{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bfddfbfd-39b6-427b-8dd3-e0b827149101",
   "metadata": {},
   "source": [
    "# Advanced CySolver Examples\n",
    "This notebook showcases some of the advanced functionality of the CySolver backend distributed with CyRK. \n",
    "In order to run these cells you need to make sure Cython, Jupyter, CyRK, and Numpy are installed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5f95089-c7af-481b-87ba-5ab10caf14d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Cython extension is already loaded. To reload it, use:\n",
      "  %reload_ext Cython\n",
      "CyRK version: 0.17.0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('dark_background')\n",
    "# Hack the cython magic so that it can set the correct C++ standard.\n",
    "%load_ext Cython\n",
    "import sys\n",
    "import platform\n",
    "import numpy as np\n",
    "import CyRK\n",
    "print(\"CyRK version:\", CyRK.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c75057f-cd71-470a-a625-9e75c6d95225",
   "metadata": {},
   "source": [
    "## Hack IPython Magic\n",
    "In order to make this notebook work across platforms without changes we need to hack the cython cells to include some additional header information. This is done using the script `jupyter_cyhack.py` file contained in the same directory as these demos. Hopefully it works fine for you, but keep in mind that the hack below is required to get these cells to work. So if you are making your own cythonized notebook you will want to see what headers should be included for your operating system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "614ec761-20b6-4a2f-a359-af6284a99156",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Cython.Build import IpythonMagic\n",
    "\n",
    "from jupyter_cyhack import build_hack\n",
    "\n",
    "# Get the current cython Ipython parser.\n",
    "original_cython_parser = IpythonMagic.CythonMagics.cython\n",
    "# Apply hacks to headers and compile lines.\n",
    "patched_cython_parser = build_hack(original_cython_parser)\n",
    "# Patch it in\n",
    "IpythonMagic.CythonMagics.cython = patched_cython_parser\n",
    "# Reload\n",
    "ip = get_ipython()\n",
    "ip.register_magics(IpythonMagic.CythonMagics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb5fc83",
   "metadata": {},
   "source": [
    "## Parallelizing `cysolve_ivp`\n",
    "Below is an example of how you can parallelize over `cysolve_ivp` using Cython's\n",
    "[prange](https://cython.readthedocs.io/en/latest/src/userguide/parallelism.html).\n",
    "\n",
    "Note that in this example we use `cysolve_ivp_noreturn` instead of `cysolve_ivp`. It should work with both. Please\n",
    "report a bug if it does not!\n",
    "\n",
    "In this example we build a unique `CySolveOutput` for each job and batch send them to threads to work through the jobs.\n",
    "In the next example we show how you can instead have one `CySolveOutput` per thread."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "938d76cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_b079f79e5601112d690eb8710dc64b86fc48f37951916b65c17d7897418586c0.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b079f79e5601112d690eb8710dc64b86fc48f37951916b65c17d7897418586c0.cpp(25554): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b079f79e5601112d690eb8710dc64b86fc48f37951916b65c17d7897418586c0.cpp(25555): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b079f79e5601112d690eb8710dc64b86fc48f37951916b65c17d7897418586c0.cpp(25678): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b079f79e5601112d690eb8710dc64b86fc48f37951916b65c17d7897418586c0.cpp(28820): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b079f79e5601112d690eb8710dc64b86fc48f37951916b65c17d7897418586c0.cpp(28827): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b079f79e5601112d690eb8710dc64b86fc48f37951916b65c17d7897418586c0.cpp(29327): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_b079f79e5601112d690eb8710dc64b86fc48f37951916b65c17d7897418586c0.cpp(29328): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_b079f79e5601112d690eb8710dc64b86fc48f37951916b65c17d7897418586c0.cp312-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_b079f79e5601112d690eb8710dc64b86fc48f37951916b65c17d7897418586c0.cp312-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "\n",
      "Starting prange Demo!\n",
      "Working with N=1...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 1900.1 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 1.9ms/loop.\n",
      "Total Time: 1900.120ms.\n",
      "\n",
      "\n",
      "Working with N=2...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 984.2 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 1.0ms/loop.\n",
      "Total Time: 984.242ms (-48.2 % change from N=1; -48.2 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=3...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 641.9 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 0.6ms/loop.\n",
      "Total Time: 641.888ms (-34.8 % change from N=2; -66.2 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=4...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 491.1 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 0.5ms/loop.\n",
      "Total Time: 491.051ms (-23.5 % change from N=3; -74.2 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=5...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 389.7 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 0.4ms/loop.\n",
      "Total Time: 389.738ms (-20.6 % change from N=4; -79.5 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=6...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 338.3 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 0.3ms/loop.\n",
      "Total Time: 338.321ms (-13.2 % change from N=5; -82.2 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=7...\n",
      "CyRK prange test `run_prange_constant_args_test` finished in 298.4 ms (If you are seeing this then tests passed too).\n",
      "\tAvg time: 0.3ms/loop.\n",
      "Total Time: 298.442ms (-11.8 % change from N=6; -84.3 % change from N=1).\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%cython --force\n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "from cython.parallel cimport prange\n",
    "\n",
    "from libcpp.vector cimport vector\n",
    "from libcpp.memory cimport unique_ptr, make_unique\n",
    "from libcpp.utility cimport move\n",
    "\n",
    "from CyRK.cy.cysolver_api cimport cysolve_ivp_noreturn, CySolverResult, CySolveOutput, ODEMethod\n",
    "from CyRK.cy.cysolver_test cimport lotkavolterra_diffeq\n",
    "\n",
    "cdef extern from \"<chrono>\" namespace \"std::chrono\" nogil:\n",
    "    cdef cppclass microseconds:\n",
    "        long long count()\n",
    "    \n",
    "    # We need a generic duration type to handle the result of subtraction\n",
    "    cdef cppclass duration \"std::chrono::high_resolution_clock::duration\":\n",
    "        pass\n",
    "\n",
    "    microseconds duration_cast_microseconds \"std::chrono::duration_cast<std::chrono::microseconds>\"(duration)\n",
    "\n",
    "cdef extern from \"<chrono>\" namespace \"std::chrono::high_resolution_clock\" nogil:\n",
    "    cdef cppclass time_point:\n",
    "        duration operator-(time_point)\n",
    "    \n",
    "    time_point now()\n",
    "\n",
    "# Note the example below uses a `def` vs. `cdef` function but it should work with either\n",
    "\n",
    "def run_prange(int num_threads = 2):\n",
    "    \"\"\"Run a prange test where args are common across all runs.\"\"\"\n",
    "    \n",
    "    cdef int num_runs = 1000\n",
    "    cdef double t_start = 0.0\n",
    "    # Pick a large time so each time step takes a significant amount of time so it can benefit from parallelize.\n",
    "    cdef double t_end = 2000.0\n",
    "    \n",
    "    cdef vector[vector[double]] y0_vecs = vector[vector[double]]()\n",
    "    y0_vecs.resize(num_runs)\n",
    "    cdef size_t i\n",
    "    for i in range(num_runs):\n",
    "        # Set initial conditions; all the same for these tests\n",
    "        # 2 dependent variables\n",
    "        y0_vecs[i].resize(2)\n",
    "        y0_vecs[i][0] = 10.0\n",
    "        y0_vecs[i][1] = 5.0\n",
    "    \n",
    "    # --- CHANGE: Single constant vector instead of vector[vector] ---\n",
    "    cdef vector[char] constant_args = vector[char]()\n",
    "    # arg vector\n",
    "    constant_args.resize(4 * 8)  # Number of double * size of double\n",
    "    \n",
    "    # Recast <char*> args to double pointer for storage of args\n",
    "    cdef double* arg_dbl_ptr = <double*> constant_args.data()\n",
    "    arg_dbl_ptr[0] = 1.5\n",
    "    arg_dbl_ptr[1] = 1.0\n",
    "    arg_dbl_ptr[2] = 3.0\n",
    "    arg_dbl_ptr[3] = 1.0\n",
    "\n",
    "    cdef vector[CySolveOutput] results = vector[CySolveOutput]()\n",
    "    results.resize(num_runs)\n",
    "    for i in range(num_runs):\n",
    "        results[i] = move(make_unique[CySolverResult](ODEMethod.RK45))\n",
    "\n",
    "    # Main Loop\n",
    "    cdef time_point start_time, end_time\n",
    "    cdef Py_ssize_t p_i  # prange has to used a signed integer type\n",
    "\n",
    "    start_time = now()\n",
    "    for p_i in prange(num_runs, nogil=True, num_threads=num_threads, use_threads_if=num_threads>1, schedule='static'):\n",
    "        cysolve_ivp_noreturn(\n",
    "            results[p_i].get(), # Pass in CySolverResult pointer which is found with CySolveOutput::get()\n",
    "            lotkavolterra_diffeq,\n",
    "            t_start,\n",
    "            t_end,\n",
    "            y0_vecs[p_i],\n",
    "            1.0e-5, # rtol\n",
    "            1.0e-8, # atol\n",
    "            constant_args # Pass the single shared vector\n",
    "        )\n",
    "    end_time = now()\n",
    "\n",
    "    # Check results\n",
    "    cdef CySolverResult* reference_result_ptr = results[0].get()\n",
    "    # Check the reference first.\n",
    "    assert reference_result_ptr.success\n",
    "    assert reference_result_ptr.size > 0\n",
    "\n",
    "    cdef CySolverResult* check_result_ptr\n",
    "    cdef size_t j\n",
    "    for i in range(num_runs):\n",
    "\n",
    "        if i == 0:\n",
    "            continue\n",
    "        check_result_ptr = results[i].get()\n",
    "\n",
    "        assert check_result_ptr.success\n",
    "        assert check_result_ptr.size > 0\n",
    "        assert reference_result_ptr.size == check_result_ptr.size\n",
    "\n",
    "        for j in range(reference_result_ptr.size):\n",
    "            assert check_result_ptr.time_domain_vec[j] == reference_result_ptr.time_domain_vec[j]\n",
    "            assert check_result_ptr.solution[2 * j + 0] == reference_result_ptr.solution[2 * j + 0]\n",
    "            assert check_result_ptr.solution[2 * j + 1] == reference_result_ptr.solution[2 * j + 1]\n",
    "\n",
    "\n",
    "    cdef double loop_time_ms = <double>(<int>duration_cast_microseconds(end_time - start_time).count()) / 1000.0\n",
    "    print(f\"CyRK prange test `run_prange_constant_args_test` finished in {loop_time_ms:0.1f} ms (If you are seeing this then tests passed too).\")\n",
    "    print(f\"\\tAvg time: {loop_time_ms/num_runs:0.1f}ms/loop.\")\n",
    "\n",
    "    return loop_time_ms\n",
    "\n",
    "import os\n",
    "cdef int cpu_count = os.cpu_count()\n",
    "cdef double result\n",
    "cdef int threads\n",
    "cdef double t1_result, last_result\n",
    "print(\"\\n\\nStarting prange Demo!\")\n",
    "for threads in range(1, min(cpu_count, 8)): \n",
    "    print(f\"Working with N={threads}...\")\n",
    "    result = run_prange(threads)\n",
    "    if threads == 1:\n",
    "        print(f\"Total Time: {result:0.3f}ms.\\n\\n\")\n",
    "        t1_result = result\n",
    "    else:\n",
    "        print(f\"Total Time: {result:0.3f}ms ({100.0*(result - last_result)/last_result:0.1f} % change from N={threads-1}; {100.0*(result - t1_result)/t1_result:0.1f} % change from N=1).\\n\\n\")\n",
    "    last_result = result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "989d46bf",
   "metadata": {},
   "source": [
    "### Batching `CySolveOutput`s Over Multiple Threads\n",
    "In the parallelized example above we created a `CySolveOutput` for each of our runs. This is safe because each thread\n",
    "is guaranteed to only work with a unique `CySolveOutput` so it can write data as it wishes. However, `CySolveOutput`\n",
    "does carry a little overhead that may cause memory issues for problems with many dependent `y` variables. \n",
    "\n",
    "It may be advantageous to make use of \"Re-Uses\" (see other examples in this demo) with `cysolve_ivp_noreturn`. Where\n",
    "only one `CySolveOutput` per thread is built and used. However, to do any useful work we still have to build storage\n",
    "arrays for our `y`, `t`, and perhaps other metadata like integration success, for each job. That is a large part of\n",
    "the memory footprint of `CySolveOutput` in the first place. **To emphasize** many problems will not see an improvement\n",
    "moving from a global `CySolveOutput` to a local one with multithreaded workflows (as we see in the example below)!\n",
    "\n",
    "Examples where this approach may be faster (you should still test both approaches!):\n",
    "- The ODE system has many dependent `y` variables so the internal structures of `CySolveOutput` start to take up a\n",
    "  considerable memory footprint.\n",
    "- You only care about the output at the end of the integration or at certain time steps: then the storage arrays would\n",
    "  be much smaller than dealing with the storage arrays inside `CySolveOutput`.\n",
    "\n",
    "\n",
    "_The example below is just one example of an approach you could take._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78ef4f83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_161526d290fbaed164bb95774fbc88f57d73a0433fdc7e9e0c59e7bd722866c8.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_161526d290fbaed164bb95774fbc88f57d73a0433fdc7e9e0c59e7bd722866c8.cpp(25738): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_161526d290fbaed164bb95774fbc88f57d73a0433fdc7e9e0c59e7bd722866c8.cpp(25739): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_161526d290fbaed164bb95774fbc88f57d73a0433fdc7e9e0c59e7bd722866c8.cpp(25862): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_161526d290fbaed164bb95774fbc88f57d73a0433fdc7e9e0c59e7bd722866c8.cpp(29004): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_161526d290fbaed164bb95774fbc88f57d73a0433fdc7e9e0c59e7bd722866c8.cpp(29011): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_161526d290fbaed164bb95774fbc88f57d73a0433fdc7e9e0c59e7bd722866c8.cpp(29511): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_161526d290fbaed164bb95774fbc88f57d73a0433fdc7e9e0c59e7bd722866c8.cpp(29512): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_161526d290fbaed164bb95774fbc88f57d73a0433fdc7e9e0c59e7bd722866c8.cp312-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_161526d290fbaed164bb95774fbc88f57d73a0433fdc7e9e0c59e7bd722866c8.cp312-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "\n",
      "Starting Batched prange Demo!\n",
      "Working with N=1...\n",
      "Starting Batched Run (1 threads; jobs per thread = 1000)...\n",
      "Batched test finished in 1897.6 ms.\n",
      "\tAvg time: 1.898ms/loop.\n",
      "Total Time: 1897.574ms.\n",
      "\n",
      "\n",
      "Working with N=2...\n",
      "Starting Batched Run (2 threads; jobs per thread = 500)...\n",
      "Batched test finished in 979.7 ms.\n",
      "\tAvg time: 0.980ms/loop.\n",
      "Total Time: 979.657ms (-48.4 % change from N=1; -48.4 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=3...\n",
      "Starting Batched Run (3 threads; jobs per thread = 333)...\n",
      "Batched test finished in 666.8 ms.\n",
      "\tAvg time: 0.667ms/loop.\n",
      "Total Time: 666.806ms (-31.9 % change from N=2; -64.9 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=4...\n",
      "Starting Batched Run (4 threads; jobs per thread = 250)...\n",
      "Batched test finished in 505.2 ms.\n",
      "\tAvg time: 0.505ms/loop.\n",
      "Total Time: 505.152ms (-24.2 % change from N=3; -73.4 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=5...\n",
      "Starting Batched Run (5 threads; jobs per thread = 200)...\n",
      "Batched test finished in 414.2 ms.\n",
      "\tAvg time: 0.414ms/loop.\n",
      "Total Time: 414.221ms (-18.0 % change from N=4; -78.2 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=6...\n",
      "Starting Batched Run (6 threads; jobs per thread = 166)...\n",
      "Batched test finished in 357.3 ms.\n",
      "\tAvg time: 0.357ms/loop.\n",
      "Total Time: 357.297ms (-13.7 % change from N=5; -81.2 % change from N=1).\n",
      "\n",
      "\n",
      "Working with N=7...\n",
      "Starting Batched Run (7 threads; jobs per thread = 142)...\n",
      "Batched test finished in 326.5 ms.\n",
      "\tAvg time: 0.327ms/loop.\n",
      "Total Time: 326.510ms (-8.6 % change from N=6; -82.8 % change from N=1).\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%cython --force\n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "\n",
    "from cython.parallel cimport prange\n",
    "\n",
    "from libc.string cimport memcpy\n",
    "\n",
    "from libcpp cimport bool as cpp_bool\n",
    "from libcpp.vector cimport vector\n",
    "from libcpp.memory cimport unique_ptr, make_unique\n",
    "from libcpp.utility cimport move\n",
    "\n",
    "from CyRK.cy.cysolver_api cimport cysolve_ivp_noreturn, CySolverResult, ODEMethod\n",
    "from CyRK.cy.cysolver_test cimport lotkavolterra_diffeq\n",
    "\n",
    "cdef extern from \"<chrono>\" namespace \"std::chrono\" nogil:\n",
    "    cdef cppclass microseconds:\n",
    "        long long count()\n",
    "    cdef cppclass duration \"std::chrono::high_resolution_clock::duration\":\n",
    "        pass\n",
    "    microseconds duration_cast_microseconds \"std::chrono::duration_cast<std::chrono::microseconds>\"(duration)\n",
    "\n",
    "cdef extern from \"<chrono>\" namespace \"std::chrono::high_resolution_clock\" nogil:\n",
    "    cdef cppclass time_point:\n",
    "        duration operator-(time_point)\n",
    "    time_point now()\n",
    "\n",
    "cdef void single_thread_run(\n",
    "        bint* success_storage_ptr,\n",
    "        vector[double]* t_storage_vec_ptr,\n",
    "        vector[double]* y_storage_vec_ptr,\n",
    "        vector[double]* y0_vec_ptr,\n",
    "        size_t num_dy,\n",
    "        vector[char]& args_vec,\n",
    "        double t_start,\n",
    "        double t_end,\n",
    "        size_t num_jobs\n",
    "        ) noexcept nogil:\n",
    "\n",
    "    # Thread-Local Allocation\n",
    "    # This solver exists only for this thread and is reused for every job in the batch.\n",
    "    # For this example we will make it easy and just hard code the integration method.\n",
    "    cdef unique_ptr[CySolverResult] solver_uptr = make_unique[CySolverResult](ODEMethod.RK45)\n",
    "    cdef CySolverResult* solver_ptr = solver_uptr.get()\n",
    "\n",
    "    cdef size_t solution_size\n",
    "\n",
    "    # Have this thread work on each job sequentially\n",
    "    cdef size_t job_idx\n",
    "    for job_idx in range(num_jobs):\n",
    "        # All arrays will be provided starting at this particular thread's first job.\n",
    "        # It does not need to know any details about what the other threads are doing.\n",
    "        # So we start from 0 and end when we are done with our work.\n",
    "\n",
    "        # Reuse the solver pointer\n",
    "        cysolve_ivp_noreturn(\n",
    "            solver_ptr,\n",
    "            lotkavolterra_diffeq,  # Again this is hardcoded, as are many of the other arguments\n",
    "            t_start,\n",
    "            t_end,\n",
    "            y0_vec_ptr[job_idx],\n",
    "            1.0e-5, # rtol\n",
    "            1.0e-8, # atol\n",
    "            args_vec\n",
    "        )\n",
    "\n",
    "        # D. Extract Results to Shared Arrays\n",
    "        # Store success status\n",
    "        success_storage_ptr[job_idx] = solver_ptr.success\n",
    "        \n",
    "        if solver_ptr.success:\n",
    "            solution_size = solver_ptr.time_domain_vec.size()\n",
    "            # Copy the time_domain vector from the solution into our storage array.\n",
    "            t_storage_vec_ptr[job_idx].resize(solution_size)\n",
    "            memcpy(t_storage_vec_ptr[job_idx].data(), solver_ptr.time_domain_vec.data(), solution_size * sizeof(double))\n",
    "\n",
    "            # Repeat for y's\n",
    "            # There are multiple y's stored in a flattened solution array in the solver_ptr. But we don't actually\n",
    "            #  care about their shape right now because we want to copy the exact shape over to our final array. \n",
    "            # All we care about is the total size so we can use memcpy.\n",
    "            # We use num_dy not num_y because the user might be capturing extra outputs\n",
    "            y_storage_vec_ptr[job_idx].resize(solution_size * num_dy)\n",
    "            memcpy(y_storage_vec_ptr[job_idx].data(), solver_ptr.solution.data(), solution_size * num_dy * sizeof(double))\n",
    "\n",
    "\n",
    "def run_prange_batched(int num_threads=2):\n",
    "\n",
    "    if num_threads < 1:\n",
    "        num_threads = 1\n",
    "\n",
    "    cdef int num_runs = 1000\n",
    "    cdef double t_start = 0.0\n",
    "    cdef double t_end = 2000.0\n",
    "\n",
    "    # 1. Setup Inputs\n",
    "    cdef vector[vector[double]] y0_vecs = vector[vector[double]]()\n",
    "    y0_vecs.resize(num_runs)\n",
    "    cdef size_t num_dy = 2\n",
    "    cdef size_t num_y = 2\n",
    "    cdef size_t i\n",
    "    for i in range(num_runs):\n",
    "        y0_vecs[i].resize(2)\n",
    "        y0_vecs[i][0] = 10.0\n",
    "        y0_vecs[i][1] = 5.0\n",
    "    \n",
    "    # Setup Constant Args\n",
    "    cdef vector[char] constant_args = vector[char]()\n",
    "    constant_args.resize(4 * 8)\n",
    "    cdef double* arg_dbl_ptr = <double*>constant_args.data()\n",
    "    arg_dbl_ptr[0] = 1.5\n",
    "    arg_dbl_ptr[1] = 1.0\n",
    "    arg_dbl_ptr[2] = 3.0\n",
    "    arg_dbl_ptr[3] = 1.0\n",
    "\n",
    "    # Setup Output storage for our full solution domain (Lightweight vectors instead of full CySolverResult objects)\n",
    "    # We pre-allocate these so threads can write to them without locking.\n",
    "    # Since each thread writes to a unique index 'job_idx', this is thread-safe.\n",
    "    cdef vector[vector[double]] final_time_domains = vector[vector[double]](num_runs)\n",
    "    cdef vector[vector[double]] final_ys           = vector[vector[double]](num_runs)\n",
    "\n",
    "    # Important! We use cpp_bool a lot throughout CyRK but c++ bool is just a bit and can't have a memory address.\n",
    "    # Since we need to take the pointer addresses of elements of this vector we need to use another struct. bint should work.\n",
    "    cdef vector[bint] success_flags = vector[bint](num_runs)\n",
    "\n",
    "    # Determine each thread's starting index and number of jobs\n",
    "    cdef Py_ssize_t thread_idx\n",
    "    cdef vector[size_t] thread_jobs        = vector[size_t](num_threads)\n",
    "    cdef vector[size_t] thread_start_index = vector[size_t](num_threads)\n",
    "\n",
    "    cdef size_t start_idx\n",
    "    cdef size_t end_idx\n",
    "    for thread_idx in range(num_threads):\n",
    "        # Integer division chunking: (i*N)//T to ((i+1)*N)//T\n",
    "        start_idx = (thread_idx * num_runs) // num_threads\n",
    "        end_idx   = ((thread_idx + 1) * num_runs) // num_threads\n",
    "        \n",
    "        thread_start_index[thread_idx] = start_idx\n",
    "        thread_jobs[thread_idx]        = end_idx - start_idx\n",
    "    \n",
    "    # Loop over the number of threads, NOT the number of runs.\n",
    "    print(f\"Starting Batched Run ({num_threads} threads; jobs per thread = {num_runs // num_threads})...\")\n",
    "    cdef time_point start_time, end_time\n",
    "    start_time = now()\n",
    "    for thread_idx in prange(num_threads, nogil=True, schedule='static'):\n",
    "        single_thread_run(\n",
    "            &success_flags[thread_start_index[thread_idx]],\n",
    "            &final_time_domains[thread_start_index[thread_idx]],\n",
    "            &final_ys[thread_start_index[thread_idx]],\n",
    "            &y0_vecs[thread_start_index[thread_idx]],\n",
    "            num_dy,\n",
    "            constant_args,\n",
    "            t_start,\n",
    "            t_end,\n",
    "            thread_jobs[thread_idx]\n",
    "            )\n",
    "    end_time = now()\n",
    "\n",
    "    # 3. Validation\n",
    "    for i in range(num_runs):\n",
    "        assert success_flags[i] == True\n",
    "        \n",
    "    cdef double loop_time_ms = <double>(<int>duration_cast_microseconds(end_time - start_time).count()) / 1000.0\n",
    "    print(f\"Batched test finished in {loop_time_ms:0.1f} ms.\")\n",
    "    print(f\"\\tAvg time: {loop_time_ms/num_runs:0.3f}ms/loop.\")\n",
    "    \n",
    "    return loop_time_ms\n",
    "\n",
    "import os\n",
    "cdef int cpu_count = os.cpu_count()\n",
    "cdef double result\n",
    "cdef int threads\n",
    "cdef double t1_result, last_result\n",
    "print(\"\\n\\nStarting Batched prange Demo!\")\n",
    "for threads in range(1, min(cpu_count, 8)): \n",
    "    print(f\"Working with N={threads}...\")\n",
    "    result = run_prange_batched(threads)\n",
    "    if threads == 1:\n",
    "        print(f\"Total Time: {result:0.3f}ms.\\n\\n\")\n",
    "        t1_result = result\n",
    "    else:\n",
    "        print(f\"Total Time: {result:0.3f}ms ({100.0*(result - last_result)/last_result:0.1f} % change from N={threads-1}; {100.0*(result - t1_result)/t1_result:0.1f} % change from N=1).\\n\\n\")\n",
    "    last_result = result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24fa5c2c-a9ee-43dd-a483-51385acfb1d8",
   "metadata": {},
   "source": [
    "## Arbitrary Additional Arguments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74810433-eb2b-4b95-b47f-27269060a6d8",
   "metadata": {},
   "source": [
    "### Additional Args = Array of Doubles\n",
    "See discussion in \"Advanced CySolver.md\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "350bd407-9563-4fa4-983e-02046bdcc051",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_31382ce44063f0dd07dd67fcf2e5fc782bfe4eca24cc5b848ceff7d9914b376a.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_31382ce44063f0dd07dd67fcf2e5fc782bfe4eca24cc5b848ceff7d9914b376a.cpp(25021): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_31382ce44063f0dd07dd67fcf2e5fc782bfe4eca24cc5b848ceff7d9914b376a.cpp(25022): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_31382ce44063f0dd07dd67fcf2e5fc782bfe4eca24cc5b848ceff7d9914b376a.cpp(25145): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_31382ce44063f0dd07dd67fcf2e5fc782bfe4eca24cc5b848ceff7d9914b376a.cpp(28009): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_31382ce44063f0dd07dd67fcf2e5fc782bfe4eca24cc5b848ceff7d9914b376a.cpp(28010): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_31382ce44063f0dd07dd67fcf2e5fc782bfe4eca24cc5b848ceff7d9914b376a.cpp(28630): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_31382ce44063f0dd07dd67fcf2e5fc782bfe4eca24cc5b848ceff7d9914b376a.cpp(28637): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_31382ce44063f0dd07dd67fcf2e5fc782bfe4eca24cc5b848ceff7d9914b376a.cp312-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_31382ce44063f0dd07dd67fcf2e5fc782bfe4eca24cc5b848ceff7d9914b376a.cp312-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "\n",
      "Integration success = True \n",
      "\tNumber of adaptive time steps required: 152\n",
      "Integration message: Integration completed without issue.\n"
     ]
    }
   ],
   "source": [
    "%%cython --force\n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "np.import_array()\n",
    "\n",
    "from libc.math cimport sin\n",
    "from libcpp.utility cimport move\n",
    "from libcpp.vector cimport vector\n",
    "\n",
    "from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType, MAX_STEP, CySolveOutput, PreEvalFunc, ODEMethod\n",
    "\n",
    "cdef void pendulum_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:\n",
    "    # Arguments are passed in as char pointers.\n",
    "    # These point to preallocated and instantiated memory in the CySolver class that is filled with user-provided data.\n",
    "\n",
    "    # The user can then recast the generic char pointers back to original format of args_ptr. In this case, an array of doubles. \n",
    "    # Seg faults will occur if this function recasts to the incorrect type.\n",
    "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
    "    cdef double l = args_dbl_ptr[0]\n",
    "    cdef double m = args_dbl_ptr[1]\n",
    "    cdef double g = args_dbl_ptr[2]\n",
    "\n",
    "    cdef double coeff_1 = (-3. * g / (2. * l))\n",
    "    cdef double coeff_2 = (3. / (m * l**2))\n",
    "\n",
    "    # Unpack y\n",
    "    cdef double y0, y1, torque\n",
    "    y0 = y_ptr[0]\n",
    "    y1 = y_ptr[1]\n",
    "\n",
    "    # External torque\n",
    "    torque = 0.1 * sin(t)\n",
    "\n",
    "    dy_ptr[0] = y1\n",
    "    dy_ptr[1] = coeff_1 * sin(y0) + coeff_2 * torque\n",
    "\n",
    "\n",
    "# Now we define a function that builds our inputs and calls `cysolve_ivp` to solve the problem\n",
    "def run():\n",
    "    cdef DiffeqFuncType diffeq = pendulum_diffeq\n",
    "\n",
    "    # Define time domain\n",
    "    cdef double t_start = 0.0\n",
    "    cdef double t_end = 10.0\n",
    "    \n",
    "    # Define initial conditions\n",
    "    cdef vector[double] y0_vec = vector[double](2)\n",
    "    y0_vec[0] = 0.01\n",
    "    y0_vec[1] = 0.0\n",
    "    \n",
    "    # Define our arguments in a char vector\n",
    "    # The size of the vector is the size of the underlying object(s) x number of them.\n",
    "    cdef vector[char] args_vec = vector[char](sizeof(double) * 3)\n",
    "    # To make it easier to populate the args_vec we recast it as a double pointer.\n",
    "    cdef double* args_dbl_ptr = <double*>args_vec.data()\n",
    "    args_dbl_ptr[0] = 1.0\n",
    "    args_dbl_ptr[1] = 1.0\n",
    "    args_dbl_ptr[2] = 9.81\n",
    "\n",
    "    cdef CySolveOutput result = cysolve_ivp(\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        method = ODEMethod.RK45, # Integration method\n",
    "        rtol = 1.0e-6,\n",
    "        atol = 1.0e-8,\n",
    "        args_vec = args_vec\n",
    "        )\n",
    "\n",
    "    # If we want to pass the solution back to python we need to wrap it in a CyRK `WrapCySolverResult` class.\n",
    "    cdef WrapCySolverResult pysafe_result = WrapCySolverResult()\n",
    "    pysafe_result.set_cyresult_pointer(move(result))\n",
    "\n",
    "    return pysafe_result\n",
    "\n",
    "result_reg = run()\n",
    "print(\"\\n\\nIntegration success =\", result_reg.success, \"\\n\\tNumber of adaptive time steps required:\", result_reg.size)\n",
    "print(\"Integration message:\", result_reg.message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c87d9c2d-a280-4a5f-bcf1-ed432dc42368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot(result_reg.t, result_reg.y[0], c='C0')\n",
    "ax2.plot(result_reg.t, result_reg.y[1], c='C3')\n",
    "ax.set_ylabel(\"Angle [Rad]\", c='C0')\n",
    "ax.set_xlabel(\"Time [s]\")\n",
    "ax2.set_ylabel(\"Angular Velocity [Rad s-1]\", c='C3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6741d823-f135-4c6d-bb2d-47cde7960b66",
   "metadata": {},
   "source": [
    "### Additional Args = Cython C-Struct\n",
    "See discussion in \"Advanced CySolver.md\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e57015f3-0ccf-4a02-becc-60027ca7741f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_d4bc01dc0f3accbc4586830a5c49ad06d41619e57ccc498e020a9d049f406bdf.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_d4bc01dc0f3accbc4586830a5c49ad06d41619e57ccc498e020a9d049f406bdf.cpp(25074): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_d4bc01dc0f3accbc4586830a5c49ad06d41619e57ccc498e020a9d049f406bdf.cpp(25075): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_d4bc01dc0f3accbc4586830a5c49ad06d41619e57ccc498e020a9d049f406bdf.cpp(25198): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_d4bc01dc0f3accbc4586830a5c49ad06d41619e57ccc498e020a9d049f406bdf.cpp(28062): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_d4bc01dc0f3accbc4586830a5c49ad06d41619e57ccc498e020a9d049f406bdf.cpp(28063): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_d4bc01dc0f3accbc4586830a5c49ad06d41619e57ccc498e020a9d049f406bdf.cpp(28683): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_d4bc01dc0f3accbc4586830a5c49ad06d41619e57ccc498e020a9d049f406bdf.cpp(28690): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_d4bc01dc0f3accbc4586830a5c49ad06d41619e57ccc498e020a9d049f406bdf.cp312-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_d4bc01dc0f3accbc4586830a5c49ad06d41619e57ccc498e020a9d049f406bdf.cp312-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "\n",
      "Integration success = True \n",
      "\tNumber of adaptive time steps required: 112\n",
      "Integration message: Integration completed without issue.\n"
     ]
    }
   ],
   "source": [
    "%%cython --force \n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "np.import_array()\n",
    "\n",
    "from libc.string cimport memcpy\n",
    "from libc.math cimport sin\n",
    "from libcpp cimport bool as cpp_bool\n",
    "from libcpp.utility cimport move\n",
    "from libcpp.vector cimport vector\n",
    "\n",
    "from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType,MAX_STEP, CySolveOutput, PreEvalFunc, ODEMethod\n",
    "\n",
    "cdef struct PendulumArgs:\n",
    "    # Structure that contains heterogeneous types\n",
    "    cpp_bool use_drag\n",
    "    double g\n",
    "    double l\n",
    "    double m\n",
    "\n",
    "cdef void pendulum_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:\n",
    "    # Arg pointer still must be listed as a char pointer or it will not work with cysolve_ivp.\n",
    "    # But now the user can recast that char pointer to the structure they wish.\n",
    "    cdef PendulumArgs* pendulum_args_ptr = <PendulumArgs*>args_ptr\n",
    "    # And easily access its members which can be many heterogeneous types.\n",
    "    cdef double l = pendulum_args_ptr.l\n",
    "    cdef double m = pendulum_args_ptr.m\n",
    "    cdef double g = pendulum_args_ptr.g\n",
    "    cdef double use_drag = pendulum_args_ptr.use_drag\n",
    "\n",
    "    cdef double coeff_1 = (-3. * g / (2. * l))\n",
    "    cdef double coeff_2 = (3. / (m * l**2))\n",
    "\n",
    "    # Unpack y\n",
    "    cdef double y0, y1, torque\n",
    "    y0 = y_ptr[0]\n",
    "    y1 = y_ptr[1]\n",
    "\n",
    "    # External torque\n",
    "    torque = 0.1 * sin(t)\n",
    "\n",
    "    dy_ptr[0] = y1\n",
    "    dy_ptr[1] = coeff_1 * sin(y0) + coeff_2 * torque\n",
    "\n",
    "    if use_drag:\n",
    "        dy_ptr[1] -= 1.5 * y1\n",
    "\n",
    "# Now we define a function that builds our inputs and calls `cysolve_ivp` to solve the problem\n",
    "def run():\n",
    "    cdef DiffeqFuncType diffeq = pendulum_diffeq\n",
    "\n",
    "    # Define time domain\n",
    "    cdef double t_start = 0.0\n",
    "    cdef double t_end = 10.0\n",
    "    \n",
    "    # Define initial conditions\n",
    "    cdef vector[double] y0_vec = vector[double](2)\n",
    "    y0_vec[0] = 0.01\n",
    "    y0_vec[1] = 0.0\n",
    "    \n",
    "    # Define our arguments.\n",
    "    # We now have a a structure that we need to allocate memory for.\n",
    "    # For this example, let's do it on the stack. \n",
    "    cdef PendulumArgs pendulum_args = PendulumArgs(True, 9.81, 1.0, 1.0)\n",
    "    # We need to pass in a char vector to cysolve_ivp, so let's create a vector of the correct size\n",
    "    cdef vector[char] args_vec = vector[char](sizeof(PendulumArgs))\n",
    "    # Then we need to copy over the contents to this vector.\n",
    "    memcpy(args_vec.data(), &pendulum_args, sizeof(PendulumArgs))\n",
    "\n",
    "    cdef CySolveOutput result = cysolve_ivp(\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        method = ODEMethod.RK45,\n",
    "        rtol = 1.0e-6,\n",
    "        atol = 1.0e-8,\n",
    "        args_vec = args_vec\n",
    "        )\n",
    "\n",
    "    # If we want to pass the solution back to python we need to wrap it in a CyRK `WrapCySolverResult` class.\n",
    "    cdef WrapCySolverResult pysafe_result = WrapCySolverResult()\n",
    "    pysafe_result.set_cyresult_pointer(move(result))\n",
    "\n",
    "    return pysafe_result\n",
    "\n",
    "result_drag = run()\n",
    "print(\"\\n\\nIntegration success =\", result_drag.success, \"\\n\\tNumber of adaptive time steps required:\", result_drag.size)\n",
    "print(\"Integration message:\", result_drag.message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4044656e-c299-40e1-b54f-ce6f01931a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot(result_drag.t, result_drag.y[0], c='C0')\n",
    "ax2.plot(result_drag.t, result_drag.y[1], c='C3')\n",
    "ax.set_ylabel(\"Angle [Rad]\", c='C0')\n",
    "ax.set_xlabel(\"Time [s]\")\n",
    "ax2.set_ylabel(\"Angular Velocity [Rad s-1]\", c='C3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864ebd48-b2b1-41a7-819d-ff0110b4d315",
   "metadata": {},
   "source": [
    "## Pre-Evaluation Functions\n",
    "See discussion in \"Advanced CySolver.md\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13202bf2-766d-4d59-977c-c0e9a96fae87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_9138999a69b1ea35f98f8614c03d533204edb47e27667122523e452dcfd94b6b.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_9138999a69b1ea35f98f8614c03d533204edb47e27667122523e452dcfd94b6b.cpp(25254): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_9138999a69b1ea35f98f8614c03d533204edb47e27667122523e452dcfd94b6b.cpp(25255): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_9138999a69b1ea35f98f8614c03d533204edb47e27667122523e452dcfd94b6b.cpp(25378): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_9138999a69b1ea35f98f8614c03d533204edb47e27667122523e452dcfd94b6b.cpp(28242): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_9138999a69b1ea35f98f8614c03d533204edb47e27667122523e452dcfd94b6b.cpp(28243): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_9138999a69b1ea35f98f8614c03d533204edb47e27667122523e452dcfd94b6b.cpp(28863): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_9138999a69b1ea35f98f8614c03d533204edb47e27667122523e452dcfd94b6b.cpp(28870): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_9138999a69b1ea35f98f8614c03d533204edb47e27667122523e452dcfd94b6b.cp312-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_9138999a69b1ea35f98f8614c03d533204edb47e27667122523e452dcfd94b6b.cp312-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code\n",
      "\n",
      "Integration success = True \n",
      "\tNumber of adaptive time steps required: 112\n",
      "Integration message: Integration completed without issue.\n"
     ]
    }
   ],
   "source": [
    "%%cython --force \n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "np.import_array()\n",
    "\n",
    "from libc.math cimport sin\n",
    "from libcpp.utility cimport move\n",
    "from libcpp.vector cimport vector\n",
    "\n",
    "from CyRK cimport cysolve_ivp, WrapCySolverResult, DiffeqFuncType, MAX_STEP, CySolveOutput, PreEvalFunc, ODEMethod\n",
    "\n",
    "cdef void pendulum_preeval_nodrag(char* output_ptr, double time, double* y_ptr, char* args_ptr) noexcept nogil:\n",
    "    # Unpack args (in this example we do not need these but they follow the same rules as the Arbitrary Args section discussed above)\n",
    "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
    "\n",
    "    # External torque\n",
    "    cdef double torque = 0.1 * sin(time)\n",
    "\n",
    "    # Convert output pointer to double pointer so we can store data\n",
    "    cdef double* output_dbl_ptr = <double*>output_ptr\n",
    "    output_dbl_ptr[0] = torque\n",
    "    output_dbl_ptr[1] = 0.0  # No Drag\n",
    "\n",
    "cdef void pendulum_preeval_withdrag(char* output_ptr, double time, double* y_ptr, char* args_ptr) noexcept nogil:\n",
    "    # Unpack args (in this example we do not need these but they follow the same rules as the Arbitrary Args section discussed above)\n",
    "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
    "\n",
    "    # External torque\n",
    "    cdef double torque = 0.1 * sin(time)\n",
    "\n",
    "    # Convert output pointer to double pointer so we can store data\n",
    "    cdef double* output_dbl_ptr = <double*>output_ptr\n",
    "    output_dbl_ptr[0] = torque\n",
    "    output_dbl_ptr[1] = -1.5 * y_ptr[1]  # With Drag\n",
    "\n",
    "\n",
    "cdef void pendulum_preeval_diffeq(double* dy_ptr, double t, double* y_ptr, char* args_ptr, PreEvalFunc pre_eval_func) noexcept nogil:\n",
    "\n",
    "    # Build other parameters that do not depend on the pre-eval func\n",
    "    cdef double* args_dbl_ptr = <double*>args_ptr\n",
    "    cdef double l = args_dbl_ptr[0]\n",
    "    cdef double m = args_dbl_ptr[1]\n",
    "    cdef double g = args_dbl_ptr[2]\n",
    "\n",
    "    cdef double coeff_1 = (-3. * g / (2. * l))\n",
    "    cdef double coeff_2 = (3. / (m * l**2))\n",
    "\n",
    "    # Make stack allocated storage for pre eval output\n",
    "    cdef double[4] pre_eval_storage\n",
    "    cdef double* pre_eval_storage_ptr = &pre_eval_storage[0]\n",
    "\n",
    "    # Cast storage to char so we can call function\n",
    "    cdef char* pre_eval_storage_char_ptr = <char*>pre_eval_storage_ptr\n",
    "\n",
    "    # Call Pre-Eval Function\n",
    "    # Note that even though CyRK calls this function a \"pre-eval\" function, it can be placed anywhere inside the diffeq function. \n",
    "    pre_eval_func(pre_eval_storage_char_ptr, t, y_ptr, args_ptr)\n",
    "\n",
    "    cdef double y0 = y_ptr[0]\n",
    "    cdef double y1 = y_ptr[1]\n",
    "\n",
    "    # Use results of pre-eval function to update dy. Note that we are using the double* not the char* here.\n",
    "    # Even though pre_eval_func was passed the char* it updated the memory that the double* pointed to so we can use it below.\n",
    "    dy_ptr[0] = y1\n",
    "    dy_ptr[1] = coeff_1 * sin(y0) + coeff_2 * pre_eval_storage_ptr[0] + pre_eval_storage_ptr[1]\n",
    "\n",
    "\n",
    "# Now we define a function that builds our inputs and calls `cysolve_ivp` to solve the problem\n",
    "def run():\n",
    "    cdef DiffeqFuncType diffeq = pendulum_preeval_diffeq\n",
    "    \n",
    "    # Setup pointer to pre-eval function\n",
    "    cdef PreEvalFunc pre_eval_func\n",
    "    \n",
    "    cdef bint use_drag = True\n",
    "    if use_drag:\n",
    "        pre_eval_func = pendulum_preeval_withdrag\n",
    "    else:\n",
    "        pre_eval_func = pendulum_preeval_nodrag\n",
    "    \n",
    "    # Define time domain\n",
    "    cdef double t_start = 0.0\n",
    "    cdef double t_end   = 10.0\n",
    "    \n",
    "    # Define initial conditions\n",
    "    cdef vector[double] y0_vec = vector[double](2)\n",
    "    y0_vec[0] = 0.01\n",
    "    y0_vec[1] = 0.0\n",
    "\n",
    "    # Define our arguments.\n",
    "    cdef vector[char] args_vec = vector[char](sizeof(double) * 3)\n",
    "    cdef double* args_dbl_ptr = <double*>args_vec.data()\n",
    "    args_dbl_ptr[0] = 1.0\n",
    "    args_dbl_ptr[1] = 1.0\n",
    "    args_dbl_ptr[2] = 9.81\n",
    "\n",
    "    cdef CySolveOutput result = cysolve_ivp(\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        method = ODEMethod.RK45, # Integration method\n",
    "        rtol = 1.0e-6,\n",
    "        atol = 1.0e-8,\n",
    "        args_vec = args_vec,\n",
    "        num_extra = 0,\n",
    "        max_num_steps = 100_000_000,\n",
    "        max_ram_MB = 2000,\n",
    "        dense_output = False,\n",
    "        t_eval_vec = vector[double](), # C++ / Cython require you to provide args in order even if they unused like t_eval_vec here.\n",
    "        pre_eval_func = pre_eval_func\n",
    "        )\n",
    "\n",
    "    # If we want to pass the solution back to python we need to wrap it in a CyRK `WrapCySolverResult` class.\n",
    "    cdef WrapCySolverResult pysafe_result = WrapCySolverResult()\n",
    "    pysafe_result.set_cyresult_pointer(move(result))\n",
    "\n",
    "    return pysafe_result\n",
    "\n",
    "result_preeval = run()\n",
    "print(\"\\n\\nIntegration success =\", result_preeval.success, \"\\n\\tNumber of adaptive time steps required:\", result_preeval.size)\n",
    "print(\"Integration message:\", result_preeval.message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ced2203e-13c2-41b1-83f9-b8e3d5e2d751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot(result_preeval.t, result_preeval.y[0], c='C0')\n",
    "ax2.plot(result_preeval.t, result_preeval.y[1], c='C3')\n",
    "ax.set_ylabel(\"Angle [Rad]\", c='C0')\n",
    "ax.set_xlabel(\"Time [s]\")\n",
    "ax2.set_ylabel(\"Angular Velocity [Rad s-1]\", c='C3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adca897a-8134-44ca-904b-93bec9e946ee",
   "metadata": {},
   "source": [
    "## PySolver and CySolver Solution Reuses\n",
    "See discussion in \"CySolver & PySolver Reuses.md\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "222a5b4b-33e8-4641-8a01-d439c34aedd3",
   "metadata": {},
   "source": [
    "### PySolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e5c9d6c0-3ae7-4231-a7f1-4852c57ddfeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Was Integration was successful? True\n",
      "Integration completed without issue.\n",
      "Size of solution:  360\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Was Integration was successful? True\n",
      "Integration completed without issue.\n",
      "Size of solution:  601\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Note if using this format, `dy` must be the first argument. Additionally, a special flag must be set to True when calling pysolve_ivp, see below.\n",
    "def cy_diffeq(dy, t, y):\n",
    "    dy[0] = (1. - 0.01 * y[1]) * y[0]\n",
    "    dy[1] = (0.02 * y[0] - 1.) * y[1]\n",
    "\n",
    "# Since this is pure python we can njit it safely\n",
    "from numba import njit\n",
    "cy_diffeq = njit(cy_diffeq)\n",
    "    \n",
    "import numpy as np\n",
    "from CyRK import pysolve_ivp\n",
    "\n",
    "initial_conds = np.asarray((20., 20.), dtype=np.float64, order='C')\n",
    "time_span = (0., 50.)\n",
    "rtol = 1.0e-7\n",
    "atol = 1.0e-8\n",
    "\n",
    "result = \\\n",
    "    pysolve_ivp(cy_diffeq, time_span, initial_conds,\n",
    "                method=\"RK45\", rtol=rtol, atol=atol, pass_dy_as_arg=True)\n",
    "\n",
    "# Use the result's memory allocation to re-call pysolve_ivp\n",
    "result = \\\n",
    "    pysolve_ivp(cy_diffeq, time_span, initial_conds,\n",
    "                method=\"RK45\", rtol=rtol, atol=atol, pass_dy_as_arg=True,\n",
    "                solution_reuse=result) # Set the reuse argument.\n",
    "\n",
    "print(\"Was Integration was successful?\", result.success)\n",
    "print(result.message)\n",
    "print(\"Size of solution: \", result.size)\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(result.t, result.y[0], c='C0')\n",
    "ax.plot(result.t, result.y[1], c='C3')\n",
    "plt.show()\n",
    "\n",
    "# We can change parameters too\n",
    "new_initial_conds = np.asarray((3., 0.1), dtype=np.float64, order='C')\n",
    "result = \\\n",
    "    pysolve_ivp(cy_diffeq, time_span, new_initial_conds,\n",
    "                method=\"RK45\", rtol=rtol, atol=atol, pass_dy_as_arg=True,\n",
    "                solution_reuse=result) # Set the reuse argument.\n",
    "\n",
    "print(\"Was Integration was successful?\", result.success)\n",
    "print(result.message)\n",
    "print(\"Size of solution: \", result.size)\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(result.t, result.y[0], c='C0')\n",
    "ax.plot(result.t, result.y[1], c='C3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbbbea83-b993-4bd3-9694-6deb01d07704",
   "metadata": {},
   "source": [
    "### CySolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87c45d91-94ad-41cb-8880-59459494e05f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_e8dc2852613e7617e71ec8816dba3ba288779843c465bd29263e43cbadf90870.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_e8dc2852613e7617e71ec8816dba3ba288779843c465bd29263e43cbadf90870.cpp(24996): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_e8dc2852613e7617e71ec8816dba3ba288779843c465bd29263e43cbadf90870.cpp(24997): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_e8dc2852613e7617e71ec8816dba3ba288779843c465bd29263e43cbadf90870.cpp(25120): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_e8dc2852613e7617e71ec8816dba3ba288779843c465bd29263e43cbadf90870.cpp(28761): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_e8dc2852613e7617e71ec8816dba3ba288779843c465bd29263e43cbadf90870.cpp(28768): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_e8dc2852613e7617e71ec8816dba3ba288779843c465bd29263e43cbadf90870.cpp(29126): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_e8dc2852613e7617e71ec8816dba3ba288779843c465bd29263e43cbadf90870.cpp(29127): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_e8dc2852613e7617e71ec8816dba3ba288779843c465bd29263e43cbadf90870.cp312-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_e8dc2852613e7617e71ec8816dba3ba288779843c465bd29263e43cbadf90870.cp312-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code"
     ]
    }
   ],
   "source": [
    "%%cython --force\n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "from libcpp.utility cimport move\n",
    "from libcpp.vector cimport vector\n",
    "\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "np.import_array()\n",
    "\n",
    "from CyRK cimport cysolve_ivp, cysolve_ivp_noreturn, DiffeqFuncType, WrapCySolverResult, CySolveOutput, PreEvalFunc\n",
    "\n",
    "cdef void cython_diffeq(double* dy, double t, double* y, char* args, PreEvalFunc pre_eval_func) noexcept nogil:\n",
    "\n",
    "    # Build Coeffs\n",
    "    cdef double coeff_1 = (1. - 0.01 * y[1])\n",
    "    cdef double coeff_2 = (0.02 * y[0] - 1.)\n",
    "    \n",
    "    # Store results\n",
    "    dy[0] = coeff_1 * y[0]\n",
    "    dy[1] = coeff_2 * y[1]\n",
    "\n",
    "# Import the required functions from CyRK\n",
    "from CyRK cimport cysolve_ivp, DiffeqFuncType, WrapCySolverResult, CySolveOutput\n",
    "\n",
    "# Let's get the integration number for the RK45 method\n",
    "from CyRK cimport ODEMethod\n",
    "\n",
    "def run_reuse_cysolver(tuple t_span, double[::1] y0):\n",
    "    \n",
    "    # Cast our diffeq to the accepted format\n",
    "    cdef DiffeqFuncType diffeq = cython_diffeq\n",
    "    \n",
    "    # Convert the python user input to pure C types\n",
    "    cdef size_t num_y          = len(y0)\n",
    "    cdef double t_start        = t_span[0]\n",
    "    cdef double t_end          = t_span[1]\n",
    "    cdef vector[double] y0_vec = vector[double](num_y)\n",
    "    cdef size_t yi\n",
    "    for yi in range(num_y):\n",
    "        y0_vec[yi] = y0[yi]\n",
    "\n",
    "    # Run the integrator!\n",
    "    cdef CySolveOutput result = cysolve_ivp(\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        method = ODEMethod.RK45,\n",
    "        rtol = 1.0e-7,\n",
    "        atol = 1.0e-8\n",
    "    )\n",
    "\n",
    "    # Run again using the noreturn version so we can reuse the integration storage.\n",
    "    cysolve_ivp_noreturn(\n",
    "        result.get(),  # result is a unique pointer; we need to pass the raw pointer to the baseline_cysolve_ivp\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        rtol = 1.0e-7,\n",
    "        atol = 1.0e-8\n",
    "    )\n",
    "\n",
    "    # Like with PySolver, we can change the inputs \n",
    "    cdef double t_end_2 = 2.0 * t_end\n",
    "    cysolve_ivp_noreturn(\n",
    "        result.get(),  # result is a unique pointer; we need to pass the raw pointer to the baseline_cysolve_ivp\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end_2,\n",
    "        y0_vec,\n",
    "        rtol = 1.0e-7,\n",
    "        atol = 1.0e-8\n",
    "    )\n",
    "\n",
    "    # The CySolveOutput is not accesible via Python. We need to wrap it first\n",
    "    cdef WrapCySolverResult pysafe_result = WrapCySolverResult()\n",
    "    pysafe_result.set_cyresult_pointer(move(result))\n",
    "\n",
    "    return pysafe_result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d2b3fb62-108b-4ac2-92ed-62c7166ead5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------\n",
      "CyRK v0.17.0 - WrapCySolverResult Diagnostic.\n",
      "----------------------------------------------------\n",
      "# of y:             2.\n",
      "# of dy:            2.\n",
      "# of events:        0.\n",
      "Success:            True.\n",
      "Error Code:         1.\n",
      "Status:             Integration completed without issue..\n",
      "Size:               705.\n",
      "Steps Taken:        704.\n",
      "Event Termination:  False.\n",
      "Integrator Message:\n",
      "\tIntegration completed without issue.\n",
      "\n",
      "----------------- CySolverResult -------------------\n",
      "Capture Extra:          False.\n",
      "Capture Dense Output:   False.\n",
      "Integration Direction:  Forward.\n",
      "Integration Method:     Explicit Runge-Kutta method of order 5(4).\n",
      "# of Interpolates:      0.\n",
      "\n",
      "---- Additional Argument Info ----\n",
      "args size (bytes):   0.\n",
      "args size (doubles): 0.\n",
      "Args Pointer is Null.\n",
      "End of Additional Argument Info.\n",
      "\n",
      "------------------ CySolverBase --------------------\n",
      "Integration Method: 4.\n",
      "# of y:             2.\n",
      "# of dy:            2.\n",
      "PySolver:           False.\n",
      "---- Current State Info ----\n",
      "t_now: 100.0.\n",
      "y_now:\n",
      "\ty0  = 1.40214e+02.\n",
      "\ty1  = 3.76947e+01.\n",
      "dy_now:\n",
      "\tdy0 = 8.73608e+01.\n",
      "\tdy1 = 6.80120e+01.\n",
      "End of Current State Info.\n",
      "\n",
      "-------------- Diagnostic Complete -----------------\n",
      "\n",
      "Was Integration was successful? True\n",
      "Integration completed without issue.\n",
      "Size of solution:  705\n"
     ]
    },
    {
     "data": {
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8cuMdyAjAsdp/HsGexQAGNUIwv3sAlNT/S9CJvPfee/TrF198kRx00EHkX/7lX8gtt9xCu3igXMOzKCNGjCCrV6+mX8Pngw8+OPT3wffZ92TW1dVFP4pu0Xo2tLjC4oINb8+HOE2LmY2FD0LsEk/RDmQ8oISeFQoAG062Hi6HsufBSgv2NTndeAdhSMjtHciIbCN2SSMAbNjC9iq/q2l3E8lWPAcFtCgATBYsWEBBxNy5c/3vTZkyhUyYMIFqVMDg84wZM8iwYcP8n5k3bx4FNAsXLiQ7u/n1bC4bsx38+ToyZkdAkQ9CVEYJOQsqWhcPTMbsLMB7gWfBLnFsQpzXgymwZ9oPbHFq0HmHLSiP67WwS16diOVhDEv1tH/605+Se++9l7z//vtUqAodO3PmzCEf+chHqDbk+uuvJ1dccQXZuHEj/fXVV19NZ6VABw/YAw88QIHIjTfeSC666CKqO/nJT35Crr322p2CIdENuqxnngU7vpXSlg98PRuDHg0r4PHqpmGghFlKiGdBmCUeAEpdBejiAT86vavsUdYpp7uAddpcX239vTBdELpujC/LYgq5+bVRBD8ghvUWYc+W/OfhSjwCg6Frf/zjH8moUaMo6/Hqq69ScMI6dy688EKK8m6//XbKqkCHzvnnn+//efjeySefTLUrwKbAsDbQsHz/+98nu4KxYMc6E+CyPNv1bH6WAWvZ5Ke62rJqwQHED4LCKK10IfrBz2NhwADTDyoa9p4HpuYCABsLuijrg/ejt5c0kzrr+yWUVHh7FvQP9svD3EGIuWe55IZ1eGHEMB7Md2L6UcUBR8T3gmGp4OAXv/hF5fehi+eCCy6gHzID9uWkk04iu6KFDqFeD6BYXkj8LIMdiEr8oFUwoEfZ7cZ4jEEBgm4JgFJfIQCbn50iAzYfKCH60RfK1mvQyrJsz1I/amrJjr5uFPDK9kpDUUB0UfxAbTMuBaWm3QSguLt4TM2YQEK6fLBj4lQMP3i6ODiQ8bIPdMYgRBcz4IgNlArAbKH7EbyXwI9adNYTzDr7ypdlUfdKvKSB23aNCwxqeCYH8b1gmAMoprNTpBIPX0cGs05bs3kb3AGEOcocO8gIh08hHsjogE2od0Ce0ukDFNslnuDfY/okDD/4siwqaOT2bOBHdUFYz2rkMnVf7K6zXdkcQDE1FAxpg4d94OhiyxkI3z0TKOARu0WwSytcFw/zowG5QwK3xBOUAP1DCHk+jV/WQNSN0W4zVpq1zeQIdEE4IDoeR9HBPCsPI3cidrkSj7M8hoJ1I5d44AAqlYjvh+0N7rcscqgfkx7lARsqbc2VeHC1MNwaxSx58doPDIaNZxzZ+rDsB1+WBSCNxWyJdEHQ/sx3GVn1g64Npl/DvGgUYhg++9qLuDawzDEoxsoJyCUeQCdeRxGOH3wXD37XCv9OMPyo9v1AFuuGgFIBRMOOxg8NBAPDKq+IyrIYfgQTXEukuwDJDTbrWcN38SAyWxjmAEpeDzJC0wbUJE5phQU7PwNBKjXxwAACD58t2gVsvFgXOQtC1VzEaWt0+rwA4mXMi+l4H8CwgLQowcIU6/JsErb2AyuOFknYjmEOoORk/MHLLyQsmtYPdmglnvgBhBPsBDQtchdPEQSIdIIrIjAQAjaEEk+IYUOi8XkfwPBKPEE5lGdQUGMH4jDBYrc715DdwRxAMTBsiafisLpnWIknEHfhlZow6eKQ4A6TMRC1CqK29+J2aoRKXohB1y8nlIJygvW9wvkAhjWDhC/LgmGJ20UTXBsQ12gfNutZFWejHYPiLNMi4qdSYirxWTaGJdblS03gCys5oWWnyAKzcFaI31aLDtgEANZ266Q8qcAbDYCqQYnEDqySqEj7gSPkDt4L1hqNdgBixXMscwyKkRIPN87c8sZiswzwSzxByyL1A0s0zAkQCzGYDDkrDHUmILZOCgEb4lBDzC4vXhcEhgWkebaR+oHU0hpmPTGHTYavQUBjcqq82NGLy+RgmAMoBoYthVprkerI0RIPZhcPWDCsDccP/iCEd8Vnz1b84Jgc1GxMwFxglrzKWhjcUkJcC4O9Z5G1MMwPpDb0sC4IT7/GtztjzciJtV0j7lkMcwDFQImnrD7H6p6JdPFgAYMIXYw9+p8v8aCU3risEDPYhSa4onZI8IANt5SALV7mNRdgeM8jHDvQ/Yjo17CYHH5toOrX+hyD4qzCRdTjbWqsbKwo9exgbDayWFcwbwPjUC7a5XihaaGIV8iHOjWQ9orvB1JphQl1oyUeTGE7GNbUUj6G8cMm8YAS9o3s1Z4frs3YWQ40HBj6NEjkEk9tTVUxmByua4V1aVA/EDs1CqGF4QboFeY2Y8RbhDFvu47uWR8YFGVEAbYfyLdM82C+sQ7zZvg+VKCEYa7EY4ymLYoSHysbC9PFWFkQaxVkYl20e05E7c6Yl+NxOinUIVgA2HrwSzyADbDanaOsZ2HKslgscFSTg53cRGY51SFdJtmLHDswzAEUA5Q1GFY2JhenIrU7YzM5vhizGFkhL/wDkazla04iomG8e074/cIG6GHtFb8si1xKAO0apvYjWpbFHrlfmGSvILOceumsHryhhhjmAIphBTwmLUn9QBoYV5SJtnz9FlWTExojzt0ybf1iumJ08QhvVS5KKQGtJR95QFrkTiC0dmfJe8FigXnWE5NR6kEWtmPY7vG/RBSn4tG0xZipEM2CrN9zEgv+2BexBcOnyn5UI05wLcAFaIitk7EJrmh7RTLqHm3kfnSvIN0nFn0viDeyA7vFmDZU9rXHlXicVTjUpwgHYay0ghxksJkc7HtOwiUNfrYDphYG7wI0/sZa/FJCQbQfflkWe4JrZAp1Lc51HawsiwYcZY0P1vVr1QJhuyvxOMu4iIpEjwbBH+lOoKgfaK2TuNqgGFAqwDAu7AMZWzQsKyVYH7nvlxLCXTxYewW9LCvRr9nX5Mi0MNXowvbq6irrwyYxzJV4DNyaW4R6dryLp7Yg9X0cJscP/tgdAREhJNpV9vzFY7V2xbrRm7+LMMQPDGuAXrBnsa+nkMQw7NiB/jxw251rBNdC7C4sigMohuvZtoFB/EbSYmRBhWvhRM/WcTtX+NuMweqqa3BGzPNTOouik0KcnIoJDGJ+YIP5wtzjFZ2GjVOWLYN5TmC/G7QaO4BiiB4NSgk4dGCMMSiI8A87S0YvrRSEPucvQLO9PqIlHqyym6+DiZRlsbUwhSvLIk5w5f3A1H4UQgvTB7fClyj7Sf3YDYa1OYCS8wEUm7dRkLt47AODgtyMGqHP0QSIEvEy3kVsEbGuxecRneCKTeFj37gd8wN5dhH2BNcg2QsnWXW1yMkeVtJZFWWB8cTtts0BFOODyXCCDBv6hAcMijEXJipARLvnJEIXo90vwgEl/p4TqwwKJ3jBbJ2UjwbAGmpYjNHu2DODpNoP6zODoskeTgdNbbSbaDdqNXYAxcBQH1yaVlJKsH0BWqxVsA+lQyIYTNZXzHtOkIAj5lwYXqiLOyNHDKLxroUIg1frI9WLdl2H/16QBtfJRjYURAvT4BgUZxXP20Cmrdk9J3VFuQANreSFLfwryCWOBQh2MrBm+1COUedIFH7sNuOe3b3EE06ysO6MisaOopSHu7irMnZ1cwyKsRIPzj0n8aFPuNqPnoIwOQGzhTwEC3vSsKzLy2qJR+wDFlCKd/HgUPh+WRb7vijkLh6pNgjpWogghuEK7HuQgSOGOYBiqIunm8252F1b9GJ0MdLziJQTsO45iQIlNPFyjfhQtpmNyW7ctl3miYN5bAofmcmJlGXRSxp+dxVWiSfSxYN1J1C1DEg7gOIs9SKKHkA4tGRUC4M9vhtr7kdMYIY99Amxi4cdQNjrIwrm+XtObO6XWCs8WpeG7O4Z3BvI8a8gYJocbNYTebhiVTGANIY5BsX0vA3rUxijAjNcJT52iUd+CGEHu17U+SOYWpjoXqF+IBzKcgofu5SAK9aNz3KyPZ8GX8gtHv2PNFyxuhgXnmKYAyi72j0W0S4e9BbOYgCDoPSGG/zhNmOs7DQ8wRWvtTZKWWOV3uK35gb3nPCzWnafazKignKsjrdIWRarxCOb8Gv5vdR5/x620B/DHEAxTAfavuck3qJXjFZBrLkfxRf+YU1wja4PhO4ZD6xhdWrIuniw5sJg7xXZPBbM6xh4P4oy9NLm2qjizg7sDkAMcwDFGNruQbnnJFpaKdywJey7eJAH6GEGu+gEV7QunsgaxbrnJHqRJH/PCcZcGPxyqLjjDfM6hrIf+EMNi8h61jkGxVmlBxBaNhYZz1yUCa7FCXZI46oRhZDRCa5Y2o8o24g1QC86xK9814n9Q1k+OXV3LfHgMxciPzA0ObVcUhETtjsGxVn24UI4sx0KcyNpUVo4o3QxWoeETPhnX+vAz9zoRrjnJArWsLL1qIC67AfmXJgwMMAuy6KVVmT3ViEL2zuLJmyvcSUeZxm1Dvw9JzY3FmsljYkgd1ORbCwrLMgEV5/JQWAMWEsv1hCsaG0f61AObjPmxbp4o/+j+jWYWcO3hmMlWbZLCdH1gXFflJr1xLu3CvNqCAxzGhSDtDVqCye65kLcZoydFTKaFu2ek5jwrxbNBywaPwoai7JXCjP6n9OvYTyPaDkU+zZjPAYFv925RlDiwZqxhWG7/v8QSWCGFexkXTzW7zlh47vRJ9pGhX/IQ7AQtTBC7QdKaSXexYO6V7g9i8FsxUoaaOVhya3Ku/twxYII2/tcicdZ5dqPvkLUs6OoH/sQwmudxA8yvB+YnRpCEF2AkgZ2h4SoxGNzr7DBZGzEPJucav95iDUX6NoP7A5ARGBQEynZUz98EO1KPM5yoK1t1nBlWgcs9Tn2cKEiDFtSC91qkSe44pV4eKCEcc9JtL0XDyiJgCPe4DrsbpHYRFtkoBT1A+Peqh5ubQTJ3q5fAEn1P/z2t79NnnvuObJ161ayZs0acscdd5ApU6aEfubRRx8lpVIp9PGrX/0q9DPjxo0jd911F2lra6N/z+WXX05qdnJFsijIoIiqIkwOf88Jhvoc+wZOWYmnKLMMUCanioABCrsmGHW/O2thkJkcWVkWDmSbwyaLdl0H5siGWtUa3cnPTB1Llb4dffTR5NprryXPP/88qa2tJT/96U/JAw88QPbdd1+yY8cO/+d+85vfkO9///v+r/nvAZ159913k9WrV5PDDjuMjBo1ivzxj38k3d3d5Dvf+Q7ZWS2q/cC7yl4g1u3tpR0BGDM3inIBWjA2Gy9TDwkQEdg1ZXsvMlBCpc9FZVnkkhfGoRx9HrwWBvzgy8U2Sl7R+SPoo+4Ru3h6hWB+1y/xpPofnnDCCaFfn3POOWTdunVk1qxZ5MknnwwBEmBGRHbcccdRQPPhD3+YrF27lrzyyivke9/7HrnsssvID3/4QwpUdkYTIV2MQ1lIn/f2khZSh3oIsU0FrZ0QkPnnhHGLMEaXRtmP6MV0uKUVjPZeFduI0SHBQGNRgAGaWFdRHm6wCFCK0N7Lt3fHWU/clvwudxePng0YMIB+3rhxY+j3zzrrLApcXnvtNcqyNDU1+d+bPXs2/X0AJ8zuv/9++ndNmzZN+O/U19eT1tbW0EfRTNWZgNKpIZztUI1+9wwYBpMTlHjwDmRs4Z8/9wOZLlbeZowxSVbE5CBrcjDfS7QcavtQlt2EjiHkRh+uWFUMYTuWZV51VVVV5KqrriJPPfUUeeONN/zf/9Of/kSWLVtGVq5cSfbbbz/KjOy9997kE5/4BP3+yJEjY+wK+zV8T2QXX3wxZVeKbML7RVBKPIKaJcY9JzG6ODzbYYclpiyagWCWNMKTddnleEWhi5FLGiglnjhQwmEuVBNt8cqy8AnKK/AsMEpemCMKwqwn3rUQtQURtmNZ5icNWpTp06eTI444IvT71113nf/166+/TlatWkUeeeQRsueee5LFixdn+rcuvfRScsUVV/i/BgZlxYoVpEgmvEIe8Z6TcIdED/ohBJ8h4EAWj8Hk4JZ4goDL4gxGiUcEonHbe+OdCRgdEthlWSFwRLwKIdrSCgDFLmsg0X4ggDXqhw+UPPYVGcx37UYMSqZVd/XVV5OTTz6ZHHPMMYlA4dlnn6WfJ0+eTD+DOHbEiBGhn2G/hu+JrKuri2zbti30UTRjC5oN08G65yQ65AjrKnuRH8GhjBD8ES9AE05wRcwK2bwN3o+iaD+KMxfGflkWvQMwMsEVT3ch7ryzOWxSNMEVI45WR1quw344gCIEJ6eddho59thjydKlSxMf8AEHHEA/A5MCNn/+fDJjxgwybNgw/2fmzZtHtmzZQhYuXEh2VhMFGYxZF8FdPAJKEEX7gdzVJGl3hgBk656T4gwmE3TxIL4TEVDC0cIIJtoiaC6E7CuGHyIgXYDbnW36IRK24zQ9VHk+8HHUlXikZZ0zzzyTnHrqqZTFYMwHgIuOjg5axoHv33PPPWTDhg1Ug3LllVeSxx9/nApjwaAtGYDIjTfeSC666CKqO/nJT35C/25gSnZWE2s/8IYtYQv/oq2C6DfWRoIM9aO2hrR391jzAb20oixp4F3HgHUgB2sU+XmIBPaITI4IKFm9JkPSxcPWx/aubqslHr8sizg8rxf5ChUsS7Xqzj//fDJw4EAKOKAcwz7OOOMM+n0AGNA+DCBk0aJF5L/+67/I7bffTk455RT/74CsCcpDvb29lE256aab6BwUfm7KzmgB0sU9kKOTZPH8kIu7bOkMIMZEO1cw7jkRZ0EF6RZB1KCIARvCQYhe4pEDJbvCdrk2CKPEw55HaNikpXXqM6/ctQOY91b1IccOLKtN27mjsuXLl5M5c+Yk/j3vv/8+Oemkk8iuZKouHmwBYlFmXdjeWPwE1x7Ee06KkgUp755Bae8tbruzzXtOVOJljPciLMuiz+spD5u0tWcLMzOoWr42bArKsWzXH+aPOsEVv3sG+56TPt4Py4ey6A4cDKpWJT7EKfFg3yIs1zrYBQbxveIzBhhZMrJeS1SWxREvi9aH7T0rB/PAyvIaFetgvteVeJylXkiCmQoIDIqKxscOMrYP5ZDQDTErVIkPATTauudENcEVZeR+QYABdndVoMnBLr3JBZk2/RAOFERKbkRgDQco9fm/hzX6H8Mcg2K0VRBhKJjwvhUMoKSg8S0zF9idGqqAiwHYsLt41FoYjGnH2MBAUeJBLyfgaD9kftgC0iowb7c8XOX5gSvkxjIHUHLv4ilGzRK7vi/U5FjPguIaFOqHf1FfNfrEUpysEJcuFl/H0IN4zwlu8C8CmOf9CJWaLE/WFU1wxRg2KQLz8H5YfLfOvvbhgmgscwDFxm3G2OJUxCFYokMZhUFBPIQC8SFyNqYou9md0lkU0XAxuquit+ZSPwoy4ZcBA9trtDglnvClplgscO9u2sXjAIqVYUu4w6dwskI5k2OLpg0mp4aDjO1DmR1A/GAyds9J2Q/bnQnYI9VFtDXC3USi4YqIHW88YOsuzJwcu2XqsLAdr6tJBNYwWeBeAZiH7/HPa1c0B1B2uSvk5V08GPecYLZOinzAOJRFWRBGsAu0MAWZnCro0rDZaSaiz3GF7dglHnlrLcYEV8yRDaLrGDBieo2iA9CmH1jmAEpeD1LBGKDPHylIF4/tkldxsiAxULIe7JjGQKD9sOqHsCW/GHNhcG67FnXxIFwmWYBygqwsW4QuHuqH5ZENtYoOwN1BKOsASt4CM+R2MGE9G2PYksdOYN4/IxIf8n7YYg1E5S7MwXWi7gibjJJo3gYboIc9M8gX62IAJezkRlGmtg6i0fdKMVjPGsEahf3Lns+urkNxAGUXbRUM34FjN8jwl/CJ71uxXL+NBpmCMTnW24wFwwTtArZizNsoTJtxwUYUYMYwURwN3QyPqP3g/bD1XqolzwPj4kIMcwDFQgsn+rAlJO2H/HZnvFkG1A/rwU7C5FhurRWVEmCZ2B6SVpQuHtHN3zgj5uX3VmHf7mx7CrXIB4wYJmNfbTNbtTXxsizW3CAM27X/dxZNGGQQhW7C24xRWgUFTI7l7plokLGdBYky9XDnil2xrswP2wBWtFca6+yPuufLshillQAo9aEK20VlWawSj7QcWhDWExPMh88Wx6A4q1Scit3FYxkYsOxDPtHW8j0W0SBTuHo2thbG9qwL9VwYW/eciMoJKILyAlwWyF+3IJxdVFOQvWIbREu1MLZZz1LYD4R2eAxzDIoN7YfVIViCAWm222oTWgWttdVK6rf2s0I1MMBkLqgf1gfXyTN16oftcoLoYk2MSbKIwvak9l58thFHryX3o3q3Yl+xbNf+31m0IgCDwoyYT2oVtJ19yIABshbG+j0nCXSxLZ2BqtMMpUMCkTHg34twxDxyWdY2yycH88VgcoKkE68lP9wh6ko8ziqdJIsywVV0VbndUkJU3IWVfcSDjF3NRZLwD/PiMYyppSLABuvEv+fEevDHLvHg36ocnuCKN2lYdMM0rn5NUh62DdhK4hKPm4PiTHMhCTQoGBegCe/AsSzGTBhyZG1ze8+iL+oH2gRXcRdPYTokEC9AQ+mQELGNqMMV8bUfZT/wytRsr8jbavEmuIb8sAyie5CF7VjmSjwWplJi3HMiZnJsAQOZ9sO25kIyrtoDBrbbjGWtgvYZA+QhWAXRwqhKPDbvOVHeWIvAeoo0OZj6JAwmpyisZ60UzDuRrLNUC6lY95yImBx7GgPxQdjdY/dCODld3LdbCv9qEvywr4XBnhaaINZFpPExhe2hqxAsl2WTu3h2M/1adTHAPJY5BiWvB6kABigdI6L7RWyjfulwIeRszHL9NvFAtrw25EOf7A7BkgEl28yWqMSDTeOjtfciz9sQ3fxd9gNpz6KzntVqRsmVeJxVqv2wtaBDI+YRR91LJ7gitdXKbjO2X9LAnY6ZfAjZBtEF0cKUxPecWC8nIHa8JbF8tt9JXPthWUdXI94rzI86ZDDfjTClHMMcg2LpnhMbC0k+Yh5H+xE7CAs29AkbsFnPkquL0TopGjFP/cAaoId8z4mqi8e+sB13pLqsLBswBnZ1dLG1Yb3EU10IJgfLHEAx2Lli+56TkNBNdBeP7VuEZQch8oVfWFmh7EC2l40VRQtTDGDg36qMTeMrGBRYG/yUV+tg3tev4Wo/unoLpv2wDOb7pB2RDqA4q6RmaTErlA1Is1/iKUbdVHogF2AwWSG7eNC1QcWY0mmz1Zgvy8pG/9sAbDIBdTfSMEHsttpEPxB1UliXWmKYY1CsZYU2SjzqcdUwY8DGPSeyWQaFKfH0FEWTgyOSxdfCJIF5u3Ny4tm6vfUhZT0ti3Wl7axYtwjLmBzbLDDyvVW1BWk4wDIHUHahhRQOdvF2Z9tMTvRZ2L5FOKmujj2YDE+8XCqG5kI2J8f6fBq8Qzk0wVXCoFjRryWVeJDLsvZn5BQMzPfhsp5Y5gCKpSy53nKJh48zYbq4ZrebFCobm12YwWTWL8eTvRfc0gorJ9TV2r7NWCLWtQgM0P0oCJhPYj2LUg61rtcqyUb/O4DiLM1Ckman9oJMD9fezHxih5KdbqJiZEHBbcay7AO3lGC/xJPUxVNbjEPI0nsRDVe0fZW9jPW0z+Qk3Jpba0esmyTkxhaU227vrS2IsB3LHINiKVu3GeyiixlLrIueBTEKXwKUbE/plAYZ662TBQFKsgF6yOJlm4JMXjeGqXdIKvGA1VXvRkxO9c7B5DQ4gOJMbyGpOwLsAANxwKV+ILQ7y9gkW/ecFIYxkB2E1ksr6o4Ae7MuCtIhIR1c14NS4pHe1VQA1tN+DBN33qFrP4oClHpdF4+zHG6stRtkxAHXNpOTNDnVlh9Jbca2D8K+wgv/7E4tjQ/jwumQiJUAMVjPSFk2Wl7BHlpna50GHYB4pfIitffWSMG83XIolrkST95KfEm2brO0oirx2Al2au2H7ewUe95G8sh9S4PJpHQx0hAsGZhHvoLAKjCQ+ED9sPheZF2IoWGTFtud5WVZXIE9mtC/D/d2Zyzbtf93lowXumEOfZIFGfslHnE2xj8bm/V9WZCxzxjI5m3Y1cJgz7qQ6R2Ckhcuje8DA5sljYgPtg9lGWikflh8L0naj8Y6ZGE71tTlEu6wSSxzACXHIKMc+lSLG2RsZkEyzQVYR7f9DgnMziqdYGf9CoLYgWz3ArRE0bAFP+CVFGGgoIxdw9KvCf2w+TwStB9gdRYAPbsGoa+oItleV+JxlqnEI5sWWosbZGxmQapSk9Vgpz6AamuqQ6PGjfmRpLmw3cUTK2kgXYCGWOIJJRWSLh5s1tMmkNbyw0qJR639sPdeinGLcE0imN+1OYZd+39XgBKPVQ1KQUo8OnSxjfKKnC62e8u0TIxp/Z4T6fhurBIP3uh/2QTXsh82SzxyYTtOiUee3Nhg2JLKskVhcmyD+T7kPYtlDqAYvKTPtiBTJ8jYAAYygZn1biLJleloo/+R57EkDX2yD5T6CsmgoIB5UVKBMIUam8mRlTSg1MI6nWxeQSDXryGD+V7GerouHmfaA8EUjIGVEk9ykLEClLSEf9XozAX2IRRk6sgTXLE6JFAv6RPfImxfFCovy2LoxkR+dBXND4uxFLu9tyahA9CJZJ1VxBjYnI4pu2vFtlhXJj6kfqCUeESMks0pnZIhflhzUApT4sG750Q1wdXXGRSki8du5x1ukiW7zRgruZEx4hDjbAybrJWCeXs6KUxzJR7D4lT/ArSiaD+sBBmxAp76YVGQKaOLbR/KiUHGMjCQASXsG2ttTgsNlWVlg+uKUuKxWQ4VJlkY7CtyV5PsdvqQFgaPFe+yLGzHslQr7tvf/jZ57rnnyNatW8maNWvIHXfcQaZMmRL6mYaGBnLNNdeQ9evXk23btpHbbruNDB8+PPQz48aNI3fddRdpa2ujf8/ll19OanbiB60HDHCDjF2aVjXRts+iJqcYHRLyejaOWBfzvijeD8xpoXxZNro8rAq5dTreLO7Z6DuxDwx0GBSLwEBykaR1oNSHq4XBslSnxNFHH02uvfZacuihh5J58+aRuro68sADD5Dm5mb/Z6688kpyyimnkNNPP53+/OjRo8lf/vKX4B+sriZ33303qa+vJ4cddhg5++yzyTnnnEMuueQSsrOaSvths8SjnKmAIE4VBzt7ugulaNgqja+eZWAfOIoHpOEL/zCGGqqE3NVok1N5P6xk6hrlYZvsq0qDYle/Fr8ZnmkNMXV0XZY7ALEs1Yo74YQTQr8GYLFu3Toya9Ys8uSTT5L+/fuTL3zhC+TMM88kjz76KP2Zc889lyxatIgccsgh5NlnnyXHHXcc2XfffcmHP/xhsnbtWvLKK6+Q733ve+Syyy4jP/zhD0l3dzfZ2Uy9qewtJFkpwfoFaCrtB8rQJ+S5MAndM+x5bEMWye5O7c562g+8Vnjb48x9RknBNtbV2ijxyNnorsIwOT2kqbrOcomnL+YDmNOgKGzAgAH088aNG+lnACrAjDz00EP+z7z11ltk2bJlZPbs2fTX8Pm1116j4ITZ/fffT/+uadOmCf8d+DtbW1tDH0UymfiwKFemUz8Q7tMQBhmUe06KUeKJdnnx95wUAShhX4CGAQyKMsFVvFcwxLqK52HhvbAJrko22mqJpyAlrz4xmLc1bBLLMkPiqqoqctVVV5GnnnqKvPHGG/T3Ro4cSTo7O8mWLVtCPws6E/ge+xn4dfT77Hsiu/jii6nuhX2sWLGCFMlkKNd+HTmZycGeJMvuOcG8zZj6gTBzQ9xNhCD8Q7y0kB8xL7ubyMYoc+XkVH8wGV4pgfqBAthwJ8mqBtf5bHRBYqlNFrgHeZYTlmXegaBFmT59Ovn0pz9NTNull15Ky0fsY8yYMaRIplrMKJcFCulRi0yOhtDNbrArxgVoQk0OxhAsSYnHxqEcvrcKT/inPgiLVeKxynoil2VVoxIwyrLiEmCPtc7M2gQwv6uXeTJFo6uvvpqcfPLJ5JhjjgmxGatXr6ZdPKz0w2zEiBH0e+xn4NfR77Pviayrq4t2BPEfRbLCXNJXECZHJ8jYKa0UY/S/1nwaxHHmNseIh++tKuGLQgsDorHbe4sF5tUlHnvvpQ+Z9ayWxA7bwyaxrDoLODnttNPIscceS5YuXRr63oIFCyiYmDt3rv970IY8YcIEMn/+fPpr+DxjxgwybNgw/2egIwjKQgsXLiQ7oym1HxibuzDtzqqJtngXfvF+2AWOuOPMkya42ngvhbm3StXeiwCi1X7YA2ziDkD7dxOphjxi3wyPMvq/JL8Zflcu8dSmLetAh86pp55KWQzGfAC46OjooPqQ66+/nlxxxRVUOAu/BkDz9NNP0w4eMGhLBiBy4403kosuuojqTn7yk5/QvxvAzc5oqlZBjK4VdYteMdqd7QoQVXRxUVo48YIdu+cEBHem34vy3iqrXSsa4tTd8BZhkR8sW0cvUyP4gZ3c1Cb40VhXu0szKKmi8/nnn08/P/7447F24z/84Q/06wsvvJAe1Lfffjst90CHDvtzYPA9KA/96le/omwKDGuDP/v973+f7KwWZB/Iiu8aeatg0bQf2FM6gw4JG0LIgggQlVlybxmgGAZsoRHziBegaQ0EQ94rgVi3KGAeuyyLcMt0QVjxPuW9Zg6g+J07SQZdPBdccAH9kNn7779PTjrpJLKrmEr7UYTbe9HEusJsrCD3nBRkwi+OiFq8TltIXSEYFOjyAV9FQGrXK/GodGMYs4sKAgzQ34silvYWLHbU7roAxd3FY1r7gVJawRXryoRd9qeF6mhQbE60LWYXj83yihok9VnXwognuNqc/ixnX2221RZl2KSqPFw4jRL2FQS9dgcsYpgDKKY3d1FaBa1ePFawLKggF6AVJdipgXQtog/27jlR39NkcWaQ6joGlD2rup4Cl33FuQpBMVkXmfXs3g1KPA6g2Jrgih1kMNpqkbUwbMIi/u3OGloYbBG1pUNI5QO8JxaIjbc7F0UnVVWQm78LAgyUk3UL0hFZtA7ABlficaZcRIWZc1GUiaXJmxtbc2EVKOmsDxvZaY0Oa2C6xCMPuGU/7LwXFetZmAs+bc7b0GA9d8cOQHyNUnUhkk4scwyK6WCHUOJRdYtgj7pHESAqNDnodyRZYnL4+zrEgM1OB40KzNs8DFXiVAbWwFfT95yo22qLAeZt6td0bpnGZoFt6tdqC6KjwzIHUKxd0mfzgitscaoiyNgctqQYGIfRPYMp/OMnuGICadVeoX5YOoR0NBd2nwc2+1qMtlqdsiy6NghBv9ar1BXuusf4rvs/s2g6JQ27WSH2DZzJWaHVWRcFofHVswzsdM8klXjMj7qXgzWbrZNJM2GY2Xoe6LfmqlryC3Dzd8gPmyUvJetpsSzbhydsxzQHUCyVeGwcQqq6qU/TWryqXBX8baD+wrROajA5xksrivkjPGAz394rB/M2WQP1rB7unhNELYzdkkZR2nuLpf1QxjDDrHgVV13EFLZjmgMoeTxE1QEUap2sRQv+VmnJojA5BZsGiSnIVE1wtVl6C/aKmEHpKkCJhy/zGGc9NcrDRRHY717tvTr6tWKwnvWuxONMj5aMb25YVwF7YWu2QzHEuqpM3W6JB3c6plqA2Id+SZ9N+lw1eMqqFkYhkg35Yassq+qeqa0JZdNYIwqwtR82Z0oF7d94TE6N50NyJ6Ir8ThTLaTE1kk7h1BQWkEGBkrhnz3UrzNAryhdPLbWBphomVovrfThMiiqtWEz+AclDTkwsKlfwx42qVMetjvRFq/xoSYkbFcM0HNtxs4qCna2hmApx3f3+fec8AvfpB+qOrLp+i31Q1FOsHtJXxFKPJ4PnL4CJyuUX2hJ/bAElPxSU4IWps7aXBj5GrUJ2JRttbtVey/+DeS1SWVZi1chYJnToNioZ1sr8SRn6nZnXYiCDMIFaMgti8qSl2W6OJkxwOtawSjxSIGStRJPcsebHYZNp1ukKHu2erdjPXt309uMHUAxrP2w2jqpDDL2ZzuIWgVtov7iXEGQzORgdq3wfthiUGRAydZkXRWrxfthvsQjX6PwW7bKK8pblTHanRX3Z9nRr6mmUNsXtveJGCVLQm5McwDF8ARXm1NLVa2CAFoYYLDF5ChLPNitgjbHiOvcZmzrAJIdyD12OiRUQ/xwtDBFYVCKq8mxKcZUj2zwDmSrWhi8O4FqqhLKshbL1FjmAIrhCa52SzwJTI6lWRfqLAjhAjTEMeK6E1wx21lxgBIyMFAIucProxoVONrT5OgNmzStyVEPvbQvsFcL/XcPMI9pDqBY6OKxPkYcub6vnMfi+QATEk3fc6I6hGwF/qRZBrbuOUkWchejxGOvQyKBQbHcxYMeO3yBfZJ+DQ9I40yhVnTP7CZgHtMcQLEgQLRW39dtnbTG5OAK/5RKfGugkZ9lgCi42wkmuBaqzdjyHJSkPWtvREFCN5FhwFatUeKxeV0HZrtzjTaYdwDFWSX1bMsalKTgb1pnoDPLADsDsQcaE0o81oJdUvnPdhdP0m3Gpq+FKMZeSYodAcNmegq1fH2AQJPpIKy1O6sE9hb3rLrdefcA85jmGBSbUymtdfHg+qEKMqF7TnaDMeJJswxsvZNq3QmuBdHCmJ6TkygatgXYErQw1gBbUjnBMpOj0q/Z0tBRPxS6sTpsAXWPvdH/WOYAigXth/3WSeRW0kTAZrdND7OLJ2mWQdEmuJoGBkmlFf8CNOy5MJa1H1LgaEl3kQgcLesuVFoYANs88DflA/aIgprEd+JKPM5ybRXEo2lDftQWI/hjtpKyIAPZh0mtrirg8n5gd/F0FSRTt9WpURT6PBCU4+5Z7dH/BWh3Nv08ku6tsj2bpgf5nWCaY1AsdvFYq6sn0PjoMyasZyBJY8RrERkD210auMFOdfN3yA9rJR5s4KjXxWMeOCaN/u+zql/DFNgXhfWsSXwnrs3YWR7aD0tZUHVRgIEuYEMUyfKtkyaDf5I41XamLtegMAofV/th6wI0Xe0Heiup9RJPQukNUUQNz4j9vsn1wU9wVU+0dSUe0+YYFAuTZAMBoukST1LwtwSUEgbX2UL+LNvDbJ0sXqbeV4zSinRt9BVM+4E8+t/S9NSilBNUE1xtxTD+qg5RjmVfn9Qn/L4r8TjTXEgFmUqpma3bUp9jzoXhdSWJwj8LdDG+GLMgmXoSu2arU8PXBvUp23vxR//bAWxJZVn7rbV4sUPXB4hzJm+Gr00oy9oStmOaY1CsdvHg1tXtBRl89XlSq6At1iBJjGl7YqkcKFnK1JP2irXR/8WY4JpclrXVWquroysGkDaqQdH0wbgfVcUQtmParvs/s2QfOvE4ctApJ6BPpZx33ufJpA/th+7HWZf9iAwcOkSrc8WUH1XV1eRrv/+l/+ukQ7nREDhobO1H/vnXVyYIl3uMB7shY8eQz/7sB1pAqdHggTzxQ/uRj174Va01atKPD50wjxxxxsfVfvSaXRtgc790NtnnsIO1yrImn8cZl3yHDBs3VulHhwU/vvK7a0mNtwdk7AXzwxSgr29qIudfd7Vyr3Rwe9bU+hg4Yjg554r/VMYO5ofJNYptDqBUaP902Y98GjiJtja1uWvq6sjxX/1SYrbOFnRTnZkF3X/4MDLzxOMSSzyBH3VG/Nhj/+lk4oxp/q9lz6PdsDD0wFNOIINHDKdf9xHZOwmyMVPv5eR/u4A0t7SoD6Bu88Hu81f/nNR5/8ekd2LSj3+6/BJSm8DktPvPwxyIPvHr5yX7wfaKoefRPKA/Ofi0kxM1KMFhaGbPjtlnCpl84If8AylJzG1qr8yYezQZNm60co3Cu2KTdU29F0g4+/VvTXgnvUZ9KII5gFKBNbQ0089V3kKWBf8g2JlZSCMnT9Tzww92hoDBATPCfsgOIe95mNpYE2fu7/ugzEC6zQa7UXtN8v0oSQZLQdmNMU1NBrMx/3lI6GAbwQ4Ow2rPjaS9YuqdAJgPr9GkvWLGj6Hjy4xFFXseMsag2yyIHr9fGchXJ+xZH6DU1Rhj15gPerHUjB9j953q+9GnGJAUAGkzfgwaNYJUs7gl+Td8HwztlSKYAygV2IDhw8oPMelANs1cDBsWCrqy7MP05h4wbGj4eSRkYw2Ggl3/YUPDAAXpEOo/fGgQdBXlG9OsQWO/Fv95VDc0SHzopp+b682A1yov2Ad+1KPQ1v298mMA5tXg1VTwZ7EjyQ/TsSPqh6wsazqpAD/CexaHURowAvzwfqGYVhsAaTP7pWXQoGCv1Iv3Snt3ec/uymUeB1AqsAEefV/lHYBJjEGjwU0VBkoJNK2toIvEoABAqebAkbyG67EGBp9HEoPCBxpzpTceKNUq34kpLUzLoIH0c7W3Nqskgd04mB8eBtF9kvdifI16fgRJRQKjZCp2DNP0w0KSxQOUxOdhzI8gudFjUMytj6qE5Ib5YPJ5YJsDKDkyKLKga7qUEA0ySXV19KBrISv0g4zi52wAJexg19DcTBpbWtCDHTwLMOZHleT/aloLE2VQqiWMkWmdVMA29qHrxtKUeKywjVrC0DrjyU1JsWdNamGqa2tIv8GD/OchS25saGGwzQGUCqx1yKBQsKuRUHGmN3erF3TRS01DvaDrbfBkoFRn7HloARSDwKC6poa0DhkcBF0Fg2ISwLYOCx/IMoACt0yzkpyJddoaBQZJDIpB0Ej//YQ9a1pj0BoBbLJDyPjziJW8EkTUhgAb+KFTljUO2IYFfpQUM05MJjf9Bg+m7edVKcrDjkFxJhT98cFOXt83u4iYH1VJTI6FjgDeD6xSExVjkmTmosMrrZjwo8lT4OuUeEwKVIM16r0LxYFrkj5vGRjeK0QGUDwfTGlhmqJ7RaKFMV2Wbe4ffh41kthhWgsTjWHJ3VU1xvzQEbab1NHVNTaQuoYGUuXFjhI3S8nm84jF85rkPes0KM4qDnZNtoJdY6PSD1NZED2US6XEVkGTQAno0abWfqTK+7dVNK1J4BgFKHoiWQPBLuKHrLRi+r00eWtUt7RiakhaFLDV1BcjqahpStizhmJHNIb1kSqcGAZJhcfe6LCeJt6LvzZY7NASyZpMKkrl31A8cxtt+ZjmSjw5AAM/yCQwKKZaBf1g522sWkmw80sJBv3gsyDZYWgyC2pqLR/Ifv1WxaDYZC5qcDoCosGuShFQTbYtRg/k6jp1acXYe9Hcs8b3igcc2XupkwGUHst+NDep/TCwNoC5qK2vD9hGVVLRbQ9E6yUVBvZsNLmpVSQVhgX22OYASo6IOzHYGVpETQNa9RgUg5k6MBd8Oyv1o7kRnbmQdWkYz4KiJR7FHTcmD6F40FUFO3PZmM/y9anLkCEtjIX3ImU9DTMocfZVzaDYYnKqGxusAwM/jpLkPetPf7YA5lUMikmRbJQRJwqgtKsPa3MAJYcFTfq8aaBIwj8/K/QCe5W0b94gMIgwF/TrBJ2ByWDX19mZTNMaZLaYH6WurkQ/TGphAj+6kzUoRoFSeX2UGEOioYUxApTYnvVG+0u7iQx38fhAie3ZBD9MPAvGXNCyLANKktgRAANzILq3w9uzVVVlvyyvUfZO+tie1WI9ze2Vvm7PD5096xgUZ7KFxA6hRHGqoZY0YC54oFRdX2t/c3uBv3PrtsA3SX3fZEcA86Ntw0Zf6Mamh8r8MMlctG/eUvYDSYnP1mjX9u3l39AQ3Jmsq/e079Cuqxt5Lz6A9fashKLn58KY1MIQ7/9aJdHk2NBc9DHwSmOYfdGwv2fXbyj7U1VF6pJ0dEb0Wt6e3bRZe8+aFMl2sViqJZI1e4kjljkGpQKD+RJgJW+RyOr7bBHBHTV1Od882dDsgROuvVeWBZnsnmHPopsdhAr63KQYk/nR4QWZvuoqUi+t75ur3zLQ2LVlGypN29SvH/3cvb2Nfi4pRbLm/GDPo7e9IxmgGOxcYX6wpIIkgHkTzwMAs88QMCan3j6IZnula1uQVNQklbwM+tHp7RUo8QC7o/TDwNpo8NZGB0sqqjXAvMG90rVNI6kwPBcG21KflkceeSS58847yYoVK0ipVCKnnnpq6Ps33HAD/X3+49577w39zKBBg8hNN91EtmzZQjZt2kR++9vfkhZvke5MBkOwqHmLRDqS2GCwa/TuA+rp6vJFsjI/TAY7di9Rd5uXIcMZlKCFMRJkPD+6vANZlY0ZPZB9P/SZCyOD2tj62NGe2MVjUiTL9kqfR+PL2MZQfd/Q4LpwUmFfC8PWBp9UJMWORkt7VjoqwSC7xvzo7Siv0RKpIvUIWhg/djDAVlNNL3VUJ3t15t9LTbJuzJV4PAMg8corr5CvfrV8bbrIAJCMHDnS//jMZz4T+v7NN99Mpk2bRubNm0dOPvlkctRRR5Hf/OY3ZGcyyIBqvM1alZAFwZXp/oVwOW9wtpg723b4IrOkYGeirh4chDu0aVqTQaa3o8OvZycGOyNBtyUElACgyIKdDWDQ096u3RHQaDArZKUVotFNZPIwDEor9XI/DK1Tf8/uaA9mKMnanQ3OhWFrtNvbs9SPBAbFKIj22DVgPWWxw6QWprFZkNzIGh8sgOhuL6lQ7RXTg+uwLfX/6r777qMfKuvs7CRr1qwRfm/q1KnkhBNOIAceeCBZsGAB/b2vfe1r5J577iHf/OY3yapVq8jOYH6gAwOAUlsjrd+yhQRBJu8NzvzooAClbFUJWZCJemUQZNr9zS0FBgbrtyzIUIBSk8CgmGQuWJCBYDewpRx0GxpIFwMKAj+MAjb4d/s16Wk/DAKDvq5OQpoapG3G1A9D76W+qYlO6KTW2wMCLlKj8qOnh/RrqM8d0AcAZUf5ducq+VwYnn0FLQwkO7mvUZ5BkexZk5m6v2f52JEwF8YogwKs58B+5eSmqVG4Z20wSj2g12ptJlVIwvZdVoMyZ84cClAWLVpEfvnLX5LBgwf735s9ezYt6zBwAvbQQw+Rvr4+csghhwj/vvr6etLa2hr6wDa/brqjPfGGVpMLiQWZzrY2H20m1ZHNaGHCqJ8Cg6ThUwaZC1ZKKGnUs43S5xyjhKGFCYJdcmnFaPBnpRVPkKkzj8UUcwGxJkn7Ebp1O2cg7ceOth3+1FIpMDBYHo6ynuCJvBxqfq/wXTz1iaMSDIJoj+Xrq9LQwhhNKjrKv6HQl5huQ9/lAAqwK5/73OfI3Llzybe+9S1y9NFH05IPy1yg5LN27drQn+nt7SUbN26k3xPZxRdfTLZu3ep/gP4F2/gsqCbhWmyTh7KP+kN1ZLUS36QWptcDKBQYYNCjzA+vzbjM5MiAknlg4LcZQ7BLqu8bBAZ9nR2JwMDU7c5wLxEDZ33eM6/C3Cs72oM9q0gqTDFbfFmWQR9ZiceoFsYruzF9krIsa2PPehqUskjW/lyY6J6lAnuEbiK2Z3s9gALtztJORMNDQLEt9//VLbfc4n/9+uuvk1dffZUsXryYsiqPPPJIpr/z0ksvJVdccYX/a2BQsEFKWPuh7loxeSj7ItkQQBFvqq7eshamurqKbvCtTA+QI3PRA6WV+iolMDA5YyKcjbXiBd0IY1CuqzcogYGRkpefFTJGqYYGu14PKNjITuv56aQa2g9TQ7AahXtW/E5MlrzYXqHJjfd7MgaF+tHtlZoMsa8+QKmuTjyQTWhhGKNE92xrk2Z52Fw3kT8HRac8bLIc2sF0dNUU4LOEyuYVBLt8m/GSJUvIunXryOTJk+mvV69eTYYPHx76mZqaGloGgu+JrKuri2zbti30gW2iLEg2SdakmClgUNq47hkNP3JuS2sQ1pElY7MttE6WfAalmjTIRv8zYGAyG/OCSknxPEzOhQkYJTYwTl5qMqXJ8TUG8Pd7pRUl22i6HMq0H4rbjKkf3Yb92N5Garyx7jUqgGLoUA5pHfw1qgbz9M/lDKSDA7kzea9Y0En5SQUAA8nof6OjEpigvDtIbpLeiyvxZLQxY8aQIUOG+OLX+fPn0zbjmTNn+j9z7LHH0hLQs88+S3YWa+SDHcEMdoymTW4VpH4YBkq97fw0SLXwD5icekX7bSV++FlQdZX0vZgFSuFukT6E6Zg1tbXBv+n7UU1qpROPzZS8QuVQ9psqDYohoMSYi442AAYkUYNieq90c0mFSmBvquTll2W9TF25Rg2Wh0MCas+PGoS5MNGxETSGJU0HN5FUMD96e/2261rJ+jB9Q/1O2Wa8//770w+wiRMn0q/HjRtHv3f55ZdTseuECRMo8Pjb3/5G3n33XXL//ffTnwfhLGhSrrvuOnLQQQeRww47jFxzzTXkz3/+807TwRNjUFiwS+gIADPVEdDTxinNNYSQprKxvk7GoFTLAQoX7PIua/iaC1ZHVgClQPhnro7c5x36Kj9MaWH4TjPWCq9zCJk6gCgwYP7U4QEDnvXUAQZ51/d9gMKm6iZqg8y+F1/rAMBA4kdPXx/p6e0zEzt8nVSg10oGBuZZT2AuZNoPU3ultqGBJhahPVstfy9OJBsxaA9++eWX6QfYlVdeSb++5JJLqNh1v/32o4Pc3n77bXL99dfTbh0Y7gZlGmZnnXUWBSoPP/wwbS9+6qmnyJe//GWyM1k4G/MQiiobMySqirb3gsk2FfXD0KEc7RZRMRdMC2PyEArEqRoHssHSCvHKSErAZvgA6u7o9Ndo+b3UWRXJ+poLXvsBbfmyIViGQTRlcrznoQIGxrQwzfGkQlXyMlYejogxy2tUJ8kyBJRY90y1Csyb37N+aUWDBc6/w0uUVFRL3wvbKyauYyiCpV5pjz/+OKlSXId9/PHHJ/4d0GYMIGVntjCDUn4eslkGJsfMB4O4IBtrJb2wqXQ6EwwF3RBdnACUWurr8tfCxOrIiuyDlTRyn7fRSDtXwKoYXUyDbtI9JzXGQHStBwZUDIoxYNAc3yss+AN4sgWUGgV+aJV4TAFHKMu2NqAPjKPC9gRgwGJYqwmxbrR7hpZ46q3OhYEzjenXQuXQpKnchuI5gOhmb8/qlMtdiceZeCHpBjvTQYanaTEEiBHth+ogtJEVBllQtaJ+22tEC+P70Nfn31arAmymxLr8gcwAik5d3RhzwWk/KI0vPYS6zZaatreRal+DogEMDDEGPTu8ORe6omFDfrAOr8SkwlT7d2TPqg5kU3NhQqJcXoOCxHrye7ZPI7kxdes2trnLAnMIunXeQlJNxzR1IPuXsLEZAopNFfYjb+aCjTIPBqSp/DChhYGSQYOnuve7ZzTo4rzfS2jehs9cJM8yMBvsGHOhAAaGtDCN3LTjGhibmnAYmtZJQXZa67OeeGCe7VkwWdnNqBYmcj9SUnJjblRC+AJHVVJhai6MD9Z6e0m1V36mwCBJnFpnY89WJQI2E2u03+BByiTThjmAUullcFz3jDIbM97e2xEAAw3hn6l5LMEURrnAjPphYIMzcKLbPcNrYfIsr4iCjDIbM5QF+YzBjjYOKKlKXoaB0g7NrNAwu1ZmPTWSCsPAgA01LJdl8UYU+EMNNZOKPP2AUqg/H4gTp2r5keN7SQsMzO9Zbq/Q5CbhjqSc18bhn/4k+dHj95BPfPffCaY5gFLxaPfkCa42pmOyMdGqA8ioFiai/VAdyNQPA7oL/1n09HCllYSgawA48oO4dLQfpkoa/Ej1Gg1gYKrUxAODALBB0K3FEclyfkBZVqapM11aYdqPcveM/fKwP5iML/FoJFl5vhe+04zdMF3C8IPTfoTKoZp3JBkvy9ar267z74Zs8p8HpjmAktEaImOiwWokUwdtTJLlAYpycxtY0Py8jVI3p0HRabs2HWQUB7Kpsgbzo9zhFfgho/HZOwEtTJ53JImZnOT2b6Oj3XXEusa6iQT1fUVZw3RJI7xndcqyZoAjP0xQR4NiYs/2dHX5t7EnPQ8TyR5/6WqoLGt5LoxsjdZanqHE+4FpDqBUmq1zQjca7CQLxVinhj9DgNWRNRkDA5ub+sFNYdSjiw0wF/zmpkOOVMGfjZmvNTJSPaT9SKBpsYOd8bp6jFFKmAtjsnvGM73SmyntR3BvlVZ7b47PA/49xmCVNMuyJthX0RpVzUGhP2vgefB7tkajLMvPhck3lnLXIFQFowGw1qhjUHZSC9pqAwYFAo10sI9NJb5Wy2KdmXkbLAtKYC5MZKe+GHM7tNUmz/0wDdh0D2QQ/pmYCyPVftgWp0q7iexqUBrZ1GVuZpBKd2E6O+3RZVAMXIUQSip8BkWdVJgAsCHmgrWgKwakWd2zsFcUcdJEDBOzjdXJAntDWphOrkKAYY5ByetKbO+6cpv0OYjL2LyNoHsmiTEwkQXF522UOwKQsjGafSQzBsa0MKIDOanUZEILI/IDGCXpgWxKCxMcQkHwB/q8DmWCK9/eqypFmhLJsvtdWPeMbjnUiJAbOs2830tKKkzEMJ+50NRrmfKDXRkSZhvla5T6YQAciPZsn8ZoAGOaHFfi2TktGFcdDCYjGvS5sSyIG0xmnbng6MCQxkCnnm1A+xENMnoiWRullTq7WpiUMxWMzYUJiXWr0OvqvR1BiUevUyPfeRtw7xj9t/1px2XBsFSsa1D7ocvyGRenZtizJsS6cXGqzlUIJjoAuWRPYw5K/kmWJ5Ll7ovCMMegZDAIJIH2gwMoCgalwyDq58ftqy7aAtth6yBMqCP7fphuFVTMMghdI28AOEaDnYq23mGgvOIzF3w9W5EVsncC1myYPtfpTMjTh5A4lWc9NcqyJtYGzNvg73wBs+lHWCeVXHajfhh4LyLdWBL7aka/JmuFT04qTLyXcptxVSJgAy1MtzcS30gMcyWenc/4K7jZ3TMBQKlXBv88N7dIjAkBFzI0VvqJ+1He3C02DmQlUPL8qK8zfhCq2q7b2Hsx4EcsyCgZpe78n4eQtpYfQqCFYcEuXz/iWaGKyWHvJE8f+HkbrHsmqSzb5rGSRvbKjvbQOyE6fpjYKymYCz+GGd6zSUkF88PM8wj0a0lstJFYKivx1Gv4kePzYGecE8nuhMZQP523wW6r9b4nOwxNBF1ZKQEt2NESD8dcKDdV/tmHTGCm9MPA8wjd+aIZZPz1YRg4loO/ArCZPJRjXU3qNQraD7ae8vKBvy+qN6EsawK8+rNpIiBamdx0mdgrXLeIZlJhZG0IZ+SomYs2g3uW10kltV3biumlJD9MvhfHoOx8JhN2gcmQf5uJICMZLgQmmzxoJNilHC5kDCjJgozKDyPAQDYgzfLzEGSF5RJPMlAykiVrlryYD3m+F3/eRnc3qfLaQ1lSIQv+Png10bXCdZpBK7zKDxMHYYh9rdFLKkweyLoXWloF8wnJjXkwX+35kQAcTQJpp0HZ+UxIw3nfkx1CRksanB+9JIFBscbkqEfumwgy/ORU7WBnmFHSGZBG/TBZeouKl1U0fs5BF0AIO3ih5FXHus4UfsANtey+lbzei5DF8feKvVKTSPvB7uOVAUczB2FwAIU63urkk3VN+MHuEoM1Wq1bamJJlikWOHLjttQPE2A+1HBQpdUBmPee5cuhrotnJ7RwKaEqAgxsBhn+bhEWdMtms64eFnbpHYRWS00aYt18hW5B8NeZWGos6EoZJXtZMlsbYN0cXZzoR87rVFz+09srIMaslhzaubTCe99LSiqM6KQiA9LAqiVCbZNrNFriUQnKg+QmTz2fQKybMEPJSKmJAbYU2qC2nPcsr7F0JZ6d0ITBzvtejUaQySnWCQVmvd6gNJt19bASP0D9gMSlYl1rpSb1LANrAsQEoGSE2fJbBfVmKlA/cn4vvCiUjTLXuZjOFFAK75UE5sJAV1NYuMySmwT21ZL2g3UTJbKvVnRSeHs2pF9TzCUy+TxCM4Oq7GpQWNyAcmgvtwcwzLUZ5yR084OMZJFs92Ye5Bn8mR/8YvaDbmJd3bzy3HapKcg+ilHPDg2uI3YZg/qmRh8chuaPJIxVz/t5iEA0ClASHcje91RzUNiEXzOlpnRJhTl9UnTPJrDABkpeYQ2K3TWa5VoI6kfOjBI/vqKTv4G8OmlEQb5+FGVIG5gDKDmhXD/I1GkEOwO0te+Hl6TaFNzJRJBgUiGk0RbOyBwUJLFuNCu02e7sz+np6yNd7eGWVpv1fVGrs1bJK+d16k873h4E/iCp0PDDIGBL3LPcGs2LfRWVZX0/klhgZIG92XJoeECazkyp3JIKrrTSsT1yN5FFHR3f4YVtDqDkhba9zW036HL1bKbELyVkY0aCHVvQwcA4X5OTAJSsaC5UB1DOmTqwFsBeRLVBSUEmb2aLrY0uT/eRuqvJIGNA/Ugq8ZjSoKQ4kM0+j+Ag7E3asxzNntdVCCJg0Fsqc0qy/WKiJb8S5sJ8K7zd5IYfX9HT2anvR94gmisNY5sDKDkpvgOa1t5C4udtsM3d4wc7NTDINdgJmRx10DVSasowYt6kwCxVHdkgMAALMzm4wCAYkGZPrCvuvFOvUepHzgBWNG8j2CuSsizfdm2y1ORlWXgalEC/BpbUhp7Xs6iurSF1DQ3ZBeW5leyDvQJWq+tH7kApnNxgmgMouWUf5e/ZrFmKWgUxgx29kZQBJa9NNEk0bL7UVK0U6+Z9ILMDiAnMdCanGvEjQtOGB6Spgm6+7fDh7oiqYI0mALbcS00CrYMOg2KM9eT2Sm8C+wqPyxTDxuuTguTGngZFdsFn2Q875WFWhoxpPxKmP+cezyPajxq+3dliJ6LPoLgSz85pvjiVDluKAAONhZR3kOmIBn9FkDEd7LRpa4PzNuhMBW5z60z4bTbGXOjNQcn/QA7uaUpT4sn7fhFxSaP8Pb1W0vyzU5/19NYoFqPkjyhISCqoHzlflSEa8sgYlKSkAg7wvC7IE5VlARjY7ERka6O7s5P09fTqj7rPeZCfivWstcmg+He8uRLPTmkiMWZPmmCXO+IOgr/PXNgsJwiCHfMjSYMCN+fmcUEeP28jKk61WWqKBpn0o+7NBDt+QJrNtmv+XiKf5dMphxryQ1gOxdgrIT+8vaJqaTXEKPFMju9HQlKR136pbWggNd7e56cdJ5WaTO/Z8C3C+GXZUtKoBEMaFFfi2UlNWM9O2FT8Qupno46s1cJZm+uC5sd3B0FGI9jl8DzYs+ju6KQ3xeq2f+cfZKKlleTR7kYP5B07QmJo63V1QXtvUinBhB/ismyyH6ZKTaI9a7NjRMS+BkmFOC6AAJ/dNJ3HOuWTCjgM2fPoLvVa1aDwewUMa9S93I8qLTCf+xp1JZ6d00S0dVL2YbauLmAuLNXVo/M2/CDT16v0IxTsctjgvMaA/rvseXj3rtgqNUXryKnFunmLMbkyJPWj2nJdXVn+s69BCQGlFEmFmXJoBBhYovFh3gbfeRfEDsYC2wGOPPNaKpUCJoft2YS2a2AF62qqje5ZMMby2BoNwEortZoCe3OMUjmWYpoTyeZUR0YJdorSiq26enTeRjTI2Co18fM2wKLBP0mDUltTTeolQtpKNjc/IA0CXdI9JyaBAfUjgcmxok/yD0IEYBBqM06xV/I+hHaISk21Vp5HfRM3yjz0XvqS/chxncpKGswPW5dJysqySbOc8t4r/D1NLCalGv2fcyci9ph7MAdQchJ2JW2q3LMgEKpx/eo1mqWV3IFBbN6GHl2cdwunLNj19PRaLTUFkzEl2Zgs2BVlgqtB+jx6ICsH1xnTa/FgXsOP3MW68WnHaWJHnsAASqFQEtVlG/Nep7I9253gB3y/u7c3/z0rKK2U/bBfHmbxPA3raaIsi20OoFSIdGNttZaCHQMn8dKKhh8WsiAdJifPUlMgxoyUeBKeB3y/ywMxuQZdgQZF5YeNAxllQFqzoK3W10klg+i8u4lESYWtsmz0lth4WdbOkEcpMPDKsraSLF5ATf9dltx44EMnuTHCoGh2eTEfGmprQ4Ail73iCcp1WM+8S031PjPvGJSd0hrYnS9cfd8v8ehMLc0F9bf48zZ6uro4P9SMQf5ZkFgUGmRjdg7DKOpnAaO7t8dyqUneKqjTOplbsJNNcNV8FmZ0UizwqzUGJqaWiu5p8hmUWktJRaTTLDiQ9ZOKvLUf/IHMgIGtJCu6ZwMGRQMo5bhO4+XhsB9JAnsTQCm0Z6vU12QYa3d2Itmdz/h5G6GhTzolDRP06HamdYhqP+zQ5/xMmNTZWJ7AgAONvB86tHWepSZ2EAZiXS/49yTU1XNu4eT9CJXdLAe7YH1sT8c2GmJyAChBa7sumDexV2DeBowzj7fk2ynL+msjuld0ysN5sq+yPasj9M8zuekXFdiz5MaLYRI/Ont6/bif63vh9mxIv8aBFrM3f4fXB6a5Ek/GRcQosCBTTz6Qt+dJj/qLOSIKZQeypWvCo5vbP4S8solWXT3XLh5ZFlRriT4XB13G5Mj86Ort9d9dnkwO38XDAj8b6411F4/WQZijH1BW8edtCJ6HtaRCBgw0mIsdRkF0Cj/yBEqxYYL6sbTNxp5l+jVL+g9fCxPtvKvSu3i1qa7OH1BZkR8R4IhpDqBUMEK81NeXqo6c72IOKGv670ZKPMqZCnlmQWzqoL+5o3VkOxqUKOqviQSZGo0hWLkyF9vDA9L8YGdp9gdf0mD1bAYM2P0jNoNdjMnR1UnluFeiNzvrdJqZKMtGu0V8UajtMmRkzwZg3k4M85MKGTCox/UjVckrT6DEMfN93rUQ1K+ETsS8ZltFtUGY5gBKXvRomiwoT4EZO5AjdWStYJcjgxIIzFIE3Tzp834yLYx6Hgv1w4RYt60tNCAtyArtlN6CUffxeRu6dfVKg1250yw4DOPvRKWTym8gGM/iwLwN3W4R8wdhRPthS68VY1+jjIGdA5kH0bwfPT1MN2bZj0js6PL8sFUeFrfk9/nfl/nR3t3jDwrNM9lzDMpOaLK7Vlj3jO0MOUrTatGjBkpN0SDTzYKMpbkwPD0aei9+sLNF0/KX4wX4v4vdY6JResuTxheVNKgfdeaDHS8K7UjZaZankDt+EIY1KNgHYdK1EOYy9TCTEyRZGoxSjnslCpR0ytQmmC150mmJBeaSX35t9LDYYWG/QCmUdZqxswXTHIOS0yVsWnSgAcGdVHluDRiISzw6DMoOC4I71kKsE2RyCXahkkawvXRo6zw7V0S390LghzIH9cNCsPNvdu7qCt3s7O8VzbtnKq008WwSWJoST76lJklJwzYwkLDAqcqQBpIbBpS6erwD2VIMC/R8baEBaV0ei2dPgyIqh5bo/in7Yf69RDvNsM0BlMyZqXggmFZrnIlNFdVc2AIGkhHz3SzIaJWacqybsmDnl5oYTWsnC+K7q4QMigVBJhwwfqdZJBvr9d69DQGiTBTaq9OCzpWamhRAJovoL1ZqsjWrJ3ogV4VZPluXJ0bZ13hLvt35I0yvFTyPFOLUPJ4HV4bkdVc6sdRIeXh7W6jjrdcDSjY0WwHL104H+WGbAygVH4RRgZlGa1yum0pSR7ZVz5a1GafxwwhNW4WSBfFBJi2Dktfz4DvNYMKvPyCtVPLpYq3Lx3IKdvEuDY+5ULROMmCQy/NokRzItst/XGdV2qTCyB04GdjGXEte0tihM7sox3u8JKwnSyqUbGNOyV74TrNIiaery1qpqUgzUMAcQMn7QLYVZBJbBetQ6+qpWgXrDPqhpYXpySXIwGHLd3nxwc4HBhqllUr94MuQZVFoIJKFUoutYBeI7aIC6iAzkw1Jg+aFvGZdSLtFUnTPGGU9dYTteWouYnM/wntF6xJHI9qgqDjVvgYllFSw5MaCrpDtWWAtuto7Inu2J9mPnPZKkQSyYA6gVHgQBq2C+kr8fDd3eHKqzxjo1PfznCQbDf46wi4LQrfu7hTMVqVBhrt+oBzsgtk0PRazdb+zigEDriMgDYNS8fNgB3Lk+gEeoNjororuldhsGo1nAcPdmiosRVbCGBiZ/izt4rEksI/NLgqXeGwkN9BuD+xF9IZpsC6dKdQ5s54x4XJfkFTY6K6KgmhscwAlt959jVJCd37Bjh/EJfTDsuYimD/iASU/2Nlt4YzR1t3J9GiQJdfmev1AMH+ECzIWDmSpkFuTQckNGETKkDU14bbaRD98IF1r5Hkw8KoGBuX9lA9wlAGDFGVII5NkPWCQZm2YZFC8/6uN8jCb41T2I8x6dvulFfO6sfjQumB2UdDFY55RKtIU2UwA5cgjjyR33nknWbFiBaWPTz311NjP/OhHPyIrV64kO3bsIA8++CCZPHly6PuDBg0iN910E9myZQvZtGkT+e1vf0tavAdTdJMOSNPp0sjx7oa40C1Mj1qrq0v86E6B+vspnpn29QPe3xE/hJKD/3YvEFXqh2p0t06QYZOGK/VDpscp17PZe9EJ/vXGypA6z6Mtr+chyU51GBQYlNWe0zoNRhSIdVI6XSv98pwyLCvxaL2THP3IokHJaY2yziq41Zm/fiB9510+azQmKO/TYz1zix2RCsFOB1AASLzyyivkq1/9qvD7F110Efn6179OzjvvPHLIIYeQtrY2cv/995MGbrz2zTffTKZNm0bmzZtHTj75ZHLUUUeR3/zmN2RnMNmwpQAYyA9kqKtv7ywfhq0N+RxC0WmQrJSgWszbuAO50hbO4DCMDDnyg678/7nVexZ5ikJBfQ7/J3bfig5gY++kX0NOWofo9QMcMNDyo+IgEz0I0zE52/Jao4o7X7T86MrHj/hBGO2eUf/9bJ22Vrg+pPM2NEorzAcAVxWXmiTvxd+zdclro1+F74S/fiBelk0G0ds6O+nn1pxLGnwZMmAbazXWRqXgVcyu9Wqynv5eqc93r2Bb6pV+33330Q+ZfeMb3yA/+clPKMsC9rnPfY6sWbOGfOxjHyO33HILmTp1KjnhhBPIgQceSBYsWEB/5mtf+xq55557yDe/+U2yatUqUmSLZmP1nvK6o7ubNCUsIraQYHNXvJAiE1wDP5KDDNtUcIgDi8LQd1qrqqriEHd5QdelCDLsQO6vuBsmy/UDdd4cg3CJJznI5OaHF+zYmHvongnqyDoHcl4lnmiw4+hihUYpeB55g2jWTcQJ/5TrtDMfPxQt+TWcFqHPAwqiQ3lEv5bcGDb2XmJzPxLaSGGAHuxZiB0wUC+zHxIWuDsNaMyJ5WPXD/B+dHYlr428wKsUNIbYxnqN55G3kJufg8LeS71x4Bg933YpDcrEiRPJqFGjyEMPPeT/3tatW8mzzz5LZs+eTX8Nn6Gsw8AJGPw8LFRgXERWX19PWltbQx9FaTNmhyHfzgoHt8zyYlCiNC0DBp0aBzIENzZCuRI/6pt4USi7e4b5kRx08zuQw5u7rjq4Z6ZTQwHvMwY507TsncBFgOkO5EqBUphBYeCV+qHD5ORW8grf6eGD11691sntnR5tnTNQ8t8LV3JVHULbDR2GsdiR8LzZfqkEsEErKxNzR7P1zpQMSiV3NTUIDsJgQFryGg2Yiwr3SiTRY+8E1qiOOHVbR15+hEsrLIZ19+kxOey9VArmo7OLdimAMnLkSPoZGBPe4Nfse/B57dq1oe/39vaSjRs3+j8TtYsvvpgCHfYB+hcsiwUZbyHxwa5a0jqZJyUYOwzZIaSBtvM6lNlihsO3xztcWfBnTI7KD/Ys4GI6vvZbOasV/F2dGpqL3N5JVOhWIziQ682XeKJBxgcGmvXs3Eo8EWDAA6Wg7ToZsOXFNjLmgu2VDo451DkMKwaOEVEoix06YD6vLDl6/QD1w1unHV5yo1ob7FlQPypgDaIgmmc9dfzwE72cBeWivaJVWjEEXrtp7PD2ioL1zJvZ2iUZFFN26aWXkv79+/sfY8aMQfMlFmQYY8AFOx2KtBKkS+9L8IJlgLgjWZACJOWVgfiju7mWNBb8OzUU8Czglv2oJOiKwVrZDx2BWV515HAnER/sQIBH/VBmp925llaiLF85K9QpAeZTWpGxjfR5aDA5eewVUemNAVjQJ+mM/g+AQaUdI2GgxPzoYHslYWKuv04rSSo8H7o7O/3MnAFHVlpRvRMAl6xDL9c9y7OeXTaBQXjPsmdB94pGspdbOTQym8b3o4/ToNRrgPmcy6G7FEBZvXo1/TxixIjQ78Ov2ffg8/Dhw0Pfr6mpIYMHD/Z/JmpdXV1k27ZtoQ8siwcZlo0Fh636EMo3C4pSkwwYJDEowaFcQRYkoAODoJscZKDOyzokKtng0U0VlBJ4elSDMciLufBAYxDserVKGnkHmUD7EQS7Ho3slAGlvLuJQiUenc6EnOrqshIgH/xNlwBrGxp8sO4DWB8YJD+LvA5D0Z71k5uuzsT4FQaODbntWZ719AGbhm6sobbW32e53sMDe1ZDG+TH84o7vCRlyN4ghqn1fN25ArZdssSzZMkSKnKdO3eu/3ugFwFtyfz58+mv4TO0Gc+cOdP/mWOPPZZUV1dTrUrRTdZKWj4M9Wu4eWxu/r6EKG2dFGTyoK1FLWk+be1fcJUQ7NjGqmCDR2nJMD2qE+y8wFxTWYdEdHKqsJ6tweTkFex8EF0bX6M6HRJ5iVOD98JpYVKIuXPTBgkYpaC7KlkrVdleaY6JQgNg0J04+j+v0puIwo8CpSShP3svlZV4IgeykPVM1gXl9TyE5dAUeq282FdhiafbYpIVYRuxrTZLmzE/1wSEsfvvvz/VkHzwwQfkqquuIt/97nfJO++8QwHLj3/8YzoT5a9//Sv9+UWLFpF7772XXHfddbQVua6ujlxzzTXkz3/+c+E7eEAUCkBKGuy6u2mg06nhVkTTChaRH+y8w0W3np0HXcyCDIjm/PZeXYDS1UWGk+ZcGKUOodaBHcjJ46rBKumQiDM5gdCN+aGndciJto76oSlOza29V1JqKs+FYetDBxhk9wMO/Og6FWanhjskeGAA86P459HuMRfsvTA9V9wPxrA15CqCjAKlpD2bR/ddsFeiAmo9USh0pEFnE4wogPWxYUd7RX74pXIvdsCsHp2kIi+dVPyqDp71TOFHzkBppwMo0B782GOP+b++8sor6eff//735NxzzyWXX345BTEw12TgwIHkqaeeIscff7x/cIKdddZZFJQ8/PDDNKO4/fbb6eyUohtbRMBawGCfuM4AFlKTccQtagXzhW5sNHdNDf2Q3UjpA5TGHEsrHE3b7qnbVXXTvLL1uPiQeycawQ7Oi60dnaR/YwP1Y21GgViUHg2B1z59TQ7rkIAhYZXV9yNMDu0I0JiTk9s8lihtzdX3NebksA6J/EWh7BDis9Pk91KJIFN0CZvPXHgsIgNsUoDi7e3KtB9xPxigb+/o0EtucohhwdA6VfdMstAfAEoeSVaQ3PCxQ183Rkc21Nf5A+QqZXIY69mjyTbm1mlWsDbj1ADl8ccfV7bRgv3gBz+gHzKDNmMAKTubRUV/fJAp6wzsdEgI/WClFU54CoFGClDyENxFNxVH07KsMLnEk4cfCmCgmRXC8wCAUtlhGClp8ECpL5nJiXZI8L+urL7PGKV0dLE5kSxfVzdcWvEOIGCO2L8p8kNPrFuJoFzFXPB71iyNH/WDZz398rD24Lr8YwdlPVPs2ZGkJadkL9KVqakbg+ngMLIBZtrAfskKUKJamMCPPt8P1V7Je3bRLimS3dVNFGT8jgAO+auHYOXAGCj82NFZzoK0hX8GxKk8UNKtZ+fL5PBah2QFfLhtsZK2a7ESPyx0qzXeISGb+xEW6+rMp6lAFFpf7x8w0WGC5aCrMQQrx1Z4ETAo71mddvjK6fMom0T98LuJdJmtHP0Qtff6e1adt+Yxy0kqbA+19yaVqSt/HsH6UDE55pOsuCYnznoq2UZuZAO7D21XKPE4gFJpaYXXGWgg/zw6JKJBJjwHJdBPqGnrTmM0LUy8DLqJ7LVOCpmLnrSiYQNBhhdjag7jyoVRitLnIdq63miHRPT6gRhQSjGMq5L2XlHbZJj1TM5O/YFxeQAlAevJd1eZ1gbFypBcUtHhJTdshEGiHyYOZE3QmLfAPsrklN9JcmmF+sHE7Tmy4qF25279KdSVPA+41RlkAUUSyTqAUsGBLEPcqmw9D81FtNU5fCj30hkHyYN9cuyeibVv6lHn1I9cBXdtcY2B7uC6HLVB0ZbF8JXpmoxSRj9C1w9EstMQo6Q6kHPokOAFsnD9QHwOSpe2H3mUVlhnVVRnoDP6P4+Sl4r11O2uygNER4FSqCzLlRRh9L/cjxxiWORADolCNdZGKMlqzE+Tw7fC92omWXmuD1GS1aOR+IK/bDhm1v3C9izIArraAyYe0xxASWFNrf3o5/bt2/3f89uMdUVVeRyEnh88UKqvTSdAzKXUFJn7kUX7EWTJJjY3n6lrdjVV0iERY3K4TF3jCoI8OiTqvTHmSaJQFWBjHRJlP+pzGSEu7a7SKa3kwVx4d0VFdQbBMC6zHRIqkWz4UNaYnmqI9ezixLl6MzdyAI6RoXX0nfjzRyzMY4ndn8XNQdFlUCrcswAGgb0QvZcuzVlOeXS98eMrimIOoKSwpv796ef2rdsEdDEnZtJoM65kUzX3L99FtGPr1vK/x9G0unqHPO6QaPL8YM9DNP1QdzjZgAqCrswPqjFIOdthQGMOz8MbJMjrk3QOZLAtFQLHZm+Nwlr0rx8IlXh0gSN7L9meR5N3X1b7NgGY59lGxRrd4q1REHFmnbnBkoqOrYEfaWdM5Lln+efBM1s6o/+3eJ2DlfnRP+RHmPXky8P1xtaoaK+IWU/1O2frIw8/dmyN+BEqu6mTikqfR1O/8hrlk1++xNOj64e3PrICWD8BRxyEGjUHUCo4CKM0bTB5MDnI5HIQbmGbKniNIUpQcRhu9vwYmIcfbHOHAm4Q7GotPY8gyMS1DkkH8mavxTIrUAL2hF0v4D8PH7DplVZCz6OpobJnsaUMXuNzUNL5AZ1NlfgRBvPVcSZH8V46enp80fDAxnKGmR3Mb1Oynjp7trK9Ek9uRKyn6lDOd89ujYE1oPZ1Rv/7eyXPPcuxnjrNBmBbKvQDZuT4h7IgduhqYSqNYU0D+vvlP3ajNr9Ge1L6kXmveH6wc6UI5gBKBcxFTJCpka0HWVA9yXoZqAwYUD80ZzvkEWRYNsaCDH85Hs+gqIJuHgDFzwpZ0BXOQUkKdsyPxoreCTx7Vr8N396bzo+sQSYa+GP0uUaJJ4/DsFkEUPjSisbwKd6PzMFfBAwE2akaGHT4gKIx4Y4rXWDAs566JZ48bruOrg++7Aamc//MlvbKgVJ0fYRZrWSwFl6jGfeKB05CTI4gdiQnNxUCA9G54oP5Xu3kptI9y/YK7we2OYBSAXMR6wjQCLpsEUHffNaugCgw4MdE6wpUcy1pCIBBibsgT+VHpZsbpvuyjRsv8ejPVKh8c8sZgyzBLmtpRQgMQp0J6YLdgByDXXgIli5gqwxIR9eoNEtWDsEqz7rIA7BFWb40o//zYFBiwIDbs2A664OVNCpiLqRlWdAFdWmN/vf9yFrS8NYoCHUZc8GPKNAV68KQx8rYRoF0IDQHpTtVWTarH6LYgW0OoGSqm8bryLp0MdDWnd7BXflhGAYGEERh+KiOEJJlhbnQxVvCQQZQP/2sUU7wmYvMmos4cyFicpLmoFRa0hBtbvEcFE3tR67Bjp+DoqnJqRigKIBSaKihHSaHZ5SEDJvm+qh4z26RsZ7JTA7zobm+LgRwMvkhYD3BdJicSsFrI6e5YABWNAeF+qFgrBiTk5X1FO9ZQbtznX22MQDzvdpr1ESShW0OoGQR/gmzMX7in52FJKVptUo8TEBZQ5oT/BUZqM7Z/zOaFQLqp370aNDFlZZWBii0Dlz9Fu5QYj3+Kj/ypEeFJR7DJQ0VXcwPE9TV5FQMDEJaGFF7r54AMbNYVxcoae7ZvDQ5cdZT/66mrM+j3ILeTwoMwBjrqVWWrZDlg46mgLmIA4MkP4ywnoI7cMyXVgQgWjQHpdbuuVIEcwAlp4XE07RJwa5SNb6qfgumM6UT2khBrJh1QbNnAf/n4HbWQBTKvmeatlbRo+XNHQR1HZ1BnqWV2lBHgB1gIBZyx8W6xtcoE9zJGKWUmpyKRcNCTY5eFw/vR3bAFgawzAfGeup03sHdTJX4AeMJ2GWnUdYTwBr1R+N58GxjlqmlScBAtwPQ757Jk10TjUpIGv1f4V4RPw/BHBRdPypco45B2UlNBgzSCN3AKgkydY0N/kKV1ZF1gh31o4JygpAxkNDFKj+YD411taRBMRwqDWPAi0JDdLEy6FamyRF3z4huaE0qreTjRwgoZRHrdprICuPtvfpCyPyCbnj6sx7rWcnzoMxFtFuEEwzrJhWVMo7sncCcC8aUyJObZGCQ9VBuHiBYG7XBXoHbnnW6mvJiPZPKsqYZFNEaDc8M6tb0I5/kxolkd1KLBv8GjqbthPs0NOjRSoWhbFPBv8WGPrGDHXwA0z2EKiknJGkuqI9aY9U76ZAo6keGYKdmDPoohcxaJ9VamAo3t6DUJMrUk9dGPkxOmDEIZkwEZTcM0TCfJfdY7a5iZVnonAtrlNL5kSU7hWFxjLmIt8JHxam6z6O+ghkoYrAGptNdBT6zQX7Z9qya9dR9HmyvZO2IVIF5fvpzchz1/MgRGIhKXjXaa8NpUHY7q21o8O+oCGjaAKCEka452lrVpRHVoCQPOsp+KIs2NwNsnSwr1ED+QG8zYWhefrD3wsTIOte3swMZJtpmoa2jnVXsLhvqRwoGxUxbrXdrborpmEyAWDldLGC2eoLJurqlpiwiamAuZHqt+HvRmwuTJalgexZE3Ozfi+4V/bbrjux+CEB0LLnRnkGSfc+K1obvR290zyaznlk7IlkZkgcG/PNgz4Lq15Sj/7uMlWW7wA/Nkft5aYNciWcnNPbyKHPhjSNmixkyU6gP697dUEmWrAIGPl2sDZSybyw/U98WHzzFgp3OTIVKBYhiYBAN/uYFiKLN7R9CPT36rYKV0sW+9mNrHChRPzSDnYESD38op52DkuVA5pkLGeup0/HG+5EFKIky5Ohe0e6uqmA8gKgsGwNK2lNcKykPK9ZGFCgZ7IhU7tnenvAsJw0mJ88uHh6w9WqO/nci2d3Y2Kbq4FqMG2qCDBlMOzvNobQiPJAZMEg5YyILMGDTD8UDsNIBpbwZlFiw09DCAKXL7jrJ5oeIMeBG7mtqYXyw1tBQEW2dBAz0mZzGbMxFRHMRA0qae2VrDnulu6PTXwPMh7Tt35WUmkQag2hSoa93yD4XJmh1jjMGaZObSvwQdXjF/EjZEZl3eZifP6K7Z4HJbkp4fyo/RGXZrhRzUPKb/uwGte10Jq7tB2JMMN3BPpVkp0nCLuqHBQGiMOhGmAvdOyQqyZLFbbXRzoQe40yOEBhwWZD26H9vbcD9M1kuyVNmY5Qu1mzvrYAxgAvpWEu3rHtG5zbjvA5koYA6wlyYZJQYmA/7Iduz9eZZT0F5OA1zQf2oiMlRlGVTsJ6VAliRsD3YKz2R0f91xjsiQ6wn9156UyYVWXyAEha7ONGVeHZCEwGDOHORdrBPY76LOeXmZkF3UAV+COni6PMweJeFkEGJAaV0OoMsz0MpGuaCTNJhCM+OXZs+yLvhNI2xbhF5K7xuSSO71oG9k+7OTv/CwqgmpyflXhmUF4iWaD8Sg78/FCynUoKE9UzsvMuDQRH4kVW/lmWNJmku0vjhr48MfgjLwxnXRyWx1PdDBJR6g+RGt9Osqa4udUckm/EVHUSKbW4OSsbbN8HqIyUe3SCz2Zt4OrDJTIlHV2ewyfejggN5i0awS3gezI9KgoyqxKN7KG/KIejK3ovu6H+wjex5pAx2wFwwZiQU7EIlHr1gt8k7kKHjJe0traq2SepHT1DfrzG4NsQHclBmSgNeN3mzfgbn5UfGg5CtjUr8EB3I8c47M2s0STcWbbs2tVdkyV69vz6y+ZE2lgLT2NivRSH07w2xjVA+VZXK2ZUMaZ8HPyUdmKOimAMoOYwjjm2qhIOQLeYhTU05100jQSZBiR/4kW8W1JnyeWzIwQ9x/TYdbb1xh3cINecU7KJ+aN4kvLE9mx8s8MeZi3hHQJIPIEDc4fk7OOU6VYE16keKIVj+AdTUmFqTkzSkLc38EbZGBzc35dr6HZuDkvQ8djCAksUPEQscPZC99ZGQ3Ph+5LRXsnY1Zd0riesjJZPDYkfaGBa+sHC7MJb2aOrXoCPSj+kp12kRO3jAHEDJIejGFnPCQbhhR870qERgpnsgD6oAKKmU+LoMSgAMKgFsIno0nTYoAErp/GhoDpgLZZasMfq/EgAru0sjPPTJG/1fU6Mc/Q+2IWPwV4HXaIdEMljr8FtJ0wohVWW3OHOhx+RkA9HJ5WHd0f/+gdxktjyczKCwAzmf2MEzBmA9mp0rWQ9kemGhSMhdIaOUNoYxfVLH9rYQc8HWRzfHeqZZp2nXh2jycxHMAZQcmQtdUai/uTNlY6K6aTgL0kX9DCgNyZB9COdccJsqzUyFSmhr3w++1CTTwtTpZ+tZ1gZ/YaFKo6QNHDMCg+hdGqIuHqPBTlHSKE8KDfZKUuCHvcW6q9LulyZhbb/8f+6KzchJ2ivtvianOiWVI2bXIuVhzVJCZUxOPHb4M3KiDEriGq2kLJusG/PbnROTm4x7NsRciGcXhWK6LpOTca9Ep7eKRLI6yQ1bp2n9ELWgF8EcQKngosD4YLKeVIuopT6DmEmohYmUmnQPQv8AyotBEQfdZNo626aqb2ryD1nxRVvZmJz0B6GauYgyObWGmBwZTQvXCFA/uAFpeus0K5OjEP2lFFBTPyoEjqI9Gy3xJB1ATJ8E3VVpuySEJR5utDv1w3ApQc6+eoAt6kciUGJ7pTE9c5FillNy6a29ojXa0dbmX1goAmxpGZSsIFoVO0rc6H/tmO5KPLuXicWpYuYiKTOFYUusLS0PGl/W3qtdv81NgxLJxjSfRxDssh3I/IWFlYz+D4Jd5YwB70ea0f8h5iI1gyIYIc7dFwV+8MFYXwuTVYOS3Fab5EP4UM62PoSlhGjZLWFtwMhx1tKaGcBqsHxpxJhpJx6rmK3oGtUWp6aNXxxzsSMHceqmjAyKrKQRSzoNM0pJsaPTj6V6yU1WNlqWZGGbY1ByOJDTXvhVyUJKcwdO4oHsBX7IsJsTfpa3+qZGP6CrWhZ1xalZOzXY/TdSejSl4C5rsBMxBqLWSW3A5jNKKQ9kxbwN/nmAiDaNiDrPEk9MQK2x7oLstPKgy8+54P2ww+QklxJqNZmctJ0aPHMhG+2exo/MolBvr8A9YjxY9oFS2jJ1RiZHBgwqZpRyEJRDBTF+fUlPKnY+K4iOloexzQGUHMWpugxK3tlplsmpYNu7uv2AkGaDMx/4CwvLfkjarnW1MBlp2iTmQhco+aWVjExOIlBKyeTkkQVFR7uHAKxu6S0Pkax07kedsnWS+uE9j6zAQDXnIg2Tw1qN0zM58hHz8ZZ89dro7Stl0gY19uM0F1tySG48H2CoIc/SZQYGkWRPP8nKVqaWHcjxLh49P1hyk7+gvDdlO3zWpMKJZHdqEwUZaaugRlboI90UC4m/sFBVR9Y9gLJmyTLGQD64Tu9AhkOsXwKY0Qt22eagBFqYfDQosk6NxHksvni5cg1K9L6oNJocX5CZkT4XlVaiuiCw6sTOlazPQzV1uS/V/JGsonIAX8HwvHjsCNaG3kGYVWfA1kbnjnZ/Ho+qpKEzVp3dQp6GyZEKuaOMkmbnXdb5NNI9m1k3llUkK5/jJBQv15pNbpxIdie1lkED6Ocdm1WTU/UEd1mDTMvA/v7Bzy4sFGcf3gFUq+NH+kNZVr+N0pK6MxXau3v86amp/GBAiRMMV5IlZy5pSOvZUeCoq8nJ1tKqMxMm1fCpCrNTnRJPmnJCdp1BvNOMdfGkSSqyMDnhCwu3K7pWuvT9yJDciMT1avGy2g8Au6zclAY4SjUX0Vjao1nS8PYK3ELOMw/JfsjY1/AgP222MWPDgbjsFr4vKk2JJ2By8kmysM2VeDQndLKFsX3TJv/32cVQ7ZGplHrBLv1h2G/QIPq5bfNmquxm1ugt6A7mh+Yk2aw1y36DBpb92LQ59PuNGefCZJ250TK47Mf2DcE7qURwx3xIOz3V94N7HrVwRbsnYowCJV0mJ+2B7K8Pbo1GgUHYD81ZFymDHfOD3yuy0e6pOiRSBH+4W4QBFP69xG+6zqKFSb9XYM4F/3+OX6ypz6AEzFYKP7w12rYxvGfjJZ4UzyPDOm0Z7K2NjZu0yrLJo/87/YaDNLE02LMRPyQMm267c9rkRhRLo3qcdMlNNgZFFtOxzQGUFAEX6FG4GVUGDHRLCSEhZBoGxVtEfMAFa2JDwrwglybYZRGoyvxgz8MHbGmEkBl0KMGBLH4e8fei9gMOT7j4K/0hFA+6DKzx70U72GWcnuoHXe4Q8kE0N+xJ9xDKOlOBrQ/+MPTXqPdOUrVOZqjvtwwoM55w2RtfigzWRvqybBZGqd/gwcKDMPpedEsaobJGiuch88OPYRn8yDKJWnYQNkTWRzDeXSOGZWBy+gnWKMy3YQxbLOlMZFDK76S5vs5/pln3bGPEB7C0k5fTMjlBTA+vD2xzACXFImpLBAbpRbJDmyvPgqJMTq/mwDiw9d4hNKwljR/xDFn1PHSCP/MjzfOQbSq2wQOAojf6n/ejUkYJLuxiFjBbesCA+QDTU1MdhgI/oiA6TdDdkOGd1DU2kAbv50Vso9gP9fNY5wmxU60Nb43u2LyF3oMUA9Hev52mLMv8SLdXxHs2CubTzIVZ18b2bHPFwCDOAuu1wof2bBo//NghSSoiSZbO81jfliWWlv3YFkoqaqVJReKtyp1dPuORKZZyfqiSitqEmM6eRRofoMOrZeCAGFAqgjmAkpGyFl48lqLNeK2nIUkTZFoS/OjoTjcwLqsfouxDhPx1B7WBrWN+9GvOD7BFssLaBn0/hqd6L3FGyQdJXJAJSm9qP3r6+nz6XNcPEGO2DIwDNiFA0dQ7sLUBMzd0BwqyvQL/Bq+TCtaoIOgmPA/mx3DvOng9PyRso6QsCzqRpDHz67Zn2bMsQ04A8947qUuxRlP54e2VbQl++FoYDT+C95L+efBlSOF70dwrWf0QlUOZD6Kkwlwsjcf0KHgNgfmE6x4YiAY9oO5AQSiFsmsvQD5QJHMAJc/sw5svQW+d9IRxMlu7PcumUjM5Hd7QKVaGYh0/uW9uSRbU6B14bHOzeRs6fqxpa6OfR1QIDGB4FRPrtmfxY3vZj+FpgNIQ73ls2CgtaYT80AgcaQ+hxtZWnzFLKvHo+gFX2ftZYXNz5oww5Af/PDr0/MgCGuU6qTBQ4ku2SX5UchDKgFKWvVLRgSxhcrL4kQUo+esjohvzk5vIe0mzV9LsWWE51L8GodfveOvyykc6fqz1Y5gekAZWht1kLGJfO7m94vuR8F6gTL3ZK/OM8P5u3b0CpVB+Nk0RzAGUjIs5XEoID8DSWUiZsg+BGDMc7MJ+aG0qdiCnyE6lWVA0+Bs8kMOH4UYhTcuCbpogk/YwpGJMryMgHGQEwCAFcFyT0g/GJsGNqLwYUwSUevzg35h7VijbK1HtR/gwbNTyAfQBAzSzQh80RoBSdM9Cpg46lTR7NgtzEd0rjJEKgFKHVoYc3rNZwHzkeUSAY1cKYMDA/Ih+lSUVoP1oiAKlVHsl/fMQl0Pj2o90yV660hvbs1De47tnomAtrR9pY6lMuFwEcwClIjFmXZim9S41Y9NW82dQ1EE3ln2YZlASSjxpsqC8slN2EGYNdqk3t1dWgZtIRZfS8aUVdiDXawCDtH4kMgYVMjnaQElAnYu0H2mAI2SF0K2RhmHzy6ExJqcuM5PD1ijok6BLS8daJXslChyz7JUsYF7KvmaIHZmSCmE5lNN+WGByErsyM7CNWZgcviuTN2FS0Wkulha1gwfMAZQcso8OrjNBd4OzRQR/h25La3KJJ5yN6W2q9KIqaV098jx0aUnqBwNsmrQkAEAGAkPdIsyH7h56a27Wza1L0/o6GBBjcq3f0XeS9RDSzU5l+iR/jXJBtysLgNUOunraj6xAulLAFtVchNZHgh8gbGctrbr7Rdp5JynxUC1MgjaI7dl0SUUSC8z86NAG0WkPQih7N3tizLaEjjcfGDQZOJC9jiaYgq3qyqR+eN/Xeh4eo6TPoKjLoR0Vxg7dPcvWaFSfVARzAKUiJX5cCKmLuGHxscvHdDeWLNhFW/SYDyD6gxKEDk0LSnydrLAsxhyg1n5kqCOvSctceM8C/q/8uH1VkNEqaaQUQsrYtYAxyHggp/VDs8MrtR8pg39Q4hEzKGwOSphRyp/J0QUGvB9J6wPwJxMhVsooRdlX/qBMZF+9ZwEtrbqTl2VJVnR9ZNJ+aIJ5iBtsaF3bli0xH0Laj3ZzyY0crIliB/NDXySrzfINTojn3QINigEGhbF8jkHZSS1x/kjFSFd3YzGhmwRxi4R/iVlhB+n1avA6bXowcZB1O4jaWUVamDQljfSBP/mdZGIM0h6EOlmQnxXmn50mrVGR9qM+hR/ps0JJ+S8jfe7rLlLuFdlAsFBSkeFQrlST0xDteOvp8aenJq3THd3d/rwenfUB5YxGD0Tw+wVm7EQPw1TaDw8YQNwAHYnuGo2KMYWdZqkE9jmxazmVeFInN2liR4NBDUrBZqCAOQZFw/oPHUI/b1u/IfT7oo0VlDXyPYSA9mWTMbdx3SKicgIv/EsCB5CxMMpYp5zAAr9MjEm/53cTpRD+ec+ipb6OfiT6MWSwkJZstFxHbvX80AGvqUpeGYGBjvYji85At9Qk80Oo/WhPvz6GV0qfV/hestb3pQBWtE5TdJvprA/2LOJiTIFei9srSZc4wtRSuI8HJibrzA1K806yMTkp14ZM5J+RbWSALb0fmwvhR1vBZqCAOYCSYLBJW4eUAcrW9eu1EXd9U76HIQNJAD6il/SJ+uZNZYX9hw2ln7euWy+h8APtRxofICPckSIr7D/MA43rNgg3d6iUkIFB0c0KmR/R59FQp8jU07Q7awYZ9l62rJWsURFtrbM2/GCnx1z0H8rWh/i9ZBfreroLTaAkSyrUJR790psOkwMJBRNjxvyoUKMUAMcW/TUajV8icSrHvibNQoGbldlo9RGVxA5BHGWgMY0WprWhPjTLRGatnh/R2CGKo0FpJQULrLtG/dgh8aM7vmd1WM+0gI3tla2RNVoEcwAlwUDUBfMlgJGIMhfCmmWK2ik7hEZqBJnWYeJFxI9n5mn8dBSpvh+yAzna6hwV/ukMOlqd4nkkASXG4pT90A92oDGArBCmuOoIIVu9Azl+ANXJ22pTCP90noUqyKi0HzprI807UR2GqtZJnfeSxg844NnlZ9L1kdGPNamAwVC/lMDfIBzyI4MWJtzim2aviNcG3AcEYIP3IW0M0/IjYY1mLWls6+zyk5tRKfzYogGU0oBXtkYBGIAmT9cPWSxtz/g8/L3i3aKtvT7WriO7PED5wQ9+QLsZ+I8333zT/35DQwO55ppryPr168m2bdvIbbfdRoYPH06KaizgQpCJDrFhpYgdGWnaVdvKC2mUxkJimWkU9fPlEFaXTov8V3q3rI7un8KPSJBp9jYV1MeZpRmCBbZqW9mPMd7hkiXYMT+E4lQNHyBQM8A2urU1cxakeh46a2OF907glladLq/+w9n60GD5UjyPldv01wZ0aTC6eGuMyfEAW8a9stIrTYxOsVdAaAmX9In8yBr8mR9jdPxggV+QVIgOQzamQO+9lP9fYzTeS6vPJq1P3CvQLs+mlua9PmRJRaV7lvdDK5ZK/QgPmszCvkKXFyQ3OuwFY3KkyV53Nj9YHB3dqpdUsPURjWG7LIPy+uuvk5EjR/ofRxxxhP+9K6+8kpxyyink9NNPJ0cffTQZPXo0+ctf/kKKav6BHGFPwFq8Bb3dG8ucVgi5YluKoCthUJgPkPVnpYtXpdjcrdIDmT2L4EBOI/wLB5kUTE7kIGSAjfcjzeYOATYtP1iQWSf0oy0EUPTZNXiXbNy9HmATZ8msy0O4RlMcQKAxSBp3D+AERmbDIRetqwv9SFFqYgey3gEkZnH4/cKD+Sx7JY8DObpO03SurEwROwYMH+b5IU5ueB/S7he2V3SAEgPRsT2riKPAvCZN5aZ++MlNv8zlP2HsSLFXQM/H2Aut5EaSZAXPozuTH+xZABhPugQWZsKwu7NE+2WXBCg9PT1kzZo1/seGDeUX0L9/f/KFL3yB/Ou//it59NFHyYsvvkjOPfdccvjhh5NDDjmEFNECrcP6WBYEbX4x5iJNkEmzuSXBLthUweZOmxWuYJtbJ9hJ/OjnlXAY1ZrJD5/JadUurUQ3VQvzgwcGKYR/4awwRZCJBf/6+EGYIsjwDFvSIQTjslltOvY82IEsGpCm8U7gtmsmsE3yw98rGzaGLujLAziyAxlKPEnaID8zjRyEPFASApQ0azQNUIqBxnpxUpFpr/TLfhCyNcqtjZAfOuyrn61nP5DZ82gTaC7SxlKtPTtcFsNEa0OfiU4DlKAT0r+wUBJL2/g9267/TqCcu94rRSbFdAZeoemBZ7x3aYCy1157kRUrVpD33nuP3HTTTWTcuHH092fNmkXq6+vJQw895P/sW2+9RZYtW0Zmz54t/fvgz7S2toY+bFlwEIop/FiWnIouThHspJs7fgCF6uoaTE6aze0Hf2n2EQFKLPjr+JECKMmCv+og1L0EjQX/xCBTV+e3TsaAo7c+RAehjtaB+qGZJTPwKgoyPojuzM4o6R7K/YcNk4rtGMMm3CtamosdtB2+tiaZPpftFT6pyMp6BgeyfmklbVKhI7APaPwUrGdEYxCwSZI9m6bEk4ptjDBKrFQu2bPp/EjzXsQscNaybKgUmRA7mA9QToMhj0L2tSvbuUL90GT6gvJf8co7RgDKs88+S8455xxy/PHHk6985Stk4sSJ5MknnyT9+vWj5Z7Ozk6yhRvSAwYsC3xPZhdffDHZunWr/wHgx5YlHYSQBQlFVVolnu3+kLQk+pwFmSja9gN/BTRtqqxQFnQFB1BaP3Q3N+2sYhtLJwtKKfzTDf6qziphFpSCuQgxKAnPQxb4ZX6kWaNpskJZZxXvx4486POMz0OaVKSq77f5h2rSbbEyP5KSivyTG3X5LxY7siRZWqyn+DAUlf9Auxjc8JwfQIFhcewWb3mJJ1s5tOyHHuspazYIx/Suiv1IKokG3X/FE8gaASj33XcfFb6+9tpr5IEHHiAnnngiGThwIPnUpz6V+e+89NJLaXmIfYwZM4bYMmlpRYC2wboZFZc3fS4JMoFQN+xHmsOQHcg69Dl7HrHN3RDPgtLXTvU2dzMfZCLaINHmTiv8Y4AtaXPLOqtkwS64pC/fbEwV7IT1/ZRZoa5GSQWU8tQGJTFs0jUqSSrS7BUoyWzQ1AbJhdwy7Ye+JoclN4Obm0LzTNJocuQalBR+aAIl2lnl/Uz0MBTptcp+pIgdmiUvxgDDLJZoZ5UIsLHSCsQb0Fcl+pGS9RSDeUGS1Z4uufHF3EmxY3djUKIGbMnbb79NJk+eTFavXk27eAYMKI9KZzZixAj6PZl1dXXRjh/+w5bJ67eSzZ1iemo6+lwSZGTBLkWWDF0rOvQ5lIvY9eCxICMNuvrjzHU7NdizEAWZ5GCnQ+Prbm55kPHpYgFjoCv809UGyTqrpHRxSiZHVxskY9cA84reS5qbc0NASRuwicWHcs1FWhF1v0ydVXnoteDyRPZOVesUDlU21FCmk4olWSlKkWyvwCA/VWstW6OdO9pJp6ePiPkReR5pZqGs0I0dipkfovWRln3VBkoqP0QC+85sXU26QKmIHTxWAEpLSwuZNGkSWbVqFVmwYAEFG3PnzvW/P2XKFDJhwgQyf/58UkQbMLLcAr151eqUArN0C0kVZEDrwBbS5tVrxRR+BSUeaK1l9LkqKxwwovws4O6beJBJCP5aNG1An6vU50zYJaIlA7q4cto6KUMeMCLZj0pKTfp+DBcOaZMxbFnX6NiEoOu/F0kZUsbk6IL55d7zGJf0PPyulXXCvRLVfqRltnzgOCDheQwTP49EvZauINPPkuXPo3XoYDqHCIB8dIJrUpKlN2m4nc5RgdbaUf36JcZRMctXW/me5eKoigQe6O0VkYDaj+mcXovNk0rLKI1NWKO+H4oSz/aMnWa8H9oxLLJXdlmA8vOf/5wcddRRFHSA8PWOO+4gvb295H//93+pfuT6668nV1xxBZkzZw6ZOXMmueGGG8jTTz9NtStFM8g+WLCLAgO/xBPdVCmpuA887cJ47wI+1SKCRRq9P8IXmHVnp2nB3t9c9mPCwPI4fZENGjWCft60crV0c1dCFwN9vtoDKRMGqPwYVfZjlcCPJE2Ohh/LvHcCIEk1g2TQqJFyPxLEujp+sHcyXvFOwAay96J4HiI/dIHBsk1lzdh4xTsBGzR6pHB98K3wQr2W5l55398ren5sjPgh6rrL5IcnapwQYYJ5gw4NeMcw4HFLLKnIh8lh61S5Vzxt35Y162KdVYkaFE1t0AdbtiW+l0Ej5WtUpJNKu2fhQAYWGO4WUt1ErtwrOWiD/DWasFd8P1auSqfXasgvnodimCCm75IAZezYsRSMQHfOrbfeSluMDz30UDqYDezCCy8kd911F7n99tvJE088QUs7H//4x0kRDehAaAcD/YKsnl1xkNEBBorNLaLweT/qc/RjoBfsNq1eE/dDBthSZoXLWPBX+eFt7s2r4n4Eh1A0O9UHjvAs2bhoLcAm8EPEsIHwL00JkB1AcAWB6n4i5keU5YNMEga9sf9T1mAXrA35gRx+L6vNHMgeUFL50dS/P2loblYmFdG9kuamWOqHFpgf6Zf/omVIGVBKK6L2/RiUvFc2CUroos6qLEnWUm/P7pFxz0pjmBc7dO5q6unr8xk2nfeSJrlJsz4ArAEQh3esujok8EPxPLrjAnudG+rDcVR3z8b92CUBymc+8xkqYm1sbKTtxfDrxYsX+9+HLp4LLriADBkyhHb2fOITn6BdPEW0gd4i2rJmLT1ceGOZ9VaOAsxCxS3VWEjMj80CYMD82Cahi3U2t+6CVjEoIiV+RYfhIA0/BJuqVVbySqkN0jmUZQdyODuVPI9GPZ0BCKmpH4qMbKAHYGOMAVdaEc1UgCscdIR/bI2ObG2RCjJh4FNz//7pypD+ZYHJrd9lP/RZPhBP8/Q8u6ul0gw5LYhWMgYVlGXBlm7KZ8/G1yibaJvfXgky9VUKgCJru06X3Oyh4YcKKG2vYM929fb65SYdoBT1A5oUktjXeo338r7HasGaHyx5fgB2mHRABGCLYO4uHoWpDsL+3mLd2tFVURbEgszEjJl6f+/f2RqZf5E2K1xSIXPBWi63RA4FX+iWY5BRAYMBEj/SAkedQ0iVBQ3w3suWyPrwu7wa8zmEAICyG5WjAJb5AOO3pVoYDT82d3T660v2PNizgHZr0CgJ90r0naQGjVv8unq9BFgFe2W1dK8A8KvEj6VpDmTRGvWAUmyNZjyQlWt09CjpnvX3Sod5PwImR7VXxHs2NQuswygpYsdWD6DF/cgnhoFAnpXtN0b8YCA6+jx6u7tpN6IucIRyOROVy9Yp+AD6JHjf2zeE9UlFMQdQFKY6kP1NFTsI2YGcfNEcv6lU9dtKDmRGdyf6sWlrcvbhl3hW6wM2ryWzXuM6dj74q+hiP/gLGaUG/1DljR2akOmn8kPC5IBwmemTosEOZtqwu1ai78X3Q/e9sOAvCbrsQO5oayPtnmgyaW0As8DKDg2atxQnlTWCmrr8QI69k7Z072T9jnYfaI0f0KrUn4gPZDHrmdYPBhpBkFlXU506Ux/gHTDRpML3Q3Nt6AAlxq4JgYHnR/y9tKXasyx26AA2VXIj27P1afeKDBhUVZGBnlhXDByZHx3C96Lvh3qvQJcZsBe93T0x6QB7FjB+ossDJL4fXixlo+l1/ZDFUp+ZL2h5B8wBFIUFB6H8QI6ifnY5GWvH1alZgrgL7k2AVj2hHwrthyz4d3jouVH7AKqMQZFtbvY8mhQK/zRBhhcuiw5DtsG3yt5LyuchA2wq4TJ7FlCLjh6Gwfrol8shFOhP5BlydG3wQbdR81BOei8q8DrQOwhje8U7CIFtBMCXhx++TkoB5mN7Zbu3VzRvf4VL4eAAgc4VWbeGOlOX7Fm2NjT9YM8CuppkLb4yfRL1g5WpI360b0u3Z4MytQ4LrEhuOiUxTDOWLvWBUn+5cLmhgTIRolksMGZBxHqy9dGk6UfiXvFE/ptBOhARLsuY19A6TRtLJUmWKtErijmAojAf9Udq6qHNLT2A9BYzL+6SHYZ+VijwI2AuIkHGX8x6fkDNEg5UEFWKxF2J2YcUsG1Plamz7FTGXKiEyxCkmSh0S4SmTR38ExgllXCZPQvQBUWkS/6h3Kh50ygLMnsOynAgS8p/bCx+2Y90QEnmh4q5kB5A3E3Duut0CSuJDlYDNmHZLQm8avrAZ6cTB2XwQ8K+pgXRMBoAbryFg3Wcp/9Jw2z5wLFTtmfTMwYioKQSLqsOZbZX0iY3E73rJ2SMAbT2Rm+m58uh0Y7IYM+m2yvJa0MfrIWSPW0Aq+eHY1B2Uhs8djT9vOGDFdoHcgAM9BYR2OKN5Qx8iqcliAKDwWPKiHvD8hXam7sz5aYCOpG1yO01pHyJVXToFGQfUBoQzf2Q1W/b/U2lCVA2b6WMEtRiYbJt1AaPG+PrLeLC5aBWHQNKKbOxd72ZEZOHDBLOVRgydkzqTD3kh+Yh9O6GzdJ3AjZ47Ch5acXL1KMHUDgr7JfqeUj9GKPhR+R5QPboB//UfgxW7llxqSkZGOgM0KN+bFD7EayPVdrl0IDJ0VsbsPwXe+yd6L009W/1hctiHZ2EyWHAQDN2wJ1R0L1XV1MjBPRDvHcCwICNrxeVQ2Psq7dXGlLu2YkDBwhLb0MUazQoM4V9KPuRDjgGa0O2V+RrVFbu4tepbrL3jqYfGwXC5f1HDiOfO2A6meUlHljmGBSJQaAa6h2G69//QCFO7RJvquYmrXYwsLe9ce1ThsYXEpQzKDDo7lFmp9Ggy2jaNFmhyo+h48bSzxtXrIplH3CAs24iOTDQB0osS54ydLDUj/XLlks3N+gUgJkSAUfdAxl8gAFUQP2OEVxOOWS858f7y1MxF2lLPOydTB48SHgNwbDx5Ys41wnWKNMYbBH5kRLAvr2erQ3xgTzUex4qP1RBVxfAvr2+HHSnSIIuex6iPSsDjoxNSsMavOUFf9FeAcag2eu6Eq0P2WGYdq+E34t8z0JC0dVe1i/orFP/QNb0A4ASAweiJEu5NrhyaLQT0U9uNGMYzELZ3tlFGaU9BSzK0AnyvRLEUUVppTXdngWwJrpjbegEFjvkz0O4Z7dvT7lXvLUhAdGBH/E1euKUSeS3px1PvnTgfgTTHECRGAADGEkOpQQhLSkBBizwp0HcbymCPzsIgT1hKm6tIJPyQOaD/96iIONt7vUfLBe2TUI9XpWNpQq6iuA/LOPmZoySbjYGAMdntoR+eMFumfwgFDEXPlDSLvFspTQ+ZJmi4U/++hABJQnLF84Km1OtDQj8Ihp/KAMGouchKYdmAWzv+CB6sPAiOGANpGyjpMQDHRKsc0V3v6iCP1ujoDEQXWEv6yZiB7Ju3Ahnyen2bG11tV8OlWpy+mXwQwSUPD82vL9Crhnr7IyVQzsZY5DFDwGAZUBJBRq3iBiUlCVAuHkb3m11dRWZJAJKLMkS7dkmFUBp8/zQBUqb/AaMJu6izJgfgj07xBPisnunsMwBFImxxbxx+cqYkAlskCQbA3aBqa1TZ6eK7EMUZFSUIE+PQpkozeYWMhfMD+EBVPahq6eXtreF/UjP5LBDSBR0hyg2t6p+Gwj/cgr+qiCjAAZ+0NU8hGBS53sqoMQYFAUwUJV4dIPdB1u3Uhq/vjZO47cMGkjLATA1dcPylemAUsrDkAElEY3PwBqU/0TAQC1ATHcYau0VwdpQlUPZ2oD5NLpt6G8xRkkEDPw9K9grXDtr1I8smpwghsmZHNWeFTEXaVlP6ocCwDIQLQTzOYpTqR+KpNMHjio/VGC+Vc8PABcMYOw1OPxeoMuUNRuIzpahHkBZ5wBKMS0pyDCEuT5yJ00W9oIF3UmDB8Zo/ICyFgcZODDo9yMLiWVj0Oeu2y6oDDIKP9hi3iCgkbNkYyoanzEXSj8EmypLkFHR+CqaVuVHwKDo+yFjDaArAZ4rAAMovcnW6MYdiqywtXIan61RAAZRjUGiHynLCTAEa5uExlftFd4P8TpNB2DZXoEBelEaX5WZAnPB7pmKrg9oq2UsadoSoDK5UaxRAI3Rcmi2A1kO2Hy2UeHHRuGeTbdGQ8BRwaCI/BjS4vkhWBs+s5VDuRzEwuzCQhEwGKJ6HtvS6ddUQGmIJ1+ADsToeALejw1tjkEppKlQP4CIwd6ckygwyJKBgDiV0fjRnnWfwhcsZtZtA3XXKHMBsy6gPJXGD7ap9hw8MJadKoOdt7nXtyk2VYogwzbVVO9WWhGDIgoy7HkI30lKwV3IDy+gMIPBaBBo4DARAYOhnh+i7CPtgUz98ILu3pEgw0AjaJNEwIDdTL0uMjgte5Zc9mPqsMGSMlOcwgcb5nVwQHtu3I8s5QT2XqLPQ742QutD6Yd+qzFM+QUaP3oYqkorQ5obfc3FRm+Srrgkmo7JARo/eh2CCrD5a1TwLBjbCIPadHV0b/nvRLRnx0iZC7Y2VGtUVxdE/Vgnjh3wd7CBhutVfghiWGcleyXyPNgahYsbWUwK+eHFUmHsaMsClMR7VpXoUT9ZAu4YlGLasInjlSgXApM0G0t5KAONv8hrmZ3uzddIwxjIaLhgY2nS+Fu20YwK1Ph88IcSkQqwKYOMt6lq6+q0x+6/4d00CowSG8cNBmOZQXwMwGCTABj4m1t4AKVrWQR7fU25W2lG5J2wAwg6I0C/EPNDxa75Wpjmiv3w9TiS8p9/CG3PBxi8vlbmx7gEP1iwywcovb6mvD5mjBS/F9FBCGP/2QEuBkrps3W2TmPrQ6HHYaARAj/s+0oBLLAw7Nbr6cOHhv1QxA5/jQpiB9NrpcnW31y3gYIuuA6BH1MAz1MFDJTJTYYSz+veO4FnwZPRbI3CFQjRm9iT1qjfmdmaX+yQ7pVmDRDdmoMf49V++BUCwfOwaU6DIrGRk/ekn1e/G9wjJKLwe/tK8oWUgop7ZXV5Ie3vzRoBg3kfw/eY4PmxJFVGmLWG+6onCOb9gHY0yKZASChmDORBBoIBlCDAGjUPZQiay727JHjANnLyxPL3318eu4CN+uFnQaoDKM2BvJ62PEPQ5YfojZxUXhtrFsffSTg7lYPXNO+ErY39RgwLBd0RzI/3xH4MUwBYfw5KqrXh+REBBiMmTZT6Af6qstO0wj+wV7w1Cs+Dt5GeH6vfWyoFr8BURm/cDvmRas96fnB7BcD8iEl70K/XLF4qP4AkgT9o8dX341XvEOL9AFAAomHYe2uXvh/3QxE7IAFIq6ODzrn3vBIg/17YXoHyX/QKBD6GCZObDDo6aDjo7Omh2hZeK+XvFcE7oX6o1mjKNmP+newzbEiIjWZrdM17Ej/8JEu0ZzP4sVoMUFgslftRfh5OJFtAg8OYMQZJAEVkfs0yBdJlwQ76z/1/Z8I4KpiD4Cm6KJABAxmD4m+sFMGOHYYzuGA3cq/y5l67eJmwk0hVSoBZJf7U0jSHobfBeaA0cvIk+nnVO+8J/4ySHmXDp5qbtS7IA2vv7vFnGuw3Iv48RGuD+qHIPjLV9zdspAcrdF3wuoskP1SHUBZGia1RCLr8XTijFGAehoGxCZ2ibD3ITtMHXf5AhlLE8IkMzL8nB68yYFCRH8NCg9HgAIGSm5C5UBxAoUM51SEUjx0j95rks0nRSxNDfshiR4Yky9+zo+J7ZZVsjfpso7ykQXV0mleHgJ6GMVt87FCt0XCSJWc90w7y29zeQTWCPBs9MskPxTrtTDnkkX8nMM+JZ6MDP+J7BTp+GNvoSjwFtOET9/DpwOgY8xDqlzAXaYeCgb2yKp6NJW0qhvplQCnLYSgCSmwxrxIs5vBBqPYjG5MzLPXmFgUZdhCmrWn7zBYfdJkf76iBQV5MDrB0ryuCrgiw8WJMMWBL7weUAEF3ASXAfb0aP5TtmAZFtD4YWINgHb1bJCujxIIuaKVYNwokFDAWAA4T0bwgdiDL1mgWRilgcuIgGjJ1EZj394oUKGUAjoLY4e9ZKZhXs69ZSl4iZsuPYW+r/RAdyNCJBfOfsgLHUHKzl3rPKsvUbI2meBbyJEsODJLWaXuGNbqBY6MZiwIJ2og995ACxyEeGISuzGiHl21zJR6BjUrITIHyl9WyswYZtphhxDtrHWZZkAwYjPAA0FpuZHilNUvR5k4CSoxBWZtnsPOCrnhzq/0QZafQ/t3liRJTAUcBUEp6HiNUJZ4MpQQRYINnyS6CE/nBAh2UqABU5JEhh56HB9hGTtqDZrgg+hPdiBocQBKdVMr5NGDw/3nfG+PNDmW2V9a8uyQ2YRhsuPf/lK1RxvKlOYQWrt1An+/wfs1ktPfnkmLHiKS9knKAHh874ABiM2ry2rNp2vJZ7DhAAOZXJe3Z7TI/MpQifWAwTBuwwTukfii0MACA4SMr0wdgnolkVwkAG7SfN9TWKkTD21OfKyJmC3yA/weU3MRaPnmzgW1zAEVgfhYkqe2zC8IgoxRZFlEViFNZOeFgb3w521QQdEU2zrvR9QNBmxhYh7fp0wQZoEehnAAiKTbsiAV/WfbBngdD6nE/0g0FA1uwco0PUBpra2kNmtVNZUF3rPc8lsueR4ahcQu8cdSHeOO6W4cOoXM/IDtes2SZkLJmo7uhJTbmg7c2IIvRpa3BXmB+jBsdqu3DmGqR6I+9E5iuKRRjZlijYAtWsOfB1ugk5Ttha3SF5J2kvZjO98N7Hod6zyPpQE7yIyit6K9R6Jxjgl3mRxKIHusN20vyI81egU4eiB9Ay8/wZlskAQN+fQj9YCXRFLHjBW9tQOcKS7JGsdghSbKCPSvxI0N55fnlq0J7Bf4su1dt9XuLhSUNJgoVxQ6mx0nrxwve4c/WxoiJE+i+B1Ye2PmowaWPjPWA8rIsnqcR2If8GBtdo2Iwn7RXbJoDKAIbu+/e9POKRe+oD+SEIJOWEnzaa9M8fHy5LW/MPlPo55VvJfghA0rbyr/fKBjVLjOg4dlheNj4MRT1s9r+qrffFf4ZtqCXb92q9ANGgOsa3JAKGwRquAeOGUn1OKAfAdpXNhOGDcCSAUf/vUhuoBXZc8tX0QvEgNka07+f/07WS2r7LOCu2rZdWNIAFofR1s0p/Hh6WXltHDxmFC3fMD9koJFNnf3Au18pau1bK12j5UxwzNQpysx0nO+Heo2meSe8H4d5wT/JD/Zekvxgd9do++Hd0zV7fMQPSUnDT242y/xga1TfDwCg8z8oD8g7bMIYegiOmjKZ/nq19HmU/352/5bMjzRrFNgYAEvQ4QiHMtz4DWAeBO1rFsfBPOiYRnnrL3GdpvDjpVVraZIFTAC0gI/23gncmyVq7WXAACYMi4YJwqDO4L2k2LPvl9/JTNAl1damWKPiZ9HB4nlLi3b7d2ivsHPF82N1xr1i0xxAiRhk6mP3nUq//uCNhcoXuELyApluBQZppbF/sOA/YSzpN2QQGTx6FFXhL1/4VqaFxFA6GwyUZUGPnTqFdhNtXb+Bju4WZR+srg43IouMUf+s3VDX5nsbHADbuOn70K+Xv/mWsLbPDkLIPqK3kcaeRwo/oOODlTUOGzeGjJ9W9uP918RrY1wCaKR+bCz70S+FH2+u30BLGyCUBVHm+On7lv14XeKHN09HBqKhJAMGVG+aw5AdhPsMH0IGNzWS8TPUfvgZsiTobvfeSavkjp+kvTJ7/BjaKTQuwY9xiXtlUyY/GHAEwAbsy3Cvtv/BG2+q/ZC8F7ZG0/ox3wdsY2hXFQj94VBdJ+jggTLQGA8YyNap/16GZI8dbI0CiBaBeQD8YO3d3dJyAtsr/VP4AYnB8x5rMJvz44PX38wEosPrdEjmJIut0ax+7Niy1U9u0sTSaJLl71npGlWDV5vmAErEhu0xntJ4kOnKWrD8LEgSZLasW+/P7ciyuQ8aM5LsOX0a/Xrt4qXC9jxoXRvh1dVlh9C2deXZKq2CgWcq+4cfdANg8EHCgQzTPUXZB7vFlN2InMoPjynhg0zSAaQCBsyP1rTvxcuSITtNPAgTgAHvx4AUzwOY2FDw94PdwkxlSOgygYAH1j/F+oCD5C1vXR22xzg/G5MBNsbkyMArAF/GXOjOyQF7edVaOnofqPkD996LgnAI3jLWc5wHwmTZqb9Gh2ZbGweMHE72mrEv1ePAuH8GAHmjQCohS95WYewoAwMPzL+xSEjhj+rXj3ZWwWWYqyX6ta3eO67Ej3Fsz8oSPQ0wv9UTh6eNYQxIQwzTBdGyeM6v07TJHp9k6cYw2Z4tlUoBYEuxTiHJetVLLg8bP5aMm6YX01UxzJY5gBIxtqlWSDJ1GGvNFtJS79ZdabBLubmhhx86P5rq6sjcA6YpMzG4iwSoVJg/IOsI2LqeBd20m3sFHboE45H3mz5VibYnem2vTLSY5+b2GaXxY8geCZvK90OB+n1gkDboeoDt6D3GBUFG6scAv81QZgFwzBb8j5080S+7ybKxPTX8yLpO2Xs58YDp9M6YHVu3Coej8c9Dtj5gzDa7qK9/CtYA2kmf9bQGpxz0Ifp55TvvCjN1AAYTPOAoWx9sjQJzCTeZ6xocJvB/gwP/+Jn7KfcsAAMQQUI2K9InVbJXnl+xmgIOOGwPmrGP8iCETJrpPkT6JOrHunWZgAFbo1CKnMSY6MQ9qwIGGdco27MTx5Nx07wYJvGD7ZX3N2+R++ENKcyaZM2ZNIGMmjJJK3YoY9jarMle+XkcP2MfWqaCUrms+SJpz9o0B1AiNmG/acoDGWqacHMv0O2y7IMdQMDEpBFCgj3i1WrneiJE2QEEsyjAFq3bELsFlNmWtdk2N1w8yHQoczwFuizI7Dt8iD9JMu+DEEora7a3UW3JQd6o5vclz0PPj2yM0iNL3qeHCgyNG9VYvuFapgti70XpR8ZD6MF3y4zesRPHk5reXjoFsk0SVKca9OMBz4/jJo7z16goUwetDBNas0nJymw9JXvxoMdwzpswRrlGYWBXc30d1SUskSQVQOFDORXKmS1egE7rx4c9EWLSXoGBZt298QtIQ3s25QEEZc0nvUsBjx09QnkQptkrAzIkWZC4gVD8sJFDlUBJxw8WS9PGjieWfUDfNxy2k7yLLJcvXCTxY2iKvTI00145csJY0lgqlUvlgplW1A/v//jmWpUf67P58Y63Z/ccT+lYWirv6RWCeZ0YZsscQInYnrMOoJ+XLHhZ+MDY3QoADGQGJRmmPk9Dn4PdtaiMamd6b2bxi6+I/dBYRNu8TQVCtTSiKrC73yr7MaOrkzJJy157Q/hzbDEv9EBIngAFzrx73y6LQCdvWE/r86I7iXg/tDZ3Sj8AjDIqf+KqVTTQiUbc80FmoXfQ5Pk8Xl69lpYGmmtryNh168iyV14X/hzogthANy3gmPIwhAMZZiSMb6gnA7dtk/oB4ARmpoD48AOt0tuQTGt0v/paUtvTQ5a+/Jrw59jMFjg8ZYwBrHFWlkkb/JkfM2GLlUpSP4K9otqz5WcBgnD4SGP3eHtlf2/K8rJX1H6oD2TvnaQEr7wfe2/eRHUwslK5lh/+XknnBzDLjy39wN+z0FUl6nbT92NDJj9ANPzO+o2kvrqajF+7RrpXQBfELhY04cdjS9+nz2REfR0ZumWL1A8ohYLODfY3u0Ud0xxA4QxErfuOHEZa29rIYglAAXGgDroMqLjwiOEku//dJaS7r48M3b6d1K1cKVVa62wqEOtCXR7q4mkFqizojlu3lqx5/U2h+l0bGGQMMrwfEGTefeZ56c8xP95QACU/Gxua3Y89Vq0i7zz7gvBngOlh9exF3kWDymwsJUDhg7/Kj72HDKblPxjCJRsmWInuAvRGkKGCTVwt90M3E8sK2ODvXbJ5K6krlWjwf/f5BZkz5HC2nm59PLx4Geno6SUDOzpI69q1Us2Fjh+gfWNt+VkB2+gN68nmt94WtrLqg2iPbYS1lDG52WP1arLk+ReFpXL+eaj82JJxjfJ+qNYolOzhzq+yH/kzF2B3eXsWYpjMj0mDBtLyH4CIZVvkpaZtGcF8Z08vedgDimU/nleyWjC9OnrLNYY5gMLZTz92AvmnBx8k+zz3vJQ6/9CoEYlZUCX0OZRX3ugqZ0AjXnxJSJ3zw5BUmzurqIoN94Gqa11vLxnyipjFAQp/GgsyyqxwY6aOEbCHFi8jXVVVZGBbG6mVUMUwJAtaCmFoFmTJeTMGYHd5wQ6Yi82SDJlNagTRn0wwTP3wS2/pgdL9S8sM0l4rlpNlz78k/Bk2GCpxjWY8kMEeW1/OrvZatowse1XMrrER8InAIONeAfuHN0dlwqK3yBZvIJXMDxXLV8khBDMrXvQmbo588SUhdU798NaHas9WAtgg413eVyI1pRIZ/rJ4zwKFP2MEAwby97Jj85ZMHSNgjy/9gLRXVZF+HR2k+XXx2oApx0wXpFfiyZ5UjN6wgeyQMAYA1qBkv3FHu7Rkz/uRFhiA3eOVefZcuZIsf0G9Z99UlOwrKTWBPeLN7Jny/vtkyYuvqveKYm3YNAdQOFvoXfy338b1oXsL/IdVVeXPKGEiLBOH4eujy7XsQ7ZvC10Oxw8DY9kHU6ubOAwXji1rDA7vFLcAfmjUcEoHwuZWaQyydozQP1tbR94bXX7mR/aIxy4ftcdYv7NDNOAomo2l7RgB2zpgAFk1eDAN/kdKdg3zA0TGyr+rgqxw2dChZHtjI2nq6iKH929R++EJ4/LOxsDeGDqM9FZXkxFbt5IZns4k7kd5/bDymMyydoyAvTq8nDBM3bTJH7YVtSO9G2TZXIpEP7Ls2VFlzdhB27b601x5a22o95ObxD27Ppv+A+yNseV3P7t9h/RAhrEAbVxnhzS52cDeSzoWuK+mhrw9puzHEd3xKcb09704CqXyjYJJx9FnAclNs9cRpmttLS3k/eHlg/8o0puwZ5PWRjaBPdjKIUPIFijZ9fSQI5vFcedIzw8mMjaxRt8cOox019SQwW3bycwhA9V7JWHP2jIHUDj79e//l26Ypqoq8tkDpsceFmQeA5saaU39ZYnQiRnL5gaNLE8w1DXQi2yZfSjprKsjo+vryHHe7Ze8geCKXaWt2txgbHbJoNHp/IBWtCXTpxGAbIcOGUT2FnRYgEIeDMR5KtRfiR/7HDmbLNyzPPnwY3uO9ydUig5CyNxUBmUq1rI9yDssdG36sUeThXuUZ1x8/oBpwkNI148t3rOAWRc1dXEgrLJ9jjmKLBpf7uD554PLeqmsfmxma9SbspnGJhxzJHnPA9IiP0AHA50cYE8s+cDI2oCZRa3HHEXWDBxIaqsIOXfmDKEOZnT/flQw+ezylVrvJe3zgPkn7UccTnbAvUS1NeTUqXvFfgYSG+j0AZYjaQDWlozPA+5XWX7AAaS3qors179faOQ8s6M9YTMchDKhbqXvZcrsg8miyeVBcSeMG+2PsxfFjqQ1ClovVqpK68f0uUeThRPKe/az0/cOXXDJbI6mH+xZQAdMmmmyvh9e7Pjng8R7ds4eZT+e0IwdgzPs2UnHHEne8YDjeYI9C4w4S8CT9qwtcwAlYr98rkzB/fsRB9Ppf7yduk858Dz1/gp6gZvK1r9ffsFDPTCha9PmHEn6GhrIs/3K2cL3jzks9jMf3ae8+R/TWES+H+PLgUnXZnx4DtnW3EJerik/g+8cPTv2M6fsXW6be2xJfBhU1NjAqGFp/Zh3DFk5dChZ2tNHs9ALDzsw9H0ACid67XuP6zwPr9Mh9fOYN4e8PXYc2dzTQ2vWZ3ndXsxgaBnb3El+QMAFnQFM/BzidX7oGPz89GOOJK/vOZF09fWRY/ecQI6IrC8oI0AbKYjckrLCdcvK7wTu9EnDKEEr7sSZ+5NX9yw/988dMM2n65l9ZPJEOqAKWhUXSzpnon4M87I3XRs7bR/q+wKP6fvXww6MMZ+nTC3vlWeWr6R1eB0/YGpxGtv3qMNJdWMjeXZQGcR/d87sGPN5srdXHtfZK56+J/0aPYbsaGwkC+rL7/J7c+Kxw/djqYYf3mGZds/uN28OWTtoEHm7r0Rj6DePODj0fXg2J7HYsTjZj/XLMvrx4TkURK/r7iFj+reSz88KA1hYKzA6gPqR8F5AYMvYnDTvBUD0jLlzyMI9JpL23l46fv+4yWWwwmzy4IFU2wgl6qe8+JS0NloGDUxVLocrLfY69CDy6qTyc//U9KmhW5bBjpk4njLioFtTaflsmgMoEfvdgtfo7AgQO37ziIOCB1VVRf5p//Kh9OdXxVoI0YE8fI9ytptmc4P96o23KA170NhR5Mz9ynMNmBDz4/uWh2P9ScsPb3NPKCN0XZsx92j6+f+9+Lq/oGd7Y8WZABIGqEH77W1vvJXsBwsyXqagY3BoAoMCEe1nz7xIf+9rh87y5xawgxAyZNhUDy1equGHdxh6gUnHIHMbt+9UqoX5r6fLQswfHnt4iM05c/99qcjtxZWrlTqYqB9snomOTTrwQzQwreroIr9/sayD+flH5tDMh9k5Hotw56J36fpRGYioYYYJiKiHjiuDKx2bfsxR9M88t2YdbX2GTp3LP1Jet8wYm3HLa+K2cNEBBGADJqCm3St/eXcp7ZSA0sV3IofyuR8qM6H/++qbKfZKejAPdt0bb9Nbm6Ed/Yuz9ve/31xXR87wZpP8WeN5+GB+QvoDGezKlxbSww7A2bHQVuoZgMgPT9qDzji65bVFRvYKCGohyYI9e9lz5UaD8w46IHQYHjVhHAX5oNO6950l+oAtRewA3cweH9qPlpt+/vQLfpIF5XFmp0+fSg9kGDwI5WH956HvBwx2hJH/G3t6yf94z+Oy4+ZQcW50z97/7tJERryrvd1n54elSH73PeowUltXR17buJnc+eY7VHdz+fHHhH6GAThYG0mMuC1zAEUwJvm7Dz5Bv/6Po2aTeZPKaPerh3yIZqagt/jrm+I7aXhb610kN3jMKDrMSsdgIe992CH06yfuf4Rc9uQz9OtfnPRhX2AHByMMcoPyDrucS8cPdt24ju1xwH704ISOgjvve4T84aXXaVfIHz5xkj/98dLjygDmnrffI2skN5EK/fAuqtKx/Y87lrZawmTO3z34GKU/IaDcdPop9O4doGx/8uEj6c/e9MrCRMqa92OUd8mdjh38sZPp5/eef4n89xPzaesgANjrTzuBTvQF9uRbR5bf2+884GDieRz0sZPo59ceeZz852NP0/bnWWNG+uAAZvR8cVZ5WNjvJCK4PP145YFHyH88+DgdEHbavlPI1w6d6WdiJ0wp/32/f0ksTuQN9EmsxXfEnvGSpuwgnHXy8fTrlx96jHzrgcfp1xfOPpB81GNNIODuPWwI7Tr6v9f1D+QBw4dpZ6ctAweQaXOOoF/Pf+BR8pPH59Ov4Z3AeHOw/zj6UJpYwGWgSaUEHqCkeSej996LTvUFYet99z9M/uf5skgW1igbvHXpvKP9riPVAL9K/ABwAloRYBtuuv9Rct87S+hMlJtOP5nuE2A8fzrvKB+sya6l4G3tkqWhCyF17MCPnkBB9NJXXiPXPv40eWPNenr7++8/cSIFB8DGAtOVas96M6rSPA8WO9547Ely2RPP0LlO00YMJb84ca4/QPD8g8uDBm9YoLdn13jPg13UqWMHeX68+sAj5LsPPUlLnsfvNdGPW3B30mne/V4s+SmCOYAisFteX0RufuUNWjP+21kfJ0988Uzy84+U0eZ3H36S3mKaZEDjg7AKaHkIHjp2yMc/Sn/+3edfpMHh5089Ryk/CG7gw9Nf/ify1UPKh8C/3/eY1t8JA8VgSNHAEcMpNa9jsz/1Mfr5pXsfpJqNf733EVo7B4D2wlc+R148/2xy4pQ9SWdPD/nOQ09q/Z0rFr1NP0MQBdpTxw474zT6+dnb76Sfv3DHvfRQhsD/ygXnkpe+eg7NVoE9ufzJZ9P54W1GnYPwkE98lH79zO1/oyLcc/9yDy2hQIb68lfPJQvOP5sGP2i1/r3HOCX68WbwPHQPQgBs1I//+xtZta2NnH/nA/TXFxw6kyz4ytnkyS+dRQ+D+99ZQh56b1kqP8buU74gM8lgLe+x/wx6EL7wt3vIK6vXkR888g/6vf864Vjyjy+dRf56Vvm9/eq5lyiYS/VevIs6dQ5CABJwEL7x6JO0y+q3L7xCgfStZ5xK98s1J82jP3vJo/+g476TDMpu7CJKdmGoTuAHAScMI4P/w9XPLCAPvbeU3iz8yLmfJk996SxykXcIfOv+x7QyU5jZAcMA4aCHBEfHDjvj4/Tzaw8/RjsQL37wcboeobTx7D9/ljx/3ufIJ6fvTRnP73gJWJLBMC92IzEMsEvjx3N/+Tu9ZO+f/3YfWbt9B+1Sgb3y8vnnUFYYmKafemBOf8/qvROIL4eeXo5hz952J01czr79bnrnz3GTJ5JXL/g8een8c+idMxDXfv38y0b2CmiTZp50HP36mdvvpPeEffmv91MG6/Oz9iMvfuUcMv/L/0STrieXfkDufOvddH7sq+cHlCynHHoQPQeeu+MuOgKBAfoff/hI6sM9n/0k3Tt/fOl12sFZFHMARWL//LcHyP++upCCFECX8PKue+EVcr0mygVjl/yx8egqgyB36CdPpV/Pv/UO+hl0Lqf96Q5aH4VJmCwj+/7DT9IsSMeAEmSZ0LhpyX7AYCZ2EDI/IAM97ve30PbIwc1NFBQAODnvbw9olTPYnUIwXhkEZjoUKYh02UH47B1lgAJZH/gBF3BB4AUBJACWz952F938ad7JyEl7ajFb+82dQw9CAJyvPfSYf/kWvBegqMEH8AW0Fv9029+FNxiL/Shn9OyuoyQ79JMf8w9C9mdvX/g2+eId99F3MWPkMNq6+drqdeSf/3a/1t/JPw9dP44483T/IGTixf/31HPkx48+TQMvHD7A8D26+H3tg5D3g13GqOsHHIRwWy7Y1+5+iNzw4mv+bbqwdyHRuNorD6Y5lNl9JUnglYHo+beU9woAkNP//DcKEgEsHuxNhL708fnk717ba5LB/4fdeMuu3lAZABl2ED59y1/oZwDSH/nDreSllWuosH//UcMpqIZnBAP/dGzj8pW0BAjrjo1pTxLpsoPwmdv+Rn8PgPS8399Cp8uObG2hjBY0GZzzl3vo99KsDSh56dwmPPXIw8jQcWPpNQov3fcg/T04dE++8XbKgAOjNH5gf3rj+Fn/93dl5594bUzVSrKAPQEGePV7S8hir7343ncW+2AJmBQoS0JjxhfuuE+7rLIixRoFO+Izn6Sf33ziaXqjM0seLn7gcVoKBBYWQNI/li0n37zvUVIk04PFu6HBQXP27ffQ4AbtgW+sXZ/YAhY1mCi579GHk8kHzyRP3nyr8mdnn/4xehDCAnrt4TK6BYND8Ljf30rmTIS67SDywopVNGtN5cdLr9LgAX68+UQ525XZ3C9+jtQ1NJAlL70aulMEOg9m/eoP5IS99qQzR6DckmbSIJ1G++rrZPLBs8ikg2b6pQWZfeSrX/RZHHYbMhj83/f5xfXko1MnkYaaWjrVVDW/IGowZhqeMXRq7DnzAPLW03LmBe5kmfeVL9Cvn/7z7f5BCAb/7uQrf0PLCZCV3vPOYuXsk6jBs4UsGfwYMnYM2bBcvrYA1M0550z6dXQd/fHl18mD7y2hJZXN7Z10kJsOw8ds6ctlwD1h/+lU8yO6y4bZkHFjKXVO/bgp7MePH3uaAvo5e44nK7ZspwMHZVNbRbbEm5g86eAyQ6gyWEOTD5pJ29cZiGaAHsAZBF8A82+v35TYFSHy44CPzKV75ZHr/6j82YM+eiJ9dwDU2EEIBtqfU266nXbb7T1sMHlx5Rr6kdYP0D3B//OV+x9W/uwx555FD0JgGt7j5mzAvpj9m5uoTmtU/xY6GkE3oWC29CUvhh00S3qrOrOPfPVL9PPrjzzhH4Rstsf0q39HRfUt9fXk4cVLyYqt4sGPshLgmsVLaQybdOBM8vojj2vFDmAtICliBt2Ge111HTl16mQKBmDPQoKjaysXvU06d7RTHRiUzFe9LQeckPwc+4XP0q+fuvn/Ygz9o0veJyfuvSfZ3tlN7n77PW2QBAaxmTEoEBvYYD+ZbIAlvk/9Kbxn/+sfz5Pb33ibzJ00gZaeADwlNX/YNsegJBjoPIA5SQtOwN6e/xz9PPngA2kWosqAPvzlc+jXD/3m98Ix6tCxA+xNWnDC+zH1iHgnDm/AbABQArvvmt/Evg+LF6h0yFKzjEF+e355euE+CX6A2nyfIw+jgOCBX/8u9n04gG99/S1y4ytvpAInzN555gWt5wGlnZGTJtIA+cRNt8S+D4DkxpffIP/72pupwAkY6HvYSPSpIARW2NwvnU3XCATpl+4JDkJmkImCuPsvC99OBU7AAChCCyWAUjiUVXbyhedTqv/NJ5+mtf2ovbtxM/ntC6/SQJcGnIDB5GZgyyDzVTFsUAI9+V+/Sr+GLJ21f/IGgkfwIy04AXvHm1YMV16oRs0DfX/c+eWD8OHf/jF0EPIHIviRFpykWaNQAjrizE/Rr++75rrY9+E9wPuA9ZEWnIC97T2PJD+A7QTmFdiT+6+9TpjwAeMHgDoNOIm+l6lHHqr8uZknf4QCO7hq5NEbbop9H9hg0Kvd/OrCVOAEDOLR4gUvaT2PY845i871Af3cc3f8Pfb9tW07aDkYGgzSgBOWZMG+hb0ALd0qO/HrX6F7G4Ari7+8Ld28hZ4rENeLBk7AHEAxaEDFw2IClAtZiMxOu/hf6Zh9oHWf++tdufux6Oln6Y2xcNjKNA+w2M/40X9QIAUH0LvPiceGV2Is89n7iEOppkIW+E//wbfo15Ady+7eqcQYQ3XA8XOlY7yho+TkC8sHIYAkVZaS1SDTBJt10kekPwM07tGf+wz9+q4rrqV1fQw/9jvuWLLfvGNokL77ql/m7gNond72xm/PVPhx9Nmfoc8E6HsA83kbgEAI/hDUWXeOyE755teorgs0K/P/76+5+wGjyOGQBQAy8UNl4bOI4Tv9B9+mnU+gW1v4+FO5+/HGo0/4zBZk4yKDf/9Tl/wH/Rp0SaChMbVn9593rDTZg/L0xy76Bv36ketvpF1qpvxgAm2RQTlsrpdw3vOLX4WY17xj6ayT5XsFkjxgPAE03nXFNWRnNAdQDBpMY3zhznvp13POOUtYtzzqc5+mARlKILf+8FLpmOxKDAaUsUPomM//k/BnPvbtC+lsCwiKt//458SEQfAHDQW0ux352TNi34fs/J8uu4TS5htXriL3/OLXRvx46x/PUFoeMpwDTy6XLHgDQHnuLy4jTa396KVaT/0pTNHmZS/f+yAt80B5hV1SyRscCOdcdSl9LlDqMnEAgT3/17vp5/2PmysUZY6aMpmc4R1Aj/7uJiW1XYm94Plx2KdOo0A1alOPOJSc+PXz6Nd3/vy/pXfNVOyHv2fPpCAgarNPP43M/mSZaYQ9qyqLZTVgZF65/xH69THnivfsSd84n2bQ8LP/96OfERO2ccUqCn6gI+bos8tlxmhi8+mffI+WX0CwfOf/u9qIH8AAQEwAJpGJ1qMg6ZwrL6XlF2gMeOyGm4348eqDj1L2E4TDsB6jBo0I51z1Mxrj3njsKfLyfQ8ZXaP7zjlSOKoAfu/Mn/3ALzHJbrYuujmAYtie/NOttG45Yb9p5JjPfzZ48LU15PgLvkxO/fd/ob/++xXXkPcld5rkYZBRgH3ohHlk/4/M9X+/vqmJnPHj75DDP/0J+uv//Y8fh+rHufvx2z/6FCjQwsyAQfr81T+nTBOwPX+48D/8qa95G2Q0LIBBuQC0Fczg6/Ouv4bWd6H19cZ//54R1gIMDlgQeYJBJsyzSsASXPCH/6FMDmT0t11yGTHJ9IEWp6aulh42/NA2CMLn/+5a0tjSQlm1+3/1W2N+vPrQY1TQDWvhE9/99xA4gNZmAI1wID7/t7uNMI3Mnr71L5ShgUPoOE+DBAb/NpTbPvn9i+iv77v2OvLe8/oC3LT22O9vpmt12jFHkgM/emJI3wDPB7QnYLf+8Kf+LBmTexbElqAfYwZg4ewrf0pLO1Ce++O/fYe0b01uX85isAcBHIOdcMGXKSBiBjquf77uv8keB8ygot4//Ot/GGEtwGBdwPoA+/h3/j10TxEA+Qt+/2tapgRN2Z+/+2NiytYsXkpZFACOsGchjjPb65ADyfk3/JJe6QGl2LuuvJbsrAYpffEKTwnW2tpKtm7dSvr370+2bVOPjS6CQesdBBRW0920YhXZa/ZBZPDocrYKVPW9V/+PcT+Alp5z9pmU8oPWTChbwCA0OBDg9+AQZC29Ju3cX/yMjo4HkSMcSrAI951zBK35Qzb4+wu/TRY9VZ4BY8qAlfjazdfRejVcCQ+bvbFfP7KPN9AI6OH/+fK/+C2OpgyC/L/dfiMtF0Bb+sLH/0HH8O99eDk7gxLCr75wAS0VmjRoRbzwlhsoEIHBWABG4ICGoA8Gge66r/yr9FbrvAyG0cFhA+8HRJkgJgaGafSUyX4Ge9NF3zd2ADEDQPCZ//yen70DcNrr0AMpuwf2+I1/Jnde/gti2kB4etx5n6dfQ0a+Y8sWujbYxYp//dmViQL8POysn/2QMr0ARKCDC+IFlBCAZQQW8MZvfi9RvFqpAUCEgxdKXpC8vPrgY6QB7iU7+nBa9gHw8JuvXGg00QMDdu/fbvujL5Be+NhT9G4ceC8AGIDp+fUXvqYUvudhg0aNpH5AZxNoXUBrOHzPCWTSrPJcFdg/v/nnf5FefLsznN8OoFiyuV88mxx/wZfoJmNGKdHLf0HpexsGGenH/+Pf/FkFzOAQBIrYhO5EZEDHQikHMkPe4DC65fv/aayEEDXIfiArhwOQN2ATbv3BpcZBAU/Hfv6/L4+JQ1+8+37yl59eYSwrjRqU+M6+4qehrBAOpKf+fBu556pfUUBpw4Dhg5ISL1KFAwmA/KM33GyM0YraUZ/9NNUhAbPEDFg1qOc//7d7rPgAZeFT/v3r5OjPfjpWern9J5cbB/LMAACceekP/BEEzFa+/S655Xv/6be+mzZgGc++6lL/EGYGZSiIHdAabcOAaf38f18WG9oGwwtv/8nPjehfRDZ+xr7knCt/FtIHAXgHATmsUyhHFc0cQCmoAeIGpqCpXwtZ9e5isuip+UL1v2mDYVt7H3YwvagOAguou0EDY9vgQIRW31Kpj3a0QCeHbQPQBs8C7nXpbu8g7z6/wB+EZNPgXQBgG7nnHpSmXvSPZ43S9jIDUACXm8EdQQCgoS2djda2aaAlAJEqMAVwGIP+ht2IbdPgmgM6HbV/K1mzZBmdJQGzhWwbHIRTDz+U1DY20PX59tPPGmeRRAZgHlp9ATgBqwbzPUBrZ9Pg34ZOPzice7q6KcP1wev2NRZQpt/3qCPI6CmTSPv2NvpOoPRi2+qbGsn0Y4+idwQBo7PoyflGy/SVmgMozpw5c+bMmbOdGqCgimTPP/98smTJEtLe3k6eeeYZctBBweV8zpw5c+bMmbPd19AAyqc+9SlyxRVXkB/96Edk5syZ5JVXXiH3338/GTZM3GvvzJkzZ86cOdu9rITx8cwzz5Suvvpq/9dVVVWl5cuXl771rW8l/tnW1tYSGHzG8t99uGfg1oBbA24NuDXg1gBJ9QzSnN8oDEpdXR2ZNWsWeeihYIgNCK3g17Nnq0cIO3PmzJkzZ852fUO5LHDo0KGktraWrFkTbuOEX0+dOjX28/X19aSBGx4FIhtnzpw5c+bM2a5rO8Uk2YsvvpiqftnHihVmB+A4c+bMmTNnznZDgLJ+/XrS09NDRowYEfp9+PXq1fH+7UsvvZS2JLGPMWPK0xydOXPmzJkzZ7umoQCU7u5usmDBAjJ37tzQ8B349fz582M/39XVRful+Q9nzpw5c+bM2a5rKBoUMGgx/sMf/kBeeOEF8txzz5FvfOMbpKWlhdxwww1YLjlz5syZM2fOdneAcuutt9KZJ5dccgkZOXIkefnll8nxxx9P1q5di+WSM2fOnDlz5qwg5i4LdObMmTNnzpxZsZ1m1L0zZ86cOXPmzJnIHEBx5syZM2fOnBXO0DQoeZgb2ObMmTNnzpztmud27c78H3QD25w5c+bMmbOd8xxP0qDslCJZsNGjRxuZhwIPDYAPDINz81bMmXvOdsw9Z/ecdyVz63nXeNbwd69cuXLXZFDAdP5zlZgbCGfH3HN2z3lXMree3XPe1WybgeGoun+fE8k6c+bMmTNnzgpnDqA4c+bMmTNnzgpnDqBErLOzk/zwhz+kn52ZM/ec7Zh7zu4570rm1vPu9ax3WpGsM2fOnDlz5mzXNcegOHPmzJkzZ84KZw6gOHPmzJkzZ84KZw6gOHPmzJkzZ84KZw6gOHPmzJkzZ84KZw6gcHb++eeTJUuWkPb2dvLMM8+Qgw46CO/N7AL27W9/mzz33HP0au01a9aQO+64g0yZMiX0Mw0NDeSaa64h69evp8N7brvtNjJ8+HA0n3cF+9a3vkVKpRK58sor/d9zzzm/CdY33ngjXa87duwgr776Kpk1a1boZ370ox/RQZLw/QcffJBMnjw5p39997Hq6mpyySWXkMWLF9Pn+O6775Lvfve7sZ9zzzqdHXnkkeTOO++kE2IhRpx66qmpn+mgQYPITTfdRLZs2UI2bdpEfvvb35KWlhZiyqCLZ7f/+NSnPlXq6OgonXPOOaV99tmn9D//8z+ljRs3loYNG7bbP5us6+Pee+8tnX322aV99923tN9++5Xuuuuu0tKlS0vNzc3+z/zyl78sLVu2rHTMMceUZs6cWXr66adLTz31lHvmGZ/5gQceWFq8eHHp5ZdfLl155ZXuOecY2wYOHFhasmRJ6Xe/+13poIMOKu2xxx6lefPmlfbcc0//Zy666KLSpk2bSh/96EdLM2bMKP31r38tvffee6WGhga3plM864svvri0bt260oknnliaMGFC6ROf+ERp69atpa997WvuWZPsa/j4448v/fjHPy597GMfK4Gdeuqpoe/rrN977rmn9NJLL5UOPvjg0uGHH156++23SzfffLOp9e3ACTyDZ555pnT11Vf7D6aqqqq0fPny0re+9S0XWHJaI0OHDqWb4sgjj6S/7t+/f6mzs5MGH/Yze++9N/2ZQw45xD33lM+3paWl9NZbb5Xmzp1bevTRR32A4p5zPuv30ksvLT3xxBPKn1m5cmXp3/7t3/xfw7Nvb28vnXHGGW49p3jWf//730u//e1vQ7932223lW688Ub3rEk+61kEUJLW79SpU+mfmzVrlv8zH/nIR0q9vb2lUaNG5b7GXYmHEFJXV0dp2oceeiiglUol+uvZs2cbo652NxswYAD9vHHjRvoZnnl9fX3oub/11ltk2bJl7rlnsGuvvZbcfffd5OGHHw79vnvO+dhHP/pR8sILL5Bbb72VlixffPFF8sUvftH//sSJE8moUaNC6xnKm88++6xbzynt6aefJnPnziV77bUX/fV+++1HjjjiCHLvvfe6Z23IdNYvfIayzoIFC/yfgZ/v6+sjhxxySO4+7bSXBeZpQ4cOJbW1tTTo8Aa/njp1Kppfu5JVVVWRq666ijz11FPkjTfeoL83cuRIOqUQapnR5w7fc6ZvZ5xxBpk5c6ZQN+Wecz625557kq985SvkiiuuID/96U/ps/7v//5v0tXVRf74xz/6a1YUR9x6Tmc/+9nPSP/+/cmiRYtIb28vqampId/5znfIn/70J39Nu2edr+k8U/i8du3a0Pfh/UDSaWKNO4DizFp2P336dJoFOcvXxo4dS37xi1+QefPmuSsaDAs3gUGBgxLs5Zdfpmv6vPPOowDFWX72qU99ipx11lnkzDPPpAnNAQccQBMcEG+6Z737mCvxEEIV+T09PWTEiBGhhwO/Xr16Nda72WXs6quvJieffDI55phjqHqcGTxb6C5hpR9m7rmnMyjhwDODkkN3dzf9mDNnDvn6179Ov4YMyD3nym3VqlVk4cKFod978803yfjx4+nXLFa4OFK5/fznP6csyi233EJef/112jUCXWkXX3yxe9aGTGf9wudolyWwW4MHDzZyVjqAQggN4lBTg5onX5KAX8+fPz/3h767gZPTTjuNHHvssWTp0qWh78EzB3qcf+7QhjxhwgT33FMYaE4gk4csk308//zz5Oabb6ZfQ9bvnnPl9o9//IPsvffeod+D9QqaKTAYUQAghl/Pra2ttDbv4kg6a25uprqGaCkBWCz3rM2YzvqFz9BmDOVkZhDb4b2AVsWEOXU5IbTNGNTKn/vc56hS+de//jVtMx4+fLh7PhnXyLXXXktb1o466qjSiBEj/I/GxsZQmzG0Hs+ZM4e2Gf/jH/+gH25dVrYv+S4e95zza+Hu6uqiLbCTJk0qfeYznylt3769dOaZZ4baNCFunHLKKaXp06eX7rjjDtdmnOFZ33DDDaUPPvjAbzOGtti1a9eWfvazn7lnTSrr9Nt///3pB9g3vvEN+vW4ceO01y+0GS9YsIC22h922GG0c9C1GVsAUl/96lfpYQnzUKDtGPq83UGZ/XnKDGajsJ+BhX/NNdeUNmzYQIP97bffTkGMe+75AhT3nPOJESeddFLp1VdfpcnMwoULS1/84hdjP/OjH/2otGrVKvozDz74YGmvvfZy6znlc+7Xrx9dvxCPd+zYUXr33Xfp/I66ujr3rEn29Xv00UcLYzIAQt31O2jQIApIYC7N5s2bS9dffz0FPiZidpX3hTNnzpw5c+bMWWHMaVCcOXPmzJkzZ4UzB1CcOXPmzJkzZ4UzB1CcOXPmzJkzZ4UzB1CcOXPmzJkzZ4UzB1CcOXPmzJkzZ4UzB1CcOXPmzJkzZ4UzB1CcOXPmzJkzZ4UzB1CcOXPmzJkzZ4UzB1CcOXPmzJkzZ4UzB1CcOXPmzJkzZ4UzB1CcOXPmzJkzZ4UzB1CcOXPmzJkzZ6Ro9v8BZkz5QxE1UHMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assume we are working in a Jupyter notebook so we don't need to import `run_cysolver` if it was defined in an earlier cell.\n",
    "# from my_cython_code import run_cysolver\n",
    "\n",
    "import numpy as np\n",
    "initial_conds = np.asarray((20., 20.), dtype=np.float64, order='C')\n",
    "time_span = (0., 50.)\n",
    "\n",
    "result = run_reuse_cysolver(time_span, initial_conds)\n",
    "result.print_diagnostics()\n",
    "\n",
    "print(\"Was Integration was successful?\", result.success)\n",
    "print(result.message)\n",
    "print(\"Size of solution: \", result.size)\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(result.t, result.y[0], c='C0')\n",
    "ax.plot(result.t, result.y[1], c='C3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8af8fd-fa2d-4d08-bfae-f858eb3274a0",
   "metadata": {},
   "source": [
    "## Using Events with CySolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "96d98276-9dd6-42e8-a7c3-e8d1db5060a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content of stdout:\n",
      "_cython_magic_be0781bd329588d8007d63393b0fe6608ce0b7554b2f6b534e40a773d1238870.cpp\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_be0781bd329588d8007d63393b0fe6608ce0b7554b2f6b534e40a773d1238870.cpp(25643): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_be0781bd329588d8007d63393b0fe6608ce0b7554b2f6b534e40a773d1238870.cpp(25644): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_be0781bd329588d8007d63393b0fe6608ce0b7554b2f6b534e40a773d1238870.cpp(25767): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_be0781bd329588d8007d63393b0fe6608ce0b7554b2f6b534e40a773d1238870.cpp(28631): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_be0781bd329588d8007d63393b0fe6608ce0b7554b2f6b534e40a773d1238870.cpp(28632): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_be0781bd329588d8007d63393b0fe6608ce0b7554b2f6b534e40a773d1238870.cpp(29252): warning C4551: function call missing argument list\n",
      "C:\\Users\\joepr\\.ipython\\cython\\_cython_magic_be0781bd329588d8007d63393b0fe6608ce0b7554b2f6b534e40a773d1238870.cpp(29259): warning C4551: function call missing argument list\n",
      "   Creating library C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_be0781bd329588d8007d63393b0fe6608ce0b7554b2f6b534e40a773d1238870.cp312-win_amd64.lib and object C:\\Users\\joepr\\.ipython\\cython\\Users\\joepr\\.ipython\\cython\\_cython_magic_be0781bd329588d8007d63393b0fe6608ce0b7554b2f6b534e40a773d1238870.cp312-win_amd64.exp\n",
      "Generating code\n",
      "Finished generating code"
     ]
    }
   ],
   "source": [
    "%%cython --force\n",
    "# cython: boundscheck=False, wraparound=False, nonecheck=False, cdivision=True, initializedcheck=False\n",
    "\n",
    "from libcpp cimport bool as cpp_bool\n",
    "from libcpp.vector cimport vector\n",
    "from libcpp.limits cimport numeric_limits\n",
    "\n",
    "import numpy as np\n",
    "cimport numpy as cnp\n",
    "cnp.import_array()\n",
    "\n",
    "from CyRK cimport CyrkErrorCodes, PreEvalFunc, MAX_STEP, DiffeqFuncType, Event, EventFunc, cysolve_ivp_noreturn, WrapCySolverResult, ODEMethod, CySolverResult\n",
    "from CyRK.cy.cysolver_test cimport lorenz_diffeq\n",
    "\n",
    "cdef double lorenz_event_func_1(double t, double* y_ptr, char* unused_args) noexcept nogil:\n",
    "\n",
    "    # Check if t greater than or equal to 5.0\n",
    "    if t >= 5.0:\n",
    "        return 0.0\n",
    "    else:\n",
    "        return 1.0\n",
    "\n",
    "cdef double lorenz_event_func_2(double t, double* y_ptr, char* unused_args) noexcept nogil:\n",
    "\n",
    "    # Check y values.\n",
    "    # In the time span [0,10]: \n",
    "    #    y_0  starts at 1, spikes then goes below zero and oscillates with a min below -10. Have this return if y_0 < -10\n",
    "    if y_ptr[0] < -10.0:\n",
    "        return 0.0\n",
    "    elif y_ptr[2] > 30.0:\n",
    "        return 0.0\n",
    "    else:\n",
    "        return 1.0\n",
    "\n",
    "cdef double lorenz_event_func_3(double t, double* y_ptr, char* args) noexcept nogil:\n",
    "\n",
    "    # Use arguments to set threshold\n",
    "    cdef double* args_dbl_ptr = <double*>args\n",
    "\n",
    "    # We won't actually use the args but lets just check they are correct.\n",
    "    cdef cpp_bool args_correct = False\n",
    "    if args_dbl_ptr[0] == 10.0 and args_dbl_ptr[1] == 28.0 and args_dbl_ptr[2] == 8.0 / 3.0:\n",
    "        args_correct = True\n",
    "\n",
    "    # Then return events if args are correct and t greater than 4\n",
    "    if args_correct and t >= 4.0:\n",
    "        if t <= 4.1:\n",
    "            return 0.0\n",
    "        elif t >= 4.5 and t <= 4.6:\n",
    "            return 0.0\n",
    "        elif t >= 5.0 and t <= 5.1:\n",
    "            return 0.0\n",
    "        elif t >= 5.5 and t <= 5.6:\n",
    "            return 0.0\n",
    "        elif t >= 6.0 and t <= 6.1:\n",
    "            return 0.0\n",
    "        else:\n",
    "            return 1.0\n",
    "    else:\n",
    "        return 1.0\n",
    "\n",
    "def run_cysolver_with_events(bint use_termination = False):\n",
    "    \n",
    "    # Inputs for lorenz diffeq\n",
    "    cdef double t_start = 0.0\n",
    "    cdef double t_end = 10.0\n",
    "    \n",
    "    cdef size_t num_y = 3\n",
    "    cdef vector[double] y0_vec = vector[double](num_y)\n",
    "    y0_vec[0] = 1.0\n",
    "    y0_vec[1] = 0.0\n",
    "    y0_vec[2] = 0.0\n",
    "    \n",
    "    cdef vector[double] t_eval_vec = vector[double]()\n",
    "    cdef vector[char] args_vec = vector[char](3 * sizeof(double))\n",
    "    args_dbl_ptr = <double*>args_vec.data()\n",
    "    args_dbl_ptr[0] = 10.0\n",
    "    args_dbl_ptr[1] = 28.0\n",
    "    args_dbl_ptr[2] = 8.0 / 3.0\n",
    "    \n",
    "    cdef PreEvalFunc pre_eval_func = NULL\n",
    "    \n",
    "    # Build events\n",
    "    cdef vector[Event] events_vec = vector[Event]()\n",
    "    # Set the maximum number of events allowed before termination to the max size of size_t (effectively infinite)\n",
    "    cdef size_t max_allowed = numeric_limits[size_t].max()\n",
    "    cdef int direction = 0\n",
    "    if use_termination:\n",
    "        events_vec.emplace_back(lorenz_event_func_1, 1, 0)\n",
    "    else:\n",
    "        events_vec.emplace_back(lorenz_event_func_1, max_allowed, direction)\n",
    "    events_vec.emplace_back(lorenz_event_func_2, max_allowed, direction)\n",
    "    events_vec.emplace_back(lorenz_event_func_3, max_allowed, direction)\n",
    "    \n",
    "    cdef ODEMethod integration_method = ODEMethod.RK45\n",
    "    cdef WrapCySolverResult solution = WrapCySolverResult()\n",
    "    solution.build_cyresult(integration_method)\n",
    "    cdef CySolverResult* solution_ptr = solution.cyresult_uptr.get()\n",
    "    \n",
    "    cdef DiffeqFuncType diffeq = lorenz_diffeq\n",
    "    cdef size_t num_extra = 0\n",
    "    cdef cpp_bool use_dense = False\n",
    "    \n",
    "     # Run Solver\n",
    "    cysolve_ivp_noreturn(\n",
    "        solution_ptr,\n",
    "        diffeq,\n",
    "        t_start,\n",
    "        t_end,\n",
    "        y0_vec,\n",
    "        rtol = 1.0e-6,\n",
    "        atol = 1.0e-6,\n",
    "        args_vec = args_vec,\n",
    "        num_extra = num_extra,\n",
    "        max_num_steps = 100000,\n",
    "        max_ram_MB = 2000,\n",
    "        dense_output = use_dense,\n",
    "        t_eval_vec = t_eval_vec,\n",
    "        pre_eval_func = pre_eval_func,\n",
    "        events_vec = events_vec,\n",
    "        rtols_vec = vector[double](),\n",
    "        atols_vec = vector[double](),\n",
    "        max_step = MAX_STEP,\n",
    "        first_step = 0.0,\n",
    "        expected_size = 128\n",
    "        )\n",
    "    \n",
    "    solution.finalize()\n",
    "    return solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "caef3357-0bb1-44e9-b5ee-bf62d08d2978",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution Succeeded: True\n",
      "27.3 μs ± 376 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare to the solve_ivp and pysolve_ivp examples in \"1 - Getting Started\" Notebook\n",
    "\n",
    "# CyRK v0.16.0\n",
    "#    Terminate off: 61.1us; 62.0us; 62.6us\n",
    "#    Terminate on:  34.4us; 33.7us; 34.4us\n",
    "# CyRK v0.17.0\n",
    "#    Terminate off: 57.9us; 56.9us; 58.5us\n",
    "#    Terminate on:  29.4us; 30.6us; 29.6us\n",
    "terminate = True\n",
    "\n",
    "solution = run_cysolver_with_events(terminate)\n",
    "print(\"Solution Succeeded:\", solution.success)\n",
    "\n",
    "%timeit run_cysolver_with_events(terminate)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(solution.t, solution.y[0], label='y0', c='C0')\n",
    "ax.plot(solution.t, solution.y[1], label='y1', c='C3')\n",
    "ax.plot(solution.t, solution.y[2], label='y2', c='C6')\n",
    "# Event 1 will be dots.\n",
    "# Event 2 will be X's\n",
    "# Event 3 will be +'s\n",
    "ax.scatter(solution.t_events[1], solution.y_events[1][0, :], c='C0', marker='o')\n",
    "ax.scatter(solution.t_events[0], solution.y_events[0][1, :], c='C3', marker='x', s=120)\n",
    "ax.scatter(solution.t_events[1], solution.y_events[1][2, :], c='C6', marker='o')\n",
    "ax.scatter(solution.t_events[2], solution.y_events[2][0, :], c='C0', marker='+', s=120)\n",
    "ax.set(xlabel='Time', ylabel='y')\n",
    "ax.legend(loc=\"upper right\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}