VAE_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from __future__ import division, print_function, absolute_import\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import struct\n",
    "from scipy import signal\n",
    "import pywt\n",
    "\n",
    "%pylab inline\n",
    "\n",
    "pwd = os.getcwd()\n",
    "fb = open(pwd + \"//\" + \"input_data\" + \"//\" + \"other\" + \"//\" + \"drop\" + \"//\" + \"0\", \"rb\")\n",
    "x = 0\n",
    "data1 = []\n",
    "data2 = []\n",
    "\n",
    "interval = 5\n",
    "dt = 0.0000001 * interval\n",
    "fs = 10000000 / interval\n",
    "start = int(250000 / interval)\n",
    "end = int(350000 / interval)\n",
    "fig_size = 20\n",
    "\n",
    "while True:\n",
    "    data = fb.read(4)\n",
    "    if not data:\n",
    "        break\n",
    "    if x % interval == 0:\n",
    "        ch1, ch2 = struct.unpack('<HH', data)\n",
    "        ch1 = (float(ch1) - 8192) / 8192 * 2.5\n",
    "        ch2 = (float(ch2) - 8192) / 8192 * 2.5\n",
    "        ch1 = float(ch1)\n",
    "        ch2 = float(ch2)\n",
    "        data1.append(ch1)\n",
    "        data2.append(ch2)\n",
    "    x = x + 1\n",
    "\n",
    "fb.close()\n",
    "\n",
    "data1 = data1[start:end]\n",
    "data2 = data2[start:end]\n",
    "\n",
    "wavelet = 'morl'\n",
    "c = pywt.central_frequency(wavelet)\n",
    "fa = np.arange(400000, 20000 - 1, -20000)\n",
    "scales = np.array(float(c)) * fs / np.array(fa)\n",
    "\n",
    "[cfs1,frequencies1] = pywt.cwt(data1,scales,wavelet,dt)\n",
    "[cfs2,frequencies2] = pywt.cwt(data2,scales,wavelet,dt)\n",
    "power1 = (abs(cfs1)) ** 2\n",
    "power2 = (abs(cfs2)) ** 2\n",
    "\n",
    "length_now = len(power2[0])\n",
    "power1 = np.reshape(power1,(len(power1),fig_size,int(length_now/fig_size)))\n",
    "power2 = np.reshape(power2,(len(power2),fig_size,int(length_now/fig_size)))\n",
    "power1 = np.log10(np.mean(power1,axis=2))\n",
    "power2 = np.log10(np.mean(power2,axis=2))\n",
    "\n",
    "mx = power1.max()\n",
    "mn = power1.min()\n",
    "power1 = (power1-mn) / (mx-mn)\n",
    "power1 = power1.flatten()\n",
    "\n",
    "mx = power2.max()\n",
    "mn = power2.min()\n",
    "power2 = (power2-mn) / (mx-mn)\n",
    "power2 = power2.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-2-c5cf4ba91634>:7: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "WARNING:tensorflow:`tf.train.start_queue_runners()` was called when no queue runners were defined. You can safely remove the call to this deprecated function.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/training/queue_runner_impl.py:391: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "INFO:tensorflow:Restoring parameters from saver/VAE/VAE\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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F16+8238PxvLiPD12ZW6Wr0/760XG2werv7Uaz4Ln4GNo2+afO/YeiTbYmwKAjFw7dmwZYu/v6O99FDfI/b2dXqGLiCRCDV1EJBFq6CIiiVBDFxFJhBq6iEgi1NBFRBKhhi4ikohoDt3MngbwRwCWQwgf3v7clwH8JYDL2zf72xDC87FzVbWA1oFuX9tZavJ56O9dXqbr1z72oLt2zws/ocfeOMwz7qsH/Fz1mwf8jDrA8/WrDZ5DL43nrpcm/AxyLE/LsuAA0An+16qo+LGrWzyDXFb+64z2lj8bnB13N7tb20D7nv7moS8d57P837fWputvHvFrbL3J36uw2uTvCVif8L9Wq3nk3OS9Cq3AZ/3H8trtrl8HbA0AqsDPzbLksRorurz2Y9f2bHZ7e8tQL8+A0wAeucvnvxZCOLn9X7TgRcbQaai2JSHRhh5CeAnA6hD2IjJUqm1JzSA/Q3/SzF4xs6fNjP88RGR/UW3LvtRvQ/8GgPcBOAngAoCvejc0syfM7IyZnSnbK31eTmRo+qzty97NRIamr4YeQrgUQuiGECoA3wTwELntUyGEUyGEU3mT/+OPyKj1X9uHhrdJEUdfDd3M7r3tj58BcHZ3tiMyWqpt2c96iS1+G8DDABbNbAnAlwA8bGYnAQQArwP4Qi8XKyeA1aN+bPHoq36kpzL+vecDP3qNrtPoYcGilMD0VT6+9FjpH786x2NhK7Nz7lpsZHAe+L7r8NcrbNFjYyNIM/gRvXPX+cjW9Y3I+FJyt7PMv2632lkkbDdru8oCOtP+2NWFJf/YrOKjYnGF119W+Y/JfOsGPZaNbwaAMvProBMZn9up+fHB3Ph9boHHGluht3Gyd712xq9ddfuLFgJAZv7XIrbO1ixy3luiDT2E8NhdPv2PPZ1dZIyptiU1eqeoiEgi1NBFRBKhhi4ikgg1dBGRRKihi4gkQg1dRCQRvc1k3CXlRIWVo4W7Xkz542LrVclP/t+/pMvTE2Ss5fdf5ef+jSN0efL1K+7aR1d5hvjVj7zHXZs50KHHLs3x0bzv7vhzp1bqPB9/X7VG19dq/ljV9RmeIf5V8LP3AB8xWq/5GeIayajvte4EsHrEr1E2Ubi5sclP/p/n6PKHjvm5/41p/rW474JfuwDw5lE2mpePQV6ZmXXX2hM8R742wUfzruR+/XYiba2IrLdyf2+x0bwtNicZfDRvPfPfNxLLt791u55uJSIiY08NXUQkEWroIiKJUEMXEUmEGrqISCLU0EVEEqGGLiKSiKHm0PPJCgcf9Oczl5P+r2/8+t/9CT3351fc3xR204PktyW9cokf+/z/8vUuma/8sfvpoQ+SnPDCwev82PwCXW90/JnnZ0+8mx7b3OTZ6Iv3+NnnlcgM+I/N8fcMzHT9a89vtt21c+CP117aqle4cL+/79ZBkpuejMz2/udX+HrXzyhPfer99NCpXyzT9XtqZDb4h4/xbc37WfKKDb0HsLJIfn8BgIuLfv11JnhWvCIz3gFgbcrfdyw/v57zbH4n8/fGfv/Bf5n//p3b6RW6iEgi1NBFRBKhhi4ikgg1dBGRRKihi4gkQg1dRCQRQ40tTjdKPHTCj0kVU/e5a+sH/dGSAPAff/PndP3w2jV37djvfIAee+Df/oeuX/rjj7prR95Yocf+/P3vctdWZ3n8r17ykcI/O3TcXTva9h8PAHjh8Ifoeg4/qpmDf61+BP/rDABrVcNdW5jecNeukZG+e21qqosTH/JHDhdTB921c8f4eOb7//rj/OIdvw6uv/cwPfTs75+i6w++cdFda7Z5tJWN3l25xx+tCwBljcwbBtCa9Gtkvc7roIice53UUSeLjN4FH1fcgR9bbAd/bbPHVq1X6CIiiVBDFxFJhBq6iEgi1NBFRBKhhi4ikgg1dBGRRKihi4gkwkLwR2/u+sXMLgO4fXbqIgAe1B4N7WtnxmVf94cQDo3iwnfU9rg8HnfSvnZuXPbWU20PtaG/4+JmZ0II/J0NI6B97cy47mtUxvXx0L52bpz3djf6kYuISCLU0EVEEjHqhv7UiK/v0b52Zlz3NSrj+nhoXzs3znt7h5H+DF1ERHbPqF+hi4jILhlJQzezR8zsF2b2mpl9cRR7uBsze93MfmJmPzazMyPey9NmtmxmZ2/73IKZ/cDMXt3+/4Ex2deXzez89uP2YzP7w2Hva1yotqP7UF3voaE3dDOrAfg6gD8A8EEAj5nZB4e9D+KTIYSTYxBVOg3gkTs+90UAL4YQTgB4cfvPw3Ya79wXAHxt+3E7GUJ4fsh7Gguq7Z6chup6z4ziFfpDAF4LIZwLIRQAvgPg0RHsY6yFEF4CsHrHpx8F8Mz2x88A+PRQNwV3X3KTajtCdb23RtHQjwN447Y/L21/bhwEAP9uZj80sydGvZm7OBJCuLD98UUA/FfdDNeTZvbK9l9dh/5X5jGh2u6P6nqX6B9F3+7jIYTfws2/Mv+VmX1i1BvyhJvxpHGJKH0DwPsAnARwAcBXR7sduYt9Uduq68GMoqGfB3D7L9K8b/tzIxdCOL/9/2UAz+LmX6HHySUzuxcAtv/v/4LWIQohXAohdEMIFYBvYvwet2FRbfdHdb1LRtHQXwZwwszeY2Z1AJ8D8NwI9vE2ZjZtZrO3PgbwKQBn+VFD9xyAx7c/fhzA90a4l7fcejJu+wzG73EbFtV2f1TXu6S3XyW9i0IIpZk9CeD7AGoAng4h/HTY+7iLIwCeNTPg5uPyrRDCC6PajJl9G8DDABbNbAnAlwB8BcB3zezzuDnZ77Njsq+Hzewkbv5V+XUAXxj2vsaBajtOdb239E5REZFE6B9FRUQSoYYuIpIINXQRkUSooYuIJEINXUQkEWroIiKJUEMXEUmEGrqISCL+H5fjjSDeOp7BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "249.12445 240.92908\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:  \n",
    "    \n",
    "    tf.local_variables_initializer().run()\n",
    "    tf.global_variables_initializer().run()\n",
    "    \n",
    "    coord = tf.train.Coordinator()\n",
    "    thread = tf.train.start_queue_runners(sess=sess,coord=coord)\n",
    "    \n",
    "    saver = tf.train.import_meta_graph('saver/VAE/VAE.meta')\n",
    "    saver.restore(sess,'saver/VAE/VAE')  \n",
    "    y = tf.get_collection('output_y')[0]\n",
    "    l = tf.get_collection('output_y')[1]\n",
    "    \n",
    "    graph = tf.get_default_graph()\n",
    "    x = graph.get_tensor_by_name(\"input_x:0\")\n",
    "    \n",
    "    outputdata1,loss1 = sess.run([y,l],feed_dict ={x:[power1]})\n",
    "    outputdata2,loss2 = sess.run([y,l],feed_dict ={x:[power2]})\n",
    "    \n",
    "    power1 = np.reshape(power1,(20,20))\n",
    "    power2 = np.reshape(power2,(20,20))\n",
    "    outputdata1 = np.reshape(outputdata1,(20,20))\n",
    "    outputdata2 = np.reshape(outputdata2,(20,20))\n",
    "    \n",
    "    \n",
    "    \n",
    "    plt.subplot(1,2,1)\n",
    "    plt.imshow(power1,cmap=plt.get_cmap('rainbow'))\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.imshow(outputdata1,cmap=plt.get_cmap('rainbow'))\n",
    "    plt.show()\n",
    "    \n",
    "    plt.subplot(1,2,1)\n",
    "    plt.imshow(power2,cmap=plt.get_cmap('rainbow'))\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.imshow(outputdata2,cmap=plt.get_cmap('rainbow'))\n",
    "    plt.show()\n",
    "    \n",
    "    print(loss1,loss2)\n",
    "    \n",
    "    coord.request_stop()\n",
    "    coord.join(thread)"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}