GAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import h5py\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import glob\n",
    "import os\n",
    "\n",
    "\n",
    "from IPython import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1000, 4)"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load(\"class_0_seqs_v2.npy\", allow_pickle=True).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_labels = np.load(\"centered_genes_labels.npy\", allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_labels_1 = np.load(\"centered_genes_labels_1.npy\", allow_pickle=True)\n",
    "train_data_labels_2 = np.load(\"centered_genes_labels_2.npy\", allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = np.load(\"centered_genes.npy\", allow_pickle = True)\n",
    "train_data_1 = np.load(\"centered_genes_1.npy\", allow_pickle = True)\n",
    "train_data_2 = np.load(\"centered_genes_2.npy\", allow_pickle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_final = np.concatenate((train_data, train_data_1, train_data_2), axis = 0)\n",
    "\n",
    "train_data_labels_final = np.concatenate((train_data_labels,train_data_labels_1, train_data_labels_2), axis = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "centered_genes = train_data_final.reshape(train_data_final.shape[0], 1000, 4, 1)\n",
    "centered_genes = centered_genes.astype('float32')\n",
    "\n",
    "type1_mask = [0,2,3]\n",
    "type2_mask = [0,1,3]\n",
    "\n",
    "baseline_celltype0 = np.mean(train_data_labels_final[:,1:,:], axis = 1)\n",
    "baseline_celltype1 = np.mean(train_data_labels_final[:,type1_mask,:], axis = 1)\n",
    "baseline_celltype2 = np.mean(train_data_labels_final[:,type2_mask,:], axis = 1)\n",
    "baseline_celltype3 = np.mean(train_data_labels_final[:,:3,:], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_0 = [1 if i > 10*j else 0 for i,j in zip(train_data_labels_final[:,0,:], baseline_celltype0)]\n",
    "cell_1 = [1 if i > 10*j else 0 for i,j in zip(train_data_labels_final[:,1,:], baseline_celltype1)]\n",
    "cell_2 = [1 if i > 10*j else 0 for i,j in zip(train_data_labels_final[:,2,:], baseline_celltype2)]\n",
    "cell_3 = [1 if i > 10*j else 0 for i,j in zip(train_data_labels_final[:,3,:], baseline_celltype3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_0 = np.where(np.array(cell_0) == 1)\n",
    "idx_1 = np.where(np.array(cell_1) == 1)\n",
    "idx_2 = np.where(np.array(cell_2) == 1)\n",
    "idx_3 = np.where(np.array(cell_3) == 1)\n",
    "\n",
    "centered_genes = np.squeeze(centered_genes[idx_3,:,:,:], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4439, 1000, 4, 1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centered_genes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 256\n",
    "BUFFER_SIZE = 60000\n",
    "EPOCHES = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BatchDataset shapes: (None, 1000, 4, 1), types: tf.float32>"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset = tf.data.Dataset.from_tensor_slices(centered_genes).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
    "\n",
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_generator_model():\n",
    "    model = tf.keras.Sequential()\n",
    "    model.add(keras.layers.Dense(500*2*256, use_bias = False, input_shape = (100,)))\n",
    "    model.add(keras.layers.BatchNormalization())\n",
    "    model.add(keras.layers.ReLU())\n",
    "    \n",
    "    model.add(keras.layers.Reshape((500,2,256))) #Dimensions might be off\n",
    "    assert model.output_shape == (None, 500, 2, 256)\n",
    "    \n",
    "    model.add(keras.layers.Conv2DTranspose(128, (5,5), strides = (1,1), padding = 'same', use_bias = False))\n",
    "    assert model.output_shape == (None, 500, 2, 128)\n",
    "    model.add(keras.layers.BatchNormalization())\n",
    "    model.add(keras.layers.ReLU())\n",
    "    \n",
    "    model.add(keras.layers.Conv2DTranspose(64, (5,5), strides = (2,2), padding = 'same', use_bias = False))\n",
    "    assert model.output_shape == (None, 1000, 4, 64)\n",
    "    model.add(keras.layers.BatchNormalization())\n",
    "    model.add(keras.layers.ReLU())\n",
    "    \n",
    "    model.add(keras.layers.Conv2DTranspose(1, (5,5), strides = (1,1), padding = 'same', use_bias = False, activation = 'tanh'))\n",
    "    assert model.output_shape == (None, 1000, 4, 1)\n",
    "    \n",
    "    return model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8cc67b3550>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generator = make_generator_model()\n",
    "\n",
    "noise = tf.random.normal([1,100])\n",
    "generated_image = generator(noise, training = False)\n",
    "\n",
    "#generated_image\n",
    "plt.imshow(generated_image[0, :10, :, 0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_discriminator_model():\n",
    "    model = tf.keras.Sequential()\n",
    "    model.add(layers.Conv2D(64, (5,5), strides = (2,2), padding = 'same', input_shape = [1000,4,1]))\n",
    "    model.add(layers.LeakyReLU())\n",
    "    model.add(layers.Dropout(0.3))\n",
    "    \n",
    "    model.add(layers.Conv2D(128, (5,5), strides = (2,2), padding = 'same'))\n",
    "    model.add(layers.ReLU())\n",
    "    model.add(layers.Dropout(0.3))\n",
    "    \n",
    "    model.add(layers.Flatten())\n",
    "    model.add(layers.Dense(1)) #predicts the 6 outputs???\n",
    "    \n",
    "    return model\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([[-0.00010447]], shape=(1, 1), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "discriminator = make_discriminator_model()\n",
    "decision = discriminator(generated_image)\n",
    "\n",
    "print(decision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator_loss(real_output, fake_output):\n",
    "    real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
    "    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
    "    total_loss = real_loss + fake_loss\n",
    "    return total_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator_loss(fake_output):\n",
    "    return cross_entropy(tf.ones_like(fake_output), fake_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
    "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "checkpoint_dir = './training_checkpoints'\n",
    "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
    "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
    "                                 discriminator_optimizer=discriminator_optimizer,\n",
    "                                 generator=generator,\n",
    "                                 discriminator=discriminator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 10\n",
    "noise_dim = 100\n",
    "num_examples_to_generate = 100\n",
    "\n",
    "# We will reuse this seed overtime (so it's easier)\n",
    "# to visualize progress in the animated GIF)\n",
    "seed = tf.random.normal([num_examples_to_generate, noise_dim])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_step(images):\n",
    "    noise = tf.random.normal([BATCH_SIZE, noise_dim])\n",
    "\n",
    "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
    "        generated_images = generator(noise, training=True)\n",
    "        \n",
    "        real_output = discriminator(images, training=True)\n",
    "        fake_output = discriminator(generated_images, training=True)\n",
    "\n",
    "        gen_loss = generator_loss(fake_output)\n",
    "        disc_loss = discriminator_loss(real_output, fake_output)\n",
    "\n",
    "    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
    "    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
    "\n",
    "    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
    "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataset, epochs):\n",
    "    for epoch in range(epochs):\n",
    "        print(\"STARTING EPOCH\")\n",
    "        start = time.time()\n",
    "        \n",
    "        i = 0\n",
    "        for image_batch in dataset:\n",
    "            train_step(image_batch)\n",
    "            print('BATCH', i)\n",
    "            i = i + 1\n",
    "\n",
    "        #Produce images for the GIF as we go\n",
    "        display.clear_output(wait=True)\n",
    "        generate_and_save_images(generator,\n",
    "                                 epoch + 1,\n",
    "                                 seed)\n",
    "\n",
    "        # Save the model every 15 epochs\n",
    "        if (epoch + 1) % 15 == 0:\n",
    "            checkpoint.save(file_prefix = checkpoint_prefix)\n",
    "\n",
    "        print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
    "\n",
    "    # Generate after the final epoch\n",
    "    display.clear_output(wait=True)\n",
    "    generate_and_save_images(generator,\n",
    "                           epochs,\n",
    "                           seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_and_save_images(model, epoch, test_input):\n",
    "  # Notice `training` is set to False.\n",
    "  # This is so all layers run in inference mode (batchnorm).\n",
    "    predictions = model(test_input, training=False)\n",
    "\n",
    "    fig = plt.figure(figsize=(5,20))\n",
    "\n",
    "    for i in range(predictions.shape[0]):\n",
    "        plt.subplot(5, 20, i+1)\n",
    "        plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\n",
    "        plt.axis('off')\n",
    "\n",
    "    plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
    "    plt.show()\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x1440 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(train_dataset, EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x1440 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(100, 1000, 4, 1)"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_3 = generate_and_save_images(generator, 20, seed)\n",
    "class_3.numpy().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot(a, num_classes):\n",
    "    return np.squeeze(np.eye(num_classes)[a.reshape(-1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_3 = class_3[:,:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_3_temp = np.argmax(class_3, axis = 2)\n",
    "final_seqs = np.zeros((100,1000,4))\n",
    "\n",
    "for i in range(class_0_temp.shape[0]):\n",
    "    final_seqs[i,:,:] = one_hot(class_3_temp[i,:],4)\n",
    "\n",
    "np.save(\"class_3_seqs_v2.npy\", final_seqs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "num must be 1 <= num <= 8, not 9",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-212-e9c35e148a6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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----> 7\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\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      8\u001b[0m     \u001b[0mseq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mone_hot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\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[1;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m700\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msubplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1083\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1084\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\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   1085\u001b[0m     \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1086\u001b[0m     \u001b[0mbyebye\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/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1365\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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   1368\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/matplotlib/axes/_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     raise ValueError(\n\u001b[1;32m     59\u001b[0m                         (\"num must be 1 <= num <= {maxn}, not {num}\"\n\u001b[0;32m---> 60\u001b[0;31m                         ).format(maxn=rows*cols, num=num))\n\u001b[0m\u001b[1;32m     61\u001b[0m                 self._subplotspec = GridSpec(\n\u001b[1;32m     62\u001b[0m                         rows, cols, figure=self.figure)[int(num) - 1]\n",
      "\u001b[0;31mValueError\u001b[0m: num must be 1 <= num <= 8, not 9"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a = np.load(\"class_3_seqs_v2.npy\", allow_pickle=True)\n",
    "b = centered_genes[:,:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
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     ]
    }
   ],
   "source": [
    "identical = 0\n",
    "\n",
    "for i in range(a.shape[0]):\n",
    "    print(i)\n",
    "    for j in range(b.shape[0]): \n",
    "        if (np.array_equal(a[i,:,:], b[j,:,:])):\n",
    "            identical = identical + 1\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11208, 1000, 4)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}