWWz2OxBr9cjJiam1MkeoOAm+fr168jIyLBhZPZx7949xMbGljrZAxS0AoyJiZH1uFaiKGL8+PH49ttvLVrP9BuZO3cuPv30U1uEZjd5eXm4du2aRQ+28/LyEBMTI/uH4Rs3bsQbb7wBURQtPo4PHz6MoUOHynp8H6PRiOvXr5c62QMUXBvFxcXJur4zyc7ORkxMTKmTPaZ1rl27VqjLnyNZnPDZtGkT2rZtCx8fH6xfvx579+7FtGnTily8urm54eTJk4X+tmzZUmiZvLw8tGnTBlu2bMGbb76Jffv2YcaMGfjhhx/QokWLYivHtWvXFinX39/f0s0gIiqzTZs2YceOHVbdtE+bNg0XL16UMCr7MBqNGD16NO7du1emC3iDwYD09HSMHj1alhdAly9ftupJrdFoxK5du7BhwwYJo7Kv+Pj4Ml/AJyQkyPbiPzc3F8OHD4dOpyvTsWswGBAfHy/bJ/2//vorPvzwQ6vKWL58OQ4fPixRRPYXGxtr0UW/iSiKiIuLsyhRUt7k5ubi5s2bZar3TOvK1fbt27F582ar6vx33nkHf//9t4RR2Y8oioiNjS2x6+rDGAwGxMbGyjbhd/36dUyaNKnM6xuNRhw4cABr1qyRMCr7SkhIKLHbti3XLQ9MD6vKUufrdDrcuHGjXDzgsyjhEx8fj1GjRmH06NHYvHkzevbsifbt22PkyJGYOXNm4YIVCjRv3rzQX4MGDQotc/z4cVy+fBkLFizAuHHj0K5dO4wePRpLly5FfHw8Dh06VCSGunXrFinXHs2lyHnMnz8f3t7eSE5OdnQodrN161YsX77cYZ9v+s6dQUZGhiQ3bEaj0aqLCEf59ttv8csvv1h18WYwGIp9CCAHb7zxhiQXrlOnTkV6eroEEdlXenq6RS17HiSKomxv/FauXImrV69afexv27YNP//8s4SR2Z6phYO13bEUCgXGjx9fphtHR0tOTrbqxsXUDVKu4uPjrbpxycjIsKiFQHmRk5ODyZMnSzJUxMSJEyWIyP7u3LlTpkSniU6nk21vjKlTp0qSqH3rrbdked+RmZlp9e/W2nOHI92+fduq+iovLw9JSUkSRlQ2FtXcq1evRlZWFqZNmybJh5sSNQ/279NqtQDgFH0+icqDbdu2YeXKlQ77/GHDhhWbwJWjrVu3WtScvyQGgwEnTpyQXSufFStWSDIGh0KhwIoVKySIyH4uXbqEY8eOSZLwyc7OlmXCS4oLl7y8PNkluwwGA1auXCnJRatKpcKqVaskiMp+jh07hitXrlh97JuSHj/++KNEkdmPFDdrmZmZJQ7yXJ5lZmZK0jLv7t27EkRjXzt27EBqaqrVv32DwYDo6GicO3dOosjsw2g0SnLsp6WlyS7Re/36dRw4cECSOj8vLw+bNm2SICr7qqh1PlCQqLTmAZdJcnKywxNeFl21//zzz/Dz88OFCxfQsGFDqFQqBAUFYcyYMUV2ZE5ODkJCQqBUKlGlShXzbA73a9myJZo0aYJZs2YhOjoamZmZOHPmDGbMmIHGjRujU6dORWLo0aMHlEol/Pz80LdvX9k2jyRyBqW9AKxcuTKaNWtm42js45tvvpFsUHilUmnRAJCOdv36dZw5c0aSrlhGoxHnzp3DtWvXJIjMPrZv3y7pwKObN2+WrCx7yMvLk6w7ltye9P/++++SPaHW6/XYu3dvibOclEfffvutZDOuyO28BxQkPKQai0GO41hJ9Xs1TWEvJ1u2bJFsoHGVSiW7Y//evXuS1PmiKMruvL9z507JrvdEUZRdna/T6SSrp+S274GCmKVI1JiGMnAki7t0ZWdnY8CAARg4cCAOHTqEKVOmYP369ejWrZv5S2nQoAE+/PBDfP3119i/fz9eeuklrF27Fi1btiz0ZFylUuHIkSOIiIhAs2bN4OXlhSZNmkCr1eLgwYOFumqFhISYBzo9cuQI5s6di+joaDRv3lx22XIqH+7cuYPhw4ejSpUqqFGjBsaNG1cok/v555+jS5cuiIiIQEhICJo3b45ly5YVadp57tw5DBgwABEREQgICECtWrXQv3//Ujfd3r9/P7y9vXH69Gnza7t27YK3tzf69+9faNmnn34aQ4YMsSjGbt264cCBA4iNjYW3t7f5zyQ/Px+LFi1CkyZNEBAQgOrVq2Ps2LFFnsTVrVsXAwYMwPfff49WrVohMDAQCxYsKNU2Ftely2g0YtmyZebPjYiIwKhRo4p8b926dcNTTz2F06dPo3PnzggODkb9+vWxZMkSu48BYzAYcO7cOcky9UajEWfOnJGkLHs4e/asLMq0ldOnT0t2zImiiPPnz8vqiaeU/fDldtN35swZSWeXMhqNsnpg9fvvv0t2rBoMBvzxxx+SlGUvUh6vchzPoqJuvyiKkp739Xo9Tp06JUlZ9lJR9z1QUOdLOevzf//9Z1XXOHuryHU+4Fzbb9HjGqPRiNzcXLz77ruYPn06AKBdu3bQaDR4/fXXcfjwYXTq1KnIuBRRUVFo1KgR+vfvjy+++ML8vk6nw8CBA/H333/jiy++wGOPPYaYmBi89957iIqKwk8//WTu7tWlSxd06dLFXGabNm3QvXt31KtXDzNnzsSuXbus+iKo4hkyZAj69u2LoUOH4p9//sHs2bMBwNzNJCYmBgMGDEB4eDg0Gg3Onz+PDz/8EJcuXTIvk5WVhd69eyM8PByLFy9GUFAQEhMTcfz48VJ3+2nVqhXUajWOHj2KJk2aAACOHj0KNzc3/PLLL9DpdFCr1UhKSsK///6LESNGmNctTYxLlizBhAkTEBMTg40bNxb6bKPRiEGDBuHkyZOYOHEinnrqKcTFxWH+/Pno1q0bjh07VmgqyXPnzuHixYuYOnUqwsPD4e7uXsZvH5g0aRLWrl2LUaNGoUuXLoiNjcV7772HEydO4Pjx44UGY09MTMTIkSPx2muvYfr06di9ezdmzZqFkJAQDB48uMwxWOr27duSNscXRRH//fefZOXZ2pUrV6BSqSS78VOpVLh8+bIkZdnDhQsXJG2Wm5eXh4SEBFStWlWyMm1JytkmTAMfy2WK7itXrkCpVEqaZL58+TKeeuopycqzpZiYGEnLi4uLg8FgkM1U3VLepJWXWVtKSxTFCrv9KSkpks+udunSJUnLszUp972ckh1AQZ0v5WDTBoMBN27cQK1atSQr05ak3F96vR56vV6ylqL2IOX2O7orr0Xfur+/Py5fvozOnTsXer1r1654/fXXcebMmWK7YQHAs88+Cw8PD/z222/m19asWYN9+/YhOjoaTZs2BQC0bt0arVq1Qo0aNbBs2TK8++67JcZTrVo1tGrVqlCZRKU1dOhQ8wB67du3x7Vr17BhwwYsX74cgiDg/fffNy9rNBrRokUL+Pn5Ydy4cZg3bx58fX1x6dIlpKSkYPny5ejevbt5+b59+5Y6Dk9PTzRt2hRHjx7F5MmTAQBHjhzBqFGj8Mknn+CPP/5Ay5YtceTIEYiiiHbt2pnXLU2MtWvXho+PD1xcXIp0q9qxYwcOHTqEDRs2oFevXubX69ati3bt2mHjxo0YOXKk+fWkpCT88ccfiIyMLPX2FefSpUtYu3YtRo4cWWjWl/r166NDhw5Yvnx5oYHgU1JS8O2335rPE+3bt8eJEyewbds2uyZ8bDHDipxmbdHpdJI+7RIEQVYX/7bYV3Lafqn7oDu6T7sldDqd5PHKad/boiWaTqeTTcJHyn0vp+PeFuS0/RW9zgcgy9k0pVLR939FrvMB5zrvW/RorX79+sW+btqIRz2pE0Wx0DJ//vknlEolGjduXGi5iIgI+Pv7l6q584NlEpVWt27dCv27bt26yM3NNQ9Qdu7cOQwcOBDh4eHQarXw8/PD6NGjYTAYcOXKFQAFx6pWq8XMmTOxZs0aXLhwoUyxtG3bFr/99htycnIQGxuLa9euoV+/fqhfvz5++uknAAWtfsLCwlCzZk3zeqWJ8WH2798PrVaLrl27mrPver0e9evXR3BwME6cOFHkO7I22QPAPEPNCy+8UOj1pk2b4rHHHsPRo0cLvR4cHGxO9pjUqVMHcXFxVsdiiQcHmJeCr6+v5GXailarlfxpl5+fn2Tl2Zot9pWc9r+UN+eCIMiq7tZqtZImOwF57XsvLy9Jy3NxcYGLi4ukZdqSlMe+XJJcJlL/VuW0/d7e3pL/7k0T08iFlC0y5HTOB1jnS90aR06/fUDaeB3dssmiX16/fv0AAPv27Sv0+t69ewEAzZs3L3Hdb7/9FtnZ2YWWCQ0NNY9af79Lly4hOTkZVapUeWg8MTEx+OWXXx76uUQlefBG03TxmZOTg7i4OHTp0gW3bt3CwoULceDAARw9ehSLFy8G8P9N83x8fLBv3z7Uq1cPs2fPRrNmzVCrVi3MmzfPoix++/btkZeXh5MnT+LIkSPw9/dHgwYN0K5dO3Py49ixY4Va95Q2xodJSkpCWloa/P394efnV+gvMTGxyMwMwcHBpd6mhzEN4B4SElLkvUqVKhUZ4L24pICLi4vdm0j6+vqiUqVKkpWnUqnM3fjkoH79+pI+7TMajSU+SCiPGjduLGmlHRwcXKjrYnkn5cyZLi4ukt9I2VL9+vUlfzIrp2O/YcOGku6vunXrymr/S3nsy3EG2oq6/e7u7qhWrZpk5SmVSlnV+QAKdesvT2XZg9R1vlarlfQa0takrvPllvBzpmPfoqP4mWeeQc+ePTFnzhwYjUY0b94cp06dwuzZs9GjRw+0atUKN27cwODBgzFo0CDUrFkTgiDg2LFjWLZsGerUqVOoe8jw4cOxdOlS9OvXD2+//TYee+wxXLt2DfPnz4eHhwfGjBljXrZTp05o06YN6tevD29vb5w/fx6LFi2CIAiYO3eudN8IEYA9e/YgKysLGzZsKDS+xvnz54ssW6dOHaxbtw6iKOLvv//Gxo0bsXDhQri5ueGNN94o1ec1bdoUnp6eOHr0KGJjY9G2bVsIgoC2bdvik08+wenTpxEXF1co4WNJjCUxJXd27NhR7PsPPtWV6gLdlMC5ffs2KleuXOi9W7duleub4Hbt2mHbtm2SdHHQ6/Vo1aqVBFHZR6NGjeDq6ipZos3FxQWNGjWSpCx7aNWqFTZs2CBJWSqVCm3btpWkLHvx8PCAQqGQJOkndYsRW2vRogUEQZCsWXZQUBAiIiIkKcse2rRpg+PHj0uy/UqlslBdJgdSHq+enp6SlWUvXl5ekgxgKgiC7La/ffv2iIuLk6TONxqNsqrzgYLj9c6dO5KVJSetWrXC6tWrJSlLqVSidevWskp0u7u7Q6lUStKyW277HiiIWapZFR29/Ran2rZs2YLXX38dn3/+Obp27YqVK1di0qRJ+PbbbwEUNH8MDg7GkiVL0K9fP/Tu3Rvbt2/HhAkT8Msvv8DDw8NcVlhYGKKjo9GhQwcsXLgQXbt2xZw5c/Dkk0/ijz/+wGOPPWZetl69etiyZQuGDh2Kzp07Y9GiRejQoQNOnTqFunXrSvBVEP0/0wn5/ibnoihi3bp1D12nXr16WLBgAbRarUWzx6nVarRo0QJHjhzBsWPH0KFDBwAFNxkqlQpz586FIAiFLpItidHFxaXYEeK7dOmClJQUGAwGNG7cuMifFN23imO60d2yZUuh10+fPo2LFy+W6xvhl19+WbLxLDw8PCwa78nRPDw8MHjwYEmauapUKgwaNEhWN/59+vSRLF69Xl/oAYgcKBQKSbojCIIgq2btQMH1SseOHSU59hUKBUaOHCmrC/8hQ4ZIFq/RaMTQoUMlKcte1Gq1JL99hUIhu2MfKGjdKsX+9/HxkV23jpdeekmyOt/FxQUDBgyQpCx7cXd3l6Slh0ajKTJba3nXvXt3yX6vBoNBdnW+lHW1nLrvm3h7exeaMbys3NzcHN6y0eJ2am5ubliwYEGJ0zH7+vqW2FqgODVr1sT69esfudzSpUtLXSaRtTp06ACNRoOXX34ZEydORF5eHlavXo20tLRCy+3btw+rV69Gjx49UK1aNYiiiN27dyMtLQ3t27e36DPbtWuHGTNmmP8fKPi9PfXUU/jpp59Qt25dBAYGWhwjADzxxBP4/vvvsXr1ajRs2BAKhQKNGzdG//79sXXrVvTv3x9jx45FkyZNoFarER8fj+PHj6N79+7o2bOnRdtRGpGRkRg+fDg+++wzKBQKREVFmWfpqlKlCl599VXJP1MqzZo1Q+vWrfHrr79a9dRDEAS8/vrrVs105gjjx4/Hhg0brH7iIwgCJkyYIFFU9uHu7o5JkyZh7ty5VrV0UKlUaNasmWxmaLpfUFAQ7t27Z9X+9/f3h0ajkTAq+5g6dSoOHz5sVRmCIMDLy0t2F/6VKlXCsGHDsG7dOqtaeCmVSvTv3x/Vq1eXMDr7CAkJQWZmplW//eDgYNl1awAKzlmBgYFWtfRQKpWSdQu3p4YNG5pnDra2zh8/frxNxgK0tZCQEFy/ft2qMuS4711cXDBlyhTztXlZKZVKNGzYsFw/zCxJYGAg0tLSrEp6+vn5yWrMNhNBEBAUFIT4+HiryikP3fjkV+sQ2UGtWrXw9ddfIy0tDUOGDMGUKVNQv359LFq0qNByNWvWhI+PD5YtW4ZBgwZh2LBh+PPPP7Fy5Uq89NJLFn2mKclTo0aNQl20TK8/2AS+tDECwNixY9GnTx/MmTMHHTt2NJelVCqxZcsWvPnmm9i9ezdeeOEFDB48GEuXLoWrqyueeOIJi7bBEkuXLsXs2bPx448/4rnnnsOcOXPQoUMHHDx4sFx36RIEAStWrIBGoynzE0+lUonHH3+81F3+ypOaNWti1qxZVpczc+ZMm7Ugs6WJEyeiTp06ZX5KLQgClEolVq5cKasWHiYqlQqhoaFlXt/FxUWWF/5AwTiFY8eOteqGXRRFLF26FAEBARJGZh9z5sxBcHBwmY99pVIJrVaLhQsXShyZfbi4uBQ77lxpeXh4lOu67VECAwOtGociJCREkqfljvDxxx/D1dW1zL99pVKJGjVqYNq0aRJHZh+enp5WnbO0Wq0sE10AzA9Dra3zP//8c1kme5VKpdV1vjXnTUfz9fW16tgNCgoqFw92BdHR84TZiD0vpN3c3LB582Y8//zzxXabIWm4ubkhMTHR0WEQAQB+/PFHDBo0CEaj0aIn3kqlEgEBATh06BDCw8NtGKHtGI1GjBs3Dps2bbJ4XUEQMHDgQKxatUqWFz9AwYDpnTp1wp07dyx64qtQKCAIAjZv3owuXbrYMELbS0lJwa1btyxq7eDi4oLq1as7fLYKa+Tl5WHgwIE4evRomVq6TJ48Ge+++64NIrOP8+fPo0uXLsjOzrbo2FcqlXBxccGePXuKzLgoN3fu3LG4pYuHhwfCw8Nle84zMRgMuH79usXXupUqVZJ1sgsomCm1f//+0Ov1Ftf5vr6+OHjwIGrUqGHDCG0vISGhyKQaj+Lj44MqVarI8gGHSUJCAqKiopCQkGDReU8QBAiCgPXr16NXr142jND2UlNTkZCQYHGdX61aNdkmek1EUURsbCwyMjIsWs/f398urXtK091Y3jUPEZGDPPPMM/juu+/g5+dX6ic/pnGeDh8+LNtkD1CQuFixYgXeeOMN89OrR1EqlRAEARMnTsTKlStlfeMTFhaGQ4cOoUGDBqVex3TRv3PnTtkne4CCJtrVq1cvdTPtgIAA1KxZU9bJHqDgAnbLli144YUXAJRummGlUgmVSoX58+dj5syZtg7RpurVq4eDBw+iWrVqpf4NKxQKVK5cGfv375d9sgcoeGIbHh5eqpsYQRAQHBxs0fdVnimVSkRERCAwMLBUN/AajQbVq1eXfbIHKGhlvXv3bgQFBVlU5z/++OM4dOiQ7JM9QMHsylWqVCnV9isUCoSGhiIsLEzWyR6gYLsPHTpk0fnL1KJx27Ztsk/2AAUtXSIiIkpd5/v7+6NGjRqyT/YABb/j8PBwVKpUqVTncZVKhbCwsHLRlcuELXwkwBY+9iHHFj6laf0h5xsgZ9++0khNTcWcOXPw9ddfIz8/HyqVqlBfZ9O/AwICMGnSJIwdO9apvpPff/8db731Fv744w8olUqIomg+JkwtWgwGA5588knMmzcPzZs3d3DE0jEYDFi5ciWWLFmCu3fvlrjvNRoNhgwZgpkzZ8py4MKHEUURGRkZSE1NRU5Ojnn7BUGAi4sLvLy84OvrK8sxex7lwIEDePfdd/Hvv/8WOfbv/3eHDh0wf/58m3aRtbfc3Fx8+OGHWLVqFdLT06FWqwtNW2/6t4eHB0aNGoVp06aVi2btUhJFEWlpabh37x5ycnLMT/4FQYCrqyu8vb3h6+vrVOf7++l0OqSkpCAjIwN5eXnmJ/8qlQpubm7QarXw9vaW/c3+g+7du4d58+Zh3bp1yM3NLfHY9/X1xcSJE/Haa685xU3v/YxGI1JTU5Geno6cnJxCdb6rqyt8fHyg1WplN0D3oxiNRnzxxRf48MMPkZiYWGydbzAYzJNSzJkzxymSnQ9KT09HWloacnJyzMe+IAjQaDTw8vKCn5+fU9b5QMF1X2pqKu7du4e8vLxCdf79x749E/ylaeHDhI8EmPCxDzkmfMaMGfPIbi/p6el2ikZ6zr59lkhLS8P333+PM2fO4J9//kFubi58fHzQsGFDNG/eHJ07d3a6i777/fPPPzhw4ADOnj2L2NhYiKKIqlWrolGjRujcubNTz6ao0+nw448/4uTJkzh37hzS0tLMY2A1adIEPXv2lOXMPGVhMBggiqK5RZezE0UR0dHR+Omnn/Dnn38iISEBCoUCERERaNSoEbp16+YUT/ZLkpOTgx9++AHR0dH466+/kJmZCQ8PD9StWxdPPvkkevToUWh2VmdW0Y79+4miCIPBUOoWn87g3r172LNnD06dOoV//vkHOTk58Pb2RoMGDfDUU0+ha9euTnvT+yBT0sNZk5sP0uv1OHjwIE6ePIk///wTqamp0Gg0qFOnDho3boyePXs6ZaKnODzvOf68x4SPnTDhYx9yTPjcuHEDycnJD12mcePGdopGes6+fUREREREROVRaRI+FSMVS+Qg4eHhsh6r5VGcffuIiIiIiIjkymkTPvbsRqLX6/Hzzz8jPj6+wjRndIT7+8kSERERERERUcnkP2UAEREREREREREVwoQPEREREREREZGTYcKHiIiIiIiIiMjJMOFDRERERERERORkmPAhIiIiIiIiInIyTPgQERERERERETkZp51D3MvLy26fpdPpAACenp5Qq9V2+9yKRqfT8fslIiIiIiIiKgW28CEiIiIiIiIicjJM+BARERERERERORkmfIiIiIiIiIiInAwTPkREREREREREToYJHyIiIiIiIiIiJ8OEDxERERERERGRk2HCh4iIiIiIiIjIyTDhQ0RERERERETkZJjwISIiIiIiIiJyMkz4EBERERERERE5GZWjA7AFURSRkZFht8/T6XTIzs5Geno61Gq13T63otHpdFCr1fDy8oIgCI4Oh4iIiIiIiKjccsqET0ZGBnx8fBwdBtnInTt3EBgY6OgwiIiIiIiIiMotp0z4eHl54d69e3b7vPT0dISFhSEuLg7e3t52+9yKxvQ9azQaR4dCREREREREVK45ZcJHEASHJF68vb2Z8LEDduciIiIiIiIiejgO2kxERERERERE5GSY8CEiIiIiIiIicjJM+EjAxcUF7777LlxcXBwdilPj90xERERERERUOoIoiqKjgyAiIiIiIiIiIumwhQ8RERERERERkZNhwoeIiIiIiIiIyMkw4UNERERERERE5GSY8CEiIiIiIiIicjIVNuFz9OhRCIJQ7N9vv/1WaNkzZ86gU6dO8PT0hFarRd++fXHt2rViy/3kk09Qu3ZtuLi4oHr16pg9ezZ0Ol2R5e7cuYOXXnoJAQEBcHd3x9NPP43Dhw/bZFvlLjMzE6+//jpCQ0Ph6uqKhg0b4ptvvnF0WERERERERETllsrRATja/Pnz0b59+0Kv1a1b1/z/Fy5cQLt27dCwYUNs3boVubm5mDlzJlq3bo0///wTgYGB5mXnzZuHd955B9OnT8czzzyD6OhovP3224iPj8fnn39uXi4vLw8dO3ZEWloaPvroIwQFBWH58uXo0qULDh06hLZt29p+w2Wkb9++iI6OxoIFC1CrVi1s2rQJzz//PIxGIwYPHuzo8IiIiIiIiIjKnQo7LfvRo0fRvn17bNu2Df379y9xueeeew5HjhzB1atX4e3tDQC4ceMGIiMjMWnSJCxcuBAAkJycjCpVqmDo0KH47LPPzOvPnz8fb7/9Nv7++2888cQTAIAVK1bg1Vdfxa+//oqnn34aAKDX69GgQQN4enri999/t9Vmy87evXvRvXt3c5LH5JlnnsE///yD2NhYKJVKB0ZIREREREREVP5U2C5dpaHX67Fnzx7069fPnOwBgPDwcLRv3x47d+40v7Z//37k5uZi+PDhhcoYPnw4RFHEd999Z35t586deOyxx8zJHgBQqVQYMmQI/vjjD8THx9tuo2Rm586d8PT0xIABAwq9Pnz4cCQkJDA5RkRERERERFSMCp/wefXVV6FSqeDt7Y3OnTvjxIkT5veuXr2KnJwc1K9fv8h69evXx5UrV5CbmwsA+PvvvwEA9erVK7RcpUqVEBAQYH7ftGxJZQLAP//8Y/2GOYm///4bjz/+OFSqwr0PTd/V/d8rERERERERERWosAkfHx8fTJw4EZ999hmOHDmCjz76CHFxcWjXrh0OHDgAoKCbFgD4+fkVWd/Pzw+iKCI1NdW8rIuLCzw8PIpd1lSWadmSyrz/c4nfFREREREREVFZVNhBmxs1aoRGjRqZ/926dWs8++yzqFevHqZOnYrOnTub3xMEocRy7n+vtMtZumxFx++KiIiIiIiIyDIVtoVPcbRaLXr06IG//voLOTk58Pf3B1B8K5KUlBQIggCtVgsA8Pf3R25uLrKzs4td9v5WKv7+/iWWCRTfoqii4ndFREREREREZDkmfB5gmrRMEATUqFEDbm5uOH/+fJHlzp8/j5o1a8LV1RXA/4/d8+Cyt2/fxt27dwtN9V6vXr0SywQKTwtf0dWrVw///fcf9Hp9odf5XRERERERERGVjAmf+6SmpmLPnj1o2LAhXF1doVKp0LNnT+zYsQMZGRnm5WJjY3HkyBH07dvX/FqXLl3g6uqKdevWFSpz3bp1EAQBffr0Mb/27LPP4sKFC4VmmNLr9diwYQOeeuophIaG2mwb5ebZZ59FZmYmtm/fXuj1r776CqGhoXjqqaccFBkRERERERFR+VVhx/AZPHgwqlatiqZNmyIgIACXL1/G4sWLkZiYWChpM3v2bDz55JPo0aMHpk+fjtzcXMycORMBAQGYPHmyeTk/Pz+8/fbbeOedd+Dn54dnnnkG0dHRmDVrFkaOHIknnnjCvOzLL7+M5cuXY8CAAViwYAGCgoKwYsUKXLx4EYcOHbLn11Dude3aFVFRURg7dizS09NRs2ZNbN68Gfv378eGDRugVCodHSIRERERERFRuSOIpj5MFcyCBQuwZcsWxMTEIDMzE35+fmjVqhX+97//4cknnyy07OnTpzFt2jScPHkSKpUKHTp0wIcffogaNWoUKffjjz/G8uXLcf36dYSEhGD48OF46623oFarCy2XmJiIqVOnYs+ePcjOzkbDhg0xd+5cdOrUyabbLUeZmZl46623sHXrVqSkpKB27dr43//+h0GDBjk6NCIiIiIiIqJyqcImfIiIiIiIiIiInBXH8CEiIiIiIiIicjJM+BARERERERERORkmfIiIiIiIiIiInAwTPkREREREREREToYJHyIiIiIiIiIiJ8OEDxERERERERGRk2HCh4iIiIiIiIjIyTDhQ0RERERERETkZJjwISIiIiIiIiJyMkz4EBERERERERE5GSZ8iIiIiIiIiIiczP8BWiyEvC3bL6wAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1400x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create pivot dataframe\n",
"df_upset = pd.pivot_table(\n",
" gro_df, \n",
" index=[\"has_protein\", \"has_lipid\", \"has_nucleic\", \"has_water_ion\", \"has_glucid\"], \n",
" values=[\"file_name\"], \n",
" aggfunc=len\n",
")\n",
"# Convert pivot dataframe to Pandas Series with multi-index\n",
"sr_upset = pd.Series(index=df_upset.index, data=df_upset.values.flatten(), name=\"value\")\n",
"plt.rcParams.update({\"font.size\": 12})\n",
"fig = plt.figure(figsize=(14, 8))\n",
"UPplot(sr_upset, fig=fig, show_counts=True, element_size=None, sort_by=\"cardinality\")\n",
"filename = \"fig/gromacs_system_composition\"\n",
"plt.savefig(f\"{filename}.png\", dpi=300, bbox_inches=\"tight\")\n",
"plt.savefig(f\"{filename}.svg\", bbox_inches=\"tight\")"
]
},
{
"cell_type": "markdown",
"id": "47023754-0908-40a4-ae65-77af054963e2",
"metadata": {},
"source": [
"Systems without protein, lipid, nucleic acid, water, ion nor glucid:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "70f4335c-8740-4e3b-b38d-543833911da5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_origin</th>\n",
" <th>dataset_id</th>\n",
" <th>file_name</th>\n",
" <th>atom_number</th>\n",
" <th>has_protein</th>\n",
" <th>has_nucleic</th>\n",
" <th>has_lipid</th>\n",
" <th>has_glucid</th>\n",
" <th>has_water_ion</th>\n",
" <th>dataset_url</th>\n",
" <th>k_particles</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1706</th>\n",
" <td>zenodo</td>\n",
" <td>7125315</td>\n",
" <td>3b_START.gro</td>\n",
" <td>5583.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://zenodo.org/record/7125315</td>\n",
" <td>5.583</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1707</th>\n",
" <td>zenodo</td>\n",
" <td>7125315</td>\n",
" <td>3c_START.gro</td>\n",
" <td>5844.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://zenodo.org/record/7125315</td>\n",
" <td>5.844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1708</th>\n",
" <td>zenodo</td>\n",
" <td>7125315</td>\n",
" <td>4a_START.gro</td>\n",
" <td>4374.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://zenodo.org/record/7125315</td>\n",
" <td>4.374</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1709</th>\n",
" <td>zenodo</td>\n",
" <td>7125315</td>\n",
" <td>4b_START.gro</td>\n",
" <td>4410.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://zenodo.org/record/7125315</td>\n",
" <td>4.410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1710</th>\n",
" <td>zenodo</td>\n",
" <td>7125315</td>\n",
" <td>DMF.gro</td>\n",
" <td>12.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://zenodo.org/record/7125315</td>\n",
" <td>0.012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8666</th>\n",
" <td>osf</td>\n",
" <td>4aghb</td>\n",
" <td>PtB-b-force field/em4_nojump.gro</td>\n",
" <td>18432.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://osf.io/4aghb/</td>\n",
" <td>18.432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8667</th>\n",
" <td>osf</td>\n",
" <td>4aghb</td>\n",
" <td>PtB-b-force field/pr_nvt.gro</td>\n",
" <td>18432.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://osf.io/4aghb/</td>\n",
" <td>18.432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8668</th>\n",
" <td>osf</td>\n",
" <td>4aghb</td>\n",
" <td>PtB-b-force field/pr_nvt_nojump.gro</td>\n",
" <td>18432.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://osf.io/4aghb/</td>\n",
" <td>18.432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8669</th>\n",
" <td>osf</td>\n",
" <td>4aghb</td>\n",
" <td>PtB-b-force field/Production_10ns_PTI_noVs.gro</td>\n",
" <td>18432.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://osf.io/4aghb/</td>\n",
" <td>18.432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8670</th>\n",
" <td>osf</td>\n",
" <td>4aghb</td>\n",
" <td>Pt-free-force field/MOL_GMX.gro</td>\n",
" <td>61.0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>https://osf.io/4aghb/</td>\n",
" <td>0.061</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1127 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" dataset_origin dataset_id \\\n",
"1706 zenodo 7125315 \n",
"1707 zenodo 7125315 \n",
"1708 zenodo 7125315 \n",
"1709 zenodo 7125315 \n",
"1710 zenodo 7125315 \n",
"... ... ... \n",
"8666 osf 4aghb \n",
"8667 osf 4aghb \n",
"8668 osf 4aghb \n",
"8669 osf 4aghb \n",
"8670 osf 4aghb \n",
"\n",
" file_name atom_number \\\n",
"1706 3b_START.gro 5583.0 \n",
"1707 3c_START.gro 5844.0 \n",
"1708 4a_START.gro 4374.0 \n",
"1709 4b_START.gro 4410.0 \n",
"1710 DMF.gro 12.0 \n",
"... ... ... \n",
"8666 PtB-b-force field/em4_nojump.gro 18432.0 \n",
"8667 PtB-b-force field/pr_nvt.gro 18432.0 \n",
"8668 PtB-b-force field/pr_nvt_nojump.gro 18432.0 \n",
"8669 PtB-b-force field/Production_10ns_PTI_noVs.gro 18432.0 \n",
"8670 Pt-free-force field/MOL_GMX.gro 61.0 \n",
"\n",
" has_protein has_nucleic has_lipid has_glucid has_water_ion \\\n",
"1706 False False False False False \n",
"1707 False False False False False \n",
"1708 False False False False False \n",
"1709 False False False False False \n",
"1710 False False False False False \n",
"... ... ... ... ... ... \n",
"8666 False False False False False \n",
"8667 False False False False False \n",
"8668 False False False False False \n",
"8669 False False False False False \n",
"8670 False False False False False \n",
"\n",
" dataset_url k_particles \n",
"1706 https://zenodo.org/record/7125315 5.583 \n",
"1707 https://zenodo.org/record/7125315 5.844 \n",
"1708 https://zenodo.org/record/7125315 4.374 \n",
"1709 https://zenodo.org/record/7125315 4.410 \n",
"1710 https://zenodo.org/record/7125315 0.012 \n",
"... ... ... \n",
"8666 https://osf.io/4aghb/ 18.432 \n",
"8667 https://osf.io/4aghb/ 18.432 \n",
"8668 https://osf.io/4aghb/ 18.432 \n",
"8669 https://osf.io/4aghb/ 18.432 \n",
"8670 https://osf.io/4aghb/ 0.061 \n",
"\n",
"[1127 rows x 11 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(gro_df\n",
" .query(\"has_protein == False\")\n",
" .query(\"has_water_ion == False\")\n",
" .query(\"has_lipid == False\")\n",
" .query(\"has_nucleic == False\")\n",
" .query(\"has_glucid == False\")\n",
" #.to_csv(\"systems_without_protein_water_ion_lipid_nucleic_acid_glucid.tsv\", sep=\"\\t\", index=False)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "96931c40-60a9-4d63-9ac9-c92e981e536b",
"metadata": {},
"source": [
"## Figure 4\n",
"\n",
"### 4.A. Simulation temperature"
]
},
{
"cell_type": "code",
"execution_count": 171,
"id": "8918dec6-334e-4d81-af43-6d440a086d83",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset has 9496 mdp files\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_origin</th>\n",
" <th>dataset_id</th>\n",
" <th>file_name</th>\n",
" <th>dt</th>\n",
" <th>nsteps</th>\n",
" <th>temperature</th>\n",
" <th>thermostat</th>\n",
" <th>barostat</th>\n",
" <th>dataset_url</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>zenodo</td>\n",
" <td>1043926</td>\n",
" <td>mono.mdp</td>\n",
" <td>0.002</td>\n",
" <td>100000000.0</td>\n",
" <td>298.0</td>\n",
" <td>v-rescale</td>\n",
" <td>no</td>\n",
" <td>https://zenodo.org/record/1043926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>zenodo</td>\n",
" <td>1043946</td>\n",
" <td>mono.mdp</td>\n",
" <td>0.002</td>\n",
" <td>100000000.0</td>\n",
" <td>298.0</td>\n",
" <td>v-rescale</td>\n",
" <td>no</td>\n",
" <td>https://zenodo.org/record/1043946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>zenodo</td>\n",
" <td>3463130</td>\n",
" <td>md.mdp</td>\n",
" <td>0.020</td>\n",
" <td>2500000.0</td>\n",
" <td>310.0</td>\n",
" <td>v-rescale</td>\n",
" <td>parrinello-rahman</td>\n",
" <td>https://zenodo.org/record/3463130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>zenodo</td>\n",
" <td>1167532</td>\n",
" <td>md.mdp</td>\n",
" <td>0.002</td>\n",
" <td>100000000.0</td>\n",
" <td>298.0</td>\n",
" <td>nose-hoover</td>\n",
" <td>parrinello-rahman</td>\n",
" <td>https://zenodo.org/record/1167532</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>zenodo</td>\n",
" <td>3434100</td>\n",
" <td>1-POPC512_ECC-lipid14-CaCL_978mM_md_mdout.mdp</td>\n",
" <td>0.002</td>\n",
" <td>NaN</td>\n",
" <td>313.0</td>\n",
" <td>v-rescale</td>\n",
" <td>parrinello-rahman</td>\n",
" <td>https://zenodo.org/record/3434100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_origin dataset_id file_name \\\n",
"0 zenodo 1043926 mono.mdp \n",
"1 zenodo 1043946 mono.mdp \n",
"2 zenodo 3463130 md.mdp \n",
"3 zenodo 1167532 md.mdp \n",
"4 zenodo 3434100 1-POPC512_ECC-lipid14-CaCL_978mM_md_mdout.mdp \n",
"\n",
" dt nsteps temperature thermostat barostat \\\n",
"0 0.002 100000000.0 298.0 v-rescale no \n",
"1 0.002 100000000.0 298.0 v-rescale no \n",
"2 0.020 2500000.0 310.0 v-rescale parrinello-rahman \n",
"3 0.002 100000000.0 298.0 nose-hoover parrinello-rahman \n",
"4 0.002 NaN 313.0 v-rescale parrinello-rahman \n",
"\n",
" dataset_url \n",
"0 https://zenodo.org/record/1043926 \n",
"1 https://zenodo.org/record/1043946 \n",
"2 https://zenodo.org/record/3463130 \n",
"3 https://zenodo.org/record/1167532 \n",
"4 https://zenodo.org/record/3434100 "
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mdp_df = pd.read_csv(\"../data/gromacs_mdp_files_info.tsv\", sep=\"\\t\")\n",
"mdp_df = pd.merge(\n",
" mdp_df,\n",
" df[[\"dataset_origin\", \"dataset_id\", \"dataset_url\"]].drop_duplicates(),\n",
" on=[\"dataset_origin\", \"dataset_id\"],\n",
" how=\"left\"\n",
")\n",
"print(f\"Dataset has {len(mdp_df)} mdp files\")\n",
"mdp_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "cb357266-8bff-4456-a6ba-ea42d2c2d956",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1000x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_temperature_histogram(df):\n",
" \"\"\"Create plot for temperature distribution\"\"\"\n",
" bins=200\n",
" plt.rcParams.update({\"font.size\": 16})\n",
" fig, ax = plt.subplots(2, sharex=True, figsize=(10,7))\n",
" ax[0].hist(df[\"temperature\"], bins=bins, color=\"tab:orange\")\n",
" ax[0].set_ylabel(\"Number of files\")\n",
" ax[0].set_xlim(0, 810)\n",
" ax[0].annotate(\"310 K\\n37 °C\", xy=(310, 2400), xytext=(450, 2750), color=\"gray\",\n",
" arrowprops={\"arrowstyle\":\"-|>\", \"lw\":2, \"color\":\"gray\", \"connectionstyle\":\"arc3\"}, \n",
" horizontalalignment=\"left\", verticalalignment=\"top\")\n",
" ax[0].annotate(\"298 K\\n25 °C\", xy=(298, 800), xytext=(190, 1200), color=\"gray\",\n",
" arrowprops={\"arrowstyle\":\"-|>\", \"lw\":2, \"color\":\"gray\"}, \n",
" horizontalalignment=\"right\", verticalalignment=\"bottom\")\n",
"\n",
" ax[1].hist(df[\"temperature\"], bins=bins, color=\"tab:orange\")\n",
" ax[1].set_ylabel(\"Number of files\")\n",
" ax[1].set_ylim(0, 300)\n",
" ax[1].set_xlabel(\"Temperature (K)\")\n",
" filename = \"fig/gromacs_temperature_histogram\"\n",
" plt.savefig(f\"{filename}.png\", dpi=300, bbox_inches=\"tight\")\n",
" plt.savefig(f\"{filename}.svg\", bbox_inches=\"tight\")\n",
"\n",
"plot_temperature_histogram(mdp_df)"
]
},
{
"cell_type": "markdown",
"id": "11ae8e2e-5693-4788-bb4c-82ed1ec53928",
"metadata": {},
"source": [
"### Systems with lowest temperatures"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "657c4d44-7012-442f-91e3-ce0cf3526a77",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_origin</th>\n",
" <th>dataset_id</th>\n",
" <th>file_name</th>\n",
" <th>dt</th>\n",
" <th>nsteps</th>\n",
" <th>temperature</th>\n",
" <th>thermostat</th>\n",
" <th>barostat</th>\n",
" <th>dataset_url</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9127</th>\n",
" <td>figshare</td>\n",
" <td>8846045</td>\n",
" <td>Tutorial/mdp/3_run.mdp</td>\n",
" <td>0.0300</td>\n",
" <td>1000000.0</td>\n",
" <td>0.0</td>\n",
" <td>Berendsen</td>\n",
" <td>berendsen</td>\n",
" <td>https://acs.figshare.com/articles/dataset/Nucleation_Mechanisms_of_Self-Assembled_Physisorbed_Monolayers_on_Graphite/8846045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9059</th>\n",
" <td>figshare</td>\n",
" <td>7370684</td>\n",
" <td>md_files/2-MD-runs/mdp/1_grompp_minimisation.mdp</td>\n",
" <td>0.0020</td>\n",
" <td>5000.0</td>\n",
" <td>0.0</td>\n",
" <td>v-rescale</td>\n",
" <td>no</td>\n",
" <td>https://acs.figshare.com/articles/dataset/Stereospecific_1_3_H_Transfer_of_Indenols_Proceeds_via_Persistent_Ion-Pairs_Anchored_by_NH__Interactions/7370684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9126</th>\n",
" <td>figshare</td>\n",
" <td>8846045</td>\n",
" <td>Tutorial/mdp/2_nvt.mdp</td>\n",
" <td>0.0300</td>\n",
" <td>5000.0</td>\n",
" <td>0.0</td>\n",
" <td>Berendsen</td>\n",
" <td>no</td>\n",
" <td>https://acs.figshare.com/articles/dataset/Nucleation_Mechanisms_of_Self-Assembled_Physisorbed_Monolayers_on_Graphite/8846045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9125</th>\n",
" <td>figshare</td>\n",
" <td>8846045</td>\n",
" <td>Tutorial/mdp/2_npt.mdp</td>\n",
" <td>0.0300</td>\n",
" <td>10000.0</td>\n",
" <td>0.0</td>\n",
" <td>Berendsen</td>\n",
" <td>berendsen</td>\n",
" <td>https://acs.figshare.com/articles/dataset/Nucleation_Mechanisms_of_Self-Assembled_Physisorbed_Monolayers_on_Graphite/8846045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9026</th>\n",
" <td>figshare</td>\n",
" <td>10269416</td>\n",
" <td>input_files/MD/production_REMD.mdp</td>\n",
" <td>0.0005</td>\n",
" <td>60000000.0</td>\n",
" <td>3.0</td>\n",
" <td>nose-hoover</td>\n",
" <td>Parrinello-Rahman</td>\n",
" <td>https://acs.figshare.com/articles/dataset/Adding_Anisotropy_to_the_Standard_Quasi-Harmonic_Approximation_Still_Fails_in_Several_Ways_to_Capture_Organic_Crystal_Thermodynamics/10269416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9025</th>\n",
" <td>figshare</td>\n",
" <td>10269416</td>\n",
" <td>input_files/MD/production.mdp</td>\n",
" <td>0.0005</td>\n",
" <td>10000000.0</td>\n",
" <td>10.0</td>\n",
" <td>nose-hoover</td>\n",
" <td>Parrinello-Rahman</td>\n",
" <td>https://acs.figshare.com/articles/dataset/Adding_Anisotropy_to_the_Standard_Quasi-Harmonic_Approximation_Still_Fails_in_Several_Ways_to_Capture_Organic_Crystal_Thermodynamics/10269416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7216</th>\n",
" <td>figshare</td>\n",
" <td>21644393</td>\n",
" <td>SI/Carbamazepine/GAFFTIP3P/Carbamazepine/Freeze_10/equilibration.mdp</td>\n",
" <td>0.0010</td>\n",
" <td>5000000.0</td>\n",
" <td>10.0</td>\n",
" <td>berendsen</td>\n",
" <td>no</td>\n",
" <td>https://acs.figshare.com/articles/dataset/A_Comparison_of_Methods_for_Computing_Relative_Anhydrous_Hydrate_Stability_with_Molecular_Simulation/21644393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7215</th>\n",
" <td>figshare</td>\n",
" <td>21644393</td>\n",
" <td>SI/Carbamazepine/GAFFTIP3P/Carbamazepine/Freeze_10/production.mdp</td>\n",
" <td>0.0010</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>berendsen</td>\n",
" <td>no</td>\n",
" <td>https://acs.figshare.com/articles/dataset/A_Comparison_of_Methods_for_Computing_Relative_Anhydrous_Hydrate_Stability_with_Molecular_Simulation/21644393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7081</th>\n",
" <td>figshare</td>\n",
" <td>21644393</td>\n",
" <td>SI/Carbamazepine/GAFFTIP3P/CarbamazepineLiq48/Freeze_10/equilibration.mdp</td>\n",
" <td>0.0010</td>\n",
" <td>5000000.0</td>\n",
" <td>10.0</td>\n",
" <td>berendsen</td>\n",
" <td>berendsen</td>\n",
" <td>https://acs.figshare.com/articles/dataset/A_Comparison_of_Methods_for_Computing_Relative_Anhydrous_Hydrate_Stability_with_Molecular_Simulation/21644393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5479</th>\n",
" <td>figshare</td>\n",
" <td>21644393</td>\n",
" <td>SI/Gandotinib/GAFFTIP3P/Gandotinib/Freeze_10/equilibration.mdp</td>\n",
" <td>0.0010</td>\n",
" <td>5000000.0</td>\n",
" <td>10.0</td>\n",
" <td>berendsen</td>\n",
" <td>no</td>\n",
" <td>https://acs.figshare.com/articles/dataset/A_Comparison_of_Methods_for_Computing_Relative_Anhydrous_Hydrate_Stability_with_Molecular_Simulation/21644393</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_origin dataset_id \\\n",
"9127 figshare 8846045 \n",
"9059 figshare 7370684 \n",
"9126 figshare 8846045 \n",
"9125 figshare 8846045 \n",
"9026 figshare 10269416 \n",
"9025 figshare 10269416 \n",
"7216 figshare 21644393 \n",
"7215 figshare 21644393 \n",
"7081 figshare 21644393 \n",
"5479 figshare 21644393 \n",
"\n",
" file_name \\\n",
"9127 Tutorial/mdp/3_run.mdp \n",
"9059 md_files/2-MD-runs/mdp/1_grompp_minimisation.mdp \n",
"9126 Tutorial/mdp/2_nvt.mdp \n",
"9125 Tutorial/mdp/2_npt.mdp \n",
"9026 input_files/MD/production_REMD.mdp \n",
"9025 input_files/MD/production.mdp \n",
"7216 SI/Carbamazepine/GAFFTIP3P/Carbamazepine/Freeze_10/equilibration.mdp \n",
"7215 SI/Carbamazepine/GAFFTIP3P/Carbamazepine/Freeze_10/production.mdp \n",
"7081 SI/Carbamazepine/GAFFTIP3P/CarbamazepineLiq48/Freeze_10/equilibration.mdp \n",
"5479 SI/Gandotinib/GAFFTIP3P/Gandotinib/Freeze_10/equilibration.mdp \n",
"\n",
" dt nsteps temperature thermostat barostat \\\n",
"9127 0.0300 1000000.0 0.0 Berendsen berendsen \n",
"9059 0.0020 5000.0 0.0 v-rescale no \n",
"9126 0.0300 5000.0 0.0 Berendsen no \n",
"9125 0.0300 10000.0 0.0 Berendsen berendsen \n",
"9026 0.0005 60000000.0 3.0 nose-hoover Parrinello-Rahman \n",
"9025 0.0005 10000000.0 10.0 nose-hoover Parrinello-Rahman \n",
"7216 0.0010 5000000.0 10.0 berendsen no \n",
"7215 0.0010 0.0 10.0 berendsen no \n",
"7081 0.0010 5000000.0 10.0 berendsen berendsen \n",
"5479 0.0010 5000000.0 10.0 berendsen no \n",
"\n",
" dataset_url \n",
"9127 https://acs.figshare.com/articles/dataset/Nucleation_Mechanisms_of_Self-Assembled_Physisorbed_Monolayers_on_Graphite/8846045 \n",
"9059 https://acs.figshare.com/articles/dataset/Stereospecific_1_3_H_Transfer_of_Indenols_Proceeds_via_Persistent_Ion-Pairs_Anchored_by_NH__Interactions/7370684 \n",
"9126 https://acs.figshare.com/articles/dataset/Nucleation_Mechanisms_of_Self-Assembled_Physisorbed_Monolayers_on_Graphite/8846045 \n",
"9125 https://acs.figshare.com/articles/dataset/Nucleation_Mechanisms_of_Self-Assembled_Physisorbed_Monolayers_on_Graphite/8846045 \n",
"9026 https://acs.figshare.com/articles/dataset/Adding_Anisotropy_to_the_Standard_Quasi-Harmonic_Approximation_Still_Fails_in_Several_Ways_to_Capture_Organic_Crystal_Thermodynamics/10269416 \n",
"9025 https://acs.figshare.com/articles/dataset/Adding_Anisotropy_to_the_Standard_Quasi-Harmonic_Approximation_Still_Fails_in_Several_Ways_to_Capture_Organic_Crystal_Thermodynamics/10269416 \n",
"7216 https://acs.figshare.com/articles/dataset/A_Comparison_of_Methods_for_Computing_Relative_Anhydrous_Hydrate_Stability_with_Molecular_Simulation/21644393 \n",
"7215 https://acs.figshare.com/articles/dataset/A_Comparison_of_Methods_for_Computing_Relative_Anhydrous_Hydrate_Stability_with_Molecular_Simulation/21644393 \n",
"7081 https://acs.figshare.com/articles/dataset/A_Comparison_of_Methods_for_Computing_Relative_Anhydrous_Hydrate_Stability_with_Molecular_Simulation/21644393 \n",
"5479 https://acs.figshare.com/articles/dataset/A_Comparison_of_Methods_for_Computing_Relative_Anhydrous_Hydrate_Stability_with_Molecular_Simulation/21644393 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(mdp_df\n",
" .query(\"temperature < 15\")\n",
" .sort_values(by=\"temperature\", ascending=True)\n",
" .head(10)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "e19abe41-01b1-4cb7-81e3-74ebce691ae9",
"metadata": {},
"source": [
"### Focus on spikes at 400, 600 & 800 K"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "8e389f33-9738-4d40-826a-d0ee75e295e2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dataset_origin dataset_id\n",
"figshare 21644393 2160\n",
" 4040661 1\n",
" 6384443 2\n",
"zenodo 20544 3\n",
"dtype: int64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(mdp_df\n",
" .query(\"temperature == 400 | temperature == 600 | temperature == 800\")\n",
" .groupby([\"dataset_origin\", \"dataset_id\"])\n",
" .size()\n",
")"
]
},
{
"cell_type": "markdown",
"id": "1e936895-68cd-475a-996f-6a9eb9cee714",
"metadata": {},
"source": [
"We dig more with dataset 21644393:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "57e02c4b-cc54-45a2-ada6-636130112805",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"temperature\n",
"400.0 1924\n",
"300.0 256\n",
"600.0 128\n",
"800.0 108\n",
"250.0 96\n",
"dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(mdp_df\n",
" .query(\"dataset_id == '21644393'\")\n",
" .value_counts(subset=\"temperature\")\n",
" .head()\n",
")"
]
},
{
"cell_type": "markdown",
"id": "988a17b0-7eae-4023-9020-be1082cec3ca",
"metadata": {},
"source": [
"Dataset [21644393](https://acs.figshare.com/articles/dataset/A_Comparison_of_Methods_for_Computing_Relative_Anhydrous_Hydrate_Stability_with_Molecular_Simulation/21644393) in Figshare:\n",
"> A Comparison of Methods for Computing Relative Anhydrous–Hydrate Stability with Molecular Simulation\n",
"\n",
"This dataset has actually far more mdp files:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "67fde0cb-28d6-420e-8a48-417a9baf6898",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5256"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(mdp_df\n",
" .query(\"dataset_id == '21644393'\")\n",
" .shape[0]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "500e472b-febe-41cd-91f2-cc197c233523",
"metadata": {},
"source": [
"5256 mdp files for a single dataset!"
]
},
{
"cell_type": "markdown",
"id": "5daeeeda-e70f-489a-85fc-928d8a031e55",
"metadata": {},
"source": [
"### Hightest temperatures"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "d44a4517-346c-4f6a-ba1f-344b701f9d68",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_origin</th>\n",
" <th>dataset_id</th>\n",
" <th>file_name</th>\n",
" <th>dt</th>\n",
" <th>nsteps</th>\n",
" <th>temperature</th>\n",
" <th>thermostat</th>\n",
" <th>barostat</th>\n",
" <th>dataset_url</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9120</th>\n",
" <td>figshare</td>\n",
" <td>2267512</td>\n",
" <td>6P_crystal_reproduction_files/reproduction_files/NVT/nvt_annealing.mdp</td>\n",
" <td>0.001</td>\n",
" <td>6000000.0</td>\n",
" <td>1500.0</td>\n",
" <td>V-rescale</td>\n",
" <td>no</td>\n",
" <td>https://acs.figshare.com/articles/dataset/Growth_and_Characterization_of_Molecular_Crystals_of_i_para_i_Sexiphenyl_by_All_Atom_Computer_Simulations/2267512</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_origin dataset_id \\\n",
"9120 figshare 2267512 \n",
"\n",
" file_name \\\n",
"9120 6P_crystal_reproduction_files/reproduction_files/NVT/nvt_annealing.mdp \n",
"\n",
" dt nsteps temperature thermostat barostat \\\n",
"9120 0.001 6000000.0 1500.0 V-rescale no \n",
"\n",
" dataset_url \n",
"9120 https://acs.figshare.com/articles/dataset/Growth_and_Characterization_of_Molecular_Crystals_of_i_para_i_Sexiphenyl_by_All_Atom_Computer_Simulations/2267512 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mdp_df.query(\"temperature > 800\")"
]
},
{
"cell_type": "markdown",
"id": "4cac7bdd-da46-4cb5-991a-227e12866ed1",
"metadata": {},
"source": [
"Dataset [2267512](https://figshare.com/articles/dataset/Growth_and_Characterization_of_Molecular_Crystals_of_i_para_i_Sexiphenyl_by_All_Atom_Computer_Simulations/2267512) at 1500 K:\n",
"\n",
"> Growth and Characterization of Molecular Crystals of para-Sexiphenyl by All-Atom Computer Simulations"
]
},
{
"cell_type": "markdown",
"id": "872a189e-1e15-44f6-ba77-c2b725d07632",
"metadata": {},
"source": [
"## 4.B. Cumulated distribution of simulation time"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "f44c7b76-2095-4089-bf57-c73fa3c7e14d",
"metadata": {},
"outputs": [],
"source": [
"# Compute simulation time:\n",
"# dt is in ps\n",
"# https://manual.gromacs.org/documentation/current/user-guide/mdp-options.html#mdp-dt\n",
"mdp_df[\"time\"] = mdp_df[\"dt\"] * mdp_df[\"nsteps\"] / 1000\n",
"# in ns\n",
"# 1 µs = 1 000 ns\n",
"\n",
"# Attribute all-atom (AA) or corase-grain (CG) simulation type\n",
"mdp_df[\"simulation_type\"] = np.where(mdp_df[\"dt\"] <= 0.01, \"AA\", \"GG\")"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "4652869e-f608-4367-a7ad-f0fe0c960378",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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kXF1dce7cOSQmJkIqlaJNmzbo378/vLy8dJq5c+fOkEqlSElJQVpaGkpKSmBlZYV27dqhd+/eapeTEJq7uzu6d++OtLQ0ZGVl4c6dO5DJZHB1dUVQUBC6d+9e57qBYhjr6mc6ASiLR3UGDhyoUjQC2o1PUFAQZs+ejZMnT+Lu3buoqKiAk5MTevTooVxPUVc8PDwwaNAgJCcnIzs7G6mpqTAzM0OrVq3QtWtXdOjQQafn15a2vyvq06lTJxgbG+P8+fMoLCyEo6Mjpk2bBkdHR2zevBmPHj1S/v/dp08f2NjYKNe9lMvlsLa2RocOHdCnTx+VW7uJ6lX4AMi9U/f+9Mt6i4J2I4A2/fR3PgDoNBOw1O0z20T1qmMCO12TKHS58BY1WV5eHmxtbZGbm6t2SnoiIhKXy5cvY8+ePRgwYAAGDhwodBxqaXLv1r//QhhwYlXznKvzM03rH7oQcG7fPFmIDIRQtQGvNBIRERFRlc+D9XMeJ39gwjf6ORcRNRmLRiIiIiLSjeBJwJjPam+X1D27MxE9eVg0EhEREZH2jEzr3mfpDFg46C8LEekEi0YiIiID0rlzZ3Tu3FnoGCSk2HAg67rm7fPSgEs/V70OeLp5s3j2AuZGNO8xieiJw6KRiIiIyJDE7Qbi9mjXN+HXxrWfdxRwrmcpGN5mSiQKLBqJiIiISD0TC8DUWugURCQwFo1EREREQjn2EVAhb1yfzHjdZCEiqgOLRiIDcObMGVhaWj6xC5e3JKdPn4adnR2Cg/U07XwLkZGRgQsXLuDpp5v5eSmqJS0tDVeuXMHo0aOFjkLN4fQaQF7UtGM4tQdcAhpup1AA8XurXg98S7NbSy2dm5aNiFoEFo1EBiA+Ph52dnYsGvUgPj4eLi4uLBob6c6dO7h8+TKLRj24c+cOi0ZSFTwRGPSW0CmIqAXj08tERERERERUJ15pJCIiItKXoofAT+P++l5erLq/7WDAvJHrGrYKanouIqJ6sGgkIiIi0pfKCuBeTN37By8F3LvpLw8RkQZYNBIRERHVdOxj4Nxa3Ry76IFujktEpEMsGokMQJcuXWBhYSF0DFHo2rUrbGxshI5hcNq0aYPQ0FChY4iCr68vSktLhY7RsskLWdwREdXAopHIALi6usLExEToGKLg5uYGMzMzoWMYHGtra3h7ewsdQxQ41i3QiJWApVPVa/s2wmYhIlKDs6cSGYCDBw/i5MmTQscQhQMHDuD06dNCxzA4sbGx2Lp1q9AxRCEmJgbbtm0TOobhUCiA8tLGfVVW6Ddjx2l/fVk0chIcIiI94JVGIgNQWVmJyspKoWOIAsdaOxw3/eFYN5K8CPiwddOO4TsI6Pda8+RRx5S3xBPRk41FIxEREVF9rF2BNv2FTkFEJBjenkpERERERER1YtFIREREREREdeLtqUQGwNbWlstA6AnHWjs2NjYcNz3hWGugsgIoK6x6LS+qvX/2fsDaTfPjmVo3Ty4iIgMlUSgUCqFDUN3y8vJga2uL3NxcvkkgIiLSxP044Ns+de9/JQaw89JfHiKiZiJUbcDbU4kMwK5du3DkyBGhY4jC9u3bERkZKXQMg5OQkIAvv/xS6BiicO3aNXz99ddCxyAiIhHh7alEBiAvLw9SKT/j0Ye8vDyYmZkJHcPg5OXlIS8vT+gYopCfn8+xJiIiveK7UCIiIiIiIqoTrzQSERFRy/fqtb9eW7kKl4OIyACxaCQiIqKWz9ZD6ARERAaLt6cSGQCpVMpnGvWEY60djpv+cKyJiEjfeKWRyACMHDkSMplM6BiiMHr0aJiamgodw+CEhITA0dFR6Bii0KFDB7i4uAgdg4iIRIQfVRIZgIyMDDx48EDoGKKQnp6O7OxsoWMYnPz8fCQlJQkdQxTy8vKQnJwsdAz9qawEKsob91VZLnRqIqIWhVcaiQzApUuXYGdnB39/f6GjtHgXL16Ei4sL/Pz8hI5iUJKSkhAVFYXBgwcLHaXFS0pKQnR0NAYOHCh0lL+U5AFxe3Rz7NNrgOybujk2ERFphEUjERERNU1RNrD3X0KnICIiHeHtqURERERERFQnFo1ERERERERUJ96eSkRERM2vdRdA0oyfTZcVAnkZgGdPYOTK5jsuERE1iEUjkQEIDAyEpaWl0DFEISgoCLa2tkLHMDienp7o3Lmz0DFEwWDGes4BQGYudAoiImoGLBqJDED79u1hbMx/rvoQEBAAExMToWMYHEdHR3Tr1k3oGKLg7OyMLl26CB2DiIhEhM80EhmAX3/9FceOHRM6hijs3bsXJ06cEDqGwYmNjcXGjRuFjiEKV69exc8//yx0DCIiEhEWjUQGoLy8HOXlXKxaHzjW2uG46Q/HmoiI9I33uxEREYmJQlH1VfXNn6+buK00T3/5iYhI71g0EhERGYKsG8DBfwM5joC8FfC/KerbleYDd85UvZZI/yrsiIiItMSikYiIyBCU5AK3DgHoAsDpz9cNUFTqOhUREYkAn2kkMgAWFhawsLAQOoYocKy1w3HTHwuUwALFQscgIiIRkSgUCt6z8gTLy8uDra0tcnNzYWNjI3QcEkh5eTkkEgmMjIyEjtLiyeVySKVSjnUjKRQKyOVyLleiS3f+ANYPRSUkKIcxTCAXOlH93r7HdRqJiJqZULUBb08lMgAHDx6EjY0N+vXrJ3SUFu/AgQNwcnJC3759hY5iUG7fvo1jx45h3rx5Qkdp8W7BB6fRA3OMdgODl9bfOOsGUCkHus8FTK0BieTPHZI/X//5ffVr5X512yT1b3u8j7FZU39UIiJ6QrBoJDIAmZmZKCsrEzqGKGRmZgodwSA9fPgQ9+7dEzqGKDyEHe7BBTAyAUIXCh2HiIhEgM80EhERERERUZ1YNBIREREREVGdWDQSERERERFRnQyiaJw9ezYkEkm9XyUlJWr7RkdHY/z48XB2doa5uTmCgoKwYsWKOttXi4+PxzPPPAM3NzeYmZmhbdu2WLRoEXJycurtl5aWhhdeeAGenp4wNTWFl5cXXnzxRaSlpWn74xMp/56T7nGstcNx05+qqWY48TkREemPQU2E065dO7i4uKjdJ5XWrn83bdqE5557DhUVFXB3d4enpydiY2OxbNky7Nu3D8eOHVO7rlhkZCTGjBmD4uJiODs7Izg4GAkJCfjss8/wyy+/ICoqCq1atarVLy4uDv369cPDhw9ha2uLkJAQ3L59G2vXrsWuXbtw6tQpBAQENH0gSHQGDx4MU1NToWOIwpAhQ7jeoBaCgoJgaWkpdAxRCMIN2CBf6BhERCQiBnGlsdq///1vnDp1Su3X42uDJScnY+7cuaioqMAnn3yCO3fu4OLFi7h58ybat2+PP/74A0uWLKl1jvz8fEyfPh3FxcVYsGAB0tLScOHCBaSmpiI0NBSJiYmYO3durX4VFRWYOnUqHj58iMmTJyM9PR0XLlxAWloaJk2ahOzsbEyfPh2VlZU6Gx9quYqKihq8Ok7Ng2OtHblcjvx8FjL6IIcx8sECnYiI9MegisbGWLVqFUpLSzF8+HAsXrxYeduUt7c3fvzxRwDA2rVrcf/+fZV+3333HbKyshAYGIjVq1dDJpMBABwdHbF582YYGxtj//79uHjxokq/8PBwxMXFwdHRERs2bFBeqbC0tERYWBgcHR1x9epV7NmzR9c/OrVA0dHRtf7OkW5ERUXh8uXLQscwODdu3MChQ4eEjiEKN+CLI+CarUREpD8tsmhUKBT45ZdfAEDtVcG+ffsiICAAcrm8VhEXHh4OoOo5SiMjI5V9Xl5eGDp0KABg586davtNmzYN1tbWKvusra0xdepUAMCOHTu0/bGIiIiIiIj0zqCKxp07d2LChAkYPHgwZsyYga+++gq5ubm12qWmpiIjIwMAEBoaqvZY1dvPnj2r3FZeXo4LFy40uh8AnDlzRqt+RERERERETzKDmghn//79Kt9v27YN77zzDjZv3oyRI0cqt9+8eRMAYGpqitatW6s9lq+vr0pboOo5SLlcrrJfk35lZWVITU3VqF/1OapveyUiIiIiInqSGUTR2LZtW3z44YcYM2YM2rRpA4lEgujoaPznP//B2bNnMWHCBJw6dQrdu3cHADx69AgAYGdnV+cU8Pb29iptH39dvV+Tfrm5ucoJbhrqV1lZiby8PDg6OqptV1paitLSUuX3eXl5atsREZGBy7kDlBUCisqqLyj+eq2oBBQK1T8zrwmdmIiIRMogisb//Oc/tbYNGzYMAwYMQL9+/XDu3Dm88cYbOHLkCAAoZz58fEbVmqqXLyguLlZuqzljYl19m9rv8b6PW7lyJd57770695M4tWnTptazsqQbvr6+dX74Q3VzdXVF+/bthY6hPw+TgB9HNtyuPgX3tOrmiiz4I7Fp5yYiImoEgyga62JiYoIVK1ZgxIgROHbsGB49egR7e3uYmZkBqLpttC7VV/PMzc2V26r7Vfet+X1j+tV3vsf7Pu6tt97Ca6+9pvw+Ly8Pnp6edbYncejevXutiZlIN3r27AljY4P+1SgId3d35URhoqCo1LroayoPZMAOvAuFiIj0x6AmwlGnT58+AKpu+0xMrPrktfoqQU5ODhQKhdp+1beX1ryiUPN1zdtPG+pna2sLqVSqUT+pVAobG5s6fx5TU1PY2NiofBHt2bMHR48eFTqGKISHh+PYsWNCxzA4MTExWLdundAxROEqArEeM4SOQUREImLwH6fXnFCmvLwcANCuXTsAVVf30tPT4e7uXqtfdYFZ3RYAfHx8IJPJIJfLkZiYCDc3N436mZiYwMvLC8nJyUhMTFQWsur6VZ+DqDHKysrqvXJOzYdjrZ0WM26pZ4HIDxpuJ6/7MYMmkcoAifSxL8mfX1Xfl5Xbo0xuCli10k0GIiKixxh80Xjt2l8TA3h4eACoWk/R1dUV9+7dw+nTpzFt2rRa/U6fPg0A6NWrl3KbsbExunbtirNnz+L06dNql89Q16/6++TkZJw+fRrPPPOMxv2IiOgJUpQNJB3Xru+sHYBRE/6z6ugH2Hk13O7MGSAyElhwUftzERERNYLB35762WefAQACAgKUVxQlEgkmTpwIAFi/fn2tPlFRUUhISIBMJsO4ceNU9k2aNAkAEBYWhoqKCpV9qampOHz4MABg8uTJavtt374d+fn5Kvvy8/OxY8cOAMCUKVMa/0MSEdGTz3cg0Haw9l+aFIxEREQCeOKLxkOHDuGtt95CUlKSyvbc3FwsWLAAW7ZsAQAsW7ZMZf/ixYthYmKCiIgIrFq1SvlsY0pKCp5//nkAwLx58+Dq6qrS76WXXoKTkxPi4+Px2muvKddtzM7OxqxZs1BeXo5Ro0ahW7duKv0mT56MgIAAZGdnY86cOSgqKgIAFBYWYs6cOcjOzkZISAgmTJjQPANDRERERESkB0/87amFhYX46KOP8NFHH8Hd3R2tW7eGXC5HXFwcysrKIJFIsGzZMsycOVOlX5s2bbBu3TrMmTMHS5YswZo1a+Di4oLY2FjI5XJ069YNq1atqnU+GxsbbN26FU8//TS+/PJLbNmyBV5eXoiPj0dRURF8fHzw448/1upnZGSEHTt2oH///ti1axcOHz4MPz8/3Lp1C7m5uXBwcMC2bduUE+YQNYapqanKsi2kOxxr7bTYcTN3AAYs0aytVD8zHLfYsSYioieWRFHX9KJPiDt37uD7779HdHQ0bt26haysLCgUCri5uaFfv36YP39+vc8JRkVFYeXKlYiKikJhYSF8fHwwc+ZMvPHGG2qX1Kh27do1vP/++zh69ChycnLg7u6OiRMnYunSpfWu4Xbnzh0sX74cBw4cQFZWFpydnTF69GgsW7ZM+cxlY+Tl5cHW1ha5ubmcSVXEioqKYGRkxDeKesCx1k5FRQUKCwsN//dUwm/A1hofQtq3ARZeFiyOOhUVFSgqKuLarUREIiRUbfDEF41ix6KRAODIkSOwtrZGz549hY7S4kVERMDBwQHdu3cXOopBSUlJwenTpzFr1iyhozSNARSNSUlJOHfuHKZPny50FCIi0jOhaoMn/vZUIqqahMnOzk7oGKKQmpqKkpISoWMYnPv37yuXFiLd4lgTEZG+8QE7IiIiIiIiqhOLRiIiIiIiIqoTi0YiIiIiIiKqE4tGIiIiIiIiqhMnwiEyAE899VS9S8RQ8+nXrx8sLS2FjmFw2rdv/2SuQ3vmWyD/nubtH97WXZZmEhAQABMTE6FjEBGRiGhdNObl5SE5ORmOjo5wd3dX2RceHo5169YhPT0d3bp1w/Lly7Vao5CIqhgbG8PYmJ/x6INMJuNYa0EqlT6ZhcylTcD9GKFTNKsndqyJiKjF0vpj4dWrV6NLly44ePCgyvaNGzdi6tSpOHjwIGJiYhAWFobQ0FDk5eU1OSyRWB0/fhxnz54VOoYoREZG4ty5c0LHMDgJCQnYu3ev0DFEIS4uDvv27RM6BhERiYjWH6cfOnQIRkZGmDZtmsr2d999FwDw5ptvonfv3lizZg2OHTuGb775Bm+++WaTwhKJlUKhgEKhEDqGKHCctaOTccu4Avzyf007Rua15slCREQkYloXjcnJyWjdujWsrKyU2y5evIiUlBQMHjwYH374IQAgNDQU7u7u2LVrF4tGIiICcu8CmQkNt7tzpvmLPokU6DBV8/aWzs17fiIiIgOkddGYnZ2Nzp07q2w7fvw4JBIJJkyYoNzm6OgIf39/pKSkaHsqIiJ6ksXsBO7eBxQVwKX/Ndz+4k/AHYFut564FujYiKKRiIiItC8aTUxM8PDhQ5VtJ06cAAD0799fZbu5uTkKCwu1PRURET3JDr4NFLQCMADY80+h0xAREVEz07poDAgIwIULF3Djxg34+/vj0aNHOHToEBwdHdGxY0eVtunp6XBxcWlyWCKxcnNzg42NjdAxRMHNzQ1OTk5CxzA4TngID2To/kST1zetv0eP5skhICcnJ85ITkREeqV10fjMM8/g/PnzGD58OKZMmYIjR46guLgYL7zwgkq7lJQUpKWlYfjw4U0OSyRWgwcPhkQiETqGKAwdOhRGRkZCxzA4PrgLNzRhRk8bDYogU2ugwxTtz9FC+Pr61lrqioiISJe0Lhr/9a9/4eTJkwgPD8fq1asBAL169cI777yj0u7nn38GUPVGjIi0Ex4eDjs7O4wcOVLoKC3ezp074eLigmHDhgkdxaBcgz8iMACvYy3g6AeYNuLKeKsgYPzXugvXwsTExCAyMhKvvPKK0FGIiEgktC4ajYyMsHPnTly8eBE3b96Ep6cn+vTpU+tqiK+vLz7//HNMmcJPh4m0VVxcDFNTU6FjiEJxcTGKi4uFjmFwimGGYphVfTP6U6DtIGEDtWD8O0qkP0Xyojr3KVD3UkP1LUNUb7969jV0XG376SpPQ321OWZDx33i8ugga35+fr15dEXrorFa165d0bVr1zr3z5o1q6mnICIiIqI/Xc26isyiTJ0c+9VjrwIA3K1Ub4Gu+Qa35htaldd1tFF92ci+AmWo3ldczg9o6MlSUVwhyHmbXDRWS09PR1paGoqLi2vNnkpEREREQEFZAT678FmTjrHzxs5mSlO3tII0nZ+DiAxHk4vGb7/9FqtXr0ZiYiIAQCKRoLy8XLn/9ddfR3R0NLZu3QovL6+mno6IiIhIp0rKS1BWWaaTYz8sfqiXoo+IqDlpXTQqFArMmDEDO3dW/eLz8fHBgwcPUFBQoNKuV69e+PzzzxEeHs6H9om0JJPJYGzcbDcGUD041tqRoRzGKG+4ITWZsbEx/47q2DdXvsGG2A1CxyAiemJIFFo+UfvDDz/ghRdeQHBwMDZv3owOHTqgX79+iIqKQkXFX/faFhYWws7ODgMHDsShQ4eaLbhY5OXlwdbWFrm5uVynT8Ryc3NhbGwMS0tLoaO0eDk5OZDJZBzrxvi0PeQFD/AItnBBNvD33ZwIR4fkcjlycnLg7OwsdBSDUFZRhhVnVjSqz+5bu3UTRod8bHya9XjJeckAgMXdF8PbxrvOZZ8k+Gt7zTYq22u8Vn3ZuL5NylDH9lrHqqNdVnEWKior4GfvB3Mj83r7NZameZq7r7bHbejYDfbVVWaBMjV07Hr7apE5Ly8PrRxb6b020PqjyvXr10MqlWLHjh0ICAios52lpSXatm2rvH2ViBovNjYWVlZW6NSpk9BRWryYmBjY2dmhQ4cOQkcxKJlwxHl0wnhECB2lxbt37x4uX76MsWPHCh1FKb0gHX8/8HflZCOKP/8H/DW5iLoJRx5vp/jrAI0/zmP7FVAACqBc8eRfAR/VZhQczBy07h/aOhT9PPo1YyIielKVGevm1vmGaF00Xrt2Db6+vvUWjNXs7e1x5coVbU9FJHo3btyAnZ0di0Y9uH79OlxcXFg0NlIaXBGDABaNepCWlobY2NgnqmisUFTobDZPMXiz55tNKhqJiHRN66KxsrJS43Xj8vLyuMYcERERGaRebr3wQegHOju+rYmtzo5NRNQctC4a27Rpg1u3bqGgoABWVlZ1trt37x6uX7+Onj17ansqIiLSFXkJUJILQAEoKgHFn39Coea1Qn27SrmgPwJRYw3zHgY3SzeN2wc7BqOVZSsdJiIierJpXTSOGzcOK1euxLJly7B69eo6273++utQKBSYOHGitqciIiJNFWQBleXA9d+AuN2AsTlgagVUVvxZ7D32dbO5bif1aKbjUF3SCtKwKX4TylPLIa+Q45M/PlHbrkhehF03dwEA2tq2rfe5wOrnCFWeJ6y5/fHnDRWqzwxWb69Q1F5s+r+D/wsTIxPlRA/VkzpIIKk1+UPNbcp2j33/+Da17SRQPUaNNjWP42vrC5mRTO34ERFRbVoXjYsWLcLGjRuxZs0a3LlzB3PnzkVJSQkAICkpCTExMfjyyy9x9OhR+Pr6Yv78+c0WmoiI6vDTOCAzTugUpAMPih/g57if4Zfrh+DKYOyMa3itv9u5t/WQTL1ebr1gZmwm2PmJiKj5aF002tvb4+DBgxg/fjx27dqF8PBw5T4/Pz8AVZ9M+vr6Yv/+/Zy+nqgJunfvDgsLC6FjiEKPHj1gbW0tdIzaMq78ebWw+vZQNVcOKysEKxjbIgUDcEaQc4vNffP7MIKR0DGIiEhEmrQ6cHBwMK5evYr169fjl19+QUxMDHJzc2FlZYWgoCBMmjQJL774IgtGoiZydHSEiYmJ0DFEwcnJCWZmT+DVkbUD/3yG8EkjASQSWKAMbsgGpCaARCp0qBat1KgUOSY5QscgIiIRaVLRCAAWFhZ4+eWX8fLLLzdHHiJS49ChQ7Czs8P48eOFjtLiHTx4EM7Ozk/UcgbNZuBbVQXd419So6qCVF4MmNsDHacDEsmfxd+ff0okqq+V26pcO38eBw8exNtvvy3YjycWnoWeCH4UjN98fsPYtvX/PY3OiEZeaR4W9VgEWxPb2s8E1ngGsPq5wrqeKazeV6tfzT41nk2USfnMIBFRS9HkopGIdK+yshKVlU/iVaaWp7KyUjlZSIswZjUQMhkwkgEmurvro8WN2xNMoqgq0kyMTLA8dLnQcYiISARYNBIRGSKZJWBkDEiMal81rHk10MYdMLcTOi0REREZMI2Kxueff77JJ5JIJFi/fn2Tj0NERADmRgCuIUKnICIiIhHQqGgMCwtr8olYNBIRERERERkejYrGDRs26DoHEdXD3t4etra2QscQBXt7e9jZ2Qkdw+DY2trCwcFB6BiiUGRchAJZgdAxiIhIRDQqGp977jld5yCieowbN045IyHp1sSJEznWWvD390e7du2EjiEK6RbpyLDIgDnMhY5CREQiwYlwiAxAeHg4bG1tMWzYMKGjtHg7duyAs7MzBg8eLHQUg5KQkICDBw/ilVdeETqKwSgoK0B5ZXmj2gOAR5EHgh8F46TPSV1FIyIiUsGikcgA5Ofnw8jISOgYopCfnw9zc17Baaz8/HwUFBj+LZP3Cu/hwv0LDbbLKMzAmotrAACBDoFanSv+YbxW/czKzWBWYaZVXyIiIm1oVDT+9NNPAKqeWaleXLx6W2M8++yzje5DRESkL/HZ8Xjz5JuN66Nl8UdERGQoNCoaZ8+eDYlEgvbt2yuLxuptjcGikYiIiIiIyLBoVDQ+++yzkEgkcHNzq7WNiIiIiIiIWi6t12lsjrUbiUgzRkZGkEqlQscQBY61dlrquBlLjeFr61vnfgUUuPnoJgBged/lMJY2fqqA/LJ8lFaUIsgxCG3t2jbYPuZiDM6eOov9k/Y3+lxERETa0Oi/bs8//zzat2+PN954Q7ktNTUVZmZmcHFx0Vk4IqoyatQoyGQyoWOIwpgxY2Bqaip0DIMTEhICZ2dnoWM0OzdLN+wat0voGCr6dOsDPy8/OJk7CR2FiIhEQqOPhcPCwrB/v+onmj4+Ppg6dapOQhGRqtTUVGRkZAgdQxRSUlJw7949oWMYnNzcXFy/fl3oGKKQk5ODGzduCB2DiIhERKOi0djYGKWlpbW2KxSKZg9ERLVdvXoVCQkJQscQhStXrrD40UJycjLOnj0rdAxRSE5Oxrlz54SOQUREIqLR7akeHh6Ii4tDeno6WrduretMREQtV2YC8McPtbf/sa7qT7fOgMyi9n5FpU5jEREREdVFo6Jx0qRJWL16Ndq3b4+QkBDlwtcxMTEYPHiwRieSSCQ4cuSI9kmJiJ4UexcAxY+qCjmF4s8/q78qarxWAMknq/rY+wASKfAwsf5jZ1zWdXpRefnIy0jMbWDMaygqL9JhGiIiIsOkUdG4YsUKZGRkYNu2bSq3H+Xm5uLYsWManYjLcxDRE23/60D+PSDbEyiIA7Zuq7ttwq+NP/6jZK2jkfYyCjOQmp8qdAwiIiKDplHRaG5ujk2bNuHrr7/GzZs3UVRUhEGDBqFDhw748ssvdZ2RiEj3bh/98yrgTKD4AZBzSOhEoveo5BHO3z/fpGNcf8TnU4mIiJqqUQtK2dnZoUePHsrvbW1tMWDAgGYPRUSqQkJCYGlpKXQMUeiABNgiT78n7f78X6/vngfuXQU6zQI8utXdx+bJer7cy8sL3bt3b9ZjJucl47VjrzXrMVsCb29vdOtWz98NIiKiZtb4VYj/lJSUBDMzs+bMQkR18PX1hbGx1v9cqRHaIhkmkDeuk+8gwHdA1TOLEikgMarxWlL1590/gKKHQMAYwMLxr77GZkC7oc37Q+hZflk+jK2M4Rvgi7yyhgvu3Td3Y9X5VQAAE6lJne3KKsuaLWO14d7DMdp3tMbtzY3Nmz1DUzk4OCA4OFjoGEREJCJavwv19vZuzhxEVI/9+/fDzs4O48aNEzpKi7cPw+CCBxgT4gA4t9esU8fpgH0DvxN7zG16uCfU+N3jYZVphY4PO2KP955G9dVFYVifwV6DMcRriF7P2dyuXr2KyMhILFmyROgoREQkErx0QWQAKioqUFFRIXQMUaiAESpgBARPBAKfFjqOwZAqpJAqNFr6t0l8bHya1N9KZtU8QQTE3wdERKRvLBqJiMggWMussW/iPqFjEBERiQ6LRiIiEpRMKsPPo39usJ2RxEgPaYiIiOhxLBqJiKhZfdL/E3RrpfnsnsZSYziYOegwERERETUFi0YiA2BpacklN/TEEkWwRJHQMQxOiVEJSo1KAQB2pnZwsXAROFHLxd8HRESkbxoVjc8//zz8/f3x5ptvKrelpqbCzMwMLi58Y0Cka5MnT4ZEIhE6hihMwz5IoAAwX+goBiXNIg0Z5hlCxxCFkJAQBAYGCh2DiIhERKOp7sLCwvDbb7+pbPPx8cHUqVN1EoqIVO3fvx8nT54UOoYo7MMwRKF5F6kXA9diVwy6N0joGKJw/fp1bNiwQegYREQkIhpdaTQ2NkZpaWmt7QqFotkDEVFt2dnZnGJfTx7AAVJUCh3D4FiWW8KmzEboGKKQk5ODBw8eCB2DiIhERKMrjR4eHoiLi0N6erqu8xAREREREdETRKMrjZMmTcLq1avRvn17hISEwNzcHAAQExODwYMHa3QiiUSCI0eOaJ+UiIiIiIiI9E6jonHFihXIyMjAtm3bcPbsWeX23NxcHDt2TKMTcRIPIiIiIiIiw6NR0Whubo5Nmzbh66+/xs2bN1FUVIRBgwahQ4cO+PLLL3WdkUj0JBIJP3jREwkUf86eSo2hgALgX1G94O8DIiLSt0at02hnZ4cePXoov7e1tcWAAQOaPRQRqRo6dChMTU2FjiEKw3Ec5igBMEfoKAblruVdFBsXCx1DFEJCQmBvby90DCIiEpFGFY01JSUlwczMrDmzEFEdcnNzYW5uDmdnZ6GjtHg5sEUFjOAkdBAtbU3YigfFD1CpqIQCCuWfCkXV65rbKxWVOH/vPG7n3gYAhDiGKPcBqNVfoVCo7K/el12SDfNKc1jLrZEBrtWoa8XFxcjOzhY6BhERiYjWRaO3t3etbQUFBcjPz4e1tTWsrKyaFIyI/nLu3DnY2dnB19dX6Cgt3ll0gQsewEfoIFradn0bbuXc0qpvbHas1ud1LXZF8KNg3LC9ofUxSDO3bt3CsWPH0KdPH6GjEBGRSGhdNFaLjY3Fp59+ioiICNy/f1+5vVWrVhg5ciRee+01hISENPU0RESitjx6OSoUFcorhMqrhgoFKhQVyiuA2haMRERERHVpUtG4fv16/POf/4RcLodCoTpxxL179xAWFobNmzfj66+/xty5c5sUlIhIzHbd3KW8LZSIiIhIn7QuGs+ePYsXX3wRlZWVGD16NBYuXIjg4GC0atUK9+/fx7Vr17BmzRr89ttveOmll9ChQwf07NmzObMTEVEDjCXGGNVmFCQSCaQSKaQSKST46zUASCVSlFeWIyIlAvam9vhHx39AgqoZOqvbSiABJIAU0qpjQaryfXpcOm6du4VvhnyDAIcAgX9qIiIiak5aF42rVq2CQqHAhx9+iDfffFNlX+vWrdG6dWsMGzYMn3zyCd58802sWrUKO3bsaHJgIiLS3If9PsSoNqM0aruszzKtz3Mu4xwSJYno59FP62MQERHRk0nrovHUqVNwdnauVTA+bvHixVi9ejVOnjyp7amIRM/Pz4+TS+mJH5Jgj1yhYzRogt8EOJk7Ka8YVl/xM5IY/XVVEVK0d2ivlzytW7dGUFCQXs4ldq1bt0ZgYKDQMYiISES0LhofPXqEzp07N9hOIpHA29sbV65c0fZUai1duhQffPABAGDFihVYunSp2nbR0dH46KOPEBUVhYKCArRp0wYzZ87E4sWL610yJD4+Hu+//z6OHj2KR48ewd3dHRMnTsTSpUthZ2dXZ7+0tDS89957OHDgADIzM9GqVSuMGjUKy5Ytg7u7e5N+ZhKvzp07w8jISOgYotAVsZChXOgYDfpb4N/0VhBqwtXVlev26ombmxv69eMVXSIi0h+pth1dXFxw69YtlJfX/+ZKLpfj1q1bcHJqvlXP4uPjsWrVqgbbbdq0Cf369cPevXthamqKwMBA3Lp1C8uWLUP//v1RVFSktl9kZCS6deuGzZs3o6KiAsHBwbh37x4+++wzdOvWTWWW2Jri4uLQsWNHrFu3Dvn5+QgJCUFeXh7Wrl2LTp06ISEhoUk/N4nX3r17cfToUaFjiMJujMQx9BY6hsGJjY3FDz/8IHQMUbh69So2bNggdAwiIhIRrYvGgQMHIicnB2+88Ua97d544w3k5ORg8ODB2p5KhUKhwIsvvgiZTFbvMZOTkzF37lxUVFTgk08+wZ07d3Dx4kXcvHkT7du3xx9//IElS5bU6pefn4/p06ejuLgYCxYsQFpaGi5cuIDU1FSEhoYiMTFR7UywFRUVmDp1Kh4+fIjJkycjPT0dFy5cQFpaGiZNmoTs7GxMnz4dlZWc/ZAar6ysDHK5XOgYolAGGeSQCR3D4PDvqP7I5XKONRER6ZXWReNbb70FExMTfPHFF+jevTs2bNiAs2fPIikpCWfPnsWGDRvQrVs3rFmzBiYmJg0++6ip9evX4+TJk1i2bBk8PT3rbLdq1SqUlpZi+PDhWLx4MSQSCQDA29sbP/74IwBg7dq1ta4afvfdd8jKykJgYCBWr14NmazqzaOjoyM2b94MY2Nj7N+/HxcvXlTpFx4ejri4ODg6OmLDhg2wsLAAAFhaWiIsLAyOjo64evUq9uzZ0yzjQEREREREpA9aF41BQUHYtm0brK2tcfHiRcybNw99+/aFn58f+vbti3nz5uHSpUuwtrbG9u3bm2WChKysLLzxxhsICgrCq6++Wmc7hUKBX375BQDUXhXs27cvAgICIJfLaxVx4eHhAIDZs2fXeobMy8sLQ4cOBQDs3LlTbb9p06bB2tpaZZ+1tTWmTp0KAJxBloiIiIiIDIrWRSMAjBs3DvHx8fj3v/+N7t27w8bGBlKpFDY2NujevTuWLl2K+Ph4jB07tlnCvvrqq3j48CG++eYb5RVAdVJTU5GRkQEACA0NVdumevvZs2eV28rLy3HhwoVG9wOAM2fOaNWPiIiIiIjoSab17KnV3Nzc8P777+P9999vjjx1OnLkCDZt2oS//e1vDc7Qd/PmTQCAqakpWrdurbaNr6+vSlug6jnI6udEqvdr0q+srAypqaka9as+R31FL9HjzMzM6p3tl5qPGUphhlKhYxgc/h3VH441ERHpW5OLRn0oKSnBSy+9BFtbW3z66acNtn/06BEAwM7OTvks4+Ps7e1V2j7+unq/Jv1yc3OVE9w01K+yshJ5eXlwdHRU2660tBSlpX+9Yc3Ly1PbjsRl4sSJkEqbdGMAaWgK9sMIFQBeFDqKQQkJCUHbtm2FjiEKHTp0QLt27YSOQUREImIQ70Lff/993Lp1Cx988AFatWrVYPuSkhIAgImJSZ1tTE1NAQDFxcW1+tXXt6n9Hu/7uJUrV8LW1lb5Vd9kPyQeJ06cqDX5EulGJPrgEoKFjmFwUlJSlM92k24lJydj9+7dQscgIiIReeKLxuo1Gbt27Yr/+7//06hP9W07ZWVldbapvppnbm5eq199fZva7/G+j3vrrbeQm5ur/Lpz506dbUk80tLSlM/pkm6lwQ334CJ0DIOTlZWFlJQUoWOIQlZWlvKRCCIiIn144m9PnT9/PsrLy/Htt99qfHte9a2gOTk5UCgUam9Rrb69tObtpDVfP3r0CG5ubhr1s7W1hVQqRWVlpcptq+r6VU8UVBdTU1OVq5JERERERERCeuKvNF66dAkSiQTjxo2Dq6uryte2bdsAAB9//DFcXV3Ro0cPAFA+61FaWor09HS1x01MTFRpCwA+Pj7KCWqq92vSz8TEBF5eXhr1q3kOIiIiIiKiJ90Tf6URACoqKnD//v069xcUFKCgoEB5m6iXlxdcXV1x7949nD59GtOmTavV5/Tp0wCAXr16KbcZGxuja9euOHv2LE6fPq12+Qx1/aq/T05OxunTp/HMM89o3I+IxOXmo5v4Ke6nWtt339oNAGhj2wZmRrVnxqxUVOo6GhEREZFaT3zRmJOTU+e+2bNnY+PGjVixYgWWLl2q3C6RSDBx4kR8++23WL9+fa2iMSoqCgkJCZDJZBg3bpzKvkmTJuHs2bMICwvD66+/DiMjI+W+1NRUHD58GAAwefLkWv22bduG7du34+OPP4a1tbVyX35+Pnbs2AEAmDJlSuMGgIieOG+efBO5pbmoVFSiUlEJhUKBStR4rahEJapexzyIAQA4mTtBCikyizPrPXZSbpI+fgQiIiIijT3xRaO2Fi9ejPXr1yMiIgKrVq3CokWLIJFIkJKSgueffx4AMG/ePLi6uqr0e+mll7Bq1SrEx8fjtddew6effgqZTIbs7GzMmjUL5eXlGDVqFLp166bSb/LkyQgICEBCQgLmzJmDn376CRYWFigsLMScOXOQnZ2NkJAQTJgwQV9DQC1I//79uS6bji01r0BWK2eYlsahUlqC/Qk/Anf3qW0blR7V6OM/KH7Q1IhPtICAgHpnrKbmExgYWO+EakRERM2txRaNbdq0wbp16zBnzhwsWbIEa9asgYuLC2JjYyGXy9GtWzesWrWqVj8bGxts3boVTz/9NL788kts2bIFXl5eiI+PR1FREXx8fPDjjz/W6mdkZIQdO3agf//+2LVrFw4fPgw/Pz/cunULubm5cHBwwLZt27jWHtET6pKRAqkm5nCFKUqMFMjJuwnk3RQ6lkFRKBRCRyAiIiIdaHLRGBERgQMHDiAxMREFBQV1vmmQSCQ4cuRIU0/XKM8++yz8/PywcuVKREVFIS4uDr6+vpg5cybeeOONOq/cDBkyBOfPn8f777+Po0ePIiYmBu7u7pg4cSKWLl2qMnNqTSEhIbhy5QqWL1+OAwcOICYmBs7Ozpg2bRqWLVsGDw8PXf641IKdOHECdnZ2mDhxotBRWrzAnEDkmeThgukFvZ3zuaDnlK/jHsbhds5tjGozCkGOQXX2cbV0rXOfEBISEhAREYEuXboIHaXFi4+PR2RkJDp16iR0FCIiEgmti8aioiJMmjQJhw4dAtDwJ8zqlr1oqrCwMISFhdXbpm/fvti3T/0tZvUJDg7Gli1bGt3P09MT69ata3Q/IjJcgzwHobdbb0glUkglUkgkEkjx12sjiRHisuNQIC9AaOtQ2Jj+teyOidQE3V27C5ieiIiIqH5aF43/+c9/EBERARsbG8ybNw89evSAi4sLb78kohZhvFM3tPMZpFHbET4jGrzyN7bt2OaIRURERKR3WheNO3bsgJGRESIiItCzZ8/mzEREJLhB9oEYEvxcww2JiIiIWjitLwtmZWWhXbt2LBiJiIiIiIhaMK2vNHp4eMDYuMVOvkr0RPHw8FBZ+5N056HZQ+Qb5wsdw+C4uLjAx8dH6Bii4OzsDG9vb6FjEBGRiGh9pfGZZ55BXFwcbt++3Zx5iEiNfv36oWvXrkLHEIU42zikWKUIHcPgeHl5cR1aPfHx8cH48eOFjkFERCKiddH473//G0899RQmTJiAS5cuNWcmInrML7/8ovcla8SqV1YvBOcECx3D4MTGxuK7774TOoYoxMTEYO3atULHICIiEdH6/tKXXnoJnp6eiI6ORo8ePdC5c2e0bdsWlpaWattLJBKsX79e66BEYlZSUoKSkhKhY4iCrFIGWaVM6BgGh39H9YdjTURE+qZ10RgWFgaJRKJcn/HixYu4ePFine1ZNBIRERERERkerYvGDRs2NGcOIqLmk3EFiNvTuD6KCgBGOolDREREZMi0Lhqfe47rlxGRjhx8G3hwU/v+Nw82vo+HG1g0EhEREdXGNTOIDICJiQlkMhE9Z5d6Bkg7L8ipK6QVKJeUC3JuQya6v6MCkslkHGsiItKrZisab9y4gRs3biA/Px/W1tbw9/eHv79/cx2eSNTGjRsHI6MWcBXsx1FAWUHD7e5d1X2WOpxzOocKSQWADoJlMEQhISHw9PQUOoYodOzYEW3atBE6BhERiUiTi8bvv/8eH3/8MVJSaq9r5uPjgzfffBP/+Mc/mnoaIlG7dOkSrK2t0aVLF6Gj/KWssOqrMVKjdJNFEyFT6t9fdAVQlMI33xcFsgLA3EE/uVqIe/fu4dy5c5g0aZLQUVq8jIwMXLx4ketiEhGR3jSpaJwzZw5++uknKBQKmJqawtPTE61atcL9+/dx584dJCUl4aWXXkJUVBQnziFqgtu3b8POzu7JKhrP/whELNXPuRz9gC5/176/lQvQeVb9bcLHAPmpaFXcCuYV5oCjr/bnE6H09HTExcWxaNSD9PR0xMfHs2gkIiK90bpo3Lx5MzZu3AhLS0u88847eOmll2BlZaXcX1BQgO+++w7Lly/HTz/9hOHDh2PmzJnNEpqIRCZ0IdD1WaFTEBEREYmS1kXjunXrIJFIsGvXLgwfPrzWfisrKyxatAgdO3bEyJEjsW7dOhaNRE+SzASgvASAAlBUAgrUeP3nn1A89vrPfVAAt440PUOPeYBHz4bbeXRv1GGLy4uRV5rXqD4ViopGtSciIiISC62LxitXrsDX11dtwVjT8OHD4efnh0uXLml7KiLShc3TgJzazyLrVcfpgGftovFS5iXkl+X/taE4DbibpvFh993eh9+Tf2+OhERERESip3XRWFJSAjs7O43a2tjY4O7du9qeiogMQesuwDO76m0Sdn0ryirlf23IvgA8ulyr3VeXvmrmcERERESkLa2LRi8vL8TGxuLBgwdwcnKqs11WVhauXbsGb29vbU9FJHo9e/aEhYWF0DHqZ+EIWDrW2+T7hP+hQK7BkhsCumlzEyXGJULHMDh+fn4oL+f6lvrQrl07oSMQEZHIaF00jhs3Dp9++immT5+OrVu3wtnZuVabzMxMzJgxA2VlZRg/fnyTghKJma2tLUxMTHR7EiMTQCoDJBJAIgUg+fN1ze+lyu8PmEix1Vz6Z2cJgHvAgefqPcWTXjACQLFxMcqkZULHMDhmZmZwdKz/QwNqHmZmZnBw4JIwRESkP1oXjW+++Sa2bt2KY8eOwdvbG1OnTkVQUBBcXFyQmZmJuLg47NixAyUlJfD09MQbb7zRnLmJROXw4cOws7PT7RT7034C2o/SuHnmtY24eP7TvzZU5AGZF3UQrEpry9aN7lOuKEexvBht7dri66Ffa9Rn609b4ejsiP4e/Rt9PjG7du0aIiIi8PbbbwsdpcWLjY1FZGQk3nzzTaGjEBGRSGhdNDo4OODo0aOYOXMmLly4gJ9//hkSiUS5X6FQAAB69OiBzZs381NRoiZQKBTKf1MtiYOZAzo5d2qw3YyAGejbuq8eEgFSSCGTyCCTyvRyvpaipf4dfRJxrImISN+0LhqBqmdY/vjjDxw5cgQRERG4ceMGCgoKYGVlBX9/f4wYMQKDBw9urqxE1Iyet1Ig3arq6l2lBFBc/BCVVz8FFEAlKqvemEKh/LNSUanyfaG8sMkZ1gxag84unZt8HCIiIiLSnSYVjdWGDBmCIUOGNMehiKgh8mLg5Geq2xKPA3fPAWZ2gFcfjQ6TLqlAmnGNXwFlOUATH+X7T+//NKq9h7VH005IRERERDrXLEUjEenIwyQgLw0oyQMKyoDkU0BxDnBilfr2JTnAjQOaHduj8c8I1ifUPRTT2k9r1mMSERERkfBYNBI9yc7/CER9CUcMg+39POD2EqETtXhOTk58BlsL9vb2amfRpubHsSYiIn3TqGj09fUFUPUMY0REhMo2TUkkEty+fbuR8YgIAMbgCCTQ7cQX/+c+FJ0Cp0ACCSSSP78ggVRStayGVCL9a9+ff0ohVX5vZWKl03z6MnbsWJVJvUgzfn5+aNOmjdAxRMHf3x9t27YVOgYREYmIRkVjcnIygKq1oR7fpim+CSPS3i6Mhh3yMBwn1DfwDgWsXYGyoj9vT5UAQxp4vjB1J1D+17qJQa17ItQ9tPlCG6jt27fDxcWFz2k3UlxcHCIiIvDqq68KHaXFq15yY8GCBUJHISIikdCoaExKSgIAyGSyWtuISPcKYQEZygEjE8DUutb+konfocDUolHHrLj/u0rRCGu3psZsEQoLC1FY2PSZYcWG46Y/HGsiItI3jYpGb29vjbYRkY6FTEbemE8w9pexKpsf7hkjUCAiIiIiaum0ngjnxIkTsLW1RadODS/MffXqVeTk5KB///7ano5IlH4rvovjzo4wzZFBYWSKM0XXkXfiDTwseSh0NCIiIiISCa2LxoEDB6Jfv344fvx4g20XLlyIkydPory8XNvTEYlSgjwHv1lZYkC+FEXGxvij/AGQdkroWEREREQkItKmdFYoNJ/NsTFtiUhVpaQSFZIKoWOIgpGREYyMjISOYXA4bvrDsSYiIn3TyzqN2dnZMDc318epiFqkS46X6i0aP3zqQ7hYuEBeKUeBvACWxpYIcQpp1DksZZZNjdkijB07FqampkLHMDgdOnSAq6ur0DFEoWPHjnB3dxc6BhERiYjGRWNeXh5ycnJUtpWWluLOnTt1XkUsLi7G8ePHERsbq9Gzj0SkXqviVigxKoG5ySMMDJ5Va/8gz0EtZp1Eod26dQu2trYIDAwUOopBefjwIa5evcpiRg+ys7MRExOD1q1bCx2FiIhEQuOi8fPPP8fy5ctVtp0/fx4+Pj4a9Z87d26jghHRXzwKPVBkXAQ3uzIs7LpQ6DgtWmxsLFxcXFg0NlJqaiouXLiAUaNGCR2lxUtNTcXFixcxYsQIoaMQEZFIaFw02tnZwcvLS/l9amoqTExM6rwdSSKRwNzcHL6+vpg+fTr+9re/NT0tERERERER6ZXGRePChQuxcOFfVzikUil69OiBEydO6CQYERERERERCU/riXA2bNiAVq1aNWcWIiIiIiIiesJoXTQ+99xzzZmDiIiIiIiInkB6WXKDiJom1SoVpdJSuAkdRAQ6deoEW1tboWMYHB8fH/Tq1UvoGKLg4+ODnj17Ch2DiIhEROuisbELC0skEpSXl2t7OiJRyzbNRrmkHADXUtQ1Ly8vmJmZCR3D4Nja2sLf31/oGKJgZ2eHdu3aCR2DiIhERKptR4VC0aivysrK5sxNJCqdH3ZGYC6XgNCH3377DSdPnhQ6hsGJjY3Fpk2bhI4hClevXsWWLVuEjkFERCKiddFYWVlZ51dBQQEuX76Mf/7zn7CwsMB3333HopGoCSQKCaQKrf+5UiNUVFTw95UWOG76U/3fWiIiIn3RyTONFhYW6NixI7766it0794dzz//PDw9PbnoMxERERERkYHR+aWL5557Dq6urli5cqWuT0VERERERETNTC/3u7m5ueHy5cv6OBURERERERE1I50vuVFYWIjr1683erZVoidOaX7Vl7Yq5MDV7VWvrV0BiaThPkXZAIASoxKUGJVof27SmLW1NaysrISOYXA4bvpjZWXFsSYiIr3SadEYHx+P1157DUVFRRg5cqQuT0WkvT9+AJJPN9zuWrjuszzO3g6ws8FZ57MAgDZw1n8GkZk2bZrQEQxSQEAA2rdvL3QMUQgODkZQUJDQMYiISES0Lhp9fX3r3KdQKJCVlYXi4mIoFApYWVnhww8/1PZURLp194IwBWEjdH/QHUXGRYBLltBRWrzw8HA4Ozujf//+QkcxKDdu3MDhw4fxz3/+U+goLV5CQgKOHz+Ol156SegoREQkEloXjcnJyQ22sbW1xYgRI/Dee+/xE2iiJrAst4QEEgAsGnXt0aNHkMlkQscwOLm5uXj06JHQMUSBY01ERPqmddGYlJRU5z6JRAJLS0s4Ojpqe3givYmrKECGhXmD7S6bmuKsuRl85XJ4y+WNPk+JRIIf7WwBAF1KSiABAKkMMDats89dSSWAGuuxmfA5JiIiIiLSL62LRm9v7+bMQSSYTWXp2NtK82cF401NmnzOS2ZmNb4r17yja4cmn5uIiIiIqDH0suQGERERERERGaZmmT31+PHjOHjwIG7cuIH8/HxYW1vD398fw4cPx8CBA5vjFESippAooJAohI4hClKpFBJNlkMhFRw3/ZFIJBxrIiLSqyYVjTdv3sSzzz6Lc+fOAaiaNbWaRCLBxx9/jB49euCnn36Cv79/05IS6Ym1xBj2Vq3r3J9TmoO8sjwAwFCvoVqdI/JOJCoUFfhX53/B3sy+wfbFD4shNZaivQcnlNK1ESNGwNy84WdcSVVwcDDs7OyEjiEKHTp0gJOTk9AxiIhIRLQuGu/cuYOnnnoKWVlZMDMzw5QpUxAYGIhWrVohMzMT8fHx2LFjB86dO4d+/frh/Pnz8PT0bM7sRDox2cIHr0/6RegYKq5evQoLCwv4ufkJHaXFy8rKgo2NDRwcHISOYlAKCwuRnp4OPz/+HdW16rFu27at0FGIiEgktC4a3377bWRlZWHYsGH43//+B2fn2hOJfPbZZ3jmmWdw6NAhLF26FBs3bmxSWCKxunDhAuzs7PiGXA/Onz8PFxeXeteipdoSExNx4sQJrm+pB7dv38apU6fQr18/oaMQEZFIaD0RzsGDB2Fubo6tW7eqLRgBwMnJCVu3boW5uTl+//13rUMSERERERGRMLQuGgsKChAcHAx7+/qfx7K3t0dwcDAKCgq0PRUREREREREJROuisW3btsjKytKobVZWFm+rIyIiIiIiMkBaF43/+Mc/kJKSgh07dtTbbufOnUhJScE//vEPbU9FREREREREAtF6IpyXX34ZCQkJ+Pvf/47o6GjMnz9f5Wri7du38c033+Cbb77B/Pnz8a9//atZAhOJkb+/P6ysrISOIQrt27fn0hFacHd3R4cOHYSOIQoeHh4ICQkROgYREYmIRkVjfbMIVlZWYs2aNVizZg1kMhkcHR2RnZ0NuVxedQJjY/z2229o27Ytbt++3TypiUQmJCQExsZNWlaVNNShQwfIZDKhYxgcFxcX9OnTR+gYotCqVSv07t1b6BhERCQiGt2empycXOdXeXk5FAoFFAoFysrKkJGRgbKyMuU2uVyubEtE2tm3bx+OHj0qdAxR2LNnD44fPy50DIMTGxuLH3/8UegYonDlyhWEhYUJHYOIiEREo0sXSUlJus5BRPWQy+UoLy8XOoYocKy1w3HTn/Lyco41ERHplUZFo7e3t65zEBERERER0RNI69lTiYiIiIiIqOVj0UhERERERER10uj21MGDBwOouk11w4YNKts0JZFIcOTIkUbGIyIAMDc3h7m5udAxRIFjrR2Om/5wrImISN80KhqPHTsGAAgICKi1TVMSiaRR7YnoL5MmTYJUyhsD9GHq1Kkcay0EBwerrNVLutOhQwe0b99e6BhERCQiGhWNkZGRAAALC4ta24hI944cOQIbGxuEhoYKHaXFO3ToEBwdHbkOXiMlJyfj+PHjmDNnjtBRWrzbt28jOjoazz77rNBRiIhIJDQqGgcMGKDRNiLSjXv37qGkpEToGKKQkZGBiooKoWMYnAcPHiAtLU3oGKKQnZ3NsSYiIr3S+h6s5cuXY8WKFSgrK2vOPGrt3r0bL774Irp16wY3NzeYmJjAzs4Offv2xZo1a+rNEB0djfHjx8PZ2Rnm5uYICgrCihUrGnwDHh8fj2eeeQZubm4wMzND27ZtsWjRIuTk5NTbLy0tDS+88AI8PT1hamoKLy8vvPjii/wPPBERERERGSSti8YVK1Zg69atMDExac48an366adYu3Ytrl27BnNzc3Tq1AlWVlaIjo7GK6+8gr59+6ot5jZt2oR+/fph7969MDU1RWBgIG7duoVly5ahf//+KCoqUnu+yMhIdOvWDZs3b0ZFRQWCg4Nx7949fPbZZ+jWrRvu37+vtl9cXBw6duyIdevWIT8/HyEhIcjLy8PatWvRqVMnJCQkNOewEBERERER6ZzWRWOrVq1UnnHUpXnz5iEyMhL5+flITEzEH3/8gbt37yI6OhoeHh64cOEC3n77bZU+ycnJmDt3LioqKvDJJ5/gzp07uHjxIm7evIn27dvjjz/+wJIlS2qdKz8/H9OnT0dxcTEWLFiAtLQ0XLhwAampqQgNDUViYiLmzp1bq19FRQWmTp2Khw8fYvLkyUhPT8eFCxeQlpaGSZMmITs7G9OnT0dlZaXOxomIiIiIiKi5aV00Dh06FNeuXUNubm5z5lFr9uzZGDhwIGQymcr23r17Y/Xq1QCqbmGtadWqVSgtLcXw4cOxePFi5eyt3t7e+PHHHwEAa9eurXXV8LvvvkNWVhYCAwOxevVq5TkdHR2xefNmGBsbY//+/bh48aJKv/DwcMTFxcHR0REbNmxQFtSWlpYICwuDo6Mjrl69ij179jTPoJCoSCQSzkCsJxxn7XDciIiIWi6ti8Z3330XZmZmmDNnDoqLi5szU6NULwNS81ZThUKBX375BQDUXhXs27cvAgICIJfLaxVx4eHhAKoKVSMjI5V9Xl5eGDp0KABg586davtNmzYN1tbWKvusra0xdepUAMCOHTsa9wMSoWriqV69egkdQxQGDRqEnj17Ch3D4AQEBGD8+PFCxxCFoKAgjB07VugYREQkIhrNnqrOiRMn8NJLL2HVqlXw8/PD5MmTERgYCEtLyzr76GJ68OjoaABA165dldtSU1ORkZEBAHUuURAaGoqEhAScPXsWL7zwAgCgvLwcFy5caLDf77//jrNnz6psP3PmTIP9vvvuu1r9iDQhl8thbKz1P1dqBLlcjvLycqFjGJzKykqUlpYKHUMUKisr9TIJHRERUTWt34XOnj0bEokECoUCGRkZ+Prrrxvs01xFY0VFBTIyMrB37168+eabsLS0xMqVK5X7b968CQAwNTVF69at1R7D19dXpS1Q9RykXC5X2a9Jv7KyMqSmpmrUr/ocj99qW620tFTljVdeXp7adiQup0+fhp2dHTw9PYWO0uKdPHkSLi4u8PDwEDqKQbl+/ToiIiLQvXt3oaO0eAkJCYiMjFT5sJSIiEiXtC4an332Wb0/w/LFF1/g1VdfVdk2YcIErFixAiEhIcptjx49AgDY2dnVmdHe3l6l7eOvq/dr0i83N1c5wU1D/SorK5GXlwdHR0e17VauXIn33ntP7T4iIiIiIiJ907poDAsLa8YYmnF3d0doaCjkcjlSUlJw//59REZGYsuWLVi+fLnyGcTqNRjrWw7E1NQUAFSex6y5dmNdfZva7/G+j3vrrbfw2muvKb/Py8vj1SUiIiIiIhKMQT0kNXXqVOWEMgBw9uxZvPjii/jwww/x8OFDfPvttwAAMzMzAKj3mY/qW0DNzc2V26r7Vfet+X1j+tV3vsf7Ps7U1FSlwCQiIiIiIhKS1rOn+vr6YsaMGRq1nTlzJtq2bavtqerUq1cv/PbbbzA1NcXatWuRkpIC4K9bQXNycqBQKNT2rb69tObtpDVf17z9tKF+tra2kEqlGvWTSqWwsbFp+IcjIiIiIiJ6AmhdNCYnJyM9PV2jtvfu3UNycrK2p6pX69at0blzZ1RWVuLKlSsAgHbt2gGourpXV8bExESVtgDg4+OjnKCmer8m/UxMTODl5aVRv5rnINKUl5cX3N3dhY4hCt7e3hxrLbRq1UonHw5SbRxrIiLSN62LxsYoKSnR6XIB1dPjV//p5eUFV1dXAFWzTqpTvb3m2nfGxsbK2ega06/m943tR6SJPn36oFOnTkLHEIXQ0FCVibVIMx4eHhgzZozQMUTBy8sLI0eOFDoGERGJiM6LxgcPHiAuLg6tWrXSyfGTk5OVVxir31RLJBJMnDgRALB+/fpafaKiopCQkACZTIZx48ap7Js0aRKAqol+KioqVPalpqbi8OHDAIDJkyer7bd9+3bk5+er7MvPz8eOHTsAAFOmTGn8D0mit3v3bhw5ckToGKKwa9cuHDt2TOgYBic2Nhbff/+90DFE4erVq/jhhx+EjkFERCKicdG4ceNGDB48WPkFADExMSrbHv/q06cP2rZti4KCAgwbNkyrgBcuXMA777yj9rbP33//HaNGjUJ5eTlGjx6tcrvO4sWLYWJigoiICKxatUr5bGNKSgqef/55AMC8efOUVySrvfTSS3ByckJ8fDxee+015bqN2dnZmDVrFsrLyzFq1Ch069ZNpd/kyZMREBCA7OxszJkzB0VFRQCAwsJCzJkzB9nZ2QgJCcGECRO0GgcSt8fX7yTd4Vhrh+OmPxxrIiLSN43vGU1OTlb59F0ikSA3N1ejT+QHDx6Mjz76SJt8yM/Px/Lly7F8+XK4urrCw8MDZWVlSE1NRU5ODgCgR48e2Lhxo0q/Nm3aYN26dZgzZw6WLFmCNWvWwMXFBbGxsZDL5ejWrRtWrVpV63w2NjbYunUrnn76aXz55ZfYsmULvLy8EB8fj6KiIvj4+ODHH3+s1c/IyAg7duxA//79sWvXLhw+fBh+fn64desWcnNz4eDggG3btiknzCEiIiIiIjIEGheNs2fPxsCBAwEACoUCgwcPRocOHfDll1+qbS+RSGBubo42bdrAyclJ64CdOnXCmjVrcOTIEVy7dg0JCQkoKyuDo6Mj+vTpg2nTpuFvf/ub2mcmn332Wfj5+WHlypWIiopCXFwcfH19MXPmTLzxxhtql9QAgCFDhuD8+fN4//33cfToUcTExMDd3R0TJ07E0qVLVWZOrSkkJARXrlzB8uXLceDAAcTExMDZ2RnTpk3DsmXL4OHhofU4EBERERERCUHjotHb2xve3t7K7/v3749OnTphwIABOglWzd7eHgsWLMCCBQu06t+3b1/s27ev0f2Cg4OxZcuWRvfz9PTEunXrGt2PiIiIiIjoSaT1lKacKIJIf0xMTGBiYiJ0DFHgWGuH46Y/HGsiItI3nayDkZeXhwMHDiA9PR1du3bV+dVIopZuwoQJMDIyEjqGKEyaNEmnSwS1VB06dICPj4/QMUShY8eO8PX1FToGERGJiNazsmzbtg1du3atNe13QkICQkJCMGvWLCxatAiDBw/G7Nmzm5qTSNT++OMPxMbGCh1DFM6ePYv4+HihYxictLQ0RERECB1DFO7evcsleIiISK+aVDReuXIF/fv3V9n+yiuv4O7du/D19cX48eNhZWWFn3/+Gb/99luTwxKJVVJSEu7cuSN0DFHgWGvn3r17uHHjhtAxRIFjTURE+qZ10XjlyhU4ODjA399fuS0jIwOHDh2Cl5cXYmJiEB4ejn379kGhUODrr79ulsBERERERESkP1oXjVlZWfDy8lLZFhkZCYVCgVmzZimXs+jfvz+8vb15uxcREREREZEB0rpoLCsrQ0VFhcq2kydPQiKRYNCgQSrbW7VqhYyMDG1PRURERERERALRumh0d3fH7du3UVRUpNz2+++/w9jYGKGhoSpt8/PzYWtrq31KIiIiIiIiEoTW88oPHToUP/zwA15++WW8+uqr2LlzJ1JSUjBixAhYWFgo2xUXF+PmzZvo1KlTswQmEqM+ffrA3Nxc6Bii0LdvX1hZWQkdw+D4+/tDoVAIHUMU/P39uQQPERHpldZF49tvv43w8HCEhYUhLCwMCoUCMpkM7733nkq7ffv2oby8HP369WtyWCKxMjc3Vz4nTLplYWHBsdaCsbExrK2thY4hCjKZjB9sEBGRXml9e6qXlxfOnz+P+fPnY/jw4Zg3bx7OnTuHnj17qrQ7duwYOnXqhPHjxzc5LJFYRUZGIjo6WugYonDkyBGcPXtW6BgGJz4+Hr/88ovQMUTh2rVr2LNnj9AxiIhIRLS+0ggA3t7e+Oqrr+pt88033zTlFEQEQKFQ8NY/PeFYa4fjpl8cayIi0ietrzQSERERERFRy8eikYiIiIiIiOqk0e2pvr6+AAA/Pz9ERESobNOURCLB7du3GxmPiIiIiIiIhKRR0ZicnAwAKjMKVm/TlEQiaVR7IvqLi4sLbGxshI4hCi4uLnBychI6hsFxcHCAm5ub0DFEwcHBAa6urkLHICIiEdGoaExKSgJQNc3349uISPeGDx/OD170ZNSoUZBKeed+Y7Vt2xaenp5CxxAFPz8/eHt7Cx2DiIhERKOiUd1/nPgfLCL9CQ8Ph52dHUaMGCF0lBZvx44dcHFxwdChQ4WOYlCuXbuGiIgIvPbaa0JHafFiY2MRGRmJhQsXCh2FiIhEoklLbhCRfhQVFcHExEToGKJQVFSEoqIioWMYHI6b/nCsiYhI33gPFhEREREREdVJoyuNgwcPbvKJJBIJjhw50uTjEBERERERkf5oVDQeO3aszn3Vk3MoFIp693ESDyIiIiIiIsOjUdEYGRmpdvupU6ewfPly2Nvb4/nnn0dgYCBatWqFzMxMxMfH48cff8SjR4+wbNkyhIaGNmtwIjExNjaGsTEfQdYHjrV2OG76w7EmIiJ90+i/OgMGDKi17cKFC/jggw8wefJkbNiwAaamprXavPPOO5gzZw7ef/99nD59uulpiUTq6aef5ptEPRk3bhwnHdJCSEgIWrduLXQMUejYsSM8PDyEjkFERCKi9UQ47777LmQyGX744Qe1BSMAmJiYYN26dZDJZHjnnXe0DkkkdtevX0dycrLQMUQhPj4eKSkpQscwONnZ2Th//rzQMUQhKysLFy9eFDoGERGJiNZFY3R0NNq3bw8LC4t621lYWKB9+/aIiorS9lREohcfH49bt24JHUMU4uPjcfv2baFjGJw7d+7g8uXLQscQhTt37uDKlStCxyAiIhHRumgsKSlBenq6Rm3T09NRUlKi7amIiIiIiIhIIFoXjR07dkRGRga+++67ett9//33SE9PR8eOHbU9FREREREREQlE66Jx8eLFUCgU+Ne//oWZM2fi+PHjyMzMhEKhQGZmJk6cOIFZs2bhn//8JyQSCRYvXtycuYmIiIiIiEgPtJ6OceLEifjoo4/w9ttvY/v27di+fXutNgqFAlKpFB988AEmTpzYpKBERERERESkf02aw3/JkiUYPnw4PvvsMxw6dAiZmZnKfS4uLhg2bBheffVVdO3atclBicSsS5cuDU46Rc2ja9eusLGxETqGwWnTpg3X49UTX19flJaWCh2DiIhEpMkLv3Xu3Bk///wzACA3NxcFBQWwsrKCra1tk8MRURVXV1euHagnbm5uMDMzEzqGwbG2toa3t7fQMUSBY01ERPqm9TON6tja2sLd3Z0FI1EzO3jwIE6ePCl0DFE4cOAATp8+LXQMgxMbG4utW7cKHUMUYmJisG3bNqFjEBGRiDRr0UhEulFZWYnKykqhY4gCx1o7HDf94VgTEZG+sWgkIiIiIiKiOrFoJCIiIiIiojqxaCQiIiIiIqI6sWgkMgC2trZcBkJPONbasbGx4bjpCceaiIj0rclLbhCR7k2aNEnoCKIxdepUoSMYpICAAAQEBAgdQxSCgoIQFBQkdAwiIhIRXmkkMgC7du3CkSNHhI4hCtu3b0dkZKTQMQxOQkICvvzyS6FjiMK1a9fw9ddfCx2DiIhEhFcaiQxAXl4epFJ+xqMPeXl5MDMzEzqGwcnLy0NeXp7QMUQhPz+fY01ERHqlUdG4fPnyJp9IIpHgP//5T5OPQ0RERERERPqjUdH47rvvQiKRQKFQ1NonkUga7K9QKFg0EhERERERGSCNisZ33nlH7fbs7Gx8//33kMvl6N27NwIDA9GqVStkZmYiPj4e0dHRMDExwYsvvggHB4dmDU5ERERERES6p3XRmJmZiZ49eyIgIABhYWHo0qVLrTaXL1/Gc889h7179+LcuXNNT0skUlKplM806gnHWjscN/3hWBMRkb5pPRHO0qVLkZ6ejhMnTsDLy0ttm86dO2Pv3r1o164dli5diu+//17roERiNnLkSMhkMqFjiMLo0aNhamoqdAyDExISAkdHR6FjiEKHDh3g4uIidAwiIhIRrT+q/O233xASElJnwVjN29sbISEh2L9/v7anIhK9jIwMPHjwQOgYopCeno7s7GyhYxic/Px8JCUlCR1DFPLy8pCcnCx0DCIiEhGti8bs7GwUFxdr1LakpIRvwoia4NKlS7h27ZrQMUTh4sWLiIuLEzqGwUlKSkJUVJTQMUQhKSkJ0dHRQscgIiIR0bpo9PHxwY0bNxr8D1d0dDQSEhLg4+Oj7amIiIiIiIhIIFoXjXPmzIFCocDYsWPx/fffo6SkRGV/SUkJ1q5di3HjxkEikWDOnDlNDktERERERET6pfVEOK+//jpOnTqFX3/9FfPnz8drr70Gb29vODs7IysrC6mpqSguLoZCocCYMWPw+uuvN2duIiIiIiIi0gOtrzQaGRlh9+7d+OKLL+Dp6Yni4mIkJCTg5MmTSEhIQFFRETw8PPD5559jz549MDIyas7cREREREREpAdaX2kEqtaKWrBgARYsWID4+HjcuHEDBQUFsLKygr+/PwICAiCRSJorK5FoBQYGwtLSUugYohAUFARbW1uhYxgcT09PdO7cWegYosCxJiIifWtS0VhTYGAgAgMDm+twRFRD+/btYWzcbP9cqR4BAQEwMTEROobBcXR0RLdu3YSOIQrOzs7o0qWL0DGIiEhEmu1daHp6OtLS0lBcXIz+/fs312GJCMCvv/4KOzs7jB07VugoLd7evXvh4uKC0aNHCx3FoMTGxiIiIgJvvvmm0FFavKtXryIyMhKLFy8WOoog5HI5KioqhI5BRNRoRkZGkMlkQsfQSpOLxm+//RarV69GYmIiAEAikaC8vFy5//XXX0d0dDS2bt0KLy+vpp6OSJTKy8tV/l2R7nCstcNx0x+xjnVeXh4ePHiA0tJSoaMQEWnN1NQUTk5OsLGxETpKo2hdNCoUCsyYMQM7d+4EULVu44MHD1BQUKDSrlevXvj8888RHh6OV155pUlhiYiISHzy8vKQlpYGKysrODk5QSaTcc4EIjIoCoUCcrkcubm5SEtLAwCDKhy1LhrXr1+PHTt2IDg4GJs3b0aHDh3Qr18/REVFqbQbM2YMjIyMsH//fhaNRERE1GgPHjyAlZUVPDw8WCwSkcEyNzeHtbU17t69iwcPHhhU0aj1khvr16+HVCrFjh070KFDhzrbWVpaom3btsrbV4mIiIg0JZfLUVpaCltbWxaMRGTwJBIJbG1tUVpaCrlcLnQcjWldNF67dg2+vr4ICAhosK29vT0yMjK0PRWR6FlYWMDCwkLoGKLAsdYOx01/xDbW1ZPeGOrkEUREj6v+fWZIk3ppfXtqZWUlTE1NNWqbl5encVsiqm3y5Mn8hF1Ppk6dCqlU68/TRCs4OBj+/v5CxxCFkJAQjT6wbWn4O5CIWgpD/H2m9TujNm3a4NatW7UmvnncvXv3cP36da7hSNQEBw8erPW8MOnGgQMHcPbsWaFjGJzbt2/jp59+EjqGKNy6dQubNm0SOgYREYmI1kXjuHHjUFpaimXLltXb7vXXX4dCocDEiRO1PRWR6GVmZuLBgwdCxxAFjrV2Hj58iHv37gkdQxQ41kREpG9aF42LFi1C69atsWbNGkydOhW///47SkpKAABJSUnYu3cvhg4dii1btqBNmzaYP39+s4UmIiIiIiIi/dC6aLS3t8fBgwfRpk0b7Nq1C2PGjMHFixcBAH5+fpg4cSKOHj0KX19f7N+/H5aWls0WmoiIiIhqO3bsGCQSCd59912V7QMHDtTJc1QSiQQDBw5s9uNqKywsDBKJBGFhYQZ9DqInTZNmewgODsbVq1exZs0aDBgwAA4ODjAyMoKtrS369OmDTz/9FFeuXEH79u2bKy8RERERkc4kJydDIpFg9uzZQkchemJoPXtqNQsLC7z88st4+eWXmyMPEakhkUgMcqYtQ8Sx1g7HTX841vQkiY+PF9USMAAwceJE9O7dG25ubkJHIdIbrYvGEydOwNbWFp06dWqw7dWrV5GTk4P+/ftrezoiURs8eDCXrdGTIUOGiO4NUHMICgriYwh6EhQUBBsbG6FjEAGAKJd/sbW1ha2trdAxiPRK69tTBw4ciAULFmjUduHChRg8eLC2pyISvaKiIuVEU6RbHGvtyOVy5OfnCx1DFDjWf6qsBAofGM5XZaVWP2ZZWRm++uorjBgxAp6enjA1NYWLiwsmTZqES5cuNfOg/mXXrl0YMGAAXFxcYGZmBk9PT4wcORK7d+9WaafumcbZs2dDIpEgMTERn376Kfz9/WFubo6goCBs3boVQNXf42XLlqFNmzYwMzNDx44dcfDgwVo5fHx84OPjozZjY57T/OWXXzBz5kz4+fnBwsICtra26NevH3bt2qXSLiwsDG3atAEAbNy4UXllXyKR4NixY8o2dT3TGBUVhTFjxsDBwQFmZmYICAjAu+++i6Kiolptq8cuKysLzz//PFxcXGBubo7evXsrz1VTRkYGFi5ciHbt2sHc3BwODg7o0KED5s+fj7y8PI3GgUhbTbo9VaFQ6KQtEamKjo6GnZ0dvL29hY7S4kVFRcHFxQVeXl5CRzEoN27cwKFDh9CrVy+ho7R4N27cQGRkJHr27Cl0FGEVPwRWtRU6heYW3wYsnRrd7eHDh3jllVfQr18/jB49Gvb29khMTMTevXtx4MABnDhxAj169GjWqN9++y3mz58PNzc3TJw4EY6OjsjIyMC5c+ewe/duTJgwQaPjvPbaazh79izGjh0LIyMjbN26FbNmzYK9vT2+/vprxMbGYvTo0SgpKcHmzZsxbtw4JCQkKIu25vTWW2/BxMQETz31FNzc3JCVlYW9e/diypQp+PLLL5WPWXXu3BkLFy7EmjVr0KlTJ5Wfta7itdquXbswY8YMmJiYYPr06XBxccHhw4fx3nvvISIiApGRkbXuGsrJyUFoaChsbGzwzDPPIDMzE9u2bcOIESNw4cIFhISEAKj6QDM0NBTJyckYPnw4Jk6ciLKyMiQmJiIsLAxLlizhHQikU01+plET2dnZMDc318epiIiIiFoMe3t7pKamwt3dXWX7tWvX0Lt3b/z73//GoUOHmvWcP/zwA0xMTHDlyhU4Ozur7MvOztb4OHFxcbh69aryGLNnz0bv3r0xY8YMhISEICYmRnlb+4gRIzB9+nR88cUXWLNmTfP9MH/67bff4Ovrq7KtoKAAffv2xX/+8x/MnTsXFhYW6Ny5M1555RWsWbMGnTt3rjULbV3y8/Mxb948GBkZITo6Gh07dgRQddHkb3/7GzZv3oxVq1Zh6dKlKv2uXLmC+fPn46uvvoJUWnUD4ODBgzFv3jz897//xXfffQcAOHLkCJKSkvDqq69i9erVtc7NR1hI1zS+PTUvLw+pqanKLwAoLS3FnTt3VLbX/Lp+/TrWrl2L2NhYtGvXTmc/BBEREVFLZGpqWqtgBKpmsB80aBBOnDgBuVze7OeVyWSQyWS1tjs6Omp8jLffflul6OzVqxd8fX2Rk5ODDz74QOU56MmTJ0Mmk+HKlStNC16HxwtGALCyssLs2bORm5uLP/74o0nH3717N3JycvD8888rC0ag6hbUjz76CMbGxmpvZ7W0tMTHH3+sLBgB4LnnnoOxsbHaTOouwlhbW8PExKRJ+YkaonHR+Pnnn6NNmzbKLwA4f/48fHx8VLbX/AoKCsL//d//AQDmzp2rVUCFQoFTp05h8eLF6N27N+zs7GBiYoLWrVtj8uTJiIyMrLd/dHQ0xo8fD2dnZ+X99CtWrGjwmaX4+Hg888wzcHNzg5mZGdq2bYtFixYhJyen3n5paWl44YUXlM8deHl54cUXX0RaWlpjf3QiIiIiXL58GbNmzYKXlxdMTEyUz9jt27cPZWVlePDgQbOeb9q0aSgsLERISAgWLVqEX3/9tcH3P+p06dKl1rbqGUc7d+6sst3IyAguLi46e7+UmZmJ1157DYGBgbCwsFCO4euvvw4ASE9Pb9Lxq58vVbdmpaenJ9q2bYvbt2/Xeh65Xbt2sLKyUtlmbGyMVq1aqYx5//794erqipUrV2LMmDH45ptvcPXqVT7+RXqj8e2pdnZ2Ks/4pKamwsTEBK6urmrbSyQSmJubw9fXF9OnT8ff/vY3rQIePXoUQ4cOBQBIpVL4+fnB0tISN2/eRHh4OMLDw7F06VKsWLGiVt9NmzbhueeeQ0VFBdzd3eHp6YnY2FgsW7YM+/btw7Fjx9TOkhgZGYkxY8aguLgYzs7OCA4ORkJCAj777DP88ssviIqKQqtWrWr1i4uLQ79+/fDw4UPY2toiJCQEt2/fxtq1a7Fr1y6cOnVKlLOMERERNTtzh6rnBA2FuYNW3aKiopSTCQ4fPlxZZEgkEuzevRtXrlxBaWlpcybFkiVL4OjoiO+++w6rV6/GZ599BmNjY4wePRpffPGFxs8cqnvGztjYuN59urhq+vDhQ/To0QOpqakIDQ3F0KFDYWdnByMjI1y+fBl79uxp8hhWT0Sj7v0hALi6uuL69evIy8uDtbW1cntds7AaGxujoqJCpV10dDTeeecd7Nu3D7/99hsAwMPDA2+99Rbmz5/fpPxEDdG4aFy4cCEWLlyo/F4qlaJHjx44ceKEToJVUygU8PPzw2uvvYYZM2bA3t4eQNVsYu+++y5WrlyJ999/H7169cLTTz+t7JecnIy5c+eioqICn3zyCRYtWgSJRIKUlBSMGDECf/zxB5YsWYL//ve/KufLz8/H9OnTUVxcjAULFuDTTz+FTCZDdnY2xo8fj9OnT2Pu3Ln49ddfVfpVVFRg6tSpePjwISZPnoyffvoJFhYWKCwsxLPPPovw8HBMnz4dly5dUrkFgUgTbdq0UfmPDOmOr6+v8vcMac7V1RXt27cXOoYouLq6wt/fX+gYwpNKtZpYxtB88MEHKC0txalTpxAaGqqy78yZMzq5nVMikWDevHmYN28esrOzcfLkSWzZsgXbt2/HzZs3ERMTAyMjo2Y/rzpSqRRlZWVq9+Xm5mp0jPXr1yM1NRXvv/8+3n77bZV9H330Efbs2dPknNVF8P3799Xur97elMlqfHx8sHHjRlRUVCAmJgYRERH48ssv8c9//hP29vaYOXOm1scmaojW1cuGDRvw73//uzmzqNWzZ0/Ex8fj//7v/1TeyJmYmODDDz/EqFGjAADr1q1T6bdq1SqUlpZi+PDhWLx4sXJKZm9vb/z4448AgLVr19b6x/3dd98hKysLgYGBWL16tfJ+fkdHR2zevBnGxsbYv38/Ll68qNIvPDwccXFxcHR0xIYNG5RXMC0tLREWFgZHR0dcvXq1WX4xkfh0794dQUFBQscQhZ49eyIwMFDoGAbH3d1deVcI6ZaHhweGDBkidAzSk9u3b8PBwaFWwVhUVFTrvYguODo6YsKECdi2bRsGDx6M+Ph43Lp1S+fnrWZvb4/MzEyUl5erbC8sLMTNmzc1Osbt21VXpMeNG1dr38mTJ2ttqy6Ia17pa0j1rbjqlspIS0vD7du34evr2ywfABsZGaFz585YsmQJtmzZAgDYu3dvk49LVB+ti8bnnnsOI0eObM4satnY2ChvZVBn2LBhAKqmIK+mUCjwyy+/AFD/LGXfvn0REBAAuVxeq4gLDw8HUDXD1+Ofonl5eSnfFO3cuVNtv2nTptX6hWBtbY2pU6cCAHbs2FHnz0JUlz179uDo0aNCxxCF8PBwtf/Rp/rFxMTU+vCOdOPq1atYv3690DFIT7y9vfHo0SNcu3ZNua2iogKLFi1CVlaWTs558ODBWkWaXC7Hw4cPAaifjEVXunfvDrlcjk2bNim3KRQKvPXWWygsLNToGNXLVZ06dUpl++bNm5W3edZkb28PiUSCu3fvapxz/PjxsLW1xYYNG1T+v6rOKpfLMXv2bI2P97jY2FikpKTU2l598YOrFJCu6WXJDV2qntCm5j+W1NRUZGRkAECtT+aqhYaGIiEhAWfPnsULL7wAACgvL8eFCxca7Pf777/j7NmzKtvPnDnTYL/vvvuuVj8iTZSVldV5ew41L461djhu+sOxFpeXX34ZEREReOqppzBt2jSYmZnh2LFjSEtLw8CBA3XyIdf06dNhYWGBp556Ct7e3pDL5Th06BDi4uIwffp0va5j+69//QsbNmzAvHnzcOjQITg7O+PkyZPIyclBp06dNLo99+9//zs+/vhjvPzyy4iMjIS3tzeuXr2Kw4cPY9KkScoP/qtZWVkpH8GaM2cO2rVrB6lUqpyMSB0bGxusW7cOM2fORK9evTB9+nQ4OzvjyJEjOH/+PHr27InFixdrPQ6HDx/G66+/jtDQUAQEBMDR0VG5Xqe5uTn+9a9/aX1sIk00+eG6n3/+GSNHjoSbmxtMTU1hZGSk9qu+q4XaUigUyit3NYu16tsVTE1N0bp1a7V9q6dernlrQ3JysvIBbHVTM9fVr6ysTLkMSUP9ap6DiIiIqD5PP/00du7cCV9fX/zvf//D5s2bERAQgHPnzimvoDW3lStXokePHjh37hz++9//4n//+x+sra3x/fff43//+59OzlmXDh064Pfff0e3bt2wc+dO/PzzzwgKCsLp06dhZ2en0TE8PDxw/PhxDBkyBIcPH8b333+P0tJSREREYOzYsWr7VL+/3b17N5YuXYq33noLiYmJ9Z5n6tSpiIyMRP/+/REeHo7PP/8ceXl5+M9//oOjR4/CzMyssT++0ogRI/Dyyy+joKBAeezz589jxowZuHDhArp27ar1sYk0oXUlV1FRgYkTJ2L//v0aTferiymB161bh0uXLsHExASvvPKKcvujR48AVM34Wv0s4+Oqn4+sbvv467omwlDXLzc3F5WVlRr1q6ysRF5eXp3rHJWWlqrM4FU9GxcRERGJ0+TJkzF58uRa28PCwmqt/Tdw4EC177kac0Xy//7v/5RLpjVE3bnU5dIkR3JystrtgwcPVt7R1dCxZs+erfY20E6dOuHgwYNqj6+uvb+/P/bv319n+7puNe3Xrx/69eundt/j6ntv/PhYBAYG4osvvtDouES6oPWVxm+++Qa//vor+vfvj1u3biE0NBQSiQRyuRyJiYn45Zdf0Lt3b5ibm+OHH35QFlXN5eLFi8rZXN9//320bdtWua/6ltX6Fjo1NTUFABQXF9fqV1/fpvZ7vO/jVq5cCVtbW+WXp6dnnW2JiIiIiIh0TeuicdOmTTAyMsKGDRtUbsk0MjKCj48Pxo8fj6ioKMybNw8vvPACDh061CyBASApKQlPP/00SkpKMGvWLCxatEhlf/Xl//qe+ai+mlfzWciatw3U1bep/R7v+7i33noLubm5yq87d+7U2ZbEw9TUVOWDB9IdjrV2OG76w7EmIiJ90/r21ISEBPj4+MDHxwcAlLeBVlRUqMw6+sknn2Djxo1YtWqVcqbTprh37x6GDRuGjIwMjBkzBmFhYbVuQa2+FTQnJwcKhULtLarVt5fWvJ205utHjx7Bzc1No362traQSqWorKxUuW1VXT+pVFrvGj18M0DqTJgwQW9rYond5MmTOdZaCAkJ0XjBb2qajh07ws/PT+gYREQkIlpfaSwrK1N5Lq96XcLq6ZirmZqawt/fXzkraVM8fPgQw4YNw+3btzFgwADs2LFDuY5iTe3atQNQdXUvPT1d7bGqH2aubgtULZpafby6HnZW18/ExEQ5m1ZD/Wqeg0hT0dHROlnAmWo7deoUYmJihI5hcO7evYtff/1V6BiikJqaqnaZACIiIl3Rumh0d3dHZmam8vvqokndG9u7d++iqKhI21MBAAoKCjB69GjExsaiR48e2LdvX523eXp5ecHV1RUAcPr0abVtqrf36tVLuc3Y2Fg5+1Rj+tX8vrH9iDSRmpqKtLQ0oWOIQmpqap0fNlHd7t+/3+DMgtQ8ONZERKRvWheNwcHByMjIUC4fMWjQICgUCrzzzjvIzc1Vtvvggw9w7949BAUFaR2ytLQU48ePx9mzZxEcHIzff/8d1tbWdbaXSCSYOHEiAKhdADkqKgoJCQmQyWQYN26cyr5JkyYBqJr5q6KiQmVfamoqDh8+DAC1ZjGr7rd9+3bk5+er7MvPz1cuDTJlypQGf14iIiIiIqInhdZF49ixY1FaWqpSRPn7+yM6OhoeHh7o0aMHvL29sWzZMkgkklqT1WiqoqICM2bMwNGjR9G2bVscOnQIDg4ODfZbvHgxTExMEBERgVWrVimnNU5JScHzzz8PAJg3b57yimS1l156CU5OToiPj8drr72mLIqzs7Mxa9YslJeXY9SoUejWrZtKv8mTJyMgIADZ2dmYM2eO8spqYWEh5syZg+zsbISEhGDChAlajQMREREREZEQtJ4IZ8qUKTAzM1MuCWFiYoJDhw7hueeew7Fjx5TPMNrb22PFihWYOXOmVufZvn07du/eDaBqEpmpU6eqbefm5qa8mgcAbdq0wbp16zBnzhwsWbIEa9asgYuLC2JjYyGXy9GtWzesWrWq1nFsbGywdetWPP300/jyyy+xZcsWeHl5IT4+HkVFRfDx8cGPP/5Yq5+RkRF27NiB/v37Y9euXTh8+DD8/Pxw69Yt5ObmwsHBAdu2bYNUqnWdTkREREREpHdaF422trZ45plnVLZ5enri6NGjyMjIQEpKCszNzREcHAxjY61Po7JUxc2bN3Hz5k217by9vWtte/bZZ+Hn54eVK1ciKioKcXFx8PX1xcyZM/HGG2+oLJVR05AhQ3D+/Hm8//77OHr0KGJiYuDu7o6JEydi6dKlKjOn1hQSEoIrV65g+fLlOHDgAGJiYuDs7Ixp06Zh2bJl8PDw0GIEiIiIiIiIhKN9NVcPNzc3tctVaGP27NmYPXu21v379u2Lffv2NbpfcHAwtmzZ0uh+np6eWLduXaP7EdXnqaeeqvNDDmpe/fr1g6WlpdAxDE779u15J4WeBAQEwMTEROgYREQkIvwvPJEBMDY2btIVe9KcTCbjWGtBKpWykNETjjUREembRu+MTpw40Swn69+/f7Mch0hsjh8/Djs7O06kpAeRkZFwdnauNbMy1S8hIQEHDx5Ex44dhY7S4sXFxSEyMhIhISFCRyEiIpHQqGgcOHAgJBJJk04kkUhQXl7epGMQiZVCoVDOAEy6xXHWDseN6Mlw7NgxDBo0CO+88w7effdd5faBAwfi+PHj/LeqhXfffRfvvfceIiMjMXDgQKHjAAB8fHwAAMnJyQZ9DjIcGhWN/fv3b3LRSEREREQtU1hYGObMmYMNGzY0aS4K0p/Zs2dj48aNSEpKUhaIRHXRqGg8duyYjmMQERERET15/vWvf2HGjBnw8vISOopeHTlyROgI9AThbA9ERERERHVwcnKCk5OT0DH0rm3btkJHoCcIZ08lMgBubm5o1aqV0DFEgWOtHScnJ65Fqycca3EpKyvDV199hREjRsDT0xOmpqZwcXHBpEmTcOnSpSfivLNnz8acOXMAAHPmzIFEIlF+1ZSamoq5c+fC3d0dJiYm8PDwwNy5c3Hnzp1a56+eT6O0tBT//ve/4eXlBXNzc3Tr1g2HDx8GAOTn52PBggVwd3eHmZkZ+vTpg/Pnz2v8M2ZkZGDhwoVo164dzM3N4eDggA4dOmD+/PnIy8tTtnv33XchkUhU7rxLTk6GRCLB7NmzER8fj6effhp2dnawt7fHzJkz8eDBAwDA2bNnMWzYMNjY2MDe3h7/+Mc/UFhYqJIjLCwMEokEYWFhtTIeO3YMEolE5fnUuqSnp+Odd95B79694eLiAlNTU/j4+GD+/PnIzMxUaevj44ONGzcCANq0aaP8/6vmM5s+Pj5qb1stKirCu+++i4CAAJiZmcHBwQFjxoxBVFRUrbY1x2779u3o2rUrzM3N4ebmhgULFqC4uLhWn127dmHAgAFwcXGBmZkZPD09MXLkSOzevbvBMSDd0fpKY2pqaqP7iO2yPlFzGTx4MJ8r1pOhQ4fCyMhI6BgGx8fHp9nW56X6+fr6wt3dXegYgqtUVCKnNEfoGBqzM7WDVNL4z+ofPnyIV155Bf369cPo0aNhb2+PxMRE7N27FwcOHMCJEyfQo0ePZs/bmPNOmDABOTk52LNnD8aPH4/OnTvXOt7Nmzfx1FNPITMzE2PHjkVwcDCuXbuGH3/8Eb/++itOnz4NPz+/Wv2mT5+OmJgYjBs3DsXFxdi0aROefvppREVF4cUXX0RJSQmmTJmCrKwsbNu2DSNGjEBSUhJsbGzq/fmKiooQGhqK5ORkDB8+HBMnTkRZWRkSExMRFhaGJUuWNHgMAEhKSkLfvn3RvXt3zJs3D+fPn8fWrVtx584dfPzxxxg2bBiGDRuGF154AceOHcMPP/wAADpZ0/vEiRP47LPPMGTIEPTq1QsymQyXLl3Ct99+i4MHD+LixYuwtbUFALzyyisICwvDlStXsHDhQtjZ2QFAg882lpaWYsiQIThz5gy6du2KV155BZmZmdi2bRsiIiKwbds2TJo0qVa/r7/+GgcOHMD48eMxcOBA/P777/jqq6+QnZ2NTZs2Kdt9++23mD9/Ptzc3DBx4kQ4OjoiIyMD586dw+7duzmLvIC0Lhp9fHwa9SaWs6cSaS88PBx2dnYYOXKk0FFavJ07d8LFxQXDhg0TOopBuXbtGiIiIvD6668LHaXFi4mJQWRkJF555RWhowgqpzQHA7YNEDqGxo5PPw4HM4dG97O3t0dqamqtDwquXbuG3r1749///jcOHTrUXDG1Om/NonHChAlqJ8J56aWXkJmZie+//x4vvPCCcvvatWvx4osv4qWXXlJeQazpwYMHuHr1KiwtLQEAw4cPx4wZMzBkyBAMGzYMmzdvVq6t27lzZ7zxxhtYv349Xn311Xp/viNHjiApKQmvvvoqVq9erbIvPz8fpqamDQ8Sqgq1L774AgsXLgRQNZP0008/jd9++w1PP/00tmzZgvHjxwMA5HI5unfvjo0bN+L9999v9rtaBg8ejHv37sHKykpl+08//YTnnnsO//3vf/H2228DqCoaL1++jCtXruCVV17ReCKcTz75BGfOnMEzzzyDn3/+WVkLvPLKK+jZsyfmzZuHYcOGwdraWqXfoUOHcOHCBbRv3x4A8MEHH6Bz587YsmULVq1ahdatWwMAfvjhB5iYmODKlStwdnZWOUZ2dnajx4Saj9a3p3p5edX55ejoqFwiwNjYGF5eXvD09GzO3ESiUlxcrPYWDmp+HGvtcNz0h2MtLqampmqvLAcHB2PQoEE4ceIE5HL5E33eO3fu4OjRowgKCsI//vEPlX3/+Mc/EBgYiCNHjqi9TfWDDz5QFowAMGXKFMhkMuTk5ODTTz9VFowAMHPmTADAlStXNMoFAObm5rW2WVtbw8TERKP+vr6+ePnll5XfSyQSzJgxAwDQpUsXZcEIADKZDFOmTIFcLkd8fLzGGTXl4uJSq2AEgL///e+wsbFRW5Q3VlhYGGQyGT766COVi0cdO3bE7Nmz8ejRI+zZs6dWv4ULFyoLRqBq3GfOnAmFQoELFy6otJXJZJDJZLWO4ejo2OT8pD2ti8bk5GQkJSWp/crMzEROTg5WrVoFc3NzzJo1C0lJSc2Zm4iIiEgULl++jFmzZsHLywsmJibK58/27duHsrIy5fNzjZGTk4N333231pcuzlv9DOSAAQNq3aUmkUjQv39/AOqLvS5duqh8b2RkBBcXF9jZ2dV67Kn6Fvm0tLQGM/Xv3x+urq5YuXIlxowZg2+++QZXr15t9DqWnTp1glSq+na6Ooe623Qbk1Eb4eHhGDFiBJydnWFsbAyJRAKpVIq8vDykp6c36dh5eXlITEyEn5+f2ueqq5+HvHz5cq19Xbt2rbWt+hg5OTnKbdOmTUNhYSFCQkKwaNEi/Prrryr7STg6mz3VxsYGr7/+OoKDgzFmzBgEBATg73//u65OR0RERNTiREVFYfDgwQCqbs1s164drKysIJFIsHv3bly5cgWlpaWNPm5OTg7ee++9WturC8fmPG/1pDJ13Y7p6uoKAMjNza21T91zhcbGxspn8x7fDkCjK6C2traIjo7GO++8g3379uG3334DUFXIvPXWW5g/f36Dx6gvX0P7dHF1+LPPPsOiRYvg7OyM4cOHw8PDQ3kl9YsvvtDq70lNTfn/sb7/vyoqKpTblixZAkdHR3z33XdYvXo1PvvsMxgbG2P06NH44osv0KZNmyb9DKQ9nS+5MXLkSHh7e2PNmjUsGomIiKhZ2Jna4fj040LH0JidqZ1W/T744AOUlpbi1KlTCA0NVdl35syZRt2KWZOPj0+9V9Wa87zVxdP9+/fV7q/ersnEM82pegbRiooKxMTEICIiAl9++SX++c9/KmdB1YfqK5Xq5v5QV4CpU15ejhUrVqB169a4fPmyyvOACoUCn3zySZNz6uP/R4lEgnnz5mHevHnIzs7GyZMn8f/t3XlcFPX/B/DXstwgNyiC3J6A930gHnmfaR7ZNzUzzfLIPLI0NS0tM+2XZaYZX7M805Q8U1C88E5FPABFDEWFuOQ+5vcH393E3cXdZdlh2dfz8aBkZz4zrxk+4r53Zj6fLVu2YPv27YiLi8O1a9c4WJ1I9DJPo4ODA27evKmPXRHVSGZmZuWe26Cqw3OtHZ43/TE1NeW5BmAiMdFqYBlDk5CQACcnJ4XCLTc3F5cuXao2+5W9kX/2qpGM7DbNqKgoCIJQ7hZVQRBw4sSJcuvpm1QqRfPmzdG8eXN06NABISEh2Lt3r96KRkdHRwDKb1lVd1qV1NRUZGZmokePHgoDyFy4cEHpc9AV/cyUsbOzg5+fH+Lj45GcnKzwzOvx42Uf4ujq5+js7IwhQ4ZgyJAhSE1NRUREBOLj48s9G0n6U+XzND5+/Bg3btyApaVlVe+KqMYaOHCg/DYhqlqDBw9G166GMyJjdREUFIQ33nhD7BhGoVmzZkpHp6SaydvbG+np6bh+/br8tZKSEsyaNQtPnjypNvt1cior4P/++2+FZV5eXujWrZt8io1nbdy4EdevX0f37t31OmhiTEwM7t27p/C67GqZsgFyqkrLli0hkUiwdetW5Ofny1+Pi4vD119/rdY23NzcYGVlhUuXLiE3N1f+enp6ermBep5V0c9MlbFjx6KoqAjz5s0rd6U6JiYGP/30E+zt7Ss1LcahQ4cUrrgWFRXhn3/+AaDfnwuVV2UfVaampuL8+fOYP38+CgsLy40eRUSaiYmJga2tLZo1ayZ2lBrv2rVrcHBwQHBwsNhRDMrjx49x4cIF/q7Xg5SUFPz1118YOHCg2FFID6ZOnYrDhw+jc+fOGDFiBCwtLXHs2DEkJycjNDS03ITzYu63Q4cOsLKywurVq5GVlSW/2vXBBx8AKJt/r3Pnzpg4cSLCw8PRpEkTxMbGYu/evXB1dcXatWur5DhUOXLkCN5//3106tQJjRo1grOzs3weSisrK7z77rt6y+Lh4YGRI0di69ataNWqFfr06YPHjx9j9+7d6NOnD3777bcXbsPExARTpkzBypUr0axZMwwcOBBZWVk4cOAAvL295VNaPKt79+748ssvMWnSJLzyyiuwsbGBl5cXXn31VZX7mTNnDvbt24eff/4ZN27cQI8ePeRzZBYVFWHTpk0K021oYuTIkbC2tkbnzp3h7e2NoqIi/Pnnn4iNjcXIkSM557uItL7SKJVKK/yqXbs2BgwYgMuXL6N27dpYvny5LnMTGZXbt2/jzp07YscwCrdu3eJoz1pITk7GtWvXxI5hFJKTkxETEyN2DNKTAQMGYOfOnfDz88PmzZvx66+/olGjRjh37hy8vb2rzX6dnJywc+dO1K9fH2vXrsW8efMwb948+fKGDRviwoULGDduHM6dO4cVK1bg3LlzGDduHM6fP48GDRpU2bEo07t3b0ydOhVPnz7Frl27sGrVKly4cAGjRo3CxYsXlY72WZV+/PFHTJ06FWlpafj2229x9epV/PDDDxoVr8uWLcOnn34KiUSC7777Dn/++SdGjRqFw4cPK53Com/fvvjiiy9QWlqKzz//HPPmzcMPP/xQ4T4sLS0RERGBBQsWICsrC6tWrcKuXbsQEhKCY8eO4ZVXXtH42J8/hjZt2uDcuXNYs2YNNm/ejFq1amHdunXYvHlzpbZNlSMRNB1b+H+eH174eTY2NvDz80Pfvn0xa9YsuLi4aBXQ2GVlZcHe3h6ZmZl6f0DcWHz0aw/sLXos/36cTQDeH75bxESKfvrpJzg4OGDo0KFiR6nxNmzYADc3NwwaNEjsKAbl3LlzOHz4MObPny92lBovOjoakZGR5d6Q12T5+fm4e/cufH19+agLEdUIlfm9JlZtoPXtqaWlpbrMQURERERERNVQlQ+EQ0RERERERIaLRSMRERERERGppJPRUzMyMnD37l08ffq0woliQ0JCdLE7IqPTunVrWFtbix3DKLRp06ZSI78ZK39/f05Voif+/v5KJwEnIiKqKpUqGiMiIvDRRx/h3LlzL1xXIpHwHzkiLTk7O8Pc3FzsGEbBxcWFg21owdraGu7u7mLHMAo2NjY810REpFdaF4379+/HkCFDUFxcDEtLS/j6+sLV1RUSiUSX+YgIwJ9//gkHBwfOgacHhw4dgqurK+fA09D169dx6NAhfPTRR2JHqfGuXbuGyMhI+fx3REREVU3rovHjjz9GSUkJJk2ahOXLl8Pe3l6XuYjoGaWlpRyxWE9KS0srvM2elON50x9BEHiuiYhIr7QuGmNjY+Hi4oK1a9fqMg8RERERERFVI1oXjY6OjvDw8NBlFiIiIiIiIqpmtC4ae/XqhZ07dyInJwc2Nja6zETGrKQIKC3Rvn1WMrBpSNmfTS3Ua2OaDVhzkBkiIiIiImW0LhoXL16MAwcOYNKkSdiwYQNHGyTdOLIIOLNGv/t0cQJQvYtGR0dHPjesJ46OjnBwcBA7hsGxt7eHk5OT2DGMAs81ERHpm9ZFo5eXF6KiovD6668jICAAo0ePhr+/f4Vzyb3++uva7o4M3e3DQNbfL17vYliVRzFEgwYN4sjEejJ06FCeay00aNAA9evXFzuGUWjUqBEaNmwodgwiIjIilZqn8dy5c7h//z4ePnyIr7766oXrs2g0Yud+AOL/rPLdZJpI8IudnUZtbhnA/Ie7du2Cvb09XnrpJbGj1Hg7duyAq6srunfvLnYUg3Lz5k0cOnQIM2bMEDtKjXf9+nVERkZi6tSpYkchIiIjoXXRuG3bNnkR6OnpieDgYM7TaCweXgG2/UezNhn31Frta0d7/GJXS4tQZfJMTLRuK+faqPLb0LHs7GxIpVKxYxiF7OxsWFlZiR3D4GRnZ+Pp06dixzAKT58+5bkmqkZCQ0Nx/Phx0afCqS45ZBITE+Hr64uxY8ciLCzMYPdBZbQuGpctWwaJRIJly5Zh1qxZMNHFm3UyDMWFaheBMvNdnHBBjedek80qdfFbN2xcxE5ARERERAB8fHwAlBWIJB6t36Hfvn0bHh4emDNnji7zkAHIKHqKaBvVz64qs6eWbRWlISIiIhLPpk2bkJubK3aMapNDnzw8PHDjxg0OFqgHWheNzs7OqF27ti6zkIG4l/sIs90M52pciGcILKRqTr8BoIFjgypMQ0RERDWJl5eX2BEAVJ8c+mRmZoZGjarfY0U1kdb3lA4cOBAxMTFIS0vTZR6icgIcAvBrv18r9fVN92/wVehXan8N9B8o9mErkEqlvAVcT3iutcPzpj8mJiY810boxIkTGDp0KGrXrg0LCwvUq1cPL7/8Mk6ePFluvdzcXCxatAiNGjWCpaUlnJyc0L9/f5w+fVphmw8ePMDChQvRvn17uLm5wcLCAj4+PpgyZQoeP36ssP64ceMgkUhw584drFq1CoGBgbCwsMC4ceMAAPn5+Vi5ciWaNWsGe3t72Nrawt/fH6NHj8a1a9cUtrdnzx706NEDjo6OsLS0RFBQEL788kuUlGg2X3NxcTGWLVsGf39/WFpaIiAgAMuWLcOdO3cgkUjk+WR8fHzg4+ODjIwMTJs2DfXq1YOpqan8mbiLFy/i3XffRVBQEOzt7WFlZYXg4GAsX74cRUVFCvsPDQ1VGNMjLCwMEokEYWFhOHr0KDp37gwbGxs4Oztj7NixGr1/vnTpEoYPHw4vLy9YWFigdu3a6NChA5YvX65RjvDwcLRr1w7W1tbw8PDAggULUFpaCgD45Zdf0KJFC1hZWcHLywtffvmlQg7Zz1/ZbaKLFi2CRCLBsWPHXng86p7fxMRESCQS3Lt3D/fu3YNEIpF/LVq0qNw6z/+MASApKQkTJkyAh4cHzM3N4enpiQkTJuD+/fsK68rOXXFxMZYsWQJfX19YWFigQYMG+O677xTW17Sv1wRaX2lcunQpjhw5glGjRmHz5s286kgaaVW7FcYHjn/henVs6qChE4eW79u3L8zMzMSOYRT69+8PCwv1r0xTmaCgILi6uoodwyg0bdoU7u7uYscQnVBaipKMDLFjqE3q4ACJlsX+t99+i6lTp8LKygpDhw6Fl5cXkpOTcfLkSezcuROdO3cGABQUFKBHjx6Ijo5Gy5YtMWPGDDx+/Bjbtm3D4cOHsW3bNrz88svy7UZFRWHlypXo0aMH2rVrBzMzM1y+fBlr167FoUOHcOnSJaW3/U2dOhXR0dHo378/BgwYIH8POHbsWGzfvh1NmzbF+PHjYWFhgaSkJERGRqJ3794IDg6Wb+PDDz/EsmXL4OnpiWHDhsHOzg5RUVGYPXs2zp49ix07dqh9ft544w38/PPP8Pf3xzvvvIOCggKsXr0aZ86cUdmmoKAA3bt3R3Z2NgYOHAhzc3P5caxfvx7h4eEICQlBv379kJubi2PHjmHevHk4f/48fvvtN7WzhYeH448//sDAgQPx9ttvIyoqCps2bUJCQoJCwa/MX3/9hY4dO0IqlWLw4MHw9vZGRkYGrl+/jvXr1+ODDz5QK8fu3btx+PBhDBkyBJ06dcK+ffuwdOlSCIIAR0dHfPLJJxg8eDBCQkLw22+/Yfbs2XB3d8eYMWPUPlZ1qXt+HRwcsHDhQqxevRoAyo3OHRoaWuE+4uLi0LlzZzx+/BgDBw5EYGAgrl+/jo0bN+KPP/7AqVOnEBAQoNBu9OjROHv2LPr27QupVIrt27fjnXfegZmZGSZOnChfT5O+XlNoXTSuWbMG/fr1w9q1a+Hv74++fftWOE+jRCLBggULtA5K1ZdEEODnoPgXryKD/Qeja72uVZSo5klKSoK1tTUnndeDe/fuwc7Ojs9HaCgzMxO3bt0yytuj9C0jIwO3b99GvXr1xI4iqpKMDMR17CR2DLXVP30Kpk5OGre7du0apk+fDnd3d5w6dUo+KAgACIKAhw8fyr//4osvEB0djTFjxuDnn3+WX3WaMWMG2rZtizfffBMvvfQSatUqG6W8e/fuSElJga1t+XEHNm3ahLFjx2LNmjX46KOPFDJdvXoVly9fLvf3PTMzEzt27EDr1q0RHR1dbsTvkpISZGdny7//888/sWzZMvTt2xc7d+6Uv3cUBAFTpkzB999/j99++w3Dhg174fk5evQofv75Z7Ru3RpRUVHy0a/nz5+PFi1aqGyXkpKCpk2b4tSpUwojZs+bNw/ffvttuWMQBAFvvvkmNm7ciFOnTqFTJ/X63t69e3Hs2DH5+iUlJejZsyeOHTuG6OhotG/fvsL2P//8MwoKCrBnzx4MGjSo3DJNrlYeOHAAp06dQps2bQAAixcvRkBAAFatWgU7OztcvnwZfn5+AIBZs2YhICAAX3zxRZUUjeqeXwcHByxatEh+BVh2dVEdkydPxuPHj7Fu3Tq89dZb8td/+OEHTJo0CZMnT8aRI0cU2t2/fx8xMTGw+9/0bdOnT0dQUBBWrlwpLxo16es1idZFo+wytCAIKCoqUvmpi2wdFo01l5UA/D7kd7Fj1GhXr16Fg4MDGjduLHaUGu/KlStwc3Pj5OkaSkxMxNmzZzmXqB4kJibi3Llz6NGjh9hRSA++//57lJSUYOnSpeUKRqDsPVbdunXl34eFhcHMzAzLly8vd5ti06ZNMW7cOKxbtw579uzBa6+9BgBwc3NTus///Oc/mDp1Ko4cOaK0aJw9e7bCB0Sy93sWFhYKU0RJpdJyH3quWbMGALBu3bpyFxskEgmWL1+OdevWYcuWLWoVjZs3bwYALFiwoFzxV6dOHUyfPh3z5s1T2XbFihVKp1jy9vZWeE0ikeCdd97Bxo0bceTIEbWLxldffbXculKpFGPHjsWxY8dw/vz5FxaNMspyOjs7q9UWAMaMGSMvGAGgVq1aGDBgADZu3Ii5c+fKC0YAqFevHjp37ozjx4+juLgYpqa6Hdlel+dXmfv37yMiIgJNmjQpd3UQACZOnIjVq1fj6NGjuH//vsKHb8uWLZMXjADQsGFDdOrUCcePH0d2djZq1aqlUV+vSbTuBQsXLtRlDiIiIiJ6zrlz5wAAvXr1qnC9rKws3LlzB40bN4anp6fC8tDQUKxbtw5//fWXvGgEgF27dmHdunW4dOkS0tPTyz1P+ODBA6X7atu2rcJrdnZ26NOnDw4ePIiWLVti+PDh6NKlC9q1awdzc/Ny60ZHR8PGxgY//vij0u1bWVnh5s2bAMqurMtuT3yW7KrTlStXAAAdO3ZUWEfZazKWlpYqbyEsLCzEmjVrsHXrVty8eRNPnz4tN/ehqvOiTMuWLRVek/18MtS4vXr48OFYvXo1hgwZghEjRuCll15C586dNb6rQ9lVV9lt7s2bN1e6rKSkBI8ePYKHh4dG+3oRXZ5fZS5fvgwA6Nq1q8IznhKJBCEhIbhx4wauXLmiUDS+6OdVq1Ytjfp6TcKi0djdOw3cP6tZm9SYqslCRERE5WRkZEAikbzwOdasrCwAUDnGRJ06dQCU3Vons3LlSsyaNQuurq7o1asXPD095Ve0Vq9ejYKCAqXbUrWPnTt34rPPPsOWLVvkVyhr1aqFN954A5999pn8quI///yD4uJiLF68WOXx5OTkACg7fmXryYrGrKwsmJiYKL3qVtF4G25ubgoFhczw4cMRHh6OBg0aYOTIkXBzc4OZmRkyMjLw9ddfqzwvyih71EF25U6dAX86dOiAiIgILFu2DFu2bJHfqtmqVSusWLEC3bp1UyvHs1fPns9R0TJlA/9Uli7PrzLa/F2QUffnpW5fr0mqwUzqpHfpiUBeetmfT64C4g5r1t7CHKhbR+exiIiI1CV1cED906fEjqE2qZa3rDk4OMifXazoio/sjf+jR4+ULpe9LltPNkpk3bp18ddff5UbyEoQBHzxxRcq96Wq2LKxscGnn36KTz/9FHfv3kVkZCS+//57fP3118jLy8O6devkGSQSCVJTUys48jI+Pj7lrkI9z87ODqWlpUhLS4OLS/npwFSdi4qO4fz58wgPD0fv3r2xb9++crcfRkdH4+uvv35hZl3r2rUrunbtiry8PJw9exbh4eH47rvv0L9/f1y7dg3+/v56ySEbtbm4uFhhmbICTBl9nF9N/y5oQ92+XpOwaKxJHt8AitSY1HXXJCAtrurzEBERVRGJiYlWA8sYmrZt2+LChQs4fPgwxo9XPeq4nZ0d/Pz8EB8fj+TkZIUC8/jx4wD+vRUxNTUVmZmZ6NGjh8LIxxcuXEBeXl6lcvv6+sLX1xejR4+Gm5sb9u7dK38j3a5dOxw4cABxcXGoX79+pfbTrFkzXL58GadPn1YYKEbZNCMvkpCQAKBsJO3nn1c7ceKE9kF1wMrKCqGhoQgNDYWDgwM+/vhjHDlyRG9Fo6OjIwAgOTlZYeRR2S2hL6LN+ZVKpSgsLFQ7p6yPR0VFycdVkREEQb4fZbflaqOivl6TaF00fvLJJxqtz4Fw9GD3ZODhXy9cbbGzIw55KT7voK4S5R/OURUKCgqCjY2N2DGMQnBwMEdO1YKXlxdat24tdgyj4O3tjVatWokdg/Rk8uTJWLduHebPn4/u3buXG0REEASkpKTIb10dO3YsFi5ciHnz5uG///2v/M1yTEwMfvrpJ9jb22PIkCEAym7PtLKywqVLl5Cbmyu/nS49PR1Tp07VOOeTJ09w9+5dhecd09PTUVBQUK4wnTZtGg4cOIA33ngDv//+u8KtpSkpKUhPT1dr8LcxY8YgLCwMS5YsQa9evWBpaSnfhjZXrWTn9+TJk+XOw/Xr17Fs2TKNt1dZJ06cQLNmzRSuismulikbIKeqyH7Hh4WFoWvXf0fA37lzp/xDiRfR5vw6OTkhJiYG+fn58p9vRby8vNCtWzdERkZi48aNmDBhgnzZxo0bcf36dXTv3l3rEag16es1iU5GT33e85f8OXpq5R3/Nhg2lhXP7zTdyRbw9YJUEGBVwa0cT3U9KbSJ9MXrUKX4+fnpfPQyUs7f379GP8heVZycnGrkvFTVkZOTEwIDA8WOQXoSHByM1atXY9q0aQgMDMSQIUPg7e2NlJQUREVFoX///vKBYubMmYN9+/bh559/xo0bN9CjRw88efIE27ZtQ1FRETZt2iSfbsPExARTpkyRT1A+cOBAZGVl4cCBA/D29i43Kqs6kpOT0a5dOwQGBqJly5bw8PBAWloa9uzZg6KiIsyZM0e+bp8+fbBgwQIsWbIEAQEB6NOnD7y9vZGWlob4+HicOHECS5cuVato7NmzJ8aMGYNffvkFwcHBGDx4MAoKCrB9+3a0a9cO4eHh8tsq1dG2bVu0bdsW27dvx8OHD9G+fXskJSVh79696N+/P3bu3KnReamslStX4s8//0S3bt3g5+cHS0tLXLp0CUePHkVAQACGDh2qtyxDhgyBr68vwsLCcP/+fbRo0QI3btxAREQE+vXrh/37979wG9qc3+7du+PChQsYOHAgunTpAnNzc3Tu3Fk+P6kya9euRefOnTFx4kSEh4ejSZMmiI2Nxd69e+Hq6oq1a9dqfR406es1SZUMhJOTk4P4+HgcOnQIJSUlmDJlCj+5r6S5jjaQWqlXnJVIJHiq4l79KmH64k99qHL27dsHBwcHhVtvSPfCw8Ph5uaG/v37ix3FoFy7dg2HDx+ucHh70o2rV68iMjKyxr4xIUXvvvuufK64AwcO4OnTp3Bzc0O7du0wYsQI+XqWlpaIiIjA559/jm3btmHVqlWwtrZGSEgIPvzwQ4U32cuWLYOTkxPCwsLw3XffoXbt2hg1ahQWL16MoKAgjTL6+Phg0aJFiIiIwJEjR+TPGLZs2RLvvfeewuivn3zyCUJCQvB///d/OHr0KDIyMuDs7AxfX18sWrRIo/kBw8LC0KhRI2zcuBHffPMNPD09MWPGDPTo0QPh4eEaPbsmlUrxxx9/4IMPPsDBgwdx/vx51K9fH19++aV8Xkl9evvtt2Fvb4+zZ8/Kb7f08vLC/PnzMWPGDPmHAPpgZWWFo0eP4r333kNERIR8nsmoqCj88ccfahWN2pzfBQsWID09HX/88QciIiJQWlqKhQsXVlg0NmzYEBcuXMDixYtx8OBB7Nu3D66urhg3bhwWLlyodNoPdWna12sKiVDR08WV9Pfff2PMmDFITU3FmTNnKvXAqbHKysqCvb09Gq9trHbRqG/WptY4O0bDEVhJIz/99BMcHBz0+omisdqwYQPc3NxYoGvo3LlzOHz4MObPny92lBovOjoakZGRRlOg5+fn4+7du/D19VXr1jQimQ0bNmDixIn47rvv8Pbbb4sdh0iuMr/XZLVBZmamXmurKr3fzdPTE1u3boW3tzcWLFggyohT9GIt3FpgRssZWrc3kej4dlciIiIiNaWkpKB27drlHo9KTk7G0qVLIZVKMWDAABHTEdUMVf6QlLu7O4KCgrB7924WjZVgKgjwtFLvwdr7eU8AAOPqj0CPgIEvXN/N2g11bTV7doGIiIioOli+fDn27duHLl26wM3NDUlJSfjjjz+QnZ2NRYsWaT3gCRH9Sy8ja2RmZuLJkyf62FWNVbtUgv0jIsSOQURERFSt9OnTB7Gxsdi3bx/S09NhaWmJpk2bYsqUKXj11VfFjkdUI1R50XjgwAHcvXtXYT4XIlKfjY0Np9zQE55r7fC86Q/PNVF5ffr0QZ8+fcSOQVSjaV00btq0SeUyQRDw5MkTnD9/Hr///jskEgnGjh2r7a6IjN6wYcMUprKhqjFixAieay00adIEDRs2FDuGUQgKClJrKgIiIiJd0bpoHDdu3AvfWMkGZp0wYYLRjPJGVBX27dsHe3v7cpPpUtUIDw+Hi4tLhUN5k6L4+HhERERg0qRJYkep8W7duoUTJ05g4sSJYkchIiIjoXXR+Prrr6ssGiUSCWxsbODn54e+ffuiUaNGWgckIiAtLQ0lJSVixzAKqampGk0ETWXS09P57LqeZGRkIDU1VewYeleFM4QREemVIf4+07poDAsL02EMIiIiIkVSadkcxUVFRbCyshI5DRFR5RUVFQH49/ebIeDH6URERFRtmZmZwcLCApmZmQb56TwR0bMEQUBmZiYsLCxgZmYmdhy1aXSl8fr160hISICbmxvat2//wvXPnDmDJ0+eICAgAE2aNNE6JBERERkvFxcXJCcn4++//4a9vT3MzMw4YBURGRRBEFBUVITMzEw8ffoUHh4eYkfSiNpFY25uLnr16oXU1FRERkaq1UYQBAwfPhx169bFrVu3YGFhoXVQImMmkUj4BklPeK61w/OmP8Z4ru3s7ACUPXOcnJwschoiIu1ZWFjAw8ND/nvNUKhdNG7ZsgUPHz7E5MmT0bFjR7XadOzYERMnTsT333+PrVu3ctoNIi317NmTH7roSa9evfjclBYCAwMN7h9AQxUUFARHR0exY+idnZ0d7OzsUFRUxIHBiMggSaVSg7ol9VlqF42y+RanTZum0Q5mzJiBtWvX4rfffmPRSKSlzMxMWFlZwdXVVewoNV5GRgZKSkrg4uIidhSDkp+fj9TUVM7VqAd5eXlIS0sTO4ZozMzMDPZNFxGRoVJ7IJzLly/D3d1d4+kz6tevDw8PD1y+fFnjcERU5ty5c7hy5YrYMYzC2bNncfXqVbFjGJz4+Hi1H12gyomPj8exY8fEjkFEREZE7aIxNTVV6wc269ata5RzShERERERERk6tYtGS0tL5OXlabWTvLw8mJuba9WWiIiIiIiIxKN20eju7o6EhAQUFBRotIOCggIkJCSgbt26GocjIiIiIiIicaldNHbp0gX5+fnYuXOnRjvYsWMH8vLy0KVLF43DERERERERkbjULhrHjRsHQRAwd+5c3L9/X602SUlJmDNnDiQSCUdOJaqEgIAA+Pj4iB3DKAQEBMDb21vsGAanbt26aNKkidgxjELdunXRuHFjsWMQEZERUbto7NixI1555RU8ePAA7dq1w44dO1BaWqp03dLSUmzfvh3t27fHo0ePMGzYMHTq1ElnoYmMTfPmzTmVgZ60bNkSDRo0EDuGwalTpw66du0qdgyj4O7uzrt3iIhIr9SepxEAwsLCkJycjNOnT2PUqFFwdXVFp06d4OvrCxsbG+Tk5ODu3bs4ffo0Hj9+DEEQ0KFDB4SFhVVRfCLjsHfvXtjb22PAgAFiR6nxfv/9d7i6uqJv375iRzEoMTExOHToEObOnSt2lBrv6tWriIyMxKxZs8SOQkRERkKjotHKygrHjh3DokWL8M033+Dx48fYvXs3JBKJfB1BEAAAtra2mDp1KhYtWsRJeIkqqbCwEEVFRWLHMAo819rhedOfoqIinmsiItIrjYpGADA1NcXSpUsxZ84c7Nu3D6dPn0ZycjKys7NRq1YteHh4oGPHjujXrx/s7e2rIjMRERERERHpicZFo4ydnR1Gjx6N0aNH6zIPERERERERVSNqD4RDRERERERExodFI5EBsLS0hKWlpdgxjALPtXZ43vSH55qIiPTNIIrGu3fvYv369Zg4cSKaNWsGU1NTSCQSLF269IVtz5w5g8GDB8PV1RVWVlZo0qQJlixZgvz8/Arb3bhxA2PGjIG7uzssLS3h7++PWbNmISMjo8J2ycnJeOutt1CvXj1YWFjAy8sLkyZNQnJysiaHTFTO0KFD0aNHD7FjGIXhw4ejW7duYscwOEFBQXj77bfFjmEUgoOD8dZbb4kdg4iIjIhBFI1ff/013nrrLWzYsAFXr15FSUmJWu1++eUXdOnSBXv37oWFhQUaN26M+Ph4fPzxxwgJCUFubq7SdpGRkWjVqhV+/fVXlJSUIDAwECkpKVi5ciVatWqFR48eKW0XGxuLpk2bYv369cjOzkZQUBCysrLwww8/oFmzZrh586bW54CMW1RUFC5duiR2DKMQGRmJy5cvix3D4Ny7dw+7du0SO4ZRSExMxO+//y52DCIiMiIGUTS6uLhgwIAB+OSTT3DgwAEMGzbshW0SExMxYcIElJSU4IsvvsD9+/dx6dIlxMXFoWHDhjh//jzmzJmj0C47OxsjR45EXl4epk2bhuTkZFy8eBFJSUno1KkT7ty5gwkTJii0KykpwSuvvIJ//vkHw4YNw4MHD3Dx4kUkJyfj5ZdfRlpaGkaOHInS0lKdnBMyLsnJyXj48KHYMYxCcnIyUlJSxI5hcJ48eYJ79+6JHcMoPHnyBElJSWLHICIiI2IQReP8+fMRHh6OBQsWoE+fPrC1tX1hmxUrVqCgoAC9evXC7Nmz5XNJent7Y+PGjQCAH374QeGq4ffff48nT56gcePG+Oqrr+RzTDo7O+PXX3+Fqakp9u3bp3DVZ9euXYiNjYWzszN++uknWFtbAwBsbGwQFhYGZ2dnXL16FXv27Kn0+SAiIiIiItIXgygaNSUIAnbv3g0ASq8KduzYEY0aNUJRUZFCESe7vWrcuHGQSqXllnl5eaFnz54AgJ07dyptN2LECNSqVavcslq1auGVV14BAOzYsUPbwyIiIiIiItI7redprM6SkpLkt/J16tRJ6TqdOnXCzZs3cfbsWfmAAsXFxbh48eIL2x08eBBnz54t93p0dPQL233//fcK7YiIiIioeirNyYEgyL4TIP/m2f//78/Cvyuqt54g/4/S9SAI5ff93PK8q9cgFBXBzKMuJKY18i09KZH79Kko+62RPSwuLg4AYGFhgbp16ypdx8/Pr9y6QNlzkEVFReWWq9OusLBQ/nzJi9rJ9iG77fV5BQUFKCgokH+flZWldD0iIiIiY5IdGQmhsEiv+0yePl2v+yN6kadqDgiqazWyaExPTwcAODg4yJ9lfJ6jo2O5dZ//s2y5Ou0yMzPlA9y8qF1paSmysrLg7OysdL1ly5Zh8eLFSpeR8QoJCeG8bHrStWtX2NjYiB3D4DRq1Ajm5uZixzAKjRs3hpWVldgxiDRS/M8/KExIqNQ2/n57io7SEJGmamTRKJuDsaI3MBYWFgCAvLw8hXYVta1su+fbPm/evHmYOXOm/PusrCzUq1dP5fpERNVFuVuziIiekXvuPJJnzBA7BhFpqUYWjbIrMoWFhSrXkd0C+uyntc9eySksLFR6ZUeddhXt7/m2z7OwsChXYBIBZfM0Ojg4YOjQoWJHqfGOHz8ONzc3DBo0SOwoBuXmzZs4fPgwWrRoIXaUGu/GjRuIjIxEs2bNxI5CpCB1/XpkHzqs8Hp+TIwIaYyPyXODMVLNY8LbU3VHditoRkYGBEFQeouq7PbSZ28nffbP6enpcHd3V6udvb09TExMUFpaWu62VWXtTExMYGdnp+khEREREVV7xQ8f6rVANFcxloSuFT95AqGkBPW+XQNzX19A/t5SAvzvj/L3m8/+//nXVK33/Pr/26hEomK959aXAICpKSQmNXJiBHpGVlYWYG+v9/3WyKKxfv36AMqu7j148AAeHh4K69y5c6fcugDg4+MDMzMzFBUV4c6dO0qLRmXtzM3N4eXlhcTERNy5cwcdOnRQ2U62DyIiIiJDk7l3Lx5/uVLl8uLHj9XelrSSb3w9v10D69atK7UNIlJPjSwavby8UKdOHaSkpODUqVMYMWKEwjqnTp0CALRr107+mqmpKVq2bImzZ8/i1KlTSqfPUNZO9n1iYiJOnTqFMWPGqN2OiIiIqLq4M6TixyAKbt7UyX7M/fzgv3+fTrZFRFWvRhaNEokEQ4cOxdq1a/Hjjz8qFI2nT5/GzZs3YWZmpvDc0ssvv4yzZ88iLCwM77//PqRSqXxZUlISjhw5AgAYNmyYQrtt27Zh+/bt+Pzzz1HrmXvKs7OzsWPHDgDA8OHDdXqsRERERLqiq6JQRmJlhToff6zwurSWrU73Q0RVq8be+Dx79myYm5vj8OHDWLFihXxUv3v37uGNN94AALz55puoU6dOuXaTJ0+Gi4sLbty4gZkzZ8rnbUxLS8Orr76K4uJi9O3bF61atSrXbtiwYWjUqBHS0tIwfvx45ObmAgBycnIwfvx4pKWlISgoCEOGDKniI6eayNPTU+nt0qR7PNfacXNzg4+Pj9gxjIKrqyu8vb3FjkGkFvshg+EwdIjCV62ePcWORkQakAgGMEb6qVOnMHjwYPn3T58+RUFBAaytrcuNRHr58uVy01Ns2rQJ48ePR2lpKTw8PODm5oaYmBgUFRWhVatWOH78uNL52I4ePYoBAwYgPz8frq6u8PLywo0bN5CbmwsfHx+cOXNGodgEgJiYGISEhCA9PR329vYICAhAfHw8MjMz4eTkhBMnTqBJkyYaHXtWVhbs7e3Rc01j/PlOrEZtqebIz8+HiYkJ58HTg7y8PEilUp5rDZWWliI3Nxe2trx6UNVKSkqQn5/P+USN2JM13yL3wgW11y9MTERxSgoAQKpiPmmZEhUD+lWk3o8bVC4zq1MHFv7+Gm+TiJST1QaZmZl6HVzTIG5PLSoqQlpamsLrubm58it6QNk/pM96/fXXERAQgGXLluH06dOIjY2Fn58fRo8ejblz56qcLL1Hjx64cOECli5dioiICFy7dg0eHh4YOnQo5s+fX27k1GcFBQXhypUr+OSTT3DgwAFcu3YNrq6uGDFiBD7++GN4enpW4iyQMdu9ezccHBzQt29fsaPUeL/99hvc3NzQq1cvsaMYlJiYGBw+fBizZs0SO0qNd+3aNURGRuK9994TOwqJpCAuDrnR0Vq11bQodH7rLVjUD1C5XOrgCFslY0AQUc1iEEVjaGio1pNGd+zYEeHh4Rq3CwwMxJYtWzRuV69ePaxfv17jdkQVyc/PR35+vtgxjALPtXZ43vSH57pmSduwAUJxsUZtsg8dqqI0iuwHDoDFMyPGE5FxMoiikYiIiKgmevLtdxDy8sSOQURUIRaNRERERAbMxN4eru+8o8aaAnIvXQZKSuD42mvySekrYqZkrmsiMj4sGomIiIgMmNNrr8Hp9f+ot+7rr1dxGiKqiVg0EhkAc3NzmJmZiR3DKPBca4fnTX/MzMx4rqtY7uXLOp+vUKYkMxNPVn8NALBq3hzCc8+nWrduDRN7e422ae7nq7N8RETKsGgkMgCDBg2CVCoVO4ZRGDJkCN+QayEoKKjclEdUdZo2bQpfXxYJVenp0aNI2/Bjle8n76+/FF5zmzsHVsHBVb5vIiJNmIgdgIhe7PLly7h165bYMYzCpUuXcPv2bbFjGJyUlBQcP35c7BhG4eHDh4iKihI7BhERGRFeaSQyAAkJCXBwcECLFi3EjlLjxcfHw83NDc2aNRM7ikF58OABYmNj8fLLL4sdpcZ78OABbty4gSFDhogdxSCU5ubidvsOGrURCgurKA0RkWFi0UhEREQ1mi6KQOvWrXWQpLzcCxcAAG6zZ8GkVi0AHK2UiKonFo1EREREFbAfPBh1P18udgwiItHwmUYiIiIiIiJSiVcaiYiIqEYpSknBk1WrAABCUbHCcs/v18LU1VXt7UntHXQVjYjIILFoJDIAbdu2hbW1tdgxjEL79u1ha2srdgyDExAQgOJixTfnpHv169cXO0K1V5KVhcw9e1Uut6xfn88OEhFpgEUjkQGwt7eHubm52DGMgp2dHQt0LVhaWsLZ2VnsGEbB0tISTk5OYscgIiIjwmcaiQzAkSNHcPr0abFjGIU///wTZ86cETuGwbl+/Tp27twpdgyjEBMTg127dokdg4iIjAivNBIZAEEQIAiC2DGMAs+1dnje9IfnWjvOb06Q/9mEt6ATEWmERSMRERHVeG6zZokdgYjIYPH2VCIiIiIiIlKJRSMRERERERGpxNtTiQyAs7Mz7O3txY5hFFxcXDgypRYcHR3hqsG8d6Q9nmsiItI3Fo1EBqB///6QSCRixzAKAwcO5LnWQkBAAHx9fcWOYRQaNGgAf39/sWOUI5SWojQ3r0q2nXPyJLKPHIHE1BQSCwu12pRkZlZJFiIiY8WikcgA/Pbbb3BwcECvXr3EjlLjbd++HW5ubujRo4fYUQxKbGwsDh8+jPfee0/sKDVeTEwMIiMjMW3aNLGjyBUlJyPhJf5+IiKqqVg0EhmAnJwcmJmZiR3DKOTk5CAnJ0fsGAaH501/eK6JiEjfOBAOERERERERqcQrjURERGRQHMeMETsCEZFRYdFIREREOud3YD9M1By4RhMSCwuYOjvrfLtERKQai0YiAyCVSiGVSsWOYRR4rrXD81b1ChIS8GjZcmTa2kBiZ4ekNyeKHUmuNF9x5FQzd3eYWFqKkIaIiHSNRSORAejfvz8HwtGTgQMHwqIKro7UdMHBwahTp47YMWq00uxs5Jw8CXdTU9ja1ULOP+liRyIiIiPBgXCIDEBCQgLu378vdgyjEB8fj7///lvsGAbnn3/+wdWrV8WOYRSya9VCkre32DGIiMiIsGgkMgDXr1/H7du3xY5hFGJiYhAXFyd2DIOTlJSEixcvih3DKKS6ueKOv7/YMYiIyIjw9lQiIiIDJDEzg+t774kdQyUJn3ElIqoxWDQSERFpqOjxYzxe/jkAAYIgAAIAQShbKAj/+/P/lgH/LpcvK/tegOw1lG8ryF+Qb780O7tcBomZGZzfGF+lx0lERASwaCQiItKYkJuLrP37xY5BRESkF3ymkYiIiIiIiFTilUYiA9C0aVPY2NiIHcMoNGvWDPb29mLHMDg+Pj5o166d2DEq7enx40h+f9YL1yt9+lQPaZRze/QY9S04MBYREekPi0YiA1CvXj2Ym5uLHcMoeHl5wZITkmvM3t4eDRo0EDtGpQklJVoXhM4TJ0Jibg5IJIDkfy9KJJBIJGWvyV6UfS9B2TLZa5D8uwz4d/mzywA4lJaiVlEh3IbbaXuYREREGmHRSGQADh48CHt7ewwePFjsKDXe/v374erqigEDBogdxaDExMTg0KFD+PDDD8WOIhrXqe+WFY1V7Ny5c4iMjMTcN9+s8n0REREBLBqJDEJJSQlKS0vFjmEUeK61UxXnTSguRml+fqW2kX89FqV5uTCxtILE9MVTQBTExVdqf/pQWlrKPkpERHrFopGIyAAU3Lmr0fpZBw8ga284pPb2kDo5qVwv5+RJCEVFAACzunW1zvekrjsEf3/Ed++h9TaeV/Tggc62pS1TNzd4fL1azZX5TyoREdVM/BeOiAhA2k9hKMnIQFHOU+Q9fozHCXfEjlRO2rp1Vb6PyhRpJTbWgCBUi0JPlySWlrBu0ULsGERERKJi0UhEBCB96xYU3UtC8Us9UZCZibRz58WORERERFQtsGgkMgC1atWCra2t2DGMglVePizzKvccnTEypPNmosGUKtJq+PfO1taWvw+IiEivWDQSGYBhw4aJHcFodDx5UuwIVcb1vfdULhMKCpBz9iykdnZwHDVS4217CgLaATAZ0L8SCVVkKylF8ZMnkJhKYdOpU6W2JbW3h4mVlY6SiSMwMBBNmjQROwYRERkRFo1EBmDPnj2wt7dHt27dxI5S40V37AD7zEy0srSCWZ06YsdRIJSUAADc3p8JM3d39RtKJJBIKx491BVTtc5169YtHDlyBO+8847W2yD13Lx5E8ePH8fkyZPFjkJEREaCRSORAUhPT4cgCGLHMAo5NjYwLS6G8+uvo1bPnmLHMRiZmZlIT08XO4ZR4LkmIiJ9MxE7ABEREREREVVfLBqJiIiIiIhIJRaNREREREREpBKLRiIDYGJiAhMT/nXVB4kgQMLnRzVmYmICiUQidgyjIJFIeK6JiEivOBAOkQHo1asXzM3NxY5hFFpcugyzwkKxYxicwMBAODg4iB3DKAQHB8PFxUXsGEREZER46YLIADx58oSjJepJlr0dcm1sxI5hcHJycvDgwQOxYxgFnmsiItI3Fo1EBuDixYu4du2a2DGMQnxAAJK8vcSOYXDu3LmDqKgosWMYhYSEBJw8eVLsGEREZERYNBIREREREZFKLBqJiIiIiIhIJRaNREREREREpBKLRiIiIiIiIlKJU24QGYAGDRrA1tZW7BhGwSM5GTY5OWLHMDgeHh4IDg4WO4ZR8PT0RFBQkNgxiIjIiLBoJDIAQUFBMDXlX1d98LqXBGlxsdgxDI6bmxs6dOggdgyjULt2bbRv317sGEREZER4eyqRAQgPD0dERITYMYzCuXZtERsUKHYMgxMTE4ONGzeKHcMoXLlyBWFhYWLHICIiI8KikcgAFBUVoZhXv/SiRCpFiVQqdgyDwz6qP8XFxTzXRESkVywaiYiIiIiISCUWjURERERERKQSi0YiIiIiIiJSiUUjkQGwsrKClZWV2DGMgnlhIcwLC8WOYXDYR/WH55qIiPSNY/gTGYCXX34ZJib8jEcfOpw6DZPSUrFjGJzAwEAEBASIHcMoBAcHo2HDhmLHICIiI8J3oUQG4OjRozh79qzYMYzC1ebNkMDiR2OJiYnYunWr2DGMQkJCArZv3y52DCIiMiK80khkAFJSUpCfny92DKOQ7uiIUl7V1VhqaiqSk5PFjmEU0tLSeK6JiEiv+M6IiIiIiIiIVGLRSERERERERCqxaCQiIiIiIiKVWDQSGQCJRAKJRCJ2DKMgAQBB7BSGh/2TiIio5uJAOEQGoGvXrrC0tBQ7ht4kjnkN+Vev6nWfQlERACDo6jVYFBTodd81QaNGjYyqj4qpSZMmsLW1FTsGEREZERaNRNVYcWoqSjIykJucjFJzcxTk5Ohs24mvjkFpVhakzs6QmJnpbLu6UJySIt6+TaUwLeZNGJoqLS1FAYttvSgtLUVhYaHYMYiIyIiwaKwC+/fvx1dffYVLly6hoKAADRs2xPjx4/HOO+9wgnbSSNpPP+GfHzfiWI/usMnJQbto3c/VWJKWpvNtGrIbTZrAPjMTzcQOYmBu3bqFw4cPo3Xr1mJHqfFu3ryJyMhItGzZUuwoRERkJFg06tjy5csxb948AICfnx9sbW1x5coVTJs2DUeOHMHu3btZOBqo4n/+QfGTVL3us+B2nF73R0RERET0PBaNOnTmzBl8+OGHMDExwebNmzF69GgAwJUrV9C7d2/s3bsXX331FWbNmiVy0n+VPM2BkJcrdgy5zH37kBN1AiY21jCxtta4fVHyA+ReuAAAkLq46DRbSap+C0b6l+Oro2E/bJhe9mUREQFbOztYt22rl/0RERERVXcsGnVo6dKlEAQBEydOlBeMANCsWTN89dVXGDNmDJYvX47p06fDrJo8Q/bPxh+R+t1asWNUCRZ5mnF89VXYdusmdgylLOoHwKxOHb3sy+TMGZg6OkJqZ6eX/RERERFVdywadSQrKwtHjhwBAEyYMEFh+SuvvIK3334baWlpiIyMRK9evTTaft1HAvKuX69wneSZM1F0Lwlmnp4wsVJvFMOCuHiNcpA4TAQBEkGA/eDBqPv5crHj1GgmJia8hVwLPG/6w3NNRET6xqJRRy5fvozCwkJYWloqHZzAzMwMbdq0wdGjR3H27FmNi8Z3t5QgcftwtdYt+vtvjbZNlaCnN27NL/8F0+JiwL2uXvZnzPr16wcLCwuxYxicoKAgODs7ix3DKAQHB8PNzU3sGEREZERYNOpIXFzZgCVeXl4wNVV+Wv38/HD06FH5uqQe54lvat5IEJB74SKE4mK4Tp8GiYqfSWVIHRxg2bixzrdL4qqjp9tgaxpLS0v4+vqKHcMoWFlZwcfHR+wYRERkRFg06kh6ejoAwNHRUeU6smWydZUpKCgoN9dZZmYmAOBpaYkuYr6QVds2qPvZZ3rZlzpMzM1hYmOjVVvLt8r+X6rDPM8qAVCYlVVFWyciIiIiKi/rf+89BUHQ635ZNOpIfn4+AMDc3FzlOrJb3vLy8lSus2zZMixevFjh9e537lQyoZri44Bff9XPvoiIiIiISGPZ2dmwt7fX2/5YNOqIpWXZwDOFhYUq15FdQbSyslK5zrx58zBz5kz59xkZGfD29kZSUpJeO4a62rRpg/Pnz1fLbWvTXt026qxX0TqaLsvKykK9evVw//592FXDUT3ZD7Rbh/1Af9uuzv2gouXsB7rftqbb0GR9bX/OL1puiP0AqN59wRD7gapl1b0v1KR+oEmbqnqPIAgCWrVqhbp19TvOBYtGHVHn1lN1bmG1sLBQOgiHvb19tfxFIJVKqyxXZbetTXt126izXkXraLvMzs6O/UAP7dkPtMN+oP06qpazH+h+25puQ5P1tf05v2i5IfYDoHr3BUPsBy9aVl37Qk3qB5q0qcr3CObm5nofRZtjdutI/fr1AQBJSUkoLi5Wus6d/91iKlu3JnjnnXeq7ba1aa9uG3XWq2gdbZdVV+wH2q3DfqC/bVfnflDRcvYD3W9b021osr62P+cXLTfEfgBU775giP1A0xzVRU3qB5q0qWnvESSCvp+irKGysrLg4uKCoqIinD17Fm3bti23vKioCC4uLsjKysKhQ4fUnnIjKysL9vb2yMzMrJafHpF+sB8QwH5AZdgPCGA/oH+xL5A+8EqjjtjZ2aFnz54AgB9//FFh+Y4dO5CVlQVnZ2eEhoaqvV0LCwssXLiQ88YZOfYDAtgPqAz7AQHsB/Qv9gXSB15p1KFTp06hS5cukEgk2Lx5M0aPHg0AuHLlCnr37o1Hjx7h888/x5w5c0ROSkREREREpB4WjTr26aefYv78+QAAPz8/2NraIiYmBqWlpejfvz/27NkDqVQqckoiIiIiIiL18PZUHfvoo48QHh6O7t27Iy0tDfHx8QgODsbq1av1UjAmJiZi8ODBqFWrFhwdHfGf//wHqampVbpPql7+/vtvTJ06Fe3atYOlpSUkEonYkUgkO3fuxNChQ+Hl5QVra2sEBgZi5cqVKCoqEjsa6dGhQ4cQGhoKNzc3WFhYwNvbGxMnTkRycrLY0UgkxcXFaNq0KSQSCbZu3Sp2HNKjY8eOQSKRKHy1bt1a7GhUzXHKjSowYMAADBgwQO/7ffr0Kbp16wZnZ2ds2bIFeXl5+OCDD9C/f3+cOXNG70Pzkjji4+OxY8cOtGnTBu3atUNUVJTYkUgkX375JerVq4fly5fD3d0dp0+fxvz583H16lX897//FTse6ck///yDtm3bYtq0aXB2dkZcXBw++eQTREREICYmpsK5g6lm+vrrr/HkyROxY5CINmzYgMDAQPn3tra2IqYhQ8CisQZZt24dHj58iNOnT8Pd3R0A4OPjg7Zt22LPnj0YOnSoyAlJH0JCQpCSkgIAWL58OYtGIxYeHg5XV1f59926dYMgCFiwYAG++OIL1K5dW8R0pC+jR4+WP2MPAF27doWXlxd69+6NqKgo9O7dW8R0pG/JyclYvHgx1qxZg7Fjx4odh0QSGBiI9u3bix2DDAgvPdUgf/zxB7p16yYvGAGgTZs2aNCgAcLDw0VMRvrEK8ok82zBKNOqVSsAwIMHD/Qdh6oRZ2dnAICpKT87NjbTp0/HoEGDEBISInYUIjIgfHdZxe7evYv169dj4sSJaNasGUxNTSGRSLB06VK12u/fvx89e/aEk5MTbGxs0LJlS3zzzTcoLS1VWDc2NrbcrQYygYGBuHHjRqWPhbSnz35A1ZvYfeHEiRMwNzeHv79/ZQ6DKkmMflBSUoKCggLExsZi9uzZaNGiBbp27aqrQyIt6LsfHDx4EIcPH8aKFSt0eRhUSWL8Phg8eDCkUinq1KmDSZMmIT09XVeHQzWVQFVq+vTpAgCFryVLlryw7bJly+Tr+/n5CU2bNhVMTEwEAMKgQYOEkpKScuubmZkp3e6ECROEBg0a6OyYSHP67AfK2lL1IVZfEARBiI2NFaytrYV3331XV4dDWhKjHzRs2FDernXr1kJKSoquD4s0pM9+kJeXJ/j7+wtffvmlIAiCcPfuXQGAsGXLlio5NlKfPvvBpUuXhFmzZgnh4eFCZGSk8Nlnnwm1atUSmjdvLhQWFlbVIVINwCuNVczFxQUDBgzAJ598ggMHDmDYsGFqtTtz5gw+/PBDmJiY4Ndff0VCQgKuXLmCS5cuoXbt2ti7dy+++uorhXbKRsoUOKuK6PTdD6j6EqsvpKWlYejQofD398fy5ct1dTikJTH6wW+//YYzZ84gLCwM2dnZeOmll5CVlaXLwyIN6bMffPbZZzA3N8e0adOq4lCoEvTZD1q0aIEVK1ZgwIABCA0Nxbx58/DLL7/gr7/+ws6dO6vi8KimELtqNTZjx45V69Ojfv36CQCEt956S2HZL7/8IgAQnJ2dy30q5ObmJrz//vsK6w8dOlRo37595cOTzlRlP3gWrzRWf/roC9nZ2ULbtm0Fb29vITk5WWfZSXf09TtB5v79+4JUKhVWrFhRqdykW1XVDxITEwULCwth586dQnp6upCeni5cuXJFACD8+OOPQkZGRpUcD2lH378PSktLBRsbG2HmzJmVyk01G680VkNZWVk4cuQIAGDChAkKy1955RXY2dkhLS0NkZGR8tcDAwMRGxursH5sbCwaN25cdYGpSmjbD6jmqUxfKCgowNChQ3Hnzh0cOnQIdevW1Utm0j1d/k7w9PREnTp1EB8fXyVZqepo0w/u3r2LgoICDB8+HI6OjnB0dESzZs3k2+BIyoanKt4jcF5nqgiLxmro8uXLKCwshKWlJVq2bKmw3MzMDG3atAEAnD17Vv76gAEDEBkZKZ9uAQAuXryIW7duYeDAgVUfnHRK235ANY+2faGkpASjR49GdHQ09u/fj4YNG+otM+meLn8nJCQk4MGDBxwQyQBp0w+aN2+OyMjIcl9btmwBACxYsACHDx/W3wGQTujy98HevXuRk5MjX59IGY61XQ3FxcUBALy8vFQOh+7n54ejR4/K1wWAiRMn4ptvvsGgQYOwcOFC5OfnY+7cuWjbti0GDx6sl+ykO9r2AwDy5xJiYmLKfd+kSRM0adKkqiJTFdG2L7zzzjvYvXs3lixZgpKSEkRHR8uX+fv7K52Sg6ovbfvB0KFD0apVKzRt2hS2traIjY3Fl19+CU9PT6VXKKh606YfODg4IDQ0tNw6iYmJAMr+XeD0G4ZH298Hr732Gvz8/NCyZUvY2trizJkz+OKLL9C6dWu1n6Uk48SisRqSDXvs6Oioch3ZsmeHSK5VqxYiIiIwffp0jBw5EqamphgwYABWrVrFufsMkLb9ACi7LUXZ9wsXLsSiRYt0mJL0Qdu+cPDgQQBlVxIWLFhQbv2ffvoJ48aN03FSqkra9oP27dtj27ZtWLFiBYqLi+Hl5YVhw4Zh7ty5cHJyqtrQpHOV+beBag5t+0FgYCB+/fVXrF69Gvn5+fD09MSkSZPw8ccfc95WqhB7RzWUn58PADA3N1e5joWFBQAgLy+v3Ou+vr7Yu3dv1YUjvalMPxA4Ym6Nom1fkF1JoJpB234wd+5czJ07t2rDkd5U5t+GZ/n4+PDfCgOmbT+YN28e5s2bV7XhqEbi5adqyNLSEgBQWFiocp2CggIAgJWVlV4ykf6xH5AM+wIB7AdUhv2AAPYD0j8WjdWQOreVqHNbAhk29gOSYV8ggP2AyrAfEMB+QPrHorEaql+/PgAgKSkJxcXFSte5c+dOuXWp5mE/IBn2BQLYD6gM+wEB7Aekfywaq6EWLVrAzMwM+fn5uHTpksLyoqIinD9/HgDQrl07fccjPWE/IBn2BQLYD6gM+wEB7AekfywaqyE7Ozv07NkTAPDjjz8qLN+xYweysrLg7OysMIQ21RzsByTDvkAA+wGVYT8ggP2A9I9FYzX10UcfQSKRYMOGDfIJeAHgypUrmDlzJgBgzpw5FY6aRYaP/YBk2BcIYD+gMuwHBLAfkJ4JVKVOnjwpODs7y78sLCwEAIK1tXW515OSkhTaLl26VAAgABD8/PyEpk2bCiYmJgIAoX///kJxcbEIR0TaYD8gGfYFEgT2AyrDfkCCwH5AhoFFYxWLjIyU/2Wu6Ovu3btK24eHhwvdu3cX7O3tBWtra6FZs2bC6tWr+UvAwLAfkAz7AgkC+wGVYT8gQWA/IMMgEQTO7EpERERERETK8ZlGIiIiIiIiUolFIxEREREREanEopGIiIiIiIhUYtFIREREREREKrFoJCIiIiIiIpVYNBIREREREZFKLBqJiIiIiIhIJRaNREREREREpBKLRiIiIiIiIlKJRSMRERERERGpxKKRiIiIiIiIVGLRSERELxQaGgqJRIJjx46JHaVCPj4+kEgkSExM1Mv+jh07BolEgtDQUL3sr6osWrQIEokEixYtEjtKpcXExEAqlWLy5Mmi7L979+5wcHBAWlqaKPsnIqoKLBqJiIxMUlISZs6ciaCgINjY2MDKygpeXl7o2LEjZs+ejUOHDokdsdpYtGiRwRdSiYmJWLRoEcLCwsSOohdz586FVCrFvHnzRNn/ggULkJmZiaVLl4qyfyKiqsCikYjIiERERCAoKAirVq3CzZs34ebmhqZNm8LCwgLR0dH48ssvMWbMGIV2Xl5eaNiwIaytrUVILZ7Fixdj8eLFKpdbW1ujYcOG8PLy0mMqzSQmJmLx4sUVFo0uLi5o2LAhXFxc9BesCpw4cQL79+/HmDFj4O3tLUqGbt26oX379vjuu++QlJQkSgYiIl0zFTsAERHpR1ZWFkaOHIns7Gz0798f3377bbk31hkZGdizZw+2b9+u0HbTpk36jGow2rZti5s3b4odo9LeffddvPvuu2LHqLQ1a9YAAMaOHStqjrFjxyI6Ohrr16/HkiVLRM1CRKQLvNJIRGQk9u/fj9TUVNjZ2WH79u0KV2IcHBwwduxY7Nu3T6SERNp78uQJfv/9d9StWxchISGiZhk+fDhMTU0RFhaGkpISUbMQEekCi0YiIiNx584dAECDBg00vs1U1UA448aNg0QiQVhYGO7du4fXXnsNtWvXhq2tLTp06IA///xTvu61a9cwbNgwuLm5wdraGiEhIYiOjlbYV2JiIiQSCXx8fFTmkUgkkEgkaudPSUnBN998g969e8PHxweWlpZwdHRE165d8fPPPyusLxsY5vn9yb5kA+28aCCcpKQkvP322/D19YWFhQVcXFzQt29fHDhwQOn6zw5Ik5mZiRkzZsDLywsWFhYICAjAkiVLUFxcrPZxh4aGolu3bgCA48ePlzuGZ8+vqoFwwsLCIJFIMG7cOOTl5WHevHnw8/ODlZUVGjZsiG+++Ua+blpaGqZPnw5vb29YWloiMDDwhc9RHjp0CIMGDULt2rVhYWEBT09PjB8/HgkJCWofo8zu3btRWFiIvn37wsRE8e3Ns8dSUFCARYsWISAgAJaWlqhXrx5mzpyJnJwcpdsODw9H79694eLiAjMzM7i6uqJp06aYOnUqbty4obC+i4sL2rVrh7///hunTp3S+FiIiKob3p5KRGQk7OzsAABxcXHIyMiAg4ODzrZ99+5dzJ49G3l5eWjUqBHu3buH6Oho9OvXD4cOHYK5uTn69OkDMzMz+Pv7Iz4+HidOnECPHj1w7tw5BAYG6iyLMhs2bMCCBQtgZWWFunXrIjg4GI8fP0ZUVBSioqJw+vRprF27Vr6+l5cXOnXqJH/D36lTp3Lbs7S0fOE+z549iz59+iAjIwM2NjYIDg7Go0ePcPDgQRw8eBALFizAJ598orRtZmYmOnTogLi4OAQFBUEqlSIhIQEff/wxkpKSsH79erWOOzg4GGlpaYiJiYGdnR2Cg4Ply9zd3dXaBgAUFhaW+1kJgoDbt29j2rRpSE9Px+TJk9G5c2fcu3cPgYGBKC4uRmxsLMaPHw9BEDB+/HiFbc6YMQNff/01AMDNzQ2BgYFISEhAWFgYdu3ahQMHDqBjx45qZ4yKigJQdstwRYqKitCrVy+cOHECTZo0gY+PD+Li4rBq1SrExMTg8OHD5dZfs2YNpk6dCgCoU6cOmjdvjszMTMTFxeHatWvw9/dH48aNFfbTtm1bnDp1ClFRUaJf+SQiqjSBiIiMwq1btwQTExMBgNCqVSth586dQkZGhlptu3btKgAQIiMjy70+duxYAYBgZmYmjBo1SsjKyhIEQRBKSkqEKVOmCACEZs2aCT4+PsLMmTOFgoICQRAEIT8/Xxg4cKAAQBgxYkS5bd69e1cAIHh7e6vMA0BQ9k+Yt7e3AEC4e/duuddPnDghRERECMXFxeVev3LlitC4cWMBgHDs2DG19yMTGRkpABC6du1a7vWcnBzBy8tLfnyy8yIIghAWFiZIpVIBgLB///5y7RYuXCg/nyEhIUJycrJ82d69e+Xtbty4oTKTuhmV7XfhwoXlXv/pp5/keYKDg4U7d+7Il23ZskUAIFhZWQm9evUSunXrJjx69Ei+/NNPPxUACO7u7grn/fvvvxcACL6+vuX6VHFxsbB06VIBgODp6Snk5eWpfZy+vr4CAOHChQtKlz97LE2aNBFu3bolX3bmzBnBzs5OACAcOHBA/npRUZHg6OgomJqaCrt37y63vaKiIiE8PFw4fvy40v39+uuvAgChd+/eah8DEVF1xaKRiMiIyN7Iy74kEonQsGFDYdy4ccLWrVuF/Px8pe1eVDS6u7sLOTk55ZZlZGQIlpaWAgChRYsWQmlpabnlN2/eFAAIdnZ25V6viqKxIkeOHBEACBMnTlR7PzKqCrL169cLAITatWsrLXxkBXWXLl3KvS4r3qysrIT79+8rtHv55ZcFAMJXX32l5tHppmiUSCTCpUuXFNp16NBBnvfZAlcQygpADw8PAUC5tgUFBUKdOnUEqVSqdJuCIAjDhg0TAAibNm1S6xhLS0sFU1NTAYBCDmXHcv78eYXlM2fOFAAI06ZNk7/28OFDef/VlOy8N2rUSOO2RETVDZ9pJCIyIh9++CEiIiLQr18/mJubQxAE3Lp1C2FhYRg1ahQaNGig8NyiOkaPHq3wnKS9vT18fX0BAOPHj1d4BrFhw4awsrJCVlaWXiZCz87Oxvr16zF27Fj06tULXbp0QefOnfHBBx8AAK5cuaKzfclucZw4caLSW1mnT58OADh9+rTS5+j69OkDT09PhdfbtGkD4N/nU/WlRYsWaNGihcLrzZs3BwD07dsXdevWLbdMKpWiadOmAMrnPXPmDFJSUtCyZUul2wSAQYMGASh7DlMdGRkZ8mc9nZycKly3efPmaN26tcLrys6tq6srLCwscPv2bY37hyzHkydPNGpHRFQd8ZlGIiIj061bN3Tr1g15eXm4cOECzp49i/379+PYsWNISkpCv379cOnSJTRq1Ejtbfr7+yt93dXVFTdu3KhweVJSEp4+fQpnZ2etjkcdly9fxoABA/DgwQOV6/zzzz8629/t27cBAE2aNFG6vH79+jA3N0dhYSESEhLkxZWMqvPl5uYGAHj69KnOsqqjop+fOsufzXvt2jUAZQMede7cWWm7jIwMAEBycrJa+fLz8+V/Njc3r3BdTc6tVCrFtGnTsGLFCrRs2RKdOnVCt27d5B84VPRsq5WVFQAgLy9PrWMgIqrOWDQSERkpKysrdOnSBV26dMGsWbNw8uRJ9OnTBzk5OVi5cqXag60AUDkaq+zq4ouWC4KgYXr1lZSUYMSIEXjw4AH69euHuXPnIjAwEA4ODpBKpYiPj0f9+vVRVFSks33KCg9ZIfI8iUQCV1dXJCcnIzs7W2G5jY2N0nayUUGr8nwpo8ufb2ZmJoCyK3AvugqnbsH17NXFzMxMODo6qlxX03O7fPlyeHh44Ntvv8WJEydw4sQJAGUDS02ZMgWLFi2ChYWFwvZkH0K4uLiodQxERNUZb08lIiIAQOfOnTFlyhQAwLlz50TL8aJCUtW0CKqcO3cO8fHx8Pb2xq5duxASEgJnZ2dIpVIAwP379ysXWAlbW1sAwOPHj5UuFwRBXjDVqlVL5/uvzmTnZsyYMRDKxlZQ+aXurdIWFhby0YF1ecUYKCsmp0+fjtu3b+Pu3bv473//i1GjRiE/Px/Lly/H+++/r7SdLIfsaisRkSFj0UhERHJ+fn4AyqZYEIvsSpCqq1Dx8fEabU82p2KrVq2UXhHS5bOMMg0aNAAAxMbGKl0eFxeHwsJCSKVSlbdL6oom81nqg+yW3ZiYGJ1uV/Z8pbJ5E3XFx8cHr7/+OrZs2YK9e/cCADZu3IjS0lKFdWU/+5YtW1ZZHiIifWHRSERkJFJTU194W+Pp06cBlD1zJxZnZ2fY29sjLy8P169fV1i+YcMGjbYne7bs0aNHCsuKioqwevXqF7bV9Lm03r17AwDWr19f7nk7mf/7v/8DUDb/o6rbJXWluj1b16VLF7i4uODKlStaDbqkiuz5yAsXLuhsmxVp3749gLLzmp6errBcdrW+S5cueslDRFSVWDQSERmJzZs3o3nz5li/fr3CaKUZGRn4+OOPsXnzZgBQOhm7vkgkEnnRNXPmzHIDk/z3v//Fxo0bNdpe+/btYWpqilOnTmHTpk3y1zMzMzFmzBilxaSM7MqruqN4yowePRpeXl549OgRxo0bV+4YNm/ejHXr1gGAfOTWqiQbwTY2NrZajORpaWmJTz75BADwyiuvYPfu3QofZsTExGDu3Lk4deqU2tvt1asXAODkyZM6yxobG4tJkybh/Pnz5TIWFBTg008/BQB4e3srDOIkCAJOnz4NqVSKHj166CwPEZFYWDQSERkJiUSCq1ev4q233oKLiwv8/PzQrl07NGjQALVr18aSJUsgCAJmzZqFoUOHipp18eLFsLW1xeHDh1GnTh20atUKdevWxbhx47By5UqNtlWnTh3MmDEDADB27Fh4e3ujdevWcHd3x++//45Vq1apbDty5EgAwIABA9CyZUuEhoYiNDQUKSkpFe7T2toa27dvh729PbZt24Y6deqgTZs28PLywn/+8x8UFxdj/vz56Nu3r0bHog1XV1d0794dT58+hb+/P9q3b4/Q0FCMGjWqyvetyttvv40PPvgAqampePnll+Hi4oK2bduiVatWcHZ2RnBwML744gulgwSpEhISgoCAABw7dqzCDwI0UVhYiB9++AFt27aFk5MTWrVqhZYtW6J27dpYsWIFzM3NsXbtWoV2p0+fxt9//43evXsrTEVCRGSIWDQSERmJKVOmICIiArNnz0bHjh1RUlKCv/76C8nJyfD29sbrr7+OEydOYMWKFWJHRaNGjRAVFYU+ffrAxMQEt27dgq+vL8LDwzF58mSNt/fFF19g9erVaNSoEVJSUnDv3j307NkTJ06cQJ8+fVS2++CDD7Bw4UIEBAQgNjYWx48fx/Hjx5Xecvq8du3a4cqVK5g0aRJcXFxw9epVPH36FL169cK+ffuwZMkSjY9DW7/++ivGjRsHOzs7XLx4EcePH0d0dLTe9q/MsmXLcOrUKbz66quwsbHBlStXkJiYCE9PT7zxxhvYt2+fRlfpJBIJJk6ciJKSEmzbtk0nGevXr4/169fjlVdegaurK27fvo24uDh4eHhg8uTJiI2NVVr4b9myBUDZPJ1ERDWBRND3uN1EREREVSArKwv+/v5wcnLCjRs35NNo6FNGRgZ8fHzg5eWFv/76S5QMRES6xt9kREREVCPY2dlh/vz5uH37NrZu3SpKhq+//hqZmZlYvnw5C0YiqjFMxQ5AREREpCtvv/02srKylE6DoQ+Ojo746quv0K9fP1H2T0RUFXh7KhEREREREanE+yaIiIiIiIhIJRaNREREREREpBKLRiIiIiIiIlKJRSMRERERERGpxKKRiIiIiIiIVGLRSERERERERCqxaCQiIiIiIiKVWDQSERERERGRSiwaiYiIiIiISCUWjURERERERKTS/wOlYafa53/vCAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_simulation_time_distribution(df):\n",
" logbins = np.geomspace(df[\"time\"].min()+1, df[\"time\"].max(), 1_000)\n",
"\n",
" plt.rcParams.update({\"font.size\": 16})\n",
" fig, ax = plt.subplots(figsize=(10,7))\n",
" values, bins, _ = ax.hist(\n",
" df[\"time\"], \n",
" bins=logbins,\n",
" color=\"tab:orange\", linewidth=3,\n",
" histtype=\"step\", cumulative=True,\n",
" label = \"all simulations\"\n",
" )\n",
" values, bins, _ = ax.hist(\n",
" df[ df[\"simulation_type\"] == \"AA\" ][\"time\"], \n",
" bins=logbins,\n",
" color=\"tab:green\", linewidth=3,\n",
" histtype=\"step\", cumulative=True,\n",
" label = \"all-atom simulations\"\n",
" )\n",
" values, bins, _ = ax.hist(\n",
" df[ df[\"simulation_type\"] == \"GG\" ][\"time\"], \n",
" bins=logbins,\n",
" color=\"tab:red\", linewidth=3,\n",
" histtype=\"step\", cumulative=True,\n",
" label = \"coarse-grain simulations\"\n",
" )\n",
" ax.set_xlabel(\"Simulation time (ns)\")\n",
" ax.set_ylabel(\"Cumulated distribution of files\")\n",
" ax.set_xscale(\"log\")\n",
" ax.set_xlim(1, df[\"time\"].max() - df[\"time\"].max() * 0.1)\n",
" text_y_position = 5600\n",
" ax.annotate(\"10 ns\", xy=(10, 0), xytext=(10, text_y_position), color=\"gray\",\n",
" arrowprops={\"arrowstyle\":\"-\", \"linestyle\":\"--\", \"lw\":1, \"color\":\"gray\"}, \n",
" horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=15)\n",
" ax.annotate(\"50 ns\", xy=(50, 0), xytext=(50, text_y_position), color=\"gray\",\n",
" arrowprops={\"arrowstyle\":\"-\", \"linestyle\":\"--\", \"lw\":1, \"color\":\"gray\"}, \n",
" horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=15)\n",
" ax.annotate(\"200 ns\", xy=(200, 0), xytext=(200, text_y_position), color=\"gray\",\n",
" arrowprops={\"arrowstyle\":\"-\", \"linestyle\":\"--\", \"lw\":1, \"color\":\"gray\"}, \n",
" horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=15)\n",
" ax.annotate(\"1 µs\", xy=(1000, 0), xytext=(1000, text_y_position), color=\"gray\",\n",
" arrowprops={\"arrowstyle\":\"-\", \"linestyle\":\"--\", \"lw\":1, \"color\":\"gray\"}, \n",
" horizontalalignment=\"center\", verticalalignment=\"bottom\", fontsize=15)\n",
" legend_elements = [\n",
" Line2D([0], [0], color=\"tab:orange\", lw=3, label=\"all simulations\"),\n",
" Line2D([0], [0], color=\"tab:green\", lw=3, label=\"all-atom simulations\"),\n",
" Line2D([0], [0], color=\"tab:red\", lw=3, label=\"coarse-grain simulations\")\n",
" ] \n",
" fig.legend(handles=legend_elements, loc=(0.61, 0.30), prop={\"size\":14})\n",
" filename = \"fig/gromacs_simulation_time\"\n",
" plt.savefig(f\"{filename}.png\", dpi=300, bbox_inches=\"tight\")\n",
" plt.savefig(f\"{filename}.svg\", bbox_inches=\"tight\")\n",
"\n",
"plot_simulation_time_distribution(mdp_df)"
]
},
{
"cell_type": "markdown",
"id": "b621f780-eff2-4eb7-888e-95aa9036aa7c",
"metadata": {},
"source": [
"### Simulations above 50 µs"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "5c360e16-9d0f-4bfd-8ad6-7c60be490dbd",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_origin</th>\n",
" <th>dataset_id</th>\n",
" <th>file_name</th>\n",
" <th>dt</th>\n",
" <th>nsteps</th>\n",
" <th>temperature</th>\n",
" <th>thermostat</th>\n",
" <th>barostat</th>\n",
" <th>dataset_url</th>\n",
" <th>time</th>\n",
" <th>simulation_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>925</th>\n",
" <td>zenodo</td>\n",
" <td>7017263</td>\n",
" <td>degrader-ternary-complex-prediction-paper-final/hremd/scripts/config/md.mdp</td>\n",
" <td>0.002</td>\n",
" <td>5.000000e+11</td>\n",
" <td>300.0</td>\n",
" <td>V-rescale</td>\n",
" <td>Parrinello-Rahman</td>\n",
" <td>https://zenodo.org/record/7017263</td>\n",
" <td>1000000.0</td>\n",
" <td>AA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>654</th>\n",
" <td>zenodo</td>\n",
" <td>846428</td>\n",
" <td>md.mdp</td>\n",
" <td>0.020</td>\n",
" <td>5.000000e+09</td>\n",
" <td>315.0</td>\n",
" <td>v-rescale</td>\n",
" <td>Parrinello-rahman</td>\n",
" <td>https://zenodo.org/record/846428</td>\n",
" <td>100000.0</td>\n",
" <td>GG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2066</th>\n",
" <td>zenodo</td>\n",
" <td>4451274</td>\n",
" <td>SARS-COV2_spike_simulations_Dform_Gform/md.mdp</td>\n",
" <td>0.002</td>\n",
" <td>5.000000e+10</td>\n",
" <td>310.0</td>\n",
" <td>No</td>\n",
" <td>Berendsen</td>\n",
" <td>https://zenodo.org/record/4451274</td>\n",
" <td>100000.0</td>\n",
" <td>AA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9287</th>\n",
" <td>osf</td>\n",
" <td>a43z2</td>\n",
" <td>hGBP1 dimer Martini-MD/cg_md_dimer_traj.mdp</td>\n",
" <td>0.030</td>\n",
" <td>2.500000e+09</td>\n",
" <td>310.0</td>\n",
" <td>v-rescale</td>\n",
" <td>parrinello-rahman</td>\n",
" <td>https://osf.io/a43z2/</td>\n",
" <td>75000.0</td>\n",
" <td>GG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>zenodo</td>\n",
" <td>4115972</td>\n",
" <td>md.mdp</td>\n",
" <td>0.020</td>\n",
" <td>2.500000e+09</td>\n",
" <td>310.0</td>\n",
" <td>V-rescale</td>\n",
" <td>Parrinello-Rahman</td>\n",
" <td>https://zenodo.org/record/4115972</td>\n",
" <td>50000.0</td>\n",
" <td>GG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>317</th>\n",
" <td>zenodo</td>\n",
" <td>4116245</td>\n",
" <td>md.mdp</td>\n",
" <td>0.020</td>\n",
" <td>2.500000e+09</td>\n",
" <td>310.0</td>\n",
" <td>V-rescale</td>\n",
" <td>Parrinello-Rahman</td>\n",
" <td>https://zenodo.org/record/4116245</td>\n",
" <td>50000.0</td>\n",
" <td>GG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>346</th>\n",
" <td>zenodo</td>\n",
" <td>4293255</td>\n",
" <td>md_300.mdp</td>\n",
" <td>0.002</td>\n",
" <td>2.500000e+10</td>\n",
" <td>300.0</td>\n",
" <td>Nose-Hoover</td>\n",
" <td>Parrinello-Rahman</td>\n",
" <td>https://zenodo.org/record/4293255</td>\n",
" <td>50000.0</td>\n",
" <td>AA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>379</th>\n",
" <td>zenodo</td>\n",
" <td>2653859</td>\n",
" <td>md.mdp</td>\n",
" <td>0.025</td>\n",
" <td>2.000000e+09</td>\n",
" <td>298.0</td>\n",
" <td>v-rescale</td>\n",
" <td>parrinello-rahman</td>\n",
" <td>https://zenodo.org/record/2653859</td>\n",
" <td>50000.0</td>\n",
" <td>GG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td>zenodo</td>\n",
" <td>4115152</td>\n",
" <td>md.mdp</td>\n",
" <td>0.020</td>\n",
" <td>2.500000e+09</td>\n",
" <td>310.0</td>\n",
" <td>V-rescale</td>\n",
" <td>Parrinello-Rahman</td>\n",
" <td>https://zenodo.org/record/4115152</td>\n",
" <td>50000.0</td>\n",
" <td>GG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>zenodo</td>\n",
" <td>4196842</td>\n",
" <td>md.mdp</td>\n",
" <td>0.020</td>\n",
" <td>2.500000e+09</td>\n",
" <td>310.0</td>\n",
" <td>V-rescale</td>\n",
" <td>Parrinello-Rahman</td>\n",
" <td>https://zenodo.org/record/4196842</td>\n",
" <td>50000.0</td>\n",
" <td>GG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>649</th>\n",
" <td>zenodo</td>\n",
" <td>4445375</td>\n",
" <td>md.mdp</td>\n",
" <td>0.025</td>\n",
" <td>2.000000e+09</td>\n",
" <td>310.0</td>\n",
" <td>V-rescale</td>\n",
" <td>berendsen</td>\n",
" <td>https://zenodo.org/record/4445375</td>\n",
" <td>50000.0</td>\n",
" <td>GG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1355</th>\n",
" <td>zenodo</td>\n",
" <td>7457014</td>\n",
" <td>parameter_files/20fs_prod.mdp</td>\n",
" <td>0.020</td>\n",
" <td>2.500000e+09</td>\n",
" <td>310.0</td>\n",
" <td>v-rescale</td>\n",
" <td>parrinello-rahman</td>\n",
" <td>https://zenodo.org/record/7457014</td>\n",
" <td>50000.0</td>\n",
" <td>GG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1356</th>\n",
" <td>zenodo</td>\n",
" <td>7457014</td>\n",
" <td>parameter_files/20fs_prod_nopull.mdp</td>\n",
" <td>0.020</td>\n",
" <td>2.500000e+09</td>\n",
" <td>310.0</td>\n",
" <td>v-rescale</td>\n",
" <td>parrinello-rahman</td>\n",
" <td>https://zenodo.org/record/7457014</td>\n",
" <td>50000.0</td>\n",
" <td>GG</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_origin dataset_id \\\n",
"925 zenodo 7017263 \n",
"654 zenodo 846428 \n",
"2066 zenodo 4451274 \n",
"9287 osf a43z2 \n",
"286 zenodo 4115972 \n",
"317 zenodo 4116245 \n",
"346 zenodo 4293255 \n",
"379 zenodo 2653859 \n",
"394 zenodo 4115152 \n",
"565 zenodo 4196842 \n",
"649 zenodo 4445375 \n",
"1355 zenodo 7457014 \n",
"1356 zenodo 7457014 \n",
"\n",
" file_name \\\n",
"925 degrader-ternary-complex-prediction-paper-final/hremd/scripts/config/md.mdp \n",
"654 md.mdp \n",
"2066 SARS-COV2_spike_simulations_Dform_Gform/md.mdp \n",
"9287 hGBP1 dimer Martini-MD/cg_md_dimer_traj.mdp \n",
"286 md.mdp \n",
"317 md.mdp \n",
"346 md_300.mdp \n",
"379 md.mdp \n",
"394 md.mdp \n",
"565 md.mdp \n",
"649 md.mdp \n",
"1355 parameter_files/20fs_prod.mdp \n",
"1356 parameter_files/20fs_prod_nopull.mdp \n",
"\n",
" dt nsteps temperature thermostat barostat \\\n",
"925 0.002 5.000000e+11 300.0 V-rescale Parrinello-Rahman \n",
"654 0.020 5.000000e+09 315.0 v-rescale Parrinello-rahman \n",
"2066 0.002 5.000000e+10 310.0 No Berendsen \n",
"9287 0.030 2.500000e+09 310.0 v-rescale parrinello-rahman \n",
"286 0.020 2.500000e+09 310.0 V-rescale Parrinello-Rahman \n",
"317 0.020 2.500000e+09 310.0 V-rescale Parrinello-Rahman \n",
"346 0.002 2.500000e+10 300.0 Nose-Hoover Parrinello-Rahman \n",
"379 0.025 2.000000e+09 298.0 v-rescale parrinello-rahman \n",
"394 0.020 2.500000e+09 310.0 V-rescale Parrinello-Rahman \n",
"565 0.020 2.500000e+09 310.0 V-rescale Parrinello-Rahman \n",
"649 0.025 2.000000e+09 310.0 V-rescale berendsen \n",
"1355 0.020 2.500000e+09 310.0 v-rescale parrinello-rahman \n",
"1356 0.020 2.500000e+09 310.0 v-rescale parrinello-rahman \n",
"\n",
" dataset_url time simulation_type \n",
"925 https://zenodo.org/record/7017263 1000000.0 AA \n",
"654 https://zenodo.org/record/846428 100000.0 GG \n",
"2066 https://zenodo.org/record/4451274 100000.0 AA \n",
"9287 https://osf.io/a43z2/ 75000.0 GG \n",
"286 https://zenodo.org/record/4115972 50000.0 GG \n",
"317 https://zenodo.org/record/4116245 50000.0 GG \n",
"346 https://zenodo.org/record/4293255 50000.0 AA \n",
"379 https://zenodo.org/record/2653859 50000.0 GG \n",
"394 https://zenodo.org/record/4115152 50000.0 GG \n",
"565 https://zenodo.org/record/4196842 50000.0 GG \n",
"649 https://zenodo.org/record/4445375 50000.0 GG \n",
"1355 https://zenodo.org/record/7457014 50000.0 GG \n",
"1356 https://zenodo.org/record/7457014 50000.0 GG "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mdp_df.query(\"time >= 50_000\").sort_values(by=\"time\", ascending=False)"
]
},
{
"cell_type": "markdown",
"id": "0e244a01-29cf-4a34-8f71-a41d0d482719",
"metadata": {},
"source": [
"### Proportions of AT/CG simulations"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "b33f96d3-2154-4c5a-b41e-7d5fdd81718d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"simulation_type\n",
"AA 0.918109\n",
"GG 0.081891\n",
"dtype: float64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Below 1 µs\n",
"(mdp_df\n",
" .query(\"time < 1_000\")\n",
" .value_counts(subset=\"simulation_type\", normalize=True)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "09f84f2c-527c-46c2-98e1-ebb5766dd8cf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"simulation_type\n",
"GG 0.667614\n",
"AA 0.332386\n",
"dtype: float64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Above 1 µs\n",
"(mdp_df\n",
" .query(\"time >= 1_000\")\n",
" .value_counts(subset=\"simulation_type\", normalize=True)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "220ba060-0c22-42e7-b657-629ee65d81b6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"simulation_type\n",
"GG 0.878378\n",
"AA 0.121622\n",
"dtype: float64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Above 10 µs\n",
"(mdp_df\n",
" .query(\"time >= 10_000\")\n",
" .value_counts(subset=\"simulation_type\", normalize=True)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "8cc283af-7bc5-4d16-b86c-a9be37ce89c2",
"metadata": {},
"source": [
"## 4.C. Thermostat and barostat combinaison"
]
},
{
"cell_type": "code",
"execution_count": 172,
"id": "f6c3519c-f21a-41ba-b2d3-95f970559419",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"633\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dataset_origin</th>\n",
" <th>dataset_id</th>\n",
" <th>file_name</th>\n",
" <th>dt</th>\n",
" <th>nsteps</th>\n",
" <th>temperature</th>\n",
" <th>thermostat</th>\n",
" <th>barostat</th>\n",
" <th>dataset_url</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>zenodo</td>\n",
" <td>13853</td>\n",
" <td>mdrun.mdp</td>\n",
" <td>0.002</td>\n",
" <td>25000000.0</td>\n",
" <td>303.0</td>\n",
" <td>NaN</td>\n",
" <td>berendsen</td>\n",
" <td>https://zenodo.org/record/13853</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>zenodo</td>\n",
" <td>15550</td>\n",
" <td>mdrun.mdp</td>\n",
" <td>0.002</td>\n",
" <td>25000000.0</td>\n",
" <td>323.0</td>\n",
" <td>NaN</td>\n",
" <td>berendsen</td>\n",
" <td>https://zenodo.org/record/15550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>333</th>\n",
" <td>zenodo</td>\n",
" <td>3899875</td>\n",
" <td>minimization.mdp</td>\n",
" <td>NaN</td>\n",
" <td>10000.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>https://zenodo.org/record/3899875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>337</th>\n",
" <td>zenodo</td>\n",
" <td>4034250</td>\n",
" <td>minimization.mdp</td>\n",
" <td>NaN</td>\n",
" <td>10000.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>https://zenodo.org/record/4034250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>341</th>\n",
" <td>zenodo</td>\n",
" <td>3898344</td>\n",
" <td>minimization.mdp</td>\n",
" <td>NaN</td>\n",
" <td>10000.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>https://zenodo.org/record/3898344</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dataset_origin dataset_id file_name dt nsteps \\\n",
"39 zenodo 13853 mdrun.mdp 0.002 25000000.0 \n",
"79 zenodo 15550 mdrun.mdp 0.002 25000000.0 \n",
"333 zenodo 3899875 minimization.mdp NaN 10000.0 \n",
"337 zenodo 4034250 minimization.mdp NaN 10000.0 \n",
"341 zenodo 3898344 minimization.mdp NaN 10000.0 \n",
"\n",
" temperature thermostat barostat dataset_url \n",
"39 303.0 NaN berendsen https://zenodo.org/record/13853 \n",
"79 323.0 NaN berendsen https://zenodo.org/record/15550 \n",
"333 NaN NaN NaN https://zenodo.org/record/3899875 \n",
"337 NaN NaN NaN https://zenodo.org/record/4034250 \n",
"341 NaN NaN NaN https://zenodo.org/record/3898344 "
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Mdp files without thermostat\n",
"print(mdp_df[ mdp_df[\"thermostat\"].isna() ].shape[0])\n",
"mdp_df[ mdp_df[\"thermostat\"].isna() ].head()"
]
},
{
"cell_type": "code",
"execution_count": 173,
"id": "5d98b407-0c58-4aa9-93be-da52e63c06cc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Remaining files: 8780\n"
]
}
],
"source": [
"# Clean barostat and thermostat names\n",
"thermostats = {\n",
" \"berendsen\": \"Berendsen_t\",\n",
" \"nosehoover\": \"Nose-Hoover\",\n",
" \"vrescale\": \"V-rescale\",\n",
" \"no\": \"No temperature\"\n",
"}\n",
"# Add a space after Berendsen to differentiate from Berendsen's thermostat.\n",
"barostats = {\n",
" \"berendsen\": \"Berendsen_p\",\n",
" \"parrinellorahman\": \"Parrinello-Rahman\",\n",
" \"no\": \"No pressure\"\n",
"}\n",
"def clean_thermostat_barostat(value, conversion_dict):\n",
" if not type(value) is str:\n",
" return None\n",
" raw_str = value.lower().lower().replace(\"-\", \"\").replace(\"_\", \"\")\n",
" return conversion_dict.get(raw_str, None)\n",
"\n",
"mdp_df[\"thermostat\"] = mdp_df[\"thermostat\"].apply(lambda x: clean_thermostat_barostat(x, thermostats))\n",
"mdp_df[\"barostat\"] = mdp_df[\"barostat\"].apply(lambda x: clean_thermostat_barostat(x, barostats))\n",
"mdp_df = mdp_df.dropna(subset=[\"thermostat\", \"barostat\"])\n",
"\n",
"print(f\"Remaining files: {mdp_df.shape[0]}\")"
]
},
{
"cell_type": "code",
"execution_count": 174,
"id": "6de0b996-d5ed-4ad8-a0c5-a5e09b3a9dfb",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1000x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_thermostat_barostat_sankey_graph(df):\n",
" \"\"\"Plot sankey graph with thermostats and barostats.\"\"\"\n",
" colorDict = {\n",
" \"Berendsen_t\": \"#e66101\",\n",
" \"Nose-Hoover\": \"#fdb863\",\n",
" \"V-rescale\": \"#b2abd2\",\n",
" \"No temperature\": \"#5e3c99\",\n",
" \"Parrinello-Rahman\": \"#b3b3b3\",\n",
" \"No pressure\": \"#b3b3b3\",\n",
" \"Berendsen_p\": \"#b3b3b3\",\n",
" }\n",
" plt.rcParams.update({\"font.family\": \"sans-serif\"})\n",
" fig, ax = plt.subplots(figsize=(10,7))\n",
" ax = sankey(\n",
" df[\"thermostat\"], df[\"barostat\"], aspect=10, colorDict=colorDict,\n",
" leftLabels=[\"Berendsen_t\", \"Nose-Hoover\", \"V-rescale\", \"No temperature\"],\n",
" rightLabels=[\"Berendsen_p\",\"Parrinello-Rahman\",\"No pressure\"],\n",
" fontsize=14\n",
" )\n",
" plt.figtext(0.00, 0.88, \"Thermostat\", fontsize=16, fontweight=\"bold\", fontfamily=\"sans-serif\")\n",
" plt.figtext(0.88, 0.88, \"Barostat\", fontsize=16, fontweight=\"bold\", fontfamily=\"sans-serif\")\n",
" filename = \"fig/gromacs_thermostat_barostat\"\n",
" plt.savefig(f\"{filename}.png\", dpi=300, bbox_inches=\"tight\")\n",
" plt.savefig(f\"{filename}.svg\", bbox_inches=\"tight\")\n",
"\n",
"\n",
"plot_thermostat_barostat_sankey_graph(mdp_df)"
]
},
{
"cell_type": "code",
"execution_count": 175,
"id": "776a87e7-243a-48e3-989c-7d87d9b9e8dc",
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" <tr style=\"text-align: right;\">\n",
" <th>barostat</th>\n",
" <th>Berendsen_p</th>\n",
" <th>No pressure</th>\n",
" <th>Parrinello-Rahman</th>\n",
" <th>Total</th>\n",
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" <tr>\n",
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" <tr>\n",
" <th>Berendsen_t</th>\n",
" <td>305</td>\n",
" <td>127</td>\n",
" <td>0</td>\n",
" <td>432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>No temperature</th>\n",
" <td>1</td>\n",
" <td>1931</td>\n",
" <td>98</td>\n",
" <td>2030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nose-Hoover</th>\n",
" <td>10</td>\n",
" <td>139</td>\n",
" <td>416</td>\n",
" <td>565</td>\n",
" </tr>\n",
" <tr>\n",
" <th>V-rescale</th>\n",
" <td>1581</td>\n",
" <td>1931</td>\n",
" <td>2241</td>\n",
" <td>5753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Total</th>\n",
" <td>1897</td>\n",
" <td>4128</td>\n",
" <td>2755</td>\n",
" <td>8780</td>\n",
" </tr>\n",
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"text/plain": [
"barostat Berendsen_p No pressure Parrinello-Rahman Total\n",
"thermostat \n",
"Berendsen_t 305 127 0 432\n",
"No temperature 1 1931 98 2030\n",
"Nose-Hoover 10 139 416 565\n",
"V-rescale 1581 1931 2241 5753\n",
"Total 1897 4128 2755 8780"
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(index=mdp_df[\"thermostat\"], columns=mdp_df[\"barostat\"], margins=True, margins_name=\"Total\")"
]
},
{
"cell_type": "code",
"execution_count": 176,
"id": "742127cc-0d0f-48eb-b567-f3322200b1a4",
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" <th>thermostat</th>\n",
" <th>barostat</th>\n",
" <th>value</th>\n",
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" <td>Berendsen_p</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>No temperature</td>\n",
" <td>No pressure</td>\n",
" <td>1931</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>No temperature</td>\n",
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" <th>6</th>\n",
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" <tr>\n",
" <th>8</th>\n",
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" <td>1581</td>\n",
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" <th>10</th>\n",
" <td>V-rescale</td>\n",
" <td>No pressure</td>\n",
" <td>1931</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>V-rescale</td>\n",
" <td>Parrinello-Rahman</td>\n",
" <td>2241</td>\n",
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"text/plain": [
" thermostat barostat value\n",
"0 Berendsen_t Berendsen_p 305\n",
"1 Berendsen_t No pressure 127\n",
"2 Berendsen_t Parrinello-Rahman 0\n",
"3 No temperature Berendsen_p 1\n",
"4 No temperature No pressure 1931\n",
"5 No temperature Parrinello-Rahman 98\n",
"6 Nose-Hoover Berendsen_p 10\n",
"7 Nose-Hoover No pressure 139\n",
"8 Nose-Hoover Parrinello-Rahman 416\n",
"9 V-rescale Berendsen_p 1581\n",
"10 V-rescale No pressure 1931\n",
"11 V-rescale Parrinello-Rahman 2241"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs = pd.crosstab(index=mdp_df[\"thermostat\"], columns=mdp_df[\"barostat\"]).stack().reset_index()\n",
"pairs.columns = [\"thermostat\", \"barostat\", \"value\"]\n",
"pairs"
]
},
{
"cell_type": "code",
"execution_count": 181,
"id": "15ed07bd-5a7b-4596-ae4a-407d7993533c",
"metadata": {},
"outputs": [
{
"data": {
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"{'Berendsen_t': 0,\n",
" 'No temperature': 1,\n",
" 'Nose-Hoover': 2,\n",
" 'V-rescale': 3,\n",
" 'Berendsen_p': 4,\n",
" 'No pressure': 5,\n",
" 'Parrinello-Rahman': 6}"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels = pd.concat([pairs[\"thermostat\"], pairs[\"barostat\"]]).drop_duplicates().reset_index(drop=True)\n",
"labels_dict = dict(zip(labels.values, labels.index))\n",
"labels_dict"
]
},
{
"cell_type": "code",
"execution_count": 182,
"id": "beb48bc9-3bc9-4c4a-926e-582b280ccf32",
"metadata": {},
"outputs": [],
"source": [
"pairs[\"thermostat_index\"] = pairs[\"thermostat\"].apply(lambda x: labels_dict[x])\n",
"pairs[\"barostat_index\"] = pairs[\"barostat\"].apply(lambda x: labels_dict[x])"
]
},
{
"cell_type": "code",
"execution_count": 183,
"id": "35c3fb07-0f20-439a-b025-68e2f72223dd",
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},
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},
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},
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},
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},
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},
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},
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",
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{\"responsive\": true} ).then(function(){\n",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"link = {\"source\": pairs[\"thermostat_index\"].to_list(),\n",
" \"target\": pairs[\"barostat_index\"].to_list(),\n",
" \"value\": pairs[\"value\"].to_list()}\n",
"node = {\n",
" \"label\": [\"Berendsen\", \"No temperature\", \"Nose-Hoover\", \"V-rescale\", \"Berendsen\", \"No pressure\", \"Parrinello-Rahman\"],\n",
" \"pad\": 20,\n",
" \"thickness\": 15\n",
"}\n",
"data=go.Sankey(link=link, node=node)\n",
"fig = go.Figure(data)\n",
"fig.update_layout(font_size=15, autosize=False, width=700)\n",
"fig.update_yaxes(ticklabelposition = \"outside\")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d408b20-8d9f-45a0-aea6-31425e123aec",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "725ec08d-769c-4fc3-a874-5846e58e47d8",
"metadata": {},
"source": [
"## Watermark"
]
},
{
"cell_type": "code",
"execution_count": 185,
"id": "fe6f995e-28f2-4335-b3d3-2cc0da977ca8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The watermark extension is already loaded. To reload it, use:\n",
" %reload_ext watermark\n",
"Python implementation: CPython\n",
"Python version : 3.9.15\n",
"IPython version : 8.6.0\n",
"\n",
"Compiler : GCC 11.2.0\n",
"OS : Linux\n",
"Release : 5.15.0-58-generic\n",
"Machine : x86_64\n",
"Processor : x86_64\n",
"CPU cores : 8\n",
"Architecture: 64bit\n",
"\n",
"\n",
"jupyterlab: 3.5.2\n",
"upsetplot : 0.8.0\n",
"\n",
"numpy : 1.24.1\n",
"yaml : 6.0\n",
"pandas : 1.5.2\n",
"plotly : 5.12.0\n",
"matplotlib: 3.5.3\n",
"\n",
"Watermark: 2.3.1\n",
"\n",
"conda environment: mdda\n",
"\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark --python --machine\n",
"print(\"\")\n",
"# Versions for jupyterlab, imported packages and watermark itself\n",
"%watermark --packages jupyterlab,upsetplot --iversions --watermark\n",
"# Name of conda environment\n",
"%watermark --conda"
]
},
{
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"execution_count": null,
"id": "4b3a3497-f778-41c9-a560-0080d7fb6a06",
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"outputs": [],
"source": []
}
],
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"language": "python",
"name": "python3"
},
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"name": "ipython",
"version": 3
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"name": "python",
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"pygments_lexer": "ipython3",
"version": "3.9.15"
},
"toc-autonumbering": false,
"toc-showcode": false,
"toc-showmarkdowntxt": false,
"toc-showtags": false
},
"nbformat": 4,
"nbformat_minor": 5
}
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