repeat-check.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from rnncomp.dataman import *\n",
    "from rnncomp.augman import *\n",
    "from rnncomp.constants import *\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dims': 2, 'SEED': 0, 'class_type': 'flat', 'n_classes': 3, 't_len': 0.1}\n",
      "(3, 1, 2, 100)\n"
     ]
    }
   ],
   "source": [
    "dat, desc = mk_cls_dataset(t_len=0.1, dims=2, n_classes=3, freq=5, class_type=\"flat\", save_dir=None)\n",
    "print(desc)\n",
    "print(dat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3, 2, 100)\n"
     ]
    }
   ],
   "source": [
    "f_dat = np.tile(dat, (1, 3, 1, 1))\n",
    "print(f_dat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "assert np.all(f_dat[1, 1, 1] == f_dat[1, 2, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.625  0.625  0.625  0.625  0.625  0.625  0.625  0.625  0.625  0.625]\n",
      "[ 0.625  0.625  0.625  0.625  0.625  0.625  0.625  0.625  0.625  0.625]\n",
      "[ 0.25  0.25  0.25  0.25  0.25  0.25  0.25  0.25  0.25  0.25]\n",
      "[ 0.25  0.25  0.25  0.25  0.25  0.25  0.25  0.25  0.25  0.25]\n"
     ]
    }
   ],
   "source": [
    "print(f_dat[1, 1, 1, :10])\n",
    "print(f_dat[1, 2, 1, :10])\n",
    "print(f_dat[0, 1, 1, :10])\n",
    "print(f_dat[0, 2, 1, :10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9, 2, 110)\n",
      "(9, 3)\n"
     ]
    }
   ],
   "source": [
    "nengo_dat, nengo_cor = make_run_args_nengo(f_dat)\n",
    "print(nengo_dat.shape)\n",
    "print(nengo_cor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 2, 110)\n",
      "(3, 3)\n"
     ]
    }
   ],
   "source": [
    "dd, cc = make_run_args_nengo(dat)\n",
    "print(dd.shape)\n",
    "print(cc.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(330, 1, 2)\n",
      "(330, 1, 3)\n"
     ]
    }
   ],
   "source": [
    "ad, ac = make_run_args_ann(dd, cc)\n",
    "print(ad.shape)\n",
    "print(ac.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(990, 1, 2)\n",
      "(990, 1, 3)\n"
     ]
    }
   ],
   "source": [
    "ann_dat, ann_cor = make_run_args_ann(nengo_dat, nengo_cor)\n",
    "print(ann_dat.shape)\n",
    "print(ann_cor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3, 2, 100)\n",
      "(3, 3, 2, 100)\n"
     ]
    }
   ],
   "source": [
    "print(f_dat.shape)\n",
    "res_dat = np.array(aug(f_dat, desc, 3, add_rand_noise, kwargs={\"scale\": 0.01, \"sig\": True}, save_dir=None))\n",
    "print(res_dat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9ced7bf650>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEDCAYAAAAyZm/jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X+MJsdd5/F3VT8/ZmZ3vLHDLF4HE0TMV1YQ3J2ji8kS\n4sRYkONOCigJnAKcISF3FxnF/NDpUA4ucZAIuig4lzsklEtMxEE4zrIcExGCLyGBxFgIm1+O7kwR\nOXHQ2oY18Y/d+fE8T3fX/VHVTz8zOzvPzM7uzmb785J6u6p/PNNP7dPfrqqu52kQERERERERERER\nEREREREREREREZED5OZtYGZ3AjcCEbg9hPDQzLprgd8G+sCfhxDeZmavBu4GvpA3eySE8PbzfeAi\nInJuejutNLObgOtCCMfN7HrgLuD4zCbvA94bQrjPzP57vhBE4LMhhDdesKMWEZFz5uesvxm4FyCE\n8ChwpZkdBjAzD7wS+Hhe/5MhhL9jF60IERE5OPMC/9XA0zP5k8CxnF4BTgF3mtnnzOyXZrZ7qZnd\nl5ffcv4OV0RE9mte4N/KkbpymvSLgPcDNwH/zMy+DwjAu0IIrwNuBT5sZjt2KYmIyMUzLyA/Qar1\nN64Bnszpp4HHQwhfAjCzTwPfGkL4BOnmLiGEx8zsKdIF4vGz/ZG6rqNz6iESEdkLd46Bc17gvx+4\nA/igmd0AnAghrAKEEEoze8zMrgshfBF4GfBRM3sT8C0hhDvM7ChwFDgx5+A5efLUuRz/ZWdlZVll\nkaksWiqLlspi/3bs6gkhPAg8bGYPkLp0bjOzW83s+/MmPwX8el7/bAjh48DvAi8zs88D9wFvCyGU\nF+4tiIjIXlwS/SsxxqgreKLaTEtl0VJZtFQWraNHrzinGL7Xm7siIvI1ToFfRKRjFPhFRDpG4+tF\n5KKrY0xfCIrtF4O24/I/Lqc17Pv8UOAXuYzVMTKua8ZVZFLXjOs0n2yat+myjkximpd1TTlNR8oY\nqZqpjlQRqpiW13l5XUNNWlfn5RGoI0QicU6gnycFf3A4HOAdeOfSHEfRpF1KF95ROFLaOXo+z2fT\n3tH3jp7zbdo7+t7Td45+4eg7T987BkWe+3Ze+K+9i5EC/yUuxkhdpynWbbrJN+tjnU+yOu0zXR5z\nPqdhZlkk1bimta9mG6bb7o2jqZA5N5N3qabmZubg8N6dsc77nPYOanj22bW8zOF8Wt/km7Qv3GVT\nE6xiZFzVbFQ1o2aqa75cljz9zOp0+biOeV5vnlc50OdgX8b9hNmd9XKQbYJq4RzeQw+/KQCnAJ3/\nX8n/38wGcdh+gGH6XE4/k6SLSa9fMB5XRCL19AKTLjh1czGq6+mFKV2k4r4uODspHAy8T1Ph8twz\nzPnhNO2n6WGxeVqYWda7CBeSSyLw/+mJrzJeG7NQ+E3ToPD4C3BCT4NpVVNVkaqqqcqUTsvafFXV\nm7erauoqUpU1dZ3mVZ2XVZvndT37mmle13FTOuVzukofzbKspkH+Ap63lx1fzFwMvN+cL1LNzBcp\n7b2jKNI2Rd62KBy+SK/T8+RlpH2LSFH4nCe9Vi8Hvh44F4neMXGO0sHEwSTCBMc4RsYRxkTGNYxq\n2KgioxpGdZ5XaflkH//fBZGBh76PLLnIkX5K911k4CN9B32X8r2c7zV5F+k1eQ9Fk6emyMsL2uWO\nfHE+K7c57dplmwL99Pxu566pLWzZ16VaAldcscjzz2+Aa+r9btPrtPu7dhvn8kXBUeGoIpS1o8RR\nR0cZU9mXEcoayuhm8un/bTJN53ydLrKTZllVs1rWPFuXTOpz/4/suXSxWJi9MOTpyKDHTceuYljs\n7/bsJRH4P/RXXz7LmsiAij5VmseU7sc09WJFP5b0Yk2vLinqil5VUdQVRVVRVCW+qog1OciSphxg\nY3Sb5wDRzfQ9uuYwUnqm5sEZ+XbZNB/bD3+zzDtH4cEVDuc93s/WdB29XkEd65T34J3Ptd22Zjyt\nFU9ryuT1M7Wp/Llw3k1rVjjwxPacAHCxPcVczCsibnrEzfo230zpXItEaqaF1kxNcwKAeroskl47\nxrh5+03ra2KMFIWjqqotrxfz64FrXnfTMc3+3fw+mvfjwM1sk8ort458wZg+E/qM6DOOAzboM26m\n2GfMIM3LvGzUZ8Rguk1NwV4VlAyYMGDCEUoGLqX7edmACX1Xziwr01G6PM/79ygp3Eyw2VxU+xKB\nSZ4uBU/P32TPPDDM075fqXC5EjBg4gaUsZfSscckf1am89hjTC9/xnI69hjXPTaqgudij3LL5+r6\n5YJvPHLlvo7ykgj83+X/bObkyifYTH5En1UGjN0ibcTavdmTKJ1IM+nmZJo54aYnlEvzJt+n5AI0\nQC4PF6pczviE5qtcdFSuSP97Ln1WJq7PJM/Hsc+E5kTrTfPtyVVMT7AxBXW19xpUkSskQyoOx/W2\nUpLnvbqtoDSVkl6dKiNFntjUfQd1lSon0FZEmnQTJIgLWyoiW9LTnTdXUmYrJGerj06705ybqXD4\n3J22uZLicyd7UxHxuTtuWgHxbctgWkFxMxUUF6dddNPlZ6mYNJUMgOFCj/GouQzli/imdxTBNYU4\nWzHI67apdLQVkdlKTJ0qIrlCcrZ90z5nLndEPDUDRgzYaJe79lg2V0xirpgw3c75/H/vcuWicgyf\n/wG4HAL/tbyC8aikKOK0yZ1qxalJnZrgDudqau9SMxpH6SPj6BgTmUQYRxjF3Hyu0nyjiozrPqO6\nz0YNz6Vz7Uy7bJn1XaTvIwPHpubzwKcmcNOUbprYzbKeg56vp83rnmuazTNpIkeWh6yvjihcunG1\n2XbR1c3M3Fma0bP7zvaHbzrDpk3i6brmpGxG/eZmw7ZN6Xzq1bjUnM7N5xJHWUOV502+zM3oSR1T\ns7pO/4eTaRM64vsFp9YnbVO6nunDrupzrsw6Up/sQs9zxZam9EKvaWIXm7odm20Wi2KavlA39WJs\nuh/brsYjR5Y4efJU7oJsuxS365KcdldWm7szp92P5eYux9muxqqumZQzXZBVc1FK6babNM0Pzt5b\nVwdp9n7U2boi2/U+xcDCt/keFN6zsNTnyqPX7vt4Lon668X+yYay3nzzbCPfQJveTKsioxxcmptr\n4y031GZvol2oz7+DM0YmNKMVmlEMu7mBBtvdRNt84ywtaW/0zo7CqGlHaDQ3z5p5NTOio7qAcaAZ\nQTEsPIM8umI4czNt9qZZexOt6Sstcl+pY6Eo6Oca7deSS/VnCtpBB/U2Aw/y+hg3DUaYXR4jm5Y3\ngw+mF5U4O/ggpVMf//oeBiC0rYrpPJ8fWwcXpPV54AFsasFs6mKdGWTgHNPA3XTFeu+n3bXOXbjB\nB+f6kw2XRI3/Yut5z2HvOdw/P69X5uFwo6odJrd52FxKlzFO82VdT4fOVXmoXFlHin7B+mgyHT5X\n54DajEyo83Z1jJsC8jRYx2mj9Jw0n6I04KZtwnuaIXJp3vOO4exojnyB6uWhcoVPF6q+93neLE/p\nfh4O18vbDIqUnh0ud2xlmeefWaPvL59RO5cbn2oeFBfxu6CX6kXwa0knA//51vOOni9Y7O2/+Xk+\nP9SbLgLxzIvBtMYzk7+UAuzysM/GPkcviMiZFPgvY013T8oc5JGIyKVE1SkRkY5R4BcR6Zi5XT1m\ndidwI6mL+PYQwkMz664FfhvoA38eQnjbvH1ERORg7VjjN7ObgOtCCMeBtwAf2LLJ+4D3hhBuBCoz\nu3YX+4iIyAGa19VzM3AvQAjhUeBKMzsMYGYeeCXw8bz+J0MIf7fTPiIicvDmBf6r2fzTGCeBYzm9\nApwC7jSzz5nZL+1iHxEROWB7Hc7paIeDO+BFwPuBx4HfM7Pvm7PPWa2sLO/xUC5fKouWyqKlsmip\nLPZnXuB/glSDb1wDPJnTTwOPhxC+BGBmnwa+dc4+Z6Vv4iX6VmJLZdFSWbRUFvs3r6vnfuANAGZ2\nA3AihLAKEEIogcfM7Lq87cuAR3faR0REDt6ONf4QwoNm9rCZPQBUwG1mdivwXAjhY8BPAR/JN3r/\nOoTwcYCt+1zYtyAiIntxSXyR/2L/OuelTM3YlsqipbJoqSxa5/rrnPrmrohIxyjwi4h0jAK/iEjH\nKPCLiHSMAr+ISMco8IuIdIwCv4hIxyjwi4h0jAK/iEjHKPCLiHSMAr+ISMco8IuIdIwCv4hIxyjw\ni4h0jAK/iEjHKPCLiHTM3Ietm9mdwI2kB6bfHkJ4aGbdl4GvkJ60BfDDgAF3A1/Iyx4JIbz9PB6z\niIjsw46B38xuAq4LIRw3s+uBu4DjM5tE4LUhhLWZfQz4TAjhBy/EAYuIyP7M6+q5GbgXIITwKHCl\nmR3ess12j/66JB7pKCIiZ5oX+K8Gnp7JnwSObdnm18zsc2b2npllLzWz+/LyW87HgYqIyPkxt49/\nC0fq3mn8AvBJ4BngY2b2euBB4F0hhLvN7JuBz5jZS0II5U4vvLKyvMdDuXypLFoqi5bKoqWy2J95\ngf8JUq2/cQ3wZJMJIfxmkzazTwDfFkK4h3RzlxDCY2b2FPAi4PGd/tDJk6f2duSXqZWVZZVFprJo\nqSxaKov9m9fVcz/wBgAzuwE4EUJYzfkjZvZHZraYt30V8IiZvcnM3pm3OQocBU5ckKMXEZE927HG\nH0J40MweNrMHSEM2bzOzW4HnQggfM7N7gD8xs9PAX4QQ7sk3fz9qZp8HCuBt87p5RETk4rkkRt/E\nGKOabomasS2VRUtl0VJZtI4eveKcYri+uSsi0jEK/CIiHaPALyLSMQr8IiIdo8AvItIxCvwiIh2j\nwC8i0jEK/CIiHaPALyLSMQr8IiIdo8AvItIxCvwiIh2jwC8i0jEK/CIiHaPALyLSMQr8IiIdM/dh\n62Z2J3Aj6SHrt4cQHppZ92XgK6SncwG8KYTw5E77iIjIwdox8JvZTcB1IYTjZnY9cBdwfGaTCLw2\nhLC2h31EROQAzevquRm4FyCE8ChwZX6m7qytj/7azT4iInJA5gX+q4GnZ/IngWNbtvk1M/ucmb1n\nD/uIiMgBmdvHv4Ujde80fgH4JPAM8DEze/0u9tnWysryHg/l8qWyaKksWiqLlspif+YF/idINfjG\nNcCTTSaE8JtN2sw+AXzbvH3O5uTJU7s43MvfysqyyiJTWbRUFi2Vxf7N6+q5H3gDgJndAJwIIazm\n/BEz+yMzW8zbvgp4ZKd9RETk4O1Y4w8hPGhmD5vZA6Qhm7eZ2a3AcyGEj5nZPcCfmNlp4C9CCPcA\nbN3nAr8HERHZg60jcg5EjDGq6ZaoGdtSWbRUFi2VRevo0SvOKYbrm7siIh2jwC8i0jEK/CIiHaPA\nLyLSMQr8IiIdo8AvItIxCvwiIh2jwC8i0jEK/CIiHaPALyLSMQr8IiIdo8AvItIxCvwiIh2jwC8i\n0jEK/CIiHTP3mbtmdidwI+m5ubeHEB7aZpv3AN8RQniNmb0auBv4Ql79SAjh7efvkEVEZD92DPxm\ndhNwXQjhuJldD9wFHN+yzUuB7wLGM4s/E0L4wfN9sCIisn/zunpuBu4FCCE8ClxpZoe3bPNe4B1s\nfprXJfFkLxEROdO8wH818PRM/iRwrMmY2Y8Bfwg8vmW/l5rZfWb2OTO75XwcqIiInB97vbnrSH39\nmNlVwI8A72dzDT8A7wohvA64Ffiwmc29lyAiIhfHvID8BKnW37gGeDKnX5PXfR4YAi8xs/eFEH6W\ndHOXEMJjZvYU8CLObBVssrKyvPejv0ypLFoqi5bKoqWy2J95gf9+4A7gg2Z2A3AihLAKEEK4B7gH\nwMxeDHwkhPCzZvbDpBvCd5jZUeAocGLegZw8eWofb+PysbKyrLLIVBYtlUVLZbF/Owb+EMKDZvaw\nmT0AVMBtZnYr8FwI4WMzm067gID7gI+a2eeBAnhbCKG8AMcuIiLn4JIYfRNjjLqCJ6rNtFQWLZVF\nS2XROnr0inOK4frmrohIxyjwi4h0jAK/iEjHKPCLiHSMAr+ISMco8IuIdIwCv4hIxyjwi4h0jAK/\niEjHKPCLiHSMAr+ISMco8IuIdIwCv4hIxyjwi4h0jAK/iEjHKPCLiHTM3Iegm9mdwI2kJ2zdHkJ4\naJtt3gN8RwjhNbvdR0REDsaONX4zu4n0/NzjwFuAD2yzzUuB7yI/enE3+4iIyMGZ19VzM3AvQAjh\nUeBKMzu8ZZv3Au+gfYzjd+9iHxEROSDzAv/VwNMz+ZPAsSZjZj8G/CHw+Mw2X7/TPiIicrDm9vFv\n4Wi7dK4CfgT4XuDa3ewjIiIHb17gf4JU629cAzyZ06/J6z4PDIGXmNmvzNnnrFZWlnd5yJc/lUVL\nZdFSWbRUFvszL/DfD9wBfNDMbgBOhBBWAUII9wD3AJjZi4GPhBB+xsxecbZ9dnLy5Kl9vI3Lx8rK\nssoiU1m0VBYtlcX+7djHH0J4EHjYzB4A3g/cZma3mtn3b9l02p2z3T7n/7BFRORcufmbXHgxxqgr\neKLaTEtl0VJZtFQWraNHrzinGK5v7oqIdIwCv4hIxyjwi4h0jAK/iEjHKPCLiHSMAr+ISMco8IuI\ndIwCv4hIxyjwi4h0jAK/iEjHKPCLiHSMAr+ISMco8IuIdIwCv4hIxyjwi4h0jAK/iEjHzH3Yupnd\nCdxIesLW7SGEh2bWvRV4M1ABfxVCuM3MXg3cDXwhb/ZICOHt5/vARUTk3OwY+M3sJuC6EMJxM7se\nuAs4ntctAT8EvDKEUJnZp/PzdiPw2RDCGy/wsYuIyDmY19VzM3AvQAjhUeBKMzuc82shhFty0F8C\njgBPcok8zlFERLY3L/BfDTw9kz8JHJvdwMx+Dvgi8DshhC/nxS81s/vM7HNmdsv5OlgREdm/uX38\nWzhSV85UCOGXzez9wCfM7AEgAO8KIdxtZt8MfMbMXhJCKHd64ZWV5T0eyuVLZdFSWbRUFi2Vxf7M\nC/xPkGr9jWtI3TmY2VXAt4cQPhtC2DCz3we+M4TwJ6Sbu4QQHjOzp4AXAY/v9IdOnjx1jm/h8rKy\nsqyyyFQWLZVFS2Wxf/O6eu4H3gBgZjcAJ0IIq3ldH/iwmR3K+ZcDj5rZm8zsnXmfo8BR4MR5P3IR\nETknO9b4QwgPmtnDuQunAm4zs1uB50IIHzOzd5O6ckrgL0MIH883fz9qZp8HCuBt87p5RETk4rkk\nRuDEGKOabomasS2VRUtl0VJZtI4eveKcYri+uSsi0jEK/CIiHaPALyLSMQr8IiIdo8AvItIxCvwi\nIh2jwC8i0jEK/CIiHaPALyLSMQr8IiIdo8AvItIxCvwiIh2jwC8i0jEK/CIiHaPALyLSMQr8IiId\nM/dh62Z2J3Aj6SHrt4cQHppZ91bgzaSnc/1VCOG2efuIiMjB2rHGb2Y3AdeFEI4DbwE+MLNuCfgh\n4JUhhFcC15vZK3baR0REDt68rp6bgXsBQgiPAlfmZ+oSQlgLIdwSQqjyReAI8NRO+4iIyMGbF/iv\nBp6eyZ8Ejs1uYGY/B3wR+J0Qwpd2s4+IiBycuX38WzhSv/1UCOGXzez9wCfM7IHd7LOdlZXlPR7K\n5Utl0VJZtFQWLZXF/swL/E+QavCNa4AnAczsKuDbQwifDSFsmNnvA9+50z47OXny1F6O+7K1srKs\nsshUFi2VRUtlsX/zunruB94AYGY3ACdCCKt5XR/4sJkdyvmXA4/O2UdERA7YjjX+EMKDZvZw7sKp\ngNvM7FbguRDCx8zs3cBnzKwE/jKE8HGArftc4PcgIiJ74A76AABijFFNt0TN2JbKoqWyaKksWkeP\nXnFOMVzf3BUR6RgFfhGRjlHgFxHpGAV+EZGOUeAXEekYBX4RkY5R4BcR6RgFfhGRjlHgFxHpGAV+\nEZGOUeAXEekYBX4RkY5R4BcR6RgFfhGRjlHgFxHpmL0+c1dELkMxRuJ4TD0aUY82iBsj6vGIejQi\njkbU4zFx3MwnxMk4pcsJcZKnsqSeTKAsiWVFrEpimae6hqoi1hVUNbGuiFUNsYY6EmMNMaZpJ87z\nWOGJOPAO5z14j/NFmhcFrvBQ9FK619sy9XH9NPl+Hzfo43p9/GCAGwxw/QF+OMQN0ryZ3HCIHy7g\nFxZwRXFx/lMuoLmB38zuBG4kPTD99hDCQzPrXgP8EulJW38D/ARwE3A38IW82SMhhLef5+MWkSxW\nFfXaGtXaapqvr1OvreX0GvX6ejttpHm1vk7c2KAebVBvpGlu0D0XTfD1Hlf0oMjB2Re4Xj8Fb+fB\nuenkHJz5jKiYDi9GCg/VpEoXi7om1nW6AFUVsarSsiZdVef9LbleD7+wiF9MF4KUXpxZlvNLSxSL\nS/ilJfziIsVSShdLh3DDIc4d3HOwdgz8ZnYTcF0I4biZXQ/cBRyf2eSDwKtDCCfM7H8DrwXWgM+G\nEN6424N4633/kbquz1jucPm/300LyeHwLq1xLk94nHN4HN55vHN4V+S5x7uCwvmc9hTNOgo8Huc8\nDp/SFLjocM06ivS3YgF4XCzwOe0pIHpc9HlZgYtpG6LH1Wm7OkZijNQx16zyvI7QfKDrGCHC4uKA\ntbUxsfmg78A5piWEa/Pp/ElrnCOVl3f4vNznk8s7h8/L0zzlC3/mvPC+TRftst7WdOEpvJum/QF+\nuL+WNDXuavU01enT1KurVKurVKspfboec/rpZ/Py01Rra9Rrq1Sra8TRxp7/3mzw6n/d1+EXFnOt\ntqnZDnGD4eba72CYasiDAb7f1JBzjbnfx/VzjTrXrimKCxLc9vIErthcBMpy2jqpx2OqyZjJeINy\ntE452phO1WiDKrd06vHGtMUTR2MYj2E0xo3GMJ7gN1Zxzz+LH032/CjD2jsmg4LJsGA8KBgNC8ZD\n384HBaOBZzT0rA/TfDQs8EtL/Pt/+ma+bvGqvRfcjHk1/puBewFCCI+a2ZVmdjiEcDqvf1kI4fmc\nPglcRQr8e/K9n3ySjWHBRn7DKV2wMfBsDD0bg4L1Bc/EQ3QunSTUpApAJKZUXpbTLs1xF6AWswcx\nOqh9mqIn1kXOF8Q8p/bEuN3ys6Wb1zlzG6LjEnmiJpAuLs1FoF84ej1Pz/s8z/m8rig8/Z6nX6Rl\ny4eHTCYl/WZ5s27LfLouT72Z15ldXviLc0sr1nWqVZ8+TXX6VArUp05T56BenT49DfBNuj59mliW\nu/4bTY1ycPQo/tChac2yqVX6xZlaZt7WLy5SLCziFhbw/f4FLIG9iTFSxYpxNWFST9p5PWZcTRhX\nY8b1hElePnym4KvPndqybbO+nC4vm3Sd03nbSV3mOLGNXp6WdjpiDwzzBMRIv4wMJpHhJM0H43qa\nHk4iw3HNcJznk8hCTi+MJhw5NaI4s967rUnfUx/7B/imCxv4rwYensmfBI4BfwvQBH0zOwZ8D/Dz\nwD8BXmpm95EuBHeEED610x859sTqrg62dJ51P2S9WGC9GLJWDHM+T3ndqDdkMlhkMlik6g/o9Qp6\nvZgDEBS9mGqtRSR1DeZ5EfGuBh/xRbpoeB/BVeAjztXEPEFF7SpwNTUVkZqakpqKmooqVtSUVLGi\noqSKKV3GkipOKOMGVdz9ib5bDkff9+m5ZurR8z0KevRcn8K184IehevhSWlPgY+93NrppZZLXaSW\nzcwFx9UFMXrqylHXkbKOVFVNVUeqKlJWNWXOl9N8pKprJmXNpKpZG5XT7crq4lycvXNnXCTOuEDk\n9PSi4mGhHrNQbbAw2WBQjhhM1umN1+mN1umN1vAba/j1NdzGKm59DdbXYJsW7LbHtLhIcfgwvW+4\nluLQIYrDhykOHU7B/PDhtOzQYV74ohWenziKpUP4paUL3s/cBOMURMscMFNwHddbAvR0Pp7mx9Nt\nx0zqMgXvasKkHjOuSybTfNq2jruMfHvQ9z36vp+nHovDBfq+R8/3Gfg+PZ/OjX6eelumvutR+CL3\nDBQ4csUq+jSvPTF6YgXEIp8PUFeOqoaqcjS9TXUJZQXPV5FJGSnLyKSqKcuayaSkGo9xG+v40TrF\naJ1ivE5/vEF/ssFgssGw2mCxGuMcLNdH9l02e72562DzpdLMjgK/C7wthPCMmQXgXSGEu83sm4HP\nmNlLQghnjXL/90f/EwvViIV8Ug0mG/THa/RG6xQb6cRy66sM1lcZrK1yxeoqcf2Z3R1wUdA7fJje\n8jL9K5ZTerhMb/kw/eWcX07re4cPpfzhZYrFhQveBxdj3HRSjKsJ43KmlpOXjcoxo2o8XTYqZ9LV\neLrPqBpvs26d1TKlzysHru8YFAMGvpfmRZ9+0adf9BgWAwZFPvGKdOI1817RY1CkE7DvB/R8gXe5\nmyx3lxGLmZPLEWtHXfnpiVXXkapylCXUFZQllGXMc5iUNeNJzbisUno8gY0NWF/Fb6zjVlPw7o/X\n6Y03GJTrLFRjFqsRi9WIhbqZj3fVforQVkIGL0wVE7/QVkpyxWSjN2TcX6QcLlINF/G9gn4Per2m\nUuLo9aBXQXEK/FrEf/U0xROncLlC4jx4V+OKCK4GXwPtPLpUKUkt4CpXRnLFJFZUVLki0k5lXVJu\nmk/OXivehybo9n2fgV9gqbecKyrp89BWTnoUrk9BkSopro+PuXKSKyWeXm7l+lwZaVvNTRAuy6YC\nkisZZaSsU8AdzSybVFWal1UbkMuaSVUSL0AFbSvnoN8rGPSW6S8dYXCkYND3eZln0C8Y9AuOHB5g\n3/ZNLC8N9vX35gX+J0i1/sY1wJNNxsyuAD4BvKOp1YcQniDd3CWE8JiZPQW8CHj8bH/krW+4Ydd9\ndo1Ylrm5vJqa0zNN5u2a06Nnn2X9xInd38DyPtWuDi1Na1mzzegzmtTTmzj55s7CQhpxsGsFBQXf\nuLKSysLRNjuHeyqabaWLzGxNrJ3P1tS2zlONrW1GN/uVTXqmCb46XmdSn6LMzenzyde5CT2JDCap\n2Zya0TWD8UxzehJZHEeuzOnhOLIwSundqjyMhgWjQwXPDRdTOnc/jjZ1QxasD31KDzzTTsfc/RiZ\nQBwTeT7na7brhlzfa2E0lePzcN8yVqkLctplGD3UC8R6aUu3op8G2VgVbboutul+3JKe2X5/3ZAR\nKPO0fw4Q/ISJAAAHxklEQVRy92K6R5W6Ax1Lwx69pXTPqle0XYcp7abdjLPLN7UWi6ZlWUzTg17R\nbjfTVTnotX93txXNjdURG6ujfb33eYH/fuAO4INmdgNwIoQw2y/zPuDOEML9zQIzexPwLSGEO3Jr\n4ChwYl9HuQ3X69E78gJ6R16w63029b2urqY+19WUrk6fbkdG5Btr9doa1eppyn/8xz31v06PcTic\nXgSm03CYLgrNjbTBcJp2gwHxhUc4vVGl4WX95kZaH9cf5JtnW26geb+rD4xzuXZeDOA8du/GGNMw\nvaqaDumLkwn1ZMJkkm6eTUYbVOMNyo18Ay3fNKtGG2kI4cZGuoE2nsBoBOMxbjyhmJTE9RF+XOLL\nvUe5sucZDws2lvs8v/Um2iDdLBvl+0jNzbONoWdSMI1Ps5eLSJwZbtBIJ+yQduABzQCEsww8cM5T\n5AEFzaCDwqcBB4UrKFyBc81ggzQtLQ4ZrVfTfOp68JtaSY6ZdGzWF+1gg+hx9NK8LlJLCqjTKIM0\n+KBuL17NYIT0/9xc0FKhbG0NxAizH8NNgw6m83bggc+rvXfTgQjNAIOtAw+cdxTTvOPKFyxx+vRG\nO/igSIG72Dogodhm8IH3FDl4e3/p3Au72Oa+czN7D/AqUv3iNuAG4DngD4BngAdnNv8t4H8BHyX1\n7xekPv5P7vQ3YoxxrzX+i60ej6ejKOr1tTSqYn2Nem095bcZMtcMk6s3UjqOz3N3C6Thb6mPAJeH\nylEUafhcHtu8uyFzkZgDQBpmVKeTvq7TsLmqbsdiVzPjss/hgrib9+SHQ3pLSzAYppuRCwupVdW0\nqJYWz2htFU0L7FAaRud6l8/XVPYykuVyp7JoHT16xTldvS6JS97XQuA/H2Jdp2FiuYZbj0fTC0I9\nGhHHIw4NPc9/9VRaNh5PvxxTT8a5Np2HpeUvxjTBd3bccqzTF2TSeOatX5KBVGXbpusj11ZxTL8Y\ng3NpzLX3aQy2n7mw9NJYbFf02vRsq2SQWirTFsugn4YBDgd5aOBgOnwwtXoGaVjhYIBzTif4DJVF\nS2XROtfAf/lUib4GOO8pFhcpFhfPus3KyjKFPtQicgHpt3pERDpGgV9EpGMU+EVEOkaBX0SkYxT4\nRUQ6RoFfRKRjFPhFRDpGgV9EpGMU+EVEOkaBX0SkYxT4RUQ6RoFfRKRjFPhFRDpGgV9EpGPm/iyz\nmd0J3Ej6JffbQwgPzax7DfBLpIe0/A3wEyGEuNM+IiJysHas8ZvZTcB1IYTjwFuAD2zZ5IPAG0II\nrwSWgdfuYh8RETlA87p6bgbuBQghPApcaWaHZ9a/LITQPE/3JPDCXewjIiIHaF7gvxp4eiZ/EjjW\nZEIIzwOY2THge4BPzNtHREQO1l5v7jryU1sbZnYU+F3gbSGEr+5mHxEROTjzbu4+QarBN64Bnmwy\nZnYFqZb/jhDCp3azz3acc5fEQ99FRLpgXo3/fuANAGZ2A3AihLA6s/59wJ0hhPv3sI+IiByguTVt\nM3sP8CrSkM3bgBuA54A/AJ4BHpzZ/LdCCB/auk8I4ZHzfeAiIiIiIiIiIiIiIiIiIl1y4MMou/67\nPmb2X4BXkobWvgd4CPifpBFXTwI/GkIYH9wRXlxmtgh8AXg38Id0tCzM7IeB/wCUwH8GHqGDZZG/\n9f8bwAuAIXAH8P/oUFmY2beTfg3hV0IIv2pm17LN+8+fmduBGvhgCOGus73mgf46Z9d/1yf/yN23\n5vf/WuC/kj7Y/y2E8Crgi8CbD/AQD8LP037z+910sCzM7IWkYP+dwL8CXkd3Pxc/BjwaQriZNEz8\nA3SoLMxsiTRs/g9ovwh7xnlhZoeAXwC+G3g18NNmduXZXvegf5a567/r88fAD+b0c8Ah4CbSN6EB\nPg7ccgDHdSDM7HrgeuD38qKulsUtwKdCCKshhKdCCP+OdDJ3sSz+nvQbYABXkX4C5tV0pyxGpIv/\n388s2+68eDnwZyGEUyGEDeABUsVhWwcd+Dv9uz4hhGrmy21vIQW8wyGESV7WqfIA3gv8NG0X5KGO\nlsWLgSUzu8/M/tjMvpuOlkUI4W7gWjP7W+AzwM/QobLIMWK0ZfF27//qnG78AzuUy0EH/q06+bs+\nZvY64MeBn9yy6sDvwVwsZvZvgD8OIXwlL9r63jtTFqTz8irgB0hdHb++ZX1nysLMfgT4SgjhW0g1\n219lc4zoTFmcxdne/47lctCBf8+/63O5MbPvBd4B/Iv8a6enzWyYV7+IVEZd8H3AG83sQeAnSH39\np8xsIa/vUlk8BTwYQqhDCI8Bp+huWRwn/QwMIYS/Br4BWO1oWTS2ixFbY+k3ACe27tg46MDf6d/1\nMbMjpO6NfxlCeDYv/hS5TIDXA79/EMd2sYUQ/nUI4eUhhFcAHwJ+Efg0qQygQ2VBOi9uNjOXb/Qe\nIn0uulgWXySN+sPMXgycBv4P3SsLR1uL3y5G/Cnwz83sSL5Pehz43E4vdqC6/Ls+ZvZvgXcCIS+K\npKb9h4AF4MvAj4cQqoM4voNiZu8EvkQKgL9BB8sifzbekrO/SBrm27myyKNV7gK+njTk+eeBR+lI\nWZjZdwD/AzhKGtr7j6QRgB9hy/s3s9eThgBH4AMhhN8+iGMWERERERERERERERERERERERERERER\nEREREem0/w8qHmJV8iw0FwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ced894410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(res_dat[0, 0, 1])\n",
    "plt.plot(res_dat[0, 1, 1])\n",
    "plt.plot(res_dat[0, 2, 1])\n",
    "\n",
    "plt.plot(res_dat[1, 0, 1])\n",
    "plt.plot(res_dat[1, 1, 1])\n",
    "plt.plot(res_dat[1, 2, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 2, 110)\n",
      "(3, 3, 3)\n"
     ]
    }
   ],
   "source": [
    "ad, ac = dat_repshuf(dd, cc)\n",
    "print(ad.shape)\n",
    "print(ac.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9, 2, 110)\n",
      "(9, 3)\n"
     ]
    }
   ],
   "source": [
    "rd, rc = post_arggen_repeat(dd, cc)\n",
    "print(rd.shape)\n",
    "print(rc.shape)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}