notebooks/braille_reading_ffsnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "6fc1e0b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import os\n",
    "import pickle\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import seaborn as sn\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# %matplotlib qt\n",
    "# warnings.filterwarnings(\"ignore\")  # supress warnings from matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "5c4eaa07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set variables\n",
    "use_seed = False\n",
    "threshold = 1  # possible values are: 1, 2, 5, 10\n",
    "# set the number of epochs you want to train the network (default = 300)\n",
    "epochs = 300 # 400\n",
    "save_fig = True  # set True to save the plots\n",
    "\n",
    "global use_trainable_out\n",
    "use_trainable_out = False\n",
    "global use_trainable_tc\n",
    "use_trainable_tc = True\n",
    "global use_dropout\n",
    "use_dropout = False\n",
    "global batch_size\n",
    "batch_size = 128  # 512\n",
    "global lr\n",
    "lr = 0.0015\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "id": "c8c451f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create folder to safe plots later\n",
    "if save_fig:\n",
    "    path = '../plots'\n",
    "    isExist = os.path.exists(path)\n",
    "\n",
    "    if not isExist:\n",
    "        os.makedirs(path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "f6ff6b13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single GPU detected. Setting up the simulation there.\n"
     ]
    }
   ],
   "source": [
    "# check for available GPU and distribute work\n",
    "if torch.cuda.device_count() > 1:\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    gpu_sel = 1\n",
    "    gpu_av = [torch.cuda.is_available()\n",
    "              for ii in range(torch.cuda.device_count())]\n",
    "    print(\"Detected {} GPUs. The load will be shared.\".format(\n",
    "        torch.cuda.device_count()))\n",
    "    for gpu in range(len(gpu_av)):\n",
    "        if True in gpu_av:\n",
    "            if gpu_av[gpu_sel]:\n",
    "                device = torch.device(\"cuda:\"+str(gpu))\n",
    "                torch.cuda.set_per_process_memory_fraction(0.5, device=device)\n",
    "                print(\"Selected GPUs: {}\" .format(\"cuda:\"+str(gpu)))\n",
    "            else:\n",
    "                device = torch.device(\"cuda:\"+str(gpu_av.index(True)))\n",
    "        else:\n",
    "            device = torch.device(\"cpu\")\n",
    "            print(\"No GPU detected. Running on CPU.\")\n",
    "else:\n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.empty_cache()\n",
    "\n",
    "        print(\"Single GPU detected. Setting up the simulation there.\")\n",
    "        device = torch.device(\"cuda:0\")\n",
    "        # torch.cuda.set_per_process_memory_fraction(0.5, device=device)\n",
    "    else:\n",
    "        device = torch.device(\"cpu\")\n",
    "        print(\"No GPU detected. Running on CPU.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "a2291f6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shuffle data randomly\n"
     ]
    }
   ],
   "source": [
    "# use fixed seed for reproducable results\n",
    "if use_seed:\n",
    "    seed = 42  # \"Answer to the Ultimate Question of Life, the Universe, and Everything\"\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    print(\"Seed set to {}\".format(seed))\n",
    "else:\n",
    "    print(\"Shuffle data randomly\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "88a7d1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = torch.float\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "03bcccc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "letters = ['Space', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\n",
    "           'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "03ad5dfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_extract(params, file_name, taxels=None, letter_written=letters):\n",
    "\n",
    "    max_time = int(51*25)  # ms\n",
    "    time_bin_size = int(params['time_bin_size'])  # ms\n",
    "    global time\n",
    "    time = range(0, max_time, time_bin_size)\n",
    "\n",
    "    global time_step\n",
    "    time_step = time_bin_size*0.001\n",
    "    data_steps = len(time)\n",
    "\n",
    "    data_dict = pd.read_pickle(file_name)\n",
    "\n",
    "    # Extract data\n",
    "    data = []\n",
    "    labels = []\n",
    "    bins = 1000  # ms conversion\n",
    "    nchan = len(data_dict['events'][1])  # number of channels per sensor\n",
    "    # loop over all trials\n",
    "    for i, sample in enumerate(data_dict['events']):\n",
    "        events_array = np.zeros(\n",
    "            [nchan, round((max_time/time_bin_size)+0.5), 2])\n",
    "        # loop over sensors (taxel)\n",
    "        for taxel in range(len(sample)):\n",
    "            # loop over On and Off channels\n",
    "            for event_type in range(len(sample[taxel])):\n",
    "                if sample[taxel][event_type]:\n",
    "                    indx = bins*(np.array(sample[taxel][event_type]))\n",
    "                    indx = np.array((indx/time_bin_size).round(), dtype=int)\n",
    "                    events_array[taxel, indx, event_type] = 1\n",
    "        if taxels != None:\n",
    "            events_array = np.reshape(np.transpose(events_array, (1, 0, 2))[\n",
    "                                      :, taxels, :], (events_array.shape[1], -1))\n",
    "            selected_chans = 2*len(taxels)\n",
    "        else:\n",
    "            events_array = np.reshape(np.transpose(\n",
    "                events_array, (1, 0, 2)), (events_array.shape[1], -1))\n",
    "            selected_chans = 2*nchan\n",
    "        data.append(events_array)\n",
    "        labels.append(letter_written.index(data_dict['letter'][i]))\n",
    "\n",
    "    # return data,labels\n",
    "    data = np.array(data)\n",
    "    labels = np.array(labels)\n",
    "    # print(labels)\n",
    "    data = torch.tensor(data, dtype=dtype)\n",
    "    labels = torch.tensor(labels, dtype=torch.long)\n",
    "\n",
    "    x_train, x_test, y_train, y_test = train_test_split(\n",
    "        data, labels, test_size=0.10, shuffle=True, stratify=labels)\n",
    "\n",
    "    ds_train = TensorDataset(x_train, y_train)\n",
    "    ds_test = TensorDataset(x_test, y_test)\n",
    "\n",
    "    return ds_train, ds_test, labels, selected_chans, data_steps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "ebfd634a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_snn(inputs, layers):\n",
    "\n",
    "    if use_trainable_out and use_trainable_tc:\n",
    "        w1, w2, alpha1, beta1, alpha2, beta2, out_scale, out_offset = layers\n",
    "    elif use_trainable_tc:\n",
    "        w1, w2, alpha1, beta1, alpha2, beta2 = layers\n",
    "    elif use_trainable_out:\n",
    "        w1, w2, out_scale, out_offset = layers\n",
    "    else:\n",
    "        w1, w2 = layers\n",
    "    if use_dropout:\n",
    "        dropout = nn.Dropout(p = 0.25) # using dropout on n % of spikes\n",
    "    if use_trainable_tc:\n",
    "        alpha1, beta1 = torch.abs(alpha1), torch.abs(beta1)\n",
    "        alpha2, beta2 = torch.abs(alpha2), torch.abs(beta2)\n",
    "\n",
    "    bs = inputs.shape[0]\n",
    "    \n",
    "    h1 = torch.einsum(\n",
    "        \"abc,cd->abd\", (inputs.tile((nb_input_copies,)), w1))\n",
    "    if use_dropout:\n",
    "        h1 = dropout(h1)\n",
    "    if use_trainable_tc:\n",
    "        spk_rec, mem_rec = feedforward_layer.compute_activity_tc(bs, nb_hidden, h1, alpha1, beta1, nb_steps)\n",
    "    else:\n",
    "        spk_rec, mem_rec = feedforward_layer.compute_activity(bs, nb_hidden, h1, nb_steps)\n",
    "    \n",
    "    # Readout layer\n",
    "    h2 = torch.einsum(\"abc,cd->abd\", (spk_rec, w2))\n",
    "    if use_dropout:\n",
    "        h2 = dropout(h2)\n",
    "    if use_trainable_tc:\n",
    "        s_out_rec, out_rec = feedforward_layer.compute_activity_tc(bs, nb_outputs, h2, alpha2, beta2, nb_steps)\n",
    "    else:\n",
    "        s_out_rec, out_rec = feedforward_layer.compute_activity(bs, nb_outputs, h2, nb_steps)\n",
    "\n",
    "    if use_trainable_out:\n",
    "        # trainable output spike scaling\n",
    "        # mean_firing_rate = torch.div(torch.sum(s_out_rec,1), s_out_rec.shape[1]) # mean firing rate\n",
    "        # s_out_rec = mean_firing_rate*layers[5] + layers[6]\n",
    "        s_out_rec = torch.sum(s_out_rec, 1)*out_scale + \\\n",
    "            out_offset  # sum spikes\n",
    "\n",
    "    other_recs = [mem_rec, spk_rec, out_rec]\n",
    "    layers_update = layers\n",
    "\n",
    "    return s_out_rec, other_recs, layers_update\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "5c4b7f8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_layers(file, map_location, requires_grad=True, variable=False):\n",
    "\n",
    "    if variable:\n",
    "        lays = file\n",
    "        for ii in lays:\n",
    "            ii.requires_grad = requires_grad\n",
    "    else:\n",
    "        lays = torch.load(file, map_location=map_location)\n",
    "        for ii in lays:\n",
    "            ii.requires_grad = requires_grad\n",
    "    return lays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "42d208c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(params, dataset, lr=0.0015, nb_epochs=300, opt_parameters=None, layers=None, dataset_test=None):\n",
    "\n",
    "    if (opt_parameters != None) & (layers != None):\n",
    "        parameters = opt_parameters  # The paramters we want to optimize\n",
    "        layers = layers\n",
    "    elif (opt_parameters != None) & (layers == None):\n",
    "        parameters = opt_parameters\n",
    "        if use_trainable_out and use_trainable_tc:\n",
    "            layers = [w1, w2, alpha1, beta1, alpha2, out_scale, out_offset]\n",
    "        elif use_trainable_out:\n",
    "            layers = [w1, w2, out_scale, out_offset]\n",
    "        elif use_trainable_tc:\n",
    "            layers = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "        else:\n",
    "            layers = [w1, w2, alpha1, beta1, alpha2, beta2,]\n",
    "    elif (opt_parameters == None) & (layers != None):\n",
    "        if use_trainable_out and use_trainable_tc:\n",
    "            layers = [w1, w2, alpha1, beta1, alpha2,\n",
    "                        beta2, out_scale, out_offset]\n",
    "        elif use_trainable_out:\n",
    "            layers = [w1, w2, out_scale, out_offset]\n",
    "        elif use_trainable_tc:\n",
    "            layers = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "        else:\n",
    "            layers = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "        layers = layers\n",
    "    elif (opt_parameters == None) & (layers == None):\n",
    "        if use_trainable_out and use_trainable_tc:\n",
    "            parameters = [w1, w2, alpha1, beta1, alpha2,\n",
    "                        beta2, out_scale, out_offset]\n",
    "            layers = [w1, w2, alpha1, beta1, alpha2,\n",
    "                        beta2, out_scale, out_offset]\n",
    "        elif use_trainable_out:\n",
    "            parameters = [w1, w2, out_scale, out_offset]\n",
    "            layers = [w1, w2, out_scale, out_offset]\n",
    "        elif use_trainable_tc:\n",
    "            parameters = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "            layers = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "        else:\n",
    "            parameters = [w1, w2, alpha1, beta1,\n",
    "                        alpha2, beta2]\n",
    "            layers = [w1, w2, alpha1, beta1,\n",
    "                        alpha2, beta2]\n",
    "\n",
    "    # The log softmax function across output units\n",
    "    log_softmax_fn = nn.LogSoftmax(dim=1)\n",
    "    loss_fn = nn.NLLLoss()  # The negative log likelihood loss function\n",
    "\n",
    "    generator = DataLoader(dataset, batch_size=batch_size,\n",
    "                           shuffle=True, num_workers=2)\n",
    "\n",
    "    # The optimization loop\n",
    "    loss_hist = []\n",
    "    accs_hist = [[], []]\n",
    "    for e in range(nb_epochs):\n",
    "        # learning rate decreases over epochs\n",
    "        optimizer = torch.optim.Adamax(parameters, lr=lr, betas=(0.9, 0.995))\n",
    "        # if e > nb_epochs/2:\n",
    "        #     lr = lr * 0.9\n",
    "        local_loss = []\n",
    "        # accs: mean training accuracies for each batch\n",
    "        accs = []\n",
    "        for x_local, y_local in generator:\n",
    "            x_local, y_local = x_local.to(device), y_local.to(device)\n",
    "            spks_out, recs, layers_update = run_snn(x_local, layers)\n",
    "            # [mem_rec, spk_rec, out_rec]\n",
    "            _, spk_rec, _ = recs\n",
    "\n",
    "            # with output spikes\n",
    "            if use_trainable_out:\n",
    "                m = spks_out\n",
    "            else:\n",
    "                m = torch.sum(spks_out, 1)  # sum over time\n",
    "            # cross entropy loss on the active read-out layer\n",
    "            log_p_y = log_softmax_fn(m)\n",
    "\n",
    "            # TODO change to loop!\n",
    "            # Here we can set up our regularizer loss\n",
    "            # reg_loss = params['reg_spikes']*torch.mean(torch.sum(spks1,1)) # L1 loss on spikes per neuron (original)\n",
    "            # L1 loss on total number of spikes (hidden layer 1)\n",
    "            reg_loss = params['reg_spikes']*torch.mean(torch.sum(spk_rec, 1))\n",
    "            # L1 loss on total number of spikes (output layer)\n",
    "            # reg_loss += params['reg_spikes']*torch.mean(torch.sum(spks_out, 1))\n",
    "            # print(\"L1: \", reg_loss)\n",
    "            # reg_loss += params['reg_neurons']*torch.mean(torch.sum(torch.sum(spks1,dim=0),dim=0)**2) # e.g., L2 loss on total number of spikes (original)\n",
    "            # L2 loss on spikes per neuron (hidden layer 1)\n",
    "            reg_loss += params['reg_neurons'] * \\\n",
    "                torch.mean(torch.sum(torch.sum(spk_rec, dim=0), dim=0)**2)\n",
    "            # L2 loss on spikes per neuron (output layer)\n",
    "            # reg_loss += params['reg_neurons'] * \\\n",
    "            #     torch.mean(torch.sum(torch.sum(spks_out, dim=0), dim=0)**2)\n",
    "            # print(\"L1 + L2: \", reg_loss)\n",
    "\n",
    "            # Here we combine supervised loss and the regularizer\n",
    "            loss_val = loss_fn(log_p_y, y_local) + reg_loss\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss_val.backward()\n",
    "            optimizer.step()\n",
    "            local_loss.append(loss_val.item())\n",
    "\n",
    "            # compare to labels\n",
    "            _, am = torch.max(m, 1)  # argmax over output units\n",
    "            tmp = np.mean((y_local == am).detach().cpu().numpy())\n",
    "            accs.append(tmp)\n",
    "\n",
    "        mean_loss = np.mean(local_loss)\n",
    "        loss_hist.append(mean_loss)\n",
    "\n",
    "        # mean_accs: mean training accuracy of current epoch (average over all batches)\n",
    "        mean_accs = np.mean(accs)\n",
    "        accs_hist[0].append(mean_accs)\n",
    "\n",
    "        # Calculate test accuracy in each epoch\n",
    "        if dataset_test is not None:\n",
    "            test_acc = compute_classification_accuracy(\n",
    "                params,\n",
    "                dataset_test,\n",
    "                layers=layers_update,\n",
    "                early=True\n",
    "            )\n",
    "            accs_hist[1].append(test_acc)  # only safe best test\n",
    "\n",
    "        if dataset_test is None:\n",
    "            # save best training\n",
    "            if mean_accs >= np.max(accs_hist[0]):\n",
    "                best_acc_layers = []\n",
    "                for ii in layers_update:\n",
    "                    best_acc_layers.append(ii.detach().clone())\n",
    "        else:\n",
    "            # save best test\n",
    "            if np.max(test_acc) >= np.max(accs_hist[1]):\n",
    "                best_acc_layers = []\n",
    "                for ii in layers_update:\n",
    "                    best_acc_layers.append(ii.detach().clone())\n",
    "\n",
    "        # plt.figure(\"live plot\")\n",
    "        # plt.title(\"Epoch: {}\" .format(e+1))\n",
    "        # plt.subplot(1, 2, 1)\n",
    "        # plt.plot(range(1, len(accs_hist[0])+1),\n",
    "        #          100*np.array(accs_hist[0]), color='blue')\n",
    "        # plt.plot(range(1, len(accs_hist[1])+1),\n",
    "        #          100*np.array(accs_hist[1]), color='orange')\n",
    "        # plt.xlabel(\"Epoch\")\n",
    "        # plt.ylabel(\"Accuracy (%)\")\n",
    "        # plt.ylim(0, 105)\n",
    "        # plt.legend([\"Training\", \"Test\"], loc='lower right')\n",
    "        # plt.subplot(1, 2, 2)\n",
    "        # plt.plot(range(1, len(loss_hist)+1), np.array(loss_hist), color='blue')\n",
    "        # plt.xlabel(\"Epoch\")\n",
    "        # plt.ylabel(\"Loss\")\n",
    "        # plt.legend([\"Training\"], loc='lower right')\n",
    "        # # to avoid clearing last plot\n",
    "        # if (e != epochs-1):\n",
    "        #     plt.draw()\n",
    "        #     plt.pause(0.1)\n",
    "        #     plt.cla()\n",
    "        # else:\n",
    "        #     plt.close(\"live plot\")\n",
    "\n",
    "        print(\"Epoch {}/{} done. Train accuracy: {:.2f}%, Test accuracy: {:.2f}%, Loss: {:.5f}.\".format(\n",
    "            e + 1, nb_epochs, accs_hist[0][-1]*100, accs_hist[1][-1]*100, loss_hist[-1]))\n",
    "\n",
    "    return loss_hist, accs_hist, best_acc_layers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "cb74d6b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_and_train(params, ds_train, ds_test, epochs=epochs):\n",
    "\n",
    "    global nb_input_copies\n",
    "    # Num of spiking neurons used to encode each channel\n",
    "    nb_input_copies = params['nb_input_copies']\n",
    "\n",
    "    # Network parameters\n",
    "    global nb_inputs\n",
    "    nb_inputs = nb_channels*nb_input_copies\n",
    "    global nb_outputs\n",
    "    nb_outputs = len(np.unique(labels))\n",
    "    global nb_hidden\n",
    "    nb_hidden = 450\n",
    "    global nb_steps\n",
    "    nb_steps = data_steps\n",
    "\n",
    "    tau_mem = params['tau_mem']  # ms\n",
    "    tau_syn = tau_mem/params['tau_ratio']\n",
    "    \n",
    "    if not use_trainable_tc:\n",
    "        global alpha\n",
    "        global beta\n",
    "    alpha = float(np.exp(-time_step/tau_syn))\n",
    "    beta = float(np.exp(-time_step/tau_mem))\n",
    "\n",
    "    fwd_weight_scale = params['fwd_weight_scale']\n",
    "    rec_weight_scale = fwd_weight_scale*params['weight_scale_factor']\n",
    "\n",
    "    # Spiking network\n",
    "    layers = []\n",
    "    \n",
    "    # recurrent layer\n",
    "    w1, v1 = recurrent_layer.create_layer(\n",
    "        nb_inputs, nb_hidden, fwd_weight_scale, rec_weight_scale)\n",
    "\n",
    "    # readout layer\n",
    "    w2 = feedforward_layer.create_layer(\n",
    "        nb_hidden, nb_outputs, fwd_weight_scale)\n",
    "    \n",
    "    if use_trainable_tc:\n",
    "        # time constants\n",
    "        alpha1, beta1 = trainable_time_constants.create_time_constants(\n",
    "            nb_hidden, alpha, beta, use_trainable_tc)\n",
    "\n",
    "        alpha2, beta2 = trainable_time_constants.create_time_constants(\n",
    "            nb_outputs, alpha, beta, use_trainable_tc)\n",
    "\n",
    "\n",
    "    layers.append(w1), layers.append(w2), layers.append(v1)\n",
    "    if use_trainable_tc:\n",
    "        layers.append(alpha1), layers.append(beta1), layers.append(alpha2), layers.append(beta2)\n",
    "\n",
    "    if use_trainable_out:\n",
    "        # include trainable output for readout layer (linear: y = out_scale * x + out_offset)\n",
    "        out_scale = torch.empty(\n",
    "            (nb_outputs),  device=device, dtype=dtype, requires_grad=True)\n",
    "        torch.nn.init.ones_(out_scale)\n",
    "        layers.append(out_scale)\n",
    "        out_offset = torch.empty(\n",
    "            (nb_outputs),  device=device, dtype=dtype, requires_grad=True)\n",
    "        torch.nn.init.zeros_(out_offset)\n",
    "        layers.append(out_offset)\n",
    "\n",
    "    layers_init = []\n",
    "    for ii in layers:\n",
    "        layers_init.append(ii.detach().clone())\n",
    "\n",
    "    if use_trainable_out and use_trainable_tc:\n",
    "        opt_parameters = [w1, w2, alpha1, beta1, alpha2, beta2, out_scale, out_offset]\n",
    "    elif use_trainable_tc:\n",
    "        opt_parameters = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "    elif use_trainable_out:\n",
    "        opt_parameters = [w1, w2, out_scale, out_offset]\n",
    "    else:\n",
    "        opt_parameters = [w1, w2]\n",
    "\n",
    "    # a fixed learning rate is already defined within the train function, that's why here it is omitted\n",
    "    loss_hist, accs_hist, best_layers = train(\n",
    "        params, ds_train, lr=lr, nb_epochs=epochs, opt_parameters=opt_parameters, layers=layers, dataset_test=ds_test)\n",
    "\n",
    "    # best training and test at best training\n",
    "    acc_best_train = np.max(accs_hist[0])  # returns max value\n",
    "    acc_best_train = acc_best_train*100\n",
    "    idx_best_train = np.argmax(accs_hist[0])  # returns index of max value\n",
    "    acc_test_at_best_train = accs_hist[1][idx_best_train]*100\n",
    "\n",
    "    # best test and training at best test\n",
    "    acc_best_test = np.max(accs_hist[1])\n",
    "    acc_best_test = acc_best_test*100\n",
    "    idx_best_test = np.argmax(accs_hist[1])\n",
    "    acc_train_at_best_test = accs_hist[0][idx_best_test]*100\n",
    "\n",
    "    # TODO track time constants!!!\n",
    "    print(\"Final results: \")\n",
    "    print(\"Best training accuracy: {:.2f}% and according test accuracy: {:.2f}% at epoch: {}\".format(\n",
    "        acc_best_train, acc_test_at_best_train, idx_best_train+1))\n",
    "    print(\"Best test accuracy: {:.2f}% and according train accuracy: {:.2f}% at epoch: {}\".format(\n",
    "        acc_best_test, acc_train_at_best_test, idx_best_test+1))\n",
    "    print(\"------------------------------------------------------------------------------------\\n\")\n",
    "    return loss_hist, accs_hist, best_layers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "9f786ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_classification_accuracy(params, dataset, layers=None, early=False):\n",
    "    \"\"\" Computes classification accuracy on supplied data in batches. \"\"\"\n",
    "\n",
    "    generator = DataLoader(dataset, batch_size=batch_size,\n",
    "                           shuffle=False, num_workers=2)\n",
    "    accs = []\n",
    "\n",
    "    for x_local, y_local in generator:\n",
    "        x_local, y_local = x_local.to(device), y_local.to(device)\n",
    "        if layers == None:\n",
    "            if use_trainable_out and use_trainable_tc:\n",
    "                layers = [w1, w2, alpha1, beta1, alpha2,\n",
    "                          beta2, out_scale, out_offset]\n",
    "            elif use_trainable_out:\n",
    "                layers = [w1, w2, out_scale, out_offset]\n",
    "            elif use_trainable_tc:\n",
    "                layers = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "            else:\n",
    "                layers = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "            spks_out, _, _ = run_snn(x_local, layers)\n",
    "        else:\n",
    "            spks_out, _, _ = run_snn(x_local, layers)\n",
    "        # with output spikes\n",
    "        if use_trainable_out:\n",
    "            m = spks_out\n",
    "        else:\n",
    "            m = torch.sum(spks_out, 1)  # sum over time\n",
    "        _, am = torch.max(m, 1)     # argmax over output units\n",
    "        # compare to labels\n",
    "        tmp = np.mean((y_local == am).detach().cpu().numpy())\n",
    "        accs.append(tmp)\n",
    "\n",
    "    return np.mean(accs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "0fe0a143",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ConfusionMatrix(dataset, save, layers=None, labels=letters):\n",
    "\n",
    "    generator = DataLoader(dataset, batch_size=batch_size,\n",
    "                           shuffle=False, num_workers=2)\n",
    "    accs = []\n",
    "    trues = []\n",
    "    preds = []\n",
    "    for x_local, y_local in generator:\n",
    "        x_local, y_local = x_local.to(device), y_local.to(device)\n",
    "        if layers == None:\n",
    "            if use_trainable_out and use_trainable_tc:\n",
    "                layers = [w1, w2, alpha1, beta1, alpha2,\n",
    "                          beta2, out_scale, out_offset]\n",
    "            elif use_trainable_out:\n",
    "                layers = [w1, w2, out_scale, out_offset]\n",
    "            elif use_trainable_tc:\n",
    "                layers = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "            else:\n",
    "                layers = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "            spks_out, _, _ = run_snn(x_local, layers)\n",
    "        else:\n",
    "            spks_out, _, _ = run_snn(x_local, layers)\n",
    "        # with output spikes\n",
    "        if use_trainable_out:\n",
    "            m = spks_out\n",
    "        else:\n",
    "            m = torch.sum(spks_out, 1)  # sum over time\n",
    "        _, am = torch.max(m, 1)     # argmax over output units\n",
    "        # compare to labels\n",
    "        tmp = np.mean((y_local == am).detach().cpu().numpy())\n",
    "        accs.append(tmp)\n",
    "        trues.extend(y_local.detach().cpu().numpy())\n",
    "        preds.extend(am.detach().cpu().numpy())\n",
    "\n",
    "    cm = confusion_matrix(trues, preds, normalize='true')\n",
    "    cm_df = pd.DataFrame(cm, index=[ii for ii in labels], columns=[\n",
    "                         jj for jj in labels])\n",
    "    plt.figure(figsize=(12, 9))\n",
    "    sn.heatmap(cm_df,\n",
    "               annot=True,\n",
    "               fmt='.1g',\n",
    "               cbar=False,\n",
    "               square=False,\n",
    "               cmap=\"YlGnBu\")\n",
    "    plt.xlabel('\\nPredicted')\n",
    "    plt.ylabel('True\\n')\n",
    "    plt.xticks(rotation=0)\n",
    "    if save:\n",
    "        if use_trainable_out:\n",
    "            plt.savefig(\"../plots/rsnn_1layers_train_tc_output_optimized_thr_\" +\n",
    "                        str(threshold) + \"_cm.png\", dpi=300)\n",
    "        else:\n",
    "            plt.savefig(\"../plots/rsnn_1layers_train_tc_thr_\" +\n",
    "                        str(threshold) + \"_cm.png\", dpi=300)\n",
    "    else:\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "6bfd799d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def NetworkActivity(dataset, save, layers=None, labels=letters):\n",
    "\n",
    "    generator = DataLoader(dataset, batch_size=batch_size,\n",
    "                           shuffle=False, num_workers=2)\n",
    "    accs = []\n",
    "    trues = []\n",
    "    preds = []\n",
    "    for x_local, y_local in generator:\n",
    "        x_local, y_local = x_local.to(device), y_local.to(device)\n",
    "        if layers == None:\n",
    "            if use_trainable_out and use_trainable_tc:\n",
    "                layers = [w1, w2, alpha1, beta1, alpha2,\n",
    "                          beta2, out_scale, out_offset]\n",
    "            elif use_trainable_out:\n",
    "                layers = [w1, w2, out_scale, out_offset]\n",
    "            elif use_trainable_tc:\n",
    "                layers = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "            else:\n",
    "                layers = [w1, w2, alpha1, beta1, alpha2, beta2]\n",
    "            spks_out, recs, _ = run_snn(x_local, layers)\n",
    "        else:\n",
    "            spks_out, recs, _ = run_snn(x_local, layers)\n",
    "\n",
    "        # [mem_rec, spk_rec, mem_rec2, spk_rec2, out_rec]\n",
    "        _, spk_rec, _, spk_rec3, _ = recs\n",
    "\n",
    "    nb_plt = 4\n",
    "    gs = GridSpec(1, nb_plt)\n",
    "\n",
    "    # hidden layer\n",
    "    fig = plt.figure(figsize=(7, 3), dpi=150)\n",
    "    plt.title(\"Hidden layer 1\")\n",
    "    for i in range(nb_plt):\n",
    "        plt.subplot(gs[i])\n",
    "        plt.imshow(spk_rec[i].detach().cpu().numpy().T,\n",
    "                   cmap=plt.cm.gray_r, origin=\"lower\")\n",
    "        if i == 0:\n",
    "            plt.xlabel(\"Time\")\n",
    "            plt.ylabel(\"Units\")\n",
    "        sn.despine()\n",
    "    if save:\n",
    "        if use_trainable_out:\n",
    "            plt.savefig(\"../plots/rsnn_1layers_train_tc_output\" +\n",
    "                        \"_thr_\" + str(threshold) + \"_rp_layer_1.png\", dpi=300)\n",
    "        else:\n",
    "            plt.savefig(\"../plots/rsnn_1layers_train_tc_thr_\" +\n",
    "                        str(threshold) + \"_rp_layer_1.png\", dpi=300)\n",
    "\n",
    "    # output layer\n",
    "    fig = plt.figure(figsize=(7, 3), dpi=150)\n",
    "    plt.title(\"Output layer\")\n",
    "    for i in range(nb_plt):\n",
    "        plt.subplot(gs[i])\n",
    "        plt.imshow(spks_out[i].detach().cpu().numpy().T,\n",
    "                   cmap=plt.cm.gray_r, origin=\"lower\")\n",
    "        if i == 0:\n",
    "            plt.xlabel(\"Time\")\n",
    "            plt.ylabel(\"Units\")\n",
    "        sn.despine()\n",
    "    if save:\n",
    "        if use_trainable_out:\n",
    "            plt.savefig(\"../plots/rsnn_1layers_train_tc_output\" +\n",
    "                        \"_thr_\" + str(threshold) + \"_rp_output_layer.png\", dpi=300)\n",
    "        else:\n",
    "            plt.savefig(\"../plots/rsnn_1layers_train_tc_thr_\" +\n",
    "                        str(threshold) + \"_rp_output_layer.png\", dpi=300)\n",
    "    else:\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "9b201b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data and parameters\n",
    "file_dir_data = '../data/'\n",
    "file_type = 'data_braille_letters_th_'\n",
    "file_thr = str(threshold)\n",
    "file_name = file_dir_data + file_type + file_thr + '.pkl'\n",
    "\n",
    "file_dir_params = '../parameters/'\n",
    "param_filename = 'parameters_th' + str(threshold) + '.txt'\n",
    "file_name_parameters = file_dir_params + param_filename\n",
    "params = {}\n",
    "with open(file_name_parameters) as file:\n",
    "    for line in file:\n",
    "        (key, value) = line.split()\n",
    "        if key == 'time_bin_size' or key == 'nb_input_copies':\n",
    "            params[key] = int(value)\n",
    "        else:\n",
    "            params[key] = np.double(value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "faabd621",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SurrGradSpike(torch.autograd.Function):\n",
    "    \"\"\"\n",
    "    Here we implement our spiking nonlinearity which also implements \n",
    "    the surrogate gradient. By subclassing torch.autograd.Function, \n",
    "    we will be able to use all of PyTorch's autograd functionality.\n",
    "    Here we use the normalized negative part of a fast sigmoid \n",
    "    as this was done in Zenke & Ganguli (2018).\n",
    "    \"\"\"\n",
    "\n",
    "    scale = params['scale']\n",
    "\n",
    "    @staticmethod\n",
    "    def forward(ctx, input):\n",
    "        \"\"\"\n",
    "        In the forward pass we compute a step function of the input Tensor\n",
    "        and return it. ctx is a context object that we use to stash information which \n",
    "        we need to later backpropagate our error signals. To achieve this we use the \n",
    "        ctx.save_for_backward method.\n",
    "        \"\"\"\n",
    "        ctx.save_for_backward(input)\n",
    "        out = torch.zeros_like(input)\n",
    "        out[input > 0] = 1.0\n",
    "        return out\n",
    "\n",
    "    @staticmethod\n",
    "    def backward(ctx, grad_output):\n",
    "        \"\"\"\n",
    "        In the backward pass we receive a Tensor we need to compute the \n",
    "        surrogate gradient of the loss with respect to the input. \n",
    "        Here we use the normalized negative part of a fast sigmoid \n",
    "        as this was done in Zenke & Ganguli (2018).\n",
    "        \"\"\"\n",
    "        input, = ctx.saved_tensors\n",
    "        grad_input = grad_output.clone()\n",
    "        grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\n",
    "        return grad\n",
    "\n",
    "\n",
    "spike_fn = SurrGradSpike.apply\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "d7be3b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "class feedforward_layer:\n",
    "    '''\n",
    "    class to initialize and compute spiking feedforward layer\n",
    "    '''\n",
    "    def create_layer(nb_inputs, nb_outputs, scale):\n",
    "        ff_layer = torch.empty((nb_inputs, nb_outputs),  device=device, dtype=dtype, requires_grad=True)\n",
    "        torch.nn.init.normal_(ff_layer, mean=0.0, std=scale/np.sqrt(nb_inputs))\n",
    "        return ff_layer\n",
    "    \n",
    "    def compute_activity(nb_input, nb_neurons, input_activity, nb_steps):\n",
    "        syn = torch.zeros((nb_input,nb_neurons), device=device, dtype=dtype)\n",
    "        mem = torch.zeros((nb_input,nb_neurons), device=device, dtype=dtype)\n",
    "        out = torch.zeros((nb_input, nb_neurons), device=device, dtype=dtype)\n",
    "        mem_rec = []\n",
    "        spk_rec = []\n",
    "\n",
    "        # Compute feedforward layer activity\n",
    "        for t in range(nb_steps):\n",
    "            mthr = mem-1.0\n",
    "            out = spike_fn(mthr)\n",
    "            rst_out = out.detach()\n",
    "\n",
    "            new_syn = alpha*syn + input_activity[:,t]\n",
    "            new_mem = (beta*mem + syn)*(1.0-rst_out)\n",
    "\n",
    "            mem_rec.append(mem)\n",
    "            spk_rec.append(out)\n",
    "\n",
    "            mem = new_mem\n",
    "            syn = new_syn\n",
    "\n",
    "        # Now we merge the recorded membrane potentials into a single tensor\n",
    "        mem_rec = torch.stack(mem_rec,dim=1)\n",
    "        spk_rec = torch.stack(spk_rec,dim=1)\n",
    "        return spk_rec, mem_rec\n",
    "    \n",
    "    def compute_activity_tc(nb_input, nb_neurons, input_activity, alpha, beta, nb_steps):\n",
    "        syn = torch.zeros((nb_input,nb_neurons), device=device, dtype=dtype)\n",
    "        mem = torch.zeros((nb_input,nb_neurons), device=device, dtype=dtype)\n",
    "        out = torch.zeros((nb_input, nb_neurons), device=device, dtype=dtype)\n",
    "        mem_rec = []\n",
    "        spk_rec = []\n",
    "\n",
    "        # Compute feedforward layer activity\n",
    "        for t in range(nb_steps):\n",
    "            mthr = mem-1.0\n",
    "            out = spike_fn(mthr)\n",
    "            rst_out = out.detach()\n",
    "\n",
    "            new_syn = torch.abs(alpha)*syn + input_activity[:,t]\n",
    "            new_mem = (torch.abs(beta)*mem + syn)*(1.0-rst_out)\n",
    "\n",
    "            mem_rec.append(mem)\n",
    "            spk_rec.append(out)\n",
    "\n",
    "            mem = new_mem\n",
    "            syn = new_syn\n",
    "\n",
    "        # Now we merge the recorded membrane potentials into a single tensor\n",
    "        mem_rec = torch.stack(mem_rec,dim=1)\n",
    "        spk_rec = torch.stack(spk_rec,dim=1)\n",
    "        return spk_rec, mem_rec\n",
    "\n",
    "\n",
    "class recurrent_layer:\n",
    "    '''\n",
    "    class to initialize and compute spiking recurrent layer\n",
    "    '''\n",
    "    def create_layer(nb_inputs, nb_outputs, fwd_scale, rec_scale):\n",
    "        ff_layer = torch.empty((nb_inputs, nb_outputs),  device=device, dtype=dtype, requires_grad=True)\n",
    "        torch.nn.init.normal_(ff_layer, mean=0.0, std=fwd_scale/np.sqrt(nb_inputs))\n",
    "        \n",
    "        rec_layer = torch.empty((nb_outputs, nb_outputs),  device=device, dtype=dtype, requires_grad=True)\n",
    "        torch.nn.init.normal_(rec_layer, mean=0.0, std=rec_scale/np.sqrt(nb_inputs))\n",
    "        return ff_layer,  rec_layer\n",
    "    \n",
    "    def compute_activity(nb_input, nb_neurons, input_activity, layer, nb_steps):\n",
    "        syn = torch.zeros((nb_input,nb_neurons), device=device, dtype=dtype)\n",
    "        mem = torch.zeros((nb_input,nb_neurons), device=device, dtype=dtype)\n",
    "        out = torch.zeros((nb_input, nb_neurons), device=device, dtype=dtype)\n",
    "        mem_rec = []\n",
    "        spk_rec = []\n",
    "\n",
    "        # Compute recurrent layer activity\n",
    "        for t in range(nb_steps):\n",
    "            # input activity plus last step output activity\n",
    "            h1 = input_activity[:,t] + torch.einsum(\"ab,bc->ac\", (out, layer))\n",
    "            mthr = mem-1.0\n",
    "            out = spike_fn(mthr)\n",
    "            rst = out.detach() # We do not want to backprop through the reset\n",
    "\n",
    "            new_syn = alpha*syn + h1\n",
    "            new_mem = (beta*mem + syn)*(1.0-rst)\n",
    "\n",
    "            mem_rec.append(mem)\n",
    "            spk_rec.append(out)\n",
    "        \n",
    "            mem = new_mem\n",
    "            syn = new_syn\n",
    "\n",
    "        # Now we merge the recorded membrane potentials into a single tensor\n",
    "        mem_rec = torch.stack(mem_rec,dim=1)\n",
    "        spk_rec = torch.stack(spk_rec,dim=1)\n",
    "        return spk_rec, mem_rec\n",
    "    \n",
    "    def compute_activity_tc(nb_input, nb_neurons, input_activity, layer, alpha, beta, nb_steps):\n",
    "        syn = torch.zeros((nb_input,nb_neurons), device=device, dtype=dtype)\n",
    "        mem = torch.zeros((nb_input,nb_neurons), device=device, dtype=dtype)\n",
    "        out = torch.zeros((nb_input, nb_neurons), device=device, dtype=dtype)\n",
    "        mem_rec = []\n",
    "        spk_rec = []\n",
    "\n",
    "        # Compute recurrent layer activity\n",
    "        for t in range(nb_steps):\n",
    "            # input activity plus last step output activity\n",
    "            h1 = input_activity[:,t] + torch.einsum(\"ab,bc->ac\", (out, layer))\n",
    "            mthr = mem-1.0\n",
    "            out = spike_fn(mthr)\n",
    "            rst = out.detach() # We do not want to backprop through the reset\n",
    "\n",
    "            new_syn = torch.abs(alpha)*syn + h1\n",
    "            new_mem = (torch.abs(beta)*mem + syn)*(1.0-rst)\n",
    "\n",
    "            mem_rec.append(mem)\n",
    "            spk_rec.append(out)\n",
    "        \n",
    "            mem = new_mem\n",
    "            syn = new_syn\n",
    "\n",
    "        # Now we merge the recorded membrane potentials into a single tensor\n",
    "        mem_rec = torch.stack(mem_rec,dim=1)\n",
    "        spk_rec = torch.stack(spk_rec,dim=1)\n",
    "        return spk_rec, mem_rec\n",
    "\n",
    "\n",
    "class trainable_time_constants:\n",
    "    def create_time_constants(nb_neurons, alpha_mean, beta_mean, trainable):\n",
    "        alpha = torch.empty((nb_neurons),  device=device,\n",
    "                             dtype=dtype, requires_grad=trainable)\n",
    "        torch.nn.init.normal_(\n",
    "            alpha, mean=alpha_mean, std=alpha_mean/10)\n",
    "        \n",
    "        beta = torch.empty((nb_neurons),  device=device,\n",
    "                            dtype=dtype, requires_grad=trainable)\n",
    "        torch.nn.init.normal_(\n",
    "            beta, mean=beta_mean, std=beta_mean/10)\n",
    "        return alpha, beta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f8fff8c",
   "metadata": {},
   "source": [
    "### Train and test the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "cd9869ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training data 4860\n",
      "Number of testing data 540\n",
      "Number of outputs 27\n",
      "Number of timesteps 255\n",
      "Input duration 1.275000s\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "CUDA error: out of memory\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_26591/802524866.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;31m# initialize and train network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m     loss_hist, acc_hist, best_layers = build_and_train(\n\u001b[0m\u001b[1;32m     19\u001b[0m         params, ds_train, ds_test, epochs=epochs)\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_26591/2063636374.py\u001b[0m in \u001b[0;36mbuild_and_train\u001b[0;34m(params, ds_train, ds_test, epochs)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;31m# recurrent layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m     w1, v1 = recurrent_layer.create_layer(\n\u001b[0m\u001b[1;32m     34\u001b[0m         nb_inputs, nb_hidden, fwd_weight_scale, rec_weight_scale)\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_26591/343255150.py\u001b[0m in \u001b[0;36mcreate_layer\u001b[0;34m(nb_inputs, nb_outputs, fwd_scale, rec_scale)\u001b[0m\n\u001b[1;32m     68\u001b[0m     '''\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfwd_scale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrec_scale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0mff_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequires_grad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mff_layer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfwd_scale\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: out of memory\nCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1."
     ]
    }
   ],
   "source": [
    "acc_train_list = []\n",
    "acc_test_list = []\n",
    "max_epochs = 5\n",
    "for epoch in range(max_epochs):\n",
    "    # reload and shuffle data each epoch\n",
    "    ds_train, ds_test, labels, nb_channels, data_steps = load_and_extract(\n",
    "        params, file_name, letter_written=letters)\n",
    "\n",
    "    if epoch == 0:\n",
    "        print(\"Number of training data %i\" % len(ds_train))\n",
    "        print(\"Number of testing data %i\" % len(ds_test))\n",
    "        print(\"Number of outputs %i\" % len(np.unique(labels)))\n",
    "        print(\"Number of timesteps %i\" % data_steps)\n",
    "        print(\"Input duration %fs\" % (data_steps*time_step))\n",
    "        print(\"---------------------------\\n\")\n",
    "\n",
    "    # initialize and train network\n",
    "    loss_hist, acc_hist, best_layers = build_and_train(\n",
    "        params, ds_train, ds_test, epochs=epochs)\n",
    "\n",
    "    # safe overall best layer\n",
    "    if epoch == 0:\n",
    "        very_best_layer = best_layers\n",
    "        best_acc = max(acc_hist[1])\n",
    "    else:\n",
    "        if max(acc_hist[1]) > best_acc:\n",
    "            very_best_layer = best_layers\n",
    "            best_acc = max(acc_hist[1])\n",
    "\n",
    "    acc_train_list.append(acc_hist[0])\n",
    "    acc_test_list.append(acc_hist[1])\n",
    "\n",
    "print(\"*************************\")\n",
    "print(\"* Best: \", best_acc*100)\n",
    "print(\"*************************\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1e2701b",
   "metadata": {},
   "source": [
    "### Lets plot the training curve and the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4aa7b76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calc mean and std\n",
    "acc_mean_train = np.mean(acc_train_list, axis=0)\n",
    "acc_std_train = np.std(acc_train_list, axis=0)\n",
    "acc_mean_test = np.mean(acc_test_list, axis=0)\n",
    "acc_std_test = np.std(acc_test_list, axis=0)\n",
    "best_trial, best_val_idx = np.where(np.max(acc_test_list) == acc_test_list)\n",
    "best_trial, best_val_idx = best_trial[0], best_val_idx[0]\n",
    "plt.figure()\n",
    "# plot best trial\n",
    "plt.plot(range(1, len(acc_train_list[best_trial])+1), 100*np.array(\n",
    "    acc_train_list[best_trial]), color='blue', linestyle='dashed')\n",
    "plt.plot(range(1, len(acc_test_list[best_trial])+1), 100*np.array(\n",
    "    acc_test_list[best_trial]), color='orangered', linestyle='dashed')\n",
    "# plot mean and std\n",
    "plt.plot(range(1, len(acc_mean_train)+1),\n",
    "         100*np.array(acc_mean_train), color='blue')\n",
    "plt.plot(range(1, len(acc_mean_test)+1), 100 *\n",
    "         np.array(acc_mean_test), color='orangered')\n",
    "plt.fill_between(range(1, len(acc_mean_train)+1), 100*(acc_mean_train+acc_std_train), 100*(\n",
    "    acc_mean_train-acc_std_train), color='cornflowerblue')\n",
    "plt.fill_between(range(1, len(acc_mean_test)+1), 100*(\n",
    "    acc_mean_test+acc_std_test), 100*(acc_mean_test-acc_std_test), color='sandybrown')\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy (%)\")\n",
    "plt.ylim((0, 105))\n",
    "plt.legend([\"Training\", \"Test\"], loc='lower right')\n",
    "if save_fig:\n",
    "    plt.savefig(\"../plots/rsnn_1layers_train_tc_thr_\" +\n",
    "                str(threshold)+\"_acc.png\", dpi=300)\n",
    "else:\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ad5ddfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AVmsIAN0TOuNwRFGQX+KTPnxZXzL0lWGGHiRDP/Ul48Ja3URdKfUzwB/wyN939OwZT37+QYqKSqisrCI9PYvExP5Nrte9RxyFhWUUHzhMVVU169dsY9DQXp441Fq79pZyeUwoMdEhBFj8SL6xKxmbCtzGHHSe5Ia+nQDoEmclMNCfiqPfNznT0+dJMvSdYYYePJWR0OMKCgpKOHCglKrKatau2cyQoX3cxgwZ2oeVH2wEYP26rfS/rjtKKSoqjuNy1QBwoKiUwgInMTFRPunDl/UlQ18ZZuhBMvRTXzIuzOKhY9O7tkqpL3/4swImaprm8kRhi8Wfp56awuTJM3G5ahg3bhjx8XHNqjdt+m08eO9CXC6NMSkD6NI1msWvrOKqhFgGD+1F7q4Cpj38OsePn2LThl28tjCdpR/OaHSGy6Xx9EubePOFm/H3U7yX/hV5+Ud46O5+7NpbRubnBTy78AvmTBvM/9zWCzSNx+duaHJPP/blyfMkGfrOMEMPnsqwWPyZ/sREfnfPPFw1NYxNGUzX+BgWLniP7gmdGZrYl5Rxg5n+2GKSk6YSag1m3vP3A5CTvZdFC5Zjsfij/BQzZk4i1Bp8kUTv9OHL+pKhrwwz9CAZ+qkvGRemPLmygLl87fUTc7yq4OKDmqlv4jderb9v41Cv1hfCqM64PPsQeH3a+Hv2tjghhBC+cmW9TwK2ultfhBBCCCGEMAKZqAshhBBCCKFDMlEXQgghhBBCh2SiLoQQQgghhA7Jw6QN8v7DpGbQNnam1zNOFz7t9QwhhBBCCN+Rh0mFEEIIIYQwDJmoCyGEEEIIoUMyURdCCCGEEEKHZKIuhBBCCCGEDrWaibpSyq6UekcptV8plaOUWq2UutITtbOyckhKmsLw4amkpS3zRElTZiz+y70UbFtM9vp5Hq/9IzOcJ7NkmKEHydBPfcnQV4YZepAM/dSXjIa1iom6UkoBK4ANmqZ10TStL/C/gK25tV0uF7NnL2bJklmkpy9k1aos8vIKm1vWlBn/WPYZt9z5rEdrns8s58kMGWboQTL0U18y9JVhhh4kQz/1JePCWsVEHRgKVGmatvjHH2iatkPTtI3NLbxz5z7i4qJxOOwEBgaQnDyIjIwtzS1ryozPt+6l4uhJj9Y8n1nOkxkyzNCDZOinvmToK8MMPUiGfupLxoW1lol6DyDHG4WdznLs9g612zZbBE5nuWT4gFnOkxkyzNCDZOinvmToK8MMPUiGfupLxoW1lol6oyilUpVS2Uqp7LS0d319OEIIIYQQohWz+PoAWkgu8MuLDdI0LQ1IO7vVuG8mtdkiKCk5XLvtdJZjs0U06SDNnuFtZjlPZsgwQw+SoZ/6kqGvDDP0IBn6qS8ZF9ZaPlHPBNoopVJ//IFSqpdSamBzC/fsGU9+/kGKikqorKwiPT2LxMT+zS1rygxvM8t5MkOGGXqQDP3Ulwx9ZZihB8nQT33JuLBW8Ym6pmmaUioFmK+Uegz4HsgHHm5ubYvFn6eemsLkyTNxuWoYN24Y8fFxzS1ryoy3FzzAwOuvokNYCHlbXuGZF9/j7Xc3eKy+Wc6TGTLM0INk6Ke+ZOgrwww9SIZ+6kvGhSlNa9QdHq1Q4259ae3axs70esbpwqe9niGEEEII4TtXqvp+2lpufRFCCCGEEMJQZKIuhBBCCCGEDslEXQghhBBCCB2SiboQQgghhBA6JA+TNkgeJm2MM65jXs9ISNnj9Yxtyzt6PaN9gGefLveFlrjebfxDvZ4hhBBC6Is8TCqEEEIIIYRhyERdCCGEEEIIHZKJuhBCCCGEEDokE3UhhBBCCCF0yPQTdaWUSyn1pVIqVym1Qyn1R6WUR/vOysohKWkKw4enkpa2zJOlDZWxaeMORt/8CMlJU3nj9ZV19ldWVvHo1AUkJ03l9gkzKS4uA6C4uIxre09ifMp0xqdM55lZbzYqb1CfjqxbNJaM11K4d1yPOvujO7Tj/+bcxMr5o1j119EM7tvpknv6z6Y9jBs1m5SRs3hrybo6+7dl5/Hb8c8y4OoHyVi3/ZLrN0Sud8v0IBnGqS8Z+sowQw+SoZ/6ktEw00/UgdOapl2jaVoCMBwYCXjse+9dLhezZy9myZJZpKcvZNWqLPLyCj1V3jAZLlcNc+e8zauvTeODj+axZvVm9ucVu415f/kG2rdvR/rHL3LHxBHMf+Gd2n0xDhvLVsxl2Yq5PDnrrovm+fkpZt07gLuf/oQRv/+QUYM609XhvlrI7yf0YvXnBYx5eBUP/yWLp6cMaHQ/P/Y0b85SXn71PpaunMG61Tl8s/+Q2xh7dBgz59xB0s39Lqn2hXPlerdED5LhuQwz9CAZ+qkvGfrKMEMPRs5oDRP1WpqmlQKpwP1KqXqXwblUO3fuIy4uGofDTmBgAMnJg8jI2OKJ0obK2L1rP7GxNmIcUQQEWhgxcgCfZua4jdmQuY0xYwcCMPym/mzZnEtTlwe9Or4DBYeOU+Q8SVV1Dekbv2XYdQ63MZqmEdw2AICQoEBKK05dUkburnwcsR2IcXQgIMDC8JF9+Cxzp9uYjp0iiO/WCeXnkZcTINe7pXqQDM9lmKEHydBPfcnQV4YZejByRquaqANomvYN4A9EeaKe01mO3d6hdttmi8DpLPdEaUNlOJ1HsNnDz/2+PZzS0iMNjrFY/AkOCeLo0ZPA2dshbrv1CSbdOYec7L0XzbNFBHHo8He12yWHT2GLaOc25q//3sEtQ65g05u/ZMnMG3k67dLeLGWlx7DZw85l2sIoK/X+OuJyvVumB8nwXIYZepAM/dSXDH1lmKEHI2dYmntQQjRXZKSVdRnzsVpD2JP7LQ898BIrVj5LcHBQs+qOHtSZ9zPzeOODPfTuFskLfxjIyAc+RL7jy7e8db2FEEIIs2l1n6grpa4AXEBpPftSlVLZSqnstLR3G1XPZougpORw7bbTWY7NFuGpwzVMhs0WhrOk4tzvl1QQFRXW4JjqahcnT5zCag0mMDAAqzUEgO4JnXE4oijIL7lgnrP8FNEdzn2Cbu8QhLP8O7cx44fHs3pTPgDbvyojMNCfsPaXNbqnyKhQnCXnPiV2Oo8QGeX9b82U690yPUiG5zLM0INk6Ke+ZOgrwww9GDmjVU3UlVKRwGLgFa2em2U1TUvTNK2fpmn9UlMnNKpmz57x5OcfpKiohMrKKtLTs0hM7O/R4zZCRkKPKygoKOHAgVKqKqtZu2YzQ4b2cRszZGgfVn6wEYD167bS/7ruKKWoqDiOy1UDwIGiUgoLnMTEXPjOpJ37DhPXsT0xtmACLH4kD+xMxpYDbmMOlp3k+l7RAHSJCaVNgD8Vx75vdE/de8RRWFhG8YHDVFVVs37NNgYN7dXo328qud4t04NkeC7DDD1Ihn7qS4a+MszQg5EzWsOtL22VUl8CAUA18A/gRU8Vt1j8eeqpKUyePBOXq4Zx44YRHx/nqfKGybBY/Jn+xER+d888XDU1jE0ZTNf4GBYueI/uCZ0ZmtiXlHGDmf7YYpKTphJqDWbe8/cDkJO9l0ULlmOx+KP8FDNmTiLUGnzBPFeNxtOvbeFvs4bh7+fHsk/2sa/oKA/dfg2788rJ2FrEn9/M5k/338CkW7qjafDYy59f8jmZNv02Hrx3IS6XxpiUAXTpGs3iV1ZxVUIsg4f2IndXAdMefp3jx0+xacMuXluYztIPZ1xSTn25cr2934NkeC7DDD1Ihn7qS4a+MszQg5EzlCdXYTCXr+XENMIZl/cfrkxI2eP1jG3LO3o9o32AZ/8HwRda4nq38ff+7UVCCCGEvlxZ7/JxrerWFyGEEEIIIYxCJupCCCGEEELokEzUhRBCCCGE0CGZqAshhBBCCKFDMlEXQgghhBBCh1rD8ozCiyrOXPoX1VyqvJXXez2j65gvvJ6Rt9L4q77IiizCiGS1IiGEUckn6kIIIYQQQuiQTNSFEEIIIYTQIZmoCyGEEEIIoUOtZqKulHIppb4875/HPVU7KyuHpKQpDB+eSlraMk+VNXzGls/3csfY57h9zJ/555uZdfbvyNnPPb9+icR+09iwfkez88DzPQzq05F1i8aS8VoK947rUWd/dId2/N+cm1g5fxSr/jqawX07NTsTjHm9W7q+ZOgrwyg9bNq4g9E3P0Jy0lTeeH1lnf2VlVU8OnUByUlTuX3CTIqLywAoLi7j2t6TGJ8ynfEp03lm1ps+7cPXGWboQTL0U18yGtZqJurAaU3Trjnvn2c9UdTlcjF79mKWLJlFevpCVq3KIi+v0BOlDZ3hctXw8rMreO6Vyby9/FEy124nf7/7g6dR0WE8/vQEho3o3dzD/yHTsz34+Slm3TuAu5/+hBG//5BRgzrT1eH+wNjvJ/Ri9ecFjHl4FQ//JYunpwxobhuGvN4tXV8y9JVhlB5crhrmznmbV1+bxgcfzWPN6s3szyt2G/P+8g20b9+O9I9f5I6JI5j/wju1+2IcNpatmMuyFXN5ctZdPuvD1xlm6EEy9FNfMi6sNU3UvWLnzn3ExUXjcNgJDAwgOXkQGRlbWn3G3t2FdHJE0DEmgoAAC4lJ1/D5hly3MdEdw+lyZUeUn2ru4QOe7+Hq+A4UHDpOkfMkVdU1pG/8lmHXOdzGaJpGcNsAAEKCAimtONWsHsCY17ul60uGvjKM0sPuXfuJjbUR44giINDCiJED+DQzx23MhsxtjBk7EIDhN/Vny+ZcNE3TVR++zjBDD5Khn/qScWGtaaLe9ie3vkzwRFGnsxy7vUPtts0WgdNZ7onShs4oKz1GpM1aux1ps1JW5t0l0jzdgy0iiEOHv6vdLjl8CltEO7cxf/33Dm4ZcgWb3vwlS2beyNNpzX/TG/F6t3R9ydBXhlF6cDqPYLOHn6thD6e09EiDYywWf4JDgjh69CRw9vaX2259gkl3ziEne6/P+vB1hhl6kAz91JeMC2tN66if1jTtGl8fhDCX0YM6835mHm98sIfe3SJ54Q8DGfnAh3jwAzghhA5ERlpZlzEfqzWEPbnf8tADL7Fi5bMEBwf5+tCEECbWmj5RvyilVKpSKlsplZ2W9m6jfsdmi6Ck5HDtttNZjs0W4dHjMmJGZFQoZc6jtdtlzqNERnr3C0E83YOz/BTRHc59gm7vEISz/Du3MeOHx7N6Uz4A278qIzDQn7D2lzU5E4x5vVu6vmToK8MoPdhsYThLKs7VKKkgKiqswTHV1S5OnjiF1RpMYGAAVmsIAN0TOuNwRFGQf+lf+GaUc+XL+pKhrwwz9GDkDJmon0fTtDRN0/ppmtYvNbVxd8b07BlPfv5BiopKqKysIj09i8TE/h49LiNmdEtwcKDwMIeKy6mqqibz4y+5YUiCB4+4Lk/3sHPfYeI6tifGFkyAxY/kgZ3J2HLAbczBspNc3ysagC4xobQJ8Kfi2Pe66sMXGWboQTL0U99TGQk9rqCgoIQDB0qpqqxm7ZrNDBnax23MkKF9WPnBRgDWr9tK/+u6o5SiouI4LlcNAAeKSikscBITE+WTPnydYYYeJEM/9SXjwlrTrS9tlVJfnre9VtO0Zi/RaLH489RTU5g8eSYuVw3jxg0jPt6zXxVvxAyLxZ+HHkvh0ftep6ZGY+Qt19K5i503F62lW3cHPx+SwN7cQmZMfZuTx0/xRdYe3lq8jreWP6qbHlw1Gk+/toW/zRqGv58fyz7Zx76iozx0+zXszisnY2sRf34zmz/dfwOTbumOpsFjL3/e5Dxv9eGLDDP0IBn6qe+pDIvFn+lPTOR398zDVVPD2JTBdI2PYeGC9+ie0JmhiX1JGTeY6Y8tJjlpKqHWYOY9fz8AOdl7WbRgORaLP8pPMWPmJEKtwT7pw9cZZuhBMvRTXzIuTHnyaXZz+VpOTCMcOvWV1zOig7p5PaPrmC+8npG38nqvZwgh6jrj8u6D7ABt/L17a58QwuyurHcJPLn1RQghhBBCCB2SiboQQgghhBA6JBN1IYQQQgghdEgm6kIIIYQQQuiQTNSFEEIIIYTQIVn1pUGy6ktjHK8q8HpG+wDPLp/kK/3ecXo9I/tXNq9neNsXpfu8nnF9VLzXM8xA3t9CCNFSZNUXIYQQQgghDEMm6kIIIYQQQuiQTNSFEEIIIYTQoVYzUVdKuZRSX573z+Weqp2VlUNS0hSGD08lLW2Zp8oaPuM/m/YwbtRsUkbO4q0l6+rs35adx2/HP8uAqx8kY932ZueBMc/T9fYwlt/clxXJ/Zh4VUy9Y4Y5OrB0ZF/eHdmHOdd75ptavX2uPF1/55b/8vjtf2bar/7Eqv/LqLN/7TsbmP7b55gx8S8899CrHC6paHYmGPM11RIZ8v5u3Rlm6EEy9FNfMhrWaibqwGlN06457598TxR1uVzMnr2YJUtmkZ6+kFWrssjLK/REaUNnuFw1zJuzlJdfvY+lK2ewbnUO3+w/5DbGHh3GzDl3kHRzv+Ye/g+ZxjtPfgoe69eFBz/LZfyaHJJiI+ncPshtjCP4MiZ1d3D3JzuYsGYbL2z7prlteP1cebp+jauGf7z4PlOfT2XuPx5jyyfbKP62xG1M3JWdmLnkD8x5+1GuHdKLpa+uam4bhnxNtUSGvL9bd4YZepAM/dSXjAtrTRN1r9i5cx9xcdE4HHYCAwNITh5ERsaWVp+RuysfR2wHYhwdCAiwMHxkHz7L3Ok2pmOnCOK7dUL51fug8yUz4nlKCA+h6MT3FH/3PdU1GusKyxjcKdxtTEoXO0v3HeREVTUAR85UNasH8P658nT9b/5biK1TB6I6RmAJsHDdjb3Zvmm325ir+sTT5rJAALokxFFRerQ5LQDGfE21RIa8v1t3hhl6kAz91JeMC2tNE/W25932ssJTRZ3Ocuz2DrXbNlsETme5p8obNqOs9Bg2e9h59cIoKz3WrGO8GCOep6i2bXCeOlO7XXq6kqi2bdzGxIa0JS6kLW/c2Iu/Dbua6887r03l7XPl6fpHyo4RHmWt3Q6LtHLkcMOvp6z0LfQacFWT835kxNdUS2TI+7t1Z5ihB8nQT33JuLDWNFE//9aXFF8fjBCN5a8UjpC2pGbu4okv9vJE/3iCA/x9fVi69Z+Ps/l2bxEjfz3U14cihBBCNEtrmqhflFIqVSmVrZTKTkt7t1G/Y7NFUFJyuHbb6SzHZovw6HEZMSMyKhRnyZHz6h0hMiq0Wcd4MUY8T6Wnz2ALOvcJelTbQEpPn/nJmEqyiitwaRoHvztD4YnTxIa0bXImeP9cebp+WGSo260sR8qOEtah7uspN/trPvrHJzz87N0EBFqanPcjI76mWiJD3t+tO8MMPUiGfupLxoXJRP08mqalaZrWT9O0fqmpExr1Oz17xpOff5CiohIqK6tIT88iMbG/R4/LiBnde8RRWFhG8YHDVFVVs37NNgYN7eXBI67LiOdpT8UJHCGX0bFdGyx+iptiI8kqdl+tZMOBcvr+MAkKDbQQG9KW4pPf66oPb9fv/DMHzgNllB0sp7qqmi0Z2+n9ix5uYwq+PsBbf1nGQ3++m/ZhIc1tATDma6olMuT93bozzNCDZOinvmRcWPM/cmrlLBZ/nnpqCpMnz8TlqmHcuGHEx3v2K7GNmGGx+DNt+m08eO9CXC6NMSkD6NI1msWvrOKqhFgGD+1F7q4Cpj38OsePn2LThl28tjCdpR/O0E0PLZHh0uAvOftZMLgH/n6Kld84+eb4Ke7tEcd/K06QdbCCL0qOMMBuZenIvtRoGn/98luOVVbrqg9v1/e3+PPbP9zK839Mo6amhoHJ/enU2c77S9bQ+WcOev+iB+8u+ogzp8+w8Km3AYiwhfHws3frqg+zZMj7u3VnmKEHydBPfcm4MKVpmocOz2y+lhPTCMerCrye0T7As28kX+n3jtPrGdm/snk9w9u+KN3n9Yzro+K9nmEG8v4WQoiWcmW9S2TJrS9CCCGEEELokEzUhRBCCCGE0CGZqAshhBBCCKFDMlEXQgghhBBCh2SiLoQQQgghhA7Jqi8NklVfhPF0/uNer9bfOy/aq/UB2vh794tzAP579GuvZ1xlvdLrGd52xnXM6xktcb2FEEL/ZNUXIYQQQgghDEMm6kIIIYQQQuiQTNSFEEIIIYTQoVYxUVdK2ZRS/1JKfaOUylFKfaGUSvFU/aysHJKSpjB8eCppacs8VdZ0GWbowagZg7pFkvFYIp/+741MSexaZ/+MMQmkTx1M+tTBZD6eyI45Iy9ac9PGHYy++RGSk6byxusr6+yvrKzi0akLSE6ayu0TZlJcXAZAcXEZ1/aexPiU6YxPmc4zs95scl+ePk/bvtjLfeOfZcq4uSx/O6PO/g//9Rn3T5jHQ795nid//yqlhyqanQnGeE2Z8XpLhn7rS4a+MszQg1EzTD9RV0op4AMgS9O0KzRN6wv8CojxRH2Xy8Xs2YtZsmQW6ekLWbUqi7y8Qk+UNlWGGXowaoafgtm39uJ/Xt/MTfMyGdO7E11twW5j5qzMJfnFz0h+8TPe3vQta3cdusgx1jB3ztu8+to0PvhoHmtWb2Z/XrHbmPeXb6B9+3akf/wid0wcwfwX3qndF+OwsWzFXJatmMuTs+5qUl+ePk8uVw2v/eV9npp/DwvemcbGddsp+qbEbcwVV3bihbcf5uV/PsINiVfz9iurmpx3Llf/rykzXm/J0G99ydBXhhl6MHKG6SfqQCJQqWna4h9/oGlagaZpCzxRfOfOfcTFReNw2AkMDCA5eRAZGVs8UdpUGWbowagZV8eGUVD+HUUVp6hyaXy0vZjhCfYGx4/u3YmPthc3uB9g9679xMbaiHFEERBoYcTIAXyameM2ZkPmNsaMHQjA8Jv6s2VzLp5cZcrT52nfnkKiYyKwd4ogIMDCL4b3ZktWrtuYnv260uayQAC69YilvLT5q6IY4TVlxustGfqtLxn6yjBDD0bOaA0T9QRgm7eKO53l2O0dardttgicznLJaOH6ktEwe+hlHDp6una75Nj32EPb1ju2U1hbHOFB/Gdf2UWO8Qg2e/i5Y7SHU1p6pMExFos/wSFBHD16Ejh7O8Rttz7BpDvnkJPdtCUlPX2eKkqP0cFmrd2OiAqloqzhifgnK7fS5/qfNTnvR0Z4TZnxekuGfutLhr4yzNCDkTMszT0oo1FKLQR+wdlP2a/19fEIoSejrunEmp0HqfHitwhERlpZlzEfqzWEPbnf8tADL7Fi5bMEBwd5L9TDNqzJIe+/Rfxp8e99fSi6Z4brLYQQvtIaPlHPBfr8uKFp2u+BG4HInw5USqUqpbKVUtlpae82qrjNFkFJyeHabaezHJstotkHbbYMM/Rg1IySY98TbT33Cbo99DJKjp2ud+zo3p1YeZHbXs4eYxjOknMPUjpLKoiKCmtwTHW1i5MnTmG1BhMYGIDVGgJA94TOOBxRFOS73wveGJ4+T+FRoRx2Hq3dLi89Rnhk3S/j2bH1a9576xOmP38XAYHN/6zDCK8pM15vydBvfcnQV4YZejByRmuYqGcClymlfnfez+r9KEfTtDRN0/ppmtYvNXVCo4r37BlPfv5BiopKqKysIj09i8TE/h44bHNlmKEHo2bsLDrK5R3aERMeRIC/YnTvTnyS66wz7oqoYELbBrAt/0g9Vdwl9LiCgoISDhwopaqymrVrNjNkaB+3MUOG9mHlBxsBWL9uK/2v645SioqK47hcNQAcKCqlsMBJTEzUJffl6fMUf5WDQ0WHcR4sp6qqmk3rt9N/UILbmG++OsCiZ99j+l/uwhoe0uSs8xnhNWXG6y0Z+q0vGfrKMEMPRs4w/a0vmqZpSqmxwEtKqWlAGfAd8Jgn6lss/jz11BQmT56Jy1XDuHHDiI+P80RpU2WYoQejZrhqNGa+v4u/pw7ATymWbS1kn/MEf0jqxq4DR2sn7aOv6cRHX1780/Qfj3H6ExP53T3zcNXUMDZlMF3jY1i44D26J3RmaGJfUsYNZvpji0lOmkqoNZh5z98PQE72XhYtWI7F4o/yU8yYOYlQa/BFEus/Bk+eJ3+LP/c8citPP5iGq0Zj2Oj+xF5h51+vraXrVTH0H9SDtxas4vtTZ5g3/e8ARNqtPPH83U3O9EYf3sgw4/WWDP3Wlwx9ZZihByNnKE8+lW8uX8uJEYbT+Y9Ne1CvsfbOi/ZqfYA2/nVvN/G0/x792usZV1mv9HqGt51xNX9Vm4tpiesthBD6d6Wq76et4dYXIYQQQgghDEcm6kIIIYQQQuiQTNSFEEIIIYTQIZmoCyGEEEIIoUMyURdCCCGEEEKHZNWXBsmqL40hq2e0Lt5eVQbg2xd+5vUM0brI6jWNI+dJCF+SVV+EEEIIIYQwDJmoCyGEEEIIoUMyURdCCCGEEEKHWsVEXSl18ifb/6OUesVT9bOyckhKmsLw4amkpS3zVFnDZ2z7Yi/3jX+WKePmsvztjDr7P/zXZ9w/YR4P/eZ5nvz9q5Qeqmh2phHPk1kyBnWLJOOxRD793xuZkti1zv4ZYxJInzqY9KmDyXw8kR1zRjY704jnyawZRulh08YdjL75EZKTpvLG6yvr7K+srOLRqQtITprK7RNmUlxcBkBxcRnX9p7E+JTpjE+ZzjOz3vRpH97OkPMkGS1ZXzIa1iom6t7kcrmYPXsxS5bMIj19IatWZZGXV9jqM1yuGl77y/s8Nf8eFrwzjY3rtlP0TYnbmCuu7MQLbz/My/98hBsSr+btV1bpqgfJaDw/BbNv7cX/vL6Zm+ZlMqZ3J7ragt3GzFmZS/KLn5H84me8velb1u46pKseJEO/9T2V4XLVMHfO27z62jQ++Ggea1ZvZn9esduY95dvoH37dqR//CJ3TBzB/Bfeqd0X47CxbMVclq2Yy5Oz7vJZH97OkPMkGS1ZXzIuTCbqzbRz5z7i4qJxOOwEBgaQnDyIjIwtrT5j355ComMisHeKICDAwi+G92ZLVq7bmJ79utLmskAAuvWIpby0eSsOGPE8mSXj6tgwCsq/o6jiFFUujY+2FzM8wd7g+NG9O/HR9uIG9zeGEc+TWTOM0sPuXfuJjbUR44giINDCiJED+DQzx23MhsxtjBk7EIDhN/Vny+ZcPLk6mhHOlZwnyWjJ+pJxYa1lot5WKfXlj/8Asz1V2Oksx27vULtts0XgdJZ7qrxhMypKj9HBZq3djogKpaKs4Yn4Jyu30uf65i3LZ8TzZJYMe+hlHDp6una75Nj32EPb1ju2U1hbHOFB/GdfWZPzwJjnyawZRunB6TyCzR5+roY9nNLSIw2OsVj8CQ4J4ujRs3dPFheXcdutTzDpzjnkZDdtqVIjnCs5T5LRkvUl48IszT0ogzitado1P24opf4H6OezoxFuNqzJIe+/Rfxp8e99fSiiBYy6phNrdh6kRr6pQBhIZKSVdRnzsVpD2JP7LQ898BIrVj5LcHCQrw9NV+Q8CeFZreUT9UZRSqUqpbKVUtlpae826ndstghKSg7Xbjud5dhsER49LiNmhEeFcth5tHa7vPQY4ZF1v+hix9avee+tT5j+/F0EBDbvvxuNeJ7MklFy7Huirec+QbeHXkbJsdP1jh3duxMrm3nbCxjzPJk1wyg92GxhOEvOPbTuLKkgKiqswTHV1S5OnjiF1RpMYGAAVmsIAN0TOuNwRFGQ7/7cTUv14e0MOU+S0ZL1JePCZKJ+Hk3T0jRN66dpWr/U1AmN+p2ePePJzz9IUVEJlZVVpKdnkZjY36PHZcSM+KscHCo6jPNgOVVV1Wxav53+gxLcxnzz1QEWPfse0/9yF9bwkOa2YMjzZJaMnUVHubxDO2LCgwjwV4zu3YlPcp11xl0RFUxo2wC25R+pp8qlMeJ5MmuGUXpI6HEFBQUlHDhQSlVlNWvXbGbI0D5uY4YM7cPKDzYCsH7dVvpf1x2lFBUVx3G5agA4UFRKYYGTmJgon/Th7Qw5T5LRkvUl48Jay60vXmOx+PPUU1OYPHkmLlcN48YNIz4+rtVn+Fv8ueeRW3n6wTRcNRrDRvcn9go7/3ptLV2viqH/oB68tWAV3586w7zpfwcg0m7liefv1k0PktF4rhqNme/v4u+pA/BTimVbC9nnPMEfkrqx68DR2kn76Gs68dGXzf803Rs9SIZ+63sqw2LxZ/oTE/ndPfNw1dQwNmUwXeNjWLjgPbondGZoYl9Sxg1m+mOLSU6aSqg1mHnP3w9ATvZeFi1YjsXij/JTzJg5iVBr8EUSvdOHtzPkPElGS9aXjAtTnnxK21y+lhPTCP89+rXXM66yXun1DNE4nf/YtAfDLsW3LzTvoWIhfuqMq3krSjVGG/+6t/YZjZwnIXzpSlXfT+XWFyGEEEIIIXRIJupCCCGEEELokEzUhRBCCCGE0CGZqAshhBBCCKFDMlEXQgghhBBCh2TVlwbJqi/Cs2RFhcaJf+3SvxzlUu271+71DCFEXS/tLvB6xh96eHbJPSFahqz6IoQQQgghhGHIRF0IIYQQQggdkom6EEIIIYQQOtQqJ+pKqZOerJeVlUNS0hSGD08lLW2ZJ0ubKsMMPXgqY9PGHYy++RGSk6byxusr6+yvrKzi0akLSE6ayu0TZlJcXAZAcXEZ1/aexPiU6YxPmc4zs970aR8tWX+gI4yPJ/Tjk19dS+o1jnrHjLyiA2tu68vq8X15MdEz33BqlNeUrzPM0INk+K5+4fY9/PuB2fzr97PY/v66OvtzP97I0j/8iWV//DMfPPEiFUWHmp0J5rgWLZFhhh6MmtEqJ+qe5HK5mD17MUuWzCI9fSGrVmWRl1coGS1c30gZLlcNc+e8zauvTeODj+axZvVm9ucVu415f/kG2rdvR/rHL3LHxBHMf+Gd2n0xDhvLVsxl2Yq5PDnrLp/10ZL1/RTM+nlXJq/ezcil2YzqGklXa5DbmLj2lzGldywTPtjBzctymPOf/c1twzCvKV9nmKEHyfBd/RpXDZteX0ryE/cxYf4M8jbl1JmIxw/sx20vPcH4F/6Xa8YO44u33m9uG6a4Fi2RYYYejJwhE/Vm2rlzH3Fx0TgcdgIDA0hOHkRGxhbJaOH6RsrYvWs/sbE2YhxRBARaGDFyAJ9m5riN2ZC5jTFjBwIw/Kb+bNmciydXaDLa9e4VFULB8dMUnfieqhqN9Lwybrw8wm3MhKui+b/cgxyvrAag4vuqZvUAxnlN+TrDDD1Ihu/ql+bl097egfb2DvgHWOjyiz7k/7+dbmMCg9rW/rnq+0pQ9S6QcUnMcC1aIsMMPRg5QybqzeR0lmO3d6jdttkicDrLJaOF6xspw+k8gs0efq6GPZzS0iMNjrFY/AkOCeLo0bN3bBUXl3HbrU8w6c455GTv9VkfLVnfHtSGQyfP1G6XfHcGW7tAtzGXh7alc2hb3rnlapaNvYaBjrAm5/3IKK8pX2eYoQfJ8F397yqOEdzh3Ps1ODyM78rrLme7e81n/Ou+WWz+xwf8/K5fNjnvR2a4Fi2RYYYejJxhae5BCSFaTmSklXUZ87FaQ9iT+y0PPfASK1Y+S3Bw0MV/2eQsfoq40Lb89qOd2Nu14V9jriZ5WTYnKl2+PjQhhAf0GDmYHiMHs2/j/2Pb8rUkPnCnrw9JCK+TT9TPo5RKVUplK6Wy09LebdTv2GwRlJQcrt12Osux2SIu8BuXzgwZZujBUxk2WxjOkopzNUoqiIoKa3BMdbWLkydOYbUGExgYgNUaAkD3hM44HFEU5F/6FwQZ7XqXnDpDdHCb2m17uzY4v6t0H/PdGTILyqmu0Thw4nu+PXaKy0Pb/rTUJTHKa8rXGWboQTJ8V79deCgnD5/7W8WTFUdoF9Hwl7d1/Xlf8rfubHB/Y5nhWrREhhl6MHKGTNTPo2lamqZp/TRN65eaOqFRv9OzZzz5+QcpKiqhsrKK9PQsEhP7e/S4zJBhhh48lZHQ4woKCko4cKCUqspq1q7ZzJChfdzGDBnah5UfbARg/bqt9L+uO0opKiqO43LVAHCgqJTCAicxMVE+6aMl6+8qPcHloW2JCbmMAD9FctdIMgrc/zpxfX45/aOtAIRdZqFzaBBFx79vThuGeU35OsMMPUiG7+pHdY3j2KEyjjsP46qqZv+mbVzer5fbmKMHS2v/XJCTS2h0ZJPzfmSGa9ESGWbowcgZcutLM1ks/jz11BQmT56Jy1XDuHHDiI/37NcXmyHDDD14KsNi8Wf6ExP53T3zcNXUMDZlMF3jY1i44D26J3RmaGJfUsYNZvpji0lOmkqoNZh5z98PQE72XhYtWI7F4o/yU8yYOYlQa7BP+mjJ+i4Nnt6Ux5s398BfKd77qoS8I6d4qF8cu8pOkFlQwcaiI/wiJow1t/XFVQPPbf6Go2eqddWHWTPM0INk+K6+n78/v5h8G+nPLESr0eiWOIDw2Gj+379XEdk1lsuv7cXuNVkU79yLn8WfNu2CGHp/8297McO1aIkMM/Rg5AzlyZUkzOVrOTHCo8646j4c5Wlt/Bv+62KjiH/t0m/luVT77rV7PUMIUddLuwu8nvGHHp6dfAnRMq6sdykjufVFCCGEEEIIHZKJuhBCCCGEEDokE3UhhBBCCCF0SCbqQgghhBBC6JBM1IUQQgghhNAhWfWlQbLqizCe41XeXVGhfYA5VlPocud2r2dsfyPc6xlmuB6yGpIQQoCs+iKEEEIIIYSByERdCCGEEEIIHZKJuhBCCCGEEDrUaifqSqmTnqqVlZVDUtIUhg9PJS1tmafKmi7DDD0YNeM/m/YwbtRsUkbO4q0l6+rs35adx2/HP8uAqx8kY51n7t824nka1NPO+udGkvmXm7l31M/q7I+OCOKfjw9h5TM3kT4niSG9oi85wxfXAozx/t60cQejb36E5KSpvPH6yjr7KyureHTqApKTpnL7hJkUF5cBUFxcxrW9JzE+ZTrjU6bzzKw3fdpHa8gwQw+SoZ/6ktGwVjtR9xSXy8Xs2YtZsmQW6ekLWbUqi7y8Qslo4fqScaF6Ncybs5SXX72PpStnsG51Dt/sP+Q2xh4dxsw5d5B0c7/mHv4PmcY7T35KMevOvtz1fBZJj69l9IA4unZs7zbm/jHdSd9axJgn1/HQoi94emLfSzzmlr8WZ3P1//52uWqYO+dtXn1tGh98NI81qzezP6/Ybcz7yzfQvn070j9+kTsmjmD+C+/U7otx2Fi2Yi7LVszlyVl3+ayP1pBhhh4kQz/1JePCZKLeTDt37iMuLhqHw05gYADJyYPIyNgiGS1cXzIalrsrH0dsB2IcHQgIsDB8ZB8+y9zpNqZjpwjiu3VC+dX70PklM+J5urpLOAWlJygq+44qVw2rNhcyrE8ntzGaBsFtAwAICQqg9OjpS8rwxbUAY7y/d+/aT2ysjRhHFAGBFkaMHMCnmTluYzZkbmPM2IEADL+pP1s25+LJlcuM+Lr1RYYZepAM/dSXjAuTiXozOZ3l2O0dardttgicznLJaOH6ktGwstJj2Oxh59ULo6zUu0viGfE82cLacqj83MS7pOIUtrC2bmNeXrGbsTfEsWn+aN744yCe/se2S8rwxbUAY7y/nc4j2OznlrS02cMpLT3S4BiLxZ/gkCCOHj17F2NxcRm33foEk+6cQ072Xp/10RoyzNCDZOinvmRcmKW5ByWEEK3F6OtjWb4xnzfWfkXvrhE8f+91jJy+Fvk6Ct+KjLSyLmM+VmsIe3K/5aEHXmLFymcJDg7y9aEJIUSzyCfq51FKpSqlspVS2Wlp7zbqd2y2CEpKDtduO53l2GwRHj0uM2SYoQejZkRGheIsOffJpNN5hMgo734BjBHPk/PIaaIjzn2Cbg8PwnnE/daW8YOuYPXWs/cbbs8rp02AP+HBbRqd4YtrAcZ4f9tsYThLKs7VKKkgKiqswTHV1S5OnjiF1RpMYGAAVmsIAN0TOuNwRFGQX+KTPlpDhhl6kAz91JeMC5OJ+nk0TUvTNK2fpmn9UlMnNOp3evaMJz//IEVFJVRWVpGenkViYn+PHpcZMszQg1EzuveIo7CwjOIDh6mqqmb9mm0MGtrLg0dclxHP085vKrjcFkJMh3YE+PsxakAsGdvdH2Y8VH6KG7rbAOjSMYQ2Af6UnzjT6AxfXAswxvs7occVFBSUcOBAKVWV1axds5khQ/u4jRkytA8rP9gIwPp1W+l/XXeUUlRUHMflqgHgQFEphQVOYmKifNJHa8gwQw+SoZ/6knFhypMP4hiFUsoCODVNu8B/5nzd6BPz2WfZzJ37Oi5XDePGDeN3v2vcJP9SmCHDDD3oPeN4VUG9P/88K5cXn3sPl0tjTMoA7rp3BItfWcVVCbEMHtqL3F0FTHv4dY4fP0WbQAvhHdqz9MMZdepcylfW6/k8dbmz/mUPh/SKZsZve+OnFO9lfcOij/7Lw7f2YNe3FWRsP0jXju2Ze9e1BF1mQdM0nnt3B5t2O+uttf2N8Hp/7qlrAfq6Hk2tf8Z17h79jZ99ybxn/w9XTQ1jUwaTOuUWFi54j+4JnRma2JczZyqZ/thi9v43n1BrMPOev58YRxTr121l0YLlWCz+KD/FffePc5vkt/Fv/N9a6Pl1q6cMM/QgGfqpLxkAV9a7gkBrnahfDbyuadoF/jOn8RN1IfSioYm6p1zKxFDPGpqoe1JDE3VPMsP1OH+i7i2XMlEXQgjfqH+i3upufVFKTQH+DdT/EZUQQgghhBA60OpWfdE0bTGw2NfHIYQQQgghxIW0uk/UhRBCCCGEMAKZqAshhBBCCKFDMlEXQgghhBBCh1rlqi+NI6u+CCGaLn7gp17P2LdxqFfrHzr1lVfrA0QHdfN6hhBC6J+s+iKEEEIIIYRhyERdCCGEEEIIHZKJuhBCCCGEEDokE3UhhBBCCCF0qNVM1JVSJ8/7881Kqa+VUh75/u2srBySkqYwfHgqaWnLPFHSlBlm6EEy9FPfqBkD+zv4+J8T+OTfvyL1N9fU2R8dFcw/Xh7Fh2+M46O3fsngAY5mZ4Ln+9jy+V7uGPsct4/5M/98M7PO/h05+7nn1y+R2G8aG9bvaHYeGPN6mzXDDD1Ihn7qS0bDWs1E/UdKqRuBvwIjNU0raG49l8vF7NmLWbJkFunpC1m1Kou8vMLmH6jJMszQg2Top75RM/z8FLOm/pzJj6xm5B1LGTWsK10vt7qNuW9iH9Z8+g233L2cP8z6hFlTBzazC8/34XLV8PKzK3julcm8vfxRMtduJ39/iduYqOgwHn96AsNG9G7u4f+QabzrbdYMM/QgGfqpLxkX1qom6kqpQcDrwChN0/Z7oubOnfuIi4vG4bATGBhAcvIgMjK2eKK0qTLM0INk6Ke+UTN6XRVFQfFxig6doKq6hvSMPG78xeXugzSN4KAAAILbtaH08HfN6OAsT/exd3chnRwRdIyJICDAQmLSNXy+IddtTHTHcLpc2RHlV++KY5fMiNfbrBlm6EEy9FNfMi6sNU3U2wAfAGM1TdvrqaJOZzl2e4fabZstAqez3FPlTZNhhh4kQz/1jZphjwziUGntXXiUlH2HrUM7tzF//VsOY26KZ+Py37DkLyOZPf/zJuf9yNN9lJUeI9Jmrd2OtFkpKzvWnEO8KCNeb7NmmKEHydBPfcm4sNY0Ua8C/gPc3dAApVSqUipbKZWdlvZuyx2ZEEL8YNSwLry/5msGjvsnkx9dw/NPJqI886G0EEIIg2lNE/Ua4Dagv1Jqen0DNE1L0zStn6Zp/VJTJzSqqM0WQUnJ4dptp7Mcmy3CE8drqgwz9CAZ+qlv1IySslNERwXXbtsj2+H8ya0t45N/xupPz96Z92WukzaB/oSFXtbkTPB8H5FRoZQ5j9ZulzmPEhkZ2pxDvCgjXm+zZpihB8nQT33JuLDWNFFH07RTQDLwG6VUg5+sX4qePePJzz9IUVEJlZVVpKdnkZjY3xOlTZVhhh4kQz/1jZqxa28pl8eEEhMdQoDFj+Qbu5Kxyf2Z9oPOk9zQtxMAXeKsBAb6U3H0e1310S3BwYHCwxwqLqeqqprMj7/khiEJzTrGizHi9TZrhhl6kAz91JeMC7N46NgMQ9O0CqXUCCBLKVWmadrK5tSzWPx56qkpTJ48E5erhnHjhhEf75FVH02VYYYeJEM/9Y2a4XJpPP3SJt584Wb8/RTvpX9FXv4RHrq7H7v2lpH5eQHPLvyCOdMG8z+39QJN4/G5G3TXh8Xiz0OPpfDofa9TU6Mx8pZr6dzFzpuL1tKtu4OfD0lgb24hM6a+zcnjp/giaw9vLV7HW8sf1U0PkqHf+pKhrwwz9GDkDKVpmocOz2y+lhMjhGiy+IGfej1j38ahXq1/6NRXXq0PEB3UzesZQgihf1fW+zRSq7r1RQghhBBCCKOQiboQQgghhBA6JBN1IYQQQgghdEgm6kIIIYQQQuiQPEzaIHmYVBiPtx/+kwf/Gu+My7vf1AnQfdI3Xq2//++9vVpftD4t8b5o4+/dNf2F8A55mFQIIYQQQgjDkIm6EEIIIYQQOiQTdSGEEEIIIXRIJupCCCGEEELoUKuaqCulTnqjblZWDklJUxg+PJW0tGXeiDBFhhl6MGrGls/3csfY57h9zJ/555uZdfbvyNnPPb9+icR+09iwfkez88CY58lbGZs27mD0zY+QnDSVN15fWWd/ZWUVj05dQHLSVG6fMJPi4jIAiovLuLb3JManTGd8ynSemfVmo/IG9bSz/rmRZP7lZu4d9bM6+6Mjgvjn40NY+cxNpM9JYkiv6Cb1dT6jXAvJ0E/9ln5feKuP1pBhhh6MmtGqJure4HK5mD17MUuWzCI9fSGrVmWRl1coGS1cXzIuVK+Gl59dwXOvTObt5Y+SuXY7+ftL3MZERYfx+NMTGDbCM6t8GPE8eSvD5aph7py3efW1aXzw0TzWrN7M/rxitzHvL99A+/btSP/4Re6YOIL5L7xTuy/GYWPZirksWzGXJ2fdddE8P6WYdWdf7no+i6TH1zJ6QBxdO7Z3G3P/mO6kby1izJPreGjRFzw9se8l9VS3R2NcC8nQT/2Wfl94q4/WkGGGHoycIRP1Ztq5cx9xcdE4HHYCAwNITh5ERsYWyWjh+pLRsL27C+nkiKBjTAQBARYSk67h8w25bmOiO4bT5cqOKL96V4e6ZEY8T97K2L1rP7GxNmIcUQQEWhgxcgCfZua4jdmQuY0xYwcCMPym/mzZnEtTl869uks4BaUnKCr7jipXDas2FzKsTye3MZoGwW0DAAgJCqD06OkmZf3IKNdCMvRTv6XfF97qozVkmKEHI2fIRL2ZnM5y7PYOtds2WwROZ7lktHB9yWhYWekxIm3W2u1Im5WyMu+uZWzE8+StDKfzCDZ7+Lka9nBKS480OMZi8Sc4JIijR8/eqVdcXMZttz7BpDvnkJO996J5trC2HCo/N/EuqTiFLayt25iXV+xm7A1xbJo/mjf+OIin/7Htknr6KaNcC8nQT/2Wfl94q4/WkGGGHoycIRP18yilUpVS2Uqp7LS0d319OEKIVi4y0sq6jPksff9PPPrYb3h82iJOnjzV7Lqjr49l+cZ8fvHwR9z9QhbP33sdyjN/mSKE13nrfSGEHslE/TyapqVpmtZP07R+qakTGvU7NlsEJSWHa7edznJstgiPHpcZMszQg1EzIqNCKXMerd0ucx4lMtK739xnxPPkrQybLQxnScW5GiUVREWFNTimutrFyROnsFqDCQwMwGoNAaB7QmccjigK8t2fL/gp55HTREec+wTdHh6E84j7rS3jB13B6q1n75vcnldOmwB/woPbXFJf7sdvjGshGfqp39LvC2/10RoyzNCDkTNkot5MPXvGk59/kKKiEiorq0hPzyIxsb9ktHB9yWhYtwQHBwoPc6i4nKqqajI//pIbhiR48IjrMuJ58lZGQo8rKCgo4cCBUqoqq1m7ZjNDhvZxGzNkaB9WfrARgPXrttL/uu4opaioOI7LVQPAgaJSCgucxMREXTBv5zcVXG4LIaZDOwL8/Rg1IJaM7e4P6R0qP8UN3W0AdOkYQpsAf8pPnLmkvs5nlGshGfqp39LvC2/10RoyzNCDkTMsHjo2owhSSh04b/tFTdNebE5Bi8Wfp56awuTJM3G5ahg3bhjx8XHNPEzzZZihB6NmWCz+PPRYCo/e9zo1NRojb7mWzl3svLloLd26O/j5kAT25hYyY+rbnDx+ii+y9vDW4nW8tfxR3fRg5AyLxZ/pT0zkd/fMw1VTw9iUwXSNj2HhgvfontCZoYl9SRk3mOmPLSY5aSqh1mDmPX8/ADnZe1m0YDkWiz/KTzFj5iRCrcEXzHPVaDz99228NW0wfkrxXtY37Cs+zsO39mDXtxVkbD/I3H9/ydy7rmXSiG5omsa015v3sJNRroVk6Kd+S78vvNVHa8gwQw9GzlCefILaXL6WEyMM59Cpr7xaPzqom1frm8kZl3cf2AXoPukbr9bf/3fPLNcpxI9a4n3Rxt+7t/YJ4R1X1vukkNz6IoQQQgghhA7JRF0IIYQQQggdkom6EEIIIYQQOiQTdSGEEEIIIXToohN1ddZvlVJP/bAdq5Ty7Ho2QgghhBBCCDcXXfVFKfUqUAMkapp2lVIqDFinadq1LXGAviOrvgjj8faKCrKaQuOZYXWLLndu92p9MM/KMserCrye0cbP6t368v4WwofqX/WlMeuoX6dpWh+l1HYATdOOKKUCPXpsQgghhBBCCDeNuUe9SinlD2gASqlIzn7CLoQQQgghhPCSxkzU/wqsAKKUUn8CNgFzvXpUQgghhBBCtHIXnahrmvZPYBrwZ+AQMFbTtGXePjBPUEppSqn/O2/bopQqU0qt8mROVlYOSUlTGD48lbQ075waM2SYoQcjZWzauIPRNz9CctJU3nh9ZZ39lZVVPDp1AclJU7l9wkyKi8sAKC4u49rekxifMp3xKdN5ZtabPuvBLBm+vhae6uN8g3raWf/cSDL/cjP3jvpZnf3REUH88/EhrHzmJtLnJDGkV3SzM41yvc/3n017GDdqNikjZ/HWknV19m/LzuO3459lwNUPkrGu8c8E+Po1ZcRrIRn6rS8ZDWvMqi+xwCngI2Al8N0PPzOC74AeSqm2P2wPB4o9GeByuZg9ezFLlswiPX0hq1ZlkZdX6MkIU2SYoQcjZbhcNcyd8zavvjaNDz6ax5rVm9mf5/7Sf3/5Btq3b0f6xy9yx8QRzH/hndp9MQ4by1bMZdmKuTw56y6f9GCWDF9fC0/1cT4/pZh1Z1/uej6LpMfXMnpAHF07tncbc/+Y7qRvLWLMk+t4aNEXPD2xb5PzvNFDS2S4XDXMm7OUl1+9j6UrZ7BudQ7f7D/kNsYeHcbMOXeQdHO/S6or72/JaKkMM/Rg5IzG3PqSDqz64d8ZwDfAmmaltqzVQPIPf/418G9PFt+5cx9xcdE4HHYCAwNITh5ERsYWT0aYIsMMPRgpY/eu/cTG2ohxRBEQaGHEyAF8mpnjNmZD5jbGjB0IwPCb+rNlcy4XWwWqJXswS4avr4Wn+jjf1V3CKSg9QVHZd1S5ali1uZBhfTq5jdE0CG4bAEBIUAClR0/rqoeWyMjdlY8jtgMxjg4EBFgYPrIPn2XudBvTsVME8d06ofzqXfChXr5+TRnxWkiGfutLxoU15taXnpqm9frh3/FAf+CLZqW2rHeAXymlLgN6AR69Kk5nOXZ7h9ptmy0Cp7PckxGmyDBDD0bKcDqPYLOHn6thD6e09EiDYywWf4JDgjh69CRw9q/Hb7v1CSbdOYec7L0+6cEsGb6+Fp7q43y2sLYcKj838S6pOIUtrK3bmJdX7GbsDXFsmj+aN/44iKf/sa3JeWCc632+stJj2Oxh59ULo6y0+ct2+vo1ZcRrIRn6rS8ZF9aY5RndaJq2TSl1XbNSW5CmaTuVUpdz9tP01T4+HCF0LzLSyrqM+VitIezJ/ZaHHniJFSufJTg4yNeH1uoY+VqMvj6W5RvzeWPtV/TuGsHz917HyOlr8eBfFIgmMPJrSojWqDH3qE89759HlFL/Ag62wLF50krgeS5y24tSKlUpla2Uyk5Le7dRhW22CEpKDtduO53l2GwRzTlWU2aYoQcjZdhsYThLKs7VKKkgKiqswTHV1S5OnjiF1RpMYGAAVmsIAN0TOuNwRFGQX9LiPZglw9fXwlN9nM955DTREec+QbeHB+E84n5ry/hBV7B669l7M7fnldMmwJ/w4DZNzjTK9T5fZFQozpJzn3Q7nUeIjGr+lwr5+jVlxGshGfqtLxkX1ph71EPO+6cNZ+9Vv6VZqS3vTeBpTdN2XWiQpmlpmqb10zStX2rqhEYV7tkznvz8gxQVlVBZWUV6ehaJif09ccymyjBDD0bKSOhxBQUFJRw4UEpVZTVr12xmyNA+bmOGDO3Dyg82ArB+3Vb6X9cdpRQVFcdxuc5+VcKBolIKC5zExES1eA9myfD1tfBUH+fb+U0Fl9tCiOnQjgB/P0YNiCVju/vDjIfKT3FDdxsAXTqG0CbAn/ITZ5qcaZTrfb7uPeIoLCyj+MBhqqqqWb9mG4OG9mr2cfr6NWXEayEZ+q0vGRd2wVtffviioxBN0x5pVoqPaZp2gLPrwXucxeLPU09NYfLkmbhcNYwbN4z4+DjJaOH6klG3xvQnJvK7e+bhqqlhbMpgusbHsHDBe3RP6MzQxL6kjBvM9McWk5w0lVBrMPOevx+AnOy9LFqwHIvFH+WnmDFzEqHW4BbvwSwZvr4WnurjfK4ajaf/vo23pg3GTyney/qGfcXHefjWHuz6toKM7QeZ++8vmXvXtUwa0Q1N05j2evMeDzLK9f5pvWnTb+PBexficmmMSRlAl67RLH5lFVclxDJ4aC9ydxUw7eHXOX78FJs27OK1heks/XDGRevK+1syWirDDD0YOUM19BS4UsqiaVq1UuoLTdOub1aKIX0td1IKwznjav6DahfSxr/5f23fWnj7WoD3r0eXOxu/rndT7f97b69ntITjVQVez2jjZ/VufXl/C+FDV9a79NOFPlHfCvQBvlRKrQSWcXZdcgA0TXvfo8cnhBBCCCGEqNWYVV8uA8qBREAD1A//lom6EEIIIYQQXnKhiXqUUmoqsJtzE/QfyW0hQgghhBBCeNGFJur+QDDuE/QfyURdCCGEEEIIL7rQRP2QpmmzW+xIhBBCCCGEELUutOrLdk3TzPE4fpPIqi9CmFVLrNDRPsCzy36ZVdcxX3g9I3dFd69nyIopQviGGVbYOqv+VV8u9IVHN3rpSIQQQgghhBAX0eBEXdO0iob2CSGEEEIIIbzrQp+oCyGEEEIIIXykVUzUlVKaUuqF87YfUUrN8lT9rKwckpKmMHx4KmlpyzxV1nQZZuhBMvRT3xsZ/9m0h3GjZpMychZvLVlXZ/+27Dx+O/5ZBlz9IBnrPPetnUY8V96uP6hPR9YtGkvGayncO65Hnf3RHdrxf3NuYuX8Uaz662gG9+3UqLqbNu5g9M2PkJw0lTdeX1lnf2VlFY9OXUBy0lRunzCT4uIyAIqLy7i29yTGp0xnfMp0npn1ZpN7k+stGUbLMEoPZnx/t4qJOnAGuFUp1cHThV0uF7NnL2bJklmkpy9k1aos8vIKJaOF60uGvjKM2IPLVcO8OUt5+dX7WLpyButW5/DN/kNuY+zRYcyccwdJN/dr7uGfl2u8c+Xt+n5+iln3DuDupz9hxO8/ZNSgznR1uD/M9fsJvVj9eQFjHl7Fw3/J4ukpAxpxnDXMnfM2r742jQ8+msea1ZvZn1fsNub95Rto374d6R+/yB0TRzD/hXdq98U4bCxbMZdlK+by5Ky7mtSbXG/JMFqGUXow6/u7tUzUq4E04A+eLrxz5z7i4qJxOOwEBgaQnDyIjIwtktHC9SVDXxlG7CF3Vz6O2A7EODoQEGBh+Mg+fJa5021Mx04RxHfrhPKr9+H8JjHiufJ2/avjO1Bw6DhFzpNUVdeQvvFbhl3ncBujaRrBbQMACAkKpLTi1EXr7t61n9hYGzGOKAICLYwYOYBPM3PcxmzI3MaYsQMBGH5Tf7ZszqWh1dGaQq63ZBgtwyg9mPX93Vom6gALgd8opTy6xo7TWY7dfu6DepstAqez3JMRpsgwQw+SoZ/63sgoKz2GzR52Xr0wykq9v+yXEc+Vt+vbIoI4dPi72u2Sw6ewRbRzG/PXf+/gliFXsOnNX7Jk5o08nXbx/zN0Oo9gs4efy7GHU1p6pMExFos/wSFBHD16Ejj71+O33foEk+6cQ0723ib1JtdbMoyWYZQezPr+vtAXHpmKpmnHlVJ/Bx4ETvv6eIQQQjTd6EGdeT8zjzc+2EPvbpG88IeBjHzgQzz44ZibyEgr6zLmY7WGsCf3Wx564CVWrHyW4OAg7wQKIVqMnt/frekTdYD5wN1Au/p2KqVSlVLZSqnstLR3G1XQZougpORw7bbTWY7NFuGBQzVXhhl6kAz91PdGRmRUKM6Sc5++OJ1HiIzy/pdcGPFcebu+s/wU0R3O/c+0vUMQzvLv3MaMHx7P6k35AGz/qozAQH/C2l92keMMw1lybuVhZ0kFUVFhDY6prnZx8sQprNZgAgMDsFpDAOie0BmHI4qC/JJL7k2ut2QYLcMoPZj1/d2qJuo/rA2/lLOT9fr2p2ma1k/TtH6pqRMaVbNnz3jy8w9SVFRCZWUV6elZJCb29+BRmyPDDD1Ihn7qeyOje484CgvLKD5wmKqqatav2cagob08eMT1M+K58nb9nfsOE9exPTG2YAIsfiQP7EzGlgNuYw6WneT6XtEAdIkJpU2APxXHvr9g3YQeV1BQUMKBA6VUVVazds1mhgzt4zZmyNA+rPxgIwDr122l/3XdUUpRUXEcl6sGgANFpRQWOImJibrk3uR6S4bRMozSg1nf363m1pfzvADc76liFos/Tz01hcmTZ+Jy1TBu3DDi4z371eFmyDBDD5Khn/reyLBY/Jk2/TYevHchLpfGmJQBdOkazeJXVnFVQiyDh/Yid1cB0x5+nePHT7Fpwy5eW5jO0g9n6KoPX2R4ur6rRuPp17bwt1nD8PfzY9kn+9hXdJSHbr+G3XnlZGwt4s9vZvOn+29g0i3d0TR47OXPG3Wc05+YyO/umYerpoaxKYPpGh/DwgXv0T2hM0MT+5IybjDTH1tMctJUQq3BzHv+7P9d5GTvZdGC5Vgs/ig/xYyZkwi1Bl9yb3K9JcNoGUbpwazvb+XJp13N5Ws5MUKY1PGqAq9ntA/w7P+RmVXXMV94PSN3RXevZ7Tx9/5tUkKIus64vP/Qf8u8v6+sdzmxVnXrixBCCCGEEEYhE3UhhBBCCCF0SCbqQgghhBBC6JBM1IUQQgghhNAhmagLIYQQQgihQ7LqS4Nk1ZfWxDxPjYvGaInr3RK8/Zoyy3m6cp73+yj431ivZ3ibWa63/G+tMCZZ9UUIIYQQQgjDkIm6EEIIIYQQOiQTdSGEEEIIIXTI4usDaAlKqRhgIdCds/9xsgp4VNO0Sk/Uz8rK4U9/ep2amhrGjx9Oaup4T5Q1XYZReti0cQfP/fkf1LhquPWXQ7j7njFu+ysrq3ji8cXsyf2WUGsIf3nxfjp1iqS4uIyxo6Zx+eXRAPS6uitPzrrLZ334OsMoPbTE9TbDa8qM52nwFRHMHHYl/n6Kd74s5tXN7t9Y++SNV3J9XBgAbQP8iAgKpNdLnzXuhF2AEd4bZrze9THCtdBDhhl6MGqG6SfqSikFvA+8qmnaLUopfyAN+BPwaHPru1wuZs9ezN/+9gw2WwS//OVUEhOvo2tXzz1YZIYMo/TgctUwd87bpC15HJstnF9PeIohQ/vSpWun2jHvL99A+/btSP/4Rdas/oL5L7zDX158AIAYh41lK+b6vA9fZxilh5a43mZ4TZnxPPkpeOambvzmne2UHP+elf/Tn0/2HWZf+Xe1Y57J+Lr2z//T10GCLaTR9RvuU//vDTNe7/qPQf/XQg8ZZujByBmt4daXROB7TdP+BqBpmgv4A3CXUiqoucV37txHXFw0DoedwMAAkpMHkZGxpbllTZdhlB5279pPbKyNGEcUAYEWRowcwKeZOW5jNmRuY8zYgQAMv6k/Wzbn4snVk4xyrnxZ31MZLXG9zfCaMuN5uqZjKPlHTlN09DRVNRof/dfJ8CsjGxw/pruND/eUNCnrfEZ4b5jxetfHCNdCDxlm6MHIGa1hop4AuL37NU07DhQCXZtb3Oksx27vULtts0XgdJY3t6zpMozSg9N5BJs9/FwNezilpUcaHGOx+BMcEsTRoycBKC4u47Zbn2DSnXPIyd7rsz58nWGUHlriepvhNWXG82QPbsOh49/Xbh868T32kDb1ju3U/jIc1rb8p6DionUvxgjvDTNe7/qPQf/XQg8ZZujByBmmv/VFiJYSGWllXcZ8rNYQ9uR+y0MPvMSKlc8SHNzsv7gROtQS19sMrykznKfR3W2s3ltKjXy7xkWZ4XoLoSet4RP1PUDf83+glGoPxAJ5P/l5qlIqWymVnZb2bqOK22wRlJQcrt12Osux2SKafdBmyzBKDzZbGM6Sc5+aOUsqiIoKa3BMdbWLkydOYbUGExgYgNV69h7W7gmdcTiiKMi/9L8qN8q58mV9T2W0xPU2w2vKjOep5OQZottfVrsdHXIZJSfO1Dt2zFV2VnrgtpezPej/vWHG611/n/q/FnrIMEMPRs5oDRP1DCBIKXUnwA8Pk74AvKVp2qnzB2qalqZpWj9N0/qlpk5oVPGePePJzz9IUVEJlZVVpKdnkZjY36MNmCHDKD0k9LiCgoISDhwopaqymrVrNjNkaB+3MUOG9mHlBxsBWL9uK/2v645SioqK47hcNQAcKCqlsMBJTEyUT/rwdYZRemiJ622G15QZz9OOg8fpHNYWR+hlBPgpRl9lY/2+sjrjuoQH0f4yCznFnvnWTiO8N8x4vetjhGuhhwwz9GDkDNPf+qJpmqaUSgEWKaWe5Ox/nKwGpnuivsXiz1NPTWHy5Jm4XDWMGzeM+Pg4T5Q2VYZRerBY/Jn+xER+d888XDU1jE0ZTNf4GBYueI/uCZ0ZmtiXlHGDmf7YYpKTphJqDWbe8/cDkJO9l0ULlmOx+KP8FDNmTiLUGuyTPnydYZQeWuJ6m+E1Zcbz5NI0nlr/FX//VW/8lWLpzoPsO/wdUwdewc5Dx/kk7+ynYqO72/nov85Gn6vGnEu9vzfMeL29cZ5aS4YZejByhvLkE9Tm8rWcmFbkjMszn5ZdSBv/UK9niMZpievdErz9mjLLebpynvf7KPhfzy3x5itmud7yv7XCmK5U9f20Ndz6IoQQQgghhOHIRF0IIYQQQggdkom6EEIIIYQQOiQTdSGEEEIIIXRIJupCCCGEEELokKz60iBZ9UUIX5AVePTjeFWB1zPaB3h2eTRf6feO55ZwbEjmuO+9Wr+Nn9Wr9QEqznjmi6MuJDqom9czhPA8WfVFCCGEEEIIw5CJuhBCCCGEEDokE3UhhBBCCCF0qNVM1JVSLqXUl0qp3UqpZUqpIE/VzsrKISlpCsOHp5KWtsxTZU2XYYYeJKNl62/auIPRNz9CctJU3nh9ZZ39lZVVPDp1AclJU7l9wkyKi8sAKC4u49rekxifMp3xKdN5ZtabPu3DjBn/2bSHcaNmkzJyFm8tWVdn/7bsPH47/lkGXP0gGeu2NzsPjHmerreHsfzmvqxI7sfEq2LqHTPM0YGlI/vy7sg+zLn+0u+v9sa1aOn33pbP93LH2Oe4fcyf+eebmXX278jZzz2/fonEftPYsH5Ho2o2hhFfU77IMEMPRs1oNRN14LSmaddomtYDqASmeKKoy+Vi9uzFLFkyi/T0haxalUVeXqEnSpsqwww9SEbL1ne5apg7521efW0aH3w0jzWrN7M/r9htzPvLN9C+fTvSP36ROyaOYP4L79Tui3HYWLZiLstWzOXJWXf5rA8zZrhcNcybs5SXX72PpStnsG51Dt/sP+Q2xh4dxsw5d5B0c7/mHv4PmcY7T34KHuvXhQc/y2X8mhySYiPp3N79MyJH8GVM6u7g7k92MGHNNl7Y9s0lHrPnr0VLv/dcrhpefnYFz70ymbeXP0rm2u3k73d/6DQqOozHn57AsBG9G9VDYxjxNeWLDDP0YOSM1jRRP99GoKsnCu3cuY+4uGgcDjuBgQEkJw8iI2OLJ0qbKsMMPUhGy9bfvWs/sbE2YhxRBARaGDFyAJ9m5riN2ZC5jTFjBwIw/Kb+bNmciydXsjLDtfBGRu6ufByxHYhxdCAgwMLwkX34LHOn25iOnSKI79YJ5VfvQgaXzIjnKSE8hKIT31P83fdU12isKyxjcKdwtzEpXews3XeQE1XVABw5U3VJGd64Fi393tu7u5BOjgg6xkQQEGAhMekaPt+Q6zYmumM4Xa7s6LHXExjzNeWLDDP0YOSMVjdRV0pZgJHALk/UczrLsds71G7bbBE4neWeKG2qDDP0IBktW9/pPILNfm5SY7OHU1p6pMExFos/wSFBHD16Ejj7V/C33foEk+6cQ072Xp/1YcaMstJj2Oxh59ULo6zUu8tqGvE8RbVtg/PUmdrt0tOVRLVt4zYmNqQtcSFteePGXvxt2NVcf955bQxvXIuWfu+VlR4j0mat3Y60WSkr8/4yrUZ8Tfkiwww9GDnD0tyDMpC2Sqkvf/jzRuANHx6LEMKLIiOtrMuYj9Uawp7cb3nogZdYsfJZgoM99miKEB7hrxSOkLakZu7CFhRI2o1X86s1OZyscvn60JpE3ntCeFZr+kT9x3vUr9E07QFN0yp/OkAplaqUylZKZaelvduoojZbBCUlh2u3nc5ybLYIzx21STLM0INktGx9my0MZ0nFuRolFURFhTU4prraxckTp7BagwkMDMBqDQGge0JnHI4oCvIv/YtWzHAtvJERGRWKs+TcJ6xO5xEio7z7JVJGPE+lp89gCzr3CXpU20BKT5/5yZhKsoorcGkaB787Q+GJ08SGtG10hjeuRUu/9yKjQilzHq3dLnMeJTLS+19KZsTXlC8yzNCDkTNa00T9ojRNS9M0rZ+maf1SUyc06nd69ownP/8gRUUlVFZWkZ6eRWJif48elxkyzNCDZLRs/YQeV1BQUMKBA6VUVVazds1mhgzt4zZmyNA+rPxgIwDr122l/3XdUUpRUXEcl6sGgANFpRQWOImJifJJH2bM6N4jjsLCMooPHKaqqpr1a7YxaGgvDx5xXUY8T3sqTuAIuYyO7dpg8VPcFBtJVnGF25gNB8rp+8PEOjTQQmxIW4pPNv4bSL1xLVr6vdctwcGBwsMcKi6nqqqazI+/5IYhCc3qoTGM+JryRYYZejByhvLkg1d6ppQ6qWlacON/4+tGn5jPPstm7tzXcblqGDduGL/7XeMm+ZfCDBlm6EEyvF//jOvcvakbP/uSec/+H66aGsamDCZ1yi0sXPAe3RM6MzSxL2fOVDL9scXs/W8+odZg5j1/PzGOKNav28qiBcuxWPxRfor77h/nNtFo49/4T+vMcC2amnG8qqDBfZ9n5fLic+/hcmmMSRnAXfeOYPErq7gqIZbBQ3uRu6uAaQ+/zvHjp2gTaCG8Q3uWfjijTp32AXFe7eFSNTWj3zvOen/+8+gwpva+An8/xcpvnLy5p4h7e8Tx34oTZB08O2n/wzWduT46nBpN4809RawrLKu3Vua4+ifwnroWbfystX/21nuv4kz9n65v3vhfXnn+Q2pqNEbeci13TB7Gm4vW0q27g58PSWBvbiEzpr7NyeOnCGwTQHhECG8tf7TeWtFBjV/iUs+vKT1lmKEH/WdcWe+T0q1mon7pGj9RF0J4zvkTdW+5lIl6a3ahibqnXMpEXc8amqh7UkMTdU85f6LuLQ1N1D3pUibqQuhH/RN1ufVFCCGEEEIIHZKJuhBCCCGEEDokE3UhhBBCCCF0SCbqQgghhBBC6JBM1IUQQgghhNAhWfWlQbLqizCe9779xqv1f9n5Cq/WF/rSEivwtASzrPIT/5p3V0zZd6/dq/WFEBciq74IIYQQQghhGDJRF0IIIYQQQodkoi6EEEIIIYQOtYqJulIqRin1oVJqn1LqG6XUK0qpNp6qn5WVQ1LSFIYPTyUtbZmnypouwww9GDXj6+z/8tLdf+KFSc/w2bvr6+zfkr6Jv055lgX3zSNt6nxKC5p/L6wRz5NkNGzTxh2MvvkRkpOm8sbrK+vsr6ys4tGpC0hOmsrtE2ZSXFwGQHFxGdf2nsT4lOmMT5nOM7Pe9GnGxRjhWvzUQEcYH0/oxye/upbUaxz1jhl5RQfW3NaX1eP78mLiz5qdacTzJBn6rS8ZDTP9RF0ppYD3gQ80TYsH4oG2wDxP1He5XMyevZglS2aRnr6QVauyyMsr9ERpU2WYoQejZtS4avho4TImzrmXh9L+l50bttWZiF89pB8PLn6cBxZNY+D4G1mdtkJXPUiGbzNcrhrmznmbV1+bxgcfzWPN6s3szyt2G/P+8g20b9+O9I9f5I6JI5j/wju1+2IcNpatmMuyFXN5ctZdPsu4eJ/6vxY/5adg1s+7Mnn1bkYuzWZU10i6WoPcxsS1v4wpvWOZ8MEObl6Ww5z/7NdVD5Kh7wwz9GDkDNNP1IFE4HtN0/4GoGmaC/gDcKdSKri5xXfu3EdcXDQOh53AwACSkweRkbGluWVNl2GGHoyaceCrAsKjIwmP7oAlwEKvwX347xe73MZc1u6y2j9Xfl8Jqt6HzxvNiOdJMhq2e9d+YmNtxDiiCAi0MGLkAD7NzHEbsyFzG2PGDgRg+E392bI5l0tZVawlMi7GCNfip3pFhVBw/DRFJ76nqkYjPa+MGy+PcBsz4apo/i/3IMcrqwGo+L5KVz1Ihr4zzNCDkTNaw0Q9AXD7X3tN044D+UDX5hZ3Osux2zvUbttsETid5c0ta7oMM/Rg1Izj5ccIjbTWbrfvYOVYed1l9zav3MgLk2bz8RsrGfW7W5ucB8Y8T5Jxod8/gs0efu737eGUlh5pcIzF4k9wSBBHj54Ezt6actutTzDpzjnkZO/1WcbF+9T/tfgpe1AbDp08U7td8t0ZbO0C3cZcHtqWzqFteeeWq1k29hoGOsKanAfGPE+Sod/6knFhluYelBDCHAaMGciAMQPZ8Wk2G/69jl8+8ltfH5IwgchIK+sy5mO1hrAn91seeuAlVqx8luDgoIv/so4yjMzip4gLbctvP9qJvV0b/jXmapKXZXOi0uXrQxNCXERr+ER9D9D3/B8opdoDduCrn/w8VSmVrZTKTkt7t1HFbbYISkoO1247neXYbBEX+I1LZ4YMM/Rg1Iz2EaEcKztau3388FFCIxr+Apieg/uw5z+7GtzfGEY8T5Jxod8Pw1lSce73SyqIigprcEx1tYuTJ05htQYTGBiA1RoCQPeEzjgcURTk131YuSUyLt6n/q/FT5WcOkN08Lm1Eezt2uD8rtJ9zHdnyCwop7pG48CJ7/n22CkuD23b5EwjnifJ0G99ybiw1jBRzwCClFJ3Aiil/IEXgFc0TTt9/kBN09I0TeunaVq/1NQJjSres2c8+fkHKSoqobKyivT0LBIT+3u0ATNkmKEHo2Z06hZL+cEyKkrKqa6qZudn2/jZgB5uYw4Xl9b++aute4joFNnkPDDmeZKMhiX0uIKCghIOHCilqrKatWs2M2RoH7cxQ4b2YeUHGwFYv24r/a/rjlKKiorjuFw1ABwoKqWwwElMTJRPMi7GCNfip3aVnuDy0LbEhFxGgJ8iuWskGQXuf9W+Pr+c/tFWAMIus9A5NIii49/rpgfJ0HeGGXowcobpb33RNE1TSqUAC5VSTwKRwLuapv3JE/UtFn+eemoKkyfPxOWqYdy4YcTHx3mitKkyzNCDUTP8/f0Zfd843nriVbSaGvrcNADb5dF88vfVdIp3cNX1Pdm8ciP7t3+Nn8WftsFt+eUff6OrHiTDtxkWiz/Tn5jI7+6Zh6umhrEpg+kaH8PCBe/RPaEzQxP7kjJuMNMfW0xy0lRCrcHMe/5+AHKy97JowXIsFn+Un2LGzEmEWus+x98SGd4+T77IcGnw9KY83ry5B/5K8d5XJeQdOcVD/eLYVXaCzIIKNhYd4RcxYay5rS+uGnhu8zccPVOtmx4kQ98ZZujByBnKk0/MG4FS6gbg30CKpmnbGh75des6McIU3vv2G6/W/2XnK7xaX+jLGVfdh46NqI1/w7d6GUn8a83/foML2Xev3av1hRAXcmW9y62Z/hP1n9I07T+AZ/8TSgghhBBCCA9rDfeoCyGEEEIIYTgyURdCCCGEEEKHZKIuhBBCCCGEDslEXQghhBBCCB1qdau+NJ6s+iKMx9urdJhl9QzROIdOfXXxQc0UHdTN6xktsXrNmZqjXs/wtsTll3k9I/tXNq9nCGFM9a/6Ip+oCyGEEEIIoUMyURdCCCGEEEKHZKIuhBBCCCGEDrWKibpSyqWU+lIptVsp9ZFSyurJ+llZOSQlTWH48FTS0pZ5srSpMszQg5EyNm3cweibHyE5aSpvvL6yzv7KyioenbqA5KSp3D5hJsXFZQAUF5dxbe9JjE+ZzviU6Twz602f9SAZ+s3Y8vle7hj7HLeP+TP/fDOzzv4dOfu559cvkdhvGhvW72h2HhjzffGfTXsYN2o2KSNn8daSdXX2b8vO47fjn2XA1Q+SsW57k3pqiYzr7WEsv7kvK5L7MfGqmHrHDHN0YOnIvrw7sg9zrm/+swdGfF+YNcMMPRg1o1VM1IHTmqZdo2laD6AC+L2nCrtcLmbPXsySJbNIT1/IqlVZ5OUVeqq8aTLM0IORMlyuGubOeZtXX5vGBx/NY83qzezPK3Yb8/7yDbRv3470j1/kjokjmP/CO7X7Yhw2lq2Yy7IVc3ly1l0+6UEy9JvhctXw8rMreO6Vyby9/FEy124nf7/719tHRYfx+NMTGDaid3MP/4dM470vXK4a5s1Zysuv3sfSlTNYtzqHb/Yfchtjjw5j5pw7SLq53yX10pIZfgoe69eFBz/LZfyaHJJiI+ncPshtjCP4MiZ1d3D3JzuYsGYbL2z7pklZPzLi+8KsGWbowcgZrWWifr4vgE6eKrZz5z7i4qJxOOwEBgaQnDyIjIwtnipvmgwz9GCkjN279hMbayPGEUVAoIURIwfwaWaO25gNmdsYM3YgAMNv6s+Wzbl4ahUoo5wnyWiavbsL6eSIoGNMBAEBFhKTruHzDbluY6I7htPlyo4ov3oXMrhkRnxf5O7KxxHbgRhHBwICLAwf2YfPMne6jenYKYL4bp2afJ5aIiMhPISiE99T/N33VNdorCssY3CncLcxKV3sLN13kBNV1QAcOVPVpKwfGfF9YdYMM/Rg5IxWNVFXSvkDNwJ1/76ziZzOcuz2DrXbNlsETme5p8qbJsMMPRgpw+k8gs1+7v9IbfZwSkuPNDjGYvEnOCSIo0dPAmf/mv+2W59g0p1zyMne65MeJEO/GWWlx4i0WWu3I21Wysq8uwSiEd8XZaXHsNnDzjvmMMpKPXueWiIjqm0bnKfO1G6Xnq4kqm0btzGxIW2JC2nLGzf24m/Drub6846pKYz4vjBrhhl6MHKGpbkHZRBtlVJfcvaT9P8C6317OELoV2SklXUZ87FaQ9iT+y0PPfASK1Y+S3Bw0MV/WQiTkvfFhfkrhSOkLamZu7AFBZJ249X8ak0OJ6tcvj40IQyttXyiflrTtGuAOEDRwD3qSqlUpVS2Uio7Le3dRhW22SIoKTlcu+10lmOzRTT/iE2WYYYejJRhs4XhLKk4V6OkgqiosAbHVFe7OHniFFZrMIGBAVitIQB0T+iMwxFFQb77/cct0YNk6DcjMiqUMufR2u0y51EiI737ZVhGfF9ERoXiLDn3ib3TeYTIKM+ep5bIKD19BlvQuU/Qo9oGUnr6zE/GVJJVXIFL0zj43RkKT5wmNqRtkzON+L4wa4YZejByRmuZqAOgadop4EHgj0qpOn+boGlamqZp/TRN65eaOqFRNXv2jCc//yBFRSVUVlaRnp5FYmJ/jx63GTLM0IORMhJ6XEFBQQkHDpRSVVnN2jWbGTK0j9uYIUP7sPKDjQCsX7eV/td1RylFRcVxXK4aAA4UlVJY4CQmJqrFe5AM/WZ0S3BwoPAwh4rLqaqqJvPjL7lhSIIHj7guI74vuveIo7CwjOIDh6mqqmb9mm0MGtrrko75YloiY0/FCRwhl9GxXRssfoqbYiPJKq5wG7PhQDl9f/gPhNBAC7EhbSk++X2TM434vjBrhhl6MHKG8tTDY3qmlDqpaVrwedsfAUs1TftHw7/1daNPzGefZTN37uu4XDWMGzeM3/2ucZP8S2GGDDP0oPeM878qfeNnXzLv2f/DVVPD2JTBpE65hYUL3qN7QmeGJvblzJlKpj+2mL3/zSfUGsy85+8nxhHF+nVbWbRgORaLP8pPcd/942onM238G/9JnZ7Pk2Q0LuPQqa8a3Ld543955fkPqanRGHnLtdwxeRhvLlpLt+4Ofj4kgb25hcyY+jYnj58isE0A4REhvLX80Tp1ooMav4yfXt8XAGdqjtab/XlWLi8+9x4ul8aYlAHcde8IFr+yiqsSYhk8tBe5uwqY9vDrHD9+ijaBFsI7tGfphzMafU48mZG4/LIGM34eHcbU3lfg76dY+Y2TN/cUcW+POP5bcYKsg2cn7X+4pjPXR4dTo2m8uaeIdYVldepk/8rW6L70+r5ojRlm6EH/GVfW+7R3q5ioN03jJ+pC6MX5ExJvuJSJujC+C03UPeVSJupN5e33BTQ8UTeSC03UPeVSJupCtC71T9Rb1a0vQgghhBBCGIVM1IUQQgghhNAhmagLIYQQQgihQzJRF0IIIYQQQodkoi6EEEIIIYQOyaovDZJVX4TwBbOs0NE+IM7rGd7WEtfCLCsJHa8q8HqGGV5Tnf+41+sZ377wM69nCOF5suqLEEIIIYQQhiETdSGEEEIIIXRIJupCCCGEEELokEzUhRBCCCGE0KFWM1FXSj2hlMpVSu1USn2plLrOU7WzsnJISprC8OGppKUt81RZ02WYoQfJaNn6mzbuYPTNj5CcNJU3Xl9ZZ39lZRWPTl1ActJUbp8wk+LiMgCKi8u4tvckxqdMZ3zKdJ6Z9Waj8v6zaQ/jRs0mZeQs3lqyrs7+bdl5/Hb8swy4+kEy1m1vUk/1McL1bulr4Y0efJFh1teUp+sP6hZJxmOJfPq/NzIlsWud/TPGJJA+dTDpUweT+XgiO+aMbHYmGPM15YsMM/Rg1IxWMVFXSl0PjAL6aJrWCxgGFHmitsvlYvbsxSxZMov09IWsWpVFXl6hJ0qbKsMMPUhGy9Z3uWqYO+dtXn1tGh98NI81qzezP6/Ybcz7yzfQvn070j9+kTsmjmD+C+/U7otx2Fi2Yi7LVszlyVl3NSpv3pylvPzqfSxdOYN1q3P4Zv8htzH26DBmzrmDpJv7XVIvF87V//Vu6WvhjR58kWHW15Sn6/spmH1rL/7n9c3cNC+TMb070dUW7DZmzspckl/8jOQXP+PtTd+ydtehBqo1nhFfU77IMEMPRs5oFRN1IBo4rGnaGQBN0w5rmnbQE4V37txHXFw0DoedwMAAkpMHkZGxxROlTZVhhh4ko2Xr7961n9hYGzGOKAICLYwYOYBPM3PcxmzI3MaYsQMBGH5Tf7ZszqWpS87m7srHEduBGEcHAgIsDB/Zh88yd7qN6dgpgvhunVB+9a6i1SRGuN4tfS280YMvMsz6mvJ0/atjwygo/46iilNUuTQ+2l7M8AR7g+NH9+7ER9uLG9zfWEZ8Tfkiwww9GDmjtUzU1wEOpdTXSqlFSqnBnirsdJZjt3eo3bbZInA6yz1V3jQZZuhBMlq2vtN5BJs9/FwNezilpUcaHGOx+BMcEsTRoyeBs7dc3HbrE0y6cw452Rdfu7ms9Bg2e9h5xxxGWan31xE3wvVu6WvhjR58kWHW15Sn69tDL+PQ0dO12yXHvsce2rbesZ3C2uIID+I/+8qanPcjI76mfJFhhh6MnNEqJuqapp0E+gKpQBnwrlLqf346TimVqpTKVkplp6W928JHKYTwlMhIK+sy5rP0/T/x6GO/4fFpizh58pSvD6tVkmshPGnUNZ1Ys/MgNfKVhKKVaBUTdQBN01yapm3QNG0mcD8wrp4xaZqm9dM0rV9q6oRG1bXZIigpOVy77XSWY7NFeOqwTZNhhh4ko2Xr22xhOEsqztUoqSAqKqzBMdXVLk6eOIXVGkxgYABWawgA3RM643BEUZBfcsG8yKhQnCXnPiV2Oo8QGeX9b800wvVu6WvhjR58kWHW15Sn65cc+55o67lP0O2hl1Fy7HS9Y0f37sRKD9z2AsZ8Tfkiwww9GDmjVUzUlVLdlFLx5/3oGsAj3/fcs2c8+fkHKSoqobKyivT0LBIT+3uitKkyzNCDZLRs/YQeV1BQUMKBA6VUVVazds1mhgzt4zZmyNA+rPxgIwDr122l/3XdUUpRUXEcl6sGgANFpRQWOImJibpgXvcecRQWllF84DBVVdWsX7ONQUN7XdIxN4URrndLXwtv9OCLDLO+pjxdf2fRUS7v0I6Y8CAC/BWje3fik1xnnXFXRAUT2jaAbflH6qly6Yz4mvJFhhl6MHKGxUPHpnfBwAKllBWoBvI4extMs1ks/jz11BQmT56Jy1XDuHHDiI+P80RpU2WYoQfJaNn6Fos/05+YyO/umYerpoaxKYPpGh/DwgXv0T2hM0MT+5IybjDTH1tMctJUQq3BzHv+fgBysveyaMFyLBZ/lJ9ixsxJhFqDL5o3bfptPHjvQlwujTEpA+jSNZrFr6ziqoRYBg/tRe6uAqY9/DrHj59i04ZdvLYwnaUfzmjyefoxV+/Xu6WvhTd68EWGWV9Tnq7vqtGY+f4u/p46AD+lWLa1kH3OE/whqRu7DhytnbSPvqYTH33pmU/TwZivKV9kmKEHI2coTz6Vby5fy4kRwgfOuLz/sN2ZmqNez2gf4Nn/A/CFlrgWbfy9fytISzhe5ZG/pL0gM7ymOv+xaQ8TX4pvX/iZ1zOE8Lwr6136qVXc+iKEEEIIIYTRyERdCCGEEEIIHZKJuhBCCCGEEDokE3UhhBBCCCF0SB4mbZA8TCqEEEIYTZc7t3s9Y//fe3s9oyUe5vY2szwsfujUV17PiA4aLQ+TCiGEEEIIYRQyURdCCCGEEEKHZKIuhBBCCCGEDslEXQghhBBCCB1qFRN1pVSEUurLH/4pUUoVn7cd2Nz6WVk5JCVNYfjwVNLSlnnikE2ZYYYeJEM/9SVDXxlm6EEy9FPfGxmDetpZ/9xIMv9yM/eOqvvtpdERQfzz8SGsfOYm0uckMaRXdLMzwTN9bNq4g9E3P0Jy0lTeeH1lnf2VlVU8OnUByUlTuX3CTIqLywAoLi7j2t6TGJ8ynfEp03lm1ps+qd8YRnxNbfl8L3eMfY7bx/yZf76ZWWf/jpz93PPrl0jsN40N63c0KaNVTNQ1TSvXNO0aTdOuARYDL/24rWlaZXNqu1wuZs9ezJIls0hPX8iqVVnk5RV65LjNlGGGHiRDP/UlQ18ZZuhBMvRT3xsZfkox686+3PV8FkmPr2X0gDi6dmzvNub+Md1J31rEmCfX8dCiL3h6Yt/mtuGRPlyuGubOeZtXX5vGBx/NY83qzezPK3Yb8/7yDbRv3470j1/kjokjmP/CO7X7Yhw2lq2Yy7IVc3ly1l0tXr9xPRrvNeVy1fDysyt47pXJvL38UTLXbid/f4nbmKjoMB5/egLDRjR9laBWMVH3pp079xEXF43DYScwMIDk5EFkZGyRjBauLxn6yjBDD5Khn/qSoa8MI/ZwdZdwCkpPUFT2HVWuGlZtLmRYn05uYzQNgtsGABASFEDp0dPN6gE808fuXfuJjbUR44giINDCiJED+DQzx23MhsxtjBk7EIDhN/Vny+ZcGrv8trfrN4YRX1N7dxfSyRFBx5gIAgIsJCZdw+cbct3GRHcMp8uVHVF+9a682CgyUW8mp7Mcu71D7bbNFoHTWS4ZLVxfMvSVYYYeJEM/9SVDXxlG7MEW1pZD5ecm3iUVp7CFtXUb8/KK3Yy9IY5N80fzxh8H8fQ/tjU570ee6MPpPILNHn6uhj2c0tIjDY6xWPwJDgni6NGTwNnbU2679Qkm3TmHnOy9LV6/cT0a7zVVVnqMSJu1djvSZqWszPNr38tE/TxKqVSlVLZSKjst7V1fH44QQgghWsjo62NZvjGfXzz8EXe/kMXz916HavoHoboQGWllXcZ8lr7/Jx597Dc8Pm0RJ0+eMkx9IRN1N5qmpWma1k/TtH6pqRMa9Ts2WwQlJYdrt53Ocmy2CI8elxkyzNCDZOinvmToK8MMPUiGfup7I8N55DTREec+QbeHB+E84n5ry/hBV7B669l7lrfnldMmwJ/w4DZNzgTP9GGzheEsqThXo6SCqKiwBsdUV7s4eeIUVmswgYEBWK0hAHRP6IzDEUVBfkmDv+uN+o3r0XivqcioUMqcR2u3y5xHiYz0/DexykS9mXr2jCc//yBFRSVUVlaRnp5FYmJ/yWjh+pKhrwwz9CAZ+qkvGfrKMGIPO7+p4HJbCDEd2hHg78eoAbFkbHd/YPJQ+Slu6G4DoEvHENoE+FN+4ozP+0jocQUFBSUcOFBKVWU1a9dsZsjQPm5jhgztw8oPNgKwft1W+l/XHaUUFRXHcblqADhQVEphgZOYmKgWrd8YRnxNdUtwcKDwMIeKy6mqqibz4y+5YUiCB4/4LIvHK7YyFos/Tz01hcmTZ+Jy1TBu3DDi4+Mko4XrS4a+MszQg2Top75k6CvDiD24ajSe/vs23po2GD+leC/rG/YVH+fhW3uw69sKMrYfZO6/v2TuXdcyaUQ3NE1j2uvNf5jRE31YLP5Mf2Iiv7tnHq6aGsamDKZrfAwLF7xH94TODE3sS8q4wUx/bDHJSVMJtQYz7/n7AcjJ3suiBcuxWPxRfooZMycRag1u0fotdZ5aOsNi8eehx1J49L7XqanRGHnLtXTuYufNRWvp1t3Bz4cksDe3kBlT3+bk8VN8kbWHtxav463lj15SjvLkU7vm8rWcGCGEEMJguty53esZ+//e9OX2GuuMy/MPJra0Nv6evxXEFw6d+srrGdFBo+t9IkJufRFCCCGEEEKHZKIuhBBCCCGEDslEXQghhBBCCB2SiboQQgghhBA6JA+TNuB41Xqvn5j2AZ59olkIIYQQ3tcSD6zu+dsVXq3fEg96tsQDsebp41p5mFQIIYQQQgijkIm6EEIIIYQQOiQTdSGEEEIIIXRIJupCCCGEEELoUKuYqCulLldK7f7Jz2YppR651Fr/2bSHcaNmkzJyFm8tWVdn/7bsPH47/lkGXP0gGes897BJVlYOSUlTGD48lbS0ZR6r25IZZuhBMvRTXzL0lWGGHiRDP/WNmjGop531z40k8y83c++on9XZHx0RxD8fH8LKZ24ifU4SQ3pFN6rupo07GH3zIyQnTeWN11fW2V9ZWcWjUxeQnDSV2yfMpLi4DIDi4jKu7T2J8SnTGZ8ynWdmvdmkvjxxnnzdg1H7aBUTdU9xuWqYN2cpL796H0tXzmDd6hy+2X/IbYw9OoyZc+4g6eZ+Hsx1MXv2YpYsmUV6+kJWrcoiL6/QY/VbIsMMPUiGfupLhr4yzNCDZOinvlEz/JRi1p19uev5LJIeX8voAXF07djebcz9Y7qTvrWIMU+u46FFX/D0xL6NOM4a5s55m1dfm8YHH81jzerN7M8rdhvz/vINtG/fjvSPX+SOiSOY/8I7tftiHDaWrZjLshVzeXLWXZfclyfOk697MHIfMlG/BLm78nHEdiDG0YGAAAvDR/bhs8ydbmM6doogvlsnlF+9q+w0yc6d+4iLi8bhsBMYGEBy8iAyMrZ4rH5LZJihB8nQT33J0FeGGXqQDP3UN2rG1V3CKSg9QVHZd1S5ali1uZBhfTq5jdE0CG4bAEBIUAClR09ftO7uXfuJjbUR44giINDCiJED+DQzx23MhsxtjBk7EIDhN/Vny+ZcPLX8tifOk697MHIfMlG/BGWlx7DZw2q3bbYwykq9v7am01mO3d7hvNwInM5yQ2WYoQfJ0E99ydBXhhl6kAz91Ddqhi2sLYfKz028SypOYQtr6zbm5RW7GXtDHJvmj+aNPw7i6X9sa8RxHsFmDz+XYw+ntPRIg2MsFn+CQ4I4evQkcPaWi9tufYJJd84hJ3vvJfflifPk6x6M3Iflko7QuBr6Txn5tichhBBCtIjR18eyfGM+b6z9it5dI3j+3usYOX0t3vruychIK+sy5mO1hrAn91seeuAlVqx8luDgIO8EeoEZeoCm99FaPlEvB8J+8rNw4PD5P1BKpSqlspVS2X9bkl6nSGRUKM6Sc//l5HQeITLK+9+IZbNFUFJy7lCdznJstghDZZihB8nQT33J0FeGGXqQDP3UN2qG88hpoiPOfYJuDw/CecT91pbxg65g9daz90VvzyunTYA/4cFtLnKcYThLKs7llFQQFRXW4JjqahcnT5zCag0mMDAAqzUEgO4JnXE4oijIL7mkvjxxnnzdg5H7aBUTdU3TTgKHlFKJAEqpcGAEsOkn49I0TeunaVq/SZOT69Tp3iOOwsIyig8cpqqqmvVrtjFoaC+vH3/PnvHk5x+kqKiEysoq0tOzSEzsb6gMM/QgGfqpLxn6yjBDD5Khn/pGzdj5TQWX20KI6dCOAH8/Rg2IJWO7+4OGh8pPcUN3GwBdOobQJsCf8hNnLlg3occVFBSUcOBAKVWV1axds5khQ/u4jRkytA8rP9gIwPp1W+l/XXeUUlRUHMflqgHgQFEphQVOYmKiLqkvT5wnX/dg5D6UJ2/U1zOlVHdgIec+Wf+Lpmn/bGj88ar19Z6Yz7NyefG593C5NMakDOCue0ew+JVVXJUQy+ChvcjdVcC0h1/n+PFTtAm0EN6hPUs/nFFvRvuAuEYf/2efZTN37uu4XDWMGzeM3/1uQqN/Vy8ZZuhBMvRTXzL0lWGGHiRDP/X1ntHlzvqXXx7SK5oZv+2Nn1K8l/UNiz76Lw/f2oNd31aQsf0gXTu2Z+5d1xJ0mQVN03ju3R1s2u2st9aev11R++eNn33JvGf/D1dNDWNTBpM65RYWLniP7gmdGZrYlzNnKpn+2GL2/jefUGsw856/nxhHFOvXbWXRguVYLP4oP8V994+rnVi28W/8HQFNPU9nXOee4/NGD+bq49p6VyFpNRP1S9XQRN2TLmWiLoQQQgh9aGii7knnT9S94VImuE11/gTXW8zTR/0T9VZx64sQQgghhBBGIxN1IYQQQgghdEgm6kIIIYQQQuiQTNSFEEIIIYTQIZmoCyGEEEIIoUOy6kuDvpYTI4QQQog6WmIVkB5Dtnm1/r6NQ71aH2TVl0shq74IIYQQQghhIDJRF0IIIYQQQodkoi6EEEIIIYQOmX6irpT6VCmV9JOfPayUetVTGVlZOSQlTWH48FTS0pZ5qqzpMszQg2Top75k6CvDDD1Ihn7qGylj08YdjL75EZKTpvLG6yvr7K+srOLRqQtITprK7RNmUlxcBkBxcRnX9p7E+JTpjE+ZzjOz3mxU3sD+Dj7+5wQ++fevSP3NNXX2R0cF84+XR/HhG+P46K1fMniAo0l9nc+I58ksfZh+og78G/jVT372qx9+3mwul4vZsxezZMks0tMXsmpVFnl5hZ4obaoMM/QgGfqpLxn6yjBDD5Khn/pGynC5apg7521efW0aH3w0jzWrN7M/r9htzPvLN9C+fTvSP36ROyaOYP4L79Tui3HYWLZiLstWzOXJWXddNM/PTzFr6s+Z/MhqRt6xlFHDutL1cqvbmPsm9mHNp99wy93L+cOsT5g1deAl9VS3R+OdJzP10Rom6u8ByUqpQACl1OVAR2CjJ4rv3LmPuLhoHA47gYEBJCcPIiNjiydKmyrDDD1Ihn7qS4a+MszQg2Top76RMnbv2k9srI0YRxQBgRZGjBzAp5k5bmM2ZG5jzNizk+XhN/Vny+ZcmrriXq+roigoPk7RoRNUVdeQnpHHjb+43H2QphEcFABAcLs2lB7+rklZPzLieTJTH6afqGuaVgFsBUb+8KNfAUs1D119p7Mcu71D7bbNFoHTWe6J0qbKMEMPkqGf+pKhrwwz9CAZ+qlvpAyn8wg2e/i5GvZwSkuPNDjGYvEnOCSIo0dPAmdvh7jt1ieYdOcccrL3XjTPHhnEodKTtdslZd9h69DObcxf/5bDmJvi2bj8Nyz5y0hmz//8knr6KSOeJzP1YbmkIzSuH29/+fCHf9/t28MRQgghRGsWGWllXcZ8rNYQ9uR+y0MPvMSKlc8SHBzUrLqjhnXh/TVf8+a7O7kmwcbzTyZy851LMerX5njrPLW0pvZh+k/Uf/AhcKNSqg8QpGlaTn2DlFKpSqn/396dh0dVnm8c/z5kQSBAAiQTIAFRohUQFZHigiyCiFgrpYjaqrUipdaV1uWHVtBSpbigVVwQrXZzoYgiiKAgBRSxgLKEUgVlJwuERURNmLy/P2YSJglLIGfIyeT+XFeu5My8ee7zzEwyb868c7LYzBZPmPBapQoHAk3JydlWup2bu51AoKkX+xxTGbHQgzL8U18Z/sqIhR6U4Z/6NSkjEEghN6dgf42cAtLSUg46Zt++IHu+3ktychKJiQkkJzcEoF37NmRmprF+Xc4h83Ly99I8Lal0Oz21AbnllrYM6v8D3vlgLQCfZedSNzGOlMbHHVFfZfe/5t1OsdRHrZioO+f2AB8AL3KIN5E65yY45zo75zoPHTq4UrVPPTWLdeu2sHFjDoWFRUyfPo9evbp4s+MxlBELPSjDP/WV4a+MWOhBGf6pX5My2nc4gfXrc9i0KY+iwn28O+NjevTsVGZMj56dmPpm6G1x7836hC4/bIeZUVCwm2CwGIBNG/PYsD6XjIy0Q+atWJ3H8RmNyWjekIT4OvS/oC2zF6wvM2ZL7h7OObMlACe2TiYxMY6Cnd8dUV+RauLtFEt91JalLxCaoE+h4hlgqiQ+Po777hvGkCEjCQaLGTiwN1lZrb2MiImMWOhBGf6prwx/ZcRCD8rwT/2alBEfH8eIe67l1zeMJVhczGUDutM2K4PxT/6Ldu3b0LPXmQwY2J0Rdz1L/77DaZycxNhHbgJgyeLVPP3kZOLj47A6xr0jr6NxctIh84JBx/3jFvDioxcTV8f41/T/sWbdDm69vjMrVucz58P1jBm/kNF3ducXl3cE57j7wblHexOV9ljTbqdY6sO8fEdtbPlcN4yIiIhU8H1wV9QzOvRYGtX6X8zvGdX6cGxup7pxjaOecWz6OMsOdHmtWPoiIiIiIlLTaKIuIiIiIuJDmqiLiIiIiPiQJuoiIiIiIj6kibqIiIiIiA/prC8HpbO+iIiISGzK6vZB1DPmzWwR9Yzm9U+OesaxcZLO+iIiIiIiUlNooi4iIiIi4kOaqIuIiIiI+FDMTtTNbJyZ3RaxPdPMJkZsP2pmw73ImjdvCX37DqNPn6FMmDDJi5IxmRELPSjDP/WV4a+MWOhBGf6pr4zqzejWJZOZ/xjM+69cwdCfnV7h+uZpSfztiUt464WBvP3ST+neNfOI6i/6cDVXX/Ynrrr0If7x4pwK1y9bspYbrhxHr853Mve9ZUfbRgU18b6I2Yk68CFwDoCZ1QGaAe0jrj8H+KiqIcFgkAceeJaJE0cxffp4pk2bx5o1G6paNuYyYqEHZfinvjL8lRELPSjDP/WVUb0ZdeoYo4afy5DfvUO/q1/nkt5taXt8cpkxN17biRkffMmPr5/M7aPeZ9Twbkewv8U8MWYKf3pqCC9PvoM5737KurU5ZcakNU/h7vsH0/uiM466j4q5Ne++gNieqH8EnB3+uj2wEvjazFLMrC5wCrC0qiHLl39B69bNycxMJzExgf79z2f27EVVLRtzGbHQgzL8U18Z/sqIhR6U4Z/6yqjejI6npLF+8242bv2aon3FTJ+9hgvOO77sIOdIqp8AQFKDuuRt+6bS9Vev3EDLzKa0yGhKQkI8vfqezodzs8uMad6iCSee1AKrc8AToRyVmnhfQAxP1J1zW4B9ZtaK0NHzhcAiQpP3zsAK51xhVXNyc7eTnt6sdDsQaEpu7vaqlo25jFjoQRn+qa8Mf2XEQg/K8E99ZVRvRnpqfbbm7Sndzsn/hkCzBmXG/PkvS7j0wizmT/4ZEx/uxwOPf1jp+vl5u0gNJJdupwaSyc/fddT7W1k18b6AGJ6oh31EaJJeMlFfGLFd+UeViIiIiABwSe8TeWPG53Qb+A+G3DGDR37fC/Pu4LdEiPWJesk69VMJLX35mNAR9QOuTzezoWa22MwWT5jwWqUCAoGm5ORsK93Ozd1OINDUg12PrYxY6EEZ/qmvDH9lxEIPyvBPfWVUb0ZO/l6apyWVbqenNiC33NKWQf1/wDsfrAXgs+xc6ibGkdL4uErVT01rTH7uztLt/NydpKY2Pur9rayaeF9A7E/UPwIuAQqcc0HnXAGQTGiyXmGi7pyb4Jzr7JzrPHTo4EoFnHpqFuvWbWHjxhwKC4uYPn0evXp18bCF2MiIhR6U4Z/6yvBXRiz0oAz/1FdG9WasWJ3H8RmNyWjekIT4OvS/oC2zF6wvM2ZL7h7OObMlACe2TiYxMY6Cnd9Vqv7J7TPZtGEbWzdvp6hoH3NmfsY5Pdof/hurqCbeFwDxHu2bX60gdLaXf5a7LMk5t+3A33Jk4uPjuO++YQwZMpJgsJiBA3uTldXai9IxlRELPSjDP/WV4a+MWOhBGf6pr4zqzQgGHfePW8CLj15MXB3jX9P/x5p1O7j1+s6sWJ3PnA/XM2b8Qkbf2Z1fXN4RnOPuB+ce0f7eetcA7rjxeYqLHf1+fBZtTkznxaff5eR2mZzboz2rszdw7/CX2bN7LwvnreKlZ2fx0uQ7jrqnktyadl8AmHPOo92LNZ/rhhEREZGYlNXtg6hnzJvZIuoZzeufHPWMY+OkA67yj/WlLyIiIiIiNZIm6iIiIiIiPqSJuoiIiIiID2miLiIiIiLiQ5qoi4iIiIj4UKyfnlFERETEU98Ho/8v7+vGRfefAB2LM7J0u6Ig6hlrpkY9olrpiLqIiIiIiA9poi4iIiIi4kOaqIuIiIiI+FDMT9TNLNPMvjKzJuHtlPD28V5lzJu3hL59h9Gnz1AmTJjkVdmYy4iFHpThn/rK8FdGLPSgDP/Ur0kZC+Yv40cX/47+fYfzwvMVF0wXFhZxx/An6d93OFcNHsnmzfkAbN6cz1lnXMegASMYNGAEfxj1YrX2EWnRh6u5+rI/cdWlD/GPF+dUuH7ZkrXccOU4enW+k7nvLTvi+ud3asGspy9j9nMD+NXADhWub96sAX8ffSFTH7+EaX/+Ed3PbHlUfZRXUx5TkWJ+ou6c2wg8A4wJXzQGmOCcW+dF/WAwyAMPPMvEiaOYPn0806bNY82aDV6UjqmMWOhBGf6prwx/ZcRCD8rwT/2alBEMFvPg6Jd55rk7efPtscx452PWrtlcZswbk+fSqFEDps98jKuvvYjHH3219LqMzACTpjzIpCkP8vtRv6y2Psr39MSYKfzpqSG8PPkO5rz7KevW5pQZk9Y8hbvvH0zvi8444vp16hijftWV6+9/n4t+8xaXnN+Gtpll3zj7m8EdeefD9Vx62zRue3ge9w/retT9lKgpj6nyYn6iHjYO6GpmtwHnAY94VXj58i9o3bo5mZnpJCYm0L//+cyevcir8jGTEQs9KMM/9ZXhr4xY6EEZ/qlfkzJWrlhLq1YBMjLTSEiM56J+XflgzpIyY+bOWcqll3UDoM+FXVj0cTbOOV/1EWn1yg20zGxKi4ymJCTE06vv6Xw4N7vMmOYtmnDiSS2wOnbE9U/Lasb6rbvZmLuHon3FTJ//Fb1/mFlmjHOOpHoJADSsn0hewd6j7qdETXlMlVcrJurOuSLgDkIT9tvC257Izd1Oenqz0u1AoCm5udu9Kh8zGbHQgzL8U18Z/sqIhR6U4Z/6NSkjN3cHgfQm+2ukNyEvb8dBx8THx5HUsD47d+4BQstfLv/JPVx3zWiWLF5dbX1Eys/bRWoguXQ7NZBMfr53p6MMNK3P1m3flG7nbNtLoGmDMmP+/MoyftzjBBa8+FMmjryA+ydUfUJdUx5T5dWm86j3A7YCHYD3qnlfREREpBZLTU1m1uzHSU5uyKrsr7j15nFMmTqGpKT61b1r1e5H57fhjTlreOHNVZxxciqP3t6Nfje/hYcvRNQYteKIupmdDvQBugK3m1nzg4wbamaLzWzxhAmvVap2INCUnJxtpdu5udsJBJpWfadjLCMWelCGf+orw18ZsdCDMvxTvyZlBAIp5Obs/6c+uTkFpKWlHHTMvn1B9ny9l+TkJBITE0hObghAu/ZtyMxMY/26smvBj1UfkVLTGpOfu7N0Oz93J6mp3v3zpdzte2nebP8R9PRm9cnd/k2ZMYP6ZPHOgnUAfPq/fBIT40hpdFyVcmvKY6q8mJ+om5kRejPpbc65DcDDHGSNunNugnOus3Ou89ChgytV/9RTs1i3bgsbN+ZQWFjE9Onz6NWri2f7HysZsdCDMvxTXxn+yoiFHpThn/o1KaN9hxNYvz6HTZvyKCrcx7szPqZHz05lxvTo2Ympb84H4L1Zn9Dlh+0wMwoKdhMMFgOwaWMeG9bnkpGRVi19RDq5fSabNmxj6+btFBXtY87MzzinR/ujrlfe8i+20bpFIzICSSTE16F/tzbMXrSpzJgt+Xs4u2PomOqJGY2pmxBHwa7vqpRbUx5T5dWGpS83ABuccyXLXZ4GrjOz7s65f1e1eHx8HPfdN4whQ0YSDBYzcGBvsrJaV7VszGXEQg/K8E99ZfgrIxZ6UIZ/6tekjPj4OEbccy2/vmEsweJiLhvQnbZZGYx/8l+0a9+Gnr3OZMDA7oy461n69x1O4+Qkxj5yEwBLFq/m6ScnEx8fh9Ux7h15HY2Tk6qlj/L1br1rAHfc+DzFxY5+Pz6LNiem8+LT73Jyu0zO7dGe1dkbuHf4y+zZvZeF81bx0rOzeGnyHZWqHyx23P/cIv4yqjdxdeow6f0v+GLjTm696nRWrtnO7E828tCLi/njTedw3Y/b4Rzc9cSHR91PZF814TFVnnn5zuPY8rluGBEREang+6B3b648mLpx3i03OZCte/8X1foA3a4oOPygKloz9eyoZxwbJx3wFDoxv/RFRERERKQm0kRdRERERMSHNFEXEREREfEhTdRFRERERHxIE3URERERER+qDadnFBEREfFMtM/Iciy8+mXV/oFQZWRPaRf1jJ7v5Ec9460+e6Oe0SjhpANeriPqIiIiIiI+pIm6iIiIiIgPaaIuIiIiIuJDtWKibiELzKxfxGWDzOxdL+rPm7eEvn2H0afPUCZMmORFyZjMiIUelOGf+srwV0Ys9KAM/9RXRvVmbPh0Fa/c/AD//M0oPn1jVoXrs2fO5/Xb/8ik3z7Em/c8RsHGrYetuWD+Mn508e/o33c4Lzw/tcL1hYVF3DH8Sfr3Hc5Vg0eyeXNo7fnmzfmcdcZ1DBowgkEDRvCHUS9WqoezmiXz8vmd+Hv3Tlx5QssK1/dtmcaUC7rw/Hmn8fx5p3FxRqBSdSN9tGAVAy95gAH9RvHSxIq309LFa/j5oDF0Pe0WZs/69IjrQy2ZqDvnHDAMeMzMjjOzJOBB4DdVrR0MBnnggWeZOHEU06ePZ9q0eaxZs6GqZWMuIxZ6UIZ/6ivDXxmx0IMy/FNfGdWbURwsZsHzr9P/nhsZ/Pi9rFmwpMJEPKtbZy4fdw+DHv0/Tr+sNwtfeuMw+1jMg6Nf5pnn7uTNt8cy452PWbtmc5kxb0yeS6NGDZg+8zGuvvYiHn/01dLrMjIDTJryIJOmPMjvR/3ysD3UAW5tfwJ3/yebX8z7lAtapNI6qV6FcR9s3cYNC5Zxw4JlvLMp97B1y/c0dvTrPPHMjbw+9V5mvbOEL9eWvZ3Sm6cwcvTV9L248xHVLt9LreCcWwm8DdwF3Af81Tm3tqp1ly//gtatm5OZmU5iYgL9+5/P7NmLqlo25jJioQdl+Ke+MvyVEQs9KMM/9ZVRvRl5a9bRKL0ZjdKbEZcQz4nndWLdf5aXGZNYf/+kt+i7QjA7ZM2VK9bSqlWAjMw0EhLjuahfVz6Ys6TMmLlzlnLpZd0A6HNhFxZ9nE3oOOuR+0FyQ7bs/Y6t337PPueYszWfcwNNjqrWwWSvWEdmq2ZkZDYjISGePv068e85ZW+nFi2bknVyS6zOoW+fQ6k1E/Ww+4GrgH7AWC8K5uZuJz29Wel2INCU3NztXpSOqYxY6EEZ/qmvDH9lxEIPyvBPfWVUb8Y3BbtIapZSup3UJIVvtu+qMG7ljH/zzxtH8fHf3uTcX/70MPu4g0D6/olyIL0JeXk7DjomPj6OpIb12blzDxBa/nL5T+7humtGs2Tx6sP20Oy4RPK+Kyzdzv+2kGZ161YYd356UyaedzqjzjiZ1OMSD1s3Un7eLgLp+2+nQCCF/LyKt1NV1arzqDvnvjGz14A9zrnvq3t/RERERGqiDv2606Ffd76Y/x+WTn6XXjdfE5Wc1NRkZs1+nOTkhqzK/opbbx7HlKljSEqqX6W6C/MKmLM1n6Jix48yA9zdMYvffpLt0V57p7YdUQcoDn9UYGZDzWyxmS2eMOG1ShULBJqSk7OtdDs3dzuBQFNPdjSWMmKhB2X4p74y/JURCz0owz/1lVG9GQ2aNGbPtv1Hu/cU7KBB04P/g6e2557Juk+WH/T60D6mkJtTsH8fcwpIS0s56Jh9+4Ls+XovyclJJCYmkJzcEIB27duQmZnG+nU5h8zb9l0haRFHyFPrJbLt+7LHZ3cX7aOoOLS0ZvrGXE5qnHTImuWlpjUmN2f/7ZSbu4PUNO//EVZtnKgflHNugnOus3Ou89Chgyv1PaeemsW6dVvYuDGHwsIipk+fR69eXTzdr1jIiIUelOGf+srwV0Ys9KAM/9RXRvVmpLVtza6t+ezO3UawaB9rFyzl+M4dy4zZuSWv9Ov1S7Jp3Dz1kDXbdziB9etz2LQpj6LCfbw742N69OxUZkyPnp2Y+uZ8AN6b9QldftgOM6OgYDfBYOj46qaNeWxYn0tGRtoh81bv+pqWDeqRXq8u8Wb0ap7KR7kFZcY0qZtQ+vU5gSZs2PPtIWuW165DazZsyGfzpm0UFe3jvRlLOb9nx8N/4xGqVUtfoiE+Po777hvGkCEjCQaLGTiwN1lZrZVxjOsrw18ZsdCDMvxTXxn+yoiFHpRxcHXi4jhvyOVM/8N4XLHj5F5dadKqOf95ZRqpbVtx/FkdWTljHpuXr6ZOfBx1G9Sn502HXvYSHx/HiHuu5dc3jCVYXMxlA7rTNiuD8U/+i3bt29Cz15kMGNidEXc9S/++w2mcnMTYR24CYMni1Tz95GTi4+OwOsa9I6+jcfKhj34XO/hz9peM7dKeOsCMTXms2/Mt12W14n+79vBRXgE/Ob4F56Y1Iegcu4v2MWb5F0d0O8XHx3HniMu55VfjCQYdlw7oyoltm/PsU9M4pX0ruvfsSPaK9dx52/Ps3r2XBXNX8Nz46bz+1r1HlGNH+47a2Pe5bhgRERGJSeNWro96xo2nJEc946KZhYcfVEVv9dkb9YxGCX0OeGoYLX0REREREfEhTdRFRERERHxIE3URERERER/SRF1ERERExIc0URcRERER8SGd9eWgdNYXERERqWhh3pGdyu9onJ2WFdX6u4uif9aXunWSo55xLJw76buoZyy+opvO+iIiIiIiUlNooi4iIiIi4kOaqIuIiIiI+FCtmKib2QAz+6zcR7GZ9fOi/rx5S+jbdxh9+gxlwoRJXpSMyYxY6EEZ/qmvDH9lxEIPyvBP/ZqasXzRf7n7qoe484o/Mu3vsytc/+6rcxnx8z9x77UP86dbn2FbTkGVM8H7Pj5asIqBlzzAgH6jeGnirArXL128hp8PGkPX025h9qxPK1Vzwfxl/Oji39G/73BeeH5qhesLC4u4Y/iT9O87nKsGj2Tz5nwANm/O56wzrmPQgBEMGjCCP4x6sVozIp2dnsLki89kSv/OXHtKxgHH9M5sxuv9zuS1fp0YffbJlaobqVZM1J1zU5xzp5d8AE8D84GZVa0dDAZ54IFnmThxFNOnj2fatHmsWbOhqmVjLiMWelCGf+orw18ZsdCDMvxTv6ZmFAeL+dtjbzD8kaE8+Le7WPT+UjZ/lVNmTOuTWjJy4u2MfvkOzurRkdefmVbVNjzvIxgsZuzo13nimRt5feq9zHpnCV+u3VpmTHrzFEaOvpq+F3eudM0HR7/MM8/dyZtvj2XGOx+zds3mMmPemDyXRo0aMH3mY1x97UU8/uirpddlZAaYNOVBJk15kN+P+mW1ZUSqY3BX5xO55d/ZDJqxhL6tUmnTqH6ZMZlJx3Fdu0yuf38Zg2cs5dGlXx62boWcI/6OGs7MTgLuA652zhVXtd7y5V/QunVzMjPTSUxMoH//85k9e1HVdzTGMmKhB2X4p74y/JURCz0owz/1a2rGl//dQKBlM9JaNCU+IZ4fXnAGny5YWWbMKZ2yqHtcIgAntm9NQd7OqrQAeN9H9op1ZLZqRkZmMxIS4unTrxP/nrO8zJgWLZuSdXJLrM4BT1RSwcoVa2nVKkBGZhoJifFc1K8rH8xZUmbM3DlLufSybgD0ubALiz7O5kjOTHgsMiK1b9KQjV9/x+ZvvmNfsWPWhny6t2xSZsyAE9N5/YstfF20D4Ad3xcdcU6tmqibWQLwT+C3zjlP/jTPzd1Oenqz0u1AoCm5udu9KB1TGbHQgzL8U18Z/sqIhR6U4Z/6NTVjR/4umqQll26npCazY9uug46fN30RHbuectR5JbzuIz9vF4H0lIh6KeTnHbyPyu3jDgLp+yexgfQm5OXtOOiY+Pg4khrWZ+fOPUBoacrlP7mH664ZzZLFq6stI1Javbrk7v2+dDvv20LS6tUtM6ZVw3q0bliPFy7oyF96n8bZEbdrZcUf8XfUbH8Asp1zr1X3joiIiEjt9NHMxXy1eiP/9+RN1b0rvpeamsys2Y+TnNyQVdlfcevN45gydQxJSfUP/83VnBFnRmbDegyds4JA/UQmXHAaV8xYwp6iYKVr1Joj6mbWAxgIHPSnwsyGmtliM1s8YULl5vKBQFNycraVbufmbicQaFq1nY3BjFjoQRn+qa8Mf2XEQg/K8E/9mpqRktq4zFKWHfk7SWnWuMK47MWf8/bf3ue2MdeTkFj146Ve95Ga1pjcnP1HonNzd5CaVrGPI9vHFHIj3jibm1NAWlrKQcfs2xdkz9d7SU5OIjExgeTkhgC0a9+GzMw01q8ru/b/WGVEyvv2ewL19x9BT6uXSN6335cbU8i8zQUEnWPLN9+z4etvadWw3iHrllcrJupmlgL8BbjGOff1wcY55yY45zo75zoPHTq4UrVPPTWLdeu2sHFjDoWFRUyfPo9evbp4tOexkxELPSjDP/WV4a+MWOhBGf6pX1Mz2vwgk9xN+eRv2c6+on0smv0pZ5zXocyY9Z9v4qWHJ3HrQ9fTKKVhVVsAvO+jXYfWbNiQz+ZN2ygq2sd7M5Zyfs+OVdrH9h1OYP36HDZtyqOocB/vzviYHj07lRnTo2cnpr45H4D3Zn1Clx+2w8woKNhNMBh6S+GmjXlsWJ9LRkZatWREWlXwNZkNj6NFg7rE1zEubJXKvM1lz+Izd9N2zgz/kdM4MZ5WDeuxec+R/ZfT2rL0ZRiQBjxjVuaNDw9VdRlMfHwc9903jCFDRhIMFjNwYG+yslpXpWRMZsRCD8rwT31l+CsjFnpQhn/q19SMuPg4fn77T3jktxMoLi6mW/8utGyTzhsTZ9DmB5mccV4HXnv6bb7/9nvG3/cyAE0DKdw25npf9REfH8edIy7nll+NJxh0XDqgKye2bc6zT03jlPat6N6zI9kr1nPnbc+ze/deFsxdwXPjp/P6W/cesuaIe67l1zeMJVhczGUDutM2K4PxT/6Ldu3b0LPXmQwY2J0Rdz1L/77DaZycxNhHQgsglixezdNPTiY+Pg6rY9w78joaJydVS0akoIOHl6zlye4diKtjTP0yly937+VXHVrz34KvmbelgIU5O+ianszr/c6k2Dn+/NlX7Crcd0T3hx3tu11j3+e6YURERKSChXlfRD3j7LSsqNbfXbQ+qvUB6tZJjnrGsXDupCM7Cn40Fl/R7YCn0KkVS19ERERERGoaTdRFRERERHxIE3URERERER/SRF1ERERExIc0URcRERER8SPnnD48+ACGKqP2ZMRCD8rwT31l+CsjFnpQhn/qK8NfGTWtBx1R985QZdSqjFjoQRn+qa8Mf2XEQg/K8E99Zfgro0b1oIm6iIiIiIgPaaIuIiIiIuJDmqh7Z4IyalVGLPSgDP/UV4a/MmKhB2X4p74y/JVRo3qw8KJ3ERERERHxER1RFxERERHxIU3UD8DM7jGzbDNbbmafmdkPfbBPl5mZM7MfRKl+MNzrMjNbambnRCEj3cxeNbO1ZrbEzN4xs5M8rF/SQ3a4j9+amaeP8YiMko+7vax/kIzjo5ARMLN/mtmX4ftioZkN8LD+nnLbvzCzp7yqf6isaIlWTmRdM7vYzD43s9bRyoiG8O+mv0dsx5tZvplN8zjj0Yjt35nZKK/qh2tmmNlbZvZF+PfUE2aW6HFGyc/3SjObZGb1vawfzojs40sze8rM6npYP7KHt80s2ava5XKi9lxsZk0jfsfmmNnmiG1P7nMzO97MVpa7bJSZ/c6L+uF6H5hZ33KX3WZmz3hQe5yZ3RaxPdPMJkZsP2pmw6uaE66VaWZfmVmT8HZKePt4j+qbmS0ws34Rlw0ys3e9qB+uN6Dcc/dnZlYcmXk0NFEvx8zOBi4BOjnnOgK9gY3Vu1cAXAksCH+Ohm+dc6c7504D/g94yMviZmbAFGCuc+5E59yZ4ZyAhzElPbQH+gD9gJEe1o/MKPkY43H9A2Ws87J4+L54E5jnnDshfF9cAWR4mSNHxswuAP4M9HPOra/u/TlC3wAdzKxeeLsPsNnjjO+Bn5hZM4/rAqU/F28AbzrnsoCTgCTgjx5Hlfx8dwAKgWFeFj9AH1lAPWCshzGRPRQAv/GwNhD952Ln3PaS37HAs8C4iN+5hV7lHAOvEPr9HemK8OVV9SFwDkD4oFczoH3E9ecAH3mQg3NuI/AMUPKcOgaY4NXznwut8x4GPGZmx5lZEvAgHj52nXNTIp+7gaeB+cDMqtTVRL2i5sA259z3AM65bc65LWa2zszGmtkKM/vEzNoCmNmPzGyRmX1qZu+bWSB8eZKZ/SU8frmZDQxffmH46OXS8NGUpMPtUHjMecD1VPyBjIZGwA6Pa/YEipxzz5Zc4Jxb5pyb73FOSe08QucxvSn8xCX79QIKy90X651zT1bjPtVqZnY+8DxwiXNubXXvz1F6B+gf/vpKvJkoRNpH6A1at3tct0Qv4Dvn3F8AnHPBcNYvo3HUO2w+0Nbjmgfr45rKPN8chYVAyyjUPeBzcRRyarp/Af1LXgUIH4FuQeixVVUfAWeHv24PrAS+Dh/trgucAiz1IKfEOKBr+Cj+ecAjHtbGObcSeBu4C7gP+Gu0ft9aaLXAfcDVzrniqtTSRL2iWUCmhV5+ftrMukdct8s5dyrwFPB4+LIFQFfn3BnAq8Cd4ct/XzI+fDRgTvhI0L1Ab+dcJ2AxUJmXjX4MvOuc+xzYbmZnVrHHA6kXfplmNTAR+IPH9TsASzyueUjOuS+BOCDNw7Ilt1PJx2APax8oY0oU6rfH21+uB1LmdgIeiHJeTVaX0CsclznnVlfzvlTFq8AVZnYc0BFYFIWM8cDPzKxxFGq3p9zvKOfcbmAD3k+mMbN4Qq/6rfC49MH6WIfHfZhZHHABMNXLumGHei6WMOdcAfAJoccShA7mve48OFNI+A+jfWbWitDR84WEfq7PBjoDK7x89cE5VwTcQWjCflt422v3A1cRur28fJWplJklAP8Efuuc21DVevFV36XY4pzbE54IdyN0FPg1278O+ZWIz+PCX2eExzQHEoGvwpf3JuLot3Nuh5ldArQDPgwf5E0k9MA/nCuBJ8Jfvxre9nrS+234pZqSlxz/amYdvPhhjzGlt1MNzyhlZuMJHb0odM6d5VHZMj2Y2S8I/WKXiooIHbm6Hri1mvflqDnnloeP5l1J6Oh6NDJ2m9lfgVuAb6ORcQzUC//xCqGjni9U474crZIeWgL/Bd7zOuBgz8XOuZe8zoqigz1/ev28WrL85a3w5+s9rP0RoUn6OcBjhO7zc4BdhJbGeK0fsJXQwb1oPK6+MbPXgD0lr9ZEwR+AbOfca14U0xH1A3DOBZ1zc51zI4GbgIElV0UOC39+EngqfKT9V8BxhyhtwHsRa5jaOecO+QMVfmNFL2Cima0j9Nfm5dFczuGcW0hoLVqqh2WzgWi8EnBQZnYCEATyjmVuDZANdCrZcM79htBRMS/vb6m8YuByoIuZjajunamiqYRervZ62UukxwlNRBp4XHcV5X5HmVkjoBWwxsOcyPeg3ByF9dAH6yMd+J9HGSV/iLcm9Lzm+Rp1OORzcU2xHUgpd1kTYJvHOW8BF5hZJ6C+c87LA3kl69RPJbT05WNCR9Q9W59ewsxOJ/T+lq7A7eEDoNFQHP7wnJn1IPQ4vcmrmpqol2NmJ5tZVsRFpwMlb+waHPG55Eh4Y/a/aeraiO97j4hfXmaWQugBfm7E+vYGdviznvwU+JtzrrVz7njnXCaho/bdjqixI2ChM8vEEfol45U5QF0zGxqR09HMotKHmaUSeoPQU3pVoII5wHFm9uuIy6K1BlcqwTm3l9D67p+ZmZdHw461F4H7nXNeL+coFX6p/3W8PWoIMBuob2bXQOmyjkeBl8L3T01xsD6ecs55+ipE+Ha5BfhteCmPZw7zXFwjOOf2AFvNrBeUHni7iNCSWa9zPiD08+f1H8kfEXpTb0H4D6cCIJnQZN2ziXr44OMzhJa8bAAexuM16tEWnuf9BbjGOfe1V3U1Ua8oCXjZzFaZ2XJCS1VGha9LCV92K/vf0DQKmGRmSyj7V/Lo8PiVZrYM6Omcywd+AbwSrrMQONzpFq8kdLaUSJPx/uwvpWuKgdeAa8NvQvJEeLI8AOhtodOeZRM6s0yOVxns7yEbeJ/QGsf7PawfmVHyEY2zvkRV+L64DOhuodNffQK8TOgNNnIA4UlItF4mBUonoBcB95rZpR6Xr29mmyI+PDmlWnnOuU3OuT9Ho3Y5jxJ61c8zEb+jBpnZF8DnwHdAjXqVI6KPn4b72A4UO+e8PntNSd6nwHK8f0461HNxTXIN8Pvwc+scQn/IRuMNjK8Ap+H9RH0FoZ+1j8tdtss55+UrAzcAG5xzJctdngZOqWHvTRhG6D1xz3j5Xjb9Z9JKCi876ezxA1NEagAzOw143jnXpbr3ReRIWOh/YrwCDHDORftN5CLiMb2ZVETkEMxsGKGX92+r5l0ROWLOuY8IrSUXkRpIR9RFRERERHxIa9RFRERERHxIE3URERERER/SRF1ERERExIc0URcRERER8SFN1EVEREREfEgTdRERERERH9JEXURERETEhzRRFxERERHxIU3URURERER8SBN1EREREREf0kRdRERERMSHNFEXEREREfEhTdRFRERERHxIE3URERERER/SRF1ERERExIc0URcRqWXMLGhmn5nZSjObZGb1q1DrJTP7afjriWbW7hBje5jZOUeRsc7Mmh3tPoqI1FSaqIuI1D7fOudOd851AAqBYZFXmln80RR1zg1xzq06xJAewBFP1EVEaitN1EVEarf5QNvw0e75ZjYVWGVmcWb2sJn9x8yWm9mvACzkKTP7n5m9D6SVFDKzuWbWOfz1RWa21MyWmdlsMzue0B8Et4eP5nczs1QzmxzO+I+ZnRv+3qZmNsvMss1sImDH+DYREfGFozpqIiIiNV/4yHk/4N3wRZ2ADs65r8xsKLDLOXeWmdUFPjSzWcAZwMlAOyAArAJeLFc3FXgeOD9cq4lzrsDMngX2OOceCY/7JzDOObfAzFoBM4FTgJHAAufcA2bWH7g+qjeEiIhPaaIuIlL71DOzz8JfzwdeILQk5RPn3Ffhyy8EOpasPwcaA1nA+cArzrkgsMXM5hygfldgXkkt51zBQfajN9DOrPSAeSMzSwpn/CT8vdPNbMfRtSkiUrNpoi4iUvt865w7PfKC8GT5m8iLgJudczPLjbvYw/2oA3R1zn13gH0REan1tEZdREQOZCbwazNLADCzk8ysATAPGBxew94c6HmA7/0YON/M2oS/t0n48q+BhhHjZgE3l2yY2enhL+cBV4Uv6wekeNWUiEhNoom6iIgcyERC68+XmtlK4DlCr8JOAb4IX/dXYGH5b3TO5QNDgTfMbBnwWviqt4EBJW8mBW4BOoffrLqK/WefuZ/QRD+b0BKYDVHqUUTE18w5V937ICIiIiIi5eiIuoiIiIiID2miLiIiIiLiQ5qoi4iIiIj4kCbqIiIiIiI+pIm6iIiIiIgPaaIuIiIiIuJDmqiLiIiIiPiQJuoiIiIiIj70/9otTh2+LJ+kAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the confusion matrix\n",
    "ConfusionMatrix(ds_test, layers=very_best_layer, save=save_fig)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f07656",
   "metadata": {},
   "source": [
    "### Lets create some raster plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c4a533b",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid shape (27,) for image data",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mf:\\GitHub\\tactile_braille_reading_playground\\notebooks\\braille_reading_rsnn_3layer_ultimate.ipynb Zelle 25\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/GitHub/tactile_braille_reading_playground/notebooks/braille_reading_rsnn_3layer_ultimate.ipynb#X33sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# plotting the network activity\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/GitHub/tactile_braille_reading_playground/notebooks/braille_reading_rsnn_3layer_ultimate.ipynb#X33sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m NetworkActivity(ds_test, layers\u001b[39m=\u001b[39;49mvery_best_layer, save\u001b[39m=\u001b[39;49msave_fig)\n",
      "\u001b[1;32mf:\\GitHub\\tactile_braille_reading_playground\\notebooks\\braille_reading_rsnn_3layer_ultimate.ipynb Zelle 25\u001b[0m in \u001b[0;36mNetworkActivity\u001b[1;34m(dataset, save, layers, labels)\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/GitHub/tactile_braille_reading_playground/notebooks/braille_reading_rsnn_3layer_ultimate.ipynb#X33sZmlsZQ%3D%3D?line=91'>92</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(nb_plt):\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/GitHub/tactile_braille_reading_playground/notebooks/braille_reading_rsnn_3layer_ultimate.ipynb#X33sZmlsZQ%3D%3D?line=92'>93</a>\u001b[0m     plt\u001b[39m.\u001b[39msubplot(gs[i])\n\u001b[1;32m---> <a href='vscode-notebook-cell:/f%3A/GitHub/tactile_braille_reading_playground/notebooks/braille_reading_rsnn_3layer_ultimate.ipynb#X33sZmlsZQ%3D%3D?line=93'>94</a>\u001b[0m     plt\u001b[39m.\u001b[39;49mimshow(spks_out[i]\u001b[39m.\u001b[39;49mdetach()\u001b[39m.\u001b[39;49mcpu()\u001b[39m.\u001b[39;49mnumpy()\u001b[39m.\u001b[39;49mT,\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/GitHub/tactile_braille_reading_playground/notebooks/braille_reading_rsnn_3layer_ultimate.ipynb#X33sZmlsZQ%3D%3D?line=94'>95</a>\u001b[0m                cmap\u001b[39m=\u001b[39;49mplt\u001b[39m.\u001b[39;49mcm\u001b[39m.\u001b[39;49mgray_r, origin\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mlower\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/GitHub/tactile_braille_reading_playground/notebooks/braille_reading_rsnn_3layer_ultimate.ipynb#X33sZmlsZQ%3D%3D?line=95'>96</a>\u001b[0m     \u001b[39mif\u001b[39;00m i \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/GitHub/tactile_braille_reading_playground/notebooks/braille_reading_rsnn_3layer_ultimate.ipynb#X33sZmlsZQ%3D%3D?line=96'>97</a>\u001b[0m         plt\u001b[39m.\u001b[39mxlabel(\u001b[39m\"\u001b[39m\u001b[39mTime\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\smuel\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\_api\\deprecation.py:456\u001b[0m, in \u001b[0;36mmake_keyword_only.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m name_idx:\n\u001b[0;32m    451\u001b[0m     warn_deprecated(\n\u001b[0;32m    452\u001b[0m         since, message\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mPassing the \u001b[39m\u001b[39m%(name)s\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m%(obj_type)s\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    453\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mpositionally is deprecated since Matplotlib \u001b[39m\u001b[39m%(since)s\u001b[39;00m\u001b[39m; the \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    454\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mparameter will become keyword-only \u001b[39m\u001b[39m%(removal)s\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m    455\u001b[0m         name\u001b[39m=\u001b[39mname, obj_type\u001b[39m=\u001b[39m\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m{\u001b[39;00mfunc\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m()\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 456\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\smuel\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\pyplot.py:2640\u001b[0m, in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[0;32m   2634\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39mimshow)\n\u001b[0;32m   2635\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mimshow\u001b[39m(\n\u001b[0;32m   2636\u001b[0m         X, cmap\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, norm\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, aspect\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, interpolation\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[0;32m   2637\u001b[0m         alpha\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, vmin\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, vmax\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, origin\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, extent\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m,\n\u001b[0;32m   2638\u001b[0m         interpolation_stage\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, filternorm\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, filterrad\u001b[39m=\u001b[39m\u001b[39m4.0\u001b[39m,\n\u001b[0;32m   2639\u001b[0m         resample\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, url\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m-> 2640\u001b[0m     __ret \u001b[39m=\u001b[39m gca()\u001b[39m.\u001b[39mimshow(\n\u001b[0;32m   2641\u001b[0m         X, cmap\u001b[39m=\u001b[39mcmap, norm\u001b[39m=\u001b[39mnorm, aspect\u001b[39m=\u001b[39maspect,\n\u001b[0;32m   2642\u001b[0m         interpolation\u001b[39m=\u001b[39minterpolation, alpha\u001b[39m=\u001b[39malpha, vmin\u001b[39m=\u001b[39mvmin,\n\u001b[0;32m   2643\u001b[0m         vmax\u001b[39m=\u001b[39mvmax, origin\u001b[39m=\u001b[39morigin, extent\u001b[39m=\u001b[39mextent,\n\u001b[0;32m   2644\u001b[0m         interpolation_stage\u001b[39m=\u001b[39minterpolation_stage,\n\u001b[0;32m   2645\u001b[0m         filternorm\u001b[39m=\u001b[39mfilternorm, filterrad\u001b[39m=\u001b[39mfilterrad, resample\u001b[39m=\u001b[39mresample,\n\u001b[0;32m   2646\u001b[0m         url\u001b[39m=\u001b[39murl, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m({\u001b[39m\"\u001b[39m\u001b[39mdata\u001b[39m\u001b[39m\"\u001b[39m: data} \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m {}),\n\u001b[0;32m   2647\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   2648\u001b[0m     sci(__ret)\n\u001b[0;32m   2649\u001b[0m     \u001b[39mreturn\u001b[39;00m __ret\n",
      "File \u001b[1;32mc:\\Users\\smuel\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\_api\\deprecation.py:456\u001b[0m, in \u001b[0;36mmake_keyword_only.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m name_idx:\n\u001b[0;32m    451\u001b[0m     warn_deprecated(\n\u001b[0;32m    452\u001b[0m         since, message\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mPassing the \u001b[39m\u001b[39m%(name)s\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m%(obj_type)s\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    453\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mpositionally is deprecated since Matplotlib \u001b[39m\u001b[39m%(since)s\u001b[39;00m\u001b[39m; the \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    454\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mparameter will become keyword-only \u001b[39m\u001b[39m%(removal)s\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m    455\u001b[0m         name\u001b[39m=\u001b[39mname, obj_type\u001b[39m=\u001b[39m\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m{\u001b[39;00mfunc\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m()\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 456\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\smuel\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\__init__.py:1412\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1409\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[0;32m   1410\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m   1411\u001b[0m     \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m-> 1412\u001b[0m         \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39m\u001b[39mmap\u001b[39m(sanitize_sequence, args), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1414\u001b[0m     bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1415\u001b[0m     auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[0;32m   1416\u001b[0m                   \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n",
      "File \u001b[1;32mc:\\Users\\smuel\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\axes\\_axes.py:5488\u001b[0m, in \u001b[0;36mAxes.imshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m   5481\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_aspect(aspect)\n\u001b[0;32m   5482\u001b[0m im \u001b[39m=\u001b[39m mimage\u001b[39m.\u001b[39mAxesImage(\u001b[39mself\u001b[39m, cmap, norm, interpolation,\n\u001b[0;32m   5483\u001b[0m                       origin, extent, filternorm\u001b[39m=\u001b[39mfilternorm,\n\u001b[0;32m   5484\u001b[0m                       filterrad\u001b[39m=\u001b[39mfilterrad, resample\u001b[39m=\u001b[39mresample,\n\u001b[0;32m   5485\u001b[0m                       interpolation_stage\u001b[39m=\u001b[39minterpolation_stage,\n\u001b[0;32m   5486\u001b[0m                       \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m-> 5488\u001b[0m im\u001b[39m.\u001b[39;49mset_data(X)\n\u001b[0;32m   5489\u001b[0m im\u001b[39m.\u001b[39mset_alpha(alpha)\n\u001b[0;32m   5490\u001b[0m \u001b[39mif\u001b[39;00m im\u001b[39m.\u001b[39mget_clip_path() \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   5491\u001b[0m     \u001b[39m# image does not already have clipping set, clip to axes patch\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\smuel\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\image.py:715\u001b[0m, in \u001b[0;36m_ImageBase.set_data\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m    711\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_A \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_A[:, :, \u001b[39m0\u001b[39m]\n\u001b[0;32m    713\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_A\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m2\u001b[39m\n\u001b[0;32m    714\u001b[0m         \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_A\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m3\u001b[39m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_A\u001b[39m.\u001b[39mshape[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m] \u001b[39min\u001b[39;00m [\u001b[39m3\u001b[39m, \u001b[39m4\u001b[39m]):\n\u001b[1;32m--> 715\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mInvalid shape \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m for image data\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    716\u001b[0m                     \u001b[39m.\u001b[39mformat(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_A\u001b[39m.\u001b[39mshape))\n\u001b[0;32m    718\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_A\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m3\u001b[39m:\n\u001b[0;32m    719\u001b[0m     \u001b[39m# If the input data has values outside the valid range (after\u001b[39;00m\n\u001b[0;32m    720\u001b[0m     \u001b[39m# normalisation), we issue a warning and then clip X to the bounds\u001b[39;00m\n\u001b[0;32m    721\u001b[0m     \u001b[39m# - otherwise casting wraps extreme values, hiding outliers and\u001b[39;00m\n\u001b[0;32m    722\u001b[0m     \u001b[39m# making reliable interpretation impossible.\u001b[39;00m\n\u001b[0;32m    723\u001b[0m     high \u001b[39m=\u001b[39m \u001b[39m255\u001b[39m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39missubdtype(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_A\u001b[39m.\u001b[39mdtype, np\u001b[39m.\u001b[39minteger) \u001b[39melse\u001b[39;00m \u001b[39m1\u001b[39m\n",
      "\u001b[1;31mTypeError\u001b[0m: Invalid shape (27,) for image data"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x450 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x450 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x450 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the network activity\n",
    "NetworkActivity(ds_test, layers=very_best_layer, save=save_fig)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "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": "3.8.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}