MNIST_RuntimeMonitoring.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Runtime Monitoring Neuron Activation Patterns (1) - MNIST CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by fixing the random seed to ensure reproducability. In all our examples, we use 42. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.random\n",
    "# Fix the number for repeatability (we have also stored the trained model)\n",
    "numpy.random.seed(42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put these at the top of every notebook, to get automatic reloading and inline plotting\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import all required library, and set the device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import numpy as np\n",
    "import h5py\n",
    "import scipy.misc\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.autograd  import Variable\n",
    "from torch.autograd.gradcheck import zero_gradients\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "import torchvision.transforms as T\n",
    "from torchvision.models.inception import inception_v3\n",
    "\n",
    "from PIL import Image\n",
    "from scipy.misc import imsave\n",
    "import os\n",
    "\n",
    "\n",
    "# Device configuration\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define hyper-parameters. The parameter \"sizeOfNeuronsToMonitor\" is the number of neurons we will monitor, which is the 2nd to last layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyper-parameters \n",
    "num_classes = 10\n",
    "num_epochs = 5\n",
    "batch_size = 64\n",
    "learning_rate = 0.001\n",
    "sizeOfNeuronsToMonitor = 40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then , prepare the training and test set. MNIST can be directly downloaded. Notice that we do not normalize the input data, but just rely on small learning rates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST dataset \n",
    "train_dataset = torchvision.datasets.MNIST(root='data/mnist', \n",
    "                                           train=True, \n",
    "                                           transform=transforms.ToTensor(),  \n",
    "                                           download=True)\n",
    "\n",
    "test_dataset = torchvision.datasets.MNIST(root='data/mnist', \n",
    "                                          train=False, \n",
    "                                          transform=transforms.ToTensor())\n",
    "\n",
    "# Data loader\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
    "                                           batch_size=batch_size, \n",
    "                                           shuffle=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
    "                                          batch_size=batch_size, \n",
    "                                          shuffle=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a utility function to display MNIST images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import util\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "util.displayMNIST(images[0].numpy())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the network \n",
    "\n",
    "To use run-time monitoring, apart from standard \"forward()\" function, we additionally define another function to return values of intermediate layers. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class NeuralNet(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(NeuralNet, self).__init__()\n",
    " \n",
    "        self.conv1 = nn.Conv2d(1, 40, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(40, 20, 5)\n",
    "        self.fc1 = nn.Linear(20 * 4 * 4, 160)\n",
    "        self.fc2 = nn.Linear(160, 80)\n",
    "        self.fc3 = nn.Linear(80, sizeOfNeuronsToMonitor)\n",
    "        self.fc4 = nn.Linear(sizeOfNeuronsToMonitor, num_classes)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Original 28x28x1 -(conv)-> 24x24x40 -(pool)-> 12x12x40\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        # Original 12x12x40 -(conv)-> 8x8x20 -(pool)-> 4x4x20\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        # Flatten it to an array of inputs\n",
    "        x = x.view(-1, 20 * 4 * 4)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = F.relu(self.fc3(x))\n",
    "        out = self.fc4(x)\n",
    "        return out \n",
    "  \n",
    "    # Here we add another function, which does the same forward computation but also extracts intermediate layer results\n",
    "    def forwardWithIntermediate(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 20 * 4 * 4)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        intermediateValues = x\n",
    "        x = F.relu(x)\n",
    "        out = self.fc4(x)\n",
    "        return out, intermediateValues    \n",
    "    \n",
    "net = NeuralNet()\n",
    "net.eval()\n",
    "\n",
    "# Loss and optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the model (if you have a pretrained one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup model to be attacked\n",
    "# net = NeuralNet()\n",
    "net.load_state_dict(torch.load('models/1_model_MNIST_CNN.ckpt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "furtherTrain = False\n",
    "\n",
    "if furtherTrain: \n",
    "\n",
    "    # Train the model\n",
    "    total_step = len(train_loader)\n",
    "    for epoch in range(num_epochs):\n",
    "        for i, (images, labels) in enumerate(train_loader):  \n",
    "            # Move tensors to the configured device\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            # Forward pass\n",
    "            outputs = net(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "\n",
    "            # Backward and optimize\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            if (i+1) % 100 == 0:\n",
    "                print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' \n",
    "                       .format(epoch+1, num_epochs, i+1, total_step, loss.item()))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute accuracy on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 98.81 %\n"
     ]
    }
   ],
   "source": [
    "# In test phase, we don't need to compute gradients (for memory efficiency)\n",
    "\n",
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    outofActivationPattern = 0\n",
    "    outofActivationPatternAndResultWrong = 0\n",
    "    \n",
    "    total = 0\n",
    "    for images, labels in test_loader:\n",
    "        labels = labels.to(device)\n",
    "        outputs = net(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        \n",
    "\n",
    "    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Runtime Monitoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Trigger neuron on-off activation pattern monitoring\n",
    "\n",
    "Here we pretend that test set is the \"real data\" after deployment, so we only record neuron activation patterns inside the training set. \n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nndependability.rv import napmonitor\n",
    "monitor = napmonitor.NAP_Monitor(num_classes, sizeOfNeuronsToMonitor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trigger the function addAllNeuronPatternsToClass() to record all visited patterns for each batch (internally the monitor will filter incorrect results). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the all train images: 99.34166666666667 %\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for images, labels in train_loader:\n",
    "        labels = labels.to(device)\n",
    "        outputs, intermediateValues = net.forwardWithIntermediate(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        # Add the batch of neuron activation patterns to the monitor\n",
    "        monitor.addAllNeuronPatternsToClass(intermediateValues.numpy(), predicted.numpy(), labels.numpy(), -1)\n",
    "\n",
    "                \n",
    "    print('Accuracy of the network on the all train images: {} %'.format(100 * correct / total))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the state of run-time monitoring \n",
    "\n",
    "Following code saves the napmonitor for using it as runtime monitor in the deployment code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('napmonitor.pkl', 'wb') as output:\n",
    "    pickle.dump(monitor, output, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the state of run-time monitoring \n",
    "Following code is a sample of how to load the monitor during deployment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pickle\n",
    "\n",
    "# with open('napmonitor.pkl', 'rb') as input:\n",
    "#     monitor = pickle.load(input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perform run-time monitoring and examine if an activation pattern has been visited before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 98.81 %\n",
      "Out-of-activation pattern on the 10000 test images: 7.66 %\n",
      "Out-of-activation pattern & misclassified / out-of-activation pattern : 10.704960835509139 %\n"
     ]
    }
   ],
   "source": [
    "# We don't need to compute gradients (for memory efficiency)\n",
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    outofActivationPattern = 0\n",
    "    outofActivationPatternAndResultWrong = 0\n",
    "    \n",
    "    total = 0\n",
    "    for images, labels in test_loader:\n",
    "        labels = labels.to(device)\n",
    "        outputs, intermediateValues = net.forwardWithIntermediate(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "                \n",
    "        # Additional processing for runtime monitoring\n",
    "        \n",
    "        predictedNp = predicted.numpy()\n",
    "        \n",
    "        result = (predicted == labels)\n",
    "        res = result.numpy()\n",
    "               \n",
    "        # Iterate over each image in the batch\n",
    "        for exampleIndex in range(intermediateValues.shape[0]):   \n",
    "            if not monitor.isPatternContained(intermediateValues.numpy()[exampleIndex,:], predicted.numpy()[exampleIndex]):\n",
    "                outofActivationPattern = outofActivationPattern +1\n",
    "                if res[exampleIndex] == False :\n",
    "                    outofActivationPatternAndResultWrong = outofActivationPatternAndResultWrong + 1\n",
    "          \n",
    "    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))\n",
    "    print('Out-of-activation pattern on the 10000 test images: {} %'.format(100 * outofActivationPattern / total))\n",
    "    print('Out-of-activation pattern & misclassified / out-of-activation pattern : {} %'.format(100 * outofActivationPatternAndResultWrong / (outofActivationPattern)))\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-activation pattern & misclassified / mis-classified: 68.90756302521008 %\n"
     ]
    }
   ],
   "source": [
    "print('Out-of-activation pattern & misclassified / mis-classified: {} %'.format(100 * outofActivationPatternAndResultWrong / (total - correct)))\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  B. Produce adversarial training example using iterative FGSM and check if the activation pattern is within"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 108x108 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataiter = iter(test_loader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "\n",
    "\n",
    "util.displayMNIST(images[0].numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import util"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The iterative FGSM attack is within util.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = images[0].unsqueeze(0)\n",
    "label = labels[0].unsqueeze(0)\n",
    "\n",
    "adv_img, noise, attackSuccessful = util.iterative_FGSM_attack(img, label, net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iterative FGSM is not effective\n"
     ]
    }
   ],
   "source": [
    "if attackSuccessful :\n",
    "    util.displayMNIST(adv_img[0].numpy())\n",
    "else:\n",
    "    print(\"iterative FGSM is not effective\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the noise value is not within [0, 1]. Therefore, we add each pixel  with 0.5 to display the effect. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "if attackSuccessful :\n",
    "    util.displayMNIST(noise[0].numpy()+0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if the adversarial image produces any similar activation pattern. After flowing the pattern into the monitor, the monitor diagnoses that it has not seen anything similar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-5.8602, -0.5502, -0.4174,  0.7075, -0.0347, -3.4367, -9.9213, 10.5300,\n",
      "         -2.3333,  2.3564]])\n"
     ]
    }
   ],
   "source": [
    "out, intermediateValues = net.forwardWithIntermediate(adv_img)        \n",
    "print(out.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adv example is within activation pattern\n"
     ]
    }
   ],
   "source": [
    "_, predicted = torch.max(out.data, 1)\n",
    "#iv = intermediateValues.detach().numpy()\n",
    "#mat = np.zeros(intermediateValues.shape)\n",
    "#ivabs = np.greater(iv, mat)\n",
    "\n",
    "# Only perform analysis when the monitor takes all classes\n",
    "for exampleIndex in range(intermediateValues.detach().numpy().shape[0]):      \n",
    "    if not monitor.isPatternContained(intermediateValues.detach().numpy()[exampleIndex,:], predicted.numpy()[exampleIndex]):\n",
    "        print(\"Adv example is not within activation pattern\")\n",
    "    else:\n",
    "        print(\"Adv example is within activation pattern\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, try to generate adversarial images for each image of the test set, and check the percentage of being out of patterns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def performAdversarialExamination(loader, monitor):\n",
    "    notInNeuronActivationPattern = 0\n",
    "    totalPerturbed = 0\n",
    "    for images, labels in loader:\n",
    "        # print(list(images.size())[0])\n",
    "        for i in range(list(images.size())[0]):\n",
    "\n",
    "            adv_img, noise, attackSuccessful = util.iterative_FGSM_attack(images[i].unsqueeze(0), labels[i].unsqueeze(0), net)\n",
    "\n",
    "            if (attackSuccessful) :\n",
    "                totalPerturbed = totalPerturbed + 1\n",
    "\n",
    "                out, intermediateValues = net.forwardWithIntermediate(adv_img)  \n",
    "                _, predicted = torch.max(out.data, 1)\n",
    "                # iv = intermediateValues.detach().numpy()\n",
    "                # mat = np.zeros(intermediateValues.shape)\n",
    "                # ivabs = np.greater(iv, mat)\n",
    "\n",
    "                for exampleIndex in range(intermediateValues.shape[0]): \n",
    "                    # if not monitor.isOnOffPatternContained(ivabs[exampleIndex,:], predicted.numpy()[exampleIndex]):\n",
    "                    if not monitor.isPatternContained(intermediateValues.detach().numpy()[exampleIndex,:], predicted.numpy()[exampleIndex]):\n",
    "                        notInNeuronActivationPattern = notInNeuronActivationPattern +1\n",
    "\n",
    "\n",
    "    print('not in neuron activation pattern / all total perturbed: {} %'.format(100 * notInNeuronActivationPattern / totalPerturbed))\n",
    "    print('all total perturbed = '+str(totalPerturbed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "isTestAdv = False\n",
    "if isTestAdv:\n",
    "    performAdversarialExamination(test_loader, monitor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Enlarge the region by allowing perturbation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One may now enlarge the memorized pattern by allowing 1-bit flip (thus out-of-activation will drop), and run it again "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "monitor.enlargeSetByOneBitFluctuation(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 98.81 %\n",
      "Out-of-activation pattern on the 10000 test images: 2.01 %\n",
      "Out-of-activation pattern & misclassified / out-of-activation pattern : 21.890547263681594 %\n"
     ]
    }
   ],
   "source": [
    "# We don't need to compute gradients (for memory efficiency)\n",
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    outofActivationPattern = 0\n",
    "    outofActivationPatternAndResultWrong = 0\n",
    "    \n",
    "    total = 0\n",
    "    for images, labels in test_loader:\n",
    "        labels = labels.to(device)\n",
    "        outputs, intermediateValues = net.forwardWithIntermediate(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "                \n",
    "        # Additional processing for runtime monitoring\n",
    "        \n",
    "        predictedNp = predicted.numpy()\n",
    "        \n",
    "        result = (predicted == labels)\n",
    "        res = result.numpy()\n",
    "               \n",
    "        # Iterate over each image in the batch\n",
    "        for exampleIndex in range(intermediateValues.shape[0]):   \n",
    "            if not monitor.isPatternContained(intermediateValues.numpy()[exampleIndex,:], predicted.numpy()[exampleIndex]):\n",
    "                outofActivationPattern = outofActivationPattern +1\n",
    "                if res[exampleIndex] == False :\n",
    "                    outofActivationPatternAndResultWrong = outofActivationPatternAndResultWrong + 1\n",
    "          \n",
    "    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))\n",
    "    print('Out-of-activation pattern on the 10000 test images: {} %'.format(100 * outofActivationPattern / total))\n",
    "    print('Out-of-activation pattern & misclassified / out-of-activation pattern : {} %'.format(100 * outofActivationPatternAndResultWrong / (outofActivationPattern)))\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-activation pattern & misclassified / mis-classified: 36.97478991596638 %\n"
     ]
    }
   ],
   "source": [
    "print('Out-of-activation pattern & misclassified / mis-classified: {} %'.format(100 * outofActivationPatternAndResultWrong / (total - correct)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "if isTestAdv:\n",
    "    performAdversarialExamination(test_loader, monitor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "monitor.enlargeSetByOneBitFluctuation(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 98.81 %\n",
      "Out-of-activation pattern on the 10000 test images: 0.6 %\n",
      "Out-of-activation pattern & misclassified / out-of-activation pattern : 31.666666666666668 %\n"
     ]
    }
   ],
   "source": [
    "# We don't need to compute gradients (for memory efficiency)\n",
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    outofActivationPattern = 0\n",
    "    outofActivationPatternAndResultWrong = 0\n",
    "    \n",
    "    total = 0\n",
    "    for images, labels in test_loader:\n",
    "        labels = labels.to(device)\n",
    "        outputs, intermediateValues = net.forwardWithIntermediate(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "                \n",
    "        # Additional processing for runtime monitoring\n",
    "        \n",
    "        predictedNp = predicted.numpy()\n",
    "        \n",
    "        result = (predicted == labels)\n",
    "        res = result.numpy()\n",
    "               \n",
    "        # Iterate over each image in the batch\n",
    "        for exampleIndex in range(intermediateValues.shape[0]):   \n",
    "            if not monitor.isPatternContained(intermediateValues.numpy()[exampleIndex,:], predicted.numpy()[exampleIndex]):\n",
    "                outofActivationPattern = outofActivationPattern +1\n",
    "                if res[exampleIndex] == False :\n",
    "                    outofActivationPatternAndResultWrong = outofActivationPatternAndResultWrong + 1\n",
    "          \n",
    "    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))\n",
    "    print('Out-of-activation pattern on the 10000 test images: {} %'.format(100 * outofActivationPattern / total))\n",
    "    print('Out-of-activation pattern & misclassified / out-of-activation pattern : {} %'.format(100 * outofActivationPatternAndResultWrong / (outofActivationPattern)))\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-activation pattern & misclassified / mis-classified: 15.966386554621849 %\n"
     ]
    }
   ],
   "source": [
    "print('Out-of-activation pattern & misclassified / mis-classified: {} %'.format(100 * outofActivationPatternAndResultWrong / (total - correct)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "if isTestAdv:\n",
    "    performAdversarialExamination(test_loader, monitor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Trigger runtime neuron interval pattern monitoring "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Understand the max value for output of each neuron in the layer under analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.976089   12.64085674 14.45319462 20.14255142 12.10131168  7.20467901\n",
      "  14.01826191 15.72563648  7.57931471 12.96555805 13.73955441  3.02725601\n",
      "  12.84442425 12.07688522  0.          8.21488285 19.87310982  0.\n",
      "  10.62583733  8.91006088 10.26775837 10.06238365 11.35547543 15.94989872\n",
      "  11.2846899  12.3040657  14.48820591 14.17330456 11.95500755  4.9770484\n",
      "  17.16381836  0.         13.73954868 12.10352421  3.96824312 11.53234386\n",
      "  25.40777206 14.69727707  9.12612629  5.32893896]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "maximumValues = np.zeros([1, sizeOfNeuronsToMonitor])\n",
    "\n",
    "# We don't need to compute gradients (for memory efficiency)\n",
    "with torch.no_grad():\n",
    "    \n",
    "    total = 0\n",
    "    for images, labels in test_loader:\n",
    "        labels = labels.to(device)\n",
    "        outputs, intermediateValues = net.forwardWithIntermediate(images)\n",
    "    \n",
    "    for exampleIndex in range(intermediateValues.shape[0]):   \n",
    "        maximumValues = np.maximum(intermediateValues.numpy()[exampleIndex,:], maximumValues)\n",
    "    \n",
    "    print(maximumValues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, prepare two threshold for fine-grained monitor, which is based on further partitioning the >0 domain to 0-33% MaxInTraining, 33%-66% MaxInTraining, and >66% MaxInTraining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold1 = (maximumValues*1)/3\n",
    "threshold2 = (maximumValues*2)/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "runIntervalMonitor = True\n",
    "\n",
    "from nndependability.rv import nipmonitor\n",
    "intervalmonitor = nipmonitor.NIP_Monitor(num_classes, sizeOfNeuronsToMonitor, threshold1, threshold2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we only do it on class 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-305712a2ccd0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m             \u001b[0;31m# Add the batch of neuron activation patterns to the monitor, with only the 0-th class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m             \u001b[0mintervalmonitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddAllNeuronPatternsToClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintermediateValues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassToAnalyze\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/nn-dependability-kit/nndependability/rv/nipmonitor.py\u001b[0m in \u001b[0;36maddAllNeuronPatternsToClass\u001b[0;34m(self, neuronValuesNp, predictedNp, labelsNp, classToKeep)\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m                 \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictedNp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexampleIndex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlabelsNp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexampleIndex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlabelsNp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexampleIndex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mclassToKeep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddDiscretePatternToClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexampleIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictedNp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexampleIndex\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     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/nn-dependability-kit/nndependability/rv/nipmonitor.py\u001b[0m in \u001b[0;36maddDiscretePatternToClass\u001b[0;34m(self, neuronDiscretePattern, classIndex)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0;31m# Add into BDD\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoveredPatterns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclassIndex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoveredPatterns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclassIndex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepareConstraint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneuronDiscretePattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassIndex\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    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/autoref.py\u001b[0m in \u001b[0;36madd_expr\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_wrapper\u001b[0;34m(bdd, *args, **kwargs)\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_ReorderingContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Reordering needed...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0;31m# disable reordering requests while swapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36madd_expr\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m   1108\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0m_try_to_reorder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1109\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1110\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1112\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_expr\u001b[0;34m(e, bdd)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0m_parsers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m     \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_parsers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0mu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfalse\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'false'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, op, u, v, w)\u001b[0m\n\u001b[1;32m   1142\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mop\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'and'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/\\\\'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'&'\u001b[0m\u001b[0;34m,\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-> 1144\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1145\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mop\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'xor'\u001b[0m\u001b[0;34m,\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   1146\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_wrapper\u001b[0;34m(bdd, *args, **kwargs)\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_ReorderingContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Reordering needed...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0;31m# disable reordering requests while swapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36mite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0;31m# wrap so reordering can delete unreferenced nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_or_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    704\u001b[0m         \u001b[0;31m# cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite_table\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36mfind_or_add\u001b[0;34m(self, i, v, w)\u001b[0m\n\u001b[1;32m    739\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    740\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_succ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 741\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ref\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    742\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_min_free\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_free_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    743\u001b[0m         \u001b[0;31m# increment reference counters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "classToAnalyze = 2\n",
    "# Use below if we want to analyze the complete class\n",
    "# classToAnalyze = -1\n",
    "\n",
    "if runIntervalMonitor: \n",
    "\n",
    "    with torch.no_grad():\n",
    "        for images, labels in train_loader:\n",
    "            labels = labels.to(device)\n",
    "            outputs, intermediateValues = net.forwardWithIntermediate(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "            # Add the batch of neuron activation patterns to the monitor, with only the 0-th class\n",
    "            intervalmonitor.addAllNeuronPatternsToClass(intermediateValues.numpy(), predicted.numpy(), labels.numpy(), classToAnalyze)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_code\u001b[0;34m(self, code_obj, result, async_)\u001b[0m\n\u001b[1;32m   3266\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3267\u001b[0;31m                     \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_global_ns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3268\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-38-6d934eaf2f8b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     27\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mclassToAnalyze\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpredicted\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexampleIndex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mclassToAnalyze\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mintervalmonitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misPatternContained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintermediateValues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexampleIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexampleIndex\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[0m\u001b[1;32m     29\u001b[0m                         \u001b[0moutofActivationPattern\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutofActivationPattern\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/nn-dependability-kit/nndependability/rv/nipmonitor.py\u001b[0m in \u001b[0;36misPatternContained\u001b[0;34m(self, neuronValuesNp, classIndex)\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoveredPatterns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclassIndex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepareConstraint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfalse\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/autoref.py\u001b[0m in \u001b[0;36madd_expr\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_wrapper\u001b[0;34m(bdd, *args, **kwargs)\u001b[0m\n\u001b[1;32m     81\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_ReorderingContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Reordering needed...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36madd_expr\u001b[0;34m(self, e)\u001b[0m\n\u001b[1;32m   1109\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1110\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_expr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_expr\u001b[0;34m(e, bdd)\u001b[0m\n\u001b[1;32m    203\u001b[0m     \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_parsers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/_parser.py\u001b[0m in \u001b[0;36madd_ast\u001b[0;34m(t, bdd)\u001b[0m\n\u001b[1;32m    240\u001b[0m             \u001b[0moperands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0madd_ast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mbdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'bool'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, op, u, v, w)\u001b[0m\n\u001b[1;32m   1143\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mop\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'and'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/\\\\'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'&'\u001b[0m\u001b[0;34m,\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-> 1144\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1145\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mop\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'xor'\u001b[0m\u001b[0;34m,\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~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_wrapper\u001b[0;34m(bdd, *args, **kwargs)\u001b[0m\n\u001b[1;32m     81\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_ReorderingContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Reordering needed...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36mite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0;31m# wrap so reordering can delete unreferenced nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_or_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_or_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0mv0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_top_cofactor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0mv0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_top_cofactor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0mv0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_top_cofactor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0mv0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_top_cofactor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0mv0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_top_cofactor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0mv0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_top_cofactor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0mv0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_top_cofactor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    702\u001b[0m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_or_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_or_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    701\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_or_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36m_ite\u001b[0;34m(self, g, u, v)\u001b[0m\n\u001b[1;32m    702\u001b[0m         \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_or_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    704\u001b[0m         \u001b[0;31m# cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/dd/bdd.py\u001b[0m in \u001b[0;36mfind_or_add\u001b[0;34m(self, i, v, w)\u001b[0m\n\u001b[1;32m    736\u001b[0m         \u001b[0mu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_min_free\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0mu\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_succ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    738\u001b[0m         \u001b[0;31m# add node\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31m<class 'str'>\u001b[0m: (<class 'MemoryError'>, MemoryError())",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_code\u001b[0;34m(self, code_obj, result, async_)\u001b[0m\n\u001b[1;32m   3282\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3283\u001b[0m                 \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror_in_exec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3284\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshowtraceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrunning_compiled_code\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3285\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3286\u001b[0m             \u001b[0moutflag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2021\u001b[0m                                             value, tb, tb_offset=tb_offset)\n\u001b[1;32m   2022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2023\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_showtraceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2024\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_pdb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2025\u001b[0m                         \u001b[0;31m# drop into debugger\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/ipykernel/zmqshell.py\u001b[0m in \u001b[0;36m_showtraceback\u001b[0;34m(self, etype, evalue, stb)\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0;34mu'traceback'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mstb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m             \u001b[0;34mu'ename'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0municode_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 546\u001b[0;31m             \u001b[0;34mu'evalue'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mpy3compat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msafe_unicode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevalue\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    547\u001b[0m         }\n\u001b[1;32m    548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/ipython_genutils/py3compat.py\u001b[0m in \u001b[0;36msafe_unicode\u001b[0;34m(e)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \"\"\"\n\u001b[1;32m     64\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0municode_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mUnicodeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "if runIntervalMonitor: \n",
    "    \n",
    "    # We don't need to compute gradients (for memory efficiency)\n",
    "    with torch.no_grad():\n",
    "        correct = 0\n",
    "        outofActivationPattern = 0\n",
    "        outofActivationPatternAndResultWrong = 0\n",
    "\n",
    "        total = 0\n",
    "        for images, labels in test_loader:\n",
    "            labels = labels.to(device)\n",
    "            outputs, intermediateValues = net.forwardWithIntermediate(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "            # Additional processing for runtime monitoring\n",
    "\n",
    "            predictedNp = predicted.numpy()\n",
    "\n",
    "            result = (predicted == labels)\n",
    "            res = result.numpy()\n",
    "\n",
    "            # Iterate over each image in the batch\n",
    "            for exampleIndex in range(intermediateValues.shape[0]): \n",
    "                if (classToAnalyze == -1) or (predicted.numpy()[exampleIndex] == classToAnalyze): \n",
    "                    if not intervalmonitor.isPatternContained(intermediateValues.numpy()[exampleIndex,:], predicted.numpy()[exampleIndex]):\n",
    "                        outofActivationPattern = outofActivationPattern +1\n",
    "                        if res[exampleIndex] == False :\n",
    "                            outofActivationPatternAndResultWrong = outofActivationPatternAndResultWrong + 1\n",
    "\n",
    "        print('Result on interval-based monitor ([0,1,2,3]:= [<=0, 0-33%, 33%-66%, >66%] in max training value: ')      \n",
    "        print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-activation pattern & misclassified / out-of-activation pattern for class 2: 1.5337423312883436 %\n"
     ]
    }
   ],
   "source": [
    "if classToAnalyze == -1:\n",
    "        print('Out-of-activation pattern on the 10000 test images: {} %'.format(100 * outofActivationPattern / total))\n",
    "        print('Out-of-activation pattern & misclassified / out-of-activation pattern : {} %'.format(100 * outofActivationPatternAndResultWrong / (outofActivationPattern)))\n",
    "else:\n",
    "        print('Out-of-activation pattern & misclassified / out-of-activation pattern for class '+str(classToAnalyze)+': {} %'.format(100 * outofActivationPatternAndResultWrong / (outofActivationPattern)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.save(net.state_dict(), 'local/model_MNIST_CNN.ckpt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Acknowledgements \n",
    "\n",
    "During the development of this jupyter notebook, I got some code from existing github repositories:\n",
    "\n",
    "- PyTorch tutorials as in https://pytorch.org/\n",
    "- https://github.com/Lextal/adv-attacks-pytorch-101/ for implementing iterative FGSM on PyTorch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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