matty/20190103a/20190103a-Client.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `20190103a` - New board tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not loading RPi.GPIO as not on RPi\n"
     ]
    }
   ],
   "source": [
    "from pyUn0 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/20190103a-1.json\n",
      "./data/20190103a-2.json\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_clean(\"./\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data acquired\n"
     ]
    },
    {
     "data": {
      "image/png": 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f7uryUBJ3yKO99046p6KvftXDz/e+l0zbfnuf96c/TaYdf7z0pz+VD90nndR7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\n/wGgdlhsNENTzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2255829690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7acaMNxOsqmE7dlRo2LDnakwbOvTKGs/Hj/+Zdt99WgGrAgAAAIDWixYYMRFg\nNO7++8/VLrt8VWVlc2tMf+ONWxKqqHEvv/xSo8vsuuvEAlQCAAAAAJBitsAws3mSNkiqlFTh7qPN\nrKekeyUNlTRP0inu3mLb1hNgNK5jxw8kSWVlGxOupCmSvdQrAAAAAKCmXLTA+JK77+Puo6Pnl0ma\n7O4jJU2OnrdYbdsSYDQuhAFmVTWmjhjxlySKyYpZ/QHG1q3bC1gJAAAAAEDKTxeSEyWNjx6Pl3RS\nHvZRNGiBUb+nnvqX/vGPU5UKMAYMmJlsQU3QUIBx330HSao7yCcAAAAAIH/iBhgu6Skzm2FmF0TT\n+rr7kujxUkl9M61oZheY2XQzm75ixYqYZSSnrIyrkNSnffujNWLExJI70a+qci1ffka98wcNer2A\n1QAAAAAApPgBxqHuvp+kYyRdaGZfSJ/p7q4QctTh7je6+2h3H11eXh6zjOR06CBt2ZJ0Fcgld1fv\n3p80uMyKFWvUt++cOtNfeumVfJUFAAAAAK1arADD3RdF98slPShpjKRlZtZPkqL75XGLLGa9ekmr\nVyddBXKpqqqq0WVefPHxjNMrKg7JdTkAAAAAAMUIMMyss5l1ST2WdJSkdyQ9IunsaLGzJT0ct8hi\n1qOHtHZt0lUUN7OMjXCKVlVVadULAAAAAK1BnMuo9pX0YDTYYZmku9z9STN7TdJEMztP0nxJp8Qv\ns3h16CCtabEXic2NAQPeSrqEJsmmBUbPnvWPkQEAAAAAyL1mBxju/pGkz2WYvkrSEXGKKiXt20vb\nuapmi5JNgAEAAAAAKKx8XEa1VenQQdq2LekqWqZPPhmTyH7vvvvSRPYLAAAAAKgfAUZMtMBIzrJl\nN+ZluyNGXJuX7QIAAAAAmo8AIyZaYCTn1FP/I+kSMmIQUAAAAADIPQKMmGiBkdn99w9NbN8ffZRs\nF5CJE29IdP8AAAAA0BIRYMREC4zMeveen9i+d911ZGL7lqRNm95PdP8AAAAA0BIRYMREC4zic8gh\nx2vJklGJ7d99R2L7BgAAAICWigAjpg4dQoDhDHuQMytWDI29jXHj3opfSLMRYAAAAABArhFgxNS+\nfbjfwTlrUWnXrizBvdMkBwAAAAByjQAjpg4dwn1rHAdj8uRnNGHCNXnYssVb2+KtH19VwvsHAAAA\ngJaHACOmTp3C/caNydaRhLZtj9SgQT/O+XbdUwFE4frl7L//phxujf5EAAAAAJBrBBgx9e4d7let\nSraOlqm1E/ezAAAgAElEQVR5LSma0wKjS5dOzdoXAAAAAKAwCDBi6to13K9fn2wdhfLee3O0Zcs2\nTZ/+Zo3pK1eu1erV6yRJd911bcy9JN0FBAAAAABQbAgwYmrXLty3hkE8V6xYo6VLR+rOO7+rjRv3\nrTHvnXd6aObM7pKk/v2/F2s/qS4kO3Yc1qz1kx8DAwAAAACQawQYMbWmAGNjNNBHjx7P1Jj+4osv\n52V/Y8acp6FDF+Rl2/k0YsQEVVRUJl0GAAAAALQoBBgxtaYAoz6VlYfmZbtmpqFDBzZrvcasXHnb\nzsdLl+7R5H00JtWdBgAAAACQGwQYMbXGAMOs/qtsTJ48ORd7yME2GjZu3Nk7Hx9//Bs53/6aNWty\nvk0AAAAAaM0IMGJqjQFGz56L6p23bNkPClJD+/b/btLyK1bcUu+8zp07xi2njk2bal6WdcaMt/Tc\nc6bnnjNt2rQl5/sDAAAAgJaOACOm1hhgNKR//1mxt5EaxNO9ZkuPOXNO3fn44IMPrHf9TF1Iyst3\nV2Vl2wb3u2jR/zalzAZt2rShxvN33rlw5+PXXuukp59+Kmf7AgAAAIDWgAAjpvbtw/22bcnWUQgv\nvDAh0f2ff/49sdY3q2pwft++e8bafrrKykP1wAPh9Vq8eLmGDKk50Gm7dl9RVVX9XXEAAAAAADUR\nYMTUt2+4X7o02ToKYciQnxZoT/HGwKhvEM82bRoODHJ9+dVevc6SJC1fvjzj/K1bW0HqBQAAAAA5\nQoAR0667Sr16SfPmJV1Jy5HqQpLjreZhm4374IOP6p23Y0dFASsBAAAAgNJGgJEDQ4ZI8+cnXUXr\ntn37k9q8eVdJuW9JEcfixcPrnbdt2/YCVgIAAAAApY0AIweGDm3ZLTB27KjQzTf/MNEali0b2eD8\no476Sp1p8+ZdufNx7QFBM8tP8DF//rsZpz/11Pi87A8AAAAAWiICjBwYPFhasCDpKvLn6aef0PDh\nfy74flOhQ4cOr+qLX3yl3uUWL96rxvM2bUIQMXTo2BrT58w5J6f1Zatbt1MzTh848EcFrgQAAAAA\nShcBRg507Spt3Chl9SV/CXKvTHT/Bx00Rrvt1luStG5deY15w4Yt1Fe/+u/oWe0WFDWfn3vuzXmq\nEAAAAACQbwQYOdCxY7jfujXZOvKlqqrhy48W0p57vqb16+/b+XzIkAHq1m3XRtcbM+ZAtW2bn8O9\nc+cZ2rbtca1YMSwv2wcAAAAASGVJF9ASdOoU7rdsqQ4zWpbcNS1ZuHBfDRz4RrPXHzFiiEaMGNLg\nMtWDeIa65837gsaObfyNae7gnwccsJ8kadGif+vDD3dr0rpVVb6zywsAAAAAoH60wMiBVGixeXO4\nf+QR6a23kqsn17p0GZeT7RxwwGZt3frpeudv3Vra6c+AAX2bvM6tt/44D5UAAAAAQMtDgJEDqQBj\ny5ZwNZITT5T22Ud67bVEyyq4ysqnG5zfuXNHnXLKTRnnLVt2gwYOfF1Ll16n3F0NpGnbGTCg4ZYd\n2di8+eEmLd+jxz2x9wkAAAAArQEBRg5UVIT7n/9cOuSQ6um33JJMPUk54ogvN7pM166dM04/9dQL\ntNden9Zpp30312XVa5ddpmndunt3Pt9jj+Hq0GFqneU6dZpe7zb69/+oxvN99z2oSTV07LiuScsD\nAAAAQGtFgJEDS5aE+3vvlRYvrp5+/fXS6tXJ1JSURYv+L+kSsnbggQfoxBNPqTGtf/+BdZYbM2b/\nerex2259ajxv06b+X6mFC/etM61jx02aNev9xkoFAAAAgFaPACMHunatf97rrxeujnx45pmGu4XU\nNnbsqTnbt5fAdWlrD/y5666dMi63ZMkonXFG5oNh5syTc14XAAAAALQ0BBg5cP759c878si601at\nkh57TJo9O3815UpZ2VFNWr7YQ4dPPvmd5s49tt75lZWVWW+roqJMXbrUDCw6d+6o7ds71Fl2y5b+\nkqTly+uOAdKv32zdfvsfst4vAAAAALRGBBg50KHu+WoNu+8uXXJJeLxypdS7t3TccdKoUbnZ/44d\n0n/+p7RwYW62t3Xrdj311L+yWnbu3OMlSR9/PFaS1KtXj9wUEUMqROnff7AkyewrO+edddZ/67zz\nHqt33YqK7AOM+fO/n3H6ggV1x/EYOfJKSdIpp2ROuwYP/m9NmnRHnenvvvuBPvlkcYY1AAAAAKB1\nIcDIkWeeqfl80aLqxx9/LP3pT+HxX/6S+30/+2wYb+M732l4ubVrJTPp9tvrX6ayskpTp3ZQ+/ZH\n68EH+ze4vZUrb9V55z2isWNd5547RZLUsWMHzZlzToPrLVlS81KqGzd2r7VEbq5C8qlPDdPw4Yt1\nxhmXZb1OVVV1gPHxx4drl12mNXm/Z511dZ1phx126M7HI0cuzbhez55n6uGHJ9aYtnz5HvroowG6\n4Qbpu98N7yFatk2bwhWNZs8OnxsbNoTp7tUDBr/xhnTRRdLvfifNnZvb/W/fLm3dKs2ZE/ZZ2/vv\nhyAWAIB8+e1vpTFjkq4CQDEiwMiRI46Q/pXWaKFfv7rLrFol/c//1Jx2//3Vj1evlgYPDiFDQz0Z\nFi2S3n67+nnbtuF+8WJpwYL615s/P9z/8Y81p69fX32ictttP9o5vUePJfVvTNLee38x4/ROnfZr\ncL2TT35Lc+aEwTPnzBmnUaPeanD5OAYN6qe2bbM/zLt06bLz8bnnTtaBBx5QY/6KFcPSnmUOWtq1\nK2twHwMG9NWiRf+bcV63bqdq8uSQhk2YUB2EfPe70g03SD16SJddlvnEUgonn1VVYeyVxRkabqxb\nJ11zjfTSSw2WiAKqqKj+fd+wQdp1V6lTp9BC65JLwhg7mzdL//3fUrt2Uv/+0n77SX/7m3T55dX/\n4FVVhc+Q7dvD58AHHzS8323bwj+I06dLb0W/grfdFlqUdewojRwZ9vfoo2HbCxZIBxwgffrTUnl5\nCELvvDNvLwsAoBW74grptdeSrgJAMSLAyKHUeBe77x5CiHvvrTn/0UfrrvONb1Q//t73qgOIz3++\n5nLu0rXXhhPZgQOlvfeWfvObML0sOl9+880QgOzYEb4lveyy8HjevHCClAo6tmyRHn44nMx+8IHU\nrZt09NFh3vDh2V1FZPDg+frUp4ZlnHfaaRftfLx+fe8683fZpb2kjpKk7t2/qt13H5zVPgthwIC+\nkqTKyrYZ57u30YIFf8w4rz5z5/6gzrRvfeviepdv2/ZIzZ+/SIMG/STj/N//vroVjXv4Nn7hwvC4\nQwdp3Dhp//2lAQPCMlVVYcyV4cOl7t2lH/9YOuyw6gFm16yRHnpI+sxnwnH729+GdVLrpodpqRYA\nxeD997O7ys/KldK//x1aQj34YG5PuidNal6rmMWLpYsvDq93u3bhd/jii+sfELhz5+rgcUmtXHH1\naunYY8Pvd69e4RgYPFjaYw9pxQrpnnvCJZ1Tode2beG93mWX8A/iAQdI++wjnXyydO65NbddWSkd\nf3yYftRRIexIOfts6Ywz6ta6dGn18VPMNm+WPvqo8eUAAMkppv87ABQHAowcMgutMFLfbveude5+\nzjmZ19u6NXzzetdd1dNmzAj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2255b18090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not an image\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (2560,) and (0,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-d052131ca7f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_fft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#OK\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_npz\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[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_detail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m35\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0;31m#y.mkSpectrum()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0;31m#y.mk2DArray() #@todo - DeltaAcq issue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/kelu/ultrasound/echomods/matty/20190103a/pyUn0.py\u001b[0m in \u001b[0;36mplot_detail\u001b[0;34m(self, nb_line, Start, Stop)\u001b[0m\n\u001b[1;32m    738\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 740\u001b[0;31m         \u001b[0mfile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"images/detail_\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miD\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"-\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mN\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    741\u001b[0m         \u001b[0mfile_name\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mStart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"-\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mStop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"-line\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_line\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\".jpg\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    742\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3315\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3316\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3317\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\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   3318\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3319\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1896\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1897\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1898\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\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   1899\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1404\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1406\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\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[0m\u001b[1;32m   1407\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1408\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_grab_next_args\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    405\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\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[0m\u001b[1;32m    408\u001b[0m                     \u001b[0;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    242\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\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[0my\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[0m\n\u001b[1;32m    243\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 244\u001b[0;31m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m    245\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (2560,) and (0,)"
     ]
    }
   ],
   "source": [
    "for MyDataFile in os.listdir(\"./data/\"):\n",
    "    if MyDataFile.endswith(\"2.json\"):\n",
    "        y = us_json()\n",
    "        y.JSONprocessing(\"./data/\"+MyDataFile)\n",
    "        y.create_fft() #OK\n",
    "        y.save_npz()\n",
    "        y.plot_detail(0,35,40) #@todo correct \n",
    "        #@todo make a showfirstline method too\n",
    "        #y.mkSpectrum()\n",
    "        #y.mk2DArray() #@todo - DeltaAcq issue\n",
    "        #y.mkImg() #@todo - DeltaAcq issue\n",
    "        #print y.create_title_text() #@todo - DeltaAcq issue\n",
    "        print MyDataFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'LengthAcq': 200000,\n",
       " u'LengthAcq_Real': 25600,\n",
       " u'PeriodAcq': 2500000,\n",
       " u'PeriodAcq_Real': 250000,\n",
       " u'Pon': 200,\n",
       " u'Pon_Real': 20,\n",
       " u'PulsesDelay': 300,\n",
       " u'PulsesDelay_Real': 30}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 9360, 9360)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y.tdac),len(y.t),len(y.tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'16': 75,\n",
       " u'17': 78,\n",
       " u'18': 82,\n",
       " u'19': 86,\n",
       " u'20': 90,\n",
       " u'208': 30,\n",
       " u'21': 93,\n",
       " u'22': 97,\n",
       " u'222': 0,\n",
       " u'224': 20,\n",
       " u'229': 100,\n",
       " u'23': 101,\n",
       " u'230': 0,\n",
       " u'231': 3,\n",
       " u'232': 208,\n",
       " u'233': 144,\n",
       " u'234': 1,\n",
       " u'235': 1,\n",
       " u'237': 3,\n",
       " u'238': 3,\n",
       " u'239': 1,\n",
       " u'24': 105,\n",
       " u'25': 108,\n",
       " u'26': 112,\n",
       " u'27': 116,\n",
       " u'28': 120,\n",
       " u'29': 123,\n",
       " u'30': 127,\n",
       " u'31': 131,\n",
       " u'32': 135,\n",
       " u'33': 138,\n",
       " u'34': 142,\n",
       " u'35': 146,\n",
       " u'36': 150,\n",
       " u'37': 153,\n",
       " u'38': 157,\n",
       " u'39': 161,\n",
       " u'40': 165,\n",
       " u'41': 168,\n",
       " u'42': 172,\n",
       " u'43': 176,\n",
       " u'44': 180,\n",
       " u'45': 183,\n",
       " u'46': 187,\n",
       " u'47': 191,\n",
       " u'48': 195,\n",
       " u'49': 198,\n",
       " u'50': 202,\n",
       " u'51': 206,\n",
       " u'52': 210,\n",
       " u'53': 213,\n",
       " u'54': 217,\n",
       " u'55': 0,\n",
       " u'56': 0}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.Registers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.8125"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1000/128.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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