diff --git a/examples/notebook_luminosity_function_binaries.ipynb b/examples/notebook_luminosity_function_binaries.ipynb
index 8325ab08f0772fc969f2acf5f9306c04ca64562d..39836e3db1e168549fc650cc9548752c9abdcddf 100644
--- a/examples/notebook_luminosity_function_binaries.ipynb
+++ b/examples/notebook_luminosity_function_binaries.ipynb
@@ -23,6 +23,7 @@
"source": [
"import os\n",
"import math\n",
+ "\n",
"from binarycpython.utils.grid import Population\n",
"\n",
"# help(Population) # Uncomment this line to see the public functions of this object"
@@ -49,7 +50,6 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "adding: max_evolution_time=0.1 to BSE_options\n",
"verbosity is 1\n"
]
}
@@ -67,122 +67,51 @@
"# All other values that you set are put in a custom_options dict\n",
"population.set(\n",
" # binary_c physics options\n",
- " max_evolution_time=0.1, # maximum stellar evolution time in Myr\n",
+ " max_evolution_time=0.1, # maximum stellar evolution time in Myr. We do this to capture only ZAMS\n",
" )\n",
"\n",
"# We can access the options through \n",
"print(\"verbosity is\", population.grid_options['verbosity'])"
]
},
- {
- "cell_type": "markdown",
- "id": "f9a65554-36ab-4a04-96ca-9f1422c307fd",
- "metadata": {},
- "source": [
- "## Adding grid variables\n",
- "The main purpose of the Population object is to handle the population synthesis side of running a set of stars. The main method to do this with binarycpython, as is the case with Perl binarygrid, is to use grid variables. These are loops over a predefined range of values, where a probability will be assigned to the systems based on the chosen probability distributions.\n",
- "\n",
- "Usually we use either 1 mass grid variable, or a trio of mass, mass ratio and period (other notebooks cover these examples). We can, however, also add grid sampling for e.g. eccentricity, metallicity or other parameters. \n",
- "\n",
- "To add a grid variable to the population object we use `population.add_grid_variable`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "68c84521-9ae8-4020-af7a-5334173db969",
- "metadata": {},
- "outputs": [],
- "source": [
- "# help(population.add_grid_variable)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bd75cebe-2152-4025-b680-dc020b80889b",
- "metadata": {},
- "source": [
- "All the distribution functions that we can use are stored in the `binarycpython.utils.distribution_functions` or `binarycpython/utils/distribution_functions.py` on git. If you uncomment the help statement below you can see which functions are available now:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "048db541-3e92-4c5d-a25c-9c5a34b9c857",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "import binarycpython.utils.distribution_functions\n",
- "# help(binarycpython.utils.distribution_functions)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2a9104fc-4136-4e53-8604-f24ad52fbe56",
- "metadata": {},
- "source": [
- "First let us set up some global variables that will be useful throughout.\n",
- "\n",
- " * The resolution is the number of stars we simulate in our model population.\n",
- " * The massrange is a list of the min and max masses\n",
- " * The total_probability is the theoretical integral of a probability density function, i.e. 1.0.\n",
- " * The binwidth sets the resolution of the final distribution. If set to 0.5, the bins in log*L* are 0.5dex wide."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "aba3fe4e-18f2-4bb9-8e5c-4c6007ab038b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set resolution and mass range that we simulate\n",
- "resolution = {\"M_1\": 40} # start with resolution = 10, and increase later if you want \"more accurate\" data\n",
- "massrange = (0.07, 100.0) # we work with stars of mass 0.07 to 100 Msun\n",
- "total_probability = 1.0 # theoretical integral of the mass probability density function over all masses \n",
- "# distribution binwidths : \n",
- "# (log10) luminosity distribution\n",
- "binwidth = { 'luminosity' : 1.0 }"
- ]
- },
{
"cell_type": "markdown",
"id": "1b3a007b-5c17-42a7-a981-7e268e6f545c",
"metadata": {},
"source": [
- "The next cell contains an example of adding the mass grid variable, sampling the phase space in linear mass *M*_1."
+ "Now that we have set up the population object, we need to add grid variables to describe the population of stars we want to run. For more information on this see the \"notebook_population.ipynb\". Here we add three grid variables:\n",
+ "- Primary mass sampled with log sampling\n",
+ "- Mass ratio sampled with linear sampling\n",
+ "- Period sampled with log10 sampling"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 3,
"id": "47979841-2c26-4b26-8945-603d013dc93a",
"metadata": {},
"outputs": [],
"source": [
- "# Set up the binary grid in \"cubic\" M1 - M2=q*M1 - log10 period space\n",
- "\n",
- "population = Population()\n",
- "\n",
"# resolution on each side of the cube, with more stars for the primary mass\n",
"nres = 10\n",
- "resolution = {\"M_1\": 4*nres,\n",
- " \"q\": nres,\n",
- " \"per\": nres}\n",
+ "resolution = {\n",
+ " \"M_1\": 4 * nres,\n",
+ " \"q\": nres,\n",
+ " \"per\": nres\n",
+ "}\n",
+ "\n",
+ "binwidth = { 'luminosity' : 1.0}\n",
"\n",
- "massrange = [0.07,100]\n",
+ "massrange = [0.07, 100]\n",
"logperrange = [0.15, 5.5]\n",
"\n",
"population.add_grid_variable(\n",
" name=\"lnm1\",\n",
" longname=\"Primary mass\",\n",
" valuerange=massrange,\n",
- " samplerfunc=\"const(math.log({min}), math.log({max}), {res})\".format(min=massrange[0],max=massrange[1],res=resolution[\"M_1\"]),\n",
+ " samplerfunc=\"self.const_linear(math.log({min}), math.log({max}), {res})\".format(min=massrange[0],max=massrange[1],res=resolution[\"M_1\"]),\n",
" precode=\"M_1=math.exp(lnm1)\",\n",
- " probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
+ " probdist=\"self.three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
" dphasevol=\"dlnm1\",\n",
" parameter_name=\"M_1\",\n",
" condition=\"\", # Impose a condition on this grid variable. Mostly for a check for yourself\n",
@@ -193,8 +122,8 @@
" name=\"q\",\n",
" longname=\"Mass ratio\",\n",
" valuerange=[\"0.1/M_1\", 1],\n",
- " samplerfunc=\"const({}/M_1, 1, {})\".format(massrange[0],resolution['q']),\n",
- " probdist=\"flatsections(q, [{{'min': {}/M_1, 'max': 1.0, 'height': 1}}])\".format(massrange[0]),\n",
+ " samplerfunc=\"self.const_linear({}/M_1, 1, {})\".format(massrange[0],resolution['q']),\n",
+ " probdist=\"self.flatsections(q, [{{'min': {}/M_1, 'max': 1.0, 'height': 1}}])\".format(massrange[0]),\n",
" dphasevol=\"dq\",\n",
" precode=\"M_2 = q * M_1\",\n",
" parameter_name=\"M_2\",\n",
@@ -206,12 +135,12 @@
" name=\"log10per\", # in days\n",
" longname=\"log10(Orbital_Period)\",\n",
" valuerange=[0.15, 5.5],\n",
- " samplerfunc=\"const({}, {}, {})\".format(logperrange[0],logperrange[1],resolution[\"per\"]),\n",
+ " samplerfunc=\"self.const_linear({}, {}, {})\".format(logperrange[0],logperrange[1],resolution[\"per\"]),\n",
" precode=\"\"\"orbital_period = 10.0 ** log10per\n",
"sep = calc_sep_from_period(M_1, M_2, orbital_period)\n",
"sep_min = calc_sep_from_period(M_1, M_2, 10**{})\n",
"sep_max = calc_sep_from_period(M_1, M_2, 10**{})\"\"\".format(logperrange[0],logperrange[1]),\n",
- " probdist=\"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**{}), math.log10(10**{}), {})\".format(logperrange[0],logperrange[1],-0.55),\n",
+ " probdist=\"self.sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**{}), math.log10(10**{}), {})\".format(logperrange[0],logperrange[1],-0.55),\n",
" parameter_name=\"orbital_period\",\n",
" dphasevol=\"dlog10per\",\n",
" )"
@@ -232,10 +161,55 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 4,
"id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "adding: C_logging_code=\n",
+ "// select ZAMS\n",
+ "if(stardata->model.model_number == 0 &&\n",
+ " stardata->model.time == 0)\n",
+ "{\n",
+ " // loop over the stars individually (equivalent to a resolved binary) \n",
+ " Foreach_star(star)\n",
+ " {\n",
+ " // select main-sequence stars\n",
+ " if(star->stellar_type <= MAIN_SEQUENCE)\n",
+ " {\n",
+ " /* Note that we use Printf - with a capital P! */\n",
+ " Printf(\"ZERO_AGE_MAIN_SEQUENCE_STAR%d %30.12e %g %g %g %g\\n\",\n",
+ " star->starnum,\n",
+ " stardata->model.time, // 1\n",
+ " stardata->common.zero_age.mass[0], // 2\n",
+ " star->mass, // 3\n",
+ " star->luminosity, // 4\n",
+ " stardata->model.probability // 5\n",
+ " );\n",
+ " }\n",
+ " }\n",
+ " \n",
+ " // unresolved MS-MS binary\n",
+ " if(stardata->star[0].stellar_type <= MAIN_SEQUENCE &&\n",
+ " stardata->star[1].stellar_type <= MAIN_SEQUENCE) \n",
+ " {\n",
+ " Printf(\"ZERO_AGE_MAIN_SEQUENCE_STAR%d %30.12e %g %g %g %g\\n\",\n",
+ " 2,\n",
+ " stardata->model.time, // 1\n",
+ " stardata->common.zero_age.mass[0] + stardata->common.zero_age.mass[1], // 2\n",
+ " stardata->star[0].mass + stardata->star[1].mass, // 3\n",
+ " stardata->star[0].luminosity + stardata->star[1].luminosity, // 4\n",
+ " stardata->model.probability // 5\n",
+ " );\n",
+ " }\n",
+ "}\n",
+ " to grid_options\n"
+ ]
+ }
+ ],
"source": [
"# Create custom logging statement\n",
"#\n",
@@ -298,7 +272,7 @@
"\n",
"population.set(\n",
" C_logging_code=custom_logging_statement\n",
- ")\n"
+ ")"
]
},
{
@@ -311,10 +285,18 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 5,
"id": "fd197154-a8ce-4865-8929-008d3483101a",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "adding: parse_function=<function parse_function at 0x7f777242c4c0> to grid_options\n"
+ ]
+ }
+ ],
"source": [
"# import the bin_data function so we can construct finite-resolution probability distributions\n",
"# import the datalinedict to make a dictionary from each line of data from binary_c\n",
@@ -374,7 +356,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 6,
"id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
"metadata": {
"tags": []
@@ -384,367 +366,130 @@
"name": "stdout",
"output_type": "stream",
"text": [
+ "adding: verbosity=1 to grid_options\n",
"adding: num_cores=4 to grid_options\n",
"Running the population now, this may take a little while...\n",
"Creating and loading custom logging functionality\n",
+ "Do dry run? True\n",
"Doing dry run to calculate total starcount and probability\n",
"Generating grid code\n",
- "Generating grid code\n",
- "Saving grid code to grid_options\n",
- "Writing grid code to /tmp/binary_c_python-izzard/binary_c_grid_25014bc73b334765a1c09a4e4a97ed66.py [dry_run = True]\n",
- "Symlinked grid code to /tmp/binary_c_python-izzard/binary_c_grid-latest0 \n",
- "Loading grid code function from /tmp/binary_c_python-izzard/binary_c_grid_25014bc73b334765a1c09a4e4a97ed66.py\n",
+ "Save grid code to grid_options\n",
+ "Write grid code to /tmp/binary_c_python-david/binary_c_grid_58bfc73fabfb43b18ae455666fe4d8f8.py [dry_run = True]\n",
+ "Symlinked grid code to /tmp/binary_c_python-david/binary_c_grid-latest0 \n",
+ "Load grid code function from /tmp/binary_c_python-david/binary_c_grid_58bfc73fabfb43b18ae455666fe4d8f8.py\n",
"Grid code loaded\n",
- "Dry run of the grid\n",
- "Grid has handled 4000 stars with a total probability of 0.648566\n",
- "****************************************\n",
- "* Total starcount for this run is 4000 *\n",
- "* Total probability is 0.648566 *\n",
- "****************************************\n",
- "\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 09:56:53,685 DEBUG Process-2] --- Setting up processor: process-0\n",
- "[2021-11-01 09:56:53,690 DEBUG Process-3] --- Setting up processor: process-1\n",
- "[2021-11-01 09:56:53,694 DEBUG Process-4] --- Setting up processor: process-2\n",
- "[2021-11-01 09:56:53,698 DEBUG MainProcess] --- setting up the system_queue_filler now\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating grid code\n",
+ "Doing a dry run of the grid.\n",
+ "Grid has handled 3159 stars with a total probability of 0.645748\n",
+ "**********************************\n",
+ "* Dry run *\n",
+ "* Total starcount is 3159 *\n",
+ "* Total probability is 0.645748 *\n",
+ "**********************************\n",
+ "\n",
+ "[2022-06-18 12:49:01,996 DEBUG Process-2] --- Setting up processor: process-0\n",
+ "[2022-06-18 12:49:02,008 DEBUG Process-3] --- Setting up processor: process-1\n",
+ "[2022-06-18 12:49:02,019 DEBUG Process-4] --- Setting up processor: process-2\n",
+ "[2022-06-18 12:49:02,025 DEBUG MainProcess] --- setting up the system_queue_filler now\n",
"Generating grid code\n",
- "Saving grid code to grid_options\n",
- "Writing grid code to /tmp/binary_c_python-izzard/binary_c_grid_25014bc73b334765a1c09a4e4a97ed66.py [dry_run = False]\n",
- "Symlinked grid code to /tmp/binary_c_python-izzard/binary_c_grid-latest1 \n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 09:56:53,707 DEBUG Process-5] --- Setting up processor: process-3\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading grid code function from /tmp/binary_c_python-izzard/binary_c_grid_25014bc73b334765a1c09a4e4a97ed66.py\n",
+ "[2022-06-18 12:49:02,027 DEBUG Process-5] --- Setting up processor: process-3\n",
+ "Save grid code to grid_options\n",
+ "Write grid code to /tmp/binary_c_python-david/binary_c_grid_58bfc73fabfb43b18ae455666fe4d8f8.py [dry_run = False]\n",
+ "Symlinked grid code to /tmp/binary_c_python-david/binary_c_grid-latest1 \n",
+ "Load grid code function from /tmp/binary_c_python-david/binary_c_grid_58bfc73fabfb43b18ae455666fe4d8f8.py\n",
"Grid code loaded\n",
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- "2795/4000 69.9% complete 10:01:52 ETA= 4.8m tpr=9.48e-01 ETF=10:06:38 mem:635.1MB M1=10 M2=9.8 P=1.2e+3\n",
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- "2875/4000 71.9% complete 10:02:16 ETA= 4.7m tpr=1.01e+00 ETF=10:07:01 mem:636.5MB M1=12 M2=9.3 P=1.2e+3\n",
- "2891/4000 72.3% complete 10:02:21 ETA= 4.8m tpr=1.03e+00 ETF=10:07:07 mem:636.5MB M1=12 M2=12 P=9\n",
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- "2947/4000 73.7% complete 10:02:31 ETA= 4.6m tpr=1.05e+00 ETF=10:07:07 mem:636.7MB M1=15 M2=6.7 P=1.5e+4\n",
- "2965/4000 74.1% complete 10:02:37 ETA= 4.5m tpr=1.05e+00 ETF=10:07:09 mem:636.8MB M1=15 M2=9.7 P=1.2e+3\n",
- "2982/4000 74.5% complete 10:02:42 ETA= 4.5m tpr=1.06e+00 ETF=10:07:11 mem:637.3MB M1=15 M2=13 P=31\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 10:02:46,515 DEBUG MainProcess] --- Signaling stop to processes\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "3010/4000 75.2% complete 10:02:47 ETA= 4.4m tpr=1.07e+00 ETF=10:07:12 mem:637.8MB M1=18 M2=2.7 P=2.6\n",
- "3035/4000 75.9% complete 10:02:52 ETA= 4.3m tpr=1.07e+00 ETF=10:07:11 mem:637.8MB M1=18 M2=6.3 P=1.2e+3\n",
- "3059/4000 76.5% complete 10:02:57 ETA= 4.2m tpr=1.07e+00 ETF=10:07:09 mem:637.8MB M1=18 M2=9.8 P=1.7e+5\n",
- "3078/4000 77.0% complete 10:03:02 ETA= 4.1m tpr=1.07e+00 ETF=10:07:08 mem:638.7MB M1=18 M2=13 P=5e+4\n",
- "3100/4000 77.5% complete 10:03:08 ETA= 4.0m tpr=1.08e+00 ETF=10:07:10 mem:638.9MB M1=21 M2=1.1 P=2.6\n",
- "3124/4000 78.1% complete 10:03:13 ETA= 3.9m tpr=1.08e+00 ETF=10:07:09 mem:639.1MB M1=21 M2=5.4 P=3.6e+2\n",
- "3148/4000 78.7% complete 10:03:18 ETA= 3.8m tpr=1.08e+00 ETF=10:07:08 mem:639.7MB M1=21 M2=9.7 P=5e+4\n",
- "3174/4000 79.3% complete 10:03:24 ETA= 3.7m tpr=1.07e+00 ETF=10:07:05 mem:640.1MB M1=21 M2=16 P=3.6e+2\n",
- "3197/4000 79.9% complete 10:03:29 ETA= 3.6m tpr=1.07e+00 ETF=10:07:04 mem:640.4MB M1=21 M2=20 P=1.5e+4\n",
- "3231/4000 80.8% complete 10:03:34 ETA= 3.4m tpr=1.07e+00 ETF=10:07:00 mem:640.9MB M1=26 M2=9 P=9\n",
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- "3294/4000 82.3% complete 10:03:50 ETA= 3.0m tpr=1.03e+00 ETF=10:06:51 mem:641.4MB M1=26 M2=24 P=3.6e+2\n",
- "3321/4000 83.0% complete 10:03:55 ETA= 2.9m tpr=1.02e+00 ETF=10:06:48 mem:641.6MB M1=31 M2=7.7 P=9\n",
- "3348/4000 83.7% complete 10:04:00 ETA= 2.7m tpr=1.01e+00 ETF=10:06:45 mem:641.9MB M1=31 M2=14 P=5e+4\n",
- "3373/4000 84.3% complete 10:04:05 ETA= 2.6m tpr=1.01e+00 ETF=10:06:44 mem:641.9MB M1=31 M2=23 P=1.1e+2\n",
- "3394/4000 84.8% complete 10:04:10 ETA= 2.5m tpr=1.01e+00 ETF=10:06:43 mem:642.2MB M1=31 M2=29 P=3.6e+2\n",
- "3422/4000 85.5% complete 10:04:15 ETA= 2.4m tpr=1.00e+00 ETF=10:06:40 mem:642.5MB M1=37 M2=9.3 P=31\n",
- "3445/4000 86.1% complete 10:04:21 ETA= 2.3m tpr=9.93e-01 ETF=10:06:38 mem:642.7MB M1=37 M2=17 P=1.2e+3\n",
- "3464/4000 86.6% complete 10:04:26 ETA= 2.2m tpr=9.90e-01 ETF=10:06:39 mem:642.8MB M1=37 M2=24 P=3.6e+2\n",
- "3483/4000 87.1% complete 10:04:31 ETA= 2.1m tpr=9.96e-01 ETF=10:06:40 mem:642.8MB M1=37 M2=31 P=1.1e+2\n",
- "3509/4000 87.7% complete 10:04:37 ETA= 2.0m tpr=9.91e-01 ETF=10:06:38 mem:642.8MB M1=44 M2=2.3 P=1.7e+5\n",
- "3533/4000 88.3% complete 10:04:42 ETA= 1.9m tpr=9.81e-01 ETF=10:06:36 mem:642.8MB M1=44 M2=16 P=1.1e+2\n",
- "3550/4000 88.8% complete 10:04:47 ETA= 1.8m tpr=9.71e-01 ETF=10:06:36 mem:642.8MB M1=44 M2=24 P=2.6\n",
- "3568/4000 89.2% complete 10:04:52 ETA= 1.7m tpr=9.65e-01 ETF=10:06:37 mem:642.8MB M1=44 M2=29 P=5e+4\n",
- "3588/4000 89.7% complete 10:04:58 ETA= 1.7m tpr=9.64e-01 ETF=10:06:37 mem:643.1MB M1=44 M2=38 P=5e+4\n",
- "3622/4000 90.5% complete 10:05:03 ETA= 1.5m tpr=9.68e-01 ETF=10:06:34 mem:643.2MB M1=53 M2=13 P=313623/4000 90.6% complete 10:05:03 ETA= 1.5m tpr=9.68e-01 ETF=10:06:34 mem:643.2MB M1=53 M2=13 P=1.1e+2\n",
+ "3145/3159 99.6% complete 12:49:05 ETA= 0.0s tpr=6.36e-03 ETF=12:49:05 mem:584.7MB M1=91 M2=76 P=6.7e+23146/3159 99.6% complete 12:49:05 ETA= 0.0s tpr=6.36e-03 ETF=12:49:05 mem:584.7MB M1=91 M2=76 P=2.6e+3\n",
+ "\n",
+ "3147/3159 99.6% complete 12:49:05 ETA= 0.0s tpr=6.37e-03 ETF=12:49:05 mem:584.7MB M1=91 M2=76 P=1e+4\n",
+ "[2022-06-18 12:49:05,913 DEBUG MainProcess] --- Signalling processes to stop\n",
+ "Do join of subprocesses ...\n",
+ "[2022-06-18 12:49:05,941 DEBUG Process-2] --- Process-0 is finishing.\n",
+ "[2022-06-18 12:49:05,942 DEBUG Process-3] --- Process-1 is finishing.\n",
+ "[2022-06-18 12:49:05,941 DEBUG Process-5] --- Process-3 is finishing.\n",
+ "[2022-06-18 12:49:05,942 DEBUG Process-4] --- Process-2 is finishing.\n",
+ "process 0 free memory and return process 1 free memory and return process 3 free memory and return process 2 free memory and return \n",
+ "\n",
"\n",
- "3640/4000 91.0% complete 10:05:08 ETA= 1.4m tpr=9.52e-01 ETF=10:06:33 mem:643.6MB M1=53 M2=24 P=2.6\n",
- "3657/4000 91.4% complete 10:05:13 ETA= 1.4m tpr=9.50e-01 ETF=10:06:34 mem:643.7MB M1=53 M2=29 P=1.5e+4\n",
- "3676/4000 91.9% complete 10:05:18 ETA= 1.3m tpr=9.53e-01 ETF=10:06:35 mem:643.7MB M1=53 M2=40 P=4.2e+3\n",
- "3706/4000 92.7% complete 10:05:23 ETA= 1.2m tpr=9.66e-01 ETF=10:06:34 mem:643.9MB M1=64 M2=3.2 P=4.2e+3\n",
- "3726/4000 93.2% complete 10:05:29 ETA= 1.1m tpr=9.50e-01 ETF=10:06:34 mem:644.5MB M1=64 M2=16 P=4.2e+3\n",
- "3746/4000 93.7% complete 10:05:34 ETA= 1.0m tpr=9.48e-01 ETF=10:06:34 mem:644.5MB M1=64 M2=29 P=4.2e+3\n",
- "3763/4000 94.1% complete 10:05:39 ETA= 55.8s tpr=9.42e-01 ETF=10:06:35 mem:644.5MB M1=64 M2=41 P=1.1e+2\n",
- "3786/4000 94.7% complete 10:05:45 ETA= 50.8s tpr=9.49e-01 ETF=10:06:35 mem:644.6MB M1=64 M2=54 P=4.2e+3\n",
- "3811/4000 95.3% complete 10:05:50 ETA= 45.5s tpr=9.63e-01 ETF=10:06:35 mem:645.0MB M1=76 M2=11 P=9\n",
- "3832/4000 95.8% complete 10:05:55 ETA= 39.9s tpr=9.51e-01 ETF=10:06:35 mem:645.0MB M1=76 M2=27 P=31\n",
- "3849/4000 96.2% complete 10:06:00 ETA= 35.5s tpr=9.42e-01 ETF=10:06:35 mem:645.1MB M1=76 M2=34 P=1.7e+5\n",
- "3875/4000 96.9% complete 10:06:05 ETA= 29.4s tpr=9.40e-01 ETF=10:06:34 mem:645.4MB M1=76 M2=57 P=1.2e+3\n",
- "3905/4000 97.6% complete 10:06:10 ETA= 22.2s tpr=9.36e-01 ETF=10:06:32 mem:645.5MB M1=91 M2=4.6 P=1.2e+3\n",
- "3930/4000 98.2% complete 10:06:15 ETA= 16.5s tpr=9.41e-01 ETF=10:06:32 mem:645.5MB M1=91 M2=32 P=2.6\n",
- "3931/4000 98.3% complete 10:06:15 ETA= 16.2s tpr=9.41e-01 ETF=10:06:31 mem:645.5MB M1=91 M2=32 P=9\n",
- "3954/4000 98.8% complete 10:06:20 ETA= 10.6s tpr=9.19e-01 ETF=10:06:31 mem:645.8MB M1=91 M2=50 P=3.6e+2\n",
- "3977/4000 99.4% complete 10:06:25 ETA= 5.2s tpr=9.06e-01 ETF=10:06:30 mem:645.8MB M1=91 M2=69 P=1.5e+4\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 10:06:29,167 DEBUG Process-3] --- Process-1 is finishing.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "process 1 free memory and return \n",
"****************************************************\n",
- "* Process 1 finished: *\n",
- "* generator started at 2021-11-01T09:56:53.690194 *\n",
- "* generator finished at 2021-11-01T10:06:29.176751 *\n",
- "* total: 9m 35.49s *\n",
- "* of which 9m 34.96s with binary_c *\n",
- "* Ran 1001 systems *\n",
- "* with a total probability of 0.160675 *\n",
+ "* Process 0 finished: *\n",
+ "* generator started at 2022-06-18T12:49:01.996087 *\n",
+ "* generator finished at 2022-06-18T12:49:05.948339 *\n",
+ "* total: 3.95s *\n",
+ "* of which 3.64s with binary_c *\n",
+ "* Ran 792 systems *\n",
+ "* with a total probability of 0.161354 *\n",
"* This thread had 0 failing systems *\n",
"* with a total failed probability of 0 *\n",
"* Skipped a total of 0 zero-probability systems *\n",
"* *\n",
"****************************************************\n",
- "\n",
- "process 1 queue put output_dict \n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 10:06:29,186 DEBUG Process-3] --- Process-1 is finished.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "process 1 return \n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 10:06:29,342 DEBUG Process-5] --- Process-3 is finishing.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "process 3 free memory and return \n",
"****************************************************\n",
- "* Process 3 finished: *\n",
- "* generator started at 2021-11-01T09:56:53.706780 *\n",
- "* generator finished at 2021-11-01T10:06:29.345842 *\n",
- "* total: 9m 35.64s *\n",
- "* of which 9m 35.06s with binary_c *\n",
- "* Ran 1001 systems *\n",
- "* with a total probability of 0.155662 *\n",
+ "* Process 2 finished: *\n",
+ "* generator started at 2022-06-18T12:49:02.018956 *\n",
+ "* generator finished at 2022-06-18T12:49:05.948532 *\n",
+ "* total: 3.93s *\n",
+ "* of which 3.64s with binary_c *\n",
+ "* Ran 791 systems *\n",
+ "* with a total probability of 0.158204 *\n",
"* This thread had 0 failing systems *\n",
"* with a total failed probability of 0 *\n",
"* Skipped a total of 0 zero-probability systems *\n",
"* *\n",
"****************************************************\n",
- "\n",
- "process 3 queue put output_dict \n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 10:06:29,350 DEBUG Process-5] --- Process-3 is finished.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "process 3 return \n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 10:06:29,429 DEBUG Process-2] --- Process-0 is finishing.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "process 0 free memory and return \n",
"****************************************************\n",
- "* Process 0 finished: *\n",
- "* generator started at 2021-11-01T09:56:53.684890 *\n",
- "* generator finished at 2021-11-01T10:06:29.433207 *\n",
- "* total: 9m 35.75s *\n",
- "* of which 9m 35.15s with binary_c *\n",
- "* Ran 1025 systems *\n",
- "* with a total probability of 0.162454 *\n",
+ "* Process 3 finished: *\n",
+ "* generator started at 2022-06-18T12:49:02.027113 *\n",
+ "* generator finished at 2022-06-18T12:49:05.949028 *\n",
+ "* total: 3.92s *\n",
+ "* of which 3.63s with binary_c *\n",
+ "* Ran 784 systems *\n",
+ "* with a total probability of 0.166051 *\n",
"* This thread had 0 failing systems *\n",
"* with a total failed probability of 0 *\n",
"* Skipped a total of 0 zero-probability systems *\n",
"* *\n",
"****************************************************\n",
"\n",
- "process 0 queue put output_dict \n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 10:06:29,437 DEBUG Process-2] --- Process-0 is finished.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "process 0 return \n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 10:06:29,449 DEBUG Process-4] --- Process-2 is finishing.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "process 2 free memory and return \n",
- "****************************************************\n",
- "* Process 2 finished: *\n",
- "* generator started at 2021-11-01T09:56:53.694517 *\n",
- "* generator finished at 2021-11-01T10:06:29.453059 *\n",
- "* total: 9m 35.76s *\n",
- "* of which 9m 35.25s with binary_c *\n",
- "* Ran 973 systems *\n",
- "* with a total probability of 0.169775 *\n",
+ "\n",
+ "\n",
+ "\n",
+ "process 2 queue put output_dict \n",
+ "process 3 queue put output_dict process 0 queue put output_dict ****************************************************\n",
+ "* Process 1 finished: *\n",
+ "* generator started at 2022-06-18T12:49:02.007947 *\n",
+ "* generator finished at 2022-06-18T12:49:05.955484 *\n",
+ "* total: 3.95s *\n",
+ "* of which 3.62s with binary_c *\n",
+ "* Ran 792 systems *\n",
+ "* with a total probability of 0.160139 *\n",
"* This thread had 0 failing systems *\n",
"* with a total failed probability of 0 *\n",
"* Skipped a total of 0 zero-probability systems *\n",
"* *\n",
"****************************************************\n",
"\n",
- "process 2 queue put output_dict \n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[2021-11-01 10:06:29,456 DEBUG Process-4] --- Process-2 is finished.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "process 2 return \n",
- "****************************************************************\n",
- "* Population-25014bc73b334765a1c09a4e4a97ed66 finished! *\n",
- "* The total probability is 0.648566. *\n",
- "* It took a total of 9m 35.99s to run 4000 systems on 4 cores *\n",
- "* = 38m 23.97s of CPU time. *\n",
- "* Maximum memory use 856.406 MB *\n",
- "****************************************************************\n",
"\n",
- "There were no errors found in this run.\n",
+ "\n",
+ "[2022-06-18 12:49:05,959 DEBUG Process-4] --- Process-2 is finished.\n",
+ "[2022-06-18 12:49:05,960 DEBUG Process-5] --- Process-3 is finished.\n",
+ "[2022-06-18 12:49:05,961 DEBUG Process-2] --- Process-0 is finished.\n",
+ "process 2 return process 3 return \n",
+ "process 1 queue put output_dict \n",
+ "process 0 return \n",
+ "\n",
+ "[2022-06-18 12:49:05,968 DEBUG Process-3] --- Process-1 is finished.\n",
+ "process 1 return \n",
+ "Joined subprocesses.\n",
+ "************************************************************\n",
+ "* Population-58bfc73fabfb43b18ae455666fe4d8f8 finished! *\n",
+ "* The total probability is 0.645748. *\n",
+ "* It took a total of 4.41s to run 3159 systems on 4 cores *\n",
+ "* = 17.66s of CPU time. *\n",
+ "* Maximum memory use 584.672 MB *\n",
+ "************************************************************\n",
+ "\n",
+ "No failed systems were found in this run.\n",
+ "Do analytics\n",
+ "Added analytics to metadata\n",
"Done population run!\n"
]
}
@@ -754,9 +499,10 @@
"population.set(\n",
" # verbose output is not required \n",
" verbosity=1,\n",
+ "\n",
" # set number of threads (i.e. number of CPU cores we use)\n",
" num_cores=4,\n",
- " )\n",
+ ")\n",
"\n",
"# Evolve the population - this is the slow, number-crunching step\n",
"print(\"Running the population now, this may take a little while...\")\n",
@@ -777,7 +523,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
"metadata": {},
"outputs": [
@@ -785,7 +531,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "{'population_name': '25014bc73b334765a1c09a4e4a97ed66', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6485656144116352, 'total_count': 4000, 'start_timestamp': 1635760613.6602514, 'end_timestamp': 1635761189.652638, 'total_mass_run': 82563.09295167374, 'total_probability_weighted_mass_run': 0.6438124832773024, 'zero_prob_stars_skipped': 0}\n"
+ "{'population_id': '58bfc73fabfb43b18ae455666fe4d8f8', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6457484448453049, 'total_count': 3159, 'start_timestamp': 1655552941.9314468, 'end_timestamp': 1655552946.3461084, 'time_elapsed': 4.414661645889282, 'total_mass_run': 65199.55913120549, 'total_probability_weighted_mass_run': 0.6433998017038131, 'zero_prob_stars_skipped': 0}\n"
]
}
],
@@ -795,7 +541,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
"metadata": {},
"outputs": [
@@ -805,13 +551,13 @@
"[None]"
]
},
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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vv0rjxs0ybbNjx1bi4uJYvPhvFi/+O8P6Y8eOZlrAunHjEMaM+YzY2FgSExM4c+YUY8bce0idtdfOS3Tq9BJms5lt2zYzduwXTJkykR9/nHPf83kYen3G59HZOevnJyt33yMqlcpWfyu9x1F6sjJd+uPExAQUReHbb79nzpxZbN26idWrV6DRaGjQoDFDh4544FkQ27Rpx/vvr+HUqRMkJSURGRmR5RBAa2y38fLK2FvJx8eH2Ni4TLdp2rQ5ZrOZf/5ZyJw5PzNr1kwKFChI374DaNo0Yy2yO93r3k+XJ4+P3eM7r9OTcvt2XIbjpB/r7sTk3a9hRVEwm5/CLy8HkGSVEMKhLKmJpJ7ZSfSR7aTovLFUaEPBIkXRO8uvJyGEEEI8GUEFPRj0UqX7N/yXRqPCaMxYTNuR9HrXhyri/aQlJiYwZMgAihcvxa+/LiAwsAgqlYpdu7azefPG+27v6upGo0ZN2LRpHX5+fphMpkxrQ93p6tUrfPXVl1SoUJF+/QZl2W7lymUUKhTA8OEf2S03mYx88MFgli5dlGmyKji4Pmq1hm3bNhMbG0O5chXInz9/hnabN29g4sSv+OGHWXY9u1QqFQ0bNuHw4UOsWLH03hfgDtaEgv39lZyclEXrp8vd3Zpoiom5ZTeUMTraWpPL09MLgLx58zF06IcMGfIB586dYdOmDcyfPxdvb2/ef3/4Ax2rWrWa+PnlZ+PGdSQlJVGmTNl7zrjn6enFqVMnMyyPi8s8UZUuJKQlISEtSUhIYO/e3cyfP5cvvxxJ5cpVbDWqHlV8/G27x+nX6c4hgBmf24fr6eTu7sGtWxl7gkZH37Q9H88DKbAuhHAYi8VM4t+fYtg1H4/EMHxjDuOz5Ss2/fg1o2ZuYMaSY6zaHcbxS7dISDbcf4dCCCGEEOKpCAu7RFxcHK+80pWiRYNQqawfJXfv3gmQ6Ux5d2vTpj2nTp3kn38WUq9eg3v2yElLS+OTTz5Eq9XyxRdfZVmE/ebNm+zZs4uQkBZUrVrd7qdGjdrUrVufDRvWEh8fn2Hb9KGAmzdvZNOmDTRtmvkQwKJFixEXF8eCBb9nuv7y5TCKFbv/cMY7jxsZGWm37MiRww+8/ZNUuXIVANats697lV4Hq2LFypw4cYx27Zpz8uRxFEWhRIlS9O7dj6CgYrbZC++Wfn/cvax163Zs2bKJHTu20apV1r2qAKpXr8HVq5ftZqSMjY3l+PFjWW7z+ecfM2LEUADc3Nxo0qQZ3bu/jclksiWAMovtQe3Zs8vu8aZNG3BycqJMGesQUFdX1wzX5OjRULvH9zt+5cpV2bFjm12SKzk5mR07tlGxYuVHjj2nka4LQohnxpwSjyXhFuq81m+kFEWFJqgGhiOriTO74KlKRqOYaeB8mlqW82y6VJalJ8uSig4AHw9nAvO7W3/8rP96uuoceUpCCCGEEM+FgIAiuLq6MmfOzygKqFRqNm/eYOtR9CC9R6pUqUb+/AU5fPggX3/97T3bzpz5HWfOnKJPn/5ERUXZaizdydvbm82bN9yzl1bLlm3YvHkDa9asoHPnLhnWN2nSjDFjPsdisWQ5zDAwsAivvvo6//vfr0RGXqd581b4+voRE3OLNWtWcvDgfr799of7nn+6unWD+e23Ofz66xzKlSvP9u1bOHAgYxH8ZyEoqDgtWrTip59+IDU1hfLlK3Ds2FHmzfuFFi1aUbRoEAaDARcXF0aN+pS33upNnjw+7N+/l7Nnz9Cly+uZ7tfNzZ1bt26xa9cOSpQoRd681h5NrVq1Zc6cn9FqtfftWdeiRRv++usPPvhgMH36vIter2fu3F8wm7Mezlu9eg3GjRvNd999S5069YiPv80vv/xIQEAgQUHFbbGdOXOaQ4cOULbsw9WPO378KBMnfkXjxs04eHA/f//9Jz169MLNzQ2AunXrs3btakqXLkuhQoVZtWoZV69evue1uVuPHr3o3ftNBg16h9de6wbA/PnzSE5OshVTfx5IskoI8VRZLGZM105hOLkZ46WDqLwKoH/xS1thQl25phxO9OWnfRYKqaPpG3AGt7hzOClGWrocIdjpNKuSK7E9tTTRt1OIvp3CwTM3bPv3ctPZElfp/3q7Oz1QEUUhhBBCCPFg3Nzc+OqrSfzww1RGjvwAvd6VEiVK8d13PzJ06CCOHDlsmykuK9YZ1OqyZcsmatbMfAa4dKdPnwKsSaustGrVlhMnjlG8eEkCA4tk2qZ27bp4eXmzZMmiTJNV9epZZwUsW7a8LaGSmX79BlGqVFmWL1/MlCkTSUhIwN3dg0qVqjBz5pwHLmoP0K3bW8TGxvK//83DaDRSt249PvzwEz788MELlj9JI0Z8hr9/YVasWMq8eb/g55eft97qTdeu1kSJVqvlm2++Y8aMaUyZMomEhHgKFSrM8OEf06JF60z32apVW3bs2MqIEUPo3bufbV8FC/pTpEhRihYtdt9aV1qtlilTZjB16iQmT/4aRVF44YWOFCzon+VQwLZtO5Camso///zNP//8hZOTM9Wr16Rfv0G2Qu5durzGt99OZMiQAUyZMv2hrlX37r04ceIow4a9h5eXF337DrDNoggwYMD7GI1Gvv9+Cmq1mmbNmtO3b3++/vqrLK/N3UnSYsWK8/33PzNz5neMHv0ZKpWKSpWqMGPGbIoVK/5Q8eZkisXyNEoH5i7R0Qm5pkhZTpAvnzs3bmTspityFnNSLIbT2zGc3orltv03YfqOn6POV8T2eM3ecP7ceA6A799vgPbGaVL3/oX55iUA4go34JBnE8Ij4gmLjOdmXMo9j+2u19olsALyu5PP0/mJJbDkHhXZndyjIruTe1Q8LRERYeTPn3G2uIeVHWtW5QZms5muXV+kceNm9OnzrqPDydFy6j16/fo1XnmlA5MmTaVGjdr330DkaFFRl/H1zXxSCZVKwcfHLcttpWeVEOKJsZjNmK4cw3BqC8aww2C5o4uuWoOmaA20pRuiypvxTaSiS8JisX7bofIvg1P7kVjCDpIWuoqCjV+ikLO7rW182Ekum30Ji0ogLCKesIh4ImP+63oen2Tg2MVbHLt4y7ZM76S5I3nlRqCfO3559KikB5YQQgghxFOVkJDAggX/49ixo9y4EUWnTi85OiTxjJ05c4rt27eyceN6goKKU716LUeHJLI5SVYJIZ6YlI0zMF7Ya7dM5e2PtkwjtMXroDhnnjmPNl3HufJWUs9WxmKxEJEYxZi936BT6+ge/CqVnN25lRLDwjNLcU5LpebxvRTxKkqh6i+QL9BIA20e8jn5ceuWhUsR8YRF3uZyZCLXohNt0ygnpRo5GRbDybAY23GddGoCfN3shhAW8NGjfoyii0IIIYQQwp6zszNLlvyNxQIfffSZ3axz4vmQlpbGH3/Mx8/Pj08/HSUlO8R9SbJKCPFILGYT5ltXbMXSATSBla3JKo0OTVAtdGUaovItdt8/RjEm64wZGr8wUkypJBmtvaTSTGloVdZfU7Gptwm9eRyAMmoVpsizXFk/hdmF8wDQt2J3KgSUxcU7kaUJ83Ap6cygUq+iNxbk+NXL7I3bSkqSQnyYP8YkN9CkYXC9xfnbGs5e9wCTDrCg1ago7OtuN4zQP58rGrUksIQQQgghHoVGo2HJkjX3byhyrfLlK7J27RZHhyFyEElWCSEeivl2FIZTWzGc2Y4lLQm316eg6FwA0BStjpMhFW3xWig6/UPvW+1h7fXk5eRJm6IhJBmTyefyX6HLAq5+JBmS8CxaGk7tIln9XxJMfWQd5hr5SDJYE13JxhRcdE4U9/PErL/B2kOXwB3e69oMvdGPfZdPsTl+IwDGMzUxxOZB5RaLuswerpm0XDpbBfOhPCjOCWgLncNNq6e4U2VK+RaiWDFXIpPC8XR2pbC7Py4aF8wWMwqKfEskhBBCCCGEEI9JklVCiPuymIwYLx3EcGoLpqvH7dYZzu1GV7YxAIpGZ/v/wyimrcyB7dYhgi71nHFxcqN10RC7NkGegYysNcT22FypAyUOLOL9i7tJVin4pt4g8cJRXEvXpVWh+iRjxsfF2xoXCvn1viQak/B0dsPfzYM4tSubj1r39WGXWmjSvNkZHsrW24DGgEalIQ1QnJJR54kgGdh33Je9h5NQed7AqdQBAEqmtKKsb3EU95ssufoneq0L71TqQRGPAK7EX2Nd+Gb0GheaBjQkr0se4lLjuXQ7DL3GhcLu/jhrnCXRJYQQQgghhBB3kGSVECJL5tjrpJ3agvHMDiwp9rM2qfyKoyvdEE1QTYfEpnLLg0fDnrhWak3a3r8xXjoAFiNeJ7bS6Gp+XF8ei6JYh+6V8C7GJ7WH2m1fyrs4H9ccTKIhiYLufjipdTRwLY4+silJhmSa12lMcqKGXeFH2RVzjlRzCs4qJ5IBRW207efI2duEHjmLyjsCpxJmEgyJLNkaTql8YHS7xv7IwwDULWgtInnpdhg/Hp0HwAfVBxLgUYjj0af46eiv6DUuDKjSC3+3AoTdvsymy9vRa/WEBDTE29mL2NQ4LsdfxUXjQoC7Pzq1DovFIkkuIYQQQgghRK4iySohRKbSjq0jded8+4VOrmhL1EVbuiHqPIWe2LFuma6j8T8LgMFcB5eH+NWk9iqIS/MBmKIukLr3L0zXTqKr0NyWqMqKs8aJgm757ZYVcPWjXVAL22NvF3gxb21exDqtrrmZhRuxyUTGJ3LgfHGu3oohAieSsGBJ0WO4FoSiNhB6NYHQY+dQeUWiDdCj0hr4e0M4JfzSSHa9Ydu/XmsdPplkSMZkMRFvSLDV6IpKusm+yEMANPSvA8C52IvMPv4/AEbWGkIBVz8O3TjKvBN/oNe4MKhqX/z0+bgQF8b2q7vJ4+xNff86eDr9N5OiEEIIIYQQQmR3kqwSQgBgjo1A5fVf8kZdsOx//y9QCm3phmiKVkfR6J74sWPMEWj9zwNgMpseaR9q3yBc2gzHdO0k6gIlbcstJiPJqyejLVkPTfHa901i3YtKUfDz1lO+pB8VA/JZ92+xcOt2KmGR8VyKiCc8Mp5LTvHcNqZhjvUjNdYPgCMkceTUBVAbUJzq4OEB82PCKep3GxdvHfULBGNW0nDXudmO5ePsTZIxGb3WWv8rvR4XgF6TnuhKwmA2EpcWj06lBSAiMZI9EdZhis4aJ5oFNHzkcxZCCCGEEEKIZ02SVUI8xyyGVIzn95B2agvmqPPoO36OOl8RANR5/HGq+xqaQuVReRVwbKAPSFEUNP5l7Zal19kyXT2O6sgqnGq8hLpwhSc2dE5RFHw8nfHxdKZqyXy25bEJqYRFxBMWGW/799btVDBpsSR5EpcEoRExhJ6N+XcLNzxddUQdPUOAnzuBfv4MLPcePh7Otlir+lUk0KMQiYYk3LSuABR0K0DTgAYkGZJx/XeZSlHhqtFTMk9xmhZuAFhnVtSpn3yiUQghhBBCCCGeNElWCfEcMt24hOHUZgzndoMhxbbccGoz6nzdbY915UMy2frJK6atwoHt1qFqzvWcn+i+FZ0LiosHluTbmKMvk7z6G9QFSuFU8yXUfsWf6LHu5OXmhFdxJyoV/282w9tJaYTbklcJhEfEExX7X2+puMQ0jpyP5sj5aNsyNxctgX5uBOR3J9DPncD8PhTOUwjVvwmsIM9AgjwD7Y5du0B1auWvZi3crigkGpKYuP87ahWoRovAJlLjSgghhBBC5FjZuWaro2Nz9PGfJElWCfGcsKQlYTi3G8PJLZijw+zWKXovtCWD0ZZu4KDonh5tibpoilQj7ega0kJXgSEZ0/XTJC0ZjaZIVXQ1OqP2LvhMYvHQ6yhf1IfyRX1sy5JSDIRFJhD27xDCsMh4IqKTsPy7PiHZwPFLMRy/FGPbxsVJTYCvO4H5//3xcyd/Hj0q1X9/mBRFQa2oAZh/8i+ikm+y7MIa/PS+VPGt8EzOVwghhBBPzvnz55g3bxaHDh3k9u04PD09qVSpKm+80YMSJUrefwfZyMqVyxg79gsWLVqBr6/fMz9+cHD1e67v0aMXb7/dx27Zd999yx9//Mbrr3enb9/+GbYZM+ZzVq1ajp9ffv7+e3mm+x016hPWrFlFq1Zt+fjjz23LQ0MPM3/+XI4fP0JSUhLe3nmoUaMW3bq9hb9/1nVin+V17N+/N2q1hilTfniqx7nb3ed46dJFxo8fxfTpvzzxY40Z8zlHjhzmzz8XA9C5czuqV6/Jhx9+8kDb79ixjY0b1/HJJ18+1eNkJbNrExxcnZ49+9K9e8/H2rcjSLJKiOeAMeIsySu/BmPafwsVBXXhitZaVAGVUFRqh8V3Z4F1k6UuT/pXk6J1wqnqC2jLNibt0HIMxzeA2Yjx0kGMYYfQlAjGud7rKFqnJ3rcB6F31lIm0Jsygd62ZSlpRi5HWRNY6UMIr91MwmyxprCSU02cvhzL6cuxtm10WpU1geXnTkB+NwL93CmY1xWNWkXboBZcSbhOEY/CVM5X/lmfohBCCCEe07lzZ3nnnbeoUKES778/DC8vb27ciOKvv36nT58eTJ06g/Ll5cuoBzVjxuxMl0+fPpUjRw5TsWIlu+VGo5G1a1dRrFhxVq5cRs+efdFoMr5fVRSFyMgITpw4Rtmy9u+5UlNT2bZta4Zt9u7dzbBhg2jUqCkffvgJrq5uXL16hf/9bx69e7/Jjz/OvWfC6lkZMuRDh/TYqVMnmBkzZuPtnQeAzZs3cPTokWdy7LFjv8bV1e2B2y9Y8Dsmk/G+7bp370lSUuLjhJapzK7NjBmz8fN79gnhJ0GSVULkQpaUBNBoUTTW5Is6bwD8m4xSXPOgLd0Aban6qNx87rWbZ+bOAuvGRyyw/iBUzu4413kVXYXmpO7/B+PZHWCxYI65Ak+hcPyjctZpKFHIixKFvGzL0gwmrtxItKuBdfVGAkaT5d/1Zs5djePc1TjbNk46NZ0aBNGsWiGGVx+ATq1FURSSDEn8fOw3OhZvS2H3Z9OrTAghhBCPbsGC/+HtnYevv56CWv3fF4zBwQ157bXOzJ37M19/PcWBEeYsmSX2Fi36i9DQQ3Tr9hY1atS2W7d79w5iYm4xatR43n23J1u3bqZJk2YZ9lGgQEHS0tLYvHlDhmTV7t07UavV+PnZz0b9229zqFChEl98Mda2rGrV6tSpU4+XX+7AH3/MZ8iQDx7ndJ+IokWDHHJcb29vvL2979/wKShZsvRT2e+zTD7m5CS2JKuEyCUsFgum66cxnNqC8eI+nOq9ga60dRY4ReOEU+0uqPTeqAuVR1E9+ox4uYHKzQeXRj0xVWxF2r6FaMuH2H1TZLx+GnXeIg7paZUVnVZNUEEPggp62JYZTWaupiewIuMJj4jnclQCaUYzAKlpJn5ff5aL127zZsvS6NRqTGYTPx/7jdMx5/jmwPcMrzGQAq4589sWIYQQ4nkRE3MLi8X6fu9Oer2egQMHk5KSYrd8y5ZNzJ07i0uXLuDu7kHz5q3o1esddLr/vpw7duwoP/88nRMnjqPT6ahVqw79+79n68ESFRXJzJnfc+DAPuLjb1O6dFl69uxLlSrVALh+/RovvfQCY8ZMYM2aVezbtxuNRkujRk0ZNGgIzs7WOqRms5l5835h6dJ/iIuLpWbN2lSqVCXDOS5e/DdLl/5DePglzGYLRYoUoVu3t2jUqClgHQ42ceJXDBw4hFmzZmIymXj77T5MnjyBP/9cbJcAWLz4b6ZOncTixavw8PC87/U9efI406Z9Q7VqNejZs2+G9StWLKNUqTJUqlSZsmXLs2TJokyTVYqi0KhRUzZt2ki/foPs1m3cuJaGDRtz6NABu+W3bt3Cw8ODu+XNm4/Bg4fh4eF13/jTZTZU7+DB/Qwc2Jfvv/+ZSpUqM2vWTDZv3kCPHr35+efpRERcp0SJUnz88WdcvnyZGTOmce3aVYoVK8GwYSMoUaJUpvsODq7O0KEjOHHiGFu3bsZkMlG7dl0GDx5uu4cAVq9ewYIF/yM8PAx3dw+aNm1Or159cXKy3h8xMTFMnTqJAwf2kZCQQEBAIK+80pVWrdoC9sMAly1bzOzZP9mO36NHLy5evMCpUyf466+ldu/nP/vsI65evcLPP8/L9Frdvn2b776bzLZtW7BYLLzwQkfMZrNdm7uH561bt5rffpvL5cvh6PV6atasTb9+A8mbNx/9+/fm8OGDttimTp0BwMCBfRk27CPmzp1FYmIC48dPZsWKpXbDAAEMhjQmTvyKdetWo9FoaNw4hH79BqDXu2Yay4Ncm7ff7pNhGOCTel0/C8/3J1YhcgFz8m3SQleSuGAEycvHYTy3C0xGDCe32LXTlW6IJqBitkxUBWkrk7y3Jcl7W+KseXYJInUef1xaDLKbQdCcfJvk1ZNJ/GMYacc3YDHfvyuvo2jUKgLzu9OgUkHeaF6Kj7tV5/vBDRj1dk16tC6Nt7v1Wu4+EcmYX/cTFZOESlFR1qcUCgrlfErjp893n6MIIYQQwtFq167L9etX6dv3LRYt+ouwsEu2dY0bN7N9sAdYu3Y1H388jKCgYowdO5Fu3XqwZMkivvjiY1ubM2dOMWBAb8xmM5988gXvvz+c0NBDDB/+PgA3b96kV69unDhxjH79BvHFF1/h5OTMe+/148CBfXaxjRs3moIF/fnqq0l07foGy5cv5tdf/xtm98MPU5k9+yfatevA2LET8fDwZMaM7+z28ddffzB58gQaNWrChAnf8tlno1CrNXz++cfcuBFla2cwGFi48A8++uhTBgx4n5CQluh0OtauXWW3v9WrV1C3bv0HSlTFx8fz6acj8PT04vPPx6C6671yTEwMu3Ztp0WL1gC0bt2Wgwf3ceXK5Uz316RJM65fv8qpUydty1JSUti5cztNmzbP0L527bocOXKYQYPeYeXKZVy7dtW2rm3bDjRo0Oi+5/CwIiKu8+OPP9Cz5zt88smXXLkSzrBh7zFt2jd06/YWX3wxlsjI64wa9ek99zNjxjQARo36in79BrJjxza++26ybf2sWTMZM+ZzKleuytixE3nlla4sWbKI4cMH2xKvo0Z9wqVLFxg6dAQTJ06hZMlSjBnzOQcP7s9wvHbtOtC+fad/jz2bdu060KZNOyIirhMaesjWLjExgW3btti9Lu5kNpsZMmQAu3btoH//9xg58nOOHg1lw4a1WZ7rkSOHGT36Mxo1asKkSVMZMOB9DhzYyxdfjASswyTLlClLyZKlmDFjNqVK/dcra86cnxk0aAjvvTcsQ4+7dOvXr+XSpYt8+uloevToxZo1K/n00xFZxvMg1+ZuT/J1/SxIzyohciCLxYzp6glrL6pLB+HOoXMqDZqi1dCWbpirZoN4VgyntoIhBYshhdQdv5J2dA1O1TuhKVYTRcl+ib67qVUq/PO54Z/PjUrF8jJjyTFOhcdy5UYiX87ZT+8XytKsWEMKu/lT1DMAlaIiyZDEuvAttC7SDK1a6+hTEEIIIZ4Kw+ltGM5sv287bclgNOUa2h6n7JyPOTr8vts51emKOu9/M/QmLfsq031rS9V/wIj/06nTy0RHR/PHH/P55pvxAHh5eVOrVm06d+5CmTLlAGvPqxkzplG3bn1GjvzCtr2vrx8jRgz9tx5TZebN+wVv7zxMnDjV1tvKw8ODr78ey5Url1myZBHx8Qn8+ONc27C1unWD6d79VaZPn2bXW6Vevfr07/8eANWr12Tfvj3s3LmNXr3eIT4+noUL/+DVV9+gR49eANSqVYebN2+yZ89O2z6uX79K167d6NbtLduy/PkL8vbbr3P06BFbLyaLxUL37j2pUyfY1q5+/YasWbPKtv/Ll8M5duwIEyZ8+0DXduzYz4mKimTq1Jl2PYLSrV27EoCQkJYANG3agqlTJ7N06aIMvacAKlSoRL58vmzevIHSpcsAsHPndpydXahaNWNh9969+5GYmMjKlUttCQNfXz/q1KnHK690JSCgyAOdx8NITk5m+PCPbPEcPnyQv/9ewJQp06lWrQYAly9f5vvvvyUpKQm9Xp/pfooXL8lHH30GQI0a1h5qW7duBuD27Tjmz59Lx46dGThwCAA1a9YmXz4/PvtsBLt27aBu3WAOHz5I9+49bUm5ypWr4unphVab8T2pr68f+fL5Av8Nb/PxyUu+fL6sWbOSypWrArBx43rAQkhIi0zj3r17JydPHmfSpGnUqlUHgGrVavLSS+2yvGahoYdxcnLmtdfevOM148mpUyewWCwULRqEXu+GyWTMMPSuU6eXadiwSZb7BvDy8mLSpKm2HmcajYaJE8dx9uxpW++2e8ns2tztzz/nP5HX9bOS/T95CSHsWFITSfzjA5JXTsR4YZ8tUaXyKoBT7S64vj4Zl6bvoPEvm2MSVekF1jX+ZzE6uCeTrnJrnBv3RnG39jiy3I4iZeMMkv75AuOVYxm632dnHq46hnSpTMtaAQAkpRqZ8tcRlmy/SAnvYujUOkxmE7OOzWdt2CamHPqRZGOyg6MWQgghng5z/E1M10/f98ccf9N+u+jwB9rOkpZkt92D7PtBKYpC7979WLx4FZ99Npq2bduj1+tZs2YVvXt35++/FwAQHh5GVFQkwcENMBqNtp+aNeug1WrZt28PAEeOhFK7dl27YYE1atRiwYIlFCpUmMOHD1KxYiW7+koqlYqmTZtz+vRJu+LQFSrYFyPPl8+X5GTrsMTjx49iNBoJDm5o1+buIXQDBw6hT593iY+P59ixo6xZs5JFi6znZDQa7Nre/cG9TZv2XLkSzvHjxwBrryofHx9bEuJe/vjjN7Zt20KfPv2pVKlypm1WrlxGjRq1UKvVxMfHA9beUCtXLsdgMGRonz4UcPPmDbZlGzeupVGjpnb1xtLpdDo++OBjFi1awYcffkLz5q0wm80sWbKIN998lW3bNt/3PB5FuXL/9fBJT9Ld2evH09PaKy0hIT7Lfdz93Pv6+pGSYn0vefz4MdLS0mjWzD5h1LhxU7RarW04ZJUq1Zk1ayYjR37A8uWLuXXrFu++OyjDvrOiVqtp2bINmzZtIC3NOpnUqlXL79mzLjT0EDqdk9094uLiQu3a9bI8TpUqVUlJSaZbt1eYMeM7QkMPUbNmbXr06HXfz1wPMltnnTrBtkQVQHBwI8Dao+tJeVKv62dFelYJkc1ZzGYwpqLoXABQnFxRnN2xxN8AtRZNUE20ZRqi9iuRY5JTd7uzwLrprrHiz5qiqNCWqIsmqCaGk5tIO7gUS0o85pthJK+ciLpgGVJadAdtzqjzpFapeLlxcYoW8OCXFSdJNZhYsv0iF6/fple7sihqA2lm6xstTyd3nNTZp06XEEII8SSp3POiLnD/Hgoq97z2j30CHmj/is6+90lmx7p73w/Lw8ODkJCWtl4+Z86cYtSoT/n++ymEhLQkLi4WgAkTxjBhwpgM29+8aU2WxcXFZtqLKF18/G0CAjKed548PlgsFpKS/kvM3V3DRqVSYbFY38/dvn0bIEOBbB8f++tw9eoVJkwYy4EDe9FqtQQEFKF48RJAxjpdLi4udo+rV6+Jr68fa9asoGzZcqxdu4oWLVpnmhi607FjR5kx4zsaNGhM165vZNrm1KmTnD9/jvPnz9GqVeMM67ds2ZghGQPWZNxff/3O2bOn8fcvzK5dO/jmm+8ytLuTj09e2rZtT9u27QFrrakvv/yEiRPHERzc8Im+z1er1XaJkXR3X9v7cXKyf9+oKIrt+YqPtz73dz/XKpUKLy9vEhISAPjii7HMm/cLGzeuY/PmDahUKqpXr8Xw4R+RP3+BB4qjdet2/PrrbHbs2ErJkqU5ejSUCRMmZ9n+9u3beHl5ZVh+d6x3Kl++Il9/PYU//5zPn3/O57ff5pAnjw/duvWgc+cu94zPxSXznml3uvv1mB5f+nV6Ep7U6/pZkWSVENmUOf6mtbv66W1oAivjHNzNtk5XpS2WxFtoi9dBcXJ1YJS5l6LWoCsfgrZkMGlH15B2ZDUYUjBdO8m1OSNw7TLhsd9wPks1SvtSMK8r3y06SuStJI6cj2bUnP2826kCA6v0Zu2ljTQLbIRKUZFsTOZS3GXK+Nz/WyAhhBAip9CWqv9IQ/Cc6772SMfTt3vwejP3EhUVSa9eb9KrV1/atu1gt65kydL06tWPjz4ayvXr13BzcwNg4MDBVKxYOcO+PD29AHBzcyM2NsZundlsZvfunZQpUxZ3d3eio6MzbB8dbU12eXh42v5/L+kfuG/dirYrgH779n+zF5vNZoYNG4RO58TPP8+jePGSaDQaLl68wJo1K+97DJVKRatWbVm2bDHNm7fi+vVrtGqV9XCu9ON/9tkI8ucvYBvGlpmVK5fi6urKV19NyrDuyy8/YcmSRZkmq8qXr4ivrx+bNm2gaNEgPDw8M30+jh8/xocfDubTT7/MMANh1arV6dr1DaZO/Yb4+NsPVH9LURTMd82snZzsmF7z7u7ugPWeufO5N5vNxMTcst0bbm5u9Os3kH79BhIefolt27YwZ87PfPPNhHsmnO5UuHAAFStWZuPG9Vy9eoU8eXyoWTPrnnVeXl7ExsZkKJly532ZmVq16lCrVh1SUlI4cGAff/31O99+O5Hy5SvZhnw+qvRee+liYm4B/yWxrM+tfbIoOdm+N+f9PKnX9bMiwwCFyEYsZiOGi/tJWjWJxN+HkXZwCZbEWxjO7cJiTLW10xapiq5cs1yTqCqmreKQAusPQtG54FStA65dJqAtHwIqDW7lgnNUoiqdf15XPulWnSolrLFHxSYzZt5+Dpy8SZug5jjdMSzw+9BZrAvbnKOGPQohhBC5kY9PXtRqNYsW/UVqamqG9eHhl3B2dsbfvxCBgUXx8vLm+vXrlC5d1vbj6enF9OnTuHTpIgAVK1Zmz55dGI3/lV84ejSU4cPfIzw8jMqVq3HkSChRUZG29WazmY0b11GmTFm74YP3Ur58RZycnNi0ab3d8h07ttr+HxcXS3h4GO3adaB06bJoNNb+FLt377Qd935at25HTMwtZsz4jjJlylK0aFCWbS0WC6NHf0ZMTAyjRo2zJfjulpaWxrp1a6hfvxFVq1bP8BMS0pJDhw4QHn4pw7bWoYBN2LJlI5s3b6BJk2aZ9owqXDiA5OQk/vrrj0zPMzw8jHz5fB8oUQXg6upq95zBkx1G9jDKlauATqdj/fo1dss3bdqA0WikYsVKREVF0qlTG9v9ERBQhNdee5Pq1WtlOI90WfWYa9PmBXbv3snGjetp0aLVPXvWVatWg7S0NLZv/29CKoPBwN69u7Pc5ocfptKrVzcsFgvOzs7Uq1efd999D8AWq1r96OmV/fv3YjL9l2hMvybpdbj0elciI+/93N6vN+GTel0/K9KzSohswBwXieHUFgxntmNJvm23TpWvKNrSDYGcOcTvgeSAfIjKxQPnuq+hq9CcPHk9ibnjvWLKll9QnN3QVW6T7ROIemcN73aqwMpdYfyz9QJpRjM/LjvBheu3eblxcSKSIjkfdwkLFi7GhWHBgpKb7z0hhBAim1Or1QwePJyPPx5Oz55v8OKLLxMYWJSUlBT27dvN338voE+f/rakS69e7/DNN+NRqRRq165LXFwcv/zyIwkJCZQsaR2a+OabPXnnnbcYPvx9XnzxZZKTk5g58weqVKlGhQqV8PcvzOrVKxg06B3eeqs3er0r//xjnYXw66+nPHDser2e7t178tNP03FycqZKlWrs3LmdHTu22dp4e+ehQIGCLFz4B3nz5sPV1ZW9e3exYMHvgHUmvfvx9y9EpUpVOHz4IIMHf3DPtgsX/snOndvp3LkLqalpHDt2NEMbV1dXLlw4T3z87Ux7TgG0bNmG33//lSVL/mHAgPczrG/SJIQFC37n6tUrfP/9z5nuw8PDg379BvHNN+N5992etGvXkYIF/UlISGDr1k2sXr2Czz8fe9/zT1e3bn22b9/KtGmTqVevPkeOHGb16hUPvP2T5OHhyauvvsG8eb+g0WioU6ceFy9eYNasmVSuXJVateqiUqnIn78A3347kcTERPz9C3Hq1El2797Bm2++nel+3dysPbbWrVtN+fIVKVCgIGCdFfPbbydy5swpPvnky3vGVr16TWrWrMPYsV/Sp080fn5+/PXXH8TGxpA3b+YzZdeoUZPff/+VMWM+p0WLVhgMRv73v3l4eXlRpUo1W2yhoYc4cGDfAxVFv9ONG5F89tkIOnTozNmzZ/jpp+m0bt2OgADrpA116wbz229z+PXXOZQrV57t27dw4ID9jIlZXZt0r7zy2hN5XT8rkqwSwsGS1061zuh3J50L2uJ10JZuaDerTG6VXmAdwGiuhxP3/lbAkVTu+dB4uMMNa1dd080wDKet3w6mndyMrnJrdOVDULJZD7E7qRSFtnWLUKSAOzOXHCcxxcj6/VcIj4jnnQ7lGVK1H8svrqVb2S62YYFGswl3XebfPAohhBDi6QoObsjMmXP4/fd5zJs3m5iYW+h0OkqWLM2XX46jYcP/6im1b98JV1dX/ve/efzzz0L0elcqV65Knz7v2mrylC5dhilTpvPjjz/wyScf4u7uRnBwQ/r06Y9KpSJv3rxMnz6L6dOnMnHiV5jNZkqXLsvkyd9nOqPdvbzxRg9cXFxYsOB3/vxzPuXLV6R///eYOHGcrc3YsROZMmUio0d/hk6npUiRIMaPn8zUqZMIDT1Ex46d73ucunXrc+LEsSyTS+nOnDkFwMKFf7Bw4R+ZtqlcuSpOTs54eXlRvXrNTNsUK1acEiVKsnr1cvr0eTfD+nLlKuDnlx+VSm1XzPxunTq9REBAIAsX/sHMmd8RFxeHXu9K2bLlmDJlui0R8iDatHmBq1evsGrVchYvXkjlytUYPXo877yTeeLnaevV6x3y5MnD338v4J9/FuLtnYf27Tvx1lt9UKmsvZDGjJnw70x0M4iLi8XX14+33urNa6+9mek+69dvxMqVSxkz5nNeeKGjLTmp1+upUqUqMTEx9+xZl27s2K+ZPn0qP/88ndTUNJo2DeGFFzqxc+e2TNvXqFGbzz8fw/z58/joo+EoikKlSpWZOnWGbchjx46dOX78KEOHDmTkyC/uWRfubh06dCY+/jYjRgzBycmZl17qYjfzXrdubxEbG8v//jcPo9FI3br1+PDDT/jww8H3vTbpnuTr+llQLDLG476ioxMwm+UyPSv58rlz40bWs07kdBZjql0iI2XbHAwnNwOg9iuBtkxDNEE1snWy40n7bttiThqsXb2/qvM5Hg9QhNCR7rxHTTfDSNnxK+bIc7b1it4LXbUOaEvVR1Fl38QbwM3YZL775yjhkdbijV5uOvp1rEBxf2t3c5PZxPQjs4lKukHfij0o6Jb/XrsT2URu/z0qcj65R8XTEhERRv78j/9Fn0ajwmh07KQv4uEMHNgXH5+8fPbZaEeH8kzIPWovKSmJjh1b8e677/HCCx0dHY74V1TUZXx9C2e6TqVS8PHJ+stw6VklxDNgMaZivLAPw8ktWLDg2n6kbZ22TGPQOKEt3QC1t78Do8wmctiIM3XeQPQvfIwp/DCpexdijrmKJSmW1G1zSDuyGqcaL6IpWj3bztSY18uFj16vxq9rT7PjaASxCWmMn3+Qrs1K0KiKPweiQjl56wwAa8I20qNcVwdHLIQQQgjxn9mzf+LSpQscOnSAn36a5+hwxDN2/fo1Vq9ewe7dO3F2dqZ581aODkk8IZKsEuIpMkWHYzi5BcO5nZD230wcpltXUeexJqbUeQOfi6F+91JMW4WDOzwAcKqf83qUKYqCJrAK6sKVMJ7bSer+f7AkRGOJiyBl/feo8hXFuVEv1N4F778zB9Bp1bzVugxBBT3537ozmMwWfl17hgvXbvN684q8EBTLgahQXi3VCQCD2YhGUWfbBJwQQgghnh/bt2/l6tUrDBgw+LFnZBM5j6Ko+OuvP3B1deWzz8bg7Ozs6JDEEyLJKiGeMEtaMobzezCc2oL5xkW7dYqLB9qSwShO2XuYm3g0ikqFtmQwmqCaGE5sIu3QMiypCZhjr6O4uDs6vHtSFIXGVfwp7OvGD/8cJTYhjR3HIrh8I4F3O9ahSUADtCoNZouZn47Ow1PnziulOqJRyZ8RIYQQQjjOrFm/OjoE4UD58+dn5coNjg5DPAXyKUOIJyj18ArSDi0Dw52zliioC5dHW7ohmsDKKPLhPoPoHFRg/UEoGh26ii3Qlm5A2pFVKDoXVM7/JatMN8NQtE6oPLNf/afi/p581r0G05cc58zlWMIjE/hyzj76tC9H+aI+rL60gePR1sKkHk4etAu6dxFTIYQQQgghhHhY8qlZiMdgMRvtkk+KTm9LVCmu3mhL1UdbqgEq97yOCjFHiDFfR+t/HgCTJfcUilR0LjhV72S3zGKxkLJtDuabYWhLN0BXrQMqvZdjAsyCp5sTQ7tU5q9N51m3/zKJKUYm/xlKhwZBBFetybHoUxhMBkICGjo6VCGEEEIIIUQuJMkqIR6BOfY6qYeXY7pyHNcuE1A0OgC0xWtjunocbclg1IUrZPuZ4MSzZ446bxseaji5GcOZnegqNEdXqRWKk6uDo/uPRq3i1WYlKFrQnTmrTpFmMPPP1gtcup6XPi17YtGk4axxxmwxM+/En9TIX5VyPqUcHbYQQgghhBAiF1A5OgAhcqKklRMxntmBJSkW48X9tuWKzgWXkP7/DveTRNWDKqatSvLeliTvbYmzWufocJ4qtV9x9J2+QF24gnWBKY20w8tJ+GM4aaGrsBjTHBvgXWqXzc/IN6rj6+UCwKGzNxk3P5TEeOt3HUvOr2Jf5CGmh/7CkRvHHRmqEEIIIYQQIpeQZJUQj8CSeAsAVb6i2bLukMje1HkD0bcagkvbD1D5BlkXpiaSuudPEv/8kLRTW7CYTY4N8g6FfN34tHt1KhXzASDyVhKj5+5n36koAj0Ko1Vpye/qSwnvYg6OVAghhBBCCJEbSLJKiMegKVQedXqyQTyyW/8WWNf4n8WUjZI0T5umYBn07T/BOWQAKq8CgDURmrp1NsZzux0cnT29s5YBnSvSIbgoCpBqMDF98THOHdMzqEof+lbsjsu/wwJXXFxHoiHJ0SELIYQQQgghcihJVgkhHO6WyVpgXet/HpPl+UlWASiKgrZoNfSdR+Pc4C0U1zwoHn5oiteytbFYLA6M8D8qReGF4KIMeqkieifrMMDVe8P5a8VNdGbrbIdLz69m5cV1fL1/GjeTox0ZrhBCCCGEECKHkmSVEI8ie+QORC6iqNRoSzfA9ZVxuLQcZDfLpOHkJpJWTcJ0M8yBEf6nYrG8fNq9OoXyuQFwKjyWL+bs49zVWKJTrENk1YoaV63ekWEKIYQQQojnRHb5cjczjo7N0cd/VJKsEkI4XDFtFVuBdV0uL7B+P4pGh9qroO2xxZBC2oHFmC4fJWnRZyRvmIH5dpQDI7Ty9dbzcbdq1C7nB0BMfCoT/neIYoZGtA9qRd+KPXDRuGC2mDl684SDoxVCCCFytv79ezNoUL8s1zdsWItZs2Y+w4ge35gxn/PKKx2eybE6d27HuHGj7hlLcHD1LH86d26XYZsLF84RHFydTp3aYDJlHBlw8OB+2/YHDuzL9Lj79++1tblTbGwsU6dO4uWX29O4cR1atWrCoEHvsGXLpvuea3BwdebM+fm+7R7XypXLCA6uTlRU5FM/1t3uPEeDwcC0aZNZt271Ez9O+nMYGnoYgFmzZtKwYa17b3SHGzeiGD78PSIirj/V42Qls2vzLF93j0tz/yZCiCwpiqMjyHXkkt7FbEJTtDqGk1vAYsJ4fjfGC/vQlmmErmo7VHovh4XmpFXTq21Zggp48OfGcxhNFuatOUP9igVoXMga17ILa1gbton6/nV4qcQLqGWWTCGEEEJkM92796R9+xczLD98+AAzZnxHpUqVM6xbsWIZRYsGcenSRXbt2k5wcMNM960oCps2baBatRoZ1m3YsC7DspSUFPr1exuAbt16ULBgIRISEtiwYS0ffzyMgQOH8PLLrz7kGT55deoEM2PGbLy98zzzY8+YMRs/v3+/MI25xZ9/zuejjz576sdt164DtWvXe+D2Bw/uZ+fO7bz//vB7titVqjQzZswmKOjJ1kLO7Np0796TpKTEJ3qcp0WSVUIIh0svsA5gMgejRRIa6RQnV5yDu6Gr0ILUfX9jvLAXLCYMJzZgOLMdXYXm6Cq1RtG5OCY+RaFZ9cIE+LkzffEx4hLT2HbkOpejEnirXXH2XN8PwJmY86SZ03BROSZOIYQQQois+PsXwt+/kN2yiIjr/PHHbwQGFmHIkBF264xGI2vXruLVV19n27YtLFmyKMtkVYUKldi2bRODBw9HpVLZ7WPr1o2UKFGSs2fP2JZv2rSe8PAwFixYQsGC/rblDRo0Ii0tlVmzZvDiiy+j0Th2kJS3tzfe3t4OOXb58hUcclxfXz98ff2e+H5dXd2e2TndfZ9nZzIMUIhHoC5QCnWBUqjc8jo6lFwhxnxngXWzo8PJllSefrg064e+4+eoC5W3LjSmknZoGYm/D8Nwfq9D4ytZ2ItPu9eguL8nAJci4pnw2zE65H+D0t4l/p0t0DosMCYl1qGxCiGEELldcHB1Fi/+m7Fjv6Bly8aEhDTgk08+JCbmlq1N//69GT36M0aMGErTpvUYPvx9AFJTU/j++yl07NiaJk3q0qNHV7Zv32K3/1OnTjJo0Du0aNGQkJAGDBrUj2PHjtq12bVrO337vkVISAPatg1h/PjRxMXFZhrv2LFf0KFDK8xm+/eB48aNonPndraaO4cPH+Tdd3vRtGk92rRpyvjxo4mPj7fb5ty5s7z3Xj9CQurz4ottWbt21UNfP6PRyCeffEhqaiqjRo1Hr7evw7lr13ZiYm5Ru3Y9WrRoxZ49u4iIiMh0X02aNCM6OprQ0EN2y/fv34vBYMzQUyf9OcqszlC3bm/x5ps9MRgMD3QeWQ3Vu3NY5PXr1wgOrs6WLRsZNmwQzZoF07Fja5YsWcTNmzf56KNhNGsWTKdObViw4H9Z7nvMmM8ZPHgAy5cvpkuXjjRuXIfu3buyZ88uu2NfunSRESOG0LZtCCEh9RkyZCDnzp21a7Ngwe907foiTZrUpUOHVkycOI7ExATb+vRhgNevX6NTpzaA9R7q3LkdO3duz3To5Z49uwgOrs6FC+ezvF6LF/9Nly6daNKkHv379yYy0v45vXt43tWrV/jgg/dp3bopTZvWo0+fHuzatd12fUaN+hSAl156gTFjPrdd+2nTJjNgQB+aNKnHpEnjMwwDTLdp03peeaWDLZ6TJ49nGcuDXBvIOAzQaDSyYMH/eP31l2nSpB4vvdSeuXNn2Q1t7d+/NxMmjGHevF/o1KkNTZrU5Z133uLUqadb6kOSVUI8An27D9G3G4G2dANHhyKeM+p8RdC3HopLm+Go8hUFwJKagOLs5uDIwNvdieFdq9C0qvUbm4RkAzP/vkDxtObkc/EBYMWFtYzZO5lTt87ea1dCCCGEeEwzZkwDYNSor+jXbyA7dmzju+8m27VZt241np6ejB8/mVde6YrFYuGjj4azdOkiXn31DcaOnUiJEqUYMWIo27ZtBiAxMYGhQwfg6enF6NET+OKLsaSkJDN06ABbQmHFiqUMG/Ye/v6FGDVqHL1792PHjm0MGNCHlJSUDLG2bNmGmzdv2CV0DAYDW7ZsIiSkJYqicPjwQd57rx96vZ5Ro8bTr98gdu7czuDB/TEajYC1RlD//r1ITEzg009H0bNnX6ZPn8aNGw9X7/P776dw8uRxhg4dQVBQsQzrV65cRsmSpQgKKkbTpi3QaLQsW/ZPpvsqUaI0hQoVZvPmDXbLN25cR3BwA5ycnOyW16hRG7VaTf/+vZkz52eOHz9mO78yZcrRtesbODs7P9T5PIhx40ZTrlwFxo37huLFS/DNN+MZOLAPQUHFGD9+MmXKlGXq1G/umaA4ceIof/wxn549+zJ27ETUajUjRw4nIcF6X5w/f45evbpx8+ZNhg0bwciRXxAXF8s777zNxYsXAOs9OX36VDp1eolJk6bRvXtP1qxZyZQpkzIcz8cnL+PGfQPAm2++zdixX1OrVh18fPKyZs1Ku7arVi2ndOmymT6fAH///ScTJ35F3br1GDduEmXLlmfChDFZnqvZbGb48PdISUnhk0++ZNy4SXh6evLhh0O4evUKdeoE89ZbvQEYM+Zrunfvadt24cI/KF++Il99NZFWrdpkun+TycTEiV/x6qtv8OWXY0lNTWXgwL5cvXoly5jud20yM27cKKZPn0aTJs0YP34SLVu2ZvbsnzKc+8aN6/4d0jiMzz4bQ3R0NCNHfpAhwfwkyTBAIYTDFdNW5fBOLwB0DbSODSaH0PiXRd3hU4wX92O6egKNf1nbOnPybUyR59AEVkF5xkXANGoVrzUvSdGC7sxdfRqD0cxfm85z8dptWjb0ZnXYRgAWnl3KRzXfR6XIdyZCCCGejV3X99uGp79Xte89l+2N2I/Fcv92j7rsWShevKStVk2NGnDy5HG2bt1s18bJyZkhQz5Eq7W+/9q3bzd79uxk9OjxNGrUFIDatesSHx/P999PpX79Rly8eJHY2FheeqkLFSpUAiAwsAhLliwiKSkJFxc9M2d+T926wXzyyZe2YxUrVoI+fbqzYsVSXnzxZbs4qlSphq+vHxs2rKVKlWqAtSdMfPxtWra0fpifOfM7ihQJYvz4ybbhdCVLluKtt15n48Z1NG/eigULfsdkMjNx4lQ8Pb0ACAgoQp8+3R/4um3ZspG//vqd9u070aJF6wzrY2JusXPndvr3t/ZEc3d3p379hixfvoQePXqh0WT8iN24cTNWrVrOe+8NQ1EUDAYDW7duZuTILzh37oxd2xIlSvL552OYPPlrfv55Bj//PANnZ2cqVapKmzYv0KRJswc+l4cRHNzAllBxc3Nn164dlClTjp49rfds8eIl2LJlE8ePH6V06bKZ7iMhIYFffplvG77o4uJC//69OXRoP/XrN2L27J9wdnZh6tQZuLhYS0PUqFGbV17pwKxZMxk9ejyHDx+kQIGCdOr0MiqViipVqqHX67l9Oy7D8XQ6HSVLlgKsw9tKliwNWJOfixcvZMiQD3ByciYxMYFt2zbTr9+gTOO2WCzMmTOLpk2bM3DgEABq1qxNUlIiixf/nek2MTG3CAu7xJtv9qROHWvvuDJlyjN79o+kpqbaDS0tWbIUBQr8N4GSv38h+vR51/b44MH9mR5j+PCRNGzYGLAOJ+3cuR0LF/7JoEFDMm3/INfmThcunGf16hX06zeIrl3fAKzPh7OzM9OnT+OVV7oSFFQcAJPJzDffTEOvdwUgKSmRMWM+5/z5c5QoUfK+8TwK+ZQghBA5lKIoaINq4Fz/TbvlaYeWkbJ2KklLx2COe/YztADULV+Aj16vRl5P6zd/+0/fYNbfV+gQ0AlPnTu9K3RDpagwW8yYzBln0BFCCCGetFvJtzgbe4GzsRfuu+xMzIO1e9RlD+tBvny6u016Iimdr68fKSnJdsuKFClqS1QB7N+/D7VaTe3a9TAajbaf4OAGXLkSzvXr1wgKKoaXlzfDh7/P11+PZcuWTeTJ40O/fgPJl8+X8PAwbt2KplmzFnbHKleuPIUKFebQoQOZxt68eSs2b95o60W0YcNaSpUqQ2BgEVJSUjh+/Bh16wZjNpttcRUtWoz8+Quwb98eAEJDD1GhQiVboir9uH5++e97/cA6rOurr76kVKkyDBo0NNM26T126tSpR3x8PPHx8TRq1ITo6Jvs2LEt022aNGnGzZs3OHIkFIC9e3ejKAq1atXJtH3jxs1YtGgFkyZNo0uX1wkMLMq+fbv59NMP+eyzEZkOEXxcZcuWt/0/T548GZalX9O7h13eyccnr12drXz5fAFITrb2pgsNPURwcANbogpAr9cTHNyAw4et90XVqtUJDw/j7bdfZ/bsnzh16gQhIS3p3LnLA59LmzbtSExMZPv2rYB1OJ3FYiEkpEWm7cPDw4iJuUX9+vZ1x5o0CcnyGHny+FCkSBATJoxm9OjPWLt2NWazmQEDBmfZeyvdgyR3NBqNXTyenl5UqFCJI0cO33fbB5Xek/Hu69K8eSsADh06aFtWrFhxW6IKsNXuuvt3ypMkPauEeASp+63dfNUFS6MpWMbB0eR89gXW60uB9cdgMaZhPG99w2aOPEfSigno249E5frsC2AG5nfn0+41+HHZcY5duMX16CT+WarmzdZv46vPB8DKi+u4dPsyb5V7Db1Wiq8LIYR4evK45KGEV9ADLSvpHcSd+YCH2fZBlj0sFxcX21Cqu5lMJkwmE87O9n9H7x5epihKhiSHi4t9Labbt+MwmUw0axac6bFu3rxBgQIF+eGHn5g7dxYbNqxjyZJFODk50bJlGwYNGmrrAZMnj0+G7b2989jVHrpTixat+e23ORw4sI9KlaqwfftWevV6B4D4+NuYzWbmzfuFefN+ybBtoUKF/43/NoULF86w3sfn/nVmDQYDn346AkVRMWrUOHQ6XabtVq5chslkokuXjhnWLVmyyNYT5k4lSpSicOEANm/eQKVKldm4cS0NGjSySxTeTaPRUKtWHVtC6+bNm3z77QQ2bFhHq1btCA7O/Dl6VHfX5QLskkoP4u7hiek94Cz/1qS9fTsuy/si/f5u2rQ5ZrOZf/5ZyJw5PzNr1kwKFChI374DaNo06+TRnQICilChQkXWrFlJ06bNWb16JfXqNcDDwzPT9un3rJeX/fvle903iqLw7bffM2fOLLZu3cTq1SvQaDQ0aNCYoUNH4OHhkeW2d7/uMuPl5W1XkB+sRe2vXbt6320fVPp53z2jY/rjO1+rTk72z216ctxsfvKJ03SSrBLiEaQdXAKADiRZ9QSkF1gHMCMF1h+HotHh+vJXpO77G8OJjVgSokle/Q36diNQdPf/w/ikubloea9zJZZsv8iynZdISTMxc/EprtRJpniZFFZdstZvmHfyT/pW7P7M4xNCCPH8qFOgOnUKVH+gZfUL18RoND/Stg+y7GHlyePDpUsXM11348YNAHx8MiYBHparqxtubm58++0Pma4PCAj8998ifPLJKEwmEydPHmf16pUsXryQQoUCqF27LgC3bkVn2D46+qZdb507FS0aRMmSpdm0aT2JiYmkpaXaeny4urqiKAqvvvp6pr1d0hMtXl5e3Lp1K8P6zIaQ3W3atG84c+YU48Z9Y9c76E6nTp3gwoXz9O7dj/LlK9qtW7NmJStXLuPatauZbt+4cTNWr15B37792b59K6NGjc/0GH369CAwsIhtCGe6vHnz8sEHn7B580YuXbrwQMmq/xIK9u+vk5OT7rvt0+Du7pHlfXFnb7iQkJaEhLQkISGBvXt3M3/+XL78ciSVK1d5oMQjQJs2LzBx4jguXbpIaOghJkyYnGXb9GPffe9kNSFAurx58zF06IcMGfIB586dYdOmDcyfPxdvb2/ef3/4A8WZlYSEeCwWi12PyejoaNsMjIqiZHhek5Ie7nl1d7cm1GJibtl6wVmPcxPA7jlxBBkGKIQQuYzi5IpTvTfQlmkEgDn6Mslrp2ExPdjMMU+aSqXQsUEQA16sgIuTtdfcil1hrN2USFnvMrhoXOhYPPPikkIIIYSw1nS6du1qpsWtt27diEqlolKlqo99nMqVq5KQkIBaraZ06bK2n+PHjzF37ixAYcuWTbRt24zo6Juo1WrKl6/I0KEf4ubmTlRUJIGBRciTx4f169fY7fvEiWNcu3aVihUrZ3n8li1bs2vXdjZuXEuNGrVsPTz0eldKlCjF5cvhdnEVLhzAjz/+wPHjxwCoVq0GR44ctn3YBrh48cJ9e6Ns3LieRYv+4rXX3qRevfpZtluxYhnOzs689NKrVK1a3e7nlVdew2KxsGzZ4ky3bdy4GVFRkcydOwutVke1ajUybZc/fwE2bVqfaSHt8PAwwDok60GkD9uKjPyvLERY2CXi4u6fvHsaKleuyo4d20hO/m/oWHJyMjt2bLPdF59//jEjRliHYLq5udGkSTO6d38bk8mUaaJLpcp8REaTJs3RarVMnPgVefL4ULNm5kMuAQoXDsDX149Nm9bbLc9qWCdY7+d27Zpz8uRxFEWhRIlS9O7dj6CgYrYZEu/uGfUwUlJS7CYcuHnzJkeOhFK5srWmm6urKxaLxW6mx7uHCGZ1bdJVrlwFgHXr7F+r6a/de71WnwVJVgkhHC5IU5XkvS1J3tsSrUoKrD8JiqLgVK8bmiLWN66maydJ2fSTrRu2I1QpkY9P3qyBf17rG6eTFxO4uKskrwX2wO/fYYGrL21gx9U9DotRCCGEyI6aNm1OyZKlGTp0EAsW/I+DB/ezdetmvv12Ij/8MJUuXV4jf/4Hq8t0L3XrBlOhQiU++GAwS5Ys4uDB/cydO4tp077Bzc0dvV5PxYqVMJstjBgxlK1bN3PgwD4mTBhDUlIiDRs2RqVS0bv3O+zcuZ1Roz5l9+6dLFu2mBEjhhAQEEirVm2zPH6zZi2IjY1l27YtNG9uX9y8V6932LFjG2PGfM7u3TvZtm0zgwcP4OjRUEqVshaPfvnlV3Fzc2fw4P5s2bKRDRvW8uGHg9Fosn5/GRUVyfjxoyhcOIC6dYM5duxopj9paWmsX7+GevXqZzo8LiioGCVLlmbFiqW2ult3KlGiJAEBgfzvf/No1KgJanXmiYTevfvh4qKnd+83mTPnZ/bt28PBg/uZP38uH3zwPnXr1qdGjdpZns+dqlatjpOTE1OnTmLXrh1s2LCWESOGZDkc7mnr0cM6U+OgQe+wZctGtmzZyKBB75CcnGQr7l69eg22bdvMd999y4ED+9i8eQM//TSdgIBAW7HvO6X3ujtwYK8taQnW3naNGjXl8OGDtGjRKsvrDdb3ze+8M4Bt2zYzYcIY9u7dzS+//MjixQuz3KZEiVK4uLgwatSnrF+/hoMH9/Pjjz9w9uwZ2+QEbm7ugLVof1jYpYe6VlqtljFjPmfDhnVs3bqZIUMG4ObmxssvvwpAnTrWnnXjxo1i//69rFixlIkTv7KrK5XVtUkXFFScFi1a8dNPPzB79k/s27eb2bN/4uefZ9CiRSuKFn284cuPS4YBCiFELqWoVDg36Uvyiq8xRZ7FeGEvqXpPnOp0feazBKbLn0fPx92qMXvlKfadiuLW7VSmL7hEtxZOuPhFsuyC9ZucRGMSzQMz1nwQQgghnkcajYbvvvuRuXNn8c8/C4mKikSr1VK4cCDDhn1EmzYvPJHjqFQqJk2ayk8/zWD27J+Ii4vF19ePbt3eolu3twBrPZtvvvmOH3/8gXHjRpGSkkJQUDFGj55A5crWL8natu2As7ML8+fPZcSIIbi7exAc3IA+fd69Zx2kPHl8qFGjFqGhh2nQoJHdujp16jFp0lR++eUnPv54GDqdE2XKlGPatJm2JIanpxc//PAzU6dOYvToz9HrXejatRsbNqzL8phXrlwmMTGRxMRE+vXrmWW7vn37Ex9/m6ZNMy/SDdZZ6KZOncS2bZszHULVuHEz5s61zjqXFX//Qvzyy3x++20Oa9eu4rff5mCxWChUKICuXbvZkhUPwt3dnTFjvmbGjO/46KOh5M9fgB49erN69YoH3seTVKxYcb7//mdmzvyO0aM/+7dHYBVmzJht6y3Wtm0HUlNT+eefv/nnn79wcnKmevWa9Os3KNOEk4uLC126vM7ixQvZtWsnS5eusc3IWLduMKtWLadVq3b3jS0kpCUqlYo5c35m9eoVBAUVZ9iwj/j8848zba/Vavnmm++YMWMaU6ZMIiEhnkKFCjN8+Me2WSSrVKlGjRq1mDnzew4ePHDPoYh38/LypmfPvvzwwxRiYmKoXLkKo0aNs/U2DAgIZOTIL5g7dxbDhg0iMLAoH3zwMZMnf33Pa3O3ESM+w9+/MCtWLGXevF/w88vPW2/1pmvXbg8c69OiWJ7GVAK5THR0wlMtHCbs5cvnzo0bWc8ykR3E/9gdAF3V9jhVz1hcUTycedt3sf3iEQCmdnkL53sUm8wOcsI9eidLSgJJS8dijr0GgEvbDxxea81isbB232X+2nQe879/hqpV1RDusgmTxcTQav3J7+p7n72IrOS0e1Q8f+QeFU9LREQY+fMHPvZ+NBqVXc0qIbIbuUfvb+zYL7h8OYzp0zMW5RfPRlTUZXx9M056ANZSIT4+blluK8MAhRAOl15gXet/HosUWH/iFGc3XFoPQXHNg1PtVx2eqAJrd+sWNQMY2qUy7nprcvLAQSP68EZ0Lf6aLVG17epubiRlrE8ghBBCCCFEZhYs+J0JE8awatVyunR5w9HhiEckySohhHgOqNx8cH15LLqKWXddd4TSgd581r0GRQtYZyMJu2xi3sIoTofHcDDqCH+cXsTXB6ZxPvaSYwMVQgghhBA5QmjoQdatW8Orr75Ow4ZSViKneq5qVqWlpdGnTx+6detG48Zy0wqRXRTTVCV0pxcA2obZewhgTqZone0eG6+dQtHoUPs6tnhiHg9nPnytKr+vP8Pmw9e4nWTg698PU7GudSYfk9mMqzbr+hZCCCGEEEKkGzPm6/s3Etnec9Oz6tSpU7z22mscPHjQ0aEIIe4iFeGePcOFfSSvnEjy6smY4yIcHQ5ajYpuLUvTo1VpNGoVZouFwzt8KJRcjzdKdyG/qx8Ap26dxezAGQ2FEEIIIYQQT99zk6z6/fff6devHxUrVnR0KCIX0FVtj65qe9QFSzs6lFzhlvkaGv+zaPzPYrKYHB3Oc8GSEg9mI5aUeJJWTsKcFOvokACoX6kgI16vio+HEwBnj7rz9/J4Im8lcSjqKNMO/8TMI3NINqY4OFIhhBBCCCHE05JrklWLFy+mbNmyGX7i460zzXzxxRcy9E88MU7VO+JUvWO2KFSdG9gVWJcJSp8JXdkm6Kpap7m2xN8gedVkLGnJDo7KqmgBDz7tXoOyRbwBuHojkS/n7mPpmfUAnIu9RFxqnCNDFEIIkY3JewkhhHC8x/1dnGuSVR06dODEiRMZftzd3R0dmhBCZEu6ah3RlmoAgDk6jOR132ExGR0clZW7XsfglyvTurZ1+vHkVBNhO8uRTylC93Kv2oYFxqXedmSYQgghshm1WoPBkOboMIQQ4rlnMKTi5KR75O1zTbJKCJFzFdNUI3lvS5L3tkSjeq7mfXAoRVFwqv8m6oBKAJiuHidly89YsklNKJVKoXOjYrzbsTxOOjWYNYTvKcX6jWkkJBs4fOMYn+0ax+7r+x0dqhBCiGzCzc2L2NgbpKWlSg8rIYR4xiwWCyaTkcTEeGJjb5I3b95H3pd8KhTiIVksZpKXjwdAW6o+2pLBDo5IiEenqNS4NOtH0vIJmKPOYzy3m1S9F861uzg6NJtqpXwp4OPK9/8c5Xp0EkcvRPPFvJ2YSm3AYDby15kllPUphYdOetIKIcTzzsXFFYC4uJuYHqO3sEqlwmzOHl/eCJEZuUdFdqVSqdFqdXh7++Ll5cWNG/GPtJ9sl6w6efIknTt3ZsOGDeTPn99u3fLly5k+fTqXL1/G39+fPn360KFDB8cEKp5rpuunAVAXLOvgSHKHW6araPzPAmC2NEA6fT5bisYJfcv3SVoyGnNcBIYjq1F55kdXppGjQ7MpmNeVkd2q88uKkxw4c4PoGBO6U5VwLXWUHuVesSWqzBYzKkXuHyGEeJ65uLjaklaPKl8+90f+gCXEsyD3qMjtstU7+vPnz9OnTx+MxozfgqxcuZKhQ4cSHBzM999/T82aNfnggw9YvXr1Qx3j119/lULrQmQzdxZYN2eTIWjPG8XZDZfWQ1H0XqjyFUVTpKqjQ8rAxUlDv47l6dyoGIoCabHexOyrS+ghNUaTmdAbxxm/byrRyTGODlUIIYQQQgjxGBRLNhjMbTQa+fPPP5k0aRJarZbY2Fi2bNli17MqJCSE8uXLM3nyZNuy9957j9OnT7Nq1SpHhC2eUxaLmYtjXwLAu8EreNd/2cER5XxfLP2V48k7AZjXaQrO2kcvxCceT1r0VTTueVDpXBwdyj0dPhPFhF8PEJ9kLaJbvJiGm75rSTWl4eeWj8mtPkOjUjs4SiGEEEIIIcSjyBbDAA8cOMDEiRN5++238fPzY+TIkXbrL1++THh4OIMHD7Zb3qJFC1atWsXly5cpXLjwU4svOjoBs9nhOb3nRnbv0npn8enExDSM2TjWnKKwuRL793oAENsoGbUq1cER3Vt2v0cfjwfEGQHr+VmMqZgTolF7FXRsWHfx93bhkzer8f0/xwiLiOfceQOuFAKfC3Qo2pqY6CRHh+hQufseFbmB3KMiu5N7VGR3co+KnOBe96lKpeDj45blttliGGCxYsVYv349/fv3R63O+E34hQsXAChatKjd8sBA65TmFy9efPpBCiHEc8acEk/S8gkkLxuH+XaUo8PJIK+nCx+9XpXgigUAhcTzJTEcr8eNcE8sFgtHb55g6fnVMrRUCCGEEEKIHCZb9Ky633SG8fHWTJybm33WzdXVWjgxISHh6QQmhHgmbpmv2RVYV2ePPPpzzxQeijnqPABJKyehb/8xKhcPB0dlT6tR06NVaYIKejB/7RmMie7MX3eG49cvccl9NammNGJSY3mzbPaZ3VAIIYQQQghxbzniE+H9ymqpVDniNIQQWbh1R4H1bFBGT/xLWzIYXeU2AFhuR5K8ejIWQ4qDo8pIURQaVfbnw9er4u3uBEDo2RiMyU6AQpV8FRwboBBCCCGEEOKh5Igsj7u7dUryxMREu+XpParS1wshhHiydDU6oylRDwDzjYskr/8eiznjjK3ZQbGCnnzWvQalA7ywpLqScLQmSlgVuG2drONE9GkuxoU5OEohhBBCCCHE/eSIZFV6rarw8HC75WFhYXbrhRA5UzFNNZL3tiR5b0vUMoNbtqIoCs4Ne6AubO2dZLp8lJQts7NtDzgPVx1DulSmRc3CYNKSFOnLlL9C+d+2/cw69hvfHprJ/sjDjg5TCCGEEEIIcQ85IlkVGBhIoUKFWL16td3ytWvXUqRIEQoWzF6zVIncTsGl7Qe4tP0Abcm6jg4m11FQHB2CuIui0uDS7F1U+axfDBjP7iBt30IHR5U1tUrFK01K0Ld9OZy0aizAplOnSDGmYTQb0aiyRblGIYQQQgghRBZyzDv2d999lxEjRuDp6UmjRo3YsGEDq1atYvLkyY4OTTxnFEVBU7CMo8PIVewLrDdEhfSuym4UrTMuLd8nackYLLcjSTu8AkXvha58iKNDy1LNMn7453Xlu0VHiYwuSKpBh4dPKnkpAkDY7cvkd/XDSa1zbKBCCCGEEEIIOzkmWdWpUyfS0tL45Zdf+OuvvyhcuDDjx4+ndevWjg5NCPGYYv4tsA73n1BBOI7KxQN96yEkLRmNJTURxTn71wv0z+fGJ2/W4OflJzh8DmJvw+h5++kYko+1MX+QT5+XPhXexNvZy9GhCiGEEEIIIf6V7ZJVnTp1olOnTpmu69KlC126yPTjQgjhKCoPX1xaDcGSkoCmUDlHh/NA9M4a+r9YgRW7wli89QJpBjOLTmxA45vK5firhMdfkWSVEEIIIYQQ2Ui2S1YJkd1ZzCYSfn4bAF31jjhVbe/giHK+YupqHN2VBwB1IxkCmN2p8wZmWGYxpKBonR0QzYNRKQrt6hahSH53flx6nMSwsmBR8NF7UURfEoD4tATcdW4OjlQIIYQQQgiRIwqsC5F9STFwIdKOrSdxwQjM8TccHcp9VQjy4dPuNQjI54EhrBwRJwvy5Zx97L1wni92T2DFxXUyFFUIIYQQQggHk2SVEMLh0gusa/zPYsbs6HDEQzBGnCV1529YEmNIWjkJc0q8o0O6r3xeLox4oxp1yuUHFGLiU5h9/HeSjSmsvLiOi7fDHB2iEEIIIYQQzzVJVgkhHC7Gcg2t/3lbkXWRc2jyl0BbsSUAlrgIkldPxmJIdXBU9+ekVdOzbRleCymJWqUi7XxFzCl6ChqqUEhfGACzRRKnQgghhBBCOIIkq4QQQjwWp1ovoyleGwBz1AWSN/yAxWxycFT3pygKTasVYnjXKnho8pB6rC7nD/ny1W8HORkZzti9kwm/fcXRYQohhBBCCPHckWSVEMLhiqmrk7y3Jcl7W6JS5NdSTqMoKpwb9kTtb50d0BQeSuq2OTmm9lOJQl583r0GJQr6AAphN2L47uBsridGMvngdGJT4xwdohBCCCGEEM8V+VQohHC4nJHSEPeiqDW4hPRH9e9MgYbT20jbv8jBUT04Tzcnhr1ahWbVCoFZTVpEYSwWCFBVwlPn4ejwhBBCCCGEeK5IskoI4XB2BdalTlCOpehccGk5GMU9HwBph5aRdnyDg6N6cBq1iq4hJenVrhzq6CBSj9fh6E4fvv/nGOGxEfx+6m/STAZHhymEEEIIIUSuJ8kqIYTD3VlgXXF0MOKxqPSe6FsPQXF2B8AYHoolhyUg65TLz8fdqpNX5wcoHDx/ja93/8j2a3uYeWSOo8MTQgghhBAi15NklRBCiCdK5Zkfl5bvoy3TCJcWA1FyYB2ywr5ufNq9BhWL+YBiwZCiBUBvyI/531pcZ2LOSU9AIYQQQgghngKNowMQIqdRVGrce89xdBi5SjF1dY7t8gFA1TjnJTZERmrfINS+QY4O47G4OmsZ2LkiS7e7s3SnBk3+i+yIdOLS0b1Uq6piXcxfFHTNT7eyXSjsXtDR4QohhBBCCJFryKdCIYQQT505/gZJKyZgToh2dCgPRaUodKgfxMAXK+MUUwrMGq7eSGT1+S0ARCTewFWtByDRkITJbHJkuEIIIYQQQuQK0rNKCOFw0aaraPzPAmCxNEJRpHJVbmJOvk3SkjFYkmJJXjkJ/QsfoTi7OTqsh1K5eF7G963L5sNXWbvvMrcvVESTGAaKhVGzjtG8RmEuu2zlWtI1WhVpRq0C1RwdshBCCCGEEDmW9KwS4iFZLGaM105ivHYSc/xNR4eTK9xZYN2CxdHhiCdMcXZHE1QDAHPsNZLWfIvFmObgqB6e3llD69qBTOhbhzealcUrqRzGa8WJS0xj4e5QQm8e5UZyNEdunLJtY7HI/SyEEEIIIcTDkmSVEA/LbCZ5+XiSl4/HcG6Xo6PJdaRXVe6jKApOdV5FE1QTAHPkOVI2TMeSQ4fM6bRqGlctxFd9atOrbVn887piSXUhLbw05lRnDmzxZP66M1y8EcWXe75m65VdGMxGR4cthBBCCCFEjiHDAIUQDldMXZ3ju/MCoGoiOfTcSFFUODfuRXJKPKZrJzGGHSJ1+6841X8zxyYo1SoVdcrnp1Y5P0LP3WTlLm/OhwYAKjYcuMK2m+tR+93kzzP/UMi9IEGegY4OWQghhBBCiBxBPhUKIYR4JhS1FpfmA1D5FAbAcGozaQeXODiqx6dSFKqUyMdHb1Rj+KvVKFc0DwCmBHfMKS6Y4vKwasNtLl6/zZYrO1kfvoUUY6qDoxZCCCGEECL7kp5VQgiHu2W+s8B64xzb00bcn6LT49JqCEmLR2FJiCbtwGIUvRe6Mo0cHdpjUxSF0oHelA705lLEbVbsysfBowWwaNI4YLjBgXPXca26FbMqjeM3TzGoah9HhyyEEEIIIUS2JD2rhHhoUjD5SYuxXLcVWBe5n0rvhb71UBQn64yAqXsWYElJcHBUT1aR/B6827ECo3vWIbh0UdQqBcUpGWOqFoCoc34cOnMDg8nI+vAtJBmSHRyxEEIIIYQQ2Yf0rBLisUgPICEehcqrAC6t3id5wwxcQvqjOLs5OqSnooCPK2+1KUOH+kVZvTecrYc9SHWL5HqsO9MuHMWn6A2S8h1g1cX1DKzSm0CPwo4OWQghhBBCCIeTZJUQwuHuLLCuNJEE4PNC7VsM11e+QlHl/j9FeTyc6dqsJG3rFmH9/itsPHCFpFQj8eorqIHUFBWnz5goUMnEzdQbuOvccNflzgSeEEIIIYQQ95P7PyEIIYTItu5MVFksFtJCV6EtUQeVq7cDo3p6PPQ6OjUIolWtADYfvsqavVoSblwFlZk/Dl1gxc5wXCvsIUmJpXlgI1oXDXF0yEIIIYQQQjxzUrNKCOFw6QXW04usi+ePxWwmZcss0vYuIHnlJCypiY4O6alycdLQqlYgX79Tl9dr18fHUgSARNUN4iw3MJgNHL94i7jENAAS0nL39RBCCCGEEOJO0rNKiIemoC5QCgCVWx4Hx5I7xFiuSXH1552ioGicADDHXCF57VRcWg1B0egcHNjTpdWoaVTFn/qVCrDvVBQrdl3i+skaaApc4uR5L4Yf3EnlihpOaldQp2ANWhdphqeTh6PDFkIIIYQQ4qmSZJUQD0lRa9C3G+HoMHIVi0yw+NxTFAWnuq9hSY7DeHE/puunSdn0I85N+6Gocn8nYLVKRe2y+alVxo/Q88VZuSuMc6Y4DJgJjd+F2tvEjit7qexWC09/DywWC4oi9d2EEEIIIUTuJMkqIYTDFVPX4MTOfNYHTRwbi3AcRaXCuXFvklPiMV0/jfHiflJ3zsep3uvPTWJGURQqF89LpWI+nLkcy4rdYRy/VgwUsKQ58fW+01QpEY2myDFcXdS0KNIEP30+R4cthBBCCCHEEyXJKiFEtvF8pCPEvSgaHS7NB5K09CvMMVcwnNiA4uqFU5V2jg7tmVIUhVIB3pQK8CYsohgrd5dk/6UIAA6HhePkFYoSZ+HG7XgG13r7uUnmCSGEEEKI50PuH1shxBNmMZtI3f8Pqfv/wXj9tKPDyRWkwLq4k+LkikvrISiu1ppwafv+xnB6m4OjcpzA/O6806E8Y3rXpX7FAqhUCuYYXywWOLE3L6Pm7mfHiUvMPv4/rsRfc3S4QgghhBBCPDZJVgnxsMwm0g4uIe3gEkyRklx5EtILrGukyLr4l8rVG5fWQ8HJFYCUrbMx3bzk2KAcLH8ePT1al2H8W01p5P0C5uONsSR5cCkinrkHV7M/8jBf7fuWS3FXHB2qEEIIIYQQj0WSVUIIIbIltXdB9C3eA7UWXcWWqHwCHB1StpDHw5kuTUswsVczXqhXBFdnDYraiMWiYE7wZNr8Syzddp4DEUe5GBfm6HCFEEIIIYR4aFKzSgjhcEH/FlhXQAqsCzvq/CVwfWkMKg9fR4eS7bi5aOlQP4iWtQLYcrgIqw+d5HZqMqlJafy0JBSXyltBm0ptvxq8Ue4lR4crhBBCCCHEA5NklRBCiGzt7kSVKfoyirMbKldvB0WUvTjrNLSoGUCTqoXYeew6q/aEc9N4BYs6DQXYtTcN56hzNKvuT7QpgmKeRaQguxBCCCGEyNYkWSWEcLj0AuvWj8/StUpkzXjtJMlrpqLyyIu+3QgUnd7RIWUbWo2KhpX9qV+xIGeuxfPbhnxEqU5jjPRjVWQ468/vRRN0mMKuAfSs2JW8LnkcHbIQQgghhBCZkppVQjwW6Z3wJKQXWFcXlALr4t6MF/eDIRlz9GWS107DYjI4OqRsR6VSqF/Fn1HdGtK/bmdKFrL2QFP5XgQgPDaChesucyUqgURDEhaLxZHhCiGEEEIIkYH0rBJCCJFjONXpiiXhFsawQ5iunSRl0084N+2Losh3L3dTFIWKxXyoWMyHM5djWbrHlTPXDmJJc2ZvVDR7T0STp8pB3N1UvFiqFeXzlnF0yEIIIYQQQgDSs0oIkQ0UU9cgeW9LUve3dHQoIptTVGqcm76D2q8EAMYLe0nd9bv0DrqPkoW9GNq5FiNDXqeaTw0UBVTut0jWRhGVGsFvO3dx7EI0FotFrqUQQgghhHA46VklhMgG5MOxeHCKRodLi0EkLRuLOeYahmPrUPTeOFVu7ejQsr0AP3f6ti9PxwZJLN9zln1XY1DlCyfqTAG+ORGKfyEz5oADtC8RQvX8lVFJjzUhhBBCCOEA8i5UiIelUqGr2h5d1fao85dwdDS5QnqBdXXBc44OReQQirMbLq2GoPw7I2Da3gUYzuxwcFQ5h5+3nrdbVuKrDt1ooHsdJ8VaqD7K6QixxmjmnviD1YdOYTSZHRypEEIIIYR4HknPKiEekqLS4FS9o6PDyFXSC6zL6CPxMFRuPri0GkrS0jGQlkTKll9Q9J5oCpV3dGg5hre7E12alKJtnSA27L/MuvBwzGm3MN3Oy8J9EWzYFUORSpFUCChAcKEaaFTytkEIIYQQQjx98q5TCCFEjqXO449Li0Ekr/waxc0HlYevo0PKkdxctLSvH0TLtEA2Hg5n3aWLxAIxKXEkp+zh1DkLOy4cZ1CtN3Fz0To6XCGEEEIIkctJskoI4XDF1DU4udMXlaJAU0dHI3IaTYFSuLR4H5VPYVQuHo4OJ0dz0qlpVbMoIdUC2XUsgqWhB0kwOKE4pXDxmDfDDu4kuHJe8gTcpGnRuujUkrgSQgghhBBPniSrhHhIFmMayasmAaAt0xht8doOjkgIoSlUzu6xxWyEtBQUZzcHRZSzadQq6lcqSL0KBdh7uhpLQvcQmeBBKia2XN6JlrOsCdvIO+V6Uyq/v6PDFUIIIYQQuYwkq4R4WBYLpuunAdAEVHJwMLlD9L8F1hUUoLGjwxE5nMWQSvL677EkxaFv9yGKzsXRIeVYKpVC7TIFqFW6Pccv3mL5rkuEuUcBkJqkYcLcU9QoHUO1SjrKFwrARePs4IiFEEIIIURuIMkqIYTDxUqBdfEEpR1fh+nyEQCS132HS8v3UdTy5+5xKIpC+SAfygf5cOZKEAsObuXSlVQsFoW9pyII1W1Fc85MQ7/GdK4gY3mFEEIIIcTjUTk6ACGEEOJJ0lVshfrfXo+mq8dJ2fIzFovZwVHlHiULeTPyhfZ83rk1tcv6oc4TicopBbMqjTV7LzPutwOEnrtJYlqio0MVQgghhBA5lHzVLIRwuCBVDU7u8kWtkgLr4vEpKjUuzfqRtHwC5qjzGM/tJlXvhXPtLo4OLVcp5OtG7xfK0T6mCL/vy8vphCOYbhbijCWOc+vX4Vz8KOXcq/BG5Ta4OekdHa4QQgghhMhBpGeVEEKIXEfROOHS8j1UnvkBMBxZTdqR1Q6OKnfy83blveYt+Kr5QFrVLIKzTmUd1qsycjTuEKPm7WfL4aukGUyODlUIIYQQQuQQkqwSQjjcLYu1wLqqwFlHhyJyEZWzOy6th6DovQBI3f0HhnO7HRtULubl5sRLjYsz4Z261PRqAIneGCOKcCPayNzVpxmyYgoTt/1GRHy0o0MVQgghhBDZnCSrhBAOF2O5itb/POqC5x0dishlVO75cGk1GLTWGQFTNv+E8cpxB0eVu7m56OhRvwGTWgzjxTItyOPhhOIai9kjgouGI4xa9SeLt10gIdng6FCFEEIIIUQ2JckqIYQQuZraJwCXFgNBpQGzGXNchKNDei446zS0qBHIuD516FCvOJokXyxmhZQrgSzdcYlhs9YxasMszkRdcXSoQgghhBAim5EC60IIhyumqsmpXX5SYF08NZqCZXBu0hssFrTFajk6nOeKRq2iXdWKtKlSgW0nzrHlSiyXIuIx5ztLhHKVb4+eoXLqy7SvXQa/PFKIXQghhBBCSLJKiIen1uLS9gMAVB6+Dg5GCPGgtEE1MyyzWMwoinQyfhZUikLDciVoUNbC8Uu3mHvyOEmAOSYfu8/FsefIbgIrRlG5aEFalqyDTq11dMhCCCGEEMJBJFklxENSVCo0Bcs4Ooxc5ZblChr/sygoQGNHhyOeE4bzezEcW2ctwq51dnQ4zw1FUShf1Ieviw5g38XzbIm7xgkMWNRpRGgPs+b6QXZcOspb5bpSsrAXiqI4OmQhhBBCCPGMSbJKCOFwMZZr1qnuzfKhVDwbxivHSNkwHbCQvO47XFq+h6KSP4nPWo2ixahRtBhXbyTw1979nDFpQZ1K9MV8jA89RFAhF9xLnKNjmcYEeBRydLhCCCGEEOIZkbEPQgjHszg6APG8URcsjbpweQBMV46RsuUXLBa5ER3FP58b77VpxGe1PqCUqRnqpHwAhBtPcCbxKOP3T2Xp4QOYzGYHRyqEEEIIIZ4F+RpZiIdkSUsmYc47ADjVegVdpVYOjijnC1LV5NTufwusN3N0NOJ5oKg0uDR7l6Tl4zHfuIjx7E7S9F441XrZ0aE91/y83RgY0py4umms33+ZjddPY7EoWFL0LF4dy47duylXJZkCfhqCC9XCRSPDN4UQQgghciPpWSXE45BRa0+UlKYRz5Kidcal5fsoHn4ApIWuJO3YOgdHJQA8XXW82LAYX3foRRPn19Feqwwo3IhNZlf0NhZfWMGX26eSnGp0dKhCCCGEEOIpkGSVEMLh0gusqwqcdXQo4jmjcvFA33oIiosHAKk7/4fh/F4HRyXS6Z01dK5XgYnd2/JaSEm88/w3VPPmxbwM+2Enf285z2/HF3E25oIM5RRCCCGEyCUkWSWEcLgYy1W0/udR5T/v6FDEc0jl4YtLqyGgdQYspGz6EeO1k44OS9xBp1XTtFohxr/dhNcL98Y1oi6mG4VISjWy6sR+dkXu5ttDM9hwcZejQxVCCCGEEE+AJKuEEEI899R5A3EJGQAqNZiNpB1aLr10siGNWkW9CgUZ92p7BnSoQtECHijOiVjMKiwmNX/+k8AvK06yJ+wkqy6uJz4twdEhCyGEEEKIRyAF1oUQDhekqsnp3fnRqKXAunAcTaFyODfqheHsTlya9UORImrZlkpRqFIyH5VL5OVUWBBL95zm7K1wzAYN249eZ2/KAdTeN1gftpVx9T9Bq9Y6OmQhhBBCCPEQJFklxGORD7NC5Cba4rXRFKsliaocQlEUyhTJQ5kidbh4vRwrd4Vx8GwEii4VgISIfExdeJw2tQO5aD5IYfeClPUphUqRjuVCCCGEENmZJKuEEA5nK7COCmjs6HDEc+7ORJU5JZ60fYtwqt0FRevkwKjE/RQt4MG7nSpw7WYQK/cUZO+pU5hSnTieeosTV6/gXGkbKBaaBTSiY/HWjg5XCCGEEELcgySrhBAOF2u5htb/PBaz9HYQ2Yc5MYak5eOxxEVgTryFS/OBKCq1o8MS91Ewrys925SjY1wxVu8NZ1voNYz6eCxmFYraxN6dGvKnRFAiSMf2a7tpWKgu3s5ejg5bCCGEEELcQZJVQgghRCYUZ3dUbj6Y4iIwhYeSum0OTg3ekiGCOYSPpzOvhZSkXb0irN9/mQ2HfElziSDyloafrp3Ao9h5DD5n2RC+lc/qDCevSx5HhyyEEEIIIf4lySohHpKic8G99xxHh5Gr/FdgXSUF1kW2oag1uIT0J2n5OMw3wzCc3gZaZ5yqtkdxdnN0eOIBeeh1dGpQjFa1Atl86Cpr9l3mdmIaKao41IAl0Ys9h2/TuIoHh28dwkmtpXK+CqilF50QQgghhMNIskoI4XgWRwcgROYUnQsuLQeTtGQ0lvgbGI6tw3ByE5oi1dCWqo/avyyKFOvOEVycNLSqHUiz6oXYfuQ6q/Y4E301EhQLfx+/wMo9F9FW3ISBFCrlLUfvim86OmQhhBBCiOeWvMMWQjicrcB6/rOODkWIDFR6T/Sth6LyKmhdYDJiPL+H5JUTSVr4KRaL2bEBioei1ahpXLUQX/WpTc+mdSio9wcgRRVLmsEEQHKkLzfjkjGYjSw6t5zIxChHhiyEEEII8dyRnlVCPCSL2YQp4gwAKg9fVG4+Do4o54tFCqyL7E3l6Yf+pdGYrp3CcHorxov7wWRE7Rdk17PKfPsGit4TRaNzYLTiQahVKuqUy0+tsn4cORfNit2XOH/YDbXPdUKjtRw7tJviFeIJd9rOhvCtDKjci9J5Sjg6bCGEEEKI54Ikq4R4WMZUkpePB8Cp9qvoKrZwcEBCiGdBUVRo/Mui8S+LJTURw/k9qP2K27VJ2fQjppiraIvXQVu6Puq8RRwTrHhgKkWhcom8VCruw5nLsazYFcaxm7cwWSycv3UFbQFQm51RJflAHjgefZq41Diq+1VBp9Y6OnwhhBBCiFxJklVCCIcLUmpyek9+tBopsC5yBsXJFV3ZJnbLzLHXMUVah7IaTmzAcGIDKp8AtKXqoy1eR4qyZ3OKolAqwJtSAd5cirjNyl1hHDgNphuFUJySGbf/MKUDvUgO2EJU6nU2XN7GyJqDZXZIIYQQQoinQMbcCCGEEE+A4p4X56b9UBcqD1gTGObocFJ3zifht/dIXv8DxivHpMZVDlAkvwf9OlZgdK9a1CtZAiXeF4BTVyOJjL8FQH5VkG1uiFUX1xMef8VB0QohhBBC5D7Ss0oI4XDpBdYVRQ00cnQ4QjwSRa1FW6wm2mI1MSdEYzizHcPp7Vjib4DZiPHCXowX9qIOqIy+5XuODlc8gAI+rrzVugwdgouyZu9ltoReJSW0IWrvSHbFO3Pp8B5qVXNiTexall9cyyslO9KgUB1Hhy2EEEIIkeNJskqIh2Wx3L+NeCgxXJUC6yJXUbn54FS1Pboq7TBdP43hVHpRdgOawhXs2ppuXELlXVCKsmdjeTycebVZCdrWDWT9/itsOKAjyWDkenQSyw6fRBeooChQyqsUANcSIjgVc5Y6BWrgonF2cPRCCCGEEDmPJKuEeBxSqkQIcQ+KokJTsAyagmWwpL6O4fwetMVq2dZbjGkkrfwaLBa0xWujLd0AlU+g1EHKptz1Ojo2CKJlrQC2HL7Gmn3hxEUFkhLry//Zu+/wrOr7/+PPc+6ZvROyyALC3nuDiiwXzqq1Wrt3v7W1tra142vHr62181s71Fq1jqqogBMQQfbeKyELSEII2bnn+f0RjaWuoCEn4/W4rl4XOeckvm64m9x535/z+pgxtdy9Zy8Xjc+mOmYDW6q3saL4FX409Q4NrERERETOkYZVImK7fGMShzam41bBuvRi71bKHizZAb4mAAL7VhLYtxIzKRtX4UyVsndjER4n8yf154JxWazbc4IXNpRSVRNBAwGeWnOEiBGHIQLyYvLbB1VrKzaQFpnKgPg8DSNFREREPoCGVSIiIjZx5o7Be+EXCBx8nVDZHsAiXFOG742H8W14DGfuWFyDZ+LIGIph6jbZ7sblNJk9OpMZI9PZcqCaZetLKK9upGX3VMy4anaEInmo6iAzxiXx5OFnCYSDzM2ewZUDL7E7uoiIiEi3pmGViNhOBevSVxkOF678ibjy3yplX0fg4OvvKGV3jbgY75SP2R1X3oPDNJk0NI2JQ1LZXVTDsvUlHC43CAOrtlfwevEO3APDYMCI5CEANAeaWVOxnmkZk4hxawWdiIiIyH/SsErkXJkOHOltJbpGVKLNYXqHMypYF3mzlP1S3GMWv6OU3ZU/4axrg2W7cKQPVil7N2MYBiMLkhlZkMyhsjMsW1/C7qIagmdSCG6fhSPxJCtON+GYUkdRcDvPFb3IimOv8u0JXyU9Ks3u+CIiIiLdhoZVIufIcHmJvOQOu2OISC/136XswZIdmKkF7efDZ07SsuLX4I5sK2UvnImZrFL27mZQdjyDsuMprWxg+YYSNh+oIlSVw86qGnYeqSFx5G7wQrI3kX6RqQDsrN6LwzAZmlSIaWh4LyIiIn2XhlUiYrt8JnFoUzpulwrWRf6T4YnCNWjaWccCR95o+4O/+e1S9sRsXINVyt4d9U+L4XOXDeeKmc2s2FDKG3tOEAxZnN41HDMmk5YkD1vjqxk9KImnDj/HqdbTDE8azOdHfdLu6CIiIiK20bBKRESkB3GPuQQzMZvAwTVvl7KfPruUvXniPKzofJWydyNpCZHcvGAwl03P46XNpazefhxfQyInG+CPx/aQ0i9Aa04DAEOTBgMQtsK8UvIaE/qNIcEbb2N6ERERka6lYZXIObKCfvw7lgHgzB6BI22AzYl6vtOoYF2ko9pK2Sfgyp/wnqXsJ4s24Zl5C+7Bs+yOK/8lIcbDtXMHsmhKLq9uLeeVLWU0tQapPumC6pnEZlXSHJ2GLzXE4fpDLC1awXPFL/KZETcxInmo3fFFREREuoSGVSLnKujHv20pAIY3WsOqTlBLuQrWRT6Ed5SyH3ydYNEWDCxceePbr7NCAYLFW3DmjlMpezcRHeHisul5XDwxmzU7jvPi5jJqG6C+JIsnSkpYsf442aNLAXCZTgbE5wFQXFfK6dbTjE4ZgcN02PkQRERERM4bDatERER6uLNK2afdSLT/JE2eqPbzwWPbaV35Z3BH4BowBVfhDMzkXJWydwNet5N5E/szZ2wW6/eeZMWGEiprW2hsCbB/fTqeuBjyCl34Wk0iomHFsVfYW3OAtMhU7pz0PypiFxERkV5JwyoRsV0+kzi8KUMF6yKdwHBHEpk5iqbqhvZjwaJNbX/wt5xdyl44A9fAqSpl7wZcTpOZozKYPiKdLQerWL6+hNKqRnx10WzfBLu3vsHkEYkUR7atthqUUNA+qHq9YgOD4vNJi0q18yGIiIiIdBoNq0Sk2zDQKg+R88E797MESyYROPg6ofLdYL1Zyr7+EXwbH8eZOwZX4UwcmcNUym4z0zSYOCSNCYNT2VN8mmXrSzhUdoZgyGLtjhoMcxoFwxsZEjkWgOrmGh47+DQWFlcNvJQ52dNtfgQiIiIiH52GVSJiu7cK1jEcgAqhRTrb2aXspwkcWvtfpeybCRZtJvKyO9XD100YhsGI/CRG5CdxpLyOZeuPsfNoDVbYwZFdcdy76ygjC86QPaQGCwuAwoS2f7vTrbUcOH2ECWmjcTlcdj4MERERkQ9FwyoRsd0ZKtoK1kMqCxY538zoxP8oZT9E4OAagkVbMGOSMFML2q8L11cRqjyCM2+8StltNiArjq9ePYqyqkaWbyhh0/5KLAt2Ha1h11HIy11AfqGP9Kg0AFaXrePVsjUsPbqcOyd9gxi3bvMUERGRnkXDKhERkT6orZR9MM6MwVjTbiTccOqswvXAgdfw71gG6x7CVTAZ1+CZKmW3WXZqNJ+9dBhXzMjjhY2lrN19gmDIoviYRfExN4d3bGbBpP7sqz8IQHpUWvugakf1HlIiksiMTrfzIYiIiIh0iIZVImK7PGsShzdn4HE54CK704j0PYY7EkdS//aPLcsiWLqr7QN/C4H9qwjsX4WZmIWrcCbOgVMwvTE2pZXUhEhumj+YS6fn8dLmMlZtr8DnD1Fa2cifn91HasJkJo/xMa5/NgD+kJ9H9j9JU7CZudkzuHLgJTY/AhEREZH3p2GViIiInMUwDCKv+AHBku3/Vcpe/mYp+2M4c8bgGjwTR+ZwlbLbJD7awzVzBrBoSg4rt5bz8pZyGlsCVNX6qFoJOzZXMW+Ch7ScRpqDLQDkxeUAEAgF2HhyKxP6jcXj0G2eIiIi0r1oWCVyrpxu3GMvA8CRmm9zmN6h9q2CdVMF6yLdheFwnl3KfnhdWyl7fRWEQwSLtxAs3UH0jfeCJ8ruuH1alNfFJdPymDehP2t2HueFTaXUNviobfDx2MojRHmdTBt3PZ7U44xKHgbAlsodPHrwKZYeXcFXxnyW7JgMmx+FiIiIyNs0rBI5R4bTjWf8FXbH6FVqKVfBukg3ZkYn4hlzCe7RiwmdOEjg4OsEizbjzB2L8R+DquDx/ViNp3Hmj8dwemxM3Dd53A4umpDNnLGZrN97khUbSjl5upmm1iAvr6vB44rEP7qIiyf2Z0f1HgBcppP0qFQAjpwpxmU6yYnNtvNhiIiIiGhYJSIiIh1jGMZ/lLLfgOVvPeu8f8cyQuV7YN0/cQ2YjKtwBmZKnkrZu5jTYTJjZAbThqez7VA1yzaUUHKyAV8gxEuby1i5rZwpwydydeEIoiIdOE0nlmXx78PPUtpQweiU4Xx6xE12PwwRERHpwzSsEhHb5TOZI5sy8bhVsC7SUxjuSAx3ZPvHVqCVcE1p2weB/y5ln4Fz4FSVsncx0zQYPziVcYUp7DtWy7L1xzhQeoZgyOL1nZWs3QUTBqeSNrkBT3QL5Y0nAMiI6tf+NTad3MaI5CFEOCPsehgiIiLSB2lYJXKOLH8zLS/eC4Br+EW48sbbnEhExH6Gy0vU9b8mWLqDwIE1/1XK/ii+jY+3lbIXzsSRPRzDUCl7VzEMg2F5iQzLS+RoRR3L1pew48gpLAs27a9i0/4qRuQncfP4L1Bu7WVG1hQAiutKeXDfv/A6PNwy7HqGJw+x+ZGIiIhIX6Fhlcg5ssIhQicOAuDMm2Bzmt6hvWDdcKKCdZGey3A4ceWNx5U3nnBTLYFDb5WyV7aXsodPlxOZ/VO7o/ZZBZlxfOWqkZRXN7JiQwkb91URtix2F9Wwu6iGAVmp5Fo+RhZEs7VqBwD+cICsNwvYK5urafA3UhCXq9s7RURE5LzRsEpEbKeCdZHex4xKwDNmMe7RiwidPETg4BqCRZtxDZ551pDDv381GAau/Aln3VYo51dWSjSfvmQYl8/I54VNpby+8wTBUJgj5XXc++QuslKiWTh5Ip8dMYDK5iriPXEAvHhsJRtPbiU3tj//M/bzOEx93xYREZHOp2GViIiInDeGYeBML8SZXog19cazzlnhEP4tT2O11OFb90+cueNwDZqKI3MYhoYgXSIlPoKPzyvk0qm5vLSljFXbKmj1hyivbuS+5/aRGh/B/MkDCWSGCRFg55u7CCZ449sHVXtO7ScvLocol4aNIiIi0jk0rBIR271VsO5VwbpIr2a4zy7pthqq4a1VVqEAwaMbCB7dgBEZj3PAZFyDpuNIzLIhad8TF+3h6tkDWDQ5h5XbKnh5SxkNzQGqzrTwjxcOsnRtMRdP6M/tY/+HzdWb2/urGvyN/GXPQxgYXDXwEqZnTrb5kYiIiEhvoGGVyLmyLLsTiIj0CmZcP6Ku/xWhin0EDq0jeGwrhAJYzWcI7HqBwK4XMJNycA2ZjXvoHLvj9gmRXheLp+Zy0YRs1u46wQsbS6ip91HX6OfxVUdYtt7J3LEDSOyXBsC2ql0Ew0EA0iJTAGgONFPSUM7ghIHqtRIREZEPRcMqkY9Cr8E7xWmrTAXrIn2UYTpwZo/AmT0Cy99CoGgTwcNvtG9kEa4pIXTyIGhY1aU8LgcXjMti1ugMNu6rZPmGEk7UNNPUGuS5N47x4uZSZo3KZN6EMaSNTmH3qX0MiM8HYO3xjSw9uoKMqH58afSnifPE2PxoREREpKfRsEpEbFdrVKhgXUQw3BG4B8/CPXgW4fpqAoffIHB4Ha5B08+6ruWVP2J4onANmoaZWqDVO+eR02EybUQ6U4b3Y/uhUyzfcIziEw34A2Fe3lLGym3lTBnej4WTL8IwDCzLYsOJLQBYWMS6owEoqishOSKRWLcGVyIiIvLBNKwSkW5Dv26KyFvM2BQ84y7DPfZS4O3br8ONNQSLNgMWgf2rMOLScA2chmvgVMyYZNvy9namYTCuMIWxg5LZX1LLsvUl7C+pJRS2WLvrBOt2nWBcYQqLpuTyjXFfZF3FRpIjkzAMg7AV5oG9j1Dnq+einDkszp9n98MRERGRbk7DKhGxXb41maObM4nwqGBdRM7Wtmrq7VG2FWjF0X8kobLdYIWx6irxb3kK/5ancKQPxjVoGs688e8oc5fOYRgGQ3MTGZqbSNHxepatP8b2w6ewgC0Hq9lysJpheYksnjKaQSnxABw8fYSa1lqA9pVVYSvM/ppDFCYOwDRMmx6NiIiIdFcaVomcI8MdScTi24G2cmAREek6joRMIud/nXDzGYJHNhI4vJZwTRkAoRMHCJ04AGsfwj3yYjwTrrQ5be+WnxHLl68cScWpJlZsKGHD3krClsXe4tPsLT5NQWYsiybnMqJgAF8f+3nWVmxkUvo4ALYd38Pvd/6V1MhkPjX842RGp9v8aERERKQ70bBK5BwZDifOjCF2x+hVanmzYN1UwbqIdIwZGY975MW4R15MqKa0bTfBI+uxWuoh5H/HyiqrtRHDG21T2t4tMzmKTy0eyuUz8nhxYxlrdh0nEAxztKKe3/57F5kpUSycnMPHh1yDw2xbRfVK0VoA6n0NJHoTADjZVIXH4SbBG2/XQxEREZFuQsMqEbFdrVGOK/MohPQtSUTOnSOpP44p/bEmXUOofC+BQ2txDpjSft4Kh2h64jsYUYlttwkWTMKMiLUxce+UHBfBDfMGccm03Pby9RZfiIrqJv7y3D6eXlPEgkn9mT4yna9MvoXndq0kZIWJcHoBePLwsxysPcKU9PFcP/gqmx+NiIiI2Em/GYqIiEivYJgOnP1H4uw/8qzjofK9WC31WC31+E4dw7f+X23XDZqGs/8oDIfLpsS9U2yUmytnFbBgUg6rtpfz8uYy6psDnKpr5aGXDvHsumNcMXsAUwZNJcLT9lL0VEsN+08fAsBhvP3y9OiZY+TGZuMwtVusiIhIX9Lrh1UPPPAATz75JIZh0L9/f37yk5+QkJBgdyzpwcIt9TQ99BUAPNNuxD3sQpsT9Xx51mSObs5q+6VFBesi0snMxEzcYy8jcHgdVsMpsEIES7YTLNkOnihcBZNwDZqGmZL/ZqG7dIZIr5NFU3K5aHw2a3ef4IWNpZyqa6Wuyc8Dy/bx+CtO5o7L5MLx2SRHJvGt8V9mZdnrzMmeBkBV8ynu2fYnEr3xXD/4KgYnDrT5EYmIiEhX6dXbr2zdupUnn3ySxx57jOeee478/Hx+9atf2R1LehX9UiMi0t2Z0Ul4xl9B1HW/IOKSO3AVzgRX261n+JoI7FtJ8zM/xrfxMXuD9lJul4O5Y7O4+zOT+fTioWQmRwHQ7Avy/BslfOuPb/Dwy4eItlK4Zdj1pEamAPB6xXosLGpaa4nztN22Wedr4FRLjW2PRURERLrGOa+samlpISKirbS0traW5cuXY5omCxYsID4+vrPzfSTx8fF8//vfJyqq7UXR0KFDefzxx21OJSL/ra1g/QiYLmCm3XFEpJcyDBNneiHO9EI8024geGw7gcPrCJXvAcvCmTXirOuDZbtw9BuE8dZgSz4Sp8NkyvB+TBqWxrGqJh558QBFx+vxB8O8urWc1dsrmDwsjYWTc0hPimJx/sWkRCRzvOkk6VFpALxSuppVZWsZmTKMW4Zdj8vs9TcJiIiI9Ekd/glfX1/P17/+derr63niiSdobGzkyiuv5MSJE1iWxR//+EceeeQRsrOzz2fed3jmmWf4zne+847jGzdupKCggIKCAgAaGxv54x//yPXXX9+l+UTkg9UaFSpYF5EuZTg9uAZMxjVgMuGmWoLFW3D8x06v4cYaWlbcA043zrzxuAZNw5ExGMPo1YvSu4RpGEwank5eahQHSs+wfP0x9h6rJRS2WLf7JG/sPsnYQSksnJLDzKy3i/L9oQBvHN+MhUVLoKV9UFXecJx+Uak4NbgSERHpNTr8U/03v/kNGzdu5DOf+QwATz75JMePH+db3/oWw4cP55vf/Ca/+c1vuvw2u8svv5zLL7/8fa+prKzk85//PGPHjuVjH/tY1wQTkY6z0B2VImIbMyoB9/CzC/OCRzcCFgR9BA+vI3h4XdtuggOntvVbxafbE7YXMQyDITkJDMlJoPhEPcs3lLDtYDUWsPVQNVsPVTM0N4FFk3MYnJOA2+Hif8Z9npVlrzMmpW0VnC/k597tf8ZlulgyYBHj+42x90GJiIhIp+jwsGrlypXceOONfOUrbcXSr7zyCklJSXzyk58E4IYbbuD+++8/Pyk/ggMHDvC5z32Oa665hi984Qt2x5HewLLsTtDr5FuTKdqkgnUR6T5cI+ZhxmcQOLSWYMkOCAexmk7j3/E8/h3PY6bk4xo0DVfBJAxvtN1xe7y89Fi+eMUITtQ0sWJDKev3niQUtth3rJZ9x2rJS49l8ZQcRg3sx8eHXNP+eZtPbqM52AK04HhzZVUgFKCmtZZ+Uak2PRoRERH5qDo8rKqpqWHgwLZdWBoaGtixYwcLFy5sP5+QkEBLS0vnJ/wIKioquPnmm/ne977HokWL7I4jvZF2jepU+tsUke7CMJ04c0bjzBmN5WsicHQjgcNvEK48AkC4ughfdRGE/LhHLrA5be+RnhTFJxcN4bLpeby4qZQ1O4/jD4YpPlHP757aTUZyFAsn92fikDScDpNJ/cbhMBzsqN7NqJRhAGyu3M7DB55kaFIhNw6+hjhPjM2PSkRERM5Vh4sX0tLSKCsrA9pWVYVCIWbPnt1+ftu2baSnf/gl8fv372fYsGGcPHnyHeeef/55Fi1axMiRI1mwYAHPPPNMh77mAw88QEtLC/fddx+XXXYZl112Gd/4xjc+dEYROT/aCtYPY6UesTuKiMg7GJ4o3EPnEnXZnURd8zPcYy7BiE4Cw8Q54O1OJSscpHXDY4Sqi7G0CvcjSYrzcv1Fg/jFF6ayeGoukZ6291ePn2rir8/v544/b+DVreVYYZMpGRP4/KhPYhomlmWxqmxt27WNJ4l2RQJQ3VxDIBSw7fGIiIjIuenwyqo5c+bw4IMP0tjYyLJly4iLi2Pu3LlUVlbyl7/8haVLl37o2+yOHj3KZz/7WYLB4DvOLV++nNtuu41PfOITTJ8+nVdeeYXbb78dr9fL/Pnz3/frfve73+W73/3uh8okIl2n1ihXwbqI9AhmfD88E67EPf4KwqcrMCPj28+FyvYQ2LWCwK4VmAkZOAdOwzVgCmZ0on2Be7jYSDdLZuazYFJ/Vu+o4KVNZdQ1+ampb+Xhlw/x3LpiLpqQzZwxWUR6nRiGwa3Db2R1+TrSIlNwmA4sy+Jvex7ijK+ei3PnMid7ut0PS0RERD5Ah38z/OY3v0lLSwtPPvkkaWlp3HXXXXi9Xg4dOsTDDz/MpZde2l6+3lHBYJDHHnuMX/3qV7hcrne95p577mHBggXccccdAMyYMYO6ujruvffeDxxWdZakJHVRdLWUlO67ZD/YGKLpzT/HRHuI7cZZewqnywGhtj9353/7/9RTckrfpedoF0iNO+vD2oOVtGAAFuHa4/g3PYF/05NE5I0kesQsogonYbq99mTths71OXpTVgLXXTyEVzeX8u9VR6g83Ux9c4B/v1bEio2lLJqWx6UzChiRUsCI3IL2z9tffZiyxuMAmB6r/b97vP4kGbH9Ou8BSa+j76PS3ek5Kj3Bh32eGlYH16kfPnyYAQMGYPxXR4/f7+fMmTOkpp57ieXGjRv53Oc+x6233kpaWhp33nknr732Gv36tb1wKCsr48ILL+Q3v/kNCxa83QexYsUKvva1r/HKK6+QnZ19zv/dc1VT00g4rOX8XSUlJYbq6ga7Y0gXevjlQ7y6tZxIj5Pff32m3XE+kJ6j0t3pOWqfcFMtgcPrCR5eS7j2+NknXV6ceeNxj5yPIzHLnoDdxEd9jobCYTbvr2L5hhLKq5vaj7ucJjNGpjN/Yn+S4yPevDbEtqpdrKlYz2dG3ESMO5qiuhJ+tfUPDEoYwLWDLqNfVNpHfkzSu+j7qHR3eo5KT/B+z1PTNN53YVCHV1bdfPPNXHHFFdx2221nHXe73R9qUAVQUFDQvqvgU0899Y7zRUVFAOTl5Z11PCcnB4Di4uIuGVaJiIiIdIQZlYBn9ELcoxYQPnWMwKF1BI9swPI1QqCV4KG1uAZOtTtmj+cwTSYP68ekoWnsPFrD8vUlHKmoIxAMs3JbBau3H2fS0DQWTskhMzmKCf3GMKHfmPbPX1X2OgBHzxQT4WwbatX5GvA6PXgcblsek4iIiLytw8Oq5uZmsrI6913A5OTk9z3f0NA2gYuOPnvaFhUVBUBjY2On5hERe7QVrB8B0wV0/5VVIiIfxDAMHCl5OFLysCZfR7BsF8FD6widLseRMbj9unBDNS0r/4xr4FRc+RMxvKoeOBeGYTB6QDKjCpI4VHaGZetL2FN8mrBlsX7vSdbvPcmYgcksnJJDQcbbt21eVrCQOE8soXCYOE8sAE8fWcbemv3MzJzC4vyL33E3gYiIiHSdDg+rPvGJT3D//fczbNgwRowYcT4ztfugOxRNs8ObGYp0GisUJFR5GAAzrh9mVILNiXq+M28WrFvBd++uExHpyQyHE1fuWFy5Y7HCQQzj7dcvgcPrCVcewVd5BN8bj+DMGY1r0DQc2SMwTG060VGGYVDYP4HC/gmUnGxg2YYSth6owgK2Hz7F9sOnGJKTwMIpOQzNSSA5IpGrBl7a/vlnfHVsq9pJyApR3VLTPqg63VpLolc/50VERLpah18F7dmzh6qqKq655hq8Xi/x8fHvGBYZhsErr7zSaeFiYtqKuJqams46/taKqrfOi3Qly9dIy/M/B8Az/SbcQ+fanEhERHqK/x5AGe4IjKhErKbTEA4SLN5CsHgLhjcG54DJuAZNw0zK0Sqfc5DTL4YvXD6ck6ebWbGhhDf2nCQUtthfUsv+klpy+8WwaEoOYwalYL759xrjiuaWYdezsux15mTPAKDB38gP1/+C7JgsrhiwiIL4XBsflYiISN/S4WGVz+dj+PDh5zPLO7zVVVVaWkphYWH78ZKSkrPOi0jPlmdNoWhTNlFeJ8yzO42ISNdxD78I17ALCB0/0NZvVbwFgj6s1gYCe14msOdlzIQsvLM/hSMl1+64PUq/xEhuWTiEy6bn8dLmMlbvqMAfCHPsZAN/eHoP6UmRLJiUw+RhaTgdDsakjmBM6tt3D6ypWE/QClFcX9J+rDnQQtAKEuvWG6YiIiLnU4eHVQ899ND5zPGucnJyyMrK4oUXXuCiiy5qP/7SSy+Rm5tLRkZGl2cSkfNAm22KSB9mGCbOzKE4M4diTf84weKtBA6vI1SxH7AInzmOEZ3Yfr0VDkE4iOH02Be6B0mM9XLdBQNZNCWHV7eW8+rWcppag5yoaebvy/ezdG0RF0/sz4xRGXhcjvbPm5o+gVA4RGlDOflxbZv7rKlYz/LilxmbOoqPDV6iMnYREZHzpFPLEPbt28fQoUM780vyxS9+kTvuuIO4uDhmz57Nq6++yooVK7jnnns69b8jIvapNdoK1i0VrItIH2e4vLgGTcM1aBrhxhoCh9djNZ/BjIhtvyZUuouWVffhyp+Ac9A0HP0GntWDJe8uJtLN5TPyuXhif17bcZwXN5dS1+inpt7HI68c5rk3jnHh+GwuGJtJpNdFgjeeSwvmt39+KBzi9Yr1hKwQx5tO4DbbehZPNFWSEpGEUx1jIiIinabDP1X9fj+//e1vef3112lubiYcDrefC4VCNDU10djYyP79+zs14JIlS/D7/fz973/niSeeIDs7m5///OcsXLiwU/87ImIfFayLiLyTGZ2EZ8zidxwPHFoLgRYCB9cQOLgGIyalbTfBgVMx49JsSNqzRHiczJ/UnwvGZfHGnhOs2FBK1ZkWGpoDPL2miBUbSpgzJpN5E7KJi3579ZppmHxy2A28Vr6OIUmFGIZBKBziDzv+RsgKsTh/HtMyJtn4yERERHqPDg+r7r33Xv72t7/Rr18/YmNjOXToEOPHj6e6upqKigq8Xi/f/e53P3SQJUuWsGTJknc9d91113Hdddd96K8tcv6o8FZERLqWq3A6WGGCpbvACmE1VOPfthT/tqU40gtxj1qII3ukStk/gMtpMmt0JjNGZrDlYBXL1pdQVtVIqz/Eio2lvLylnOkj05k7JpOs1GgMw6AgPvesovXdNfup9Z0B3t7FOmyFKW0oJycmW/8GIiIiH1KHh1UvvPACEydO5IEHHqC6uppZs2bx/e9/n0GDBvHaa6/xxS9+EZdLqyJE5NzlhadQtEUF6yIiHeHMGYMzZwzhlnqCRzcSOPwG4epiAEInDtJy4iBmUjYR876CGZNic9ruzzQNJg5JY8LgVHYX1bBsfQmHy+sIhsKs3l7B6u0VZCZHMWloGhOHppEaH9H+ucMSC7lpyLVsPLmVCf3GArD71D7u2/0P+sdkcfPQ60iLSrXroYmIiPRYHR5WVVZWcsstt2CaJmlpaSQlJbF9+3YGDRrErFmzuOKKK3j88ce59tprz2deEREREQHMiFjcwy/CPfwiQqcrCBx6ncD+1RBoxfK3YEQl2B2xRzEMg5EFyYwsSOZQ2RmWbyhh19EaACpONfHUmiKeWlNEfkZs2+BqcCpx0R4mpY9jUvq49q/zesUGAKpbThHniQPaeq0inF7i3/xYRERE3l+Hh1Ver/eslVP9+/fn0KFD7R+PHDmSF198sXPTiUifcNooxZl5VAXrIiIfkiMxE8fk6/CMuQT/3lcxo5Mw/qPwO3DwdSxfE64hszFcXhuT9gyDsuMZlB1P1ZkWNu2rZOP+SiqqmwAoOl5P0fF6/vXqYYbkJDBpSBrjClOI9La9Tr5xyNWsrdiI03TgfXPHxicPPcuhM0eZkj6e6wdfZdvjEhER6Sk6PKwaMmQIa9asaV85lZ+fz/bt29vPV1ZW6r586RMM04kjvRAAMzLe3jC9hArWRUQ6h+GJwjP20rOOWaEgvi1PYTXV4tv+XNtqrGEXYnijbUrZc6TGR7B4ai6Lp+ZSXtXIxv2VbNxXyam6ViwL9h2rZd+xWh566SAj8pOYNDSNUQOSWZz/9j3tp1pqOFB7GAC36W4/vq/mIAPj83E59LNPRETkv3V4WHX99dfz9a9/neuvv5777ruPRYsW8e9//5s77riD/Px8HnjgAUaPHn0eo4p0D4Y3mshL7rA7hoiISIdYjacwnB4sAF8T/q3P4N+5AteQ2bhHzsfU7YIdkpUaTVZqNEtm5nP0eD0b91WyeX8l9c0BgiGL7YdPsf3wKTxuB2MHJjNpaBpDcxNJjkjim+O/xOqydczMmgJAZVMVf9j5N6JdUdw45GpGJA+1+dGJiIh0Lx0eVi1YsIDGxkbuv/9+IiIimDp1KjfccAMPP/wwABkZGXz7298+b0FFpPfKC0+heEt/oiNcKlgXEelkZlw/Iq++m+Cxrfh3PE/4VAkEfQR2v0hg76u4Bk3DPWohZlya3VF7BMMwGJAZx4DMOK67YAAHSs6wcV8lWw9V0+IL4vOHWL+3kvV7K4mOcDF+cCqThqRy09DrMN+8C2H9iS0ANAaaSI1IBqCmpZZa3xkK4nJ1t4KIiPR5hvXWPrsf0vHjx6mrq6OgoAC32/3Bn9AD1dQ0Eg5/pL8mOQcpKTFUVzfYHUO60D9fOsjKbRVER7j47Vdn2B3nA+k5Kt2dnqPyXizLIlSxF//25wmdOPD2CcPAPeEqPKMXdUmO3vgcDQRD7Dp6mo37K9l55BSBYPis8wkxHiYNSWPS0DQyUrzsrN5DaWMFSwYsBuCJQ0tZXb6O7OgM/mfcF3A7eufr6p6iNz5HpXfRc1R6gvd7npqmQVLSe1cSdHhl1U033cTnP/95pkyZctbxjIwMMjIyWLlyJb/61a9YtmxZR7+kSI9k+Vvw73oBaNs+3JGSa2+gXqDWKMOZeZSw6Qa6/7BKRKSnMgwDZ9ZwnFnDCVUewbf9eUKlO8CycKTk2R2vR3M5HYwrTGFcYQotviDbD1ezcV8Ve4tPE7Ysaht8vLCplBc2ldIvMZJJQ9OYOrStAzMUDrGlcgcAUa6o9kHV7lP7yIxOJ9GrWzVFRKRvec9hVUtLC7W1te0fb9q0iYsuuoicnJx3XBsOh1mzZg3l5eXnJ6VIN2IFWvFvWwqAEZWgYVUnqFXBuohIl3OkDSBy/tcInS4jWLQZR8aQ9nPh5jO0rroP98j5OLJG6La0cxThcTJ1eDpTh6dT3+xn64EqNuyr5HB5HQAnTzezdG0xS9cWk9MvhklD0vji0C+yt34neXH9AWgN+nhg77/whXwsyruIBXkX2vmQREREutT7Dqsuv/xyGhralmwZhsHdd9/N3Xff/a7XW5bFtGnTzk9KERERETkvHInZOBKzzzrm3/UioYp9tFTsw0zKwT16Ec688RimaVPKnis20s2csVnMGZtFTV0rmw5UsnFvJaVVjQCUnGyg5GQDxioYlJ1AxNBIsiICHGo4SGuoFYCM6H4A+EJ+tlRuZ0LaGN0mKCIivdp7DqsSExP5f//v/7F7924sy+IPf/gDF110EYWFhe+41jRNEhMTWbSoa3oORKR3yQtP4ZgK1kVEug3D5QWXFwKthGtKaH31jxhxabhHLcQ1cBqGo8NNEvIfkuK8LJiUw4JJOZyoaWLjvko27KukqrYFCzhYdoaDZWd4+OVDDMtLZOHAG2n0FrfvFrjp5Db+dfApnjmynK+P/Xz7EEtERKS3ed9XGrNmzWLWrFlAW5H6ddddx6hRo7okmIiIiIjYwzPuMtzDL8S/9xUCu1/G8jVi1VXiW3M//q3P4B45H9fg2Rguj91Re6z0pCgun5HPZdPzOHaygY37Ktm0v5IzjX5CYYtdR2vYdRTczjhqD+5j0pA0djTtAcDj8JAWmQLAgdOHMTAYlFCg2zVFRKTX6PDbYj/96U/f9fjhw4cxTZOCgoJOCyUifYsK1kVEuh/DE4Vn7GW4R8wncOA1/LtewGo6jdVUi2/9o/h3LCPquv+ngdVHZBgGeemx5KXHcs2cARwqO8OGfZVsPVhFU2sQfzDMpv1VbNpfRYRnAPlDMhmQGoOBiWVZPHXkeSoaTzAkcRBfGv0pux+OiIhIpzinNdz33XcfxcXF/PSnPyUcDvO5z32O119/HYCpU6fy29/+lqioqPMSVER6rzNGmQrWRUS6KcPlwT1iHq6hcwkefgPfzuVYdSdxZA0/a1BlWZZW9nxEpmkwOCeBwTkJ3DhvEHuKT7NxXyXbD1fjD4Rp8YXZu8PD3h1+Vq9ex/DBHiod1QAMiM8H2v4dXil9jTGpI0iOSLLz4YiIiHxoHR5W/fWvf+XXv/41M2a0rXpYsWIFa9as4eKLL2bgwIH85S9/4Q9/+APf+ta3zltYEemlLLsDiIjIBzEcTlyDZ+IcNJ3gsS3vKGVvffWPGJ4o3KMWYsam2pSy93A6TEYPSGb0gGR8/hDbj1SzaV8Vu4tqCIUt6hr9rNviB+dM4rIrOeNIpSK6iRZnFc8cXc7Soyu4eeh1jO83xu6HIiIics46PKx6+umnueiii/jd734HwPLly4mIiODnP/85Xq+XpqYmXnjhBQ2rROSc5YWncmxrjgrWRUR6AMM0ceVPPOtY6HQ5waLNAAQOvIazYBLu0YveMdCSD8fjdjB5aD8mD+1HY0uArQer2LivkoOlZ7CCbuqKs3mpuIqX1leROLAUEsA0TAYlDgCgtKGcY3WlTOw3Dq9Tt22KiEj31+FhVVlZGTfffDMAgUCA9evXM3HiRLxeLwAFBQWcOnXqvIQUkb5Bd4+IiPRQhoGj/yhCpTvBsgge2UDwyAYc/UfjGbMYR9oAuxP2GtERLmaNzmTW6ExqG3xs3l/Jxv2VFJ9oAOD04f4YEQmYUXX8vvwQk4amcdS5mp01u1hW/DI/nvod3A7ddi8iIt1bh4dVsbGxNDY2ArBx40aam5uZOXNm+/nS0lKSk5M7P6FIN2O4vLjHXgaAIznX3jC9RK1RijOziLBDBesiIj2RIyGTyPlfJ1RTin/HMoJFm8CyCJXuoLl0B470wbjHLMaROUy9Vp0oIcbDvIn9mTexP5W1zWzcV8nGfZWcqIFQSwxHqOPI8Vo8w49iRkCSmU0oaIADVpauoV9UGoMTB2Iapt0PRURE5CwdHlaNGTOGf/7zn2RmZvJ///d/OJ1O5s2bRyAQYNWqVTz66KNceOGF5zOrSLdguCPwjL/C7hi9yhmjHFfmUcJBt91RRETkI3Ak9Sfigs8THr8E/84VBA6thXCQ0IkDtJw4gGf6J3APnWN3zF4pLSGSS6flccnUXMqqGtm4r5JN+yupqffh2z0dM76Kg75IvvrGWoYNiORI3HLChJmTPZ2rBl5qd3wREZGzdHhY9Z3vfIdbb72Vr3zlKxiGwbe+9S1SUlLYuHEjX/nKV8jPz+erX/3q+cwqIiIiIj2AGZeGd+bNuMddhn/3iwT2rQLDwFXwdteVZYUhHMZwnNPm1PIBDMOgf1oM/dNiuHJ2AUfK69i4v5LN+900tgQIEmb3ySLc0QaGA8oPx7LHUUNWhotXSlczM2sKqZEpdj8MERHp4wzLsjq8D1cwGGTfvn2kpaWRlpYGQF1dHevWrWPOnDlERESct6B2qqlpJBzWdmVdJSUlhurqBrtjSBf6x4sHWb29gphIF/d+pfvfBqjnqHR3eo5Kd2O1NhI6VYIzaxjQ9hw9vmkVvjcewT1qAa7BMzFU/H1eBUNh9pfUsnFfJVsPVeMLteJIqCR0KhMwiMopJpx2EIA7JnydrJh0ewPbTN9HpbvTc1R6gvd7npqmQVJS9Ht+7jm9leV0Ohk5cuRZx+Li4li4cOG5fBmRHi3cUk/rK38AwD1qAc7+o+0NJCIi0s0Z3uj2QRWAZVn4dyzDajqN742H8W97Ftfwi3APuwDDE2Vj0t7L6TAZkZ/EiPwkbgqE2HW0ho37KtlZW0MwFMbvaMAJhJti+M1DR5k0tAlvv+NERRpMyRhPhLN3viktIiLdk9Zdi5yrUIDQibZ3HsMDp9ocpndQwbqISB9jhXENmY3f14RVX4XV2oB/y1P4dy7HPXQurhHzMCPj7U7Za7ldDsYPTmX84FSaW4NsO1TNxv2J7N9dCo4Apxv9rNhwDM+oNZieVl49uomvjv48qQmRdkcXEZE+QsMqEbHdGaNMBesiIn2IYTpwD56Fa9AMgsWb8e94nnBNGQRa8e9cjn/PS7gKZ+IeuQAzVv1J51Ok18n0kelMH5lOXdNQthyoYuO+So6eOgFW2y6BVUXJfHvjBnLTo3Dl7WFO3gQmZg3XLoIiInLeaFglIiIiIrYwTBNXwSSc+RMJle3Cv2MZoZOHIBQksG8loRMHibzqJxiGYXfUPiEuys0F47K4YFwWp860sGHfMNYe20lVTdutmaWtR/EED/LQ4YOs2DKJC/KnMK4whSivy+bkIiLS27znsGr16tUMHz6c5OTkrswjIn1QXngaJVtziY10wTy704iISFczDANn/1E4+48ieOJg29CqbBfukfPPGlSFW+oxI2JtTNp3JMdHsHhqHoun5lFR3cjG/ZW8XnEcX8gBlkHZ4RgeOHiAf254nZTsBmZmTWX24EI8bofd0UVEpBd4z2HVbbfdxre//W2uuuoqAG666SY+//nPM2XKlC4LJyJ9RMc3JRURkV7OmV6IM72Q0KkSzITM9uNW0Efzk3diJmTiHr0YR+ZQrbjqIpkp0SxJieYKK5/95dWsOXiAAxEm9U1+zNRiznhPs/RkEc+8ciFjBqQxcWgaw/MScTp0m6CIiHw47zmssiyLrVu3smjRIiIiIti0aRPXXHNNV2YT6fYM9CK5M9QaZTgziwipYF1ERN7kSM456+PAgdexWuoJtdTTcnw/Zkoe7tGLceaOwVB3UpcwDIOh2akMzU4lPNdib0k1jx7ZQx2nCZ3KJOCHDfsq2dr8Kq4NMYxJHsu0If0Z1D8eU4NFERE5B+85rJo3bx5PP/00zzzzTPuxb37zm3zzm998zy9mGAb79u3r1IAi0vudMVWwLiIi78+ZO5ZwQzWB/asg6CdcXUzry7/DjM/APXohzgGTMUzVsXYV0zQYkZfKiLyvcLyhiqLyJnZHNrGz/BiO1HIsYNPJZtY9OoSEGA8TBqcyaWgauf1itCJOREQ+0Hv+RP/hD3/IsGHDOHToEH6/n6VLlzJu3Diys7O7Mp+IiIiICGZ0It4pH8M9ZjGBPa/g3/sK+JoInzlO6+q/Ymx5GvfIBbgGz8Rw6s2PrpQRk0rGEJg+BPZUeXh4/x7qQ6cJV7etjjsTqGF1/eu88mwOaY48lswoYOygZA2tRETkPb3nsMrtdnPjjTe2f/zMM89w7bXXcskll3RJMBHpO/JCU9sK1qPcKlgXEZH3ZXpj8Iy/AvfI+QQOrMa/60Ws5jNYjTX43vgnjuQcHP0G2h2zzxqeOpC7U26nrKGChMmpbDlYzQvly2mMOo0j7jQnd0Xxh6ebGZAVx7VzBlCQGWd3ZBER6YY6vFb6wIED7X8+deoUx48fx+VykZaWRmJi4nkJJ9IdGd4YIhbfDoAZn25zGhERkb7JcEe0raQaegGBw2/g37EMMzrxrEFVuKUeLAszUgORrmQYBv1jswCYMyaT5rgcVpaVEW2lcJp4mghS3LKPny4tYlzGEK6cXUBqfITNqUVEpDs5pxv79+zZw49+9CN279591vFRo0bx3e9+lxEjRnRqOJHuyHC6cWYMsTtGr3LaKMWZWUzI4QGm2x1HRER6EMPpxj1kNq7CGVitDWed829dSuDgGlyDZ+IeuQAzJtmmlH3bovx5XNB/Fk2BJryTY3jmjYOsC72C4Qiy42Ql2+6rZu7YLC6Zlkt0hMvuuCIi0g10eFh18OBBPv7xjwNwzTXXUFBQQDgcpqioiOeee46bbrqJxx9/nIEDtexaRM5NnVmugnUREflIDNOBERnf/rHV2kjg4BoIBQjsfZXAvtU4B0zGPXoRjoQM+4L2UV6nB6/TA8CYUS627DbxhyFUl0I4bPHy1hLWHijikglDuGBcJi6nw+bEIiJipw4Pq37zm98QFRXFY489RmZm5lnnvvCFL3DVVVfx+9//nnvvvbfTQ4qIiIiInBNPFBELb8O//TlC5XvAChE8vI7g4Tdw5o5tG1ql5tudsk8amlTIXVNuZ2vldnIHj+KJVUc54tuF1f8ATx0u4tXtw7hy5iAmDknDVAm7iEif1OFh1ZYtW7jlllveMagC6NevHx/72Md46KGHOjWcSHcUbjhF06O3AeCd+Ulcg2fanKjny32zYD1OBesiItJJDMPAmV6IM72Q0Klj+HcsI1i0BbAIHttK8NhWHJnDcI9ZrNv7bRDniWFu/7bXUF+/djjfWfssLeEwjqST1FQM5L5n9/HSpjKunTuAwv4JNqcVEZGuZnb0Qr/fT1RU1Huej46OprW1tVNCifQYerdPRESk23Mk5xJx4ReJuuZuXIUzwGi7xSxUsZfgkY02pxO3080XxtxMbmx/JifMJDbCA1gcj17DL19Yxr1P7uRETZPdMUVEpAt1eFg1ZMgQnn/+eYLB4DvOBQIBnnvuOQYNGtSp4USkb6g1SnFmHiaUdMTuKCIi0ouZ8el4Z91K1Md+gWv4ReDy4h698KxrgmW7scLvfL0r51d+XC63jfsiN02ay08/O4Xxk4M4EitxD9jFnvrtfO+vm3joxYPUNfntjioiIl2gw7cBfupTn+JLX/oSN954I7fccgu5ubkAFBUV8cADD7B3717uueee85VTRHqxM2YZrsyjhAIeu6OIiEgfYEYn4Z16A54JV2G43v7ZE6oupmXFrzBiknGPXICrcAaGU5t/dBXjzRXrER4nEwencuxQFL5AmNZTGViWxeoDB3jj6CEWjh7BvAnZeFwqYRcR6a06PKy68MIL+d73vscvf/lLvva1r7UftywLj8fD7bffzvz5889HRhERERGRTvefgyoA/96VAFgNp/Ctewj/tqW4Ry3ANfQCDa262Ph+YxiePITK5mqM4fE8vvoIRzwbMWJqeb70GKu2j+GKGflMG56OaaqWQUSkt+nwsArghhtuYNGiRaxfv57y8nIsyyIrK4upU6cSHx9/niKKdDeW3QF6ndzgNEq35hEX7YaL7U4jIiJ9lXf6xwmk5uHfuQKroRqrpR7fhsfw73oR99hLcRXOxHCc08tn+Qi8Ti85sdkQC9cvSufuTWfaXoWFTc40+Ll/+QFe2lzKtXMHMjwvye64IiLSic75p218fDwLFiw4H1lERERERGxjON24h87FNXgWwaMb8W9/jvCZE1jNZ/Ct/Qf+nSvwjLsM54CpGGaHq1+lE2RE9+POSd9gefEr9E+cxPLTJ6gLnqYmcyX3rs6ncNMorp1TSHZqtN1RRUSkE+inrIjY7oypgnUREek+DNOBa+BUIq/6Cd5Zt2LEJANgNVTT+sYjEGixOWHf1C8qlU8Ov54LRxfw089Oof/ocgy3H1f/A+w/Wc5df9/E35ftp7bBZ3dUERH5iDSsEhHbtResJx61O4qIiEg7w3TgKpxB1DU/wzP9JozIeNyjFmB4otqvCbfUY1mqCOhqHpeDS4fOIMmTRKo1EKM1FgtYf3Ijdzz8PE+tKaLFp10dRUR6Kt10LyLdgF7ki4hI92U4nG23Bw6aftZxywrTsuz/gcuDZ/wSnJlDbUrYN41JHcHI5KH4Qn7qxoR55LVdHI09gGGGeaG0mjU7hnHZjHxmjkrHods2RUR6lA4Pq8LhMKa+yYtgxqQQ85kH7I7Rq7QVrOerYF1ERLq1/94RMFi0hfDpMgBalv0CR8YQPBOuxJE2wI54fZLDdBBpRhCZBBfMiKF0j4OgFSZ8JoX65gAPvbyPl7Yf4poZwxk9IBnD0M6BIiI9QYenT5dddhkPPvjg+cwiIn2cXj6KiEhP4swcinv0YnhziBU6vp/mpT+h+YV7CNWU2pyu7xmdMpwfTv0W1wy6nE/NnUZSrBdn2jHqsl/kTxv/zc8f2ULxiXq7Y4qISAd0eGXVsWPHiIiIOJ9ZRKSPaitYP0bQ6QGmf+D1IiIi3YHhjcYz8Spcwy/Cv+N5AvtWQThIqHQnzaU7ceZPxDP+Csz4dLuj9hnxnjhmZU0FYGh+DN97YxlBwjjiqzm0p54fP7iFyUPTWDIzn+R4/W4jItJddXhl1fTp03nppZfw+/3nM49It2cF/QSP7yd4fD/h5jN2x+kVVLAuIiI9mRkZh3fqDURd93Ncg2eB0fYSO1i0iaYnvkPw5CGbE/ZNsd4ovjTmk2RFZTLCO62tt8oIsy24jO8+8TSPrTxMc2vA7pgiIvIuOryyavDgwTz44IPMmDGDESNGkJSU9I4OK8MwuPvuuzs9pEh3YjWfoeX5nwPgnf1pzEHTbE4kIiIi3YEZnYR35i24Ry3Et/UZgkc2YMam4UgtsDtanzUwoYBvT/wKANXjWvjzG8s5GXEK4k/xytEwa3ed4JJpecwdm4nToX5eEZHuosPDqj/96U/tf167du27XqNhlYh8GDmBtoL1eBWsi4hIL2DGpREx97OERi/C8jdjmI72c/69rxJuqMY9ehGmN8bGlH3HW6XqqQmRXDg2l38f2oPfZxA63Y8mK8hjG7bw8u59XDN1DOMLU1TCLiLSDXR4WHXgwIHzmUNEREREpFdxJGad9bHlb8G/9Rms1gYC+1fjHjEP98j5GO5ImxL2PVPSxzM6ZTinWmqoynHx+Ooj1Gesoymygb9uO8JLmydx7ZyBpKRokCgiYqcPtdY1HA5z6tQp9VeJSKdoK1g/TChJnVUiItJ7Wa2Nb5etB1rxb3uWxke/iW/H81gBn73h+pAIp5fsmEzGFabyqavTMaMaMQywgi6OVtRz9z+38pMH3qDydLPdUUVE+qxzGlaVlJTw5S9/mXHjxjFjxgy2bt3K+vXrufrqq9myZcv5yigivdxbBetBFayLiEgvZsamEHHJHUQsvA0zJa/toK8J/6YnafrXN/HvfgkrqDeDu9LAxDy+O/HrjE0ZwwXZM3E6TIyoM+x0Psb3lz7OQy8foKFZ/yYiIl2tw8OqY8eOcfXVV7Np0yZmzJjRftzhcFBUVMQnP/lJduzYcT4yioiIiIj0CoZh4MwaTuTl38c778uYCW23Clot9fjWP0LTY98mWLrD3pB9TEZ0P24d8TGunTOYuz89iaTBxRguP47s/azefYRv/3k9yzeU4A+E7I4qItJndHhY9etf/xqv18vy5cu56667sCwLgIkTJ7J8+XKSk5P5/e9/f96CikjvlRucRsum+XiPqF1dRET6BsMwcOWOI/LKH+Gd+1mM2DQArKbT4I6yOV3flRTn5ZqRF5DgSSCupRDLH0mLL8QzB17h9n8uY/2ek4Tf/D1IRETOnw4XrG/YsIFbbrmFpKQkamtrzzqXlpbG9ddfz1//+tdODygivZ9e8omISF9lmCauAVNw5k8gcGgd4aoinP0Gtp8PN9USqirCmTtWu9R1AcMwGJ82mguHTOZk5RkODW3k0bXbqcs6iN84yP1bq3hp8yiumVPAkNxEu+OKiPRaHR5W+f1+YmNj3/O8y+XC51MxpIicu7aC9WMEnV5gmt1xREREupxhOnEPngWDZ5113L/9OQL7VmKm5OEZvwRH1nANrbqAy+HC6/IyssBLc2Qij+x3ELJChGpTKWlt4P89sZmheXF8bPYIMpO1Ek5EpLN1eFg1ePBgVq5cyQ033PCOc8FgkGeffZbCwsJODSfSLTlcONLbnutGxHsPcKXj6t4qWA947Y4iIiLSbVhBH8Hitk2MwtXFtKz4FY5+g3BPuBJnul53d5XJ6eMoTChgd/VBTrtTeGFTKeF+RymKK+NHK3YyOWUGS6YXEBftsTuqiEiv0eFh1Wc/+1m+8IUvcNttt3HBBRcAUFFRwauvvsrf/vY39u3bx29+85vzlVOk2zCjEoi85A67Y4iIiEgvZzg9RF37M/y7XsS/+0UItBI6eYiW536KI2t420qr1Hy7Y/YJCd54ZmZPgmwYPzyOn217AcsIY8Sc5vUdJ9i0t5r5k/ozf2J/PG6H3XFFRHo8w7I63hD41FNPcffdd9PU1IRlWRiGgWVZeDwevv71r3PzzTefx6j2qalpJBxWq05XSUmJobq6we4Y0oX+vnw/a3edICHGw6++2P1vA9RzVLo7PUelu9Nz9NyFWxvw71hGYO+rEAq0H3fmjsU9fgmOxCwb0/U+H/QcPXD6ME8cfB7XyZEcOmiAGcQ9aBueugKWjJrKjJEZmKZu15TzR99HpSd4v+epaRokJUW/5+d2eGUVwJIlS5g3bx7r1q2jrKyMcDhMZmYmU6dOJSEh4dxSi4iIiIhIh5jeGLyTr8M94uK2HqsDr0E4RPDYNgxPNI5Zn7Q7Yp8yOHEgd07+GoZhsO/Yae7f+izNsacJxp7moQ0hXtmax9WzBzAiP1EdYyIiH8I5DasAoqOjmTdvHqdPn8Y0TQ2ppM+xWhvx73kZAGfeOBxJ/W1O1POdMVSwLiIi0hFmVALe6TfhHrUA39ZnCRZtxD3usrOuCbc2YHpjbErYd7w1hBqam8glzkKePnyUQIuH8JkUKmjitytWk5+azI2zxtI/Tf8eIiLn4pyGVUePHuXee+9l7dq1tLS0ABATE8MFF1zAV7/6Vfr163deQop0J5avCf+2pQCYcWkaVnWCM45SFayLiIicAzMmhYjZt2JNvhbD+/ZtFKGqozQ/91Ncg2fjHrMYMzLevpB9yMysKYxPG01N8xl2JfhYvrGYcP4uyj2t/O9LB5iQMIMlM/NJjNVrHRGRjujwsGr37t3cdNNNBAIBZs6cSf/+/bEsi+LiYp599lnWrFnDo48+Sv/++sVdRERERKQr/OegCsC3+SkIBQnsfYXAwTW4h12Ie9TCd1wnnS/SFUFkXATZUyAtp4n797eAAeGAmzf2nGTzgUouGJ/BJVMKiPCc8w0uIiJ9Soe/S/7yl78kOjqahx9++B0DqUOHDnHTTTfx85//nD/84Q+dHlJEerfcwHTKtxWQGKstn0VERD4Kz4Ql+IBQxV4I+vHvXI5/3yrcI+fjHjEPwx1hd8Q+YXzGMPrFfI0VR1+j9cwQtledJhRVxerWlax5rJDLhs5kzpgsnA7T7qgiIt1Sh7877ty5k5tuuuldV04NGjSIm266ifXr13dqOBERERER6ThHagGRi75JxOLbMdMGtB0MtODf+jRNj34T/84VWEG/vSH7iKyYDD49+mN8eclovnX9aKLyjmC4fYTT9/Lo6r1872+b2HaomnPYnF1EpM/o8LAqNjaWUCj0nuejoqLwenUPtvQFekHR2c6YJTgzDxNIOGJ3FBERkV7BmTGEyEu/S8T8r2Mm5QBg+RrxbXyM5qd/hGWFbU7YtwzKjuf6UfOJcsTiqR0EQQ+Vp5v5vw1P88PHX+Lo8Tq7I4qIdCsdvg3whhtu4P777+fCCy9kwIABZ52rrKzkoYce4pprrun0gCLS+51xlOHKPEpIBesiIiKdxjAMnP1H4cgeQbB4K/4tTxM+cxznwCkYhm4/60qmYTIpfRxjU0cSCIVZu6OSZ3dsx8o6QjVH+NkLxxmXNIElswpIjdetmiIi7zmsuuOOO95xzOfzcfnllzNjxgzy8vIwDIOKigrWrFmDx6OuGRERERGR7sYwTFz5E3DmjiN4dAPO3HHt56xwmNZX/oCzYCLO/AkaYp1nLocLlwPmTeyPI7WMp4schMMW4TOpbKqqYuvRcmaMymDJ1CFER7jsjisiYpv3HFY9/fTT7/lJq1atYtWqVWcda25u5s9//jNf+9rXOi2ciPQNOYFplG8rIEkF6yIiIueNYZq4Bk4961iwaCPBY1sJHtuKueN5POOX4Og/GsMwbErZd1yQO43RaUPZdeIIh5oj2bivEjPzIBvCr7HxqQEsLriQC8dn43JqgCgifc97DqsOHDjQlTlERERERKSrhcMY3his1gbCNWW0vHgvZmoBnglX4swcane6Xi8pIoE5+ROYkw8TR0Xz1yMvgmER8tbyxOqjrNpewZWzCpg4JFUDRBHpUzSmFzlHhica99jLcI+9DDMx2+44vcIZs/TNgvWjdkcRERHpU1yDphF13S9wj18C7raupHDVUVqW/YLm539OqFKbn3SVMTk5fHH0J0l2pRFXPxKAU0313H/4b9z5+LMcKDltc0IRka7T4YJ1gGeeeYZ169ZRXV1NOPzOHUQMw+DBBx/stHAi3ZHhjcYz/gq7Y/QqdY5SXJlHCQZUKCoiItLVDHcEnrGX4h52Af6dK/DveRmCPkLH99O89Cc4+o8iYvanMbzRdkft9YYmFXLX9EGEplq8vvM4/z7yPFbMGc7ErOOXzwUYlT6Iq2YXkJ4UZXdUEZHzqsPDqnvuuYc///nPuFwukpKSME0tyhIRERER6S0MTxSeiVfhGjEP//bnCexfCaEgVn01uCPtjtdnGIaB02EwZ2wW/oQhLC8uJ9AQTbgxge2HT7Gr6iATC3K5dtooYqPcdscVETkvOjysevrpp5k+fTq/+93viIjQ6gcR6Tw5/umUbxuggnUREZFuwIyIxTv1etwj5+Pf/iyOrBEY//FGdbBsF2ZcP8zYVBtT9g0X581iWtZ4Tp6pY42zjnV7y3Dl7WS7uYXtT+9iQf4FzJuQjcflsDuqiEin6vCwqrGxkYsvvliDKunzwo01tK66DwD3mEtxZg2zOVFvouJQERGR7sKMTsQ74+azjln+FlpX3oflb8E1eCbusZdiRiXYE7CPiHZFMSAligGLMsge1MAzZX4A/C0enl5TxPo9J/nspUPJ6Rdrc1IRkc7T4Xv5ZsyYwYYNG85nFpGeIRggdOIgoRMHsVrr7U7TK5xxlLxZsK4SVxERke4sWLYby9cIVojA/lU0/etbtK5/lHCLXhN1hYsGTuT28V9hWMwY+pkDAKgKHeNnG//A0k17sCzL5oQiIp2jwyurvve973HLLbfwjW98gwsvvJCkpKR33T51woQJnRpQRHq/M44yFayLiIj0AK6CiZhxqfg2P0WobBeEAgR2v0hg/2rcI+bhHjkfw6Py7/Opf2wWX5jwMcLjLJ5Zf5BXGlZhuH28ePoxSp6+klsXDiPK67I7pojIR9LhYdXx48dpaGhg2bJlLF++/B3nLcvCMAz279/fqQFFRERERKT7cCTnErngfwiePIx/85OEThyEoA//9ufw730V96gFuIdfhOHy2h21VzNNg0WT8zm1YwQ767YQKB/IjlOnuevkJj6xqIDhOf3sjigi8qF1eFj1ox/9iPr6em699VZyc3NxOjv8qSIi7yvHP52KbQNIitWLWhERkZ7C2W8gjsXfJlSxD9/mfxOuLgJ/M/7N/8aZMQRH2gC7I/Z6Hoebz4y7hoOnJvFi7Rl2nKqh1ijnj4eeZ3jJdD47bQEOh3ZxF5Gep8MTp8OHD/OlL32JT3/60+czj0gPoC4AEREREQDDMHBmDcOROZRQyQ58W57CiE48a1Bl+VvA6cIw9Wb3+VKYnMOgJf15eWsJz5xaheEIsce3ll88mcAXFo0jLlo7LotIz9Lhnxj9+vXDNDWVFzmbdq/rDG0F66UEXRHAVLvjiIiIyDkyDANn7hgcOaPA13zWOd/GxwhW7MMz7nKcBZMx9DvFeWEYBvPG5+I5eiVPHHkaf1kBh0+18oO/b2LRhbFcOGTku3YOi4h0Rx3+SfGpT32KBx98kCNHtFuXiHSuOkcprsyjBBKO2h1FREREPgLDMDG80e0fh+urCBx4Hau+itZV99H87+8RPK6O2/NpVsFofjLjW0zsNw6ARtdxnjn5MN9f9Qdqms/YG05EpIM6vLLqwIEDGIbBpZdeSnZ2NsnJyTgcjrOuMQyDBx98sNNDioiIiIhIz2NEJeCZ8jH825/DaqkjXFtBy/M/xzlgCp7J12JGxtsdsVeKj4zi04uHMaR/Ao+UbACgJlTB757eyRcXTSQlXjswi0j31uFh1apVq3A4HPTr149AIMCJEyfOZy4R6UNy/DOo2DaQ5DgVrIuIiPQmhsOFe/iFuAbPwL/7Zfzbn4Wgn+CR9QRLd+CZcCWuIXN1a+B5Mn1kBpn9PsPvNvyLhuoYSqtD3HX/ZqbN9LN4xCRi3TF2RxQReVcdHlatXLnyfOYQ6TGM6EQiFt8OgBmfYXOa3sFSZ72IiEivZjg9eMYsxjVwCr43HiF4bCv4W/Ct+yeBg6/jnf0ZHImZdsfslfJSk/n5ws/z2KojrKyuwOc5ybq6LWx8fQ2fGXkjw1IG2R1RROQd9BaGyDkynB6cGUNwZgzBjIyzO06v0FawfphAgjrxREREejMzOomIeV8mYv7XMGJSAAjXHsdwumxO1ru5nA5uvKiQLy8ZgSepBoBAOMDDy8upONVkczoRkXfq8Mqqm266qUPX/eMf//jQYUSkb2ovWA+oP0FERKQvcPYfTVTGUPw7ngenFzM2tf2c5WsCd6R2rjsPxgxK4Udpt3DPyyuorG3kZA38+IHNjJxWw7SCQkalDrc7oogIcA7DqvLy8nccC4fD1NbW4vP5yMzMZODAgZ0aTkREREREeifD6cYzfslZxyzLouWVP0IogGf6TTgSs2xK13slxXn54ZLLWLr2GMveOEYwspq9LZvZu2cji3Lms7Bgrt0RRUQ+emdVKBTi1Vdf5c477+TWW2/ttGAi3VXozHGaH/8OAN65n8M1YLLNiXq+HP8Mjm9XwbqIiEhfFyzeQqhiLwDN//4BrhHz8Iy7DMOl1widyWGaLJmZz5D+8fzf6lcJBJ1gWKxeHWZIZD05/aIxDTXGiIh9PvJ3IIfDwbx587j66qv55S9/2RmZRERERESkD3JmDcc1fB4YBlghArtW0PT4dwgUb8HSjiydbkhuIj+++jJy6xfjPzKaU9Umdz+0lV+vfZh/7n+clmCL3RFFpI/qtHF5bm4uBw4c6KwvJyJ9iArWRUREBMBwR+Cdej2RV9yFmTYAAKvpNK0v/56WF+4hXF9lc8LeJzbKzW1XTuGqsVNwmAZW1CmKA7tZf2IL/9j7pN3xRKSP6pRhld/v59lnnyUpKakzvpyI9DHtBevxRXZHERERkW7AkZxD5KXfwTPzFvBEARAq20XTE9/Bt3UpVtBvc8LexTQM5k/qzx03jiPenUCoLgkr5ODAhlQOlNTiD/lpDfrsjikifchH3g3Q7/dTXFxMfX09X/7ylzstmIj0Qdr0R0RERN5kGCbuwbNw5o7Fv/EJAgfXQChI4MBq3CMvtjter5SfEcuPPj6b+19IY9u+IlpbXPy/R7czcHI5ze7jfHzI1QxMKLA7poj0AR9pN0Bo66zKz89n8eLFXH/99Z0WTKTbUl1Cp8vxT+f49oGkxKs8VURERM5memPwzvokrsIZtK79B+6xl55VuG5ZYQyVgXeaSK+TL1w2nDU7E3nklcMEPbWUhfdAKzx9+AW+OeELGIbeYRSR8+sj7wYo0qfpB7WIiIhIl3D0G0jkkrvgPwZTVtBP89If4xo4HdfwCzFMh235ehPDMJg1OpOCzDj+tHQPVSWDcWUcpXRLHjsTahiaH0tF40ny4vrbHVVEeqkOD6tERM6XtoL1MvyuSGCq3XFERESkm/rvYZR/x/OEa8rw1TxK4NBaPNNvwtlvoE3pep+slGi+/4kJPPpKPGt2ZkHYyW//vYuCCeWcMPYyt/8MLs2fj9PUr5Ui0rne87vK73//+w/1Bb/0pS996DAi0je1FawXEfBH2h1FREREehBHv0KMuDSsukrCp8toefZ/cRXOwD3pGkxvjN3xegWPy8HNCwYzNDeBB184QEu4mYrwPgyHxcGaYswC3YIpIp3vIw+r/vt+ZQ2rRERERESkKzizhhF11U/w71yBf/tzEAoQOPg6gWPb8Ey8Gtfgmeqz6iQTh6SRmx7Ln5fu4djeqbhy91K6P59N0VWMHZzEmoo3mJU1DZdWWYlIJ3jP7ySvvvrqB35yY2Mj99xzD6tXr8bpdL7njoEivYkjIYOYzzxgd4xepb9/Bse3DyI1PsLuKCIiItLDGA4XnrGX4howmdZ1/yRUtgt8Tfhef4DAwTV4p38CR3KO3TF7hdT4CO64cRxPvVbEC5uiAbjvuX3kVJRR5dzLxhNb+eLoW4n3xNmcVER6uvccVmVmZr7vJy5fvpyf/exnVFVVMXbsWO666y4GDRrU6QFFREREREQ+iBmbSsT8rxM8tg3fGw9jNZ0mXFWEb/0jRF5yh93xeg2nw+SauQMYnJPAX5/fR2Orj5O+MkwnEHYQ69btlyLy0Z3zmtiysjI+9alP8Y1vfAO/389PfvITHnnkEQ2qRORDaytYP4w//ojdUURERKQHMwwDV944oq65G/eoheBw4Zl6o92xeqWRBUn88JMTGZydiG/fZALlAyjZXMBr24/jC/r5+56HqWg8YXdMEemhOnxDcSAQ4L777uMvf/kLPp+PK664gm9+85skJCScz3wi3Y4V8BGqLgLATMjEjIi1OVHPV+9sK1j3+6PsjiIiIiK9gOHy4pl0Da6R8896rRZurMG37p94Jl2DGZ9uY8LeISHGw23XjWHZ+mM8s9bEsuChlw6x8uQrnPbuY0f1Hr429nPkx+k2TBE5Nx0aVm3YsIEf/vCHFBcXM3DgQH7wgx8wfvz4851NpFsKN5yi5fmfA+C98AuY+RNtTiQiIiIi7+a/31T0rX+UYMl2gmW7cI9aiHvMYgynx6Z0vYNpGlwyLY/C/gn8+dm91Da0Ulnjw5UBKd40cmKyAAhbYUyV3YtIB73vsOr06dPcfffdLFu2DK/Xyze+8Q1uueUWnE7t8CAinad/6wyO71PBuoiIiJw/VjiEERELGBAO4d/+HIEj6/FOvRFnzmi74/V4g7Lj+eEnJ/L3ZfvZccQgdCaF0rCLF4wyLpyQwT3b/8T4tNHMzZ6hoZWIfKD3nDo9+uij/OY3v6G+vp65c+dy5513kp6upbIich4ZdgcQERGR3sowHXin34Rr0HRa1/6D8KljWA2naHnxNzhzx+KZegNmdJLdMXu06AgXX75yBK9uLefxVUcIhiz+/VoR606tpC6qgrKGCiKdkUzNmGB3VBHp5t5zWPXDH/6w/c8rV65k5cqVH/jFDMNg3759nZNMRPqMtoL1MvzuSGCK3XFERESkF3Ok5hN5+fcJ7F+Fb/OT4G8heGwbwfI9uMdehnvExXZH7NEMw+DC8dkMzIrn/5buobK2haqSODwFUaTGxDKp31gA6nwNxLijtMpKRN7Vew6rLr/8cgxDyxxE5PyrU8G6iIiIdCHDNHEPuwBn3nh8Gx8jePgNCPrxb3oCAq2w8Ga7I/Z4Of1i+P7NE/jnSwdZvxdadk+h1Bng6eAxFk/L5nc77iPaFcWNQ64hOSLR7rgi0s2857DqZz/7WVfmEBERERER6VJmZBwRcz5DsHAGvrUPYbXUa2VVJ4rwOPn0JcMYmpvIQy8dxO93snxDCVtq36AhrhKALZU7mJ871+akItLd9Pqm9HvvvZcXXngBwzCYOXMm3/rWtzBNLTUV6U5yfDM4sX0QqQkqWBcREZGu58wYguPKHxE+cwLDG91+PFR5hNCpY7iGzMXQ7xAf2rQR6eRnxPJ/S/dSVtVI1eE0InLrSE73cVH/WQCcaKrE6/CQ4I23N6yIdAu9+jvua6+9xrp163j22Wd57rnn2LFjB6+++qrdsUTkv1h2BxAREZE+z3A4cSRlt39shYO0vv4AvnX/pPmZHxGqKrIxXc+XnhTFnTeN44KxWRB20lI0lLI3RvLIy0do9vn4+56H+cnGX7PxxFa7o4pIN9CrV1bNmjWLqVOn4nK5OH36NA0NDcTFxdkdS0T+S91bBeuuKFSwLiIiIt2B1XgaK9AKQPjUMZqf+TGuoXPwTLgSw6OezQ/D5XRww7xBDMlN4P7l+2lqhVXbK9hzei+NaScBqGk9bXNKEekOevzKqmeeeYahQ4e+438NDQ0AuFwu/vKXv3DBBReQnJzM6NGj7Q0sPZ7hdONIL8SRXojhjbE7Tq9Q5yzBlXmUQPxRu6OIiIiIAGDGphJ19f/iHnMJmA7AIrBvJU2PfZvAoXVYltaGf1hjB6Vw1y0TGZDVtpCguiSe0NHxZLjymNd/DgBlDcfZdHKb/p5F+ijD6iP/7w8EAnz7298mMTGR7373u+f0uTU1jYTDfeKvqVtISYmhurrB7hjShb774p844yrG9Efxu/k/sDvOB9JzVLo7PUelu9NzVLq7/36Ohs+coHXdQ4Qq9rUfc6QX4pl2E47ETDsi9gqhcJila4tZ9kZJey3E5KFpfOyiAn67648cbzrJmNSR3DrsBu1U/1/0fVR6gvd7npqmQVJS9Lueg16wsur9HDp0iH372n6guFwuFi9ezIEDB2xOJSL/rb9vJi2b5hNdMs/uKCIiIiLvYManE7Hwm3jnfg4jom01UOjEQZr//X2CJw7anK7ncpgmS2YW8I3rRhMX5QZgw75KfvSv1zjT2vYLbr/IVA2qRPqgXj2sKioq4s4778Tv9xMKhVi+fDkTJkywO5aIiIiIiPQwhmHgGjCZqGt/imv4RWAYmAkZONIG2B2txxuam8hdn5zI8LxEAGoqXZzZOpkB7rFcnNN2W2B5w3Hu3/sIjf4mO6OKSBfpNsOq/fv3M2zYME6ePPmOc88//zyLFi1i5MiRLFiwgGeeeaZDX3P+/PnMmjWLyy+/nMsvv5zo6Gg+97nPdXJy6WvCzXX4tjyNb8vThGor7I7TK5xxHMOZeRhfnDqrREREpHsz3JF4p95A5BV34Z19K4bpaD8XLN9DuL7KxnQ9V1yUm69dM4qr5xTgMA1Cfhe716byx6f2UdvYzD/2P8aWyh3cvekefCG/3XFF5DzrFrsBHj16lM9+9rMEg8F3nFu+fDm33XYbn/jEJ5g+fTqvvPIKt99+O16vl/nz53/g1/7qV7/KV7/61fMRW/ooq7Ue/7alAJiJWTgS1FPwUdU5S3FlFhPwa2cdERER6RkcyTlnfRxubaDl1T9B0I97zGLcoxZiOFw2peuZTMNgwaQcBmXF8+dn93KqrpWdR2v40T9OkzWurdtmSsYEPI62WwZD4RCO/xgWikjvYeuwKhgM8thjj/GrX/0Kl+vdv5Hfc889LFiwgDvuuAOAGTNmUFdXx7333tuhYVVneL/SLzk/UlK67y57fiuK5jf/HBsXQXQ3ztpTOMw3F3ka3fvf/j/1lJzSd+k5Kt2dnqPS3Z3rc7Rh11aafG23qPm3PI1VtIGkiz9NZP6o8xGvV0tJiWF4YRq/f3wH63Ydp67eomF1PjNnDeKGcRfjcbooOVPOL9b+H7eOvY6xGcPtjmwLfR+VnuDDPk9tHVZt3bqVX/7yl9x6662kpaVx5513nnW+rKyM0tJS/ud//ues4xdffDErVqygrKyM7Ozs855TuwF2re6+s0Wo9u375OvrWmjpxll7iszmaVTsH0haYmS3/rd/S3d/joroOSrdnZ6j0t19qOdo+jgiLv0uvrUPEj5dTuD0CU4++iOc+RPxTPkYZlTC+Qnbi31yQSEF6TE8+uphAsEwq1eHOHH0DW5dPJi/HLyf6qYafrH2T9w1+VskRSTaHbdL6fuo9AQ9djfAgoICXnnlFb70pS/hcLxz+WZRUREAeXl5Zx3PyWlbcltcXHz+Q4qIiIiIiHSAs99AIpf8EM/kj4HLC0CwaBNNj9+Bf9eLWOGQzQl7FsMwmD0mk+/dNJ70pEgADpad4YcPbKHAPQqvw8u8/rPbB1W1rWdsTCsincnWYVVycjJJSUnveb6hoW0CFx199rQtKqqt16axsfH8hRN5L1pk1+nqnCU4Mw/jV8G6iIiI9HCG6cA98mKirvkpzvw3dyIPtOLb8CjNT92F5W+xN2APlJUazfc/MYGZo9IBaGoJ8sILFiODV3BR/7kAHG88yV0bfsGjB5+iNeizM66IdIJusxvgu7Gs958KmGa3ji99gWHYnaBXqHOW4Mo8qmGViIiI9BpmVAIRF36RiAXfwIhNazsWl4bhjrA5Wc/kcTu4ecEQPnvpMLzutrtyXttcy88f3kllbTOPH3qGYDjIuoqNVDZrR0aRnq5bT3tiYtqKuJqams46/taKqrfOi4iIiIiIdEfO7BFEXfVj3OOX4Jly/VnnQlVHsaywTcl6pklD07jrlgnk9mv7XbDkZAM/vH8zw5xzGBifz0U5s8mJbes1PnD6ML6Q3864IvIhdeth1VtdVaWlpWcdLykpOeu8iPRs/Vtn0rJpPjGl8+yOIiIiItLpDKcbz9hLMaPfLgEP1ZTRvPR/aV76v4ROldiYrudJTYjkOx8fx8UT24ZSrf4Qjy6vIObkTC7KvABouy3wTzv/zk833UNxXen7fTkR6Ya69bAqJyeHrKwsXnjhhbOOv/TSS+Tm5pKRkWFTMhERERERkQ/Pv20pWGHCVUdpfvouWt94WH1W58DpMLl27kC+dvVIoiNcAKzbdZL/fWgb5VWNvFbxBkErxKmW06h0VqTncdod4IN88Ytf5I477iAuLo7Zs2fz6quvsmLFCu655x67o0kfZUTE4h57GQBmvAamnaGtYL0cnzsamGx3HBEREZHzzjv7U/hiUgjsfgmsEIE9LxMs2oxnysdw5k/EUDdqh4wsSOaHn5zIX57by4HSM5yoaeZHD27hugsmcEVBEq0hH3lxbbvJbzixhX5RqeTG9rc5tYh8kG4/rFqyZAl+v5+///3vPPHEE2RnZ/Pzn/+chQsX2h1N+igzMg7P+CvsjtGrtBWsH8Pvj/7gi0VERER6AcPlxTv5WlyDpuFb+w9CJw9hNZ+h9dU/4TiwBu+0j2PG97M7Zo+QEOPhtuvG8Pz6YyxdW0wwFOafLx1mXGEKtywYDMCJpkoePfgUoXCIqwZeyuzsaTanFpH3022GVUuWLGHJkiXveu66667juuuu6+JEIiIiIiIi55cjMYuIS+4geHgdvg2PYbU2EKrYS9OTd+IeeymesZfaHbFHME2DS6flUZgdz33P7aO2wcfWg9UcO9HA5y4bximzvH23+f6xmTanFZEP0q07q0Skb8huaStYjy1TwbqIiIj0PYZh4Bo0nahrfopryBzAgHAQwiG7o/U4hf0T+OEnJzJ6QDIANfWt/PSf26gpSeZb47/MNYMuIz8uF4BVZWtZVvwyIf09i3Q73WZllUhPEa6vovW1vwHgHr8EZ3qhzYlEREREpDcwvNF4Z3wCV+EM/DuW4R69qP2cZVlYLfWYkXE2JuwZoiNcfPnKEbyypZzHVx0hFLZ4cvVR9pck8qnF4wE42VTFM0eXEwwHOdF4kk+N+LjNqUXkP2lllcg5sgKthE4cJHTiIFZro91xeoW2gvXD+GKP2h1FRERExHaO1Hwi5n0Zw+luPxYs3kLTv76Jb8dyrFDQxnQ9g2EYXDQhm+/eNI7UhAgA9haf5gd/38TeY6cJW2FSI5IxMJiTPQOAsBXWKiuRbkLDKhGxXb2rBFfmUXxxGlaJiIiI/DcrFMC3/lEI+vFvepzmp75P8PgBu2P1CLn9YvnBzROYPCwNgPomP7/+1w7Wb23iG2O/xOdH3UJBfC4Aq8vX8attf6TBrzekReymYZWIiIiIiEg3ZjhceOd8GjM+A4Bw7XFanv8ZLavuI9xSb3O67i/C4+TTi4dy66IhuF0mFrBsfQm//tdu0pw5AFQ1V/Ps0RWU1Jfx8IEn2svYRcQeGlaJiO2yVLAuIiIi8r6cGUOIvPJHuCdeDW/eHhg8/AbNT3yXwLFtNqfr/gzDYNqIdH5w8wSyUqIBOFJRx11/38zWg1UkeOIZnDiIKFckE/uNwzAMmxOL9G0qWBc5V3qXRURERERsYDiceEYvwlUwCd8bDxMs2Y7V2kDrS78lVDgTz9TrMVxeu2N2a+lJUXzvE+N4bOURVm6roNkX5A9P72HO2Eyum3ElpgNi3TF2xxTp87SySuSj0BsunaLOeUwF6yIiIiIdZMYkE3HxV/HO/Ry428rDAwfXECzeanOynsHldHDjvEK+eMVwIj1t6zdWbavg14/sp6mh7VfkbVW7+PuehwlbYTujivRZWlklIrZrK1gvwefXu1giIiIiHeUaMBlHv4G0rv4rhsuLc+BUuyP1KOMKU8npF8Ofn93L0Yp6yqsb+eEDm1m40ODFE8sAGBCfx8ws/b2KdDWtrBIREREREemhzOgkIhZ9E++cz5zVsxQo2kT4zEkbk/UMyXER3H79WBZNycEA/IEwu7Z5SPQmEOWKJEa3BIrYQiurRMR22S2z2LK9ivSkSLujiIiIiPQ4hmG23w4IEDpdQeuqv4Bh4Jn8MVxDZqsw/H04HSZXziogFLZ4YWMpRWXNXDtyMZMH5RLn0bBKxA5aWSVyjsy4NCIW307E4ttx9Btkd5zeQaX1IiIiIp0mdHwfhAIQ9ONb+yAtL/6GcHOd3bG6vcVTcoiOcAGw6o1GopxRQFt/1Zry9XZGE+lzNKwSOUeGy4szYwjOjCGYXr3T0hnqnCVtBetxKlgXERER+ajcwy8iYtG3MKISAQiV7qT5yTsJHttuc7LuLdLr4tJpuQBU1bawansFS4+u4G97/sm/Dz/L8UbdVinSVTSsEhHbtRWsH8UXW2R3FBEREZFewZk5lKirfoyzYDIAVmsDLS/dS+uav2MFWm1O133NHpNJWkLbLZXPri1mUOwgDAw8Dg+1Pq1OE+kqGlaJiIiIiIj0QoYniogLPod37mfbO60CB9bQ9O/vE6o8YnO67snpMLlq9gAAmlqD7N5jcdPQa/nupP9hWFKhzelE+g4Nq0TOUejUMRruu5mG+24mcGyb3XF6heyWmbRsmk9c+Ty7o4iIiIj0Oq4BU4i66ic40gcDYNVXETiiDqb3MnZQMgOz4gB4ZUsZ+d4hxHliAdhRtZvS+nI744n0CRpWiXwEBtpVRURERES6PzM6iYjF38Iz+VrMpGw8k66xO1K3ZRgG18xtW10VDFn8e00RlmXxz/1P8Jc9D/Hgvn8RCAVsTinSu2lYJSK2e6tgvTVWBesiIiIi54thmLhHLiDyirswnJ7248Hj+/HvX42lHZrbFWTEMXFIKgAb91VSfKKB5Ii2wvp6fwMnmirtjCfS6zntDiAi0lawXoLPr90VRURERM43w3S0/9nyNdG66i9YTacJlmzHO/OTmJFxNqbrPq6cVcC2Q9UEQxaPrzzMbR+bRXOwhbnZM4j36O9I5HzSyioREREREZE+KtxwCmhbURUq3Unzk3cSLNlub6huIiU+ggvHZQNwqLyOnUdqWTJgcfugas+p/bQGfXZGFOm1NKwSEdtlvVmwHq+CdREREZEu5UjOIeqqn+DMnwiA1dpAy4v30rrmfqxAq83p7Ldoag5R3rYbkp5YfYRgKIw/5OfBff/iT7vu55mjy21OKNI7aVglIiIiIiLShxmeKLwXfB7vnM+AOwKAwIHXaPr3DwhV9e1O0Sivi0un5QFQVdvC6u0VOE0nta1nANhauYMGf6ONCUV6Jw2rRMR2da5jKlgXERERsZFhGLgGTiXqqp/gSC8EwKqvpHnp/+Lb8jRWOGRzQvvMGZtJanzbEO/Zdcdo9YW4ccg1jEkZwXcn/Q8x7mibE4r0PhpWiYjt6p0luDKP4tOwSkRERMRWZnQSEYtuxzPpGjAdYIUJnTgAGHZHs43TYXLV7AIAGlsCLFtfQnJEIp8a8fH2/qrS+nI7I4r0OhpWiYiIiIiISDvDNHGPWkjkFT/ATBuAd/anMcy+/avjuMIUBmS2DaZe3lLOqTMtALQEW/jHvsf4+ZbfsqN6j50RRXqVvv0dR+RDcCTnEvOZB4j5zAM4c8fYHadXyG6eRcum+cRVqGBdREREpLtwJPUn8tLvYsYktx8LN5yi5dX/I9xcZ2OyrmcYBtfOHQBAMBTmqTVFAPhCfnaf2gfA0qPLCVth2zKK9CYaVomIiIiIiMi7Moy3b/+zwmFaV/+F4NENND95J8GS7TYm63oFmXFMGJwKwIZ9lRSfqCfeE8c1gy5nSOIgvjL6M5iGfsUW6Qz6f5KI2K69YD1GnVUiIiIi3VYogBHRdiuc1dpAy4v30rrmAaxAq83Bus6VswtwmG0DvMdWHsGyLManjeaLo24lwRsPQJ2vwcaEIr2DhlUi58jytxA8vp/g8f2EW/WDqDPUu94qWC+yO4qIiIiIvAfD5cF7wefxzvkMuNp2xwscWE3Tv39AqKpvvOmYGh/BBeOyADhUdoYdh09hGAaGYdASbOGhfY/z442/5Iyvb90mKdLZNKwSOUfhupO0PP9zWp7/OeHKvvFDWUREREQE2m4LdA2cStRVP8aRXgiAVV9J89L/xbflaaxwyOaE59/iqblEeZ0APL76KMFQW0/VkTPFbDi5hZZgC08efs7OiCI9noZVImK7rDcL1uMrLrI7ioiIiIh0gBmTTMSi2/FMugZMB1hh/NuW0rz0fwk31tgd77yKjnBxydRcACpPN/PajuMAjEgeyqR+4xiSOIgrByy2MaFIz6dhlYiIiIiIiJwzwzRxj1pI5OXfx0zIBMBqPoPh8tqc7PybMzaLlPi2x7l0bTHNrUEAritcclZ/VSAUsCuiSI+mYZWI2K5eBesiIiIiPZYjOYfIK36Aa/g8vHM+jeGJaj9nWZaNyc4fl9PkqtkDAGhsCbB8QwkAbofrzf6qVv65/wl+v/OvhK2wnVFFeiQNq0TOVS/9gWuntwrWW1WwLiIiItIjGU433qnX48wY0n7MCvpoef5nBEt22BfsPBpfmEJBRiwAL20uo6bu7V0RXy5ZzfoTmzlyppjXyt+wK6JIj6VhlchHYdgdQERERESke/JteJzQiYO0vPgbWl9/ACvgsztSpzIMg2vnDgQgGArz1Jq37xKYlzOH5IgkBicMZHTKcLsiivRYGlaJiO0ym2aqYF1ERESkl3GkFYArAoDA/tU0PfV9QlW9ayX9gKw4xhemALB+byXHTtYD4HV6+PrYz/HF0W/3V/XWWyJFzgcNq0SkG9FSNREREZHewjVwKlFX/RhHeiEAVl0lzUt/gm/rUqxwyOZ0neeq2QU4zLbXsY+vPNI+lIr3xGEaJi3BVh7e/yQvlqy0M6ZIj6JhlYjYTgXrIiIiIr2TGZNMxKLb8Uy6BkwHWGH8W5+m+dm7CddV2h2vU6QmRHLBuCwADpSeYeeRmrPO37/3Ed44sYnlxa9Q1nDcjogiPY6GVSLnyu3FkV6II70QwxNtd5peod6tgnURERGR3sowTdyjFhJ5+fcxEzIBCFcdpenf3ydQvNXmdJ1j8dRcIj1OAB5fdYRg6O0dABfnzcM0TAri84h687ZIEXl/TrsDiPQ0jvgMIi+5w+4YIiIiIiI9iiM5h8grfoBv0xME9rwMVggzrp/dsTpFdISLS6bl8tjKI5w83czrO48zZ2zbaqv+sVl8Y9wX6B+ThWlovYhIR+j/KSJiu6ymWSpYFxEREekDDKcb79QbiFh4G97pn8CRmNl+rqf3WM0dm0VynBeAZ9YW0+ILtp/Lje2PaZi0Blt59MC/KaorsSumSI+gYZWIiIiIiIh0KWfWcFyFM9o/tiyL1pd/T+vrD2AFfDYm+/BcTpOrZhcA0NAcYPmGswdSoXCIX2z5PWuPb+ShfY/hD/ntiCnSI2hYJXKOwk21+LY8jW/L04TPnLQ7Tq/QXrAeq4J1ERERkb4ocHANwZLtBPavpump7xOq6pldphMGp5KfEQvAS5vLOF3f2n7OYTqYkj4eaNspsCXYM4dyIl1BwyqRc2Q11eLfthT/tqWE63vHDiZ2ay9Yj+mZL0pERERE5KNxZg7F0W8QAFZdJc1Lf4Jv69Ied2ugYRhcO3cAAIFgmKfWnP369oL+M7ll2PV8ecynifPE2BFRpEfQsEpERERERERsZcakELH427gnXg2mA6ww/q1P0/zs3YTrq+yOd04GZsUzrjAFgPV7TlJysqH9nGmYjE8b/WZ/lY+lR1fQEmx9ry8l0mdpWCUitnurYD3huArWRURERPoqwzTxjF5E5OXfx0zIACBcdZSmJ7+H/8BrWJZlc8KOu2p2AQ7TwAIeW3n4HdnP+Oq4e9M9vFSyiqcOP2dPSJFuTMMqERERERER6TYcyTlEXnEXruFvvpEZ9OFbcz++NX+3N9g5SEuIZM7Ytp0OD5SeYdfRmrPOx7pjSIlIAqC6pYZAOPiOryHSl2lYJXLOes47Oj1F3ZsF6y0xKlgXERERETCcbrxTbyBi4W0YkfEAOPqPtjXTubp0Wh4RHicAj686Qigcbj9nGiY3DrmaqwdexlfGfAaX6bQrpki3pGGVyEdi2B2gV2hQwbqIiIiIvAtn1nCirvoJnumfwJU3rv24FQ5iBbr3bnrRES4umZoLwImaZl7feeKs8wneeGZnT2vvr3rj+GYbUop0TxpWiYiIiIiISLdleKNxD51z1jH/1qU0PfV9QlXd+83OC8ZlkhznBeCZ14to8b3zdr/yhuP8dNM9PHzgCbZW7uzqiCLdkoZVImK7zMa2gvVEFayLiIiIyAcInSrBv+N5rLpKmpf+BN/WpVjhkN2x3pXL6eDKWQUA1DcHWLGx9B3XxLhjaAm17Qi4tUrDKhHQsEpERERERER6EDMxG/eEK8F0gBXGv/Vpmp+9m3B9ld3R3tXEIankpccC8NKmUk7Xt551Ps4Tw/WFV3LlgMV8aviNdkQU6XY0rBI5R0ZkAu6xl+EeexlmXKrdcXqFercK1kVERESkYwzTxDN6MZGXfx8zPgOAcNVRmp78Hv4Dr2FZ3WtDJMMwuHbuAAD8wTBPr3nnrYujU0cwt/9MTMPEF/JTVHesi1OKdC8aVomcIzM6Ec/4K/CMvwIzrp/dcXqFBvcxXJlHaVHBuoiIiIh0kCM5h8gld+Ea/maVRNCHb839tL70W8It9bZm+2+DsuMZOygFgDf2nKTkZMO7XldUV8Ldm+7hDzv+Rk1LbVdGFOlWNKwSERERERGRHslwuvFOvYGIhbdhRMYDECzZTvNTP+h2uwVeNbsAh2lgAY+vOvKuK8CaA82caqmhNeTjtYp1XR9SpJvQsEpEbJfZOLutYP2ECtZFRERE5Nw5s4YTddVPcOaNB8BVOBPD5bE51dn6JUYye0wmAPtLatldVPOOa4YnD2FW1lSWDFjM5QULuzqiSLfhtDuASE8Tqq3At/YfAHgmXYMjtcDmRCIiIiIiYnij8V74RYIl23H2H3nWuXB9FWas/X2zl07L5Y09J2jxhXh81VGG5SXiMM9eQ3LNoMvb/+wL+WkJthDvievipCL20soqkXPlbyF04iChEwexfE12p+kV6t3FbQXr0SpYFxEREZEPzzAMXLljMcy312UEijbT9Ngd+LYtxQqHbEwHMZFuFk/JBeD4qSZe33XiPa8tqjvGTzfdw193/5OwFe6ihCLdg4ZVImK7BncJrsyjtMYU2x1FRERERHoRK+jHt+6fYIXwb3ma5ud+Sri+ytZMF47PIim27RbFZ14vpsUXfNfrtlXuorqlhuL6Ejad3NaVEUVsp2GViNiue20uLCIiIiK9heF0E7HgfzDjMwAIVx6h6d/fx3/gtXctOO8KLqeDK2e1VYnUN/l5YWPpu153acF8MqPTuWLAIib2G9uVEUVsp2GViNgus2GWCtZFRERE5LxwJOcQueQuXMMubDsQaMW35n5aX/4d4ZZ6WzJNHJpGbr8YAF7cVEptwzt3LnQ73Nw+/itc2H8WpmHiD/kJht99FZZIb6NhlYiIiIiIiPRqhtONd9qNRCz4BkZkPADBY9tofvJOgqU7uzyPaRhcO3cAAP5gmKfXFL3rdQ7TAUBxXQk/3fwbXjj2apdlFLGThlUiH4lhd4Beod59TAXrIiIiInLeObNHEHXVT3DmjQfAaqmn5YV7CJ083OVZCvsnMGZgMgDrdp+gtLLhXa+zLIvHDj1DVfMpXixZRUXje5eyi/QWGlaJiO0aPMdwZR6lJfrd31ESEREREekshjca74VfxDv70+Dy4swdi5k2wJYsV80uwDQMLODxVUfetUfLMAxuHHw1XoeHS/Pnkx6V1vVBRbqY84MvEREREREREek9DMPANWgajvRB4PRgGPbcMZGeFMXsMRms3FbBvmO17Ck+zYj8pHdclxWTwY+n3kGkKxKAcDjc1VFFupRWVomI7TIaZrcVrJ9UwbqIiIiIdB0zJgUzIhbLChNuqiXcVIsVaO3SDJdOz8PrbuumenzlEULvMYh6a1BVXFfKN178MYdrdVeC9F4aVomcIzMhg4jFtxOx+HYcKXl2x+ldVAEmIiIiInbwNdP08NdpevjrBA6+3qX/6dhIN4um5ABQcaqJdbtPvue1zYFmfrfjPirqT/LQ/sdpDb5zF0GR3kDDKpFzZLgjcWYMwZkxBMMbbXecXqHeXayCdRERERHpsy4an01irAeAp9cU0eoPvut1ka5ILsmfj9N0Mj1zEm6HqytjinQZDatExHaNnhIVrIuIiIhIn+V2ObhyZgEAdU1+XthY+p7Xzsqayi/n38m8nDmYhn6ll95Jz2wRERERERERm00alkZOWgwAL2wqpbbh3W/xMw2TjJi2HQFL6sv47fb7aA40d1lOka6gYZXIOQqePEzDfTfTcN/NBMv32B2nV1DBuoiIiIj0daZhcM3cAQD4A2Geef397zoobSjnl1v/wMHaIzx+6NmuiCjSZTSsEhEREREREekGhuQkMHpAMgBrd52grKrxPa/Njs5kSOIgHIaD9KhULMvqqpgi552GVSJiOxWsi4iIiIi0uWp2AaZhYAFPrDryntcZhsH1g6/k9glf4eLcuRiGttaW3kPDKhGxXYPnWFvBelSx3VFERERERGyVkRzFrNEZAOwpPs2eopr3vDbeE0dmdDrQ1l/11JHntcJKegUNq0RERERERES6kcum5+F1OwB4fNURwuH3H0Btq9rFL7f+gVdL17C5cntXRBQ5rzSsEhHbZTbMoWXTfJIqL7Q7ioiIiIj0RYaBERmPERkPTrfdaYiNcrNwcg4A5dVNrNt94n2vHxRfQJQzEofhoEk7A0ov4LQ7gIiIiIiIiIidDE8U0Tf+xu4YZ5k3IZtV2yuobfDx1OtFTByShufN1Vb/LdodxS3DrifKFUlWTEYXJxXpfFpZJSK2e6tgvTnq/bfnFRERERHpK9wuB1fOygegrtHPi5tK3/f6wsQB7YOq0vpytlXtOu8ZRc4XDatExHbtBesxGlaJiIiIiLxl8rB+9E+LBmDFxlLONPr+f3t3HldVnf9x/H0v+6IgiAqILFqM4q6Je64papmMpmYzTVpjjdP8nPJXav7KmnqoYzNW066tUzkOZjaaWy5ppVbi0ihgiaCAG7EJiGz3/v4gGO/gwlXgXOD1fDx4dDvnnsPb643gzfd8zjWP+SLtay2Nf1l/T/ynMi9ceTg74MgoqwAAAAAATZq1vEylx/ao9NgeleeeMjpOFbPJpMlDO0iSikvLtfbLa989u41XK1msFpVZypRy/kRdRwTqBDOrADs5t7lJzX77rtExGpWg80P17+NZCg9sZnQUAAAANEVlxbq4/Q1Jklu/u+Xk6zhznzqG+albe38dSs7Sl9+f0ojebRUQcOXvm3/hd5PubD9Gv/C7WSHMr0IDxcoqAAAAAAAc2KShHWQ2mWS1SnE7kq/5/JGhQ6qKqpP56TpTeK6uIwK1irIKgOHy3RiwDgAAAFxJUEsvDe5eUT79+3iWDhytWfm0KXWblu57We8l/EPllvK6jAjUKsoqwE7W4kKVnUpU2alEWYsLjY7TKFQOWL/gTVkFAAAAXM74geFyc3WSJL297ogsFus1jykquyiL1aL0glM6nsf8KjQclFWAncqz01W0fomK1i9R+U98wQcAAAAal2uXQEbw8XLVmL6hkqTU0+e1+/CZax4zLvw29WjVVf/b+/e6qUVEXUcEag1lFQDDBZ4foqJvR6vl2ZFGRwEAAAAc1m23hKhFMzdJ0ppdySouvfqlfS5OLrq/8z1q16ytJCmj4LRKLWV1nhO4UZRVAAAAAAA0AG4uToodXLFCKregRJu/PVmj48ot5dqYslWLv3tRnx3fUpcRgVpBWQXAcP8ZsH7tO5sAAAAATVm/qDaKCPKRJG3ce1J5BcU1Ou77n47IYrVoR/pXyi3Oq8uIwA2jrAJguIKfB6wXeaUYHQUAAABwaGazSdNvj5IkFZeWa+1X1/4e2snspF93mqJ2zYL1aM/fydfNp65jAjeEsgqA8RxzhiUAAACaCrOznNv3lXP7vjL7BBqd5pq63Rygru39JUm7Dp1SRmbBNY8J9Gqtx3r/Qe2aV8yvyis+X6cZgRtBWQXAcIHnh1YMWM8cYXQUAAAANEEmFzd5DH9QHsMflHO7rkbHqZFJQ9rLZJKsVinui5qN0zCZTCq3lGtT6jY9uXuRkrJ/rOOUwPWhrAIAAAAAoIEJDvDW4G5BkqTvk7N0JDW7RsflFudpc+p2lVnLtTLpY5Vbrn5HQcAIlFUADMeAdQAAAMB+dw4Ml5uLkyTpn9uPyWK59nwNfw8/TegwTm08W2l652lyMjvVdUzAbs5GBwAaGpOrh5wCIyseu3kanKZxyHdPlUvz07pQ4md0FAAAADRB1rJiFe/+SJLkHNFHzm2jDE5UMz7eborp205rv0xR2rkC7TlyRgO6XHvm1qDgvuoX2FsuTi6SpNLy0qrHgCNgZRVgJyf/dvK8fZ48b58np5ZhRscBAAAAcKMs5SpN2qnSpJ2yZKcbncYuo25pJ19vV0nSml3HVVx67cv6TCaTXJxcfp5ftV1P712qgpLCuo4K1BhlFQDDBeb9PGD9HAPWAQAAAHu4uTppwuAISVJOfrG2fJdW42OPZCVp3fFNyinO1T9/WFtHCQH7UVYBcBgmowMAAAAADdCAzoFqG+AtSdqw94TyCktqdFyXlp3UrWWUWnu20rB2g+oyImAXyirATpb8TBXv+0TF+z6R5Xym0XEahcoB64UMWAcAAADsZjabNHlYB0lScUm5Pv0qpUbHmUwm3dNxkube8j8Ka95OkmS1XntIO1DXKKsAO1nys1Sy/1OV7P9UloKfjI7TKBS4p8olOFkXPGv2P1UAAACg7jTMsiYq3E+dIypuWLTr4Cll/FSzGVSeLp5ydXKRxWrRltQdWnH47xRWMBxlFQAAAACgiWscAynuGtpBJpNksVq1escxu47dlLpNnx7fqIOZh7Xn9L46SgjUDGUVAMNVDlgPyGTAOgAAAHC92gZ4a1DXQEnSoeQsJaZm1/jYIW0HyMe1uVp7BijIu3VdRQRqhLIKgPFYZgwAAADUijsHRcjVpeJH/VU7jslSw++1PV089fvu92vuLbOr5lcBRqGsAuxGsVLb8t0rB6wfNzoKAAAA0KD5erspJjpUknTybIH2HjlT42ODvNtUza/6/MQX2pW+p65iAlflbHQAAChwS5VL8zO6UOJvdBQAAAA0RU4uchv0m4qHrdobm6UWjO7TTl8czFBeQYk+3nlcvSNbydXFqcbHv5fwD+07e1AuZhdF+nVQa8+AOkwLVMfKKgAAAABAk2ZycpZrxyFy7ThETv4hRse5YW6uTpowKEKSlJNfrM/3pdl1/MCgaElSC3cflZSX1Ho+4FpYWQXAcIF5Q3UkNUdBwc2NjgIAAAA0CgO7BOrzfWnKyCzUZ3tOaFDXIDX3cq3RsTe1aK8HuvxanfxulqtTzY4BahMrqwAAAAAAaGTMZpMmD+0gSbpYUq5Pv06x6/juAZ3l6uQqi9WirSd3KqPgdF3EBC6Lsgqwk7lZS7n2HC/XnuNlbtbS6DiNwvnKAeueDFgHAABA/bOWXlTBqsdVsOpxlSbtMjpOrekc4a+ocD9J0s4Dp3Q6q9Cu4y1Wi1468KY+OfaZ3kv4h8osZXURE6iGsgqwk7lZgNx6T5Bb7wkyN2PQYG0odEuVS3CyLnjZ99seAAAAoFZYrbLmna34KLGv0HF0dw3tIJMki9WquB3Jdh1rNpnV3jdcklRSXqLc4vN1kBCojplVAAAAAAA0UiGtvDWga6C++v60Dh77SUkncvSL0BY1Pj4mbLhczS4aEjJQbsyvQj1pMiur3n//fcXGxhodA8BltMkbqqJvR6tV5gijowAAAACNzoRBEXJ1qfjxf9WOY7JYrTU+1tnsrFFhw+T28/yqrzL2qqS8tK6iApKaSFmVkJCg5cuXGx0DjUT5T6m6sG6RLqxbpPKsk0bHAQAAAICratHMTaP7tJMknTiTr28SqK2EBwAAJa1JREFUztp9joKSQr2w/w2tPLpG645vqu2IgI1GX1YVFhbqySef1COPPGJ0FDQS1uILKj99VOWnj8paUmR0nEYhnwHrAAAAcBQ1X3TUoIyObqfmXhWX8a3ZmayS0nK7jvdwdpfFWnHM4axElZSX1HpGoFKDL6vWrl2rTp06VfvIz8+XJC1cuFDTp09XUFCQwUkBXElB5YB1yioAAAAYwWQyOkGdc3d11oRBFcPSs84Xa2t8ul3HO5md9KtOkzW07UDNvWW2XJlfhTrU4MuqO++8UwkJCdU+mjVrpjVr1sjFxUVjxowxOiaAmmj83yMAAAAAhhnYNVDBLb0kSZ/tSdX5C/atjmrtGaCJN99RNb8qMfuHuogJNPyy6mrWrVunQ4cOafz48VqwYIGSk5P1m9/8xuhYAP5Lm9yKAesBPzFgHQAAAKgrTmaz7hrWQZJUVFyudV+lXtd5sopy9OKBN/TywRU6kpVUiwmBCs5GB6hL77zzTtXjb775RkuWLNG7775rXCAAAAAAAAzUOdxPUWEtdCQ1R18czNCwXsEK9Pey+zzp+ackSZtTd6iTX6RMTeBSStQfh1lZlZiYqKioKJ05c6bavvXr12vs2LHq2rWrYmJitHbt2voPCKDOFDBgHQAAAEZydpXXXYvldddiuUQOMjpNnTKZTJo0tINMksotVq3+Itnuc/h7tNAvb7pDg4L76XfdplNUodY5RFmVnJysmTNnqqysrNq+DRs2aM6cORo4cKBeeeUV9enTR48//rg2bbLvVpnR0dFas2ZNbUUGUIsK3FMYsA4AAADDmExmmX3byOzbRiZ3b6Pj1Ll2rZtpQJdASdKBH3/S0ZM5dp+jf9AtmhI5Qe7ObrJYLTp3IbO2Y6IJM/QywLKyMq1atUp/+ctf5OLictnnLFu2TDExMZo3b54kadCgQcrLy9OLL76o0aNH10tOf//G/8XK0QQENDM6whUVFXqq6OfHvr4e8nDgrA2FyVzxmxiTTA79d3+phpITTRfvUTg63qNwdLxH4ehu9D16/4Qu+jbpnEpKy/Xxlyn6yx9CZDbbv0LqXMFPeu3b95V2/rT+Ovr/5OPe/IZyoXG53vepoWVVfHy8nn/+ec2YMUOtW7fWggULbPanpaXp5MmTeuSRR2y2jxo1Shs3blRaWppCQkLqPGdWVoEsFmudfx5UCAhopszMfKNjXFFZ7oWqx7m5RSrwcNysDUXr7KFKPJGjtm19HPrvvpKjv0cB3qNwdLxH4eh4jzY9VqtF1gt5kiSTq4dMLu4GJ7q62nqPjrolROt2p+pYWq4+23VMfaPa2H2O/WcTlZD5oyTp/X1rNSVywg3nQuNwtfep2Wy66sIgQ8uq9u3ba+vWrfL397/sJXrHj1dcEhQeHm6zPTQ0VJKUkpJSL2UVcCmnlqHyGPd4xWO/tganAQAAAHDDyktV+OEfJUmufe6SW/cxBgeqH6Oj22nnoVM6X1iij3cmq1dkgFycnew6R6/W3XUw87A8XTx1Z/um8bqh7hk6s6ply5by9/e/4v78/IoGztvbtm3z8qq4U0FBQUHdhQOuwOTmJeegjnIO6iiTm/13zUB1DFgHAAAA6p+Hm7PuHFSxOCTrfLG27ku/rvPcF3W3pkbGVs2vKioruvZBwFU4xID1K7Far37pndns0PEB1FDlgPVCjxSjowAAAABNyqCugQpqWfFL+PV7UpV/ocTuc5hNFT+bZxVl66UDb+qtwx9e8+d54Gocuu1p1qxiEFdhYaHN9soVVZX7AQAAAACA/ZzMZt01tL0kqai4XP/6OvW6z7Xl5Bf6Mfe4ErN/0N7T+2opIZoihy6rKmdVnTx50mb7iRMnbPYD9aks/Yjy3/yN8t/8jcpOHzU6TqPQOmeoir4drVZZw42OAgAAADQ5XSL81TG0hSTpiwMZOpN94RpHXN6d7WPUws1XA4Ki1aNVl9qMiCbGocuq0NBQtW3bVps2bbLZvmXLFoWFhSkoKMigZMDPTPbf2hVXxqsJAAAA1D+TyaTJwzrIJKncYtXqL5Kv6zwezh6a3+ePuvsXv5S7s7ssVossVkvthkWTYOjdAGti1qxZmjdvnnx8fDRkyBBt27ZNGzdu1LJly4yOBqCWVAxY/0kFHn6SehkdBwAAAGhy2rVupv6d2+jrw2e0/4dM/ZCWq5tDfO0+j6eLhyQpqyhHHyTFKco/UiPa3VrLadHYOXxZFRsbq5KSEr399tuKi4tTSEiIlixZojFjuCUm0FgUeqTIxfecCovzjI4CAAAANFkTBkfo26RzKi2zaNX2H/XEr3vLfB1Xk1itVr3x73eVUXBapwvPUFbBbg5TVsXGxio2Nvay+6ZMmaIpU6bUcyIAAAAAAJoOv+buGtUnROt3n1DK6Xx9l3hO0Z1a230ek8mk7gGdVVR2Uc5mpzpIisbOYcoqoOH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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
@@ -880,21 +626,13 @@
" * Alter the metallicity, does this make much of a difference?\n",
" * Change the binary fraction. Here we assume a 100% binary fraction, but a real population is a mixture of single and binary stars.\n",
" * How might you go about comparing these computed observations to real stars?\n",
- " * What about evolved stars? Here we consider only the *zero-age* main sequnece. What about other main-sequence stars? What about stars in later phases of stellar evolution?"
+ " * What about evolved stars? Here we consider only the *zero-age* main sequence. What about other main-sequence stars? What about stars in later phases of stellar evolution?"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "abb096b0-7c57-43d6-a9f7-56bdd21dc542",
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -908,7 +646,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.4"
+ "version": "3.9.9"
}
},
"nbformat": 4,