2DofArm_simulation_testing_learned_readouts-C-STP_ON.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the coefficients in a real simulation where the feedback is generated by the readout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Makes possible to show the output from matplotlib inline\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Makes the figures in the PNG format:\n",
    "# For more information see %config InlineBackend\n",
    "%config InlineBackend.figure_formats=set([u'png'])\n",
    "\n",
    "# plt.rcParams['figure.figsize'] = 5, 10\n",
    "\n",
    "import numpy\n",
    "import sys\n",
    "import os\n",
    "\n",
    "import save_load_file as slf\n",
    "\n",
    "from generates_lsm_start import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main functions to the 2 joint arm simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from simulation_2DoF_Arm_physics import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start of the simulation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Experiment identifier\n",
    "sim_sets = [\"set_A\", \"set_B\", \"set_C\", \"set_D\"]\n",
    "\n",
    "sim_set = sim_sets[2]\n",
    "\n",
    "base_dir = \"2DofArm_simulation_data\"\n",
    "\n",
    "trajectories = [[[0.75,0.25],[0.0,0.5]],\\\n",
    "                [[0.25,0.60],[-0.25,0.60]],\\\n",
    "                [[-0.10,0.75],[-0.10,0.25]],\\\n",
    "                [[-0.75,0.50],[-0.40,0.00]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# First the arm parametres according to Joshi/Maass paper\n",
    "aparams = {\n",
    "    'l1' : 0.5, # metres\n",
    "    'l2' : 0.5,\n",
    "    'lc1' : 0.25,\n",
    "    'lc2' : 0.25,\n",
    "    'm1' : 1.0, # kg\n",
    "    'm2' : 1.0,\n",
    "    'i1' : 0.03, # kg*m*m\n",
    "    'i2' : 0.03\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Total time spent during the movement (in seconds)\n",
    "MT = 0.5\n",
    "# Simulation time step (in seconds)\n",
    "time_step = 2/1000.0\n",
    "# Number of neurons at each input layer (defines the resolution of the system)\n",
    "Ninput = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t_mov=numpy.arange(0, MT, time_step) # t starts in 0s and steps time_step(s) until reaches MT(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Reads all the experiments to check which range of torques is necessary to control the arm\n",
    "# These values are based ONLY in the 4 trajectories used!\n",
    "torques_all_1 = numpy.array([]);\n",
    "torques_all_2 = numpy.array([]);\n",
    "number_of_trajectories = len(trajectories)\n",
    "for i in range(1,number_of_trajectories+1):\n",
    "    states,torques = slf.load_from_file_gz(\"./\"+base_dir+\"/\"+sim_set+\"/States_Torques_movement\"+str(i)+\".gzpickle\")\n",
    "    torques_all_1 = numpy.concatenate((torques_all_1,torques[:,0])) # torques joint 1\n",
    "    torques_all_2 = numpy.concatenate((torques_all_2,torques[:,1])) # torques joint 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creates the 50 possible discrete values of torques for each joint (probably will be different)\n",
    "torques_values_1=numpy.linspace(torques_all_1.min(),torques_all_1.max(),num=50)\n",
    "torques_values_2=numpy.linspace(torques_all_2.min(),torques_all_2.max(),num=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creates the 50 possible discrete values of angles for each joint\n",
    "teta1_values=numpy.linspace(-numpy.pi/6,numpy.pi,num=50)\n",
    "teta2_values=numpy.linspace(0,numpy.pi,num=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# These are the 50 possible values that the input neurons will represent (x,y) \n",
    "x_values=numpy.linspace(-1,1,num=50)\n",
    "y_values=numpy.linspace(-1,1,num=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loads the coefficients generated by the linear regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "[c_tau1,r_tau1] = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/coefficients_residues_tau1_08-05-15.pickle\")\n",
    "[c_tau2,r_tau2] = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/coefficients_residues_tau2_08-05-15.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# plt.figure(figsize =(20,10))\n",
    "# plt.plot(c_tau1,'v',label=\"Tau1\")\n",
    "# plt.plot(c_tau2,'s',label=\"Tau2\")\n",
    "# plt.title(\"Coefficients - \" + sim_set)\n",
    "# plt.xlabel(\"Readout index\")\n",
    "# plt.ylabel(\"Weight value\")\n",
    "# plt.legend()\n",
    "# plt.savefig(os.getcwd()+\"/\"+base_dir+\"/\"+\"coefficients_\"+sim_set+\".pdf\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DON'T FORGET TO SET PROPERLY THE SIMULATION'S VARIABLES!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def new_experiment():\n",
    "    global run_number\n",
    "    run_number+=1\n",
    "    #####DEBUG\n",
    "    #####Reads exactly the same input spikes using during the training to check if the system is reproducing the same simulation!\n",
    "    # inputs_training = slf.load_from_file_gz(\"./\"+base_dir+\"/\"+sim_set+\"/States_Torques_movement\"+str(tji)+\"_INPUTS_LSM_1.gzpickle\")\n",
    "    # tau_temp = []\n",
    "    # teta_temp = []\n",
    "    # torque_idx = []\n",
    "    #####END_DEBUG\n",
    "\n",
    "    import numpy\n",
    "    import time\n",
    "    import save_load_file as slf\n",
    "\n",
    "    import step_by_step_brian\n",
    "    reload(sys.modules['step_by_step_brian']) # Makes sure the interpreter is going to reload the module\n",
    "    step_by_step_brian_sim = step_by_step_brian.step_by_step_brian_sim\n",
    "\n",
    "\n",
    "    #\n",
    "    # It's necessary to uncomment the import statement according to which parameters should be used\n",
    "    #\n",
    "\n",
    "    # Simulator based on Joshi/Maass 2006 parameters\n",
    "    import simulation_2DoF_Arm_md \n",
    "    reload(sys.modules['simulation_2DoF_Arm_md']) # Makes sure the interpreter is going to reload the module\n",
    "    simulation_2DoF_Arm = simulation_2DoF_Arm_md.simulation_2DoF_Arm\n",
    "\n",
    "    import membrane_lowpass_md\n",
    "    reload(sys.modules['membrane_lowpass_md']) # Makes sure the interpreter is going to reload the module\n",
    "    membrane_lowpass = membrane_lowpass_md.membrane_lowpass\n",
    "\n",
    "\n",
    "    # Calculates the initial teta1 and teta2 angles according to the start position\n",
    "    teta1_0, teta2_0 = hand_to_joints(numpy.array([[xstart,ystart]]),aparams,ang_error=0.01)[0]\n",
    "\n",
    "    step_size = 2\n",
    "    \n",
    "#     noiselevel=0\n",
    "    noiselevellist = [1.0,2.0,10.0]\n",
    "\n",
    "    ################## Simulation's variables\n",
    "    simulation_2DoF_Arm.func_globals['my_seed']=93200         # This seed is important to always repeat the liquid's structure\n",
    "    simulation_2DoF_Arm.func_globals['input_gain']=70.0    # Base value used within the input weights\n",
    "    simulation_2DoF_Arm.func_globals['noisy']=1            # Controls if the liquid is going to use the noisy currents/input weights\n",
    "    simulation_2DoF_Arm.func_globals['gaussian_pop']=1         # Controls which type of input weight configuration is used (1=>gaussian distributed)\n",
    "    simulation_2DoF_Arm.func_globals['w_SD']=3.0           # The width of the gaussian used above.\n",
    "    simulation_2DoF_Arm.func_globals['stratification']=1   # Type of connections: stratified or 30% random\n",
    "    simulation_2DoF_Arm.func_globals['STP_OFF']=False      # Uses or not STP (False means it uses STP)\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['i_noiseA'] = noiselevellist[noiselevel]*1.0/100     # amplitude (dimensionless) of the random currents (updated every time step).\n",
    "    simulation_2DoF_Arm.func_globals['offset_noise'] = (13.5/100,14.5/100) \n",
    "                              # Range of the uniform distribution used to generate the constant offset current.\n",
    "                              # Maass 2002, should be drawn from a uniform distr [14.975nA,15.025nA]\n",
    "                              # Joshi/Maass 2005 does [13.5nA,14.5nA]. Maybe it is to avoid too many spikes without inputs...\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['initial_volt'] = (13.5,14.9) \n",
    "                              # Range of the uniform distribution used to generate the initial membrane voltage.\n",
    "                              # Maass 2002, should be drawn from a uniform distr [13.5mV,15.0mV]\n",
    "                              # Joshi/Maass 2005 does [13.5mV,14.9mV], this way it's impossible to generate spikes (14.9mV is below threshold)\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['random_reset'] = (13.8,14.5)    # Range of the uniform distribution used to generate the constant reset voltages.\n",
    "    simulation_2DoF_Arm.func_globals['Net_shape'] = (20,5,6)          # Defines the shape of the network (liquid state machine)\n",
    "    simulation_2DoF_Arm.func_globals['Number_of_input_layers'] = 6    # It means I will have 6 input layers...\n",
    "    simulation_2DoF_Arm.func_globals['Number_of_neurons_inputs'] = 50 # ...with 50 neurons each.\n",
    "    simulation_2DoF_Arm.func_globals['lbd_value'] = 1.2               # lbd controls the connection probability\n",
    "\n",
    "    simulation_2DoF_Arm.func_globals['noisy_currents'] = True # Injects noisy currents the neurons\n",
    "    simulation_2DoF_Arm.func_globals['noisy_input_weights'] = False # Injects noise into the input weights\n",
    "    simulation_2DoF_Arm.func_globals['noisy_input'] = True # Ignores the random seed for the input noise\n",
    "    simulation_2DoF_Arm.func_globals['input_noise_level'] = 0 # 1/SNR\n",
    "\n",
    "    # STP parameters\n",
    "    simulation_2DoF_Arm.func_globals['AII']=47.0  # Joshi/Maass2006=>47.0 / Maass2002=>2.8\n",
    "    simulation_2DoF_Arm.func_globals['AIE']=47.0  # Joshi/Maass2006=>47.0 / Maass2002=>3.0\n",
    "    simulation_2DoF_Arm.func_globals['AEI']=150.0 # Joshi/Maass2006=>150.0 / Maass2002=>1.6\n",
    "    simulation_2DoF_Arm.func_globals['AEE']=70.0  # Joshi/Maass2006=>70.0 / Maass2002=>1.2\n",
    "    \n",
    "\n",
    "    indices2kill=numpy.arange(600)\n",
    "    numpy.random.shuffle(indices2kill)\n",
    "#     decimationlevel=0\n",
    "    decimationlevellist=[0,6,60]\n",
    "    simulation_2DoF_Arm.func_globals['killed_neurons']=indices2kill[:decimationlevellist[decimationlevel]] #Sets the indices of the neurons I whant to 'kill'\n",
    "    \n",
    "    \n",
    "\n",
    "    cheat_feedback = 0 #Uses the torque values from the mechanical simulation instead of the calculated ones by the LSM\n",
    "    using_spikes = 0 #Uses only the spikes without the membrane low-pass filtering\n",
    "    membrane_time_constant = 30E-3 #time constant used with the membrane low-pass filter\n",
    "\n",
    "    delta_t = 200 # Time delay imposed to the joint angles - proprioceptive (in ms)\n",
    "    ################## END OF Simulation's variables\n",
    "\n",
    "    # Starting the simulation object!\n",
    "    # Initializing the simulation...\n",
    "    s = step_by_step_brian_sim(simulation_2DoF_Arm, step_size)\n",
    "\n",
    "    init_t = time.time()\n",
    "\n",
    "\n",
    "    # The input (one big neurongroup with 300 neurons) will be divided like this:\n",
    "    # 6 groups of 50 neurons.\n",
    "    # - Group 1: xdest => offset:0\n",
    "    # - Group 2: ydest => offset:50\n",
    "    # - Group 3: teta1 => offset:100\n",
    "    # - Group 4: teta2 => offset:150\n",
    "    # - Group 5: tau1  => offset:200\n",
    "    # - Group 6: tau2  => offset:250\n",
    "    input_spikes = [x_lsm, y_lsm] # At the first step the system only receives the X,Y destination position\n",
    "\n",
    "    delta_t_idx = int(delta_t/(1000*time_step)) # Number of steps the delay (delta_t) takes\n",
    "\n",
    "    m_v=membrane_lowpass(600,membrane_time_constant) # Initialize the membrane\n",
    "\n",
    "    teta = [] # Used to save the calculated teta1 and teta2 angles\n",
    "    torques = [] # Used to save the calculated tau1 and tau2 torques\n",
    "    velocities = [] # Used to save the calculated joint velocities\n",
    "    OutputSpks_save = []\n",
    "    OutputSpks_Filtered_save = []\n",
    "\n",
    "\n",
    "    tau1 = 0 # Initial values of the torques (because of the type of velocity curve, it's always zero at the beginning)\n",
    "    tau2 = 0\n",
    "\n",
    "    teta1 = teta1_0 # Initial values of the joint angles\n",
    "    teta2 = teta2_0\n",
    "\n",
    "    if delta_t==0:\n",
    "        teta.append([teta1_0,teta2_0]) # otherwise the generation of joint_states_idx will raise an error.\n",
    "\n",
    "    # Runs the simulation going step-by-step (250 steps = 500ms)\n",
    "    for step_i in range(250):\n",
    "\n",
    "        # Because the LSM accepts only indexes, I have to convert the real values to indexes\n",
    "        # torques_values_1 and torques_values_2 are numpy.arrays with all the possible torque values.\n",
    "        if cheat_feedback:\n",
    "            torques_normalized_idx = [abs(torques_values_1-torques_all_1[(tji-1)*250:(tji+1)*250][step_i]).argmin(), abs(torques_values_2-torques_all_2[(tji-1)*250:(tji+1)*250][step_i]).argmin()]\n",
    "        else:\n",
    "            torques_normalized_idx = [abs(torques_values_1-tau1).argmin(), abs(torques_values_2-tau2).argmin()]\n",
    "\n",
    "    #####DEBUG\n",
    "    #     tau_temp.append(torques_normalized_idx)\n",
    "    #     torque_idx.append(torques_normalized_idx)\n",
    "    #####END_DEBUG\n",
    "\n",
    "        if step_i>=delta_t_idx: # Tests if the delayed angles can be feed to the LSM\n",
    "            # teta1_values and teta2_values  are numpy.arrays with all the possible joint angles.\n",
    "            joint_states_idx = [abs(teta1_values-teta[step_i-delta_t_idx][0]).argmin(), abs(teta2_values-teta[step_i-delta_t_idx][1]).argmin()]\n",
    "\n",
    "            # Here I start to send the teta1 and teta2 delayed inputs\n",
    "            # It is necessary to convert the values of teta1, teta2, tau1 and tau2 to input indexes!!!\n",
    "            input_spikes = [x_lsm, y_lsm, joint_states_idx[0]+100, joint_states_idx[1]+150, torques_normalized_idx[0]+200, torques_normalized_idx[1]+250]\n",
    "    #####DEBUG\n",
    "    #         teta_temp.append(joint_states_idx)\n",
    "    #####END_DEBUG\n",
    "        else:\n",
    "            # It is necessary to convert the values of teta1, teta2, tau1 and tau2 to input indexes!!!\n",
    "            input_spikes = [x_lsm, y_lsm, torques_normalized_idx[0]+200, torques_normalized_idx[1]+250]\n",
    "\n",
    "    #####DEBUG\n",
    "    #####Replaces the calculated inputs by the ones used during the training!\n",
    "    #     input_spikes = inputs_training[step_i]\n",
    "    #####END_DEBUG\n",
    "\n",
    "        # Here the simulator is receiving the input spikes (and returning the output ones):\n",
    "        r, current_t, received_spikes, OutputSpks, t = s.run_step(input_spikes) # Sends the spikes and reads the output\n",
    "\n",
    "\n",
    "        if OutputSpks!=[]:\n",
    "            OutputSpks_save.append([OutputSpks[0],t_mov[step_i]])\n",
    "        else:\n",
    "            OutputSpks_save.append([[],t_mov[step_i]])\n",
    "\n",
    "        if using_spikes:\n",
    "            Output = numpy.zeros(600); Output[OutputSpks]=1 # Using the output of the liquid directly\n",
    "        else:\n",
    "            m_v.process_spikes(OutputSpks,t_mov[step_i]) # Send the spikes to the membrane filter\n",
    "            OutputSpks_Filtered = m_v.check_values(t_mov[step_i]) # Reads the values of the membranes\n",
    "            OutputSpks_Filtered_save.append(OutputSpks_Filtered)        \n",
    "            Output = OutputSpks_Filtered # Using the low-pass (membrane) filtered version of the liquid's output\n",
    "\n",
    "        torques.append([tau1,tau2])\n",
    "        tau1=(Output.dot(c_tau1)+r_tau1) # Calculates the torques based on the membrane filtered output of the liquid\n",
    "        tau2=(Output.dot(c_tau2)+r_tau2) # These are real numbers, not input indexes!\n",
    "\n",
    "    #####DEBUG\n",
    "    #     tau1=torques_all_1[(tji-1)*250:(tji+1)*250][step_i]\n",
    "    #     tau2=torques_all_2[(tji-1)*250:(tji+1)*250][step_i]\n",
    "    #####END_DEBUG\n",
    "\n",
    "\n",
    "    #####DEBUG    \n",
    "    ##### Moving averaging the torques before sending to the simulator\n",
    "    #     ws=2 #window size\n",
    "    #     temp_tau1_v = numpy.concatenate((numpy.array(torques)[step_i-ws:,0],[tau1[-1]]))\n",
    "    #     temp_tau2_v = numpy.concatenate((numpy.array(torques)[step_i-ws:,1],[tau2[-1]]))\n",
    "    #     tau1[-1]=moving_average(temp_tau1_v, window=len(temp_tau1_v))[-1]\n",
    "    #     tau2[-1]=moving_average(temp_tau2_v, window=len(temp_tau2_v))[-1] \n",
    "    #####END_DEBUG\n",
    "\n",
    "\n",
    "        if step_i>0: # Because the simulator needs two time steps to be able to calculate the next state\n",
    "            state = odeint_arms(twojointarm, state, [t_mov[step_i-1], t_mov[step_i]], aparams, [tau1,tau2])[1]\n",
    "            teta1,teta2 = state[0],state[1] \n",
    "            teta.append([teta1,teta2])\n",
    "            velocities.append([state[2],state[3]])\n",
    "        else:\n",
    "            state = [teta1_0,teta2_0,0,0]\n",
    "            teta.append([teta1_0,teta2_0])        \n",
    "            velocities.append([0,0])\n",
    "\n",
    "    print \"Total simulation time (seconds):\",time.time()-init_t\n",
    "\n",
    "    slf.save_to_file(numpy.array(torques),\"./\"+base_dir+\"/\"+sim_set+\"/torques_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "    slf.save_to_file(numpy.array(teta),\"./\"+base_dir+\"/\"+sim_set+\"/teta_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "    slf.save_to_file(numpy.array(OutputSpks_save),\"./\"+base_dir+\"/\"+sim_set+\"/OutputSpks_save_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "    slf.save_to_file(numpy.array(OutputSpks_Filtered_save),\"./\"+base_dir+\"/\"+sim_set+\"/OutputSpks_Filtered_save_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# %%capture\n",
    "\n",
    "# # Here I'm going to run 5 trials for each experiment\n",
    "# for tji in [1,2,3,4]: #[1,2,3,4] are the training set trajectories\n",
    "#     x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "    \n",
    "#     for decimationlevel in range(3): #[0,6,60] neurons\n",
    "#         for noiselevel in range(3):  #[1.0,2.0,10.0] multiplication\n",
    "#             run_number=5 #<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n",
    "#             while(run_number<15): #trials for each situation\n",
    "#                 new_experiment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIiKy6+4C5gHnxVsK3gicHV+riP+/O1Aev+0f39AyrwPV8XM/Xn/QOZcL5AG/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IrK4Wv6J+9YU3udn8rDrGU1x/PKmrEp3MIiLqsM6p5mCrtxWs32v37hNzR9fAQ7xpXd0WYxgv4U\n7SIPBs4DdgISeBoIivXR+gOjgYEUFXfPAquBteXHS8p9FwPfAh4CHgcez2Rh2cJxx/Ic6z6/H9ge\nmEdRzfeXTNZ2dP6SJEmSJEk9xfv82hibIuyyjaEkbWLtViXVVEANekUMu+RudnjNsbztt4Nanej4\nZTvzM/6XYBBHNrcvLBzLHtzAmsoJ9CNIVlaOLeVvFAHKATVjTbEQcpua7bOZThHcHFEz9thbgsEL\nVsB9tX9f+q9ZBgyv2T6QEfSr8/doF45gJ97CW2n9s5jIWH7I+UxkRM32RZ9+nKfGvYadhx7I8U27\nNI9tRJtFgAiGAj8C9gVGAIOA54DfUoRcWzX/JOCjFOHWGuB7wBvLsUfKsX7lx3eA/SnCq9OBh4GX\nAXuXYdra8nzPAs8DcyhCr92BPYG7gDUR/BV4EHgFsEP5WL8q5zmSYr2z4cDycvuTwDPluU8pz7kY\nOA14NpO+8Y4XSZIkSZIkqQsMuySpm1QFKEBVFdZe8aa4lJ04o9V2gONW7s6Ij+5OsLryQVazkLWs\nhDZhF0ATi4DtarbP428U4dTYmrGlvFiO1VqRj3DDyDG8fe76c05lJnMPuIYVxx3Kdd9YyYlPrw+h\nrtt5GU33v5Mh8/+ZqiBsBU1UhV1z+Us5h51qxp7hFoawFXBQzdigNgHYOjuteRPbvXMNR6/YsdX2\niYxlCpNjdFS0bRz0ihj+2VtZ9undGDj1EHa4bND6tcNOvSubzngTQATTgL2Au4FTWq4dFsG88su7\ngUltxp5vMXb4urEIZgCHUlSG3ZXJKS2OmUYRdt0NzS0j9y0/3grsWm6bD3wRmAt8iSJUGwKMAn4J\n7ExRmXYQsG15zFPA2gjmU1SObUfxmmAZ8AOKkGwORcj3wfKxlrV9XpIkSZIkSVJfYNglSR3QoSqt\nabyapaxmIru1OsmxvIxb+XeG91tDVVe6edxZBkO1gdFCnqAIhl5dM7aCmUxl2zZt/WayiMvLr8d0\naGw5FzK732C+9ZYfMPyhR1kxDJ7797WsOO0S4HqefPjf+N+//yv9h4xi9ZqFNM16f86dfVOMjpWV\n51vI95la03aw/Tks5DKSybU/JNaFe6Nqts/nAQav2BqorUrbhcPYkTdzeJvQ7fiVu3Plj45g3gvf\nZdxXx3Hc2hZrh/1ixxj9ngkAjBk4mNz7BeLhxSxedRC0aos4CfgmcFZFIFRvbFn5+W7grI045jfA\nbyI4niKAahuetQzVJrQJ3KYBR7E+PFtOEXJtB0wB9qMIUY+hqArbqfx4A0UYRzmf5kBOkiRJkiRJ\nPSMi1gJjM7PT69RHxEXAmMx8V7dNrP5jjQe+n5m7bWjfbnk81+ySpNZi1O4nMuSlVzIwhrN6xTyW\nPf/BnPv4jyoDrZ/yDDtwI89zDMfw0pqTXccKTmRwzfbvveoFlu+wLR+4bWDN2BSms4jLK9apmsms\nMvhpb2xrzmEQQ1lJE4u4vLk9XxHUdXwsuJWikuh5itZ812WyohzblooQp975OjOHyp/7VGbyNN+v\nWLOr+DlszWROqwgLv/OKxxncbzSTHqxdo+x2ktU0Va5fdiX3MKomSHyUWZy7obaI7an38+vsce2d\nbwNjrYKwNuf8C0U1Wc2YJEmSJElSS331Pn9ELIHm5RzWLQGxbkmQD2TmDztxzqOBT1DcN1kO/AT4\nSGYuaeeYSRRrwr+cYqmJ+4HPZeZvNuLxuiPsurA8xwbDroi4Cng6M/+tk481njph16ZYs8uwS9IW\nqyagWNDvMpo+cih7XH8eJz2xvvL1hh2Xsu3zn2Mx7+Y49q450TRmkazgaMbUjF3NfN5Z0VrwO+P+\nypLln+GlT3+hKqzpbDDULT+YNlq02gO4tmWrvZ7S0fCsMiC7brcVzDz3Rnb64p6c/kLtGmVXcytD\n2Za3V6xf9iOSkyraPdYPJpuDsG76EWxSGwjCRgIzgXMy+X5vzE+SJEmSJDWGRrjPHxGPA2dm5m1d\nPM+pFMtK3EmxpMQPgCcz8+w6+58HfIyiu88twErgSOAfMvNjG/F4PVrZZdjVSY3wSyCp8XSo7eD1\nOzexZEhw+mNDak50C/NIlnMkO9eMXcsdrGR5ZSXRldzDyH7bt2qLd91OS3j+uVM2FGj1Fe1V/fRl\nMTomMOigKfQfuB2rhsC8k+7MpjMPrVsp1l5F2P+xgFOa17ta75esYCULOYoda8amMD0fyaM6W/XV\nl0Twj8C3gVdksry35yNJkiRJkvqmRrjP3zLsiogDgEuBvYEm4HrgvMxcFRF7AI8BAzJzbXnsDIoA\n59sV530bcHFm1iw/EhHbALOAMzLz+jrzqjuXcrw57IqIocBngRMo1mh/ADgMOJA2AVNEPAG8t3y+\nF9Ei7IqIHwFvBoYCfwLOzsyHIuIDwFcpquFWArdl5nERsTNwOXAIsAT4n8y8vDzXUODrwETgWeAq\nYHJPhV2u2SVps1UZatw8cO8YteNH2J6zW20HOOGZoVxftYgWsJAHWMlyqAi7ypCqcs2pBSddzXOn\nfJznv9rEgBy6fm2rIuwoQ4++Hny0txZVn5VzcloEd7E+qDuuefvogCmVFWHVa4etYCHwupoHeZHf\nM4xRUBF2jWTfODy+wO6c2qrF5VTGxOjyb3aDhGCZ3BbBfRRl9p/v7flIkiRJkiR1k9XAucA9wG7A\nz4APUoROVZL17RDbOhT4S52xN1FUf93YTXP5MvCK8rxzgAOgzn3N+vMF+ClwBkWg9SXgamC/zLwi\nIt5EUdn1GYCI6Af8uHwOp5Rz/EVE/C0zbwUuBF4G7AmMAKZv4LG7lWGXpE0mgu9RvJvgUWBSdwcl\nEQwERrDdAccyfO0XGdh/BCsHJfNO/iVN5yS7HHIYE381otVBx616KdOW38CQoPJau5yFUNF2sGnk\nIJbO/VJlENIyKGkZoDzz70+y7N8+Cfwzs0+4jQYMjADK+fZ468JuUhnU1QsZ6wVhAEytqAabyxdZ\nxWRgn5pHXs1yFvFejmkThE1kLFfxObZnRJvzjYnRQV8NvIDzgT9EcFUmz/T2ZCRJkiRJUmOJ4Apg\nL2AZnbxX2B3naCkz723x7ZMRcQVFaFUv7KozrzgMeDdULI9RGAm8uK5CrCtzKUOn9wBvzMxny813\nlWMdmTaZeVWL814MnBsRW2Xm4nWbW+z+BmBUZn62/P7xiPhf4B3ArcBJFJVhC4AFEXEp8JkOTagL\nDLskdbsI1l1w3wEMBMZRBA5dCkwieCNFQLE10A9IBt28nJ1fGNxqja3rH59A/+tvYuBdiyneRdDa\nysX3MZ+5wFtrxlYwk6ls2yqEuGHr53nm8u1Ycep0iHOrKoJgfYBSrn/0beD1wIGZrOuj26iBUcPq\nTFBXLwjrcDXYLD7MKC6gquprOK9hYps1wCYylimcA0zri60PM3ksgv8FvgCc3ptzkSRJkiRJDWkv\n1q8LP7+DuUyVbrjfGHsBlwD7A8MoMpN7OniOAykqok7IzEfr7DYXGBUR/eoFXh2YyyiKKrGZHZln\nxeP1Bz4HnAjswPrKsFHA4opDXgrsHBHzW2zrT7FmGRQdsZ5uMfZUV+bXUYZdkrpVBK+g+EMzGPg9\nRc/X1cCnunje9zPopv9ih0v6M6DfAFYNhnkn38mu72vipDZrLJ0wexC3zP4nVrKw8mRLmFO2Hdyj\ndYvDfo+zfO2FzKJ1qLFg2VdZcepFwKSck1Nop+1gBDdQtM2bAxyQyfNded7qOzpaDZZzclqMi8mV\nJ1vLAqoqCHfkgDguvsNLOYyj2aV5e4uqr14Owj4H/C2CAzL5Qw89piRJkiRJ2jwsKz93el34NmvL\nn9UNc/o68EfglMxcGhEfplgHC2Bp+XkYxfpUADu1nk/sB9xMsRbX7e08zu+AFcDbKNbi6uhcWnoR\nWA6MBf7cZmxpOd918+tPEWRVmUSxvtY/ZeaTEbEtMI/11Vxt22I9BTyemXvVOd+zwO7AX8vvd6+z\n3yZh2CWpW0QwBPgERR/ZiykuzltRBF9zKHq2vmuD52l7I3/+dt9g7rxjGHztW9nntIUct2r9RfKW\nW1/HGoZUnmghf+FFvlTZeq5lRc73B51HvvzVxN/+zOKVl7QIDVqFBxHMB66N4KbM5j9uLcf7UfTU\nPYaimu2lFIs1Ws21Bai79toiLmtnDbDasGsJT7KSt3BMi6ALiqqvqzk3Rgc169D1YPvDTBZH8Cng\nKxEcnNlzfZclSZIkSVLD64514bt7bfkRFFVMyyJib+BsKN68npkvRMRs4F1lS8HTgTHrDoyIV1Ks\nS/WhzPbvy2Tmwoj4DPD/ImI18HNgFUXnqfGZ+bH25tLmXGsj4krgkoh4V7nPARRB2d+BIRExoXyM\nT1IUJdR77iuAeRExnNp12udQrL+1zh+AxRFxAcV9z5UU64YNycx7gGuBT0TE78tzn9Pez6S7RWbf\nuE8VEZmZXS9clNTjIphKcWFeBPxjJg+1GR8OPAi8P5Of1z3P6JhQcyP/ul1XMPc9DzD6syM4Pveu\nOehq5vPOitBgCtPzkTyqDM8q2w524nleDTyWyb+12b4jcBVFeLEcGE8X3qGizUvVv0GgKrSaySwm\nM4oLOLm5pH+921nLChZwJNvXjE1hOou4vCcqvspg9w/AJZn8oLvPL0mSJEmSGlcj3OePiMeBMzPz\ntog4BLgC2BW4D7gdeEtm/kO575HA1yju+32bosXg9zPzyoj4DsWb+5tanP6JzHxVO489CfgIRUi0\nmKJN4ecy866NmMsaYFxmPhYRQyiWmjiJIli6HzgiM1dExOnlWH/gS8C/AO8rn++FwJjMfHcZcF0N\n/CNFm8XPUNzjXPcYY4EfAXsAt2fm2yPiJcB/A2+hCNEeBj5dnnso8A2KarHZ5bkmZ2ZNhVe9fydd\n+fdj2CWpSyL4APBVimomgGsza6uZIjiaYjHFV7FjvKXqpnyMi+mc1qYlIcDPWc5aFnBE6zJhAL7H\nA2zL0KrQoLtv9EewG8Ufjv0zeaLcdjjwnfLjYmA43fvuEm2m6gWxdX8PfsAvGMFoJlL7gulq/s5W\n9Gvze/Aoszh3EwVeBwPXAHtnNpf0S5IkSZKkLZz3+bUxNkXYZRtDSZ1SVnd8kSKp/y3F4pJ1e+Vm\n8tMITmfoJd9hV/Zvc1N+bIyP/RjJvpUPNp/fs5LlUBF29WM2s7i8ar2krj7HiufwdASXAl+K4DTg\ns8CpwGmZrOvJuwBbF2ojdLj14QL+h7VMhoqwayBjmUi/VtsmMpYpnAN0+zpfmfwmghXAExHMpXin\nzgKK0vflwHsoFjOdC5zU08FvBNuUcxpXzmtSZs8uiipJkiRJkqSeY9glqcMiGAZMAUYCBwFraFPN\nVHVzHfLDbPfTx5nIoFYnnMgYbuV8Vg5YAKtrH3Bd67eqAGB9sLXJ1ysqfZmiX+18ioUp35TJYz30\n2NoCNK8nVxHgxuig8vdgLcuoCsG25+VxYHyIXTl3E6zzNYeiT/Uo4L0UpfWDgSHA6yiuDwBzIriH\nomf03yhK43egWDD1g8AsYFFm8csfwVBg5/Jjl/Lz6eX5VgA/pFiIdSnF7+AZFP2jBwFPUCzOui2w\nlqKMH2BmBAuAmeXH3sBQikVXT8hkThd+DpIkSZIkSeplXQq7yr6Qd1Dc3BoE3JyZn6jY7zLgKGAZ\ncEZm3teVx5XUeyLYCZhK0Y/11ExWlEPN1UyVa29NZSz7xFUsHbEM2oRdAC/uNJsnvrYLa943j7fP\nW78m0c39HmfR2svbCwA2zTOtlsmyCGYCrwGGUfS/tZJL3apegFvv94Ct61R8Jf0YyBc5imGttndP\n1dfC8nPN+nQRTKP4u383cBpFVebLy4+WQdjvKBYz3TqCtUCUH7PKj9nAMxSh1S7lMW8Hbi23Dafo\nlb3ufE8AbyqP/UmLORxOEcKNKT8OBl5aHjM7gsco1hV8sDx+GEWgPcl2pJIkSZIkSX1fl9fsiohh\nmbksIgYAvwbOz8xftxifAHwoMydExBuBSzPzwIrz2MtT6uMieCXFDeRvA5/NJMREdcEAACAASURB\nVCsruLZmcuWaQ7cwnxXMbRWCrfOtQ5cxe8Y/smOMZKtB55EvfzXxtz+zeOUlPR1obUibG/mHezNc\nva1OwDyTWUxmFBdwMofWHPQT/sYiLmIE/9GZtb4i2JY669NtYKzm9yeCAO4E3lzu1mrtv/Z+5+qN\ndWAOE4AdgX3Lj3+hqFaDohLtQ8CvWgT7kiRJkiSpDu/za2NsijW7uhx2tZjEMIoqr9Mz86EW278B\n3J6Z/1d+/zBwaGbOaXO8vwRSH1beHH4rReXDWzJZUHmD/Sc8xWoGczyja05yLXfwIl9i58Hf4PgV\nuzVvv370Yp4Z8J58cdb1m/6ZdF17N9Gl3lIGz7WtD8fF9Mrw+WfMYSAjeWtFlfcUpucjeVR3r/UF\n9X9/NhBodSpY6+gc2szjIeBmYDzwSopKr1UUrRP/E3gWWAQsBj5DUSm2FKvBJEmSJElbMO/za2P0\nybArIvoB91K0Bfp6Zl7QZvzHwBcy87fl978APpaZf2yzn78EUh8VIy78Ktv87mwGruzHqsEw7+Q7\ns+nMQ+veRL+G5byDITXbW95AH3jINQyIEaxeu5imWe/LuY//qCeei7Slabfqawc+zkkcUnPQjdzD\ns1xYcdxGVX11ap59JESumkcE2wO/AvYpd3sWeATYGtgK2B0YWI7dlMnbenTSkiRJkiT1Ed7n18bY\nFGFXl9bsAsjMtcBrI2Ib4JaIGJ+ZM9rOse1hVeeKiItafDuj4jySelAE/Rly6bWMvepYTnyqX/PA\nzb/YLQ553wWM4pWVB67kEaYytObm+iIuh3LdoeBe4FCKm8UnAoZd0ibQ3np3MS4mVx60Ffsygh9x\nWP21vrp9nkWw1Ovr31XNI5N5ETxJEXa110pxLnBoBJ8HvpLJ8z03c0mSJEmSpMYSEeMpuup0WZfD\nrnUyc2FE/BR4PTCjxdBsYLcW3+9abqs6x0XdNR9JXRPBcGAKI6/5B058amCrwePWvoxb+RQr69zI\n7cdsZnF51c31FnstKz/fDZy1KZ6DpEL5u1cbUC3iMqYyprLq6yX8O7B/zTGDGLop2hs2gEnUrzxr\nHgO2A84HHo7gauDLwFPA9hStDvdo8TEBWFGOn2r7Q0mSJEmStCUpC55mrPs+Ii7s7Lm61MYwIkYB\nqzNzQUQMBW4BLs7MX7bYZwLwocycEBEHAl/JzAMrzmV5o9SLWt28XjE0mXXlLqw88be8ZtBrOT5f\nU3PAuvW36rRH29CN777Sskza0nV4ra8baWIoTRzJ9s3bNmF7w0YVwU7AR4BzKd5clMDDwEzgifLj\nn4GXl4f8Bfgnq8EkSZIkSY3M+/zdLyI+AeyZme/vzn034lxrgbGZ+VhXz1Vx7r61ZldEvAr4LtCv\n/Ph+Zv5XRJwFkJnfLPf7KnAkxcLt78nMeyvO5S+B1Esq1/SZNriJl69YwkyGcDhb1RzUcv2tihvl\nPTl/Sd2v7lpfS1nNqc0BzXqtrwlbWtVXXRH8Cnhz+e21metbJLZof/gg8EdgInAzcHkmf2x7LkmS\nJEmS+rq+ep8/Ipawfnml4cByYE35/Qcy84edOOfRwCeAfcvz/QT4SGYuaeeYM4CPAnsCi4AbgU9k\n5sKOPv6mtkWFXd2pr/4SSJubqhvRbM3kygqOa/k1L/KFzlZvSWpsVWE2o7iAkzm0Zuefs5BtmMIc\nJnAsL2vevoVXfbUItKrW+mpV4RrBSOB9wKeB1cCfgOOtfpUkSZIkNYpGuM8fEY8DZ2bmbV08z6kU\n63ffCQwBfgA8mZln19n/o8C/Au8Gfkmx5NPXgB2AgzNzVcUx/TNzTdvtPcGwq5Ma4ZdAanSVlRo/\n4wXWMJxjGFZzwLXckQ/meKu3JK1Tt73hDdzLEHZmAjvVjG3BVV+dadkawR3AP5Tf/gnYP5NeeWEr\nSZIkSVJH1A0xuuGeQHfdV2gZdkXEAcClwN5AE3A9cF5mroqIPYDHgAGZubY8dgZFh7tvV5z3bRTL\nPL26YmxrYDZF57vrWmwfDjwOfCwzvxMRFwGvLOcyETgP2A0Yk5nvKo95N/AfFBVqX6F44+x7y+dz\n0bp9W8z/jHL/YcD/ZObny/PUfe7leEOFXQO6Pi1JDWNrJrcKugCOYgemDFlZVNq2sZImgPKPxmZ9\nQ1rSRlrEZUxlTE2153P8G6O4ACrCrhHsWKct4pgYHWzOgVcZcJ2ywR1bW1p+/jNFS4PfRvDeTB7s\n1slJkiRJktQDuuOewCa8r7CaYr3teyhCpZ8BH6QIgaok69shtnUoxZrcVQ6iqP66odXJMpdGxDTg\nMOA75eaJwIllYDUE+Ni6/SNiH+D/AUdQdJH5PLBzm/m1dTCwF8W64X+IiOsz8290/Ln3af16ewKS\nul+MjgkxLqbHvjEjxsX0GB0T4uJ4HaN4VeUBK0Y3cXO0TuinMpNFXN4T85XUOHJOTmMW5zKF6VzL\nHUxhenNb05VVqTkwnL0Zy//VhO0TGcvWnNMT824wk4BrKV4kjweuBGZE8OkIBvbmxCRJkiRJ6rCq\nN+B39J5Ad5yjQmbem5l/yMy1mfkkcAVULN+wARFxGEV7ws/U2WUU8OK6CrE2nivH1/ltZk4t57cc\naFnpdCIwNTN/W1ZgfYbWAVdVVdTFmbkiM/9M0UHmteW5u+W59xVWdkmbmcp3Ofyc8cxkIatYVHnQ\n4CfvYjaXMcVWhZI2rG61Z72qr9mcy858DnhNzTGDGLoltjdsT0U12Dcj+BlFO8RnI5hJ0RN8kut5\nSZIkSZL6vEEMqdy+C0fGxbFx6yztUvfcQzs5KwAiYi/gEmB/ijZ/AygqnTpyjgOBq4ETMvPROru9\nCIyKiH4VgddLgBdafD+rnYfbueV4ZjZFxNwNTPG5Fl8vo2h/2C3PvS8x7JIaVN2bw1XvcjiMwUzh\nvvJGdOsg7OZ+j7No7WW2KpTUVTknp8XooCo4j3FxDlVhV7IXu/N1jmH35m1bQHvDjsrkqQgmAI8A\nB5Sbv0nHWyRKkiRJktSz6nWCmc30vDCP2phTxJSYDhVriJfLsHTB14E/AqeULQU/DJxQjq1bZmAY\nsKT8utXyDRGxH3AzcEZm3t7O4/wOWFGe+0ctjh8BHAl8osW+7QWAz1C0I1x3/FBgZDv7t6e9595w\nDLukBlRZvfVjXh7HxQxeUqfUdBBDeD5vYfGX/8BzP9+TgSv6sWoIzDvp6Ww60xvKkrpFh6q+fsxT\nDGBwq6ALijYEUzin8jxbsEwygr8DY4BngbN6eUqSJEmSJG1YvU4wHVlCpTvOUW0EsBhYFhF7A2cD\nzwNk5gsRMRt4V0RcAZxO8f/kAETEK4HpwIcy23/DbmYujIiLgcsjYhFwG0W92teAp4Hvb+R8rwd+\nFxFvogiqLqK6deHGqPvcG5Fhl9THRfBG4BbgUYqLzSTGVlRvHcseTOOtLOV+4MCaE60YvAaYRtP5\nQ2g6fwbwjxSLGB63aZ+BJNWv+mIUFwCjaw4YxvbQThXrlmsS8F3gTcBYGri9gCRJkiRpy9BeJ5ie\nPEcd51OsVXUBcB9wDfCWFuPvpwikPg98G/hNi7GPUlRVXRkRV5bbnsjMV1U+h8z/KlsOfpkiNFsE\n3AicWq6/BUVVV9vKruZtmflgRJxTznM48BWKe8Yr6hzfXpXYhp77xrWY7CMis2/MNyIyMzubQEqb\npQiOZ9BNV7HDZUMYsHYwqwbDvJPvZOz7BnAiB9UccC138CJfqqn6umnILB6aMpiVJ3wH+BRFav9N\n4CzXe5HUm2JcTOe0ijYEv2Alu/FXZjKaCS1aBEzlUWZx7hYeeBHBOylaHOyf2fyCVpIkSZKkXuV9\n/p5VtkGcD4zNzCd7ez4bq96/k678+zHskvqoCM5h0I0Xsc+pyzh+xa7NA7ewhJUM5Vj61xw0hen5\nSB5VVkIU73JYOmYHnv3yLqw8/oxMburJ5yBJG1LZlnUqM5nDeYziP3gbr645qLzW9eQ8+5oIguLd\nXw9m8qneno8kSZIkSeB9/p4QEccCv6RoX/jfwBsyc//enVXHGHZJDSCCbSkuNkspFi6c1JHqqQj6\nAV8CjmHPEXN499J/qNnpKv7E9gyvuTk8i8nrqh0iuBI4GhgKHJrJfV14WpK0ybQK6Fu0IYh9YwYn\nV6xDeDN/4jj242sctSW3OIxgJ+BPwNGZtjOUJEmSJPU+7/NvehHxLeBEirDrbuCDmflI786qYzZF\n2OWaXVI3imAHivW19gEGl5u/CZzS7nHNa9L0G8au+43jhfPmcsQv3s5zS6tv2g6LhczKT9brUVvO\n4ySKdoUAH9/QHCSpt5TXrtrr3UqWVx4wlD2ZyVO8jCEcxajm7VMZE6ODLSXwyuS5CD4CXBVhO0NJ\nkiRJkrYEmfl+irXE1IKVXVI3iWAX4OfADcAbgbcCfwfe2F5lV2ULr1tYwl7AQ8znaHarOejKfR7N\nJx8cV2ceuwO3UoTZYyjS/cNdm0tSo6nb4nAWk9mRCzmRA2oO2sJaHJbtDG8A/prJJ3t7PpIkSZKk\nLZv3+bUxNkVlV7+uT0tSBHsCdwLfzeTTFFVVMygqq9qvoNyaya1u5AIcwQh+x108yT8zlUdbjd00\n4Ame+9wOEexXMY+9gV9RVJO9HrgWgy5JDSrn5DRmcS5TmM613MEUpje3a11LU+VBW/GSGB0TYlxM\nj31jRoyL6TE6JvTw1HtMJgmcDZwZwRt6ez6SJEmSJEm9wTaGUhdF8AqKSqovZPI1gDJceksE/wVc\nEcEJ7Bg1a8vwQX7Ntv32grW1Jx7AwJyT02J00Lpd4erLWXn8KIq2VW/IZGU5j/2BnwCfyOSq8iy2\nLpTU0Drc4nAtezGOGzmMQc3bNvP2hmU7ww8Dt0TwELCIDq4XKUmSJEmS1MhsYyh1QVldNQ24IJPv\nV4wPBn7PsE/NYO/PH92qgms689mLATwwcjDHzR3U9tj2WnGVbat+DPwxkwsjGE9RxfWBTG7qjucm\nSX1Z3RaHC1jIu3ldzQGbeXvD8u/Ck9Dc+vb6TE7sxSlJkiRJkrZA3ufXxtgUbQyt7JI6KYKbgAnA\nAxTBU41MVkTwTra9614m0jrQOpLtuGq/BTz3gduJs8fU3LBdxOX1HjuTjOADwP0RNAHnAadkcnvX\nn5kk9X3Vla9cziguqDxgFK+Ji+MVfI2Xta2y3Rwqvsq/C3+hCLsWAG+I4B3A/5WtDiVJkiRJ6hER\n4f+HqsdZ2SV1QgRvpFgba2C56drMomVgjI4JNe0Kdxj9TU6as2vNib7/yjnMfGAXdowj2Lr1DduN\nufkawW3Am4G7gaNtWSVpSxfjYjqncUTNwI95lH0ZyaMM5nCGNW+fyqPM4tzNIfCKYFuKNRvPAl4L\n/DewGjg/k1/15twkSZIkSZI2pCs5kWGX1EERvBa4BXgKeD1F0HR4Jgsq22r9nOU0EUxkcM3JpsQt\n+cjaI7swlxnAoeW3zYGbJG2p6rY3nMVktuHDvJPDag7aTFscRtAPmAR8HVhB0eZwQiZzenVikiRJ\nkiRJFbqSE/Xr7slIm7MI9gF+BvwLcBjFOlmHN1dUbc3kVjdYAQ5jCHP5Czf3f6LV9pv7P8GivKyL\nU1pWfr6b4p38krRFyzk5jVmcyxSmcy13MIXpzGJyzslpDKR2fUSAbdkzLo4hMTomxLiYHvvGjBgX\n02N0TOjh6XerTNZmMgW4DxgJvA54KoIrI3hrBP17d4aSJEmSJEndo0trdkXEbsD3gB2BBK7IbH3z\nPiLGAzcDj5Wbrs/Mz3blcaXeEMFY4FbgX9kxlrE115StCq+J0VzGB7mN7diz8uDhLGH2ms/wrUOu\nYUC/rVg1BOad9FQ2ndnVtlmTKFtW2cJQkgplS8La6+tKllceMIDteYzZvIzkKEY2b5/KmBgdbAYt\nDpeUn+8GTgeOAv4T2CWCF4E1wPPAyf4tkSRJkiRJjahLbQwjYidgp8y8PyJGAH8Ejs/Mv7bYZzxw\nXmZO3MC5bGOoPiuC3YE7gc+zY8yqaZH1M17k5QR/Zy1HskPNCcoWWRFMo7jJ2Nz6sIeegiRt8dpt\ncTiKT3EyB9UctBm0OGy5llfLvzsR7EXRlnePctNiYCrF36i7gfszmyuIJUmSJEmSNqlea2OYmc9l\n5v3l10uAvwI7V+xqiKWGFcFLgF8C/5PJFZWtCo9iFH/kbzzGGUzl0VZjU5nJIi4vv5tE29aHkqQe\n0W6LQ1hVedBQtm309oaZLMjklLZ/dzL5O8VrN4B7KN6McRuwN3AZsDCCeRE8EFGnclmSJEmSJKkP\n6FIbw5YiYg9gP+D3bYYSOCgi/gTMBs7PzIe663GlTSmC7wInAk8D3wVgMMMrd05W5ZycFqMDpnAO\ngxjKSppYxOXrWmCVNxpP6ZnZS5La6nCLw/68njFcwxFs1bxt82lvCLXtcH8DXAkQwZ3AIcB2wMMR\n3A78H3BTJvN6ab6SJEmSJEk1uiXsKlsYXgecW1Z4tXQvsFtmLouIo4CbgL2643GlTSlG7XwCO+8z\niYGrB7Bq8MtZNPEncfGHbmQ7Dqg8YCVN0M6NVElS37WIy5jKmJoWh0tYwvG8ptW+ExnLFM5hM7jW\nb+BNGC3X+joeeDNwMvD1CJYAM4EjMpm/yScqSZIkSZLUji6t2QUQEQOBnwA/y8yvbMT+jwP7Z+a8\nNtsTuLjFphmZOaNLk5M6KXYYcSw7bXMNJz4zrHnjL1jDLtzFY/yANXykas2XzeRd/pK0RYrRMYGt\nW1fmMooLOJlDa3a+kT/m/fn68pjJDGIIK1nOIi7bXP4WtLPW168ogi8o1vm6APie63tJkiRJkqSO\niIjxwPgWmy7s7JpdXQq7IiIoWrvNzcyP1NlnNPB8ZmZEHABcm5l7VOzX6YXHpO4UQbDb62Zx5n21\n689NYXo+kkdV3RDdXG5uSpLWi3ExndM4ombgF6xgF/7ITHbjGHZr3j6VR5nFuZvz34QIplGs73U3\n8O/A+4GDgG8B/y+T2b04PUmSJEmS1KC6khN1tY3hwcBpwJ8j4r5y2yeB3QEy85sU6x2dHRGrgWXA\nO7r4mFK3qXpHPv1WjGebedtVHjCIoWCrQknaYtRrb/gi/8pSPs9xLYIu2KxaHLaj7TpfP4lgLDAZ\nmBnBYmAWMDGTp3txnpIkSZIkaQvR5TaG3cXKLvW0GB0T2JVLW93A/PHwRbx06yE882wTR7JNzUFl\nZVdPzlOS1LvqVfPGvjGjssXhtdyRD+b4np9p74vg1xRvhgJYDdwCXA9MzWRur01MkiRJkiT1eb1Z\n2SU1rq2Z3CroAjh26f9n797jtK7r/P8/XiDDgDCgqEMKghw8oeZp0zTTSkXGRDMVRWurbTNLcNvK\nave3Jb92O+xubWpZWlbabLVkB0fF8VSglSWlpHkKEA+gYqk4ouAIvL9/fD4Mc81cw2lmrs8cHvfb\n7boN1+dwzautvc3n+rw+79ezhuvWPcBzfJqGNo2wBpbSxOWVLlOSVKwOV/M2s7bsCcPZN+bEwVzB\n7n01z2szmvKfC8lW978FOB34Wr7i6zVgJXBaSjxXTImSJEmSJKmvsdml/quK6rLbN7z2QlqZ5kVt\nQL25XJKkDpQfcfgYu3M7j3EHk6jiRIa12jcxaoM+/rek7YjDHwI/jGAocA8wBZgArIjg98Cv89cZ\nwHiykdcz83MlSZIkSZK2imMM1adEcA0wGVhFq5tl7bK5hvBjhvAf1LF7uw9xVKEkaSt1OOJw77iN\nczm+3Qn9+G9MBPOAaWSrvk4H9iVb+XU08DZgYH7oXUBdSqwuok5JkiRJklQMxxhKQAQ7Au8Gdsw3\nXQnMKJvNdTsnsI67aBj4OtPXj2vZft3o1TQ966hCSdJW6XDE4SAGlT1hKDtBmYcw+seIw7arvpYD\ntwNEcDNwErACGAA8G8F9wND83JW44kuSJEmSJHXAZpf6hAgC+B7wIlmz61Xgk0D5bK7jGcAPBrzG\n8mtfZeVVr7IDQ1m3/iXWLP/H9Hyfv9koSepuHeV5jeDQOCMaGMuBnML4lu39YMRh3qia0cHuc2jV\nCMsfYDka+A4wNj9mXgRvTYl13V+tJEmSJEnqTQYUXYDURT4NjAMOB+YC/wtcEyOeqqaGMeVP2f9g\nXpv5ACvmj+GJ+XNZcdf49Pyyn1SsYklS35XleS0p2dbAUp7jfTzBQSWNLoDpTKKGWRWssEdJiVUp\nMWPjyq2UeCUlbgX+nB+y8f+WSyL4eAQjCilUkiRJkiT1SK7sUq8XwcnAR9np0M8w6r5r8pFQI2n+\n5EhObnyKRQwue+LakYOB96fEq3T8pLkkSdssrUzzojagvkye15T4ENkDGqWqGFL5Snu8ktGHEfwd\n8DHgXyNYAawB/oYjDiVJkiRJ6tcipVR0DUDngsfUf0WwL3Anw8/5MpN//OE2uVzraZr5NIuPuJ/9\nLtqnZN9Pxq3j6Z3en164r76AsiVJ/VhMjkbOY2q7HdfzMH/lM1RzQT/L8tpmEYwBfsumEYeLgLqU\neKa4qiRJkiRJUmd0pk/kyi71WhGMBK4HPk3tj88qk8s1kB/85FHW/O8YHl57N02fWsLAkbvx8mEH\nsHrYx9NLv7DRJUmqvGzE4cSSv1s38hQjGciOXMfxra7P+kGW1/ZIieUR/Jms2fUo8BDwUAR/AOqB\nn6dEU5E1SpIkSZKkynFll3qlCAYCDcBjXBKzaeABpjOl3YFzWcBD6RzgN8DVwIeB2Snx84oWLElS\nK1EbddSUjjikhtllV3zV05gWp2kFlNmj5Q+9tB5xOAQ4BTgXOAl4BXiO7G//XSmxvrBiJUmSJEnS\nFrmyS/1G1EYdg994JeOH7krzkAHs+tC/ArdTXSb7BKCZNSnxTARTgT8BzwL/GMGvzPaQJBUlX6lV\nslorpsTFZQ8exm7Q0iCb7YjDTP53fEar92uAucDcCH4NHA3sRLYKfF0E84HdgMGY8yVJkiRJUp9i\ns0uFiuAaYALwMlu46RS1UccYLmX6n8a0bLwtvsxCvskyvkYDXy0ZCdXAUpq4HCAlFuejjY4B9iJ7\nEnwGkiT1FM2sLbt9GFNiRixgT8bxzlYPdzjicHM2jjBcCJwIDAXeDvw3UJvveyiCbwJ3A/c49lCS\nJEmSpN7LMYYqTASjgKeAIfmmuSl13ICKydG4ufFO5UZCtb4BGME8YBr5jS+f5pYk9SSbHupo8+DG\n81zMKL7cLpsSHHHYgbYjDltt33gtsIis8fVG4M3AIcBrZA/fLAHO8DpBkiRJkqTKcoyhep0IBgM/\nB55h08quf9rsSUMH75Hdh2qjKmuWlRsJ1cZMytz4kiSpJ0gr07yoDahv/+BGTInZUKbZVdXywIha\naTvisJW21wL/CxBBFdkKr0OBccA9ERyfEk9WqGRJkiRJktQJNrtUcREEcDVZaPypZDedEnB1BKel\nRHNJLsk61rP/oGaGpSllP7CZNVvzezdz40uSpB6hwwc3OhpxWMXOMScGcAUnmee1ZR1dC6REcwQr\n87eLgDuBRRH8AvhySjxawTIlSZIkSdI2coyhKi6CS8hGCL0tJV7Ntw0CfgI0s8vga9mz+X9KxjXN\nG7qOJdMWMf6nI9uNd1rObG/oSZL6srIjDm9kBfvwMkEVj1LNyezesq+BJSznIv8+br22ow8j2Bm4\nMH+tzl8rgHNcIS5JkiRJUtfrTJ/IZpcqKoL3AnOAI1NqeYJ6475q4EbGHrIv/7Boj3Yn18ctNKXL\nNpfLJUlSX1Uum5KPcDM/4z5O543tTjDPq0tEMAy4j01jJB8HZgO3pdTBijtJkiRJkrTNzOxSrxDB\nsWRh8Me1bXQBpMTaOPJrZ9P87PKyH1CVqrcil0uSpD6po7+BMTfKrzIyz6tLpMTqCBaTNbsWAT8C\nPgH8IIK/As1ko5lPT4kXi6tUkiRJkqT+y2aXKiKCfYC5wMyUeAhanlDflC+yB3cxjfczL14Cdmv3\nIVuZzSVJUr/SUZ5XNaNjbJxKNReY5dVpM2k14hD4zwhGk2V77Z+/novgD8D9+etEYGfgZbLrH0cf\nSpIkSZLUTTo1xjAixgLXkjUmEnBVSumyMsddRpbR9CrwvpTSfWWOcYxhHxXBNcBZwGLgrSmxqmz2\nyO2sYxD/zoMsZI+BX+fU9Xu17Ltu9Gqee3aGN+gkSSpV9m/qDTzJcNawjgkcz6CW7WZ5dakI5pFd\n4y4EzgT2BA7KX2cDNfmhfwROSYlniqhTkiRJkqTeoLDMrogYDYxOKS2KiGFkX+RPSyk93OqYOuDC\nlFJdRBwBXJpSOrLMZ9ns6oMiGA48DQzLN81NiRkxORo5j6ntTsjzRaI26qg64hoGVu/CuvUvsWb5\nP6bnl/2kkrVLktRblM3zqmH25v7WFlBmnxPBSEpXfLXet7ERthS4FzgBeBS4HjgYqCV7EMxVX5Ik\nSZIkUWBmV0rpWeDZ/N+rI+JhYHfg4VaHTQeuyY/5fUSMjIjalFK7zCb1LRFUA78AXiRrdi0Ezgdg\nKDuVPSnPF0kr07wIJtPBDSRJkrRJuTyvmBIXlz14CCMqUVN/kF+fzOhgd8nowwiqgGOBU4HTgKr8\nuCs38xmSJEmSJGkrdFlmV0SMBw4Bft9m1x7AU63eLwfGADa7+rAIdgB+SPXVg9jjI4tJ+1QTj7zM\nq68fE3PYlxoOKXtiq1yuLdxAkiRJm9NRltcIDo858Rmu5mEG82HzvLpH2+uYlGgGbgNui2B/4G3A\nX9j4IJAkSZIkSdpuXdLsykcYXgdclFJaXe6QNu+3f3aierwIAriSwT8Yx/4fHMF0JsIDAG/nNo7m\nUR7hZS6ggU+X5Is0sJQmLi+qbkmS+pQmLqOBie3+1m7gizzMBxjH/8/xra4FG5gYtYENr4o4Hfgp\ncCCwC7iCXZIkSZKkzuh0sysiBpF9Wa9PKf2izCErgLGt3o/Jt5X7rEtavZ2fUprf2fpUiC8BBzDm\nAy8wnUNL9pzAYOp5Ji1OV0dtPEN9ab6IN9gkSeoaaWWaF7VBub+1MTnO/62BXQAAIABJREFU5Lw2\n14HTmUQ9s2gzDlFdL1/19Y4IPgr8PII3p0S5B8YkSZIkSeqzIuI44Liu+KxONbsiIoCrgYdSSl/r\n4LAG4ELgxxFxJLCqo7yulNIlnalHxYvgYuAU4BgGr/tp2YNa5XLhDTVJkrpNh39rq6gue0I1wwGi\nNuqoYbYjDrvdFcBhwPciOCslpx9IkiRJkvqPfMHT/I3vI+Jz2/tZnV3ZdTRwHnB/RNyXb/sXYE+A\nlNKVKaV5EVEXEUuAV4D3d/J3qoeKYAFwBPBbpvzfDgzJ/nfQTqtcLkmSVICO8rxG8ndxcnyDMUzN\nxhDnHHHYLVIiRfAR4E7gYuDLBZckSZIkSVKvFCn1jAdIIyKllNpme6mXiOEXfI6apZ9jUHMwYC0c\nvuQVBjy/iCXszins1XJgA0tZzmxvlkmSVJyojTrGcGm7PK+BfJWBfJ6T2LndSfU0psVpWiXr7C8i\nGAv8Hnh/StxSdD2SJEmSJBWhM32iTmd2STHi1AsZ98C/ceayTf8jnBev8DhfAMpmhRRVqyRJ2kKe\n15Q4Czi23Un5GGJ1vZR4KoKzgZ9EcFRKLC26JkmSJEmSehObXeqUCMawR9N/ceaygSU76tJu1DMr\nfwLc5pYkST1Mh3leHY04fJ3XzPLqPilxZwRLgAcieAr4DHBnSvyt4NIkSZIkSerxbHZpu0UwnIGv\n3UjtfWugTNC9T4BLktT7NHEZDUwsGXF4C6+wF4eziqupY3TLdrO8utrrwBBgb+CbQHUELwF/BMaQ\nXbs/A8xMiVWFVSlJkiRJUg9js0tbrfRp7niN0ZfszlnX1PL7l9aXPaGZNRUuUZIkdVJHIw6pZQ7v\n5vCSg6cziXpm4SrurvJq/nMhcCLQBEwADgP+CxgLHAzMj+B4V31JkiRJkpSx2aWt0j7IPsFtl8Bf\n4gqWcRMNZULum7i8mGolSVJnlBtxGFPi4rIHu5K7K80ErgTOb7VyawmwJIK/J2t2PQw8lG+bB1wF\nLEiJVETBkiRJkiT1BDa7tHVqmF3SzAI4IUF9mrC5kPuCqpUkSV2toyyvQYyIORFcwTTzvDonb3DN\n6GB3SSMsgp2B84BvAHtEsAp4Fjg1JVZWpGBJkiRJknqISKlnPAQaESmlFEXXofJiSsznLI5tt2Mu\nC9KD6bjKVyRJkiqp/Spv4CZWsA+vs54XWcwo3smeLfsaWMJyLrLh1b0iCLJMr0PyTeuABcAv89cf\nUmJdQeVJkiRJkrTVOtMnGtDVxajviTkxgmHsXXanuVySJPULaWWax3Iuop5G5rKAehp5gg8xib15\nhCEljS7I8rxqmFVQuf1GPr7w2fztQmAi8DVgV7KVYK9E8GQEt0cwsqAyJUmSJEnqVo4xVImojbqS\nEUSjuYPj+QhvGPYI143ajTOeGNhy8PUDltG0wVwuSZL6iXJZXgAxN1YC+7Y7wTyvSmmb9fUkcCNA\nBHcDR5Llfd0TwTtS4qnCKpUkSZIkqRvY7OqnIqgHTiALOT8tJVaVHU90O8dzx4ivcNeqUxg659v8\n4AuTSfscRDx6Py83f9XRRJIkqcM8r0RAmYdpzPPqUlvI+nox/7kIuAv4UwQ/Bb6cEksqUZ8kSZIk\nSd3NzK5+KIIRwApgx3zT3SlxVEyORs5jarsTvvPm51n+2/qU+KdK1ilJknqHsg/M3Mzf2JcqXuJm\nnuRwpjOxZZ95XhWTjy5sWfUVwSjgIuBiYBXZg0/vyhtmkiRJkiQVpjN9Iptd/UwEVWTjhyYDewIP\nAsOABvYf8EbOSm9td9K1b3yexxbVpsT6ihYrSZJ6jXz11iyqGEIza2jicj7C/cxjIXWMbndCPY1p\ncZpWQKkCIrgLeEv+9hHgyJR4qcCSJEmSJEn9XGf6RI4x7EciCOBqYDVwCPBN4HwgqH7hWgaPOhL+\nVubEB++10SVJkjZnM3lej0KZZpd5XkV7Of+5iOzhp0cj+DxwVUq8XlxZkiRJkiRtuwFFF6CK+g9g\nEqN2u4ZJ8UP2j1omxY85Is7lU6P2pnbkMq4bV9rUun7g47y87mvFlCtJknq9jvK8Xue1CleiUjOB\nucDbUuI8YCpwGvBABNPzh6QkSZIkSeoVHGPYT0RwAfAxdt73s4x/9PMlmRq3s56hfCXdkj4VI879\nBMP++iUGvTaAdetWsebp89Pzy35SXOWSJKk3K5vndSuvMoG/Mon3cgXDqGE2VVTTzFqauMwsr2Lk\nDa6pwA/zTX/CPC9JkiRJUoWY2aXNimA68C3gGCbFNziPqe0OapWbEcFvgKPyPXNTYkbFipUkSX1O\nB3leQ3iMb7GYwUxleMvBDSxhORfZ8CpOBAuAjTmufwTelBIbCixJkiRJktQPmNmlDkXwc+BkYCHw\nPFVUlz2wNDdjYzj5QrJML0mSpO3WYZ7XPnEBM3lHycbpTKKeWeWOV8W8kv/8M5CAX0bwwZRYUmBN\nkiRJkiR1yMyuPiyCA8kaXYOAo2DDlVSXCYgHaGZNq3cbMxxOdGyNJEnqNjt08OBV6UM4qryN14LH\nAEcCDcDvIvhYBAMLrUySJEmSpDJsdvVREexH1c/ns8cxaxh3HOx5RBNnVe3FfsCNPFFycANLaeLy\njW9TYlVKzLDRJUmSulUza8tuH8qeMT5Oj8nRGFNifkyOxqiNugpX12+1vhZMifUp8VXgzcBpwNMR\nPBTBrRGMLLhUSZIkSZKALsjsiojvkq0eei6ldGCZ/ccB1wOP5Zt+mlL69zLHmdnVRSKYRNXP7ma/\n96/nXU21LTsaWcVT/D2vs65tboa5GJIkqdKiNuoYw6VMZ1LLxht4nB1pYgP7c3yrlV9meRUuggHA\ng8C++abngC8ADSmxrLDCJEmSJEl9Qmf6RF3R7DoGWA1cu5lm1z+nlKZv4XNsdnWBCMYBCxg3YTXv\nXzal3QH1NKbFaVrlK5MkSWovaqOu7UM41DCb85ja7mCvYwoXwTxgGvBH4KvAO4B3kmUBvwwsAc5w\nQoAkSZIkaVt1pk9UPidhG6SU7oqI8Vs4zCZWBUSwB/BL4CsMW3ZW2YPMwJAkST1IvlKrZLVWTImL\nyx48mKGVqEmbNRO4Ejg/b2j9MM/xWggcAowD7ong7SmxvMA6JUmSJEn9SCUyuxJwVET8KSLmRcT+\nFfid/U4Eo4E7gG9xSdzOCA4ue2AzaypamCRJ0rbqKMtrBIfEnDgqaqPOPK9ilMt2TYn1wLP520XA\nrcD9EVwVwYQi6pQkSZIk9S+dXtm1Fe4FxqaUXo2IacAvgL0r8Hv7haiNOgYf8m3GDxvN67GWyfe+\nAVhADd+hgXeWZGA0sJQmLi+uWkmSpK3QxGU0MLHddcworuMxGpjIIKZS02rfxKgNzPMqVMmKrwgu\nAS4iW+X1AvAi8Dww0xGHkiRJkqSu1unMLoB8jOEN5TK7yhy7DDgspfRCm+0JmNNq0/yU0vxOF9eH\nRW3UsceAr3Pqhr1aNt42ILFuw+z0u/T1chkY3gSSJEm9QUfXMbFP3M5M3tHuBPO8eqQIRpCt9hqf\nb5qbEjOKq0iSJEmS1FNExHHAca02fa6wzK4tiYha4LmUUoqIN5E12F4od2xK6ZLurqdPGV71cU5t\n3qtk2wkbgnpOBr5eLgNDkiSpN+jwOmaHDq5fzSXtkVLipQgeJmt2vQasjqA6pQ5GVUqSJEmS+o18\nwdP8je8j4nPb+1mdzuyKiB8BvwX2iYinIuIDEXF+RJyfH3IG8EBELAK+Bpzd2d8piGAUGw4+ouxO\nb/ZIkqS+qqM8ryHsHnOiyjyvHmkmMBeYDAwHfhvRakSlJEmSJEmd1OmVXSmlc7aw/xvANzr7e/qD\nCEYBf8zfLgbOLJdpkB234XaGPrkW2LHdBzWzplsLlSRJKkq5PK8beILJrGIJf2EcO3Aye7TsM8+r\ncPn17AyACGYAHyVreF2QEj8ttDhJkiRJUp/QJZldXSEi0vbOYuwLItgJuAPYDVpu0DwMvCUlXmh1\n3M7ssOYOZr5zAOt/OYJHgXcyruWDGljKcmZ7Q0eSJPVV5fK8+Ag38zPu43Te2O4E87x6nAgOJ1vt\n9QqwCngZmFnuQS9JkiRJUv/QmT5Rt2d2acsiGAncCvwS2J+s2XU/WZj34hj61dvY/TO7UpUGM+7g\nN3LYE83s9dyvCN7PbRxDffvw9gL/40iSJHWrjvK8Ym6Ub5Q44rnHSYk/RHAY2cNdB+Sbb4lgqg0v\nSZIkSdK2stlVsAhGALcAvwE+CYwArgTOT4lVsdPR72Pskkt5d3NNdsZCuJkX+TXfTSvTy3yO8uHt\nkiRJ/U1HeV6JyFeDzaaKappZSxOX+YBQsVLixQjuBaYBjwF/A56I4C6yUd2DgCZc8SVJkiRJ2gLH\nGBYoguFkja57gVkp0e6/jJgcjZzH1HYnO45HkiSpRNRGHWO4tCTP62aeZyeqeZG1TGNUy/YGlrCc\ni2x4FSufcLDpQa+gBngncCmwS37YT1LirKJqlCRJkiRVRmf6RAO6uhhtnQiGka3Iuh+YXa7RBcDg\nDsbuOI5HkiSpRFqZ5rGci6inkbksoJ5GlvFenuDekkYXwHQmUcOsgkpVLiVWpcSMjSu3UqIpJX4I\nLMwPWQ2MjmB8UTVKkiRJkno+m10FiOC7wHJgHPCZlNhQ9rg5MYrhLRkGpZpZ030VSpIk9U5pZZqX\nFqdp6cF0XFqcpqWVaR5R/lrLh4d6tJnAXLLr5RuBhRF8MIJ+NQlCkiRJkrR1bHZVWASDgNPIsrnG\nAt9q2VcbdTE5GmNKzI/949cs5QHewC9pYEnJhzSwlCYur2jhkiRJvVVHWV47UANtrsEmR2PURl1F\n61M7rVZ8vZAS/wm8DbgAuCmCPQouT5IkSZLUw5jZVUH5k6jfIwvh3o1sPMuJKbGqbMbEPFbyOB8A\noIZZVDGEZtbQxOXmS0iSJG2dstdZN7GCfUm8znIWM5pTWo3JM8+rR8ofGvsM8Gng8fw1c+MIREmS\nJElS79aZPpHNrgqK4PPAicC7gP8hD+IGiMnRyHlMbXdSPY1pcZpW0UIlSZL6mKiNurYPD/ER7uAG\nHuQUJrY7wWuwHiuCPwCH5W+vS4kzi6xHkiRJktQ1OtMn2qGri1F5EZwPnA0cnRLPATNKDhjcQWaE\nWRKSJEmdlq/SardSK+bGcijT7PIarCd7Lv/ZBOwSwaiUeL7IgiRJkiRJxTKzqwIiOAW4BJiWN7pK\n98+JXajhwLInN7Ome6uTJEnqxzrK81pHc4Ur0dabCcwFJgB/AO6J4KBiS5IkSZIkFclmVzeL4Ajg\nu8CpKbGk3f45sS/wO0ZzOw1t9jewlCYur0ylkiRJ/VATl7W7BruF1RzBQTEnpkVt1MXkaIwpMT8m\nR2PURl1BlSqXEqtSYkZKPJ8SnwQ+C9wRwRlF1yZJkiRJKoaZXd0ogsnAncAHU+ImaMmLmE0V1Qxk\nRw5hMhP4WPpc+l65LAmD0SVJkrpXB3le61jK91nKcE5kWMvBDSxhORd5jdazRHAo8HPgebLxhq8C\nMzfm40qSJEmSer7O9IlsdnWTCK4FzgQWA29NiVVRG3WM4VKmM6nlwJtYwRN8yBsmkiRJPUvsHbdx\nLse321FPY1qcphVQkjYjgt2AvwAj8k1zU2qTkytJkiRJ6rE60ydyjGE3iKAaOBWoBg4ErgSghtkl\njS6Ak9mDGmZVukZJkiRtwSAGld1exZAKV6KtkGfj/i5/uxr4dIHlSJIkSZIqaIeiC+hrIgiyjK5X\ngBpgIXA+AIMZWvYkb5hIkiT1PM2sLbt9KGNizziNwXyYKqppZi1NXOZK/R7hbOAq4Fng5ghOSonH\niy1JkiRJktTdbHZ1vc8CE4BDgUuB81NiVcyJ3ajhoLJnNLOmgvVJkiRpazRxGQ1MLFmZfwOPM4y1\njGMux7da+dXAxKgNbHgVK8/oOgsggtnAryM4JSXuK7YySZIkSVJ3MrOrC0VwDvBF4IiUWNmyfU7s\nDcxjEX/gSQ4ruWHSwFKWM9sbI5IkST1P1EYdNcyiiiE0s4YmLqeG2ZzH1HYHm+XV40TwbuCbwLkp\ncVvR9UiSJEmSOtaZPpEru7pIBEeRreR6B7vFYTGZ2VRRzQCGcCiTmcDF6efpO1EbddSX3jCx0SVJ\nktQz5ddpJddqMSUuLnuwo6l7nJT4aQTPAddF8BAQwKvAzHwVmCRJkiSpD+j0yq6I+C5wMvBcSunA\nDo65DJhG9sXyfSmldmNEevPKrgj2An4L/AO7BYzh0pLVWzfxNE/wjza1JEmSer+YHI1lV3bdwgtM\n5WiuYAI1+YNP5nn1CBHsB9wLVOeb5qbEjAJLkiRJkiS1UfTKru8BlwPXltsZEXXApJTS5Ig4gmyM\nyJFd8Ht7hAhGADcCX0yJeTGZxpJGF8DJ7E49s2jzVLAkSZJ6oXJZXg0s5Q3cxjJ+zwTWcRI7t9pn\nnlfBUuLhCBYAU4F1wPIIhqXE6oJLkyRJkiR1gQGd/YCU0l3Ai5s5ZDpwTX7s74GREVHb2d/bE0Sw\nA/BnYCRwUgQjqWp5WrSUY20kSZL6hLQyzWM5F1FPI3NZQD2NLGd2ujFdwO+5r6TRBTCdSdQwq6By\ntcnZwFzgIGBX4JEI3hPR+e9EkiRJkqRiVSKzaw/gqVbvlwNjgJUV+N3d7cvAjsBOwO4MeuXbDGef\nskc2s6aShUmSJKn7lMvyAjp+lMwHnwqXZ3RtHF343gjeTJa5+98RLCf7fmKWlyRJkiT1QpV6irHt\njMXOBYX1ABG8FzgV+CMAQ/52L5/eaTTjWUoDS0sObmApTVxe+SolSZJUUc2sLbu9ilExJyJqoy4m\nR2NMifkxORqjNuoqXKFyKXE32Xj1F4FDyTKGryq0KEmSJEnSdqnEyq4VwNhW78fk29qJiEtavZ2f\nUprffWVtvwiOAL7CiLrPssvtZzBw4vOMemRvHuJXHMi7+BVTqWcWVQyhmTU0cbkZDZIkSf1AuTyv\nG3mKKQzmL9zPntTwTvZs2WeeV6FSYkMEjwH7AC8Du0cwNqWSyRSSJEmSpG4QEccBx3XJZ6XU+UVW\nETEeuCGldGCZfXXAhSmluog4EvhaSunIMsellFLbFWA9TgS7A/cw7MKr2fsbM9sEky9hORd5s0KS\nJKn/itqoo6b0wSc+wi1czwOcyn7tTqinMS1O0wooVUAEI4ErgQuADwEfAy5IiZ8VWpgkSZIk9TOd\n6RN1utkVET8CjgV2IZtz/zlgEEBK6cr8mK8DJwGvAO9PKd1b5nN6fLMrgmpgAdDApDiG85ja7iBv\nVkiSJKmMmBLzOYtj2+2Yy4L0YDqu8hWpnAjeBPwIuB34WEq8WnBJkiRJktQvdKZP1Okxhimlc7bi\nmAs7+3uKFkEA3wKeAL7AcP5U9kDDxyVJklROR3le61mfrwabTRXVNLOWJi5zWkAxUuKeCA4BvgGs\niGAp8BwwMyVWFVudJEmSJKmcSmR29RX/BLwReAuXxDnczj5lj2pmTUWrkiRJUu9QLs+rkVWM502M\n5HtMY7eW7WZ5FSolmoD3RPAQcFi++bvA6cVVJUmSJEnqyICiC+gNIrgJ+BKwiot3+SjwZdbyTzSw\npOTABpbSxOVF1ChJkqSeLa1M81jORdTTyFwWUE8jj3EuT/FASaMLYDqTqGFWQaVqk8fzn38F3hzB\nzHzigyRJkiSpB3Fl1xbEyBM/xO5MY9BrwbDlx7FswxFMYf+0MD0etfEE9aXh4z59K0mSpI7k14ol\n14sxJS4ue7DjsXuCmcCVwPnAvmRjzT8QwUdT4tFCK5MkSZIktbDZtRkxap8ZjF37dc54ctPTmzcM\neI4F7M/neLzczQpJkiRpm3SU5TWQoRWuRG3kGV0z8re/i+Bw4ELgNxE8CazOX+Z5SZIkSVKBIqVU\ndA0ARERKKfWYkSARVDHmqGf54N07tdtZT2NanKYVUJYkSZL6mKiNOsZwaUmW1008w37swARu5kc0\nsp6/p4pqmllLE5c5TaBYEewB3Afsmm+am1JLU0ySJEmStB060ydyZVcZ+Rz+bzJ49YayBzhSRpIk\nSV0krUzzojZoOx6bk7mT+7mWXfkBxzOw5YQGJkZtYMOrOCmxIoI/ANOAV4GxERyaEvcWXJokSZIk\n9Us2u8r7BNUvHM6uDwwou7eZNRWuR5IkSX1YR+OxY3IM5bxWjS6A6UyinlnljldFbczzugA4Hbgp\nghuBf02J5wqtTJIkSZJ6mAiqge8AewEv0cXj4G12tRHBqQx97mP8854reYS7aWDvkpEyDSylicsL\nLFGSJEn9RRXVZbcPZliFK1EbbfK8vhPBT4B/A56I4GlgCTDDLC9JkiRJvVEEA4D/BfYFNgDXAjsC\nu+SvY4FhwDrgD8BaYD1wCDACCOBpYGR+/MZ+VFX+80rounHwNrvIcxJqmM3Aml3Y88CDOHrh39ih\n+VYO4NPcybS2I2UcGSNJkqSKaGZt2e07cXjMidO5grXUMNs8r+KlxEvAJyI4BngTMAG4O4LjU2JF\nsdVJkiRJUnkRXAUcSNYvagAmAVOA/YCBwOD80JHA/wHLgUVkTa09830jgO/lxx/Apnzj+4BTgeeB\n1cBNZOPgFwLnd+l/jpRSV37edutM8Finfm+5QPCbeZFlnOeNAkmSJBWp7LVqA0vZkasYx0d4jF05\nkaGt9i1hORd5HVucCOaRfXm7H/gNcDZwA/CVlLi/yNokSZIk9V8RBPAGskbWAa1+Hg4t4/P/AvwX\n8CDwEPAjNjWnTmw9uaLVd5+SfR1tz/eNJFvRdX65KRid6RPZ7JocjZzH1HY76mlMi9O0StcjSZIk\ntZZPIWg3aSD2jls5lxPaneB1bKHafnmLYCeyJxZnkY3++CvwLF08n16SJEmS2opgZ+Ak4GSyrOGB\nwMvAdWSrrh4EPgsczzY2pzrat6WG1ubr3f4+kWMMBw0eDq+1317FkMoXI0mSJJXKV2m1X6k1qGXO\neSmvYwvVJsuLlHgR+FIE/wP8iWzUB8D3gdMqXqAkSZKkPitfvXUAWXPrZOAgYD7Z+MAJwJHAzsDI\nlPhWfs6ZdNCcavv9Zmv2be6c7tSvm10R7MaE/Q/OGphtNLOm8hVJkiRJW6mjPK/h7BNzYgxXcJB5\nXj1HSrwWwWPAPsBK4KgIPgh8NyU2FFudJEmSpN4igu8DhwJB1sTaBdgjf+0NJLLvHP8MzEsp++4Y\n0fKwXUleVlHNqa7Wb5tdEezKuAV3c+TDwTyepY7RLTsbWEoTlxdYniRJkrR5TVxGAxPb5Hk9xp78\njmU8yF40M41dWu2bGLWBDa9CzSR/YhIYD3wLeH8EF5jnJUmSJPU/EdQDewKr2cKo8wgOBj4KnMum\n3k4AlwEr8tc3gKOAccCMlPhZq49o+T7SF0eq95vMrjzrIHuy9bXBG2j+5L68539GsMOaGXxrw4Zy\nOQjdVYskSZLUFTrM89o/7mQGx7Q7wTyvHiWCAcAHgX8HngFeYiu+5EqSJEnqvSIYBZwJnAccwabG\n1Z+BDwP3pMTr+bFVwLvJmlzjyJpVxwHvoHzG1jxgWrl9vUFn+kT9otkVtVHHGC4teer19kgMTJ9N\n89O/d8fvlCRJkooSU2I+Z3Fsux1zWZAeTMdVviJtTgS7AQ8Au+Wb5qbU+8eISJIkScpEUE2WofUe\n4G3AzUA9WRPrJOAvwK3A0cAkYBUwgGxE4T3A14CGlFgXwUg6WKG1uX29QWf6RP1jjGENs0saXQDH\np6CeowuqSJIkSeo+HeV5DWbXeEO8k2FcaJZXz5ESz0XwR7InMNcBCwouSZIkSVIXiGBPslytfckm\nOXwWeG9KNOX7f02b5lS+8utOYP/8Y55pPY5wcxlbfSV/a3v0j2ZXFdUdbB9S4UokSZKk7lcuz+tG\nnmJndmQo13ECg1u2m+XVU2ycn/8/wHcjOAL4aEqsLrYsSZIkSdsqginAxcA7gVfIejGjgGNT4oqN\nx5VrTqXE8xE8QdbsWkiW+astGFB0ARXR0ZOtzaypcCWSJElSt0sr0zyWcxH1NDKXBdTTyJN8mCd4\npKTRBTCdSdQwq6BSlUuJVSkxIyV+B/wdsB5YGMGBBZcmSZIkaStF8OYIrgfuAB4lG0n453z3tjSu\nZgJz6YW5W0XpHyu7djzhdW67A07YsGlbA0tp4vLiipIkSZK6T75Sq2S1VkyJi8se7MSDHiUlXgE+\nEMF7gHsiWA48AZzhF11JkiSpGK1GEu5GNn7818Dr+e4j8u07AP8GnJ1SttgmomWKw1bnaPXncYTb\nq9MruyLipIh4JCIWR8Snyuw/LiJeioj78tf/19nfuU31nf6ef+XERXW8vPd/lDzZupzZjmqRJElS\nv9LRxIORHBJz4uiojbqYHI0xJebH5GiM2qircIVqJSV+ADxA9jToO4AnIvhWBOdEsEex1UmSJEn9\nQwSjIvgKcB9QQ9bU2h0YBzTmr3X5vqHAERsbXVAyxcEH17pRp1Z2RcRA4OvA8cAKYGFENKSUHm5z\n6IKU0vTO/K6trqk26qhhNlVUM6B6Vw6o2pfff/wj6U+fvbISv1+SJEnqscpleTWwlF34GY/xCyYy\nmKkMb7XPPK/i/S3/uRD4OHAYcBZweQSDgFXAI+CXZ0mSJKkrRbAjcBHwz2QjBQ8Argb2JLs+P2nj\nNXi+emtfzNgqTKSUtv/kiDcDn0spnZS//zRASulLrY45Dvh4SumULXxWSinFdhdD3ugaw6UlX96v\n3/FFVrxynl/QJUmSpJaHw2ZRxRCaWUMTl6eVaV7sHbdxLse3O6GexrQ4TSugVAERjKTMyJMIBgC/\nBw7PNy1IieMqX6EkSZLUt0TwbeCtZCu35gGfSonF+b6Ors/Lbte26UyfqLOZXXsAT7V6v5xsNmVr\nCTgqIv5EtvrrEymlhzr5e8urYXZJowvg1Fd2op5ZtMkrkCRJkvqjclleAAxiUNkTzPMqVEez+lNi\nQwR/zd/+BZgQwbXAP6fUshpMkiRJ0laIIIAjgXOAv4eW70evb2yHv4yCAAAgAElEQVR0wWavz83Y\nKlhnm11bsyzsXmBsSunViJgG/ALYu9yBEXFJq7fzU0rzt6maKqo72O4XdEmSJGlzOsrzqmG/mBMT\nuIJ9W8aFN7OWJi5zekLhWoKuyTIC5gB/juATwP+mtFXf1yRJkqR+KW9wHUTW4DobWAP8CPgtcCyO\nJOx2+WTA47riszrb7FoBjG31fizZ6q4WKaWXW/375oi4IiJ2Tim90PbDUkqXdKqaga3yBVpr3hQG\nJ0mSJKmM8nlejzGGu1jGIiawjpPYqdU+87wKVubp0Y9H8CPg28AXI1gNvAj8A/BoSmwooExJkiSp\nR8gzuA4EDgZmk40pDLIHyE4F7k+JFMFlOJKwIvIFT/M3vo+Iz23vZ3U2s2sH4FHgHcDTwD3AOSml\nh1sdUws8l1JKEfEmYG5KaXyZz+pUZlfMiZNYGnO5f/SOvOuZAS07rh+wjBUbLvRLuCRJkrR5HeZ5\n7RfzOZtj251gnlePFMEg4CFoaVyuJfsS/yQwJP/3YuBdfnmXJElSXxTBaLKm1hxgL2AoMAB4GFhE\nlsm18Xp5bkqOIOwJCsvsSimti4gLgVuAgcDVKaWHI+L8fP+VwBnABRGxDniVbDlgl4o5cQKJa/n1\nbc+y8s7b+MGX9iHtcxDx6P283PxVG12SJEnSlnWY5zWg/bGA48J7qJR4PYLFZF/eFwInAq+RPbk6\nl+xp1j2AuyN4U0q83OGHSZIkSWXkD1jNBaYArwP1QDPZt4eBwOnASOA54NSUeH4LnzcA+CGwH/BX\n4IyteTArH0X4I7JxhIOBZcABZJlbi4DdgV3zw69LiTPz8+ax6XrZUYV9QKdWdnWlbenY5U+cZnkB\nAxjKoezNH388nwdnrAXOcTa9JEmS1HVicjRyHlPb7ZjH07zKZ1jLTLO8epYIRlJm9Er+pX4a2Rf/\nh4C3Af8GfD8l1hdRqyRJknq+CKqBN5GtiHorcCRZY2vH/JDFwA3ABmA9WQ7Wnvm+dcDdwO/z1+nA\nZKCKLB9rCvBGsmbV4Pycl8maaXcCdwGP5yMGhwKHA0flrzcDw1ud92uybNvl+fEbr38XAiduvDbu\n6HpZxerMyq5e1+yK2qhjDJeWZAncMORFHrh2Dc1n7JcSTd1aqCRJktTPlL0Gb+AxBrOcgbyF41ut\n/WpgCcu5yIZXz9T2S30EhwNfIxvr8rGUWFBogZIkSSpMBCPIpgHsmb82/vvtwC5AE/AD4DbgN2Sr\nudo1kvLPat1kOgPYm6xBdgQwlWzlFcCfgE+QPYx1batzPgYcAhxD1lwbDiSysdyLyBpgvyVrol21\nmTpsavUi/avZ1dFTpdfs9Jv02Atv6ZbiJEmSpH6uXJ4XNcwue21ullevko9+ORO4mizf6yGyUTPe\nDJAkSeoHItgX+BWwG1kU0d3AY2SZr0+QNaMOzg9vybfaXCNpC/u2abVVfr36O7KVZSU1bOl3qXcp\nLLOrEFVUl90+5MV1Fa5EkiRJ6jfK5XnFlLi47MHDqIU248cdcdhj5WPg50ZwIZuenP1DBG9JiWeL\nrU6SJEndJYIq4NPAbOBFYDQwDHgxJT7c6rhzyZpdJflWeWNpBmVsbh/ZmMF2zamOzsnHEW7M/GqX\nsbWF36V+ovc1uzqquZk1Fa5DkiRJ6t+aWVt2+zD2ixnxK8YynlMY37K9gYlRG9jw6rFW5z/vIxsJ\n82AEXwf+OyVeLq4sSZIkdbUI3gx8G1hGNi7wSmASZZpJdNCc2l7b2Zzq0hrU9wzY8iE9R8yJvTmc\nfZnHypId1w9YRhOXF1SWJEmS1D81cRkNLCnZ1sBSnuJcljK2pNEFMJ1J1DCrghVq28wkCwF/e0pc\nCBwGTAD+EsGvI1gQwbx8TIwkSZJ6oQhq8geafgZ8HpieEk+x6VrwxLbNpJRYlRIzimwy9YQa1LP1\nmsyumBPjgQXAHP7rU1MYcd8/Mei1AbxeDS+ceWda8w/HVqpWSZIkSZlyWV5pZZoXU2I+Z9H+Gn0u\nC9KD6bjKV6rtFcHBwC+BnfJN16XEmQWWJEmSpG0Uwf7ANcCBwF+BY1Li8UKLktrok5ldJfP9NwB/\nx95M5AtckpqBs3mFX5PNkl8InFposZIkSVI/VS7LC+h4xOEODI85EVzBNPO8eoeUWBTB78hCxF8C\n3hzBR4GrU+rgv2dJkiQVKoIA3gicAbybLIsrgMHAGODLmHOlPqRHNruiNuoYw6VMZ1LLxpt5np99\nYizwL8DxwNM4o1OSJEnqmbIRhxNLrulv4hkOYmf+wgOMYwQnM6Zln3lePV1LRgKwN/BvwL9E8J/A\nt1Pi1SKLkyRJ6s8iGAaMy1/jgQvI8rc2AFcD7yNbNHIjsAflc7mkXq1HjjGMydHIeUxtd9BVb3+N\np+84PCX+XPECJUmSJG2TciMO+Qi38AsWcRoHtDuhnsa0OE0roFRthwgOJWt6nQisBJYAZ/kwoiRJ\nUveK4A1AI7AX2Uqt9cATwOP560Sy7FWAuSllK7jy7FUXkKjH6ntjDKuoLru95g8PpBU2uiRJkqTe\noKMRhzE3ni97QhVDursmdZ2UuBd4VwQLgcPJbrbcE8GJ5j9IkiR1vQhGA58C/h54BRie72ppaOXH\nzSNrdpWs4MobXI4uVJ80oOgCynqd5rLb1zX9rcKVSJIkSepqHeV5DWH32CveHZOjMabE/JgcjVEb\ndRWuTtvur/nPRWTNzT9G8OMIDiuwJkmSpD4jgt0i+G/gIbJ7+lOAB/Ld5UYSzgTmAie6gkv9RY9b\n2RVzYiCHMoRbWc2JDGvZcf2AZTRtuLzA0iRJkiR1hXJ5XjfwBDuyhiH8H8czsGW7WV69QUueV0qs\niuCzwAeB+RFsAJ4luwFzV0qsL7BOSZKkXiOCIGtYHQGMBr4HHJgSK/L9Jddgrc91BZf6ox6V2cUl\nDAC+DYzjO3ydDe/4CYPWDeL1anjhzDvTmn84tug6JUmSJHVe2TyvGmaXze41y6tXimAB8Nb8bRPw\nOnAHcCtwa0o8VVRtkiRJPU3e3NoHeFv+Oo5sTOHGyJ+SUYVSX9R3Mrtu5DH2YS2T+TuWp6OBZmAQ\n2VLMU4stTpIkSVJXKZfnFVPi4rIH1zAm5kRwBdOoYTZVVNPMWpq4zBVfPdor+c+FZCHpw4Dj839/\nPYIE/A24BLg5JZ4pokhJkqQiRFANHAy8CZgF7AmsB34B3AR8AvgWMI3yowoltdKzml3vZDw3sIxb\nqt8GfBH4EFmTq91STEmSJEl9TEdZXtWMYzEPMY4aTmb3lu2OOOzp2o7WWQV8H/h+BPOBY4GxwOeB\n/45gJTAfmEz2BHMTMNPvgpIkqbeLYBDwQ+AAYCjwPNkqrkeBe8iaXFX54QNT4pr8vA5HFUoq1dPG\nGGa+N+FBnlj6N+BtKdEzCpQkSZLUraI26hjDpSVZXg0sZQUXMZov8C4OaneSIw57pQjmsekp5ROB\nl4GDyMb1/CswKj/0T8A5wCN+N5QkST1dBFcBB5ItMrmJ7CGeA/OfCRiSH/pL4JSUeDU/r+TayMaW\n+qu+M8Zwoxg9GXivX2YkSZKk/iOtTPOiNqC+NMsrrUzzYkp8suxJVS03DNS7lHtK+T7gvghOILvZ\n8xeyGz63AK9H8CqwAXgGONubQJIkqSeIYBTZqOapwLlsWqE1HPgS8FXgYeA6NjW03r2x0ZVzBZf6\ntTzTeXZnPqNnNrvWr/lrStxbdBmSJEmSKqtclhfQ8YjD4ewbc+IArmBP87x6j/wmTkcB6yU3e/Kw\n9gPJ8iv2IlsB9mgElwJ3AH9MiXUVKFuSJIkIvg+8kayZ9RIwCVgA3ApMIBvV3G6F1uZGEm7h2kjq\n00omfFyy/Z/T85pdPxm/gdUDPlN0GZIkSZJ6kCYuo4GJbUYcPsYe/JLHuIuJ7MBUhrXaZ55XL9X2\nZk8+8eP+CB4ha3bdC3wZOBL4NjA2gpfIVn29BFwM/BlYmRIbKly+JEnqxfKHbHYEaoCvAfsBA8ly\ntSaQ5WyNyrcB/ArYNSWa8/PrsaElbZsaZpd8z9tOnc7sioiTyP4ffyDwnZTSl8sccxnZEs1Xgfel\nlO4rc0xizGHP8PzZv0qvfuLcThUlSZIkqc/JR1u0H3G4T9zOTN7R7gTzvPqUCEZS5uZRBLVkN5r2\nyze9AKwDRgBPkmVjbABWAqelxNP/r707D7OrrvM8/v6mksoGIYRAQggQSHCjcRQbsFWQRmRPAEEW\niW3bDtqjEsfuEdS2BZ+ZUYfnGXtabRtFnVEDBAjEhH1rAW1pBUFFWYSQQCob2fdakvzmj3Nqyznn\nJrUkVZW8X89TT1Wd3z3LvVX31K3zud/vb08etyRJ6h8iqAcmkVViHZN/Ph8YAwwCmsiqtZrI3kAz\nmvY5tn4DfA54Efg+zq8l9Zo4Lv6di3kXANdBn8zZFRF1wLfJepIuBp6MiHkppec73OYcYEpK6ZiI\nOAn4V7J34BU1PLUd+HhPjkmSJEnS3qmyxeHgiv9rRjIWOvR/t8XhgFb1buiUWB7BQrKwq+2iUwQj\ngCOA28jaIB4BvBrBr4FHgbeRBWLbyf5PPZTsotcU4CSyN3QuAE5PieW79c5Jknqs1t/73h5zewPj\nseX19DtGfv6zjH7ibxmShjNh2CBWfmIZzRf9DniJEf99CAc+OJrBdSNpGQqrL/stjR89JSW2xrg4\nh/oTf0Td8OFs3bqOLYv/V1q14GcxLs5h/yFDSW9aQbywgQ0t74L+/1jsi9vr7r72Vv3158gnGcNo\n/rw37mNP2xieCLycUloIEBGzyNLw5zvcZhrwI4CU0q8iYnREjEsplf2z8KWU2NTDY5IkSZK0L6me\nz+utcVn8O0dwOOdxeNtyWxzujQpzYOSTvr8QQQNZ2PUk2f+rbwb+Eng3WdgFWcB1G/AcMA/4Ctn/\nu38GLIzga8B3UmLlHrtHkgCI4MfAG8i6BX0cWAk05x/f6TD2oR2rK/IqjiPJWqAenX++mOy530Q2\nv85qYCOwCTiPrH3Z68DlVmv0jj0SagBt8720yv/e9/rYIXEChzPd7e2hx3bOiONi1Ee/BUMGc/TI\nT3LhpkPbxn464q1xwEVfJzY0cvTgL3D+1kltY7OPOI2NX2zh6B9v56KlI9uW3/Hsfiy/uOPvzP5t\nY3MfHsfivzkjxrWO/XpsPnIA8/hqHBJvyY6vZQo8C3Aa8zjC37N+uL1u7qu756a+Dox2aXv98ef4\nICfzMsvYxjXM41M9bWXYozaGEXExcGZK6cr8++nASSmlqzrc5i7gaymlX+bfPwxck1L6zQ7bSpDu\no+TFiSRJkiRVqfjnbT4r+RwH8VXO502FlWxxuM+o0f7wXipaEO0w9mngSrIL5LOAf0qJP+25eyDt\nOyLYDzieLGw+Kf98GO1z4zTlH0OA+g7LIQu/VuefRwND89stAl4mq9RcAEynve3pb4BbgP3yj+nA\n+HxsKfBPwGPA0ymxtVfvbD/WmxdfK/5Gv8wiZpZcEH2ZBj4DlF1ErT22jvV8mOMLd+ZWHgfquJR3\nF8bu5Gmgjg/wnwpjc3keiNLXELPYzGWMKCy/l0XAIM7hsMLY/bwOBGdxcMkxtnApQwrLH2QtieDM\ntjdmdBxbDwRndAhqWt3GNi7p9NzIPMQmEnAGI0vGNuZj+xXGbmUrl5YUTGTHAGcwqmRsHQkqjr36\nft06aDuXbh9UWH7PqK2QEuduKD5Od4/eSopg6prifZ5FM5dRX1h+H0tJwDkcWhi7l8UApT/HWTRy\nGcOKx8erJOA8jiweHwsAOI+jCmN3MR+AqUwujN3CJi4v+VnN40UApvHGwtjcvADl/LZzXLub2ciH\nSn6+P+VZEnAhxxXG5vBbElH6HLmZ9Xyo5Gd/B08BwUW8ozA2m18DwcWcUBi7iXVcUfI7cTu/JBFc\nwl8Uxm7jFySi9Pl9K4+TCC7j5MLYTFYznTGF5bfwbyRuZH++ytQOP6+7eIU1XEMiMYbrmcrRbWPz\nmM9ibuIwrmBah59jT85n3Tk/LuGzAEzg/+xwHPNZxmcZyVVcwftLHotVTOegwvKbeAgIruD0wtjN\nPESqGKva3s08AgQf4rSSfT2Q/pTOamtZ/zJn9UkbQ2BXk7IdD65ivevOhiW/iLhxNvBoSunR7h+a\nJEmSpH1BfjENZpbM53VsfAZKLlTVM3xfbF+yL6oxGXyhGqzG2K8j+EfgU8BvIxgMbAOWAVvILq43\nkV2Ub10+zfaHEkRQRzZHzr8Ak8muCd1Ddk1qZP7xTuAgYBjwDPAEMBf4B7J54ncWTD9NFkg3koVg\nt5EFZQC/Sqn9HBDBe9ih7WmHsWPz7T1DFnSdSDY3zxsj2Ew2h893gN+SzduzKCW29/xR6r5+++7/\nuzgmjouxHMSVhXfqT2MKN/PfmLbDhfdpTOEe/pWgrhA0TGMKDzKLoI737xA0TWMKcyoeoLGcQNV1\nyP04snJsKKNIFWODKYYxANvZUnEU0MyKfF/FsGsQzVASdm3OQxJ4e2FsEy/lXxUDhazSsRiCbeCP\nZNdoi0HDxrYuXcWxQWwp3V6tY9ichziUBJDrDtvC9iFDYWHxyGNQC2wfWli+df1z+VdvLYxtX/tC\n/tWfFcYGsxVKwq6tbc/7Yti1nY3FA2vbXtW18K2Fq9+tyn9bMnXUUbxunqmvuG4/pCQAazW0JCxq\nHys+DgDDOaTyGEZyWOXYsJLAF2BUSXDX6oCSgK79OIpBHMCBJT/zVgeV/H61Gtv2N6BoJAeWLh/H\nyWznZE7f4fk4laN5hFkAvG+HIHkak7mdLzNth21NYwr/xj0kKMwsvLOx27muG9u7q2JsMv/G3Mrf\nz5ElwRTAhJL5kFsdWmOsanvjObVynUbGR8R1ALzOr4CzKm+7Ez0NuxZDh3Yg2dcNO7nNxHxZievy\nFxrfs7JLkiRJ0i6rnM+rqsXhYN7BJL7f6R29tjfcp9QIwUrHUmIZ8I8RnAKcQnZh8jngarILaUOB\n79E+P1hDBI8CDwOPAM+kxLbdcmekPhZBAGPIWgV+Pf88FFhFFnCtILtY3nqB8X1kIdKm/OMtZM8b\ngFdSYkaHbXclmG5dZ1X+5ZPAJ3ZlnYqxn+Tb+3fgXWQVYx8HXgXeCIyJYAtZiLEJuAl4BViSf/x9\nfr82sQudjHqldd/cQcd0q/3UXbwx3hFf42A+WhpOzeJqppWETA/ELKCOM1PnsakczUN8j0EVYWDV\nhXdoZnvFuXLdmNfZPmwILDmiMLZpyEZoKV4sX8pjZBfrzyyMvc6TlWMreTYfm1gYa2QjlFT3rOWV\nfJ03FMbWsygfKwYyTWyBkgBjI8upCho2saJyrJkmysKpzayuXGfjkHUwqC5730bh+Mq3V+sYVh87\nmu3D94OnimMbDthMbF4NJZU1zVs3kJ07dliHJfm+isHHehqoemwb2Qwlocw6Xs3XKVZArWVBPlYM\nZhrZBAwvLF/DS/k6xaBnNS/mY8XKrtW8kI8VK8K2UP5YrOIPVP1uruT3+diEku2tB8YWlq/gGaqf\nI7+pHNvM2tLtLedXless54ka21tTur1l/KJynaU8XmPs0cqxTawq3dcSHqGe4cB7C2Or+EX+VXEs\nsQ5KgsaVPFa5zioezwP13tle7bHH8/+Jyh6LlZQ9Fot5kKrHr9ZY1faW5JViZesMZWlK6brWbyPi\n2sJtdlGtbHlXPAUcExGTIqKe7J+BeTvcZh7wVwAR8U5gbcV8XbDDO2okSZIkqUfW803m8XKnZfOY\nTwsrCq1rpjGFUVyFVFvrPNNPAlekxB9T4pmU+A/a3/z5JNk8YN8mq/Z6CGiKYG0EsyL4YgQXRvCm\niJJ380u9KIK6/PfumQieiuDjEXwygi9E8PUIno3ghQj+LW/7ubPtHZHfdlEEyyL4PbAWmA/cQHYx\nehJZxcRS4JCUOBL4j3wTTwKnpMQ3U+IHKTGLrNVg61incCol1qbEpWXXi2qMfYisuqtwnamb21vX\n4fj+PCXelxITgUPIWiMeRBZqXQD8BXAVQ2+9kwlnXMGRp57ChDPPpv6uFRG8FsNufzUOO60pjnpP\nSxz2l5tj5Fd/EMH7Y8xJH2Ei/8x0zuQS3st0zmQi/xzj4pw4ePT5HFb3L53Gxh98M0On3FwIps7f\nfjSj3zybg466tTS0mrTfHI4aMbcwNpVJDB32r4waUV4NUTe0vIpj9VvqWfVn5YFHwylDmf++8nPc\nxgPK11ly8iEsPbnYPg5g3ZT92VheNMDmw5uZfVjnyqrbj9zG4utXsexTv2Lu4IWdxuYOWsCK8+5i\n2d8+wZwDVnQamzNyKStOn8OaQ28sfQ2xjm8Xls8ZtYLFX3+dhhsGFY7jjkPWs+Qrr7Lsb59g7pDX\ndml7c5nPmoO/x6oTbmbu0EWdxu4cs5rF31hPw79s447xnauQbj+8iaWf3crsI5o7L5+0jdfmvIdX\n557C7Ud3rk66/ejEa7PfxaJZJ3D7pM5B422TYcmXRnP7UZ1Dy2x77+LVn76nsL3Z4zey4q/+nTUn\n/6Bw7HMHLaDxuRlsWXh18T4PWlD6WMxjPuv5VuVrulpj/X17A/nY+/v2uruvqjfKNbOlxlhLl9dp\nYnOvbm9nY/3959hLejRnF0BEnE1WUl4H/CCl9LWI+ARASum7+W2+TVZ+tgn4aErp6ZLtpO72YpQk\nSZKkKm393zu0OGQsV3NJyTsfb+fn6Q/pFFscqkrVHGC1xvIKr9bftyeBR8neTf5mslBgA1mlyDkp\nsWT33gPtrSK4kSxoqiNrEzgZOJasOiFor0R5jawSdh2wHvgb2qsRNgIzySoSf5YSqyOYBJxK9jt8\nKlkFStD+zu2HgEtTYk1+HKXz4XXnudNfVD63x8U51J/4I+qGj2Xr1nVsWXxlWrXg9tJWgHMHv8pr\nh9zJ4Ssv4oLm9sqk2Ue08NL/fJ6DfvhGPv6zYhXHLW/ZTl1LcMlLxWtmd5L4QEllzQN169helzi7\nuRhe3nnga6TUzEVrpxTGZo16iqZBzXxk7bsKYz9hFR8uaU81k/uBYHrJu/VnxgOsPuqHHLnoes5v\naa9cmT1hC4vHzeLIF8/kgs0TOi1/7cRriKatHPXwNVzQcZ3xG3l9WVZxW6xmW8Di7Z8GoP6kH1E3\nbCxbt29g7bu/z6avNQFvo37OSYy94QDqt0Dz8GDFp7bQMm0hsIhhN8BBt57C4DSclmGw8hNrab5w\nJTCR+rnbGPvtodQ3DaJ5GKz85CaaLxhE/d3DGfvtQQxpJFvnyhU0X/R94BX2/9jBjHrxagYPHk3L\nMFj10VdouvQ+YAr1s9/K2O8fypAt0DIssfK/rKf5go3Uzx7E2O8enO1nOKz89Daap24DVjP0tmYO\nunEiQ1oGZ/v6WAPNH7wBWM3Ivz+S0U9+ksGD9s/2dcUzNH34bzjgnL9gvw1fY3DdAdnyy/6Dpr+e\nCrRw0CGnMWzK9xlcP4aWYbD6g4+nLR97L7T+Trc+htvWsaXhSlYvmMeYyecz4tDvUtd6ny77Vfv2\nxp/GsKNvrNze/vV/R3rjW4kXf8+G5m90qlYsGSt73bZDhWOXxvr79gbysff37XVnX5VzATfkFcdl\nY4v4SUmb19rr7MntNTBjp/e5H/wcW/UkJ+px2NVbDLskSZIk7SlxTNxfemHuYZoZyx0s4p2dJqbO\nJ4U28FJ3VF38z8ceh7bJ01vIgob/B/w8VcwXE8H3yNpkbWYXWqNp75LPgXUUWZD1lvzzsWRBV2sH\nnxfJ2gn+EXierMppZ/NePQl8FjgJOD3/gPY5th4CHsu3d0+N7fXr4KpKzTmsdn1uq5dp4DOMYkbp\n35hb2MDlJe3gHmIzddRzWsl0I/fzOolmzi5pWXYza/lQSTVezQBqJ2Pr+VZpmNSw/Ufdufja9ljt\nEKC0hYIly6E8dOk0VhWg1A5Vf0lWeQdw2w5zuRXO0x3ac95H+1xWc4HpZPPDzdtxnVrb6zD2KO1v\ngJgLfJqsXd0tHfZzR0pcvIvbqwqYu7xOrcewu9uTBpr+ENTtjuBvoDDskiRJkqQuqHzXJlxPHV/i\n3E7zDmdmcn96KZ29Bw9Te4mdXHzteIHww8BU4K/J5gRZRzbH0SDg92St0saTze3RGmosA2YDLwF/\nAj5C1j7OIGwvEMEwsnlojgfenn+8A9hGVpU1C3iaLNT6H8AZdDGAqnFh+zGy+emgGAzsfYFWWXC1\niJklAc/LrOOL1DODS3lPYScPsIo6hnF6yRxMd9DCRSWtS+/k1zSxicv5y8JYrXDqhzzFWEbvlnf/\n92LFDdT8PdtjFX/dCXhqrdfdY+9mONWd53CvPrYDuTpT0sBh2CVJkiRJXVT5Tspj49HSFoe38Vj6\nYzrVFofqTWUXCPNqgncAd0Jb8PoEcDVZuPUvZKHGs2QBxwTgGLJqr5Npn9D+ReA6sgunr1RViqn/\niOBwslaB/wBMJGs9+DzwG+AZsmDrq9AWsrSFUHsyGOjPerkSaxOXl4RWj7AVaOF9DC+MzeX3NLGR\nSyi2ApzJSqa3tX/suLy8ompXg6t99N3/XdXd58hueG51OZySpH2FYZckSZIk9ZLKFocPsp79uIGV\nXMS0tvltbHGo3aab1QSt67wI3AocR9YWawRZtVcj2fxgF3sxtffkrSXfBDSTtQLcRja31T+SVeJt\nAb4AvE42n/lmsjaDbyar4vsD8C5gf7I53d4GbcHGTtut7ab71K8vvJeFWkBZKJQFWgfwWa5oa83Y\n7n5WMpjhFZVYTVxEcR6t23iMZhq71ApwJ/Ow9MY8J5IkDXSGXZIkSZLUSypbHB7AT2jkM5zJgYWV\nbHGo3aA320xFMB54mGxuJ4DlZNVDd6bEml498H1ABCOBE8kqrN4DvA+oy4c3A6+RhVpTgAPy5WvI\nKvNGkAVhY2hvR/k08FfAcymRutvObG/TpSqtu1jARpq4nDcVNvQwzdQxhL+keN1pHn+gkfW9WYlV\nK7gytJIkqZphlyRJkiT1ou60OGQl19veUP1ZhwDlN8A3gRanJrkAABPiSURBVPOB08lCmCay6qMb\nga3AEGAwcBlZWLMEuGhvD1d2FEE9WTvBI4FrgcnAfkA98DvgF/nHVWSPZVfm4NlnAq1a7V+7FGjd\nQwOjmckqPsA03lDY0Z1s4wNtoWO7O3iCLWxgOmcUxnZTJZYkSeo6wy5JkiRJ2gMqWxzeyQaGsYlz\nGN+2zPaG6mcq5gcbBfwK2qphFgAPQT4nEXyA9nnD1gI3APcDv0yJlj139LtPBMPJ5jt7E9m8aBPJ\nwr4twMHAUrLWj28EDslXuyMlLu6wjS7PwbO3BVrQjdaCZWP3spSDmcNyzuU8jizs5D6WAy2czcTC\n2E2s4Yry6lsrsSRJ6v8MuyRJkiRpD6hscbiFQVzKUYUVbG+oAWAXq4+eAr5E1rLvbLL2fGvIQqEW\nsgCsCUj5x2lkc1GtAi5NiUU19h9kLf2+Bxydb+erZGFTkLX6uwoYBWwAPrSzcCiCQcAdZAHWVmBm\nPjQsP67zgEPz7+uA+cALwJ8DR+S3vQe4ICW27uxx2pd0qRJrHi+zlvX8FccXNjSPF6ljBOe2hant\n7mER0MK5HF0YqzVf1g95irGMthJLkqSBqSc50eDePhhJkiRJ2lvlF0thZueLpYzlaigJu/ZnQnwl\nBvEdzrLFofqxD1FdYbTj2APAP0ZwCPAoMCm/3TuBH0PbnEijaH9OvBLBUuCPwASyNoCDycKlw8ha\nBG7Ll43M17kR+D1ZcLY9337r3FePRHB+SjR0PNAIhgCnABcCFwAHks2PBfCfgTlAI7COLOQak4/N\nTokP5tu4lyzsehKY3hp07cLjtFfpYqA1OcYFjGJGp+UA05jCHTSX7mQII9lOY+nYJl6hmUYoCbta\nz7vzmFwItRq5lgYK5+jW823+2XOvJEl7IcMuSZIkSeqCsoulcUzMKL3xcI5iPg0cRT1nc1Db8vzi\nsIGX+oM8uLm0K2Mp8XoEC4E3U14RdjpZ2PUkcCZZ8HQs8M+0h2CvkQVIr6bEul2sMHsZ+BPwuwgW\nkIVhI8iCsOFkFVpzyObP+kaN7Z1MNv/Wk8CVHe5aZaBV63EaqLrQdrB2oHUv32UI+5fupIUWsjnO\nOlvFH8jC0SmFsVqBVnt7wcpQCwMtSZL2ObYxlCRJkqQeqmxv2MAMDubLfJCTCivlLQ6rKij25PFL\n3dGd+ah2Emjt8vYiGAycDPyErDoM4O6UmNqT49uXdLnt4N0soJ5RnNEhuG8fe4lG1nIxJxTGarQW\nBMqCNdsOSpK0j3LOLkmSJEnqY1UXZePYeJRLeG9hhft4lS18hcF8sXCxuYHPeEFXe6PeDpmcR2vn\nKlsSHhP3l857dQfNXFRSiXUvC2hkPR/gPxXGZnI/6/lWZegPVIVWBlqSJKmVYZckSZIk9VOVF5Tv\n4VWGciinl1xUzqu+9sTxSQOZFVqZLs2xdS/LOJRHWMEZnMHBhY3NYhOXtc2d1m4ngZaVWJIkqacM\nuyRJkiSpn9pJi8Nr+CCnFFa6jcdYyfW2N5TU0S7PsXU3r3IAP2YNlzGNYwobupv5tLCJC3lrYaxG\n20EDLUmStDsZdkmSJElSP1bZ4rCq6uunNDOMzZzF6LZltjeU9gldqtK6m1fZSAuXdVjW6n5WkGjk\nbA4vjLUG6t1oOyhJkrS79CQnGtzbByNJkiRJ6iy/SFy8ULyebzKPyYWLzZtp4gLe0um205jCTK4C\n7q26GL6b74akXtKlQGsek+O4OJhxXNVpOcB5HMkdtJTuZD3P0UwjlIRdzWzJ9wczK0MtzymSJGnA\nMOySJEmSpD5SdbGZsVxdusJ43h1T43sczvuZyqS25fOYHOMCAy+p/+hyoDUu4EA+Vwi0pjGFh/ke\nic2lO2pkI3BgYXnr+aQsUF/Pt6BGEC9JkjTAGHZJkiRJUh8qu9gcx8SM0huv40WaOYepHNZpuVVf\nUp/Z5Xm0WgOtUcwoDbQe4acE5W17VvNEXqVVbHvaxHzmlcyx1doutXb1liRJ0l7BsEuSJEmS+puq\n9obLuTav+jqssM4Y3hDHx39mIteUXWD34rbU+6qqtFjL+tJA6wF+zBBGlW5sLU+yhQ2UBVq1qrQa\nuZYGKgMtq7ckSdK+wLBLkiRJkvqZWtUYlVVfiSGM5NucztBOy/Oqr7aKEiu+pC6rrJisqtKqmker\nkcWs4Q/AewtjW1hfq+2gc2xJkiRV63bYFRFjgFuBI4GFwCUppbUlt1sIrAe2AS0ppRO7u09JkiRJ\n2ldUVmNUVX01MCOv+ipeRB/Mm5jIN5nG5A7rWPElddClObbu4+1xUfyccZxQurGs5eCQwvINLMkD\nrcO6E2hZpSVJklQuUkrdWzHiemBlSun6iLgGODCl9PmS2y0A3pFSWr2T7aWUUnlvakmSJElSm/yi\nfFnV1/1ML2mBdistXFpy4X0m96eX0tnO86V9yS7PsXU3iziIW1nJRUzlqMKG7uJPtLCJD/D2wtgP\neYqxJfNoNTCjLUAreQ7vhrsrSZI0YPQkJ+pJ2PUC8N6U0vKIGA88mlJ6U8ntFgB/nlJatZPtGXZJ\nkiRJUg9UzB80n81s5jKOK6xwP0sYwY2s48NM5egO67xMA5/x4rsGqi5Vad3NIjazjUuYVNjQfSwj\n0cg5JWO38Rgrub70OddA1m7UQEuSJGmX9SQn6smcXeNSSsvzr5cD4ypul4CHI2Ib8N2U0o092Kck\nSZIkqUJVCzRGMQNKwq5mNgBXMZUxnZbn83wB91r1pf6qS4HWPKbEO+NoDuXKwhxb53E4d9BcupMN\nvJi3JJxUGGtmi/NoSZIk9Q81w66IeAgYXzL0Dx2/SSmliKgqEXt3SmlpRBwMPBQRL6SUft69w5Uk\nSZIk1VI2p0+MCyrm+fq7ynm+xnFSnB//lyM5nXOZ2GG9trm+DMK0J+xy28F5TI4jYygH87lCoDWN\nyTzIVxlBY+lOGtkE1BeWtwbGZc+f9XwLnEdLkiSpP6gZdqWU3l81FhHLI2J8SmlZRBwKvF6xjaX5\n5xURMQc4ESgNuyLiug7fPppSerT24UuSJEmSdqZW9UkcEzNKV9rAQpo5rVPQBVnV10381xgX5WFD\nHoTt1jukvU4Xq7Qms55NJYHWFB5hNoktpTtZy9N5lVZxXrsm5jOvZI6t1udJ7eotSZIkdUNEnAqc\n2ivb6sGcXdcDq1JK/ysiPg+MTil9fofbjADqUkobImIk8CDwlZTSgyXbc84uSZIkSdrDKuf5amAG\nY7maS0qqvn7GdprZwJkcUBibyf1trROt+FIHXQy0XmYpf8doPselnFzY2J0kPkDxGsLtPE4TW5he\nEmi1/m46x5YkSVK/1Fdzdn0duC0iPgYsBC7JD2YCcGNK6VyyFoh3RkTrvm4qC7okSZIkSX2jW1Vf\nS3iE/TgYeFthbChv50hu5FwmtC2z9eE+pUttB8cFHFjadnAKj/BTqJhLawtrgQMLy5vYXKvtoHNs\nSZIk7Z26XdnV26zskiRJkqT+pWbV1yhmlFbPzGILlzG8sPxOnibxLYbwD4UKngY+YxA2sHS5SmsD\njVzBnxU29AjbCLZzGkMKY7fzc5rYXPp79kOeYmxJ28EGZnT4XbJKS5IkaQDpq8ouSZIkSdJerGbV\n17igtHoGNgPHFTa2P5MIbuB0hnZaPo0p3MLf15oDDMAQbPeoFTB2KdC6izfEO+OfGM/flFZp3cn2\n0gNYzRM0sYnyebQ2VVZpNXItDVRWaOWf/R2RJEnaRxh2SZIkSZIqVYUGVUEYo5hBWdi1nF9Tz3Ao\nmQNsHKdwMO/hdOo7LZ/GFP4v/52DGFUWgu2sGmxfqxTr6mMB1A4Ydxy7h2PjjLiJiVxYCLSmchQP\n8j/Zr7Lt4DrK2w5utO2gJEmSesqwS5IkSZLULWVBWGXFV3sQVrSEhxnOAcBfFMb243im7bBsGlOY\nzZfjrXEIE/kS05jcYV/VYc0uVIrtyfCs21VVu94+sOZjwVrWl1Zh3cN3GMRQzmZ8p7Fzmch9/DVD\naCm9Q2t5mmYaKa/Sms+8kraDuxBoWaUlSZKknTHskiRJkiT1mm61PlzPt6AiCNvOGsoqgkbzZhI3\n8P6Stojz+Ce201wa5PyQr5TM9bRrAVnF2E5DqF4Kp+KQOIHDmV66zihmlN7fOXyNQQypaC1YZStb\n2Qw7hF0AG3ghD7QmFsZaq/tsOyhJkqQ9zLBLkiRJktSrutr6sGYQ1lTR/m4Zv6xsiziE4QzigNKD\n25/jmcagTsuygOwbbGdbaSh0K9ewne2lYzO5qnK+se6EU7fxBYLBpWO38DmmMbKw/CFupW6H0K/V\nCA4lVVRibakIEtfwEhDAmwtjtQIt2w5KkiSpjxh2SZIkSZL2mC7PAQYwrxAk1W6LuIo/koU1xXZ6\niQ1QEoQNYSRBXen2xnISsUNA1moC72c87+N0hnRangVXXy4N1h5hHoMqtncQx5PYVjpWX/E//EZe\noJGNwKmFsRX8huyxmFAYq9FaMP+6W4GWVVqSJEna0wy7JEmSJEn9QlVI0s22iOVjjawDji/sfBV/\nIAuFDi2MLeVnVIVny3g0rzB7V8ld2giMKixdzRN5K8DTS/b1eOW+trABSiq4NrEyr7aa2MXHomZr\nQQMtSZIkDRSGXZIkSZKkfq07bRGhPKwBqivFsq+7Fp6t5RuVFWbNNJcub2JjHk5N6tK+1vET5hXa\nIu5StVV3WgsaaEmSJGmgiJRSXx8DABGRUkrR18chSZIkSdq7xbg4h1GVoVCXx2JcnFMyZ9d8FvGT\nkjm75tPAjLb1urOvinUkSZKkgawnOZFhlyRJkiRJPWQ4JUmSJPWMYZckSZIkSZIkSZIGrJ7kRIN6\n+2AkSZIkSZIkSZKkPcWwS5IkSZIkSZIkSQOWYZckSZIkSZIkSZIGLMMuSZIkSZIkSZIkDViGXZIk\nSZIkSZIkSRqwDLskSZIkSZIkSZI0YBl2SZIkSZIkSZIkacAy7JIkSZIkSZIkSdKA1e2wKyI+GBF/\njIhtEXF8jdudFREvRMRLEXFNd/cnSZIkSZIkSZIk7agnlV3PAhcCj1fdICLqgG8DZwFvAS6PiDf3\nYJ+SpA4i4tS+PgZJGkg8b0pS13nulKSu89wpSXtWt8OulNILKaU/7eRmJwIvp5QWppRagFnA+d3d\npySp4NS+PgBJGmBO7esDkKQB6NS+PgBJGoBO7esDkKR9ye6es+swYFGH7xvyZZIkSZIkSZIkSVKP\nDa41GBEPAeNLhr6YUrprF7afunVUkiRJkiRJkiRJ0i6oGXallN7fw+0vBg7v8P3hZNVdpSLCcEyS\nuigiru3rY5CkgcTzpiR1nedOSeo6z52StOfUDLu6ICqWPwUcExGTgCXApcDlZTdMKVVtQ5IkSZIk\nSZIkSSrV7Tm7IuLCiFgEvBO4JyLuy5dPiIh7AFJKW4FPAw8AzwG3ppSe7/lhS5IkSZIkSZIkSRAp\n2TlQkiRJkiRJkiRJA1O3K7t6S0ScFREvRMRLEXFNXx+PJPVXEbEwIn4fEc9ExK/zZWMi4qGI+FNE\nPBgRo/v6OCWpL0XEDyNieUQ822FZ5bkyIr6Qvw59ISLO6JujlqS+U3HevC4iGvLXnc9ExNkdxjxv\nStrnRcThEfGziPhjRPwhImbky33dKUkVapw7e+W1Z59WdkVEHfAicDqwGHgSuNxWh5JUFBELgHek\nlFZ3WHY9sDKldH3+hoEDU0qf77ODlKQ+FhEnAxuBH6eUjsuXlZ4rI+ItwM3ACcBhwMPAG1JK2/vo\n8CVpj6s4b14LbEgpfWOH23relCQgIsYD41NKv42I/YDfABcAH8XXnZJUqsa58xJ64bVnX1d2nQi8\nnFJamFJqAWYB5/fxMUlSfxY7fD8N+FH+9Y/I/kBI0j4rpfRzYM0Oi6vOlecDt6SUWlJKC4GXyV6f\nStI+o+K8CcXXneB5U5IASCktSyn9Nv96I/A82YVYX3dKUoUa507ohdeefR12HQYs6vB9A+13TpLU\nWQIejoinIuLKfNm4lNLy/OvlwLi+OTRJ6teqzpUTyF5/tvK1qCS1uyoifhcRP+jQhsvzpiTtICIm\nAW8HfoWvOyVpl3Q4d/5HvqjHrz37Ouzqux6KkjTwvDul9HbgbOBTecuZNinrS+t5VZJq2IVzpedR\nSYJ/BY4C3gYsBf53jdt63pS0z8rbcN0BfCaltKHjmK87Jalcfu6cTXbu3Egvvfbs67BrMXB4h+8P\np3NSJ0nKpZSW5p9XAHPIynaX5/1uiYhDgdf77gglqd+qOlfu+Fp0Yr5MkvZpKaXXUw74Pu3tYjxv\nSlIuIoaQBV0/SSn9NF/s605JqqHDuXNm67mzt1579nXY9RRwTERMioh64FJgXh8fkyT1OxExIiL2\nz78eCZwBPEt2zvxIfrOPAD8t34Ik7dOqzpXzgMsioj4ijgKOAX7dB8cnSf1KfoG21YVkrzvB86Yk\nARARAfwAeC6l9H86DPm6U5IqVJ07e+u15+DeP+Rdl1LaGhGfBh4A6oAfpJSe78tjkqR+ahwwJ/ub\nwGDgppTSgxHxFHBbRHwMWAhc0neHKEl9LyJuAd4LjI2IRcCXga9Tcq5MKT0XEbcBzwFbgU/m7yST\npH1GyXnzWuDUiHgbWZuYBcAnwPOmJHXwbmA68PuIeCZf9gV83SlJtZSdO78IXN4brz3D86okSZIk\nSZIkSZIGqr5uYyhJkiRJkiRJkiR1m2GXJEmSJEmSJEmSBizDLkmSJEmSJEmSJA1Yhl2SJEmSJEmS\nJEkasAy7JEmSJEmSJEmSNGAZdkmSJEmSJEmSJGnAMuySJEmSJEmSJEnSgGXYJUmSJEmSJEmSpAHr\n/wPmfg3xvs46XAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8498690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_number=1\n",
    "tji=1\n",
    "noiselevel=0\n",
    "decimationlevel=0\n",
    "x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "\n",
    "torques = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/torques_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "\n",
    "ymin = numpy.array([torques_all_1.min(),torques_all_2.min()]).min()\n",
    "ymax = numpy.array([torques_all_1.max(),torques_all_2.max()]).max()\n",
    "plt.figure(figsize =(30,10))\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(torques[:,0],'v-',label=\"Tau1 Calculated\")\n",
    "plt.plot(torques_all_1[(tji-1)*250:(tji+1)*250],'s-',label=\"Tau1 Original\")\n",
    "plt.title(\"Tau1\")\n",
    "plt.xlim(0,250)\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(torques[:,1],'.-',label=\"Tau2 Calculated\")\n",
    "plt.plot(torques_all_2[(tji-1)*250:(tji+1)*250],'o-',label=\"Tau2 Original\")\n",
    "plt.xlim(0,250)\n",
    "plt.title(\"Tau2\")\n",
    "# plt.ylim(ymin,ymax)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ggegCAGggugAAGoguAIAGogsAoIHoAgBoILoAABqILgCABqILAKCB6AIAaCC6AAAaiC4A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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7bf8f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_number=1\n",
    "tji=1\n",
    "noiselevel=0\n",
    "decimationlevel=0\n",
    "x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "\n",
    "teta = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/teta_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "\n",
    "H,E = joints_to_hand(teta,aparams)\n",
    "\n",
    "plt.figure(figsize =(10,10))\n",
    "plt.plot(H[:,0],H[:,1],'.',markersize=1)\n",
    "plt.plot([xstart,xdest],[ystart,ydest],'.-')\n",
    "plt.xlim(-1,1)\n",
    "plt.ylim(-1,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/P7ZKc84+AAAAfLzx672/8ejZf+2H99S2ertZa+0Pt9b+6O3//4iIfLuI/J8i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\n41WvplxmPy35eM25pW3PmJF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x77ea650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_number=1\n",
    "tji=1\n",
    "noiselevel=0\n",
    "decimationlevel=0\n",
    "x_lsm,y_lsm,xstart,ystart,xdest,ydest = generates_lsm_start(tji,trajectories,x_values,y_values)\n",
    "\n",
    "OutputSpks_save = slf.load_from_file(\"./\"+base_dir+\"/\"+sim_set+\"/OutputSpks_save_\"+str(tji)+\"_noise_level_\"+str(noiselevel)+\"_killed_neurons_\"+str(decimationlevel)+\"_run_\"+str(run_number)+\".pickles\")\n",
    "\n",
    "y = numpy.array([j for i in OutputSpks_save for j in i[0]])\n",
    "x = numpy.array([i[1] for i in OutputSpks_save for j in i[0]])\n",
    "\n",
    "plt.figure(figsize =(20,10))\n",
    "plt.plot(x*1000,y,'.',markersize=1)\n",
    "# plt.xlim(-1,251)\n",
    "plt.ylim(-1,601)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}